diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index d0be62529..74c3fa18a 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -72,7 +72,7 @@ Include additional illustrative plots describing input data, methods, testing, a
 - [ ] No large data file(s) added/modified
 - [ ] No substantive impact on runtime for full-US reference case
 - [ ] No substantive impact on folder size for full-US reference case
-- [ ] No change to process flow (runbatch.py, d_solve_iterate.py)
+- [ ] No change to process flow (runreeds.py, reeds/core/solve/solve.py)
 - [ ] No change to code organization
 - [ ] No change to package requirements (environment.yml or Project.toml)
 
diff --git a/.github/labeler.yaml b/.github/labeler.yaml
index e0558a5b2..5268fb035 100644
--- a/.github/labeler.yaml
+++ b/.github/labeler.yaml
@@ -9,6 +9,7 @@ dependencies:
           - "Project.toml"
           - "Manifest.toml"
           - ".github/dependabot.yml"
+          - "instantiate.jl"
 
 github_actions:
   - changed-files:
@@ -42,15 +43,8 @@ tests:
 model_changes:
   - changed-files:
       - any-glob-to-any-file:
-          - "*.gms"
-          - "**/*.gms"
-          - "runbatch.py"
-          - "d_solve*.py"
           - "reeds/**"
-          - "ReEDS_Augur/**"
-          - "Augur.py"
-          - "instantiate.jl"
-          - "reeds2pras/**"
+          - "runreeds.py"
 
 data_changes:
   - changed-files:
@@ -60,19 +54,11 @@ data_changes:
           - "postprocessing/**/inputs/**"
           - "**/*.csv"
           - "**/*.h5"
-          - "sources.csv"
-          - "sources_documentation.md"
 
 hourlize:
   - changed-files:
       - any-glob-to-any-file: "hourlize/**"
 
-input_processing:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "input_processing/**"
-          - "preprocessing/**"
-
 postprocessing:
   - changed-files:
       - any-glob-to-any-file: "postprocessing/**"
diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml
index 24f72ed6c..6c3ccc09c 100644
--- a/.github/workflows/build-docs.yaml
+++ b/.github/workflows/build-docs.yaml
@@ -26,14 +26,14 @@ jobs:
     permissions:
       contents: write # Required for peaceiris/actions-gh-pages publish to gh-pages.
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with: 
           fetch-depth: 1
           lfs: true
           persist-credentials: false
 
       - name: Set up Python 3.12
-        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5
+        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
         with: 
           python-version: "3.12"
 
@@ -50,21 +50,13 @@ jobs:
             sphinxcontrib-bibtex==2.6.2 \
             sphinx_design==0.6.1
 
-      - name: Set variables for internal github repo
-        run: |
-          echo "BASE_URL=https://github.com/ReEDS-Model/ReEDS" >> "$GITHUB_ENV"
-          cd "$GITHUB_WORKSPACE/docs/source/documentation_tools/"
-          sh generate_sources_md_file.sh
-          cd "$GITHUB_WORKSPACE"
-          python docs/source/documentation_tools/generate_markdown.py --githubURL "https://github.com/ReEDS-Model/ReEDS/blob/main" --reedsPath "$GITHUB_WORKSPACE"
-
       - name: Build Sphinx documentation
         env: 
           GAMSLICE: ${{ secrets.GAMSLICE }}
         run: sphinx-build docs/source docs/build/
 
       - name: Deploy to GitHub Pages
-        uses: peaceiris/actions-gh-pages@373f7f263a76c20808c831209c920827a82a2847 # v3
+        uses: peaceiris/actions-gh-pages@84c30a85c19949d7eee79c4ff27748b70285e453 # v4.1.0
         if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
         with: 
           github_token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/labeler.yaml b/.github/workflows/labeler.yaml
index a1bcfcbfb..61731895b 100644
--- a/.github/workflows/labeler.yaml
+++ b/.github/workflows/labeler.yaml
@@ -22,7 +22,7 @@ jobs:
     timeout-minutes: 5
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@634933edcd8ababfe52f92936142cc22ac488b1b # v6.0.1
+      - uses: actions/labeler@f27b608878404679385c85cfa523b85ccb86e213 # v6.1.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           configuration-path: .github/labeler.yaml
diff --git a/.github/workflows/python-app.yaml b/.github/workflows/python-app.yaml
index f7bb2cc21..34008584a 100644
--- a/.github/workflows/python-app.yaml
+++ b/.github/workflows/python-app.yaml
@@ -34,7 +34,7 @@ jobs:
       contents: read
       actions: write # Required to save/update micromamba cache.
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           persist-credentials: false
       - name: Setup ReEDS environments
@@ -52,7 +52,7 @@ jobs:
       contents: read
       actions: write # Required to save/update Zenodo data cache.
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           fetch-depth: 1
           lfs: false
@@ -115,7 +115,7 @@ jobs:
     env:
       batch: test
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           fetch-depth: 1
           lfs: false
@@ -195,7 +195,7 @@ jobs:
       - name: Run ReEDS model
         env:
           SCENARIO: ${{ matrix.scenario }}
-        run: python runbatch.py -b "$batch" -c test -s "$SCENARIO"
+        run: python runreeds.py -b "$batch" -c test -s "$SCENARIO"
 
       - name: Print GAMS log
         if: runner.debug == '1'
@@ -203,7 +203,7 @@ jobs:
 
       - name: Check ReEDS outputs
         if: ${{ hashFiles(format('{0}/outputs/cap_ivrt.csv', env.RUN_FOLDER)) == '' }}
-        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v7
         with:
           script: |
             core.setFailed('ReEDS run failed. Check log to diagnose.')
@@ -215,7 +215,7 @@ jobs:
 
       - name: Upload ReEDS failure diagnostics
         if: ${{ failure() }}
-        uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
         with:
           name: ${{ format('{0}-failure-diagnostics', matrix.scenario) }}
           compression-level: 9
@@ -230,7 +230,7 @@ jobs:
             ${{ env.RUN_FOLDER }}/ReEDS_Augur/PRAS/PRAS_*i*.log
 
       - name: Save ReEDS run
-        uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
         with:
           name: ${{ matrix.scenario }}
           compression-level: 9
@@ -264,17 +264,17 @@ jobs:
             solve-year: 2026
 
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           persist-credentials: false
       - name: Retrieve ReEDS runs for ${{ matrix.scenario }}
-        uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
         with:
           name: ${{ matrix.scenario }}
           path: ${{ format('{0}-{1}', matrix.scenario, matrix.solve-year) }}
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7
+        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
         with:
           python-version: "3.11"
           enable-cache: true
@@ -290,7 +290,7 @@ jobs:
           R2X_WEATHER_YEAR: "2012"
           R2X_SYSTEM_JSON: ${{ format('{0}_system.json', matrix.scenario) }}
         run: |
-          uvx --from "r2x-reeds>=0.3.5" python scripts/run_r2x.py \
+          uvx --from "r2x-reeds>=0.3.5" python postprocessing/run_r2x.py \
             --reeds-run-path "$R2X_REEDS_RUN_PATH" \
             --scenario "$R2X_SCENARIO" \
             --solve-year "$R2X_SOLVE_YEAR" \
@@ -298,7 +298,7 @@ jobs:
             --system-json "$R2X_SYSTEM_JSON"
 
       - name: Save R2X system
-        uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
         with:
           name: r2x-${{ matrix.scenario }}
           compression-level: 9
diff --git a/.github/workflows/workflow-quality.yaml b/.github/workflows/workflow-quality.yaml
index d35249e9d..c818cb3ad 100644
--- a/.github/workflows/workflow-quality.yaml
+++ b/.github/workflows/workflow-quality.yaml
@@ -23,12 +23,12 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 5
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           persist-credentials: false
 
       - name: Install uv
-        uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
+        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
         with:
           version: "0.9.4"
 
@@ -40,7 +40,7 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 5
     steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           persist-credentials: false
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index dce9669c8..dc8b30486 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -6,4 +6,4 @@ For questions or general discussion, please open a [discussion](https://github.c
 
 To contribute code, fork the repository, make your changes, and submit a pull request. Please review our [Developer Guide](https://reeds-model.github.io/ReEDS/developer_best_practices.html) before getting started.
 
-**Note:** if you're considering making a change that might lead to meaningful differences in model capability, workflow, or outputs, we recommend initiating a [discussion](https://github.com/ReEDS-Model/ReEDS/discussions).
\ No newline at end of file
+**Note:** if you're considering making a change that might lead to meaningful differences in model capability, workflow, or outputs, we recommend initiating a [discussion](https://github.com/ReEDS-Model/ReEDS/discussions).
diff --git a/README.md b/README.md
index 31242f4ae..86a63cd07 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,8 @@ This GitHub repository contains the source code for NLR's ReEDS model.
 The ReEDS model source code is available at no cost from the National Laboratory of the Rockies.
 The ReEDS model can be downloaded or cloned from [https://github.com/ReEDS-Model/ReEDS](https://github.com/ReEDS-Model/ReEDS).
 
+If you want to use the latest stable version of ReEDS, download or check out the latest stable release [here](https://github.com/ReEDS-Model/ReEDS/releases/latest).
+
 **For more information about the model, see the [ReEDS Documentation](https://reeds-model.github.io/ReEDS).**
 
 ReEDS training videos are available on the [NLR Learning YouTube channel](https://youtube.com/playlist?list=PLmIn8Hncs7bG558qNlmz2QbKhsv7QCKiC&si=NgGBaL_MxNcYiIEX).
@@ -63,10 +65,10 @@ A step-by-step guide for getting started with ReEDS is available [here](https://
     These files are downloaded automatically as needed during a ReEDS run, but to finish all the internet-requiring steps up front, you can download them all by running `python reeds/remote.py`.
     Additional details on remote files and other topics can be found in the [user guide](https://reeds-model.github.io/ReEDS/user_guide.html#large-input-files).
 5. Run ReEDS on a test case from the root of the cloned repository:
-    1. For interactive setup: `python runbatch.py`
-    2. For one-line operation: `python runbatch.py -b v20250314_main -c test`.
+    1. For interactive setup: `python runreeds.py`
+    2. For one-line operation: `python runreeds.py -b v20250314_main -c test`.
     In this example, "v20250314_main" is the prefix for this batch of cases, and "test" is the suffix of the cases file, in this case `cases_test.csv`, located in the root of the repository.
-    Run `python runbatch.py -h` for information on other optional command-line arguments for ReEDS.
+    Run `python runreeds.py -h` for information on other optional command-line arguments for ReEDS.
 
 
 
diff --git a/cases.csv b/cases.csv
index 317faeb06..fa0dc507e 100644
--- a/cases.csv
+++ b/cases.csv
@@ -29,15 +29,15 @@ plantchar_other,Cost and performance characteristics for lfill-gas and o-g-s (in
 plantchar_upv,UPV cost and performance inputs (inputs/plant_characteristics/{plantchar_upv}.csv). The options for plantchar_upv start with 'upv_' and the switch should be set to the entire file name (ex: upv_ATB_2023_advanced).,upv_ATB_(2023|2024)_(conservative|moderate|advanced),upv_ATB_2024_moderate,
 ccsflexscen,"Flexible CCS scenario (inputs\plant_characteristics\{ccsflexscen}_cost/perf.csv).The options for ccsflexscen start with 'ccsflex_' and the switch should be set to the file name, dropping '_cost' or '_perf' (ex: ccsflex_ATB_2020)",ccsflex_ATB_2020,ccsflex_ATB_2020,
 ccs_upgrade_cost_case,case in upgrade_cost_ccs_coal and upgrade_cost_ccs_gas to use for ccs retrofit costs,atb22_1p2_moderate; atb22_1p2_advanced,atb22_1p2_moderate,
-coalscen,"Coal price scenario (inputs\fuelprices\{coalscen}.csv). The options for coalscen start with 'coal_' and the switch should be set to the file name, dropping  'coal' (ex: AEO_2025_reference)",AEO_(2023|2025)_reference,AEO_2025_reference,
-demandscen,Demand scenario (see inputs\load),N/A,AEO_2025_reference,
+coalscen,"Coal price scenario (inputs\fuelprices\{coalscen}.csv). The options for coalscen start with 'coal_' and the switch should be set to the file name, dropping  'coal' (ex: AEO_2025_reference)",AEO_2025_reference; AEO_2026_baseline; AEO_2026_altelec,AEO_2026_baseline,
+demandscen,Demand scenario (see inputs\load),N/A,AEO_2026_baseline,
 dr_shedscen,DR shed scenario,demo_data_January_2025,demo_data_January_2025,
 evmcscen,EVMC scenario for load and supply curve and flexibility,Baseline,Baseline,
 geodiscov,Annual discover rates for new geothermal sites,BAU; TI,BAU,
 geohydrosupplycurve,Geohydro Supply Curve,ATB_2023,ATB_2023,
 egssupplycurve,EGS Supply Curve,ATB_2023; reV,reV,
 egsnearfieldsupplycurve,EGS (near field) Supply Curve,ATB_2023,ATB_2023,
-ngscen,Natural gas price inputs (see inputs\fuelprices),N/A,AEO_2025_reference,
+ngscen,Natural gas price inputs (see inputs\fuelprices),N/A,AEO_2026_baseline,
 pshsupplycurve,PSH Supply Curve,(8|10|12)hr_(ref|wEphemeral|wExist|wEph|wExist_wEph)_(apr2025|mar2024),12hr_ref_apr2025,
 pvbscen,PVB cost-sharing scenario,benchmark2020,benchmark2020,
 dacscen,Electrical DAC cost and performance inputs (see inputs/consume/[dacscen].csv starting with 'dac_elec_').,BVRE_2021_(high|mid|low),BVRE_2021_mid,
@@ -45,9 +45,9 @@ h2combustionfuelscen,H2-CT/CC Fuel Price Scenarios (only used if endogenous H2 p
 upgradescen,Exogenous upgrade capital cost specification (see inputs\upgrades). 'default' calculates cost using diff between from- and to- tech,N/A,default,
 retscen,Retirement Scenario,Nuke60RetireYear; Nuke80RetireYear; NukeEarlyRetireYear; NukeRefRetireYear,Nuke80RetireYear,
 supplycurve,Wind and Solar Supply Curves,default; 0; naris; 2018,default,
-uraniumscen,Uranium price scenario,AEO_(2023|2025)_reference,AEO_2025_reference,
+uraniumscen,Uranium price scenario,AEO_2025_reference; AEO_2026_baseline,AEO_2026_baseline,
 GSw_Region,Specify column from inputs/userinput/modeled_regions.csv or from {column of hierarchy.csv}/{period-delimited entries to keep from that column},N/A,country/USA,
-GSw_ZoneSet,Set of geospatial zones to use as balancing areas (described in inputs/zones/README.md),^(z48|z54|z69|z132|z134|z3109|UTcounty|PJMcounty)$,z132
+GSw_ZoneSet,Set of geospatial zones to use as balancing areas (described in inputs/zones/README.md),^(z48|z54|z69|z132|z134|z3109|UTcounty|PJMcounty)$,z132,
 capcredit_hierarchy_level,Select level at which to aggregate net load for capacity-credit calculation,r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region,transreg,
 construction_schedules_suffix,File suffix for construction schedules,default,default,
 construction_times_suffix,File suffix for construction times by technology,default,default,
@@ -85,7 +85,7 @@ GSw_BinOM,Turn on/off binned FOM and VOM for each historical binned vintage,0; 1
 GSw_Biopower,Turn on/off biopower,0; 1,1,
 GSw_BioSupply,Multiplier to adjust total biomass supply,N/A,1,
 GSw_BioTransportCost,Biomass collection and transport costs ($2004 per dry ton),N/A,22,
-GSw_calc_powfrac,Switch to compute powfrac in e_report and e_powfrac_calc - dramatically reduces calculation times with hourly resolution,0; 1,0,
+GSw_calc_powfrac,Compute powfrac in report.gms and powfrac_calc.gms - dramatically increases calculation times with hourly resolution,0; 1,0,
 GSw_Canada,"Turn canada off [0], or use flexible (dispatchable-hydro-like) representation [1]",0; 1,1,
 GSw_CarbTax,Turn on/off CO2 tax,0; 1,0,
 GSw_CarbTaxOption,Choose the co2_tax input csv file (see inputs\emission_constraints),,default,
@@ -143,6 +143,7 @@ GSw_GasCurve,"Select natural gas supply curve (0 = cendiv, 1 = national + cendiv
 GSw_GasCC_H_1x1,Turn on/off both gas-CC_H_1x1 and gas-CC_H_1x1-CCS_mod/max plant options,0; 1,0,
 GSw_GasCC_H_2x1,Turn on/off both gas-CC_H_2x1 and gas-CC_H_2x1-CCS_mod/max plant options,0; 1,0,
 GSw_GasCT_Aero,Turn on/off gas-CT_aeroderivative technology option,0; 1,0,
+GSw_GasRegionSmooth,Exponential decay length [km] to use when smoothing regional (census division) gas prices together (if 0 then no smoothing is applied and r:cendiv map is used 1:1),float,150,
 GSw_GasSector,Select sector scope of gas curves,electric_sector; energy_sector,electric_sector,
 GSw_GenMandate,Turn on/off national Gen Requirement. 2 turns on and applies constraint at busbar.,0; 1; 2,0,
 GSw_GenMandateList,Select technology list for national generation standard (see inputs/national_generation/nat_gen_tech_frac.csv),RE; Nuclear; NuclearCCS,RE,
@@ -179,6 +180,7 @@ GSw_HourlyClusterRegionLevel,Indicate the region hierarchy level at which to agg
 GSw_HourlyClusterWeights,/-delimited relative weights for load and RE profiles used in clustering,^load_\d*.?\d+\/upv_\d*.?\d+\/wind-ons_\d*.?\d+\/wind-ofs_\d*.?\d+$,load_1/upv_1/wind-ons_1/wind-ofs_0,
 GSw_HourlyClusterYear,Year to use when clustering EFS and/or climate-modified data,N/A,2035,
 GSw_HourlyClusterTimestep,Indicate the time resolution to use in clustering algorithm (period=day or wek depending on GSw_HourlyType),hour; period,period,
+GSw_HourlyClusterMapMethod,How to map actual periods to representative periods: 'milp' minimizes sum of absolute errors but is slow; 'bestfirst' is orders of magnitude faster when using many weather years in GSw_HourlyWeatherYears,^(milp|bestfirst)$,milp,
 GSw_HourlyMinRElevel,Indicate the region hierarchy level at which to include minimum wind and solar capacity factor days (or False to ignore min-wind/solar CF days),false; False; FALSE; r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region; ccreg,interconnect,
 GSw_HourlyNormProfiles,Indicate whether to normalize load/wind/solar profiles before identification of representative periods,0; 1,0,
 GSw_HourlyNumClusters,Number of clusters to create (i.e. number of representative days or weks),int,33,
@@ -296,7 +298,7 @@ GSw_SitingGeo,Specify the Geothermal siting scenario,reference,reference,
 GSw_SitingUPV,Specify the UPV siting scenario,open; reference; limited,reference,
 GSw_SitingWindOfs,Specify the offshore wind siting scenario,open; reference; limited,reference,
 GSw_SitingWindOns,Specify the onshore wind siting scenario,open; reference; limited,reference,
-GSw_SkipAugurYear,Last year in which to skip running the Augur module,N/A,2020,
+GSw_SkipRAyear,Last year in which to skip running the resource adequacy (RA) calculations,N/A,2020,
 GSw_SpurCostMult,Multiplier for spur-line costs,float,1,
 GSw_SpurScen,Spur-line scenario: 0 to include in resource supply curve; 1 to model endoegenously,0; 1,0,
 GSw_SpurShare,Indicate whether wind-ons and upv are allowed to share spur line capacity,0; 1,0,
@@ -317,6 +319,7 @@ GSw_TCPhaseout_trigger_f,The fraction of emissions from the ref year used to det
 GSw_TransCapMaxEachType,[GW] maximum transmission capacity allowed across each individual BA-BA interface for each trtype (constraint is off if set to 0; must be 0 or >= existing capacity (26.13 for AC)),N/A,0,
 GSw_TransCapMaxAllTypes,[GW] maximum transmission capacity allowed across each individual BA-BA interface for all trtypes together (constraint is off if set to 0; must be 0 or >= existing capacity (26.13 for AC)),N/A,0,
 GSw_TransCapMaxTypes,Transmission types to limit according to GSw_TransCapMaxEachType and GSw_TransCapMaxAllTypes [0 = none; 1 = all; 2 = VSC; 3 = all DC],0; 1; 2; 3,1,
+GSw_TransConductor,Which conductor to assume for new AC transmission ('acss' is a mix of ACSS and ACSR as specified by the MISO Transmission Cost Estimation Guide; 'acsr' is just ACSR),^(acss|acsr)$,acsr,
 GSw_TransIntraCost,[USD2004/kW] intra-zone network reinforcement cost for all technologies (100 USD2022/kW using gas cost from slide 14 of https://emp.lbl.gov/publications/generator-interconnection-costs),N/A,65,
 GSw_TransInvMaxLongTerm,[TW-mile/year] maximum transmission capacity investment per year IN/AFTER firstyear_trans_longterm (constraint is OFF if set to 0 meaning unlimited trans investment; annual max since 2009 is ~3.64),N/A,0,
 GSw_TransInvMaxNearTerm,[TW-mile/year] maximum transmission capacity investment per year BEFORE firstyear_trans_longterm (constraint is OFF if set to 0 meaning unlimited trans investment; annual mean since 2009 is ~1.4),float,1.4,
@@ -329,9 +332,9 @@ GSw_TransHurdleLevel2,Apply hurdle rates from inputs/transmission/cost_hurdle_in
 GSw_TransHurdleRate,Turn on (1) or off (0) hurdle rates between hierarchy regions as defined by GSw_TransHurdleLevel1/GSw_TransHurdleLevel2 and inputs/transmission/cost_hurdle_intra.csv,0; 1,0,
 GSw_TransNetworkSource,Network source for the initial transmission network used in the model,NARIS2024,NARIS2024,
 GSw_TransRestrict,hierarchy level within which to allow new transmission lines,r; nercr; transreg; transgrp; cendiv; st; interconnect; country; usda_region,country,
-GSw_TransScen,Transmission expansion scenario (options in inputs/transmission),N/A,default,
-GSw_TransSquiggliness,Transmission distance multiplier to approximate the squigglier paths followed by actual lines (only applied to NEW lines),float,1,
-GSw_TransUpgradeMethod,Method for calculating AC transmission upgrade costs,500kv,500kv,
+GSw_TransScen,Additional candidate interfaces for transmission expansion between zones,^(none|NTP_(MT|P2P))$,none,
+GSw_TransSquigglinessMin,Minimum squiggliness (straight-line length multiplier) to apply for interzonal transmission; the default value is from the MISO Transmission Cost Estimation Guide (https://www.misoenergy.org/planning/transmission-planning/mtep),float,1.3,
+GSw_TransUpgradeMethod,Method for calculating interzonal AC transmission expansion costs,^(greenfield|500kv)$,greenfield,
 GSw_TransGroupContraint,Indicate whether to apply flow constraint on groups of transmission interfaces,0; 1,1,
 GSw_TransGroupDerate,[fraction] amount by which to derate the capacity of grouped transmission flows for new transmission additions (0 = no derate),float,0.15,
 GSw_Upgrades,Switch to turn upgrades on or off - not to be used with water constraints - value of 2 indicates persistent upgrades,0; 1; 2,1,
@@ -355,9 +358,9 @@ cleanup_level,How aggressively to clean up the case folder (see postprocessing/c
 diagnose,Write A and B matrix of the model [0 = no diagnose ; 1 = diagnose ],0; 1,0,
 diagnose_year,Year in which to start report diagnose,N/A,2022,
 dump_alldata,switch to automatically dump data from final solve year into .gdx file,0; 1,0,
-file_replacements,"List of files to replace from run folder, e.g: inputs_case/national_gen_frac.csv << //nrelnas01/ReEDS/some proj/national_gen_frac.csv || c_supplymodel.gms << //nrelnas01/ReEDS/some proj/c_supplymodel.gms",N/A,none,
-input_processing_only,Only run input_processing scripts; stop before creating and solving model,0; 1,0,
-keep_augur_files,Indicate whether to keep (1) or delete (0) Augur csv and h5 files after Augur finishes. If you plan to run PRAS via ReEDS2PRAS then set this switch to 1.,0; 1,0,
+file_replacements,"List of files to replace from run folder, e.g: inputs_case/national_gen_frac.csv << //nrelnas01/ReEDS/some_project/national_gen_frac.csv || c_model.gms << //nrelnas01/ReEDS/some_project/c_model.gms",N/A,none,
+input_processing_only,Only run input_processing scripts; stop before creating and solving model (1) or stop right after Monte Carlo sampling (2),0; 1; 2,0,
+keep_resource_adequacy_files,Indicate whether to keep (1) or delete (0) resource adequacy csv and h5 files after RA calculations finish,0; 1,0,
 keep_g00_files,Keep (1) or delete (0) .g00 files for completed solve years,0; 1,0,
 keep_run_terminal,"0=close run terminal, 1=keep run terminal open",0; 1,0,
 land_use_analysis,switch to turn on/off land-use analysis. Requires the `reeds_to_rev` switch also be activated,0; 1,0,
@@ -372,8 +375,8 @@ pras_max_unitsize_prm,Cap the upper bound of disaggregated unit size by zone at
 pras_samples,Indicate how many Monte Carlo samples to use in PRAS (if 0 then the .pras file is generated but PRAS is not run),int,100,
 pras_scheduled_outage,Include scheduled outages in PRAS (if 0 then a default outage rate of 0 is applied),0; 1,0,
 pras_seed,Seed to use for PRAS samples; can be any positive integer. If 0 the seed is set randomly,int,1,
-pras_singlethread,Force single-thread operation for PRAS (much slower but sometimes helps avoid Julia hangs and errors),^[0-1]$,0
-pras_trans_contingency,Indicate whether to use n-0 (0) or n-1 (1) transmission capacities in PRAS,0; 1,0,
+pras_singlethread,Force single-thread operation for PRAS (much slower but sometimes helps avoid Julia hangs and errors),^[0-1]$,0,
+pras_trans_contingency,Indicate whether to use energy (0) or PRM (1) transmission capacities in PRAS,0; 1,0,
 pras_unitsize_source,Data source for characteristic unit sizes in ReESD2PRAS,atb; r2x,atb,
 pras_vre_combine,Combine VRE into a single VRE tech in ReEDS2PRAS,0; 1,0,
 reeds_to_rev,switch to turn on/off ReV outputs,0; 1,1,
@@ -381,3 +384,4 @@ resource_adequacy_years,_-delimited years to include in resource adequacy calcul
 MCS_runs,Number of Monte Carlo simulation runs. Set to 0 to disable MCS. Set to an integer N > 0 to run N ReEDS simulations with sampling.,int,0,
 MCS_dist,Name of the `.yaml` file defining the MCS distributions. Use 'default' or a string beginning with 'user' to reference `inputs/userinput/mcs_distributions_{MCS_dist}.yaml`,N/A,default,
 MCS_dist_groups,Define the distribution groups to use in a Monte Carlo Simulation. Each entry should refer to a key from `inputs/userinput/mcs_distributions_{MCS_dist}.yaml` with multiple entries separated by a dot (e.g. Example1.Example2.Example3),N/A,tech,
+MCS_lhs,Option to use random sampling (0) or latin hypercube sampling (1) for Monte Carlo simulation,0; 1,0,
diff --git a/cases_examples.csv b/cases_examples.csv
index d30998827..f479e4ad0 100644
--- a/cases_examples.csv
+++ b/cases_examples.csv
@@ -6,7 +6,6 @@ GSw_ZoneSet,,,,,,,z69,z54,z54,z3109,PJMcounty,z3109
 GSw_GasCurve,,2,2,2,2,,,,,2,2,2
 GSw_HourlyType,,,wek,year,,,,,,,,
 GSw_OpRes,,,,0,,,,,0,,,
-GSw_TransSquiggliness,,,,,,,1.07,1.07,1.07,,,
 GSw_HourlyNumClusters,,,,,,20,,,20,,,
 GSw_PRM_CapCredit,,,,,1,,,,,,,
 GSw_StartCost,,,,,,,,,0,,,
diff --git a/cases_small.csv b/cases_small.csv
index 14ec14616..c4812e970 100644
--- a/cases_small.csv
+++ b/cases_small.csv
@@ -27,7 +27,7 @@ GSw_Refurb,0
 GSw_PRM_CapCredit,0
 GSw_Retire,0
 GSw_RGGI,0
-GSw_SkipAugurYear,2030
+GSw_SkipRAyear,2030
 GSw_StateCap,0
 GSw_StateRPS,0
 GSw_Storage,0
diff --git a/cases_standardscenarios.csv b/cases_standardscenarios.csv
index af208fd73..0f5e323d4 100644
--- a/cases_standardscenarios.csv
+++ b/cases_standardscenarios.csv
@@ -44,6 +44,6 @@ GSw_TransHurdleLevel2,Region whose hurdle rate rate2 is applied to (defined in c
 GSw_TransHurdleRate,Turn on / off hurdle rate,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 GSw_TransInvMaxLongTerm,[TW-mile/year] maximum transmission capacity investment per year IN/AFTER firstyear_trans_longterm (constraint is OFF if set to 0 meaning unlimited trans investment; annual max since 2009 is ~3.64),0,,,,,,,,,,,,,,1.07,,,,,,,,,,,,,,,,,,,,,,,,1.07,,,,,,,,,,,,,,,,,,,,,,,,,,1.07,,,,,,,,
 GSw_TransRestrict,hierarchy level within which to allow new transmission lines,country,,,,,,,,,,,,,,transreg,,,,,,,,,,,,,,,,,,,,,,,,transreg,,,,,,,,,,,,,,,,,,,,,,,,,,transreg,,,,,,,,
-GSw_TransScen,"Select Transmission Scenario [default, LCC_all, LCC_SeamsD3_certain, etc]",default,,,,,,,,,,,,,VSC_all,,,,,,,,,,,,,,,,,,,,,,,,VSC_all,,,,,,,,,,,,,,,,,,,,,,,,,,VSC_all,,,,,,,,,
+GSw_TransScen,"Select Transmission Scenario [default, LCC_all, LCC_SeamsD3_certain, etc]",default,,,,,,,,,,,,,NTP_MT,,,,,,,,,,,,,,,,,,,,,,,,NTP_MT,,,,,,,,,,,,,,,,,,,,,,,,,,NTP_MT,,,,,,,,,
 GSw_UpgradeCost_Mult,CCS upgrade cost scenario: 0=mid 1=adv 2=con,0,,,,,,,,,,,2,1,,,,,,,,,,,,,,,,,,,,,,,2,1,,,,,,,,,,,,,,,,,,,,,,,,,2,1,,,,,,,,,,
 GSw_Upgrades,Turn on/off upgrades,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
diff --git a/cases_test.csv b/cases_test.csv
index e97c4207a..a87de9670 100644
--- a/cases_test.csv
+++ b/cases_test.csv
@@ -1,62 +1,65 @@
-,Default Value,Pacific,USA_defaults,Mid_Case,USA_decarb,github_Pacific,github_Everything,github_MA_county_CC,Pacific_CC,Pacific_weks,Pacific_full_year,Interday_storage,Pacific_2020,Pacific_rep7,WY_county,WECC_county,PJM_county_CC,NYVT_mixed,OR_water,MonteCarlo,Everything,Simple,USA_fast,USA_faster,Pacific_DR,Pacific_MGA,Pacific_LoadSite95,MARICTNYNJPAOH_Offshore,R2P
-ignore,1,0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_Region,cendiv/Pacific,,country/USA,country/USA,country/USA,,st/ID.WY.NE.IA.IL,st/MA,,,,,,,st/WY,interconnect/western,transreg/PJM,st/NY.VT,st/OR,st/NE.NY.PA,st/ID.WY.NE.IA.IL,st/KS,country/USA,country/USA,,,,st/MA.RI.CT.NY.NJ.PA.OH,
-endyear,2032,,2050,2050,2050,2029,2060,2026,,,,,,,,,,,2035,2030,2060,2035,2050,2050,,,,,
-yearset,,,,,,,2010..2060..10,,,,,,,,,,,,,2010..2050..5,2010..2060..10,,,2010_2025_2050,,,,,
-GSw_ZoneSet,,,,,,,z54,z3109,,,,,,,z3109,z3109,z3109,PJMcounty,,,z54,,z54,z48,,,,,
-GSw_GasCurve,2,,1,1,,,,,,,,,,,,,,,,,,,1,1,,,,,
-GSw_Geothermal,,,,2,,,,,,,,,,,,,,,,,,0,,0,,,,,
-GSw_GrowthPenalties,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_Upstream,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_TransHurdleRate,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,
-distpvscen,,,,,stscen2023_mid_case_95_by_2035,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_AnnualCap,,,,,2,,1,,,,,,,,,,,,,,1,,,,,,,,
-GSw_AnnualCapScen,,,,,start2024_90pct2035_100pct2045,,start2027_95pct2035,,,,,,,,,,,,,,start2027_95pct2035,,,,,,,,
-GSw_LoadProfiles,,,,,EER2025_100by2050,EER2025_IRAlow,EER2025_IRAlow,EER2025_IRAlow,,,,,historic,,,,,,,,EER2025_100by2050,,,,historic,,,,
-GSw_NG_CRF_penalty,,,,,ramp_2045,,ramp_2023_2035,,,,,,,,,,,,,,ramp_2023_2035,,,,,,,,
-GSw_PRM_NetImportLimit,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_RetirePenalty,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,
-GSw_FakeData,,,,,,1,1,1,,,,,,,,,,,,,,,,,,,,,
-GSw_PRM_CapCredit,,,,,,,,1,1,,,,,,,,1,,,,,,,,,,,,
-GSw_PRM_scenario,,,,,,,,,static,,,,,,,,static,,,,,,,,,,,,
-GSw_HourlyType,,,,,,,,,,wek,year,,,,,,,,,,,,,,,,,,
-GSw_InterDayLinkage,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,
-GSw_HourlyWeatherYears,,,,,,,2012_2013,,,,,,2020,2007_2008_2009_2010_2011_2012_2013,,,,,,,2012_2013,,,,2018,,,,
-GSw_WaterCapacity,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,
-GSw_WaterMain,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,
-GSw_WaterUse,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,
-resource_adequacy_years,,,,,,,2011_2012_2013_2021_2022_2023,,,,,,,,,,,,,,2011_2012_2013_2021_2022_2023,,,,,,,,
-GSw_HourlyClusterAlgorithm,,,,,,,,,,,,,,,,,,,,user,,,,,,,,,
-MCS_runs,,,,,,,,,,,,,,,,,,,,2,,,,,,,,,
-MCS_dist_groups,,,,,,,,,,,,,,,,,,,,tech.hydro.nuclear.gas.coal.ng_fuel_price,,,,,,,,,
-GSw_PRM_StressIterateMax,,,,,,,,0,,,,,,,,0,0,,,1,,,,,,,,,
-GSw_ReducedResource,,,,,,,1,,,,,,,,,,,,,,1,,,,,,,,
-GSw_SitingUPV,,,,,,limited,limited,limited,,,,,,,,,,,,,limited,,,,,,,,
-GSw_SitingWindOfs,,,,,,limited,limited,limited,,,,,,,,,,,,,open,,,,,,,,
-GSw_SitingWindOns,,,,,,limited,limited,limited,,,,,,,,,,,,,limited,,,,,,,,
-GSw_TransScen,,,,,,,VSC_all,,,,,,,,,,,,,,VSC_all,,,,,,,,
-GSw_CO2_Detail,,,,,,,1,,,,,,,,,,,,,,1,,,,,,,,
-GSw_DAC,,,,,,,1,,,,,,,,,,,,,,1,,,,,,,,
-GSw_NoFossilOffsetCDR,,,,,,,1,,,,,,,,,,,,,,1,,,,,,,,
-GSw_Biopower,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
-GSw_HourlyChunkLengthRep,,,,,,,,,,,,,,,,,,,,,,6,4,4,,,,,
-GSw_HourlyChunkLengthStress,,,,,,,,,,,,,,,,,,,,,,6,4,4,,,,,
-GSw_LfillGas,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
-GSw_Nuclear,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
-GSw_OpRes,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
-GSw_StartCost,,,,,,,,,,,,,,,,,,,,,,0,0,0,,,,,
-GSw_H2,,,,,,,,,,,,,,,,,,,,,,,0,0,,,,,
-GSw_H2_PTC,,,,,,,,,,,,,,,,,,,,,,,0,0,,,,,
-GSw_H2Combustion,,,,,,,,,,,,,,,,,,,,,,,,0,,,,,
-GSw_DRShed,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,
-GSw_MGA_CostDelta,,,,,,,,,,,,,,,,,,,,,,,,,,0.01,,,
-GSw_LoadSiteCF,,,,,,,,,,,,,,,,,,,,,,,,,,,0.95,,
-GSw_OffshoreZones,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
-GSw_OffshoreBackbone,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
-GSw_OffshoreBackflow,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
-pras_agg_ogs_lfillgas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-pras_existing_unit_size,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
-pras_scheduled_outage,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
-pras_unitsize_source,,,,,,,,,,,,,,,,,,,,,,,,,,,,,r2x
-pras_vre_combine,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
-pras_samples,,,,,,10,10,10,,,,,,,,,,,,,,,10,10,,,,,
+,Default Value,Pacific,USA_defaults,Mid_Case,USA_decarb,github_Pacific,github_Everything,github_MA_county_CC,Pacific_CC,Pacific_weks,Pacific_full_year,Interday_storage,Pacific_2020,Pacific_rep15,WY_county,WECC_county,PJM_county_CC,NYVT_mixed,OR_water,MonteCarlo_Random,MonteCarlo_LHS,Everything,Simple,USA_fast,USA_faster,Pacific_DR,Pacific_MGA,Pacific_LoadSite95,MARICTNYNJPAOH_Offshore,R2P
+ignore,1,0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_Region,cendiv/Pacific,,country/USA,country/USA,country/USA,,st/ID.WY.NE.IA.IL,st/MA,,,,,,,st/WY,interconnect/western,transreg/PJM,st/NY.VT,st/OR,st/NE.NY.PA,st/NE.NY.PA,st/ID.WY.NE.IA.IL,st/KS,country/USA,country/USA,,,,st/MA.RI.CT.NY.NJ.PA.OH,
+endyear,2032,,2050,2050,2050,2029,2060,2026,,,,,,,,,,,2035,2030,2030,2060,2035,2050,2050,,,,,
+yearset,,,,,,,2010..2060..10,,,,,,,,,,,,,2010..2050..5,2010..2050..5,2010..2060..10,,,2010_2025_2050,,,,,
+GSw_ZoneSet,,,,,,,z54,z3109,,,,,,,z3109,z3109,z3109,PJMcounty,,,,z54,,z54,z48,,,,,
+GSw_GasCurve,2,,1,1,,,,,,,,,,,,,,,,,,,,1,1,,,,,
+GSw_Geothermal,,,,2,,,,,,,,,,,,,,,,,,,0,,0,,,,,
+GSw_GrowthPenalties,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_Upstream,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_TransHurdleRate,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,
+distpvscen,,,,,stscen2023_mid_case_95_by_2035,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_AnnualCap,,,,,2,,1,,,,,,,,,,,,,,,1,,,,,,,,
+GSw_AnnualCapScen,,,,,start2024_90pct2035_100pct2045,,start2027_95pct2035,,,,,,,,,,,,,,,start2027_95pct2035,,,,,,,,
+GSw_LoadProfiles,,,,,EER2025_100by2050,EER2025_IRAlow,EER2025_IRAlow,EER2025_IRAlow,,,,,historic,,,,,,,,,EER2025_100by2050,,,,historic,,,,
+GSw_NG_CRF_penalty,,,,,ramp_2045,,ramp_2023_2035,,,,,,,,,,,,,,,ramp_2023_2035,,,,,,,,
+GSw_PRM_NetImportLimit,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_RetirePenalty,,,,,0,,,,,,,,,,,,,,,,,,,,,,,,,
+GSw_FakeData,,,,,,1,1,1,,,,,,,,,,,,,,,,,,,,,,
+GSw_PRM_CapCredit,,,,,,,,1,1,,,,,,,,1,,,,,,,,,,,,,
+GSw_PRM_scenario,,,,,,,,,static,,,,,,,,static,,,,,,,,,,,,,
+GSw_PRM_UpdateMethod,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,
+GSw_HourlyType,,,,,,,,,,wek,year,,,,,,,,,,,,,,,,,,,
+GSw_InterDayLinkage,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,
+GSw_HourlyWeatherYears,,,,,,,2012_2013,,,,,,2020,2007_2008_2009_2010_2011_2012_2013_2016_2017_2018_2019_2020_2021_2022_2023,,,,,,,,2012_2013,,,,2018,,,,
+GSw_HourlyClusterMapMethod,,,,,,,,,,,,,,bestfirst,,,,,,,,,,,,,,,,
+GSw_WaterCapacity,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,
+GSw_WaterMain,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,
+GSw_WaterUse,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,
+resource_adequacy_years,,,,,,,2011_2012_2013_2021_2022_2023,,,,,,,,,,,,,,,2011_2012_2013_2021_2022_2023,,,,,,,,
+GSw_HourlyClusterAlgorithm,,,,,,,,,,,,,,,,,,,,user,user,,,,,,,,,
+MCS_runs,,,,,,,,,,,,,,,,,,,,2,2,,,,,,,,,
+MCS_dist_groups,,,,,,,,,,,,,,,,,,,,tech.hydro.nuclear.gas.coal.load_country,upv_tri.nuclear_tri.ng_fuel_price_tri.load_country_unif,,,,,,,,,
+MCS_lhs,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,
+GSw_PRM_StressIterateMax,,,,,,,,0,,,,,,,,0,0,,,1,1,,,,,,,,,
+GSw_ReducedResource,,,,,,,1,,,,,,,,,,,,,,,1,,,,,,,,
+GSw_SitingUPV,,,,,,limited,limited,limited,,,,,,,,,,,,,,limited,,,,,,,,
+GSw_SitingWindOfs,,,,,,limited,limited,limited,,,,,,,,,,,,,,open,,,,,,,,
+GSw_SitingWindOns,,,,,,limited,limited,limited,,,,,,,,,,,,,,limited,,,,,,,,
+GSw_TransScen,,,,,,,NTP_MT,,,,,,,,,,,,,,,NTP_MT,,,,,,,,
+GSw_CO2_Detail,,,,,,,1,,,,,,,,,,,,,,,1,,,,,,,,
+GSw_DAC,,,,,,,1,,,,,,,,,,,,,,,1,,,,,,,,
+GSw_NoFossilOffsetCDR,,,,,,,1,,,,,,,,,,,,,,,1,,,,,,,,
+GSw_Biopower,,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
+GSw_HourlyChunkLengthRep,,,,,,,,,,,,,,,,,,,,,,,6,4,4,,,,,
+GSw_HourlyChunkLengthStress,,,,,,,,,,,,,,,,,,,,,,,6,4,4,,,,,
+GSw_LfillGas,,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
+GSw_Nuclear,,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
+GSw_OpRes,,,,,,,,,,,,,,,,,,,,,,,0,,0,,,,,
+GSw_StartCost,,,,,,,,,,,,,,,,,,,,,,,0,0,0,,,,,
+GSw_H2,,,,,,,,,,,,,,,,,,,,,,,,0,0,,,,,
+GSw_H2_PTC,,,,,,,,,,,,,,,,,,,,,,,,0,0,,,,,
+GSw_H2Combustion,,,,,,,,,,,,,,,,,,,,,,,,,0,,,,,
+GSw_DRShed,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,
+GSw_MGA_CostDelta,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01,,,
+GSw_LoadSiteCF,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.95,,
+GSw_OffshoreZones,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
+GSw_OffshoreBackbone,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
+GSw_OffshoreBackflow,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,
+pras_agg_ogs_lfillgas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
+pras_existing_unit_size,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
+pras_scheduled_outage,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0
+pras_unitsize_source,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,r2x
+pras_vre_combine,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1
+pras_samples,,,,,,10,10,10,,,,,,,,,,,,,,,,10,10,,,,,
diff --git a/createmodel.gms b/createmodel.gms
deleted file mode 100644
index 36ab4603e..000000000
--- a/createmodel.gms
+++ /dev/null
@@ -1,5 +0,0 @@
-$include b_inputs.gms
-$include c_supplymodel.gms
-$include c_supplyobjective.gms
-$include c_mga.gms
-$include d_solveprep.gms
diff --git a/docs/source/documentation_tools/generate_markdown.py b/docs/scripts/generate_markdown.py
similarity index 84%
rename from docs/source/documentation_tools/generate_markdown.py
rename to docs/scripts/generate_markdown.py
index 8bbcf9ab5..0c3ea7e7a 100644
--- a/docs/source/documentation_tools/generate_markdown.py
+++ b/docs/scripts/generate_markdown.py
@@ -1,6 +1,9 @@
 #!/usr/bin/env python
 # coding: utf-8
 
+# Note: this script does not need to be run manually, 
+# sources.md is updated automatically when the documentation is built with `make html`
+
 # In[1]:
 
 import os
@@ -10,7 +13,7 @@
 
 def slugify(text: str) -> str:
     """
-    Convert a string to a stable anchor id for markdow.
+    Convert a string to a stable anchor id for markdown.
     Lowercase, replace spaces and slashes with hyphens, remove special characters except hyphens, and collapse multiple hyphens into one.
 
     Args:
@@ -86,9 +89,6 @@ def write_file_entries(data, main_file, file_entries, indent, githubURL):
                 if unit:
                     main_file.write(f"{indent}  - **Units:** {unit}\n\n")
 
-                
-                main_file.write("---\n\n")
-
 #Function to generate Table of Contents using folder hierarchy
 def write_folder_hierarchy(main_file, folder_hierarchy, depth=1, parent_folder=None):
     """
@@ -153,39 +153,44 @@ def write_folder_sections(data, main_file,folder_hierarchy, githubURL, depth=1,
             write_folder_sections(data, main_file, contents, githubURL, depth + 1, full_path)         
 
 
-def main():
+def main(app=None):
     """
     Main function to generate markdown documentation for ReEDS sources.
     Reads sources.csv, builds folder hierarchy, and writes a markdown file with file metadata and structure.
     """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--githubURL', '-g', type=str, default='', help='base github url')
-    parser.add_argument('--reedsPath', '-r', type=str, default='', help='path to reeds directory' )
-    args = parser.parse_args()
-    githubURL = args.githubURL
-    reedsPath = args.reedsPath
+    # When invoked by Sphinx (builder-inited), an app object is passed in.
+    # In that context, do not parse CLI args from sys.argv.
+    if app is None:
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--githubURL', '-g', type=str, default='', help='base github url')
+        parser.add_argument('--reedsPath', '-r', type=str, default='', help='path to reeds directory' )
+        args = parser.parse_args()
+        githubURL = args.githubURL
+        reedsPath = args.reedsPath
+    else:
+        githubURL = os.environ.get("https://github.com/reeds-model/reeds/", "")
+        reedsPath = ''
 
     #Conversion of latest version of sources.csv to markdown/readme format
 
     # Description holder file
     desc_holder = 'sources.csv'
-    desc_holder = desc_holder.replace("\\","/")
 
-
-    #Setting correct path to main ReEDS folder        
-    current_path = os.getcwd()
+    #Setting correct path to main ReEDS folder
     if reedsPath != '':
         reeds_path = reedsPath
+    elif app is not None and hasattr(app, "srcdir"):
+        reeds_path = os.path.dirname(os.path.dirname(app.srcdir))
     else: 
         reeds_path = os.path.dirname(os.path.dirname(os.path.dirname(current_path)))
         reeds_path = reeds_path.replace("\\","/")
 
+    reeds_docs_path = os.path.join(reeds_path, "docs")
                
-    desc_file_path = os.path.join(reeds_path, desc_holder).replace("\\","/")
-
+    desc_file_path = os.path.join(reeds_docs_path, desc_holder).replace("\\","/")
 
-    #Dataframe to store the newly generated sources.csv data
-    with open(desc_file_path, "r") as csv_file:
+    # Dataframe to store the newly generated sources.csv data
+    with open(desc_file_path, "r", newline="", encoding="utf-8") as csv_file:
         reader = csv.DictReader(csv_file)
         data = list(reader)
 
@@ -219,12 +224,13 @@ def main():
         current_folder["files"].append((file_name, file_ext, rel_file_path))
         
     #Generate separate readme for ReEDS 2.0 Sources files
-    main_readme_file = "sources_documentation.md"
-    main_readme_file_path = os.path.join(reeds_path, main_readme_file).replace("\\","/")
+    main_readme_file = "sources.md"
+    main_readme_file_path = os.path.join(reeds_docs_path, "source", main_readme_file).replace("\\","/")
 
     #Open markdown file for entries
-    with open(main_readme_file_path, "w") as main_file:
-        main_file.write("## Table of Contents\n\n")    
+    with open(main_readme_file_path, "w", encoding="utf-8") as main_file:
+        main_file.write("# Sources Documentation\n")
+        main_file.write("## Table of Contents\n")    
 
                             
         main_file.write("\n")    
@@ -243,7 +249,7 @@ def main():
             main_file.write("\n## Files \n\n")
             write_file_entries(data, main_file, folder_hierarchy["files"], "", githubURL)
 
-    print("Sources Documentation Markdown file has been generated!")
+    print("sources.md has been updated!")
 
 if __name__ == "__main__":
     main()
diff --git a/docs/source/conf.py b/docs/source/conf.py
index c68f998ba..c457ebc99 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,5 +1,8 @@
 import datetime
 import os
+import sys
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "scripts"))
+import generate_markdown
 
 # Configuration file for the Sphinx documentation builder.
 #
@@ -13,7 +16,11 @@
 copyright = "2024, NREL"
 author = "NLR"
 
-# -- General configuration ---------------------------------------------------
+# --setup function to run generate_markdown.py on 'make html' ---------------------------------
+def setup(app):
+    app.connect("builder-inited", generate_markdown.main)
+
+# -- General configuration ----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = ["myst_parser", "sphinxcontrib.bibtex", "sphinx_design"]
@@ -32,15 +39,10 @@
 # get GAMSLICE from environment variable
 gamslice_secret = os.getenv("GAMSLICE", "")
 
-# get BASE_URL from environment variable
-base_url = os.environ.get("BASE_URL", "https://github.com/ReEDS-Model/ReEDS")
-github_releases_url = base_url + "/releases"
 
 myst_enable_extensions = ["substitution", "dollarmath", "html_image"]
 
 myst_substitutions = {
-    "base_github_url": base_url,
-    "github_releases_url": github_releases_url,
     "gamslice": gamslice_secret,
     "cite_date_last_updated": datetime.date.today().strftime("%Y, %B"),
 }
@@ -58,10 +60,6 @@
 
 html_theme_options = {"navigation_depth": 3, "logo_only": True}
 
-html_context = {
-    "base_url": base_url,
-}
-
 html_logo = "_static/reeds-logo-white.png"
 
 html_favicon = "_static/reeds-icon.png"
diff --git a/docs/source/developer_best_practices.md b/docs/source/developer_best_practices.md
index 9547bc02f..a86d254cd 100644
--- a/docs/source/developer_best_practices.md
+++ b/docs/source/developer_best_practices.md
@@ -3,7 +3,7 @@
 
 The following naming, rounding, and coding conventions apply to all new code contributed to ReEDS. Because these conventions were established after initial development, you may notice inconsistencies in the existing codebase--that's expected. The goal is consistency going forward, not comprehensiveness.
 
-Using the Ruff Python linter is recommended to improve code quality. To get started with Ruff, see the guide on [Installing Ruff](https://docs.astral.sh/ruff/installation/). Once installed, you can check for errors using the following command from the base ReEDS-2.0 directory: `ruff check`. If you need more information on a specific error, see the [Ruff Rules](https://docs.astral.sh/ruff/rules/).
+Using the Ruff Python linter is recommended to improve code quality. To get started with Ruff, see the guide on [Installing Ruff](https://docs.astral.sh/ruff/installation/). Once installed, you can check for errors using the following command from the base ReEDS directory: `ruff check`. If you need more information on a specific error, see the [Ruff Rules](https://docs.astral.sh/ruff/rules/).
 
 Since we have not yet adopted strict formatting guidelines, do not make code *formatting* changes to existing scripts using Ruff; use only the linter.
 
@@ -17,7 +17,7 @@ Since we have not yet adopted strict formatting guidelines, do not make code *fo
 -------------| --------------- | ------------ |
 Folders | lowercase | `inputs`, `docs` | 
 Files | typically lowercase with underscores (acronyms often left as uppercase); output files noun first |  `battery_ATB_2024_moderate.csv`, `gen_ann` not `ann_gen` |
-GAMS Files | letter-underscore prefix by category, alpha-ordered; numbering can help communicate ordering when multiple files share a category | `d1_financials.gms`, `d2_varfix.gms` |
+GAMS Files | letter-underscore prefix by category, alpha-ordered; numbering can help communicate ordering when multiple files share a category | `2_financials.gms`, `5_varfix.gms` |
 Parameters | lowercase with underscores, noun first; costs prefixed with "cost" | `curt_marg` not `marg_curt`, `cost_cap` |
 Variables | all caps, noun first | `INV`, `INV_TRANS` |
 Equations (model constraints) | prefixed with `eq_`, lowercase with underscores | `eq_reserve_margin` |
@@ -107,7 +107,7 @@ _Some exceptions to this might exist due to number scaling (e.g., emission rates
   * GAMS functions such as sum, max, smax, etc. should use {}; Example: avg_outage(i) = sum{h,hours(h)*outage(i,h)} / 8760 ;
   * When including the semicolon on the end of a line there should be a space between the semicolon and the last character of the line (see previous example)
   * When using `/ /` for a parameter declaration, place the closing semicolon on the same line as the final slash: `/ ;`
-  * Sums outside of equations (e.g., in e_reports) need not be split over multiple lines if they do not exceed the line limit
+  * Sums outside of equations (e.g., in `report.gms`) need not be split over multiple lines if they do not exceed the line limit
   * Do not use hard-coded numbers in equations or calculations. Values should be assigned to an appropriate parameter name that is subsequently used in the code.
   * Large input data tables should be loaded from individual data files for each table, preferably in *.csv format. Large data tables should not be manually written into the code but can be written dynamically by scripts or inserted with a $include statement.
   * Compile-time conditionals should always use a tag (period + tag name) to clearly define the relationships between compile-time conditional statements. Failure to do so hurts readability sometimes leads to compilation errors. Example:
@@ -162,21 +162,22 @@ _Some exceptions to this might exist due to number scaling (e.g., emission rates
 
 * If preprocessing is needed to create an input file that is placed in the ReEDS repository, the preprocessing scripts or workbooks should be included in the [ReEDS_Input_Processing repository](https://github.com/ReEDS-Model/ReEDS_Input_Processing). Data from external sources should be downloaded programmatically when possible.
 
-* Any scripts that preprocess data after a ReEDS run is started should be placed in the input_processing folder.
+* Any scripts that preprocess data after a ReEDS run is started should be placed in the `reeds/input_processing` folder.
   * Input processing scripts should start with a block of descriptive comments describing the purpose and methodology, and internal functions should use docstrings and liberal comments on functionality and assumptions.
 
 * Any costs read into b_inputs should already be in 2004$.  Cost adjustments in preprocessing scripts should rely on the deflator.csv file rather than have hard-coded conversions.
 
 * In general, if inputs require calculations before they are ingested into b_inputs, those calculations should be done in Python rather than in GAMS. GAMS can be used for calculations where the GAMS syntax simplifies the calculation or where upstream dependencies make it challenging for the calculations to happen in Python preprocessing scripts.
 
-* In Python, file paths should be added using os.path.join() rather than writing out the filepath with slashes.
+* In Python, file paths should be specified using `from pathlib import Path` and `Path(reeds.io.reeds_path, 'foldername', 'maybe_more_foldernames', 'filename.extension')` instead of writing out the filepath as a string with slashes.
+Use absolute filepaths whenever possible.
 
 * Data column headers should use the ReEDS set names when practical. 
   * Example: data that include regions should use "r" for the column name rather than "ba", "reeds_ba", or "region".
 
 * Preprocessing scripts in input_processing should not change the working directory or use relative filepaths; absolute filepaths should be used wherever possible.
 
-* When feasible, inputs used in the objective function (c_supplyobjective.gms) should be included in tests/objective_function_params.yaml. Inputs included in this .yaml file will be checked for missing values using input_processing/check_inputs.py.
+* When feasible, inputs used in the objective function (`d_objective.gms`) should be included in tests/objective_function_params.yaml. Inputs included in this .yaml file will be checked for missing values using input_processing/check_inputs.py.
 
 #### Input Data
 
@@ -227,7 +228,7 @@ The goal is to keep ReEDS as lightweight as possible to facilitate faster clonin
 
 ## Version Control and Testing
 ### ReEDS Versioning & Releases
-This section outlines the current ReEDS approach to versioning. You can find current and past ReEDS versions here: {{ '[ReEDS-2.0 Releases]({}/releases)'.format(base_github_url) }}
+This section outlines the current ReEDS approach to versioning. You can find current and past ReEDS versions here: '[ReEDS Releases](https://github.com/reeds-model/reeds/releases)
 
 #### Versioning overview
 
@@ -298,11 +299,6 @@ Lastly, comparison reports are created for the USA scenario to compare the curre
 
 ## Documentation Guidelines
 
-When making changes to ReEDS, you should generate and update the sources.csv and sources_documentation.md files before merging.
-
-```{include} documentation_tools/README.md
-```
-
 ### Updating the ReEDS Documentation
 
 The ReEDS documentation lives in the "docs/source" folder within the repo. Depending on the changes you're making to the model, please update the documentation here accordingly. See the [Getting Started with Markdown in VSCode](markdown_cheat_sheet.md) if you're unfamiliar with markdown.
@@ -314,6 +310,8 @@ To edit the ReEDS documentation:
 3. Commit and push your changes as you normally would.
    - **When your pull request gets merged into main, there is a github action that will automatically recompile the documentation with your changes and publish the updated site.**
 
+Make sure to update the `docs/sources.csv` file when applicable.
+
 ### Viewing the Documentation Locally
 
 If you would like to see what the documentation will look like when developing locally, there are two approaches:
@@ -332,7 +330,7 @@ If you would like to see what the documentation will look like when developing l
 2. Run the command `make html` to build the documentation locally
    - Ensure you have the 'reeds2' environment activated
   
-3. Open `/ReEDS-2.0/docs/build/html/index.html` to view the documentation.
+3. Open `/ReEDS/docs/build/html/index.html` to view the documentation.
    - If you make changes and wish to see how they are reflected in the documentation, you can run the `make html` command again and refresh the window you already have open
 
 4. If you would like to remove the generated html files, you can run the command `make clean` from the "docs/" folder
@@ -348,7 +346,7 @@ If you would like to see what the documentation will look like when developing l
 2. Request access to the ReEDS Zotero library from Brian, Patrick, or Wesley
 
 3. If you add any new references to the ReEDS Zotero library and cite them in the ReEDS docs, you'll need to:
-   a. Replace the contents of ReEDS-2.0/docs/source/references.bib with the contents of the .bib file at {your path}. Make sure to export your .bib file in a "Better BibTex" format.
+   a. Replace the contents of ReEDS/docs/source/references.bib with the contents of the .bib file at {your path}. Make sure to export your .bib file in a "Better BibTex" format.
 
 
 To add an in-text citation, find the citation key of the citation you would like to add in Zotero.
@@ -596,8 +594,8 @@ When using the python debugger, you will need to set a configuration. Here's an
             "program": "${file}",
             "console": "integratedTerminal",
             "args": [
-                "/Users/kminderm/ReEDS-2.0",
-                "/Users/kminderm/ReEDS-2.0/runs/main_Pacific/inputs_case"
+                "/Users/kminderm/ReEDS",
+                "/Users/kminderm/ReEDS/runs/main_Pacific/inputs_case"
             ],
             "purpose": ["debug-in-terminal"]
         },
@@ -629,7 +627,7 @@ Additionally, if you want to re-run a given scenario without having to run all o
 * To avoid the prompts when kicking off a run, you can use the command line arguments: 
    * The following example runs the scenarios in cases_test.csv with the batch name '20240717_test'. The '-r -1' means that all cases will run simultaneously.  
    ```
-   python runbatch.py -c test -b 20240717_test -r -1
+   python runreeds.py -c test -b 20240717_test -r -1
    ```
     * All options for command line arguments that can be used: 
         | Flag | |
diff --git a/docs/source/documentation_tools/README.md b/docs/source/documentation_tools/README.md
deleted file mode 100644
index 07edd3f7c..000000000
--- a/docs/source/documentation_tools/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### How to Use Sources Documentation
-
-1. Before running the .bat script, please ensure the sources.csv file is closed. If open, the script will be unable to replace the file and will throw an error.
-2. Run **generate_sources_md_file.bat** script (for Mac and Linux users **generate_sources_md_file.sh**)located within the documentation_tools folder (ReEDS-2.0/docs/source/documentation_tools). You will need navigate to that directory prior to running.
-3. This will run the first script **generate_new_sources.py**. generating a new **sources.csv** file at the top directory of the repository, please note,the existing sources.csv in your Repository root will be renamed to sources_{timestamp}.csv format. This can be deleted manually if no longer required; or can be held on to if required for comparison. Tree change files are generated in the documentations_tools folder to indicate files not included in the prior sources file (**sources_files_added.csv**), files removed from the prior sources file (**sources_files_deleted.csv**), and files not included in the sources file because they aren't committed (**sources_untracked_files.csv**). These change files should not be committed and can be deleted when no longer needed.
-4. Once this has finished running, please proceed to update relevant fields in the *sources.csv* file
-5. Once relevant fields have been updated, please save sources.csv and close it.
-6. Run **generate_markdown.bat** (for Mac and Linux users **generate_markdown.sh**)located within the documentation_tools folder. This will generate a README file *sources_documentation.md* with all the Source files and their details for the Repository by running the script **generate_markdown.py**. The markdown file will be generated in the ReEDS-2.0/docs/source/ location.
-7. Commit and push the updated **sources.csv** and **sources_documentation.md** files.
----
-
-#### How to Update Relevant Fields in sources.csv
-1. Once prompted by the .bat script, open **sources.csv** (found at the Repository root).
-2. Using the Added Files List, **sources_files_added.csv** (found within the documentation_tools folder) which displays all the input files added by the user, enter relevant details in corresponding columns of **sources.csv**. Fields that do not apply can be left blank. Do not add new columns to sources.csv without also updating the scripts to support the expanded fields.
-3. Save the sources.csv and close the file.
diff --git a/docs/source/documentation_tools/generate_markdown.bat b/docs/source/documentation_tools/generate_markdown.bat
deleted file mode 100644
index 4941604d1..000000000
--- a/docs/source/documentation_tools/generate_markdown.bat
+++ /dev/null
@@ -1 +0,0 @@
-python generate_markdown.py
\ No newline at end of file
diff --git a/docs/source/documentation_tools/generate_markdown.sh b/docs/source/documentation_tools/generate_markdown.sh
deleted file mode 100644
index f7f7fb2d4..000000000
--- a/docs/source/documentation_tools/generate_markdown.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/sh
-python generate_markdown.py
\ No newline at end of file
diff --git a/docs/source/documentation_tools/generate_new_sources.py b/docs/source/documentation_tools/generate_new_sources.py
deleted file mode 100644
index b2552cf96..000000000
--- a/docs/source/documentation_tools/generate_new_sources.py
+++ /dev/null
@@ -1,248 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-#author: akarmaka
-#This file processes the existing sources.csv file from the repo to check for any changes 
-# and generates a new version of sources.csv containing details about each input and script files
-
-import os
-import csv
-import pandas as pd
-from datetime import datetime
-import re
-
-timestamp = datetime.now().strftime("%Y%m%d%H%M")
-
-
-# Description holder file
-desc_holder = 'sources.csv'
-desc_holder = desc_holder.replace("\\","/")
-
-
-#Setting correct path to main ReEDS folder        
-current_path = os.getcwd()
-reeds_path = os.path.dirname(os.path.dirname(os.path.dirname(current_path)))
-reeds_path = reeds_path.replace("\\","/")
-#current_path = current_path.replace("/postprocessing/documentation_tools","")
-
-
-#dir_path = current_path.replace("\\","/")
-#Descriptor file path            
-desc_file_path = os.path.join(reeds_path, desc_holder).replace("\\","/")
-
-
-
-#file extensions dictionary to filter for
-extensions = ['.csv', '.h5', '.xlsx', 'csv.gz'] #['.csv', '.py', 'csv.gz', '.gms', '.h5', '.r']
-
-#folders to be excluded for source.csv population
-omit_folders = ['.git', '.github', 'runs']
-
-#Temp files to be ignored
-ignore_files = ['sources_interim', 'sources_{timestamp}', 'sources_files_added', 'sources_files_deleted', 'sources_untracked_files', 'local_sources_files_added']
-timestamp_pattern = re.compile(r'^sources_\d{12}$')
-
-
-
-descriptions = {}
-indices = {}
-citations = {}
-dollar_year = {}
-filetypes = {}
-units = {}
-
-        
-#Use for reading sources.csv if re-running the script!        
-with open(desc_file_path, "r") as csv_file:
-    read = csv.DictReader(csv_file)
-    for row in read:
-        file_loc = row["FileName_new"] 
-        ext = row["FileExtension"]
-        rel_path = row["RelativeFilePath"]
-        #full_path = row["FullFilePath"]
-        description = row["Description_new"]
-        index = row["Indices"]
-        dollar_yr = row["DollarYear"]
-        citation = row["Citation"]
-        filetype = row["Filetype"]
-        unit = row["Units"]
-
-        descriptions[rel_path] = description
-        indices[rel_path] = index
-        citations[rel_path] = citation
-        dollar_year[rel_path] = dollar_yr
-        filetypes[rel_path] = filetype
-        units[rel_path] = unit
-
-
-
-#Saving the latest sources.csv for the interim
-interim_sources_csv = "sources_interim.csv"
-interim_sources_csv_path = os.path.join(reeds_path, interim_sources_csv).replace("\\","/")
-
-
-#Open csv file to store new sources.csv data
-with open(interim_sources_csv_path, "w", newline="") as csv_file:
-    writer = csv.writer(csv_file)
-    
-    #Specifying headers
-    writer.writerow(["RelativeFilePath", "RelativeFolderPath", "FileName_new", "FileExtension", "Description_new", "Indices", "DollarYear", "Citation", "Filetype", "Units"])
-    
-    data = []
-    #To navigate entire directory and subdirectories within it
-    for root, dirs, files in os.walk(reeds_path, topdown=True):
-        
-        dirs[:] = [d for d in dirs if d not in omit_folders]
-
-        files.sort(key=str.casefold)
-        
-        for file_name in files:
-            
-            #Store file paths
-            file_path = os.path.join(root, file_name)
-            file_path = file_path.replace("\\", "/")
-                       
-            #Store relative paths
-            relative_path = os.path.relpath(file_path, reeds_path)
-            relative_path = "\\"+ relative_path
-            relative_path = relative_path.replace("\\", "/")
-            
-            rel_folder_path = os.path.dirname(relative_path).replace("\\", "/")
-
-            #Store file names and their respective extensions
-            file_name, file_ext = os.path.splitext(file_name)
-
-            if file_name in ignore_files or timestamp_pattern.match(file_name):
-                continue
-
-            #Filter files based on extensions present in our dictionary
-            if file_ext in extensions:
-                
-                description = descriptions.get(relative_path, '')
-                index = indices.get(relative_path, '')
-                
-                citation = citations.get(relative_path, '')
-                dollar_yr = dollar_year.get(relative_path, '')
-                filetype = filetypes.get(relative_path, '')
-                unit = units.get(relative_path, '')
-
-                data.append({"RelativeFilePath" : relative_path, "RelativeFolderPath": rel_folder_path, "FileName_new": file_name, "FileExtension": file_ext, "Description_new": description, "Index": index, "DollarYear": dollar_yr, "Citation": citation, "Filetype": filetype, "Units": unit})
-
-    sorted_data = sorted(data, key=lambda x: x["RelativeFilePath"].casefold())
-
-    for row in sorted_data:
-        relative_file_path = row["RelativeFilePath"]
-        relative_folder_path = row["RelativeFolderPath"]
-        file_name = row["FileName_new"]
-        file_ext = row["FileExtension"]
-        description = row["Description_new"]
-        index = row["Index"]
-        dollar_yr = row["DollarYear"]
-        citation = row["Citation"]
-        filetype = row["Filetype"]
-        unit = row["Units"]
-
-        writer.writerow([relative_file_path, relative_folder_path, file_name, file_ext, description, index, dollar_yr, citation, filetype, unit])
-            
-
-            
-#Function to compare the 2 csv files to generate an added and deleted files list
-def compare_sources_csv(old_csv_file, new_csv_file): #, tracking_file):
-    with open(old_csv_file, 'r') as file:
-        old_csv = csv.DictReader(file)
-        old_files = [row['RelativeFilePath'] for row in old_csv]
-        
-    with open(new_csv_file, 'r') as file:
-        new_csv = csv.DictReader(file)
-        new_files = [row['RelativeFilePath'] for row in new_csv]
-
-         
-        added_files = set(new_files) - set(old_files)
-        # tracked_and_added_files = added_files.intersection(tracked_files)
-
-        
-        deleted_files = set(old_files) - set(new_files)
-        # tracked_and_deleted_files = set(deleted_files) - set(tracked_files)
-        
-        with open('local_sources_files_added.csv', 'w', newline='') as file:
-            writer = csv.writer(file)
-            writer.writerow(['RelativeFilePath'])
-            writer.writerows([[file_path] for file_path in added_files])
-            
-        with open('sources_files_deleted.csv', 'w', newline='') as file:
-            writer = csv.writer(file)
-            writer.writerow(['RelativeFilePath'])
-            writer.writerows([[file_path] for file_path in deleted_files])
-            
-#Call the compare function which compares local versions of old vs new sources.csv
-compare_sources_csv(desc_file_path, interim_sources_csv_path)
-
-
-#Read the tracked files list which contains all the files committed to the branch
-tracked_file = "tracked_files_list.txt"
-tracked_file_path = os.path.join(reeds_path, tracked_file).replace("\\","/")
-
-df_tracked_file_list = pd.read_csv(tracked_file_path, header=None, names=['RelativeFilePath'])
-df_tracked_file_list['RelativeFilePath'] = df_tracked_file_list['RelativeFilePath'].str.replace('\\', '/', regex=False)
-df_tracked_file_list['RelativeFilePath'] = "/" + df_tracked_file_list['RelativeFilePath']
-
-df_sources_added_file = pd.read_csv("local_sources_files_added.csv", header=0)
-
-#Function to compare local version of files added and the files committed
-def compare_commited_files(tracked_sources_added, tracked_list):
-    
-    #Comparison to generate added and tracked files (tracked = committed)
-    df_added_tracked_files = pd.merge(tracked_sources_added, tracked_list, on='RelativeFilePath', how='inner')
-    df_added_tracked_files.to_csv('sources_files_added.csv', sep=',', encoding='utf-8', index=False)
-
-    #Comparison to generate untracked files
-    df_untracked_files = tracked_sources_added[~tracked_sources_added['RelativeFilePath'].isin(tracked_list['RelativeFilePath'])]
-    df_untracked_files.to_csv('sources_untracked_files.csv', sep=',', encoding='utf-8', index=False)
-    
-
-#Call function to compare local vs committed versions of files
-compare_commited_files(df_sources_added_file, df_tracked_file_list)
-
-
-
-#Function to rename files
-def rename_file(reeds_path, old_sources_csv, new_sources_csv):
-    old_file_path = os.path.join(reeds_path, old_sources_csv).replace("\\", "/")
-    
-    new_file_path = os.path.join(reeds_path, new_sources_csv).replace("\\", "/")
-    
-    os.rename(old_file_path, new_file_path)
-    
-    
-    
-#Old sources csv is the original sources.csv file
-old_sources_csv = "sources.csv"
-#Adding a timestamp for log purposes and to compare with the latest sources.csv if needed
-new_sources_csv = f"sources_{timestamp}.csv"
-
-#Renaming original sources to sources file with a timestamp; to be kept for comparison if needed
-rename_file(reeds_path, old_sources_csv, new_sources_csv)
-
-#Renaming latest sources.csv to take the place of the original sources.csv
-rename_file(reeds_path, interim_sources_csv, old_sources_csv)
-
-
-#Removing the tracked files list generated by git comamnd
-os.remove(tracked_file_path)
-
-
-
-
-print("Added & Deleted Files list generated")
-
-print("New Sources File list generated")
-
-
-# In[ ]:
-
-
-
-
diff --git a/docs/source/documentation_tools/generate_sources_md_file.bat b/docs/source/documentation_tools/generate_sources_md_file.bat
deleted file mode 100644
index 5245fe80e..000000000
--- a/docs/source/documentation_tools/generate_sources_md_file.bat
+++ /dev/null
@@ -1,7 +0,0 @@
-FOR /F "tokens=*" %%g IN ('git rev-parse --abbrev-ref HEAD') do (SET VAR=%%g)
-cd ..
-cd ..
-cd ..
-git ls-tree -r %VAR% --name-only > tracked_files_list.txt
-cd docs\source\documentation_tools
-python generate_new_sources.py
\ No newline at end of file
diff --git a/docs/source/documentation_tools/generate_sources_md_file.sh b/docs/source/documentation_tools/generate_sources_md_file.sh
deleted file mode 100644
index 2df4b886a..000000000
--- a/docs/source/documentation_tools/generate_sources_md_file.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/sh
-branch=$(git rev-parse --abbrev-ref HEAD)
-cd ..
-cd ..
-cd ..
-git ls-tree -r $branch --name-only > tracked_files_list.txt
-cd docs/source/documentation_tools
-python generate_new_sources.py
\ No newline at end of file
diff --git a/docs/source/faq.md b/docs/source/faq.md
index 445771a37..584a9a4a6 100644
--- a/docs/source/faq.md
+++ b/docs/source/faq.md
@@ -6,6 +6,7 @@
   - [Table of Contents](#table-of-contents)
     - [How much are the GAMS licensing fees?](#how-much-are-the-gams-licensing-fees)
     - [Is there a trial version of the GAMS license so that I can test ReEDS?](#is-there-a-trial-version-of-the-gams-license-so-that-i-can-test-reeds)
+    - [What if the GAMS community license isn't enough to run my ReEDS case?](#what-if-the-gams-community-license-isnt-enough-to-run-my-reeds-case)
     - [What computer hardware is necessary to run ReEDS?](#what-computer-hardware-is-necessary-to-run-reeds)
     - [Can I configure a ReEDS case to run as an isolated interconnect?](#can-i-configure-a-reeds-case-to-run-as-an-isolated-interconnect)
     - [How do I change the spatial resolution of a ReEDS case?](#how-do-i-change-the-spatial-resolution-of-a-reeds-case)
@@ -26,6 +27,12 @@ Please contact GAMS for more information.
 
 We have created a reduced size version of the ReEDS model that has less than 5,000 rows and columns, and therefore should be compatible with the GAMS community license ([https://www.gams.com/try_gams/](https://www.gams.com/try_gams/) -- Please contact GAMS if you need additional information regarding the community license). You can run this reduced model version by using the cases_small.csv input file. This reduced model uses a smaller technology subset, smaller geographic extent, and simplifies several model constraints.
 
+### What if the GAMS community license isn't enough to run my ReEDS case?
+
+If you'd like to test ReEDS without the community license row/column limit, GAMS offers time-limited evaluation licenses. To request an evaluation license, [email GAMS](mailto:sales@gams.com?subject=Request%20for%20Evaluation%20License&body=Hello%2C%20%0A%0AI'd%20like%20to%20request%20an%20evaluation%20license%20for%20GAMS.%20I%20plan%20to%20use%20it%20to%20run%20the%20ReEDS.%20%0A%0AContact%20information%3A%20%0A-%20Full%20name%3A%20%0A-%20Company%3A%0A-%20Department%3A%20%0A-%20Full%20postal%20address%3A%0A-%20Country%3A%0A%0ALicense%20information%20(select%20one)%3A%20%0A-%20%5B%20%5D%20Single%20user%20-%20one%20named%20individual%0A-%20%5B%20%5D%20Shared%20team%20-%20N%20concurrent%20sessions%2C%20shared%20across%20a%20group%0A-%20%5B%20%5D%20Server%2Fmachine%20-%20tied%20to%20a%20specific%20machine%20(e.g.%20for%20batch%2FHPC%20use)%0A%0ANumber%20of%20users%2Fmachines%3A%20%0ANumber%20of%20cores%20on%20the%20machine%20that%20you%E2%80%99ll%20be%20using%3A%0ARequired%20solvers%3A%20GAMS%2FCPLEX%0A%0AAny%20additional%20comments%20regarding%20your%20setup%3A%20%0A%0AThank%20you%2C%20%0A%5BYour%20Name%5D).
+
+To see more about the different GAMS licenses, see their [general licensing information page](https://www.gams.com/sales/licensing/).
+
 ### What computer hardware is necessary to run ReEDS?
 
 Running ReEDS using the reference case (located in cases.csv) is feasible on a laptop with 32GB of RAM. However, for running ReEDS with enhanced features such as explicit H<sub>2</sub> modeling, CO<sub>2</sub> networks, increased temporal/spatial resolution, etc., it is recommended to utilize a higher-performance workstation or High-Performance Computing (HPC) machine.
@@ -80,7 +87,7 @@ If you'd like to reduce the model solve time, consider making some of the follow
 
 Every year we target June 1 for the bulk of model changes to be completed, which allows us to meet the hard deadline for having a working, updated version of the model by June 30. We typically make minor updates to the model over the summer and tag a final version for that year in August or September. This version is then used to produce the Standard Scenarios and Cambium data products.
 
-Additionally, changes are made throughout the year and a new version is created and published roughly every month. You can find current and past ReEDS versions here: {{ '[ReEDS-2.0 Releases]({}/releases)'.format(base_github_url) }}
+Additionally, changes are made throughout the year and a new version is created and published roughly every month. You can find current and past ReEDS versions here: '[ReEDS Releases](https://github.com/reeds-model/reeds/releases)
 
 If you would like to run ReEDS with a previous version, you can either download the source code directly or check out that version using the tag.
 
diff --git a/docs/source/figs/docs/input-fuel-price-assumptions.png b/docs/source/figs/docs/input-fuel-price-assumptions.png
index e7f11dbd2..ff78ea7a4 100644
--- a/docs/source/figs/docs/input-fuel-price-assumptions.png
+++ b/docs/source/figs/docs/input-fuel-price-assumptions.png
@@ -1,3 +1,3 @@
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+oid sha256:d08a78cb84884a3f1feca173cdbf384e9987fc7b393dac55c7b8ed79e4ebe727
+size 71406
diff --git a/docs/source/figs/docs/interconnection-cost-distribution.png b/docs/source/figs/docs/interconnection-cost-distribution.png
deleted file mode 100644
index d2202fe97..000000000
--- a/docs/source/figs/docs/interconnection-cost-distribution.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/docs/source/figs/docs/lcp-p134.png b/docs/source/figs/docs/lcp-p134.png
deleted file mode 100644
index e0e8f6203..000000000
--- a/docs/source/figs/docs/lcp-p134.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/docs/source/figs/docs/local-generation-interconnection-components.png b/docs/source/figs/docs/local-generation-interconnection-components.png
index ce8b5effa..fc22317d5 100644
--- a/docs/source/figs/docs/local-generation-interconnection-components.png
+++ b/docs/source/figs/docs/local-generation-interconnection-components.png
@@ -1,3 +1,3 @@
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+oid sha256:fa4a399a49be66024f86692a7fc50821425ee259b4f9a3c285fe5c62992f0c25
+size 871554
diff --git a/docs/source/figs/docs/new-ac-transmission-cost-assumptions.png b/docs/source/figs/docs/new-ac-transmission-cost-assumptions.png
deleted file mode 100644
index 33161c0b7..000000000
--- a/docs/source/figs/docs/new-ac-transmission-cost-assumptions.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/docs/source/figs/docs/supplycurve-cost.png b/docs/source/figs/docs/supplycurve-cost.png
index 9450f36e5..6d2c9592a 100644
--- a/docs/source/figs/docs/supplycurve-cost.png
+++ b/docs/source/figs/docs/supplycurve-cost.png
@@ -1,3 +1,3 @@
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-oid sha256:cbb3c10b834d53a7181237ed372956e43867f3551f84bcfa02fc3a3c4ceedb00
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+oid sha256:72c3c1ae14cec079f37d2ad8048bfb2b148ec6b9b93cb6ba2bc1efda4bb49501
+size 1361007
diff --git a/docs/source/figs/docs/transmission-cost-ac.png b/docs/source/figs/docs/transmission-cost-ac.png
new file mode 100644
index 000000000..922b8de91
--- /dev/null
+++ b/docs/source/figs/docs/transmission-cost-ac.png
@@ -0,0 +1,3 @@
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+size 1880028
diff --git a/docs/source/figs/docs/transmission-cost-input-data.png b/docs/source/figs/docs/transmission-cost-input-data.png
deleted file mode 100644
index 456407288..000000000
--- a/docs/source/figs/docs/transmission-cost-input-data.png
+++ /dev/null
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diff --git a/docs/source/figs/docs/transmission-hvdc_b2b.png b/docs/source/figs/docs/transmission-hvdc_b2b.png
new file mode 100644
index 000000000..3b7e28551
--- /dev/null
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@@ -0,0 +1,3 @@
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diff --git a/docs/source/figs/docs/transmission-land-terrain.png b/docs/source/figs/docs/transmission-land-terrain.png
new file mode 100644
index 000000000..aaaa85d1f
--- /dev/null
+++ b/docs/source/figs/docs/transmission-land-terrain.png
@@ -0,0 +1,3 @@
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diff --git a/docs/source/figs/readme/anaconda-intel.png b/docs/source/figs/readme/anaconda-intel.png
deleted file mode 100644
index 895f9cbc5..000000000
--- a/docs/source/figs/readme/anaconda-intel.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/docs/source/model_documentation.md b/docs/source/model_documentation.md
index 54b58d3c5..0d4b14beb 100644
--- a/docs/source/model_documentation.md
+++ b/docs/source/model_documentation.md
@@ -489,6 +489,11 @@ Choices are `optimized` (for the method described above), `hierarchical` (for hi
 The default value of 33 periods is chosen as a trade-off between runtime and accuracy (both of which increase with the number of representative periods modeled).
 When using `GSw_HourlyClusterAlgorithm = optimized`, fewer periods may be required.
 If more periods are desired than the number identified by the optimized method, either set `GSw_HourlyClusterRegionLevel` to a finer region level (such as `r` or `st`) or set `GSw_HourlyClusterAlgorithm` to `hierarchical`.
+- `GSw_HourlyClusterMapMethod` (default `milp`): How to map actual periods to representative periods.
+'milp' minimizes the sum of absolute errors between representative and actual periods using a Mixed Integer Linear Program (MILP), but is slow;
+'bestfirst' is orders of magnitude faster when using many weather years in `GSw_HourlyWeatherYears`.
+The `bestfirst` algorithm iteratively matches representative periods to actual periods that are closest in (feature × region)-dimensional space,
+working its way out from each representative period until the representative period is mapped to a number of actual periods equal to its weight.
 ```
 
 
@@ -1572,7 +1577,7 @@ Note the difference in color scales.
 
 
 Scheduled (planned and maintenance) outage rates are derived from the NERC GADS database {cite}`nercGeneratingAvailabilityData2023`.
-Scheduled outage rates for combined cycle, combustion turbine, nuclear, steam (coal), and hydro technologies are measured and applied at monthly resolution using GADS data from 2013 to 2023 {cite}`murphyGridReliabilityStatistics2025`.
+Scheduled outage rates for combined cycle, combustion turbine, nuclear, steam (coal), and hydro technologies are measured and applied at monthly resolution using GADS data from 2013 to 2023 {cite}`murphyGridReliabilityStatistics2025` (see the [scheduled outages](https://github.com/NatLabRockies/grid-reliability-statistics/blob/main/notebooks/scheduled_outages.ipynb) notebook for specific details).
 Scheduled outage rates for other technologies are measured as time-independent average values using GADS data from 2014 to 2018;
 scheduled outages for these technologies are applied only during spring and fall, with the outage rates during those months scaled to reproduce the measured time-independent averages.
 {numref}`figure-outage_scheduled` shows scheduled outage rates for the technologies used by default.
@@ -1601,13 +1606,13 @@ Air temperature, relative humidity, cold/icing shutoff indicators, modeled wind
 
 ## Fuel Prices
 
-Natural gas, coal, and uranium prices in ReEDS are based on the AEO2025.
-Coal prices are taken from the Reference scenario, with any missing values from the Reference scenario forward-filled using prior years.
+Natural gas, coal, and uranium prices are based on the AEO2026 {cite}`eiaAnnualEnergyOutlook2026`.
+Coal prices are taken from the Alternative Electricity scenario because it provides a complete dataset through 2050 relative to the Counterfactual Baseline scenario (which has coal fully phasing out in many regions).
 Coal prices are provided for each of the nine EIA census divisions.
-Default natural gas prices and demand levels are from the AEO2025 Reference scenarios.
+Default natural gas prices and demand levels are from the AEO2026 Counterfactual Baseline scenarios.
 Low and high natural gas price alternatives are taken from the High and Low Oil and Gas Resource and Technology scenarios, respectively.
-ReEDS includes only a single national uranium price trajectory based on the AEO2025 Reference scenario.
-Base fuel price trajectories are shown in {numref}`figure-input-fuel-price-assumptions` for the AEO2025 {cite}`eiaAnnualEnergyOutlook2025`.
+ReEDS includes only a single national uranium price trajectory based on the AEO2026 Counterfactual Baseline scenario.
+Base fuel price trajectories are shown in {numref}`figure-input-fuel-price-assumptions`.
 Biomass fuel prices are represented using supply curves as described in the [Biopower section](#biopower).
 
 ```{figure} figs/docs/input-fuel-price-assumptions.png
@@ -1627,7 +1632,7 @@ Although there is no explicit representation of natural gas demand beyond the el
 For details, see the [Natural Gas Supply Curves](#natural-gas-supply-curves) section of the appendix.
 
 [^ref32]: Supply curves are nonlinear in practice, but a linear regression approximation has been observed to be satisfactory under most conditions.
-The elasticity coefficients are derived from all scenarios of AEO2018, but the price-demand setpoints are taken from any one single scenario of the AEO.
+The elasticity coefficients are derived from AEO2026 scenarios via a demeaned fixed-effects ordinary least squares regression, and the price-demand setpoints are taken from any one single scenario of the AEO.
 
 ReEDS includes options for other types of fuel supply curve representations.
 Supply curves can be national-only, census-division-only, or static.
@@ -1638,7 +1643,11 @@ In the static case, fuel prices are not responsive to demand.
 The switch `GSw_GasCurve` controls the choice of natural gas supply curve.
 0 = census-division-only, 1 = national + census division, 2 = static, 3 = national-only
 
-The file `inputs/fuelprices/cendivweights.csv` contains the weights applied to the fuel prices to help smooth the prices across census divisions when setting `GSw_GasCurve` to 1. This file was created by taking an input file of county-level spatial resolution and assigning a weight to each balancing area.  The highest weight is farthest from the census region border and an exponential decay length of 150 km is applied, blending the weight values across balancing areas and census regions.
+Gas prices vary by census division.
+To avoid sharp boundaries between regions, the gas price in each model zone can be defined as the average over multiple census divisions, weighted by the distance from the zone centroid to the census division boundary.
+The distance weighting is controlled by the `GSw_GasRegionSmooth` switch;
+an exponential decay length of 150 km is used by default.
+If `GSw_GasRegionSmooth` is set to 0, the 1:1 zone:census-division mapping in {numref}`figure-hierarchy` is instead used directly.
 ```
 
 The natural gas fuel prices also include a seasonal price adjustor, making winter prices higher than the natural gas prices seen during the other seasons of the year.
@@ -1648,7 +1657,7 @@ For details, see the [Seasonal Natural Gas Price Adjustments section](#seasonal-
 ## Electricity Demand
 
 End-use electricity demand is an exogenous input to ReEDS represented by hourly profiles.
-The available load profile options fall into three categories: 1) load projections from Evolved Energy Research, 2) load projections developed as part of the Electrification Futures Study and 3) historic load multiplied by annual load growth factors from AEO.
+The available load profile options fall into three categories: 1) load projections from Evolved Energy Research, 2) load projections developed as part of the Electrification Futures Study and 3) historic load multiplied by state-level annual load growth factors from AEO.
 When applicable, ReEDS will modify the exogenously specified profiles by applying a load shape adjustment method that incorporates analysis from other modeling tools or by adding load from endogenously built electricity-consuming technologies.
 
 ReEDS includes interzonal transmission system losses in the optimization.
@@ -1776,18 +1785,20 @@ Transmission costs are described first, followed by the separate representations
 Estimated costs for transmission lines are generated by defining a high-geographic-resolution cost surface, then using a least-cost-path algorithm to identify a representative transmission line path between two points {cite}`lopezRenewableEnergyTechnical2025`.
 The final \$/MW cost for a particular route is then given by the integrated \$/MW-mile values for the cost surface points traversed by the least-cost route.
 
-Base voltage-dependent \$/MW-mile transmission costs are taken from four sources:
-Southern California Edison for California Independent System Operator (CAISO) and the Northeast,
-Western Electricity Coordinating Council/Transmission Expansion Planning Policy Committee (WECC/TEPPC) for non-CAISO areas in the Western Interconnection,
-Midcontinent Independent System Operator (MISO) for the Midwest, and a representative utility for the Southeast.
-These base transmission costs are shown on the left side of {numref}`figure-transmission-cost-input-data`.
-Cost multipliers based on terrain type (hilly, with land slope between 2% and 8%, and mountainous, with ≥8% land slope) and land class (pasture/farmland, wetland, suburban, urban, and forest) are taken from the same four sources.
-A separate 90-m-resolution CONUS dataset of terrain and land classes is then combined with the base transmission costs, terrain multipliers, and land class multipliers to generate the cost surface used in the least-cost-path routing algorithm.
+Base voltage-dependent \$/mile transmission costs are taken from the Midcontinent Independent System Operator (MISO) Transmission Cost Estimation Guide {cite}`misoTransmissionCostEstimation2025`.
+The MISO Guide provides material, installation, and overhead cost estimates for transmission structures, conductors, and terrain adders, as well as right-of-way (ROW) width by voltage.
+We use MISO terrain cost adders for forests and wetlands.
+The MISO terrain adder for mountainous terrain is applied to "low mountain" areas from the USGS Global Mountain Explorer K3 dataset {cite}`sayreNewHighResolutionMap2018`;
+for "high mountain" areas (which are not found in the MISO footprint),
+we instead use an estimated 2.5× multiplier on base line costs from Southern California Edison 2023 {cite}`caisoParticipatingTransmissionOwner2023`,
+resulting in a \$310,000/ROW-acre adder (2024 dollars) for high mountain areas when averaged over 230–765 kV base costs.
+An additional adder for the direct cost of ROW land is taken from {cite}`nolteHighresolutionLandValue2020`.
+The land and terrain cost adders are implemented at 90-meter resolution across the CONUS ({numref}`figure-transmission-land-terrain`) and combined with the base per-mile costs to generate the cost surface used in the least-cost-path routing algorithm.
 
-```{figure} figs/docs/transmission-cost-input-data.png
-:name: figure-transmission-cost-input-data
+```{figure} figs/docs/transmission-land-terrain.png
+:name: figure-transmission-land-terrain
 
-Overview of transmission cost input data used in reV model calculations, used to generate ReEDS model inputs.
+Land and terrain cost adders used in reV transmission cost calculations, used to generate ReEDS model inputs.
 ```
 
 Transmission of all types (local and interzonal) incurs FOM costs, which are approximated as 1.5% of the upfront capital cost per year {cite}`weidnerEnergyTechnologyReference2014`.
@@ -1812,19 +1823,14 @@ Substation POIs incur a substation upgrade cost of \$15/kW; transmission line PO
 
 Network reinforcement represents upgrades to the existing transmission network required to avoid congestion when moving power from the POI for a new generator to load centers.
 It is intended to represent the costs associated with interconnection queues, which represent a major bottleneck for the deployment of new wind and solar in the United States. {cite}`gormanGridConnectionBarriers2025`.
-Network reinforcement costs are approximated by tracing a path along existing transmission lines from each wind/solar POI to each zone "center" within the same state;
-the zone center is usually taken as the largest population center in the model zone but is sometimes (for zones without large urban centers) assigned to a high-voltage substation within the zone.[^ref35]
-A cost for each reinforcement route is calculated using the cost surface described above, with capital expenditure (CAPEX) costs multiplied by 50% to approximate the lower cost for reconductoring compared to greenfield transmission construction.
+Network reinforcement costs are approximated by tracing a path along existing transmission lines from each wind/solar POI to a nearby urban center.
+An urban center is defined as the centroid of a ≥100 km<sup>2</sup> urban area from [2020 U.S. Census data](https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2025&layergroup=Urban+Areas).
+POIs are assumed to connect to the urban center that minimizes the combined interconnection cost;
+the least-cost urban center for a reV site is usually, but not always, in the same state as the reV site.
+A cost for each reinforcement route is calculated using the cost surface described above.
 The single lowest-cost route for each POI is then selected; the associated reinforcement cost [\$/MW] and transmission distance [MW-miles] are incurred for every MW of new wind/solar capacity added at all reV sites associated with that POI.
-(This heuristic method of tracing a path from the POI to the largest load center in the zone is highly simplified and does not represent all the considerations involved in an actual interconnection study.)
-
-[^ref35]: Some zone centers are also manually adjusted.
-For example, Vancouver and Portland are the largest population centers in the southern Washington and northern Oregon regions, respectively.
-However, these centers are only about 10 miles apart.
-Modeling such a short distance between these nodes could create a bias for interzonal transmission investments between Washington and Oregon.
-Therefore, Yakima was used in lieu of Vancouver as the node location for southern Washington.
-
-{numref}`figure-local-generation-interconnection-components` illustrates the concepts of spur lines and reinforcement lines, and {numref}`figure-interconnection-cost-distribution` shows the resulting distribution of interconnection costs for land-based wind and utility-scale PV under the three siting regimes.
+(This heuristic method of tracing a path from the POI to an urban center is highly simplified and does not represent all the considerations involved in an actual interconnection study.)
+{numref}`figure-local-generation-interconnection-components` illustrates the concepts of spur lines and reinforcement lines.
 
 ```{figure} figs/docs/local-generation-interconnection-components.png
 :name: figure-local-generation-interconnection-components
@@ -1832,12 +1838,6 @@ Therefore, Yakima was used in lieu of Vancouver as the node location for souther
 Illustration of local generation interconnection components.
 ```
 
-```{figure} figs/docs/interconnection-cost-distribution.png
-:name: figure-interconnection-cost-distribution
-
-Interconnection cost distribution for land-based wind (blue) and utility-scale PV (orange) under different siting assumptions.
-```
-
 {numref}`figure-supplycurve-cost` shows maps of the estimated spur line costs, reinforcement costs, total interconnection costs, and total supply curve costs (including land-cost adders) by reV site.
 
 ```{figure} figs/docs/supplycurve-cost.png
@@ -1924,30 +1924,16 @@ import reeds
 GSw_ZoneSet = 'z132'
 reeds.inputs.get_itls(GSw_ZoneSet=GSw_ZoneSet)
 ```
-```
 
 
 ##### HVDC and B2B
 
-Existing HVDC and B2B connection capacities are taken from project websites and are listed in {numref}`dc-transmission-connections`.
-
-```{table} Existing HVDC and B2B connection capacity
-:name: dc-transmission-connections
-
-| **Project** | **Type** | **Capacity (MW)** |
-|-----|-----|----:|
-| Pacific DC Intertie | LCC | 2,780 |
-| Intermountain Power Project | LCC | 1,920 |
-| CU HVDC and Square Butte | LCC | 1,500 |
-| Welsh Intertie (ERCOT-East) | B2B | 600 |
-| Oklaunion Intertie (ERCOT-East) | B2B | 220 |
-| Lamar Intertie (West-East) | B2B | 210 |
-| Artesia Intertie (West-ERCOT) | B2B | 200 |
-| Blackwater Intertie (West-East) | B2B | 200 |
-| Miles City Intertie (West-East) | B2B | 200 |
-| Rapid City Intertie (West-East) | B2B | 200 |
-| Virginia Smith Intertie (West-East) | B2B | 200 |
-| Segall Intertie (West-East) | B2B | 110 |
+Existing and planned HVDC and B2B connection capacities are taken from project websites and are shown in {numref}`figure-transmission-hvdc_b2b`.
+
+```{figure} figs/docs/transmission-hvdc_b2b.png
+:name: figure-transmission-hvdc_b2b
+
+Existing (red) and planned (orange) HVDC lines and B2B converters (gray circles).
 ```
 
 
@@ -1958,20 +1944,13 @@ Existing HVDC and B2B connection capacities are taken from project websites and
 
 #### New transmission capacity
 
-The cost of new interzonal transmission capacity between each pair of model zones is calculated in the reV model using the base costs shown in {numref}`figure-transmission-cost-input-data`.
-For each pair of zones, a [least-cost path](https://github.com/NatLabRockies/reVX/tree/main/reVX/least_cost_xmission) between the two zone "centers" (the same "centers" described in the [Network reinforcement](#network-reinforcement) section) is determined.
-(Example paths from Maine to each of the other ReEDS zones are shown in {numref}`figure-lcp-p134`.)
+The cost of new interzonal transmission capacity between each pair of model zones is calculated in the reV model as described in the [Transmission Costs](transmission-costs) section.
+For each pair of zones, a [least-cost path](https://github.com/NatLabRockies/reVRt) between the two zone "centers" is determined.
+(Example paths between connected zones for two different model resolutions are shown on the right side of {numref}`figure-transmission-cost-ac`.)
 The integrated \$/mile cost along the least-cost path determines the \$/MW cost for expanding the interface capacity between the linked zones;
 the length of the least-cost path determines the distance (used in the calculation of transmission losses within the model,
 and of the total TW-miles of transmission capacity calculated in postprocessing).
 
-```{figure} figs/docs/lcp-p134.png
-:name: figure-lcp-p134
-
-Example least-cost paths from Maine to each of the other model zones.
-Least-cost paths are determined between each pair of zones.
-```
-
 By default, endogenous expansion of interzonal transmission capacity is allowed to begin 8 years after the present year.
 All components and types of interzonal transmission (including both per-mile line costs and AC/DC converters) use a financial multiplier calculated using a 40-year capital recovery period.
 
@@ -1986,18 +1965,24 @@ For example:
 
 ##### AC and B2B
 
-New AC transmission capacity uses base costs representative of single-circuit 500-kV lines.
-By default, interfaces with existing AC capacity can be expanded endogenously.
-Interfaces crossing between the three asynchronous interconnections that are currently linked by B2B capacity can also be expanded endogenously.
-B2B connections are modeled as AC lines on either side of an AC/DC/AC converter, so the per-mile costs and distances use AC values.
-{numref}`figure-new-ac-transmission-cost-assumptions` shows the estimated per-mile interzonal transmission costs for each expandable interface, calculated using the cost surfaces described in the [Transmission costs](#transmission-costs) section and visualized using the least-cost paths described in the [New transmission capacity](#new-transmission-capacity) section.
+New AC transmission is assumed to be able to be added between pairs of model zones that are currently connected by AC transmission (referred to as "interfaces").
+The voltage for new AC transmission varies by interface and is taken as the voltage of the highest-voltage line that currently crosses the interface, with a floor of 138 kV.
+{numref}`figure-transmission-cost-ac` shows the voltage of existing transmission lines ({cite}`HIFLD`, top)
+alongside the assumed voltage levels for new expansion (left)
+and the estimated per-MW-mile expansion costs (right)
+for each expandable interface at two different model resolutions,
+calculated using the cost surfaces described in the [Transmission costs](#transmission-costs) section and visualized using the least-cost paths described in the [New transmission capacity](#new-transmission-capacity) section.
 
-```{figure} figs/docs/new-ac-transmission-cost-assumptions.png
-:name: figure-new-ac-transmission-cost-assumptions
+```{figure} figs/docs/transmission-cost-ac.png
+:name: figure-transmission-cost-ac
 
 Modeled per-mile costs for new AC and B2B transmission additions.
 ```
 
+Interfaces crossing between the three asynchronous interconnections that are currently linked by B2B capacity can also be expanded endogenously.
+B2B connections are modeled as AC lines on either side of two LCC AC/DC converters,
+so the per-mile costs and distances use AC values.
+
 As discussed in the [Existing transmission capacity](#existing-transmission-capacity) section,
 two levels of flow constraints are applied: one at the model zone level and one at the planning subregion level, with existing AC capacity between planning subregions assessed at the $n - 1$ contingency level.
 As an approximation of contingency considerations for new transmission capacity (which cannot be assessed directly because interzonal transmission capacity, like all other variables in ReEDS, is represented linearly rather than as individual units or transmission lines),
@@ -2041,8 +2026,8 @@ Existing HVDC connections (all of which use LCC at the time of this writing) are
 
 ```{admonition} HVDC scenarios
 - Additional candidate point-to-point HVDC connections can be allowed using the `GSw_TransScen` switch.
-For example, the point-to-point connections shown in {numref}`figure-transmission-lcc-vsc` can be enabled by setting `GSw_TransScen=LCC_1000miles_demand1_wind1_subferc_20230629`.
-- Multiterminal HVDC expansion can be turned on by setting `GSw_TransScen=VSC_all`.
+For example, the point-to-point connections shown in {numref}`figure-transmission-lcc-vsc` can be enabled by setting `GSw_TransScen=NTP_P2P`.
+- Multiterminal HVDC expansion can be turned on by setting `GSw_TransScen=NTP_MT`.
 ```
 
 
@@ -2460,11 +2445,12 @@ Adapted from {cite}`maiIncorporatingStressfulGrid2024`.
 
 The calculation of capacity credit for VRE is described in the [VRE capacity credit](#vre-capacity-credit) section;
 the method for storage is described in the [storage capacity credit](#storage-capacity-credit) section.
-Thermal generators are given a capacity credit of 100% in ReEDS;
-technology-specific [outage rates](#outage-rates) for thermal generators are not considered in this method
-and are instead assumed to be factored into the [planning reserve margin](#planning-reserve-margins).
 
 
+#### Thermal generation capacity credit
+
+For thermal generators (i.e. combined cycle, combustion turbine, nuclear (conventional and SMR), and steam (coal)), ReEDS estimates a seasonal capacity credit for each region/technology combination based on temperature-dependent forced outage rates (described in detail in [outage rates](#outage-rates)). To calculate each technology's contribution to the seasonal reserve margin, its nameplate capacity is multiplied by $(1 - \bar{FOR})$, where $\bar{FOR}$ is the mean forced outage rate during the top 20 net load hours cross all modeled [weather years](#weather-years) for each season. 
+
 
 #### VRE capacity credit
 
@@ -3139,7 +3125,7 @@ For these states, the nuclear power plants are not allowed to retire until after
 The policy end dates are taken from EIA {cite:year}`eiaElectricPowerMonthly2019a`.
 
 In addition, there are [several states that do not allow new nuclear power](https://www.ncsl.org/environment-and-natural-resources/states-restrictions-on-new-nuclear-power-facility-construction).
-These states include California, Connecticut, Illinois, Maine, Massachusetts, Minnesota, New Jersey, New York (Long Island only), Oregon, Rhode Island, and Vermont.
+These states include California, Illinois, Maine, Massachusetts, Minnesota, New York (Long Island only), Oregon, Rhode Island, and Vermont. Nuclear ban is not imposed for Connecticut since Act25-127 allows construction of advanced nuclear facilities in municipalities with referendums.
 
 
 ### Other Policy Capabilities
@@ -3499,13 +3485,27 @@ P_{r,t} = \alpha + \alpha_r + \alpha_t + \alpha_{r,t} + \beta_{\text{nat}}Q_{\te
 where $P_{r,t}$ is the price of natural gas (in \$/MMBtu) in region $r$ and year $t$; the $\alpha$ parameters are the intercept terms of the supply curves with adjustments made based on region ($\alpha_r$), year ($\alpha_t$), and the region-year interaction ($\alpha_{r,t}$); $\beta_{\text{nat}}$ is the coefficient for the national NG demand ($Q_{\text{nat}}$, in quads); and $\beta_r$ is the coefficient for the regional NG demand ($Q_{r,t}$) in region $r$.
 Note that the four $\alpha$ parameters in {eq}`ng-price-consumption` can in practice be represented using only $\alpha_{r,t}$.
 
-The $\beta$ terms are regressed from AEO2014 scenarios, with 9 of the 31 AEO2014 scenarios removed as outliers {cite}`eiaAnnualEnergyOutlook2014`.
-These outlier scenarios typically include cases of very low or very high natural gas resource availability, which are useful for estimating NG price as a function of supply but not for estimating NG price as a function of demand within a given supply scenario.
+The $\beta$ terms are regressed from AEO scenarios using a demeaned fixed-effects ordinary least squares approach in two stages.
+
+**Stage 1 — Beta regression.** The price-consumption relationship is modeled as:
+
+$$\text{price}(r, t, s) = \alpha_1(r, t) + \beta_{\text{reg}}(r) \cdot Q_{\text{reg}}(r, t, s) + \beta_{\text{nat}} \cdot Q_{\text{nat}}(t, s)$$
+
+where $s$ indexes AEO scenarios, $r$ indexes census divisions, $t$ indexes years, $\alpha_1(r,t)$ is a fixed effect shared across scenarios, $Q_{\text{reg}}$ is regional electric-sector NG demand (quads), and $Q_{\text{nat}}$ is national total NG demand (quads).
+The scenarios included are those that primarily vary demand-side assumptions (e.g., high/low macroeconomic growth, high/low zero-carbon technology costs, alternative electrification pathways); High/Low Oil & Gas Supply and High/Low Oil Price scenarios are excluded to avoid distorting the structural demand-price elasticities with supply-side variation.
+
+To remove the fixed effect $\alpha_1(r,t)$, each variable is demeaned by subtracting its mean across scenarios for each $(r,t)$ group:
+
+$$d\text{Price} = \text{Price} - \overline{\text{Price}}_{(r,t)}$$
+$$dQ_{\text{reg}} = Q_{\text{reg}} - \overline{Q}_{\text{reg},(r,t)}$$
+$$dQ_{\text{nat}} = Q_{\text{nat}} - \overline{Q}_{\text{nat},(r,t)}$$
+
+yielding the demeaned regression:
+
+$$d\text{Price}(r, t, s) = \beta_{\text{reg}}(r) \cdot dQ_{\text{reg}}(r, t, s) + \beta_{\text{nat}} \cdot dQ_{\text{nat}}(t, s)$$
+
+The 9 regional $\beta_{\text{reg}}$ values and 1 national $\beta_{\text{nat}}$ are then estimated jointly via ordinary least squares.
 The national and regional $\beta$ terms are reported in {numref}`figure-census-division-values`.
-We made a specific post hoc adjustment to the regression model's outputs for one region: The $\beta_r$ term for the West North Central division was originally an order of magnitude higher than the other $\beta_r$ values because the West North Central usage in the electricity sector is so low (0.05 quad[^ref65] in 2013, compared to ~0.5 quad or more in most regions).
-The overall natural gas usage (i.e., not just electricity sector usage) in West North Central is similar to the usage in East North Central, so intuitively it makes sense to have a $\beta_r$ for West North Central relatively close to that of East North Central.
-We therefore manually adjusted the West North Central $\beta_r$ term to be 0.6 (in 2004\$/MMBtu/quad) and recalculated the $\alpha$ terms with the new $\beta$ to achieve the AEO2014 target prices.
-The situation in West North Central whereby such a small fraction of NG demand goes to electricity is unique; we do not believe the other regions warrant similar treatment.
 
 [^ref65]: A quad is a quadrillion Btu, or 10<sup>15</sup> Btu.
 
@@ -3517,9 +3517,22 @@ The "National" value at the far left is $\beta_{\text{nat}}$.
 A $\beta$ of 0.2 means that if demand increases by 1 quad, the price will increase by \$0.20/MMBtu (see {eq}`ng-price-consumption`).
 ```
 
-The $\alpha$ terms are then regressed for each scenario assuming the same $\beta$ values for all scenarios.
-Although the $\beta$ terms are derived from AEO2014 data, $\alpha$ terms are regressed using the most recent AEO data.
-Thus, we assume natural gas price elasticity has remained constant, whereas price projections shift over time as represented by the $\alpha$ values.
+**Stage 2 — Alpha regression.** Using the $\beta_{\text{reg}}$ and $\beta_{\text{nat}}$ from Stage 1, the $\alpha$ intercept is computed for the three AEO cases used as ReEDS inputs ($c$): Reference, High Oil & Gas Supply (HOG), and Low Oil & Gas Supply (LOG):
+
+$$\text{price}(r, t, c) = \alpha_c(r, t, c) + \beta_{\text{reg}}(r) \cdot Q_{\text{reg}}(r, t, c) + \beta_{\text{nat}} \cdot Q_{\text{nat}}(t, c)$$
+
+Here $\alpha_c(r, t, c)$ captures the remaining price component for each region, year, and case after accounting for the regional and national quantity effects.
+It is solved as the residual: $\alpha_c = \text{price} - \beta_{\text{reg}} \cdot Q_{\text{reg}} - \beta_{\text{nat}} \cdot Q_{\text{nat}}$.
+
+For the ReEDS start year (2010), the supply curve regression is not applied; instead, the $\beta$ contributions are zeroed and $\alpha$ absorbs the full AEO price level.
+This provides a fixed initial price point from which the supply curve responds to subsequent demand changes, consistent with how ReEDS initializes its NG price model.
+
+All monetary values are deflated to 2004 dollars.
+
+```{seealso}
+The natural gas price regression implementation can be found in the
+[`aeo_updates/natural_gas_price_regression`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/aeo_updates/natural_gas_price_regression) folder of the ReEDS_Input_Processing repository.
+```
 
 #### Comparison of elasticities from regression approach to literature values
 
@@ -3751,7 +3764,9 @@ For all ReEDS system cost results, we assume the operational costs for the nonmo
 The marginal electricity prices in ReEDS are taken as the shadow prices from the constraints in the model that are directly impacted by the need to serve electricity. These include the load balance constraint, the operating reserve requirement, the RPS and CES requirements, and the planning reserve margin requirement (applied either in stress periods or seasonally via capacity credits). Taken together, these values show the total marginal cost of serving electricity in a given region and time slice. Weighted average versions are also calculated to report national annual marginal electricity prices. These marginal prices are most analogous to wholesale electricity prices but within a model that has full coordination and foresight.
 
 ```{admonition} Marginal Price Outputs
-Marginal electricity price outputs are calculated with `reqt_price` in `e_report.gms` and are outputs in `reqt_price.csv`. The quantity required by the model is reported in the `reqt_quant.csv` output. These two taken together can be used to calculate a $/MWh electricity price, which is done in make of the ReEDS outputs and reported as "Bulk System Electricity Price" in bokehpivot HTML outputs and in other output locations.
+Marginal electricity price outputs are recorded in `reqt_price.csv`.
+The quantity required by the model is reported in `reqt_quant.csv`.
+These two can be combined to calculate a \$/MWh electricity price.
 ```
 
 
diff --git a/docs/source/postprocessing_tools.md b/docs/source/postprocessing_tools.md
index 0c9367b7a..3c76961c3 100644
--- a/docs/source/postprocessing_tools.md
+++ b/docs/source/postprocessing_tools.md
@@ -82,19 +82,19 @@ The first case in the list is treated as the base case, and other cases are all
 Example for two cases:
 
 ```bash
-python postprocessing/compare_cases.py /Users/username/github/ReEDS-2.0/runs/v20250310_main_USA /Users/username/github/ReEDS-2.0/runs/v20250310_newthing_USA
+python postprocessing/compare_cases.py /Users/username/github/ReEDS/runs/v20250310_main_USA /Users/username/github/ReEDS/runs/v20250310_newthing_USA
 ```
 
 Example for three cases:
 
 ```bash
-python postprocessing/compare_cases.py /Users/username/github/ReEDS-2.0/runs/v20250310_main_USA /Users/username/github/ReEDS-2.0/runs/v20250310_newthing1_USA /Users/username/github/ReEDS-2.0/runs/v20250310_newthing2_USA
+python postprocessing/compare_cases.py /Users/username/github/ReEDS/runs/v20250310_main_USA /Users/username/github/ReEDS/runs/v20250310_newthing1_USA /Users/username/github/ReEDS/runs/v20250310_newthing2_USA
 ```
 
 Example for a .csv file of cases:
 
 ```bash
-python postprocessing/compare_cases.py /Users/username/github/ReEDS-2.0/postprocessing/example.csv
+python postprocessing/compare_cases.py /Users/username/github/ReEDS/postprocessing/example.csv
 ```
 
 ### Run PRAS: `postprocessing/run_reeds2pras.py`
@@ -103,10 +103,10 @@ The PRAS model is typically run multiple times during each ReEDS case (as long a
 This script reruns PRAS on a finished ReEDS case (provided by the single required command-line argument) and allows the settings to be changed.
 For example, to use a different number of samples than are specified by the default `pras_samples` switch, use the `-s/--samples` command-line argument.
 
-### Run a dispatch model: `run_pcm.py`
+### Run a dispatch model: `postprocessing/run_pcm.py`
 
 This script reruns a completed ReEDS case as a dispatch simulation at higher time resolution.
-The operational constraints in `c_supplymodel.gms` are used directly, but the investment and capacity variables are fixed to their previously optimized values; only the operational variables are re-optimized.
+The operational constraints in `c_model.gms` are used directly, but the investment and capacity variables are fixed to their previously optimized values; only the operational variables are re-optimized.
 365 representative 1-day periods at 1-hour resolution are used by default, but these settings can be changed using the `-s/--switch_mods` switch.
 
 This approach is distinct from the [R2X](https://github.com/NatLabRockies/R2X) tool, which formats the results of a ReEDS case as inputs to a separate production cost modeling tool such as [Sienna](https://github.com/NREL-Sienna) or [PLEXOS](https://www.energyexemplar.com/plexos).
diff --git a/docs/source/reeds_training_homework.md b/docs/source/reeds_training_homework.md
index f984daad6..6630275c4 100644
--- a/docs/source/reeds_training_homework.md
+++ b/docs/source/reeds_training_homework.md
@@ -18,14 +18,14 @@ orphan: true
 5. Add a new column with 'ERCOT_BA' in row 1.
    - Copy the contents of rows 2-6 from the ERCOT_county column
    - Replace 'z3109' with 'z134' in row 5 (`GSw_ZoneSet`)
-   - Save `cases_examples.csv` in the ReEDS-2.0 folder (you can overwrite the existing file)
+   - Save `cases_examples.csv` in the ReEDS folder (you can overwrite the existing file)
 
 6. Open VS Code
-   - Open the folder where your repo is located (File -> Open Folder -> navigate to ReEDS-2.0 folder)
+   - Open the folder where your repo is located (File -> Open Folder -> navigate to ReEDS folder)
    - Open a new terminal and activate the reeds2 environment
 
 7. Start a new run
-   - `python runbatch.py`
+   - `python runreeds.py`
    - when prompted for case file name, enter `examples`
    - when prompted for how many simultaneous runs you would like to execute, enter 2
    - The 'ERCOT_county' and 'ERCOT_BA' runs should start
@@ -33,7 +33,7 @@ orphan: true
 
 ### 1. Review the results
 
-1. Navigate to 'ReEDS-2.0/runs/{your run name}/outputs/reeds-report' and open 'report.html'
+1. Navigate to 'ReEDS/runs/{your run name}/outputs/reeds-report' and open 'report.html'
 2. Learn to create your own plots of the results using bokehpivot
    - See [Bokeh Pivot](../postprocessing_tools.md#bokehpivot)
 
@@ -67,14 +67,14 @@ Create an informal slide deck with the following results:
 
 4. Change the ‘ignore’ row value to be 1 for all columns except ‘Western_BA_Decarb’ which you can either set to 0 or leave blank
 
-5. Save `cases_examples.csv` in ReEDS-2.0 folder
+5. Save `cases_examples.csv` in ReEDS folder
 
 6. Open VS Code
-   - Open the folder where your repo is located (File -> Open Folder -> navigate to ReEDS-2.0 folder)
+   - Open the folder where your repo is located (File -> Open Folder -> navigate to ReEDS folder)
    - Open a new terminal and activate the reeds2 environment
 
 7. Start a new run
-   - `python runbatch.py`
+   - `python runreeds.py`
    - when prompted for case file name, enter `examples`
    - The 'Western_BA_Decarb' run should start
 
diff --git a/docs/source/references.bib b/docs/source/references.bib
index c83ffd8d7..1fd019de6 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -82,6 +82,21 @@ @techreport{2021DistributedEnergy2021
   institution = {California Public Utilities Commission}
 }
 
+@article{2022_marcy_etal,
+  title = {Comparison of Temporal Resolution Selection Approaches in Energy Systems Models},
+  author = {Marcy, Cara and Goforth, Teagan and Nock, Destenie and Brown, Maxwell},
+  year = 2022,
+  month = jul,
+  journal = {Energy},
+  volume = {251},
+  pages = {123969},
+  issn = {0360-5442},
+  doi = {10.1016/j.energy.2022.123969},
+  url = {https://www.sciencedirect.com/science/article/pii/S0360544222008726},
+  urldate = {2026-05-12},
+  abstract = {Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.}
+}
+
 @article{2023_mirletz_etal,
   title = {Unfounded Concerns about Photovoltaic Module Toxicity and Waste Are Slowing Decarbonization},
   author = {Mirletz, Heather and Hieslmair, Henry and Ovaitt, Silvana and Curtis, Taylor L. and Barnes, Teresa M.},
@@ -123,6 +138,21 @@ @article{2024_wuijts_etal
   langid = {english}
 }
 
+@article{2025_catania_etal,
+  title = {The Impact of Temporal Clustering on Long-Term Energy System Models},
+  author = {Catania, Matteo and Muliere, Giuseppe and Fattori, Fabrizio and Colbertaldo, Paolo},
+  year = 2025,
+  month = dec,
+  journal = {Applied Energy},
+  volume = {399},
+  pages = {126354},
+  issn = {0306-2619},
+  doi = {10.1016/j.apenergy.2025.126354},
+  url = {https://www.sciencedirect.com/science/article/pii/S0306261925010840},
+  urldate = {2026-05-12},
+  abstract = {The field of energy system modelling is experiencing significant development, driven by the urgent need to redesign the national energy systems to achieve carbon neutrality. A growing interest regards long-term energy system models, which enable tracking the pathway and not only the final need for installations. The increase in complexity may easily lead them to face computational limits. Therefore, modelling approaches are required that cluster data to reduce the size of the problem while limiting errors and inaccuracies. This article studies the impact of temporal clustering on the performances of a sector-integrated energy system model, considering the double-layer clustering scheme operating on two distinct temporal scales: intra-year and inter-year. The former is addressed through typical-day clustering (k-means and k-medoids), while the latter introduces multi-year gaps between representative years. This methodology is implemented in the open-source framework oemof, which is customized to the dual clustering approach. The study addresses a sector-integrated energy system, built on the Italian structure, with a multi-vector and multi-sector perspective along the 2020--2050 horizon. The impact is investigated by comparing multiple options with varying number of typical days and multi-year gap, comparing each configuration with a benchmark without clustering. The approach yields coherent representations of the energy system evolution, simultaneously reducing the memory usage down to 4~\%. The outcomes show the benefits of balancing the number of representative years with the number of representative days, suggesting that such a trade-off leads to significant computational advantages. Although literature shows that time-series reduction is case-dependent, the double-layer clustering scheme appears promising for application on even more complex models, where a full-hour resolution would be computationally intractable.}
+}
+
 @techreport{2025_wilson_etal,
   title = {Power {{Demand Forecasts Revised Up}} for {{Third Year Running}}, {{Led}} by {{Data Centers}}},
   author = {Wilson, John D and Meyer, Sophie and Zimmerman, Zach and Gramlich, Rob},
@@ -4055,6 +4085,14 @@ @techreport{caiso20242025TransmissionPlan2025
   url = {https://www.caiso.com/documents/iso-board-approved-2024-2025-transmission-plan.pdf}
 }
 
+@misc{caisoParticipatingTransmissionOwner2023,
+  title = {Participating Transmission Owner per Unit Costs},
+  author = {{CAISO}},
+  year = 2023,
+  month = feb,
+  url = {https://stakeholdercenter.caiso.com/RecurringStakeholderProcesses/Participating-transmission-owner-per-unit-costs-2023}
+}
+
 @techreport{caitlinmurphyPotentialRoleConcentrating,
   title = {The {{Potential Role}} of {{Concentrating Solar Power}} in a {{Low-Cost Solar Future}} (in Review)},
   author = {{Caitlin Murphy} and {Yinong Sun} and {Wesley Cole} and {Galen Maclaurin} and {Craig Turchi} and {Mark Mehos}}
@@ -8692,6 +8730,17 @@ @techreport{eiaAnnualEnergyOutlook2025
   url = {https://www.eia.gov/outlooks/aeo/pdf/2025/AEO2025-narrative.pdf}
 }
 
+@techreport{eiaAnnualEnergyOutlook2026,
+  title = {Annual {{Energy Outlook}} 2026},
+  author = {EIA},
+  year = 2026,
+  month = apr,
+  address = {Washington, D.C.},
+  institution = {U.S. Energy Information Administration},
+  url = {https://www.eia.gov/outlooks/aeo/},
+  langid = {english}
+}
+
 @techreport{eiaAssumptionsAnnualEnergy2015,
   title = {Assumptions to the {{Annual Energy Outlook}} 2015},
   author = {EIA},
@@ -21417,6 +21466,23 @@ @article{nockHolisticMulticriteriaDecision2019
   abstract = {Designing policies to achieve a more sustainable electricity system requires policy-makers to weigh different electricity futures against a wide range of societal, economic, environmental, and technical implications. There is controversy on multiple fronts, as no technology satisfies all the demands of sustainability. Moreover, electricity systems include combinations of interacting technologies, meaning it is not enough to analyze technologies individually. We present a methodology for evaluating the sustainability of a region's electric generation portfolio, using multi-criteria decision analysis. Our framework focuses on long-term capacity planning for resource adequacy and sustainability. We used a regional electricity model and pay close attention to controversies involving offshore wind, natural gas pipelines, and the retirement of nuclear plants. We evaluate a set of generation portfolios under nine illustrative stakeholder preference scenarios across seven sustainability metrics. We find that under many stakeholder preferences, increasing offshore wind from 1.6 to 10\,GW and eliminating oil generation scores well. If stakeholders are concerned about the full range of sustainability metrics -- including costs, climate change, pollution, land-use, jobs, and safety alongside water conservation and nuclear concerns -- then the most sustainable solution is to increase nuclear (to 9.2\,GW from 3.5) alongside wind, and back them up with base levels of natural gas and hydro (18.7 and 3.3\,GW respectively).}
 }
 
+@article{nolteHighresolutionLandValue2020,
+  title = {High-Resolution Land Value Maps Reveal Underestimation of Conservation Costs in the {{United States}}},
+  author = {Nolte, Christoph},
+  year = 2020,
+  month = nov,
+  journal = {Proceedings of the National Academy of Sciences},
+  volume = {117},
+  number = {47},
+  pages = {29577--29583},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.2012865117},
+  url = {https://pnas.org/doi/full/10.1073/pnas.2012865117},
+  urldate = {2026-05-21},
+  abstract = {Significance             This paper presents high-resolution maps of the estimated value of private lands in the contiguous United States. The estimates permit the prediction of the cost of conservation interventions at a much higher accuracy than proxies used in previous nationwide conservation planning studies. A close look at two recent studies on US-wide floodplain protection and species conservation planning for climate change suggests that the use of previous cost proxies led to the underestimation of policy budgets necessary to achieve environmental goals by a factor of 2 and a factor of 37.5, respectively. This can undermine the validity of findings. Future analyses of conservation policies can and should use high-resolution cost estimates in their justification and spatial prioritization of interventions.           ,              The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I developed high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimate conservation costs, especially at the expensive tail of the distribution. This has led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges and can help improve the targeting of public ecosystem service investments.},
+  langid = {english}
+}
+
 @techreport{northamericanelectricreliabilitycorporation2021LongTermReliability2021,
   title = {2021 {{Long-Term Reliability Assessment}}},
   shorttitle = {{{NERC}}\_{{LTRA}}\_2021},
@@ -24614,6 +24680,23 @@ @techreport{saucerStaffAnalysisUplift2014
   url = {http://www.ferc.gov/legal/staff-reports/2014/08-13-14-uplift.pdf}
 }
 
+@article{sayreNewHighResolutionMap2018,
+  title = {A {{New High-Resolution Map}} of {{World Mountains}} and an {{Online Tool}} for {{Visualizing}} and {{Comparing Characterizations}} of {{Global Mountain Distributions}}},
+  author = {Sayre, Roger and Frye, Charlie and Karagulle, Deniz and Krauer, J{\"u}rg and Breyer, Sean and Aniello, Peter and Wright, Dawn J. and Payne, Davnah and Adler, Carolina and Warner, Harumi and VanSistine, D. Paco and Cress, Jill},
+  year = 2018,
+  month = aug,
+  journal = {Mountain Research and Development},
+  volume = {38},
+  number = {3},
+  pages = {240--249},
+  issn = {0276-4741, 1994-7151},
+  doi = {10.1659/MRD-JOURNAL-D-17-00107.1},
+  url = {http://www.bioone.org/doi/10.1659/MRD-JOURNAL-D-17-00107.1},
+  urldate = {2026-05-21},
+  copyright = {http://www.bioone.org/page/resources/researchers/rights\_and\_permissions},
+  langid = {english}
+}
+
 @article{schaberParametricStudyVariable2012,
   title = {Parametric Study of Variable Renewable Energy Integration in {{Europe}}: {{Advantages}} and Costs of Transmission Grid Extensions},
   shorttitle = {Parametric Study of Variable Renewable Energy Integration in {{Europe}}},
@@ -25329,6 +25412,22 @@ @article{shehabiDataCenterDesign2011
   abstract = {The rapidly increasing electricity demand for data center operation has motivated efforts to better understand current data center energy use and to identify strategies that reduce the environmental impact of these buildings. This paper builds on previous data center energy modeling efforts by characterizing local climate and mechanical equipment differences among data centers and then evaluating their consequences for building energy use. Cities in the United States with significant data center activity are identified. Representative climate conditions for these cities are applied to data center energy models for several different prototypical space types. Results indicate that widespread, effective economizer use in data centers could reduce energy demand for data centers by about 20--25\%, equivalent to an energy efficiency resource in the US of {$\sim$}13--17 billion kWh per year. Almost half of the potential savings would result from better airflow management and proper control sequences. The total energy savings potential of economizers, although substantial, is constrained by their limited potential for use in server closets and server rooms, which together are estimated to account for about 30\% of all data center energy demand. Incorporating economizer use into the mechanical systems of larger data centers would increase the variation in energy efficiency among geographic regions, indicating that as data center buildings become more energy efficient, their locations will have an increasing effect on overall energy demand. Differences among regions become even more important when accounting for greenhouse-gas emissions. Future data center development could consider site location, along with efficiency measures, to limit the environmental impact attributable to this increasingly prominent economic sector.}
 }
 
+@article{sheikholeslamiProgressiveLatinHypercube2017,
+  title = {Progressive {{Latin Hypercube Sampling}}: {{An}} Efficient Approach for Robust Sampling-Based Analysis of Environmental Models},
+  shorttitle = {Progressive {{Latin Hypercube Sampling}}},
+  author = {Sheikholeslami, Razi and Razavi, Saman},
+  year = 2017,
+  month = jul,
+  journal = {Environmental Modelling \& Software},
+  volume = {93},
+  pages = {109--126},
+  issn = {1364-8152},
+  doi = {10.1016/j.envsoft.2017.03.010},
+  url = {https://www.sciencedirect.com/science/article/pii/S1364815216305096},
+  urldate = {2025-10-13},
+  abstract = {Efficient sampling strategies that scale with the size of the problem, computational budget, and users' needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses.}
+}
+
 @techreport{sherlockRenewableElectricityProduction2015,
   title = {The {{Renewable Electricity Production Tax Credit}}: {{In Brief}}},
   author = {Sherlock, Molly F.},
@@ -27442,6 +27541,23 @@ @article{tsoHierarchicalClusteringDecomposition2020
   langid = {english}
 }
 
+@article{turanORACLERigorousMetric2026,
+  title = {{{ORACLE}}: {{A}} Rigorous Metric and Method to Explore All near-Optimal Designs for Energy Systems},
+  shorttitle = {{{ORACLE}}},
+  author = {Turan, Evren M. and Moret, Stefano and Bardow, Andr{\'e}},
+  year = 2026,
+  month = jul,
+  journal = {Computers \& Chemical Engineering},
+  volume = {210},
+  pages = {109630},
+  issn = {00981354},
+  doi = {10.1016/j.compchemeng.2026.109630},
+  url = {https://linkinghub.elsevier.com/retrieve/pii/S0098135426000839},
+  urldate = {2026-04-29},
+  abstract = {Optimization models are fundamental tools for providing quantitative insights to decision-makers. However, models, objectives, and constraints do not capture all real-world factors accurately. Thus, instead of the single optimal solution, real-world stakeholders are often interested in the near-optimal space --- solutions that lie within a specified margin of the optimal objective value. Solutions in the near-optimal space can then be assessed regarding desirable non-modelled or qualitative aspects. The near-optimal space is usually explored by so-called Modelling to Generate Alternatives (MGA) methods. However, current MGA approaches mainly employ heuristics, which do not measure or guarantee convergence.},
+  langid = {english}
+}
+
 @article{turconiEmissionsCyclingThermal2014,
   title = {Emissions from Cycling of Thermal Power Plants in Electricity Systems with High Penetration of Wind Power: {{Life}} Cycle Assessment for {{Ireland}}},
   shorttitle = {Emissions from Cycling of Thermal Power Plants in Electricity Systems with High Penetration of Wind Power},
diff --git a/docs/source/setup.md b/docs/source/setup.md
index 2c178e4b4..fb4bc5c7b 100644
--- a/docs/source/setup.md
+++ b/docs/source/setup.md
@@ -85,12 +85,6 @@ Screenshot of a test of Python in the terminal window
 #### MacOS
 Download the latest version of Anaconda: [https://www.anaconda.com/download](https://www.anaconda.com/download)
 
-**IMPORTANT**: Make sure to download the Intel version even if your machine has an Apple Silicon / ARM processor. 
-
-```{figure} figs/readme/anaconda-intel.png
-:name: figure-anaconda-intel
-```
-
 During Installation, select to install Anaconda for your machine only.
 
 ```{figure} figs/readme/anaconda-install-mac.png
@@ -132,6 +126,8 @@ When creating the reeds2 environment locally, you might run into an SSL error th
 ### GAMS Configuration
 NLR uses GAMS versions 51.3.0 and 49.6.0; however, older versions might also work. A valid GAMS license must be installed.
 
+For more information on getting a trial license for GAMS, see the [FAQ](faq.md#table-of-contents)
+
 1. Install GAMS: [https://www.gams.com/download/](https://www.gams.com/download/)
     **If installing on Mac:** on the "Installation" page, click "customize" and ensure the box to "Add GAMS to PATH" is checked.
 
@@ -162,7 +158,7 @@ The ReEDS source code is hosted on GitHub at <https://github.com/ReEDS-Model/ReE
 git lfs install
 ```
 
-3. Clone the ReEDS-2.0 repository on your desktop. Alternatively, download a ZIP from GitHub ({numref}`figure-github-download`).
+3. Clone the ReEDS repository on your desktop. Alternatively, download a ZIP from GitHub ({numref}`figure-github-download`).
 
 ```{figure} figs/readme/github-download.png
 :name: figure-github-download
@@ -291,7 +287,7 @@ If you experience issues, try the following:
 **Quick Start:**
 1. Navigate to the ReEDS directory from the command line
 2. Activate environment: `conda activate reeds2`
-3. Run the model: `python runbatch.py`
+3. Run the model: `python runreeds.py`
 4. Follow the prompts for batch configuration
 5. Check for a successful run: 
    1. Look for CSV files in `runs/[batchname_scenario]/outputs` (a successful run should have 100+ csv files in the outputs folder)
diff --git a/docs/source/sources.md b/docs/source/sources.md
index 49f33c068..15e577e5f 100644
--- a/docs/source/sources.md
+++ b/docs/source/sources.md
@@ -1,7 +1,2355 @@
-## Sources and Data
+# Sources Documentation
+## Table of Contents
 
-```{eval-rst}
-.. include:: ../../sources_documentation.md
-    :parser: myst_parser.sphinx_
-```
-<!-- -->
\ No newline at end of file
+  - [hourlize](#hourlize)
+    - [inputs](#hourlize-inputs)
+      - [load](#hourlize-inputs-load)
+      - [resource](#hourlize-inputs-resource)
+    - [tests](#hourlize-tests)
+      - [data](#hourlize-tests-data)
+        - [r2r_expanded](#hourlize-tests-data-r2r-expanded)
+        - [r2r_from_config](#hourlize-tests-data-r2r-from-config)
+        - [r2r_integration](#hourlize-tests-data-r2r-integration)
+        - [r2r_integration_geothermal](#hourlize-tests-data-r2r-integration-geothermal)
+  - [inputs](#inputs)
+    - [canada_imports](#inputs-canada-imports)
+    - [capacity_exogenous](#inputs-capacity-exogenous)
+    - [climate](#inputs-climate)
+      - [GFDL-ESM2M_RCP4p5_WM](#inputs-climate-gfdl-esm2m-rcp4p5-wm)
+      - [HadGEM2-ES_RCP2p6](#inputs-climate-hadgem2-es-rcp2p6)
+      - [HadGEM2-ES_rcp45_AT](#inputs-climate-hadgem2-es-rcp45-at)
+      - [HadGEM2-ES_RCP4p5](#inputs-climate-hadgem2-es-rcp4p5)
+      - [HadGEM2-ES_rcp85_AT](#inputs-climate-hadgem2-es-rcp85-at)
+      - [HadGEM2-ES_RCP8p5](#inputs-climate-hadgem2-es-rcp8p5)
+      - [IPSL-CM5A-LR_RCP8p5_WM](#inputs-climate-ipsl-cm5a-lr-rcp8p5-wm)
+    - [consume](#inputs-consume)
+    - [ctus](#inputs-ctus)
+    - [degradation](#inputs-degradation)
+    - [demand_response](#inputs-demand-response)
+    - [dgen_model_inputs](#inputs-dgen-model-inputs)
+      - [stscen2023_electrification](#inputs-dgen-model-inputs-stscen2023-electrification)
+      - [stscen2023_highng](#inputs-dgen-model-inputs-stscen2023-highng)
+      - [stscen2023_highre](#inputs-dgen-model-inputs-stscen2023-highre)
+      - [stscen2023_lowng](#inputs-dgen-model-inputs-stscen2023-lowng)
+      - [stscen2023_lowre](#inputs-dgen-model-inputs-stscen2023-lowre)
+      - [stscen2023_mid_case](#inputs-dgen-model-inputs-stscen2023-mid-case)
+      - [stscen2023_mid_case_95_by_2035](#inputs-dgen-model-inputs-stscen2023-mid-case-95-by-2035)
+      - [stscen2023_mid_case_95_by_2050](#inputs-dgen-model-inputs-stscen2023-mid-case-95-by-2050)
+      - [stscen2023_taxcredit_extended2050](#inputs-dgen-model-inputs-stscen2023-taxcredit-extended2050)
+    - [disaggregation](#inputs-disaggregation)
+    - [emission_constraints](#inputs-emission-constraints)
+    - [financials](#inputs-financials)
+    - [fuelprices](#inputs-fuelprices)
+    - [geothermal](#inputs-geothermal)
+    - [growth_constraints](#inputs-growth-constraints)
+    - [hydro](#inputs-hydro)
+    - [load](#inputs-load)
+    - [national_generation](#inputs-national-generation)
+    - [outages](#inputs-outages)
+    - [plant_characteristics](#inputs-plant-characteristics)
+    - [profiles_cf](#inputs-profiles-cf)
+    - [profiles_demand](#inputs-profiles-demand)
+    - [remote](#inputs-remote)
+    - [reserves](#inputs-reserves)
+    - [sets](#inputs-sets)
+    - [shapefiles](#inputs-shapefiles)
+    - [state_policies](#inputs-state-policies)
+    - [storage](#inputs-storage)
+    - [supply_curve](#inputs-supply-curve)
+    - [techs](#inputs-techs)
+    - [temporal](#inputs-temporal)
+    - [transmission](#inputs-transmission)
+    - [upgrades](#inputs-upgrades)
+    - [userinput](#inputs-userinput)
+    - [valuestreams](#inputs-valuestreams)
+    - [waterclimate](#inputs-waterclimate)
+    - [zones](#inputs-zones)
+  - [postprocessing](#postprocessing)
+    - [air_quality](#postprocessing-air-quality)
+      - [rcm_data](#postprocessing-air-quality-rcm-data)
+    - [bokehpivot](#postprocessing-bokehpivot)
+      - [in](#postprocessing-bokehpivot-in)
+        - [reeds2](#postprocessing-bokehpivot-in-reeds2)
+    - [combine_runs](#postprocessing-combine-runs)
+    - [land_use](#postprocessing-land-use)
+      - [inputs](#postprocessing-land-use-inputs)
+    - [plots](#postprocessing-plots)
+    - [retail_rate_module](#postprocessing-retail-rate-module)
+      - [calc_historical_capex](#postprocessing-retail-rate-module-calc-historical-capex)
+      - [inputs](#postprocessing-retail-rate-module-inputs)
+    - [reValue](#postprocessing-revalue)
+    - [tableau](#postprocessing-tableau)
+  - [preprocessing](#preprocessing)
+    - [atb_updates_processing](#preprocessing-atb-updates-processing)
+      - [input_files](#preprocessing-atb-updates-processing-input-files)
+  - [reeds2pras](#reeds2pras)
+    - [test](#reeds2pras-test)
+      - [reeds_cases](#reeds2pras-test-reeds-cases)
+        - [test](#reeds2pras-test-reeds-cases-test)
+  - [ReEDS_Augur](#reeds-augur)
+  - [tests](#tests)
+    - [data](#tests-data)
+      - [county](#tests-data-county)
+
+
+## Input Files
+- [cases.csv](/cases.csv)
+  - **File Type:** Switches file
+  - **Description:** Contains the configuration settings for the ReEDS run(s).
+  - **Dollar year:** 2004
+- [cases_examples.csv](/cases_examples.csv)
+- [cases_small.csv](/cases_small.csv)
+  - **Description:** Contains settings to run ReEDS at a smaller scale to test operability of the ReEDS model. Turns off several technologies and reduces the model size to significantly improve solve times.
+- [cases_standardscenarios.csv](/cases_standardscenarios.csv)
+  - **File Type:** StdScen Cases file
+  - **Description:** Contains the configuration settings for the Standard Scenarios ReEDS runs.
+- [cases_test.csv](/cases_test.csv)
+  - **Description:** Contains the configuration settings for doing test runs including the default Pacific census division test case.
+- [e_report_params.csv](/e_report_params.csv)
+  - **Description:** Contains a parameter list used in the model along with descriptions of what they are and units used.
+- [runfiles.csv](/runfiles.csv)
+  - **Description:** Contains the locations of input data that is copied from the repository into the runs folder for each respective case.
+- [sources.csv](/sources.csv)
+  - **Description:** CSV file containing a list of all input files (csv, h5, csv.gz)
+
+<a id='hourlize'></a>
+### hourlize
+
+
+<a id='hourlize-inputs'></a>
+#### hourlize/inputs
+
+
+<a id='hourlize-inputs-load'></a>
+##### hourlize/inputs/load
+
+  - [dummy_agg_op_datacenters.csv](/hourlize/inputs/load/dummy_agg_op_datacenters.csv)
+  - [legacy_ba_state_map.csv](/hourlize/inputs/load/legacy_ba_state_map.csv)
+  - [legacy_ba_timezone.csv](/hourlize/inputs/load/legacy_ba_timezone.csv)
+
+<a id='hourlize-inputs-resource'></a>
+##### hourlize/inputs/resource
+
+  - [egs_resource_classes.csv](/hourlize/inputs/resource/egs_resource_classes.csv)
+  - [geohydro_resource_classes.csv](/hourlize/inputs/resource/geohydro_resource_classes.csv)
+  - [offshore_zone_names.csv](/hourlize/inputs/resource/offshore_zone_names.csv)
+  - [rev_sc_columns.csv](/hourlize/inputs/resource/rev_sc_columns.csv)
+  - [state_abbrev.csv](/hourlize/inputs/resource/state_abbrev.csv)
+    - **Description:** Contains state names and codesfor the US.
+  - [upv_resource_classes.csv](/hourlize/inputs/resource/upv_resource_classes.csv)
+    - **Description:** Contains information related to UPV class segregation based on mean irradiance levels.
+  - [wind-ofs_resource_classes.csv](/hourlize/inputs/resource/wind-ofs_resource_classes.csv)
+    - **File Type:** supply curve input
+    - **Description:** Contains information related to Offshore wind class segregation and turbine type (fixed vs floating) based on water depth and site lcoe
+    - **Indices:** n/a
+  - [wind-ons_resource_classes.csv](/hourlize/inputs/resource/wind-ons_resource_classes.csv)
+    - **Description:** Contains information related to Onshore wind class segregation based on mean wind speeds.
+
+<a id='hourlize-tests'></a>
+#### hourlize/tests
+
+
+<a id='hourlize-tests-data'></a>
+##### hourlize/tests/data
+
+
+<a id='hourlize-tests-data-r2r-expanded'></a>
+###### hourlize/tests/data/r2r_expanded
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1-expected-results'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1/expected_results
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/expected_results/df_sc_out_upv_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1-reeds'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1/reeds
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-1-supply-curves'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_1/supply_curves
+
+  - [upv_supply_curve_raw_unpacked.csv](/hourlize/tests/data/r2r_expanded/upv_case_1/supply_curves/upv_supply_curve_raw_unpacked.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2-expected-results'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2/expected_results
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/expected_results/df_sc_out_upv_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2-reeds'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2/reeds
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-2-supply-curves'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_2/supply_curves
+
+  - [upv_supply_curve_raw_unpacked.csv](/hourlize/tests/data/r2r_expanded/upv_case_2/supply_curves/upv_supply_curve_raw_unpacked.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3-expected-results'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3/expected_results
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/expected_results/df_sc_out_upv_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3-reeds'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3/reeds
+
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-upv-case-3-supply-curves'></a>
+###### hourlize/tests/data/r2r_expanded/upv_case_3/supply_curves
+
+  - [upv_supply_curve_raw_unpacked.csv](/hourlize/tests/data/r2r_expanded/upv_case_3/supply_curves/upv_supply_curve_raw_unpacked.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1
+
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-expected-results'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/expected_results
+
+  - [df_sc_out_wind-ons_reduced.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/expected_results/df_sc_out_wind-ons_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-reeds'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds
+
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-reeds-inputs-case-supplycurve-metadata'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/supplycurve_metadata
+
+  - [rev_supply_curves.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/inputs_case/supplycurve_metadata/rev_supply_curves.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-expanded-wind-ons-case-1-supply-curves'></a>
+###### hourlize/tests/data/r2r_expanded/wind-ons_case_1/supply_curves
+
+  - [wind-ons_supply_curve_raw.csv](/hourlize/tests/data/r2r_expanded/wind-ons_case_1/supply_curves/wind-ons_supply_curve_raw.csv)
+
+<a id='hourlize-tests-data-r2r-from-config'></a>
+###### hourlize/tests/data/r2r_from_config
+
+
+<a id='hourlize-tests-data-r2r-from-config-expected-results'></a>
+###### hourlize/tests/data/r2r_from_config/expected_results
+
+
+<a id='hourlize-tests-data-r2r-from-config-expected-results-multiple-priority-inputs'></a>
+###### hourlize/tests/data/r2r_from_config/expected_results/multiple_priority_inputs
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/multiple_priority_inputs/df_sc_out_upv_reduced.csv)
+  - [df_sc_out_wind-ofs_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/multiple_priority_inputs/df_sc_out_wind-ofs_reduced.csv)
+  - [df_sc_out_wind-ons_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/multiple_priority_inputs/df_sc_out_wind-ons_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-from-config-expected-results-no-bin-constraint'></a>
+###### hourlize/tests/data/r2r_from_config/expected_results/no_bin_constraint
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/no_bin_constraint/df_sc_out_upv_reduced.csv)
+  - [df_sc_out_wind-ofs_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/no_bin_constraint/df_sc_out_wind-ofs_reduced.csv)
+  - [df_sc_out_wind-ons_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/no_bin_constraint/df_sc_out_wind-ons_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-from-config-expected-results-priority-inputs'></a>
+###### hourlize/tests/data/r2r_from_config/expected_results/priority_inputs
+
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/priority_inputs/df_sc_out_upv_reduced.csv)
+  - [df_sc_out_wind-ofs_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/priority_inputs/df_sc_out_wind-ofs_reduced.csv)
+  - [df_sc_out_wind-ons_reduced.csv](/hourlize/tests/data/r2r_from_config/expected_results/priority_inputs/df_sc_out_wind-ons_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-integration'></a>
+###### hourlize/tests/data/r2r_integration
+
+
+<a id='hourlize-tests-data-r2r-integration-expected-results'></a>
+###### hourlize/tests/data/r2r_integration/expected_results
+
+  - [df_sc_out_upv.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_upv.csv)
+  - [df_sc_out_upv_reduced.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_upv_reduced.csv)
+  - [df_sc_out_upv_reduced_simul_fill.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_upv_reduced_simul_fill.csv)
+  - [df_sc_out_wind-ofs.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_wind-ofs.csv)
+  - [df_sc_out_wind-ofs_reduced.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_wind-ofs_reduced.csv)
+  - [df_sc_out_wind-ons.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_wind-ons.csv)
+  - [df_sc_out_wind-ons_reduced.csv](/hourlize/tests/data/r2r_integration/expected_results/df_sc_out_wind-ons_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-integration-reeds'></a>
+###### hourlize/tests/data/r2r_integration/reeds
+
+
+<a id='hourlize-tests-data-r2r-integration-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_integration/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_integration/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_integration/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_integration/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_integration/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_integration/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-integration-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_integration/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_integration/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_integration/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_integration/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_integration/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_integration/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves
+
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-upv-reference'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/upv_reference
+
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-upv-reference-results'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/upv_reference/results
+
+  - [upv_supply_curve_raw.csv](/hourlize/tests/data/r2r_integration/supply_curves/upv_reference/results/upv_supply_curve_raw.csv)
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-wind-ofs-0-open-moderate'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/wind-ofs_0_open_moderate
+
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-wind-ofs-0-open-moderate-results'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/wind-ofs_0_open_moderate/results
+
+  - [wind-ofs_supply_curve_raw.csv](/hourlize/tests/data/r2r_integration/supply_curves/wind-ofs_0_open_moderate/results/wind-ofs_supply_curve_raw.csv)
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-wind-ons-reference'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/wind-ons_reference
+
+
+<a id='hourlize-tests-data-r2r-integration-supply-curves-wind-ons-reference-results'></a>
+###### hourlize/tests/data/r2r_integration/supply_curves/wind-ons_reference/results
+
+  - [wind-ons_supply_curve_raw.csv](/hourlize/tests/data/r2r_integration/supply_curves/wind-ons_reference/results/wind-ons_supply_curve_raw.csv)
+
+<a id='hourlize-tests-data-r2r-integration-geothermal'></a>
+###### hourlize/tests/data/r2r_integration_geothermal
+
+
+<a id='hourlize-tests-data-r2r-integration-geothermal-expected-results'></a>
+###### hourlize/tests/data/r2r_integration_geothermal/expected_results
+
+  - [df_sc_out_egs_allkm.csv](/hourlize/tests/data/r2r_integration_geothermal/expected_results/df_sc_out_egs_allkm.csv)
+  - [df_sc_out_egs_allkm_reduced.csv](/hourlize/tests/data/r2r_integration_geothermal/expected_results/df_sc_out_egs_allkm_reduced.csv)
+  - [df_sc_out_geohydro_allkm.csv](/hourlize/tests/data/r2r_integration_geothermal/expected_results/df_sc_out_geohydro_allkm.csv)
+  - [df_sc_out_geohydro_allkm_reduced.csv](/hourlize/tests/data/r2r_integration_geothermal/expected_results/df_sc_out_geohydro_allkm_reduced.csv)
+
+<a id='hourlize-tests-data-r2r-integration-geothermal-reeds'></a>
+###### hourlize/tests/data/r2r_integration_geothermal/reeds
+
+
+<a id='hourlize-tests-data-r2r-integration-geothermal-reeds-inputs-case'></a>
+###### hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case
+
+  - [hierarchy_original.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case/hierarchy_original.csv)
+  - [maxage.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case/maxage.csv)
+  - [rev_paths.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case/rev_paths.csv)
+  - [site_bin_map.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case/site_bin_map.csv)
+  - [switches.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/inputs_case/switches.csv)
+
+<a id='hourlize-tests-data-r2r-integration-geothermal-reeds-outputs'></a>
+###### hourlize/tests/data/r2r_integration_geothermal/reeds/outputs
+
+  - [cap.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/outputs/cap.csv)
+  - [cap_exog.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/outputs/cap_exog.csv)
+  - [cap_new_bin_out.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/outputs/cap_new_bin_out.csv)
+  - [cap_new_ivrt_refurb.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/outputs/cap_new_ivrt_refurb.csv)
+  - [systemcost.csv](/hourlize/tests/data/r2r_integration_geothermal/reeds/outputs/systemcost.csv)
+
+<a id='hourlize-tests-data-r2r-integration-geothermal-supply-curves'></a>
+###### hourlize/tests/data/r2r_integration_geothermal/supply_curves
+
+  - [egs_supply_curve_raw.csv](/hourlize/tests/data/r2r_integration_geothermal/supply_curves/egs_supply_curve_raw.csv)
+  - [geohydro_supply_curve_raw.csv](/hourlize/tests/data/r2r_integration_geothermal/supply_curves/geohydro_supply_curve_raw.csv)
+
+<a id='inputs'></a>
+### inputs
+
+  - [county2zone.csv](/inputs/county2zone.csv)
+  - [hierarchy.csv](/inputs/hierarchy.csv)
+  - [hierarchy_agg125.csv](/inputs/hierarchy_agg125.csv)
+  - [hierarchy_agg54.csv](/inputs/hierarchy_agg54.csv)
+  - [hierarchy_agg69.csv](/inputs/hierarchy_agg69.csv)
+  - [remote_files.csv](/inputs/remote_files.csv)
+  - [scalars.csv](/inputs/scalars.csv)
+  - [tech-subset-table.csv](/inputs/tech-subset-table.csv)
+    - **Description:** Maps all technologies to specific subsets of technologies
+
+<a id='inputs-canada-imports'></a>
+#### inputs/canada_imports
+
+  - [can_exports.csv](/inputs/canada_imports/can_exports.csv)
+    - **File Type:** Input
+    - **Description:** Annual exports to Canada by BA
+    - **Indices:** r,t
+    - **Units:** MWh
+
+  - [can_exports_szn_frac.csv](/inputs/canada_imports/can_exports_szn_frac.csv)
+    - **File Type:** Input
+    - **Description:** Fraction of annual exports to Canada by season
+    - **Indices:** N/A
+    - **Units:** rate (unitless)
+
+  - [can_imports.csv](/inputs/canada_imports/can_imports.csv)
+    - **File Type:** Input
+    - **Description:** Annual imports from Canada by BA
+    - **Indices:** r,t
+    - **Units:** MWh
+
+  - [can_imports_quarter_frac.csv](/inputs/canada_imports/can_imports_quarter_frac.csv)
+    - **File Type:** Input
+    - **Description:** Fraction of annual imports from Canada by season
+    - **Indices:** N/A
+    - **Units:** rate (unitless)
+
+
+<a id='inputs-capacity-exogenous'></a>
+#### inputs/capacity_exogenous
+
+  - [cappayments.csv](/inputs/capacity_exogenous/cappayments.csv)
+  - [cappayments_ba.csv](/inputs/capacity_exogenous/cappayments_ba.csv)
+  - [demonstration_plants.csv](/inputs/capacity_exogenous/demonstration_plants.csv)
+    - **File Type:** Prescribed capacity
+    - **Description:** Nuclear-smr demonstration plants; active when GSw_NuclearDemo=1
+    - **Indices:** t,r,i,coolingwatertech,ctt,wst,value
+    - **Citation:** See 'notes' column in the file and https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0
+    - **Units:** MW
+
+  - [exog_cap_geohydro_allkm_reference.csv](/inputs/capacity_exogenous/exog_cap_geohydro_allkm_reference.csv)
+  - [exog_cap_geohydro_reference.csv](/inputs/capacity_exogenous/exog_cap_geohydro_reference.csv)
+  - [exog_cap_upv_limited.csv](/inputs/capacity_exogenous/exog_cap_upv_limited.csv)
+  - [exog_cap_upv_open.csv](/inputs/capacity_exogenous/exog_cap_upv_open.csv)
+  - [exog_cap_upv_reference.csv](/inputs/capacity_exogenous/exog_cap_upv_reference.csv)
+  - [exog_cap_wind-ons_limited.csv](/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv)
+  - [exog_cap_wind-ons_open.csv](/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv)
+  - [exog_cap_wind-ons_reference.csv](/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv)
+  - [interconnection_queues.csv](/inputs/capacity_exogenous/interconnection_queues.csv)
+  - [prescribed_builds_wind-ofs_meshed_limited.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_meshed_limited.csv)
+  - [prescribed_builds_wind-ofs_meshed_open.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_meshed_open.csv)
+  - [prescribed_builds_wind-ofs_meshed_reference.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_meshed_reference.csv)
+  - [prescribed_builds_wind-ofs_radial_limited.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_limited.csv)
+  - [prescribed_builds_wind-ofs_radial_open.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_open.csv)
+  - [prescribed_builds_wind-ofs_radial_reference.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_reference.csv)
+  - [prescribed_builds_wind-ons_limited.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ons_limited.csv)
+  - [prescribed_builds_wind-ons_open.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ons_open.csv)
+  - [prescribed_builds_wind-ons_reference.csv](/inputs/capacity_exogenous/prescribed_builds_wind-ons_reference.csv)
+  - [ReEDS_generator_database_final_EIA-NEMS.csv](/inputs/capacity_exogenous/ReEDS_generator_database_final_EIA-NEMS.csv)
+    - **File Type:** Input
+    - **Description:** EIA-NEMS database of existing generators
+
+<a id='inputs-climate'></a>
+#### inputs/climate
+
+  - [climate_heuristics_finalyear.csv](/inputs/climate/climate_heuristics_finalyear.csv)
+  - [climate_heuristics_yearfrac.csv](/inputs/climate/climate_heuristics_yearfrac.csv)
+
+<a id='inputs-climate-gfdl-esm2m-rcp4p5-wm'></a>
+##### inputs/climate/GFDL-ESM2M_RCP4p5_WM
+
+  - [hydadjann.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/hydadjann.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the GFDL-ESM2M_RCP4p5_WM climate scenario
+    - **Indices:** r,t
+    - **Units:** multipliers (unitless)
+
+  - [hydadjsea.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/hydadjsea.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual/monthly non-dispatchable hydropower for the GFDL-ESM2M_RCP4p5_WM climate scenario
+    - **Indices:** r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMult.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the GFDL-ESM2M_RCP4p5_WM climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the GFDL-ESM2M_RCP4p5_WM climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the GFDL-ESM2M_RCP4p5_WM climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-hadgem2-es-rcp2p6'></a>
+##### inputs/climate/HadGEM2-ES_RCP2p6
+
+  - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP2p6/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP2p6 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/HadGEM2-ES_RCP2p6/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the HadGEM2-ES_RCP2p6 climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/HadGEM2-ES_RCP2p6/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the HadGEM2-ES_RCP2p6 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-hadgem2-es-rcp45-at'></a>
+##### inputs/climate/HadGEM2-ES_rcp45_AT
+
+  - [hydadjann.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/hydadjann.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the HadGEM2-ES_rcp45_AT climate scenario
+    - **Indices:** r,t
+    - **Units:** multipliers (unitless)
+
+  - [hydadjsea.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/hydadjsea.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual/monthly non-dispatchable hydropower for the HadGEM2-ES_rcp45_AT climate scenario
+    - **Indices:** r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_rcp45_AT climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the HadGEM2-ES_rcp45_AT climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the HadGEM2-ES_rcp45_AT climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-hadgem2-es-rcp4p5'></a>
+##### inputs/climate/HadGEM2-ES_RCP4p5
+
+  - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP4p5/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP4p5 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/HadGEM2-ES_RCP4p5/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the HadGEM2-ES_RCP4p5 climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/HadGEM2-ES_RCP4p5/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the HadGEM2-ES_RCP4p5 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-hadgem2-es-rcp85-at'></a>
+##### inputs/climate/HadGEM2-ES_rcp85_AT
+
+  - [hydadjann.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/hydadjann.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the HadGEM2-ES_rcp85_AT climate scenario
+    - **Indices:** r,t
+    - **Units:** multipliers (unitless)
+
+  - [hydadjsea.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/hydadjsea.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual/monthly non-dispatchable hydropower for the HadGEM2-ES_rcp85_AT climate scenario
+    - **Indices:** r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_rcp85_AT climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the HadGEM2-ES_rcp85_AT climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the HadGEM2-ES_rcp85_AT climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-hadgem2-es-rcp8p5'></a>
+##### inputs/climate/HadGEM2-ES_RCP8p5
+
+  - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP8p5/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP8p5 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/HadGEM2-ES_RCP8p5/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the HadGEM2-ES_RCP8p5 climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/HadGEM2-ES_RCP8p5/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the HadGEM2-ES_RCP8p5 climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-climate-ipsl-cm5a-lr-rcp8p5-wm'></a>
+##### inputs/climate/IPSL-CM5A-LR_RCP8p5_WM
+
+  - [hydadjann.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/hydadjann.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the IPSL-CM5A-LR_RCP8p5_WM climate scenario
+    - **Indices:** r,t
+    - **Units:** multipliers (unitless)
+
+  - [hydadjsea.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/hydadjsea.csv)
+    - **Description:** Climate-impact capacity factor multipliers for annual/monthly non-dispatchable hydropower for the IPSL-CM5A-LR_RCP8p5_WM climate scenario
+    - **Indices:** r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMult.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/UnappWaterMult.csv)
+    - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the IPSL-CM5A-LR_RCP8p5_WM climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterMultAnn.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/UnappWaterMultAnn.csv)
+    - **Description:** Climate-impact water availability multipliers for annual unappropriated fresh surface water for the IPSL-CM5A-LR_RCP8p5_WM climate scenario
+    - **Indices:** wst,r,t
+    - **Units:** multipliers (unitless)
+
+  - [UnappWaterSeaAnnDistr.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/UnappWaterSeaAnnDistr.csv)
+    - **Description:** Fractional distribution of unappropriated fresh surface water for each month of a given year for the  IPSL-CM5A-LR_RCP8p5_WM climate scenario
+    - **Indices:** wst,r,month,t
+    - **Units:** multipliers (unitless)
+
+
+<a id='inputs-consume'></a>
+#### inputs/consume
+
+  - [consume_char_low.csv](/inputs/consume/consume_char_low.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost (capex, FOM, VOM) and efficiency (gas and electrical) as well as storage and transmission adder (stortran_adder) inputs for various H2 producing technologies, under Conservative assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** Units vary based on the parameter - see commented text in b_inputs.gms.
+    - **Citation:** N/A
+  - [consume_char_ref.csv](/inputs/consume/consume_char_ref.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost (capex, FOM, VOM) and efficiency (gas and electrical) as well as storage and transmission adder (stortran_adder) inputs for various H2 producing technologies, under Reference assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** Units vary based on the parameter - see commented text in b_inputs.gms.
+    - **Citation:** N/A
+  - [dac_elec_BVRE_2021_high.csv](/inputs/consume/dac_elec_BVRE_2021_high.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using High assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a](https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a)
+  - [dac_elec_BVRE_2021_low.csv](/inputs/consume/dac_elec_BVRE_2021_low.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using Low assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a](https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a)
+  - [dac_elec_BVRE_2021_mid.csv](/inputs/consume/dac_elec_BVRE_2021_mid.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using Mid assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a](https://www.netl.doe.gov/energy-analysis/details?id=d5860604-fbc7-44bb-a756-76db47d8b85a)
+  - [dac_gas_BVRE_2021_high.csv](/inputs/consume/dac_gas_BVRE_2021_high.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using High assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987](https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987)
+  - [dac_gas_BVRE_2021_low.csv](/inputs/consume/dac_gas_BVRE_2021_low.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using Low assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987](https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987)
+  - [dac_gas_BVRE_2021_mid.csv](/inputs/consume/dac_gas_BVRE_2021_mid.csv)
+    - **File Type:** Inputs
+    - **Description:** DAC costs (capex [$/(metric ton CO2/hr)], FOM [$/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time, using Mid assumptions.
+    - **Indices:** i,t
+    - **Dollar year:** As specified in inputs/consume/dollaryear
+    - **Citation:** [https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987](https://netl.doe.gov/energy-analysis/details?id=36385f18-3eaa-4f96-9983-6e2b607f6987)
+  - [dollaryear.csv](/inputs/consume/dollaryear.csv)
+    - **File Type:** Inputs
+    - **Description:** Dollar year for various Beyond VRE scenarios. 
+    - **Indices:** N/A
+    - **Dollar year:** Stated in document.
+    - **Citation:** N/A
+  - [h2_demand_county_share.csv](/inputs/consume/h2_demand_county_share.csv)
+    - **File Type:** Inputs
+    - **Description:** The fraction of national hydrogen demand in that year that corresponds to each county. Demand estimates come from https://data.openei.org/submissions/5655. 2021 demand shares correspond to the "Reference" scenario with light-duty vehicles / biofuels / methanol demand removed and 2050 shares correspond to the "Low Cost Electrolysis" scenario.
+    - **Indices:** r,t
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [h2_exogenous_demand.csv](/inputs/consume/h2_exogenous_demand.csv)
+    - **File Type:** Inputs
+    - **Description:** Exogenous hydrogen demand by industries other than the power sector per year
+    - **Indices:** t
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [h2_transport_and_storage_costs.csv](/inputs/consume/h2_transport_and_storage_costs.csv)
+    - **File Type:** Inputs
+    - **Description:** Transport and storage costs of hydrogen per year
+    - **Indices:** t
+    - **Dollar year:** 2004
+    - **Citation:** N/A
+
+<a id='inputs-ctus'></a>
+#### inputs/ctus
+
+  - [co2_site_char.csv](/inputs/ctus/co2_site_char.csv)
+    - **Dollar year:** 2018
+  - [cs.csv](/inputs/ctus/cs.csv)
+
+<a id='inputs-degradation'></a>
+#### inputs/degradation
+
+  - [degradation_annual_default.csv](/inputs/degradation/degradation_annual_default.csv)
+
+<a id='inputs-demand-response'></a>
+#### inputs/demand_response
+
+  - [dr_shed_avail_scalar.csv](/inputs/demand_response/dr_shed_avail_scalar.csv)
+  - [dr_shed_capacity_scalar_demo_data_January_2025.csv](/inputs/demand_response/dr_shed_capacity_scalar_demo_data_January_2025.csv)
+  - [dr_shed_hourly.h5](/inputs/demand_response/dr_shed_hourly.h5)
+  - [ev_load_Baseline.h5](/inputs/demand_response/ev_load_Baseline.h5)
+    - **File Type:** inputs
+    - **Description:** Baseline electricity load from EV charging by timeslice h and year t
+    - **Units:** MW
+
+  - [evmc_rsc_Baseline.csv](/inputs/demand_response/evmc_rsc_Baseline.csv)
+  - [evmc_shape_decrease_profile_Baseline.h5](/inputs/demand_response/evmc_shape_decrease_profile_Baseline.h5)
+  - [evmc_shape_increase_profile_Baseline.h5](/inputs/demand_response/evmc_shape_increase_profile_Baseline.h5)
+  - [evmc_storage_decrease_profile_Baseline.h5](/inputs/demand_response/evmc_storage_decrease_profile_Baseline.h5)
+  - [evmc_storage_increase_profile_Baseline.h5](/inputs/demand_response/evmc_storage_increase_profile_Baseline.h5)
+  - [evmc_storage_profile_energy_Baseline.h5](/inputs/demand_response/evmc_storage_profile_energy_Baseline.h5)
+
+<a id='inputs-dgen-model-inputs'></a>
+#### inputs/dgen_model_inputs
+
+
+<a id='inputs-dgen-model-inputs-stscen2023-electrification'></a>
+##### inputs/dgen_model_inputs/stscen2023_electrification
+
+  - [distpvcap_stscen2023_electrification.csv](/inputs/dgen_model_inputs/stscen2023_electrification/distpvcap_stscen2023_electrification.csv)
+
+<a id='inputs-dgen-model-inputs-stscen2023-highng'></a>
+##### inputs/dgen_model_inputs/stscen2023_highng
+
+  - [distpvcap_stscen2023_highng.csv](/inputs/dgen_model_inputs/stscen2023_highng/distpvcap_stscen2023_highng.csv)
+    - **File Type:** distribution PV inputs 
+    - **Description:** Setting for distpv scenario capacity - from standard scenarios 2023 with high NG (including distpv) costs
+
+<a id='inputs-dgen-model-inputs-stscen2023-highre'></a>
+##### inputs/dgen_model_inputs/stscen2023_highre
+
+  - [distpvcap_stscen2023_highre.csv](/inputs/dgen_model_inputs/stscen2023_highre/distpvcap_stscen2023_highre.csv)
+    - **File Type:** distribution PV inputs 
+    - **Description:** Setting for distpv scenario capacity - from standard scenarios 2023 with high RE (including distpv) costs
+
+<a id='inputs-dgen-model-inputs-stscen2023-lowng'></a>
+##### inputs/dgen_model_inputs/stscen2023_lowng
+
+  - [distpvcap_stscen2023_lowng.csv](/inputs/dgen_model_inputs/stscen2023_lowng/distpvcap_stscen2023_lowng.csv)
+    - **File Type:** distribution PV inputs 
+    - **Description:** Setting for distpv scenario capacity - from standard scenarios 2023 with low NG (including distpv) costs
+
+<a id='inputs-dgen-model-inputs-stscen2023-lowre'></a>
+##### inputs/dgen_model_inputs/stscen2023_lowre
+
+  - [distpvcap_stscen2023_lowre.csv](/inputs/dgen_model_inputs/stscen2023_lowre/distpvcap_stscen2023_lowre.csv)
+    - **File Type:** distribution PV inputs 
+    - **Description:** Setting for distpv scenario capacity - from standard scenarios 2023 with low RE (including distpv) costs
+
+<a id='inputs-dgen-model-inputs-stscen2023-mid-case'></a>
+##### inputs/dgen_model_inputs/stscen2023_mid_case
+
+  - [distpvcap_stscen2023_mid_case.csv](/inputs/dgen_model_inputs/stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv)
+    - **File Type:** distribution PV inputs 
+
+<a id='inputs-dgen-model-inputs-stscen2023-mid-case-95-by-2035'></a>
+##### inputs/dgen_model_inputs/stscen2023_mid_case_95_by_2035
+
+  - [distpvcap_stscen2023_mid_case_95_by_2035.csv](/inputs/dgen_model_inputs/stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv)
+    - **File Type:** distribution PV inputs 
+
+<a id='inputs-dgen-model-inputs-stscen2023-mid-case-95-by-2050'></a>
+##### inputs/dgen_model_inputs/stscen2023_mid_case_95_by_2050
+
+  - [distpvcap_stscen2023_mid_case_95_by_2050.csv](/inputs/dgen_model_inputs/stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv)
+    - **File Type:** distribution PV inputs 
+
+<a id='inputs-dgen-model-inputs-stscen2023-taxcredit-extended2050'></a>
+##### inputs/dgen_model_inputs/stscen2023_taxcredit_extended2050
+
+  - [distpvcap_stscen2023_taxcredit_extended2050.csv](/inputs/dgen_model_inputs/stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv)
+    - **File Type:** distribution PV inputs 
+
+<a id='inputs-disaggregation'></a>
+#### inputs/disaggregation
+
+  - [county_population.csv](/inputs/disaggregation/county_population.csv)
+    - **Description:** The population of each county, relative values are used as multipliers for downselecting data. Data come from the U.S. Census Bureau 2021 county population estimates (https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-total.html).
+    - **Indices:** FIPS
+  - [county_state_lpf.csv](/inputs/disaggregation/county_state_lpf.csv)
+  - [disagg_hydroexist.csv](/inputs/disaggregation/disagg_hydroexist.csv)
+    - **Description:** The hydropower capacity fraction of each county within a given ReEDS BA, used as multipliers for downselecting data
+    - **Indices:** r
+
+<a id='inputs-emission-constraints'></a>
+#### inputs/emission_constraints
+
+  - [ccs_link.csv](/inputs/emission_constraints/ccs_link.csv)
+  - [ccs_link_water.csv](/inputs/emission_constraints/ccs_link_water.csv)
+  - [co2_cap.csv](/inputs/emission_constraints/co2_cap.csv)
+    - **Description:** Annual nationwide carbon cap
+  - [co2_tax.csv](/inputs/emission_constraints/co2_tax.csv)
+    - **Description:** Annual co2 tax
+  - [county_co2_share_egrid_2022.csv](/inputs/emission_constraints/county_co2_share_egrid_2022.csv)
+  - [csapr_group1_ex.csv](/inputs/emission_constraints/csapr_group1_ex.csv)
+  - [csapr_group2_ex.csv](/inputs/emission_constraints/csapr_group2_ex.csv)
+  - [csapr_ozone_season.csv](/inputs/emission_constraints/csapr_ozone_season.csv)
+  - [emitrate.csv](/inputs/emission_constraints/emitrate.csv)
+    - **Description:** Emission rates for thermal generators with values from Table 5 of https://docs.nlr.gov/docs/fy25osti/93005.pdf
+    - **Indices:** i,e
+  - [gwp.csv](/inputs/emission_constraints/gwp.csv)
+  - [h2_leakage_rate.csv](/inputs/emission_constraints/h2_leakage_rate.csv)
+  - [methane_leakage_rate.csv](/inputs/emission_constraints/methane_leakage_rate.csv)
+  - [ng_crf_penalty.csv](/inputs/emission_constraints/ng_crf_penalty.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost adjustment for NG techs in scenarios with national decarbonization targets
+    - **Indices:** allt
+    - **Dollar year:** N/A
+    - **Citation:** [https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220](https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220)
+    - **Units:** rate (unitless)
+
+  - [rggi_states.csv](/inputs/emission_constraints/rggi_states.csv)
+    - **Description:** Participating RGGI states
+    - **Citation:** [https://www.rggi.org/program-overview-and-design/elements](https://www.rggi.org/program-overview-and-design/elements)
+  - [rggicon.csv](/inputs/emission_constraints/rggicon.csv)
+    - **Description:** CO2 caps for RGGI states in metric tons
+    - **Citation:** [https://www.rggi.org/allowance-tracking/allowance-distribution](https://www.rggi.org/allowance-tracking/allowance-distribution)
+  - [state_cap.csv](/inputs/emission_constraints/state_cap.csv)
+
+<a id='inputs-financials'></a>
+#### inputs/financials
+
+  - [cap_penalty.csv](/inputs/financials/cap_penalty.csv)
+  - [construction_schedules_default.csv](/inputs/financials/construction_schedules_default.csv)
+  - [construction_times_default.csv](/inputs/financials/construction_times_default.csv)
+  - [currency_incentives.csv](/inputs/financials/currency_incentives.csv)
+  - [deflator.csv](/inputs/financials/deflator.csv)
+    - **Description:** Dollar year deflator to convert values to 2004$
+  - [depreciation_schedules_default.csv](/inputs/financials/depreciation_schedules_default.csv)
+  - [energy_communities.csv](/inputs/financials/energy_communities.csv)
+  - [financials_hydrogen.csv](/inputs/financials/financials_hydrogen.csv)
+  - [financials_sys_ATB2023.csv](/inputs/financials/financials_sys_ATB2023.csv)
+  - [financials_sys_ATB2024.csv](/inputs/financials/financials_sys_ATB2024.csv)
+  - [financials_tech_ATB2023.csv](/inputs/financials/financials_tech_ATB2023.csv)
+  - [financials_tech_ATB2023_CRP20.csv](/inputs/financials/financials_tech_ATB2023_CRP20.csv)
+  - [financials_tech_ATB2024.csv](/inputs/financials/financials_tech_ATB2024.csv)
+  - [financials_transmission_30ITC_0pen_2022_2031.csv](/inputs/financials/financials_transmission_30ITC_0pen_2022_2031.csv)
+  - [financials_transmission_default.csv](/inputs/financials/financials_transmission_default.csv)
+  - [incentives_annual.csv](/inputs/financials/incentives_annual.csv)
+  - [incentives_biennial.csv](/inputs/financials/incentives_biennial.csv)
+  - [incentives_ira.csv](/inputs/financials/incentives_ira.csv)
+  - [incentives_ira_45q_45v_extension.csv](/inputs/financials/incentives_ira_45q_45v_extension.csv)
+  - [incentives_noira.csv](/inputs/financials/incentives_noira.csv)
+  - [incentives_none.csv](/inputs/financials/incentives_none.csv)
+  - [incentives_obbba.csv](/inputs/financials/incentives_obbba.csv)
+  - [incentives_obbba_conservative.csv](/inputs/financials/incentives_obbba_conservative.csv)
+  - [inflation_default.csv](/inputs/financials/inflation_default.csv)
+    - **Description:** Annual inflation factors from 1914 through 2200; historical values use the avg-avg values from https://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008/
+    - **Indices:** t
+  - [nuclear_energy_communities.csv](/inputs/financials/nuclear_energy_communities.csv)
+    - **Description:** "Counties belonging to metropolitan statistical areas (MSAs) for which at least 0.17 percent of direct employment has been related to nuclear power at any point since 2010. These are determined partly by following the process described in Section 2.6 of https://home.treasury.gov/system/files/8861/EnergyCommunities_Data_Documentation.pdf and substituting in the NAICS code for nuclear electric power generation (221113) and partly by determining counties that belong to MSAs where the number of people employed by national labs engaged in nuclear research and development (PNNL, INL, ORNL, SNL, LLNL, Argonne, and LANL) has been at least 0.17 percent of the MSA's total employment at any point since 2010."
+  - [reg_cap_cost_diff_default.csv](/inputs/financials/reg_cap_cost_diff_default.csv)
+    - **File Type:** parameter
+    - **Description:** region-specific differences for capital cost of all resources. Add to 1 to produce a multiplier
+    - **Indices:** i,r
+  - [retire_penalty.csv](/inputs/financials/retire_penalty.csv)
+  - [supply_chain_adjust.csv](/inputs/financials/supply_chain_adjust.csv)
+  - [tc_phaseout_schedule_ira2022.csv](/inputs/financials/tc_phaseout_schedule_ira2022.csv)
+
+<a id='inputs-fuelprices'></a>
+#### inputs/fuelprices
+
+  - [alpha_AEO_2023_HOG.csv](/inputs/fuelprices/alpha_AEO_2023_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology scenario census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2023
+  - [alpha_AEO_2023_LOG.csv](/inputs/fuelprices/alpha_AEO_2023_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology scenario census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2023
+  - [alpha_AEO_2023_reference.csv](/inputs/fuelprices/alpha_AEO_2023_reference.csv)
+    - **File Type:** Input
+    - **Description:** reference census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2023
+  - [alpha_AEO_2025_HOG.csv](/inputs/fuelprices/alpha_AEO_2025_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology scenario census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2025
+  - [alpha_AEO_2025_LOG.csv](/inputs/fuelprices/alpha_AEO_2025_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology scenario census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2025
+  - [alpha_AEO_2025_reference.csv](/inputs/fuelprices/alpha_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** reference census division alpha values, used in the calculation of natural gas demand curves
+    - **Indices:** allt,cendiv
+    - **Dollar year:** 2004
+    - **Citation:** AEO 2025
+  - [cd_beta0.csv](/inputs/fuelprices/cd_beta0.csv)
+    - **File Type:** Input
+    - **Description:** reference census division beta levels electric sector
+    - **Indices:** cendiv
+    - **Dollar year:** 2004
+  - [cd_beta0_allsector.csv](/inputs/fuelprices/cd_beta0_allsector.csv)
+    - **File Type:** Input
+    - **Description:** reference census division beta levels all sectors
+    - **Indices:** cendiv
+    - **Dollar year:** 2004
+  - [cendivweights.csv](/inputs/fuelprices/cendivweights.csv)
+    - **Description:** weights to smooth gas prices between census regions to avoid abrupt price changes at the cendiv borders
+    - **Indices:** r,cendiv
+  - [coal_AEO_2025_reference.csv](/inputs/fuelprices/coal_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** AEO2025 Reference case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic
+    - **Indices:** t,cendiv
+    - **Dollar year:** 2024
+    - **Citation:** AEO2025
+    - **Units:** $/MMBtu
+
+  - [coal_AEO_2026_altelec.csv](/inputs/fuelprices/coal_AEO_2026_altelec.csv)
+    - **File Type:** Input
+    - **Description:** AEO2026 Alternative Electricity case census division fuel price of coal with missing New England values set to Mid Atlantic
+    - **Indices:** t,cendiv
+    - **Dollar year:** 2025
+    - **Citation:** AEO2026
+    - **Units:** $/MMBtu
+
+  - [coal_AEO_2026_baseline.csv](/inputs/fuelprices/coal_AEO_2026_baseline.csv)
+    - **File Type:** Input
+    - **Description:** AEO2026 Counterfactual Baseline case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic
+    - **Indices:** t,cendiv
+    - **Dollar year:** 2025
+    - **Citation:** AEO2026
+    - **Units:** $/MMBtu
+
+  - [dollaryear.csv](/inputs/fuelprices/dollaryear.csv)
+    - **Description:** Dollar year mapping for each fuel price scenario
+  - [h2-combustion_10.csv](/inputs/fuelprices/h2-combustion_10.csv)
+    - **Description:** price of hydrogen for combustion technologies (h2-ct and cc) at $10/MMBtu for all years
+  - [h2-combustion_30.csv](/inputs/fuelprices/h2-combustion_30.csv)
+    - **Description:** price of hydrogen for combustion technologies (h2-ct and cc) at $30/MMBtu for all years
+  - [h2-combustion_reference.csv](/inputs/fuelprices/h2-combustion_reference.csv)
+    - **Description:** price of hydrogen for combustion technologies (h2-ct and cc) at $20/MMBtu for all years
+  - [ng_AEO_2023_HOG.csv](/inputs/fuelprices/ng_AEO_2023_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_AEO_2023_LOG.csv](/inputs/fuelprices/ng_AEO_2023_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_AEO_2023_reference.csv](/inputs/fuelprices/ng_AEO_2023_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_AEO_2025_HOG.csv](/inputs/fuelprices/ng_AEO_2025_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_AEO_2025_LOG.csv](/inputs/fuelprices/ng_AEO_2025_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_AEO_2025_reference.csv](/inputs/fuelprices/ng_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference scenario census division fuel price of natural gas
+    - **Indices:** cendiv,t
+    - **Dollar year:** 2004
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** 2004$/MMBtu
+
+  - [ng_demand_AEO_2023_HOG.csv](/inputs/fuelprices/ng_demand_AEO_2023_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_demand_AEO_2023_LOG.csv](/inputs/fuelprices/ng_demand_AEO_2023_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_demand_AEO_2023_reference.csv](/inputs/fuelprices/ng_demand_AEO_2023_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_demand_AEO_2025_HOG.csv](/inputs/fuelprices/ng_demand_AEO_2025_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_demand_AEO_2025_LOG.csv](/inputs/fuelprices/ng_demand_AEO_2025_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_demand_AEO_2025_reference.csv](/inputs/fuelprices/ng_demand_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference census division natural gas demand for the electric sector, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2023_HOG.csv](/inputs/fuelprices/ng_tot_demand_AEO_2023_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2023_LOG.csv](/inputs/fuelprices/ng_tot_demand_AEO_2023_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2023_reference.csv](/inputs/fuelprices/ng_tot_demand_AEO_2023_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2023: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2025_HOG.csv](/inputs/fuelprices/ng_tot_demand_AEO_2025_HOG.csv)
+    - **File Type:** Input
+    - **Description:** High Oil and Gas Resource and Technology census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2025_LOG.csv](/inputs/fuelprices/ng_tot_demand_AEO_2025_LOG.csv)
+    - **File Type:** Input
+    - **Description:** Low Oil and Gas Resource and Technology census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [ng_tot_demand_AEO_2025_reference.csv](/inputs/fuelprices/ng_tot_demand_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** Reference census division natural gas demand across all sectors, used in the calculation of natural gas demand curves
+    - **Indices:** cendiv,t
+    - **Citation:** AEO2025: https://www.eia.gov/outlooks/aeo/
+    - **Units:** Quads
+
+  - [uranium_AEO_2023_reference.csv](/inputs/fuelprices/uranium_AEO_2023_reference.csv)
+  - [uranium_AEO_2025_reference.csv](/inputs/fuelprices/uranium_AEO_2025_reference.csv)
+
+<a id='inputs-geothermal'></a>
+#### inputs/geothermal
+
+  - [geo_discovery_BAU.csv](/inputs/geothermal/geo_discovery_BAU.csv)
+  - [geo_discovery_factor_ATB_2023.csv](/inputs/geothermal/geo_discovery_factor_ATB_2023.csv)
+  - [geo_discovery_factor_reV.csv](/inputs/geothermal/geo_discovery_factor_reV.csv)
+  - [geo_discovery_TI.csv](/inputs/geothermal/geo_discovery_TI.csv)
+  - [geo_rsc_ATB_2023.csv](/inputs/geothermal/geo_rsc_ATB_2023.csv)
+
+<a id='inputs-growth-constraints'></a>
+#### inputs/growth_constraints
+
+  - [gbin_min.csv](/inputs/growth_constraints/gbin_min.csv)
+  - [growth_bin_size_mult.csv](/inputs/growth_constraints/growth_bin_size_mult.csv)
+  - [growth_limit_absolute.csv](/inputs/growth_constraints/growth_limit_absolute.csv)
+    - **Description:** Maximum expected annual builds for wind, batteries, and UPV from 2024-2026 using observed record builds.
+    - **Units:** MW/year
+
+  - [growth_penalty.csv](/inputs/growth_constraints/growth_penalty.csv)
+
+<a id='inputs-hydro'></a>
+#### inputs/hydro
+
+  - [cap_existing_hydro.h5](/inputs/hydro/cap_existing_hydro.h5)
+    - **File Type:** Input
+    - **Description:** Annual capacities for hydro plants spanning 2007-2022, which come from ORNL's Existing Hydropower Assets dataset.
+    - **Indices:** t
+    - **Units:** MW
+
+  - [hyd_fom.csv](/inputs/hydro/hyd_fom.csv)
+    - **Description:** Regional FOM costs for hydro
+  - [hydcf_fixed.h5](/inputs/hydro/hydcf_fixed.h5)
+    - **File Type:** Input
+    - **Description:** Fixed monthly zonal hydro capacity factor data partially created by ORNL and partially derived from ORNL's Existing Hydropower Assets dataset.
+    - **Indices:** i,month
+    - **Units:** unitless
+
+  - [hydro_mingen.csv](/inputs/hydro/hydro_mingen.csv)
+  - [net_gen_existing_hydro.h5](/inputs/hydro/net_gen_existing_hydro.h5)
+    - **File Type:** Input
+    - **Description:** Monthly net generation values for hydro plants spanning 2007-2022, which come from ORNL's Existing Hydropower Assets dataset.
+    - **Indices:** t,month
+    - **Units:** MWh
+
+  - [SeaCapAdj_hy.csv](/inputs/hydro/SeaCapAdj_hy.csv)
+
+<a id='inputs-load'></a>
+#### inputs/load
+
+  - [cangrowth.csv](/inputs/load/cangrowth.csv)
+    - **Description:** Canada load growth multiplier
+  - [demand_AEO_2023_high.csv](/inputs/load/demand_AEO_2023_high.csv)
+    - **Description:** Load growth projection from the AEO2023 High Economic Growth scenario
+    - **Units:** unitless
+
+  - [demand_AEO_2023_low.csv](/inputs/load/demand_AEO_2023_low.csv)
+    - **Description:** Load growth projection from the AEO2023 Low Economic Growth scenario
+    - **Units:** unitless
+
+  - [demand_AEO_2023_reference.csv](/inputs/load/demand_AEO_2023_reference.csv)
+    - **Description:** Load growth projection from the AEO2023 Reference scenario
+    - **Units:** unitless
+
+  - [demand_AEO_2025_high.csv](/inputs/load/demand_AEO_2025_high.csv)
+    - **Description:** Load growth projection from the AEO2025 High Economic Growth scenario
+    - **Units:** unitless
+
+  - [demand_AEO_2025_low.csv](/inputs/load/demand_AEO_2025_low.csv)
+    - **Description:** Load growth projection from the AEO2025 Low Economic Growth scenario
+    - **Units:** unitless
+
+  - [demand_AEO_2025_reference.csv](/inputs/load/demand_AEO_2025_reference.csv)
+    - **Description:** Load growth projection from the AEO2025 Reference scenario
+    - **Units:** unitless
+
+  - [EIA_loadbystate.csv](/inputs/load/EIA_loadbystate.csv)
+  - [loadsite_country_test.csv](/inputs/load/loadsite_country_test.csv)
+  - [mex_growth_rate.csv](/inputs/load/mex_growth_rate.csv)
+    - **Description:** Mexico load growth multiplier
+
+<a id='inputs-national-generation'></a>
+#### inputs/national_generation
+
+  - [gen_mandate_tech_list.csv](/inputs/national_generation/gen_mandate_tech_list.csv)
+  - [gen_mandate_trajectory.csv](/inputs/national_generation/gen_mandate_trajectory.csv)
+  - [national_rps_frac_allScen.csv](/inputs/national_generation/national_rps_frac_allScen.csv)
+
+<a id='inputs-outages'></a>
+#### inputs/outages
+
+  - [temperature_celsius-st.h5](/inputs/outages/temperature_celsius-st.h5)
+
+<a id='inputs-plant-characteristics'></a>
+#### inputs/plant_characteristics
+
+  - [battery_ATB_2024_advanced.csv](/inputs/plant_characteristics/battery_ATB_2024_advanced.csv)
+    - **Dollar year:** 2021
+  - [battery_ATB_2024_conservative.csv](/inputs/plant_characteristics/battery_ATB_2024_conservative.csv)
+    - **Dollar year:** 2021
+  - [battery_ATB_2024_moderate.csv](/inputs/plant_characteristics/battery_ATB_2024_moderate.csv)
+    - **Dollar year:** 2021
+  - [beccs_BVRE_2021_high.csv](/inputs/plant_characteristics/beccs_BVRE_2021_high.csv)
+  - [beccs_BVRE_2021_low.csv](/inputs/plant_characteristics/beccs_BVRE_2021_low.csv)
+  - [beccs_BVRE_2021_mid.csv](/inputs/plant_characteristics/beccs_BVRE_2021_mid.csv)
+  - [beccs_lowcost.csv](/inputs/plant_characteristics/beccs_lowcost.csv)
+  - [beccs_reference.csv](/inputs/plant_characteristics/beccs_reference.csv)
+  - [biopower_ATB_2024_moderate.csv](/inputs/plant_characteristics/biopower_ATB_2024_moderate.csv)
+  - [ccsflex_ATB_2020_cost.csv](/inputs/plant_characteristics/ccsflex_ATB_2020_cost.csv)
+  - [ccsflex_ATB_2020_perf.csv](/inputs/plant_characteristics/ccsflex_ATB_2020_perf.csv)
+  - [coal-ccs_ATB_2024_advanced.csv](/inputs/plant_characteristics/coal-ccs_ATB_2024_advanced.csv)
+  - [coal-ccs_ATB_2024_conservative.csv](/inputs/plant_characteristics/coal-ccs_ATB_2024_conservative.csv)
+  - [coal-ccs_ATB_2024_moderate.csv](/inputs/plant_characteristics/coal-ccs_ATB_2024_moderate.csv)
+  - [coal_ATB_2024_moderate.csv](/inputs/plant_characteristics/coal_ATB_2024_moderate.csv)
+  - [cost_opres_default.csv](/inputs/plant_characteristics/cost_opres_default.csv)
+  - [cost_opres_market.csv](/inputs/plant_characteristics/cost_opres_market.csv)
+  - [csp_ATB_2023_advanced.csv](/inputs/plant_characteristics/csp_ATB_2023_advanced.csv)
+  - [csp_ATB_2023_conservative.csv](/inputs/plant_characteristics/csp_ATB_2023_conservative.csv)
+  - [csp_ATB_2023_moderate.csv](/inputs/plant_characteristics/csp_ATB_2023_moderate.csv)
+  - [csp_ATB_2024_advanced.csv](/inputs/plant_characteristics/csp_ATB_2024_advanced.csv)
+  - [csp_ATB_2024_conservative.csv](/inputs/plant_characteristics/csp_ATB_2024_conservative.csv)
+  - [csp_ATB_2024_moderate.csv](/inputs/plant_characteristics/csp_ATB_2024_moderate.csv)
+  - [csp_SunShot2030.csv](/inputs/plant_characteristics/csp_SunShot2030.csv)
+    - **Description:** Csp costs from the SunShot2030 cost scenario
+  - [dollaryear.csv](/inputs/plant_characteristics/dollaryear.csv)
+    - **Description:** Dollar year mapping for each plant cost scenario
+  - [dr_shed_capcost_demo_data_IEF_January_2025.csv](/inputs/plant_characteristics/dr_shed_capcost_demo_data_IEF_January_2025.csv)
+  - [dr_shed_fom.csv](/inputs/plant_characteristics/dr_shed_fom.csv)
+  - [dr_shed_vom.csv](/inputs/plant_characteristics/dr_shed_vom.csv)
+  - [evmc_shape_Baseline.csv](/inputs/plant_characteristics/evmc_shape_Baseline.csv)
+  - [evmc_storage_Baseline.csv](/inputs/plant_characteristics/evmc_storage_Baseline.csv)
+  - [gas-ccs_ATB_2024_advanced.csv](/inputs/plant_characteristics/gas-ccs_ATB_2024_advanced.csv)
+  - [gas-ccs_ATB_2024_conservative.csv](/inputs/plant_characteristics/gas-ccs_ATB_2024_conservative.csv)
+  - [gas-ccs_ATB_2024_moderate.csv](/inputs/plant_characteristics/gas-ccs_ATB_2024_moderate.csv)
+  - [gas_ATB_2024_moderate.csv](/inputs/plant_characteristics/gas_ATB_2024_moderate.csv)
+  - [geo_ATB_2023_advanced.csv](/inputs/plant_characteristics/geo_ATB_2023_advanced.csv)
+  - [geo_ATB_2023_conservative.csv](/inputs/plant_characteristics/geo_ATB_2023_conservative.csv)
+  - [geo_ATB_2023_moderate.csv](/inputs/plant_characteristics/geo_ATB_2023_moderate.csv)
+  - [geo_ATB_2024_advanced.csv](/inputs/plant_characteristics/geo_ATB_2024_advanced.csv)
+  - [geo_ATB_2024_conservative.csv](/inputs/plant_characteristics/geo_ATB_2024_conservative.csv)
+  - [geo_ATB_2024_moderate.csv](/inputs/plant_characteristics/geo_ATB_2024_moderate.csv)
+  - [h2-combustion_ATB_2023.csv](/inputs/plant_characteristics/h2-combustion_ATB_2023.csv)
+  - [h2-combustion_ATB_2024.csv](/inputs/plant_characteristics/h2-combustion_ATB_2024.csv)
+    - **Description:** Hydrogen CT and CC plant costs generated in preprocessing from moderate case NREL ATB 2024 data
+  - [heat_rate_adj.csv](/inputs/plant_characteristics/heat_rate_adj.csv)
+    - **Description:** Heat rate adjustment multiplier by technology
+  - [heat_rate_penalty_spin.csv](/inputs/plant_characteristics/heat_rate_penalty_spin.csv)
+  - [hydro_ATB_2019_constant.csv](/inputs/plant_characteristics/hydro_ATB_2019_constant.csv)
+    - **Description:** Hydro costs from the 2019 ATB constant cost scenario
+  - [hydro_ATB_2019_low.csv](/inputs/plant_characteristics/hydro_ATB_2019_low.csv)
+    - **Description:** Hydro costs from the 2019 ATB low cost scenario
+  - [hydro_ATB_2019_mid.csv](/inputs/plant_characteristics/hydro_ATB_2019_mid.csv)
+    - **Description:** Hydro costs from the 2019 ATB mid cost scenario
+  - [maxage.csv](/inputs/plant_characteristics/maxage.csv)
+    - **Description:** Maximum age allowed for each technology
+  - [maxdailycf.csv](/inputs/plant_characteristics/maxdailycf.csv)
+    - **Description:** maximum daily capacity factor--dr_shed input supply curves are based on one 4-hour event per day
+  - [min_retire_age.csv](/inputs/plant_characteristics/min_retire_age.csv)
+    - **Description:** Minimum retirement age for given technology
+  - [minCF.csv](/inputs/plant_characteristics/minCF.csv)
+    - **Description:** minimum annual capacity factor for each tech fleet - applied to i-rto
+  - [mingen_fixed.csv](/inputs/plant_characteristics/mingen_fixed.csv)
+  - [minloadfrac0.csv](/inputs/plant_characteristics/minloadfrac0.csv)
+    - **Description:** characteristics/minloadfrac0 database of minloadbed generator cs
+  - [mttr.csv](/inputs/plant_characteristics/mttr.csv)
+  - [nuclear-smr_ATB_2024_advanced.csv](/inputs/plant_characteristics/nuclear-smr_ATB_2024_advanced.csv)
+  - [nuclear-smr_ATB_2024_conservative.csv](/inputs/plant_characteristics/nuclear-smr_ATB_2024_conservative.csv)
+  - [nuclear-smr_ATB_2024_moderate.csv](/inputs/plant_characteristics/nuclear-smr_ATB_2024_moderate.csv)
+  - [nuclear_ATB_2024_advanced.csv](/inputs/plant_characteristics/nuclear_ATB_2024_advanced.csv)
+  - [nuclear_ATB_2024_conservative.csv](/inputs/plant_characteristics/nuclear_ATB_2024_conservative.csv)
+  - [nuclear_ATB_2024_moderate.csv](/inputs/plant_characteristics/nuclear_ATB_2024_moderate.csv)
+  - [ofs-wind_ATB_2023_advanced.csv](/inputs/plant_characteristics/ofs-wind_ATB_2023_advanced.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 advanced ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2004
+  - [ofs-wind_ATB_2023_conservative.csv](/inputs/plant_characteristics/ofs-wind_ATB_2023_conservative.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2004
+  - [ofs-wind_ATB_2023_moderate.csv](/inputs/plant_characteristics/ofs-wind_ATB_2023_moderate.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2004
+  - [ofs-wind_ATB_2023_moderate_noFloating.csv](/inputs/plant_characteristics/ofs-wind_ATB_2023_moderate_noFloating.csv)
+  - [ofs-wind_ATB_2024_advanced.csv](/inputs/plant_characteristics/ofs-wind_ATB_2024_advanced.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 advanced ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2022
+  - [ofs-wind_ATB_2024_conservative.csv](/inputs/plant_characteristics/ofs-wind_ATB_2024_conservative.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2022
+  - [ofs-wind_ATB_2024_moderate.csv](/inputs/plant_characteristics/ofs-wind_ATB_2024_moderate.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year 
+    - **Dollar year:** 2022
+  - [ofs-wind_ATB_2024_moderate_noFloating.csv](/inputs/plant_characteristics/ofs-wind_ATB_2024_moderate_noFloating.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 moderate_noFloating ofs-wind capital (5x floating capital cost), fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year
+    - **Dollar year:** 2022
+  - [ons-wind_ATB_2023_advanced.csv](/inputs/plant_characteristics/ons-wind_ATB_2023_advanced.csv)
+  - [ons-wind_ATB_2023_conservative.csv](/inputs/plant_characteristics/ons-wind_ATB_2023_conservative.csv)
+  - [ons-wind_ATB_2023_moderate.csv](/inputs/plant_characteristics/ons-wind_ATB_2023_moderate.csv)
+  - [ons-wind_ATB_2024_advanced.csv](/inputs/plant_characteristics/ons-wind_ATB_2024_advanced.csv)
+    - **File Type:** Inputs file
+    - **Description:** Advanced cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind
+    - **Dollar year:** 2022
+  - [ons-wind_ATB_2024_conservative.csv](/inputs/plant_characteristics/ons-wind_ATB_2024_conservative.csv)
+    - **File Type:** Inputs file
+    - **Description:** Conservative cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind
+    - **Dollar year:** 2022
+  - [ons-wind_ATB_2024_moderate.csv](/inputs/plant_characteristics/ons-wind_ATB_2024_moderate.csv)
+    - **File Type:** Inputs file
+    - **Description:** Moderate cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind
+    - **Dollar year:** 2022
+  - [other_plantchar.csv](/inputs/plant_characteristics/other_plantchar.csv)
+  - [outage_forced_static.csv](/inputs/plant_characteristics/outage_forced_static.csv)
+    - **File Type:** Inputs file
+    - **Description:** Forced outage rates by technology
+  - [outage_forced_temperature_murphy2019.csv](/inputs/plant_characteristics/outage_forced_temperature_murphy2019.csv)
+  - [outage_scheduled_monthly.csv](/inputs/plant_characteristics/outage_scheduled_monthly.csv)
+  - [outage_scheduled_static.csv](/inputs/plant_characteristics/outage_scheduled_static.csv)
+    - **Description:** Scheduled outage rate by technology
+  - [pvb_benchmark2020.csv](/inputs/plant_characteristics/pvb_benchmark2020.csv)
+  - [ramprate.csv](/inputs/plant_characteristics/ramprate.csv)
+    - **Description:** Generator ramp rates by technology
+  - [startcost.csv](/inputs/plant_characteristics/startcost.csv)
+  - [unitsize_atb.csv](/inputs/plant_characteristics/unitsize_atb.csv)
+  - [upv_ATB_2023_advanced.csv](/inputs/plant_characteristics/upv_ATB_2023_advanced.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 advanced UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [upv_ATB_2023_conservative.csv](/inputs/plant_characteristics/upv_ATB_2023_conservative.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 conservative UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [upv_ATB_2023_moderate.csv](/inputs/plant_characteristics/upv_ATB_2023_moderate.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2023 moderate UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [upv_ATB_2024_advanced.csv](/inputs/plant_characteristics/upv_ATB_2024_advanced.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 advanced UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [upv_ATB_2024_conservative.csv](/inputs/plant_characteristics/upv_ATB_2024_conservative.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 conservative UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [upv_ATB_2024_moderate.csv](/inputs/plant_characteristics/upv_ATB_2024_moderate.csv)
+    - **File Type:** Inputs file
+    - **Description:** 2024 moderate UPV capital, FOM and VOM costs, and capacity factor improvement multipliers by year
+    - **Dollar year:** 2004
+  - [years_until_endogenous.csv](/inputs/plant_characteristics/years_until_endogenous.csv)
+
+<a id='inputs-profiles-cf'></a>
+#### inputs/profiles_cf
+
+  - [cf_distpv_county.h5](/inputs/profiles_cf/cf_distpv_county.h5)
+  - [cf_upv_limited_ba.h5](/inputs/profiles_cf/cf_upv_limited_ba.h5)
+  - [cf_upv_limited_county.h5](/inputs/profiles_cf/cf_upv_limited_county.h5)
+  - [cf_upv_open_ba.h5](/inputs/profiles_cf/cf_upv_open_ba.h5)
+  - [cf_upv_open_county.h5](/inputs/profiles_cf/cf_upv_open_county.h5)
+  - [cf_upv_reference_ba.h5](/inputs/profiles_cf/cf_upv_reference_ba.h5)
+  - [cf_upv_reference_county.h5](/inputs/profiles_cf/cf_upv_reference_county.h5)
+  - [cf_wind-ofs_meshed_limited_ba.h5](/inputs/profiles_cf/cf_wind-ofs_meshed_limited_ba.h5)
+  - [cf_wind-ofs_meshed_open_ba.h5](/inputs/profiles_cf/cf_wind-ofs_meshed_open_ba.h5)
+  - [cf_wind-ofs_meshed_reference_ba.h5](/inputs/profiles_cf/cf_wind-ofs_meshed_reference_ba.h5)
+  - [cf_wind-ofs_radial_limited_ba.h5](/inputs/profiles_cf/cf_wind-ofs_radial_limited_ba.h5)
+  - [cf_wind-ofs_radial_limited_county.h5](/inputs/profiles_cf/cf_wind-ofs_radial_limited_county.h5)
+  - [cf_wind-ofs_radial_open_ba.h5](/inputs/profiles_cf/cf_wind-ofs_radial_open_ba.h5)
+  - [cf_wind-ofs_radial_open_county.h5](/inputs/profiles_cf/cf_wind-ofs_radial_open_county.h5)
+  - [cf_wind-ofs_radial_reference_ba.h5](/inputs/profiles_cf/cf_wind-ofs_radial_reference_ba.h5)
+  - [cf_wind-ofs_radial_reference_county.h5](/inputs/profiles_cf/cf_wind-ofs_radial_reference_county.h5)
+  - [cf_wind-ons_limited_ba.h5](/inputs/profiles_cf/cf_wind-ons_limited_ba.h5)
+  - [cf_wind-ons_limited_county.h5](/inputs/profiles_cf/cf_wind-ons_limited_county.h5)
+  - [cf_wind-ons_open_ba.h5](/inputs/profiles_cf/cf_wind-ons_open_ba.h5)
+  - [cf_wind-ons_open_county.h5](/inputs/profiles_cf/cf_wind-ons_open_county.h5)
+  - [cf_wind-ons_reference_ba.h5](/inputs/profiles_cf/cf_wind-ons_reference_ba.h5)
+  - [cf_wind-ons_reference_county.h5](/inputs/profiles_cf/cf_wind-ons_reference_county.h5)
+
+<a id='inputs-profiles-demand'></a>
+#### inputs/profiles_demand
+
+  - [demand_EER2023_100by2050.h5](/inputs/profiles_demand/demand_EER2023_100by2050.h5)
+  - [demand_EER2023_Baseline_AEO2022.h5](/inputs/profiles_demand/demand_EER2023_Baseline_AEO2022.h5)
+  - [demand_EER2023_IRAlow.h5](/inputs/profiles_demand/demand_EER2023_IRAlow.h5)
+  - [demand_EER2023_IRAmoderate.h5](/inputs/profiles_demand/demand_EER2023_IRAmoderate.h5)
+  - [demand_EER2025_100by2050.h5](/inputs/profiles_demand/demand_EER2025_100by2050.h5)
+  - [demand_EER2025_Baseline_AEO2023.h5](/inputs/profiles_demand/demand_EER2025_Baseline_AEO2023.h5)
+  - [demand_EER2025_IRAlow.h5](/inputs/profiles_demand/demand_EER2025_IRAlow.h5)
+  - [demand_EFS_Baseline.h5](/inputs/profiles_demand/demand_EFS_Baseline.h5)
+  - [demand_EFS_Clean2035.h5](/inputs/profiles_demand/demand_EFS_Clean2035.h5)
+  - [demand_EFS_Clean2035_LTS.h5](/inputs/profiles_demand/demand_EFS_Clean2035_LTS.h5)
+  - [demand_EFS_Clean2035clip1pct.h5](/inputs/profiles_demand/demand_EFS_Clean2035clip1pct.h5)
+  - [demand_EFS_HIGH.h5](/inputs/profiles_demand/demand_EFS_HIGH.h5)
+  - [demand_EFS_MEDIUM.h5](/inputs/profiles_demand/demand_EFS_MEDIUM.h5)
+  - [demand_EFS_MEDIUMStretch2040.h5](/inputs/profiles_demand/demand_EFS_MEDIUMStretch2040.h5)
+  - [demand_EFS_MEDIUMStretch2046.h5](/inputs/profiles_demand/demand_EFS_MEDIUMStretch2046.h5)
+  - [demand_EFS_REFERENCE.h5](/inputs/profiles_demand/demand_EFS_REFERENCE.h5)
+  - [demand_historic.h5](/inputs/profiles_demand/demand_historic.h5)
+
+<a id='inputs-remote'></a>
+#### inputs/remote
+
+  - [cf_distpv_county_18421977.h5](/inputs/remote/cf_distpv_county_18421977.h5)
+  - [cf_upv_limited_ba_18407660.h5](/inputs/remote/cf_upv_limited_ba_18407660.h5)
+  - [cf_upv_limited_county_18407660.h5](/inputs/remote/cf_upv_limited_county_18407660.h5)
+  - [cf_upv_open_ba_18407660.h5](/inputs/remote/cf_upv_open_ba_18407660.h5)
+  - [cf_upv_open_county_18407660.h5](/inputs/remote/cf_upv_open_county_18407660.h5)
+  - [cf_upv_reference_ba_18407660.h5](/inputs/remote/cf_upv_reference_ba_18407660.h5)
+  - [cf_upv_reference_county_18407660.h5](/inputs/remote/cf_upv_reference_county_18407660.h5)
+  - [cf_wind-ofs_meshed_limited_ba_18423723.h5](/inputs/remote/cf_wind-ofs_meshed_limited_ba_18423723.h5)
+  - [cf_wind-ofs_meshed_open_ba_18423723.h5](/inputs/remote/cf_wind-ofs_meshed_open_ba_18423723.h5)
+  - [cf_wind-ofs_meshed_reference_ba_18423723.h5](/inputs/remote/cf_wind-ofs_meshed_reference_ba_18423723.h5)
+  - [cf_wind-ofs_radial_limited_ba_18423723.h5](/inputs/remote/cf_wind-ofs_radial_limited_ba_18423723.h5)
+  - [cf_wind-ofs_radial_limited_county_18423723.h5](/inputs/remote/cf_wind-ofs_radial_limited_county_18423723.h5)
+  - [cf_wind-ofs_radial_open_ba_18423723.h5](/inputs/remote/cf_wind-ofs_radial_open_ba_18423723.h5)
+  - [cf_wind-ofs_radial_open_county_18423723.h5](/inputs/remote/cf_wind-ofs_radial_open_county_18423723.h5)
+  - [cf_wind-ofs_radial_reference_ba_18423723.h5](/inputs/remote/cf_wind-ofs_radial_reference_ba_18423723.h5)
+  - [cf_wind-ofs_radial_reference_county_18423723.h5](/inputs/remote/cf_wind-ofs_radial_reference_county_18423723.h5)
+  - [cf_wind-ons_limited_ba_18422200.h5](/inputs/remote/cf_wind-ons_limited_ba_18422200.h5)
+  - [cf_wind-ons_limited_county_18422200.h5](/inputs/remote/cf_wind-ons_limited_county_18422200.h5)
+  - [cf_wind-ons_open_ba_18422200.h5](/inputs/remote/cf_wind-ons_open_ba_18422200.h5)
+  - [cf_wind-ons_open_county_18422200.h5](/inputs/remote/cf_wind-ons_open_county_18422200.h5)
+  - [cf_wind-ons_reference_ba_18422200.h5](/inputs/remote/cf_wind-ons_reference_ba_18422200.h5)
+  - [cf_wind-ons_reference_county_18422200.h5](/inputs/remote/cf_wind-ons_reference_county_18422200.h5)
+  - [demand_EER2023_100by2050_18423998.h5](/inputs/remote/demand_EER2023_100by2050_18423998.h5)
+  - [demand_EER2023_Baseline_AEO2022_18423998.h5](/inputs/remote/demand_EER2023_Baseline_AEO2022_18423998.h5)
+  - [demand_EER2023_IRAlow_18423998.h5](/inputs/remote/demand_EER2023_IRAlow_18423998.h5)
+  - [demand_EER2023_IRAmoderate_18423998.h5](/inputs/remote/demand_EER2023_IRAmoderate_18423998.h5)
+  - [demand_EER2025_100by2050_18435264.h5](/inputs/remote/demand_EER2025_100by2050_18435264.h5)
+  - [demand_EER2025_Baseline_AEO2023_18435264.h5](/inputs/remote/demand_EER2025_Baseline_AEO2023_18435264.h5)
+  - [demand_EER2025_IRAlow_18435264.h5](/inputs/remote/demand_EER2025_IRAlow_18435264.h5)
+  - [demand_EFS_Baseline_18461543.h5](/inputs/remote/demand_EFS_Baseline_18461543.h5)
+  - [demand_EFS_Clean2035_18461543.h5](/inputs/remote/demand_EFS_Clean2035_18461543.h5)
+  - [demand_EFS_Clean2035_LTS_18461543.h5](/inputs/remote/demand_EFS_Clean2035_LTS_18461543.h5)
+  - [demand_EFS_Clean2035clip1pct_18461543.h5](/inputs/remote/demand_EFS_Clean2035clip1pct_18461543.h5)
+  - [demand_EFS_HIGH_18461543.h5](/inputs/remote/demand_EFS_HIGH_18461543.h5)
+  - [demand_EFS_MEDIUM_18461543.h5](/inputs/remote/demand_EFS_MEDIUM_18461543.h5)
+  - [demand_EFS_MEDIUMStretch2040_18461543.h5](/inputs/remote/demand_EFS_MEDIUMStretch2040_18461543.h5)
+  - [demand_EFS_MEDIUMStretch2046_18461543.h5](/inputs/remote/demand_EFS_MEDIUMStretch2046_18461543.h5)
+  - [demand_EFS_REFERENCE_18461543.h5](/inputs/remote/demand_EFS_REFERENCE_18461543.h5)
+  - [demand_historic_18462671.h5](/inputs/remote/demand_historic_18462671.h5)
+
+<a id='inputs-reserves'></a>
+#### inputs/reserves
+
+  - [ccseason_dates.csv](/inputs/reserves/ccseason_dates.csv)
+  - [opres_periods.csv](/inputs/reserves/opres_periods.csv)
+  - [orperc.csv](/inputs/reserves/orperc.csv)
+  - [peak_net_imports.csv](/inputs/reserves/peak_net_imports.csv)
+  - [prm_annual.csv](/inputs/reserves/prm_annual.csv)
+    - **Description:** Annual planning reserve margin by NERC region
+  - [ramptime.csv](/inputs/reserves/ramptime.csv)
+
+<a id='inputs-sets'></a>
+#### inputs/sets
+
+  - [aclike.csv](/inputs/sets/aclike.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of AC transmission capacity types
+  - [allt.csv](/inputs/sets/allt.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of all potential years
+  - [bioclass.csv](/inputs/sets/bioclass.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of bio tech classes
+  - [ccsflex_cat.csv](/inputs/sets/ccsflex_cat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of flexible ccs performance parameter categories
+  - [climate_param.csv](/inputs/sets/climate_param.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of parameters defined in climate_heuristics_finalyear
+  - [consumecat.csv](/inputs/sets/consumecat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of categories for consuming facility characteristics
+  - [csapr_cat.csv](/inputs/sets/csapr_cat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of CSAPR regulation categories
+  - [csapr_group.csv](/inputs/sets/csapr_group.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of CSAPR trading groups
+  - [ctt.csv](/inputs/sets/ctt.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of cooling technology types
+  - [e.csv](/inputs/sets/e.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of emission categories used in model
+  - [eall.csv](/inputs/sets/eall.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of emission categories used in reporting
+  - [etype.csv](/inputs/sets/etype.csv)
+  - [f.csv](/inputs/sets/f.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of fuel types
+  - [flex_type.csv](/inputs/sets/flex_type.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of demand flexibility types
+  - [fuel2tech.csv](/inputs/sets/fuel2tech.csv)
+    - **File Type:** GAMS set
+    - **Description:** mapping between fuel types and generations
+  - [fuelbin.csv](/inputs/sets/fuelbin.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of gas usage brackets
+  - [gb.csv](/inputs/sets/gb.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of gas price bins
+  - [gbin.csv](/inputs/sets/gbin.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of growth bins
+  - [geotech.csv](/inputs/sets/geotech.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of geothermal technology categories
+  - [h2_st.csv](/inputs/sets/h2_st.csv)
+    - **File Type:** GAMS set
+    - **Description:** defines investments needed to store and transport H2
+  - [h2_stor.csv](/inputs/sets/h2_stor.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of H2 storage options
+  - [hintage_char.csv](/inputs/sets/hintage_char.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of characteristics available in hintage_data
+  - [i.csv](/inputs/sets/i.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of technologies
+  - [i_geotech.csv](/inputs/sets/i_geotech.csv)
+    - **File Type:** GAMS set
+    - **Description:** crosswalk between an individual geothermal technology and its category
+  - [i_h2_ptc_gen.csv](/inputs/sets/i_h2_ptc_gen.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of technologies which can produce energy for electrolyzers claiming the hydrogen production tax credit due to their low lifecycle carbon emissions
+  - [i_p.csv](/inputs/sets/i_p.csv)
+    - **File Type:** GAMS set
+    - **Description:** mapping from technologies to the products they produce
+  - [i_subtech.csv](/inputs/sets/i_subtech.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of categories for subtechs
+  - [i_water_nocooling.csv](/inputs/sets/i_water_nocooling.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of technologies that use water, but are not differentiated by cooling tech and water source
+  - [lcclike.csv](/inputs/sets/lcclike.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of transmission capacity types where lines are bundled with AC/DC converters
+  - [month.csv](/inputs/sets/month.csv)
+    - **File Type:** GAMS set
+  - [noretire.csv](/inputs/sets/noretire.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of technologies that will never be retired
+  - [notvsc.csv](/inputs/sets/notvsc.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of transmission capacity types that are not VSC
+  - [ofstype.csv](/inputs/sets/ofstype.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of offshore types used in offshore requirement constraint (eq_RPS_OFSWind)
+  - [ofstype_i.csv](/inputs/sets/ofstype_i.csv)
+    - **File Type:** GAMS set
+    - **Description:** crosswalk between ofstype and i
+  - [orcat.csv](/inputs/sets/orcat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of operating reserve categories
+  - [ortype.csv](/inputs/sets/ortype.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of types of operating reserve constraints
+  - [p.csv](/inputs/sets/p.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of products produced
+  - [pcat.csv](/inputs/sets/pcat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of prescribed technology categories
+  - [plantcat.csv](/inputs/sets/plantcat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of categories for plant characteristics
+  - [prepost.csv](/inputs/sets/prepost.csv)
+    - **File Type:** GAMS set
+  - [prescriptivelink0.csv](/inputs/sets/prescriptivelink0.csv)
+    - **File Type:** GAMS set
+    - **Description:** initial set of prescribed categories and their technologies - used in assigning prescribed builds
+  - [pvb_agg.csv](/inputs/sets/pvb_agg.csv)
+    - **File Type:** GAMS set
+    - **Description:** crosswalk between hybrid pv+battery configurations and technology options
+  - [pvb_config.csv](/inputs/sets/pvb_config.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of hybrid pv+battery configurations
+  - [quarter.csv](/inputs/sets/quarter.csv)
+    - **File Type:** GAMS set
+  - [resourceclass.csv](/inputs/sets/resourceclass.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of renewable resource classes
+  - [RPSCat.csv](/inputs/sets/RPSCat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of RPS constraint categories, including clean energy standards
+  - [sc_cat.csv](/inputs/sets/sc_cat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of supply curve categories (capacity and cost)
+  - [sdbin.csv](/inputs/sets/sdbin.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of storage durage bins
+  - [sw.csv](/inputs/sets/sw.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of surface water types where access is based on consumption not withdrawal
+  - [tg.csv](/inputs/sets/tg.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of technology groups
+  - [tg_rsc_cspagg.csv](/inputs/sets/tg_rsc_cspagg.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of csp technologies that belong to the same class
+  - [tg_rsc_upvagg.csv](/inputs/sets/tg_rsc_upvagg.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of pv and pvb technologies that belong to the same class
+  - [trancap_fut_cat.csv](/inputs/sets/trancap_fut_cat.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of categories of near-term transmission projects that describe the likelihood of being completed
+  - [trtype.csv](/inputs/sets/trtype.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of transmission capacity types
+  - [unitspec_upgrades.csv](/inputs/sets/unitspec_upgrades.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of upgraded technologies that get unit-specific characteristics
+  - [upgrade_hintage_char.csv](/inputs/sets/upgrade_hintage_char.csv)
+    - **File Type:** GAMS set
+    - **Description:** set to operate over in extension of hintage_data characteristics when sw_upgrades = 1
+  - [w.csv](/inputs/sets/w.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of water withdrawal or consumption options for water techs
+  - [wst.csv](/inputs/sets/wst.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of water source types
+  - [wst_climate.csv](/inputs/sets/wst_climate.csv)
+    - **File Type:** GAMS set
+    - **Description:** set of water sources affected by climate change
+  - [yearafter.csv](/inputs/sets/yearafter.csv)
+    - **File Type:** GAMS set
+    - **Description:** set to loop over for the final year calculation
+
+<a id='inputs-shapefiles'></a>
+#### inputs/shapefiles
+
+  - [state_fips_codes.csv](/inputs/shapefiles/state_fips_codes.csv)
+    - **Description:** Mapping of states to FIPS codes and postcal code abbreviations
+
+<a id='inputs-state-policies'></a>
+#### inputs/state_policies
+
+  - [acp_disallowed.csv](/inputs/state_policies/acp_disallowed.csv)
+    - **Description:** List of states which do not allow alternative compliance payments in place of meeting RPS or CES requirements 
+  - [acp_prices.csv](/inputs/state_policies/acp_prices.csv)
+  - [ces_fraction.csv](/inputs/state_policies/ces_fraction.csv)
+    - **Description:** Annual compliance for states with a CES policy
+  - [forced_retirements.csv](/inputs/state_policies/forced_retirements.csv)
+    - **Description:** List of regions with mandatory retirement policies for certain technologies
+  - [hydrofrac_policy.csv](/inputs/state_policies/hydrofrac_policy.csv)
+  - [ng_crf_penalty_st.csv](/inputs/state_policies/ng_crf_penalty_st.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost adjustment for NG techs in states where all NG techs must be retired by a certain year
+    - **Indices:** allt,st
+    - **Dollar year:** N/A
+    - **Citation:** [https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220](https://github.nrel.gov/ReEDS/ReEDS-2.0/pull/1220)
+    - **Units:** rate (unitless)
+
+  - [nuclear_subsidies.csv](/inputs/state_policies/nuclear_subsidies.csv)
+  - [offshore_req_default.csv](/inputs/state_policies/offshore_req_default.csv)
+    - **File Type:** Inputs
+    - **Description:** default state mandates of offshore wind capacity, updated in November 2025
+    - **Indices:** st,allt
+    - **Units:** MW
+
+  - [oosfrac.csv](/inputs/state_policies/oosfrac.csv)
+    - **Description:** Defines the fraction of renewable and clean energy credits can be purchased from out of state (oos). Applied for RPS and CES
+  - [recstyle.csv](/inputs/state_policies/recstyle.csv)
+    - **Description:** Indication for how to apply state requirement (0 = end-use sales, 1 = bus-bar sales, 2 = generation). Default is 0.
+  - [rectable.csv](/inputs/state_policies/rectable.csv)
+    - **Description:** Table defining which states are allowed to trade RECs
+  - [rps_fraction.csv](/inputs/state_policies/rps_fraction.csv)
+    - **Description:** Indicates what fraction of sales or generation (based on recstyle.csv) must be from renewable energy 
+  - [storage_mandates.csv](/inputs/state_policies/storage_mandates.csv)
+    - **Description:** Energy storage mandates by region
+  - [techs_banned_ces.csv](/inputs/state_policies/techs_banned_ces.csv)
+    - **Description:** Indicates which technolgies are not eligible to contribute to CES 
+  - [techs_banned_imports_rps.csv](/inputs/state_policies/techs_banned_imports_rps.csv)
+  - [techs_banned_rps.csv](/inputs/state_policies/techs_banned_rps.csv)
+    - **Description:** Indicates which technolgies are not eligible to contribute to RPS
+  - [unbundled_limit_ces.csv](/inputs/state_policies/unbundled_limit_ces.csv)
+    - **Description:** Limit on fraction of credits towards CES which can be purchased unbundled from other states 
+  - [unbundled_limit_rps.csv](/inputs/state_policies/unbundled_limit_rps.csv)
+    - **Description:** Limit on fraction of credits towards RPS which can be purchased unbundled from other states 
+
+<a id='inputs-storage'></a>
+#### inputs/storage
+
+  - [cap_existing_psh.csv](/inputs/storage/cap_existing_psh.csv)
+    - **Description:** County-wide PSH operational capacity, pump capacity, and max energy, based on plant-level data from https://www.hydropower.org/hydropower-pumped-storage-tool
+    - **Units:** MW/MWh
+
+  - [PSH_supply_curves_durations.csv](/inputs/storage/PSH_supply_curves_durations.csv)
+  - [storage_duration.csv](/inputs/storage/storage_duration.csv)
+
+<a id='inputs-supply-curve'></a>
+#### inputs/supply_curve
+
+  - [bio_supplycurve.csv](/inputs/supply_curve/bio_supplycurve.csv)
+    - **Description:** Regional biomass supply and costs by resource class
+    - **Dollar year:** 2015
+  - [dollaryear.csv](/inputs/supply_curve/dollaryear.csv)
+  - [dr_shed_cap.csv](/inputs/supply_curve/dr_shed_cap.csv)
+  - [dr_shed_cost.csv](/inputs/supply_curve/dr_shed_cost.csv)
+  - [hyd_add_upg_cap.csv](/inputs/supply_curve/hyd_add_upg_cap.csv)
+  - [hydcap.csv](/inputs/supply_curve/hydcap.csv)
+  - [hydcost.csv](/inputs/supply_curve/hydcost.csv)
+  - [interconnection_land.h5](/inputs/supply_curve/interconnection_land.h5)
+  - [interconnection_offshore.h5](/inputs/supply_curve/interconnection_offshore.h5)
+  - [PSH_supply_curves_capacity_10hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_ref_apr2025.csv)
+  - [PSH_supply_curves_capacity_10hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 10 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_10hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_10hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 10 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_10hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_capacity_10hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 10 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_10hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_10hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_10hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 10 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_12hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_ref_apr2025.csv)
+  - [PSH_supply_curves_capacity_12hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 12 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_12hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_12hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 12 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_12hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_capacity_12hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 12 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_12hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_12hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_12hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 12 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_8hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_ref_apr2025.csv)
+  - [PSH_supply_curves_capacity_8hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 8 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_8hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_8hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 8 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_8hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_capacity_8hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 8 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_capacity_8hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_capacity_8hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_capacity_8hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve capacity assuming 8 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_10hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_ref_apr2025.csv)
+  - [PSH_supply_curves_cost_10hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 10 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_10hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_10hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 10 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_10hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_cost_10hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 10 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_10hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_10hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_10hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 10 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_12hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_ref_apr2025.csv)
+  - [PSH_supply_curves_cost_12hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 12 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_12hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_12hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 12 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_12hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_cost_12hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 12 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_12hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_12hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_12hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 12 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_8hr_ref_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_ref_apr2025.csv)
+  - [PSH_supply_curves_cost_8hr_ref_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_ref_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 8 hour duration and reference exclusions as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_8hr_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_8hr_wEphemeral_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wEphemeral_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 8 hour duration and allowing sites on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_8hr_wExist_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wExist_apr2025.csv)
+  - [PSH_supply_curves_cost_8hr_wExist_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wExist_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 8 hour duration and allowing sites using existing reservoirs as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [PSH_supply_curves_cost_8hr_wExist_wEph_apr2025.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wExist_wEph_apr2025.csv)
+  - [PSH_supply_curves_cost_8hr_wExist_wEph_mar2024.csv](/inputs/supply_curve/PSH_supply_curves_cost_8hr_wExist_wEph_mar2024.csv)
+    - **Description:** PSH supply curve cost assuming 8 hour duration and allowing sites  using existing reservoirs and on ephemeral streams as used in 2024 Annual Technology Baseline
+    - **Dollar year:** 2004
+    - **Citation:** [https://www.nlr.gov/gis/psh-supply-curves.html](https://www.nlr.gov/gis/psh-supply-curves.html)
+  - [rev_paths.csv](/inputs/supply_curve/rev_paths.csv)
+  - [sc_point_gid_old2new.csv](/inputs/supply_curve/sc_point_gid_old2new.csv)
+  - [sitemap.h5](/inputs/supply_curve/sitemap.h5)
+  - [supplycurve_egs-reference.csv](/inputs/supply_curve/supplycurve_egs-reference.csv)
+  - [supplycurve_upv-limited.csv](/inputs/supply_curve/supplycurve_upv-limited.csv)
+    - **Description:** UPV supply curve from reV for the limited siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+    - **Units:** capacity numbers are in MW_DC and cost numbers are in $/MW_AC
+
+  - [supplycurve_upv-open.csv](/inputs/supply_curve/supplycurve_upv-open.csv)
+    - **Description:** UPV supply curve from reV for the open siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+    - **Units:** capacity numbers are in MW_DC and cost numbers are in $/MW_AC
+
+  - [supplycurve_upv-reference.csv](/inputs/supply_curve/supplycurve_upv-reference.csv)
+    - **Description:** UPV supply curve from reV for the reference siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+    - **Units:** capacity numbers are in MW_DC and cost numbers are in $/MW_AC
+
+  - [supplycurve_wind-ofs-limited.csv](/inputs/supply_curve/supplycurve_wind-ofs-limited.csv)
+    - **Description:** Offshore sind supply curve from reV for the limited siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [supplycurve_wind-ofs-open.csv](/inputs/supply_curve/supplycurve_wind-ofs-open.csv)
+    - **Description:** Offshore wind supply curve from reV for the open siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [supplycurve_wind-ofs-reference.csv](/inputs/supply_curve/supplycurve_wind-ofs-reference.csv)
+    - **Description:** Offshore wind supply curve from reV for the reference siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [supplycurve_wind-ons-limited.csv](/inputs/supply_curve/supplycurve_wind-ons-limited.csv)
+    - **Description:** Land-based wind supply curve from reV for the limited siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [supplycurve_wind-ons-open.csv](/inputs/supply_curve/supplycurve_wind-ons-open.csv)
+    - **Description:** Land-based wind supply curve from reV for the open siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [supplycurve_wind-ons-reference.csv](/inputs/supply_curve/supplycurve_wind-ons-reference.csv)
+    - **Description:** Land-based wind supply curve from reV for the reference siting scenario
+    - **Dollar year:** specified in inputs/supply_curve/dollaryear.csv
+    - **Citation:** [https://docs.nlr.gov/docs/fy25osti/91900.pdf](https://docs.nlr.gov/docs/fy25osti/91900.pdf)
+  - [trans_intra_cost_adder.csv](/inputs/supply_curve/trans_intra_cost_adder.csv)
+
+<a id='inputs-techs'></a>
+#### inputs/techs
+
+  - [tech_resourceclass.csv](/inputs/techs/tech_resourceclass.csv)
+  - [techs_default.csv](/inputs/techs/techs_default.csv)
+    - **Description:** List of technologies to be used in the model
+  - [techs_subsetForTesting.csv](/inputs/techs/techs_subsetForTesting.csv)
+    - **Description:** Short list of technologies for testing
+
+<a id='inputs-temporal'></a>
+#### inputs/temporal
+
+  - [month2quarter.csv](/inputs/temporal/month2quarter.csv)
+  - [period_szn_user.csv](/inputs/temporal/period_szn_user.csv)
+  - [reeds_region_tz_map.csv](/inputs/temporal/reeds_region_tz_map.csv)
+  - [stressperiods_user.csv](/inputs/temporal/stressperiods_user.csv)
+
+<a id='inputs-transmission'></a>
+#### inputs/transmission
+
+  - [cost_hurdle_country.csv](/inputs/transmission/cost_hurdle_country.csv)
+    - **File Type:** GAMS set
+    - **Description:** Cost for transmission hurdle rate by country
+    - **Indices:** country
+    - **Dollar year:** 2004
+  - [cost_hurdle_intra.csv](/inputs/transmission/cost_hurdle_intra.csv)
+  - [rev_transmission_basecost.csv](/inputs/transmission/rev_transmission_basecost.csv)
+    - **File Type:** inputs
+    - **Description:** Unweighted average base cost across the four regions for which we have transmission cost data.
+    - **Indices:** Transreg
+    - **Dollar year:** 2004
+  - [transmission_capacity_future_ba_baseline.csv](/inputs/transmission/transmission_capacity_future_ba_baseline.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the baseline case at BA resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_ba_default.csv](/inputs/transmission/transmission_capacity_future_ba_default.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the default case at BA resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_ba_LCC_all.csv](/inputs/transmission/transmission_capacity_future_ba_LCC_all.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the LCC_all case at BA resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_ba_VSC_all.csv](/inputs/transmission/transmission_capacity_future_ba_VSC_all.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the VSC_all_case at BA resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_county_baseline.csv](/inputs/transmission/transmission_capacity_future_county_baseline.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the baseline case at county resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_county_default.csv](/inputs/transmission/transmission_capacity_future_county_default.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the default case at county resolution
+    - **Indices:** r,rr
+  - [transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csv](/inputs/transmission/transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csv)
+    - **File Type:** inputs
+    - **Description:** Future transmission capacity additions for the LCC_1000miles_demand1_wind1_subferc_20230629 case at BA resolution
+    - **Indices:** r,rr
+  - [transmission_cost_ac_500kv_ba.h5](/inputs/transmission/transmission_cost_ac_500kv_ba.h5)
+    - **Description:** Transmission costs for new 500 kV AC at BA resolution
+  - [transmission_cost_ac_500kv_county.h5](/inputs/transmission/transmission_cost_ac_500kv_county.h5)
+    - **Description:** Transmission costs for new 500 kV AC at county resolution
+  - [transmission_cost_dc_ba.csv](/inputs/transmission/transmission_cost_dc_ba.csv)
+    - **Description:** Transmission costs for new 500 kV DC at BA resolution
+  - [transmission_cost_dc_county.csv](/inputs/transmission/transmission_cost_dc_county.csv)
+    - **Description:** Transmission costs for new 500 kV DC at county resolution
+  - [transmission_distance_ba.h5](/inputs/transmission/transmission_distance_ba.h5)
+    - **Description:** Length of least-cost transmission paths between zones at BA resolution
+  - [transmission_distance_county.h5](/inputs/transmission/transmission_distance_county.h5)
+    - **Description:** Length of least-cost transmission paths between zones at county resolution
+
+<a id='inputs-upgrades'></a>
+#### inputs/upgrades
+
+  - [i_coolingtech_watersource_upgrades.csv](/inputs/upgrades/i_coolingtech_watersource_upgrades.csv)
+    - **File Type:** Inputs
+    - **Description:** List of cooling technologies for water sources that can be upgraded.
+    - **Indices:** i
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [i_coolingtech_watersource_upgrades_link.csv](/inputs/upgrades/i_coolingtech_watersource_upgrades_link.csv)
+    - **File Type:** Inputs
+    - **Description:** List of cooling technologies for water sources that can be upgraded + their to, from, ctt (cooling technology type) and wst (water source type)
+    - **Indices:** i, ctt, wst
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [upgrade_costs_ccs_coal.csv](/inputs/upgrades/upgrade_costs_ccs_coal.csv)
+  - [upgrade_costs_ccs_gas.csv](/inputs/upgrades/upgrade_costs_ccs_gas.csv)
+  - [upgrade_link.csv](/inputs/upgrades/upgrade_link.csv)
+    - **File Type:** Inputs
+    - **Description:** Techs that can be upgraded including the original technology, the technology it is upgrading to, and the delta.
+    - **Indices:** i
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [upgrade_mult_atb23_ccs_adv.csv](/inputs/upgrades/upgrade_mult_atb23_ccs_adv.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost adjustment (advanced) over various years for upgrade technologies
+    - **Indices:** i,t
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [upgrade_mult_atb23_ccs_con.csv](/inputs/upgrades/upgrade_mult_atb23_ccs_con.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost adjustment (conservative) over various years for upgrade technologies
+    - **Indices:** i,t
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [upgrade_mult_atb23_ccs_mid.csv](/inputs/upgrades/upgrade_mult_atb23_ccs_mid.csv)
+    - **File Type:** Inputs
+    - **Description:** Cost adjustment (Mid) over various years for upgrade technologies
+    - **Indices:** i,t
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+  - [upgradelink_water.csv](/inputs/upgrades/upgradelink_water.csv)
+    - **File Type:** Inputs
+    - **Description:** Water techs that can be upgraded including the original technology, the technology it is upgrading to, and the delta
+    - **Indices:** i
+    - **Dollar year:** N/A
+    - **Citation:** N/A
+
+<a id='inputs-userinput'></a>
+#### inputs/userinput
+
+  - [futurefiles.csv](/inputs/userinput/futurefiles.csv)
+  - [ivt_default.csv](/inputs/userinput/ivt_default.csv)
+  - [ivt_small.csv](/inputs/userinput/ivt_small.csv)
+  - [ivt_step.csv](/inputs/userinput/ivt_step.csv)
+    - **Description:** ivt steps for endyears beyond 2050
+  - [modeled_regions.csv](/inputs/userinput/modeled_regions.csv)
+    - **Description:** Sets of BA regions that a user can model in a run. Each column is a different region option and can be specified in cases using GSw_Region.
+  - [windows_2100.csv](/inputs/userinput/windows_2100.csv)
+    - **Description:** Window size for using window solve method to 2100
+  - [windows_default.csv](/inputs/userinput/windows_default.csv)
+    - **Description:** Window size for using window solve method
+  - [windows_step10.csv](/inputs/userinput/windows_step10.csv)
+    - **Description:** Window size for beyond2050step10
+  - [windows_step5.csv](/inputs/userinput/windows_step5.csv)
+    - **Description:** Window size for beyond2050step5
+
+<a id='inputs-valuestreams'></a>
+#### inputs/valuestreams
+
+  - [var_map.csv](/inputs/valuestreams/var_map.csv)
+
+<a id='inputs-waterclimate'></a>
+#### inputs/waterclimate
+
+  - [cost_cap_mult.csv](/inputs/waterclimate/cost_cap_mult.csv)
+  - [cost_vom_mult.csv](/inputs/waterclimate/cost_vom_mult.csv)
+  - [heat_rate_mult.csv](/inputs/waterclimate/heat_rate_mult.csv)
+  - [i_coolingtech_watersource.csv](/inputs/waterclimate/i_coolingtech_watersource.csv)
+  - [i_coolingtech_watersource_link.csv](/inputs/waterclimate/i_coolingtech_watersource_link.csv)
+  - [tg_rsc_cspagg_tmp.csv](/inputs/waterclimate/tg_rsc_cspagg_tmp.csv)
+  - [unapp_water_sea_distr.csv](/inputs/waterclimate/unapp_water_sea_distr.csv)
+  - [wat_access_cap_cost.csv](/inputs/waterclimate/wat_access_cap_cost.csv)
+  - [water_req_psh_10h_1_51.csv](/inputs/waterclimate/water_req_psh_10h_1_51.csv)
+  - [water_with_cons_rate.csv](/inputs/waterclimate/water_with_cons_rate.csv)
+
+<a id='inputs-zones'></a>
+#### inputs/zones
+
+  - [hierarchy_offshore.csv](/inputs/zones/hierarchy_offshore.csv)
+
+<a id='postprocessing'></a>
+### postprocessing
+
+  - [example.csv](/postprocessing/example.csv)
+
+<a id='postprocessing-air-quality'></a>
+#### postprocessing/air_quality
+
+  - [scenarios.csv](/postprocessing/air_quality/scenarios.csv)
+
+<a id='postprocessing-air-quality-rcm-data'></a>
+##### postprocessing/air_quality/rcm_data
+
+  - [counties_ACS_high_stack_2017.csv](/postprocessing/air_quality/rcm_data/counties_ACS_high_stack_2017.csv)
+  - [counties_H6C_high_stack_2017.csv](/postprocessing/air_quality/rcm_data/counties_H6C_high_stack_2017.csv)
+  - [states_ACS_high_stack_2017.csv](/postprocessing/air_quality/rcm_data/states_ACS_high_stack_2017.csv)
+  - [states_H6C_high_stack_2017.csv](/postprocessing/air_quality/rcm_data/states_H6C_high_stack_2017.csv)
+
+<a id='postprocessing-bokehpivot'></a>
+#### postprocessing/bokehpivot
+
+  - [reeds_scenarios.csv](/postprocessing/bokehpivot/reeds_scenarios.csv)
+    - **Description:** Example data for ReEDS scenarios, each scenario with a custom style 
+
+<a id='postprocessing-bokehpivot-in'></a>
+##### postprocessing/bokehpivot/in
+
+  - [example_custom_styles.csv](/postprocessing/bokehpivot/in/example_custom_styles.csv)
+    - **Description:** Examples of custom styles used for bokehpivot
+  - [example_data_US_electric_power_generation.csv](/postprocessing/bokehpivot/in/example_data_US_electric_power_generation.csv)
+    - **Description:** Example data for US electric power generation
+  - [gis_centroid_rb.csv](/postprocessing/bokehpivot/in/gis_centroid_rb.csv)
+  - [gis_nercr.csv](/postprocessing/bokehpivot/in/gis_nercr.csv)
+  - [gis_nercr_new.csv](/postprocessing/bokehpivot/in/gis_nercr_new.csv)
+  - [gis_rb.csv](/postprocessing/bokehpivot/in/gis_rb.csv)
+  - [gis_rs.csv](/postprocessing/bokehpivot/in/gis_rs.csv)
+  - [gis_rto.csv](/postprocessing/bokehpivot/in/gis_rto.csv)
+  - [gis_st.csv](/postprocessing/bokehpivot/in/gis_st.csv)
+  - [state_code.csv](/postprocessing/bokehpivot/in/state_code.csv)
+    - **Description:** Abbreviation and code for each state
+
+<a id='postprocessing-bokehpivot-in-reeds2'></a>
+###### postprocessing/bokehpivot/in/reeds2
+
+  - [class_map.csv](/postprocessing/bokehpivot/in/reeds2/class_map.csv)
+    - **Description:** Class mapping for bokehpivot postprocessing
+  - [class_style.csv](/postprocessing/bokehpivot/in/reeds2/class_style.csv)
+    - **Description:** Custom styles for classes in bokehpivot 
+  - [con_adj_map.csv](/postprocessing/bokehpivot/in/reeds2/con_adj_map.csv)
+  - [con_adj_style.csv](/postprocessing/bokehpivot/in/reeds2/con_adj_style.csv)
+  - [cost_cat_map.csv](/postprocessing/bokehpivot/in/reeds2/cost_cat_map.csv)
+  - [cost_cat_style.csv](/postprocessing/bokehpivot/in/reeds2/cost_cat_style.csv)
+  - [ctt_map.csv](/postprocessing/bokehpivot/in/reeds2/ctt_map.csv)
+  - [ctt_style.csv](/postprocessing/bokehpivot/in/reeds2/ctt_style.csv)
+  - [hours.csv](/postprocessing/bokehpivot/in/reeds2/hours.csv)
+    - **Description:** Hours for each of the 17 timeslices
+  - [m_bar_width.csv](/postprocessing/bokehpivot/in/reeds2/m_bar_width.csv)
+  - [m_map.csv](/postprocessing/bokehpivot/in/reeds2/m_map.csv)
+  - [m_style.csv](/postprocessing/bokehpivot/in/reeds2/m_style.csv)
+  - [process_style.csv](/postprocessing/bokehpivot/in/reeds2/process_style.csv)
+  - [tech_ctt_wst.csv](/postprocessing/bokehpivot/in/reeds2/tech_ctt_wst.csv)
+  - [tech_map.csv](/postprocessing/bokehpivot/in/reeds2/tech_map.csv)
+  - [tech_style.csv](/postprocessing/bokehpivot/in/reeds2/tech_style.csv)
+    - **Description:** Custom colors for each technology used by bokehpivot
+  - [trtype_map.csv](/postprocessing/bokehpivot/in/reeds2/trtype_map.csv)
+  - [trtype_style.csv](/postprocessing/bokehpivot/in/reeds2/trtype_style.csv)
+  - [wst_map.csv](/postprocessing/bokehpivot/in/reeds2/wst_map.csv)
+  - [wst_style.csv](/postprocessing/bokehpivot/in/reeds2/wst_style.csv)
+
+<a id='postprocessing-combine-runs'></a>
+#### postprocessing/combine_runs
+
+  - [combinefiles.csv](/postprocessing/combine_runs/combinefiles.csv)
+  - [runlist.csv](/postprocessing/combine_runs/runlist.csv)
+
+<a id='postprocessing-land-use'></a>
+#### postprocessing/land_use
+
+
+<a id='postprocessing-land-use-inputs'></a>
+##### postprocessing/land_use/inputs
+
+  - [federal_land_categories.csv](/postprocessing/land_use/inputs/federal_land_categories.csv)
+  - [field_definitions.csv](/postprocessing/land_use/inputs/field_definitions.csv)
+  - [nlcd_categories.csv](/postprocessing/land_use/inputs/nlcd_categories.csv)
+  - [nlcd_combined_categories.csv](/postprocessing/land_use/inputs/nlcd_combined_categories.csv)
+  - [usgs_categories.csv](/postprocessing/land_use/inputs/usgs_categories.csv)
+  - [usgs_combined_categories.csv](/postprocessing/land_use/inputs/usgs_combined_categories.csv)
+
+<a id='postprocessing-plots'></a>
+#### postprocessing/plots
+
+  - [scghg_annual.csv](/postprocessing/plots/scghg_annual.csv)
+  - [transmission-interface-coords.csv](/postprocessing/plots/transmission-interface-coords.csv)
+
+<a id='postprocessing-retail-rate-module'></a>
+#### postprocessing/retail_rate_module
+
+  - [capital_financing_assumptions.csv](/postprocessing/retail_rate_module/capital_financing_assumptions.csv)
+  - [df_f861_contiguous.csv](/postprocessing/retail_rate_module/df_f861_contiguous.csv)
+  - [df_f861_state.csv](/postprocessing/retail_rate_module/df_f861_state.csv)
+  - [inputs.csv](/postprocessing/retail_rate_module/inputs.csv)
+  - [inputs_default.csv](/postprocessing/retail_rate_module/inputs_default.csv)
+  - [load_by_state_eia.csv](/postprocessing/retail_rate_module/load_by_state_eia.csv)
+    - **Description:** End use load by state since 1960
+  - [map_i_to_tech.csv](/postprocessing/retail_rate_module/map_i_to_tech.csv)
+    - **Description:** Maps i to tech with custom coloring for each
+
+<a id='postprocessing-retail-rate-module-calc-historical-capex'></a>
+##### postprocessing/retail_rate_module/calc_historical_capex
+
+  - [existing_transmission_cost_bystate_USD2024.csv](/postprocessing/retail_rate_module/calc_historical_capex/existing_transmission_cost_bystate_USD2024.csv)
+
+<a id='postprocessing-retail-rate-module-inputs'></a>
+##### postprocessing/retail_rate_module/inputs
+
+  - [Electric O & M Expenses-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20O%20&%20M%20Expenses-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
+  - [Electric Operating Revenues-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20Operating%20Revenues-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
+  - [Electric Plant in Service-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20Plant%20in%20Service-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
+  - [f861_cust_counts.csv](/postprocessing/retail_rate_module/inputs/f861_cust_counts.csv)
+  - [overwrite-utility-energy_sales.csv](/postprocessing/retail_rate_module/inputs/overwrite-utility-energy_sales.csv)
+  - [state-meanbiaserror_rate-aggregation.csv](/postprocessing/retail_rate_module/inputs/state-meanbiaserror_rate-aggregation.csv)
+  - [Table_9.8_Average_Retail_Prices_of_Electricity.xlsx](/postprocessing/retail_rate_module/inputs/Table_9.8_Average_Retail_Prices_of_Electricity.xlsx)
+    - **Description:** Historical EIA861 rates (annual and monthly)
+
+<a id='postprocessing-revalue'></a>
+#### postprocessing/reValue
+
+  - [scenarios.csv](/postprocessing/reValue/scenarios.csv)
+
+<a id='postprocessing-tableau'></a>
+#### postprocessing/tableau
+
+  - [tables_to_aggregate.csv](/postprocessing/tableau/tables_to_aggregate.csv)
+
+<a id='preprocessing'></a>
+### preprocessing
+
+
+<a id='preprocessing-atb-updates-processing'></a>
+#### preprocessing/atb_updates_processing
+
+
+<a id='preprocessing-atb-updates-processing-input-files'></a>
+##### preprocessing/atb_updates_processing/input_files
+
+  - [batt_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/batt_plant_char_format.csv)
+  - [conv_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/conv_plant_char_format.csv)
+  - [csp_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/csp_plant_char_format.csv)
+  - [geo_fom_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/geo_fom_plant_char_format.csv)
+  - [h2-combustion_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/h2-combustion_plant_char_format.csv)
+    - **Description:** Plant characteristics for which the H2-CC and CT ATB estimates are made using Gas-CC and CT data in preprocessing
+  - [ofs-wind_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/ofs-wind_plant_char_format.csv)
+  - [ofs-wind_rsc_mult_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/ofs-wind_rsc_mult_plant_char_format.csv)
+  - [ons-wind_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/ons-wind_plant_char_format.csv)
+  - [upv_plant_char_format.csv](/preprocessing/atb_updates_processing/input_files/upv_plant_char_format.csv)
+
+<a id='reeds2pras'></a>
+### reeds2pras
+
+
+<a id='reeds2pras-test'></a>
+#### reeds2pras/test
+
+
+<a id='reeds2pras-test-reeds-cases'></a>
+##### reeds2pras/test/reeds_cases
+
+
+<a id='reeds2pras-test-reeds-cases-test'></a>
+###### reeds2pras/test/reeds_cases/test
+
+  - [cases_reeds2pras.csv](/reeds2pras/test/reeds_cases/test/cases_reeds2pras.csv)
+  - [meta.csv](/reeds2pras/test/reeds_cases/test/meta.csv)
+
+<a id='reeds2pras-test-reeds-cases-test-inputs-case'></a>
+###### reeds2pras/test/reeds_cases/test/inputs_case
+
+  - [hydcapadj.csv](/reeds2pras/test/reeds_cases/test/inputs_case/hydcapadj.csv)
+  - [hydcf.csv](/reeds2pras/test/reeds_cases/test/inputs_case/hydcf.csv)
+  - [mttr.csv](/reeds2pras/test/reeds_cases/test/inputs_case/mttr.csv)
+  - [outage_forced_hourly.h5](/reeds2pras/test/reeds_cases/test/inputs_case/outage_forced_hourly.h5)
+  - [outage_forced_static.csv](/reeds2pras/test/reeds_cases/test/inputs_case/outage_forced_static.csv)
+  - [outage_scheduled_hourly.h5](/reeds2pras/test/reeds_cases/test/inputs_case/outage_scheduled_hourly.h5)
+  - [resources.csv](/reeds2pras/test/reeds_cases/test/inputs_case/resources.csv)
+  - [tech-subset-table.csv](/reeds2pras/test/reeds_cases/test/inputs_case/tech-subset-table.csv)
+  - [unitdata.csv](/reeds2pras/test/reeds_cases/test/inputs_case/unitdata.csv)
+  - [unitsize.csv](/reeds2pras/test/reeds_cases/test/inputs_case/unitsize.csv)
+
+<a id='reeds2pras-test-reeds-cases-test-reeds-augur'></a>
+###### reeds2pras/test/reeds_cases/test/ReEDS_Augur
+
+
+<a id='reeds2pras-test-reeds-cases-test-reeds-augur-augur-data'></a>
+###### reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data
+
+  - [cap_converter_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/cap_converter_2035.csv)
+  - [charge_eff_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/charge_eff_2035.csv)
+  - [discharge_eff_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/discharge_eff_2035.csv)
+  - [energy_cap_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/energy_cap_2035.csv)
+  - [max_cap_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/max_cap_2035.csv)
+  - [max_unitsize_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/max_unitsize_2035.csv)
+  - [pras_load_2035.h5](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/pras_load_2035.h5)
+  - [pras_vre_gen_2035.h5](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/pras_vre_gen_2035.h5)
+  - [tran_cap_2035.csv](/reeds2pras/test/reeds_cases/test/ReEDS_Augur/augur_data/tran_cap_2035.csv)
+
+<a id='reeds-augur'></a>
+### ReEDS_Augur
+
+  - [augur_switches.csv](/ReEDS_Augur/augur_switches.csv)
+
+<a id='tests'></a>
+### tests
+
+
+<a id='tests-data'></a>
+#### tests/data
+
+
+<a id='tests-data-county'></a>
+##### tests/data/county
+
+  - [csp.h5](/tests/data/county/csp.h5)
+    - **Description:** Subset of county-level data for the github runner county test
+  - [distpv.h5](/tests/data/county/distpv.h5)
+    - **Description:** Subset of county-level data for the github runner county test
+  - [upv.h5](/tests/data/county/upv.h5)
+    - **Description:** Subset of county-level data for the github runner county test
+  - [wind-ofs.h5](/tests/data/county/wind-ofs.h5)
+    - **Description:** Subset of county-level data for the github runner county test
+  - [wind-ons.h5](/tests/data/county/wind-ons.h5)
+    - **Description:** Subset of county-level data for the github runner county test
+
+## Files 
+
+- [cases.csv](/cases.csv)
+  - **File Type:** Switches file
+  - **Description:** Contains the configuration settings for the ReEDS run(s).
+  - **Dollar year:** 2004
+- [cases_examples.csv](/cases_examples.csv)
+- [cases_small.csv](/cases_small.csv)
+  - **Description:** Contains settings to run ReEDS at a smaller scale to test operability of the ReEDS model. Turns off several technologies and reduces the model size to significantly improve solve times.
+- [cases_standardscenarios.csv](/cases_standardscenarios.csv)
+  - **File Type:** StdScen Cases file
+  - **Description:** Contains the configuration settings for the Standard Scenarios ReEDS runs.
+- [cases_test.csv](/cases_test.csv)
+  - **Description:** Contains the configuration settings for doing test runs including the default Pacific census division test case.
+- [e_report_params.csv](/e_report_params.csv)
+  - **Description:** Contains a parameter list used in the model along with descriptions of what they are and units used.
+- [runfiles.csv](/runfiles.csv)
+  - **Description:** Contains the locations of input data that is copied from the repository into the runs folder for each respective case.
+- [sources.csv](/sources.csv)
+  - **Description:** CSV file containing a list of all input files (csv, h5, csv.gz)
diff --git a/docs/source/user_guide.md b/docs/source/user_guide.md
index 2e847370c..4ee15efb6 100644
--- a/docs/source/user_guide.md
+++ b/docs/source/user_guide.md
@@ -1,5 +1,34 @@
 # User guide
 
+## General Suggestions
+### Document and Communicate Inputs
+Document and communicate inputs used and any changes made to the model. For example, published reports or journal articles should include enough information so that others can reproduce your work.
+
+### Emphasize Aggregated Results
+Emphasize aggregated results rather than results for a specific region or year, given that aggregate results will be more robust.  For example, the magnitude of new wind builds identified across a multistate region will be substantially more robust result than wind builds in a single ReEDS region. Similarly, the magnitude of natural gas combined cycle capacity additions across a multiyear period (e.g., 2030-2040) will be more robust result than the amount added in a single year (e.g., 2032).
+
+We recommend this practice because:
+- ReEDS results at their finest resolution can be sensitive to small changes in assumptions. For example, small differences in natural gas prices between two adjacent regions could lead to large disparities in gas-based capacity expansion in those regions that ignore practical constraints that might produce more even deployment.
+- ReEDS was designed as a national-scale model. That level of geographic scope means that many local conditions important to power plant build decisions might not be captured.
+
+### Emphasize Trends or Scenario Differences
+Emphasize trends or scenario differences over absolute results.  For example, the result that photovoltaic (PV) deployment is highly sensitive to natural gas price is more robust than the specific amounts of PV capacity deployed under high and low gas price scenarios.
+
+### Understand Calculations Behind Reported Numbers
+Do not rely on parameter names or labels alone when interpreting results. Explore the underlying calculations to ensure a robust understanding of the parameter and what it means. For example, reported electricity prices represent the marginal cost of meeting an additional infinitesimally small amount of load. They do not consider existing real-world market structure, rate design, or distribution costs, and thus should not be interpreted as estimates of retail rates.
+
+### Employ Scenario Sensitivity Analysis
+Always employ scenario sensitivity analysis. ReEDS is a deterministic model, so uncertainties are not captured in individual model runs.
+
+### Deliberately Choose Model Inputs
+Be deliberate in how you choose model inputs.  The ReEDS default values are not necessarily “most likely,” and “high” and “low” input options are not based on specific probabilities of occurrence.
+
+### Keep Questions Within Model's Scope
+Ensure that your questions are within the scope of the model. ReEDS is primarily meant to inform decisions at the investment or planning timescales in the bulk power sector.  For other questions at different timescales (such as unit commitment questions) or touching other sectors (such as distributed generation adoption), other models should be used.  However, ReEDS might be used to inform inputs or identify analysis questions for these other models.
+
+### Cautiously Use Near-Term Results
+Use near-term results with caution.  We have designed ReEDS primarily to consider long-term questions, and as such, not all near-term power sector changes are represented.
+
 ## Large input files
 
 Large input files,
@@ -46,7 +75,7 @@ Here is partial list of remotely hosted files used by ReEDS:
       affinity='euclidean', linkage='ward')
   ```
 
-  - If set to 'optimized', then a two-step custom optimization is performed using the `hourly_repperiods.optimize_period_weights()` and `hourly_repperiods.assign_representative_days()` functions to minimize the deviation in regional load and PV/wind CF between the weighted representative periods and the full year.
+  - If set to 'optimized', then a two-step custom optimization is performed using the `hourly_repperiods.optimize_period_weights()` and `hourly_repperiods.match_act2rep_milp()` functions to minimize the deviation in regional load and PV/wind CF between the weighted representative periods and the full year.
   - If set to a string containing the substring 'user', then instead of optimizing the choice of representative periods for this run, the model reads a user-supplied file at `inputs/temporal/period_szn_{GSw_HourlyClusterAlgorithm}.csv`.
       - So if you want to use the example period:szn map, set `GSw_HourlyClusterAlgorithm=user` and provide `inputs/temporal/period_szn_user.csv`.
       - If you want to specify a different period:szn map, then create a file with your label in the filename and set `GSw_HourlyClusterAlgorithm` to that same label (which must contain the substring 'user'). For example, for `GSw_HourlyClusterAlgorithm=user_myname_20230130`, provide `inputs/temporal/period_szn_user_myname_20230130.csv`.
@@ -71,38 +100,6 @@ Here is partial list of remotely hosted files used by ReEDS:
 
 
 
-## Hourly Resolution
-
-The model can be run at hourly resolution using the following switch settings:
-
-- `GSw_Hourly = 1`
-  - Turn on hourly resolution
-- `GSw_Canada = 2`
-  - Turn on hourly resolution for Canadian imports/exports
-- `GSw_AugurCurtailment = 0`
-  - Turn off the Augur calculation of curtailment
-- `GSw_StorageArbitrageMult = 0`
-  - Turn off the Augur calculation of storage arbitrage value
-- `GSw_Storage_in_Min = 0`
-  - Turn off the Augur calculation of storage charging
-- `capcredit_szn_hours = 3`
-  - The current default hourly representation is 18 representative 5-day weeks. Each representative period is treated as a 'season' and is thus active in the planning-reserve margin constraint. In h17 ReEDS we set `capcredit_szn_hours = 10`, giving 40 total hours considered for planning reserves (the top 10 hours in each of the 4 quarterly seasons). 18 'seasons' with 10 hours each would give 180 hours, so we switch to 3 hours per 'season' (for 54 hours total).
-
-To further reduce solve time, you can make the following changes:
-
-- `yearset = 2010_2015_2020_2025_2030_2035_2040_2045_2050`
-  - Solve in 5-year steps
-- `GSw_OpRes = 0`
-  - Turn off operating reserves
-- `GSw_MinLoading = 0`
-  - Turn off the sliding-window representation of minimum-generation limits
-- `GSw_PVB = 0`
-  - Turn off PV-battery hybrids
-- `GSw_calc_powfrac = 0`
-  - Turn off a post-processing calculation of power flows
-
-
-
 ## Electricity Demand Profiles
 
 ### Switch options for GSw_LoadProfiles
@@ -386,14 +383,19 @@ In addition, the `GSw_ReducedResource` switch allows for a uniform reduction of
 1. `GSw_TransInvMaxLongTerm`: Limit on annual transmission deployment nationwide **IN/AFTER** `firstyear_trans_longterm`, measured in TW-miles
 1. `GSw_TransInvMaxNearTerm`: Limit on annual transmission deployment nationwide **BEFORE** `firstyear_trans_longterm`, measured in TW-miles
 1. `GSw_TransInvPRMderate`: By default, adding 1 MW of transmission capacity between two zones increases the energy transfer capability by 1 MW but the PRM trading capability by only 0.85 MW; here you can adjust that derate
+1. `GSw_TransConductor`: Conductor type assumed for new interzonal AC transmission.
+`acss` applies the assumptions from the [MISO Transmission Cost Estimation Guide](https://www.misoenergy.org/planning/transmission-planning/mtep), using either ACSS or ACSR depending on voltage level;
+`acsr` uses ACSR for all voltage levels, reducing the power capacity of some representative lines and increasing the \$/MW cost.
 1. `GSw_TransCostMult`: Applies to interzonal transmission capacity (including AC/DC converters) but not FOM costs
-1. `GSw_TransSquiggliness`: Somewhat similar to `GSw_TransCostMult`, but scales the distance for each inter-zone interface. So turning it up to 1.3 will increase costs and losses by 1.3, and for the same amount of GW it will increase TWmiles by 1.3.
+1. `GSw_TransSquigglinessMin`: Minimum squiggliness (straight-line length multiplier) to apply for interzonal transmission; the default value of 1.3 is from the [MISO Transmission Cost Estimation Guide](https://www.misoenergy.org/planning/transmission-planning/mtep).
+The cost and length of representative interzonal transmission routes that are straighter than `GSw_TransSquigglinessMin` are scaled up to match `GSw_TransSquigglinessMin`
+(i.e., if a representative route is 11 miles long and the straight-line distance between its endpoints is 10 miles, giving a squiggliness factor of 1.1, its cost and length are scaled up by 1.3 / 1.1 = 1.18).
 1. `GSw_TransHurdle`: Intra-US hurdle rate for interzonal flows, measured in $2004/MWh
 1. `GSw_TransHurdleLevel`: Indicate the level of hierarchy.csv between which to apply the hurdle rate specified by `GSw_TransHurdle`. i.e. if set to ‘st’, intra-state flows will have no hurdle rates but inter-state flows will have hurdle rates specified by `GSw_TransHurdle`.
-1. `GSw_TransRestrict`: Indicate the level of hierarchy.csv within which to allow transmission expansion. i.e. if set to ‘st’, no inter-state expansion is allowed.
-1. `GSw_TransScen`: Indicate the inputs/transmission/transmission_capacity_future_{`GSw_TransScen`}.csv file to use, which includes the list of interfaces that can be expanded.
-Note that the full list of expandable interfaces is indicated by this file plus transmission_capacity_future_default.csv (currently planned additions) plus existing AC and DC interfaces (which can be expanded by default).
-Applies to AC, LCC, and VSC.
+1. `GSw_TransRestrict`: Spatial hierarchy level within which to allow transmission expansion. For example, if set to `st`, no inter-state expansion is allowed.
+1. `GSw_TransScen`: Which `inputs/transmission/hvdc_planned-{GSw_TransScen}.csv` file to use.
+This file provides a list of additional transmission lines that can be built.
+The full list of candidate lines is indicated by this file plus `hvdc_planned-baseline.csv` (currently planned additions) plus existing AC and DC interfaces (which can be expanded by default).
 1. `GSw_PRM_hierarchy_level`: Level of hierarchy.csv within which to calculate net load, used for capacity credit. Larger levels indicate more planning coordination between regions.
 1. `GSw_PRMTRADE_level`: Level of hierarchy.csv within which to allow PRM trading. By default it’s set to ‘country’, indicating no limits. If set to ‘r’, no PRM trading is allowed.
 
@@ -421,7 +423,7 @@ Some of the behavior of ReEDS2PRAS and PRAS (used for the stress periods resourc
 - ReEDS2PRAS technology representation
   - `pras_hydro_energylim` (default 1): Model hydro as energy-limited in PRAS (1) or like a thermal generator (0)
   - `pras_include_h2dac` (default 0): If set to 1, include demand associated with H2 production & DAC in PRAS
-  - `pras_trans_contingency` (default 0): Use n-0 (0) or n-1 (1) transmission capacities in PRAS
+  - `pras_trans_contingency` (default 0): Use energy (0) or PRM (1) transmission capacities in PRAS
 
 If a ReEDS case raises an out-of-memory error in ReEDS2PRAS/PRAS, the memory use can be reduced using some or all of the following settings:
 
@@ -448,9 +450,11 @@ For example, `default` will use `inputs/userinput/mcs_distributions_default.yaml
 3. Set `MCS_dist_groups` to one or more YAML group names. Separate multiple groups with a dot.
    For example `tech.load_st.ng_fuel_price`.
 
-4. Run ReEDS as usual. Each Monte Carlo draw will create its own run using the sampled inputs.
+4. Choose the sampling method; `MCS_lhs=1` uses a Latin Hypercube sampling method and `MCS_lhs=0` uses random sampling.
+
+5. Run ReEDS as usual. Each Monte Carlo draw will create its own run using the sampled inputs.
 
-These three switches (`MCS_runs`,`MCS_dist`, and  `MCS_dist_groups`) are the only required controls.
+These four switches (`MCS_runs`, `MCS_dist`, `MCS_dist_groups`, and `MCS_lhs`) are the only required controls.
 All other settings live in the YAML file (`inputs/userinput/mcs_distributions_{MCS_dist}.yaml`).
 
 ### YAML distribution file format
@@ -555,6 +559,31 @@ Each state receives its own weighted combination of the two load scenarios.\
 
 This enables state level uncertainty in siting supply curves for wind and solar technologies through a random draw between `limited` and `reference` conditions.
 
+### Sampling method
+
+There are two sampling approaches available for Monte Carlo analysis: random sampling and Latin Hypercube sampling. 
+
+Random sampling uses the numpy `random` method for the relevant distribution to sample a set of weights. 
+These weights are applied to the assignment values specified in the distribution group to generate the value for each run. 
+To ensure reproducibility, the Monte Carlo run number is used as the seed value. This means that, for a given 
+run configuration, run MC001 will always have the same sampled values. A global seed value (set by `MCS_seed` in `inputs/scalars.csv`) 
+can be used to shift the seed values for a batch of runs; this can be useful for extending a set of runs; 
+for example, if you ran already 200 runs and now want to add 100 more, set the seed value to 200 to generate 
+new sampled runs.
+
+The second approach, Latin Hypercube sampling (LHS), utilizes a quasi-Monte Carlo sampling method that is 
+designed to improve efficiency by reducing overlap of the sampled values. An overview of this method
+can be found in {cite}`sheikholeslamiProgressiveLatinHypercube2017`. For this method, an NxD matrix is generated upfront
+for all model runs based on the number of samples (N) and the independent dimensions being sampled (D). 
+The values in this matrix represent sampling of the cumulative probability distribution functions, 
+and are later used by the inverse CDF (percent point) functions to derive the actual sample values. 
+A single global seed value (set by `MCS_seed` in `inputs/scalars.csv`) is used for all runs, 
+resulting in unique sampling matrices for a given set of values of N and D.
+
+The LHS method tends to result in sampling values that converge on the true input distributions for lower numbers of samples.
+However, it does not currently support sampling for any spatial resolution besides country 
+or using multivariate distributions such as the Dirichlet.
+
 ### Tips
 
 - Use multiple distribution groups to sample switches independently.
@@ -600,7 +629,7 @@ Options are `capacity`, `transmission`, `rasharing`, and `co2`.
 - `GSw_MGA_SubObjective` (default `fossil`): Technology subset to minimize or maximize the capacity of (only used for `GSw_MGA_Objective = capacity`).
 Options are the column names in the `inputs/tech-subset-table.csv` file.
 
-Users familiar with GAMS can add alternative objective functions to the `c_mga.gms` file and associated options to the `GSw_MGA_Objective` switch in `cases.csv`.
+Users familiar with GAMS can add alternative objective functions to the `d_mga.gms` file and associated options to the `GSw_MGA_Objective` switch in `cases.csv`.
 
 
 
@@ -717,7 +746,6 @@ This section provides guidance on identifying and resolving common issues encoun
   - What to look for:
     - `1_inputs.lst`: errors will be preceded by `****`
     - `{batch_prefix}_{case}_{year}i0.lst`: there should be one file for each year of the model run
-    - `Augur_errors_{year}`: this file will appear in the event that there is an augur-related issue
 
 - GAMS Workfiles
   - Path: `/runs/{batch_prefix}_{case}/g00files/`
@@ -733,10 +761,10 @@ This section provides guidance on identifying and resolving common issues encoun
       - these files should contain data, an error message "GDX file not found" indicates an issue with the reporting script at the end of the model
     - `reeds-report/` and `reeds-report-reduced/`: if these folders are not present, it can indicate a problem with the post-processing scripts
 
-- Augur Data
-  - Path: `/runs/{batch_prefix}_{case}/ReEDS_Augur/augur_data/`
+- Resource adequacy data
+  - Path: `/runs/{batch_prefix}_{case}/handoff/reeds_data/`
   - What to look for:
-    - `ReEDS_Augur_{year}.gdx`: there should be a file for each year of the model run =
+    - `ccdata_{year}.gdx`: there should be a file for each year of the model run =
     - `reeds_data_{year}.gdx`: there should be a file for each year of the model run
 
 - Case Inputs
diff --git a/sources.csv b/docs/sources.csv
similarity index 98%
rename from sources.csv
rename to docs/sources.csv
index 22a5f39bd..e0ca5e3cc 100644
--- a/sources.csv
+++ b/docs/sources.csv
@@ -1,5 +1,5 @@
 RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,Indices,DollarYear,Citation,Filetype,Units
-/cases.csv,/,cases,.csv,Contains the configuration settings for the ReEDS run(s).,,2004,https://github.nrel.gov/ReEDS/ReEDS-2.0/blob/38e6610a8c6a92291804598c95c11b707bf187b9/cases.csv,Switches file,
+/cases.csv,/,cases,.csv,Contains the configuration settings for the ReEDS run(s).,,2004,,Switches file,
 /cases_examples.csv,/,cases_examples,.csv,,,,,,
 /cases_small.csv,/,cases_small,.csv,Contains settings to run ReEDS at a smaller scale to test operability of the ReEDS model. Turns off several technologies and reduces the model size to significantly improve solve times.,,,,,
 /cases_standardscenarios.csv,/,cases_standardscenarios,.csv,Contains the configuration settings for the Standard Scenarios ReEDS runs.,,,,StdScen Cases file,
@@ -258,9 +258,9 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/fuelprices/alpha_AEO_2025_reference.csv,/inputs/fuelprices,alpha_AEO_2025_reference,.csv,"reference census division alpha values, used in the calculation of natural gas demand curves","allt,cendiv",2004,AEO 2025,Input,
 /inputs/fuelprices/cd_beta0.csv,/inputs/fuelprices,cd_beta0,.csv,reference census division beta levels electric sector,cendiv,2004,,Input,
 /inputs/fuelprices/cd_beta0_allsector.csv,/inputs/fuelprices,cd_beta0_allsector,.csv,reference census division beta levels all sectors,cendiv,2004,,Input,
-/inputs/fuelprices/cendivweights.csv,/inputs/fuelprices,cendivweights,.csv,weights to smooth gas prices between census regions to avoid abrupt price changes at the cendiv borders,"r,cendiv",,,,
-/inputs/fuelprices/coal_AEO_2023_reference.csv,/inputs/fuelprices,coal_AEO_2023_reference,.csv,reference case census division fuel price of coal,"t,cendiv",2022,,,
-/inputs/fuelprices/coal_AEO_2025_reference.csv,/inputs/fuelprices,coal_AEO_2025_reference,.csv,reference case census division fuel price of coal with missing values forward-filled from earlier years,"t,cendiv",2024,,,
+/inputs/fuelprices/coal_AEO_2025_reference.csv,/inputs/fuelprices,coal_AEO_2025_reference,.csv,AEO2025 Reference case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic,"t,cendiv",2024,AEO2025,Input,$/MMBtu
+/inputs/fuelprices/coal_AEO_2026_altelec.csv,/inputs/fuelprices,coal_AEO_2026_altelec,.csv,AEO2026 Alternative Electricity case census division fuel price of coal with missing New England values set to Mid Atlantic,"t,cendiv",2025,AEO2026,Input,$/MMBtu
+/inputs/fuelprices/coal_AEO_2026_baseline.csv,/inputs/fuelprices,coal_AEO_2026_baseline,.csv,AEO2026 Counterfactual Baseline case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic,"t,cendiv",2025,AEO2026,Input,$/MMBtu
 /inputs/fuelprices/dollaryear.csv,/inputs/fuelprices,dollaryear,.csv,Dollar year mapping for each fuel price scenario,,,,,
 /inputs/fuelprices/h2-combustion_10.csv,/inputs/fuelprices,h2-combustion_10,.csv,price of hydrogen for combustion technologies (h2-ct and cc) at $10/MMBtu for all years,,,,,
 /inputs/fuelprices/h2-combustion_30.csv,/inputs/fuelprices,h2-combustion_30,.csv,price of hydrogen for combustion technologies (h2-ct and cc) at $30/MMBtu for all years,,,,,
@@ -306,14 +306,14 @@ RelativeFilePath,RelativeFolderPath,FileName_new,FileExtension,Description_new,I
 /inputs/hydro/net_gen_existing_hydro.h5,/inputs/hydro,net_gen_existing_hydro,.h5,"Monthly net generation values for hydro plants spanning 2007-2022, which come from ORNL's Existing Hydropower Assets dataset.","t,month",,,Input,MWh
 /inputs/hydro/SeaCapAdj_hy.csv,/inputs/hydro,SeaCapAdj_hy,.csv,,,,,,
 /inputs/load/cangrowth.csv,/inputs/load,cangrowth,.csv,Canada load growth multiplier,,,,,
-/inputs/load/demand_AEO_2023_high.csv,/inputs/load,demand_AEO_2023_high,.csv,Load growth projection from the AEO2023 High Economic Growth scenario,,,,,unitless
-/inputs/load/demand_AEO_2023_low.csv,/inputs/load,demand_AEO_2023_low,.csv,Load growth projection from the AEO2023 Low Economic Growth scenario,,,,,unitless
-/inputs/load/demand_AEO_2023_reference.csv,/inputs/load,demand_AEO_2023_reference,.csv,Load growth projection from the AEO2023 Reference scenario,,,,,unitless
 /inputs/load/demand_AEO_2025_high.csv,/inputs/load,demand_AEO_2025_high,.csv,Load growth projection from the AEO2025 High Economic Growth scenario,,,,,unitless
 /inputs/load/demand_AEO_2025_low.csv,/inputs/load,demand_AEO_2025_low,.csv,Load growth projection from the AEO2025 Low Economic Growth scenario,,,,,unitless
 /inputs/load/demand_AEO_2025_reference.csv,/inputs/load,demand_AEO_2025_reference,.csv,Load growth projection from the AEO2025 Reference scenario,,,,,unitless
-/inputs/load/EIA_loadbystate.csv,/inputs/load,EIA_loadbystate,.csv,,,,,,
-/inputs/load/loadsite_country_test.csv,/inputs/load,loadsite_country_test,.csv,,,,,,
+/inputs/load/demand_AEO_2026_high.csv,/inputs/load,demand_AEO_2026_high,.csv,Load growth projection from the AEO2026 High Economic Growth scenario,,,,,unitless
+/inputs/load/demand_AEO_2026_low.csv,/inputs/load,demand_AEO_2026_low,.csv,Load growth projection from the AEO2026 Low Economic Growth scenario,,,,,unitless
+/inputs/load/demand_AEO_2026_baseline.csv,/inputs/load,demand_AEO_2026_baseline,.csv,Load growth projection from the AEO2026 Counterfactual Baseline scenario,,,,,unitless
+/inputs/load/EIA_loadbystate.csv,/inputs/load,EIA_loadbystate,.csv,,,,,/
+/inputs/load/loadsite_country_test.csv,/inputs/load,loadsite_country_test,.csv,,,,,/
 /inputs/load/mex_growth_rate.csv,/inputs/load,mex_growth_rate,.csv,Mexico load growth multiplier,,,,,
 /inputs/national_generation/gen_mandate_tech_list.csv,/inputs/national_generation,gen_mandate_tech_list,.csv,,,,,,
 /inputs/national_generation/gen_mandate_trajectory.csv,/inputs/national_generation,gen_mandate_trajectory,.csv,,,,,,
diff --git a/sources_documentation.md b/docs/sources_documentation.md
similarity index 97%
rename from sources_documentation.md
rename to docs/sources_documentation.md
index b4b86651a..21237a2ee 100644
--- a/sources_documentation.md
+++ b/docs/sources_documentation.md
@@ -63,6 +63,7 @@
     - [userinput](#inputs-userinput)
     - [valuestreams](#inputs-valuestreams)
     - [waterclimate](#inputs-waterclimate)
+    - [zones](#inputs-zones)
   - [postprocessing](#postprocessing)
     - [air_quality](#postprocessing-air-quality)
       - [rcm_data](#postprocessing-air-quality-rcm-data)
@@ -96,10 +97,6 @@
   - **File Type:** Switches file
   - **Description:** Contains the configuration settings for the ReEDS run(s).
   - **Dollar year:** 2004
-  - **Citation:** [https://github.nrel.gov/ReEDS/ReEDS-2.0/blob/38e6610a8c6a92291804598c95c11b707bf187b9/cases.csv](https://github.nrel.gov/ReEDS/ReEDS-2.0/blob/38e6610a8c6a92291804598c95c11b707bf187b9/cases.csv)
----
-
-- [cases_county.csv](/cases_county.csv)
 ---
 
 - [cases_examples.csv](/cases_examples.csv)
@@ -690,9 +687,6 @@
   - [hierarchy_agg69.csv](/inputs/hierarchy_agg69.csv)
 ---
 
-  - [hierarchy_offshore.csv](/inputs/zones/hierarchy_offshore.csv)
----
-
   - [remote_files.csv](/inputs/remote_files.csv)
 ---
 
@@ -828,13 +822,9 @@
 ---
 
 
-
 <a id='inputs-climate-gfdl-esm2m-rcp4p5-wm'></a>
 ##### inputs/climate/GFDL-ESM2M_RCP4p5_WM
 
-  - [HDDCDD.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/HDDCDD.csv)
----
-
   - [hydadjann.csv](/inputs/climate/GFDL-ESM2M_RCP4p5_WM/hydadjann.csv)
     - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the GFDL-ESM2M_RCP4p5_WM climate scenario
     - **Indices:** r,t
@@ -874,9 +864,6 @@
 <a id='inputs-climate-hadgem2-es-rcp2p6'></a>
 ##### inputs/climate/HadGEM2-ES_RCP2p6
 
-  - [HDDCDD.csv](/inputs/climate/HadGEM2-ES_RCP2p6/HDDCDD.csv)
----
-
   - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP2p6/UnappWaterMult.csv)
     - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP2p6 climate scenario
     - **Indices:** wst,r,month,t
@@ -902,9 +889,6 @@
 <a id='inputs-climate-hadgem2-es-rcp45-at'></a>
 ##### inputs/climate/HadGEM2-ES_rcp45_AT
 
-  - [HDDCDD.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/HDDCDD.csv)
----
-
   - [hydadjann.csv](/inputs/climate/HadGEM2-ES_rcp45_AT/hydadjann.csv)
     - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the HadGEM2-ES_rcp45_AT climate scenario
     - **Indices:** r,t
@@ -944,9 +928,6 @@
 <a id='inputs-climate-hadgem2-es-rcp4p5'></a>
 ##### inputs/climate/HadGEM2-ES_RCP4p5
 
-  - [HDDCDD.csv](/inputs/climate/HadGEM2-ES_RCP4p5/HDDCDD.csv)
----
-
   - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP4p5/UnappWaterMult.csv)
     - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP4p5 climate scenario
     - **Indices:** wst,r,month,t
@@ -972,9 +953,6 @@
 <a id='inputs-climate-hadgem2-es-rcp85-at'></a>
 ##### inputs/climate/HadGEM2-ES_rcp85_AT
 
-  - [HDDCDD.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/HDDCDD.csv)
----
-
   - [hydadjann.csv](/inputs/climate/HadGEM2-ES_rcp85_AT/hydadjann.csv)
     - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the HadGEM2-ES_rcp85_AT climate scenario
     - **Indices:** r,t
@@ -1014,9 +992,6 @@
 <a id='inputs-climate-hadgem2-es-rcp8p5'></a>
 ##### inputs/climate/HadGEM2-ES_RCP8p5
 
-  - [HDDCDD.csv](/inputs/climate/HadGEM2-ES_RCP8p5/HDDCDD.csv)
----
-
   - [UnappWaterMult.csv](/inputs/climate/HadGEM2-ES_RCP8p5/UnappWaterMult.csv)
     - **Description:** Climate-impact water availability multipliers for annual/monthly unappropriated fresh surface water for the HadGEM2-ES_RCP8p5 climate scenario
     - **Indices:** wst,r,month,t
@@ -1042,9 +1017,6 @@
 <a id='inputs-climate-ipsl-cm5a-lr-rcp8p5-wm'></a>
 ##### inputs/climate/IPSL-CM5A-LR_RCP8p5_WM
 
-  - [HDDCDD.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/HDDCDD.csv)
----
-
   - [hydadjann.csv](/inputs/climate/IPSL-CM5A-LR_RCP8p5_WM/hydadjann.csv)
     - **Description:** Climate-impact capacity factor multipliers for annual dispatchable hydropower for the IPSL-CM5A-LR_RCP8p5_WM climate scenario
     - **Indices:** r,t
@@ -1205,7 +1177,7 @@
   - [dr_shed_avail_scalar.csv](/inputs/demand_response/dr_shed_avail_scalar.csv)
 ---
 
-  - [dr_shed_capacity_scalar_demo_data_IEF_January_2025.csv](/inputs/demand_response/dr_shed_capacity_scalar_demo_data_IEF_January_2025.csv)
+  - [dr_shed_capacity_scalar_demo_data_January_2025.csv](/inputs/demand_response/dr_shed_capacity_scalar_demo_data_January_2025.csv)
 ---
 
   - [dr_shed_hourly.h5](/inputs/demand_response/dr_shed_hourly.h5)
@@ -1563,21 +1535,34 @@
     - **Dollar year:** 2004
 ---
 
-  - [cendivweights.csv](/inputs/fuelprices/cendivweights.csv)
-    - **Description:** weights to smooth gas prices between census regions to avoid abrupt price changes at the cendiv borders
-    - **Indices:** r,cendiv
+  - [coal_AEO_2025_reference.csv](/inputs/fuelprices/coal_AEO_2025_reference.csv)
+    - **File Type:** Input
+    - **Description:** AEO2025 Reference case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic
+    - **Indices:** t,cendiv
+    - **Dollar year:** 2024
+    - **Citation:** AEO2025
+    - **Units:** $/MMBtu
+
 ---
 
-  - [coal_AEO_2023_reference.csv](/inputs/fuelprices/coal_AEO_2023_reference.csv)
-    - **Description:** reference case census division fuel price of coal
+  - [coal_AEO_2026_altelec.csv](/inputs/fuelprices/coal_AEO_2026_altelec.csv)
+    - **File Type:** Input
+    - **Description:** AEO2026 Alternative Electricity case census division fuel price of coal with missing New England values set to Mid Atlantic
     - **Indices:** t,cendiv
-    - **Dollar year:** 2022
+    - **Dollar year:** 2025
+    - **Citation:** AEO2026
+    - **Units:** $/MMBtu
+
 ---
 
-  - [coal_AEO_2025_reference.csv](/inputs/fuelprices/coal_AEO_2025_reference.csv)
-    - **Description:** reference case census division fuel price of coal with missing values forward-filled from earlier years
+  - [coal_AEO_2026_baseline.csv](/inputs/fuelprices/coal_AEO_2026_baseline.csv)
+    - **File Type:** Input
+    - **Description:** AEO2026 Counterfactual Baseline case census division fuel price of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic
     - **Indices:** t,cendiv
-    - **Dollar year:** 2024
+    - **Dollar year:** 2025
+    - **Citation:** AEO2026
+    - **Units:** $/MMBtu
+
 ---
 
   - [dollaryear.csv](/inputs/fuelprices/dollaryear.csv)
@@ -1852,40 +1837,41 @@
 
   - [cangrowth.csv](/inputs/load/cangrowth.csv)
     - **Description:** Canada load growth multiplier
+
 ---
 
-  - [demand_AEO_2023_high.csv](/inputs/load/demand_AEO_2023_high.csv)
-    - **Description:** Load growth projection from the AEO2023 High Economic Growth scenario
+  - [demand_AEO_2025_high.csv](/inputs/load/demand_AEO_2025_high.csv)
+    - **Description:** Load growth projection from the AEO2025 High Economic Growth scenario
     - **Units:** unitless
 
 ---
 
-  - [demand_AEO_2023_low.csv](/inputs/load/demand_AEO_2023_low.csv)
-    - **Description:** Load growth projection from the AEO2023 Low Economic Growth scenario
+  - [demand_AEO_2025_low.csv](/inputs/load/demand_AEO_2025_low.csv)
+    - **Description:** Load growth projection from the AEO2025 Low Economic Growth scenario
     - **Units:** unitless
 
 ---
 
-  - [demand_AEO_2023_reference.csv](/inputs/load/demand_AEO_2023_reference.csv)
-    - **Description:** Load growth projection from the AEO2023 Reference scenario
+  - [demand_AEO_2025_reference.csv](/inputs/load/demand_AEO_2025_reference.csv)
+    - **Description:** Load growth projection from the AEO2025 Reference scenario
     - **Units:** unitless
 
 ---
 
-  - [demand_AEO_2025_high.csv](/inputs/load/demand_AEO_2025_high.csv)
-    - **Description:** Load growth projection from the AEO2025 High Economic Growth scenario
+  - [demand_AEO_2026_high.csv](/inputs/load/demand_AEO_2026_high.csv)
+    - **Description:** Load growth projection from the AEO2026 High Economic Growth scenario
     - **Units:** unitless
 
 ---
 
-  - [demand_AEO_2025_low.csv](/inputs/load/demand_AEO_2025_low.csv)
-    - **Description:** Load growth projection from the AEO2025 Low Economic Growth scenario
+  - [demand_AEO_2026_low.csv](/inputs/load/demand_AEO_2026_low.csv)
+    - **Description:** Load growth projection from the AEO2026 Low Economic Growth scenario
     - **Units:** unitless
 
 ---
 
-  - [demand_AEO_2025_reference.csv](/inputs/load/demand_AEO_2025_reference.csv)
-    - **Description:** Load growth projection from the AEO2025 Reference scenario
+  - [demand_AEO_2026_baseline.csv](/inputs/load/demand_AEO_2026_reference.csv)
+    - **Description:** Load growth projection from the AEO2026 Counterfactual Baseline scenario
     - **Units:** unitless
 
 ---
@@ -2926,12 +2912,13 @@
   - [cap_existing_psh.csv](/inputs/storage/cap_existing_psh.csv)
     - **Description:** County-wide PSH operational capacity, pump capacity, and max energy, based on plant-level data from https://www.hydropower.org/hydropower-pumped-storage-tool
     - **Units:** MW/MWh
+
 ---
 
   - [PSH_supply_curves_durations.csv](/inputs/storage/PSH_supply_curves_durations.csv)
 ---
 
-  - [storinmaxfrac.csv](/inputs/storage/storinmaxfrac.csv)
+  - [storage_duration.csv](/inputs/storage/storage_duration.csv)
 ---
 
 
@@ -3340,34 +3327,6 @@
     - **Indices:** r,rr
 ---
 
-  - [transmission_capacity_init_AC_ba_NARIS2024.csv](/inputs/transmission/transmission_capacity_init_AC_ba_NARIS2024.csv)
-    - **Description:** Initial AC transmission capacity from the NARIS 2024 system at the BA resolution - 'NARIS2024' is a better starting point for future-oriented studies, but it becomes increasingly inaccurate for years earlier than 2024
----
-
-  - [transmission_capacity_init_AC_ba_REFS2009.csv](/inputs/transmission/transmission_capacity_init_AC_ba_REFS2009.csv)
-    - **Description:** Initial AC transmission capacity from the 2009 transmission system for ReEDS at the BA resolution - 'REFS2009' does not include direction-dependent capacities or differentiated capacities for energy and PRM trading but it better represents historical additions between 2010-2024
----
-
-  - [transmission_capacity_init_AC_county_NARIS2024.csv](/inputs/transmission/transmission_capacity_init_AC_county_NARIS2024.csv)
-    - **Description:** Initial AC transmission capacity modified from the NARIS 2024 file to eliminate most supply (with county transmission) demand mismatches for the 2024 solve year
----
-
-  - [transmission_capacity_init_AC_county_NARIS2024_base.csv](/inputs/transmission/transmission_capacity_init_AC_county_NARIS2024_base.csv)
-    - **Description:** Initial AC transmission capacity from the NARIS 2024 system at the county resolution
----
-
-  - [transmission_capacity_init_AC_transgrp_NARIS2024.csv](/inputs/transmission/transmission_capacity_init_AC_transgrp_NARIS2024.csv)
-    - **Description:** Initial AC transmission capacity from the NARIS 2024 system at the transgrp resolution
----
-
-  - [transmission_capacity_init_nonAC_ba.csv](/inputs/transmission/transmission_capacity_init_nonAC_ba.csv)
-    - **Description:** Initial non-AC transmission capacity at the BA resolution
----
-
-  - [transmission_capacity_init_nonAC_county.csv](/inputs/transmission/transmission_capacity_init_nonAC_county.csv)
-    - **Description:** Initial non-AC transmission capacity at the county resolution
----
-
   - [transmission_cost_ac_500kv_ba.h5](/inputs/transmission/transmission_cost_ac_500kv_ba.h5)
     - **Description:** Transmission costs for new 500 kV AC at BA resolution
 ---
@@ -3537,6 +3496,13 @@
 ---
 
 
+<a id='inputs-zones'></a>
+#### inputs/zones
+
+  - [hierarchy_offshore.csv](/inputs/zones/hierarchy_offshore.csv)
+---
+
+
 <a id='postprocessing'></a>
 ### postprocessing
 
@@ -3764,12 +3730,15 @@
 ##### postprocessing/retail_rate_module/inputs
 
   - [Electric O & M Expenses-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20O%20&%20M%20Expenses-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
 ---
 
   - [Electric Operating Revenues-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20Operating%20Revenues-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
 ---
 
   - [Electric Plant in Service-IOU-1993-2019.csv](/postprocessing/retail_rate_module/inputs/Electric%20Plant%20in%20Service-IOU-1993-2019.csv)
+    - **Description:** values taken from FERC Form 1 -- see https://docs.nlr.gov/docs/fy22osti/78224.pdf sections 2.2.2 and 2.2.3
 ---
 
   - [f861_cust_counts.csv](/postprocessing/retail_rate_module/inputs/f861_cust_counts.csv)
@@ -3976,7 +3945,6 @@
   - **File Type:** Switches file
   - **Description:** Contains the configuration settings for the ReEDS run(s).
   - **Dollar year:** 2004
-  - **Citation:** [https://github.nrel.gov/ReEDS/ReEDS-2.0/blob/38e6610a8c6a92291804598c95c11b707bf187b9/cases.csv](https://github.nrel.gov/ReEDS/ReEDS-2.0/blob/38e6610a8c6a92291804598c95c11b707bf187b9/cases.csv)
 ---
 
 - [cases_examples.csv](/cases_examples.csv)
diff --git a/interim_report.py b/helpers/interim_report.py
similarity index 100%
rename from interim_report.py
rename to helpers/interim_report.py
diff --git a/interim_report_batch.py b/helpers/interim_report_batch.py
similarity index 85%
rename from interim_report_batch.py
rename to helpers/interim_report_batch.py
index 95a62e0ee..0f295a9a0 100644
--- a/interim_report_batch.py
+++ b/helpers/interim_report_batch.py
@@ -1,8 +1,8 @@
 '''
 This script allows for batch re-running of just the reporting 
-functions (e_report.gms and e_report_dump.py) for a set of jobs 
+functions (report.gms and report_dump.py) for a set of jobs 
 with a common batch prefix, specified as a command line argument.
-It will make new copies of e_report.gms/e_report_dump.py to 
+It will make new copies of report.gms/report_dump.py to 
 each run folder, and can thus be useful to re-run reporting
 for a large set of existing runs using new report scripts.
 
@@ -18,6 +18,9 @@
 from glob import glob
 import argparse
 import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent))
+import reeds
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--batch_name', '-b', type=str, default='', help='Prefix for batch of runs')
@@ -31,7 +34,7 @@
 if len(case_list) == 0:
     sys.exit(f"No cases found with {args.batch_name} prefix.")
 # list of new report files to copy
-report_files = ["e_report.gms", "e_report_dump.py"]
+report_files = ["report.gms", "report_dump.py"]
 
 # loop over cases to copy files and run 'interim_report.py' for each one
 for case in case_list:
@@ -44,7 +47,10 @@
     interim_report = os.path.join(case, "interim_report.py")
     if os.name=='posix':
         if hpc:
-            shutil.copy("srun_template.sh", os.path.join(case, "interim_report.sh"))
+            shutil.copy(
+                Path(reeds.io.reeds_path,'reeds','hpc','srun_template.sh'),
+                os.path.join(case, "interim_report.sh")
+            )
             with open(os.path.join(case, "interim_report.sh"), 'a') as SPATH:
                 #add the name for easy tracking of the case
                 SPATH.writelines("\n#SBATCH --job-name=" + case_name + "_interim_report" + "\n\n")
diff --git a/restart_runs.py b/helpers/restart_runs.py
similarity index 78%
rename from restart_runs.py
rename to helpers/restart_runs.py
index dee9a85c6..f1132e2e3 100644
--- a/restart_runs.py
+++ b/helpers/restart_runs.py
@@ -1,46 +1,55 @@
 #%% Imports
 import os
+import sys
 import shutil
 import subprocess
 import argparse
-import pandas as pd
 from glob import glob
-from runbatch import submit_slurm_parallel_jobs
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent))
+import reeds
+from runreeds import submit_slurm_parallel_jobs
 from runstatus import get_run_status
 
 #%% Argument inputs
 parser = argparse.ArgumentParser(description='Restart failed runs on the HPC')
 parser.add_argument('batch_name', type=str, help='batch name (case prefix) to search for')
-parser.add_argument('--copy_cplex', '-c', type=int, default=0,
-                    help='Which cplex.opt file to copy (or 0 for none)')
+parser.add_argument(
+    '--copy_solver_settings', '-c', action='store_true',
+    help='Copy the solver settings file used by this run from the repo to the run folder',
+)
 parser.add_argument('--copy_srun_template', '-s', action='store_true',
                     help='Copy current srun_template.sh to sbatch file')
 parser.add_argument('--force', '-f', action='store_true',
                     help='Proceed without double-checking')
 parser.add_argument('--more_copyfiles', '-m', type=str, default='',
-                    help=',-delimited list of additional files to copy from reeds_path')
+                    help=',-delimited list of additional relative filepaths to copy from reeds_path')
+parser.add_argument('--copy_reeds', '-r', action='store_true',
+                    help='Copy the reeds/ model folder from the repo to the run')
 parser.add_argument('--include_finished', '-i', action='store_true',
                     help='Also restart finished runs (e.g. to redo postprocessing)')
 
 args = parser.parse_args()
 batch_name = args.batch_name
-copy_cplex = args.copy_cplex
+copy_solver_settings = args.copy_solver_settings
 copy_srun_template = args.copy_srun_template
 force = args.force
 more_copyfiles = [i for i in args.more_copyfiles.split(',') if len(i)]
+copy_reeds = args.copy_reeds
 include_finished = args.include_finished
 
 # #%% Inputs for debugging
 # batch_name = 'v20231113_yamM0'
-# copy_cplex = 1
+# copy_solver_settings = True
 # copy_srun_template = True
 # force = True
-# more_copyfiles = ['e_report.gms']
+# more_copyfiles = ['report.gms']
+# copy_reeds = False
 # include_finished = False
 
 ###### Procedure
 #%% Shared parameters
-reeds_path = os.path.dirname(os.path.abspath(__file__))
+reeds_path = reeds.io.reeds_path
 #%% Get all runs
 dictruns = get_run_status(reeds_path, batch_name)
 
@@ -63,18 +72,9 @@
         quit()
 
 
-#%% Get the cplex file to copy
-if copy_cplex:
-    if copy_cplex == 1:
-        cplex_file = os.path.join(reeds_path,'cplex.opt')
-    else:
-        cplex_file = os.path.join(reeds_path,f'cplex.op{copy_cplex}')
-else:
-    cplex_file = None
-
 #%% Copy the header from the srun_template.sh file if desired
 if copy_srun_template:
-    srun_template = os.path.join(reeds_path,'srun_template.sh')
+    srun_template = os.path.join(reeds_path,'reeds','hpc','srun_template.sh')
     writelines_srun = list()
     with open(srun_template, 'r') as f:
         for line in f:
@@ -86,13 +86,18 @@
 for case in runs_failed:
     casename = os.path.basename(case)
 
-    #%% Copy the cplex file if desired
-    if copy_cplex:
-        shutil.copy(cplex_file, os.path.join(case,''))
+    #%% Copy the solver settings file if desired
+    if copy_solver_settings:
+        fpath_settings = Path(
+            reeds.io.reeds_path, 'reeds', 'solver', reeds.io.get_optfile(case)
+        )
+        shutil.copy(fpath_settings, os.path.join(case,''))
 
     #%% Copy additional files if desired
     for f in more_copyfiles:
-        shutil.copy(os.path.join(reeds_path,f), os.path.join(case,f))
+        shutil.copy(f, Path(case, f))
+    if copy_reeds:
+        shutil.copytree(Path(reeds_path, 'reeds'), Path(case, 'reeds'), dirs_exist_ok=True)
 
     #%% Make a backup copy of the original bash and sbatch scripts
     callfile = os.path.join(case,f'call_{casename}.sh')
@@ -106,14 +111,16 @@
     if any([os.path.basename(i).startswith('report') for i in lstfiles]):
         restart_tag = '# Output processing'
     elif len(lstfiles) < 2: 
-        # If there is only 1 lst file, then it is an environment.csv so the run failed during inputs processing 
+        # If there is only 1 lst file, then it is an environment.csv,
+        #  so the run failed during inputs processing 
         restart_tag = '# Input processing'
     elif len(lstfiles) == 2: 
-        # If there are only 2 lst files, then one of them will be environment.csv and the other will be 1_inputs.lst so the run failed during the model compilation 
+        # If there are only 2 lst files, then one of them will be environment.csv and
+        # the other will be 1_inputs.lst, so the run failed during the model compilation 
         restart_tag = '# Compile model'
     else:
         # Drop environment and inputs .lst files
-        lstfiles = [l for l in lstfiles if ("environment.csv" not in l) and ('1_Inputs.lst' not in l)]
+        lstfiles = [i for i in lstfiles if ("environment.csv" not in i) and ('1_Inputs.lst' not in i)]
         lastfile = lstfiles[-1]
         restart_year = int(os.path.splitext(lastfile)[0].split('_')[-1].split('i')[0])
         restart_tag = f'# Year: {restart_year}'
diff --git a/runstatus.py b/helpers/runstatus.py
similarity index 96%
rename from runstatus.py
rename to helpers/runstatus.py
index 8e44d134f..470a6f440 100644
--- a/runstatus.py
+++ b/helpers/runstatus.py
@@ -5,6 +5,7 @@
 import subprocess
 import argparse
 from glob import glob
+from pathlib import Path
 
 # #%% Inputs for debugging
 # batch_name = 'v20250812_mcK0'
@@ -91,11 +92,9 @@ def get_run_status(reeds_path, batch_name):
     include_finished = args.include_finished
     verbose = args.verbose
 
-    #%% Shared parameters
-    reeds_path = os.path.dirname(os.path.abspath(__file__))
-    dictruns = get_run_status(reeds_path, batch_name)
-
     #%%### Loop through categories and runs and report their status
+    reeds_path = Path(__file__).parent.parent
+    dictruns = get_run_status(reeds_path, batch_name)
     for key, runs in dictruns.items():
         text = f'{key}: {len(runs)}'
         print(f"\n{text}\n{'-'*len(text)}")
@@ -116,7 +115,7 @@ def get_run_status(reeds_path, batch_name):
                 ### Get last .lst file
                 lstfiles = sorted(glob(os.path.join(fullcase,'lstfiles','*')))
                 if any([os.path.basename(i).startswith('report') for i in lstfiles]):
-                    last_lst = 'e_report.gms'
+                    last_lst = 'report.gms'
                     penultimatefile = None
                 else:
                     if len(lstfiles) > 1:
@@ -176,8 +175,10 @@ def get_run_status(reeds_path, batch_name):
                         elif "dual objective limit exceeded" in slurm: 
                             errortext = "(hit dual obj. limit)"
                         # check if infeasible
-                        elif "d_solveoneyear.gms failed with return code 3" in slurm:
+                        elif "3_solve_oneyear.gms failed with return code 3" in slurm:
                             errortext = "(infeasible)"
+                        elif "Out of Memory" in slurm or "OOM Killed" in slurm:
+                            errortext = "(out of memory)"
                         else:
                             errortext = ""
                     print(f"{case:<{longest}}: failed in {last_lst} {errortext}")
diff --git a/hourlize/README.md b/hourlize/README.md
index 3089fce66..a926f6d32 100644
--- a/hourlize/README.md
+++ b/hourlize/README.md
@@ -30,7 +30,7 @@ Hourlize is run by a call to `run_hourlize.py`, which assembles information on t
 1. Update settings in `config_base.json` as needed ([details](#config-jsons)), including `load_source`.
 1. If running on the HPC, specify run allocation or other submission settings in `inputs/configs/srun_template.sh` ([details](#config-jsons)).
 1. Run using `run_hourlize.py load` ([details](#running-hourlize)).
-1. Outputs are written to `ReEDS-2.0/inputs/load`.
+1. Outputs are written to `ReEDS/inputs/load`.
     1. If the results are intended to be used on the main branch, then after testing and quality control, upload the new profiles to the remote host as described in `preprocessing/README.md`.
     1. If the results are not intended for general use, they can still be used locally in ReEDS by setting the `GSw_LoadProfiles` switch to the absolute filepath of the resulting hourly demand file.
 
@@ -56,7 +56,7 @@ rsync -aPu [username]@kestrel.nlr.gov://shared-projects/rev/projects/seto/fy23/r
 
 ### 2. Update the rev_paths files
 
-Update the reV paths file at `ReEDS-2.0/inputs/supply_curve/rev_paths.csv`. Typically this means updating the information for whichever techs (e.g., upv, wind-ons, wind-ofs) and access cases (e.g., reference, open, limited) you want to run.
+Update the reV paths file at `ReEDS/inputs/supply_curve/rev_paths.csv`. Typically this means updating the information for whichever techs (e.g., upv, wind-ons, wind-ofs) and access cases (e.g., reference, open, limited) you want to run.
 
 Some details on the additional columns to update:
 
@@ -140,7 +140,7 @@ Each entry should be given a `casename` for the supply curve run in the format [
 
 * Supported values for `tech`: upv, wind-ofs, wind-ons.
 * Typical values for `access_case`: reference, open, or limited.
-  * Other values allowed but must match values in `access_case` column of the rev_paths file (typically at `ReEDS-2.0/inputs/supply_curve/rev_paths.csv` but can be specified in `config_base.json`).
+  * Other values allowed but must match values in `access_case` column of the rev_paths file (typically at `ReEDS/inputs/supply_curve/rev_paths.csv` but can be specified in `config_base.json`).
 
 To link a case to a custom set of config files users can add entries for `config_base` and `config_tech` in the case definition. For example, adding `config_base:test` would link that case to the settings in `config_base_test.json` instead of the typical `config_base.json` file.
 
diff --git a/hourlize/agg_buildings/agg_buildings.py b/hourlize/agg_buildings/agg_buildings.py
index fd0c0581a..d081df6d9 100644
--- a/hourlize/agg_buildings/agg_buildings.py
+++ b/hourlize/agg_buildings/agg_buildings.py
@@ -8,7 +8,7 @@
 
 #Inputs
 bldg_type = 'com' # 'com' for comstock or 'res' for resstock
-bldg_path = r'/projects/geohc/jm_ReEDS-2.0/postprocessing/reValue/parallel_agg/outputs/outputs_2025-01-20-15-17-29'
+bldg_path = r'/projects/geohc/jm_ReEDS/postprocessing/reValue/parallel_agg/outputs/outputs_2025-01-20-15-17-29'
 bldg_tech = 'upgrade0'
 
 shutil.copy2(os.path.realpath(__file__), bldg_path)
diff --git a/pyproject.toml b/hourlize/pyproject.toml
similarity index 100%
rename from pyproject.toml
rename to hourlize/pyproject.toml
diff --git a/hourlize/qaqc/summarize_supply_curves.py b/hourlize/qaqc/summarize_supply_curves.py
index 26a7aa87e..4522abcf3 100644
--- a/hourlize/qaqc/summarize_supply_curves.py
+++ b/hourlize/qaqc/summarize_supply_curves.py
@@ -93,7 +93,7 @@ def load_raw_supply_curves(rev_paths):
     today = datetime.datetime.now().strftime("%Y-%m-%d")
 
     site.addsitedir(os.path.join(reeds_path))
-    import runbatch as rb
+    import runreeds
 
     ## read list of rev_path files
     rev_paths = []
@@ -105,7 +105,7 @@ def load_raw_supply_curves(rev_paths):
             sys.exit(1)
         else:
             rev_path = pd.read_csv(rp)
-            rev_path = rb.get_rev_paths(rev_path, resolution)
+            rev_path = runreeds.get_rev_paths(rev_path, resolution)
             # subset to base name for sc_path
             rev_path['sc_folder'] = rev_path['sc_path'].apply(lambda row: os.path.basename(row))
             # subset to relevant columns and techs
diff --git a/requirements_dev.txt b/hourlize/requirements_dev.txt
similarity index 63%
rename from requirements_dev.txt
rename to hourlize/requirements_dev.txt
index 21614b6ef..10ea561c5 100644
--- a/requirements_dev.txt
+++ b/hourlize/requirements_dev.txt
@@ -1,3 +1,3 @@
-pre-commit>=3.1.1,<3.2
+pre-commit>=4.6.0,<4.7
 pylint>=2.17.0,<2.18
 pytest>=7.2.2,<7.3
diff --git a/hourlize/resource.py b/hourlize/resource.py
index 1566e5f5f..c4cfa97d3 100644
--- a/hourlize/resource.py
+++ b/hourlize/resource.py
@@ -402,7 +402,7 @@ def get_supply_curve_and_preprocess(tech, original_sc_file, reeds_path, hourlize
     print(f"Mapping county to zone from {reg_map_file}")
     df['county'] = 'p' + df.FIPS.astype(str).map('{:>05}'.format)
     # map the regions from county to zone using the mapping file supplied
-    county2zone = pd.read_csv(reg_map_file, dtype={'FIPS':str}, index_col='FIPS').ba
+    county2zone = pd.read_csv(reg_map_file, dtype={'FIPS':str}, index_col='FIPS').r
     ## Offshore zones have ba in FIPS column, so fall back to FIPS value if not in county2zone
     df['ba'] = df.FIPS.map(lambda x: county2zone.get(x,x))
 
@@ -426,7 +426,12 @@ def get_supply_curve_and_preprocess(tech, original_sc_file, reeds_path, hourlize
             'Unique ID':'UID', 'StartYear':'Commercial.Online.Year',
         }).copy()
         df_exist = df_exist[(df_exist['tech'].str.lower().str.contains(tech.lower())) & (df_exist['RetireYear'] > start_year)].reset_index(drop=True)
-        df_exist['LONG'] = df_exist['LONG'] * -1
+        if (df_exist['LONG'] > 0).any():
+            err = (
+                f'Some longitudes in {existing_sites} are positive; '
+                'for the contiguous US they should all be negative'
+            )
+            raise ValueError(err)
         df_exist.sort_values('cap', ascending=False, inplace=True)
 
         #Map the state codes of df_exist to the state names in df
diff --git a/hourlize/simulate_PV_AC.py b/hourlize/simulate_PV_AC.py
index bf1ba8641..b440ccdc0 100644
--- a/hourlize/simulate_PV_AC.py
+++ b/hourlize/simulate_PV_AC.py
@@ -20,7 +20,7 @@
 
 ### Filepaths
 revpath = os.path.expanduser(os.path.join('~','Projects','reV',''))
-reeds_path = os.path.expanduser(os.path.join('~','github2','ReEDS-2.0',''))
+reeds_path = os.path.expanduser(os.path.join('~','github2','ReEDS',''))
 ### reV scenario
 path_sim = os.path.join(revpath,'scenarios_aux','scen_032_ed1','')
 profile_sim = 'dc_profiles'
diff --git a/input_processing/aggregate_regions.py b/input_processing/aggregate_regions.py
deleted file mode 100644
index 48679a777..000000000
--- a/input_processing/aggregate_regions.py
+++ /dev/null
@@ -1,1182 +0,0 @@
-"""
-prbrown 20220421
-Notes to user:
---------------
-* This script loops over files in runs/{}/inputs_case/ and agg/disaggregates
-  them based on the directions given in runfiles.csv.
-    * If new files have been added to inputs_case, you'll need to add rows with
-      processing directions to runfiles.csv.
-    * The column names should be self-explanatory; most likely there's also at least
-      one similarly-formatted file in inputs_case that you can copy the settings for.
-* Some files are agg/disaggregated in other scripts:
-    * WriteHintage.py (these files are handled upstream since
-      aggregation affects the clustering of generators into (b/h/v)intages):
-        * hintage_data.csv
-"""
-
-#%% ===========================================================================
-### --- IMPORTS ---
-### ===========================================================================
-
-import argparse
-import numpy as np
-import os
-import pandas as pd
-import gdxpds
-import shutil
-import sys
-import datetime
-from glob import glob
-from warnings import warn
-import h5py
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import reeds
-## Time the operation of this script
-tic = datetime.datetime.now()
-
-
-#%% ===========================================================================
-### --- FUNCTIONS AND DICTIONARIES ---
-### ===========================================================================
-
-the_unnamer = {'Unnamed: {}'.format(i): '' for i in range(1000)}
-
-aggfuncmap = {
-    'mode': pd.Series.mode,
-}
-
-def logprint(filepath, message):
-    print('{:-<45}> {}'.format(filepath+' ', message))
-
-
-def refilter_regions(df, region_cols, region_col, val_r_all):
-    '''
-    This function is used to filter by region again for cases when only a subset of a model
-    balancing area is represented
-    '''
-    dfout = df.copy()
-    if ('*r' in region_cols) and ('rr' in region_cols):
-        dfout = dfout.loc[dfout.index.get_level_values('*r').isin(val_r_all)]
-        dfout = dfout.loc[dfout.index.get_level_values('rr').isin(val_r_all)]
-    else:
-        dfout = dfout.loc[dfout.index.get_level_values(region_col).isin(val_r_all)]
-
-    return dfout
-
-
-def aggreg_methods(
-    df, row, aggfunc, region_cols, region_col,
-    r2aggreg, r_ba, disagg_data, sw, indexnames, columns,
-):
-    df1 = df.copy()
-    ### Pre-aggregation: Map old regions to new regions
-    if aggfunc in ['sum','mean','first','min','sc_cat','resources']:
-        # Exception for dr_shed_hourly becuase the wide column contains tech|region
-        if 'dr_shed_hourly' in row.name:
-            # Separate tech and region from the 'wide' column
-            df1[['tech', 'region']] = df1['wide'].str.split('|', expand=True)
-            # Map regions to aggregated regions
-            df1['region'] = df1['region'].map(r2aggreg)
-            # Recombine tech and aggregated region into the 'wide' column
-            df1['wide'] = df1['tech'] + '|' + df1['region']   
-        else:
-            for c in region_cols:
-                df1[c] = df1[c].map(lambda x: r2aggreg.get(x,x))
-            if row.i_col:
-                df1[row.i_col] = df1[row.i_col].map(lambda x: new_classes.get(x,x))
-
-    if aggfunc == 'sc_cat':
-        ## Weight cost by cap; if there's no cap, use 1 MW as weight
-        for cost_type in sc_cost_types:
-            ## Geothermal doesn't have all sc_cost_types
-            if cost_type in df1:
-                df1[f'cap_times_{cost_type}'] = df1['cap'].fillna(1).replace(0,1) * df1[cost_type]
-        ## Sum everything
-        df1 = df1.groupby(row.fix_cols+[region_col]).sum()
-        ## Divide cost*cap by cap
-        for cost_type in sc_cost_types:
-            if cost_type in df1:
-                df1[cost_type] = df1[f'cap_times_{cost_type}'] / df1['cap'].fillna(1).replace(0,1)
-                df1.drop([f'cap_times_{cost_type}'], axis=1, inplace=True)
-    elif aggfunc == 'trans_lookup':
-        ## Get data for anchor zones
-        for c in region_cols:
-            df1 = df1.loc[df1[c].isin(aggreg2anchorreg)].copy()
-        ## Map to aggregated regions
-        for c in region_cols:
-            df1[c] = df1[c].map(anchorreg2aggreg)
-    elif aggfunc == 'mean_cap':
-        df1 = (
-            df1.rename(columns={'value':columns[-1]})
-               .merge((rscweight_nobin.rename(columns={'i':'*i'}) if '*i' in row.fix_cols
-                       else rscweight_nobin),
-                       on=['r',('*i' if '*i' in row.fix_cols else 'i')], how='left')
-            ## There are some nan's because we subtract existing capacity from the supply curve.
-            ## Fill them with 1 MW for now, but it would be better to change that procedure.
-            ## Note also that the weighting will be off
-               .fillna(1)
-            )
-        ### Similar procedure as above for aggfunc == 'sc_cat'
-        if row.i_col:
-            df1[row.i_col] = df1[row.i_col].map(lambda x: new_classes.get(x,x))
-        df1 = (
-            df1.assign(r=df1.r.map(r_ba))
-               .assign(cap_times_cf=df1.cf*df1.MW)
-               .groupby(row.fix_cols+region_cols).sum()
-            )
-        df1.cf = df1.cap_times_cf / df1.MW
-        df1 = df1.drop(['cap_times_cf','MW'], axis=1)
-    elif aggfunc == 'resources':
-        ### Special case: Rebuild the 'resources' column as {tech}|{region}
-        df1 = (
-            df1.assign(resource=df1.i+'|'+df1.r)
-               .drop_duplicates()
-            )
-        ### Special case: If calculating capacity credit by r, replace ccreg with r
-        if sw['capcredit_hierarchy_level'] == 'r':
-            df1 = df1.assign(ccreg=df1.r).drop_duplicates()
-    elif aggfunc in ['recf', 'csp']:
-        ## Get correct rscweight_nobin tech value
-        rsctech = os.path.splitext(row.name)[0].split('_')[1]
-        rscweight_nobin_tech = rscweight_nobin.loc[rscweight_nobin['i'].str.contains(rsctech)]
-        ### Region is embedded in the 'resources' column as {tech}|{region}
-        col2r = dict(zip(columns, [c.split('|')[-1] for c in columns]))
-        col2i = dict(zip(columns, [c.split('|')[0] for c in columns]))
-        df1 = df1.rename(columns={'value':'cf'})
-        df1['r'] = df1[region_col].map(col2r)
-        df1['i'] = df1[region_col].map(col2i)
-        ## rscweight_nobin data from writesupplycurves.py has tech values of {rsctech}|{class}
-        ## so replicate this in order to merge for capacities
-        df1['i'] = f'{rsctech}_' + df1['i']
-
-        ## Get capacities
-        df1 = df1.merge(rscweight_nobin_tech, on=['r','i'], how='left')
-        ## Similar procedure as above for aggfunc == 'sc_cat'
-        df1['i'] = df1['i'].map(lambda x: new_classes.get(x,x))
-        df1 = df1.rename(columns={'value':'cf'})
-        df1 = (
-            df1
-            .assign(r=df1.r.map(r_ba))
-            .assign(cap_times_cf=df1.cf*df1.MW)
-            .groupby(indexnames + ['i','r']).sum()
-        )
-        df1.cf = df1.cap_times_cf / df1.MW
-        df1 = df1.rename(columns={'cf':'value'}).reset_index()
-        # Revert i column so rsctech is not included in the name.
-        # This ensures the resource h5 files will be in the same format when read in recf.py
-        # regardless of the spatial resolution
-        df1['i'] = df1['i'].str.replace(f'{rsctech}_','')
-        ### Remake the resources (column names) with new regions
-        df1['wide'] = df1.i + '|' + df1.r
-        df1 = df1.set_index(indexnames + ['wide'])[['value']].astype(np.float32)
-    elif aggfunc in ['sum','mean','first','min']:
-        # Exception for dr_shed_hourly becuase the wide column contains tech|region
-        if 'dr_shed_hourly' in row.name : 
-            # Group by relevant columns and aggregate values
-            df1 = df1.groupby(['datetime', 'wide', 'year'], as_index=False).sum().drop(columns=['tech', 'region'])    
-        else:
-            df1 = df1.groupby(row.fix_cols+region_cols).agg(aggfunc)
-
-    ### Disaggregation methods --------------------------------------------------------------------
-    elif aggfunc == 'uniform':
-        for rcol in region_cols:
-            df1 = (
-                df1.merge(r_ba, left_on=rcol, right_on='ba', how='inner')
-                   .drop(columns=[rcol,'ba'])
-                   .rename(columns={'FIPS':rcol})
-                )
-        # if the fixed column is wide, then 'wide' needs to be an index as well
-        if (len(row.fix_cols) == 1) and (row.fix_cols[0] == 'wide'):
-            df1.set_index([region_col,'wide'],inplace=True)
-        else:
-            df1.set_index(row.fix_cols+region_cols,inplace=True)
-        df1 = refilter_regions(df1, region_cols,region_col, val_r_all)
-    elif aggfunc in ['population','geosize','hydroexist']:
-        if 'sc_cat' in columns:
-            # Split cap and cost
-            df1_cap = df1[df1['sc_cat']=='cap']
-            df1_cost = df1[df1['sc_cat']=='cost']
-
-            # Disaggregate cap using the selected aggfunc
-            fracdata= disagg_data[aggfunc]
-            rcol = region_cols[0]
-            df1cols = (df1.columns)
-            valcol = df1cols[-1]
-            # Identify the columns to merge from the fracdata
-            fracdata_mergecols = (['PCA_REG'] + [col for col in fracdata.columns
-                                    if col not in ['PCA_REG','nonUS_PCA','FIPS','fracdata']])
-            # Identify the columns to merge from the original data
-            df1_mergecols = [rcol] + [col for col in df1cols if col in fracdata_mergecols]
-            # Merge the datasets using PCA_REG
-            df1_cap = pd.merge(fracdata, df1_cap, left_on='PCA_REG', right_on= df1_mergecols, how='inner')
-            # Apply the weights to create a new value
-            df1_cap['new_value'] = (df1_cap['fracdata'].multiply(df1_cap[valcol], axis='index'))
-            # Clean up dataframe before grabbing final values
-            df1_cap.drop(columns=[valcol]+[rcol],inplace=True)
-            df1_cap.rename(columns={'new_value':valcol,'FIPS':rcol},inplace=True)
-            new_index = df1cols[:-1]
-            df1_cap = df1_cap.set_index(new_index.to_list())
-            # Keep cost uniform, so map costs to all subregions with the PCA_REG
-            df1_cost = pd.merge(fracdata, df1_cost, left_on='PCA_REG', right_on= df1_mergecols, how='inner')
-            df1_cost['new_value'] = df1_cost[valcol]
-            df1_cost.drop(columns=[valcol]+[rcol],inplace=True)
-            df1_cost.rename(columns={'new_value':valcol,'FIPS':rcol},inplace=True)
-            new_index = df1cols[:-1]
-            df1_cost = df1_cost.set_index(new_index.to_list())
-            # Combine cap and cost to get back into original format
-            df1 = pd.concat([df1_cap, df1_cost])
-            df1 = refilter_regions(df1, region_cols, region_col,val_r_all)
-
-        elif row.name =='dr_shed_hourly.h5' :
-            # separate tech | region
-            rcol = df1.wide.str.split('|',expand=True)[1]
-            fracdata = disagg_data[aggfunc]
-            df1cols = (df1.columns)
-            valcol = df1cols[-1]
-            # Identify the columns to merge from the fracdata
-            fracdata_mergecols = ['PCA_REG'] + [col for col in fracdata.columns
-                                    if col not in ['PCA_REG','nonUS_PCA','FIPS','fracdata']]
-            # Identify the columns to merge from the original data
-            df1_mergecols = [rcol] + [col for col in df1cols if col in fracdata_mergecols]
-            # Merge the datasets using PCA_REG
-            df1 = pd.merge(fracdata, df1, left_on=fracdata_mergecols, right_on=df1_mergecols, how='inner')
-            # Multiply BA shed by population fraction
-            df1['new_value'] = (df1['fracdata'].multiply(df1[valcol], axis='index'))
-            # Clean up dataframe   
-            df1['wide'] = df1.wide.str.split('|',expand=True)[0] +'|' + df1['FIPS']         
-            df1 = (df1.drop(columns=[valcol,'PCA_REG','fracdata','FIPS'])
-                      .rename(columns={'new_value':valcol}))            
-        else:
-            # Disaggregate cap using the selected aggfunc
-            fracdata = disagg_data[aggfunc]
-            rcol = region_cols[0]
-            df1cols = (df1.columns)
-            valcol = df1cols[-1]
-            # Identify the columns to merge from the fracdata
-            fracdata_mergecols = ['PCA_REG'] + [col for col in fracdata.columns
-                                    if col not in ['PCA_REG','nonUS_PCA','FIPS','fracdata']]
-            # Identify the columns to merge from the original data
-            df1_mergecols = [rcol] + [col for col in df1cols if col in fracdata_mergecols]
-            # Merge the datasets using PCA_REG
-            df1 = pd.merge(fracdata, df1, left_on=fracdata_mergecols, right_on=df1_mergecols, how='inner')
-            # Clean up dataframe before grabbing final values
-            df1['new_value'] = (df1['fracdata'].multiply(df1[valcol], axis='index'))
-            df1 = (df1.drop(columns=[valcol]+[rcol])
-                      .rename(columns={'new_value':valcol,'FIPS':rcol}))
-            new_index = df1cols[:-1]
-            df1 = df1.set_index(new_index.to_list())
-            df1 = refilter_regions(df1, region_cols,region_col, val_r_all)
-
-    else:
-        raise ValueError(f'Invalid choice of aggfunc: {aggfunc} for {row.name}')
-
-    ## Filter by regions again for cases when only a subset of a model balancing area is represented
-    if agglevel == 'county':
-        if ('*r' in region_cols) and ('rr' in region_cols):
-            df1 = df1.loc[df1.index.get_level_values('*r').isin(val_r_all)]
-            df1 = df1.loc[df1.index.get_level_values('rr').isin(val_r_all)]
-        elif row.name =='dr_shed_hourly.h5':
-            # separate tech | region to filter
-            df1 = df1.loc[df1.wide.str.split('|',expand=True)[1].isin(val_r_all)]
-        else:
-            df1 = df1.loc[df1.index.get_level_values(region_col).isin(val_r_all)]
-
-    ################################
-    ### Put back in original format ###
-
-    if row.name == 'dr_shed_hourly.h5':
-        dfout = df1.set_index(['datetime','year','wide']).unstack('wide')['value'].reset_index()
-    elif (aggfunc == 'sc_cat') and (not row.wide):
-        dfout = df1.stack().rename(row.key).reset_index()[columns]
-    elif (aggfunc == 'sc_cat') and row.wide and (len(row.fix_cols) == 1):
-        dfout = df1.stack().rename('value').unstack('wide').reset_index()[columns].fillna(0)
-    elif not row.wide:
-        dfout = df1.reset_index()[columns]
-    elif (row.wide) and (region_col != 'wide') and (len(row.fix_cols) == 1) and (row.fix_cols[0] == 'wide'):
-        dfout = df1.unstack('wide')['value'].reset_index()
-    elif (row.wide) and (region_col != 'wide') and (len(row.fix_cols) > 1) and ('wide' in row.fix_cols):
-        # In some cases disaggregating to county level can lead to empty
-        # dataframes, so address that here
-        if df1.empty:
-            dfout = pd.DataFrame(columns = columns)
-        else:
-            dfout = df1.unstack('wide')['value'].reset_index()[columns]
-    elif row.wide and (region_col == 'wide') and len(row.fix_cols):
-        if (len(row.fix_cols) == 1):
-            dfout = (
-                df1.reset_index()
-                .set_index(row.fix_cols)
-                .pivot(columns='wide', values='value')
-                .reset_index()
-            )
-        else:
-            dfout = df1.unstack('wide')['value'].reset_index()
-
-    ### Drop rows where r and rr are the same
-    if row.key == 'drop_dup_r':
-        dfout = dfout.loc[dfout[region_cols[0]] != dfout[region_cols[1]]].copy()
-
-    ### Other special-case processing
-    if (row.name == 'hierarchy.csv') and (sw['capcredit_hierarchy_level'] == 'r'):
-        dfout = dfout.assign(ccreg=dfout[region_col]).drop_duplicates()
-        dfout['ccreg'].to_csv(os.path.join(inputs_case, 'ccreg.csv'), index=False)
-
-    dfout.rename(columns=the_unnamer, inplace=True)
-
-    return dfout
-
-
-def reshape_to_long(
-    dfin,
-    filepath,
-    row,
-    indexnames,
-    aggfunc,
-    region_col,
-    region_cols,
-):
-    if (aggfunc == 'sc_cat') and (not row.wide):
-        ### Supply-curve format. Expect an sc_cat column with 'cap' and 'cost' values.
-        ## 'cap' values are summed; 'cost' values use the 'cap'-weighted mean
-        df = dfin.pivot(
-            index=row.fix_cols+region_cols,
-            columns='sc_cat',
-            values=row.key,
-        ).reset_index()
-    elif (aggfunc == 'sc_cat') and row.wide and (len(row.fix_cols) == 1):
-        ### Supply-curve format. Expect an sc_cat column with 'cap' and 'cost' values.
-        ## Some value other than region is in wide format
-        ## So turn region and the wide value into the index
-        df = dfin.set_index(region_cols+['sc_cat']).stack().rename('value')
-        df.index = df.index.rename(region_cols+['sc_cat','wide'])
-        ## Make value columns for 'cap' and 'cost'
-        df = df.unstack('sc_cat').reset_index()
-    elif not row.wide:
-        ### File is already long so don't do anything
-        df = dfin.copy()
-    elif (
-        row.wide
-        and (region_col != 'wide')
-        and (len(row.fix_cols) == 1)
-        and (row.fix_cols[0] == 'wide')
-    ):
-        ## Some value other than region is in wide format
-        ## So turn region and the wide value into the index
-        df = dfin.set_index(region_col).stack().rename('value')
-        df.index = df.index.rename([region_col, 'wide'])
-        ## Turn index into columns
-        df = df.reset_index()
-    elif (
-        row.wide
-        and (region_col != 'wide')
-        and (len(row.fix_cols) > 1)
-        and ('wide' in row.fix_cols)
-    ):
-        ## Some value other than region is in wide format
-        ## So turn region, other fix_cols, and the wide value into the index
-        df = (
-            dfin
-            .set_index([region_col]+[c for c in row.fix_cols if c != 'wide'])
-            .stack()
-            .rename('value')
-        )
-        df.index = df.index.rename([region_col]+row.fix_cols)
-        ## Turn index into columns
-        df = df.reset_index()
-    elif row.wide and (region_col == 'wide') and len(row.fix_cols):
-        ### File has some fixed columns and then regions in wide format
-        df = (
-            ## Turn all identifying columns into indices, with region as the last index
-            dfin.set_index(row.fix_cols).stack()
-            ## Name the region column 'wide'
-            .rename_axis(row.fix_cols+['wide']).rename('value')
-            ## Turn index into columns
-            .reset_index()
-        )
-
-    return df
-
-
-def separate_wide_mixed_data(dfin,columns,fix_cols,agglevel_list):
-    # Separate mixed BA and county data in wide format
-
-    reg_cols =  [col for col in columns if col not in fix_cols]
-    if all('|' in col for col in reg_cols):
-        #Find columns with regions that are being solved at BA or aggreg resolution
-        # Some inputs files have tech and regions combined as column header and
-        # need to be filtered differently
-        keep_col = []
-        for col in dfin.columns:
-            new_col = col.split('|')
-            for part in new_col:
-                if part in agglevel_list :
-                    keep_col.append(col)
-
-        df_sep_in = dfin[fix_cols + keep_col]
-
-    else:
-        col_list = fix_cols + agglevel_list
-        #Check if data exists for all BAs in agglevel list
-        #Loop through each ba in ba region list and add to region_list if it doesn't appear in input data
-        regions_list = []
-        for ba in  agglevel_list :
-            if ba not in dfin.columns :
-                regions_list.append(ba)
-        #If there is a ba for which there is no data, exclude this ba from the column list
-        if len(regions_list) >0 :
-            reduced_ba_regions = [x for x in agglevel_list if x not in regions_list]
-            df_sep_in = dfin[fix_cols +  reduced_ba_regions]
-        #If not, rewrite col_list to exclude BAs for which there is no data
-        else:
-            df_sep_in = dfin[col_list]
-
-    return df_sep_in
-
-
-def agg_disagg(filepath, r2aggreg_glob, r_ba_glob, runfiles_row):
-    """
-    filepath: input file to be aggregated/disaggregated/ignored
-    r2aggreg_glob: ba to aggreg mapping needed for single resolution aggreg cases.
-                   For mixed resolutions runs the r2aggreg parameter is re-defined separately
-                   within the agg_disagg function for data that are being aggregated and disaggregated
-    r_ba_glob: r to ba mapping for single resolution runs. For mixed resolution runs r_ba is re-defined
-               separately within the agg_disagg function for data that are aggregated and disaggregated
-    row : consists of data in runfiles.csv row that correspond with the filepath
-
-    """
-    #%% Continue loop
-    row = runfiles_row.copy()
-    filetic = datetime.datetime.now()
-    filename = row.name
-    if row['aggfunc']=='ignore' and row['disaggfunc']=='ignore':
-        if verbose > 1:
-            logprint(filepath, 'ignored')
-        return
-    ### Ensure the correct aggfunc/disaggfunc is chosen for the given agglevel
-     # This will never be true for mixed resolution runs where agglevel is assigned more than one value
-    elif ((agglevel in ['ba','aggreg'] and row['aggfunc']=='ignore')
-        or (agglevel in ['county'] and row['disaggfunc']=='ignore')):
-        if verbose > 1:
-            logprint(filepath, 'ignored')
-        return
-    elif (row['aggfunc']!='ignore') or (row['disaggfunc']!='ignore'):
-        pass
-
-    # In mixed resolution runs, some of the inputfiles will contain mixed ba-county data
-    # created in copy_files. This data does not need to be aggregated or disaggregated
-    # if the resolution selected is ['ba','county'], so skip the file.
-    if agglevel_variables['lvl'] == 'mult':
-        list_check = ['county','ba']
-        list_check= sorted(list_check)
-        agglevel_check = sorted(agglevel)
-        if list_check == agglevel_check :
-            if row['disaggfunc']=='ignore':
-                return
-
-    # If the file isn't in inputs_case, skip it
-    if filename not in inputfiles:
-        if verbose > 1:
-            logprint(filepath, 'skipped since not in inputs_case')
-        return
-
-    #%%##############
-    ### Settings ###
-
-    ### header: 0 if file has column labels, otherwise 'None'
-    header = (None if row['header'] in ['None','none','',None,np.nan]
-              else 'keepindex' if row['header'] == 'keepindex'
-              else int(row['header']))
-    ### region_col: usually 'r', 'rb', or 'region', or 'wide' if file uses regions as columns
-    region_col = row['region_col']
-    # Some datasets have both rb regions and cendiv regions.  Only use the r
-    # regions for these datasets
-    if region_col == 'r_cendiv':
-        region_col = 'r'
-    region_cols = region_col.split(',')
-    # Assign variable to track if region data exists in two columns
-    two_col = False
-    if region_col == '*r,rr' or region_col =='r,rr' or region_col == 'transgrp,transgrpp':
-        two_col = True
-        region_col = region_col.split(',')
-
-    # If solving at mixed resolutions, both disagg and agg functions could be required
-    # Check if one of the desired spatial resolutions is county
-    if agglevel_variables['lvl'] == 'mult':
-        for val in agglevel:
-            if val in ['county']:
-                aggfunc_agg = aggfuncmap.get(row['aggfunc'], row['aggfunc'])
-                aggfunc_disagg = aggfuncmap.get(row['disaggfunc'], row['disaggfunc'])
-    #Single resolution procedure
-    else:
-        ### Set aggfunc to the aggregation setting if using ba or aggreg,
-        ### and set to the disaggregation setting if using county
-        if agglevel in ['ba','aggreg']:
-            aggfunc = aggfuncmap.get(row['aggfunc'], row['aggfunc'])
-        elif agglevel in ['county']:
-            aggfunc = aggfuncmap.get(row['disaggfunc'], row['disaggfunc'])
-
-    ### wide: 1 if any parameters are in wide format, otherwise 0
-    row.wide = int(row.wide)
-    ### Get the filetype of the input file from the filename string
-    filetype = os.path.splitext(filename)[1].strip('.')
-    ### key: only used for gdx files, indicating the parameter name.
-    ### gdx files need a separate line in runfiles.csv for each parameter.
-    ### fix_cols: indicate columns to use as for fields that should be projected
-    ### independently to future years (e.g. r, szn, tech)
-    row.fix_cols = (
-        [] if row.fix_cols in ['None', 'none', '', None, np.nan]
-        else row.fix_cols.split(',')
-    )
-    ### i_col: indicate technology column. Only used/needed if aggregating techs.
-    if row.i_col in ['None', 'none', '', np.nan]:
-        row.i_col = None
-    # indexnames will get overwritten for h5 files but needs to be defined for the aggreg_methods function
-    indexnames = None
-
-
-    #%%###################
-    ### Load the file ###
-
-    if filetype in ['csv', 'gz']:
-        # Some csv files are empty and therefore cannot be opened.  If that is
-        # the case, then skip them.
-        try:
-            dfin = pd.read_csv(
-                os.path.join(inputs_case, filepath), header=header,
-                dtype={'FIPS':str, 'fips':str, 'cnty_fips':str},
-            )
-        except pd.errors.EmptyDataError:
-            return
-    elif filetype == 'h5':
-        # Skip empty files
-        with h5py.File(os.path.join(inputs_case, filepath), 'r') as f:
-            if len(f.keys()) == 0:
-                return
-
-        dfin = reeds.io.read_file(os.path.join(inputs_case, filepath)).copy()
-        if header == 'keepindex':
-            indexnames = (dfin.index.names)
-            if (len(indexnames) == 1) and (not indexnames[0]):
-                indexnames = ['index']
-            dfin = dfin.reset_index()
-    elif filetype == 'gdx':
-        ### Read in the full gdx file, but only change the 'key' parameter
-        ### given in runfiles. That's wasteful, but there are currently no
-        ### active gdx files.
-        dfall = gdxpds.to_dataframes(os.path.join(inputs_case, filepath))
-        dfin = dfall[row.key]
-    else:
-        raise Exception(f'Unsupported filetype: {filepath}')
-
-    dfin.rename(columns={c:str(c) for c in dfin.columns}, inplace=True)
-    columns = dfin.columns.tolist()
-    ### Stop now if it's empty
-    if dfin.empty:
-        if verbose > 1:
-            logprint(filepath, 'empty')
-        return
-
-
-    #%%############################
-    ### Reshape to long format ###
-
-    ########## Mixed Resolution Procedure ##########
-    if agglevel_variables['lvl'] == 'mult':
-        ########## BA ##########
-        # If desired resolution is ['county', 'ba'], then BA data are already in correct format
-        # Filter dataframe to regions being solved at BA resolution
-        for val in agglevel:
-            if val in ['ba']:
-                if region_col != 'wide':
-                    if two_col :
-                        df_agg_in = dfin[dfin[region_col[0]].isin(
-                                            agglevel_variables['ba_regions'] + agglevel_variables['ba_transgrp'])]
-                        df_agg_in =  df_agg_in[ df_agg_in[region_col[1]].isin(agglevel_variables['ba_regions'])]
-                    else:
-                        df_agg_in = dfin[dfin[region_col].isin(agglevel_variables['ba_regions'])]
-                else:
-                    col_list = row.fix_cols + agglevel_variables['ba_regions']
-                    # Check if data exists for all BAs in agglevel_variables['ba_regions'] list
-                    # Loop through each ba in ba region list and add to region_list if it doesn't appear in input data
-                    regions_list = []
-                    for ba in  agglevel_variables['ba_regions'] :
-                        if ba not in dfin.columns :
-                            regions_list.append(ba)
-                    # If there is a ba for which there is no data, exclude this ba from the column list
-                    if len(regions_list) >1 :
-                        reduced_ba_regions = [x for x in agglevel_variables['ba_regions'] if x not in regions_list]
-                        df_agg_in = dfin[row.fix_cols +  reduced_ba_regions]
-                    #If not, rewrite col_list to exclude BAs for which there is no data
-                    else:
-                        df_agg_in = dfin[[c for c in col_list if c in dfin]].copy()
-
-                df_agg = df_agg_in
-
-            elif val in ['aggreg']:
-                # Subset to include regions being solved at Aggreg
-                # If column headers are region names, need to filter after reformatting
-                if region_col != 'wide':
-                    if two_col :
-                        df_agg_in = dfin[dfin[region_col[0]].isin(
-                                            agglevel_variables['ba_regions'] + agglevel_variables['ba_transgrp'])]
-                        df_agg_in =  df_agg_in[ df_agg_in[region_col[1]].isin(agglevel_variables['ba_regions'])]
-                    else:
-                        df_agg_in = dfin[dfin[region_col].isin(agglevel_variables['ba_regions'])]
-
-                # Clause to separate mixed BA and county data in wide format
-                elif region_col == 'wide':
-                    df_agg_in = separate_wide_mixed_data(
-                        dfin,
-                        columns,
-                        row.fix_cols,
-                        agglevel_variables['ba_regions'],
-                    )
-
-                #Set aggfunc to aggregation function
-                aggfunc = aggfunc_agg
-
-                ##### Reformat BA Data ####
-                df_agg = reshape_to_long(
-                    df_agg_in,
-                    filepath,
-                    row,
-                    indexnames,
-                    aggfunc,
-                    region_col,
-                    region_cols,
-                )
-
-        ########## County ##########
-        # When aggfunc_disagg is set to ignore the county-level data do not need to be reformatted.
-        # Filter county data from BA data that will be aggregated to aggreg
-        if aggfunc_disagg == 'ignore':
-            if region_col != 'wide':
-                if two_col:
-                    # County transmission data will have county-ba interfaces created in copy_files
-                    # To maintain these interfaces the filtering of the data needs to ensure that BA-BA interfaces
-                    # are dropped but county-county and county-BA interfaces are kept
-                    df_disagg_list = []
-                    for idx, tx_row in dfin.iterrows():
-                        cond1 = ((tx_row[region_col[0]] in agglevel_variables['ba_regions']+agglevel_variables['ba_transgrp'])
-                                    and (tx_row[region_col[1]] in agglevel_variables['county_regions']+agglevel_variables['county_transgrp']))
-                        cond2 = ((tx_row[region_col[1]] in agglevel_variables['ba_regions']+ agglevel_variables['ba_transgrp'])
-                                    and (tx_row[region_col[0]] in agglevel_variables['county_regions']+agglevel_variables['county_transgrp']))
-                        cond3 = ((tx_row[region_col[0]] in agglevel_variables['county_regions']+agglevel_variables['county_transgrp'])
-                                    and (tx_row[region_col[1]] in agglevel_variables['county_regions']+agglevel_variables['county_transgrp']))
-
-                        if cond1 or cond2 or cond3:
-                            df_disagg_list.append(tx_row)
-
-                    df_disagg_in = pd.DataFrame(df_disagg_list).drop_duplicates()
-
-                elif filename == 'county2zone.csv':
-                    df_disagg_in = dfin[dfin[region_col].isin(agglevel_variables['county_regions2ba'])]
-                else:
-                    df_disagg_in = dfin[dfin[region_col].isin(agglevel_variables['county_regions'])]
-            # Clause to separate mixed BA and county data in wide format
-            elif region_col == 'wide':
-                df_disagg_in = separate_wide_mixed_data(
-                    dfin,
-                    columns,
-                    row.fix_cols,
-                    agglevel_variables['county_regions'],
-                )
-
-            # Files where aggfunc_disagg is 'ignore' but aggfunc_agg is NOT 'ignore'
-            # indicate that the file will already have county-level data
-            # (e.g. csp.h5, recf.h5, load.h5). Copy the separated county-level columns
-            # to df_disagg so the script can add them back to dfout once the ba-level data
-            # is aggregated
-            if aggfunc_agg != 'ignore':
-                df_disagg = df_disagg_in.copy()
-
-        else:
-            # Need to read in input data at BA level to be disaggregated
-            # If column headers are region names, need to filter after reformatting
-            if region_col != 'wide':
-                if two_col :
-                    df_disagg_in = dfin[dfin[region_col[0]].isin(agglevel_variables['county_regions2ba'])]
-                    df_disagg_in = df_disagg_in[df_disagg_in[region_col[1]]
-                                    .isin(agglevel_variables['county_regions2ba'])]
-                else:
-                    df_disagg_in = dfin[dfin[region_col].isin(agglevel_variables['county_regions2ba'])]
-            else:
-                col_list = row.fix_cols + agglevel_variables['county_regions2ba']
-                # Check if data exists for all BAs in county_regions2ba list
-                regions_list = []
-                for ba in agglevel_variables['county_regions2ba']:
-                    if ba not in dfin.columns :
-                        regions_list.append(ba)
-                # If there is a ba for which there is no data, exclude this ba from the column list
-                if len(regions_list) >1 :
-                    reduced_ba_regions = [x for x in agglevel_variables['county_regions2ba'] if x not in regions_list]
-                    df_disagg_in = dfin[row.fix_cols + reduced_ba_regions]
-                else:
-                    df_disagg_in = dfin[col_list]
-
-            #Set aggfunc to disaggregation function
-            aggfunc = aggfunc_disagg
-
-            #####Reformat county Data ####
-            df_disagg = reshape_to_long(
-                df_disagg_in,
-                filepath,
-                row,
-                indexnames,
-                aggfunc,
-                region_col,
-                region_cols,
-            )
-
-    ########### Single Resolution Procedure ###########
-    else:
-        df = reshape_to_long(
-            dfin,
-            filepath,
-            row,
-            indexnames,
-            aggfunc,
-            region_col,
-            region_cols,
-        )
-
-        #If the file is empty, move on to the next one as there is nothing to aggregate
-        if df.empty:
-            if verbose > 1:
-                logprint(filepath, 'empty')
-            return
-
-    #%%#######################################
-    ### Aggregate/Dissaggregate by Region ###
-
-    ########## Mixed Resolution Procedure ##########
-    if agglevel_variables['lvl'] == 'mult':
-        ########## BA, Aggreg ##########
-        # If there are no BA level data, set BA addition to False
-        # This will prevent appending an empty dataframe to the county level data below
-        if df_agg.empty:
-             ba_addition = False
-        else:
-            ba_addition = True
-            # Set aggregation function to aggfunc column value (Done in Settings section above)
-            aggfunc = aggfunc_agg
-            # Check if solving at aggreg, otherwise aggregating BA data to lower resolution is not necessary
-            if 'aggreg' in agglevel:
-                r2aggreg = r_aggreg
-                r_ba = r_aggreg
-
-                # If aggfunc is not 'ignore', perform aggregation
-                if aggfunc != 'ignore':
-                    df_agg = aggreg_methods(
-                        df_agg, row, aggfunc, region_cols, region_col,
-                        r2aggreg, r_ba, disagg_data, sw, indexnames, columns,
-                    )
-
-        ########## County ##########
-
-        # If there are no county level data, set county addition to False
-        # This will prevent appending an empty dataframe to the BA level data below
-        if df_disagg.empty:
-             county_addition = False
-        else:
-            county_addition = True
-            # Set aggregation function to aggfunc column value (Done in Settings section above)
-            aggfunc = aggfunc_disagg
-            # If aggfunc is not 'ignore', perform disaggregation
-            if aggfunc != 'ignore':
-                # Ensure correct r_ba is used
-                r_ba = r_ba_for_county
-                r2aggreg = r_ba
-
-                # Disagg for county
-                df_disagg = aggreg_methods(
-                    df_disagg, row, aggfunc, region_cols, region_col,
-                    r2aggreg, r_ba, disagg_data, sw, indexnames, columns,
-                )
-
-        # Combine aggregated and disaggregated data
-        if ba_addition and county_addition:
-            # Combined BA and county data
-            if region_col == 'wide':
-                dfout = pd.merge(df_agg, df_disagg, how='inner', on=row.fix_cols)
-            else:
-                dfout = pd.concat([df_agg,df_disagg])
-        # Aggregated data only
-        if ba_addition and not county_addition:
-            dfout = df_agg.copy()
-        # Disaggregated data only
-        if not ba_addition and county_addition:
-            dfout = df_disagg.copy()
-
-        # If neither data exists, skip file
-        if not ba_addition and not county_addition:
-            if verbose > 1:
-                logprint(filepath, 'empty')
-            return
-
-
-    ########## Single Resolution Procedure ##########
-    else:
-        if agglevel_variables['lvl'] == 'county':
-            r2aggreg = r_county
-        else:
-            r2aggreg = r2aggreg_glob
-        r_ba = r_ba_glob
-        dfout = aggreg_methods(
-            df, row, aggfunc, region_cols, region_col,
-            r2aggreg, r_ba, disagg_data, sw, indexnames, columns,
-        )
-
-    #%%####################
-    ### Data Write-Out ###
-
-    if filetype in ['csv','gz']:
-        dfout.round(decimals).to_csv(
-            os.path.join(inputs_case, filepath),
-            header=(False if header is None else True),
-            index=False,
-        )
-    elif filetype == 'h5':
-        if header == 'keepindex':
-            dfwrite = dfout.sort_values(indexnames).set_index(indexnames)
-            dfwrite.columns.name = None
-        else:
-            dfwrite = dfout
-        reeds.io.write_profile_to_h5(dfwrite, filepath, inputs_case)
-    elif filetype == 'gdx':
-        ### Overwrite the projected parameter
-        dfall[row.key] = dfout.round(decimals)
-        ### Write the whole file
-        gdxpds.to_gdx(dfall, inputs_case+filepath)
-
-    if verbose > 1:
-        now = datetime.datetime.now()
-        logprint(
-            filepath,
-            'aggregated ({:.1f} seconds)'.format((now-filetic).total_seconds()))
-
-
-#%% ===========================================================================
-### --- PROCEDURE ---
-### ===========================================================================
-
-#%% Parse arguments
-parser = argparse.ArgumentParser(description='Extend inputs to arbitrary future year')
-parser.add_argument('reeds_path', help='path to ReEDS directory')
-parser.add_argument('inputs_case', help='path to inputs_case directory')
-
-args = parser.parse_args()
-reeds_path = args.reeds_path
-inputs_case = os.path.join(args.inputs_case)
-
-# #%%## Settings for testing
-# reeds_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-# inputs_case = os.path.join(
-#     reeds_path,'runs','v20250301_hydroM0_Pacific','inputs_case')
-
-#%% Settings for debugging
-### Set debug == True to copy the original files to a new folder (inputs_case_original).
-### If debug == False, the original files are overwritten.
-debug = True
-### missing: 'raise' or 'warn'
-missing = 'raise'
-### verbose: 0, 1, 2
-verbose = 2
-
-#%%#################
-### FIXED INPUTS ###
-decimals = 6
-### anchortype: 'load' sets rb with largest 2010 load as anchor reg;
-### 'size' sets largest rb as anchor reg
-anchortype = 'size'
-### Types of cost data in files that use sc_cat
-sc_cost_types = ['cost', 'cost_cap', 'cost_trans']
-
-######################
-### DERIVED INPUTS ###
-### Get the case name (ReEDS-2.0/runs/{casename}/inputscase/)
-casename = inputs_case.split(os.sep)[-3]
-
-#%% Set up logger
-log = reeds.log.makelog(
-    scriptname=__file__,
-    logpath=os.path.join(inputs_case, '..', 'gamslog.txt'),
-)
-
-#%% Inputs from switches
-sw = pd.read_csv(
-    os.path.join(inputs_case, 'switches.csv'), header=None, index_col=0).squeeze(1)
-endyear = int(sw.endyear)
-GSw_CSP_Types = [int(i) for i in sw.GSw_CSP_Types.split('_')]
-
-scalars = pd.read_csv(
-        os.path.join(inputs_case, 'scalars.csv'),
-        header=None, usecols=[0,1], index_col=0).squeeze(1)
-
-# Use agglevel_variables function to obtain spatial resolution variables
-agglevel_variables = reeds.spatial.get_agglevel_variables(reeds_path, inputs_case)
-agglevel = agglevel_variables['agglevel']
-# Regions present in the current run
-val_r_all = sorted(
-    pd.read_csv(
-         os.path.join(inputs_case, 'val_r_all.csv'), header=None,
-    ).squeeze(1).tolist()
-)
-#%%
-#DEBUG: Copy the original inputs_case files
-if debug and (agglevel != 'ba'):
-    print('Copying original inputs_case file...')
-    import distutils.dir_util
-    os.makedirs(inputs_case+'_original', exist_ok=True)
-    distutils.dir_util.copy_tree(inputs_case, inputs_case+'_original', verbose=0)
-#%%
-### Mixed Resolution Procedure ###
-if agglevel_variables['lvl'] == 'mult' :
-    # Get the various region maps created in copy_files.py
-    #Need to store separate r_ba values for county and BA data
-    r_county = pd.read_csv(os.path.join(inputs_case,'r_county.csv'), index_col='county').squeeze()
-    r_ba = pd.read_csv(os.path.join(inputs_case,'r_ba.csv'))
-    r_ba_for_county = pd.read_csv(os.path.join(inputs_case,'r_ba.csv')).rename(columns={'r':'FIPS'})
-    r_ba_for_ba = pd.read_csv(os.path.join(inputs_case,'r_ba.csv')).set_index('ba').squeeze()
-
-    for val in agglevel_variables['agglevel'] :
-        if val == 'aggreg':
-            ### Get map from BA to aggreg
-            r_aggreg = pd.read_csv(os.path.join(inputs_case,'rb_aggreg.csv')).set_index('ba')['aggreg'].to_dict()
-
-
-### Single Resolution Procedure ###
-else:
-    # Get the various region maps created in copy_files.py
-    r_county = pd.read_csv(os.path.join(inputs_case,'r_county.csv'), index_col='county').squeeze()
-    r_ba = pd.read_csv(os.path.join(inputs_case,'r_ba.csv'))
-    # r_ba needs to be in different formats depending on whether you are aggregating
-    # or disaggregating
-    if agglevel in ['county']:
-        r_ba.rename(columns={'r':'FIPS'}, inplace=True)
-    elif agglevel in ['ba','aggreg']:
-        r_ba = r_ba.set_index('ba').squeeze()
-        ### Make all-regions-to-aggreg map
-        r2aggreg = pd.concat([r_county, r_ba])
-
-#####################################################
-### If using default 134 regions, exit the script ###
-
-if agglevel == 'ba':
-    print('all valid regions are BA, so skip aggregate_regions.py')
-    quit()
-else:
-    print('Starting aggregate_regions.py', flush=True)
-    ## Read in disaggregation data
-    disagg_data = {
-        'population': pd.read_csv(
-            os.path.join(inputs_case,'disagg_population.csv'),
-            header=0,
-            dtype={'fracdata':np.float32},
-            usecols=["PCA_REG", "FIPS", "fracdata"]
-        ),
-        'geosize': pd.read_csv(
-            os.path.join(inputs_case,'disagg_geosize.csv'),
-            header=0,
-            usecols=["PCA_REG", "FIPS", "fracdata"]
-        ),
-        'hydroexist': pd.read_csv(
-            os.path.join(inputs_case,'disagg_hydroexist.csv'),
-            header=0,
-            usecols=["PCA_REG", "FIPS", "i", "fracdata"]
-        )
-    }
-
-#%%######################################################
-### Get the "anchor" zone for each aggregation region ###
-
-# For transmission we want to use the old endpoints to avoid requiring a new run of the
-# reV least-cost-paths procedure.
-
-if 'aggreg' in agglevel:
-    if agglevel_variables['lvl'] == 'mult':
-        r_ba = r_aggreg
-
-    # Written in reeds.io.get_zonemap()
-    reeds.io.get_zonemap(reeds.io.standardize_case(inputs_case))
-    aggreg2anchorreg = pd.read_csv(os.path.join(inputs_case, 'aggreg2anchorreg.csv'))
-    aggreg2anchorreg = aggreg2anchorreg.set_index('aggreg')
-    aggreg2anchorreg = aggreg2anchorreg.squeeze()
-    anchorreg2aggreg = pd.Series(index=aggreg2anchorreg.values, data=aggreg2anchorreg.index)    
-    
-    ### Get RSC VRE available capacity to use in capacity-weighted averages
-    ### We need the original un-aggregated supply curves, so run writesupplycurves again
-    # rscweight = pd.read_csv(os.path.join(inputs_case, 'rsc_combined.csv'))
-
-    # Read generator database and create rsc_wsc (for use in writesupplycurves function call below)
-    gendb = pd.read_csv(os.path.join(inputs_case,'unitdata.csv'))
-    import writecapdat
-    from writecapdat import create_rsc_wsc
-    # Set the 'r' column for the generator database
-    # Create the 'r_col' column
-    gendb = gendb.assign(r=gendb.reeds_ba.map(r_ba))
-    startyear = int(sw.startyear)
-    rsc_wsc = create_rsc_wsc(gendb, TECH=writecapdat.TECH, scalars=scalars,startyear=startyear)
-
-    import writesupplycurves
-    rscweight = writesupplycurves.main(
-        reeds_path, inputs_case, AggregateRegions=0, rsc_wsc_dat=rsc_wsc, write=False)
-    rscweight = (
-        rscweight.loc[(rscweight.sc_cat=='cap')]
-        .rename(columns={'*i':'i'})
-        .drop_duplicates(subset=['i','r','rscbin'])
-        [['i','r','rscbin','value']].rename(columns={'value':'MW'})
-    ).copy()
-
-    ## Add PVB values to rscweight_nobin in case we need them
-    rscweight_nobin = rscweight.groupby(['i','r'], as_index=False).sum(numeric_only=True)
-    pvbtechs = [f'pvb{i}' for i in sw.GSw_PVB_Types.split('_')]
-    tocopy = rscweight_nobin.loc[rscweight_nobin.i.str.startswith('upv')].copy()
-    rscweight_nobin = pd.concat(
-        [rscweight_nobin]
-        + [tocopy.assign(i=tocopy.i.str.replace('upv',pvbtech)) for pvbtech in pvbtechs]
-    )
-    ## Remove duplicate CSP values for different solar multiples
-    rscweight_nobin.i.replace(
-        {f'csp{i+1}_{c+1}': f'csp_{c+1}'
-         for i in GSw_CSP_Types
-         for c in range(int(sw.GSw_NumCSPclasses))},
-        inplace=True
-    )
-    rscweight_nobin.drop_duplicates(['i','r'], inplace=True)
-
-# rscweight_nobin required to be defined for aggreg_methods function call to work
-else:
-    rscweight_nobin=None
-
-#%% Get the mapping to reduced-resolution technology classes
-original_num_classes = {**{f'csp{i}':12 for i in range(1,5)}}
-new_classes = {}
-for tech in [f'csp{i}' for i in range(1,5)]:
-    GSw_NumClasses = int(sw['GSw_Num{}classes'.format(tech.upper().strip('1234'))])
-    ## Spread the new classes roughly evenly out over the old classes
-    num_in_step = original_num_classes[tech] // GSw_NumClasses
-    remainder = original_num_classes[tech] % GSw_NumClasses
-    new_classes[tech] = sorted(
-        list(np.ravel([[i]*num_in_step for i in range(1,GSw_NumClasses+1)]))
-        + list(range(1,remainder+1))
-    )
-    new_classes[tech] = dict(zip(
-        [f'{tech}_{i}' for i in range(1,original_num_classes[tech]+1)],
-        [f'{tech}_{i}' for i in new_classes[tech]]
-    ))
-### Combine all the new classes into one dictionary
-new_classes = {k:v for d in new_classes for k,v in new_classes[d].items()}
-
-#%% Get the settings file
-runfiles = (
-    pd.read_csv(
-        os.path.join(reeds_path, 'runfiles.csv'),
-        dtype={'fix_cols':str}, index_col='filename',
-        comment='#',
-    ).fillna({'fix_cols':''})
-    .rename(columns={'wide (1 if any parameters are in wide format)':'wide',
-                     'header (0 if file has column labels)':'header'})
-    )
-#%% If any files are missing, stop and alert the user
-inputfiles = sorted([
-    f.split('inputs_case'+os.sep)[1]
-    for f in glob(os.path.join(inputs_case,'**'), recursive=True)
-    if 'metadata' not in f
-])
-## Drop the directories and backup h17 files
-inputfiles = [f for f in inputfiles if (('.' in f) and not f.endswith('_h17.csv'))]
-missingfiles = [f for f in inputfiles if (os.path.basename(f) not in runfiles.index.values)
-]
-if any(missingfiles):
-    if missing == 'raise':
-        raise Exception(
-            'Missing aggregation method for:\n{}\n'
-            '>>> Need to add entries for these files to runfiles.csv'
-            .format('\n'.join(missingfiles))
-        )
-    else:
-        from warnings import warn
-        warn(
-            'Missing aggregation directions for:\n{}\n'
-            '>>> For this run, these files are copied without modification'
-            .format('\n'.join(missingfiles))
-        )
-        for f in missingfiles:
-            shutil.copy(os.path.join(inputs_case, f), os.path.join(inputs_case, f))
-            print(f'copied {f}, which is missing from runfiles.csv')
-
-#%% Maps (special case)
-mapsfile = os.path.join(inputs_case, 'maps.gpkg')
-if os.path.exists(mapsfile):
-    os.remove(mapsfile)
-dfmap = reeds.io.get_dfmap(os.path.dirname(inputs_case))
-for level in dfmap:
-    dfmap[level].rename_axis(level).to_file(mapsfile, layer=level)
-
-dfmap = reeds.io.get_dfmap(os.path.dirname(inputs_case))
-
-### Aggregate or disaggregate the 'r' map; none of the rest should change
-# Mixed resolution maps are patched together in the get_zonemap() function
-if agglevel_variables['lvl'] == 'mult' :
-    pass
-
-#Single resolution procedure
-else:
-    match agglevel:
-        case 'aggreg':
-            r2aggreg = pd.read_csv(
-                os.path.join(inputs_case, 'hierarchy_original.csv')
-            ).rename(columns={'ba':'r'}).set_index('r').aggreg
-        case 'county':
-            aggreg2anchorreg = r2aggreg = r_county.copy()
-
-
-    dfmap_r_agg = dfmap['r'].reset_index().rename(columns={'rb':'r', 'ba':'r'})
-    dfmap_r_agg.r = dfmap_r_agg.r.map(r2aggreg)
-    dfmap_r_agg = dfmap_r_agg.dissolve('r').loc[aggreg2anchorreg.index].copy()
-
-    ## Map endpoints to anchor regions
-    for j in ['x','y']:
-        dfmap_r_agg[j] = dfmap['r'][j].loc[dfmap_r_agg[j].index.map(aggreg2anchorreg)].values
-        dfmap_r_agg[f'centroid_{j}'] = dfmap_r_agg.centroid.x if j == 'x' else dfmap_r_agg.centroid.y
-
-    ## Overwrite the non-aggregated zone map
-    dfmap['r'] = dfmap_r_agg.drop(columns='county', errors='ignore')
-
-    ## Write the aggregated maps
-    mapsfile = os.path.join(inputs_case, 'maps.gpkg')
-    if os.path.exists(mapsfile):
-        os.remove(mapsfile)
-    for level in dfmap:
-        (
-            dfmap[level]
-            .drop(columns='aggreg', errors='ignore')
-            .rename_axis(level)
-            .to_file(mapsfile, layer=level)
-        )
-
-#%%
-if agglevel_variables['lvl'] == 'mult' or agglevel == 'county':
-    r2aggreg_glob = None
-else:
-    r2aggreg_glob = r2aggreg
-r_ba_glob = r_ba
-
-# loop over inputfiles from runfiles and call aggregation/disaggregation function
-for filepath in inputfiles:
-    ### For debugging: Specify a file
-    # filepath = ''
-    ### Get the appropriate row from runfiles
-    row = runfiles.loc[os.path.basename(filepath)]
-    try:
-        agg_disagg(filepath, r2aggreg_glob, r_ba_glob, row)
-    except Exception as err:
-        print(f"Error processing {filepath}")
-        raise Exception(err)
-
-#%% Finish
-reeds.log.toc(tic=tic, year=0, process='input_processing/aggregate_regions.py',
-    path=os.path.join(inputs_case,'..'))
-
-print('Finished aggregate_regions.py')
diff --git a/input_processing/fuelcostprep.py b/input_processing/fuelcostprep.py
deleted file mode 100644
index d697464c0..000000000
--- a/input_processing/fuelcostprep.py
+++ /dev/null
@@ -1,175 +0,0 @@
-'''
-The purpose of this script is to write out fuel costs for the following fuels at 
-census division level:
-    - coal
-    - uranium
-    - H2 (for H2-CT/CC tech)
-    - natural gas
-Additionally, this script also writes out natural gas demand (total NG demand as 
-well as NG demand for electricity generation) and natural gas alphas
-'''
-#%% ===========================================================================
-### --- IMPORTS ---
-### ===========================================================================
-
-import pandas as pd
-import os
-import sys
-import argparse
-import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import reeds
-# Time the operation of this script
-tic = datetime.datetime.now()
-
-#%% Parse arguments
-parser = argparse.ArgumentParser(description="""This file organizes fuel cost data by techonology""")
-
-parser.add_argument("reeds_path", help='ReEDS-2.0 directory')
-parser.add_argument("inputs_case", help='ReEDS-2.0/runs/{case}/inputs_case directory')
-
-args = parser.parse_args()
-reeds_path = args.reeds_path
-inputs_case = args.inputs_case
-
-# #%% Settings for testing
-# reeds_path = 'd:\\Danny_ReEDS\\ReEDS-2.0'
-# reeds_path = os.getcwd()
-# inputs_case = os.path.join('runs','nd5_ND','inputs_case')
-
-#%% Set up logger
-log = reeds.log.makelog(
-    scriptname=__file__,
-    logpath=os.path.join(inputs_case,'..','gamslog.txt'),
-)
-print("Starting fuelcostprep.py")
-
-#%% Inputs from switches
-sw = reeds.io.get_switches(inputs_case)
-
-# Load valid regions
-val_r = pd.read_csv(
-    os.path.join(inputs_case, 'val_r.csv'), header=None).squeeze(1).tolist()
-val_cendiv = pd.read_csv(
-    os.path.join(inputs_case, 'val_cendiv.csv'), header=None).squeeze(1).tolist()
-
-r_cendiv = pd.read_csv(os.path.join(inputs_case,"r_cendiv.csv"))
-
-dollaryear = pd.read_csv(os.path.join(inputs_case, "dollaryear_fuel.csv"))
-deflator = pd.read_csv(os.path.join(inputs_case,'deflator.csv'))
-deflator.columns = ["Dollar.Year","Deflator"]
-dollaryear = dollaryear.merge(deflator,on="Dollar.Year",how="left")
-
-#%% ===========================================================================
-### --- PROCEDURE: FUEL PRICE CALCULATIONS ---
-### ===========================================================================
-
-####################
-#    -- Coal --    #
-####################
-coal = pd.read_csv(os.path.join(inputs_case, 'coal_price.csv'))
-coal = coal.melt(id_vars = ['year']).rename(columns={'variable':'cendiv'})
-coal = coal.loc[coal['cendiv'].isin(val_cendiv)]
-
-# Adjust prices to 2004$
-deflate = dollaryear.loc[dollaryear['Scenario'] == sw.coalscen,'Deflator'].values[0]
-coal.loc[:,'value'] = coal['value'] * deflate
-
-coal = coal.merge(r_cendiv,on='cendiv',how='left')
-coal = coal.drop('cendiv', axis=1)
-coal = coal[['year','r','value']].rename(columns={'year':'t','value':'coal'})
-coal.coal = coal.coal.round(6)
-
-#######################
-#    -- Uranium --    #
-#######################
-uranium = pd.read_csv(os.path.join(inputs_case, 'uranium_price.csv'))
-
-# Adjust prices to 2004$
-deflate = dollaryear.loc[dollaryear['Scenario'] == sw.uraniumscen,'Deflator'].values[0]
-uranium.loc[:,'cost'] = uranium['cost'] * deflate
-uranium = pd.concat([uranium.assign(r=i) for i in val_r], ignore_index=True)
-uranium = uranium[['year','r','cost']].rename(columns={'year':'t','cost':'uranium'})
-uranium.uranium = uranium.uranium.round(6)
-
-#############################
-#    -- H2-Combustion --    #
-#############################
-# note that these fuel inputs are not used when H2 production is run endogenously in ReEDS (GSw_H2 > 0)
-h2fuel = pd.read_csv(os.path.join(inputs_case, 'hydrogen_price.csv'), index_col='year')
-
-#Adjust prices to 2004$
-deflate = dollaryear.loc[dollaryear['Scenario'] == sw.h2combustionfuelscen,'Deflator'].squeeze()
-h2fuel['cost'] = h2fuel['cost'] * deflate
-# Reshape from [:,[t,cost]] to [:,[t,r,cost]]
-h2fuel = (
-    pd.concat({r:h2fuel for r in val_r}, axis=0, names=['r'])
-    .reset_index().rename(columns={'year':'t','cost':'h2fuel'})
-    [['t','r','h2fuel']]
-    .round(6)
-)
-
-###########################
-#    -- Natural Gas --    #
-###########################
-
-ngprice = pd.read_csv(os.path.join(inputs_case,'natgas_price_cendiv.csv'))
-ngprice = ngprice.melt(id_vars=['year']).rename(columns={'variable':'cendiv'})
-ngprice = ngprice.loc[ngprice['cendiv'].isin(val_cendiv)]
-
-# Adjust prices to 2004$
-deflate = dollaryear.loc[dollaryear['Scenario'] == sw.ngscen,'Deflator'].values[0]
-ngprice.loc[:,'value'] = ngprice['value'] * deflate
-
-# Save Natural Gas prices by census region
-ngprice_cendiv = ngprice.copy()
-ngprice_cendiv = ngprice_cendiv.pivot_table(index='cendiv',columns='year',values='value')
-ngprice_cendiv = ngprice_cendiv.round(6)
-
-# Map census regions to model regions
-ngprice = ngprice.merge(r_cendiv,on='cendiv',how='left')
-ngprice = ngprice.drop('cendiv', axis=1)
-ngprice = ngprice[['year','r','value']].rename(columns={'year':'t','value':'naturalgas'})
-ngprice.naturalgas = ngprice.naturalgas.round(6)
-
-# Combine all fuel data
-fuel = coal.merge(uranium,on=['t','r'],how='left')
-fuel = fuel.merge(ngprice,on=['t','r'],how='left')
-fuel = fuel.merge(h2fuel,on=['t','r'],how='left')
-fuel = fuel.sort_values(['t','r'])
-
-#%%#################################### 
-### Natural Gas Demand Calculations ###
-
-# Natural Gas demand
-ngdemand = pd.read_csv(os.path.join(inputs_case,'ng_demand_elec.csv'), index_col='year')
-ngdemand = ngdemand[ngdemand.columns[ngdemand.columns.isin(val_cendiv)]]
-ngdemand = ngdemand.transpose()
-ngdemand = ngdemand.round(6)
-
-# Total Natural Gas demand
-ngtotdemand = pd.read_csv(os.path.join(inputs_case, 'ng_demand_tot.csv'), index_col='year')
-ngtotdemand = ngtotdemand[ngtotdemand.columns[ngtotdemand.columns.isin(val_cendiv)]]
-ngtotdemand = ngtotdemand.transpose()
-ngtotdemand = ngtotdemand.round(6)
-
-### Natural Gas Alphas (already in 2004$)
-alpha = pd.read_csv(os.path.join(inputs_case, 'alpha.csv'), index_col='t')
-alpha = alpha[alpha.columns[alpha.columns.isin(val_cendiv)]]
-alpha = alpha.round(6)
-
-#%%###################
-### Data Write-Out ###
-######################
-
-fuel.to_csv(os.path.join(inputs_case,'fprice.csv'),index=False)
-ngprice_cendiv.to_csv(os.path.join(inputs_case,'gasprice_ref.csv'))
-
-ngdemand.to_csv(os.path.join(inputs_case,'ng_demand_elec.csv'))
-ngtotdemand.to_csv(os.path.join(inputs_case,'ng_demand_tot.csv'))
-alpha.to_csv(os.path.join(inputs_case,'alpha.csv'))
-
-reeds.log.toc(tic=tic, year=0, process='input_processing/fuelcostprep.py', 
-    path=os.path.join(inputs_case,'..'))
-
-print('Finished fuelcostprep.py')
diff --git a/input_processing/transmission.py b/input_processing/transmission.py
deleted file mode 100644
index 913ea293b..000000000
--- a/input_processing/transmission.py
+++ /dev/null
@@ -1,641 +0,0 @@
-#%% ===========================================================================
-### --- IMPORTS ---
-### ===========================================================================
-
-import argparse
-import geopandas as gpd
-import pandas as pd
-import numpy as np
-import os
-import sys
-import datetime
-from pathlib import Path
-sys.path.append(str(Path(__file__).parent.parent))
-import reeds
-tic = datetime.datetime.now()
-
-#%% Parse arguments
-parser = argparse.ArgumentParser(description="Format and write climate inputs")
-parser.add_argument('reeds_path', help='ReEDS directory')
-parser.add_argument('inputs_case', help='output directory (inputs_case)')
-
-args = parser.parse_args()
-reeds_path = args.reeds_path
-inputs_case = args.inputs_case
-
-# #%% Settings for testing ###
-# reeds_path = reeds.io.reeds_path
-# inputs_case = str(Path(reeds_path,'runs','v20260409_itlM0_WECC_county','inputs_case'))
-
-#%%#################
-### FIXED INPUTS ###
-
-decimals = 5
-drop_canmex = True
-dollar_year = 2004
-weight = 'cost'
-
-costcol = f'USD{dollar_year}perMW'
-
-#%% Set up logger
-log = reeds.log.makelog(
-    scriptname=__file__,
-    logpath=os.path.join(inputs_case,'..','gamslog.txt'),
-)
-print('Starting transmission.py', flush=True)
-
-#%% Inputs from switches
-sw = reeds.io.get_switches(inputs_case)
-
-## networksource must end in a 4-digit year indicating the year represented by the network
-trans_init_year = int(sw.GSw_TransNetworkSource[-4:])
-
-valid_regions = {}
-for level in ['r','itlgrp','transgrp']:
-    valid_regions[level] = pd.read_csv(
-        os.path.join(inputs_case, f'val_{level}.csv'), header=None).squeeze(1).tolist()
-
-
-#%% ===========================================================================
-### --- FUNCTIONS ---
-### ===========================================================================
-def get_trancap_init(case, networksource='NARIS2024', level='r'):
-    """
-    AC capacity is defined for each direction and calculated using the scripts at
-    https://github.nrel.gov/ReEDS/TSC
-    """
-    sw = reeds.io.get_switches(case)
-    trancap_init_ac = (
-        reeds.inputs.get_itls(case, level=level, GSw_ZoneSet=sw.GSw_ZoneSet)
-        [['r', 'rr', 'MW_forward', 'MW_reverse']]
-        .assign(trtype='AC')
-    )
-    ### DEPRECATED: p19 is islanded with NARIS transmission data, so connect it manually
-    if (
-        (networksource == 'NARIS2024')
-        and (level != 'transgrp')
-        and ('p19' in valid_regions['r'])
-        and ('p20' in valid_regions['r'])
-    ):
-        trancap_init_ac = pd.concat([
-            trancap_init_ac,
-            pd.Series({
-                'r':'p19',
-                'rr':'p20',
-                'MW_forward':0.001,
-                'MW_reverse':0.001,
-                'trtype':'AC',
-            }).to_frame().T
-        ], ignore_index=True)
-
-    ### DC
-    if level == 'r':
-        ## transgrp capacity is only defined for AC
-        hvdc = reeds.inputs.map_hvdc_lines_to_interfaces(case).assign(trtype='LCC')
-        b2b = reeds.inputs.get_b2b(case).assign(trtype='B2B')
-        ## DC capacity is only defined in one direction,
-        ## so duplicate it for the opposite direction
-        trancap_init_nonac_undup = pd.concat([hvdc, b2b])[['r', 'rr', 'trtype', 'MW']]
-        trancap_init_nonac = pd.concat([
-            trancap_init_nonac_undup,
-            trancap_init_nonac_undup.rename(columns={'r':'rr', 'rr':'r'})
-        ], axis=0)
-    else:
-        trancap_init_nonac = pd.DataFrame(columns=['r', 'rr', 'trtype', 'MW'])
-
-    ### Initial trading limit, using contingency levels specified by contingency level
-    ### (but assuming full capacity of DC is available for both energy and capcity)
-    dfout = (
-        pd.concat(
-            [
-                ## AC
-                pd.concat([
-                    ## Forward direction
-                    (trancap_init_ac[['r', 'rr', 'trtype', 'MW_forward']]
-                    .rename(columns={'MW_forward':'MW'})),
-                    ## Reverse direction
-                    (trancap_init_ac[['r', 'rr', 'trtype', 'MW_reverse']]
-                    .rename(columns={'r':'rr', 'rr':'r', 'MW_reverse':'MW'}))
-                ], axis=0),
-                ## DC
-                trancap_init_nonac[['r', 'rr', 'trtype', 'MW']]
-            ],
-            axis=0
-        )
-        ## Drop entries with zero capacity
-        .replace(0.,np.nan).dropna()
-        .groupby(['r', 'rr', 'trtype']).sum().reset_index()
-    )
-    dfout = dfout.loc[
-        dfout['r'].isin(valid_regions[level])
-        & dfout['rr'].isin(valid_regions[level])
-    ].copy()
-    
-    ## Get alias for level (e.g. rr, transgrpp)
-    levell = level + level[-1]
-    return dfout.rename(columns={'r':level, 'rr':levell})
-
-
-def calculate_adjacent_routes(dfzones):
-    routes_adjacent = dfzones.copy()
-    routes_adjacent['r_adj'] = routes_adjacent.apply(
-        axis=1,
-        func=lambda x: (
-            routes_adjacent.loc[(
-                routes_adjacent.touches(x['geometry'])
-                | routes_adjacent.overlaps(x['geometry'])
-            )]
-            .index
-            .values
-            .tolist()
-        )
-    )
-    # Reformat so that each row represents a pair of regions
-    routes_adjacent = (
-        routes_adjacent.drop(columns='geometry')
-        .explode('r_adj')
-        .reset_index(names=['r'])
-        .rename(columns={'r': '*r', 'r_adj': 'rr'})
-        [['*r', 'rr']]
-        .dropna()
-    )
-
-    return routes_adjacent
-
-
-def calculate_co2_storage_routes(dfzones, co2_storage_sites):
-    # Determine the storage sites that are within 200 miles
-    # of each region's transmission endpoint
-    region_centroids = (
-        gpd.GeoDataFrame(
-            dfzones[['x', 'y']],
-            geometry=gpd.points_from_xy(dfzones.x, dfzones.y),
-            crs=dfzones.crs
-        )
-        [['geometry']]
-        .rename_axis(index='*r')
-        .reset_index()
-    )
-    region_centroids['cs'] = region_centroids.apply(
-        axis=1,
-        func=lambda x: (
-            co2_storage_sites.loc[(
-                co2_storage_sites.distance(x['geometry']) / 1609.34 <= 200
-            )]
-            ['cs']
-            .tolist()
-        )
-    )
-
-    # Calculate the lengths of the spurlines between regions and storage sites,
-    # excluding routes not completely within the U.S.
-    routes_cs = (
-        region_centroids.explode('cs')
-        .merge(
-            co2_storage_sites[['cs', 'geometry']],
-            on='cs',
-            suffixes=('_region', '_site')
-        )
-        .assign(
-            geometry=lambda x: (
-                gpd.GeoSeries(x['geometry_region'])
-                .shortest_line(gpd.GeoSeries(x['geometry_site']))
-            )
-        )
-        [['*r', 'cs', 'geometry']]
-    )
-    routes_cs = gpd.GeoDataFrame(routes_cs, geometry='geometry', crs=dfzones.crs)
-    routes_cs = routes_cs.loc[(
-        routes_cs.within(
-            reeds.io.get_dfmap(levels=['country'])['country'].loc['USA','geometry']
-        )
-        | (routes_cs.length == 0)
-    )]
-    routes_cs['distance_m'] = routes_cs.length
-    routes_cs['miles'] = (routes_cs['distance_m'] / 1609.34).round(2)
-
-    return routes_cs
-
-
-#%% ===========================================================================
-### --- PROCEDURE ---
-### ===========================================================================
-
-#%% Limits on PRMTRADE across nercr boundaries
-if not int(sw.GSw_PRM_NetImportLimit):
-    ## No limit
-    firm_import_limit = pd.DataFrame(columns=['*nercr','t','fraction']).set_index(['*nercr','t'])
-else:
-    limits = pd.Series(
-        {int(i.split('_')[0]): i.split('_')[1] for i in sw.GSw_PRM_NetImportLimitScen.split('/')}
-    )
-
-    solveyears = pd.read_csv(
-        os.path.join(inputs_case,'modeledyears.csv')
-    ).columns.astype(int).tolist()
-    startyear = min(solveyears)
-    endyear = max(solveyears)
-    allyears = range(startyear, max(endyear, limits.index.max())+1)
-
-    ## calculate the historical net_firm_import fraction for each region and drop negative values
-    peak_net_imports = pd.read_csv(
-        os.path.join(inputs_case,'peak_net_imports.csv'),
-        index_col=['nercr']
-    )
-    net_firm_import_frac = (
-        peak_net_imports.MW / peak_net_imports.MW_TotalDemand
-    ).clip(lower=0)
-    nercrs = net_firm_import_frac.index
-
-    _dfout = {}
-    for key, val in limits.items():
-        ## If 'hist' is in GSw_PRM_NetImportLimitScen,
-        ## all years up until that year use the historical regional max
-        if val == 'hist':
-            for y in range(startyear, key+1):
-                _dfout[y] = net_firm_import_frac
-        ## If 'histmax', all prior years use the historical max across all regions
-        elif val == 'histmax':
-            for y in range(startyear, key+1):
-                _dfout[y] = net_firm_import_frac.clip(lower=net_firm_import_frac.max())
-        else:
-            ## Input values are percentages so convert to fractions
-            _dfout[key] = pd.Series(index=nercrs, data=float(val) / 100)
-
-    firm_import_limit = (
-        pd.concat(_dfout, names=('t',)).unstack('nercr')
-        ## Linear interpolation between values; flat projections before and after
-        .reindex(allyears).interpolate('linear').bfill().ffill()
-        .loc[solveyears]
-        .unstack('t').rename('fraction').rename_axis(['*nercr','t'])
-    )
-
-firm_import_limit.to_csv(os.path.join(inputs_case, 'firm_import_limit.csv'))
-
-
-#%% Load the transmission scalars
-scalars = reeds.io.get_scalars(inputs_case)
-### Put some in dicts for easier access
-tranloss_permile = {
-    'AC': scalars['tranloss_permile_ac'],
-    ### B2B converters are AC-AC/DC-DC/AC-AC, so use AC per-mile losses
-    'B2B': scalars['tranloss_permile_ac'],
-    'LCC': scalars['tranloss_permile_dc'],
-    'VSC': scalars['tranloss_permile_dc'],
-}
-tranloss_fixed = {
-    'AC': 1 - scalars['converter_efficiency_ac'],
-    'B2B': 1 - scalars['converter_efficiency_lcc'],
-    'LCC': 1 - scalars['converter_efficiency_lcc'],
-    'VSC': 1 - scalars['converter_efficiency_vsc'],
-}
-
-
-#%% Get single-link distances and losses
-interface_params = pd.read_csv(
-    os.path.join(inputs_case,'transmission_distance.csv'),
-)
-interface_params['r_rr'] = interface_params.r + '_' + interface_params.rr
-
-# Apply the distance multiplier
-interface_params['miles'] = interface_params['miles'] * float(sw.GSw_TransSquiggliness)
-
-# Make sure there are no duplicates
-if interface_params[['r','rr']].duplicated().sum():
-    print(
-        interface_params.loc[
-            interface_params[['r','rr']].duplicated(keep=False)
-        ].sort_values(['r','rr'])
-    )
-    raise Exception('Duplicate entries in transmission_distance.csv')
-
-### Calculate losses
-def getloss(row, trtype='AC'):
-    """
-    Fixed losses are entered as per-endpoint values (e.g. for each AC/DC converter station
-    on a LCC DC line). There are two endpoints per line, so multiply fixed losses by 2.
-    Note that this approach only applies for LCC DC lines; VSC AC/DC losses are applied later.
-    """
-    return row.miles * tranloss_permile[trtype] + tranloss_fixed[trtype] * 2
-
-trtypes = ['AC', 'LCC', 'B2B', 'VSC']
-interface_params = pd.concat(
-    {
-        trtype:
-        interface_params.assign(loss=interface_params.apply(getloss, args=(trtype,), axis=1))
-        for trtype in trtypes
-    },
-    axis=0,
-    names=('trtype',),
-).reset_index(level='trtype').set_index(['r','rr','trtype'])
-
-
-#%% Include distances for existing lines
-transmission_distance = interface_params.miles.copy()
-
-#%% Write the line-specific transmission FOM costs [$/MW/year]
-trans_fom_region_mult = int(scalars['trans_fom_region_mult'])
-trans_fom_frac = scalars['trans_fom_frac']
-
-### For simplicity we just take the unweighted average base cost across
-### the four regions for which we have transmission cost data.
-### Future work should identify a better assumption.
-rev_transcost_base = pd.read_csv(
-    os.path.join(inputs_case,'rev_transmission_basecost.csv'),
-    header=[0], skiprows=[1],
-).replace({'500ACsingle':'AC','500DCbipole':'LCC'}).set_index('Voltage')
-
-transfom_USDperMWmileyear = {
-    trtype: (
-        rev_transcost_base.loc[trtype][['TEPPC','SCE','MISO','Southeast']].mean()
-        * trans_fom_frac
-    )
-    for trtype in ['AC','LCC']
-}
-### B2B is treated like (AC line)-(AC/DC converter)-(AC/DC converter)-(AC line) so uses AC line FOM
-transfom_USDperMWmileyear['B2B'] = transfom_USDperMWmileyear['AC']
-transfom_USDperMWmileyear['VSC'] = transfom_USDperMWmileyear['LCC']
-
-if trans_fom_region_mult:
-    ### Multiply line-specific $/MW by FOM fraction to get $/MW/year
-    transmission_line_fom = interface_params[costcol] * trans_fom_frac
-    ### Use regional average * distance_initial for existing lines
-    append = transmission_distance.loc[
-        transmission_distance.reset_index().trtype.isin(
-            ['AC','LCC','B2B','VSC']).set_axis(transmission_distance.index)
-    ]
-else:
-    ### Multiply $/MW/mile/year by distance [miles] to get $/MW/year for ALL lines
-    transmission_line_fom = (
-        transmission_distance.reset_index().trtype.map(transfom_USDperMWmileyear)
-        * transmission_distance.values
-    ).set_axis(transmission_distance.index).rename('USDperMWyear')
-
-
-#%%### Write files for ReEDS (adding * to make GAMS read column names as comment)
-### transmission_distance
-transmission_distance.round(3).reset_index().rename(columns={'r':'*r'}).to_csv(
-    os.path.join(inputs_case,'transmission_miles.csv'), index=False)
-
-### tranloss
-tranloss = interface_params['loss'].reset_index()
-tranloss.round(decimals).rename(columns={'r':'*r'}).to_csv(
-    os.path.join(inputs_case,'tranloss.csv'), index=False, header=True)
-
-### transmission_line_fom
-transmission_line_fom.round(2).rename_axis(('*r','rr','trtype')).to_csv(
-    os.path.join(inputs_case,'transmission_line_fom.csv'))
-
-#%% Write the initial capacities
-case = Path(inputs_case).parent
-trancap_init = {}
-for captype, level in [
-    ('energy', 'r'),
-    ('transgroup', 'transgrp'),
-]:
-    trancap_init[captype] = get_trancap_init(
-        case=case, networksource=sw.GSw_TransNetworkSource, level=level)
-    ### TEMPORARY 20260402: Drop county interfaces with no distance/cost
-    if (level == 'r') and (sw.GSw_RegionResolution in ['county', 'mixed']):
-        indices = ['r', 'rr', 'trtype']
-        drop = (
-            trancap_init[captype]
-            .merge(transmission_line_fom.reset_index(), on=indices, how='left')
-        )
-        drop = list(drop.loc[drop.USDperMWyear.isnull(), indices].itertuples(index=False))
-        trancap_init[captype] = trancap_init[captype].set_index(indices).drop(drop).reset_index()
-    trancap_init[captype].rename(columns={level:'*'+level}).round(3).to_csv(
-        os.path.join(inputs_case,f'trancap_init_{captype}.csv'),
-        index=False,
-    )
-trancap_init['energy'].rename(columns={'r':'*r'}).round(3).to_csv(
-    os.path.join(inputs_case,'trancap_init_prm.csv'),
-    index=False,
-)
-### TEMPORARY 20260402: Skip itlgrp functionality until we fix it
-# ### Also write itlgrp capacity
-# trancap_itlgrp = trancap_init['energy'].copy()
-# ## Map counties to itlgrp's
-# hierarchy_itlgrp = pd.read_csv(os.path.join(inputs_case, 'hierarchy_itlgrp.csv'))
-# itl_d = dict(zip(hierarchy_itlgrp['*r'], hierarchy_itlgrp['itlgrp']))
-# for r in ['r', 'rr']:
-#     trancap_itlgrp[r] = trancap_itlgrp[r].map(lambda x: itl_d.get(x,x))
-# trancap_itlgrp.rename(columns={'r':'*itlgrp', 'rr':'itlgrpp'}).round(3).to_csv(
-#     os.path.join(inputs_case, 'trancap_init_itlgrp.csv'),
-#     index=False,
-# )
-
-
-#%%### Future transmission capacity
-## note that '0' is used as a filler value in the t column for firstyear_trans, which is defined
-## in inputs/scalars.csv. So we replace it whenever we load a transmission_capacity_future file.
-trancap_fut = (
-    pd.concat(
-        [
-            pd.read_csv(
-                os.path.join(inputs_case, 'transmission_capacity_future_baseline.csv'),
-                comment='#',
-            ),
-            pd.read_csv(
-                os.path.join(inputs_case, 'transmission_capacity_future.csv'),
-                comment='#',
-            )
-        ],
-        axis=0,
-        ignore_index=True,
-    )
-    .astype({'t': int})
-    .drop(['Notes', 'notes', 'Note', 'note'], axis=1, errors='ignore')
-    .replace({'t': {0: int(scalars['firstyear_trans_longterm'])}})
-)
-
-### Drop prospective AC lines from years <= trans_init_year
-trancap_fut = trancap_fut.drop(
-    trancap_fut.loc[
-        (trancap_fut.t <= trans_init_year)
-        & (trancap_fut.trtype == 'AC')
-    ].index,
-).copy()
-
-trancap_fut.rename(columns={'r':'*r'}).astype({'t':int}).round(3).to_csv(
-    os.path.join(inputs_case,'trancap_fut.csv'), index=False)
-
-
-#%%### Transmission upgrade supply curve
-transmission_cost_ac = pd.read_csv(
-    os.path.join(inputs_case, 'transmission_cost_ac.csv')
-)
-### Interfaces are always defined with the zones sorted in lexicographic order
-reverse_interfaces = transmission_cost_ac.loc[
-    transmission_cost_ac.apply(lambda row: row.r > row.rr, axis=1)
-]
-for i, row in reverse_interfaces.iterrows():
-    transmission_cost_ac.loc[i, ['r', 'rr']] = transmission_cost_ac.loc[i, ['rr', 'r']].values
-    transmission_cost_ac.loc[i, ['USD2004perMW_forward', 'USD2004perMW_reverse']] = (
-        transmission_cost_ac.loc[i, ['USD2004perMW_reverse', 'USD2004perMW_forward']].values
-    )
-
-_test = transmission_cost_ac.apply(lambda row: row.r < row.rr, axis=1)
-if not _test.all():
-    print(transmission_cost_ac.loc[~_test])
-    err = (
-        "Must have r < rr in AC transmission cost inputs but the interfaces "
-        "listed above are out of order"
-    )
-    raise ValueError(err)
-
-labels = {
-    'binwidth_USD2004': 'binwidth',
-    'USD2004perMW_forward': 'forward',
-    'USD2004perMW_reverse': 'reverse',
-}
-for col, label in labels.items():
-    transmission_cost_ac[['r','rr','tscbin',col]].rename(columns={'r':'*r'}).round(2).to_csv(
-        os.path.join(inputs_case, f'tsc_{label}.csv'),
-        index=False,
-    )
-transmission_cost_ac.tscbin.drop_duplicates().to_csv(
-    os.path.join(inputs_case, 'tscbin.csv'),
-    index=False,
-    header=False,
-)
-
-
-#%% DC and B2B transmission cost
-## Get DC line cost
-transmission_cost_dc = pd.read_csv(os.path.join(inputs_case, 'transmission_cost_dc.csv'))
-
-## B2B is: (zone center)--------(AC/DC converter)(DC/AC converter)--------(zone center)
-##                         ^ AC line                                 ^ AC line
-## so use AC per-mile costs.
-b2b_links = trancap_init['energy'].loc[
-    (trancap_init['energy'].trtype=='B2B')
-    & (trancap_init['energy'].r < trancap_init['energy'].rr)
-].set_index(['r','rr']).index
-## Take the weighted average of the whole supply curve (for the default 500 kV assumption
-## the supply curve only has one bin per interface, so it doesn't matter; when we add the
-## full supply curve, we'll need to include entries for these B2B-containing interfaces).
-df = transmission_cost_ac.set_index(['r','rr']).loc[b2b_links].copy()
-df['cost_weighted'] = (
-    df.binwidth_USD2004
-    * df.USD2004perMW_forward
-)
-transmission_cost_b2b = (
-    df.groupby(['r','rr','tscbin']).cost_weighted.sum()
-    / df.groupby(['r','rr','tscbin']).binwidth_USD2004.sum()
-).reset_index(level='tscbin', drop=True).rename('USD2004perMW').reset_index()
-## Add the reverse direction and write it
-transmission_cost_b2b = pd.concat([
-    transmission_cost_b2b,
-    transmission_cost_b2b.rename(columns={'r':'rr', 'rr':'r'})
-])
-
-### Write the combined cost table
-transmission_cost_nonac = (
-    pd.concat({
-        'LCC': transmission_cost_dc,
-        'B2B': transmission_cost_b2b,
-        'VSC': transmission_cost_dc,
-    }, names=('trtype','drop'))
-    .reset_index('drop', drop=True)
-    .reset_index()
-    .rename(columns={'r':'*r'})
-    [['*r','rr','trtype','USD2004perMW']]
-)
-transmission_cost_nonac.round(2).to_csv(
-    os.path.join(inputs_case, 'transmission_cost_nonac.csv'),
-    index=False,
-)
-
-
-#%%### Hurdle rates
-hurdle_levels = [1, 2]
-cost_hurdle_intra = (
-    pd.read_csv(os.path.join(inputs_case, 'cost_hurdle_intra.csv'))
-    .rename(columns={'t':'*t'}).set_index('*t').round(3)
-)
-cost_hurdle_rate = {
-    i: (
-        cost_hurdle_intra[sw[f'GSw_TransHurdleLevel{i}']] if int(sw.GSw_TransHurdleRate)
-        else pd.Series(name='region').rename_axis('*t')
-    )
-    for i in hurdle_levels
-}
-for i in hurdle_levels:
-    cost_hurdle_rate[i].to_csv(os.path.join(inputs_case, f'cost_hurdle_rate{i}.csv'))
-
-
-#%%### H2 pipeline cost multipliers
-# Calculate H2 pipeline cost multipliers by dividing the [$/mile] cost of DC transmission
-# between each pair of regions by the minimum interface [$/mile] cost for DC transmission
-# and subtracting 1 to get a fractional adder (which is then added to 1 in b_inputs.gms)
-fpath = os.path.join(inputs_case, 'pipeline_cost_mult.csv')
-if len(transmission_cost_nonac):
-    dc_cost_permile = (
-        transmission_cost_nonac.rename(columns={'*r':'r'})
-        .set_index(['trtype','r','rr']).loc['LCC'].squeeze(1)
-        / interface_params.xs('LCC', 0, 'trtype').miles
-    )
-    pipeline_cost_mult = dc_cost_permile.rename('multiplier') / dc_cost_permile.min() - 1
-
-    pipeline_cost_mult.reset_index().rename(columns={'r':'*r'}).round(3).to_csv(
-        fpath,
-        index=False,
-    )
-else:
-    pd.DataFrame(columns=['*r','rr','multiplier']).to_csv(fpath, index=False)
-
-
-# Get model regions
-dfzones = reeds.io.get_dfmap(
-    os.path.dirname(inputs_case),
-    levels=['r'],
-    exclude_water_areas=True
-)['r']
-
-#%%### Adjacent regions
-# Determine which pairs of model regions are adjacent to each other
-routes_adjacent = calculate_adjacent_routes(dfzones)
-routes_adjacent.to_csv(
-    os.path.join(inputs_case,'routes_adjacent.csv'),
-    index=False
-)
-
-#%%### CO2 storage sites
-# Determine spurline routes from model regions to carbon storage sites
-co2_storage_sites = reeds.io.get_co2_storage_sites()
-routes_cs = calculate_co2_storage_routes(dfzones, co2_storage_sites)
-routes_cs[['*r', 'cs']].to_csv(
-    os.path.join(inputs_case, 'r_cs.csv'), index=False
-)
-routes_cs[['*r', 'cs', 'miles']].to_csv(
-    os.path.join(inputs_case,'r_cs_distance_mi.csv'),
-    index=False
-)
-
-# Determine sites that have valid routes to model regions
-val_cs = pd.Series(routes_cs['cs'].unique())
-val_cs.to_csv(os.path.join(inputs_case, 'val_cs.csv'), header=False, index=False)
-
-# Subset CO2 site characteristics data to valid sites
-co2_site_char = pd.read_csv(os.path.join(inputs_case, 'co2_site_char.csv'))
-co2_site_char = co2_site_char.loc[co2_site_char['cs'].isin(val_cs)]
-co2_site_char.to_csv(os.path.join(inputs_case, 'co2_site_char.csv'), index=False)
-
-# Create WKT file of region-to-site spurlines
-r_cs_spurlines = (
-    routes_cs.loc[routes_cs['distance_m'] > 0]
-    .rename(columns={'*r': 'ba_str', 'cs': 'FmnID'})
-    .to_crs('EPSG:4326')
-    .assign(WKT=lambda x: x['geometry'].to_wkt())
-    [['ba_str', 'FmnID', 'distance_m', 'WKT']]
-)
-r_cs_spurlines.to_csv(
-    os.path.join(inputs_case,'ctus_r_cs_spurlines_200mi.csv'),
-    index=False
-)
-
-#%% Finish the timer
-reeds.log.toc(tic=tic, year=0, process='input_processing/transmission.py',
-    path=os.path.join(inputs_case,'..'))
-print('Finished transmission.py', flush=True)
diff --git a/inputs/capacity_exogenous/exog_cap_upv_limited.csv b/inputs/capacity_exogenous/exog_cap_upv_limited.csv
index 6db8be148..bc1a463a5 100644
--- a/inputs/capacity_exogenous/exog_cap_upv_limited.csv
+++ b/inputs/capacity_exogenous/exog_cap_upv_limited.csv
@@ -1,1496 +1,1556 @@
 *tech,year,sc_point_gid,capacity
-upv_1,2010,48781,0.4
-upv_3,2010,84310,0.9
-upv_3,2010,85680,1.4
-upv_4,2010,86359,0.5
-upv_4,2010,86362,1.0
-upv_4,2010,87047,1.2
-upv_4,2010,87733,2.0
-upv_3,2010,89096,0.9
-upv_4,2010,89781,1.0
-upv_1,2010,90103,0.9
-upv_4,2010,90462,0.5
-upv_4,2010,91147,0.7
-upv_4,2010,94564,0.5
-upv_4,2010,95247,0.3
-upv_4,2010,95248,0.5
-upv_4,2010,95939,3.0
-upv_4,2010,96617,0.9
-upv_4,2010,97298,0.3
-upv_4,2010,97301,1.0
-upv_4,2010,97982,0.5
-upv_4,2010,97986,1.1
-upv_4,2010,98669,1.0
-upv_1,2010,99683,3.0
-upv_4,2010,101403,0.9
-upv_4,2010,102084,0.4
-upv_1,2010,102329,9.0
-upv_4,2010,104135,0.3
-upv_4,2010,106189,0.9
-upv_3,2010,110429,1.5
-upv_4,2010,113026,0.9
-upv_5,2010,113707,0.4
-upv_4,2010,113710,0.9
-upv_4,2010,115758,0.3
-upv_4,2010,122598,0.8
-upv_5,2010,123282,0.9
-upv_5,2010,123964,0.5
-upv_5,2010,125333,0.9
-upv_4,2010,126161,1.9
-upv_5,2010,126751,10.0
-upv_4,2010,128894,0.1
-upv_5,2010,129527,3.0
-upv_4,2010,129578,0.4
-upv_4,2010,129582,5.4
-upv_4,2010,130262,0.9
-upv_4,2010,130263,1.5
-upv_5,2010,130894,0.2
-upv_5,2010,134314,3.7
-upv_5,2010,136294,21.0
-upv_2,2010,137257,1.0
-upv_2,2010,138622,0.5
-upv_2,2010,139990,0.5
-upv_5,2010,147307,3.0
-upv_4,2010,194735,25.0
-upv_1,2011,48781,0.4
-upv_3,2011,84310,0.9
-upv_3,2011,85680,1.4
-upv_4,2011,86359,0.5
-upv_4,2011,86362,1.0
-upv_4,2011,87047,1.2
-upv_4,2011,87733,2.0
-upv_3,2011,89096,0.9
-upv_4,2011,89781,1.0
-upv_1,2011,90103,0.9
-upv_4,2011,90462,0.5
-upv_4,2011,91147,0.7
-upv_4,2011,94564,0.5
-upv_4,2011,95247,0.3
-upv_4,2011,95248,0.5
-upv_4,2011,95939,3.0
-upv_4,2011,96617,0.9
-upv_4,2011,97298,0.3
-upv_4,2011,97301,1.0
-upv_4,2011,97982,0.5
-upv_4,2011,97986,1.1
-upv_4,2011,98669,1.0
-upv_1,2011,99683,3.0
-upv_4,2011,101403,0.9
-upv_4,2011,102084,0.4
-upv_1,2011,102329,9.0
-upv_4,2011,104135,0.3
-upv_4,2011,106189,0.9
-upv_3,2011,110429,1.5
-upv_4,2011,113026,0.9
-upv_5,2011,113707,0.4
-upv_4,2011,113710,0.9
-upv_4,2011,115758,0.3
-upv_4,2011,122598,0.8
-upv_5,2011,123282,0.9
-upv_5,2011,123964,0.5
-upv_5,2011,125333,0.9
-upv_4,2011,126161,1.9
-upv_5,2011,126751,10.0
-upv_4,2011,128894,0.1
-upv_5,2011,129527,3.0
-upv_4,2011,129578,0.4
-upv_4,2011,129582,5.4
-upv_4,2011,130262,0.9
-upv_4,2011,130263,1.5
-upv_5,2011,130894,0.2
-upv_5,2011,134314,3.7
-upv_5,2011,136294,21.0
-upv_2,2011,137257,1.0
-upv_2,2011,138622,0.5
-upv_2,2011,139990,0.5
-upv_5,2011,147307,3.0
-upv_4,2011,194735,25.0
-upv_1,2012,48781,0.4
-upv_3,2012,84310,0.9
-upv_3,2012,85680,1.4
-upv_4,2012,86359,0.5
-upv_4,2012,86362,1.0
-upv_4,2012,87047,1.2
-upv_4,2012,87733,2.0
-upv_3,2012,89096,0.9
-upv_4,2012,89781,1.0
-upv_1,2012,90103,0.9
-upv_4,2012,90462,0.5
-upv_4,2012,91147,0.7
-upv_4,2012,94564,0.5
-upv_4,2012,95247,0.3
-upv_4,2012,95248,0.5
-upv_4,2012,95939,3.0
-upv_4,2012,96617,0.9
-upv_4,2012,97298,0.3
-upv_4,2012,97301,1.0
-upv_4,2012,97982,0.5
-upv_4,2012,97986,1.1
-upv_4,2012,98669,1.0
-upv_1,2012,99683,3.0
-upv_4,2012,101403,0.9
-upv_4,2012,102084,0.4
-upv_1,2012,102329,9.0
-upv_4,2012,104135,0.3
-upv_4,2012,106189,0.9
-upv_3,2012,110429,1.5
-upv_4,2012,113026,0.9
-upv_5,2012,113707,0.4
-upv_4,2012,113710,0.9
-upv_4,2012,115758,0.3
-upv_4,2012,122598,0.8
-upv_5,2012,123282,0.9
-upv_5,2012,123964,0.5
-upv_5,2012,125333,0.9
-upv_4,2012,126161,1.9
-upv_5,2012,126751,10.0
-upv_4,2012,128894,0.1
-upv_5,2012,129527,3.0
-upv_4,2012,129578,0.4
-upv_4,2012,129582,5.4
-upv_4,2012,130262,0.9
-upv_4,2012,130263,1.5
-upv_5,2012,130894,0.2
-upv_5,2012,134314,3.7
-upv_5,2012,136294,21.0
-upv_2,2012,137257,1.0
-upv_2,2012,138622,0.5
-upv_2,2012,139990,0.5
-upv_5,2012,147307,3.0
-upv_4,2012,194735,25.0
-upv_1,2013,48781,0.4
-upv_3,2013,84310,0.9
-upv_3,2013,85680,1.4
-upv_4,2013,86359,0.5
-upv_4,2013,86362,1.0
-upv_4,2013,87047,1.2
-upv_4,2013,87733,2.0
-upv_3,2013,89096,0.9
-upv_4,2013,89781,1.0
-upv_1,2013,90103,0.9
-upv_4,2013,90462,0.5
-upv_4,2013,91147,0.7
-upv_4,2013,94564,0.5
-upv_4,2013,95247,0.3
-upv_4,2013,95248,0.5
-upv_4,2013,95939,3.0
-upv_4,2013,96617,0.9
-upv_4,2013,97298,0.3
-upv_4,2013,97301,1.0
-upv_4,2013,97982,0.5
-upv_4,2013,97986,1.1
-upv_4,2013,98669,1.0
-upv_1,2013,99683,3.0
-upv_4,2013,101403,0.9
-upv_4,2013,102084,0.4
-upv_1,2013,102329,9.0
-upv_4,2013,104135,0.3
-upv_4,2013,106189,0.9
-upv_3,2013,110429,1.5
-upv_4,2013,113026,0.9
-upv_5,2013,113707,0.4
-upv_4,2013,113710,0.9
-upv_4,2013,115758,0.3
-upv_4,2013,122598,0.8
-upv_5,2013,123282,0.9
-upv_5,2013,123964,0.5
-upv_5,2013,125333,0.9
-upv_4,2013,126161,1.9
-upv_5,2013,126751,10.0
-upv_4,2013,128894,0.1
-upv_5,2013,129527,3.0
-upv_4,2013,129578,0.4
-upv_4,2013,129582,5.4
-upv_4,2013,130262,0.9
-upv_4,2013,130263,1.5
-upv_5,2013,130894,0.2
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diff --git a/inputs/capacity_exogenous/exog_cap_upv_open.csv b/inputs/capacity_exogenous/exog_cap_upv_open.csv
index 0886870ef..4aab16eb9 100644
--- a/inputs/capacity_exogenous/exog_cap_upv_open.csv
+++ b/inputs/capacity_exogenous/exog_cap_upv_open.csv
@@ -1,1526 +1,1526 @@
 *tech,year,sc_point_gid,capacity
-upv_1,2010,52200,0.4
-upv_1,2010,98999,3.0
-upv_2,2010,102419,0.9
-upv_4,2010,103504,10.0
-upv_2,2010,105841,2.3
-upv_2,2010,107802,9.0
-upv_4,2010,109747,1.9
-upv_5,2010,110303,0.5
-upv_5,2010,111674,0.9
-upv_5,2010,113043,1.0
-upv_5,2010,113725,0.7
-upv_5,2010,114409,0.9
-upv_5,2010,114414,1.4
-upv_5,2010,115091,0.5
-upv_5,2010,115774,0.5
-upv_4,2010,117140,0.3
-upv_5,2010,117144,0.9
-upv_5,2010,117831,1.1
-upv_5,2010,117832,1.2
-upv_5,2010,117837,2.0
-upv_4,2010,119321,1.5
-upv_4,2010,119875,0.4
-upv_4,2010,119876,0.5
-upv_4,2010,121244,0.5
-upv_4,2010,121375,5.4
-upv_4,2010,122610,0.4
-upv_4,2010,123294,0.5
-upv_4,2010,123298,0.9
-upv_4,2010,124661,0.3
-upv_4,2010,125349,0.8
-upv_5,2010,127396,0.3
-upv_5,2010,127403,1.0
-upv_5,2010,129464,3.0
-upv_5,2010,130131,0.3
-upv_5,2010,130136,1.0
-upv_4,2010,130263,0.1
-upv_5,2010,130819,0.9
-upv_5,2010,130821,1.0
-upv_4,2010,130947,0.4
-upv_4,2010,130948,1.5
-upv_4,2010,131631,0.9
-upv_2,2010,131794,0.5
-upv_2,2010,132478,0.5
-upv_2,2010,132480,1.0
-upv_5,2010,132947,3.0
-upv_5,2010,133554,0.9
-upv_5,2010,134238,0.9
-upv_5,2010,136289,0.9
-upv_5,2010,136973,0.9
-upv_5,2010,137681,21.0
-upv_5,2010,139101,0.2
-upv_5,2010,142521,3.7
-upv_5,2010,146624,3.0
-upv_4,2010,198159,25.0
-upv_1,2011,52200,0.4
-upv_1,2011,98999,3.0
-upv_2,2011,102419,0.9
-upv_4,2011,103504,10.0
-upv_2,2011,105841,2.3
-upv_2,2011,107802,9.0
-upv_4,2011,109747,1.9
-upv_5,2011,110303,0.5
-upv_5,2011,111674,0.9
-upv_5,2011,113043,1.0
-upv_5,2011,113725,0.7
-upv_5,2011,114409,0.9
-upv_5,2011,114414,1.4
-upv_5,2011,115091,0.5
-upv_5,2011,115774,0.5
-upv_4,2011,117140,0.3
-upv_5,2011,117144,0.9
-upv_5,2011,117831,1.1
-upv_5,2011,117832,1.2
-upv_5,2011,117837,2.0
-upv_4,2011,119321,1.5
-upv_4,2011,119875,0.4
-upv_4,2011,119876,0.5
-upv_4,2011,121244,0.5
-upv_4,2011,121375,5.4
-upv_4,2011,122610,0.4
-upv_4,2011,123294,0.5
-upv_4,2011,123298,0.9
-upv_4,2011,124661,0.3
-upv_4,2011,125349,0.8
-upv_5,2011,127396,0.3
-upv_5,2011,127403,1.0
-upv_5,2011,129464,3.0
-upv_5,2011,130131,0.3
-upv_5,2011,130136,1.0
-upv_4,2011,130263,0.1
-upv_5,2011,130819,0.9
-upv_5,2011,130821,1.0
-upv_4,2011,130947,0.4
-upv_4,2011,130948,1.5
-upv_4,2011,131631,0.9
-upv_2,2011,131794,0.5
-upv_2,2011,132478,0.5
-upv_2,2011,132480,1.0
-upv_5,2011,132947,3.0
-upv_5,2011,133554,0.9
-upv_5,2011,134238,0.9
-upv_5,2011,136289,0.9
-upv_5,2011,136973,0.9
-upv_5,2011,137681,21.0
-upv_5,2011,139101,0.2
-upv_5,2011,142521,3.7
-upv_5,2011,146624,3.0
-upv_4,2011,198159,25.0
-upv_1,2012,52200,0.4
-upv_1,2012,98999,3.0
-upv_2,2012,102419,0.9
-upv_4,2012,103504,10.0
-upv_2,2012,105841,2.3
-upv_2,2012,107802,9.0
-upv_4,2012,109747,1.9
-upv_5,2012,110303,0.5
-upv_5,2012,111674,0.9
-upv_5,2012,113043,1.0
-upv_5,2012,113725,0.7
-upv_5,2012,114409,0.9
-upv_5,2012,114414,1.4
-upv_5,2012,115091,0.5
-upv_5,2012,115774,0.5
-upv_4,2012,117140,0.3
-upv_5,2012,117144,0.9
-upv_5,2012,117831,1.1
-upv_5,2012,117832,1.2
-upv_5,2012,117837,2.0
-upv_4,2012,119321,1.5
-upv_4,2012,119875,0.4
-upv_4,2012,119876,0.5
-upv_4,2012,121244,0.5
-upv_4,2012,121375,5.4
-upv_4,2012,122610,0.4
-upv_4,2012,123294,0.5
-upv_4,2012,123298,0.9
-upv_4,2012,124661,0.3
-upv_4,2012,125349,0.8
-upv_5,2012,127396,0.3
-upv_5,2012,127403,1.0
-upv_5,2012,129464,3.0
-upv_5,2012,130131,0.3
-upv_5,2012,130136,1.0
-upv_4,2012,130263,0.1
-upv_5,2012,130819,0.9
-upv_5,2012,130821,1.0
-upv_4,2012,130947,0.4
-upv_4,2012,130948,1.5
-upv_4,2012,131631,0.9
-upv_2,2012,131794,0.5
-upv_2,2012,132478,0.5
-upv_2,2012,132480,1.0
-upv_5,2012,132947,3.0
-upv_5,2012,133554,0.9
-upv_5,2012,134238,0.9
-upv_5,2012,136289,0.9
-upv_5,2012,136973,0.9
-upv_5,2012,137681,21.0
-upv_5,2012,139101,0.2
-upv_5,2012,142521,3.7
-upv_5,2012,146624,3.0
-upv_4,2012,198159,25.0
-upv_1,2013,52200,0.4
-upv_1,2013,98999,3.0
-upv_2,2013,102419,0.9
-upv_4,2013,103504,10.0
-upv_2,2013,105841,2.3
-upv_2,2013,107802,9.0
-upv_4,2013,109747,1.9
-upv_5,2013,110303,0.5
-upv_5,2013,111674,0.9
-upv_5,2013,113043,1.0
-upv_5,2013,113725,0.7
-upv_5,2013,114409,0.9
-upv_5,2013,114414,1.4
-upv_5,2013,115091,0.5
-upv_5,2013,115774,0.5
-upv_4,2013,117140,0.3
-upv_5,2013,117144,0.9
-upv_5,2013,117831,1.1
-upv_5,2013,117832,1.2
-upv_5,2013,117837,2.0
-upv_4,2013,119321,1.5
-upv_4,2013,119875,0.4
-upv_4,2013,119876,0.5
-upv_4,2013,121244,0.5
-upv_4,2013,121375,5.4
-upv_4,2013,122610,0.4
-upv_4,2013,123294,0.5
-upv_4,2013,123298,0.9
-upv_4,2013,124661,0.3
-upv_4,2013,125349,0.8
-upv_5,2013,127396,0.3
-upv_5,2013,127403,1.0
-upv_5,2013,129464,3.0
-upv_5,2013,130131,0.3
-upv_5,2013,130136,1.0
-upv_4,2013,130263,0.1
-upv_5,2013,130819,0.9
-upv_5,2013,130821,1.0
-upv_4,2013,130947,0.4
-upv_4,2013,130948,1.5
-upv_4,2013,131631,0.9
-upv_2,2013,131794,0.5
-upv_2,2013,132478,0.5
-upv_2,2013,132480,1.0
-upv_5,2013,132947,3.0
-upv_5,2013,133554,0.9
-upv_5,2013,134238,0.9
-upv_5,2013,136289,0.9
-upv_5,2013,136973,0.9
-upv_5,2013,137681,21.0
-upv_5,2013,139101,0.2
-upv_5,2013,142521,3.7
-upv_5,2013,146624,3.0
-upv_4,2013,198159,25.0
-upv_1,2014,52200,0.4
-upv_1,2014,98999,3.0
-upv_2,2014,102419,0.9
-upv_4,2014,103504,10.0
-upv_2,2014,105841,2.3
-upv_2,2014,107802,9.0
-upv_4,2014,109747,1.9
-upv_5,2014,110303,0.5
-upv_5,2014,111674,0.9
-upv_5,2014,113043,1.0
-upv_5,2014,113725,0.7
-upv_5,2014,114409,0.9
-upv_5,2014,114414,1.4
-upv_5,2014,115091,0.5
-upv_5,2014,115774,0.5
-upv_4,2014,117140,0.3
-upv_5,2014,117144,0.9
-upv_5,2014,117831,1.1
-upv_5,2014,117832,1.2
-upv_5,2014,117837,2.0
-upv_4,2014,119321,1.5
-upv_4,2014,119875,0.4
-upv_4,2014,119876,0.5
-upv_4,2014,121244,0.5
-upv_4,2014,121375,5.4
-upv_4,2014,122610,0.4
-upv_4,2014,123294,0.5
-upv_4,2014,123298,0.9
-upv_4,2014,124661,0.3
-upv_4,2014,125349,0.8
-upv_5,2014,127396,0.3
-upv_5,2014,127403,1.0
-upv_5,2014,129464,3.0
-upv_5,2014,130131,0.3
-upv_5,2014,130136,1.0
-upv_4,2014,130263,0.1
-upv_5,2014,130819,0.9
-upv_5,2014,130821,1.0
-upv_4,2014,130947,0.4
-upv_4,2014,130948,1.5
-upv_4,2014,131631,0.9
-upv_2,2014,131794,0.5
-upv_2,2014,132478,0.5
-upv_2,2014,132480,1.0
-upv_5,2014,132947,3.0
-upv_5,2014,133554,0.9
-upv_5,2014,134238,0.9
-upv_5,2014,136289,0.9
-upv_5,2014,136973,0.9
-upv_5,2014,137681,21.0
-upv_5,2014,139101,0.2
-upv_5,2014,142521,3.7
-upv_5,2014,146624,3.0
-upv_4,2014,198159,25.0
-upv_1,2015,52200,0.4
-upv_1,2015,98999,3.0
-upv_2,2015,102419,0.9
-upv_4,2015,103504,10.0
-upv_2,2015,105841,2.3
-upv_2,2015,107802,9.0
-upv_4,2015,109747,1.9
-upv_5,2015,110303,0.5
-upv_5,2015,111674,0.9
-upv_5,2015,113043,1.0
-upv_5,2015,113725,0.7
-upv_5,2015,114409,0.9
-upv_5,2015,114414,1.4
-upv_5,2015,115091,0.5
-upv_5,2015,115774,0.5
-upv_4,2015,117140,0.3
-upv_5,2015,117144,0.9
-upv_5,2015,117831,1.1
-upv_5,2015,117832,1.2
-upv_5,2015,117837,2.0
-upv_4,2015,119321,1.5
-upv_4,2015,119875,0.4
-upv_4,2015,119876,0.5
-upv_4,2015,121244,0.5
-upv_4,2015,121375,5.4
-upv_4,2015,122610,0.4
-upv_4,2015,123294,0.5
-upv_4,2015,123298,0.9
-upv_4,2015,124661,0.3
-upv_4,2015,125349,0.8
-upv_5,2015,127396,0.3
-upv_5,2015,127403,1.0
-upv_5,2015,129464,3.0
-upv_5,2015,130131,0.3
-upv_5,2015,130136,1.0
-upv_4,2015,130263,0.1
-upv_5,2015,130819,0.9
-upv_5,2015,130821,1.0
-upv_4,2015,130947,0.4
-upv_4,2015,130948,1.5
-upv_4,2015,131631,0.9
-upv_2,2015,131794,0.5
-upv_2,2015,132478,0.5
-upv_2,2015,132480,1.0
-upv_5,2015,132947,3.0
-upv_5,2015,133554,0.9
-upv_5,2015,134238,0.9
-upv_5,2015,136289,0.9
-upv_5,2015,136973,0.9
-upv_5,2015,137681,21.0
-upv_5,2015,139101,0.2
-upv_5,2015,142521,3.7
-upv_5,2015,146624,3.0
-upv_4,2015,198159,25.0
-upv_1,2016,52200,0.4
-upv_1,2016,98999,3.0
-upv_2,2016,102419,0.9
-upv_4,2016,103504,10.0
-upv_2,2016,105841,2.3
-upv_2,2016,107802,9.0
-upv_4,2016,109747,1.9
-upv_5,2016,110303,0.5
-upv_5,2016,111674,0.9
-upv_5,2016,113043,1.0
-upv_5,2016,113725,0.7
-upv_5,2016,114409,0.9
-upv_5,2016,114414,1.4
-upv_5,2016,115091,0.5
-upv_5,2016,115774,0.5
-upv_4,2016,117140,0.3
-upv_5,2016,117144,0.9
-upv_5,2016,117831,1.1
-upv_5,2016,117832,1.2
-upv_5,2016,117837,2.0
-upv_4,2016,119321,1.5
-upv_4,2016,119875,0.4
-upv_4,2016,119876,0.5
-upv_4,2016,121244,0.5
-upv_4,2016,121375,5.4
-upv_4,2016,122610,0.4
-upv_4,2016,123294,0.5
-upv_4,2016,123298,0.9
-upv_4,2016,124661,0.3
-upv_4,2016,125349,0.8
-upv_5,2016,127396,0.3
-upv_5,2016,127403,1.0
-upv_5,2016,129464,3.0
-upv_5,2016,130131,0.3
-upv_5,2016,130136,1.0
-upv_4,2016,130263,0.1
-upv_5,2016,130819,0.9
-upv_5,2016,130821,1.0
-upv_4,2016,130947,0.4
-upv_4,2016,130948,1.5
-upv_4,2016,131631,0.9
-upv_2,2016,131794,0.5
-upv_2,2016,132478,0.5
-upv_2,2016,132480,1.0
-upv_5,2016,132947,3.0
-upv_5,2016,133554,0.9
-upv_5,2016,134238,0.9
-upv_5,2016,136289,0.9
-upv_5,2016,136973,0.9
-upv_5,2016,137681,21.0
-upv_5,2016,139101,0.2
-upv_5,2016,142521,3.7
-upv_5,2016,146624,3.0
-upv_4,2016,198159,25.0
-upv_1,2017,52200,0.4
-upv_1,2017,98999,3.0
-upv_2,2017,102419,0.9
-upv_4,2017,103504,10.0
-upv_2,2017,105841,2.3
-upv_2,2017,107802,9.0
-upv_4,2017,109747,1.9
-upv_5,2017,110303,0.5
-upv_5,2017,111674,0.9
-upv_5,2017,113043,1.0
-upv_5,2017,113725,0.7
-upv_5,2017,114409,0.9
-upv_5,2017,114414,1.4
-upv_5,2017,115091,0.5
-upv_5,2017,115774,0.5
-upv_4,2017,117140,0.3
-upv_5,2017,117144,0.9
-upv_5,2017,117831,1.1
-upv_5,2017,117832,1.2
-upv_5,2017,117837,2.0
-upv_4,2017,119321,1.5
-upv_4,2017,119875,0.4
-upv_4,2017,119876,0.5
-upv_4,2017,121244,0.5
-upv_4,2017,121375,5.4
-upv_4,2017,122610,0.4
-upv_4,2017,123294,0.5
-upv_4,2017,123298,0.9
-upv_4,2017,124661,0.3
-upv_4,2017,125349,0.8
-upv_5,2017,127396,0.3
-upv_5,2017,127403,1.0
-upv_5,2017,129464,3.0
-upv_5,2017,130131,0.3
-upv_5,2017,130136,1.0
-upv_4,2017,130263,0.1
-upv_5,2017,130819,0.9
-upv_5,2017,130821,1.0
-upv_4,2017,130947,0.4
-upv_4,2017,130948,1.5
-upv_4,2017,131631,0.9
-upv_2,2017,131794,0.5
-upv_2,2017,132478,0.5
-upv_2,2017,132480,1.0
-upv_5,2017,132947,3.0
-upv_5,2017,133554,0.9
-upv_5,2017,134238,0.9
-upv_5,2017,136289,0.9
-upv_5,2017,136973,0.9
-upv_5,2017,137681,21.0
-upv_5,2017,139101,0.2
-upv_5,2017,142521,3.7
-upv_5,2017,146624,3.0
-upv_4,2017,198159,25.0
-upv_1,2018,52200,0.4
-upv_1,2018,98999,3.0
-upv_2,2018,102419,0.9
-upv_4,2018,103504,10.0
-upv_2,2018,105841,2.3
-upv_2,2018,107802,9.0
-upv_4,2018,109747,1.9
-upv_5,2018,110303,0.5
-upv_5,2018,111674,0.9
-upv_5,2018,113043,1.0
-upv_5,2018,113725,0.7
-upv_5,2018,114409,0.9
-upv_5,2018,114414,1.4
-upv_5,2018,115091,0.5
-upv_5,2018,115774,0.5
-upv_4,2018,117140,0.3
-upv_5,2018,117144,0.9
-upv_5,2018,117831,1.1
-upv_5,2018,117832,1.2
-upv_5,2018,117837,2.0
-upv_4,2018,119321,1.5
-upv_4,2018,119875,0.4
-upv_4,2018,119876,0.5
-upv_4,2018,121244,0.5
-upv_4,2018,121375,5.4
-upv_4,2018,122610,0.4
-upv_4,2018,123294,0.5
-upv_4,2018,123298,0.9
-upv_4,2018,124661,0.3
-upv_4,2018,125349,0.8
-upv_5,2018,127396,0.3
-upv_5,2018,127403,1.0
-upv_5,2018,129464,3.0
-upv_5,2018,130131,0.3
-upv_5,2018,130136,1.0
-upv_4,2018,130263,0.1
-upv_5,2018,130819,0.9
-upv_5,2018,130821,1.0
-upv_4,2018,130947,0.4
-upv_4,2018,130948,1.5
-upv_4,2018,131631,0.9
-upv_2,2018,131794,0.5
-upv_2,2018,132478,0.5
-upv_2,2018,132480,1.0
-upv_5,2018,132947,3.0
-upv_5,2018,133554,0.9
-upv_5,2018,134238,0.9
-upv_5,2018,136289,0.9
-upv_5,2018,136973,0.9
-upv_5,2018,137681,21.0
-upv_5,2018,139101,0.2
-upv_5,2018,142521,3.7
-upv_5,2018,146624,3.0
-upv_4,2018,198159,25.0
-upv_1,2019,52200,0.4
-upv_1,2019,98999,3.0
-upv_2,2019,102419,0.9
-upv_4,2019,103504,10.0
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+upv_2,2035,102418,0.9
+upv_4,2035,103446,0.9
+upv_4,2035,104125,1.4
+upv_4,2035,105495,0.9
+upv_5,2035,105496,2.0
+upv_4,2035,105504,3.0
+upv_4,2035,106864,0.9
+upv_4,2035,106865,1.0
+upv_2,2035,107212,2.3
+upv_4,2035,107556,1.0
+upv_4,2035,110291,0.7
+upv_4,2035,110975,1.1
+upv_4,2035,111098,1.9
+upv_4,2035,111099,1.5
+upv_4,2035,111758,0.4
+upv_4,2035,112441,0.9
+upv_4,2035,114398,1.0
+upv_4,2035,117132,1.0
+upv_4,2035,117823,0.5
+upv_4,2035,118507,0.5
+upv_4,2035,119988,1.5
+upv_4,2035,120556,0.5
+upv_4,2035,121245,0.5
+upv_5,2035,126136,0.1
+upv_5,2035,126819,5.4
+upv_5,2035,127428,10.0
+upv_5,2035,129463,0.3
+upv_2,2035,129751,0.5
+upv_5,2035,130148,0.3
+upv_5,2035,130149,0.3
+upv_2,2035,130434,0.5
+upv_5,2035,130835,0.3
+upv_5,2035,132866,0.4
+upv_5,2035,133549,0.4
+upv_5,2035,134232,0.8
+upv_5,2035,136992,0.9
+upv_3,2035,137305,1.0
+upv_5,2035,141094,0.9
+upv_5,2035,143144,0.9
+upv_5,2035,144510,0.9
+upv_5,2035,146580,21.0
+upv_5,2035,148628,0.9
+upv_5,2035,148629,0.9
+upv_5,2035,158237,3.0
+upv_4,2035,191310,25.0
+upv_3,2036,52863,0.4
+upv_2,2036,100277,9.0
+upv_2,2036,100371,3.0
+upv_4,2036,102071,1.2
+upv_2,2036,102418,0.9
+upv_4,2036,103446,0.9
+upv_4,2036,104125,1.4
+upv_4,2036,105495,0.9
+upv_5,2036,105496,2.0
+upv_4,2036,105504,3.0
+upv_4,2036,106864,0.9
+upv_4,2036,106865,1.0
+upv_2,2036,107212,2.3
+upv_4,2036,107556,1.0
+upv_4,2036,110291,0.7
+upv_4,2036,110975,1.1
+upv_4,2036,111098,1.9
+upv_4,2036,111099,1.5
+upv_4,2036,111758,0.4
+upv_4,2036,112441,0.9
+upv_4,2036,114398,1.0
+upv_4,2036,117132,1.0
+upv_4,2036,117823,0.5
+upv_4,2036,118507,0.5
+upv_4,2036,119988,1.5
+upv_4,2036,120556,0.5
+upv_4,2036,121245,0.5
+upv_5,2036,126136,0.1
+upv_5,2036,126819,5.4
+upv_5,2036,127428,10.0
+upv_5,2036,129463,0.3
+upv_2,2036,129751,0.5
+upv_5,2036,130148,0.3
+upv_5,2036,130149,0.3
+upv_2,2036,130434,0.5
+upv_5,2036,130835,0.3
+upv_5,2036,132866,0.4
+upv_5,2036,133549,0.4
+upv_5,2036,134232,0.8
+upv_5,2036,136992,0.9
+upv_3,2036,137305,1.0
+upv_5,2036,141094,0.9
+upv_5,2036,143144,0.9
+upv_5,2036,144510,0.9
+upv_5,2036,146580,21.0
+upv_5,2036,148628,0.9
+upv_5,2036,148629,0.9
+upv_5,2036,158237,3.0
+upv_4,2036,191310,25.0
+upv_3,2037,52863,0.4
+upv_2,2037,100277,9.0
+upv_2,2037,100371,3.0
+upv_4,2037,102071,1.2
+upv_2,2037,102418,0.9
+upv_4,2037,103446,0.9
+upv_4,2037,104125,1.4
+upv_4,2037,105495,0.9
+upv_5,2037,105496,2.0
+upv_4,2037,105504,3.0
+upv_4,2037,106864,0.9
+upv_4,2037,106865,1.0
+upv_2,2037,107212,2.3
+upv_4,2037,107556,1.0
+upv_4,2037,110291,0.7
+upv_4,2037,110975,1.1
+upv_4,2037,111098,1.9
+upv_4,2037,111099,1.5
+upv_4,2037,111758,0.4
+upv_4,2037,112441,0.9
+upv_4,2037,114398,1.0
+upv_4,2037,117132,1.0
+upv_4,2037,117823,0.5
+upv_4,2037,118507,0.5
+upv_4,2037,119988,1.5
+upv_4,2037,120556,0.5
+upv_4,2037,121245,0.5
+upv_5,2037,126136,0.1
+upv_5,2037,126819,5.4
+upv_5,2037,127428,10.0
+upv_5,2037,129463,0.3
+upv_2,2037,129751,0.5
+upv_5,2037,130148,0.3
+upv_5,2037,130149,0.3
+upv_2,2037,130434,0.5
+upv_5,2037,130835,0.3
+upv_5,2037,132866,0.4
+upv_5,2037,133549,0.4
+upv_5,2037,134232,0.8
+upv_5,2037,136992,0.9
+upv_3,2037,137305,1.0
+upv_5,2037,141094,0.9
+upv_5,2037,143144,0.9
+upv_5,2037,144510,0.9
+upv_5,2037,146580,21.0
+upv_5,2037,148628,0.9
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+upv_4,2037,191310,25.0
+upv_2,2038,100277,9.0
+upv_2,2038,100371,3.0
+upv_4,2038,102071,1.2
+upv_2,2038,102418,0.9
+upv_4,2038,104125,1.4
+upv_4,2038,105495,0.9
+upv_5,2038,105496,2.0
+upv_4,2038,105504,3.0
+upv_4,2038,106864,0.9
+upv_2,2038,107212,2.3
+upv_4,2038,107556,1.0
+upv_4,2038,110291,0.7
+upv_4,2038,110975,1.1
+upv_4,2038,111098,1.9
+upv_4,2038,111099,1.5
+upv_4,2038,111758,0.4
+upv_4,2038,112441,0.9
+upv_4,2038,114398,1.0
+upv_4,2038,117132,1.0
+upv_4,2038,120556,0.5
+upv_4,2038,121245,0.5
+upv_5,2038,127428,10.0
+upv_5,2038,129463,0.3
+upv_2,2038,129751,0.5
+upv_5,2038,130148,0.3
+upv_5,2038,130149,0.3
+upv_2,2038,130434,0.5
+upv_5,2038,130835,0.3
+upv_5,2038,132866,0.4
+upv_5,2038,133549,0.4
+upv_5,2038,134232,0.8
+upv_5,2038,136992,0.9
+upv_3,2038,137305,1.0
+upv_5,2038,144510,0.9
+upv_5,2038,146580,21.0
+upv_5,2038,148628,0.9
+upv_5,2038,158237,3.0
+upv_4,2038,191310,25.0
+upv_2,2039,100277,9.0
+upv_4,2039,102071,1.2
+upv_2,2039,102418,0.9
+upv_4,2039,104125,1.4
+upv_5,2039,105496,2.0
+upv_4,2039,105504,3.0
+upv_2,2039,107212,2.3
+upv_4,2039,110291,0.7
+upv_4,2039,111099,1.5
+upv_4,2039,111758,0.4
+upv_4,2039,112441,0.9
+upv_4,2039,114398,1.0
+upv_4,2039,117132,1.0
+upv_4,2039,120556,0.5
+upv_4,2039,121245,0.5
+upv_5,2039,134232,0.8
+upv_5,2039,136992,0.9
+upv_5,2039,144510,0.9
+upv_5,2039,146580,21.0
+upv_5,2039,158237,3.0
+upv_4,2039,191310,25.0
diff --git a/inputs/capacity_exogenous/exog_cap_upv_reference.csv b/inputs/capacity_exogenous/exog_cap_upv_reference.csv
index 9080301d3..c7cdbcf77 100644
--- a/inputs/capacity_exogenous/exog_cap_upv_reference.csv
+++ b/inputs/capacity_exogenous/exog_cap_upv_reference.csv
@@ -1,1526 +1,1584 @@
 *tech,year,sc_point_gid,capacity
-upv_1,2010,46046,0.4
-upv_1,2010,98999,3.0
-upv_1,2010,100961,9.0
-upv_1,2010,102419,0.9
-upv_2,2010,104472,2.3
-upv_3,2010,109747,1.9
-upv_5,2010,110304,0.9
-upv_5,2010,111673,1.0
-upv_5,2010,112357,1.0
-upv_5,2010,112359,1.2
-upv_4,2010,113035,0.5
-upv_5,2010,113044,1.4
-upv_4,2010,115090,0.7
-upv_4,2010,115772,0.5
-upv_4,2010,115774,0.9
-upv_4,2010,117137,0.3
-upv_5,2010,117151,2.0
-upv_4,2010,117826,1.0
-upv_5,2010,118547,10.0
-upv_4,2010,119193,0.9
-upv_4,2010,119321,1.5
-upv_4,2010,119872,0.3
-upv_4,2010,120557,0.5
-upv_4,2010,121247,1.1
-upv_4,2010,121375,5.4
-upv_4,2010,121928,0.9
-upv_4,2010,122607,0.4
-upv_4,2010,123976,0.5
-upv_4,2010,123979,0.8
-upv_5,2010,124658,0.3
-upv_5,2010,127393,0.3
-upv_5,2010,128084,1.0
-upv_5,2010,128761,0.5
-upv_5,2010,128778,3.0
-upv_5,2010,129446,0.5
-upv_5,2010,129449,0.9
-upv_4,2010,130263,0.1
-upv_4,2010,130947,0.4
-upv_4,2010,130948,1.5
-upv_5,2010,131579,3.0
-upv_4,2010,131631,0.9
-upv_5,2010,132184,0.9
-upv_5,2010,132863,0.4
-upv_5,2010,132868,0.9
-upv_5,2010,133630,0.2
-upv_2,2010,134529,0.5
-upv_4,2010,134919,0.9
-upv_2,2010,135213,0.5
-upv_2,2010,135215,1.0
-upv_4,2010,135603,0.9
-upv_5,2010,143833,21.0
-upv_5,2010,145940,3.7
-upv_5,2010,154830,3.0
-upv_4,2010,194737,25.0
-upv_1,2011,46046,0.4
-upv_1,2011,98999,3.0
-upv_1,2011,100961,9.0
-upv_1,2011,102419,0.9
-upv_2,2011,104472,2.3
-upv_3,2011,109747,1.9
-upv_5,2011,110304,0.9
-upv_5,2011,111673,1.0
-upv_5,2011,112357,1.0
-upv_5,2011,112359,1.2
-upv_4,2011,113035,0.5
-upv_5,2011,113044,1.4
-upv_4,2011,115090,0.7
-upv_4,2011,115772,0.5
-upv_4,2011,115774,0.9
-upv_4,2011,117137,0.3
-upv_5,2011,117151,2.0
-upv_4,2011,117826,1.0
-upv_5,2011,118547,10.0
-upv_4,2011,119193,0.9
-upv_4,2011,119321,1.5
-upv_4,2011,119872,0.3
-upv_4,2011,120557,0.5
-upv_4,2011,121247,1.1
-upv_4,2011,121375,5.4
-upv_4,2011,121928,0.9
-upv_4,2011,122607,0.4
-upv_4,2011,123976,0.5
-upv_4,2011,123979,0.8
-upv_5,2011,124658,0.3
-upv_5,2011,127393,0.3
-upv_5,2011,128084,1.0
-upv_5,2011,128761,0.5
-upv_5,2011,128778,3.0
-upv_5,2011,129446,0.5
-upv_5,2011,129449,0.9
-upv_4,2011,130263,0.1
-upv_4,2011,130947,0.4
-upv_4,2011,130948,1.5
-upv_5,2011,131579,3.0
-upv_4,2011,131631,0.9
-upv_5,2011,132184,0.9
-upv_5,2011,132863,0.4
-upv_5,2011,132868,0.9
-upv_5,2011,133630,0.2
-upv_2,2011,134529,0.5
-upv_4,2011,134919,0.9
-upv_2,2011,135213,0.5
-upv_2,2011,135215,1.0
-upv_4,2011,135603,0.9
-upv_5,2011,143833,21.0
-upv_5,2011,145940,3.7
-upv_5,2011,154830,3.0
-upv_4,2011,194737,25.0
-upv_1,2012,46046,0.4
-upv_1,2012,98999,3.0
-upv_1,2012,100961,9.0
-upv_1,2012,102419,0.9
-upv_2,2012,104472,2.3
-upv_3,2012,109747,1.9
-upv_5,2012,110304,0.9
-upv_5,2012,111673,1.0
-upv_5,2012,112357,1.0
-upv_5,2012,112359,1.2
-upv_4,2012,113035,0.5
-upv_5,2012,113044,1.4
-upv_4,2012,115090,0.7
-upv_4,2012,115772,0.5
-upv_4,2012,115774,0.9
-upv_4,2012,117137,0.3
-upv_5,2012,117151,2.0
-upv_4,2012,117826,1.0
-upv_5,2012,118547,10.0
-upv_4,2012,119193,0.9
-upv_4,2012,119321,1.5
-upv_4,2012,119872,0.3
-upv_4,2012,120557,0.5
-upv_4,2012,121247,1.1
-upv_4,2012,121375,5.4
-upv_4,2012,121928,0.9
-upv_4,2012,122607,0.4
-upv_4,2012,123976,0.5
-upv_4,2012,123979,0.8
-upv_5,2012,124658,0.3
-upv_5,2012,127393,0.3
-upv_5,2012,128084,1.0
-upv_5,2012,128761,0.5
-upv_5,2012,128778,3.0
-upv_5,2012,129446,0.5
-upv_5,2012,129449,0.9
-upv_4,2012,130263,0.1
-upv_4,2012,130947,0.4
-upv_4,2012,130948,1.5
-upv_5,2012,131579,3.0
-upv_4,2012,131631,0.9
-upv_5,2012,132184,0.9
-upv_5,2012,132863,0.4
-upv_5,2012,132868,0.9
-upv_5,2012,133630,0.2
-upv_2,2012,134529,0.5
-upv_4,2012,134919,0.9
-upv_2,2012,135213,0.5
-upv_2,2012,135215,1.0
-upv_4,2012,135603,0.9
-upv_5,2012,143833,21.0
-upv_5,2012,145940,3.7
-upv_5,2012,154830,3.0
-upv_4,2012,194737,25.0
-upv_1,2013,46046,0.4
-upv_1,2013,98999,3.0
-upv_1,2013,100961,9.0
-upv_1,2013,102419,0.9
-upv_2,2013,104472,2.3
-upv_3,2013,109747,1.9
-upv_5,2013,110304,0.9
-upv_5,2013,111673,1.0
-upv_5,2013,112357,1.0
-upv_5,2013,112359,1.2
-upv_4,2013,113035,0.5
-upv_5,2013,113044,1.4
-upv_4,2013,115090,0.7
-upv_4,2013,115772,0.5
-upv_4,2013,115774,0.9
-upv_4,2013,117137,0.3
-upv_5,2013,117151,2.0
-upv_4,2013,117826,1.0
-upv_5,2013,118547,10.0
-upv_4,2013,119193,0.9
-upv_4,2013,119321,1.5
-upv_4,2013,119872,0.3
-upv_4,2013,120557,0.5
-upv_4,2013,121247,1.1
-upv_4,2013,121375,5.4
-upv_4,2013,121928,0.9
-upv_4,2013,122607,0.4
-upv_4,2013,123976,0.5
-upv_4,2013,123979,0.8
-upv_5,2013,124658,0.3
-upv_5,2013,127393,0.3
-upv_5,2013,128084,1.0
-upv_5,2013,128761,0.5
-upv_5,2013,128778,3.0
-upv_5,2013,129446,0.5
-upv_5,2013,129449,0.9
-upv_4,2013,130263,0.1
-upv_4,2013,130947,0.4
-upv_4,2013,130948,1.5
-upv_5,2013,131579,3.0
-upv_4,2013,131631,0.9
-upv_5,2013,132184,0.9
-upv_5,2013,132863,0.4
-upv_5,2013,132868,0.9
-upv_5,2013,133630,0.2
-upv_2,2013,134529,0.5
-upv_4,2013,134919,0.9
-upv_2,2013,135213,0.5
-upv_2,2013,135215,1.0
-upv_4,2013,135603,0.9
-upv_5,2013,143833,21.0
-upv_5,2013,145940,3.7
-upv_5,2013,154830,3.0
-upv_4,2013,194737,25.0
-upv_1,2014,46046,0.4
-upv_1,2014,98999,3.0
-upv_1,2014,100961,9.0
-upv_1,2014,102419,0.9
-upv_2,2014,104472,2.3
-upv_3,2014,109747,1.9
-upv_5,2014,110304,0.9
-upv_5,2014,111673,1.0
-upv_5,2014,112357,1.0
-upv_5,2014,112359,1.2
-upv_4,2014,113035,0.5
-upv_5,2014,113044,1.4
-upv_4,2014,115090,0.7
-upv_4,2014,115772,0.5
-upv_4,2014,115774,0.9
-upv_4,2014,117137,0.3
-upv_5,2014,117151,2.0
-upv_4,2014,117826,1.0
-upv_5,2014,118547,10.0
-upv_4,2014,119193,0.9
-upv_4,2014,119321,1.5
-upv_4,2014,119872,0.3
-upv_4,2014,120557,0.5
-upv_4,2014,121247,1.1
-upv_4,2014,121375,5.4
-upv_4,2014,121928,0.9
-upv_4,2014,122607,0.4
-upv_4,2014,123976,0.5
-upv_4,2014,123979,0.8
-upv_5,2014,124658,0.3
-upv_5,2014,127393,0.3
-upv_5,2014,128084,1.0
-upv_5,2014,128761,0.5
-upv_5,2014,128778,3.0
-upv_5,2014,129446,0.5
-upv_5,2014,129449,0.9
-upv_4,2014,130263,0.1
-upv_4,2014,130947,0.4
-upv_4,2014,130948,1.5
-upv_5,2014,131579,3.0
-upv_4,2014,131631,0.9
-upv_5,2014,132184,0.9
-upv_5,2014,132863,0.4
-upv_5,2014,132868,0.9
-upv_5,2014,133630,0.2
-upv_2,2014,134529,0.5
-upv_4,2014,134919,0.9
-upv_2,2014,135213,0.5
-upv_2,2014,135215,1.0
-upv_4,2014,135603,0.9
-upv_5,2014,143833,21.0
-upv_5,2014,145940,3.7
-upv_5,2014,154830,3.0
-upv_4,2014,194737,25.0
-upv_1,2015,46046,0.4
-upv_1,2015,98999,3.0
-upv_1,2015,100961,9.0
-upv_1,2015,102419,0.9
-upv_2,2015,104472,2.3
-upv_3,2015,109747,1.9
-upv_5,2015,110304,0.9
-upv_5,2015,111673,1.0
-upv_5,2015,112357,1.0
-upv_5,2015,112359,1.2
-upv_4,2015,113035,0.5
-upv_5,2015,113044,1.4
-upv_4,2015,115090,0.7
-upv_4,2015,115772,0.5
-upv_4,2015,115774,0.9
-upv_4,2015,117137,0.3
-upv_5,2015,117151,2.0
-upv_4,2015,117826,1.0
-upv_5,2015,118547,10.0
-upv_4,2015,119193,0.9
-upv_4,2015,119321,1.5
-upv_4,2015,119872,0.3
-upv_4,2015,120557,0.5
-upv_4,2015,121247,1.1
-upv_4,2015,121375,5.4
-upv_4,2015,121928,0.9
-upv_4,2015,122607,0.4
-upv_4,2015,123976,0.5
-upv_4,2015,123979,0.8
-upv_5,2015,124658,0.3
-upv_5,2015,127393,0.3
-upv_5,2015,128084,1.0
-upv_5,2015,128761,0.5
-upv_5,2015,128778,3.0
-upv_5,2015,129446,0.5
-upv_5,2015,129449,0.9
-upv_4,2015,130263,0.1
-upv_4,2015,130947,0.4
-upv_4,2015,130948,1.5
-upv_5,2015,131579,3.0
-upv_4,2015,131631,0.9
-upv_5,2015,132184,0.9
-upv_5,2015,132863,0.4
-upv_5,2015,132868,0.9
-upv_5,2015,133630,0.2
-upv_2,2015,134529,0.5
-upv_4,2015,134919,0.9
-upv_2,2015,135213,0.5
-upv_2,2015,135215,1.0
-upv_4,2015,135603,0.9
-upv_5,2015,143833,21.0
-upv_5,2015,145940,3.7
-upv_5,2015,154830,3.0
-upv_4,2015,194737,25.0
-upv_1,2016,46046,0.4
-upv_1,2016,98999,3.0
-upv_1,2016,100961,9.0
-upv_1,2016,102419,0.9
-upv_2,2016,104472,2.3
-upv_3,2016,109747,1.9
-upv_5,2016,110304,0.9
-upv_5,2016,111673,1.0
-upv_5,2016,112357,1.0
-upv_5,2016,112359,1.2
-upv_4,2016,113035,0.5
-upv_5,2016,113044,1.4
-upv_4,2016,115090,0.7
-upv_4,2016,115772,0.5
-upv_4,2016,115774,0.9
-upv_4,2016,117137,0.3
-upv_5,2016,117151,2.0
-upv_4,2016,117826,1.0
-upv_5,2016,118547,10.0
-upv_4,2016,119193,0.9
-upv_4,2016,119321,1.5
-upv_4,2016,119872,0.3
-upv_4,2016,120557,0.5
-upv_4,2016,121247,1.1
-upv_4,2016,121375,5.4
-upv_4,2016,121928,0.9
-upv_4,2016,122607,0.4
-upv_4,2016,123976,0.5
-upv_4,2016,123979,0.8
-upv_5,2016,124658,0.3
-upv_5,2016,127393,0.3
-upv_5,2016,128084,1.0
-upv_5,2016,128761,0.5
-upv_5,2016,128778,3.0
-upv_5,2016,129446,0.5
-upv_5,2016,129449,0.9
-upv_4,2016,130263,0.1
-upv_4,2016,130947,0.4
-upv_4,2016,130948,1.5
-upv_5,2016,131579,3.0
-upv_4,2016,131631,0.9
-upv_5,2016,132184,0.9
-upv_5,2016,132863,0.4
-upv_5,2016,132868,0.9
-upv_5,2016,133630,0.2
-upv_2,2016,134529,0.5
-upv_4,2016,134919,0.9
-upv_2,2016,135213,0.5
-upv_2,2016,135215,1.0
-upv_4,2016,135603,0.9
-upv_5,2016,143833,21.0
-upv_5,2016,145940,3.7
-upv_5,2016,154830,3.0
-upv_4,2016,194737,25.0
-upv_1,2017,46046,0.4
-upv_1,2017,98999,3.0
-upv_1,2017,100961,9.0
-upv_1,2017,102419,0.9
-upv_2,2017,104472,2.3
-upv_3,2017,109747,1.9
-upv_5,2017,110304,0.9
-upv_5,2017,111673,1.0
-upv_5,2017,112357,1.0
-upv_5,2017,112359,1.2
-upv_4,2017,113035,0.5
-upv_5,2017,113044,1.4
-upv_4,2017,115090,0.7
-upv_4,2017,115772,0.5
-upv_4,2017,115774,0.9
-upv_4,2017,117137,0.3
-upv_5,2017,117151,2.0
-upv_4,2017,117826,1.0
-upv_5,2017,118547,10.0
-upv_4,2017,119193,0.9
-upv_4,2017,119321,1.5
-upv_4,2017,119872,0.3
-upv_4,2017,120557,0.5
-upv_4,2017,121247,1.1
-upv_4,2017,121375,5.4
-upv_4,2017,121928,0.9
-upv_4,2017,122607,0.4
-upv_4,2017,123976,0.5
-upv_4,2017,123979,0.8
-upv_5,2017,124658,0.3
-upv_5,2017,127393,0.3
-upv_5,2017,128084,1.0
-upv_5,2017,128761,0.5
-upv_5,2017,128778,3.0
-upv_5,2017,129446,0.5
-upv_5,2017,129449,0.9
-upv_4,2017,130263,0.1
-upv_4,2017,130947,0.4
-upv_4,2017,130948,1.5
-upv_5,2017,131579,3.0
-upv_4,2017,131631,0.9
-upv_5,2017,132184,0.9
-upv_5,2017,132863,0.4
-upv_5,2017,132868,0.9
-upv_5,2017,133630,0.2
-upv_2,2017,134529,0.5
-upv_4,2017,134919,0.9
-upv_2,2017,135213,0.5
-upv_2,2017,135215,1.0
-upv_4,2017,135603,0.9
-upv_5,2017,143833,21.0
-upv_5,2017,145940,3.7
-upv_5,2017,154830,3.0
-upv_4,2017,194737,25.0
-upv_1,2018,46046,0.4
-upv_1,2018,98999,3.0
-upv_1,2018,100961,9.0
-upv_1,2018,102419,0.9
-upv_2,2018,104472,2.3
-upv_3,2018,109747,1.9
-upv_5,2018,110304,0.9
-upv_5,2018,111673,1.0
-upv_5,2018,112357,1.0
-upv_5,2018,112359,1.2
-upv_4,2018,113035,0.5
-upv_5,2018,113044,1.4
-upv_4,2018,115090,0.7
-upv_4,2018,115772,0.5
-upv_4,2018,115774,0.9
-upv_4,2018,117137,0.3
-upv_5,2018,117151,2.0
-upv_4,2018,117826,1.0
-upv_5,2018,118547,10.0
-upv_4,2018,119193,0.9
-upv_4,2018,119321,1.5
-upv_4,2018,119872,0.3
-upv_4,2018,120557,0.5
-upv_4,2018,121247,1.1
-upv_4,2018,121375,5.4
-upv_4,2018,121928,0.9
-upv_4,2018,122607,0.4
-upv_4,2018,123976,0.5
-upv_4,2018,123979,0.8
-upv_5,2018,124658,0.3
-upv_5,2018,127393,0.3
-upv_5,2018,128084,1.0
-upv_5,2018,128761,0.5
-upv_5,2018,128778,3.0
-upv_5,2018,129446,0.5
-upv_5,2018,129449,0.9
-upv_4,2018,130263,0.1
-upv_4,2018,130947,0.4
-upv_4,2018,130948,1.5
-upv_5,2018,131579,3.0
-upv_4,2018,131631,0.9
-upv_5,2018,132184,0.9
-upv_5,2018,132863,0.4
-upv_5,2018,132868,0.9
-upv_5,2018,133630,0.2
-upv_2,2018,134529,0.5
-upv_4,2018,134919,0.9
-upv_2,2018,135213,0.5
-upv_2,2018,135215,1.0
-upv_4,2018,135603,0.9
-upv_5,2018,143833,21.0
-upv_5,2018,145940,3.7
-upv_5,2018,154830,3.0
-upv_4,2018,194737,25.0
-upv_1,2019,46046,0.4
-upv_1,2019,98999,3.0
-upv_1,2019,100961,9.0
-upv_1,2019,102419,0.9
-upv_2,2019,104472,2.3
-upv_3,2019,109747,1.9
-upv_5,2019,110304,0.9
-upv_5,2019,111673,1.0
-upv_5,2019,112357,1.0
-upv_5,2019,112359,1.2
-upv_4,2019,113035,0.5
-upv_5,2019,113044,1.4
-upv_4,2019,115090,0.7
-upv_4,2019,115772,0.5
-upv_4,2019,115774,0.9
-upv_4,2019,117137,0.3
-upv_5,2019,117151,2.0
-upv_4,2019,117826,1.0
-upv_5,2019,118547,10.0
-upv_4,2019,119193,0.9
-upv_4,2019,119321,1.5
-upv_4,2019,119872,0.3
-upv_4,2019,120557,0.5
-upv_4,2019,121247,1.1
-upv_4,2019,121375,5.4
-upv_4,2019,121928,0.9
-upv_4,2019,122607,0.4
-upv_4,2019,123976,0.5
-upv_4,2019,123979,0.8
-upv_5,2019,124658,0.3
-upv_5,2019,127393,0.3
-upv_5,2019,128084,1.0
-upv_5,2019,128761,0.5
-upv_5,2019,128778,3.0
-upv_5,2019,129446,0.5
-upv_5,2019,129449,0.9
-upv_4,2019,130263,0.1
-upv_4,2019,130947,0.4
-upv_4,2019,130948,1.5
-upv_5,2019,131579,3.0
-upv_4,2019,131631,0.9
-upv_5,2019,132184,0.9
-upv_5,2019,132863,0.4
-upv_5,2019,132868,0.9
-upv_5,2019,133630,0.2
-upv_2,2019,134529,0.5
-upv_4,2019,134919,0.9
-upv_2,2019,135213,0.5
-upv_2,2019,135215,1.0
-upv_4,2019,135603,0.9
-upv_5,2019,143833,21.0
-upv_5,2019,145940,3.7
-upv_5,2019,154830,3.0
-upv_4,2019,194737,25.0
-upv_1,2020,46046,0.4
-upv_1,2020,98999,3.0
-upv_1,2020,100961,9.0
-upv_1,2020,102419,0.9
-upv_2,2020,104472,2.3
-upv_3,2020,109747,1.9
-upv_5,2020,110304,0.9
-upv_5,2020,111673,1.0
-upv_5,2020,112357,1.0
-upv_5,2020,112359,1.2
-upv_4,2020,113035,0.5
-upv_5,2020,113044,1.4
-upv_4,2020,115090,0.7
-upv_4,2020,115772,0.5
-upv_4,2020,115774,0.9
-upv_4,2020,117137,0.3
-upv_5,2020,117151,2.0
-upv_4,2020,117826,1.0
-upv_5,2020,118547,10.0
-upv_4,2020,119193,0.9
-upv_4,2020,119321,1.5
-upv_4,2020,119872,0.3
-upv_4,2020,120557,0.5
-upv_4,2020,121247,1.1
-upv_4,2020,121375,5.4
-upv_4,2020,121928,0.9
-upv_4,2020,122607,0.4
-upv_4,2020,123976,0.5
-upv_4,2020,123979,0.8
-upv_5,2020,124658,0.3
-upv_5,2020,127393,0.3
-upv_5,2020,128084,1.0
-upv_5,2020,128761,0.5
-upv_5,2020,128778,3.0
-upv_5,2020,129446,0.5
-upv_5,2020,129449,0.9
-upv_4,2020,130263,0.1
-upv_4,2020,130947,0.4
-upv_4,2020,130948,1.5
-upv_5,2020,131579,3.0
-upv_4,2020,131631,0.9
-upv_5,2020,132184,0.9
-upv_5,2020,132863,0.4
-upv_5,2020,132868,0.9
-upv_5,2020,133630,0.2
-upv_2,2020,134529,0.5
-upv_4,2020,134919,0.9
-upv_2,2020,135213,0.5
-upv_2,2020,135215,1.0
-upv_4,2020,135603,0.9
-upv_5,2020,143833,21.0
-upv_5,2020,145940,3.7
-upv_5,2020,154830,3.0
-upv_4,2020,194737,25.0
-upv_1,2021,46046,0.4
-upv_1,2021,98999,3.0
-upv_1,2021,100961,9.0
-upv_1,2021,102419,0.9
-upv_2,2021,104472,2.3
-upv_3,2021,109747,1.9
-upv_5,2021,110304,0.9
-upv_5,2021,111673,1.0
-upv_5,2021,112357,1.0
-upv_5,2021,112359,1.2
-upv_4,2021,113035,0.5
-upv_5,2021,113044,1.4
-upv_4,2021,115090,0.7
-upv_4,2021,115772,0.5
-upv_4,2021,115774,0.9
-upv_4,2021,117137,0.3
-upv_5,2021,117151,2.0
-upv_4,2021,117826,1.0
-upv_5,2021,118547,10.0
-upv_4,2021,119193,0.9
-upv_4,2021,119321,1.5
-upv_4,2021,119872,0.3
-upv_4,2021,120557,0.5
-upv_4,2021,121247,1.1
-upv_4,2021,121375,5.4
-upv_4,2021,121928,0.9
-upv_4,2021,122607,0.4
-upv_4,2021,123976,0.5
-upv_4,2021,123979,0.8
-upv_5,2021,124658,0.3
-upv_5,2021,127393,0.3
-upv_5,2021,128084,1.0
-upv_5,2021,128761,0.5
-upv_5,2021,128778,3.0
-upv_5,2021,129446,0.5
-upv_5,2021,129449,0.9
-upv_4,2021,130263,0.1
-upv_4,2021,130947,0.4
-upv_4,2021,130948,1.5
-upv_5,2021,131579,3.0
-upv_4,2021,131631,0.9
-upv_5,2021,132184,0.9
-upv_5,2021,132863,0.4
-upv_5,2021,132868,0.9
-upv_5,2021,133630,0.2
-upv_2,2021,134529,0.5
-upv_4,2021,134919,0.9
-upv_2,2021,135213,0.5
-upv_2,2021,135215,1.0
-upv_4,2021,135603,0.9
-upv_5,2021,143833,21.0
-upv_5,2021,145940,3.7
-upv_5,2021,154830,3.0
-upv_4,2021,194737,25.0
-upv_1,2022,46046,0.4
-upv_1,2022,98999,3.0
-upv_1,2022,100961,9.0
-upv_1,2022,102419,0.9
-upv_2,2022,104472,2.3
-upv_3,2022,109747,1.9
-upv_5,2022,110304,0.9
-upv_5,2022,111673,1.0
-upv_5,2022,112357,1.0
-upv_5,2022,112359,1.2
-upv_4,2022,113035,0.5
-upv_5,2022,113044,1.4
-upv_4,2022,115090,0.7
-upv_4,2022,115772,0.5
-upv_4,2022,115774,0.9
-upv_4,2022,117137,0.3
-upv_5,2022,117151,2.0
-upv_4,2022,117826,1.0
-upv_5,2022,118547,10.0
-upv_4,2022,119193,0.9
-upv_4,2022,119321,1.5
-upv_4,2022,119872,0.3
-upv_4,2022,120557,0.5
-upv_4,2022,121247,1.1
-upv_4,2022,121375,5.4
-upv_4,2022,121928,0.9
-upv_4,2022,122607,0.4
-upv_4,2022,123976,0.5
-upv_4,2022,123979,0.8
-upv_5,2022,124658,0.3
-upv_5,2022,127393,0.3
-upv_5,2022,128084,1.0
-upv_5,2022,128761,0.5
-upv_5,2022,128778,3.0
-upv_5,2022,129446,0.5
-upv_5,2022,129449,0.9
-upv_4,2022,130263,0.1
-upv_4,2022,130947,0.4
-upv_4,2022,130948,1.5
-upv_5,2022,131579,3.0
-upv_4,2022,131631,0.9
-upv_5,2022,132184,0.9
-upv_5,2022,132863,0.4
-upv_5,2022,132868,0.9
-upv_5,2022,133630,0.2
-upv_2,2022,134529,0.5
-upv_4,2022,134919,0.9
-upv_2,2022,135213,0.5
-upv_2,2022,135215,1.0
-upv_4,2022,135603,0.9
-upv_5,2022,143833,21.0
-upv_5,2022,145940,3.7
-upv_5,2022,154830,3.0
-upv_4,2022,194737,25.0
-upv_1,2023,46046,0.4
-upv_1,2023,98999,3.0
-upv_1,2023,100961,9.0
-upv_1,2023,102419,0.9
-upv_2,2023,104472,2.3
-upv_3,2023,109747,1.9
-upv_5,2023,110304,0.9
-upv_5,2023,111673,1.0
-upv_5,2023,112357,1.0
-upv_5,2023,112359,1.2
-upv_4,2023,113035,0.5
-upv_5,2023,113044,1.4
-upv_4,2023,115090,0.7
-upv_4,2023,115772,0.5
-upv_4,2023,115774,0.9
-upv_4,2023,117137,0.3
-upv_5,2023,117151,2.0
-upv_4,2023,117826,1.0
-upv_5,2023,118547,10.0
-upv_4,2023,119193,0.9
-upv_4,2023,119321,1.5
-upv_4,2023,119872,0.3
-upv_4,2023,120557,0.5
-upv_4,2023,121247,1.1
-upv_4,2023,121375,5.4
-upv_4,2023,121928,0.9
-upv_4,2023,122607,0.4
-upv_4,2023,123976,0.5
-upv_4,2023,123979,0.8
-upv_5,2023,124658,0.3
-upv_5,2023,127393,0.3
-upv_5,2023,128084,1.0
-upv_5,2023,128761,0.5
-upv_5,2023,128778,3.0
-upv_5,2023,129446,0.5
-upv_5,2023,129449,0.9
-upv_4,2023,130263,0.1
-upv_4,2023,130947,0.4
-upv_4,2023,130948,1.5
-upv_5,2023,131579,3.0
-upv_4,2023,131631,0.9
-upv_5,2023,132184,0.9
-upv_5,2023,132863,0.4
-upv_5,2023,132868,0.9
-upv_5,2023,133630,0.2
-upv_2,2023,134529,0.5
-upv_4,2023,134919,0.9
-upv_2,2023,135213,0.5
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+upv_5,2039,144511,0.9
+upv_5,2039,146580,21.0
+upv_5,2039,158237,3.0
+upv_4,2039,191310,25.0
diff --git a/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv b/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv
index a24d169a7..32e472b3a 100644
--- a/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv
+++ b/inputs/capacity_exogenous/exog_cap_wind-ons_limited.csv
@@ -1,21187 +1,18420 @@
 *tech,year,sc_point_gid,capacity
-wind-ons_1,2010,45361,60.0
-wind-ons_1,2010,46046,60.0
-wind-ons_1,2010,46728,44.0
-wind-ons_2,2010,49465,42.0
-wind-ons_6,2010,52969,9.7
-wind-ons_6,2010,54250,60.0
-wind-ons_4,2010,54935,168.6
-wind-ons_5,2010,55021,101.4
-wind-ons_6,2010,55705,104.2
-wind-ons_6,2010,55748,31.5
-wind-ons_5,2010,56302,156.0
-wind-ons_6,2010,56985,38.9
-wind-ons_5,2010,56986,186.0
-wind-ons_6,2010,57116,40.5
-wind-ons_7,2010,57316,42.0
-wind-ons_6,2010,57669,122.8
-wind-ons_6,2010,57670,66.0
-wind-ons_6,2010,57831,0.93
-wind-ons_5,2010,58352,7.2
-wind-ons_5,2010,58354,96.0
-wind-ons_7,2010,58484,40.5
-wind-ons_6,2010,58515,0.93
-wind-ons_7,2010,58516,1.3
-wind-ons_6,2010,59036,78.0
-wind-ons_4,2010,59037,149.4
-wind-ons_6,2010,59199,0.93
-wind-ons_7,2010,59201,2.0
-wind-ons_4,2010,59720,35.0
-wind-ons_3,2010,59721,13.0
-wind-ons_6,2010,59883,0.93
-wind-ons_7,2010,59885,2.0
-wind-ons_7,2010,59886,2.0
-wind-ons_7,2010,59888,12.0
-wind-ons_6,2010,59889,30.0
-wind-ons_7,2010,59899,126.0
-wind-ons_7,2010,60052,4.5
-wind-ons_1,2010,60405,138.0
-wind-ons_6,2010,60567,0.93
-wind-ons_7,2010,60569,2.0
-wind-ons_7,2010,60572,11.8
-wind-ons_7,2010,60583,79.5
-wind-ons_8,2010,60738,57.0
-wind-ons_2,2010,61088,136.3
-wind-ons_2,2010,61089,124.2
-wind-ons_6,2010,61252,1.0
-wind-ons_6,2010,61253,1.9
-wind-ons_7,2010,61255,4.2
-wind-ons_7,2010,61256,11.2
-wind-ons_8,2010,61423,66.0
-wind-ons_7,2010,61935,0.93
-wind-ons_7,2010,61936,0.97
-wind-ons_6,2010,61937,1.7
-wind-ons_7,2010,61940,10.4
-wind-ons_7,2010,61941,2.0
-wind-ons_7,2010,62105,4.5
-wind-ons_3,2010,62454,8.0
-wind-ons_2,2010,62455,94.0
-wind-ons_3,2010,62456,176.9
-wind-ons_7,2010,62619,0.93
-wind-ons_8,2010,62620,0.97
-wind-ons_7,2010,62621,1.7
-wind-ons_8,2010,62622,2.0
-wind-ons_7,2010,62626,91.1
-wind-ons_1,2010,63138,9.9
-wind-ons_3,2010,63139,76.5
-wind-ons_8,2010,63303,0.93
-wind-ons_8,2010,63304,0.97
-wind-ons_7,2010,63305,1.7
-wind-ons_6,2010,63306,2.0
-wind-ons_6,2010,63307,3.0
-wind-ons_7,2010,63308,9.2
-wind-ons_8,2010,63310,85.5
-wind-ons_8,2010,63311,100.7
-wind-ons_7,2010,63313,150.0
-wind-ons_6,2010,63987,0.9
-wind-ons_6,2010,63989,1.7
-wind-ons_7,2010,63992,8.4
-wind-ons_7,2010,63993,25.0
-wind-ons_8,2010,63995,100.5
-wind-ons_2,2010,64506,10.0
-wind-ons_8,2010,64596,135.0
-wind-ons_6,2010,64671,0.9
-wind-ons_6,2010,64672,0.97
-wind-ons_6,2010,64673,1.7
-wind-ons_6,2010,64674,2.0
-wind-ons_7,2010,64675,2.1
-wind-ons_6,2010,64676,8.0
-wind-ons_7,2010,64678,51.0
-wind-ons_6,2010,64679,100.5
-wind-ons_6,2010,64680,90.0
-wind-ons_1,2010,65189,3.3
-wind-ons_1,2010,65190,25.0
-wind-ons_2,2010,65191,42.9
-wind-ons_6,2010,65355,0.9
-wind-ons_7,2010,65357,1.7
-wind-ons_6,2010,65358,2.0
-wind-ons_6,2010,65360,5.2
-wind-ons_5,2010,65363,100.0
-wind-ons_1,2010,65874,12.0
-wind-ons_2,2010,65875,49.3
-wind-ons_6,2010,66039,0.9
-wind-ons_7,2010,66040,0.97
-wind-ons_6,2010,66046,49.5
-wind-ons_6,2010,66186,24.0
-wind-ons_8,2010,66692,48.0
-wind-ons_7,2010,66723,0.9
-wind-ons_5,2010,66729,19.98
-wind-ons_5,2010,66866,5.2
-wind-ons_8,2010,67376,48.0
-wind-ons_6,2010,67407,0.9
-wind-ons_7,2010,68061,31.5
-wind-ons_6,2010,68091,0.7
-wind-ons_7,2010,68744,49.5
-wind-ons_6,2010,68775,0.7
-wind-ons_7,2010,68776,0.97
-wind-ons_6,2010,68777,1.5
-wind-ons_4,2010,68778,2.0
-wind-ons_8,2010,68902,40.5
-wind-ons_8,2010,69428,49.5
-wind-ons_8,2010,69429,84.0
-wind-ons_6,2010,69460,0.97
-wind-ons_5,2010,69461,1.5
-wind-ons_4,2010,69462,2.0
-wind-ons_6,2010,69582,20.0
-wind-ons_6,2010,69583,66.0
-wind-ons_9,2010,69587,52.0
+wind-ons_9,2010,50181,101.4
+wind-ons_8,2010,50865,104.2
+wind-ons_8,2010,51155,42.0
+wind-ons_6,2010,52179,44.0
+wind-ons_6,2010,52862,84.6
+wind-ons_7,2010,52863,144.0
+wind-ons_8,2010,55742,40.5
+wind-ons_6,2010,56415,29.0
+wind-ons_7,2010,56416,60.0
+wind-ons_8,2010,56426,118.5
+wind-ons_9,2010,56427,40.5
+wind-ons_7,2010,57100,60.0
+wind-ons_8,2010,57997,57.0
+wind-ons_5,2010,58348,7.2
+wind-ons_5,2010,58391,9.7
+wind-ons_5,2010,59696,136.3
+wind-ons_5,2010,59699,199.0
+wind-ons_5,2010,59707,5.0
+wind-ons_6,2010,59708,90.0
+wind-ons_6,2010,59716,149.4
+wind-ons_8,2010,59717,156.0
+wind-ons_7,2010,59718,78.0
+wind-ons_9,2010,59827,7.5
+wind-ons_7,2010,59831,11.9
+wind-ons_6,2010,60379,246.0
+wind-ons_7,2010,60380,16.2
+wind-ons_6,2010,60381,94.0
+wind-ons_5,2010,60382,98.9
+wind-ons_7,2010,60383,204.0
+wind-ons_4,2010,60393,176.9
+wind-ons_3,2010,60394,122.8
+wind-ons_9,2010,60509,4.5
+wind-ons_9,2010,60511,108.0
+wind-ons_8,2010,61065,163.3
+wind-ons_6,2010,61066,125.4
+wind-ons_2,2010,61075,9.9
+wind-ons_3,2010,61076,25.0
+wind-ons_6,2010,61077,41.0
+wind-ons_7,2010,61747,81.0
+wind-ons_8,2010,61749,221.0
+wind-ons_6,2010,61750,102.9
+wind-ons_7,2010,61762,54.0
+wind-ons_6,2010,61763,48.3
+wind-ons_8,2010,61877,2.6
+wind-ons_7,2010,62085,66.0
+wind-ons_6,2010,62431,24.0
+wind-ons_7,2010,62432,100.8
+wind-ons_5,2010,62434,102.9
+wind-ons_5,2010,62441,10.0
+wind-ons_5,2010,63117,76.5
+wind-ons_6,2010,63119,100.5
+wind-ons_5,2010,63120,98.7
+wind-ons_5,2010,63121,72.0
+wind-ons_8,2010,63304,25.0
+wind-ons_4,2010,63801,24.6
+wind-ons_5,2010,63802,25.2
+wind-ons_4,2010,63804,3.3
+wind-ons_4,2010,63805,9.9
+wind-ons_4,2010,63806,9.9
+wind-ons_8,2010,63935,18.0
+wind-ons_5,2010,64489,6.6
+wind-ons_4,2010,64490,8.3
+wind-ons_6,2010,64617,7.5
+wind-ons_8,2010,64636,49.5
+wind-ons_9,2010,64637,60.0
+wind-ons_5,2010,65174,5.0
+wind-ons_4,2010,65175,8.0
+wind-ons_7,2010,65242,135.0
+wind-ons_9,2010,65298,48.0
+wind-ons_9,2010,65299,50.6
+wind-ons_7,2010,65300,31.5
+wind-ons_9,2010,65321,60.0
+wind-ons_5,2010,65860,1.7
+wind-ons_9,2010,65987,42.0
+wind-ons_8,2010,66005,148.5
+wind-ons_9,2010,66199,4.5
+wind-ons_8,2010,66689,48.0
+wind-ons_7,2010,66698,0.97
+wind-ons_8,2010,66699,0.93
+wind-ons_7,2010,67381,0.7
+wind-ons_8,2010,67922,100.7
+wind-ons_8,2010,68065,0.7
+wind-ons_8,2010,68220,54.0
+wind-ons_8,2010,68253,4.5
+wind-ons_7,2010,68905,55.5
+wind-ons_8,2010,69586,102.0
+wind-ons_9,2010,69587,4.5
+wind-ons_9,2010,69589,42.0
 wind-ons_9,2010,70067,30.0
-wind-ons_7,2010,70112,50.6
-wind-ons_8,2010,70113,118.5
-wind-ons_8,2010,70114,12.0
-wind-ons_6,2010,70143,0.4
-wind-ons_5,2010,70144,0.97
-wind-ons_5,2010,70145,1.3
-wind-ons_4,2010,70146,2.0
 wind-ons_7,2010,70198,1.8
-wind-ons_6,2010,70266,11.5
-wind-ons_6,2010,70267,42.0
-wind-ons_8,2010,70269,54.0
-wind-ons_3,2010,70661,40.8
-wind-ons_1,2010,70662,11.0
-wind-ons_8,2010,70796,61.5
-wind-ons_8,2010,70797,120.0
-wind-ons_8,2010,70798,168.0
-wind-ons_6,2010,70828,0.97
-wind-ons_6,2010,70829,1.3
-wind-ons_6,2010,70830,2.0
-wind-ons_7,2010,70952,9.0
-wind-ons_7,2010,70953,46.5
-wind-ons_5,2010,71344,25.2
-wind-ons_5,2010,71345,60.0
-wind-ons_8,2010,71479,2.6
-wind-ons_8,2010,71481,149.0
-wind-ons_6,2010,71512,0.97
-wind-ons_6,2010,71513,1.3
-wind-ons_6,2010,71514,2.0
-wind-ons_7,2010,71635,45.5
-wind-ons_6,2010,71637,46.5
-wind-ons_9,2010,71639,66.0
-wind-ons_6,2010,72027,9.9
-wind-ons_8,2010,72163,4.5
-wind-ons_8,2010,72165,94.5
-wind-ons_6,2010,72195,0.04
-wind-ons_6,2010,72196,0.97
-wind-ons_6,2010,72197,1.3
-wind-ons_5,2010,72198,2.0
-wind-ons_6,2010,72319,30.55
-wind-ons_5,2010,72712,98.7
-wind-ons_8,2010,72847,11.9
-wind-ons_8,2010,72849,54.0
-wind-ons_6,2010,72879,0.02
-wind-ons_5,2010,72880,0.97
-wind-ons_6,2010,72881,1.3
-wind-ons_7,2010,73003,16.5
-wind-ons_6,2010,73004,2.5
-wind-ons_7,2010,73005,87.5
-wind-ons_4,2010,73396,58.7
-wind-ons_7,2010,73531,18.0
-wind-ons_5,2010,73564,0.97
-wind-ons_6,2010,73689,72.0
-wind-ons_3,2010,74079,24.0
-wind-ons_4,2010,74080,102.3
-wind-ons_8,2010,74372,45.5
-wind-ons_6,2010,74374,54.0
-wind-ons_2,2010,74762,8.3
-wind-ons_7,2010,74961,11.88
-wind-ons_7,2010,75056,34.5
-wind-ons_6,2010,75058,97.5
-wind-ons_2,2010,75446,9.9
-wind-ons_3,2010,75447,21.0
-wind-ons_7,2010,75645,11.0
-wind-ons_7,2010,75740,30.0
-wind-ons_2,2010,76131,90.0
-wind-ons_7,2010,76329,3.6
-wind-ons_6,2010,76330,54.0
-wind-ons_5,2010,76813,1.7
-wind-ons_4,2010,76815,42.0
-wind-ons_6,2010,77015,24.0
-wind-ons_5,2010,77497,6.6
-wind-ons_3,2010,77499,54.0
-wind-ons_6,2010,77698,13.5
-wind-ons_8,2010,78477,30.55
-wind-ons_8,2010,78482,60.0
-wind-ons_8,2010,78957,99.0
-wind-ons_7,2010,79009,25.0
-wind-ons_7,2010,79093,33.0
-wind-ons_10,2010,79161,16.5
-wind-ons_4,2010,79167,96.0
-wind-ons_7,2010,79641,99.0
-wind-ons_7,2010,79693,40.5
-wind-ons_9,2010,79755,72.0
-wind-ons_8,2010,79845,6.6
-wind-ons_8,2010,79851,30.0
-wind-ons_5,2010,80234,60.0
-wind-ons_3,2010,80235,54.0
-wind-ons_8,2010,80377,50.4
-wind-ons_8,2010,80439,73.2
-wind-ons_3,2010,80917,41.0
-wind-ons_3,2010,80918,71.4
-wind-ons_9,2010,81061,51.0
-wind-ons_9,2010,81145,30.0
-wind-ons_3,2010,81601,60.0
-wind-ons_2,2010,81602,114.0
-wind-ons_6,2010,81692,4.9
-wind-ons_7,2010,81829,3.2
-wind-ons_5,2010,82326,21.0
-wind-ons_2,2010,82328,16.5
-wind-ons_4,2010,82376,19.5
-wind-ons_6,2010,82598,1.8
-wind-ons_5,2010,83060,25.0
-wind-ons_3,2010,83652,24.6
-wind-ons_3,2010,83653,48.9
-wind-ons_3,2010,83654,156.0
-wind-ons_3,2010,83693,10.5
-wind-ons_5,2010,83744,28.8
-wind-ons_8,2010,83824,0.97
-wind-ons_2,2010,84337,54.0
-wind-ons_5,2010,84428,41.4
-wind-ons_8,2010,84508,0.93
-wind-ons_8,2010,84509,1.3
-wind-ons_6,2010,84512,3.9
-wind-ons_3,2010,85020,60.0
-wind-ons_5,2010,85112,39.0
-wind-ons_8,2010,85192,0.93
-wind-ons_7,2010,85193,1.3
-wind-ons_8,2010,85195,2.1
-wind-ons_8,2010,85196,3.8
-wind-ons_7,2010,85197,11.6
-wind-ons_3,2010,85704,10.5
-wind-ons_5,2010,85796,60.0
-wind-ons_7,2010,85881,9.8
-wind-ons_7,2010,85882,20.0
-wind-ons_9,2010,85907,44.55
-wind-ons_2,2010,86367,13.1
-wind-ons_4,2010,86428,8.4
-wind-ons_4,2010,86480,99.0
-wind-ons_9,2010,86591,30.0
-wind-ons_3,2010,87051,16.2
-wind-ons_6,2010,87070,5.0
-wind-ons_9,2010,87244,0.93
-wind-ons_9,2010,87247,2.0
-wind-ons_9,2010,87275,12.9
-wind-ons_7,2010,87302,29.0
-wind-ons_9,2010,87928,0.93
-wind-ons_9,2010,87929,1.2
-wind-ons_8,2010,87930,1.7
-wind-ons_9,2010,87931,2.0
-wind-ons_10,2010,88057,26.5
-wind-ons_5,2010,88531,12.14
-wind-ons_9,2010,88612,0.93
-wind-ons_9,2010,88614,1.7
-wind-ons_7,2010,88616,2.1
-wind-ons_8,2010,88617,4.2
-wind-ons_7,2010,88618,15.6
-wind-ons_6,2010,88619,43.06
-wind-ons_7,2010,88620,99.0
-wind-ons_7,2010,88671,36.0
-wind-ons_5,2010,89215,17.0
-wind-ons_9,2010,89296,0.93
-wind-ons_9,2010,89297,1.0
-wind-ons_9,2010,89298,1.7
-wind-ons_9,2010,89299,2.0
-wind-ons_8,2010,89300,2.1
-wind-ons_8,2010,89301,4.2
-wind-ons_7,2010,89302,13.5
-wind-ons_8,2010,89304,98.8
-wind-ons_7,2010,89305,13.5
-wind-ons_8,2010,89425,37.5
-wind-ons_5,2010,89899,24.8
-wind-ons_8,2010,89977,0.6
-wind-ons_9,2010,89979,2.0
-wind-ons_8,2010,89980,10.5
-wind-ons_8,2010,89981,43.5
-wind-ons_8,2010,89982,78.0
-wind-ons_8,2010,89987,150.0
-wind-ons_6,2010,89988,1.0
-wind-ons_5,2010,90105,54.0
-wind-ons_3,2010,90532,18.9
-wind-ons_7,2010,90583,29.4
-wind-ons_9,2010,90661,0.5
-wind-ons_9,2010,90666,76.5
-wind-ons_7,2010,90671,150.0
-wind-ons_6,2010,90672,126.0
-wind-ons_2,2010,91216,21.0
-wind-ons_2,2010,91218,48.0
-wind-ons_8,2010,91267,39.0
-wind-ons_8,2010,91354,123.0
-wind-ons_8,2010,91355,108.0
-wind-ons_8,2010,91356,48.8
-wind-ons_8,2010,91951,48.86
-wind-ons_8,2010,92034,75.0
-wind-ons_8,2010,92036,52.0
-wind-ons_8,2010,92037,104.3
-wind-ons_8,2010,92039,42.0
-wind-ons_6,2010,92158,54.0
-wind-ons_8,2010,92723,60.0
-wind-ons_8,2010,93319,99.0
-wind-ons_7,2010,93402,66.0
-wind-ons_7,2010,93404,69.0
-wind-ons_10,2010,93526,46.5
-wind-ons_9,2010,94056,1.3
-wind-ons_8,2010,94116,42.57
-wind-ons_8,2010,94117,43.7
-wind-ons_10,2010,94203,26.0
-wind-ons_9,2010,94207,27.0
-wind-ons_9,2010,94208,54.0
-wind-ons_8,2010,94687,142.2
-wind-ons_9,2010,94740,1.3
-wind-ons_6,2010,94887,15.0
-wind-ons_9,2010,94892,26.0
-wind-ons_6,2010,94895,48.0
-wind-ons_6,2010,95370,1.8
-wind-ons_8,2010,95459,60.0
-wind-ons_8,2010,95461,199.7
-wind-ons_3,2010,95572,34.5
-wind-ons_6,2010,96054,16.8
-wind-ons_8,2010,96137,35.0
-wind-ons_7,2010,96143,150.0
-wind-ons_10,2010,96258,52.5
-wind-ons_9,2010,96792,8.91
-wind-ons_7,2010,96827,150.0
-wind-ons_8,2010,96828,160.5
-wind-ons_8,2010,96832,84.0
-wind-ons_8,2010,96850,1.5
-wind-ons_8,2010,96940,29.4
-wind-ons_9,2010,96943,64.5
-wind-ons_9,2010,96944,48.0
-wind-ons_6,2010,97422,25.0
-wind-ons_10,2010,97476,8.91
-wind-ons_8,2010,97506,62.2
-wind-ons_8,2010,97513,198.0
-wind-ons_8,2010,97516,60.0
-wind-ons_8,2010,97564,48.0
-wind-ons_9,2010,97624,24.0
-wind-ons_7,2010,97627,30.0
-wind-ons_10,2010,98160,8.91
-wind-ons_8,2010,98189,29.7
-wind-ons_9,2010,98192,72.0
-wind-ons_9,2010,98308,9.0
-wind-ons_7,2010,98309,37.8
-wind-ons_7,2010,98311,32.5
-wind-ons_6,2010,98790,42.0
-wind-ons_10,2010,98845,54.27
-wind-ons_9,2010,98876,27.0
-wind-ons_6,2010,99474,64.08
-wind-ons_9,2010,99529,59.4
-wind-ons_8,2010,99557,21.0
-wind-ons_8,2010,99559,80.0
-wind-ons_8,2010,99560,60.0
-wind-ons_9,2010,99568,42.0
-wind-ons_8,2010,99612,36.0
-wind-ons_9,2010,99677,30.0
-wind-ons_3,2010,100042,17.0
-wind-ons_8,2010,100243,80.0
-wind-ons_8,2010,100244,36.0
-wind-ons_8,2010,100252,114.0
-wind-ons_7,2010,100725,4.5
-wind-ons_8,2010,100925,20.0
-wind-ons_8,2010,100928,2.2
-wind-ons_8,2010,101606,1.2
-wind-ons_8,2010,101610,49.6
-wind-ons_8,2010,101611,80.0
-wind-ons_8,2010,101612,98.0
-wind-ons_8,2010,102293,16.2
-wind-ons_7,2010,102323,8.4
-wind-ons_4,2010,102776,3.7
-wind-ons_3,2010,102777,18.0
-wind-ons_9,2010,102893,1.3
-wind-ons_8,2010,102974,0.9
-wind-ons_8,2010,102977,15.0
-wind-ons_8,2010,102979,78.0
-wind-ons_6,2010,103007,2.5
-wind-ons_8,2010,103013,60.0
-wind-ons_9,2010,103577,15.92
-wind-ons_9,2010,103661,14.7
-wind-ons_6,2010,103693,46.5
-wind-ons_7,2010,103697,13.5
-wind-ons_8,2010,103713,54.0
-wind-ons_7,2010,104375,1.5
-wind-ons_7,2010,104376,80.0
-wind-ons_7,2010,104377,100.5
-wind-ons_8,2010,104397,5.3
-wind-ons_8,2010,104398,78.0
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+wind-ons_8,2010,70270,40.5
+wind-ons_9,2010,70271,60.0
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+wind-ons_8,2010,83806,0.93
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+wind-ons_4,2010,84352,18.9
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+wind-ons_9,2010,84483,103.5
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+wind-ons_9,2010,85169,49.5
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+wind-ons_9,2010,85171,4.2
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+wind-ons_8,2010,85173,0.97
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+wind-ons_4,2010,85722,10.5
+wind-ons_9,2010,85853,98.8
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+wind-ons_9,2010,85856,2.0
+wind-ons_9,2010,85859,0.9
+wind-ons_8,2010,85860,0.04
+wind-ons_9,2010,85907,78.0
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+wind-ons_9,2010,86536,51.0
+wind-ons_9,2010,86539,2.0
+wind-ons_9,2010,86540,2.0
+wind-ons_9,2010,86541,1.5
+wind-ons_9,2010,86542,43.06
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+wind-ons_9,2010,86591,42.0
+wind-ons_8,2010,87199,1.3
+wind-ons_9,2010,87221,85.5
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+wind-ons_9,2010,87225,2.0
+wind-ons_9,2010,87226,1.3
+wind-ons_10,2010,87227,15.6
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+wind-ons_9,2010,87276,54.0
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+wind-ons_8,2010,87905,205.5
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+wind-ons_9,2010,87909,1.3
+wind-ons_9,2010,87911,1.3
+wind-ons_10,2010,87912,54.0
+wind-ons_9,2010,87914,100.5
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+wind-ons_8,2010,87930,7.1
+wind-ons_7,2010,87945,30.0
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+wind-ons_9,2010,88030,120.0
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+wind-ons_9,2010,88594,8.4
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+wind-ons_9,2010,88612,16.5
+wind-ons_9,2010,88613,100.7
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+wind-ons_7,2010,89273,11.6
+wind-ons_8,2010,89274,2.0
+wind-ons_9,2010,89276,2.0
+wind-ons_9,2010,89277,20.0
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+wind-ons_9,2010,89284,1.7
+wind-ons_9,2010,89285,1.3
+wind-ons_8,2010,89294,13.5
+wind-ons_9,2010,89296,36.0
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+wind-ons_9,2010,89959,1.5
+wind-ons_9,2010,89960,1.3
+wind-ons_8,2010,89961,2.0
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+wind-ons_9,2010,89965,0.9
+wind-ons_9,2010,89968,1.7
+wind-ons_9,2010,89969,0.9
+wind-ons_9,2010,89975,29.7
+wind-ons_9,2010,89976,80.0
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+wind-ons_8,2010,89978,26.0
+wind-ons_8,2010,89980,108.0
+wind-ons_8,2010,89981,168.0
+wind-ons_9,2010,90571,99.0
+wind-ons_10,2010,90572,99.0
+wind-ons_8,2010,90644,2.0
+wind-ons_8,2010,90645,98.2
+wind-ons_8,2010,90646,49.6
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+wind-ons_9,2010,90654,10.5
+wind-ons_9,2010,90659,54.0
+wind-ons_9,2010,90660,26.0
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+wind-ons_9,2010,90665,24.0
+wind-ons_9,2010,90666,99.0
+wind-ons_8,2010,91253,17.0
+wind-ons_9,2010,91257,99.0
+wind-ons_8,2010,91327,1.2
+wind-ons_8,2010,91329,0.6
+wind-ons_8,2010,91330,100.0
+wind-ons_9,2010,91340,66.0
+wind-ons_9,2010,91341,162.0
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+wind-ons_9,2010,92024,150.0
+wind-ons_9,2010,92025,37.0
+wind-ons_8,2010,92034,0.5
+wind-ons_8,2010,92125,36.0
+wind-ons_6,2010,92133,28.0
+wind-ons_10,2010,92146,42.0
+wind-ons_9,2010,92156,54.0
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+wind-ons_9,2010,92709,44.0
+wind-ons_9,2010,92711,42.0
+wind-ons_8,2010,92810,42.0
+wind-ons_9,2010,92829,58.5
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+wind-ons_8,2010,93433,8.55
+wind-ons_8,2010,93434,42.57
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+wind-ons_8,2010,94734,8.91
+wind-ons_9,2010,94750,76.5
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+wind-ons_9,2010,95433,0.7
+wind-ons_9,2010,95434,104.3
+wind-ons_9,2010,95449,108.0
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+wind-ons_9,2010,96117,0.7
+wind-ons_9,2010,96118,160.5
+wind-ons_9,2010,96122,123.0
+wind-ons_10,2010,96124,62.2
+wind-ons_9,2010,96129,35.0
+wind-ons_9,2010,96130,150.0
+wind-ons_9,2010,96132,13.7
+wind-ons_10,2010,96133,78.0
+wind-ons_8,2010,96137,0.9
+wind-ons_7,2010,96156,40.5
+wind-ons_8,2010,96157,60.0
+wind-ons_10,2010,96770,59.4
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+wind-ons_9,2010,96805,78.0
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+wind-ons_9,2010,97498,16.2
+wind-ons_9,2010,97624,102.0
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+wind-ons_10,2010,98184,78.0
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+wind-ons_10,2010,98776,25.0
+wind-ons_10,2010,98777,25.0
+wind-ons_10,2010,98778,28.8
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+wind-ons_9,2010,98857,150.0
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+wind-ons_10,2010,99459,24.8
+wind-ons_10,2010,99460,41.4
+wind-ons_9,2010,99462,99.0
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+wind-ons_8,2010,99584,37.5
+wind-ons_8,2010,100113,15.92
+wind-ons_10,2010,100143,16.8
+wind-ons_8,2010,100265,28.2
+wind-ons_8,2010,100268,96.0
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+wind-ons_7,2010,100953,48.0
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+wind-ons_8,2010,101634,80.0
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+wind-ons_7,2010,102317,2.5
+wind-ons_8,2010,102318,2.5
+wind-ons_10,2010,102396,36.0
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+wind-ons_6,2010,103007,49.5
+wind-ons_10,2010,103080,80.0
+wind-ons_9,2010,103643,150.0
+wind-ons_9,2010,103647,174.8
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+wind-ons_7,2010,103684,53.0
+wind-ons_8,2010,103690,99.0
+wind-ons_7,2010,103691,51.0
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+wind-ons_10,2010,103764,39.0
+wind-ons_9,2010,103767,30.0
+wind-ons_10,2010,103768,29.4
+wind-ons_7,2010,104368,72.0
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 wind-ons_7,2010,104417,1.8
-wind-ons_8,2010,105060,60.0
-wind-ons_8,2010,105061,42.0
-wind-ons_7,2010,105065,120.9
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+wind-ons_10,2010,104447,54.0
+wind-ons_7,2010,104450,42.0
+wind-ons_10,2010,104451,34.5
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+wind-ons_8,2010,105051,150.0
+wind-ons_7,2010,105101,1.8
+wind-ons_10,2010,105131,8.5
 wind-ons_7,2010,105162,7.5
-wind-ons_6,2010,105644,60.0
-wind-ons_9,2010,105710,0.9
-wind-ons_8,2010,105713,10.5
-wind-ons_9,2010,105717,48.0
-wind-ons_8,2010,105744,16.2
-wind-ons_8,2010,105765,34.0
-wind-ons_9,2010,106394,0.7
-wind-ons_8,2010,106398,42.0
-wind-ons_8,2010,106449,51.0
-wind-ons_7,2010,106452,60.0
-wind-ons_7,2010,106453,108.0
-wind-ons_8,2010,107080,2.3
-wind-ons_8,2010,107088,30.0
-wind-ons_7,2010,107115,102.0
-wind-ons_7,2010,107136,97.7
-wind-ons_7,2010,107137,56.0
-wind-ons_2,2010,107567,46.9
-wind-ons_8,2010,107696,1.5
-wind-ons_7,2010,107697,221.0
-wind-ons_9,2010,107797,3.0
-wind-ons_8,2010,108380,78.0
-wind-ons_9,2010,108446,0.7
-wind-ons_9,2010,108453,30.0
-wind-ons_9,2010,108480,48.0
-wind-ons_7,2010,108522,1.8
-wind-ons_9,2010,109164,48.0
-wind-ons_2,2010,109236,30.0
-wind-ons_5,2010,109239,60.0
-wind-ons_2,2010,109620,23.6
-wind-ons_8,2010,109848,7.5
-wind-ons_10,2010,109918,60.0
-wind-ons_2,2010,110304,36.0
-wind-ons_3,2010,110305,3.0
-wind-ons_8,2010,110532,10.5
-wind-ons_7,2010,110538,144.0
-wind-ons_8,2010,110555,28.0
-wind-ons_8,2010,110557,42.0
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+wind-ons_7,2010,105639,151.8
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+wind-ons_7,2010,105821,9.0
+wind-ons_4,2010,106179,30.0
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+wind-ons_5,2010,106323,29.7
+wind-ons_5,2010,106324,22.5
+wind-ons_7,2010,106495,10.5
+wind-ons_5,2010,106503,37.8
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+wind-ons_5,2010,107004,13.5
+wind-ons_7,2010,107119,130.5
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+wind-ons_8,2010,107549,150.0
+wind-ons_9,2010,107550,60.0
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+wind-ons_8,2010,107794,108.0
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+wind-ons_9,2010,108423,0.7
+wind-ons_7,2010,108473,40.5
+wind-ons_8,2010,108476,60.9
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+wind-ons_8,2010,109156,60.0
+wind-ons_9,2010,109162,54.0
+wind-ons_8,2010,109163,159.8
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+wind-ons_3,2010,109601,126.0
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+wind-ons_9,2010,109797,146.0
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+wind-ons_8,2010,110480,5.0
+wind-ons_9,2010,110481,50.4
 wind-ons_6,2010,110597,30.0
-wind-ons_9,2010,110602,14.0
-wind-ons_1,2010,110981,7.5
-wind-ons_1,2010,110982,20.5
-wind-ons_7,2010,111115,4.0
-wind-ons_7,2010,111116,151.8
-wind-ons_9,2010,111154,0.7
-wind-ons_8,2010,111216,2.5
-wind-ons_7,2010,111222,213.8
-wind-ons_8,2010,111238,66.0
-wind-ons_9,2010,111838,0.7
-wind-ons_8,2010,111922,48.0
-wind-ons_1,2010,112348,3.0
-wind-ons_3,2010,112433,18.9
-wind-ons_7,2010,112484,199.5
-wind-ons_8,2010,112590,90.0
-wind-ons_1,2010,113032,6.5
-wind-ons_1,2010,113033,18.4
-wind-ons_6,2010,113333,46.0
-wind-ons_1,2010,113716,8.7
-wind-ons_7,2010,113851,29.7
-wind-ons_7,2010,113976,114.0
-wind-ons_5,2010,113995,1.8
-wind-ons_3,2010,114398,0.1
-wind-ons_8,2010,114639,46.0
-wind-ons_8,2010,114640,48.0
-wind-ons_7,2010,114660,16.5
-wind-ons_3,2010,115082,1.5
-wind-ons_2,2010,115083,4.2
-wind-ons_1,2010,115084,15.5
-wind-ons_1,2010,115085,24.0
-wind-ons_1,2010,115086,38.0
-wind-ons_9,2010,115267,54.0
-wind-ons_10,2010,115268,54.0
-wind-ons_8,2010,115321,54.0
+wind-ons_6,2010,110598,36.0
+wind-ons_9,2010,110601,20.0
+wind-ons_10,2010,111285,90.0
+wind-ons_6,2010,111286,54.0
+wind-ons_1,2010,111651,36.0
+wind-ons_9,2010,111855,50.4
+wind-ons_2,2010,112337,2.0
+wind-ons_2,2010,112339,18.0
+wind-ons_9,2010,112540,54.0
+wind-ons_9,2010,112541,2.7
+wind-ons_6,2010,113333,4.0
+wind-ons_7,2010,113938,1.7
+wind-ons_2,2010,115076,17.0
+wind-ons_5,2010,115139,203.5
 wind-ons_5,2010,115382,54.0
-wind-ons_3,2010,115766,1.6
-wind-ons_3,2010,115767,7.4
-wind-ons_1,2010,115770,42.6
-wind-ons_10,2010,115951,42.0
-wind-ons_7,2010,116027,42.0
-wind-ons_5,2010,116066,12.0
-wind-ons_3,2010,116451,9.1
-wind-ons_1,2010,116453,29.0
-wind-ons_7,2010,116689,5.0
-wind-ons_10,2010,116755,24.0
-wind-ons_3,2010,117135,13.6
-wind-ons_1,2010,117138,43.4
-wind-ons_8,2010,117373,42.0
-wind-ons_4,2010,117416,1.8
-wind-ons_9,2010,118004,60.0
-wind-ons_4,2010,118502,8.0
-wind-ons_3,2010,118504,28.7
-wind-ons_9,2010,118638,1.5
-wind-ons_4,2010,119869,3.0
-wind-ons_4,2010,119871,22.0
-wind-ons_3,2010,120553,6.5
-wind-ons_9,2010,120739,54.0
-wind-ons_3,2010,121237,8.3
-wind-ons_9,2010,121423,4.8
-wind-ons_9,2010,121424,34.5
-wind-ons_6,2010,121480,60.0
-wind-ons_3,2010,121920,2.4
-wind-ons_8,2010,122059,162.0
-wind-ons_9,2010,122107,99.0
-wind-ons_9,2010,122108,96.0
-wind-ons_7,2010,122163,54.0
-wind-ons_1,2010,122604,3.5
-wind-ons_4,2010,122691,203.5
-wind-ons_9,2010,122791,42.0
-wind-ons_9,2010,122792,9.0
-wind-ons_6,2010,122846,18.0
-wind-ons_7,2010,123532,54.0
-wind-ons_10,2010,124160,112.2
-wind-ons_7,2010,124214,3.0
-wind-ons_1,2010,124656,11.7
-wind-ons_1,2010,124657,21.7
-wind-ons_1,2010,125340,16.5
-wind-ons_1,2010,125341,23.1
-wind-ons_1,2010,125343,45.0
-wind-ons_7,2010,125478,22.5
-wind-ons_9,2010,125527,100.5
-wind-ons_6,2010,125582,1.7
-wind-ons_9,2010,126212,114.0
-wind-ons_1,2010,127393,30.75
-wind-ons_9,2010,128213,1.5
-wind-ons_9,2010,128899,201.0
-wind-ons_9,2010,128946,14.0
-wind-ons_4,2010,129442,9.6
-wind-ons_2,2010,129443,20.5
-wind-ons_9,2010,129582,75.0
-wind-ons_1,2010,130125,3.4
-wind-ons_1,2010,130127,22.2
-wind-ons_1,2010,130128,30.75
-wind-ons_10,2010,130314,36.0
-wind-ons_10,2010,130317,36.0
-wind-ons_1,2010,130809,7.3
-wind-ons_1,2010,130810,17.3
-wind-ons_1,2010,130812,33.6
-wind-ons_10,2010,130825,34.5
-wind-ons_10,2010,130998,44.0
-wind-ons_6,2010,131107,27.0
-wind-ons_1,2010,131492,1.8
-wind-ons_2,2010,132176,2.1
-wind-ons_2,2010,132177,11.6
-wind-ons_10,2010,132193,54.0
-wind-ons_6,2010,132413,138.0
-wind-ons_3,2010,132860,5.4
-wind-ons_1,2010,132861,15.3
-wind-ons_1,2010,132862,22.4
-wind-ons_1,2010,132863,31.0
-wind-ons_3,2010,132869,54.0
-wind-ons_8,2010,132994,0.7
-wind-ons_6,2010,133097,8.0
-wind-ons_2,2010,133545,20.0
-wind-ons_1,2010,133546,25.3
-wind-ons_1,2010,133547,38.9
-wind-ons_9,2010,133733,25.5
-wind-ons_9,2010,134362,80.0
-wind-ons_9,2010,134420,36.0
-wind-ons_7,2010,134521,0.7
-wind-ons_1,2010,134918,50.0
-wind-ons_10,2010,135046,90.0
-wind-ons_6,2010,135149,56.7
+wind-ons_10,2010,116620,3.0
+wind-ons_10,2010,116621,42.0
+wind-ons_9,2010,116622,90.0
+wind-ons_10,2010,116755,12.0
+wind-ons_1,2010,117138,0.1
+wind-ons_10,2010,117304,60.0
+wind-ons_3,2010,117820,1.8
+wind-ons_10,2010,117989,6.0
+wind-ons_4,2010,118501,1.6
+wind-ons_3,2010,118504,4.2
+wind-ons_2,2010,119183,1.5
+wind-ons_3,2010,119184,2.4
+wind-ons_4,2010,119185,3.7
+wind-ons_2,2010,119867,3.5
+wind-ons_3,2010,119868,6.5
+wind-ons_4,2010,119869,7.5
+wind-ons_4,2010,119870,8.7
+wind-ons_1,2010,120551,7.4
+wind-ons_3,2010,120552,8.3
+wind-ons_3,2010,120553,13.6
+wind-ons_10,2010,120721,148.5
+wind-ons_10,2010,120722,100.8
+wind-ons_2,2010,121232,3.0
+wind-ons_1,2010,121233,4.5
+wind-ons_3,2010,121236,15.3
+wind-ons_3,2010,121920,17.3
+wind-ons_1,2010,122603,18.4
+wind-ons_9,2010,122751,99.0
+wind-ons_1,2010,124657,60.3
+wind-ons_1,2010,124658,76.9
+wind-ons_6,2010,124792,1.5
+wind-ons_1,2010,125344,75.0
+wind-ons_7,2010,125476,4.0
+wind-ons_10,2010,126182,78.0
+wind-ons_10,2010,126867,22.5
+wind-ons_9,2010,127525,75.0
+wind-ons_9,2010,127527,162.0
+wind-ons_9,2010,128262,54.0
+wind-ons_10,2010,128912,112.2
+wind-ons_9,2010,128946,54.0
+wind-ons_1,2010,129439,22.0
+wind-ons_2,2010,130123,23.1
+wind-ons_1,2010,130124,28.7
+wind-ons_9,2010,130266,1.5
+wind-ons_1,2010,130808,29.0
+wind-ons_1,2010,130809,54.0
+wind-ons_10,2010,130978,44.0
+wind-ons_10,2010,130979,50.0
+wind-ons_2,2010,131491,25.3
+wind-ons_1,2010,131492,59.6
+wind-ons_1,2010,131497,60.0
+wind-ons_2,2010,132176,46.9
+wind-ons_2,2010,132177,60.0
+wind-ons_1,2010,132861,28.5
+wind-ons_10,2010,133045,42.0
+wind-ons_2,2010,133545,13.0
+wind-ons_10,2010,134396,18.9
+wind-ons_9,2010,135080,25.5
+wind-ons_7,2010,135191,27.0
 wind-ons_6,2010,135192,0.7
-wind-ons_1,2010,135603,6.3
-wind-ons_2,2010,135606,67.0
-wind-ons_2,2010,135607,76.9
-wind-ons_7,2010,135833,50.4
-wind-ons_7,2010,136517,50.4
-wind-ons_1,2010,136972,60.0
-wind-ons_7,2010,137885,5.0
-wind-ons_8,2010,140517,0.6
-wind-ons_4,2010,140665,0.7
-wind-ons_5,2010,141885,20.0
-wind-ons_9,2010,143309,40.5
-wind-ons_9,2010,143310,30.0
-wind-ons_10,2010,143994,42.0
-wind-ons_2,2010,144525,60.0
-wind-ons_9,2010,144677,25.5
-wind-ons_9,2010,144678,26.9
-wind-ons_1,2010,145201,96.0
-wind-ons_9,2010,145361,25.5
-wind-ons_9,2010,146045,25.5
-wind-ons_9,2010,146046,39.0
-wind-ons_10,2010,146673,204.0
-wind-ons_10,2010,146729,18.9
-wind-ons_9,2010,146730,60.0
-wind-ons_9,2010,148100,39.0
-wind-ons_2,2010,148620,49.8
-wind-ons_1,2010,148621,150.0
-wind-ons_9,2010,148724,0.6
-wind-ons_9,2010,148784,120.0
-wind-ons_8,2010,148786,90.0
-wind-ons_4,2010,149303,135.0
-wind-ons_9,2010,149470,61.4
-wind-ons_9,2010,150150,94.5
-wind-ons_3,2010,150679,12.0
-wind-ons_3,2010,150680,30.0
-wind-ons_7,2010,150776,30.0
-wind-ons_7,2010,150836,106.5
-wind-ons_3,2010,151364,48.0
-wind-ons_7,2010,151518,48.0
-wind-ons_6,2010,151520,90.0
-wind-ons_9,2010,152144,42.0
-wind-ons_7,2010,152202,26.3
-wind-ons_6,2010,152205,48.0
-wind-ons_9,2010,152828,100.0
-wind-ons_8,2010,152888,11.2
-wind-ons_8,2010,153553,10.0
-wind-ons_9,2010,154237,10.0
-wind-ons_9,2010,154238,79.8
-wind-ons_9,2010,154239,66.0
-wind-ons_7,2010,154256,17.0
-wind-ons_6,2010,154258,60.0
-wind-ons_6,2010,154260,57.5
-wind-ons_9,2010,154921,10.0
-wind-ons_9,2010,154923,48.0
-wind-ons_8,2010,154935,2.0
-wind-ons_6,2010,154944,76.5
-wind-ons_9,2010,155624,42.0
-wind-ons_7,2010,155628,48.0
-wind-ons_8,2010,156308,30.0
-wind-ons_6,2010,156312,66.0
-wind-ons_1,2010,156884,63.0
-wind-ons_9,2010,156972,3.0
-wind-ons_9,2010,156977,42.0
-wind-ons_9,2010,156989,42.0
-wind-ons_8,2010,156992,48.0
-wind-ons_7,2010,156994,54.0
-wind-ons_6,2010,156995,78.0
-wind-ons_6,2010,156996,42.0
-wind-ons_9,2010,157656,10.0
-wind-ons_9,2010,157658,20.0
-wind-ons_6,2010,157678,60.0
-wind-ons_6,2010,157679,60.0
-wind-ons_6,2010,157680,54.0
-wind-ons_10,2010,158340,10.0
-wind-ons_10,2010,158341,13.5
-wind-ons_8,2010,158360,48.0
-wind-ons_7,2010,158364,60.0
-wind-ons_9,2010,159039,30.0
-wind-ons_8,2010,160392,10.0
-wind-ons_7,2010,160393,36.0
-wind-ons_6,2010,161784,60.0
-wind-ons_6,2010,162403,48.0
-wind-ons_8,2010,162445,25.5
-wind-ons_9,2010,163130,60.0
-wind-ons_9,2010,163812,10.0
-wind-ons_10,2010,164496,8.8
-wind-ons_9,2010,164497,54.0
-wind-ons_8,2010,165184,48.0
-wind-ons_7,2010,165185,30.0
-wind-ons_7,2010,165200,24.0
-wind-ons_10,2010,165866,20.5
-wind-ons_10,2010,165867,4.5
-wind-ons_10,2010,165868,60.0
-wind-ons_8,2010,165879,48.0
-wind-ons_10,2010,166550,12.0
-wind-ons_10,2010,166551,48.0
-wind-ons_8,2010,166556,24.0
-wind-ons_10,2010,167234,72.0
-wind-ons_10,2010,167235,60.0
-wind-ons_7,2010,167247,24.0
-wind-ons_7,2010,167248,84.0
-wind-ons_5,2010,167254,60.0
-wind-ons_8,2010,167916,0.16
-wind-ons_5,2010,167938,40.5
-wind-ons_5,2010,167942,60.0
-wind-ons_8,2010,168601,3.0
-wind-ons_10,2010,168602,37.2
-wind-ons_10,2010,168603,54.0
-wind-ons_9,2010,168612,54.0
-wind-ons_6,2010,168619,30.0
-wind-ons_6,2010,168622,66.0
-wind-ons_5,2010,168625,48.0
-wind-ons_5,2010,168626,60.0
-wind-ons_8,2010,169285,54.0
-wind-ons_10,2010,169287,66.0
-wind-ons_8,2010,169294,54.0
-wind-ons_9,2010,169296,54.0
-wind-ons_5,2010,169307,54.0
-wind-ons_5,2010,169309,90.0
-wind-ons_5,2010,169310,54.0
-wind-ons_8,2010,169980,60.0
-wind-ons_5,2010,169987,60.0
-wind-ons_7,2010,169990,30.0
-wind-ons_6,2010,169991,31.5
-wind-ons_5,2010,169993,78.0
-wind-ons_5,2010,169994,66.0
-wind-ons_8,2010,170654,36.0
-wind-ons_8,2010,170664,29.0
-wind-ons_6,2010,170671,30.0
-wind-ons_5,2010,170677,25.0
-wind-ons_5,2010,170678,9.0
-wind-ons_9,2010,171337,27.0
-wind-ons_8,2010,171344,54.0
-wind-ons_8,2010,171352,36.0
-wind-ons_5,2010,171365,36.0
-wind-ons_8,2010,172021,36.0
-wind-ons_6,2010,172030,50.2
-wind-ons_8,2010,172036,38.0
-wind-ons_5,2010,172049,156.0
-wind-ons_7,2010,172714,84.0
-wind-ons_7,2010,172719,36.0
-wind-ons_5,2010,172726,57.0
-wind-ons_5,2010,172733,107.0
-wind-ons_7,2010,173398,36.0
-wind-ons_7,2010,173403,60.0
-wind-ons_7,2010,177500,36.0
-wind-ons_5,2010,177521,204.0
-wind-ons_5,2010,178205,74.0
-wind-ons_7,2010,178868,42.0
-wind-ons_5,2010,178889,85.2
-wind-ons_7,2010,179548,24.0
-wind-ons_6,2010,179573,48.0
-wind-ons_7,2010,180236,6.3
-wind-ons_6,2010,180257,54.0
-wind-ons_7,2010,180917,60.0
-wind-ons_6,2010,180941,42.0
-wind-ons_6,2010,181601,18.0
-wind-ons_6,2010,181625,54.0
-wind-ons_6,2010,182284,54.0
-wind-ons_6,2010,182966,31.58
-wind-ons_6,2010,182967,54.0
-wind-ons_6,2010,183650,60.0
-wind-ons_6,2010,183651,60.0
-wind-ons_6,2010,183652,54.0
-wind-ons_6,2010,183653,12.0
-wind-ons_6,2010,184332,2.64
-wind-ons_6,2010,184334,42.0
-wind-ons_6,2010,184336,17.5
-wind-ons_7,2010,184337,30.0
-wind-ons_6,2010,184343,48.0
-wind-ons_6,2010,185018,13.2
-wind-ons_7,2010,185019,6.0
-wind-ons_6,2010,185027,114.0
-wind-ons_5,2010,185028,138.0
-wind-ons_7,2010,185702,54.0
-wind-ons_7,2010,185705,54.0
-wind-ons_6,2010,185710,115.5
-wind-ons_6,2010,185711,18.0
-wind-ons_6,2010,185712,48.0
-wind-ons_7,2010,186386,60.0
-wind-ons_8,2010,187068,0.92
-wind-ons_6,2010,187086,21.6
-wind-ons_7,2010,187757,54.0
-wind-ons_6,2010,187770,60.0
-wind-ons_7,2010,188436,0.6
-wind-ons_8,2010,188437,59.4
-wind-ons_8,2010,188438,29.5
-wind-ons_8,2010,189120,0.08
-wind-ons_9,2010,189121,54.0
-wind-ons_7,2010,189122,34.5
-wind-ons_6,2010,189126,150.0
-wind-ons_7,2010,189130,60.0
-wind-ons_7,2010,189131,60.0
-wind-ons_7,2010,189804,0.07
-wind-ons_7,2010,189806,48.0
-wind-ons_7,2010,189807,60.0
-wind-ons_6,2010,189811,153.9
-wind-ons_7,2010,189814,48.0
-wind-ons_7,2010,189815,120.0
-wind-ons_7,2010,190488,0.07
-wind-ons_8,2010,190489,4.8
-wind-ons_7,2010,190492,144.0
-wind-ons_6,2010,190495,144.0
-wind-ons_6,2010,190498,59.7
-wind-ons_8,2010,191174,60.0
-wind-ons_7,2010,191175,54.0
-wind-ons_6,2010,191179,8.0
-wind-ons_6,2010,191180,160.84
-wind-ons_8,2010,191858,22.0
-wind-ons_7,2010,191859,54.0
-wind-ons_8,2010,192543,58.5
-wind-ons_8,2010,193228,22.5
-wind-ons_7,2010,196645,37.5
-wind-ons_7,2010,197329,34.3
-wind-ons_8,2010,197330,60.0
-wind-ons_9,2010,197332,54.0
-wind-ons_7,2010,198013,32.52
-wind-ons_7,2010,198014,79.92
-wind-ons_8,2010,198016,72.5
-wind-ons_8,2010,198698,78.0
-wind-ons_8,2010,198699,101.2
-wind-ons_8,2010,199381,17.64
-wind-ons_8,2010,199382,75.0
-wind-ons_9,2010,199384,124.7
-wind-ons_8,2010,200066,73.5
-wind-ons_9,2010,200068,60.0
-wind-ons_8,2010,200750,69.53
-wind-ons_9,2010,200752,64.2
-wind-ons_9,2010,201435,98.8
-wind-ons_9,2010,202120,121.9
-wind-ons_9,2010,202804,120.6
-wind-ons_9,2010,203488,120.0
-wind-ons_1,2011,45361,60.0
-wind-ons_1,2011,46046,60.0
-wind-ons_1,2011,46728,44.0
-wind-ons_2,2011,49465,42.0
-wind-ons_6,2011,52969,9.7
-wind-ons_6,2011,54250,60.0
-wind-ons_4,2011,54935,168.6
-wind-ons_5,2011,55021,101.4
-wind-ons_6,2011,55705,104.2
-wind-ons_6,2011,55748,31.5
-wind-ons_5,2011,56302,156.0
-wind-ons_6,2011,56985,38.9
-wind-ons_5,2011,56986,186.0
-wind-ons_6,2011,57116,40.5
-wind-ons_7,2011,57316,42.0
-wind-ons_6,2011,57669,122.8
-wind-ons_6,2011,57670,66.0
-wind-ons_6,2011,57831,0.93
-wind-ons_5,2011,58352,7.2
-wind-ons_5,2011,58354,96.0
-wind-ons_7,2011,58484,40.5
-wind-ons_6,2011,58515,0.93
-wind-ons_7,2011,58516,1.3
-wind-ons_6,2011,59036,78.0
-wind-ons_4,2011,59037,149.4
-wind-ons_6,2011,59199,0.93
-wind-ons_7,2011,59201,2.0
-wind-ons_4,2011,59720,35.0
-wind-ons_3,2011,59721,13.0
-wind-ons_6,2011,59883,0.93
-wind-ons_7,2011,59885,2.0
-wind-ons_7,2011,59886,2.0
-wind-ons_7,2011,59888,12.0
-wind-ons_6,2011,59889,30.0
-wind-ons_7,2011,59899,126.0
-wind-ons_7,2011,60052,4.5
-wind-ons_1,2011,60405,138.0
-wind-ons_6,2011,60567,0.93
-wind-ons_7,2011,60569,2.0
-wind-ons_7,2011,60572,11.8
-wind-ons_7,2011,60583,79.5
-wind-ons_8,2011,60738,57.0
-wind-ons_2,2011,61088,136.3
-wind-ons_2,2011,61089,124.2
-wind-ons_6,2011,61252,1.0
-wind-ons_6,2011,61253,1.9
-wind-ons_7,2011,61255,4.2
-wind-ons_7,2011,61256,11.2
-wind-ons_8,2011,61423,66.0
-wind-ons_7,2011,61935,0.93
-wind-ons_7,2011,61936,0.97
-wind-ons_6,2011,61937,1.7
-wind-ons_7,2011,61940,10.4
-wind-ons_7,2011,61941,2.0
-wind-ons_7,2011,62105,4.5
-wind-ons_3,2011,62454,8.0
-wind-ons_2,2011,62455,94.0
-wind-ons_3,2011,62456,176.9
-wind-ons_7,2011,62619,0.93
-wind-ons_8,2011,62620,0.97
-wind-ons_7,2011,62621,1.7
-wind-ons_8,2011,62622,2.0
-wind-ons_7,2011,62626,91.1
-wind-ons_1,2011,63138,9.9
-wind-ons_3,2011,63139,76.5
-wind-ons_8,2011,63303,0.93
-wind-ons_8,2011,63304,0.97
-wind-ons_7,2011,63305,1.7
-wind-ons_6,2011,63306,2.0
-wind-ons_6,2011,63307,3.0
-wind-ons_7,2011,63308,9.2
-wind-ons_8,2011,63310,85.5
-wind-ons_8,2011,63311,100.7
-wind-ons_7,2011,63313,150.0
-wind-ons_6,2011,63987,0.9
-wind-ons_6,2011,63989,1.7
-wind-ons_7,2011,63992,8.4
-wind-ons_7,2011,63993,25.0
-wind-ons_8,2011,63995,100.5
-wind-ons_2,2011,64506,10.0
-wind-ons_8,2011,64596,135.0
-wind-ons_6,2011,64671,0.9
-wind-ons_6,2011,64672,0.97
-wind-ons_6,2011,64673,1.7
-wind-ons_6,2011,64674,2.0
-wind-ons_7,2011,64675,2.1
-wind-ons_6,2011,64676,8.0
-wind-ons_7,2011,64678,51.0
-wind-ons_6,2011,64679,100.5
-wind-ons_6,2011,64680,90.0
-wind-ons_1,2011,65189,3.3
-wind-ons_1,2011,65190,25.0
-wind-ons_2,2011,65191,42.9
-wind-ons_6,2011,65355,0.9
-wind-ons_7,2011,65357,1.7
-wind-ons_6,2011,65358,2.0
-wind-ons_6,2011,65360,5.2
-wind-ons_5,2011,65363,100.0
-wind-ons_1,2011,65874,12.0
-wind-ons_2,2011,65875,49.3
-wind-ons_6,2011,66039,0.9
-wind-ons_7,2011,66040,0.97
-wind-ons_6,2011,66046,49.5
-wind-ons_6,2011,66186,24.0
-wind-ons_8,2011,66692,48.0
-wind-ons_7,2011,66723,0.9
-wind-ons_5,2011,66729,19.98
-wind-ons_5,2011,66866,5.2
-wind-ons_8,2011,67376,48.0
-wind-ons_6,2011,67407,0.9
-wind-ons_7,2011,68061,31.5
-wind-ons_6,2011,68091,0.7
-wind-ons_7,2011,68744,49.5
-wind-ons_6,2011,68775,0.7
-wind-ons_7,2011,68776,0.97
-wind-ons_6,2011,68777,1.5
-wind-ons_4,2011,68778,2.0
-wind-ons_8,2011,68902,40.5
-wind-ons_8,2011,69428,49.5
-wind-ons_8,2011,69429,84.0
-wind-ons_6,2011,69460,0.97
-wind-ons_5,2011,69461,1.5
-wind-ons_4,2011,69462,2.0
-wind-ons_6,2011,69582,20.0
-wind-ons_6,2011,69583,66.0
-wind-ons_9,2011,69587,52.0
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+wind-ons_4,2010,135875,0.7
+wind-ons_5,2010,135876,0.7
+wind-ons_9,2010,136424,10.0
+wind-ons_10,2010,136428,114.0
+wind-ons_10,2010,136429,3.0
+wind-ons_10,2010,136444,54.0
+wind-ons_9,2010,136447,25.5
+wind-ons_10,2010,137112,79.8
+wind-ons_10,2010,137129,66.0
+wind-ons_10,2010,137130,94.5
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+wind-ons_10,2010,137813,101.2
+wind-ons_10,2010,137814,25.5
+wind-ons_9,2010,138476,10.0
+wind-ons_10,2010,139159,10.0
+wind-ons_10,2010,139160,49.5
+wind-ons_10,2010,139161,10.0
+wind-ons_9,2010,139843,10.0
+wind-ons_9,2010,140527,8.8
+wind-ons_7,2010,142576,0.16
+wind-ons_10,2010,142582,79.5
+wind-ons_10,2010,142585,80.0
+wind-ons_10,2010,142599,123.0
+wind-ons_4,2010,143197,63.0
+wind-ons_9,2010,143260,160.84
+wind-ons_10,2010,143265,10.0
+wind-ons_9,2010,143280,98.9
+wind-ons_10,2010,143287,40.5
+wind-ons_10,2010,143288,106.5
+wind-ons_2,2010,144525,9.1
+wind-ons_3,2010,144528,5.4
+wind-ons_6,2010,145198,43.4
+wind-ons_2,2010,145200,45.0
+wind-ons_1,2010,145201,42.6
+wind-ons_2,2010,145210,6.5
+wind-ons_3,2010,145212,3.4
+wind-ons_8,2010,145291,90.0
+wind-ons_10,2010,145303,20.0
+wind-ons_10,2010,145304,80.0
+wind-ons_5,2010,145883,38.9
+wind-ons_3,2010,145884,33.6
+wind-ons_3,2010,145885,31.0
+wind-ons_2,2010,145896,2.1
+wind-ons_10,2010,146025,30.0
+wind-ons_6,2010,146566,30.75
+wind-ons_3,2010,146567,30.75
+wind-ons_8,2010,146666,36.0
+wind-ons_10,2010,146707,44.3
+wind-ons_10,2010,146708,51.0
+wind-ons_4,2010,147250,21.7
+wind-ons_5,2010,147251,22.4
+wind-ons_5,2010,147252,22.2
+wind-ons_7,2010,147335,100.0
+wind-ons_7,2010,147350,168.0
+wind-ons_10,2010,147392,48.0
+wind-ons_10,2010,147393,151.4
+wind-ons_3,2010,147934,20.5
+wind-ons_3,2010,147935,20.5
+wind-ons_2,2010,147936,20.0
+wind-ons_1,2010,147937,16.5
+wind-ons_2,2010,148619,11.7
+wind-ons_2,2010,148620,11.6
+wind-ons_1,2010,148621,9.6
+wind-ons_4,2010,149303,50.0
+wind-ons_5,2010,149304,8.0
+wind-ons_4,2010,149305,7.3
+wind-ons_8,2010,149408,0.6
+wind-ons_9,2010,149409,0.7
+wind-ons_7,2010,150093,0.6
+wind-ons_2,2010,150675,3.0
+wind-ons_6,2010,151454,30.0
+wind-ons_8,2010,151455,90.0
+wind-ons_10,2010,151477,57.0
+wind-ons_10,2010,154214,150.0
+wind-ons_9,2010,154927,112.5
+wind-ons_9,2010,158996,180.0
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+wind-ons_8,2010,159000,91.2
+wind-ons_10,2010,159023,60.0
+wind-ons_7,2010,159682,84.0
+wind-ons_9,2010,159707,60.0
+wind-ons_9,2010,160368,70.5
+wind-ons_9,2010,160371,63.0
+wind-ons_9,2010,160380,60.3
+wind-ons_10,2010,160381,38.0
+wind-ons_9,2010,160383,102.0
+wind-ons_10,2010,161054,60.0
+wind-ons_10,2010,161055,120.0
+wind-ons_10,2010,161063,54.0
+wind-ons_10,2010,161065,60.0
+wind-ons_10,2010,161066,48.0
+wind-ons_10,2010,161739,126.0
+wind-ons_10,2010,161740,123.0
+wind-ons_10,2010,161742,37.5
+wind-ons_8,2010,161744,47.2
+wind-ons_8,2010,161746,26.0
+wind-ons_10,2010,161749,96.0
+wind-ons_9,2010,162422,72.0
+wind-ons_10,2010,162423,174.0
+wind-ons_10,2010,162424,35.0
+wind-ons_10,2010,162425,132.0
+wind-ons_9,2010,162426,54.0
+wind-ons_8,2010,162427,38.0
+wind-ons_9,2010,162428,60.0
+wind-ons_9,2010,162429,6.6
+wind-ons_9,2010,162430,48.0
+wind-ons_10,2010,163107,126.5
+wind-ons_10,2010,163108,65.0
+wind-ons_10,2010,163109,60.0
+wind-ons_10,2010,163110,114.0
+wind-ons_10,2010,163111,20.2
+wind-ons_10,2010,163112,54.0
+wind-ons_10,2010,163113,54.0
+wind-ons_10,2010,163116,30.0
+wind-ons_9,2010,163781,24.0
+wind-ons_9,2010,163782,96.0
+wind-ons_8,2010,163786,34.3
+wind-ons_7,2010,163790,73.5
+wind-ons_8,2010,163791,3.0
+wind-ons_10,2010,163792,222.0
+wind-ons_10,2010,163793,19.0
+wind-ons_10,2010,163795,232.5
+wind-ons_10,2010,163796,67.5
+wind-ons_10,2010,163797,96.0
+wind-ons_10,2010,163809,32.52
+wind-ons_8,2010,164458,0.92
+wind-ons_10,2010,164469,121.9
+wind-ons_9,2010,164470,58.8
+wind-ons_10,2010,164476,84.0
+wind-ons_10,2010,164479,213.0
+wind-ons_10,2010,164480,246.0
+wind-ons_10,2010,164481,53.0
+wind-ons_10,2010,164493,17.64
+wind-ons_10,2010,164494,2.64
+wind-ons_8,2010,165141,59.4
+wind-ons_9,2010,165142,91.58
+wind-ons_10,2010,165153,153.9
+wind-ons_10,2010,165154,89.5
+wind-ons_10,2010,165155,124.2
+wind-ons_6,2010,165158,0.08
+wind-ons_7,2010,165159,0.07
+wind-ons_9,2010,165161,80.5
+wind-ons_7,2010,165162,98.8
+wind-ons_9,2010,165163,169.5
+wind-ons_10,2010,165164,96.0
+wind-ons_10,2010,165165,60.0
+wind-ons_9,2010,165167,30.0
+wind-ons_8,2010,165826,0.6
+wind-ons_10,2010,165838,124.7
+wind-ons_9,2010,165839,112.5
+wind-ons_9,2010,165840,231.5
+wind-ons_10,2010,165841,214.5
+wind-ons_9,2010,165842,79.92
+wind-ons_6,2010,165843,69.53
+wind-ons_6,2010,165844,0.07
+wind-ons_8,2010,165849,84.0
+wind-ons_7,2010,166524,186.0
+wind-ons_9,2010,166525,149.5
+wind-ons_7,2010,170617,86.0
+wind-ons_9,2010,170618,192.0
+wind-ons_10,2010,171315,60.0
+wind-ons_9,2010,171984,82.0
+wind-ons_7,2010,171985,78.0
+wind-ons_10,2010,172670,82.5
+wind-ons_10,2010,172671,167.7
+wind-ons_10,2010,173353,150.0
+wind-ons_9,2010,173365,36.0
+wind-ons_10,2010,173366,54.0
+wind-ons_7,2010,191863,36.0
+wind-ons_8,2010,193229,144.0
+wind-ons_9,2010,198700,201.6
+wind-ons_8,2010,199383,258.0
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+wind-ons_9,2011,50181,101.4
+wind-ons_8,2011,50865,104.2
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+wind-ons_6,2011,52179,44.0
+wind-ons_6,2011,52862,84.6
+wind-ons_7,2011,52863,144.0
+wind-ons_8,2011,55742,40.5
+wind-ons_6,2011,56415,29.0
+wind-ons_7,2011,56416,60.0
+wind-ons_8,2011,56426,118.5
+wind-ons_9,2011,56427,40.5
+wind-ons_7,2011,57100,60.0
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+wind-ons_5,2011,58348,7.2
+wind-ons_5,2011,58391,9.7
+wind-ons_5,2011,59696,136.3
+wind-ons_5,2011,59699,199.0
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+wind-ons_6,2011,59708,90.0
+wind-ons_6,2011,59716,149.4
+wind-ons_8,2011,59717,156.0
+wind-ons_7,2011,59718,78.0
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+wind-ons_7,2011,59831,11.9
+wind-ons_6,2011,60379,246.0
+wind-ons_7,2011,60380,16.2
+wind-ons_6,2011,60381,94.0
+wind-ons_5,2011,60382,98.9
+wind-ons_7,2011,60383,204.0
+wind-ons_4,2011,60393,176.9
+wind-ons_3,2011,60394,122.8
+wind-ons_9,2011,60509,4.5
+wind-ons_9,2011,60511,108.0
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+wind-ons_3,2011,61076,25.0
+wind-ons_6,2011,61077,41.0
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+wind-ons_7,2011,61762,54.0
+wind-ons_6,2011,61763,48.3
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+wind-ons_7,2011,62085,66.0
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+wind-ons_5,2011,63120,98.7
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+wind-ons_5,2011,63802,25.2
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+wind-ons_4,2011,63805,9.9
+wind-ons_4,2011,63806,9.9
+wind-ons_8,2011,63935,18.0
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+wind-ons_4,2011,64490,8.3
+wind-ons_6,2011,64617,7.5
+wind-ons_8,2011,64636,49.5
+wind-ons_9,2011,64637,60.0
+wind-ons_5,2011,65174,5.0
+wind-ons_4,2011,65175,8.0
+wind-ons_7,2011,65242,135.0
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+wind-ons_9,2011,65299,50.6
+wind-ons_7,2011,65300,31.5
+wind-ons_9,2011,65321,60.0
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+wind-ons_9,2011,65987,42.0
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+wind-ons_8,2011,66689,48.0
+wind-ons_7,2011,66698,0.97
+wind-ons_8,2011,66699,0.93
+wind-ons_7,2011,67381,0.7
+wind-ons_8,2011,67922,100.7
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+wind-ons_8,2011,68220,54.0
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+wind-ons_7,2011,68905,55.5
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+wind-ons_9,2011,69589,42.0
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-wind-ons_7,2011,70112,50.6
-wind-ons_8,2011,70113,118.5
-wind-ons_8,2011,70114,12.0
-wind-ons_6,2011,70143,0.4
-wind-ons_5,2011,70144,0.97
-wind-ons_5,2011,70145,1.3
-wind-ons_4,2011,70146,2.0
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-wind-ons_6,2011,70266,11.5
-wind-ons_6,2011,70267,42.0
-wind-ons_8,2011,70269,54.0
-wind-ons_3,2011,70661,40.8
-wind-ons_1,2011,70662,11.0
-wind-ons_8,2011,70796,61.5
-wind-ons_8,2011,70797,120.0
-wind-ons_8,2011,70798,168.0
-wind-ons_6,2011,70828,0.97
-wind-ons_6,2011,70829,1.3
-wind-ons_6,2011,70830,2.0
-wind-ons_7,2011,70952,9.0
-wind-ons_7,2011,70953,46.5
-wind-ons_5,2011,71344,25.2
-wind-ons_5,2011,71345,60.0
-wind-ons_8,2011,71479,2.6
-wind-ons_8,2011,71481,149.0
-wind-ons_6,2011,71512,0.97
-wind-ons_6,2011,71513,1.3
-wind-ons_6,2011,71514,2.0
-wind-ons_7,2011,71635,45.5
-wind-ons_6,2011,71637,46.5
-wind-ons_9,2011,71639,66.0
-wind-ons_6,2011,72027,9.9
-wind-ons_8,2011,72163,4.5
-wind-ons_8,2011,72165,94.5
-wind-ons_6,2011,72195,0.04
-wind-ons_6,2011,72196,0.97
-wind-ons_6,2011,72197,1.3
-wind-ons_5,2011,72198,2.0
-wind-ons_6,2011,72319,30.55
-wind-ons_5,2011,72712,98.7
-wind-ons_8,2011,72847,11.9
-wind-ons_8,2011,72849,54.0
-wind-ons_6,2011,72879,0.02
-wind-ons_5,2011,72880,0.97
-wind-ons_6,2011,72881,1.3
-wind-ons_7,2011,73003,16.5
-wind-ons_6,2011,73004,2.5
-wind-ons_7,2011,73005,87.5
-wind-ons_4,2011,73396,58.7
-wind-ons_7,2011,73531,18.0
-wind-ons_5,2011,73564,0.97
-wind-ons_6,2011,73689,72.0
-wind-ons_3,2011,74079,24.0
-wind-ons_4,2011,74080,102.3
-wind-ons_8,2011,74372,45.5
-wind-ons_6,2011,74374,54.0
-wind-ons_2,2011,74762,8.3
-wind-ons_7,2011,74961,11.88
-wind-ons_7,2011,75056,34.5
-wind-ons_6,2011,75058,97.5
-wind-ons_2,2011,75446,9.9
-wind-ons_3,2011,75447,21.0
-wind-ons_7,2011,75645,11.0
-wind-ons_7,2011,75740,30.0
-wind-ons_2,2011,76131,90.0
-wind-ons_7,2011,76329,3.6
-wind-ons_6,2011,76330,54.0
-wind-ons_5,2011,76813,1.7
-wind-ons_4,2011,76815,42.0
-wind-ons_6,2011,77015,24.0
-wind-ons_5,2011,77497,6.6
-wind-ons_3,2011,77499,54.0
-wind-ons_6,2011,77698,13.5
-wind-ons_8,2011,78477,30.55
-wind-ons_8,2011,78482,60.0
-wind-ons_8,2011,78957,99.0
-wind-ons_7,2011,79009,25.0
-wind-ons_7,2011,79093,33.0
-wind-ons_10,2011,79161,16.5
-wind-ons_4,2011,79167,96.0
-wind-ons_7,2011,79641,99.0
-wind-ons_7,2011,79693,40.5
-wind-ons_9,2011,79755,72.0
-wind-ons_8,2011,79845,6.6
-wind-ons_8,2011,79851,30.0
-wind-ons_5,2011,80234,60.0
-wind-ons_3,2011,80235,54.0
-wind-ons_8,2011,80377,50.4
-wind-ons_8,2011,80439,73.2
-wind-ons_3,2011,80917,41.0
-wind-ons_3,2011,80918,71.4
-wind-ons_9,2011,81061,51.0
-wind-ons_9,2011,81145,30.0
-wind-ons_3,2011,81601,60.0
-wind-ons_2,2011,81602,114.0
-wind-ons_6,2011,81692,4.9
-wind-ons_7,2011,81829,3.2
-wind-ons_5,2011,82326,21.0
-wind-ons_2,2011,82328,16.5
-wind-ons_4,2011,82376,19.5
-wind-ons_6,2011,82598,1.8
-wind-ons_5,2011,83060,25.0
-wind-ons_3,2011,83652,24.6
-wind-ons_3,2011,83653,48.9
-wind-ons_3,2011,83654,156.0
-wind-ons_3,2011,83693,10.5
-wind-ons_5,2011,83744,28.8
-wind-ons_8,2011,83824,0.97
-wind-ons_2,2011,84337,54.0
-wind-ons_5,2011,84428,41.4
-wind-ons_8,2011,84508,0.93
-wind-ons_8,2011,84509,1.3
-wind-ons_6,2011,84512,3.9
-wind-ons_3,2011,85020,60.0
-wind-ons_5,2011,85112,39.0
-wind-ons_8,2011,85192,0.93
-wind-ons_7,2011,85193,1.3
-wind-ons_8,2011,85195,2.1
-wind-ons_8,2011,85196,3.8
-wind-ons_7,2011,85197,11.6
-wind-ons_3,2011,85704,10.5
-wind-ons_5,2011,85796,60.0
-wind-ons_7,2011,85881,9.8
-wind-ons_7,2011,85882,20.0
-wind-ons_9,2011,85907,44.55
-wind-ons_2,2011,86367,13.1
-wind-ons_4,2011,86428,8.4
-wind-ons_4,2011,86480,99.0
-wind-ons_9,2011,86591,30.0
-wind-ons_3,2011,87051,16.2
-wind-ons_6,2011,87070,5.0
-wind-ons_9,2011,87244,0.93
-wind-ons_9,2011,87247,2.0
-wind-ons_9,2011,87275,12.9
-wind-ons_7,2011,87302,29.0
-wind-ons_9,2011,87928,0.93
-wind-ons_9,2011,87929,1.2
-wind-ons_8,2011,87930,1.7
-wind-ons_9,2011,87931,2.0
-wind-ons_10,2011,88057,26.5
-wind-ons_5,2011,88531,12.14
-wind-ons_9,2011,88612,0.93
-wind-ons_9,2011,88614,1.7
-wind-ons_7,2011,88616,2.1
-wind-ons_8,2011,88617,4.2
-wind-ons_7,2011,88618,15.6
-wind-ons_6,2011,88619,43.06
-wind-ons_7,2011,88620,99.0
-wind-ons_7,2011,88671,36.0
-wind-ons_5,2011,89215,17.0
-wind-ons_9,2011,89296,0.93
-wind-ons_9,2011,89297,1.0
-wind-ons_9,2011,89298,1.7
-wind-ons_9,2011,89299,2.0
-wind-ons_8,2011,89300,2.1
-wind-ons_8,2011,89301,4.2
-wind-ons_7,2011,89302,13.5
-wind-ons_8,2011,89304,98.8
-wind-ons_7,2011,89305,13.5
-wind-ons_8,2011,89425,37.5
-wind-ons_5,2011,89899,24.8
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+wind-ons_9,2011,105636,126.0
+wind-ons_9,2011,105637,201.0
+wind-ons_8,2011,105638,60.0
+wind-ons_7,2011,105639,151.8
+wind-ons_6,2011,105785,1.8
+wind-ons_7,2011,105821,9.0
+wind-ons_4,2011,106179,30.0
+wind-ons_5,2011,106314,79.5
+wind-ons_5,2011,106321,60.0
+wind-ons_5,2011,106323,29.7
+wind-ons_5,2011,106324,22.5
+wind-ons_7,2011,106495,10.5
+wind-ons_5,2011,106503,37.8
+wind-ons_6,2011,106505,15.0
+wind-ons_1,2011,106943,18.9
+wind-ons_5,2011,107004,13.5
+wind-ons_7,2011,107119,130.5
+wind-ons_7,2011,107121,106.0
+wind-ons_7,2011,107123,199.7
+wind-ons_8,2011,107549,150.0
+wind-ons_9,2011,107550,60.0
+wind-ons_1,2011,107555,63.0
+wind-ons_8,2011,107793,42.0
+wind-ons_8,2011,107794,108.0
+wind-ons_7,2011,107803,100.0
+wind-ons_8,2011,107804,200.0
+wind-ons_7,2011,107805,101.3
+wind-ons_1,2011,108239,24.0
+wind-ons_9,2011,108422,0.7
+wind-ons_9,2011,108423,0.7
+wind-ons_7,2011,108473,40.5
+wind-ons_8,2011,108476,60.9
+wind-ons_7,2011,108489,99.0
+wind-ons_4,2011,108917,60.0
+wind-ons_1,2011,108923,15.5
+wind-ons_8,2011,109156,60.0
+wind-ons_9,2011,109162,54.0
+wind-ons_8,2011,109163,159.8
+wind-ons_1,2011,109600,19.8
+wind-ons_3,2011,109601,126.0
+wind-ons_2,2011,109607,13.1
+wind-ons_9,2011,109797,146.0
+wind-ons_6,2011,109914,30.0
+wind-ons_8,2011,110480,5.0
+wind-ons_9,2011,110481,50.4
 wind-ons_6,2011,110597,30.0
-wind-ons_9,2011,110602,14.0
-wind-ons_1,2011,110981,7.5
-wind-ons_1,2011,110982,20.5
-wind-ons_7,2011,111115,4.0
-wind-ons_7,2011,111116,151.8
-wind-ons_9,2011,111154,0.7
-wind-ons_8,2011,111216,2.5
-wind-ons_7,2011,111222,213.8
-wind-ons_8,2011,111238,66.0
-wind-ons_9,2011,111838,0.7
-wind-ons_8,2011,111922,48.0
-wind-ons_1,2011,112348,3.0
-wind-ons_3,2011,112433,18.9
-wind-ons_7,2011,112484,199.5
-wind-ons_8,2011,112590,90.0
-wind-ons_1,2011,113032,6.5
-wind-ons_1,2011,113033,18.4
-wind-ons_6,2011,113333,46.0
-wind-ons_1,2011,113716,8.7
-wind-ons_7,2011,113851,29.7
-wind-ons_7,2011,113976,114.0
-wind-ons_5,2011,113995,1.8
-wind-ons_3,2011,114398,0.1
-wind-ons_8,2011,114639,46.0
-wind-ons_8,2011,114640,48.0
-wind-ons_7,2011,114660,16.5
-wind-ons_3,2011,115082,1.5
-wind-ons_2,2011,115083,4.2
-wind-ons_1,2011,115084,15.5
-wind-ons_1,2011,115085,24.0
-wind-ons_1,2011,115086,38.0
-wind-ons_9,2011,115267,54.0
-wind-ons_10,2011,115268,54.0
-wind-ons_8,2011,115321,54.0
+wind-ons_6,2011,110598,36.0
+wind-ons_9,2011,110601,20.0
+wind-ons_10,2011,111285,90.0
+wind-ons_6,2011,111286,54.0
+wind-ons_1,2011,111651,36.0
+wind-ons_9,2011,111855,50.4
+wind-ons_2,2011,112337,2.0
+wind-ons_2,2011,112339,18.0
+wind-ons_9,2011,112540,54.0
+wind-ons_9,2011,112541,2.7
+wind-ons_6,2011,113333,4.0
+wind-ons_7,2011,113938,1.7
+wind-ons_2,2011,115076,17.0
+wind-ons_5,2011,115139,203.5
 wind-ons_5,2011,115382,54.0
-wind-ons_3,2011,115766,1.6
-wind-ons_3,2011,115767,7.4
-wind-ons_1,2011,115770,42.6
-wind-ons_10,2011,115951,42.0
-wind-ons_7,2011,116027,42.0
-wind-ons_5,2011,116066,12.0
-wind-ons_3,2011,116451,9.1
-wind-ons_1,2011,116453,29.0
-wind-ons_7,2011,116689,5.0
-wind-ons_10,2011,116755,24.0
-wind-ons_3,2011,117135,13.6
-wind-ons_1,2011,117138,43.4
-wind-ons_8,2011,117373,42.0
-wind-ons_4,2011,117416,1.8
-wind-ons_9,2011,118004,60.0
-wind-ons_4,2011,118502,8.0
-wind-ons_3,2011,118504,28.7
-wind-ons_9,2011,118638,1.5
-wind-ons_4,2011,119869,3.0
-wind-ons_4,2011,119871,22.0
-wind-ons_3,2011,120553,6.5
-wind-ons_9,2011,120739,54.0
-wind-ons_3,2011,121237,8.3
-wind-ons_9,2011,121423,4.8
-wind-ons_9,2011,121424,34.5
-wind-ons_6,2011,121480,60.0
-wind-ons_3,2011,121920,2.4
-wind-ons_8,2011,122059,162.0
-wind-ons_9,2011,122107,99.0
-wind-ons_9,2011,122108,96.0
-wind-ons_7,2011,122163,54.0
-wind-ons_1,2011,122604,3.5
-wind-ons_4,2011,122691,203.5
-wind-ons_9,2011,122791,42.0
-wind-ons_9,2011,122792,9.0
-wind-ons_6,2011,122846,18.0
-wind-ons_7,2011,123532,54.0
-wind-ons_10,2011,124160,112.2
-wind-ons_7,2011,124214,3.0
-wind-ons_1,2011,124656,11.7
-wind-ons_1,2011,124657,21.7
-wind-ons_1,2011,125340,16.5
-wind-ons_1,2011,125341,23.1
-wind-ons_1,2011,125343,45.0
-wind-ons_7,2011,125478,22.5
-wind-ons_9,2011,125527,100.5
-wind-ons_6,2011,125582,1.7
-wind-ons_9,2011,126212,114.0
-wind-ons_1,2011,127393,30.75
-wind-ons_9,2011,128213,1.5
-wind-ons_9,2011,128899,201.0
-wind-ons_9,2011,128946,14.0
-wind-ons_4,2011,129442,9.6
-wind-ons_2,2011,129443,20.5
-wind-ons_9,2011,129582,75.0
-wind-ons_1,2011,130125,3.4
-wind-ons_1,2011,130127,22.2
-wind-ons_1,2011,130128,30.75
-wind-ons_10,2011,130314,36.0
-wind-ons_10,2011,130317,36.0
-wind-ons_1,2011,130809,7.3
-wind-ons_1,2011,130810,17.3
-wind-ons_1,2011,130812,33.6
-wind-ons_10,2011,130825,34.5
-wind-ons_10,2011,130998,44.0
-wind-ons_6,2011,131107,27.0
-wind-ons_1,2011,131492,1.8
-wind-ons_2,2011,132176,2.1
-wind-ons_2,2011,132177,11.6
-wind-ons_10,2011,132193,54.0
-wind-ons_6,2011,132413,138.0
-wind-ons_3,2011,132860,5.4
-wind-ons_1,2011,132861,15.3
-wind-ons_1,2011,132862,22.4
-wind-ons_1,2011,132863,31.0
-wind-ons_3,2011,132869,54.0
-wind-ons_8,2011,132994,0.7
-wind-ons_6,2011,133097,8.0
-wind-ons_2,2011,133545,20.0
-wind-ons_1,2011,133546,25.3
-wind-ons_1,2011,133547,38.9
-wind-ons_9,2011,133733,25.5
-wind-ons_9,2011,134362,80.0
-wind-ons_9,2011,134420,36.0
-wind-ons_7,2011,134521,0.7
-wind-ons_1,2011,134918,50.0
-wind-ons_10,2011,135046,90.0
-wind-ons_6,2011,135149,56.7
+wind-ons_10,2011,116620,3.0
+wind-ons_10,2011,116621,42.0
+wind-ons_9,2011,116622,90.0
+wind-ons_10,2011,116755,12.0
+wind-ons_1,2011,117138,0.1
+wind-ons_10,2011,117304,60.0
+wind-ons_3,2011,117820,1.8
+wind-ons_10,2011,117989,6.0
+wind-ons_4,2011,118501,1.6
+wind-ons_3,2011,118504,4.2
+wind-ons_2,2011,119183,1.5
+wind-ons_3,2011,119184,2.4
+wind-ons_4,2011,119185,3.7
+wind-ons_2,2011,119867,3.5
+wind-ons_3,2011,119868,6.5
+wind-ons_4,2011,119869,7.5
+wind-ons_4,2011,119870,8.7
+wind-ons_1,2011,120551,7.4
+wind-ons_3,2011,120552,8.3
+wind-ons_3,2011,120553,13.6
+wind-ons_10,2011,120721,148.5
+wind-ons_10,2011,120722,100.8
+wind-ons_2,2011,121232,3.0
+wind-ons_1,2011,121233,4.5
+wind-ons_3,2011,121236,15.3
+wind-ons_3,2011,121920,17.3
+wind-ons_1,2011,122603,18.4
+wind-ons_9,2011,122751,99.0
+wind-ons_1,2011,124657,60.3
+wind-ons_1,2011,124658,76.9
+wind-ons_6,2011,124792,1.5
+wind-ons_1,2011,125344,75.0
+wind-ons_7,2011,125476,4.0
+wind-ons_10,2011,126182,78.0
+wind-ons_10,2011,126867,22.5
+wind-ons_9,2011,127525,75.0
+wind-ons_9,2011,127527,162.0
+wind-ons_9,2011,128262,54.0
+wind-ons_10,2011,128912,112.2
+wind-ons_9,2011,128946,54.0
+wind-ons_1,2011,129439,22.0
+wind-ons_2,2011,130123,23.1
+wind-ons_1,2011,130124,28.7
+wind-ons_9,2011,130266,1.5
+wind-ons_1,2011,130808,29.0
+wind-ons_1,2011,130809,54.0
+wind-ons_10,2011,130978,44.0
+wind-ons_10,2011,130979,50.0
+wind-ons_2,2011,131491,25.3
+wind-ons_1,2011,131492,59.6
+wind-ons_1,2011,131497,60.0
+wind-ons_2,2011,132176,46.9
+wind-ons_2,2011,132177,60.0
+wind-ons_1,2011,132861,28.5
+wind-ons_10,2011,133045,42.0
+wind-ons_2,2011,133545,13.0
+wind-ons_10,2011,134396,18.9
+wind-ons_9,2011,135080,25.5
+wind-ons_7,2011,135191,27.0
 wind-ons_6,2011,135192,0.7
-wind-ons_1,2011,135603,6.3
-wind-ons_2,2011,135606,67.0
-wind-ons_2,2011,135607,76.9
-wind-ons_7,2011,135833,50.4
-wind-ons_7,2011,136517,50.4
-wind-ons_1,2011,136972,60.0
-wind-ons_7,2011,137885,5.0
-wind-ons_8,2011,140517,0.6
-wind-ons_4,2011,140665,0.7
-wind-ons_5,2011,141885,20.0
-wind-ons_9,2011,143309,40.5
-wind-ons_9,2011,143310,30.0
-wind-ons_10,2011,143994,42.0
-wind-ons_2,2011,144525,60.0
-wind-ons_9,2011,144677,25.5
-wind-ons_9,2011,144678,26.9
-wind-ons_1,2011,145201,96.0
-wind-ons_9,2011,145361,25.5
-wind-ons_9,2011,146045,25.5
-wind-ons_9,2011,146046,39.0
-wind-ons_10,2011,146673,204.0
-wind-ons_10,2011,146729,18.9
-wind-ons_9,2011,146730,60.0
-wind-ons_9,2011,148100,39.0
-wind-ons_2,2011,148620,49.8
-wind-ons_1,2011,148621,150.0
-wind-ons_9,2011,148724,0.6
-wind-ons_9,2011,148784,120.0
-wind-ons_8,2011,148786,90.0
-wind-ons_4,2011,149303,135.0
-wind-ons_9,2011,149470,61.4
-wind-ons_9,2011,150150,94.5
-wind-ons_3,2011,150679,12.0
-wind-ons_3,2011,150680,30.0
-wind-ons_7,2011,150776,30.0
-wind-ons_7,2011,150836,106.5
-wind-ons_3,2011,151364,48.0
-wind-ons_7,2011,151518,48.0
-wind-ons_6,2011,151520,90.0
-wind-ons_9,2011,152144,42.0
-wind-ons_7,2011,152202,26.3
-wind-ons_6,2011,152205,48.0
-wind-ons_9,2011,152828,100.0
-wind-ons_8,2011,152888,11.2
-wind-ons_8,2011,153553,10.0
-wind-ons_9,2011,154237,10.0
-wind-ons_9,2011,154238,79.8
-wind-ons_9,2011,154239,66.0
-wind-ons_7,2011,154256,17.0
-wind-ons_6,2011,154258,60.0
-wind-ons_6,2011,154260,57.5
-wind-ons_9,2011,154921,10.0
-wind-ons_9,2011,154923,48.0
-wind-ons_8,2011,154935,2.0
-wind-ons_6,2011,154944,76.5
-wind-ons_9,2011,155624,42.0
-wind-ons_7,2011,155628,48.0
-wind-ons_8,2011,156308,30.0
-wind-ons_6,2011,156312,66.0
-wind-ons_1,2011,156884,63.0
-wind-ons_9,2011,156972,3.0
-wind-ons_9,2011,156977,42.0
-wind-ons_9,2011,156989,42.0
-wind-ons_8,2011,156992,48.0
-wind-ons_7,2011,156994,54.0
-wind-ons_6,2011,156995,78.0
-wind-ons_6,2011,156996,42.0
-wind-ons_9,2011,157656,10.0
-wind-ons_9,2011,157658,20.0
-wind-ons_6,2011,157678,60.0
-wind-ons_6,2011,157679,60.0
-wind-ons_6,2011,157680,54.0
-wind-ons_10,2011,158340,10.0
-wind-ons_10,2011,158341,13.5
-wind-ons_8,2011,158360,48.0
-wind-ons_7,2011,158364,60.0
-wind-ons_9,2011,159039,30.0
-wind-ons_8,2011,160392,10.0
-wind-ons_7,2011,160393,36.0
-wind-ons_6,2011,161784,60.0
-wind-ons_6,2011,162403,48.0
-wind-ons_8,2011,162445,25.5
-wind-ons_9,2011,163130,60.0
-wind-ons_9,2011,163812,10.0
-wind-ons_10,2011,164496,8.8
-wind-ons_9,2011,164497,54.0
-wind-ons_8,2011,165184,48.0
-wind-ons_7,2011,165185,30.0
-wind-ons_7,2011,165200,24.0
-wind-ons_10,2011,165866,20.5
-wind-ons_10,2011,165867,4.5
-wind-ons_10,2011,165868,60.0
-wind-ons_8,2011,165879,48.0
-wind-ons_10,2011,166550,12.0
-wind-ons_10,2011,166551,48.0
-wind-ons_8,2011,166556,24.0
-wind-ons_10,2011,167234,72.0
-wind-ons_10,2011,167235,60.0
-wind-ons_7,2011,167247,24.0
-wind-ons_7,2011,167248,84.0
-wind-ons_5,2011,167254,60.0
-wind-ons_8,2011,167916,0.16
-wind-ons_5,2011,167938,40.5
-wind-ons_5,2011,167942,60.0
-wind-ons_8,2011,168601,3.0
-wind-ons_10,2011,168602,37.2
-wind-ons_10,2011,168603,54.0
-wind-ons_9,2011,168612,54.0
-wind-ons_6,2011,168619,30.0
-wind-ons_6,2011,168622,66.0
-wind-ons_5,2011,168625,48.0
-wind-ons_5,2011,168626,60.0
-wind-ons_8,2011,169285,54.0
-wind-ons_10,2011,169287,66.0
-wind-ons_8,2011,169294,54.0
-wind-ons_9,2011,169296,54.0
-wind-ons_5,2011,169307,54.0
-wind-ons_5,2011,169309,90.0
-wind-ons_5,2011,169310,54.0
-wind-ons_8,2011,169980,60.0
-wind-ons_5,2011,169987,60.0
-wind-ons_7,2011,169990,30.0
-wind-ons_6,2011,169991,31.5
-wind-ons_5,2011,169993,78.0
-wind-ons_5,2011,169994,66.0
-wind-ons_8,2011,170654,36.0
-wind-ons_8,2011,170664,29.0
-wind-ons_6,2011,170671,30.0
-wind-ons_5,2011,170677,25.0
-wind-ons_5,2011,170678,9.0
-wind-ons_9,2011,171337,27.0
-wind-ons_8,2011,171344,54.0
-wind-ons_8,2011,171352,36.0
-wind-ons_5,2011,171365,36.0
-wind-ons_8,2011,172021,36.0
-wind-ons_6,2011,172030,50.2
-wind-ons_8,2011,172036,38.0
-wind-ons_5,2011,172049,156.0
-wind-ons_7,2011,172714,84.0
-wind-ons_7,2011,172719,36.0
-wind-ons_5,2011,172726,57.0
-wind-ons_5,2011,172733,107.0
-wind-ons_7,2011,173398,36.0
-wind-ons_7,2011,173403,60.0
-wind-ons_7,2011,177500,36.0
-wind-ons_5,2011,177521,204.0
-wind-ons_5,2011,178205,74.0
-wind-ons_7,2011,178868,42.0
-wind-ons_5,2011,178889,85.2
-wind-ons_7,2011,179548,24.0
-wind-ons_6,2011,179573,48.0
-wind-ons_7,2011,180236,6.3
-wind-ons_6,2011,180257,54.0
-wind-ons_7,2011,180917,60.0
-wind-ons_6,2011,180941,42.0
-wind-ons_6,2011,181601,18.0
-wind-ons_6,2011,181625,54.0
-wind-ons_6,2011,182284,54.0
-wind-ons_6,2011,182966,31.58
-wind-ons_6,2011,182967,54.0
-wind-ons_6,2011,183650,60.0
-wind-ons_6,2011,183651,60.0
-wind-ons_6,2011,183652,54.0
-wind-ons_6,2011,183653,12.0
-wind-ons_6,2011,184332,2.64
-wind-ons_6,2011,184334,42.0
-wind-ons_6,2011,184336,17.5
-wind-ons_7,2011,184337,30.0
-wind-ons_6,2011,184343,48.0
-wind-ons_6,2011,185018,13.2
-wind-ons_7,2011,185019,6.0
-wind-ons_6,2011,185027,114.0
-wind-ons_5,2011,185028,138.0
-wind-ons_7,2011,185702,54.0
-wind-ons_7,2011,185705,54.0
-wind-ons_6,2011,185710,115.5
-wind-ons_6,2011,185711,18.0
-wind-ons_6,2011,185712,48.0
-wind-ons_7,2011,186386,60.0
-wind-ons_8,2011,187068,0.92
-wind-ons_6,2011,187086,21.6
-wind-ons_7,2011,187757,54.0
-wind-ons_6,2011,187770,60.0
-wind-ons_7,2011,188436,0.6
-wind-ons_8,2011,188437,59.4
-wind-ons_8,2011,188438,29.5
-wind-ons_8,2011,189120,0.08
-wind-ons_9,2011,189121,54.0
-wind-ons_7,2011,189122,34.5
-wind-ons_6,2011,189126,150.0
-wind-ons_7,2011,189130,60.0
-wind-ons_7,2011,189131,60.0
-wind-ons_7,2011,189804,0.07
-wind-ons_7,2011,189806,48.0
-wind-ons_7,2011,189807,60.0
-wind-ons_6,2011,189811,153.9
-wind-ons_7,2011,189814,48.0
-wind-ons_7,2011,189815,120.0
-wind-ons_7,2011,190488,0.07
-wind-ons_8,2011,190489,4.8
-wind-ons_7,2011,190492,144.0
-wind-ons_6,2011,190495,144.0
-wind-ons_6,2011,190498,59.7
-wind-ons_8,2011,191174,60.0
-wind-ons_7,2011,191175,54.0
-wind-ons_6,2011,191179,8.0
-wind-ons_6,2011,191180,160.84
-wind-ons_8,2011,191858,22.0
-wind-ons_7,2011,191859,54.0
-wind-ons_8,2011,192543,58.5
-wind-ons_8,2011,193228,22.5
-wind-ons_7,2011,196645,37.5
-wind-ons_7,2011,197329,34.3
-wind-ons_8,2011,197330,60.0
-wind-ons_9,2011,197332,54.0
-wind-ons_7,2011,198013,32.52
-wind-ons_7,2011,198014,79.92
-wind-ons_8,2011,198016,72.5
-wind-ons_8,2011,198698,78.0
-wind-ons_8,2011,198699,101.2
-wind-ons_8,2011,199381,17.64
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-wind-ons_8,2015,122059,162.0
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-wind-ons_1,2015,125343,45.0
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+wind-ons_10,2018,93394,54.0
+wind-ons_7,2018,93402,75.0
+wind-ons_7,2018,93403,0.9
+wind-ons_8,2018,93433,8.55
+wind-ons_8,2018,93434,42.57
+wind-ons_8,2018,93523,10.5
+wind-ons_8,2018,94116,13.5
+wind-ons_8,2018,94117,12.9
+wind-ons_8,2018,94118,3.6
+wind-ons_8,2018,94734,8.91
+wind-ons_9,2018,94750,76.5
+wind-ons_9,2018,94891,24.0
+wind-ons_9,2018,95417,8.91
+wind-ons_10,2018,95418,54.27
+wind-ons_9,2018,95433,0.7
+wind-ons_9,2018,95434,104.3
+wind-ons_9,2018,95449,108.0
+wind-ons_10,2018,96102,8.91
+wind-ons_9,2018,96117,0.7
+wind-ons_9,2018,96118,160.5
+wind-ons_9,2018,96122,123.0
+wind-ons_10,2018,96124,62.2
+wind-ons_9,2018,96129,35.0
+wind-ons_9,2018,96130,150.0
+wind-ons_9,2018,96132,13.7
+wind-ons_10,2018,96133,78.0
+wind-ons_8,2018,96137,0.9
+wind-ons_7,2018,96156,40.5
+wind-ons_8,2018,96157,60.0
+wind-ons_10,2018,96770,59.4
+wind-ons_8,2018,96802,15.0
+wind-ons_9,2018,96805,78.0
+wind-ons_8,2018,96940,26.0
+wind-ons_9,2018,97408,1.3
+wind-ons_9,2018,97498,16.2
+wind-ons_9,2018,97624,102.0
+wind-ons_10,2018,98091,99.0
+wind-ons_10,2018,98092,19.5
+wind-ons_9,2018,98093,4.9
+wind-ons_10,2018,98184,78.0
+wind-ons_6,2018,98745,48.86
+wind-ons_10,2018,98775,1.8
+wind-ons_10,2018,98776,25.0
+wind-ons_10,2018,98777,25.0
+wind-ons_10,2018,98778,28.8
+wind-ons_9,2018,98856,45.0
+wind-ons_9,2018,98857,150.0
+wind-ons_9,2018,98868,72.0
+wind-ons_9,2018,98869,150.0
+wind-ons_6,2018,99428,64.08
+wind-ons_8,2018,99429,142.2
+wind-ons_7,2018,99430,12.14
+wind-ons_10,2018,99459,24.8
+wind-ons_10,2018,99460,41.4
+wind-ons_9,2018,99462,99.0
+wind-ons_10,2018,99539,54.0
+wind-ons_8,2018,99545,14.7
+wind-ons_8,2018,99584,37.5
+wind-ons_8,2018,100113,15.92
+wind-ons_10,2018,100143,16.8
+wind-ons_8,2018,100265,28.2
+wind-ons_8,2018,100268,96.0
+wind-ons_8,2018,100269,54.0
+wind-ons_8,2018,100952,30.0
+wind-ons_7,2018,100953,48.0
+wind-ons_7,2018,101628,1.5
+wind-ons_8,2018,101634,80.0
+wind-ons_10,2018,102207,42.0
+wind-ons_8,2018,102313,1.5
+wind-ons_7,2018,102317,2.5
+wind-ons_8,2018,102318,2.5
+wind-ons_10,2018,102396,36.0
+wind-ons_9,2018,102892,29.4
+wind-ons_6,2018,103007,49.5
+wind-ons_10,2018,103080,80.0
+wind-ons_9,2018,103643,150.0
+wind-ons_9,2018,103647,174.8
+wind-ons_8,2018,103683,8.4
+wind-ons_7,2018,103684,53.0
+wind-ons_8,2018,103690,99.0
+wind-ons_7,2018,103691,51.0
+wind-ons_7,2018,103692,10.5
+wind-ons_10,2018,103764,39.0
+wind-ons_9,2018,103767,30.0
+wind-ons_10,2018,103768,29.4
+wind-ons_7,2018,104368,72.0
+wind-ons_7,2018,104374,106.0
 wind-ons_7,2018,104417,1.8
-wind-ons_8,2018,105060,60.0
-wind-ons_8,2018,105061,42.0
-wind-ons_7,2018,105065,120.9
+wind-ons_8,2018,104418,1.8
+wind-ons_10,2018,104447,54.0
+wind-ons_7,2018,104450,42.0
+wind-ons_10,2018,104451,34.5
+wind-ons_3,2018,104809,16.2
+wind-ons_8,2018,105051,150.0
+wind-ons_7,2018,105101,1.8
+wind-ons_10,2018,105131,8.5
 wind-ons_7,2018,105162,7.5
-wind-ons_6,2018,105644,60.0
-wind-ons_9,2018,105710,0.9
-wind-ons_8,2018,105713,10.5
-wind-ons_9,2018,105717,48.0
-wind-ons_8,2018,105744,16.2
-wind-ons_8,2018,105765,34.0
-wind-ons_9,2018,106394,0.7
-wind-ons_8,2018,106398,42.0
-wind-ons_8,2018,106449,51.0
-wind-ons_7,2018,106452,60.0
-wind-ons_7,2018,106453,108.0
-wind-ons_8,2018,107080,2.3
-wind-ons_8,2018,107088,30.0
-wind-ons_7,2018,107115,102.0
-wind-ons_7,2018,107136,97.7
-wind-ons_7,2018,107137,56.0
-wind-ons_2,2018,107567,46.9
-wind-ons_8,2018,107696,1.5
-wind-ons_7,2018,107697,221.0
-wind-ons_9,2018,107797,3.0
-wind-ons_8,2018,108380,78.0
-wind-ons_9,2018,108446,0.7
-wind-ons_9,2018,108453,30.0
-wind-ons_9,2018,108480,48.0
-wind-ons_7,2018,108522,1.8
-wind-ons_9,2018,109164,48.0
-wind-ons_2,2018,109236,30.0
-wind-ons_5,2018,109239,60.0
-wind-ons_2,2018,109620,23.6
-wind-ons_8,2018,109848,7.5
-wind-ons_10,2018,109918,60.0
-wind-ons_2,2018,110304,36.0
-wind-ons_3,2018,110305,3.0
-wind-ons_8,2018,110532,10.5
-wind-ons_7,2018,110538,144.0
-wind-ons_8,2018,110555,28.0
-wind-ons_8,2018,110557,42.0
+wind-ons_8,2018,105631,221.0
+wind-ons_9,2018,105636,126.0
+wind-ons_9,2018,105637,201.0
+wind-ons_8,2018,105638,60.0
+wind-ons_7,2018,105639,151.8
+wind-ons_6,2018,105785,1.8
+wind-ons_7,2018,105821,9.0
+wind-ons_4,2018,106179,30.0
+wind-ons_5,2018,106314,79.5
+wind-ons_5,2018,106321,60.0
+wind-ons_5,2018,106323,29.7
+wind-ons_5,2018,106324,22.5
+wind-ons_7,2018,106495,10.5
+wind-ons_5,2018,106503,37.8
+wind-ons_6,2018,106505,15.0
+wind-ons_1,2018,106943,18.9
+wind-ons_5,2018,107004,13.5
+wind-ons_7,2018,107119,130.5
+wind-ons_7,2018,107121,106.0
+wind-ons_7,2018,107123,199.7
+wind-ons_8,2018,107549,150.0
+wind-ons_9,2018,107550,60.0
+wind-ons_1,2018,107555,63.0
+wind-ons_8,2018,107793,42.0
+wind-ons_8,2018,107794,108.0
+wind-ons_7,2018,107803,100.0
+wind-ons_8,2018,107804,200.0
+wind-ons_7,2018,107805,101.3
+wind-ons_1,2018,108239,24.0
+wind-ons_9,2018,108422,0.7
+wind-ons_9,2018,108423,0.7
+wind-ons_7,2018,108473,40.5
+wind-ons_8,2018,108476,60.9
+wind-ons_7,2018,108489,99.0
+wind-ons_4,2018,108917,60.0
+wind-ons_1,2018,108923,15.5
+wind-ons_8,2018,109156,60.0
+wind-ons_9,2018,109162,54.0
+wind-ons_8,2018,109163,159.8
+wind-ons_1,2018,109600,19.8
+wind-ons_3,2018,109601,126.0
+wind-ons_2,2018,109607,13.1
+wind-ons_9,2018,109797,146.0
+wind-ons_6,2018,109914,30.0
+wind-ons_8,2018,110480,5.0
+wind-ons_9,2018,110481,50.4
 wind-ons_6,2018,110597,30.0
-wind-ons_9,2018,110602,14.0
-wind-ons_1,2018,110981,7.5
-wind-ons_1,2018,110982,20.5
-wind-ons_7,2018,111115,4.0
-wind-ons_7,2018,111116,151.8
-wind-ons_9,2018,111154,0.7
-wind-ons_8,2018,111216,2.5
-wind-ons_7,2018,111222,213.8
-wind-ons_8,2018,111238,66.0
-wind-ons_9,2018,111838,0.7
-wind-ons_8,2018,111922,48.0
-wind-ons_1,2018,112348,3.0
-wind-ons_3,2018,112433,18.9
-wind-ons_7,2018,112484,199.5
-wind-ons_8,2018,112590,90.0
-wind-ons_1,2018,113032,6.5
-wind-ons_1,2018,113033,18.4
-wind-ons_6,2018,113333,46.0
-wind-ons_1,2018,113716,8.7
-wind-ons_7,2018,113851,29.7
-wind-ons_7,2018,113976,114.0
-wind-ons_5,2018,113995,1.8
-wind-ons_3,2018,114398,0.1
-wind-ons_8,2018,114639,46.0
-wind-ons_8,2018,114640,48.0
-wind-ons_7,2018,114660,16.5
-wind-ons_3,2018,115082,1.5
-wind-ons_2,2018,115083,4.2
-wind-ons_1,2018,115084,15.5
-wind-ons_1,2018,115085,24.0
-wind-ons_1,2018,115086,38.0
-wind-ons_9,2018,115267,54.0
-wind-ons_10,2018,115268,54.0
-wind-ons_8,2018,115321,54.0
+wind-ons_6,2018,110598,36.0
+wind-ons_9,2018,110601,20.0
+wind-ons_10,2018,111285,90.0
+wind-ons_6,2018,111286,54.0
+wind-ons_1,2018,111651,36.0
+wind-ons_9,2018,111855,50.4
+wind-ons_2,2018,112337,2.0
+wind-ons_2,2018,112339,18.0
+wind-ons_9,2018,112540,54.0
+wind-ons_9,2018,112541,2.7
+wind-ons_6,2018,113333,4.0
+wind-ons_7,2018,113938,1.7
+wind-ons_2,2018,115076,17.0
+wind-ons_5,2018,115139,203.5
 wind-ons_5,2018,115382,54.0
-wind-ons_3,2018,115766,1.6
-wind-ons_3,2018,115767,7.4
-wind-ons_1,2018,115770,42.6
-wind-ons_10,2018,115951,42.0
-wind-ons_7,2018,116027,42.0
-wind-ons_5,2018,116066,12.0
-wind-ons_3,2018,116451,9.1
-wind-ons_1,2018,116453,29.0
-wind-ons_7,2018,116689,5.0
-wind-ons_10,2018,116755,24.0
-wind-ons_3,2018,117135,13.6
-wind-ons_1,2018,117138,43.4
-wind-ons_8,2018,117373,42.0
-wind-ons_4,2018,117416,1.8
-wind-ons_9,2018,118004,60.0
-wind-ons_4,2018,118502,8.0
-wind-ons_3,2018,118504,28.7
-wind-ons_9,2018,118638,1.5
-wind-ons_4,2018,119869,3.0
-wind-ons_4,2018,119871,22.0
-wind-ons_3,2018,120553,6.5
-wind-ons_9,2018,120739,54.0
-wind-ons_3,2018,121237,8.3
-wind-ons_9,2018,121423,4.8
-wind-ons_9,2018,121424,34.5
-wind-ons_6,2018,121480,60.0
-wind-ons_3,2018,121920,2.4
-wind-ons_8,2018,122059,162.0
-wind-ons_9,2018,122107,99.0
-wind-ons_9,2018,122108,96.0
-wind-ons_7,2018,122163,54.0
-wind-ons_1,2018,122604,3.5
-wind-ons_4,2018,122691,203.5
-wind-ons_9,2018,122791,42.0
-wind-ons_9,2018,122792,9.0
-wind-ons_6,2018,122846,18.0
-wind-ons_7,2018,123532,54.0
-wind-ons_10,2018,124160,112.2
-wind-ons_7,2018,124214,3.0
-wind-ons_1,2018,124656,11.7
-wind-ons_1,2018,124657,21.7
-wind-ons_1,2018,125340,16.5
-wind-ons_1,2018,125341,23.1
-wind-ons_1,2018,125343,45.0
-wind-ons_7,2018,125478,22.5
-wind-ons_9,2018,125527,100.5
-wind-ons_6,2018,125582,1.7
-wind-ons_9,2018,126212,114.0
-wind-ons_1,2018,127393,30.75
-wind-ons_9,2018,128213,1.5
-wind-ons_9,2018,128899,201.0
-wind-ons_9,2018,128946,14.0
-wind-ons_4,2018,129442,9.6
-wind-ons_2,2018,129443,20.5
-wind-ons_9,2018,129582,75.0
-wind-ons_1,2018,130125,3.4
-wind-ons_1,2018,130127,22.2
-wind-ons_1,2018,130128,30.75
-wind-ons_10,2018,130314,36.0
-wind-ons_10,2018,130317,36.0
-wind-ons_1,2018,130809,7.3
-wind-ons_1,2018,130810,17.3
-wind-ons_1,2018,130812,33.6
-wind-ons_10,2018,130825,34.5
-wind-ons_10,2018,130998,44.0
-wind-ons_6,2018,131107,27.0
-wind-ons_1,2018,131492,1.8
-wind-ons_2,2018,132176,2.1
-wind-ons_2,2018,132177,11.6
-wind-ons_10,2018,132193,54.0
-wind-ons_6,2018,132413,138.0
-wind-ons_3,2018,132860,5.4
-wind-ons_1,2018,132861,15.3
-wind-ons_1,2018,132862,22.4
-wind-ons_1,2018,132863,31.0
-wind-ons_3,2018,132869,54.0
-wind-ons_8,2018,132994,0.7
-wind-ons_6,2018,133097,8.0
-wind-ons_2,2018,133545,20.0
-wind-ons_1,2018,133546,25.3
-wind-ons_1,2018,133547,38.9
-wind-ons_9,2018,133733,25.5
-wind-ons_9,2018,134362,80.0
-wind-ons_9,2018,134420,36.0
-wind-ons_7,2018,134521,0.7
-wind-ons_1,2018,134918,50.0
-wind-ons_10,2018,135046,90.0
-wind-ons_6,2018,135149,56.7
+wind-ons_10,2018,116620,3.0
+wind-ons_10,2018,116621,42.0
+wind-ons_9,2018,116622,90.0
+wind-ons_10,2018,116755,12.0
+wind-ons_1,2018,117138,0.1
+wind-ons_10,2018,117304,60.0
+wind-ons_3,2018,117820,1.8
+wind-ons_10,2018,117989,6.0
+wind-ons_4,2018,118501,1.6
+wind-ons_3,2018,118504,4.2
+wind-ons_2,2018,119183,1.5
+wind-ons_3,2018,119184,2.4
+wind-ons_4,2018,119185,3.7
+wind-ons_2,2018,119867,3.5
+wind-ons_3,2018,119868,6.5
+wind-ons_4,2018,119869,7.5
+wind-ons_4,2018,119870,8.7
+wind-ons_1,2018,120551,7.4
+wind-ons_3,2018,120552,8.3
+wind-ons_3,2018,120553,13.6
+wind-ons_10,2018,120721,148.5
+wind-ons_10,2018,120722,100.8
+wind-ons_2,2018,121232,3.0
+wind-ons_1,2018,121233,4.5
+wind-ons_3,2018,121236,15.3
+wind-ons_3,2018,121920,17.3
+wind-ons_1,2018,122603,18.4
+wind-ons_9,2018,122751,99.0
+wind-ons_1,2018,124657,60.3
+wind-ons_1,2018,124658,76.9
+wind-ons_6,2018,124792,1.5
+wind-ons_1,2018,125344,75.0
+wind-ons_7,2018,125476,4.0
+wind-ons_10,2018,126182,78.0
+wind-ons_10,2018,126867,22.5
+wind-ons_9,2018,127525,75.0
+wind-ons_9,2018,127527,162.0
+wind-ons_9,2018,128262,54.0
+wind-ons_10,2018,128912,112.2
+wind-ons_9,2018,128946,54.0
+wind-ons_1,2018,129439,22.0
+wind-ons_2,2018,130123,23.1
+wind-ons_1,2018,130124,28.7
+wind-ons_9,2018,130266,1.5
+wind-ons_1,2018,130808,29.0
+wind-ons_1,2018,130809,54.0
+wind-ons_10,2018,130978,44.0
+wind-ons_10,2018,130979,50.0
+wind-ons_2,2018,131491,25.3
+wind-ons_1,2018,131492,59.6
+wind-ons_1,2018,131497,60.0
+wind-ons_2,2018,132176,46.9
+wind-ons_2,2018,132177,60.0
+wind-ons_1,2018,132861,28.5
+wind-ons_10,2018,133045,42.0
+wind-ons_2,2018,133545,13.0
+wind-ons_10,2018,134396,18.9
+wind-ons_9,2018,135080,25.5
+wind-ons_7,2018,135191,27.0
 wind-ons_6,2018,135192,0.7
-wind-ons_1,2018,135603,6.3
-wind-ons_2,2018,135606,67.0
-wind-ons_2,2018,135607,76.9
-wind-ons_7,2018,135833,50.4
-wind-ons_7,2018,136517,50.4
-wind-ons_1,2018,136972,60.0
-wind-ons_7,2018,137885,5.0
-wind-ons_8,2018,140517,0.6
-wind-ons_4,2018,140665,0.7
-wind-ons_5,2018,141885,20.0
-wind-ons_9,2018,143309,40.5
-wind-ons_9,2018,143310,30.0
-wind-ons_10,2018,143994,42.0
-wind-ons_2,2018,144525,60.0
-wind-ons_9,2018,144677,25.5
-wind-ons_9,2018,144678,26.9
-wind-ons_1,2018,145201,96.0
-wind-ons_9,2018,145361,25.5
-wind-ons_9,2018,146045,25.5
-wind-ons_9,2018,146046,39.0
-wind-ons_10,2018,146673,204.0
-wind-ons_10,2018,146729,18.9
-wind-ons_9,2018,146730,60.0
-wind-ons_9,2018,148100,39.0
-wind-ons_2,2018,148620,49.8
-wind-ons_1,2018,148621,150.0
-wind-ons_9,2018,148724,0.6
-wind-ons_9,2018,148784,120.0
-wind-ons_8,2018,148786,90.0
-wind-ons_4,2018,149303,135.0
-wind-ons_9,2018,149470,61.4
-wind-ons_9,2018,150150,94.5
-wind-ons_3,2018,150679,12.0
-wind-ons_3,2018,150680,30.0
-wind-ons_7,2018,150776,30.0
-wind-ons_7,2018,150836,106.5
-wind-ons_3,2018,151364,48.0
-wind-ons_7,2018,151518,48.0
-wind-ons_6,2018,151520,90.0
-wind-ons_9,2018,152144,42.0
-wind-ons_7,2018,152202,26.3
-wind-ons_6,2018,152205,48.0
-wind-ons_9,2018,152828,100.0
-wind-ons_8,2018,152888,11.2
-wind-ons_8,2018,153553,10.0
-wind-ons_9,2018,154237,10.0
-wind-ons_9,2018,154238,79.8
-wind-ons_9,2018,154239,66.0
-wind-ons_7,2018,154256,17.0
-wind-ons_6,2018,154258,60.0
-wind-ons_6,2018,154260,57.5
-wind-ons_9,2018,154921,10.0
-wind-ons_9,2018,154923,48.0
-wind-ons_8,2018,154935,2.0
-wind-ons_6,2018,154944,76.5
-wind-ons_9,2018,155624,42.0
-wind-ons_7,2018,155628,48.0
-wind-ons_8,2018,156308,30.0
-wind-ons_6,2018,156312,66.0
-wind-ons_1,2018,156884,63.0
-wind-ons_9,2018,156972,3.0
-wind-ons_9,2018,156977,42.0
-wind-ons_9,2018,156989,42.0
-wind-ons_8,2018,156992,48.0
-wind-ons_7,2018,156994,54.0
-wind-ons_6,2018,156995,78.0
-wind-ons_6,2018,156996,42.0
-wind-ons_9,2018,157656,10.0
-wind-ons_9,2018,157658,20.0
-wind-ons_6,2018,157678,60.0
-wind-ons_6,2018,157679,60.0
-wind-ons_6,2018,157680,54.0
-wind-ons_10,2018,158340,10.0
-wind-ons_10,2018,158341,13.5
-wind-ons_8,2018,158360,48.0
-wind-ons_7,2018,158364,60.0
-wind-ons_9,2018,159039,30.0
-wind-ons_8,2018,160392,10.0
-wind-ons_7,2018,160393,36.0
-wind-ons_6,2018,161784,60.0
-wind-ons_6,2018,162403,48.0
-wind-ons_8,2018,162445,25.5
-wind-ons_9,2018,163130,60.0
-wind-ons_9,2018,163812,10.0
-wind-ons_10,2018,164496,8.8
-wind-ons_9,2018,164497,54.0
-wind-ons_8,2018,165184,48.0
-wind-ons_7,2018,165185,30.0
-wind-ons_7,2018,165200,24.0
-wind-ons_10,2018,165866,20.5
-wind-ons_10,2018,165867,4.5
-wind-ons_10,2018,165868,60.0
-wind-ons_8,2018,165879,48.0
-wind-ons_10,2018,166550,12.0
-wind-ons_10,2018,166551,48.0
-wind-ons_8,2018,166556,24.0
-wind-ons_10,2018,167234,72.0
-wind-ons_10,2018,167235,60.0
-wind-ons_7,2018,167247,24.0
-wind-ons_7,2018,167248,84.0
-wind-ons_5,2018,167254,60.0
-wind-ons_8,2018,167916,0.16
-wind-ons_5,2018,167938,40.5
-wind-ons_5,2018,167942,60.0
-wind-ons_8,2018,168601,3.0
-wind-ons_10,2018,168602,37.2
-wind-ons_10,2018,168603,54.0
-wind-ons_9,2018,168612,54.0
-wind-ons_6,2018,168619,30.0
-wind-ons_6,2018,168622,66.0
-wind-ons_5,2018,168625,48.0
-wind-ons_5,2018,168626,60.0
-wind-ons_8,2018,169285,54.0
-wind-ons_10,2018,169287,66.0
-wind-ons_8,2018,169294,54.0
-wind-ons_9,2018,169296,54.0
-wind-ons_5,2018,169307,54.0
-wind-ons_5,2018,169309,90.0
-wind-ons_5,2018,169310,54.0
-wind-ons_8,2018,169980,60.0
-wind-ons_5,2018,169987,60.0
-wind-ons_7,2018,169990,30.0
-wind-ons_6,2018,169991,31.5
-wind-ons_5,2018,169993,78.0
-wind-ons_5,2018,169994,66.0
-wind-ons_8,2018,170654,36.0
-wind-ons_8,2018,170664,29.0
-wind-ons_6,2018,170671,30.0
-wind-ons_5,2018,170677,25.0
-wind-ons_5,2018,170678,9.0
-wind-ons_9,2018,171337,27.0
-wind-ons_8,2018,171344,54.0
-wind-ons_8,2018,171352,36.0
-wind-ons_5,2018,171365,36.0
-wind-ons_8,2018,172021,36.0
-wind-ons_6,2018,172030,50.2
-wind-ons_8,2018,172036,38.0
-wind-ons_5,2018,172049,156.0
-wind-ons_7,2018,172714,84.0
-wind-ons_7,2018,172719,36.0
-wind-ons_5,2018,172726,57.0
-wind-ons_5,2018,172733,107.0
-wind-ons_7,2018,173398,36.0
-wind-ons_7,2018,173403,60.0
-wind-ons_7,2018,177500,36.0
-wind-ons_5,2018,177521,204.0
-wind-ons_5,2018,178205,74.0
-wind-ons_7,2018,178868,42.0
-wind-ons_5,2018,178889,85.2
-wind-ons_7,2018,179548,24.0
-wind-ons_6,2018,179573,48.0
-wind-ons_7,2018,180236,6.3
-wind-ons_6,2018,180257,54.0
-wind-ons_7,2018,180917,60.0
-wind-ons_6,2018,180941,42.0
-wind-ons_6,2018,181601,18.0
-wind-ons_6,2018,181625,54.0
-wind-ons_6,2018,182284,54.0
-wind-ons_6,2018,182966,31.58
-wind-ons_6,2018,182967,54.0
-wind-ons_6,2018,183650,60.0
-wind-ons_6,2018,183651,60.0
-wind-ons_6,2018,183652,54.0
-wind-ons_6,2018,183653,12.0
-wind-ons_6,2018,184332,2.64
-wind-ons_6,2018,184334,42.0
-wind-ons_6,2018,184336,17.5
-wind-ons_7,2018,184337,30.0
-wind-ons_6,2018,184343,48.0
-wind-ons_6,2018,185018,13.2
-wind-ons_7,2018,185019,6.0
-wind-ons_6,2018,185027,114.0
-wind-ons_5,2018,185028,138.0
-wind-ons_7,2018,185702,54.0
-wind-ons_7,2018,185705,54.0
-wind-ons_6,2018,185710,115.5
-wind-ons_6,2018,185711,18.0
-wind-ons_6,2018,185712,48.0
-wind-ons_7,2018,186386,60.0
-wind-ons_8,2018,187068,0.92
-wind-ons_6,2018,187086,21.6
-wind-ons_7,2018,187757,54.0
-wind-ons_6,2018,187770,60.0
-wind-ons_7,2018,188436,0.6
-wind-ons_8,2018,188437,59.4
-wind-ons_8,2018,188438,29.5
-wind-ons_8,2018,189120,0.08
-wind-ons_9,2018,189121,54.0
-wind-ons_7,2018,189122,34.5
-wind-ons_6,2018,189126,150.0
-wind-ons_7,2018,189130,60.0
-wind-ons_7,2018,189131,60.0
-wind-ons_7,2018,189804,0.07
-wind-ons_7,2018,189806,48.0
-wind-ons_7,2018,189807,60.0
-wind-ons_6,2018,189811,153.9
-wind-ons_7,2018,189814,48.0
-wind-ons_7,2018,189815,120.0
-wind-ons_7,2018,190488,0.07
-wind-ons_8,2018,190489,4.8
-wind-ons_7,2018,190492,144.0
-wind-ons_6,2018,190495,144.0
-wind-ons_6,2018,190498,59.7
-wind-ons_8,2018,191174,60.0
-wind-ons_7,2018,191175,54.0
-wind-ons_6,2018,191179,8.0
-wind-ons_6,2018,191180,160.84
-wind-ons_8,2018,191858,22.0
-wind-ons_7,2018,191859,54.0
-wind-ons_8,2018,192543,58.5
-wind-ons_8,2018,193228,22.5
-wind-ons_7,2018,196645,37.5
-wind-ons_7,2018,197329,34.3
-wind-ons_8,2018,197330,60.0
-wind-ons_9,2018,197332,54.0
-wind-ons_7,2018,198013,32.52
-wind-ons_7,2018,198014,79.92
-wind-ons_8,2018,198016,72.5
-wind-ons_8,2018,198698,78.0
-wind-ons_8,2018,198699,101.2
-wind-ons_8,2018,199381,17.64
-wind-ons_8,2018,199382,75.0
-wind-ons_9,2018,199384,124.7
-wind-ons_8,2018,200066,73.5
-wind-ons_9,2018,200068,60.0
-wind-ons_8,2018,200750,69.53
-wind-ons_9,2018,200752,64.2
-wind-ons_9,2018,201435,98.8
-wind-ons_9,2018,202120,121.9
-wind-ons_9,2018,202804,120.6
-wind-ons_9,2018,203488,120.0
-wind-ons_1,2019,45361,60.0
-wind-ons_1,2019,46046,60.0
-wind-ons_1,2019,46728,44.0
-wind-ons_2,2019,49465,42.0
-wind-ons_6,2019,52969,9.7
-wind-ons_6,2019,54250,60.0
-wind-ons_4,2019,54935,168.6
-wind-ons_5,2019,55021,101.4
-wind-ons_6,2019,55705,104.2
-wind-ons_6,2019,55748,31.5
-wind-ons_5,2019,56302,156.0
-wind-ons_6,2019,56985,38.9
-wind-ons_5,2019,56986,186.0
-wind-ons_6,2019,57116,40.5
-wind-ons_7,2019,57316,42.0
-wind-ons_6,2019,57669,122.8
-wind-ons_6,2019,57670,66.0
-wind-ons_6,2019,57831,0.93
-wind-ons_5,2019,58352,7.2
-wind-ons_5,2019,58354,96.0
-wind-ons_7,2019,58484,40.5
-wind-ons_6,2019,58515,0.93
-wind-ons_7,2019,58516,1.3
-wind-ons_6,2019,59036,78.0
-wind-ons_4,2019,59037,149.4
-wind-ons_6,2019,59199,0.93
-wind-ons_7,2019,59201,2.0
-wind-ons_4,2019,59720,35.0
-wind-ons_3,2019,59721,13.0
-wind-ons_6,2019,59883,0.93
-wind-ons_7,2019,59885,2.0
-wind-ons_7,2019,59886,2.0
-wind-ons_7,2019,59888,12.0
-wind-ons_6,2019,59889,30.0
-wind-ons_7,2019,59899,126.0
-wind-ons_7,2019,60052,4.5
-wind-ons_1,2019,60405,138.0
-wind-ons_6,2019,60567,0.93
-wind-ons_7,2019,60569,2.0
-wind-ons_7,2019,60572,11.8
-wind-ons_7,2019,60583,79.5
-wind-ons_8,2019,60738,57.0
-wind-ons_2,2019,61088,136.3
-wind-ons_2,2019,61089,124.2
-wind-ons_6,2019,61252,1.0
-wind-ons_6,2019,61253,1.9
-wind-ons_7,2019,61255,4.2
-wind-ons_7,2019,61256,11.2
-wind-ons_8,2019,61423,66.0
-wind-ons_7,2019,61935,0.93
-wind-ons_7,2019,61936,0.97
-wind-ons_6,2019,61937,1.7
-wind-ons_7,2019,61940,10.4
-wind-ons_7,2019,61941,2.0
-wind-ons_7,2019,62105,4.5
-wind-ons_3,2019,62454,8.0
-wind-ons_2,2019,62455,94.0
-wind-ons_3,2019,62456,176.9
-wind-ons_7,2019,62619,0.93
-wind-ons_8,2019,62620,0.97
-wind-ons_7,2019,62621,1.7
-wind-ons_8,2019,62622,2.0
-wind-ons_7,2019,62626,91.1
-wind-ons_1,2019,63138,9.9
-wind-ons_3,2019,63139,76.5
-wind-ons_8,2019,63303,0.93
-wind-ons_8,2019,63304,0.97
-wind-ons_7,2019,63305,1.7
-wind-ons_6,2019,63306,2.0
-wind-ons_6,2019,63307,3.0
-wind-ons_7,2019,63308,9.2
-wind-ons_8,2019,63310,85.5
-wind-ons_8,2019,63311,100.7
-wind-ons_7,2019,63313,150.0
-wind-ons_6,2019,63987,0.9
-wind-ons_6,2019,63989,1.7
-wind-ons_7,2019,63992,8.4
-wind-ons_7,2019,63993,25.0
-wind-ons_8,2019,63995,100.5
-wind-ons_2,2019,64506,10.0
-wind-ons_8,2019,64596,135.0
-wind-ons_6,2019,64671,0.9
-wind-ons_6,2019,64672,0.97
-wind-ons_6,2019,64673,1.7
-wind-ons_6,2019,64674,2.0
-wind-ons_7,2019,64675,2.1
-wind-ons_6,2019,64676,8.0
-wind-ons_7,2019,64678,51.0
-wind-ons_6,2019,64679,100.5
-wind-ons_6,2019,64680,90.0
-wind-ons_1,2019,65189,3.3
-wind-ons_1,2019,65190,25.0
-wind-ons_2,2019,65191,42.9
-wind-ons_6,2019,65355,0.9
-wind-ons_7,2019,65357,1.7
-wind-ons_6,2019,65358,2.0
-wind-ons_6,2019,65360,5.2
-wind-ons_5,2019,65363,100.0
-wind-ons_1,2019,65874,12.0
-wind-ons_2,2019,65875,49.3
-wind-ons_6,2019,66039,0.9
-wind-ons_7,2019,66040,0.97
-wind-ons_6,2019,66046,49.5
-wind-ons_6,2019,66186,24.0
-wind-ons_8,2019,66692,48.0
-wind-ons_7,2019,66723,0.9
-wind-ons_5,2019,66729,19.98
-wind-ons_5,2019,66866,5.2
-wind-ons_8,2019,67376,48.0
-wind-ons_6,2019,67407,0.9
-wind-ons_7,2019,68061,31.5
-wind-ons_6,2019,68091,0.7
-wind-ons_7,2019,68744,49.5
-wind-ons_6,2019,68775,0.7
-wind-ons_7,2019,68776,0.97
-wind-ons_6,2019,68777,1.5
-wind-ons_4,2019,68778,2.0
-wind-ons_8,2019,68902,40.5
-wind-ons_8,2019,69428,49.5
-wind-ons_8,2019,69429,84.0
-wind-ons_6,2019,69460,0.97
-wind-ons_5,2019,69461,1.5
-wind-ons_4,2019,69462,2.0
-wind-ons_6,2019,69582,20.0
-wind-ons_6,2019,69583,66.0
-wind-ons_9,2019,69587,52.0
+wind-ons_9,2018,135740,10.0
+wind-ons_4,2018,135875,0.7
+wind-ons_5,2018,135876,0.7
+wind-ons_9,2018,136424,10.0
+wind-ons_10,2018,136428,114.0
+wind-ons_10,2018,136429,3.0
+wind-ons_10,2018,136444,54.0
+wind-ons_9,2018,136447,25.5
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+wind-ons_10,2018,137129,66.0
+wind-ons_10,2018,137130,94.5
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+wind-ons_10,2018,137813,101.2
+wind-ons_10,2018,137814,25.5
+wind-ons_9,2018,138476,10.0
+wind-ons_10,2018,139159,10.0
+wind-ons_10,2018,139160,49.5
+wind-ons_10,2018,139161,10.0
+wind-ons_9,2018,139843,10.0
+wind-ons_9,2018,140527,8.8
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+wind-ons_10,2018,142582,79.5
+wind-ons_10,2018,142585,80.0
+wind-ons_10,2018,142599,123.0
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+wind-ons_10,2018,143265,10.0
+wind-ons_9,2018,143280,98.9
+wind-ons_10,2018,143287,40.5
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+wind-ons_3,2018,145212,3.4
+wind-ons_8,2018,145291,90.0
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+wind-ons_10,2018,145304,80.0
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+wind-ons_3,2018,146567,30.75
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+wind-ons_5,2018,147251,22.4
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+wind-ons_7,2018,147335,100.0
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+wind-ons_3,2018,147935,20.5
+wind-ons_2,2018,147936,20.0
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+wind-ons_1,2018,148621,9.6
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+wind-ons_9,2018,149409,0.7
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+wind-ons_8,2039,89977,78.0
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+wind-ons_9,2039,90571,99.0
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diff --git a/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv b/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv
index 931368d99..2b86ec4bb 100644
--- a/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv
+++ b/inputs/capacity_exogenous/exog_cap_wind-ons_open.csv
@@ -1,15683 +1,15387 @@
 *tech,year,sc_point_gid,capacity
-wind-ons_2,2010,43995,180.0
-wind-ons_3,2010,44678,44.0
-wind-ons_1,2010,46730,48.6
-wind-ons_2,2010,47413,7.2
-wind-ons_1,2010,48097,95.0
-wind-ons_3,2010,49465,176.9
-wind-ons_6,2010,52200,156.0
-wind-ons_7,2010,52969,9.7
-wind-ons_6,2010,53567,78.0
-wind-ons_6,2010,55619,204.0
-wind-ons_7,2010,55705,104.2
-wind-ons_6,2010,56986,98.9
-wind-ons_6,2010,57670,136.3
-wind-ons_8,2010,58001,42.0
-wind-ons_6,2010,58354,199.0
-wind-ons_7,2010,58527,48.0
-wind-ons_8,2010,59202,0.97
-wind-ons_7,2010,59203,1.3
-wind-ons_8,2010,59204,2.0
-wind-ons_8,2010,59211,102.0
-wind-ons_9,2010,59215,72.0
-wind-ons_8,2010,59370,57.0
-wind-ons_3,2010,59721,94.0
-wind-ons_8,2010,59886,0.97
-wind-ons_8,2010,59887,1.3
-wind-ons_7,2010,59888,2.0
-wind-ons_7,2010,59889,3.8
-wind-ons_8,2010,59890,11.2
-wind-ons_8,2010,59899,133.5
-wind-ons_9,2010,60055,66.0
-wind-ons_2,2010,60405,122.8
-wind-ons_7,2010,60570,0.97
-wind-ons_7,2010,60571,1.3
-wind-ons_7,2010,60572,2.0
-wind-ons_7,2010,60573,3.0
-wind-ons_7,2010,60574,10.4
-wind-ons_8,2010,60575,25.0
-wind-ons_8,2010,60737,4.5
-wind-ons_3,2010,61089,149.4
-wind-ons_7,2010,61254,0.93
-wind-ons_8,2010,61255,1.2
-wind-ons_7,2010,61256,2.0
-wind-ons_8,2010,61261,100.5
-wind-ons_5,2010,61773,262.2
-wind-ons_8,2010,61906,2.6
-wind-ons_7,2010,61938,0.93
-wind-ons_7,2010,61940,2.0
-wind-ons_8,2010,61942,9.8
-wind-ons_8,2010,61943,20.0
-wind-ons_8,2010,61944,51.0
-wind-ons_8,2010,61945,100.5
-wind-ons_8,2010,61946,103.5
-wind-ons_8,2010,62544,30.0
-wind-ons_9,2010,62590,4.5
-wind-ons_7,2010,62622,0.93
-wind-ons_7,2010,62624,2.0
-wind-ons_8,2010,62625,2.1
-wind-ons_8,2010,62626,9.2
-wind-ons_8,2010,62629,100.0
-wind-ons_9,2010,62790,4.5
-wind-ons_3,2010,63140,9.9
-wind-ons_3,2010,63141,100.5
-wind-ons_9,2010,63274,11.9
-wind-ons_6,2010,63306,0.93
-wind-ons_8,2010,63308,1.9
-wind-ons_8,2010,63309,2.1
-wind-ons_9,2010,63310,8.4
-wind-ons_9,2010,63311,19.98
-wind-ons_8,2010,63312,49.5
-wind-ons_7,2010,63314,100.7
-wind-ons_5,2010,63825,102.9
-wind-ons_9,2010,63912,101.4
-wind-ons_9,2010,63958,18.0
-wind-ons_7,2010,63990,0.93
-wind-ons_7,2010,63991,0.97
-wind-ons_8,2010,63992,1.7
-wind-ons_8,2010,63993,2.1
-wind-ons_8,2010,63994,8.0
-wind-ons_6,2010,63997,99.0
-wind-ons_1,2010,64508,25.0
-wind-ons_3,2010,64510,41.0
-wind-ons_8,2010,64596,135.0
-wind-ons_7,2010,64674,0.93
-wind-ons_7,2010,64675,0.97
-wind-ons_7,2010,64676,1.7
-wind-ons_7,2010,64677,2.0
-wind-ons_8,2010,64678,5.2
-wind-ons_2,2010,65192,81.0
-wind-ons_8,2010,65326,31.5
-wind-ons_7,2010,65358,0.93
-wind-ons_8,2010,65359,0.97
-wind-ons_7,2010,65360,1.7
-wind-ons_8,2010,65361,2.0
-wind-ons_6,2010,65363,15.6
-wind-ons_8,2010,66042,0.93
-wind-ons_7,2010,66043,0.97
-wind-ons_7,2010,66044,1.7
-wind-ons_7,2010,66045,2.0
-wind-ons_6,2010,66046,4.2
-wind-ons_4,2010,66560,102.3
-wind-ons_8,2010,66694,40.5
-wind-ons_7,2010,66726,0.93
-wind-ons_8,2010,66727,0.97
-wind-ons_7,2010,66728,1.7
-wind-ons_5,2010,67244,125.4
-wind-ons_8,2010,67378,40.5
-wind-ons_8,2010,67379,118.5
-wind-ons_8,2010,67410,0.9
-wind-ons_8,2010,67411,0.97
-wind-ons_6,2010,67412,1.7
-wind-ons_7,2010,67538,54.0
-wind-ons_5,2010,67927,72.0
-wind-ons_6,2010,67928,163.3
-wind-ons_6,2010,67929,180.0
-wind-ons_8,2010,68094,0.9
-wind-ons_9,2010,68095,0.97
-wind-ons_6,2010,68222,30.55
-wind-ons_3,2010,68610,6.6
-wind-ons_8,2010,68746,48.0
-wind-ons_8,2010,68747,149.0
-wind-ons_6,2010,68778,0.9
-wind-ons_6,2010,68906,16.5
-wind-ons_4,2010,68919,5.2
-wind-ons_1,2010,69294,9.9
-wind-ons_2,2010,69295,100.8
-wind-ons_8,2010,69430,48.0
-wind-ons_8,2010,69432,180.0
-wind-ons_5,2010,69462,0.9
-wind-ons_5,2010,69591,81.0
-wind-ons_1,2010,69979,102.9
-wind-ons_9,2010,70115,115.5
-wind-ons_5,2010,70146,0.7
-wind-ons_2,2010,70662,25.2
-wind-ons_8,2010,70798,49.5
-wind-ons_8,2010,70799,120.0
-wind-ons_7,2010,70830,0.7
-wind-ons_5,2010,70959,45.5
-wind-ons_6,2010,71345,1.7
-wind-ons_8,2010,71482,49.5
-wind-ons_8,2010,71483,148.5
-wind-ons_7,2010,71643,16.5
-wind-ons_6,2010,71644,106.5
-wind-ons_8,2010,72166,50.6
-wind-ons_7,2010,72198,0.4
-wind-ons_5,2010,72328,97.5
-wind-ons_8,2010,72850,61.5
-wind-ons_5,2010,73696,45.5
-wind-ons_5,2010,74380,30.0
-wind-ons_5,2010,74764,5.0
-wind-ons_3,2010,74765,98.7
-wind-ons_4,2010,75065,100.5
-wind-ons_4,2010,75448,9.9
-wind-ons_3,2010,75449,100.7
-wind-ons_7,2010,76334,11.0
-wind-ons_6,2010,76433,68.5
-wind-ons_4,2010,76816,24.6
-wind-ons_6,2010,76857,8.4
-wind-ons_7,2010,77117,30.55
-wind-ons_3,2010,77500,76.5
-wind-ons_9,2010,77705,145.2
-wind-ons_6,2010,77801,6.6
-wind-ons_4,2010,77802,100.5
-wind-ons_3,2010,78183,8.0
-wind-ons_3,2010,78225,21.0
-wind-ons_8,2010,78326,51.0
-wind-ons_8,2010,78504,24.0
-wind-ons_9,2010,78957,17.0
-wind-ons_5,2010,79170,14.5
-wind-ons_4,2010,79551,10.0
-wind-ons_8,2010,79641,25.0
-wind-ons_9,2010,79755,44.55
-wind-ons_9,2010,79756,67.7
-wind-ons_4,2010,79854,34.5
-wind-ons_3,2010,80235,41.0
-wind-ons_8,2010,80325,39.0
-wind-ons_9,2010,80439,42.57
-wind-ons_5,2010,80538,11.5
-wind-ons_8,2010,81009,99.0
-wind-ons_9,2010,81123,30.0
-wind-ons_9,2010,81124,54.0
-wind-ons_4,2010,81223,87.5
-wind-ons_3,2010,81602,8.3
-wind-ons_6,2010,81693,99.0
-wind-ons_7,2010,81908,112.0
-wind-ons_2,2010,82328,21.0
-wind-ons_9,2010,82491,13.5
-wind-ons_9,2010,82523,1.8
-wind-ons_9,2010,82524,59.0
-wind-ons_8,2010,82591,37.5
-wind-ons_9,2010,82610,1.5
-wind-ons_9,2010,83175,12.9
-wind-ons_10,2010,83295,1.8
-wind-ons_8,2010,83827,0.97
-wind-ons_7,2010,83828,1.3
-wind-ons_9,2010,83859,11.88
-wind-ons_3,2010,84337,3.3
-wind-ons_6,2010,84428,1.3
-wind-ons_8,2010,84510,0.04
-wind-ons_7,2010,84511,0.97
-wind-ons_7,2010,84512,1.3
-wind-ons_6,2010,84513,2.0
-wind-ons_9,2010,84543,3.6
-wind-ons_6,2010,85112,12.14
-wind-ons_8,2010,85194,0.02
-wind-ons_8,2010,85195,0.93
-wind-ons_8,2010,85196,1.3
-wind-ons_7,2010,85197,2.0
-wind-ons_7,2010,85198,2.1
-wind-ons_5,2010,85796,19.5
-wind-ons_9,2010,85879,0.93
-wind-ons_8,2010,85880,1.3
-wind-ons_8,2010,85881,1.7
-wind-ons_8,2010,85882,2.0
-wind-ons_8,2010,85945,69.0
-wind-ons_7,2010,86012,20.0
-wind-ons_4,2010,86389,24.0
-wind-ons_5,2010,86480,25.0
-wind-ons_9,2010,86563,0.93
-wind-ons_9,2010,86565,1.7
-wind-ons_8,2010,86566,2.0
-wind-ons_8,2010,86567,4.2
-wind-ons_8,2010,86568,13.5
-wind-ons_7,2010,86569,43.06
-wind-ons_5,2010,87164,29.4
-wind-ons_9,2010,87247,0.93
-wind-ons_8,2010,87248,1.0
-wind-ons_8,2010,87249,1.7
-wind-ons_8,2010,87250,2.0
-wind-ons_7,2010,87251,4.2
-wind-ons_7,2010,87254,98.8
-wind-ons_8,2010,87313,51.2
-wind-ons_8,2010,87931,0.93
-wind-ons_8,2010,87932,1.0
-wind-ons_7,2010,87933,1.5
-wind-ons_7,2010,87934,2.0
-wind-ons_5,2010,87936,12.0
-wind-ons_6,2010,87937,32.0
-wind-ons_5,2010,88532,99.0
-wind-ons_9,2010,88615,0.9
-wind-ons_8,2010,88616,0.97
-wind-ons_8,2010,88617,1.5
-wind-ons_8,2010,88618,2.0
-wind-ons_7,2010,88620,11.8
-wind-ons_6,2010,88622,91.1
-wind-ons_6,2010,89216,99.0
-wind-ons_9,2010,89299,0.9
-wind-ons_9,2010,89300,0.97
-wind-ons_9,2010,89301,1.3
-wind-ons_9,2010,89302,2.0
-wind-ons_9,2010,89303,3.9
-wind-ons_8,2010,89304,11.6
-wind-ons_7,2010,89306,85.5
-wind-ons_3,2010,89851,64.5
-wind-ons_8,2010,89953,40.5
-wind-ons_4,2010,90534,18.9
-wind-ons_9,2010,90584,142.2
-wind-ons_9,2010,90637,50.4
-wind-ons_6,2010,90672,1.2
-wind-ons_7,2010,90674,35.0
-wind-ons_7,2010,90675,80.0
-wind-ons_7,2010,90789,9.0
-wind-ons_5,2010,90790,64.5
-wind-ons_8,2010,91321,25.0
-wind-ons_9,2010,91357,2.3
-wind-ons_7,2010,91359,80.0
-wind-ons_9,2010,91951,4.9
-wind-ons_10,2010,92005,1.3
-wind-ons_9,2010,92040,0.9
-wind-ons_8,2010,92041,2.0
-wind-ons_8,2010,92042,29.7
-wind-ons_7,2010,92043,80.0
-wind-ons_10,2010,92635,16.8
-wind-ons_9,2010,92689,1.3
-wind-ons_8,2010,92724,0.7
-wind-ons_8,2010,92726,21.0
-wind-ons_7,2010,92842,34.5
-wind-ons_8,2010,92843,75.0
-wind-ons_9,2010,93319,24.8
-wind-ons_8,2010,93408,0.7
-wind-ons_8,2010,93410,20.0
-wind-ons_8,2010,93411,78.0
-wind-ons_9,2010,93526,26.0
-wind-ons_8,2010,93528,102.0
-wind-ons_8,2010,93543,66.0
-wind-ons_2,2010,93953,10.5
-wind-ons_9,2010,94003,28.8
-wind-ons_8,2010,94092,0.6
-wind-ons_7,2010,94095,75.0
-wind-ons_7,2010,94211,52.5
-wind-ons_9,2010,94228,60.0
-wind-ons_9,2010,94687,41.4
-wind-ons_9,2010,94776,0.5
-wind-ons_7,2010,94778,10.5
-wind-ons_7,2010,94779,66.0
-wind-ons_4,2010,94896,80.0
-wind-ons_7,2010,95371,64.08
-wind-ons_9,2010,95462,10.5
-wind-ons_9,2010,95464,100.0
-wind-ons_9,2010,95465,150.0
-wind-ons_9,2010,95487,2.5
-wind-ons_8,2010,95539,1.8
-wind-ons_7,2010,96055,99.0
-wind-ons_7,2010,96223,1.8
-wind-ons_6,2010,96263,30.0
-wind-ons_8,2010,96832,99.0
-wind-ons_5,2010,96947,24.0
-wind-ons_8,2010,97516,99.0
-wind-ons_9,2010,98199,49.6
-wind-ons_8,2010,98200,98.2
-wind-ons_8,2010,98201,123.0
-wind-ons_7,2010,98205,66.0
-wind-ons_9,2010,98324,7.5
-wind-ons_7,2010,98790,1.8
-wind-ons_8,2010,98884,98.0
-wind-ons_8,2010,98886,160.5
-wind-ons_7,2010,99000,37.8
-wind-ons_7,2010,99474,15.92
-wind-ons_8,2010,99684,29.4
-wind-ons_7,2010,99686,100.5
-wind-ons_8,2010,100254,150.0
-wind-ons_8,2010,100297,106.0
-wind-ons_7,2010,100328,1.8
-wind-ons_9,2010,100368,15.0
-wind-ons_7,2010,100369,62.5
-wind-ons_8,2010,100935,43.5
-wind-ons_8,2010,100938,150.0
-wind-ons_8,2010,100939,199.7
-wind-ons_7,2010,100942,234.0
-wind-ons_8,2010,101619,42.0
-wind-ons_8,2010,101622,150.0
-wind-ons_9,2010,101644,76.2
-wind-ons_8,2010,101665,100.0
-wind-ons_10,2010,102210,42.0
-wind-ons_8,2010,102306,150.0
-wind-ons_8,2010,102307,199.0
-wind-ons_8,2010,102328,51.0
-wind-ons_9,2010,102894,48.86
-wind-ons_8,2010,102990,150.0
-wind-ons_8,2010,102991,174.8
-wind-ons_8,2010,103012,10.5
-wind-ons_9,2010,103637,59.4
-wind-ons_8,2010,103696,2.5
-wind-ons_8,2010,104380,1.5
-wind-ons_6,2010,104433,1.8
-wind-ons_8,2010,105040,78.0
-wind-ons_7,2010,105041,104.3
-wind-ons_8,2010,105065,150.0
-wind-ons_8,2010,105086,199.7
-wind-ons_9,2010,105689,54.27
-wind-ons_9,2010,105721,0.9
-wind-ons_9,2010,105724,76.5
-wind-ons_8,2010,105770,130.5
-wind-ons_9,2010,106329,199.5
-wind-ons_9,2010,106407,16.2
-wind-ons_9,2010,107013,201.0
-wind-ons_9,2010,107056,0.7
-wind-ons_8,2010,107091,15.0
-wind-ons_8,2010,107092,62.2
-wind-ons_8,2010,107117,100.5
-wind-ons_8,2010,107775,14.7
-wind-ons_5,2010,107873,66.0
-wind-ons_8,2010,108381,221.0
-wind-ons_10,2010,108424,0.7
-wind-ons_8,2010,108485,80.0
-wind-ons_5,2010,108558,100.0
-wind-ons_5,2010,108559,164.0
-wind-ons_8,2010,109169,72.0
-wind-ons_8,2010,109748,22.5
-wind-ons_8,2010,109793,8.91
-wind-ons_8,2010,109853,53.0
-wind-ons_9,2010,109875,200.0
-wind-ons_10,2010,110477,8.91
-wind-ons_8,2010,110537,49.5
-wind-ons_8,2010,111116,29.7
-wind-ons_10,2010,111161,8.91
-wind-ons_8,2010,111221,8.4
-wind-ons_8,2010,111905,1.5
-wind-ons_3,2010,112433,18.9
-wind-ons_8,2010,112484,60.0
-wind-ons_8,2010,112591,217.5
-wind-ons_8,2010,113275,210.9
-wind-ons_7,2010,113295,99.0
-wind-ons_8,2010,113852,79.5
-wind-ons_8,2010,113958,106.0
-wind-ons_9,2010,114589,148.5
-wind-ons_8,2010,114642,99.0
-wind-ons_9,2010,116588,151.8
-wind-ons_10,2010,118009,112.2
-wind-ons_5,2010,118084,101.3
-wind-ons_9,2010,118693,150.0
-wind-ons_7,2010,118746,1.7
-wind-ons_8,2010,119323,1.5
-wind-ons_9,2010,120061,105.0
-wind-ons_7,2010,120116,213.8
-wind-ons_9,2010,120691,4.0
-wind-ons_10,2010,121429,100.8
-wind-ons_7,2010,121483,100.5
-wind-ons_5,2010,122691,203.5
-wind-ons_9,2010,122797,100.5
-wind-ons_9,2010,124849,99.0
-wind-ons_6,2010,125479,75.0
-wind-ons_10,2010,126217,96.0
-wind-ons_7,2010,127626,5.0
-wind-ons_9,2010,128898,1.5
-wind-ons_9,2010,128899,162.0
-wind-ons_7,2010,129679,146.0
-wind-ons_2,2010,130161,7.4
-wind-ons_7,2010,130423,27.0
-wind-ons_2,2010,130846,18.0
-wind-ons_9,2010,131005,50.0
-wind-ons_4,2010,131106,0.7
-wind-ons_6,2010,131107,0.7
-wind-ons_3,2010,131790,0.7
-wind-ons_4,2010,132213,15.5
-wind-ons_3,2010,132215,38.0
-wind-ons_6,2010,132311,204.0
-wind-ons_9,2010,132373,44.0
-wind-ons_7,2010,132415,56.7
-wind-ons_7,2010,133099,50.4
-wind-ons_6,2010,133580,7.5
-wind-ons_7,2010,133582,24.0
-wind-ons_7,2010,133783,50.4
-wind-ons_7,2010,134265,18.4
-wind-ons_7,2010,134266,28.7
-wind-ons_8,2010,134269,28.5
-wind-ons_7,2010,134950,30.75
-wind-ons_10,2010,135730,0.6
-wind-ons_5,2010,136317,22.0
-wind-ons_5,2010,136318,36.0
-wind-ons_9,2010,136478,99.0
-wind-ons_5,2010,137000,17.0
-wind-ons_3,2010,137007,54.0
-wind-ons_9,2010,137098,20.0
-wind-ons_9,2010,137162,98.9
-wind-ons_8,2010,137782,90.0
-wind-ons_3,2010,138366,3.5
-wind-ons_4,2010,139050,4.5
-wind-ons_2,2010,139051,13.1
-wind-ons_2,2010,139052,20.5
-wind-ons_9,2010,139214,94.5
-wind-ons_8,2010,139215,151.4
-wind-ons_5,2010,139733,2.4
-wind-ons_5,2010,139734,6.5
-wind-ons_8,2010,140582,74.3
-wind-ons_2,2010,142469,5.4
-wind-ons_3,2010,143153,7.3
-wind-ons_4,2010,143154,16.2
-wind-ons_5,2010,143155,23.1
-wind-ons_5,2010,143157,36.0
-wind-ons_6,2010,143318,40.5
-wind-ons_4,2010,143836,3.0
-wind-ons_3,2010,143837,8.7
-wind-ons_2,2010,143841,60.0
-wind-ons_5,2010,143845,19.8
-wind-ons_9,2010,143937,0.7
-wind-ons_3,2010,144519,1.6
-wind-ons_3,2010,144521,9.1
-wind-ons_3,2010,144523,29.0
-wind-ons_3,2010,144524,46.9
-wind-ons_2,2010,144525,67.0
-wind-ons_2,2010,144526,60.0
-wind-ons_6,2010,144686,25.5
-wind-ons_2,2010,145202,0.1
-wind-ons_2,2010,145204,4.2
-wind-ons_3,2010,145205,11.6
-wind-ons_3,2010,145206,20.0
-wind-ons_4,2010,145207,30.75
-wind-ons_3,2010,145214,150.0
-wind-ons_10,2010,145305,30.0
-wind-ons_6,2010,145370,25.5
-wind-ons_4,2010,145891,33.6
-wind-ons_7,2010,145892,59.6
-wind-ons_3,2010,145896,135.0
-wind-ons_10,2010,145989,80.0
-wind-ons_6,2010,146054,25.5
-wind-ons_2,2010,146570,1.5
-wind-ons_2,2010,146574,22.2
-wind-ons_6,2010,146576,24.3
-wind-ons_2,2010,146581,150.0
-wind-ons_10,2010,146673,90.0
-wind-ons_5,2010,146738,25.5
-wind-ons_3,2010,147255,3.0
-wind-ons_2,2010,147257,17.3
-wind-ons_2,2010,147258,25.3
-wind-ons_3,2010,147259,43.4
-wind-ons_6,2010,147260,3.0
-wind-ons_6,2010,147422,18.9
-wind-ons_6,2010,147423,123.0
-wind-ons_3,2010,147939,3.4
-wind-ons_3,2010,147940,9.6
-wind-ons_2,2010,147948,72.0
-wind-ons_8,2010,148107,120.0
-wind-ons_3,2010,148622,1.8
-wind-ons_3,2010,148623,3.7
-wind-ons_3,2010,148624,11.7
-wind-ons_3,2010,148625,20.5
-wind-ons_3,2010,148626,31.0
-wind-ons_4,2010,148628,76.9
-wind-ons_5,2010,149306,2.1
-wind-ons_5,2010,149307,6.5
-wind-ons_4,2010,149308,13.6
-wind-ons_4,2010,149309,21.7
-wind-ons_3,2010,149310,2.9
-wind-ons_3,2010,149311,50.0
-wind-ons_3,2010,149991,8.0
-wind-ons_3,2010,149992,15.3
-wind-ons_3,2010,149993,22.4
-wind-ons_3,2010,149994,42.6
-wind-ons_7,2010,150159,106.5
-wind-ons_3,2010,150675,8.3
-wind-ons_2,2010,150676,16.5
-wind-ons_3,2010,150678,45.0
-wind-ons_5,2010,150843,101.2
-wind-ons_10,2010,152144,0.6
-wind-ons_6,2010,154946,8.8
-wind-ons_7,2010,155630,3.0
-wind-ons_7,2010,155631,79.8
-wind-ons_6,2010,155633,160.84
-wind-ons_9,2010,156248,100.0
-wind-ons_7,2010,156316,114.0
-wind-ons_2,2010,156884,63.0
-wind-ons_6,2010,156999,59.4
-wind-ons_6,2010,157000,114.0
-wind-ons_6,2010,157002,186.0
-wind-ons_7,2010,157682,0.16
-wind-ons_6,2010,157683,58.8
-wind-ons_6,2010,157684,112.5
-wind-ons_6,2010,157687,209.0
-wind-ons_7,2010,158366,0.07
-wind-ons_7,2010,158367,57.0
-wind-ons_7,2010,158368,112.5
-wind-ons_6,2010,159056,249.0
-wind-ons_6,2010,159735,49.5
-wind-ons_7,2010,160420,101.2
-wind-ons_6,2010,160421,150.0
-wind-ons_7,2010,161103,37.5
-wind-ons_6,2010,161106,180.0
-wind-ons_6,2010,161108,241.0
-wind-ons_6,2010,161787,34.3
-wind-ons_6,2010,161789,150.0
-wind-ons_6,2010,161792,232.5
-wind-ons_6,2010,161793,222.0
-wind-ons_7,2010,162471,32.52
-wind-ons_6,2010,162472,98.8
-wind-ons_6,2010,162473,150.0
-wind-ons_7,2010,162475,200.0
-wind-ons_6,2010,162476,231.5
-wind-ons_6,2010,162477,77.0
-wind-ons_6,2010,163159,200.0
-wind-ons_6,2010,163840,91.58
-wind-ons_7,2010,164524,91.2
-wind-ons_7,2010,164526,170.2
-wind-ons_7,2010,165208,91.2
-wind-ons_7,2010,165892,89.5
-wind-ons_6,2010,165894,169.5
-wind-ons_6,2010,166575,17.64
-wind-ons_7,2010,166576,84.0
-wind-ons_6,2010,166577,149.5
-wind-ons_6,2010,166580,223.5
-wind-ons_6,2010,167259,10.0
-wind-ons_7,2010,167260,82.5
-wind-ons_6,2010,167262,167.7
-wind-ons_6,2010,167265,246.0
-wind-ons_7,2010,167943,10.0
-wind-ons_6,2010,167944,82.0
-wind-ons_6,2010,167948,214.5
-wind-ons_6,2010,167949,32.0
-wind-ons_6,2010,168627,10.0
-wind-ons_6,2010,168631,197.0
-wind-ons_6,2010,168632,213.0
-wind-ons_6,2010,169311,10.0
-wind-ons_6,2010,169312,80.5
-wind-ons_6,2010,169995,10.0
-wind-ons_5,2010,169996,80.0
-wind-ons_5,2010,169997,144.0
-wind-ons_5,2010,169998,162.3
-wind-ons_6,2010,170679,10.0
-wind-ons_6,2010,171363,10.0
-wind-ons_6,2010,171364,79.92
-wind-ons_6,2010,172049,130.5
-wind-ons_6,2010,173416,79.5
-wind-ons_6,2010,173417,126.5
-wind-ons_6,2010,174784,78.0
-wind-ons_6,2010,174785,124.7
-wind-ons_6,2010,175468,75.0
-wind-ons_6,2010,175469,124.2
-wind-ons_6,2010,176152,73.5
-wind-ons_5,2010,176835,2.64
-wind-ons_6,2010,176836,69.53
-wind-ons_6,2010,176837,121.9
-wind-ons_6,2010,176838,153.9
-wind-ons_6,2010,177521,120.6
-wind-ons_5,2010,177522,152.0
-wind-ons_6,2010,178203,0.92
-wind-ons_6,2010,178205,120.0
-wind-ons_6,2010,178887,0.6
-wind-ons_6,2010,178888,63.0
-wind-ons_6,2010,178889,120.0
-wind-ons_6,2010,178891,201.6
-wind-ons_6,2010,179574,180.0
-wind-ons_7,2010,180255,0.08
-wind-ons_6,2010,180257,120.0
-wind-ons_6,2010,180261,55.2
-wind-ons_7,2010,180939,0.07
-wind-ons_6,2010,180945,174.0
-wind-ons_7,2010,181629,54.0
-wind-ons_2,2011,43995,180.0
-wind-ons_3,2011,44678,44.0
-wind-ons_1,2011,46730,48.6
-wind-ons_2,2011,47413,7.2
-wind-ons_1,2011,48097,95.0
-wind-ons_3,2011,49465,176.9
-wind-ons_6,2011,52200,156.0
-wind-ons_7,2011,52969,9.7
-wind-ons_6,2011,53567,78.0
-wind-ons_6,2011,55619,204.0
-wind-ons_7,2011,55705,104.2
-wind-ons_6,2011,56986,98.9
-wind-ons_6,2011,57670,136.3
-wind-ons_8,2011,58001,42.0
-wind-ons_6,2011,58354,199.0
-wind-ons_7,2011,58527,48.0
-wind-ons_8,2011,59202,0.97
-wind-ons_7,2011,59203,1.3
-wind-ons_8,2011,59204,2.0
-wind-ons_8,2011,59211,102.0
-wind-ons_9,2011,59215,72.0
-wind-ons_8,2011,59370,57.0
-wind-ons_3,2011,59721,94.0
-wind-ons_8,2011,59886,0.97
-wind-ons_8,2011,59887,1.3
-wind-ons_7,2011,59888,2.0
-wind-ons_7,2011,59889,3.8
-wind-ons_8,2011,59890,11.2
-wind-ons_8,2011,59899,133.5
-wind-ons_9,2011,60055,66.0
-wind-ons_2,2011,60405,122.8
-wind-ons_7,2011,60570,0.97
-wind-ons_7,2011,60571,1.3
-wind-ons_7,2011,60572,2.0
-wind-ons_7,2011,60573,3.0
-wind-ons_7,2011,60574,10.4
-wind-ons_8,2011,60575,25.0
-wind-ons_8,2011,60737,4.5
-wind-ons_3,2011,61089,149.4
-wind-ons_7,2011,61254,0.93
-wind-ons_8,2011,61255,1.2
-wind-ons_7,2011,61256,2.0
-wind-ons_8,2011,61261,100.5
-wind-ons_5,2011,61773,262.2
-wind-ons_8,2011,61906,2.6
-wind-ons_7,2011,61938,0.93
-wind-ons_7,2011,61940,2.0
-wind-ons_8,2011,61942,9.8
-wind-ons_8,2011,61943,20.0
-wind-ons_8,2011,61944,51.0
-wind-ons_8,2011,61945,100.5
-wind-ons_8,2011,61946,103.5
-wind-ons_8,2011,62544,30.0
-wind-ons_9,2011,62590,4.5
-wind-ons_7,2011,62622,0.93
-wind-ons_7,2011,62624,2.0
-wind-ons_8,2011,62625,2.1
-wind-ons_8,2011,62626,9.2
-wind-ons_8,2011,62629,100.0
-wind-ons_9,2011,62790,4.5
-wind-ons_3,2011,63140,9.9
-wind-ons_3,2011,63141,100.5
-wind-ons_9,2011,63274,11.9
-wind-ons_6,2011,63306,0.93
-wind-ons_8,2011,63308,1.9
-wind-ons_8,2011,63309,2.1
-wind-ons_9,2011,63310,8.4
-wind-ons_9,2011,63311,19.98
-wind-ons_8,2011,63312,49.5
-wind-ons_7,2011,63314,100.7
-wind-ons_5,2011,63825,102.9
-wind-ons_9,2011,63912,101.4
-wind-ons_9,2011,63958,18.0
-wind-ons_7,2011,63990,0.93
-wind-ons_7,2011,63991,0.97
-wind-ons_8,2011,63992,1.7
-wind-ons_8,2011,63993,2.1
-wind-ons_8,2011,63994,8.0
-wind-ons_6,2011,63997,99.0
-wind-ons_1,2011,64508,25.0
-wind-ons_3,2011,64510,41.0
-wind-ons_8,2011,64596,135.0
-wind-ons_7,2011,64674,0.93
-wind-ons_7,2011,64675,0.97
-wind-ons_7,2011,64676,1.7
-wind-ons_7,2011,64677,2.0
-wind-ons_8,2011,64678,5.2
-wind-ons_2,2011,65192,81.0
-wind-ons_8,2011,65326,31.5
-wind-ons_7,2011,65358,0.93
-wind-ons_8,2011,65359,0.97
-wind-ons_7,2011,65360,1.7
-wind-ons_8,2011,65361,2.0
-wind-ons_6,2011,65363,15.6
-wind-ons_8,2011,66042,0.93
-wind-ons_7,2011,66043,0.97
-wind-ons_7,2011,66044,1.7
-wind-ons_7,2011,66045,2.0
-wind-ons_6,2011,66046,4.2
-wind-ons_4,2011,66560,102.3
-wind-ons_8,2011,66694,40.5
-wind-ons_7,2011,66726,0.93
-wind-ons_8,2011,66727,0.97
-wind-ons_7,2011,66728,1.7
-wind-ons_5,2011,67244,125.4
-wind-ons_8,2011,67378,40.5
-wind-ons_8,2011,67379,118.5
-wind-ons_8,2011,67410,0.9
-wind-ons_8,2011,67411,0.97
-wind-ons_6,2011,67412,1.7
-wind-ons_7,2011,67538,54.0
-wind-ons_5,2011,67927,72.0
-wind-ons_6,2011,67928,163.3
-wind-ons_6,2011,67929,180.0
-wind-ons_8,2011,68094,0.9
-wind-ons_9,2011,68095,0.97
-wind-ons_6,2011,68222,30.55
-wind-ons_3,2011,68610,6.6
-wind-ons_8,2011,68746,48.0
-wind-ons_8,2011,68747,149.0
-wind-ons_6,2011,68778,0.9
-wind-ons_6,2011,68906,16.5
-wind-ons_4,2011,68919,5.2
-wind-ons_1,2011,69294,9.9
-wind-ons_2,2011,69295,100.8
-wind-ons_8,2011,69430,48.0
-wind-ons_8,2011,69432,180.0
-wind-ons_5,2011,69462,0.9
-wind-ons_5,2011,69591,81.0
-wind-ons_1,2011,69979,102.9
-wind-ons_9,2011,70115,115.5
-wind-ons_5,2011,70146,0.7
-wind-ons_2,2011,70662,25.2
-wind-ons_8,2011,70798,49.5
-wind-ons_8,2011,70799,120.0
-wind-ons_7,2011,70830,0.7
-wind-ons_5,2011,70959,45.5
-wind-ons_6,2011,71345,1.7
-wind-ons_8,2011,71482,49.5
-wind-ons_8,2011,71483,148.5
-wind-ons_7,2011,71643,16.5
-wind-ons_6,2011,71644,106.5
-wind-ons_8,2011,72166,50.6
-wind-ons_7,2011,72198,0.4
-wind-ons_5,2011,72328,97.5
-wind-ons_8,2011,72850,61.5
-wind-ons_5,2011,73696,45.5
-wind-ons_5,2011,74380,30.0
-wind-ons_5,2011,74764,5.0
-wind-ons_3,2011,74765,98.7
-wind-ons_4,2011,75065,100.5
-wind-ons_4,2011,75448,9.9
-wind-ons_3,2011,75449,100.7
-wind-ons_7,2011,76334,11.0
-wind-ons_6,2011,76433,68.5
-wind-ons_4,2011,76816,24.6
-wind-ons_6,2011,76857,8.4
-wind-ons_7,2011,77117,30.55
-wind-ons_3,2011,77500,76.5
-wind-ons_9,2011,77705,145.2
-wind-ons_6,2011,77801,6.6
-wind-ons_4,2011,77802,100.5
-wind-ons_3,2011,78183,8.0
-wind-ons_3,2011,78225,21.0
-wind-ons_8,2011,78326,51.0
-wind-ons_8,2011,78504,24.0
-wind-ons_9,2011,78957,17.0
-wind-ons_5,2011,79170,14.5
-wind-ons_4,2011,79551,10.0
-wind-ons_8,2011,79641,25.0
-wind-ons_9,2011,79755,44.55
-wind-ons_9,2011,79756,67.7
-wind-ons_4,2011,79854,34.5
-wind-ons_3,2011,80235,41.0
-wind-ons_8,2011,80325,39.0
-wind-ons_9,2011,80439,42.57
-wind-ons_5,2011,80538,11.5
-wind-ons_8,2011,81009,99.0
-wind-ons_9,2011,81123,30.0
-wind-ons_9,2011,81124,54.0
-wind-ons_4,2011,81223,87.5
-wind-ons_3,2011,81602,8.3
-wind-ons_6,2011,81693,99.0
-wind-ons_7,2011,81908,112.0
-wind-ons_2,2011,82328,21.0
-wind-ons_9,2011,82491,13.5
-wind-ons_9,2011,82523,1.8
-wind-ons_9,2011,82524,59.0
-wind-ons_8,2011,82591,37.5
-wind-ons_9,2011,82610,1.5
-wind-ons_9,2011,83175,12.9
-wind-ons_10,2011,83295,1.8
-wind-ons_8,2011,83827,0.97
-wind-ons_7,2011,83828,1.3
-wind-ons_9,2011,83859,11.88
-wind-ons_3,2011,84337,3.3
-wind-ons_6,2011,84428,1.3
-wind-ons_8,2011,84510,0.04
-wind-ons_7,2011,84511,0.97
-wind-ons_7,2011,84512,1.3
-wind-ons_6,2011,84513,2.0
-wind-ons_9,2011,84543,3.6
-wind-ons_6,2011,85112,12.14
-wind-ons_8,2011,85194,0.02
-wind-ons_8,2011,85195,0.93
-wind-ons_8,2011,85196,1.3
-wind-ons_7,2011,85197,2.0
-wind-ons_7,2011,85198,2.1
-wind-ons_5,2011,85796,19.5
-wind-ons_9,2011,85879,0.93
-wind-ons_8,2011,85880,1.3
-wind-ons_8,2011,85881,1.7
-wind-ons_8,2011,85882,2.0
-wind-ons_8,2011,85945,69.0
-wind-ons_7,2011,86012,20.0
-wind-ons_4,2011,86389,24.0
-wind-ons_5,2011,86480,25.0
-wind-ons_9,2011,86563,0.93
-wind-ons_9,2011,86565,1.7
-wind-ons_8,2011,86566,2.0
-wind-ons_8,2011,86567,4.2
-wind-ons_8,2011,86568,13.5
-wind-ons_7,2011,86569,43.06
-wind-ons_5,2011,87164,29.4
-wind-ons_9,2011,87247,0.93
-wind-ons_8,2011,87248,1.0
-wind-ons_8,2011,87249,1.7
-wind-ons_8,2011,87250,2.0
-wind-ons_7,2011,87251,4.2
-wind-ons_7,2011,87254,98.8
-wind-ons_8,2011,87313,51.2
-wind-ons_8,2011,87931,0.93
-wind-ons_8,2011,87932,1.0
-wind-ons_7,2011,87933,1.5
-wind-ons_7,2011,87934,2.0
-wind-ons_5,2011,87936,12.0
-wind-ons_6,2011,87937,32.0
-wind-ons_5,2011,88532,99.0
-wind-ons_9,2011,88615,0.9
-wind-ons_8,2011,88616,0.97
-wind-ons_8,2011,88617,1.5
-wind-ons_8,2011,88618,2.0
-wind-ons_7,2011,88620,11.8
-wind-ons_6,2011,88622,91.1
-wind-ons_6,2011,89216,99.0
-wind-ons_9,2011,89299,0.9
-wind-ons_9,2011,89300,0.97
-wind-ons_9,2011,89301,1.3
-wind-ons_9,2011,89302,2.0
-wind-ons_9,2011,89303,3.9
-wind-ons_8,2011,89304,11.6
-wind-ons_7,2011,89306,85.5
-wind-ons_3,2011,89851,64.5
-wind-ons_8,2011,89953,40.5
-wind-ons_4,2011,90534,18.9
-wind-ons_9,2011,90584,142.2
-wind-ons_9,2011,90637,50.4
-wind-ons_6,2011,90672,1.2
-wind-ons_7,2011,90674,35.0
-wind-ons_7,2011,90675,80.0
-wind-ons_7,2011,90789,9.0
-wind-ons_5,2011,90790,64.5
-wind-ons_8,2011,91321,25.0
-wind-ons_9,2011,91357,2.3
-wind-ons_7,2011,91359,80.0
-wind-ons_9,2011,91951,4.9
-wind-ons_10,2011,92005,1.3
-wind-ons_9,2011,92040,0.9
-wind-ons_8,2011,92041,2.0
-wind-ons_8,2011,92042,29.7
-wind-ons_7,2011,92043,80.0
-wind-ons_10,2011,92635,16.8
-wind-ons_9,2011,92689,1.3
-wind-ons_8,2011,92724,0.7
-wind-ons_8,2011,92726,21.0
-wind-ons_7,2011,92842,34.5
-wind-ons_8,2011,92843,75.0
-wind-ons_9,2011,93319,24.8
-wind-ons_8,2011,93408,0.7
-wind-ons_8,2011,93410,20.0
-wind-ons_8,2011,93411,78.0
-wind-ons_9,2011,93526,26.0
-wind-ons_8,2011,93528,102.0
-wind-ons_8,2011,93543,66.0
-wind-ons_2,2011,93953,10.5
-wind-ons_9,2011,94003,28.8
-wind-ons_8,2011,94092,0.6
-wind-ons_7,2011,94095,75.0
-wind-ons_7,2011,94211,52.5
-wind-ons_9,2011,94228,60.0
-wind-ons_9,2011,94687,41.4
-wind-ons_9,2011,94776,0.5
-wind-ons_7,2011,94778,10.5
-wind-ons_7,2011,94779,66.0
-wind-ons_4,2011,94896,80.0
-wind-ons_7,2011,95371,64.08
-wind-ons_9,2011,95462,10.5
-wind-ons_9,2011,95464,100.0
-wind-ons_9,2011,95465,150.0
-wind-ons_9,2011,95487,2.5
-wind-ons_8,2011,95539,1.8
-wind-ons_7,2011,96055,99.0
-wind-ons_7,2011,96223,1.8
-wind-ons_6,2011,96263,30.0
-wind-ons_8,2011,96832,99.0
-wind-ons_5,2011,96947,24.0
-wind-ons_8,2011,97516,99.0
-wind-ons_9,2011,98199,49.6
-wind-ons_8,2011,98200,98.2
-wind-ons_8,2011,98201,123.0
-wind-ons_7,2011,98205,66.0
-wind-ons_9,2011,98324,7.5
-wind-ons_7,2011,98790,1.8
-wind-ons_8,2011,98884,98.0
-wind-ons_8,2011,98886,160.5
-wind-ons_7,2011,99000,37.8
-wind-ons_7,2011,99474,15.92
-wind-ons_8,2011,99684,29.4
-wind-ons_7,2011,99686,100.5
-wind-ons_8,2011,100254,150.0
-wind-ons_8,2011,100297,106.0
-wind-ons_7,2011,100328,1.8
-wind-ons_9,2011,100368,15.0
-wind-ons_7,2011,100369,62.5
-wind-ons_8,2011,100935,43.5
-wind-ons_8,2011,100938,150.0
-wind-ons_8,2011,100939,199.7
-wind-ons_7,2011,100942,234.0
-wind-ons_8,2011,101619,42.0
-wind-ons_8,2011,101622,150.0
-wind-ons_9,2011,101644,76.2
-wind-ons_8,2011,101665,100.0
-wind-ons_10,2011,102210,42.0
-wind-ons_8,2011,102306,150.0
-wind-ons_8,2011,102307,199.0
-wind-ons_8,2011,102328,51.0
-wind-ons_9,2011,102894,48.86
-wind-ons_8,2011,102990,150.0
-wind-ons_8,2011,102991,174.8
-wind-ons_8,2011,103012,10.5
-wind-ons_9,2011,103637,59.4
-wind-ons_8,2011,103696,2.5
-wind-ons_8,2011,104380,1.5
-wind-ons_6,2011,104433,1.8
-wind-ons_8,2011,105040,78.0
-wind-ons_7,2011,105041,104.3
-wind-ons_8,2011,105065,150.0
-wind-ons_8,2011,105086,199.7
-wind-ons_9,2011,105689,54.27
-wind-ons_9,2011,105721,0.9
-wind-ons_9,2011,105724,76.5
-wind-ons_8,2011,105770,130.5
-wind-ons_9,2011,106329,199.5
-wind-ons_9,2011,106407,16.2
-wind-ons_9,2011,107013,201.0
-wind-ons_9,2011,107056,0.7
-wind-ons_8,2011,107091,15.0
-wind-ons_8,2011,107092,62.2
-wind-ons_8,2011,107117,100.5
-wind-ons_8,2011,107775,14.7
-wind-ons_5,2011,107873,66.0
-wind-ons_8,2011,108381,221.0
-wind-ons_10,2011,108424,0.7
-wind-ons_8,2011,108485,80.0
-wind-ons_5,2011,108558,100.0
-wind-ons_5,2011,108559,164.0
-wind-ons_8,2011,109169,72.0
-wind-ons_8,2011,109748,22.5
-wind-ons_8,2011,109793,8.91
-wind-ons_8,2011,109853,53.0
-wind-ons_9,2011,109875,200.0
-wind-ons_10,2011,110477,8.91
-wind-ons_8,2011,110537,49.5
-wind-ons_8,2011,111116,29.7
-wind-ons_10,2011,111161,8.91
-wind-ons_8,2011,111221,8.4
-wind-ons_8,2011,111905,1.5
-wind-ons_3,2011,112433,18.9
-wind-ons_8,2011,112484,60.0
-wind-ons_8,2011,112591,217.5
-wind-ons_8,2011,113275,210.9
-wind-ons_7,2011,113295,99.0
-wind-ons_8,2011,113852,79.5
-wind-ons_8,2011,113958,106.0
-wind-ons_9,2011,114589,148.5
-wind-ons_8,2011,114642,99.0
-wind-ons_9,2011,116588,151.8
-wind-ons_10,2011,118009,112.2
-wind-ons_5,2011,118084,101.3
-wind-ons_9,2011,118693,150.0
-wind-ons_7,2011,118746,1.7
-wind-ons_8,2011,119323,1.5
-wind-ons_9,2011,120061,105.0
-wind-ons_7,2011,120116,213.8
-wind-ons_9,2011,120691,4.0
-wind-ons_10,2011,121429,100.8
-wind-ons_7,2011,121483,100.5
-wind-ons_5,2011,122691,203.5
-wind-ons_9,2011,122797,100.5
-wind-ons_9,2011,124849,99.0
-wind-ons_6,2011,125479,75.0
-wind-ons_10,2011,126217,96.0
-wind-ons_7,2011,127626,5.0
-wind-ons_9,2011,128898,1.5
-wind-ons_9,2011,128899,162.0
-wind-ons_7,2011,129679,146.0
-wind-ons_2,2011,130161,7.4
-wind-ons_7,2011,130423,27.0
-wind-ons_2,2011,130846,18.0
-wind-ons_9,2011,131005,50.0
-wind-ons_4,2011,131106,0.7
-wind-ons_6,2011,131107,0.7
-wind-ons_3,2011,131790,0.7
-wind-ons_4,2011,132213,15.5
-wind-ons_3,2011,132215,38.0
-wind-ons_6,2011,132311,204.0
-wind-ons_9,2011,132373,44.0
-wind-ons_7,2011,132415,56.7
-wind-ons_7,2011,133099,50.4
-wind-ons_6,2011,133580,7.5
-wind-ons_7,2011,133582,24.0
-wind-ons_7,2011,133783,50.4
-wind-ons_7,2011,134265,18.4
-wind-ons_7,2011,134266,28.7
-wind-ons_8,2011,134269,28.5
-wind-ons_7,2011,134950,30.75
-wind-ons_10,2011,135730,0.6
-wind-ons_5,2011,136317,22.0
-wind-ons_5,2011,136318,36.0
-wind-ons_9,2011,136478,99.0
-wind-ons_5,2011,137000,17.0
-wind-ons_3,2011,137007,54.0
-wind-ons_9,2011,137098,20.0
-wind-ons_9,2011,137162,98.9
-wind-ons_8,2011,137782,90.0
-wind-ons_3,2011,138366,3.5
-wind-ons_4,2011,139050,4.5
-wind-ons_2,2011,139051,13.1
-wind-ons_2,2011,139052,20.5
-wind-ons_9,2011,139214,94.5
-wind-ons_8,2011,139215,151.4
-wind-ons_5,2011,139733,2.4
-wind-ons_5,2011,139734,6.5
-wind-ons_8,2011,140582,74.3
-wind-ons_2,2011,142469,5.4
-wind-ons_3,2011,143153,7.3
-wind-ons_4,2011,143154,16.2
-wind-ons_5,2011,143155,23.1
-wind-ons_5,2011,143157,36.0
-wind-ons_6,2011,143318,40.5
-wind-ons_4,2011,143836,3.0
-wind-ons_3,2011,143837,8.7
-wind-ons_2,2011,143841,60.0
-wind-ons_5,2011,143845,19.8
-wind-ons_9,2011,143937,0.7
-wind-ons_3,2011,144519,1.6
-wind-ons_3,2011,144521,9.1
-wind-ons_3,2011,144523,29.0
-wind-ons_3,2011,144524,46.9
-wind-ons_2,2011,144525,67.0
-wind-ons_2,2011,144526,60.0
-wind-ons_6,2011,144686,25.5
-wind-ons_2,2011,145202,0.1
-wind-ons_2,2011,145204,4.2
-wind-ons_3,2011,145205,11.6
-wind-ons_3,2011,145206,20.0
-wind-ons_4,2011,145207,30.75
-wind-ons_3,2011,145214,150.0
-wind-ons_10,2011,145305,30.0
-wind-ons_6,2011,145370,25.5
-wind-ons_4,2011,145891,33.6
-wind-ons_7,2011,145892,59.6
-wind-ons_3,2011,145896,135.0
-wind-ons_10,2011,145989,80.0
-wind-ons_6,2011,146054,25.5
-wind-ons_2,2011,146570,1.5
-wind-ons_2,2011,146574,22.2
-wind-ons_6,2011,146576,24.3
-wind-ons_2,2011,146581,150.0
-wind-ons_10,2011,146673,90.0
-wind-ons_5,2011,146738,25.5
-wind-ons_3,2011,147255,3.0
-wind-ons_2,2011,147257,17.3
-wind-ons_2,2011,147258,25.3
-wind-ons_3,2011,147259,43.4
-wind-ons_6,2011,147260,3.0
-wind-ons_6,2011,147422,18.9
-wind-ons_6,2011,147423,123.0
-wind-ons_3,2011,147939,3.4
-wind-ons_3,2011,147940,9.6
-wind-ons_2,2011,147948,72.0
-wind-ons_8,2011,148107,120.0
-wind-ons_3,2011,148622,1.8
-wind-ons_3,2011,148623,3.7
-wind-ons_3,2011,148624,11.7
-wind-ons_3,2011,148625,20.5
-wind-ons_3,2011,148626,31.0
-wind-ons_4,2011,148628,76.9
-wind-ons_5,2011,149306,2.1
-wind-ons_5,2011,149307,6.5
-wind-ons_4,2011,149308,13.6
-wind-ons_4,2011,149309,21.7
-wind-ons_3,2011,149310,2.9
-wind-ons_3,2011,149311,50.0
-wind-ons_3,2011,149991,8.0
-wind-ons_3,2011,149992,15.3
-wind-ons_3,2011,149993,22.4
-wind-ons_3,2011,149994,42.6
-wind-ons_7,2011,150159,106.5
-wind-ons_3,2011,150675,8.3
-wind-ons_2,2011,150676,16.5
-wind-ons_3,2011,150678,45.0
-wind-ons_5,2011,150843,101.2
-wind-ons_10,2011,152144,0.6
-wind-ons_6,2011,154946,8.8
-wind-ons_7,2011,155630,3.0
-wind-ons_7,2011,155631,79.8
-wind-ons_6,2011,155633,160.84
-wind-ons_9,2011,156248,100.0
-wind-ons_7,2011,156316,114.0
-wind-ons_2,2011,156884,63.0
-wind-ons_6,2011,156999,59.4
-wind-ons_6,2011,157000,114.0
-wind-ons_6,2011,157002,186.0
-wind-ons_7,2011,157682,0.16
-wind-ons_6,2011,157683,58.8
-wind-ons_6,2011,157684,112.5
-wind-ons_6,2011,157687,209.0
-wind-ons_7,2011,158366,0.07
-wind-ons_7,2011,158367,57.0
-wind-ons_7,2011,158368,112.5
-wind-ons_6,2011,159056,249.0
-wind-ons_6,2011,159735,49.5
-wind-ons_7,2011,160420,101.2
-wind-ons_6,2011,160421,150.0
-wind-ons_7,2011,161103,37.5
-wind-ons_6,2011,161106,180.0
-wind-ons_6,2011,161108,241.0
-wind-ons_6,2011,161787,34.3
-wind-ons_6,2011,161789,150.0
-wind-ons_6,2011,161792,232.5
-wind-ons_6,2011,161793,222.0
-wind-ons_7,2011,162471,32.52
-wind-ons_6,2011,162472,98.8
-wind-ons_6,2011,162473,150.0
-wind-ons_7,2011,162475,200.0
-wind-ons_6,2011,162476,231.5
-wind-ons_6,2011,162477,77.0
-wind-ons_6,2011,163159,200.0
-wind-ons_6,2011,163840,91.58
-wind-ons_7,2011,164524,91.2
-wind-ons_7,2011,164526,170.2
-wind-ons_7,2011,165208,91.2
-wind-ons_7,2011,165892,89.5
-wind-ons_6,2011,165894,169.5
-wind-ons_6,2011,166575,17.64
-wind-ons_7,2011,166576,84.0
-wind-ons_6,2011,166577,149.5
-wind-ons_6,2011,166580,223.5
-wind-ons_6,2011,167259,10.0
-wind-ons_7,2011,167260,82.5
-wind-ons_6,2011,167262,167.7
-wind-ons_6,2011,167265,246.0
-wind-ons_7,2011,167943,10.0
-wind-ons_6,2011,167944,82.0
-wind-ons_6,2011,167948,214.5
-wind-ons_6,2011,167949,32.0
-wind-ons_6,2011,168627,10.0
-wind-ons_6,2011,168631,197.0
-wind-ons_6,2011,168632,213.0
-wind-ons_6,2011,169311,10.0
-wind-ons_6,2011,169312,80.5
-wind-ons_6,2011,169995,10.0
-wind-ons_5,2011,169996,80.0
-wind-ons_5,2011,169997,144.0
-wind-ons_5,2011,169998,162.3
-wind-ons_6,2011,170679,10.0
-wind-ons_6,2011,171363,10.0
-wind-ons_6,2011,171364,79.92
-wind-ons_6,2011,172049,130.5
-wind-ons_6,2011,173416,79.5
-wind-ons_6,2011,173417,126.5
-wind-ons_6,2011,174784,78.0
-wind-ons_6,2011,174785,124.7
-wind-ons_6,2011,175468,75.0
-wind-ons_6,2011,175469,124.2
-wind-ons_6,2011,176152,73.5
-wind-ons_5,2011,176835,2.64
-wind-ons_6,2011,176836,69.53
-wind-ons_6,2011,176837,121.9
-wind-ons_6,2011,176838,153.9
-wind-ons_6,2011,177521,120.6
-wind-ons_5,2011,177522,152.0
-wind-ons_6,2011,178203,0.92
-wind-ons_6,2011,178205,120.0
-wind-ons_6,2011,178887,0.6
-wind-ons_6,2011,178888,63.0
-wind-ons_6,2011,178889,120.0
-wind-ons_6,2011,178891,201.6
-wind-ons_6,2011,179574,180.0
-wind-ons_7,2011,180255,0.08
-wind-ons_6,2011,180257,120.0
-wind-ons_6,2011,180261,55.2
-wind-ons_7,2011,180939,0.07
-wind-ons_6,2011,180945,174.0
-wind-ons_7,2011,181629,54.0
-wind-ons_2,2012,43995,180.0
-wind-ons_3,2012,44678,44.0
-wind-ons_1,2012,46730,48.6
-wind-ons_2,2012,47413,7.2
-wind-ons_1,2012,48097,95.0
-wind-ons_3,2012,49465,176.9
-wind-ons_6,2012,52200,156.0
-wind-ons_7,2012,52969,9.7
-wind-ons_6,2012,53567,78.0
-wind-ons_6,2012,55619,204.0
-wind-ons_7,2012,55705,104.2
-wind-ons_6,2012,56986,98.9
-wind-ons_6,2012,57670,136.3
-wind-ons_8,2012,58001,42.0
-wind-ons_6,2012,58354,199.0
-wind-ons_7,2012,58527,48.0
-wind-ons_8,2012,59202,0.97
-wind-ons_7,2012,59203,1.3
-wind-ons_8,2012,59204,2.0
-wind-ons_8,2012,59211,102.0
-wind-ons_9,2012,59215,72.0
-wind-ons_8,2012,59370,57.0
-wind-ons_3,2012,59721,94.0
-wind-ons_8,2012,59886,0.97
-wind-ons_8,2012,59887,1.3
-wind-ons_7,2012,59888,2.0
-wind-ons_7,2012,59889,3.8
-wind-ons_8,2012,59890,11.2
-wind-ons_8,2012,59899,133.5
-wind-ons_9,2012,60055,66.0
-wind-ons_2,2012,60405,122.8
-wind-ons_7,2012,60570,0.97
-wind-ons_7,2012,60571,1.3
-wind-ons_7,2012,60572,2.0
-wind-ons_7,2012,60573,3.0
-wind-ons_7,2012,60574,10.4
-wind-ons_8,2012,60575,25.0
-wind-ons_8,2012,60737,4.5
-wind-ons_3,2012,61089,149.4
-wind-ons_7,2012,61254,0.93
-wind-ons_8,2012,61255,1.2
-wind-ons_7,2012,61256,2.0
-wind-ons_8,2012,61261,100.5
-wind-ons_5,2012,61773,262.2
-wind-ons_8,2012,61906,2.6
-wind-ons_7,2012,61938,0.93
-wind-ons_7,2012,61940,2.0
-wind-ons_8,2012,61942,9.8
-wind-ons_8,2012,61943,20.0
-wind-ons_8,2012,61944,51.0
-wind-ons_8,2012,61945,100.5
-wind-ons_8,2012,61946,103.5
-wind-ons_8,2012,62544,30.0
-wind-ons_9,2012,62590,4.5
-wind-ons_7,2012,62622,0.93
-wind-ons_7,2012,62624,2.0
-wind-ons_8,2012,62625,2.1
-wind-ons_8,2012,62626,9.2
-wind-ons_8,2012,62629,100.0
-wind-ons_9,2012,62790,4.5
-wind-ons_3,2012,63140,9.9
-wind-ons_3,2012,63141,100.5
-wind-ons_9,2012,63274,11.9
-wind-ons_6,2012,63306,0.93
-wind-ons_8,2012,63308,1.9
-wind-ons_8,2012,63309,2.1
-wind-ons_9,2012,63310,8.4
-wind-ons_9,2012,63311,19.98
-wind-ons_8,2012,63312,49.5
-wind-ons_7,2012,63314,100.7
-wind-ons_5,2012,63825,102.9
-wind-ons_9,2012,63912,101.4
-wind-ons_9,2012,63958,18.0
-wind-ons_7,2012,63990,0.93
-wind-ons_7,2012,63991,0.97
-wind-ons_8,2012,63992,1.7
-wind-ons_8,2012,63993,2.1
-wind-ons_8,2012,63994,8.0
-wind-ons_6,2012,63997,99.0
-wind-ons_1,2012,64508,25.0
-wind-ons_3,2012,64510,41.0
-wind-ons_8,2012,64596,135.0
-wind-ons_7,2012,64674,0.93
-wind-ons_7,2012,64675,0.97
-wind-ons_7,2012,64676,1.7
-wind-ons_7,2012,64677,2.0
-wind-ons_8,2012,64678,5.2
-wind-ons_2,2012,65192,81.0
-wind-ons_8,2012,65326,31.5
-wind-ons_7,2012,65358,0.93
-wind-ons_8,2012,65359,0.97
-wind-ons_7,2012,65360,1.7
-wind-ons_8,2012,65361,2.0
-wind-ons_6,2012,65363,15.6
-wind-ons_8,2012,66042,0.93
-wind-ons_7,2012,66043,0.97
-wind-ons_7,2012,66044,1.7
-wind-ons_7,2012,66045,2.0
-wind-ons_6,2012,66046,4.2
-wind-ons_4,2012,66560,102.3
-wind-ons_8,2012,66694,40.5
-wind-ons_7,2012,66726,0.93
-wind-ons_8,2012,66727,0.97
-wind-ons_7,2012,66728,1.7
-wind-ons_5,2012,67244,125.4
-wind-ons_8,2012,67378,40.5
-wind-ons_8,2012,67379,118.5
-wind-ons_8,2012,67410,0.9
-wind-ons_8,2012,67411,0.97
-wind-ons_6,2012,67412,1.7
-wind-ons_7,2012,67538,54.0
-wind-ons_5,2012,67927,72.0
-wind-ons_6,2012,67928,163.3
-wind-ons_6,2012,67929,180.0
-wind-ons_8,2012,68094,0.9
-wind-ons_9,2012,68095,0.97
-wind-ons_6,2012,68222,30.55
-wind-ons_3,2012,68610,6.6
-wind-ons_8,2012,68746,48.0
-wind-ons_8,2012,68747,149.0
-wind-ons_6,2012,68778,0.9
-wind-ons_6,2012,68906,16.5
-wind-ons_4,2012,68919,5.2
-wind-ons_1,2012,69294,9.9
-wind-ons_2,2012,69295,100.8
-wind-ons_8,2012,69430,48.0
-wind-ons_8,2012,69432,180.0
-wind-ons_5,2012,69462,0.9
-wind-ons_5,2012,69591,81.0
-wind-ons_1,2012,69979,102.9
-wind-ons_9,2012,70115,115.5
-wind-ons_5,2012,70146,0.7
-wind-ons_2,2012,70662,25.2
-wind-ons_8,2012,70798,49.5
-wind-ons_8,2012,70799,120.0
-wind-ons_7,2012,70830,0.7
-wind-ons_5,2012,70959,45.5
-wind-ons_6,2012,71345,1.7
-wind-ons_8,2012,71482,49.5
-wind-ons_8,2012,71483,148.5
-wind-ons_7,2012,71643,16.5
-wind-ons_6,2012,71644,106.5
-wind-ons_8,2012,72166,50.6
-wind-ons_7,2012,72198,0.4
-wind-ons_5,2012,72328,97.5
-wind-ons_8,2012,72850,61.5
-wind-ons_5,2012,73696,45.5
-wind-ons_5,2012,74380,30.0
-wind-ons_5,2012,74764,5.0
-wind-ons_3,2012,74765,98.7
-wind-ons_4,2012,75065,100.5
-wind-ons_4,2012,75448,9.9
-wind-ons_3,2012,75449,100.7
-wind-ons_7,2012,76334,11.0
-wind-ons_6,2012,76433,68.5
-wind-ons_4,2012,76816,24.6
-wind-ons_6,2012,76857,8.4
-wind-ons_7,2012,77117,30.55
-wind-ons_3,2012,77500,76.5
-wind-ons_9,2012,77705,145.2
-wind-ons_6,2012,77801,6.6
-wind-ons_4,2012,77802,100.5
-wind-ons_3,2012,78183,8.0
-wind-ons_3,2012,78225,21.0
-wind-ons_8,2012,78326,51.0
-wind-ons_8,2012,78504,24.0
-wind-ons_9,2012,78957,17.0
-wind-ons_5,2012,79170,14.5
-wind-ons_4,2012,79551,10.0
-wind-ons_8,2012,79641,25.0
-wind-ons_9,2012,79755,44.55
-wind-ons_9,2012,79756,67.7
-wind-ons_4,2012,79854,34.5
-wind-ons_3,2012,80235,41.0
-wind-ons_8,2012,80325,39.0
-wind-ons_9,2012,80439,42.57
-wind-ons_5,2012,80538,11.5
-wind-ons_8,2012,81009,99.0
-wind-ons_9,2012,81123,30.0
-wind-ons_9,2012,81124,54.0
-wind-ons_4,2012,81223,87.5
-wind-ons_3,2012,81602,8.3
-wind-ons_6,2012,81693,99.0
-wind-ons_7,2012,81908,112.0
-wind-ons_2,2012,82328,21.0
-wind-ons_9,2012,82491,13.5
-wind-ons_9,2012,82523,1.8
-wind-ons_9,2012,82524,59.0
-wind-ons_8,2012,82591,37.5
-wind-ons_9,2012,82610,1.5
-wind-ons_9,2012,83175,12.9
-wind-ons_10,2012,83295,1.8
-wind-ons_8,2012,83827,0.97
-wind-ons_7,2012,83828,1.3
-wind-ons_9,2012,83859,11.88
-wind-ons_3,2012,84337,3.3
-wind-ons_6,2012,84428,1.3
-wind-ons_8,2012,84510,0.04
-wind-ons_7,2012,84511,0.97
-wind-ons_7,2012,84512,1.3
-wind-ons_6,2012,84513,2.0
-wind-ons_9,2012,84543,3.6
-wind-ons_6,2012,85112,12.14
-wind-ons_8,2012,85194,0.02
-wind-ons_8,2012,85195,0.93
-wind-ons_8,2012,85196,1.3
-wind-ons_7,2012,85197,2.0
-wind-ons_7,2012,85198,2.1
-wind-ons_5,2012,85796,19.5
-wind-ons_9,2012,85879,0.93
-wind-ons_8,2012,85880,1.3
-wind-ons_8,2012,85881,1.7
-wind-ons_8,2012,85882,2.0
-wind-ons_8,2012,85945,69.0
-wind-ons_7,2012,86012,20.0
-wind-ons_4,2012,86389,24.0
-wind-ons_5,2012,86480,25.0
-wind-ons_9,2012,86563,0.93
-wind-ons_9,2012,86565,1.7
-wind-ons_8,2012,86566,2.0
-wind-ons_8,2012,86567,4.2
-wind-ons_8,2012,86568,13.5
-wind-ons_7,2012,86569,43.06
-wind-ons_5,2012,87164,29.4
-wind-ons_9,2012,87247,0.93
-wind-ons_8,2012,87248,1.0
-wind-ons_8,2012,87249,1.7
-wind-ons_8,2012,87250,2.0
-wind-ons_7,2012,87251,4.2
-wind-ons_7,2012,87254,98.8
-wind-ons_8,2012,87313,51.2
-wind-ons_8,2012,87931,0.93
-wind-ons_8,2012,87932,1.0
-wind-ons_7,2012,87933,1.5
-wind-ons_7,2012,87934,2.0
-wind-ons_5,2012,87936,12.0
-wind-ons_6,2012,87937,32.0
-wind-ons_5,2012,88532,99.0
-wind-ons_9,2012,88615,0.9
-wind-ons_8,2012,88616,0.97
-wind-ons_8,2012,88617,1.5
-wind-ons_8,2012,88618,2.0
-wind-ons_7,2012,88620,11.8
-wind-ons_6,2012,88622,91.1
-wind-ons_6,2012,89216,99.0
-wind-ons_9,2012,89299,0.9
-wind-ons_9,2012,89300,0.97
-wind-ons_9,2012,89301,1.3
-wind-ons_9,2012,89302,2.0
-wind-ons_9,2012,89303,3.9
-wind-ons_8,2012,89304,11.6
-wind-ons_7,2012,89306,85.5
-wind-ons_3,2012,89851,64.5
-wind-ons_8,2012,89953,40.5
-wind-ons_4,2012,90534,18.9
-wind-ons_9,2012,90584,142.2
-wind-ons_9,2012,90637,50.4
-wind-ons_6,2012,90672,1.2
-wind-ons_7,2012,90674,35.0
-wind-ons_7,2012,90675,80.0
-wind-ons_7,2012,90789,9.0
-wind-ons_5,2012,90790,64.5
-wind-ons_8,2012,91321,25.0
-wind-ons_9,2012,91357,2.3
-wind-ons_7,2012,91359,80.0
-wind-ons_9,2012,91951,4.9
-wind-ons_10,2012,92005,1.3
-wind-ons_9,2012,92040,0.9
-wind-ons_8,2012,92041,2.0
-wind-ons_8,2012,92042,29.7
-wind-ons_7,2012,92043,80.0
-wind-ons_10,2012,92635,16.8
-wind-ons_9,2012,92689,1.3
-wind-ons_8,2012,92724,0.7
-wind-ons_8,2012,92726,21.0
-wind-ons_7,2012,92842,34.5
-wind-ons_8,2012,92843,75.0
-wind-ons_9,2012,93319,24.8
-wind-ons_8,2012,93408,0.7
-wind-ons_8,2012,93410,20.0
-wind-ons_8,2012,93411,78.0
-wind-ons_9,2012,93526,26.0
-wind-ons_8,2012,93528,102.0
-wind-ons_8,2012,93543,66.0
-wind-ons_2,2012,93953,10.5
-wind-ons_9,2012,94003,28.8
-wind-ons_8,2012,94092,0.6
-wind-ons_7,2012,94095,75.0
-wind-ons_7,2012,94211,52.5
-wind-ons_9,2012,94228,60.0
-wind-ons_9,2012,94687,41.4
-wind-ons_9,2012,94776,0.5
-wind-ons_7,2012,94778,10.5
-wind-ons_7,2012,94779,66.0
-wind-ons_4,2012,94896,80.0
-wind-ons_7,2012,95371,64.08
-wind-ons_9,2012,95462,10.5
-wind-ons_9,2012,95464,100.0
-wind-ons_9,2012,95465,150.0
-wind-ons_9,2012,95487,2.5
-wind-ons_8,2012,95539,1.8
-wind-ons_7,2012,96055,99.0
-wind-ons_7,2012,96223,1.8
-wind-ons_6,2012,96263,30.0
-wind-ons_8,2012,96832,99.0
-wind-ons_5,2012,96947,24.0
-wind-ons_8,2012,97516,99.0
-wind-ons_9,2012,98199,49.6
-wind-ons_8,2012,98200,98.2
-wind-ons_8,2012,98201,123.0
-wind-ons_7,2012,98205,66.0
-wind-ons_9,2012,98324,7.5
-wind-ons_7,2012,98790,1.8
-wind-ons_8,2012,98884,98.0
-wind-ons_8,2012,98886,160.5
-wind-ons_7,2012,99000,37.8
-wind-ons_7,2012,99474,15.92
-wind-ons_8,2012,99684,29.4
-wind-ons_7,2012,99686,100.5
-wind-ons_8,2012,100254,150.0
-wind-ons_8,2012,100297,106.0
-wind-ons_7,2012,100328,1.8
-wind-ons_9,2012,100368,15.0
-wind-ons_7,2012,100369,62.5
-wind-ons_8,2012,100935,43.5
-wind-ons_8,2012,100938,150.0
-wind-ons_8,2012,100939,199.7
-wind-ons_7,2012,100942,234.0
-wind-ons_8,2012,101619,42.0
-wind-ons_8,2012,101622,150.0
-wind-ons_9,2012,101644,76.2
-wind-ons_8,2012,101665,100.0
-wind-ons_10,2012,102210,42.0
-wind-ons_8,2012,102306,150.0
-wind-ons_8,2012,102307,199.0
-wind-ons_8,2012,102328,51.0
-wind-ons_9,2012,102894,48.86
-wind-ons_8,2012,102990,150.0
-wind-ons_8,2012,102991,174.8
-wind-ons_8,2012,103012,10.5
-wind-ons_9,2012,103637,59.4
-wind-ons_8,2012,103696,2.5
-wind-ons_8,2012,104380,1.5
-wind-ons_6,2012,104433,1.8
-wind-ons_8,2012,105040,78.0
-wind-ons_7,2012,105041,104.3
-wind-ons_8,2012,105065,150.0
-wind-ons_8,2012,105086,199.7
-wind-ons_9,2012,105689,54.27
-wind-ons_9,2012,105721,0.9
-wind-ons_9,2012,105724,76.5
-wind-ons_8,2012,105770,130.5
-wind-ons_9,2012,106329,199.5
-wind-ons_9,2012,106407,16.2
-wind-ons_9,2012,107013,201.0
-wind-ons_9,2012,107056,0.7
-wind-ons_8,2012,107091,15.0
-wind-ons_8,2012,107092,62.2
-wind-ons_8,2012,107117,100.5
-wind-ons_8,2012,107775,14.7
-wind-ons_5,2012,107873,66.0
-wind-ons_8,2012,108381,221.0
-wind-ons_10,2012,108424,0.7
-wind-ons_8,2012,108485,80.0
-wind-ons_5,2012,108558,100.0
-wind-ons_5,2012,108559,164.0
-wind-ons_8,2012,109169,72.0
-wind-ons_8,2012,109748,22.5
-wind-ons_8,2012,109793,8.91
-wind-ons_8,2012,109853,53.0
-wind-ons_9,2012,109875,200.0
-wind-ons_10,2012,110477,8.91
-wind-ons_8,2012,110537,49.5
-wind-ons_8,2012,111116,29.7
-wind-ons_10,2012,111161,8.91
-wind-ons_8,2012,111221,8.4
-wind-ons_8,2012,111905,1.5
-wind-ons_3,2012,112433,18.9
-wind-ons_8,2012,112484,60.0
-wind-ons_8,2012,112591,217.5
-wind-ons_8,2012,113275,210.9
-wind-ons_7,2012,113295,99.0
-wind-ons_8,2012,113852,79.5
-wind-ons_8,2012,113958,106.0
-wind-ons_9,2012,114589,148.5
-wind-ons_8,2012,114642,99.0
-wind-ons_9,2012,116588,151.8
-wind-ons_10,2012,118009,112.2
-wind-ons_5,2012,118084,101.3
-wind-ons_9,2012,118693,150.0
-wind-ons_7,2012,118746,1.7
-wind-ons_8,2012,119323,1.5
-wind-ons_9,2012,120061,105.0
-wind-ons_7,2012,120116,213.8
-wind-ons_9,2012,120691,4.0
-wind-ons_10,2012,121429,100.8
-wind-ons_7,2012,121483,100.5
-wind-ons_5,2012,122691,203.5
-wind-ons_9,2012,122797,100.5
-wind-ons_9,2012,124849,99.0
-wind-ons_6,2012,125479,75.0
-wind-ons_10,2012,126217,96.0
-wind-ons_7,2012,127626,5.0
-wind-ons_9,2012,128898,1.5
-wind-ons_9,2012,128899,162.0
-wind-ons_7,2012,129679,146.0
-wind-ons_2,2012,130161,7.4
-wind-ons_7,2012,130423,27.0
-wind-ons_2,2012,130846,18.0
-wind-ons_9,2012,131005,50.0
-wind-ons_4,2012,131106,0.7
-wind-ons_6,2012,131107,0.7
-wind-ons_3,2012,131790,0.7
-wind-ons_4,2012,132213,15.5
-wind-ons_3,2012,132215,38.0
-wind-ons_6,2012,132311,204.0
-wind-ons_9,2012,132373,44.0
-wind-ons_7,2012,132415,56.7
-wind-ons_7,2012,133099,50.4
-wind-ons_6,2012,133580,7.5
-wind-ons_7,2012,133582,24.0
-wind-ons_7,2012,133783,50.4
-wind-ons_7,2012,134265,18.4
-wind-ons_7,2012,134266,28.7
-wind-ons_8,2012,134269,28.5
-wind-ons_7,2012,134950,30.75
-wind-ons_10,2012,135730,0.6
-wind-ons_5,2012,136317,22.0
-wind-ons_5,2012,136318,36.0
-wind-ons_9,2012,136478,99.0
-wind-ons_5,2012,137000,17.0
-wind-ons_3,2012,137007,54.0
-wind-ons_9,2012,137098,20.0
-wind-ons_9,2012,137162,98.9
-wind-ons_8,2012,137782,90.0
-wind-ons_3,2012,138366,3.5
-wind-ons_4,2012,139050,4.5
-wind-ons_2,2012,139051,13.1
-wind-ons_2,2012,139052,20.5
-wind-ons_9,2012,139214,94.5
-wind-ons_8,2012,139215,151.4
-wind-ons_5,2012,139733,2.4
-wind-ons_5,2012,139734,6.5
-wind-ons_8,2012,140582,74.3
-wind-ons_2,2012,142469,5.4
-wind-ons_3,2012,143153,7.3
-wind-ons_4,2012,143154,16.2
-wind-ons_5,2012,143155,23.1
-wind-ons_5,2012,143157,36.0
-wind-ons_6,2012,143318,40.5
-wind-ons_4,2012,143836,3.0
-wind-ons_3,2012,143837,8.7
-wind-ons_2,2012,143841,60.0
-wind-ons_5,2012,143845,19.8
-wind-ons_9,2012,143937,0.7
-wind-ons_3,2012,144519,1.6
-wind-ons_3,2012,144521,9.1
-wind-ons_3,2012,144523,29.0
-wind-ons_3,2012,144524,46.9
-wind-ons_2,2012,144525,67.0
-wind-ons_2,2012,144526,60.0
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-wind-ons_2,2012,145202,0.1
-wind-ons_2,2012,145204,4.2
-wind-ons_3,2012,145205,11.6
-wind-ons_3,2012,145206,20.0
-wind-ons_4,2012,145207,30.75
-wind-ons_3,2012,145214,150.0
-wind-ons_10,2012,145305,30.0
-wind-ons_6,2012,145370,25.5
-wind-ons_4,2012,145891,33.6
-wind-ons_7,2012,145892,59.6
-wind-ons_3,2012,145896,135.0
-wind-ons_10,2012,145989,80.0
-wind-ons_6,2012,146054,25.5
-wind-ons_2,2012,146570,1.5
-wind-ons_2,2012,146574,22.2
-wind-ons_6,2012,146576,24.3
-wind-ons_2,2012,146581,150.0
-wind-ons_10,2012,146673,90.0
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-wind-ons_3,2012,147255,3.0
-wind-ons_2,2012,147257,17.3
-wind-ons_2,2012,147258,25.3
-wind-ons_3,2012,147259,43.4
-wind-ons_6,2012,147260,3.0
-wind-ons_6,2012,147422,18.9
-wind-ons_6,2012,147423,123.0
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-wind-ons_3,2012,147940,9.6
-wind-ons_2,2012,147948,72.0
-wind-ons_8,2012,148107,120.0
-wind-ons_3,2012,148622,1.8
-wind-ons_3,2012,148623,3.7
-wind-ons_3,2012,148624,11.7
-wind-ons_3,2012,148625,20.5
-wind-ons_3,2012,148626,31.0
-wind-ons_4,2012,148628,76.9
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-wind-ons_5,2012,149307,6.5
-wind-ons_4,2012,149308,13.6
-wind-ons_4,2012,149309,21.7
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-wind-ons_3,2012,149311,50.0
-wind-ons_3,2012,149991,8.0
-wind-ons_3,2012,149992,15.3
-wind-ons_3,2012,149993,22.4
-wind-ons_3,2012,149994,42.6
-wind-ons_7,2012,150159,106.5
-wind-ons_3,2012,150675,8.3
-wind-ons_2,2012,150676,16.5
-wind-ons_3,2012,150678,45.0
-wind-ons_5,2012,150843,101.2
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-wind-ons_6,2012,154946,8.8
-wind-ons_7,2012,155630,3.0
-wind-ons_7,2012,155631,79.8
-wind-ons_6,2012,155633,160.84
-wind-ons_9,2012,156248,100.0
-wind-ons_7,2012,156316,114.0
-wind-ons_2,2012,156884,63.0
-wind-ons_6,2012,156999,59.4
-wind-ons_6,2012,157000,114.0
-wind-ons_6,2012,157002,186.0
-wind-ons_7,2012,157682,0.16
-wind-ons_6,2012,157683,58.8
-wind-ons_6,2012,157684,112.5
-wind-ons_6,2012,157687,209.0
-wind-ons_7,2012,158366,0.07
-wind-ons_7,2012,158367,57.0
-wind-ons_7,2012,158368,112.5
-wind-ons_6,2012,159056,249.0
-wind-ons_6,2012,159735,49.5
-wind-ons_7,2012,160420,101.2
-wind-ons_6,2012,160421,150.0
-wind-ons_7,2012,161103,37.5
-wind-ons_6,2012,161106,180.0
-wind-ons_6,2012,161108,241.0
-wind-ons_6,2012,161787,34.3
-wind-ons_6,2012,161789,150.0
-wind-ons_6,2012,161792,232.5
-wind-ons_6,2012,161793,222.0
-wind-ons_7,2012,162471,32.52
-wind-ons_6,2012,162472,98.8
-wind-ons_6,2012,162473,150.0
-wind-ons_7,2012,162475,200.0
-wind-ons_6,2012,162476,231.5
-wind-ons_6,2012,162477,77.0
-wind-ons_6,2012,163159,200.0
-wind-ons_6,2012,163840,91.58
-wind-ons_7,2012,164524,91.2
-wind-ons_7,2012,164526,170.2
-wind-ons_7,2012,165208,91.2
-wind-ons_7,2012,165892,89.5
-wind-ons_6,2012,165894,169.5
-wind-ons_6,2012,166575,17.64
-wind-ons_7,2012,166576,84.0
-wind-ons_6,2012,166577,149.5
-wind-ons_6,2012,166580,223.5
-wind-ons_6,2012,167259,10.0
-wind-ons_7,2012,167260,82.5
-wind-ons_6,2012,167262,167.7
-wind-ons_6,2012,167265,246.0
-wind-ons_7,2012,167943,10.0
-wind-ons_6,2012,167944,82.0
-wind-ons_6,2012,167948,214.5
-wind-ons_6,2012,167949,32.0
-wind-ons_6,2012,168627,10.0
-wind-ons_6,2012,168631,197.0
-wind-ons_6,2012,168632,213.0
-wind-ons_6,2012,169311,10.0
-wind-ons_6,2012,169312,80.5
-wind-ons_6,2012,169995,10.0
-wind-ons_5,2012,169996,80.0
-wind-ons_5,2012,169997,144.0
-wind-ons_5,2012,169998,162.3
-wind-ons_6,2012,170679,10.0
-wind-ons_6,2012,171363,10.0
-wind-ons_6,2012,171364,79.92
-wind-ons_6,2012,172049,130.5
-wind-ons_6,2012,173416,79.5
-wind-ons_6,2012,173417,126.5
-wind-ons_6,2012,174784,78.0
-wind-ons_6,2012,174785,124.7
-wind-ons_6,2012,175468,75.0
-wind-ons_6,2012,175469,124.2
-wind-ons_6,2012,176152,73.5
-wind-ons_5,2012,176835,2.64
-wind-ons_6,2012,176836,69.53
-wind-ons_6,2012,176837,121.9
-wind-ons_6,2012,176838,153.9
-wind-ons_6,2012,177521,120.6
-wind-ons_5,2012,177522,152.0
-wind-ons_6,2012,178203,0.92
-wind-ons_6,2012,178205,120.0
-wind-ons_6,2012,178887,0.6
-wind-ons_6,2012,178888,63.0
-wind-ons_6,2012,178889,120.0
-wind-ons_6,2012,178891,201.6
-wind-ons_6,2012,179574,180.0
-wind-ons_7,2012,180255,0.08
-wind-ons_6,2012,180257,120.0
-wind-ons_6,2012,180261,55.2
-wind-ons_7,2012,180939,0.07
-wind-ons_6,2012,180945,174.0
-wind-ons_7,2012,181629,54.0
-wind-ons_2,2013,43995,180.0
-wind-ons_3,2013,44678,44.0
-wind-ons_1,2013,46730,48.6
-wind-ons_2,2013,47413,7.2
-wind-ons_1,2013,48097,95.0
-wind-ons_3,2013,49465,176.9
-wind-ons_6,2013,52200,156.0
-wind-ons_7,2013,52969,9.7
-wind-ons_6,2013,53567,78.0
-wind-ons_6,2013,55619,204.0
-wind-ons_7,2013,55705,104.2
-wind-ons_6,2013,56986,98.9
-wind-ons_6,2013,57670,136.3
-wind-ons_8,2013,58001,42.0
-wind-ons_6,2013,58354,199.0
-wind-ons_7,2013,58527,48.0
-wind-ons_8,2013,59202,0.97
-wind-ons_7,2013,59203,1.3
-wind-ons_8,2013,59204,2.0
-wind-ons_8,2013,59211,102.0
-wind-ons_9,2013,59215,72.0
-wind-ons_8,2013,59370,57.0
-wind-ons_3,2013,59721,94.0
-wind-ons_8,2013,59886,0.97
-wind-ons_8,2013,59887,1.3
-wind-ons_7,2013,59888,2.0
-wind-ons_7,2013,59889,3.8
-wind-ons_8,2013,59890,11.2
-wind-ons_8,2013,59899,133.5
-wind-ons_9,2013,60055,66.0
-wind-ons_2,2013,60405,122.8
-wind-ons_7,2013,60570,0.97
-wind-ons_7,2013,60571,1.3
-wind-ons_7,2013,60572,2.0
-wind-ons_7,2013,60573,3.0
-wind-ons_7,2013,60574,10.4
-wind-ons_8,2013,60575,25.0
-wind-ons_8,2013,60737,4.5
-wind-ons_3,2013,61089,149.4
-wind-ons_7,2013,61254,0.93
-wind-ons_8,2013,61255,1.2
-wind-ons_7,2013,61256,2.0
-wind-ons_8,2013,61261,100.5
-wind-ons_5,2013,61773,262.2
-wind-ons_8,2013,61906,2.6
-wind-ons_7,2013,61938,0.93
-wind-ons_7,2013,61940,2.0
-wind-ons_8,2013,61942,9.8
-wind-ons_8,2013,61943,20.0
-wind-ons_8,2013,61944,51.0
-wind-ons_8,2013,61945,100.5
-wind-ons_8,2013,61946,103.5
-wind-ons_8,2013,62544,30.0
-wind-ons_9,2013,62590,4.5
-wind-ons_7,2013,62622,0.93
-wind-ons_7,2013,62624,2.0
-wind-ons_8,2013,62625,2.1
-wind-ons_8,2013,62626,9.2
-wind-ons_8,2013,62629,100.0
-wind-ons_9,2013,62790,4.5
-wind-ons_3,2013,63140,9.9
-wind-ons_3,2013,63141,100.5
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-wind-ons_8,2013,63308,1.9
-wind-ons_8,2013,63309,2.1
-wind-ons_9,2013,63310,8.4
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-wind-ons_8,2013,63312,49.5
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-wind-ons_9,2013,63958,18.0
-wind-ons_7,2013,63990,0.93
-wind-ons_7,2013,63991,0.97
-wind-ons_8,2013,63992,1.7
-wind-ons_8,2013,63993,2.1
-wind-ons_8,2013,63994,8.0
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-wind-ons_8,2013,64596,135.0
-wind-ons_7,2013,64674,0.93
-wind-ons_7,2013,64675,0.97
-wind-ons_7,2013,64676,1.7
-wind-ons_7,2013,64677,2.0
-wind-ons_8,2013,64678,5.2
-wind-ons_2,2013,65192,81.0
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-wind-ons_7,2013,65358,0.93
-wind-ons_8,2013,65359,0.97
-wind-ons_7,2013,65360,1.7
-wind-ons_8,2013,65361,2.0
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-wind-ons_8,2013,66042,0.93
-wind-ons_7,2013,66043,0.97
-wind-ons_7,2013,66044,1.7
-wind-ons_7,2013,66045,2.0
-wind-ons_6,2013,66046,4.2
-wind-ons_4,2013,66560,102.3
-wind-ons_8,2013,66694,40.5
-wind-ons_7,2013,66726,0.93
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-wind-ons_6,2013,169995,10.0
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-wind-ons_5,2013,169997,144.0
-wind-ons_5,2013,169998,162.3
-wind-ons_6,2013,170679,10.0
-wind-ons_6,2013,171363,10.0
-wind-ons_6,2013,171364,79.92
-wind-ons_6,2013,172049,130.5
-wind-ons_6,2013,173416,79.5
-wind-ons_6,2013,173417,126.5
-wind-ons_6,2013,174784,78.0
-wind-ons_6,2013,174785,124.7
-wind-ons_6,2013,175468,75.0
-wind-ons_6,2013,175469,124.2
-wind-ons_6,2013,176152,73.5
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-wind-ons_6,2013,176836,69.53
-wind-ons_6,2013,176837,121.9
-wind-ons_6,2013,176838,153.9
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-wind-ons_5,2013,177522,152.0
-wind-ons_6,2013,178203,0.92
-wind-ons_6,2013,178205,120.0
-wind-ons_6,2013,178887,0.6
-wind-ons_6,2013,178888,63.0
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-wind-ons_6,2013,179574,180.0
-wind-ons_7,2013,180255,0.08
-wind-ons_6,2013,180257,120.0
-wind-ons_6,2013,180261,55.2
-wind-ons_7,2013,180939,0.07
-wind-ons_6,2013,180945,174.0
-wind-ons_7,2013,181629,54.0
-wind-ons_2,2014,43995,180.0
-wind-ons_3,2014,44678,44.0
-wind-ons_1,2014,46730,48.6
-wind-ons_2,2014,47413,7.2
-wind-ons_1,2014,48097,95.0
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-wind-ons_6,2014,52200,156.0
-wind-ons_7,2014,52969,9.7
-wind-ons_6,2014,53567,78.0
-wind-ons_6,2014,55619,204.0
-wind-ons_7,2014,55705,104.2
-wind-ons_6,2014,56986,98.9
-wind-ons_6,2014,57670,136.3
-wind-ons_8,2014,58001,42.0
-wind-ons_6,2014,58354,199.0
-wind-ons_7,2014,58527,48.0
-wind-ons_8,2014,59202,0.97
-wind-ons_7,2014,59203,1.3
-wind-ons_8,2014,59204,2.0
-wind-ons_8,2014,59211,102.0
-wind-ons_9,2014,59215,72.0
-wind-ons_8,2014,59370,57.0
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-wind-ons_7,2014,59888,2.0
-wind-ons_7,2014,59889,3.8
-wind-ons_8,2014,59890,11.2
-wind-ons_8,2014,59899,133.5
-wind-ons_9,2014,60055,66.0
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-wind-ons_7,2014,60572,2.0
-wind-ons_7,2014,60573,3.0
-wind-ons_7,2014,60574,10.4
-wind-ons_8,2014,60575,25.0
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-wind-ons_8,2014,61255,1.2
-wind-ons_7,2014,61256,2.0
-wind-ons_8,2014,61261,100.5
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-wind-ons_7,2014,61938,0.93
-wind-ons_7,2014,61940,2.0
-wind-ons_8,2014,61942,9.8
-wind-ons_8,2014,61943,20.0
-wind-ons_8,2014,61944,51.0
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-wind-ons_8,2014,61946,103.5
-wind-ons_8,2014,62544,30.0
-wind-ons_9,2014,62590,4.5
-wind-ons_7,2014,62622,0.93
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-wind-ons_8,2014,62625,2.1
-wind-ons_8,2014,62626,9.2
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-wind-ons_8,2014,63309,2.1
-wind-ons_9,2014,63310,8.4
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-wind-ons_7,2014,64676,1.7
-wind-ons_7,2014,64677,2.0
-wind-ons_8,2014,64678,5.2
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-wind-ons_8,2014,65326,31.5
-wind-ons_7,2014,65358,0.93
-wind-ons_8,2014,65359,0.97
-wind-ons_7,2014,65360,1.7
-wind-ons_8,2014,65361,2.0
-wind-ons_6,2014,65363,15.6
-wind-ons_8,2014,66042,0.93
-wind-ons_7,2014,66043,0.97
-wind-ons_7,2014,66044,1.7
-wind-ons_7,2014,66045,2.0
-wind-ons_6,2014,66046,4.2
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-wind-ons_7,2014,66726,0.93
-wind-ons_8,2014,66727,0.97
-wind-ons_7,2014,66728,1.7
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-wind-ons_8,2014,67378,40.5
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-wind-ons_8,2014,67411,0.97
-wind-ons_6,2014,67412,1.7
-wind-ons_7,2014,67538,54.0
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-wind-ons_6,2014,67929,180.0
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-wind-ons_9,2014,68095,0.97
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-wind-ons_8,2014,71483,148.5
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-wind-ons_9,2014,82523,1.8
-wind-ons_9,2014,82524,59.0
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-wind-ons_9,2014,82610,1.5
-wind-ons_9,2014,83175,12.9
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-wind-ons_3,2014,138366,3.5
-wind-ons_4,2014,139050,4.5
-wind-ons_2,2014,139051,13.1
-wind-ons_2,2014,139052,20.5
-wind-ons_9,2014,139214,94.5
-wind-ons_8,2014,139215,151.4
-wind-ons_5,2014,139733,2.4
-wind-ons_5,2014,139734,6.5
-wind-ons_8,2014,140582,74.3
-wind-ons_2,2014,142469,5.4
-wind-ons_3,2014,143153,7.3
-wind-ons_4,2014,143154,16.2
-wind-ons_5,2014,143155,23.1
-wind-ons_5,2014,143157,36.0
-wind-ons_6,2014,143318,40.5
-wind-ons_4,2014,143836,3.0
-wind-ons_3,2014,143837,8.7
-wind-ons_2,2014,143841,60.0
-wind-ons_5,2014,143845,19.8
-wind-ons_9,2014,143937,0.7
-wind-ons_3,2014,144519,1.6
-wind-ons_3,2014,144521,9.1
-wind-ons_3,2014,144523,29.0
-wind-ons_3,2014,144524,46.9
-wind-ons_2,2014,144525,67.0
-wind-ons_2,2014,144526,60.0
-wind-ons_6,2014,144686,25.5
-wind-ons_2,2014,145202,0.1
-wind-ons_2,2014,145204,4.2
-wind-ons_3,2014,145205,11.6
-wind-ons_3,2014,145206,20.0
-wind-ons_4,2014,145207,30.75
-wind-ons_3,2014,145214,150.0
-wind-ons_10,2014,145305,30.0
-wind-ons_6,2014,145370,25.5
-wind-ons_4,2014,145891,33.6
-wind-ons_7,2014,145892,59.6
-wind-ons_3,2014,145896,135.0
-wind-ons_10,2014,145989,80.0
-wind-ons_6,2014,146054,25.5
-wind-ons_2,2014,146570,1.5
-wind-ons_2,2014,146574,22.2
-wind-ons_6,2014,146576,24.3
-wind-ons_2,2014,146581,150.0
-wind-ons_10,2014,146673,90.0
-wind-ons_5,2014,146738,25.5
-wind-ons_3,2014,147255,3.0
-wind-ons_2,2014,147257,17.3
-wind-ons_2,2014,147258,25.3
-wind-ons_3,2014,147259,43.4
-wind-ons_6,2014,147260,3.0
-wind-ons_6,2014,147422,18.9
-wind-ons_6,2014,147423,123.0
-wind-ons_3,2014,147939,3.4
-wind-ons_3,2014,147940,9.6
-wind-ons_2,2014,147948,72.0
-wind-ons_8,2014,148107,120.0
-wind-ons_3,2014,148622,1.8
-wind-ons_3,2014,148623,3.7
-wind-ons_3,2014,148624,11.7
-wind-ons_3,2014,148625,20.5
-wind-ons_3,2014,148626,31.0
-wind-ons_4,2014,148628,76.9
-wind-ons_5,2014,149306,2.1
-wind-ons_5,2014,149307,6.5
-wind-ons_4,2014,149308,13.6
-wind-ons_4,2014,149309,21.7
-wind-ons_3,2014,149310,2.9
-wind-ons_3,2014,149311,50.0
-wind-ons_3,2014,149991,8.0
-wind-ons_3,2014,149992,15.3
-wind-ons_3,2014,149993,22.4
-wind-ons_3,2014,149994,42.6
-wind-ons_7,2014,150159,106.5
-wind-ons_3,2014,150675,8.3
-wind-ons_2,2014,150676,16.5
-wind-ons_3,2014,150678,45.0
-wind-ons_5,2014,150843,101.2
-wind-ons_10,2014,152144,0.6
-wind-ons_6,2014,154946,8.8
-wind-ons_7,2014,155630,3.0
-wind-ons_7,2014,155631,79.8
-wind-ons_6,2014,155633,160.84
-wind-ons_9,2014,156248,100.0
-wind-ons_7,2014,156316,114.0
-wind-ons_2,2014,156884,63.0
-wind-ons_6,2014,156999,59.4
-wind-ons_6,2014,157000,114.0
-wind-ons_6,2014,157002,186.0
-wind-ons_7,2014,157682,0.16
-wind-ons_6,2014,157683,58.8
-wind-ons_6,2014,157684,112.5
-wind-ons_6,2014,157687,209.0
-wind-ons_7,2014,158366,0.07
-wind-ons_7,2014,158367,57.0
-wind-ons_7,2014,158368,112.5
-wind-ons_6,2014,159056,249.0
-wind-ons_6,2014,159735,49.5
-wind-ons_7,2014,160420,101.2
-wind-ons_6,2014,160421,150.0
-wind-ons_7,2014,161103,37.5
-wind-ons_6,2014,161106,180.0
-wind-ons_6,2014,161108,241.0
-wind-ons_6,2014,161787,34.3
-wind-ons_6,2014,161789,150.0
-wind-ons_6,2014,161792,232.5
-wind-ons_6,2014,161793,222.0
-wind-ons_7,2014,162471,32.52
-wind-ons_6,2014,162472,98.8
-wind-ons_6,2014,162473,150.0
-wind-ons_7,2014,162475,200.0
-wind-ons_6,2014,162476,231.5
-wind-ons_6,2014,162477,77.0
-wind-ons_6,2014,163159,200.0
-wind-ons_6,2014,163840,91.58
-wind-ons_7,2014,164524,91.2
-wind-ons_7,2014,164526,170.2
-wind-ons_7,2014,165208,91.2
-wind-ons_7,2014,165892,89.5
-wind-ons_6,2014,165894,169.5
-wind-ons_6,2014,166575,17.64
-wind-ons_7,2014,166576,84.0
-wind-ons_6,2014,166577,149.5
-wind-ons_6,2014,166580,223.5
-wind-ons_6,2014,167259,10.0
-wind-ons_7,2014,167260,82.5
-wind-ons_6,2014,167262,167.7
-wind-ons_6,2014,167265,246.0
-wind-ons_7,2014,167943,10.0
-wind-ons_6,2014,167944,82.0
-wind-ons_6,2014,167948,214.5
-wind-ons_6,2014,167949,32.0
-wind-ons_6,2014,168627,10.0
-wind-ons_6,2014,168631,197.0
-wind-ons_6,2014,168632,213.0
-wind-ons_6,2014,169311,10.0
-wind-ons_6,2014,169312,80.5
-wind-ons_6,2014,169995,10.0
-wind-ons_5,2014,169996,80.0
-wind-ons_5,2014,169997,144.0
-wind-ons_5,2014,169998,162.3
-wind-ons_6,2014,170679,10.0
-wind-ons_6,2014,171363,10.0
-wind-ons_6,2014,171364,79.92
-wind-ons_6,2014,172049,130.5
-wind-ons_6,2014,173416,79.5
-wind-ons_6,2014,173417,126.5
-wind-ons_6,2014,174784,78.0
-wind-ons_6,2014,174785,124.7
-wind-ons_6,2014,175468,75.0
-wind-ons_6,2014,175469,124.2
-wind-ons_6,2014,176152,73.5
-wind-ons_5,2014,176835,2.64
-wind-ons_6,2014,176836,69.53
-wind-ons_6,2014,176837,121.9
-wind-ons_6,2014,176838,153.9
-wind-ons_6,2014,177521,120.6
-wind-ons_5,2014,177522,152.0
-wind-ons_6,2014,178203,0.92
-wind-ons_6,2014,178205,120.0
-wind-ons_6,2014,178887,0.6
-wind-ons_6,2014,178888,63.0
-wind-ons_6,2014,178889,120.0
-wind-ons_6,2014,178891,201.6
-wind-ons_6,2014,179574,180.0
-wind-ons_7,2014,180255,0.08
-wind-ons_6,2014,180257,120.0
-wind-ons_6,2014,180261,55.2
-wind-ons_7,2014,180939,0.07
-wind-ons_6,2014,180945,174.0
-wind-ons_7,2014,181629,54.0
-wind-ons_2,2015,43995,180.0
-wind-ons_3,2015,44678,44.0
-wind-ons_1,2015,46730,48.6
-wind-ons_2,2015,47413,7.2
-wind-ons_1,2015,48097,95.0
-wind-ons_3,2015,49465,176.9
-wind-ons_6,2015,52200,156.0
-wind-ons_7,2015,52969,9.7
-wind-ons_6,2015,53567,78.0
-wind-ons_6,2015,55619,204.0
-wind-ons_7,2015,55705,104.2
-wind-ons_6,2015,56986,98.9
-wind-ons_6,2015,57670,136.3
-wind-ons_8,2015,58001,42.0
-wind-ons_6,2015,58354,199.0
-wind-ons_7,2015,58527,48.0
-wind-ons_8,2015,59202,0.97
-wind-ons_7,2015,59203,1.3
-wind-ons_8,2015,59204,2.0
-wind-ons_8,2015,59211,102.0
-wind-ons_9,2015,59215,72.0
-wind-ons_8,2015,59370,57.0
-wind-ons_3,2015,59721,94.0
-wind-ons_8,2015,59886,0.97
-wind-ons_8,2015,59887,1.3
-wind-ons_7,2015,59888,2.0
-wind-ons_7,2015,59889,3.8
-wind-ons_8,2015,59890,11.2
-wind-ons_8,2015,59899,133.5
-wind-ons_9,2015,60055,66.0
-wind-ons_2,2015,60405,122.8
-wind-ons_7,2015,60570,0.97
-wind-ons_7,2015,60571,1.3
-wind-ons_7,2015,60572,2.0
-wind-ons_7,2015,60573,3.0
-wind-ons_7,2015,60574,10.4
-wind-ons_8,2015,60575,25.0
-wind-ons_8,2015,60737,4.5
-wind-ons_3,2015,61089,149.4
-wind-ons_7,2015,61254,0.93
-wind-ons_8,2015,61255,1.2
-wind-ons_7,2015,61256,2.0
-wind-ons_8,2015,61261,100.5
-wind-ons_5,2015,61773,262.2
-wind-ons_8,2015,61906,2.6
-wind-ons_7,2015,61938,0.93
-wind-ons_7,2015,61940,2.0
-wind-ons_8,2015,61942,9.8
-wind-ons_8,2015,61943,20.0
-wind-ons_8,2015,61944,51.0
-wind-ons_8,2015,61945,100.5
-wind-ons_8,2015,61946,103.5
-wind-ons_8,2015,62544,30.0
-wind-ons_9,2015,62590,4.5
-wind-ons_7,2015,62622,0.93
-wind-ons_7,2015,62624,2.0
-wind-ons_8,2015,62625,2.1
-wind-ons_8,2015,62626,9.2
-wind-ons_8,2015,62629,100.0
-wind-ons_9,2015,62790,4.5
-wind-ons_3,2015,63140,9.9
-wind-ons_3,2015,63141,100.5
-wind-ons_9,2015,63274,11.9
-wind-ons_6,2015,63306,0.93
-wind-ons_8,2015,63308,1.9
-wind-ons_8,2015,63309,2.1
-wind-ons_9,2015,63310,8.4
-wind-ons_9,2015,63311,19.98
-wind-ons_8,2015,63312,49.5
-wind-ons_7,2015,63314,100.7
-wind-ons_5,2015,63825,102.9
-wind-ons_9,2015,63912,101.4
-wind-ons_9,2015,63958,18.0
-wind-ons_7,2015,63990,0.93
-wind-ons_7,2015,63991,0.97
-wind-ons_8,2015,63992,1.7
-wind-ons_8,2015,63993,2.1
-wind-ons_8,2015,63994,8.0
-wind-ons_6,2015,63997,99.0
-wind-ons_1,2015,64508,25.0
-wind-ons_3,2015,64510,41.0
-wind-ons_8,2015,64596,135.0
-wind-ons_7,2015,64674,0.93
-wind-ons_7,2015,64675,0.97
-wind-ons_7,2015,64676,1.7
-wind-ons_7,2015,64677,2.0
-wind-ons_8,2015,64678,5.2
-wind-ons_2,2015,65192,81.0
-wind-ons_8,2015,65326,31.5
-wind-ons_7,2015,65358,0.93
-wind-ons_8,2015,65359,0.97
-wind-ons_7,2015,65360,1.7
-wind-ons_8,2015,65361,2.0
-wind-ons_6,2015,65363,15.6
-wind-ons_8,2015,66042,0.93
-wind-ons_7,2015,66043,0.97
-wind-ons_7,2015,66044,1.7
-wind-ons_7,2015,66045,2.0
-wind-ons_6,2015,66046,4.2
-wind-ons_4,2015,66560,102.3
-wind-ons_8,2015,66694,40.5
-wind-ons_7,2015,66726,0.93
-wind-ons_8,2015,66727,0.97
-wind-ons_7,2015,66728,1.7
-wind-ons_5,2015,67244,125.4
-wind-ons_8,2015,67378,40.5
-wind-ons_8,2015,67379,118.5
-wind-ons_8,2015,67410,0.9
-wind-ons_8,2015,67411,0.97
-wind-ons_6,2015,67412,1.7
-wind-ons_7,2015,67538,54.0
-wind-ons_5,2015,67927,72.0
-wind-ons_6,2015,67928,163.3
-wind-ons_6,2015,67929,180.0
-wind-ons_8,2015,68094,0.9
-wind-ons_9,2015,68095,0.97
-wind-ons_6,2015,68222,30.55
-wind-ons_3,2015,68610,6.6
-wind-ons_8,2015,68746,48.0
-wind-ons_8,2015,68747,149.0
-wind-ons_6,2015,68778,0.9
-wind-ons_6,2015,68906,16.5
-wind-ons_4,2015,68919,5.2
-wind-ons_1,2015,69294,9.9
-wind-ons_2,2015,69295,100.8
-wind-ons_8,2015,69430,48.0
-wind-ons_8,2015,69432,180.0
-wind-ons_5,2015,69462,0.9
-wind-ons_5,2015,69591,81.0
-wind-ons_1,2015,69979,102.9
-wind-ons_9,2015,70115,115.5
-wind-ons_5,2015,70146,0.7
-wind-ons_2,2015,70662,25.2
-wind-ons_8,2015,70798,49.5
-wind-ons_8,2015,70799,120.0
-wind-ons_7,2015,70830,0.7
-wind-ons_5,2015,70959,45.5
-wind-ons_6,2015,71345,1.7
-wind-ons_8,2015,71482,49.5
-wind-ons_8,2015,71483,148.5
-wind-ons_7,2015,71643,16.5
-wind-ons_6,2015,71644,106.5
-wind-ons_8,2015,72166,50.6
-wind-ons_7,2015,72198,0.4
-wind-ons_5,2015,72328,97.5
-wind-ons_8,2015,72850,61.5
-wind-ons_5,2015,73696,45.5
-wind-ons_5,2015,74380,30.0
-wind-ons_5,2015,74764,5.0
-wind-ons_3,2015,74765,98.7
-wind-ons_4,2015,75065,100.5
-wind-ons_4,2015,75448,9.9
-wind-ons_3,2015,75449,100.7
-wind-ons_7,2015,76334,11.0
-wind-ons_6,2015,76433,68.5
-wind-ons_4,2015,76816,24.6
-wind-ons_6,2015,76857,8.4
-wind-ons_7,2015,77117,30.55
-wind-ons_3,2015,77500,76.5
-wind-ons_9,2015,77705,145.2
-wind-ons_6,2015,77801,6.6
-wind-ons_4,2015,77802,100.5
-wind-ons_3,2015,78183,8.0
-wind-ons_3,2015,78225,21.0
-wind-ons_8,2015,78326,51.0
-wind-ons_8,2015,78504,24.0
-wind-ons_9,2015,78957,17.0
-wind-ons_5,2015,79170,14.5
-wind-ons_4,2015,79551,10.0
-wind-ons_8,2015,79641,25.0
-wind-ons_9,2015,79755,44.55
-wind-ons_9,2015,79756,67.7
-wind-ons_4,2015,79854,34.5
-wind-ons_3,2015,80235,41.0
-wind-ons_8,2015,80325,39.0
-wind-ons_9,2015,80439,42.57
-wind-ons_5,2015,80538,11.5
-wind-ons_8,2015,81009,99.0
-wind-ons_9,2015,81123,30.0
-wind-ons_9,2015,81124,54.0
-wind-ons_4,2015,81223,87.5
-wind-ons_3,2015,81602,8.3
-wind-ons_6,2015,81693,99.0
-wind-ons_7,2015,81908,112.0
-wind-ons_2,2015,82328,21.0
-wind-ons_9,2015,82491,13.5
-wind-ons_9,2015,82523,1.8
-wind-ons_9,2015,82524,59.0
-wind-ons_8,2015,82591,37.5
-wind-ons_9,2015,82610,1.5
-wind-ons_9,2015,83175,12.9
-wind-ons_10,2015,83295,1.8
-wind-ons_8,2015,83827,0.97
-wind-ons_7,2015,83828,1.3
-wind-ons_9,2015,83859,11.88
-wind-ons_3,2015,84337,3.3
-wind-ons_6,2015,84428,1.3
-wind-ons_8,2015,84510,0.04
-wind-ons_7,2015,84511,0.97
-wind-ons_7,2015,84512,1.3
-wind-ons_6,2015,84513,2.0
-wind-ons_9,2015,84543,3.6
-wind-ons_6,2015,85112,12.14
-wind-ons_8,2015,85194,0.02
-wind-ons_8,2015,85195,0.93
-wind-ons_8,2015,85196,1.3
-wind-ons_7,2015,85197,2.0
-wind-ons_7,2015,85198,2.1
-wind-ons_5,2015,85796,19.5
-wind-ons_9,2015,85879,0.93
-wind-ons_8,2015,85880,1.3
-wind-ons_8,2015,85881,1.7
-wind-ons_8,2015,85882,2.0
-wind-ons_8,2015,85945,69.0
-wind-ons_7,2015,86012,20.0
-wind-ons_4,2015,86389,24.0
-wind-ons_5,2015,86480,25.0
-wind-ons_9,2015,86563,0.93
-wind-ons_9,2015,86565,1.7
-wind-ons_8,2015,86566,2.0
-wind-ons_8,2015,86567,4.2
-wind-ons_8,2015,86568,13.5
-wind-ons_7,2015,86569,43.06
-wind-ons_5,2015,87164,29.4
-wind-ons_9,2015,87247,0.93
-wind-ons_8,2015,87248,1.0
-wind-ons_8,2015,87249,1.7
-wind-ons_8,2015,87250,2.0
-wind-ons_7,2015,87251,4.2
-wind-ons_7,2015,87254,98.8
-wind-ons_8,2015,87313,51.2
-wind-ons_8,2015,87931,0.93
-wind-ons_8,2015,87932,1.0
-wind-ons_7,2015,87933,1.5
-wind-ons_7,2015,87934,2.0
-wind-ons_5,2015,87936,12.0
-wind-ons_6,2015,87937,32.0
-wind-ons_5,2015,88532,99.0
-wind-ons_9,2015,88615,0.9
-wind-ons_8,2015,88616,0.97
-wind-ons_8,2015,88617,1.5
-wind-ons_8,2015,88618,2.0
-wind-ons_7,2015,88620,11.8
-wind-ons_6,2015,88622,91.1
-wind-ons_6,2015,89216,99.0
-wind-ons_9,2015,89299,0.9
-wind-ons_9,2015,89300,0.97
-wind-ons_9,2015,89301,1.3
-wind-ons_9,2015,89302,2.0
-wind-ons_9,2015,89303,3.9
-wind-ons_8,2015,89304,11.6
-wind-ons_7,2015,89306,85.5
-wind-ons_3,2015,89851,64.5
-wind-ons_8,2015,89953,40.5
-wind-ons_4,2015,90534,18.9
-wind-ons_9,2015,90584,142.2
-wind-ons_9,2015,90637,50.4
-wind-ons_6,2015,90672,1.2
-wind-ons_7,2015,90674,35.0
-wind-ons_7,2015,90675,80.0
-wind-ons_7,2015,90789,9.0
-wind-ons_5,2015,90790,64.5
-wind-ons_8,2015,91321,25.0
-wind-ons_9,2015,91357,2.3
-wind-ons_7,2015,91359,80.0
-wind-ons_9,2015,91951,4.9
-wind-ons_10,2015,92005,1.3
-wind-ons_9,2015,92040,0.9
-wind-ons_8,2015,92041,2.0
-wind-ons_8,2015,92042,29.7
-wind-ons_7,2015,92043,80.0
-wind-ons_10,2015,92635,16.8
-wind-ons_9,2015,92689,1.3
-wind-ons_8,2015,92724,0.7
-wind-ons_8,2015,92726,21.0
-wind-ons_7,2015,92842,34.5
-wind-ons_8,2015,92843,75.0
-wind-ons_9,2015,93319,24.8
-wind-ons_8,2015,93408,0.7
-wind-ons_8,2015,93410,20.0
-wind-ons_8,2015,93411,78.0
-wind-ons_9,2015,93526,26.0
-wind-ons_8,2015,93528,102.0
-wind-ons_8,2015,93543,66.0
-wind-ons_2,2015,93953,10.5
-wind-ons_9,2015,94003,28.8
-wind-ons_8,2015,94092,0.6
-wind-ons_7,2015,94095,75.0
-wind-ons_7,2015,94211,52.5
-wind-ons_9,2015,94228,60.0
-wind-ons_9,2015,94687,41.4
-wind-ons_9,2015,94776,0.5
-wind-ons_7,2015,94778,10.5
-wind-ons_7,2015,94779,66.0
-wind-ons_4,2015,94896,80.0
-wind-ons_7,2015,95371,64.08
-wind-ons_9,2015,95462,10.5
-wind-ons_9,2015,95464,100.0
-wind-ons_9,2015,95465,150.0
-wind-ons_9,2015,95487,2.5
-wind-ons_8,2015,95539,1.8
-wind-ons_7,2015,96055,99.0
-wind-ons_7,2015,96223,1.8
-wind-ons_6,2015,96263,30.0
-wind-ons_8,2015,96832,99.0
-wind-ons_5,2015,96947,24.0
-wind-ons_8,2015,97516,99.0
-wind-ons_9,2015,98199,49.6
-wind-ons_8,2015,98200,98.2
-wind-ons_8,2015,98201,123.0
-wind-ons_7,2015,98205,66.0
-wind-ons_9,2015,98324,7.5
-wind-ons_7,2015,98790,1.8
-wind-ons_8,2015,98884,98.0
-wind-ons_8,2015,98886,160.5
-wind-ons_7,2015,99000,37.8
-wind-ons_7,2015,99474,15.92
-wind-ons_8,2015,99684,29.4
-wind-ons_7,2015,99686,100.5
-wind-ons_8,2015,100254,150.0
-wind-ons_8,2015,100297,106.0
-wind-ons_7,2015,100328,1.8
-wind-ons_9,2015,100368,15.0
-wind-ons_7,2015,100369,62.5
-wind-ons_8,2015,100935,43.5
-wind-ons_8,2015,100938,150.0
-wind-ons_8,2015,100939,199.7
-wind-ons_7,2015,100942,234.0
-wind-ons_8,2015,101619,42.0
-wind-ons_8,2015,101622,150.0
-wind-ons_9,2015,101644,76.2
-wind-ons_8,2015,101665,100.0
-wind-ons_10,2015,102210,42.0
-wind-ons_8,2015,102306,150.0
-wind-ons_8,2015,102307,199.0
-wind-ons_8,2015,102328,51.0
-wind-ons_9,2015,102894,48.86
-wind-ons_8,2015,102990,150.0
-wind-ons_8,2015,102991,174.8
-wind-ons_8,2015,103012,10.5
-wind-ons_9,2015,103637,59.4
-wind-ons_8,2015,103696,2.5
-wind-ons_8,2015,104380,1.5
-wind-ons_6,2015,104433,1.8
-wind-ons_8,2015,105040,78.0
-wind-ons_7,2015,105041,104.3
-wind-ons_8,2015,105065,150.0
-wind-ons_8,2015,105086,199.7
-wind-ons_9,2015,105689,54.27
-wind-ons_9,2015,105721,0.9
-wind-ons_9,2015,105724,76.5
-wind-ons_8,2015,105770,130.5
-wind-ons_9,2015,106329,199.5
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-wind-ons_9,2015,107013,201.0
-wind-ons_9,2015,107056,0.7
-wind-ons_8,2015,107091,15.0
-wind-ons_8,2015,107092,62.2
-wind-ons_8,2015,107117,100.5
-wind-ons_8,2015,107775,14.7
-wind-ons_5,2015,107873,66.0
-wind-ons_8,2015,108381,221.0
-wind-ons_10,2015,108424,0.7
-wind-ons_8,2015,108485,80.0
-wind-ons_5,2015,108558,100.0
-wind-ons_5,2015,108559,164.0
-wind-ons_8,2015,109169,72.0
-wind-ons_8,2015,109748,22.5
-wind-ons_8,2015,109793,8.91
-wind-ons_8,2015,109853,53.0
-wind-ons_9,2015,109875,200.0
-wind-ons_10,2015,110477,8.91
-wind-ons_8,2015,110537,49.5
-wind-ons_8,2015,111116,29.7
-wind-ons_10,2015,111161,8.91
-wind-ons_8,2015,111221,8.4
-wind-ons_8,2015,111905,1.5
-wind-ons_3,2015,112433,18.9
-wind-ons_8,2015,112484,60.0
-wind-ons_8,2015,112591,217.5
-wind-ons_8,2015,113275,210.9
-wind-ons_7,2015,113295,99.0
-wind-ons_8,2015,113852,79.5
-wind-ons_8,2015,113958,106.0
-wind-ons_9,2015,114589,148.5
-wind-ons_8,2015,114642,99.0
-wind-ons_9,2015,116588,151.8
-wind-ons_10,2015,118009,112.2
-wind-ons_5,2015,118084,101.3
-wind-ons_9,2015,118693,150.0
-wind-ons_7,2015,118746,1.7
-wind-ons_8,2015,119323,1.5
-wind-ons_9,2015,120061,105.0
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-wind-ons_9,2015,120691,4.0
-wind-ons_10,2015,121429,100.8
-wind-ons_7,2015,121483,100.5
-wind-ons_5,2015,122691,203.5
-wind-ons_9,2015,122797,100.5
-wind-ons_9,2015,124849,99.0
-wind-ons_6,2015,125479,75.0
-wind-ons_10,2015,126217,96.0
-wind-ons_7,2015,127626,5.0
-wind-ons_9,2015,128898,1.5
-wind-ons_9,2015,128899,162.0
-wind-ons_7,2015,129679,146.0
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-wind-ons_7,2015,130423,27.0
-wind-ons_2,2015,130846,18.0
-wind-ons_9,2015,131005,50.0
-wind-ons_4,2015,131106,0.7
-wind-ons_6,2015,131107,0.7
-wind-ons_3,2015,131790,0.7
-wind-ons_4,2015,132213,15.5
-wind-ons_3,2015,132215,38.0
-wind-ons_6,2015,132311,204.0
-wind-ons_9,2015,132373,44.0
-wind-ons_7,2015,132415,56.7
-wind-ons_7,2015,133099,50.4
-wind-ons_6,2015,133580,7.5
-wind-ons_7,2015,133582,24.0
-wind-ons_7,2015,133783,50.4
-wind-ons_7,2015,134265,18.4
-wind-ons_7,2015,134266,28.7
-wind-ons_8,2015,134269,28.5
-wind-ons_7,2015,134950,30.75
-wind-ons_10,2015,135730,0.6
-wind-ons_5,2015,136317,22.0
-wind-ons_5,2015,136318,36.0
-wind-ons_9,2015,136478,99.0
-wind-ons_5,2015,137000,17.0
-wind-ons_3,2015,137007,54.0
-wind-ons_9,2015,137098,20.0
-wind-ons_9,2015,137162,98.9
-wind-ons_8,2015,137782,90.0
-wind-ons_3,2015,138366,3.5
-wind-ons_4,2015,139050,4.5
-wind-ons_2,2015,139051,13.1
-wind-ons_2,2015,139052,20.5
-wind-ons_9,2015,139214,94.5
-wind-ons_8,2015,139215,151.4
-wind-ons_5,2015,139733,2.4
-wind-ons_5,2015,139734,6.5
-wind-ons_8,2015,140582,74.3
-wind-ons_2,2015,142469,5.4
-wind-ons_3,2015,143153,7.3
-wind-ons_4,2015,143154,16.2
-wind-ons_5,2015,143155,23.1
-wind-ons_5,2015,143157,36.0
-wind-ons_6,2015,143318,40.5
-wind-ons_4,2015,143836,3.0
-wind-ons_3,2015,143837,8.7
-wind-ons_2,2015,143841,60.0
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-wind-ons_9,2015,143937,0.7
-wind-ons_3,2015,144519,1.6
-wind-ons_3,2015,144521,9.1
-wind-ons_3,2015,144523,29.0
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-wind-ons_2,2015,144526,60.0
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-wind-ons_2,2015,145204,4.2
-wind-ons_3,2015,145205,11.6
-wind-ons_3,2015,145206,20.0
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-wind-ons_3,2015,145214,150.0
-wind-ons_10,2015,145305,30.0
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-wind-ons_3,2015,145896,135.0
-wind-ons_10,2015,145989,80.0
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-wind-ons_2,2015,146581,150.0
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-wind-ons_2,2015,147257,17.3
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-wind-ons_3,2015,148623,3.7
-wind-ons_3,2015,148624,11.7
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-wind-ons_3,2015,149993,22.4
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-wind-ons_7,2015,155631,79.8
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-wind-ons_6,2015,157683,58.8
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-wind-ons_7,2015,158367,57.0
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-wind-ons_6,2015,160421,150.0
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-wind-ons_6,2015,161106,180.0
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-wind-ons_6,2015,161789,150.0
-wind-ons_6,2015,161792,232.5
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-wind-ons_6,2015,162472,98.8
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-wind-ons_7,2015,166576,84.0
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-wind-ons_6,2015,167944,82.0
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-wind-ons_6,2015,168631,197.0
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-wind-ons_6,2015,169312,80.5
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-wind-ons_6,2015,178205,120.0
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-wind-ons_6,2015,178888,63.0
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-wind-ons_6,2015,180257,120.0
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-wind-ons_8,2016,59887,1.3
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-wind-ons_7,2016,64675,0.97
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-wind-ons_3,2017,148624,11.7
-wind-ons_3,2017,148625,20.5
-wind-ons_3,2017,148626,31.0
-wind-ons_4,2017,148628,76.9
-wind-ons_5,2017,149306,2.1
-wind-ons_5,2017,149307,6.5
-wind-ons_4,2017,149308,13.6
-wind-ons_4,2017,149309,21.7
-wind-ons_3,2017,149310,2.9
-wind-ons_3,2017,149311,50.0
-wind-ons_3,2017,149991,8.0
-wind-ons_3,2017,149992,15.3
-wind-ons_3,2017,149993,22.4
-wind-ons_3,2017,149994,42.6
-wind-ons_7,2017,150159,106.5
-wind-ons_3,2017,150675,8.3
-wind-ons_2,2017,150676,16.5
-wind-ons_3,2017,150678,45.0
-wind-ons_5,2017,150843,101.2
-wind-ons_10,2017,152144,0.6
-wind-ons_6,2017,154946,8.8
-wind-ons_7,2017,155630,3.0
-wind-ons_7,2017,155631,79.8
-wind-ons_6,2017,155633,160.84
-wind-ons_9,2017,156248,100.0
-wind-ons_7,2017,156316,114.0
-wind-ons_2,2017,156884,63.0
-wind-ons_6,2017,156999,59.4
-wind-ons_6,2017,157000,114.0
-wind-ons_6,2017,157002,186.0
-wind-ons_7,2017,157682,0.16
-wind-ons_6,2017,157683,58.8
-wind-ons_6,2017,157684,112.5
-wind-ons_6,2017,157687,209.0
-wind-ons_7,2017,158366,0.07
-wind-ons_7,2017,158367,57.0
-wind-ons_7,2017,158368,112.5
-wind-ons_6,2017,159056,249.0
-wind-ons_6,2017,159735,49.5
-wind-ons_7,2017,160420,101.2
-wind-ons_6,2017,160421,150.0
-wind-ons_7,2017,161103,37.5
-wind-ons_6,2017,161106,180.0
-wind-ons_6,2017,161108,241.0
-wind-ons_6,2017,161787,34.3
-wind-ons_6,2017,161789,150.0
-wind-ons_6,2017,161792,232.5
-wind-ons_6,2017,161793,222.0
-wind-ons_7,2017,162471,32.52
-wind-ons_6,2017,162472,98.8
-wind-ons_6,2017,162473,150.0
-wind-ons_7,2017,162475,200.0
-wind-ons_6,2017,162476,231.5
-wind-ons_6,2017,162477,77.0
-wind-ons_6,2017,163159,200.0
-wind-ons_6,2017,163840,91.58
-wind-ons_7,2017,164524,91.2
-wind-ons_7,2017,164526,170.2
-wind-ons_7,2017,165208,91.2
-wind-ons_7,2017,165892,89.5
-wind-ons_6,2017,165894,169.5
-wind-ons_6,2017,166575,17.64
-wind-ons_7,2017,166576,84.0
-wind-ons_6,2017,166577,149.5
-wind-ons_6,2017,166580,223.5
-wind-ons_6,2017,167259,10.0
-wind-ons_7,2017,167260,82.5
-wind-ons_6,2017,167262,167.7
-wind-ons_6,2017,167265,246.0
-wind-ons_7,2017,167943,10.0
-wind-ons_6,2017,167944,82.0
-wind-ons_6,2017,167948,214.5
-wind-ons_6,2017,167949,32.0
-wind-ons_6,2017,168627,10.0
-wind-ons_6,2017,168631,197.0
-wind-ons_6,2017,168632,213.0
-wind-ons_6,2017,169311,10.0
-wind-ons_6,2017,169312,80.5
-wind-ons_6,2017,169995,10.0
-wind-ons_5,2017,169996,80.0
-wind-ons_5,2017,169997,144.0
-wind-ons_5,2017,169998,162.3
-wind-ons_6,2017,170679,10.0
-wind-ons_6,2017,171363,10.0
-wind-ons_6,2017,171364,79.92
-wind-ons_6,2017,172049,130.5
-wind-ons_6,2017,173416,79.5
-wind-ons_6,2017,173417,126.5
-wind-ons_6,2017,174784,78.0
-wind-ons_6,2017,174785,124.7
-wind-ons_6,2017,175468,75.0
-wind-ons_6,2017,175469,124.2
-wind-ons_6,2017,176152,73.5
-wind-ons_5,2017,176835,2.64
-wind-ons_6,2017,176836,69.53
-wind-ons_6,2017,176837,121.9
-wind-ons_6,2017,176838,153.9
-wind-ons_6,2017,177521,120.6
-wind-ons_5,2017,177522,152.0
-wind-ons_6,2017,178203,0.92
-wind-ons_6,2017,178205,120.0
-wind-ons_6,2017,178887,0.6
-wind-ons_6,2017,178888,63.0
-wind-ons_6,2017,178889,120.0
-wind-ons_6,2017,178891,201.6
-wind-ons_6,2017,179574,180.0
-wind-ons_7,2017,180255,0.08
-wind-ons_6,2017,180257,120.0
-wind-ons_6,2017,180261,55.2
-wind-ons_7,2017,180939,0.07
-wind-ons_6,2017,180945,174.0
-wind-ons_7,2017,181629,54.0
-wind-ons_2,2018,43995,180.0
-wind-ons_3,2018,44678,44.0
-wind-ons_1,2018,46730,48.6
-wind-ons_2,2018,47413,7.2
-wind-ons_1,2018,48097,95.0
-wind-ons_3,2018,49465,176.9
-wind-ons_6,2018,52200,156.0
-wind-ons_7,2018,52969,9.7
-wind-ons_6,2018,53567,78.0
-wind-ons_6,2018,55619,204.0
-wind-ons_7,2018,55705,104.2
-wind-ons_6,2018,56986,98.9
-wind-ons_6,2018,57670,136.3
-wind-ons_8,2018,58001,42.0
-wind-ons_6,2018,58354,199.0
-wind-ons_7,2018,58527,48.0
-wind-ons_8,2018,59202,0.97
-wind-ons_7,2018,59203,1.3
-wind-ons_8,2018,59204,2.0
-wind-ons_8,2018,59211,102.0
-wind-ons_9,2018,59215,72.0
-wind-ons_8,2018,59370,57.0
-wind-ons_3,2018,59721,94.0
-wind-ons_8,2018,59886,0.97
-wind-ons_8,2018,59887,1.3
-wind-ons_7,2018,59888,2.0
-wind-ons_7,2018,59889,3.8
-wind-ons_8,2018,59890,11.2
-wind-ons_8,2018,59899,133.5
-wind-ons_9,2018,60055,66.0
-wind-ons_2,2018,60405,122.8
-wind-ons_7,2018,60570,0.97
-wind-ons_7,2018,60571,1.3
-wind-ons_7,2018,60572,2.0
-wind-ons_7,2018,60573,3.0
-wind-ons_7,2018,60574,10.4
-wind-ons_8,2018,60575,25.0
-wind-ons_8,2018,60737,4.5
-wind-ons_3,2018,61089,149.4
-wind-ons_7,2018,61254,0.93
-wind-ons_8,2018,61255,1.2
-wind-ons_7,2018,61256,2.0
-wind-ons_8,2018,61261,100.5
-wind-ons_5,2018,61773,262.2
-wind-ons_8,2018,61906,2.6
-wind-ons_7,2018,61938,0.93
-wind-ons_7,2018,61940,2.0
-wind-ons_8,2018,61942,9.8
-wind-ons_8,2018,61943,20.0
-wind-ons_8,2018,61944,51.0
-wind-ons_8,2018,61945,100.5
-wind-ons_8,2018,61946,103.5
-wind-ons_8,2018,62544,30.0
-wind-ons_9,2018,62590,4.5
-wind-ons_7,2018,62622,0.93
-wind-ons_7,2018,62624,2.0
-wind-ons_8,2018,62625,2.1
-wind-ons_8,2018,62626,9.2
-wind-ons_8,2018,62629,100.0
-wind-ons_9,2018,62790,4.5
-wind-ons_3,2018,63140,9.9
-wind-ons_3,2018,63141,100.5
-wind-ons_9,2018,63274,11.9
-wind-ons_6,2018,63306,0.93
-wind-ons_8,2018,63308,1.9
-wind-ons_8,2018,63309,2.1
-wind-ons_9,2018,63310,8.4
-wind-ons_9,2018,63311,19.98
-wind-ons_8,2018,63312,49.5
-wind-ons_7,2018,63314,100.7
-wind-ons_5,2018,63825,102.9
-wind-ons_9,2018,63912,101.4
-wind-ons_9,2018,63958,18.0
-wind-ons_7,2018,63990,0.93
-wind-ons_7,2018,63991,0.97
-wind-ons_8,2018,63992,1.7
-wind-ons_8,2018,63993,2.1
-wind-ons_8,2018,63994,8.0
-wind-ons_6,2018,63997,99.0
-wind-ons_1,2018,64508,25.0
-wind-ons_3,2018,64510,41.0
-wind-ons_8,2018,64596,135.0
-wind-ons_7,2018,64674,0.93
-wind-ons_7,2018,64675,0.97
-wind-ons_7,2018,64676,1.7
-wind-ons_7,2018,64677,2.0
-wind-ons_8,2018,64678,5.2
-wind-ons_2,2018,65192,81.0
-wind-ons_8,2018,65326,31.5
-wind-ons_7,2018,65358,0.93
-wind-ons_8,2018,65359,0.97
-wind-ons_7,2018,65360,1.7
-wind-ons_8,2018,65361,2.0
-wind-ons_6,2018,65363,15.6
-wind-ons_8,2018,66042,0.93
-wind-ons_7,2018,66043,0.97
-wind-ons_7,2018,66044,1.7
-wind-ons_7,2018,66045,2.0
-wind-ons_6,2018,66046,4.2
-wind-ons_4,2018,66560,102.3
-wind-ons_8,2018,66694,40.5
-wind-ons_7,2018,66726,0.93
-wind-ons_8,2018,66727,0.97
-wind-ons_7,2018,66728,1.7
-wind-ons_5,2018,67244,125.4
-wind-ons_8,2018,67378,40.5
-wind-ons_8,2018,67379,118.5
-wind-ons_8,2018,67410,0.9
-wind-ons_8,2018,67411,0.97
-wind-ons_6,2018,67412,1.7
-wind-ons_7,2018,67538,54.0
-wind-ons_5,2018,67927,72.0
-wind-ons_6,2018,67928,163.3
-wind-ons_6,2018,67929,180.0
-wind-ons_8,2018,68094,0.9
-wind-ons_9,2018,68095,0.97
-wind-ons_6,2018,68222,30.55
-wind-ons_3,2018,68610,6.6
-wind-ons_8,2018,68746,48.0
-wind-ons_8,2018,68747,149.0
-wind-ons_6,2018,68778,0.9
-wind-ons_6,2018,68906,16.5
-wind-ons_4,2018,68919,5.2
-wind-ons_1,2018,69294,9.9
-wind-ons_2,2018,69295,100.8
-wind-ons_8,2018,69430,48.0
-wind-ons_8,2018,69432,180.0
-wind-ons_5,2018,69462,0.9
-wind-ons_5,2018,69591,81.0
-wind-ons_1,2018,69979,102.9
-wind-ons_9,2018,70115,115.5
-wind-ons_5,2018,70146,0.7
-wind-ons_2,2018,70662,25.2
-wind-ons_8,2018,70798,49.5
-wind-ons_8,2018,70799,120.0
-wind-ons_7,2018,70830,0.7
-wind-ons_5,2018,70959,45.5
-wind-ons_6,2018,71345,1.7
-wind-ons_8,2018,71482,49.5
-wind-ons_8,2018,71483,148.5
-wind-ons_7,2018,71643,16.5
-wind-ons_6,2018,71644,106.5
-wind-ons_8,2018,72166,50.6
-wind-ons_7,2018,72198,0.4
-wind-ons_5,2018,72328,97.5
-wind-ons_8,2018,72850,61.5
-wind-ons_5,2018,73696,45.5
-wind-ons_5,2018,74380,30.0
-wind-ons_5,2018,74764,5.0
-wind-ons_3,2018,74765,98.7
-wind-ons_4,2018,75065,100.5
-wind-ons_4,2018,75448,9.9
-wind-ons_3,2018,75449,100.7
-wind-ons_7,2018,76334,11.0
-wind-ons_6,2018,76433,68.5
-wind-ons_4,2018,76816,24.6
-wind-ons_6,2018,76857,8.4
-wind-ons_7,2018,77117,30.55
-wind-ons_3,2018,77500,76.5
-wind-ons_9,2018,77705,145.2
-wind-ons_6,2018,77801,6.6
-wind-ons_4,2018,77802,100.5
-wind-ons_3,2018,78183,8.0
-wind-ons_3,2018,78225,21.0
-wind-ons_8,2018,78326,51.0
-wind-ons_8,2018,78504,24.0
-wind-ons_9,2018,78957,17.0
-wind-ons_5,2018,79170,14.5
-wind-ons_4,2018,79551,10.0
-wind-ons_8,2018,79641,25.0
-wind-ons_9,2018,79755,44.55
-wind-ons_9,2018,79756,67.7
-wind-ons_4,2018,79854,34.5
-wind-ons_3,2018,80235,41.0
-wind-ons_8,2018,80325,39.0
-wind-ons_9,2018,80439,42.57
-wind-ons_5,2018,80538,11.5
-wind-ons_8,2018,81009,99.0
-wind-ons_9,2018,81123,30.0
-wind-ons_9,2018,81124,54.0
-wind-ons_4,2018,81223,87.5
-wind-ons_3,2018,81602,8.3
-wind-ons_6,2018,81693,99.0
-wind-ons_7,2018,81908,112.0
-wind-ons_2,2018,82328,21.0
-wind-ons_9,2018,82491,13.5
-wind-ons_9,2018,82523,1.8
-wind-ons_9,2018,82524,59.0
-wind-ons_8,2018,82591,37.5
-wind-ons_9,2018,82610,1.5
-wind-ons_9,2018,83175,12.9
-wind-ons_10,2018,83295,1.8
-wind-ons_8,2018,83827,0.97
-wind-ons_7,2018,83828,1.3
-wind-ons_9,2018,83859,11.88
-wind-ons_3,2018,84337,3.3
-wind-ons_6,2018,84428,1.3
-wind-ons_8,2018,84510,0.04
-wind-ons_7,2018,84511,0.97
-wind-ons_7,2018,84512,1.3
-wind-ons_6,2018,84513,2.0
-wind-ons_9,2018,84543,3.6
-wind-ons_6,2018,85112,12.14
-wind-ons_8,2018,85194,0.02
-wind-ons_8,2018,85195,0.93
-wind-ons_8,2018,85196,1.3
-wind-ons_7,2018,85197,2.0
-wind-ons_7,2018,85198,2.1
-wind-ons_5,2018,85796,19.5
-wind-ons_9,2018,85879,0.93
-wind-ons_8,2018,85880,1.3
-wind-ons_8,2018,85881,1.7
-wind-ons_8,2018,85882,2.0
-wind-ons_8,2018,85945,69.0
-wind-ons_7,2018,86012,20.0
-wind-ons_4,2018,86389,24.0
-wind-ons_5,2018,86480,25.0
-wind-ons_9,2018,86563,0.93
-wind-ons_9,2018,86565,1.7
-wind-ons_8,2018,86566,2.0
-wind-ons_8,2018,86567,4.2
-wind-ons_8,2018,86568,13.5
-wind-ons_7,2018,86569,43.06
-wind-ons_5,2018,87164,29.4
-wind-ons_9,2018,87247,0.93
-wind-ons_8,2018,87248,1.0
-wind-ons_8,2018,87249,1.7
-wind-ons_8,2018,87250,2.0
-wind-ons_7,2018,87251,4.2
-wind-ons_7,2018,87254,98.8
-wind-ons_8,2018,87313,51.2
-wind-ons_8,2018,87931,0.93
-wind-ons_8,2018,87932,1.0
-wind-ons_7,2018,87933,1.5
-wind-ons_7,2018,87934,2.0
-wind-ons_5,2018,87936,12.0
-wind-ons_6,2018,87937,32.0
-wind-ons_5,2018,88532,99.0
-wind-ons_9,2018,88615,0.9
-wind-ons_8,2018,88616,0.97
-wind-ons_8,2018,88617,1.5
-wind-ons_8,2018,88618,2.0
-wind-ons_7,2018,88620,11.8
-wind-ons_6,2018,88622,91.1
-wind-ons_6,2018,89216,99.0
-wind-ons_9,2018,89299,0.9
-wind-ons_9,2018,89300,0.97
-wind-ons_9,2018,89301,1.3
-wind-ons_9,2018,89302,2.0
-wind-ons_9,2018,89303,3.9
-wind-ons_8,2018,89304,11.6
-wind-ons_7,2018,89306,85.5
-wind-ons_3,2018,89851,64.5
-wind-ons_8,2018,89953,40.5
-wind-ons_4,2018,90534,18.9
-wind-ons_9,2018,90584,142.2
-wind-ons_9,2018,90637,50.4
-wind-ons_6,2018,90672,1.2
-wind-ons_7,2018,90674,35.0
-wind-ons_7,2018,90675,80.0
-wind-ons_7,2018,90789,9.0
-wind-ons_5,2018,90790,64.5
-wind-ons_8,2018,91321,25.0
-wind-ons_9,2018,91357,2.3
-wind-ons_7,2018,91359,80.0
-wind-ons_9,2018,91951,4.9
-wind-ons_10,2018,92005,1.3
-wind-ons_9,2018,92040,0.9
-wind-ons_8,2018,92041,2.0
-wind-ons_8,2018,92042,29.7
-wind-ons_7,2018,92043,80.0
-wind-ons_10,2018,92635,16.8
-wind-ons_9,2018,92689,1.3
-wind-ons_8,2018,92724,0.7
-wind-ons_8,2018,92726,21.0
-wind-ons_7,2018,92842,34.5
-wind-ons_8,2018,92843,75.0
-wind-ons_9,2018,93319,24.8
-wind-ons_8,2018,93408,0.7
-wind-ons_8,2018,93410,20.0
-wind-ons_8,2018,93411,78.0
-wind-ons_9,2018,93526,26.0
-wind-ons_8,2018,93528,102.0
-wind-ons_8,2018,93543,66.0
-wind-ons_2,2018,93953,10.5
-wind-ons_9,2018,94003,28.8
-wind-ons_8,2018,94092,0.6
-wind-ons_7,2018,94095,75.0
-wind-ons_7,2018,94211,52.5
-wind-ons_9,2018,94228,60.0
-wind-ons_9,2018,94687,41.4
-wind-ons_9,2018,94776,0.5
-wind-ons_7,2018,94778,10.5
-wind-ons_7,2018,94779,66.0
-wind-ons_4,2018,94896,80.0
-wind-ons_7,2018,95371,64.08
-wind-ons_9,2018,95462,10.5
-wind-ons_9,2018,95464,100.0
-wind-ons_9,2018,95465,150.0
-wind-ons_9,2018,95487,2.5
-wind-ons_8,2018,95539,1.8
-wind-ons_7,2018,96055,99.0
-wind-ons_7,2018,96223,1.8
-wind-ons_6,2018,96263,30.0
-wind-ons_8,2018,96832,99.0
-wind-ons_5,2018,96947,24.0
-wind-ons_8,2018,97516,99.0
-wind-ons_9,2018,98199,49.6
-wind-ons_8,2018,98200,98.2
-wind-ons_8,2018,98201,123.0
-wind-ons_7,2018,98205,66.0
-wind-ons_9,2018,98324,7.5
-wind-ons_7,2018,98790,1.8
-wind-ons_8,2018,98884,98.0
-wind-ons_8,2018,98886,160.5
-wind-ons_7,2018,99000,37.8
-wind-ons_7,2018,99474,15.92
-wind-ons_8,2018,99684,29.4
-wind-ons_7,2018,99686,100.5
-wind-ons_8,2018,100254,150.0
-wind-ons_8,2018,100297,106.0
-wind-ons_7,2018,100328,1.8
-wind-ons_9,2018,100368,15.0
-wind-ons_7,2018,100369,62.5
-wind-ons_8,2018,100935,43.5
-wind-ons_8,2018,100938,150.0
-wind-ons_8,2018,100939,199.7
-wind-ons_7,2018,100942,234.0
-wind-ons_8,2018,101619,42.0
-wind-ons_8,2018,101622,150.0
-wind-ons_9,2018,101644,76.2
-wind-ons_8,2018,101665,100.0
-wind-ons_10,2018,102210,42.0
-wind-ons_8,2018,102306,150.0
-wind-ons_8,2018,102307,199.0
-wind-ons_8,2018,102328,51.0
-wind-ons_9,2018,102894,48.86
-wind-ons_8,2018,102990,150.0
-wind-ons_8,2018,102991,174.8
-wind-ons_8,2018,103012,10.5
-wind-ons_9,2018,103637,59.4
-wind-ons_8,2018,103696,2.5
-wind-ons_8,2018,104380,1.5
-wind-ons_6,2018,104433,1.8
-wind-ons_8,2018,105040,78.0
-wind-ons_7,2018,105041,104.3
-wind-ons_8,2018,105065,150.0
-wind-ons_8,2018,105086,199.7
-wind-ons_9,2018,105689,54.27
-wind-ons_9,2018,105721,0.9
-wind-ons_9,2018,105724,76.5
-wind-ons_8,2018,105770,130.5
-wind-ons_9,2018,106329,199.5
-wind-ons_9,2018,106407,16.2
-wind-ons_9,2018,107013,201.0
-wind-ons_9,2018,107056,0.7
-wind-ons_8,2018,107091,15.0
-wind-ons_8,2018,107092,62.2
-wind-ons_8,2018,107117,100.5
-wind-ons_8,2018,107775,14.7
-wind-ons_5,2018,107873,66.0
-wind-ons_8,2018,108381,221.0
-wind-ons_10,2018,108424,0.7
-wind-ons_8,2018,108485,80.0
-wind-ons_5,2018,108558,100.0
-wind-ons_5,2018,108559,164.0
-wind-ons_8,2018,109169,72.0
-wind-ons_8,2018,109748,22.5
-wind-ons_8,2018,109793,8.91
-wind-ons_8,2018,109853,53.0
-wind-ons_9,2018,109875,200.0
-wind-ons_10,2018,110477,8.91
-wind-ons_8,2018,110537,49.5
-wind-ons_8,2018,111116,29.7
-wind-ons_10,2018,111161,8.91
-wind-ons_8,2018,111221,8.4
-wind-ons_8,2018,111905,1.5
-wind-ons_3,2018,112433,18.9
-wind-ons_8,2018,112484,60.0
-wind-ons_8,2018,112591,217.5
-wind-ons_8,2018,113275,210.9
-wind-ons_7,2018,113295,99.0
-wind-ons_8,2018,113852,79.5
-wind-ons_8,2018,113958,106.0
-wind-ons_9,2018,114589,148.5
-wind-ons_8,2018,114642,99.0
-wind-ons_9,2018,116588,151.8
-wind-ons_10,2018,118009,112.2
-wind-ons_5,2018,118084,101.3
-wind-ons_9,2018,118693,150.0
-wind-ons_7,2018,118746,1.7
-wind-ons_8,2018,119323,1.5
-wind-ons_9,2018,120061,105.0
-wind-ons_7,2018,120116,213.8
-wind-ons_9,2018,120691,4.0
-wind-ons_10,2018,121429,100.8
-wind-ons_7,2018,121483,100.5
-wind-ons_5,2018,122691,203.5
-wind-ons_9,2018,122797,100.5
-wind-ons_9,2018,124849,99.0
-wind-ons_6,2018,125479,75.0
-wind-ons_10,2018,126217,96.0
-wind-ons_7,2018,127626,5.0
-wind-ons_9,2018,128898,1.5
-wind-ons_9,2018,128899,162.0
-wind-ons_7,2018,129679,146.0
-wind-ons_2,2018,130161,7.4
-wind-ons_7,2018,130423,27.0
-wind-ons_2,2018,130846,18.0
-wind-ons_9,2018,131005,50.0
-wind-ons_4,2018,131106,0.7
-wind-ons_6,2018,131107,0.7
-wind-ons_3,2018,131790,0.7
-wind-ons_4,2018,132213,15.5
-wind-ons_3,2018,132215,38.0
-wind-ons_6,2018,132311,204.0
-wind-ons_9,2018,132373,44.0
-wind-ons_7,2018,132415,56.7
-wind-ons_7,2018,133099,50.4
-wind-ons_6,2018,133580,7.5
-wind-ons_7,2018,133582,24.0
-wind-ons_7,2018,133783,50.4
-wind-ons_7,2018,134265,18.4
-wind-ons_7,2018,134266,28.7
-wind-ons_8,2018,134269,28.5
-wind-ons_7,2018,134950,30.75
-wind-ons_10,2018,135730,0.6
-wind-ons_5,2018,136317,22.0
-wind-ons_5,2018,136318,36.0
-wind-ons_9,2018,136478,99.0
-wind-ons_5,2018,137000,17.0
-wind-ons_3,2018,137007,54.0
-wind-ons_9,2018,137098,20.0
-wind-ons_9,2018,137162,98.9
-wind-ons_8,2018,137782,90.0
-wind-ons_3,2018,138366,3.5
-wind-ons_4,2018,139050,4.5
-wind-ons_2,2018,139051,13.1
-wind-ons_2,2018,139052,20.5
-wind-ons_9,2018,139214,94.5
-wind-ons_8,2018,139215,151.4
-wind-ons_5,2018,139733,2.4
-wind-ons_5,2018,139734,6.5
-wind-ons_8,2018,140582,74.3
-wind-ons_2,2018,142469,5.4
-wind-ons_3,2018,143153,7.3
-wind-ons_4,2018,143154,16.2
-wind-ons_5,2018,143155,23.1
-wind-ons_5,2018,143157,36.0
-wind-ons_6,2018,143318,40.5
-wind-ons_4,2018,143836,3.0
-wind-ons_3,2018,143837,8.7
-wind-ons_2,2018,143841,60.0
-wind-ons_5,2018,143845,19.8
-wind-ons_9,2018,143937,0.7
-wind-ons_3,2018,144519,1.6
-wind-ons_3,2018,144521,9.1
-wind-ons_3,2018,144523,29.0
-wind-ons_3,2018,144524,46.9
-wind-ons_2,2018,144525,67.0
-wind-ons_2,2018,144526,60.0
-wind-ons_6,2018,144686,25.5
-wind-ons_2,2018,145202,0.1
-wind-ons_2,2018,145204,4.2
-wind-ons_3,2018,145205,11.6
-wind-ons_3,2018,145206,20.0
-wind-ons_4,2018,145207,30.75
-wind-ons_3,2018,145214,150.0
-wind-ons_10,2018,145305,30.0
-wind-ons_6,2018,145370,25.5
-wind-ons_4,2018,145891,33.6
-wind-ons_7,2018,145892,59.6
-wind-ons_3,2018,145896,135.0
-wind-ons_10,2018,145989,80.0
-wind-ons_6,2018,146054,25.5
-wind-ons_2,2018,146570,1.5
-wind-ons_2,2018,146574,22.2
-wind-ons_6,2018,146576,24.3
-wind-ons_2,2018,146581,150.0
-wind-ons_10,2018,146673,90.0
-wind-ons_5,2018,146738,25.5
-wind-ons_3,2018,147255,3.0
-wind-ons_2,2018,147257,17.3
-wind-ons_2,2018,147258,25.3
-wind-ons_3,2018,147259,43.4
-wind-ons_6,2018,147260,3.0
-wind-ons_6,2018,147422,18.9
-wind-ons_6,2018,147423,123.0
-wind-ons_3,2018,147939,3.4
-wind-ons_3,2018,147940,9.6
-wind-ons_2,2018,147948,72.0
-wind-ons_8,2018,148107,120.0
-wind-ons_3,2018,148622,1.8
-wind-ons_3,2018,148623,3.7
-wind-ons_3,2018,148624,11.7
-wind-ons_3,2018,148625,20.5
-wind-ons_3,2018,148626,31.0
-wind-ons_4,2018,148628,76.9
-wind-ons_5,2018,149306,2.1
-wind-ons_5,2018,149307,6.5
-wind-ons_4,2018,149308,13.6
-wind-ons_4,2018,149309,21.7
-wind-ons_3,2018,149310,2.9
-wind-ons_3,2018,149311,50.0
-wind-ons_3,2018,149991,8.0
-wind-ons_3,2018,149992,15.3
-wind-ons_3,2018,149993,22.4
-wind-ons_3,2018,149994,42.6
-wind-ons_7,2018,150159,106.5
-wind-ons_3,2018,150675,8.3
-wind-ons_2,2018,150676,16.5
-wind-ons_3,2018,150678,45.0
-wind-ons_5,2018,150843,101.2
-wind-ons_10,2018,152144,0.6
-wind-ons_6,2018,154946,8.8
-wind-ons_7,2018,155630,3.0
-wind-ons_7,2018,155631,79.8
-wind-ons_6,2018,155633,160.84
-wind-ons_9,2018,156248,100.0
-wind-ons_7,2018,156316,114.0
-wind-ons_2,2018,156884,63.0
-wind-ons_6,2018,156999,59.4
-wind-ons_6,2018,157000,114.0
-wind-ons_6,2018,157002,186.0
-wind-ons_7,2018,157682,0.16
-wind-ons_6,2018,157683,58.8
-wind-ons_6,2018,157684,112.5
-wind-ons_6,2018,157687,209.0
-wind-ons_7,2018,158366,0.07
-wind-ons_7,2018,158367,57.0
-wind-ons_7,2018,158368,112.5
-wind-ons_6,2018,159056,249.0
-wind-ons_6,2018,159735,49.5
-wind-ons_7,2018,160420,101.2
-wind-ons_6,2018,160421,150.0
-wind-ons_7,2018,161103,37.5
-wind-ons_6,2018,161106,180.0
-wind-ons_6,2018,161108,241.0
-wind-ons_6,2018,161787,34.3
-wind-ons_6,2018,161789,150.0
-wind-ons_6,2018,161792,232.5
-wind-ons_6,2018,161793,222.0
-wind-ons_7,2018,162471,32.52
-wind-ons_6,2018,162472,98.8
-wind-ons_6,2018,162473,150.0
-wind-ons_7,2018,162475,200.0
-wind-ons_6,2018,162476,231.5
-wind-ons_6,2018,162477,77.0
-wind-ons_6,2018,163159,200.0
-wind-ons_6,2018,163840,91.58
-wind-ons_7,2018,164524,91.2
-wind-ons_7,2018,164526,170.2
-wind-ons_7,2018,165208,91.2
-wind-ons_7,2018,165892,89.5
-wind-ons_6,2018,165894,169.5
-wind-ons_6,2018,166575,17.64
-wind-ons_7,2018,166576,84.0
-wind-ons_6,2018,166577,149.5
-wind-ons_6,2018,166580,223.5
-wind-ons_6,2018,167259,10.0
-wind-ons_7,2018,167260,82.5
-wind-ons_6,2018,167262,167.7
-wind-ons_6,2018,167265,246.0
-wind-ons_7,2018,167943,10.0
-wind-ons_6,2018,167944,82.0
-wind-ons_6,2018,167948,214.5
-wind-ons_6,2018,167949,32.0
-wind-ons_6,2018,168627,10.0
-wind-ons_6,2018,168631,197.0
-wind-ons_6,2018,168632,213.0
-wind-ons_6,2018,169311,10.0
-wind-ons_6,2018,169312,80.5
-wind-ons_6,2018,169995,10.0
-wind-ons_5,2018,169996,80.0
-wind-ons_5,2018,169997,144.0
-wind-ons_5,2018,169998,162.3
-wind-ons_6,2018,170679,10.0
-wind-ons_6,2018,171363,10.0
-wind-ons_6,2018,171364,79.92
-wind-ons_6,2018,172049,130.5
-wind-ons_6,2018,173416,79.5
-wind-ons_6,2018,173417,126.5
-wind-ons_6,2018,174784,78.0
-wind-ons_6,2018,174785,124.7
-wind-ons_6,2018,175468,75.0
-wind-ons_6,2018,175469,124.2
-wind-ons_6,2018,176152,73.5
-wind-ons_5,2018,176835,2.64
-wind-ons_6,2018,176836,69.53
-wind-ons_6,2018,176837,121.9
-wind-ons_6,2018,176838,153.9
-wind-ons_6,2018,177521,120.6
-wind-ons_5,2018,177522,152.0
-wind-ons_6,2018,178203,0.92
-wind-ons_6,2018,178205,120.0
-wind-ons_6,2018,178887,0.6
-wind-ons_6,2018,178888,63.0
-wind-ons_6,2018,178889,120.0
-wind-ons_6,2018,178891,201.6
-wind-ons_6,2018,179574,180.0
-wind-ons_7,2018,180255,0.08
-wind-ons_6,2018,180257,120.0
-wind-ons_6,2018,180261,55.2
-wind-ons_7,2018,180939,0.07
-wind-ons_6,2018,180945,174.0
-wind-ons_7,2018,181629,54.0
-wind-ons_2,2019,43995,180.0
-wind-ons_3,2019,44678,44.0
-wind-ons_1,2019,46730,48.6
-wind-ons_2,2019,47413,7.2
-wind-ons_1,2019,48097,95.0
-wind-ons_3,2019,49465,176.9
-wind-ons_6,2019,52200,156.0
-wind-ons_7,2019,52969,9.7
-wind-ons_6,2019,53567,78.0
-wind-ons_6,2019,55619,204.0
-wind-ons_7,2019,55705,104.2
-wind-ons_6,2019,56986,98.9
-wind-ons_6,2019,57670,136.3
-wind-ons_8,2019,58001,42.0
-wind-ons_6,2019,58354,199.0
-wind-ons_7,2019,58527,48.0
-wind-ons_8,2019,59202,0.97
-wind-ons_7,2019,59203,1.3
-wind-ons_8,2019,59204,2.0
-wind-ons_8,2019,59211,102.0
-wind-ons_9,2019,59215,72.0
-wind-ons_8,2019,59370,57.0
-wind-ons_3,2019,59721,94.0
-wind-ons_8,2019,59886,0.97
-wind-ons_8,2019,59887,1.3
-wind-ons_7,2019,59888,2.0
-wind-ons_7,2019,59889,3.8
-wind-ons_8,2019,59890,11.2
-wind-ons_8,2019,59899,133.5
-wind-ons_9,2019,60055,66.0
-wind-ons_2,2019,60405,122.8
-wind-ons_7,2019,60570,0.97
-wind-ons_7,2019,60571,1.3
-wind-ons_7,2019,60572,2.0
-wind-ons_7,2019,60573,3.0
-wind-ons_7,2019,60574,10.4
-wind-ons_8,2019,60575,25.0
-wind-ons_8,2019,60737,4.5
-wind-ons_3,2019,61089,149.4
-wind-ons_7,2019,61254,0.93
-wind-ons_8,2019,61255,1.2
-wind-ons_7,2019,61256,2.0
-wind-ons_8,2019,61261,100.5
-wind-ons_5,2019,61773,262.2
-wind-ons_8,2019,61906,2.6
-wind-ons_7,2019,61938,0.93
-wind-ons_7,2019,61940,2.0
-wind-ons_8,2019,61942,9.8
-wind-ons_8,2019,61943,20.0
-wind-ons_8,2019,61944,51.0
-wind-ons_8,2019,61945,100.5
-wind-ons_8,2019,61946,103.5
-wind-ons_8,2019,62544,30.0
-wind-ons_9,2019,62590,4.5
-wind-ons_7,2019,62622,0.93
-wind-ons_7,2019,62624,2.0
-wind-ons_8,2019,62625,2.1
-wind-ons_8,2019,62626,9.2
-wind-ons_8,2019,62629,100.0
-wind-ons_9,2019,62790,4.5
-wind-ons_3,2019,63140,9.9
-wind-ons_3,2019,63141,100.5
-wind-ons_9,2019,63274,11.9
-wind-ons_6,2019,63306,0.93
-wind-ons_8,2019,63308,1.9
-wind-ons_8,2019,63309,2.1
-wind-ons_9,2019,63310,8.4
-wind-ons_9,2019,63311,19.98
-wind-ons_8,2019,63312,49.5
-wind-ons_7,2019,63314,100.7
-wind-ons_5,2019,63825,102.9
-wind-ons_9,2019,63912,101.4
-wind-ons_9,2019,63958,18.0
-wind-ons_7,2019,63990,0.93
-wind-ons_7,2019,63991,0.97
-wind-ons_8,2019,63992,1.7
-wind-ons_8,2019,63993,2.1
-wind-ons_8,2019,63994,8.0
-wind-ons_6,2019,63997,99.0
-wind-ons_1,2019,64508,25.0
-wind-ons_3,2019,64510,41.0
-wind-ons_8,2019,64596,135.0
-wind-ons_7,2019,64674,0.93
-wind-ons_7,2019,64675,0.97
-wind-ons_7,2019,64676,1.7
-wind-ons_7,2019,64677,2.0
-wind-ons_8,2019,64678,5.2
-wind-ons_2,2019,65192,81.0
-wind-ons_8,2019,65326,31.5
-wind-ons_7,2019,65358,0.93
-wind-ons_8,2019,65359,0.97
-wind-ons_7,2019,65360,1.7
-wind-ons_8,2019,65361,2.0
-wind-ons_6,2019,65363,15.6
-wind-ons_8,2019,66042,0.93
-wind-ons_7,2019,66043,0.97
-wind-ons_7,2019,66044,1.7
-wind-ons_7,2019,66045,2.0
-wind-ons_6,2019,66046,4.2
-wind-ons_4,2019,66560,102.3
-wind-ons_8,2019,66694,40.5
-wind-ons_7,2019,66726,0.93
-wind-ons_8,2019,66727,0.97
-wind-ons_7,2019,66728,1.7
-wind-ons_5,2019,67244,125.4
-wind-ons_8,2019,67378,40.5
-wind-ons_8,2019,67379,118.5
-wind-ons_8,2019,67410,0.9
-wind-ons_8,2019,67411,0.97
-wind-ons_6,2019,67412,1.7
-wind-ons_7,2019,67538,54.0
-wind-ons_5,2019,67927,72.0
-wind-ons_6,2019,67928,163.3
-wind-ons_6,2019,67929,180.0
-wind-ons_8,2019,68094,0.9
-wind-ons_9,2019,68095,0.97
-wind-ons_6,2019,68222,30.55
-wind-ons_3,2019,68610,6.6
-wind-ons_8,2019,68746,48.0
-wind-ons_8,2019,68747,149.0
-wind-ons_6,2019,68778,0.9
-wind-ons_6,2019,68906,16.5
-wind-ons_4,2019,68919,5.2
-wind-ons_1,2019,69294,9.9
-wind-ons_2,2019,69295,100.8
-wind-ons_8,2019,69430,48.0
-wind-ons_8,2019,69432,180.0
-wind-ons_5,2019,69462,0.9
-wind-ons_5,2019,69591,81.0
-wind-ons_1,2019,69979,102.9
-wind-ons_9,2019,70115,115.5
-wind-ons_5,2019,70146,0.7
-wind-ons_2,2019,70662,25.2
-wind-ons_8,2019,70798,49.5
-wind-ons_8,2019,70799,120.0
-wind-ons_7,2019,70830,0.7
-wind-ons_5,2019,70959,45.5
-wind-ons_6,2019,71345,1.7
-wind-ons_8,2019,71482,49.5
-wind-ons_8,2019,71483,148.5
-wind-ons_7,2019,71643,16.5
-wind-ons_6,2019,71644,106.5
-wind-ons_8,2019,72166,50.6
-wind-ons_7,2019,72198,0.4
-wind-ons_5,2019,72328,97.5
-wind-ons_8,2019,72850,61.5
-wind-ons_5,2019,73696,45.5
-wind-ons_5,2019,74380,30.0
-wind-ons_5,2019,74764,5.0
-wind-ons_3,2019,74765,98.7
-wind-ons_4,2019,75065,100.5
-wind-ons_4,2019,75448,9.9
-wind-ons_3,2019,75449,100.7
-wind-ons_7,2019,76334,11.0
-wind-ons_6,2019,76433,68.5
-wind-ons_4,2019,76816,24.6
-wind-ons_6,2019,76857,8.4
-wind-ons_7,2019,77117,30.55
-wind-ons_3,2019,77500,76.5
-wind-ons_9,2019,77705,145.2
-wind-ons_6,2019,77801,6.6
-wind-ons_4,2019,77802,100.5
-wind-ons_3,2019,78183,8.0
-wind-ons_3,2019,78225,21.0
-wind-ons_8,2019,78326,51.0
-wind-ons_8,2019,78504,24.0
-wind-ons_9,2019,78957,17.0
-wind-ons_5,2019,79170,14.5
-wind-ons_4,2019,79551,10.0
-wind-ons_8,2019,79641,25.0
-wind-ons_9,2019,79755,44.55
-wind-ons_9,2019,79756,67.7
-wind-ons_4,2019,79854,34.5
-wind-ons_3,2019,80235,41.0
-wind-ons_8,2019,80325,39.0
-wind-ons_9,2019,80439,42.57
-wind-ons_5,2019,80538,11.5
-wind-ons_8,2019,81009,99.0
-wind-ons_9,2019,81123,30.0
-wind-ons_9,2019,81124,54.0
-wind-ons_4,2019,81223,87.5
-wind-ons_3,2019,81602,8.3
-wind-ons_6,2019,81693,99.0
-wind-ons_7,2019,81908,112.0
-wind-ons_2,2019,82328,21.0
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-wind-ons_9,2019,82523,1.8
-wind-ons_9,2019,82524,59.0
-wind-ons_8,2019,82591,37.5
-wind-ons_9,2019,82610,1.5
-wind-ons_9,2019,83175,12.9
-wind-ons_10,2019,83295,1.8
-wind-ons_8,2019,83827,0.97
-wind-ons_7,2019,83828,1.3
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-wind-ons_8,2019,84510,0.04
-wind-ons_7,2019,84511,0.97
-wind-ons_7,2019,84512,1.3
-wind-ons_6,2019,84513,2.0
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-wind-ons_6,2019,85112,12.14
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-wind-ons_8,2019,85195,0.93
-wind-ons_8,2019,85196,1.3
-wind-ons_7,2019,85197,2.0
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-wind-ons_8,2019,85880,1.3
-wind-ons_8,2019,85881,1.7
-wind-ons_8,2019,85882,2.0
-wind-ons_8,2019,85945,69.0
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-wind-ons_9,2019,86563,0.93
-wind-ons_9,2019,86565,1.7
-wind-ons_8,2019,86566,2.0
-wind-ons_8,2019,86567,4.2
-wind-ons_8,2019,86568,13.5
-wind-ons_7,2019,86569,43.06
-wind-ons_5,2019,87164,29.4
-wind-ons_9,2019,87247,0.93
-wind-ons_8,2019,87248,1.0
-wind-ons_8,2019,87249,1.7
-wind-ons_8,2019,87250,2.0
-wind-ons_7,2019,87251,4.2
-wind-ons_7,2019,87254,98.8
-wind-ons_8,2019,87313,51.2
-wind-ons_8,2019,87931,0.93
-wind-ons_8,2019,87932,1.0
-wind-ons_7,2019,87933,1.5
-wind-ons_7,2019,87934,2.0
-wind-ons_5,2019,87936,12.0
-wind-ons_6,2019,87937,32.0
-wind-ons_5,2019,88532,99.0
-wind-ons_9,2019,88615,0.9
-wind-ons_8,2019,88616,0.97
-wind-ons_8,2019,88617,1.5
-wind-ons_8,2019,88618,2.0
-wind-ons_7,2019,88620,11.8
-wind-ons_6,2019,88622,91.1
-wind-ons_6,2019,89216,99.0
-wind-ons_9,2019,89299,0.9
-wind-ons_9,2019,89300,0.97
-wind-ons_9,2019,89301,1.3
-wind-ons_9,2019,89302,2.0
-wind-ons_9,2019,89303,3.9
-wind-ons_8,2019,89304,11.6
-wind-ons_7,2019,89306,85.5
-wind-ons_3,2019,89851,64.5
-wind-ons_8,2019,89953,40.5
-wind-ons_4,2019,90534,18.9
-wind-ons_9,2019,90584,142.2
-wind-ons_9,2019,90637,50.4
-wind-ons_6,2019,90672,1.2
-wind-ons_7,2019,90674,35.0
-wind-ons_7,2019,90675,80.0
-wind-ons_7,2019,90789,9.0
-wind-ons_5,2019,90790,64.5
-wind-ons_8,2019,91321,25.0
-wind-ons_9,2019,91357,2.3
-wind-ons_7,2019,91359,80.0
-wind-ons_9,2019,91951,4.9
-wind-ons_10,2019,92005,1.3
-wind-ons_9,2019,92040,0.9
-wind-ons_8,2019,92041,2.0
-wind-ons_8,2019,92042,29.7
-wind-ons_7,2019,92043,80.0
-wind-ons_10,2019,92635,16.8
-wind-ons_9,2019,92689,1.3
-wind-ons_8,2019,92724,0.7
-wind-ons_8,2019,92726,21.0
-wind-ons_7,2019,92842,34.5
-wind-ons_8,2019,92843,75.0
-wind-ons_9,2019,93319,24.8
-wind-ons_8,2019,93408,0.7
-wind-ons_8,2019,93410,20.0
-wind-ons_8,2019,93411,78.0
-wind-ons_9,2019,93526,26.0
-wind-ons_8,2019,93528,102.0
-wind-ons_8,2019,93543,66.0
-wind-ons_2,2019,93953,10.5
-wind-ons_9,2019,94003,28.8
-wind-ons_8,2019,94092,0.6
-wind-ons_7,2019,94095,75.0
-wind-ons_7,2019,94211,52.5
-wind-ons_9,2019,94228,60.0
-wind-ons_9,2019,94687,41.4
-wind-ons_9,2019,94776,0.5
-wind-ons_7,2019,94778,10.5
-wind-ons_7,2019,94779,66.0
-wind-ons_4,2019,94896,80.0
-wind-ons_7,2019,95371,64.08
-wind-ons_9,2019,95462,10.5
-wind-ons_9,2019,95464,100.0
-wind-ons_9,2019,95465,150.0
-wind-ons_9,2019,95487,2.5
-wind-ons_8,2019,95539,1.8
-wind-ons_7,2019,96055,99.0
-wind-ons_7,2019,96223,1.8
-wind-ons_6,2019,96263,30.0
-wind-ons_8,2019,96832,99.0
-wind-ons_5,2019,96947,24.0
-wind-ons_8,2019,97516,99.0
-wind-ons_9,2019,98199,49.6
-wind-ons_8,2019,98200,98.2
-wind-ons_8,2019,98201,123.0
-wind-ons_7,2019,98205,66.0
-wind-ons_9,2019,98324,7.5
-wind-ons_7,2019,98790,1.8
-wind-ons_8,2019,98884,98.0
-wind-ons_8,2019,98886,160.5
-wind-ons_7,2019,99000,37.8
-wind-ons_7,2019,99474,15.92
-wind-ons_8,2019,99684,29.4
-wind-ons_7,2019,99686,100.5
-wind-ons_8,2019,100254,150.0
-wind-ons_8,2019,100297,106.0
-wind-ons_7,2019,100328,1.8
-wind-ons_9,2019,100368,15.0
-wind-ons_7,2019,100369,62.5
-wind-ons_8,2019,100935,43.5
-wind-ons_8,2019,100938,150.0
-wind-ons_8,2019,100939,199.7
-wind-ons_7,2019,100942,234.0
-wind-ons_8,2019,101619,42.0
-wind-ons_8,2019,101622,150.0
-wind-ons_9,2019,101644,76.2
-wind-ons_8,2019,101665,100.0
-wind-ons_10,2019,102210,42.0
-wind-ons_8,2019,102306,150.0
-wind-ons_8,2019,102307,199.0
-wind-ons_8,2019,102328,51.0
-wind-ons_9,2019,102894,48.86
-wind-ons_8,2019,102990,150.0
-wind-ons_8,2019,102991,174.8
-wind-ons_8,2019,103012,10.5
-wind-ons_9,2019,103637,59.4
-wind-ons_8,2019,103696,2.5
-wind-ons_8,2019,104380,1.5
-wind-ons_6,2019,104433,1.8
-wind-ons_8,2019,105040,78.0
-wind-ons_7,2019,105041,104.3
-wind-ons_8,2019,105065,150.0
-wind-ons_8,2019,105086,199.7
-wind-ons_9,2019,105689,54.27
-wind-ons_9,2019,105721,0.9
-wind-ons_9,2019,105724,76.5
-wind-ons_8,2019,105770,130.5
-wind-ons_9,2019,106329,199.5
-wind-ons_9,2019,106407,16.2
-wind-ons_9,2019,107013,201.0
-wind-ons_9,2019,107056,0.7
-wind-ons_8,2019,107091,15.0
-wind-ons_8,2019,107092,62.2
-wind-ons_8,2019,107117,100.5
-wind-ons_8,2019,107775,14.7
-wind-ons_5,2019,107873,66.0
-wind-ons_8,2019,108381,221.0
-wind-ons_10,2019,108424,0.7
-wind-ons_8,2019,108485,80.0
-wind-ons_5,2019,108558,100.0
-wind-ons_5,2019,108559,164.0
-wind-ons_8,2019,109169,72.0
-wind-ons_8,2019,109748,22.5
-wind-ons_8,2019,109793,8.91
-wind-ons_8,2019,109853,53.0
-wind-ons_9,2019,109875,200.0
-wind-ons_10,2019,110477,8.91
-wind-ons_8,2019,110537,49.5
-wind-ons_8,2019,111116,29.7
-wind-ons_10,2019,111161,8.91
-wind-ons_8,2019,111221,8.4
-wind-ons_8,2019,111905,1.5
-wind-ons_3,2019,112433,18.9
-wind-ons_8,2019,112484,60.0
-wind-ons_8,2019,112591,217.5
-wind-ons_8,2019,113275,210.9
-wind-ons_7,2019,113295,99.0
-wind-ons_8,2019,113852,79.5
-wind-ons_8,2019,113958,106.0
-wind-ons_9,2019,114589,148.5
-wind-ons_8,2019,114642,99.0
-wind-ons_9,2019,116588,151.8
-wind-ons_10,2019,118009,112.2
-wind-ons_5,2019,118084,101.3
-wind-ons_9,2019,118693,150.0
-wind-ons_7,2019,118746,1.7
-wind-ons_8,2019,119323,1.5
-wind-ons_9,2019,120061,105.0
-wind-ons_7,2019,120116,213.8
-wind-ons_9,2019,120691,4.0
-wind-ons_10,2019,121429,100.8
-wind-ons_7,2019,121483,100.5
-wind-ons_5,2019,122691,203.5
-wind-ons_9,2019,122797,100.5
-wind-ons_9,2019,124849,99.0
-wind-ons_6,2019,125479,75.0
-wind-ons_10,2019,126217,96.0
-wind-ons_7,2019,127626,5.0
-wind-ons_9,2019,128898,1.5
-wind-ons_9,2019,128899,162.0
-wind-ons_7,2019,129679,146.0
-wind-ons_2,2019,130161,7.4
-wind-ons_7,2019,130423,27.0
-wind-ons_2,2019,130846,18.0
-wind-ons_9,2019,131005,50.0
-wind-ons_4,2019,131106,0.7
-wind-ons_6,2019,131107,0.7
-wind-ons_3,2019,131790,0.7
-wind-ons_4,2019,132213,15.5
-wind-ons_3,2019,132215,38.0
-wind-ons_6,2019,132311,204.0
-wind-ons_9,2019,132373,44.0
-wind-ons_7,2019,132415,56.7
-wind-ons_7,2019,133099,50.4
-wind-ons_6,2019,133580,7.5
-wind-ons_7,2019,133582,24.0
-wind-ons_7,2019,133783,50.4
-wind-ons_7,2019,134265,18.4
-wind-ons_7,2019,134266,28.7
-wind-ons_8,2019,134269,28.5
-wind-ons_7,2019,134950,30.75
-wind-ons_10,2019,135730,0.6
-wind-ons_5,2019,136317,22.0
-wind-ons_5,2019,136318,36.0
-wind-ons_9,2019,136478,99.0
-wind-ons_5,2019,137000,17.0
-wind-ons_3,2019,137007,54.0
-wind-ons_9,2019,137098,20.0
-wind-ons_9,2019,137162,98.9
-wind-ons_8,2019,137782,90.0
-wind-ons_3,2019,138366,3.5
-wind-ons_4,2019,139050,4.5
-wind-ons_2,2019,139051,13.1
-wind-ons_2,2019,139052,20.5
-wind-ons_9,2019,139214,94.5
-wind-ons_8,2019,139215,151.4
-wind-ons_5,2019,139733,2.4
-wind-ons_5,2019,139734,6.5
-wind-ons_8,2019,140582,74.3
-wind-ons_2,2019,142469,5.4
-wind-ons_3,2019,143153,7.3
-wind-ons_4,2019,143154,16.2
-wind-ons_5,2019,143155,23.1
-wind-ons_5,2019,143157,36.0
-wind-ons_6,2019,143318,40.5
-wind-ons_4,2019,143836,3.0
-wind-ons_3,2019,143837,8.7
-wind-ons_2,2019,143841,60.0
-wind-ons_5,2019,143845,19.8
-wind-ons_9,2019,143937,0.7
-wind-ons_3,2019,144519,1.6
-wind-ons_3,2019,144521,9.1
-wind-ons_3,2019,144523,29.0
-wind-ons_3,2019,144524,46.9
-wind-ons_2,2019,144525,67.0
-wind-ons_2,2019,144526,60.0
-wind-ons_6,2019,144686,25.5
-wind-ons_2,2019,145202,0.1
-wind-ons_2,2019,145204,4.2
-wind-ons_3,2019,145205,11.6
-wind-ons_3,2019,145206,20.0
-wind-ons_4,2019,145207,30.75
-wind-ons_3,2019,145214,150.0
-wind-ons_10,2019,145305,30.0
-wind-ons_6,2019,145370,25.5
-wind-ons_4,2019,145891,33.6
-wind-ons_7,2019,145892,59.6
-wind-ons_3,2019,145896,135.0
-wind-ons_10,2019,145989,80.0
-wind-ons_6,2019,146054,25.5
-wind-ons_2,2019,146570,1.5
-wind-ons_2,2019,146574,22.2
-wind-ons_6,2019,146576,24.3
-wind-ons_2,2019,146581,150.0
-wind-ons_10,2019,146673,90.0
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-wind-ons_3,2019,147255,3.0
-wind-ons_2,2019,147257,17.3
-wind-ons_2,2019,147258,25.3
-wind-ons_3,2019,147259,43.4
-wind-ons_6,2019,147260,3.0
-wind-ons_6,2019,147422,18.9
-wind-ons_6,2019,147423,123.0
-wind-ons_3,2019,147939,3.4
-wind-ons_3,2019,147940,9.6
-wind-ons_2,2019,147948,72.0
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-wind-ons_3,2019,148622,1.8
-wind-ons_3,2019,148623,3.7
-wind-ons_3,2019,148624,11.7
-wind-ons_3,2019,148625,20.5
-wind-ons_3,2019,148626,31.0
-wind-ons_4,2019,148628,76.9
-wind-ons_5,2019,149306,2.1
-wind-ons_5,2019,149307,6.5
-wind-ons_4,2019,149308,13.6
-wind-ons_4,2019,149309,21.7
-wind-ons_3,2019,149310,2.9
-wind-ons_3,2019,149311,50.0
-wind-ons_3,2019,149991,8.0
-wind-ons_3,2019,149992,15.3
-wind-ons_3,2019,149993,22.4
-wind-ons_3,2019,149994,42.6
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-wind-ons_3,2019,150675,8.3
-wind-ons_2,2019,150676,16.5
-wind-ons_3,2019,150678,45.0
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-wind-ons_6,2019,154946,8.8
-wind-ons_7,2019,155630,3.0
-wind-ons_7,2019,155631,79.8
-wind-ons_6,2019,155633,160.84
-wind-ons_9,2019,156248,100.0
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-wind-ons_2,2019,156884,63.0
-wind-ons_6,2019,156999,59.4
-wind-ons_6,2019,157000,114.0
-wind-ons_6,2019,157002,186.0
-wind-ons_7,2019,157682,0.16
-wind-ons_6,2019,157683,58.8
-wind-ons_6,2019,157684,112.5
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-wind-ons_7,2019,158367,57.0
-wind-ons_7,2019,158368,112.5
-wind-ons_6,2019,159056,249.0
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-wind-ons_7,2019,160420,101.2
-wind-ons_6,2019,160421,150.0
-wind-ons_7,2019,161103,37.5
-wind-ons_6,2019,161106,180.0
-wind-ons_6,2019,161108,241.0
-wind-ons_6,2019,161787,34.3
-wind-ons_6,2019,161789,150.0
-wind-ons_6,2019,161792,232.5
-wind-ons_6,2019,161793,222.0
-wind-ons_7,2019,162471,32.52
-wind-ons_6,2019,162472,98.8
-wind-ons_6,2019,162473,150.0
-wind-ons_7,2019,162475,200.0
-wind-ons_6,2019,162476,231.5
-wind-ons_6,2019,162477,77.0
-wind-ons_6,2019,163159,200.0
-wind-ons_6,2019,163840,91.58
-wind-ons_7,2019,164524,91.2
-wind-ons_7,2019,164526,170.2
-wind-ons_7,2019,165208,91.2
-wind-ons_7,2019,165892,89.5
-wind-ons_6,2019,165894,169.5
-wind-ons_6,2019,166575,17.64
-wind-ons_7,2019,166576,84.0
-wind-ons_6,2019,166577,149.5
-wind-ons_6,2019,166580,223.5
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-wind-ons_7,2019,167260,82.5
-wind-ons_6,2019,167262,167.7
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-wind-ons_6,2019,167944,82.0
-wind-ons_6,2019,167948,214.5
-wind-ons_6,2019,167949,32.0
-wind-ons_6,2019,168627,10.0
-wind-ons_6,2019,168631,197.0
-wind-ons_6,2019,168632,213.0
-wind-ons_6,2019,169311,10.0
-wind-ons_6,2019,169312,80.5
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-wind-ons_5,2019,169996,80.0
-wind-ons_5,2019,169997,144.0
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-wind-ons_6,2019,170679,10.0
-wind-ons_6,2019,171363,10.0
-wind-ons_6,2019,171364,79.92
-wind-ons_6,2019,172049,130.5
-wind-ons_6,2019,173416,79.5
-wind-ons_6,2019,173417,126.5
-wind-ons_6,2019,174784,78.0
-wind-ons_6,2019,174785,124.7
-wind-ons_6,2019,175468,75.0
-wind-ons_6,2019,175469,124.2
-wind-ons_6,2019,176152,73.5
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-wind-ons_6,2019,176836,69.53
-wind-ons_6,2019,176837,121.9
-wind-ons_6,2019,176838,153.9
-wind-ons_6,2019,177521,120.6
-wind-ons_5,2019,177522,152.0
-wind-ons_6,2019,178203,0.92
-wind-ons_6,2019,178205,120.0
-wind-ons_6,2019,178887,0.6
-wind-ons_6,2019,178888,63.0
-wind-ons_6,2019,178889,120.0
-wind-ons_6,2019,178891,201.6
-wind-ons_6,2019,179574,180.0
-wind-ons_7,2019,180255,0.08
-wind-ons_6,2019,180257,120.0
-wind-ons_6,2019,180261,55.2
-wind-ons_7,2019,180939,0.07
-wind-ons_6,2019,180945,174.0
-wind-ons_7,2019,181629,54.0
-wind-ons_2,2020,43995,180.0
-wind-ons_3,2020,44678,44.0
-wind-ons_1,2020,46730,48.6
-wind-ons_2,2020,47413,7.2
-wind-ons_1,2020,48097,95.0
-wind-ons_3,2020,49465,176.9
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-wind-ons_7,2020,52969,9.7
-wind-ons_6,2020,53567,78.0
-wind-ons_6,2020,55619,204.0
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-wind-ons_6,2020,56986,98.9
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-wind-ons_6,2020,58354,199.0
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-wind-ons_7,2020,59203,1.3
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-wind-ons_8,2020,59211,102.0
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-wind-ons_8,2020,59886,0.97
-wind-ons_8,2020,59887,1.3
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-wind-ons_7,2020,59889,3.8
-wind-ons_8,2020,59890,11.2
-wind-ons_8,2020,59899,133.5
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-wind-ons_7,2020,60571,1.3
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-wind-ons_7,2020,60573,3.0
-wind-ons_7,2020,60574,10.4
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-wind-ons_8,2020,61255,1.2
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-wind-ons_8,2020,63992,1.7
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-wind-ons_8,2020,64596,135.0
-wind-ons_7,2020,64674,0.93
-wind-ons_7,2020,64675,0.97
-wind-ons_7,2020,64676,1.7
-wind-ons_7,2020,64677,2.0
-wind-ons_8,2020,64678,5.2
-wind-ons_2,2020,65192,81.0
-wind-ons_8,2020,65326,31.5
-wind-ons_7,2020,65358,0.93
-wind-ons_8,2020,65359,0.97
-wind-ons_7,2020,65360,1.7
-wind-ons_8,2020,65361,2.0
-wind-ons_6,2020,65363,15.6
-wind-ons_8,2020,66042,0.93
-wind-ons_7,2020,66043,0.97
-wind-ons_7,2020,66044,1.7
-wind-ons_7,2020,66045,2.0
-wind-ons_6,2020,66046,4.2
-wind-ons_4,2020,66560,102.3
-wind-ons_8,2020,66694,40.5
-wind-ons_7,2020,66726,0.93
-wind-ons_8,2020,66727,0.97
-wind-ons_7,2020,66728,1.7
-wind-ons_5,2020,67244,125.4
-wind-ons_8,2020,67378,40.5
-wind-ons_8,2020,67379,118.5
-wind-ons_8,2020,67410,0.9
-wind-ons_8,2020,67411,0.97
-wind-ons_6,2020,67412,1.7
-wind-ons_7,2020,67538,54.0
-wind-ons_5,2020,67927,72.0
-wind-ons_6,2020,67928,163.3
-wind-ons_6,2020,67929,180.0
-wind-ons_8,2020,68094,0.9
-wind-ons_9,2020,68095,0.97
-wind-ons_6,2020,68222,30.55
-wind-ons_3,2020,68610,6.6
-wind-ons_8,2020,68746,48.0
-wind-ons_8,2020,68747,149.0
-wind-ons_6,2020,68778,0.9
-wind-ons_6,2020,68906,16.5
-wind-ons_4,2020,68919,5.2
-wind-ons_1,2020,69294,9.9
-wind-ons_2,2020,69295,100.8
-wind-ons_8,2020,69430,48.0
-wind-ons_8,2020,69432,180.0
-wind-ons_5,2020,69462,0.9
-wind-ons_5,2020,69591,81.0
-wind-ons_1,2020,69979,102.9
-wind-ons_9,2020,70115,115.5
-wind-ons_5,2020,70146,0.7
-wind-ons_2,2020,70662,25.2
-wind-ons_8,2020,70798,49.5
-wind-ons_8,2020,70799,120.0
-wind-ons_7,2020,70830,0.7
-wind-ons_5,2020,70959,45.5
-wind-ons_6,2020,71345,1.7
-wind-ons_8,2020,71482,49.5
-wind-ons_8,2020,71483,148.5
-wind-ons_7,2020,71643,16.5
-wind-ons_6,2020,71644,106.5
-wind-ons_8,2020,72166,50.6
-wind-ons_7,2020,72198,0.4
-wind-ons_5,2020,72328,97.5
-wind-ons_8,2020,72850,61.5
-wind-ons_5,2020,73696,45.5
-wind-ons_5,2020,74380,30.0
-wind-ons_5,2020,74764,5.0
-wind-ons_3,2020,74765,98.7
-wind-ons_4,2020,75065,100.5
-wind-ons_4,2020,75448,9.9
-wind-ons_3,2020,75449,100.7
-wind-ons_7,2020,76334,11.0
-wind-ons_6,2020,76433,68.5
-wind-ons_4,2020,76816,24.6
-wind-ons_6,2020,76857,8.4
-wind-ons_7,2020,77117,30.55
-wind-ons_3,2020,77500,76.5
-wind-ons_9,2020,77705,145.2
-wind-ons_6,2020,77801,6.6
-wind-ons_4,2020,77802,100.5
-wind-ons_3,2020,78183,8.0
-wind-ons_3,2020,78225,21.0
-wind-ons_8,2020,78326,51.0
-wind-ons_8,2020,78504,24.0
-wind-ons_9,2020,78957,17.0
-wind-ons_5,2020,79170,14.5
-wind-ons_4,2020,79551,10.0
-wind-ons_8,2020,79641,25.0
-wind-ons_9,2020,79755,44.55
-wind-ons_9,2020,79756,67.7
-wind-ons_4,2020,79854,34.5
-wind-ons_3,2020,80235,41.0
-wind-ons_8,2020,80325,39.0
-wind-ons_9,2020,80439,42.57
-wind-ons_5,2020,80538,11.5
-wind-ons_8,2020,81009,99.0
-wind-ons_9,2020,81123,30.0
-wind-ons_9,2020,81124,54.0
-wind-ons_4,2020,81223,87.5
-wind-ons_3,2020,81602,8.3
-wind-ons_6,2020,81693,99.0
-wind-ons_7,2020,81908,112.0
-wind-ons_2,2020,82328,21.0
-wind-ons_9,2020,82491,13.5
-wind-ons_9,2020,82523,1.8
-wind-ons_9,2020,82524,59.0
-wind-ons_8,2020,82591,37.5
-wind-ons_9,2020,82610,1.5
-wind-ons_9,2020,83175,12.9
-wind-ons_10,2020,83295,1.8
-wind-ons_8,2020,83827,0.97
-wind-ons_7,2020,83828,1.3
-wind-ons_9,2020,83859,11.88
-wind-ons_3,2020,84337,3.3
-wind-ons_6,2020,84428,1.3
-wind-ons_8,2020,84510,0.04
-wind-ons_7,2020,84511,0.97
-wind-ons_7,2020,84512,1.3
-wind-ons_6,2020,84513,2.0
-wind-ons_9,2020,84543,3.6
-wind-ons_6,2020,85112,12.14
-wind-ons_8,2020,85194,0.02
-wind-ons_8,2020,85195,0.93
-wind-ons_8,2020,85196,1.3
-wind-ons_7,2020,85197,2.0
-wind-ons_7,2020,85198,2.1
-wind-ons_5,2020,85796,19.5
-wind-ons_9,2020,85879,0.93
-wind-ons_8,2020,85880,1.3
-wind-ons_8,2020,85881,1.7
-wind-ons_8,2020,85882,2.0
-wind-ons_8,2020,85945,69.0
-wind-ons_7,2020,86012,20.0
-wind-ons_4,2020,86389,24.0
-wind-ons_5,2020,86480,25.0
-wind-ons_9,2020,86563,0.93
-wind-ons_9,2020,86565,1.7
-wind-ons_8,2020,86566,2.0
-wind-ons_8,2020,86567,4.2
-wind-ons_8,2020,86568,13.5
-wind-ons_7,2020,86569,43.06
-wind-ons_5,2020,87164,29.4
-wind-ons_9,2020,87247,0.93
-wind-ons_8,2020,87248,1.0
-wind-ons_8,2020,87249,1.7
-wind-ons_8,2020,87250,2.0
-wind-ons_7,2020,87251,4.2
-wind-ons_7,2020,87254,98.8
-wind-ons_8,2020,87313,51.2
-wind-ons_8,2020,87931,0.93
-wind-ons_8,2020,87932,1.0
-wind-ons_7,2020,87933,1.5
-wind-ons_7,2020,87934,2.0
-wind-ons_5,2020,87936,12.0
-wind-ons_6,2020,87937,32.0
-wind-ons_5,2020,88532,99.0
-wind-ons_9,2020,88615,0.9
-wind-ons_8,2020,88616,0.97
-wind-ons_8,2020,88617,1.5
-wind-ons_8,2020,88618,2.0
-wind-ons_7,2020,88620,11.8
-wind-ons_6,2020,88622,91.1
-wind-ons_6,2020,89216,99.0
-wind-ons_9,2020,89299,0.9
-wind-ons_9,2020,89300,0.97
-wind-ons_9,2020,89301,1.3
-wind-ons_9,2020,89302,2.0
-wind-ons_9,2020,89303,3.9
-wind-ons_8,2020,89304,11.6
-wind-ons_7,2020,89306,85.5
-wind-ons_3,2020,89851,64.5
-wind-ons_8,2020,89953,40.5
-wind-ons_4,2020,90534,18.9
-wind-ons_9,2020,90584,142.2
-wind-ons_9,2020,90637,50.4
-wind-ons_6,2020,90672,1.2
-wind-ons_7,2020,90674,35.0
-wind-ons_7,2020,90675,80.0
-wind-ons_7,2020,90789,9.0
-wind-ons_5,2020,90790,64.5
-wind-ons_8,2020,91321,25.0
-wind-ons_9,2020,91357,2.3
-wind-ons_7,2020,91359,80.0
-wind-ons_9,2020,91951,4.9
-wind-ons_10,2020,92005,1.3
-wind-ons_9,2020,92040,0.9
-wind-ons_8,2020,92041,2.0
-wind-ons_8,2020,92042,29.7
-wind-ons_7,2020,92043,80.0
-wind-ons_10,2020,92635,16.8
-wind-ons_9,2020,92689,1.3
-wind-ons_8,2020,92724,0.7
-wind-ons_8,2020,92726,21.0
-wind-ons_7,2020,92842,34.5
-wind-ons_8,2020,92843,75.0
-wind-ons_9,2020,93319,24.8
-wind-ons_8,2020,93408,0.7
-wind-ons_8,2020,93410,20.0
-wind-ons_8,2020,93411,78.0
-wind-ons_9,2020,93526,26.0
-wind-ons_8,2020,93528,102.0
-wind-ons_8,2020,93543,66.0
-wind-ons_2,2020,93953,10.5
-wind-ons_9,2020,94003,28.8
-wind-ons_8,2020,94092,0.6
-wind-ons_7,2020,94095,75.0
-wind-ons_7,2020,94211,52.5
-wind-ons_9,2020,94228,60.0
-wind-ons_9,2020,94687,41.4
-wind-ons_9,2020,94776,0.5
-wind-ons_7,2020,94778,10.5
-wind-ons_7,2020,94779,66.0
-wind-ons_4,2020,94896,80.0
-wind-ons_7,2020,95371,64.08
-wind-ons_9,2020,95462,10.5
-wind-ons_9,2020,95464,100.0
-wind-ons_9,2020,95465,150.0
-wind-ons_9,2020,95487,2.5
-wind-ons_8,2020,95539,1.8
-wind-ons_7,2020,96055,99.0
-wind-ons_7,2020,96223,1.8
-wind-ons_6,2020,96263,30.0
-wind-ons_8,2020,96832,99.0
-wind-ons_5,2020,96947,24.0
-wind-ons_8,2020,97516,99.0
-wind-ons_9,2020,98199,49.6
-wind-ons_8,2020,98200,98.2
-wind-ons_8,2020,98201,123.0
-wind-ons_7,2020,98205,66.0
-wind-ons_9,2020,98324,7.5
-wind-ons_7,2020,98790,1.8
-wind-ons_8,2020,98884,98.0
-wind-ons_8,2020,98886,160.5
-wind-ons_7,2020,99000,37.8
-wind-ons_7,2020,99474,15.92
-wind-ons_8,2020,99684,29.4
-wind-ons_7,2020,99686,100.5
-wind-ons_8,2020,100254,150.0
-wind-ons_8,2020,100297,106.0
-wind-ons_7,2020,100328,1.8
-wind-ons_9,2020,100368,15.0
-wind-ons_7,2020,100369,62.5
-wind-ons_8,2020,100935,43.5
-wind-ons_8,2020,100938,150.0
-wind-ons_8,2020,100939,199.7
-wind-ons_7,2020,100942,234.0
-wind-ons_8,2020,101619,42.0
-wind-ons_8,2020,101622,150.0
-wind-ons_9,2020,101644,76.2
-wind-ons_8,2020,101665,100.0
-wind-ons_10,2020,102210,42.0
-wind-ons_8,2020,102306,150.0
-wind-ons_8,2020,102307,199.0
-wind-ons_8,2020,102328,51.0
-wind-ons_9,2020,102894,48.86
-wind-ons_8,2020,102990,150.0
-wind-ons_8,2020,102991,174.8
-wind-ons_8,2020,103012,10.5
-wind-ons_9,2020,103637,59.4
-wind-ons_8,2020,103696,2.5
-wind-ons_8,2020,104380,1.5
-wind-ons_6,2020,104433,1.8
-wind-ons_8,2020,105040,78.0
-wind-ons_7,2020,105041,104.3
-wind-ons_8,2020,105065,150.0
-wind-ons_8,2020,105086,199.7
-wind-ons_9,2020,105689,54.27
-wind-ons_9,2020,105721,0.9
-wind-ons_9,2020,105724,76.5
-wind-ons_8,2020,105770,130.5
-wind-ons_9,2020,106329,199.5
-wind-ons_9,2020,106407,16.2
-wind-ons_9,2020,107013,201.0
-wind-ons_9,2020,107056,0.7
-wind-ons_8,2020,107091,15.0
-wind-ons_8,2020,107092,62.2
-wind-ons_8,2020,107117,100.5
-wind-ons_8,2020,107775,14.7
-wind-ons_5,2020,107873,66.0
-wind-ons_8,2020,108381,221.0
-wind-ons_10,2020,108424,0.7
-wind-ons_8,2020,108485,80.0
-wind-ons_5,2020,108558,100.0
-wind-ons_5,2020,108559,164.0
-wind-ons_8,2020,109169,72.0
-wind-ons_8,2020,109748,22.5
-wind-ons_8,2020,109793,8.91
-wind-ons_8,2020,109853,53.0
-wind-ons_9,2020,109875,200.0
-wind-ons_10,2020,110477,8.91
-wind-ons_8,2020,110537,49.5
-wind-ons_8,2020,111116,29.7
-wind-ons_10,2020,111161,8.91
-wind-ons_8,2020,111221,8.4
-wind-ons_8,2020,111905,1.5
-wind-ons_3,2020,112433,18.9
-wind-ons_8,2020,112484,60.0
-wind-ons_8,2020,112591,217.5
-wind-ons_8,2020,113275,210.9
-wind-ons_7,2020,113295,99.0
-wind-ons_8,2020,113852,79.5
-wind-ons_8,2020,113958,106.0
-wind-ons_9,2020,114589,148.5
-wind-ons_8,2020,114642,99.0
-wind-ons_9,2020,116588,151.8
-wind-ons_10,2020,118009,112.2
-wind-ons_5,2020,118084,101.3
-wind-ons_9,2020,118693,150.0
-wind-ons_7,2020,118746,1.7
-wind-ons_8,2020,119323,1.5
-wind-ons_9,2020,120061,105.0
-wind-ons_7,2020,120116,213.8
-wind-ons_9,2020,120691,4.0
-wind-ons_10,2020,121429,100.8
-wind-ons_7,2020,121483,100.5
-wind-ons_5,2020,122691,203.5
-wind-ons_9,2020,122797,100.5
-wind-ons_9,2020,124849,99.0
-wind-ons_6,2020,125479,75.0
-wind-ons_10,2020,126217,96.0
-wind-ons_7,2020,127626,5.0
-wind-ons_9,2020,128898,1.5
-wind-ons_9,2020,128899,162.0
-wind-ons_7,2020,129679,146.0
-wind-ons_2,2020,130161,7.4
-wind-ons_7,2020,130423,27.0
-wind-ons_2,2020,130846,18.0
-wind-ons_9,2020,131005,50.0
-wind-ons_4,2020,131106,0.7
-wind-ons_6,2020,131107,0.7
-wind-ons_3,2020,131790,0.7
-wind-ons_4,2020,132213,15.5
-wind-ons_3,2020,132215,38.0
-wind-ons_6,2020,132311,204.0
-wind-ons_9,2020,132373,44.0
-wind-ons_7,2020,132415,56.7
-wind-ons_7,2020,133099,50.4
-wind-ons_6,2020,133580,7.5
-wind-ons_7,2020,133582,24.0
-wind-ons_7,2020,133783,50.4
-wind-ons_7,2020,134265,18.4
-wind-ons_7,2020,134266,28.7
-wind-ons_8,2020,134269,28.5
-wind-ons_7,2020,134950,30.75
-wind-ons_10,2020,135730,0.6
-wind-ons_5,2020,136317,22.0
-wind-ons_5,2020,136318,36.0
-wind-ons_9,2020,136478,99.0
-wind-ons_5,2020,137000,17.0
-wind-ons_3,2020,137007,54.0
-wind-ons_9,2020,137098,20.0
-wind-ons_9,2020,137162,98.9
-wind-ons_8,2020,137782,90.0
-wind-ons_3,2020,138366,3.5
-wind-ons_4,2020,139050,4.5
-wind-ons_2,2020,139051,13.1
-wind-ons_2,2020,139052,20.5
-wind-ons_9,2020,139214,94.5
-wind-ons_8,2020,139215,151.4
-wind-ons_5,2020,139733,2.4
-wind-ons_5,2020,139734,6.5
-wind-ons_8,2020,140582,74.3
-wind-ons_2,2020,142469,5.4
-wind-ons_3,2020,143153,7.3
-wind-ons_4,2020,143154,16.2
-wind-ons_5,2020,143155,23.1
-wind-ons_5,2020,143157,36.0
-wind-ons_6,2020,143318,40.5
-wind-ons_4,2020,143836,3.0
-wind-ons_3,2020,143837,8.7
-wind-ons_2,2020,143841,60.0
-wind-ons_5,2020,143845,19.8
-wind-ons_9,2020,143937,0.7
-wind-ons_3,2020,144519,1.6
-wind-ons_3,2020,144521,9.1
-wind-ons_3,2020,144523,29.0
-wind-ons_3,2020,144524,46.9
-wind-ons_2,2020,144525,67.0
-wind-ons_2,2020,144526,60.0
-wind-ons_6,2020,144686,25.5
-wind-ons_2,2020,145202,0.1
-wind-ons_2,2020,145204,4.2
-wind-ons_3,2020,145205,11.6
-wind-ons_3,2020,145206,20.0
-wind-ons_4,2020,145207,30.75
-wind-ons_3,2020,145214,150.0
-wind-ons_10,2020,145305,30.0
-wind-ons_6,2020,145370,25.5
-wind-ons_4,2020,145891,33.6
-wind-ons_7,2020,145892,59.6
-wind-ons_3,2020,145896,135.0
-wind-ons_10,2020,145989,80.0
-wind-ons_6,2020,146054,25.5
-wind-ons_2,2020,146570,1.5
-wind-ons_2,2020,146574,22.2
-wind-ons_6,2020,146576,24.3
-wind-ons_2,2020,146581,150.0
-wind-ons_10,2020,146673,90.0
-wind-ons_5,2020,146738,25.5
-wind-ons_3,2020,147255,3.0
-wind-ons_2,2020,147257,17.3
-wind-ons_2,2020,147258,25.3
-wind-ons_3,2020,147259,43.4
-wind-ons_6,2020,147260,3.0
-wind-ons_6,2020,147422,18.9
-wind-ons_6,2020,147423,123.0
-wind-ons_3,2020,147939,3.4
-wind-ons_3,2020,147940,9.6
-wind-ons_2,2020,147948,72.0
-wind-ons_8,2020,148107,120.0
-wind-ons_3,2020,148622,1.8
-wind-ons_3,2020,148623,3.7
-wind-ons_3,2020,148624,11.7
-wind-ons_3,2020,148625,20.5
-wind-ons_3,2020,148626,31.0
-wind-ons_4,2020,148628,76.9
-wind-ons_5,2020,149306,2.1
-wind-ons_5,2020,149307,6.5
-wind-ons_4,2020,149308,13.6
-wind-ons_4,2020,149309,21.7
-wind-ons_3,2020,149310,2.9
-wind-ons_3,2020,149311,50.0
-wind-ons_3,2020,149991,8.0
-wind-ons_3,2020,149992,15.3
-wind-ons_3,2020,149993,22.4
-wind-ons_3,2020,149994,42.6
-wind-ons_7,2020,150159,106.5
-wind-ons_3,2020,150675,8.3
-wind-ons_2,2020,150676,16.5
-wind-ons_3,2020,150678,45.0
-wind-ons_5,2020,150843,101.2
-wind-ons_10,2020,152144,0.6
-wind-ons_6,2020,154946,8.8
-wind-ons_7,2020,155630,3.0
-wind-ons_7,2020,155631,79.8
-wind-ons_6,2020,155633,160.84
-wind-ons_9,2020,156248,100.0
-wind-ons_7,2020,156316,114.0
-wind-ons_2,2020,156884,63.0
-wind-ons_6,2020,156999,59.4
-wind-ons_6,2020,157000,114.0
-wind-ons_6,2020,157002,186.0
-wind-ons_7,2020,157682,0.16
-wind-ons_6,2020,157683,58.8
-wind-ons_6,2020,157684,112.5
-wind-ons_6,2020,157687,209.0
-wind-ons_7,2020,158366,0.07
-wind-ons_7,2020,158367,57.0
-wind-ons_7,2020,158368,112.5
-wind-ons_6,2020,159056,249.0
-wind-ons_6,2020,159735,49.5
-wind-ons_7,2020,160420,101.2
-wind-ons_6,2020,160421,150.0
-wind-ons_7,2020,161103,37.5
-wind-ons_6,2020,161106,180.0
-wind-ons_6,2020,161108,241.0
-wind-ons_6,2020,161787,34.3
-wind-ons_6,2020,161789,150.0
-wind-ons_6,2020,161792,232.5
-wind-ons_6,2020,161793,222.0
-wind-ons_7,2020,162471,32.52
-wind-ons_6,2020,162472,98.8
-wind-ons_6,2020,162473,150.0
-wind-ons_7,2020,162475,200.0
-wind-ons_6,2020,162476,231.5
-wind-ons_6,2020,162477,77.0
-wind-ons_6,2020,163159,200.0
-wind-ons_6,2020,163840,91.58
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+wind-ons_9,2018,87908,2.0
+wind-ons_9,2018,87909,1.3
+wind-ons_9,2018,87910,1.0
+wind-ons_9,2018,87911,0.9
+wind-ons_10,2018,87912,0.02
+wind-ons_10,2018,87914,100.5
+wind-ons_9,2018,87929,100.5
+wind-ons_9,2018,87957,42.57
+wind-ons_9,2018,87958,44.55
+wind-ons_9,2018,88030,126.0
+wind-ons_8,2018,88036,37.5
+wind-ons_9,2018,88587,11.6
+wind-ons_9,2018,88588,2.0
+wind-ons_9,2018,88589,2.0
+wind-ons_10,2018,88592,1.5
+wind-ons_9,2018,88593,2.0
+wind-ons_9,2018,88594,8.4
+wind-ons_9,2018,88595,1.0
+wind-ons_10,2018,88596,99.0
+wind-ons_9,2018,88599,2.1
+wind-ons_9,2018,88602,1.3
+wind-ons_9,2018,88612,100.0
+wind-ons_9,2018,88613,100.7
+wind-ons_9,2018,88641,11.88
+wind-ons_9,2018,88642,13.5
+wind-ons_9,2018,88714,100.5
+wind-ons_8,2018,88719,87.5
+wind-ons_8,2018,89273,2.0
+wind-ons_9,2018,89274,2.0
+wind-ons_9,2018,89276,1.3
+wind-ons_10,2018,89277,20.0
+wind-ons_10,2018,89280,0.9
+wind-ons_9,2018,89284,1.7
+wind-ons_9,2018,89285,0.9
+wind-ons_9,2018,89287,1.3
+wind-ons_9,2018,89295,9.8
+wind-ons_9,2018,89296,13.5
+wind-ons_9,2018,89297,91.1
+wind-ons_9,2018,89326,54.0
+wind-ons_10,2018,89888,39.0
+wind-ons_9,2018,89955,3.8
+wind-ons_8,2018,89956,2.0
+wind-ons_8,2018,89957,1.7
+wind-ons_9,2018,89958,1.7
+wind-ons_9,2018,89959,1.3
+wind-ons_9,2018,89960,1.3
+wind-ons_9,2018,89961,2.0
+wind-ons_9,2018,89962,2.0
+wind-ons_9,2018,89963,0.9
+wind-ons_9,2018,89968,1.7
+wind-ons_9,2018,89969,0.9
+wind-ons_9,2018,89981,270.0
+wind-ons_10,2018,90571,99.0
+wind-ons_10,2018,90572,99.0
+wind-ons_9,2018,90644,2.0
+wind-ons_9,2018,90645,98.2
+wind-ons_9,2018,90646,49.6
+wind-ons_9,2018,90647,43.5
+wind-ons_9,2018,90648,10.5
+wind-ons_9,2018,90654,10.5
+wind-ons_9,2018,90657,20.0
+wind-ons_9,2018,90659,80.0
+wind-ons_9,2018,90660,80.0
+wind-ons_9,2018,90661,78.0
+wind-ons_9,2018,90662,80.0
+wind-ons_8,2018,90665,30.0
+wind-ons_9,2018,90666,99.0
+wind-ons_9,2018,91253,17.0
+wind-ons_10,2018,91257,99.0
+wind-ons_9,2018,91327,1.2
+wind-ons_9,2018,91329,0.6
+wind-ons_9,2018,91330,100.0
+wind-ons_9,2018,91340,66.0
+wind-ons_9,2018,91341,199.0
+wind-ons_9,2018,91344,29.7
+wind-ons_8,2018,91348,0.5
+wind-ons_9,2018,92025,150.0
+wind-ons_8,2018,92146,100.5
+wind-ons_8,2018,92156,64.5
+wind-ons_9,2018,92706,2.3
+wind-ons_9,2018,92710,98.0
+wind-ons_9,2018,92711,42.0
+wind-ons_9,2018,93385,21.0
+wind-ons_8,2018,93401,75.0
+wind-ons_8,2018,93417,30.0
+wind-ons_9,2018,94734,8.91
+wind-ons_10,2018,94750,76.5
+wind-ons_8,2018,94771,0.9
+wind-ons_9,2018,94890,24.0
+wind-ons_10,2018,95417,8.91
+wind-ons_10,2018,95418,54.27
+wind-ons_9,2018,95433,0.7
+wind-ons_9,2018,95434,104.3
+wind-ons_10,2018,95438,62.2
+wind-ons_9,2018,95449,199.7
+wind-ons_8,2018,95454,0.9
+wind-ons_10,2018,96102,8.91
+wind-ons_9,2018,96117,0.7
+wind-ons_9,2018,96118,160.5
+wind-ons_10,2018,96122,123.0
+wind-ons_9,2018,96130,150.0
+wind-ons_9,2018,96131,16.2
+wind-ons_8,2018,96157,100.5
+wind-ons_10,2018,96770,59.4
+wind-ons_9,2018,96802,15.0
+wind-ons_9,2018,96806,78.0
+wind-ons_9,2018,96814,35.0
+wind-ons_9,2018,97408,1.3
+wind-ons_8,2018,97624,102.0
+wind-ons_9,2018,97625,26.0
+wind-ons_10,2018,98091,99.0
+wind-ons_10,2018,98092,19.5
+wind-ons_9,2018,98093,4.9
+wind-ons_10,2018,98184,150.0
+wind-ons_7,2018,98745,48.86
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+wind-ons_10,2018,98776,25.0
+wind-ons_10,2018,98777,25.0
+wind-ons_10,2018,98778,28.8
+wind-ons_9,2018,98857,150.0
+wind-ons_9,2018,98869,150.0
+wind-ons_7,2018,99428,64.08
+wind-ons_9,2018,99429,142.2
+wind-ons_7,2018,99430,12.14
+wind-ons_10,2018,99459,24.8
+wind-ons_10,2018,99460,41.4
+wind-ons_9,2018,99462,99.0
+wind-ons_10,2018,99540,99.0
+wind-ons_9,2018,99545,14.7
+wind-ons_8,2018,100113,15.92
+wind-ons_10,2018,100143,16.8
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+wind-ons_9,2018,100268,217.5
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+wind-ons_8,2018,100306,1.8
+wind-ons_8,2018,100949,2.5
+wind-ons_8,2018,100950,80.0
+wind-ons_8,2018,100989,1.8
+wind-ons_8,2018,100990,1.8
+wind-ons_8,2018,101634,2.5
+wind-ons_10,2018,102396,75.0
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+wind-ons_8,2018,102995,1.5
+wind-ons_8,2018,102996,1.5
+wind-ons_8,2018,103006,10.5
+wind-ons_6,2018,103079,62.5
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+wind-ons_9,2018,103573,42.0
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+wind-ons_10,2018,103647,174.8
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+wind-ons_7,2018,103684,53.0
+wind-ons_8,2018,103689,49.5
+wind-ons_8,2018,103690,99.0
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+wind-ons_8,2018,105051,150.0
+wind-ons_9,2018,105130,29.4
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+wind-ons_9,2018,105631,221.0
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+wind-ons_9,2018,105637,201.0
+wind-ons_9,2018,105638,60.0
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+wind-ons_8,2018,105813,9.0
+wind-ons_9,2018,105815,52.5
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+wind-ons_7,2018,106182,13.1
+wind-ons_1,2018,106259,18.9
+wind-ons_8,2018,106315,79.5
+wind-ons_7,2018,106321,29.7
+wind-ons_8,2018,106495,15.0
+wind-ons_9,2018,106498,37.8
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+wind-ons_9,2018,107053,0.7
+wind-ons_8,2018,107119,130.5
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+wind-ons_9,2018,107182,30.0
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+wind-ons_9,2018,107550,145.8
+wind-ons_9,2018,107737,0.7
+wind-ons_8,2018,107803,100.0
+wind-ons_9,2018,107804,200.0
+wind-ons_8,2018,107805,101.3
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+wind-ons_9,2018,109162,213.8
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+wind-ons_9,2018,109797,146.0
+wind-ons_8,2018,109841,100.5
+wind-ons_8,2018,110480,5.0
+wind-ons_9,2018,110481,50.4
+wind-ons_9,2018,110601,100.0
+wind-ons_5,2018,110970,18.0
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+wind-ons_9,2018,111855,50.4
+wind-ons_8,2018,111967,66.0
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+wind-ons_5,2018,115139,203.5
+wind-ons_8,2018,115304,1.7
+wind-ons_10,2018,116621,96.0
+wind-ons_10,2018,117305,105.0
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+wind-ons_10,2018,120722,100.8
+wind-ons_9,2018,122751,99.0
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+wind-ons_8,2018,125476,4.0
+wind-ons_10,2018,126865,100.5
+wind-ons_9,2018,127525,75.0
+wind-ons_10,2018,127527,162.0
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+wind-ons_1,2018,129455,13.6
+wind-ons_7,2018,129457,8.3
+wind-ons_10,2018,129625,150.0
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+wind-ons_3,2018,130139,29.0
+wind-ons_3,2018,130140,76.9
+wind-ons_1,2018,130820,3.0
+wind-ons_1,2018,130821,22.0
+wind-ons_10,2018,130825,135.0
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+wind-ons_10,2018,130949,1.5
+wind-ons_10,2018,130978,44.0
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+wind-ons_1,2018,131504,18.4
+wind-ons_1,2018,131505,46.9
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+wind-ons_1,2018,132187,1.8
+wind-ons_2,2018,132188,17.3
+wind-ons_8,2018,132189,59.6
+wind-ons_3,2018,132195,28.7
+wind-ons_4,2018,132196,3.5
+wind-ons_10,2018,132872,6.5
+wind-ons_6,2018,132878,88.5
+wind-ons_4,2018,132879,25.3
+wind-ons_7,2018,133556,1.6
+wind-ons_7,2018,133557,15.3
+wind-ons_6,2018,133559,67.0
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+wind-ons_10,2018,134242,4.5
+wind-ons_7,2018,134244,23.1
+wind-ons_5,2018,134245,7.5
+wind-ons_5,2018,134246,3.7
+wind-ons_9,2018,134389,18.9
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+wind-ons_9,2018,135076,25.5
+wind-ons_7,2018,135191,27.0
+wind-ons_7,2018,135192,0.7
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+wind-ons_10,2018,135758,94.5
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+wind-ons_4,2018,135875,0.7
+wind-ons_3,2018,135876,0.7
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+wind-ons_10,2018,136444,25.5
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+wind-ons_10,2018,139160,49.5
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+wind-ons_5,2018,139726,9.6
+wind-ons_4,2018,139727,7.3
+wind-ons_5,2018,139728,3.4
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+wind-ons_7,2018,142465,20.0
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+wind-ons_10,2018,142595,98.9
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+wind-ons_9,2018,161053,130.5
+wind-ons_10,2018,161055,120.0
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+wind-ons_9,2018,163782,120.0
+wind-ons_8,2018,163786,34.3
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+wind-ons_10,2018,164469,121.9
+wind-ons_9,2018,164470,58.8
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+wind-ons_10,2018,164479,169.5
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+wind-ons_9,2018,165141,59.4
+wind-ons_9,2018,165142,91.58
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+wind-ons_10,2018,165838,124.7
+wind-ons_10,2018,165839,112.5
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+wind-ons_9,2018,166527,0.07
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+wind-ons_9,2018,170618,270.0
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+wind-ons_10,2018,171998,150.0
+wind-ons_10,2018,172670,82.5
+wind-ons_10,2018,172671,167.7
+wind-ons_10,2018,173353,150.0
+wind-ons_8,2018,193229,180.0
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+wind-ons_9,2019,50181,101.4
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+wind-ons_10,2038,165839,112.5
+wind-ons_10,2038,165840,231.5
+wind-ons_10,2038,165842,79.92
+wind-ons_7,2038,165843,0.08
+wind-ons_8,2038,166524,186.0
+wind-ons_10,2038,166526,69.53
+wind-ons_9,2038,166527,0.07
+wind-ons_10,2038,171998,150.0
+wind-ons_8,2038,193229,180.0
+wind-ons_9,2038,198700,201.6
+wind-ons_9,2038,199383,252.0
+wind-ons_10,2038,199384,31.2
+wind-ons_9,2039,50181,101.4
+wind-ons_6,2039,52862,44.0
+wind-ons_7,2039,57100,149.0
+wind-ons_9,2039,57997,57.0
+wind-ons_5,2039,59696,136.3
+wind-ons_9,2039,59827,4.5
+wind-ons_7,2039,60379,262.2
+wind-ons_6,2039,60382,98.9
+wind-ons_9,2039,60511,115.5
+wind-ons_8,2039,61065,163.3
+wind-ons_8,2039,61067,102.9
+wind-ons_5,2039,61070,9.9
+wind-ons_4,2039,61072,5.0
+wind-ons_4,2039,61073,8.0
+wind-ons_6,2039,61753,98.7
+wind-ons_6,2039,61754,72.0
+wind-ons_6,2039,61755,3.3
+wind-ons_6,2039,61756,9.9
+wind-ons_5,2039,61757,9.9
+wind-ons_6,2039,61762,102.3
+wind-ons_8,2039,62085,66.0
+wind-ons_8,2039,62432,100.8
+wind-ons_6,2039,62440,10.0
+wind-ons_6,2039,62441,8.3
+wind-ons_5,2039,62442,1.7
+wind-ons_6,2039,63125,6.6
+wind-ons_9,2039,64637,49.5
+wind-ons_9,2039,65302,49.5
+wind-ons_9,2039,65321,120.0
+wind-ons_8,2039,68253,4.5
+wind-ons_8,2039,68902,106.5
+wind-ons_9,2039,69589,97.5
+wind-ons_8,2039,79020,2.0
+wind-ons_9,2039,79704,2.0
+wind-ons_9,2039,82507,59.0
+wind-ons_9,2039,83096,25.0
+wind-ons_5,2039,84351,8.4
+wind-ons_5,2039,84352,18.9
+wind-ons_9,2039,84482,50.4
+wind-ons_10,2039,85169,49.5
+wind-ons_10,2039,85350,1.5
+wind-ons_10,2039,86519,51.0
+wind-ons_8,2039,87273,3.6
+wind-ons_9,2039,87274,12.9
+wind-ons_8,2039,87345,112.0
+wind-ons_9,2039,87929,100.5
+wind-ons_9,2039,88030,126.0
+wind-ons_8,2039,88036,37.5
+wind-ons_9,2039,88642,13.5
+wind-ons_8,2039,88719,87.5
+wind-ons_9,2039,89326,54.0
+wind-ons_10,2039,89888,39.0
+wind-ons_10,2039,90571,99.0
+wind-ons_10,2039,90572,99.0
+wind-ons_9,2039,90648,10.5
+wind-ons_9,2039,90654,10.5
+wind-ons_9,2039,90661,78.0
+wind-ons_9,2039,90662,80.0
+wind-ons_9,2039,90666,99.0
+wind-ons_9,2039,91253,17.0
+wind-ons_9,2039,91330,100.0
+wind-ons_9,2039,91340,66.0
+wind-ons_9,2039,91341,199.0
+wind-ons_8,2039,91348,0.5
+wind-ons_8,2039,92146,100.5
+wind-ons_9,2039,94734,8.91
+wind-ons_10,2039,95417,8.91
+wind-ons_10,2039,95418,54.27
+wind-ons_9,2039,95449,199.7
+wind-ons_8,2039,95454,0.9
+wind-ons_10,2039,96102,8.91
+wind-ons_8,2039,96157,100.5
+wind-ons_8,2039,97624,102.0
+wind-ons_10,2039,98184,150.0
+wind-ons_10,2039,98778,28.8
+wind-ons_9,2039,99462,99.0
+wind-ons_9,2039,100268,217.5
+wind-ons_10,2039,102396,75.0
+wind-ons_8,2039,102995,1.5
+wind-ons_8,2039,102996,1.5
+wind-ons_8,2039,103006,10.5
+wind-ons_6,2039,103079,62.5
+wind-ons_9,2039,103573,42.0
+wind-ons_8,2039,103689,49.5
+wind-ons_8,2039,103691,51.0
+wind-ons_8,2039,104374,106.0
+wind-ons_8,2039,105639,151.8
+wind-ons_8,2039,105640,22.5
+wind-ons_9,2039,105815,52.5
+wind-ons_8,2039,106865,150.0
+wind-ons_8,2039,107121,106.0
+wind-ons_8,2039,107123,199.7
+wind-ons_8,2039,107803,100.0
+wind-ons_9,2039,107804,200.0
+wind-ons_8,2039,107805,101.3
+wind-ons_8,2039,108489,99.0
+wind-ons_9,2039,109797,146.0
+wind-ons_8,2039,109841,100.5
+wind-ons_5,2039,115139,203.5
+wind-ons_10,2039,120721,148.5
+wind-ons_9,2039,122751,99.0
+wind-ons_10,2039,130825,135.0
+wind-ons_10,2039,130978,44.0
+wind-ons_10,2039,130979,50.0
+wind-ons_10,2039,136428,114.0
+wind-ons_10,2039,137126,101.2
+wind-ons_10,2039,139160,49.5
+wind-ons_4,2039,140413,6.5
+wind-ons_10,2039,142582,79.5
+wind-ons_10,2039,142595,98.9
+wind-ons_5,2039,143197,63.0
+wind-ons_8,2039,147335,100.0
+wind-ons_10,2039,147394,99.0
+wind-ons_9,2039,159707,120.0
+wind-ons_10,2039,161739,249.0
+wind-ons_10,2039,162424,197.0
+wind-ons_8,2039,163106,73.5
+wind-ons_9,2039,163107,75.0
+wind-ons_10,2039,163112,101.2
+wind-ons_9,2039,164458,0.92
+wind-ons_9,2039,165141,59.4
+wind-ons_9,2039,165142,91.58
+wind-ons_6,2039,165158,0.07
+wind-ons_9,2039,165826,0.6
+wind-ons_10,2039,165840,231.5
+wind-ons_10,2039,166526,69.53
+wind-ons_9,2039,166527,0.07
+wind-ons_10,2039,171998,150.0
+wind-ons_8,2039,193229,180.0
+wind-ons_9,2039,198700,201.6
diff --git a/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv b/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv
index fda87bae4..5568d220c 100644
--- a/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv
+++ b/inputs/capacity_exogenous/exog_cap_wind-ons_reference.csv
@@ -1,16768 +1,15941 @@
 *tech,year,sc_point_gid,capacity
-wind-ons_1,2010,46046,114.0
-wind-ons_2,2010,46729,44.0
-wind-ons_1,2010,46730,28.2
-wind-ons_2,2010,48781,156.0
-wind-ons_4,2010,49464,7.2
-wind-ons_3,2010,49465,228.6
-wind-ons_5,2010,50148,94.0
-wind-ons_3,2010,51516,54.0
-wind-ons_7,2010,52200,168.0
-wind-ons_7,2010,52969,9.7
-wind-ons_6,2010,53567,122.8
-wind-ons_5,2010,54935,31.0
-wind-ons_6,2010,55619,234.0
-wind-ons_7,2010,55705,104.2
-wind-ons_6,2010,56302,98.9
-wind-ons_6,2010,57670,62.9
-wind-ons_6,2010,58354,174.0
-wind-ons_8,2010,58684,4.5
-wind-ons_4,2010,59037,95.0
-wind-ons_8,2010,59201,0.93
-wind-ons_8,2010,59203,2.0
-wind-ons_3,2010,59721,136.3
-wind-ons_7,2010,59854,18.0
-wind-ons_7,2010,59885,0.93
-wind-ons_8,2010,59886,1.3
-wind-ons_7,2010,59889,10.4
-wind-ons_8,2010,59899,180.0
-wind-ons_1,2010,60405,95.4
-wind-ons_7,2010,60569,0.93
-wind-ons_7,2010,60570,1.2
-wind-ons_7,2010,60571,2.0
-wind-ons_7,2010,60572,2.1
-wind-ons_7,2010,60574,25.0
-wind-ons_8,2010,60583,25.5
-wind-ons_9,2010,60738,42.0
-wind-ons_8,2010,60739,66.0
-wind-ons_2,2010,61089,30.0
-wind-ons_7,2010,61253,0.93
-wind-ons_7,2010,61255,1.9
-wind-ons_7,2010,61256,2.1
-wind-ons_7,2010,61257,9.2
-wind-ons_5,2010,61772,78.0
-wind-ons_7,2010,61937,0.93
-wind-ons_7,2010,61939,1.7
-wind-ons_8,2010,61940,2.1
-wind-ons_7,2010,61941,8.4
-wind-ons_8,2010,61942,20.0
-wind-ons_9,2010,61943,51.0
-wind-ons_9,2010,61944,60.0
-wind-ons_9,2010,62107,57.0
-wind-ons_7,2010,62544,30.0
-wind-ons_8,2010,62590,31.5
-wind-ons_7,2010,62621,0.93
-wind-ons_7,2010,62623,1.7
-wind-ons_7,2010,62624,2.0
-wind-ons_8,2010,62625,8.0
-wind-ons_9,2010,62628,40.5
-wind-ons_4,2010,63139,6.6
-wind-ons_3,2010,63140,98.7
-wind-ons_7,2010,63305,0.93
-wind-ons_7,2010,63307,1.7
-wind-ons_7,2010,63308,2.0
-wind-ons_8,2010,63309,5.2
-wind-ons_8,2010,63310,19.98
-wind-ons_9,2010,63311,49.5
-wind-ons_8,2010,63312,100.5
-wind-ons_7,2010,63314,150.0
-wind-ons_9,2010,63474,4.5
-wind-ons_4,2010,63823,9.9
-wind-ons_3,2010,63824,30.9
-wind-ons_4,2010,63825,60.0
-wind-ons_8,2010,63912,101.4
-wind-ons_8,2010,63958,40.5
-wind-ons_6,2010,63989,0.93
-wind-ons_7,2010,63990,1.0
-wind-ons_7,2010,63991,1.7
-wind-ons_8,2010,63992,2.0
-wind-ons_7,2010,63996,100.0
-wind-ons_8,2010,64596,135.0
-wind-ons_8,2010,64642,40.5
-wind-ons_8,2010,64643,115.5
-wind-ons_7,2010,64673,0.93
-wind-ons_7,2010,64674,0.97
-wind-ons_7,2010,64675,1.7
-wind-ons_7,2010,64677,4.2
-wind-ons_2,2010,65191,24.0
-wind-ons_7,2010,65357,0.9
-wind-ons_7,2010,65358,0.97
-wind-ons_8,2010,65359,1.7
-wind-ons_7,2010,65360,2.0
-wind-ons_8,2010,65361,4.2
-wind-ons_7,2010,65362,15.6
-wind-ons_5,2010,65363,43.06
-wind-ons_1,2010,65874,1.7
-wind-ons_2,2010,65875,72.0
-wind-ons_8,2010,66010,48.0
-wind-ons_8,2010,66011,118.5
-wind-ons_7,2010,66041,0.9
-wind-ons_7,2010,66042,0.97
-wind-ons_7,2010,66043,1.5
-wind-ons_7,2010,66044,2.0
-wind-ons_8,2010,66045,4.2
-wind-ons_2,2010,66559,100.5
-wind-ons_4,2010,66560,101.0
-wind-ons_8,2010,66694,48.0
-wind-ons_8,2010,66695,6.0
-wind-ons_7,2010,66725,0.9
-wind-ons_7,2010,66726,0.97
-wind-ons_8,2010,66727,1.3
-wind-ons_7,2010,66728,2.0
-wind-ons_2,2010,67243,35.4
-wind-ons_8,2010,67379,149.0
-wind-ons_8,2010,67409,0.9
-wind-ons_8,2010,67410,0.97
-wind-ons_8,2010,67411,1.3
-wind-ons_6,2010,67412,2.0
-wind-ons_7,2010,67538,54.0
-wind-ons_8,2010,68062,49.5
-wind-ons_7,2010,68093,0.7
-wind-ons_8,2010,68094,0.97
-wind-ons_6,2010,68221,30.55
-wind-ons_6,2010,68222,100.5
-wind-ons_8,2010,68746,49.5
-wind-ons_7,2010,68777,0.7
-wind-ons_5,2010,68778,0.97
-wind-ons_7,2010,68905,16.5
-wind-ons_6,2010,68906,19.5
-wind-ons_4,2010,68919,5.2
-wind-ons_1,2010,69293,8.3
-wind-ons_8,2010,69430,50.6
-wind-ons_8,2010,69431,114.0
-wind-ons_5,2010,69462,0.97
-wind-ons_5,2010,69591,52.0
-wind-ons_1,2010,69979,60.0
-wind-ons_8,2010,70114,61.5
-wind-ons_8,2010,70115,148.5
-wind-ons_5,2010,70145,0.4
-wind-ons_5,2010,70146,0.97
-wind-ons_6,2010,70274,68.5
-wind-ons_1,2010,70662,31.3
-wind-ons_7,2010,70830,0.97
-wind-ons_7,2010,70959,52.5
-wind-ons_6,2010,71345,24.6
-wind-ons_7,2010,71514,0.97
-wind-ons_8,2010,71643,78.0
-wind-ons_3,2010,72028,3.3
-wind-ons_8,2010,72165,2.6
-wind-ons_7,2010,72197,0.04
-wind-ons_6,2010,72198,0.97
-wind-ons_6,2010,72345,24.0
-wind-ons_5,2010,72712,9.9
-wind-ons_8,2010,72849,4.5
-wind-ons_6,2010,72881,0.02
-wind-ons_8,2010,73533,11.9
-wind-ons_8,2010,73535,180.0
-wind-ons_5,2010,74764,41.0
-wind-ons_4,2010,76132,102.3
-wind-ons_7,2010,76334,42.57
-wind-ons_6,2010,76857,18.9
-wind-ons_8,2010,77702,30.0
-wind-ons_5,2010,77800,11.5
-wind-ons_5,2010,77801,81.0
-wind-ons_8,2010,78957,19.5
-wind-ons_3,2010,79169,45.5
-wind-ons_4,2010,79551,72.0
-wind-ons_8,2010,79641,39.0
-wind-ons_9,2010,79755,43.2
-wind-ons_3,2010,79853,30.55
-wind-ons_3,2010,80235,132.0
-wind-ons_5,2010,80538,87.5
-wind-ons_5,2010,80539,126.0
-wind-ons_4,2010,80918,25.0
-wind-ons_8,2010,81009,99.0
-wind-ons_9,2010,81122,12.9
-wind-ons_10,2010,81124,102.0
-wind-ons_3,2010,81223,60.0
-wind-ons_3,2010,81601,5.0
-wind-ons_2,2010,81602,76.5
-wind-ons_9,2010,81806,11.88
-wind-ons_9,2010,81807,67.7
-wind-ons_4,2010,81906,68.5
-wind-ons_3,2010,82285,9.9
-wind-ons_2,2010,82286,100.7
-wind-ons_9,2010,82490,11.0
-wind-ons_6,2010,82590,34.5
-wind-ons_3,2010,82970,102.9
-wind-ons_9,2010,83175,54.0
-wind-ons_6,2010,83274,16.5
-wind-ons_3,2010,83653,10.0
-wind-ons_6,2010,83744,1.3
-wind-ons_9,2010,83859,44.55
-wind-ons_3,2010,84337,25.2
-wind-ons_5,2010,84428,4.9
-wind-ons_8,2010,84510,0.97
-wind-ons_7,2010,84512,2.0
-wind-ons_7,2010,84513,2.1
-wind-ons_9,2010,84542,3.6
-wind-ons_9,2010,84574,1.8
-wind-ons_6,2010,84643,45.5
-wind-ons_9,2010,84657,1.5
-wind-ons_8,2010,84658,1.8
-wind-ons_3,2010,85020,8.0
-wind-ons_2,2010,85021,81.0
-wind-ons_3,2010,85063,21.0
-wind-ons_6,2010,85112,17.0
-wind-ons_8,2010,85194,0.93
-wind-ons_9,2010,85195,1.3
-wind-ons_8,2010,85196,2.0
-wind-ons_8,2010,85197,2.0
-wind-ons_7,2010,85198,9.8
-wind-ons_3,2010,85705,100.8
-wind-ons_5,2010,85796,25.0
-wind-ons_8,2010,85878,0.93
-wind-ons_9,2010,85879,1.3
-wind-ons_8,2010,85880,1.7
-wind-ons_8,2010,85881,2.0
-wind-ons_7,2010,86012,100.5
-wind-ons_4,2010,86389,90.0
-wind-ons_4,2010,86480,28.8
-wind-ons_8,2010,86562,0.93
-wind-ons_9,2010,86563,1.3
-wind-ons_9,2010,86564,1.7
-wind-ons_8,2010,86565,2.0
-wind-ons_8,2010,86567,13.5
-wind-ons_7,2010,86569,99.0
-wind-ons_5,2010,87164,41.4
-wind-ons_9,2010,87246,0.93
-wind-ons_8,2010,87248,1.7
-wind-ons_8,2010,87249,2.0
-wind-ons_8,2010,87250,3.9
-wind-ons_7,2010,87251,12.0
-wind-ons_7,2010,87252,32.0
-wind-ons_7,2010,87253,98.8
-wind-ons_7,2010,87254,60.0
-wind-ons_9,2010,87279,13.5
-wind-ons_8,2010,87312,59.0
-wind-ons_2,2010,87798,8.4
-wind-ons_5,2010,87848,99.0
-wind-ons_9,2010,87901,1.3
-wind-ons_9,2010,87930,0.93
-wind-ons_9,2010,87931,1.0
-wind-ons_7,2010,87932,1.5
-wind-ons_8,2010,87934,3.8
-wind-ons_7,2010,87935,11.8
-wind-ons_7,2010,87938,43.5
-wind-ons_8,2010,87996,51.2
-wind-ons_8,2010,88064,37.5
-wind-ons_5,2010,88532,99.0
-wind-ons_9,2010,88585,1.3
-wind-ons_9,2010,88614,0.9
-wind-ons_8,2010,88616,1.3
-wind-ons_8,2010,88617,2.0
-wind-ons_8,2010,88618,3.0
-wind-ons_7,2010,88619,11.6
-wind-ons_8,2010,88621,91.1
-wind-ons_8,2010,88748,30.0
-wind-ons_9,2010,89269,25.0
-wind-ons_9,2010,89298,0.9
-wind-ons_9,2010,89299,0.97
-wind-ons_9,2010,89300,1.3
-wind-ons_9,2010,89301,2.0
-wind-ons_8,2010,89303,11.2
-wind-ons_7,2010,89305,85.5
-wind-ons_6,2010,89306,100.7
-wind-ons_7,2010,89419,9.0
-wind-ons_9,2010,89953,40.5
-wind-ons_9,2010,89986,1.2
-wind-ons_7,2010,89988,10.5
-wind-ons_6,2010,89990,99.0
-wind-ons_8,2010,90049,69.0
-wind-ons_7,2010,90104,37.8
-wind-ons_9,2010,90637,50.4
-wind-ons_8,2010,90670,0.9
-wind-ons_6,2010,90672,10.5
-wind-ons_7,2010,90674,98.2
-wind-ons_7,2010,90675,150.0
-wind-ons_7,2010,90789,62.5
-wind-ons_8,2010,91267,12.14
-wind-ons_8,2010,91354,0.7
-wind-ons_8,2010,91358,98.0
-wind-ons_8,2010,91472,26.0
-wind-ons_6,2010,91473,52.5
-wind-ons_5,2010,91474,3.0
-wind-ons_6,2010,91485,20.0
-wind-ons_9,2010,91951,16.8
-wind-ons_8,2010,92038,0.7
-wind-ons_8,2010,92041,49.6
-wind-ons_9,2010,92156,24.0
-wind-ons_6,2010,92169,6.6
-wind-ons_1,2010,92585,10.5
-wind-ons_8,2010,92722,0.6
-wind-ons_7,2010,92727,123.0
-wind-ons_2,2010,93270,64.5
-wind-ons_9,2010,93319,25.0
-wind-ons_9,2010,93373,51.0
-wind-ons_9,2010,93406,0.5
-wind-ons_7,2010,93409,43.5
-wind-ons_8,2010,93525,34.5
-wind-ons_10,2010,93526,64.5
-wind-ons_9,2010,93527,14.0
-wind-ons_2,2010,93953,21.0
-wind-ons_8,2010,94093,42.0
-wind-ons_7,2010,94094,80.0
-wind-ons_10,2010,94209,29.4
-wind-ons_8,2010,94211,72.0
-wind-ons_6,2010,94212,102.0
-wind-ons_9,2010,94687,48.86
-wind-ons_7,2010,94778,80.0
-wind-ons_9,2010,94854,1.8
-wind-ons_4,2010,94896,100.5
-wind-ons_7,2010,95371,99.0
-wind-ons_9,2010,95462,80.0
-wind-ons_10,2010,95577,15.0
-wind-ons_7,2010,96055,99.0
-wind-ons_8,2010,96147,104.3
-wind-ons_4,2010,96264,66.0
-wind-ons_6,2010,96739,142.2
-wind-ons_8,2010,96830,78.0
-wind-ons_7,2010,96946,30.0
-wind-ons_8,2010,97513,29.7
-wind-ons_9,2010,97514,4.5
-wind-ons_8,2010,97516,160.5
-wind-ons_8,2010,97517,138.0
-wind-ons_7,2010,98106,1.8
-wind-ons_8,2010,98198,75.0
-wind-ons_7,2010,98275,1.8
-wind-ons_7,2010,98790,15.92
-wind-ons_8,2010,98880,2.3
-wind-ons_8,2010,98881,20.0
-wind-ons_8,2010,99008,7.5
-wind-ons_8,2010,99566,66.0
-wind-ons_9,2010,99569,60.0
-wind-ons_8,2010,99573,246.0
-wind-ons_9,2010,100158,24.8
-wind-ons_8,2010,100249,16.2
-wind-ons_9,2010,100253,1.7
-wind-ons_8,2010,100257,54.0
-wind-ons_9,2010,100842,29.4
-wind-ons_8,2010,100900,8.91
-wind-ons_9,2010,100936,150.0
-wind-ons_8,2010,100981,162.0
-wind-ons_9,2010,101526,42.0
-wind-ons_9,2010,101584,8.91
-wind-ons_8,2010,101620,150.0
-wind-ons_8,2010,101621,199.0
-wind-ons_8,2010,101665,130.5
-wind-ons_10,2010,102210,64.08
-wind-ons_9,2010,102268,8.91
-wind-ons_8,2010,102303,100.0
-wind-ons_8,2010,102304,150.0
-wind-ons_8,2010,102305,174.8
-wind-ons_8,2010,102327,1.5
-wind-ons_8,2010,102349,84.0
-wind-ons_6,2010,102380,1.8
-wind-ons_9,2010,102984,2.0
-wind-ons_8,2010,102985,15.0
-wind-ons_8,2010,102988,150.0
-wind-ons_6,2010,103064,1.8
-wind-ons_8,2010,103669,14.7
-wind-ons_8,2010,103670,62.2
-wind-ons_8,2010,103671,99.0
-wind-ons_9,2010,103672,84.0
-wind-ons_8,2010,103717,101.3
-wind-ons_8,2010,104356,66.0
-wind-ons_8,2010,104401,99.0
-wind-ons_8,2010,105064,99.0
-wind-ons_9,2010,105719,0.9
-wind-ons_9,2010,106329,42.0
-wind-ons_9,2010,107013,54.0
-wind-ons_8,2010,107116,53.0
-wind-ons_9,2010,107741,59.4
-wind-ons_8,2010,107775,78.0
-wind-ons_8,2010,107800,49.5
-wind-ons_8,2010,107802,217.5
-wind-ons_4,2010,107872,60.0
-wind-ons_8,2010,108380,22.5
-wind-ons_8,2010,108381,221.0
-wind-ons_9,2010,108425,54.27
-wind-ons_8,2010,108458,35.0
-wind-ons_8,2010,108459,72.0
-wind-ons_8,2010,108484,10.5
-wind-ons_8,2010,108486,213.8
-wind-ons_5,2010,108556,6.0
-wind-ons_5,2010,108557,100.0
-wind-ons_5,2010,108559,150.0
-wind-ons_8,2010,109168,2.5
-wind-ons_8,2010,109169,150.0
-wind-ons_8,2010,109170,210.9
-wind-ons_6,2010,109243,14.0
-wind-ons_8,2010,109748,29.7
-wind-ons_7,2010,109826,21.0
-wind-ons_8,2010,109852,1.5
-wind-ons_9,2010,109874,100.0
-wind-ons_10,2010,110475,0.7
-wind-ons_8,2010,110559,180.0
-wind-ons_10,2010,111159,0.7
-wind-ons_8,2010,111800,60.0
-wind-ons_8,2010,111905,100.5
-wind-ons_2,2010,112433,18.9
-wind-ons_7,2010,112611,37.7
-wind-ons_7,2010,113168,79.5
-wind-ons_8,2010,113273,80.0
-wind-ons_8,2010,113957,76.2
-wind-ons_7,2010,113979,22.0
-wind-ons_9,2010,114588,105.0
-wind-ons_8,2010,114641,51.0
-wind-ons_9,2010,115272,100.8
-wind-ons_9,2010,115904,151.8
-wind-ons_8,2010,115956,99.0
-wind-ons_8,2010,116009,8.4
-wind-ons_5,2010,116032,20.0
-wind-ons_9,2010,116640,96.0
-wind-ons_8,2010,116693,1.7
-wind-ons_9,2010,117272,157.5
-wind-ons_8,2010,118062,106.0
-wind-ons_9,2010,118639,1.5
-wind-ons_9,2010,118640,147.0
-wind-ons_9,2010,118692,100.5
-wind-ons_7,2010,119430,100.5
-wind-ons_8,2010,120007,4.0
-wind-ons_9,2010,120060,112.2
-wind-ons_7,2010,120798,72.0
-wind-ons_5,2010,122691,203.5
-wind-ons_7,2010,122850,2.5
-wind-ons_9,2010,123427,75.0
-wind-ons_7,2010,126941,5.0
-wind-ons_9,2010,127584,50.0
-wind-ons_10,2010,128214,1.5
-wind-ons_10,2010,128215,162.0
-wind-ons_9,2010,128952,44.0
-wind-ons_9,2010,129637,150.0
-wind-ons_7,2010,130423,27.0
-wind-ons_9,2010,131005,148.5
-wind-ons_6,2010,131105,0.7
-wind-ons_5,2010,131106,0.7
-wind-ons_6,2010,131107,0.7
-wind-ons_6,2010,132311,204.0
-wind-ons_6,2010,132414,56.7
-wind-ons_7,2010,133098,50.4
-wind-ons_7,2010,133782,50.4
-wind-ons_7,2010,134467,146.0
-wind-ons_9,2010,135793,98.9
-wind-ons_9,2010,135794,60.0
-wind-ons_5,2010,136323,36.0
-wind-ons_9,2010,136414,20.0
-wind-ons_9,2010,136478,91.4
-wind-ons_4,2010,136993,1.5
-wind-ons_2,2010,136994,2.4
-wind-ons_3,2010,136995,7.5
-wind-ons_4,2010,137679,8.3
-wind-ons_3,2010,137680,18.0
-wind-ons_8,2010,137782,90.0
-wind-ons_9,2010,137845,99.0
-wind-ons_5,2010,138362,3.0
-wind-ons_4,2010,138364,18.4
-wind-ons_3,2010,138366,38.0
-wind-ons_4,2010,139046,4.2
-wind-ons_3,2010,139049,28.7
-wind-ons_4,2010,139730,6.5
-wind-ons_5,2010,139731,15.5
-wind-ons_4,2010,139733,30.75
-wind-ons_9,2010,139897,94.5
-wind-ons_4,2010,140416,22.0
-wind-ons_10,2010,141779,0.1
-wind-ons_8,2010,141780,1.6
-wind-ons_7,2010,141781,3.4
-wind-ons_8,2010,141949,74.3
-wind-ons_7,2010,142465,4.5
-wind-ons_6,2010,142466,13.1
-wind-ons_3,2010,142468,29.0
-wind-ons_2,2010,142469,42.6
-wind-ons_6,2010,143149,6.5
-wind-ons_8,2010,143253,0.6
-wind-ons_6,2010,143835,22.2
-wind-ons_4,2010,143836,31.0
-wind-ons_5,2010,143844,30.0
-wind-ons_8,2010,143937,0.7
-wind-ons_1,2010,144517,8.7
-wind-ons_4,2010,144518,17.0
-wind-ons_3,2010,144519,24.0
-wind-ons_3,2010,144529,150.0
-wind-ons_6,2010,144685,40.5
-wind-ons_3,2010,145200,3.5
-wind-ons_1,2010,145201,9.6
-wind-ons_2,2010,145203,25.3
-wind-ons_10,2010,145305,30.0
-wind-ons_3,2010,145884,5.4
-wind-ons_2,2010,145885,11.7
-wind-ons_1,2010,145886,20.5
-wind-ons_3,2010,145890,67.0
-wind-ons_3,2010,145891,88.5
-wind-ons_10,2010,145989,80.0
-wind-ons_9,2010,146053,25.5
-wind-ons_3,2010,146568,7.3
-wind-ons_3,2010,146569,15.3
-wind-ons_2,2010,146580,114.0
-wind-ons_10,2010,146673,100.0
-wind-ons_5,2010,146737,25.5
-wind-ons_5,2010,146738,123.0
-wind-ons_5,2010,147252,8.0
-wind-ons_4,2010,147253,16.2
-wind-ons_4,2010,147254,22.4
-wind-ons_3,2010,147255,33.6
-wind-ons_1,2010,147257,60.3
-wind-ons_6,2010,147421,25.5
-wind-ons_6,2010,147422,120.0
-wind-ons_4,2010,147935,3.0
-wind-ons_2,2010,147936,9.1
-wind-ons_1,2010,147937,17.3
-wind-ons_2,2010,147938,23.1
-wind-ons_2,2010,147940,46.9
-wind-ons_3,2010,147941,63.0
-wind-ons_8,2010,148105,25.5
-wind-ons_7,2010,148618,1.8
-wind-ons_3,2010,148619,3.7
-wind-ons_2,2010,148620,11.6
-wind-ons_2,2010,148621,20.0
-wind-ons_3,2010,148623,38.9
-wind-ons_5,2010,148629,135.0
-wind-ons_6,2010,148789,18.9
-wind-ons_5,2010,149304,13.6
-wind-ons_5,2010,149305,20.5
-wind-ons_4,2010,149307,43.4
-wind-ons_4,2010,149308,50.0
-wind-ons_9,2010,149474,106.5
-wind-ons_6,2010,149986,2.1
-wind-ons_8,2010,149987,7.4
-wind-ons_7,2010,149989,21.7
-wind-ons_5,2010,149990,30.75
-wind-ons_2,2010,149991,45.0
-wind-ons_2,2010,149992,59.6
-wind-ons_3,2010,149993,76.9
-wind-ons_7,2010,150158,101.2
-wind-ons_8,2010,150672,16.5
-wind-ons_5,2010,150682,61.8
-wind-ons_5,2010,151366,54.0
-wind-ons_10,2010,152144,0.6
-wind-ons_6,2010,154261,80.0
-wind-ons_7,2010,154944,10.0
-wind-ons_6,2010,154946,126.5
-wind-ons_8,2010,155564,90.0
-wind-ons_7,2010,155628,10.0
-wind-ons_7,2010,155629,80.5
-wind-ons_6,2010,155633,156.0
-wind-ons_7,2010,156312,10.0
-wind-ons_6,2010,156313,79.8
-wind-ons_7,2010,156314,124.7
-wind-ons_6,2010,156317,44.0
-wind-ons_6,2010,156318,204.0
-wind-ons_2,2010,156884,63.0
-wind-ons_7,2010,156996,10.0
-wind-ons_6,2010,156998,124.2
-wind-ons_6,2010,157000,170.2
-wind-ons_6,2010,157002,9.0
-wind-ons_6,2010,157680,10.0
-wind-ons_7,2010,157681,79.92
-wind-ons_7,2010,158364,10.0
-wind-ons_7,2010,158365,79.5
-wind-ons_7,2010,158366,121.9
-wind-ons_7,2010,158368,168.0
-wind-ons_7,2010,159050,60.0
-wind-ons_6,2010,159055,216.0
-wind-ons_7,2010,159732,8.8
-wind-ons_7,2010,159733,75.0
-wind-ons_6,2010,159734,60.6
-wind-ons_6,2010,159735,150.0
-wind-ons_7,2010,159736,1.5
-wind-ons_6,2010,159739,25.0
-wind-ons_7,2010,160417,73.5
-wind-ons_7,2010,160418,120.0
-wind-ons_6,2010,160423,204.0
-wind-ons_7,2010,161100,3.0
-wind-ons_7,2010,161101,69.53
-wind-ons_7,2010,161102,120.0
-wind-ons_7,2010,161103,150.0
-wind-ons_6,2010,161104,167.7
-wind-ons_6,2010,161106,180.0
-wind-ons_6,2010,161107,28.5
-wind-ons_7,2010,161784,2.64
-wind-ons_6,2010,161787,90.0
-wind-ons_6,2010,161790,29.0
-wind-ons_6,2010,161791,78.0
-wind-ons_7,2010,162471,60.0
-wind-ons_6,2010,162475,153.5
-wind-ons_7,2010,163837,63.0
-wind-ons_7,2010,163838,120.0
-wind-ons_7,2010,164520,0.92
-wind-ons_7,2010,164521,78.0
-wind-ons_7,2010,165204,0.6
-wind-ons_7,2010,165206,114.0
-wind-ons_7,2010,165208,162.3
-wind-ons_6,2010,165209,192.0
-wind-ons_6,2010,165890,114.0
-wind-ons_6,2010,165893,5.0
-wind-ons_5,2010,166572,0.16
-wind-ons_6,2010,166573,59.4
-wind-ons_6,2010,166574,112.5
-wind-ons_7,2010,167256,0.08
-wind-ons_5,2010,167257,58.8
-wind-ons_5,2010,167258,112.5
-wind-ons_6,2010,167940,0.07
-wind-ons_7,2010,167941,57.0
-wind-ons_7,2010,167943,149.5
-wind-ons_6,2010,168624,0.07
-wind-ons_6,2010,168628,160.84
-wind-ons_5,2010,168630,200.0
-wind-ons_6,2010,168633,216.0
-wind-ons_5,2010,169310,101.2
-wind-ons_5,2010,169313,180.0
-wind-ons_6,2010,169317,83.0
-wind-ons_5,2010,169993,49.5
-wind-ons_5,2010,169997,6.0
-wind-ons_5,2010,169999,210.0
-wind-ons_5,2010,170000,228.0
-wind-ons_5,2010,170683,13.5
-wind-ons_5,2010,170684,21.0
-wind-ons_6,2010,171361,37.5
-wind-ons_5,2010,171362,98.8
-wind-ons_5,2010,171363,126.0
-wind-ons_6,2010,172045,34.3
-wind-ons_5,2010,172047,18.0
-wind-ons_6,2010,172729,32.52
-wind-ons_6,2010,172730,91.58
-wind-ons_5,2010,172735,198.0
-wind-ons_6,2010,173414,91.2
-wind-ons_6,2010,173419,16.5
-wind-ons_6,2010,173421,180.0
-wind-ons_6,2010,174098,91.2
-wind-ons_6,2010,174099,130.5
-wind-ons_7,2010,174101,180.0
-wind-ons_5,2010,174105,98.0
-wind-ons_6,2010,174784,153.9
-wind-ons_5,2010,174789,73.2
-wind-ons_7,2010,175465,17.64
-wind-ons_6,2010,175466,89.5
-wind-ons_6,2010,175468,152.0
-wind-ons_5,2010,175473,150.0
-wind-ons_6,2010,176149,10.0
-wind-ons_5,2010,176150,84.0
-wind-ons_5,2010,176153,180.0
-wind-ons_5,2010,176154,201.6
-wind-ons_6,2010,176157,60.0
-wind-ons_6,2010,176834,60.0
-wind-ons_6,2010,177518,22.5
-wind-ons_6,2010,178202,82.0
-wind-ons_1,2011,46046,114.0
-wind-ons_2,2011,46729,44.0
-wind-ons_1,2011,46730,28.2
-wind-ons_2,2011,48781,156.0
-wind-ons_4,2011,49464,7.2
-wind-ons_3,2011,49465,228.6
-wind-ons_5,2011,50148,94.0
-wind-ons_3,2011,51516,54.0
-wind-ons_7,2011,52200,168.0
-wind-ons_7,2011,52969,9.7
-wind-ons_6,2011,53567,122.8
-wind-ons_5,2011,54935,31.0
-wind-ons_6,2011,55619,234.0
-wind-ons_7,2011,55705,104.2
-wind-ons_6,2011,56302,98.9
-wind-ons_6,2011,57670,62.9
-wind-ons_6,2011,58354,174.0
-wind-ons_8,2011,58684,4.5
-wind-ons_4,2011,59037,95.0
-wind-ons_8,2011,59201,0.93
-wind-ons_8,2011,59203,2.0
-wind-ons_3,2011,59721,136.3
-wind-ons_7,2011,59854,18.0
-wind-ons_7,2011,59885,0.93
-wind-ons_8,2011,59886,1.3
-wind-ons_7,2011,59889,10.4
-wind-ons_8,2011,59899,180.0
-wind-ons_1,2011,60405,95.4
-wind-ons_7,2011,60569,0.93
-wind-ons_7,2011,60570,1.2
-wind-ons_7,2011,60571,2.0
-wind-ons_7,2011,60572,2.1
-wind-ons_7,2011,60574,25.0
-wind-ons_8,2011,60583,25.5
-wind-ons_9,2011,60738,42.0
-wind-ons_8,2011,60739,66.0
-wind-ons_2,2011,61089,30.0
-wind-ons_7,2011,61253,0.93
-wind-ons_7,2011,61255,1.9
-wind-ons_7,2011,61256,2.1
-wind-ons_7,2011,61257,9.2
-wind-ons_5,2011,61772,78.0
-wind-ons_7,2011,61937,0.93
-wind-ons_7,2011,61939,1.7
-wind-ons_8,2011,61940,2.1
-wind-ons_7,2011,61941,8.4
-wind-ons_8,2011,61942,20.0
-wind-ons_9,2011,61943,51.0
-wind-ons_9,2011,61944,60.0
-wind-ons_9,2011,62107,57.0
-wind-ons_7,2011,62544,30.0
-wind-ons_8,2011,62590,31.5
-wind-ons_7,2011,62621,0.93
-wind-ons_7,2011,62623,1.7
-wind-ons_7,2011,62624,2.0
-wind-ons_8,2011,62625,8.0
-wind-ons_9,2011,62628,40.5
-wind-ons_4,2011,63139,6.6
-wind-ons_3,2011,63140,98.7
-wind-ons_7,2011,63305,0.93
-wind-ons_7,2011,63307,1.7
-wind-ons_7,2011,63308,2.0
-wind-ons_8,2011,63309,5.2
-wind-ons_8,2011,63310,19.98
-wind-ons_9,2011,63311,49.5
-wind-ons_8,2011,63312,100.5
-wind-ons_7,2011,63314,150.0
-wind-ons_9,2011,63474,4.5
-wind-ons_4,2011,63823,9.9
-wind-ons_3,2011,63824,30.9
-wind-ons_4,2011,63825,60.0
-wind-ons_8,2011,63912,101.4
-wind-ons_8,2011,63958,40.5
-wind-ons_6,2011,63989,0.93
-wind-ons_7,2011,63990,1.0
-wind-ons_7,2011,63991,1.7
-wind-ons_8,2011,63992,2.0
-wind-ons_7,2011,63996,100.0
-wind-ons_8,2011,64596,135.0
-wind-ons_8,2011,64642,40.5
-wind-ons_8,2011,64643,115.5
-wind-ons_7,2011,64673,0.93
-wind-ons_7,2011,64674,0.97
-wind-ons_7,2011,64675,1.7
-wind-ons_7,2011,64677,4.2
-wind-ons_2,2011,65191,24.0
-wind-ons_7,2011,65357,0.9
-wind-ons_7,2011,65358,0.97
-wind-ons_8,2011,65359,1.7
-wind-ons_7,2011,65360,2.0
-wind-ons_8,2011,65361,4.2
-wind-ons_7,2011,65362,15.6
-wind-ons_5,2011,65363,43.06
-wind-ons_1,2011,65874,1.7
-wind-ons_2,2011,65875,72.0
-wind-ons_8,2011,66010,48.0
-wind-ons_8,2011,66011,118.5
-wind-ons_7,2011,66041,0.9
-wind-ons_7,2011,66042,0.97
-wind-ons_7,2011,66043,1.5
-wind-ons_7,2011,66044,2.0
-wind-ons_8,2011,66045,4.2
-wind-ons_2,2011,66559,100.5
-wind-ons_4,2011,66560,101.0
-wind-ons_8,2011,66694,48.0
-wind-ons_8,2011,66695,6.0
-wind-ons_7,2011,66725,0.9
-wind-ons_7,2011,66726,0.97
-wind-ons_8,2011,66727,1.3
-wind-ons_7,2011,66728,2.0
-wind-ons_2,2011,67243,35.4
-wind-ons_8,2011,67379,149.0
-wind-ons_8,2011,67409,0.9
-wind-ons_8,2011,67410,0.97
-wind-ons_8,2011,67411,1.3
-wind-ons_6,2011,67412,2.0
-wind-ons_7,2011,67538,54.0
-wind-ons_8,2011,68062,49.5
-wind-ons_7,2011,68093,0.7
-wind-ons_8,2011,68094,0.97
-wind-ons_6,2011,68221,30.55
-wind-ons_6,2011,68222,100.5
-wind-ons_8,2011,68746,49.5
-wind-ons_7,2011,68777,0.7
-wind-ons_5,2011,68778,0.97
-wind-ons_7,2011,68905,16.5
-wind-ons_6,2011,68906,19.5
-wind-ons_4,2011,68919,5.2
-wind-ons_1,2011,69293,8.3
-wind-ons_8,2011,69430,50.6
-wind-ons_8,2011,69431,114.0
-wind-ons_5,2011,69462,0.97
-wind-ons_5,2011,69591,52.0
-wind-ons_1,2011,69979,60.0
-wind-ons_8,2011,70114,61.5
-wind-ons_8,2011,70115,148.5
-wind-ons_5,2011,70145,0.4
-wind-ons_5,2011,70146,0.97
-wind-ons_6,2011,70274,68.5
-wind-ons_1,2011,70662,31.3
-wind-ons_7,2011,70830,0.97
-wind-ons_7,2011,70959,52.5
-wind-ons_6,2011,71345,24.6
-wind-ons_7,2011,71514,0.97
-wind-ons_8,2011,71643,78.0
-wind-ons_3,2011,72028,3.3
-wind-ons_8,2011,72165,2.6
-wind-ons_7,2011,72197,0.04
-wind-ons_6,2011,72198,0.97
-wind-ons_6,2011,72345,24.0
-wind-ons_5,2011,72712,9.9
-wind-ons_8,2011,72849,4.5
-wind-ons_6,2011,72881,0.02
-wind-ons_8,2011,73533,11.9
-wind-ons_8,2011,73535,180.0
-wind-ons_5,2011,74764,41.0
-wind-ons_4,2011,76132,102.3
-wind-ons_7,2011,76334,42.57
-wind-ons_6,2011,76857,18.9
-wind-ons_8,2011,77702,30.0
-wind-ons_5,2011,77800,11.5
-wind-ons_5,2011,77801,81.0
-wind-ons_8,2011,78957,19.5
-wind-ons_3,2011,79169,45.5
-wind-ons_4,2011,79551,72.0
-wind-ons_8,2011,79641,39.0
-wind-ons_9,2011,79755,43.2
-wind-ons_3,2011,79853,30.55
-wind-ons_3,2011,80235,132.0
-wind-ons_5,2011,80538,87.5
-wind-ons_5,2011,80539,126.0
-wind-ons_4,2011,80918,25.0
-wind-ons_8,2011,81009,99.0
-wind-ons_9,2011,81122,12.9
-wind-ons_10,2011,81124,102.0
-wind-ons_3,2011,81223,60.0
-wind-ons_3,2011,81601,5.0
-wind-ons_2,2011,81602,76.5
-wind-ons_9,2011,81806,11.88
-wind-ons_9,2011,81807,67.7
-wind-ons_4,2011,81906,68.5
-wind-ons_3,2011,82285,9.9
-wind-ons_2,2011,82286,100.7
-wind-ons_9,2011,82490,11.0
-wind-ons_6,2011,82590,34.5
-wind-ons_3,2011,82970,102.9
-wind-ons_9,2011,83175,54.0
-wind-ons_6,2011,83274,16.5
-wind-ons_3,2011,83653,10.0
-wind-ons_6,2011,83744,1.3
-wind-ons_9,2011,83859,44.55
-wind-ons_3,2011,84337,25.2
-wind-ons_5,2011,84428,4.9
-wind-ons_8,2011,84510,0.97
-wind-ons_7,2011,84512,2.0
-wind-ons_7,2011,84513,2.1
-wind-ons_9,2011,84542,3.6
-wind-ons_9,2011,84574,1.8
-wind-ons_6,2011,84643,45.5
-wind-ons_9,2011,84657,1.5
-wind-ons_8,2011,84658,1.8
-wind-ons_3,2011,85020,8.0
-wind-ons_2,2011,85021,81.0
-wind-ons_3,2011,85063,21.0
-wind-ons_6,2011,85112,17.0
-wind-ons_8,2011,85194,0.93
-wind-ons_9,2011,85195,1.3
-wind-ons_8,2011,85196,2.0
-wind-ons_8,2011,85197,2.0
-wind-ons_7,2011,85198,9.8
-wind-ons_3,2011,85705,100.8
-wind-ons_5,2011,85796,25.0
-wind-ons_8,2011,85878,0.93
-wind-ons_9,2011,85879,1.3
-wind-ons_8,2011,85880,1.7
-wind-ons_8,2011,85881,2.0
-wind-ons_7,2011,86012,100.5
-wind-ons_4,2011,86389,90.0
-wind-ons_4,2011,86480,28.8
-wind-ons_8,2011,86562,0.93
-wind-ons_9,2011,86563,1.3
-wind-ons_9,2011,86564,1.7
-wind-ons_8,2011,86565,2.0
-wind-ons_8,2011,86567,13.5
-wind-ons_7,2011,86569,99.0
-wind-ons_5,2011,87164,41.4
-wind-ons_9,2011,87246,0.93
-wind-ons_8,2011,87248,1.7
-wind-ons_8,2011,87249,2.0
-wind-ons_8,2011,87250,3.9
-wind-ons_7,2011,87251,12.0
-wind-ons_7,2011,87252,32.0
-wind-ons_7,2011,87253,98.8
-wind-ons_7,2011,87254,60.0
-wind-ons_9,2011,87279,13.5
-wind-ons_8,2011,87312,59.0
-wind-ons_2,2011,87798,8.4
-wind-ons_5,2011,87848,99.0
-wind-ons_9,2011,87901,1.3
-wind-ons_9,2011,87930,0.93
-wind-ons_9,2011,87931,1.0
-wind-ons_7,2011,87932,1.5
-wind-ons_8,2011,87934,3.8
-wind-ons_7,2011,87935,11.8
-wind-ons_7,2011,87938,43.5
-wind-ons_8,2011,87996,51.2
-wind-ons_8,2011,88064,37.5
-wind-ons_5,2011,88532,99.0
-wind-ons_9,2011,88585,1.3
-wind-ons_9,2011,88614,0.9
-wind-ons_8,2011,88616,1.3
-wind-ons_8,2011,88617,2.0
-wind-ons_8,2011,88618,3.0
-wind-ons_7,2011,88619,11.6
-wind-ons_8,2011,88621,91.1
-wind-ons_8,2011,88748,30.0
-wind-ons_9,2011,89269,25.0
-wind-ons_9,2011,89298,0.9
-wind-ons_9,2011,89299,0.97
-wind-ons_9,2011,89300,1.3
-wind-ons_9,2011,89301,2.0
-wind-ons_8,2011,89303,11.2
-wind-ons_7,2011,89305,85.5
-wind-ons_6,2011,89306,100.7
-wind-ons_7,2011,89419,9.0
-wind-ons_9,2011,89953,40.5
-wind-ons_9,2011,89986,1.2
-wind-ons_7,2011,89988,10.5
-wind-ons_6,2011,89990,99.0
-wind-ons_8,2011,90049,69.0
-wind-ons_7,2011,90104,37.8
-wind-ons_9,2011,90637,50.4
-wind-ons_8,2011,90670,0.9
-wind-ons_6,2011,90672,10.5
-wind-ons_7,2011,90674,98.2
-wind-ons_7,2011,90675,150.0
-wind-ons_7,2011,90789,62.5
-wind-ons_8,2011,91267,12.14
-wind-ons_8,2011,91354,0.7
-wind-ons_8,2011,91358,98.0
-wind-ons_8,2011,91472,26.0
-wind-ons_6,2011,91473,52.5
-wind-ons_5,2011,91474,3.0
-wind-ons_6,2011,91485,20.0
-wind-ons_9,2011,91951,16.8
-wind-ons_8,2011,92038,0.7
-wind-ons_8,2011,92041,49.6
-wind-ons_9,2011,92156,24.0
-wind-ons_6,2011,92169,6.6
-wind-ons_1,2011,92585,10.5
-wind-ons_8,2011,92722,0.6
-wind-ons_7,2011,92727,123.0
-wind-ons_2,2011,93270,64.5
-wind-ons_9,2011,93319,25.0
-wind-ons_9,2011,93373,51.0
-wind-ons_9,2011,93406,0.5
-wind-ons_7,2011,93409,43.5
-wind-ons_8,2011,93525,34.5
-wind-ons_10,2011,93526,64.5
-wind-ons_9,2011,93527,14.0
-wind-ons_2,2011,93953,21.0
-wind-ons_8,2011,94093,42.0
-wind-ons_7,2011,94094,80.0
-wind-ons_10,2011,94209,29.4
-wind-ons_8,2011,94211,72.0
-wind-ons_6,2011,94212,102.0
-wind-ons_9,2011,94687,48.86
-wind-ons_7,2011,94778,80.0
-wind-ons_9,2011,94854,1.8
-wind-ons_4,2011,94896,100.5
-wind-ons_7,2011,95371,99.0
-wind-ons_9,2011,95462,80.0
-wind-ons_10,2011,95577,15.0
-wind-ons_7,2011,96055,99.0
-wind-ons_8,2011,96147,104.3
-wind-ons_4,2011,96264,66.0
-wind-ons_6,2011,96739,142.2
-wind-ons_8,2011,96830,78.0
-wind-ons_7,2011,96946,30.0
-wind-ons_8,2011,97513,29.7
-wind-ons_9,2011,97514,4.5
-wind-ons_8,2011,97516,160.5
-wind-ons_8,2011,97517,138.0
-wind-ons_7,2011,98106,1.8
-wind-ons_8,2011,98198,75.0
-wind-ons_7,2011,98275,1.8
-wind-ons_7,2011,98790,15.92
-wind-ons_8,2011,98880,2.3
-wind-ons_8,2011,98881,20.0
-wind-ons_8,2011,99008,7.5
-wind-ons_8,2011,99566,66.0
-wind-ons_9,2011,99569,60.0
-wind-ons_8,2011,99573,246.0
-wind-ons_9,2011,100158,24.8
-wind-ons_8,2011,100249,16.2
-wind-ons_9,2011,100253,1.7
-wind-ons_8,2011,100257,54.0
-wind-ons_9,2011,100842,29.4
-wind-ons_8,2011,100900,8.91
-wind-ons_9,2011,100936,150.0
-wind-ons_8,2011,100981,162.0
-wind-ons_9,2011,101526,42.0
-wind-ons_9,2011,101584,8.91
-wind-ons_8,2011,101620,150.0
-wind-ons_8,2011,101621,199.0
-wind-ons_8,2011,101665,130.5
-wind-ons_10,2011,102210,64.08
-wind-ons_9,2011,102268,8.91
-wind-ons_8,2011,102303,100.0
-wind-ons_8,2011,102304,150.0
-wind-ons_8,2011,102305,174.8
-wind-ons_8,2011,102327,1.5
-wind-ons_8,2011,102349,84.0
-wind-ons_6,2011,102380,1.8
-wind-ons_9,2011,102984,2.0
-wind-ons_8,2011,102985,15.0
-wind-ons_8,2011,102988,150.0
-wind-ons_6,2011,103064,1.8
-wind-ons_8,2011,103669,14.7
-wind-ons_8,2011,103670,62.2
-wind-ons_8,2011,103671,99.0
-wind-ons_9,2011,103672,84.0
-wind-ons_8,2011,103717,101.3
-wind-ons_8,2011,104356,66.0
-wind-ons_8,2011,104401,99.0
-wind-ons_8,2011,105064,99.0
-wind-ons_9,2011,105719,0.9
-wind-ons_9,2011,106329,42.0
-wind-ons_9,2011,107013,54.0
-wind-ons_8,2011,107116,53.0
-wind-ons_9,2011,107741,59.4
-wind-ons_8,2011,107775,78.0
-wind-ons_8,2011,107800,49.5
-wind-ons_8,2011,107802,217.5
-wind-ons_4,2011,107872,60.0
-wind-ons_8,2011,108380,22.5
-wind-ons_8,2011,108381,221.0
-wind-ons_9,2011,108425,54.27
-wind-ons_8,2011,108458,35.0
-wind-ons_8,2011,108459,72.0
-wind-ons_8,2011,108484,10.5
-wind-ons_8,2011,108486,213.8
-wind-ons_5,2011,108556,6.0
-wind-ons_5,2011,108557,100.0
-wind-ons_5,2011,108559,150.0
-wind-ons_8,2011,109168,2.5
-wind-ons_8,2011,109169,150.0
-wind-ons_8,2011,109170,210.9
-wind-ons_6,2011,109243,14.0
-wind-ons_8,2011,109748,29.7
-wind-ons_7,2011,109826,21.0
-wind-ons_8,2011,109852,1.5
-wind-ons_9,2011,109874,100.0
-wind-ons_10,2011,110475,0.7
-wind-ons_8,2011,110559,180.0
-wind-ons_10,2011,111159,0.7
-wind-ons_8,2011,111800,60.0
-wind-ons_8,2011,111905,100.5
-wind-ons_2,2011,112433,18.9
-wind-ons_7,2011,112611,37.7
-wind-ons_7,2011,113168,79.5
-wind-ons_8,2011,113273,80.0
-wind-ons_8,2011,113957,76.2
-wind-ons_7,2011,113979,22.0
-wind-ons_9,2011,114588,105.0
-wind-ons_8,2011,114641,51.0
-wind-ons_9,2011,115272,100.8
-wind-ons_9,2011,115904,151.8
-wind-ons_8,2011,115956,99.0
-wind-ons_8,2011,116009,8.4
-wind-ons_5,2011,116032,20.0
-wind-ons_9,2011,116640,96.0
-wind-ons_8,2011,116693,1.7
-wind-ons_9,2011,117272,157.5
-wind-ons_8,2011,118062,106.0
-wind-ons_9,2011,118639,1.5
-wind-ons_9,2011,118640,147.0
-wind-ons_9,2011,118692,100.5
-wind-ons_7,2011,119430,100.5
-wind-ons_8,2011,120007,4.0
-wind-ons_9,2011,120060,112.2
-wind-ons_7,2011,120798,72.0
-wind-ons_5,2011,122691,203.5
-wind-ons_7,2011,122850,2.5
-wind-ons_9,2011,123427,75.0
-wind-ons_7,2011,126941,5.0
-wind-ons_9,2011,127584,50.0
-wind-ons_10,2011,128214,1.5
-wind-ons_10,2011,128215,162.0
-wind-ons_9,2011,128952,44.0
-wind-ons_9,2011,129637,150.0
-wind-ons_7,2011,130423,27.0
-wind-ons_9,2011,131005,148.5
-wind-ons_6,2011,131105,0.7
-wind-ons_5,2011,131106,0.7
-wind-ons_6,2011,131107,0.7
-wind-ons_6,2011,132311,204.0
-wind-ons_6,2011,132414,56.7
-wind-ons_7,2011,133098,50.4
-wind-ons_7,2011,133782,50.4
-wind-ons_7,2011,134467,146.0
-wind-ons_9,2011,135793,98.9
-wind-ons_9,2011,135794,60.0
-wind-ons_5,2011,136323,36.0
-wind-ons_9,2011,136414,20.0
-wind-ons_9,2011,136478,91.4
-wind-ons_4,2011,136993,1.5
-wind-ons_2,2011,136994,2.4
-wind-ons_3,2011,136995,7.5
-wind-ons_4,2011,137679,8.3
-wind-ons_3,2011,137680,18.0
-wind-ons_8,2011,137782,90.0
-wind-ons_9,2011,137845,99.0
-wind-ons_5,2011,138362,3.0
-wind-ons_4,2011,138364,18.4
-wind-ons_3,2011,138366,38.0
-wind-ons_4,2011,139046,4.2
-wind-ons_3,2011,139049,28.7
-wind-ons_4,2011,139730,6.5
-wind-ons_5,2011,139731,15.5
-wind-ons_4,2011,139733,30.75
-wind-ons_9,2011,139897,94.5
-wind-ons_4,2011,140416,22.0
-wind-ons_10,2011,141779,0.1
-wind-ons_8,2011,141780,1.6
-wind-ons_7,2011,141781,3.4
-wind-ons_8,2011,141949,74.3
-wind-ons_7,2011,142465,4.5
-wind-ons_6,2011,142466,13.1
-wind-ons_3,2011,142468,29.0
-wind-ons_2,2011,142469,42.6
-wind-ons_6,2011,143149,6.5
-wind-ons_8,2011,143253,0.6
-wind-ons_6,2011,143835,22.2
-wind-ons_4,2011,143836,31.0
-wind-ons_5,2011,143844,30.0
-wind-ons_8,2011,143937,0.7
-wind-ons_1,2011,144517,8.7
-wind-ons_4,2011,144518,17.0
-wind-ons_3,2011,144519,24.0
-wind-ons_3,2011,144529,150.0
-wind-ons_6,2011,144685,40.5
-wind-ons_3,2011,145200,3.5
-wind-ons_1,2011,145201,9.6
-wind-ons_2,2011,145203,25.3
-wind-ons_10,2011,145305,30.0
-wind-ons_3,2011,145884,5.4
-wind-ons_2,2011,145885,11.7
-wind-ons_1,2011,145886,20.5
-wind-ons_3,2011,145890,67.0
-wind-ons_3,2011,145891,88.5
-wind-ons_10,2011,145989,80.0
-wind-ons_9,2011,146053,25.5
-wind-ons_3,2011,146568,7.3
-wind-ons_3,2011,146569,15.3
-wind-ons_2,2011,146580,114.0
-wind-ons_10,2011,146673,100.0
-wind-ons_5,2011,146737,25.5
-wind-ons_5,2011,146738,123.0
-wind-ons_5,2011,147252,8.0
-wind-ons_4,2011,147253,16.2
-wind-ons_4,2011,147254,22.4
-wind-ons_3,2011,147255,33.6
-wind-ons_1,2011,147257,60.3
-wind-ons_6,2011,147421,25.5
-wind-ons_6,2011,147422,120.0
-wind-ons_4,2011,147935,3.0
-wind-ons_2,2011,147936,9.1
-wind-ons_1,2011,147937,17.3
-wind-ons_2,2011,147938,23.1
-wind-ons_2,2011,147940,46.9
-wind-ons_3,2011,147941,63.0
-wind-ons_8,2011,148105,25.5
-wind-ons_7,2011,148618,1.8
-wind-ons_3,2011,148619,3.7
-wind-ons_2,2011,148620,11.6
-wind-ons_2,2011,148621,20.0
-wind-ons_3,2011,148623,38.9
-wind-ons_5,2011,148629,135.0
-wind-ons_6,2011,148789,18.9
-wind-ons_5,2011,149304,13.6
-wind-ons_5,2011,149305,20.5
-wind-ons_4,2011,149307,43.4
-wind-ons_4,2011,149308,50.0
-wind-ons_9,2011,149474,106.5
-wind-ons_6,2011,149986,2.1
-wind-ons_8,2011,149987,7.4
-wind-ons_7,2011,149989,21.7
-wind-ons_5,2011,149990,30.75
-wind-ons_2,2011,149991,45.0
-wind-ons_2,2011,149992,59.6
-wind-ons_3,2011,149993,76.9
-wind-ons_7,2011,150158,101.2
-wind-ons_8,2011,150672,16.5
-wind-ons_5,2011,150682,61.8
-wind-ons_5,2011,151366,54.0
-wind-ons_10,2011,152144,0.6
-wind-ons_6,2011,154261,80.0
-wind-ons_7,2011,154944,10.0
-wind-ons_6,2011,154946,126.5
-wind-ons_8,2011,155564,90.0
-wind-ons_7,2011,155628,10.0
-wind-ons_7,2011,155629,80.5
-wind-ons_6,2011,155633,156.0
-wind-ons_7,2011,156312,10.0
-wind-ons_6,2011,156313,79.8
-wind-ons_7,2011,156314,124.7
-wind-ons_6,2011,156317,44.0
-wind-ons_6,2011,156318,204.0
-wind-ons_2,2011,156884,63.0
-wind-ons_7,2011,156996,10.0
-wind-ons_6,2011,156998,124.2
-wind-ons_6,2011,157000,170.2
-wind-ons_6,2011,157002,9.0
-wind-ons_6,2011,157680,10.0
-wind-ons_7,2011,157681,79.92
-wind-ons_7,2011,158364,10.0
-wind-ons_7,2011,158365,79.5
-wind-ons_7,2011,158366,121.9
-wind-ons_7,2011,158368,168.0
-wind-ons_7,2011,159050,60.0
-wind-ons_6,2011,159055,216.0
-wind-ons_7,2011,159732,8.8
-wind-ons_7,2011,159733,75.0
-wind-ons_6,2011,159734,60.6
-wind-ons_6,2011,159735,150.0
-wind-ons_7,2011,159736,1.5
-wind-ons_6,2011,159739,25.0
-wind-ons_7,2011,160417,73.5
-wind-ons_7,2011,160418,120.0
-wind-ons_6,2011,160423,204.0
-wind-ons_7,2011,161100,3.0
-wind-ons_7,2011,161101,69.53
-wind-ons_7,2011,161102,120.0
-wind-ons_7,2011,161103,150.0
-wind-ons_6,2011,161104,167.7
-wind-ons_6,2011,161106,180.0
-wind-ons_6,2011,161107,28.5
-wind-ons_7,2011,161784,2.64
-wind-ons_6,2011,161787,90.0
-wind-ons_6,2011,161790,29.0
-wind-ons_6,2011,161791,78.0
-wind-ons_7,2011,162471,60.0
-wind-ons_6,2011,162475,153.5
-wind-ons_7,2011,163837,63.0
-wind-ons_7,2011,163838,120.0
-wind-ons_7,2011,164520,0.92
-wind-ons_7,2011,164521,78.0
-wind-ons_7,2011,165204,0.6
-wind-ons_7,2011,165206,114.0
-wind-ons_7,2011,165208,162.3
-wind-ons_6,2011,165209,192.0
-wind-ons_6,2011,165890,114.0
-wind-ons_6,2011,165893,5.0
-wind-ons_5,2011,166572,0.16
-wind-ons_6,2011,166573,59.4
-wind-ons_6,2011,166574,112.5
-wind-ons_7,2011,167256,0.08
-wind-ons_5,2011,167257,58.8
-wind-ons_5,2011,167258,112.5
-wind-ons_6,2011,167940,0.07
-wind-ons_7,2011,167941,57.0
-wind-ons_7,2011,167943,149.5
-wind-ons_6,2011,168624,0.07
-wind-ons_6,2011,168628,160.84
-wind-ons_5,2011,168630,200.0
-wind-ons_6,2011,168633,216.0
-wind-ons_5,2011,169310,101.2
-wind-ons_5,2011,169313,180.0
-wind-ons_6,2011,169317,83.0
-wind-ons_5,2011,169993,49.5
-wind-ons_5,2011,169997,6.0
-wind-ons_5,2011,169999,210.0
-wind-ons_5,2011,170000,228.0
-wind-ons_5,2011,170683,13.5
-wind-ons_5,2011,170684,21.0
-wind-ons_6,2011,171361,37.5
-wind-ons_5,2011,171362,98.8
-wind-ons_5,2011,171363,126.0
-wind-ons_6,2011,172045,34.3
-wind-ons_5,2011,172047,18.0
-wind-ons_6,2011,172729,32.52
-wind-ons_6,2011,172730,91.58
-wind-ons_5,2011,172735,198.0
-wind-ons_6,2011,173414,91.2
-wind-ons_6,2011,173419,16.5
-wind-ons_6,2011,173421,180.0
-wind-ons_6,2011,174098,91.2
-wind-ons_6,2011,174099,130.5
-wind-ons_7,2011,174101,180.0
-wind-ons_5,2011,174105,98.0
-wind-ons_6,2011,174784,153.9
-wind-ons_5,2011,174789,73.2
-wind-ons_7,2011,175465,17.64
-wind-ons_6,2011,175466,89.5
-wind-ons_6,2011,175468,152.0
-wind-ons_5,2011,175473,150.0
-wind-ons_6,2011,176149,10.0
-wind-ons_5,2011,176150,84.0
-wind-ons_5,2011,176153,180.0
-wind-ons_5,2011,176154,201.6
-wind-ons_6,2011,176157,60.0
-wind-ons_6,2011,176834,60.0
-wind-ons_6,2011,177518,22.5
-wind-ons_6,2011,178202,82.0
-wind-ons_1,2012,46046,114.0
-wind-ons_2,2012,46729,44.0
-wind-ons_1,2012,46730,28.2
-wind-ons_2,2012,48781,156.0
-wind-ons_4,2012,49464,7.2
-wind-ons_3,2012,49465,228.6
-wind-ons_5,2012,50148,94.0
-wind-ons_3,2012,51516,54.0
-wind-ons_7,2012,52200,168.0
-wind-ons_7,2012,52969,9.7
-wind-ons_6,2012,53567,122.8
-wind-ons_5,2012,54935,31.0
-wind-ons_6,2012,55619,234.0
-wind-ons_7,2012,55705,104.2
-wind-ons_6,2012,56302,98.9
-wind-ons_6,2012,57670,62.9
-wind-ons_6,2012,58354,174.0
-wind-ons_8,2012,58684,4.5
-wind-ons_4,2012,59037,95.0
-wind-ons_8,2012,59201,0.93
-wind-ons_8,2012,59203,2.0
-wind-ons_3,2012,59721,136.3
-wind-ons_7,2012,59854,18.0
-wind-ons_7,2012,59885,0.93
-wind-ons_8,2012,59886,1.3
-wind-ons_7,2012,59889,10.4
-wind-ons_8,2012,59899,180.0
-wind-ons_1,2012,60405,95.4
-wind-ons_7,2012,60569,0.93
-wind-ons_7,2012,60570,1.2
-wind-ons_7,2012,60571,2.0
-wind-ons_7,2012,60572,2.1
-wind-ons_7,2012,60574,25.0
-wind-ons_8,2012,60583,25.5
-wind-ons_9,2012,60738,42.0
-wind-ons_8,2012,60739,66.0
-wind-ons_2,2012,61089,30.0
-wind-ons_7,2012,61253,0.93
-wind-ons_7,2012,61255,1.9
-wind-ons_7,2012,61256,2.1
-wind-ons_7,2012,61257,9.2
-wind-ons_5,2012,61772,78.0
-wind-ons_7,2012,61937,0.93
-wind-ons_7,2012,61939,1.7
-wind-ons_8,2012,61940,2.1
-wind-ons_7,2012,61941,8.4
-wind-ons_8,2012,61942,20.0
-wind-ons_9,2012,61943,51.0
-wind-ons_9,2012,61944,60.0
-wind-ons_9,2012,62107,57.0
-wind-ons_7,2012,62544,30.0
-wind-ons_8,2012,62590,31.5
-wind-ons_7,2012,62621,0.93
-wind-ons_7,2012,62623,1.7
-wind-ons_7,2012,62624,2.0
-wind-ons_8,2012,62625,8.0
-wind-ons_9,2012,62628,40.5
-wind-ons_4,2012,63139,6.6
-wind-ons_3,2012,63140,98.7
-wind-ons_7,2012,63305,0.93
-wind-ons_7,2012,63307,1.7
-wind-ons_7,2012,63308,2.0
-wind-ons_8,2012,63309,5.2
-wind-ons_8,2012,63310,19.98
-wind-ons_9,2012,63311,49.5
-wind-ons_8,2012,63312,100.5
-wind-ons_7,2012,63314,150.0
-wind-ons_9,2012,63474,4.5
-wind-ons_4,2012,63823,9.9
-wind-ons_3,2012,63824,30.9
-wind-ons_4,2012,63825,60.0
-wind-ons_8,2012,63912,101.4
-wind-ons_8,2012,63958,40.5
-wind-ons_6,2012,63989,0.93
-wind-ons_7,2012,63990,1.0
-wind-ons_7,2012,63991,1.7
-wind-ons_8,2012,63992,2.0
-wind-ons_7,2012,63996,100.0
-wind-ons_8,2012,64596,135.0
-wind-ons_8,2012,64642,40.5
-wind-ons_8,2012,64643,115.5
-wind-ons_7,2012,64673,0.93
-wind-ons_7,2012,64674,0.97
-wind-ons_7,2012,64675,1.7
-wind-ons_7,2012,64677,4.2
-wind-ons_2,2012,65191,24.0
-wind-ons_7,2012,65357,0.9
-wind-ons_7,2012,65358,0.97
-wind-ons_8,2012,65359,1.7
-wind-ons_7,2012,65360,2.0
-wind-ons_8,2012,65361,4.2
-wind-ons_7,2012,65362,15.6
-wind-ons_5,2012,65363,43.06
-wind-ons_1,2012,65874,1.7
-wind-ons_2,2012,65875,72.0
-wind-ons_8,2012,66010,48.0
-wind-ons_8,2012,66011,118.5
-wind-ons_7,2012,66041,0.9
-wind-ons_7,2012,66042,0.97
-wind-ons_7,2012,66043,1.5
-wind-ons_7,2012,66044,2.0
-wind-ons_8,2012,66045,4.2
-wind-ons_2,2012,66559,100.5
-wind-ons_4,2012,66560,101.0
-wind-ons_8,2012,66694,48.0
-wind-ons_8,2012,66695,6.0
-wind-ons_7,2012,66725,0.9
-wind-ons_7,2012,66726,0.97
-wind-ons_8,2012,66727,1.3
-wind-ons_7,2012,66728,2.0
-wind-ons_2,2012,67243,35.4
-wind-ons_8,2012,67379,149.0
-wind-ons_8,2012,67409,0.9
-wind-ons_8,2012,67410,0.97
-wind-ons_8,2012,67411,1.3
-wind-ons_6,2012,67412,2.0
-wind-ons_7,2012,67538,54.0
-wind-ons_8,2012,68062,49.5
-wind-ons_7,2012,68093,0.7
-wind-ons_8,2012,68094,0.97
-wind-ons_6,2012,68221,30.55
-wind-ons_6,2012,68222,100.5
-wind-ons_8,2012,68746,49.5
-wind-ons_7,2012,68777,0.7
-wind-ons_5,2012,68778,0.97
-wind-ons_7,2012,68905,16.5
-wind-ons_6,2012,68906,19.5
-wind-ons_4,2012,68919,5.2
-wind-ons_1,2012,69293,8.3
-wind-ons_8,2012,69430,50.6
-wind-ons_8,2012,69431,114.0
-wind-ons_5,2012,69462,0.97
-wind-ons_5,2012,69591,52.0
-wind-ons_1,2012,69979,60.0
-wind-ons_8,2012,70114,61.5
-wind-ons_8,2012,70115,148.5
-wind-ons_5,2012,70145,0.4
-wind-ons_5,2012,70146,0.97
-wind-ons_6,2012,70274,68.5
-wind-ons_1,2012,70662,31.3
-wind-ons_7,2012,70830,0.97
-wind-ons_7,2012,70959,52.5
-wind-ons_6,2012,71345,24.6
-wind-ons_7,2012,71514,0.97
-wind-ons_8,2012,71643,78.0
-wind-ons_3,2012,72028,3.3
-wind-ons_8,2012,72165,2.6
-wind-ons_7,2012,72197,0.04
-wind-ons_6,2012,72198,0.97
-wind-ons_6,2012,72345,24.0
-wind-ons_5,2012,72712,9.9
-wind-ons_8,2012,72849,4.5
-wind-ons_6,2012,72881,0.02
-wind-ons_8,2012,73533,11.9
-wind-ons_8,2012,73535,180.0
-wind-ons_5,2012,74764,41.0
-wind-ons_4,2012,76132,102.3
-wind-ons_7,2012,76334,42.57
-wind-ons_6,2012,76857,18.9
-wind-ons_8,2012,77702,30.0
-wind-ons_5,2012,77800,11.5
-wind-ons_5,2012,77801,81.0
-wind-ons_8,2012,78957,19.5
-wind-ons_3,2012,79169,45.5
-wind-ons_4,2012,79551,72.0
-wind-ons_8,2012,79641,39.0
-wind-ons_9,2012,79755,43.2
-wind-ons_3,2012,79853,30.55
-wind-ons_3,2012,80235,132.0
-wind-ons_5,2012,80538,87.5
-wind-ons_5,2012,80539,126.0
-wind-ons_4,2012,80918,25.0
-wind-ons_8,2012,81009,99.0
-wind-ons_9,2012,81122,12.9
-wind-ons_10,2012,81124,102.0
-wind-ons_3,2012,81223,60.0
-wind-ons_3,2012,81601,5.0
-wind-ons_2,2012,81602,76.5
-wind-ons_9,2012,81806,11.88
-wind-ons_9,2012,81807,67.7
-wind-ons_4,2012,81906,68.5
-wind-ons_3,2012,82285,9.9
-wind-ons_2,2012,82286,100.7
-wind-ons_9,2012,82490,11.0
-wind-ons_6,2012,82590,34.5
-wind-ons_3,2012,82970,102.9
-wind-ons_9,2012,83175,54.0
-wind-ons_6,2012,83274,16.5
-wind-ons_3,2012,83653,10.0
-wind-ons_6,2012,83744,1.3
-wind-ons_9,2012,83859,44.55
-wind-ons_3,2012,84337,25.2
-wind-ons_5,2012,84428,4.9
-wind-ons_8,2012,84510,0.97
-wind-ons_7,2012,84512,2.0
-wind-ons_7,2012,84513,2.1
-wind-ons_9,2012,84542,3.6
-wind-ons_9,2012,84574,1.8
-wind-ons_6,2012,84643,45.5
-wind-ons_9,2012,84657,1.5
-wind-ons_8,2012,84658,1.8
-wind-ons_3,2012,85020,8.0
-wind-ons_2,2012,85021,81.0
-wind-ons_3,2012,85063,21.0
-wind-ons_6,2012,85112,17.0
-wind-ons_8,2012,85194,0.93
-wind-ons_9,2012,85195,1.3
-wind-ons_8,2012,85196,2.0
-wind-ons_8,2012,85197,2.0
-wind-ons_7,2012,85198,9.8
-wind-ons_3,2012,85705,100.8
-wind-ons_5,2012,85796,25.0
-wind-ons_8,2012,85878,0.93
-wind-ons_9,2012,85879,1.3
-wind-ons_8,2012,85880,1.7
-wind-ons_8,2012,85881,2.0
-wind-ons_7,2012,86012,100.5
-wind-ons_4,2012,86389,90.0
-wind-ons_4,2012,86480,28.8
-wind-ons_8,2012,86562,0.93
-wind-ons_9,2012,86563,1.3
-wind-ons_9,2012,86564,1.7
-wind-ons_8,2012,86565,2.0
-wind-ons_8,2012,86567,13.5
-wind-ons_7,2012,86569,99.0
-wind-ons_5,2012,87164,41.4
-wind-ons_9,2012,87246,0.93
-wind-ons_8,2012,87248,1.7
-wind-ons_8,2012,87249,2.0
-wind-ons_8,2012,87250,3.9
-wind-ons_7,2012,87251,12.0
-wind-ons_7,2012,87252,32.0
-wind-ons_7,2012,87253,98.8
-wind-ons_7,2012,87254,60.0
-wind-ons_9,2012,87279,13.5
-wind-ons_8,2012,87312,59.0
-wind-ons_2,2012,87798,8.4
-wind-ons_5,2012,87848,99.0
-wind-ons_9,2012,87901,1.3
-wind-ons_9,2012,87930,0.93
-wind-ons_9,2012,87931,1.0
-wind-ons_7,2012,87932,1.5
-wind-ons_8,2012,87934,3.8
-wind-ons_7,2012,87935,11.8
-wind-ons_7,2012,87938,43.5
-wind-ons_8,2012,87996,51.2
-wind-ons_8,2012,88064,37.5
-wind-ons_5,2012,88532,99.0
-wind-ons_9,2012,88585,1.3
-wind-ons_9,2012,88614,0.9
-wind-ons_8,2012,88616,1.3
-wind-ons_8,2012,88617,2.0
-wind-ons_8,2012,88618,3.0
-wind-ons_7,2012,88619,11.6
-wind-ons_8,2012,88621,91.1
-wind-ons_8,2012,88748,30.0
-wind-ons_9,2012,89269,25.0
-wind-ons_9,2012,89298,0.9
-wind-ons_9,2012,89299,0.97
-wind-ons_9,2012,89300,1.3
-wind-ons_9,2012,89301,2.0
-wind-ons_8,2012,89303,11.2
-wind-ons_7,2012,89305,85.5
-wind-ons_6,2012,89306,100.7
-wind-ons_7,2012,89419,9.0
-wind-ons_9,2012,89953,40.5
-wind-ons_9,2012,89986,1.2
-wind-ons_7,2012,89988,10.5
-wind-ons_6,2012,89990,99.0
-wind-ons_8,2012,90049,69.0
-wind-ons_7,2012,90104,37.8
-wind-ons_9,2012,90637,50.4
-wind-ons_8,2012,90670,0.9
-wind-ons_6,2012,90672,10.5
-wind-ons_7,2012,90674,98.2
-wind-ons_7,2012,90675,150.0
-wind-ons_7,2012,90789,62.5
-wind-ons_8,2012,91267,12.14
-wind-ons_8,2012,91354,0.7
-wind-ons_8,2012,91358,98.0
-wind-ons_8,2012,91472,26.0
-wind-ons_6,2012,91473,52.5
-wind-ons_5,2012,91474,3.0
-wind-ons_6,2012,91485,20.0
-wind-ons_9,2012,91951,16.8
-wind-ons_8,2012,92038,0.7
-wind-ons_8,2012,92041,49.6
-wind-ons_9,2012,92156,24.0
-wind-ons_6,2012,92169,6.6
-wind-ons_1,2012,92585,10.5
-wind-ons_8,2012,92722,0.6
-wind-ons_7,2012,92727,123.0
-wind-ons_2,2012,93270,64.5
-wind-ons_9,2012,93319,25.0
-wind-ons_9,2012,93373,51.0
-wind-ons_9,2012,93406,0.5
-wind-ons_7,2012,93409,43.5
-wind-ons_8,2012,93525,34.5
-wind-ons_10,2012,93526,64.5
-wind-ons_9,2012,93527,14.0
-wind-ons_2,2012,93953,21.0
-wind-ons_8,2012,94093,42.0
-wind-ons_7,2012,94094,80.0
-wind-ons_10,2012,94209,29.4
-wind-ons_8,2012,94211,72.0
-wind-ons_6,2012,94212,102.0
-wind-ons_9,2012,94687,48.86
-wind-ons_7,2012,94778,80.0
-wind-ons_9,2012,94854,1.8
-wind-ons_4,2012,94896,100.5
-wind-ons_7,2012,95371,99.0
-wind-ons_9,2012,95462,80.0
-wind-ons_10,2012,95577,15.0
-wind-ons_7,2012,96055,99.0
-wind-ons_8,2012,96147,104.3
-wind-ons_4,2012,96264,66.0
-wind-ons_6,2012,96739,142.2
-wind-ons_8,2012,96830,78.0
-wind-ons_7,2012,96946,30.0
-wind-ons_8,2012,97513,29.7
-wind-ons_9,2012,97514,4.5
-wind-ons_8,2012,97516,160.5
-wind-ons_8,2012,97517,138.0
-wind-ons_7,2012,98106,1.8
-wind-ons_8,2012,98198,75.0
-wind-ons_7,2012,98275,1.8
-wind-ons_7,2012,98790,15.92
-wind-ons_8,2012,98880,2.3
-wind-ons_8,2012,98881,20.0
-wind-ons_8,2012,99008,7.5
-wind-ons_8,2012,99566,66.0
-wind-ons_9,2012,99569,60.0
-wind-ons_8,2012,99573,246.0
-wind-ons_9,2012,100158,24.8
-wind-ons_8,2012,100249,16.2
-wind-ons_9,2012,100253,1.7
-wind-ons_8,2012,100257,54.0
-wind-ons_9,2012,100842,29.4
-wind-ons_8,2012,100900,8.91
-wind-ons_9,2012,100936,150.0
-wind-ons_8,2012,100981,162.0
-wind-ons_9,2012,101526,42.0
-wind-ons_9,2012,101584,8.91
-wind-ons_8,2012,101620,150.0
-wind-ons_8,2012,101621,199.0
-wind-ons_8,2012,101665,130.5
-wind-ons_10,2012,102210,64.08
-wind-ons_9,2012,102268,8.91
-wind-ons_8,2012,102303,100.0
-wind-ons_8,2012,102304,150.0
-wind-ons_8,2012,102305,174.8
-wind-ons_8,2012,102327,1.5
-wind-ons_8,2012,102349,84.0
-wind-ons_6,2012,102380,1.8
-wind-ons_9,2012,102984,2.0
-wind-ons_8,2012,102985,15.0
-wind-ons_8,2012,102988,150.0
-wind-ons_6,2012,103064,1.8
-wind-ons_8,2012,103669,14.7
-wind-ons_8,2012,103670,62.2
-wind-ons_8,2012,103671,99.0
-wind-ons_9,2012,103672,84.0
-wind-ons_8,2012,103717,101.3
-wind-ons_8,2012,104356,66.0
-wind-ons_8,2012,104401,99.0
-wind-ons_8,2012,105064,99.0
-wind-ons_9,2012,105719,0.9
-wind-ons_9,2012,106329,42.0
-wind-ons_9,2012,107013,54.0
-wind-ons_8,2012,107116,53.0
-wind-ons_9,2012,107741,59.4
-wind-ons_8,2012,107775,78.0
-wind-ons_8,2012,107800,49.5
-wind-ons_8,2012,107802,217.5
-wind-ons_4,2012,107872,60.0
-wind-ons_8,2012,108380,22.5
-wind-ons_8,2012,108381,221.0
-wind-ons_9,2012,108425,54.27
-wind-ons_8,2012,108458,35.0
-wind-ons_8,2012,108459,72.0
-wind-ons_8,2012,108484,10.5
-wind-ons_8,2012,108486,213.8
-wind-ons_5,2012,108556,6.0
-wind-ons_5,2012,108557,100.0
-wind-ons_5,2012,108559,150.0
-wind-ons_8,2012,109168,2.5
-wind-ons_8,2012,109169,150.0
-wind-ons_8,2012,109170,210.9
-wind-ons_6,2012,109243,14.0
-wind-ons_8,2012,109748,29.7
-wind-ons_7,2012,109826,21.0
-wind-ons_8,2012,109852,1.5
-wind-ons_9,2012,109874,100.0
-wind-ons_10,2012,110475,0.7
-wind-ons_8,2012,110559,180.0
-wind-ons_10,2012,111159,0.7
-wind-ons_8,2012,111800,60.0
-wind-ons_8,2012,111905,100.5
-wind-ons_2,2012,112433,18.9
-wind-ons_7,2012,112611,37.7
-wind-ons_7,2012,113168,79.5
-wind-ons_8,2012,113273,80.0
-wind-ons_8,2012,113957,76.2
-wind-ons_7,2012,113979,22.0
-wind-ons_9,2012,114588,105.0
-wind-ons_8,2012,114641,51.0
-wind-ons_9,2012,115272,100.8
-wind-ons_9,2012,115904,151.8
-wind-ons_8,2012,115956,99.0
-wind-ons_8,2012,116009,8.4
-wind-ons_5,2012,116032,20.0
-wind-ons_9,2012,116640,96.0
-wind-ons_8,2012,116693,1.7
-wind-ons_9,2012,117272,157.5
-wind-ons_8,2012,118062,106.0
-wind-ons_9,2012,118639,1.5
-wind-ons_9,2012,118640,147.0
-wind-ons_9,2012,118692,100.5
-wind-ons_7,2012,119430,100.5
-wind-ons_8,2012,120007,4.0
-wind-ons_9,2012,120060,112.2
-wind-ons_7,2012,120798,72.0
-wind-ons_5,2012,122691,203.5
-wind-ons_7,2012,122850,2.5
-wind-ons_9,2012,123427,75.0
-wind-ons_7,2012,126941,5.0
-wind-ons_9,2012,127584,50.0
-wind-ons_10,2012,128214,1.5
-wind-ons_10,2012,128215,162.0
-wind-ons_9,2012,128952,44.0
-wind-ons_9,2012,129637,150.0
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-wind-ons_7,2014,64675,1.7
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-wind-ons_7,2014,65357,0.9
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-wind-ons_7,2014,65360,2.0
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-wind-ons_9,2014,86564,1.7
-wind-ons_8,2014,86565,2.0
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-wind-ons_9,2014,87930,0.93
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-wind-ons_9,2014,89269,25.0
-wind-ons_9,2014,89298,0.9
-wind-ons_9,2014,89299,0.97
-wind-ons_9,2014,89300,1.3
-wind-ons_9,2014,89301,2.0
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-wind-ons_7,2014,156314,124.7
-wind-ons_6,2014,156317,44.0
-wind-ons_6,2014,156318,204.0
-wind-ons_2,2014,156884,63.0
-wind-ons_7,2014,156996,10.0
-wind-ons_6,2014,156998,124.2
-wind-ons_6,2014,157000,170.2
-wind-ons_6,2014,157002,9.0
-wind-ons_6,2014,157680,10.0
-wind-ons_7,2014,157681,79.92
-wind-ons_7,2014,158364,10.0
-wind-ons_7,2014,158365,79.5
-wind-ons_7,2014,158366,121.9
-wind-ons_7,2014,158368,168.0
-wind-ons_7,2014,159050,60.0
-wind-ons_6,2014,159055,216.0
-wind-ons_7,2014,159732,8.8
-wind-ons_7,2014,159733,75.0
-wind-ons_6,2014,159734,60.6
-wind-ons_6,2014,159735,150.0
-wind-ons_7,2014,159736,1.5
-wind-ons_6,2014,159739,25.0
-wind-ons_7,2014,160417,73.5
-wind-ons_7,2014,160418,120.0
-wind-ons_6,2014,160423,204.0
-wind-ons_7,2014,161100,3.0
-wind-ons_7,2014,161101,69.53
-wind-ons_7,2014,161102,120.0
-wind-ons_7,2014,161103,150.0
-wind-ons_6,2014,161104,167.7
-wind-ons_6,2014,161106,180.0
-wind-ons_6,2014,161107,28.5
-wind-ons_7,2014,161784,2.64
-wind-ons_6,2014,161787,90.0
-wind-ons_6,2014,161790,29.0
-wind-ons_6,2014,161791,78.0
-wind-ons_7,2014,162471,60.0
-wind-ons_6,2014,162475,153.5
-wind-ons_7,2014,163837,63.0
-wind-ons_7,2014,163838,120.0
-wind-ons_7,2014,164520,0.92
-wind-ons_7,2014,164521,78.0
-wind-ons_7,2014,165204,0.6
-wind-ons_7,2014,165206,114.0
-wind-ons_7,2014,165208,162.3
-wind-ons_6,2014,165209,192.0
-wind-ons_6,2014,165890,114.0
-wind-ons_6,2014,165893,5.0
-wind-ons_5,2014,166572,0.16
-wind-ons_6,2014,166573,59.4
-wind-ons_6,2014,166574,112.5
-wind-ons_7,2014,167256,0.08
-wind-ons_5,2014,167257,58.8
-wind-ons_5,2014,167258,112.5
-wind-ons_6,2014,167940,0.07
-wind-ons_7,2014,167941,57.0
-wind-ons_7,2014,167943,149.5
-wind-ons_6,2014,168624,0.07
-wind-ons_6,2014,168628,160.84
-wind-ons_5,2014,168630,200.0
-wind-ons_6,2014,168633,216.0
-wind-ons_5,2014,169310,101.2
-wind-ons_5,2014,169313,180.0
-wind-ons_6,2014,169317,83.0
-wind-ons_5,2014,169993,49.5
-wind-ons_5,2014,169997,6.0
-wind-ons_5,2014,169999,210.0
-wind-ons_5,2014,170000,228.0
-wind-ons_5,2014,170683,13.5
-wind-ons_5,2014,170684,21.0
-wind-ons_6,2014,171361,37.5
-wind-ons_5,2014,171362,98.8
-wind-ons_5,2014,171363,126.0
-wind-ons_6,2014,172045,34.3
-wind-ons_5,2014,172047,18.0
-wind-ons_6,2014,172729,32.52
-wind-ons_6,2014,172730,91.58
-wind-ons_5,2014,172735,198.0
-wind-ons_6,2014,173414,91.2
-wind-ons_6,2014,173419,16.5
-wind-ons_6,2014,173421,180.0
-wind-ons_6,2014,174098,91.2
-wind-ons_6,2014,174099,130.5
-wind-ons_7,2014,174101,180.0
-wind-ons_5,2014,174105,98.0
-wind-ons_6,2014,174784,153.9
-wind-ons_5,2014,174789,73.2
-wind-ons_7,2014,175465,17.64
-wind-ons_6,2014,175466,89.5
-wind-ons_6,2014,175468,152.0
-wind-ons_5,2014,175473,150.0
-wind-ons_6,2014,176149,10.0
-wind-ons_5,2014,176150,84.0
-wind-ons_5,2014,176153,180.0
-wind-ons_5,2014,176154,201.6
-wind-ons_6,2014,176157,60.0
-wind-ons_6,2014,176834,60.0
-wind-ons_6,2014,177518,22.5
-wind-ons_6,2014,178202,82.0
-wind-ons_1,2015,46046,114.0
-wind-ons_2,2015,46729,44.0
-wind-ons_1,2015,46730,28.2
-wind-ons_2,2015,48781,156.0
-wind-ons_4,2015,49464,7.2
-wind-ons_3,2015,49465,228.6
-wind-ons_5,2015,50148,94.0
-wind-ons_3,2015,51516,54.0
-wind-ons_7,2015,52200,168.0
-wind-ons_7,2015,52969,9.7
-wind-ons_6,2015,53567,122.8
-wind-ons_5,2015,54935,31.0
-wind-ons_6,2015,55619,234.0
-wind-ons_7,2015,55705,104.2
-wind-ons_6,2015,56302,98.9
-wind-ons_6,2015,57670,62.9
-wind-ons_6,2015,58354,174.0
-wind-ons_8,2015,58684,4.5
-wind-ons_4,2015,59037,95.0
-wind-ons_8,2015,59201,0.93
-wind-ons_8,2015,59203,2.0
-wind-ons_3,2015,59721,136.3
-wind-ons_7,2015,59854,18.0
-wind-ons_7,2015,59885,0.93
-wind-ons_8,2015,59886,1.3
-wind-ons_7,2015,59889,10.4
-wind-ons_8,2015,59899,180.0
-wind-ons_1,2015,60405,95.4
-wind-ons_7,2015,60569,0.93
-wind-ons_7,2015,60570,1.2
-wind-ons_7,2015,60571,2.0
-wind-ons_7,2015,60572,2.1
-wind-ons_7,2015,60574,25.0
-wind-ons_8,2015,60583,25.5
-wind-ons_9,2015,60738,42.0
-wind-ons_8,2015,60739,66.0
-wind-ons_2,2015,61089,30.0
-wind-ons_7,2015,61253,0.93
-wind-ons_7,2015,61255,1.9
-wind-ons_7,2015,61256,2.1
-wind-ons_7,2015,61257,9.2
-wind-ons_5,2015,61772,78.0
-wind-ons_7,2015,61937,0.93
-wind-ons_7,2015,61939,1.7
-wind-ons_8,2015,61940,2.1
-wind-ons_7,2015,61941,8.4
-wind-ons_8,2015,61942,20.0
-wind-ons_9,2015,61943,51.0
-wind-ons_9,2015,61944,60.0
-wind-ons_9,2015,62107,57.0
-wind-ons_7,2015,62544,30.0
-wind-ons_8,2015,62590,31.5
-wind-ons_7,2015,62621,0.93
-wind-ons_7,2015,62623,1.7
-wind-ons_7,2015,62624,2.0
-wind-ons_8,2015,62625,8.0
-wind-ons_9,2015,62628,40.5
-wind-ons_4,2015,63139,6.6
-wind-ons_3,2015,63140,98.7
-wind-ons_7,2015,63305,0.93
-wind-ons_7,2015,63307,1.7
-wind-ons_7,2015,63308,2.0
-wind-ons_8,2015,63309,5.2
-wind-ons_8,2015,63310,19.98
-wind-ons_9,2015,63311,49.5
-wind-ons_8,2015,63312,100.5
-wind-ons_7,2015,63314,150.0
-wind-ons_9,2015,63474,4.5
-wind-ons_4,2015,63823,9.9
-wind-ons_3,2015,63824,30.9
-wind-ons_4,2015,63825,60.0
-wind-ons_8,2015,63912,101.4
-wind-ons_8,2015,63958,40.5
-wind-ons_6,2015,63989,0.93
-wind-ons_7,2015,63990,1.0
-wind-ons_7,2015,63991,1.7
-wind-ons_8,2015,63992,2.0
-wind-ons_7,2015,63996,100.0
-wind-ons_8,2015,64596,135.0
-wind-ons_8,2015,64642,40.5
-wind-ons_8,2015,64643,115.5
-wind-ons_7,2015,64673,0.93
-wind-ons_7,2015,64674,0.97
-wind-ons_7,2015,64675,1.7
-wind-ons_7,2015,64677,4.2
-wind-ons_2,2015,65191,24.0
-wind-ons_7,2015,65357,0.9
-wind-ons_7,2015,65358,0.97
-wind-ons_8,2015,65359,1.7
-wind-ons_7,2015,65360,2.0
-wind-ons_8,2015,65361,4.2
-wind-ons_7,2015,65362,15.6
-wind-ons_5,2015,65363,43.06
-wind-ons_1,2015,65874,1.7
-wind-ons_2,2015,65875,72.0
-wind-ons_8,2015,66010,48.0
-wind-ons_8,2015,66011,118.5
-wind-ons_7,2015,66041,0.9
-wind-ons_7,2015,66042,0.97
-wind-ons_7,2015,66043,1.5
-wind-ons_7,2015,66044,2.0
-wind-ons_8,2015,66045,4.2
-wind-ons_2,2015,66559,100.5
-wind-ons_4,2015,66560,101.0
-wind-ons_8,2015,66694,48.0
-wind-ons_8,2015,66695,6.0
-wind-ons_7,2015,66725,0.9
-wind-ons_7,2015,66726,0.97
-wind-ons_8,2015,66727,1.3
-wind-ons_7,2015,66728,2.0
-wind-ons_2,2015,67243,35.4
-wind-ons_8,2015,67379,149.0
-wind-ons_8,2015,67409,0.9
-wind-ons_8,2015,67410,0.97
-wind-ons_8,2015,67411,1.3
-wind-ons_6,2015,67412,2.0
-wind-ons_7,2015,67538,54.0
-wind-ons_8,2015,68062,49.5
-wind-ons_7,2015,68093,0.7
-wind-ons_8,2015,68094,0.97
-wind-ons_6,2015,68221,30.55
-wind-ons_6,2015,68222,100.5
-wind-ons_8,2015,68746,49.5
-wind-ons_7,2015,68777,0.7
-wind-ons_5,2015,68778,0.97
-wind-ons_7,2015,68905,16.5
-wind-ons_6,2015,68906,19.5
-wind-ons_4,2015,68919,5.2
-wind-ons_1,2015,69293,8.3
-wind-ons_8,2015,69430,50.6
-wind-ons_8,2015,69431,114.0
-wind-ons_5,2015,69462,0.97
-wind-ons_5,2015,69591,52.0
-wind-ons_1,2015,69979,60.0
-wind-ons_8,2015,70114,61.5
-wind-ons_8,2015,70115,148.5
-wind-ons_5,2015,70145,0.4
-wind-ons_5,2015,70146,0.97
-wind-ons_6,2015,70274,68.5
-wind-ons_1,2015,70662,31.3
-wind-ons_7,2015,70830,0.97
-wind-ons_7,2015,70959,52.5
-wind-ons_6,2015,71345,24.6
-wind-ons_7,2015,71514,0.97
-wind-ons_8,2015,71643,78.0
-wind-ons_3,2015,72028,3.3
-wind-ons_8,2015,72165,2.6
-wind-ons_7,2015,72197,0.04
-wind-ons_6,2015,72198,0.97
-wind-ons_6,2015,72345,24.0
-wind-ons_5,2015,72712,9.9
-wind-ons_8,2015,72849,4.5
-wind-ons_6,2015,72881,0.02
-wind-ons_8,2015,73533,11.9
-wind-ons_8,2015,73535,180.0
-wind-ons_5,2015,74764,41.0
-wind-ons_4,2015,76132,102.3
-wind-ons_7,2015,76334,42.57
-wind-ons_6,2015,76857,18.9
-wind-ons_8,2015,77702,30.0
-wind-ons_5,2015,77800,11.5
-wind-ons_5,2015,77801,81.0
-wind-ons_8,2015,78957,19.5
-wind-ons_3,2015,79169,45.5
-wind-ons_4,2015,79551,72.0
-wind-ons_8,2015,79641,39.0
-wind-ons_9,2015,79755,43.2
-wind-ons_3,2015,79853,30.55
-wind-ons_3,2015,80235,132.0
-wind-ons_5,2015,80538,87.5
-wind-ons_5,2015,80539,126.0
-wind-ons_4,2015,80918,25.0
-wind-ons_8,2015,81009,99.0
-wind-ons_9,2015,81122,12.9
-wind-ons_10,2015,81124,102.0
-wind-ons_3,2015,81223,60.0
-wind-ons_3,2015,81601,5.0
-wind-ons_2,2015,81602,76.5
-wind-ons_9,2015,81806,11.88
-wind-ons_9,2015,81807,67.7
-wind-ons_4,2015,81906,68.5
-wind-ons_3,2015,82285,9.9
-wind-ons_2,2015,82286,100.7
-wind-ons_9,2015,82490,11.0
-wind-ons_6,2015,82590,34.5
-wind-ons_3,2015,82970,102.9
-wind-ons_9,2015,83175,54.0
-wind-ons_6,2015,83274,16.5
-wind-ons_3,2015,83653,10.0
-wind-ons_6,2015,83744,1.3
-wind-ons_9,2015,83859,44.55
-wind-ons_3,2015,84337,25.2
-wind-ons_5,2015,84428,4.9
-wind-ons_8,2015,84510,0.97
-wind-ons_7,2015,84512,2.0
-wind-ons_7,2015,84513,2.1
-wind-ons_9,2015,84542,3.6
-wind-ons_9,2015,84574,1.8
-wind-ons_6,2015,84643,45.5
-wind-ons_9,2015,84657,1.5
-wind-ons_8,2015,84658,1.8
-wind-ons_3,2015,85020,8.0
-wind-ons_2,2015,85021,81.0
-wind-ons_3,2015,85063,21.0
-wind-ons_6,2015,85112,17.0
-wind-ons_8,2015,85194,0.93
-wind-ons_9,2015,85195,1.3
-wind-ons_8,2015,85196,2.0
-wind-ons_8,2015,85197,2.0
-wind-ons_7,2015,85198,9.8
-wind-ons_3,2015,85705,100.8
-wind-ons_5,2015,85796,25.0
-wind-ons_8,2015,85878,0.93
-wind-ons_9,2015,85879,1.3
-wind-ons_8,2015,85880,1.7
-wind-ons_8,2015,85881,2.0
-wind-ons_7,2015,86012,100.5
-wind-ons_4,2015,86389,90.0
-wind-ons_4,2015,86480,28.8
-wind-ons_8,2015,86562,0.93
-wind-ons_9,2015,86563,1.3
-wind-ons_9,2015,86564,1.7
-wind-ons_8,2015,86565,2.0
-wind-ons_8,2015,86567,13.5
-wind-ons_7,2015,86569,99.0
-wind-ons_5,2015,87164,41.4
-wind-ons_9,2015,87246,0.93
-wind-ons_8,2015,87248,1.7
-wind-ons_8,2015,87249,2.0
-wind-ons_8,2015,87250,3.9
-wind-ons_7,2015,87251,12.0
-wind-ons_7,2015,87252,32.0
-wind-ons_7,2015,87253,98.8
-wind-ons_7,2015,87254,60.0
-wind-ons_9,2015,87279,13.5
-wind-ons_8,2015,87312,59.0
-wind-ons_2,2015,87798,8.4
-wind-ons_5,2015,87848,99.0
-wind-ons_9,2015,87901,1.3
-wind-ons_9,2015,87930,0.93
-wind-ons_9,2015,87931,1.0
-wind-ons_7,2015,87932,1.5
-wind-ons_8,2015,87934,3.8
-wind-ons_7,2015,87935,11.8
-wind-ons_7,2015,87938,43.5
-wind-ons_8,2015,87996,51.2
-wind-ons_8,2015,88064,37.5
-wind-ons_5,2015,88532,99.0
-wind-ons_9,2015,88585,1.3
-wind-ons_9,2015,88614,0.9
-wind-ons_8,2015,88616,1.3
-wind-ons_8,2015,88617,2.0
-wind-ons_8,2015,88618,3.0
-wind-ons_7,2015,88619,11.6
-wind-ons_8,2015,88621,91.1
-wind-ons_8,2015,88748,30.0
-wind-ons_9,2015,89269,25.0
-wind-ons_9,2015,89298,0.9
-wind-ons_9,2015,89299,0.97
-wind-ons_9,2015,89300,1.3
-wind-ons_9,2015,89301,2.0
-wind-ons_8,2015,89303,11.2
-wind-ons_7,2015,89305,85.5
-wind-ons_6,2015,89306,100.7
-wind-ons_7,2015,89419,9.0
-wind-ons_9,2015,89953,40.5
-wind-ons_9,2015,89986,1.2
-wind-ons_7,2015,89988,10.5
-wind-ons_6,2015,89990,99.0
-wind-ons_8,2015,90049,69.0
-wind-ons_7,2015,90104,37.8
-wind-ons_9,2015,90637,50.4
-wind-ons_8,2015,90670,0.9
-wind-ons_6,2015,90672,10.5
-wind-ons_7,2015,90674,98.2
-wind-ons_7,2015,90675,150.0
-wind-ons_7,2015,90789,62.5
-wind-ons_8,2015,91267,12.14
-wind-ons_8,2015,91354,0.7
-wind-ons_8,2015,91358,98.0
-wind-ons_8,2015,91472,26.0
-wind-ons_6,2015,91473,52.5
-wind-ons_5,2015,91474,3.0
-wind-ons_6,2015,91485,20.0
-wind-ons_9,2015,91951,16.8
-wind-ons_8,2015,92038,0.7
-wind-ons_8,2015,92041,49.6
-wind-ons_9,2015,92156,24.0
-wind-ons_6,2015,92169,6.6
-wind-ons_1,2015,92585,10.5
-wind-ons_8,2015,92722,0.6
-wind-ons_7,2015,92727,123.0
-wind-ons_2,2015,93270,64.5
-wind-ons_9,2015,93319,25.0
-wind-ons_9,2015,93373,51.0
-wind-ons_9,2015,93406,0.5
-wind-ons_7,2015,93409,43.5
-wind-ons_8,2015,93525,34.5
-wind-ons_10,2015,93526,64.5
-wind-ons_9,2015,93527,14.0
-wind-ons_2,2015,93953,21.0
-wind-ons_8,2015,94093,42.0
-wind-ons_7,2015,94094,80.0
-wind-ons_10,2015,94209,29.4
-wind-ons_8,2015,94211,72.0
-wind-ons_6,2015,94212,102.0
-wind-ons_9,2015,94687,48.86
-wind-ons_7,2015,94778,80.0
-wind-ons_9,2015,94854,1.8
-wind-ons_4,2015,94896,100.5
-wind-ons_7,2015,95371,99.0
-wind-ons_9,2015,95462,80.0
-wind-ons_10,2015,95577,15.0
-wind-ons_7,2015,96055,99.0
-wind-ons_8,2015,96147,104.3
-wind-ons_4,2015,96264,66.0
-wind-ons_6,2015,96739,142.2
-wind-ons_8,2015,96830,78.0
-wind-ons_7,2015,96946,30.0
-wind-ons_8,2015,97513,29.7
-wind-ons_9,2015,97514,4.5
-wind-ons_8,2015,97516,160.5
-wind-ons_8,2015,97517,138.0
-wind-ons_7,2015,98106,1.8
-wind-ons_8,2015,98198,75.0
-wind-ons_7,2015,98275,1.8
-wind-ons_7,2015,98790,15.92
-wind-ons_8,2015,98880,2.3
-wind-ons_8,2015,98881,20.0
-wind-ons_8,2015,99008,7.5
-wind-ons_8,2015,99566,66.0
-wind-ons_9,2015,99569,60.0
-wind-ons_8,2015,99573,246.0
-wind-ons_9,2015,100158,24.8
-wind-ons_8,2015,100249,16.2
-wind-ons_9,2015,100253,1.7
-wind-ons_8,2015,100257,54.0
-wind-ons_9,2015,100842,29.4
-wind-ons_8,2015,100900,8.91
-wind-ons_9,2015,100936,150.0
-wind-ons_8,2015,100981,162.0
-wind-ons_9,2015,101526,42.0
-wind-ons_9,2015,101584,8.91
-wind-ons_8,2015,101620,150.0
-wind-ons_8,2015,101621,199.0
-wind-ons_8,2015,101665,130.5
-wind-ons_10,2015,102210,64.08
-wind-ons_9,2015,102268,8.91
-wind-ons_8,2015,102303,100.0
-wind-ons_8,2015,102304,150.0
-wind-ons_8,2015,102305,174.8
-wind-ons_8,2015,102327,1.5
-wind-ons_8,2015,102349,84.0
-wind-ons_6,2015,102380,1.8
-wind-ons_9,2015,102984,2.0
-wind-ons_8,2015,102985,15.0
-wind-ons_8,2015,102988,150.0
-wind-ons_6,2015,103064,1.8
-wind-ons_8,2015,103669,14.7
-wind-ons_8,2015,103670,62.2
-wind-ons_8,2015,103671,99.0
-wind-ons_9,2015,103672,84.0
-wind-ons_8,2015,103717,101.3
-wind-ons_8,2015,104356,66.0
-wind-ons_8,2015,104401,99.0
-wind-ons_8,2015,105064,99.0
-wind-ons_9,2015,105719,0.9
-wind-ons_9,2015,106329,42.0
-wind-ons_9,2015,107013,54.0
-wind-ons_8,2015,107116,53.0
-wind-ons_9,2015,107741,59.4
-wind-ons_8,2015,107775,78.0
-wind-ons_8,2015,107800,49.5
-wind-ons_8,2015,107802,217.5
-wind-ons_4,2015,107872,60.0
-wind-ons_8,2015,108380,22.5
-wind-ons_8,2015,108381,221.0
-wind-ons_9,2015,108425,54.27
-wind-ons_8,2015,108458,35.0
-wind-ons_8,2015,108459,72.0
-wind-ons_8,2015,108484,10.5
-wind-ons_8,2015,108486,213.8
-wind-ons_5,2015,108556,6.0
-wind-ons_5,2015,108557,100.0
-wind-ons_5,2015,108559,150.0
-wind-ons_8,2015,109168,2.5
-wind-ons_8,2015,109169,150.0
-wind-ons_8,2015,109170,210.9
-wind-ons_6,2015,109243,14.0
-wind-ons_8,2015,109748,29.7
-wind-ons_7,2015,109826,21.0
-wind-ons_8,2015,109852,1.5
-wind-ons_9,2015,109874,100.0
-wind-ons_10,2015,110475,0.7
-wind-ons_8,2015,110559,180.0
-wind-ons_10,2015,111159,0.7
-wind-ons_8,2015,111800,60.0
-wind-ons_8,2015,111905,100.5
-wind-ons_2,2015,112433,18.9
-wind-ons_7,2015,112611,37.7
-wind-ons_7,2015,113168,79.5
-wind-ons_8,2015,113273,80.0
-wind-ons_8,2015,113957,76.2
-wind-ons_7,2015,113979,22.0
-wind-ons_9,2015,114588,105.0
-wind-ons_8,2015,114641,51.0
-wind-ons_9,2015,115272,100.8
-wind-ons_9,2015,115904,151.8
-wind-ons_8,2015,115956,99.0
-wind-ons_8,2015,116009,8.4
-wind-ons_5,2015,116032,20.0
-wind-ons_9,2015,116640,96.0
-wind-ons_8,2015,116693,1.7
-wind-ons_9,2015,117272,157.5
-wind-ons_8,2015,118062,106.0
-wind-ons_9,2015,118639,1.5
-wind-ons_9,2015,118640,147.0
-wind-ons_9,2015,118692,100.5
-wind-ons_7,2015,119430,100.5
-wind-ons_8,2015,120007,4.0
-wind-ons_9,2015,120060,112.2
-wind-ons_7,2015,120798,72.0
-wind-ons_5,2015,122691,203.5
-wind-ons_7,2015,122850,2.5
-wind-ons_9,2015,123427,75.0
-wind-ons_7,2015,126941,5.0
-wind-ons_9,2015,127584,50.0
-wind-ons_10,2015,128214,1.5
-wind-ons_10,2015,128215,162.0
-wind-ons_9,2015,128952,44.0
-wind-ons_9,2015,129637,150.0
-wind-ons_7,2015,130423,27.0
-wind-ons_9,2015,131005,148.5
-wind-ons_6,2015,131105,0.7
-wind-ons_5,2015,131106,0.7
-wind-ons_6,2015,131107,0.7
-wind-ons_6,2015,132311,204.0
-wind-ons_6,2015,132414,56.7
-wind-ons_7,2015,133098,50.4
-wind-ons_7,2015,133782,50.4
-wind-ons_7,2015,134467,146.0
-wind-ons_9,2015,135793,98.9
-wind-ons_9,2015,135794,60.0
-wind-ons_5,2015,136323,36.0
-wind-ons_9,2015,136414,20.0
-wind-ons_9,2015,136478,91.4
-wind-ons_4,2015,136993,1.5
-wind-ons_2,2015,136994,2.4
-wind-ons_3,2015,136995,7.5
-wind-ons_4,2015,137679,8.3
-wind-ons_3,2015,137680,18.0
-wind-ons_8,2015,137782,90.0
-wind-ons_9,2015,137845,99.0
-wind-ons_5,2015,138362,3.0
-wind-ons_4,2015,138364,18.4
-wind-ons_3,2015,138366,38.0
-wind-ons_4,2015,139046,4.2
-wind-ons_3,2015,139049,28.7
-wind-ons_4,2015,139730,6.5
-wind-ons_5,2015,139731,15.5
-wind-ons_4,2015,139733,30.75
-wind-ons_9,2015,139897,94.5
-wind-ons_4,2015,140416,22.0
-wind-ons_10,2015,141779,0.1
-wind-ons_8,2015,141780,1.6
-wind-ons_7,2015,141781,3.4
-wind-ons_8,2015,141949,74.3
-wind-ons_7,2015,142465,4.5
-wind-ons_6,2015,142466,13.1
-wind-ons_3,2015,142468,29.0
-wind-ons_2,2015,142469,42.6
-wind-ons_6,2015,143149,6.5
-wind-ons_8,2015,143253,0.6
-wind-ons_6,2015,143835,22.2
-wind-ons_4,2015,143836,31.0
-wind-ons_5,2015,143844,30.0
-wind-ons_8,2015,143937,0.7
-wind-ons_1,2015,144517,8.7
-wind-ons_4,2015,144518,17.0
-wind-ons_3,2015,144519,24.0
-wind-ons_3,2015,144529,150.0
-wind-ons_6,2015,144685,40.5
-wind-ons_3,2015,145200,3.5
-wind-ons_1,2015,145201,9.6
-wind-ons_2,2015,145203,25.3
-wind-ons_10,2015,145305,30.0
-wind-ons_3,2015,145884,5.4
-wind-ons_2,2015,145885,11.7
-wind-ons_1,2015,145886,20.5
-wind-ons_3,2015,145890,67.0
-wind-ons_3,2015,145891,88.5
-wind-ons_10,2015,145989,80.0
-wind-ons_9,2015,146053,25.5
-wind-ons_3,2015,146568,7.3
-wind-ons_3,2015,146569,15.3
-wind-ons_2,2015,146580,114.0
-wind-ons_10,2015,146673,100.0
-wind-ons_5,2015,146737,25.5
-wind-ons_5,2015,146738,123.0
-wind-ons_5,2015,147252,8.0
-wind-ons_4,2015,147253,16.2
-wind-ons_4,2015,147254,22.4
-wind-ons_3,2015,147255,33.6
-wind-ons_1,2015,147257,60.3
-wind-ons_6,2015,147421,25.5
-wind-ons_6,2015,147422,120.0
-wind-ons_4,2015,147935,3.0
-wind-ons_2,2015,147936,9.1
-wind-ons_1,2015,147937,17.3
-wind-ons_2,2015,147938,23.1
-wind-ons_2,2015,147940,46.9
-wind-ons_3,2015,147941,63.0
-wind-ons_8,2015,148105,25.5
-wind-ons_7,2015,148618,1.8
-wind-ons_3,2015,148619,3.7
-wind-ons_2,2015,148620,11.6
-wind-ons_2,2015,148621,20.0
-wind-ons_3,2015,148623,38.9
-wind-ons_5,2015,148629,135.0
-wind-ons_6,2015,148789,18.9
-wind-ons_5,2015,149304,13.6
-wind-ons_5,2015,149305,20.5
-wind-ons_4,2015,149307,43.4
-wind-ons_4,2015,149308,50.0
-wind-ons_9,2015,149474,106.5
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-wind-ons_8,2015,149987,7.4
-wind-ons_7,2015,149989,21.7
-wind-ons_5,2015,149990,30.75
-wind-ons_2,2015,149991,45.0
-wind-ons_2,2015,149992,59.6
-wind-ons_3,2015,149993,76.9
-wind-ons_7,2015,150158,101.2
-wind-ons_8,2015,150672,16.5
-wind-ons_5,2015,150682,61.8
-wind-ons_5,2015,151366,54.0
-wind-ons_10,2015,152144,0.6
-wind-ons_6,2015,154261,80.0
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-wind-ons_6,2015,154946,126.5
-wind-ons_8,2015,155564,90.0
-wind-ons_7,2015,155628,10.0
-wind-ons_7,2015,155629,80.5
-wind-ons_6,2015,155633,156.0
-wind-ons_7,2015,156312,10.0
-wind-ons_6,2015,156313,79.8
-wind-ons_7,2015,156314,124.7
-wind-ons_6,2015,156317,44.0
-wind-ons_6,2015,156318,204.0
-wind-ons_2,2015,156884,63.0
-wind-ons_7,2015,156996,10.0
-wind-ons_6,2015,156998,124.2
-wind-ons_6,2015,157000,170.2
-wind-ons_6,2015,157002,9.0
-wind-ons_6,2015,157680,10.0
-wind-ons_7,2015,157681,79.92
-wind-ons_7,2015,158364,10.0
-wind-ons_7,2015,158365,79.5
-wind-ons_7,2015,158366,121.9
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-wind-ons_7,2015,159050,60.0
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-wind-ons_7,2015,159732,8.8
-wind-ons_7,2015,159733,75.0
-wind-ons_6,2015,159734,60.6
-wind-ons_6,2015,159735,150.0
-wind-ons_7,2015,159736,1.5
-wind-ons_6,2015,159739,25.0
-wind-ons_7,2015,160417,73.5
-wind-ons_7,2015,160418,120.0
-wind-ons_6,2015,160423,204.0
-wind-ons_7,2015,161100,3.0
-wind-ons_7,2015,161101,69.53
-wind-ons_7,2015,161102,120.0
-wind-ons_7,2015,161103,150.0
-wind-ons_6,2015,161104,167.7
-wind-ons_6,2015,161106,180.0
-wind-ons_6,2015,161107,28.5
-wind-ons_7,2015,161784,2.64
-wind-ons_6,2015,161787,90.0
-wind-ons_6,2015,161790,29.0
-wind-ons_6,2015,161791,78.0
-wind-ons_7,2015,162471,60.0
-wind-ons_6,2015,162475,153.5
-wind-ons_7,2015,163837,63.0
-wind-ons_7,2015,163838,120.0
-wind-ons_7,2015,164520,0.92
-wind-ons_7,2015,164521,78.0
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-wind-ons_7,2015,165206,114.0
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-wind-ons_6,2015,165209,192.0
-wind-ons_6,2015,165890,114.0
-wind-ons_6,2015,165893,5.0
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-wind-ons_6,2015,166573,59.4
-wind-ons_6,2015,166574,112.5
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-wind-ons_5,2015,167257,58.8
-wind-ons_5,2015,167258,112.5
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-wind-ons_7,2015,167941,57.0
-wind-ons_7,2015,167943,149.5
-wind-ons_6,2015,168624,0.07
-wind-ons_6,2015,168628,160.84
-wind-ons_5,2015,168630,200.0
-wind-ons_6,2015,168633,216.0
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-wind-ons_5,2015,169313,180.0
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-wind-ons_5,2015,169999,210.0
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-wind-ons_5,2015,171362,98.8
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-wind-ons_5,2015,172047,18.0
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-wind-ons_5,2015,172735,198.0
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-wind-ons_6,2015,173421,180.0
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-wind-ons_6,2015,174099,130.5
-wind-ons_7,2015,174101,180.0
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-wind-ons_6,2015,175466,89.5
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-wind-ons_5,2015,176150,84.0
-wind-ons_5,2015,176153,180.0
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-wind-ons_3,2016,49465,228.6
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-wind-ons_6,2016,56302,98.9
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-wind-ons_8,2016,59899,180.0
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-wind-ons_7,2016,60570,1.2
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-wind-ons_7,2016,63307,1.7
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-wind-ons_7,2016,65358,0.97
-wind-ons_8,2016,65359,1.7
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-wind-ons_7,2016,66043,1.5
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-wind-ons_7,2016,84512,2.0
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-wind-ons_3,2017,145890,67.0
-wind-ons_3,2017,145891,88.5
-wind-ons_10,2017,145989,80.0
-wind-ons_9,2017,146053,25.5
-wind-ons_3,2017,146568,7.3
-wind-ons_3,2017,146569,15.3
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-wind-ons_10,2017,146673,100.0
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-wind-ons_5,2017,146738,123.0
-wind-ons_5,2017,147252,8.0
-wind-ons_4,2017,147253,16.2
-wind-ons_4,2017,147254,22.4
-wind-ons_3,2017,147255,33.6
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-wind-ons_6,2017,147422,120.0
-wind-ons_4,2017,147935,3.0
-wind-ons_2,2017,147936,9.1
-wind-ons_1,2017,147937,17.3
-wind-ons_2,2017,147938,23.1
-wind-ons_2,2017,147940,46.9
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-wind-ons_2,2017,148620,11.6
-wind-ons_2,2017,148621,20.0
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-wind-ons_5,2017,149304,13.6
-wind-ons_5,2017,149305,20.5
-wind-ons_4,2017,149307,43.4
-wind-ons_4,2017,149308,50.0
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-wind-ons_8,2017,149987,7.4
-wind-ons_7,2017,149989,21.7
-wind-ons_5,2017,149990,30.75
-wind-ons_2,2017,149991,45.0
-wind-ons_2,2017,149992,59.6
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-wind-ons_8,2017,150672,16.5
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-wind-ons_5,2017,151366,54.0
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-wind-ons_6,2017,154946,126.5
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-wind-ons_7,2017,155628,10.0
-wind-ons_7,2017,155629,80.5
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-wind-ons_7,2017,156312,10.0
-wind-ons_6,2017,156313,79.8
-wind-ons_7,2017,156314,124.7
-wind-ons_6,2017,156317,44.0
-wind-ons_6,2017,156318,204.0
-wind-ons_2,2017,156884,63.0
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-wind-ons_6,2017,156998,124.2
-wind-ons_6,2017,157000,170.2
-wind-ons_6,2017,157002,9.0
-wind-ons_6,2017,157680,10.0
-wind-ons_7,2017,157681,79.92
-wind-ons_7,2017,158364,10.0
-wind-ons_7,2017,158365,79.5
-wind-ons_7,2017,158366,121.9
-wind-ons_7,2017,158368,168.0
-wind-ons_7,2017,159050,60.0
-wind-ons_6,2017,159055,216.0
-wind-ons_7,2017,159732,8.8
-wind-ons_7,2017,159733,75.0
-wind-ons_6,2017,159734,60.6
-wind-ons_6,2017,159735,150.0
-wind-ons_7,2017,159736,1.5
-wind-ons_6,2017,159739,25.0
-wind-ons_7,2017,160417,73.5
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-wind-ons_6,2017,160423,204.0
-wind-ons_7,2017,161100,3.0
-wind-ons_7,2017,161101,69.53
-wind-ons_7,2017,161102,120.0
-wind-ons_7,2017,161103,150.0
-wind-ons_6,2017,161104,167.7
-wind-ons_6,2017,161106,180.0
-wind-ons_6,2017,161107,28.5
-wind-ons_7,2017,161784,2.64
-wind-ons_6,2017,161787,90.0
-wind-ons_6,2017,161790,29.0
-wind-ons_6,2017,161791,78.0
-wind-ons_7,2017,162471,60.0
-wind-ons_6,2017,162475,153.5
-wind-ons_7,2017,163837,63.0
-wind-ons_7,2017,163838,120.0
-wind-ons_7,2017,164520,0.92
-wind-ons_7,2017,164521,78.0
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-wind-ons_7,2017,165206,114.0
-wind-ons_7,2017,165208,162.3
-wind-ons_6,2017,165209,192.0
-wind-ons_6,2017,165890,114.0
-wind-ons_6,2017,165893,5.0
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-wind-ons_6,2017,166573,59.4
-wind-ons_6,2017,166574,112.5
-wind-ons_7,2017,167256,0.08
-wind-ons_5,2017,167257,58.8
-wind-ons_5,2017,167258,112.5
-wind-ons_6,2017,167940,0.07
-wind-ons_7,2017,167941,57.0
-wind-ons_7,2017,167943,149.5
-wind-ons_6,2017,168624,0.07
-wind-ons_6,2017,168628,160.84
-wind-ons_5,2017,168630,200.0
-wind-ons_6,2017,168633,216.0
-wind-ons_5,2017,169310,101.2
-wind-ons_5,2017,169313,180.0
-wind-ons_6,2017,169317,83.0
-wind-ons_5,2017,169993,49.5
-wind-ons_5,2017,169997,6.0
-wind-ons_5,2017,169999,210.0
-wind-ons_5,2017,170000,228.0
-wind-ons_5,2017,170683,13.5
-wind-ons_5,2017,170684,21.0
-wind-ons_6,2017,171361,37.5
-wind-ons_5,2017,171362,98.8
-wind-ons_5,2017,171363,126.0
-wind-ons_6,2017,172045,34.3
-wind-ons_5,2017,172047,18.0
-wind-ons_6,2017,172729,32.52
-wind-ons_6,2017,172730,91.58
-wind-ons_5,2017,172735,198.0
-wind-ons_6,2017,173414,91.2
-wind-ons_6,2017,173419,16.5
-wind-ons_6,2017,173421,180.0
-wind-ons_6,2017,174098,91.2
-wind-ons_6,2017,174099,130.5
-wind-ons_7,2017,174101,180.0
-wind-ons_5,2017,174105,98.0
-wind-ons_6,2017,174784,153.9
-wind-ons_5,2017,174789,73.2
-wind-ons_7,2017,175465,17.64
-wind-ons_6,2017,175466,89.5
-wind-ons_6,2017,175468,152.0
-wind-ons_5,2017,175473,150.0
-wind-ons_6,2017,176149,10.0
-wind-ons_5,2017,176150,84.0
-wind-ons_5,2017,176153,180.0
-wind-ons_5,2017,176154,201.6
-wind-ons_6,2017,176157,60.0
-wind-ons_6,2017,176834,60.0
-wind-ons_6,2017,177518,22.5
-wind-ons_6,2017,178202,82.0
-wind-ons_1,2018,46046,114.0
-wind-ons_2,2018,46729,44.0
-wind-ons_1,2018,46730,28.2
-wind-ons_2,2018,48781,156.0
-wind-ons_4,2018,49464,7.2
-wind-ons_3,2018,49465,228.6
-wind-ons_5,2018,50148,94.0
-wind-ons_3,2018,51516,54.0
-wind-ons_7,2018,52200,168.0
-wind-ons_7,2018,52969,9.7
-wind-ons_6,2018,53567,122.8
-wind-ons_5,2018,54935,31.0
-wind-ons_6,2018,55619,234.0
-wind-ons_7,2018,55705,104.2
-wind-ons_6,2018,56302,98.9
-wind-ons_6,2018,57670,62.9
-wind-ons_6,2018,58354,174.0
-wind-ons_8,2018,58684,4.5
-wind-ons_4,2018,59037,95.0
-wind-ons_8,2018,59201,0.93
-wind-ons_8,2018,59203,2.0
-wind-ons_3,2018,59721,136.3
-wind-ons_7,2018,59854,18.0
-wind-ons_7,2018,59885,0.93
-wind-ons_8,2018,59886,1.3
-wind-ons_7,2018,59889,10.4
-wind-ons_8,2018,59899,180.0
-wind-ons_1,2018,60405,95.4
-wind-ons_7,2018,60569,0.93
-wind-ons_7,2018,60570,1.2
-wind-ons_7,2018,60571,2.0
-wind-ons_7,2018,60572,2.1
-wind-ons_7,2018,60574,25.0
-wind-ons_8,2018,60583,25.5
-wind-ons_9,2018,60738,42.0
-wind-ons_8,2018,60739,66.0
-wind-ons_2,2018,61089,30.0
-wind-ons_7,2018,61253,0.93
-wind-ons_7,2018,61255,1.9
-wind-ons_7,2018,61256,2.1
-wind-ons_7,2018,61257,9.2
-wind-ons_5,2018,61772,78.0
-wind-ons_7,2018,61937,0.93
-wind-ons_7,2018,61939,1.7
-wind-ons_8,2018,61940,2.1
-wind-ons_7,2018,61941,8.4
-wind-ons_8,2018,61942,20.0
-wind-ons_9,2018,61943,51.0
-wind-ons_9,2018,61944,60.0
-wind-ons_9,2018,62107,57.0
-wind-ons_7,2018,62544,30.0
-wind-ons_8,2018,62590,31.5
-wind-ons_7,2018,62621,0.93
-wind-ons_7,2018,62623,1.7
-wind-ons_7,2018,62624,2.0
-wind-ons_8,2018,62625,8.0
-wind-ons_9,2018,62628,40.5
-wind-ons_4,2018,63139,6.6
-wind-ons_3,2018,63140,98.7
-wind-ons_7,2018,63305,0.93
-wind-ons_7,2018,63307,1.7
-wind-ons_7,2018,63308,2.0
-wind-ons_8,2018,63309,5.2
-wind-ons_8,2018,63310,19.98
-wind-ons_9,2018,63311,49.5
-wind-ons_8,2018,63312,100.5
-wind-ons_7,2018,63314,150.0
-wind-ons_9,2018,63474,4.5
-wind-ons_4,2018,63823,9.9
-wind-ons_3,2018,63824,30.9
-wind-ons_4,2018,63825,60.0
-wind-ons_8,2018,63912,101.4
-wind-ons_8,2018,63958,40.5
-wind-ons_6,2018,63989,0.93
-wind-ons_7,2018,63990,1.0
-wind-ons_7,2018,63991,1.7
-wind-ons_8,2018,63992,2.0
-wind-ons_7,2018,63996,100.0
-wind-ons_8,2018,64596,135.0
-wind-ons_8,2018,64642,40.5
-wind-ons_8,2018,64643,115.5
-wind-ons_7,2018,64673,0.93
-wind-ons_7,2018,64674,0.97
-wind-ons_7,2018,64675,1.7
-wind-ons_7,2018,64677,4.2
-wind-ons_2,2018,65191,24.0
-wind-ons_7,2018,65357,0.9
-wind-ons_7,2018,65358,0.97
-wind-ons_8,2018,65359,1.7
-wind-ons_7,2018,65360,2.0
-wind-ons_8,2018,65361,4.2
-wind-ons_7,2018,65362,15.6
-wind-ons_5,2018,65363,43.06
-wind-ons_1,2018,65874,1.7
-wind-ons_2,2018,65875,72.0
-wind-ons_8,2018,66010,48.0
-wind-ons_8,2018,66011,118.5
-wind-ons_7,2018,66041,0.9
-wind-ons_7,2018,66042,0.97
-wind-ons_7,2018,66043,1.5
-wind-ons_7,2018,66044,2.0
-wind-ons_8,2018,66045,4.2
-wind-ons_2,2018,66559,100.5
-wind-ons_4,2018,66560,101.0
-wind-ons_8,2018,66694,48.0
-wind-ons_8,2018,66695,6.0
-wind-ons_7,2018,66725,0.9
-wind-ons_7,2018,66726,0.97
-wind-ons_8,2018,66727,1.3
-wind-ons_7,2018,66728,2.0
-wind-ons_2,2018,67243,35.4
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-wind-ons_8,2018,67409,0.9
-wind-ons_8,2018,67410,0.97
-wind-ons_8,2018,67411,1.3
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-wind-ons_7,2018,67538,54.0
-wind-ons_8,2018,68062,49.5
-wind-ons_7,2018,68093,0.7
-wind-ons_8,2018,68094,0.97
-wind-ons_6,2018,68221,30.55
-wind-ons_6,2018,68222,100.5
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-wind-ons_5,2018,68778,0.97
-wind-ons_7,2018,68905,16.5
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-wind-ons_5,2018,69591,52.0
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-wind-ons_8,2018,70115,148.5
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-wind-ons_5,2018,70146,0.97
-wind-ons_6,2018,70274,68.5
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-wind-ons_7,2018,70830,0.97
-wind-ons_7,2018,70959,52.5
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-wind-ons_7,2018,71514,0.97
-wind-ons_8,2018,71643,78.0
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-wind-ons_8,2018,72165,2.6
-wind-ons_7,2018,72197,0.04
-wind-ons_6,2018,72198,0.97
-wind-ons_6,2018,72345,24.0
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-wind-ons_8,2018,73533,11.9
-wind-ons_8,2018,73535,180.0
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-wind-ons_3,2018,79853,30.55
-wind-ons_3,2018,80235,132.0
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-wind-ons_10,2018,81124,102.0
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-wind-ons_8,2018,84658,1.8
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-wind-ons_9,2018,85195,1.3
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-wind-ons_8,2018,85197,2.0
-wind-ons_7,2018,85198,9.8
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-wind-ons_9,2018,85879,1.3
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-wind-ons_8,2018,85881,2.0
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-wind-ons_9,2018,86563,1.3
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-wind-ons_9,2018,87930,0.93
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-wind-ons_9,2018,89298,0.9
-wind-ons_9,2018,89299,0.97
-wind-ons_9,2018,89300,1.3
-wind-ons_9,2018,89301,2.0
-wind-ons_8,2018,89303,11.2
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-wind-ons_10,2018,95577,15.0
-wind-ons_7,2018,96055,99.0
-wind-ons_8,2018,96147,104.3
-wind-ons_4,2018,96264,66.0
-wind-ons_6,2018,96739,142.2
-wind-ons_8,2018,96830,78.0
-wind-ons_7,2018,96946,30.0
-wind-ons_8,2018,97513,29.7
-wind-ons_9,2018,97514,4.5
-wind-ons_8,2018,97516,160.5
-wind-ons_8,2018,97517,138.0
-wind-ons_7,2018,98106,1.8
-wind-ons_8,2018,98198,75.0
-wind-ons_7,2018,98275,1.8
-wind-ons_7,2018,98790,15.92
-wind-ons_8,2018,98880,2.3
-wind-ons_8,2018,98881,20.0
-wind-ons_8,2018,99008,7.5
-wind-ons_8,2018,99566,66.0
-wind-ons_9,2018,99569,60.0
-wind-ons_8,2018,99573,246.0
-wind-ons_9,2018,100158,24.8
-wind-ons_8,2018,100249,16.2
-wind-ons_9,2018,100253,1.7
-wind-ons_8,2018,100257,54.0
-wind-ons_9,2018,100842,29.4
-wind-ons_8,2018,100900,8.91
-wind-ons_9,2018,100936,150.0
-wind-ons_8,2018,100981,162.0
-wind-ons_9,2018,101526,42.0
-wind-ons_9,2018,101584,8.91
-wind-ons_8,2018,101620,150.0
-wind-ons_8,2018,101621,199.0
-wind-ons_8,2018,101665,130.5
-wind-ons_10,2018,102210,64.08
-wind-ons_9,2018,102268,8.91
-wind-ons_8,2018,102303,100.0
-wind-ons_8,2018,102304,150.0
-wind-ons_8,2018,102305,174.8
-wind-ons_8,2018,102327,1.5
-wind-ons_8,2018,102349,84.0
-wind-ons_6,2018,102380,1.8
-wind-ons_9,2018,102984,2.0
-wind-ons_8,2018,102985,15.0
-wind-ons_8,2018,102988,150.0
-wind-ons_6,2018,103064,1.8
-wind-ons_8,2018,103669,14.7
-wind-ons_8,2018,103670,62.2
-wind-ons_8,2018,103671,99.0
-wind-ons_9,2018,103672,84.0
-wind-ons_8,2018,103717,101.3
-wind-ons_8,2018,104356,66.0
-wind-ons_8,2018,104401,99.0
-wind-ons_8,2018,105064,99.0
-wind-ons_9,2018,105719,0.9
-wind-ons_9,2018,106329,42.0
-wind-ons_9,2018,107013,54.0
-wind-ons_8,2018,107116,53.0
-wind-ons_9,2018,107741,59.4
-wind-ons_8,2018,107775,78.0
-wind-ons_8,2018,107800,49.5
-wind-ons_8,2018,107802,217.5
-wind-ons_4,2018,107872,60.0
-wind-ons_8,2018,108380,22.5
-wind-ons_8,2018,108381,221.0
-wind-ons_9,2018,108425,54.27
-wind-ons_8,2018,108458,35.0
-wind-ons_8,2018,108459,72.0
-wind-ons_8,2018,108484,10.5
-wind-ons_8,2018,108486,213.8
-wind-ons_5,2018,108556,6.0
-wind-ons_5,2018,108557,100.0
-wind-ons_5,2018,108559,150.0
-wind-ons_8,2018,109168,2.5
-wind-ons_8,2018,109169,150.0
-wind-ons_8,2018,109170,210.9
-wind-ons_6,2018,109243,14.0
-wind-ons_8,2018,109748,29.7
-wind-ons_7,2018,109826,21.0
-wind-ons_8,2018,109852,1.5
-wind-ons_9,2018,109874,100.0
-wind-ons_10,2018,110475,0.7
-wind-ons_8,2018,110559,180.0
-wind-ons_10,2018,111159,0.7
-wind-ons_8,2018,111800,60.0
-wind-ons_8,2018,111905,100.5
-wind-ons_2,2018,112433,18.9
-wind-ons_7,2018,112611,37.7
-wind-ons_7,2018,113168,79.5
-wind-ons_8,2018,113273,80.0
-wind-ons_8,2018,113957,76.2
-wind-ons_7,2018,113979,22.0
-wind-ons_9,2018,114588,105.0
-wind-ons_8,2018,114641,51.0
-wind-ons_9,2018,115272,100.8
-wind-ons_9,2018,115904,151.8
-wind-ons_8,2018,115956,99.0
-wind-ons_8,2018,116009,8.4
-wind-ons_5,2018,116032,20.0
-wind-ons_9,2018,116640,96.0
-wind-ons_8,2018,116693,1.7
-wind-ons_9,2018,117272,157.5
-wind-ons_8,2018,118062,106.0
-wind-ons_9,2018,118639,1.5
-wind-ons_9,2018,118640,147.0
-wind-ons_9,2018,118692,100.5
-wind-ons_7,2018,119430,100.5
-wind-ons_8,2018,120007,4.0
-wind-ons_9,2018,120060,112.2
-wind-ons_7,2018,120798,72.0
-wind-ons_5,2018,122691,203.5
-wind-ons_7,2018,122850,2.5
-wind-ons_9,2018,123427,75.0
-wind-ons_7,2018,126941,5.0
-wind-ons_9,2018,127584,50.0
-wind-ons_10,2018,128214,1.5
-wind-ons_10,2018,128215,162.0
-wind-ons_9,2018,128952,44.0
-wind-ons_9,2018,129637,150.0
-wind-ons_7,2018,130423,27.0
-wind-ons_9,2018,131005,148.5
-wind-ons_6,2018,131105,0.7
-wind-ons_5,2018,131106,0.7
-wind-ons_6,2018,131107,0.7
-wind-ons_6,2018,132311,204.0
-wind-ons_6,2018,132414,56.7
-wind-ons_7,2018,133098,50.4
-wind-ons_7,2018,133782,50.4
-wind-ons_7,2018,134467,146.0
-wind-ons_9,2018,135793,98.9
-wind-ons_9,2018,135794,60.0
-wind-ons_5,2018,136323,36.0
-wind-ons_9,2018,136414,20.0
-wind-ons_9,2018,136478,91.4
-wind-ons_4,2018,136993,1.5
-wind-ons_2,2018,136994,2.4
-wind-ons_3,2018,136995,7.5
-wind-ons_4,2018,137679,8.3
-wind-ons_3,2018,137680,18.0
-wind-ons_8,2018,137782,90.0
-wind-ons_9,2018,137845,99.0
-wind-ons_5,2018,138362,3.0
-wind-ons_4,2018,138364,18.4
-wind-ons_3,2018,138366,38.0
-wind-ons_4,2018,139046,4.2
-wind-ons_3,2018,139049,28.7
-wind-ons_4,2018,139730,6.5
-wind-ons_5,2018,139731,15.5
-wind-ons_4,2018,139733,30.75
-wind-ons_9,2018,139897,94.5
-wind-ons_4,2018,140416,22.0
-wind-ons_10,2018,141779,0.1
-wind-ons_8,2018,141780,1.6
-wind-ons_7,2018,141781,3.4
-wind-ons_8,2018,141949,74.3
-wind-ons_7,2018,142465,4.5
-wind-ons_6,2018,142466,13.1
-wind-ons_3,2018,142468,29.0
-wind-ons_2,2018,142469,42.6
-wind-ons_6,2018,143149,6.5
-wind-ons_8,2018,143253,0.6
-wind-ons_6,2018,143835,22.2
-wind-ons_4,2018,143836,31.0
-wind-ons_5,2018,143844,30.0
-wind-ons_8,2018,143937,0.7
-wind-ons_1,2018,144517,8.7
-wind-ons_4,2018,144518,17.0
-wind-ons_3,2018,144519,24.0
-wind-ons_3,2018,144529,150.0
-wind-ons_6,2018,144685,40.5
-wind-ons_3,2018,145200,3.5
-wind-ons_1,2018,145201,9.6
-wind-ons_2,2018,145203,25.3
-wind-ons_10,2018,145305,30.0
-wind-ons_3,2018,145884,5.4
-wind-ons_2,2018,145885,11.7
-wind-ons_1,2018,145886,20.5
-wind-ons_3,2018,145890,67.0
-wind-ons_3,2018,145891,88.5
-wind-ons_10,2018,145989,80.0
-wind-ons_9,2018,146053,25.5
-wind-ons_3,2018,146568,7.3
-wind-ons_3,2018,146569,15.3
-wind-ons_2,2018,146580,114.0
-wind-ons_10,2018,146673,100.0
-wind-ons_5,2018,146737,25.5
-wind-ons_5,2018,146738,123.0
-wind-ons_5,2018,147252,8.0
-wind-ons_4,2018,147253,16.2
-wind-ons_4,2018,147254,22.4
-wind-ons_3,2018,147255,33.6
-wind-ons_1,2018,147257,60.3
-wind-ons_6,2018,147421,25.5
-wind-ons_6,2018,147422,120.0
-wind-ons_4,2018,147935,3.0
-wind-ons_2,2018,147936,9.1
-wind-ons_1,2018,147937,17.3
-wind-ons_2,2018,147938,23.1
-wind-ons_2,2018,147940,46.9
-wind-ons_3,2018,147941,63.0
-wind-ons_8,2018,148105,25.5
-wind-ons_7,2018,148618,1.8
-wind-ons_3,2018,148619,3.7
-wind-ons_2,2018,148620,11.6
-wind-ons_2,2018,148621,20.0
-wind-ons_3,2018,148623,38.9
-wind-ons_5,2018,148629,135.0
-wind-ons_6,2018,148789,18.9
-wind-ons_5,2018,149304,13.6
-wind-ons_5,2018,149305,20.5
-wind-ons_4,2018,149307,43.4
-wind-ons_4,2018,149308,50.0
-wind-ons_9,2018,149474,106.5
-wind-ons_6,2018,149986,2.1
-wind-ons_8,2018,149987,7.4
-wind-ons_7,2018,149989,21.7
-wind-ons_5,2018,149990,30.75
-wind-ons_2,2018,149991,45.0
-wind-ons_2,2018,149992,59.6
-wind-ons_3,2018,149993,76.9
-wind-ons_7,2018,150158,101.2
-wind-ons_8,2018,150672,16.5
-wind-ons_5,2018,150682,61.8
-wind-ons_5,2018,151366,54.0
-wind-ons_10,2018,152144,0.6
-wind-ons_6,2018,154261,80.0
-wind-ons_7,2018,154944,10.0
-wind-ons_6,2018,154946,126.5
-wind-ons_8,2018,155564,90.0
-wind-ons_7,2018,155628,10.0
-wind-ons_7,2018,155629,80.5
-wind-ons_6,2018,155633,156.0
-wind-ons_7,2018,156312,10.0
-wind-ons_6,2018,156313,79.8
-wind-ons_7,2018,156314,124.7
-wind-ons_6,2018,156317,44.0
-wind-ons_6,2018,156318,204.0
-wind-ons_2,2018,156884,63.0
-wind-ons_7,2018,156996,10.0
-wind-ons_6,2018,156998,124.2
-wind-ons_6,2018,157000,170.2
-wind-ons_6,2018,157002,9.0
-wind-ons_6,2018,157680,10.0
-wind-ons_7,2018,157681,79.92
-wind-ons_7,2018,158364,10.0
-wind-ons_7,2018,158365,79.5
-wind-ons_7,2018,158366,121.9
-wind-ons_7,2018,158368,168.0
-wind-ons_7,2018,159050,60.0
-wind-ons_6,2018,159055,216.0
-wind-ons_7,2018,159732,8.8
-wind-ons_7,2018,159733,75.0
-wind-ons_6,2018,159734,60.6
-wind-ons_6,2018,159735,150.0
-wind-ons_7,2018,159736,1.5
-wind-ons_6,2018,159739,25.0
-wind-ons_7,2018,160417,73.5
-wind-ons_7,2018,160418,120.0
-wind-ons_6,2018,160423,204.0
-wind-ons_7,2018,161100,3.0
-wind-ons_7,2018,161101,69.53
-wind-ons_7,2018,161102,120.0
-wind-ons_7,2018,161103,150.0
-wind-ons_6,2018,161104,167.7
-wind-ons_6,2018,161106,180.0
-wind-ons_6,2018,161107,28.5
-wind-ons_7,2018,161784,2.64
-wind-ons_6,2018,161787,90.0
-wind-ons_6,2018,161790,29.0
-wind-ons_6,2018,161791,78.0
-wind-ons_7,2018,162471,60.0
-wind-ons_6,2018,162475,153.5
-wind-ons_7,2018,163837,63.0
-wind-ons_7,2018,163838,120.0
-wind-ons_7,2018,164520,0.92
-wind-ons_7,2018,164521,78.0
-wind-ons_7,2018,165204,0.6
-wind-ons_7,2018,165206,114.0
-wind-ons_7,2018,165208,162.3
-wind-ons_6,2018,165209,192.0
-wind-ons_6,2018,165890,114.0
-wind-ons_6,2018,165893,5.0
-wind-ons_5,2018,166572,0.16
-wind-ons_6,2018,166573,59.4
-wind-ons_6,2018,166574,112.5
-wind-ons_7,2018,167256,0.08
-wind-ons_5,2018,167257,58.8
-wind-ons_5,2018,167258,112.5
-wind-ons_6,2018,167940,0.07
-wind-ons_7,2018,167941,57.0
-wind-ons_7,2018,167943,149.5
-wind-ons_6,2018,168624,0.07
-wind-ons_6,2018,168628,160.84
-wind-ons_5,2018,168630,200.0
-wind-ons_6,2018,168633,216.0
-wind-ons_5,2018,169310,101.2
-wind-ons_5,2018,169313,180.0
-wind-ons_6,2018,169317,83.0
-wind-ons_5,2018,169993,49.5
-wind-ons_5,2018,169997,6.0
-wind-ons_5,2018,169999,210.0
-wind-ons_5,2018,170000,228.0
-wind-ons_5,2018,170683,13.5
-wind-ons_5,2018,170684,21.0
-wind-ons_6,2018,171361,37.5
-wind-ons_5,2018,171362,98.8
-wind-ons_5,2018,171363,126.0
-wind-ons_6,2018,172045,34.3
-wind-ons_5,2018,172047,18.0
-wind-ons_6,2018,172729,32.52
-wind-ons_6,2018,172730,91.58
-wind-ons_5,2018,172735,198.0
-wind-ons_6,2018,173414,91.2
-wind-ons_6,2018,173419,16.5
-wind-ons_6,2018,173421,180.0
-wind-ons_6,2018,174098,91.2
-wind-ons_6,2018,174099,130.5
-wind-ons_7,2018,174101,180.0
-wind-ons_5,2018,174105,98.0
-wind-ons_6,2018,174784,153.9
-wind-ons_5,2018,174789,73.2
-wind-ons_7,2018,175465,17.64
-wind-ons_6,2018,175466,89.5
-wind-ons_6,2018,175468,152.0
-wind-ons_5,2018,175473,150.0
-wind-ons_6,2018,176149,10.0
-wind-ons_5,2018,176150,84.0
-wind-ons_5,2018,176153,180.0
-wind-ons_5,2018,176154,201.6
-wind-ons_6,2018,176157,60.0
-wind-ons_6,2018,176834,60.0
-wind-ons_6,2018,177518,22.5
-wind-ons_6,2018,178202,82.0
-wind-ons_1,2019,46046,114.0
-wind-ons_2,2019,46729,44.0
-wind-ons_1,2019,46730,28.2
-wind-ons_2,2019,48781,156.0
-wind-ons_4,2019,49464,7.2
-wind-ons_3,2019,49465,228.6
-wind-ons_5,2019,50148,94.0
-wind-ons_3,2019,51516,54.0
-wind-ons_7,2019,52200,168.0
-wind-ons_7,2019,52969,9.7
-wind-ons_6,2019,53567,122.8
-wind-ons_5,2019,54935,31.0
-wind-ons_6,2019,55619,234.0
-wind-ons_7,2019,55705,104.2
-wind-ons_6,2019,56302,98.9
-wind-ons_6,2019,57670,62.9
-wind-ons_6,2019,58354,174.0
-wind-ons_8,2019,58684,4.5
-wind-ons_4,2019,59037,95.0
-wind-ons_8,2019,59201,0.93
-wind-ons_8,2019,59203,2.0
-wind-ons_3,2019,59721,136.3
-wind-ons_7,2019,59854,18.0
-wind-ons_7,2019,59885,0.93
-wind-ons_8,2019,59886,1.3
-wind-ons_7,2019,59889,10.4
-wind-ons_8,2019,59899,180.0
-wind-ons_1,2019,60405,95.4
-wind-ons_7,2019,60569,0.93
-wind-ons_7,2019,60570,1.2
-wind-ons_7,2019,60571,2.0
-wind-ons_7,2019,60572,2.1
-wind-ons_7,2019,60574,25.0
-wind-ons_8,2019,60583,25.5
-wind-ons_9,2019,60738,42.0
-wind-ons_8,2019,60739,66.0
-wind-ons_2,2019,61089,30.0
-wind-ons_7,2019,61253,0.93
-wind-ons_7,2019,61255,1.9
-wind-ons_7,2019,61256,2.1
-wind-ons_7,2019,61257,9.2
-wind-ons_5,2019,61772,78.0
-wind-ons_7,2019,61937,0.93
-wind-ons_7,2019,61939,1.7
-wind-ons_8,2019,61940,2.1
-wind-ons_7,2019,61941,8.4
-wind-ons_8,2019,61942,20.0
-wind-ons_9,2019,61943,51.0
-wind-ons_9,2019,61944,60.0
-wind-ons_9,2019,62107,57.0
-wind-ons_7,2019,62544,30.0
-wind-ons_8,2019,62590,31.5
-wind-ons_7,2019,62621,0.93
-wind-ons_7,2019,62623,1.7
-wind-ons_7,2019,62624,2.0
-wind-ons_8,2019,62625,8.0
-wind-ons_9,2019,62628,40.5
-wind-ons_4,2019,63139,6.6
-wind-ons_3,2019,63140,98.7
-wind-ons_7,2019,63305,0.93
-wind-ons_7,2019,63307,1.7
-wind-ons_7,2019,63308,2.0
-wind-ons_8,2019,63309,5.2
-wind-ons_8,2019,63310,19.98
-wind-ons_9,2019,63311,49.5
-wind-ons_8,2019,63312,100.5
-wind-ons_7,2019,63314,150.0
-wind-ons_9,2019,63474,4.5
-wind-ons_4,2019,63823,9.9
-wind-ons_3,2019,63824,30.9
-wind-ons_4,2019,63825,60.0
-wind-ons_8,2019,63912,101.4
-wind-ons_8,2019,63958,40.5
-wind-ons_6,2019,63989,0.93
-wind-ons_7,2019,63990,1.0
-wind-ons_7,2019,63991,1.7
-wind-ons_8,2019,63992,2.0
-wind-ons_7,2019,63996,100.0
-wind-ons_8,2019,64596,135.0
-wind-ons_8,2019,64642,40.5
-wind-ons_8,2019,64643,115.5
-wind-ons_7,2019,64673,0.93
-wind-ons_7,2019,64674,0.97
-wind-ons_7,2019,64675,1.7
-wind-ons_7,2019,64677,4.2
-wind-ons_2,2019,65191,24.0
-wind-ons_7,2019,65357,0.9
-wind-ons_7,2019,65358,0.97
-wind-ons_8,2019,65359,1.7
-wind-ons_7,2019,65360,2.0
-wind-ons_8,2019,65361,4.2
-wind-ons_7,2019,65362,15.6
-wind-ons_5,2019,65363,43.06
-wind-ons_1,2019,65874,1.7
-wind-ons_2,2019,65875,72.0
-wind-ons_8,2019,66010,48.0
-wind-ons_8,2019,66011,118.5
-wind-ons_7,2019,66041,0.9
-wind-ons_7,2019,66042,0.97
-wind-ons_7,2019,66043,1.5
-wind-ons_7,2019,66044,2.0
-wind-ons_8,2019,66045,4.2
-wind-ons_2,2019,66559,100.5
-wind-ons_4,2019,66560,101.0
-wind-ons_8,2019,66694,48.0
-wind-ons_8,2019,66695,6.0
-wind-ons_7,2019,66725,0.9
-wind-ons_7,2019,66726,0.97
-wind-ons_8,2019,66727,1.3
-wind-ons_7,2019,66728,2.0
-wind-ons_2,2019,67243,35.4
-wind-ons_8,2019,67379,149.0
-wind-ons_8,2019,67409,0.9
-wind-ons_8,2019,67410,0.97
-wind-ons_8,2019,67411,1.3
-wind-ons_6,2019,67412,2.0
-wind-ons_7,2019,67538,54.0
-wind-ons_8,2019,68062,49.5
-wind-ons_7,2019,68093,0.7
-wind-ons_8,2019,68094,0.97
-wind-ons_6,2019,68221,30.55
-wind-ons_6,2019,68222,100.5
-wind-ons_8,2019,68746,49.5
-wind-ons_7,2019,68777,0.7
-wind-ons_5,2019,68778,0.97
-wind-ons_7,2019,68905,16.5
-wind-ons_6,2019,68906,19.5
-wind-ons_4,2019,68919,5.2
-wind-ons_1,2019,69293,8.3
-wind-ons_8,2019,69430,50.6
-wind-ons_8,2019,69431,114.0
-wind-ons_5,2019,69462,0.97
-wind-ons_5,2019,69591,52.0
-wind-ons_1,2019,69979,60.0
-wind-ons_8,2019,70114,61.5
-wind-ons_8,2019,70115,148.5
-wind-ons_5,2019,70145,0.4
-wind-ons_5,2019,70146,0.97
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-wind-ons_1,2019,70662,31.3
-wind-ons_7,2019,70830,0.97
-wind-ons_7,2019,70959,52.5
-wind-ons_6,2019,71345,24.6
-wind-ons_7,2019,71514,0.97
-wind-ons_8,2019,71643,78.0
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-wind-ons_8,2019,72165,2.6
-wind-ons_7,2019,72197,0.04
-wind-ons_6,2019,72198,0.97
-wind-ons_6,2019,72345,24.0
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-wind-ons_8,2019,72849,4.5
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-wind-ons_8,2019,73533,11.9
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-wind-ons_8,2019,77702,30.0
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-wind-ons_5,2019,77801,81.0
-wind-ons_8,2019,78957,19.5
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-wind-ons_3,2019,80235,132.0
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-wind-ons_8,2019,81009,99.0
-wind-ons_9,2019,81122,12.9
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-wind-ons_3,2019,81223,60.0
-wind-ons_3,2019,81601,5.0
-wind-ons_2,2019,81602,76.5
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-wind-ons_9,2019,81807,67.7
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-wind-ons_9,2019,83175,54.0
-wind-ons_6,2019,83274,16.5
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-wind-ons_6,2019,83744,1.3
-wind-ons_9,2019,83859,44.55
-wind-ons_3,2019,84337,25.2
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-wind-ons_7,2019,84512,2.0
-wind-ons_7,2019,84513,2.1
-wind-ons_9,2019,84542,3.6
-wind-ons_9,2019,84574,1.8
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-wind-ons_8,2019,84658,1.8
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-wind-ons_8,2019,85194,0.93
-wind-ons_9,2019,85195,1.3
-wind-ons_8,2019,85196,2.0
-wind-ons_8,2019,85197,2.0
-wind-ons_7,2019,85198,9.8
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-wind-ons_8,2019,85878,0.93
-wind-ons_9,2019,85879,1.3
-wind-ons_8,2019,85880,1.7
-wind-ons_8,2019,85881,2.0
-wind-ons_7,2019,86012,100.5
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-wind-ons_8,2019,86562,0.93
-wind-ons_9,2019,86563,1.3
-wind-ons_9,2019,86564,1.7
-wind-ons_8,2019,86565,2.0
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-wind-ons_8,2019,87249,2.0
-wind-ons_8,2019,87250,3.9
-wind-ons_7,2019,87251,12.0
-wind-ons_7,2019,87252,32.0
-wind-ons_7,2019,87253,98.8
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-wind-ons_8,2019,87312,59.0
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-wind-ons_9,2019,87930,0.93
-wind-ons_9,2019,87931,1.0
-wind-ons_7,2019,87932,1.5
-wind-ons_8,2019,87934,3.8
-wind-ons_7,2019,87935,11.8
-wind-ons_7,2019,87938,43.5
-wind-ons_8,2019,87996,51.2
-wind-ons_8,2019,88064,37.5
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-wind-ons_9,2019,88585,1.3
-wind-ons_9,2019,88614,0.9
-wind-ons_8,2019,88616,1.3
-wind-ons_8,2019,88617,2.0
-wind-ons_8,2019,88618,3.0
-wind-ons_7,2019,88619,11.6
-wind-ons_8,2019,88621,91.1
-wind-ons_8,2019,88748,30.0
-wind-ons_9,2019,89269,25.0
-wind-ons_9,2019,89298,0.9
-wind-ons_9,2019,89299,0.97
-wind-ons_9,2019,89300,1.3
-wind-ons_9,2019,89301,2.0
-wind-ons_8,2019,89303,11.2
-wind-ons_7,2019,89305,85.5
-wind-ons_6,2019,89306,100.7
-wind-ons_7,2019,89419,9.0
-wind-ons_9,2019,89953,40.5
-wind-ons_9,2019,89986,1.2
-wind-ons_7,2019,89988,10.5
-wind-ons_6,2019,89990,99.0
-wind-ons_8,2019,90049,69.0
-wind-ons_7,2019,90104,37.8
-wind-ons_9,2019,90637,50.4
-wind-ons_8,2019,90670,0.9
-wind-ons_6,2019,90672,10.5
-wind-ons_7,2019,90674,98.2
-wind-ons_7,2019,90675,150.0
-wind-ons_7,2019,90789,62.5
-wind-ons_8,2019,91267,12.14
-wind-ons_8,2019,91354,0.7
-wind-ons_8,2019,91358,98.0
-wind-ons_8,2019,91472,26.0
-wind-ons_6,2019,91473,52.5
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-wind-ons_6,2019,91485,20.0
-wind-ons_9,2019,91951,16.8
-wind-ons_8,2019,92038,0.7
-wind-ons_8,2019,92041,49.6
-wind-ons_9,2019,92156,24.0
-wind-ons_6,2019,92169,6.6
-wind-ons_1,2019,92585,10.5
-wind-ons_8,2019,92722,0.6
-wind-ons_7,2019,92727,123.0
-wind-ons_2,2019,93270,64.5
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-wind-ons_9,2019,93373,51.0
-wind-ons_9,2019,93406,0.5
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-wind-ons_8,2019,93525,34.5
-wind-ons_10,2019,93526,64.5
-wind-ons_9,2019,93527,14.0
-wind-ons_2,2019,93953,21.0
-wind-ons_8,2019,94093,42.0
-wind-ons_7,2019,94094,80.0
-wind-ons_10,2019,94209,29.4
-wind-ons_8,2019,94211,72.0
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-wind-ons_9,2019,94687,48.86
-wind-ons_7,2019,94778,80.0
-wind-ons_9,2019,94854,1.8
-wind-ons_4,2019,94896,100.5
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-wind-ons_9,2019,95462,80.0
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-wind-ons_7,2019,96055,99.0
-wind-ons_8,2019,96147,104.3
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-wind-ons_8,2019,96830,78.0
-wind-ons_7,2019,96946,30.0
-wind-ons_8,2019,97513,29.7
-wind-ons_9,2019,97514,4.5
-wind-ons_8,2019,97516,160.5
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-wind-ons_7,2019,98106,1.8
-wind-ons_8,2019,98198,75.0
-wind-ons_7,2019,98275,1.8
-wind-ons_7,2019,98790,15.92
-wind-ons_8,2019,98880,2.3
-wind-ons_8,2019,98881,20.0
-wind-ons_8,2019,99008,7.5
-wind-ons_8,2019,99566,66.0
-wind-ons_9,2019,99569,60.0
-wind-ons_8,2019,99573,246.0
-wind-ons_9,2019,100158,24.8
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-wind-ons_9,2019,100253,1.7
-wind-ons_8,2019,100257,54.0
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-wind-ons_8,2019,100900,8.91
-wind-ons_9,2019,100936,150.0
-wind-ons_8,2019,100981,162.0
-wind-ons_9,2019,101526,42.0
-wind-ons_9,2019,101584,8.91
-wind-ons_8,2019,101620,150.0
-wind-ons_8,2019,101621,199.0
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-wind-ons_8,2019,102303,100.0
-wind-ons_8,2019,102304,150.0
-wind-ons_8,2019,102305,174.8
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-wind-ons_8,2019,102985,15.0
-wind-ons_8,2019,102988,150.0
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-wind-ons_8,2019,103669,14.7
-wind-ons_8,2019,103670,62.2
-wind-ons_8,2019,103671,99.0
-wind-ons_9,2019,103672,84.0
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-wind-ons_8,2019,105064,99.0
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-wind-ons_9,2019,106329,42.0
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-wind-ons_8,2019,107116,53.0
-wind-ons_9,2019,107741,59.4
-wind-ons_8,2019,107775,78.0
-wind-ons_8,2019,107800,49.5
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-wind-ons_8,2019,108380,22.5
-wind-ons_8,2019,108381,221.0
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-wind-ons_8,2019,108458,35.0
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-wind-ons_8,2019,109169,150.0
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-wind-ons_8,2019,109852,1.5
-wind-ons_9,2019,109874,100.0
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-wind-ons_8,2019,110559,180.0
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-wind-ons_9,2019,117272,157.5
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-wind-ons_7,2019,167943,149.5
-wind-ons_6,2019,168624,0.07
-wind-ons_6,2019,168628,160.84
-wind-ons_5,2019,168630,200.0
-wind-ons_6,2019,168633,216.0
-wind-ons_5,2019,169310,101.2
-wind-ons_5,2019,169313,180.0
-wind-ons_6,2019,169317,83.0
-wind-ons_5,2019,169993,49.5
-wind-ons_5,2019,169997,6.0
-wind-ons_5,2019,169999,210.0
-wind-ons_5,2019,170000,228.0
-wind-ons_5,2019,170683,13.5
-wind-ons_5,2019,170684,21.0
-wind-ons_6,2019,171361,37.5
-wind-ons_5,2019,171362,98.8
-wind-ons_5,2019,171363,126.0
-wind-ons_6,2019,172045,34.3
-wind-ons_5,2019,172047,18.0
-wind-ons_6,2019,172729,32.52
-wind-ons_6,2019,172730,91.58
-wind-ons_5,2019,172735,198.0
-wind-ons_6,2019,173414,91.2
-wind-ons_6,2019,173419,16.5
-wind-ons_6,2019,173421,180.0
-wind-ons_6,2019,174098,91.2
-wind-ons_6,2019,174099,130.5
-wind-ons_7,2019,174101,180.0
-wind-ons_5,2019,174105,98.0
-wind-ons_6,2019,174784,153.9
-wind-ons_5,2019,174789,73.2
-wind-ons_7,2019,175465,17.64
-wind-ons_6,2019,175466,89.5
-wind-ons_6,2019,175468,152.0
-wind-ons_5,2019,175473,150.0
-wind-ons_6,2019,176149,10.0
-wind-ons_5,2019,176150,84.0
-wind-ons_5,2019,176153,180.0
-wind-ons_5,2019,176154,201.6
-wind-ons_6,2019,176157,60.0
-wind-ons_6,2019,176834,60.0
-wind-ons_6,2019,177518,22.5
-wind-ons_6,2019,178202,82.0
-wind-ons_1,2020,46046,114.0
-wind-ons_2,2020,46729,44.0
-wind-ons_1,2020,46730,28.2
-wind-ons_2,2020,48781,156.0
-wind-ons_4,2020,49464,7.2
-wind-ons_3,2020,49465,228.6
-wind-ons_5,2020,50148,94.0
-wind-ons_3,2020,51516,54.0
-wind-ons_7,2020,52200,168.0
-wind-ons_7,2020,52969,9.7
-wind-ons_6,2020,53567,122.8
-wind-ons_5,2020,54935,31.0
-wind-ons_6,2020,55619,234.0
-wind-ons_7,2020,55705,104.2
-wind-ons_6,2020,56302,98.9
-wind-ons_6,2020,57670,62.9
-wind-ons_6,2020,58354,174.0
-wind-ons_8,2020,58684,4.5
-wind-ons_4,2020,59037,95.0
-wind-ons_8,2020,59201,0.93
-wind-ons_8,2020,59203,2.0
-wind-ons_3,2020,59721,136.3
-wind-ons_7,2020,59854,18.0
-wind-ons_7,2020,59885,0.93
-wind-ons_8,2020,59886,1.3
-wind-ons_7,2020,59889,10.4
-wind-ons_8,2020,59899,180.0
-wind-ons_1,2020,60405,95.4
-wind-ons_7,2020,60569,0.93
-wind-ons_7,2020,60570,1.2
-wind-ons_7,2020,60571,2.0
-wind-ons_7,2020,60572,2.1
-wind-ons_7,2020,60574,25.0
-wind-ons_8,2020,60583,25.5
-wind-ons_9,2020,60738,42.0
-wind-ons_8,2020,60739,66.0
-wind-ons_2,2020,61089,30.0
-wind-ons_7,2020,61253,0.93
-wind-ons_7,2020,61255,1.9
-wind-ons_7,2020,61256,2.1
-wind-ons_7,2020,61257,9.2
-wind-ons_5,2020,61772,78.0
-wind-ons_7,2020,61937,0.93
-wind-ons_7,2020,61939,1.7
-wind-ons_8,2020,61940,2.1
-wind-ons_7,2020,61941,8.4
-wind-ons_8,2020,61942,20.0
-wind-ons_9,2020,61943,51.0
-wind-ons_9,2020,61944,60.0
-wind-ons_9,2020,62107,57.0
-wind-ons_7,2020,62544,30.0
-wind-ons_8,2020,62590,31.5
-wind-ons_7,2020,62621,0.93
-wind-ons_7,2020,62623,1.7
-wind-ons_7,2020,62624,2.0
-wind-ons_8,2020,62625,8.0
-wind-ons_9,2020,62628,40.5
-wind-ons_4,2020,63139,6.6
-wind-ons_3,2020,63140,98.7
-wind-ons_7,2020,63305,0.93
-wind-ons_7,2020,63307,1.7
-wind-ons_7,2020,63308,2.0
-wind-ons_8,2020,63309,5.2
-wind-ons_8,2020,63310,19.98
-wind-ons_9,2020,63311,49.5
-wind-ons_8,2020,63312,100.5
-wind-ons_7,2020,63314,150.0
-wind-ons_9,2020,63474,4.5
-wind-ons_4,2020,63823,9.9
-wind-ons_3,2020,63824,30.9
-wind-ons_4,2020,63825,60.0
-wind-ons_8,2020,63912,101.4
-wind-ons_8,2020,63958,40.5
-wind-ons_6,2020,63989,0.93
-wind-ons_7,2020,63990,1.0
-wind-ons_7,2020,63991,1.7
-wind-ons_8,2020,63992,2.0
-wind-ons_7,2020,63996,100.0
-wind-ons_8,2020,64596,135.0
-wind-ons_8,2020,64642,40.5
-wind-ons_8,2020,64643,115.5
-wind-ons_7,2020,64673,0.93
-wind-ons_7,2020,64674,0.97
-wind-ons_7,2020,64675,1.7
-wind-ons_7,2020,64677,4.2
-wind-ons_2,2020,65191,24.0
-wind-ons_7,2020,65357,0.9
-wind-ons_7,2020,65358,0.97
-wind-ons_8,2020,65359,1.7
-wind-ons_7,2020,65360,2.0
-wind-ons_8,2020,65361,4.2
-wind-ons_7,2020,65362,15.6
-wind-ons_5,2020,65363,43.06
-wind-ons_1,2020,65874,1.7
-wind-ons_2,2020,65875,72.0
-wind-ons_8,2020,66010,48.0
-wind-ons_8,2020,66011,118.5
-wind-ons_7,2020,66041,0.9
-wind-ons_7,2020,66042,0.97
-wind-ons_7,2020,66043,1.5
-wind-ons_7,2020,66044,2.0
-wind-ons_8,2020,66045,4.2
-wind-ons_2,2020,66559,100.5
-wind-ons_4,2020,66560,101.0
-wind-ons_8,2020,66694,48.0
-wind-ons_8,2020,66695,6.0
-wind-ons_7,2020,66725,0.9
-wind-ons_7,2020,66726,0.97
-wind-ons_8,2020,66727,1.3
-wind-ons_7,2020,66728,2.0
-wind-ons_2,2020,67243,35.4
-wind-ons_8,2020,67379,149.0
-wind-ons_8,2020,67409,0.9
-wind-ons_8,2020,67410,0.97
-wind-ons_8,2020,67411,1.3
-wind-ons_6,2020,67412,2.0
-wind-ons_7,2020,67538,54.0
-wind-ons_8,2020,68062,49.5
-wind-ons_7,2020,68093,0.7
-wind-ons_8,2020,68094,0.97
-wind-ons_6,2020,68221,30.55
-wind-ons_6,2020,68222,100.5
-wind-ons_8,2020,68746,49.5
-wind-ons_7,2020,68777,0.7
-wind-ons_5,2020,68778,0.97
-wind-ons_7,2020,68905,16.5
-wind-ons_6,2020,68906,19.5
-wind-ons_4,2020,68919,5.2
-wind-ons_1,2020,69293,8.3
-wind-ons_8,2020,69430,50.6
-wind-ons_8,2020,69431,114.0
-wind-ons_5,2020,69462,0.97
-wind-ons_5,2020,69591,52.0
-wind-ons_1,2020,69979,60.0
-wind-ons_8,2020,70114,61.5
-wind-ons_8,2020,70115,148.5
-wind-ons_5,2020,70145,0.4
-wind-ons_5,2020,70146,0.97
-wind-ons_6,2020,70274,68.5
-wind-ons_1,2020,70662,31.3
-wind-ons_7,2020,70830,0.97
-wind-ons_7,2020,70959,52.5
-wind-ons_6,2020,71345,24.6
-wind-ons_7,2020,71514,0.97
-wind-ons_8,2020,71643,78.0
-wind-ons_3,2020,72028,3.3
-wind-ons_8,2020,72165,2.6
-wind-ons_7,2020,72197,0.04
-wind-ons_6,2020,72198,0.97
-wind-ons_6,2020,72345,24.0
-wind-ons_5,2020,72712,9.9
-wind-ons_8,2020,72849,4.5
-wind-ons_6,2020,72881,0.02
-wind-ons_8,2020,73533,11.9
-wind-ons_8,2020,73535,180.0
-wind-ons_5,2020,74764,41.0
-wind-ons_4,2020,76132,102.3
-wind-ons_7,2020,76334,42.57
-wind-ons_6,2020,76857,18.9
-wind-ons_8,2020,77702,30.0
-wind-ons_5,2020,77800,11.5
-wind-ons_5,2020,77801,81.0
-wind-ons_8,2020,78957,19.5
-wind-ons_3,2020,79169,45.5
-wind-ons_4,2020,79551,72.0
-wind-ons_8,2020,79641,39.0
-wind-ons_9,2020,79755,43.2
-wind-ons_3,2020,79853,30.55
-wind-ons_3,2020,80235,132.0
-wind-ons_5,2020,80538,87.5
-wind-ons_5,2020,80539,126.0
-wind-ons_4,2020,80918,25.0
-wind-ons_8,2020,81009,99.0
-wind-ons_9,2020,81122,12.9
-wind-ons_10,2020,81124,102.0
-wind-ons_3,2020,81223,60.0
-wind-ons_3,2020,81601,5.0
-wind-ons_2,2020,81602,76.5
-wind-ons_9,2020,81806,11.88
-wind-ons_9,2020,81807,67.7
-wind-ons_4,2020,81906,68.5
-wind-ons_3,2020,82285,9.9
-wind-ons_2,2020,82286,100.7
-wind-ons_9,2020,82490,11.0
-wind-ons_6,2020,82590,34.5
-wind-ons_3,2020,82970,102.9
-wind-ons_9,2020,83175,54.0
-wind-ons_6,2020,83274,16.5
-wind-ons_3,2020,83653,10.0
-wind-ons_6,2020,83744,1.3
-wind-ons_9,2020,83859,44.55
-wind-ons_3,2020,84337,25.2
-wind-ons_5,2020,84428,4.9
-wind-ons_8,2020,84510,0.97
-wind-ons_7,2020,84512,2.0
-wind-ons_7,2020,84513,2.1
-wind-ons_9,2020,84542,3.6
-wind-ons_9,2020,84574,1.8
-wind-ons_6,2020,84643,45.5
-wind-ons_9,2020,84657,1.5
-wind-ons_8,2020,84658,1.8
-wind-ons_3,2020,85020,8.0
-wind-ons_2,2020,85021,81.0
-wind-ons_3,2020,85063,21.0
-wind-ons_6,2020,85112,17.0
-wind-ons_8,2020,85194,0.93
-wind-ons_9,2020,85195,1.3
-wind-ons_8,2020,85196,2.0
-wind-ons_8,2020,85197,2.0
-wind-ons_7,2020,85198,9.8
-wind-ons_3,2020,85705,100.8
-wind-ons_5,2020,85796,25.0
-wind-ons_8,2020,85878,0.93
-wind-ons_9,2020,85879,1.3
-wind-ons_8,2020,85880,1.7
-wind-ons_8,2020,85881,2.0
-wind-ons_7,2020,86012,100.5
-wind-ons_4,2020,86389,90.0
-wind-ons_4,2020,86480,28.8
-wind-ons_8,2020,86562,0.93
-wind-ons_9,2020,86563,1.3
-wind-ons_9,2020,86564,1.7
-wind-ons_8,2020,86565,2.0
-wind-ons_8,2020,86567,13.5
-wind-ons_7,2020,86569,99.0
-wind-ons_5,2020,87164,41.4
-wind-ons_9,2020,87246,0.93
-wind-ons_8,2020,87248,1.7
-wind-ons_8,2020,87249,2.0
-wind-ons_8,2020,87250,3.9
-wind-ons_7,2020,87251,12.0
-wind-ons_7,2020,87252,32.0
-wind-ons_7,2020,87253,98.8
-wind-ons_7,2020,87254,60.0
-wind-ons_9,2020,87279,13.5
-wind-ons_8,2020,87312,59.0
-wind-ons_2,2020,87798,8.4
-wind-ons_5,2020,87848,99.0
-wind-ons_9,2020,87901,1.3
-wind-ons_9,2020,87930,0.93
-wind-ons_9,2020,87931,1.0
-wind-ons_7,2020,87932,1.5
-wind-ons_8,2020,87934,3.8
-wind-ons_7,2020,87935,11.8
-wind-ons_7,2020,87938,43.5
-wind-ons_8,2020,87996,51.2
-wind-ons_8,2020,88064,37.5
-wind-ons_5,2020,88532,99.0
-wind-ons_9,2020,88585,1.3
-wind-ons_9,2020,88614,0.9
-wind-ons_8,2020,88616,1.3
-wind-ons_8,2020,88617,2.0
-wind-ons_8,2020,88618,3.0
-wind-ons_7,2020,88619,11.6
-wind-ons_8,2020,88621,91.1
-wind-ons_8,2020,88748,30.0
-wind-ons_9,2020,89269,25.0
-wind-ons_9,2020,89298,0.9
-wind-ons_9,2020,89299,0.97
-wind-ons_9,2020,89300,1.3
-wind-ons_9,2020,89301,2.0
-wind-ons_8,2020,89303,11.2
-wind-ons_7,2020,89305,85.5
-wind-ons_6,2020,89306,100.7
-wind-ons_7,2020,89419,9.0
-wind-ons_9,2020,89953,40.5
-wind-ons_9,2020,89986,1.2
-wind-ons_7,2020,89988,10.5
-wind-ons_6,2020,89990,99.0
-wind-ons_8,2020,90049,69.0
-wind-ons_7,2020,90104,37.8
-wind-ons_9,2020,90637,50.4
-wind-ons_8,2020,90670,0.9
-wind-ons_6,2020,90672,10.5
-wind-ons_7,2020,90674,98.2
-wind-ons_7,2020,90675,150.0
-wind-ons_7,2020,90789,62.5
-wind-ons_8,2020,91267,12.14
-wind-ons_8,2020,91354,0.7
-wind-ons_8,2020,91358,98.0
-wind-ons_8,2020,91472,26.0
-wind-ons_6,2020,91473,52.5
-wind-ons_5,2020,91474,3.0
-wind-ons_6,2020,91485,20.0
-wind-ons_9,2020,91951,16.8
-wind-ons_8,2020,92038,0.7
-wind-ons_8,2020,92041,49.6
-wind-ons_9,2020,92156,24.0
-wind-ons_6,2020,92169,6.6
-wind-ons_1,2020,92585,10.5
-wind-ons_8,2020,92722,0.6
-wind-ons_7,2020,92727,123.0
-wind-ons_2,2020,93270,64.5
-wind-ons_9,2020,93319,25.0
-wind-ons_9,2020,93373,51.0
-wind-ons_9,2020,93406,0.5
-wind-ons_7,2020,93409,43.5
-wind-ons_8,2020,93525,34.5
-wind-ons_10,2020,93526,64.5
-wind-ons_9,2020,93527,14.0
-wind-ons_2,2020,93953,21.0
-wind-ons_8,2020,94093,42.0
-wind-ons_7,2020,94094,80.0
-wind-ons_10,2020,94209,29.4
-wind-ons_8,2020,94211,72.0
-wind-ons_6,2020,94212,102.0
-wind-ons_9,2020,94687,48.86
-wind-ons_7,2020,94778,80.0
-wind-ons_9,2020,94854,1.8
-wind-ons_4,2020,94896,100.5
-wind-ons_7,2020,95371,99.0
-wind-ons_9,2020,95462,80.0
-wind-ons_10,2020,95577,15.0
-wind-ons_7,2020,96055,99.0
-wind-ons_8,2020,96147,104.3
-wind-ons_4,2020,96264,66.0
-wind-ons_6,2020,96739,142.2
-wind-ons_8,2020,96830,78.0
-wind-ons_7,2020,96946,30.0
-wind-ons_8,2020,97513,29.7
-wind-ons_9,2020,97514,4.5
-wind-ons_8,2020,97516,160.5
-wind-ons_8,2020,97517,138.0
-wind-ons_7,2020,98106,1.8
-wind-ons_8,2020,98198,75.0
-wind-ons_7,2020,98275,1.8
-wind-ons_7,2020,98790,15.92
-wind-ons_8,2020,98880,2.3
-wind-ons_8,2020,98881,20.0
-wind-ons_8,2020,99008,7.5
-wind-ons_8,2020,99566,66.0
-wind-ons_9,2020,99569,60.0
-wind-ons_8,2020,99573,246.0
-wind-ons_9,2020,100158,24.8
-wind-ons_8,2020,100249,16.2
-wind-ons_9,2020,100253,1.7
-wind-ons_8,2020,100257,54.0
-wind-ons_9,2020,100842,29.4
-wind-ons_8,2020,100900,8.91
-wind-ons_9,2020,100936,150.0
-wind-ons_8,2020,100981,162.0
-wind-ons_9,2020,101526,42.0
-wind-ons_9,2020,101584,8.91
-wind-ons_8,2020,101620,150.0
-wind-ons_8,2020,101621,199.0
-wind-ons_8,2020,101665,130.5
-wind-ons_10,2020,102210,64.08
-wind-ons_9,2020,102268,8.91
-wind-ons_8,2020,102303,100.0
-wind-ons_8,2020,102304,150.0
-wind-ons_8,2020,102305,174.8
-wind-ons_8,2020,102327,1.5
-wind-ons_8,2020,102349,84.0
-wind-ons_6,2020,102380,1.8
-wind-ons_9,2020,102984,2.0
-wind-ons_8,2020,102985,15.0
-wind-ons_8,2020,102988,150.0
-wind-ons_6,2020,103064,1.8
-wind-ons_8,2020,103669,14.7
-wind-ons_8,2020,103670,62.2
-wind-ons_8,2020,103671,99.0
-wind-ons_9,2020,103672,84.0
-wind-ons_8,2020,103717,101.3
-wind-ons_8,2020,104356,66.0
-wind-ons_8,2020,104401,99.0
-wind-ons_8,2020,105064,99.0
-wind-ons_9,2020,105719,0.9
-wind-ons_9,2020,106329,42.0
-wind-ons_9,2020,107013,54.0
-wind-ons_8,2020,107116,53.0
-wind-ons_9,2020,107741,59.4
-wind-ons_8,2020,107775,78.0
-wind-ons_8,2020,107800,49.5
-wind-ons_8,2020,107802,217.5
-wind-ons_4,2020,107872,60.0
-wind-ons_8,2020,108380,22.5
-wind-ons_8,2020,108381,221.0
-wind-ons_9,2020,108425,54.27
-wind-ons_8,2020,108458,35.0
-wind-ons_8,2020,108459,72.0
-wind-ons_8,2020,108484,10.5
-wind-ons_8,2020,108486,213.8
-wind-ons_5,2020,108556,6.0
-wind-ons_5,2020,108557,100.0
-wind-ons_5,2020,108559,150.0
-wind-ons_8,2020,109168,2.5
-wind-ons_8,2020,109169,150.0
-wind-ons_8,2020,109170,210.9
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-wind-ons_8,2020,109748,29.7
-wind-ons_7,2020,109826,21.0
-wind-ons_8,2020,109852,1.5
-wind-ons_9,2020,109874,100.0
-wind-ons_10,2020,110475,0.7
-wind-ons_8,2020,110559,180.0
-wind-ons_10,2020,111159,0.7
-wind-ons_8,2020,111800,60.0
-wind-ons_8,2020,111905,100.5
-wind-ons_2,2020,112433,18.9
-wind-ons_7,2020,112611,37.7
-wind-ons_7,2020,113168,79.5
-wind-ons_8,2020,113273,80.0
-wind-ons_8,2020,113957,76.2
-wind-ons_7,2020,113979,22.0
-wind-ons_9,2020,114588,105.0
-wind-ons_8,2020,114641,51.0
-wind-ons_9,2020,115272,100.8
-wind-ons_9,2020,115904,151.8
-wind-ons_8,2020,115956,99.0
-wind-ons_8,2020,116009,8.4
-wind-ons_5,2020,116032,20.0
-wind-ons_9,2020,116640,96.0
-wind-ons_8,2020,116693,1.7
-wind-ons_9,2020,117272,157.5
-wind-ons_8,2020,118062,106.0
-wind-ons_9,2020,118639,1.5
-wind-ons_9,2020,118640,147.0
-wind-ons_9,2020,118692,100.5
-wind-ons_7,2020,119430,100.5
-wind-ons_8,2020,120007,4.0
-wind-ons_9,2020,120060,112.2
-wind-ons_7,2020,120798,72.0
-wind-ons_5,2020,122691,203.5
-wind-ons_7,2020,122850,2.5
-wind-ons_9,2020,123427,75.0
-wind-ons_7,2020,126941,5.0
-wind-ons_9,2020,127584,50.0
-wind-ons_10,2020,128214,1.5
-wind-ons_10,2020,128215,162.0
-wind-ons_9,2020,128952,44.0
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+wind-ons_8,2018,82434,0.97
+wind-ons_9,2018,82435,0.93
+wind-ons_9,2018,82436,0.93
+wind-ons_8,2018,82507,59.0
+wind-ons_7,2018,82576,30.0
+wind-ons_10,2018,82610,1.8
+wind-ons_7,2018,82980,21.0
+wind-ons_9,2018,83093,40.5
+wind-ons_8,2018,83096,25.0
+wind-ons_9,2018,83116,11.8
+wind-ons_9,2018,83117,1.7
+wind-ons_8,2018,83118,1.3
+wind-ons_9,2018,83119,0.97
+wind-ons_9,2018,83120,0.93
+wind-ons_9,2018,83121,0.93
+wind-ons_9,2018,83123,1.7
+wind-ons_8,2018,83261,11.5
+wind-ons_6,2018,83664,21.0
+wind-ons_10,2018,83801,1.7
+wind-ons_9,2018,83802,2.1
+wind-ons_9,2018,83803,2.1
+wind-ons_9,2018,83804,0.97
+wind-ons_9,2018,83805,0.93
+wind-ons_8,2018,83807,1.7
+wind-ons_9,2018,83944,34.5
+wind-ons_5,2018,84351,8.4
+wind-ons_5,2018,84352,18.9
+wind-ons_7,2018,84375,64.5
+wind-ons_10,2018,84482,50.4
+wind-ons_10,2018,84483,103.5
+wind-ons_10,2018,84485,3.9
+wind-ons_10,2018,84486,4.2
+wind-ons_9,2018,84487,4.2
+wind-ons_9,2018,84488,0.97
+wind-ons_9,2018,84489,0.93
+wind-ons_9,2018,84576,69.0
+wind-ons_10,2018,85167,150.0
+wind-ons_10,2018,85168,11.2
+wind-ons_9,2018,85169,49.5
+wind-ons_9,2018,85170,2.1
+wind-ons_10,2018,85171,4.2
+wind-ons_9,2018,85172,1.5
+wind-ons_9,2018,85173,0.97
+wind-ons_8,2018,85259,51.2
+wind-ons_10,2018,85351,1.5
+wind-ons_5,2018,85722,10.5
+wind-ons_9,2018,85853,98.8
+wind-ons_10,2018,85854,2.0
+wind-ons_9,2018,85855,2.0
+wind-ons_10,2018,85856,1.7
+wind-ons_10,2018,85859,0.04
+wind-ons_9,2018,85907,145.2
+wind-ons_9,2018,86519,51.0
+wind-ons_9,2018,86535,5.2
+wind-ons_9,2018,86536,51.0
+wind-ons_9,2018,86539,2.0
+wind-ons_9,2018,86540,2.0
+wind-ons_10,2018,86541,0.93
+wind-ons_10,2018,86542,43.06
+wind-ons_9,2018,86559,32.0
+wind-ons_9,2018,86560,3.0
+wind-ons_8,2018,87199,1.3
+wind-ons_9,2018,87219,9.2
+wind-ons_9,2018,87221,85.5
+wind-ons_9,2018,87222,12.0
+wind-ons_9,2018,87223,10.4
+wind-ons_9,2018,87224,2.0
+wind-ons_9,2018,87225,1.3
+wind-ons_10,2018,87226,0.9
+wind-ons_10,2018,87227,15.6
+wind-ons_10,2018,87228,1.2
+wind-ons_10,2018,87229,19.98
+wind-ons_9,2018,87243,8.0
+wind-ons_8,2018,87273,12.9
+wind-ons_9,2018,87274,13.5
+wind-ons_9,2018,87275,67.7
+wind-ons_8,2018,87345,112.0
+wind-ons_9,2018,87346,6.6
+wind-ons_8,2018,87883,1.3
+wind-ons_9,2018,87905,205.5
+wind-ons_10,2018,87907,2.0
+wind-ons_9,2018,87908,2.0
+wind-ons_9,2018,87909,1.3
+wind-ons_9,2018,87910,1.0
+wind-ons_9,2018,87911,0.9
+wind-ons_10,2018,87912,0.02
+wind-ons_9,2018,87914,100.5
+wind-ons_9,2018,87929,100.5
+wind-ons_8,2018,87956,3.6
+wind-ons_9,2018,87957,42.57
+wind-ons_9,2018,87958,44.55
+wind-ons_9,2018,87959,11.88
+wind-ons_8,2018,88026,20.0
+wind-ons_9,2018,88030,126.0
+wind-ons_8,2018,88036,37.5
+wind-ons_9,2018,88587,11.6
+wind-ons_8,2018,88588,2.0
+wind-ons_9,2018,88589,2.0
+wind-ons_10,2018,88592,1.5
+wind-ons_9,2018,88593,2.0
+wind-ons_9,2018,88594,8.4
+wind-ons_9,2018,88595,1.0
+wind-ons_9,2018,88596,99.0
+wind-ons_9,2018,88599,2.1
+wind-ons_9,2018,88602,1.3
+wind-ons_9,2018,88612,100.0
+wind-ons_9,2018,88613,100.7
+wind-ons_9,2018,88714,100.5
+wind-ons_9,2018,88719,87.5
+wind-ons_9,2018,89273,2.0
+wind-ons_9,2018,89274,2.0
+wind-ons_9,2018,89276,1.3
+wind-ons_9,2018,89277,20.0
+wind-ons_9,2018,89280,0.9
+wind-ons_9,2018,89284,1.7
+wind-ons_9,2018,89285,0.9
+wind-ons_9,2018,89287,1.3
+wind-ons_8,2018,89295,9.8
+wind-ons_9,2018,89296,13.5
+wind-ons_9,2018,89297,91.1
+wind-ons_10,2018,89326,54.0
+wind-ons_10,2018,89888,39.0
+wind-ons_9,2018,89955,3.8
+wind-ons_8,2018,89956,2.0
+wind-ons_8,2018,89957,1.7
+wind-ons_9,2018,89958,1.7
+wind-ons_9,2018,89959,1.3
+wind-ons_9,2018,89960,1.3
+wind-ons_9,2018,89961,2.0
+wind-ons_9,2018,89962,2.0
+wind-ons_9,2018,89963,0.9
+wind-ons_9,2018,89968,1.7
+wind-ons_9,2018,89969,0.9
+wind-ons_9,2018,89981,264.0
+wind-ons_10,2018,90571,99.0
+wind-ons_10,2018,90572,99.0
+wind-ons_9,2018,90644,2.0
+wind-ons_9,2018,90645,98.2
+wind-ons_9,2018,90646,49.6
+wind-ons_9,2018,90647,43.5
+wind-ons_9,2018,90648,10.5
+wind-ons_9,2018,90654,10.5
+wind-ons_9,2018,90657,20.0
+wind-ons_9,2018,90659,80.0
+wind-ons_9,2018,90660,80.0
+wind-ons_9,2018,90661,78.0
+wind-ons_9,2018,90662,80.0
+wind-ons_8,2018,90665,36.0
+wind-ons_9,2018,90666,99.0
+wind-ons_9,2018,91253,17.0
+wind-ons_10,2018,91257,99.0
+wind-ons_9,2018,91327,1.2
+wind-ons_9,2018,91329,0.6
+wind-ons_9,2018,91330,100.0
+wind-ons_9,2018,91340,66.0
+wind-ons_9,2018,91341,199.0
+wind-ons_9,2018,91344,29.7
+wind-ons_8,2018,91348,0.5
+wind-ons_9,2018,92025,150.0
+wind-ons_8,2018,92146,100.5
+wind-ons_9,2018,92156,64.5
+wind-ons_9,2018,92706,2.3
+wind-ons_9,2018,92710,98.0
+wind-ons_9,2018,92711,42.0
+wind-ons_9,2018,93385,21.0
+wind-ons_8,2018,93401,75.0
+wind-ons_8,2018,93417,30.0
+wind-ons_9,2018,94734,8.91
+wind-ons_9,2018,94750,76.5
+wind-ons_8,2018,94771,0.9
+wind-ons_10,2018,94890,24.0
+wind-ons_10,2018,95417,8.91
+wind-ons_10,2018,95418,54.27
+wind-ons_9,2018,95433,0.7
+wind-ons_9,2018,95434,104.3
+wind-ons_9,2018,95438,62.2
+wind-ons_9,2018,95449,199.7
+wind-ons_8,2018,95454,0.9
+wind-ons_10,2018,96102,8.91
+wind-ons_9,2018,96117,0.7
+wind-ons_9,2018,96118,160.5
+wind-ons_9,2018,96122,123.0
+wind-ons_9,2018,96130,150.0
+wind-ons_9,2018,96131,16.2
+wind-ons_8,2018,96157,100.5
+wind-ons_10,2018,96770,59.4
+wind-ons_9,2018,96802,15.0
+wind-ons_9,2018,96806,78.0
+wind-ons_9,2018,96814,35.0
+wind-ons_10,2018,97408,1.3
+wind-ons_9,2018,97624,102.0
+wind-ons_10,2018,97625,26.0
+wind-ons_10,2018,98091,99.0
+wind-ons_10,2018,98092,19.5
+wind-ons_9,2018,98093,4.9
+wind-ons_10,2018,98184,150.0
+wind-ons_7,2018,98745,48.86
+wind-ons_10,2018,98775,1.8
+wind-ons_10,2018,98776,25.0
+wind-ons_10,2018,98777,25.0
+wind-ons_10,2018,98778,28.8
+wind-ons_9,2018,98857,150.0
+wind-ons_9,2018,98869,150.0
+wind-ons_7,2018,99428,64.08
+wind-ons_8,2018,99429,142.2
+wind-ons_8,2018,99430,12.14
+wind-ons_10,2018,99459,24.8
+wind-ons_10,2018,99460,41.4
+wind-ons_9,2018,99462,99.0
+wind-ons_10,2018,99540,99.0
+wind-ons_9,2018,99545,14.7
+wind-ons_9,2018,100113,15.92
+wind-ons_10,2018,100143,16.8
+wind-ons_8,2018,100267,76.2
+wind-ons_9,2018,100268,186.0
+wind-ons_9,2018,100269,31.5
+wind-ons_7,2018,100305,1.8
+wind-ons_8,2018,100306,1.8
+wind-ons_8,2018,100949,2.5
+wind-ons_8,2018,100950,80.0
+wind-ons_8,2018,100989,1.8
+wind-ons_8,2018,100990,1.8
+wind-ons_7,2018,101627,1.5
+wind-ons_8,2018,101634,2.5
+wind-ons_8,2018,102313,1.5
+wind-ons_7,2018,102395,39.0
+wind-ons_10,2018,102396,36.0
+wind-ons_10,2018,102888,29.4
+wind-ons_8,2018,103006,48.0
+wind-ons_7,2018,103007,10.5
+wind-ons_8,2018,103079,62.5
+wind-ons_10,2018,103080,80.0
+wind-ons_9,2018,103573,42.0
+wind-ons_10,2018,103643,150.0
+wind-ons_10,2018,103647,174.8
+wind-ons_8,2018,103683,8.4
+wind-ons_7,2018,103684,53.0
+wind-ons_8,2018,103690,99.0
+wind-ons_8,2018,103691,51.0
+wind-ons_8,2018,104368,72.0
+wind-ons_8,2018,104373,1.5
+wind-ons_8,2018,104374,106.0
+wind-ons_10,2018,104447,52.5
+wind-ons_5,2018,104448,29.4
+wind-ons_9,2018,104479,7.5
+wind-ons_8,2018,105051,150.0
+wind-ons_3,2018,105497,13.1
+wind-ons_8,2018,105631,221.0
+wind-ons_7,2018,105635,199.5
+wind-ons_9,2018,105637,201.0
+wind-ons_8,2018,105638,60.0
+wind-ons_8,2018,105639,151.8
+wind-ons_8,2018,105640,22.5
+wind-ons_9,2018,105811,9.0
+wind-ons_10,2018,105812,30.0
+wind-ons_5,2018,106179,16.2
+wind-ons_6,2018,106182,63.0
+wind-ons_2,2018,106259,18.9
+wind-ons_7,2018,106315,79.5
+wind-ons_6,2018,106321,29.7
+wind-ons_8,2018,106495,15.0
+wind-ons_9,2018,106865,120.0
+wind-ons_9,2018,106866,60.0
+wind-ons_9,2018,107053,0.7
+wind-ons_8,2018,107119,130.5
+wind-ons_8,2018,107121,106.0
+wind-ons_8,2018,107123,199.7
+wind-ons_7,2018,107179,34.5
+wind-ons_9,2018,107180,37.8
+wind-ons_5,2018,107546,15.5
+wind-ons_9,2018,107549,150.0
+wind-ons_10,2018,107550,30.0
+wind-ons_9,2018,107737,0.7
+wind-ons_9,2018,107793,42.0
+wind-ons_9,2018,107794,108.0
+wind-ons_8,2018,107803,100.0
+wind-ons_8,2018,107804,200.0
+wind-ons_8,2018,107805,101.3
+wind-ons_7,2018,108232,85.8
+wind-ons_9,2018,108476,60.0
+wind-ons_8,2018,108489,99.0
+wind-ons_2,2018,108915,24.0
+wind-ons_9,2018,109162,54.0
+wind-ons_9,2018,109163,159.8
+wind-ons_9,2018,109164,0.9
+wind-ons_3,2018,109600,38.0
+wind-ons_9,2018,109797,146.0
+wind-ons_8,2018,109841,100.5
+wind-ons_8,2018,110480,5.0
+wind-ons_9,2018,110481,50.4
+wind-ons_9,2018,110601,100.0
+wind-ons_3,2018,110971,18.0
+wind-ons_9,2018,111283,30.0
+wind-ons_10,2018,111285,138.0
+wind-ons_4,2018,111286,26.0
+wind-ons_9,2018,111855,50.4
+wind-ons_9,2018,111967,36.0
+wind-ons_9,2018,112540,56.7
+wind-ons_7,2018,114621,1.7
+wind-ons_3,2018,115076,17.0
+wind-ons_5,2018,115139,203.5
+wind-ons_10,2018,116621,96.0
+wind-ons_10,2018,117305,105.0
+wind-ons_10,2018,120721,148.5
+wind-ons_10,2018,120722,100.8
+wind-ons_9,2018,122751,99.0
+wind-ons_6,2018,124792,1.5
+wind-ons_7,2018,125476,4.0
+wind-ons_10,2018,126865,100.5
+wind-ons_9,2018,127525,75.0
+wind-ons_10,2018,127527,162.0
+wind-ons_2,2018,128086,21.0
+wind-ons_8,2018,128087,60.0
+wind-ons_1,2018,128762,7.4
+wind-ons_1,2018,128769,54.0
+wind-ons_10,2018,128912,112.2
+wind-ons_9,2018,128946,24.0
+wind-ons_1,2018,129446,8.7
+wind-ons_9,2018,129630,126.0
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+wind-ons_1,2018,130132,10.9
+wind-ons_1,2018,130816,66.0
+wind-ons_9,2018,130949,1.5
+wind-ons_10,2018,130978,44.0
+wind-ons_10,2018,130979,50.0
+wind-ons_1,2018,131497,3.0
+wind-ons_1,2018,132182,18.4
+wind-ons_1,2018,132183,59.6
+wind-ons_1,2018,132867,46.9
+wind-ons_4,2018,133551,23.1
+wind-ons_1,2018,134233,1.6
+wind-ons_1,2018,134235,22.0
+wind-ons_1,2018,134236,54.0
+wind-ons_9,2018,134389,18.9
+wind-ons_1,2018,134918,2.4
+wind-ons_1,2018,134919,15.3
+wind-ons_1,2018,134920,29.0
+wind-ons_10,2018,135074,120.0
+wind-ons_9,2018,135075,25.5
+wind-ons_9,2018,135076,25.5
+wind-ons_7,2018,135191,27.0
+wind-ons_6,2018,135192,0.7
+wind-ons_1,2018,135603,6.5
+wind-ons_1,2018,135604,17.3
+wind-ons_2,2018,135605,60.3
+wind-ons_2,2018,135606,28.5
+wind-ons_9,2018,135740,10.0
+wind-ons_10,2018,135758,94.5
+wind-ons_10,2018,135760,25.5
+wind-ons_4,2018,135875,0.7
+wind-ons_3,2018,135876,0.7
+wind-ons_1,2018,136288,8.3
+wind-ons_3,2018,136290,13.0
+wind-ons_10,2018,136424,10.0
+wind-ons_10,2018,136428,114.0
+wind-ons_10,2018,136429,3.0
+wind-ons_10,2018,136444,25.5
+wind-ons_1,2018,136972,1.8
+wind-ons_1,2018,136973,7.5
+wind-ons_1,2018,136978,60.0
+wind-ons_10,2018,137112,79.8
+wind-ons_10,2018,137126,101.2
+wind-ons_2,2018,137657,1.5
+wind-ons_1,2018,137658,3.7
+wind-ons_3,2018,137668,28.7
+wind-ons_6,2018,137669,25.3
+wind-ons_1,2018,138344,4.2
+wind-ons_5,2018,138352,4.5
+wind-ons_7,2018,138353,3.5
+wind-ons_6,2018,138355,0.1
+wind-ons_10,2018,138476,10.0
+wind-ons_3,2018,139042,9.1
+wind-ons_10,2018,139159,10.0
+wind-ons_10,2018,139160,49.5
+wind-ons_10,2018,139161,10.0
+wind-ons_4,2018,139726,30.75
+wind-ons_4,2018,139730,3.4
+wind-ons_9,2018,139843,10.0
+wind-ons_1,2018,140407,9.6
+wind-ons_9,2018,140527,8.8
+wind-ons_1,2018,141091,20.5
+wind-ons_3,2018,141092,38.9
+wind-ons_1,2018,141775,20.5
+wind-ons_1,2018,141776,33.6
+wind-ons_7,2018,141777,43.4
+wind-ons_7,2018,141781,42.6
+wind-ons_1,2018,142459,16.5
+wind-ons_1,2018,142460,21.7
+wind-ons_1,2018,142461,31.0
+wind-ons_8,2018,142464,45.0
+wind-ons_7,2018,142465,22.4
+wind-ons_6,2018,142466,11.7
+wind-ons_10,2018,142582,79.5
+wind-ons_10,2018,142584,80.0
+wind-ons_10,2018,142595,98.9
+wind-ons_10,2018,142599,123.0
+wind-ons_10,2018,142604,40.5
+wind-ons_1,2018,143144,7.3
+wind-ons_3,2018,143145,22.2
+wind-ons_3,2018,143146,30.75
+wind-ons_6,2018,143149,20.0
+wind-ons_5,2018,143150,5.4
+wind-ons_5,2018,143197,63.0
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+wind-ons_10,2018,143260,160.84
+wind-ons_10,2018,143288,106.5
+wind-ons_2,2018,143828,2.1
+wind-ons_3,2018,143829,6.5
+wind-ons_6,2018,143830,11.6
+wind-ons_1,2018,143832,8.0
+wind-ons_10,2018,143950,10.0
+wind-ons_6,2018,144515,3.0
+wind-ons_9,2018,145291,90.0
+wind-ons_10,2018,145303,20.0
+wind-ons_10,2018,145304,80.0
+wind-ons_8,2018,147335,100.0
+wind-ons_9,2018,147350,204.0
+wind-ons_10,2018,147392,74.3
+wind-ons_10,2018,147393,151.4
+wind-ons_10,2018,147394,99.0
+wind-ons_10,2018,148725,0.6
+wind-ons_7,2018,149302,50.0
+wind-ons_9,2018,149409,0.7
+wind-ons_8,2018,150093,0.6
+wind-ons_7,2018,151454,30.0
+wind-ons_9,2018,151455,90.0
+wind-ons_10,2018,151477,57.0
+wind-ons_10,2018,154214,150.0
+wind-ons_10,2018,154925,112.5
+wind-ons_9,2018,158996,180.0
+wind-ons_8,2018,158999,91.2
+wind-ons_8,2018,159000,91.2
+wind-ons_8,2018,159682,84.0
+wind-ons_9,2018,159707,120.0
+wind-ons_9,2018,160371,63.0
+wind-ons_7,2018,161052,70.5
+wind-ons_9,2018,161053,60.0
+wind-ons_10,2018,161055,120.0
+wind-ons_10,2018,161066,162.3
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+wind-ons_10,2018,161749,200.0
+wind-ons_10,2018,162423,209.0
+wind-ons_10,2018,162424,197.0
+wind-ons_10,2018,162432,200.0
+wind-ons_8,2018,163106,73.5
+wind-ons_9,2018,163107,75.0
+wind-ons_10,2018,163108,126.5
+wind-ons_10,2018,163109,37.5
+wind-ons_10,2018,163110,98.8
+wind-ons_10,2018,163111,152.0
+wind-ons_10,2018,163112,101.2
+wind-ons_9,2018,163782,120.0
+wind-ons_8,2018,163786,34.3
+wind-ons_10,2018,163792,241.0
+wind-ons_10,2018,163793,144.0
+wind-ons_10,2018,163795,232.5
+wind-ons_10,2018,163796,170.2
+wind-ons_10,2018,163797,120.6
+wind-ons_10,2018,163798,114.0
+wind-ons_10,2018,163809,32.52
+wind-ons_9,2018,164458,0.92
+wind-ons_10,2018,164469,121.9
+wind-ons_9,2018,164470,58.8
+wind-ons_10,2018,164476,80.5
+wind-ons_10,2018,164479,169.5
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+wind-ons_10,2038,163809,32.52
+wind-ons_9,2038,164458,0.92
+wind-ons_10,2038,164469,121.9
+wind-ons_9,2038,164470,58.8
+wind-ons_10,2038,164479,169.5
+wind-ons_10,2038,164493,17.64
+wind-ons_10,2038,164494,2.64
+wind-ons_9,2038,165141,59.4
+wind-ons_9,2038,165142,91.58
+wind-ons_10,2038,165153,153.9
+wind-ons_6,2038,165158,0.07
+wind-ons_7,2038,165159,0.07
+wind-ons_9,2038,165826,0.6
+wind-ons_10,2038,165838,124.7
+wind-ons_10,2038,165839,112.5
+wind-ons_10,2038,165840,231.5
+wind-ons_10,2038,165842,79.92
+wind-ons_8,2038,165843,0.08
+wind-ons_8,2038,166524,186.0
+wind-ons_10,2038,166526,69.53
+wind-ons_10,2038,171998,150.0
+wind-ons_8,2038,193229,180.0
+wind-ons_9,2038,198700,201.6
+wind-ons_9,2038,199383,252.0
+wind-ons_9,2038,199384,31.2
+wind-ons_9,2039,50181,101.4
+wind-ons_7,2039,52179,44.0
+wind-ons_7,2039,56416,89.0
+wind-ons_8,2039,57100,60.0
+wind-ons_9,2039,57997,57.0
+wind-ons_6,2039,59696,136.3
+wind-ons_9,2039,59827,4.5
+wind-ons_7,2039,60379,240.0
+wind-ons_7,2039,60380,22.2
+wind-ons_6,2039,60382,98.9
+wind-ons_9,2039,60511,115.5
+wind-ons_8,2039,61065,163.3
+wind-ons_7,2039,61067,102.9
+wind-ons_5,2039,61070,98.7
+wind-ons_5,2039,61071,1.7
+wind-ons_4,2039,61072,5.0
+wind-ons_4,2039,61073,8.0
+wind-ons_6,2039,61754,72.0
+wind-ons_6,2039,61755,3.3
+wind-ons_5,2039,61756,9.9
+wind-ons_6,2039,61757,9.9
+wind-ons_8,2039,61762,54.0
+wind-ons_7,2039,61763,48.3
+wind-ons_8,2039,62085,66.0
+wind-ons_8,2039,62432,100.8
+wind-ons_7,2039,62438,9.9
+wind-ons_6,2039,62440,10.0
+wind-ons_6,2039,62441,8.3
+wind-ons_6,2039,63125,6.6
+wind-ons_9,2039,64637,49.5
+wind-ons_10,2039,65302,49.5
+wind-ons_9,2039,65321,120.0
+wind-ons_8,2039,68253,4.5
+wind-ons_8,2039,68902,106.5
+wind-ons_9,2039,69589,97.5
+wind-ons_8,2039,79020,2.0
+wind-ons_8,2039,79704,2.0
+wind-ons_8,2039,82507,59.0
+wind-ons_8,2039,83096,25.0
+wind-ons_5,2039,84351,8.4
+wind-ons_5,2039,84352,18.9
+wind-ons_10,2039,84482,50.4
+wind-ons_9,2039,85169,49.5
+wind-ons_10,2039,85351,1.5
+wind-ons_9,2039,86519,51.0
+wind-ons_8,2039,87273,12.9
+wind-ons_9,2039,87274,13.5
+wind-ons_8,2039,87345,112.0
+wind-ons_9,2039,87929,100.5
+wind-ons_8,2039,87956,3.6
+wind-ons_9,2039,88030,126.0
+wind-ons_8,2039,88036,37.5
+wind-ons_9,2039,88719,87.5
+wind-ons_10,2039,89326,54.0
+wind-ons_10,2039,89888,39.0
+wind-ons_10,2039,90571,99.0
+wind-ons_10,2039,90572,99.0
+wind-ons_9,2039,90648,10.5
+wind-ons_9,2039,90654,10.5
+wind-ons_9,2039,90661,78.0
+wind-ons_9,2039,90662,80.0
+wind-ons_9,2039,90666,99.0
+wind-ons_9,2039,91253,17.0
+wind-ons_9,2039,91330,100.0
+wind-ons_9,2039,91340,66.0
+wind-ons_9,2039,91341,199.0
+wind-ons_8,2039,91348,0.5
+wind-ons_8,2039,92146,100.5
+wind-ons_9,2039,94734,8.91
+wind-ons_10,2039,95417,8.91
+wind-ons_10,2039,95418,54.27
+wind-ons_9,2039,95449,199.7
+wind-ons_8,2039,95454,0.9
+wind-ons_10,2039,96102,8.91
+wind-ons_8,2039,96157,100.5
+wind-ons_9,2039,97624,102.0
+wind-ons_10,2039,98184,150.0
+wind-ons_10,2039,98778,28.8
+wind-ons_9,2039,99462,99.0
+wind-ons_9,2039,100268,186.0
+wind-ons_9,2039,100269,31.5
+wind-ons_7,2039,101627,1.5
+wind-ons_8,2039,102313,1.5
+wind-ons_7,2039,102395,39.0
+wind-ons_10,2039,102396,36.0
+wind-ons_8,2039,103006,48.0
+wind-ons_7,2039,103007,10.5
+wind-ons_8,2039,103079,62.5
+wind-ons_9,2039,103573,42.0
+wind-ons_8,2039,103691,51.0
+wind-ons_8,2039,104373,1.5
+wind-ons_8,2039,104374,106.0
+wind-ons_10,2039,104447,52.5
+wind-ons_8,2039,105639,151.8
+wind-ons_8,2039,105640,22.5
+wind-ons_9,2039,106865,120.0
+wind-ons_8,2039,107121,106.0
+wind-ons_8,2039,107123,199.7
+wind-ons_10,2039,107550,30.0
+wind-ons_8,2039,107803,100.0
+wind-ons_8,2039,107804,200.0
+wind-ons_8,2039,107805,101.3
+wind-ons_8,2039,108489,99.0
+wind-ons_9,2039,109797,146.0
+wind-ons_8,2039,109841,100.5
+wind-ons_5,2039,115139,203.5
+wind-ons_10,2039,120721,148.5
+wind-ons_9,2039,122751,99.0
+wind-ons_2,2039,128086,21.0
+wind-ons_8,2039,128087,60.0
+wind-ons_1,2039,128769,54.0
+wind-ons_10,2039,130978,44.0
+wind-ons_10,2039,130979,50.0
+wind-ons_10,2039,136428,114.0
+wind-ons_10,2039,137126,101.2
+wind-ons_10,2039,139160,49.5
+wind-ons_10,2039,142582,79.5
+wind-ons_10,2039,142595,98.9
+wind-ons_5,2039,143197,63.0
+wind-ons_3,2039,143829,6.5
+wind-ons_8,2039,147335,100.0
+wind-ons_10,2039,147394,99.0
+wind-ons_9,2039,159707,120.0
+wind-ons_10,2039,161739,249.0
+wind-ons_10,2039,162424,197.0
+wind-ons_8,2039,163106,73.5
+wind-ons_9,2039,163107,75.0
+wind-ons_10,2039,163112,101.2
+wind-ons_9,2039,164458,0.92
+wind-ons_9,2039,165141,59.4
+wind-ons_9,2039,165142,91.58
+wind-ons_6,2039,165158,0.07
+wind-ons_7,2039,165159,0.07
+wind-ons_9,2039,165826,0.6
+wind-ons_10,2039,165840,231.5
+wind-ons_10,2039,166526,69.53
+wind-ons_10,2039,171998,150.0
+wind-ons_8,2039,193229,180.0
+wind-ons_9,2039,198700,201.6
diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_limited.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_limited.csv
index 81bb62304..21dcea72d 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_limited.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_limited.csv
@@ -1,48 +1,41 @@
 sc_point_gid,year,capacity
-94940,2016,29.3
-122272,2021,12.0
-85377,2024,65.0
-86061,2024,65.0
-86745,2024,64.03
-88114,2024,65.0
-88798,2024,0.97
-85379,2025,530.84
-86063,2025,236.47
-86064,2025,32.69
-86746,2025,357.5
-87430,2025,357.5
-85378,2026,179.41
-86062,2026,462.0
-86747,2026,264.97
-87431,2026,17.63
-92117,2026,3.4
-92118,2026,3.4
-92800,2026,3.4
-92801,2026,3.4
-92802,2026,3.4
-93485,2026,3.4
-112017,2026,476.38
-112018,2026,0.1
-112701,2026,346.42
-112702,2026,15.62
-113385,2026,120.0
-113386,2026,1.52
-114070,2026,5.96
-96303,2027,269.89
-96304,2027,372.18
-96305,2027,114.05
-96306,2027,18.47
-96987,2027,32.14
-96988,2027,3.27
-121588,2027,74.13
-121589,2027,160.09
-122273,2027,529.55
-122274,2027,42.83
-122957,2027,489.18
-122958,2027,16.98
-123641,2027,462.32
-123642,2027,11.08
-124325,2027,480.78
-124326,2027,0.39
-125009,2027,349.64
-125010,2027,23.04
+90132,2016,1.78
+90817,2016,12.34
+90818,2016,13.65
+91501,2016,1.52
+122945,2021,12.0
+88088,2024,11.44
+88772,2024,36.77
+89455,2024,65.0
+91509,2024,53.56
+92192,2024,28.23
+92193,2024,65.0
+89456,2025,459.85
+90138,2025,357.5
+90139,2025,357.5
+90140,2025,340.15
+90822,2026,10.51
+91504,2026,462.0
+91505,2026,451.49
+95531,2026,3.4
+96213,2026,3.4
+96214,2026,3.4
+96215,2026,3.4
+96897,2026,3.4
+96898,2026,3.4
+110637,2026,498.28
+111319,2026,5.57
+111320,2026,342.14
+111321,2026,120.0
+97644,2027,133.62
+97645,2027,146.42
+98328,2027,47.34
+98329,2027,225.63
+98330,2027,257.0
+121578,2027,186.98
+121579,2027,134.7
+122261,2027,474.04
+122262,2027,530.84
+122263,2027,530.84
+122946,2027,319.98
+122947,2027,462.61
diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_open.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_open.csv
index ae1e44540..87218c344 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_open.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_open.csv
@@ -1,38 +1,36 @@
 sc_point_gid,year,capacity
-95625,2016,29.3
-120920,2021,12.0
-87437,2024,23.85
-88121,2024,51.85
-89490,2024,65.0
-90174,2024,65.0
-90859,2024,41.15
-91543,2024,13.15
-86753,2025,296.75
-86754,2025,303.44
-86755,2025,145.99
-87439,2025,184.16
-88123,2025,166.41
-88806,2025,357.5
-90175,2025,60.75
-87438,2026,327.12
-88122,2026,367.46
-88807,2026,134.88
-89491,2026,94.54
-92118,2026,3.4
-92802,2026,3.4
-93486,2026,3.4
-93487,2026,3.4
-94170,2026,3.4
-94171,2026,3.4
-114769,2026,378.33
-115453,2026,300.32
-116137,2026,167.35
-116821,2026,120.0
-101794,2027,511.34
-102479,2027,294.81
-103163,2027,3.86
-120921,2027,530.84
-120922,2027,530.84
-120923,2027,530.84
-120924,2027,519.27
-121605,2027,528.2
+90816,2016,29.3
+122945,2021,12.0
+87401,2024,31.85
+88086,2024,65.0
+88771,2024,33.15
+89455,2024,65.0
+90139,2024,65.0
+87400,2025,267.59
+88085,2025,89.91
+89456,2025,528.96
+90138,2025,357.5
+90140,2025,271.04
+90822,2026,10.51
+91504,2026,462.0
+91505,2026,451.49
+96898,2026,3.4
+96899,2026,3.4
+97582,2026,3.4
+97583,2026,3.4
+97584,2026,3.4
+98267,2026,3.4
+110637,2026,305.6
+111319,2026,177.65
+111320,2026,362.75
+111321,2026,120.0
+97644,2027,173.53
+97645,2027,171.69
+98328,2027,57.12
+98329,2027,242.68
+98330,2027,164.98
+121578,2027,516.63
+122262,2027,530.84
+122263,2027,530.84
+122946,2027,530.84
+122947,2027,530.84
diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_reference.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_reference.csv
index 87b4e34c4..599e97851 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_reference.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ofs_radial_reference.csv
@@ -1,40 +1,39 @@
 sc_point_gid,year,capacity
-95625,2016,29.3
-120920,2021,12.0
-86752,2024,65.0
-88121,2024,51.85
-88805,2024,65.0
-90174,2024,65.0
-91543,2024,13.15
-86754,2025,386.35
-86755,2025,145.99
-87437,2025,298.05
-87439,2025,184.16
-88123,2025,83.49
-88806,2025,357.5
-89490,2025,18.3
-90859,2025,41.15
-86753,2026,34.55
-87438,2026,327.12
-88122,2026,272.16
-88807,2026,134.88
-89491,2026,94.54
-90175,2026,60.75
-92118,2026,3.4
-92802,2026,3.4
-93486,2026,3.4
-93487,2026,3.4
-94170,2026,3.4
-94171,2026,3.4
-114769,2026,378.33
-115453,2026,300.32
-116137,2026,167.35
-116821,2026,120.0
-101794,2027,511.34
-102479,2027,294.81
-103163,2027,3.86
-120921,2027,530.84
-120922,2027,530.84
-120923,2027,530.84
-120924,2027,519.27
-121605,2027,528.2
+90816,2016,29.3
+122945,2021,12.0
+88088,2024,11.44
+88771,2024,33.15
+88772,2024,31.85
+89455,2024,65.0
+91509,2024,53.56
+92192,2024,65.0
+88086,2025,50.32
+89456,2025,528.96
+90138,2025,357.5
+90139,2025,307.18
+90140,2025,271.04
+90822,2026,10.51
+91504,2026,462.0
+91505,2026,451.49
+96898,2026,3.4
+96899,2026,3.4
+97582,2026,3.4
+97583,2026,3.4
+97584,2026,3.4
+98267,2026,3.4
+110637,2026,305.6
+111319,2026,177.65
+111320,2026,362.75
+111321,2026,120.0
+97644,2027,173.53
+97645,2027,171.69
+98328,2027,57.12
+98329,2027,242.68
+98330,2027,164.98
+121578,2027,186.98
+121579,2027,134.7
+122261,2027,474.04
+122262,2027,530.84
+122263,2027,530.84
+122946,2027,319.98
+122947,2027,462.61
diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ons_limited.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ons_limited.csv
index 5bde5f453..b5bc53c0c 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ons_limited.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ons_limited.csv
@@ -1,2064 +1,1745 @@
 sc_point_gid,year,capacity
-55064,2010,19.5
-55065,2010,96.0
-56301,2010,90.0
-56432,2010,36.8
-60404,2010,100.7
-60579,2010,54.0
-60739,2010,66.0
-61087,2010,6.0
-61263,2010,146.0
-61771,2010,50.0
-61944,2010,28.8
-62790,2010,25.5
-63997,2010,60.0
-65361,2010,20.0
-65873,2010,9.0
-71480,2010,62.4
-73532,2010,6.4
-74763,2010,62.0
-74764,2010,131.0
-77014,2010,20.0
-78183,2010,30.0
-81643,2010,22.5
-81644,2010,62.25
-81693,2010,174.0
-82285,2010,98.7
-83282,2010,1.7
-83797,2010,99.0
-83798,2010,210.0
-83968,2010,3.0
-85745,2010,16.8
-87113,2010,21.0
-88532,2010,26.0
-89306,2010,60.0
-89784,2010,1.0
-91900,2010,21.0
-93398,2010,1.5
-93953,2010,62.25
-94003,2010,111.0
-96818,2010,1.5
-100213,2010,60.0
-100922,2010,1.5
-105066,2010,222.0
-105082,2010,60.0
-105750,2010,78.0
-105766,2010,45.7
-106962,2010,1.5
-107189,2010,36.0
-108501,2010,1.0
-108550,2010,34.0
-109131,2010,1.5
-109914,2010,30.0
-110598,2010,28.5
-111221,2010,200.0
-111283,2010,42.0
-113975,2010,103.5
-114659,2010,102.3
-115219,2010,51.0
-117137,2010,36.8
-124158,2010,12.5
-128262,2010,48.0
-131729,2010,48.0
-135103,2010,39.2
-135730,2010,96.0
-135787,2010,60.0
-137202,2010,54.0
-138570,2010,66.0
-141155,2010,32.55
-143937,2010,6.4
-145942,2010,32.55
-146566,2010,101.2
-148102,2010,151.8
-151519,2010,54.0
-152203,2010,45.2
-159024,2010,10.0
-159708,2010,10.0
-172721,2010,36.0
-174773,2010,48.0
-175457,2010,54.0
-176141,2010,42.0
-176142,2010,15.6
-183666,2010,54.0
-185034,2010,72.0
-185718,2010,60.0
-188452,2010,20.1
-189810,2010,42.0
-190494,2010,60.0
-191178,2010,48.0
-198017,2010,24.0
-203489,2010,48.0
-204173,2010,42.0
-204857,2010,36.0
-55617,2011,50.0
-58684,2011,20.0
-59200,2011,1.2
-59214,2011,54.0
-59884,2011,1.2
-59898,2011,147.3
-60568,2011,1.2
-60584,2011,205.5
-61772,2011,148.8
-61939,2011,4.0
-62107,2011,60.0
-62623,2011,4.0
-62624,2011,9.9
-62625,2011,25.64
-63309,2011,25.3
-63991,2011,2.5
-68235,2011,40.0
-69459,2011,0.5
-70827,2011,0.26
-71511,2011,0.1
-73688,2011,51.3
-74078,2011,3.0
-74221,2011,11.1
-75741,2011,74.0
-76360,2011,30.0
-77044,2011,60.0
-77728,2011,12.0
-78326,2011,66.0
-79756,2011,54.0
-80440,2011,108.0
-80958,2011,12.0
-81642,2011,12.0
-82327,2011,22.5
-82515,2011,30.0
-83009,2011,10.5
-83199,2011,48.0
-83277,2011,1.6
-83694,2011,21.0
-83883,2011,12.0
-83961,2011,1.5
-85021,2011,199.2
-85338,2011,15.0
-86366,2011,1.5
-86429,2011,19.5
-87112,2011,10.5
-87250,2011,16.0
-87934,2011,16.0
-87935,2011,44.0
-88613,2011,1.2
-89848,2011,13.5
-89849,2011,22.4
-89978,2011,1.6
-90533,2011,22.5
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+202797,2016,58.0
+203478,2016,72.0
+55712,2017,150.0
+66667,2017,99.3
+67560,2017,22.8
+72027,2017,10.0
+72711,2017,10.0
+72712,2017,10.0
 73395,2017,10.0
-77017,2017,42.0
-78411,2017,30.0
-79549,2017,10.0
-80233,2017,10.0
-81600,2017,10.0
-82284,2017,10.0
-86617,2017,32.9
-93434,2017,56.0
-94083,2017,1.8
-94092,2017,60.0
-94776,2017,66.0
-96107,2017,2.3
-96879,2017,41.6
-97504,2017,3.0
-98188,2017,3.0
-98872,2017,3.0
-99527,2017,2.3
-100895,2017,2.3
-100924,2017,3.0
-101608,2017,3.0
-102292,2017,3.0
-102976,2017,3.0
-105081,2017,28.0
-105712,2017,3.0
-106434,2017,216.0
-106470,2017,66.0
-107118,2017,62.0
-108448,2017,1.8
-108457,2017,18.0
-109132,2017,1.8
-109141,2017,26.0
-110554,2017,72.0
-111158,2017,29.7
-111664,2017,1.9
-111842,2017,60.0
-113206,2017,1.7
-113715,2017,1.9
-113952,2017,0.1
-115344,2017,119.7
-118691,2017,160.6
-119375,2017,120.0
-124161,2017,28.2
-124845,2017,150.0
-126213,2017,86.0
-128898,2017,75.0
-130318,2017,114.0
-131497,2017,46.0
-132311,2017,234.0
-132416,2017,48.0
-133100,2017,60.0
-133784,2017,42.0
-134469,2017,150.0
-137782,2017,181.7
-139834,2017,64.0
-139898,2017,23.3
-145305,2017,142.6
-146734,2017,144.0
-147418,2017,10.5
-147936,2017,143.0
-150842,2017,60.0
-151526,2017,192.0
-152210,2017,45.8
-152211,2017,162.0
-152887,2017,60.0
-152895,2017,54.0
-153571,2017,39.1
-154927,2017,155.4
-154947,2017,60.0
-156998,2017,132.0
-157682,2017,48.0
-158983,2017,29.9
-161102,2017,30.0
-165208,2017,42.0
-165870,2017,60.0
-166574,2017,40.0
-166576,2017,42.0
-167260,2017,60.0
-167917,2017,50.4
-167922,2017,66.0
-167944,2017,60.0
-168606,2017,25.2
-169996,2017,54.0
-172046,2017,45.0
-172048,2017,18.0
-172730,2017,204.0
-174783,2017,60.0
-175467,2017,84.0
-176147,2017,48.0
-176151,2017,66.0
-176831,2017,120.0
-177515,2017,36.0
-178199,2017,24.0
-178203,2017,36.0
-179571,2017,7.0
-186393,2017,162.0
-187761,2017,1.0
+73396,2017,10.0
+78415,2017,41.6
+79098,2017,32.9
+81229,2017,30.0
+83209,2017,74.5
+83800,2017,200.0
+83889,2017,48.0
+83890,2017,52.0
+94786,2017,98.0
+98865,2017,3.0
+98866,2017,3.0
+98867,2017,3.0
+99547,2017,170.0
+99552,2017,3.0
+99553,2017,3.0
+100237,2017,3.0
+100921,2017,3.0
+101575,2017,2.3
+101576,2017,2.3
+101609,2017,3.0
+102944,2017,2.3
+104330,2017,1.8
+105071,2017,46.0
+105753,2017,54.0
+105784,2017,66.0
+106364,2017,1.7
+107822,2017,119.7
+108436,2017,1.8
+108437,2017,1.8
+110483,2017,222.0
+111167,2017,60.0
+111168,2017,18.0
+111220,2017,0.1
+111826,2017,54.0
+111896,2017,120.0
+111897,2017,84.0
+112338,2017,46.0
+112513,2017,35.7
+112580,2017,60.0
+112581,2017,14.0
+119866,2017,1.9
+120546,2017,1.9
+126844,2017,75.0
+126865,2017,28.6
+127549,2017,192.0
+127550,2017,60.0
+128234,2017,54.0
+128913,2017,72.0
+129596,2017,10.2
+129601,2017,42.0
+130282,2017,78.0
+130284,2017,122.0
+133721,2017,154.5
+135776,2017,42.0
+136460,2017,126.0
+136461,2017,60.0
+137144,2017,60.0
+137145,2017,9.8
+138506,2017,41.3
+138507,2017,42.0
+139191,2017,60.0
+139874,2017,48.0
+139875,2017,48.0
+139876,2017,60.0
+141923,2017,3.1
+142607,2017,42.0
+143290,2017,54.0
+145971,2017,22.0
+146655,2017,276.0
+146672,2017,132.0
+147356,2017,10.6
+147357,2017,181.7
+147366,2017,163.0
+148046,2017,234.0
+148047,2017,42.0
+149302,2017,11.0
+149987,2017,132.0
+151476,2017,151.2
+152161,2017,50.4
+156247,2017,29.9
+156960,2017,210.0
+157644,2017,40.0
+159684,2017,155.4
+159685,2017,199.0
+160369,2017,54.0
+163810,2017,100.5
+180889,2017,149.3
+195279,2017,60.0
 195280,2017,108.0
-196648,2017,41.3
-199383,2017,100.5
-202803,2017,95.3
-59808,2018,2.7
-63990,2018,2.0
-67331,2018,2.7
-68060,2018,48.0
-72848,2018,100.0
-74373,2018,49.9
-74854,2018,1.6
-75057,2018,78.4
-75538,2018,2.7
-77590,2018,25.0
-77641,2018,20.0
-78274,2018,79.7
-78325,2018,20.0
-79776,2018,44.0
-87251,2018,44.6
-88742,2018,30.0
-92035,2018,90.0
-92721,2018,103.5
-92724,2018,120.0
-93408,2018,50.0
-93409,2018,200.0
-96144,2018,169.0
-96145,2018,96.0
-96791,2018,2.3
-96825,2018,60.0
-96829,2018,103.2
-96831,2018,240.0
-97475,2018,2.3
-97509,2018,40.0
-97515,2018,10.0
-98673,2018,1.8
-99531,2018,60.0
-100041,2018,2.8
-100211,2018,2.3
-100212,2018,30.0
-100215,2018,78.0
-102263,2018,2.5
-104378,2018,60.0
-105062,2018,59.0
-105102,2018,100.0
-105785,2018,1.5
-106198,2018,1.2
-106432,2018,185.0
-107138,2018,198.0
-107741,2018,36.0
-107822,2018,2.4
-108423,2018,64.5
-108425,2018,222.1
-109890,2018,1.5
-110433,2018,270.0
-110980,2018,1.8
-111906,2018,212.0
-113214,2018,60.0
-114679,2018,1.5
-115956,2018,54.0
-117136,2018,21.6
-117323,2018,186.0
-119185,2018,1.9
-120008,2018,54.3
-120692,2018,258.0
-120744,2018,102.0
-121428,2018,60.0
-122112,2018,54.0
-122796,2018,29.3
-124659,2018,42.0
-125531,2018,54.0
-126899,2018,3.5
-133738,2018,78.0
-134422,2018,120.6
-142569,2018,50.9
-145200,2018,128.7
-147420,2018,132.0
-148104,2018,115.3
-151521,2018,130.0
-154922,2018,44.0
-154926,2018,42.0
-156290,2018,36.0
-167244,2018,94.7
-167250,2018,3.0
-167928,2018,102.0
-167930,2018,20.0
-168604,2018,54.0
-168618,2018,30.0
-169288,2018,42.0
-169972,2018,52.4
-169998,2018,186.0
-170666,2018,30.0
-170682,2018,78.0
-171366,2018,36.0
-172034,2018,60.0
-172718,2018,36.0
-172722,2018,36.0
-173402,2018,54.0
-174090,2018,42.0
-174774,2018,54.0
-175458,2018,36.0
-176838,2018,186.0
-177522,2018,114.0
-180918,2018,54.0
-182286,2018,24.0
-182970,2018,30.0
-185020,2018,54.0
-185023,2018,60.0
-185707,2018,54.0
-187756,2018,60.0
-188440,2018,34.4
-188443,2018,46.0
-63140,2019,98.0
-63449,2019,4.8
-66009,2019,28.7
-66693,2019,78.0
-70799,2019,98.0
-71483,2019,102.0
-73713,2019,24.0
-74310,2019,18.0
-75448,2019,102.0
-79783,2019,60.0
-80378,2019,200.0
-80467,2019,35.3
-81146,2019,15.9
-81915,2019,2.3
-81916,2019,5.0
-82430,2019,2.1
-82599,2019,2.3
-83113,2019,97.4
-83114,2019,198.0
-85256,2019,48.0
-86619,2019,60.0
-87252,2019,60.0
+196648,2017,81.0
+202116,2017,35.3
+202118,2017,6.0
+202119,2017,222.0
+205534,2017,36.0
+205535,2017,24.0
+60513,2018,60.0
+60514,2018,40.0
+65238,2018,2.7
+65921,2018,2.7
+66606,2018,2.7
+68667,2018,1.6
+70030,2018,25.0
+70031,2018,79.7
+70120,2018,2.0
+71445,2018,48.0
+76418,2018,19.9
+77102,2018,60.0
+79152,2018,60.0
+79153,2018,18.4
+81068,2018,44.6
+82513,2018,44.0
+88569,2018,20.0
+88570,2018,20.0
+89979,2018,48.0
+90662,2018,25.2
+90663,2018,66.0
+90664,2018,60.0
+97475,2018,156.0
+97476,2018,144.0
+98160,2018,18.1
+98187,2018,90.0
+98835,2018,202.5
+99546,2018,170.0
+102278,2018,103.5
+102294,2018,200.0
+102315,2018,212.0
+102898,2018,30.0
+102977,2018,109.0
+103649,2018,28.0
+103650,2018,126.0
+103661,2018,60.0
+104334,2018,96.0
+104362,2018,54.0
+104410,2018,54.0
+104414,2018,42.0
+104994,2018,2.5
+105016,2018,100.0
+105047,2018,65.0
+106438,2018,200.4
+108413,2018,2.3
+108414,2018,2.3
+108517,2018,4.0
+109096,2018,2.3
+109885,2018,1.5
+110568,2018,1.5
+110569,2018,1.5
+113944,2018,17.0
+113945,2018,168.0
+116575,2018,252.0
+117259,2018,222.0
+117260,2018,108.3
+117809,2018,1.8
+117821,2018,2.8
+118493,2018,1.8
+122778,2018,71.3
+122779,2018,66.0
+122780,2018,102.0
+122781,2018,60.0
+127393,2018,48.0
+128924,2018,3.5
+129608,2018,240.0
+130122,2018,21.6
+130127,2018,2.7
+132860,2018,43.2
+133547,2018,78.0
+136458,2018,60.0
+136459,2018,37.3
+137140,2018,60.0
+137143,2018,54.0
+137811,2018,12.6
+137824,2018,36.0
+138495,2018,102.0
+138496,2018,84.0
+143976,2018,130.0
+148034,2018,50.9
+150677,2018,1.9
+151464,2018,60.0
+151465,2018,88.4
+154209,2018,54.0
+154882,2018,150.0
+154894,2018,26.0
+157628,2018,204.0
+158312,2018,96.0
+167203,2018,192.0
+167888,2018,4.7
+167912,2018,74.0
+168596,2018,126.0
+169956,2018,10.0
+170640,2018,150.0
+171309,2018,192.0
+171310,2018,108.0
+172004,2018,78.0
+172005,2018,70.4
+200067,2018,201.0
+62435,2019,200.0
+66663,2019,106.7
+70778,2019,200.0
+78496,2019,28.8
+79008,2019,200.1
+79684,2019,146.0
+79685,2019,54.0
+82598,2019,2.3
+83282,2019,5.0
+83798,2019,97.4
+83891,2019,15.9
+84574,2019,60.0
+85329,2019,2.3
+85852,2019,100.0
+87300,2019,144.0
+87301,2019,17.3
 87394,2019,3.0
-87936,2019,40.0
-89354,2019,48.0
-92040,2019,171.5
-92725,2019,20.0
-94209,2019,18.0
-94210,2019,72.0
-95462,2019,200.0
-96138,2019,54.0
-96148,2019,22.0
-96822,2019,12.0
-97511,2019,140.0
-97514,2019,180.0
-99569,2019,54.0
-100253,2019,150.0
-100254,2019,24.0
-100936,2019,168.0
-100937,2019,36.0
-101579,2019,2.5
-101620,2019,131.3
-102305,2019,54.0
-102989,2019,60.0
-104380,2019,24.0
-105064,2019,170.3
-105749,2019,145.2
-109791,2019,160.0
-113274,2019,60.0
-113903,2019,228.0
-114587,2019,47.6
-116587,2019,59.4
-121236,2019,1.8
-121425,2019,199.3
-138466,2019,220.5
-146047,2019,36.0
-148099,2019,54.0
-148619,2019,131.1
-148783,2019,10.0
-153556,2019,2.0
-154240,2019,18.0
-154254,2019,48.0
-154924,2019,42.0
-154938,2019,126.0
-155615,2019,42.0
-156292,2019,66.0
-156295,2019,60.0
-156299,2019,48.0
-156306,2019,26.2
-156979,2019,24.0
-156983,2019,54.0
-157657,2019,30.2
-159741,2019,60.0
-160425,2019,54.0
-161109,2019,60.0
-164527,2019,54.0
-165897,2019,36.0
-166579,2019,54.0
-167239,2019,42.0
-167265,2019,48.0
-167947,2019,42.0
-168631,2019,78.0
-168633,2019,36.0
-169295,2019,39.8
-169315,2019,48.0
-169317,2019,54.0
-169999,2019,26.4
-170001,2019,2.3
-172720,2019,30.0
-172725,2019,60.0
-173409,2019,54.0
-174093,2019,60.0
-174102,2019,60.0
-174779,2019,52.1
-174786,2019,54.0
-175461,2019,36.1
-175463,2019,174.0
-175464,2019,153.6
-175470,2019,150.0
-175471,2019,7.1
-176140,2019,54.0
-176148,2019,30.0
-176154,2019,36.0
-176155,2019,60.0
-176832,2019,54.0
-176839,2019,90.0
-177523,2019,54.0
-178890,2019,54.0
-179574,2019,54.0
-180258,2019,84.0
-180942,2019,48.0
-181612,2019,36.0
-181626,2019,60.0
-181628,2019,96.0
-182296,2019,66.0
-183661,2019,144.0
-184345,2019,55.5
-187767,2019,14.0
-188445,2019,30.9
-189127,2019,32.0
-189129,2019,132.0
-200751,2019,99.4
-46729,2020,60.0
-55618,2020,144.0
-58353,2020,76.8
-59854,2020,60.0
-60055,2020,72.6
-60538,2020,36.0
-60571,2020,4.6
-62628,2020,127.5
-62630,2020,60.0
-63228,2020,80.0
-63312,2020,122.5
-63314,2020,140.0
-63822,2020,10.0
-63823,2020,100.0
-65326,2020,24.0
-66011,2020,1.4
-66559,2020,74.0
-66695,2020,138.0
-67379,2020,60.0
-68746,2020,102.0
-69430,2020,48.0
-69976,2020,10.0
-70205,2020,60.0
-72711,2020,10.0
-73533,2020,30.0
-76130,2020,10.0
-77731,2020,60.0
-78414,2020,42.0
-78415,2020,48.0
-79010,2020,192.2
-79098,2020,48.0
-79099,2020,6.6
-79782,2020,40.0
-80325,2020,133.0
-81009,2020,200.0
-82286,2020,126.0
-84481,2020,103.5
-84482,2020,220.0
-85166,2020,64.2
-85941,2020,54.0
+89353,2019,48.0
+89983,2019,66.0
+92013,2019,71.3
+92014,2019,228.0
+92140,2019,48.0
+92824,2019,42.0
+94063,2019,200.0
+97472,2019,22.0
+98155,2019,138.0
+98169,2019,200.0
+100211,2019,2.5
+100948,2019,14.3
+101610,2019,140.0
+101631,2019,180.0
+102295,2019,171.5
+105017,2019,216.0
+105701,2019,184.0
+106425,2019,55.2
+107109,2019,36.0
+107110,2019,60.0
+107111,2019,54.0
+117124,2019,1.8
+117279,2019,53.6
+117280,2019,222.0
+126146,2019,59.4
+126899,2019,156.0
+126900,2019,43.3
+130128,2019,54.0
+132863,2019,77.1
+143943,2019,42.1
+143944,2019,168.0
+146673,2019,220.5
+149472,2019,100.0
+150805,2019,99.4
+151489,2019,198.0
+152173,2019,2.2
+152175,2019,42.0
+152840,2019,128.0
+152859,2019,141.8
+153539,2019,104.3
+153540,2019,246.0
+155596,2019,30.2
+159683,2019,158.0
+170626,2019,92.4
+170627,2019,162.0
+171311,2019,48.0
+171998,2019,19.5
+171999,2019,150.0
+172000,2019,30.0
+174728,2019,240.0
+175412,2019,60.0
+191173,2019,60.0
+191174,2019,60.0
+191175,2019,13.1
+191179,2019,102.9
+191858,2019,120.0
+191859,2019,54.0
+191864,2019,60.0
+195269,2019,252.0
+195270,2019,48.0
+196646,2019,76.1
+196647,2019,150.0
+201422,2019,3.6
+201423,2019,234.0
+202109,2019,200.0
+50794,2020,136.8
+54346,2020,96.0
+54347,2020,103.4
+54925,2020,144.0
+59765,2020,80.0
+61227,2020,4.6
+62439,2020,60.0
+62440,2020,34.0
+63122,2020,66.0
+63123,2020,140.0
+63470,2020,72.6
+63807,2020,10.0
+63809,2020,10.0
+64491,2020,10.0
+64492,2020,10.0
+72837,2020,150.0
+72838,2020,150.0
+80332,2020,103.5
+80375,2020,192.2
+83115,2020,200.0
+83776,2020,234.0
+83777,2020,16.2
+85940,2020,40.0
 86624,2020,90.0
-87248,2020,2.5
-87249,2020,5.0
-87848,2020,198.8
-89983,2020,78.0
-89985,2020,68.0
-89990,2020,60.0
-90663,2020,1.8
-90665,2020,24.0
-90667,2020,12.0
-90670,2020,130.9
-92690,2020,186.0
-92726,2020,141.3
-93411,2020,96.0
-94777,2020,199.8
-94779,2020,48.0
-95463,2020,106.0
-96055,2020,106.9
-96147,2020,208.0
-96738,2020,19.95
-97512,2020,152.2
-98106,2020,32.55
-98874,2020,60.0
-100158,2020,79.0
-100899,2020,2.0
-101526,2020,124.0
-101581,2020,66.0
-102350,2020,42.0
-103034,2020,54.0
-104261,2020,23.0
-104262,2020,144.0
-105747,2020,96.0
-105767,2020,60.0
-107081,2020,7.9
-107082,2020,18.0
-107799,2020,114.0
-107801,2020,178.0
-107802,2020,240.0
-108454,2020,42.0
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-109108,2020,228.0
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-112526,2020,45.0
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+87906,2020,127.5
+88590,2020,122.5
+88668,2020,150.0
+88670,2020,168.6
+89292,2020,2.5
+89957,2020,5.0
+90574,2020,133.0
+90643,2020,1.8
+90656,2020,72.0
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+91260,2020,198.8
+91339,2020,127.8
+92019,2020,90.0
+92020,2020,250.0
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+93367,2020,220.0
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+95448,2020,15.3
+97407,2020,250.9
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+101612,2020,138.0
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+104256,2020,58.0
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+107139,2020,5.1
+107750,2020,2.0
+107795,2020,88.0
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+107821,2020,142.6
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+108480,2020,48.0
+108481,2020,60.0
+108488,2020,170.6
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+109841,2020,60.0
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+111853,2020,132.0
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 113272,2020,54.0
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+113946,2020,28.0
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+118633,2020,72.0
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+118639,2020,168.0
 118640,2020,84.0
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-148787,2020,54.0
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-152896,2020,90.0
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 80529,2027,2.4
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-79642,2029,64.2
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-81010,2029,216.0
-86481,2029,192.0
-87165,2029,180.0
-87849,2029,228.0
-94004,2029,258.0
-94688,2029,246.0
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diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ons_open.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ons_open.csv
index 7850e4bd2..d64658232 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ons_open.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ons_open.csv
@@ -1,1316 +1,1230 @@
 sc_point_gid,year,capacity
-50148,2010,6.0
-50832,2010,90.0
-54251,2010,100.7
-56632,2010,25.5
-60579,2010,66.0
-61263,2010,82.8
-61265,2010,200.0
-62108,2010,66.0
-62456,2010,50.0
-62627,2010,20.0
-63275,2010,102.4
-64509,2010,161.0
-64642,2010,19.5
-66010,2010,36.8
-68611,2010,98.7
-73534,2010,62.4
-80961,2010,62.25
-81062,2010,99.0
-81644,2010,16.8
-81807,2010,20.0
-83654,2010,62.0
-83745,2010,200.0
-84664,2010,3.0
-85021,2010,9.0
-86032,2010,1.7
-87115,2010,21.0
-87799,2010,22.5
-88586,2010,210.0
-89988,2010,1.5
-91218,2010,21.0
-93954,2010,62.25
-96739,2010,111.0
-100981,2010,102.3
-102985,2010,1.5
-103014,2010,258.0
-103669,2010,1.5
-103698,2010,42.0
-105037,2010,1.5
-106454,2010,103.5
-106962,2010,1.5
-107874,2010,100.5
-109170,2010,200.0
-112684,2010,70.0
-113213,2010,60.0
-113979,2010,1.0
-117400,2010,105.7
-120060,2010,12.5
-122743,2010,51.0
-131689,2010,48.0
-133742,2010,99.2
-134426,2010,99.2
-134953,2010,23.2
-135152,2010,168.0
-135634,2010,36.8
-135637,2010,78.0
-138364,2010,1.0
-138466,2010,102.4
-138531,2010,151.8
-141155,2010,32.55
-151413,2010,32.55
-172047,2010,10.0
-172731,2010,10.0
-175471,2010,200.1
-178207,2010,201.6
-181625,2010,150.0
-182309,2010,150.0
-54935,2011,148.8
-56302,2011,50.0
-59369,2011,20.0
-59891,2011,25.3
-60583,2011,201.3
-60584,2011,205.5
-60739,2011,60.0
-61258,2011,9.9
-61939,2011,1.2
-61941,2011,2.5
-62623,2011,1.2
-63307,2011,1.2
-63995,2011,16.0
-64679,2011,16.0
-64680,2011,44.0
-65193,2011,199.2
-65498,2011,40.0
-70275,2011,66.0
-71514,2011,0.5
-76338,2011,102.0
-77022,2011,60.0
-77541,2011,12.0
-78909,2011,21.0
-79593,2011,22.5
-80557,2011,1.5
-80918,2011,3.0
-80960,2011,10.5
-81907,2011,8.0
-83012,2011,21.3
-83142,2011,0.26
-83209,2011,90.0
-83696,2011,22.5
-83826,2011,0.1
-83979,2011,1.6
-84663,2011,1.5
-85063,2011,10.5
-85328,2011,51.3
-85347,2011,1.5
-85352,2011,15.0
-85747,2011,13.5
-85748,2011,79.2
-86431,2011,19.5
-86564,2011,1.2
-87218,2011,159.3
-87935,2011,4.0
-88619,2011,4.0
-89305,2011,25.64
-89850,2011,12.0
-89990,2011,36.72
-90735,2011,102.0
-91902,2011,22.4
-93373,2011,1.6
-94057,2011,11.1
-94096,2011,100.0
-94210,2011,3.2
-95579,2011,38.0
-96908,2011,50.0
-98206,2011,222.0
-98881,2011,1.6
-98890,2011,221.2
-98960,2011,99.0
-99566,2011,4.08
-99569,2011,119.6
-100933,2011,1.6
-101013,2011,252.0
-101617,2011,1.6
-102268,2011,1.5
-102301,2011,1.5
-102952,2011,1.5
-103697,2011,150.4
-104353,2011,1.5
-104355,2011,33.7
-105066,2011,239.4
-106373,2011,40.5
-107742,2011,79.9
-109241,2011,52.9
-109242,2011,97.6
-112683,2011,10.0
-114052,2011,40.0
-114536,2011,124.15
-115220,2011,124.15
-118747,2011,150.4
-119430,2011,1.5
-119431,2011,150.0
-126847,2011,123.48
-127478,2011,102.0
-127531,2011,128.52
-129476,2011,2.0
-133578,2011,1.5
-135636,2011,78.2
-136321,2011,60.0
-137691,2011,84.0
-140428,2011,72.0
-141796,2011,54.0
-143151,2011,1.0
-144529,2011,66.0
-145213,2011,150.0
-145897,2011,150.0
-145942,2011,10.0
-146055,2011,130.0
-147946,2011,102.5
-148627,2011,49.5
-149997,2011,42.0
-150685,2011,42.0
-152211,2011,100.8
-154947,2011,99.8
-156998,2011,2.1
-163155,2011,44.09
-164455,2011,50.4
-168628,2011,81.31
-173415,2011,10.0
-174099,2011,9.9
-174100,2011,78.2
-174783,2011,6.6
-176153,2011,121.97
-178204,2011,66.13
-45362,2012,105.3
-55021,2012,40.0
-56389,2012,184.6
-59037,2012,4.3
-59038,2012,138.7
-59214,2012,114.0
-59898,2012,86.0
-60054,2012,50.6
-60738,2012,34.5
-61176,2012,10.0
-62457,2012,204.0
-64643,2012,105.0
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-65195,2012,98.0
-65878,2012,74.0
-65879,2012,192.0
-66011,2012,105.0
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-66183,2012,65.0
-66562,2012,216.0
-68062,2012,45.0
-68613,2012,66.0
-68699,2012,10.0
-74398,2012,10.4
-76222,2012,9.6
-77136,2012,28.6
-77137,2012,60.0
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-77820,2012,48.0
-78386,2012,2.4
-79873,2012,1.5
-80277,2012,23.0
-81645,2012,115.8
-83275,2012,15.0
-84380,2012,23.0
-84578,2012,120.0
-84665,2012,3.0
-84669,2012,28.5
-85064,2012,39.9
-85348,2012,2.0
-85946,2012,110.4
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-86034,2012,4.0
-86630,2012,100.8
-87253,2012,36.0
-87312,2012,14.0
-88483,2012,23.0
-88621,2012,30.0
-88682,2012,81.6
-88764,2012,1.5
-89167,2012,23.0
-90049,2012,32.0
-90105,2012,30.0
-90733,2012,28.0
-90734,2012,64.0
-91356,2012,0.9
-91358,2012,30.6
-91418,2012,59.4
-91474,2012,50.4
-92159,2012,75.0
-92170,2012,4.1
-92586,2012,23.0
-92845,2012,139.4
-93270,2012,23.0
-93527,2012,70.4
-93545,2012,215.3
-94093,2012,1.6
-94777,2012,1.6
-94895,2012,48.2
-95461,2012,1.6
-96145,2012,1.6
-96148,2012,99.0
-96264,2012,70.4
-96829,2012,1.6
-96830,2012,5.9
-97513,2012,1.6
-97514,2012,5.5
-98197,2012,1.6
-99008,2012,0.1
-99565,2012,1.6
-99571,2012,200.0
-99685,2012,69.0
-100249,2012,1.6
-100250,2012,3.4
-100251,2012,44.5
-102380,2012,1.0
-102822,2012,150.0
-102989,2012,105.6
-103672,2012,80.0
-104321,2012,64.0
-106405,2012,0.7
-106409,2012,100.9
-107741,2012,32.04
-108486,2012,200.0
-109109,2012,15.9
-109142,2012,1.6
-109826,2012,1.6
-109854,2012,192.3
-111845,2012,7.96
-113168,2012,71.0
-113274,2012,109.5
-115326,2012,81.0
-116011,2012,200.0
-116032,2012,200.0
-117325,2012,108.0
-117956,2012,200.0
-120745,2012,104.0
-121375,2012,14.5
-122799,2012,288.0
-123482,2012,201.0
-123483,2012,182.4
-125533,2012,98.9
-126218,2012,200.0
-126901,2012,83.95
-127585,2012,83.95
-128953,2012,65.5
-129637,2012,65.5
-130267,2012,200.0
-131634,2012,14.5
-133058,2012,165.6
-134952,2012,24.2
-134954,2012,54.0
-136368,2012,99.2
-138376,2012,48.0
-139732,2012,1.0
-139744,2012,60.0
-139898,2012,80.0
-140416,2012,1.5
-141100,2012,1.6
-141113,2012,138.0
-141268,2012,270.0
-141797,2012,25.4
-141798,2012,240.0
-141951,2012,148.8
-141952,2012,20.76
-142474,2012,54.0
-142479,2012,42.0
-142480,2012,51.0
-142634,2012,59.8
-143163,2012,108.0
-143835,2012,1.6
-143846,2012,36.0
-144002,2012,40.0
-144530,2012,78.0
-145211,2012,84.0
-145212,2012,132.0
-145371,2012,132.0
-145888,2012,6.0
-145895,2012,26.0
-147254,2012,1.6
-147264,2012,140.0
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-147943,2012,49.0
-148106,2012,2.94
-148621,2012,1.0
-148630,2012,102.5
-149305,2012,1.0
-149314,2012,36.0
-149476,2012,235.2
-149989,2012,1.5
-149998,2012,48.0
-150001,2012,72.0
-150160,2012,227.5
-150681,2012,48.0
-150684,2012,114.0
-151365,2012,54.0
-151368,2012,72.0
-151527,2012,100.8
-153512,2012,27.3
-156315,2012,61.2
-159053,2012,150.0
-164523,2012,30.0
-165207,2012,20.0
-171365,2012,135.4
-172048,2012,79.6
-172732,2012,99.8
-172734,2012,161.0
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-174787,2012,200.0
-180256,2012,92.3
-180259,2012,201.6
-180943,2012,203.0
-83960,2013,94.4
-87314,2013,100.3
-89366,2013,74.8
-99567,2013,44.6
-99644,2013,3.4
-111846,2013,74.8
-120007,2013,2.0
-120062,2013,249.8
-122059,2013,25.1
-129582,2013,2.0
-130266,2013,2.0
-130950,2013,2.0
-163157,2013,150.0
-52884,2014,266.8
-53653,2014,10.0
-58000,2014,8.5
-60492,2014,9.7
-62628,2014,50.0
-84577,2014,74.8
-86628,2014,20.0
-86629,2014,62.4
-87217,2014,19.5
-87997,2014,50.4
-88681,2014,49.6
-90051,2014,111.0
-94223,2014,16.2
-96833,2014,138.6
-98204,2014,250.0
-100253,2014,119.6
-100900,2014,1.9
-104402,2014,200.0
-105690,2014,186.0
-106374,2014,14.6
-110432,2014,28.6
-111800,2014,34.0
-112530,2014,73.1
-113368,2014,40.0
-118640,2014,200.6
-135111,2014,199.0
-136414,2014,9.9
-138530,2014,98.6
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-159738,2014,181.7
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-163161,2014,252.0
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-165895,2014,200.0
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-58685,2015,25.7
-61424,2015,148.0
-61948,2015,195.8
-65327,2015,147.9
-69431,2015,107.5
-72852,2015,201.8
-79010,2015,80.0
-79694,2015,94.3
-87393,2015,5.0
-94094,2015,16.4
-94780,2015,100.0
-96831,2015,58.0
-98198,2015,4.7
-98882,2015,4.7
-100937,2015,115.0
-101620,2015,89.1
-102304,2015,89.0
-102987,2015,39.0
-102988,2015,85.8
-105723,2015,20.7
-106406,2015,1.9
-111162,2015,73.4
-112529,2015,6.8
-114736,2015,30.0
-118063,2015,175.0
-122113,2015,150.0
-122167,2015,98.0
-124850,2015,200.5
-128215,2015,150.0
-130321,2015,50.7
-130322,2015,198.6
-130951,2015,250.0
-137164,2015,282.0
-137846,2015,98.8
-137848,2015,17.0
-139899,2015,150.4
-141266,2015,74.0
-146577,2015,37.2
-147941,2015,19.8
-147945,2015,85.9
-149313,2015,99.0
-149990,2015,2.5
-150680,2015,42.0
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-153579,2015,100.0
-153581,2015,71.7
-154948,2015,200.0
-156314,2015,2.1
-156318,2015,194.0
-157001,2015,150.0
-158984,2015,250.0
-159052,2015,110.0
-159055,2015,207.2
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-159739,2015,204.0
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-161671,2015,30.0
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-164525,2015,150.0
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-169316,2015,211.2
-172735,2015,200.0
-175467,2015,3.8
-179572,2015,78.0
-179576,2015,39.7
-180260,2015,210.0
-180942,2015,165.0
-182308,2015,62.0
-182992,2015,48.0
-54337,2016,25.0
-60053,2016,9.1
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-83208,2016,50.0
+49418,2010,6.0
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+53547,2010,90.0
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+87202,2011,159.3
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+87904,2011,9.9
+88462,2011,19.5
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+89279,2011,205.5
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+91345,2011,1.6
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+94035,2011,1.5
+95462,2011,36.72
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+96146,2011,4.08
+98853,2011,100.0
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+106407,2011,1.5
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+107118,2011,150.4
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+108476,2011,150.0
+108483,2011,150.4
+109235,2011,52.9
+109601,2011,78.2
+109915,2011,10.0
+110600,2011,40.0
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+112568,2011,1.5
+113333,2011,97.6
+114455,2011,102.0
+115209,2011,123.48
+115892,2011,128.52
+131507,2011,150.0
+131508,2011,150.0
+132192,2011,168.0
+132875,2011,150.0
+133561,2011,102.0
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+139036,2011,1.0
+140525,2011,2.1
+140550,2011,130.0
+141095,2011,49.5
+143261,2011,78.2
+144661,2011,100.8
+146709,2011,99.8
+154894,2011,10.0
+156967,2011,66.13
+156968,2011,121.97
+157652,2011,9.9
+158948,2011,50.4
+172667,2011,6.6
+173351,2011,81.31
+173352,2011,44.09
+48814,2012,184.6
+51491,2012,4.3
+54933,2012,105.3
+58349,2012,282.0
+59033,2012,60.7
+59081,2012,40.0
+61068,2012,270.0
+61069,2012,265.0
+61752,2012,20.0
+62436,2012,282.0
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+63469,2012,34.5
+65922,2012,9.6
+65980,2012,105.0
+65981,2012,45.0
+66665,2012,105.0
+66869,2012,10.4
+66874,2012,50.6
+67295,2012,10.0
+67296,2012,10.0
+67553,2012,88.6
+68229,2012,65.0
+68903,2012,215.3
+69593,2012,10.0
+72238,2012,28.0
+73708,2012,48.0
+79874,2012,4.0
+82594,2012,28.5
+82611,2012,1.5
+82981,2012,23.0
+82982,2012,23.0
+83206,2012,59.4
+83209,2012,64.0
+83665,2012,23.0
+83666,2012,23.0
+83691,2012,115.8
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+84349,2012,23.0
+84350,2012,23.0
+84572,2012,14.0
+84578,2012,32.0
+84664,2012,1.5
+85036,2012,39.9
+85236,2012,100.8
+85939,2012,120.0
+86032,2012,3.0
+87302,2012,110.4
+87395,2012,1.5
+87903,2012,200.0
+87941,2012,2.4
+87942,2012,2.4
+87986,2012,81.6
+88026,2012,15.0
+89275,2012,30.0
+89286,2012,36.0
+90087,2012,4.1
+92721,2012,3.4
+93405,2012,30.6
+93520,2012,70.4
+94197,2012,69.0
+94204,2012,70.4
+94735,2012,7.96
+94770,2012,0.9
+95419,2012,32.04
+96123,2012,80.0
+96132,2012,99.0
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+97585,2012,1.0
+98178,2012,1.6
+98861,2012,1.6
+98862,2012,1.6
+98863,2012,1.6
+98872,2012,105.6
+99544,2012,1.6
+100229,2012,5.5
+100230,2012,44.5
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+100251,2012,5.9
+100345,2012,48.2
+100951,2012,109.5
+101711,2012,30.0
+102928,2012,15.9
+102963,2012,200.0
+102965,2012,1.6
+103612,2012,64.0
+103648,2012,100.9
+103679,2012,81.0
+103763,2012,75.0
+104130,2012,1.0
+104329,2012,1.6
+104332,2012,1.6
+104333,2012,1.6
+104364,2012,200.0
+105015,2012,1.6
+105018,2012,1.6
+106424,2012,192.3
+106864,2012,102.5
+106866,2012,38.0
+107008,2012,71.0
+107183,2012,139.4
+107548,2012,36.0
+107861,2012,50.4
+108234,2012,54.0
+108499,2012,200.0
+108917,2012,78.2
+110341,2012,150.0
+110537,2012,200.0
+115893,2012,200.0
+116577,2012,200.0
+120720,2012,201.0
+125461,2012,14.5
+126145,2012,14.5
+126176,2012,65.5
+126860,2012,65.5
+126861,2012,165.6
+126864,2012,83.95
+127548,2012,108.0
+128233,2012,83.95
+128913,2012,98.9
+129603,2012,104.0
+130141,2012,162.0
+130296,2012,206.4
+130310,2012,200.0
+130980,2012,264.0
+131506,2012,120.0
+131509,2012,132.0
+132191,2012,150.0
+132193,2012,168.0
+132874,2012,189.0
+132876,2012,150.0
+133558,2012,140.0
+133560,2012,102.0
+134406,2012,59.8
+135059,2012,40.0
+135742,2012,20.0
+135744,2012,80.0
+135772,2012,235.2
+136302,2012,1.6
+136303,2012,1.6
+136304,2012,1.5
+136343,2012,99.2
+136988,2012,1.5
+139184,2012,227.5
+139724,2012,1.6
+141090,2012,1.0
+141774,2012,1.0
+141926,2012,2.94
+142459,2012,1.6
+142463,2012,49.0
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+142609,2012,14.76
+142610,2012,276.0
+143149,2012,6.0
+143266,2012,79.6
+143945,2012,161.0
+143977,2012,100.8
+145338,2012,148.8
+149988,2012,265.4
+150807,2012,135.4
+156285,2012,161.0
+158299,2012,27.3
+158338,2012,150.0
+158995,2012,61.2
+159005,2012,30.0
+184314,2012,99.8
+195951,2012,92.3
+202802,2012,203.0
+203486,2012,201.6
+204171,2012,200.0
+84574,2013,74.8
+85940,2013,100.3
+87344,2013,94.4
+98873,2013,44.6
+106461,2013,3.4
+107009,2013,25.1
+113206,2013,74.8
+125458,2013,2.0
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+126142,2013,2.0
+126143,2013,2.0
+128223,2013,249.8
+170649,2013,150.0
+55653,2014,10.0
+59031,2014,266.8
+65923,2014,9.7
+66876,2014,8.5
+68752,2014,50.0
+80369,2014,19.5
+83890,2014,62.4
+83894,2014,20.0
+84573,2014,74.8
+84575,2014,49.6
+85256,2014,111.0
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+94029,2014,1.9
+97494,2014,250.0
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+100202,2014,200.6
+104297,2014,73.1
+105019,2014,119.6
+106322,2014,34.0
+106323,2014,28.6
+108550,2014,40.0
+109874,2014,200.0
+115894,2014,200.6
+117808,2014,1.0
+119860,2014,1.0
+130823,2014,90.0
+132877,2014,138.0
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+137799,2014,264.0
+138498,2014,98.6
+139182,2014,199.0
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+141906,2014,282.0
+142583,2014,181.7
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+146671,2014,9.9
+146681,2014,200.0
+147356,2014,9.9
+150139,2014,150.0
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+156969,2014,67.46
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+157610,2014,10.0
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+159680,2014,211.0
+198015,2014,188.0
+53682,2015,147.9
+54574,2015,148.0
+66664,2015,201.8
+66875,2015,25.7
+72129,2015,107.5
+74880,2015,94.3
+87383,2015,5.0
+87928,2015,195.8
+91995,2015,80.0
+92695,2015,89.1
+92696,2015,115.0
+92697,2015,85.8
+93379,2015,89.0
+93380,2015,100.0
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+100201,2015,73.4
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+103649,2015,1.9
+105063,2015,175.0
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+109169,2015,98.0
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+116588,2015,150.0
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+119344,2015,200.5
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+131510,2015,79.2
+132194,2015,99.0
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+135743,2015,200.0
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+135748,2015,282.0
+136456,2015,98.8
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+143257,2015,194.0
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+149424,2015,150.0
+150108,2015,200.0
+150824,2015,100.0
+152142,2015,250.0
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+154909,2015,150.0
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+161047,2015,211.2
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+197319,2015,249.7
+198004,2015,78.0
+202792,2015,200.0
+202795,2015,110.0
+204856,2015,165.0
+56337,2016,25.0
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+62447,2016,10.0
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+62786,2016,51.0
+65316,2016,190.2
+67345,2016,104.0
+68024,2016,150.0
+68708,2016,150.0
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+69585,2016,77.7
+75600,2016,78.0
+83207,2016,50.0
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+84665,2016,7.9
 85341,2016,1.5
-85342,2016,1.5
-85351,2016,7.9
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 86025,2016,1.5
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-86710,2016,1.5
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-87848,2016,80.0
-88078,2016,1.5
+86709,2016,1.5
+87394,2016,1.5
+87397,2016,1.5
+87913,2016,200.0
+88079,2016,1.5
+88080,2016,1.5
 88762,2016,1.5
 88763,2016,1.5
-92158,2016,38.3
-92727,2016,79.7
-93409,2016,1.7
-96147,2016,61.9
-98276,2016,100.8
-98883,2016,48.3
-98885,2016,119.6
-99568,2016,92.8
-102175,2016,1.7
-102305,2016,108.7
-102986,2016,3.0
-103013,2016,184.0
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-105722,2016,3.0
-107057,2016,35.8
-108460,2016,60.0
-109792,2016,1.7
-109927,2016,103.0
-111243,2016,150.0
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-113218,2016,246.0
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-122114,2016,208.3
-123481,2016,100.0
-124111,2016,60.8
-124165,2016,100.0
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-127732,2016,112.0
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-129785,2016,42.0
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-133743,2016,200.0
-134468,2016,195.8
-136479,2016,198.0
-138532,2016,264.0
-139216,2016,34.0
-140583,2016,150.0
-141950,2016,64.8
-144003,2016,147.0
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-147424,2016,3.9
-149475,2016,108.0
-151528,2016,225.0
-154196,2016,50.0
-157005,2016,108.0
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-164528,2016,230.0
-165211,2016,200.0
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-179573,2016,120.0
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-181628,2016,192.0
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-61907,2017,99.3
-68063,2017,150.0
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-76132,2017,10.0
-78389,2017,98.0
-82286,2017,10.0
-82970,2017,10.0
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-85260,2017,32.9
-85261,2017,74.5
-85705,2017,10.0
-87998,2017,100.0
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-88440,2017,10.0
-89989,2017,3.0
-90673,2017,3.0
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-103670,2017,3.0
-104320,2017,2.3
-104354,2017,3.0
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-105038,2017,3.0
-107090,2017,1.8
-107138,2017,100.0
-107774,2017,1.8
-108458,2017,1.8
-109108,2017,1.7
-110478,2017,89.7
-111907,2017,26.0
-111927,2017,119.7
-112589,2017,0.1
-118748,2017,252.0
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-127586,2017,200.0
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-132375,2017,276.0
-132895,2017,1.9
-132995,2017,22.0
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-133101,2017,252.0
-133785,2017,48.0
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-137847,2017,154.5
-139049,2017,1.9
-139150,2017,142.6
-139900,2017,258.0
-140584,2017,39.8
-141202,2017,276.0
-143840,2017,46.0
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-150683,2017,143.0
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-156317,2017,151.2
-157688,2017,228.0
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-167945,2017,149.3
-168633,2017,258.0
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-174104,2017,228.0
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-176841,2017,210.0
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-180940,2017,29.3
-181624,2017,66.0
-59808,2018,2.7
-62591,2018,100.0
-63996,2018,44.6
-66729,2018,2.0
-67331,2018,1.6
-68015,2018,2.7
-70067,2018,25.0
-70114,2018,48.0
-75538,2018,2.7
-78274,2018,79.7
-78486,2018,79.9
-84644,2018,78.4
-89365,2018,44.0
-94741,2018,20.0
-96109,2018,20.0
-96150,2018,170.0
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-97520,2018,250.0
-98202,2018,169.0
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-100936,2018,90.0
-101623,2018,199.2
-103065,2018,100.0
-105117,2018,1.5
-105725,2018,103.5
-105749,2018,119.0
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-105801,2018,1.5
-107093,2018,100.0
-107740,2018,2.5
-108425,2018,30.0
-110433,2018,276.0
-110476,2018,2.3
-111160,2018,2.3
-111164,2018,252.0
-111844,2018,2.3
-111848,2018,66.1
-113215,2018,202.5
-114664,2018,200.4
-117379,2018,185.0
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-120692,2018,264.0
-120746,2018,180.0
-121430,2018,63.5
-128271,2018,264.0
-128792,2018,1.8
-128955,2018,35.3
-132898,2018,21.6
-135795,2018,198.6
-138365,2018,1.8
-142468,2018,1.9
-143844,2018,42.0
-146571,2018,2.8
-146575,2018,43.2
-146739,2018,130.0
-148792,2018,247.3
-150682,2018,26.7
-151366,2018,60.0
-154880,2018,50.9
-158369,2018,150.0
-158373,2018,120.0
-164527,2018,200.0
-167261,2018,148.4
-168629,2018,148.4
-170680,2018,80.0
-170683,2018,196.7
-174786,2018,160.0
-174790,2018,240.0
-175474,2018,60.0
-177523,2018,201.0
-178894,2018,180.0
-63313,2019,100.0
-65877,2019,186.0
-66695,2019,106.7
-68612,2019,14.0
-72168,2019,200.0
-75767,2019,28.8
-79874,2019,2.3
-80378,2019,97.4
-82611,2019,2.3
-83892,2019,48.0
-83893,2019,75.9
-84482,2019,200.1
-84659,2019,3.0
-85166,2019,200.0
+89278,2016,13.0
+89446,2016,1.5
+91328,2016,61.9
+92011,2016,79.7
+92012,2016,108.7
+92624,2016,80.0
+95412,2016,106.0
+96096,2016,294.0
+96801,2016,27.6
+96816,2016,3.0
+97485,2016,119.6
+97486,2016,92.8
+98170,2016,60.0
+100233,2016,3.0
+100886,2016,35.8
+102351,2016,100.8
+102834,2016,1.7
+104331,2016,1.7
+105062,2016,184.0
+107120,2016,150.0
+107751,2016,48.3
+107864,2016,38.3
+109092,2016,1.7
+111286,2016,103.0
+115277,2016,195.8
+115947,2016,72.0
+122689,2016,62.1
+124843,2016,199.0
+126829,2016,60.8
+127727,2016,208.0
+128925,2016,100.0
+128926,2016,208.3
+129610,2016,100.0
+130283,2016,200.0
+133720,2016,150.0
+133721,2016,147.0
+134408,2016,200.0
+135750,2016,198.0
+135773,2016,64.8
+138493,2016,225.0
+141243,2016,282.0
+141244,2016,16.0
+143953,2016,200.0
+144639,2016,174.0
+147361,2016,230.4
+147398,2016,108.0
+148767,2016,249.9
+150791,2016,282.0
+151475,2016,18.0
+152143,2016,50.0
+152177,2016,230.0
+153529,2016,257.0
+154926,2016,125.0
+156961,2016,230.0
+163785,2016,120.0
+195952,2016,200.0
+202793,2016,62.0
+202796,2016,246.0
+202797,2016,4.0
+203477,2016,138.0
+55712,2017,150.0
+66667,2017,99.3
+67560,2017,22.8
+72028,2017,10.0
+72711,2017,10.0
+72712,2017,10.0
+73395,2017,10.0
+73396,2017,10.0
+81226,2017,30.0
+82523,2017,41.6
+82524,2017,74.5
+83800,2017,200.0
+83889,2017,100.0
+83892,2017,32.9
+88078,2017,1.5
+94786,2017,98.0
+99547,2017,170.0
+99550,2017,3.0
+99551,2017,3.0
+99552,2017,3.0
+100234,2017,3.0
+100235,2017,3.0
+100236,2017,3.0
+100918,2017,3.0
+101575,2017,2.3
+101576,2017,2.3
+101609,2017,3.0
+102260,2017,2.3
+104330,2017,1.8
+104410,2017,66.0
+107049,2017,1.7
+107806,2017,100.0
+107822,2017,119.7
+108436,2017,1.8
+108437,2017,1.8
+109602,2017,46.0
+110483,2017,276.0
+111167,2017,24.0
+111218,2017,0.1
+111828,2017,89.7
+111896,2017,258.0
+111897,2017,20.0
+120545,2017,1.9
+126844,2017,75.0
+127549,2017,276.0
+127550,2017,4.6
+129599,2017,178.2
+130967,2017,200.0
+133722,2017,154.5
+134928,2017,1.9
+136460,2017,270.0
+136461,2017,27.8
+139191,2017,35.3
+139875,2017,264.0
+144663,2017,99.1
+145971,2017,4.0
+146655,2017,294.0
+146672,2017,142.6
+147357,2017,181.7
+147366,2017,163.0
+148046,2017,276.0
+149986,2017,143.0
+151476,2017,50.4
+152160,2017,151.2
+156247,2017,29.9
+156960,2017,250.0
+159684,2017,155.4
+160369,2017,253.0
+163810,2017,100.5
+180889,2017,149.3
+195280,2017,249.0
+202119,2017,228.0
+204857,2017,95.3
+60513,2018,100.0
+65238,2018,2.7
+65921,2018,2.7
+66606,2018,2.7
+68667,2018,1.6
+70030,2018,25.0
+70031,2018,79.7
+70120,2018,2.0
+71445,2018,48.0
+77101,2018,79.9
+81068,2018,44.6
+85941,2018,44.0
+88569,2018,20.0
+88570,2018,20.0
+90076,2018,78.4
+90663,2018,199.2
+97475,2018,282.0
+97476,2018,36.1
+98187,2018,90.0
+98835,2018,202.5
+99546,2018,170.0
+102278,2018,103.5
+102294,2018,200.0
+102315,2018,212.0
+103582,2018,30.0
+103650,2018,250.0
+103661,2018,169.0
+103719,2018,100.0
+104363,2018,119.0
+105016,2018,100.0
+105678,2018,2.5
+106438,2018,200.4
+108413,2018,2.3
+108509,2018,1.5
+109097,2018,2.3
+109098,2018,2.3
+109192,2018,1.5
+109193,2018,1.5
+112578,2018,185.0
+116575,2018,276.0
+117259,2018,258.0
+117260,2018,48.3
+119175,2018,1.8
+119177,2018,1.8
+122780,2018,294.0
+122781,2018,5.3
+129453,2018,2.8
+129608,2018,243.5
+130824,2018,128.7
+131511,2018,21.6
+133562,2018,43.2
+137825,2018,247.3
+138495,2018,198.6
+140407,2018,1.9
+143976,2018,130.0
+147351,2018,50.9
+151464,2018,148.4
+154209,2018,80.0
+154882,2018,150.0
+157628,2018,252.0
+158312,2018,48.0
+167203,2018,196.7
+168596,2018,200.0
+170640,2018,160.0
+171309,2018,276.0
+171310,2018,24.0
+172004,2018,148.4
+200067,2018,201.0
+62435,2019,200.0
+66663,2019,106.7
+70778,2019,200.0
+78496,2019,28.8
+79008,2019,200.1
+79685,2019,200.0
+82593,2019,2.3
+83798,2019,97.4
+83961,2019,2.3
+85257,2019,75.9
+85332,2019,5.0
+85342,2019,3.0
 85343,2019,3.0
-85350,2019,5.0
+85852,2019,100.0
+86026,2019,3.0
+86027,2019,3.0
 86028,2019,3.0
-86712,2019,3.0
-87397,2019,3.0
-87398,2019,3.0
-87999,2019,161.3
-88080,2019,3.0
-94212,2019,90.0
-95463,2019,66.0
-95466,2019,171.5
-96149,2019,140.0
-96151,2019,14.0
-98203,2019,200.0
-98889,2019,252.0
-99573,2019,47.3
-99574,2019,264.0
-100258,2019,136.0
-101584,2019,2.5
-101645,2019,48.0
-102308,2019,186.0
-102329,2019,146.3
-106434,2019,205.2
-107058,2019,160.0
-118694,2019,270.0
-119378,2019,5.6
-123427,2019,59.4
-128270,2019,199.3
-140518,2019,220.5
-144528,2019,131.1
-145203,2019,1.8
-152895,2019,100.0
-157003,2019,210.1
-158370,2019,183.8
-161107,2019,200.2
-161788,2019,99.4
-163839,2019,30.2
-167947,2019,199.5
-167950,2019,258.0
-168634,2019,44.4
-170687,2019,240.0
-171371,2019,110.3
-172733,2019,128.0
-173419,2019,200.0
-173420,2019,226.1
-174788,2019,237.6
-175470,2019,158.0
-176158,2019,240.0
-176842,2019,258.0
-177526,2019,42.0
-178210,2019,60.0
-178890,2019,162.9
-178895,2019,234.0
-179579,2019,73.1
-46046,2020,144.0
-48781,2020,136.8
-59371,2020,72.6
-61257,2020,2.5
-61264,2020,200.0
-61947,2020,127.5
-62631,2020,122.5
-63228,2020,80.0
-63824,2020,10.0
-65362,2020,4.6
-65876,2020,100.0
-66559,2020,10.0
-67380,2020,199.4
-68748,2020,150.0
-69296,2020,200.0
-69978,2020,10.0
-72167,2020,150.0
-78867,2020,10.0
-81746,2020,103.5
-82377,2020,133.0
-83061,2020,200.0
-83798,2020,192.2
-83894,2020,150.0
-85883,2020,5.0
-87315,2020,168.6
-89270,2020,250.2
-89367,2020,130.0
-91268,2020,250.9
-92690,2020,220.0
-92725,2020,1.8
-96146,2020,7.9
-96836,2020,190.0
-97423,2020,124.0
-97517,2020,130.9
-98791,2020,198.8
-99570,2020,152.2
-100158,2020,19.95
-100252,2020,90.0
-100255,2020,199.8
-100256,2020,201.3
-100842,2020,23.0
-101526,2020,32.55
-101621,2020,110.0
-101625,2020,250.0
-102303,2020,42.0
-102309,2020,60.0
-102350,2020,264.0
-102381,2020,125.1
-103578,2020,79.0
-104381,2020,150.0
-104382,2020,250.0
-107118,2020,202.0
-107776,2020,60.0
-108507,2020,134.6
-108557,2020,56.2
-109065,2020,17.0
-109749,2020,264.0
-109794,2020,99.0
-112531,2020,230.0
-112590,2020,150.0
-112611,2020,102.0
-113214,2020,66.0
-115327,2020,202.0
-116010,2020,79.0
-116694,2020,65.0
-116716,2020,142.6
-117272,2020,171.7
-119324,2020,282.0
-125534,2020,200.1
-126903,2020,276.0
-127587,2020,24.0
-129638,2020,198.8
-131006,2020,195.8
-131635,2020,213.1
-131731,2020,145.5
-132265,2020,288.0
-132417,2020,129.5
-132418,2020,246.0
-133100,2020,236.5
-133784,2020,220.0
-134467,2020,22.0
-137052,2020,62.0
-142635,2020,148.4
-145372,2020,228.0
-146056,2020,22.0
-148108,2020,248.2
-148793,2020,240.0
-149477,2020,63.5
-154264,2020,200.0
-155564,2020,90.0
-156320,2020,240.0
-157004,2020,10.0
-159740,2020,242.8
-160422,2020,180.0
-167951,2020,210.0
-168635,2020,16.9
-173422,2020,264.0
-174102,2020,160.7
-174106,2020,36.0
-178896,2020,192.0
-179575,2020,201.6
-180258,2020,151.2
-180941,2020,142.6
-181623,2020,50.4
-59213,2021,186.0
-60581,2021,14.0
-61266,2021,200.0
-62106,2021,15.3
-72803,2021,239.8
-72853,2021,270.0
-73537,2021,28.8
-75749,2021,79.7
-79642,2021,288.0
-82430,2021,153.6
-83114,2021,154.0
-85850,2021,18.0
-86027,2021,3.0
-86534,2021,282.0
-87394,2021,1.5
-88683,2021,150.0
-88684,2021,174.0
-89368,2021,209.5
-93320,2021,215.2
-94225,2021,10.0
-94227,2021,60.0
-94910,2021,54.0
-97515,2021,54.0
-100941,2021,250.0
-101697,2021,249.8
-103034,2021,252.0
-104356,2021,80.0
-107697,2021,205.0
-107801,2021,93.0
-109110,2021,135.0
-109191,2021,50.4
-109827,2021,7.9
-110538,2021,185.0
-110539,2021,246.0
-111223,2021,56.0
-111847,2021,250.7
-115348,2021,200.0
-119377,2021,128.0
-125535,2021,264.0
-126163,2021,104.0
-126219,2021,36.6
-129639,2021,276.0
-130323,2021,21.6
-130845,2021,10.0
-131007,2021,270.0
-131047,2021,145.6
-131691,2021,24.4
-132416,2021,23.5
-133583,2021,46.2
-133679,2021,276.0
-134267,2021,46.8
-134268,2021,60.0
-134427,2021,199.0
-134469,2021,252.0
-137001,2021,25.2
-139834,2021,165.0
-141886,2021,270.0
-142636,2021,264.0
-143320,2021,24.0
-144687,2021,140.0
-145889,2021,14.4
-145890,2021,21.3
-147357,2021,100.0
-147425,2021,258.0
-148109,2021,93.8
-148791,2021,118.3
-149409,2021,8.0
-149995,2021,60.2
-150093,2021,49.0
-150679,2021,61.2
-151363,2021,4.5
-156932,2021,105.0
-157616,2021,141.2
-158300,2021,216.0
-159054,2021,182.4
-159668,2021,264.0
-160352,2021,80.0
-160424,2021,242.5
-162474,2021,175.3
-163771,2021,33.8
-165212,2021,225.6
-170004,2021,198.0
-170688,2021,222.0
-171367,2021,198.5
-171372,2021,105.0
-172054,2021,258.0
-172056,2021,258.0
-172738,2021,42.0
-172740,2021,242.6
-173423,2021,264.0
-174107,2021,76.0
-174791,2021,240.0
-175475,2021,98.4
-176151,2021,3.4
-176839,2021,200.2
-176840,2021,17.8
-177524,2021,222.0
-178209,2021,38.6
-178893,2021,60.0
-179577,2021,174.0
-181627,2021,137.4
-182311,2021,72.0
-57316,2022,20.0
-61259,2022,20.0
-61262,2022,110.0
-62630,2022,101.9
-65280,2022,282.0
-65875,2022,8.0
-67245,2022,201.3
-73487,2022,83.5
-90050,2022,72.8
-96835,2022,200.0
-97519,2022,200.0
-101624,2022,200.0
-106408,2022,70.0
-108459,2022,7.9
-109243,2022,115.0
-110479,2022,264.0
-111163,2022,34.0
-112528,2022,2.8
-112532,2022,270.0
-113212,2022,2.8
-113216,2022,30.0
-115904,2022,145.0
-120115,2022,121.3
-130160,2022,2.8
-132374,2022,177.66
-133057,2022,19.74
-140417,2022,2.8
-144005,2022,198.0
-144689,2022,216.0
-145373,2022,130.46
-146057,2022,252.0
-146741,2022,202.54
-147944,2022,70.9
-148041,2022,145.0
-150161,2022,252.0
-150845,2022,51.2
-157686,2022,184.0
-158371,2022,209.0
-160425,2022,252.0
-161109,2022,47.2
-163842,2022,171.8
-167266,2022,212.4
-169318,2022,252.0
-169319,2022,210.0
-170002,2022,49.3
-170003,2022,156.6
-170685,2022,28.2
-171369,2022,240.0
-172057,2022,216.0
-172736,2022,220.0
-172741,2022,264.0
-173424,2022,258.0
-173425,2022,246.0
-174108,2022,242.0
-174109,2022,228.0
-174792,2022,246.0
-174793,2022,73.0
-175476,2022,222.0
-176155,2022,200.2
-176159,2022,240.0
-176160,2022,23.6
-176843,2022,96.0
-181631,2022,90.0
-72119,2023,102.8
-79073,2023,91.6
-79171,2023,111.8
-79855,2023,106.0
-80539,2023,101.2
-82592,2023,107.5
-85262,2023,112.1
-85947,2023,3.0
-87902,2023,200.0
-87938,2023,91.5
-89954,2023,200.0
-90052,2023,222.0
-93544,2023,130.0
-97591,2023,1.5
-98888,2023,224.4
-99613,2023,180.0
-100370,2023,87.6
-100940,2023,200.0
-101012,2023,1.5
-101696,2023,1.5
-104262,2023,105.7
-106433,2023,108.3
-107802,2023,200.5
-107822,2023,88.0
-111222,2023,165.0
-118062,2023,25.98
-119379,2023,72.0
-119432,2023,233.82
-120747,2023,48.0
-120799,2023,105.5
-121431,2023,264.0
-122115,2023,220.0
-124166,2023,200.6
-129583,2023,199.0
-135110,2023,99.5
-141839,2023,40.2
-144004,2023,240.0
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-145210,2023,95.3
-151529,2023,258.0
-152097,2023,198.0
-152213,2023,43.7
-160419,2023,50.0
-165896,2023,223.9
-169999,2023,196.8
-170686,2023,252.0
-171370,2023,49.0
-175472,2023,239.6
-176844,2023,216.0
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-178212,2023,47.5
-57073,2024,207.9
-70800,2024,198.8
-71435,2024,100.0
-84429,2024,108.4
-85113,2024,282.0
-89900,2024,124.3
-90535,2024,160.0
-90736,2024,200.0
-91952,2024,282.0
-98107,2024,190.0
-99475,2024,47.8
-103671,2024,38.7
-110559,2024,200.0
-126363,2024,60.0
-127047,2024,60.0
-130468,2024,21.0
-131581,2024,163.0
-131837,2024,48.0
-146107,2024,184.5
-146626,2024,216.0
-147942,2024,30.0
-150677,2024,27.0
-151364,2024,85.5
-152896,2024,201.5
-153580,2024,201.5
-154263,2024,100.0
-155632,2024,114.9
-159736,2024,102.0
-160423,2024,202.5
-163156,2024,98.6
-169313,2024,148.0
-170001,2024,252.0
-170681,2024,140.0
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-171366,2024,163.2
-172053,2024,13.5
-172055,2024,258.0
-172739,2024,92.2
-175473,2024,228.0
-176157,2024,28.0
-177527,2024,246.0
-178206,2024,151.5
-178211,2024,62.8
-179578,2024,43.0
-180262,2024,162.0
-180946,2024,42.0
-181630,2024,54.0
-51516,2025,126.0
-59216,2025,162.0
-59218,2025,72.0
-59900,2025,98.0
-59901,2025,82.0
-59902,2025,246.0
-61423,2025,58.8
-61860,2025,16.8
-63826,2025,144.0
-66561,2025,56.0
-69383,2025,16.8
-70751,2025,80.0
-71484,2025,200.0
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diff --git a/inputs/capacity_exogenous/prescribed_builds_wind-ons_reference.csv b/inputs/capacity_exogenous/prescribed_builds_wind-ons_reference.csv
index f1d10e0d9..546be503e 100644
--- a/inputs/capacity_exogenous/prescribed_builds_wind-ons_reference.csv
+++ b/inputs/capacity_exogenous/prescribed_builds_wind-ons_reference.csv
@@ -1,1509 +1,1351 @@
 sc_point_gid,year,capacity
-52199,2010,6.0
-52883,2010,90.0
-55618,2010,50.0
-59211,2010,8.0
-59369,2010,25.5
-60055,2010,66.0
-60538,2010,19.5
-61261,2010,60.0
-61945,2010,88.8
-61948,2010,192.0
-62456,2010,100.7
-62626,2010,20.0
-63274,2010,36.8
-64508,2010,150.0
-66558,2010,9.0
-69294,2010,36.0
-70798,2010,62.4
-72850,2010,102.4
-75448,2010,62.7
-76816,2010,11.0
-77541,2010,21.0
-78183,2010,62.0
-78225,2010,22.5
-78386,2010,20.0
-79010,2010,99.0
-80277,2010,62.25
-80552,2010,1.7
-81693,2010,111.0
-83061,2010,200.0
-85347,2010,3.0
-87902,2010,210.0
-89850,2010,21.0
-92586,2010,62.25
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 83893,2012,54.0
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 85341,2016,1.5
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 88078,2016,1.5
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 107822,2017,119.7
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-146567,2018,1.9
-146570,2018,21.6
-148628,2018,128.7
-150670,2018,2.8
-151527,2018,198.6
-154945,2018,80.0
-156999,2018,150.0
-158369,2018,198.0
-159053,2018,2.0
-160425,2018,210.0
-161109,2018,90.0
-164529,2018,228.0
-165213,2018,72.0
-167261,2018,196.7
-168627,2018,148.4
-169311,2018,148.4
-170680,2018,78.0
-171364,2018,60.0
-172048,2018,22.0
-174102,2018,9.0
-174786,2018,192.0
-63275,2019,106.7
-64680,2019,100.0
-72168,2019,200.0
-77135,2019,28.8
-77500,2019,200.0
-78326,2019,97.4
-82524,2019,75.9
-84482,2019,200.1
-84659,2019,3.0
-85166,2019,200.0
-85259,2019,48.0
-85262,2019,161.3
+108436,2017,1.8
+108437,2017,1.8
+109602,2017,42.0
+110483,2017,252.0
+110970,2017,4.0
+111167,2017,48.0
+111220,2017,0.1
+111828,2017,89.7
+111896,2017,246.0
+111897,2017,32.0
+120546,2017,1.9
+126844,2017,75.0
+127549,2017,270.0
+127550,2017,10.6
+129599,2017,178.2
+130967,2017,200.0
+133721,2017,154.5
+136460,2017,258.0
+136461,2017,39.8
+139191,2017,41.3
+139717,2017,1.9
+139875,2017,258.0
+144663,2017,99.1
+145971,2017,10.0
+146655,2017,288.0
+146672,2017,142.6
+147357,2017,181.7
+147366,2017,163.0
+148046,2017,270.0
+148047,2017,6.0
+149986,2017,143.0
+151476,2017,50.4
+152160,2017,151.2
+156247,2017,29.9
+156960,2017,250.0
+159684,2017,155.4
+159685,2017,37.0
+160369,2017,216.0
+163810,2017,100.5
+180889,2017,149.3
+195280,2017,216.0
+195281,2017,33.0
+202119,2017,228.0
+205541,2017,95.3
+60513,2018,100.0
+65238,2018,2.7
+65921,2018,2.7
+66606,2018,2.7
+68667,2018,1.6
+70030,2018,25.0
+70031,2018,79.7
+70120,2018,2.0
+71445,2018,48.0
+77101,2018,24.0
+77102,2018,55.9
+81068,2018,44.6
+85941,2018,44.0
+88569,2018,20.0
+88570,2018,20.0
+90076,2018,78.4
+90663,2018,199.2
+97475,2018,270.0
+97476,2018,48.1
+98187,2018,90.0
+98835,2018,202.5
+99546,2018,170.0
+102278,2018,103.5
+102294,2018,200.0
+102315,2018,212.0
+103582,2018,30.0
+103650,2018,228.0
+103661,2018,169.0
+103719,2018,100.0
+104334,2018,22.0
+104362,2018,54.0
+105016,2018,100.0
+105047,2018,65.0
+105678,2018,2.5
+106438,2018,200.4
+108413,2018,2.3
+108414,2018,2.3
+108509,2018,1.5
+109096,2018,2.3
+109192,2018,1.5
+109193,2018,1.5
+112578,2018,185.0
+116575,2018,270.0
+117259,2018,258.0
+117260,2018,54.3
+117808,2018,1.8
+119859,2018,1.8
+122779,2018,137.3
+122780,2018,102.0
+122781,2018,60.0
+126029,2018,2.8
+129608,2018,243.5
+134923,2018,56.7
+136295,2018,72.0
+136975,2018,43.2
+137660,2018,21.6
+137825,2018,247.3
+138495,2018,198.6
+141090,2018,1.9
+143976,2018,130.0
+147351,2018,50.9
+151464,2018,148.4
+154209,2018,80.0
+154882,2018,150.0
+157628,2018,264.0
+158312,2018,36.0
+167203,2018,196.7
+168596,2018,200.0
+170640,2018,160.0
+171309,2018,252.0
+171310,2018,48.0
+172004,2018,148.4
+200067,2018,201.0
+62435,2019,200.0
+66663,2019,106.7
+70778,2019,200.0
+78496,2019,28.8
+79008,2019,200.1
+79684,2019,128.0
+79685,2019,72.0
+83281,2019,5.0
+83798,2019,97.4
+84645,2019,2.3
+85257,2019,75.9
+85329,2019,2.3
 85342,2019,3.0
-85345,2019,2.3
-85348,2019,5.0
+85852,2019,100.0
+86025,2019,3.0
 86026,2019,3.0
-86030,2019,2.3
+86616,2019,161.3
+86709,2019,3.0
+86710,2019,3.0
+87308,2019,48.0
+87394,2019,3.0
 87395,2019,3.0
-87398,2019,3.0
-88079,2019,3.0
-88763,2019,3.0
-92159,2019,90.0
-98202,2019,120.0
-98205,2019,228.0
-98886,2019,80.0
-98889,2019,16.0
-100250,2019,66.0
-100258,2019,84.0
-100941,2019,198.0
-100942,2019,72.0
-101622,2019,200.0
-101625,2019,101.3
-102989,2019,171.5
-103636,2019,2.5
-105040,2019,140.0
-105749,2019,194.3
-109794,2019,160.0
-116695,2019,205.2
-121375,2019,59.4
-124165,2019,199.3
-125534,2019,246.0
-126218,2019,29.6
-140518,2019,220.5
-142474,2019,41.1
-149302,2019,1.8
-150680,2019,36.0
-151364,2019,54.0
-152210,2019,100.0
-160422,2019,210.1
-163161,2019,192.0
-163841,2019,102.0
-163845,2019,108.0
-164525,2019,97.5
-167262,2019,200.2
-170678,2019,99.4
-171365,2019,183.8
-172732,2019,158.0
-172738,2019,121.1
-173413,2019,30.2
-173422,2019,186.0
-174783,2019,128.0
-174787,2019,226.1
-174790,2019,210.0
-175474,2019,92.4
-175475,2019,126.0
-176159,2019,224.3
-176841,2019,60.0
-177520,2019,162.9
-177525,2019,54.0
-178207,2019,39.6
-178209,2019,48.0
-178891,2019,198.0
-179577,2019,138.0
-180941,2019,56.0
-181625,2019,144.0
-45362,2020,132.0
-48097,2020,12.0
-56986,2020,136.8
-61946,2020,68.0
-62108,2020,72.6
-62629,2020,127.5
-62631,2020,132.0
-63228,2020,80.0
-63313,2020,122.5
-63993,2020,4.6
-64507,2020,10.0
-65192,2020,200.0
-68748,2020,192.0
-69432,2020,7.4
-70661,2020,10.0
-70799,2020,150.0
-72851,2020,150.0
-73396,2020,10.0
-78867,2020,60.0
-79694,2020,103.5
-82377,2020,133.0
-82430,2020,6.2
-83114,2020,186.0
-83745,2020,154.9
-84578,2020,162.0
-85704,2020,10.0
-85882,2020,5.0
-86630,2020,150.0
-87314,2020,130.0
-88440,2020,40.0
-88586,2020,10.0
-89270,2020,204.0
-89302,2020,2.5
-89900,2020,138.8
-89954,2020,46.2
-89987,2020,1.8
-90051,2020,6.6
-90584,2020,200.0
-91356,2020,7.9
-92042,2020,90.0
-92043,2020,130.9
-92635,2020,23.0
-92690,2020,210.0
-94003,2020,32.55
-94777,2020,42.0
-95539,2020,71.1
-96150,2020,123.3
-96833,2020,199.8
-98107,2020,60.0
-98791,2020,96.0
-99474,2020,19.95
-99568,2020,48.0
-99572,2020,240.0
-99644,2020,54.0
-100252,2020,104.2
-100256,2020,10.0
-100940,2020,210.0
-101624,2020,40.0
-102350,2020,216.0
-102894,2020,79.0
-102991,2020,78.0
-104262,2020,124.0
-104354,2020,60.0
-105750,2020,246.0
-106432,2020,79.0
-106434,2020,4.0
-107776,2020,110.0
-108485,2020,150.0
-108507,2020,182.6
-108555,2020,56.2
-109065,2020,29.0
-109190,2020,102.0
-109749,2020,258.0
-110477,2020,66.0
-110538,2020,148.0
-111162,2020,99.0
-111222,2020,202.0
-113215,2020,230.0
-113295,2020,142.6
-116588,2020,171.7
-116694,2020,150.0
-118063,2020,54.0
-119324,2020,270.0
-119377,2020,102.0
-120061,2020,98.1
-121482,2020,65.0
-125533,2020,198.8
-126901,2020,195.8
-128955,2020,246.0
-129639,2020,54.0
-131635,2020,219.1
-131730,2020,13.5
-132265,2020,270.0
-132417,2020,165.5
-132418,2020,210.0
-133100,2020,234.0
-133743,2020,246.0
-133784,2020,2.5
-134427,2020,2.2
-134466,2020,22.0
-134468,2020,174.0
-135152,2020,46.0
-136519,2020,78.0
-137052,2020,80.0
-137203,2020,54.0
-138530,2020,148.4
-146739,2020,174.0
-147423,2020,26.0
-152897,2020,198.0
-153581,2020,105.5
-154264,2020,250.0
-154880,2020,90.0
-156319,2020,20.8
-159057,2020,234.0
-159741,2020,66.0
-167264,2020,132.0
-167948,2020,118.0
-169996,2020,160.7
-172739,2020,270.0
-173417,2020,180.0
-173423,2020,148.9
-174788,2020,222.0
-175467,2020,82.6
-176151,2020,103.2
-176835,2020,48.0
-176838,2020,201.6
-177519,2020,60.0
-180937,2020,14.4
-182989,2020,36.0
-58000,2021,15.3
-59212,2021,2.0
-60580,2021,108.0
-61264,2021,92.0
-61265,2021,198.0
-67380,2021,54.0
-68064,2021,4.8
-72803,2021,239.8
-73537,2021,240.0
-77802,2021,124.0
-78958,2021,245.2
-79642,2021,258.0
-80378,2021,153.6
-81062,2021,154.0
-83959,2021,79.7
-85850,2021,48.0
-85946,2021,6.0
-85947,2021,126.0
-86534,2021,252.0
-86631,2021,53.5
-86710,2021,3.0
-88684,2021,72.0
-90052,2021,60.0
-90735,2021,144.0
-90736,2021,72.0
-91357,2021,54.0
-98204,2021,228.0
-98276,2021,198.0
-98888,2021,22.0
-103034,2021,240.0
-103065,2021,51.8
-104402,2021,110.0
-105037,2021,7.9
-106433,2021,185.0
-107091,2021,80.0
-107697,2021,205.0
-109191,2021,62.4
-110478,2021,135.0
-111847,2021,240.0
-111907,2021,86.0
-112531,2021,10.7
-115348,2021,90.0
-118748,2021,216.0
-120062,2021,60.0
-120114,2021,93.0
-120744,2021,80.0
-121430,2021,114.0
-122114,2021,123.6
-122798,2021,138.0
-123482,2021,156.4
-124111,2021,104.0
-127587,2021,180.0
-128271,2021,120.6
-132416,2021,71.5
-133057,2021,48.0
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-135835,2021,145.6
-138363,2021,10.0
-138365,2021,25.2
-139732,2021,21.3
-139734,2021,46.2
-141266,2021,140.0
-141886,2021,270.0
-143150,2021,14.4
-143154,2021,54.0
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-147357,2021,141.2
-148041,2021,165.0
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-148625,2021,64.5
-149409,2021,26.0
-150093,2021,49.0
-150159,2021,199.0
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-150676,2021,61.2
-151528,2021,84.0
-151529,2021,240.0
-152212,2021,204.0
-152213,2021,111.8
-156248,2021,100.0
-156932,2021,105.0
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-158371,2021,2.5
-158373,2021,168.0
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-167263,2021,222.0
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-167947,2021,3.6
-167950,2021,228.0
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-177521,2021,198.5
-178211,2021,120.0
-178895,2021,132.0
-180258,2021,17.4
-180942,2021,60.0
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-180944,2021,146.6
-181626,2021,132.0
-181627,2021,162.0
-181628,2021,126.0
-182311,2021,30.0
-60053,2022,20.0
-61258,2022,20.0
-63141,2022,96.0
-63997,2022,110.0
-65280,2022,270.0
-65876,2022,45.3
-69295,2022,60.0
-73487,2022,95.5
-75447,2022,8.0
-84576,2022,72.8
-88622,2022,101.9
-97518,2022,200.0
-98882,2022,16.0
-99570,2022,198.0
-100254,2022,2.0
-100938,2022,200.0
-104353,2022,7.9
-109827,2022,54.0
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-109927,2022,115.0
-110479,2022,192.0
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-112528,2022,2.8
-112532,2022,216.0
-113212,2022,2.8
-113216,2022,84.0
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-128269,2022,177.66
-129636,2022,19.74
-137678,2022,2.8
-139150,2022,145.0
-143832,2022,2.8
-146741,2022,252.0
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-148793,2022,120.0
-149309,2022,70.9
-149477,2022,102.0
-150161,2022,96.0
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-154265,2022,234.0
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-162474,2022,192.0
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-174103,2022,220.0
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-174791,2022,120.6
-174793,2022,144.0
-175476,2022,174.0
-176158,2022,192.0
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-178212,2022,72.0
-72119,2023,102.8
-78486,2023,111.8
-79170,2023,46.0
-79854,2023,101.2
-81123,2023,91.6
-81907,2023,60.0
-83209,2023,112.1
-83276,2023,130.0
-83798,2023,200.0
-84644,2023,107.5
-87218,2023,200.0
-87315,2023,195.0
-87937,2023,91.5
-91420,2023,30.0
-92843,2023,87.6
-96222,2023,1.5
-96834,2023,200.0
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-101011,2023,1.5
-101623,2023,8.4
-103013,2023,60.0
-103578,2023,105.7
-103748,2023,1.5
-107138,2023,180.0
-111221,2023,108.3
-114589,2023,168.0
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-115325,2023,25.98
-115327,2023,5.82
-116031,2023,88.0
-117325,2023,32.6
-118746,2023,105.5
-118747,2023,140.5
-121431,2023,132.0
-121483,2023,165.0
-122115,2023,228.0
-122799,2023,180.0
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-128899,2023,199.0
-137161,2023,99.5
-137164,2023,186.0
-139900,2023,90.0
-140584,2023,25.7
-141839,2023,238.2
-146575,2023,95.3
-152896,2023,204.0
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-166577,2023,196.8
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-169309,2023,50.0
-169315,2023,1.9
-170687,2023,222.0
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-176155,2023,204.0
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-177526,2023,246.0
-178210,2023,55.0
-57073,2024,207.9
-70800,2024,198.8
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-87999,2024,200.0
-89216,2024,124.3
-89851,2024,48.0
-91268,2024,125.8
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-93954,2024,64.0
-97423,2024,190.0
-99475,2024,204.0
-99613,2024,60.0
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-107774,2024,38.7
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-143319,2024,120.0
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-144687,2024,21.5
-145371,2024,198.0
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-146107,2024,126.0
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-148622,2024,27.0
-148626,2024,85.5
-149306,2024,30.0
-153578,2024,100.0
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-164522,2024,114.9
-164528,2024,210.0
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-166578,2024,202.5
-168626,2024,102.0
-169995,2024,148.0
-170686,2024,246.0
-171370,2024,62.8
-172046,2024,98.6
-172731,2024,140.0
-174100,2024,163.2
-176152,2024,151.5
-176843,2024,204.0
-177527,2024,146.2
-178894,2024,54.0
-180261,2024,85.0
-180945,2024,114.0
-181629,2024,48.0
-50832,2025,126.0
-59216,2025,60.0
-59218,2025,60.0
-59370,2025,58.8
-59900,2025,200.0
-59901,2025,88.0
-59902,2025,252.0
-61860,2025,16.8
-69383,2025,16.8
-70751,2025,80.0
-71484,2025,200.0
-73536,2025,250.0
-74765,2025,200.0
-76906,2025,16.8
-77590,2025,16.8
-78487,2025,60.0
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+89983,2019,66.0
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+50794,2020,136.8
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+147337,2026,276.0
+147338,2026,264.0
+147339,2026,249.0
+148020,2026,270.0
+148021,2026,270.0
+148022,2026,276.0
+148023,2026,288.0
+148704,2026,246.0
+148705,2026,228.0
+148706,2026,282.0
+148707,2026,161.2
+149389,2026,276.0
+149390,2026,294.0
+168578,2026,180.0
+81890,2027,2.4
+81891,2027,2.4
+83945,2027,2.4
+84627,2027,2.4
+84628,2027,2.4
+165195,2027,8.0
+165879,2027,162.0
+91402,2028,198.0
+97401,2029,22.2
+98084,2029,288.0
+98086,2029,258.0
+98771,2029,276.0
+99452,2029,294.0
+99455,2029,270.0
+100137,2029,9.6
+100138,2029,276.0
+98085,2030,126.0
+98768,2030,288.0
+98769,2030,294.0
+98770,2030,294.0
+99453,2030,294.0
+99454,2030,7.2
+98087,2031,276.0
+98767,2031,7.5
+100136,2031,270.0
diff --git a/inputs/financials/deflator.csv b/inputs/financials/deflator.csv
index 101627347..ecb551879 100644
--- a/inputs/financials/deflator.csv
+++ b/inputs/financials/deflator.csv
@@ -19,4 +19,5 @@
 2021,0.697349107
 2022,0.645693617
 2023,0.620262841
-2024,0.602782158
\ No newline at end of file
+2024,0.602782158
+2025,0.587506977
diff --git a/inputs/financials/inflation_default.csv b/inputs/financials/inflation_default.csv
index e03e99d12..9c48c4d7a 100644
--- a/inputs/financials/inflation_default.csv
+++ b/inputs/financials/inflation_default.csv
@@ -110,7 +110,7 @@ t,inflation_rate
 2022,1.08
 2023,1.041
 2024,1.029
-2025,1.025
+2025,1.026
 2026,1.025
 2027,1.025
 2028,1.025
@@ -285,4 +285,4 @@ t,inflation_rate
 2197,1.025
 2198,1.025
 2199,1.025
-2200,1.025
\ No newline at end of file
+2200,1.025
diff --git a/inputs/fuelprices/alpha_AEO_2023_HOG.csv b/inputs/fuelprices/alpha_AEO_2023_HOG.csv
deleted file mode 100644
index 2f5eafb66..000000000
--- a/inputs/fuelprices/alpha_AEO_2023_HOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-t,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,4.683876245,4.71156506,4.274324972,4.662090985,5.211405877,4.1378747,3.986709024,4.348852094,4.172503321
-2011,2.927163154,3.139824575,2.713908868,3.215775136,3.269097461,2.200280714,2.394996374,2.715813594,2.732003942
-2012,1.605684949,1.539768007,1.171484003,1.557710892,1.872629539,0.770146231,1.005573641,1.280376125,1.538252508
-2013,3.546416522,2.465455972,2.147142227,2.536507122,2.475824496,1.757503198,1.895343172,2.147623013,2.255814284
-2014,3.981166979,2.771478218,2.900041807,3.264085534,2.910622138,2.192987906,2.397177205,2.451128905,2.665740094
-2015,1.866609289,0.793683669,0.711922578,1.236132388,1.415156094,0.4291966,0.681535093,0.824554304,1.041525884
-2016,1.511995403,0.618041657,0.538910349,0.605065277,0.92749023,0.03958865,0.273632214,0.325044504,0.635908364
-2017,1.752855961,1.045269588,1.060862953,1.305793636,1.247641785,0.604537223,0.835410412,0.863670098,1.070924185
-2018,1.895133633,0.911394505,0.748657435,1.054078237,0.969653484,0.372757278,0.606488069,0.735525512,0.981903697
-2019,1.538998435,0.349026379,-0.050798824,0.296724644,0.427097309,-0.234165051,-0.12211007,0.012360555,0.503512031
-2020,1.131327992,-0.120499163,-0.502936102,-0.060480798,0.04534619,-0.604442719,-0.456998584,-0.279584188,0.187550732
-2021,3.099946994,1.451435828,1.339318581,1.779968453,2.080717873,1.50718388,1.530154484,1.760697529,2.353867574
-2022,3.5483212,1.782893754,2.022578711,2.799330397,3.078594876,2.571023858,2.549719316,2.545103468,3.029576834
-2023,2.378373501,1.342328269,1.194028745,1.543992146,1.795413259,1.21350831,1.240247497,1.375339501,1.643354892
-2024,1.415759125,0.711487689,0.544815363,0.912607162,1.081253039,0.503380546,0.580832448,0.716566802,0.926648337
-2025,0.86896598,0.329908868,0.170962886,0.54914465,0.698455234,0.172915338,0.232570699,0.352137113,0.60709127
-2026,0.460816439,0.108736805,-0.025044089,0.378299206,0.48196658,0.002818775,0.064363626,0.175025659,0.473505341
-2027,0.295242588,0.021483752,-0.066627881,0.342151721,0.413857651,-0.056176854,0.040800551,0.15133452,0.417098106
-2028,0.262804572,0.003469295,-0.025111811,0.324465557,0.436045412,-0.027426097,0.091883052,0.213712798,0.452411879
-2029,0.330810532,0.047941078,0.014145689,0.374311831,0.469207476,0.024323461,0.147288646,0.259758376,0.459137798
-2030,0.487999406,0.089810956,0.090009359,0.47352068,0.568945618,0.165111229,0.260363189,0.374120936,0.560577526
-2031,0.531704901,0.144769905,0.189264337,0.566940564,0.670661923,0.303152329,0.387230116,0.507373136,0.651157026
-2032,0.614475942,0.189059916,0.254383481,0.649718934,0.756694441,0.409273837,0.500458592,0.611808416,0.74375019
-2033,0.595860037,0.231144058,0.290699029,0.721644547,0.853783273,0.515351776,0.604655316,0.709149685,0.814816621
-2034,0.687671967,0.23984968,0.252719511,0.713428691,0.853355956,0.493272404,0.579143869,0.692475031,0.820217025
-2035,0.688618291,0.239608067,0.193577208,0.710595821,0.853304469,0.467499768,0.564303131,0.676427021,0.799429788
-2036,0.630531932,0.222782046,0.164367685,0.699986132,0.838525615,0.443571747,0.531800535,0.648784936,0.781340342
-2037,0.670133927,0.22439025,0.139073547,0.684761884,0.819496569,0.412815438,0.491417911,0.617261801,0.748010018
-2038,0.677501221,0.202673927,0.115306709,0.659895207,0.793321836,0.368401467,0.46653595,0.574809514,0.693242995
-2039,0.685677689,0.181367314,0.089257716,0.634890973,0.754710511,0.338072814,0.437220655,0.538387827,0.628826676
-2040,0.663161299,0.133757723,0.048581057,0.635222883,0.750282,0.329584063,0.438588794,0.540049385,0.671006833
-2041,0.621626742,0.114612937,0.0122987,0.634976364,0.73155049,0.318077522,0.438433797,0.53143665,0.674348524
-2042,0.597758548,0.074189031,-0.062732438,0.597490648,0.681640195,0.25193692,0.378071502,0.469340303,0.636869132
-2043,0.554992924,0.021447521,-0.137180564,0.539438775,0.623824931,0.189041038,0.317142697,0.434409579,0.584442828
-2044,0.478832608,-0.028908711,-0.183096913,0.507148422,0.596737646,0.171569504,0.29653036,0.419260507,0.573426613
-2045,0.472675955,-0.065172789,-0.249513284,0.454332407,0.553144233,0.129348749,0.248599829,0.362175087,0.5311348
-2046,0.459113622,-0.0873525,-0.284538069,0.425872565,0.517731206,0.087629096,0.217264439,0.349836749,0.543325144
-2047,0.454512094,-0.107481749,-0.31891037,0.408522145,0.504956896,0.057126698,0.21245973,0.330709159,0.549329938
-2048,0.371481364,-0.145184974,-0.356202621,0.396274684,0.495556285,0.052774366,0.208994657,0.321163871,0.537664581
-2049,0.368620379,-0.144843119,-0.384449063,0.363913833,0.460959371,0.021673888,0.174287712,0.302717598,0.547422724
-2050,0.219455025,-0.245117945,-0.429637538,0.302709642,0.404923042,-0.032787876,0.126034285,0.256061635,0.529273232
diff --git a/inputs/fuelprices/alpha_AEO_2023_LOG.csv b/inputs/fuelprices/alpha_AEO_2023_LOG.csv
deleted file mode 100644
index d4ea013ec..000000000
--- a/inputs/fuelprices/alpha_AEO_2023_LOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-t,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,4.683876245,4.71156506,4.274324972,4.662090985,5.211405877,4.1378747,3.986709024,4.348852094,4.172503321
-2011,2.927163154,3.139824575,2.713908868,3.215775136,3.269097461,2.200280714,2.394996374,2.715813594,2.732003942
-2012,1.605684949,1.539768007,1.171484003,1.557710892,1.872629539,0.770146231,1.005573641,1.280376125,1.538252508
-2013,3.546416522,2.465455972,2.147142227,2.536507122,2.475824496,1.757503198,1.895343172,2.147623013,2.255814284
-2014,3.981166979,2.771478218,2.900041807,3.264085534,2.910622138,2.192987906,2.397177205,2.451128905,2.665740094
-2015,1.866609289,0.793683669,0.711922578,1.236132388,1.415156094,0.4291966,0.681535093,0.824554304,1.041525884
-2016,1.511995403,0.618041657,0.538910349,0.605065277,0.92749023,0.03958865,0.273632214,0.325044504,0.635908364
-2017,1.752855961,1.045269588,1.060862953,1.305793636,1.247641785,0.604537223,0.835410412,0.863670098,1.070924185
-2018,1.89616394,0.912424903,0.749646281,1.055016421,0.972495521,0.37346896,0.607192968,0.736448209,0.982907525
-2019,1.538998435,0.349026379,-0.050798824,0.296724644,0.427097309,-0.234165051,-0.12211007,0.012360555,0.503512031
-2020,1.131327992,-0.120499163,-0.502936102,-0.060480798,0.04534619,-0.604442719,-0.456998584,-0.279584188,0.187550732
-2021,3.068159868,1.381751721,1.319745304,1.800063823,2.109641706,1.539344083,1.536209999,1.787792541,2.342268678
-2022,3.548349475,1.782713104,2.023076655,2.802518831,3.078450757,2.571856656,2.549358736,2.546820857,3.028562116
-2023,3.086104413,2.085727809,2.049896549,2.414475599,2.774066954,2.27101223,2.212939948,2.206933439,2.490271355
-2024,2.754738099,2.108790427,2.060027034,2.309078114,2.75219749,2.225476672,2.167142465,2.148960252,2.278834587
-2025,2.451340284,2.00300384,1.988511913,2.268955465,2.613947056,2.143772933,2.144175139,2.145789546,2.302323968
-2026,2.171663195,1.904923625,1.921823309,2.210327057,2.448999976,2.034432134,2.094175797,2.159365139,2.32203846
-2027,2.118090524,1.907323723,1.909618565,2.136759654,2.406883172,1.962842212,2.064369629,2.1338014,2.213967909
-2028,2.163440361,1.944105735,2.004738011,2.173018966,2.437919324,2.006019648,2.120842444,2.238628502,2.283045415
-2029,2.23281392,1.981375255,2.070601854,2.209752297,2.528833436,2.097831256,2.229207489,2.31830443,2.355914258
-2030,2.191276887,2.027825648,2.169291551,2.302797954,2.592872922,2.206804627,2.344928377,2.391860273,2.446421047
-2031,2.299497715,2.042086526,2.260425433,2.394584091,2.784348949,2.383140942,2.520588956,2.533436138,2.55724639
-2032,2.390894689,2.156006747,2.415581446,2.542815251,2.889148714,2.626326149,2.737317451,2.801520712,2.752312683
-2033,2.559871116,2.260907806,2.526585505,2.637727647,3.033742885,2.797877751,2.900660939,2.962957832,2.927582414
-2034,2.646952609,2.377546254,2.605189968,2.763973248,3.131908952,2.919599156,3.02030061,3.128588935,3.071178068
-2035,2.674438612,2.412418618,2.680710286,2.865947965,3.192847627,3.016645935,3.114190229,3.226548364,3.193433939
-2036,2.687030442,2.446319647,2.725959932,2.937436791,3.225334854,3.108504576,3.191167515,3.316761879,3.219216942
-2037,2.699296674,2.445544341,2.7349285,2.977164118,3.262065839,3.140853966,3.218491187,3.348696469,3.260436243
-2038,2.718025943,2.453089488,2.760804582,2.978303645,3.257961996,3.183101899,3.261026558,3.400837555,3.287686969
-2039,2.741540064,2.45762393,2.762436729,2.995086349,3.28082865,3.185535824,3.292126358,3.401142146,3.334387043
-2040,2.763483775,2.482259857,2.787719291,3.008139317,3.285546672,3.236405324,3.325071988,3.435097567,3.382535853
-2041,2.761927758,2.492487511,2.772918586,3.0020529,3.250059321,3.196117916,3.289848952,3.397510663,3.369051543
-2042,2.818363532,2.477579976,2.761201593,2.96925191,3.195286359,3.155217923,3.23512101,3.389106058,3.342264148
-2043,2.830154745,2.440889972,2.712444645,2.924951837,3.121219696,3.095141472,3.15643394,3.363554197,3.233819667
-2044,2.724631947,2.438438198,2.726260248,2.961510031,3.138287053,3.130380252,3.201376762,3.473062553,3.312614814
-2045,2.680811727,2.360969748,2.685956408,2.911371985,3.120010738,3.108945422,3.183170914,3.488570554,3.312267815
-2046,2.659688787,2.283724708,2.678004922,2.916684594,3.107402435,3.145058391,3.210138275,3.534638427,3.331679523
-2047,2.680296554,2.283565275,2.688894764,2.9234292,3.117304564,3.132474148,3.228325483,3.583398418,3.353270118
-2048,2.666998502,2.306314664,2.700002602,2.944712278,3.133728991,3.181695412,3.263771235,3.598109144,3.442880132
-2049,2.73738344,2.336432205,2.713812193,2.980074951,3.183663523,3.226118088,3.322981907,3.614685933,3.387974081
-2050,2.685812706,2.386382653,2.753951513,3.007949656,3.18681604,3.256809359,3.334284905,3.654682187,3.465140959
diff --git a/inputs/fuelprices/alpha_AEO_2023_reference.csv b/inputs/fuelprices/alpha_AEO_2023_reference.csv
deleted file mode 100644
index cf16f2f8e..000000000
--- a/inputs/fuelprices/alpha_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-t,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,4.683876245,4.71156506,4.274324972,4.662090985,5.211405877,4.1378747,3.986709024,4.348852094,4.172503321
-2011,2.927163154,3.139824575,2.713908868,3.215775136,3.269097461,2.200280714,2.394996374,2.715813594,2.732003942
-2012,1.605684949,1.539768007,1.171484003,1.557710892,1.872629539,0.770146231,1.005573641,1.280376125,1.538252508
-2013,3.546416522,2.465455972,2.147142227,2.536507122,2.475824496,1.757503198,1.895343172,2.147623013,2.255814284
-2014,3.981166979,2.771478218,2.900041807,3.264085534,2.910622138,2.192987906,2.397177205,2.451128905,2.665740094
-2015,1.866609289,0.793683669,0.711922578,1.236132388,1.415156094,0.4291966,0.681535093,0.824554304,1.041525884
-2016,1.511995403,0.618041657,0.538910349,0.605065277,0.92749023,0.03958865,0.273632214,0.325044504,0.635908364
-2017,1.752855961,1.045269588,1.060862953,1.305793636,1.247641785,0.604537223,0.835410412,0.863670098,1.070924185
-2018,1.891368595,0.908357825,0.743503087,1.046700981,0.967476207,0.366374599,0.600744147,0.729693444,0.977735257
-2019,1.538998435,0.349026379,-0.050798824,0.296724644,0.427097309,-0.234165051,-0.12211007,0.012360555,0.503512031
-2020,1.131327992,-0.120499163,-0.502936102,-0.060480798,0.04534619,-0.604442719,-0.456998584,-0.279584188,0.187550732
-2021,3.075877487,1.398019115,1.327918267,1.782788162,2.108454636,1.535214545,1.528837853,1.791394058,2.338489683
-2022,3.530959727,1.810285687,2.032214295,2.770363335,3.069669258,2.553076859,2.539895847,2.55123637,3.032822275
-2023,2.630788801,1.590930997,1.498762795,1.8473453,2.163456948,1.608520852,1.616022831,1.700451687,1.972810526
-2024,1.787949032,1.058016135,0.936048743,1.277819031,1.489235842,0.959368296,1.007749018,1.130333095,1.311057564
-2025,1.248400541,0.73586776,0.615051229,0.977161541,1.153806664,0.674834527,0.706217936,0.845637543,1.072704977
-2026,0.939900973,0.500307804,0.407008846,0.769426233,0.875720737,0.446306053,0.486952361,0.666580219,0.88613363
-2027,0.719402448,0.405608889,0.313212985,0.704948519,0.803618879,0.391432084,0.444641767,0.606963582,0.807221358
-2028,0.60214515,0.347187793,0.310449976,0.635903951,0.779850275,0.401404601,0.462252387,0.600546938,0.7970851
-2029,0.739053772,0.364722973,0.325646369,0.666014839,0.821428448,0.452659673,0.516493705,0.634005477,0.802626426
-2030,0.721601799,0.378028204,0.396996125,0.763872498,0.91410208,0.571543081,0.642902707,0.740354727,0.861333363
-2031,0.712164001,0.438058518,0.476347576,0.851315494,1.031315142,0.688863509,0.787888321,0.877224444,0.960192379
-2032,0.867074337,0.499267164,0.522045946,0.951222357,1.112169307,0.823567214,0.921421898,1.006385417,1.043309896
-2033,0.882680859,0.571510559,0.573744257,1.056799155,1.254705962,0.973237858,1.066437274,1.144457959,1.184152296
-2034,0.951059717,0.635512678,0.64468822,1.151603524,1.350473583,1.095689955,1.179959075,1.292861455,1.324347669
-2035,1.020739025,0.684403534,0.699795137,1.236667995,1.444739907,1.178277698,1.279456895,1.407216696,1.430566185
-2036,1.063172428,0.714417186,0.716284787,1.256519077,1.458870453,1.172772183,1.29380594,1.438229071,1.44934944
-2037,1.095663924,0.74626128,0.756244765,1.279409877,1.490888575,1.192112,1.286341065,1.444216946,1.466819362
-2038,1.173711248,0.798903527,0.788066318,1.328683716,1.548123804,1.272526723,1.371303078,1.541587202,1.575761006
-2039,1.195328466,0.805087254,0.787747273,1.285006092,1.507742338,1.216998932,1.305419894,1.477419479,1.498464462
-2040,1.221997287,0.819076847,0.825134585,1.367463275,1.569893435,1.303791925,1.40828944,1.578165566,1.580315141
-2041,1.317704926,0.847501553,0.836419699,1.407428997,1.615045567,1.33743083,1.452222222,1.608700541,1.61994839
-2042,1.315551986,0.814603328,0.792155473,1.4077078,1.574900128,1.323081608,1.42961391,1.586778029,1.603928454
-2043,1.27096979,0.777286806,0.751233086,1.357992614,1.526839076,1.272196783,1.372820202,1.525963299,1.567433403
-2044,1.189964176,0.741690928,0.730781676,1.344304925,1.437527768,1.204730596,1.350525197,1.494963803,1.521291338
-2045,1.22164458,0.731263184,0.695964414,1.329090681,1.429021242,1.186345714,1.334691885,1.47517591,1.504779383
-2046,1.170329544,0.705852228,0.680647508,1.288131139,1.421934601,1.208826432,1.337086097,1.446353897,1.443434263
-2047,1.134646485,0.663503903,0.648765904,1.252664866,1.399669011,1.178428495,1.305693604,1.429666933,1.442586009
-2048,1.079713795,0.620235244,0.618800264,1.23256016,1.381481808,1.142729594,1.2848104,1.40261478,1.418996732
-2049,1.080000131,0.593222888,0.563934889,1.196506351,1.326411126,1.098902336,1.227830817,1.365933734,1.40410657
-2050,1.101558511,0.594964294,0.541451841,1.17470387,1.318490502,1.101328842,1.205941228,1.360383639,1.392740356
diff --git a/inputs/fuelprices/alpha_AEO_2025_HOG.csv b/inputs/fuelprices/alpha_AEO_2025_HOG.csv
index 4b56e5f5d..6f26bdf40 100644
--- a/inputs/fuelprices/alpha_AEO_2025_HOG.csv
+++ b/inputs/fuelprices/alpha_AEO_2025_HOG.csv
@@ -1,42 +1,42 @@
 t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,4.274323852,4.137873616,4.711563826,4.348850955,4.683875017,4.172502228,5.211404511,4.662089763,3.98670798
-2011,2.713907854,2.200279761,3.13982344,2.715812526,2.927162052,2.732002912,3.269096259,3.215774001,2.394995423
-2012,1.171483321,0.770145572,1.53976723,1.280375365,1.605684139,1.538251721,1.872628631,1.557710128,1.005572996
-2013,2.147141336,1.757502341,2.465454989,2.147622061,3.546415247,2.255813341,2.475823471,2.53650614,1.895342333
-2014,2.900040717,2.192986927,2.771477152,2.451127873,3.981165594,2.665739043,2.910620997,3.264084365,2.397176235
-2015,0.711921989,0.429195995,0.79368306,0.824553628,1.866608395,1.041525202,1.415155271,1.236131689,0.681534507
-2016,0.538909779,0.039588121,0.618041078,0.325043944,1.51199459,0.635907785,0.927489517,0.605064724,0.273631725
-2017,1.060862282,0.604536582,1.045268931,0.863669435,1.752855115,1.070923523,1.247641015,1.30579293,0.83540981
-2018,0.744844045,0.368943897,0.907581091,0.731712085,1.891319959,0.97809027,0.965839997,1.050264807,0.602674726
-2019,-0.05079931,-0.234165571,0.349025816,0.012359993,1.53899757,0.503511439,0.427096661,0.296724114,-0.122110521
-2020,-0.502936486,-0.604443157,-0.120499625,-0.279584681,1.131327219,0.18755021,0.045345631,-0.060481249,-0.456998958
-2021,1.339013268,1.506878452,1.451130497,1.760392045,3.099641249,2.353562024,2.080412336,1.779663069,1.529849135
-2022,2.022577512,2.571022473,1.782892643,2.545102098,3.548319654,3.029575429,3.078593391,2.799329057,2.549718018
-2023,-0.532509151,-0.639984414,-0.127585003,-0.29602426,1.19784926,0.198578163,0.048011954,-0.064037547,-0.483870497
-2024,-0.98043835,-0.970854984,-0.797897045,-0.826460764,0.24941196,-0.169578388,-0.173104378,-0.503171848,-0.835942279
-2025,-0.768032603,-0.823242104,-0.594639859,-0.680994103,0.178015009,-0.185703727,-0.18001519,-0.308753265,-0.686137419
-2026,-1.036195147,-1.121568495,-0.733756411,-0.855258807,0.034676441,-0.332708855,-0.407940325,-0.463246799,-0.918098645
-2027,-1.131170618,-1.238480642,-0.805002502,-0.875633817,-0.135804118,-0.362822822,-0.388009041,-0.514167828,-0.97982876
-2028,-1.105236943,-1.126636448,-0.803632518,-0.767528068,-0.239839431,-0.252311396,-0.298775925,-0.419941429,-0.86988835
-2029,-1.11567918,-1.087975365,-0.803388621,-0.714058442,-0.29003843,-0.199262871,-0.264342387,-0.394562141,-0.811701553
-2030,-1.12478993,-1.004541956,-0.818886737,-0.648582215,-0.387204599,-0.07767145,-0.224907114,-0.343743315,-0.757444806
-2031,-1.163689556,-0.930202212,-0.812944633,-0.579801507,-0.394443732,0.009081337,-0.28326791,-0.365003581,-0.705143876
-2032,-1.281359207,-0.940268212,-0.865409006,-0.547673121,-0.390953194,0.098568309,-0.197459128,-0.359504799,-0.699547227
-2033,-1.229451561,-0.893348846,-0.781756537,-0.41557648,-0.314569121,0.220068911,-0.229906662,-0.269359185,-0.626850263
-2034,-1.183347108,-0.862399359,-0.717397635,-0.325393578,-0.230587757,0.306099474,-0.194982746,-0.214521969,-0.588453389
-2035,-1.130417495,-0.781452072,-0.649927507,-0.218491916,-0.128809924,0.36551396,-0.127913876,-0.13180846,-0.519649696
-2036,-1.015631429,-0.658035876,-0.564743953,-0.162141708,-0.02546988,0.444711407,-0.020665995,0.01615838,-0.385213062
-2037,-0.992298421,-0.622388115,-0.521530822,-0.100314862,0.020532266,0.472568545,0.022290135,0.035912096,-0.31784594
-2038,-0.812844388,-0.534801745,-0.435779003,0.112338685,0.138556022,0.569904764,0.120813364,0.148873804,-0.238478613
-2039,-0.704471082,-0.418050492,-0.344238809,0.275339963,0.160241954,0.689367935,0.21185856,0.22315311,-0.142122098
-2040,-0.703614125,-0.401309663,-0.376745022,0.302023845,0.116040333,0.731250959,0.202252156,0.261771476,-0.11725936
-2041,-0.732512549,-0.419115726,-0.393657416,0.343843801,0.17301562,0.739134265,0.194810132,0.287240779,-0.12008712
-2042,-0.796180127,-0.413962899,-0.390411414,0.340348516,0.183436718,0.754583544,0.185548432,0.271212584,-0.113878106
-2043,-0.913182306,-0.529957802,-0.494781799,0.230187082,0.086264907,0.696070938,0.115444399,0.184882378,-0.181605352
-2044,-1.015904371,-0.581785243,-0.600994199,0.122932442,-0.068155989,0.614235173,0.063592426,0.113344093,-0.227208693
-2045,-1.098516593,-0.639881841,-0.679749219,0.067107054,-0.157882154,0.63787519,0.027198506,0.069326837,-0.253461505
-2046,-1.14119007,-0.698756109,-0.739644575,0.071752876,-0.192537851,0.619404758,-0.001478989,0.064277231,-0.263831328
-2047,-1.155432179,-0.745504469,-0.779834904,0.038801499,-0.231737717,0.627727305,0.086252108,0.043628081,-0.295940933
-2048,-1.132644074,-0.732276816,-0.79209341,0.053735263,-0.23766312,0.67057009,0.107552391,0.053313444,-0.269915327
-2049,-1.224774229,-0.776804013,-0.838981981,-0.021675882,-0.280933762,0.630302407,0.042285076,0.033514511,-0.317431645
-2050,-1.230454535,-0.815888879,-0.868686511,-0.076279579,-0.317240188,0.631157758,0.008900914,0.023620292,-0.331612009
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
+2011,3.232510,2.927834,3.561499,3.072886,1.816976,2.283109,3.674277,3.611184,3.128780
+2012,1.835805,1.657892,2.046154,1.711281,0.608251,0.855820,2.358643,2.023828,1.846135
+2013,2.717594,2.513758,2.919933,2.527450,2.752755,1.493476,2.911481,2.958827,2.646791
+2014,3.473377,2.970544,3.226643,2.832534,3.290931,1.899157,3.347307,3.696555,3.142849
+2015,1.425834,1.389863,1.335855,1.280899,1.075644,0.350386,1.933619,1.745417,1.571988
+2016,1.317182,1.059534,1.178246,0.798265,0.754709,0.154124,1.462783,1.120631,1.171322
+2017,1.772829,1.557646,1.563513,1.303398,1.026074,0.634368,1.739925,1.783923,1.649825
+2018,1.570148,1.430502,1.494355,1.231903,1.135924,0.575436,1.526521,1.592755,1.569901
+2019,0.871148,0.898527,0.989434,0.538674,1.046551,0.198684,1.038924,0.883068,0.937050
+2020,0.455373,0.555079,0.537067,0.269587,0.582678,-0.114265,0.676152,0.539248,0.622535
+2021,2.207225,2.555236,2.060217,2.261829,2.557165,1.958033,2.666629,2.345259,2.531617
+2022,3.035049,3.666597,2.443258,3.100001,2.984546,2.729747,3.716462,3.401063,3.614057
+2023,0.482152,0.587715,0.568648,0.285439,0.616932,-0.120976,0.715908,0.570962,0.659140
+2024,0.210737,0.345082,-0.031321,-0.191626,-0.468010,-0.546024,0.497425,0.184579,0.398380
+2025,0.344876,0.410939,0.112763,-0.100039,-0.454252,-0.445625,0.499549,0.320929,0.468171
+2026,0.197131,0.224597,0.019707,-0.232231,-0.252073,-0.439900,0.316351,0.206801,0.318176
+2027,0.157925,0.175124,-0.033279,-0.229435,-0.258877,-0.439578,0.353443,0.160226,0.277408
+2028,0.207416,0.262914,-0.037449,-0.125625,-0.289135,-0.368992,0.437009,0.252221,0.367318
+2029,0.242622,0.314815,-0.032144,-0.066422,-0.322310,-0.365759,0.477042,0.276503,0.415802
+2030,0.269754,0.378667,-0.033219,0.001634,-0.251269,-0.312151,0.533248,0.334502,0.493271
+2031,0.268865,0.424748,-0.023406,0.069528,-0.182167,-0.296495,0.481149,0.299335,0.550534
+2032,0.332092,0.490504,-0.010662,0.153234,-0.176144,-0.183365,0.630270,0.340568,0.644607
+2033,0.371831,0.539430,0.064029,0.282865,-0.082352,-0.174894,0.590180,0.450130,0.707330
+2034,0.403914,0.565403,0.122283,0.370414,-0.011003,-0.123494,0.619309,0.500722,0.733782
+2035,0.439723,0.607326,0.171979,0.464788,0.056341,-0.103677,0.668940,0.566309,0.777152
+2036,0.495611,0.667947,0.217097,0.464172,0.093461,0.082731,0.737113,0.691647,0.818150
+2037,0.497352,0.683358,0.236787,0.502848,0.092945,0.041974,0.757634,0.689038,0.805579
+2038,0.536568,0.734539,0.280015,0.692031,0.198916,0.112107,0.813723,0.770227,0.833305
+2039,0.576221,0.788379,0.334648,0.836469,0.222122,0.184849,0.867698,0.812897,0.877299
+2040,0.575600,0.801046,0.307851,0.861927,0.164746,0.224821,0.862221,0.855858,0.902691
+2041,0.575753,0.810443,0.307528,0.924016,0.221233,0.204696,0.870508,0.895945,0.930069
+2042,0.557631,0.813421,0.317525,0.923621,0.198786,0.243462,0.867713,0.881745,0.943432
+2043,0.489477,0.749603,0.235768,0.831754,0.097936,0.237156,0.819954,0.812074,0.912820
+2044,0.424122,0.713009,0.142980,0.730459,0.027747,0.171369,0.782351,0.753046,0.886083
+2045,0.370421,0.681674,0.076273,0.676002,0.070819,0.217207,0.758601,0.713242,0.876443
+2046,0.349860,0.669778,0.029397,0.695452,0.043637,0.199327,0.743219,0.715050,0.883537
+2047,0.310455,0.649055,-0.008888,0.660313,-0.012972,0.230160,0.833784,0.696182,0.856814
+2048,0.296592,0.657421,-0.029444,0.662000,-0.033075,0.294965,0.847091,0.695373,0.868570
+2049,0.245406,0.617461,-0.064672,0.593020,-0.062187,0.265435,0.791758,0.700461,0.841883
+2050,0.225173,0.585727,-0.095693,0.525706,-0.061527,0.281430,0.756995,0.701374,0.823963
diff --git a/inputs/fuelprices/alpha_AEO_2025_LOG.csv b/inputs/fuelprices/alpha_AEO_2025_LOG.csv
index 26aabc5cc..aed84e948 100644
--- a/inputs/fuelprices/alpha_AEO_2025_LOG.csv
+++ b/inputs/fuelprices/alpha_AEO_2025_LOG.csv
@@ -1,42 +1,42 @@
 t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,4.274323852,4.137873616,4.711563826,4.348850955,4.683875017,4.172502228,5.211404511,4.662089763,3.98670798
-2011,2.713907854,2.200279761,3.13982344,2.715812526,2.927162052,2.732002912,3.269096259,3.215774001,2.394995423
-2012,1.171483321,0.770145572,1.53976723,1.280375365,1.605684139,1.538251721,1.872628631,1.557710128,1.005572996
-2013,2.147141336,1.757502341,2.465454989,2.147622061,3.546415247,2.255813341,2.475823471,2.53650614,1.895342333
-2014,2.900040717,2.192986927,2.771477152,2.451127873,3.981165594,2.665739043,2.910620997,3.264084365,2.397176235
-2015,0.711921989,0.429195995,0.79368306,0.824553628,1.866608395,1.041525202,1.415155271,1.236131689,0.681534507
-2016,0.538909779,0.039588121,0.618041078,0.325043944,1.51199459,0.635907785,0.927489517,0.605064724,0.273631725
-2017,1.060862282,0.604536582,1.045268931,0.863669435,1.752855115,1.070923523,1.247641015,1.30579293,0.83540981
-2018,0.744844045,0.368943897,0.907581091,0.731712085,1.891319959,0.97809027,0.965839997,1.050264807,0.602674726
-2019,-0.05079931,-0.234165571,0.349025816,0.012359993,1.53899757,0.503511439,0.427096661,0.296724114,-0.122110521
-2020,-0.502936486,-0.604443157,-0.120499625,-0.279584681,1.131327219,0.18755021,0.045345631,-0.060481249,-0.456998958
-2021,1.339013268,1.506878452,1.451130497,1.760392045,3.099641249,2.353562024,2.080412336,1.779663069,1.529849135
-2022,2.022577512,2.571022473,1.782892643,2.545102098,3.548319654,3.029575429,3.078593391,2.799329057,2.549718018
-2023,-0.532509151,-0.639984414,-0.127585003,-0.29602426,1.19784926,0.198578163,0.048011954,-0.064037547,-0.483870497
-2024,-0.98043835,-0.970854984,-0.797897045,-0.826460764,0.24941196,-0.169578388,-0.173104378,-0.503171848,-0.835942279
-2025,0.052912347,0.113159081,0.185832293,0.161823469,0.955399892,0.653191142,0.692091392,0.498099459,0.210786236
-2026,0.556012968,0.624866906,0.729716837,0.663235045,1.284286039,0.981862843,1.148832942,0.930332328,0.669647066
-2027,0.595668629,0.668752887,0.719462147,0.752688233,1.132383363,1.036836693,1.224917486,1.014220049,0.734990993
-2028,0.796412876,0.953933139,0.896830035,1.056970476,1.232606419,1.371676599,1.479329909,1.259202804,1.02890512
-2029,1.111198575,1.293318833,1.169328708,1.434981293,1.506767232,1.714382863,1.799820966,1.630624506,1.40507752
-2030,1.624631315,1.914038917,1.607756941,2.051424368,1.908047696,2.312961168,2.357971365,2.197522216,2.025384349
-2031,2.144015375,2.457545852,2.088333892,2.60763497,2.423713715,2.868033337,2.899154008,2.683917773,2.544368052
-2032,2.518251646,2.779102749,2.497367693,3.024042215,2.762036509,3.308341303,3.216629878,3.104966108,2.911778666
-2033,2.900049856,3.122204004,2.839648332,3.277357215,3.164897967,3.556434482,3.546488438,3.38646984,3.216543732
-2034,3.13033574,3.336484366,3.053955501,3.528533519,3.448965882,3.756716609,3.764636038,3.532169596,3.423182165
-2035,3.28923173,3.496637516,3.217568582,3.725506621,3.569688021,3.883580827,3.925258211,3.63475068,3.528751732
-2036,3.50200606,3.755010381,3.302281095,4.034303793,3.679293187,4.108459204,4.172998911,3.817156067,3.805995797
-2037,3.56007468,3.878931778,3.326596686,4.148920869,3.709662046,4.181199131,4.374773066,3.877242411,3.978559309
-2038,3.557736822,3.790783498,3.294901605,4.234711145,3.654516998,4.213765479,4.390260918,3.869885912,4.004404973
-2039,3.510828398,3.971830604,3.292919956,4.442288713,3.614536407,4.400752657,4.521430317,3.765212507,4.183593094
-2040,3.59954139,4.018541581,3.282332061,4.496705794,3.61592126,4.46254287,4.502009885,3.891563236,4.206410136
-2041,3.616017028,4.254986675,3.310999111,4.365744794,3.566487529,4.499110556,4.561121267,3.907281182,4.228592633
-2042,3.577042894,4.16210071,3.218622838,4.284416413,3.528534439,4.365228817,4.51813159,3.867928974,4.189358822
-2043,3.512391369,4.050880803,3.151557517,4.160762049,3.436118054,4.290786168,4.443401353,3.823488298,4.105407548
-2044,3.530099587,4.190478951,3.16642434,4.323243132,3.278930648,4.415172024,4.447622443,3.826256132,4.25844936
-2045,3.558146104,4.181646248,3.193102426,4.425419433,3.212186567,4.510703161,4.361376262,3.880564294,4.312538143
-2046,3.676942375,4.218296072,3.244401657,4.470433846,3.311696366,4.550329027,4.402971167,4.018728175,4.363276133
-2047,3.802600296,4.434405046,3.373934476,4.678081991,3.409348328,4.739334211,4.547975478,4.125399971,4.536786995
-2048,3.939109135,4.547880878,3.498263579,4.903968007,3.521662983,4.90486377,4.720125074,4.271648533,4.754947245
-2049,4.090728437,4.694276742,3.684932297,5.061472141,3.69662486,5.031985777,4.866316779,4.42996429,4.945171269
-2050,4.25101812,4.870025212,3.869244455,5.254610422,3.872221233,5.191618081,5.057361725,4.583699117,5.121867358
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
+2011,3.232510,2.927834,3.561499,3.072886,1.816976,2.283109,3.674277,3.611184,3.128780
+2012,1.835805,1.657892,2.046154,1.711281,0.608251,0.855820,2.358643,2.023828,1.846135
+2013,2.717594,2.513758,2.919933,2.527450,2.752755,1.493476,2.911481,2.958827,2.646791
+2014,3.473377,2.970544,3.226643,2.832534,3.290931,1.899157,3.347307,3.696555,3.142849
+2015,1.425834,1.389863,1.335855,1.280899,1.075644,0.350386,1.933619,1.745417,1.571988
+2016,1.317182,1.059534,1.178246,0.798265,0.754709,0.154124,1.462783,1.120631,1.171322
+2017,1.772829,1.557646,1.563513,1.303398,1.026074,0.634368,1.739925,1.783923,1.649825
+2018,1.570148,1.430502,1.494355,1.231903,1.135924,0.575436,1.526521,1.592755,1.569901
+2019,0.871148,0.898527,0.989434,0.538674,1.046551,0.198684,1.038924,0.883068,0.937050
+2020,0.455373,0.555079,0.537067,0.269587,0.582678,-0.114265,0.676152,0.539248,0.622535
+2021,2.207225,2.555236,2.060217,2.261829,2.557165,1.958033,2.666629,2.345259,2.531617
+2022,3.035049,3.666597,2.443258,3.100001,2.984546,2.729747,3.716462,3.401063,3.614057
+2023,0.482152,0.587715,0.568648,0.285439,0.616932,-0.120976,0.715908,0.570962,0.659140
+2024,0.215051,0.339058,-0.029711,-0.191824,-0.484707,-0.544057,0.490094,0.187075,0.400083
+2025,1.169469,1.322634,0.889994,0.743120,0.320668,0.468185,1.370276,1.126793,1.365049
+2026,1.435401,1.591261,1.277484,1.117014,0.993648,0.752048,1.674898,1.432441,1.515472
+2027,1.502568,1.668365,1.273043,1.214643,0.980905,0.838834,1.757396,1.511037,1.596232
+2028,1.681291,1.917707,1.426261,1.497429,1.115336,1.103785,1.990057,1.729195,1.845684
+2029,1.977326,2.224647,1.682775,1.860526,1.380942,1.387974,2.297298,2.076482,2.187165
+2030,2.498073,2.814701,2.120230,2.476454,1.840662,1.950263,2.855960,2.597319,2.806373
+2031,2.922170,3.231337,2.540952,2.981482,2.351311,2.424394,3.339785,3.044480,3.234114
+2032,3.379454,3.625742,2.992064,3.420201,2.669670,2.877220,3.697091,3.481538,3.664041
+2033,3.661245,3.901682,3.288055,3.630120,3.059481,3.098685,3.982265,3.739282,3.892631
+2034,3.835762,4.056546,3.470360,3.855987,3.327007,3.313662,4.168600,3.868858,4.057258
+2035,3.940490,4.191552,3.607970,4.031437,3.407640,3.491140,4.302966,3.954335,4.110478
+2036,4.100178,4.374171,3.658682,4.314109,3.491305,3.745742,4.517574,4.105921,4.338926
+2037,4.073614,4.422630,3.640128,4.387943,3.563082,3.863371,4.676863,4.119122,4.451877
+2038,4.013947,4.282977,3.579627,4.456922,3.496389,3.936299,4.663879,4.087316,4.440391
+2039,3.965983,4.428681,3.570535,4.655245,3.442832,4.161951,4.789079,3.972211,4.607058
+2040,4.031987,4.429668,3.551047,4.705194,3.426911,4.199913,4.760876,4.088288,4.625246
+2041,4.022913,4.621356,3.562317,4.565469,3.355756,4.239417,4.802329,4.099254,4.622140
+2042,3.963444,4.518745,3.460866,4.480272,3.316872,4.109517,4.750108,4.055829,4.571450
+2043,3.895821,4.415660,3.392686,4.357078,3.212400,4.036913,4.675000,4.008246,4.488237
+2044,3.905402,4.547160,3.403920,4.514365,3.171646,4.149064,4.676325,4.001124,4.638910
+2045,3.930714,4.517120,3.423385,4.607649,3.150600,4.239064,4.583925,4.048498,4.686094
+2046,4.060075,4.547413,3.472359,4.655728,3.230419,4.276129,4.623913,4.183568,4.733054
+2047,4.195563,4.762990,3.606305,4.865448,3.330979,4.463472,4.773827,4.287413,4.918477
+2048,4.353308,4.887999,3.740855,5.097561,3.450908,4.618085,4.956059,4.443471,5.152434
+2049,4.524340,5.046874,3.934228,5.257141,3.627606,4.741983,5.109198,4.607130,5.350720
+2050,4.696611,5.230470,4.124450,5.452070,3.805277,4.890810,5.306124,4.764436,5.537398
diff --git a/inputs/fuelprices/alpha_AEO_2025_reference.csv b/inputs/fuelprices/alpha_AEO_2025_reference.csv
index d4b272f1e..b4f06e40d 100644
--- a/inputs/fuelprices/alpha_AEO_2025_reference.csv
+++ b/inputs/fuelprices/alpha_AEO_2025_reference.csv
@@ -1,42 +1,42 @@
 t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,4.274323852,4.137873616,4.711563826,4.348850955,4.683875017,4.172502228,5.211404511,4.662089763,3.98670798
-2011,2.713907854,2.200279761,3.13982344,2.715812526,2.927162052,2.732002912,3.269096259,3.215774001,2.394995423
-2012,1.171483321,0.770145572,1.53976723,1.280375365,1.605684139,1.538251721,1.872628631,1.557710128,1.005572996
-2013,2.147141336,1.757502341,2.465454989,2.147622061,3.546415247,2.255813341,2.475823471,2.53650614,1.895342333
-2014,2.900040717,2.192986927,2.771477152,2.451127873,3.981165594,2.665739043,2.910620997,3.264084365,2.397176235
-2015,0.711921989,0.429195995,0.79368306,0.824553628,1.866608395,1.041525202,1.415155271,1.236131689,0.681534507
-2016,0.538909779,0.039588121,0.618041078,0.325043944,1.51199459,0.635907785,0.927489517,0.605064724,0.273631725
-2017,1.060862282,0.604536582,1.045268931,0.863669435,1.752855115,1.070923523,1.247641015,1.30579293,0.83540981
-2018,0.744844045,0.368943897,0.907581091,0.731712085,1.891319959,0.97809027,0.965839997,1.050264807,0.602674726
-2019,-0.05079931,-0.234165571,0.349025816,0.012359993,1.53899757,0.503511439,0.427096661,0.296724114,-0.122110521
-2020,-0.502936486,-0.604443157,-0.120499625,-0.279584681,1.131327219,0.18755021,0.045345631,-0.060481249,-0.456998958
-2021,1.339013268,1.506878452,1.451130497,1.760392045,3.099641249,2.353562024,2.080412336,1.779663069,1.529849135
-2022,2.022577512,2.571022473,1.782892643,2.545102098,3.548319654,3.029575429,3.078593391,2.799329057,2.549718018
-2023,-0.532509151,-0.639984414,-0.127585003,-0.29602426,1.19784926,0.198578163,0.048011954,-0.064037547,-0.483870497
-2024,-0.98043835,-0.970854984,-0.797897045,-0.826460764,0.24941196,-0.169578388,-0.173104378,-0.503171848,-0.835942279
-2025,-0.52819743,-0.544954094,-0.364465457,-0.380743829,0.4057343,0.140529278,0.088194268,-0.057326573,-0.401319367
-2026,-0.465745558,-0.503565246,-0.244647485,-0.329594468,0.465901522,0.118632356,0.134348213,0.00276949,-0.353490546
-2027,-0.60910673,-0.633849481,-0.382984347,-0.401536027,0.207308822,0.029139093,0.131107721,-0.090945121,-0.442583888
-2028,-0.588079036,-0.536960819,-0.395102922,-0.274490896,0.11499856,0.146713132,0.132266087,-0.001674653,-0.33007523
-2029,-0.545583666,-0.404123451,-0.346646228,-0.119320869,0.121739737,0.315791676,0.337388025,0.102201865,-0.193050543
-2030,-0.476091769,-0.231107456,-0.282627877,0.045458597,0.082704877,0.530028996,0.355402751,0.216452227,-0.045046144
-2031,-0.34293968,-0.085672544,-0.157222903,0.217521081,0.245500001,0.727859059,0.454564801,0.354426447,0.101707135
-2032,-0.357496338,0.000996721,-0.118939824,0.411087001,0.347272001,0.926298989,0.550062013,0.499679291,0.251342592
-2033,-0.039765156,0.37078581,0.176920078,0.829605338,0.659433819,1.253068631,0.88795023,0.822599456,0.622420364
-2034,0.226329793,0.643459238,0.378944364,1.109005789,0.849509583,1.490841199,1.111641178,1.071100679,0.873285411
-2035,0.400582762,0.798521248,0.486496653,1.231188653,0.956336957,1.600242156,1.24150963,1.18906337,1.00599674
-2036,0.529137723,0.878711845,0.556324442,1.286356263,1.073878571,1.611268808,1.330406368,1.282819223,1.079182422
-2037,0.619581703,0.977189346,0.642360523,1.379844931,1.109231091,1.63514802,1.443915076,1.344425666,1.175351626
-2038,0.644996148,1.036771855,0.657619113,1.449425365,1.106828763,1.663033164,1.478514282,1.371837858,1.23205881
-2039,0.667498242,1.057493733,0.640304195,1.486178012,1.049280039,1.663509755,1.492638659,1.340928458,1.255224892
-2040,0.636506088,1.057402615,0.60726062,1.560843437,1.071272461,1.738099138,1.497165404,1.382821333,1.285107406
-2041,0.63026753,1.06289159,0.600739321,1.656160053,1.102500381,1.85195936,1.487970852,1.445079335,1.331573977
-2042,0.659355882,1.086350497,0.643720882,1.741491916,1.178588391,1.942596228,1.525303979,1.526151016,1.407446106
-2043,0.650493083,1.171082158,0.651588131,1.825132512,1.213557969,2.018934423,1.572033268,1.548628348,1.4929318
-2044,0.619964702,1.190578977,0.641349235,1.84598426,1.169946954,2.06686629,1.580400242,1.563972235,1.549872097
-2045,0.629957603,1.198247143,0.617662284,1.863519383,1.073891114,2.066413572,1.557172336,1.554965218,1.580982932
-2046,0.62336646,1.245834881,0.663565312,1.92846816,1.077198853,2.153361385,1.58422958,1.617653538,1.624024136
-2047,0.653981243,1.255161617,0.651534809,1.975678245,1.110388695,2.187158843,1.549607939,1.595458054,1.676194521
-2048,0.639989147,1.19558043,0.621494769,1.908731602,1.009885846,2.186944555,1.485533103,1.551619692,1.673625366
-2049,0.612782816,1.074819349,0.595109099,1.835866405,1.062356589,2.1868416,1.431040401,1.509072786,1.652639006
-2050,0.596768254,0.990168988,0.539601544,1.863104555,1.029118559,2.169856186,1.368640206,1.525061938,1.654538352
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
+2011,3.232510,2.927834,3.561499,3.072886,1.816976,2.283109,3.674277,3.611184,3.128780
+2012,1.835805,1.657892,2.046154,1.711281,0.608251,0.855820,2.358643,2.023828,1.846135
+2013,2.717594,2.513758,2.919933,2.527450,2.752755,1.493476,2.911481,2.958827,2.646791
+2014,3.473377,2.970544,3.226643,2.832534,3.290931,1.899157,3.347307,3.696555,3.142849
+2015,1.425834,1.389863,1.335855,1.280899,1.075644,0.350386,1.933619,1.745417,1.571988
+2016,1.317182,1.059534,1.178246,0.798265,0.754709,0.154124,1.462783,1.120631,1.171322
+2017,1.772829,1.557646,1.563513,1.303398,1.026074,0.634368,1.739925,1.783923,1.649825
+2018,1.570148,1.430502,1.494355,1.231903,1.135924,0.575436,1.526521,1.592755,1.569901
+2019,0.871148,0.898527,0.989434,0.538674,1.046551,0.198684,1.038924,0.883068,0.937050
+2020,0.455373,0.555079,0.537067,0.269587,0.582678,-0.114265,0.676152,0.539248,0.622535
+2021,2.207225,2.555236,2.060217,2.261829,2.557165,1.958033,2.666629,2.345259,2.531617
+2022,3.035049,3.666597,2.443258,3.100001,2.984546,2.729747,3.716462,3.401063,3.614057
+2023,0.482152,0.587715,0.568648,0.285439,0.616932,-0.120976,0.715908,0.570962,0.659140
+2024,0.202311,0.323449,-0.041746,-0.207917,-0.505133,-0.576337,0.557962,0.174344,0.391092
+2025,0.602813,0.692976,0.347671,0.205100,-0.229539,-0.115501,0.772238,0.572750,0.749541
+2026,0.613034,0.694058,0.431180,0.230798,0.116280,-0.054895,0.782696,0.607796,0.728853
+2027,0.540627,0.615471,0.312819,0.178473,0.025300,-0.086562,0.799770,0.519140,0.672565
+2028,0.581150,0.707458,0.295013,0.304707,-0.005586,-0.041584,0.796321,0.597140,0.769714
+2029,0.669987,0.822394,0.342341,0.459622,0.067221,0.070685,1.001846,0.689225,0.892482
+2030,0.746032,0.956262,0.407551,0.616757,0.201912,0.206840,1.023289,0.790169,1.043240
+2031,0.851821,1.056561,0.511255,0.763157,0.270865,0.321341,1.103114,0.911957,1.155028
+2032,1.019251,1.236409,0.615433,1.014594,0.383170,0.526482,1.263653,1.080553,1.392116
+2033,1.250259,1.509990,0.857661,1.402480,0.667851,0.878486,1.550552,1.359765,1.683922
+2034,1.397943,1.689997,1.009215,1.640171,0.851366,1.045899,1.725658,1.597190,1.869690
+2035,1.492374,1.788715,1.079863,1.722907,0.910406,1.135310,1.819346,1.688441,1.911618
+2036,1.541326,1.811399,1.117161,1.746874,0.978942,1.165524,1.874950,1.757318,1.912304
+2037,1.536159,1.834123,1.155582,1.796241,0.991961,1.212068,1.942654,1.773991,1.941018
+2038,1.487182,1.834754,1.140687,1.836029,0.941332,1.255656,1.946024,1.777185,1.955180
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+2041,1.485430,1.837854,1.079738,2.034826,0.940136,1.503686,1.952438,1.852105,2.062283
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+2044,1.519776,1.964234,1.123951,2.219031,1.041327,1.774132,2.050695,1.987807,2.287435
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+2047,1.514791,2.018503,1.127624,2.347354,1.059170,1.939243,2.015636,1.996091,2.409184
+2048,1.495751,1.965800,1.098686,2.275422,0.948902,1.943456,1.953655,1.948797,2.405515
+2049,1.466790,1.856363,1.069420,2.194083,0.957797,1.949716,1.896208,1.903059,2.375852
+2050,1.448063,1.769984,1.008986,2.211597,0.950400,1.936905,1.828244,1.910854,2.347436
diff --git a/inputs/fuelprices/alpha_AEO_2026_HOG.csv b/inputs/fuelprices/alpha_AEO_2026_HOG.csv
new file mode 100644
index 000000000..1f5e52b63
--- /dev/null
+++ b/inputs/fuelprices/alpha_AEO_2026_HOG.csv
@@ -0,0 +1,42 @@
+t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
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+2012,1.861207,1.802179,2.019255,2.135136,1.299775,1.533531,2.668667,2.094801,2.026640
+2013,2.738112,2.628958,2.894765,2.942602,3.327790,2.185216,3.196929,3.017899,2.807178
+2014,3.494080,3.092879,3.199281,3.246415,3.820127,2.593936,3.634713,3.744689,3.299712
+2015,1.453247,1.548003,1.307021,1.760199,1.691627,1.054068,2.286350,1.809065,1.760347
+2016,1.349029,1.232448,1.146193,1.287225,1.364421,0.752212,1.836327,1.200097,1.354203
+2017,1.801610,1.721088,1.535469,1.744506,1.601754,1.182392,2.113329,1.855590,1.810900
+2018,1.604343,1.609369,1.464344,1.717984,1.757134,1.144658,1.910176,1.671963,1.775599
+2019,0.910407,1.084621,0.955677,1.167664,1.575510,0.745265,1.461099,0.976959,1.163360
+2020,0.496623,0.744497,0.495862,0.878256,1.145742,0.441472,1.095534,0.640109,0.850163
+2021,2.243688,2.721406,2.018844,2.864669,3.094409,2.537661,3.041219,2.427009,2.743093
+2022,3.080724,3.831824,2.391515,3.704427,3.556702,3.287192,4.112245,3.505542,3.831477
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+2024,0.256799,0.527445,-0.089505,0.556717,0.197963,0.095968,0.980185,0.305383,0.645424
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+2033,0.604105,1.038929,0.096617,1.241048,0.260556,0.787401,1.485130,0.834930,1.292949
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+2038,0.243586,0.790218,-0.176348,1.184959,0.112350,0.500813,1.046034,0.572945,1.149587
+2039,0.148291,0.703505,-0.268235,1.048065,0.079598,0.346407,0.950178,0.465093,1.064718
+2040,0.053479,0.624793,-0.392519,0.962721,-0.059601,0.232990,0.871614,0.376114,0.993441
+2041,-0.061608,0.523068,-0.545699,0.842347,-0.218848,0.137274,0.764985,0.257277,0.918969
+2042,-0.185466,0.416798,-0.707355,0.708869,-0.378413,-0.003099,0.647987,0.124114,0.823727
+2043,-0.286196,0.323207,-0.825149,0.597777,-0.512458,-0.137443,0.550221,0.005823,0.746389
+2044,-0.371072,0.229335,-0.931948,0.492979,-0.637612,-0.263946,0.450141,-0.118332,0.668595
+2045,-0.441924,0.162548,-1.032834,0.432171,-0.676116,-0.335872,0.378631,-0.193050,0.613378
+2046,-0.497549,0.103904,-1.097480,0.395583,-0.713449,-0.365369,0.314151,-0.237493,0.574419
+2047,-0.556369,0.048921,-1.158374,0.344603,-0.767542,-0.438947,0.253196,-0.302063,0.540740
+2048,-0.603926,-0.006141,-1.211544,0.289875,-0.820429,-0.507049,0.195107,-0.328176,0.507863
+2049,-0.665558,-0.069103,-1.276104,0.225292,-0.865166,-0.588833,0.131508,-0.403639,0.468287
+2050,-0.715123,-0.120909,-1.330371,0.164523,-0.920714,-0.682461,0.074984,-0.490857,0.430522
diff --git a/inputs/fuelprices/alpha_AEO_2026_LOG.csv b/inputs/fuelprices/alpha_AEO_2026_LOG.csv
new file mode 100644
index 000000000..0219c1079
--- /dev/null
+++ b/inputs/fuelprices/alpha_AEO_2026_LOG.csv
@@ -0,0 +1,42 @@
+t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
+2011,3.250640,3.043907,3.538973,3.435104,2.518591,2.781113,3.928042,3.661574,3.296519
+2012,1.861207,1.802179,2.019255,2.135136,1.299775,1.533531,2.668667,2.094801,2.026640
+2013,2.738112,2.628958,2.894765,2.942602,3.327790,2.185216,3.196929,3.017899,2.807178
+2014,3.494080,3.092879,3.199281,3.246415,3.820127,2.593936,3.634713,3.744689,3.299712
+2015,1.453247,1.548003,1.307021,1.760199,1.691627,1.054068,2.286350,1.809065,1.760347
+2016,1.349029,1.232448,1.146193,1.287225,1.364421,0.752212,1.836327,1.200097,1.354203
+2017,1.801610,1.721088,1.535469,1.744506,1.601754,1.182392,2.113329,1.855590,1.810900
+2018,1.604343,1.609369,1.464344,1.717984,1.757134,1.144658,1.910176,1.671963,1.775599
+2019,0.910407,1.084621,0.955677,1.167664,1.575510,0.745265,1.461099,0.976959,1.163360
+2020,0.496623,0.744497,0.495862,0.878256,1.145742,0.441472,1.095534,0.640109,0.850163
+2021,2.243688,2.721406,2.018844,2.864669,3.094409,2.537661,3.041219,2.427009,2.743093
+2022,3.080724,3.831824,2.391515,3.704427,3.556702,3.287192,4.112245,3.505542,3.831477
+2023,0.525828,0.788272,0.525021,0.929898,1.213108,0.467436,1.159950,0.677752,0.900153
+2024,0.256799,0.527445,-0.089505,0.556717,0.197963,0.095968,0.980185,0.305383,0.645424
+2025,0.849352,1.206806,0.587947,1.040695,1.082937,0.647898,1.705951,0.878566,1.272325
+2026,1.271322,1.604584,0.902877,1.466624,1.150798,1.036835,1.896542,1.283954,1.801210
+2027,1.778635,2.202551,1.379729,1.984390,1.422653,1.564029,2.469879,1.858564,2.202648
+2028,2.393737,2.801213,1.864329,2.641150,1.984482,2.259690,2.978362,2.506162,2.793915
+2029,2.917074,3.242736,2.364850,3.104513,2.468448,2.763274,3.365883,3.035462,3.163091
+2030,3.143560,3.483422,2.602218,3.394860,2.697164,3.045638,3.601954,3.326393,3.372951
+2031,3.361073,3.706062,2.830859,3.643465,2.864192,3.281765,3.843580,3.564746,3.655872
+2032,3.678378,3.972975,3.030698,4.061855,3.090680,3.640292,4.103140,3.926319,4.039783
+2033,3.854392,4.151818,3.266007,4.265576,3.320014,3.868352,4.298088,4.108464,4.159946
+2034,4.016388,4.326855,3.449482,4.434075,3.534281,4.047557,4.496548,4.252373,4.255850
+2035,4.197675,4.538563,3.676480,4.626949,3.799912,4.248096,4.737640,4.382622,4.397422
+2036,4.482252,4.808703,3.960529,4.946172,4.056435,4.551676,5.074204,4.669082,4.708541
+2037,4.766913,5.093013,4.240087,5.223659,4.373952,4.850293,5.387953,4.933817,5.008427
+2038,4.924970,5.252117,4.453101,5.356710,4.587928,4.978115,5.577611,5.068652,5.157384
+2039,5.186362,5.481660,4.668341,5.577910,4.787981,5.155541,5.766134,5.106292,5.380505
+2040,5.334884,5.593291,4.846008,5.787268,4.914851,5.325949,5.876168,5.176427,5.494554
+2041,5.721343,5.986905,5.201336,6.218070,5.331300,5.784100,6.149723,5.585408,5.919292
+2042,6.004181,6.257741,5.503065,6.510048,5.630000,6.040577,6.428461,5.860687,6.170805
+2043,6.190326,6.437477,5.712630,6.691883,5.815721,6.192059,6.619925,6.035113,6.336945
+2044,6.388643,6.611998,5.895344,6.876992,5.888823,6.326224,6.818350,6.222943,6.513248
+2045,6.694716,6.893358,6.175327,7.186384,6.153762,6.608804,7.112264,6.490861,6.791655
+2046,6.962092,7.120543,6.414420,7.505726,6.427409,6.872441,7.345939,6.758920,7.003431
+2047,7.128275,7.292545,6.559214,7.708294,6.592273,7.024219,7.515275,6.879106,7.168189
+2048,7.269464,7.457916,6.683449,7.877018,6.761227,7.136751,7.668647,7.012994,7.307539
+2049,7.378307,7.576050,6.800232,7.980815,6.911001,7.215116,7.788373,7.123527,7.415736
+2050,7.497430,7.694907,6.927563,8.055626,7.068169,7.339126,7.932874,7.212858,7.536294
diff --git a/inputs/fuelprices/alpha_AEO_2026_baseline.csv b/inputs/fuelprices/alpha_AEO_2026_baseline.csv
new file mode 100644
index 000000000..9f04a25c6
--- /dev/null
+++ b/inputs/fuelprices/alpha_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+t,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,4.274321,4.137875,4.711561,4.348849,4.683875,4.172505,5.211406,4.662091,3.986710
+2011,3.250640,3.043907,3.538973,3.435104,2.518591,2.781113,3.928042,3.661574,3.296519
+2012,1.861207,1.802179,2.019255,2.135136,1.299775,1.533531,2.668667,2.094801,2.026640
+2013,2.738112,2.628958,2.894765,2.942602,3.327790,2.185216,3.196929,3.017899,2.807178
+2014,3.494080,3.092879,3.199281,3.246415,3.820127,2.593936,3.634713,3.744689,3.299712
+2015,1.453247,1.548003,1.307021,1.760199,1.691627,1.054068,2.286350,1.809065,1.760347
+2016,1.349029,1.232448,1.146193,1.287225,1.364421,0.752212,1.836327,1.200097,1.354203
+2017,1.801610,1.721088,1.535469,1.744506,1.601754,1.182392,2.113329,1.855590,1.810900
+2018,1.604343,1.609369,1.464344,1.717984,1.757134,1.144658,1.910176,1.671963,1.775599
+2019,0.910407,1.084621,0.955677,1.167664,1.575510,0.745265,1.461099,0.976959,1.163360
+2020,0.496623,0.744497,0.495862,0.878256,1.145742,0.441472,1.095534,0.640109,0.850163
+2021,2.243688,2.721406,2.018844,2.864669,3.094409,2.537661,3.041219,2.427009,2.743093
+2022,3.080724,3.831824,2.391515,3.704427,3.556702,3.287192,4.112245,3.505542,3.831477
+2023,0.525828,0.788272,0.525021,0.929898,1.213108,0.467436,1.159950,0.677752,0.900153
+2024,0.256799,0.527445,-0.089505,0.556717,0.197963,0.095968,0.980185,0.305383,0.645424
+2025,0.852401,1.200867,0.601804,1.055427,1.100276,0.651410,1.676951,0.869033,1.279185
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+2027,1.069620,1.391995,0.716659,1.317888,0.803180,0.939826,1.879705,1.156444,1.459920
+2028,1.121800,1.434658,0.719824,1.420702,0.864588,1.072397,1.690256,1.229853,1.509028
+2029,1.239455,1.563456,0.796138,1.556980,0.935078,1.209064,1.814477,1.337114,1.600793
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+2032,1.701904,2.265472,0.929750,2.331625,1.092690,1.951962,2.567253,1.990277,2.424819
+2033,1.628033,2.249207,0.983800,2.379130,1.146902,1.974265,2.416365,2.021673,2.403020
+2034,1.505045,2.141583,0.943273,2.329366,1.132332,1.808836,2.308355,1.938153,2.290749
+2035,1.427770,2.128535,0.895384,2.319018,1.080194,1.781731,2.287886,1.885064,2.291685
+2036,1.399536,2.148171,0.845248,2.385127,1.083786,1.773895,2.504856,1.871273,2.361992
+2037,1.421311,2.163694,0.886161,2.419176,1.144225,1.783674,2.315196,1.898142,2.399047
+2038,1.445013,2.152901,0.929833,2.399261,1.152753,1.781693,2.306061,1.915447,2.392285
+2039,1.483199,2.136147,0.948910,2.338296,1.186466,1.755008,2.260834,1.888120,2.390423
+2040,1.483271,2.149574,0.931508,2.322414,1.192576,1.754340,2.278851,1.895158,2.383420
+2041,1.454831,2.093792,0.956656,2.341682,1.207616,1.745758,2.245404,1.878740,2.371799
+2042,1.417710,1.996432,0.944770,2.270628,1.191591,1.653727,2.343205,1.805271,2.286511
+2043,1.351047,1.892081,0.892979,2.148028,1.128883,1.525319,2.232732,1.693067,2.179623
+2044,1.272927,1.790872,0.784756,2.051051,0.985737,1.422989,2.115356,1.594616,2.102605
+2045,1.193022,1.682490,0.666874,1.986795,0.906896,1.359777,1.856207,1.527544,2.046563
+2046,1.135599,1.615208,0.569800,1.988558,0.853422,1.382786,1.848290,1.513098,2.041603
+2047,1.073197,1.551866,0.497443,1.934622,0.772307,1.292597,1.715850,1.443065,1.967115
+2048,1.008579,1.489174,0.437942,1.868772,0.699996,1.217253,1.649481,1.376723,1.892655
+2049,0.949038,1.429534,0.384605,1.820173,0.653817,1.154207,1.583388,1.312196,1.823080
+2050,0.897455,1.375311,0.340709,1.764423,0.563710,1.110525,1.528480,1.250993,1.768898
diff --git a/inputs/fuelprices/cd_beta0.csv b/inputs/fuelprices/cd_beta0.csv
index d55314b02..5dd0e2a9b 100644
--- a/inputs/fuelprices/cd_beta0.csv
+++ b/inputs/fuelprices/cd_beta0.csv
@@ -1,10 +1,10 @@
 *cendiv,value
-East_North_Central,0.262956
-East_South_Central,0.608412
-Mid_Atlantic,0.144978
-Mountain,0.545321
-New_England,0.502176
-Pacific,0.196842
-South_Atlantic,0.161863
-West_North_Central,0.578654
-West_South_Central,0.084872
\ No newline at end of file
+East_North_Central,0.013440
+East_South_Central,-0.026099
+Mid_Atlantic,0.185420
+Mountain,0.049527
+New_England,2.359082
+Pacific,0.704760
+South_Atlantic,0.029744
+West_North_Central,0.513493
+West_South_Central,-0.126897
diff --git a/inputs/fuelprices/cendivweights.csv b/inputs/fuelprices/cendivweights.csv
deleted file mode 100644
index 757f50650..000000000
--- a/inputs/fuelprices/cendivweights.csv
+++ /dev/null
@@ -1,3110 +0,0 @@
-r,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central,MEX
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-p09150,0.007,0.001,0.342,0.0,0.596,0.0,0.053,0.0,0.0,0
-p09160,0.011,0.002,0.426,0.0,0.493,0.0,0.068,0.0,0.0,0
-p09170,0.009,0.002,0.395,0.0,0.51,0.0,0.084,0.0,0.0,0
-p09180,0.007,0.001,0.402,0.0,0.531,0.0,0.059,0.0,0.0,0
-p09190,0.011,0.002,0.436,0.0,0.455,0.0,0.096,0.0,0.0,0
diff --git a/inputs/fuelprices/coal_AEO_2023_reference.csv b/inputs/fuelprices/coal_AEO_2023_reference.csv
deleted file mode 100644
index 571f605db..000000000
--- a/inputs/fuelprices/coal_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,2.933735194,3.382809097,3.502493226,2.314340557,4.504423263,2.385940608,4.385317937,1.85970541,2.285751166
-2011,3.203480163,3.409253326,3.509603077,2.391638928,4.642067641,2.485830298,4.553617554,2.024450009,2.415802415
-2012,3.11066407,3.250927737,3.525429695,2.543215993,5.449355623,2.459193901,4.483100258,2.115921823,2.610854902
-2013,2.995232488,3.103959196,3.178807089,2.354784347,5.021946148,2.580974321,4.190595231,2.168394252,2.669385698
-2014,2.920920174,3.180912534,3.377719533,2.388418262,3.526904973,2.727483096,3.708653037,2.362663875,2.805954955
-2015,2.745637928,2.946253891,3.269140872,2.257861915,3.464668299,2.592909986,3.401020188,2.278341507,2.704545989
-2016,2.548716332,2.717756527,3.023450085,2.223525064,3.196426803,2.364342789,3.271397257,2.07858473,2.607290578
-2017,2.426319368,2.575553511,2.573845728,2.134236045,3.108542581,2.357180947,3.133898383,1.923868858,2.337804716
-2018,2.424782829,2.578136153,2.652967572,2.144457114,3.331991898,2.463977963,3.070038248,1.994668465,2.41669474
-2019,2.260990651,2.230596125,3.077274942,2.162248779,3.112754867,2.311691296,2.817693036,2.081341278,2.185757877
-2020,2.21296752,2.06861148,2.5775226,2.154141,3.016400676,2.0504124,2.86801236,1.67514696,1.93352832
-2021,2.17984716,2.04817572,2.44783728,2.1961152,3.010839854,2.06690508,2.8091502,1.70676936,1.95455592
-2022,2.131114,2.008735,2.08875,2.152007,3.503936544,2.271631,2.589649,1.757149,2.07165
-2023,2.084341,1.95972,2.063801,2.052156,3.462083899,2.216407,2.447239,1.704537,2.006735
-2024,2.094711,1.942129,2.055524,2.040424,3.448199,2.217564,2.384869,1.720433,1.971271
-2025,2.147902,1.940953,2.069094,1.785091,3.470963055,2.294237,2.38643,1.758175,2.029351
-2026,2.106584,1.950878,2.081482,1.78466,3.491744271,2.240102,2.310191,1.746945,2.030092
-2027,2.093268,1.936479,2.084447,1.759975,3.496718141,2.215008,2.238824,1.734488,2.009337
-2028,2.076573,1.914195,2.096089,1.630492,3.516247922,2.183337,2.20904,1.714732,1.988276
-2029,2.019474,1.901134,2.120943,1.656592,3.557941202,2.158459,2.205446,1.707366,1.967524
-2030,2.034066,1.890836,2.118054,1.585909,3.553094824,2.135439,2.220501,1.702108,1.949285
-2031,2.019142,1.879298,2.100696,1.561774,3.523976293,2.113487,2.225052,1.687947,1.933424
-2032,2.004324,1.867954,2.088863,1.331491,3.504126105,2.092947,2.218937,1.677946,1.918301
-2033,2.004538,1.821609,2.085079,1.235665,3.497778339,2.076913,2.230854,1.663786,1.910883
-2034,2.010873,1.820305,2.077316,1.245699,3.484755689,2.066158,2.243947,1.665542,1.908152
-2035,2.009272,1.818837,2.071161,1.26009,3.474430505,2.055682,2.240432,1.649977,1.907058
-2036,2.01058,1.816898,2.062794,1.262006,3.460394628,2.046597,2.220953,1.64956,1.908665
-2037,2.014499,1.815858,1.894991,1.233206,3.178900403,2.039273,2.216686,1.651096,1.912143
-2038,2.018082,1.815616,1.895638,1.297967,3.179985763,2.032225,2.212141,1.624405,1.9155
-2039,2.015666,1.814015,1.895452,1.298403,3.179673743,2.023474,2.203706,1.6216,1.915044
-2040,2.009565,1.809758,1.897631,1.327411,3.183329076,2.010775,2.193242,1.620807,1.90916
-2041,2.007825,1.806802,1.903817,1.339005,3.193706264,1.991629,2.191758,1.615358,1.905341
-2042,2.004388,1.807186,1.911305,1.342167,3.206267594,1.982403,2.194301,1.609909,1.900726
-2043,2.004722,1.80927,1.919849,1.339854,3.220600393,1.976773,2.19754,1.605855,1.900378
-2044,2.003707,1.807819,1.927319,1.333778,3.233131527,1.970719,2.187804,1.603431,1.900143
-2045,2.000554,1.810795,1.933412,1.333587,3.243352705,1.96448,2.191054,1.600661,1.89916
-2046,1.997813,1.809327,1.941033,1.344362,3.256137145,1.955443,2.196399,1.596756,1.895768
-2047,1.995896,1.809096,1.949271,1.338551,3.269956621,1.945345,2.186981,1.594289,1.892605
-2048,1.993917,1.80929,1.954113,1.319891,3.278079211,1.935027,2.178619,1.592399,1.890472
-2049,1.992319,1.812262,1.963171,1.328315,3.29327426,1.925027,2.186695,1.591273,1.889601
-2050,1.990264,1.814798,1.962928,1.32453,3.29286662,1.914853,2.187768,1.589481,1.888194
diff --git a/inputs/fuelprices/coal_AEO_2026_altelec.csv b/inputs/fuelprices/coal_AEO_2026_altelec.csv
new file mode 100644
index 000000000..1e828924f
--- /dev/null
+++ b/inputs/fuelprices/coal_AEO_2026_altelec.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,3.224292,3.717842,3.84938,2.543553,4.950541,2.622244,4.819639,2.04389,2.512132
+2011,3.520753,3.746905,3.857194,2.628507,5.101818,2.732027,5.004607,2.224951,2.655063
+2012,3.418744,3.572899,3.874588,2.795096,5.989059,2.702752,4.927106,2.325483,2.869434
+2013,3.29188,3.411375,3.493636,2.588002,5.519319,2.836594,4.605631,2.383152,2.933761
+2014,3.210208,3.49595,3.712249,2.624967,3.876209,2.997613,4.075958,2.596662,3.083856
+2015,3.017566,3.238051,3.592916,2.48148,3.807809,2.849712,3.737857,2.503988,2.972404
+2016,2.801141,2.986923,3.322892,2.443743,3.513001,2.598507,3.595396,2.284448,2.865516
+2017,2.666622,2.830636,2.828759,2.345611,3.416412,2.590636,3.444279,2.114409,2.569341
+2018,2.664933,2.833475,2.915717,2.356844,3.661992,2.70801,3.374095,2.19222,2.656044
+2019,2.484919,2.451514,3.382048,2.376398,3.421042,2.540641,3.096757,2.287477,2.402235
+2020,2.43214,2.273487,2.8328,2.367487,3.315145,2.253485,3.15206,1.841053,2.125025
+2021,2.395739,2.251027,2.690271,2.413618,3.309033,2.271611,3.087368,1.875808,2.148135
+2022,2.342179,2.20768,2.29562,2.365142,3.850966,2.496613,2.846128,1.931177,2.276826
+2023,2.290774,2.153811,2.2682,2.255401,3.804968,2.43592,2.689613,1.873354,2.205482
+2024,2.741502,2.402605,2.257186,2.097527,3.720559,2.183604,3.720559,2.07623,2.219229
+2025,2.696674,2.478701,2.608568,2.471174,2.608568,2.552525,3.730905,1.797321,2.073799
+2026,2.664435,2.407584,2.586529,2.348744,2.586529,2.614501,3.63111,1.802897,2.066433
+2027,2.589188,2.342201,2.591892,2.413531,2.591892,2.639512,3.349658,1.87295,2.204021
+2028,2.600635,2.343247,2.558006,2.315373,2.558006,2.653075,3.229552,1.895456,2.254482
+2029,2.592192,2.343984,2.5345,2.305234,2.5345,2.661994,3.219424,1.891759,2.256867
+2030,2.58273,2.339671,2.528745,2.351599,2.528745,2.648286,3.230093,1.888688,2.240847
+2031,2.563478,2.319687,2.509408,1.983413,2.509408,2.626866,3.249327,1.869732,2.1265
+2032,2.565951,2.315695,2.517221,1.97391,2.517221,2.624111,3.206125,1.865713,2.153457
+2033,2.550725,2.31492,2.488566,1.985037,2.488566,2.625256,3.139448,1.868353,2.140137
+2034,2.546102,2.307602,2.432665,1.993185,2.432665,2.623763,3.122672,1.86952,2.118297
+2035,2.545326,2.300222,2.414622,1.983858,2.414622,2.623056,3.110127,1.853602,2.105928
+2036,2.549724,2.298846,2.402569,1.984207,2.402569,2.632611,3.102695,1.859341,2.116696
+2037,2.566749,2.301688,2.537275,2.001463,2.537275,2.648494,3.10704,1.877252,2.296756
+2038,2.580917,2.303939,2.53267,2.003024,2.53267,2.666843,3.116571,1.89193,2.309759
+2039,2.596149,2.30511,2.520226,2.010546,2.520226,2.686291,3.121426,1.906917,2.343749
+2040,2.610074,2.304987,2.59136,2.033732,2.59136,2.701919,3.094911,1.920162,2.358888
+2041,2.624459,2.325017,2.577563,2.050715,2.577563,2.719554,3.109776,1.933933,2.366216
+2042,2.638127,2.337488,2.573824,2.069321,2.573824,2.735152,3.118578,1.947265,2.367849
+2043,2.651603,2.348259,2.574839,2.082585,2.574839,2.749991,3.119632,1.960137,2.359388
+2044,2.662392,2.339498,2.543496,2.093113,2.543496,2.76328,3.10563,1.970987,2.3502
+2045,2.671464,2.333806,2.398671,2.104572,2.398671,2.776207,3.080047,1.981464,2.358245
+2046,2.685396,2.338267,2.364229,2.10537,2.364229,2.791546,3.083927,1.995102,2.361401
+2047,2.700309,2.350808,2.352297,2.118672,2.352297,2.808261,3.093796,2.008181,2.34796
+2048,2.713355,2.359891,2.342263,2.130679,2.342263,2.82296,3.106794,2.020095,2.356044
+2049,2.728504,2.386774,2.261732,2.145234,2.261732,2.841563,3.120025,2.034285,2.368422
+2050,2.730916,2.404197,2.270392,2.152194,2.270392,2.857189,3.147612,2.048594,2.372569
diff --git a/inputs/fuelprices/coal_AEO_2026_baseline.csv b/inputs/fuelprices/coal_AEO_2026_baseline.csv
new file mode 100644
index 000000000..540c6b491
--- /dev/null
+++ b/inputs/fuelprices/coal_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,3.224292,3.717842,3.84938,2.543553,4.950541,2.622244,4.819639,2.04389,2.512132
+2011,3.520753,3.746905,3.857194,2.628507,5.101818,2.732027,5.004607,2.224951,2.655063
+2012,3.418744,3.572899,3.874588,2.795096,5.989059,2.702752,4.927106,2.325483,2.869434
+2013,3.29188,3.411375,3.493636,2.588002,5.519319,2.836594,4.605631,2.383152,2.933761
+2014,3.210208,3.49595,3.712249,2.624967,3.876209,2.997613,4.075958,2.596662,3.083856
+2015,3.017566,3.238051,3.592916,2.48148,3.807809,2.849712,3.737857,2.503988,2.972404
+2016,2.801141,2.986923,3.322892,2.443743,3.513001,2.598507,3.595396,2.284448,2.865516
+2017,2.666622,2.830636,2.828759,2.345611,3.416412,2.590636,3.444279,2.114409,2.569341
+2018,2.664933,2.833475,2.915717,2.356844,3.661992,2.70801,3.374095,2.19222,2.656044
+2019,2.484919,2.451514,3.382048,2.376398,3.421042,2.540641,3.096757,2.287477,2.402235
+2020,2.43214,2.273487,2.8328,2.367487,3.315145,2.253485,3.15206,1.841053,2.125025
+2021,2.395739,2.251027,2.690271,2.413618,3.309033,2.271611,3.087368,1.875808,2.148135
+2022,2.342179,2.20768,2.29562,2.365142,3.850966,2.496613,2.846128,1.931177,2.276826
+2023,2.290774,2.153811,2.2682,2.255401,3.804968,2.43592,2.689613,1.873354,2.205482
+2024,2.741502,2.402605,2.257186,2.097527,3.720559,2.183604,3.720559,2.07623,2.219229
+2025,2.697991,2.478127,2.610825,2.459127,2.610825,2.547656,3.728853,1.797461,2.074193
+2026,2.678993,2.411469,2.609556,2.32962,2.609556,2.61463,3.648981,1.803064,2.073156
+2027,2.588603,2.349643,2.577045,2.393302,2.577045,2.638686,3.350258,1.872733,2.19397
+2028,2.600813,2.340981,2.55784,2.303188,2.55784,2.651147,3.241633,1.903674,2.247226
+2029,2.590306,2.345229,2.52293,2.296779,2.52293,2.660305,3.237127,1.898496,2.228578
+2030,2.58059,2.333144,2.518117,2.439893,2.518117,2.64647,3.173115,1.882441,2.237984
+2031,2.561357,2.318848,2.417819,1.968821,2.417819,2.623801,3.210659,1.861227,2.197295
+2032,2.589286,2.358742,2.065758,2.877009,2.065758,2.629092,3.019993,1.857804,2.410654
+2033,2.589104,2.357557,2.059539,2.86228,2.059539,2.633603,3.016599,1.867814,2.416533
+2034,2.580561,2.353566,2.046952,2.852402,2.046952,2.633142,2.98306,1.87117,2.416349
+2035,2.567396,2.346812,2.032553,2.839798,2.032553,2.627857,2.967977,1.870677,2.412443
+2036,2.561525,2.345188,2.02427,2.848332,2.02427,2.638476,2.937563,1.87957,2.422616
+2037,2.578872,2.342707,2.780539,2.865445,2.780539,2.654427,2.939031,1.891564,2.43772
+2038,2.579348,2.346733,2.792268,2.840283,2.792268,2.647292,2.948851,1.906695,2.456602
+2039,2.570332,2.443903,2.100127,2.840283,2.100127,2.627124,2.847225,1.926676,2.482872
+2040,2.560712,2.433394,2.095068,2.840283,2.095068,2.612675,2.837137,1.940794,2.496761
+2041,2.55301,2.430461,2.078361,2.840283,2.078361,2.620838,2.82242,1.94982,2.506227
+2042,2.54104,2.297371,2.07276,2.840283,2.07276,2.601753,2.81536,1.958098,2.511419
+2043,2.539342,2.29952,2.06317,2.840283,2.06317,2.619181,2.806508,1.968459,2.522819
+2044,2.485923,2.256552,1.027306,2.840283,1.027306,2.548782,2.550491,1.977339,2.533907
+2045,2.467135,2.251078,1.026189,2.840283,1.026189,2.524989,2.106682,1.98793,1.905336
+2046,2.416172,2.025176,1.01394,2.840283,1.01394,2.51396,2.106682,1.99214,1.905336
+2047,2.416172,2.025176,1.01394,2.840283,1.01394,2.504147,2.106682,2.000725,1.905336
+2048,2.416172,2.025176,1.01394,2.840283,1.01394,2.505794,2.106682,2.010402,1.905336
+2049,2.416172,2.025176,1.01394,2.840283,1.01394,2.536341,2.106682,2.023221,1.905336
+2050,2.416172,2.025176,1.01394,2.840283,1.01394,2.546657,2.106682,2.036995,1.905336
diff --git a/inputs/fuelprices/dollaryear.csv b/inputs/fuelprices/dollaryear.csv
index 07d900ef5..73356adb2 100644
--- a/inputs/fuelprices/dollaryear.csv
+++ b/inputs/fuelprices/dollaryear.csv
@@ -2,10 +2,11 @@ Scenario,Dollar.Year
 10,2021
 30,2021
 40,2021
-AEO_2023_HOG,2022
-AEO_2023_LOG,2022
-AEO_2023_reference,2022
 AEO_2025_HOG,2024
 AEO_2025_LOG,2024
 AEO_2025_reference,2024
+AEO_2026_HOG,2025
+AEO_2026_LOG,2025
+AEO_2026_baseline,2025
+AEO_2026_altelec,2025
 reference,2021
diff --git a/inputs/fuelprices/ng_AEO_2023_HOG.csv b/inputs/fuelprices/ng_AEO_2023_HOG.csv
deleted file mode 100644
index e8e701a5b..000000000
--- a/inputs/fuelprices/ng_AEO_2023_HOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,7.254022839,7.296905121,6.619741716,7.220283525,8.071019659,6.408418159,6.17430453,6.73516352,6.462048271
-2011,6.512955062,6.704334968,5.988900482,6.710142049,7.106759955,5.634676031,5.621728827,6.309847915,6.089580856
-2012,4.785045081,4.593918388,4.031404624,4.515513278,5.362398399,3.89411839,3.814678938,4.492850642,4.650872048
-2013,7.531933862,5.808286039,5.269445079,5.804202108,6.056879108,5.065083145,4.958719009,5.62619107,5.570066919
-2014,8.186418578,6.301208804,6.442909545,6.906918787,6.738007505,5.784351523,5.73372937,6.099062163,6.210729589
-2015,5.287712488,3.594435715,3.480682938,4.133926504,4.86341651,3.577200704,3.46307524,3.998814787,4.031247202
-2016,4.806153933,3.417760704,3.364793019,3.267776851,4.214270494,3.126378192,2.891618367,3.308831191,3.423379194
-2017,4.999383429,3.883245859,3.968796936,4.172463461,4.548750041,3.790405833,3.56084764,3.916442785,3.910755611
-2018,5.490397848,3.947354124,3.811708854,4.046354284,4.381288283,3.758426256,3.533045491,4.025154942,4.030401773
-2019,5.112507766,3.332110171,2.869852701,3.129800793,3.826569062,3.07335447,2.661432605,3.31938752,3.49969717
-2020,4.569274515,2.728053484,2.271868716,2.664482157,3.300639817,2.587141565,2.215244778,2.909860469,3.085398521
-2021,7.41965076,4.97011248,4.86566784,5.28217524,6.1920126,5.54604948,5.0796126,5.87752308,6.2665974
-2022,8.335512,5.764782,6.289096,7.121054,7.975896,7.385187,6.871352,7.296973,7.50605
-2023,6.264496,4.775036,4.737991,4.904904,5.751236,5.111089,4.585498,5.130088,5.106378
-2024,4.667513,3.713414,3.650828,3.822969,4.545685,3.927958,3.460164,3.988934,3.894965
-2025,3.792009,3.128483,3.093338,3.293304,3.963094,3.349671,2.935345,3.427662,3.433308
-2026,3.096217,2.738202,2.783703,3.015167,3.561762,3.003823,2.643772,3.162569,3.212006
-2027,2.743373,2.506775,2.610604,2.880163,3.326975,2.842243,2.523217,2.992285,3.020594
-2028,2.568799,2.359876,2.521249,2.667128,3.186194,2.743173,2.471957,2.913741,2.96571
-2029,2.642757,2.398751,2.571409,2.698899,3.209596,2.792022,2.513328,2.88477,2.93603
-2030,2.820677,2.399597,2.594927,2.773508,3.271268,2.860831,2.616809,3.001502,3.026502
-2031,2.831078,2.428467,2.687971,2.867895,3.377957,2.981458,2.75662,3.132992,3.101371
-2032,2.927299,2.459232,2.742608,2.965772,3.469411,3.080561,2.881675,3.268645,3.202971
-2033,2.854083,2.471838,2.768556,3.045159,3.572864,3.204013,2.995042,3.354263,3.261777
-2034,2.996028,2.467479,2.719951,3.029478,3.560909,3.163378,2.952709,3.299999,3.265246
-2035,3.017485,2.485497,2.67271,3.051855,3.585225,3.170303,2.963984,3.287593,3.246768
-2036,2.945943,2.461514,2.657525,3.050234,3.581397,3.151036,2.933581,3.27743,3.236876
-2037,3.055769,2.515285,2.680943,3.066206,3.606004,3.15711,2.92211,3.288228,3.220642
-2038,3.132951,2.550968,2.726869,3.111265,3.644009,3.211144,2.952675,3.36894,3.18658
-2039,3.175226,2.551472,2.736724,3.096908,3.61496,3.200227,2.942791,3.344298,3.098051
-2040,3.187366,2.537154,2.746766,3.148762,3.664401,3.239599,3.001496,3.44908,3.223347
-2041,3.138763,2.522159,2.721535,3.176383,3.659496,3.263091,3.020883,3.451127,3.243899
-2042,3.155247,2.516264,2.691384,3.175348,3.644848,3.236653,2.984807,3.447298,3.23084
-2043,3.133455,2.483737,2.646098,3.14356,3.614261,3.206888,2.940549,3.459749,3.192569
-2044,3.030737,2.452481,2.632752,3.141205,3.617012,3.217957,2.947701,3.5047,3.209046
-2045,3.053465,2.430056,2.594021,3.089114,3.58581,3.165094,2.906031,3.462365,3.176212
-2046,3.055674,2.416504,2.567769,3.079568,3.551434,3.138617,2.886255,3.497965,3.220326
-2047,3.065563,2.40404,2.543906,3.067466,3.555176,3.13667,2.882709,3.496337,3.248628
-2048,2.952746,2.368247,2.542724,3.071799,3.562545,3.157641,2.911675,3.528552,3.256435
-2049,2.94491,2.365337,2.490885,3.026026,3.497939,3.086339,2.858459,3.497436,3.273947
-2050,2.745981,2.289692,2.4608,2.968234,3.442903,3.051196,2.82423,3.495311,3.285128
diff --git a/inputs/fuelprices/ng_AEO_2023_LOG.csv b/inputs/fuelprices/ng_AEO_2023_LOG.csv
deleted file mode 100644
index 163d5128a..000000000
--- a/inputs/fuelprices/ng_AEO_2023_LOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,7.254022839,7.296905121,6.619741716,7.220283525,8.071019659,6.408418159,6.17430453,6.73516352,6.462048271
-2011,6.512955062,6.704334968,5.988900482,6.710142049,7.106759955,5.634676031,5.621728827,6.309847915,6.089580856
-2012,4.785045081,4.593918388,4.031404624,4.515513278,5.362398399,3.89411839,3.814678938,4.492850642,4.650872048
-2013,7.531933862,5.808286039,5.269445079,5.804202108,6.056879108,5.065083145,4.958719009,5.62619107,5.570066919
-2014,8.186418578,6.301208804,6.442909545,6.906918787,6.738007505,5.784351523,5.73372937,6.099062163,6.210729589
-2015,5.287712488,3.594435715,3.480682938,4.133926504,4.86341651,3.577200704,3.46307524,3.998814787,4.031247202
-2016,4.806153933,3.417760704,3.364793019,3.267776851,4.214270494,3.126378192,2.891618367,3.308831191,3.423379194
-2017,4.999383429,3.883245859,3.968796936,4.172463461,4.548750041,3.790405833,3.56084764,3.916442785,3.910755611
-2018,5.490397848,3.947354124,3.811708854,4.046354284,4.381288283,3.758426256,3.533045491,4.025154942,4.030401773
-2019,5.112507766,3.332110171,2.869852701,3.129800793,3.826569062,3.07335447,2.661432605,3.31938752,3.49969717
-2020,4.569274515,2.728053484,2.271868716,2.664482157,3.300639817,2.587141565,2.215244778,2.909860469,3.085398521
-2021,7.3701468,4.8624624,4.83796368,5.31360324,6.23540916,5.60250972,5.08998492,5.9188536,6.24981744
-2022,8.335549,5.76459,6.28925,7.127634,7.975394,7.385318,6.871107,7.298644,7.504611
-2023,7.353697,5.943807,6.089671,6.267931,7.253084,6.670957,6.085583,6.419995,6.42835
-2024,6.448774,5.568675,5.653863,5.687466,6.787796,6.133646,5.582108,5.865061,5.697881
-2025,5.744268,5.196352,5.303296,5.429222,6.306295,5.751734,5.324214,5.619208,5.553545
-2026,5.083745,4.801341,4.95651,5.12254,5.756338,5.256299,5.005971,5.393435,5.381677
-2027,4.732846,4.52384,4.63776,4.715982,5.350209,4.758035,4.663158,4.976079,4.917698
-2028,4.625924,4.358739,4.570542,4.588263,5.17415,4.55957,4.544325,4.906047,4.829637
-2029,4.622853,4.298081,4.53213,4.528111,5.21227,4.573213,4.607138,4.898719,4.830875
-2030,4.385468,4.186028,4.469368,4.524065,5.113272,4.559031,4.633702,4.821904,4.812545
-2031,4.517356,4.150454,4.556061,4.621707,5.372003,4.768547,4.864616,4.984457,4.939323
-2032,4.63586,4.305259,4.766483,4.821124,5.485615,5.104767,5.17689,5.405995,5.217563
-2033,4.898982,4.469233,4.92573,4.962501,5.711021,5.38217,5.427168,5.650806,5.478068
-2034,5.037767,4.658939,5.052215,5.162161,5.864262,5.575456,5.616404,5.914866,5.707286
-2035,5.080363,4.701237,5.164671,5.335192,5.960157,5.729566,5.762377,6.058889,5.898657
-2036,5.084612,4.741972,5.224214,5.423471,5.994436,5.860327,5.868017,6.187,5.916695
-2037,5.104743,4.740005,5.245152,5.49217,6.050889,5.923118,5.912548,6.238308,5.983891
-2038,5.150308,4.769562,5.307802,5.526766,6.053361,6.016912,5.997163,6.350635,6.042434
-2039,5.187603,4.781529,5.324528,5.542362,6.083917,6.024216,6.049494,6.342899,6.121644
-2040,5.230264,4.824027,5.373168,5.565547,6.102288,6.100887,6.103765,6.396626,6.202671
-2041,5.235667,4.850025,5.366818,5.568707,6.068235,6.055292,6.058292,6.350323,6.195572
-2042,5.327495,4.824836,5.346203,5.518661,5.972715,6.002325,5.973806,6.344037,6.154214
-2043,5.370145,4.782941,5.28771,5.463378,5.883625,5.918285,5.864641,6.349466,5.996591
-2044,5.192415,4.789168,5.317018,5.527688,5.912279,5.971885,5.936985,6.531085,6.124987
-2045,5.113031,4.652084,5.249482,5.444368,5.87785,5.925346,5.903101,6.556755,6.11711
-2046,5.074595,4.513991,5.233972,5.450544,5.850298,5.992282,5.939836,6.630671,6.140124
-2047,5.099331,4.502272,5.23372,5.464043,5.853635,5.956639,5.957829,6.700186,6.156491
-2048,5.065372,4.533745,5.237758,5.489003,5.871871,6.025952,6.008211,6.699751,6.296318
-2049,5.202069,4.603632,5.295067,5.553706,5.976315,6.109165,6.124047,6.749036,6.223396
-2050,5.126056,4.703265,5.375136,5.622306,5.99053,6.187357,6.15964,6.823635,6.36574
diff --git a/inputs/fuelprices/ng_AEO_2023_reference.csv b/inputs/fuelprices/ng_AEO_2023_reference.csv
deleted file mode 100644
index e32d49b90..000000000
--- a/inputs/fuelprices/ng_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,7.254022839,7.296905121,6.619741716,7.220283525,8.071019659,6.408418159,6.17430453,6.73516352,6.462048271
-2011,6.512955062,6.704334968,5.988900482,6.710142049,7.106759955,5.634676031,5.621728827,6.309847915,6.089580856
-2012,4.785045081,4.593918388,4.031404624,4.515513278,5.362398399,3.89411839,3.814678938,4.492850642,4.650872048
-2013,7.531933862,5.808286039,5.269445079,5.804202108,6.056879108,5.065083145,4.958719009,5.62619107,5.570066919
-2014,8.186418578,6.301208804,6.442909545,6.906918787,6.738007505,5.784351523,5.73372937,6.099062163,6.210729589
-2015,5.287712488,3.594435715,3.480682938,4.133926504,4.86341651,3.577200704,3.46307524,3.998814787,4.031247202
-2016,4.806153933,3.417760704,3.364793019,3.267776851,4.214270494,3.126378192,2.891618367,3.308831191,3.423379194
-2017,4.999383429,3.883245859,3.968796936,4.172463461,4.548750041,3.790405833,3.56084764,3.916442785,3.910755611
-2018,5.490397848,3.947354124,3.811708854,4.046354284,4.381288283,3.758426256,3.533045491,4.025154942,4.030401773
-2019,5.112507766,3.332110171,2.869852701,3.129800793,3.826569062,3.07335447,2.661432605,3.31938752,3.49969717
-2020,4.569274515,2.728053484,2.271868716,2.664482157,3.300639817,2.587141565,2.215244778,2.909860469,3.085398521
-2021,7.38234216,4.88743092,4.85184816,5.28670368,6.23408076,5.59278216,5.07799368,5.92494048,6.2433666
-2022,8.308649,5.805461,6.310704,7.077593,7.958245,7.386968,6.853481,7.297107,7.512584
-2023,6.684712,5.189338,5.242029,5.406582,6.344121,5.750495,5.191479,5.677948,5.650613
-2024,5.194127,4.200577,4.20185,4.342521,5.114598,4.528499,4.062139,4.578878,4.443596
-2025,4.213594,3.584859,3.588508,3.759445,4.456979,3.891473,3.4673,4.043263,3.987888
-2026,3.625669,3.118079,3.141228,3.367872,3.880473,3.400975,3.04443,3.622533,3.62286
-2027,3.13443,2.814363,2.83741,3.118222,3.558741,3.113628,2.828285,3.315406,3.350442
-2028,2.876058,2.652449,2.744003,2.915106,3.407456,3.008239,2.780204,3.222567,3.265682
-2029,3.03356,2.62633,2.707932,2.886118,3.420762,3.014445,2.79841,3.1882,3.213497
-2030,2.914836,2.565025,2.745545,2.976737,3.483771,3.096433,2.900386,3.22611,3.215218
-2031,2.828695,2.568714,2.79071,3.02367,3.569899,3.18959,3.039086,3.343057,3.280373
-2032,3.016352,2.602197,2.808766,3.132584,3.631186,3.328301,3.176046,3.506787,3.339648
-2033,2.997193,2.665663,2.840869,3.265977,3.805427,3.456829,3.361983,3.684196,3.520141
-2034,3.066314,2.710329,2.910858,3.384207,3.900569,3.6179,3.498266,3.861104,3.695428
-2035,3.159226,2.755018,2.989411,3.480448,4.037017,3.735771,3.636285,4.029991,3.839051
-2036,3.231528,2.787365,3.026545,3.50312,4.061968,3.726999,3.663766,4.076721,3.868771
-2037,3.293992,2.867354,3.090324,3.557522,4.130064,3.796579,3.668194,4.112775,3.90674
-2038,3.45593,2.994708,3.190867,3.701993,4.259203,3.958757,3.835625,4.302787,4.109264
-2039,3.515431,3.021759,3.207848,3.674064,4.224813,3.89638,3.761103,4.225013,4.007872
-2040,3.590063,3.091251,3.32545,3.831446,4.356409,4.079033,3.962016,4.438752,4.170026
-2041,3.765538,3.158199,3.367842,3.898807,4.448615,4.137982,4.046668,4.518301,4.24682
-2042,3.788926,3.13364,3.336906,3.910318,4.413907,4.131035,4.037673,4.522941,4.243093
-2043,3.73375,3.091184,3.287619,3.868182,4.357785,4.065986,3.967322,4.474222,4.197077
-2044,3.592151,3.050537,3.272107,3.86595,4.191014,4.027678,3.940505,4.450418,4.126357
-2045,3.651824,3.052971,3.255789,3.848908,4.19783,3.998349,3.936208,4.437391,4.117561
-2046,3.564594,3.005419,3.211098,3.811502,4.179885,4.006837,3.938043,4.41749,4.003365
-2047,3.515119,2.935276,3.18692,3.758894,4.145279,3.957955,3.901717,4.412855,4.013941
-2048,3.444422,2.87624,3.160897,3.726313,4.141592,3.937629,3.885244,4.398518,3.998891
-2049,3.45306,2.842565,3.086162,3.683216,4.068883,3.875068,3.804664,4.375498,3.98368
-2050,3.507589,2.877966,3.07016,3.663607,4.079675,3.881755,3.791144,4.387479,3.984013
diff --git a/inputs/fuelprices/ng_AEO_2025_HOG.csv b/inputs/fuelprices/ng_AEO_2025_HOG.csv
index 220c77d70..36ad6dcc8 100644
--- a/inputs/fuelprices/ng_AEO_2025_HOG.csv
+++ b/inputs/fuelprices/ng_AEO_2025_HOG.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,7.090994509,6.864627039,7.816364499,7.214633076,7.770429471,6.922075026,8.645587478,7.734288289,6.613847095
-2011,6.415244319,6.035802983,7.18160987,6.759039679,6.97660582,6.523092028,7.61268309,7.187830351,6.02193408
-2012,4.318396288,4.171336784,4.920954844,4.812692187,5.125687655,4.981962978,5.744142178,4.836968152,4.086242117
-2013,5.644571605,5.425661349,6.221772114,6.026713986,8.068124701,5.966594412,6.488062275,6.217397452,5.311725257
-2014,6.901573833,6.196133723,6.749785557,6.533248299,8.76920153,6.652865218,7.217679522,7.398615429,6.14190783
-2015,3.728469276,3.831858045,3.850319999,4.283486413,5.664139452,4.318227659,5.209638268,4.428216598,3.709608103
-2016,3.604329269,3.348941929,3.661067671,3.544383574,5.148299225,3.667086135,4.514280196,3.500406617,3.097469786
-2017,4.251331621,4.060241034,4.159690249,4.195250431,5.355284536,4.189158392,4.872571007,4.469496963,3.814340823
-2018,4.083060596,4.025984863,4.228362317,4.311701697,5.88125378,4.317322045,4.693187815,4.334410199,3.784559467
-2019,3.074154645,3.292143502,3.569319762,3.555691398,5.476462081,3.748837112,4.098978687,3.352608181,2.850897331
-2020,2.433600778,2.771317586,2.922260883,3.117010526,4.894556598,3.305044956,3.535609065,2.854163977,2.372945838
-2021,5.212049868,5.940867196,5.323929817,6.295938071,7.947848278,6.712710202,6.632815785,5.658208013,5.441225141
-2022,6.736810455,7.910931077,6.175171066,7.816437211,8.928908764,8.040398193,8.54369206,7.627994713,7.360516678
-2023,2.576696503,2.93427106,3.094089823,3.300290745,5.182356526,3.499381599,3.743502878,3.021988819,2.512475054
-2024,2.28508,2.645327,2.26953,2.841191,3.914745,3.178771,3.581676,2.605072,2.19948
+2010,7.090990,6.864630,7.816360,7.214630,7.770430,6.922080,8.645590,7.734290,6.613850
+2011,6.415240,6.035800,7.181610,6.759040,6.976610,6.523090,7.612680,7.187830,6.021930
+2012,4.318400,4.171340,4.920950,4.812690,5.125690,4.981960,5.744140,4.836970,4.086240
+2013,5.644570,5.425660,6.221770,6.026710,8.068120,5.966590,6.488060,6.217400,5.311730
+2014,6.901570,6.196130,6.749790,6.533250,8.769200,6.652870,7.217680,7.398620,6.141910
+2015,3.728470,3.831860,3.850320,4.283490,5.664140,4.318230,5.209640,4.428220,3.709610
+2016,3.604330,3.348940,3.661070,3.544380,5.148300,3.667090,4.514280,3.500410,3.097470
+2017,4.251330,4.060240,4.159690,4.195250,5.355280,4.189160,4.872570,4.469500,3.814340
+2018,4.083060,4.025980,4.228360,4.311700,5.881250,4.317320,4.693190,4.334410,3.784560
+2019,3.074150,3.292140,3.569320,3.555690,5.476460,3.748840,4.098980,3.352610,2.850900
+2020,2.433600,2.771320,2.922260,3.117010,4.894560,3.305040,3.535610,2.854160,2.372950
+2021,5.212050,5.940870,5.323930,6.295940,7.947850,6.712710,6.632820,5.658210,5.441230
+2022,6.736810,7.910930,6.175170,7.816440,8.928910,8.040400,8.543690,7.627990,7.360520
+2023,2.576700,2.934270,3.094090,3.300290,5.182360,3.499380,3.743500,3.021990,2.512480
+2024,2.298708,2.680677,2.287270,2.863694,3.942886,3.208913,3.481087,2.621773,2.210615
 2025,2.394202,2.663157,2.313296,2.777358,3.545907,2.887983,3.371045,2.708847,2.218614
 2026,2.279395,2.498695,2.298251,2.712186,3.424357,2.803907,3.222955,2.664633,2.059296
 2027,2.267412,2.484879,2.255369,2.717369,3.179948,2.824805,3.352517,2.702573,2.033904
 2028,2.342423,2.607332,2.219732,2.860942,2.963628,2.993797,3.471617,2.822133,2.178029
 2029,2.422638,2.709477,2.237025,2.964342,2.897579,3.117639,3.536583,2.905383,2.279984
 2030,2.509715,2.835151,2.309547,3.228207,2.761189,3.405689,3.637365,3.080562,2.440423
-2031,2.525936,2.905147,2.33741,3.393098,2.747252,3.586969,3.510499,3.122771,2.544189
-2032,2.816504,3.177044,2.531026,3.814344,3.035183,4.013958,3.964278,3.50146,2.843096
-2033,2.860132,3.242102,2.63399,3.950235,3.119033,4.208856,3.848393,3.516799,2.926303
+2031,2.525936,2.905147,2.337410,3.393098,2.747252,3.586969,3.510499,3.122771,2.544189
+2032,2.816504,3.177044,2.531026,3.814344,3.035183,4.013958,3.964278,3.501460,2.843096
+2033,2.860132,3.242102,2.633990,3.950235,3.119033,4.208856,3.848393,3.516799,2.926303
 2034,2.896689,3.271004,2.714677,4.049577,3.235024,4.334771,3.874689,3.576314,2.959214
-2035,2.911631,3.285653,2.718911,4.10694,3.332014,4.363559,3.886527,3.632553,2.990757
+2035,2.911631,3.285653,2.718911,4.106940,3.332014,4.363559,3.886527,3.632553,2.990757
 2036,2.902459,3.277782,2.679176,4.154849,3.345162,4.288165,3.871522,3.651698,2.995825
 2037,2.849265,3.248419,2.655793,4.159271,3.331612,4.251697,3.826073,3.584962,2.955194
-2038,2.787631,3.233404,2.610942,4.197432,3.345557,4.234464,3.80665,3.559825,2.903788
+2038,2.787631,3.233404,2.610942,4.197432,3.345557,4.234464,3.806650,3.559825,2.903788
 2039,2.754783,3.219203,2.571926,4.173839,3.217742,4.281111,3.787173,3.507277,2.895023
 2040,2.765788,3.249156,2.542018,4.291714,3.172366,4.376058,3.826042,3.599415,2.954316
 2041,2.809392,3.310269,2.589555,4.404143,3.339386,4.470148,3.902796,3.717389,3.031373
-2042,2.803964,3.32616,2.603526,4.446998,3.391913,4.516488,3.909537,3.73176,3.06811
-2043,2.752379,3.289931,2.53629,4.39126,3.33058,4.504367,3.898544,3.703559,3.063311
-2044,2.68326,3.263479,2.437677,4.328261,3.113896,4.424861,3.86422,3.646818,3.049417
-2045,2.62781,3.24825,2.36695,4.364906,2.985338,4.511312,3.847422,3.651798,3.060245
-2046,2.628173,3.277446,2.338613,4.41761,2.984034,4.539052,3.857837,3.720915,3.101784
-2047,2.562113,3.260696,2.304,4.407011,2.934076,4.557307,4.006942,3.699291,3.06158
-2048,2.51238,3.256391,2.251554,4.434324,2.892148,4.586201,4.003499,3.689516,3.06514
-2049,2.461685,3.213834,2.203854,4.389663,2.865312,4.565211,3.96656,3.655248,3.041644
-2050,2.421813,3.162359,2.150464,4.376517,2.790408,4.556719,3.907777,3.59189,3.010422
+2042,2.803964,3.326160,2.603526,4.446998,3.391913,4.516488,3.909537,3.731760,3.068110
+2043,2.752379,3.289931,2.536290,4.391260,3.330580,4.504367,3.898544,3.703559,3.063311
+2044,2.683260,3.263479,2.437677,4.328261,3.113896,4.424861,3.864220,3.646818,3.049417
+2045,2.627810,3.248250,2.366950,4.364906,2.985338,4.511312,3.847422,3.651798,3.060245
+2046,2.628173,3.277446,2.338613,4.417610,2.984034,4.539052,3.857837,3.720915,3.101784
+2047,2.562113,3.260696,2.304000,4.407011,2.934076,4.557307,4.006942,3.699291,3.061580
+2048,2.512380,3.256391,2.251554,4.434324,2.892148,4.586201,4.003499,3.689516,3.065140
+2049,2.461685,3.213834,2.203854,4.389663,2.865312,4.565211,3.966560,3.655248,3.041644
+2050,2.421813,3.162359,2.150464,4.376517,2.790408,4.556719,3.907777,3.591890,3.010422
diff --git a/inputs/fuelprices/ng_AEO_2025_LOG.csv b/inputs/fuelprices/ng_AEO_2025_LOG.csv
index a617762cf..0e670cb96 100644
--- a/inputs/fuelprices/ng_AEO_2025_LOG.csv
+++ b/inputs/fuelprices/ng_AEO_2025_LOG.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,7.090994509,6.864627039,7.816364499,7.214633076,7.770429471,6.922075026,8.645587478,7.734288289,6.613847095
-2011,6.415244319,6.035802983,7.18160987,6.759039679,6.97660582,6.523092028,7.61268309,7.187830351,6.02193408
-2012,4.318396288,4.171336784,4.920954844,4.812692187,5.125687655,4.981962978,5.744142178,4.836968152,4.086242117
-2013,5.644571605,5.425661349,6.221772114,6.026713986,8.068124701,5.966594412,6.488062275,6.217397452,5.311725257
-2014,6.901573833,6.196133723,6.749785557,6.533248299,8.76920153,6.652865218,7.217679522,7.398615429,6.14190783
-2015,3.728469276,3.831858045,3.850319999,4.283486413,5.664139452,4.318227659,5.209638268,4.428216598,3.709608103
-2016,3.604329269,3.348941929,3.661067671,3.544383574,5.148299225,3.667086135,4.514280196,3.500406617,3.097469786
-2017,4.251331621,4.060241034,4.159690249,4.195250431,5.355284536,4.189158392,4.872571007,4.469496963,3.814340823
-2018,4.083060596,4.025984863,4.228362317,4.311701697,5.88125378,4.317322045,4.693187815,4.334410199,3.784559467
-2019,3.074154645,3.292143502,3.569319762,3.555691398,5.476462081,3.748837112,4.098978687,3.352608181,2.850897331
-2020,2.433600778,2.771317586,2.922260883,3.117010526,4.894556598,3.305044956,3.535609065,2.854163977,2.372945838
-2021,5.212049868,5.940867196,5.323929817,6.295938071,7.947848278,6.712710202,6.632815785,5.658208013,5.441225141
-2022,6.736810455,7.910931077,6.175171066,7.816437211,8.928908764,8.040398193,8.54369206,7.627994713,7.360516678
-2023,2.576696503,2.93427106,3.094089823,3.300290745,5.182356526,3.499381599,3.743502878,3.021988819,2.512475054
-2024,2.28508,2.645327,2.26953,2.841191,3.914745,3.178771,3.581676,2.605072,2.19948
+2010,7.090990,6.864630,7.816360,7.214630,7.770430,6.922080,8.645590,7.734290,6.613850
+2011,6.415240,6.035800,7.181610,6.759040,6.976610,6.523090,7.612680,7.187830,6.021930
+2012,4.318400,4.171340,4.920950,4.812690,5.125690,4.981960,5.744140,4.836970,4.086240
+2013,5.644570,5.425660,6.221770,6.026710,8.068120,5.966590,6.488060,6.217400,5.311730
+2014,6.901570,6.196130,6.749790,6.533250,8.769200,6.652870,7.217680,7.398620,6.141910
+2015,3.728470,3.831860,3.850320,4.283490,5.664140,4.318230,5.209640,4.428220,3.709610
+2016,3.604330,3.348940,3.661070,3.544380,5.148300,3.667090,4.514280,3.500410,3.097470
+2017,4.251330,4.060240,4.159690,4.195250,5.355280,4.189160,4.872570,4.469500,3.814340
+2018,4.083060,4.025980,4.228360,4.311700,5.881250,4.317320,4.693190,4.334410,3.784560
+2019,3.074150,3.292140,3.569320,3.555690,5.476460,3.748840,4.098980,3.352610,2.850900
+2020,2.433600,2.771320,2.922260,3.117010,4.894560,3.305040,3.535610,2.854160,2.372950
+2021,5.212050,5.940870,5.323930,6.295940,7.947850,6.712710,6.632820,5.658210,5.441230
+2022,6.736810,7.910930,6.175170,7.816440,8.928910,8.040400,8.543690,7.627990,7.360520
+2023,2.576700,2.934270,3.094090,3.300290,5.182360,3.499380,3.743500,3.021990,2.512480
+2024,2.306225,2.671443,2.291188,2.867855,3.951021,3.213850,3.470091,2.628691,2.214949
 2025,3.757886,4.158859,3.629599,4.146041,4.825856,4.247904,4.788136,4.032818,3.699224
-2026,3.8001,4.182667,3.834864,4.11638,4.605783,4.119196,4.790429,4.000161,3.667104
-2027,3.930441,4.337811,3.83533,4.269623,4.343827,4.225954,4.924905,4.200141,3.80835
-2028,4.168205,4.68658,4.009411,4.650997,4.395217,4.694008,5.18881,4.513605,4.175693
-2029,4.62058,5.149542,4.403663,5.207363,4.783993,5.210474,5.605813,5.094242,4.716477
-2030,5.483608,6.11094,5.116742,6.219917,5.429995,6.208527,6.492614,6.184416,5.7407
-2031,6.032155,6.62288,5.623803,6.823698,6.023727,6.887148,7.070742,6.668324,6.323299
-2032,6.900975,7.39277,6.455877,7.823699,6.769484,7.795189,7.802534,7.624558,7.123114
-2033,7.243104,7.728008,6.816369,8.029579,7.239462,8.010993,8.118889,7.82219,7.409045
-2034,7.44806,7.892791,7.007079,8.258191,7.573184,8.196181,8.32683,7.872682,7.609113
-2035,7.551923,8.054188,7.163031,8.444173,7.670228,8.27807,8.486915,7.904687,7.6513
+2026,3.800100,4.182667,3.834864,4.116380,4.605783,4.119196,4.790429,4.000161,3.667104
+2027,3.930441,4.337811,3.835330,4.269623,4.343827,4.225954,4.924905,4.200141,3.808350
+2028,4.168205,4.686580,4.009411,4.650997,4.395217,4.694008,5.188810,4.513605,4.175693
+2029,4.620580,5.149542,4.403663,5.207363,4.783993,5.210474,5.605813,5.094242,4.716477
+2030,5.483608,6.110940,5.116742,6.219917,5.429995,6.208527,6.492614,6.184416,5.740700
+2031,6.032155,6.622880,5.623803,6.823698,6.023727,6.887148,7.070742,6.668324,6.323299
+2032,6.900975,7.392770,6.455877,7.823699,6.769484,7.795189,7.802534,7.624558,7.123114
+2033,7.243104,7.728008,6.816369,8.029579,7.239462,8.010993,8.118889,7.822190,7.409045
+2034,7.448060,7.892791,7.007079,8.258191,7.573184,8.196181,8.326830,7.872682,7.609113
+2035,7.551923,8.054188,7.163031,8.444173,7.670228,8.278070,8.486915,7.904687,7.651300
 2036,7.728963,8.253443,7.137417,8.753573,7.708939,8.491802,8.726404,8.052999,7.956063
 2037,7.568828,8.212115,6.949639,8.739521,7.558604,8.407489,8.848077,7.982419,8.047225
 2038,7.392748,7.899144,6.762915,8.673278,7.343988,8.322899,8.740216,7.834982,7.963167
-2039,7.297922,8.110153,6.737899,9.00624,7.251116,8.591912,8.91062,7.645207,8.226623
-2040,7.382362,8.071926,6.678354,9.026676,7.218706,8.66189,8.836534,7.822319,8.232173
-2041,7.322939,8.334088,6.644736,8.673353,7.065958,8.644938,8.86298,7.732145,8.189538
+2039,7.297922,8.110153,6.737899,9.006240,7.251116,8.591912,8.910620,7.645207,8.226623
+2040,7.382362,8.071926,6.678354,9.026676,7.218706,8.661890,8.836534,7.822319,8.232173
+2041,7.322939,8.334088,6.644736,8.673353,7.065958,8.644938,8.862980,7.732145,8.189538
 2042,7.199334,8.141396,6.447856,8.462022,6.963392,8.381509,8.753935,7.611486,8.084666
-2043,7.084161,7.972393,6.334739,8.241702,6.808531,8.252924,8.612486,7.540718,7.94293
-2044,7.090244,8.179617,6.344906,8.50786,6.503371,8.446995,8.590377,7.552183,8.183352
+2043,7.084161,7.972393,6.334739,8.241702,6.808531,8.252924,8.612486,7.540718,7.942930
+2044,7.090244,8.179617,6.344906,8.507860,6.503371,8.446995,8.590377,7.552183,8.183352
 2045,7.115554,8.105153,6.351938,8.658021,6.349429,8.574844,8.392526,7.610549,8.243489
 2046,7.327775,8.147901,6.429235,8.684699,6.509525,8.631374,8.438897,7.833263,8.316871
 2047,7.564465,8.513703,6.661896,9.064015,6.689818,8.964581,8.687522,8.053114,8.630199
-2048,7.85374,8.746612,6.915947,9.510916,6.918985,9.287422,9.020991,8.339838,9.039744
-2049,8.156782,9.02925,7.254817,9.833487,7.237922,9.528601,9.287102,8.634782,9.384081
-2050,8.458136,9.348893,7.58226,10.207071,7.553951,9.821965,9.62728,8.921481,9.705161
+2048,7.853740,8.746612,6.915947,9.510916,6.918985,9.287422,9.020991,8.339838,9.039744
+2049,8.156782,9.029250,7.254817,9.833487,7.237922,9.528601,9.287102,8.634782,9.384081
+2050,8.458136,9.348893,7.582260,10.207071,7.553951,9.821965,9.627280,8.921481,9.705161
diff --git a/inputs/fuelprices/ng_AEO_2025_reference.csv b/inputs/fuelprices/ng_AEO_2025_reference.csv
index 73b926805..de974068b 100644
--- a/inputs/fuelprices/ng_AEO_2025_reference.csv
+++ b/inputs/fuelprices/ng_AEO_2025_reference.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,7.090994509,6.864627039,7.816364499,7.214633076,7.770429471,6.922075026,8.645587478,7.734288289,6.613847095
-2011,6.415244319,6.035802983,7.18160987,6.759039679,6.97660582,6.523092028,7.61268309,7.187830351,6.02193408
-2012,4.318396288,4.171336784,4.920954844,4.812692187,5.125687655,4.981962978,5.744142178,4.836968152,4.086242117
-2013,5.644571605,5.425661349,6.221772114,6.026713986,8.068124701,5.966594412,6.488062275,6.217397452,5.311725257
-2014,6.901573833,6.196133723,6.749785557,6.533248299,8.76920153,6.652865218,7.217679522,7.398615429,6.14190783
-2015,3.728469276,3.831858045,3.850319999,4.283486413,5.664139452,4.318227659,5.209638268,4.428216598,3.709608103
-2016,3.604329269,3.348941929,3.661067671,3.544383574,5.148299225,3.667086135,4.514280196,3.500406617,3.097469786
-2017,4.251331621,4.060241034,4.159690249,4.195250431,5.355284536,4.189158392,4.872571007,4.469496963,3.814340823
-2018,4.083060596,4.025984863,4.228362317,4.311701697,5.88125378,4.317322045,4.693187815,4.334410199,3.784559467
-2019,3.074154645,3.292143502,3.569319762,3.555691398,5.476462081,3.748837112,4.098978687,3.352608181,2.850897331
-2020,2.433600778,2.771317586,2.922260883,3.117010526,4.894556598,3.305044956,3.535609065,2.854163977,2.372945838
-2021,5.212049868,5.940867196,5.323929817,6.295938071,7.947848278,6.712710202,6.632815785,5.658208013,5.441225141
-2022,6.736810455,7.910931077,6.175171066,7.816437211,8.928908764,8.040398193,8.54369206,7.627994713,7.360516678
-2023,2.576696503,2.93427106,3.094089823,3.300290745,5.182356526,3.499381599,3.743502878,3.021988819,2.512475054
-2024,2.28508,2.645327,2.26953,2.841191,3.914745,3.178771,3.581676,2.605072,2.19948
+2010,7.090990,6.864630,7.816360,7.214630,7.770430,6.922080,8.645590,7.734290,6.613850
+2011,6.415240,6.035800,7.181610,6.759040,6.976610,6.523090,7.612680,7.187830,6.021930
+2012,4.318400,4.171340,4.920950,4.812690,5.125690,4.981960,5.744140,4.836970,4.086240
+2013,5.644570,5.425660,6.221770,6.026710,8.068120,5.966590,6.488060,6.217400,5.311730
+2014,6.901570,6.196130,6.749790,6.533250,8.769200,6.652870,7.217680,7.398620,6.141910
+2015,3.728470,3.831860,3.850320,4.283490,5.664140,4.318230,5.209640,4.428220,3.709610
+2016,3.604330,3.348940,3.661070,3.544380,5.148300,3.667090,4.514280,3.500410,3.097470
+2017,4.251330,4.060240,4.159690,4.195250,5.355280,4.189160,4.872570,4.469500,3.814340
+2018,4.083060,4.025980,4.228360,4.311700,5.881250,4.317320,4.693190,4.334410,3.784560
+2019,3.074150,3.292140,3.569320,3.555690,5.476460,3.748840,4.098980,3.352610,2.850900
+2020,2.433600,2.771320,2.922260,3.117010,4.894560,3.305040,3.535610,2.854160,2.372950
+2021,5.212050,5.940870,5.323930,6.295940,7.947850,6.712710,6.632820,5.658210,5.441230
+2022,6.736810,7.910930,6.175170,7.816440,8.928910,8.040400,8.543690,7.627990,7.360520
+2023,2.576700,2.934270,3.094090,3.300290,5.182360,3.499380,3.743500,3.021990,2.512480
+2024,2.285080,2.645327,2.269530,2.841191,3.914745,3.178771,3.581676,2.605072,2.199480
 2025,2.837277,3.142428,2.724665,3.296411,3.945919,3.449765,3.846201,3.162053,2.702884
 2026,2.763278,3.055122,2.780215,3.156343,3.819109,3.232441,3.765639,3.076137,2.602545
-2027,2.703812,2.988694,2.636923,3.122164,3.43536,3.154894,3.837702,3.054238,2.549224
-2028,2.762565,3.127218,2.576812,3.269873,3.240752,3.344125,3.78544,3.195382,2.701754
-2029,2.91825,3.306478,2.643674,3.513918,3.229812,3.63497,4.085795,3.398271,2.910596
-2030,3.049576,3.512392,2.773706,3.861304,3.129285,4.01986,4.087484,3.646155,3.163967
+2027,2.703812,2.988694,2.636923,3.122164,3.435360,3.154894,3.837702,3.054238,2.549224
+2028,2.762565,3.127218,2.576812,3.269873,3.240752,3.344125,3.785440,3.195382,2.701754
+2029,2.918250,3.306478,2.643674,3.513918,3.229812,3.634970,4.085795,3.398271,2.910596
+2030,3.049576,3.512392,2.773706,3.861304,3.129285,4.019860,4.087484,3.646155,3.163967
 2031,3.170081,3.613755,2.866469,4.079747,3.326129,4.274564,4.101537,3.762861,3.303848
-2032,3.635774,4.085257,3.222987,4.733683,3.779099,4.89114,4.559186,4.423059,3.843493
+2032,3.635774,4.085257,3.222987,4.733683,3.779099,4.891140,4.559186,4.423059,3.843493
 2033,3.881344,4.388517,3.483385,5.040173,4.071227,5.191451,4.842482,4.704285,4.214373
 2034,3.987692,4.544195,3.598001,5.230925,4.168971,5.384827,4.962396,4.772688,4.409548
-2035,4.04547,4.609845,3.631619,5.303466,4.199443,5.41206,5.016607,4.784186,4.425838
-2036,4.036805,4.556694,3.610814,5.251937,4.265835,5.28292,5.044314,4.780307,4.374448
+2035,4.045470,4.609845,3.631619,5.303466,4.199443,5.412060,5.016607,4.784186,4.425838
+2036,4.036805,4.556694,3.610814,5.251937,4.265835,5.282920,5.044314,4.780307,4.374448
 2037,3.899876,4.464017,3.518157,5.176685,4.120316,5.104523,4.979167,4.672058,4.317363
-2038,3.733063,4.376263,3.376865,5.154803,3.99361,5.010477,4.909653,4.56643,4.273121
+2038,3.733063,4.376263,3.376865,5.154803,3.993610,5.010477,4.909653,4.566430,4.273121
 2039,3.681409,4.286094,3.256926,5.160571,3.816716,4.923649,4.850672,4.408439,4.233821
 2040,3.662889,4.299133,3.215503,5.306404,3.872509,5.055543,4.854046,4.491251,4.305593
 2041,3.723782,4.361627,3.252421,5.505489,3.966687,5.287903,4.880734,4.649086,4.432408
-2042,3.77397,4.390012,3.298153,5.683669,4.091134,5.408764,4.925858,4.751801,4.548378
+2042,3.773970,4.390012,3.298153,5.683669,4.091134,5.408764,4.925858,4.751801,4.548378
 2043,3.805754,4.522407,3.344024,5.836872,4.169929,5.563621,5.008918,4.807343,4.704851
 2044,3.800041,4.578125,3.347215,5.905303,4.087124,5.645613,5.012309,4.817755,4.813431
-2045,3.79805,4.596054,3.329523,5.9452,3.891919,5.636967,4.970164,4.839172,4.858135
+2045,3.798050,4.596054,3.329523,5.945200,3.891919,5.636967,4.970164,4.839172,4.858135
 2046,3.810532,4.665256,3.393556,6.013248,3.902629,5.758965,4.994895,4.945189,4.923351
-2047,3.770669,4.652761,3.383668,6.069484,3.945969,5.808951,4.917217,4.90954,5.001916
-2048,3.740844,4.571362,3.349662,6.010077,3.787596,5.813332,4.80245,4.859097,5.000763
+2047,3.770669,4.652761,3.383668,6.069484,3.945969,5.808951,4.917217,4.909540,5.001916
+2048,3.740844,4.571362,3.349662,6.010077,3.787596,5.813332,4.802450,4.859097,5.000763
 2049,3.685088,4.388964,3.274235,5.912456,3.870886,5.796697,4.690213,4.774216,4.946911
-2050,3.642668,4.235358,3.161351,5.97212,3.788044,5.74582,4.578797,4.791852,4.904408
+2050,3.642668,4.235358,3.161351,5.972120,3.788044,5.745820,4.578797,4.791852,4.904408
diff --git a/inputs/fuelprices/ng_AEO_2026_HOG.csv b/inputs/fuelprices/ng_AEO_2026_HOG.csv
new file mode 100644
index 000000000..3ca07eddc
--- /dev/null
+++ b/inputs/fuelprices/ng_AEO_2026_HOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,7.284740,7.052195,8.029929,7.411758,7.982745,7.111215,8.881817,7.945617,6.794563
+2011,6.590526,6.200719,7.377836,6.943720,7.167235,6.701323,7.820684,7.384226,6.186470
+2012,4.436393,4.285315,5.055407,4.944189,5.265741,5.118084,5.901089,4.969132,4.197890
+2013,5.798799,5.573907,6.391770,6.191380,8.288568,6.129617,6.665336,6.387280,5.456864
+2014,7.090144,6.365429,6.934217,6.711761,9.008804,6.834649,7.414891,7.600775,6.309728
+2015,3.830344,3.936559,3.955524,4.400529,5.818904,4.436219,5.351985,4.549214,3.810969
+2016,3.702813,3.440444,3.761103,3.641224,5.288969,3.767287,4.637625,3.596053,3.182103
+2017,4.367491,4.171180,4.273347,4.309878,5.501604,4.303622,5.005705,4.591622,3.918561
+2018,4.194623,4.135983,4.343893,4.429510,6.041946,4.435284,4.821424,4.452841,3.887967
+2019,3.158146,3.382092,3.666846,3.652844,5.626095,3.851271,4.210978,3.444215,2.928796
+2020,2.500094,2.847042,3.002106,3.202177,5.028296,3.395345,3.632215,2.932145,2.437787
+2021,5.354461,6.103195,5.469398,6.467966,8.165012,6.896124,6.814051,5.812811,5.589903
+2022,6.920882,8.127083,6.343897,8.030012,9.172878,8.260091,8.777133,7.836413,7.561634
+2023,2.647104,3.014444,3.178631,3.390465,5.323960,3.594995,3.845785,3.104561,2.581129
+2024,2.347516,2.717609,2.331541,2.918821,4.021704,3.265625,3.679544,2.676249,2.259577
+2025,3.286574,3.800634,3.387378,3.695329,5.345607,3.943778,4.709843,3.555411,3.311579
+2026,3.084828,3.513833,3.147799,3.556494,4.852463,3.751729,4.388223,3.362976,3.102779
+2027,2.862534,3.208148,2.785328,3.328036,3.666937,3.450739,4.032940,3.210815,2.776315
+2028,2.797957,3.162952,2.605392,3.330281,3.465783,3.458980,3.949574,3.184686,2.734897
+2029,2.850465,3.264244,2.580803,3.407689,3.167606,3.559944,4.017421,3.304868,2.837093
+2030,3.002350,3.475777,2.662569,3.680808,2.926042,3.842189,4.451139,3.534045,3.069016
+2031,3.094841,3.617326,2.699370,3.904559,2.915775,4.088018,4.593555,3.667346,3.234858
+2032,3.380701,3.970807,2.858877,4.376479,3.220855,4.569868,4.939392,4.172229,3.627450
+2033,3.330557,3.937970,2.926083,4.499977,3.376226,4.363488,4.935080,4.209935,3.608490
+2034,3.208234,3.868895,2.870562,4.575318,3.326463,4.426440,4.550412,4.190933,3.556247
+2035,3.182476,3.917942,2.851336,4.714975,3.366169,4.544697,4.708346,4.240469,3.616472
+2036,3.181453,3.951176,2.811184,4.848455,3.393479,4.679333,4.637809,4.298808,3.696667
+2037,3.152076,3.932922,2.863233,4.887789,3.464981,4.710261,4.647636,4.302949,3.704575
+2038,3.081966,3.861789,2.828988,4.774141,3.410531,4.764500,4.570607,4.243705,3.629383
+2039,3.007529,3.800644,2.790657,4.619734,3.453385,4.775273,4.504010,4.165158,3.543797
+2040,2.915007,3.731605,2.642806,4.545332,3.260410,4.720599,4.440323,4.079655,3.470656
+2041,2.794668,3.631639,2.468462,4.422882,3.059327,4.656564,4.338802,3.977866,3.384425
+2042,2.674123,3.534928,2.317640,4.290660,2.890143,4.540991,4.229695,3.853020,3.284886
+2043,2.589398,3.457415,2.249035,4.191004,2.752141,4.440181,4.150131,3.749722,3.211062
+2044,2.543918,3.390016,2.198134,4.115810,2.547546,4.342209,4.079580,3.686228,3.147907
+2045,2.486152,3.335948,2.131695,4.076365,2.426736,4.308940,4.022791,3.641717,3.098754
+2046,2.454142,3.294115,2.101854,4.076965,2.440843,4.314435,3.974510,3.623240,3.066741
+2047,2.419697,3.262509,2.082169,4.058603,2.399387,4.266572,3.935534,3.587930,3.043042
+2048,2.394982,3.220711,2.054138,4.025033,2.361834,4.200194,3.893207,3.563347,3.014104
+2049,2.352619,3.173899,2.025916,3.980294,2.339232,4.140861,3.848231,3.518968,2.969493
+2050,2.313998,3.130726,2.000027,3.923027,2.374989,4.091122,3.799017,3.463644,2.930648
diff --git a/inputs/fuelprices/ng_AEO_2026_LOG.csv b/inputs/fuelprices/ng_AEO_2026_LOG.csv
new file mode 100644
index 000000000..6530ea51a
--- /dev/null
+++ b/inputs/fuelprices/ng_AEO_2026_LOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,7.284740,7.052195,8.029929,7.411758,7.982745,7.111215,8.881817,7.945617,6.794563
+2011,6.590526,6.200719,7.377836,6.943720,7.167235,6.701323,7.820684,7.384226,6.186470
+2012,4.436393,4.285315,5.055407,4.944189,5.265741,5.118084,5.901089,4.969132,4.197890
+2013,5.798799,5.573907,6.391770,6.191380,8.288568,6.129617,6.665336,6.387280,5.456864
+2014,7.090144,6.365429,6.934217,6.711761,9.008804,6.834649,7.414891,7.600775,6.309728
+2015,3.830344,3.936559,3.955524,4.400529,5.818904,4.436219,5.351985,4.549214,3.810969
+2016,3.702813,3.440444,3.761103,3.641224,5.288969,3.767287,4.637625,3.596053,3.182103
+2017,4.367491,4.171180,4.273347,4.309878,5.501604,4.303622,5.005705,4.591622,3.918561
+2018,4.194623,4.135983,4.343893,4.429510,6.041946,4.435284,4.821424,4.452841,3.887967
+2019,3.158146,3.382092,3.666846,3.652844,5.626095,3.851271,4.210978,3.444215,2.928796
+2020,2.500094,2.847042,3.002106,3.202177,5.028296,3.395345,3.632215,2.932145,2.437787
+2021,5.354461,6.103195,5.469398,6.467966,8.165012,6.896124,6.814051,5.812811,5.589903
+2022,6.920882,8.127083,6.343897,8.030012,9.172878,8.260091,8.777133,7.836413,7.561634
+2023,2.647104,3.014444,3.178631,3.390465,5.323960,3.594995,3.845785,3.104561,2.581129
+2024,2.347516,2.717609,2.331541,2.918821,4.021704,3.265625,3.679544,2.676249,2.259577
+2025,3.263801,3.789199,3.353367,3.655058,5.294798,3.905813,4.832328,3.540977,3.288816
+2026,4.033495,4.519078,3.963801,4.433659,5.685656,4.561513,5.198661,4.260452,4.150634
+2027,4.479586,5.134305,4.249576,4.871719,5.053397,4.903999,5.751545,4.782177,4.657691
+2028,5.468980,6.094103,4.996347,5.929786,5.736206,6.002690,6.548896,5.827187,5.634982
+2029,6.310399,6.794102,5.790377,6.662073,6.381851,6.815924,7.144698,6.769516,6.258209
+2030,6.713041,7.222132,6.203698,7.181173,6.649747,7.346393,7.568275,7.371433,6.632887
+2031,7.004788,7.529885,6.481824,7.526796,6.826379,7.611009,7.898701,7.679960,7.050432
+2032,7.753469,8.184606,7.052512,8.462646,7.462828,8.514933,8.551950,8.613667,7.788092
+2033,7.940298,8.380210,7.312173,8.690731,7.706581,8.761770,8.767888,8.760403,7.947466
+2034,8.109896,8.575316,7.490126,8.864508,7.939864,8.877319,9.000998,8.820741,8.073168
+2035,8.306912,8.830894,7.736515,9.076791,8.260612,9.041146,9.300612,8.889380,8.261165
+2036,8.737354,9.240022,8.154497,9.564580,8.656876,9.496066,9.819419,9.314214,8.776074
+2037,9.195817,9.701205,8.585943,10.010752,9.160049,9.970448,10.329938,9.722696,9.260747
+2038,9.448765,9.956623,8.932805,10.219106,9.502454,10.117539,10.634950,9.927127,9.497434
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+2043,11.569989,11.936073,11.023234,12.457313,11.486072,12.094296,12.357872,11.447901,11.444778
+2044,11.909888,12.234546,11.338474,12.775576,11.489775,12.299760,12.697484,11.744664,11.745035
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+2047,13.157214,13.383212,12.439937,14.182432,12.625209,13.455883,13.874180,12.880009,12.864495
+2048,13.406144,13.672522,12.661606,14.479213,12.923044,13.656775,14.144248,13.117165,13.108910
+2049,13.595934,13.877510,12.862795,14.660414,13.178946,13.802397,14.350445,13.306535,13.296640
+2050,13.788420,14.070285,13.066490,14.776094,13.449883,14.035362,14.584373,13.447649,13.498145
diff --git a/inputs/fuelprices/ng_AEO_2026_baseline.csv b/inputs/fuelprices/ng_AEO_2026_baseline.csv
new file mode 100644
index 000000000..b0da4fbf1
--- /dev/null
+++ b/inputs/fuelprices/ng_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,7.284740,7.052195,8.029929,7.411758,7.982745,7.111215,8.881817,7.945617,6.794563
+2011,6.590526,6.200719,7.377836,6.943720,7.167235,6.701323,7.820684,7.384226,6.186470
+2012,4.436393,4.285315,5.055407,4.944189,5.265741,5.118084,5.901089,4.969132,4.197890
+2013,5.798799,5.573907,6.391770,6.191380,8.288568,6.129617,6.665336,6.387280,5.456864
+2014,7.090144,6.365429,6.934217,6.711761,9.008804,6.834649,7.414891,7.600775,6.309728
+2015,3.830344,3.936559,3.955524,4.400529,5.818904,4.436219,5.351985,4.549214,3.810969
+2016,3.702813,3.440444,3.761103,3.641224,5.288969,3.767287,4.637625,3.596053,3.182103
+2017,4.367491,4.171180,4.273347,4.309878,5.501604,4.303622,5.005705,4.591622,3.918561
+2018,4.194623,4.135983,4.343893,4.429510,6.041946,4.435284,4.821424,4.452841,3.887967
+2019,3.158146,3.382092,3.666846,3.652844,5.626095,3.851271,4.210978,3.444215,2.928796
+2020,2.500094,2.847042,3.002106,3.202177,5.028296,3.395345,3.632215,2.932145,2.437787
+2021,5.354461,6.103195,5.469398,6.467966,8.165012,6.896124,6.814051,5.812811,5.589903
+2022,6.920882,8.127083,6.343897,8.030012,9.172878,8.260091,8.777133,7.836413,7.561634
+2023,2.647104,3.014444,3.178631,3.390465,5.323960,3.594995,3.845785,3.104561,2.581129
+2024,2.347516,2.717609,2.331541,2.918821,4.021704,3.265625,3.679544,2.676249,2.259577
+2025,3.268552,3.778815,3.379013,3.678707,5.325405,3.919628,4.781505,3.541426,3.294860
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+2028,3.544278,3.991779,3.315939,4.099092,4.155579,4.283302,4.602939,3.934699,3.576442
+2029,3.747785,4.209246,3.428625,4.329078,4.123753,4.532804,4.818919,4.209109,3.767626
+2030,4.146273,4.875952,3.655290,4.929817,3.902078,5.166997,5.549663,4.832505,4.442943
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+2032,4.762383,5.617828,3.915184,5.885259,4.358751,6.052363,6.329670,5.663197,5.341123
+2033,4.535047,5.491001,3.894549,5.859532,4.339409,6.025182,5.962244,5.558170,5.265240
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+2043,4.400375,5.196852,4.125682,5.804568,4.589023,5.733068,6.009053,5.292042,5.179297
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+2046,4.197471,4.880015,3.792771,5.711275,4.196424,5.670421,5.530549,5.176457,5.060451
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+2050,3.939520,4.611669,3.592049,5.484926,4.201423,5.438996,5.144186,4.903520,4.701788
diff --git a/inputs/fuelprices/ng_demand_AEO_2023_HOG.csv b/inputs/fuelprices/ng_demand_AEO_2023_HOG.csv
deleted file mode 100644
index c0657dbd3..000000000
--- a/inputs/fuelprices/ng_demand_AEO_2023_HOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,0.420,0.890,0.326,0.124,1.544,0.567,2.049,0.641,0.989
-2011,0.456,0.965,0.395,0.117,1.671,0.639,2.188,0.567,0.766
-2012,0.448,1.154,0.656,0.171,2.043,0.797,2.337,0.663,1.045
-2013,0.372,1.066,0.475,0.140,1.883,0.629,2.075,0.651,1.069
-2014,0.34175,1.13148,0.48286,0.10807,1.89625,0.67191,2.02567,0.64891,1.07373
-2015,0.39761,1.23645,0.71331,0.14716,2.3301,0.87542,2.43411,0.75092,1.08443
-2016,0.39318,1.3445,0.91214,0.19192,2.46983,0.9619,2.35184,0.76576,0.91791
-2017,0.37141,1.19616,0.81026,0.17219,2.47589,0.9109,2.06317,0.69033,0.8409
-2018,0.400407,1.300064,1.00257,0.189613,2.535531,0.994602,2.661307,0.760259,0.871735
-2019,0.338651,1.451554,1.185716,0.228314,2.790259,1.029824,2.928261,0.988215,0.834003
-2020,0.360853,1.684218,1.262778,0.247312,2.768037,1.047204,2.940385,0.954761,0.847681
-2021,0.344592,1.654776,1.082247,0.195056,2.468727,0.913914,2.73224,0.947247,0.887034
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-2023,0.322209,1.628636,1.370934,0.204452,2.553739,0.956462,2.54281,0.792854,0.757127
-2024,0.292141,1.6204,1.373547,0.180663,2.486964,0.955863,2.380707,0.747715,0.695925
-2025,0.244,1.608202,1.404821,0.207968,2.492904,0.875996,2.424637,0.741038,0.776228
-2026,0.201186,1.530872,1.464513,0.226758,2.350623,0.820776,2.419441,0.787912,0.828612
-2027,0.18587,1.478354,1.405107,0.232876,2.171907,0.835944,2.422804,0.729788,0.764488
-2028,0.17778,1.474101,1.317673,0.157445,1.944132,0.808826,2.329015,0.662171,0.79227
-2029,0.177985,1.48089,1.368956,0.141931,1.958361,0.809067,2.230774,0.580756,0.764149
-2030,0.178273,1.488671,1.299642,0.127099,1.85046,0.720465,2.185932,0.587105,0.761249
-2031,0.169105,1.461549,1.273815,0.126945,1.847728,0.674826,2.136373,0.55783,0.711202
-2032,0.175114,1.456266,1.250309,0.133995,1.827205,0.644465,2.032429,0.570326,0.694278
-2033,0.169224,1.399408,1.27366,0.142697,1.798857,0.643373,1.970041,0.540331,0.656713
-2034,0.176368,1.345788,1.31299,0.145872,1.776953,0.642696,1.992708,0.513527,0.65969
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-2036,0.177068,1.268308,1.402719,0.150012,1.799258,0.671102,2.116937,0.522088,0.639925
-2037,0.184358,1.305971,1.451259,0.146359,1.844151,0.682652,2.179676,0.541974,0.615525
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-2039,0.189901,1.358278,1.554044,0.165178,1.916827,0.754606,2.28272,0.645166,0.520128
-2040,0.18483,1.395959,1.60827,0.165607,1.938145,0.756265,2.325557,0.705853,0.5497
-2041,0.182356,1.382635,1.640962,0.177163,1.963908,0.781381,2.340624,0.703176,0.542254
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-2044,0.155972,1.467056,1.837866,0.20969,2.094199,0.832454,2.439107,0.814076,0.502251
-2045,0.158319,1.481755,1.920597,0.208874,2.117633,0.813444,2.455262,0.832591,0.509555
-2046,0.158578,1.471896,1.932825,0.221727,2.107489,0.829494,2.498946,0.870122,0.516846
-2047,0.16147,1.489463,1.968685,0.22173,2.142432,0.861891,2.41624,0.885786,0.530741
-2048,0.14861,1.475312,2.044308,0.218968,2.127081,0.863945,2.481335,0.910914,0.53106
-2049,0.148118,1.473455,2.031857,0.227189,2.095158,0.842599,2.508403,0.911476,0.548845
-2050,0.138527,1.652081,2.032725,0.224344,2.064054,0.852848,2.515678,0.947692,0.548004
diff --git a/inputs/fuelprices/ng_demand_AEO_2023_LOG.csv b/inputs/fuelprices/ng_demand_AEO_2023_LOG.csv
deleted file mode 100644
index b432dfcd6..000000000
--- a/inputs/fuelprices/ng_demand_AEO_2023_LOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,0.420,0.890,0.326,0.124,1.544,0.567,2.049,0.641,0.989
-2011,0.456,0.965,0.395,0.117,1.671,0.639,2.188,0.567,0.766
-2012,0.448,1.154,0.656,0.171,2.043,0.797,2.337,0.663,1.045
-2013,0.372,1.066,0.475,0.140,1.883,0.629,2.075,0.651,1.069
-2014,0.34175,1.13148,0.48286,0.10807,1.89625,0.67191,2.02567,0.64891,1.07373
-2015,0.39761,1.23645,0.71331,0.14716,2.3301,0.87542,2.43411,0.75092,1.08443
-2016,0.39318,1.3445,0.91214,0.19192,2.46983,0.9619,2.35184,0.76576,0.91791
-2017,0.37141,1.19616,0.81026,0.17219,2.47589,0.9109,2.06317,0.69033,0.8409
-2018,0.400348,1.299859,1.002615,0.189721,2.524155,0.995077,2.664792,0.760402,0.871719
-2019,0.338651,1.451554,1.185716,0.228314,2.790259,1.029824,2.928261,0.988215,0.834003
-2020,0.360853,1.684218,1.262778,0.247312,2.768037,1.047204,2.940385,0.954761,0.847681
-2021,0.343424,1.653161,1.087098,0.19469,2.46062,0.920302,2.73498,0.945748,0.888837
-2022,0.366764,1.998683,1.476179,0.259339,2.604225,0.89925,2.799392,0.946573,0.850609
-2023,0.313367,1.706365,1.435218,0.221068,2.498634,0.873777,2.494542,0.795222,0.791027
-2024,0.274728,1.487367,1.214665,0.159076,2.220431,0.762225,1.955377,0.672705,0.65544
-2025,0.21798,1.407215,1.093565,0.152813,1.913679,0.693366,1.713207,0.612969,0.687514
-2026,0.194359,1.255334,1.008865,0.145144,1.57265,0.569105,1.471294,0.568226,0.709196
-2027,0.183899,1.15992,0.910513,0.108513,1.249104,0.43369,1.190893,0.428542,0.58844
-2028,0.187193,0.971313,0.825028,0.103875,1.071958,0.342827,0.98871,0.366187,0.538171
-2029,0.169133,0.87372,0.717105,0.080915,1.047165,0.308779,0.923672,0.325623,0.488475
-2030,0.137313,0.714795,0.551857,0.081094,0.848231,0.272013,0.890599,0.27539,0.455044
-2031,0.142133,0.633757,0.515065,0.075465,0.854823,0.24642,0.877888,0.254972,0.437347
-2032,0.142112,0.640003,0.498261,0.067516,0.75244,0.227977,0.875225,0.289757,0.434607
-2033,0.146954,0.657162,0.472905,0.063862,0.767643,0.242897,0.872529,0.28636,0.406404
-2034,0.146517,0.678565,0.474105,0.063729,0.755473,0.243442,0.870152,0.290248,0.414829
-2035,0.147969,0.631319,0.46575,0.081808,0.765922,0.248656,0.882823,0.282448,0.425103
-2036,0.144341,0.634269,0.470402,0.070642,0.751545,0.249641,0.874106,0.283426,0.394065
-2037,0.143115,0.621559,0.482583,0.076316,0.74149,0.260894,0.87507,0.283145,0.398235
-2038,0.144143,0.630968,0.49932,0.095366,0.71384,0.274271,0.897745,0.301872,0.400138
-2039,0.142168,0.642237,0.528256,0.081072,0.68483,0.27546,0.911073,0.289295,0.414802
-2040,0.147299,0.640714,0.540039,0.079157,0.710274,0.268246,0.90013,0.285096,0.420615
-2041,0.143352,0.637488,0.55401,0.081058,0.750263,0.274525,0.886399,0.286311,0.430134
-2042,0.15028,0.63242,0.550314,0.082974,0.711455,0.286559,0.895801,0.295421,0.433715
-2043,0.163098,0.634682,0.556694,0.081753,0.756129,0.280809,0.882713,0.331632,0.420291
-2044,0.139842,0.662481,0.566833,0.086115,0.749902,0.27576,0.874783,0.341387,0.42876
-2045,0.133843,0.616813,0.571097,0.087435,0.75281,0.268873,0.883645,0.351458,0.427164
-2046,0.133835,0.560047,0.577287,0.091524,0.743599,0.286621,0.888866,0.361269,0.422791
-2047,0.137147,0.552402,0.55921,0.105817,0.734651,0.279831,0.885683,0.365715,0.398795
-2048,0.126953,0.559868,0.540231,0.102954,0.72761,0.278259,0.892699,0.34466,0.420057
-2049,0.137295,0.575885,0.580194,0.092118,0.757378,0.272706,0.926844,0.349355,0.395341
-2050,0.122734,0.607477,0.586883,0.103555,0.734051,0.289134,0.948964,0.346366,0.420447
diff --git a/inputs/fuelprices/ng_demand_AEO_2023_reference.csv b/inputs/fuelprices/ng_demand_AEO_2023_reference.csv
deleted file mode 100644
index 5206a62cc..000000000
--- a/inputs/fuelprices/ng_demand_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,New_England,Mid_Atlantic,East_North_Central,West_North_Central,South_Atlantic,East_South_Central,West_South_Central,Mountain,Pacific
-2010,0.42,0.89,0.326,0.124,1.544,0.567,2.049,0.641,0.989
-2011,0.456,0.965,0.395,0.117,1.671,0.639,2.188,0.567,0.766
-2012,0.448,1.154,0.656,0.171,2.043,0.797,2.337,0.663,1.045
-2013,0.372,1.066,0.475,0.14,1.883,0.629,2.075,0.651,1.069
-2014,0.34175,1.13148,0.48286,0.10807,1.89625,0.67191,2.02567,0.64891,1.07373
-2015,0.39761,1.23645,0.71331,0.14716,2.3301,0.87542,2.43411,0.75092,1.08443
-2016,0.39318,1.3445,0.91214,0.19192,2.46983,0.9619,2.35184,0.76576,0.91791
-2017,0.37141,1.19616,0.81026,0.17219,2.47589,0.9109,2.06317,0.69033,0.8409
-2018,0.400312,1.294711,1.007672,0.195773,2.525427,0.998826,2.684061,0.763962,0.873542
-2019,0.338651,1.451554,1.185716,0.228314,2.790259,1.029824,2.928261,0.988215,0.834003
-2020,0.360853,1.684218,1.262778,0.247312,2.768037,1.047204,2.940385,0.954761,0.847681
-2021,0.343945,1.652881,1.090509,0.19471,2.463302,0.916938,2.731849,0.946543,0.887407
-2022,0.366805,1.990527,1.49411,0.25907,2.590068,0.931868,2.776792,0.936656,0.85512
-2023,0.328985,1.652065,1.390737,0.213204,2.549202,0.9603,2.542692,0.816922,0.789847
-2024,0.28981,1.613624,1.356609,0.182819,2.427338,0.894664,2.295436,0.744313,0.700123
-2025,0.237414,1.557385,1.32706,0.168013,2.291867,0.796825,2.115291,0.755528,0.757876
-2026,0.209522,1.497212,1.23712,0.188772,2.062988,0.745758,2.154577,0.690443,0.798458
-2027,0.198014,1.424813,1.193519,0.178736,1.786668,0.680421,2.07987,0.602823,0.767601
-2028,0.191007,1.424312,1.1498,0.150994,1.614535,0.627877,2.049199,0.589114,0.77502
-2029,0.187698,1.418441,1.13102,0.124589,1.618009,0.605361,1.9439,0.548587,0.746124
-2030,0.165197,1.38408,1.134232,0.139386,1.592795,0.575716,1.794783,0.486306,0.696908
-2031,0.168313,1.315789,1.124949,0.123157,1.50721,0.56023,1.704291,0.461348,0.650977
-2032,0.169194,1.278507,1.125497,0.13111,1.46336,0.542224,1.575679,0.481047,0.596821
-2033,0.162035,1.231041,1.100446,0.139641,1.428466,0.472701,1.568907,0.482627,0.597243
-2034,0.16265,1.154444,1.093997,0.149306,1.364966,0.481917,1.55161,0.464072,0.582221
-2035,0.162437,1.082324,1.113749,0.126248,1.386076,0.487013,1.542182,0.471909,0.562396
-2036,0.170183,1.016872,1.140848,0.116616,1.396075,0.486158,1.577925,0.469707,0.562625
-2037,0.165896,1.08454,1.107488,0.120491,1.408166,0.511788,1.581814,0.48309,0.547651
-2038,0.173774,1.133034,1.147569,0.157562,1.430346,0.514654,1.588745,0.488152,0.543932
-2039,0.181242,1.120832,1.140844,0.179323,1.462003,0.518269,1.644274,0.489798,0.537716
-2040,0.177598,1.172774,1.198637,0.172087,1.458723,0.53108,1.685609,0.515312,0.535179
-2041,0.19139,1.201287,1.21924,0.159747,1.481676,0.520815,1.686281,0.533943,0.531535
-2042,0.197107,1.219615,1.25691,0.147253,1.50238,0.513386,1.714757,0.553262,0.527623
-2043,0.195596,1.220917,1.254567,0.169363,1.515411,0.51202,1.734249,0.589283,0.512731
-2044,0.168614,1.263852,1.282365,0.185125,1.382599,0.577116,1.75609,0.612212,0.499283
-2045,0.161465,1.274607,1.335,0.174359,1.397843,0.559048,1.786944,0.613929,0.501276
-2046,0.158601,1.262726,1.297088,0.209574,1.392099,0.536975,1.814765,0.649766,0.456469
-2047,0.157522,1.212913,1.342688,0.204767,1.365172,0.528027,1.857863,0.667031,0.473731
-2048,0.157015,1.182638,1.35647,0.186671,1.403895,0.549453,1.866205,0.682171,0.495743
-2049,0.156587,1.181,1.360666,0.191372,1.420062,0.546044,1.859648,0.712082,0.49352
-2050,0.160361,1.245571,1.362169,0.186854,1.439421,0.529831,1.876195,0.714889,0.492635
diff --git a/inputs/fuelprices/ng_demand_AEO_2025_HOG.csv b/inputs/fuelprices/ng_demand_AEO_2025_HOG.csv
index 6e9d38485..a8c437989 100644
--- a/inputs/fuelprices/ng_demand_AEO_2025_HOG.csv
+++ b/inputs/fuelprices/ng_demand_AEO_2025_HOG.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,0.326,0.567,0.89,0.641,0.42,0.989,1.544,0.124,2.049
-2011,0.395,0.639,0.965,0.567,0.456,0.766,1.671,0.117,2.188
-2012,0.656,0.797,1.154,0.663,0.448,1.045,2.043,0.171,2.337
-2013,0.475,0.629,1.066,0.651,0.372,1.069,1.883,0.14,2.075
-2014,0.48286,0.67191,1.13148,0.64891,0.34175,1.07373,1.89625,0.10807,2.02567
-2015,0.71331,0.87542,1.23645,0.75092,0.39761,1.08443,2.3301,0.14716,2.43411
-2016,0.91214,0.9619,1.3445,0.76576,0.39318,0.91791,2.46983,0.19192,2.35184
-2017,0.81026,0.9109,1.19616,0.69033,0.37141,0.8409,2.47589,0.17219,2.06317
-2018,1.00257,0.994602,1.300064,0.760259,0.400407,0.871735,2.535531,0.189613,2.661307
-2019,1.185716,1.029824,1.451554,0.988215,0.338651,0.834003,2.790259,0.228314,2.928261
-2020,1.262778,1.047204,1.684218,0.954761,0.360853,0.847681,2.768037,0.247312,2.940385
-2021,1.082247,0.913914,1.654776,0.947247,0.344592,0.887034,2.468727,0.195056,2.73224
-2022,1.477695,0.90048,1.998293,0.947744,0.366773,0.850175,2.605338,0.257507,2.797009
-2023,1.337029346,1.108779595,1.783250018,1.010900947,0.382071156,0.897524643,2.930797576,0.261853946,3.113279638
-2024,1.803768,1.120752,1.947869,1.202134,0.451624,1.026979,2.771119,0.328229,3.278091
-2025,1.721987,1.101454,1.590906,1.094271,0.400232,0.854088,2.802444,0.31658,3.123186
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
+2024,1.807217,1.120767,1.951789,1.198553,0.446506,1.018170,2.772431,0.328414,3.282638
+2025,1.721987,1.101454,1.590906,1.094271,0.400232,0.854088,2.802444,0.316580,3.123186
 2026,2.042994,1.240576,1.697816,1.131739,0.311662,0.761514,2.951403,0.339554,3.375084
-2027,2.206338,1.345413,1.701938,1.092678,0.26856,0.750901,3.033981,0.389654,3.394222
-2028,2.334784,1.306726,1.644293,1.079716,0.244934,0.78061,3.01577,0.37641,3.293242
+2027,2.206338,1.345413,1.701938,1.092678,0.268560,0.750901,3.033981,0.389654,3.394222
+2028,2.334784,1.306726,1.644293,1.079716,0.244934,0.780610,3.015770,0.376410,3.293242
 2029,2.512054,1.324362,1.630497,1.073624,0.241361,0.828471,2.969723,0.398223,3.188257
-2030,2.602928,1.24977,1.778863,1.176084,0.196057,0.901304,2.868471,0.427727,3.24422
-2031,2.751464,1.181122,1.787377,1.214582,0.174589,0.966842,2.69715,0.491814,3.251625
+2030,2.602928,1.249770,1.778863,1.176084,0.196057,0.901304,2.868471,0.427727,3.244220
+2031,2.751464,1.181122,1.787377,1.214582,0.174589,0.966842,2.697150,0.491814,3.251625
 2032,3.253971,1.202948,1.845946,1.326644,0.193283,1.003485,2.864202,0.599109,3.415374
-2033,3.23993,1.22631,1.84822,1.274809,0.18547,1.094409,2.768513,0.497179,3.408022
-2034,3.205562,1.228781,1.843454,1.246807,0.187396,1.119302,2.743542,0.490385,3.36646
-2035,3.212188,1.185304,1.710656,1.19792,0.192078,1.137609,2.655394,0.484944,3.317858
-2036,3.132051,1.137773,1.642419,1.329532,0.1997,1.008565,2.550056,0.420684,2.939176
-2037,3.140409,1.144755,1.6444,1.32666,0.206521,1.047966,2.471244,0.416561,2.535467
+2033,3.239930,1.226310,1.848220,1.274809,0.185470,1.094409,2.768513,0.497179,3.408022
+2034,3.205562,1.228781,1.843454,1.246807,0.187396,1.119302,2.743542,0.490385,3.366460
+2035,3.212188,1.185304,1.710656,1.197920,0.192078,1.137609,2.655394,0.484944,3.317858
+2036,3.132051,1.137773,1.642419,1.329532,0.199700,1.008565,2.550056,0.420684,2.939176
+2037,3.140409,1.144755,1.644400,1.326660,0.206521,1.047966,2.471244,0.416561,2.535467
 2038,2.716094,1.158549,1.590891,1.171483,0.197384,1.034278,2.439109,0.376668,2.472733
 2039,2.581804,1.105213,1.437783,1.016782,0.185696,1.041979,2.377795,0.354041,2.369297
-2040,2.550396,1.084304,1.440836,1.072407,0.1913,1.048653,2.49518,0.359027,2.332093
+2040,2.550396,1.084304,1.440836,1.072407,0.191300,1.048653,2.495180,0.359027,2.332093
 2041,2.603838,1.106517,1.471445,1.044572,0.196422,1.087787,2.572892,0.366828,2.428086
 2042,2.774909,1.088461,1.400841,1.070091,0.208031,1.072913,2.560002,0.382864,2.434259
-2043,2.887747,1.150788,1.453301,1.107367,0.215927,1.04736,2.60474,0.405494,2.535568
-2044,2.989638,1.153446,1.539554,1.171576,0.195102,1.045559,2.585567,0.4108,2.570477
-2045,3.06196,1.184258,1.580606,1.259128,0.159384,1.036926,2.56146,0.439917,2.601221
-2046,3.099662,1.255747,1.648451,1.248391,0.161159,1.04817,2.573675,0.463651,2.629858
-2047,2.97911,1.305926,1.739527,1.285874,0.167061,1.030689,2.549114,0.46623,2.650514
+2043,2.887747,1.150788,1.453301,1.107367,0.215927,1.047360,2.604740,0.405494,2.535568
+2044,2.989638,1.153446,1.539554,1.171576,0.195102,1.045559,2.585567,0.410800,2.570477
+2045,3.061960,1.184258,1.580606,1.259128,0.159384,1.036926,2.561460,0.439917,2.601221
+2046,3.099662,1.255747,1.648451,1.248391,0.161159,1.048170,2.573675,0.463651,2.629858
+2047,2.979110,1.305926,1.739527,1.285874,0.167061,1.030689,2.549114,0.466230,2.650514
 2048,2.855375,1.313169,1.745558,1.325776,0.168816,1.004289,2.529676,0.474269,2.607504
-2049,2.984837,1.298944,1.680766,1.364214,0.16795,1.004726,2.625262,0.425213,2.676126
-2050,2.927017,1.317363,1.685399,1.45559,0.15661,0.990377,2.632061,0.381755,2.65857
+2049,2.984837,1.298944,1.680766,1.364214,0.167950,1.004726,2.625262,0.425213,2.676126
+2050,2.927017,1.317363,1.685399,1.455590,0.156610,0.990377,2.632061,0.381755,2.658570
diff --git a/inputs/fuelprices/ng_demand_AEO_2025_LOG.csv b/inputs/fuelprices/ng_demand_AEO_2025_LOG.csv
index 9f1228f60..6fb1bc559 100644
--- a/inputs/fuelprices/ng_demand_AEO_2025_LOG.csv
+++ b/inputs/fuelprices/ng_demand_AEO_2025_LOG.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,0.326,0.567,0.89,0.641,0.42,0.989,1.544,0.124,2.049
-2011,0.395,0.639,0.965,0.567,0.456,0.766,1.671,0.117,2.188
-2012,0.656,0.797,1.154,0.663,0.448,1.045,2.043,0.171,2.337
-2013,0.475,0.629,1.066,0.651,0.372,1.069,1.883,0.14,2.075
-2014,0.48286,0.67191,1.13148,0.64891,0.34175,1.07373,1.89625,0.10807,2.02567
-2015,0.71331,0.87542,1.23645,0.75092,0.39761,1.08443,2.3301,0.14716,2.43411
-2016,0.91214,0.9619,1.3445,0.76576,0.39318,0.91791,2.46983,0.19192,2.35184
-2017,0.81026,0.9109,1.19616,0.69033,0.37141,0.8409,2.47589,0.17219,2.06317
-2018,1.00257,0.994602,1.300064,0.760259,0.400407,0.871735,2.535531,0.189613,2.661307
-2019,1.185716,1.029824,1.451554,0.988215,0.338651,0.834003,2.790259,0.228314,2.928261
-2020,1.262778,1.047204,1.684218,0.954761,0.360853,0.847681,2.768037,0.247312,2.940385
-2021,1.082247,0.913914,1.654776,0.947247,0.344592,0.887034,2.468727,0.195056,2.73224
-2022,1.477695,0.90048,1.998293,0.947744,0.366773,0.850175,2.605338,0.257507,2.797009
-2023,1.337029346,1.108779595,1.783250018,1.010900947,0.382071156,0.897524643,2.930797576,0.261853946,3.113279638
-2024,1.803768,1.120752,1.947869,1.202134,0.451624,1.026979,2.771119,0.328229,3.278091
-2025,1.74832,1.053865,1.720835,1.072385,0.400253,0.786553,2.728033,0.311531,3.139995
-2026,1.402841,0.872166,1.49083,0.829442,0.251434,0.687765,2.304525,0.199013,2.062922
-2027,1.498055,0.930938,1.4678,0.809352,0.211858,0.664922,2.249545,0.238384,2.1327
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
+2024,1.804559,1.121850,1.954733,1.202133,0.452001,1.018694,2.774692,0.330010,3.271640
+2025,1.748320,1.053865,1.720835,1.072385,0.400253,0.786553,2.728033,0.311531,3.139995
+2026,1.402841,0.872166,1.490830,0.829442,0.251434,0.687765,2.304525,0.199013,2.062922
+2027,1.498055,0.930938,1.467800,0.809352,0.211858,0.664922,2.249545,0.238384,2.132700
 2028,1.509668,0.907154,1.385326,0.783828,0.194398,0.704409,2.034196,0.246067,1.991213
-2029,1.496857,0.871673,1.412108,0.776658,0.192247,0.741716,1.84641,0.27598,1.856138
+2029,1.496857,0.871673,1.412108,0.776658,0.192247,0.741716,1.846410,0.275980,1.856138
 2030,1.534067,0.808875,1.373499,0.770984,0.174559,0.772348,1.719123,0.437111,1.857033
 2031,1.384653,0.668381,1.197684,0.692458,0.157962,0.789767,1.451879,0.358888,1.640671
 2032,1.565061,0.734924,1.132102,0.847123,0.176228,0.815216,1.585336,0.451031,1.789956
-2033,1.333007,0.691406,1.060135,0.82023,0.166235,0.797631,1.433254,0.368381,1.579497
-2034,1.233128,0.634744,0.929961,0.759908,0.1614,0.756096,1.357102,0.308266,1.513954
-2035,1.111427,0.63708,0.89326,0.72217,0.165505,0.688957,1.358099,0.275428,1.327169
-2036,1.031229,0.549481,0.788534,0.65372,0.162913,0.632692,1.24435,0.26154,1.226203
-2037,0.854965,0.48277,0.586714,0.626503,0.137538,0.554879,1.119637,0.271243,1.115833
-2038,0.732474,0.435245,0.522783,0.52724,0.132298,0.494092,1.064564,0.25409,1.057815
-2039,0.758101,0.374653,0.54963,0.54579,0.134267,0.454257,0.993744,0.266654,1.017851
+2033,1.333007,0.691406,1.060135,0.820230,0.166235,0.797631,1.433254,0.368381,1.579497
+2034,1.233128,0.634744,0.929961,0.759908,0.161400,0.756096,1.357102,0.308266,1.513954
+2035,1.111427,0.637080,0.893260,0.722170,0.165505,0.688957,1.358099,0.275428,1.327169
+2036,1.031229,0.549481,0.788534,0.653720,0.162913,0.632692,1.244350,0.261540,1.226203
+2037,0.854965,0.482770,0.586714,0.626503,0.137538,0.554879,1.119637,0.271243,1.115833
+2038,0.732474,0.435245,0.522783,0.527240,0.132298,0.494092,1.064564,0.254090,1.057815
+2039,0.758101,0.374653,0.549630,0.545790,0.134267,0.454257,0.993744,0.266654,1.017851
 2040,0.698537,0.296413,0.527879,0.509211,0.136717,0.467174,0.974679,0.271079,1.049425
 2041,0.664124,0.238602,0.488661,0.438115,0.137923,0.449188,0.975145,0.224717,0.994796
-2042,0.615075,0.237566,0.463385,0.395162,0.13546,0.437638,0.974492,0.20615,0.978935
+2042,0.615075,0.237566,0.463385,0.395162,0.135460,0.437638,0.974492,0.206150,0.978935
 2043,0.606697,0.257154,0.473387,0.383094,0.138723,0.435116,0.925296,0.213672,0.991719
-2044,0.587082,0.247615,0.474389,0.395633,0.10313,0.442636,0.871767,0.236184,1.000719
+2044,0.587082,0.247615,0.474389,0.395633,0.103130,0.442636,0.871767,0.236184,1.000719
 2045,0.607441,0.218179,0.444759,0.407519,0.087387,0.440998,0.779892,0.234486,1.004305
 2046,0.663438,0.209494,0.450912,0.364727,0.092547,0.441239,0.730188,0.237393,0.993623
-2047,0.687064,0.198955,0.450285,0.383422,0.092992,0.446543,0.693493,0.263399,1.0473
-2048,0.734405,0.201429,0.473709,0.416587,0.09381,0.465141,0.71479,0.265423,1.086109
+2047,0.687064,0.198955,0.450285,0.383422,0.092992,0.446543,0.693493,0.263399,1.047300
+2048,0.734405,0.201429,0.473709,0.416587,0.093810,0.465141,0.714790,0.265423,1.086109
 2049,0.789578,0.213645,0.480987,0.453987,0.095297,0.473856,0.700422,0.270487,1.095479
-2050,0.816287,0.2179,0.472205,0.48647,0.096417,0.488408,0.698386,0.278686,1.125022
+2050,0.816287,0.217900,0.472205,0.486470,0.096417,0.488408,0.698386,0.278686,1.125022
diff --git a/inputs/fuelprices/ng_demand_AEO_2025_reference.csv b/inputs/fuelprices/ng_demand_AEO_2025_reference.csv
index dd3b1e917..39fc701a7 100644
--- a/inputs/fuelprices/ng_demand_AEO_2025_reference.csv
+++ b/inputs/fuelprices/ng_demand_AEO_2025_reference.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,0.326,0.567,0.890,0.641,0.420,0.989,1.544,0.124,2.049
-2011,0.395,0.639,0.965,0.567,0.456,0.766,1.671,0.117,2.188
-2012,0.656,0.797,1.154,0.663,0.448,1.045,2.043,0.171,2.337
-2013,0.475,0.629,1.066,0.651,0.372,1.069,1.883,0.140,2.075
-2014,0.48286,0.67191,1.13148,0.64891,0.34175,1.07373,1.89625,0.10807,2.02567
-2015,0.71331,0.87542,1.23645,0.75092,0.39761,1.08443,2.3301,0.14716,2.43411
-2016,0.91214,0.9619,1.3445,0.76576,0.39318,0.91791,2.46983,0.19192,2.35184
-2017,0.81026,0.9109,1.19616,0.69033,0.37141,0.8409,2.47589,0.17219,2.06317
-2018,1.00257,0.994602,1.300064,0.760259,0.400407,0.871735,2.535531,0.189613,2.661307
-2019,1.185716,1.029824,1.451554,0.988215,0.338651,0.834003,2.790259,0.228314,2.928261
-2020,1.262778,1.047204,1.684218,0.954761,0.360853,0.847681,2.768037,0.247312,2.940385
-2021,1.082247,0.913914,1.654776,0.947247,0.344592,0.887034,2.468727,0.195056,2.73224
-2022,1.477695,0.90048,1.998293,0.947744,0.366773,0.850175,2.605338,0.257507,2.797009
-2023,1.337029346,1.108779595,1.783250018,1.010900947,0.382071156,0.897524643,2.930797576,0.261853946,3.113279638
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
 2024,1.803768,1.120752,1.947869,1.202134,0.451624,1.026979,2.771119,0.328229,3.278091
-2025,1.77915,1.098819,1.629397,1.095031,0.402601,0.855043,2.839481,0.333078,3.062861
+2025,1.779150,1.098819,1.629397,1.095031,0.402601,0.855043,2.839481,0.333078,3.062861
 2026,1.709301,1.090066,1.645651,1.009045,0.307184,0.751345,2.802256,0.292994,2.831672
-2027,1.930932,1.157464,1.664455,1.0129,0.263452,0.718387,2.786452,0.347046,2.92258
+2027,1.930932,1.157464,1.664455,1.012900,0.263452,0.718387,2.786452,0.347046,2.922580
 2028,2.042475,1.160019,1.601191,0.970603,0.243434,0.776476,2.676992,0.365627,2.856328
 2029,2.250298,1.124709,1.567711,0.961866,0.223449,0.824927,2.548651,0.403164,2.763967
 2030,2.271644,1.037678,1.638802,1.036249,0.172437,0.894584,2.418587,0.456933,2.771827
 2031,2.249849,0.989105,1.537363,1.062644,0.193628,0.947843,2.196743,0.434738,2.681658
-2032,2.753094,1.045986,1.631743,1.131738,0.210223,1.00009,2.304419,0.589867,2.831487
-2033,2.61021,0.955824,1.585095,0.945356,0.20238,0.930929,2.088257,0.553112,2.650223
-2034,2.315766,0.866626,1.527352,0.872272,0.189251,0.947975,1.92241,0.410186,2.548273
-2035,2.12818,0.824893,1.546738,0.893621,0.192515,0.933309,1.878592,0.37401,2.161885
+2032,2.753094,1.045986,1.631743,1.131738,0.210223,1.000090,2.304419,0.589867,2.831487
+2033,2.610210,0.955824,1.585095,0.945356,0.202380,0.930929,2.088257,0.553112,2.650223
+2034,2.315766,0.866626,1.527352,0.872272,0.189251,0.947975,1.922410,0.410186,2.548273
+2035,2.128180,0.824893,1.546738,0.893621,0.192515,0.933309,1.878592,0.374010,2.161885
 2036,1.923508,0.771852,1.530112,0.882123,0.197367,0.888036,1.926551,0.346125,1.876698
 2037,1.718799,0.714015,1.373293,0.846004,0.189569,0.825748,1.718812,0.332811,1.742069
-2038,1.530699,0.654886,1.208284,0.834513,0.194608,0.78475,1.718832,0.307617,1.661109
-2039,1.492246,0.603034,1.129269,0.853311,0.18355,0.737564,1.680835,0.271675,1.621889
-2040,1.538328,0.603428,1.131776,0.86345,0.191371,0.723354,1.617794,0.272217,1.688687
-2041,1.606427,0.615171,1.15756,0.862811,0.192375,0.729275,1.619307,0.285775,1.746875
-2042,1.636847,0.61597,1.098378,0.91582,0.203848,0.673652,1.598931,0.264481,1.757097
+2038,1.530699,0.654886,1.208284,0.834513,0.194608,0.784750,1.718832,0.307617,1.661109
+2039,1.492246,0.603034,1.129269,0.853311,0.183550,0.737564,1.680835,0.271675,1.621889
+2040,1.538328,0.603428,1.131776,0.863450,0.191371,0.723354,1.617794,0.272217,1.688687
+2041,1.606427,0.615171,1.157560,0.862811,0.192375,0.729275,1.619307,0.285775,1.746875
+2042,1.636847,0.615970,1.098378,0.915820,0.203848,0.673652,1.598931,0.264481,1.757097
 2043,1.703603,0.590668,1.162631,0.912592,0.207948,0.706872,1.554882,0.265405,1.737841
 2044,1.783943,0.604031,1.205421,0.939069,0.183532,0.684177,1.479005,0.239437,1.767898
 2045,1.743539,0.610125,1.299167,0.952057,0.141631,0.662889,1.469072,0.278295,1.725534
-2046,1.815236,0.608258,1.281461,0.916862,0.157335,0.618836,1.423282,0.288586,1.73741
-2047,1.637457,0.593526,1.37779,0.906929,0.158988,0.640318,1.39668,0.303452,1.77373
+2046,1.815236,0.608258,1.281461,0.916862,0.157335,0.618836,1.423282,0.288586,1.737410
+2047,1.637457,0.593526,1.377790,0.906929,0.158988,0.640318,1.396680,0.303452,1.773730
 2048,1.609448,0.605255,1.420297,0.957831,0.162292,0.637655,1.344266,0.320825,1.755996
-2049,1.616478,0.636592,1.3456,0.998673,0.174212,0.629154,1.313927,0.320191,1.718013
-2050,1.626209,0.643452,1.342685,1.03689,0.165085,0.621188,1.359366,0.331866,1.536503
+2049,1.616478,0.636592,1.345600,0.998673,0.174212,0.629154,1.313927,0.320191,1.718013
+2050,1.626209,0.643452,1.342685,1.036890,0.165085,0.621188,1.359366,0.331866,1.536503
diff --git a/inputs/fuelprices/ng_demand_AEO_2026_HOG.csv b/inputs/fuelprices/ng_demand_AEO_2026_HOG.csv
new file mode 100644
index 000000000..7a1d25872
--- /dev/null
+++ b/inputs/fuelprices/ng_demand_AEO_2026_HOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
+2024,1.803770,1.120750,1.947870,1.202130,0.451620,1.026980,2.771120,0.328230,3.278090
+2025,1.648597,1.029691,1.851161,1.231434,0.417325,0.851933,2.866583,0.317455,3.039832
+2026,1.713044,1.030120,1.746961,1.266515,0.467969,0.827414,2.850809,0.292045,3.425049
+2027,1.841328,1.167425,1.646666,1.085011,0.304663,0.715407,2.930920,0.351720,3.132115
+2028,1.952928,1.225841,1.684212,1.108953,0.282638,0.710391,2.882026,0.344860,3.124128
+2029,1.985248,1.294265,1.619591,1.038270,0.208550,0.728797,3.056920,0.405253,3.261393
+2030,2.011865,1.349217,1.785222,1.155577,0.147087,0.748359,3.039507,0.486595,3.259249
+2031,2.040323,1.390848,1.692420,1.291346,0.127850,0.793124,3.143278,0.510064,3.439810
+2032,2.854984,1.451952,1.773454,1.462801,0.166115,0.858645,3.334630,0.629632,3.714446
+2033,3.015952,1.451795,1.675639,1.812109,0.174169,0.657401,3.394392,0.634252,3.823832
+2034,3.205416,1.470505,1.645214,1.901085,0.175485,0.713055,3.488226,0.648723,3.904728
+2035,3.321302,1.491139,1.679040,2.018383,0.169778,0.760828,3.590709,0.677436,3.986648
+2036,3.448392,1.515289,1.724456,2.132609,0.175985,0.815626,3.705425,0.728275,4.037294
+2037,3.572106,1.510466,1.707575,2.179991,0.162578,0.902957,3.887541,0.766930,4.127420
+2038,3.658869,1.528602,1.729452,2.032956,0.158196,1.105590,4.040754,0.781836,4.220967
+2039,3.768442,1.502221,1.833264,1.955187,0.161481,1.262494,4.263766,0.804460,4.343083
+2040,3.948766,1.498289,1.830200,2.027627,0.156339,1.323843,4.367780,0.805841,4.419831
+2041,4.019770,1.515667,1.869890,2.128645,0.155380,1.344587,4.486732,0.836198,4.574669
+2042,4.167163,1.577872,1.988616,2.224538,0.159258,1.374970,4.548312,0.853261,4.687437
+2043,4.294076,1.623893,2.140853,2.291301,0.160852,1.411695,4.607835,0.869560,4.807927
+2044,4.463818,1.684566,2.254237,2.387595,0.139318,1.430295,4.701151,0.929913,4.927483
+2045,4.541317,1.717293,2.394387,2.422866,0.110367,1.453691,4.777538,0.954616,5.002665
+2046,4.688744,1.746517,2.460590,2.464838,0.114923,1.450654,4.820875,0.952163,5.118394
+2047,4.810600,1.766934,2.527314,2.530149,0.111870,1.462748,4.858366,0.965562,5.253918
+2048,4.932360,1.800730,2.555921,2.603085,0.111630,1.459536,4.920039,0.927140,5.376229
+2049,4.997245,1.816386,2.621164,2.652108,0.109753,1.475239,4.963941,0.953469,5.553566
+2050,5.002142,1.830329,2.687166,2.658764,0.130816,1.528597,4.991158,1.007838,5.647074
diff --git a/inputs/fuelprices/ng_demand_AEO_2026_LOG.csv b/inputs/fuelprices/ng_demand_AEO_2026_LOG.csv
new file mode 100644
index 000000000..986c9dc6e
--- /dev/null
+++ b/inputs/fuelprices/ng_demand_AEO_2026_LOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
+2024,1.803770,1.120750,1.947870,1.202130,0.451620,1.026980,2.771120,0.328230,3.278090
+2025,1.679000,1.019626,1.814928,1.227479,0.415699,0.852376,2.901429,0.303772,3.039703
+2026,1.742216,0.955223,1.892826,1.270218,0.471940,0.805547,2.752043,0.280337,3.449575
+2027,1.323182,0.842020,1.519425,0.850297,0.301155,0.683090,2.449615,0.224736,2.377727
+2028,1.344027,0.866748,1.456988,0.828409,0.247602,0.660176,2.255227,0.225546,2.243650
+2029,1.413110,0.875769,1.435084,0.765199,0.215992,0.666055,2.007415,0.331048,2.030363
+2030,1.342575,0.885410,1.405076,0.836330,0.181131,0.691846,2.066261,0.431404,2.036111
+2031,1.241520,0.774044,1.294632,0.819401,0.173324,0.640920,1.972942,0.407349,1.980588
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+2033,1.349913,0.810948,1.283438,0.954536,0.176080,0.689854,2.018384,0.477644,2.231772
+2034,1.283549,0.771330,1.189227,0.854735,0.169384,0.619171,2.015351,0.386284,1.920939
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+2036,1.103241,0.639220,1.049285,0.732576,0.166770,0.554200,1.942473,0.324191,1.487373
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+2038,1.044960,0.579216,0.987886,0.692604,0.162177,0.501309,1.929736,0.294146,1.495804
+2039,1.119514,0.690582,0.956840,0.623204,0.150060,0.452780,1.804188,0.166236,1.528119
+2040,1.108948,0.679190,0.924067,0.617334,0.138701,0.453095,1.700179,0.164876,1.573129
+2041,1.098532,0.661338,0.937378,0.660225,0.140308,0.455047,1.557672,0.188367,1.585708
+2042,1.102409,0.680625,0.925667,0.684994,0.133711,0.457538,1.583407,0.190433,1.585218
+2043,1.119741,0.662954,0.927282,0.689120,0.144300,0.455408,1.605054,0.192070,1.615774
+2044,1.161269,0.661131,0.936408,0.710736,0.113996,0.435302,1.614498,0.164290,1.621199
+2045,1.182112,0.638799,0.906334,0.737419,0.100468,0.431589,1.620273,0.163450,1.599687
+2046,1.189454,0.626578,0.902110,0.734336,0.099194,0.424507,1.638262,0.165687,1.557580
+2047,1.153745,0.619159,0.882888,0.747760,0.101461,0.418307,1.653636,0.198688,1.545822
+2048,1.161926,0.633662,0.888655,0.760913,0.101902,0.419132,1.664755,0.199668,1.551339
+2049,1.205107,0.625737,0.885032,0.772766,0.101241,0.426429,1.642012,0.197028,1.551197
+2050,1.204226,0.609529,0.876168,0.757297,0.104621,0.453179,1.605915,0.196322,1.520899
diff --git a/inputs/fuelprices/ng_demand_AEO_2026_baseline.csv b/inputs/fuelprices/ng_demand_AEO_2026_baseline.csv
new file mode 100644
index 000000000..d6723a3d6
--- /dev/null
+++ b/inputs/fuelprices/ng_demand_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
+2010,0.326000,0.567000,0.890000,0.641000,0.420000,0.989000,1.544000,0.124000,2.049000
+2011,0.395000,0.639000,0.965000,0.567000,0.456000,0.766000,1.671000,0.117000,2.188000
+2012,0.656000,0.797000,1.154000,0.663000,0.448000,1.045000,2.043000,0.171000,2.337000
+2013,0.475000,0.629000,1.066000,0.651000,0.372000,1.069000,1.883000,0.140000,2.075000
+2014,0.482860,0.671910,1.131480,0.648910,0.341750,1.073730,1.896250,0.108070,2.025670
+2015,0.713310,0.875420,1.236450,0.750920,0.397610,1.084430,2.330100,0.147160,2.434110
+2016,0.912140,0.961900,1.344500,0.765760,0.393180,0.917910,2.469830,0.191920,2.351840
+2017,0.810260,0.910900,1.196160,0.690330,0.371410,0.840900,2.475890,0.172190,2.063170
+2018,1.002570,0.994600,1.300060,0.760260,0.400410,0.871740,2.535530,0.189610,2.661310
+2019,1.185720,1.029820,1.451550,0.988210,0.338650,0.834000,2.790260,0.228310,2.928260
+2020,1.262780,1.047200,1.684220,0.954760,0.360850,0.847680,2.768040,0.247310,2.940390
+2021,1.082250,0.913910,1.654780,0.947250,0.344590,0.887030,2.468730,0.195060,2.732240
+2022,1.477690,0.900480,1.998290,0.947740,0.366770,0.850180,2.605340,0.257510,2.797010
+2023,1.337030,1.108780,1.783250,1.010900,0.382070,0.897520,2.930800,0.261850,3.113280
+2024,1.803770,1.120750,1.947870,1.202130,0.451620,1.026980,2.771120,0.328230,3.278090
+2025,1.659616,1.025486,1.821352,1.210192,0.415962,0.858895,2.873841,0.322851,3.065815
+2026,1.769046,1.019964,1.820325,1.318901,0.467906,0.827082,2.829953,0.325733,3.381424
+2027,1.615740,1.040631,1.586809,1.008156,0.307878,0.721722,2.767963,0.288666,2.772255
+2028,1.720648,1.040693,1.570074,1.004709,0.270876,0.718189,2.549712,0.280728,2.720559
+2029,1.780526,1.122983,1.509975,0.958634,0.232678,0.730647,2.602069,0.383555,2.567170
+2030,1.807315,1.189790,1.591015,1.031264,0.147137,0.754155,2.698203,0.505788,2.461878
+2031,1.818539,1.153110,1.518058,1.071877,0.154523,0.762239,2.725821,0.493469,2.466089
+2032,2.332649,1.160405,1.652519,1.221084,0.171140,0.763647,2.878121,0.528790,2.773816
+2033,2.258104,1.146366,1.611316,1.138652,0.168154,0.792403,2.670293,0.461596,2.491760
+2034,2.295054,1.154389,1.598844,1.174780,0.166387,0.665455,2.691954,0.445965,2.390950
+2035,2.349791,1.135390,1.631779,1.105567,0.168846,0.661612,2.756552,0.443320,2.422616
+2036,2.441845,1.145885,1.690080,1.142129,0.173900,0.721888,2.860936,0.439980,2.462188
+2037,2.535429,1.166464,1.736329,1.215184,0.166064,0.787384,2.993963,0.450350,2.506264
+2038,2.590947,1.187185,1.741541,1.190728,0.174862,0.821557,3.036464,0.443396,2.486950
+2039,2.819755,1.099472,1.766711,1.167671,0.169670,0.774401,3.144396,0.395888,2.539166
+2040,2.885969,1.133317,1.790556,1.193164,0.158342,0.810810,3.157984,0.382803,2.436097
+2041,2.898490,1.191263,1.802938,1.245131,0.156059,0.859055,3.244412,0.387704,2.465115
+2042,2.975342,1.212832,1.800179,1.319474,0.153217,0.890109,3.393687,0.404266,2.542635
+2043,3.075706,1.240058,1.824126,1.378363,0.158617,0.920913,3.481175,0.433313,2.602159
+2044,3.161263,1.270154,1.890954,1.460349,0.139163,0.921405,3.601084,0.459030,2.685139
+2045,3.189294,1.316607,1.939437,1.509268,0.133956,0.945207,3.706220,0.460997,2.718325
+2046,3.211974,1.377722,2.003619,1.583674,0.137652,0.936823,3.787705,0.467564,2.809215
+2047,3.237010,1.423071,2.027699,1.606334,0.142889,0.956864,3.860097,0.481618,2.881419
+2048,3.263034,1.497705,2.050089,1.674062,0.144934,0.953635,3.923238,0.486595,2.922786
+2049,3.307015,1.526371,2.080405,1.742009,0.139210,0.953994,3.986758,0.484645,2.981777
+2050,3.297439,1.500245,2.142084,1.698395,0.225399,1.008947,4.038809,0.499338,2.993482
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2023_HOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2023_HOG.csv
deleted file mode 100644
index 0f8dc11b5..000000000
--- a/inputs/fuelprices/ng_tot_demand_AEO_2023_HOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.660842,1.958889,3.505682,1.985658,0.884488,2.948652,4.330067,1.971532,7.141617
-2020,4.56676,1.974559,3.616957,1.882971,0.84926,2.845356,4.201263,1.906665,7.048672
-2021,4.409971,1.841467,3.668216,1.922016,0.879326,2.946812,3.976987,1.893417,6.866061
-2022,4.923203,1.86141,4.052487,1.925796,0.92313,2.852263,4.17512,1.996832,7.176915
-2023,4.66238,1.874837,3.698862,1.719562,0.911583,2.862135,4.182066,1.889565,7.003771
-2024,4.712965,1.887711,3.676749,1.699745,0.846245,2.748235,4.056899,1.815095,6.605481
-2025,4.808831,1.835031,3.662212,1.718692,0.801614,2.860716,4.095548,1.88107,6.771583
-2026,4.908977,1.803716,3.594185,1.784055,0.760615,2.947173,3.982331,1.926108,6.889522
-2027,4.86823,1.835089,3.542237,1.739461,0.745138,2.909819,3.823046,1.947493,6.999478
-2028,4.794827,1.8198,3.55186,1.679785,0.738597,2.980421,3.610463,1.883403,6.984961
-2029,4.847705,1.825383,3.566268,1.605712,0.739958,2.973927,3.633589,1.871569,6.924437
-2030,4.770581,1.741762,3.572761,1.61748,0.741501,2.990333,3.531389,1.859264,6.921029
-2031,4.738085,1.699635,3.545746,1.5919,0.733621,2.951775,3.534227,1.860197,6.889448
-2032,4.710612,1.674164,3.542888,1.612063,0.741044,2.947041,3.521588,1.870669,6.812792
-2033,4.732515,1.678401,3.489017,1.590137,0.73765,2.927041,3.501616,1.884083,6.781031
-2034,4.776952,1.688863,3.439766,1.575678,0.747751,2.953613,3.493958,1.897016,6.870108
-2035,4.840772,1.724082,3.423015,1.576477,0.746884,2.942616,3.512699,1.914305,7.014408
-2036,4.886779,1.736828,3.369703,1.608756,0.754082,2.982888,3.541136,1.923651,7.112611
-2037,4.949911,1.75679,3.409008,1.640783,0.763908,2.989659,3.597342,1.92985,7.221704
-2038,4.998488,1.827931,3.433282,1.746756,0.768806,2.921452,3.66582,1.960354,7.315146
-2039,5.061288,1.84726,3.461702,1.762412,0.774601,2.832263,3.692025,1.967118,7.443282
-2040,5.125247,1.858579,3.501986,1.840283,0.772207,2.876755,3.725246,1.979041,7.544557
-2041,5.169348,1.896024,3.494097,1.851694,0.772836,2.898448,3.766627,2.003352,7.631004
-2042,5.267732,1.934695,3.518967,1.913971,0.777965,2.902635,3.825716,2.023869,7.755304
-2043,5.339574,1.967924,3.535349,1.95172,0.778988,2.9128,3.890703,2.050789,7.848203
-2044,5.417798,1.977184,3.584785,2.000788,0.754089,2.908925,3.933826,2.077293,7.915616
-2045,5.49749,1.959614,3.597286,2.027605,0.757322,2.939371,3.961097,2.081174,7.937524
-2046,5.520114,1.984137,3.588105,2.077916,0.759512,2.96688,3.961478,2.10513,8.039601
-2047,5.566961,2.022016,3.605681,2.106861,0.763869,3.009387,4.005132,2.114678,7.979387
-2048,5.653104,2.030382,3.596385,2.143607,0.752693,3.0524,3.998923,2.12319,8.080976
-2049,5.645404,2.012315,3.592641,2.152774,0.75286,3.08903,3.972072,2.138471,8.130584
-2050,5.663317,2.032178,3.776584,2.204069,0.745059,3.117946,3.952357,2.149287,8.204188
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2023_LOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2023_LOG.csv
deleted file mode 100644
index 5f72d0ec2..000000000
--- a/inputs/fuelprices/ng_tot_demand_AEO_2023_LOG.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.664316,1.960484,3.505027,1.984559,0.885024,2.949458,4.323184,1.971775,7.143601
-2020,4.57387,1.967167,3.610102,1.889096,0.84774,2.848881,4.212272,1.904959,7.050152
-2021,4.414782,1.847816,3.666977,1.920606,0.878255,2.948856,3.968929,1.893023,6.868584
-2022,4.921969,1.860084,4.053087,1.924723,0.923179,2.852867,4.173984,1.998839,7.178755
-2023,4.688968,1.769236,3.762858,1.713262,0.898627,2.867689,4.101022,1.882996,6.831382
-2024,4.412861,1.61232,3.484229,1.592727,0.813608,2.618943,3.694418,1.694131,5.775908
-2025,4.291225,1.548674,3.381602,1.539052,0.753545,2.655016,3.390841,1.689819,5.55677
-2026,4.215403,1.434292,3.22606,1.505826,0.729294,2.691801,3.060669,1.692376,5.365656
-2027,4.114222,1.303131,3.121192,1.373113,0.716506,2.589234,2.741685,1.655938,5.114943
-2028,4.022076,1.212429,2.935287,1.314939,0.719535,2.560855,2.566502,1.645141,4.922797
-2029,3.904353,1.179735,2.83829,1.278443,0.702002,2.523008,2.543807,1.617654,4.878305
-2030,3.732039,1.144852,2.680294,1.231844,0.671073,2.502684,2.347672,1.615437,4.867929
-2031,3.690943,1.121933,2.599766,1.216249,0.677104,2.496314,2.358272,1.60978,4.876068
-2032,3.682172,1.105237,2.604154,1.254971,0.67792,2.511045,2.258121,1.604264,4.881312
-2033,3.646841,1.122156,2.620033,1.258562,0.684441,2.497723,2.276471,1.601149,4.895258
-2034,3.632991,1.124097,2.638093,1.266249,0.685443,2.517123,2.266623,1.603735,4.912119
-2035,3.618869,1.131656,2.58872,1.264542,0.688605,2.537925,2.280862,1.62303,4.948229
-2036,3.615077,1.135067,2.589483,1.271512,0.686744,2.53008,2.269746,1.614755,4.958922
-2037,3.62762,1.148588,2.575307,1.278362,0.687396,2.547586,2.264395,1.625301,4.968882
-2038,3.636774,1.165244,2.583965,1.30486,0.690448,2.566403,2.241325,1.650259,5.018841
-2039,3.668032,1.168345,2.593171,1.299386,0.690175,2.595666,2.216114,1.641457,5.044219
-2040,3.684595,1.163503,2.590313,1.302932,0.697215,2.60734,2.246578,1.646972,5.05144
-2041,3.703439,1.174814,2.586318,1.3142,0.695232,2.639903,2.293369,1.655584,5.07185
-2042,3.708781,1.193177,2.581631,1.33487,0.70439,2.672069,2.26222,1.666314,5.119287
-2043,3.719227,1.190942,2.582599,1.380158,0.718918,2.677785,2.312643,1.670804,5.125575
-2044,3.730665,1.187394,2.609419,1.398755,0.697215,2.694914,2.308962,1.683079,5.131341
-2045,3.740067,1.183567,2.563622,1.41797,0.692845,2.714402,2.316288,1.694343,5.159649
-2046,3.750893,1.205716,2.507872,1.438353,0.694813,2.740703,2.313536,1.708017,5.190826
-2047,3.736676,1.204072,2.501286,1.454665,0.699827,2.743096,2.312001,1.732138,5.220525
-2048,3.718637,1.206687,2.506314,1.444515,0.691001,2.787571,2.309686,1.735693,5.259442
-2049,3.761524,1.204775,2.522067,1.460474,0.702651,2.789558,2.344728,1.732613,5.318783
-2050,3.779006,1.229623,2.55432,1.471585,0.689978,2.843648,2.331531,1.757863,5.389961
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2023_reference.csv b/inputs/fuelprices/ng_tot_demand_AEO_2023_reference.csv
deleted file mode 100644
index 769c1f7b2..000000000
--- a/inputs/fuelprices/ng_tot_demand_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.665439,1.962087,3.507681,1.985243,0.884965,2.949311,4.325762,1.972424,7.134515
-2020,4.5785,1.967894,3.615312,1.900725,0.849804,2.847719,4.221534,1.915619,7.066121
-2021,4.418462,1.844552,3.665614,1.921409,0.878472,2.947445,3.971677,1.893204,6.865973
-2022,4.940085,1.892741,4.044715,1.914708,0.923152,2.857152,4.159855,1.998683,7.156394
-2023,4.648726,1.861391,3.709178,1.734919,0.914369,2.86945,4.158048,1.877739,6.910273
-2024,4.647129,1.801217,3.649053,1.688023,0.838359,2.730928,3.964461,1.782738,6.415869
-2025,4.65672,1.719383,3.588987,1.715143,0.786282,2.806267,3.849951,1.787812,6.318212
-2026,4.602509,1.689707,3.53989,1.666044,0.759484,2.884556,3.647847,1.832941,6.477628
-2027,4.582214,1.637302,3.47133,1.58909,0.747313,2.877327,3.388938,1.837376,6.477735
-2028,4.548455,1.592455,3.483272,1.582061,0.741364,2.926167,3.227298,1.814092,6.505511
-2029,4.538335,1.572028,3.481173,1.550278,0.738459,2.915913,3.234772,1.787918,6.42459
-2030,4.551402,1.542259,3.448817,1.491031,0.716561,2.880794,3.210904,1.802583,6.282935
-2031,4.532803,1.526923,3.381878,1.474256,0.720571,2.841016,3.127909,1.782375,6.193381
-2032,4.524544,1.51127,3.348099,1.498738,0.722352,2.795908,3.089234,1.788848,6.07965
-2033,4.491499,1.442519,3.299821,1.506654,0.716909,2.802031,3.057165,1.796832,6.080627
-2034,4.48173,1.453742,3.223152,1.494846,0.719403,2.801301,2.997929,1.811382,6.075301
-2035,4.4979,1.461404,3.151084,1.508977,0.720952,2.786112,3.023646,1.792509,6.082915
-2036,4.518255,1.464336,3.086101,1.512955,0.730625,2.797455,3.039378,1.785394,6.150412
-2037,4.482513,1.493729,3.152291,1.532085,0.728114,2.796066,3.057254,1.793484,6.175611
-2038,4.517887,1.499213,3.196229,1.545365,0.73764,2.802267,3.082606,1.832956,6.205789
-2039,4.50979,1.50786,3.180381,1.556052,0.746798,2.815677,3.120983,1.859493,6.290382
-2040,4.566906,1.522685,3.228045,1.589684,0.744844,2.828494,3.121226,1.858755,6.352501
-2041,4.585896,1.516622,3.252863,1.617866,0.760325,2.844533,3.149406,1.854348,6.379959
-2042,4.621827,1.513537,3.26692,1.645066,0.767767,2.856831,3.175429,1.850412,6.436357
-2043,4.621121,1.517332,3.267063,1.692018,0.768026,2.858763,3.195245,1.881555,6.482502
-2044,4.650357,1.585894,3.312581,1.725517,0.742717,2.864404,3.068405,1.903816,6.524942
-2045,4.704872,1.570329,3.321653,1.738543,0.736814,2.88475,3.088467,1.898746,6.572933
-2046,4.675886,1.553363,3.309839,1.780688,0.735574,2.7657,3.089712,1.9427,6.629407
-2047,4.732585,1.549908,3.261683,1.806785,0.736256,2.792327,3.071366,1.947652,6.703919
-2048,4.750326,1.575682,3.235516,1.833318,0.73714,2.840484,3.11656,1.938674,6.745504
-2049,4.75781,1.578001,3.233698,1.874012,0.737919,2.858956,3.139706,1.952435,6.779729
-2050,4.768852,1.569669,3.297768,1.889228,0.74333,2.881607,3.169167,1.959634,6.84743
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2025_HOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2025_HOG.csv
index c58eaf8d2..cf918c855 100644
--- a/inputs/fuelprices/ng_tot_demand_AEO_2025_HOG.csv
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2025_HOG.csv
@@ -1,20 +1,20 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.660842,1.958889,3.505682,1.985658,0.884488,2.948652,4.330067,1.971532,7.141617
-2020,4.56676,1.974559,3.616957,1.882971,0.84926,2.845356,4.201263,1.906665,7.048672
-2021,4.409971,1.841467,3.668216,1.922016,0.879326,2.946812,3.976987,1.893417,6.866061
-2022,4.923203,1.86141,4.052487,1.925796,0.92313,2.852263,4.17512,1.996832,7.176915
-2023,4.66238,1.874837,3.698862,1.719562,0.911583,2.862135,4.182066,1.889565,7.003771
-2024,4.96334,2.097466,3.903117,2.15953,0.962173,2.98253,4.375371,2.048189,7.421416
-2025,5.17912,2.111273,3.654048,2.100823,0.937966,2.843258,4.469378,2.077749,7.503117
+2010,3.358270,1.354120,2.642740,4.756300,0.858180,2.707130,2.864660,1.549810,5.379280
+2011,3.525400,1.416260,2.719850,4.793510,0.915350,2.548560,2.934920,1.555520,5.529350
+2012,3.547680,1.540450,2.770950,4.903690,0.879080,2.803610,3.280120,1.491080,5.730930
+2013,3.818100,1.455900,2.891800,4.972180,0.859500,2.896840,3.261050,1.645920,5.577360
+2014,4.045160,1.544660,3.120450,5.000560,0.857730,2.784840,3.326570,1.680470,5.646740
+2015,3.886720,1.696390,3.099140,5.255470,0.905920,2.763970,3.663950,1.574980,5.951670
+2016,3.920960,1.746690,3.156140,1.581650,0.856640,2.775400,3.735140,1.638640,5.803450
+2017,3.997110,1.767730,3.113560,1.576460,0.879540,2.827700,3.888780,1.729600,5.922210
+2018,4.395240,1.912480,3.316360,1.676030,0.930980,2.952350,4.067650,1.846240,6.697610
+2019,4.665440,1.962090,3.507680,1.985240,0.884970,2.949310,4.325760,1.972420,7.134520
+2020,4.578500,1.967890,3.615310,1.900720,0.849800,2.847720,4.221530,1.915620,7.066120
+2021,4.418460,1.844550,3.665610,1.921410,0.878470,2.947450,3.971680,1.893200,6.865970
+2022,4.940080,1.892740,4.044720,1.914710,0.923150,2.857150,4.159860,1.998680,7.156390
+2023,4.648730,1.861390,3.709180,1.734920,0.914370,2.869450,4.158050,1.877740,6.910270
+2024,4.966578,2.097415,3.906965,2.155910,0.957036,2.973608,4.376607,2.048244,7.426676
+2025,5.179120,2.111273,3.654048,2.100823,0.937966,2.843258,4.469378,2.077749,7.503117
 2026,5.488719,2.230853,3.759808,2.131084,0.860033,2.739021,4.646142,2.125013,7.636079
 2027,5.671466,2.335734,3.774266,2.100113,0.818995,2.732933,4.738679,2.190878,7.659956
 2028,5.789507,2.291502,3.716908,2.087887,0.795159,2.772114,4.717777,2.173376,7.557886
@@ -25,18 +25,18 @@ year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pac
 2033,6.668369,2.200232,3.943127,2.325531,0.733747,3.145908,4.486273,2.318362,7.632854
 2034,6.625295,2.237109,3.933705,2.308635,0.735347,3.176948,4.505866,2.316933,7.758178
 2035,6.627602,2.198426,3.800751,2.271489,0.739885,3.205086,4.430108,2.316072,7.729087
-2036,6.545062,2.15605,3.739552,2.397209,0.747582,3.013469,4.338734,2.25723,7.367331
+2036,6.545062,2.156050,3.739552,2.397209,0.747582,3.013469,4.338734,2.257230,7.367331
 2037,6.556759,2.167767,3.743082,2.404191,0.757997,3.038831,4.274085,2.263292,6.954051
 2038,6.133348,2.189104,3.694221,2.272732,0.749867,3.078229,4.256666,2.231797,6.922351
-2039,6.001094,2.141802,3.544951,2.13709,0.738831,3.124984,4.208876,2.216016,6.825901
+2039,6.001094,2.141802,3.544951,2.137090,0.738831,3.124984,4.208876,2.216016,6.825901
 2040,5.973637,2.126414,3.550549,2.205422,0.745195,3.147804,4.339911,2.230641,6.780476
 2041,6.025984,2.154529,3.586475,2.189955,0.750859,3.201372,4.432344,2.242017,6.878246
-2042,6.201727,2.144847,3.519784,2.228513,0.76299,3.202829,4.433652,2.267177,6.9346
-2043,6.316656,2.21572,3.577761,2.279858,0.771564,3.196146,4.49225,2.304414,7.085598
+2042,6.201727,2.144847,3.519784,2.228513,0.762990,3.202829,4.433652,2.267177,6.934600
+2043,6.316656,2.215720,3.577761,2.279858,0.771564,3.196146,4.492250,2.304414,7.085598
 2044,6.423592,2.223858,3.673051,2.359413,0.751563,3.216992,4.486453,2.318251,7.117551
-2045,6.50085,2.262689,3.723683,2.461398,0.716803,3.226556,4.47786,2.359171,7.170787
-2046,6.540277,2.342593,3.79589,2.465548,0.718965,3.255445,4.501936,2.39281,7.244801
-2047,6.419639,2.399555,3.895167,2.51716,0.72484,3.255843,4.490632,2.406723,7.288305
-2048,6.308069,2.390973,3.907527,2.572379,0.726701,3.249705,4.457742,2.422065,7.15933
+2045,6.500850,2.262689,3.723683,2.461398,0.716803,3.226556,4.477860,2.359171,7.170787
+2046,6.540277,2.342593,3.795890,2.465548,0.718965,3.255445,4.501936,2.392810,7.244801
+2047,6.419639,2.399555,3.895167,2.517160,0.724840,3.255843,4.490632,2.406723,7.288305
+2048,6.308069,2.390973,3.907527,2.572379,0.726701,3.249705,4.457742,2.422065,7.159330
 2049,6.439577,2.408647,3.848761,2.626165,0.725793,3.269576,4.593865,2.387223,7.386389
 2050,6.387714,2.433508,3.860977,2.731677,0.713332,3.273041,4.613367,2.353932,7.393847
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2025_LOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2025_LOG.csv
index 91cfc7595..a91f2c528 100644
--- a/inputs/fuelprices/ng_tot_demand_AEO_2025_LOG.csv
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2025_LOG.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.664316,1.960484,3.505027,1.984559,0.885024,2.949458,4.323184,1.971775,7.143601
-2020,4.57387,1.967167,3.610102,1.889096,0.84774,2.848881,4.212272,1.904959,7.050152
-2021,4.414782,1.847816,3.666977,1.920606,0.878255,2.948856,3.968929,1.893023,6.868584
-2022,4.921969,1.860084,4.053087,1.924723,0.923179,2.852867,4.173984,1.998839,7.178755
-2023,4.688968,1.769236,3.762858,1.713262,0.898627,2.867689,4.101022,1.882996,6.831382
-2024,4.96334,2.097466,3.903117,2.15953,0.962173,2.98253,4.375371,2.048189,7.421416
-2025,5.087374,2.031063,3.749795,2.058202,0.930546,2.728772,4.343349,2.005418,7.37077
-2026,4.620926,1.784934,3.47533,1.781902,0.781939,2.557914,3.891988,1.842845,5.958117
+2010,3.358270,1.354120,2.642740,4.756300,0.858180,2.707130,2.864660,1.549810,5.379280
+2011,3.525400,1.416260,2.719850,4.793510,0.915350,2.548560,2.934920,1.555520,5.529350
+2012,3.547680,1.540450,2.770950,4.903690,0.879080,2.803610,3.280120,1.491080,5.730930
+2013,3.818100,1.455900,2.891800,4.972180,0.859500,2.896840,3.261050,1.645920,5.577360
+2014,4.045160,1.544660,3.120450,5.000560,0.857730,2.784840,3.326570,1.680470,5.646740
+2015,3.886720,1.696390,3.099140,5.255470,0.905920,2.763970,3.663950,1.574980,5.951670
+2016,3.920960,1.746690,3.156140,1.581650,0.856640,2.775400,3.735140,1.638640,5.803450
+2017,3.997110,1.767730,3.113560,1.576460,0.879540,2.827700,3.888780,1.729600,5.922210
+2018,4.395240,1.912480,3.316360,1.676030,0.930980,2.952350,4.067650,1.846240,6.697610
+2019,4.665440,1.962090,3.507680,1.985240,0.884970,2.949310,4.325760,1.972420,7.134520
+2020,4.578500,1.967890,3.615310,1.900720,0.849800,2.847720,4.221530,1.915620,7.066120
+2021,4.418460,1.844550,3.665610,1.921410,0.878470,2.947450,3.971680,1.893200,6.865970
+2022,4.940080,1.892740,4.044720,1.914710,0.923150,2.857150,4.159860,1.998680,7.156390
+2023,4.648730,1.861390,3.709180,1.734920,0.914370,2.869450,4.158050,1.877740,6.910270
+2024,4.964130,2.098575,3.909995,2.159559,0.962553,2.974449,4.378995,2.049968,7.414999
+2025,5.087374,2.031063,3.749795,2.058202,0.930546,2.728772,4.343349,2.005418,7.370770
+2026,4.620926,1.784934,3.475330,1.781902,0.781939,2.557914,3.891988,1.842845,5.958117
 2027,4.707423,1.835414,3.444869,1.754856,0.742323,2.526092,3.828328,1.880801,6.003426
-2028,4.696098,1.783213,3.364414,1.71399,0.725009,2.556307,3.59118,1.881701,5.845942
-2029,4.647316,1.719558,3.386134,1.696235,0.720008,2.585802,3.361863,1.897199,5.69916
-2030,4.641392,1.629336,3.335698,1.677637,0.699351,2.614153,3.190711,2.038857,5.66855
-2031,4.456717,1.47077,3.142804,1.608598,0.679255,2.628891,2.88527,1.944253,5.419774
+2028,4.696098,1.783213,3.364414,1.713990,0.725009,2.556307,3.591180,1.881701,5.845942
+2029,4.647316,1.719558,3.386134,1.696235,0.720008,2.585802,3.361863,1.897199,5.699160
+2030,4.641392,1.629336,3.335698,1.677637,0.699351,2.614153,3.190711,2.038857,5.668550
+2031,4.456717,1.470770,3.142804,1.608598,0.679255,2.628891,2.885270,1.944253,5.419774
 2032,4.594091,1.521027,3.056186,1.763594,0.694852,2.637849,2.987373,2.025953,5.548148
 2033,4.325788,1.469214,2.965106,1.746244,0.682768,2.634555,2.804255,1.940535,5.332105
 2034,4.196589,1.399418,2.821101,1.690408,0.676451,2.577221,2.729338,1.874597,5.265339
 2035,4.056654,1.387416,2.774165,1.656034,0.679203,2.517057,2.722006,1.840279,5.066918
-2036,3.955245,1.295392,2.661531,1.584508,0.675569,2.446003,2.59898,1.820973,4.959162
-2037,3.762507,1.225727,2.454186,1.567165,0.64952,2.364758,2.459701,1.83173,4.837954
-2038,3.6302,1.169368,2.387821,1.473797,0.645287,2.291434,2.406118,1.815282,4.794869
-2039,3.649924,1.127985,2.41441,1.487857,0.653401,2.252511,2.401801,1.826088,4.765383
-2040,3.580663,1.060108,2.39373,1.457779,0.655786,2.281605,2.404084,1.829875,4.802187
-2041,3.536101,1.008874,2.350435,1.39341,0.656713,2.23877,2.420143,1.785332,4.750147
-2042,3.480375,1.021078,2.321419,1.361234,0.654128,2.244678,2.43365,1.763587,4.73921
-2043,3.467469,1.043947,2.33203,1.362778,0.657176,2.247817,2.402768,1.775419,4.753344
-2044,3.439527,1.045289,2.33087,1.392945,0.621498,2.278843,2.371214,1.802037,4.766046
-2045,3.448897,1.024428,2.309155,1.404418,0.605406,2.285449,2.30251,1.802026,4.775317
+2036,3.955245,1.295392,2.661531,1.584508,0.675569,2.446003,2.598980,1.820973,4.959162
+2037,3.762507,1.225727,2.454186,1.567165,0.649520,2.364758,2.459701,1.831730,4.837954
+2038,3.630200,1.169368,2.387821,1.473797,0.645287,2.291434,2.406118,1.815282,4.794869
+2039,3.649924,1.127985,2.414410,1.487857,0.653401,2.252511,2.401801,1.826088,4.765383
+2040,3.580663,1.060108,2.393730,1.457779,0.655786,2.281605,2.404084,1.829875,4.802187
+2041,3.536101,1.008874,2.350435,1.393410,0.656713,2.238770,2.420143,1.785332,4.750147
+2042,3.480375,1.021078,2.321419,1.361234,0.654128,2.244678,2.433650,1.763587,4.739210
+2043,3.467469,1.043947,2.332030,1.362778,0.657176,2.247817,2.402768,1.775419,4.753344
+2044,3.439527,1.045289,2.330870,1.392945,0.621498,2.278843,2.371214,1.802037,4.766046
+2045,3.448897,1.024428,2.309155,1.404418,0.605406,2.285449,2.302510,1.802026,4.775317
 2046,3.494394,1.023384,2.310539,1.375992,0.609633,2.290656,2.268174,1.805467,4.763526
 2047,3.507108,1.011038,2.304937,1.401801,0.609062,2.294402,2.242798,1.829551,4.806602
 2048,3.543561,1.015026,2.319615,1.456912,0.608704,2.322879,2.267759,1.830128,4.840192
 2049,3.585941,1.036841,2.317298,1.501403,0.608873,2.333663,2.260479,1.837048,4.846149
-2050,3.602013,1.030486,2.297522,1.540658,0.608951,2.361397,2.244589,1.850036,4.88494
+2050,3.602013,1.030486,2.297522,1.540658,0.608951,2.361397,2.244589,1.850036,4.884940
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2025_reference.csv b/inputs/fuelprices/ng_tot_demand_AEO_2025_reference.csv
index f00ff1fbc..98711b385 100644
--- a/inputs/fuelprices/ng_tot_demand_AEO_2025_reference.csv
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2025_reference.csv
@@ -1,42 +1,42 @@
 year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
-2010,3.35827,1.354117,2.64274,4.756302,0.85818,2.707125,2.864664,1.549812,5.379282
-2011,3.525401,1.416257,2.719853,4.79351,0.915348,2.54856,2.934925,1.555518,5.529355
-2012,3.547677,1.540446,2.770951,4.903691,0.879083,2.803605,3.280124,1.491081,5.730931
-2013,3.818099,1.455897,2.891799,4.972179,0.859505,2.896845,3.261053,1.645916,5.577358
-2014,4.045156,1.544663,3.120449,5.000559,0.857731,2.784841,3.326574,1.68047,5.646739
-2015,3.886718,1.696394,3.099138,5.255469,0.905922,2.76397,3.663951,1.574979,5.951674
-2016,3.920965,1.74669,3.156144,1.581652,0.856641,2.775395,3.73514,1.638642,5.803449
-2017,3.997107,1.767733,3.11356,1.576464,0.879541,2.827698,3.888779,1.7296,5.922212
-2018,4.395236,1.912479,3.316355,1.676031,0.930977,2.952353,4.067651,1.846236,6.697608
-2019,4.665439,1.962087,3.507681,1.985243,0.884965,2.949311,4.325762,1.972424,7.134515
-2020,4.5785,1.967894,3.615312,1.900725,0.849804,2.847719,4.221534,1.915619,7.066121
-2021,4.418462,1.844552,3.665614,1.921409,0.878472,2.947445,3.971677,1.893204,6.865973
-2022,4.940085,1.892741,4.044715,1.914708,0.923152,2.857152,4.159855,1.998683,7.156394
-2023,4.648726,1.861391,3.709178,1.734919,0.914369,2.86945,4.158048,1.877739,6.910273
-2024,4.96334,2.097466,3.903117,2.15953,0.962173,2.98253,4.375371,2.048189,7.421416
-2025,5.156418,2.089903,3.661196,2.086673,0.935287,2.807245,4.476222,2.129834,7.23764
+2010,3.358270,1.354120,2.642740,4.756300,0.858180,2.707130,2.864660,1.549810,5.379280
+2011,3.525400,1.416260,2.719850,4.793510,0.915350,2.548560,2.934920,1.555520,5.529350
+2012,3.547680,1.540450,2.770950,4.903690,0.879080,2.803610,3.280120,1.491080,5.730930
+2013,3.818100,1.455900,2.891800,4.972180,0.859500,2.896840,3.261050,1.645920,5.577360
+2014,4.045160,1.544660,3.120450,5.000560,0.857730,2.784840,3.326570,1.680470,5.646740
+2015,3.886720,1.696390,3.099140,5.255470,0.905920,2.763970,3.663950,1.574980,5.951670
+2016,3.920960,1.746690,3.156140,1.581650,0.856640,2.775400,3.735140,1.638640,5.803450
+2017,3.997110,1.767730,3.113560,1.576460,0.879540,2.827700,3.888780,1.729600,5.922210
+2018,4.395240,1.912480,3.316360,1.676030,0.930980,2.952350,4.067650,1.846240,6.697610
+2019,4.665440,1.962090,3.507680,1.985240,0.884970,2.949310,4.325760,1.972420,7.134520
+2020,4.578500,1.967890,3.615310,1.900720,0.849800,2.847720,4.221530,1.915620,7.066120
+2021,4.418460,1.844550,3.665610,1.921410,0.878470,2.947450,3.971680,1.893200,6.865970
+2022,4.940080,1.892740,4.044720,1.914710,0.923150,2.857150,4.159860,1.998680,7.156390
+2023,4.648730,1.861390,3.709180,1.734920,0.914370,2.869450,4.158050,1.877740,6.910270
+2024,4.963340,2.097466,3.903117,2.159530,0.962173,2.982530,4.375371,2.048189,7.421416
+2025,5.156418,2.089903,3.661196,2.086673,0.935287,2.807245,4.476222,2.129834,7.237640
 2026,5.073037,2.052864,3.692177,1.988881,0.848885,2.694449,4.465045,2.030126,6.971332
 2027,5.310193,2.121335,3.721632,1.999269,0.807334,2.667008,4.458486,2.096073,7.088854
 2028,5.422043,2.120489,3.668039,1.959295,0.788003,2.730486,4.351885,2.115744,7.020803
-2029,5.623929,2.085303,3.644482,1.954141,0.767319,2.789246,4.227018,2.153275,6.95013
+2029,5.623929,2.085303,3.644482,1.954141,0.767319,2.789246,4.227018,2.153275,6.950130
 2030,5.640369,2.000149,3.723123,2.035256,0.715819,2.874931,4.102344,2.207593,6.981818
-2031,5.612779,1.956317,3.62468,2.067181,0.736064,2.935515,3.890681,2.187774,6.930852
-2032,6.106751,2.020668,3.716164,2.14524,0.752671,2.998962,4.012863,2.346345,7.100438
-2033,5.944864,1.933828,3.65934,1.967468,0.746767,2.933176,3.806603,2.309086,6.931971
-2034,5.637051,1.848481,3.596563,1.903266,0.733941,2.94572,3.651626,2.168169,6.835082
+2031,5.612779,1.956317,3.624680,2.067181,0.736064,2.935515,3.890681,2.187774,6.930852
+2032,6.106751,2.020668,3.716164,2.145240,0.752671,2.998962,4.012863,2.346345,7.100438
+2033,5.944864,1.933828,3.659340,1.967468,0.746767,2.933176,3.806603,2.309086,6.931971
+2034,5.637051,1.848481,3.596563,1.903266,0.733941,2.945720,3.651626,2.168169,6.835082
 2035,5.440754,1.810854,3.614042,1.936649,0.737544,2.938173,3.610039,2.135197,6.454259
-2036,5.228507,1.762085,3.597095,1.936809,0.742872,2.895602,3.6591,2.114645,6.180867
-2037,5.027087,1.709159,3.445315,1.914083,0.736183,2.84403,3.464379,2.105235,6.054377
-2038,4.835952,1.657892,3.288879,1.91698,0.74257,2.813577,3.479662,2.094151,6.006368
+2036,5.228507,1.762085,3.597095,1.936809,0.742872,2.895602,3.659100,2.114645,6.180867
+2037,5.027087,1.709159,3.445315,1.914083,0.736183,2.844030,3.464379,2.105235,6.054377
+2038,4.835952,1.657892,3.288879,1.916980,0.742570,2.813577,3.479662,2.094151,6.006368
 2039,4.804575,1.614115,3.218215,1.950361,0.732698,2.782817,3.457128,2.066591,5.997215
-2040,4.854189,1.621953,3.22669,1.974375,0.741344,2.780822,3.408876,2.076052,6.087286
+2040,4.854189,1.621953,3.226690,1.974375,0.741344,2.780822,3.408876,2.076052,6.087286
 2041,4.917473,1.639145,3.255468,1.985551,0.742691,2.793409,3.422421,2.094103,6.161439
-2042,4.942109,1.644759,3.196219,2.049288,0.754417,2.748004,3.412856,2.077646,6.18816
-2043,5.006926,1.624161,3.258941,2.057952,0.758587,2.793068,3.37801,2.081888,6.185266
+2042,4.942109,1.644759,3.196219,2.049288,0.754417,2.748004,3.412856,2.077646,6.188160
+2043,5.006926,1.624161,3.258941,2.057952,0.758587,2.793068,3.378010,2.081888,6.185266
 2044,5.082698,1.641373,3.302742,2.095899,0.734295,2.783741,3.311311,2.062417,6.232446
 2045,5.035407,1.651322,3.395932,2.119609,0.692461,2.772924,3.316263,2.105197,6.209975
-2046,5.099153,1.651759,3.377838,2.093664,0.70788,2.733654,3.285728,2.118033,6.23188
+2046,5.099153,1.651759,3.377838,2.093664,0.707880,2.733654,3.285728,2.118033,6.231880
 2047,4.917622,1.640283,3.474578,2.094466,0.709487,2.770767,3.267011,2.137938,6.275684
-2048,4.895315,1.657869,3.520399,2.157986,0.713195,2.78206,3.225357,2.163854,6.278858
+2048,4.895315,1.657869,3.520399,2.157986,0.713195,2.782060,3.225357,2.163854,6.278858
 2049,4.902303,1.695295,3.449674,2.213768,0.725422,2.792679,3.207612,2.172264,6.269968
-2050,4.926717,1.705988,3.45348,2.264314,0.71669,2.801707,3.263909,2.197461,6.099909
+2050,4.926717,1.705988,3.453480,2.264314,0.716690,2.801707,3.263909,2.197461,6.099909
diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2026_HOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2026_HOG.csv
new file mode 100644
index 000000000..036eb3b4a
--- /dev/null
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2026_HOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
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diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2026_LOG.csv b/inputs/fuelprices/ng_tot_demand_AEO_2026_LOG.csv
new file mode 100644
index 000000000..cb88702f8
--- /dev/null
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2026_LOG.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
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diff --git a/inputs/fuelprices/ng_tot_demand_AEO_2026_baseline.csv b/inputs/fuelprices/ng_tot_demand_AEO_2026_baseline.csv
new file mode 100644
index 000000000..c756acf99
--- /dev/null
+++ b/inputs/fuelprices/ng_tot_demand_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+year,East_North_Central,East_South_Central,Mid_Atlantic,Mountain,New_England,Pacific,South_Atlantic,West_North_Central,West_South_Central
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+2015,3.886720,1.696390,3.099140,5.255470,0.905920,2.763970,3.663950,1.574980,5.951670
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diff --git a/inputs/fuelprices/uranium_AEO_2023_reference.csv b/inputs/fuelprices/uranium_AEO_2023_reference.csv
deleted file mode 100644
index b2af956ea..000000000
--- a/inputs/fuelprices/uranium_AEO_2023_reference.csv
+++ /dev/null
@@ -1,42 +0,0 @@
-year,cost
-2010,1.022156612
-2011,1.008527857
-2012,1.0213584
-2013,0.950041604
-2014,0.957682689
-2015,0.658563508
-2016,0.770603899
-2017,0.771796784
-2018,0.774182555
-2019,0.773861723
-2020,0.775840443
-2021,0.77399712
-2022,0.710872
-2023,0.7108
-2024,0.710739
-2025,0.710677
-2026,0.710606
-2027,0.710544
-2028,0.710482
-2029,0.710411
-2030,0.710349
-2031,0.710277
-2032,0.710216
-2033,0.710154
-2034,0.710083
-2035,0.710021
-2036,0.709949
-2037,0.709888
-2038,0.709826
-2039,0.709755
-2040,0.709693
-2041,0.709631
-2042,0.70956
-2043,0.709498
-2044,0.709426
-2045,0.709365
-2046,0.709303
-2047,0.709232
-2048,0.70917
-2049,0.709108
-2050,0.709037
diff --git a/inputs/fuelprices/uranium_AEO_2026_baseline.csv b/inputs/fuelprices/uranium_AEO_2026_baseline.csv
new file mode 100644
index 000000000..2423026a8
--- /dev/null
+++ b/inputs/fuelprices/uranium_AEO_2026_baseline.csv
@@ -0,0 +1,42 @@
+year,cost
+2010,1.123391
+2011,1.108412
+2012,1.122514
+2013,1.044134
+2014,1.052532
+2015,0.723788
+2016,0.846924
+2017,0.848235
+2018,0.850858
+2019,0.850505
+2020,0.85268
+2021,0.850654
+2022,0.781277
+2023,0.781198
+2024,0.772915
+2025,0.549997
+2026,0.545068
+2027,0.544083
+2028,0.542111
+2029,0.54014
+2030,0.538169
+2031,0.535212
+2032,0.532255
+2033,0.529298
+2034,0.525355
+2035,0.521413
+2036,0.51747
+2037,0.514513
+2038,0.511556
+2039,0.508599
+2040,0.504656
+2041,0.5017
+2042,0.497757
+2043,0.4948
+2044,0.490857
+2045,0.486915
+2046,0.482972
+2047,0.480015
+2048,0.476072
+2049,0.47213
+2050,0.468187
diff --git a/inputs/load/demand_AEO_2023_high.csv b/inputs/load/demand_AEO_2023_high.csv
deleted file mode 100644
index 41c94aa6c..000000000
--- a/inputs/load/demand_AEO_2023_high.csv
+++ /dev/null
@@ -1,1969 +0,0 @@
-r,year,multiplier
-IL,2010,1.000075555
-IL,2011,0.987122063
-IL,2012,0.991686463
-IL,2013,0.979702693
-IL,2014,0.977932945
-IL,2015,0.957798834
-IL,2016,0.974658865
-IL,2017,0.948182116
-IL,2018,0.986170088
-IL,2019,0.956862312
-IL,2020,0.918089456
-IL,2021,0.943753372
-IL,2022,0.949193415
-IL,2023,0.941809654
-IL,2024,0.951388755
-IL,2025,0.960304044
-IL,2026,0.972424379
-IL,2027,0.98351345
-IL,2028,0.994210963
-IL,2029,1.002827156
-IL,2030,1.01011943
-IL,2031,1.016386369
-IL,2032,1.023272547
-IL,2033,1.029043085
-IL,2034,1.035923638
-IL,2035,1.043745445
-IL,2036,1.052976232
-IL,2037,1.061362785
-IL,2038,1.069278379
-IL,2039,1.075015907
-IL,2040,1.082058095
-IL,2041,1.08931577
-IL,2042,1.097733349
-IL,2043,1.106750841
-IL,2044,1.115949212
-IL,2045,1.125126075
-IL,2046,1.133764217
-IL,2047,1.142406217
-IL,2048,1.152045095
-IL,2049,1.16181443
-IL,2050,1.173363207
-IN,2010,1.000024006
-IN,2011,0.99839401
-IN,2012,0.992317799
-IN,2013,0.995293258
-IN,2014,1.009056042
-IN,2015,0.986174661
-IN,2016,0.978583187
-IN,2017,0.934042605
-IN,2018,0.98396742
-IN,2019,0.964542348
-IN,2020,0.918229976
-IN,2021,0.943093958
-IN,2022,0.948922166
-IN,2023,0.941538405
-IN,2024,0.951117506
-IN,2025,0.960032795
-IN,2026,0.97215313
-IN,2027,0.983242201
-IN,2028,0.993939714
-IN,2029,1.002555907
-IN,2030,1.009848181
-IN,2031,1.01611512
-IN,2032,1.023001298
-IN,2033,1.028771836
-IN,2034,1.035652389
-IN,2035,1.043474196
-IN,2036,1.052704983
-IN,2037,1.061091536
-IN,2038,1.06900713
-IN,2039,1.074744658
-IN,2040,1.081786846
-IN,2041,1.089044521
-IN,2042,1.0974621
-IN,2043,1.106479592
-IN,2044,1.115677963
-IN,2045,1.124854826
-IN,2046,1.133492968
-IN,2047,1.142134968
-IN,2048,1.151773846
-IN,2049,1.161543181
-IN,2050,1.173091958
-MI,2010,1.000031598
-MI,2011,1.013666512
-MI,2012,1.011552627
-MI,2013,0.994405594
-MI,2014,0.997105702
-MI,2015,0.989085845
-MI,2016,1.008431569
-MI,2017,0.98374973
-MI,2018,1.012562427
-MI,2019,0.977919321
-MI,2020,0.937468792
-MI,2021,0.964852082
-MI,2022,0.970742045
-MI,2023,0.963358284
-MI,2024,0.972937385
-MI,2025,0.981852674
-MI,2026,0.993973009
-MI,2027,1.00506208
-MI,2028,1.015759593
-MI,2029,1.024375786
-MI,2030,1.03166806
-MI,2031,1.037934999
-MI,2032,1.044821177
-MI,2033,1.050591715
-MI,2034,1.057472268
-MI,2035,1.065294075
-MI,2036,1.074524862
-MI,2037,1.082911415
-MI,2038,1.090827009
-MI,2039,1.096564537
-MI,2040,1.103606725
-MI,2041,1.1108644
-MI,2042,1.119281979
-MI,2043,1.128299471
-MI,2044,1.137497842
-MI,2045,1.146674705
-MI,2046,1.155312847
-MI,2047,1.163954847
-MI,2048,1.173593725
-MI,2049,1.18336306
-MI,2050,1.194911837
-OH,2010,1.000057177
-OH,2011,1.0040135
-OH,2012,0.989421437
-OH,2013,0.975579028
-OH,2014,0.978085462
-OH,2015,0.968666225
-OH,2016,0.977729512
-OH,2017,0.952215901
-OH,2018,0.993054079
-OH,2019,0.964829428
-OH,2020,0.926770864
-OH,2021,0.960163522
-OH,2022,0.97648367
-OH,2023,0.969099909
-OH,2024,0.97867901
-OH,2025,0.987594299
-OH,2026,0.999714634
-OH,2027,1.010803705
-OH,2028,1.021501218
-OH,2029,1.030117411
-OH,2030,1.037409685
-OH,2031,1.043676624
-OH,2032,1.050562802
-OH,2033,1.05633334
-OH,2034,1.063213893
-OH,2035,1.0710357
-OH,2036,1.080266487
-OH,2037,1.08865304
-OH,2038,1.096568634
-OH,2039,1.102306162
-OH,2040,1.10934835
-OH,2041,1.116606025
-OH,2042,1.125023604
-OH,2043,1.134041096
-OH,2044,1.143239467
-OH,2045,1.15241633
-OH,2046,1.161054472
-OH,2047,1.169696472
-OH,2048,1.17933535
-OH,2049,1.189104685
-OH,2050,1.200653462
-WI,2010,1.000148808
-WI,2011,0.998141201
-WI,2012,1.001294067
-WI,2013,1.005736799
-WI,2014,1.011167058
-WI,2015,0.999649852
-WI,2016,1.014844242
-WI,2017,1.00558868
-WI,2018,1.033294062
-WI,2019,1.007424368
-WI,2020,0.9829839
-WI,2021,1.012538083
-WI,2022,1.02307163
-WI,2023,1.015687869
-WI,2024,1.02526697
-WI,2025,1.034182259
-WI,2026,1.046302594
-WI,2027,1.057391665
-WI,2028,1.068089178
-WI,2029,1.076705371
-WI,2030,1.083997645
-WI,2031,1.090264584
-WI,2032,1.097150762
-WI,2033,1.1029213
-WI,2034,1.109801853
-WI,2035,1.11762366
-WI,2036,1.126854447
-WI,2037,1.135241
-WI,2038,1.143156594
-WI,2039,1.148894122
-WI,2040,1.15593631
-WI,2041,1.163193985
-WI,2042,1.171611564
-WI,2043,1.180629056
-WI,2044,1.189827427
-WI,2045,1.19900429
-WI,2046,1.207642432
-WI,2047,1.216284432
-WI,2048,1.22592331
-WI,2049,1.235692645
-WI,2050,1.247241422
-AL,2010,1.000014973
-AL,2011,0.979465346
-AL,2012,0.948512862
-AL,2013,0.966907198
-AL,2014,0.995985503
-AL,2015,0.977847217
-AL,2016,0.971067859
-AL,2017,0.94924321
-AL,2018,0.993723675
-AL,2019,0.969708811
-AL,2020,0.917994469
-AL,2021,0.942134017
-AL,2022,0.963659772
-AL,2023,0.958805492
-AL,2024,0.974994945
-AL,2025,0.98972406
-AL,2026,1.004372523
-AL,2027,1.016624138
-AL,2028,1.026513973
-AL,2029,1.03552051
-AL,2030,1.04310722
-AL,2031,1.049813122
-AL,2032,1.057322732
-AL,2033,1.0626934
-AL,2034,1.069255475
-AL,2035,1.077415232
-AL,2036,1.084820709
-AL,2037,1.092922766
-AL,2038,1.100512714
-AL,2039,1.107193166
-AL,2040,1.116147358
-AL,2041,1.124991282
-AL,2042,1.134217975
-AL,2043,1.14369235
-AL,2044,1.153493079
-AL,2045,1.164313747
-AL,2046,1.174612523
-AL,2047,1.184870397
-AL,2048,1.195231456
-AL,2049,1.20570031
-AL,2050,1.21731623
-KY,2010,1.000025676
-KY,2011,0.956961209
-KY,2012,0.951729797
-KY,2013,0.906041351
-KY,2014,0.842723377
-KY,2015,0.81280844
-KY,2016,0.797006505
-KY,2017,0.776540917
-KY,2018,0.819112523
-KY,2019,0.805677778
-KY,2020,0.767908451
-KY,2021,0.797139719
-KY,2022,0.803955017
-KY,2023,0.799100737
-KY,2024,0.81529019
-KY,2025,0.830019305
-KY,2026,0.844667768
-KY,2027,0.856919383
-KY,2028,0.866809218
-KY,2029,0.875815755
-KY,2030,0.883402465
-KY,2031,0.890108367
-KY,2032,0.897617977
-KY,2033,0.902988645
-KY,2034,0.90955072
-KY,2035,0.917710477
-KY,2036,0.925115954
-KY,2037,0.933218011
-KY,2038,0.940807959
-KY,2039,0.947488411
-KY,2040,0.956442603
-KY,2041,0.965286527
-KY,2042,0.97451322
-KY,2043,0.983987595
-KY,2044,0.993788324
-KY,2045,1.004608992
-KY,2046,1.014907768
-KY,2047,1.025165642
-KY,2048,1.035526701
-KY,2049,1.045995555
-KY,2050,1.057611475
-MS,2010,1.000016644
-MS,2011,0.992986538
-MS,2012,0.9738691
-MS,2013,0.981848499
-MS,2014,0.994455906
-MS,2015,0.980012052
-MS,2016,0.987324239
-MS,2017,0.962826342
-MS,2018,1.01438337
-MS,2019,0.985478473
-MS,2020,0.935810566
-MS,2021,0.966698457
-MS,2022,0.999933214
-MS,2023,0.995078934
-MS,2024,1.011268387
-MS,2025,1.025997502
-MS,2026,1.040645965
-MS,2027,1.05289758
-MS,2028,1.062787415
-MS,2029,1.071793952
-MS,2030,1.079380662
-MS,2031,1.086086564
-MS,2032,1.093596174
-MS,2033,1.098966842
-MS,2034,1.105528917
-MS,2035,1.113688674
-MS,2036,1.121094151
-MS,2037,1.129196208
-MS,2038,1.136786156
-MS,2039,1.143466608
-MS,2040,1.1524208
-MS,2041,1.161264724
-MS,2042,1.170491417
-MS,2043,1.179965792
-MS,2044,1.189766521
-MS,2045,1.200587189
-MS,2046,1.210885965
-MS,2047,1.221143839
-MS,2048,1.231504898
-MS,2049,1.241973752
-MS,2050,1.253589672
-TN,2010,1.000041253
-TN,2011,0.973111223
-TN,2012,0.931094148
-TN,2013,0.937006056
-TN,2014,0.968720798
-TN,2015,0.963049627
-TN,2016,0.974185592
-TN,2017,0.940219299
-TN,2018,0.995096932
-TN,2019,0.965360657
-TN,2020,0.918702072
-TN,2021,0.963295329
-TN,2022,0.989382143
-TN,2023,0.984527863
-TN,2024,1.000717316
-TN,2025,1.015446431
-TN,2026,1.030094894
-TN,2027,1.042346509
-TN,2028,1.052236344
-TN,2029,1.061242881
-TN,2030,1.068829591
-TN,2031,1.075535493
-TN,2032,1.083045103
-TN,2033,1.088415771
-TN,2034,1.094977846
-TN,2035,1.103137603
-TN,2036,1.11054308
-TN,2037,1.118645137
-TN,2038,1.126235085
-TN,2039,1.132915537
-TN,2040,1.141869729
-TN,2041,1.150713653
-TN,2042,1.159940346
-TN,2043,1.169414721
-TN,2044,1.17921545
-TN,2045,1.190036118
-TN,2046,1.200334894
-TN,2047,1.210592768
-TN,2048,1.220953827
-TN,2049,1.231422681
-TN,2050,1.243038601
-NJ,2010,1.003806079
-NJ,2011,0.977568078
-NJ,2012,0.960140816
-NJ,2013,0.958433085
-NJ,2014,0.951611606
-NJ,2015,0.973214768
-NJ,2016,0.971145997
-NJ,2017,0.949626193
-NJ,2018,0.985839841
-NJ,2019,0.96312342
-NJ,2020,0.942787292
-NJ,2021,0.95821245
-NJ,2022,0.969126193
-NJ,2023,0.951531468
-NJ,2024,0.967136271
-NJ,2025,0.972817618
-NJ,2026,0.982055657
-NJ,2027,0.991711673
-NJ,2028,1.000732555
-NJ,2029,1.008890516
-NJ,2030,1.016111909
-NJ,2031,1.023326453
-NJ,2032,1.030575642
-NJ,2033,1.038891327
-NJ,2034,1.047720736
-NJ,2035,1.058479712
-NJ,2036,1.069209208
-NJ,2037,1.081103334
-NJ,2038,1.091517445
-NJ,2039,1.101079145
-NJ,2040,1.11167124
-NJ,2041,1.122364145
-NJ,2042,1.134128476
-NJ,2043,1.146711807
-NJ,2044,1.160277941
-NJ,2045,1.174205964
-NJ,2046,1.188465752
-NJ,2047,1.20243739
-NJ,2048,1.216787004
-NJ,2049,1.231481634
-NJ,2050,1.247799082
-NY,2010,1.000394053
-NY,2011,0.996610397
-NY,2012,0.990932231
-NY,2013,1.024059333
-NY,2014,1.021427354
-NY,2015,1.033753334
-NY,2016,1.028047889
-NY,2017,1.010754732
-NY,2018,1.047069087
-NY,2019,1.019678482
-NY,2020,0.986806052
-NY,2021,0.996676569
-NY,2022,1.008809701
-NY,2023,0.991214976
-NY,2024,1.006819779
-NY,2025,1.012501126
-NY,2026,1.021739165
-NY,2027,1.031395181
-NY,2028,1.040416063
-NY,2029,1.048574024
-NY,2030,1.055795417
-NY,2031,1.063009961
-NY,2032,1.07025915
-NY,2033,1.078574835
-NY,2034,1.087404244
-NY,2035,1.09816322
-NY,2036,1.108892716
-NY,2037,1.120786842
-NY,2038,1.131200953
-NY,2039,1.140762653
-NY,2040,1.151354748
-NY,2041,1.162047653
-NY,2042,1.173811984
-NY,2043,1.186395315
-NY,2044,1.199961449
-NY,2045,1.213889472
-NY,2046,1.22814926
-NY,2047,1.242120898
-NY,2048,1.256470512
-NY,2049,1.271165142
-NY,2050,1.28748259
-PA,2010,1.000471824
-PA,2011,0.999734828
-PA,2012,0.972904808
-PA,2013,0.983441925
-PA,2014,0.986495029
-PA,2015,0.984249535
-PA,2016,0.977611612
-PA,2017,0.96239878
-PA,2018,1.002966661
-PA,2019,0.980679319
-PA,2020,0.941989911
-PA,2021,0.966748253
-PA,2022,0.977929708
-PA,2023,0.960334983
-PA,2024,0.975939786
-PA,2025,0.981621133
-PA,2026,0.990859172
-PA,2027,1.000515188
-PA,2028,1.00953607
-PA,2029,1.017694031
-PA,2030,1.024915424
-PA,2031,1.032129968
-PA,2032,1.039379157
-PA,2033,1.047694842
-PA,2034,1.056524251
-PA,2035,1.067283227
-PA,2036,1.078012723
-PA,2037,1.089906849
-PA,2038,1.10032096
-PA,2039,1.10988266
-PA,2040,1.120474755
-PA,2041,1.13116766
-PA,2042,1.142931991
-PA,2043,1.155515322
-PA,2044,1.169081456
-PA,2045,1.183009479
-PA,2046,1.197269267
-PA,2047,1.211240905
-PA,2048,1.225590519
-PA,2049,1.240285149
-PA,2050,1.256602597
-AZ,2010,1.001933241
-AZ,2011,1.034037088
-AZ,2012,1.038743358
-AZ,2013,1.050869664
-AZ,2014,1.062940341
-AZ,2015,1.081043242
-AZ,2016,1.096473158
-AZ,2017,1.092314809
-AZ,2018,1.106900389
-AZ,2019,1.105417988
-AZ,2020,1.165384295
-AZ,2021,1.161608132
-AZ,2022,1.200125229
-AZ,2023,1.187830575
-AZ,2024,1.215296644
-AZ,2025,1.237176633
-AZ,2026,1.258633871
-AZ,2027,1.278066621
-AZ,2028,1.299757067
-AZ,2029,1.32045259
-AZ,2030,1.339847835
-AZ,2031,1.358918281
-AZ,2032,1.378372902
-AZ,2033,1.398016488
-AZ,2034,1.41780171
-AZ,2035,1.438688258
-AZ,2036,1.459032791
-AZ,2037,1.485135263
-AZ,2038,1.505142535
-AZ,2039,1.526536053
-AZ,2040,1.548779094
-AZ,2041,1.572722824
-AZ,2042,1.598065029
-AZ,2043,1.623070185
-AZ,2044,1.648609595
-AZ,2045,1.674657869
-AZ,2046,1.701023537
-AZ,2047,1.728221599
-AZ,2048,1.756076109
-AZ,2049,1.784138024
-AZ,2050,1.81339945
-CO,2010,1.002079595
-CO,2011,1.013347305
-CO,2012,1.018620701
-CO,2013,1.015223108
-CO,2014,1.015930559
-CO,2015,1.030257354
-CO,2016,1.044591778
-CO,2017,1.046373042
-CO,2018,1.078115977
-CO,2019,1.080316289
-CO,2020,1.072724402
-CO,2021,1.085002865
-CO,2022,1.108470552
-CO,2023,1.096175898
-CO,2024,1.123641967
-CO,2025,1.145521956
-CO,2026,1.166979194
-CO,2027,1.186411944
-CO,2028,1.20810239
-CO,2029,1.228797913
-CO,2030,1.248193158
-CO,2031,1.267263604
-CO,2032,1.286718225
-CO,2033,1.306361811
-CO,2034,1.326147033
-CO,2035,1.347033581
-CO,2036,1.367378114
-CO,2037,1.393480586
-CO,2038,1.413487858
-CO,2039,1.434881376
-CO,2040,1.457124417
-CO,2041,1.481068147
-CO,2042,1.506410352
-CO,2043,1.531415508
-CO,2044,1.556954918
-CO,2045,1.583003192
-CO,2046,1.60936886
-CO,2047,1.636566922
-CO,2048,1.664421432
-CO,2049,1.692483347
-CO,2050,1.721744773
-ID,2010,0.999988532
-ID,2011,1.020781212
-ID,2012,1.040123183
-ID,2013,1.06197204
-ID,2014,1.019227514
-ID,2015,1.011658061
-ID,2016,1.012001618
-ID,2017,1.044334564
-ID,2018,1.043313044
-ID,2019,1.054633787
-ID,2020,1.076852921
-ID,2021,1.114399277
-ID,2022,1.149644673
-ID,2023,1.137350019
-ID,2024,1.164816088
-ID,2025,1.186696077
-ID,2026,1.208153315
-ID,2027,1.227586065
-ID,2028,1.249276511
-ID,2029,1.269972034
-ID,2030,1.289367279
-ID,2031,1.308437725
-ID,2032,1.327892346
-ID,2033,1.347535932
-ID,2034,1.367321154
-ID,2035,1.388207702
-ID,2036,1.408552235
-ID,2037,1.434654707
-ID,2038,1.454661979
-ID,2039,1.476055497
-ID,2040,1.498298538
-ID,2041,1.522242268
-ID,2042,1.547584473
-ID,2043,1.572589629
-ID,2044,1.598129039
-ID,2045,1.624177313
-ID,2046,1.650542981
-ID,2047,1.677741043
-ID,2048,1.705595553
-ID,2049,1.733657468
-ID,2050,1.762918894
-MT,2010,1.000204773
-MT,2011,1.001404969
-MT,2012,1.007225546
-MT,2013,1.020689321
-MT,2014,1.024858815
-MT,2015,1.032216882
-MT,2016,1.024695583
-MT,2017,1.069230259
-MT,2018,1.078928814
-MT,2019,1.114315989
-MT,2020,1.061472508
-MT,2021,1.089518961
-MT,2022,1.134244714
-MT,2023,1.12195006
-MT,2024,1.149416129
-MT,2025,1.171296118
-MT,2026,1.192753356
-MT,2027,1.212186106
-MT,2028,1.233876552
-MT,2029,1.254572075
-MT,2030,1.27396732
-MT,2031,1.293037766
-MT,2032,1.312492387
-MT,2033,1.332135973
-MT,2034,1.351921195
-MT,2035,1.372807743
-MT,2036,1.393152276
-MT,2037,1.419254748
-MT,2038,1.43926202
-MT,2039,1.460655538
-MT,2040,1.482898579
-MT,2041,1.506842309
-MT,2042,1.532184514
-MT,2043,1.55718967
-MT,2044,1.58272908
-MT,2045,1.608777354
-MT,2046,1.635143022
-MT,2047,1.662341084
-MT,2048,1.690195594
-MT,2049,1.718257509
-MT,2050,1.747518935
-NM,2010,1.000523205
-NM,2011,1.028599862
-NM,2012,1.035716735
-NM,2013,1.032248035
-NM,2014,1.035966265
-NM,2015,1.036013926
-NM,2016,1.03424914
-NM,2017,1.03502901
-NM,2018,1.083412511
-NM,2019,1.122725718
-NM,2020,1.121013126
-NM,2021,1.152842428
-NM,2022,1.254068236
-NM,2023,1.241773582
-NM,2024,1.269239651
-NM,2025,1.29111964
-NM,2026,1.312576878
-NM,2027,1.332009628
-NM,2028,1.353700074
-NM,2029,1.374395597
-NM,2030,1.393790842
-NM,2031,1.412861288
-NM,2032,1.432315909
-NM,2033,1.451959495
-NM,2034,1.471744717
-NM,2035,1.492631265
-NM,2036,1.512975798
-NM,2037,1.53907827
-NM,2038,1.559085542
-NM,2039,1.58047906
-NM,2040,1.602722101
-NM,2041,1.626665831
-NM,2042,1.652008036
-NM,2043,1.677013192
-NM,2044,1.702552602
-NM,2045,1.728600876
-NM,2046,1.754966544
-NM,2047,1.782164606
-NM,2048,1.810019116
-NM,2049,1.838081031
-NM,2050,1.867342457
-NV,2010,1.000962051
-NV,2011,1.006553312
-NV,2012,1.044393324
-NV,2013,1.045680486
-NV,2014,1.042589056
-NV,2015,1.073392305
-NV,2016,1.083068615
-NV,2017,1.099610175
-NV,2018,1.135252893
-NV,2019,1.117109997
-NV,2020,1.1598581
-NV,2021,1.188990178
-NV,2022,1.227146009
-NV,2023,1.214851355
-NV,2024,1.242317424
-NV,2025,1.264197413
-NV,2026,1.285654651
-NV,2027,1.305087401
-NV,2028,1.326777847
-NV,2029,1.34747337
-NV,2030,1.366868615
-NV,2031,1.385939061
-NV,2032,1.405393682
-NV,2033,1.425037268
-NV,2034,1.44482249
-NV,2035,1.465709038
-NV,2036,1.486053571
-NV,2037,1.512156043
-NV,2038,1.532163315
-NV,2039,1.553556833
-NV,2040,1.575799874
-NV,2041,1.599743604
-NV,2042,1.625085809
-NV,2043,1.650090965
-NV,2044,1.675630375
-NV,2045,1.701678649
-NV,2046,1.728044317
-NV,2047,1.755242379
-NV,2048,1.783096889
-NV,2049,1.811158804
-NV,2050,1.84042023
-UT,2010,1.000057729
-UT,2011,1.029250311
-UT,2012,1.060334249
-UT,2013,1.087399706
-UT,2014,1.072580079
-UT,2015,1.078967839
-UT,2016,1.081452849
-UT,2017,1.101037454
-UT,2018,1.12808008
-UT,2019,1.126845392
-UT,2020,1.148494104
-UT,2021,1.188625393
-UT,2022,1.207623171
-UT,2023,1.195328517
-UT,2024,1.222794586
-UT,2025,1.244674575
-UT,2026,1.266131813
-UT,2027,1.285564563
-UT,2028,1.307255009
-UT,2029,1.327950532
-UT,2030,1.347345777
-UT,2031,1.366416223
-UT,2032,1.385870844
-UT,2033,1.40551443
-UT,2034,1.425299652
-UT,2035,1.4461862
-UT,2036,1.466530733
-UT,2037,1.492633205
-UT,2038,1.512640477
-UT,2039,1.534033995
-UT,2040,1.556277036
-UT,2041,1.580220766
-UT,2042,1.605562971
-UT,2043,1.630568127
-UT,2044,1.656107537
-UT,2045,1.682155811
-UT,2046,1.708521479
-UT,2047,1.735719541
-UT,2048,1.763574051
-UT,2049,1.791635966
-UT,2050,1.820897392
-WY,2010,0.999991469
-WY,2011,1.017772879
-WY,2012,0.991764753
-WY,2013,0.996602311
-WY,2014,1.001334937
-WY,2015,0.98912506
-WY,2016,0.967550795
-WY,2017,0.980668035
-WY,2018,0.985824008
-WY,2019,0.980080343
-WY,2020,0.896595412
-WY,2021,0.923390033
-WY,2022,0.970235784
-WY,2023,0.95794113
-WY,2024,0.985407199
-WY,2025,1.007287188
-WY,2026,1.028744426
-WY,2027,1.048177176
-WY,2028,1.069867622
-WY,2029,1.090563145
-WY,2030,1.10995839
-WY,2031,1.129028836
-WY,2032,1.148483457
-WY,2033,1.168127043
-WY,2034,1.187912265
-WY,2035,1.208798813
-WY,2036,1.229143346
-WY,2037,1.255245818
-WY,2038,1.27525309
-WY,2039,1.296646608
-WY,2040,1.318889649
-WY,2041,1.342833379
-WY,2042,1.368175584
-WY,2043,1.39318074
-WY,2044,1.41872015
-WY,2045,1.444768424
-WY,2046,1.471134092
-WY,2047,1.498332154
-WY,2048,1.526186664
-WY,2049,1.554248579
-WY,2050,1.583510005
-CT,2010,1.000968603
-CT,2011,0.983536484
-CT,2012,0.971766071
-CT,2013,0.983764271
-CT,2014,0.970015984
-CT,2015,0.976408833
-CT,2016,0.962461538
-CT,2017,0.939184482
-CT,2018,0.965215025
-CT,2019,0.937909267
-CT,2020,0.916473428
-CT,2021,0.941186361
-CT,2022,0.942890166
-CT,2023,0.927054476
-CT,2024,0.952074547
-CT,2025,0.957850238
-CT,2026,0.967955381
-CT,2027,0.977142904
-CT,2028,0.988661976
-CT,2029,1.000009333
-CT,2030,1.011994738
-CT,2031,1.023527309
-CT,2032,1.035391214
-CT,2033,1.048783571
-CT,2034,1.060787428
-CT,2035,1.074307523
-CT,2036,1.088110952
-CT,2037,1.103286071
-CT,2038,1.1174865
-CT,2039,1.131683309
-CT,2040,1.146809666
-CT,2041,1.161594047
-CT,2042,1.17705719
-CT,2043,1.192400785
-CT,2044,1.207963976
-CT,2045,1.223943119
-CT,2046,1.240345285
-CT,2047,1.257173238
-CT,2048,1.272848071
-CT,2049,1.290305285
-CT,2050,1.308949333
-MA,2010,1.000706668
-MA,2011,0.974077439
-MA,2012,0.971695405
-MA,2013,0.973833487
-MA,2014,0.964579087
-MA,2015,0.971351039
-MA,2016,0.958204142
-MA,2017,0.945968936
-MA,2018,0.969373184
-MA,2019,0.935929816
-MA,2020,0.916319875
-MA,2021,0.933820818
-MA,2022,0.934808668
-MA,2023,0.918972978
-MA,2024,0.943993049
-MA,2025,0.94976874
-MA,2026,0.959873883
-MA,2027,0.969061406
-MA,2028,0.980580478
-MA,2029,0.991927835
-MA,2030,1.00391324
-MA,2031,1.015445811
-MA,2032,1.027309716
-MA,2033,1.040702073
-MA,2034,1.05270593
-MA,2035,1.066226025
-MA,2036,1.080029454
-MA,2037,1.095204573
-MA,2038,1.109405002
-MA,2039,1.123601811
-MA,2040,1.138728168
-MA,2041,1.153512549
-MA,2042,1.168975692
-MA,2043,1.184319287
-MA,2044,1.199882478
-MA,2045,1.215861621
-MA,2046,1.232263787
-MA,2047,1.24909174
-MA,2048,1.264766573
-MA,2049,1.282223787
-MA,2050,1.300867835
-ME,2010,1.000037722
-ME,2011,0.989950745
-ME,2012,1.003062171
-ME,2013,1.029134007
-ME,2014,1.041973599
-ME,2015,1.032537962
-ME,2016,0.995346166
-ME,2017,0.975947366
-ME,2018,1.076166185
-ME,2019,1.024001544
-ME,2020,0.991662026
-ME,2021,1.01632903
-ME,2022,1.047860494
-ME,2023,1.032024804
-ME,2024,1.057044875
-ME,2025,1.062820566
-ME,2026,1.072925709
-ME,2027,1.082113232
-ME,2028,1.093632304
-ME,2029,1.104979661
-ME,2030,1.116965066
-ME,2031,1.128497637
-ME,2032,1.140361542
-ME,2033,1.153753899
-ME,2034,1.165757756
-ME,2035,1.179277851
-ME,2036,1.19308128
-ME,2037,1.208256399
-ME,2038,1.222456828
-ME,2039,1.236653637
-ME,2040,1.251779994
-ME,2041,1.266564375
-ME,2042,1.282027518
-ME,2043,1.297371113
-ME,2044,1.312934304
-ME,2045,1.328913447
-ME,2046,1.345315613
-ME,2047,1.362143566
-ME,2048,1.377818399
-ME,2049,1.395275613
-ME,2050,1.413919661
-NH,2010,1.000230536
-NH,2011,0.998316077
-NH,2012,0.998725627
-NH,2013,1.015061397
-NH,2014,1.0062917
-NH,2015,1.012214509
-NH,2016,1.006114936
-NH,2017,0.998574759
-NH,2018,1.024278088
-NH,2019,0.995556828
-NH,2020,0.996379099
-NH,2021,1.015657527
-NH,2022,1.014820724
-NH,2023,0.998985034
-NH,2024,1.024005105
-NH,2025,1.029780796
-NH,2026,1.039885939
-NH,2027,1.049073462
-NH,2028,1.060592534
-NH,2029,1.071939891
-NH,2030,1.083925296
-NH,2031,1.095457867
-NH,2032,1.107321772
-NH,2033,1.120714129
-NH,2034,1.132717986
-NH,2035,1.146238081
-NH,2036,1.16004151
-NH,2037,1.175216629
-NH,2038,1.189417058
-NH,2039,1.203613867
-NH,2040,1.218740224
-NH,2041,1.233524605
-NH,2042,1.248987748
-NH,2043,1.264331343
-NH,2044,1.279894534
-NH,2045,1.295873677
-NH,2046,1.312275843
-NH,2047,1.329103796
-NH,2048,1.344778629
-NH,2049,1.362235843
-NH,2050,1.380879891
-RI,2010,1.00037454
-RI,2011,0.992284342
-RI,2012,0.989776324
-RI,2013,0.999092622
-RI,2014,0.981457765
-RI,2015,0.984484741
-RI,2016,0.968575337
-RI,2017,0.953938283
-RI,2018,0.984576662
-RI,2019,0.965493896
-RI,2020,0.980998552
-RI,2021,0.998165723
-RI,2022,1.033201009
-RI,2023,1.017365319
-RI,2024,1.04238539
-RI,2025,1.048161081
-RI,2026,1.058266224
-RI,2027,1.067453747
-RI,2028,1.078972819
-RI,2029,1.090320176
-RI,2030,1.102305581
-RI,2031,1.113838152
-RI,2032,1.125702057
-RI,2033,1.139094414
-RI,2034,1.151098271
-RI,2035,1.164618366
-RI,2036,1.178421795
-RI,2037,1.193596914
-RI,2038,1.207797343
-RI,2039,1.221994152
-RI,2040,1.237120509
-RI,2041,1.25190489
-RI,2042,1.267368033
-RI,2043,1.282711628
-RI,2044,1.298274819
-RI,2045,1.314253962
-RI,2046,1.330656128
-RI,2047,1.347484081
-RI,2048,1.363158914
-RI,2049,1.380616128
-RI,2050,1.399260176
-VT,2010,1.000388299
-VT,2011,0.993349216
-VT,2012,0.987648609
-VT,2013,1.002734417
-VT,2014,1.001408609
-VT,2015,0.996509497
-VT,2016,0.99949164
-VT,2017,0.989275982
-VT,2018,1.011857312
-VT,2019,0.997426777
-VT,2020,0.983980884
-VT,2021,1.00074034
-VT,2022,1.014103264
-VT,2023,0.998267574
-VT,2024,1.023287645
-VT,2025,1.029063336
-VT,2026,1.039168479
-VT,2027,1.048356002
-VT,2028,1.059875074
-VT,2029,1.071222431
-VT,2030,1.083207836
-VT,2031,1.094740407
-VT,2032,1.106604312
-VT,2033,1.119996669
-VT,2034,1.132000526
-VT,2035,1.145520621
-VT,2036,1.15932405
-VT,2037,1.174499169
-VT,2038,1.188699598
-VT,2039,1.202896407
-VT,2040,1.218022764
-VT,2041,1.232807145
-VT,2042,1.248270288
-VT,2043,1.263613883
-VT,2044,1.279177074
-VT,2045,1.295156217
-VT,2046,1.311558383
-VT,2047,1.328386336
-VT,2048,1.344061169
-VT,2049,1.361518383
-VT,2050,1.380162431
-CA,2010,1.004774859
-CA,2011,1.019684731
-CA,2012,1.013351818
-CA,2013,1.023981459
-CA,2014,1.034104524
-CA,2015,1.033840414
-CA,2016,1.025789692
-CA,2017,1.036639781
-CA,2018,1.037795267
-CA,2019,1.027821906
-CA,2020,1.035799006
-CA,2021,1.034164546
-CA,2022,1.06561455
-CA,2023,1.050547465
-CA,2024,1.066278862
-CA,2025,1.08912482
-CA,2026,1.111810408
-CA,2027,1.128698299
-CA,2028,1.148083922
-CA,2029,1.167674649
-CA,2030,1.185331385
-CA,2031,1.200251927
-CA,2032,1.21703038
-CA,2033,1.234489111
-CA,2034,1.253000822
-CA,2035,1.271553958
-CA,2036,1.28991981
-CA,2037,1.309383744
-CA,2038,1.329097764
-CA,2039,1.350727736
-CA,2040,1.372195084
-CA,2041,1.394702618
-CA,2042,1.417713031
-CA,2043,1.440171242
-CA,2044,1.462734763
-CA,2045,1.486113773
-CA,2046,1.509088534
-CA,2047,1.531607472
-CA,2048,1.553985661
-CA,2049,1.577596424
-CA,2050,1.602953081
-OR,2010,1.000704404
-OR,2011,1.025781596
-OR,2012,1.015643175
-OR,2013,1.036487073
-OR,2014,1.030095872
-OR,2015,1.028813207
-OR,2016,1.031964163
-OR,2017,1.091098105
-OR,2018,1.07606457
-OR,2019,1.099971107
-OR,2020,1.113998135
-OR,2021,1.182726523
-OR,2022,1.16898999
-OR,2023,1.153922905
-OR,2024,1.169654302
-OR,2025,1.19250026
-OR,2026,1.215185848
-OR,2027,1.232073739
-OR,2028,1.251459362
-OR,2029,1.271050089
-OR,2030,1.288706825
-OR,2031,1.303627367
-OR,2032,1.32040582
-OR,2033,1.337864551
-OR,2034,1.356376262
-OR,2035,1.374929398
-OR,2036,1.39329525
-OR,2037,1.412759184
-OR,2038,1.432473204
-OR,2039,1.454103176
-OR,2040,1.475570524
-OR,2041,1.498078058
-OR,2042,1.521088471
-OR,2043,1.543546682
-OR,2044,1.566110203
-OR,2045,1.589489213
-OR,2046,1.612463974
-OR,2047,1.634982912
-OR,2048,1.657361101
-OR,2049,1.680971864
-OR,2050,1.706328521
-WA,2010,0.999954043
-WA,2011,1.036996134
-WA,2012,1.021713053
-WA,2013,1.027838058
-WA,2014,1.019779181
-WA,2015,0.997596942
-WA,2016,0.984355167
-WA,2017,1.018534707
-WA,2018,0.997417722
-WA,2019,1.009611428
-WA,2020,0.962023088
-WA,2021,0.979182066
-WA,2022,1.003189075
-WA,2023,0.98812199
-WA,2024,1.003853387
-WA,2025,1.026699345
-WA,2026,1.049384933
-WA,2027,1.066272824
-WA,2028,1.085658447
-WA,2029,1.105249174
-WA,2030,1.12290591
-WA,2031,1.137826452
-WA,2032,1.154604905
-WA,2033,1.172063636
-WA,2034,1.190575347
-WA,2035,1.209128483
-WA,2036,1.227494335
-WA,2037,1.246958269
-WA,2038,1.266672289
-WA,2039,1.288302261
-WA,2040,1.309769609
-WA,2041,1.332277143
-WA,2042,1.355287556
-WA,2043,1.377745767
-WA,2044,1.400309288
-WA,2045,1.423688298
-WA,2046,1.446663059
-WA,2047,1.469181997
-WA,2048,1.491560186
-WA,2049,1.515170949
-WA,2050,1.540527606
-DE,2010,1.000740874
-DE,2011,0.991651797
-DE,2012,0.995548945
-DE,2013,0.982697513
-DE,2014,0.983258631
-DE,2015,0.997644911
-DE,2016,0.975982178
-DE,2017,0.966953201
-DE,2018,1.023907154
-DE,2019,0.999002971
-DE,2020,0.970017982
-DE,2021,1.001213296
-DE,2022,1.011565926
-DE,2023,1.007252555
-DE,2024,1.010178591
-DE,2025,1.018899084
-DE,2026,1.031205802
-DE,2027,1.044198786
-DE,2028,1.059281916
-DE,2029,1.073895603
-DE,2030,1.086539287
-DE,2031,1.098545663
-DE,2032,1.110942342
-DE,2033,1.121331372
-DE,2034,1.132302509
-DE,2035,1.145080711
-DE,2036,1.158356271
-DE,2037,1.171181212
-DE,2038,1.183280505
-DE,2039,1.194726328
-DE,2040,1.208379234
-DE,2041,1.223035607
-DE,2042,1.238718839
-DE,2043,1.254815599
-DE,2044,1.270827845
-DE,2045,1.28706934
-DE,2046,1.302853759
-DE,2047,1.319226956
-DE,2048,1.33552825
-DE,2049,1.351918697
-DE,2050,1.369601358
-FL,2010,1.000098794
-FL,2011,0.973673845
-FL,2012,0.954687732
-FL,2013,0.960183913
-FL,2014,0.978325125
-FL,2015,1.019665351
-FL,2016,1.020413339
-FL,2017,1.009696811
-FL,2018,1.033704381
-FL,2019,1.042554059
-FL,2020,1.053452573
-FL,2021,1.052191629
-FL,2022,1.134160307
-FL,2023,1.129846936
-FL,2024,1.132772972
-FL,2025,1.141493465
-FL,2026,1.153800183
-FL,2027,1.166793167
-FL,2028,1.181876297
-FL,2029,1.196489984
-FL,2030,1.209133668
-FL,2031,1.221140044
-FL,2032,1.233536723
-FL,2033,1.243925753
-FL,2034,1.25489689
-FL,2035,1.267675092
-FL,2036,1.280950652
-FL,2037,1.293775593
-FL,2038,1.305874886
-FL,2039,1.317320709
-FL,2040,1.330973615
-FL,2041,1.345629988
-FL,2042,1.36131322
-FL,2043,1.37740998
-FL,2044,1.393422226
-FL,2045,1.409663721
-FL,2046,1.42544814
-FL,2047,1.441821337
-FL,2048,1.458122631
-FL,2049,1.474513078
-FL,2050,1.492195739
-GA,2010,1.000073772
-GA,2011,0.969591747
-GA,2012,0.931397288
-GA,2013,0.928355503
-GA,2014,0.966025322
-GA,2015,0.966661637
-GA,2016,0.983214295
-GA,2017,0.950340982
-GA,2018,0.996210208
-GA,2019,0.992460799
-GA,2020,0.951173388
-GA,2021,0.979150139
-GA,2022,1.021233468
-GA,2023,1.016920097
-GA,2024,1.019846133
-GA,2025,1.028566626
-GA,2026,1.040873344
-GA,2027,1.053866328
-GA,2028,1.068949458
-GA,2029,1.083563145
-GA,2030,1.096206829
-GA,2031,1.108213205
-GA,2032,1.120609884
-GA,2033,1.130998914
-GA,2034,1.141970051
-GA,2035,1.154748253
-GA,2036,1.168023813
-GA,2037,1.180848754
-GA,2038,1.192948047
-GA,2039,1.20439387
-GA,2040,1.218046776
-GA,2041,1.232703149
-GA,2042,1.248386381
-GA,2043,1.264483141
-GA,2044,1.280495387
-GA,2045,1.296736882
-GA,2046,1.312521301
-GA,2047,1.328894498
-GA,2048,1.345195792
-GA,2049,1.361586239
-GA,2050,1.3792689
-MD,2010,1.000152142
-MD,2011,0.973981823
-MD,2012,0.947805612
-MD,2013,0.947392825
-MD,2014,0.947187257
-MD,2015,0.950872179
-MD,2016,0.949004189
-MD,2017,0.919057089
-MD,2018,0.962341236
-MD,2019,0.94198106
-MD,2020,0.886211329
-MD,2021,0.912154154
-MD,2022,0.919174068
-MD,2023,0.914860697
-MD,2024,0.917786733
-MD,2025,0.926507226
-MD,2026,0.938813944
-MD,2027,0.951806928
-MD,2028,0.966890058
-MD,2029,0.981503745
-MD,2030,0.994147429
-MD,2031,1.006153805
-MD,2032,1.018550484
-MD,2033,1.028939514
-MD,2034,1.039910651
-MD,2035,1.052688853
-MD,2036,1.065964413
-MD,2037,1.078789354
-MD,2038,1.090888647
-MD,2039,1.10233447
-MD,2040,1.115987376
-MD,2041,1.130643749
-MD,2042,1.146326981
-MD,2043,1.162423741
-MD,2044,1.178435987
-MD,2045,1.194677482
-MD,2046,1.210461901
-MD,2047,1.226835098
-MD,2048,1.243136392
-MD,2049,1.259526839
-MD,2050,1.2772095
-NC,2010,1.000056501
-NC,2011,0.961045918
-NC,2012,0.939223305
-NC,2013,0.952178915
-NC,2014,0.977037784
-NC,2015,0.982377466
-NC,2016,0.987399844
-NC,2017,0.965611665
-NC,2018,1.016095022
-NC,2019,1.002952117
-NC,2020,0.959211188
-NC,2021,0.998864028
-NC,2022,1.034915461
-NC,2023,1.03060209
-NC,2024,1.033528126
-NC,2025,1.042248619
-NC,2026,1.054555337
-NC,2027,1.067548321
-NC,2028,1.082631451
-NC,2029,1.097245138
-NC,2030,1.109888822
-NC,2031,1.121895198
-NC,2032,1.134291877
-NC,2033,1.144680907
-NC,2034,1.155652044
-NC,2035,1.168430246
-NC,2036,1.181705806
-NC,2037,1.194530747
-NC,2038,1.20663004
-NC,2039,1.218075863
-NC,2040,1.231728769
-NC,2041,1.246385142
-NC,2042,1.262068374
-NC,2043,1.278165134
-NC,2044,1.29417738
-NC,2045,1.310418875
-NC,2046,1.326203294
-NC,2047,1.342576491
-NC,2048,1.358877785
-NC,2049,1.375268232
-NC,2050,1.392950893
-SC,2010,1.000011633
-SC,2011,0.975882843
-SC,2012,0.943056809
-SC,2013,0.953025883
-SC,2014,0.989625442
-SC,2015,0.986129808
-SC,2016,0.9652528
-SC,2017,0.948534211
-SC,2018,0.99285427
-SC,2019,0.97645889
-SC,2020,0.93492418
-SC,2021,0.972652866
-SC,2022,1.01963029
-SC,2023,1.015316919
-SC,2024,1.018242955
-SC,2025,1.026963448
-SC,2026,1.039270166
-SC,2027,1.05226315
-SC,2028,1.06734628
-SC,2029,1.081959967
-SC,2030,1.094603651
-SC,2031,1.106610027
-SC,2032,1.119006706
-SC,2033,1.129395736
-SC,2034,1.140366873
-SC,2035,1.153145075
-SC,2036,1.166420635
-SC,2037,1.179245576
-SC,2038,1.191344869
-SC,2039,1.202790692
-SC,2040,1.216443598
-SC,2041,1.231099971
-SC,2042,1.246783203
-SC,2043,1.262879963
-SC,2044,1.278892209
-SC,2045,1.295133704
-SC,2046,1.310918123
-SC,2047,1.32729132
-SC,2048,1.343592614
-SC,2049,1.359983061
-SC,2050,1.377665722
-VA,2010,1.000031646
-VA,2011,0.968620938
-VA,2012,0.947298107
-VA,2013,0.971193555
-VA,2014,0.985185042
-VA,2015,0.984469086
-VA,2016,0.986956296
-VA,2017,0.980574028
-VA,2018,1.039034752
-VA,2019,1.041845506
-VA,2020,1.032190464
-VA,2021,1.103341228
-VA,2022,1.146684082
-VA,2023,1.142370711
-VA,2024,1.145296747
-VA,2025,1.15401724
-VA,2026,1.166323958
-VA,2027,1.179316942
-VA,2028,1.194400072
-VA,2029,1.209013759
-VA,2030,1.221657443
-VA,2031,1.233663819
-VA,2032,1.246060498
-VA,2033,1.256449528
-VA,2034,1.267420665
-VA,2035,1.280198867
-VA,2036,1.293474427
-VA,2037,1.306299368
-VA,2038,1.318398661
-VA,2039,1.329844484
-VA,2040,1.34349739
-VA,2041,1.358153763
-VA,2042,1.373836995
-VA,2043,1.389933755
-VA,2044,1.405946001
-VA,2045,1.422187496
-VA,2046,1.437971915
-VA,2047,1.454345112
-VA,2048,1.470646406
-VA,2049,1.487036853
-VA,2050,1.504719514
-WV,2010,1.00001102
-WV,2011,0.975268607
-WV,2012,0.962185189
-WV,2013,0.980408258
-WV,2014,1.020863806
-WV,2015,1.008618276
-WV,2016,1.001572116
-WV,2017,0.990180529
-WV,2018,1.050728457
-WV,2019,1.038335221
-WV,2020,1.001888949
-WV,2021,1.023994326
-WV,2022,1.031041855
-WV,2023,1.026728484
-WV,2024,1.02965452
-WV,2025,1.038375013
-WV,2026,1.050681731
-WV,2027,1.063674715
-WV,2028,1.078757845
-WV,2029,1.093371532
-WV,2030,1.106015216
-WV,2031,1.118021592
-WV,2032,1.130418271
-WV,2033,1.140807301
-WV,2034,1.151778438
-WV,2035,1.16455664
-WV,2036,1.1778322
-WV,2037,1.190657141
-WV,2038,1.202756434
-WV,2039,1.214202257
-WV,2040,1.227855163
-WV,2041,1.242511536
-WV,2042,1.258194768
-WV,2043,1.274291528
-WV,2044,1.290303774
-WV,2045,1.306545269
-WV,2046,1.322329688
-WV,2047,1.338702885
-WV,2048,1.355004179
-WV,2049,1.371394626
-WV,2050,1.389077287
-IA,2010,1.000038554
-IA,2011,1.004651049
-IA,2012,1.005862743
-IA,2013,1.027886541
-IA,2014,1.039229645
-IA,2015,1.038395106
-IA,2016,1.067052687
-IA,2017,1.078511261
-IA,2018,1.129684453
-IA,2019,1.126752477
-IA,2020,1.118964566
-IA,2021,1.169361839
-IA,2022,1.206986129
-IA,2023,1.196402699
-IA,2024,1.19799914
-IA,2025,1.209381583
-IA,2026,1.222881623
-IA,2027,1.235675521
-IA,2028,1.246846647
-IA,2029,1.257244285
-IA,2030,1.267690745
-IA,2031,1.277403427
-IA,2032,1.287866887
-IA,2033,1.297478931
-IA,2034,1.30790583
-IA,2035,1.319252749
-IA,2036,1.331382221
-IA,2037,1.342990745
-IA,2038,1.354199788
-IA,2039,1.362440436
-IA,2040,1.371376887
-IA,2041,1.381318063
-IA,2042,1.392934713
-IA,2043,1.40552921
-IA,2044,1.418199419
-IA,2045,1.430790287
-IA,2046,1.442873577
-IA,2047,1.45497256
-IA,2048,1.467865451
-IA,2049,1.480942849
-IA,2050,1.495167306
-KS,2010,1.000003996
-KS,2011,1.008408704
-KS,2012,0.996872918
-KS,2013,0.985877284
-KS,2014,1.003557033
-KS,2015,0.985969016
-KS,2016,1.009841384
-KS,2017,0.997121896
-KS,2018,1.040652614
-KS,2019,1.019226884
-KS,2020,0.978006388
-KS,2021,1.00325583
-KS,2022,1.044580735
-KS,2023,1.033997305
-KS,2024,1.035593746
-KS,2025,1.046976189
-KS,2026,1.060476229
-KS,2027,1.073270127
-KS,2028,1.084441253
-KS,2029,1.094838891
-KS,2030,1.105285351
-KS,2031,1.114998033
-KS,2032,1.125461493
-KS,2033,1.135073537
-KS,2034,1.145500436
-KS,2035,1.156847355
-KS,2036,1.168976827
-KS,2037,1.180585351
-KS,2038,1.191794394
-KS,2039,1.200035042
-KS,2040,1.208971493
-KS,2041,1.218912669
-KS,2042,1.230529319
-KS,2043,1.243123816
-KS,2044,1.255794025
-KS,2045,1.268384893
-KS,2046,1.280468183
-KS,2047,1.292567166
-KS,2048,1.305460057
-KS,2049,1.318537455
-KS,2050,1.332761912
-MN,2010,1.000069619
-MN,2011,1.010891741
-MN,2012,1.002939322
-MN,2013,1.012638553
-MN,2014,1.013849121
-MN,2015,0.982383
-MN,2016,0.98206703
-MN,2017,0.991326459
-MN,2018,1.014600007
-MN,2019,0.98924254
-MN,2020,0.94682989
-MN,2021,0.984855338
-MN,2022,0.983431119
-MN,2023,0.972847689
-MN,2024,0.97444413
-MN,2025,0.985826573
-MN,2026,0.999326613
-MN,2027,1.012120511
-MN,2028,1.023291637
-MN,2029,1.033689275
-MN,2030,1.044135735
-MN,2031,1.053848417
-MN,2032,1.064311877
-MN,2033,1.073923921
-MN,2034,1.08435082
-MN,2035,1.095697739
-MN,2036,1.107827211
-MN,2037,1.119435735
-MN,2038,1.130644778
-MN,2039,1.138885426
-MN,2040,1.147821877
-MN,2041,1.157763053
-MN,2042,1.169379703
-MN,2043,1.1819742
-MN,2044,1.194644409
-MN,2045,1.207235277
-MN,2046,1.219318567
-MN,2047,1.23141755
-MN,2048,1.244310441
-MN,2049,1.257387839
-MN,2050,1.271612296
-MO,2010,1.000042459
-MO,2011,0.978794789
-MO,2012,0.957869895
-MO,2013,0.969526822
-MO,2014,0.975715719
-MO,2015,0.948512272
-MO,2016,0.915254742
-MO,2017,0.890633659
-MO,2018,0.955948913
-MO,2019,0.919501066
-MO,2020,0.883885805
-MO,2021,0.908396351
-MO,2022,0.939482972
-MO,2023,0.928899542
-MO,2024,0.930495983
-MO,2025,0.941878426
-MO,2026,0.955378466
-MO,2027,0.968172364
-MO,2028,0.97934349
-MO,2029,0.989741128
-MO,2030,1.000187588
-MO,2031,1.00990027
-MO,2032,1.02036373
-MO,2033,1.029975774
-MO,2034,1.040402673
-MO,2035,1.051749592
-MO,2036,1.063879064
-MO,2037,1.075487588
-MO,2038,1.086696631
-MO,2039,1.094937279
-MO,2040,1.10387373
-MO,2041,1.113814906
-MO,2042,1.125431556
-MO,2043,1.138026053
-MO,2044,1.150696262
-MO,2045,1.16328713
-MO,2046,1.17537042
-MO,2047,1.187469403
-MO,2048,1.200362294
-MO,2049,1.213439692
-MO,2050,1.227664149
-ND,2010,1.000026861
-ND,2011,1.060289141
-ND,2012,1.135933747
-ND,2013,1.237509802
-ND,2014,1.407855742
-ND,2015,1.399301121
-ND,2016,1.429499766
-ND,2017,1.554559793
-ND,2018,1.595400015
-ND,2019,1.664100998
-ND,2020,1.684193502
-ND,2021,1.764779679
-ND,2022,1.95344026
-ND,2023,1.94285683
-ND,2024,1.944453271
-ND,2025,1.955835714
-ND,2026,1.969335754
-ND,2027,1.982129652
-ND,2028,1.993300778
-ND,2029,2.003698416
-ND,2030,2.014144876
-ND,2031,2.023857558
-ND,2032,2.034321018
-ND,2033,2.043933062
-ND,2034,2.054359961
-ND,2035,2.06570688
-ND,2036,2.077836352
-ND,2037,2.089444876
-ND,2038,2.100653919
-ND,2039,2.108894567
-ND,2040,2.117831018
-ND,2041,2.127772194
-ND,2042,2.139388844
-ND,2043,2.151983341
-ND,2044,2.16465355
-ND,2045,2.177244418
-ND,2046,2.189327708
-ND,2047,2.201426691
-ND,2048,2.214319582
-ND,2049,2.22739698
-ND,2050,2.241621437
-NE,2010,1.000011241
-NE,2011,0.994206311
-NE,2012,1.032799082
-NE,2013,1.028570162
-NE,2014,1.012529357
-NE,2015,0.988180648
-NE,2016,1.011819874
-NE,2017,1.017259301
-NE,2018,1.036882024
-NE,2019,1.018392418
-NE,2020,1.044982472
-NE,2021,1.08432271
-NE,2022,1.11546918
-NE,2023,1.10488575
-NE,2024,1.106482191
-NE,2025,1.117864634
-NE,2026,1.131364674
-NE,2027,1.144158572
-NE,2028,1.155329698
-NE,2029,1.165727336
-NE,2030,1.176173796
-NE,2031,1.185886478
-NE,2032,1.196349938
-NE,2033,1.205961982
-NE,2034,1.216388881
-NE,2035,1.2277358
-NE,2036,1.239865272
-NE,2037,1.251473796
-NE,2038,1.262682839
-NE,2039,1.270923487
-NE,2040,1.279859938
-NE,2041,1.289801114
-NE,2042,1.301417764
-NE,2043,1.314012261
-NE,2044,1.32668247
-NE,2045,1.339273338
-NE,2046,1.351356628
-NE,2047,1.363455611
-NE,2048,1.376348502
-NE,2049,1.3894259
-NE,2050,1.403650357
-SD,2010,1.000011272
-SD,2011,1.028494049
-SD,2012,1.033302949
-SD,2013,1.075182845
-SD,2014,1.087981495
-SD,2015,1.065735007
-SD,2016,1.068177818
-SD,2017,1.084403604
-SD,2018,1.132252393
-SD,2019,1.133331422
-SD,2020,1.1181258
-SD,2021,1.148577908
-SD,2022,1.182658523
-SD,2023,1.172075093
-SD,2024,1.173671534
-SD,2025,1.185053977
-SD,2026,1.198554017
-SD,2027,1.211347915
-SD,2028,1.222519041
-SD,2029,1.232916679
-SD,2030,1.243363139
-SD,2031,1.253075821
-SD,2032,1.263539281
-SD,2033,1.273151325
-SD,2034,1.283578224
-SD,2035,1.294925143
-SD,2036,1.307054615
-SD,2037,1.318663139
-SD,2038,1.329872182
-SD,2039,1.33811283
-SD,2040,1.347049281
-SD,2041,1.356990457
-SD,2042,1.368607107
-SD,2043,1.381201604
-SD,2044,1.393871813
-SD,2045,1.406462681
-SD,2046,1.418545971
-SD,2047,1.430644954
-SD,2048,1.443537845
-SD,2049,1.456615243
-SD,2050,1.4708397
-AR,2010,1.000063951
-AR,2011,0.9945394
-AR,2012,0.972405365
-AR,2013,0.968755292
-AR,2014,0.977007693
-AR,2015,0.96425511
-AR,2016,0.958547482
-AR,2017,0.956479774
-AR,2018,1.029698079
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-AR,2021,1.013711498
-AR,2022,1.030249822
-AR,2023,0.999108692
-AR,2024,1.037056041
-AR,2025,1.048835294
-AR,2026,1.066024782
-AR,2027,1.083932692
-AR,2028,1.102125934
-AR,2029,1.12005538
-AR,2030,1.135029796
-AR,2031,1.148025689
-AR,2032,1.164497945
-AR,2033,1.177251977
-AR,2034,1.192342969
-AR,2035,1.208186244
-AR,2036,1.225367044
-AR,2037,1.243907817
-AR,2038,1.261540612
-AR,2039,1.277468078
-AR,2040,1.295226878
-AR,2041,1.312351118
-AR,2042,1.329990788
-AR,2043,1.349265662
-AR,2044,1.368673934
-AR,2045,1.389620094
-AR,2046,1.410248228
-AR,2047,1.430481054
-AR,2048,1.451654361
-AR,2049,1.472452713
-AR,2050,1.494512052
-LA,2010,1.000046504
-LA,2011,1.015264133
-LA,2012,0.996189376
-LA,2013,1.009284611
-LA,2014,1.066444587
-LA,2015,1.079350387
-LA,2016,1.077156499
-LA,2017,1.074366116
-LA,2018,1.10973183
-LA,2019,1.097443496
-LA,2020,1.050598314
-LA,2021,1.070537085
-LA,2022,1.131127154
-LA,2023,1.099986024
-LA,2024,1.137933373
-LA,2025,1.149712626
-LA,2026,1.166902114
-LA,2027,1.184810024
-LA,2028,1.203003266
-LA,2029,1.220932712
-LA,2030,1.235907128
-LA,2031,1.248903021
-LA,2032,1.265375277
-LA,2033,1.278129309
-LA,2034,1.293220301
-LA,2035,1.309063576
-LA,2036,1.326244376
-LA,2037,1.344785149
-LA,2038,1.362417944
-LA,2039,1.37834541
-LA,2040,1.39610421
-LA,2041,1.41322845
-LA,2042,1.43086812
-LA,2043,1.450142994
-LA,2044,1.469551266
-LA,2045,1.490497426
-LA,2046,1.51112556
-LA,2047,1.531358386
-LA,2048,1.552531693
-LA,2049,1.573330045
-LA,2050,1.595389384
-OK,2010,1.000031118
-OK,2011,1.034626177
-OK,2012,1.025896632
-OK,2013,1.036074946
-OK,2014,1.064508284
-OK,2015,1.06042642
-OK,2016,1.063555759
-OK,2017,1.045876313
-OK,2018,1.11655916
-OK,2019,1.120518345
-OK,2020,1.077532428
-OK,2021,1.116390812
-OK,2022,1.199618489
-OK,2023,1.168477359
-OK,2024,1.206424708
-OK,2025,1.218203961
-OK,2026,1.235393449
-OK,2027,1.253301359
-OK,2028,1.271494601
-OK,2029,1.289424047
-OK,2030,1.304398463
-OK,2031,1.317394356
-OK,2032,1.333866612
-OK,2033,1.346620644
-OK,2034,1.361711636
-OK,2035,1.377554911
-OK,2036,1.394735711
-OK,2037,1.413276484
-OK,2038,1.430909279
-OK,2039,1.446836745
-OK,2040,1.464595545
-OK,2041,1.481719785
-OK,2042,1.499359455
-OK,2043,1.518634329
-OK,2044,1.538042601
-OK,2045,1.558988761
-OK,2046,1.579616895
-OK,2047,1.599849721
-OK,2048,1.621023028
-OK,2049,1.64182138
-OK,2050,1.663880719
-TX,2010,1.000100696
-TX,2011,1.049282937
-TX,2012,1.018783304
-TX,2013,1.05716162
-TX,2014,1.087569598
-TX,2015,1.09517765
-TX,2016,1.113290123
-TX,2017,1.122505904
-TX,2018,1.186050904
-TX,2019,1.200590224
-TX,2020,1.195381246
-TX,2021,1.221700967
-TX,2022,1.26497241
-TX,2023,1.23383128
-TX,2024,1.271778629
-TX,2025,1.283557882
-TX,2026,1.30074737
-TX,2027,1.31865528
-TX,2028,1.336848522
-TX,2029,1.354777968
-TX,2030,1.369752384
-TX,2031,1.382748277
-TX,2032,1.399220533
-TX,2033,1.411974565
-TX,2034,1.427065557
-TX,2035,1.442908832
-TX,2036,1.460089632
-TX,2037,1.478630405
-TX,2038,1.4962632
-TX,2039,1.512190666
-TX,2040,1.529949466
-TX,2041,1.547073706
-TX,2042,1.564713376
-TX,2043,1.58398825
-TX,2044,1.603396522
-TX,2045,1.624342682
-TX,2046,1.644970816
-TX,2047,1.665203642
-TX,2048,1.686376949
-TX,2049,1.707175301
-TX,2050,1.72923464
diff --git a/inputs/load/demand_AEO_2023_low.csv b/inputs/load/demand_AEO_2023_low.csv
deleted file mode 100644
index 1d71dccfb..000000000
--- a/inputs/load/demand_AEO_2023_low.csv
+++ /dev/null
@@ -1,1969 +0,0 @@
-r,year,multiplier
-IL,2010,1.000075555
-IL,2011,0.987122063
-IL,2012,0.991686463
-IL,2013,0.979702693
-IL,2014,0.977932945
-IL,2015,0.957798834
-IL,2016,0.974658865
-IL,2017,0.948182116
-IL,2018,0.986170088
-IL,2019,0.956862312
-IL,2020,0.918089456
-IL,2021,0.943753372
-IL,2022,0.949193415
-IL,2023,0.934606648
-IL,2024,0.936426334
-IL,2025,0.939364588
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-IL,2027,0.951056161
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-IL,2034,0.977946628
-IL,2035,0.982024212
-IL,2036,0.98549109
-IL,2037,0.989109567
-IL,2038,0.993167897
-IL,2039,0.995807857
-IL,2040,0.998063456
-IL,2041,1.001599298
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-IL,2043,1.010311999
-IL,2044,1.014386983
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-IL,2046,1.02386741
-IL,2047,1.029127029
-IL,2048,1.035191461
-IL,2049,1.041287004
-IL,2050,1.04812176
-IN,2010,1.000024006
-IN,2011,0.99839401
-IN,2012,0.992317799
-IN,2013,0.995293258
-IN,2014,1.009056042
-IN,2015,0.986174661
-IN,2016,0.978583187
-IN,2017,0.934042605
-IN,2018,0.98396742
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-IN,2020,0.918229976
-IN,2021,0.943093958
-IN,2022,0.948922166
-IN,2023,0.934335399
-IN,2024,0.936155085
-IN,2025,0.939093339
-IN,2026,0.945193232
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-IN,2032,0.970509755
-IN,2033,0.974277866
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-IN,2035,0.981752963
-IN,2036,0.985219841
-IN,2037,0.988838318
-IN,2038,0.992896648
-IN,2039,0.995536608
-IN,2040,0.997792207
-IN,2041,1.001328049
-IN,2042,1.005427171
-IN,2043,1.01004075
-IN,2044,1.014115734
-IN,2045,1.018595937
-IN,2046,1.023596161
-IN,2047,1.02885578
-IN,2048,1.034920212
-IN,2049,1.041015755
-IN,2050,1.047850511
-MI,2010,1.000031598
-MI,2011,1.013666512
-MI,2012,1.011552627
-MI,2013,0.994405594
-MI,2014,0.997105702
-MI,2015,0.989085845
-MI,2016,1.008431569
-MI,2017,0.98374973
-MI,2018,1.012562427
-MI,2019,0.977919321
-MI,2020,0.937468792
-MI,2021,0.964852082
-MI,2022,0.970742045
-MI,2023,0.956155278
-MI,2024,0.957974964
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-MI,2027,0.972604791
-MI,2028,0.976997421
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-MI,2031,0.987794933
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-MI,2033,0.996097745
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-MI,2035,1.003572842
-MI,2036,1.00703972
-MI,2037,1.010658197
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-MI,2039,1.017356487
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-MI,2047,1.050675659
-MI,2048,1.056740091
-MI,2049,1.062835634
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-OH,2010,1.000057177
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-OH,2018,0.993054079
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-OH,2021,0.960163522
-OH,2022,0.97648367
-OH,2023,0.961896903
-OH,2024,0.963716589
-OH,2025,0.966654843
-OH,2026,0.972754736
-OH,2027,0.978346416
-OH,2028,0.982739046
-OH,2029,0.986465548
-OH,2030,0.990050406
-OH,2031,0.993536558
-OH,2032,0.998071259
-OH,2033,1.00183937
-OH,2034,1.005236883
-OH,2035,1.009314467
-OH,2036,1.012781345
-OH,2037,1.016399822
-OH,2038,1.020458152
-OH,2039,1.023098112
-OH,2040,1.025353711
-OH,2041,1.028889553
-OH,2042,1.032988675
-OH,2043,1.037602254
-OH,2044,1.041677238
-OH,2045,1.046157441
-OH,2046,1.051157665
-OH,2047,1.056417284
-OH,2048,1.062481716
-OH,2049,1.068577259
-OH,2050,1.075412015
-WI,2010,1.000148808
-WI,2011,0.998141201
-WI,2012,1.001294067
-WI,2013,1.005736799
-WI,2014,1.011167058
-WI,2015,0.999649852
-WI,2016,1.014844242
-WI,2017,1.00558868
-WI,2018,1.033294062
-WI,2019,1.007424368
-WI,2020,0.9829839
-WI,2021,1.012538083
-WI,2022,1.02307163
-WI,2023,1.008484863
-WI,2024,1.010304549
-WI,2025,1.013242803
-WI,2026,1.019342696
-WI,2027,1.024934376
-WI,2028,1.029327006
-WI,2029,1.033053508
-WI,2030,1.036638366
-WI,2031,1.040124518
-WI,2032,1.044659219
-WI,2033,1.04842733
-WI,2034,1.051824843
-WI,2035,1.055902427
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-WI,2042,1.079576635
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-AL,2016,0.971067859
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-KY,2015,0.81280844
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-KY,2017,0.776540917
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-KY,2019,0.805677778
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-KY,2030,0.835046232
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-KY,2039,0.859961701
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-KY,2042,0.874909669
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-KY,2045,0.890287005
-KY,2046,0.895565355
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-KY,2048,0.907056892
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-KY,2050,0.921191831
-MS,2010,1.000016644
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-MS,2012,0.9738691
-MS,2013,0.981848499
-MS,2014,0.994455906
-MS,2015,0.980012052
-MS,2016,0.987324239
-MS,2017,0.962826342
-MS,2018,1.01438337
-MS,2019,0.985478473
-MS,2020,0.935810566
-MS,2021,0.966698457
-MS,2022,0.999933214
-MS,2023,0.988271755
-MS,2024,0.994462241
-MS,2025,1.002553856
-MS,2026,1.010959837
-MS,2027,1.017615002
-MS,2028,1.022609941
-MS,2029,1.026912068
-MS,2030,1.031024429
-MS,2031,1.035722511
-MS,2032,1.040562207
-MS,2033,1.041249038
-MS,2034,1.041955306
-MS,2035,1.044016512
-MS,2036,1.046274941
-MS,2037,1.048946382
-MS,2038,1.052426156
-MS,2039,1.055939898
-MS,2040,1.060571122
-MS,2041,1.065605271
-MS,2042,1.070887866
-MS,2043,1.076335896
-MS,2044,1.081354203
-MS,2045,1.086265202
-MS,2046,1.091543552
-MS,2047,1.097415497
-MS,2048,1.103035089
-MS,2049,1.109434585
-MS,2050,1.117170028
-TN,2010,1.000041253
-TN,2011,0.973111223
-TN,2012,0.931094148
-TN,2013,0.937006056
-TN,2014,0.968720798
-TN,2015,0.963049627
-TN,2016,0.974185592
-TN,2017,0.940219299
-TN,2018,0.995096932
-TN,2019,0.965360657
-TN,2020,0.918702072
-TN,2021,0.963295329
-TN,2022,0.989382143
-TN,2023,0.977720684
-TN,2024,0.98391117
-TN,2025,0.992002785
-TN,2026,1.000408766
-TN,2027,1.007063931
-TN,2028,1.01205887
-TN,2029,1.016360997
-TN,2030,1.020473358
-TN,2031,1.02517144
-TN,2032,1.030011136
-TN,2033,1.030697967
-TN,2034,1.031404235
-TN,2035,1.033465441
-TN,2036,1.03572387
-TN,2037,1.038395311
-TN,2038,1.041875085
-TN,2039,1.045388827
-TN,2040,1.050020051
-TN,2041,1.0550542
-TN,2042,1.060336795
-TN,2043,1.065784825
-TN,2044,1.070803132
-TN,2045,1.075714131
-TN,2046,1.080992481
-TN,2047,1.086864426
-TN,2048,1.092484018
-TN,2049,1.098883514
-TN,2050,1.106618957
-NJ,2010,1.003806079
-NJ,2011,0.977568078
-NJ,2012,0.960140816
-NJ,2013,0.958433085
-NJ,2014,0.951611606
-NJ,2015,0.973214768
-NJ,2016,0.971145997
-NJ,2017,0.949626193
-NJ,2018,0.985839841
-NJ,2019,0.96312342
-NJ,2020,0.942787292
-NJ,2021,0.95821245
-NJ,2022,0.969126193
-NJ,2023,0.94661087
-NJ,2024,0.953141523
-NJ,2025,0.952894555
-NJ,2026,0.955833775
-NJ,2027,0.96024787
-NJ,2028,0.964955523
-NJ,2029,0.969820531
-NJ,2030,0.974808256
-NJ,2031,0.979630846
-NJ,2032,0.984763547
-NJ,2033,0.990409295
-NJ,2034,0.995843153
-NJ,2035,1.001923925
-NJ,2036,1.008383775
-NJ,2037,1.015886626
-NJ,2038,1.023271185
-NJ,2039,1.029996547
-NJ,2040,1.036350279
-NJ,2041,1.043172138
-NJ,2042,1.050328208
-NJ,2043,1.058211342
-NJ,2044,1.066478823
-NJ,2045,1.075876019
-NJ,2046,1.085999933
-NJ,2047,1.094947232
-NJ,2048,1.10507376
-NJ,2049,1.116069563
-NJ,2050,1.127957642
-NY,2010,1.000394053
-NY,2011,0.996610397
-NY,2012,0.990932231
-NY,2013,1.024059333
-NY,2014,1.021427354
-NY,2015,1.033753334
-NY,2016,1.028047889
-NY,2017,1.010754732
-NY,2018,1.047069087
-NY,2019,1.019678482
-NY,2020,0.986806052
-NY,2021,0.996676569
-NY,2022,1.008809701
-NY,2023,0.986294378
-NY,2024,0.992825031
-NY,2025,0.992578063
-NY,2026,0.995517283
-NY,2027,0.999931378
-NY,2028,1.004639031
-NY,2029,1.009504039
-NY,2030,1.014491764
-NY,2031,1.019314354
-NY,2032,1.024447055
-NY,2033,1.030092803
-NY,2034,1.035526661
-NY,2035,1.041607433
-NY,2036,1.048067283
-NY,2037,1.055570134
-NY,2038,1.062954693
-NY,2039,1.069680055
-NY,2040,1.076033787
-NY,2041,1.082855646
-NY,2042,1.090011716
-NY,2043,1.09789485
-NY,2044,1.106162331
-NY,2045,1.115559527
-NY,2046,1.125683441
-NY,2047,1.13463074
-NY,2048,1.144757268
-NY,2049,1.155753071
-NY,2050,1.16764115
-PA,2010,1.000471824
-PA,2011,0.999734828
-PA,2012,0.972904808
-PA,2013,0.983441925
-PA,2014,0.986495029
-PA,2015,0.984249535
-PA,2016,0.977611612
-PA,2017,0.96239878
-PA,2018,1.002966661
-PA,2019,0.980679319
-PA,2020,0.941989911
-PA,2021,0.966748253
-PA,2022,0.977929708
-PA,2023,0.955414385
-PA,2024,0.961945038
-PA,2025,0.96169807
-PA,2026,0.96463729
-PA,2027,0.969051385
-PA,2028,0.973759038
-PA,2029,0.978624046
-PA,2030,0.983611771
-PA,2031,0.988434361
-PA,2032,0.993567062
-PA,2033,0.99921281
-PA,2034,1.004646668
-PA,2035,1.01072744
-PA,2036,1.01718729
-PA,2037,1.024690141
-PA,2038,1.0320747
-PA,2039,1.038800062
-PA,2040,1.045153794
-PA,2041,1.051975653
-PA,2042,1.059131723
-PA,2043,1.067014857
-PA,2044,1.075282338
-PA,2045,1.084679534
-PA,2046,1.094803448
-PA,2047,1.103750747
-PA,2048,1.113877275
-PA,2049,1.124873078
-PA,2050,1.136761157
-AZ,2010,1.001933241
-AZ,2011,1.034037088
-AZ,2012,1.038743358
-AZ,2013,1.050869664
-AZ,2014,1.062940341
-AZ,2015,1.081043242
-AZ,2016,1.096473158
-AZ,2017,1.092314809
-AZ,2018,1.106900389
-AZ,2019,1.105417988
-AZ,2020,1.165384295
-AZ,2021,1.161608132
-AZ,2022,1.200125229
-AZ,2023,1.178477757
-AZ,2024,1.193192
-AZ,2025,1.207897457
-AZ,2026,1.221273169
-AZ,2027,1.233213492
-AZ,2028,1.24757543
-AZ,2029,1.262454862
-AZ,2030,1.275969428
-AZ,2031,1.290323726
-AZ,2032,1.306673437
-AZ,2033,1.321772457
-AZ,2034,1.336489205
-AZ,2035,1.3503572
-AZ,2036,1.364633693
-AZ,2037,1.379642434
-AZ,2038,1.394252134
-AZ,2039,1.40857543
-AZ,2040,1.423310029
-AZ,2041,1.438493982
-AZ,2042,1.455295664
-AZ,2043,1.473349628
-AZ,2044,1.491751477
-AZ,2045,1.509629416
-AZ,2046,1.527200708
-AZ,2047,1.544939996
-AZ,2048,1.56349612
-AZ,2049,1.583063982
-AZ,2050,1.604227078
-CO,2010,1.002079595
-CO,2011,1.013347305
-CO,2012,1.018620701
-CO,2013,1.015223108
-CO,2014,1.015930559
-CO,2015,1.030257354
-CO,2016,1.044591778
-CO,2017,1.046373042
-CO,2018,1.078115977
-CO,2019,1.080316289
-CO,2020,1.072724402
-CO,2021,1.085002865
-CO,2022,1.108470552
-CO,2023,1.08682308
-CO,2024,1.101537323
-CO,2025,1.11624278
-CO,2026,1.129618492
-CO,2027,1.141558815
-CO,2028,1.155920753
-CO,2029,1.170800185
-CO,2030,1.184314751
-CO,2031,1.198669049
-CO,2032,1.21501876
-CO,2033,1.23011778
-CO,2034,1.244834528
-CO,2035,1.258702523
-CO,2036,1.272979016
-CO,2037,1.287987757
-CO,2038,1.302597457
-CO,2039,1.316920753
-CO,2040,1.331655352
-CO,2041,1.346839305
-CO,2042,1.363640987
-CO,2043,1.381694951
-CO,2044,1.4000968
-CO,2045,1.417974739
-CO,2046,1.435546031
-CO,2047,1.453285319
-CO,2048,1.471841443
-CO,2049,1.491409305
-CO,2050,1.512572401
-ID,2010,0.999988532
-ID,2011,1.020781212
-ID,2012,1.040123183
-ID,2013,1.06197204
-ID,2014,1.019227514
-ID,2015,1.011658061
-ID,2016,1.012001618
-ID,2017,1.044334564
-ID,2018,1.043313044
-ID,2019,1.054633787
-ID,2020,1.076852921
-ID,2021,1.114399277
-ID,2022,1.149644673
-ID,2023,1.127997201
-ID,2024,1.142711444
-ID,2025,1.157416901
-ID,2026,1.170792613
-ID,2027,1.182732936
-ID,2028,1.197094874
-ID,2029,1.211974306
-ID,2030,1.225488872
-ID,2031,1.23984317
-ID,2032,1.256192881
-ID,2033,1.271291901
-ID,2034,1.286008649
-ID,2035,1.299876644
-ID,2036,1.314153137
-ID,2037,1.329161878
-ID,2038,1.343771578
-ID,2039,1.358094874
-ID,2040,1.372829473
-ID,2041,1.388013426
-ID,2042,1.404815108
-ID,2043,1.422869072
-ID,2044,1.441270921
-ID,2045,1.45914886
-ID,2046,1.476720152
-ID,2047,1.49445944
-ID,2048,1.513015564
-ID,2049,1.532583426
-ID,2050,1.553746522
-MT,2010,1.000204773
-MT,2011,1.001404969
-MT,2012,1.007225546
-MT,2013,1.020689321
-MT,2014,1.024858815
-MT,2015,1.032216882
-MT,2016,1.024695583
-MT,2017,1.069230259
-MT,2018,1.078928814
-MT,2019,1.114315989
-MT,2020,1.061472508
-MT,2021,1.089518961
-MT,2022,1.134244714
-MT,2023,1.112597242
-MT,2024,1.127311485
-MT,2025,1.142016942
-MT,2026,1.155392654
-MT,2027,1.167332977
-MT,2028,1.181694915
-MT,2029,1.196574347
-MT,2030,1.210088913
-MT,2031,1.224443211
-MT,2032,1.240792922
-MT,2033,1.255891942
-MT,2034,1.27060869
-MT,2035,1.284476685
-MT,2036,1.298753178
-MT,2037,1.313761919
-MT,2038,1.328371619
-MT,2039,1.342694915
-MT,2040,1.357429514
-MT,2041,1.372613467
-MT,2042,1.389415149
-MT,2043,1.407469113
-MT,2044,1.425870962
-MT,2045,1.443748901
-MT,2046,1.461320193
-MT,2047,1.479059481
-MT,2048,1.497615605
-MT,2049,1.517183467
-MT,2050,1.538346563
-NM,2010,1.000523205
-NM,2011,1.028599862
-NM,2012,1.035716735
-NM,2013,1.032248035
-NM,2014,1.035966265
-NM,2015,1.036013926
-NM,2016,1.03424914
-NM,2017,1.03502901
-NM,2018,1.083412511
-NM,2019,1.122725718
-NM,2020,1.121013126
-NM,2021,1.152842428
-NM,2022,1.254068236
-NM,2023,1.232420764
-NM,2024,1.247135007
-NM,2025,1.261840464
-NM,2026,1.275216176
-NM,2027,1.287156499
-NM,2028,1.301518437
-NM,2029,1.316397869
-NM,2030,1.329912435
-NM,2031,1.344266733
-NM,2032,1.360616444
-NM,2033,1.375715464
-NM,2034,1.390432212
-NM,2035,1.404300207
-NM,2036,1.4185767
-NM,2037,1.433585441
-NM,2038,1.448195141
-NM,2039,1.462518437
-NM,2040,1.477253036
-NM,2041,1.492436989
-NM,2042,1.509238671
-NM,2043,1.527292635
-NM,2044,1.545694484
-NM,2045,1.563572423
-NM,2046,1.581143715
-NM,2047,1.598883003
-NM,2048,1.617439127
-NM,2049,1.637006989
-NM,2050,1.658170085
-NV,2010,1.000962051
-NV,2011,1.006553312
-NV,2012,1.044393324
-NV,2013,1.045680486
-NV,2014,1.042589056
-NV,2015,1.073392305
-NV,2016,1.083068615
-NV,2017,1.099610175
-NV,2018,1.135252893
-NV,2019,1.117109997
-NV,2020,1.1598581
-NV,2021,1.188990178
-NV,2022,1.227146009
-NV,2023,1.205498537
-NV,2024,1.22021278
-NV,2025,1.234918237
-NV,2026,1.248293949
-NV,2027,1.260234272
-NV,2028,1.27459621
-NV,2029,1.289475642
-NV,2030,1.302990208
-NV,2031,1.317344506
-NV,2032,1.333694217
-NV,2033,1.348793237
-NV,2034,1.363509985
-NV,2035,1.37737798
-NV,2036,1.391654473
-NV,2037,1.406663214
-NV,2038,1.421272914
-NV,2039,1.43559621
-NV,2040,1.450330809
-NV,2041,1.465514762
-NV,2042,1.482316444
-NV,2043,1.500370408
-NV,2044,1.518772257
-NV,2045,1.536650196
-NV,2046,1.554221488
-NV,2047,1.571960776
-NV,2048,1.5905169
-NV,2049,1.610084762
-NV,2050,1.631247858
-UT,2010,1.000057729
-UT,2011,1.029250311
-UT,2012,1.060334249
-UT,2013,1.087399706
-UT,2014,1.072580079
-UT,2015,1.078967839
-UT,2016,1.081452849
-UT,2017,1.101037454
-UT,2018,1.12808008
-UT,2019,1.126845392
-UT,2020,1.148494104
-UT,2021,1.188625393
-UT,2022,1.207623171
-UT,2023,1.185975699
-UT,2024,1.200689942
-UT,2025,1.215395399
-UT,2026,1.228771111
-UT,2027,1.240711434
-UT,2028,1.255073372
-UT,2029,1.269952804
-UT,2030,1.28346737
-UT,2031,1.297821668
-UT,2032,1.314171379
-UT,2033,1.329270399
-UT,2034,1.343987147
-UT,2035,1.357855142
-UT,2036,1.372131635
-UT,2037,1.387140376
-UT,2038,1.401750076
-UT,2039,1.416073372
-UT,2040,1.430807971
-UT,2041,1.445991924
-UT,2042,1.462793606
-UT,2043,1.48084757
-UT,2044,1.499249419
-UT,2045,1.517127358
-UT,2046,1.53469865
-UT,2047,1.552437938
-UT,2048,1.570994062
-UT,2049,1.590561924
-UT,2050,1.61172502
-WY,2010,0.999991469
-WY,2011,1.017772879
-WY,2012,0.991764753
-WY,2013,0.996602311
-WY,2014,1.001334937
-WY,2015,0.98912506
-WY,2016,0.967550795
-WY,2017,0.980668035
-WY,2018,0.985824008
-WY,2019,0.980080343
-WY,2020,0.896595412
-WY,2021,0.923390033
-WY,2022,0.970235784
-WY,2023,0.948588312
-WY,2024,0.963302555
-WY,2025,0.978008012
-WY,2026,0.991383724
-WY,2027,1.003324047
-WY,2028,1.017685985
-WY,2029,1.032565417
-WY,2030,1.046079983
-WY,2031,1.060434281
-WY,2032,1.076783992
-WY,2033,1.091883012
-WY,2034,1.10659976
-WY,2035,1.120467755
-WY,2036,1.134744248
-WY,2037,1.149752989
-WY,2038,1.164362689
-WY,2039,1.178685985
-WY,2040,1.193420584
-WY,2041,1.208604537
-WY,2042,1.225406219
-WY,2043,1.243460183
-WY,2044,1.261862032
-WY,2045,1.279739971
-WY,2046,1.297311263
-WY,2047,1.315050551
-WY,2048,1.333606675
-WY,2049,1.353174537
-WY,2050,1.374337633
-CT,2010,1.000968603
-CT,2011,0.983536484
-CT,2012,0.971766071
-CT,2013,0.983764271
-CT,2014,0.970015984
-CT,2015,0.976408833
-CT,2016,0.962461538
-CT,2017,0.939184482
-CT,2018,0.965215025
-CT,2019,0.937909267
-CT,2020,0.916473428
-CT,2021,0.941186361
-CT,2022,0.942890166
-CT,2023,0.923120571
-CT,2024,0.939666047
-CT,2025,0.940609595
-CT,2026,0.945774333
-CT,2027,0.950145809
-CT,2028,0.957805642
-CT,2029,0.965998881
-CT,2030,0.974800904
-CT,2031,0.983821238
-CT,2032,0.99355819
-CT,2033,1.004464142
-CT,2034,1.013436881
-CT,2035,1.022884166
-CT,2036,1.032329952
-CT,2037,1.042996166
-CT,2038,1.053456119
-CT,2039,1.063756857
-CT,2040,1.074383357
-CT,2041,1.0847375
-CT,2042,1.0959125
-CT,2043,1.106974833
-CT,2044,1.118147642
-CT,2045,1.12930519
-CT,2046,1.140166881
-CT,2047,1.151820428
-CT,2048,1.164152404
-CT,2049,1.176661571
-CT,2050,1.189174904
-MA,2010,1.000706668
-MA,2011,0.974077439
-MA,2012,0.971695405
-MA,2013,0.973833487
-MA,2014,0.964579087
-MA,2015,0.971351039
-MA,2016,0.958204142
-MA,2017,0.945968936
-MA,2018,0.969373184
-MA,2019,0.935929816
-MA,2020,0.916319875
-MA,2021,0.933820818
-MA,2022,0.934808668
-MA,2023,0.915039073
-MA,2024,0.931584549
-MA,2025,0.932528097
-MA,2026,0.937692835
-MA,2027,0.942064311
-MA,2028,0.949724144
-MA,2029,0.957917383
-MA,2030,0.966719406
-MA,2031,0.97573974
-MA,2032,0.985476692
-MA,2033,0.996382644
-MA,2034,1.005355383
-MA,2035,1.014802668
-MA,2036,1.024248454
-MA,2037,1.034914668
-MA,2038,1.045374621
-MA,2039,1.055675359
-MA,2040,1.066301859
-MA,2041,1.076656002
-MA,2042,1.087831002
-MA,2043,1.098893335
-MA,2044,1.110066144
-MA,2045,1.121223692
-MA,2046,1.132085383
-MA,2047,1.14373893
-MA,2048,1.156070906
-MA,2049,1.168580073
-MA,2050,1.181093406
-ME,2010,1.000037722
-ME,2011,0.989950745
-ME,2012,1.003062171
-ME,2013,1.029134007
-ME,2014,1.041973599
-ME,2015,1.032537962
-ME,2016,0.995346166
-ME,2017,0.975947366
-ME,2018,1.076166185
-ME,2019,1.024001544
-ME,2020,0.991662026
-ME,2021,1.01632903
-ME,2022,1.047860494
-ME,2023,1.028090899
-ME,2024,1.044636375
-ME,2025,1.045579923
-ME,2026,1.050744661
-ME,2027,1.055116137
-ME,2028,1.06277597
-ME,2029,1.070969209
-ME,2030,1.079771232
-ME,2031,1.088791566
-ME,2032,1.098528518
-ME,2033,1.10943447
-ME,2034,1.118407209
-ME,2035,1.127854494
-ME,2036,1.13730028
-ME,2037,1.147966494
-ME,2038,1.158426447
-ME,2039,1.168727185
-ME,2040,1.179353685
-ME,2041,1.189707828
-ME,2042,1.200882828
-ME,2043,1.211945161
-ME,2044,1.22311797
-ME,2045,1.234275518
-ME,2046,1.245137209
-ME,2047,1.256790756
-ME,2048,1.269122732
-ME,2049,1.281631899
-ME,2050,1.294145232
-NH,2010,1.000230536
-NH,2011,0.998316077
-NH,2012,0.998725627
-NH,2013,1.015061397
-NH,2014,1.0062917
-NH,2015,1.012214509
-NH,2016,1.006114936
-NH,2017,0.998574759
-NH,2018,1.024278088
-NH,2019,0.995556828
-NH,2020,0.996379099
-NH,2021,1.015657527
-NH,2022,1.014820724
-NH,2023,0.995051129
-NH,2024,1.011596605
-NH,2025,1.012540153
-NH,2026,1.017704891
-NH,2027,1.022076367
-NH,2028,1.0297362
-NH,2029,1.037929439
-NH,2030,1.046731462
-NH,2031,1.055751796
-NH,2032,1.065488748
-NH,2033,1.0763947
-NH,2034,1.085367439
-NH,2035,1.094814724
-NH,2036,1.10426051
-NH,2037,1.114926724
-NH,2038,1.125386677
-NH,2039,1.135687415
-NH,2040,1.146313915
-NH,2041,1.156668058
-NH,2042,1.167843058
-NH,2043,1.178905391
-NH,2044,1.1900782
-NH,2045,1.201235748
-NH,2046,1.212097439
-NH,2047,1.223750986
-NH,2048,1.236082962
-NH,2049,1.248592129
-NH,2050,1.261105462
-RI,2010,1.00037454
-RI,2011,0.992284342
-RI,2012,0.989776324
-RI,2013,0.999092622
-RI,2014,0.981457765
-RI,2015,0.984484741
-RI,2016,0.968575337
-RI,2017,0.953938283
-RI,2018,0.984576662
-RI,2019,0.965493896
-RI,2020,0.980998552
-RI,2021,0.998165723
-RI,2022,1.033201009
-RI,2023,1.013431414
-RI,2024,1.02997689
-RI,2025,1.030920438
-RI,2026,1.036085176
-RI,2027,1.040456652
-RI,2028,1.048116485
-RI,2029,1.056309724
-RI,2030,1.065111747
-RI,2031,1.074132081
-RI,2032,1.083869033
-RI,2033,1.094774985
-RI,2034,1.103747724
-RI,2035,1.113195009
-RI,2036,1.122640795
-RI,2037,1.133307009
-RI,2038,1.143766962
-RI,2039,1.1540677
-RI,2040,1.1646942
-RI,2041,1.175048343
-RI,2042,1.186223343
-RI,2043,1.197285676
-RI,2044,1.208458485
-RI,2045,1.219616033
-RI,2046,1.230477724
-RI,2047,1.242131271
-RI,2048,1.254463247
-RI,2049,1.266972414
-RI,2050,1.279485747
-VT,2010,1.000388299
-VT,2011,0.993349216
-VT,2012,0.987648609
-VT,2013,1.002734417
-VT,2014,1.001408609
-VT,2015,0.996509497
-VT,2016,0.99949164
-VT,2017,0.989275982
-VT,2018,1.011857312
-VT,2019,0.997426777
-VT,2020,0.983980884
-VT,2021,1.00074034
-VT,2022,1.014103264
-VT,2023,0.994333669
-VT,2024,1.010879145
-VT,2025,1.011822693
-VT,2026,1.016987431
-VT,2027,1.021358907
-VT,2028,1.02901874
-VT,2029,1.037211979
-VT,2030,1.046014002
-VT,2031,1.055034336
-VT,2032,1.064771288
-VT,2033,1.07567724
-VT,2034,1.084649979
-VT,2035,1.094097264
-VT,2036,1.10354305
-VT,2037,1.114209264
-VT,2038,1.124669217
-VT,2039,1.134969955
-VT,2040,1.145596455
-VT,2041,1.155950598
-VT,2042,1.167125598
-VT,2043,1.178187931
-VT,2044,1.18936074
-VT,2045,1.200518288
-VT,2046,1.211379979
-VT,2047,1.223033526
-VT,2048,1.235365502
-VT,2049,1.247874669
-VT,2050,1.260388002
-CA,2010,1.004774859
-CA,2011,1.019684731
-CA,2012,1.013351818
-CA,2013,1.023981459
-CA,2014,1.034104524
-CA,2015,1.033840414
-CA,2016,1.025789692
-CA,2017,1.036639781
-CA,2018,1.037795267
-CA,2019,1.027821906
-CA,2020,1.035799006
-CA,2021,1.034164546
-CA,2022,1.06561455
-CA,2023,1.04223043
-CA,2024,1.045820993
-CA,2025,1.061342932
-CA,2026,1.076180808
-CA,2027,1.085855982
-CA,2028,1.09859383
-CA,2029,1.112000722
-CA,2030,1.122984101
-CA,2031,1.133610202
-CA,2032,1.146565326
-CA,2033,1.15868115
-CA,2034,1.17087306
-CA,2035,1.182023901
-CA,2036,1.194168092
-CA,2037,1.207523409
-CA,2038,1.221676731
-CA,2039,1.236640686
-CA,2040,1.250594065
-CA,2041,1.264348028
-CA,2042,1.278660558
-CA,2043,1.2925079
-CA,2044,1.305484065
-CA,2045,1.318639468
-CA,2046,1.332241798
-CA,2047,1.346538213
-CA,2048,1.361917044
-CA,2049,1.378281912
-CA,2050,1.395507636
-OR,2010,1.000704404
-OR,2011,1.025781596
-OR,2012,1.015643175
-OR,2013,1.036487073
-OR,2014,1.030095872
-OR,2015,1.028813207
-OR,2016,1.031964163
-OR,2017,1.091098105
-OR,2018,1.07606457
-OR,2019,1.099971107
-OR,2020,1.113998135
-OR,2021,1.182726523
-OR,2022,1.16898999
-OR,2023,1.14560587
-OR,2024,1.149196433
-OR,2025,1.164718372
-OR,2026,1.179556248
-OR,2027,1.189231422
-OR,2028,1.20196927
-OR,2029,1.215376162
-OR,2030,1.226359541
-OR,2031,1.236985642
-OR,2032,1.249940766
-OR,2033,1.26205659
-OR,2034,1.2742485
-OR,2035,1.285399341
-OR,2036,1.297543532
-OR,2037,1.310898849
-OR,2038,1.325052171
-OR,2039,1.340016126
-OR,2040,1.353969505
-OR,2041,1.367723468
-OR,2042,1.382035998
-OR,2043,1.39588334
-OR,2044,1.408859505
-OR,2045,1.422014908
-OR,2046,1.435617238
-OR,2047,1.449913653
-OR,2048,1.465292484
-OR,2049,1.481657352
-OR,2050,1.498883076
-WA,2010,0.999954043
-WA,2011,1.036996134
-WA,2012,1.021713053
-WA,2013,1.027838058
-WA,2014,1.019779181
-WA,2015,0.997596942
-WA,2016,0.984355167
-WA,2017,1.018534707
-WA,2018,0.997417722
-WA,2019,1.009611428
-WA,2020,0.962023088
-WA,2021,0.979182066
-WA,2022,1.003189075
-WA,2023,0.979804955
-WA,2024,0.983395518
-WA,2025,0.998917457
-WA,2026,1.013755333
-WA,2027,1.023430507
-WA,2028,1.036168355
-WA,2029,1.049575247
-WA,2030,1.060558626
-WA,2031,1.071184727
-WA,2032,1.084139851
-WA,2033,1.096255675
-WA,2034,1.108447585
-WA,2035,1.119598426
-WA,2036,1.131742617
-WA,2037,1.145097934
-WA,2038,1.159251256
-WA,2039,1.174215211
-WA,2040,1.18816859
-WA,2041,1.201922553
-WA,2042,1.216235083
-WA,2043,1.230082425
-WA,2044,1.24305859
-WA,2045,1.256213993
-WA,2046,1.269816323
-WA,2047,1.284112738
-WA,2048,1.299491569
-WA,2049,1.315856437
-WA,2050,1.333082161
-DE,2010,1.000740874
-DE,2011,0.991651797
-DE,2012,0.995548945
-DE,2013,0.982697513
-DE,2014,0.983258631
-DE,2015,0.997644911
-DE,2016,0.975982178
-DE,2017,0.966953201
-DE,2018,1.023907154
-DE,2019,0.999002971
-DE,2020,0.970017982
-DE,2021,1.001213296
-DE,2022,1.011565926
-DE,2023,1.002937511
-DE,2024,0.997064644
-DE,2025,1.000282225
-DE,2026,1.006792967
-DE,2027,1.013796491
-DE,2028,1.02401798
-DE,2029,1.034523742
-DE,2030,1.044591575
-DE,2031,1.055162186
-DE,2032,1.065716079
-DE,2033,1.072615798
-DE,2034,1.078531766
-DE,2035,1.086204282
-DE,2036,1.094856786
-DE,2037,1.104171763
-DE,2038,1.112651599
-DE,2039,1.121220264
-DE,2040,1.13104739
-DE,2041,1.141926971
-DE,2042,1.153711624
-DE,2043,1.165530591
-DE,2044,1.177008992
-DE,2045,1.188177958
-DE,2046,1.199723216
-DE,2047,1.211351809
-DE,2048,1.223805887
-DE,2049,1.237307738
-DE,2050,1.251719031
-FL,2010,1.000098794
-FL,2011,0.973673845
-FL,2012,0.954687732
-FL,2013,0.960183913
-FL,2014,0.978325125
-FL,2015,1.019665351
-FL,2016,1.020413339
-FL,2017,1.009696811
-FL,2018,1.033704381
-FL,2019,1.042554059
-FL,2020,1.053452573
-FL,2021,1.052191629
-FL,2022,1.134160307
-FL,2023,1.125531892
-FL,2024,1.119659025
-FL,2025,1.122876606
-FL,2026,1.129387348
-FL,2027,1.136390872
-FL,2028,1.146612361
-FL,2029,1.157118123
-FL,2030,1.167185956
-FL,2031,1.177756567
-FL,2032,1.18831046
-FL,2033,1.195210179
-FL,2034,1.201126147
-FL,2035,1.208798663
-FL,2036,1.217451167
-FL,2037,1.226766144
-FL,2038,1.23524598
-FL,2039,1.243814645
-FL,2040,1.253641771
-FL,2041,1.264521352
-FL,2042,1.276306005
-FL,2043,1.288124972
-FL,2044,1.299603373
-FL,2045,1.310772339
-FL,2046,1.322317597
-FL,2047,1.33394619
-FL,2048,1.346400268
-FL,2049,1.359902119
-FL,2050,1.374313412
-GA,2010,1.000073772
-GA,2011,0.969591747
-GA,2012,0.931397288
-GA,2013,0.928355503
-GA,2014,0.966025322
-GA,2015,0.966661637
-GA,2016,0.983214295
-GA,2017,0.950340982
-GA,2018,0.996210208
-GA,2019,0.992460799
-GA,2020,0.951173388
-GA,2021,0.979150139
-GA,2022,1.021233468
-GA,2023,1.012605053
-GA,2024,1.006732186
-GA,2025,1.009949767
-GA,2026,1.016460509
-GA,2027,1.023464033
-GA,2028,1.033685522
-GA,2029,1.044191284
-GA,2030,1.054259117
-GA,2031,1.064829728
-GA,2032,1.075383621
-GA,2033,1.08228334
-GA,2034,1.088199308
-GA,2035,1.095871824
-GA,2036,1.104524328
-GA,2037,1.113839305
-GA,2038,1.122319141
-GA,2039,1.130887806
-GA,2040,1.140714932
-GA,2041,1.151594513
-GA,2042,1.163379166
-GA,2043,1.175198133
-GA,2044,1.186676534
-GA,2045,1.1978455
-GA,2046,1.209390758
-GA,2047,1.221019351
-GA,2048,1.233473429
-GA,2049,1.24697528
-GA,2050,1.261386573
-MD,2010,1.000152142
-MD,2011,0.973981823
-MD,2012,0.947805612
-MD,2013,0.947392825
-MD,2014,0.947187257
-MD,2015,0.950872179
-MD,2016,0.949004189
-MD,2017,0.919057089
-MD,2018,0.962341236
-MD,2019,0.94198106
-MD,2020,0.886211329
-MD,2021,0.912154154
-MD,2022,0.919174068
-MD,2023,0.910545653
-MD,2024,0.904672786
-MD,2025,0.907890367
-MD,2026,0.914401109
-MD,2027,0.921404633
-MD,2028,0.931626122
-MD,2029,0.942131884
-MD,2030,0.952199717
-MD,2031,0.962770328
-MD,2032,0.973324221
-MD,2033,0.98022394
-MD,2034,0.986139908
-MD,2035,0.993812424
-MD,2036,1.002464928
-MD,2037,1.011779905
-MD,2038,1.020259741
-MD,2039,1.028828406
-MD,2040,1.038655532
-MD,2041,1.049535113
-MD,2042,1.061319766
-MD,2043,1.073138733
-MD,2044,1.084617134
-MD,2045,1.0957861
-MD,2046,1.107331358
-MD,2047,1.118959951
-MD,2048,1.131414029
-MD,2049,1.14491588
-MD,2050,1.159327173
-NC,2010,1.000056501
-NC,2011,0.961045918
-NC,2012,0.939223305
-NC,2013,0.952178915
-NC,2014,0.977037784
-NC,2015,0.982377466
-NC,2016,0.987399844
-NC,2017,0.965611665
-NC,2018,1.016095022
-NC,2019,1.002952117
-NC,2020,0.959211188
-NC,2021,0.998864028
-NC,2022,1.034915461
-NC,2023,1.026287046
-NC,2024,1.020414179
-NC,2025,1.02363176
-NC,2026,1.030142502
-NC,2027,1.037146026
-NC,2028,1.047367515
-NC,2029,1.057873277
-NC,2030,1.06794111
-NC,2031,1.078511721
-NC,2032,1.089065614
-NC,2033,1.095965333
-NC,2034,1.101881301
-NC,2035,1.109553817
-NC,2036,1.118206321
-NC,2037,1.127521298
-NC,2038,1.136001134
-NC,2039,1.144569799
-NC,2040,1.154396925
-NC,2041,1.165276506
-NC,2042,1.177061159
-NC,2043,1.188880126
-NC,2044,1.200358527
-NC,2045,1.211527493
-NC,2046,1.223072751
-NC,2047,1.234701344
-NC,2048,1.247155422
-NC,2049,1.260657273
-NC,2050,1.275068566
-SC,2010,1.000011633
-SC,2011,0.975882843
-SC,2012,0.943056809
-SC,2013,0.953025883
-SC,2014,0.989625442
-SC,2015,0.986129808
-SC,2016,0.9652528
-SC,2017,0.948534211
-SC,2018,0.99285427
-SC,2019,0.97645889
-SC,2020,0.93492418
-SC,2021,0.972652866
-SC,2022,1.01963029
-SC,2023,1.011001875
-SC,2024,1.005129008
-SC,2025,1.008346589
-SC,2026,1.014857331
-SC,2027,1.021860855
-SC,2028,1.032082344
-SC,2029,1.042588106
-SC,2030,1.052655939
-SC,2031,1.06322655
-SC,2032,1.073780443
-SC,2033,1.080680162
-SC,2034,1.08659613
-SC,2035,1.094268646
-SC,2036,1.10292115
-SC,2037,1.112236127
-SC,2038,1.120715963
-SC,2039,1.129284628
-SC,2040,1.139111754
-SC,2041,1.149991335
-SC,2042,1.161775988
-SC,2043,1.173594955
-SC,2044,1.185073356
-SC,2045,1.196242322
-SC,2046,1.20778758
-SC,2047,1.219416173
-SC,2048,1.231870251
-SC,2049,1.245372102
-SC,2050,1.259783395
-VA,2010,1.000031646
-VA,2011,0.968620938
-VA,2012,0.947298107
-VA,2013,0.971193555
-VA,2014,0.985185042
-VA,2015,0.984469086
-VA,2016,0.986956296
-VA,2017,0.980574028
-VA,2018,1.039034752
-VA,2019,1.041845506
-VA,2020,1.032190464
-VA,2021,1.103341228
-VA,2022,1.146684082
-VA,2023,1.138055667
-VA,2024,1.1321828
-VA,2025,1.135400381
-VA,2026,1.141911123
-VA,2027,1.148914647
-VA,2028,1.159136136
-VA,2029,1.169641898
-VA,2030,1.179709731
-VA,2031,1.190280342
-VA,2032,1.200834235
-VA,2033,1.207733954
-VA,2034,1.213649922
-VA,2035,1.221322438
-VA,2036,1.229974942
-VA,2037,1.239289919
-VA,2038,1.247769755
-VA,2039,1.25633842
-VA,2040,1.266165546
-VA,2041,1.277045127
-VA,2042,1.28882978
-VA,2043,1.300648747
-VA,2044,1.312127148
-VA,2045,1.323296114
-VA,2046,1.334841372
-VA,2047,1.346469965
-VA,2048,1.358924043
-VA,2049,1.372425894
-VA,2050,1.386837187
-WV,2010,1.00001102
-WV,2011,0.975268607
-WV,2012,0.962185189
-WV,2013,0.980408258
-WV,2014,1.020863806
-WV,2015,1.008618276
-WV,2016,1.001572116
-WV,2017,0.990180529
-WV,2018,1.050728457
-WV,2019,1.038335221
-WV,2020,1.001888949
-WV,2021,1.023994326
-WV,2022,1.031041855
-WV,2023,1.02241344
-WV,2024,1.016540573
-WV,2025,1.019758154
-WV,2026,1.026268896
-WV,2027,1.03327242
-WV,2028,1.043493909
-WV,2029,1.053999671
-WV,2030,1.064067504
-WV,2031,1.074638115
-WV,2032,1.085192008
-WV,2033,1.092091727
-WV,2034,1.098007695
-WV,2035,1.105680211
-WV,2036,1.114332715
-WV,2037,1.123647692
-WV,2038,1.132127528
-WV,2039,1.140696193
-WV,2040,1.150523319
-WV,2041,1.1614029
-WV,2042,1.173187553
-WV,2043,1.18500652
-WV,2044,1.196484921
-WV,2045,1.207653887
-WV,2046,1.219199145
-WV,2047,1.230827738
-WV,2048,1.243281816
-WV,2049,1.256783667
-WV,2050,1.27119496
-IA,2010,1.000038554
-IA,2011,1.004651049
-IA,2012,1.005862743
-IA,2013,1.027886541
-IA,2014,1.039229645
-IA,2015,1.038395106
-IA,2016,1.067052687
-IA,2017,1.078511261
-IA,2018,1.129684453
-IA,2019,1.126752477
-IA,2020,1.118964566
-IA,2021,1.169361839
-IA,2022,1.206986129
-IA,2023,1.188968851
-IA,2024,1.180994853
-IA,2025,1.18611242
-IA,2026,1.193386777
-IA,2027,1.200179399
-IA,2028,1.205435072
-IA,2029,1.209757156
-IA,2030,1.214864464
-IA,2031,1.220855052
-IA,2032,1.228354953
-IA,2033,1.235213936
-IA,2034,1.242435461
-IA,2035,1.250216099
-IA,2036,1.257641513
-IA,2037,1.264789848
-IA,2038,1.271504185
-IA,2039,1.275992201
-IA,2040,1.279705062
-IA,2041,1.284538582
-IA,2042,1.291206079
-IA,2043,1.299267904
-IA,2044,1.306843567
-IA,2045,1.31430905
-IA,2046,1.322360486
-IA,2047,1.330651922
-IA,2048,1.339356697
-IA,2049,1.348617983
-IA,2050,1.358387654
-KS,2010,1.000003996
-KS,2011,1.008408704
-KS,2012,0.996872918
-KS,2013,0.985877284
-KS,2014,1.003557033
-KS,2015,0.985969016
-KS,2016,1.009841384
-KS,2017,0.997121896
-KS,2018,1.040652614
-KS,2019,1.019226884
-KS,2020,0.978006388
-KS,2021,1.00325583
-KS,2022,1.044580735
-KS,2023,1.026563457
-KS,2024,1.018589459
-KS,2025,1.023707026
-KS,2026,1.030981383
-KS,2027,1.037774005
-KS,2028,1.043029678
-KS,2029,1.047351762
-KS,2030,1.05245907
-KS,2031,1.058449658
-KS,2032,1.065949559
-KS,2033,1.072808542
-KS,2034,1.080030067
-KS,2035,1.087810705
-KS,2036,1.095236119
-KS,2037,1.102384454
-KS,2038,1.109098791
-KS,2039,1.113586807
-KS,2040,1.117299668
-KS,2041,1.122133188
-KS,2042,1.128800685
-KS,2043,1.13686251
-KS,2044,1.144438173
-KS,2045,1.151903656
-KS,2046,1.159955092
-KS,2047,1.168246528
-KS,2048,1.176951303
-KS,2049,1.186212589
-KS,2050,1.19598226
-MN,2010,1.000069619
-MN,2011,1.010891741
-MN,2012,1.002939322
-MN,2013,1.012638553
-MN,2014,1.013849121
-MN,2015,0.982383
-MN,2016,0.98206703
-MN,2017,0.991326459
-MN,2018,1.014600007
-MN,2019,0.98924254
-MN,2020,0.94682989
-MN,2021,0.984855338
-MN,2022,0.983431119
-MN,2023,0.965413841
-MN,2024,0.957439843
-MN,2025,0.96255741
-MN,2026,0.969831767
-MN,2027,0.976624389
-MN,2028,0.981880062
-MN,2029,0.986202146
-MN,2030,0.991309454
-MN,2031,0.997300042
-MN,2032,1.004799943
-MN,2033,1.011658926
-MN,2034,1.018880451
-MN,2035,1.026661089
-MN,2036,1.034086503
-MN,2037,1.041234838
-MN,2038,1.047949175
-MN,2039,1.052437191
-MN,2040,1.056150052
-MN,2041,1.060983572
-MN,2042,1.067651069
-MN,2043,1.075712894
-MN,2044,1.083288557
-MN,2045,1.09075404
-MN,2046,1.098805476
-MN,2047,1.107096912
-MN,2048,1.115801687
-MN,2049,1.125062973
-MN,2050,1.134832644
-MO,2010,1.000042459
-MO,2011,0.978794789
-MO,2012,0.957869895
-MO,2013,0.969526822
-MO,2014,0.975715719
-MO,2015,0.948512272
-MO,2016,0.915254742
-MO,2017,0.890633659
-MO,2018,0.955948913
-MO,2019,0.919501066
-MO,2020,0.883885805
-MO,2021,0.908396351
-MO,2022,0.939482972
-MO,2023,0.921465694
-MO,2024,0.913491696
-MO,2025,0.918609263
-MO,2026,0.92588362
-MO,2027,0.932676242
-MO,2028,0.937931915
-MO,2029,0.942253999
-MO,2030,0.947361307
-MO,2031,0.953351895
-MO,2032,0.960851796
-MO,2033,0.967710779
-MO,2034,0.974932304
-MO,2035,0.982712942
-MO,2036,0.990138356
-MO,2037,0.997286691
-MO,2038,1.004001028
-MO,2039,1.008489044
-MO,2040,1.012201905
-MO,2041,1.017035425
-MO,2042,1.023702922
-MO,2043,1.031764747
-MO,2044,1.03934041
-MO,2045,1.046805893
-MO,2046,1.054857329
-MO,2047,1.063148765
-MO,2048,1.07185354
-MO,2049,1.081114826
-MO,2050,1.090884497
-ND,2010,1.000026861
-ND,2011,1.060289141
-ND,2012,1.135933747
-ND,2013,1.237509802
-ND,2014,1.407855742
-ND,2015,1.399301121
-ND,2016,1.429499766
-ND,2017,1.554559793
-ND,2018,1.595400015
-ND,2019,1.664100998
-ND,2020,1.684193502
-ND,2021,1.764779679
-ND,2022,1.95344026
-ND,2023,1.935422982
-ND,2024,1.927448984
-ND,2025,1.932566551
-ND,2026,1.939840908
-ND,2027,1.94663353
-ND,2028,1.951889203
-ND,2029,1.956211287
-ND,2030,1.961318595
-ND,2031,1.967309183
-ND,2032,1.974809084
-ND,2033,1.981668067
-ND,2034,1.988889592
-ND,2035,1.99667023
-ND,2036,2.004095644
-ND,2037,2.011243979
-ND,2038,2.017958316
-ND,2039,2.022446332
-ND,2040,2.026159193
-ND,2041,2.030992713
-ND,2042,2.03766021
-ND,2043,2.045722035
-ND,2044,2.053297698
-ND,2045,2.060763181
-ND,2046,2.068814617
-ND,2047,2.077106053
-ND,2048,2.085810828
-ND,2049,2.095072114
-ND,2050,2.104841785
-NE,2010,1.000011241
-NE,2011,0.994206311
-NE,2012,1.032799082
-NE,2013,1.028570162
-NE,2014,1.012529357
-NE,2015,0.988180648
-NE,2016,1.011819874
-NE,2017,1.017259301
-NE,2018,1.036882024
-NE,2019,1.018392418
-NE,2020,1.044982472
-NE,2021,1.08432271
-NE,2022,1.11546918
-NE,2023,1.097451902
-NE,2024,1.089477904
-NE,2025,1.094595471
-NE,2026,1.101869828
-NE,2027,1.10866245
-NE,2028,1.113918123
-NE,2029,1.118240207
-NE,2030,1.123347515
-NE,2031,1.129338103
-NE,2032,1.136838004
-NE,2033,1.143696987
-NE,2034,1.150918512
-NE,2035,1.15869915
-NE,2036,1.166124564
-NE,2037,1.173272899
-NE,2038,1.179987236
-NE,2039,1.184475252
-NE,2040,1.188188113
-NE,2041,1.193021633
-NE,2042,1.19968913
-NE,2043,1.207750955
-NE,2044,1.215326618
-NE,2045,1.222792101
-NE,2046,1.230843537
-NE,2047,1.239134973
-NE,2048,1.247839748
-NE,2049,1.257101034
-NE,2050,1.266870705
-SD,2010,1.000011272
-SD,2011,1.028494049
-SD,2012,1.033302949
-SD,2013,1.075182845
-SD,2014,1.087981495
-SD,2015,1.065735007
-SD,2016,1.068177818
-SD,2017,1.084403604
-SD,2018,1.132252393
-SD,2019,1.133331422
-SD,2020,1.1181258
-SD,2021,1.148577908
-SD,2022,1.182658523
-SD,2023,1.164641245
-SD,2024,1.156667247
-SD,2025,1.161784814
-SD,2026,1.169059171
-SD,2027,1.175851793
-SD,2028,1.181107466
-SD,2029,1.18542955
-SD,2030,1.190536858
-SD,2031,1.196527446
-SD,2032,1.204027347
-SD,2033,1.21088633
-SD,2034,1.218107855
-SD,2035,1.225888493
-SD,2036,1.233313907
-SD,2037,1.240462242
-SD,2038,1.247176579
-SD,2039,1.251664595
-SD,2040,1.255377456
-SD,2041,1.260210976
-SD,2042,1.266878473
-SD,2043,1.274940298
-SD,2044,1.282515961
-SD,2045,1.289981444
-SD,2046,1.29803288
-SD,2047,1.306324316
-SD,2048,1.315029091
-SD,2049,1.324290377
-SD,2050,1.334060048
-AR,2010,1.000063951
-AR,2011,0.9945394
-AR,2012,0.972405365
-AR,2013,0.968755292
-AR,2014,0.977007693
-AR,2015,0.96425511
-AR,2016,0.958547482
-AR,2017,0.956479774
-AR,2018,1.029698079
-AR,2019,0.998839617
-AR,2020,0.953581892
-AR,2021,1.013711498
-AR,2022,1.030249822
-AR,2023,0.990084532
-AR,2024,1.012659364
-AR,2025,1.014543342
-AR,2026,1.022832548
-AR,2027,1.033853236
-AR,2028,1.045198473
-AR,2029,1.056602633
-AR,2030,1.065600574
-AR,2031,1.076602361
-AR,2032,1.088238654
-AR,2033,1.094978969
-AR,2034,1.102253124
-AR,2035,1.110533236
-AR,2036,1.119841193
-AR,2037,1.129717966
-AR,2038,1.140014548
-AR,2039,1.150457806
-AR,2040,1.160926953
-AR,2041,1.170651289
-AR,2042,1.181423086
-AR,2043,1.193251396
-AR,2044,1.204746681
-AR,2045,1.217152974
-AR,2046,1.230688686
-AR,2047,1.245488766
-AR,2048,1.261927764
-AR,2049,1.276723806
-AR,2050,1.292648089
-LA,2010,1.000046504
-LA,2011,1.015264133
-LA,2012,0.996189376
-LA,2013,1.009284611
-LA,2014,1.066444587
-LA,2015,1.079350387
-LA,2016,1.077156499
-LA,2017,1.074366116
-LA,2018,1.10973183
-LA,2019,1.097443496
-LA,2020,1.050598314
-LA,2021,1.070537085
-LA,2022,1.131127154
-LA,2023,1.090961864
-LA,2024,1.113536696
-LA,2025,1.115420674
-LA,2026,1.12370988
-LA,2027,1.134730568
-LA,2028,1.146075805
-LA,2029,1.157479965
-LA,2030,1.166477906
-LA,2031,1.177479693
-LA,2032,1.189115986
-LA,2033,1.195856301
-LA,2034,1.203130456
-LA,2035,1.211410568
-LA,2036,1.220718525
-LA,2037,1.230595298
-LA,2038,1.24089188
-LA,2039,1.251335138
-LA,2040,1.261804285
-LA,2041,1.271528621
-LA,2042,1.282300418
-LA,2043,1.294128728
-LA,2044,1.305624013
-LA,2045,1.318030306
-LA,2046,1.331566018
-LA,2047,1.346366098
-LA,2048,1.362805096
-LA,2049,1.377601138
-LA,2050,1.393525421
-OK,2010,1.000031118
-OK,2011,1.034626177
-OK,2012,1.025896632
-OK,2013,1.036074946
-OK,2014,1.064508284
-OK,2015,1.06042642
-OK,2016,1.063555759
-OK,2017,1.045876313
-OK,2018,1.11655916
-OK,2019,1.120518345
-OK,2020,1.077532428
-OK,2021,1.116390812
-OK,2022,1.199618489
-OK,2023,1.159453199
-OK,2024,1.182028031
-OK,2025,1.183912009
-OK,2026,1.192201215
-OK,2027,1.203221903
-OK,2028,1.21456714
-OK,2029,1.2259713
-OK,2030,1.234969241
-OK,2031,1.245971028
-OK,2032,1.257607321
-OK,2033,1.264347636
-OK,2034,1.271621791
-OK,2035,1.279901903
-OK,2036,1.28920986
-OK,2037,1.299086633
-OK,2038,1.309383215
-OK,2039,1.319826473
-OK,2040,1.33029562
-OK,2041,1.340019956
-OK,2042,1.350791753
-OK,2043,1.362620063
-OK,2044,1.374115348
-OK,2045,1.386521641
-OK,2046,1.400057353
-OK,2047,1.414857433
-OK,2048,1.431296431
-OK,2049,1.446092473
-OK,2050,1.462016756
-TX,2010,1.000100696
-TX,2011,1.049282937
-TX,2012,1.018783304
-TX,2013,1.05716162
-TX,2014,1.087569598
-TX,2015,1.09517765
-TX,2016,1.113290123
-TX,2017,1.122505904
-TX,2018,1.186050904
-TX,2019,1.200590224
-TX,2020,1.195381246
-TX,2021,1.221700967
-TX,2022,1.26497241
-TX,2023,1.22480712
-TX,2024,1.247381952
-TX,2025,1.24926593
-TX,2026,1.257555136
-TX,2027,1.268575824
-TX,2028,1.279921061
-TX,2029,1.291325221
-TX,2030,1.300323162
-TX,2031,1.311324949
-TX,2032,1.322961242
-TX,2033,1.329701557
-TX,2034,1.336975712
-TX,2035,1.345255824
-TX,2036,1.354563781
-TX,2037,1.364440554
-TX,2038,1.374737136
-TX,2039,1.385180394
-TX,2040,1.395649541
-TX,2041,1.405373877
-TX,2042,1.416145674
-TX,2043,1.427973984
-TX,2044,1.439469269
-TX,2045,1.451875562
-TX,2046,1.465411274
-TX,2047,1.480211354
-TX,2048,1.496650352
-TX,2049,1.511446394
-TX,2050,1.527370677
diff --git a/inputs/load/demand_AEO_2023_reference.csv b/inputs/load/demand_AEO_2023_reference.csv
deleted file mode 100644
index 5449baf09..000000000
--- a/inputs/load/demand_AEO_2023_reference.csv
+++ /dev/null
@@ -1,1969 +0,0 @@
-r,year,multiplier
-IL,2010,1.000075555
-IL,2011,0.987122063
-IL,2012,0.991686463
-IL,2013,0.979702693
-IL,2014,0.977932945
-IL,2015,0.957798834
-IL,2016,0.974658865
-IL,2017,0.948182116
-IL,2018,0.986170088
-IL,2019,0.956862312
-IL,2020,0.918089456
-IL,2021,0.943753372
-IL,2022,0.949193415
-IL,2023,0.945870278
-IL,2024,0.949792217
-IL,2025,0.954661562
-IL,2026,0.962564684
-IL,2027,0.970478486
-IL,2028,0.976498466
-IL,2029,0.982010049
-IL,2030,0.987208791
-IL,2031,0.991435207
-IL,2032,0.996673298
-IL,2033,1.001353593
-IL,2034,1.006408745
-IL,2035,1.012257024
-IL,2036,1.017682882
-IL,2037,1.023154562
-IL,2038,1.02855074
-IL,2039,1.032348659
-IL,2040,1.036624928
-IL,2041,1.041614801
-IL,2042,1.04722173
-IL,2043,1.053032704
-IL,2044,1.058598699
-IL,2045,1.064332846
-IL,2046,1.071596466
-IL,2047,1.079565699
-IL,2048,1.086475364
-IL,2049,1.093753034
-IL,2050,1.102989192
-IN,2010,1.000024006
-IN,2011,0.99839401
-IN,2012,0.992317799
-IN,2013,0.995293258
-IN,2014,1.009056042
-IN,2015,0.986174661
-IN,2016,0.978583187
-IN,2017,0.934042605
-IN,2018,0.98396742
-IN,2019,0.964542348
-IN,2020,0.918229976
-IN,2021,0.943093958
-IN,2022,0.948922166
-IN,2023,0.945599029
-IN,2024,0.949520968
-IN,2025,0.954390313
-IN,2026,0.962293435
-IN,2027,0.970207237
-IN,2028,0.976227217
-IN,2029,0.9817388
-IN,2030,0.986937542
-IN,2031,0.991163958
-IN,2032,0.996402049
-IN,2033,1.001082344
-IN,2034,1.006137496
-IN,2035,1.011985775
-IN,2036,1.017411633
-IN,2037,1.022883313
-IN,2038,1.028279491
-IN,2039,1.03207741
-IN,2040,1.036353679
-IN,2041,1.041343552
-IN,2042,1.046950481
-IN,2043,1.052761455
-IN,2044,1.05832745
-IN,2045,1.064061597
-IN,2046,1.071325217
-IN,2047,1.07929445
-IN,2048,1.086204115
-IN,2049,1.093481785
-IN,2050,1.102717943
-MI,2010,1.000031598
-MI,2011,1.013666512
-MI,2012,1.011552627
-MI,2013,0.994405594
-MI,2014,0.997105702
-MI,2015,0.989085845
-MI,2016,1.008431569
-MI,2017,0.98374973
-MI,2018,1.012562427
-MI,2019,0.977919321
-MI,2020,0.937468792
-MI,2021,0.964852082
-MI,2022,0.970742045
-MI,2023,0.967418908
-MI,2024,0.971340847
-MI,2025,0.976210192
-MI,2026,0.984113314
-MI,2027,0.992027116
-MI,2028,0.998047096
-MI,2029,1.003558679
-MI,2030,1.008757421
-MI,2031,1.012983837
-MI,2032,1.018221928
-MI,2033,1.022902223
-MI,2034,1.027957375
-MI,2035,1.033805654
-MI,2036,1.039231512
-MI,2037,1.044703192
-MI,2038,1.05009937
-MI,2039,1.053897289
-MI,2040,1.058173558
-MI,2041,1.063163431
-MI,2042,1.06877036
-MI,2043,1.074581334
-MI,2044,1.080147329
-MI,2045,1.085881476
-MI,2046,1.093145096
-MI,2047,1.101114329
-MI,2048,1.108023994
-MI,2049,1.115301664
-MI,2050,1.124537822
-OH,2010,1.000057177
-OH,2011,1.0040135
-OH,2012,0.989421437
-OH,2013,0.975579028
-OH,2014,0.978085462
-OH,2015,0.968666225
-OH,2016,0.977729512
-OH,2017,0.952215901
-OH,2018,0.993054079
-OH,2019,0.964829428
-OH,2020,0.926770864
-OH,2021,0.960163522
-OH,2022,0.97648367
-OH,2023,0.973160533
-OH,2024,0.977082472
-OH,2025,0.981951817
-OH,2026,0.989854939
-OH,2027,0.997768741
-OH,2028,1.003788721
-OH,2029,1.009300304
-OH,2030,1.014499046
-OH,2031,1.018725462
-OH,2032,1.023963553
-OH,2033,1.028643848
-OH,2034,1.033699
-OH,2035,1.039547279
-OH,2036,1.044973137
-OH,2037,1.050444817
-OH,2038,1.055840995
-OH,2039,1.059638914
-OH,2040,1.063915183
-OH,2041,1.068905056
-OH,2042,1.074511985
-OH,2043,1.080322959
-OH,2044,1.085888954
-OH,2045,1.091623101
-OH,2046,1.098886721
-OH,2047,1.106855954
-OH,2048,1.113765619
-OH,2049,1.121043289
-OH,2050,1.130279447
-WI,2010,1.000148808
-WI,2011,0.998141201
-WI,2012,1.001294067
-WI,2013,1.005736799
-WI,2014,1.011167058
-WI,2015,0.999649852
-WI,2016,1.014844242
-WI,2017,1.00558868
-WI,2018,1.033294062
-WI,2019,1.007424368
-WI,2020,0.9829839
-WI,2021,1.012538083
-WI,2022,1.02307163
-WI,2023,1.019748493
-WI,2024,1.023670432
-WI,2025,1.028539777
-WI,2026,1.036442899
-WI,2027,1.044356701
-WI,2028,1.050376681
-WI,2029,1.055888264
-WI,2030,1.061087006
-WI,2031,1.065313422
-WI,2032,1.070551513
-WI,2033,1.075231808
-WI,2034,1.08028696
-WI,2035,1.086135239
-WI,2036,1.091561097
-WI,2037,1.097032777
-WI,2038,1.102428955
-WI,2039,1.106226874
-WI,2040,1.110503143
-WI,2041,1.115493016
-WI,2042,1.121099945
-WI,2043,1.126910919
-WI,2044,1.132476914
-WI,2045,1.138211061
-WI,2046,1.145474681
-WI,2047,1.153443914
-WI,2048,1.160353579
-WI,2049,1.167631249
-WI,2050,1.176867407
-AL,2010,1.000014973
-AL,2011,0.979465346
-AL,2012,0.948512862
-AL,2013,0.966907198
-AL,2014,0.995985503
-AL,2015,0.977847217
-AL,2016,0.971067859
-AL,2017,0.94924321
-AL,2018,0.993723675
-AL,2019,0.969708811
-AL,2020,0.917994469
-AL,2021,0.942134017
-AL,2022,0.963659772
-AL,2023,0.96298564
-AL,2024,0.973645423
-AL,2025,0.98437557
-AL,2026,0.994880839
-AL,2027,1.003476117
-AL,2028,1.010755588
-AL,2029,1.01652564
-AL,2030,1.020859277
-AL,2031,1.025559624
-AL,2032,1.030913305
-AL,2033,1.033742011
-AL,2034,1.037913287
-AL,2035,1.04411794
-AL,2036,1.048842237
-AL,2037,1.05393103
-AL,2038,1.058672549
-AL,2039,1.063335562
-AL,2040,1.06934018
-AL,2041,1.07608669
-AL,2042,1.083031655
-AL,2043,1.090038244
-AL,2044,1.096507671
-AL,2045,1.10364202
-AL,2046,1.112249233
-AL,2047,1.121294911
-AL,2048,1.12935794
-AL,2049,1.137516308
-AL,2050,1.147392966
-KY,2010,1.000025676
-KY,2011,0.956961209
-KY,2012,0.951729797
-KY,2013,0.906041351
-KY,2014,0.842723377
-KY,2015,0.81280844
-KY,2016,0.797006505
-KY,2017,0.776540917
-KY,2018,0.819112523
-KY,2019,0.805677778
-KY,2020,0.767908451
-KY,2021,0.797139719
-KY,2022,0.803955017
-KY,2023,0.803280885
-KY,2024,0.813940668
-KY,2025,0.824670815
-KY,2026,0.835176084
-KY,2027,0.843771362
-KY,2028,0.851050833
-KY,2029,0.856820885
-KY,2030,0.861154522
-KY,2031,0.865854869
-KY,2032,0.87120855
-KY,2033,0.874037256
-KY,2034,0.878208532
-KY,2035,0.884413185
-KY,2036,0.889137482
-KY,2037,0.894226275
-KY,2038,0.898967794
-KY,2039,0.903630807
-KY,2040,0.909635425
-KY,2041,0.916381935
-KY,2042,0.9233269
-KY,2043,0.930333489
-KY,2044,0.936802916
-KY,2045,0.943937265
-KY,2046,0.952544478
-KY,2047,0.961590156
-KY,2048,0.969653185
-KY,2049,0.977811553
-KY,2050,0.987688211
-MS,2010,1.000016644
-MS,2011,0.992986538
-MS,2012,0.9738691
-MS,2013,0.981848499
-MS,2014,0.994455906
-MS,2015,0.980012052
-MS,2016,0.987324239
-MS,2017,0.962826342
-MS,2018,1.01438337
-MS,2019,0.985478473
-MS,2020,0.935810566
-MS,2021,0.966698457
-MS,2022,0.999933214
-MS,2023,0.999259082
-MS,2024,1.009918865
-MS,2025,1.020649012
-MS,2026,1.031154281
-MS,2027,1.039749559
-MS,2028,1.04702903
-MS,2029,1.052799082
-MS,2030,1.057132719
-MS,2031,1.061833066
-MS,2032,1.067186747
-MS,2033,1.070015453
-MS,2034,1.074186729
-MS,2035,1.080391382
-MS,2036,1.085115679
-MS,2037,1.090204472
-MS,2038,1.094945991
-MS,2039,1.099609004
-MS,2040,1.105613622
-MS,2041,1.112360132
-MS,2042,1.119305097
-MS,2043,1.126311686
-MS,2044,1.132781113
-MS,2045,1.139915462
-MS,2046,1.148522675
-MS,2047,1.157568353
-MS,2048,1.165631382
-MS,2049,1.17378975
-MS,2050,1.183666408
-TN,2010,1.000041253
-TN,2011,0.973111223
-TN,2012,0.931094148
-TN,2013,0.937006056
-TN,2014,0.968720798
-TN,2015,0.963049627
-TN,2016,0.974185592
-TN,2017,0.940219299
-TN,2018,0.995096932
-TN,2019,0.965360657
-TN,2020,0.918702072
-TN,2021,0.963295329
-TN,2022,0.989382143
-TN,2023,0.988708011
-TN,2024,0.999367794
-TN,2025,1.010097941
-TN,2026,1.02060321
-TN,2027,1.029198488
-TN,2028,1.036477959
-TN,2029,1.042248011
-TN,2030,1.046581648
-TN,2031,1.051281995
-TN,2032,1.056635676
-TN,2033,1.059464382
-TN,2034,1.063635658
-TN,2035,1.069840311
-TN,2036,1.074564608
-TN,2037,1.079653401
-TN,2038,1.08439492
-TN,2039,1.089057933
-TN,2040,1.095062551
-TN,2041,1.101809061
-TN,2042,1.108754026
-TN,2043,1.115760615
-TN,2044,1.122230042
-TN,2045,1.129364391
-TN,2046,1.137971604
-TN,2047,1.147017282
-TN,2048,1.155080311
-TN,2049,1.163238679
-TN,2050,1.173115337
-NJ,2010,1.003806079
-NJ,2011,0.977568078
-NJ,2012,0.960140816
-NJ,2013,0.958433085
-NJ,2014,0.951611606
-NJ,2015,0.973214768
-NJ,2016,0.971145997
-NJ,2017,0.949626193
-NJ,2018,0.985839841
-NJ,2019,0.96312342
-NJ,2020,0.942787292
-NJ,2021,0.95821245
-NJ,2022,0.969126193
-NJ,2023,0.954167539
-NJ,2024,0.96490583
-NJ,2025,0.967196271
-NJ,2026,0.972964311
-NJ,2027,0.97972324
-NJ,2028,0.985759893
-NJ,2029,0.991954311
-NJ,2030,0.997593508
-NJ,2031,1.003460712
-NJ,2032,1.009730382
-NJ,2033,1.016758712
-NJ,2034,1.024150075
-NJ,2035,1.032446169
-NJ,2036,1.040905547
-NJ,2037,1.050438027
-NJ,2038,1.059302271
-NJ,2039,1.0666725
-NJ,2040,1.07500072
-NJ,2041,1.083930956
-NJ,2042,1.093407626
-NJ,2043,1.102949886
-NJ,2044,1.112912642
-NJ,2045,1.12355524
-NJ,2046,1.135301437
-NJ,2047,1.14750987
-NJ,2048,1.159704279
-NJ,2049,1.173152232
-NJ,2050,1.18847924
-NY,2010,1.000394053
-NY,2011,0.996610397
-NY,2012,0.990932231
-NY,2013,1.024059333
-NY,2014,1.021427354
-NY,2015,1.033753334
-NY,2016,1.028047889
-NY,2017,1.010754732
-NY,2018,1.047069087
-NY,2019,1.019678482
-NY,2020,0.986806052
-NY,2021,0.996676569
-NY,2022,1.008809701
-NY,2023,0.993851047
-NY,2024,1.004589338
-NY,2025,1.006879779
-NY,2026,1.012647819
-NY,2027,1.019406748
-NY,2028,1.025443401
-NY,2029,1.031637819
-NY,2030,1.037277016
-NY,2031,1.04314422
-NY,2032,1.04941389
-NY,2033,1.05644222
-NY,2034,1.063833583
-NY,2035,1.072129677
-NY,2036,1.080589055
-NY,2037,1.090121535
-NY,2038,1.098985779
-NY,2039,1.106356008
-NY,2040,1.114684228
-NY,2041,1.123614464
-NY,2042,1.133091134
-NY,2043,1.142633394
-NY,2044,1.15259615
-NY,2045,1.163238748
-NY,2046,1.174984945
-NY,2047,1.187193378
-NY,2048,1.199387787
-NY,2049,1.21283574
-NY,2050,1.228162748
-PA,2010,1.000471824
-PA,2011,0.999734828
-PA,2012,0.972904808
-PA,2013,0.983441925
-PA,2014,0.986495029
-PA,2015,0.984249535
-PA,2016,0.977611612
-PA,2017,0.96239878
-PA,2018,1.002966661
-PA,2019,0.980679319
-PA,2020,0.941989911
-PA,2021,0.966748253
-PA,2022,0.977929708
-PA,2023,0.962971054
-PA,2024,0.973709345
-PA,2025,0.975999786
-PA,2026,0.981767826
-PA,2027,0.988526755
-PA,2028,0.994563408
-PA,2029,1.000757826
-PA,2030,1.006397023
-PA,2031,1.012264227
-PA,2032,1.018533897
-PA,2033,1.025562227
-PA,2034,1.03295359
-PA,2035,1.041249684
-PA,2036,1.049709062
-PA,2037,1.059241542
-PA,2038,1.068105786
-PA,2039,1.075476015
-PA,2040,1.083804235
-PA,2041,1.092734471
-PA,2042,1.102211141
-PA,2043,1.111753401
-PA,2044,1.121716157
-PA,2045,1.132358755
-PA,2046,1.144104952
-PA,2047,1.156313385
-PA,2048,1.168507794
-PA,2049,1.181955747
-PA,2050,1.197282755
-AZ,2010,1.001933241
-AZ,2011,1.034037088
-AZ,2012,1.038743358
-AZ,2013,1.050869664
-AZ,2014,1.062940341
-AZ,2015,1.081043242
-AZ,2016,1.096473158
-AZ,2017,1.092314809
-AZ,2018,1.106900389
-AZ,2019,1.105417988
-AZ,2020,1.165384295
-AZ,2021,1.161608132
-AZ,2022,1.200125229
-AZ,2023,1.191911432
-AZ,2024,1.213037423
-AZ,2025,1.231095975
-AZ,2026,1.246708381
-AZ,2027,1.261632958
-AZ,2028,1.278613537
-AZ,2029,1.295593247
-AZ,2030,1.310817657
-AZ,2031,1.326999127
-AZ,2032,1.344087646
-AZ,2033,1.361305619
-AZ,2034,1.378931733
-AZ,2035,1.397435641
-AZ,2036,1.416006332
-AZ,2037,1.433797969
-AZ,2038,1.450860875
-AZ,2039,1.467418759
-AZ,2040,1.484644027
-AZ,2041,1.503696376
-AZ,2042,1.523306866
-AZ,2043,1.543935396
-AZ,2044,1.564889127
-AZ,2045,1.586315697
-AZ,2046,1.614064494
-AZ,2047,1.636584984
-AZ,2048,1.65937033
-AZ,2049,1.682550452
-AZ,2050,1.707539249
-CO,2010,1.002079595
-CO,2011,1.013347305
-CO,2012,1.018620701
-CO,2013,1.015223108
-CO,2014,1.015930559
-CO,2015,1.030257354
-CO,2016,1.044591778
-CO,2017,1.046373042
-CO,2018,1.078115977
-CO,2019,1.080316289
-CO,2020,1.072724402
-CO,2021,1.085002865
-CO,2022,1.108470552
-CO,2023,1.100256755
-CO,2024,1.121382746
-CO,2025,1.139441298
-CO,2026,1.155053704
-CO,2027,1.169978281
-CO,2028,1.18695886
-CO,2029,1.20393857
-CO,2030,1.21916298
-CO,2031,1.23534445
-CO,2032,1.252432969
-CO,2033,1.269650942
-CO,2034,1.287277056
-CO,2035,1.305780964
-CO,2036,1.324351655
-CO,2037,1.342143292
-CO,2038,1.359206198
-CO,2039,1.375764082
-CO,2040,1.39298935
-CO,2041,1.412041699
-CO,2042,1.431652189
-CO,2043,1.452280719
-CO,2044,1.47323445
-CO,2045,1.49466102
-CO,2046,1.522409817
-CO,2047,1.544930307
-CO,2048,1.567715653
-CO,2049,1.590895775
-CO,2050,1.615884572
-ID,2010,0.999988532
-ID,2011,1.020781212
-ID,2012,1.040123183
-ID,2013,1.06197204
-ID,2014,1.019227514
-ID,2015,1.011658061
-ID,2016,1.012001618
-ID,2017,1.044334564
-ID,2018,1.043313044
-ID,2019,1.054633787
-ID,2020,1.076852921
-ID,2021,1.114399277
-ID,2022,1.149644673
-ID,2023,1.141430876
-ID,2024,1.162556867
-ID,2025,1.180615419
-ID,2026,1.196227825
-ID,2027,1.211152402
-ID,2028,1.228132981
-ID,2029,1.245112691
-ID,2030,1.260337101
-ID,2031,1.276518571
-ID,2032,1.29360709
-ID,2033,1.310825063
-ID,2034,1.328451177
-ID,2035,1.346955085
-ID,2036,1.365525776
-ID,2037,1.383317413
-ID,2038,1.400380319
-ID,2039,1.416938203
-ID,2040,1.434163471
-ID,2041,1.45321582
-ID,2042,1.47282631
-ID,2043,1.49345484
-ID,2044,1.514408571
-ID,2045,1.535835141
-ID,2046,1.563583938
-ID,2047,1.586104428
-ID,2048,1.608889774
-ID,2049,1.632069896
-ID,2050,1.657058693
-MT,2010,1.000204773
-MT,2011,1.001404969
-MT,2012,1.007225546
-MT,2013,1.020689321
-MT,2014,1.024858815
-MT,2015,1.032216882
-MT,2016,1.024695583
-MT,2017,1.069230259
-MT,2018,1.078928814
-MT,2019,1.114315989
-MT,2020,1.061472508
-MT,2021,1.089518961
-MT,2022,1.134244714
-MT,2023,1.126030917
-MT,2024,1.147156908
-MT,2025,1.16521546
-MT,2026,1.180827866
-MT,2027,1.195752443
-MT,2028,1.212733022
-MT,2029,1.229712732
-MT,2030,1.244937142
-MT,2031,1.261118612
-MT,2032,1.278207131
-MT,2033,1.295425104
-MT,2034,1.313051218
-MT,2035,1.331555126
-MT,2036,1.350125817
-MT,2037,1.367917454
-MT,2038,1.38498036
-MT,2039,1.401538244
-MT,2040,1.418763512
-MT,2041,1.437815861
-MT,2042,1.457426351
-MT,2043,1.478054881
-MT,2044,1.499008612
-MT,2045,1.520435182
-MT,2046,1.548183979
-MT,2047,1.570704469
-MT,2048,1.593489815
-MT,2049,1.616669937
-MT,2050,1.641658734
-NM,2010,1.000523205
-NM,2011,1.028599862
-NM,2012,1.035716735
-NM,2013,1.032248035
-NM,2014,1.035966265
-NM,2015,1.036013926
-NM,2016,1.03424914
-NM,2017,1.03502901
-NM,2018,1.083412511
-NM,2019,1.122725718
-NM,2020,1.121013126
-NM,2021,1.152842428
-NM,2022,1.254068236
-NM,2023,1.245854439
-NM,2024,1.26698043
-NM,2025,1.285038982
-NM,2026,1.300651388
-NM,2027,1.315575965
-NM,2028,1.332556544
-NM,2029,1.349536254
-NM,2030,1.364760664
-NM,2031,1.380942134
-NM,2032,1.398030653
-NM,2033,1.415248626
-NM,2034,1.43287474
-NM,2035,1.451378648
-NM,2036,1.469949339
-NM,2037,1.487740976
-NM,2038,1.504803882
-NM,2039,1.521361766
-NM,2040,1.538587034
-NM,2041,1.557639383
-NM,2042,1.577249873
-NM,2043,1.597878403
-NM,2044,1.618832134
-NM,2045,1.640258704
-NM,2046,1.668007501
-NM,2047,1.690527991
-NM,2048,1.713313337
-NM,2049,1.736493459
-NM,2050,1.761482256
-NV,2010,1.000962051
-NV,2011,1.006553312
-NV,2012,1.044393324
-NV,2013,1.045680486
-NV,2014,1.042589056
-NV,2015,1.073392305
-NV,2016,1.083068615
-NV,2017,1.099610175
-NV,2018,1.135252893
-NV,2019,1.117109997
-NV,2020,1.1598581
-NV,2021,1.188990178
-NV,2022,1.227146009
-NV,2023,1.218932212
-NV,2024,1.240058203
-NV,2025,1.258116755
-NV,2026,1.273729161
-NV,2027,1.288653738
-NV,2028,1.305634317
-NV,2029,1.322614027
-NV,2030,1.337838437
-NV,2031,1.354019907
-NV,2032,1.371108426
-NV,2033,1.388326399
-NV,2034,1.405952513
-NV,2035,1.424456421
-NV,2036,1.443027112
-NV,2037,1.460818749
-NV,2038,1.477881655
-NV,2039,1.494439539
-NV,2040,1.511664807
-NV,2041,1.530717156
-NV,2042,1.550327646
-NV,2043,1.570956176
-NV,2044,1.591909907
-NV,2045,1.613336477
-NV,2046,1.641085274
-NV,2047,1.663605764
-NV,2048,1.68639111
-NV,2049,1.709571232
-NV,2050,1.734560029
-UT,2010,1.000057729
-UT,2011,1.029250311
-UT,2012,1.060334249
-UT,2013,1.087399706
-UT,2014,1.072580079
-UT,2015,1.078967839
-UT,2016,1.081452849
-UT,2017,1.101037454
-UT,2018,1.12808008
-UT,2019,1.126845392
-UT,2020,1.148494104
-UT,2021,1.188625393
-UT,2022,1.207623171
-UT,2023,1.199409374
-UT,2024,1.220535365
-UT,2025,1.238593917
-UT,2026,1.254206323
-UT,2027,1.2691309
-UT,2028,1.286111479
-UT,2029,1.303091189
-UT,2030,1.318315599
-UT,2031,1.334497069
-UT,2032,1.351585588
-UT,2033,1.368803561
-UT,2034,1.386429675
-UT,2035,1.404933583
-UT,2036,1.423504274
-UT,2037,1.441295911
-UT,2038,1.458358817
-UT,2039,1.474916701
-UT,2040,1.492141969
-UT,2041,1.511194318
-UT,2042,1.530804808
-UT,2043,1.551433338
-UT,2044,1.572387069
-UT,2045,1.593813639
-UT,2046,1.621562436
-UT,2047,1.644082926
-UT,2048,1.666868272
-UT,2049,1.690048394
-UT,2050,1.715037191
-WY,2010,0.999991469
-WY,2011,1.017772879
-WY,2012,0.991764753
-WY,2013,0.996602311
-WY,2014,1.001334937
-WY,2015,0.98912506
-WY,2016,0.967550795
-WY,2017,0.980668035
-WY,2018,0.985824008
-WY,2019,0.980080343
-WY,2020,0.896595412
-WY,2021,0.923390033
-WY,2022,0.970235784
-WY,2023,0.962021987
-WY,2024,0.983147978
-WY,2025,1.00120653
-WY,2026,1.016818936
-WY,2027,1.031743513
-WY,2028,1.048724092
-WY,2029,1.065703802
-WY,2030,1.080928212
-WY,2031,1.097109682
-WY,2032,1.114198201
-WY,2033,1.131416174
-WY,2034,1.149042288
-WY,2035,1.167546196
-WY,2036,1.186116887
-WY,2037,1.203908524
-WY,2038,1.22097143
-WY,2039,1.237529314
-WY,2040,1.254754582
-WY,2041,1.273806931
-WY,2042,1.293417421
-WY,2043,1.314045951
-WY,2044,1.334999682
-WY,2045,1.356426252
-WY,2046,1.384175049
-WY,2047,1.406695539
-WY,2048,1.429480885
-WY,2049,1.452661007
-WY,2050,1.477649804
-CT,2010,1.000968603
-CT,2011,0.983536484
-CT,2012,0.971766071
-CT,2013,0.983764271
-CT,2014,0.970015984
-CT,2015,0.976408833
-CT,2016,0.962461538
-CT,2017,0.939184482
-CT,2018,0.965215025
-CT,2019,0.937909267
-CT,2020,0.916473428
-CT,2021,0.941186361
-CT,2022,0.942890166
-CT,2023,0.928530666
-CT,2024,0.949647785
-CT,2025,0.9527195
-CT,2026,0.959989976
-CT,2027,0.966610285
-CT,2028,0.975805642
-CT,2029,0.985526809
-CT,2030,0.995758833
-CT,2031,1.005997357
-CT,2032,1.016813023
-CT,2033,1.029225523
-CT,2034,1.039499714
-CT,2035,1.053056738
-CT,2036,1.064749619
-CT,2037,1.077443785
-CT,2038,1.089175
-CT,2039,1.101369166
-CT,2040,1.114006142
-CT,2041,1.126570595
-CT,2042,1.139904119
-CT,2043,1.152926976
-CT,2044,1.166851333
-CT,2045,1.180899404
-CT,2046,1.194522738
-CT,2047,1.208659666
-CT,2048,1.221998762
-CT,2049,1.237581452
-CT,2050,1.253449357
-MA,2010,1.000706668
-MA,2011,0.974077439
-MA,2012,0.971695405
-MA,2013,0.973833487
-MA,2014,0.964579087
-MA,2015,0.971351039
-MA,2016,0.958204142
-MA,2017,0.945968936
-MA,2018,0.969373184
-MA,2019,0.935929816
-MA,2020,0.916319875
-MA,2021,0.933820818
-MA,2022,0.934808668
-MA,2023,0.920449168
-MA,2024,0.941566287
-MA,2025,0.944638002
-MA,2026,0.951908478
-MA,2027,0.958528787
-MA,2028,0.967724144
-MA,2029,0.977445311
-MA,2030,0.987677335
-MA,2031,0.997915859
-MA,2032,1.008731525
-MA,2033,1.021144025
-MA,2034,1.031418216
-MA,2035,1.04497524
-MA,2036,1.056668121
-MA,2037,1.069362287
-MA,2038,1.081093502
-MA,2039,1.093287668
-MA,2040,1.105924644
-MA,2041,1.118489097
-MA,2042,1.131822621
-MA,2043,1.144845478
-MA,2044,1.158769835
-MA,2045,1.172817906
-MA,2046,1.18644124
-MA,2047,1.200578168
-MA,2048,1.213917264
-MA,2049,1.229499954
-MA,2050,1.245367859
-ME,2010,1.000037722
-ME,2011,0.989950745
-ME,2012,1.003062171
-ME,2013,1.029134007
-ME,2014,1.041973599
-ME,2015,1.032537962
-ME,2016,0.995346166
-ME,2017,0.975947366
-ME,2018,1.076166185
-ME,2019,1.024001544
-ME,2020,0.991662026
-ME,2021,1.01632903
-ME,2022,1.047860494
-ME,2023,1.033500994
-ME,2024,1.054618113
-ME,2025,1.057689828
-ME,2026,1.064960304
-ME,2027,1.071580613
-ME,2028,1.08077597
-ME,2029,1.090497137
-ME,2030,1.100729161
-ME,2031,1.110967685
-ME,2032,1.121783351
-ME,2033,1.134195851
-ME,2034,1.144470042
-ME,2035,1.158027066
-ME,2036,1.169719947
-ME,2037,1.182414113
-ME,2038,1.194145328
-ME,2039,1.206339494
-ME,2040,1.21897647
-ME,2041,1.231540923
-ME,2042,1.244874447
-ME,2043,1.257897304
-ME,2044,1.271821661
-ME,2045,1.285869732
-ME,2046,1.299493066
-ME,2047,1.313629994
-ME,2048,1.32696909
-ME,2049,1.34255178
-ME,2050,1.358419685
-NH,2010,1.000230536
-NH,2011,0.998316077
-NH,2012,0.998725627
-NH,2013,1.015061397
-NH,2014,1.0062917
-NH,2015,1.012214509
-NH,2016,1.006114936
-NH,2017,0.998574759
-NH,2018,1.024278088
-NH,2019,0.995556828
-NH,2020,0.996379099
-NH,2021,1.015657527
-NH,2022,1.014820724
-NH,2023,1.000461224
-NH,2024,1.021578343
-NH,2025,1.024650058
-NH,2026,1.031920534
-NH,2027,1.038540843
-NH,2028,1.0477362
-NH,2029,1.057457367
-NH,2030,1.067689391
-NH,2031,1.077927915
-NH,2032,1.088743581
-NH,2033,1.101156081
-NH,2034,1.111430272
-NH,2035,1.124987296
-NH,2036,1.136680177
-NH,2037,1.149374343
-NH,2038,1.161105558
-NH,2039,1.173299724
-NH,2040,1.1859367
-NH,2041,1.198501153
-NH,2042,1.211834677
-NH,2043,1.224857534
-NH,2044,1.238781891
-NH,2045,1.252829962
-NH,2046,1.266453296
-NH,2047,1.280590224
-NH,2048,1.29392932
-NH,2049,1.30951201
-NH,2050,1.325379915
-RI,2010,1.00037454
-RI,2011,0.992284342
-RI,2012,0.989776324
-RI,2013,0.999092622
-RI,2014,0.981457765
-RI,2015,0.984484741
-RI,2016,0.968575337
-RI,2017,0.953938283
-RI,2018,0.984576662
-RI,2019,0.965493896
-RI,2020,0.980998552
-RI,2021,0.998165723
-RI,2022,1.033201009
-RI,2023,1.018841509
-RI,2024,1.039958628
-RI,2025,1.043030343
-RI,2026,1.050300819
-RI,2027,1.056921128
-RI,2028,1.066116485
-RI,2029,1.075837652
-RI,2030,1.086069676
-RI,2031,1.0963082
-RI,2032,1.107123866
-RI,2033,1.119536366
-RI,2034,1.129810557
-RI,2035,1.143367581
-RI,2036,1.155060462
-RI,2037,1.167754628
-RI,2038,1.179485843
-RI,2039,1.191680009
-RI,2040,1.204316985
-RI,2041,1.216881438
-RI,2042,1.230214962
-RI,2043,1.243237819
-RI,2044,1.257162176
-RI,2045,1.271210247
-RI,2046,1.284833581
-RI,2047,1.298970509
-RI,2048,1.312309605
-RI,2049,1.327892295
-RI,2050,1.3437602
-VT,2010,1.000388299
-VT,2011,0.993349216
-VT,2012,0.987648609
-VT,2013,1.002734417
-VT,2014,1.001408609
-VT,2015,0.996509497
-VT,2016,0.99949164
-VT,2017,0.989275982
-VT,2018,1.011857312
-VT,2019,0.997426777
-VT,2020,0.983980884
-VT,2021,1.00074034
-VT,2022,1.014103264
-VT,2023,0.999743764
-VT,2024,1.020860883
-VT,2025,1.023932598
-VT,2026,1.031203074
-VT,2027,1.037823383
-VT,2028,1.04701874
-VT,2029,1.056739907
-VT,2030,1.066971931
-VT,2031,1.077210455
-VT,2032,1.088026121
-VT,2033,1.100438621
-VT,2034,1.110712812
-VT,2035,1.124269836
-VT,2036,1.135962717
-VT,2037,1.148656883
-VT,2038,1.160388098
-VT,2039,1.172582264
-VT,2040,1.18521924
-VT,2041,1.197783693
-VT,2042,1.211117217
-VT,2043,1.224140074
-VT,2044,1.238064431
-VT,2045,1.252112502
-VT,2046,1.265735836
-VT,2047,1.279872764
-VT,2048,1.29321186
-VT,2049,1.30879455
-VT,2050,1.324662455
-CA,2010,1.004774859
-CA,2011,1.019684731
-CA,2012,1.013351818
-CA,2013,1.023981459
-CA,2014,1.034104524
-CA,2015,1.033840414
-CA,2016,1.025789692
-CA,2017,1.036639781
-CA,2018,1.037795267
-CA,2019,1.027821906
-CA,2020,1.035799006
-CA,2021,1.034164546
-CA,2022,1.06561455
-CA,2023,1.052901834
-CA,2024,1.062075911
-CA,2025,1.081054493
-CA,2026,1.099064243
-CA,2027,1.111661314
-CA,2028,1.127326467
-CA,2029,1.142711976
-CA,2030,1.156233145
-CA,2031,1.168533145
-CA,2032,1.182727871
-CA,2033,1.196749083
-CA,2034,1.212863438
-CA,2035,1.22830388
-CA,2036,1.244637914
-CA,2037,1.2614009
-CA,2038,1.278931748
-CA,2039,1.297243245
-CA,2040,1.314673823
-CA,2041,1.332770081
-CA,2042,1.350577265
-CA,2043,1.366885006
-CA,2044,1.383098063
-CA,2045,1.399859439
-CA,2046,1.42184589
-CA,2047,1.440877629
-CA,2048,1.459828741
-CA,2049,1.480019681
-CA,2050,1.501997436
-OR,2010,1.000704404
-OR,2011,1.025781596
-OR,2012,1.015643175
-OR,2013,1.036487073
-OR,2014,1.030095872
-OR,2015,1.028813207
-OR,2016,1.031964163
-OR,2017,1.091098105
-OR,2018,1.07606457
-OR,2019,1.099971107
-OR,2020,1.113998135
-OR,2021,1.182726523
-OR,2022,1.16898999
-OR,2023,1.156277274
-OR,2024,1.165451351
-OR,2025,1.184429933
-OR,2026,1.202439683
-OR,2027,1.215036754
-OR,2028,1.230701907
-OR,2029,1.246087416
-OR,2030,1.259608585
-OR,2031,1.271908585
-OR,2032,1.286103311
-OR,2033,1.300124523
-OR,2034,1.316238878
-OR,2035,1.33167932
-OR,2036,1.348013354
-OR,2037,1.36477634
-OR,2038,1.382307188
-OR,2039,1.400618685
-OR,2040,1.418049263
-OR,2041,1.436145521
-OR,2042,1.453952705
-OR,2043,1.470260446
-OR,2044,1.486473503
-OR,2045,1.503234879
-OR,2046,1.52522133
-OR,2047,1.544253069
-OR,2048,1.563204181
-OR,2049,1.583395121
-OR,2050,1.605372876
-WA,2010,0.999954043
-WA,2011,1.036996134
-WA,2012,1.021713053
-WA,2013,1.027838058
-WA,2014,1.019779181
-WA,2015,0.997596942
-WA,2016,0.984355167
-WA,2017,1.018534707
-WA,2018,0.997417722
-WA,2019,1.009611428
-WA,2020,0.962023088
-WA,2021,0.979182066
-WA,2022,1.003189075
-WA,2023,0.990476359
-WA,2024,0.999650436
-WA,2025,1.018629018
-WA,2026,1.036638768
-WA,2027,1.049235839
-WA,2028,1.064900992
-WA,2029,1.080286501
-WA,2030,1.09380767
-WA,2031,1.10610767
-WA,2032,1.120302396
-WA,2033,1.134323608
-WA,2034,1.150437963
-WA,2035,1.165878405
-WA,2036,1.182212439
-WA,2037,1.198975425
-WA,2038,1.216506273
-WA,2039,1.23481777
-WA,2040,1.252248348
-WA,2041,1.270344606
-WA,2042,1.28815179
-WA,2043,1.304459531
-WA,2044,1.320672588
-WA,2045,1.337433964
-WA,2046,1.359420415
-WA,2047,1.378452154
-WA,2048,1.397403266
-WA,2049,1.417594206
-WA,2050,1.439571961
-DE,2010,1.000740874
-DE,2011,0.991651797
-DE,2012,0.995548945
-DE,2013,0.982697513
-DE,2014,0.983258631
-DE,2015,0.997644911
-DE,2016,0.975982178
-DE,2017,0.966953201
-DE,2018,1.023907154
-DE,2019,0.999002971
-DE,2020,0.970017982
-DE,2021,1.001213296
-DE,2022,1.011565926
-DE,2023,1.009571788
-DE,2024,1.008195052
-DE,2025,1.013789926
-DE,2026,1.022767287
-DE,2027,1.032972491
-DE,2028,1.045930594
-DE,2029,1.058284633
-DE,2030,1.069114331
-DE,2031,1.079639955
-DE,2032,1.090838431
-DE,2033,1.099781166
-DE,2034,1.108686399
-DE,2035,1.119742349
-DE,2036,1.130863369
-DE,2037,1.141829891
-DE,2038,1.151564658
-DE,2039,1.161483553
-DE,2040,1.172840697
-DE,2041,1.185615269
-DE,2042,1.199081962
-DE,2043,1.212806488
-DE,2044,1.226225642
-DE,2045,1.239252448
-DE,2046,1.253150107
-DE,2047,1.26755998
-DE,2048,1.282435138
-DE,2049,1.297844395
-DE,2050,1.315268136
-FL,2010,1.000098794
-FL,2011,0.973673845
-FL,2012,0.954687732
-FL,2013,0.960183913
-FL,2014,0.978325125
-FL,2015,1.019665351
-FL,2016,1.020413339
-FL,2017,1.009696811
-FL,2018,1.033704381
-FL,2019,1.042554059
-FL,2020,1.053452573
-FL,2021,1.052191629
-FL,2022,1.134160307
-FL,2023,1.132166169
-FL,2024,1.130789433
-FL,2025,1.136384307
-FL,2026,1.145361668
-FL,2027,1.155566872
-FL,2028,1.168524975
-FL,2029,1.180879014
-FL,2030,1.191708712
-FL,2031,1.202234336
-FL,2032,1.213432812
-FL,2033,1.222375547
-FL,2034,1.23128078
-FL,2035,1.24233673
-FL,2036,1.25345775
-FL,2037,1.264424272
-FL,2038,1.274159039
-FL,2039,1.284077934
-FL,2040,1.295435078
-FL,2041,1.30820965
-FL,2042,1.321676343
-FL,2043,1.335400869
-FL,2044,1.348820023
-FL,2045,1.361846829
-FL,2046,1.375744488
-FL,2047,1.390154361
-FL,2048,1.405029519
-FL,2049,1.420438776
-FL,2050,1.437862517
-GA,2010,1.000073772
-GA,2011,0.969591747
-GA,2012,0.931397288
-GA,2013,0.928355503
-GA,2014,0.966025322
-GA,2015,0.966661637
-GA,2016,0.983214295
-GA,2017,0.950340982
-GA,2018,0.996210208
-GA,2019,0.992460799
-GA,2020,0.951173388
-GA,2021,0.979150139
-GA,2022,1.021233468
-GA,2023,1.01923933
-GA,2024,1.017862594
-GA,2025,1.023457468
-GA,2026,1.032434829
-GA,2027,1.042640033
-GA,2028,1.055598136
-GA,2029,1.067952175
-GA,2030,1.078781873
-GA,2031,1.089307497
-GA,2032,1.100505973
-GA,2033,1.109448708
-GA,2034,1.118353941
-GA,2035,1.129409891
-GA,2036,1.140530911
-GA,2037,1.151497433
-GA,2038,1.1612322
-GA,2039,1.171151095
-GA,2040,1.182508239
-GA,2041,1.195282811
-GA,2042,1.208749504
-GA,2043,1.22247403
-GA,2044,1.235893184
-GA,2045,1.24891999
-GA,2046,1.262817649
-GA,2047,1.277227522
-GA,2048,1.29210268
-GA,2049,1.307511937
-GA,2050,1.324935678
-MD,2010,1.000152142
-MD,2011,0.973981823
-MD,2012,0.947805612
-MD,2013,0.947392825
-MD,2014,0.947187257
-MD,2015,0.950872179
-MD,2016,0.949004189
-MD,2017,0.919057089
-MD,2018,0.962341236
-MD,2019,0.94198106
-MD,2020,0.886211329
-MD,2021,0.912154154
-MD,2022,0.919174068
-MD,2023,0.91717993
-MD,2024,0.915803194
-MD,2025,0.921398068
-MD,2026,0.930375429
-MD,2027,0.940580633
-MD,2028,0.953538736
-MD,2029,0.965892775
-MD,2030,0.976722473
-MD,2031,0.987248097
-MD,2032,0.998446573
-MD,2033,1.007389308
-MD,2034,1.016294541
-MD,2035,1.027350491
-MD,2036,1.038471511
-MD,2037,1.049438033
-MD,2038,1.0591728
-MD,2039,1.069091695
-MD,2040,1.080448839
-MD,2041,1.093223411
-MD,2042,1.106690104
-MD,2043,1.12041463
-MD,2044,1.133833784
-MD,2045,1.14686059
-MD,2046,1.160758249
-MD,2047,1.175168122
-MD,2048,1.19004328
-MD,2049,1.205452537
-MD,2050,1.222876278
-NC,2010,1.000056501
-NC,2011,0.961045918
-NC,2012,0.939223305
-NC,2013,0.952178915
-NC,2014,0.977037784
-NC,2015,0.982377466
-NC,2016,0.987399844
-NC,2017,0.965611665
-NC,2018,1.016095022
-NC,2019,1.002952117
-NC,2020,0.959211188
-NC,2021,0.998864028
-NC,2022,1.034915461
-NC,2023,1.032921323
-NC,2024,1.031544587
-NC,2025,1.037139461
-NC,2026,1.046116822
-NC,2027,1.056322026
-NC,2028,1.069280129
-NC,2029,1.081634168
-NC,2030,1.092463866
-NC,2031,1.10298949
-NC,2032,1.114187966
-NC,2033,1.123130701
-NC,2034,1.132035934
-NC,2035,1.143091884
-NC,2036,1.154212904
-NC,2037,1.165179426
-NC,2038,1.174914193
-NC,2039,1.184833088
-NC,2040,1.196190232
-NC,2041,1.208964804
-NC,2042,1.222431497
-NC,2043,1.236156023
-NC,2044,1.249575177
-NC,2045,1.262601983
-NC,2046,1.276499642
-NC,2047,1.290909515
-NC,2048,1.305784673
-NC,2049,1.32119393
-NC,2050,1.338617671
-SC,2010,1.000011633
-SC,2011,0.975882843
-SC,2012,0.943056809
-SC,2013,0.953025883
-SC,2014,0.989625442
-SC,2015,0.986129808
-SC,2016,0.9652528
-SC,2017,0.948534211
-SC,2018,0.99285427
-SC,2019,0.97645889
-SC,2020,0.93492418
-SC,2021,0.972652866
-SC,2022,1.01963029
-SC,2023,1.017636152
-SC,2024,1.016259416
-SC,2025,1.02185429
-SC,2026,1.030831651
-SC,2027,1.041036855
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-VA,2010,1.000031646
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-WV,2010,1.00001102
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-WV,2013,0.980408258
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diff --git a/inputs/load/demand_AEO_2026_baseline.csv b/inputs/load/demand_AEO_2026_baseline.csv
new file mode 100644
index 000000000..188d99134
--- /dev/null
+++ b/inputs/load/demand_AEO_2026_baseline.csv
@@ -0,0 +1,1969 @@
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diff --git a/inputs/load/demand_AEO_2026_high.csv b/inputs/load/demand_AEO_2026_high.csv
new file mode 100644
index 000000000..408762581
--- /dev/null
+++ b/inputs/load/demand_AEO_2026_high.csv
@@ -0,0 +1,1969 @@
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diff --git a/inputs/load/demand_AEO_2026_low.csv b/inputs/load/demand_AEO_2026_low.csv
new file mode 100644
index 000000000..894b2fc8f
--- /dev/null
+++ b/inputs/load/demand_AEO_2026_low.csv
@@ -0,0 +1,1969 @@
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diff --git a/inputs/national_generation/README.md b/inputs/national_generation/README.md
index 566df8491..abae1d860 100644
--- a/inputs/national_generation/README.md
+++ b/inputs/national_generation/README.md
@@ -43,7 +43,7 @@ For new gas plants, this is the code implementation:
 1. `inputs/scalars.csv`
     - `caa_gas_max_cf` = 0.40.
     This is the maximum capacity factor that new gas plants (CCs or CTs) can operate at without being regulated under Clean Air Act, Section 111, expressed as a fraction.
-2. `c_supplymodel.gms` - `eq_caa_max_cf` enforces the maximum capacity factor for new gas plants.
+2. `c_model.gms` - `eq_caa_max_cf` enforces the maximum capacity factor for new gas plants.
 
 
 For existing coal plants, this is the code implementation:
@@ -57,7 +57,7 @@ For existing coal plants, this is the code implementation:
     This is the emissions rate (metric tons CO<sub>2</sub> per MWh) equivalent to average emissions from a new coal-CCS plant, assuming 90% capture rate.
     The emissions rate from a new coal-CCS plant in ReEDS is 0.051956 metric tons CO<sub>2</sub> per MWh (see `emit_rate` parameter) which assumes 95% capture.
     For 90% capture, the emissions rate is double that or 0.1039 metric tons CO<sub>2</sub> per MWh, which we use as the standard.
-2. `input_processing/WriteHintage.py`
+2. `reeds/input_processing/WriteHintage.py`
     - Coal plants are binned at the unit level if `GSw_Clean_Air_Act=1` so that each coal unit can independently choose to retire or upgrade.
     - Coal plants maintain their exogenous retirement assumption, except after 2032, when the Clean Air Act regulations begin and coal can retire endogenously.
     For example, if the NEMS data states that a plant will retire in 2029, we maintain that assumption and that plant will retire in 2029.
@@ -68,7 +68,7 @@ For existing coal plants, this is the code implementation:
     - if `caa_coal_retire_year` is not in the set of years being modeled for this run, then set it to the first year that is modeled after `caa_coal_retire_year`.
     For example, if running 5 year solves, then instead of enforcing coal retirement in 2032, it will be enforced in 2035.
 
-4. `c_supplymodel.gms`
+4. `c_model.gms`
     - `eq_caa_rate_standard(st,t)` - this constraint enforces the rate-based emissions standard by setting the maximum coal emissions rate per state under Clean Air Act Section 111.
 
 ## Assumptions
diff --git a/inputs/national_generation/gen_mandate_tech_list.csv b/inputs/national_generation/gen_mandate_tech_list.csv
deleted file mode 100644
index f56f0f0d7..000000000
--- a/inputs/national_generation/gen_mandate_tech_list.csv
+++ /dev/null
@@ -1,62 +0,0 @@
-*i,RE,Nuclear,NuclearCCS,RE_NoCombust
-can-imports,1,1,1,1
-coal-CCS_mod,0,0,1,0
-coal-CCS_max,0,0,1,0
-coal-CCS-F1,0,0,1,0
-coal-CCS-F2,0,0,1,0
-coal-CCS-F3,0,0,1,0
-Coal-IGCC,0,0,0,0
-coal-new,0,0,0,0
-CoalOldScr,0,0,0,0
-CoalOldUns,0,0,0,0
-CofireNew,0,0,0,0
-CofireOld,0,0,0,0
-Gas-CC,0,0,0,0
-Gas-CC_H_1x1,0,0,0,0
-Gas-CC_H_2x1,0,0,0,0
-Gas-CC-CCS_mod,0,0,1,0
-Gas-CC-CCS_max,0,0,1,0
-Gas-CC_H_1x1-CCS_mod,0,0,1,0
-Gas-CC_H_1x1-CCS_max,0,0,1,0
-Gas-CC_H_2x1-CCS_mod,0,0,1,0
-Gas-CC_H_2x1-CCS_max,0,0,1,0
-Gas-CC-CCS-F1,0,0,1,0
-Gas-CC-CCS-F2,0,0,1,0
-Gas-CC-CCS-F3,0,0,1,0
-Gas-CT,0,0,0,0
-Gas-CT_aero,0,0,0,0
-o-g-s,0,0,0,0
-Nuclear,0,1,1,0
-Nuclear-SMR,0,1,1,0
-H2-CT,1,1,1,0
-H2-CC,1,1,1,0
-ng-fuel-cell,0,0,0,0
-biopower,1,1,1,0
-beccs_mod,1,1,1,0
-beccs_max,1,1,1,0
-geothermal,1,1,1,1
-Hydro,1,1,1,1
-lfill-gas,1,1,1,0
-battery_li,0,0,0,0
-pumped-hydro,0,0,0,0
-pumped-hydro-flex,0,0,0,0
-distpv,1,1,1,1
-upv_1*upv_10,1,1,1,1
-wind-ofs_1*wind-ofs_10,1,1,1,1
-wind-ons_1*wind-ons_10,1,1,1,1
-csp1_1*csp1_12,1,1,1,1
-csp2_1*csp2_12,1,1,1,1
-hydD,1,1,1,1
-hydND,1,1,1,1
-hydSD,1,1,1,1
-hydSND,1,1,1,1
-hydUD,1,1,1,1
-hydUND,1,1,1,1
-hydNPD,1,1,1,1
-hydNPND,1,1,1,1
-hydED,1,1,1,1
-hydEND,1,1,1,1
-egs_allkm_1*egs_allkm_10,1,1,1,1
-geohydro_allkm_1*geohydro_allkm_10,1,1,1,1
-egs_nearfield_1*egs_nearfield_10,1,1,1,1
-dr_shed_1*dr_shed_2,1,1,1,1
diff --git a/inputs/national_generation/nat_gen_tech_frac.csv b/inputs/national_generation/nat_gen_tech_frac.csv
new file mode 100644
index 000000000..d7402e91a
--- /dev/null
+++ b/inputs/national_generation/nat_gen_tech_frac.csv
@@ -0,0 +1,139 @@
+*i,RE,Nuclear,NuclearCCS,RE_NoCombust
+can-imports,1,1,1,1
+coal-CCS_mod,0,0,1,0
+coal-CCS_max,0,0,1,0
+coal-CCS-F1,0,0,1,0
+coal-CCS-F2,0,0,1,0
+coal-CCS-F3,0,0,1,0
+Coal-IGCC,0,0,0,0
+coal-new,0,0,0,0
+CoalOldScr,0,0,0,0
+CoalOldUns,0,0,0,0
+CofireNew,0,0,0,0
+CofireOld,0,0,0,0
+Gas-CC,0,0,0,0
+Gas-CC_H_1x1,0,0,0,0
+Gas-CC_H_2x1,0,0,0,0
+Gas-CC-CCS_mod,0,0,1,0
+Gas-CC-CCS_max,0,0,1,0
+Gas-CC_H_1x1-CCS_mod,0,0,1,0
+Gas-CC_H_1x1-CCS_max,0,0,1,0
+Gas-CC_H_2x1-CCS_mod,0,0,1,0
+Gas-CC_H_2x1-CCS_max,0,0,1,0
+Gas-CC-CCS-F1,0,0,1,0
+Gas-CC-CCS-F2,0,0,1,0
+Gas-CC-CCS-F3,0,0,1,0
+Gas-CT,0,0,0,0
+Gas-CT_aero,0,0,0,0
+o-g-s,0,0,0,0
+Nuclear,0,1,1,0
+Nuclear-SMR,0,1,1,0
+H2-CT,1,1,1,0
+H2-CC,1,1,1,0
+ng-fuel-cell,0,0,0,0
+biopower,1,1,1,0
+beccs_mod,1,1,1,0
+beccs_max,1,1,1,0
+geothermal,1,1,1,1
+Hydro,1,1,1,1
+lfill-gas,1,1,1,0
+battery_li,0,0,0,0
+pumped-hydro,0,0,0,0
+pumped-hydro-flex,0,0,0,0
+distpv,1,1,1,1
+upv_1,1,1,1,1
+upv_2,1,1,1,1
+upv_3,1,1,1,1
+upv_4,1,1,1,1
+upv_5,1,1,1,1
+upv_6,1,1,1,1
+upv_7,1,1,1,1
+upv_8,1,1,1,1
+upv_9,1,1,1,1
+upv_10,1,1,1,1
+wind-ofs_1,1,1,1,1
+wind-ofs_2,1,1,1,1
+wind-ofs_3,1,1,1,1
+wind-ofs_4,1,1,1,1
+wind-ofs_5,1,1,1,1
+wind-ofs_6,1,1,1,1
+wind-ofs_7,1,1,1,1
+wind-ofs_8,1,1,1,1
+wind-ofs_9,1,1,1,1
+wind-ofs_10,1,1,1,1
+wind-ons_1,1,1,1,1
+wind-ons_2,1,1,1,1
+wind-ons_3,1,1,1,1
+wind-ons_4,1,1,1,1
+wind-ons_5,1,1,1,1
+wind-ons_6,1,1,1,1
+wind-ons_7,1,1,1,1
+wind-ons_8,1,1,1,1
+wind-ons_9,1,1,1,1
+wind-ons_10,1,1,1,1
+csp1_1,1,1,1,1
+csp1_2,1,1,1,1
+csp1_3,1,1,1,1
+csp1_4,1,1,1,1
+csp1_5,1,1,1,1
+csp1_6,1,1,1,1
+csp1_7,1,1,1,1
+csp1_8,1,1,1,1
+csp1_9,1,1,1,1
+csp1_10,1,1,1,1
+csp1_11,1,1,1,1
+csp1_12,1,1,1,1
+csp2_1,1,1,1,1
+csp2_2,1,1,1,1
+csp2_3,1,1,1,1
+csp2_4,1,1,1,1
+csp2_5,1,1,1,1
+csp2_6,1,1,1,1
+csp2_7,1,1,1,1
+csp2_8,1,1,1,1
+csp2_9,1,1,1,1
+csp2_10,1,1,1,1
+csp2_11,1,1,1,1
+csp2_12,1,1,1,1
+egs_allkm_1,1,1,1,1
+egs_allkm_2,1,1,1,1
+egs_allkm_3,1,1,1,1
+egs_allkm_4,1,1,1,1
+egs_allkm_5,1,1,1,1
+egs_allkm_6,1,1,1,1
+egs_allkm_7,1,1,1,1
+egs_allkm_8,1,1,1,1
+egs_allkm_9,1,1,1,1
+egs_allkm_10,1,1,1,1
+geohydro_allkm_1,1,1,1,1
+geohydro_allkm_2,1,1,1,1
+geohydro_allkm_3,1,1,1,1
+geohydro_allkm_4,1,1,1,1
+geohydro_allkm_5,1,1,1,1
+geohydro_allkm_6,1,1,1,1
+geohydro_allkm_7,1,1,1,1
+geohydro_allkm_8,1,1,1,1
+geohydro_allkm_9,1,1,1,1
+geohydro_allkm_10,1,1,1,1
+egs_nearfield_1,1,1,1,1
+egs_nearfield_2,1,1,1,1
+egs_nearfield_3,1,1,1,1
+egs_nearfield_4,1,1,1,1
+egs_nearfield_5,1,1,1,1
+egs_nearfield_6,1,1,1,1
+egs_nearfield_7,1,1,1,1
+egs_nearfield_8,1,1,1,1
+egs_nearfield_9,1,1,1,1
+egs_nearfield_10,1,1,1,1
+dr_shed_1,1,1,1,1
+dr_shed_2,1,1,1,1
+hydD,1,1,1,1
+hydND,1,1,1,1
+hydSD,1,1,1,1
+hydSND,1,1,1,1
+hydUD,1,1,1,1
+hydUND,1,1,1,1
+hydNPD,1,1,1,1
+hydNPND,1,1,1,1
+hydED,1,1,1,1
+hydEND,1,1,1,1
diff --git a/inputs/scalars.csv b/inputs/scalars.csv
index 5822d0b8d..fc8a69ba6 100644
--- a/inputs/scalars.csv
+++ b/inputs/scalars.csv
@@ -3,7 +3,7 @@ caa_coal_retire_year,2032,"--year-- year in which coal capacity is forced to eit
 caa_first_year,2024,"--year-- first year in which the emissions requirements under Clean Air Act, Section 111 are active"
 caa_rate_emis_standard,0.1039,"--metric tons CO2 per MWh-- rate equivalent to average emissions from a new coal-CCS plant, assuming 90% capture rate. Emissions rate from new coal-CCS plant is 0.051956 metric tons CO2 per MWh (see emit_rate) which assumes 95% capture. For 90% capture, the emissions rate is double that or 0.1039 metric tons CO2 per MWh."
 caa_gas_max_cf,0.40,"--fraction-- maximum cf that new gas plants (CCs or CTs) can operate at without being regulated under Clean Air Act, Section 111"
-co2_capture_incentive_length,12,'--years-- length for the co2 captured incentive to be extended in d_solveoneyear if an upgrade occurs
+co2_capture_incentive_length,12,'--years-- length for the co2 captured incentive to be extended in 4_post_solve_adjustments.gms if an upgrade occurs
 co2_capture_incentive_last_year_,2038,'--year-- last year the co2 captured incentive is available
 co2_emissions_2022,1539.251,"--million metric tons CO2-- 2022 emissions (used for tax credit phaseout)"
 coal_fom_adj,97.05,"--2004$/MW-year-- FOM cost annual escalation factor for coal. The product of this number and the coal plant's age is added to the plant's FOM cost. See page 17 of Assumptions to the Annual Energy Outlook 2025: Electricity Market Module."
@@ -44,6 +44,7 @@ h2_ptc_temporal_match_year,2030,"--year-- year in which the h2 production incent
 h2_storage_area_threshold,0.01,"--fraction-- fraction of a model region's land area that must overlap with the collective area of H2 storage sites of a given type for the H2 storage type to be considered available in the model region"
 h2_storage_duration,240,"--hours-- hours of H2 storage capacity associated with a given invesment in H2 storage capacity. Based on relationship in SERA of 10 days storage for a given tons/hour storage inflow (provided by Paige Jadun)."
 h2_tranloss,0.005,"--fraction-- H2 transmission losses for regional pipeline trade; based on DOE targets at https://www.energy.gov/eere/fuelcells/doe-technical-targets-hydrogen-delivery"
+hintage_unit_number,300,"--integer-- Number of hintages to use when numhintage is 'unit' or 'group'"
 hurdle_rate_floor,0.01,"--2004$/MWh-- lower bound for transmission hurdle rates (used to prevent degeneracy between transmission losses and curtailment)"
 ilr_dist,1.21,"--MW_dc/MW_ac-- inverter-loading ratio for distpv"
 ilr_utility,1.34,"--MW_dc/MW_ac-- inverter-loading ratio for UPV and DUPV"
@@ -52,6 +53,8 @@ interconnection_start,2025,"year that interconnection queue data starts"
 inverter_efficiency,0.98,"--fraction-- one-way efficiency of AC-DC or DC-AC conversion"
 lb_per_tonne,2204.62,"--lb/metric ton-- pound per metric ton"
 maxage_default,100,"--years-- maximum age for technogies (default if no entry in maxage.csv)"
+min_co2_spurline_miles,20,"--miles-- minimum distance for a CO2 spur line (used to provide a floor for pipeline distances in r_cs_distance)"
+MCS_seed,0,"global seed for sampling in Monte Carlo analysis (see mcs_sampler.py)"
 minCF_nuclear_flex,0.06,"--fraction-- fleet mincf for nuclear when using flexible nuclear"
 minloadfrac_coal,0.5,"--fraction-- coal mingen"
 minloadfrac_csp,0.15,"--fraction-- CSP mingen"
@@ -60,11 +63,12 @@ minbatteryduration,0.2,"--hours-- minimum storage duration for li-ion battery"
 molWeightCH4,16.043,"--g/mol--molecular weight of CH4"
 molWeightCO2,44.009,"--g/mol--molecular weight of CO2"
 nat_beta_energy,0.1276,"--$/MMBtu per Quad-- beta value for national natural gas supply curves; see documentation for how value is calculated"
-nat_beta_nonenergy,0.1352,"--$/MMBtu per Quad-- beta value for national natural gas supply curves; see documentation for how value is calculated"
+nat_beta_nonenergy,0.078702,"--$/MMBtu per Quad-- beta value for national natural gas supply curves; see documentation for how value is calculated"
 normfuelbinmax,1.4,"top cutoff for natural gas supply curve"
 normfuelbinmin,0.6,"bottom cutoff for natural gas supply curve"
 nuke_fom_adj,13261.21,"--2004$/MW-- FOM cost escalation factor for nuclear. This is added to the FOM cost of nuclear plants that are older than the scalar nuke_fom_adj_age_threshold. See page 17 of Assumptions to the Annual Energy Outlook 2025: Electricity Market Module."
 nuke_fom_adj_age_threshold,50,"Age beyond which the scalar nuke_fom_adj is added to the FOM cost of nuclear plants. See page 17 of Assumptions to the Annual Energy Outlook 2025: Electricity Market Module."
+power_factor_ac,0.95,"--fraction-- Power factor for AC transmission (used to convert MVA to MW)"
 reduced_resource_frac,0.5,"--fraction-- fraction by which to reduce resource when Sw_ReducedResource=1"
 reg_energy_frac,0.15,"--fraction-- fraction of regulating reserves that produces energy. from Page 7 (pdf page 16) of https://www.nlr.gov/docs/fy14osti/60568.pdf"
 RGGI_start_yr,2012,"--year-- RGGI start year"
@@ -79,10 +83,6 @@ this_year,2025,"--year-- the year it actually is in real life (CE)"
 topfuelbinwidth,2,"top fuel bin width"
 tranloss_permile_ac,0.00010,"--fraction per mile-- AC transmission losses for regional trade"
 tranloss_permile_dc,0.00005,"--fraction per mile-- DC transmission losses for regional trade"
-trans_contingency_level_energy,0,"--integer-- n-x contingency level to use for initial energy trading limits (0 means n-0)"
-trans_contingency_level_prm,0,"--integer-- n-x contingency level to use for initial capacity (PRM) trading limits (1 means n-1)"
-trans_contingency_level_transgroup,1,"--integer-- n-x contingency level to use for initial transmission interface group flow constraint"
-trans_contingency_level_itlgrp,0,"--integer-- n-x contingency level to use for initial transmission interface flow constraint between ba/aggregs"
 trans_crp,40,"--years-- transmission capital recovery period (financial lifetime / evaluation period). Applied to transmission lines, AC/DC converters, and substations."
 trans_fom_frac,0.015,"--fraction-- Transmission FOM cost expressed as fraction of upfront cost [https://publications.jrc.ec.europa.eu/repository/handle/JRC92496]"
 trans_fom_region_mult,0,"--boolean-- indicate whether to use regional multipliers on transmission FOM (1) or just the base cost (0)"
diff --git a/inputs/sets/README.md b/inputs/sets/README.md
index edfda23eb..7384cc99e 100644
--- a/inputs/sets/README.md
+++ b/inputs/sets/README.md
@@ -1,4 +1,48 @@
 # Sets
-## Notes
-* These files can have comment lines but remember to use '*' (for GAMS) instead of '#'
-* Don't use * for element expansion
+
+## Formatting guidelines
+
+- Primary sets (those that define elements that are not subsets of other sets):
+  - No header column
+  - One element per line
+  - No element-wise comments; each line should contain only the element
+- Subsets (groups of elements from other sets, either 1-dimensional or multidimensional):
+  - Include a header column specifying the relevant primary sets
+  - The header column should start with a `*`
+    - Even 1-dimensional subsets should have a header column.
+    So if the set `food` has elements `[apple, banana, cauliflower]`, the subset `fruit(food)` (specified by `fruit.csv`) has the following lines:
+      - `*food`
+      - `apple`
+      - `banana`
+- Don't use * or # for element expansion in GAMS
+- Don't use * for full-line comments; only use it for the first (header) row in subset definitions
+
+## Set-defining files
+
+- `ctt.csv`: cooling technology types
+  - `o`: once through
+  - `r`: recirculating
+  - `d`: dry cooled
+  - `p`: pond cooled
+  - `n`: no cooling (or generic placeholder)
+- `sc_cat.csv`: resource supply curve data categories
+  - `cap`: power capacity available [MW]
+  - `cost`: total supply curve cost [\$/MW]
+  - `cost_trans`: transmission (spur, point-of-interconnection, and reinforcement) component of supply curve cost [\$/MW]
+  - `cost_cap`: economies of scale, land cost, and other modifier components of supply curve cost [\$/MW]
+- `wst.csv`: water source type
+  - `fsu`: fresh surface water that is unappropriated
+  - `fsa`: fresh surface water that is appropriated
+  - `fsl`: fresh surface lake
+  - `fg`: fresh groundwater
+  - `sg`: brackish or saline groundwater
+  - `ss`: saline surface water
+  - `ww`: wastewater effluent
+
+## Special-case files
+
+- `_aliases.csv`: aliases (extra names for the same set) used in GAMS
+  - Aliases of primary sets should be added here
+  - Aliases of sets defined in `b_inputs.gms` (e.g., `h`→`hh`) should instead be defined in GAMS after the set definition
+- `_pcat.csv`: prescribed capacity categories
+  - The `pcat` set in GAMS (defined in `writecapdat.py`) includes the members of the `i` set; this file includes only the *extra* elements on top of the `i` set
diff --git a/inputs/sets/_aliases.csv b/inputs/sets/_aliases.csv
new file mode 100644
index 000000000..223ae50a5
--- /dev/null
+++ b/inputs/sets/_aliases.csv
@@ -0,0 +1,26 @@
+*parent,alias
+allh,allhh
+allh,allhhh
+allszn,allsznn
+allt,alltt
+cendiv,cendiv2
+dummy,adummy
+fuelbin,afuelbin
+gb,gbb
+i,ii
+i,iii
+itlgrp,itlgrpp
+nercr,nercrr
+pcat,ppcat
+r,n
+r,nn
+r,rr
+rscbin,arscbin
+st,ast
+t,tt
+t,ttt
+tg,tgg
+transgrp,transgrpp
+trtype,intype
+trtype,outtype
+v,vv
diff --git a/inputs/sets/_pcat.csv b/inputs/sets/_pcat.csv
new file mode 100644
index 000000000..d8a5d237d
--- /dev/null
+++ b/inputs/sets/_pcat.csv
@@ -0,0 +1,8 @@
+upv
+pvb
+wind-ons
+wind-ofs
+csp-ws
+geohydro_allkm
+egs_allkm
+egs_nearfield
diff --git a/inputs/sets/ctt.csv b/inputs/sets/ctt.csv
index b7ab9e4e7..e0c031fed 100644
--- a/inputs/sets/ctt.csv
+++ b/inputs/sets/ctt.csv
@@ -1,5 +1,5 @@
-o "once through",
-r "recirculating",
-d "dry cooled",
-p "pond cooled",
-n "no cooling (or generic placeholder)"
+o
+r
+d
+p
+n
diff --git a/inputs/sets/h2_stor.csv b/inputs/sets/h2_stor.csv
index 8716b4743..f353c7cf7 100644
--- a/inputs/sets/h2_stor.csv
+++ b/inputs/sets/h2_stor.csv
@@ -1,4 +1,4 @@
-*h2_stor
+*h2_st
 h2_storage_saltcavern
 h2_storage_hardrock
 h2_storage_undergroundpipe
diff --git a/inputs/sets/i.csv b/inputs/sets/i.csv
index 8ef1bc179..43f2dd162 100644
--- a/inputs/sets/i.csv
+++ b/inputs/sets/i.csv
@@ -5,7 +5,6 @@ beccs_max
 can-imports
 coal-CCS_mod
 coal-CCS_max
-* flexible CCS technology options that use coal as fuel
 coal-CCS-F1
 coal-CCS-F2
 coal-CCS-F3
@@ -26,7 +25,6 @@ Gas-CC_H_1x1-CCS_mod
 Gas-CC_H_1x1-CCS_max
 Gas-CC_H_2x1-CCS_mod
 Gas-CC_H_2x1-CCS_max
-* flexible CCS technology options that use natural gas as fuel
 Gas-CC-CCS-F1
 Gas-CC-CCS-F2
 Gas-CC-CCS-F3
@@ -215,17 +213,11 @@ egs_nearfield_7
 egs_nearfield_8
 egs_nearfield_9
 egs_nearfield_10
-* consuming technologies
-* electrolyzer == h2 production from electricity
 electrolyzer
-* smr = steam methane reforming
 smr
 smr_ccs
-* dac == direct air capture
 dac
-* dac_gas == direct air capture fueled by natural gas
 dac_gas
-* upgrade technologies
 Gas-CT_H2-CT
 Gas-CC_H2-CC
 CoalOldUns_CoalOldScr
@@ -252,4 +244,4 @@ CofireNew_coal-CCS_max
 CofireOld_coal-CCS_max
 hydEND_hydED
 hydED_pumped-hydro
-hydED_pumped-hydro-flex
\ No newline at end of file
+hydED_pumped-hydro-flex
diff --git a/inputs/sets/i_h2_ptc_gen.csv b/inputs/sets/i_h2_ptc_gen.csv
index 73f7da7d1..393ef407c 100644
--- a/inputs/sets/i_h2_ptc_gen.csv
+++ b/inputs/sets/i_h2_ptc_gen.csv
@@ -1,3 +1,4 @@
+*i
 Gas-CC-CCS_mod
 Gas-CC-CCS_max
 Gas-CC_H_1x1-CCS_mod
diff --git a/inputs/sets/i_subtech.csv b/inputs/sets/i_subtech.csv
index fabf8237a..cc1abc33b 100755
--- a/inputs/sets/i_subtech.csv
+++ b/inputs/sets/i_subtech.csv
@@ -9,6 +9,7 @@ COMBINED_CYCLE
 COMBUSTION_TURBINE
 FUEL_CELL
 CONV
+TEMP_DERATE
 CCS
 CCS_MOD
 CCS_MAX
diff --git a/inputs/sets/pcat.csv b/inputs/sets/pcat.csv
deleted file mode 100644
index d2696399f..000000000
--- a/inputs/sets/pcat.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-*seldom-used pound/hashtag populates
-*elements of this set with the indicated set
-#i
-upv
-pvb
-wind-ons
-wind-ofs
-csp-ws
-geohydro_allkm
-egs_allkm
-egs_nearfield
\ No newline at end of file
diff --git a/inputs/sets/sc_cat.csv b/inputs/sets/sc_cat.csv
index 3395aaebc..d24c99c68 100644
--- a/inputs/sets/sc_cat.csv
+++ b/inputs/sets/sc_cat.csv
@@ -1,9 +1,4 @@
-* capacity available
 cap
-* total supply curve cost
 cost
-* transmission component of supply curve cost
 cost_trans
-* generation capacity (e.g. economies of scale) component of supply curve cost
 cost_cap
-
diff --git a/inputs/sets/wst.csv b/inputs/sets/wst.csv
index 10d94b7ff..549c1f5a1 100644
--- a/inputs/sets/wst.csv
+++ b/inputs/sets/wst.csv
@@ -1,7 +1,7 @@
-fsu "fresh surface water that is unappropriated (formerly Unappropriated)",
-fsa "fresh surface water that is appropriated (formerly Appropriated)",
-fsl "fresh surface lake",
-fg  "fresh groundwater (formerly Potable Groundwater)",
-sg  "brackish or saline groundwater (formerly Brackish Groundwater)",
-ss  "saline surface water (new category)",
-ww  "wastewater effluent (formerly Wastewater)"
+fsu
+fsa
+fsl
+fg
+sg
+ss
+ww
diff --git a/inputs/sets/sw.csv b/inputs/sets/wst_surface.csv
similarity index 100%
rename from inputs/sets/sw.csv
rename to inputs/sets/wst_surface.csv
diff --git a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.cpg b/inputs/shapefiles/transmission_endpoints/transmission_endpoints.cpg
deleted file mode 100644
index cd89cb975..000000000
--- a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.cpg
+++ /dev/null
@@ -1 +0,0 @@
-ISO-8859-1
\ No newline at end of file
diff --git a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.dbf b/inputs/shapefiles/transmission_endpoints/transmission_endpoints.dbf
deleted file mode 100644
index 40f1ce2d6..000000000
Binary files a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.dbf and /dev/null differ
diff --git a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.prj b/inputs/shapefiles/transmission_endpoints/transmission_endpoints.prj
deleted file mode 100644
index a30e6f73c..000000000
--- a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.prj
+++ /dev/null
@@ -1 +0,0 @@
-PROJCS["North_America_Albers_Equal_Area_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-96.0],PARAMETER["Standard_Parallel_1",20.0],PARAMETER["Standard_Parallel_2",60.0],PARAMETER["Latitude_Of_Origin",40.0],UNIT["Meter",1.0]]
\ No newline at end of file
diff --git a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shp b/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shp
deleted file mode 100644
index 61cd5df41..000000000
--- a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shp
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a5f696cb260146f30d4dfd3065219a35545059dcfa2897aa472ff893803ae9ad
-size 3852
diff --git a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shx b/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shx
deleted file mode 100644
index a8a6d07c5..000000000
Binary files a/inputs/shapefiles/transmission_endpoints/transmission_endpoints.shx and /dev/null differ
diff --git a/inputs/state_policies/techs_banned.yaml b/inputs/state_policies/techs_banned.yaml
index feccb48cd..2241bfe1e 100644
--- a/inputs/state_policies/techs_banned.yaml
+++ b/inputs/state_policies/techs_banned.yaml
@@ -111,10 +111,8 @@ nuclear:
   - OR
   - CA
   - MN
-  - NJ
   - VT
   - MA
-  - CT
   - RI
   - ME
 o-g-s:
diff --git a/inputs/tech-subset-table.csv b/inputs/tech-subset-table.csv
index 2c5507ec3..8493a78c8 100644
--- a/inputs/tech-subset-table.csv
+++ b/inputs/tech-subset-table.csv
@@ -1,74 +1,74 @@
-,BIO,COAL,COAL_CCS,GAS,GAS_CC,GAS_CC_CCS,GAS_CT,COMBINED_CYCLE,COMBUSTION_TURBINE,FUEL_CELL,CONV,CCS,CCS_MOD,CCS_MAX,CCSFLEX,CCSFLEX_BYP,CCSFLEX_STO,CCSFLEX_DAC,BECCS,RE,VRE,RSC,WIND,ONSWIND,OFSWIND,UPV,distpv,PV,PVB,PVB1,PVB2,PVB3,CSP,CSP_STORAGE,CSP1,CSP2,CSP3,CSP4,STORAGE,STORAGE_HYBRID,STORAGE_STANDALONE,STORAGE_INTERDAY,THERMAL_STORAGE,BATTERY,DEMAND_FLEX,EVMC,EVMC_STORAGE,EVMC_SHAPE,COFIRE,HYDRO,HYDRO_D,HYDRO_ND,PSH,GEO,GEO_HYDRO,GEO_EGS,GEO_EGS_ALLKM,GEO_EGS_NF,GEO_BASE,GEO_EXTRA,CANADA,VRE_NO_CSP,VRE_UTILITY,VRE_DISTRIBUTED,NUCLEAR,OGS,CONSUME,H2,SMR,DAC,LFILL,H2_COMBUSTION,H2_CC,H2_CT,REFURBTECH,cf_tech,FOSSIL,BOILER,NONDISPATCH,DR_SHED
-can-imports,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,
-coal-CCS_mod,,YES,YES,,,,,,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-coal-CCS_max,,YES,YES,,,,,,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-coal-CCS-F1,,YES,YES,,,,,,,,YES,YES,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-coal-CCS-F2,,YES,YES,,,,,,,,YES,YES,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-coal-CCS-F3,,YES,YES,,,,,,,,YES,YES,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-Coal-IGCC,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-coal-new,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-CoalOldScr,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-CoalOldUns,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-CofireNew,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-CofireOld,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
-Gas-CC,,,,YES,YES,,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_1x1,,,,YES,YES,,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_2x1,,,,YES,YES,,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_1x1-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_1x1-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_2x1-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC_H_2x1-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC-CCS-F1,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC-CCS-F2,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CC-CCS-F3,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CT,,,,YES,,,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-Gas-CT_aero,,,,YES,,,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-ng-fuel-cell,,,,YES,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
-o-g-s,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,YES,YES,,
-Nuclear,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,YES,,
-Nuclear-SMR,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,YES,,
-H2-CT,,,,,,,,,YES,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,
-H2-CC,,,,,,,,YES,,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,
-biopower,YES,,,,,,,,,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
-geothermal,,,,,,,,,,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,
-beccs_mod,YES,,,,,,,,,,YES,YES,YES,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
-beccs_max,YES,,,,,,,,,,YES,YES,,YES,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
-Hydro,,,,,,,,,,,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-lfill-gas,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,
-battery_li,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-dr_shed_1*dr_shed_2,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,YES
-evmc_storage_1*evmc_storage_10,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,
-evmc_shape_1*evmc_shape_10,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,
-pumped-hydro,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,YES,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,
-pumped-hydro-flex,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,
-distpv,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,YES,
-upv_1*upv_10,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
-pvb1_1*pvb1_10,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
-pvb2_1*pvb2_10,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,,YES,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
-pvb3_1*pvb3_10,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,,,YES,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
-wind-ofs_1*wind-ofs_10,,,,,,,,,,,,,,,,,,,,YES,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
-wind-ons_1*wind-ons_10,,,,,,,,,,,,,,,,,,,,YES,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
-csp1_1*csp1_12,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,YES,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-csp2_1*csp2_12,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,YES,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-csp3_1*csp3_12,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-csp4_1*csp4_12,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydD,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydND,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
-hydSD,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydSND,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
-hydUD,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydUND,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
-hydNPD,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydNPND,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
-hydED,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
-hydEND,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
-electrolyzer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,
-smr,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,,,,,,,
-smr_ccs,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,,,,,,,
-dac,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,,,,,,,,,,
-dac_gas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,,,,,,,,,,
-egs_allkm_1*egs_allkm_10,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,YES,,,YES,,,,,,,,,,,,,,,,,,,,
-geohydro_allkm_1*geohydro_allkm_10,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,YES,,,,,,,,,,,,,,,,,,,,,
-egs_nearfield_1*egs_nearfield_10,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,
+,BIO,COAL,COAL_CCS,GAS,GAS_CC,GAS_CC_CCS,GAS_CT,COMBINED_CYCLE,COMBUSTION_TURBINE,FUEL_CELL,CONV,TEMP_DERATE,CCS,CCS_MOD,CCS_MAX,CCSFLEX,CCSFLEX_BYP,CCSFLEX_STO,CCSFLEX_DAC,BECCS,RE,VRE,RSC,WIND,ONSWIND,OFSWIND,UPV,distpv,PV,PVB,PVB1,PVB2,PVB3,CSP,CSP_STORAGE,CSP1,CSP2,CSP3,CSP4,STORAGE,STORAGE_HYBRID,STORAGE_STANDALONE,STORAGE_INTERDAY,THERMAL_STORAGE,BATTERY,DEMAND_FLEX,EVMC,EVMC_STORAGE,EVMC_SHAPE,COFIRE,HYDRO,HYDRO_D,HYDRO_ND,PSH,GEO,GEO_HYDRO,GEO_EGS,GEO_EGS_ALLKM,GEO_EGS_NF,GEO_BASE,GEO_EXTRA,CANADA,VRE_NO_CSP,VRE_UTILITY,VRE_DISTRIBUTED,NUCLEAR,OGS,CONSUME,H2,SMR,DAC,LFILL,H2_COMBUSTION,H2_CC,H2_CT,REFURBTECH,cf_tech,FOSSIL,BOILER,NONDISPATCH,DR_SHED
+can-imports,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,
+coal-CCS_mod,,YES,YES,,,,,,,,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+coal-CCS_max,,YES,YES,,,,,,,,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+coal-CCS-F1,,YES,YES,,,,,,,,YES,YES,YES,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+coal-CCS-F2,,YES,YES,,,,,,,,YES,YES,YES,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+coal-CCS-F3,,YES,YES,,,,,,,,YES,YES,YES,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+Coal-IGCC,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+coal-new,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+CoalOldScr,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+CoalOldUns,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+CofireNew,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+CofireOld,,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,
+Gas-CC,,,,YES,YES,,,YES,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_1x1,,,,YES,YES,,,YES,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_2x1,,,,YES,YES,,,YES,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_1x1-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_1x1-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_2x1-CCS_mod,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC_H_2x1-CCS_max,,,,YES,YES,YES,,YES,,,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC-CCS-F1,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC-CCS-F2,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CC-CCS-F3,,,,YES,YES,YES,,YES,,,YES,YES,YES,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CT,,,,YES,,,YES,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+Gas-CT_aero,,,,YES,,,YES,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+ng-fuel-cell,,,,YES,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,
+o-g-s,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,YES,YES,,
+Nuclear,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,YES,,
+Nuclear-SMR,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,YES,,
+H2-CT,,,,,,,,,YES,,YES,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,
+H2-CC,,,,,,,,YES,,,YES,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,
+biopower,YES,,,,,,,,,,YES,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
+geothermal,,,,,,,,,,,YES,YES,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,
+beccs_mod,YES,,,,,,,,,,YES,YES,YES,YES,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
+beccs_max,YES,,,,,,,,,,YES,YES,YES,,YES,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,
+Hydro,,,,,,,,,,,YES,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+lfill-gas,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,
+battery_li,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+dr_shed_1*dr_shed_2,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,YES
+evmc_storage_1*evmc_storage_10,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,
+evmc_shape_1*evmc_shape_10,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,
+pumped-hydro,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,YES,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pumped-hydro-flex,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,
+distpv,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,YES,
+upv_1*upv_10,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
+pvb1_1*pvb1_10,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,YES,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
+pvb2_1*pvb2_10,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,,YES,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
+pvb3_1*pvb3_10,,,,,,,,,,,,,,,,,,,,,,,YES,,,,,,,YES,,,YES,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,
+wind-ofs_1*wind-ofs_10,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
+wind-ons_1*wind-ons_10,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,YES,YES,,,,
+csp1_1*csp1_12,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,YES,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+csp2_1*csp2_12,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,YES,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+csp3_1*csp3_12,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+csp4_1*csp4_12,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,YES,YES,,,,YES,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydD,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydND,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
+hydSD,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydSND,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
+hydUD,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydUND,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
+hydNPD,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydNPND,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
+hydED,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,,
+hydEND,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,
+electrolyzer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,,,,,,,,,
+smr,,,,,,,,,,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,,,,,,,
+smr_ccs,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,YES,,,,,,,,,,,
+dac,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,,,,,,,,,,
+dac_gas,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,,YES,,,,,,,,,,
+egs_allkm_1*egs_allkm_10,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,YES,,,YES,,,,,,,,,,,,,,,,,,,,
+geohydro_allkm_1*geohydro_allkm_10,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,YES,,,,YES,,,,,,,,,,,,,,,,,,,,,
+egs_nearfield_1*egs_nearfield_10,,,,,,,,,,,,,,,,,,,,,YES,,YES,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,YES,,YES,,YES,YES,,,,,,,,,,,,,,,,,,,,,
diff --git a/inputs/transmission/README.md b/inputs/transmission/README.md
index cdb1fa8db..11921baa7 100644
--- a/inputs/transmission/README.md
+++ b/inputs/transmission/README.md
@@ -6,13 +6,33 @@
   The `osm_id` column gives the OpenStreetMap ID of the converter; for example, the "Miles City, MT" converter (with `osm_id = 137835349`) can be found at <https://www.openstreetmap.org/way/137835349>.
   This file is used to validate the interface-level B2B capacity for different spatial resolutions stored at `inputs/zones/{GSw_ZoneSet}/b2b.csv`.
 
+- `conductor_(ac|dc)*.csv`: Conductor and power rating assumptions for AC/DC transmission lines as a function of voltage from the MISO 2025 Transmission Cost Estimation Guide ([parent page](https://www.misoenergy.org/planning/transmission-planning/mtep), [description](https://cdn.misoenergy.org/MISO%20Transmission%20Cost%20Estimation%20Guide%20for%20MTEP25337433.pdf), [data workbook](https://cdn.misoenergy.org/MISO%20Transmission%20Cost%20Estimate%20Workbook%20for%20MTEP25547535.xlsx))
+  - `conductor_ac_acss.csv` and `conductor_dc.csv` use the conductors specified by the MISO guide.
+  AC lines use either ACSR or ACSS conductors depending on the voltage.
+  - `conductor_ac_acsr.csv` instead uses ACSR for all AC voltages.
+  Ampacities for 477 kcmil (Flicker) and 795 kcmil (Drake) ACSR conductors are taken from [Southwire](https://www.southwire.com/wire-cable/bare-aluminum-overhead-transmission-distribution/acsr/p/ALBARE6).
+
 - `cost_hurdle_country.csv`: Hurdle rate for transmission flows [\$/MWh] between USA/Canada and USA/Mexico.
 
 - `cost_hurdle_intra.csv`: Hurdle rate for transmission flows [\$/MWh] between ReEDS spatial hierarchy levels.
 
-- `hvdc_lines.csv`: Power capacity and start/end locations of [high-voltage direct current (HVDC) lines](https://en.wikipedia.org/wiki/List_of_HVDC_projects#North_America) in the USA.
+- `dollaryear.scv`: U.S. dollar year for cost-related input files
+
+- `hvdc_existing.csv`: Power capacity and start/end locations of [high-voltage direct current (HVDC) lines](https://en.wikipedia.org/wiki/List_of_HVDC_projects#North_America) in the USA.
 These lines are mapped to ReEDS zone interfaces during input processing.
 
+- `hvdc_planned-*.csv`: Individual planned transmission projects
+  - Files:
+    - `hvdc_planned-baseline`: Included in all runs
+      - TransWest Express: Planned online date and capacity from [CAISO 2026](https://www.caiso.com/documents/ceo-report-mar-2026.pdf); route from [TransWest Express](https://www.transwestexpress.net/about/maps.shtml)
+      - SunZia: Planned online date from [CAISO 2026](https://www.caiso.com/documents/apr-13-2026-informational-filing-of-effective-date-transmission-control-agreement-regarding-subscriber-participating-transmission-owners-sunzia-er25-169.pdf); capacity and converter type from [Hitachi](https://www.hitachienergy.com/news-and-events/customer-stories/sunzia-transmission-enabling-3-gw-of-renewable-power-across-the-u-s-southwest); endpoints from [OpenInfraMap](https://openstreetmap.org/way/1420870282)
+    - `hvdc_planned-NTP_MT`: Lines used in the "MT" scenario of the [NTP Study](https://www.energy.gov/oe/national-transmission-planning-study-0)
+    - `hvdc_planned-NTP_P2P`: Lines used in the "P2P" scenario of the [NTP Study](https://www.energy.gov/oe/national-transmission-planning-study-0)
+  - Columns:
+    - `year_online`: If set to 0, determined from `this_year` and `years_until_trans_longterm` in `inputs/scalars.csv`
+    - `trtype`: LCC or VSC (also accepts AC, but it is better to handle AC additions via the ITL calculation than to add their rated capacity directly)
+    - `certain`: If 1, the line MUST be built at the provided `MW` capacity in `year_online`; if 0, the line MAY be built at up to the provided `MW` capacity starting in `year_online`
+
 - `itl_config.yaml`: Configuration file for interface transfer limit (ITL) calculations using the [TSC](https://github.nrel.gov/ReEDS/TSC) model.
 Metadata only; not used directly in ReEDS.
 
@@ -36,22 +56,27 @@ Calculated using the [TSC](https://github.nrel.gov/pbrown/TSC) model as describe
     reeds.inputs.get_itls(GSw_ZoneSet=GSw_ZoneSet)
     ```
 
-- `rev_transmission_basecost.csv`: Base transmission costs (before terrain multipliers) used in reV.
-Sources for numeric values are:
-  - TEPPC: <https://www.wecc.org/Administrative/TEPPC_TransCapCostCalculator_E3_2019_Update.xlsx>
-  - SCE: <http://www.caiso.com/Documents/SCE2019DraftPerUnitCostGuide.xlsx>
-  - MISO: <https://cdn.misoenergy.org/20190212%20PSC%20Item%2005a%20Transmission%20Cost%20Estimation%20Guide%20for%20MTEP%202019_for%20review317692.pdf>
-    - A more recent guide with a working link (as of 20230227) is available at <https://cdn.misoenergy.org/Transmission%20Cost%20Estimation%20Guide%20for%20MTEP22337433.pdf>.
-  - Southeast: Private communication with a representative Southeastern utility
-
-- `transmission_capacity_future_(ba|county)_baseline.csv` (DEPRECATED): Historically installed (since 2010) and currently planned transmission capacity additions at 134-zone resolution.
-To be replaced with ITL-based estimates of the impacts of planned tranches of transmission system additions.
+- `newlinks_offshore_backbone.csv`: Candidate connections between offshore zones
+  - Similarly formatted files for candidate connections between offshore and coastal land-based zones are found at `inputs/zones/{GSw_ZoneSet}/newlinks_offshore_radial.csv`
 
-- `transmission_capacity_future_(ba|county)_{GSw_TransScen}.csv`: Available future routes for transmission capacity as specified by `GSw_TransScen`.
+- `transmission_cost_ac_500kv_z134.h5`: Example file illustrating the required format when using the transmission upgrade supply curve ([TSC](https://github.nrel.gov/ReEDS/TSC)) method for `GSw_ZoneSet = z134`
+  - The full method is not yet supported; when implemented, it will only be supported for a limited number of `GSw_ZoneSet` definitions
 
-- `transmission_cost_ac_500kv_(ba|county).csv` and `transmission_distance_(ba|county).csv`: Distance and cost for a representative transmission route between each pair of 134 US ReEDS zones, assuming a 500 kV single-circuit line.
-Routes are determined by the reV model using a least-cost-path algorithm accounting for terrain and land type multipliers.
-Costs represent the appropriate base cost from `rev_transmission_basecost.csv` multiplied by the appropriate terrain and land type multipliers for each 90m pixel crossed by the path.
-Endpoints are in inputs/shapefiles/transmission_endpoints and represent a point within the largest urban area in each of the 134 ReEDS zones.
+- `transmission_cost_distance.csv`: Cost [USD2024] and distance [miles] for greenfield interzonal single-circuit transmission lines of the specified polarity (AC or DC) and voltage [kV] between nodes of the specified zone hashes
+  - Node locations are described in `inputs/zones/README.md` and found in the `inputs/zones/{GSw_ZoneSet}/zonehash.csv` files
+  - Least-cost paths between nodes (and integrated land/terrain-dependent costs along those paths) are determined using the [reV Routing (reVRt) model](https://github.com/NatLabRockies/reVRt)
+  - Technical assumptions:
+    - The underlying cost model is built using the MISO 2025 Transmission Cost Estimation Guide ([parent page](https://www.misoenergy.org/planning/transmission-planning/mtep), [description](https://cdn.misoenergy.org/MISO%20Transmission%20Cost%20Estimation%20Guide%20for%20MTEP25337433.pdf), [data workbook](https://cdn.misoenergy.org/MISO%20Transmission%20Cost%20Estimate%20Workbook%20for%20MTEP25547535.xlsx))
+    - Costs for DC connections assume a 500 kV bipole architecture
+    - Costs for AC connections use interface-dependent voltage assumptions.
+    The voltage is given by the maximum voltage of an existing transmission line between the pair of zones defining the interface (using the same [NARIS](https://www.nrel.gov/docs/fy21osti/79224.pdf) dataset described above), with a floor of 138 kV.
+  - The MISO Transmission Cost Estimation Guide applies a 30% length adder to the straight-line distance between the endpoints of a candidate line to account for the "squiggliness" of line routes in practice.
+  Many of the least-cost routes from the reV model have a smaller squiggliness factor.
+  In `reeds/input_processing/transmission.py` (which is run at the beginning of each ReEDS run),
+  if the representative route between two zones has a squiggliness factor less than the user-provided `GSw_TransSquigglinessMin` switch (with a default of 1.3, matching the MISO guide),
+  the cost and distance of that route are scaled up by the ratio of (`GSw_TransSquigglinessMin` / (squiggliness of the least-cost route)),
+  such that every interzonal interface is represented by a line at least as squiggly as `GSw_TransSquigglinessMin`.
 
-- `transmission_cost_dc_(ba|county).csv`: Same as `transmission_cost_ac_500kv_(ba|county).csv` except assuming a 500 kV bipole DC line.
+- `transmission_cost_distance_lines.csv`: Similar to `transmission_cost_distance.csv`, but for individual existing and planned/possible HVDC lines, with latitude/longitude for start/end points instead of zone hashes
+  - Voltages are in kilovolts [kV]
+  - Costs and lengths (and underlying routes, not included in the file) are from the [reV Routing (reVRt) model](https://github.com/NatLabRockies/reVRt) and are not expected to exactly match the actual costs, lengths, or routes of existing lines
diff --git a/inputs/transmission/conductor_ac_acsr.csv b/inputs/transmission/conductor_ac_acsr.csv
new file mode 100644
index 000000000..c8a5d6e3d
--- /dev/null
+++ b/inputs/transmission/conductor_ac_acsr.csv
@@ -0,0 +1,9 @@
+voltage_kv,kcmil,conductor_type,conductor_quantity,amp
+69,477,ACSR,1,655
+115,795,ACSR,1,907
+138,795,ACSR,1,907
+161,795,ACSR,1,907
+230,795,ACSR,1,907
+345,795,ACSR,2,1814
+500,954,ACSR,3,3000
+765,795,ACSR,6,5000
diff --git a/inputs/transmission/conductor_ac_acss.csv b/inputs/transmission/conductor_ac_acss.csv
new file mode 100644
index 000000000..21043cd76
--- /dev/null
+++ b/inputs/transmission/conductor_ac_acss.csv
@@ -0,0 +1,9 @@
+voltage_kv,kcmil,conductor_type,conductor_quantity,amp
+69,477,ACSS,1,1175
+115,795,ACSS,1,1650
+138,795,ACSS,1,1650
+161,795,ACSS,1,1650
+230,795,ACSS,1,1650
+345,795,ACSS,2,3000
+500,954,ACSR,3,3000
+765,795,ACSR,6,5000
diff --git a/inputs/transmission/conductor_dc.csv b/inputs/transmission/conductor_dc.csv
new file mode 100644
index 000000000..e1e40d068
--- /dev/null
+++ b/inputs/transmission/conductor_dc.csv
@@ -0,0 +1,6 @@
+voltage_kv,kcmil,conductor_type,conductor_quantity,MW
+250,1590,ACSR,1,500
+400,1590,ACSR,2,1500
+500,1590,ACSR,2,2000
+600,1590,ACSR,2,2400
+640,795,ACSR,6,6000
diff --git a/inputs/transmission/dollaryear.csv b/inputs/transmission/dollaryear.csv
new file mode 100644
index 000000000..d7a36aee9
--- /dev/null
+++ b/inputs/transmission/dollaryear.csv
@@ -0,0 +1,3 @@
+filename,US_dollar_year
+transmission_cost_distance.csv,2024
+transmission_cost_distance_lines.csv,2024
diff --git a/inputs/transmission/hvdc_existing.csv b/inputs/transmission/hvdc_existing.csv
new file mode 100644
index 000000000..48500d071
--- /dev/null
+++ b/inputs/transmission/hvdc_existing.csv
@@ -0,0 +1,8 @@
+name,MW,year_online,trtype,certain,from_lon,from_lat,to_lon,to_lat
+pacific_dc_intertie,3100,1970,LCC,1,-121.11416667,45.59416667,-118.48916667,34.31083333
+square_butte,500,1977,LCC,1,-101.19583333,47.07166667,-92.29416667,46.77361111
+cu,1000,1979,LCC,1,-101.15638889,47.37333333,-93.81,45.11111111
+path_27,2400,1986,LCC,1,-112.58083333,39.50055556,-117.43722222,34.55111111
+cross_sound_cable,330,2002,LCC,1,-72.90222222,41.28666667,-72.8675,40.95916667
+neptune_cable,660,2007,LCC,1,-73.55111111,40.76055556,-74.35308333,40.47371667
+trans_bay_cable,400,2010,LCC,1,-121.89666667,38.03083333,-122.38583333,37.75472222
diff --git a/inputs/transmission/hvdc_lines.csv b/inputs/transmission/hvdc_lines.csv
deleted file mode 100644
index dca2fde1b..000000000
--- a/inputs/transmission/hvdc_lines.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-name,MW,from_lon,from_lat,to_lon,to_lat
-Pacific DC Intertie,3100,-121.11416667,45.59416667,-118.48916667,34.31083333
-Square Butte,500,-101.19583333,47.07166667,-92.29416667,46.77361111
-CU,1000,-101.15638889,47.37333333,-93.81,45.11111111
-Path 27,2400,-112.58083333,39.50055556,-117.43722222,34.55111111
-Cross Sound Cable,330,-72.90222222,41.28666667,-72.8675,40.95916667
-Neptune Cable,660,-73.55111111,40.76055556,-74.35308333,40.47371667
-Trans Bay Cable,400,-121.89666667,38.03083333,-122.38583333,37.75472222
diff --git a/inputs/transmission/hvdc_planned-NTP_MT.csv b/inputs/transmission/hvdc_planned-NTP_MT.csv
new file mode 100644
index 000000000..cbd7393f0
--- /dev/null
+++ b/inputs/transmission/hvdc_planned-NTP_MT.csv
@@ -0,0 +1,307 @@
+name,MW,year_online,trtype,certain,from_lon,from_lat,to_lon,to_lat
+z134:p1~~p2,100000,0,VSC,0,-121.52720518,47.80413219,-121.52605782,46.63558835
+z134:p1~~p4,100000,0,VSC,0,-121.52720518,47.80413219,-119.54845201,47.41580274
+z134:p2~~p3,100000,0,VSC,0,-121.52605782,46.63558835,-118.4775817,47.8997302
+z134:p2~~p4,100000,0,VSC,0,-121.52605782,46.63558835,-119.54845201,47.41580274
+z134:p2~~p5,100000,0,VSC,0,-121.52605782,46.63558835,-120.84858533,44.8758229
+z134:p2~~p14,100000,0,VSC,0,-121.52605782,46.63558835,-116.62286863,47.60393639
+z134:p3~~p4,100000,0,VSC,0,-118.4775817,47.8997302,-119.54845201,47.41580274
+z134:p3~~p5,100000,0,VSC,0,-118.4775817,47.8997302,-120.84858533,44.8758229
+z134:p3~~p14,100000,0,VSC,0,-118.4775817,47.8997302,-116.62286863,47.60393639
+z134:p3~~p17,100000,0,VSC,0,-118.4775817,47.8997302,-113.58090985,46.78287505
+z134:p5~~p6,100000,0,VSC,0,-120.84858533,44.8758229,-122.80414235,42.80374842
+z134:p5~~p7,100000,0,VSC,0,-120.84858533,44.8758229,-118.7982225,43.19385721
+z134:p5~~p14,100000,0,VSC,0,-120.84858533,44.8758229,-116.62286863,47.60393639
+z134:p5~~p15,100000,0,VSC,0,-120.84858533,44.8758229,-115.22499865,44.39350062
+z134:p6~~p7,100000,0,VSC,0,-122.80414235,42.80374842,-118.7982225,43.19385721
+z134:p6~~p8,100000,0,VSC,0,-122.80414235,42.80374842,-121.43547851,41.31103089
+z134:p6~~p9,100000,0,VSC,0,-122.80414235,42.80374842,-121.38102184,38.35087876
+z134:p7~~p8,100000,0,VSC,0,-118.7982225,43.19385721,-121.43547851,41.31103089
+z134:p7~~p12,100000,0,VSC,0,-118.7982225,43.19385721,-116.76501417,39.57265625
+z134:p7~~p15,100000,0,VSC,0,-118.7982225,43.19385721,-115.22499865,44.39350062
+z134:p8~~p9,100000,0,VSC,0,-121.43547851,41.31103089,-121.38102184,38.35087876
+z134:p8~~p12,100000,0,VSC,0,-121.43547851,41.31103089,-116.76501417,39.57265625
+z134:p9~~p10,100000,0,VSC,0,-121.38102184,38.35087876,-117.15992686,35.12105363
+z134:p9~~p12,100000,0,VSC,0,-121.38102184,38.35087876,-116.76501417,39.57265625
+z134:p10~~p11,100000,0,VSC,0,-117.15992686,35.12105363,-116.73493496,33.03408966
+z134:p10~~p12,100000,0,VSC,0,-117.15992686,35.12105363,-116.76501417,39.57265625
+z134:p10~~p13,100000,0,VSC,0,-117.15992686,35.12105363,-115.01366961,36.21387654
+z134:p10~~p27,100000,0,VSC,0,-117.15992686,35.12105363,-113.76209955,35.69784258
+z134:p10~~p28,100000,0,VSC,0,-117.15992686,35.12105363,-111.61025637,34.16709078
+z134:p12~~p13,100000,0,VSC,0,-116.76501417,39.57265625,-115.01366961,36.21387654
+z134:p12~~p15,100000,0,VSC,0,-116.76501417,39.57265625,-115.22499865,44.39350062
+z134:p12~~p25,100000,0,VSC,0,-116.76501417,39.57265625,-111.91422885,39.08215518
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diff --git a/inputs/transmission/hvdc_planned-NTP_P2P.csv b/inputs/transmission/hvdc_planned-NTP_P2P.csv
new file mode 100644
index 000000000..28eca9457
--- /dev/null
+++ b/inputs/transmission/hvdc_planned-NTP_P2P.csv
@@ -0,0 +1,196 @@
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+z134:p80~~p131,100000,0,LCC,0,-88.78740634,41.76755631,-71.79474674,42.25461052
+z134:p81~~p87,100000,0,LCC,0,-89.78738098,39.32261591,-89.93377714,32.30945125
+z134:p81~~p92,100000,0,LCC,0,-89.78738098,39.32261591,-86.34952583,35.85850386
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+z134:p81~~p99,100000,0,LCC,0,-89.78738098,39.32261591,-77.99964991,37.62459409
+z134:p81~~p112,100000,0,LCC,0,-89.78738098,39.32261591,-82.80095864,40.13748215
+z134:p81~~p127,100000,0,LCC,0,-89.78738098,39.32261591,-75.58732565,42.99934248
+z134:p85~~p92,100000,0,LCC,0,-92.02206131,34.67614755,-86.34952583,35.85850386
+z134:p85~~p94,100000,0,LCC,0,-92.02206131,34.67614755,-83.43553366,32.63997261
+z134:p85~~p101,100000,0,LCC,0,-92.02206131,34.67614755,-82.16153403,28.61064555
+z134:p85~~p103,100000,0,LCC,0,-92.02206131,34.67614755,-84.61125526,43.8762499
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+z134:p87~~p98,100000,0,LCC,0,-89.93377714,32.30945125,-77.99872674,35.44202442
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+z134:p92~~p105,100000,0,LCC,0,-86.34952583,35.85850386,-86.25403168,40.72629479
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+z134:p94~~p112,100000,0,LCC,0,-83.43553366,32.63997261,-82.80095864,40.13748215
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+z134:p98~~p112,100000,0,LCC,0,-77.99872674,35.44202442,-82.80095864,40.13748215
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+z134:p99~~p101,100000,0,LCC,0,-77.99964991,37.62459409,-82.16153403,28.61064555
+z134:p99~~p105,100000,0,LCC,0,-77.99964991,37.62459409,-86.25403168,40.72629479
+z134:p99~~p127,100000,0,LCC,0,-77.99964991,37.62459409,-75.58732565,42.99934248
+z134:p99~~p134,100000,0,LCC,0,-77.99964991,37.62459409,-69.23781577,45.35740733
+z134:p103~~p122,100000,0,LCC,0,-84.61125526,43.8762499,-76.28677225,40.53840825
+z134:p103~~p127,100000,0,LCC,0,-84.61125526,43.8762499,-75.58732565,42.99934248
+z134:p103~~p134,100000,0,LCC,0,-84.61125526,43.8762499,-69.23781577,45.35740733
+z134:p105~~p112,100000,0,LCC,0,-86.25403168,40.72629479,-82.80095864,40.13748215
+z134:p105~~p127,100000,0,LCC,0,-86.25403168,40.72629479,-75.58732565,42.99934248
+z134:p105~~p131,100000,0,LCC,0,-86.25403168,40.72629479,-71.79474674,42.25461052
+z134:p112~~p127,100000,0,LCC,0,-82.80095864,40.13748215,-75.58732565,42.99934248
+z134:p112~~p134,100000,0,LCC,0,-82.80095864,40.13748215,-69.23781577,45.35740733
+z134:p122~~p127,100000,0,LCC,0,-76.28677225,40.53840825,-75.58732565,42.99934248
+z134:p122~~p131,100000,0,LCC,0,-76.28677225,40.53840825,-71.79474674,42.25461052
+z134:p127~~p131,100000,0,LCC,0,-75.58732565,42.99934248,-71.79474674,42.25461052
+z134:p127~~p134,100000,0,LCC,0,-75.58732565,42.99934248,-69.23781577,45.35740733
diff --git a/inputs/transmission/hvdc_planned-baseline.csv b/inputs/transmission/hvdc_planned-baseline.csv
new file mode 100644
index 000000000..95c1dc73f
--- /dev/null
+++ b/inputs/transmission/hvdc_planned-baseline.csv
@@ -0,0 +1,3 @@
+name,MW,year_online,trtype,certain,from_lon,from_lat,to_lon,to_lat
+sunzia,3000,2026,VSC,1,-105.5939222,34.3491456,-111.5425066,32.8669515
+transwestexpress,3000,2032,VSC,1,-107.176147,41.747242,-112.590781,39.541825
diff --git a/inputs/transmission/newlinks_offshore_backbone.csv b/inputs/transmission/newlinks_offshore_backbone.csv
new file mode 100644
index 000000000..259ac6008
--- /dev/null
+++ b/inputs/transmission/newlinks_offshore_backbone.csv
@@ -0,0 +1,26 @@
+r,rr
+oWA,oOR_N
+oOR_N,oOR_S
+oOR_S,oCA_NNN
+oCA_NNN,oCA_NN
+oCA_NN,oCA_N
+oCA_N,oCA_S
+oTX,oLA
+oLA,oMS
+oMS,oAL
+oAL,oFL_W
+oFL_E,oGA
+oGA,oSC
+oSC,oNC_S
+oNC_S,oNC_N
+oNC_N,oVA
+oVA,oMD
+oMD,oDE
+oDE,oNJ
+oNJ,oNY
+oNY,oCT
+oCT,oRI
+oRI,oMA
+oMA,oNH
+oNY,oMA
+oNH,oME
diff --git a/inputs/transmission/rev_transmission_basecost.csv b/inputs/transmission/rev_transmission_basecost.csv
deleted file mode 100644
index a635e3071..000000000
--- a/inputs/transmission/rev_transmission_basecost.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-Voltage,Capacity,TEPPC,SCE,MISO,Southeast
-kV,MW,USD2004perMWmile,USD2004perMWmile,USD2004perMWmile,USD2004perMWmile
-69ACsingle,102,7137.35,11057.55,9105.22,5941.27
-138ACsingle,205,4260.23,7523.91,5221.91,3553.52
-230ACsingle,400,2922.49,5613.45,3135.59,2900.44
-500ACsingle,1500,1506.22,2356.88,1375.20,2001.36
-500DCbipole,3000,623.10,975.01,568.90,827.93
\ No newline at end of file
diff --git a/inputs/transmission/transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csv b/inputs/transmission/transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csv
deleted file mode 100644
index 4f1327976..000000000
--- a/inputs/transmission/transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csv
+++ /dev/null
@@ -1,196 +0,0 @@
-transmission_capacity_future_LCC_1000miles_demand1_wind1_subferc_20230629.csvr,rr,status,trtype,t,MW
-p1,p9,Possible,LCC,0,100000
-p1,p18,Possible,LCC,0,100000
-p1,p25,Possible,LCC,0,100000
-p5,p9,Possible,LCC,0,100000
-p5,p13,Possible,LCC,0,100000
-p5,p15,Possible,LCC,0,100000
-p5,p18,Possible,LCC,0,100000
-p5,p25,Possible,LCC,0,100000
-p9,p13,Possible,LCC,0,100000
-p9,p15,Possible,LCC,0,100000
-p9,p25,Possible,LCC,0,100000
-p9,p28,Possible,LCC,0,100000
-p10,p25,Possible,LCC,0,100000
-p10,p31,Possible,LCC,0,100000
-p13,p18,Possible,LCC,0,100000
-p13,p24,Possible,LCC,0,100000
-p13,p31,Possible,LCC,0,100000
-p13,p61,Possible,LCC,0,100000
-p15,p24,Possible,LCC,0,100000
-p15,p25,Possible,LCC,0,100000
-p15,p31,Possible,LCC,0,100000
-p18,p25,Possible,LCC,0,100000
-p18,p33,Possible,LCC,0,100000
-p18,p41,Possible,LCC,0,100000
-p18,p43,Possible,LCC,0,100000
-p24,p25,Possible,LCC,0,100000
-p24,p28,Possible,LCC,0,100000
-p24,p41,Possible,LCC,0,100000
-p24,p43,Possible,LCC,0,100000
-p24,p50,Possible,LCC,0,100000
-p24,p63,Possible,LCC,0,100000
-p24,p76,Possible,LCC,0,100000
-p24,p80,Possible,LCC,0,100000
-p25,p28,Possible,LCC,0,100000
-p25,p31,Possible,LCC,0,100000
-p25,p33,Possible,LCC,0,100000
-p25,p37,Possible,LCC,0,100000
-p25,p38,Possible,LCC,0,100000
-p25,p41,Possible,LCC,0,100000
-p25,p45,Possible,LCC,0,100000
-p25,p50,Possible,LCC,0,100000
-p25,p53,Possible,LCC,0,100000
-p25,p54,Possible,LCC,0,100000
-p25,p61,Possible,LCC,0,100000
-p28,p53,Possible,LCC,0,100000
-p28,p61,Possible,LCC,0,100000
-p31,p33,Possible,LCC,0,100000
-p31,p41,Possible,LCC,0,100000
-p31,p50,Possible,LCC,0,100000
-p31,p63,Possible,LCC,0,100000
-p31,p87,Possible,LCC,0,100000
-p33,p37,Possible,LCC,0,100000
-p33,p38,Possible,LCC,0,100000
-p33,p45,Possible,LCC,0,100000
-p33,p53,Possible,LCC,0,100000
-p33,p54,Possible,LCC,0,100000
-p33,p61,Possible,LCC,0,100000
-p33,p80,Possible,LCC,0,100000
-p33,p81,Possible,LCC,0,100000
-p33,p85,Possible,LCC,0,100000
-p37,p41,Possible,LCC,0,100000
-p37,p50,Possible,LCC,0,100000
-p37,p80,Possible,LCC,0,100000
-p37,p92,Possible,LCC,0,100000
-p37,p103,Possible,LCC,0,100000
-p37,p112,Possible,LCC,0,100000
-p38,p50,Possible,LCC,0,100000
-p38,p63,Possible,LCC,0,100000
-p38,p76,Possible,LCC,0,100000
-p38,p80,Possible,LCC,0,100000
-p38,p87,Possible,LCC,0,100000
-p38,p92,Possible,LCC,0,100000
-p38,p103,Possible,LCC,0,100000
-p38,p112,Possible,LCC,0,100000
-p41,p53,Possible,LCC,0,100000
-p41,p61,Possible,LCC,0,100000
-p41,p80,Possible,LCC,0,100000
-p41,p81,Possible,LCC,0,100000
-p41,p85,Possible,LCC,0,100000
-p41,p94,Possible,LCC,0,100000
-p41,p105,Possible,LCC,0,100000
-p43,p53,Possible,LCC,0,100000
-p43,p54,Possible,LCC,0,100000
-p43,p80,Possible,LCC,0,100000
-p43,p81,Possible,LCC,0,100000
-p43,p85,Possible,LCC,0,100000
-p43,p105,Possible,LCC,0,100000
-p45,p50,Possible,LCC,0,100000
-p45,p63,Possible,LCC,0,100000
-p45,p80,Possible,LCC,0,100000
-p45,p87,Possible,LCC,0,100000
-p45,p92,Possible,LCC,0,100000
-p45,p94,Possible,LCC,0,100000
-p45,p103,Possible,LCC,0,100000
-p45,p112,Possible,LCC,0,100000
-p50,p61,Possible,LCC,0,100000
-p50,p80,Possible,LCC,0,100000
-p50,p81,Possible,LCC,0,100000
-p50,p85,Possible,LCC,0,100000
-p50,p94,Possible,LCC,0,100000
-p50,p105,Possible,LCC,0,100000
-p53,p58,Possible,LCC,0,100000
-p53,p63,Possible,LCC,0,100000
-p53,p76,Possible,LCC,0,100000
-p53,p80,Possible,LCC,0,100000
-p53,p87,Possible,LCC,0,100000
-p53,p92,Possible,LCC,0,100000
-p53,p94,Possible,LCC,0,100000
-p53,p103,Possible,LCC,0,100000
-p53,p112,Possible,LCC,0,100000
-p54,p58,Possible,LCC,0,100000
-p54,p63,Possible,LCC,0,100000
-p54,p76,Possible,LCC,0,100000
-p54,p80,Possible,LCC,0,100000
-p54,p87,Possible,LCC,0,100000
-p54,p92,Possible,LCC,0,100000
-p54,p94,Possible,LCC,0,100000
-p54,p103,Possible,LCC,0,100000
-p54,p112,Possible,LCC,0,100000
-p58,p61,Possible,LCC,0,100000
-p58,p80,Possible,LCC,0,100000
-p58,p81,Possible,LCC,0,100000
-p58,p94,Possible,LCC,0,100000
-p58,p98,Possible,LCC,0,100000
-p58,p101,Possible,LCC,0,100000
-p58,p105,Possible,LCC,0,100000
-p61,p87,Possible,LCC,0,100000
-p63,p80,Possible,LCC,0,100000
-p63,p81,Possible,LCC,0,100000
-p63,p85,Possible,LCC,0,100000
-p63,p94,Possible,LCC,0,100000
-p63,p105,Possible,LCC,0,100000
-p76,p81,Possible,LCC,0,100000
-p76,p85,Possible,LCC,0,100000
-p76,p94,Possible,LCC,0,100000
-p76,p105,Possible,LCC,0,100000
-p76,p122,Possible,LCC,0,100000
-p80,p85,Possible,LCC,0,100000
-p80,p87,Possible,LCC,0,100000
-p80,p92,Possible,LCC,0,100000
-p80,p94,Possible,LCC,0,100000
-p80,p98,Possible,LCC,0,100000
-p80,p99,Possible,LCC,0,100000
-p80,p103,Possible,LCC,0,100000
-p80,p112,Possible,LCC,0,100000
-p80,p122,Possible,LCC,0,100000
-p80,p127,Possible,LCC,0,100000
-p80,p131,Possible,LCC,0,100000
-p81,p87,Possible,LCC,0,100000
-p81,p92,Possible,LCC,0,100000
-p81,p94,Possible,LCC,0,100000
-p81,p99,Possible,LCC,0,100000
-p81,p112,Possible,LCC,0,100000
-p81,p127,Possible,LCC,0,100000
-p85,p92,Possible,LCC,0,100000
-p85,p94,Possible,LCC,0,100000
-p85,p101,Possible,LCC,0,100000
-p85,p103,Possible,LCC,0,100000
-p85,p112,Possible,LCC,0,100000
-p87,p94,Possible,LCC,0,100000
-p87,p98,Possible,LCC,0,100000
-p87,p101,Possible,LCC,0,100000
-p87,p105,Possible,LCC,0,100000
-p92,p101,Possible,LCC,0,100000
-p92,p105,Possible,LCC,0,100000
-p92,p122,Possible,LCC,0,100000
-p94,p99,Possible,LCC,0,100000
-p94,p101,Possible,LCC,0,100000
-p94,p103,Possible,LCC,0,100000
-p94,p105,Possible,LCC,0,100000
-p94,p112,Possible,LCC,0,100000
-p94,p122,Possible,LCC,0,100000
-p94,p127,Possible,LCC,0,100000
-p98,p99,Possible,LCC,0,100000
-p98,p101,Possible,LCC,0,100000
-p98,p103,Possible,LCC,0,100000
-p98,p112,Possible,LCC,0,100000
-p98,p127,Possible,LCC,0,100000
-p98,p131,Possible,LCC,0,100000
-p99,p101,Possible,LCC,0,100000
-p99,p105,Possible,LCC,0,100000
-p99,p127,Possible,LCC,0,100000
-p99,p134,Possible,LCC,0,100000
-p103,p122,Possible,LCC,0,100000
-p103,p127,Possible,LCC,0,100000
-p103,p134,Possible,LCC,0,100000
-p105,p112,Possible,LCC,0,100000
-p105,p127,Possible,LCC,0,100000
-p105,p131,Possible,LCC,0,100000
-p112,p127,Possible,LCC,0,100000
-p112,p134,Possible,LCC,0,100000
-p122,p127,Possible,LCC,0,100000
-p122,p131,Possible,LCC,0,100000
-p127,p131,Possible,LCC,0,100000
-p127,p134,Possible,LCC,0,100000
diff --git a/inputs/transmission/transmission_capacity_future_ba_LCC_all.csv b/inputs/transmission/transmission_capacity_future_ba_LCC_all.csv
deleted file mode 100644
index 0c212e7eb..000000000
--- a/inputs/transmission/transmission_capacity_future_ba_LCC_all.csv
+++ /dev/null
@@ -1,8496 +0,0 @@
-r,rr,status,trtype,t,MW
-p1,p10,Possible,LCC,0,100000
-p1,p100,Possible,LCC,0,100000
-p1,p101,Possible,LCC,0,100000
-p1,p102,Possible,LCC,0,100000
-p1,p103,Possible,LCC,0,100000
-p1,p104,Possible,LCC,0,100000
-p1,p105,Possible,LCC,0,100000
-p1,p106,Possible,LCC,0,100000
-p1,p107,Possible,LCC,0,100000
-p1,p108,Possible,LCC,0,100000
-p1,p109,Possible,LCC,0,100000
-p1,p11,Possible,LCC,0,100000
-p1,p110,Possible,LCC,0,100000
-p1,p111,Possible,LCC,0,100000
-p1,p112,Possible,LCC,0,100000
-p1,p113,Possible,LCC,0,100000
-p1,p114,Possible,LCC,0,100000
-p1,p115,Possible,LCC,0,100000
-p1,p116,Possible,LCC,0,100000
-p1,p117,Possible,LCC,0,100000
-p1,p118,Possible,LCC,0,100000
-p1,p119,Possible,LCC,0,100000
-p1,p12,Possible,LCC,0,100000
-p1,p120,Possible,LCC,0,100000
-p1,p121,Possible,LCC,0,100000
-p1,p122,Possible,LCC,0,100000
-p1,p123,Possible,LCC,0,100000
-p1,p124,Possible,LCC,0,100000
-p1,p125,Possible,LCC,0,100000
-p1,p126,Possible,LCC,0,100000
-p1,p127,Possible,LCC,0,100000
-p1,p128,Possible,LCC,0,100000
-p1,p129,Possible,LCC,0,100000
-p1,p13,Possible,LCC,0,100000
-p1,p130,Possible,LCC,0,100000
-p1,p131,Possible,LCC,0,100000
-p1,p132,Possible,LCC,0,100000
-p1,p133,Possible,LCC,0,100000
-p1,p134,Possible,LCC,0,100000
-p1,p15,Possible,LCC,0,100000
-p1,p16,Possible,LCC,0,100000
-p1,p17,Possible,LCC,0,100000
-p1,p18,Possible,LCC,0,100000
-p1,p19,Possible,LCC,0,100000
-p1,p20,Possible,LCC,0,100000
-p1,p21,Possible,LCC,0,100000
-p1,p22,Possible,LCC,0,100000
-p1,p23,Possible,LCC,0,100000
-p1,p24,Possible,LCC,0,100000
-p1,p25,Possible,LCC,0,100000
-p1,p26,Possible,LCC,0,100000
-p1,p27,Possible,LCC,0,100000
-p1,p28,Possible,LCC,0,100000
-p1,p29,Possible,LCC,0,100000
-p1,p3,Possible,LCC,0,100000
-p1,p30,Possible,LCC,0,100000
-p1,p31,Possible,LCC,0,100000
-p1,p32,Possible,LCC,0,100000
-p1,p33,Possible,LCC,0,100000
-p1,p34,Possible,LCC,0,100000
-p1,p35,Possible,LCC,0,100000
-p1,p36,Possible,LCC,0,100000
-p1,p37,Possible,LCC,0,100000
-p1,p38,Possible,LCC,0,100000
-p1,p39,Possible,LCC,0,100000
-p1,p40,Possible,LCC,0,100000
-p1,p41,Possible,LCC,0,100000
-p1,p42,Possible,LCC,0,100000
-p1,p43,Possible,LCC,0,100000
-p1,p44,Possible,LCC,0,100000
-p1,p45,Possible,LCC,0,100000
-p1,p46,Possible,LCC,0,100000
-p1,p47,Possible,LCC,0,100000
-p1,p48,Possible,LCC,0,100000
-p1,p49,Possible,LCC,0,100000
-p1,p5,Possible,LCC,0,100000
-p1,p50,Possible,LCC,0,100000
-p1,p51,Possible,LCC,0,100000
-p1,p52,Possible,LCC,0,100000
-p1,p53,Possible,LCC,0,100000
-p1,p54,Possible,LCC,0,100000
-p1,p55,Possible,LCC,0,100000
-p1,p56,Possible,LCC,0,100000
-p1,p57,Possible,LCC,0,100000
-p1,p58,Possible,LCC,0,100000
-p1,p59,Possible,LCC,0,100000
-p1,p6,Possible,LCC,0,100000
-p1,p60,Possible,LCC,0,100000
-p1,p61,Possible,LCC,0,100000
-p1,p62,Possible,LCC,0,100000
-p1,p63,Possible,LCC,0,100000
-p1,p64,Possible,LCC,0,100000
-p1,p65,Possible,LCC,0,100000
-p1,p66,Possible,LCC,0,100000
-p1,p67,Possible,LCC,0,100000
-p1,p68,Possible,LCC,0,100000
-p1,p69,Possible,LCC,0,100000
-p1,p7,Possible,LCC,0,100000
-p1,p70,Possible,LCC,0,100000
-p1,p71,Possible,LCC,0,100000
-p1,p72,Possible,LCC,0,100000
-p1,p73,Possible,LCC,0,100000
-p1,p74,Possible,LCC,0,100000
-p1,p75,Possible,LCC,0,100000
-p1,p76,Possible,LCC,0,100000
-p1,p77,Possible,LCC,0,100000
-p1,p78,Possible,LCC,0,100000
-p1,p79,Possible,LCC,0,100000
-p1,p8,Possible,LCC,0,100000
-p1,p80,Possible,LCC,0,100000
-p1,p81,Possible,LCC,0,100000
-p1,p82,Possible,LCC,0,100000
-p1,p83,Possible,LCC,0,100000
-p1,p84,Possible,LCC,0,100000
-p1,p85,Possible,LCC,0,100000
-p1,p86,Possible,LCC,0,100000
-p1,p87,Possible,LCC,0,100000
-p1,p88,Possible,LCC,0,100000
-p1,p89,Possible,LCC,0,100000
-p1,p9,Possible,LCC,0,100000
-p1,p90,Possible,LCC,0,100000
-p1,p91,Possible,LCC,0,100000
-p1,p92,Possible,LCC,0,100000
-p1,p93,Possible,LCC,0,100000
-p1,p94,Possible,LCC,0,100000
-p1,p95,Possible,LCC,0,100000
-p1,p96,Possible,LCC,0,100000
-p1,p97,Possible,LCC,0,100000
-p1,p98,Possible,LCC,0,100000
-p1,p99,Possible,LCC,0,100000
-p10,p100,Possible,LCC,0,100000
-p10,p101,Possible,LCC,0,100000
-p10,p102,Possible,LCC,0,100000
-p10,p103,Possible,LCC,0,100000
-p10,p104,Possible,LCC,0,100000
-p10,p105,Possible,LCC,0,100000
-p10,p106,Possible,LCC,0,100000
-p10,p107,Possible,LCC,0,100000
-p10,p108,Possible,LCC,0,100000
-p10,p109,Possible,LCC,0,100000
-p10,p110,Possible,LCC,0,100000
-p10,p111,Possible,LCC,0,100000
-p10,p112,Possible,LCC,0,100000
-p10,p113,Possible,LCC,0,100000
-p10,p114,Possible,LCC,0,100000
-p10,p115,Possible,LCC,0,100000
-p10,p116,Possible,LCC,0,100000
-p10,p117,Possible,LCC,0,100000
-p10,p118,Possible,LCC,0,100000
-p10,p119,Possible,LCC,0,100000
-p10,p120,Possible,LCC,0,100000
-p10,p121,Possible,LCC,0,100000
-p10,p122,Possible,LCC,0,100000
-p10,p123,Possible,LCC,0,100000
-p10,p124,Possible,LCC,0,100000
-p10,p125,Possible,LCC,0,100000
-p10,p126,Possible,LCC,0,100000
-p10,p127,Possible,LCC,0,100000
-p10,p128,Possible,LCC,0,100000
-p10,p129,Possible,LCC,0,100000
-p10,p130,Possible,LCC,0,100000
-p10,p131,Possible,LCC,0,100000
-p10,p132,Possible,LCC,0,100000
-p10,p133,Possible,LCC,0,100000
-p10,p134,Possible,LCC,0,100000
-p10,p14,Possible,LCC,0,100000
-p10,p15,Possible,LCC,0,100000
-p10,p17,Possible,LCC,0,100000
-p10,p18,Possible,LCC,0,100000
-p10,p19,Possible,LCC,0,100000
-p10,p20,Possible,LCC,0,100000
-p10,p21,Possible,LCC,0,100000
-p10,p22,Possible,LCC,0,100000
-p10,p23,Possible,LCC,0,100000
-p10,p24,Possible,LCC,0,100000
-p10,p29,Possible,LCC,0,100000
-p10,p30,Possible,LCC,0,100000
-p10,p31,Possible,LCC,0,100000
-p10,p32,Possible,LCC,0,100000
-p10,p33,Possible,LCC,0,100000
-p10,p34,Possible,LCC,0,100000
-p10,p35,Possible,LCC,0,100000
-p10,p36,Possible,LCC,0,100000
-p10,p37,Possible,LCC,0,100000
-p10,p38,Possible,LCC,0,100000
-p10,p39,Possible,LCC,0,100000
-p10,p40,Possible,LCC,0,100000
-p10,p41,Possible,LCC,0,100000
-p10,p42,Possible,LCC,0,100000
-p10,p43,Possible,LCC,0,100000
-p10,p44,Possible,LCC,0,100000
-p10,p45,Possible,LCC,0,100000
-p10,p46,Possible,LCC,0,100000
-p10,p47,Possible,LCC,0,100000
-p10,p48,Possible,LCC,0,100000
-p10,p49,Possible,LCC,0,100000
-p10,p50,Possible,LCC,0,100000
-p10,p51,Possible,LCC,0,100000
-p10,p52,Possible,LCC,0,100000
-p10,p53,Possible,LCC,0,100000
-p10,p54,Possible,LCC,0,100000
-p10,p55,Possible,LCC,0,100000
-p10,p56,Possible,LCC,0,100000
-p10,p57,Possible,LCC,0,100000
-p10,p58,Possible,LCC,0,100000
-p10,p59,Possible,LCC,0,100000
-p10,p60,Possible,LCC,0,100000
-p10,p61,Possible,LCC,0,100000
-p10,p62,Possible,LCC,0,100000
-p10,p63,Possible,LCC,0,100000
-p10,p64,Possible,LCC,0,100000
-p10,p65,Possible,LCC,0,100000
-p10,p66,Possible,LCC,0,100000
-p10,p67,Possible,LCC,0,100000
-p10,p68,Possible,LCC,0,100000
-p10,p69,Possible,LCC,0,100000
-p10,p70,Possible,LCC,0,100000
-p10,p71,Possible,LCC,0,100000
-p10,p72,Possible,LCC,0,100000
-p10,p73,Possible,LCC,0,100000
-p10,p74,Possible,LCC,0,100000
-p10,p75,Possible,LCC,0,100000
-p10,p76,Possible,LCC,0,100000
-p10,p77,Possible,LCC,0,100000
-p10,p78,Possible,LCC,0,100000
-p10,p79,Possible,LCC,0,100000
-p10,p80,Possible,LCC,0,100000
-p10,p81,Possible,LCC,0,100000
-p10,p82,Possible,LCC,0,100000
-p10,p83,Possible,LCC,0,100000
-p10,p84,Possible,LCC,0,100000
-p10,p85,Possible,LCC,0,100000
-p10,p86,Possible,LCC,0,100000
-p10,p87,Possible,LCC,0,100000
-p10,p88,Possible,LCC,0,100000
-p10,p89,Possible,LCC,0,100000
-p10,p90,Possible,LCC,0,100000
-p10,p91,Possible,LCC,0,100000
-p10,p92,Possible,LCC,0,100000
-p10,p93,Possible,LCC,0,100000
-p10,p94,Possible,LCC,0,100000
-p10,p95,Possible,LCC,0,100000
-p10,p96,Possible,LCC,0,100000
-p10,p97,Possible,LCC,0,100000
-p10,p98,Possible,LCC,0,100000
-p10,p99,Possible,LCC,0,100000
-p100,p101,Possible,LCC,0,100000
-p100,p102,Possible,LCC,0,100000
-p100,p103,Possible,LCC,0,100000
-p100,p104,Possible,LCC,0,100000
-p100,p105,Possible,LCC,0,100000
-p100,p106,Possible,LCC,0,100000
-p100,p107,Possible,LCC,0,100000
-p100,p108,Possible,LCC,0,100000
-p100,p109,Possible,LCC,0,100000
-p100,p110,Possible,LCC,0,100000
-p100,p111,Possible,LCC,0,100000
-p100,p112,Possible,LCC,0,100000
-p100,p113,Possible,LCC,0,100000
-p100,p114,Possible,LCC,0,100000
-p100,p115,Possible,LCC,0,100000
-p100,p118,Possible,LCC,0,100000
-p100,p119,Possible,LCC,0,100000
-p100,p120,Possible,LCC,0,100000
-p100,p121,Possible,LCC,0,100000
-p100,p122,Possible,LCC,0,100000
-p100,p123,Possible,LCC,0,100000
-p100,p124,Possible,LCC,0,100000
-p100,p125,Possible,LCC,0,100000
-p100,p126,Possible,LCC,0,100000
-p100,p127,Possible,LCC,0,100000
-p100,p128,Possible,LCC,0,100000
-p100,p129,Possible,LCC,0,100000
-p100,p130,Possible,LCC,0,100000
-p100,p131,Possible,LCC,0,100000
-p100,p132,Possible,LCC,0,100000
-p100,p133,Possible,LCC,0,100000
-p100,p134,Possible,LCC,0,100000
-p101,p103,Possible,LCC,0,100000
-p101,p104,Possible,LCC,0,100000
-p101,p105,Possible,LCC,0,100000
-p101,p106,Possible,LCC,0,100000
-p101,p107,Possible,LCC,0,100000
-p101,p108,Possible,LCC,0,100000
-p101,p109,Possible,LCC,0,100000
-p101,p110,Possible,LCC,0,100000
-p101,p111,Possible,LCC,0,100000
-p101,p112,Possible,LCC,0,100000
-p101,p113,Possible,LCC,0,100000
-p101,p114,Possible,LCC,0,100000
-p101,p115,Possible,LCC,0,100000
-p101,p116,Possible,LCC,0,100000
-p101,p117,Possible,LCC,0,100000
-p101,p118,Possible,LCC,0,100000
-p101,p119,Possible,LCC,0,100000
-p101,p120,Possible,LCC,0,100000
-p101,p121,Possible,LCC,0,100000
-p101,p122,Possible,LCC,0,100000
-p101,p123,Possible,LCC,0,100000
-p101,p124,Possible,LCC,0,100000
-p101,p125,Possible,LCC,0,100000
-p101,p126,Possible,LCC,0,100000
-p101,p127,Possible,LCC,0,100000
-p101,p128,Possible,LCC,0,100000
-p101,p129,Possible,LCC,0,100000
-p101,p130,Possible,LCC,0,100000
-p101,p131,Possible,LCC,0,100000
-p101,p132,Possible,LCC,0,100000
-p101,p133,Possible,LCC,0,100000
-p101,p134,Possible,LCC,0,100000
-p102,p103,Possible,LCC,0,100000
-p102,p104,Possible,LCC,0,100000
-p102,p105,Possible,LCC,0,100000
-p102,p106,Possible,LCC,0,100000
-p102,p107,Possible,LCC,0,100000
-p102,p108,Possible,LCC,0,100000
-p102,p109,Possible,LCC,0,100000
-p102,p110,Possible,LCC,0,100000
-p102,p111,Possible,LCC,0,100000
-p102,p112,Possible,LCC,0,100000
-p102,p113,Possible,LCC,0,100000
-p102,p114,Possible,LCC,0,100000
-p102,p115,Possible,LCC,0,100000
-p102,p116,Possible,LCC,0,100000
-p102,p117,Possible,LCC,0,100000
-p102,p118,Possible,LCC,0,100000
-p102,p119,Possible,LCC,0,100000
-p102,p120,Possible,LCC,0,100000
-p102,p121,Possible,LCC,0,100000
-p102,p122,Possible,LCC,0,100000
-p102,p123,Possible,LCC,0,100000
-p102,p124,Possible,LCC,0,100000
-p102,p125,Possible,LCC,0,100000
-p102,p126,Possible,LCC,0,100000
-p102,p127,Possible,LCC,0,100000
-p102,p128,Possible,LCC,0,100000
-p102,p129,Possible,LCC,0,100000
-p102,p130,Possible,LCC,0,100000
-p102,p131,Possible,LCC,0,100000
-p102,p132,Possible,LCC,0,100000
-p102,p133,Possible,LCC,0,100000
-p102,p134,Possible,LCC,0,100000
-p103,p106,Possible,LCC,0,100000
-p103,p107,Possible,LCC,0,100000
-p103,p108,Possible,LCC,0,100000
-p103,p109,Possible,LCC,0,100000
-p103,p110,Possible,LCC,0,100000
-p103,p112,Possible,LCC,0,100000
-p103,p113,Possible,LCC,0,100000
-p103,p114,Possible,LCC,0,100000
-p103,p116,Possible,LCC,0,100000
-p103,p117,Possible,LCC,0,100000
-p103,p118,Possible,LCC,0,100000
-p103,p119,Possible,LCC,0,100000
-p103,p120,Possible,LCC,0,100000
-p103,p121,Possible,LCC,0,100000
-p103,p122,Possible,LCC,0,100000
-p103,p123,Possible,LCC,0,100000
-p103,p124,Possible,LCC,0,100000
-p103,p125,Possible,LCC,0,100000
-p103,p126,Possible,LCC,0,100000
-p103,p127,Possible,LCC,0,100000
-p103,p128,Possible,LCC,0,100000
-p103,p129,Possible,LCC,0,100000
-p103,p130,Possible,LCC,0,100000
-p103,p131,Possible,LCC,0,100000
-p103,p132,Possible,LCC,0,100000
-p103,p133,Possible,LCC,0,100000
-p103,p134,Possible,LCC,0,100000
-p104,p107,Possible,LCC,0,100000
-p104,p108,Possible,LCC,0,100000
-p104,p109,Possible,LCC,0,100000
-p104,p110,Possible,LCC,0,100000
-p104,p111,Possible,LCC,0,100000
-p104,p113,Possible,LCC,0,100000
-p104,p114,Possible,LCC,0,100000
-p104,p115,Possible,LCC,0,100000
-p104,p116,Possible,LCC,0,100000
-p104,p117,Possible,LCC,0,100000
-p104,p118,Possible,LCC,0,100000
-p104,p119,Possible,LCC,0,100000
-p104,p120,Possible,LCC,0,100000
-p104,p121,Possible,LCC,0,100000
-p104,p122,Possible,LCC,0,100000
-p104,p123,Possible,LCC,0,100000
-p104,p124,Possible,LCC,0,100000
-p104,p125,Possible,LCC,0,100000
-p104,p126,Possible,LCC,0,100000
-p104,p127,Possible,LCC,0,100000
-p104,p128,Possible,LCC,0,100000
-p104,p129,Possible,LCC,0,100000
-p104,p130,Possible,LCC,0,100000
-p104,p131,Possible,LCC,0,100000
-p104,p132,Possible,LCC,0,100000
-p104,p133,Possible,LCC,0,100000
-p104,p134,Possible,LCC,0,100000
-p105,p108,Possible,LCC,0,100000
-p105,p109,Possible,LCC,0,100000
-p105,p110,Possible,LCC,0,100000
-p105,p111,Possible,LCC,0,100000
-p105,p114,Possible,LCC,0,100000
-p105,p115,Possible,LCC,0,100000
-p105,p116,Possible,LCC,0,100000
-p105,p118,Possible,LCC,0,100000
-p105,p119,Possible,LCC,0,100000
-p105,p120,Possible,LCC,0,100000
-p105,p121,Possible,LCC,0,100000
-p105,p122,Possible,LCC,0,100000
-p105,p123,Possible,LCC,0,100000
-p105,p124,Possible,LCC,0,100000
-p105,p125,Possible,LCC,0,100000
-p105,p126,Possible,LCC,0,100000
-p105,p127,Possible,LCC,0,100000
-p105,p128,Possible,LCC,0,100000
-p105,p129,Possible,LCC,0,100000
-p105,p130,Possible,LCC,0,100000
-p105,p131,Possible,LCC,0,100000
-p105,p132,Possible,LCC,0,100000
-p105,p133,Possible,LCC,0,100000
-p105,p134,Possible,LCC,0,100000
-p106,p108,Possible,LCC,0,100000
-p106,p109,Possible,LCC,0,100000
-p106,p110,Possible,LCC,0,100000
-p106,p111,Possible,LCC,0,100000
-p106,p112,Possible,LCC,0,100000
-p106,p113,Possible,LCC,0,100000
-p106,p114,Possible,LCC,0,100000
-p106,p115,Possible,LCC,0,100000
-p106,p116,Possible,LCC,0,100000
-p106,p117,Possible,LCC,0,100000
-p106,p118,Possible,LCC,0,100000
-p106,p119,Possible,LCC,0,100000
-p106,p120,Possible,LCC,0,100000
-p106,p121,Possible,LCC,0,100000
-p106,p122,Possible,LCC,0,100000
-p106,p123,Possible,LCC,0,100000
-p106,p124,Possible,LCC,0,100000
-p106,p125,Possible,LCC,0,100000
-p106,p126,Possible,LCC,0,100000
-p106,p127,Possible,LCC,0,100000
-p106,p128,Possible,LCC,0,100000
-p106,p129,Possible,LCC,0,100000
-p106,p130,Possible,LCC,0,100000
-p106,p131,Possible,LCC,0,100000
-p106,p132,Possible,LCC,0,100000
-p106,p133,Possible,LCC,0,100000
-p106,p134,Possible,LCC,0,100000
-p107,p110,Possible,LCC,0,100000
-p107,p111,Possible,LCC,0,100000
-p107,p112,Possible,LCC,0,100000
-p107,p113,Possible,LCC,0,100000
-p107,p115,Possible,LCC,0,100000
-p107,p116,Possible,LCC,0,100000
-p107,p117,Possible,LCC,0,100000
-p107,p118,Possible,LCC,0,100000
-p107,p119,Possible,LCC,0,100000
-p107,p120,Possible,LCC,0,100000
-p107,p121,Possible,LCC,0,100000
-p107,p122,Possible,LCC,0,100000
-p107,p123,Possible,LCC,0,100000
-p107,p124,Possible,LCC,0,100000
-p107,p125,Possible,LCC,0,100000
-p107,p126,Possible,LCC,0,100000
-p107,p127,Possible,LCC,0,100000
-p107,p128,Possible,LCC,0,100000
-p107,p129,Possible,LCC,0,100000
-p107,p130,Possible,LCC,0,100000
-p107,p131,Possible,LCC,0,100000
-p107,p132,Possible,LCC,0,100000
-p107,p133,Possible,LCC,0,100000
-p107,p134,Possible,LCC,0,100000
-p108,p110,Possible,LCC,0,100000
-p108,p111,Possible,LCC,0,100000
-p108,p115,Possible,LCC,0,100000
-p108,p116,Possible,LCC,0,100000
-p108,p117,Possible,LCC,0,100000
-p108,p118,Possible,LCC,0,100000
-p108,p119,Possible,LCC,0,100000
-p108,p120,Possible,LCC,0,100000
-p108,p121,Possible,LCC,0,100000
-p108,p122,Possible,LCC,0,100000
-p108,p123,Possible,LCC,0,100000
-p108,p124,Possible,LCC,0,100000
-p108,p125,Possible,LCC,0,100000
-p108,p126,Possible,LCC,0,100000
-p108,p127,Possible,LCC,0,100000
-p108,p128,Possible,LCC,0,100000
-p108,p129,Possible,LCC,0,100000
-p108,p130,Possible,LCC,0,100000
-p108,p131,Possible,LCC,0,100000
-p108,p132,Possible,LCC,0,100000
-p108,p133,Possible,LCC,0,100000
-p108,p134,Possible,LCC,0,100000
-p109,p111,Possible,LCC,0,100000
-p109,p115,Possible,LCC,0,100000
-p109,p116,Possible,LCC,0,100000
-p109,p117,Possible,LCC,0,100000
-p109,p119,Possible,LCC,0,100000
-p109,p120,Possible,LCC,0,100000
-p109,p121,Possible,LCC,0,100000
-p109,p122,Possible,LCC,0,100000
-p109,p123,Possible,LCC,0,100000
-p109,p124,Possible,LCC,0,100000
-p109,p125,Possible,LCC,0,100000
-p109,p126,Possible,LCC,0,100000
-p109,p127,Possible,LCC,0,100000
-p109,p128,Possible,LCC,0,100000
-p109,p129,Possible,LCC,0,100000
-p109,p130,Possible,LCC,0,100000
-p109,p131,Possible,LCC,0,100000
-p109,p132,Possible,LCC,0,100000
-p109,p133,Possible,LCC,0,100000
-p109,p134,Possible,LCC,0,100000
-p11,p100,Possible,LCC,0,100000
-p11,p101,Possible,LCC,0,100000
-p11,p102,Possible,LCC,0,100000
-p11,p103,Possible,LCC,0,100000
-p11,p104,Possible,LCC,0,100000
-p11,p105,Possible,LCC,0,100000
-p11,p106,Possible,LCC,0,100000
-p11,p107,Possible,LCC,0,100000
-p11,p108,Possible,LCC,0,100000
-p11,p109,Possible,LCC,0,100000
-p11,p110,Possible,LCC,0,100000
-p11,p111,Possible,LCC,0,100000
-p11,p112,Possible,LCC,0,100000
-p11,p113,Possible,LCC,0,100000
-p11,p114,Possible,LCC,0,100000
-p11,p115,Possible,LCC,0,100000
-p11,p116,Possible,LCC,0,100000
-p11,p117,Possible,LCC,0,100000
-p11,p118,Possible,LCC,0,100000
-p11,p119,Possible,LCC,0,100000
-p11,p12,Possible,LCC,0,100000
-p11,p120,Possible,LCC,0,100000
-p11,p121,Possible,LCC,0,100000
-p11,p122,Possible,LCC,0,100000
-p11,p123,Possible,LCC,0,100000
-p11,p124,Possible,LCC,0,100000
-p11,p125,Possible,LCC,0,100000
-p11,p126,Possible,LCC,0,100000
-p11,p127,Possible,LCC,0,100000
-p11,p128,Possible,LCC,0,100000
-p11,p129,Possible,LCC,0,100000
-p11,p13,Possible,LCC,0,100000
-p11,p130,Possible,LCC,0,100000
-p11,p131,Possible,LCC,0,100000
-p11,p132,Possible,LCC,0,100000
-p11,p133,Possible,LCC,0,100000
-p11,p134,Possible,LCC,0,100000
-p11,p14,Possible,LCC,0,100000
-p11,p15,Possible,LCC,0,100000
-p11,p16,Possible,LCC,0,100000
-p11,p17,Possible,LCC,0,100000
-p11,p18,Possible,LCC,0,100000
-p11,p19,Possible,LCC,0,100000
-p11,p20,Possible,LCC,0,100000
-p11,p21,Possible,LCC,0,100000
-p11,p22,Possible,LCC,0,100000
-p11,p23,Possible,LCC,0,100000
-p11,p24,Possible,LCC,0,100000
-p11,p26,Possible,LCC,0,100000
-p11,p27,Possible,LCC,0,100000
-p11,p28,Possible,LCC,0,100000
-p11,p29,Possible,LCC,0,100000
-p11,p30,Possible,LCC,0,100000
-p11,p31,Possible,LCC,0,100000
-p11,p32,Possible,LCC,0,100000
-p11,p33,Possible,LCC,0,100000
-p11,p34,Possible,LCC,0,100000
-p11,p35,Possible,LCC,0,100000
-p11,p36,Possible,LCC,0,100000
-p11,p37,Possible,LCC,0,100000
-p11,p38,Possible,LCC,0,100000
-p11,p39,Possible,LCC,0,100000
-p11,p40,Possible,LCC,0,100000
-p11,p41,Possible,LCC,0,100000
-p11,p42,Possible,LCC,0,100000
-p11,p43,Possible,LCC,0,100000
-p11,p44,Possible,LCC,0,100000
-p11,p45,Possible,LCC,0,100000
-p11,p46,Possible,LCC,0,100000
-p11,p47,Possible,LCC,0,100000
-p11,p48,Possible,LCC,0,100000
-p11,p49,Possible,LCC,0,100000
-p11,p50,Possible,LCC,0,100000
-p11,p51,Possible,LCC,0,100000
-p11,p52,Possible,LCC,0,100000
-p11,p53,Possible,LCC,0,100000
-p11,p54,Possible,LCC,0,100000
-p11,p55,Possible,LCC,0,100000
-p11,p56,Possible,LCC,0,100000
-p11,p57,Possible,LCC,0,100000
-p11,p58,Possible,LCC,0,100000
-p11,p59,Possible,LCC,0,100000
-p11,p60,Possible,LCC,0,100000
-p11,p61,Possible,LCC,0,100000
-p11,p62,Possible,LCC,0,100000
-p11,p63,Possible,LCC,0,100000
-p11,p64,Possible,LCC,0,100000
-p11,p65,Possible,LCC,0,100000
-p11,p66,Possible,LCC,0,100000
-p11,p67,Possible,LCC,0,100000
-p11,p68,Possible,LCC,0,100000
-p11,p69,Possible,LCC,0,100000
-p11,p70,Possible,LCC,0,100000
-p11,p71,Possible,LCC,0,100000
-p11,p72,Possible,LCC,0,100000
-p11,p73,Possible,LCC,0,100000
-p11,p74,Possible,LCC,0,100000
-p11,p75,Possible,LCC,0,100000
-p11,p76,Possible,LCC,0,100000
-p11,p77,Possible,LCC,0,100000
-p11,p78,Possible,LCC,0,100000
-p11,p79,Possible,LCC,0,100000
-p11,p80,Possible,LCC,0,100000
-p11,p81,Possible,LCC,0,100000
-p11,p82,Possible,LCC,0,100000
-p11,p83,Possible,LCC,0,100000
-p11,p84,Possible,LCC,0,100000
-p11,p85,Possible,LCC,0,100000
-p11,p86,Possible,LCC,0,100000
-p11,p87,Possible,LCC,0,100000
-p11,p88,Possible,LCC,0,100000
-p11,p89,Possible,LCC,0,100000
-p11,p90,Possible,LCC,0,100000
-p11,p91,Possible,LCC,0,100000
-p11,p92,Possible,LCC,0,100000
-p11,p93,Possible,LCC,0,100000
-p11,p94,Possible,LCC,0,100000
-p11,p95,Possible,LCC,0,100000
-p11,p96,Possible,LCC,0,100000
-p11,p97,Possible,LCC,0,100000
-p11,p98,Possible,LCC,0,100000
-p11,p99,Possible,LCC,0,100000
-p110,p111,Possible,LCC,0,100000
-p110,p113,Possible,LCC,0,100000
-p110,p114,Possible,LCC,0,100000
-p110,p115,Possible,LCC,0,100000
-p110,p119,Possible,LCC,0,100000
-p110,p120,Possible,LCC,0,100000
-p110,p121,Possible,LCC,0,100000
-p110,p122,Possible,LCC,0,100000
-p110,p123,Possible,LCC,0,100000
-p110,p124,Possible,LCC,0,100000
-p110,p125,Possible,LCC,0,100000
-p110,p126,Possible,LCC,0,100000
-p110,p127,Possible,LCC,0,100000
-p110,p128,Possible,LCC,0,100000
-p110,p129,Possible,LCC,0,100000
-p110,p130,Possible,LCC,0,100000
-p110,p131,Possible,LCC,0,100000
-p110,p132,Possible,LCC,0,100000
-p110,p133,Possible,LCC,0,100000
-p110,p134,Possible,LCC,0,100000
-p111,p113,Possible,LCC,0,100000
-p111,p114,Possible,LCC,0,100000
-p111,p118,Possible,LCC,0,100000
-p111,p119,Possible,LCC,0,100000
-p111,p120,Possible,LCC,0,100000
-p111,p121,Possible,LCC,0,100000
-p111,p123,Possible,LCC,0,100000
-p111,p124,Possible,LCC,0,100000
-p111,p125,Possible,LCC,0,100000
-p111,p127,Possible,LCC,0,100000
-p111,p128,Possible,LCC,0,100000
-p111,p129,Possible,LCC,0,100000
-p111,p130,Possible,LCC,0,100000
-p111,p131,Possible,LCC,0,100000
-p111,p132,Possible,LCC,0,100000
-p111,p133,Possible,LCC,0,100000
-p111,p134,Possible,LCC,0,100000
-p112,p118,Possible,LCC,0,100000
-p112,p119,Possible,LCC,0,100000
-p112,p120,Possible,LCC,0,100000
-p112,p121,Possible,LCC,0,100000
-p112,p122,Possible,LCC,0,100000
-p112,p123,Possible,LCC,0,100000
-p112,p124,Possible,LCC,0,100000
-p112,p125,Possible,LCC,0,100000
-p112,p126,Possible,LCC,0,100000
-p112,p127,Possible,LCC,0,100000
-p112,p128,Possible,LCC,0,100000
-p112,p129,Possible,LCC,0,100000
-p112,p130,Possible,LCC,0,100000
-p112,p131,Possible,LCC,0,100000
-p112,p132,Possible,LCC,0,100000
-p112,p133,Possible,LCC,0,100000
-p112,p134,Possible,LCC,0,100000
-p113,p118,Possible,LCC,0,100000
-p113,p119,Possible,LCC,0,100000
-p113,p120,Possible,LCC,0,100000
-p113,p121,Possible,LCC,0,100000
-p113,p122,Possible,LCC,0,100000
-p113,p123,Possible,LCC,0,100000
-p113,p124,Possible,LCC,0,100000
-p113,p125,Possible,LCC,0,100000
-p113,p126,Possible,LCC,0,100000
-p113,p127,Possible,LCC,0,100000
-p113,p128,Possible,LCC,0,100000
-p113,p129,Possible,LCC,0,100000
-p113,p130,Possible,LCC,0,100000
-p113,p131,Possible,LCC,0,100000
-p113,p132,Possible,LCC,0,100000
-p113,p133,Possible,LCC,0,100000
-p113,p134,Possible,LCC,0,100000
-p114,p118,Possible,LCC,0,100000
-p114,p119,Possible,LCC,0,100000
-p114,p120,Possible,LCC,0,100000
-p114,p121,Possible,LCC,0,100000
-p114,p122,Possible,LCC,0,100000
-p114,p123,Possible,LCC,0,100000
-p114,p124,Possible,LCC,0,100000
-p114,p125,Possible,LCC,0,100000
-p114,p126,Possible,LCC,0,100000
-p114,p127,Possible,LCC,0,100000
-p114,p128,Possible,LCC,0,100000
-p114,p129,Possible,LCC,0,100000
-p114,p130,Possible,LCC,0,100000
-p114,p131,Possible,LCC,0,100000
-p114,p132,Possible,LCC,0,100000
-p114,p133,Possible,LCC,0,100000
-p114,p134,Possible,LCC,0,100000
-p115,p118,Possible,LCC,0,100000
-p115,p119,Possible,LCC,0,100000
-p115,p120,Possible,LCC,0,100000
-p115,p121,Possible,LCC,0,100000
-p115,p123,Possible,LCC,0,100000
-p115,p124,Possible,LCC,0,100000
-p115,p125,Possible,LCC,0,100000
-p115,p127,Possible,LCC,0,100000
-p115,p128,Possible,LCC,0,100000
-p115,p129,Possible,LCC,0,100000
-p115,p130,Possible,LCC,0,100000
-p115,p131,Possible,LCC,0,100000
-p115,p132,Possible,LCC,0,100000
-p115,p133,Possible,LCC,0,100000
-p115,p134,Possible,LCC,0,100000
-p116,p119,Possible,LCC,0,100000
-p116,p122,Possible,LCC,0,100000
-p116,p123,Possible,LCC,0,100000
-p116,p124,Possible,LCC,0,100000
-p116,p125,Possible,LCC,0,100000
-p116,p126,Possible,LCC,0,100000
-p116,p127,Possible,LCC,0,100000
-p116,p128,Possible,LCC,0,100000
-p116,p129,Possible,LCC,0,100000
-p116,p130,Possible,LCC,0,100000
-p116,p131,Possible,LCC,0,100000
-p116,p132,Possible,LCC,0,100000
-p116,p133,Possible,LCC,0,100000
-p116,p134,Possible,LCC,0,100000
-p117,p119,Possible,LCC,0,100000
-p117,p120,Possible,LCC,0,100000
-p117,p121,Possible,LCC,0,100000
-p117,p122,Possible,LCC,0,100000
-p117,p123,Possible,LCC,0,100000
-p117,p124,Possible,LCC,0,100000
-p117,p125,Possible,LCC,0,100000
-p117,p126,Possible,LCC,0,100000
-p117,p127,Possible,LCC,0,100000
-p117,p128,Possible,LCC,0,100000
-p117,p129,Possible,LCC,0,100000
-p117,p130,Possible,LCC,0,100000
-p117,p131,Possible,LCC,0,100000
-p117,p132,Possible,LCC,0,100000
-p117,p133,Possible,LCC,0,100000
-p117,p134,Possible,LCC,0,100000
-p118,p119,Possible,LCC,0,100000
-p118,p120,Possible,LCC,0,100000
-p118,p121,Possible,LCC,0,100000
-p118,p122,Possible,LCC,0,100000
-p118,p123,Possible,LCC,0,100000
-p118,p124,Possible,LCC,0,100000
-p118,p125,Possible,LCC,0,100000
-p118,p126,Possible,LCC,0,100000
-p118,p127,Possible,LCC,0,100000
-p118,p128,Possible,LCC,0,100000
-p118,p129,Possible,LCC,0,100000
-p118,p130,Possible,LCC,0,100000
-p118,p131,Possible,LCC,0,100000
-p118,p132,Possible,LCC,0,100000
-p118,p133,Possible,LCC,0,100000
-p118,p134,Possible,LCC,0,100000
-p119,p120,Possible,LCC,0,100000
-p119,p121,Possible,LCC,0,100000
-p119,p123,Possible,LCC,0,100000
-p119,p124,Possible,LCC,0,100000
-p119,p127,Possible,LCC,0,100000
-p119,p128,Possible,LCC,0,100000
-p119,p129,Possible,LCC,0,100000
-p119,p130,Possible,LCC,0,100000
-p119,p131,Possible,LCC,0,100000
-p119,p132,Possible,LCC,0,100000
-p119,p133,Possible,LCC,0,100000
-p119,p134,Possible,LCC,0,100000
-p12,p100,Possible,LCC,0,100000
-p12,p101,Possible,LCC,0,100000
-p12,p102,Possible,LCC,0,100000
-p12,p103,Possible,LCC,0,100000
-p12,p104,Possible,LCC,0,100000
-p12,p105,Possible,LCC,0,100000
-p12,p106,Possible,LCC,0,100000
-p12,p107,Possible,LCC,0,100000
-p12,p108,Possible,LCC,0,100000
-p12,p109,Possible,LCC,0,100000
-p12,p110,Possible,LCC,0,100000
-p12,p111,Possible,LCC,0,100000
-p12,p112,Possible,LCC,0,100000
-p12,p113,Possible,LCC,0,100000
-p12,p114,Possible,LCC,0,100000
-p12,p115,Possible,LCC,0,100000
-p12,p116,Possible,LCC,0,100000
-p12,p117,Possible,LCC,0,100000
-p12,p118,Possible,LCC,0,100000
-p12,p119,Possible,LCC,0,100000
-p12,p120,Possible,LCC,0,100000
-p12,p121,Possible,LCC,0,100000
-p12,p122,Possible,LCC,0,100000
-p12,p123,Possible,LCC,0,100000
-p12,p124,Possible,LCC,0,100000
-p12,p125,Possible,LCC,0,100000
-p12,p126,Possible,LCC,0,100000
-p12,p127,Possible,LCC,0,100000
-p12,p128,Possible,LCC,0,100000
-p12,p129,Possible,LCC,0,100000
-p12,p130,Possible,LCC,0,100000
-p12,p131,Possible,LCC,0,100000
-p12,p132,Possible,LCC,0,100000
-p12,p133,Possible,LCC,0,100000
-p12,p134,Possible,LCC,0,100000
-p12,p14,Possible,LCC,0,100000
-p12,p16,Possible,LCC,0,100000
-p12,p17,Possible,LCC,0,100000
-p12,p18,Possible,LCC,0,100000
-p12,p19,Possible,LCC,0,100000
-p12,p20,Possible,LCC,0,100000
-p12,p21,Possible,LCC,0,100000
-p12,p22,Possible,LCC,0,100000
-p12,p23,Possible,LCC,0,100000
-p12,p24,Possible,LCC,0,100000
-p12,p26,Possible,LCC,0,100000
-p12,p27,Possible,LCC,0,100000
-p12,p28,Possible,LCC,0,100000
-p12,p29,Possible,LCC,0,100000
-p12,p30,Possible,LCC,0,100000
-p12,p31,Possible,LCC,0,100000
-p12,p32,Possible,LCC,0,100000
-p12,p33,Possible,LCC,0,100000
-p12,p34,Possible,LCC,0,100000
-p12,p35,Possible,LCC,0,100000
-p12,p36,Possible,LCC,0,100000
-p12,p37,Possible,LCC,0,100000
-p12,p38,Possible,LCC,0,100000
-p12,p39,Possible,LCC,0,100000
-p12,p40,Possible,LCC,0,100000
-p12,p41,Possible,LCC,0,100000
-p12,p42,Possible,LCC,0,100000
-p12,p43,Possible,LCC,0,100000
-p12,p44,Possible,LCC,0,100000
-p12,p45,Possible,LCC,0,100000
-p12,p46,Possible,LCC,0,100000
-p12,p47,Possible,LCC,0,100000
-p12,p48,Possible,LCC,0,100000
-p12,p49,Possible,LCC,0,100000
-p12,p50,Possible,LCC,0,100000
-p12,p51,Possible,LCC,0,100000
-p12,p52,Possible,LCC,0,100000
-p12,p53,Possible,LCC,0,100000
-p12,p54,Possible,LCC,0,100000
-p12,p55,Possible,LCC,0,100000
-p12,p56,Possible,LCC,0,100000
-p12,p57,Possible,LCC,0,100000
-p12,p58,Possible,LCC,0,100000
-p12,p59,Possible,LCC,0,100000
-p12,p60,Possible,LCC,0,100000
-p12,p61,Possible,LCC,0,100000
-p12,p62,Possible,LCC,0,100000
-p12,p63,Possible,LCC,0,100000
-p12,p64,Possible,LCC,0,100000
-p12,p65,Possible,LCC,0,100000
-p12,p66,Possible,LCC,0,100000
-p12,p67,Possible,LCC,0,100000
-p12,p68,Possible,LCC,0,100000
-p12,p69,Possible,LCC,0,100000
-p12,p70,Possible,LCC,0,100000
-p12,p71,Possible,LCC,0,100000
-p12,p72,Possible,LCC,0,100000
-p12,p73,Possible,LCC,0,100000
-p12,p74,Possible,LCC,0,100000
-p12,p75,Possible,LCC,0,100000
-p12,p76,Possible,LCC,0,100000
-p12,p77,Possible,LCC,0,100000
-p12,p78,Possible,LCC,0,100000
-p12,p79,Possible,LCC,0,100000
-p12,p80,Possible,LCC,0,100000
-p12,p81,Possible,LCC,0,100000
-p12,p82,Possible,LCC,0,100000
-p12,p83,Possible,LCC,0,100000
-p12,p84,Possible,LCC,0,100000
-p12,p85,Possible,LCC,0,100000
-p12,p86,Possible,LCC,0,100000
-p12,p87,Possible,LCC,0,100000
-p12,p88,Possible,LCC,0,100000
-p12,p89,Possible,LCC,0,100000
-p12,p90,Possible,LCC,0,100000
-p12,p91,Possible,LCC,0,100000
-p12,p92,Possible,LCC,0,100000
-p12,p93,Possible,LCC,0,100000
-p12,p94,Possible,LCC,0,100000
-p12,p95,Possible,LCC,0,100000
-p12,p96,Possible,LCC,0,100000
-p12,p97,Possible,LCC,0,100000
-p12,p98,Possible,LCC,0,100000
-p12,p99,Possible,LCC,0,100000
-p120,p124,Possible,LCC,0,100000
-p120,p127,Possible,LCC,0,100000
-p120,p128,Possible,LCC,0,100000
-p120,p129,Possible,LCC,0,100000
-p120,p130,Possible,LCC,0,100000
-p120,p131,Possible,LCC,0,100000
-p120,p132,Possible,LCC,0,100000
-p120,p133,Possible,LCC,0,100000
-p120,p134,Possible,LCC,0,100000
-p121,p124,Possible,LCC,0,100000
-p121,p125,Possible,LCC,0,100000
-p121,p127,Possible,LCC,0,100000
-p121,p128,Possible,LCC,0,100000
-p121,p129,Possible,LCC,0,100000
-p121,p130,Possible,LCC,0,100000
-p121,p131,Possible,LCC,0,100000
-p121,p132,Possible,LCC,0,100000
-p121,p133,Possible,LCC,0,100000
-p121,p134,Possible,LCC,0,100000
-p122,p124,Possible,LCC,0,100000
-p122,p128,Possible,LCC,0,100000
-p122,p129,Possible,LCC,0,100000
-p122,p130,Possible,LCC,0,100000
-p122,p131,Possible,LCC,0,100000
-p122,p132,Possible,LCC,0,100000
-p122,p133,Possible,LCC,0,100000
-p122,p134,Possible,LCC,0,100000
-p123,p127,Possible,LCC,0,100000
-p123,p128,Possible,LCC,0,100000
-p123,p129,Possible,LCC,0,100000
-p123,p130,Possible,LCC,0,100000
-p123,p131,Possible,LCC,0,100000
-p123,p132,Possible,LCC,0,100000
-p123,p133,Possible,LCC,0,100000
-p123,p134,Possible,LCC,0,100000
-p124,p125,Possible,LCC,0,100000
-p124,p126,Possible,LCC,0,100000
-p124,p127,Possible,LCC,0,100000
-p124,p128,Possible,LCC,0,100000
-p124,p129,Possible,LCC,0,100000
-p124,p130,Possible,LCC,0,100000
-p124,p131,Possible,LCC,0,100000
-p124,p132,Possible,LCC,0,100000
-p124,p133,Possible,LCC,0,100000
-p124,p134,Possible,LCC,0,100000
-p125,p128,Possible,LCC,0,100000
-p125,p129,Possible,LCC,0,100000
-p125,p130,Possible,LCC,0,100000
-p125,p131,Possible,LCC,0,100000
-p125,p132,Possible,LCC,0,100000
-p125,p133,Possible,LCC,0,100000
-p125,p134,Possible,LCC,0,100000
-p126,p128,Possible,LCC,0,100000
-p126,p129,Possible,LCC,0,100000
-p126,p130,Possible,LCC,0,100000
-p126,p131,Possible,LCC,0,100000
-p126,p132,Possible,LCC,0,100000
-p126,p133,Possible,LCC,0,100000
-p126,p134,Possible,LCC,0,100000
-p127,p134,Possible,LCC,0,100000
-p128,p129,Possible,LCC,0,100000
-p128,p134,Possible,LCC,0,100000
-p129,p132,Possible,LCC,0,100000
-p129,p134,Possible,LCC,0,100000
-p13,p100,Possible,LCC,0,100000
-p13,p101,Possible,LCC,0,100000
-p13,p102,Possible,LCC,0,100000
-p13,p103,Possible,LCC,0,100000
-p13,p104,Possible,LCC,0,100000
-p13,p105,Possible,LCC,0,100000
-p13,p106,Possible,LCC,0,100000
-p13,p107,Possible,LCC,0,100000
-p13,p108,Possible,LCC,0,100000
-p13,p109,Possible,LCC,0,100000
-p13,p110,Possible,LCC,0,100000
-p13,p111,Possible,LCC,0,100000
-p13,p112,Possible,LCC,0,100000
-p13,p113,Possible,LCC,0,100000
-p13,p114,Possible,LCC,0,100000
-p13,p115,Possible,LCC,0,100000
-p13,p116,Possible,LCC,0,100000
-p13,p117,Possible,LCC,0,100000
-p13,p118,Possible,LCC,0,100000
-p13,p119,Possible,LCC,0,100000
-p13,p120,Possible,LCC,0,100000
-p13,p121,Possible,LCC,0,100000
-p13,p122,Possible,LCC,0,100000
-p13,p123,Possible,LCC,0,100000
-p13,p124,Possible,LCC,0,100000
-p13,p125,Possible,LCC,0,100000
-p13,p126,Possible,LCC,0,100000
-p13,p127,Possible,LCC,0,100000
-p13,p128,Possible,LCC,0,100000
-p13,p129,Possible,LCC,0,100000
-p13,p130,Possible,LCC,0,100000
-p13,p131,Possible,LCC,0,100000
-p13,p132,Possible,LCC,0,100000
-p13,p133,Possible,LCC,0,100000
-p13,p134,Possible,LCC,0,100000
-p13,p14,Possible,LCC,0,100000
-p13,p15,Possible,LCC,0,100000
-p13,p16,Possible,LCC,0,100000
-p13,p17,Possible,LCC,0,100000
-p13,p18,Possible,LCC,0,100000
-p13,p19,Possible,LCC,0,100000
-p13,p20,Possible,LCC,0,100000
-p13,p21,Possible,LCC,0,100000
-p13,p22,Possible,LCC,0,100000
-p13,p23,Possible,LCC,0,100000
-p13,p24,Possible,LCC,0,100000
-p13,p26,Possible,LCC,0,100000
-p13,p29,Possible,LCC,0,100000
-p13,p30,Possible,LCC,0,100000
-p13,p31,Possible,LCC,0,100000
-p13,p32,Possible,LCC,0,100000
-p13,p33,Possible,LCC,0,100000
-p13,p34,Possible,LCC,0,100000
-p13,p35,Possible,LCC,0,100000
-p13,p36,Possible,LCC,0,100000
-p13,p37,Possible,LCC,0,100000
-p13,p38,Possible,LCC,0,100000
-p13,p39,Possible,LCC,0,100000
-p13,p40,Possible,LCC,0,100000
-p13,p41,Possible,LCC,0,100000
-p13,p42,Possible,LCC,0,100000
-p13,p43,Possible,LCC,0,100000
-p13,p44,Possible,LCC,0,100000
-p13,p45,Possible,LCC,0,100000
-p13,p46,Possible,LCC,0,100000
-p13,p47,Possible,LCC,0,100000
-p13,p48,Possible,LCC,0,100000
-p13,p49,Possible,LCC,0,100000
-p13,p50,Possible,LCC,0,100000
-p13,p51,Possible,LCC,0,100000
-p13,p52,Possible,LCC,0,100000
-p13,p53,Possible,LCC,0,100000
-p13,p54,Possible,LCC,0,100000
-p13,p55,Possible,LCC,0,100000
-p13,p56,Possible,LCC,0,100000
-p13,p57,Possible,LCC,0,100000
-p13,p58,Possible,LCC,0,100000
-p13,p59,Possible,LCC,0,100000
-p13,p60,Possible,LCC,0,100000
-p13,p61,Possible,LCC,0,100000
-p13,p62,Possible,LCC,0,100000
-p13,p63,Possible,LCC,0,100000
-p13,p64,Possible,LCC,0,100000
-p13,p65,Possible,LCC,0,100000
-p13,p66,Possible,LCC,0,100000
-p13,p67,Possible,LCC,0,100000
-p13,p68,Possible,LCC,0,100000
-p13,p69,Possible,LCC,0,100000
-p13,p70,Possible,LCC,0,100000
-p13,p71,Possible,LCC,0,100000
-p13,p72,Possible,LCC,0,100000
-p13,p73,Possible,LCC,0,100000
-p13,p74,Possible,LCC,0,100000
-p13,p75,Possible,LCC,0,100000
-p13,p76,Possible,LCC,0,100000
-p13,p77,Possible,LCC,0,100000
-p13,p78,Possible,LCC,0,100000
-p13,p79,Possible,LCC,0,100000
-p13,p80,Possible,LCC,0,100000
-p13,p81,Possible,LCC,0,100000
-p13,p82,Possible,LCC,0,100000
-p13,p83,Possible,LCC,0,100000
-p13,p84,Possible,LCC,0,100000
-p13,p85,Possible,LCC,0,100000
-p13,p86,Possible,LCC,0,100000
-p13,p87,Possible,LCC,0,100000
-p13,p88,Possible,LCC,0,100000
-p13,p89,Possible,LCC,0,100000
-p13,p90,Possible,LCC,0,100000
-p13,p91,Possible,LCC,0,100000
-p13,p92,Possible,LCC,0,100000
-p13,p93,Possible,LCC,0,100000
-p13,p94,Possible,LCC,0,100000
-p13,p95,Possible,LCC,0,100000
-p13,p96,Possible,LCC,0,100000
-p13,p97,Possible,LCC,0,100000
-p13,p98,Possible,LCC,0,100000
-p13,p99,Possible,LCC,0,100000
-p14,p100,Possible,LCC,0,100000
-p14,p101,Possible,LCC,0,100000
-p14,p102,Possible,LCC,0,100000
-p14,p103,Possible,LCC,0,100000
-p14,p104,Possible,LCC,0,100000
-p14,p105,Possible,LCC,0,100000
-p14,p106,Possible,LCC,0,100000
-p14,p107,Possible,LCC,0,100000
-p14,p108,Possible,LCC,0,100000
-p14,p109,Possible,LCC,0,100000
-p14,p110,Possible,LCC,0,100000
-p14,p111,Possible,LCC,0,100000
-p14,p112,Possible,LCC,0,100000
-p14,p113,Possible,LCC,0,100000
-p14,p114,Possible,LCC,0,100000
-p14,p115,Possible,LCC,0,100000
-p14,p116,Possible,LCC,0,100000
-p14,p117,Possible,LCC,0,100000
-p14,p118,Possible,LCC,0,100000
-p14,p119,Possible,LCC,0,100000
-p14,p120,Possible,LCC,0,100000
-p14,p121,Possible,LCC,0,100000
-p14,p122,Possible,LCC,0,100000
-p14,p123,Possible,LCC,0,100000
-p14,p124,Possible,LCC,0,100000
-p14,p125,Possible,LCC,0,100000
-p14,p126,Possible,LCC,0,100000
-p14,p127,Possible,LCC,0,100000
-p14,p128,Possible,LCC,0,100000
-p14,p129,Possible,LCC,0,100000
-p14,p130,Possible,LCC,0,100000
-p14,p131,Possible,LCC,0,100000
-p14,p132,Possible,LCC,0,100000
-p14,p133,Possible,LCC,0,100000
-p14,p134,Possible,LCC,0,100000
-p14,p16,Possible,LCC,0,100000
-p14,p18,Possible,LCC,0,100000
-p14,p19,Possible,LCC,0,100000
-p14,p20,Possible,LCC,0,100000
-p14,p21,Possible,LCC,0,100000
-p14,p22,Possible,LCC,0,100000
-p14,p23,Possible,LCC,0,100000
-p14,p24,Possible,LCC,0,100000
-p14,p25,Possible,LCC,0,100000
-p14,p26,Possible,LCC,0,100000
-p14,p27,Possible,LCC,0,100000
-p14,p28,Possible,LCC,0,100000
-p14,p29,Possible,LCC,0,100000
-p14,p30,Possible,LCC,0,100000
-p14,p31,Possible,LCC,0,100000
-p14,p32,Possible,LCC,0,100000
-p14,p33,Possible,LCC,0,100000
-p14,p34,Possible,LCC,0,100000
-p14,p35,Possible,LCC,0,100000
-p14,p36,Possible,LCC,0,100000
-p14,p37,Possible,LCC,0,100000
-p14,p38,Possible,LCC,0,100000
-p14,p39,Possible,LCC,0,100000
-p14,p40,Possible,LCC,0,100000
-p14,p41,Possible,LCC,0,100000
-p14,p42,Possible,LCC,0,100000
-p14,p43,Possible,LCC,0,100000
-p14,p44,Possible,LCC,0,100000
-p14,p45,Possible,LCC,0,100000
-p14,p46,Possible,LCC,0,100000
-p14,p47,Possible,LCC,0,100000
-p14,p48,Possible,LCC,0,100000
-p14,p49,Possible,LCC,0,100000
-p14,p50,Possible,LCC,0,100000
-p14,p51,Possible,LCC,0,100000
-p14,p52,Possible,LCC,0,100000
-p14,p53,Possible,LCC,0,100000
-p14,p54,Possible,LCC,0,100000
-p14,p55,Possible,LCC,0,100000
-p14,p56,Possible,LCC,0,100000
-p14,p57,Possible,LCC,0,100000
-p14,p58,Possible,LCC,0,100000
-p14,p59,Possible,LCC,0,100000
-p14,p60,Possible,LCC,0,100000
-p14,p61,Possible,LCC,0,100000
-p14,p62,Possible,LCC,0,100000
-p14,p63,Possible,LCC,0,100000
-p14,p64,Possible,LCC,0,100000
-p14,p65,Possible,LCC,0,100000
-p14,p66,Possible,LCC,0,100000
-p14,p67,Possible,LCC,0,100000
-p14,p68,Possible,LCC,0,100000
-p14,p69,Possible,LCC,0,100000
-p14,p70,Possible,LCC,0,100000
-p14,p71,Possible,LCC,0,100000
-p14,p72,Possible,LCC,0,100000
-p14,p73,Possible,LCC,0,100000
-p14,p74,Possible,LCC,0,100000
-p14,p75,Possible,LCC,0,100000
-p14,p76,Possible,LCC,0,100000
-p14,p77,Possible,LCC,0,100000
-p14,p78,Possible,LCC,0,100000
-p14,p79,Possible,LCC,0,100000
-p14,p80,Possible,LCC,0,100000
-p14,p81,Possible,LCC,0,100000
-p14,p82,Possible,LCC,0,100000
-p14,p83,Possible,LCC,0,100000
-p14,p84,Possible,LCC,0,100000
-p14,p85,Possible,LCC,0,100000
-p14,p86,Possible,LCC,0,100000
-p14,p87,Possible,LCC,0,100000
-p14,p88,Possible,LCC,0,100000
-p14,p89,Possible,LCC,0,100000
-p14,p90,Possible,LCC,0,100000
-p14,p91,Possible,LCC,0,100000
-p14,p92,Possible,LCC,0,100000
-p14,p93,Possible,LCC,0,100000
-p14,p94,Possible,LCC,0,100000
-p14,p95,Possible,LCC,0,100000
-p14,p96,Possible,LCC,0,100000
-p14,p97,Possible,LCC,0,100000
-p14,p98,Possible,LCC,0,100000
-p14,p99,Possible,LCC,0,100000
-p15,p100,Possible,LCC,0,100000
-p15,p101,Possible,LCC,0,100000
-p15,p102,Possible,LCC,0,100000
-p15,p103,Possible,LCC,0,100000
-p15,p104,Possible,LCC,0,100000
-p15,p105,Possible,LCC,0,100000
-p15,p106,Possible,LCC,0,100000
-p15,p107,Possible,LCC,0,100000
-p15,p108,Possible,LCC,0,100000
-p15,p109,Possible,LCC,0,100000
-p15,p110,Possible,LCC,0,100000
-p15,p111,Possible,LCC,0,100000
-p15,p112,Possible,LCC,0,100000
-p15,p113,Possible,LCC,0,100000
-p15,p114,Possible,LCC,0,100000
-p15,p115,Possible,LCC,0,100000
-p15,p116,Possible,LCC,0,100000
-p15,p117,Possible,LCC,0,100000
-p15,p118,Possible,LCC,0,100000
-p15,p119,Possible,LCC,0,100000
-p15,p120,Possible,LCC,0,100000
-p15,p121,Possible,LCC,0,100000
-p15,p122,Possible,LCC,0,100000
-p15,p123,Possible,LCC,0,100000
-p15,p124,Possible,LCC,0,100000
-p15,p125,Possible,LCC,0,100000
-p15,p126,Possible,LCC,0,100000
-p15,p127,Possible,LCC,0,100000
-p15,p128,Possible,LCC,0,100000
-p15,p129,Possible,LCC,0,100000
-p15,p130,Possible,LCC,0,100000
-p15,p131,Possible,LCC,0,100000
-p15,p132,Possible,LCC,0,100000
-p15,p133,Possible,LCC,0,100000
-p15,p134,Possible,LCC,0,100000
-p15,p18,Possible,LCC,0,100000
-p15,p19,Possible,LCC,0,100000
-p15,p20,Possible,LCC,0,100000
-p15,p22,Possible,LCC,0,100000
-p15,p23,Possible,LCC,0,100000
-p15,p24,Possible,LCC,0,100000
-p15,p26,Possible,LCC,0,100000
-p15,p27,Possible,LCC,0,100000
-p15,p28,Possible,LCC,0,100000
-p15,p29,Possible,LCC,0,100000
-p15,p30,Possible,LCC,0,100000
-p15,p31,Possible,LCC,0,100000
-p15,p32,Possible,LCC,0,100000
-p15,p33,Possible,LCC,0,100000
-p15,p34,Possible,LCC,0,100000
-p15,p35,Possible,LCC,0,100000
-p15,p36,Possible,LCC,0,100000
-p15,p37,Possible,LCC,0,100000
-p15,p38,Possible,LCC,0,100000
-p15,p39,Possible,LCC,0,100000
-p15,p40,Possible,LCC,0,100000
-p15,p41,Possible,LCC,0,100000
-p15,p42,Possible,LCC,0,100000
-p15,p43,Possible,LCC,0,100000
-p15,p44,Possible,LCC,0,100000
-p15,p45,Possible,LCC,0,100000
-p15,p46,Possible,LCC,0,100000
-p15,p47,Possible,LCC,0,100000
-p15,p48,Possible,LCC,0,100000
-p15,p49,Possible,LCC,0,100000
-p15,p50,Possible,LCC,0,100000
-p15,p51,Possible,LCC,0,100000
-p15,p52,Possible,LCC,0,100000
-p15,p53,Possible,LCC,0,100000
-p15,p54,Possible,LCC,0,100000
-p15,p55,Possible,LCC,0,100000
-p15,p56,Possible,LCC,0,100000
-p15,p57,Possible,LCC,0,100000
-p15,p58,Possible,LCC,0,100000
-p15,p59,Possible,LCC,0,100000
-p15,p60,Possible,LCC,0,100000
-p15,p61,Possible,LCC,0,100000
-p15,p62,Possible,LCC,0,100000
-p15,p63,Possible,LCC,0,100000
-p15,p64,Possible,LCC,0,100000
-p15,p65,Possible,LCC,0,100000
-p15,p66,Possible,LCC,0,100000
-p15,p67,Possible,LCC,0,100000
-p15,p68,Possible,LCC,0,100000
-p15,p69,Possible,LCC,0,100000
-p15,p70,Possible,LCC,0,100000
-p15,p71,Possible,LCC,0,100000
-p15,p72,Possible,LCC,0,100000
-p15,p73,Possible,LCC,0,100000
-p15,p74,Possible,LCC,0,100000
-p15,p75,Possible,LCC,0,100000
-p15,p76,Possible,LCC,0,100000
-p15,p77,Possible,LCC,0,100000
-p15,p78,Possible,LCC,0,100000
-p15,p79,Possible,LCC,0,100000
-p15,p80,Possible,LCC,0,100000
-p15,p81,Possible,LCC,0,100000
-p15,p82,Possible,LCC,0,100000
-p15,p83,Possible,LCC,0,100000
-p15,p84,Possible,LCC,0,100000
-p15,p85,Possible,LCC,0,100000
-p15,p86,Possible,LCC,0,100000
-p15,p87,Possible,LCC,0,100000
-p15,p88,Possible,LCC,0,100000
-p15,p89,Possible,LCC,0,100000
-p15,p90,Possible,LCC,0,100000
-p15,p91,Possible,LCC,0,100000
-p15,p92,Possible,LCC,0,100000
-p15,p93,Possible,LCC,0,100000
-p15,p94,Possible,LCC,0,100000
-p15,p95,Possible,LCC,0,100000
-p15,p96,Possible,LCC,0,100000
-p15,p97,Possible,LCC,0,100000
-p15,p98,Possible,LCC,0,100000
-p15,p99,Possible,LCC,0,100000
-p16,p100,Possible,LCC,0,100000
-p16,p101,Possible,LCC,0,100000
-p16,p102,Possible,LCC,0,100000
-p16,p103,Possible,LCC,0,100000
-p16,p104,Possible,LCC,0,100000
-p16,p105,Possible,LCC,0,100000
-p16,p106,Possible,LCC,0,100000
-p16,p107,Possible,LCC,0,100000
-p16,p108,Possible,LCC,0,100000
-p16,p109,Possible,LCC,0,100000
-p16,p110,Possible,LCC,0,100000
-p16,p111,Possible,LCC,0,100000
-p16,p112,Possible,LCC,0,100000
-p16,p113,Possible,LCC,0,100000
-p16,p114,Possible,LCC,0,100000
-p16,p115,Possible,LCC,0,100000
-p16,p116,Possible,LCC,0,100000
-p16,p117,Possible,LCC,0,100000
-p16,p118,Possible,LCC,0,100000
-p16,p119,Possible,LCC,0,100000
-p16,p120,Possible,LCC,0,100000
-p16,p121,Possible,LCC,0,100000
-p16,p122,Possible,LCC,0,100000
-p16,p123,Possible,LCC,0,100000
-p16,p124,Possible,LCC,0,100000
-p16,p125,Possible,LCC,0,100000
-p16,p126,Possible,LCC,0,100000
-p16,p127,Possible,LCC,0,100000
-p16,p128,Possible,LCC,0,100000
-p16,p129,Possible,LCC,0,100000
-p16,p130,Possible,LCC,0,100000
-p16,p131,Possible,LCC,0,100000
-p16,p132,Possible,LCC,0,100000
-p16,p133,Possible,LCC,0,100000
-p16,p134,Possible,LCC,0,100000
-p16,p19,Possible,LCC,0,100000
-p16,p20,Possible,LCC,0,100000
-p16,p22,Possible,LCC,0,100000
-p16,p23,Possible,LCC,0,100000
-p16,p24,Possible,LCC,0,100000
-p16,p26,Possible,LCC,0,100000
-p16,p27,Possible,LCC,0,100000
-p16,p28,Possible,LCC,0,100000
-p16,p29,Possible,LCC,0,100000
-p16,p30,Possible,LCC,0,100000
-p16,p31,Possible,LCC,0,100000
-p16,p32,Possible,LCC,0,100000
-p16,p33,Possible,LCC,0,100000
-p16,p34,Possible,LCC,0,100000
-p16,p35,Possible,LCC,0,100000
-p16,p36,Possible,LCC,0,100000
-p16,p37,Possible,LCC,0,100000
-p16,p38,Possible,LCC,0,100000
-p16,p39,Possible,LCC,0,100000
-p16,p40,Possible,LCC,0,100000
-p16,p41,Possible,LCC,0,100000
-p16,p42,Possible,LCC,0,100000
-p16,p43,Possible,LCC,0,100000
-p16,p44,Possible,LCC,0,100000
-p16,p45,Possible,LCC,0,100000
-p16,p46,Possible,LCC,0,100000
-p16,p47,Possible,LCC,0,100000
-p16,p48,Possible,LCC,0,100000
-p16,p49,Possible,LCC,0,100000
-p16,p50,Possible,LCC,0,100000
-p16,p51,Possible,LCC,0,100000
-p16,p52,Possible,LCC,0,100000
-p16,p53,Possible,LCC,0,100000
-p16,p54,Possible,LCC,0,100000
-p16,p55,Possible,LCC,0,100000
-p16,p56,Possible,LCC,0,100000
-p16,p57,Possible,LCC,0,100000
-p16,p58,Possible,LCC,0,100000
-p16,p59,Possible,LCC,0,100000
-p16,p60,Possible,LCC,0,100000
-p16,p61,Possible,LCC,0,100000
-p16,p62,Possible,LCC,0,100000
-p16,p63,Possible,LCC,0,100000
-p16,p64,Possible,LCC,0,100000
-p16,p65,Possible,LCC,0,100000
-p16,p66,Possible,LCC,0,100000
-p16,p67,Possible,LCC,0,100000
-p16,p68,Possible,LCC,0,100000
-p16,p69,Possible,LCC,0,100000
-p16,p70,Possible,LCC,0,100000
-p16,p71,Possible,LCC,0,100000
-p16,p72,Possible,LCC,0,100000
-p16,p73,Possible,LCC,0,100000
-p16,p74,Possible,LCC,0,100000
-p16,p75,Possible,LCC,0,100000
-p16,p76,Possible,LCC,0,100000
-p16,p77,Possible,LCC,0,100000
-p16,p78,Possible,LCC,0,100000
-p16,p79,Possible,LCC,0,100000
-p16,p80,Possible,LCC,0,100000
-p16,p81,Possible,LCC,0,100000
-p16,p82,Possible,LCC,0,100000
-p16,p83,Possible,LCC,0,100000
-p16,p84,Possible,LCC,0,100000
-p16,p85,Possible,LCC,0,100000
-p16,p86,Possible,LCC,0,100000
-p16,p87,Possible,LCC,0,100000
-p16,p88,Possible,LCC,0,100000
-p16,p89,Possible,LCC,0,100000
-p16,p90,Possible,LCC,0,100000
-p16,p91,Possible,LCC,0,100000
-p16,p92,Possible,LCC,0,100000
-p16,p93,Possible,LCC,0,100000
-p16,p94,Possible,LCC,0,100000
-p16,p95,Possible,LCC,0,100000
-p16,p96,Possible,LCC,0,100000
-p16,p97,Possible,LCC,0,100000
-p16,p98,Possible,LCC,0,100000
-p16,p99,Possible,LCC,0,100000
-p17,p100,Possible,LCC,0,100000
-p17,p101,Possible,LCC,0,100000
-p17,p102,Possible,LCC,0,100000
-p17,p103,Possible,LCC,0,100000
-p17,p104,Possible,LCC,0,100000
-p17,p105,Possible,LCC,0,100000
-p17,p106,Possible,LCC,0,100000
-p17,p107,Possible,LCC,0,100000
-p17,p108,Possible,LCC,0,100000
-p17,p109,Possible,LCC,0,100000
-p17,p110,Possible,LCC,0,100000
-p17,p111,Possible,LCC,0,100000
-p17,p112,Possible,LCC,0,100000
-p17,p113,Possible,LCC,0,100000
-p17,p114,Possible,LCC,0,100000
-p17,p115,Possible,LCC,0,100000
-p17,p116,Possible,LCC,0,100000
-p17,p117,Possible,LCC,0,100000
-p17,p118,Possible,LCC,0,100000
-p17,p119,Possible,LCC,0,100000
-p17,p120,Possible,LCC,0,100000
-p17,p121,Possible,LCC,0,100000
-p17,p122,Possible,LCC,0,100000
-p17,p123,Possible,LCC,0,100000
-p17,p124,Possible,LCC,0,100000
-p17,p125,Possible,LCC,0,100000
-p17,p126,Possible,LCC,0,100000
-p17,p127,Possible,LCC,0,100000
-p17,p128,Possible,LCC,0,100000
-p17,p129,Possible,LCC,0,100000
-p17,p130,Possible,LCC,0,100000
-p17,p131,Possible,LCC,0,100000
-p17,p132,Possible,LCC,0,100000
-p17,p133,Possible,LCC,0,100000
-p17,p134,Possible,LCC,0,100000
-p17,p19,Possible,LCC,0,100000
-p17,p21,Possible,LCC,0,100000
-p17,p22,Possible,LCC,0,100000
-p17,p23,Possible,LCC,0,100000
-p17,p24,Possible,LCC,0,100000
-p17,p25,Possible,LCC,0,100000
-p17,p26,Possible,LCC,0,100000
-p17,p27,Possible,LCC,0,100000
-p17,p28,Possible,LCC,0,100000
-p17,p29,Possible,LCC,0,100000
-p17,p30,Possible,LCC,0,100000
-p17,p31,Possible,LCC,0,100000
-p17,p32,Possible,LCC,0,100000
-p17,p33,Possible,LCC,0,100000
-p17,p34,Possible,LCC,0,100000
-p17,p35,Possible,LCC,0,100000
-p17,p36,Possible,LCC,0,100000
-p17,p37,Possible,LCC,0,100000
-p17,p38,Possible,LCC,0,100000
-p17,p39,Possible,LCC,0,100000
-p17,p40,Possible,LCC,0,100000
-p17,p41,Possible,LCC,0,100000
-p17,p42,Possible,LCC,0,100000
-p17,p43,Possible,LCC,0,100000
-p17,p44,Possible,LCC,0,100000
-p17,p45,Possible,LCC,0,100000
-p17,p46,Possible,LCC,0,100000
-p17,p47,Possible,LCC,0,100000
-p17,p48,Possible,LCC,0,100000
-p17,p49,Possible,LCC,0,100000
-p17,p50,Possible,LCC,0,100000
-p17,p51,Possible,LCC,0,100000
-p17,p52,Possible,LCC,0,100000
-p17,p53,Possible,LCC,0,100000
-p17,p54,Possible,LCC,0,100000
-p17,p55,Possible,LCC,0,100000
-p17,p56,Possible,LCC,0,100000
-p17,p57,Possible,LCC,0,100000
-p17,p58,Possible,LCC,0,100000
-p17,p59,Possible,LCC,0,100000
-p17,p60,Possible,LCC,0,100000
-p17,p61,Possible,LCC,0,100000
-p17,p62,Possible,LCC,0,100000
-p17,p63,Possible,LCC,0,100000
-p17,p64,Possible,LCC,0,100000
-p17,p65,Possible,LCC,0,100000
-p17,p66,Possible,LCC,0,100000
-p17,p67,Possible,LCC,0,100000
-p17,p68,Possible,LCC,0,100000
-p17,p69,Possible,LCC,0,100000
-p17,p70,Possible,LCC,0,100000
-p17,p71,Possible,LCC,0,100000
-p17,p72,Possible,LCC,0,100000
-p17,p73,Possible,LCC,0,100000
-p17,p74,Possible,LCC,0,100000
-p17,p75,Possible,LCC,0,100000
-p17,p76,Possible,LCC,0,100000
-p17,p77,Possible,LCC,0,100000
-p17,p78,Possible,LCC,0,100000
-p17,p79,Possible,LCC,0,100000
-p17,p80,Possible,LCC,0,100000
-p17,p81,Possible,LCC,0,100000
-p17,p82,Possible,LCC,0,100000
-p17,p83,Possible,LCC,0,100000
-p17,p84,Possible,LCC,0,100000
-p17,p85,Possible,LCC,0,100000
-p17,p86,Possible,LCC,0,100000
-p17,p87,Possible,LCC,0,100000
-p17,p88,Possible,LCC,0,100000
-p17,p89,Possible,LCC,0,100000
-p17,p90,Possible,LCC,0,100000
-p17,p91,Possible,LCC,0,100000
-p17,p92,Possible,LCC,0,100000
-p17,p93,Possible,LCC,0,100000
-p17,p94,Possible,LCC,0,100000
-p17,p95,Possible,LCC,0,100000
-p17,p96,Possible,LCC,0,100000
-p17,p97,Possible,LCC,0,100000
-p17,p98,Possible,LCC,0,100000
-p17,p99,Possible,LCC,0,100000
-p18,p100,Possible,LCC,0,100000
-p18,p101,Possible,LCC,0,100000
-p18,p102,Possible,LCC,0,100000
-p18,p103,Possible,LCC,0,100000
-p18,p104,Possible,LCC,0,100000
-p18,p105,Possible,LCC,0,100000
-p18,p106,Possible,LCC,0,100000
-p18,p107,Possible,LCC,0,100000
-p18,p108,Possible,LCC,0,100000
-p18,p109,Possible,LCC,0,100000
-p18,p110,Possible,LCC,0,100000
-p18,p111,Possible,LCC,0,100000
-p18,p112,Possible,LCC,0,100000
-p18,p113,Possible,LCC,0,100000
-p18,p114,Possible,LCC,0,100000
-p18,p115,Possible,LCC,0,100000
-p18,p116,Possible,LCC,0,100000
-p18,p117,Possible,LCC,0,100000
-p18,p118,Possible,LCC,0,100000
-p18,p119,Possible,LCC,0,100000
-p18,p120,Possible,LCC,0,100000
-p18,p121,Possible,LCC,0,100000
-p18,p122,Possible,LCC,0,100000
-p18,p123,Possible,LCC,0,100000
-p18,p124,Possible,LCC,0,100000
-p18,p125,Possible,LCC,0,100000
-p18,p126,Possible,LCC,0,100000
-p18,p127,Possible,LCC,0,100000
-p18,p128,Possible,LCC,0,100000
-p18,p129,Possible,LCC,0,100000
-p18,p130,Possible,LCC,0,100000
-p18,p131,Possible,LCC,0,100000
-p18,p132,Possible,LCC,0,100000
-p18,p133,Possible,LCC,0,100000
-p18,p134,Possible,LCC,0,100000
-p18,p23,Possible,LCC,0,100000
-p18,p24,Possible,LCC,0,100000
-p18,p25,Possible,LCC,0,100000
-p18,p26,Possible,LCC,0,100000
-p18,p27,Possible,LCC,0,100000
-p18,p28,Possible,LCC,0,100000
-p18,p29,Possible,LCC,0,100000
-p18,p30,Possible,LCC,0,100000
-p18,p31,Possible,LCC,0,100000
-p18,p32,Possible,LCC,0,100000
-p18,p33,Possible,LCC,0,100000
-p18,p34,Possible,LCC,0,100000
-p18,p35,Possible,LCC,0,100000
-p18,p36,Possible,LCC,0,100000
-p18,p37,Possible,LCC,0,100000
-p18,p38,Possible,LCC,0,100000
-p18,p39,Possible,LCC,0,100000
-p18,p40,Possible,LCC,0,100000
-p18,p41,Possible,LCC,0,100000
-p18,p42,Possible,LCC,0,100000
-p18,p43,Possible,LCC,0,100000
-p18,p44,Possible,LCC,0,100000
-p18,p45,Possible,LCC,0,100000
-p18,p46,Possible,LCC,0,100000
-p18,p47,Possible,LCC,0,100000
-p18,p48,Possible,LCC,0,100000
-p18,p49,Possible,LCC,0,100000
-p18,p50,Possible,LCC,0,100000
-p18,p51,Possible,LCC,0,100000
-p18,p52,Possible,LCC,0,100000
-p18,p53,Possible,LCC,0,100000
-p18,p54,Possible,LCC,0,100000
-p18,p55,Possible,LCC,0,100000
-p18,p56,Possible,LCC,0,100000
-p18,p57,Possible,LCC,0,100000
-p18,p58,Possible,LCC,0,100000
-p18,p59,Possible,LCC,0,100000
-p18,p60,Possible,LCC,0,100000
-p18,p61,Possible,LCC,0,100000
-p18,p62,Possible,LCC,0,100000
-p18,p63,Possible,LCC,0,100000
-p18,p64,Possible,LCC,0,100000
-p18,p65,Possible,LCC,0,100000
-p18,p66,Possible,LCC,0,100000
-p18,p67,Possible,LCC,0,100000
-p18,p68,Possible,LCC,0,100000
-p18,p69,Possible,LCC,0,100000
-p18,p70,Possible,LCC,0,100000
-p18,p71,Possible,LCC,0,100000
-p18,p72,Possible,LCC,0,100000
-p18,p73,Possible,LCC,0,100000
-p18,p74,Possible,LCC,0,100000
-p18,p75,Possible,LCC,0,100000
-p18,p76,Possible,LCC,0,100000
-p18,p77,Possible,LCC,0,100000
-p18,p78,Possible,LCC,0,100000
-p18,p79,Possible,LCC,0,100000
-p18,p80,Possible,LCC,0,100000
-p18,p81,Possible,LCC,0,100000
-p18,p82,Possible,LCC,0,100000
-p18,p83,Possible,LCC,0,100000
-p18,p84,Possible,LCC,0,100000
-p18,p85,Possible,LCC,0,100000
-p18,p86,Possible,LCC,0,100000
-p18,p87,Possible,LCC,0,100000
-p18,p88,Possible,LCC,0,100000
-p18,p89,Possible,LCC,0,100000
-p18,p90,Possible,LCC,0,100000
-p18,p91,Possible,LCC,0,100000
-p18,p92,Possible,LCC,0,100000
-p18,p93,Possible,LCC,0,100000
-p18,p94,Possible,LCC,0,100000
-p18,p95,Possible,LCC,0,100000
-p18,p96,Possible,LCC,0,100000
-p18,p97,Possible,LCC,0,100000
-p18,p98,Possible,LCC,0,100000
-p18,p99,Possible,LCC,0,100000
-p19,p100,Possible,LCC,0,100000
-p19,p101,Possible,LCC,0,100000
-p19,p102,Possible,LCC,0,100000
-p19,p103,Possible,LCC,0,100000
-p19,p104,Possible,LCC,0,100000
-p19,p105,Possible,LCC,0,100000
-p19,p106,Possible,LCC,0,100000
-p19,p107,Possible,LCC,0,100000
-p19,p108,Possible,LCC,0,100000
-p19,p109,Possible,LCC,0,100000
-p19,p110,Possible,LCC,0,100000
-p19,p111,Possible,LCC,0,100000
-p19,p112,Possible,LCC,0,100000
-p19,p113,Possible,LCC,0,100000
-p19,p114,Possible,LCC,0,100000
-p19,p115,Possible,LCC,0,100000
-p19,p116,Possible,LCC,0,100000
-p19,p117,Possible,LCC,0,100000
-p19,p118,Possible,LCC,0,100000
-p19,p119,Possible,LCC,0,100000
-p19,p120,Possible,LCC,0,100000
-p19,p121,Possible,LCC,0,100000
-p19,p122,Possible,LCC,0,100000
-p19,p123,Possible,LCC,0,100000
-p19,p124,Possible,LCC,0,100000
-p19,p125,Possible,LCC,0,100000
-p19,p126,Possible,LCC,0,100000
-p19,p127,Possible,LCC,0,100000
-p19,p128,Possible,LCC,0,100000
-p19,p129,Possible,LCC,0,100000
-p19,p130,Possible,LCC,0,100000
-p19,p131,Possible,LCC,0,100000
-p19,p132,Possible,LCC,0,100000
-p19,p133,Possible,LCC,0,100000
-p19,p134,Possible,LCC,0,100000
-p19,p20,Possible,LCC,0,100000
-p19,p21,Possible,LCC,0,100000
-p19,p22,Possible,LCC,0,100000
-p19,p23,Possible,LCC,0,100000
-p19,p24,Possible,LCC,0,100000
-p19,p25,Possible,LCC,0,100000
-p19,p26,Possible,LCC,0,100000
-p19,p27,Possible,LCC,0,100000
-p19,p28,Possible,LCC,0,100000
-p19,p29,Possible,LCC,0,100000
-p19,p30,Possible,LCC,0,100000
-p19,p31,Possible,LCC,0,100000
-p19,p32,Possible,LCC,0,100000
-p19,p33,Possible,LCC,0,100000
-p19,p34,Possible,LCC,0,100000
-p19,p35,Possible,LCC,0,100000
-p19,p36,Possible,LCC,0,100000
-p19,p37,Possible,LCC,0,100000
-p19,p38,Possible,LCC,0,100000
-p19,p39,Possible,LCC,0,100000
-p19,p40,Possible,LCC,0,100000
-p19,p41,Possible,LCC,0,100000
-p19,p42,Possible,LCC,0,100000
-p19,p43,Possible,LCC,0,100000
-p19,p44,Possible,LCC,0,100000
-p19,p45,Possible,LCC,0,100000
-p19,p46,Possible,LCC,0,100000
-p19,p47,Possible,LCC,0,100000
-p19,p48,Possible,LCC,0,100000
-p19,p49,Possible,LCC,0,100000
-p19,p50,Possible,LCC,0,100000
-p19,p51,Possible,LCC,0,100000
-p19,p52,Possible,LCC,0,100000
-p19,p53,Possible,LCC,0,100000
-p19,p54,Possible,LCC,0,100000
-p19,p55,Possible,LCC,0,100000
-p19,p56,Possible,LCC,0,100000
-p19,p57,Possible,LCC,0,100000
-p19,p58,Possible,LCC,0,100000
-p19,p59,Possible,LCC,0,100000
-p19,p60,Possible,LCC,0,100000
-p19,p61,Possible,LCC,0,100000
-p19,p62,Possible,LCC,0,100000
-p19,p63,Possible,LCC,0,100000
-p19,p64,Possible,LCC,0,100000
-p19,p65,Possible,LCC,0,100000
-p19,p66,Possible,LCC,0,100000
-p19,p67,Possible,LCC,0,100000
-p19,p68,Possible,LCC,0,100000
-p19,p69,Possible,LCC,0,100000
-p19,p70,Possible,LCC,0,100000
-p19,p71,Possible,LCC,0,100000
-p19,p72,Possible,LCC,0,100000
-p19,p73,Possible,LCC,0,100000
-p19,p74,Possible,LCC,0,100000
-p19,p75,Possible,LCC,0,100000
-p19,p76,Possible,LCC,0,100000
-p19,p77,Possible,LCC,0,100000
-p19,p78,Possible,LCC,0,100000
-p19,p79,Possible,LCC,0,100000
-p19,p80,Possible,LCC,0,100000
-p19,p81,Possible,LCC,0,100000
-p19,p82,Possible,LCC,0,100000
-p19,p83,Possible,LCC,0,100000
-p19,p84,Possible,LCC,0,100000
-p19,p85,Possible,LCC,0,100000
-p19,p86,Possible,LCC,0,100000
-p19,p87,Possible,LCC,0,100000
-p19,p88,Possible,LCC,0,100000
-p19,p89,Possible,LCC,0,100000
-p19,p90,Possible,LCC,0,100000
-p19,p91,Possible,LCC,0,100000
-p19,p92,Possible,LCC,0,100000
-p19,p93,Possible,LCC,0,100000
-p19,p94,Possible,LCC,0,100000
-p19,p95,Possible,LCC,0,100000
-p19,p96,Possible,LCC,0,100000
-p19,p97,Possible,LCC,0,100000
-p19,p98,Possible,LCC,0,100000
-p19,p99,Possible,LCC,0,100000
-p2,p100,Possible,LCC,0,100000
-p2,p101,Possible,LCC,0,100000
-p2,p102,Possible,LCC,0,100000
-p2,p103,Possible,LCC,0,100000
-p2,p104,Possible,LCC,0,100000
-p2,p105,Possible,LCC,0,100000
-p2,p106,Possible,LCC,0,100000
-p2,p107,Possible,LCC,0,100000
-p2,p108,Possible,LCC,0,100000
-p2,p109,Possible,LCC,0,100000
-p2,p11,Possible,LCC,0,100000
-p2,p110,Possible,LCC,0,100000
-p2,p111,Possible,LCC,0,100000
-p2,p112,Possible,LCC,0,100000
-p2,p113,Possible,LCC,0,100000
-p2,p114,Possible,LCC,0,100000
-p2,p115,Possible,LCC,0,100000
-p2,p116,Possible,LCC,0,100000
-p2,p117,Possible,LCC,0,100000
-p2,p118,Possible,LCC,0,100000
-p2,p119,Possible,LCC,0,100000
-p2,p12,Possible,LCC,0,100000
-p2,p120,Possible,LCC,0,100000
-p2,p121,Possible,LCC,0,100000
-p2,p122,Possible,LCC,0,100000
-p2,p123,Possible,LCC,0,100000
-p2,p124,Possible,LCC,0,100000
-p2,p125,Possible,LCC,0,100000
-p2,p126,Possible,LCC,0,100000
-p2,p127,Possible,LCC,0,100000
-p2,p128,Possible,LCC,0,100000
-p2,p129,Possible,LCC,0,100000
-p2,p13,Possible,LCC,0,100000
-p2,p130,Possible,LCC,0,100000
-p2,p131,Possible,LCC,0,100000
-p2,p132,Possible,LCC,0,100000
-p2,p133,Possible,LCC,0,100000
-p2,p134,Possible,LCC,0,100000
-p2,p15,Possible,LCC,0,100000
-p2,p16,Possible,LCC,0,100000
-p2,p18,Possible,LCC,0,100000
-p2,p19,Possible,LCC,0,100000
-p2,p20,Possible,LCC,0,100000
-p2,p21,Possible,LCC,0,100000
-p2,p22,Possible,LCC,0,100000
-p2,p23,Possible,LCC,0,100000
-p2,p24,Possible,LCC,0,100000
-p2,p25,Possible,LCC,0,100000
-p2,p26,Possible,LCC,0,100000
-p2,p27,Possible,LCC,0,100000
-p2,p28,Possible,LCC,0,100000
-p2,p29,Possible,LCC,0,100000
-p2,p30,Possible,LCC,0,100000
-p2,p31,Possible,LCC,0,100000
-p2,p32,Possible,LCC,0,100000
-p2,p33,Possible,LCC,0,100000
-p2,p34,Possible,LCC,0,100000
-p2,p35,Possible,LCC,0,100000
-p2,p36,Possible,LCC,0,100000
-p2,p37,Possible,LCC,0,100000
-p2,p38,Possible,LCC,0,100000
-p2,p39,Possible,LCC,0,100000
-p2,p40,Possible,LCC,0,100000
-p2,p41,Possible,LCC,0,100000
-p2,p42,Possible,LCC,0,100000
-p2,p43,Possible,LCC,0,100000
-p2,p44,Possible,LCC,0,100000
-p2,p45,Possible,LCC,0,100000
-p2,p46,Possible,LCC,0,100000
-p2,p47,Possible,LCC,0,100000
-p2,p48,Possible,LCC,0,100000
-p2,p49,Possible,LCC,0,100000
-p2,p50,Possible,LCC,0,100000
-p2,p51,Possible,LCC,0,100000
-p2,p52,Possible,LCC,0,100000
-p2,p53,Possible,LCC,0,100000
-p2,p54,Possible,LCC,0,100000
-p2,p55,Possible,LCC,0,100000
-p2,p56,Possible,LCC,0,100000
-p2,p57,Possible,LCC,0,100000
-p2,p58,Possible,LCC,0,100000
-p2,p59,Possible,LCC,0,100000
-p2,p6,Possible,LCC,0,100000
-p2,p60,Possible,LCC,0,100000
-p2,p61,Possible,LCC,0,100000
-p2,p62,Possible,LCC,0,100000
-p2,p63,Possible,LCC,0,100000
-p2,p64,Possible,LCC,0,100000
-p2,p65,Possible,LCC,0,100000
-p2,p66,Possible,LCC,0,100000
-p2,p67,Possible,LCC,0,100000
-p2,p68,Possible,LCC,0,100000
-p2,p69,Possible,LCC,0,100000
-p2,p7,Possible,LCC,0,100000
-p2,p70,Possible,LCC,0,100000
-p2,p71,Possible,LCC,0,100000
-p2,p72,Possible,LCC,0,100000
-p2,p73,Possible,LCC,0,100000
-p2,p74,Possible,LCC,0,100000
-p2,p75,Possible,LCC,0,100000
-p2,p76,Possible,LCC,0,100000
-p2,p77,Possible,LCC,0,100000
-p2,p78,Possible,LCC,0,100000
-p2,p79,Possible,LCC,0,100000
-p2,p8,Possible,LCC,0,100000
-p2,p80,Possible,LCC,0,100000
-p2,p81,Possible,LCC,0,100000
-p2,p82,Possible,LCC,0,100000
-p2,p83,Possible,LCC,0,100000
-p2,p84,Possible,LCC,0,100000
-p2,p85,Possible,LCC,0,100000
-p2,p86,Possible,LCC,0,100000
-p2,p87,Possible,LCC,0,100000
-p2,p88,Possible,LCC,0,100000
-p2,p89,Possible,LCC,0,100000
-p2,p9,Possible,LCC,0,100000
-p2,p90,Possible,LCC,0,100000
-p2,p91,Possible,LCC,0,100000
-p2,p92,Possible,LCC,0,100000
-p2,p93,Possible,LCC,0,100000
-p2,p94,Possible,LCC,0,100000
-p2,p95,Possible,LCC,0,100000
-p2,p96,Possible,LCC,0,100000
-p2,p97,Possible,LCC,0,100000
-p2,p98,Possible,LCC,0,100000
-p2,p99,Possible,LCC,0,100000
-p20,p100,Possible,LCC,0,100000
-p20,p101,Possible,LCC,0,100000
-p20,p102,Possible,LCC,0,100000
-p20,p103,Possible,LCC,0,100000
-p20,p104,Possible,LCC,0,100000
-p20,p105,Possible,LCC,0,100000
-p20,p106,Possible,LCC,0,100000
-p20,p107,Possible,LCC,0,100000
-p20,p108,Possible,LCC,0,100000
-p20,p109,Possible,LCC,0,100000
-p20,p110,Possible,LCC,0,100000
-p20,p111,Possible,LCC,0,100000
-p20,p112,Possible,LCC,0,100000
-p20,p113,Possible,LCC,0,100000
-p20,p114,Possible,LCC,0,100000
-p20,p115,Possible,LCC,0,100000
-p20,p116,Possible,LCC,0,100000
-p20,p117,Possible,LCC,0,100000
-p20,p118,Possible,LCC,0,100000
-p20,p119,Possible,LCC,0,100000
-p20,p120,Possible,LCC,0,100000
-p20,p121,Possible,LCC,0,100000
-p20,p122,Possible,LCC,0,100000
-p20,p123,Possible,LCC,0,100000
-p20,p124,Possible,LCC,0,100000
-p20,p125,Possible,LCC,0,100000
-p20,p126,Possible,LCC,0,100000
-p20,p127,Possible,LCC,0,100000
-p20,p128,Possible,LCC,0,100000
-p20,p129,Possible,LCC,0,100000
-p20,p130,Possible,LCC,0,100000
-p20,p131,Possible,LCC,0,100000
-p20,p132,Possible,LCC,0,100000
-p20,p133,Possible,LCC,0,100000
-p20,p134,Possible,LCC,0,100000
-p20,p21,Possible,LCC,0,100000
-p20,p22,Possible,LCC,0,100000
-p20,p23,Possible,LCC,0,100000
-p20,p24,Possible,LCC,0,100000
-p20,p25,Possible,LCC,0,100000
-p20,p26,Possible,LCC,0,100000
-p20,p27,Possible,LCC,0,100000
-p20,p28,Possible,LCC,0,100000
-p20,p29,Possible,LCC,0,100000
-p20,p30,Possible,LCC,0,100000
-p20,p31,Possible,LCC,0,100000
-p20,p32,Possible,LCC,0,100000
-p20,p33,Possible,LCC,0,100000
-p20,p34,Possible,LCC,0,100000
-p20,p36,Possible,LCC,0,100000
-p20,p37,Possible,LCC,0,100000
-p20,p38,Possible,LCC,0,100000
-p20,p39,Possible,LCC,0,100000
-p20,p40,Possible,LCC,0,100000
-p20,p41,Possible,LCC,0,100000
-p20,p42,Possible,LCC,0,100000
-p20,p43,Possible,LCC,0,100000
-p20,p44,Possible,LCC,0,100000
-p20,p45,Possible,LCC,0,100000
-p20,p46,Possible,LCC,0,100000
-p20,p47,Possible,LCC,0,100000
-p20,p48,Possible,LCC,0,100000
-p20,p49,Possible,LCC,0,100000
-p20,p50,Possible,LCC,0,100000
-p20,p51,Possible,LCC,0,100000
-p20,p52,Possible,LCC,0,100000
-p20,p53,Possible,LCC,0,100000
-p20,p54,Possible,LCC,0,100000
-p20,p55,Possible,LCC,0,100000
-p20,p56,Possible,LCC,0,100000
-p20,p57,Possible,LCC,0,100000
-p20,p58,Possible,LCC,0,100000
-p20,p59,Possible,LCC,0,100000
-p20,p60,Possible,LCC,0,100000
-p20,p61,Possible,LCC,0,100000
-p20,p62,Possible,LCC,0,100000
-p20,p63,Possible,LCC,0,100000
-p20,p64,Possible,LCC,0,100000
-p20,p65,Possible,LCC,0,100000
-p20,p66,Possible,LCC,0,100000
-p20,p67,Possible,LCC,0,100000
-p20,p68,Possible,LCC,0,100000
-p20,p69,Possible,LCC,0,100000
-p20,p70,Possible,LCC,0,100000
-p20,p71,Possible,LCC,0,100000
-p20,p72,Possible,LCC,0,100000
-p20,p73,Possible,LCC,0,100000
-p20,p74,Possible,LCC,0,100000
-p20,p75,Possible,LCC,0,100000
-p20,p76,Possible,LCC,0,100000
-p20,p77,Possible,LCC,0,100000
-p20,p78,Possible,LCC,0,100000
-p20,p79,Possible,LCC,0,100000
-p20,p80,Possible,LCC,0,100000
-p20,p81,Possible,LCC,0,100000
-p20,p82,Possible,LCC,0,100000
-p20,p83,Possible,LCC,0,100000
-p20,p84,Possible,LCC,0,100000
-p20,p85,Possible,LCC,0,100000
-p20,p86,Possible,LCC,0,100000
-p20,p87,Possible,LCC,0,100000
-p20,p88,Possible,LCC,0,100000
-p20,p89,Possible,LCC,0,100000
-p20,p90,Possible,LCC,0,100000
-p20,p91,Possible,LCC,0,100000
-p20,p92,Possible,LCC,0,100000
-p20,p93,Possible,LCC,0,100000
-p20,p94,Possible,LCC,0,100000
-p20,p95,Possible,LCC,0,100000
-p20,p96,Possible,LCC,0,100000
-p20,p97,Possible,LCC,0,100000
-p20,p98,Possible,LCC,0,100000
-p20,p99,Possible,LCC,0,100000
-p21,p100,Possible,LCC,0,100000
-p21,p101,Possible,LCC,0,100000
-p21,p102,Possible,LCC,0,100000
-p21,p103,Possible,LCC,0,100000
-p21,p104,Possible,LCC,0,100000
-p21,p105,Possible,LCC,0,100000
-p21,p106,Possible,LCC,0,100000
-p21,p107,Possible,LCC,0,100000
-p21,p108,Possible,LCC,0,100000
-p21,p109,Possible,LCC,0,100000
-p21,p110,Possible,LCC,0,100000
-p21,p111,Possible,LCC,0,100000
-p21,p112,Possible,LCC,0,100000
-p21,p113,Possible,LCC,0,100000
-p21,p114,Possible,LCC,0,100000
-p21,p115,Possible,LCC,0,100000
-p21,p116,Possible,LCC,0,100000
-p21,p117,Possible,LCC,0,100000
-p21,p118,Possible,LCC,0,100000
-p21,p119,Possible,LCC,0,100000
-p21,p120,Possible,LCC,0,100000
-p21,p121,Possible,LCC,0,100000
-p21,p122,Possible,LCC,0,100000
-p21,p123,Possible,LCC,0,100000
-p21,p124,Possible,LCC,0,100000
-p21,p125,Possible,LCC,0,100000
-p21,p126,Possible,LCC,0,100000
-p21,p127,Possible,LCC,0,100000
-p21,p128,Possible,LCC,0,100000
-p21,p129,Possible,LCC,0,100000
-p21,p130,Possible,LCC,0,100000
-p21,p131,Possible,LCC,0,100000
-p21,p132,Possible,LCC,0,100000
-p21,p133,Possible,LCC,0,100000
-p21,p134,Possible,LCC,0,100000
-p21,p22,Possible,LCC,0,100000
-p21,p23,Possible,LCC,0,100000
-p21,p27,Possible,LCC,0,100000
-p21,p28,Possible,LCC,0,100000
-p21,p29,Possible,LCC,0,100000
-p21,p30,Possible,LCC,0,100000
-p21,p31,Possible,LCC,0,100000
-p21,p32,Possible,LCC,0,100000
-p21,p34,Possible,LCC,0,100000
-p21,p35,Possible,LCC,0,100000
-p21,p36,Possible,LCC,0,100000
-p21,p37,Possible,LCC,0,100000
-p21,p38,Possible,LCC,0,100000
-p21,p39,Possible,LCC,0,100000
-p21,p40,Possible,LCC,0,100000
-p21,p41,Possible,LCC,0,100000
-p21,p42,Possible,LCC,0,100000
-p21,p43,Possible,LCC,0,100000
-p21,p44,Possible,LCC,0,100000
-p21,p45,Possible,LCC,0,100000
-p21,p46,Possible,LCC,0,100000
-p21,p47,Possible,LCC,0,100000
-p21,p48,Possible,LCC,0,100000
-p21,p49,Possible,LCC,0,100000
-p21,p50,Possible,LCC,0,100000
-p21,p51,Possible,LCC,0,100000
-p21,p52,Possible,LCC,0,100000
-p21,p53,Possible,LCC,0,100000
-p21,p54,Possible,LCC,0,100000
-p21,p55,Possible,LCC,0,100000
-p21,p56,Possible,LCC,0,100000
-p21,p57,Possible,LCC,0,100000
-p21,p58,Possible,LCC,0,100000
-p21,p59,Possible,LCC,0,100000
-p21,p60,Possible,LCC,0,100000
-p21,p61,Possible,LCC,0,100000
-p21,p62,Possible,LCC,0,100000
-p21,p63,Possible,LCC,0,100000
-p21,p64,Possible,LCC,0,100000
-p21,p65,Possible,LCC,0,100000
-p21,p66,Possible,LCC,0,100000
-p21,p67,Possible,LCC,0,100000
-p21,p68,Possible,LCC,0,100000
-p21,p69,Possible,LCC,0,100000
-p21,p70,Possible,LCC,0,100000
-p21,p71,Possible,LCC,0,100000
-p21,p72,Possible,LCC,0,100000
-p21,p73,Possible,LCC,0,100000
-p21,p74,Possible,LCC,0,100000
-p21,p75,Possible,LCC,0,100000
-p21,p76,Possible,LCC,0,100000
-p21,p77,Possible,LCC,0,100000
-p21,p78,Possible,LCC,0,100000
-p21,p79,Possible,LCC,0,100000
-p21,p80,Possible,LCC,0,100000
-p21,p81,Possible,LCC,0,100000
-p21,p82,Possible,LCC,0,100000
-p21,p83,Possible,LCC,0,100000
-p21,p84,Possible,LCC,0,100000
-p21,p85,Possible,LCC,0,100000
-p21,p86,Possible,LCC,0,100000
-p21,p87,Possible,LCC,0,100000
-p21,p88,Possible,LCC,0,100000
-p21,p89,Possible,LCC,0,100000
-p21,p90,Possible,LCC,0,100000
-p21,p91,Possible,LCC,0,100000
-p21,p92,Possible,LCC,0,100000
-p21,p93,Possible,LCC,0,100000
-p21,p94,Possible,LCC,0,100000
-p21,p95,Possible,LCC,0,100000
-p21,p96,Possible,LCC,0,100000
-p21,p97,Possible,LCC,0,100000
-p21,p98,Possible,LCC,0,100000
-p21,p99,Possible,LCC,0,100000
-p22,p100,Possible,LCC,0,100000
-p22,p101,Possible,LCC,0,100000
-p22,p102,Possible,LCC,0,100000
-p22,p103,Possible,LCC,0,100000
-p22,p104,Possible,LCC,0,100000
-p22,p105,Possible,LCC,0,100000
-p22,p106,Possible,LCC,0,100000
-p22,p107,Possible,LCC,0,100000
-p22,p108,Possible,LCC,0,100000
-p22,p109,Possible,LCC,0,100000
-p22,p110,Possible,LCC,0,100000
-p22,p111,Possible,LCC,0,100000
-p22,p112,Possible,LCC,0,100000
-p22,p113,Possible,LCC,0,100000
-p22,p114,Possible,LCC,0,100000
-p22,p115,Possible,LCC,0,100000
-p22,p116,Possible,LCC,0,100000
-p22,p117,Possible,LCC,0,100000
-p22,p118,Possible,LCC,0,100000
-p22,p119,Possible,LCC,0,100000
-p22,p120,Possible,LCC,0,100000
-p22,p121,Possible,LCC,0,100000
-p22,p122,Possible,LCC,0,100000
-p22,p123,Possible,LCC,0,100000
-p22,p124,Possible,LCC,0,100000
-p22,p125,Possible,LCC,0,100000
-p22,p126,Possible,LCC,0,100000
-p22,p127,Possible,LCC,0,100000
-p22,p128,Possible,LCC,0,100000
-p22,p129,Possible,LCC,0,100000
-p22,p130,Possible,LCC,0,100000
-p22,p131,Possible,LCC,0,100000
-p22,p132,Possible,LCC,0,100000
-p22,p133,Possible,LCC,0,100000
-p22,p134,Possible,LCC,0,100000
-p22,p25,Possible,LCC,0,100000
-p22,p26,Possible,LCC,0,100000
-p22,p27,Possible,LCC,0,100000
-p22,p28,Possible,LCC,0,100000
-p22,p29,Possible,LCC,0,100000
-p22,p30,Possible,LCC,0,100000
-p22,p31,Possible,LCC,0,100000
-p22,p34,Possible,LCC,0,100000
-p22,p35,Possible,LCC,0,100000
-p22,p36,Possible,LCC,0,100000
-p22,p37,Possible,LCC,0,100000
-p22,p38,Possible,LCC,0,100000
-p22,p39,Possible,LCC,0,100000
-p22,p40,Possible,LCC,0,100000
-p22,p41,Possible,LCC,0,100000
-p22,p42,Possible,LCC,0,100000
-p22,p43,Possible,LCC,0,100000
-p22,p44,Possible,LCC,0,100000
-p22,p45,Possible,LCC,0,100000
-p22,p46,Possible,LCC,0,100000
-p22,p47,Possible,LCC,0,100000
-p22,p48,Possible,LCC,0,100000
-p22,p49,Possible,LCC,0,100000
-p22,p50,Possible,LCC,0,100000
-p22,p51,Possible,LCC,0,100000
-p22,p52,Possible,LCC,0,100000
-p22,p53,Possible,LCC,0,100000
-p22,p54,Possible,LCC,0,100000
-p22,p55,Possible,LCC,0,100000
-p22,p56,Possible,LCC,0,100000
-p22,p57,Possible,LCC,0,100000
-p22,p58,Possible,LCC,0,100000
-p22,p59,Possible,LCC,0,100000
-p22,p60,Possible,LCC,0,100000
-p22,p61,Possible,LCC,0,100000
-p22,p62,Possible,LCC,0,100000
-p22,p63,Possible,LCC,0,100000
-p22,p64,Possible,LCC,0,100000
-p22,p65,Possible,LCC,0,100000
-p22,p66,Possible,LCC,0,100000
-p22,p67,Possible,LCC,0,100000
-p22,p68,Possible,LCC,0,100000
-p22,p69,Possible,LCC,0,100000
-p22,p70,Possible,LCC,0,100000
-p22,p71,Possible,LCC,0,100000
-p22,p72,Possible,LCC,0,100000
-p22,p73,Possible,LCC,0,100000
-p22,p74,Possible,LCC,0,100000
-p22,p75,Possible,LCC,0,100000
-p22,p76,Possible,LCC,0,100000
-p22,p77,Possible,LCC,0,100000
-p22,p78,Possible,LCC,0,100000
-p22,p79,Possible,LCC,0,100000
-p22,p80,Possible,LCC,0,100000
-p22,p81,Possible,LCC,0,100000
-p22,p82,Possible,LCC,0,100000
-p22,p83,Possible,LCC,0,100000
-p22,p84,Possible,LCC,0,100000
-p22,p85,Possible,LCC,0,100000
-p22,p86,Possible,LCC,0,100000
-p22,p87,Possible,LCC,0,100000
-p22,p88,Possible,LCC,0,100000
-p22,p89,Possible,LCC,0,100000
-p22,p90,Possible,LCC,0,100000
-p22,p91,Possible,LCC,0,100000
-p22,p92,Possible,LCC,0,100000
-p22,p93,Possible,LCC,0,100000
-p22,p94,Possible,LCC,0,100000
-p22,p95,Possible,LCC,0,100000
-p22,p96,Possible,LCC,0,100000
-p22,p97,Possible,LCC,0,100000
-p22,p98,Possible,LCC,0,100000
-p22,p99,Possible,LCC,0,100000
-p23,p100,Possible,LCC,0,100000
-p23,p101,Possible,LCC,0,100000
-p23,p102,Possible,LCC,0,100000
-p23,p103,Possible,LCC,0,100000
-p23,p104,Possible,LCC,0,100000
-p23,p105,Possible,LCC,0,100000
-p23,p106,Possible,LCC,0,100000
-p23,p107,Possible,LCC,0,100000
-p23,p108,Possible,LCC,0,100000
-p23,p109,Possible,LCC,0,100000
-p23,p110,Possible,LCC,0,100000
-p23,p111,Possible,LCC,0,100000
-p23,p112,Possible,LCC,0,100000
-p23,p113,Possible,LCC,0,100000
-p23,p114,Possible,LCC,0,100000
-p23,p115,Possible,LCC,0,100000
-p23,p116,Possible,LCC,0,100000
-p23,p117,Possible,LCC,0,100000
-p23,p118,Possible,LCC,0,100000
-p23,p119,Possible,LCC,0,100000
-p23,p120,Possible,LCC,0,100000
-p23,p121,Possible,LCC,0,100000
-p23,p122,Possible,LCC,0,100000
-p23,p123,Possible,LCC,0,100000
-p23,p124,Possible,LCC,0,100000
-p23,p125,Possible,LCC,0,100000
-p23,p126,Possible,LCC,0,100000
-p23,p127,Possible,LCC,0,100000
-p23,p128,Possible,LCC,0,100000
-p23,p129,Possible,LCC,0,100000
-p23,p130,Possible,LCC,0,100000
-p23,p131,Possible,LCC,0,100000
-p23,p132,Possible,LCC,0,100000
-p23,p133,Possible,LCC,0,100000
-p23,p134,Possible,LCC,0,100000
-p23,p25,Possible,LCC,0,100000
-p23,p26,Possible,LCC,0,100000
-p23,p27,Possible,LCC,0,100000
-p23,p28,Possible,LCC,0,100000
-p23,p29,Possible,LCC,0,100000
-p23,p30,Possible,LCC,0,100000
-p23,p31,Possible,LCC,0,100000
-p23,p34,Possible,LCC,0,100000
-p23,p35,Possible,LCC,0,100000
-p23,p36,Possible,LCC,0,100000
-p23,p37,Possible,LCC,0,100000
-p23,p38,Possible,LCC,0,100000
-p23,p39,Possible,LCC,0,100000
-p23,p40,Possible,LCC,0,100000
-p23,p41,Possible,LCC,0,100000
-p23,p42,Possible,LCC,0,100000
-p23,p43,Possible,LCC,0,100000
-p23,p44,Possible,LCC,0,100000
-p23,p45,Possible,LCC,0,100000
-p23,p46,Possible,LCC,0,100000
-p23,p47,Possible,LCC,0,100000
-p23,p48,Possible,LCC,0,100000
-p23,p49,Possible,LCC,0,100000
-p23,p50,Possible,LCC,0,100000
-p23,p51,Possible,LCC,0,100000
-p23,p52,Possible,LCC,0,100000
-p23,p53,Possible,LCC,0,100000
-p23,p54,Possible,LCC,0,100000
-p23,p55,Possible,LCC,0,100000
-p23,p56,Possible,LCC,0,100000
-p23,p57,Possible,LCC,0,100000
-p23,p58,Possible,LCC,0,100000
-p23,p59,Possible,LCC,0,100000
-p23,p60,Possible,LCC,0,100000
-p23,p61,Possible,LCC,0,100000
-p23,p62,Possible,LCC,0,100000
-p23,p63,Possible,LCC,0,100000
-p23,p64,Possible,LCC,0,100000
-p23,p65,Possible,LCC,0,100000
-p23,p66,Possible,LCC,0,100000
-p23,p67,Possible,LCC,0,100000
-p23,p68,Possible,LCC,0,100000
-p23,p69,Possible,LCC,0,100000
-p23,p70,Possible,LCC,0,100000
-p23,p71,Possible,LCC,0,100000
-p23,p72,Possible,LCC,0,100000
-p23,p73,Possible,LCC,0,100000
-p23,p74,Possible,LCC,0,100000
-p23,p75,Possible,LCC,0,100000
-p23,p76,Possible,LCC,0,100000
-p23,p77,Possible,LCC,0,100000
-p23,p78,Possible,LCC,0,100000
-p23,p79,Possible,LCC,0,100000
-p23,p80,Possible,LCC,0,100000
-p23,p81,Possible,LCC,0,100000
-p23,p82,Possible,LCC,0,100000
-p23,p83,Possible,LCC,0,100000
-p23,p84,Possible,LCC,0,100000
-p23,p85,Possible,LCC,0,100000
-p23,p86,Possible,LCC,0,100000
-p23,p87,Possible,LCC,0,100000
-p23,p88,Possible,LCC,0,100000
-p23,p89,Possible,LCC,0,100000
-p23,p90,Possible,LCC,0,100000
-p23,p91,Possible,LCC,0,100000
-p23,p92,Possible,LCC,0,100000
-p23,p93,Possible,LCC,0,100000
-p23,p94,Possible,LCC,0,100000
-p23,p95,Possible,LCC,0,100000
-p23,p96,Possible,LCC,0,100000
-p23,p97,Possible,LCC,0,100000
-p23,p98,Possible,LCC,0,100000
-p23,p99,Possible,LCC,0,100000
-p24,p100,Possible,LCC,0,100000
-p24,p101,Possible,LCC,0,100000
-p24,p102,Possible,LCC,0,100000
-p24,p103,Possible,LCC,0,100000
-p24,p104,Possible,LCC,0,100000
-p24,p105,Possible,LCC,0,100000
-p24,p106,Possible,LCC,0,100000
-p24,p107,Possible,LCC,0,100000
-p24,p108,Possible,LCC,0,100000
-p24,p109,Possible,LCC,0,100000
-p24,p110,Possible,LCC,0,100000
-p24,p111,Possible,LCC,0,100000
-p24,p112,Possible,LCC,0,100000
-p24,p113,Possible,LCC,0,100000
-p24,p114,Possible,LCC,0,100000
-p24,p115,Possible,LCC,0,100000
-p24,p116,Possible,LCC,0,100000
-p24,p117,Possible,LCC,0,100000
-p24,p118,Possible,LCC,0,100000
-p24,p119,Possible,LCC,0,100000
-p24,p120,Possible,LCC,0,100000
-p24,p121,Possible,LCC,0,100000
-p24,p122,Possible,LCC,0,100000
-p24,p123,Possible,LCC,0,100000
-p24,p124,Possible,LCC,0,100000
-p24,p125,Possible,LCC,0,100000
-p24,p126,Possible,LCC,0,100000
-p24,p127,Possible,LCC,0,100000
-p24,p128,Possible,LCC,0,100000
-p24,p129,Possible,LCC,0,100000
-p24,p130,Possible,LCC,0,100000
-p24,p131,Possible,LCC,0,100000
-p24,p132,Possible,LCC,0,100000
-p24,p133,Possible,LCC,0,100000
-p24,p134,Possible,LCC,0,100000
-p24,p25,Possible,LCC,0,100000
-p24,p26,Possible,LCC,0,100000
-p24,p27,Possible,LCC,0,100000
-p24,p28,Possible,LCC,0,100000
-p24,p29,Possible,LCC,0,100000
-p24,p30,Possible,LCC,0,100000
-p24,p31,Possible,LCC,0,100000
-p24,p35,Possible,LCC,0,100000
-p24,p36,Possible,LCC,0,100000
-p24,p37,Possible,LCC,0,100000
-p24,p38,Possible,LCC,0,100000
-p24,p40,Possible,LCC,0,100000
-p24,p41,Possible,LCC,0,100000
-p24,p42,Possible,LCC,0,100000
-p24,p43,Possible,LCC,0,100000
-p24,p44,Possible,LCC,0,100000
-p24,p45,Possible,LCC,0,100000
-p24,p46,Possible,LCC,0,100000
-p24,p47,Possible,LCC,0,100000
-p24,p48,Possible,LCC,0,100000
-p24,p49,Possible,LCC,0,100000
-p24,p50,Possible,LCC,0,100000
-p24,p51,Possible,LCC,0,100000
-p24,p52,Possible,LCC,0,100000
-p24,p53,Possible,LCC,0,100000
-p24,p54,Possible,LCC,0,100000
-p24,p55,Possible,LCC,0,100000
-p24,p56,Possible,LCC,0,100000
-p24,p57,Possible,LCC,0,100000
-p24,p58,Possible,LCC,0,100000
-p24,p59,Possible,LCC,0,100000
-p24,p60,Possible,LCC,0,100000
-p24,p61,Possible,LCC,0,100000
-p24,p62,Possible,LCC,0,100000
-p24,p63,Possible,LCC,0,100000
-p24,p64,Possible,LCC,0,100000
-p24,p65,Possible,LCC,0,100000
-p24,p66,Possible,LCC,0,100000
-p24,p67,Possible,LCC,0,100000
-p24,p68,Possible,LCC,0,100000
-p24,p69,Possible,LCC,0,100000
-p24,p70,Possible,LCC,0,100000
-p24,p71,Possible,LCC,0,100000
-p24,p72,Possible,LCC,0,100000
-p24,p73,Possible,LCC,0,100000
-p24,p74,Possible,LCC,0,100000
-p24,p75,Possible,LCC,0,100000
-p24,p76,Possible,LCC,0,100000
-p24,p77,Possible,LCC,0,100000
-p24,p78,Possible,LCC,0,100000
-p24,p79,Possible,LCC,0,100000
-p24,p80,Possible,LCC,0,100000
-p24,p81,Possible,LCC,0,100000
-p24,p82,Possible,LCC,0,100000
-p24,p83,Possible,LCC,0,100000
-p24,p84,Possible,LCC,0,100000
-p24,p85,Possible,LCC,0,100000
-p24,p86,Possible,LCC,0,100000
-p24,p87,Possible,LCC,0,100000
-p24,p88,Possible,LCC,0,100000
-p24,p89,Possible,LCC,0,100000
-p24,p90,Possible,LCC,0,100000
-p24,p91,Possible,LCC,0,100000
-p24,p92,Possible,LCC,0,100000
-p24,p93,Possible,LCC,0,100000
-p24,p94,Possible,LCC,0,100000
-p24,p95,Possible,LCC,0,100000
-p24,p96,Possible,LCC,0,100000
-p24,p97,Possible,LCC,0,100000
-p24,p98,Possible,LCC,0,100000
-p24,p99,Possible,LCC,0,100000
-p25,p100,Possible,LCC,0,100000
-p25,p101,Possible,LCC,0,100000
-p25,p102,Possible,LCC,0,100000
-p25,p103,Possible,LCC,0,100000
-p25,p104,Possible,LCC,0,100000
-p25,p105,Possible,LCC,0,100000
-p25,p106,Possible,LCC,0,100000
-p25,p107,Possible,LCC,0,100000
-p25,p108,Possible,LCC,0,100000
-p25,p109,Possible,LCC,0,100000
-p25,p110,Possible,LCC,0,100000
-p25,p111,Possible,LCC,0,100000
-p25,p112,Possible,LCC,0,100000
-p25,p113,Possible,LCC,0,100000
-p25,p114,Possible,LCC,0,100000
-p25,p115,Possible,LCC,0,100000
-p25,p116,Possible,LCC,0,100000
-p25,p117,Possible,LCC,0,100000
-p25,p118,Possible,LCC,0,100000
-p25,p119,Possible,LCC,0,100000
-p25,p120,Possible,LCC,0,100000
-p25,p121,Possible,LCC,0,100000
-p25,p122,Possible,LCC,0,100000
-p25,p123,Possible,LCC,0,100000
-p25,p124,Possible,LCC,0,100000
-p25,p125,Possible,LCC,0,100000
-p25,p126,Possible,LCC,0,100000
-p25,p127,Possible,LCC,0,100000
-p25,p128,Possible,LCC,0,100000
-p25,p129,Possible,LCC,0,100000
-p25,p130,Possible,LCC,0,100000
-p25,p131,Possible,LCC,0,100000
-p25,p132,Possible,LCC,0,100000
-p25,p133,Possible,LCC,0,100000
-p25,p134,Possible,LCC,0,100000
-p25,p27,Possible,LCC,0,100000
-p25,p29,Possible,LCC,0,100000
-p25,p30,Possible,LCC,0,100000
-p25,p32,Possible,LCC,0,100000
-p25,p33,Possible,LCC,0,100000
-p25,p34,Possible,LCC,0,100000
-p25,p35,Possible,LCC,0,100000
-p25,p36,Possible,LCC,0,100000
-p25,p37,Possible,LCC,0,100000
-p25,p38,Possible,LCC,0,100000
-p25,p39,Possible,LCC,0,100000
-p25,p40,Possible,LCC,0,100000
-p25,p41,Possible,LCC,0,100000
-p25,p42,Possible,LCC,0,100000
-p25,p43,Possible,LCC,0,100000
-p25,p44,Possible,LCC,0,100000
-p25,p45,Possible,LCC,0,100000
-p25,p46,Possible,LCC,0,100000
-p25,p47,Possible,LCC,0,100000
-p25,p48,Possible,LCC,0,100000
-p25,p49,Possible,LCC,0,100000
-p25,p50,Possible,LCC,0,100000
-p25,p51,Possible,LCC,0,100000
-p25,p52,Possible,LCC,0,100000
-p25,p53,Possible,LCC,0,100000
-p25,p54,Possible,LCC,0,100000
-p25,p55,Possible,LCC,0,100000
-p25,p56,Possible,LCC,0,100000
-p25,p57,Possible,LCC,0,100000
-p25,p58,Possible,LCC,0,100000
-p25,p59,Possible,LCC,0,100000
-p25,p60,Possible,LCC,0,100000
-p25,p61,Possible,LCC,0,100000
-p25,p62,Possible,LCC,0,100000
-p25,p63,Possible,LCC,0,100000
-p25,p64,Possible,LCC,0,100000
-p25,p65,Possible,LCC,0,100000
-p25,p66,Possible,LCC,0,100000
-p25,p67,Possible,LCC,0,100000
-p25,p68,Possible,LCC,0,100000
-p25,p69,Possible,LCC,0,100000
-p25,p70,Possible,LCC,0,100000
-p25,p71,Possible,LCC,0,100000
-p25,p72,Possible,LCC,0,100000
-p25,p73,Possible,LCC,0,100000
-p25,p74,Possible,LCC,0,100000
-p25,p75,Possible,LCC,0,100000
-p25,p76,Possible,LCC,0,100000
-p25,p77,Possible,LCC,0,100000
-p25,p78,Possible,LCC,0,100000
-p25,p79,Possible,LCC,0,100000
-p25,p80,Possible,LCC,0,100000
-p25,p81,Possible,LCC,0,100000
-p25,p82,Possible,LCC,0,100000
-p25,p83,Possible,LCC,0,100000
-p25,p84,Possible,LCC,0,100000
-p25,p85,Possible,LCC,0,100000
-p25,p86,Possible,LCC,0,100000
-p25,p87,Possible,LCC,0,100000
-p25,p88,Possible,LCC,0,100000
-p25,p89,Possible,LCC,0,100000
-p25,p90,Possible,LCC,0,100000
-p25,p91,Possible,LCC,0,100000
-p25,p92,Possible,LCC,0,100000
-p25,p93,Possible,LCC,0,100000
-p25,p94,Possible,LCC,0,100000
-p25,p95,Possible,LCC,0,100000
-p25,p96,Possible,LCC,0,100000
-p25,p97,Possible,LCC,0,100000
-p25,p98,Possible,LCC,0,100000
-p25,p99,Possible,LCC,0,100000
-p26,p100,Possible,LCC,0,100000
-p26,p101,Possible,LCC,0,100000
-p26,p102,Possible,LCC,0,100000
-p26,p103,Possible,LCC,0,100000
-p26,p104,Possible,LCC,0,100000
-p26,p105,Possible,LCC,0,100000
-p26,p106,Possible,LCC,0,100000
-p26,p107,Possible,LCC,0,100000
-p26,p108,Possible,LCC,0,100000
-p26,p109,Possible,LCC,0,100000
-p26,p110,Possible,LCC,0,100000
-p26,p111,Possible,LCC,0,100000
-p26,p112,Possible,LCC,0,100000
-p26,p113,Possible,LCC,0,100000
-p26,p114,Possible,LCC,0,100000
-p26,p115,Possible,LCC,0,100000
-p26,p116,Possible,LCC,0,100000
-p26,p117,Possible,LCC,0,100000
-p26,p118,Possible,LCC,0,100000
-p26,p119,Possible,LCC,0,100000
-p26,p120,Possible,LCC,0,100000
-p26,p121,Possible,LCC,0,100000
-p26,p122,Possible,LCC,0,100000
-p26,p123,Possible,LCC,0,100000
-p26,p124,Possible,LCC,0,100000
-p26,p125,Possible,LCC,0,100000
-p26,p126,Possible,LCC,0,100000
-p26,p127,Possible,LCC,0,100000
-p26,p128,Possible,LCC,0,100000
-p26,p129,Possible,LCC,0,100000
-p26,p130,Possible,LCC,0,100000
-p26,p131,Possible,LCC,0,100000
-p26,p132,Possible,LCC,0,100000
-p26,p133,Possible,LCC,0,100000
-p26,p134,Possible,LCC,0,100000
-p26,p27,Possible,LCC,0,100000
-p26,p28,Possible,LCC,0,100000
-p26,p29,Possible,LCC,0,100000
-p26,p30,Possible,LCC,0,100000
-p26,p31,Possible,LCC,0,100000
-p26,p32,Possible,LCC,0,100000
-p26,p34,Possible,LCC,0,100000
-p26,p35,Possible,LCC,0,100000
-p26,p36,Possible,LCC,0,100000
-p26,p37,Possible,LCC,0,100000
-p26,p38,Possible,LCC,0,100000
-p26,p39,Possible,LCC,0,100000
-p26,p40,Possible,LCC,0,100000
-p26,p41,Possible,LCC,0,100000
-p26,p42,Possible,LCC,0,100000
-p26,p43,Possible,LCC,0,100000
-p26,p44,Possible,LCC,0,100000
-p26,p45,Possible,LCC,0,100000
-p26,p46,Possible,LCC,0,100000
-p26,p47,Possible,LCC,0,100000
-p26,p48,Possible,LCC,0,100000
-p26,p49,Possible,LCC,0,100000
-p26,p50,Possible,LCC,0,100000
-p26,p51,Possible,LCC,0,100000
-p26,p52,Possible,LCC,0,100000
-p26,p53,Possible,LCC,0,100000
-p26,p54,Possible,LCC,0,100000
-p26,p55,Possible,LCC,0,100000
-p26,p56,Possible,LCC,0,100000
-p26,p57,Possible,LCC,0,100000
-p26,p58,Possible,LCC,0,100000
-p26,p59,Possible,LCC,0,100000
-p26,p60,Possible,LCC,0,100000
-p26,p61,Possible,LCC,0,100000
-p26,p62,Possible,LCC,0,100000
-p26,p63,Possible,LCC,0,100000
-p26,p64,Possible,LCC,0,100000
-p26,p65,Possible,LCC,0,100000
-p26,p66,Possible,LCC,0,100000
-p26,p67,Possible,LCC,0,100000
-p26,p68,Possible,LCC,0,100000
-p26,p69,Possible,LCC,0,100000
-p26,p70,Possible,LCC,0,100000
-p26,p71,Possible,LCC,0,100000
-p26,p72,Possible,LCC,0,100000
-p26,p73,Possible,LCC,0,100000
-p26,p74,Possible,LCC,0,100000
-p26,p75,Possible,LCC,0,100000
-p26,p76,Possible,LCC,0,100000
-p26,p77,Possible,LCC,0,100000
-p26,p78,Possible,LCC,0,100000
-p26,p79,Possible,LCC,0,100000
-p26,p80,Possible,LCC,0,100000
-p26,p81,Possible,LCC,0,100000
-p26,p82,Possible,LCC,0,100000
-p26,p83,Possible,LCC,0,100000
-p26,p84,Possible,LCC,0,100000
-p26,p85,Possible,LCC,0,100000
-p26,p86,Possible,LCC,0,100000
-p26,p87,Possible,LCC,0,100000
-p26,p88,Possible,LCC,0,100000
-p26,p89,Possible,LCC,0,100000
-p26,p90,Possible,LCC,0,100000
-p26,p91,Possible,LCC,0,100000
-p26,p92,Possible,LCC,0,100000
-p26,p93,Possible,LCC,0,100000
-p26,p94,Possible,LCC,0,100000
-p26,p95,Possible,LCC,0,100000
-p26,p96,Possible,LCC,0,100000
-p26,p97,Possible,LCC,0,100000
-p26,p98,Possible,LCC,0,100000
-p26,p99,Possible,LCC,0,100000
-p27,p100,Possible,LCC,0,100000
-p27,p101,Possible,LCC,0,100000
-p27,p102,Possible,LCC,0,100000
-p27,p103,Possible,LCC,0,100000
-p27,p104,Possible,LCC,0,100000
-p27,p105,Possible,LCC,0,100000
-p27,p106,Possible,LCC,0,100000
-p27,p107,Possible,LCC,0,100000
-p27,p108,Possible,LCC,0,100000
-p27,p109,Possible,LCC,0,100000
-p27,p110,Possible,LCC,0,100000
-p27,p111,Possible,LCC,0,100000
-p27,p112,Possible,LCC,0,100000
-p27,p113,Possible,LCC,0,100000
-p27,p114,Possible,LCC,0,100000
-p27,p115,Possible,LCC,0,100000
-p27,p116,Possible,LCC,0,100000
-p27,p117,Possible,LCC,0,100000
-p27,p118,Possible,LCC,0,100000
-p27,p119,Possible,LCC,0,100000
-p27,p120,Possible,LCC,0,100000
-p27,p121,Possible,LCC,0,100000
-p27,p122,Possible,LCC,0,100000
-p27,p123,Possible,LCC,0,100000
-p27,p124,Possible,LCC,0,100000
-p27,p125,Possible,LCC,0,100000
-p27,p126,Possible,LCC,0,100000
-p27,p127,Possible,LCC,0,100000
-p27,p128,Possible,LCC,0,100000
-p27,p129,Possible,LCC,0,100000
-p27,p130,Possible,LCC,0,100000
-p27,p131,Possible,LCC,0,100000
-p27,p132,Possible,LCC,0,100000
-p27,p133,Possible,LCC,0,100000
-p27,p134,Possible,LCC,0,100000
-p27,p29,Possible,LCC,0,100000
-p27,p31,Possible,LCC,0,100000
-p27,p32,Possible,LCC,0,100000
-p27,p33,Possible,LCC,0,100000
-p27,p34,Possible,LCC,0,100000
-p27,p35,Possible,LCC,0,100000
-p27,p36,Possible,LCC,0,100000
-p27,p37,Possible,LCC,0,100000
-p27,p38,Possible,LCC,0,100000
-p27,p39,Possible,LCC,0,100000
-p27,p40,Possible,LCC,0,100000
-p27,p41,Possible,LCC,0,100000
-p27,p42,Possible,LCC,0,100000
-p27,p43,Possible,LCC,0,100000
-p27,p44,Possible,LCC,0,100000
-p27,p45,Possible,LCC,0,100000
-p27,p46,Possible,LCC,0,100000
-p27,p47,Possible,LCC,0,100000
-p27,p48,Possible,LCC,0,100000
-p27,p49,Possible,LCC,0,100000
-p27,p50,Possible,LCC,0,100000
-p27,p51,Possible,LCC,0,100000
-p27,p52,Possible,LCC,0,100000
-p27,p53,Possible,LCC,0,100000
-p27,p54,Possible,LCC,0,100000
-p27,p55,Possible,LCC,0,100000
-p27,p56,Possible,LCC,0,100000
-p27,p57,Possible,LCC,0,100000
-p27,p58,Possible,LCC,0,100000
-p27,p59,Possible,LCC,0,100000
-p27,p60,Possible,LCC,0,100000
-p27,p61,Possible,LCC,0,100000
-p27,p62,Possible,LCC,0,100000
-p27,p63,Possible,LCC,0,100000
-p27,p64,Possible,LCC,0,100000
-p27,p65,Possible,LCC,0,100000
-p27,p66,Possible,LCC,0,100000
-p27,p67,Possible,LCC,0,100000
-p27,p68,Possible,LCC,0,100000
-p27,p69,Possible,LCC,0,100000
-p27,p70,Possible,LCC,0,100000
-p27,p71,Possible,LCC,0,100000
-p27,p72,Possible,LCC,0,100000
-p27,p73,Possible,LCC,0,100000
-p27,p74,Possible,LCC,0,100000
-p27,p75,Possible,LCC,0,100000
-p27,p76,Possible,LCC,0,100000
-p27,p77,Possible,LCC,0,100000
-p27,p78,Possible,LCC,0,100000
-p27,p79,Possible,LCC,0,100000
-p27,p80,Possible,LCC,0,100000
-p27,p81,Possible,LCC,0,100000
-p27,p82,Possible,LCC,0,100000
-p27,p83,Possible,LCC,0,100000
-p27,p84,Possible,LCC,0,100000
-p27,p85,Possible,LCC,0,100000
-p27,p86,Possible,LCC,0,100000
-p27,p87,Possible,LCC,0,100000
-p27,p88,Possible,LCC,0,100000
-p27,p89,Possible,LCC,0,100000
-p27,p90,Possible,LCC,0,100000
-p27,p91,Possible,LCC,0,100000
-p27,p92,Possible,LCC,0,100000
-p27,p93,Possible,LCC,0,100000
-p27,p94,Possible,LCC,0,100000
-p27,p95,Possible,LCC,0,100000
-p27,p96,Possible,LCC,0,100000
-p27,p97,Possible,LCC,0,100000
-p27,p98,Possible,LCC,0,100000
-p27,p99,Possible,LCC,0,100000
-p28,p100,Possible,LCC,0,100000
-p28,p101,Possible,LCC,0,100000
-p28,p102,Possible,LCC,0,100000
-p28,p103,Possible,LCC,0,100000
-p28,p104,Possible,LCC,0,100000
-p28,p105,Possible,LCC,0,100000
-p28,p106,Possible,LCC,0,100000
-p28,p107,Possible,LCC,0,100000
-p28,p108,Possible,LCC,0,100000
-p28,p109,Possible,LCC,0,100000
-p28,p110,Possible,LCC,0,100000
-p28,p111,Possible,LCC,0,100000
-p28,p112,Possible,LCC,0,100000
-p28,p113,Possible,LCC,0,100000
-p28,p114,Possible,LCC,0,100000
-p28,p115,Possible,LCC,0,100000
-p28,p116,Possible,LCC,0,100000
-p28,p117,Possible,LCC,0,100000
-p28,p118,Possible,LCC,0,100000
-p28,p119,Possible,LCC,0,100000
-p28,p120,Possible,LCC,0,100000
-p28,p121,Possible,LCC,0,100000
-p28,p122,Possible,LCC,0,100000
-p28,p123,Possible,LCC,0,100000
-p28,p124,Possible,LCC,0,100000
-p28,p125,Possible,LCC,0,100000
-p28,p126,Possible,LCC,0,100000
-p28,p127,Possible,LCC,0,100000
-p28,p128,Possible,LCC,0,100000
-p28,p129,Possible,LCC,0,100000
-p28,p130,Possible,LCC,0,100000
-p28,p131,Possible,LCC,0,100000
-p28,p132,Possible,LCC,0,100000
-p28,p133,Possible,LCC,0,100000
-p28,p134,Possible,LCC,0,100000
-p28,p32,Possible,LCC,0,100000
-p28,p33,Possible,LCC,0,100000
-p28,p34,Possible,LCC,0,100000
-p28,p35,Possible,LCC,0,100000
-p28,p36,Possible,LCC,0,100000
-p28,p37,Possible,LCC,0,100000
-p28,p38,Possible,LCC,0,100000
-p28,p39,Possible,LCC,0,100000
-p28,p40,Possible,LCC,0,100000
-p28,p41,Possible,LCC,0,100000
-p28,p42,Possible,LCC,0,100000
-p28,p43,Possible,LCC,0,100000
-p28,p44,Possible,LCC,0,100000
-p28,p45,Possible,LCC,0,100000
-p28,p46,Possible,LCC,0,100000
-p28,p47,Possible,LCC,0,100000
-p28,p48,Possible,LCC,0,100000
-p28,p49,Possible,LCC,0,100000
-p28,p50,Possible,LCC,0,100000
-p28,p51,Possible,LCC,0,100000
-p28,p52,Possible,LCC,0,100000
-p28,p53,Possible,LCC,0,100000
-p28,p54,Possible,LCC,0,100000
-p28,p55,Possible,LCC,0,100000
-p28,p56,Possible,LCC,0,100000
-p28,p57,Possible,LCC,0,100000
-p28,p58,Possible,LCC,0,100000
-p28,p59,Possible,LCC,0,100000
-p28,p60,Possible,LCC,0,100000
-p28,p61,Possible,LCC,0,100000
-p28,p62,Possible,LCC,0,100000
-p28,p63,Possible,LCC,0,100000
-p28,p64,Possible,LCC,0,100000
-p28,p65,Possible,LCC,0,100000
-p28,p66,Possible,LCC,0,100000
-p28,p67,Possible,LCC,0,100000
-p28,p68,Possible,LCC,0,100000
-p28,p69,Possible,LCC,0,100000
-p28,p70,Possible,LCC,0,100000
-p28,p71,Possible,LCC,0,100000
-p28,p72,Possible,LCC,0,100000
-p28,p73,Possible,LCC,0,100000
-p28,p74,Possible,LCC,0,100000
-p28,p75,Possible,LCC,0,100000
-p28,p76,Possible,LCC,0,100000
-p28,p77,Possible,LCC,0,100000
-p28,p78,Possible,LCC,0,100000
-p28,p79,Possible,LCC,0,100000
-p28,p80,Possible,LCC,0,100000
-p28,p81,Possible,LCC,0,100000
-p28,p82,Possible,LCC,0,100000
-p28,p83,Possible,LCC,0,100000
-p28,p84,Possible,LCC,0,100000
-p28,p85,Possible,LCC,0,100000
-p28,p86,Possible,LCC,0,100000
-p28,p87,Possible,LCC,0,100000
-p28,p88,Possible,LCC,0,100000
-p28,p89,Possible,LCC,0,100000
-p28,p90,Possible,LCC,0,100000
-p28,p91,Possible,LCC,0,100000
-p28,p92,Possible,LCC,0,100000
-p28,p93,Possible,LCC,0,100000
-p28,p94,Possible,LCC,0,100000
-p28,p95,Possible,LCC,0,100000
-p28,p96,Possible,LCC,0,100000
-p28,p97,Possible,LCC,0,100000
-p28,p98,Possible,LCC,0,100000
-p28,p99,Possible,LCC,0,100000
-p29,p100,Possible,LCC,0,100000
-p29,p101,Possible,LCC,0,100000
-p29,p102,Possible,LCC,0,100000
-p29,p103,Possible,LCC,0,100000
-p29,p104,Possible,LCC,0,100000
-p29,p105,Possible,LCC,0,100000
-p29,p106,Possible,LCC,0,100000
-p29,p107,Possible,LCC,0,100000
-p29,p108,Possible,LCC,0,100000
-p29,p109,Possible,LCC,0,100000
-p29,p110,Possible,LCC,0,100000
-p29,p111,Possible,LCC,0,100000
-p29,p112,Possible,LCC,0,100000
-p29,p113,Possible,LCC,0,100000
-p29,p114,Possible,LCC,0,100000
-p29,p115,Possible,LCC,0,100000
-p29,p116,Possible,LCC,0,100000
-p29,p117,Possible,LCC,0,100000
-p29,p118,Possible,LCC,0,100000
-p29,p119,Possible,LCC,0,100000
-p29,p120,Possible,LCC,0,100000
-p29,p121,Possible,LCC,0,100000
-p29,p122,Possible,LCC,0,100000
-p29,p123,Possible,LCC,0,100000
-p29,p124,Possible,LCC,0,100000
-p29,p125,Possible,LCC,0,100000
-p29,p126,Possible,LCC,0,100000
-p29,p127,Possible,LCC,0,100000
-p29,p128,Possible,LCC,0,100000
-p29,p129,Possible,LCC,0,100000
-p29,p130,Possible,LCC,0,100000
-p29,p131,Possible,LCC,0,100000
-p29,p132,Possible,LCC,0,100000
-p29,p133,Possible,LCC,0,100000
-p29,p134,Possible,LCC,0,100000
-p29,p32,Possible,LCC,0,100000
-p29,p33,Possible,LCC,0,100000
-p29,p34,Possible,LCC,0,100000
-p29,p35,Possible,LCC,0,100000
-p29,p36,Possible,LCC,0,100000
-p29,p37,Possible,LCC,0,100000
-p29,p38,Possible,LCC,0,100000
-p29,p39,Possible,LCC,0,100000
-p29,p40,Possible,LCC,0,100000
-p29,p41,Possible,LCC,0,100000
-p29,p42,Possible,LCC,0,100000
-p29,p43,Possible,LCC,0,100000
-p29,p44,Possible,LCC,0,100000
-p29,p45,Possible,LCC,0,100000
-p29,p46,Possible,LCC,0,100000
-p29,p47,Possible,LCC,0,100000
-p29,p48,Possible,LCC,0,100000
-p29,p49,Possible,LCC,0,100000
-p29,p50,Possible,LCC,0,100000
-p29,p51,Possible,LCC,0,100000
-p29,p52,Possible,LCC,0,100000
-p29,p53,Possible,LCC,0,100000
-p29,p54,Possible,LCC,0,100000
-p29,p55,Possible,LCC,0,100000
-p29,p56,Possible,LCC,0,100000
-p29,p57,Possible,LCC,0,100000
-p29,p58,Possible,LCC,0,100000
-p29,p59,Possible,LCC,0,100000
-p29,p60,Possible,LCC,0,100000
-p29,p61,Possible,LCC,0,100000
-p29,p62,Possible,LCC,0,100000
-p29,p63,Possible,LCC,0,100000
-p29,p64,Possible,LCC,0,100000
-p29,p65,Possible,LCC,0,100000
-p29,p66,Possible,LCC,0,100000
-p29,p67,Possible,LCC,0,100000
-p29,p68,Possible,LCC,0,100000
-p29,p69,Possible,LCC,0,100000
-p29,p70,Possible,LCC,0,100000
-p29,p71,Possible,LCC,0,100000
-p29,p72,Possible,LCC,0,100000
-p29,p73,Possible,LCC,0,100000
-p29,p74,Possible,LCC,0,100000
-p29,p75,Possible,LCC,0,100000
-p29,p76,Possible,LCC,0,100000
-p29,p77,Possible,LCC,0,100000
-p29,p78,Possible,LCC,0,100000
-p29,p79,Possible,LCC,0,100000
-p29,p80,Possible,LCC,0,100000
-p29,p81,Possible,LCC,0,100000
-p29,p82,Possible,LCC,0,100000
-p29,p83,Possible,LCC,0,100000
-p29,p84,Possible,LCC,0,100000
-p29,p85,Possible,LCC,0,100000
-p29,p86,Possible,LCC,0,100000
-p29,p87,Possible,LCC,0,100000
-p29,p88,Possible,LCC,0,100000
-p29,p89,Possible,LCC,0,100000
-p29,p90,Possible,LCC,0,100000
-p29,p91,Possible,LCC,0,100000
-p29,p92,Possible,LCC,0,100000
-p29,p93,Possible,LCC,0,100000
-p29,p94,Possible,LCC,0,100000
-p29,p95,Possible,LCC,0,100000
-p29,p96,Possible,LCC,0,100000
-p29,p97,Possible,LCC,0,100000
-p29,p98,Possible,LCC,0,100000
-p29,p99,Possible,LCC,0,100000
-p3,p10,Possible,LCC,0,100000
-p3,p100,Possible,LCC,0,100000
-p3,p101,Possible,LCC,0,100000
-p3,p102,Possible,LCC,0,100000
-p3,p103,Possible,LCC,0,100000
-p3,p104,Possible,LCC,0,100000
-p3,p105,Possible,LCC,0,100000
-p3,p106,Possible,LCC,0,100000
-p3,p107,Possible,LCC,0,100000
-p3,p108,Possible,LCC,0,100000
-p3,p109,Possible,LCC,0,100000
-p3,p11,Possible,LCC,0,100000
-p3,p110,Possible,LCC,0,100000
-p3,p111,Possible,LCC,0,100000
-p3,p112,Possible,LCC,0,100000
-p3,p113,Possible,LCC,0,100000
-p3,p114,Possible,LCC,0,100000
-p3,p115,Possible,LCC,0,100000
-p3,p116,Possible,LCC,0,100000
-p3,p117,Possible,LCC,0,100000
-p3,p118,Possible,LCC,0,100000
-p3,p119,Possible,LCC,0,100000
-p3,p12,Possible,LCC,0,100000
-p3,p120,Possible,LCC,0,100000
-p3,p121,Possible,LCC,0,100000
-p3,p122,Possible,LCC,0,100000
-p3,p123,Possible,LCC,0,100000
-p3,p124,Possible,LCC,0,100000
-p3,p125,Possible,LCC,0,100000
-p3,p126,Possible,LCC,0,100000
-p3,p127,Possible,LCC,0,100000
-p3,p128,Possible,LCC,0,100000
-p3,p129,Possible,LCC,0,100000
-p3,p13,Possible,LCC,0,100000
-p3,p130,Possible,LCC,0,100000
-p3,p131,Possible,LCC,0,100000
-p3,p132,Possible,LCC,0,100000
-p3,p133,Possible,LCC,0,100000
-p3,p134,Possible,LCC,0,100000
-p3,p15,Possible,LCC,0,100000
-p3,p16,Possible,LCC,0,100000
-p3,p18,Possible,LCC,0,100000
-p3,p19,Possible,LCC,0,100000
-p3,p20,Possible,LCC,0,100000
-p3,p21,Possible,LCC,0,100000
-p3,p22,Possible,LCC,0,100000
-p3,p23,Possible,LCC,0,100000
-p3,p24,Possible,LCC,0,100000
-p3,p25,Possible,LCC,0,100000
-p3,p26,Possible,LCC,0,100000
-p3,p27,Possible,LCC,0,100000
-p3,p28,Possible,LCC,0,100000
-p3,p29,Possible,LCC,0,100000
-p3,p30,Possible,LCC,0,100000
-p3,p31,Possible,LCC,0,100000
-p3,p32,Possible,LCC,0,100000
-p3,p33,Possible,LCC,0,100000
-p3,p34,Possible,LCC,0,100000
-p3,p35,Possible,LCC,0,100000
-p3,p36,Possible,LCC,0,100000
-p3,p37,Possible,LCC,0,100000
-p3,p38,Possible,LCC,0,100000
-p3,p39,Possible,LCC,0,100000
-p3,p40,Possible,LCC,0,100000
-p3,p41,Possible,LCC,0,100000
-p3,p42,Possible,LCC,0,100000
-p3,p43,Possible,LCC,0,100000
-p3,p44,Possible,LCC,0,100000
-p3,p45,Possible,LCC,0,100000
-p3,p46,Possible,LCC,0,100000
-p3,p47,Possible,LCC,0,100000
-p3,p48,Possible,LCC,0,100000
-p3,p49,Possible,LCC,0,100000
-p3,p50,Possible,LCC,0,100000
-p3,p51,Possible,LCC,0,100000
-p3,p52,Possible,LCC,0,100000
-p3,p53,Possible,LCC,0,100000
-p3,p54,Possible,LCC,0,100000
-p3,p55,Possible,LCC,0,100000
-p3,p56,Possible,LCC,0,100000
-p3,p57,Possible,LCC,0,100000
-p3,p58,Possible,LCC,0,100000
-p3,p59,Possible,LCC,0,100000
-p3,p6,Possible,LCC,0,100000
-p3,p60,Possible,LCC,0,100000
-p3,p61,Possible,LCC,0,100000
-p3,p62,Possible,LCC,0,100000
-p3,p63,Possible,LCC,0,100000
-p3,p64,Possible,LCC,0,100000
-p3,p65,Possible,LCC,0,100000
-p3,p66,Possible,LCC,0,100000
-p3,p67,Possible,LCC,0,100000
-p3,p68,Possible,LCC,0,100000
-p3,p69,Possible,LCC,0,100000
-p3,p7,Possible,LCC,0,100000
-p3,p70,Possible,LCC,0,100000
-p3,p71,Possible,LCC,0,100000
-p3,p72,Possible,LCC,0,100000
-p3,p73,Possible,LCC,0,100000
-p3,p74,Possible,LCC,0,100000
-p3,p75,Possible,LCC,0,100000
-p3,p76,Possible,LCC,0,100000
-p3,p77,Possible,LCC,0,100000
-p3,p78,Possible,LCC,0,100000
-p3,p79,Possible,LCC,0,100000
-p3,p8,Possible,LCC,0,100000
-p3,p80,Possible,LCC,0,100000
-p3,p81,Possible,LCC,0,100000
-p3,p82,Possible,LCC,0,100000
-p3,p83,Possible,LCC,0,100000
-p3,p84,Possible,LCC,0,100000
-p3,p85,Possible,LCC,0,100000
-p3,p86,Possible,LCC,0,100000
-p3,p87,Possible,LCC,0,100000
-p3,p88,Possible,LCC,0,100000
-p3,p89,Possible,LCC,0,100000
-p3,p9,Possible,LCC,0,100000
-p3,p90,Possible,LCC,0,100000
-p3,p91,Possible,LCC,0,100000
-p3,p92,Possible,LCC,0,100000
-p3,p93,Possible,LCC,0,100000
-p3,p94,Possible,LCC,0,100000
-p3,p95,Possible,LCC,0,100000
-p3,p96,Possible,LCC,0,100000
-p3,p97,Possible,LCC,0,100000
-p3,p98,Possible,LCC,0,100000
-p3,p99,Possible,LCC,0,100000
-p30,p100,Possible,LCC,0,100000
-p30,p101,Possible,LCC,0,100000
-p30,p102,Possible,LCC,0,100000
-p30,p103,Possible,LCC,0,100000
-p30,p104,Possible,LCC,0,100000
-p30,p105,Possible,LCC,0,100000
-p30,p106,Possible,LCC,0,100000
-p30,p107,Possible,LCC,0,100000
-p30,p108,Possible,LCC,0,100000
-p30,p109,Possible,LCC,0,100000
-p30,p110,Possible,LCC,0,100000
-p30,p111,Possible,LCC,0,100000
-p30,p112,Possible,LCC,0,100000
-p30,p113,Possible,LCC,0,100000
-p30,p114,Possible,LCC,0,100000
-p30,p115,Possible,LCC,0,100000
-p30,p116,Possible,LCC,0,100000
-p30,p117,Possible,LCC,0,100000
-p30,p118,Possible,LCC,0,100000
-p30,p119,Possible,LCC,0,100000
-p30,p120,Possible,LCC,0,100000
-p30,p121,Possible,LCC,0,100000
-p30,p122,Possible,LCC,0,100000
-p30,p123,Possible,LCC,0,100000
-p30,p124,Possible,LCC,0,100000
-p30,p125,Possible,LCC,0,100000
-p30,p126,Possible,LCC,0,100000
-p30,p127,Possible,LCC,0,100000
-p30,p128,Possible,LCC,0,100000
-p30,p129,Possible,LCC,0,100000
-p30,p130,Possible,LCC,0,100000
-p30,p131,Possible,LCC,0,100000
-p30,p132,Possible,LCC,0,100000
-p30,p133,Possible,LCC,0,100000
-p30,p134,Possible,LCC,0,100000
-p30,p31,Possible,LCC,0,100000
-p30,p32,Possible,LCC,0,100000
-p30,p33,Possible,LCC,0,100000
-p30,p34,Possible,LCC,0,100000
-p30,p35,Possible,LCC,0,100000
-p30,p36,Possible,LCC,0,100000
-p30,p37,Possible,LCC,0,100000
-p30,p38,Possible,LCC,0,100000
-p30,p39,Possible,LCC,0,100000
-p30,p40,Possible,LCC,0,100000
-p30,p41,Possible,LCC,0,100000
-p30,p42,Possible,LCC,0,100000
-p30,p43,Possible,LCC,0,100000
-p30,p44,Possible,LCC,0,100000
-p30,p45,Possible,LCC,0,100000
-p30,p46,Possible,LCC,0,100000
-p30,p47,Possible,LCC,0,100000
-p30,p48,Possible,LCC,0,100000
-p30,p49,Possible,LCC,0,100000
-p30,p50,Possible,LCC,0,100000
-p30,p51,Possible,LCC,0,100000
-p30,p52,Possible,LCC,0,100000
-p30,p53,Possible,LCC,0,100000
-p30,p54,Possible,LCC,0,100000
-p30,p55,Possible,LCC,0,100000
-p30,p56,Possible,LCC,0,100000
-p30,p57,Possible,LCC,0,100000
-p30,p58,Possible,LCC,0,100000
-p30,p59,Possible,LCC,0,100000
-p30,p60,Possible,LCC,0,100000
-p30,p61,Possible,LCC,0,100000
-p30,p62,Possible,LCC,0,100000
-p30,p63,Possible,LCC,0,100000
-p30,p64,Possible,LCC,0,100000
-p30,p65,Possible,LCC,0,100000
-p30,p66,Possible,LCC,0,100000
-p30,p67,Possible,LCC,0,100000
-p30,p68,Possible,LCC,0,100000
-p30,p69,Possible,LCC,0,100000
-p30,p70,Possible,LCC,0,100000
-p30,p71,Possible,LCC,0,100000
-p30,p72,Possible,LCC,0,100000
-p30,p73,Possible,LCC,0,100000
-p30,p74,Possible,LCC,0,100000
-p30,p75,Possible,LCC,0,100000
-p30,p76,Possible,LCC,0,100000
-p30,p77,Possible,LCC,0,100000
-p30,p78,Possible,LCC,0,100000
-p30,p79,Possible,LCC,0,100000
-p30,p80,Possible,LCC,0,100000
-p30,p81,Possible,LCC,0,100000
-p30,p82,Possible,LCC,0,100000
-p30,p83,Possible,LCC,0,100000
-p30,p84,Possible,LCC,0,100000
-p30,p85,Possible,LCC,0,100000
-p30,p86,Possible,LCC,0,100000
-p30,p87,Possible,LCC,0,100000
-p30,p88,Possible,LCC,0,100000
-p30,p89,Possible,LCC,0,100000
-p30,p90,Possible,LCC,0,100000
-p30,p91,Possible,LCC,0,100000
-p30,p92,Possible,LCC,0,100000
-p30,p93,Possible,LCC,0,100000
-p30,p94,Possible,LCC,0,100000
-p30,p95,Possible,LCC,0,100000
-p30,p96,Possible,LCC,0,100000
-p30,p97,Possible,LCC,0,100000
-p30,p98,Possible,LCC,0,100000
-p30,p99,Possible,LCC,0,100000
-p31,p100,Possible,LCC,0,100000
-p31,p101,Possible,LCC,0,100000
-p31,p102,Possible,LCC,0,100000
-p31,p103,Possible,LCC,0,100000
-p31,p104,Possible,LCC,0,100000
-p31,p105,Possible,LCC,0,100000
-p31,p106,Possible,LCC,0,100000
-p31,p107,Possible,LCC,0,100000
-p31,p108,Possible,LCC,0,100000
-p31,p109,Possible,LCC,0,100000
-p31,p110,Possible,LCC,0,100000
-p31,p111,Possible,LCC,0,100000
-p31,p112,Possible,LCC,0,100000
-p31,p113,Possible,LCC,0,100000
-p31,p114,Possible,LCC,0,100000
-p31,p115,Possible,LCC,0,100000
-p31,p116,Possible,LCC,0,100000
-p31,p117,Possible,LCC,0,100000
-p31,p118,Possible,LCC,0,100000
-p31,p119,Possible,LCC,0,100000
-p31,p120,Possible,LCC,0,100000
-p31,p121,Possible,LCC,0,100000
-p31,p122,Possible,LCC,0,100000
-p31,p123,Possible,LCC,0,100000
-p31,p124,Possible,LCC,0,100000
-p31,p125,Possible,LCC,0,100000
-p31,p126,Possible,LCC,0,100000
-p31,p127,Possible,LCC,0,100000
-p31,p128,Possible,LCC,0,100000
-p31,p129,Possible,LCC,0,100000
-p31,p130,Possible,LCC,0,100000
-p31,p131,Possible,LCC,0,100000
-p31,p132,Possible,LCC,0,100000
-p31,p133,Possible,LCC,0,100000
-p31,p134,Possible,LCC,0,100000
-p31,p32,Possible,LCC,0,100000
-p31,p33,Possible,LCC,0,100000
-p31,p35,Possible,LCC,0,100000
-p31,p36,Possible,LCC,0,100000
-p31,p37,Possible,LCC,0,100000
-p31,p38,Possible,LCC,0,100000
-p31,p39,Possible,LCC,0,100000
-p31,p40,Possible,LCC,0,100000
-p31,p41,Possible,LCC,0,100000
-p31,p42,Possible,LCC,0,100000
-p31,p43,Possible,LCC,0,100000
-p31,p44,Possible,LCC,0,100000
-p31,p45,Possible,LCC,0,100000
-p31,p46,Possible,LCC,0,100000
-p31,p49,Possible,LCC,0,100000
-p31,p50,Possible,LCC,0,100000
-p31,p51,Possible,LCC,0,100000
-p31,p52,Possible,LCC,0,100000
-p31,p53,Possible,LCC,0,100000
-p31,p54,Possible,LCC,0,100000
-p31,p55,Possible,LCC,0,100000
-p31,p56,Possible,LCC,0,100000
-p31,p57,Possible,LCC,0,100000
-p31,p58,Possible,LCC,0,100000
-p31,p60,Possible,LCC,0,100000
-p31,p61,Possible,LCC,0,100000
-p31,p62,Possible,LCC,0,100000
-p31,p63,Possible,LCC,0,100000
-p31,p64,Possible,LCC,0,100000
-p31,p65,Possible,LCC,0,100000
-p31,p66,Possible,LCC,0,100000
-p31,p67,Possible,LCC,0,100000
-p31,p68,Possible,LCC,0,100000
-p31,p69,Possible,LCC,0,100000
-p31,p70,Possible,LCC,0,100000
-p31,p71,Possible,LCC,0,100000
-p31,p72,Possible,LCC,0,100000
-p31,p73,Possible,LCC,0,100000
-p31,p74,Possible,LCC,0,100000
-p31,p75,Possible,LCC,0,100000
-p31,p76,Possible,LCC,0,100000
-p31,p77,Possible,LCC,0,100000
-p31,p78,Possible,LCC,0,100000
-p31,p79,Possible,LCC,0,100000
-p31,p80,Possible,LCC,0,100000
-p31,p81,Possible,LCC,0,100000
-p31,p82,Possible,LCC,0,100000
-p31,p83,Possible,LCC,0,100000
-p31,p84,Possible,LCC,0,100000
-p31,p85,Possible,LCC,0,100000
-p31,p86,Possible,LCC,0,100000
-p31,p87,Possible,LCC,0,100000
-p31,p88,Possible,LCC,0,100000
-p31,p89,Possible,LCC,0,100000
-p31,p90,Possible,LCC,0,100000
-p31,p91,Possible,LCC,0,100000
-p31,p92,Possible,LCC,0,100000
-p31,p93,Possible,LCC,0,100000
-p31,p94,Possible,LCC,0,100000
-p31,p95,Possible,LCC,0,100000
-p31,p96,Possible,LCC,0,100000
-p31,p97,Possible,LCC,0,100000
-p31,p98,Possible,LCC,0,100000
-p31,p99,Possible,LCC,0,100000
-p32,p100,Possible,LCC,0,100000
-p32,p101,Possible,LCC,0,100000
-p32,p102,Possible,LCC,0,100000
-p32,p103,Possible,LCC,0,100000
-p32,p104,Possible,LCC,0,100000
-p32,p105,Possible,LCC,0,100000
-p32,p106,Possible,LCC,0,100000
-p32,p107,Possible,LCC,0,100000
-p32,p108,Possible,LCC,0,100000
-p32,p109,Possible,LCC,0,100000
-p32,p110,Possible,LCC,0,100000
-p32,p111,Possible,LCC,0,100000
-p32,p112,Possible,LCC,0,100000
-p32,p113,Possible,LCC,0,100000
-p32,p114,Possible,LCC,0,100000
-p32,p115,Possible,LCC,0,100000
-p32,p116,Possible,LCC,0,100000
-p32,p117,Possible,LCC,0,100000
-p32,p118,Possible,LCC,0,100000
-p32,p119,Possible,LCC,0,100000
-p32,p120,Possible,LCC,0,100000
-p32,p121,Possible,LCC,0,100000
-p32,p122,Possible,LCC,0,100000
-p32,p123,Possible,LCC,0,100000
-p32,p124,Possible,LCC,0,100000
-p32,p125,Possible,LCC,0,100000
-p32,p126,Possible,LCC,0,100000
-p32,p127,Possible,LCC,0,100000
-p32,p128,Possible,LCC,0,100000
-p32,p129,Possible,LCC,0,100000
-p32,p130,Possible,LCC,0,100000
-p32,p131,Possible,LCC,0,100000
-p32,p132,Possible,LCC,0,100000
-p32,p133,Possible,LCC,0,100000
-p32,p134,Possible,LCC,0,100000
-p32,p34,Possible,LCC,0,100000
-p32,p35,Possible,LCC,0,100000
-p32,p36,Possible,LCC,0,100000
-p32,p37,Possible,LCC,0,100000
-p32,p39,Possible,LCC,0,100000
-p32,p40,Possible,LCC,0,100000
-p32,p41,Possible,LCC,0,100000
-p32,p42,Possible,LCC,0,100000
-p32,p43,Possible,LCC,0,100000
-p32,p44,Possible,LCC,0,100000
-p32,p45,Possible,LCC,0,100000
-p32,p46,Possible,LCC,0,100000
-p32,p47,Possible,LCC,0,100000
-p32,p48,Possible,LCC,0,100000
-p32,p49,Possible,LCC,0,100000
-p32,p50,Possible,LCC,0,100000
-p32,p51,Possible,LCC,0,100000
-p32,p52,Possible,LCC,0,100000
-p32,p53,Possible,LCC,0,100000
-p32,p54,Possible,LCC,0,100000
-p32,p55,Possible,LCC,0,100000
-p32,p56,Possible,LCC,0,100000
-p32,p57,Possible,LCC,0,100000
-p32,p58,Possible,LCC,0,100000
-p32,p59,Possible,LCC,0,100000
-p32,p60,Possible,LCC,0,100000
-p32,p61,Possible,LCC,0,100000
-p32,p62,Possible,LCC,0,100000
-p32,p63,Possible,LCC,0,100000
-p32,p64,Possible,LCC,0,100000
-p32,p65,Possible,LCC,0,100000
-p32,p66,Possible,LCC,0,100000
-p32,p67,Possible,LCC,0,100000
-p32,p68,Possible,LCC,0,100000
-p32,p69,Possible,LCC,0,100000
-p32,p70,Possible,LCC,0,100000
-p32,p71,Possible,LCC,0,100000
-p32,p72,Possible,LCC,0,100000
-p32,p73,Possible,LCC,0,100000
-p32,p74,Possible,LCC,0,100000
-p32,p75,Possible,LCC,0,100000
-p32,p76,Possible,LCC,0,100000
-p32,p77,Possible,LCC,0,100000
-p32,p78,Possible,LCC,0,100000
-p32,p79,Possible,LCC,0,100000
-p32,p80,Possible,LCC,0,100000
-p32,p81,Possible,LCC,0,100000
-p32,p82,Possible,LCC,0,100000
-p32,p83,Possible,LCC,0,100000
-p32,p84,Possible,LCC,0,100000
-p32,p85,Possible,LCC,0,100000
-p32,p86,Possible,LCC,0,100000
-p32,p87,Possible,LCC,0,100000
-p32,p88,Possible,LCC,0,100000
-p32,p89,Possible,LCC,0,100000
-p32,p90,Possible,LCC,0,100000
-p32,p91,Possible,LCC,0,100000
-p32,p92,Possible,LCC,0,100000
-p32,p93,Possible,LCC,0,100000
-p32,p94,Possible,LCC,0,100000
-p32,p95,Possible,LCC,0,100000
-p32,p96,Possible,LCC,0,100000
-p32,p97,Possible,LCC,0,100000
-p32,p98,Possible,LCC,0,100000
-p32,p99,Possible,LCC,0,100000
-p33,p100,Possible,LCC,0,100000
-p33,p101,Possible,LCC,0,100000
-p33,p102,Possible,LCC,0,100000
-p33,p103,Possible,LCC,0,100000
-p33,p104,Possible,LCC,0,100000
-p33,p105,Possible,LCC,0,100000
-p33,p106,Possible,LCC,0,100000
-p33,p107,Possible,LCC,0,100000
-p33,p108,Possible,LCC,0,100000
-p33,p109,Possible,LCC,0,100000
-p33,p110,Possible,LCC,0,100000
-p33,p111,Possible,LCC,0,100000
-p33,p112,Possible,LCC,0,100000
-p33,p113,Possible,LCC,0,100000
-p33,p114,Possible,LCC,0,100000
-p33,p115,Possible,LCC,0,100000
-p33,p116,Possible,LCC,0,100000
-p33,p117,Possible,LCC,0,100000
-p33,p118,Possible,LCC,0,100000
-p33,p119,Possible,LCC,0,100000
-p33,p120,Possible,LCC,0,100000
-p33,p121,Possible,LCC,0,100000
-p33,p122,Possible,LCC,0,100000
-p33,p123,Possible,LCC,0,100000
-p33,p124,Possible,LCC,0,100000
-p33,p125,Possible,LCC,0,100000
-p33,p126,Possible,LCC,0,100000
-p33,p127,Possible,LCC,0,100000
-p33,p128,Possible,LCC,0,100000
-p33,p129,Possible,LCC,0,100000
-p33,p130,Possible,LCC,0,100000
-p33,p131,Possible,LCC,0,100000
-p33,p132,Possible,LCC,0,100000
-p33,p133,Possible,LCC,0,100000
-p33,p134,Possible,LCC,0,100000
-p33,p35,Possible,LCC,0,100000
-p33,p36,Possible,LCC,0,100000
-p33,p37,Possible,LCC,0,100000
-p33,p38,Possible,LCC,0,100000
-p33,p39,Possible,LCC,0,100000
-p33,p40,Possible,LCC,0,100000
-p33,p41,Possible,LCC,0,100000
-p33,p42,Possible,LCC,0,100000
-p33,p43,Possible,LCC,0,100000
-p33,p44,Possible,LCC,0,100000
-p33,p45,Possible,LCC,0,100000
-p33,p46,Possible,LCC,0,100000
-p33,p47,Possible,LCC,0,100000
-p33,p48,Possible,LCC,0,100000
-p33,p49,Possible,LCC,0,100000
-p33,p50,Possible,LCC,0,100000
-p33,p51,Possible,LCC,0,100000
-p33,p52,Possible,LCC,0,100000
-p33,p53,Possible,LCC,0,100000
-p33,p54,Possible,LCC,0,100000
-p33,p55,Possible,LCC,0,100000
-p33,p56,Possible,LCC,0,100000
-p33,p57,Possible,LCC,0,100000
-p33,p58,Possible,LCC,0,100000
-p33,p59,Possible,LCC,0,100000
-p33,p60,Possible,LCC,0,100000
-p33,p61,Possible,LCC,0,100000
-p33,p62,Possible,LCC,0,100000
-p33,p63,Possible,LCC,0,100000
-p33,p64,Possible,LCC,0,100000
-p33,p65,Possible,LCC,0,100000
-p33,p66,Possible,LCC,0,100000
-p33,p67,Possible,LCC,0,100000
-p33,p68,Possible,LCC,0,100000
-p33,p69,Possible,LCC,0,100000
-p33,p70,Possible,LCC,0,100000
-p33,p71,Possible,LCC,0,100000
-p33,p72,Possible,LCC,0,100000
-p33,p73,Possible,LCC,0,100000
-p33,p74,Possible,LCC,0,100000
-p33,p75,Possible,LCC,0,100000
-p33,p76,Possible,LCC,0,100000
-p33,p77,Possible,LCC,0,100000
-p33,p78,Possible,LCC,0,100000
-p33,p79,Possible,LCC,0,100000
-p33,p80,Possible,LCC,0,100000
-p33,p81,Possible,LCC,0,100000
-p33,p82,Possible,LCC,0,100000
-p33,p83,Possible,LCC,0,100000
-p33,p84,Possible,LCC,0,100000
-p33,p85,Possible,LCC,0,100000
-p33,p86,Possible,LCC,0,100000
-p33,p87,Possible,LCC,0,100000
-p33,p88,Possible,LCC,0,100000
-p33,p89,Possible,LCC,0,100000
-p33,p90,Possible,LCC,0,100000
-p33,p91,Possible,LCC,0,100000
-p33,p92,Possible,LCC,0,100000
-p33,p93,Possible,LCC,0,100000
-p33,p94,Possible,LCC,0,100000
-p33,p95,Possible,LCC,0,100000
-p33,p96,Possible,LCC,0,100000
-p33,p97,Possible,LCC,0,100000
-p33,p98,Possible,LCC,0,100000
-p33,p99,Possible,LCC,0,100000
-p34,p100,Possible,LCC,0,100000
-p34,p101,Possible,LCC,0,100000
-p34,p102,Possible,LCC,0,100000
-p34,p103,Possible,LCC,0,100000
-p34,p104,Possible,LCC,0,100000
-p34,p105,Possible,LCC,0,100000
-p34,p106,Possible,LCC,0,100000
-p34,p107,Possible,LCC,0,100000
-p34,p108,Possible,LCC,0,100000
-p34,p109,Possible,LCC,0,100000
-p34,p110,Possible,LCC,0,100000
-p34,p111,Possible,LCC,0,100000
-p34,p112,Possible,LCC,0,100000
-p34,p113,Possible,LCC,0,100000
-p34,p114,Possible,LCC,0,100000
-p34,p115,Possible,LCC,0,100000
-p34,p116,Possible,LCC,0,100000
-p34,p117,Possible,LCC,0,100000
-p34,p118,Possible,LCC,0,100000
-p34,p119,Possible,LCC,0,100000
-p34,p120,Possible,LCC,0,100000
-p34,p121,Possible,LCC,0,100000
-p34,p122,Possible,LCC,0,100000
-p34,p123,Possible,LCC,0,100000
-p34,p124,Possible,LCC,0,100000
-p34,p125,Possible,LCC,0,100000
-p34,p126,Possible,LCC,0,100000
-p34,p127,Possible,LCC,0,100000
-p34,p128,Possible,LCC,0,100000
-p34,p129,Possible,LCC,0,100000
-p34,p130,Possible,LCC,0,100000
-p34,p131,Possible,LCC,0,100000
-p34,p132,Possible,LCC,0,100000
-p34,p133,Possible,LCC,0,100000
-p34,p134,Possible,LCC,0,100000
-p34,p35,Possible,LCC,0,100000
-p34,p36,Possible,LCC,0,100000
-p34,p37,Possible,LCC,0,100000
-p34,p38,Possible,LCC,0,100000
-p34,p39,Possible,LCC,0,100000
-p34,p40,Possible,LCC,0,100000
-p34,p41,Possible,LCC,0,100000
-p34,p42,Possible,LCC,0,100000
-p34,p43,Possible,LCC,0,100000
-p34,p44,Possible,LCC,0,100000
-p34,p45,Possible,LCC,0,100000
-p34,p46,Possible,LCC,0,100000
-p34,p47,Possible,LCC,0,100000
-p34,p48,Possible,LCC,0,100000
-p34,p49,Possible,LCC,0,100000
-p34,p50,Possible,LCC,0,100000
-p34,p51,Possible,LCC,0,100000
-p34,p53,Possible,LCC,0,100000
-p34,p54,Possible,LCC,0,100000
-p34,p55,Possible,LCC,0,100000
-p34,p56,Possible,LCC,0,100000
-p34,p57,Possible,LCC,0,100000
-p34,p58,Possible,LCC,0,100000
-p34,p59,Possible,LCC,0,100000
-p34,p60,Possible,LCC,0,100000
-p34,p61,Possible,LCC,0,100000
-p34,p62,Possible,LCC,0,100000
-p34,p63,Possible,LCC,0,100000
-p34,p64,Possible,LCC,0,100000
-p34,p65,Possible,LCC,0,100000
-p34,p66,Possible,LCC,0,100000
-p34,p67,Possible,LCC,0,100000
-p34,p68,Possible,LCC,0,100000
-p34,p69,Possible,LCC,0,100000
-p34,p70,Possible,LCC,0,100000
-p34,p71,Possible,LCC,0,100000
-p34,p72,Possible,LCC,0,100000
-p34,p73,Possible,LCC,0,100000
-p34,p74,Possible,LCC,0,100000
-p34,p75,Possible,LCC,0,100000
-p34,p76,Possible,LCC,0,100000
-p34,p77,Possible,LCC,0,100000
-p34,p78,Possible,LCC,0,100000
-p34,p79,Possible,LCC,0,100000
-p34,p80,Possible,LCC,0,100000
-p34,p81,Possible,LCC,0,100000
-p34,p82,Possible,LCC,0,100000
-p34,p83,Possible,LCC,0,100000
-p34,p84,Possible,LCC,0,100000
-p34,p85,Possible,LCC,0,100000
-p34,p86,Possible,LCC,0,100000
-p34,p87,Possible,LCC,0,100000
-p34,p88,Possible,LCC,0,100000
-p34,p89,Possible,LCC,0,100000
-p34,p90,Possible,LCC,0,100000
-p34,p91,Possible,LCC,0,100000
-p34,p92,Possible,LCC,0,100000
-p34,p93,Possible,LCC,0,100000
-p34,p94,Possible,LCC,0,100000
-p34,p95,Possible,LCC,0,100000
-p34,p96,Possible,LCC,0,100000
-p34,p97,Possible,LCC,0,100000
-p34,p98,Possible,LCC,0,100000
-p34,p99,Possible,LCC,0,100000
-p35,p100,Possible,LCC,0,100000
-p35,p101,Possible,LCC,0,100000
-p35,p102,Possible,LCC,0,100000
-p35,p103,Possible,LCC,0,100000
-p35,p104,Possible,LCC,0,100000
-p35,p105,Possible,LCC,0,100000
-p35,p106,Possible,LCC,0,100000
-p35,p107,Possible,LCC,0,100000
-p35,p108,Possible,LCC,0,100000
-p35,p109,Possible,LCC,0,100000
-p35,p110,Possible,LCC,0,100000
-p35,p111,Possible,LCC,0,100000
-p35,p112,Possible,LCC,0,100000
-p35,p113,Possible,LCC,0,100000
-p35,p114,Possible,LCC,0,100000
-p35,p115,Possible,LCC,0,100000
-p35,p116,Possible,LCC,0,100000
-p35,p117,Possible,LCC,0,100000
-p35,p118,Possible,LCC,0,100000
-p35,p119,Possible,LCC,0,100000
-p35,p120,Possible,LCC,0,100000
-p35,p121,Possible,LCC,0,100000
-p35,p122,Possible,LCC,0,100000
-p35,p123,Possible,LCC,0,100000
-p35,p124,Possible,LCC,0,100000
-p35,p125,Possible,LCC,0,100000
-p35,p126,Possible,LCC,0,100000
-p35,p127,Possible,LCC,0,100000
-p35,p128,Possible,LCC,0,100000
-p35,p129,Possible,LCC,0,100000
-p35,p130,Possible,LCC,0,100000
-p35,p131,Possible,LCC,0,100000
-p35,p132,Possible,LCC,0,100000
-p35,p133,Possible,LCC,0,100000
-p35,p134,Possible,LCC,0,100000
-p35,p38,Possible,LCC,0,100000
-p35,p39,Possible,LCC,0,100000
-p35,p40,Possible,LCC,0,100000
-p35,p41,Possible,LCC,0,100000
-p35,p42,Possible,LCC,0,100000
-p35,p43,Possible,LCC,0,100000
-p35,p44,Possible,LCC,0,100000
-p35,p45,Possible,LCC,0,100000
-p35,p46,Possible,LCC,0,100000
-p35,p47,Possible,LCC,0,100000
-p35,p48,Possible,LCC,0,100000
-p35,p49,Possible,LCC,0,100000
-p35,p50,Possible,LCC,0,100000
-p35,p51,Possible,LCC,0,100000
-p35,p52,Possible,LCC,0,100000
-p35,p53,Possible,LCC,0,100000
-p35,p54,Possible,LCC,0,100000
-p35,p55,Possible,LCC,0,100000
-p35,p56,Possible,LCC,0,100000
-p35,p57,Possible,LCC,0,100000
-p35,p58,Possible,LCC,0,100000
-p35,p59,Possible,LCC,0,100000
-p35,p60,Possible,LCC,0,100000
-p35,p61,Possible,LCC,0,100000
-p35,p62,Possible,LCC,0,100000
-p35,p63,Possible,LCC,0,100000
-p35,p64,Possible,LCC,0,100000
-p35,p65,Possible,LCC,0,100000
-p35,p66,Possible,LCC,0,100000
-p35,p67,Possible,LCC,0,100000
-p35,p68,Possible,LCC,0,100000
-p35,p69,Possible,LCC,0,100000
-p35,p70,Possible,LCC,0,100000
-p35,p71,Possible,LCC,0,100000
-p35,p72,Possible,LCC,0,100000
-p35,p73,Possible,LCC,0,100000
-p35,p74,Possible,LCC,0,100000
-p35,p75,Possible,LCC,0,100000
-p35,p76,Possible,LCC,0,100000
-p35,p77,Possible,LCC,0,100000
-p35,p78,Possible,LCC,0,100000
-p35,p79,Possible,LCC,0,100000
-p35,p80,Possible,LCC,0,100000
-p35,p81,Possible,LCC,0,100000
-p35,p82,Possible,LCC,0,100000
-p35,p83,Possible,LCC,0,100000
-p35,p84,Possible,LCC,0,100000
-p35,p85,Possible,LCC,0,100000
-p35,p86,Possible,LCC,0,100000
-p35,p87,Possible,LCC,0,100000
-p35,p88,Possible,LCC,0,100000
-p35,p89,Possible,LCC,0,100000
-p35,p90,Possible,LCC,0,100000
-p35,p91,Possible,LCC,0,100000
-p35,p92,Possible,LCC,0,100000
-p35,p93,Possible,LCC,0,100000
-p35,p94,Possible,LCC,0,100000
-p35,p95,Possible,LCC,0,100000
-p35,p96,Possible,LCC,0,100000
-p35,p97,Possible,LCC,0,100000
-p35,p98,Possible,LCC,0,100000
-p35,p99,Possible,LCC,0,100000
-p36,p100,Possible,LCC,0,100000
-p36,p101,Possible,LCC,0,100000
-p36,p102,Possible,LCC,0,100000
-p36,p103,Possible,LCC,0,100000
-p36,p104,Possible,LCC,0,100000
-p36,p105,Possible,LCC,0,100000
-p36,p106,Possible,LCC,0,100000
-p36,p107,Possible,LCC,0,100000
-p36,p108,Possible,LCC,0,100000
-p36,p109,Possible,LCC,0,100000
-p36,p110,Possible,LCC,0,100000
-p36,p111,Possible,LCC,0,100000
-p36,p112,Possible,LCC,0,100000
-p36,p113,Possible,LCC,0,100000
-p36,p114,Possible,LCC,0,100000
-p36,p115,Possible,LCC,0,100000
-p36,p116,Possible,LCC,0,100000
-p36,p117,Possible,LCC,0,100000
-p36,p118,Possible,LCC,0,100000
-p36,p119,Possible,LCC,0,100000
-p36,p120,Possible,LCC,0,100000
-p36,p121,Possible,LCC,0,100000
-p36,p122,Possible,LCC,0,100000
-p36,p123,Possible,LCC,0,100000
-p36,p124,Possible,LCC,0,100000
-p36,p125,Possible,LCC,0,100000
-p36,p126,Possible,LCC,0,100000
-p36,p127,Possible,LCC,0,100000
-p36,p128,Possible,LCC,0,100000
-p36,p129,Possible,LCC,0,100000
-p36,p130,Possible,LCC,0,100000
-p36,p131,Possible,LCC,0,100000
-p36,p132,Possible,LCC,0,100000
-p36,p133,Possible,LCC,0,100000
-p36,p134,Possible,LCC,0,100000
-p36,p39,Possible,LCC,0,100000
-p36,p40,Possible,LCC,0,100000
-p36,p41,Possible,LCC,0,100000
-p36,p44,Possible,LCC,0,100000
-p36,p45,Possible,LCC,0,100000
-p36,p46,Possible,LCC,0,100000
-p36,p47,Possible,LCC,0,100000
-p36,p48,Possible,LCC,0,100000
-p36,p49,Possible,LCC,0,100000
-p36,p50,Possible,LCC,0,100000
-p36,p51,Possible,LCC,0,100000
-p36,p52,Possible,LCC,0,100000
-p36,p53,Possible,LCC,0,100000
-p36,p54,Possible,LCC,0,100000
-p36,p55,Possible,LCC,0,100000
-p36,p56,Possible,LCC,0,100000
-p36,p57,Possible,LCC,0,100000
-p36,p58,Possible,LCC,0,100000
-p36,p59,Possible,LCC,0,100000
-p36,p60,Possible,LCC,0,100000
-p36,p61,Possible,LCC,0,100000
-p36,p62,Possible,LCC,0,100000
-p36,p63,Possible,LCC,0,100000
-p36,p64,Possible,LCC,0,100000
-p36,p65,Possible,LCC,0,100000
-p36,p66,Possible,LCC,0,100000
-p36,p67,Possible,LCC,0,100000
-p36,p68,Possible,LCC,0,100000
-p36,p69,Possible,LCC,0,100000
-p36,p70,Possible,LCC,0,100000
-p36,p71,Possible,LCC,0,100000
-p36,p72,Possible,LCC,0,100000
-p36,p73,Possible,LCC,0,100000
-p36,p74,Possible,LCC,0,100000
-p36,p75,Possible,LCC,0,100000
-p36,p76,Possible,LCC,0,100000
-p36,p77,Possible,LCC,0,100000
-p36,p78,Possible,LCC,0,100000
-p36,p79,Possible,LCC,0,100000
-p36,p80,Possible,LCC,0,100000
-p36,p81,Possible,LCC,0,100000
-p36,p82,Possible,LCC,0,100000
-p36,p83,Possible,LCC,0,100000
-p36,p84,Possible,LCC,0,100000
-p36,p85,Possible,LCC,0,100000
-p36,p86,Possible,LCC,0,100000
-p36,p87,Possible,LCC,0,100000
-p36,p88,Possible,LCC,0,100000
-p36,p89,Possible,LCC,0,100000
-p36,p90,Possible,LCC,0,100000
-p36,p91,Possible,LCC,0,100000
-p36,p92,Possible,LCC,0,100000
-p36,p93,Possible,LCC,0,100000
-p36,p94,Possible,LCC,0,100000
-p36,p95,Possible,LCC,0,100000
-p36,p96,Possible,LCC,0,100000
-p36,p97,Possible,LCC,0,100000
-p36,p98,Possible,LCC,0,100000
-p36,p99,Possible,LCC,0,100000
-p37,p100,Possible,LCC,0,100000
-p37,p101,Possible,LCC,0,100000
-p37,p102,Possible,LCC,0,100000
-p37,p103,Possible,LCC,0,100000
-p37,p104,Possible,LCC,0,100000
-p37,p105,Possible,LCC,0,100000
-p37,p106,Possible,LCC,0,100000
-p37,p107,Possible,LCC,0,100000
-p37,p108,Possible,LCC,0,100000
-p37,p109,Possible,LCC,0,100000
-p37,p110,Possible,LCC,0,100000
-p37,p111,Possible,LCC,0,100000
-p37,p112,Possible,LCC,0,100000
-p37,p113,Possible,LCC,0,100000
-p37,p114,Possible,LCC,0,100000
-p37,p115,Possible,LCC,0,100000
-p37,p116,Possible,LCC,0,100000
-p37,p117,Possible,LCC,0,100000
-p37,p118,Possible,LCC,0,100000
-p37,p119,Possible,LCC,0,100000
-p37,p120,Possible,LCC,0,100000
-p37,p121,Possible,LCC,0,100000
-p37,p122,Possible,LCC,0,100000
-p37,p123,Possible,LCC,0,100000
-p37,p124,Possible,LCC,0,100000
-p37,p125,Possible,LCC,0,100000
-p37,p126,Possible,LCC,0,100000
-p37,p127,Possible,LCC,0,100000
-p37,p128,Possible,LCC,0,100000
-p37,p129,Possible,LCC,0,100000
-p37,p130,Possible,LCC,0,100000
-p37,p131,Possible,LCC,0,100000
-p37,p132,Possible,LCC,0,100000
-p37,p133,Possible,LCC,0,100000
-p37,p134,Possible,LCC,0,100000
-p37,p39,Possible,LCC,0,100000
-p37,p40,Possible,LCC,0,100000
-p37,p41,Possible,LCC,0,100000
-p37,p44,Possible,LCC,0,100000
-p37,p45,Possible,LCC,0,100000
-p37,p47,Possible,LCC,0,100000
-p37,p48,Possible,LCC,0,100000
-p37,p49,Possible,LCC,0,100000
-p37,p50,Possible,LCC,0,100000
-p37,p51,Possible,LCC,0,100000
-p37,p52,Possible,LCC,0,100000
-p37,p53,Possible,LCC,0,100000
-p37,p54,Possible,LCC,0,100000
-p37,p55,Possible,LCC,0,100000
-p37,p56,Possible,LCC,0,100000
-p37,p57,Possible,LCC,0,100000
-p37,p58,Possible,LCC,0,100000
-p37,p59,Possible,LCC,0,100000
-p37,p60,Possible,LCC,0,100000
-p37,p61,Possible,LCC,0,100000
-p37,p62,Possible,LCC,0,100000
-p37,p63,Possible,LCC,0,100000
-p37,p64,Possible,LCC,0,100000
-p37,p65,Possible,LCC,0,100000
-p37,p66,Possible,LCC,0,100000
-p37,p67,Possible,LCC,0,100000
-p37,p69,Possible,LCC,0,100000
-p37,p70,Possible,LCC,0,100000
-p37,p71,Possible,LCC,0,100000
-p37,p72,Possible,LCC,0,100000
-p37,p73,Possible,LCC,0,100000
-p37,p74,Possible,LCC,0,100000
-p37,p75,Possible,LCC,0,100000
-p37,p76,Possible,LCC,0,100000
-p37,p77,Possible,LCC,0,100000
-p37,p78,Possible,LCC,0,100000
-p37,p79,Possible,LCC,0,100000
-p37,p80,Possible,LCC,0,100000
-p37,p81,Possible,LCC,0,100000
-p37,p82,Possible,LCC,0,100000
-p37,p83,Possible,LCC,0,100000
-p37,p84,Possible,LCC,0,100000
-p37,p85,Possible,LCC,0,100000
-p37,p86,Possible,LCC,0,100000
-p37,p87,Possible,LCC,0,100000
-p37,p88,Possible,LCC,0,100000
-p37,p89,Possible,LCC,0,100000
-p37,p90,Possible,LCC,0,100000
-p37,p91,Possible,LCC,0,100000
-p37,p92,Possible,LCC,0,100000
-p37,p93,Possible,LCC,0,100000
-p37,p94,Possible,LCC,0,100000
-p37,p95,Possible,LCC,0,100000
-p37,p96,Possible,LCC,0,100000
-p37,p97,Possible,LCC,0,100000
-p37,p98,Possible,LCC,0,100000
-p37,p99,Possible,LCC,0,100000
-p38,p100,Possible,LCC,0,100000
-p38,p101,Possible,LCC,0,100000
-p38,p102,Possible,LCC,0,100000
-p38,p103,Possible,LCC,0,100000
-p38,p104,Possible,LCC,0,100000
-p38,p105,Possible,LCC,0,100000
-p38,p106,Possible,LCC,0,100000
-p38,p107,Possible,LCC,0,100000
-p38,p108,Possible,LCC,0,100000
-p38,p109,Possible,LCC,0,100000
-p38,p110,Possible,LCC,0,100000
-p38,p111,Possible,LCC,0,100000
-p38,p112,Possible,LCC,0,100000
-p38,p113,Possible,LCC,0,100000
-p38,p114,Possible,LCC,0,100000
-p38,p115,Possible,LCC,0,100000
-p38,p116,Possible,LCC,0,100000
-p38,p117,Possible,LCC,0,100000
-p38,p118,Possible,LCC,0,100000
-p38,p119,Possible,LCC,0,100000
-p38,p120,Possible,LCC,0,100000
-p38,p121,Possible,LCC,0,100000
-p38,p122,Possible,LCC,0,100000
-p38,p123,Possible,LCC,0,100000
-p38,p124,Possible,LCC,0,100000
-p38,p125,Possible,LCC,0,100000
-p38,p126,Possible,LCC,0,100000
-p38,p127,Possible,LCC,0,100000
-p38,p128,Possible,LCC,0,100000
-p38,p129,Possible,LCC,0,100000
-p38,p130,Possible,LCC,0,100000
-p38,p131,Possible,LCC,0,100000
-p38,p132,Possible,LCC,0,100000
-p38,p133,Possible,LCC,0,100000
-p38,p134,Possible,LCC,0,100000
-p38,p41,Possible,LCC,0,100000
-p38,p46,Possible,LCC,0,100000
-p38,p47,Possible,LCC,0,100000
-p38,p48,Possible,LCC,0,100000
-p38,p49,Possible,LCC,0,100000
-p38,p50,Possible,LCC,0,100000
-p38,p51,Possible,LCC,0,100000
-p38,p52,Possible,LCC,0,100000
-p38,p53,Possible,LCC,0,100000
-p38,p54,Possible,LCC,0,100000
-p38,p55,Possible,LCC,0,100000
-p38,p56,Possible,LCC,0,100000
-p38,p57,Possible,LCC,0,100000
-p38,p58,Possible,LCC,0,100000
-p38,p59,Possible,LCC,0,100000
-p38,p60,Possible,LCC,0,100000
-p38,p61,Possible,LCC,0,100000
-p38,p62,Possible,LCC,0,100000
-p38,p63,Possible,LCC,0,100000
-p38,p64,Possible,LCC,0,100000
-p38,p65,Possible,LCC,0,100000
-p38,p66,Possible,LCC,0,100000
-p38,p67,Possible,LCC,0,100000
-p38,p71,Possible,LCC,0,100000
-p38,p72,Possible,LCC,0,100000
-p38,p73,Possible,LCC,0,100000
-p38,p74,Possible,LCC,0,100000
-p38,p75,Possible,LCC,0,100000
-p38,p76,Possible,LCC,0,100000
-p38,p77,Possible,LCC,0,100000
-p38,p78,Possible,LCC,0,100000
-p38,p79,Possible,LCC,0,100000
-p38,p80,Possible,LCC,0,100000
-p38,p81,Possible,LCC,0,100000
-p38,p82,Possible,LCC,0,100000
-p38,p83,Possible,LCC,0,100000
-p38,p84,Possible,LCC,0,100000
-p38,p85,Possible,LCC,0,100000
-p38,p86,Possible,LCC,0,100000
-p38,p87,Possible,LCC,0,100000
-p38,p88,Possible,LCC,0,100000
-p38,p89,Possible,LCC,0,100000
-p38,p90,Possible,LCC,0,100000
-p38,p91,Possible,LCC,0,100000
-p38,p92,Possible,LCC,0,100000
-p38,p93,Possible,LCC,0,100000
-p38,p94,Possible,LCC,0,100000
-p38,p95,Possible,LCC,0,100000
-p38,p96,Possible,LCC,0,100000
-p38,p97,Possible,LCC,0,100000
-p38,p98,Possible,LCC,0,100000
-p38,p99,Possible,LCC,0,100000
-p39,p100,Possible,LCC,0,100000
-p39,p101,Possible,LCC,0,100000
-p39,p102,Possible,LCC,0,100000
-p39,p103,Possible,LCC,0,100000
-p39,p104,Possible,LCC,0,100000
-p39,p105,Possible,LCC,0,100000
-p39,p106,Possible,LCC,0,100000
-p39,p107,Possible,LCC,0,100000
-p39,p108,Possible,LCC,0,100000
-p39,p109,Possible,LCC,0,100000
-p39,p110,Possible,LCC,0,100000
-p39,p111,Possible,LCC,0,100000
-p39,p112,Possible,LCC,0,100000
-p39,p113,Possible,LCC,0,100000
-p39,p114,Possible,LCC,0,100000
-p39,p115,Possible,LCC,0,100000
-p39,p116,Possible,LCC,0,100000
-p39,p117,Possible,LCC,0,100000
-p39,p118,Possible,LCC,0,100000
-p39,p119,Possible,LCC,0,100000
-p39,p120,Possible,LCC,0,100000
-p39,p121,Possible,LCC,0,100000
-p39,p122,Possible,LCC,0,100000
-p39,p123,Possible,LCC,0,100000
-p39,p124,Possible,LCC,0,100000
-p39,p125,Possible,LCC,0,100000
-p39,p126,Possible,LCC,0,100000
-p39,p127,Possible,LCC,0,100000
-p39,p128,Possible,LCC,0,100000
-p39,p129,Possible,LCC,0,100000
-p39,p130,Possible,LCC,0,100000
-p39,p131,Possible,LCC,0,100000
-p39,p132,Possible,LCC,0,100000
-p39,p133,Possible,LCC,0,100000
-p39,p134,Possible,LCC,0,100000
-p39,p41,Possible,LCC,0,100000
-p39,p42,Possible,LCC,0,100000
-p39,p43,Possible,LCC,0,100000
-p39,p44,Possible,LCC,0,100000
-p39,p45,Possible,LCC,0,100000
-p39,p46,Possible,LCC,0,100000
-p39,p47,Possible,LCC,0,100000
-p39,p48,Possible,LCC,0,100000
-p39,p49,Possible,LCC,0,100000
-p39,p50,Possible,LCC,0,100000
-p39,p51,Possible,LCC,0,100000
-p39,p52,Possible,LCC,0,100000
-p39,p53,Possible,LCC,0,100000
-p39,p54,Possible,LCC,0,100000
-p39,p55,Possible,LCC,0,100000
-p39,p56,Possible,LCC,0,100000
-p39,p57,Possible,LCC,0,100000
-p39,p58,Possible,LCC,0,100000
-p39,p59,Possible,LCC,0,100000
-p39,p60,Possible,LCC,0,100000
-p39,p61,Possible,LCC,0,100000
-p39,p62,Possible,LCC,0,100000
-p39,p63,Possible,LCC,0,100000
-p39,p64,Possible,LCC,0,100000
-p39,p65,Possible,LCC,0,100000
-p39,p66,Possible,LCC,0,100000
-p39,p67,Possible,LCC,0,100000
-p39,p68,Possible,LCC,0,100000
-p39,p69,Possible,LCC,0,100000
-p39,p70,Possible,LCC,0,100000
-p39,p71,Possible,LCC,0,100000
-p39,p72,Possible,LCC,0,100000
-p39,p73,Possible,LCC,0,100000
-p39,p74,Possible,LCC,0,100000
-p39,p75,Possible,LCC,0,100000
-p39,p76,Possible,LCC,0,100000
-p39,p77,Possible,LCC,0,100000
-p39,p78,Possible,LCC,0,100000
-p39,p79,Possible,LCC,0,100000
-p39,p80,Possible,LCC,0,100000
-p39,p81,Possible,LCC,0,100000
-p39,p82,Possible,LCC,0,100000
-p39,p83,Possible,LCC,0,100000
-p39,p84,Possible,LCC,0,100000
-p39,p85,Possible,LCC,0,100000
-p39,p86,Possible,LCC,0,100000
-p39,p87,Possible,LCC,0,100000
-p39,p88,Possible,LCC,0,100000
-p39,p89,Possible,LCC,0,100000
-p39,p90,Possible,LCC,0,100000
-p39,p91,Possible,LCC,0,100000
-p39,p92,Possible,LCC,0,100000
-p39,p93,Possible,LCC,0,100000
-p39,p94,Possible,LCC,0,100000
-p39,p95,Possible,LCC,0,100000
-p39,p96,Possible,LCC,0,100000
-p39,p97,Possible,LCC,0,100000
-p39,p98,Possible,LCC,0,100000
-p39,p99,Possible,LCC,0,100000
-p4,p10,Possible,LCC,0,100000
-p4,p100,Possible,LCC,0,100000
-p4,p101,Possible,LCC,0,100000
-p4,p102,Possible,LCC,0,100000
-p4,p103,Possible,LCC,0,100000
-p4,p104,Possible,LCC,0,100000
-p4,p105,Possible,LCC,0,100000
-p4,p106,Possible,LCC,0,100000
-p4,p107,Possible,LCC,0,100000
-p4,p108,Possible,LCC,0,100000
-p4,p109,Possible,LCC,0,100000
-p4,p11,Possible,LCC,0,100000
-p4,p110,Possible,LCC,0,100000
-p4,p111,Possible,LCC,0,100000
-p4,p112,Possible,LCC,0,100000
-p4,p113,Possible,LCC,0,100000
-p4,p114,Possible,LCC,0,100000
-p4,p115,Possible,LCC,0,100000
-p4,p116,Possible,LCC,0,100000
-p4,p117,Possible,LCC,0,100000
-p4,p118,Possible,LCC,0,100000
-p4,p119,Possible,LCC,0,100000
-p4,p12,Possible,LCC,0,100000
-p4,p120,Possible,LCC,0,100000
-p4,p121,Possible,LCC,0,100000
-p4,p122,Possible,LCC,0,100000
-p4,p123,Possible,LCC,0,100000
-p4,p124,Possible,LCC,0,100000
-p4,p125,Possible,LCC,0,100000
-p4,p126,Possible,LCC,0,100000
-p4,p127,Possible,LCC,0,100000
-p4,p128,Possible,LCC,0,100000
-p4,p129,Possible,LCC,0,100000
-p4,p13,Possible,LCC,0,100000
-p4,p130,Possible,LCC,0,100000
-p4,p131,Possible,LCC,0,100000
-p4,p132,Possible,LCC,0,100000
-p4,p133,Possible,LCC,0,100000
-p4,p134,Possible,LCC,0,100000
-p4,p14,Possible,LCC,0,100000
-p4,p15,Possible,LCC,0,100000
-p4,p16,Possible,LCC,0,100000
-p4,p17,Possible,LCC,0,100000
-p4,p18,Possible,LCC,0,100000
-p4,p19,Possible,LCC,0,100000
-p4,p20,Possible,LCC,0,100000
-p4,p21,Possible,LCC,0,100000
-p4,p22,Possible,LCC,0,100000
-p4,p23,Possible,LCC,0,100000
-p4,p24,Possible,LCC,0,100000
-p4,p25,Possible,LCC,0,100000
-p4,p26,Possible,LCC,0,100000
-p4,p27,Possible,LCC,0,100000
-p4,p28,Possible,LCC,0,100000
-p4,p29,Possible,LCC,0,100000
-p4,p30,Possible,LCC,0,100000
-p4,p31,Possible,LCC,0,100000
-p4,p32,Possible,LCC,0,100000
-p4,p33,Possible,LCC,0,100000
-p4,p34,Possible,LCC,0,100000
-p4,p35,Possible,LCC,0,100000
-p4,p36,Possible,LCC,0,100000
-p4,p37,Possible,LCC,0,100000
-p4,p38,Possible,LCC,0,100000
-p4,p39,Possible,LCC,0,100000
-p4,p40,Possible,LCC,0,100000
-p4,p41,Possible,LCC,0,100000
-p4,p42,Possible,LCC,0,100000
-p4,p43,Possible,LCC,0,100000
-p4,p44,Possible,LCC,0,100000
-p4,p45,Possible,LCC,0,100000
-p4,p46,Possible,LCC,0,100000
-p4,p47,Possible,LCC,0,100000
-p4,p48,Possible,LCC,0,100000
-p4,p49,Possible,LCC,0,100000
-p4,p50,Possible,LCC,0,100000
-p4,p51,Possible,LCC,0,100000
-p4,p52,Possible,LCC,0,100000
-p4,p53,Possible,LCC,0,100000
-p4,p54,Possible,LCC,0,100000
-p4,p55,Possible,LCC,0,100000
-p4,p56,Possible,LCC,0,100000
-p4,p57,Possible,LCC,0,100000
-p4,p58,Possible,LCC,0,100000
-p4,p59,Possible,LCC,0,100000
-p4,p6,Possible,LCC,0,100000
-p4,p60,Possible,LCC,0,100000
-p4,p61,Possible,LCC,0,100000
-p4,p62,Possible,LCC,0,100000
-p4,p63,Possible,LCC,0,100000
-p4,p64,Possible,LCC,0,100000
-p4,p65,Possible,LCC,0,100000
-p4,p66,Possible,LCC,0,100000
-p4,p67,Possible,LCC,0,100000
-p4,p68,Possible,LCC,0,100000
-p4,p69,Possible,LCC,0,100000
-p4,p7,Possible,LCC,0,100000
-p4,p70,Possible,LCC,0,100000
-p4,p71,Possible,LCC,0,100000
-p4,p72,Possible,LCC,0,100000
-p4,p73,Possible,LCC,0,100000
-p4,p74,Possible,LCC,0,100000
-p4,p75,Possible,LCC,0,100000
-p4,p76,Possible,LCC,0,100000
-p4,p77,Possible,LCC,0,100000
-p4,p78,Possible,LCC,0,100000
-p4,p79,Possible,LCC,0,100000
-p4,p8,Possible,LCC,0,100000
-p4,p80,Possible,LCC,0,100000
-p4,p81,Possible,LCC,0,100000
-p4,p82,Possible,LCC,0,100000
-p4,p83,Possible,LCC,0,100000
-p4,p84,Possible,LCC,0,100000
-p4,p85,Possible,LCC,0,100000
-p4,p86,Possible,LCC,0,100000
-p4,p87,Possible,LCC,0,100000
-p4,p88,Possible,LCC,0,100000
-p4,p89,Possible,LCC,0,100000
-p4,p9,Possible,LCC,0,100000
-p4,p90,Possible,LCC,0,100000
-p4,p91,Possible,LCC,0,100000
-p4,p92,Possible,LCC,0,100000
-p4,p93,Possible,LCC,0,100000
-p4,p94,Possible,LCC,0,100000
-p4,p95,Possible,LCC,0,100000
-p4,p96,Possible,LCC,0,100000
-p4,p97,Possible,LCC,0,100000
-p4,p98,Possible,LCC,0,100000
-p4,p99,Possible,LCC,0,100000
-p40,p100,Possible,LCC,0,100000
-p40,p101,Possible,LCC,0,100000
-p40,p102,Possible,LCC,0,100000
-p40,p103,Possible,LCC,0,100000
-p40,p104,Possible,LCC,0,100000
-p40,p105,Possible,LCC,0,100000
-p40,p106,Possible,LCC,0,100000
-p40,p107,Possible,LCC,0,100000
-p40,p108,Possible,LCC,0,100000
-p40,p109,Possible,LCC,0,100000
-p40,p110,Possible,LCC,0,100000
-p40,p111,Possible,LCC,0,100000
-p40,p112,Possible,LCC,0,100000
-p40,p113,Possible,LCC,0,100000
-p40,p114,Possible,LCC,0,100000
-p40,p115,Possible,LCC,0,100000
-p40,p116,Possible,LCC,0,100000
-p40,p117,Possible,LCC,0,100000
-p40,p118,Possible,LCC,0,100000
-p40,p119,Possible,LCC,0,100000
-p40,p120,Possible,LCC,0,100000
-p40,p121,Possible,LCC,0,100000
-p40,p122,Possible,LCC,0,100000
-p40,p123,Possible,LCC,0,100000
-p40,p124,Possible,LCC,0,100000
-p40,p125,Possible,LCC,0,100000
-p40,p126,Possible,LCC,0,100000
-p40,p127,Possible,LCC,0,100000
-p40,p128,Possible,LCC,0,100000
-p40,p129,Possible,LCC,0,100000
-p40,p130,Possible,LCC,0,100000
-p40,p131,Possible,LCC,0,100000
-p40,p132,Possible,LCC,0,100000
-p40,p133,Possible,LCC,0,100000
-p40,p134,Possible,LCC,0,100000
-p40,p42,Possible,LCC,0,100000
-p40,p43,Possible,LCC,0,100000
-p40,p46,Possible,LCC,0,100000
-p40,p47,Possible,LCC,0,100000
-p40,p48,Possible,LCC,0,100000
-p40,p49,Possible,LCC,0,100000
-p40,p50,Possible,LCC,0,100000
-p40,p51,Possible,LCC,0,100000
-p40,p54,Possible,LCC,0,100000
-p40,p55,Possible,LCC,0,100000
-p40,p56,Possible,LCC,0,100000
-p40,p57,Possible,LCC,0,100000
-p40,p58,Possible,LCC,0,100000
-p40,p59,Possible,LCC,0,100000
-p40,p60,Possible,LCC,0,100000
-p40,p61,Possible,LCC,0,100000
-p40,p62,Possible,LCC,0,100000
-p40,p63,Possible,LCC,0,100000
-p40,p64,Possible,LCC,0,100000
-p40,p65,Possible,LCC,0,100000
-p40,p66,Possible,LCC,0,100000
-p40,p67,Possible,LCC,0,100000
-p40,p68,Possible,LCC,0,100000
-p40,p71,Possible,LCC,0,100000
-p40,p72,Possible,LCC,0,100000
-p40,p73,Possible,LCC,0,100000
-p40,p74,Possible,LCC,0,100000
-p40,p75,Possible,LCC,0,100000
-p40,p76,Possible,LCC,0,100000
-p40,p77,Possible,LCC,0,100000
-p40,p78,Possible,LCC,0,100000
-p40,p79,Possible,LCC,0,100000
-p40,p80,Possible,LCC,0,100000
-p40,p82,Possible,LCC,0,100000
-p40,p83,Possible,LCC,0,100000
-p40,p84,Possible,LCC,0,100000
-p40,p85,Possible,LCC,0,100000
-p40,p86,Possible,LCC,0,100000
-p40,p87,Possible,LCC,0,100000
-p40,p88,Possible,LCC,0,100000
-p40,p89,Possible,LCC,0,100000
-p40,p90,Possible,LCC,0,100000
-p40,p91,Possible,LCC,0,100000
-p40,p92,Possible,LCC,0,100000
-p40,p93,Possible,LCC,0,100000
-p40,p94,Possible,LCC,0,100000
-p40,p95,Possible,LCC,0,100000
-p40,p96,Possible,LCC,0,100000
-p40,p97,Possible,LCC,0,100000
-p40,p98,Possible,LCC,0,100000
-p40,p99,Possible,LCC,0,100000
-p41,p100,Possible,LCC,0,100000
-p41,p101,Possible,LCC,0,100000
-p41,p102,Possible,LCC,0,100000
-p41,p103,Possible,LCC,0,100000
-p41,p104,Possible,LCC,0,100000
-p41,p105,Possible,LCC,0,100000
-p41,p106,Possible,LCC,0,100000
-p41,p107,Possible,LCC,0,100000
-p41,p108,Possible,LCC,0,100000
-p41,p109,Possible,LCC,0,100000
-p41,p110,Possible,LCC,0,100000
-p41,p111,Possible,LCC,0,100000
-p41,p112,Possible,LCC,0,100000
-p41,p113,Possible,LCC,0,100000
-p41,p114,Possible,LCC,0,100000
-p41,p115,Possible,LCC,0,100000
-p41,p116,Possible,LCC,0,100000
-p41,p117,Possible,LCC,0,100000
-p41,p118,Possible,LCC,0,100000
-p41,p119,Possible,LCC,0,100000
-p41,p120,Possible,LCC,0,100000
-p41,p121,Possible,LCC,0,100000
-p41,p122,Possible,LCC,0,100000
-p41,p123,Possible,LCC,0,100000
-p41,p124,Possible,LCC,0,100000
-p41,p125,Possible,LCC,0,100000
-p41,p126,Possible,LCC,0,100000
-p41,p127,Possible,LCC,0,100000
-p41,p128,Possible,LCC,0,100000
-p41,p129,Possible,LCC,0,100000
-p41,p130,Possible,LCC,0,100000
-p41,p131,Possible,LCC,0,100000
-p41,p132,Possible,LCC,0,100000
-p41,p133,Possible,LCC,0,100000
-p41,p134,Possible,LCC,0,100000
-p41,p42,Possible,LCC,0,100000
-p41,p43,Possible,LCC,0,100000
-p41,p44,Possible,LCC,0,100000
-p41,p46,Possible,LCC,0,100000
-p41,p47,Possible,LCC,0,100000
-p41,p48,Possible,LCC,0,100000
-p41,p49,Possible,LCC,0,100000
-p41,p50,Possible,LCC,0,100000
-p41,p51,Possible,LCC,0,100000
-p41,p52,Possible,LCC,0,100000
-p41,p55,Possible,LCC,0,100000
-p41,p56,Possible,LCC,0,100000
-p41,p57,Possible,LCC,0,100000
-p41,p58,Possible,LCC,0,100000
-p41,p59,Possible,LCC,0,100000
-p41,p60,Possible,LCC,0,100000
-p41,p61,Possible,LCC,0,100000
-p41,p62,Possible,LCC,0,100000
-p41,p63,Possible,LCC,0,100000
-p41,p64,Possible,LCC,0,100000
-p41,p65,Possible,LCC,0,100000
-p41,p66,Possible,LCC,0,100000
-p41,p67,Possible,LCC,0,100000
-p41,p68,Possible,LCC,0,100000
-p41,p72,Possible,LCC,0,100000
-p41,p73,Possible,LCC,0,100000
-p41,p74,Possible,LCC,0,100000
-p41,p75,Possible,LCC,0,100000
-p41,p76,Possible,LCC,0,100000
-p41,p77,Possible,LCC,0,100000
-p41,p78,Possible,LCC,0,100000
-p41,p79,Possible,LCC,0,100000
-p41,p80,Possible,LCC,0,100000
-p41,p82,Possible,LCC,0,100000
-p41,p83,Possible,LCC,0,100000
-p41,p84,Possible,LCC,0,100000
-p41,p85,Possible,LCC,0,100000
-p41,p86,Possible,LCC,0,100000
-p41,p87,Possible,LCC,0,100000
-p41,p88,Possible,LCC,0,100000
-p41,p89,Possible,LCC,0,100000
-p41,p90,Possible,LCC,0,100000
-p41,p91,Possible,LCC,0,100000
-p41,p92,Possible,LCC,0,100000
-p41,p93,Possible,LCC,0,100000
-p41,p94,Possible,LCC,0,100000
-p41,p95,Possible,LCC,0,100000
-p41,p96,Possible,LCC,0,100000
-p41,p97,Possible,LCC,0,100000
-p41,p98,Possible,LCC,0,100000
-p41,p99,Possible,LCC,0,100000
-p42,p100,Possible,LCC,0,100000
-p42,p101,Possible,LCC,0,100000
-p42,p102,Possible,LCC,0,100000
-p42,p103,Possible,LCC,0,100000
-p42,p104,Possible,LCC,0,100000
-p42,p105,Possible,LCC,0,100000
-p42,p106,Possible,LCC,0,100000
-p42,p107,Possible,LCC,0,100000
-p42,p108,Possible,LCC,0,100000
-p42,p109,Possible,LCC,0,100000
-p42,p110,Possible,LCC,0,100000
-p42,p111,Possible,LCC,0,100000
-p42,p112,Possible,LCC,0,100000
-p42,p113,Possible,LCC,0,100000
-p42,p114,Possible,LCC,0,100000
-p42,p115,Possible,LCC,0,100000
-p42,p116,Possible,LCC,0,100000
-p42,p117,Possible,LCC,0,100000
-p42,p118,Possible,LCC,0,100000
-p42,p119,Possible,LCC,0,100000
-p42,p120,Possible,LCC,0,100000
-p42,p121,Possible,LCC,0,100000
-p42,p122,Possible,LCC,0,100000
-p42,p123,Possible,LCC,0,100000
-p42,p124,Possible,LCC,0,100000
-p42,p125,Possible,LCC,0,100000
-p42,p126,Possible,LCC,0,100000
-p42,p127,Possible,LCC,0,100000
-p42,p128,Possible,LCC,0,100000
-p42,p129,Possible,LCC,0,100000
-p42,p130,Possible,LCC,0,100000
-p42,p131,Possible,LCC,0,100000
-p42,p132,Possible,LCC,0,100000
-p42,p133,Possible,LCC,0,100000
-p42,p134,Possible,LCC,0,100000
-p42,p44,Possible,LCC,0,100000
-p42,p45,Possible,LCC,0,100000
-p42,p46,Possible,LCC,0,100000
-p42,p47,Possible,LCC,0,100000
-p42,p48,Possible,LCC,0,100000
-p42,p49,Possible,LCC,0,100000
-p42,p50,Possible,LCC,0,100000
-p42,p51,Possible,LCC,0,100000
-p42,p52,Possible,LCC,0,100000
-p42,p53,Possible,LCC,0,100000
-p42,p54,Possible,LCC,0,100000
-p42,p55,Possible,LCC,0,100000
-p42,p56,Possible,LCC,0,100000
-p42,p57,Possible,LCC,0,100000
-p42,p58,Possible,LCC,0,100000
-p42,p59,Possible,LCC,0,100000
-p42,p60,Possible,LCC,0,100000
-p42,p61,Possible,LCC,0,100000
-p42,p62,Possible,LCC,0,100000
-p42,p63,Possible,LCC,0,100000
-p42,p64,Possible,LCC,0,100000
-p42,p65,Possible,LCC,0,100000
-p42,p66,Possible,LCC,0,100000
-p42,p67,Possible,LCC,0,100000
-p42,p68,Possible,LCC,0,100000
-p42,p69,Possible,LCC,0,100000
-p42,p70,Possible,LCC,0,100000
-p42,p71,Possible,LCC,0,100000
-p42,p72,Possible,LCC,0,100000
-p42,p73,Possible,LCC,0,100000
-p42,p74,Possible,LCC,0,100000
-p42,p75,Possible,LCC,0,100000
-p42,p76,Possible,LCC,0,100000
-p42,p77,Possible,LCC,0,100000
-p42,p78,Possible,LCC,0,100000
-p42,p79,Possible,LCC,0,100000
-p42,p80,Possible,LCC,0,100000
-p42,p81,Possible,LCC,0,100000
-p42,p82,Possible,LCC,0,100000
-p42,p83,Possible,LCC,0,100000
-p42,p84,Possible,LCC,0,100000
-p42,p85,Possible,LCC,0,100000
-p42,p86,Possible,LCC,0,100000
-p42,p87,Possible,LCC,0,100000
-p42,p88,Possible,LCC,0,100000
-p42,p89,Possible,LCC,0,100000
-p42,p90,Possible,LCC,0,100000
-p42,p91,Possible,LCC,0,100000
-p42,p92,Possible,LCC,0,100000
-p42,p93,Possible,LCC,0,100000
-p42,p94,Possible,LCC,0,100000
-p42,p95,Possible,LCC,0,100000
-p42,p96,Possible,LCC,0,100000
-p42,p97,Possible,LCC,0,100000
-p42,p98,Possible,LCC,0,100000
-p42,p99,Possible,LCC,0,100000
-p43,p100,Possible,LCC,0,100000
-p43,p101,Possible,LCC,0,100000
-p43,p102,Possible,LCC,0,100000
-p43,p103,Possible,LCC,0,100000
-p43,p104,Possible,LCC,0,100000
-p43,p105,Possible,LCC,0,100000
-p43,p106,Possible,LCC,0,100000
-p43,p107,Possible,LCC,0,100000
-p43,p108,Possible,LCC,0,100000
-p43,p109,Possible,LCC,0,100000
-p43,p110,Possible,LCC,0,100000
-p43,p111,Possible,LCC,0,100000
-p43,p112,Possible,LCC,0,100000
-p43,p113,Possible,LCC,0,100000
-p43,p114,Possible,LCC,0,100000
-p43,p115,Possible,LCC,0,100000
-p43,p116,Possible,LCC,0,100000
-p43,p117,Possible,LCC,0,100000
-p43,p118,Possible,LCC,0,100000
-p43,p119,Possible,LCC,0,100000
-p43,p120,Possible,LCC,0,100000
-p43,p121,Possible,LCC,0,100000
-p43,p122,Possible,LCC,0,100000
-p43,p123,Possible,LCC,0,100000
-p43,p124,Possible,LCC,0,100000
-p43,p125,Possible,LCC,0,100000
-p43,p126,Possible,LCC,0,100000
-p43,p127,Possible,LCC,0,100000
-p43,p128,Possible,LCC,0,100000
-p43,p129,Possible,LCC,0,100000
-p43,p130,Possible,LCC,0,100000
-p43,p131,Possible,LCC,0,100000
-p43,p132,Possible,LCC,0,100000
-p43,p133,Possible,LCC,0,100000
-p43,p134,Possible,LCC,0,100000
-p43,p47,Possible,LCC,0,100000
-p43,p48,Possible,LCC,0,100000
-p43,p49,Possible,LCC,0,100000
-p43,p50,Possible,LCC,0,100000
-p43,p51,Possible,LCC,0,100000
-p43,p52,Possible,LCC,0,100000
-p43,p53,Possible,LCC,0,100000
-p43,p54,Possible,LCC,0,100000
-p43,p55,Possible,LCC,0,100000
-p43,p56,Possible,LCC,0,100000
-p43,p57,Possible,LCC,0,100000
-p43,p58,Possible,LCC,0,100000
-p43,p59,Possible,LCC,0,100000
-p43,p60,Possible,LCC,0,100000
-p43,p61,Possible,LCC,0,100000
-p43,p62,Possible,LCC,0,100000
-p43,p63,Possible,LCC,0,100000
-p43,p64,Possible,LCC,0,100000
-p43,p65,Possible,LCC,0,100000
-p43,p66,Possible,LCC,0,100000
-p43,p67,Possible,LCC,0,100000
-p43,p69,Possible,LCC,0,100000
-p43,p70,Possible,LCC,0,100000
-p43,p71,Possible,LCC,0,100000
-p43,p72,Possible,LCC,0,100000
-p43,p73,Possible,LCC,0,100000
-p43,p74,Possible,LCC,0,100000
-p43,p75,Possible,LCC,0,100000
-p43,p76,Possible,LCC,0,100000
-p43,p79,Possible,LCC,0,100000
-p43,p80,Possible,LCC,0,100000
-p43,p81,Possible,LCC,0,100000
-p43,p82,Possible,LCC,0,100000
-p43,p83,Possible,LCC,0,100000
-p43,p84,Possible,LCC,0,100000
-p43,p85,Possible,LCC,0,100000
-p43,p86,Possible,LCC,0,100000
-p43,p87,Possible,LCC,0,100000
-p43,p88,Possible,LCC,0,100000
-p43,p89,Possible,LCC,0,100000
-p43,p90,Possible,LCC,0,100000
-p43,p91,Possible,LCC,0,100000
-p43,p92,Possible,LCC,0,100000
-p43,p93,Possible,LCC,0,100000
-p43,p94,Possible,LCC,0,100000
-p43,p95,Possible,LCC,0,100000
-p43,p96,Possible,LCC,0,100000
-p43,p97,Possible,LCC,0,100000
-p43,p98,Possible,LCC,0,100000
-p43,p99,Possible,LCC,0,100000
-p44,p100,Possible,LCC,0,100000
-p44,p101,Possible,LCC,0,100000
-p44,p102,Possible,LCC,0,100000
-p44,p103,Possible,LCC,0,100000
-p44,p104,Possible,LCC,0,100000
-p44,p105,Possible,LCC,0,100000
-p44,p106,Possible,LCC,0,100000
-p44,p107,Possible,LCC,0,100000
-p44,p108,Possible,LCC,0,100000
-p44,p109,Possible,LCC,0,100000
-p44,p110,Possible,LCC,0,100000
-p44,p111,Possible,LCC,0,100000
-p44,p112,Possible,LCC,0,100000
-p44,p113,Possible,LCC,0,100000
-p44,p114,Possible,LCC,0,100000
-p44,p115,Possible,LCC,0,100000
-p44,p116,Possible,LCC,0,100000
-p44,p117,Possible,LCC,0,100000
-p44,p118,Possible,LCC,0,100000
-p44,p119,Possible,LCC,0,100000
-p44,p120,Possible,LCC,0,100000
-p44,p121,Possible,LCC,0,100000
-p44,p122,Possible,LCC,0,100000
-p44,p123,Possible,LCC,0,100000
-p44,p124,Possible,LCC,0,100000
-p44,p125,Possible,LCC,0,100000
-p44,p126,Possible,LCC,0,100000
-p44,p127,Possible,LCC,0,100000
-p44,p128,Possible,LCC,0,100000
-p44,p129,Possible,LCC,0,100000
-p44,p130,Possible,LCC,0,100000
-p44,p131,Possible,LCC,0,100000
-p44,p132,Possible,LCC,0,100000
-p44,p133,Possible,LCC,0,100000
-p44,p134,Possible,LCC,0,100000
-p44,p47,Possible,LCC,0,100000
-p44,p48,Possible,LCC,0,100000
-p44,p49,Possible,LCC,0,100000
-p44,p50,Possible,LCC,0,100000
-p44,p51,Possible,LCC,0,100000
-p44,p52,Possible,LCC,0,100000
-p44,p53,Possible,LCC,0,100000
-p44,p54,Possible,LCC,0,100000
-p44,p55,Possible,LCC,0,100000
-p44,p56,Possible,LCC,0,100000
-p44,p57,Possible,LCC,0,100000
-p44,p58,Possible,LCC,0,100000
-p44,p59,Possible,LCC,0,100000
-p44,p60,Possible,LCC,0,100000
-p44,p61,Possible,LCC,0,100000
-p44,p62,Possible,LCC,0,100000
-p44,p63,Possible,LCC,0,100000
-p44,p64,Possible,LCC,0,100000
-p44,p65,Possible,LCC,0,100000
-p44,p66,Possible,LCC,0,100000
-p44,p67,Possible,LCC,0,100000
-p44,p69,Possible,LCC,0,100000
-p44,p70,Possible,LCC,0,100000
-p44,p71,Possible,LCC,0,100000
-p44,p72,Possible,LCC,0,100000
-p44,p73,Possible,LCC,0,100000
-p44,p74,Possible,LCC,0,100000
-p44,p75,Possible,LCC,0,100000
-p44,p76,Possible,LCC,0,100000
-p44,p77,Possible,LCC,0,100000
-p44,p78,Possible,LCC,0,100000
-p44,p79,Possible,LCC,0,100000
-p44,p80,Possible,LCC,0,100000
-p44,p81,Possible,LCC,0,100000
-p44,p82,Possible,LCC,0,100000
-p44,p83,Possible,LCC,0,100000
-p44,p84,Possible,LCC,0,100000
-p44,p85,Possible,LCC,0,100000
-p44,p86,Possible,LCC,0,100000
-p44,p87,Possible,LCC,0,100000
-p44,p88,Possible,LCC,0,100000
-p44,p89,Possible,LCC,0,100000
-p44,p90,Possible,LCC,0,100000
-p44,p91,Possible,LCC,0,100000
-p44,p92,Possible,LCC,0,100000
-p44,p93,Possible,LCC,0,100000
-p44,p94,Possible,LCC,0,100000
-p44,p95,Possible,LCC,0,100000
-p44,p96,Possible,LCC,0,100000
-p44,p97,Possible,LCC,0,100000
-p44,p98,Possible,LCC,0,100000
-p44,p99,Possible,LCC,0,100000
-p45,p100,Possible,LCC,0,100000
-p45,p101,Possible,LCC,0,100000
-p45,p102,Possible,LCC,0,100000
-p45,p103,Possible,LCC,0,100000
-p45,p104,Possible,LCC,0,100000
-p45,p105,Possible,LCC,0,100000
-p45,p106,Possible,LCC,0,100000
-p45,p107,Possible,LCC,0,100000
-p45,p108,Possible,LCC,0,100000
-p45,p109,Possible,LCC,0,100000
-p45,p110,Possible,LCC,0,100000
-p45,p111,Possible,LCC,0,100000
-p45,p112,Possible,LCC,0,100000
-p45,p113,Possible,LCC,0,100000
-p45,p114,Possible,LCC,0,100000
-p45,p115,Possible,LCC,0,100000
-p45,p116,Possible,LCC,0,100000
-p45,p117,Possible,LCC,0,100000
-p45,p118,Possible,LCC,0,100000
-p45,p119,Possible,LCC,0,100000
-p45,p120,Possible,LCC,0,100000
-p45,p121,Possible,LCC,0,100000
-p45,p122,Possible,LCC,0,100000
-p45,p123,Possible,LCC,0,100000
-p45,p124,Possible,LCC,0,100000
-p45,p125,Possible,LCC,0,100000
-p45,p126,Possible,LCC,0,100000
-p45,p127,Possible,LCC,0,100000
-p45,p128,Possible,LCC,0,100000
-p45,p129,Possible,LCC,0,100000
-p45,p130,Possible,LCC,0,100000
-p45,p131,Possible,LCC,0,100000
-p45,p132,Possible,LCC,0,100000
-p45,p133,Possible,LCC,0,100000
-p45,p134,Possible,LCC,0,100000
-p45,p46,Possible,LCC,0,100000
-p45,p47,Possible,LCC,0,100000
-p45,p48,Possible,LCC,0,100000
-p45,p49,Possible,LCC,0,100000
-p45,p50,Possible,LCC,0,100000
-p45,p51,Possible,LCC,0,100000
-p45,p52,Possible,LCC,0,100000
-p45,p53,Possible,LCC,0,100000
-p45,p55,Possible,LCC,0,100000
-p45,p56,Possible,LCC,0,100000
-p45,p57,Possible,LCC,0,100000
-p45,p58,Possible,LCC,0,100000
-p45,p59,Possible,LCC,0,100000
-p45,p60,Possible,LCC,0,100000
-p45,p61,Possible,LCC,0,100000
-p45,p62,Possible,LCC,0,100000
-p45,p63,Possible,LCC,0,100000
-p45,p64,Possible,LCC,0,100000
-p45,p65,Possible,LCC,0,100000
-p45,p66,Possible,LCC,0,100000
-p45,p67,Possible,LCC,0,100000
-p45,p72,Possible,LCC,0,100000
-p45,p73,Possible,LCC,0,100000
-p45,p74,Possible,LCC,0,100000
-p45,p75,Possible,LCC,0,100000
-p45,p76,Possible,LCC,0,100000
-p45,p77,Possible,LCC,0,100000
-p45,p78,Possible,LCC,0,100000
-p45,p79,Possible,LCC,0,100000
-p45,p80,Possible,LCC,0,100000
-p45,p82,Possible,LCC,0,100000
-p45,p83,Possible,LCC,0,100000
-p45,p84,Possible,LCC,0,100000
-p45,p85,Possible,LCC,0,100000
-p45,p86,Possible,LCC,0,100000
-p45,p87,Possible,LCC,0,100000
-p45,p88,Possible,LCC,0,100000
-p45,p89,Possible,LCC,0,100000
-p45,p90,Possible,LCC,0,100000
-p45,p91,Possible,LCC,0,100000
-p45,p92,Possible,LCC,0,100000
-p45,p93,Possible,LCC,0,100000
-p45,p94,Possible,LCC,0,100000
-p45,p95,Possible,LCC,0,100000
-p45,p96,Possible,LCC,0,100000
-p45,p97,Possible,LCC,0,100000
-p45,p98,Possible,LCC,0,100000
-p45,p99,Possible,LCC,0,100000
-p46,p100,Possible,LCC,0,100000
-p46,p101,Possible,LCC,0,100000
-p46,p102,Possible,LCC,0,100000
-p46,p103,Possible,LCC,0,100000
-p46,p104,Possible,LCC,0,100000
-p46,p105,Possible,LCC,0,100000
-p46,p106,Possible,LCC,0,100000
-p46,p107,Possible,LCC,0,100000
-p46,p108,Possible,LCC,0,100000
-p46,p109,Possible,LCC,0,100000
-p46,p110,Possible,LCC,0,100000
-p46,p111,Possible,LCC,0,100000
-p46,p112,Possible,LCC,0,100000
-p46,p113,Possible,LCC,0,100000
-p46,p114,Possible,LCC,0,100000
-p46,p115,Possible,LCC,0,100000
-p46,p116,Possible,LCC,0,100000
-p46,p117,Possible,LCC,0,100000
-p46,p118,Possible,LCC,0,100000
-p46,p119,Possible,LCC,0,100000
-p46,p120,Possible,LCC,0,100000
-p46,p121,Possible,LCC,0,100000
-p46,p122,Possible,LCC,0,100000
-p46,p123,Possible,LCC,0,100000
-p46,p124,Possible,LCC,0,100000
-p46,p125,Possible,LCC,0,100000
-p46,p126,Possible,LCC,0,100000
-p46,p127,Possible,LCC,0,100000
-p46,p128,Possible,LCC,0,100000
-p46,p129,Possible,LCC,0,100000
-p46,p130,Possible,LCC,0,100000
-p46,p131,Possible,LCC,0,100000
-p46,p132,Possible,LCC,0,100000
-p46,p133,Possible,LCC,0,100000
-p46,p134,Possible,LCC,0,100000
-p46,p47,Possible,LCC,0,100000
-p46,p48,Possible,LCC,0,100000
-p46,p49,Possible,LCC,0,100000
-p46,p50,Possible,LCC,0,100000
-p46,p51,Possible,LCC,0,100000
-p46,p52,Possible,LCC,0,100000
-p46,p53,Possible,LCC,0,100000
-p46,p54,Possible,LCC,0,100000
-p46,p55,Possible,LCC,0,100000
-p46,p56,Possible,LCC,0,100000
-p46,p57,Possible,LCC,0,100000
-p46,p58,Possible,LCC,0,100000
-p46,p59,Possible,LCC,0,100000
-p46,p60,Possible,LCC,0,100000
-p46,p61,Possible,LCC,0,100000
-p46,p62,Possible,LCC,0,100000
-p46,p63,Possible,LCC,0,100000
-p46,p64,Possible,LCC,0,100000
-p46,p65,Possible,LCC,0,100000
-p46,p66,Possible,LCC,0,100000
-p46,p67,Possible,LCC,0,100000
-p46,p69,Possible,LCC,0,100000
-p46,p70,Possible,LCC,0,100000
-p46,p71,Possible,LCC,0,100000
-p46,p72,Possible,LCC,0,100000
-p46,p73,Possible,LCC,0,100000
-p46,p79,Possible,LCC,0,100000
-p46,p80,Possible,LCC,0,100000
-p46,p81,Possible,LCC,0,100000
-p46,p82,Possible,LCC,0,100000
-p46,p83,Possible,LCC,0,100000
-p46,p84,Possible,LCC,0,100000
-p46,p85,Possible,LCC,0,100000
-p46,p86,Possible,LCC,0,100000
-p46,p87,Possible,LCC,0,100000
-p46,p88,Possible,LCC,0,100000
-p46,p89,Possible,LCC,0,100000
-p46,p90,Possible,LCC,0,100000
-p46,p91,Possible,LCC,0,100000
-p46,p92,Possible,LCC,0,100000
-p46,p93,Possible,LCC,0,100000
-p46,p94,Possible,LCC,0,100000
-p46,p95,Possible,LCC,0,100000
-p46,p96,Possible,LCC,0,100000
-p46,p97,Possible,LCC,0,100000
-p46,p98,Possible,LCC,0,100000
-p46,p99,Possible,LCC,0,100000
-p47,p100,Possible,LCC,0,100000
-p47,p101,Possible,LCC,0,100000
-p47,p102,Possible,LCC,0,100000
-p47,p103,Possible,LCC,0,100000
-p47,p104,Possible,LCC,0,100000
-p47,p105,Possible,LCC,0,100000
-p47,p106,Possible,LCC,0,100000
-p47,p107,Possible,LCC,0,100000
-p47,p108,Possible,LCC,0,100000
-p47,p109,Possible,LCC,0,100000
-p47,p110,Possible,LCC,0,100000
-p47,p111,Possible,LCC,0,100000
-p47,p112,Possible,LCC,0,100000
-p47,p113,Possible,LCC,0,100000
-p47,p114,Possible,LCC,0,100000
-p47,p115,Possible,LCC,0,100000
-p47,p116,Possible,LCC,0,100000
-p47,p117,Possible,LCC,0,100000
-p47,p118,Possible,LCC,0,100000
-p47,p119,Possible,LCC,0,100000
-p47,p120,Possible,LCC,0,100000
-p47,p121,Possible,LCC,0,100000
-p47,p122,Possible,LCC,0,100000
-p47,p123,Possible,LCC,0,100000
-p47,p124,Possible,LCC,0,100000
-p47,p125,Possible,LCC,0,100000
-p47,p126,Possible,LCC,0,100000
-p47,p127,Possible,LCC,0,100000
-p47,p128,Possible,LCC,0,100000
-p47,p129,Possible,LCC,0,100000
-p47,p130,Possible,LCC,0,100000
-p47,p131,Possible,LCC,0,100000
-p47,p132,Possible,LCC,0,100000
-p47,p133,Possible,LCC,0,100000
-p47,p134,Possible,LCC,0,100000
-p47,p49,Possible,LCC,0,100000
-p47,p50,Possible,LCC,0,100000
-p47,p51,Possible,LCC,0,100000
-p47,p52,Possible,LCC,0,100000
-p47,p53,Possible,LCC,0,100000
-p47,p54,Possible,LCC,0,100000
-p47,p55,Possible,LCC,0,100000
-p47,p56,Possible,LCC,0,100000
-p47,p57,Possible,LCC,0,100000
-p47,p58,Possible,LCC,0,100000
-p47,p59,Possible,LCC,0,100000
-p47,p60,Possible,LCC,0,100000
-p47,p61,Possible,LCC,0,100000
-p47,p62,Possible,LCC,0,100000
-p47,p63,Possible,LCC,0,100000
-p47,p64,Possible,LCC,0,100000
-p47,p65,Possible,LCC,0,100000
-p47,p66,Possible,LCC,0,100000
-p47,p67,Possible,LCC,0,100000
-p47,p68,Possible,LCC,0,100000
-p47,p69,Possible,LCC,0,100000
-p47,p70,Possible,LCC,0,100000
-p47,p71,Possible,LCC,0,100000
-p47,p72,Possible,LCC,0,100000
-p47,p73,Possible,LCC,0,100000
-p47,p74,Possible,LCC,0,100000
-p47,p75,Possible,LCC,0,100000
-p47,p76,Possible,LCC,0,100000
-p47,p77,Possible,LCC,0,100000
-p47,p78,Possible,LCC,0,100000
-p47,p79,Possible,LCC,0,100000
-p47,p80,Possible,LCC,0,100000
-p47,p81,Possible,LCC,0,100000
-p47,p82,Possible,LCC,0,100000
-p47,p83,Possible,LCC,0,100000
-p47,p84,Possible,LCC,0,100000
-p47,p85,Possible,LCC,0,100000
-p47,p86,Possible,LCC,0,100000
-p47,p87,Possible,LCC,0,100000
-p47,p88,Possible,LCC,0,100000
-p47,p89,Possible,LCC,0,100000
-p47,p90,Possible,LCC,0,100000
-p47,p91,Possible,LCC,0,100000
-p47,p92,Possible,LCC,0,100000
-p47,p93,Possible,LCC,0,100000
-p47,p94,Possible,LCC,0,100000
-p47,p95,Possible,LCC,0,100000
-p47,p96,Possible,LCC,0,100000
-p47,p97,Possible,LCC,0,100000
-p47,p98,Possible,LCC,0,100000
-p47,p99,Possible,LCC,0,100000
-p48,p100,Possible,LCC,0,100000
-p48,p101,Possible,LCC,0,100000
-p48,p102,Possible,LCC,0,100000
-p48,p103,Possible,LCC,0,100000
-p48,p104,Possible,LCC,0,100000
-p48,p105,Possible,LCC,0,100000
-p48,p106,Possible,LCC,0,100000
-p48,p107,Possible,LCC,0,100000
-p48,p108,Possible,LCC,0,100000
-p48,p109,Possible,LCC,0,100000
-p48,p110,Possible,LCC,0,100000
-p48,p111,Possible,LCC,0,100000
-p48,p112,Possible,LCC,0,100000
-p48,p113,Possible,LCC,0,100000
-p48,p114,Possible,LCC,0,100000
-p48,p115,Possible,LCC,0,100000
-p48,p116,Possible,LCC,0,100000
-p48,p117,Possible,LCC,0,100000
-p48,p118,Possible,LCC,0,100000
-p48,p119,Possible,LCC,0,100000
-p48,p120,Possible,LCC,0,100000
-p48,p121,Possible,LCC,0,100000
-p48,p122,Possible,LCC,0,100000
-p48,p123,Possible,LCC,0,100000
-p48,p124,Possible,LCC,0,100000
-p48,p125,Possible,LCC,0,100000
-p48,p126,Possible,LCC,0,100000
-p48,p127,Possible,LCC,0,100000
-p48,p128,Possible,LCC,0,100000
-p48,p129,Possible,LCC,0,100000
-p48,p130,Possible,LCC,0,100000
-p48,p131,Possible,LCC,0,100000
-p48,p132,Possible,LCC,0,100000
-p48,p133,Possible,LCC,0,100000
-p48,p134,Possible,LCC,0,100000
-p48,p53,Possible,LCC,0,100000
-p48,p54,Possible,LCC,0,100000
-p48,p55,Possible,LCC,0,100000
-p48,p56,Possible,LCC,0,100000
-p48,p57,Possible,LCC,0,100000
-p48,p58,Possible,LCC,0,100000
-p48,p59,Possible,LCC,0,100000
-p48,p60,Possible,LCC,0,100000
-p48,p61,Possible,LCC,0,100000
-p48,p62,Possible,LCC,0,100000
-p48,p63,Possible,LCC,0,100000
-p48,p64,Possible,LCC,0,100000
-p48,p65,Possible,LCC,0,100000
-p48,p66,Possible,LCC,0,100000
-p48,p67,Possible,LCC,0,100000
-p48,p68,Possible,LCC,0,100000
-p48,p69,Possible,LCC,0,100000
-p48,p70,Possible,LCC,0,100000
-p48,p71,Possible,LCC,0,100000
-p48,p72,Possible,LCC,0,100000
-p48,p73,Possible,LCC,0,100000
-p48,p74,Possible,LCC,0,100000
-p48,p75,Possible,LCC,0,100000
-p48,p76,Possible,LCC,0,100000
-p48,p77,Possible,LCC,0,100000
-p48,p78,Possible,LCC,0,100000
-p48,p79,Possible,LCC,0,100000
-p48,p80,Possible,LCC,0,100000
-p48,p81,Possible,LCC,0,100000
-p48,p82,Possible,LCC,0,100000
-p48,p83,Possible,LCC,0,100000
-p48,p84,Possible,LCC,0,100000
-p48,p85,Possible,LCC,0,100000
-p48,p86,Possible,LCC,0,100000
-p48,p87,Possible,LCC,0,100000
-p48,p88,Possible,LCC,0,100000
-p48,p89,Possible,LCC,0,100000
-p48,p90,Possible,LCC,0,100000
-p48,p91,Possible,LCC,0,100000
-p48,p92,Possible,LCC,0,100000
-p48,p93,Possible,LCC,0,100000
-p48,p94,Possible,LCC,0,100000
-p48,p95,Possible,LCC,0,100000
-p48,p96,Possible,LCC,0,100000
-p48,p97,Possible,LCC,0,100000
-p48,p98,Possible,LCC,0,100000
-p48,p99,Possible,LCC,0,100000
-p49,p100,Possible,LCC,0,100000
-p49,p101,Possible,LCC,0,100000
-p49,p102,Possible,LCC,0,100000
-p49,p103,Possible,LCC,0,100000
-p49,p104,Possible,LCC,0,100000
-p49,p105,Possible,LCC,0,100000
-p49,p106,Possible,LCC,0,100000
-p49,p107,Possible,LCC,0,100000
-p49,p108,Possible,LCC,0,100000
-p49,p109,Possible,LCC,0,100000
-p49,p110,Possible,LCC,0,100000
-p49,p111,Possible,LCC,0,100000
-p49,p112,Possible,LCC,0,100000
-p49,p113,Possible,LCC,0,100000
-p49,p114,Possible,LCC,0,100000
-p49,p115,Possible,LCC,0,100000
-p49,p116,Possible,LCC,0,100000
-p49,p117,Possible,LCC,0,100000
-p49,p118,Possible,LCC,0,100000
-p49,p119,Possible,LCC,0,100000
-p49,p120,Possible,LCC,0,100000
-p49,p121,Possible,LCC,0,100000
-p49,p122,Possible,LCC,0,100000
-p49,p123,Possible,LCC,0,100000
-p49,p124,Possible,LCC,0,100000
-p49,p125,Possible,LCC,0,100000
-p49,p126,Possible,LCC,0,100000
-p49,p127,Possible,LCC,0,100000
-p49,p128,Possible,LCC,0,100000
-p49,p129,Possible,LCC,0,100000
-p49,p130,Possible,LCC,0,100000
-p49,p131,Possible,LCC,0,100000
-p49,p132,Possible,LCC,0,100000
-p49,p133,Possible,LCC,0,100000
-p49,p134,Possible,LCC,0,100000
-p49,p51,Possible,LCC,0,100000
-p49,p54,Possible,LCC,0,100000
-p49,p55,Possible,LCC,0,100000
-p49,p56,Possible,LCC,0,100000
-p49,p57,Possible,LCC,0,100000
-p49,p58,Possible,LCC,0,100000
-p49,p59,Possible,LCC,0,100000
-p49,p60,Possible,LCC,0,100000
-p49,p61,Possible,LCC,0,100000
-p49,p62,Possible,LCC,0,100000
-p49,p63,Possible,LCC,0,100000
-p49,p64,Possible,LCC,0,100000
-p49,p65,Possible,LCC,0,100000
-p49,p66,Possible,LCC,0,100000
-p49,p67,Possible,LCC,0,100000
-p49,p68,Possible,LCC,0,100000
-p49,p69,Possible,LCC,0,100000
-p49,p70,Possible,LCC,0,100000
-p49,p71,Possible,LCC,0,100000
-p49,p72,Possible,LCC,0,100000
-p49,p73,Possible,LCC,0,100000
-p49,p74,Possible,LCC,0,100000
-p49,p75,Possible,LCC,0,100000
-p49,p76,Possible,LCC,0,100000
-p49,p77,Possible,LCC,0,100000
-p49,p78,Possible,LCC,0,100000
-p49,p79,Possible,LCC,0,100000
-p49,p80,Possible,LCC,0,100000
-p49,p81,Possible,LCC,0,100000
-p49,p82,Possible,LCC,0,100000
-p49,p83,Possible,LCC,0,100000
-p49,p84,Possible,LCC,0,100000
-p49,p85,Possible,LCC,0,100000
-p49,p86,Possible,LCC,0,100000
-p49,p87,Possible,LCC,0,100000
-p49,p88,Possible,LCC,0,100000
-p49,p89,Possible,LCC,0,100000
-p49,p90,Possible,LCC,0,100000
-p49,p91,Possible,LCC,0,100000
-p49,p92,Possible,LCC,0,100000
-p49,p93,Possible,LCC,0,100000
-p49,p94,Possible,LCC,0,100000
-p49,p95,Possible,LCC,0,100000
-p49,p96,Possible,LCC,0,100000
-p49,p97,Possible,LCC,0,100000
-p49,p98,Possible,LCC,0,100000
-p49,p99,Possible,LCC,0,100000
-p5,p100,Possible,LCC,0,100000
-p5,p101,Possible,LCC,0,100000
-p5,p102,Possible,LCC,0,100000
-p5,p103,Possible,LCC,0,100000
-p5,p104,Possible,LCC,0,100000
-p5,p105,Possible,LCC,0,100000
-p5,p106,Possible,LCC,0,100000
-p5,p107,Possible,LCC,0,100000
-p5,p108,Possible,LCC,0,100000
-p5,p109,Possible,LCC,0,100000
-p5,p110,Possible,LCC,0,100000
-p5,p111,Possible,LCC,0,100000
-p5,p112,Possible,LCC,0,100000
-p5,p113,Possible,LCC,0,100000
-p5,p114,Possible,LCC,0,100000
-p5,p115,Possible,LCC,0,100000
-p5,p116,Possible,LCC,0,100000
-p5,p117,Possible,LCC,0,100000
-p5,p118,Possible,LCC,0,100000
-p5,p119,Possible,LCC,0,100000
-p5,p12,Possible,LCC,0,100000
-p5,p120,Possible,LCC,0,100000
-p5,p121,Possible,LCC,0,100000
-p5,p122,Possible,LCC,0,100000
-p5,p123,Possible,LCC,0,100000
-p5,p124,Possible,LCC,0,100000
-p5,p125,Possible,LCC,0,100000
-p5,p126,Possible,LCC,0,100000
-p5,p127,Possible,LCC,0,100000
-p5,p128,Possible,LCC,0,100000
-p5,p129,Possible,LCC,0,100000
-p5,p13,Possible,LCC,0,100000
-p5,p130,Possible,LCC,0,100000
-p5,p131,Possible,LCC,0,100000
-p5,p132,Possible,LCC,0,100000
-p5,p133,Possible,LCC,0,100000
-p5,p134,Possible,LCC,0,100000
-p5,p16,Possible,LCC,0,100000
-p5,p17,Possible,LCC,0,100000
-p5,p18,Possible,LCC,0,100000
-p5,p19,Possible,LCC,0,100000
-p5,p20,Possible,LCC,0,100000
-p5,p21,Possible,LCC,0,100000
-p5,p22,Possible,LCC,0,100000
-p5,p23,Possible,LCC,0,100000
-p5,p24,Possible,LCC,0,100000
-p5,p25,Possible,LCC,0,100000
-p5,p26,Possible,LCC,0,100000
-p5,p27,Possible,LCC,0,100000
-p5,p28,Possible,LCC,0,100000
-p5,p29,Possible,LCC,0,100000
-p5,p30,Possible,LCC,0,100000
-p5,p31,Possible,LCC,0,100000
-p5,p32,Possible,LCC,0,100000
-p5,p33,Possible,LCC,0,100000
-p5,p34,Possible,LCC,0,100000
-p5,p35,Possible,LCC,0,100000
-p5,p36,Possible,LCC,0,100000
-p5,p37,Possible,LCC,0,100000
-p5,p38,Possible,LCC,0,100000
-p5,p39,Possible,LCC,0,100000
-p5,p40,Possible,LCC,0,100000
-p5,p41,Possible,LCC,0,100000
-p5,p42,Possible,LCC,0,100000
-p5,p43,Possible,LCC,0,100000
-p5,p44,Possible,LCC,0,100000
-p5,p45,Possible,LCC,0,100000
-p5,p46,Possible,LCC,0,100000
-p5,p47,Possible,LCC,0,100000
-p5,p48,Possible,LCC,0,100000
-p5,p49,Possible,LCC,0,100000
-p5,p50,Possible,LCC,0,100000
-p5,p51,Possible,LCC,0,100000
-p5,p52,Possible,LCC,0,100000
-p5,p53,Possible,LCC,0,100000
-p5,p54,Possible,LCC,0,100000
-p5,p55,Possible,LCC,0,100000
-p5,p56,Possible,LCC,0,100000
-p5,p57,Possible,LCC,0,100000
-p5,p58,Possible,LCC,0,100000
-p5,p59,Possible,LCC,0,100000
-p5,p60,Possible,LCC,0,100000
-p5,p61,Possible,LCC,0,100000
-p5,p62,Possible,LCC,0,100000
-p5,p63,Possible,LCC,0,100000
-p5,p64,Possible,LCC,0,100000
-p5,p65,Possible,LCC,0,100000
-p5,p66,Possible,LCC,0,100000
-p5,p67,Possible,LCC,0,100000
-p5,p68,Possible,LCC,0,100000
-p5,p69,Possible,LCC,0,100000
-p5,p70,Possible,LCC,0,100000
-p5,p71,Possible,LCC,0,100000
-p5,p72,Possible,LCC,0,100000
-p5,p73,Possible,LCC,0,100000
-p5,p74,Possible,LCC,0,100000
-p5,p75,Possible,LCC,0,100000
-p5,p76,Possible,LCC,0,100000
-p5,p77,Possible,LCC,0,100000
-p5,p78,Possible,LCC,0,100000
-p5,p79,Possible,LCC,0,100000
-p5,p80,Possible,LCC,0,100000
-p5,p81,Possible,LCC,0,100000
-p5,p82,Possible,LCC,0,100000
-p5,p83,Possible,LCC,0,100000
-p5,p84,Possible,LCC,0,100000
-p5,p85,Possible,LCC,0,100000
-p5,p86,Possible,LCC,0,100000
-p5,p87,Possible,LCC,0,100000
-p5,p88,Possible,LCC,0,100000
-p5,p89,Possible,LCC,0,100000
-p5,p9,Possible,LCC,0,100000
-p5,p90,Possible,LCC,0,100000
-p5,p91,Possible,LCC,0,100000
-p5,p92,Possible,LCC,0,100000
-p5,p93,Possible,LCC,0,100000
-p5,p94,Possible,LCC,0,100000
-p5,p95,Possible,LCC,0,100000
-p5,p96,Possible,LCC,0,100000
-p5,p97,Possible,LCC,0,100000
-p5,p98,Possible,LCC,0,100000
-p5,p99,Possible,LCC,0,100000
-p50,p100,Possible,LCC,0,100000
-p50,p101,Possible,LCC,0,100000
-p50,p102,Possible,LCC,0,100000
-p50,p103,Possible,LCC,0,100000
-p50,p104,Possible,LCC,0,100000
-p50,p105,Possible,LCC,0,100000
-p50,p106,Possible,LCC,0,100000
-p50,p107,Possible,LCC,0,100000
-p50,p108,Possible,LCC,0,100000
-p50,p109,Possible,LCC,0,100000
-p50,p110,Possible,LCC,0,100000
-p50,p111,Possible,LCC,0,100000
-p50,p112,Possible,LCC,0,100000
-p50,p113,Possible,LCC,0,100000
-p50,p114,Possible,LCC,0,100000
-p50,p115,Possible,LCC,0,100000
-p50,p116,Possible,LCC,0,100000
-p50,p117,Possible,LCC,0,100000
-p50,p118,Possible,LCC,0,100000
-p50,p119,Possible,LCC,0,100000
-p50,p120,Possible,LCC,0,100000
-p50,p121,Possible,LCC,0,100000
-p50,p122,Possible,LCC,0,100000
-p50,p123,Possible,LCC,0,100000
-p50,p124,Possible,LCC,0,100000
-p50,p125,Possible,LCC,0,100000
-p50,p126,Possible,LCC,0,100000
-p50,p127,Possible,LCC,0,100000
-p50,p128,Possible,LCC,0,100000
-p50,p129,Possible,LCC,0,100000
-p50,p130,Possible,LCC,0,100000
-p50,p131,Possible,LCC,0,100000
-p50,p132,Possible,LCC,0,100000
-p50,p133,Possible,LCC,0,100000
-p50,p134,Possible,LCC,0,100000
-p50,p52,Possible,LCC,0,100000
-p50,p54,Possible,LCC,0,100000
-p50,p58,Possible,LCC,0,100000
-p50,p59,Possible,LCC,0,100000
-p50,p61,Possible,LCC,0,100000
-p50,p63,Possible,LCC,0,100000
-p50,p64,Possible,LCC,0,100000
-p50,p65,Possible,LCC,0,100000
-p50,p66,Possible,LCC,0,100000
-p50,p67,Possible,LCC,0,100000
-p50,p68,Possible,LCC,0,100000
-p50,p69,Possible,LCC,0,100000
-p50,p70,Possible,LCC,0,100000
-p50,p71,Possible,LCC,0,100000
-p50,p72,Possible,LCC,0,100000
-p50,p73,Possible,LCC,0,100000
-p50,p74,Possible,LCC,0,100000
-p50,p75,Possible,LCC,0,100000
-p50,p76,Possible,LCC,0,100000
-p50,p77,Possible,LCC,0,100000
-p50,p78,Possible,LCC,0,100000
-p50,p79,Possible,LCC,0,100000
-p50,p80,Possible,LCC,0,100000
-p50,p81,Possible,LCC,0,100000
-p50,p82,Possible,LCC,0,100000
-p50,p83,Possible,LCC,0,100000
-p50,p84,Possible,LCC,0,100000
-p50,p86,Possible,LCC,0,100000
-p50,p87,Possible,LCC,0,100000
-p50,p88,Possible,LCC,0,100000
-p50,p89,Possible,LCC,0,100000
-p50,p90,Possible,LCC,0,100000
-p50,p91,Possible,LCC,0,100000
-p50,p92,Possible,LCC,0,100000
-p50,p93,Possible,LCC,0,100000
-p50,p94,Possible,LCC,0,100000
-p50,p95,Possible,LCC,0,100000
-p50,p96,Possible,LCC,0,100000
-p50,p97,Possible,LCC,0,100000
-p50,p98,Possible,LCC,0,100000
-p50,p99,Possible,LCC,0,100000
-p51,p100,Possible,LCC,0,100000
-p51,p101,Possible,LCC,0,100000
-p51,p102,Possible,LCC,0,100000
-p51,p103,Possible,LCC,0,100000
-p51,p104,Possible,LCC,0,100000
-p51,p105,Possible,LCC,0,100000
-p51,p106,Possible,LCC,0,100000
-p51,p107,Possible,LCC,0,100000
-p51,p108,Possible,LCC,0,100000
-p51,p109,Possible,LCC,0,100000
-p51,p110,Possible,LCC,0,100000
-p51,p111,Possible,LCC,0,100000
-p51,p112,Possible,LCC,0,100000
-p51,p113,Possible,LCC,0,100000
-p51,p114,Possible,LCC,0,100000
-p51,p115,Possible,LCC,0,100000
-p51,p116,Possible,LCC,0,100000
-p51,p117,Possible,LCC,0,100000
-p51,p118,Possible,LCC,0,100000
-p51,p119,Possible,LCC,0,100000
-p51,p120,Possible,LCC,0,100000
-p51,p121,Possible,LCC,0,100000
-p51,p122,Possible,LCC,0,100000
-p51,p123,Possible,LCC,0,100000
-p51,p124,Possible,LCC,0,100000
-p51,p125,Possible,LCC,0,100000
-p51,p126,Possible,LCC,0,100000
-p51,p127,Possible,LCC,0,100000
-p51,p128,Possible,LCC,0,100000
-p51,p129,Possible,LCC,0,100000
-p51,p130,Possible,LCC,0,100000
-p51,p131,Possible,LCC,0,100000
-p51,p132,Possible,LCC,0,100000
-p51,p133,Possible,LCC,0,100000
-p51,p134,Possible,LCC,0,100000
-p51,p52,Possible,LCC,0,100000
-p51,p54,Possible,LCC,0,100000
-p51,p57,Possible,LCC,0,100000
-p51,p58,Possible,LCC,0,100000
-p51,p59,Possible,LCC,0,100000
-p51,p60,Possible,LCC,0,100000
-p51,p61,Possible,LCC,0,100000
-p51,p62,Possible,LCC,0,100000
-p51,p63,Possible,LCC,0,100000
-p51,p64,Possible,LCC,0,100000
-p51,p65,Possible,LCC,0,100000
-p51,p66,Possible,LCC,0,100000
-p51,p67,Possible,LCC,0,100000
-p51,p68,Possible,LCC,0,100000
-p51,p69,Possible,LCC,0,100000
-p51,p70,Possible,LCC,0,100000
-p51,p71,Possible,LCC,0,100000
-p51,p73,Possible,LCC,0,100000
-p51,p74,Possible,LCC,0,100000
-p51,p75,Possible,LCC,0,100000
-p51,p76,Possible,LCC,0,100000
-p51,p77,Possible,LCC,0,100000
-p51,p78,Possible,LCC,0,100000
-p51,p79,Possible,LCC,0,100000
-p51,p80,Possible,LCC,0,100000
-p51,p81,Possible,LCC,0,100000
-p51,p82,Possible,LCC,0,100000
-p51,p83,Possible,LCC,0,100000
-p51,p84,Possible,LCC,0,100000
-p51,p85,Possible,LCC,0,100000
-p51,p86,Possible,LCC,0,100000
-p51,p87,Possible,LCC,0,100000
-p51,p88,Possible,LCC,0,100000
-p51,p89,Possible,LCC,0,100000
-p51,p90,Possible,LCC,0,100000
-p51,p91,Possible,LCC,0,100000
-p51,p92,Possible,LCC,0,100000
-p51,p93,Possible,LCC,0,100000
-p51,p94,Possible,LCC,0,100000
-p51,p95,Possible,LCC,0,100000
-p51,p96,Possible,LCC,0,100000
-p51,p97,Possible,LCC,0,100000
-p51,p98,Possible,LCC,0,100000
-p51,p99,Possible,LCC,0,100000
-p52,p100,Possible,LCC,0,100000
-p52,p101,Possible,LCC,0,100000
-p52,p102,Possible,LCC,0,100000
-p52,p103,Possible,LCC,0,100000
-p52,p104,Possible,LCC,0,100000
-p52,p105,Possible,LCC,0,100000
-p52,p106,Possible,LCC,0,100000
-p52,p107,Possible,LCC,0,100000
-p52,p108,Possible,LCC,0,100000
-p52,p109,Possible,LCC,0,100000
-p52,p110,Possible,LCC,0,100000
-p52,p111,Possible,LCC,0,100000
-p52,p112,Possible,LCC,0,100000
-p52,p113,Possible,LCC,0,100000
-p52,p114,Possible,LCC,0,100000
-p52,p115,Possible,LCC,0,100000
-p52,p116,Possible,LCC,0,100000
-p52,p117,Possible,LCC,0,100000
-p52,p118,Possible,LCC,0,100000
-p52,p119,Possible,LCC,0,100000
-p52,p120,Possible,LCC,0,100000
-p52,p121,Possible,LCC,0,100000
-p52,p122,Possible,LCC,0,100000
-p52,p123,Possible,LCC,0,100000
-p52,p124,Possible,LCC,0,100000
-p52,p125,Possible,LCC,0,100000
-p52,p126,Possible,LCC,0,100000
-p52,p127,Possible,LCC,0,100000
-p52,p128,Possible,LCC,0,100000
-p52,p129,Possible,LCC,0,100000
-p52,p130,Possible,LCC,0,100000
-p52,p131,Possible,LCC,0,100000
-p52,p132,Possible,LCC,0,100000
-p52,p133,Possible,LCC,0,100000
-p52,p134,Possible,LCC,0,100000
-p52,p54,Possible,LCC,0,100000
-p52,p55,Possible,LCC,0,100000
-p52,p56,Possible,LCC,0,100000
-p52,p57,Possible,LCC,0,100000
-p52,p58,Possible,LCC,0,100000
-p52,p59,Possible,LCC,0,100000
-p52,p60,Possible,LCC,0,100000
-p52,p61,Possible,LCC,0,100000
-p52,p62,Possible,LCC,0,100000
-p52,p63,Possible,LCC,0,100000
-p52,p64,Possible,LCC,0,100000
-p52,p65,Possible,LCC,0,100000
-p52,p66,Possible,LCC,0,100000
-p52,p67,Possible,LCC,0,100000
-p52,p68,Possible,LCC,0,100000
-p52,p69,Possible,LCC,0,100000
-p52,p70,Possible,LCC,0,100000
-p52,p71,Possible,LCC,0,100000
-p52,p72,Possible,LCC,0,100000
-p52,p73,Possible,LCC,0,100000
-p52,p74,Possible,LCC,0,100000
-p52,p75,Possible,LCC,0,100000
-p52,p76,Possible,LCC,0,100000
-p52,p77,Possible,LCC,0,100000
-p52,p78,Possible,LCC,0,100000
-p52,p79,Possible,LCC,0,100000
-p52,p80,Possible,LCC,0,100000
-p52,p81,Possible,LCC,0,100000
-p52,p82,Possible,LCC,0,100000
-p52,p83,Possible,LCC,0,100000
-p52,p84,Possible,LCC,0,100000
-p52,p85,Possible,LCC,0,100000
-p52,p86,Possible,LCC,0,100000
-p52,p87,Possible,LCC,0,100000
-p52,p88,Possible,LCC,0,100000
-p52,p89,Possible,LCC,0,100000
-p52,p90,Possible,LCC,0,100000
-p52,p91,Possible,LCC,0,100000
-p52,p92,Possible,LCC,0,100000
-p52,p93,Possible,LCC,0,100000
-p52,p94,Possible,LCC,0,100000
-p52,p95,Possible,LCC,0,100000
-p52,p96,Possible,LCC,0,100000
-p52,p97,Possible,LCC,0,100000
-p52,p98,Possible,LCC,0,100000
-p52,p99,Possible,LCC,0,100000
-p53,p100,Possible,LCC,0,100000
-p53,p101,Possible,LCC,0,100000
-p53,p102,Possible,LCC,0,100000
-p53,p103,Possible,LCC,0,100000
-p53,p104,Possible,LCC,0,100000
-p53,p105,Possible,LCC,0,100000
-p53,p106,Possible,LCC,0,100000
-p53,p107,Possible,LCC,0,100000
-p53,p108,Possible,LCC,0,100000
-p53,p109,Possible,LCC,0,100000
-p53,p110,Possible,LCC,0,100000
-p53,p111,Possible,LCC,0,100000
-p53,p112,Possible,LCC,0,100000
-p53,p113,Possible,LCC,0,100000
-p53,p114,Possible,LCC,0,100000
-p53,p115,Possible,LCC,0,100000
-p53,p116,Possible,LCC,0,100000
-p53,p117,Possible,LCC,0,100000
-p53,p118,Possible,LCC,0,100000
-p53,p119,Possible,LCC,0,100000
-p53,p120,Possible,LCC,0,100000
-p53,p121,Possible,LCC,0,100000
-p53,p122,Possible,LCC,0,100000
-p53,p123,Possible,LCC,0,100000
-p53,p124,Possible,LCC,0,100000
-p53,p125,Possible,LCC,0,100000
-p53,p126,Possible,LCC,0,100000
-p53,p127,Possible,LCC,0,100000
-p53,p128,Possible,LCC,0,100000
-p53,p129,Possible,LCC,0,100000
-p53,p130,Possible,LCC,0,100000
-p53,p131,Possible,LCC,0,100000
-p53,p132,Possible,LCC,0,100000
-p53,p133,Possible,LCC,0,100000
-p53,p134,Possible,LCC,0,100000
-p53,p57,Possible,LCC,0,100000
-p53,p58,Possible,LCC,0,100000
-p53,p59,Possible,LCC,0,100000
-p53,p60,Possible,LCC,0,100000
-p53,p61,Possible,LCC,0,100000
-p53,p62,Possible,LCC,0,100000
-p53,p63,Possible,LCC,0,100000
-p53,p64,Possible,LCC,0,100000
-p53,p65,Possible,LCC,0,100000
-p53,p66,Possible,LCC,0,100000
-p53,p67,Possible,LCC,0,100000
-p53,p68,Possible,LCC,0,100000
-p53,p69,Possible,LCC,0,100000
-p53,p70,Possible,LCC,0,100000
-p53,p71,Possible,LCC,0,100000
-p53,p72,Possible,LCC,0,100000
-p53,p73,Possible,LCC,0,100000
-p53,p74,Possible,LCC,0,100000
-p53,p75,Possible,LCC,0,100000
-p53,p76,Possible,LCC,0,100000
-p53,p77,Possible,LCC,0,100000
-p53,p78,Possible,LCC,0,100000
-p53,p79,Possible,LCC,0,100000
-p53,p80,Possible,LCC,0,100000
-p53,p81,Possible,LCC,0,100000
-p53,p82,Possible,LCC,0,100000
-p53,p83,Possible,LCC,0,100000
-p53,p84,Possible,LCC,0,100000
-p53,p85,Possible,LCC,0,100000
-p53,p86,Possible,LCC,0,100000
-p53,p87,Possible,LCC,0,100000
-p53,p88,Possible,LCC,0,100000
-p53,p89,Possible,LCC,0,100000
-p53,p90,Possible,LCC,0,100000
-p53,p91,Possible,LCC,0,100000
-p53,p92,Possible,LCC,0,100000
-p53,p93,Possible,LCC,0,100000
-p53,p94,Possible,LCC,0,100000
-p53,p95,Possible,LCC,0,100000
-p53,p96,Possible,LCC,0,100000
-p53,p97,Possible,LCC,0,100000
-p53,p98,Possible,LCC,0,100000
-p53,p99,Possible,LCC,0,100000
-p54,p100,Possible,LCC,0,100000
-p54,p101,Possible,LCC,0,100000
-p54,p102,Possible,LCC,0,100000
-p54,p103,Possible,LCC,0,100000
-p54,p104,Possible,LCC,0,100000
-p54,p105,Possible,LCC,0,100000
-p54,p106,Possible,LCC,0,100000
-p54,p107,Possible,LCC,0,100000
-p54,p108,Possible,LCC,0,100000
-p54,p109,Possible,LCC,0,100000
-p54,p110,Possible,LCC,0,100000
-p54,p111,Possible,LCC,0,100000
-p54,p112,Possible,LCC,0,100000
-p54,p113,Possible,LCC,0,100000
-p54,p114,Possible,LCC,0,100000
-p54,p115,Possible,LCC,0,100000
-p54,p116,Possible,LCC,0,100000
-p54,p117,Possible,LCC,0,100000
-p54,p118,Possible,LCC,0,100000
-p54,p119,Possible,LCC,0,100000
-p54,p120,Possible,LCC,0,100000
-p54,p121,Possible,LCC,0,100000
-p54,p122,Possible,LCC,0,100000
-p54,p123,Possible,LCC,0,100000
-p54,p124,Possible,LCC,0,100000
-p54,p125,Possible,LCC,0,100000
-p54,p126,Possible,LCC,0,100000
-p54,p127,Possible,LCC,0,100000
-p54,p128,Possible,LCC,0,100000
-p54,p129,Possible,LCC,0,100000
-p54,p130,Possible,LCC,0,100000
-p54,p131,Possible,LCC,0,100000
-p54,p132,Possible,LCC,0,100000
-p54,p133,Possible,LCC,0,100000
-p54,p134,Possible,LCC,0,100000
-p54,p56,Possible,LCC,0,100000
-p54,p57,Possible,LCC,0,100000
-p54,p58,Possible,LCC,0,100000
-p54,p59,Possible,LCC,0,100000
-p54,p60,Possible,LCC,0,100000
-p54,p61,Possible,LCC,0,100000
-p54,p62,Possible,LCC,0,100000
-p54,p63,Possible,LCC,0,100000
-p54,p64,Possible,LCC,0,100000
-p54,p65,Possible,LCC,0,100000
-p54,p66,Possible,LCC,0,100000
-p54,p67,Possible,LCC,0,100000
-p54,p68,Possible,LCC,0,100000
-p54,p69,Possible,LCC,0,100000
-p54,p73,Possible,LCC,0,100000
-p54,p74,Possible,LCC,0,100000
-p54,p75,Possible,LCC,0,100000
-p54,p76,Possible,LCC,0,100000
-p54,p77,Possible,LCC,0,100000
-p54,p78,Possible,LCC,0,100000
-p54,p79,Possible,LCC,0,100000
-p54,p80,Possible,LCC,0,100000
-p54,p81,Possible,LCC,0,100000
-p54,p82,Possible,LCC,0,100000
-p54,p83,Possible,LCC,0,100000
-p54,p84,Possible,LCC,0,100000
-p54,p85,Possible,LCC,0,100000
-p54,p86,Possible,LCC,0,100000
-p54,p87,Possible,LCC,0,100000
-p54,p88,Possible,LCC,0,100000
-p54,p89,Possible,LCC,0,100000
-p54,p90,Possible,LCC,0,100000
-p54,p91,Possible,LCC,0,100000
-p54,p92,Possible,LCC,0,100000
-p54,p93,Possible,LCC,0,100000
-p54,p94,Possible,LCC,0,100000
-p54,p95,Possible,LCC,0,100000
-p54,p96,Possible,LCC,0,100000
-p54,p97,Possible,LCC,0,100000
-p54,p98,Possible,LCC,0,100000
-p54,p99,Possible,LCC,0,100000
-p55,p100,Possible,LCC,0,100000
-p55,p101,Possible,LCC,0,100000
-p55,p102,Possible,LCC,0,100000
-p55,p103,Possible,LCC,0,100000
-p55,p104,Possible,LCC,0,100000
-p55,p105,Possible,LCC,0,100000
-p55,p106,Possible,LCC,0,100000
-p55,p107,Possible,LCC,0,100000
-p55,p108,Possible,LCC,0,100000
-p55,p109,Possible,LCC,0,100000
-p55,p110,Possible,LCC,0,100000
-p55,p111,Possible,LCC,0,100000
-p55,p112,Possible,LCC,0,100000
-p55,p113,Possible,LCC,0,100000
-p55,p114,Possible,LCC,0,100000
-p55,p115,Possible,LCC,0,100000
-p55,p116,Possible,LCC,0,100000
-p55,p117,Possible,LCC,0,100000
-p55,p118,Possible,LCC,0,100000
-p55,p119,Possible,LCC,0,100000
-p55,p120,Possible,LCC,0,100000
-p55,p121,Possible,LCC,0,100000
-p55,p122,Possible,LCC,0,100000
-p55,p123,Possible,LCC,0,100000
-p55,p124,Possible,LCC,0,100000
-p55,p125,Possible,LCC,0,100000
-p55,p126,Possible,LCC,0,100000
-p55,p127,Possible,LCC,0,100000
-p55,p128,Possible,LCC,0,100000
-p55,p129,Possible,LCC,0,100000
-p55,p130,Possible,LCC,0,100000
-p55,p131,Possible,LCC,0,100000
-p55,p132,Possible,LCC,0,100000
-p55,p133,Possible,LCC,0,100000
-p55,p134,Possible,LCC,0,100000
-p55,p57,Possible,LCC,0,100000
-p55,p58,Possible,LCC,0,100000
-p55,p59,Possible,LCC,0,100000
-p55,p60,Possible,LCC,0,100000
-p55,p61,Possible,LCC,0,100000
-p55,p62,Possible,LCC,0,100000
-p55,p63,Possible,LCC,0,100000
-p55,p64,Possible,LCC,0,100000
-p55,p65,Possible,LCC,0,100000
-p55,p66,Possible,LCC,0,100000
-p55,p67,Possible,LCC,0,100000
-p55,p68,Possible,LCC,0,100000
-p55,p69,Possible,LCC,0,100000
-p55,p70,Possible,LCC,0,100000
-p55,p71,Possible,LCC,0,100000
-p55,p73,Possible,LCC,0,100000
-p55,p74,Possible,LCC,0,100000
-p55,p75,Possible,LCC,0,100000
-p55,p76,Possible,LCC,0,100000
-p55,p77,Possible,LCC,0,100000
-p55,p78,Possible,LCC,0,100000
-p55,p79,Possible,LCC,0,100000
-p55,p80,Possible,LCC,0,100000
-p55,p81,Possible,LCC,0,100000
-p55,p82,Possible,LCC,0,100000
-p55,p83,Possible,LCC,0,100000
-p55,p86,Possible,LCC,0,100000
-p55,p87,Possible,LCC,0,100000
-p55,p88,Possible,LCC,0,100000
-p55,p89,Possible,LCC,0,100000
-p55,p90,Possible,LCC,0,100000
-p55,p91,Possible,LCC,0,100000
-p55,p92,Possible,LCC,0,100000
-p55,p93,Possible,LCC,0,100000
-p55,p94,Possible,LCC,0,100000
-p55,p95,Possible,LCC,0,100000
-p55,p96,Possible,LCC,0,100000
-p55,p97,Possible,LCC,0,100000
-p55,p98,Possible,LCC,0,100000
-p55,p99,Possible,LCC,0,100000
-p56,p100,Possible,LCC,0,100000
-p56,p101,Possible,LCC,0,100000
-p56,p102,Possible,LCC,0,100000
-p56,p103,Possible,LCC,0,100000
-p56,p104,Possible,LCC,0,100000
-p56,p105,Possible,LCC,0,100000
-p56,p106,Possible,LCC,0,100000
-p56,p107,Possible,LCC,0,100000
-p56,p108,Possible,LCC,0,100000
-p56,p109,Possible,LCC,0,100000
-p56,p110,Possible,LCC,0,100000
-p56,p111,Possible,LCC,0,100000
-p56,p112,Possible,LCC,0,100000
-p56,p113,Possible,LCC,0,100000
-p56,p114,Possible,LCC,0,100000
-p56,p115,Possible,LCC,0,100000
-p56,p116,Possible,LCC,0,100000
-p56,p117,Possible,LCC,0,100000
-p56,p118,Possible,LCC,0,100000
-p56,p119,Possible,LCC,0,100000
-p56,p120,Possible,LCC,0,100000
-p56,p121,Possible,LCC,0,100000
-p56,p122,Possible,LCC,0,100000
-p56,p123,Possible,LCC,0,100000
-p56,p124,Possible,LCC,0,100000
-p56,p125,Possible,LCC,0,100000
-p56,p126,Possible,LCC,0,100000
-p56,p127,Possible,LCC,0,100000
-p56,p128,Possible,LCC,0,100000
-p56,p129,Possible,LCC,0,100000
-p56,p130,Possible,LCC,0,100000
-p56,p131,Possible,LCC,0,100000
-p56,p132,Possible,LCC,0,100000
-p56,p133,Possible,LCC,0,100000
-p56,p134,Possible,LCC,0,100000
-p56,p57,Possible,LCC,0,100000
-p56,p58,Possible,LCC,0,100000
-p56,p59,Possible,LCC,0,100000
-p56,p60,Possible,LCC,0,100000
-p56,p61,Possible,LCC,0,100000
-p56,p62,Possible,LCC,0,100000
-p56,p63,Possible,LCC,0,100000
-p56,p64,Possible,LCC,0,100000
-p56,p65,Possible,LCC,0,100000
-p56,p66,Possible,LCC,0,100000
-p56,p67,Possible,LCC,0,100000
-p56,p68,Possible,LCC,0,100000
-p56,p69,Possible,LCC,0,100000
-p56,p70,Possible,LCC,0,100000
-p56,p71,Possible,LCC,0,100000
-p56,p73,Possible,LCC,0,100000
-p56,p74,Possible,LCC,0,100000
-p56,p75,Possible,LCC,0,100000
-p56,p76,Possible,LCC,0,100000
-p56,p77,Possible,LCC,0,100000
-p56,p78,Possible,LCC,0,100000
-p56,p79,Possible,LCC,0,100000
-p56,p80,Possible,LCC,0,100000
-p56,p81,Possible,LCC,0,100000
-p56,p82,Possible,LCC,0,100000
-p56,p83,Possible,LCC,0,100000
-p56,p84,Possible,LCC,0,100000
-p56,p86,Possible,LCC,0,100000
-p56,p87,Possible,LCC,0,100000
-p56,p88,Possible,LCC,0,100000
-p56,p89,Possible,LCC,0,100000
-p56,p90,Possible,LCC,0,100000
-p56,p91,Possible,LCC,0,100000
-p56,p92,Possible,LCC,0,100000
-p56,p93,Possible,LCC,0,100000
-p56,p94,Possible,LCC,0,100000
-p56,p95,Possible,LCC,0,100000
-p56,p96,Possible,LCC,0,100000
-p56,p97,Possible,LCC,0,100000
-p56,p98,Possible,LCC,0,100000
-p56,p99,Possible,LCC,0,100000
-p57,p100,Possible,LCC,0,100000
-p57,p101,Possible,LCC,0,100000
-p57,p102,Possible,LCC,0,100000
-p57,p103,Possible,LCC,0,100000
-p57,p104,Possible,LCC,0,100000
-p57,p105,Possible,LCC,0,100000
-p57,p106,Possible,LCC,0,100000
-p57,p107,Possible,LCC,0,100000
-p57,p108,Possible,LCC,0,100000
-p57,p109,Possible,LCC,0,100000
-p57,p110,Possible,LCC,0,100000
-p57,p111,Possible,LCC,0,100000
-p57,p112,Possible,LCC,0,100000
-p57,p113,Possible,LCC,0,100000
-p57,p114,Possible,LCC,0,100000
-p57,p115,Possible,LCC,0,100000
-p57,p116,Possible,LCC,0,100000
-p57,p117,Possible,LCC,0,100000
-p57,p118,Possible,LCC,0,100000
-p57,p119,Possible,LCC,0,100000
-p57,p120,Possible,LCC,0,100000
-p57,p121,Possible,LCC,0,100000
-p57,p122,Possible,LCC,0,100000
-p57,p123,Possible,LCC,0,100000
-p57,p124,Possible,LCC,0,100000
-p57,p125,Possible,LCC,0,100000
-p57,p126,Possible,LCC,0,100000
-p57,p127,Possible,LCC,0,100000
-p57,p128,Possible,LCC,0,100000
-p57,p129,Possible,LCC,0,100000
-p57,p130,Possible,LCC,0,100000
-p57,p131,Possible,LCC,0,100000
-p57,p132,Possible,LCC,0,100000
-p57,p133,Possible,LCC,0,100000
-p57,p134,Possible,LCC,0,100000
-p57,p59,Possible,LCC,0,100000
-p57,p60,Possible,LCC,0,100000
-p57,p61,Possible,LCC,0,100000
-p57,p62,Possible,LCC,0,100000
-p57,p64,Possible,LCC,0,100000
-p57,p65,Possible,LCC,0,100000
-p57,p67,Possible,LCC,0,100000
-p57,p68,Possible,LCC,0,100000
-p57,p69,Possible,LCC,0,100000
-p57,p70,Possible,LCC,0,100000
-p57,p71,Possible,LCC,0,100000
-p57,p72,Possible,LCC,0,100000
-p57,p73,Possible,LCC,0,100000
-p57,p74,Possible,LCC,0,100000
-p57,p75,Possible,LCC,0,100000
-p57,p76,Possible,LCC,0,100000
-p57,p77,Possible,LCC,0,100000
-p57,p78,Possible,LCC,0,100000
-p57,p79,Possible,LCC,0,100000
-p57,p80,Possible,LCC,0,100000
-p57,p81,Possible,LCC,0,100000
-p57,p82,Possible,LCC,0,100000
-p57,p83,Possible,LCC,0,100000
-p57,p84,Possible,LCC,0,100000
-p57,p86,Possible,LCC,0,100000
-p57,p87,Possible,LCC,0,100000
-p57,p88,Possible,LCC,0,100000
-p57,p89,Possible,LCC,0,100000
-p57,p90,Possible,LCC,0,100000
-p57,p91,Possible,LCC,0,100000
-p57,p92,Possible,LCC,0,100000
-p57,p93,Possible,LCC,0,100000
-p57,p94,Possible,LCC,0,100000
-p57,p95,Possible,LCC,0,100000
-p57,p96,Possible,LCC,0,100000
-p57,p97,Possible,LCC,0,100000
-p57,p98,Possible,LCC,0,100000
-p57,p99,Possible,LCC,0,100000
-p58,p100,Possible,LCC,0,100000
-p58,p101,Possible,LCC,0,100000
-p58,p102,Possible,LCC,0,100000
-p58,p103,Possible,LCC,0,100000
-p58,p104,Possible,LCC,0,100000
-p58,p105,Possible,LCC,0,100000
-p58,p106,Possible,LCC,0,100000
-p58,p107,Possible,LCC,0,100000
-p58,p108,Possible,LCC,0,100000
-p58,p109,Possible,LCC,0,100000
-p58,p110,Possible,LCC,0,100000
-p58,p111,Possible,LCC,0,100000
-p58,p112,Possible,LCC,0,100000
-p58,p113,Possible,LCC,0,100000
-p58,p114,Possible,LCC,0,100000
-p58,p115,Possible,LCC,0,100000
-p58,p116,Possible,LCC,0,100000
-p58,p117,Possible,LCC,0,100000
-p58,p118,Possible,LCC,0,100000
-p58,p119,Possible,LCC,0,100000
-p58,p120,Possible,LCC,0,100000
-p58,p121,Possible,LCC,0,100000
-p58,p122,Possible,LCC,0,100000
-p58,p123,Possible,LCC,0,100000
-p58,p124,Possible,LCC,0,100000
-p58,p125,Possible,LCC,0,100000
-p58,p126,Possible,LCC,0,100000
-p58,p127,Possible,LCC,0,100000
-p58,p128,Possible,LCC,0,100000
-p58,p129,Possible,LCC,0,100000
-p58,p130,Possible,LCC,0,100000
-p58,p131,Possible,LCC,0,100000
-p58,p132,Possible,LCC,0,100000
-p58,p133,Possible,LCC,0,100000
-p58,p134,Possible,LCC,0,100000
-p58,p59,Possible,LCC,0,100000
-p58,p60,Possible,LCC,0,100000
-p58,p61,Possible,LCC,0,100000
-p58,p62,Possible,LCC,0,100000
-p58,p63,Possible,LCC,0,100000
-p58,p64,Possible,LCC,0,100000
-p58,p65,Possible,LCC,0,100000
-p58,p67,Possible,LCC,0,100000
-p58,p68,Possible,LCC,0,100000
-p58,p69,Possible,LCC,0,100000
-p58,p70,Possible,LCC,0,100000
-p58,p71,Possible,LCC,0,100000
-p58,p72,Possible,LCC,0,100000
-p58,p73,Possible,LCC,0,100000
-p58,p74,Possible,LCC,0,100000
-p58,p75,Possible,LCC,0,100000
-p58,p76,Possible,LCC,0,100000
-p58,p77,Possible,LCC,0,100000
-p58,p78,Possible,LCC,0,100000
-p58,p79,Possible,LCC,0,100000
-p58,p80,Possible,LCC,0,100000
-p58,p81,Possible,LCC,0,100000
-p58,p82,Possible,LCC,0,100000
-p58,p83,Possible,LCC,0,100000
-p58,p84,Possible,LCC,0,100000
-p58,p88,Possible,LCC,0,100000
-p58,p89,Possible,LCC,0,100000
-p58,p90,Possible,LCC,0,100000
-p58,p91,Possible,LCC,0,100000
-p58,p92,Possible,LCC,0,100000
-p58,p93,Possible,LCC,0,100000
-p58,p94,Possible,LCC,0,100000
-p58,p95,Possible,LCC,0,100000
-p58,p96,Possible,LCC,0,100000
-p58,p97,Possible,LCC,0,100000
-p58,p98,Possible,LCC,0,100000
-p58,p99,Possible,LCC,0,100000
-p59,p100,Possible,LCC,0,100000
-p59,p101,Possible,LCC,0,100000
-p59,p102,Possible,LCC,0,100000
-p59,p103,Possible,LCC,0,100000
-p59,p104,Possible,LCC,0,100000
-p59,p105,Possible,LCC,0,100000
-p59,p106,Possible,LCC,0,100000
-p59,p107,Possible,LCC,0,100000
-p59,p108,Possible,LCC,0,100000
-p59,p109,Possible,LCC,0,100000
-p59,p110,Possible,LCC,0,100000
-p59,p111,Possible,LCC,0,100000
-p59,p112,Possible,LCC,0,100000
-p59,p113,Possible,LCC,0,100000
-p59,p114,Possible,LCC,0,100000
-p59,p115,Possible,LCC,0,100000
-p59,p116,Possible,LCC,0,100000
-p59,p117,Possible,LCC,0,100000
-p59,p118,Possible,LCC,0,100000
-p59,p119,Possible,LCC,0,100000
-p59,p120,Possible,LCC,0,100000
-p59,p121,Possible,LCC,0,100000
-p59,p122,Possible,LCC,0,100000
-p59,p123,Possible,LCC,0,100000
-p59,p124,Possible,LCC,0,100000
-p59,p125,Possible,LCC,0,100000
-p59,p126,Possible,LCC,0,100000
-p59,p127,Possible,LCC,0,100000
-p59,p128,Possible,LCC,0,100000
-p59,p129,Possible,LCC,0,100000
-p59,p130,Possible,LCC,0,100000
-p59,p131,Possible,LCC,0,100000
-p59,p132,Possible,LCC,0,100000
-p59,p133,Possible,LCC,0,100000
-p59,p134,Possible,LCC,0,100000
-p59,p60,Possible,LCC,0,100000
-p59,p61,Possible,LCC,0,100000
-p59,p62,Possible,LCC,0,100000
-p59,p63,Possible,LCC,0,100000
-p59,p64,Possible,LCC,0,100000
-p59,p65,Possible,LCC,0,100000
-p59,p66,Possible,LCC,0,100000
-p59,p67,Possible,LCC,0,100000
-p59,p68,Possible,LCC,0,100000
-p59,p69,Possible,LCC,0,100000
-p59,p70,Possible,LCC,0,100000
-p59,p71,Possible,LCC,0,100000
-p59,p72,Possible,LCC,0,100000
-p59,p73,Possible,LCC,0,100000
-p59,p74,Possible,LCC,0,100000
-p59,p75,Possible,LCC,0,100000
-p59,p76,Possible,LCC,0,100000
-p59,p77,Possible,LCC,0,100000
-p59,p78,Possible,LCC,0,100000
-p59,p79,Possible,LCC,0,100000
-p59,p80,Possible,LCC,0,100000
-p59,p81,Possible,LCC,0,100000
-p59,p82,Possible,LCC,0,100000
-p59,p83,Possible,LCC,0,100000
-p59,p84,Possible,LCC,0,100000
-p59,p85,Possible,LCC,0,100000
-p59,p86,Possible,LCC,0,100000
-p59,p87,Possible,LCC,0,100000
-p59,p88,Possible,LCC,0,100000
-p59,p89,Possible,LCC,0,100000
-p59,p90,Possible,LCC,0,100000
-p59,p91,Possible,LCC,0,100000
-p59,p92,Possible,LCC,0,100000
-p59,p93,Possible,LCC,0,100000
-p59,p94,Possible,LCC,0,100000
-p59,p95,Possible,LCC,0,100000
-p59,p96,Possible,LCC,0,100000
-p59,p97,Possible,LCC,0,100000
-p59,p98,Possible,LCC,0,100000
-p59,p99,Possible,LCC,0,100000
-p6,p10,Possible,LCC,0,100000
-p6,p100,Possible,LCC,0,100000
-p6,p101,Possible,LCC,0,100000
-p6,p102,Possible,LCC,0,100000
-p6,p103,Possible,LCC,0,100000
-p6,p104,Possible,LCC,0,100000
-p6,p105,Possible,LCC,0,100000
-p6,p106,Possible,LCC,0,100000
-p6,p107,Possible,LCC,0,100000
-p6,p108,Possible,LCC,0,100000
-p6,p109,Possible,LCC,0,100000
-p6,p11,Possible,LCC,0,100000
-p6,p110,Possible,LCC,0,100000
-p6,p111,Possible,LCC,0,100000
-p6,p112,Possible,LCC,0,100000
-p6,p113,Possible,LCC,0,100000
-p6,p114,Possible,LCC,0,100000
-p6,p115,Possible,LCC,0,100000
-p6,p116,Possible,LCC,0,100000
-p6,p117,Possible,LCC,0,100000
-p6,p118,Possible,LCC,0,100000
-p6,p119,Possible,LCC,0,100000
-p6,p12,Possible,LCC,0,100000
-p6,p120,Possible,LCC,0,100000
-p6,p121,Possible,LCC,0,100000
-p6,p122,Possible,LCC,0,100000
-p6,p123,Possible,LCC,0,100000
-p6,p124,Possible,LCC,0,100000
-p6,p125,Possible,LCC,0,100000
-p6,p126,Possible,LCC,0,100000
-p6,p127,Possible,LCC,0,100000
-p6,p128,Possible,LCC,0,100000
-p6,p129,Possible,LCC,0,100000
-p6,p13,Possible,LCC,0,100000
-p6,p130,Possible,LCC,0,100000
-p6,p131,Possible,LCC,0,100000
-p6,p132,Possible,LCC,0,100000
-p6,p133,Possible,LCC,0,100000
-p6,p134,Possible,LCC,0,100000
-p6,p14,Possible,LCC,0,100000
-p6,p15,Possible,LCC,0,100000
-p6,p16,Possible,LCC,0,100000
-p6,p17,Possible,LCC,0,100000
-p6,p18,Possible,LCC,0,100000
-p6,p19,Possible,LCC,0,100000
-p6,p20,Possible,LCC,0,100000
-p6,p21,Possible,LCC,0,100000
-p6,p22,Possible,LCC,0,100000
-p6,p23,Possible,LCC,0,100000
-p6,p24,Possible,LCC,0,100000
-p6,p25,Possible,LCC,0,100000
-p6,p26,Possible,LCC,0,100000
-p6,p27,Possible,LCC,0,100000
-p6,p28,Possible,LCC,0,100000
-p6,p29,Possible,LCC,0,100000
-p6,p30,Possible,LCC,0,100000
-p6,p31,Possible,LCC,0,100000
-p6,p32,Possible,LCC,0,100000
-p6,p33,Possible,LCC,0,100000
-p6,p34,Possible,LCC,0,100000
-p6,p35,Possible,LCC,0,100000
-p6,p36,Possible,LCC,0,100000
-p6,p37,Possible,LCC,0,100000
-p6,p38,Possible,LCC,0,100000
-p6,p39,Possible,LCC,0,100000
-p6,p40,Possible,LCC,0,100000
-p6,p41,Possible,LCC,0,100000
-p6,p42,Possible,LCC,0,100000
-p6,p43,Possible,LCC,0,100000
-p6,p44,Possible,LCC,0,100000
-p6,p45,Possible,LCC,0,100000
-p6,p46,Possible,LCC,0,100000
-p6,p47,Possible,LCC,0,100000
-p6,p48,Possible,LCC,0,100000
-p6,p49,Possible,LCC,0,100000
-p6,p50,Possible,LCC,0,100000
-p6,p51,Possible,LCC,0,100000
-p6,p52,Possible,LCC,0,100000
-p6,p53,Possible,LCC,0,100000
-p6,p54,Possible,LCC,0,100000
-p6,p55,Possible,LCC,0,100000
-p6,p56,Possible,LCC,0,100000
-p6,p57,Possible,LCC,0,100000
-p6,p58,Possible,LCC,0,100000
-p6,p59,Possible,LCC,0,100000
-p6,p60,Possible,LCC,0,100000
-p6,p61,Possible,LCC,0,100000
-p6,p62,Possible,LCC,0,100000
-p6,p63,Possible,LCC,0,100000
-p6,p64,Possible,LCC,0,100000
-p6,p65,Possible,LCC,0,100000
-p6,p66,Possible,LCC,0,100000
-p6,p67,Possible,LCC,0,100000
-p6,p68,Possible,LCC,0,100000
-p6,p69,Possible,LCC,0,100000
-p6,p70,Possible,LCC,0,100000
-p6,p71,Possible,LCC,0,100000
-p6,p72,Possible,LCC,0,100000
-p6,p73,Possible,LCC,0,100000
-p6,p74,Possible,LCC,0,100000
-p6,p75,Possible,LCC,0,100000
-p6,p76,Possible,LCC,0,100000
-p6,p77,Possible,LCC,0,100000
-p6,p78,Possible,LCC,0,100000
-p6,p79,Possible,LCC,0,100000
-p6,p80,Possible,LCC,0,100000
-p6,p81,Possible,LCC,0,100000
-p6,p82,Possible,LCC,0,100000
-p6,p83,Possible,LCC,0,100000
-p6,p84,Possible,LCC,0,100000
-p6,p85,Possible,LCC,0,100000
-p6,p86,Possible,LCC,0,100000
-p6,p87,Possible,LCC,0,100000
-p6,p88,Possible,LCC,0,100000
-p6,p89,Possible,LCC,0,100000
-p6,p90,Possible,LCC,0,100000
-p6,p91,Possible,LCC,0,100000
-p6,p92,Possible,LCC,0,100000
-p6,p93,Possible,LCC,0,100000
-p6,p94,Possible,LCC,0,100000
-p6,p95,Possible,LCC,0,100000
-p6,p96,Possible,LCC,0,100000
-p6,p97,Possible,LCC,0,100000
-p6,p98,Possible,LCC,0,100000
-p6,p99,Possible,LCC,0,100000
-p60,p100,Possible,LCC,0,100000
-p60,p101,Possible,LCC,0,100000
-p60,p102,Possible,LCC,0,100000
-p60,p103,Possible,LCC,0,100000
-p60,p104,Possible,LCC,0,100000
-p60,p105,Possible,LCC,0,100000
-p60,p106,Possible,LCC,0,100000
-p60,p107,Possible,LCC,0,100000
-p60,p108,Possible,LCC,0,100000
-p60,p109,Possible,LCC,0,100000
-p60,p110,Possible,LCC,0,100000
-p60,p111,Possible,LCC,0,100000
-p60,p112,Possible,LCC,0,100000
-p60,p113,Possible,LCC,0,100000
-p60,p114,Possible,LCC,0,100000
-p60,p115,Possible,LCC,0,100000
-p60,p116,Possible,LCC,0,100000
-p60,p117,Possible,LCC,0,100000
-p60,p118,Possible,LCC,0,100000
-p60,p119,Possible,LCC,0,100000
-p60,p120,Possible,LCC,0,100000
-p60,p121,Possible,LCC,0,100000
-p60,p122,Possible,LCC,0,100000
-p60,p123,Possible,LCC,0,100000
-p60,p124,Possible,LCC,0,100000
-p60,p125,Possible,LCC,0,100000
-p60,p126,Possible,LCC,0,100000
-p60,p127,Possible,LCC,0,100000
-p60,p128,Possible,LCC,0,100000
-p60,p129,Possible,LCC,0,100000
-p60,p130,Possible,LCC,0,100000
-p60,p131,Possible,LCC,0,100000
-p60,p132,Possible,LCC,0,100000
-p60,p133,Possible,LCC,0,100000
-p60,p134,Possible,LCC,0,100000
-p60,p64,Possible,LCC,0,100000
-p60,p65,Possible,LCC,0,100000
-p60,p66,Possible,LCC,0,100000
-p60,p67,Possible,LCC,0,100000
-p60,p68,Possible,LCC,0,100000
-p60,p69,Possible,LCC,0,100000
-p60,p70,Possible,LCC,0,100000
-p60,p71,Possible,LCC,0,100000
-p60,p72,Possible,LCC,0,100000
-p60,p73,Possible,LCC,0,100000
-p60,p74,Possible,LCC,0,100000
-p60,p75,Possible,LCC,0,100000
-p60,p76,Possible,LCC,0,100000
-p60,p77,Possible,LCC,0,100000
-p60,p78,Possible,LCC,0,100000
-p60,p79,Possible,LCC,0,100000
-p60,p80,Possible,LCC,0,100000
-p60,p81,Possible,LCC,0,100000
-p60,p82,Possible,LCC,0,100000
-p60,p83,Possible,LCC,0,100000
-p60,p84,Possible,LCC,0,100000
-p60,p85,Possible,LCC,0,100000
-p60,p86,Possible,LCC,0,100000
-p60,p87,Possible,LCC,0,100000
-p60,p88,Possible,LCC,0,100000
-p60,p89,Possible,LCC,0,100000
-p60,p90,Possible,LCC,0,100000
-p60,p91,Possible,LCC,0,100000
-p60,p92,Possible,LCC,0,100000
-p60,p93,Possible,LCC,0,100000
-p60,p94,Possible,LCC,0,100000
-p60,p95,Possible,LCC,0,100000
-p60,p96,Possible,LCC,0,100000
-p60,p97,Possible,LCC,0,100000
-p60,p98,Possible,LCC,0,100000
-p60,p99,Possible,LCC,0,100000
-p61,p100,Possible,LCC,0,100000
-p61,p101,Possible,LCC,0,100000
-p61,p102,Possible,LCC,0,100000
-p61,p103,Possible,LCC,0,100000
-p61,p104,Possible,LCC,0,100000
-p61,p105,Possible,LCC,0,100000
-p61,p106,Possible,LCC,0,100000
-p61,p107,Possible,LCC,0,100000
-p61,p108,Possible,LCC,0,100000
-p61,p109,Possible,LCC,0,100000
-p61,p110,Possible,LCC,0,100000
-p61,p111,Possible,LCC,0,100000
-p61,p112,Possible,LCC,0,100000
-p61,p113,Possible,LCC,0,100000
-p61,p114,Possible,LCC,0,100000
-p61,p115,Possible,LCC,0,100000
-p61,p116,Possible,LCC,0,100000
-p61,p117,Possible,LCC,0,100000
-p61,p118,Possible,LCC,0,100000
-p61,p119,Possible,LCC,0,100000
-p61,p120,Possible,LCC,0,100000
-p61,p121,Possible,LCC,0,100000
-p61,p122,Possible,LCC,0,100000
-p61,p123,Possible,LCC,0,100000
-p61,p124,Possible,LCC,0,100000
-p61,p125,Possible,LCC,0,100000
-p61,p126,Possible,LCC,0,100000
-p61,p127,Possible,LCC,0,100000
-p61,p128,Possible,LCC,0,100000
-p61,p129,Possible,LCC,0,100000
-p61,p130,Possible,LCC,0,100000
-p61,p131,Possible,LCC,0,100000
-p61,p132,Possible,LCC,0,100000
-p61,p133,Possible,LCC,0,100000
-p61,p134,Possible,LCC,0,100000
-p61,p66,Possible,LCC,0,100000
-p61,p67,Possible,LCC,0,100000
-p61,p68,Possible,LCC,0,100000
-p61,p69,Possible,LCC,0,100000
-p61,p70,Possible,LCC,0,100000
-p61,p71,Possible,LCC,0,100000
-p61,p72,Possible,LCC,0,100000
-p61,p73,Possible,LCC,0,100000
-p61,p74,Possible,LCC,0,100000
-p61,p75,Possible,LCC,0,100000
-p61,p76,Possible,LCC,0,100000
-p61,p77,Possible,LCC,0,100000
-p61,p78,Possible,LCC,0,100000
-p61,p79,Possible,LCC,0,100000
-p61,p80,Possible,LCC,0,100000
-p61,p81,Possible,LCC,0,100000
-p61,p82,Possible,LCC,0,100000
-p61,p83,Possible,LCC,0,100000
-p61,p84,Possible,LCC,0,100000
-p61,p85,Possible,LCC,0,100000
-p61,p86,Possible,LCC,0,100000
-p61,p87,Possible,LCC,0,100000
-p61,p88,Possible,LCC,0,100000
-p61,p89,Possible,LCC,0,100000
-p61,p90,Possible,LCC,0,100000
-p61,p91,Possible,LCC,0,100000
-p61,p92,Possible,LCC,0,100000
-p61,p93,Possible,LCC,0,100000
-p61,p94,Possible,LCC,0,100000
-p61,p95,Possible,LCC,0,100000
-p61,p96,Possible,LCC,0,100000
-p61,p97,Possible,LCC,0,100000
-p61,p98,Possible,LCC,0,100000
-p61,p99,Possible,LCC,0,100000
-p62,p100,Possible,LCC,0,100000
-p62,p101,Possible,LCC,0,100000
-p62,p102,Possible,LCC,0,100000
-p62,p103,Possible,LCC,0,100000
-p62,p104,Possible,LCC,0,100000
-p62,p105,Possible,LCC,0,100000
-p62,p106,Possible,LCC,0,100000
-p62,p107,Possible,LCC,0,100000
-p62,p108,Possible,LCC,0,100000
-p62,p109,Possible,LCC,0,100000
-p62,p110,Possible,LCC,0,100000
-p62,p111,Possible,LCC,0,100000
-p62,p112,Possible,LCC,0,100000
-p62,p113,Possible,LCC,0,100000
-p62,p114,Possible,LCC,0,100000
-p62,p115,Possible,LCC,0,100000
-p62,p116,Possible,LCC,0,100000
-p62,p117,Possible,LCC,0,100000
-p62,p118,Possible,LCC,0,100000
-p62,p119,Possible,LCC,0,100000
-p62,p120,Possible,LCC,0,100000
-p62,p121,Possible,LCC,0,100000
-p62,p122,Possible,LCC,0,100000
-p62,p123,Possible,LCC,0,100000
-p62,p124,Possible,LCC,0,100000
-p62,p125,Possible,LCC,0,100000
-p62,p126,Possible,LCC,0,100000
-p62,p127,Possible,LCC,0,100000
-p62,p128,Possible,LCC,0,100000
-p62,p129,Possible,LCC,0,100000
-p62,p130,Possible,LCC,0,100000
-p62,p131,Possible,LCC,0,100000
-p62,p132,Possible,LCC,0,100000
-p62,p133,Possible,LCC,0,100000
-p62,p134,Possible,LCC,0,100000
-p62,p64,Possible,LCC,0,100000
-p62,p65,Possible,LCC,0,100000
-p62,p66,Possible,LCC,0,100000
-p62,p67,Possible,LCC,0,100000
-p62,p68,Possible,LCC,0,100000
-p62,p69,Possible,LCC,0,100000
-p62,p70,Possible,LCC,0,100000
-p62,p71,Possible,LCC,0,100000
-p62,p72,Possible,LCC,0,100000
-p62,p73,Possible,LCC,0,100000
-p62,p74,Possible,LCC,0,100000
-p62,p75,Possible,LCC,0,100000
-p62,p76,Possible,LCC,0,100000
-p62,p77,Possible,LCC,0,100000
-p62,p78,Possible,LCC,0,100000
-p62,p79,Possible,LCC,0,100000
-p62,p80,Possible,LCC,0,100000
-p62,p81,Possible,LCC,0,100000
-p62,p82,Possible,LCC,0,100000
-p62,p83,Possible,LCC,0,100000
-p62,p84,Possible,LCC,0,100000
-p62,p85,Possible,LCC,0,100000
-p62,p86,Possible,LCC,0,100000
-p62,p87,Possible,LCC,0,100000
-p62,p88,Possible,LCC,0,100000
-p62,p89,Possible,LCC,0,100000
-p62,p90,Possible,LCC,0,100000
-p62,p91,Possible,LCC,0,100000
-p62,p92,Possible,LCC,0,100000
-p62,p93,Possible,LCC,0,100000
-p62,p94,Possible,LCC,0,100000
-p62,p95,Possible,LCC,0,100000
-p62,p96,Possible,LCC,0,100000
-p62,p97,Possible,LCC,0,100000
-p62,p98,Possible,LCC,0,100000
-p62,p99,Possible,LCC,0,100000
-p63,p100,Possible,LCC,0,100000
-p63,p101,Possible,LCC,0,100000
-p63,p102,Possible,LCC,0,100000
-p63,p103,Possible,LCC,0,100000
-p63,p104,Possible,LCC,0,100000
-p63,p105,Possible,LCC,0,100000
-p63,p106,Possible,LCC,0,100000
-p63,p107,Possible,LCC,0,100000
-p63,p108,Possible,LCC,0,100000
-p63,p109,Possible,LCC,0,100000
-p63,p110,Possible,LCC,0,100000
-p63,p111,Possible,LCC,0,100000
-p63,p112,Possible,LCC,0,100000
-p63,p113,Possible,LCC,0,100000
-p63,p114,Possible,LCC,0,100000
-p63,p115,Possible,LCC,0,100000
-p63,p116,Possible,LCC,0,100000
-p63,p117,Possible,LCC,0,100000
-p63,p118,Possible,LCC,0,100000
-p63,p119,Possible,LCC,0,100000
-p63,p120,Possible,LCC,0,100000
-p63,p121,Possible,LCC,0,100000
-p63,p122,Possible,LCC,0,100000
-p63,p123,Possible,LCC,0,100000
-p63,p124,Possible,LCC,0,100000
-p63,p125,Possible,LCC,0,100000
-p63,p126,Possible,LCC,0,100000
-p63,p127,Possible,LCC,0,100000
-p63,p128,Possible,LCC,0,100000
-p63,p129,Possible,LCC,0,100000
-p63,p130,Possible,LCC,0,100000
-p63,p131,Possible,LCC,0,100000
-p63,p132,Possible,LCC,0,100000
-p63,p133,Possible,LCC,0,100000
-p63,p134,Possible,LCC,0,100000
-p63,p66,Possible,LCC,0,100000
-p63,p68,Possible,LCC,0,100000
-p63,p69,Possible,LCC,0,100000
-p63,p70,Possible,LCC,0,100000
-p63,p71,Possible,LCC,0,100000
-p63,p72,Possible,LCC,0,100000
-p63,p73,Possible,LCC,0,100000
-p63,p74,Possible,LCC,0,100000
-p63,p75,Possible,LCC,0,100000
-p63,p76,Possible,LCC,0,100000
-p63,p77,Possible,LCC,0,100000
-p63,p78,Possible,LCC,0,100000
-p63,p79,Possible,LCC,0,100000
-p63,p80,Possible,LCC,0,100000
-p63,p81,Possible,LCC,0,100000
-p63,p82,Possible,LCC,0,100000
-p63,p83,Possible,LCC,0,100000
-p63,p84,Possible,LCC,0,100000
-p63,p85,Possible,LCC,0,100000
-p63,p86,Possible,LCC,0,100000
-p63,p87,Possible,LCC,0,100000
-p63,p88,Possible,LCC,0,100000
-p63,p89,Possible,LCC,0,100000
-p63,p90,Possible,LCC,0,100000
-p63,p91,Possible,LCC,0,100000
-p63,p92,Possible,LCC,0,100000
-p63,p93,Possible,LCC,0,100000
-p63,p94,Possible,LCC,0,100000
-p63,p95,Possible,LCC,0,100000
-p63,p96,Possible,LCC,0,100000
-p63,p97,Possible,LCC,0,100000
-p63,p98,Possible,LCC,0,100000
-p63,p99,Possible,LCC,0,100000
-p64,p100,Possible,LCC,0,100000
-p64,p101,Possible,LCC,0,100000
-p64,p102,Possible,LCC,0,100000
-p64,p103,Possible,LCC,0,100000
-p64,p104,Possible,LCC,0,100000
-p64,p105,Possible,LCC,0,100000
-p64,p106,Possible,LCC,0,100000
-p64,p107,Possible,LCC,0,100000
-p64,p108,Possible,LCC,0,100000
-p64,p109,Possible,LCC,0,100000
-p64,p110,Possible,LCC,0,100000
-p64,p111,Possible,LCC,0,100000
-p64,p112,Possible,LCC,0,100000
-p64,p113,Possible,LCC,0,100000
-p64,p114,Possible,LCC,0,100000
-p64,p115,Possible,LCC,0,100000
-p64,p116,Possible,LCC,0,100000
-p64,p117,Possible,LCC,0,100000
-p64,p118,Possible,LCC,0,100000
-p64,p119,Possible,LCC,0,100000
-p64,p120,Possible,LCC,0,100000
-p64,p121,Possible,LCC,0,100000
-p64,p122,Possible,LCC,0,100000
-p64,p123,Possible,LCC,0,100000
-p64,p124,Possible,LCC,0,100000
-p64,p125,Possible,LCC,0,100000
-p64,p126,Possible,LCC,0,100000
-p64,p127,Possible,LCC,0,100000
-p64,p128,Possible,LCC,0,100000
-p64,p129,Possible,LCC,0,100000
-p64,p130,Possible,LCC,0,100000
-p64,p131,Possible,LCC,0,100000
-p64,p132,Possible,LCC,0,100000
-p64,p133,Possible,LCC,0,100000
-p64,p134,Possible,LCC,0,100000
-p64,p66,Possible,LCC,0,100000
-p64,p68,Possible,LCC,0,100000
-p64,p69,Possible,LCC,0,100000
-p64,p70,Possible,LCC,0,100000
-p64,p71,Possible,LCC,0,100000
-p64,p72,Possible,LCC,0,100000
-p64,p73,Possible,LCC,0,100000
-p64,p74,Possible,LCC,0,100000
-p64,p75,Possible,LCC,0,100000
-p64,p76,Possible,LCC,0,100000
-p64,p77,Possible,LCC,0,100000
-p64,p78,Possible,LCC,0,100000
-p64,p79,Possible,LCC,0,100000
-p64,p80,Possible,LCC,0,100000
-p64,p81,Possible,LCC,0,100000
-p64,p82,Possible,LCC,0,100000
-p64,p83,Possible,LCC,0,100000
-p64,p84,Possible,LCC,0,100000
-p64,p85,Possible,LCC,0,100000
-p64,p86,Possible,LCC,0,100000
-p64,p87,Possible,LCC,0,100000
-p64,p88,Possible,LCC,0,100000
-p64,p89,Possible,LCC,0,100000
-p64,p90,Possible,LCC,0,100000
-p64,p91,Possible,LCC,0,100000
-p64,p92,Possible,LCC,0,100000
-p64,p93,Possible,LCC,0,100000
-p64,p94,Possible,LCC,0,100000
-p64,p95,Possible,LCC,0,100000
-p64,p96,Possible,LCC,0,100000
-p64,p97,Possible,LCC,0,100000
-p64,p98,Possible,LCC,0,100000
-p64,p99,Possible,LCC,0,100000
-p65,p100,Possible,LCC,0,100000
-p65,p101,Possible,LCC,0,100000
-p65,p102,Possible,LCC,0,100000
-p65,p103,Possible,LCC,0,100000
-p65,p104,Possible,LCC,0,100000
-p65,p105,Possible,LCC,0,100000
-p65,p106,Possible,LCC,0,100000
-p65,p107,Possible,LCC,0,100000
-p65,p108,Possible,LCC,0,100000
-p65,p109,Possible,LCC,0,100000
-p65,p110,Possible,LCC,0,100000
-p65,p111,Possible,LCC,0,100000
-p65,p112,Possible,LCC,0,100000
-p65,p113,Possible,LCC,0,100000
-p65,p114,Possible,LCC,0,100000
-p65,p115,Possible,LCC,0,100000
-p65,p116,Possible,LCC,0,100000
-p65,p117,Possible,LCC,0,100000
-p65,p118,Possible,LCC,0,100000
-p65,p119,Possible,LCC,0,100000
-p65,p120,Possible,LCC,0,100000
-p65,p121,Possible,LCC,0,100000
-p65,p122,Possible,LCC,0,100000
-p65,p123,Possible,LCC,0,100000
-p65,p124,Possible,LCC,0,100000
-p65,p125,Possible,LCC,0,100000
-p65,p126,Possible,LCC,0,100000
-p65,p127,Possible,LCC,0,100000
-p65,p128,Possible,LCC,0,100000
-p65,p129,Possible,LCC,0,100000
-p65,p130,Possible,LCC,0,100000
-p65,p131,Possible,LCC,0,100000
-p65,p132,Possible,LCC,0,100000
-p65,p133,Possible,LCC,0,100000
-p65,p134,Possible,LCC,0,100000
-p65,p66,Possible,LCC,0,100000
-p65,p68,Possible,LCC,0,100000
-p65,p69,Possible,LCC,0,100000
-p65,p70,Possible,LCC,0,100000
-p65,p71,Possible,LCC,0,100000
-p65,p72,Possible,LCC,0,100000
-p65,p73,Possible,LCC,0,100000
-p65,p74,Possible,LCC,0,100000
-p65,p75,Possible,LCC,0,100000
-p65,p76,Possible,LCC,0,100000
-p65,p77,Possible,LCC,0,100000
-p65,p78,Possible,LCC,0,100000
-p65,p79,Possible,LCC,0,100000
-p65,p80,Possible,LCC,0,100000
-p65,p81,Possible,LCC,0,100000
-p65,p82,Possible,LCC,0,100000
-p65,p83,Possible,LCC,0,100000
-p65,p84,Possible,LCC,0,100000
-p65,p85,Possible,LCC,0,100000
-p65,p86,Possible,LCC,0,100000
-p65,p87,Possible,LCC,0,100000
-p65,p88,Possible,LCC,0,100000
-p65,p89,Possible,LCC,0,100000
-p65,p90,Possible,LCC,0,100000
-p65,p91,Possible,LCC,0,100000
-p65,p92,Possible,LCC,0,100000
-p65,p93,Possible,LCC,0,100000
-p65,p94,Possible,LCC,0,100000
-p65,p95,Possible,LCC,0,100000
-p65,p96,Possible,LCC,0,100000
-p65,p97,Possible,LCC,0,100000
-p65,p98,Possible,LCC,0,100000
-p65,p99,Possible,LCC,0,100000
-p66,p100,Possible,LCC,0,100000
-p66,p101,Possible,LCC,0,100000
-p66,p102,Possible,LCC,0,100000
-p66,p103,Possible,LCC,0,100000
-p66,p104,Possible,LCC,0,100000
-p66,p105,Possible,LCC,0,100000
-p66,p106,Possible,LCC,0,100000
-p66,p107,Possible,LCC,0,100000
-p66,p108,Possible,LCC,0,100000
-p66,p109,Possible,LCC,0,100000
-p66,p110,Possible,LCC,0,100000
-p66,p111,Possible,LCC,0,100000
-p66,p112,Possible,LCC,0,100000
-p66,p113,Possible,LCC,0,100000
-p66,p114,Possible,LCC,0,100000
-p66,p115,Possible,LCC,0,100000
-p66,p116,Possible,LCC,0,100000
-p66,p117,Possible,LCC,0,100000
-p66,p118,Possible,LCC,0,100000
-p66,p119,Possible,LCC,0,100000
-p66,p120,Possible,LCC,0,100000
-p66,p121,Possible,LCC,0,100000
-p66,p122,Possible,LCC,0,100000
-p66,p123,Possible,LCC,0,100000
-p66,p124,Possible,LCC,0,100000
-p66,p125,Possible,LCC,0,100000
-p66,p126,Possible,LCC,0,100000
-p66,p127,Possible,LCC,0,100000
-p66,p128,Possible,LCC,0,100000
-p66,p129,Possible,LCC,0,100000
-p66,p130,Possible,LCC,0,100000
-p66,p131,Possible,LCC,0,100000
-p66,p132,Possible,LCC,0,100000
-p66,p133,Possible,LCC,0,100000
-p66,p134,Possible,LCC,0,100000
-p66,p67,Possible,LCC,0,100000
-p66,p68,Possible,LCC,0,100000
-p66,p69,Possible,LCC,0,100000
-p66,p70,Possible,LCC,0,100000
-p66,p71,Possible,LCC,0,100000
-p66,p72,Possible,LCC,0,100000
-p66,p73,Possible,LCC,0,100000
-p66,p74,Possible,LCC,0,100000
-p66,p75,Possible,LCC,0,100000
-p66,p76,Possible,LCC,0,100000
-p66,p77,Possible,LCC,0,100000
-p66,p78,Possible,LCC,0,100000
-p66,p79,Possible,LCC,0,100000
-p66,p80,Possible,LCC,0,100000
-p66,p81,Possible,LCC,0,100000
-p66,p82,Possible,LCC,0,100000
-p66,p83,Possible,LCC,0,100000
-p66,p84,Possible,LCC,0,100000
-p66,p85,Possible,LCC,0,100000
-p66,p87,Possible,LCC,0,100000
-p66,p88,Possible,LCC,0,100000
-p66,p89,Possible,LCC,0,100000
-p66,p90,Possible,LCC,0,100000
-p66,p91,Possible,LCC,0,100000
-p66,p92,Possible,LCC,0,100000
-p66,p93,Possible,LCC,0,100000
-p66,p94,Possible,LCC,0,100000
-p66,p95,Possible,LCC,0,100000
-p66,p96,Possible,LCC,0,100000
-p66,p97,Possible,LCC,0,100000
-p66,p98,Possible,LCC,0,100000
-p66,p99,Possible,LCC,0,100000
-p67,p100,Possible,LCC,0,100000
-p67,p101,Possible,LCC,0,100000
-p67,p102,Possible,LCC,0,100000
-p67,p103,Possible,LCC,0,100000
-p67,p104,Possible,LCC,0,100000
-p67,p105,Possible,LCC,0,100000
-p67,p106,Possible,LCC,0,100000
-p67,p107,Possible,LCC,0,100000
-p67,p108,Possible,LCC,0,100000
-p67,p109,Possible,LCC,0,100000
-p67,p110,Possible,LCC,0,100000
-p67,p111,Possible,LCC,0,100000
-p67,p112,Possible,LCC,0,100000
-p67,p113,Possible,LCC,0,100000
-p67,p114,Possible,LCC,0,100000
-p67,p115,Possible,LCC,0,100000
-p67,p116,Possible,LCC,0,100000
-p67,p117,Possible,LCC,0,100000
-p67,p118,Possible,LCC,0,100000
-p67,p119,Possible,LCC,0,100000
-p67,p120,Possible,LCC,0,100000
-p67,p121,Possible,LCC,0,100000
-p67,p122,Possible,LCC,0,100000
-p67,p123,Possible,LCC,0,100000
-p67,p124,Possible,LCC,0,100000
-p67,p125,Possible,LCC,0,100000
-p67,p126,Possible,LCC,0,100000
-p67,p127,Possible,LCC,0,100000
-p67,p128,Possible,LCC,0,100000
-p67,p129,Possible,LCC,0,100000
-p67,p130,Possible,LCC,0,100000
-p67,p131,Possible,LCC,0,100000
-p67,p132,Possible,LCC,0,100000
-p67,p133,Possible,LCC,0,100000
-p67,p134,Possible,LCC,0,100000
-p67,p68,Possible,LCC,0,100000
-p67,p69,Possible,LCC,0,100000
-p67,p70,Possible,LCC,0,100000
-p67,p71,Possible,LCC,0,100000
-p67,p72,Possible,LCC,0,100000
-p67,p73,Possible,LCC,0,100000
-p67,p74,Possible,LCC,0,100000
-p67,p75,Possible,LCC,0,100000
-p67,p76,Possible,LCC,0,100000
-p67,p77,Possible,LCC,0,100000
-p67,p78,Possible,LCC,0,100000
-p67,p79,Possible,LCC,0,100000
-p67,p80,Possible,LCC,0,100000
-p67,p81,Possible,LCC,0,100000
-p67,p82,Possible,LCC,0,100000
-p67,p83,Possible,LCC,0,100000
-p67,p84,Possible,LCC,0,100000
-p67,p85,Possible,LCC,0,100000
-p67,p86,Possible,LCC,0,100000
-p67,p87,Possible,LCC,0,100000
-p67,p88,Possible,LCC,0,100000
-p67,p89,Possible,LCC,0,100000
-p67,p90,Possible,LCC,0,100000
-p67,p91,Possible,LCC,0,100000
-p67,p92,Possible,LCC,0,100000
-p67,p93,Possible,LCC,0,100000
-p67,p94,Possible,LCC,0,100000
-p67,p95,Possible,LCC,0,100000
-p67,p96,Possible,LCC,0,100000
-p67,p97,Possible,LCC,0,100000
-p67,p98,Possible,LCC,0,100000
-p67,p99,Possible,LCC,0,100000
-p68,p100,Possible,LCC,0,100000
-p68,p101,Possible,LCC,0,100000
-p68,p102,Possible,LCC,0,100000
-p68,p103,Possible,LCC,0,100000
-p68,p104,Possible,LCC,0,100000
-p68,p105,Possible,LCC,0,100000
-p68,p106,Possible,LCC,0,100000
-p68,p107,Possible,LCC,0,100000
-p68,p108,Possible,LCC,0,100000
-p68,p109,Possible,LCC,0,100000
-p68,p110,Possible,LCC,0,100000
-p68,p111,Possible,LCC,0,100000
-p68,p112,Possible,LCC,0,100000
-p68,p113,Possible,LCC,0,100000
-p68,p114,Possible,LCC,0,100000
-p68,p115,Possible,LCC,0,100000
-p68,p116,Possible,LCC,0,100000
-p68,p117,Possible,LCC,0,100000
-p68,p118,Possible,LCC,0,100000
-p68,p119,Possible,LCC,0,100000
-p68,p120,Possible,LCC,0,100000
-p68,p121,Possible,LCC,0,100000
-p68,p122,Possible,LCC,0,100000
-p68,p123,Possible,LCC,0,100000
-p68,p124,Possible,LCC,0,100000
-p68,p125,Possible,LCC,0,100000
-p68,p126,Possible,LCC,0,100000
-p68,p127,Possible,LCC,0,100000
-p68,p128,Possible,LCC,0,100000
-p68,p129,Possible,LCC,0,100000
-p68,p130,Possible,LCC,0,100000
-p68,p131,Possible,LCC,0,100000
-p68,p132,Possible,LCC,0,100000
-p68,p133,Possible,LCC,0,100000
-p68,p134,Possible,LCC,0,100000
-p68,p70,Possible,LCC,0,100000
-p68,p71,Possible,LCC,0,100000
-p68,p72,Possible,LCC,0,100000
-p68,p73,Possible,LCC,0,100000
-p68,p74,Possible,LCC,0,100000
-p68,p75,Possible,LCC,0,100000
-p68,p76,Possible,LCC,0,100000
-p68,p79,Possible,LCC,0,100000
-p68,p80,Possible,LCC,0,100000
-p68,p81,Possible,LCC,0,100000
-p68,p82,Possible,LCC,0,100000
-p68,p83,Possible,LCC,0,100000
-p68,p84,Possible,LCC,0,100000
-p68,p85,Possible,LCC,0,100000
-p68,p86,Possible,LCC,0,100000
-p68,p87,Possible,LCC,0,100000
-p68,p88,Possible,LCC,0,100000
-p68,p89,Possible,LCC,0,100000
-p68,p90,Possible,LCC,0,100000
-p68,p91,Possible,LCC,0,100000
-p68,p92,Possible,LCC,0,100000
-p68,p93,Possible,LCC,0,100000
-p68,p94,Possible,LCC,0,100000
-p68,p95,Possible,LCC,0,100000
-p68,p96,Possible,LCC,0,100000
-p68,p97,Possible,LCC,0,100000
-p68,p98,Possible,LCC,0,100000
-p68,p99,Possible,LCC,0,100000
-p69,p100,Possible,LCC,0,100000
-p69,p101,Possible,LCC,0,100000
-p69,p102,Possible,LCC,0,100000
-p69,p103,Possible,LCC,0,100000
-p69,p104,Possible,LCC,0,100000
-p69,p105,Possible,LCC,0,100000
-p69,p106,Possible,LCC,0,100000
-p69,p107,Possible,LCC,0,100000
-p69,p108,Possible,LCC,0,100000
-p69,p109,Possible,LCC,0,100000
-p69,p110,Possible,LCC,0,100000
-p69,p111,Possible,LCC,0,100000
-p69,p112,Possible,LCC,0,100000
-p69,p113,Possible,LCC,0,100000
-p69,p114,Possible,LCC,0,100000
-p69,p115,Possible,LCC,0,100000
-p69,p116,Possible,LCC,0,100000
-p69,p117,Possible,LCC,0,100000
-p69,p118,Possible,LCC,0,100000
-p69,p119,Possible,LCC,0,100000
-p69,p120,Possible,LCC,0,100000
-p69,p121,Possible,LCC,0,100000
-p69,p122,Possible,LCC,0,100000
-p69,p123,Possible,LCC,0,100000
-p69,p124,Possible,LCC,0,100000
-p69,p125,Possible,LCC,0,100000
-p69,p126,Possible,LCC,0,100000
-p69,p127,Possible,LCC,0,100000
-p69,p128,Possible,LCC,0,100000
-p69,p129,Possible,LCC,0,100000
-p69,p130,Possible,LCC,0,100000
-p69,p131,Possible,LCC,0,100000
-p69,p132,Possible,LCC,0,100000
-p69,p133,Possible,LCC,0,100000
-p69,p134,Possible,LCC,0,100000
-p69,p71,Possible,LCC,0,100000
-p69,p72,Possible,LCC,0,100000
-p69,p73,Possible,LCC,0,100000
-p69,p74,Possible,LCC,0,100000
-p69,p75,Possible,LCC,0,100000
-p69,p76,Possible,LCC,0,100000
-p69,p78,Possible,LCC,0,100000
-p69,p79,Possible,LCC,0,100000
-p69,p80,Possible,LCC,0,100000
-p69,p81,Possible,LCC,0,100000
-p69,p82,Possible,LCC,0,100000
-p69,p83,Possible,LCC,0,100000
-p69,p84,Possible,LCC,0,100000
-p69,p85,Possible,LCC,0,100000
-p69,p86,Possible,LCC,0,100000
-p69,p87,Possible,LCC,0,100000
-p69,p88,Possible,LCC,0,100000
-p69,p89,Possible,LCC,0,100000
-p69,p90,Possible,LCC,0,100000
-p69,p91,Possible,LCC,0,100000
-p69,p92,Possible,LCC,0,100000
-p69,p93,Possible,LCC,0,100000
-p69,p94,Possible,LCC,0,100000
-p69,p95,Possible,LCC,0,100000
-p69,p96,Possible,LCC,0,100000
-p69,p97,Possible,LCC,0,100000
-p69,p98,Possible,LCC,0,100000
-p69,p99,Possible,LCC,0,100000
-p7,p10,Possible,LCC,0,100000
-p7,p100,Possible,LCC,0,100000
-p7,p101,Possible,LCC,0,100000
-p7,p102,Possible,LCC,0,100000
-p7,p103,Possible,LCC,0,100000
-p7,p104,Possible,LCC,0,100000
-p7,p105,Possible,LCC,0,100000
-p7,p106,Possible,LCC,0,100000
-p7,p107,Possible,LCC,0,100000
-p7,p108,Possible,LCC,0,100000
-p7,p109,Possible,LCC,0,100000
-p7,p11,Possible,LCC,0,100000
-p7,p110,Possible,LCC,0,100000
-p7,p111,Possible,LCC,0,100000
-p7,p112,Possible,LCC,0,100000
-p7,p113,Possible,LCC,0,100000
-p7,p114,Possible,LCC,0,100000
-p7,p115,Possible,LCC,0,100000
-p7,p116,Possible,LCC,0,100000
-p7,p117,Possible,LCC,0,100000
-p7,p118,Possible,LCC,0,100000
-p7,p119,Possible,LCC,0,100000
-p7,p120,Possible,LCC,0,100000
-p7,p121,Possible,LCC,0,100000
-p7,p122,Possible,LCC,0,100000
-p7,p123,Possible,LCC,0,100000
-p7,p124,Possible,LCC,0,100000
-p7,p125,Possible,LCC,0,100000
-p7,p126,Possible,LCC,0,100000
-p7,p127,Possible,LCC,0,100000
-p7,p128,Possible,LCC,0,100000
-p7,p129,Possible,LCC,0,100000
-p7,p13,Possible,LCC,0,100000
-p7,p130,Possible,LCC,0,100000
-p7,p131,Possible,LCC,0,100000
-p7,p132,Possible,LCC,0,100000
-p7,p133,Possible,LCC,0,100000
-p7,p134,Possible,LCC,0,100000
-p7,p14,Possible,LCC,0,100000
-p7,p17,Possible,LCC,0,100000
-p7,p18,Possible,LCC,0,100000
-p7,p19,Possible,LCC,0,100000
-p7,p20,Possible,LCC,0,100000
-p7,p21,Possible,LCC,0,100000
-p7,p22,Possible,LCC,0,100000
-p7,p23,Possible,LCC,0,100000
-p7,p24,Possible,LCC,0,100000
-p7,p26,Possible,LCC,0,100000
-p7,p27,Possible,LCC,0,100000
-p7,p28,Possible,LCC,0,100000
-p7,p29,Possible,LCC,0,100000
-p7,p30,Possible,LCC,0,100000
-p7,p31,Possible,LCC,0,100000
-p7,p32,Possible,LCC,0,100000
-p7,p33,Possible,LCC,0,100000
-p7,p34,Possible,LCC,0,100000
-p7,p35,Possible,LCC,0,100000
-p7,p36,Possible,LCC,0,100000
-p7,p37,Possible,LCC,0,100000
-p7,p38,Possible,LCC,0,100000
-p7,p39,Possible,LCC,0,100000
-p7,p40,Possible,LCC,0,100000
-p7,p41,Possible,LCC,0,100000
-p7,p42,Possible,LCC,0,100000
-p7,p43,Possible,LCC,0,100000
-p7,p44,Possible,LCC,0,100000
-p7,p45,Possible,LCC,0,100000
-p7,p46,Possible,LCC,0,100000
-p7,p47,Possible,LCC,0,100000
-p7,p48,Possible,LCC,0,100000
-p7,p49,Possible,LCC,0,100000
-p7,p50,Possible,LCC,0,100000
-p7,p51,Possible,LCC,0,100000
-p7,p52,Possible,LCC,0,100000
-p7,p53,Possible,LCC,0,100000
-p7,p54,Possible,LCC,0,100000
-p7,p55,Possible,LCC,0,100000
-p7,p56,Possible,LCC,0,100000
-p7,p57,Possible,LCC,0,100000
-p7,p58,Possible,LCC,0,100000
-p7,p59,Possible,LCC,0,100000
-p7,p60,Possible,LCC,0,100000
-p7,p61,Possible,LCC,0,100000
-p7,p62,Possible,LCC,0,100000
-p7,p63,Possible,LCC,0,100000
-p7,p64,Possible,LCC,0,100000
-p7,p65,Possible,LCC,0,100000
-p7,p66,Possible,LCC,0,100000
-p7,p67,Possible,LCC,0,100000
-p7,p68,Possible,LCC,0,100000
-p7,p69,Possible,LCC,0,100000
-p7,p70,Possible,LCC,0,100000
-p7,p71,Possible,LCC,0,100000
-p7,p72,Possible,LCC,0,100000
-p7,p73,Possible,LCC,0,100000
-p7,p74,Possible,LCC,0,100000
-p7,p75,Possible,LCC,0,100000
-p7,p76,Possible,LCC,0,100000
-p7,p77,Possible,LCC,0,100000
-p7,p78,Possible,LCC,0,100000
-p7,p79,Possible,LCC,0,100000
-p7,p80,Possible,LCC,0,100000
-p7,p81,Possible,LCC,0,100000
-p7,p82,Possible,LCC,0,100000
-p7,p83,Possible,LCC,0,100000
-p7,p84,Possible,LCC,0,100000
-p7,p85,Possible,LCC,0,100000
-p7,p86,Possible,LCC,0,100000
-p7,p87,Possible,LCC,0,100000
-p7,p88,Possible,LCC,0,100000
-p7,p89,Possible,LCC,0,100000
-p7,p9,Possible,LCC,0,100000
-p7,p90,Possible,LCC,0,100000
-p7,p91,Possible,LCC,0,100000
-p7,p92,Possible,LCC,0,100000
-p7,p93,Possible,LCC,0,100000
-p7,p94,Possible,LCC,0,100000
-p7,p95,Possible,LCC,0,100000
-p7,p96,Possible,LCC,0,100000
-p7,p97,Possible,LCC,0,100000
-p7,p98,Possible,LCC,0,100000
-p7,p99,Possible,LCC,0,100000
-p70,p100,Possible,LCC,0,100000
-p70,p101,Possible,LCC,0,100000
-p70,p102,Possible,LCC,0,100000
-p70,p103,Possible,LCC,0,100000
-p70,p104,Possible,LCC,0,100000
-p70,p105,Possible,LCC,0,100000
-p70,p106,Possible,LCC,0,100000
-p70,p107,Possible,LCC,0,100000
-p70,p108,Possible,LCC,0,100000
-p70,p109,Possible,LCC,0,100000
-p70,p110,Possible,LCC,0,100000
-p70,p111,Possible,LCC,0,100000
-p70,p112,Possible,LCC,0,100000
-p70,p113,Possible,LCC,0,100000
-p70,p114,Possible,LCC,0,100000
-p70,p115,Possible,LCC,0,100000
-p70,p116,Possible,LCC,0,100000
-p70,p117,Possible,LCC,0,100000
-p70,p118,Possible,LCC,0,100000
-p70,p119,Possible,LCC,0,100000
-p70,p120,Possible,LCC,0,100000
-p70,p121,Possible,LCC,0,100000
-p70,p122,Possible,LCC,0,100000
-p70,p123,Possible,LCC,0,100000
-p70,p124,Possible,LCC,0,100000
-p70,p125,Possible,LCC,0,100000
-p70,p126,Possible,LCC,0,100000
-p70,p127,Possible,LCC,0,100000
-p70,p128,Possible,LCC,0,100000
-p70,p129,Possible,LCC,0,100000
-p70,p130,Possible,LCC,0,100000
-p70,p131,Possible,LCC,0,100000
-p70,p132,Possible,LCC,0,100000
-p70,p133,Possible,LCC,0,100000
-p70,p134,Possible,LCC,0,100000
-p70,p72,Possible,LCC,0,100000
-p70,p73,Possible,LCC,0,100000
-p70,p74,Possible,LCC,0,100000
-p70,p75,Possible,LCC,0,100000
-p70,p76,Possible,LCC,0,100000
-p70,p78,Possible,LCC,0,100000
-p70,p79,Possible,LCC,0,100000
-p70,p83,Possible,LCC,0,100000
-p70,p84,Possible,LCC,0,100000
-p70,p85,Possible,LCC,0,100000
-p70,p86,Possible,LCC,0,100000
-p70,p87,Possible,LCC,0,100000
-p70,p88,Possible,LCC,0,100000
-p70,p89,Possible,LCC,0,100000
-p70,p90,Possible,LCC,0,100000
-p70,p91,Possible,LCC,0,100000
-p70,p92,Possible,LCC,0,100000
-p70,p93,Possible,LCC,0,100000
-p70,p94,Possible,LCC,0,100000
-p70,p95,Possible,LCC,0,100000
-p70,p96,Possible,LCC,0,100000
-p70,p97,Possible,LCC,0,100000
-p70,p98,Possible,LCC,0,100000
-p70,p99,Possible,LCC,0,100000
-p71,p100,Possible,LCC,0,100000
-p71,p101,Possible,LCC,0,100000
-p71,p102,Possible,LCC,0,100000
-p71,p103,Possible,LCC,0,100000
-p71,p104,Possible,LCC,0,100000
-p71,p105,Possible,LCC,0,100000
-p71,p106,Possible,LCC,0,100000
-p71,p107,Possible,LCC,0,100000
-p71,p108,Possible,LCC,0,100000
-p71,p109,Possible,LCC,0,100000
-p71,p110,Possible,LCC,0,100000
-p71,p111,Possible,LCC,0,100000
-p71,p112,Possible,LCC,0,100000
-p71,p113,Possible,LCC,0,100000
-p71,p114,Possible,LCC,0,100000
-p71,p115,Possible,LCC,0,100000
-p71,p116,Possible,LCC,0,100000
-p71,p117,Possible,LCC,0,100000
-p71,p118,Possible,LCC,0,100000
-p71,p119,Possible,LCC,0,100000
-p71,p120,Possible,LCC,0,100000
-p71,p121,Possible,LCC,0,100000
-p71,p122,Possible,LCC,0,100000
-p71,p123,Possible,LCC,0,100000
-p71,p124,Possible,LCC,0,100000
-p71,p125,Possible,LCC,0,100000
-p71,p126,Possible,LCC,0,100000
-p71,p127,Possible,LCC,0,100000
-p71,p128,Possible,LCC,0,100000
-p71,p129,Possible,LCC,0,100000
-p71,p130,Possible,LCC,0,100000
-p71,p131,Possible,LCC,0,100000
-p71,p132,Possible,LCC,0,100000
-p71,p133,Possible,LCC,0,100000
-p71,p134,Possible,LCC,0,100000
-p71,p73,Possible,LCC,0,100000
-p71,p74,Possible,LCC,0,100000
-p71,p75,Possible,LCC,0,100000
-p71,p76,Possible,LCC,0,100000
-p71,p77,Possible,LCC,0,100000
-p71,p78,Possible,LCC,0,100000
-p71,p79,Possible,LCC,0,100000
-p71,p80,Possible,LCC,0,100000
-p71,p82,Possible,LCC,0,100000
-p71,p83,Possible,LCC,0,100000
-p71,p84,Possible,LCC,0,100000
-p71,p85,Possible,LCC,0,100000
-p71,p86,Possible,LCC,0,100000
-p71,p87,Possible,LCC,0,100000
-p71,p88,Possible,LCC,0,100000
-p71,p89,Possible,LCC,0,100000
-p71,p90,Possible,LCC,0,100000
-p71,p91,Possible,LCC,0,100000
-p71,p92,Possible,LCC,0,100000
-p71,p93,Possible,LCC,0,100000
-p71,p94,Possible,LCC,0,100000
-p71,p95,Possible,LCC,0,100000
-p71,p96,Possible,LCC,0,100000
-p71,p97,Possible,LCC,0,100000
-p71,p98,Possible,LCC,0,100000
-p71,p99,Possible,LCC,0,100000
-p72,p100,Possible,LCC,0,100000
-p72,p101,Possible,LCC,0,100000
-p72,p102,Possible,LCC,0,100000
-p72,p103,Possible,LCC,0,100000
-p72,p104,Possible,LCC,0,100000
-p72,p105,Possible,LCC,0,100000
-p72,p106,Possible,LCC,0,100000
-p72,p107,Possible,LCC,0,100000
-p72,p108,Possible,LCC,0,100000
-p72,p109,Possible,LCC,0,100000
-p72,p110,Possible,LCC,0,100000
-p72,p111,Possible,LCC,0,100000
-p72,p112,Possible,LCC,0,100000
-p72,p113,Possible,LCC,0,100000
-p72,p114,Possible,LCC,0,100000
-p72,p115,Possible,LCC,0,100000
-p72,p116,Possible,LCC,0,100000
-p72,p117,Possible,LCC,0,100000
-p72,p118,Possible,LCC,0,100000
-p72,p119,Possible,LCC,0,100000
-p72,p120,Possible,LCC,0,100000
-p72,p121,Possible,LCC,0,100000
-p72,p122,Possible,LCC,0,100000
-p72,p123,Possible,LCC,0,100000
-p72,p124,Possible,LCC,0,100000
-p72,p125,Possible,LCC,0,100000
-p72,p126,Possible,LCC,0,100000
-p72,p127,Possible,LCC,0,100000
-p72,p128,Possible,LCC,0,100000
-p72,p129,Possible,LCC,0,100000
-p72,p130,Possible,LCC,0,100000
-p72,p131,Possible,LCC,0,100000
-p72,p132,Possible,LCC,0,100000
-p72,p133,Possible,LCC,0,100000
-p72,p134,Possible,LCC,0,100000
-p72,p74,Possible,LCC,0,100000
-p72,p75,Possible,LCC,0,100000
-p72,p76,Possible,LCC,0,100000
-p72,p77,Possible,LCC,0,100000
-p72,p78,Possible,LCC,0,100000
-p72,p79,Possible,LCC,0,100000
-p72,p80,Possible,LCC,0,100000
-p72,p82,Possible,LCC,0,100000
-p72,p83,Possible,LCC,0,100000
-p72,p85,Possible,LCC,0,100000
-p72,p86,Possible,LCC,0,100000
-p72,p87,Possible,LCC,0,100000
-p72,p88,Possible,LCC,0,100000
-p72,p89,Possible,LCC,0,100000
-p72,p90,Possible,LCC,0,100000
-p72,p91,Possible,LCC,0,100000
-p72,p92,Possible,LCC,0,100000
-p72,p93,Possible,LCC,0,100000
-p72,p94,Possible,LCC,0,100000
-p72,p95,Possible,LCC,0,100000
-p72,p96,Possible,LCC,0,100000
-p72,p97,Possible,LCC,0,100000
-p72,p98,Possible,LCC,0,100000
-p72,p99,Possible,LCC,0,100000
-p73,p100,Possible,LCC,0,100000
-p73,p101,Possible,LCC,0,100000
-p73,p102,Possible,LCC,0,100000
-p73,p103,Possible,LCC,0,100000
-p73,p104,Possible,LCC,0,100000
-p73,p105,Possible,LCC,0,100000
-p73,p106,Possible,LCC,0,100000
-p73,p107,Possible,LCC,0,100000
-p73,p108,Possible,LCC,0,100000
-p73,p109,Possible,LCC,0,100000
-p73,p110,Possible,LCC,0,100000
-p73,p111,Possible,LCC,0,100000
-p73,p112,Possible,LCC,0,100000
-p73,p113,Possible,LCC,0,100000
-p73,p114,Possible,LCC,0,100000
-p73,p115,Possible,LCC,0,100000
-p73,p116,Possible,LCC,0,100000
-p73,p117,Possible,LCC,0,100000
-p73,p118,Possible,LCC,0,100000
-p73,p119,Possible,LCC,0,100000
-p73,p120,Possible,LCC,0,100000
-p73,p121,Possible,LCC,0,100000
-p73,p122,Possible,LCC,0,100000
-p73,p123,Possible,LCC,0,100000
-p73,p124,Possible,LCC,0,100000
-p73,p125,Possible,LCC,0,100000
-p73,p126,Possible,LCC,0,100000
-p73,p127,Possible,LCC,0,100000
-p73,p128,Possible,LCC,0,100000
-p73,p129,Possible,LCC,0,100000
-p73,p130,Possible,LCC,0,100000
-p73,p131,Possible,LCC,0,100000
-p73,p132,Possible,LCC,0,100000
-p73,p133,Possible,LCC,0,100000
-p73,p134,Possible,LCC,0,100000
-p73,p74,Possible,LCC,0,100000
-p73,p75,Possible,LCC,0,100000
-p73,p76,Possible,LCC,0,100000
-p73,p77,Possible,LCC,0,100000
-p73,p78,Possible,LCC,0,100000
-p73,p79,Possible,LCC,0,100000
-p73,p80,Possible,LCC,0,100000
-p73,p81,Possible,LCC,0,100000
-p73,p82,Possible,LCC,0,100000
-p73,p83,Possible,LCC,0,100000
-p73,p85,Possible,LCC,0,100000
-p73,p86,Possible,LCC,0,100000
-p73,p87,Possible,LCC,0,100000
-p73,p88,Possible,LCC,0,100000
-p73,p89,Possible,LCC,0,100000
-p73,p90,Possible,LCC,0,100000
-p73,p91,Possible,LCC,0,100000
-p73,p92,Possible,LCC,0,100000
-p73,p93,Possible,LCC,0,100000
-p73,p94,Possible,LCC,0,100000
-p73,p95,Possible,LCC,0,100000
-p73,p96,Possible,LCC,0,100000
-p73,p97,Possible,LCC,0,100000
-p73,p98,Possible,LCC,0,100000
-p73,p99,Possible,LCC,0,100000
-p74,p100,Possible,LCC,0,100000
-p74,p101,Possible,LCC,0,100000
-p74,p102,Possible,LCC,0,100000
-p74,p103,Possible,LCC,0,100000
-p74,p104,Possible,LCC,0,100000
-p74,p105,Possible,LCC,0,100000
-p74,p106,Possible,LCC,0,100000
-p74,p107,Possible,LCC,0,100000
-p74,p108,Possible,LCC,0,100000
-p74,p109,Possible,LCC,0,100000
-p74,p110,Possible,LCC,0,100000
-p74,p111,Possible,LCC,0,100000
-p74,p112,Possible,LCC,0,100000
-p74,p113,Possible,LCC,0,100000
-p74,p114,Possible,LCC,0,100000
-p74,p115,Possible,LCC,0,100000
-p74,p116,Possible,LCC,0,100000
-p74,p117,Possible,LCC,0,100000
-p74,p118,Possible,LCC,0,100000
-p74,p119,Possible,LCC,0,100000
-p74,p120,Possible,LCC,0,100000
-p74,p121,Possible,LCC,0,100000
-p74,p122,Possible,LCC,0,100000
-p74,p123,Possible,LCC,0,100000
-p74,p124,Possible,LCC,0,100000
-p74,p125,Possible,LCC,0,100000
-p74,p126,Possible,LCC,0,100000
-p74,p127,Possible,LCC,0,100000
-p74,p128,Possible,LCC,0,100000
-p74,p129,Possible,LCC,0,100000
-p74,p130,Possible,LCC,0,100000
-p74,p131,Possible,LCC,0,100000
-p74,p132,Possible,LCC,0,100000
-p74,p133,Possible,LCC,0,100000
-p74,p134,Possible,LCC,0,100000
-p74,p77,Possible,LCC,0,100000
-p74,p81,Possible,LCC,0,100000
-p74,p82,Possible,LCC,0,100000
-p74,p83,Possible,LCC,0,100000
-p74,p84,Possible,LCC,0,100000
-p74,p85,Possible,LCC,0,100000
-p74,p86,Possible,LCC,0,100000
-p74,p87,Possible,LCC,0,100000
-p74,p88,Possible,LCC,0,100000
-p74,p89,Possible,LCC,0,100000
-p74,p90,Possible,LCC,0,100000
-p74,p91,Possible,LCC,0,100000
-p74,p92,Possible,LCC,0,100000
-p74,p93,Possible,LCC,0,100000
-p74,p94,Possible,LCC,0,100000
-p74,p95,Possible,LCC,0,100000
-p74,p96,Possible,LCC,0,100000
-p74,p97,Possible,LCC,0,100000
-p74,p98,Possible,LCC,0,100000
-p74,p99,Possible,LCC,0,100000
-p75,p100,Possible,LCC,0,100000
-p75,p101,Possible,LCC,0,100000
-p75,p102,Possible,LCC,0,100000
-p75,p103,Possible,LCC,0,100000
-p75,p104,Possible,LCC,0,100000
-p75,p105,Possible,LCC,0,100000
-p75,p106,Possible,LCC,0,100000
-p75,p107,Possible,LCC,0,100000
-p75,p108,Possible,LCC,0,100000
-p75,p109,Possible,LCC,0,100000
-p75,p110,Possible,LCC,0,100000
-p75,p111,Possible,LCC,0,100000
-p75,p112,Possible,LCC,0,100000
-p75,p113,Possible,LCC,0,100000
-p75,p114,Possible,LCC,0,100000
-p75,p115,Possible,LCC,0,100000
-p75,p116,Possible,LCC,0,100000
-p75,p117,Possible,LCC,0,100000
-p75,p118,Possible,LCC,0,100000
-p75,p119,Possible,LCC,0,100000
-p75,p120,Possible,LCC,0,100000
-p75,p121,Possible,LCC,0,100000
-p75,p122,Possible,LCC,0,100000
-p75,p123,Possible,LCC,0,100000
-p75,p124,Possible,LCC,0,100000
-p75,p125,Possible,LCC,0,100000
-p75,p126,Possible,LCC,0,100000
-p75,p127,Possible,LCC,0,100000
-p75,p128,Possible,LCC,0,100000
-p75,p129,Possible,LCC,0,100000
-p75,p130,Possible,LCC,0,100000
-p75,p131,Possible,LCC,0,100000
-p75,p132,Possible,LCC,0,100000
-p75,p133,Possible,LCC,0,100000
-p75,p134,Possible,LCC,0,100000
-p75,p81,Possible,LCC,0,100000
-p75,p82,Possible,LCC,0,100000
-p75,p83,Possible,LCC,0,100000
-p75,p84,Possible,LCC,0,100000
-p75,p85,Possible,LCC,0,100000
-p75,p86,Possible,LCC,0,100000
-p75,p87,Possible,LCC,0,100000
-p75,p88,Possible,LCC,0,100000
-p75,p89,Possible,LCC,0,100000
-p75,p90,Possible,LCC,0,100000
-p75,p91,Possible,LCC,0,100000
-p75,p92,Possible,LCC,0,100000
-p75,p93,Possible,LCC,0,100000
-p75,p94,Possible,LCC,0,100000
-p75,p95,Possible,LCC,0,100000
-p75,p96,Possible,LCC,0,100000
-p75,p97,Possible,LCC,0,100000
-p75,p98,Possible,LCC,0,100000
-p75,p99,Possible,LCC,0,100000
-p76,p100,Possible,LCC,0,100000
-p76,p101,Possible,LCC,0,100000
-p76,p102,Possible,LCC,0,100000
-p76,p103,Possible,LCC,0,100000
-p76,p104,Possible,LCC,0,100000
-p76,p105,Possible,LCC,0,100000
-p76,p106,Possible,LCC,0,100000
-p76,p107,Possible,LCC,0,100000
-p76,p108,Possible,LCC,0,100000
-p76,p109,Possible,LCC,0,100000
-p76,p110,Possible,LCC,0,100000
-p76,p111,Possible,LCC,0,100000
-p76,p112,Possible,LCC,0,100000
-p76,p113,Possible,LCC,0,100000
-p76,p114,Possible,LCC,0,100000
-p76,p115,Possible,LCC,0,100000
-p76,p116,Possible,LCC,0,100000
-p76,p117,Possible,LCC,0,100000
-p76,p118,Possible,LCC,0,100000
-p76,p119,Possible,LCC,0,100000
-p76,p120,Possible,LCC,0,100000
-p76,p121,Possible,LCC,0,100000
-p76,p122,Possible,LCC,0,100000
-p76,p123,Possible,LCC,0,100000
-p76,p124,Possible,LCC,0,100000
-p76,p125,Possible,LCC,0,100000
-p76,p126,Possible,LCC,0,100000
-p76,p127,Possible,LCC,0,100000
-p76,p128,Possible,LCC,0,100000
-p76,p129,Possible,LCC,0,100000
-p76,p130,Possible,LCC,0,100000
-p76,p131,Possible,LCC,0,100000
-p76,p132,Possible,LCC,0,100000
-p76,p133,Possible,LCC,0,100000
-p76,p134,Possible,LCC,0,100000
-p76,p81,Possible,LCC,0,100000
-p76,p82,Possible,LCC,0,100000
-p76,p83,Possible,LCC,0,100000
-p76,p84,Possible,LCC,0,100000
-p76,p85,Possible,LCC,0,100000
-p76,p86,Possible,LCC,0,100000
-p76,p87,Possible,LCC,0,100000
-p76,p88,Possible,LCC,0,100000
-p76,p89,Possible,LCC,0,100000
-p76,p90,Possible,LCC,0,100000
-p76,p91,Possible,LCC,0,100000
-p76,p92,Possible,LCC,0,100000
-p76,p93,Possible,LCC,0,100000
-p76,p94,Possible,LCC,0,100000
-p76,p95,Possible,LCC,0,100000
-p76,p96,Possible,LCC,0,100000
-p76,p97,Possible,LCC,0,100000
-p76,p98,Possible,LCC,0,100000
-p76,p99,Possible,LCC,0,100000
-p77,p100,Possible,LCC,0,100000
-p77,p101,Possible,LCC,0,100000
-p77,p102,Possible,LCC,0,100000
-p77,p103,Possible,LCC,0,100000
-p77,p104,Possible,LCC,0,100000
-p77,p105,Possible,LCC,0,100000
-p77,p106,Possible,LCC,0,100000
-p77,p107,Possible,LCC,0,100000
-p77,p108,Possible,LCC,0,100000
-p77,p109,Possible,LCC,0,100000
-p77,p110,Possible,LCC,0,100000
-p77,p111,Possible,LCC,0,100000
-p77,p112,Possible,LCC,0,100000
-p77,p113,Possible,LCC,0,100000
-p77,p114,Possible,LCC,0,100000
-p77,p115,Possible,LCC,0,100000
-p77,p116,Possible,LCC,0,100000
-p77,p117,Possible,LCC,0,100000
-p77,p118,Possible,LCC,0,100000
-p77,p119,Possible,LCC,0,100000
-p77,p120,Possible,LCC,0,100000
-p77,p121,Possible,LCC,0,100000
-p77,p122,Possible,LCC,0,100000
-p77,p123,Possible,LCC,0,100000
-p77,p124,Possible,LCC,0,100000
-p77,p125,Possible,LCC,0,100000
-p77,p126,Possible,LCC,0,100000
-p77,p127,Possible,LCC,0,100000
-p77,p128,Possible,LCC,0,100000
-p77,p129,Possible,LCC,0,100000
-p77,p130,Possible,LCC,0,100000
-p77,p131,Possible,LCC,0,100000
-p77,p132,Possible,LCC,0,100000
-p77,p133,Possible,LCC,0,100000
-p77,p134,Possible,LCC,0,100000
-p77,p81,Possible,LCC,0,100000
-p77,p82,Possible,LCC,0,100000
-p77,p83,Possible,LCC,0,100000
-p77,p84,Possible,LCC,0,100000
-p77,p85,Possible,LCC,0,100000
-p77,p86,Possible,LCC,0,100000
-p77,p87,Possible,LCC,0,100000
-p77,p88,Possible,LCC,0,100000
-p77,p89,Possible,LCC,0,100000
-p77,p90,Possible,LCC,0,100000
-p77,p91,Possible,LCC,0,100000
-p77,p92,Possible,LCC,0,100000
-p77,p93,Possible,LCC,0,100000
-p77,p94,Possible,LCC,0,100000
-p77,p95,Possible,LCC,0,100000
-p77,p96,Possible,LCC,0,100000
-p77,p97,Possible,LCC,0,100000
-p77,p98,Possible,LCC,0,100000
-p77,p99,Possible,LCC,0,100000
-p78,p100,Possible,LCC,0,100000
-p78,p101,Possible,LCC,0,100000
-p78,p102,Possible,LCC,0,100000
-p78,p103,Possible,LCC,0,100000
-p78,p104,Possible,LCC,0,100000
-p78,p105,Possible,LCC,0,100000
-p78,p106,Possible,LCC,0,100000
-p78,p107,Possible,LCC,0,100000
-p78,p108,Possible,LCC,0,100000
-p78,p109,Possible,LCC,0,100000
-p78,p110,Possible,LCC,0,100000
-p78,p111,Possible,LCC,0,100000
-p78,p112,Possible,LCC,0,100000
-p78,p113,Possible,LCC,0,100000
-p78,p114,Possible,LCC,0,100000
-p78,p115,Possible,LCC,0,100000
-p78,p116,Possible,LCC,0,100000
-p78,p117,Possible,LCC,0,100000
-p78,p118,Possible,LCC,0,100000
-p78,p119,Possible,LCC,0,100000
-p78,p120,Possible,LCC,0,100000
-p78,p121,Possible,LCC,0,100000
-p78,p122,Possible,LCC,0,100000
-p78,p123,Possible,LCC,0,100000
-p78,p124,Possible,LCC,0,100000
-p78,p125,Possible,LCC,0,100000
-p78,p126,Possible,LCC,0,100000
-p78,p127,Possible,LCC,0,100000
-p78,p128,Possible,LCC,0,100000
-p78,p129,Possible,LCC,0,100000
-p78,p130,Possible,LCC,0,100000
-p78,p131,Possible,LCC,0,100000
-p78,p132,Possible,LCC,0,100000
-p78,p133,Possible,LCC,0,100000
-p78,p134,Possible,LCC,0,100000
-p78,p81,Possible,LCC,0,100000
-p78,p82,Possible,LCC,0,100000
-p78,p83,Possible,LCC,0,100000
-p78,p84,Possible,LCC,0,100000
-p78,p85,Possible,LCC,0,100000
-p78,p86,Possible,LCC,0,100000
-p78,p87,Possible,LCC,0,100000
-p78,p88,Possible,LCC,0,100000
-p78,p89,Possible,LCC,0,100000
-p78,p90,Possible,LCC,0,100000
-p78,p91,Possible,LCC,0,100000
-p78,p92,Possible,LCC,0,100000
-p78,p93,Possible,LCC,0,100000
-p78,p94,Possible,LCC,0,100000
-p78,p95,Possible,LCC,0,100000
-p78,p96,Possible,LCC,0,100000
-p78,p97,Possible,LCC,0,100000
-p78,p98,Possible,LCC,0,100000
-p78,p99,Possible,LCC,0,100000
-p79,p100,Possible,LCC,0,100000
-p79,p101,Possible,LCC,0,100000
-p79,p102,Possible,LCC,0,100000
-p79,p103,Possible,LCC,0,100000
-p79,p104,Possible,LCC,0,100000
-p79,p106,Possible,LCC,0,100000
-p79,p107,Possible,LCC,0,100000
-p79,p108,Possible,LCC,0,100000
-p79,p109,Possible,LCC,0,100000
-p79,p110,Possible,LCC,0,100000
-p79,p111,Possible,LCC,0,100000
-p79,p112,Possible,LCC,0,100000
-p79,p113,Possible,LCC,0,100000
-p79,p114,Possible,LCC,0,100000
-p79,p115,Possible,LCC,0,100000
-p79,p116,Possible,LCC,0,100000
-p79,p117,Possible,LCC,0,100000
-p79,p118,Possible,LCC,0,100000
-p79,p119,Possible,LCC,0,100000
-p79,p120,Possible,LCC,0,100000
-p79,p121,Possible,LCC,0,100000
-p79,p122,Possible,LCC,0,100000
-p79,p123,Possible,LCC,0,100000
-p79,p124,Possible,LCC,0,100000
-p79,p125,Possible,LCC,0,100000
-p79,p126,Possible,LCC,0,100000
-p79,p127,Possible,LCC,0,100000
-p79,p128,Possible,LCC,0,100000
-p79,p129,Possible,LCC,0,100000
-p79,p130,Possible,LCC,0,100000
-p79,p131,Possible,LCC,0,100000
-p79,p132,Possible,LCC,0,100000
-p79,p133,Possible,LCC,0,100000
-p79,p134,Possible,LCC,0,100000
-p79,p81,Possible,LCC,0,100000
-p79,p84,Possible,LCC,0,100000
-p79,p85,Possible,LCC,0,100000
-p79,p86,Possible,LCC,0,100000
-p79,p87,Possible,LCC,0,100000
-p79,p88,Possible,LCC,0,100000
-p79,p89,Possible,LCC,0,100000
-p79,p90,Possible,LCC,0,100000
-p79,p91,Possible,LCC,0,100000
-p79,p92,Possible,LCC,0,100000
-p79,p93,Possible,LCC,0,100000
-p79,p94,Possible,LCC,0,100000
-p79,p95,Possible,LCC,0,100000
-p79,p96,Possible,LCC,0,100000
-p79,p97,Possible,LCC,0,100000
-p79,p98,Possible,LCC,0,100000
-p79,p99,Possible,LCC,0,100000
-p8,p10,Possible,LCC,0,100000
-p8,p100,Possible,LCC,0,100000
-p8,p101,Possible,LCC,0,100000
-p8,p102,Possible,LCC,0,100000
-p8,p103,Possible,LCC,0,100000
-p8,p104,Possible,LCC,0,100000
-p8,p105,Possible,LCC,0,100000
-p8,p106,Possible,LCC,0,100000
-p8,p107,Possible,LCC,0,100000
-p8,p108,Possible,LCC,0,100000
-p8,p109,Possible,LCC,0,100000
-p8,p11,Possible,LCC,0,100000
-p8,p110,Possible,LCC,0,100000
-p8,p111,Possible,LCC,0,100000
-p8,p112,Possible,LCC,0,100000
-p8,p113,Possible,LCC,0,100000
-p8,p114,Possible,LCC,0,100000
-p8,p115,Possible,LCC,0,100000
-p8,p116,Possible,LCC,0,100000
-p8,p117,Possible,LCC,0,100000
-p8,p118,Possible,LCC,0,100000
-p8,p119,Possible,LCC,0,100000
-p8,p120,Possible,LCC,0,100000
-p8,p121,Possible,LCC,0,100000
-p8,p122,Possible,LCC,0,100000
-p8,p123,Possible,LCC,0,100000
-p8,p124,Possible,LCC,0,100000
-p8,p125,Possible,LCC,0,100000
-p8,p126,Possible,LCC,0,100000
-p8,p127,Possible,LCC,0,100000
-p8,p128,Possible,LCC,0,100000
-p8,p129,Possible,LCC,0,100000
-p8,p13,Possible,LCC,0,100000
-p8,p130,Possible,LCC,0,100000
-p8,p131,Possible,LCC,0,100000
-p8,p132,Possible,LCC,0,100000
-p8,p133,Possible,LCC,0,100000
-p8,p134,Possible,LCC,0,100000
-p8,p14,Possible,LCC,0,100000
-p8,p15,Possible,LCC,0,100000
-p8,p16,Possible,LCC,0,100000
-p8,p17,Possible,LCC,0,100000
-p8,p18,Possible,LCC,0,100000
-p8,p19,Possible,LCC,0,100000
-p8,p20,Possible,LCC,0,100000
-p8,p21,Possible,LCC,0,100000
-p8,p22,Possible,LCC,0,100000
-p8,p23,Possible,LCC,0,100000
-p8,p24,Possible,LCC,0,100000
-p8,p25,Possible,LCC,0,100000
-p8,p26,Possible,LCC,0,100000
-p8,p27,Possible,LCC,0,100000
-p8,p28,Possible,LCC,0,100000
-p8,p29,Possible,LCC,0,100000
-p8,p30,Possible,LCC,0,100000
-p8,p31,Possible,LCC,0,100000
-p8,p32,Possible,LCC,0,100000
-p8,p33,Possible,LCC,0,100000
-p8,p34,Possible,LCC,0,100000
-p8,p35,Possible,LCC,0,100000
-p8,p36,Possible,LCC,0,100000
-p8,p37,Possible,LCC,0,100000
-p8,p38,Possible,LCC,0,100000
-p8,p39,Possible,LCC,0,100000
-p8,p40,Possible,LCC,0,100000
-p8,p41,Possible,LCC,0,100000
-p8,p42,Possible,LCC,0,100000
-p8,p43,Possible,LCC,0,100000
-p8,p44,Possible,LCC,0,100000
-p8,p45,Possible,LCC,0,100000
-p8,p46,Possible,LCC,0,100000
-p8,p47,Possible,LCC,0,100000
-p8,p48,Possible,LCC,0,100000
-p8,p49,Possible,LCC,0,100000
-p8,p50,Possible,LCC,0,100000
-p8,p51,Possible,LCC,0,100000
-p8,p52,Possible,LCC,0,100000
-p8,p53,Possible,LCC,0,100000
-p8,p54,Possible,LCC,0,100000
-p8,p55,Possible,LCC,0,100000
-p8,p56,Possible,LCC,0,100000
-p8,p57,Possible,LCC,0,100000
-p8,p58,Possible,LCC,0,100000
-p8,p59,Possible,LCC,0,100000
-p8,p60,Possible,LCC,0,100000
-p8,p61,Possible,LCC,0,100000
-p8,p62,Possible,LCC,0,100000
-p8,p63,Possible,LCC,0,100000
-p8,p64,Possible,LCC,0,100000
-p8,p65,Possible,LCC,0,100000
-p8,p66,Possible,LCC,0,100000
-p8,p67,Possible,LCC,0,100000
-p8,p68,Possible,LCC,0,100000
-p8,p69,Possible,LCC,0,100000
-p8,p70,Possible,LCC,0,100000
-p8,p71,Possible,LCC,0,100000
-p8,p72,Possible,LCC,0,100000
-p8,p73,Possible,LCC,0,100000
-p8,p74,Possible,LCC,0,100000
-p8,p75,Possible,LCC,0,100000
-p8,p76,Possible,LCC,0,100000
-p8,p77,Possible,LCC,0,100000
-p8,p78,Possible,LCC,0,100000
-p8,p79,Possible,LCC,0,100000
-p8,p80,Possible,LCC,0,100000
-p8,p81,Possible,LCC,0,100000
-p8,p82,Possible,LCC,0,100000
-p8,p83,Possible,LCC,0,100000
-p8,p84,Possible,LCC,0,100000
-p8,p85,Possible,LCC,0,100000
-p8,p86,Possible,LCC,0,100000
-p8,p87,Possible,LCC,0,100000
-p8,p88,Possible,LCC,0,100000
-p8,p89,Possible,LCC,0,100000
-p8,p90,Possible,LCC,0,100000
-p8,p91,Possible,LCC,0,100000
-p8,p92,Possible,LCC,0,100000
-p8,p93,Possible,LCC,0,100000
-p8,p94,Possible,LCC,0,100000
-p8,p95,Possible,LCC,0,100000
-p8,p96,Possible,LCC,0,100000
-p8,p97,Possible,LCC,0,100000
-p8,p98,Possible,LCC,0,100000
-p8,p99,Possible,LCC,0,100000
-p80,p100,Possible,LCC,0,100000
-p80,p101,Possible,LCC,0,100000
-p80,p102,Possible,LCC,0,100000
-p80,p103,Possible,LCC,0,100000
-p80,p104,Possible,LCC,0,100000
-p80,p106,Possible,LCC,0,100000
-p80,p107,Possible,LCC,0,100000
-p80,p108,Possible,LCC,0,100000
-p80,p109,Possible,LCC,0,100000
-p80,p110,Possible,LCC,0,100000
-p80,p111,Possible,LCC,0,100000
-p80,p112,Possible,LCC,0,100000
-p80,p113,Possible,LCC,0,100000
-p80,p114,Possible,LCC,0,100000
-p80,p115,Possible,LCC,0,100000
-p80,p116,Possible,LCC,0,100000
-p80,p117,Possible,LCC,0,100000
-p80,p118,Possible,LCC,0,100000
-p80,p119,Possible,LCC,0,100000
-p80,p120,Possible,LCC,0,100000
-p80,p121,Possible,LCC,0,100000
-p80,p122,Possible,LCC,0,100000
-p80,p123,Possible,LCC,0,100000
-p80,p124,Possible,LCC,0,100000
-p80,p125,Possible,LCC,0,100000
-p80,p126,Possible,LCC,0,100000
-p80,p127,Possible,LCC,0,100000
-p80,p128,Possible,LCC,0,100000
-p80,p129,Possible,LCC,0,100000
-p80,p130,Possible,LCC,0,100000
-p80,p131,Possible,LCC,0,100000
-p80,p132,Possible,LCC,0,100000
-p80,p133,Possible,LCC,0,100000
-p80,p134,Possible,LCC,0,100000
-p80,p84,Possible,LCC,0,100000
-p80,p85,Possible,LCC,0,100000
-p80,p86,Possible,LCC,0,100000
-p80,p87,Possible,LCC,0,100000
-p80,p88,Possible,LCC,0,100000
-p80,p89,Possible,LCC,0,100000
-p80,p90,Possible,LCC,0,100000
-p80,p91,Possible,LCC,0,100000
-p80,p92,Possible,LCC,0,100000
-p80,p93,Possible,LCC,0,100000
-p80,p94,Possible,LCC,0,100000
-p80,p95,Possible,LCC,0,100000
-p80,p96,Possible,LCC,0,100000
-p80,p97,Possible,LCC,0,100000
-p80,p98,Possible,LCC,0,100000
-p80,p99,Possible,LCC,0,100000
-p81,p100,Possible,LCC,0,100000
-p81,p101,Possible,LCC,0,100000
-p81,p102,Possible,LCC,0,100000
-p81,p103,Possible,LCC,0,100000
-p81,p104,Possible,LCC,0,100000
-p81,p105,Possible,LCC,0,100000
-p81,p106,Possible,LCC,0,100000
-p81,p107,Possible,LCC,0,100000
-p81,p109,Possible,LCC,0,100000
-p81,p110,Possible,LCC,0,100000
-p81,p111,Possible,LCC,0,100000
-p81,p112,Possible,LCC,0,100000
-p81,p113,Possible,LCC,0,100000
-p81,p114,Possible,LCC,0,100000
-p81,p115,Possible,LCC,0,100000
-p81,p116,Possible,LCC,0,100000
-p81,p117,Possible,LCC,0,100000
-p81,p118,Possible,LCC,0,100000
-p81,p119,Possible,LCC,0,100000
-p81,p120,Possible,LCC,0,100000
-p81,p121,Possible,LCC,0,100000
-p81,p122,Possible,LCC,0,100000
-p81,p123,Possible,LCC,0,100000
-p81,p124,Possible,LCC,0,100000
-p81,p125,Possible,LCC,0,100000
-p81,p126,Possible,LCC,0,100000
-p81,p127,Possible,LCC,0,100000
-p81,p128,Possible,LCC,0,100000
-p81,p129,Possible,LCC,0,100000
-p81,p130,Possible,LCC,0,100000
-p81,p131,Possible,LCC,0,100000
-p81,p132,Possible,LCC,0,100000
-p81,p133,Possible,LCC,0,100000
-p81,p134,Possible,LCC,0,100000
-p81,p84,Possible,LCC,0,100000
-p81,p85,Possible,LCC,0,100000
-p81,p86,Possible,LCC,0,100000
-p81,p87,Possible,LCC,0,100000
-p81,p88,Possible,LCC,0,100000
-p81,p89,Possible,LCC,0,100000
-p81,p90,Possible,LCC,0,100000
-p81,p91,Possible,LCC,0,100000
-p81,p92,Possible,LCC,0,100000
-p81,p94,Possible,LCC,0,100000
-p81,p95,Possible,LCC,0,100000
-p81,p96,Possible,LCC,0,100000
-p81,p97,Possible,LCC,0,100000
-p81,p98,Possible,LCC,0,100000
-p81,p99,Possible,LCC,0,100000
-p82,p100,Possible,LCC,0,100000
-p82,p101,Possible,LCC,0,100000
-p82,p102,Possible,LCC,0,100000
-p82,p103,Possible,LCC,0,100000
-p82,p104,Possible,LCC,0,100000
-p82,p105,Possible,LCC,0,100000
-p82,p106,Possible,LCC,0,100000
-p82,p107,Possible,LCC,0,100000
-p82,p108,Possible,LCC,0,100000
-p82,p109,Possible,LCC,0,100000
-p82,p110,Possible,LCC,0,100000
-p82,p111,Possible,LCC,0,100000
-p82,p112,Possible,LCC,0,100000
-p82,p113,Possible,LCC,0,100000
-p82,p114,Possible,LCC,0,100000
-p82,p115,Possible,LCC,0,100000
-p82,p116,Possible,LCC,0,100000
-p82,p117,Possible,LCC,0,100000
-p82,p118,Possible,LCC,0,100000
-p82,p119,Possible,LCC,0,100000
-p82,p120,Possible,LCC,0,100000
-p82,p121,Possible,LCC,0,100000
-p82,p122,Possible,LCC,0,100000
-p82,p123,Possible,LCC,0,100000
-p82,p124,Possible,LCC,0,100000
-p82,p125,Possible,LCC,0,100000
-p82,p126,Possible,LCC,0,100000
-p82,p127,Possible,LCC,0,100000
-p82,p128,Possible,LCC,0,100000
-p82,p129,Possible,LCC,0,100000
-p82,p130,Possible,LCC,0,100000
-p82,p131,Possible,LCC,0,100000
-p82,p132,Possible,LCC,0,100000
-p82,p133,Possible,LCC,0,100000
-p82,p134,Possible,LCC,0,100000
-p82,p84,Possible,LCC,0,100000
-p82,p85,Possible,LCC,0,100000
-p82,p86,Possible,LCC,0,100000
-p82,p87,Possible,LCC,0,100000
-p82,p88,Possible,LCC,0,100000
-p82,p89,Possible,LCC,0,100000
-p82,p90,Possible,LCC,0,100000
-p82,p91,Possible,LCC,0,100000
-p82,p92,Possible,LCC,0,100000
-p82,p93,Possible,LCC,0,100000
-p82,p94,Possible,LCC,0,100000
-p82,p95,Possible,LCC,0,100000
-p82,p96,Possible,LCC,0,100000
-p82,p97,Possible,LCC,0,100000
-p82,p98,Possible,LCC,0,100000
-p82,p99,Possible,LCC,0,100000
-p83,p100,Possible,LCC,0,100000
-p83,p101,Possible,LCC,0,100000
-p83,p102,Possible,LCC,0,100000
-p83,p103,Possible,LCC,0,100000
-p83,p104,Possible,LCC,0,100000
-p83,p106,Possible,LCC,0,100000
-p83,p108,Possible,LCC,0,100000
-p83,p109,Possible,LCC,0,100000
-p83,p110,Possible,LCC,0,100000
-p83,p111,Possible,LCC,0,100000
-p83,p112,Possible,LCC,0,100000
-p83,p113,Possible,LCC,0,100000
-p83,p114,Possible,LCC,0,100000
-p83,p115,Possible,LCC,0,100000
-p83,p116,Possible,LCC,0,100000
-p83,p117,Possible,LCC,0,100000
-p83,p118,Possible,LCC,0,100000
-p83,p119,Possible,LCC,0,100000
-p83,p120,Possible,LCC,0,100000
-p83,p121,Possible,LCC,0,100000
-p83,p122,Possible,LCC,0,100000
-p83,p123,Possible,LCC,0,100000
-p83,p124,Possible,LCC,0,100000
-p83,p125,Possible,LCC,0,100000
-p83,p126,Possible,LCC,0,100000
-p83,p127,Possible,LCC,0,100000
-p83,p128,Possible,LCC,0,100000
-p83,p129,Possible,LCC,0,100000
-p83,p130,Possible,LCC,0,100000
-p83,p131,Possible,LCC,0,100000
-p83,p132,Possible,LCC,0,100000
-p83,p133,Possible,LCC,0,100000
-p83,p134,Possible,LCC,0,100000
-p83,p84,Possible,LCC,0,100000
-p83,p85,Possible,LCC,0,100000
-p83,p86,Possible,LCC,0,100000
-p83,p87,Possible,LCC,0,100000
-p83,p88,Possible,LCC,0,100000
-p83,p89,Possible,LCC,0,100000
-p83,p90,Possible,LCC,0,100000
-p83,p91,Possible,LCC,0,100000
-p83,p92,Possible,LCC,0,100000
-p83,p93,Possible,LCC,0,100000
-p83,p94,Possible,LCC,0,100000
-p83,p95,Possible,LCC,0,100000
-p83,p96,Possible,LCC,0,100000
-p83,p97,Possible,LCC,0,100000
-p83,p98,Possible,LCC,0,100000
-p83,p99,Possible,LCC,0,100000
-p84,p100,Possible,LCC,0,100000
-p84,p101,Possible,LCC,0,100000
-p84,p102,Possible,LCC,0,100000
-p84,p103,Possible,LCC,0,100000
-p84,p104,Possible,LCC,0,100000
-p84,p105,Possible,LCC,0,100000
-p84,p106,Possible,LCC,0,100000
-p84,p107,Possible,LCC,0,100000
-p84,p108,Possible,LCC,0,100000
-p84,p109,Possible,LCC,0,100000
-p84,p110,Possible,LCC,0,100000
-p84,p111,Possible,LCC,0,100000
-p84,p112,Possible,LCC,0,100000
-p84,p113,Possible,LCC,0,100000
-p84,p114,Possible,LCC,0,100000
-p84,p115,Possible,LCC,0,100000
-p84,p116,Possible,LCC,0,100000
-p84,p117,Possible,LCC,0,100000
-p84,p118,Possible,LCC,0,100000
-p84,p119,Possible,LCC,0,100000
-p84,p120,Possible,LCC,0,100000
-p84,p121,Possible,LCC,0,100000
-p84,p122,Possible,LCC,0,100000
-p84,p123,Possible,LCC,0,100000
-p84,p124,Possible,LCC,0,100000
-p84,p125,Possible,LCC,0,100000
-p84,p126,Possible,LCC,0,100000
-p84,p127,Possible,LCC,0,100000
-p84,p128,Possible,LCC,0,100000
-p84,p129,Possible,LCC,0,100000
-p84,p130,Possible,LCC,0,100000
-p84,p131,Possible,LCC,0,100000
-p84,p132,Possible,LCC,0,100000
-p84,p133,Possible,LCC,0,100000
-p84,p134,Possible,LCC,0,100000
-p84,p86,Possible,LCC,0,100000
-p84,p87,Possible,LCC,0,100000
-p84,p88,Possible,LCC,0,100000
-p84,p89,Possible,LCC,0,100000
-p84,p90,Possible,LCC,0,100000
-p84,p91,Possible,LCC,0,100000
-p84,p93,Possible,LCC,0,100000
-p84,p94,Possible,LCC,0,100000
-p84,p95,Possible,LCC,0,100000
-p84,p96,Possible,LCC,0,100000
-p84,p97,Possible,LCC,0,100000
-p84,p98,Possible,LCC,0,100000
-p84,p99,Possible,LCC,0,100000
-p85,p100,Possible,LCC,0,100000
-p85,p101,Possible,LCC,0,100000
-p85,p102,Possible,LCC,0,100000
-p85,p103,Possible,LCC,0,100000
-p85,p104,Possible,LCC,0,100000
-p85,p105,Possible,LCC,0,100000
-p85,p106,Possible,LCC,0,100000
-p85,p107,Possible,LCC,0,100000
-p85,p108,Possible,LCC,0,100000
-p85,p109,Possible,LCC,0,100000
-p85,p110,Possible,LCC,0,100000
-p85,p111,Possible,LCC,0,100000
-p85,p112,Possible,LCC,0,100000
-p85,p113,Possible,LCC,0,100000
-p85,p114,Possible,LCC,0,100000
-p85,p115,Possible,LCC,0,100000
-p85,p116,Possible,LCC,0,100000
-p85,p117,Possible,LCC,0,100000
-p85,p118,Possible,LCC,0,100000
-p85,p119,Possible,LCC,0,100000
-p85,p120,Possible,LCC,0,100000
-p85,p121,Possible,LCC,0,100000
-p85,p122,Possible,LCC,0,100000
-p85,p123,Possible,LCC,0,100000
-p85,p124,Possible,LCC,0,100000
-p85,p125,Possible,LCC,0,100000
-p85,p126,Possible,LCC,0,100000
-p85,p127,Possible,LCC,0,100000
-p85,p128,Possible,LCC,0,100000
-p85,p129,Possible,LCC,0,100000
-p85,p130,Possible,LCC,0,100000
-p85,p131,Possible,LCC,0,100000
-p85,p132,Possible,LCC,0,100000
-p85,p133,Possible,LCC,0,100000
-p85,p134,Possible,LCC,0,100000
-p85,p88,Possible,LCC,0,100000
-p85,p89,Possible,LCC,0,100000
-p85,p90,Possible,LCC,0,100000
-p85,p91,Possible,LCC,0,100000
-p85,p93,Possible,LCC,0,100000
-p85,p94,Possible,LCC,0,100000
-p85,p95,Possible,LCC,0,100000
-p85,p96,Possible,LCC,0,100000
-p85,p97,Possible,LCC,0,100000
-p85,p98,Possible,LCC,0,100000
-p85,p99,Possible,LCC,0,100000
-p86,p100,Possible,LCC,0,100000
-p86,p101,Possible,LCC,0,100000
-p86,p102,Possible,LCC,0,100000
-p86,p103,Possible,LCC,0,100000
-p86,p104,Possible,LCC,0,100000
-p86,p105,Possible,LCC,0,100000
-p86,p106,Possible,LCC,0,100000
-p86,p107,Possible,LCC,0,100000
-p86,p108,Possible,LCC,0,100000
-p86,p109,Possible,LCC,0,100000
-p86,p110,Possible,LCC,0,100000
-p86,p111,Possible,LCC,0,100000
-p86,p112,Possible,LCC,0,100000
-p86,p113,Possible,LCC,0,100000
-p86,p114,Possible,LCC,0,100000
-p86,p115,Possible,LCC,0,100000
-p86,p116,Possible,LCC,0,100000
-p86,p117,Possible,LCC,0,100000
-p86,p118,Possible,LCC,0,100000
-p86,p119,Possible,LCC,0,100000
-p86,p120,Possible,LCC,0,100000
-p86,p121,Possible,LCC,0,100000
-p86,p122,Possible,LCC,0,100000
-p86,p123,Possible,LCC,0,100000
-p86,p124,Possible,LCC,0,100000
-p86,p125,Possible,LCC,0,100000
-p86,p126,Possible,LCC,0,100000
-p86,p127,Possible,LCC,0,100000
-p86,p128,Possible,LCC,0,100000
-p86,p129,Possible,LCC,0,100000
-p86,p130,Possible,LCC,0,100000
-p86,p131,Possible,LCC,0,100000
-p86,p132,Possible,LCC,0,100000
-p86,p133,Possible,LCC,0,100000
-p86,p134,Possible,LCC,0,100000
-p86,p90,Possible,LCC,0,100000
-p86,p91,Possible,LCC,0,100000
-p86,p92,Possible,LCC,0,100000
-p86,p93,Possible,LCC,0,100000
-p86,p94,Possible,LCC,0,100000
-p86,p95,Possible,LCC,0,100000
-p86,p96,Possible,LCC,0,100000
-p86,p97,Possible,LCC,0,100000
-p86,p98,Possible,LCC,0,100000
-p86,p99,Possible,LCC,0,100000
-p87,p100,Possible,LCC,0,100000
-p87,p101,Possible,LCC,0,100000
-p87,p102,Possible,LCC,0,100000
-p87,p103,Possible,LCC,0,100000
-p87,p104,Possible,LCC,0,100000
-p87,p105,Possible,LCC,0,100000
-p87,p106,Possible,LCC,0,100000
-p87,p107,Possible,LCC,0,100000
-p87,p108,Possible,LCC,0,100000
-p87,p109,Possible,LCC,0,100000
-p87,p110,Possible,LCC,0,100000
-p87,p111,Possible,LCC,0,100000
-p87,p112,Possible,LCC,0,100000
-p87,p113,Possible,LCC,0,100000
-p87,p114,Possible,LCC,0,100000
-p87,p115,Possible,LCC,0,100000
-p87,p116,Possible,LCC,0,100000
-p87,p117,Possible,LCC,0,100000
-p87,p118,Possible,LCC,0,100000
-p87,p119,Possible,LCC,0,100000
-p87,p120,Possible,LCC,0,100000
-p87,p121,Possible,LCC,0,100000
-p87,p122,Possible,LCC,0,100000
-p87,p123,Possible,LCC,0,100000
-p87,p124,Possible,LCC,0,100000
-p87,p125,Possible,LCC,0,100000
-p87,p126,Possible,LCC,0,100000
-p87,p127,Possible,LCC,0,100000
-p87,p128,Possible,LCC,0,100000
-p87,p129,Possible,LCC,0,100000
-p87,p130,Possible,LCC,0,100000
-p87,p131,Possible,LCC,0,100000
-p87,p132,Possible,LCC,0,100000
-p87,p133,Possible,LCC,0,100000
-p87,p134,Possible,LCC,0,100000
-p87,p91,Possible,LCC,0,100000
-p87,p93,Possible,LCC,0,100000
-p87,p94,Possible,LCC,0,100000
-p87,p95,Possible,LCC,0,100000
-p87,p96,Possible,LCC,0,100000
-p87,p97,Possible,LCC,0,100000
-p87,p98,Possible,LCC,0,100000
-p87,p99,Possible,LCC,0,100000
-p88,p100,Possible,LCC,0,100000
-p88,p101,Possible,LCC,0,100000
-p88,p102,Possible,LCC,0,100000
-p88,p103,Possible,LCC,0,100000
-p88,p104,Possible,LCC,0,100000
-p88,p105,Possible,LCC,0,100000
-p88,p106,Possible,LCC,0,100000
-p88,p107,Possible,LCC,0,100000
-p88,p108,Possible,LCC,0,100000
-p88,p109,Possible,LCC,0,100000
-p88,p110,Possible,LCC,0,100000
-p88,p111,Possible,LCC,0,100000
-p88,p112,Possible,LCC,0,100000
-p88,p113,Possible,LCC,0,100000
-p88,p114,Possible,LCC,0,100000
-p88,p115,Possible,LCC,0,100000
-p88,p116,Possible,LCC,0,100000
-p88,p117,Possible,LCC,0,100000
-p88,p118,Possible,LCC,0,100000
-p88,p119,Possible,LCC,0,100000
-p88,p120,Possible,LCC,0,100000
-p88,p121,Possible,LCC,0,100000
-p88,p122,Possible,LCC,0,100000
-p88,p123,Possible,LCC,0,100000
-p88,p124,Possible,LCC,0,100000
-p88,p125,Possible,LCC,0,100000
-p88,p126,Possible,LCC,0,100000
-p88,p127,Possible,LCC,0,100000
-p88,p128,Possible,LCC,0,100000
-p88,p129,Possible,LCC,0,100000
-p88,p130,Possible,LCC,0,100000
-p88,p131,Possible,LCC,0,100000
-p88,p132,Possible,LCC,0,100000
-p88,p133,Possible,LCC,0,100000
-p88,p134,Possible,LCC,0,100000
-p88,p90,Possible,LCC,0,100000
-p88,p91,Possible,LCC,0,100000
-p88,p93,Possible,LCC,0,100000
-p88,p94,Possible,LCC,0,100000
-p88,p95,Possible,LCC,0,100000
-p88,p96,Possible,LCC,0,100000
-p88,p97,Possible,LCC,0,100000
-p88,p98,Possible,LCC,0,100000
-p88,p99,Possible,LCC,0,100000
-p89,p100,Possible,LCC,0,100000
-p89,p101,Possible,LCC,0,100000
-p89,p102,Possible,LCC,0,100000
-p89,p103,Possible,LCC,0,100000
-p89,p104,Possible,LCC,0,100000
-p89,p105,Possible,LCC,0,100000
-p89,p106,Possible,LCC,0,100000
-p89,p107,Possible,LCC,0,100000
-p89,p108,Possible,LCC,0,100000
-p89,p109,Possible,LCC,0,100000
-p89,p110,Possible,LCC,0,100000
-p89,p111,Possible,LCC,0,100000
-p89,p112,Possible,LCC,0,100000
-p89,p113,Possible,LCC,0,100000
-p89,p114,Possible,LCC,0,100000
-p89,p115,Possible,LCC,0,100000
-p89,p116,Possible,LCC,0,100000
-p89,p117,Possible,LCC,0,100000
-p89,p118,Possible,LCC,0,100000
-p89,p119,Possible,LCC,0,100000
-p89,p120,Possible,LCC,0,100000
-p89,p121,Possible,LCC,0,100000
-p89,p122,Possible,LCC,0,100000
-p89,p123,Possible,LCC,0,100000
-p89,p124,Possible,LCC,0,100000
-p89,p125,Possible,LCC,0,100000
-p89,p126,Possible,LCC,0,100000
-p89,p127,Possible,LCC,0,100000
-p89,p128,Possible,LCC,0,100000
-p89,p129,Possible,LCC,0,100000
-p89,p130,Possible,LCC,0,100000
-p89,p131,Possible,LCC,0,100000
-p89,p132,Possible,LCC,0,100000
-p89,p133,Possible,LCC,0,100000
-p89,p134,Possible,LCC,0,100000
-p89,p91,Possible,LCC,0,100000
-p89,p93,Possible,LCC,0,100000
-p89,p95,Possible,LCC,0,100000
-p89,p96,Possible,LCC,0,100000
-p89,p97,Possible,LCC,0,100000
-p89,p98,Possible,LCC,0,100000
-p89,p99,Possible,LCC,0,100000
-p9,p100,Possible,LCC,0,100000
-p9,p101,Possible,LCC,0,100000
-p9,p102,Possible,LCC,0,100000
-p9,p103,Possible,LCC,0,100000
-p9,p104,Possible,LCC,0,100000
-p9,p105,Possible,LCC,0,100000
-p9,p106,Possible,LCC,0,100000
-p9,p107,Possible,LCC,0,100000
-p9,p108,Possible,LCC,0,100000
-p9,p109,Possible,LCC,0,100000
-p9,p11,Possible,LCC,0,100000
-p9,p110,Possible,LCC,0,100000
-p9,p111,Possible,LCC,0,100000
-p9,p112,Possible,LCC,0,100000
-p9,p113,Possible,LCC,0,100000
-p9,p114,Possible,LCC,0,100000
-p9,p115,Possible,LCC,0,100000
-p9,p116,Possible,LCC,0,100000
-p9,p117,Possible,LCC,0,100000
-p9,p118,Possible,LCC,0,100000
-p9,p119,Possible,LCC,0,100000
-p9,p120,Possible,LCC,0,100000
-p9,p121,Possible,LCC,0,100000
-p9,p122,Possible,LCC,0,100000
-p9,p123,Possible,LCC,0,100000
-p9,p124,Possible,LCC,0,100000
-p9,p125,Possible,LCC,0,100000
-p9,p126,Possible,LCC,0,100000
-p9,p127,Possible,LCC,0,100000
-p9,p128,Possible,LCC,0,100000
-p9,p129,Possible,LCC,0,100000
-p9,p13,Possible,LCC,0,100000
-p9,p130,Possible,LCC,0,100000
-p9,p131,Possible,LCC,0,100000
-p9,p132,Possible,LCC,0,100000
-p9,p133,Possible,LCC,0,100000
-p9,p134,Possible,LCC,0,100000
-p9,p14,Possible,LCC,0,100000
-p9,p15,Possible,LCC,0,100000
-p9,p16,Possible,LCC,0,100000
-p9,p17,Possible,LCC,0,100000
-p9,p18,Possible,LCC,0,100000
-p9,p19,Possible,LCC,0,100000
-p9,p20,Possible,LCC,0,100000
-p9,p21,Possible,LCC,0,100000
-p9,p22,Possible,LCC,0,100000
-p9,p23,Possible,LCC,0,100000
-p9,p24,Possible,LCC,0,100000
-p9,p25,Possible,LCC,0,100000
-p9,p26,Possible,LCC,0,100000
-p9,p27,Possible,LCC,0,100000
-p9,p28,Possible,LCC,0,100000
-p9,p29,Possible,LCC,0,100000
-p9,p30,Possible,LCC,0,100000
-p9,p31,Possible,LCC,0,100000
-p9,p32,Possible,LCC,0,100000
-p9,p33,Possible,LCC,0,100000
-p9,p34,Possible,LCC,0,100000
-p9,p35,Possible,LCC,0,100000
-p9,p36,Possible,LCC,0,100000
-p9,p37,Possible,LCC,0,100000
-p9,p38,Possible,LCC,0,100000
-p9,p39,Possible,LCC,0,100000
-p9,p40,Possible,LCC,0,100000
-p9,p41,Possible,LCC,0,100000
-p9,p42,Possible,LCC,0,100000
-p9,p43,Possible,LCC,0,100000
-p9,p44,Possible,LCC,0,100000
-p9,p45,Possible,LCC,0,100000
-p9,p46,Possible,LCC,0,100000
-p9,p47,Possible,LCC,0,100000
-p9,p48,Possible,LCC,0,100000
-p9,p49,Possible,LCC,0,100000
-p9,p50,Possible,LCC,0,100000
-p9,p51,Possible,LCC,0,100000
-p9,p52,Possible,LCC,0,100000
-p9,p53,Possible,LCC,0,100000
-p9,p54,Possible,LCC,0,100000
-p9,p55,Possible,LCC,0,100000
-p9,p56,Possible,LCC,0,100000
-p9,p57,Possible,LCC,0,100000
-p9,p58,Possible,LCC,0,100000
-p9,p59,Possible,LCC,0,100000
-p9,p60,Possible,LCC,0,100000
-p9,p61,Possible,LCC,0,100000
-p9,p62,Possible,LCC,0,100000
-p9,p63,Possible,LCC,0,100000
-p9,p64,Possible,LCC,0,100000
-p9,p65,Possible,LCC,0,100000
-p9,p66,Possible,LCC,0,100000
-p9,p67,Possible,LCC,0,100000
-p9,p68,Possible,LCC,0,100000
-p9,p69,Possible,LCC,0,100000
-p9,p70,Possible,LCC,0,100000
-p9,p71,Possible,LCC,0,100000
-p9,p72,Possible,LCC,0,100000
-p9,p73,Possible,LCC,0,100000
-p9,p74,Possible,LCC,0,100000
-p9,p75,Possible,LCC,0,100000
-p9,p76,Possible,LCC,0,100000
-p9,p77,Possible,LCC,0,100000
-p9,p78,Possible,LCC,0,100000
-p9,p79,Possible,LCC,0,100000
-p9,p80,Possible,LCC,0,100000
-p9,p81,Possible,LCC,0,100000
-p9,p82,Possible,LCC,0,100000
-p9,p83,Possible,LCC,0,100000
-p9,p84,Possible,LCC,0,100000
-p9,p85,Possible,LCC,0,100000
-p9,p86,Possible,LCC,0,100000
-p9,p87,Possible,LCC,0,100000
-p9,p88,Possible,LCC,0,100000
-p9,p89,Possible,LCC,0,100000
-p9,p90,Possible,LCC,0,100000
-p9,p91,Possible,LCC,0,100000
-p9,p92,Possible,LCC,0,100000
-p9,p93,Possible,LCC,0,100000
-p9,p94,Possible,LCC,0,100000
-p9,p95,Possible,LCC,0,100000
-p9,p96,Possible,LCC,0,100000
-p9,p97,Possible,LCC,0,100000
-p9,p98,Possible,LCC,0,100000
-p9,p99,Possible,LCC,0,100000
-p90,p100,Possible,LCC,0,100000
-p90,p101,Possible,LCC,0,100000
-p90,p102,Possible,LCC,0,100000
-p90,p103,Possible,LCC,0,100000
-p90,p104,Possible,LCC,0,100000
-p90,p105,Possible,LCC,0,100000
-p90,p106,Possible,LCC,0,100000
-p90,p107,Possible,LCC,0,100000
-p90,p108,Possible,LCC,0,100000
-p90,p109,Possible,LCC,0,100000
-p90,p110,Possible,LCC,0,100000
-p90,p111,Possible,LCC,0,100000
-p90,p112,Possible,LCC,0,100000
-p90,p113,Possible,LCC,0,100000
-p90,p114,Possible,LCC,0,100000
-p90,p115,Possible,LCC,0,100000
-p90,p116,Possible,LCC,0,100000
-p90,p117,Possible,LCC,0,100000
-p90,p118,Possible,LCC,0,100000
-p90,p119,Possible,LCC,0,100000
-p90,p120,Possible,LCC,0,100000
-p90,p121,Possible,LCC,0,100000
-p90,p122,Possible,LCC,0,100000
-p90,p123,Possible,LCC,0,100000
-p90,p124,Possible,LCC,0,100000
-p90,p125,Possible,LCC,0,100000
-p90,p126,Possible,LCC,0,100000
-p90,p127,Possible,LCC,0,100000
-p90,p128,Possible,LCC,0,100000
-p90,p129,Possible,LCC,0,100000
-p90,p130,Possible,LCC,0,100000
-p90,p131,Possible,LCC,0,100000
-p90,p132,Possible,LCC,0,100000
-p90,p133,Possible,LCC,0,100000
-p90,p134,Possible,LCC,0,100000
-p90,p92,Possible,LCC,0,100000
-p90,p93,Possible,LCC,0,100000
-p90,p95,Possible,LCC,0,100000
-p90,p96,Possible,LCC,0,100000
-p90,p97,Possible,LCC,0,100000
-p90,p98,Possible,LCC,0,100000
-p90,p99,Possible,LCC,0,100000
-p91,p100,Possible,LCC,0,100000
-p91,p102,Possible,LCC,0,100000
-p91,p103,Possible,LCC,0,100000
-p91,p104,Possible,LCC,0,100000
-p91,p105,Possible,LCC,0,100000
-p91,p106,Possible,LCC,0,100000
-p91,p107,Possible,LCC,0,100000
-p91,p108,Possible,LCC,0,100000
-p91,p109,Possible,LCC,0,100000
-p91,p110,Possible,LCC,0,100000
-p91,p111,Possible,LCC,0,100000
-p91,p112,Possible,LCC,0,100000
-p91,p113,Possible,LCC,0,100000
-p91,p114,Possible,LCC,0,100000
-p91,p115,Possible,LCC,0,100000
-p91,p116,Possible,LCC,0,100000
-p91,p117,Possible,LCC,0,100000
-p91,p118,Possible,LCC,0,100000
-p91,p119,Possible,LCC,0,100000
-p91,p120,Possible,LCC,0,100000
-p91,p121,Possible,LCC,0,100000
-p91,p122,Possible,LCC,0,100000
-p91,p123,Possible,LCC,0,100000
-p91,p124,Possible,LCC,0,100000
-p91,p125,Possible,LCC,0,100000
-p91,p126,Possible,LCC,0,100000
-p91,p127,Possible,LCC,0,100000
-p91,p128,Possible,LCC,0,100000
-p91,p129,Possible,LCC,0,100000
-p91,p130,Possible,LCC,0,100000
-p91,p131,Possible,LCC,0,100000
-p91,p132,Possible,LCC,0,100000
-p91,p133,Possible,LCC,0,100000
-p91,p134,Possible,LCC,0,100000
-p91,p92,Possible,LCC,0,100000
-p91,p93,Possible,LCC,0,100000
-p91,p95,Possible,LCC,0,100000
-p91,p96,Possible,LCC,0,100000
-p91,p97,Possible,LCC,0,100000
-p91,p98,Possible,LCC,0,100000
-p91,p99,Possible,LCC,0,100000
-p92,p100,Possible,LCC,0,100000
-p92,p101,Possible,LCC,0,100000
-p92,p102,Possible,LCC,0,100000
-p92,p103,Possible,LCC,0,100000
-p92,p104,Possible,LCC,0,100000
-p92,p105,Possible,LCC,0,100000
-p92,p106,Possible,LCC,0,100000
-p92,p110,Possible,LCC,0,100000
-p92,p111,Possible,LCC,0,100000
-p92,p112,Possible,LCC,0,100000
-p92,p113,Possible,LCC,0,100000
-p92,p114,Possible,LCC,0,100000
-p92,p115,Possible,LCC,0,100000
-p92,p116,Possible,LCC,0,100000
-p92,p117,Possible,LCC,0,100000
-p92,p119,Possible,LCC,0,100000
-p92,p120,Possible,LCC,0,100000
-p92,p121,Possible,LCC,0,100000
-p92,p122,Possible,LCC,0,100000
-p92,p123,Possible,LCC,0,100000
-p92,p124,Possible,LCC,0,100000
-p92,p125,Possible,LCC,0,100000
-p92,p126,Possible,LCC,0,100000
-p92,p127,Possible,LCC,0,100000
-p92,p128,Possible,LCC,0,100000
-p92,p129,Possible,LCC,0,100000
-p92,p130,Possible,LCC,0,100000
-p92,p131,Possible,LCC,0,100000
-p92,p132,Possible,LCC,0,100000
-p92,p133,Possible,LCC,0,100000
-p92,p134,Possible,LCC,0,100000
-p92,p95,Possible,LCC,0,100000
-p92,p96,Possible,LCC,0,100000
-p92,p98,Possible,LCC,0,100000
-p92,p99,Possible,LCC,0,100000
-p93,p100,Possible,LCC,0,100000
-p93,p101,Possible,LCC,0,100000
-p93,p102,Possible,LCC,0,100000
-p93,p103,Possible,LCC,0,100000
-p93,p104,Possible,LCC,0,100000
-p93,p105,Possible,LCC,0,100000
-p93,p106,Possible,LCC,0,100000
-p93,p110,Possible,LCC,0,100000
-p93,p111,Possible,LCC,0,100000
-p93,p112,Possible,LCC,0,100000
-p93,p113,Possible,LCC,0,100000
-p93,p115,Possible,LCC,0,100000
-p93,p116,Possible,LCC,0,100000
-p93,p117,Possible,LCC,0,100000
-p93,p118,Possible,LCC,0,100000
-p93,p119,Possible,LCC,0,100000
-p93,p120,Possible,LCC,0,100000
-p93,p121,Possible,LCC,0,100000
-p93,p122,Possible,LCC,0,100000
-p93,p123,Possible,LCC,0,100000
-p93,p124,Possible,LCC,0,100000
-p93,p125,Possible,LCC,0,100000
-p93,p126,Possible,LCC,0,100000
-p93,p127,Possible,LCC,0,100000
-p93,p128,Possible,LCC,0,100000
-p93,p129,Possible,LCC,0,100000
-p93,p130,Possible,LCC,0,100000
-p93,p131,Possible,LCC,0,100000
-p93,p132,Possible,LCC,0,100000
-p93,p133,Possible,LCC,0,100000
-p93,p134,Possible,LCC,0,100000
-p93,p94,Possible,LCC,0,100000
-p93,p95,Possible,LCC,0,100000
-p93,p96,Possible,LCC,0,100000
-p93,p97,Possible,LCC,0,100000
-p93,p98,Possible,LCC,0,100000
-p93,p99,Possible,LCC,0,100000
-p94,p100,Possible,LCC,0,100000
-p94,p102,Possible,LCC,0,100000
-p94,p103,Possible,LCC,0,100000
-p94,p104,Possible,LCC,0,100000
-p94,p105,Possible,LCC,0,100000
-p94,p106,Possible,LCC,0,100000
-p94,p107,Possible,LCC,0,100000
-p94,p108,Possible,LCC,0,100000
-p94,p109,Possible,LCC,0,100000
-p94,p110,Possible,LCC,0,100000
-p94,p111,Possible,LCC,0,100000
-p94,p112,Possible,LCC,0,100000
-p94,p113,Possible,LCC,0,100000
-p94,p114,Possible,LCC,0,100000
-p94,p115,Possible,LCC,0,100000
-p94,p116,Possible,LCC,0,100000
-p94,p117,Possible,LCC,0,100000
-p94,p118,Possible,LCC,0,100000
-p94,p119,Possible,LCC,0,100000
-p94,p120,Possible,LCC,0,100000
-p94,p121,Possible,LCC,0,100000
-p94,p122,Possible,LCC,0,100000
-p94,p123,Possible,LCC,0,100000
-p94,p124,Possible,LCC,0,100000
-p94,p125,Possible,LCC,0,100000
-p94,p126,Possible,LCC,0,100000
-p94,p127,Possible,LCC,0,100000
-p94,p128,Possible,LCC,0,100000
-p94,p129,Possible,LCC,0,100000
-p94,p130,Possible,LCC,0,100000
-p94,p131,Possible,LCC,0,100000
-p94,p132,Possible,LCC,0,100000
-p94,p133,Possible,LCC,0,100000
-p94,p134,Possible,LCC,0,100000
-p94,p98,Possible,LCC,0,100000
-p94,p99,Possible,LCC,0,100000
-p95,p100,Possible,LCC,0,100000
-p95,p101,Possible,LCC,0,100000
-p95,p102,Possible,LCC,0,100000
-p95,p103,Possible,LCC,0,100000
-p95,p104,Possible,LCC,0,100000
-p95,p105,Possible,LCC,0,100000
-p95,p106,Possible,LCC,0,100000
-p95,p107,Possible,LCC,0,100000
-p95,p108,Possible,LCC,0,100000
-p95,p109,Possible,LCC,0,100000
-p95,p110,Possible,LCC,0,100000
-p95,p111,Possible,LCC,0,100000
-p95,p112,Possible,LCC,0,100000
-p95,p113,Possible,LCC,0,100000
-p95,p114,Possible,LCC,0,100000
-p95,p115,Possible,LCC,0,100000
-p95,p116,Possible,LCC,0,100000
-p95,p117,Possible,LCC,0,100000
-p95,p118,Possible,LCC,0,100000
-p95,p119,Possible,LCC,0,100000
-p95,p120,Possible,LCC,0,100000
-p95,p121,Possible,LCC,0,100000
-p95,p122,Possible,LCC,0,100000
-p95,p123,Possible,LCC,0,100000
-p95,p124,Possible,LCC,0,100000
-p95,p125,Possible,LCC,0,100000
-p95,p126,Possible,LCC,0,100000
-p95,p127,Possible,LCC,0,100000
-p95,p128,Possible,LCC,0,100000
-p95,p129,Possible,LCC,0,100000
-p95,p130,Possible,LCC,0,100000
-p95,p131,Possible,LCC,0,100000
-p95,p132,Possible,LCC,0,100000
-p95,p133,Possible,LCC,0,100000
-p95,p134,Possible,LCC,0,100000
-p95,p99,Possible,LCC,0,100000
-p96,p100,Possible,LCC,0,100000
-p96,p101,Possible,LCC,0,100000
-p96,p102,Possible,LCC,0,100000
-p96,p103,Possible,LCC,0,100000
-p96,p104,Possible,LCC,0,100000
-p96,p105,Possible,LCC,0,100000
-p96,p106,Possible,LCC,0,100000
-p96,p107,Possible,LCC,0,100000
-p96,p108,Possible,LCC,0,100000
-p96,p109,Possible,LCC,0,100000
-p96,p110,Possible,LCC,0,100000
-p96,p111,Possible,LCC,0,100000
-p96,p112,Possible,LCC,0,100000
-p96,p113,Possible,LCC,0,100000
-p96,p114,Possible,LCC,0,100000
-p96,p115,Possible,LCC,0,100000
-p96,p116,Possible,LCC,0,100000
-p96,p117,Possible,LCC,0,100000
-p96,p118,Possible,LCC,0,100000
-p96,p119,Possible,LCC,0,100000
-p96,p120,Possible,LCC,0,100000
-p96,p121,Possible,LCC,0,100000
-p96,p122,Possible,LCC,0,100000
-p96,p123,Possible,LCC,0,100000
-p96,p124,Possible,LCC,0,100000
-p96,p125,Possible,LCC,0,100000
-p96,p126,Possible,LCC,0,100000
-p96,p127,Possible,LCC,0,100000
-p96,p128,Possible,LCC,0,100000
-p96,p129,Possible,LCC,0,100000
-p96,p130,Possible,LCC,0,100000
-p96,p131,Possible,LCC,0,100000
-p96,p132,Possible,LCC,0,100000
-p96,p133,Possible,LCC,0,100000
-p96,p134,Possible,LCC,0,100000
-p96,p97,Possible,LCC,0,100000
-p96,p99,Possible,LCC,0,100000
-p97,p100,Possible,LCC,0,100000
-p97,p101,Possible,LCC,0,100000
-p97,p102,Possible,LCC,0,100000
-p97,p103,Possible,LCC,0,100000
-p97,p104,Possible,LCC,0,100000
-p97,p105,Possible,LCC,0,100000
-p97,p106,Possible,LCC,0,100000
-p97,p107,Possible,LCC,0,100000
-p97,p108,Possible,LCC,0,100000
-p97,p109,Possible,LCC,0,100000
-p97,p110,Possible,LCC,0,100000
-p97,p111,Possible,LCC,0,100000
-p97,p112,Possible,LCC,0,100000
-p97,p113,Possible,LCC,0,100000
-p97,p114,Possible,LCC,0,100000
-p97,p115,Possible,LCC,0,100000
-p97,p116,Possible,LCC,0,100000
-p97,p117,Possible,LCC,0,100000
-p97,p119,Possible,LCC,0,100000
-p97,p120,Possible,LCC,0,100000
-p97,p121,Possible,LCC,0,100000
-p97,p122,Possible,LCC,0,100000
-p97,p123,Possible,LCC,0,100000
-p97,p124,Possible,LCC,0,100000
-p97,p125,Possible,LCC,0,100000
-p97,p126,Possible,LCC,0,100000
-p97,p127,Possible,LCC,0,100000
-p97,p128,Possible,LCC,0,100000
-p97,p129,Possible,LCC,0,100000
-p97,p130,Possible,LCC,0,100000
-p97,p131,Possible,LCC,0,100000
-p97,p132,Possible,LCC,0,100000
-p97,p133,Possible,LCC,0,100000
-p97,p134,Possible,LCC,0,100000
-p97,p99,Possible,LCC,0,100000
-p98,p100,Possible,LCC,0,100000
-p98,p101,Possible,LCC,0,100000
-p98,p102,Possible,LCC,0,100000
-p98,p103,Possible,LCC,0,100000
-p98,p104,Possible,LCC,0,100000
-p98,p105,Possible,LCC,0,100000
-p98,p106,Possible,LCC,0,100000
-p98,p107,Possible,LCC,0,100000
-p98,p108,Possible,LCC,0,100000
-p98,p109,Possible,LCC,0,100000
-p98,p110,Possible,LCC,0,100000
-p98,p111,Possible,LCC,0,100000
-p98,p112,Possible,LCC,0,100000
-p98,p113,Possible,LCC,0,100000
-p98,p114,Possible,LCC,0,100000
-p98,p115,Possible,LCC,0,100000
-p98,p116,Possible,LCC,0,100000
-p98,p117,Possible,LCC,0,100000
-p98,p118,Possible,LCC,0,100000
-p98,p119,Possible,LCC,0,100000
-p98,p120,Possible,LCC,0,100000
-p98,p121,Possible,LCC,0,100000
-p98,p122,Possible,LCC,0,100000
-p98,p123,Possible,LCC,0,100000
-p98,p124,Possible,LCC,0,100000
-p98,p125,Possible,LCC,0,100000
-p98,p126,Possible,LCC,0,100000
-p98,p127,Possible,LCC,0,100000
-p98,p128,Possible,LCC,0,100000
-p98,p129,Possible,LCC,0,100000
-p98,p130,Possible,LCC,0,100000
-p98,p131,Possible,LCC,0,100000
-p98,p132,Possible,LCC,0,100000
-p98,p133,Possible,LCC,0,100000
-p98,p134,Possible,LCC,0,100000
-p99,p101,Possible,LCC,0,100000
-p99,p102,Possible,LCC,0,100000
-p99,p103,Possible,LCC,0,100000
-p99,p104,Possible,LCC,0,100000
-p99,p105,Possible,LCC,0,100000
-p99,p106,Possible,LCC,0,100000
-p99,p107,Possible,LCC,0,100000
-p99,p108,Possible,LCC,0,100000
-p99,p109,Possible,LCC,0,100000
-p99,p110,Possible,LCC,0,100000
-p99,p111,Possible,LCC,0,100000
-p99,p112,Possible,LCC,0,100000
-p99,p113,Possible,LCC,0,100000
-p99,p114,Possible,LCC,0,100000
-p99,p115,Possible,LCC,0,100000
-p99,p117,Possible,LCC,0,100000
-p99,p119,Possible,LCC,0,100000
-p99,p122,Possible,LCC,0,100000
-p99,p124,Possible,LCC,0,100000
-p99,p125,Possible,LCC,0,100000
-p99,p126,Possible,LCC,0,100000
-p99,p127,Possible,LCC,0,100000
-p99,p128,Possible,LCC,0,100000
-p99,p129,Possible,LCC,0,100000
-p99,p130,Possible,LCC,0,100000
-p99,p131,Possible,LCC,0,100000
-p99,p132,Possible,LCC,0,100000
-p99,p133,Possible,LCC,0,100000
-p99,p134,Possible,LCC,0,100000
\ No newline at end of file
diff --git a/inputs/transmission/transmission_capacity_future_ba_VSC_all.csv b/inputs/transmission/transmission_capacity_future_ba_VSC_all.csv
deleted file mode 100644
index 73e7d164e..000000000
--- a/inputs/transmission/transmission_capacity_future_ba_VSC_all.csv
+++ /dev/null
@@ -1,307 +0,0 @@
-r,rr,status,trtype,t,MW
-p1,p2,Possible,VSC,2032,100000
-p1,p4,Possible,VSC,2032,100000
-p2,p3,Possible,VSC,2032,100000
-p2,p4,Possible,VSC,2032,100000
-p2,p5,Possible,VSC,2032,100000
-p2,p14,Possible,VSC,2032,100000
-p3,p4,Possible,VSC,2032,100000
-p3,p5,Possible,VSC,2032,100000
-p3,p14,Possible,VSC,2032,100000
-p3,p17,Possible,VSC,2032,100000
-p5,p6,Possible,VSC,2032,100000
-p5,p7,Possible,VSC,2032,100000
-p5,p14,Possible,VSC,2032,100000
-p5,p15,Possible,VSC,2032,100000
-p6,p7,Possible,VSC,2032,100000
-p6,p8,Possible,VSC,2032,100000
-p6,p9,Possible,VSC,2032,100000
-p7,p8,Possible,VSC,2032,100000
-p7,p12,Possible,VSC,2032,100000
-p7,p15,Possible,VSC,2032,100000
-p8,p9,Possible,VSC,2032,100000
-p8,p12,Possible,VSC,2032,100000
-p9,p10,Possible,VSC,2032,100000
-p9,p12,Possible,VSC,2032,100000
-p10,p11,Possible,VSC,2032,100000
-p10,p12,Possible,VSC,2032,100000
-p10,p13,Possible,VSC,2032,100000
-p10,p27,Possible,VSC,2032,100000
-p10,p28,Possible,VSC,2032,100000
-p12,p13,Possible,VSC,2032,100000
-p12,p15,Possible,VSC,2032,100000
-p12,p25,Possible,VSC,2032,100000
-p13,p25,Possible,VSC,2032,100000
-p13,p27,Possible,VSC,2032,100000
-p13,p28,Possible,VSC,2032,100000
-p14,p15,Possible,VSC,2032,100000
-p14,p17,Possible,VSC,2032,100000
-p15,p16,Possible,VSC,2032,100000
-p15,p17,Possible,VSC,2032,100000
-p15,p21,Possible,VSC,2032,100000
-p15,p25,Possible,VSC,2032,100000
-p16,p17,Possible,VSC,2032,100000
-p16,p18,Possible,VSC,2032,100000
-p16,p21,Possible,VSC,2032,100000
-p16,p25,Possible,VSC,2032,100000
-p17,p18,Possible,VSC,2032,100000
-p17,p20,Possible,VSC,2032,100000
-p18,p19,Possible,VSC,2032,100000
-p18,p20,Possible,VSC,2032,100000
-p18,p21,Possible,VSC,2032,100000
-p18,p22,Possible,VSC,2032,100000
-p21,p24,Possible,VSC,2032,100000
-p21,p25,Possible,VSC,2032,100000
-p21,p26,Possible,VSC,2032,100000
-p22,p23,Possible,VSC,2032,100000
-p22,p24,Possible,VSC,2032,100000
-p23,p24,Possible,VSC,2032,100000
-p23,p32,Possible,VSC,2032,100000
-p24,p32,Possible,VSC,2032,100000
-p24,p33,Possible,VSC,2032,100000
-p25,p26,Possible,VSC,2032,100000
-p25,p28,Possible,VSC,2032,100000
-p25,p31,Possible,VSC,2032,100000
-p26,p33,Possible,VSC,2032,100000
-p27,p28,Possible,VSC,2032,100000
-p28,p29,Possible,VSC,2032,100000
-p28,p30,Possible,VSC,2032,100000
-p28,p31,Possible,VSC,2032,100000
-p29,p30,Possible,VSC,2032,100000
-p29,p31,Possible,VSC,2032,100000
-p31,p34,Possible,VSC,2032,100000
-p31,p59,Possible,VSC,2032,100000
-p33,p34,Possible,VSC,2032,100000
-p35,p36,Possible,VSC,2032,100000
-p36,p37,Possible,VSC,2032,100000
-p36,p38,Possible,VSC,2032,100000
-p37,p38,Possible,VSC,2032,100000
-p37,p42,Possible,VSC,2032,100000
-p37,p43,Possible,VSC,2032,100000
-p38,p39,Possible,VSC,2032,100000
-p38,p40,Possible,VSC,2032,100000
-p38,p43,Possible,VSC,2032,100000
-p38,p44,Possible,VSC,2032,100000
-p38,p45,Possible,VSC,2032,100000
-p39,p40,Possible,VSC,2032,100000
-p40,p41,Possible,VSC,2032,100000
-p40,p45,Possible,VSC,2032,100000
-p40,p52,Possible,VSC,2032,100000
-p41,p45,Possible,VSC,2032,100000
-p41,p53,Possible,VSC,2032,100000
-p41,p54,Possible,VSC,2032,100000
-p41,p70,Possible,VSC,2032,100000
-p42,p43,Possible,VSC,2032,100000
-p43,p44,Possible,VSC,2032,100000
-p43,p46,Possible,VSC,2032,100000
-p43,p68,Possible,VSC,2032,100000
-p44,p45,Possible,VSC,2032,100000
-p44,p68,Possible,VSC,2032,100000
-p45,p54,Possible,VSC,2032,100000
-p45,p68,Possible,VSC,2032,100000
-p45,p69,Possible,VSC,2032,100000
-p45,p70,Possible,VSC,2032,100000
-p46,p68,Possible,VSC,2032,100000
-p46,p74,Possible,VSC,2032,100000
-p46,p75,Possible,VSC,2032,100000
-p46,p76,Possible,VSC,2032,100000
-p46,p77,Possible,VSC,2032,100000
-p47,p48,Possible,VSC,2032,100000
-p48,p49,Possible,VSC,2032,100000
-p48,p50,Possible,VSC,2032,100000
-p49,p50,Possible,VSC,2032,100000
-p49,p52,Possible,VSC,2032,100000
-p49,p53,Possible,VSC,2032,100000
-p50,p51,Possible,VSC,2032,100000
-p50,p53,Possible,VSC,2032,100000
-p50,p56,Possible,VSC,2032,100000
-p50,p57,Possible,VSC,2032,100000
-p50,p85,Possible,VSC,2032,100000
-p51,p53,Possible,VSC,2032,100000
-p51,p55,Possible,VSC,2032,100000
-p51,p56,Possible,VSC,2032,100000
-p52,p53,Possible,VSC,2032,100000
-p53,p54,Possible,VSC,2032,100000
-p53,p55,Possible,VSC,2032,100000
-p54,p55,Possible,VSC,2032,100000
-p54,p70,Possible,VSC,2032,100000
-p54,p71,Possible,VSC,2032,100000
-p54,p72,Possible,VSC,2032,100000
-p55,p56,Possible,VSC,2032,100000
-p55,p72,Possible,VSC,2032,100000
-p55,p84,Possible,VSC,2032,100000
-p55,p85,Possible,VSC,2032,100000
-p56,p85,Possible,VSC,2032,100000
-p57,p58,Possible,VSC,2032,100000
-p57,p66,Possible,VSC,2032,100000
-p57,p85,Possible,VSC,2032,100000
-p58,p66,Possible,VSC,2032,100000
-p58,p85,Possible,VSC,2032,100000
-p58,p86,Possible,VSC,2032,100000
-p58,p87,Possible,VSC,2032,100000
-p60,p61,Possible,VSC,2032,100000
-p60,p62,Possible,VSC,2032,100000
-p60,p63,Possible,VSC,2032,100000
-p61,p62,Possible,VSC,2032,100000
-p61,p63,Possible,VSC,2032,100000
-p61,p64,Possible,VSC,2032,100000
-p61,p65,Possible,VSC,2032,100000
-p63,p64,Possible,VSC,2032,100000
-p63,p67,Possible,VSC,2032,100000
-p64,p65,Possible,VSC,2032,100000
-p64,p67,Possible,VSC,2032,100000
-p65,p67,Possible,VSC,2032,100000
-p66,p86,Possible,VSC,2032,100000
-p68,p69,Possible,VSC,2032,100000
-p68,p77,Possible,VSC,2032,100000
-p69,p70,Possible,VSC,2032,100000
-p69,p77,Possible,VSC,2032,100000
-p70,p71,Possible,VSC,2032,100000
-p70,p77,Possible,VSC,2032,100000
-p70,p80,Possible,VSC,2032,100000
-p70,p81,Possible,VSC,2032,100000
-p71,p72,Possible,VSC,2032,100000
-p71,p81,Possible,VSC,2032,100000
-p72,p73,Possible,VSC,2032,100000
-p72,p81,Possible,VSC,2032,100000
-p72,p84,Possible,VSC,2032,100000
-p73,p84,Possible,VSC,2032,100000
-p74,p75,Possible,VSC,2032,100000
-p74,p103,Possible,VSC,2032,100000
-p75,p76,Possible,VSC,2032,100000
-p75,p79,Possible,VSC,2032,100000
-p76,p77,Possible,VSC,2032,100000
-p76,p78,Possible,VSC,2032,100000
-p76,p79,Possible,VSC,2032,100000
-p77,p78,Possible,VSC,2032,100000
-p77,p80,Possible,VSC,2032,100000
-p78,p79,Possible,VSC,2032,100000
-p78,p80,Possible,VSC,2032,100000
-p79,p80,Possible,VSC,2032,100000
-p80,p81,Possible,VSC,2032,100000
-p80,p82,Possible,VSC,2032,100000
-p80,p83,Possible,VSC,2032,100000
-p80,p105,Possible,VSC,2032,100000
-p81,p82,Possible,VSC,2032,100000
-p81,p83,Possible,VSC,2032,100000
-p81,p93,Possible,VSC,2032,100000
-p81,p107,Possible,VSC,2032,100000
-p81,p108,Possible,VSC,2032,100000
-p82,p83,Possible,VSC,2032,100000
-p83,p105,Possible,VSC,2032,100000
-p83,p107,Possible,VSC,2032,100000
-p84,p85,Possible,VSC,2032,100000
-p84,p92,Possible,VSC,2032,100000
-p85,p86,Possible,VSC,2032,100000
-p85,p87,Possible,VSC,2032,100000
-p85,p92,Possible,VSC,2032,100000
-p86,p87,Possible,VSC,2032,100000
-p87,p88,Possible,VSC,2032,100000
-p87,p89,Possible,VSC,2032,100000
-p87,p90,Possible,VSC,2032,100000
-p87,p92,Possible,VSC,2032,100000
-p88,p89,Possible,VSC,2032,100000
-p88,p92,Possible,VSC,2032,100000
-p89,p90,Possible,VSC,2032,100000
-p89,p92,Possible,VSC,2032,100000
-p89,p94,Possible,VSC,2032,100000
-p90,p91,Possible,VSC,2032,100000
-p90,p94,Possible,VSC,2032,100000
-p91,p94,Possible,VSC,2032,100000
-p91,p101,Possible,VSC,2032,100000
-p92,p93,Possible,VSC,2032,100000
-p92,p94,Possible,VSC,2032,100000
-p92,p97,Possible,VSC,2032,100000
-p92,p108,Possible,VSC,2032,100000
-p92,p109,Possible,VSC,2032,100000
-p92,p118,Possible,VSC,2032,100000
-p93,p108,Possible,VSC,2032,100000
-p93,p109,Possible,VSC,2032,100000
-p94,p95,Possible,VSC,2032,100000
-p94,p96,Possible,VSC,2032,100000
-p94,p97,Possible,VSC,2032,100000
-p94,p101,Possible,VSC,2032,100000
-p95,p96,Possible,VSC,2032,100000
-p95,p97,Possible,VSC,2032,100000
-p95,p98,Possible,VSC,2032,100000
-p96,p98,Possible,VSC,2032,100000
-p97,p98,Possible,VSC,2032,100000
-p97,p118,Possible,VSC,2032,100000
-p98,p99,Possible,VSC,2032,100000
-p99,p100,Possible,VSC,2032,100000
-p99,p116,Possible,VSC,2032,100000
-p99,p118,Possible,VSC,2032,100000
-p99,p121,Possible,VSC,2032,100000
-p99,p123,Possible,VSC,2032,100000
-p100,p116,Possible,VSC,2032,100000
-p101,p102,Possible,VSC,2032,100000
-p103,p104,Possible,VSC,2032,100000
-p103,p105,Possible,VSC,2032,100000
-p103,p111,Possible,VSC,2032,100000
-p104,p105,Possible,VSC,2032,100000
-p105,p106,Possible,VSC,2032,100000
-p105,p107,Possible,VSC,2032,100000
-p105,p112,Possible,VSC,2032,100000
-p106,p107,Possible,VSC,2032,100000
-p107,p108,Possible,VSC,2032,100000
-p107,p109,Possible,VSC,2032,100000
-p107,p112,Possible,VSC,2032,100000
-p107,p114,Possible,VSC,2032,100000
-p108,p109,Possible,VSC,2032,100000
-p109,p110,Possible,VSC,2032,100000
-p109,p112,Possible,VSC,2032,100000
-p109,p114,Possible,VSC,2032,100000
-p109,p118,Possible,VSC,2032,100000
-p110,p112,Possible,VSC,2032,100000
-p110,p117,Possible,VSC,2032,100000
-p110,p118,Possible,VSC,2032,100000
-p111,p112,Possible,VSC,2032,100000
-p111,p115,Possible,VSC,2032,100000
-p111,p116,Possible,VSC,2032,100000
-p111,p122,Possible,VSC,2032,100000
-p112,p113,Possible,VSC,2032,100000
-p112,p114,Possible,VSC,2032,100000
-p112,p115,Possible,VSC,2032,100000
-p112,p116,Possible,VSC,2032,100000
-p112,p117,Possible,VSC,2032,100000
-p113,p114,Possible,VSC,2032,100000
-p115,p116,Possible,VSC,2032,100000
-p115,p122,Possible,VSC,2032,100000
-p116,p117,Possible,VSC,2032,100000
-p116,p118,Possible,VSC,2032,100000
-p116,p120,Possible,VSC,2032,100000
-p116,p121,Possible,VSC,2032,100000
-p117,p118,Possible,VSC,2032,100000
-p119,p122,Possible,VSC,2032,100000
-p120,p121,Possible,VSC,2032,100000
-p120,p122,Possible,VSC,2032,100000
-p121,p122,Possible,VSC,2032,100000
-p121,p123,Possible,VSC,2032,100000
-p122,p123,Possible,VSC,2032,100000
-p122,p125,Possible,VSC,2032,100000
-p122,p126,Possible,VSC,2032,100000
-p122,p127,Possible,VSC,2032,100000
-p123,p124,Possible,VSC,2032,100000
-p123,p125,Possible,VSC,2032,100000
-p125,p126,Possible,VSC,2032,100000
-p126,p127,Possible,VSC,2032,100000
-p127,p128,Possible,VSC,2032,100000
-p127,p129,Possible,VSC,2032,100000
-p127,p131,Possible,VSC,2032,100000
-p127,p132,Possible,VSC,2032,100000
-p128,p132,Possible,VSC,2032,100000
-p129,p130,Possible,VSC,2032,100000
-p129,p131,Possible,VSC,2032,100000
-p130,p131,Possible,VSC,2032,100000
-p130,p134,Possible,VSC,2032,100000
-p131,p132,Possible,VSC,2032,100000
-p131,p133,Possible,VSC,2032,100000
-p132,p133,Possible,VSC,2032,100000
-p20,p35,Possible,VSC,2032,100000
-p24,p39,Possible,VSC,2032,100000
-p31,p47,Possible,VSC,2032,100000
-p31,p48,Possible,VSC,2032,100000
-p32,p38,Possible,VSC,2032,100000
-p34,p52,Possible,VSC,2032,100000
-p50,p60,Possible,VSC,2032,100000
-p57,p63,Possible,VSC,2032,100000
diff --git a/inputs/transmission/transmission_capacity_future_ba_baseline.csv b/inputs/transmission/transmission_capacity_future_ba_baseline.csv
deleted file mode 100644
index 6cceaece9..000000000
--- a/inputs/transmission/transmission_capacity_future_ba_baseline.csv
+++ /dev/null
@@ -1,120 +0,0 @@
-r,rr,status,trtype,t,MW,notes
-p61,p62,Certain,AC,2010,1021
-p61,p63,Certain,AC,2010,387
-p12,p13,Certain,AC,2011,1262
-p12,p15,Certain,AC,2011,765
-p60,p63,Certain,AC,2011,606
-p99,p100,Certain,AC,2011,2706
-p100,p116,Certain,AC,2011,2308
-p115,p116,Certain,AC,2011,2077
-p2,p5,Certain,AC,2012,1275
-p40,p53,Certain,AC,2012,514
-p60,p61,Certain,AC,2012,493
-p61,p63,Certain,AC,2012,387
-p12,p13,Certain,AC,2013,1262
-p50,p51,Certain,AC,2013,866
-p60,p63,Certain,AC,2013,1819
-p61,p62,Certain,AC,2013,1021
-p61,p64,Certain,AC,2013,418
-p79,p80,Certain,AC,2013,772
-p131,p132,Certain,AC,2013,1098
-p43,p46,Certain,AC,2014,839
-p48,p50,Certain,AC,2014,908
-p49,p50,Certain,AC,2014,460
-p2,p5,Certain,AC,2015,1275
-p37,p43,Certain,AC,2015,402
-p38,p44,Certain,AC,2015,490
-p43,p44,Certain,AC,2015,615
-p45,p69,Certain,AC,2015,648
-p111,p115,Certain,AC,2015,836
-p122,p126,Certain,AC,2015,2140
-p131,p133,Certain,AC,2015,1479
-p132,p133,Certain,AC,2015,1104
-p45,p68,Certain,AC,2016,704
-p41,p54,Certain,AC,2017,665
-p46,p78,Certain,AC,2018,549
-p70,p71,Certain,AC,2018,797
-p71,p81,Certain,AC,2018,736
-p81,p82,Certain,AC,2018,1074
-p82,p83,Certain,AC,2018,1055
-p105,p107,Certain,AC,2018,4385
-p36,p38,Certain,AC,2019,464
-p5,p7,Certain,AC,2020,1974
-p7,p15,Certain,AC,2020,1215
-p42,p43,Certain,AC,2020,654
-p83,p105,Certain,AC,2020,710
-p10,p28,Possible,AC,2023,970
-p15,p16,Possible,AC,2024,1391
-p16,p21,Possible,AC,2024,1624
-p21,p24,Possible,AC,2024,3026
-p21,p33,Possible,AC,2024,1023
-p25,p26,Possible,AC,2024,2046
-p26,p33,Possible,AC,2024,1088
-p28,p31,Possible,AC,2025,3000
-p5,p15,Possible,AC,2026,1100
-### vvv The next lines are prospective lines; they were used for the National
-### Transmission Planning study but are turned off by default
-# p33,p34,Certain,AC,2030,3274,Colorado Power Pathway (2x 500 kV)
-# p12,p13,Certain,AC,2030,2000,Greenlink Nevada (525 kV)
-# p13,p25,Certain,AC,2030,1600,TransWest Express
-# p24,p25,Certain,LCC,2030,3000,TransWest Express
-# p5,p15,Certain,AC,2030,1732,Boardman to Hemingway
-### ^^^
-p80,p104,Possible,AC,0,100000,Allows Chicago to connect to rest of PJM
-p104,p111,Possible,AC,0,100000,Allows SW Michigan to connect to rest of PJM
-### vvv Candidate offshore links
-## Land-to-offshore links (these are only built for offshore wind delivery
-## so they won't be built early unless there's exogenous offshore wind)
-oWA,p1,Possible,VSC,2010,100000
-oOR_N,p5,Possible,VSC,2010,100000
-oOR_S,p6,Possible,VSC,2010,100000
-oCA_NNN,p9,Possible,VSC,2010,100000
-oCA_NN,p9,Possible,VSC,2010,100000
-oCA_N,p9,Possible,VSC,2010,100000
-oCA_S,p9,Possible,VSC,2010,100000
-oTX,p67,Possible,VSC,2010,100000
-oLA,p58,Possible,VSC,2010,100000
-oMS,p87,Possible,VSC,2010,100000
-oAL,p90,Possible,VSC,2010,100000
-oFL_W,p101,Possible,VSC,2010,100000
-oFL_E,p102,Possible,VSC,2010,100000
-oGA,p94,Possible,VSC,2010,100000
-oSC,p96,Possible,VSC,2010,100000
-oNC_S,p98,Possible,VSC,2010,100000
-oNC_N,p98,Possible,VSC,2010,100000
-oVA,p99,Possible,VSC,2010,100000
-oMD,p123,Possible,VSC,2010,100000
-oDE,p125,Possible,VSC,2010,100000
-oNJ,p126,Possible,VSC,2010,100000
-oNY,p128,Possible,VSC,2010,100000
-oCT,p132,Possible,VSC,2010,100000
-oRI,p133,Possible,VSC,2010,100000
-oMA,p131,Possible,VSC,2010,100000
-oNH,p130,Possible,VSC,2010,100000
-oME,p134,Possible,VSC,2010,100000
-## Offshore-to-offshore links
-oWA,oOR_N,Possible,VSC,0,100000
-oOR_N,oOR_S,Possible,VSC,0,100000
-oOR_S,oCA_NNN,Possible,VSC,0,100000
-oCA_NNN,oCA_NN,Possible,VSC,0,100000
-oCA_NN,oCA_N,Possible,VSC,0,100000
-oCA_N,oCA_S,Possible,VSC,0,100000
-oTX,oLA,Possible,VSC,0,100000
-oLA,oMS,Possible,VSC,0,100000
-oMS,oAL,Possible,VSC,0,100000
-oAL,oFL_W,Possible,VSC,0,100000
-oFL_E,oGA,Possible,VSC,0,100000
-oGA,oSC,Possible,VSC,0,100000
-oSC,oNC_S,Possible,VSC,0,100000
-oNC_S,oNC_N,Possible,VSC,0,100000
-oNC_N,oVA,Possible,VSC,0,100000
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diff --git a/inputs/transmission/transmission_capacity_future_ba_default.csv b/inputs/transmission/transmission_capacity_future_ba_default.csv
deleted file mode 100644
index c9e6e6370..000000000
--- a/inputs/transmission/transmission_capacity_future_ba_default.csv
+++ /dev/null
@@ -1 +0,0 @@
-r,rr,status,trtype,t,MW
\ No newline at end of file
diff --git a/inputs/transmission/transmission_capacity_future_county_baseline.csv b/inputs/transmission/transmission_capacity_future_county_baseline.csv
deleted file mode 100644
index b207e0575..000000000
--- a/inputs/transmission/transmission_capacity_future_county_baseline.csv
+++ /dev/null
@@ -1 +0,0 @@
-r,rr,status,trtype,t,MW
diff --git a/inputs/transmission/transmission_capacity_future_county_default.csv b/inputs/transmission/transmission_capacity_future_county_default.csv
deleted file mode 100644
index c9e6e6370..000000000
--- a/inputs/transmission/transmission_capacity_future_county_default.csv
+++ /dev/null
@@ -1 +0,0 @@
-r,rr,status,trtype,t,MW
\ No newline at end of file
diff --git a/inputs/transmission/transmission_cost_ac_500kv_county.h5 b/inputs/transmission/transmission_cost_ac_500kv_county.h5
deleted file mode 100644
index 795c94a46..000000000
--- a/inputs/transmission/transmission_cost_ac_500kv_county.h5
+++ /dev/null
@@ -1,3 +0,0 @@
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-size 8140792
diff --git a/inputs/transmission/transmission_cost_ac_500kv_ba.h5 b/inputs/transmission/transmission_cost_ac_500kv_z134.h5
similarity index 100%
rename from inputs/transmission/transmission_cost_ac_500kv_ba.h5
rename to inputs/transmission/transmission_cost_ac_500kv_z134.h5
diff --git a/inputs/transmission/transmission_cost_dc_ba.csv b/inputs/transmission/transmission_cost_dc_ba.csv
deleted file mode 100644
index ce734d697..000000000
--- a/inputs/transmission/transmission_cost_dc_ba.csv
+++ /dev/null
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diff --git a/inputs/transmission/transmission_cost_dc_county.csv b/inputs/transmission/transmission_cost_dc_county.csv
deleted file mode 100644
index e28d7de97..000000000
--- a/inputs/transmission/transmission_cost_dc_county.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-r,rr,USD2004perMW
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diff --git a/inputs/transmission/transmission_cost_distance.csv b/inputs/transmission/transmission_cost_distance.csv
new file mode 100644
index 000000000..0c726da78
--- /dev/null
+++ b/inputs/transmission/transmission_cost_distance.csv
@@ -0,0 +1,9313 @@
+start,end,polarity,voltage,cost_MUSD,length_miles
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diff --git a/inputs/transmission/transmission_cost_distance_lines.csv b/inputs/transmission/transmission_cost_distance_lines.csv
new file mode 100644
index 000000000..0784af3d7
--- /dev/null
+++ b/inputs/transmission/transmission_cost_distance_lines.csv
@@ -0,0 +1,499 @@
+name,from_lon,from_lat,to_lon,to_lat,polarity,voltage,cost_MUSD,length_miles
+cross_sound_cable,-72.90222222,41.28666667,-72.8675,40.95916667,dc,500,158.9,25.01
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+neptune_cable,-73.55111111,40.76055556,-74.35308333,40.47371667,dc,500,779.16,61.47
+pacific_dc_intertie,-121.11416667,45.59416667,-118.48916667,34.31083333,dc,500,2564.51,898.16
+path_27,-112.58083333,39.50055556,-117.43722222,34.55111111,dc,500,1257.08,509.74
+square_butte,-101.19583333,47.07166667,-92.29416667,46.77361111,dc,500,976.0,438.59
+sunzia,-105.5939222,34.3491456,-111.5425066,32.8669515,dc,500,923.75,376.75
+trans_bay_cable,-121.89666667,38.03083333,-122.38583333,37.75472222,dc,500,384.27,39.89
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+z134:p12~~p25,-116.76501417,39.57265625,-111.91422885,39.08215518,dc,500,748.84,322.11
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diff --git a/inputs/transmission/transmission_distance_ba.h5 b/inputs/transmission/transmission_distance_ba.h5
deleted file mode 100644
index f354ceb96..000000000
--- a/inputs/transmission/transmission_distance_ba.h5
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c6afa6468e969654ae9607da424d691536c8a260fe7e5418422fcd7d386576e2
-size 84209
diff --git a/inputs/transmission/transmission_distance_county.h5 b/inputs/transmission/transmission_distance_county.h5
deleted file mode 100644
index 6663648d6..000000000
--- a/inputs/transmission/transmission_distance_county.h5
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aa2248aade935ec1ef6d30aff93b2f20cb035e0dfd353fc43aa97e5840383112
-size 1894282
diff --git a/inputs/userinput/futurefiles.csv b/inputs/userinput/futurefiles.csv
index defb65b56..5ce71c1be 100644
--- a/inputs/userinput/futurefiles.csv
+++ b/inputs/userinput/futurefiles.csv
@@ -3,7 +3,6 @@ aclike.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 acp_disallowed.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 acp_prices.csv,.csv,0,None,wide,st,1,0,constant,None,None,done,constant
 agglevels.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-aggreg2anchorreg.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 allout_RSC.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 allt.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 alpha.csv,.csv,0,None,t,None,1,0,constant,None,None,done,linear_10
@@ -87,7 +86,6 @@ csapr_group2_ex.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 csapr_ozone_season.csv,.csv,0,None,wide,"st,csapr_cat",1,0,constant,None,None,done,constant
 csp.h5,.h5,1,None,9999,None,9999,0,constant,None,None,new,
 ctt.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-ctus_r_cs_spurlines_200mi.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 currency_incentives.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 d_szn_1yr.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 d_szn_7yr.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
@@ -163,7 +161,7 @@ gasprice_ref.csv,.csv,0,None,wide,cendiv,1,0,constant,0,None,done,linear_5
 gb.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 gbin_min.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 gbin.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-gen_mandate_tech_list.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
+nat_gen_tech_frac.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 gen_mandate_trajectory.csv,.csv,0,None,*t,None,0,0,constant,None,None,new,
 geo_discovery_factor.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 geo_discovery_rate.csv,.csv,0,None,*t,None,0,0,linear_5,None,None,new,
@@ -233,6 +231,7 @@ i.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 incentives.csv,.csv,0,None,t_start_construction,"i,country",1,0,constant,None,None,[CHECK] do we need to forecast within calc_financial_inputs?; new,
 inflation.csv,.csv,0,None,t,None,0,0,constant,None,None,done,constant
 inputs.gdx,.gdx,1,None,9999,None,9999,0,constant,None,None,done (wouldn't exist yet in normal run),constant
+inputs.h5,.h5,1,None,9999,None,9999,0,constant,None,None,new,
 interconnection_queues.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 itc_energy_comm_bonus.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 itc_frac_monetized.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new; handled in calc_financial_inputs.py,
@@ -283,7 +282,6 @@ objective_function_params.yaml,.yaml,1,None,9999,None,9999,0,constant,None,None,
 offshore_goal_fix.csv,.csv,0,None,wide,st,1,0,constant,0,None,done,constant
 offshore_goal_float.csv,.csv,0,None,wide,st,1,0,constant,0,None,done,constant
 offshore_req.csv,.csv,0,None,wide,st,1,0,constant,0,None,done,constant
-offshore.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 ofstype_i.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 ofstype.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 ofswind_rsc_mult.csv,.csv,0,None,t,None,1,0,constant,0,None,done,linear_5
@@ -393,7 +391,6 @@ retire_penalty.csv,.csv,0,None,0,None,0,None,constant,None,None,new,
 retirements_energy.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 retirements.csv,.csv,1,None,9999,None,9999,0,constant,None,None,"discuss, make sure it should just be copied",constant
 rev_paths.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-rev_transmission_basecost.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 rggi_states.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 rggicon.csv,.csv,0,None,0,None,0,None,constant,None,None,done,constant
 routes_adjacent.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
@@ -433,7 +430,6 @@ supplycurve_geohydro.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 supplycurve_upv.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 supplycurve_wind-ofs.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 supplycurve_wind-ons.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-sw.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 switches.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 tax_rate.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new; handled in calc_financial_inputs.py,
 tc_phaseout_schedule.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
@@ -458,10 +454,6 @@ trans_cap_cost_mult_noITC.csv,.csv,1,None,*t,None,0,0,constant,None,None,handled
 trans_cap_cost_mult.csv,.csv,1,None,*t,None,0,0,constant,None,None,handled in calc_financial_inputs.py,constant
 trans_intra_cost_adder.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 trans_itc_fractions.csv,.csv,1,None,9999,None,9999,0,constant,None,None,constant or expire?,constant
-transmission_capacity_future_baseline.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-transmission_capacity_future.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-transmission_cost_ac.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
-transmission_cost_dc.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 transmission_distance_cost_500kVac.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 transmission_distance_cost_500kVdc.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 transmission_distance.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
@@ -487,23 +479,10 @@ upgrade_mult_mid.csv,.csv,0,None,wide,dummy,1,0,linear_5,0,None,new,
 upgradelink_water.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 uranium_price.csv,.csv,0,None,year,None,0,0,linear_5,0,None,new,
 va_ng_crf_penalty.csv,.csv,0,None,0,None,0,None,constant,None,None,done,constant
-val_aggreg.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 val_ba.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_cendiv.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_country.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_county.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
+county.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 val_cs.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_h2ptcreg.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_hurdlereg.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_interconnect.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_itlgrp.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_nercr.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 val_r_all.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_r.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
-val_st.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_transgrp.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_transreg.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
-val_usda_region.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 var_map.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
 w.csv,.csv,1,None,9999,None,9999,0,constant,None,None,new,
 wat_access_cap_cost.csv,.csv,1,None,9999,None,9999,0,constant,None,None,done,constant
diff --git a/inputs/userinput/mcs_distribution_rules.yaml b/inputs/userinput/mcs_distribution_rules.yaml
new file mode 100644
index 000000000..f69c526ec
--- /dev/null
+++ b/inputs/userinput/mcs_distribution_rules.yaml
@@ -0,0 +1,58 @@
+# Rules for validating MCS distribution specifications.
+# Each top-level key is a valid distribution name.
+# These rules are loaded by general_mcs_dist_validation() in mcs_sampler.py
+# and checked against every distribution group in the user's mcs_distributions_*.yaml file.
+#
+# Fields:
+#   required_keys:
+#     List of keys that must be present in the distribution group entry
+#     (e.g. ['name', 'assignments_list', 'dist', 'dist_params', 'weight_r']).
+#
+#   multiplicative:
+#     true  — distribution applies a scalar multiplier to a single reference value.
+#     false — distribution blends across multiple reference files/values.
+#
+#   num_assignments:
+#     Fixed int — groups with this distribution must have exactly this many swith assignments;
+#                 for multiplicative this defines the number of required dist_params.
+#     "match_dist_params" — the number of assignments per switch must equal len(dist_params).
+#
+#   sampling_methods:
+#     List of compatible sampling methods (e.g. ['random', 'lhs']).
+#     Validated against the MCS_lhs switch setting.
+
+dirichlet:
+  required_keys: ['name', 'assignments_list', 'dist', 'dist_params', 'weight_r']
+  multiplicative: false
+  num_assignments: "match_dist_params"
+  sampling_methods: ['random']
+
+discrete:
+  required_keys: ['name', 'assignments_list', 'dist', 'dist_params', 'weight_r']
+  multiplicative: false
+  num_assignments: "match_dist_params"
+  sampling_methods: ['random', 'lhs']
+
+uniform:
+  required_keys: ['name', 'assignments_list', 'dist', 'weight_r']
+  multiplicative: false
+  num_assignments: 2
+  sampling_methods: ['lhs']
+
+triangular:
+  required_keys: ['name', 'assignments_list', 'dist', 'weight_r']
+  multiplicative: false
+  num_assignments: 3
+  sampling_methods: ['lhs']
+
+uniform_multiplier:
+  required_keys: ['name', 'assignments_list', 'dist', 'dist_params', 'weight_r']
+  multiplicative: true
+  num_assignments: 2
+  sampling_methods: ['random', 'lhs']
+
+triangular_multiplier:
+  required_keys: ['name', 'assignments_list', 'dist', 'dist_params', 'weight_r']
+  multiplicative: true
+  num_assignments: 3
+  sampling_methods: ['random', 'lhs']
diff --git a/inputs/userinput/mcs_distributions_default.yaml b/inputs/userinput/mcs_distributions_default.yaml
index 9c301df24..530c9cacb 100644
--- a/inputs/userinput/mcs_distributions_default.yaml
+++ b/inputs/userinput/mcs_distributions_default.yaml
@@ -1,4 +1,5 @@
 # Description: This file defines the distributions used for Monte Carlo Sampling (MCS) analysis.
+# For rules on the distribution entries and support see inputs/userinput/mcs_distribution_rules.yaml
 
 # Distribution definition fields:
 # 1. name: A unique identifier for the distribution group, used in the 'MCS_dist_groups' switch.
@@ -15,18 +16,23 @@
 # 3. dist: Type of distribution to use. Options are:
 #    - dirichlet (weighted average)
 #    - discrete
+#    - triangular
 #    - triangular_multiplier
+#    - uniform
 #    - uniform_multiplier
 #
 # 4. dist_params (list): Parameters that define the distribution, based on its type:
 #    - dirichlet: [alpha1, alpha2, ...] — concentration parameters
 #    - discrete: [w1, w2, ...] — prob weights (normalized internally)
+#    - triangular: not required
 #    - triangular_multiplier: [low, center, high]
+#    - uniform: not required
 #    - uniform_multiplier: [low, high]
 #
 # 5. weight_r: For some files (those with regional data) we support the possibility of applying
 #    different weights to different ReEDS hierarchies. This field specifies the hierarchy 
 #    considered (e.g. country, st, ba, transgrp, etc- weight_r: country is always supported).
+#    Note that regional weights are not currently supported with latin hypercube sampling.
 #
 # Special cases:
 # 1. switches.csv: If you want to apply a distribution to a value in switches.csv,
@@ -46,6 +52,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: gas_tri
+  assignments_list:
+    - plantchar_gas_ccs: [gas-ccs_ATB_2024_conservative, gas-ccs_ATB_2024_moderate, gas-ccs_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: coal
   assignments_list:
     - plantchar_coal_ccs: [coal-ccs_ATB_2024_conservative, coal-ccs_ATB_2024_moderate, coal-ccs_ATB_2024_advanced]
@@ -53,6 +65,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: coal_tri
+  assignments_list:
+    - plantchar_coal_ccs: [coal-ccs_ATB_2024_conservative, coal-ccs_ATB_2024_moderate, coal-ccs_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: geo
   assignments_list:
     - plantchar_geo: [geo_ATB_2024_conservative, geo_ATB_2024_moderate, geo_ATB_2024_advanced]
@@ -60,6 +78,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: geo_tri
+  assignments_list:
+    - plantchar_geo: [geo_ATB_2024_conservative, geo_ATB_2024_moderate, geo_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: hydro
   assignments_list: 
     - plantchar_hydro: [hydro_ATB_2019_constant, hydro_ATB_2019_mid, hydro_ATB_2019_low]
@@ -67,6 +91,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: hydro_tri
+  assignments_list:
+    - plantchar_hydro: [hydro_ATB_2019_constant, hydro_ATB_2019_mid, hydro_ATB_2019_low]
+  dist: triangular
+  weight_r: country
+
 - name: nuclear
   assignments_list:
     - plantchar_nuclear: [nuclear_ATB_2024_conservative, nuclear_ATB_2024_moderate, nuclear_ATB_2024_advanced]
@@ -75,6 +105,13 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: nuclear_tri
+  assignments_list:
+    - plantchar_nuclear: [nuclear_ATB_2024_conservative, nuclear_ATB_2024_moderate, nuclear_ATB_2024_advanced]
+    - plantchar_nuclear_smr: [nuclear-smr_ATB_2024_conservative, nuclear-smr_ATB_2024_moderate, nuclear-smr_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: battery
   assignments_list:
     - plantchar_battery: [battery_ATB_2024_conservative, battery_ATB_2024_moderate, battery_ATB_2024_advanced]
@@ -82,6 +119,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: battery_tri
+  assignments_list:
+    - plantchar_battery: [battery_ATB_2024_conservative, battery_ATB_2024_moderate, battery_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: upv
   assignments_list:
     - plantchar_upv: [upv_ATB_2024_conservative, upv_ATB_2024_moderate, upv_ATB_2024_advanced]
@@ -89,6 +132,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: upv_tri
+  assignments_list:
+    - plantchar_upv: [upv_ATB_2024_conservative, upv_ATB_2024_moderate, upv_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: csp
   assignments_list:
     - plantchar_csp: [csp_ATB_2024_conservative, csp_ATB_2024_moderate, csp_ATB_2024_advanced]
@@ -103,6 +152,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: ofswind_tri
+  assignments_list:
+    - plantchar_ofswind: [ofs-wind_ATB_2024_conservative, ofs-wind_ATB_2024_moderate, ofs-wind_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 - name: onswind
   assignments_list:
     - plantchar_onswind: [ons-wind_ATB_2024_conservative, ons-wind_ATB_2024_moderate, ons-wind_ATB_2024_advanced]
@@ -110,6 +165,12 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: onswind_tri
+  assignments_list:
+    - plantchar_onswind: [ons-wind_ATB_2024_conservative, ons-wind_ATB_2024_moderate, ons-wind_ATB_2024_advanced]
+  dist: triangular
+  weight_r: country
+
 # --- Combined technology sampling ---
 # These distributions sample multiple technologies together with shared correlation
 - name: tech
@@ -147,6 +208,11 @@
   dist_params: [1, 1, 1]
   weight_r: country
 
+- name: ng_fuel_price_tri
+  assignments_list:
+    - ngscen: [AEO_2025_HOG, AEO_2025_reference, AEO_2025_LOG]
+  dist: triangular
+  weight_r: country
 
 ### ---------------------------------------------------###
 ### --- Distributions for load
@@ -158,6 +224,12 @@
   dist_params: [1, 1]
   weight_r: country
 
+- name: load_country_unif
+  assignments_list: 
+    - GSw_LoadProfiles: [EER2025_IRAlow, EER2025_100by2050]
+  dist: uniform
+  weight_r: country
+
 - name: load_st
   assignments_list: 
     - GSw_LoadProfiles: [EER2025_100by2050, EER2025_IRAlow]
diff --git a/inputs/zones/PJMcounty/newlinks_offshore_radial.csv b/inputs/zones/PJMcounty/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..83a4e4f52
--- /dev/null
+++ b/inputs/zones/PJMcounty/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,p1
+oOR_N,p5
+oOR_S,p6
+oCA_NNN,p9
+oCA_NN,p9
+oCA_N,p9
+oCA_S,p9
+oTX,p67
+oLA,p58
+oMS,p87
+oAL,p90
+oFL_W,p101
+oFL_E,p102
+oGA,p94
+oSC,p96
+oNC_S,p37129
+oNC_N,p37129
+oVA,p51810
+oMD,p24047
+oDE,p10005
+oNJ,p34029
+oNY,p36103
+oCT,p132
+oRI,p133
+oMA,p131
+oNH,p130
+oME,p134
diff --git a/inputs/zones/README.md b/inputs/zones/README.md
index 54159636e..00fc6295b 100644
--- a/inputs/zones/README.md
+++ b/inputs/zones/README.md
@@ -5,6 +5,7 @@ Model zones are defined by the following user-generated files:
 - `county2zone.csv`: Maps from counties (`FIPS` column) to ReEDS zones (`ba` column)
 - `hierarchy.csv`: Maps from ReEDS zones (`r` column) to the larger region hierarchy levels
 - `b2b.csv`: Back-to-back converter (B2B) capacity [MW] between model zones
+- `newlinks_offshore_radial.csv`: Candiate connections between offshore and coastal land-based zones
 - TEMPORARY 20260402: `hierarchy_from134.csv`: The legacy-formatted `hierarchy.csv` file from the 134-zone version of ReEDS (will be removed once the rest of the spatial input processing is updated)
 
 And the following automatically generated files:
@@ -51,10 +52,15 @@ Once you're happy with your zone and hierarchy level definitions, run the follow
     - It also checks to make sure the user-specified `interconnect` for each zone matches the interconnection for the majority of network buses located within that zone.
 1. Calculate the AC interface transfer limits (ITLs) by running [TSC/interfacemax.py](https://github.nrel.gov/ReEDS/TSC/blob/pb/itldb/interfacemax.py)
     - Include the `--dbpath={path/to/ITL folder}` argument and point it to the existing directory of ITLs to avoid recalculating ITLs for interfaces that already have data
+1. Calculate the length and cost of new greenfield transmission lines between zones
+    - Write the new interzonal endpoint pairs using [TSC/analysis/zone_links.py](https://github.nrel.gov/ReEDS/TSC/blob/main/analysis/zone_links.py)
+        - Write pairs that require offshore routes (the links between land-based and offshore zones, new backbone links between offshore zones, and/or new exogenous offshore HVDC lines) into their own file, separate from routes that should be land-only
+    - Send these files (with a note on which should allow offshore paths and which should not) to a member of the reV team who can calculate the interzonal least-cost paths using the [reVRt](https://github.com/NatLabRockies/reVRt) tool
 1. Write the resulting files to the ReEDS repo by running [TSC/analysis/write_for_reeds.py](https://github.nrel.gov/ReEDS/TSC/blob/pb/itldb/analysis/write_for_reeds.py). This script:
     - Copies the `interface_r.csv` and `interface_transgrp.csv` files
     - Creates the `zonehash.csv` file
-    - Rewrites the `itl_NARIS.csv` file (existing data in the file are preserved, so you should only see new rows added to the bottom of the file)
+    - Rewrites the `itl_NARIS.csv`, `transmission_cost_distance.csv`, and `transmission_cost_distance_lines.csv` files
+    (existing data in the file are preserved, so you should only see new rows added, typically at the bottom of the file)
 1. Add the new zone definition to the choices for the `GSw_ZoneSet` switch in `cases.csv`
 1. To make sure it worked (or just to read the ITLs in general), you can run `import reeds` and then `reeds.inputs.get_itls(GSw_ZoneSet='your new zoneset name')` in Python with the `reeds2` conda environment activated.
 1. Try a ReEDS run.
diff --git a/inputs/zones/UTcounty/newlinks_offshore_radial.csv b/inputs/zones/UTcounty/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..abe9b78cc
--- /dev/null
+++ b/inputs/zones/UTcounty/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,p1
+oOR_N,p5
+oOR_S,p6
+oCA_NNN,p9
+oCA_NN,p9
+oCA_N,p9
+oCA_S,p9
+oTX,p67
+oLA,p58
+oMS,p87
+oAL,p90
+oFL_W,p101
+oFL_E,p102
+oGA,p94
+oSC,p96
+oNC_S,p98
+oNC_N,p98
+oVA,p99
+oMD,p123
+oDE,p125
+oNJ,p126
+oNY,p128
+oCT,p132
+oRI,p133
+oMA,p131
+oNH,p130
+oME,p134
diff --git a/inputs/zones/z132/newlinks_offshore_radial.csv b/inputs/zones/z132/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..abe9b78cc
--- /dev/null
+++ b/inputs/zones/z132/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,p1
+oOR_N,p5
+oOR_S,p6
+oCA_NNN,p9
+oCA_NN,p9
+oCA_N,p9
+oCA_S,p9
+oTX,p67
+oLA,p58
+oMS,p87
+oAL,p90
+oFL_W,p101
+oFL_E,p102
+oGA,p94
+oSC,p96
+oNC_S,p98
+oNC_N,p98
+oVA,p99
+oMD,p123
+oDE,p125
+oNJ,p126
+oNY,p128
+oCT,p132
+oRI,p133
+oMA,p131
+oNH,p130
+oME,p134
diff --git a/inputs/zones/z134/newlinks_offshore_radial.csv b/inputs/zones/z134/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..abe9b78cc
--- /dev/null
+++ b/inputs/zones/z134/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,p1
+oOR_N,p5
+oOR_S,p6
+oCA_NNN,p9
+oCA_NN,p9
+oCA_N,p9
+oCA_S,p9
+oTX,p67
+oLA,p58
+oMS,p87
+oAL,p90
+oFL_W,p101
+oFL_E,p102
+oGA,p94
+oSC,p96
+oNC_S,p98
+oNC_N,p98
+oVA,p99
+oMD,p123
+oDE,p125
+oNJ,p126
+oNY,p128
+oCT,p132
+oRI,p133
+oMA,p131
+oNH,p130
+oME,p134
diff --git a/inputs/zones/z3109/newlinks_offshore_radial.csv b/inputs/zones/z3109/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..3faaff3d5
--- /dev/null
+++ b/inputs/zones/z3109/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,p53073
+oOR_N,p41039
+oOR_S,p41019
+oCA_NNN,p06075
+oCA_NN,p06075
+oCA_N,p06075
+oCA_S,p06075
+oTX,p48039
+oLA,p22051
+oMS,p28047
+oAL,p01097
+oFL_W,p12057
+oFL_E,p12086
+oGA,p13051
+oSC,p45019
+oNC_S,p37129
+oNC_N,p37129
+oVA,p51810
+oMD,p24047
+oDE,p10005
+oNJ,p34029
+oNY,p36103
+oCT,p09190
+oRI,p44009
+oMA,p25009
+oNH,p33015
+oME,p23005
diff --git a/inputs/zones/z48/newlinks_offshore_radial.csv b/inputs/zones/z48/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..56c1efb1d
--- /dev/null
+++ b/inputs/zones/z48/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,WA
+oOR_N,OR
+oOR_S,OR
+oCA_NNN,CA
+oCA_NN,CA
+oCA_N,CA
+oCA_S,CA
+oTX,TX
+oLA,LA
+oMS,MS
+oAL,AL
+oFL_W,FL
+oFL_E,FL
+oGA,GA
+oSC,SC
+oNC_S,NC
+oNC_N,NC
+oVA,VA
+oMD,MD
+oDE,DE
+oNJ,NJ
+oNY,NY
+oCT,CT
+oRI,RI
+oMA,MA
+oNH,NH
+oME,ME
diff --git a/inputs/zones/z54/newlinks_offshore_radial.csv b/inputs/zones/z54/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..6435bfd3f
--- /dev/null
+++ b/inputs/zones/z54/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,WA
+oOR_N,OR
+oOR_S,OR
+oCA_NNN,CA_N
+oCA_NN,CA_N
+oCA_N,CA_N
+oCA_S,CA_N
+oTX,TX_ERCOT_E
+oLA,LA
+oMS,MS
+oAL,AL
+oFL_W,FL
+oFL_E,FL
+oGA,GA
+oSC,SC
+oNC_S,NC
+oNC_N,NC
+oVA,VA
+oMD,MD
+oDE,DE
+oNJ,NJ
+oNY,NY_NYCLI
+oCT,CT
+oRI,RI
+oMA,MA
+oNH,NH
+oME,ME
diff --git a/inputs/zones/z69/b2b.csv b/inputs/zones/z69/b2b.csv
index fbcc26923..e491edd25 100644
--- a/inputs/zones/z69/b2b.csv
+++ b/inputs/zones/z69/b2b.csv
@@ -5,4 +5,4 @@ NE,WY_WestConnect,310
 CO,KS,210
 NM_SPP,NM_WestConnect,400
 OK,TX_W_ERCOT,220
-TX_E_ERCOT,TX_SPP,600
+TX_E_ERCOT,TX_SPP_E,600
diff --git a/inputs/zones/z69/newlinks_offshore_radial.csv b/inputs/zones/z69/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..d4e4310de
--- /dev/null
+++ b/inputs/zones/z69/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,WA
+oOR_N,OR
+oOR_S,OR
+oCA_NNN,CA_N_CAISO
+oCA_NN,CA_N_CAISO
+oCA_N,CA_N_CAISO
+oCA_S,CA_N_CAISO
+oTX,TX_E_ERCOT
+oLA,LA
+oMS,MS_MISO
+oAL,AL
+oFL_W,FL_FRCC
+oFL_E,FL_FRCC
+oGA,GA
+oSC,SC
+oNC_S,NC
+oNC_N,NC
+oVA,VA
+oMD,MD
+oDE,DE
+oNJ,NJ
+oNY,NY_NYCLI
+oCT,CT
+oRI,RI
+oMA,MA
+oNH,NH
+oME,ME
diff --git a/inputs/zones/z90/newlinks_offshore_radial.csv b/inputs/zones/z90/newlinks_offshore_radial.csv
new file mode 100644
index 000000000..e24db37ad
--- /dev/null
+++ b/inputs/zones/z90/newlinks_offshore_radial.csv
@@ -0,0 +1,28 @@
+r,rr
+oWA,WA_W
+oOR_N,OR_W
+oOR_S,OR_W
+oCA_NNN,CA_N
+oCA_NN,CA_N
+oCA_N,CA_N
+oCA_S,CA_N
+oTX,TX_HOU
+oLA,LA
+oMS,MS_MISO
+oAL,AL
+oFL_W,FL_S
+oFL_E,FL_SE
+oGA,GA_S
+oSC,SC
+oNC_S,NC_SE
+oNC_N,NC_PJM
+oVA,VA_E
+oMD,MD
+oDE,DE
+oNJ,NJ
+oNY,NY_LI
+oCT,CT
+oRI,RI
+oMA,MA
+oNH,NH
+oME,ME
diff --git a/postprocessing/air_quality/health_damage_calculations.py b/postprocessing/air_quality/health_damage_calculations.py
index 7f4c9a841..01145251f 100644
--- a/postprocessing/air_quality/health_damage_calculations.py
+++ b/postprocessing/air_quality/health_damage_calculations.py
@@ -1,6 +1,6 @@
 '''
 This script can be run in one of two ways:
-1. called automatically from runbatch.py as part of a ReEDS run,
+1. called automatically from runreeds.py as part of a ReEDS run,
    in which case a single case folder is passed to the script 
 
 2. call directly to post-process a set of completed runs, with a csv file 
diff --git a/postprocessing/bokehpivot/in/reeds2/process_style.csv b/postprocessing/bokehpivot/in/reeds2/process_style.csv
index 1d12ac8fc..b2372a661 100644
--- a/postprocessing/bokehpivot/in/reeds2/process_style.csv
+++ b/postprocessing/bokehpivot/in/reeds2/process_style.csv
@@ -1,6 +1,6 @@
 order,color
 input_processing/copy_files.py,#393B79
-input_processing/aggregate_regions.py,#5254A3
+input_processing/h2_storage.py,#5254A3
 input_processing/calc_financial_inputs.py,#6B6ECF
 input_processing/fuelcostprep.py,#9C9EDE
 input_processing/writecapdat.py,#637939
@@ -15,16 +15,16 @@ input_processing/transmission.py,#7B4173
 input_processing/outage_rates.py,#A55194
 input_processing/hourly_repperiods.py,#CE6DBD
 input_processing/check_inputs.py,#DE9ED6
-createmodel.gms,#31A354
-d_solveoneyear.gms,#843C39
+a_createmodel.gms,#31A354
+3_solve_oneyear.gms,#843C39
 solver/barrier,#AD494A
 solver/crossover,#D6616B
 solver/remainder,#E7969C
-reeds_augur/prep_data.py,#3182BD
-reeds_augur/stress_periods.py,#6BAED6
-reeds_augur/capacity_credit.py,#9ECAE1
+ra/prep_data.py,#3182BD
+ra/stress_periods.py,#6BAED6
+ra/capacity_credit.py,#9ECAE1
 reeds2pras,#E6550D
 pras,#FD8D3C
-e_report.gms,#74C476
-e_report_dump.py,#A1D99B
+report.gms,#74C476
+report_dump.py,#A1D99B
 retail_rate_calculations.py,#C7E9C0
diff --git a/postprocessing/bokehpivot/reeds2.py b/postprocessing/bokehpivot/reeds2.py
index 7d8d0da49..dcb86cfca 100644
--- a/postprocessing/bokehpivot/reeds2.py
+++ b/postprocessing/bokehpivot/reeds2.py
@@ -99,7 +99,7 @@ def gather_cost_types(df):
 
 def pre_systemcost(dfs, **kw):
     df = dfs['sc'].copy()
-    sw = dfs['sw'].set_index('switch')['value'].copy()
+    sw = dfs['switches'].set_index('switch')['value'].copy()
     scalars = dfs['scalars'].set_index('scalar')['value'].copy()
     sim_years = sorted(dfs['sc']['year'].unique()) #Years Optimized
     sys_eval_years = int(sw['sys_eval_years'])
@@ -164,7 +164,7 @@ def pre_systemcost(dfs, **kw):
         ###### Add payments for pre-2010 capacity
         if ('remove_existing' not in kw) or (kw['remove_existing'] is False):
             ### Get modeled BAs
-            val_r = dfs['val_r'][0].values
+            val_r = dfs['r'].squeeze(1).values
             ### Get total historical capex in modeled BAs
             df_capex_init = dfs['df_capex_init']
             if 'maintain_ba_index' in kw and kw['maintain_ba_index'] is True:
@@ -1068,14 +1068,14 @@ def rgba2hex(rgba):
 def pre_runtime(dictin, **kw):
     """
     ### Use the code below to redefine the colormap when new scripts are added
-    augur_start = df.index.tolist().index('ReEDS_Augur/prep_data.py')
+    ra_start = df.index.tolist().index('ra/prep_data.py')
     for i, row in enumerate(df.index):
-        if i < augur_start:
+        if i < ra_start:
             colors[row.lower()] = rgba2hex(plt.cm.tab20b(i))
-        elif i < augur_start + 20:
-            colors[row.lower()] = rgba2hex(plt.cm.tab20c(i-augur_start))
+        elif i < ra_start + 20:
+            colors[row.lower()] = rgba2hex(plt.cm.tab20c(i-ra_start))
         else:
-            colors[row.lower()] = rgba2hex(plt.cm.tab20(i-augur_start-20))
+            colors[row.lower()] = rgba2hex(plt.cm.tab20(i-ra_start-20))
     """
     df = dictin['runtime'].copy()
 
@@ -1135,24 +1135,23 @@ def process_health_damage(df, **kw):
     rows = product(*allRows.values())
     df_all = pd.DataFrame.from_records(rows, columns=allRows.keys())
     df_new = df.merge(df_all, how='outer', on=['model', 'cr', 'e', 'rb', 'year'])
-    df_new['st'] = df_new['st'].interpolate(method="ffill")
     
     # sort by category and year
     df_new = df_new.sort_values(['model', 'cr', 'e', 'rb', 'year'])
 
     # interpolate any missing values 
-    df_new = df_new.groupby(['model', 'cr', 'e', 'rb']).apply(lambda group: group.interpolate(method='ffill'))
+    df_new = df_new.ffill()
 
     # sum over rb
-    df_out = df_new.groupby(['model', 'cr', 'e', 'year'])[
+    df_out = df_new.groupby(['model', 'cr', 'e', 'year'])[[
             'Emissions (thousand metric tons)', 'Health damages (billion $)', 
             'Health damages (lives)', 'Discounted health damages (billion $)'
-        ].sum().reset_index()
+    ]].sum().reset_index()
 
     # also sum over pollutant   
-    df_poll_agg = df_new.groupby(['model', 'cr', 'year'])[
+    df_poll_agg = df_new.groupby(['model', 'cr', 'year'])[[
             'Health damages (billion $)', 'Health damages (lives)', 'Discounted health damages (billion $)'
-        ].sum().reset_index()
+    ]].sum().reset_index()
 
     df_poll_agg.rename(columns={'Health damages (billion $)' : 'Total health damages (billion $)', 
                                 'Health damages (lives)' : 'Total health damages (lives)',
@@ -1174,7 +1173,7 @@ def process_social_costs(dfs, **kw):
     system_costs_agg.rename(columns={'Cost (Bil $)' : 'Cost (Bil $)-system', 
                                     'Discounted Cost (Bil $)' : 'Discounted Cost (Bil $)-system'}, inplace=True)
 
-    health_costs_agg = health_costs.groupby(['year', 'model', 'cr'])['Health damages (billion $)', 'Discounted health damages (billion $)'].sum().reset_index()
+    health_costs_agg = health_costs.groupby(['year', 'model', 'cr'])[['Health damages (billion $)', 'Discounted health damages (billion $)']].sum().reset_index()
     health_costs_agg.rename(columns={'Health damages (billion $)' : 'Cost (Bil $)-health', 
                                     'Discounted health damages (billion $)' : 'Discounted Cost (Bil $)-health'}, inplace=True)
 
@@ -2067,7 +2066,7 @@ def pre_spur(dfs, **kw):
 
     ('Health Damages from Emissions',
         {'file':'health_damages_caused_r.csv',
-        'columns': ['rb', 'st', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)'],
+        'columns': ['rb', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)'],
         'preprocess': [
             {'func': process_health_damage, 'args':{}},
         ],
@@ -2086,11 +2085,11 @@ def pre_spur(dfs, **kw):
             {'name': 'sc', 'file': 'systemcost', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
             {'name': 'q', 'file': 'reqt_quant', 'columns': ['type', 'subtype', 'rb', 'timeslice', 'year', 'q']},
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
-            {'name': 'health_damages', 'file': 'health_damages_caused_r.csv', 'columns': ['rb', 'st', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)']},
+            {'name': 'health_damages', 'file': 'health_damages_caused_r.csv', 'columns': ['rb', 'year', 'e', 'Emissions (thousand metric tons)', 'model', 'cr', 'Marginal damage ($/metric ton)', 'Health damages (billion $)', 'Health damages (lives)']},
         ],  
         'preprocess': [
             {'func': process_social_costs, 'args': {}},
@@ -2258,7 +2257,7 @@ def pre_spur(dfs, **kw):
             {'name': 'sc', 'file': 'systemcost', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
             {'name': 'pvf_cap', 'file': 'pvf_capital', 'columns': ['year', 'pvfcap']},
             {'name': 'pvf_onm', 'file': 'pvf_onm', 'columns': ['year', 'pvfonm']},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'index': ['cost_cat', 'year'],
@@ -2274,7 +2273,7 @@ def pre_spur(dfs, **kw):
     ('Sys Cost beyond final year (Bil $)',
         {'sources': [
             {'name': 'sc', 'file': 'systemcost_bulk', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'index': ['cost_cat', 'year'],
@@ -2294,9 +2293,9 @@ def pre_spur(dfs, **kw):
         {'sources': [
             {'name': 'sc', 'file': 'systemcost', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'index': ['cost_cat', 'year'],
@@ -2318,9 +2317,9 @@ def pre_spur(dfs, **kw):
         {'sources': [
             {'name': 'sc', 'file': 'systemcost_ba', 'columns': ['cost_cat', 'r', 'year', 'Cost (Bil $)']},
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'index': ['cost_cat', 'r', 'year'],
@@ -2349,9 +2348,9 @@ def pre_spur(dfs, **kw):
             {'name': 'sc', 'file': 'systemcost', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
             {'name': 'q', 'file': 'reqt_quant', 'columns': ['type', 'subtype', 'rb', 'timeslice', 'year', 'q']}, 
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'preprocess': [
@@ -2374,9 +2373,9 @@ def pre_spur(dfs, **kw):
             {'name': 'powerfrac_downstream', 'file': 'powerfrac_downstream', 'columns': ['rr', 'r', 'timeslice', 'year', 'frac']},
             {'name': 'powerfrac_upstream', 'file': 'powerfrac_upstream', 'columns': ['r', 'rr', 'timeslice', 'year', 'frac']},
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'preprocess': [
@@ -2394,9 +2393,9 @@ def pre_spur(dfs, **kw):
             {'name': 'sc', 'file': 'systemcost', 'columns': ['cost_cat', 'year', 'Cost (Bil $)']},
             {'name': 'emit', 'file': 'emit_nat', 'columns': ['year', 'CO2 (MMton)']},
             {'name': 'crf', 'file': '../inputs_case/crf.csv', 'columns': ['year', 'crf']},
-            {'name': 'val_r', 'file': '../inputs_case/val_r.csv', 'header':None},
+            {'name': 'r', 'file': '../inputs_case/val_r.csv', 'header':None},
             {'name': 'df_capex_init', 'file': '../inputs_case/df_capex_init.csv'},
-            {'name': 'sw', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
+            {'name': 'switches', 'file': '../inputs_case/switches.csv', 'header':None, 'columns': ['switch', 'value']},
             {'name': 'scalars', 'file': '../inputs_case/scalars.csv', 'header':None, 'columns': ['scalar', 'value', 'comment']},
         ],
         'preprocess': [
diff --git a/postprocessing/bokehpivot/reeds_bokeh.py b/postprocessing/bokehpivot/reeds_bokeh.py
index 105128d2f..40f6c69ad 100644
--- a/postprocessing/bokehpivot/reeds_bokeh.py
+++ b/postprocessing/bokehpivot/reeds_bokeh.py
@@ -22,7 +22,7 @@
 
 this_dir_path = os.path.dirname(os.path.realpath(__file__))
 sys.path.append(os.path.abspath(os.path.join(this_dir_path,'..','..')))
-from reeds.io import read_output
+from reeds.io import read_input, read_output
 
 logger = logging.getLogger('')
 #ReEDS globals
@@ -350,10 +350,11 @@ def get_src(scen, src):
         df_src = pd.DataFrame(data)
         df_src.columns = src['columns']
     elif src['file'].endswith('.csv'):
-        if 'header' in src and src['header'] == None:
-            df_src = pd.read_csv(filepath, low_memory=False, header=None)
-        else:
-            df_src = pd.read_csv(filepath, low_memory=False)
+        kwargs = {'header': None} if ('header' in src and src['header'] is None) else {}
+        try:
+            df_src = pd.read_csv(filepath, low_memory=False, **kwargs)
+        except FileNotFoundError:
+            df_src = read_input(scen['path'], src['name'], low_memory=False, **kwargs)
     else:
         df_src = read_output(scen['path'], filepath)
     if 'transpose' in src and src['transpose'] is True:
@@ -619,4 +620,4 @@ def df_to_lowercase(df):
     for col in df:
         if df[col].dtype == object:
             df[col] = df[col].str.lower()
-    return df
\ No newline at end of file
+    return df
diff --git a/postprocessing/cleanup_files.py b/postprocessing/cleanup_files.py
index 0e94b9e62..0ba36c28b 100644
--- a/postprocessing/cleanup_files.py
+++ b/postprocessing/cleanup_files.py
@@ -43,16 +43,15 @@
     ],
     ## Large output files. Can be regenerated without rerunning the case.
     3: [
-        os.path.join('outputs', 'Augur_plots'),
         os.path.join('outputs', 'hourly'),
         os.path.join('outputs', 'figures'),
     ],
     ## Largest output files. Would need to rerun the case to regenerate.
     4: [
         'g00files',
-        'ReEDS_Augur',
+        'handoff',
         ## The following regex matches the rep_{casename}.gdx file written by
-        ## e_report.gms (which contains the same data as outputs.h5)
+        ## report.gms (which contains the same data as outputs.h5)
         os.path.join('outputs', '^rep_.*\.gdx$'),
     ]
 }
diff --git a/postprocessing/compare_cases.py b/postprocessing/compare_cases.py
index 57e21efb5..cda0f96e7 100644
--- a/postprocessing/compare_cases.py
+++ b/postprocessing/compare_cases.py
@@ -2502,6 +2502,7 @@ def two_bars(dfplot, basecase, colors, ax, col=0, ypad=0.02):
 #%%### Copy some premade single-case plots
 level = dictin_sw[basecase]['GSw_PRM_StressThreshold'].split('_')[0]
 wide = 1 if len(hierarchy[basecase]['transreg'].unique()) > 6 else 0
+weatheryear = sw.GSw_HourlyWeatherYears.split('_')[0]
 metrics = [
     'cap',
     'rep_mean',
@@ -2516,6 +2517,8 @@ def two_bars(dfplot, basecase, colors, ax, col=0, ypad=0.02):
     (f'map_gencap_transcap-{lastyear}', None, SLIDE_HEIGHT),
     (f'plot_stressperiod_evolution-sum-{level}', SLIDE_WIDTH, None),
     (f'plot_dispatch-yearbymonth-1-{lastyear}', SLIDE_WIDTH, None),
+    ## Include both versions for backwards compatibility
+    (f'plot_dispatch-yearbymonth-1-{lastyear}-w{weatheryear}', SLIDE_WIDTH, None),
 ] + [
     (
         f"plot_techmix-transreg-{lastyear}-{units}-{reedsplots.stress_metrics_shorten(metrics)}",
diff --git a/postprocessing/example.csv b/postprocessing/example.csv
index 680e627df..560407213 100644
--- a/postprocessing/example.csv
+++ b/postprocessing/example.csv
@@ -7,8 +7,8 @@ casepath,casename,color
 # /Volumes/ReEDS/Users/pbrown/ReEDSruns/main/v20241028_mainK0_USA,Ref,C1
 # /Volumes/ReEDS/Users/pbrown/ReEDSruns/main/v20241028_mainK0_USA_decarb,Z35,plt.cm.tab20(4)
 ## kestrel
-# /projects/reedsweto/pbrown/github/ReEDS-2.0/runs/v20241028_mainK0_USA,main_Ref,plt.cm.tab20(0)
-# /projects/reedsweto/pbrown/github4/ReEDS-2.0/runs/v20241028_porK0_USA,por_Ref,plt.cm.tab20(1)
-# /projects/reedsweto/pbrown/github/ReEDS-2.0/runs/v20241028_mainK0_USA_decarb,main_Z35,plt.cm.tab20(2)
-# /projects/reedsweto/pbrown/github4/ReEDS-2.0/runs/v20241028_porK0_USA_decarb,por_Z35,plt.cm.tab20(3)
+# /projects/reedsweto/pbrown/github/ReEDS/runs/v20241028_mainK0_USA,main_Ref,plt.cm.tab20(0)
+# /projects/reedsweto/pbrown/github4/ReEDS/runs/v20241028_porK0_USA,por_Ref,plt.cm.tab20(1)
+# /projects/reedsweto/pbrown/github/ReEDS/runs/v20241028_mainK0_USA_decarb,main_Z35,plt.cm.tab20(2)
+# /projects/reedsweto/pbrown/github4/ReEDS/runs/v20241028_porK0_USA_decarb,por_Z35,plt.cm.tab20(3)
 ### Add your cases below (don't commit them) or create a new csv file with the same format
\ No newline at end of file
diff --git a/postprocessing/hybrid_pvwind_minLCOE.py b/postprocessing/hybrid_pvwind_minLCOE.py
index f574ac6a8..aa7b1f88b 100644
--- a/postprocessing/hybrid_pvwind_minLCOE.py
+++ b/postprocessing/hybrid_pvwind_minLCOE.py
@@ -304,9 +304,7 @@ def cfcorr(dspv, dswind):
 sitemap['profile'] = sitemap.i.map(lambda x: x.split('_')[1]) + '|' + sitemap.r
 sitemap['tech'] = sitemap.i.map(lambda x: x.split('_')[0])
 ### Get list of valid regions and subset to those regions
-val_r = pd.read_csv(
-    os.path.join(inp['case'],'inputs_case','val_r.csv')
-).columns.values
+val_r = reeds.io.read_input(inp['case'], 'r').squeeze(1).values
 sitemap = sitemap.loc[sitemap.r.isin(val_r)].copy()
 ### Make lookup and a single-level column version to write out
 profilemap = sitemap.pivot(columns='tech',index='x',values=['profile','i','r']).dropna()
diff --git a/postprocessing/input_diff_plots.py b/postprocessing/input_diff_plots.py
index 96b8beeb2..b18b12985 100644
--- a/postprocessing/input_diff_plots.py
+++ b/postprocessing/input_diff_plots.py
@@ -117,8 +117,8 @@ def plot_cf_diff(
     Plot absolute difference in regional CF between repo_old and repo_new.
 
     Args:
-        repo_old: Path to old ReEDS-2.0 directory
-        repo_new: Path to new ReEDS-2.0 directory
+        repo_old: Path to old ReEDS directory
+        repo_new: Path to new ReEDS directory
         diffmax: Color axis limit for difference plots [%]
     
     Returns:
@@ -248,8 +248,8 @@ def main(repo_old, repo_new, outpath):
         description='Plot differences in input data between ReEDS repos',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('old', help='path to old ReEDS-2.0 directory')
-    parser.add_argument('new', help='path to new ReEDS-2.0 directory')
+    parser.add_argument('old', help='path to old ReEDS directory')
+    parser.add_argument('new', help='path to new ReEDS directory')
     parser.add_argument(
         '--outpath', '-o', default='~/Desktop',
         help='directory path to save figures to', 
diff --git a/postprocessing/input_plots.py b/postprocessing/input_plots.py
index 5507c141a..e5b32f100 100644
--- a/postprocessing/input_plots.py
+++ b/postprocessing/input_plots.py
@@ -3,6 +3,7 @@
 import sys
 import numpy as np
 import pandas as pd
+from pathlib import Path
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 from matplotlib import patheffects as pe
@@ -98,7 +99,6 @@ def plot_profile(
     case,
     datum='demand',
     year=0,
-    region=None,
     weatheryears=None,
     color='k',
     hourly=False,
@@ -117,7 +117,7 @@ def plot_profile(
     ## Parse inputs
     sw = reeds.io.get_switches(case)
     t = reeds.io.get_years(case)[-1] if year in [0, None, 'last'] else year
-    rs = reeds.inputs.parse_regions((region if region else case), case)
+    rs = reeds.inputs.parse_regions(case)
     if weatheryears is None:
         weatheryears = sw.resource_adequacy_years_list
     elif isinstance(weatheryears, int):
@@ -407,8 +407,11 @@ def plot_units_existing(
             techs = [i for i in techs if i in onlytechs]
 
     ### Parse inputs
+    plot_settings = reeds.io.get_plot_formatting()
+    colors = plot_settings['tech_color'].squeeze(1)
+
     if markers is None:
-        techmarkers = reeds.reedsplots.techmarkers
+        techmarkers = plot_settings['tech_marker'].squeeze(1)
     elif isinstance(markers, str):
         techmarkers = dict(zip(techs, markers(len(techs))))
     elif isinstance(markers, dict):
@@ -416,13 +419,6 @@ def plot_units_existing(
     else:
         raise ValueError(f'Invalid markers ({type(markers)}): {markers}')
 
-    colors = pd.read_csv(
-        os.path.join(
-            reeds.io.reeds_path,'postprocessing','bokehpivot','in','reeds2','tech_style.csv'),
-        index_col='order',
-    ).squeeze(1)
-    colors.index = colors.index.str.lower()
-
     dfmap = reeds.io.get_dfmap(case)
 
     ## Create scale
@@ -472,6 +468,64 @@ def plot_units_existing(
     return f, ax, dfunits
 
 
+def plot_exog_prescribed_cap(
+    case=None, year=None, crs='EPSG:5070',
+    markerscale=20, colors={'wind-ons':'C0', 'upv':'C1'},
+    **kwargs,
+):
+    """Plot exogenous and prescribed capacity (only applies to upv and wind-ons)"""
+    sw = reeds.io.get_switches(case=case, **kwargs)
+    if year is None:
+        scalars = reeds.io.get_scalars(case=case)
+        year = int(scalars.this_year)
+    sitemap = reeds.io.get_sitemap().to_crs(crs)
+    dfmap = reeds.io.get_dfmap(case=case, **kwargs)
+    for key, val in dfmap.items():
+        dfmap[key] = val.to_crs(crs)
+    
+    tech_access = [
+        ('wind-ons', sw.GSw_SitingWindOns),
+        ('upv', sw.GSw_SitingUPV),
+    ]
+    infiles = ['exog_cap', 'prescribed_builds']
+    ncols = len(tech_access)
+    nrows = len(infiles)
+
+    dfin = {}
+    plt.close()
+    f,ax = plt.subplots(
+        nrows, ncols, figsize=(4*ncols, 2.5*nrows), sharex=True, sharey=True,
+        gridspec_kw={'hspace':0, 'wspace':0},
+    )
+    for row, infile in enumerate(infiles):
+        for col, (tech, access) in enumerate(tech_access):
+            fpath = Path(
+                reeds.io.reeds_path, 'inputs', 'capacity_exogenous',
+                f'{infile}_{tech}_{access}.csv'
+            )
+            if fpath.is_file():
+                dfin[tech] = pd.read_csv(fpath)
+            else:
+                continue
+            _ax = ax[row,col]
+            _ax.set_title(f'{infile} {tech} ({access})', y=0.92)
+            dfplot = (
+                dfin[tech].loc[dfin[tech].year==year].groupby('sc_point_gid').capacity.sum()
+                .to_frame()
+            )
+            dfplot = sitemap.merge(dfplot, on='sc_point_gid', how='right')
+            dfmap['st'].plot(ax=_ax, facecolor='none', edgecolor='k', lw=0.2, zorder=1e6)
+            dfmap['r'].plot(ax=_ax, facecolor='none', edgecolor='0.5', lw=0.1, zorder=1e5)
+            dfplot.plot(
+                ax=_ax, color=colors[tech], lw=0,
+                markersize=(dfplot.capacity/dfplot.capacity.max()*markerscale),
+            )
+    for row in range(nrows):
+        for col in range(ncols):
+            ax[row,col].axis('off')
+    return f, ax, dfin
+
+
 def plot_existing_unitsize(
     case=None,
     level='transreg',
@@ -763,6 +817,147 @@ def map_gas_price(
     return f, ax, dictin
 
 
+def plot_hvdc(case=None, crs='EPSG:5070', **kwargs):
+    """Plot existing/planned HVDC lines and B2B converters"""
+    ### Get maps for plot
+    dfstates = reeds.spatial.get_map('state', source='census', crs=crs)
+    dfmap = reeds.io.get_dfmap(case=case, **kwargs)
+
+    ### Get HVDC lines
+    hvdc = pd.concat({
+        'existing': reeds.inputs.get_hvdc_lines('hvdc_existing.csv').set_index('name'),
+        'planned': reeds.inputs.get_hvdc_lines('hvdc_planned-baseline.csv').set_index('name'),
+    }, names=('group',)).to_crs(crs)
+    hvdc.geometry = hvdc.buffer(hvdc.MW * 15)
+    offset = {
+        'Pacific DC Intertie': (-10, 30),
+        'Trans Bay Cable': (-1, 5),
+        'Square Butte': (0, 5),
+        'CU': (-5, -5),
+        'Path 27': (5, -5),
+        'Cross Sound Cable': (10, 0),
+        'Neptune Cable': (5, -5),
+        'TransWestExpress': (1, -10),
+        'SunZia': (1, -10),
+    }
+    colors = {'existing': 'C3', 'planned': 'C1'}
+
+    ### Get B2B converters
+    fpath = Path(reeds.io.reeds_path, 'inputs', 'transmission', 'b2b_converters.csv')
+    b2b = reeds.plots.df2gdf(pd.read_csv(fpath), crs=crs)
+    ## Rotated half-filled marker for B2B
+    t = mpl.transforms.Affine2D().rotate_deg(45)
+    markerstyle = {
+        'marker':mpl.markers.MarkerStyle('o', 'top', t),
+        'markersize':10,
+        'markerfacecolor':'0.9',
+        'markerfacecoloralt':'0.7',
+        'markeredgecolor':'k',
+    }
+
+    ### Plot it
+    plt.close()
+    f,ax = plt.subplots(figsize=(12,9))
+    ## Background
+    if (case is None) and ('GSw_ZoneSet' not in kwargs):
+        dfstates.plot(ax=ax, facecolor='none', edgecolor='k', lw=0.5)
+    else:
+        dfmap['r'].to_crs(crs).plot(ax=ax, facecolor='none', edgecolor='0.7', lw=0.5)
+        dfstates.plot(ax=ax, facecolor='none', edgecolor='k', lw=0.8)
+        icolors = {
+            'western': plt.cm.Pastel2(0),
+            'eastern': plt.cm.Pastel2(1),
+            'texas': plt.cm.Pastel2(2),
+        }
+        for interconnection, color in icolors.items():
+            dfmap['interconnect'].loc[[interconnection]].to_crs(crs).plot(
+                ax=ax, facecolor=color, edgecolor='none', zorder=-10, alpha=0.2,
+            )
+    ## HVDC
+    for group, color in colors.items():
+        hvdc.loc[group].plot(
+            ax=ax, color=color, zorder=1e6,
+            path_effects=[pe.withStroke(linewidth=0.7, foreground='w', alpha=1)],
+        )
+    for i, row in hvdc.iterrows():
+        group, name = i
+        label = f'{name}\n{row.MW} MW'
+        x, y = offset.get(name, (0, 0))
+        ha = 'right' if x < 0 else ('left' if x > 0 else 'center')
+        va = 'top' if y < 0 else ('bottom' if y > 0 else 'center')
+        ax.annotate(
+            label, (row.geometry.centroid.x, row.geometry.centroid.y),
+            xytext=(x, y), textcoords='offset points', ha=ha, va=va,
+            color=colors[group], fontsize=12, zorder=1e7,
+            path_effects=[pe.withStroke(linewidth=4, foreground='w', alpha=1)],
+        )
+    ## B2B
+    ax.plot(
+        b2b.geometry.x.values, b2b.geometry.y.values,
+        lw=0, alpha=1.0, **markerstyle,
+    )
+    ax.annotate(
+        'B2B\nconverter', (b2b.iloc[0].geometry.x, b2b.iloc[0].geometry.y),
+        xytext=(-7, 0), textcoords='offset points', ha='right', va='center',
+        color='0.5', fontsize=12, zorder=1e7,
+        path_effects=[pe.withStroke(linewidth=4, foreground='w', alpha=1)],
+    )
+    ax.axis('off')
+
+    return f, ax, {'hvdc': hvdc, 'b2b': b2b}
+
+
+def plot_voltage(case=None, crs='EPSG:5070', lw=1.5, legend=True, **kwargs):
+    """Plot AC voltage between zones"""
+    dfmap = reeds.io.get_dfmap(case=case, **kwargs)
+    for key in ['r', 'st', 'country']:
+        dfmap[key] = dfmap[key].to_crs(crs)
+
+    hierarchy = reeds.io.get_hierarchy(case, **kwargs)
+    dfplot = reeds.inputs.get_distances(case, **kwargs)
+    dfplot = dfplot.loc[
+        (dfplot.polarity == 'ac')
+        & (dfplot.r.map(hierarchy.interconnect) == dfplot.rr.map(hierarchy.interconnect))
+    ].copy()
+    def _make_line(row):
+        return shapely.LineString([[row.start_lon, row.start_lat], [row.end_lon, row.end_lat]])
+    dfplot['geometry'] = dfplot.apply(_make_line, axis=1)
+    dfplot = gpd.GeoDataFrame(dfplot, crs='EPSG:4326').to_crs(crs)
+
+    colors = {138:'C3', 161:'C1', 230:'C8', 345:'C2', 500:'C0', 765:'C4'}
+    ## Optional lighter version
+    # colors = {
+    #     138:'#e25856', 161:'#ff9a50', 230:'#caca5a',
+    #     345:'#5fb45b', 500:'#5393c4', 765:'#aa87cb',
+    # }
+
+    plt.close()
+    f,ax = plt.subplots()
+    dfmap['r'].plot(ax=ax, facecolor='none', edgecolor='0.5', lw=0.15)
+    dfmap['st'].plot(ax=ax, facecolor='none', edgecolor='k', lw=0.2)
+    dfmap['country'].plot(ax=ax, facecolor='none', edgecolor='k', lw=0.3)
+    for kv, color in colors.items():
+        dfplot.loc[dfplot.voltage==kv].plot(ax=ax, lw=lw, color=color, alpha=1)
+    ax.set_title(f"{len(dfmap['r'])} zones", y=0.9)
+    ## Legend
+    if legend:
+        handles = [
+            mpl.patches.Patch(facecolor=c, edgecolor='none', label=f'{kv} kV')
+            for kv, c in colors.items()
+        ]
+        ax.legend(
+            handles=handles,
+            loc='lower left', ncol=2, bbox_to_anchor=(0.03, 0.03),
+            frameon=False, fontsize=9,
+            handletextpad=0.2, handlelength=0.4,
+            columnspacing=0.6, labelspacing=0.2,
+            labelcolor='linecolor',
+        )
+    ax.axis('off')
+
+    return f, ax, dfplot
+
+
 def map_supplycurves(
     case=None,
     tech=None,
@@ -957,32 +1152,33 @@ def map_supplycurves(
         description='Check inputs.gdx parameters against objective_function_params.yaml',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('case', help='ReEDS-2.0/runs/{case} directory')
+    parser.add_argument('case', help='ReEDS/runs/{case} directory')
     parser.add_argument(
-        '--write', '-w', choices=['png', 'ppt', 'pptx'], default='png',
+        '--write', '-w', choices=['pdf', 'png', 'ppt', 'pptx'], default='png',
         help='Output format (png or pptx)')
     args = parser.parse_args()
     case = args.case
     write = args.write
 
     # #%% Inputs for testing
-    # case = os.path.join(reeds.io.reeds_path, 'runs', 'v20251209_scM0_USA_defaults')
+    # case = os.path.join(reeds.io.reeds_path, 'runs', 'v20260604_mainM0_USA_fast')
     # interactive = True
     # write = 'png'
 
     #%% Create output container
-    if write.strip('.') == 'png':
+    suffix = write.strip('.')
+    if suffix in ['pdf', 'png']:
         savepath = os.path.join(case, 'outputs', 'figures', 'inputs')
         os.makedirs(savepath, exist_ok=True)
 
         def saveit(savename):
-            outpath = os.path.join(savepath, savename.lower().replace(' ', '-') + '.png')
+            outpath = os.path.join(savepath, savename.lower().replace(' ', '-') + f'.{suffix}')
             plt.savefig(outpath)
             print(os.path.basename(outpath))
             if interactive:
                 plt.show()
 
-    elif write.strip('.') in ['ppt', 'pptx']:
+    elif suffix in ['ppt', 'pptx']:
         savepath = os.path.join(case, 'outputs', 'figures', 'inputs.pptx')
         prs = reeds.report_utils.init_pptx()
         def saveit(savename, **kwargs):
@@ -1057,6 +1253,12 @@ def saveit(savename, **kwargs):
     except Exception:
         print(traceback.format_exc())
 
+    try:
+        f, ax, df = plot_exog_prescribed_cap(case=case)
+        saveit('Exog and prescribed capacity')
+    except Exception:
+        print(traceback.format_exc())
+
     ## Size distribution
     try:
         f, ax, df = plot_existing_unitsize(case=case)
@@ -1084,10 +1286,24 @@ def saveit(savename, **kwargs):
     except Exception:
         print(traceback.format_exc())
 
+    ### Existing/planned HVDC lines and B2B converters
+    try:
+        f, ax, df = plot_hvdc(case)
+        saveit('HVDC and B2B')
+    except Exception:
+        print(traceback.format_exc())
+
+    ### Interzonal AC transmission voltage
+    try:
+        f, ax, df = plot_voltage(case)
+        saveit('AC voltage')
+    except Exception:
+        print(traceback.format_exc())
+
     ### Supply curves
     extras = (True if 'usa' in sw.GSw_Region.lower() else False)
     try:
-        for tech in ['upv', 'wind-ons', 'wind-ofs', 'egs']:
+        for tech in [None, 'upv', 'wind-ons', 'wind-ofs', 'egs']:
             plot_generator = map_supplycurves(
                 case=case,
                 tech=tech,
diff --git a/postprocessing/land_use/README.md b/postprocessing/land_use/README.md
index dcd956ead..e0783f5fc 100644
--- a/postprocessing/land_use/README.md
+++ b/postprocessing/land_use/README.md
@@ -7,7 +7,7 @@ estimate total land use from a ReEDS scenario for a set of specified reV charact
 
 The land_use analysis processing can be run as part of a ReEDS run by setting `land_use_analysis=1` (`reeds_to_rev` must also be enabled). By default the script runs for all supported technologies (upv and wind-ons).
 
-Alternatively, the script can be run as a standalone for a specified ReEDS run by passing the folder of the run: `python postprocessing/land_use/land_use_analysis.py /kfs3/scratch/bsergi/ReEDS-2.0/runs/[run folder]`. See the passed arguments options in the script for some additional options for running in standalone mode.
+Alternatively, the script can be run as a standalone for a specified ReEDS run by passing the folder of the run: `python postprocessing/land_use/land_use_analysis.py /kfs3/scratch/bsergi/ReEDS/runs/[run folder]`. See the passed arguments options in the script for some additional options for running in standalone mode.
 
 When running, users specify which land characterizations from the reV supply curve they want to process in `postprocessing/land_use/inputs/process_categories.json`. Each entry has a format like the following:
 
diff --git a/raw_value_streams.py b/postprocessing/raw_value_streams.py
similarity index 99%
rename from raw_value_streams.py
rename to postprocessing/raw_value_streams.py
index d2f95961a..7ed4b033e 100644
--- a/raw_value_streams.py
+++ b/postprocessing/raw_value_streams.py
@@ -3,8 +3,8 @@
 from GAMS gdx solution file to produce value streams for the variables of the model.
 '''
 
-import pandas as pd
 import gdxpds
+import pandas as pd
 import subprocess
 from datetime import datetime
 import logging
@@ -250,4 +250,4 @@ def get_df_symbols(dfs, symbols):
     df_syms = pd.concat(df_syms).reset_index(drop=True)
     for col in ['sym_name','sym_set']:
         df_syms[col] = df_syms[col].str.lower()
-    return df_syms
\ No newline at end of file
+    return df_syms
diff --git a/postprocessing/reValue/README.md b/postprocessing/reValue/README.md
index 4cd4c38f4..a49803d0a 100644
--- a/postprocessing/reValue/README.md
+++ b/postprocessing/reValue/README.md
@@ -22,7 +22,7 @@ reValue has two main purposes:
      - **netload_num_hrs**: When **res_marg_style** = *max_net_load_2012*, this allows the user to specify the number of max net load hours to assign the reserve margin prices (default in ReEDS is 20, but the default here is 50 so that technologies don't randomly align with peak net load hours as often). Higher **netload_num_hrs** mean lower reserve margin prices in each hour, such that total value of firm capacity stays constant.
      - **netload_time_style**: When **res_marg_style** = *max_net_load_2012*, This allows the user to keep reserve margin prices at the hour level (**netload_time_style**=*hour*), or to assign prices to the entire timeslice(s) containing the hour(s) (**netload_time_style**=*timeslice*).
  3. Run activated scenarios with `python reValue.py` (from the reeds2 conda environment).
- 4. Gather price and value metric outputs from output folder at *ReEDS-2.0/postprocessing/reValue/outputs_[timestamp]*. Here are the main outputs:
+ 4. Gather price and value metric outputs from output folder at *ReEDS/postprocessing/reValue/outputs_[timestamp]*. Here are the main outputs:
      - **reValue_out.csv**: This file contains the various value metrics:
          - *LVOE*: Value per unit energy
          - *LVOE_load*: Value per unit energy (load, or energy requirement, component. For *load* technology, this includes influence on operating reserves, state rps, and all model requirements that are linked to the LOAD variable)
diff --git a/postprocessing/reValue/reValue.py b/postprocessing/reValue/reValue.py
index b7062584e..064376b29 100644
--- a/postprocessing/reValue/reValue.py
+++ b/postprocessing/reValue/reValue.py
@@ -75,39 +75,39 @@ def get_prices():
     df_pq_rm_adj = df_pq_rm.rename(columns={'h':'season'})
     df_seas_h_map = df_hmap[['season','h']].drop_duplicates()
     if res_marg_style == 'max_net_load_2012':
-        #Read in net_load_2012 of the appropriate ReEDS Augur file.
+        #Read in net_load_2012 of the appropriate capacity credit file.
         #The file we want is for the highest year that is lower than r['year']
         #TODO: Perhaps we should use the file that has the same year as r['year'],
         #depending on if it represents the system of that year better.
-        aug_files = os.listdir(f'{reeds_run_path}/ReEDS_Augur/augur_data')
-        aug_file_yrs = [f for f in aug_files if 'ReEDS_Augur_' in f]
-        yrs = [int(f.replace('ReEDS_Augur_','').replace('.gdx','')) for f in aug_file_yrs]
+        aug_files = os.listdir(f'{reeds_run_path}/handoff/reeds_data')
+        aug_file_yrs = [f for f in aug_files if 'ccdata_' in f]
+        yrs = [int(f.replace('ccdata_','').replace('.gdx','')) for f in aug_file_yrs]
         yrs_less = [y for y in yrs if y < year]
         max_yr = max(yrs_less)
-        df_aug = gdxpds.to_dataframe(f'{reeds_run_path}/ReEDS_Augur/augur_data/ReEDS_Augur_{max_yr}.gdx',
+        df_ra = gdxpds.to_dataframe(f'{reeds_run_path}/handoff/reeds_data/ccdata_{max_yr}.gdx',
             'net_load_2012', old_interface=False)
-        if int(df_aug['t'][0]) != year:
-            sys.exit(f'ERROR: Augur year ({int(df_aug["t"][0])}) does not match current scenario year ({year})')
-        df_aug = df_aug.sort_values('Value', ascending=False)
-        df_aug_top = df_aug.groupby(['ccreg','ccseason'], as_index=False).head(netload_num_hrs).copy()
-        df_aug_top = df_aug_top.rename(columns={'ccseason':'season'})
+        if int(df_ra['t'][0]) != year:
+            raise ValueError(f'RA year ({int(df_ra["t"][0])}) does not match current scenario year ({year})')
+        df_ra = df_ra.sort_values('Value', ascending=False)
+        df_ra_top = df_ra.groupby(['ccreg','ccseason'], as_index=False).head(netload_num_hrs).copy()
+        df_ra_top = df_ra_top.rename(columns={'ccseason':'season'})
         if netload_time_style == 'hour':
-            df_aug_top = df_aug_top[['ccreg','season','hour']].copy()
-            df_aug_top['hour'] = df_aug_top['hour'].astype(int)
+            df_ra_top = df_ra_top[['ccreg','season','hour']].copy()
+            df_ra_top['hour'] = df_ra_top['hour'].astype(int)
             #Convert the seasonal price to an hourly price over the set of hours assigned to that season (for that ba)
             df_pq_rm_adj['price'] = df_pq_rm_adj['price'] / netload_num_hrs
             #Restrict to prices only and add ccreg column
             df_p_rm = df_pq_rm_adj[['reeds_ba','season','price']].merge(df_ba_cc_map, on='reeds_ba', how='left')
             #Merge max net load hours into df_p_rm (which will duplicate rows if there are multiple timeslices in a ba/season)
-            df_p_rm = df_p_rm.merge(df_aug_top, on=['ccreg','season'], how='left')
+            df_p_rm = df_p_rm.merge(df_ra_top, on=['ccreg','season'], how='left')
             #Hour is one-indexed. Re-index to the 2012 set of 8760 hours (43801 to 52560)
             df_p_rm_h = df_p_rm.pivot_table(index=['hour'], columns='reeds_ba', values='price')
             df_p_rm_h = df_p_rm_h.reindex(range(43801,52561)).reset_index(drop=True)
         elif netload_time_style == 'timeslice':
             #We assign reserve margin prices to the entire timeslice(s) that contain the top net load hour(s) of each season
-            df_aug_top = df_aug_top[['ccreg','season','h']].drop_duplicates()
+            df_ra_top = df_ra_top[['ccreg','season','h']].drop_duplicates()
             #Find number of total hours that we're mapping prices to in each season
-            df_seas_hrs = df_aug_top.merge(df_h_num_hrs, on=['h'], how='left')
+            df_seas_hrs = df_ra_top.merge(df_h_num_hrs, on=['h'], how='left')
             df_seas_hrs = df_seas_hrs.groupby(['ccreg','season'], as_index=False)['num_hrs'].sum()
             #Add ccreg and num_hrs columns to df_pq_rm_adj
             df_pq_rm_adj = df_pq_rm_adj.merge(df_ba_cc_map, on='reeds_ba', how='left')
@@ -116,7 +116,7 @@ def get_prices():
             df_pq_rm_adj['price'] = df_pq_rm_adj['price'] / df_pq_rm_adj['num_hrs']
             #Merge in the timeslices to which we'll be mapping prices. This might duplicate rows if there are
             #multiple timeslices in a ba/season (which is possible if netload_num_hrs is greater than 1)
-            df_pq_rm_adj = df_pq_rm_adj.merge(df_aug_top, on=['ccreg','season'], how='left')
+            df_pq_rm_adj = df_pq_rm_adj.merge(df_ra_top, on=['ccreg','season'], how='left')
             #Isolate prices with h as first column and reeds_ba as other columns
             df_p_rm = df_pq_rm_adj.pivot_table(index=['h'], columns='reeds_ba', values='price').reset_index()
             #Merge with df_hmap, duplicating prices across all hours of the chosen timeslices.
diff --git a/postprocessing/retail_rate_module/ITC-PTC_expenditures.py b/postprocessing/retail_rate_module/ITC-PTC_expenditures.py
index 3c3f346df..3c94bef08 100644
--- a/postprocessing/retail_rate_module/ITC-PTC_expenditures.py
+++ b/postprocessing/retail_rate_module/ITC-PTC_expenditures.py
@@ -3,12 +3,12 @@
 * Purpose: Calculate yearly PTC and ITC expenditures
 * Usage notes
     * To use this script, direct `reeds_path` and `runpath` under "USER INPUTS" to the
-    desired ReEDS-2.0 and {batch}_{case} run directories
-    * Outputs are saved to ReEDS-2.0/runs/{batch}_{case}/outputs/
+    desired ReEDS and {batch}_{case} run directories
+    * Outputs are saved to ReEDS/runs/{batch}_{case}/outputs/
     * To use for outputs generated before PR #527 (2cec69214baddf817b276a89e3f4072ab76cf2c9),
-    add the following lines to the start of e_report.gms and re-run (otherwise geothermal
+    add the following lines to the start of report.gms and re-run (otherwise geothermal
     won't be included)
-    e_report.gms and e_report_dump.py:
+    report.gms and report_dump.py:
     cost_cap_fin_mult_noITC(i,r,t)$geo(i) = cost_cap_fin_mult_noITC("geothermal",r,t)
     cost_cap_fin_mult_no_credits(i,r,t)$geo(i) = cost_cap_fin_mult_no_credits("geothermal",r,t)
 * Caveats
@@ -28,8 +28,8 @@
 ###################
 #%% USER INPUTS ###
 
-### Direct these paths to the desired ReEDS-2.0 and {batch}_{case} run directories
-reeds_path = os.path.expanduser(os.path.join('~','Projects','Retail','ReEDS-2.0',''))
+### Direct these paths to the desired ReEDS and {batch}_{case} run directories
+reeds_path = os.path.expanduser(os.path.join('~','Projects','Retail','ReEDS',''))
 runpath = os.path.join(reeds_path,'runs','v20201203_retail_Mid_ITCPTC','')
 ### Inflation adjustments
 input_dollar_year = 2004
diff --git a/postprocessing/retail_rate_module/README.md b/postprocessing/retail_rate_module/README.md
index 922dfbb38..e5b65e4c1 100644
--- a/postprocessing/retail_rate_module/README.md
+++ b/postprocessing/retail_rate_module/README.md
@@ -18,9 +18,9 @@ Long-Term Electricity System Models. Golden, CO: National Renewable Energy Labor
 
 ## Usage
 
-- `cd` to the REEDS-2.0/postprocessing/retail_rate_module directory
+- `cd` to the ReEDS/postprocessing/retail_rate_module directory
 - Run the retail_rate_calculations.py script, with the name of a run folder as a required argument input. For example:
-  - `D:\username\ReEDS-2.0\postprocessing\retail_rate_module> python retail_rate_calculations.py v20200806_retailtest_MidCase`
+  - `D:\username\ReEDS\postprocessing\retail_rate_module> python retail_rate_calculations.py v20200806_retailtest_MidCase`
 
 ## Outputs
 
diff --git a/postprocessing/retail_rate_module/calculate_historical_capex.py b/postprocessing/retail_rate_module/calculate_historical_capex.py
index 60699eb9a..9b8e4df79 100644
--- a/postprocessing/retail_rate_module/calculate_historical_capex.py
+++ b/postprocessing/retail_rate_module/calculate_historical_capex.py
@@ -33,22 +33,16 @@ def get_historical_units(inputs_case):
 
     # Rename techs for consistency with capital cost data from inputs.gdx
     tech_name_map = {
-        'coal-igcc': 'Coal-IGCC',
-        'coaloldscr': 'CoalOldScr',
-        'coalolduns': 'CoalOldUns',
         'csp-ns': 'csp1_1',
         'dupv': 'upv_1',
-        'gas-cc': 'Gas-CC',
-        'gas-ct': 'Gas-CT',
         'geohydro_allkm': 'geohydro_allkm_1',
-        'hydED': 'hydND',
-        'hydEND': 'hydND',
-        'nuclear': 'Nuclear',
+        'hyded': 'hydnd',
+        'hydend': 'hydnd',
         'pvb': 'upv_1',
         'upv': 'upv_1',
         'wind-ons': 'wind-ons_1'
     }
-    init_cap['i'] = init_cap['i'].replace(tech_name_map)
+    init_cap['i'] = init_cap['i'].str.lower().replace(tech_name_map)
 
     return init_cap
 
@@ -60,11 +54,13 @@ def get_earliest_cap_costs(inputs_case):
         'cost_cap',
         old_interface=False
     )
+    cost_cap.i = cost_cap.i.str.lower()
     cost_cap_energy = gdxpds.to_dataframe(
         os.path.join(inputs_case, 'inputs.gdx'),
         'cost_cap_energy',
         old_interface=False
     )
+    cost_cap_energy.i = cost_cap_energy.i.str.lower()
     cost_cap = (
         pd.concat([cost_cap, cost_cap_energy])
         .groupby(['i', 't'], as_index=False)
@@ -85,6 +81,7 @@ def get_earliest_cap_costs(inputs_case):
         'cost_cap_fin_mult_out',
         old_interface=False
     )
+    cost_cap_mult.i = cost_cap_mult.i.str.lower()
     cost_cap_mult['t'] = cost_cap_mult['t'].astype(int)
     cost_cap_mult = cost_cap_mult.rename(
         columns={'Value': 'cap_cost_mult_for_ratebase'}
@@ -114,6 +111,7 @@ def get_earliest_cap_costs(inputs_case):
         'rsc_dat',
         old_interface=False
     )
+    rsc_dat.i = rsc_dat.i.str.lower()
     cost_cap_rsc = (
         rsc_dat.loc[~rsc_dat.i.isin(cost_cap_earliest['i'])]
         .pivot_table(
diff --git a/postprocessing/retail_rate_module/ferc_distadmin.py b/postprocessing/retail_rate_module/ferc_distadmin.py
index 849588ccc..3ffbe161b 100644
--- a/postprocessing/retail_rate_module/ferc_distadmin.py
+++ b/postprocessing/retail_rate_module/ferc_distadmin.py
@@ -226,7 +226,7 @@ def get_ferc_costs(
         Indicate whether to write results as csv or pickle (default = csv), or enter False
         to skip writing of results
     inflationpath : str
-        Filepath to ReEDS-2.0/runs/{casename}/inputs_case/inflation.csv
+        Filepath to ReEDS/runs/{casename}/inputs_case/inflation.csv
         (required)
     drop_pgesce_20182019 : bool
         Indicate whether to drop 2018 and 2019 values for 'A&G Oper Injuries & Damages $'
@@ -248,9 +248,9 @@ def get_ferc_costs(
     # aggregation = 'state'
     # writeout = False
     # inflationpath = os.path.join(
-    #     os.path.expanduser('~/Documents/ReEDS_/ReEDS-2.0/runs/stdscen_091923_Mid_Case/'),
+    #     os.path.expanduser('~/Documents/ReEDS_/ReEDS/runs/stdscen_091923_Mid_Case/'),
     #     'inputs_case', 'inflation.csv')
-    # inflationpath = os.path.join('/','Users','jcarag','ReEDS','RRM_Fixes','ReEDS-2.0',
+    # inflationpath = os.path.join('/','Users','jcarag','ReEDS','RRM_Fixes','ReEDS',
     #                               'runs','stdscen_091923_DAC_100by2035','inputs_case','inflation.csv')
     # drop_pgesce_20182019 = True
     # dollar_year = 2004
diff --git a/postprocessing/retail_rate_module/mean_bias_error.py b/postprocessing/retail_rate_module/mean_bias_error.py
index a5aa94bd8..d51fdf492 100644
--- a/postprocessing/retail_rate_module/mean_bias_error.py
+++ b/postprocessing/retail_rate_module/mean_bias_error.py
@@ -1,7 +1,7 @@
 """
 This script calculates the mean bias error between the bottom-up retail rates
 and reported state-level retail rates from EIA form 861 and writes the results
-to ReEDS-2.0/postprocessing/retail_rate_module/inputs/state-meanbiaserror_rate-aggregation.csv.
+to ReEDS/postprocessing/retail_rate_module/inputs/state-meanbiaserror_rate-aggregation.csv.
 The results are written in ¢/kWh in {out_dollar_year} dollars.
 """
 
@@ -32,7 +32,7 @@
 out_dollar_year = args.out_dollar_year
 
 # #%% Settings for testing/debugging
-# reeds_path = os.path.expanduser('~/github2/ReEDS-2.0/')
+# reeds_path = os.path.expanduser('~/github2/ReEDS/')
 # run_dir = os.path.join(reeds_path,'runs','v20210716_main_Ref_2026')
 # out_dollar_year = 2022
 
diff --git a/postprocessing/retail_rate_module/retail_rate_calculations.py b/postprocessing/retail_rate_module/retail_rate_calculations.py
index c8365b99e..f29c6422f 100644
--- a/postprocessing/retail_rate_module/retail_rate_calculations.py
+++ b/postprocessing/retail_rate_module/retail_rate_calculations.py
@@ -4,9 +4,9 @@
 import argparse
 import datetime
 import itertools
+import gdxpds
 import pandas as pd
 import numpy as np
-import gdxpds
 import os
 import sys
 import urllib
@@ -462,7 +462,7 @@ def main(run_dir, inputpath='inputs.csv', write=True, verbose=0):
             'r':'receiving_region', 't':'t', 'Value':'expenditure_flow',
             'Dim1':'receiving_region','Dim2':'t','Val':'expenditure_flow'})
         )
-    ### According to e_report.gms, all international flows are load to/from Canada
+    ### According to report.gms, all international flows are load to/from Canada
     # (not capacity, reserves, rps, or Mexico)
     state_international_flows['price_type'] = 'load'
     state_international_flows['sending_state'] = 'Canadian Imports'
diff --git a/run_pcm.py b/postprocessing/run_pcm.py
similarity index 93%
rename from run_pcm.py
rename to postprocessing/run_pcm.py
index a7b58ff0a..1186f1c19 100644
--- a/run_pcm.py
+++ b/postprocessing/run_pcm.py
@@ -1,19 +1,18 @@
 # %% Imports
 import os
-import sys
 import subprocess
 import argparse
 import json
 from glob import glob
 import gdxpds
 import pandas as pd
+from pathlib import Path
 
 ## Local imports
 import reeds
-import e_report_dump
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'input_processing')))
-import hourly_repperiods
-import hourly_writetimeseries
+from reeds.input_processing import hourly_repperiods
+from reeds.input_processing import hourly_writetimeseries
+from reeds.core.terminus.report_dump import write_dfdict
 
 
 # %% Inferred inputs
@@ -64,7 +63,7 @@ def solvestring_pcm(
     savefile = f"pcm_{label}_{batch_case}_{t}i{iteration}"
     _stress_year = f"{t}i{iteration}" if stress_year in ['keep', 'default'] else stress_year
     out = (
-        "gams d_solvepcm.gms"
+        f"gams {Path('reeds','core','solve_pcm','solve_pcm.gms')}"
         + (" license=gamslice.txt" if hpc else '')
         + f" o={os.path.join('lstfiles', f'{savefile}.lst')}"
         + f" r={os.path.join('g00files', restartfile)}"
@@ -80,7 +79,7 @@ def solvestring_pcm(
             [
                 f" --{s}={sw[s]}"
                 for s in [
-                    'GSw_SkipAugurYear',
+                    'GSw_SkipRAyear',
                     'GSw_HourlyType',
                     'GSw_HourlyWrapLevel',
                     'GSw_ClimateWater',
@@ -107,7 +106,7 @@ def solvestring_pcm(
 def pcm_report_string(batch_case, sw, t, iteration=0, hpc=0, label=''):
     savefile = f"pcm_{label}_{batch_case}_{t}i{iteration}"
     out = (
-        "gams e_report.gms"
+        f"gams {Path('reeds','core','terminus','report.gms')}"
         + (' license=gamslice.txt' if hpc else '')
         + f" o={os.path.join('lstfiles', f'report_pcm_{label}_{t}_{batch_case}.lst')}"
         + f" r={os.path.join('g00files', savefile)}"
@@ -124,11 +123,11 @@ def submit_job(casepath, command_string, jobname='', joblabel='', bigmem=0):
     """
     Create a slurm job submission script for `command_string` at `casepath`,
     then submit it.
-    Uses the slurm settings from {reeds_path}/srun_template.sh.
+    Uses the slurm settings from {reeds_path}/reeds/hpc/srun_template.sh.
     """
     ### Get the SLURM boilerplate
     commands_header, commands_sbatch, commands_other = [], [], []
-    with open(os.path.join(reeds_path, 'srun_template.sh'), 'r') as f:
+    with open(os.path.join(reeds_path,'reeds','hpc','srun_template.sh'), 'r') as f:
         for line in f:
             if bigmem and ('--mem=' in line):
                 line = '#SBATCH --mem=500000'
@@ -248,7 +247,7 @@ def main(casepath, t, switch_mods=switch_mods_default, label='', overwrite=False
     ### Run GAMS LP
     result = subprocess.run(cmd_gams, shell=True)
     if result.returncode:
-        raise Exception(f'd_solvepcm.gms failed with return code {result.returncode}')
+        raise Exception(f'solve_pcm.gms failed with return code {result.returncode}')
 
     # %% Dump results to gdx
     cmd_report = pcm_report_string(
@@ -263,12 +262,12 @@ def main(casepath, t, switch_mods=switch_mods_default, label='', overwrite=False
 
     result = subprocess.run(cmd_report, shell=True)
     if result.returncode:
-        raise Exception(f'e_report.gms failed with return code {result.returncode}')
+        raise Exception(f'report.gms failed with return code {result.returncode}')
 
     # %% Dump gdx to h5
     ## Get new file names if applicable
     dfparams = pd.read_csv(
-        os.path.join(casepath, "e_report_params.csv"),
+        os.path.join(casepath, 'reeds', 'core', 'terminus', 'report_params.csv'),
         comment="#",
         index_col="param",
     )
@@ -284,7 +283,7 @@ def main(casepath, t, switch_mods=switch_mods_default, label='', overwrite=False
 
     outputs_path = os.path.join(casepath, 'outputs', f'pcm_{label}_{_t}')
     os.makedirs(outputs_path, exist_ok=True)
-    e_report_dump.write_dfdict(
+    write_dfdict(
         dfdict=dict_out,
         outputs_path=outputs_path,
         rename=rename,
@@ -298,7 +297,7 @@ def main(casepath, t, switch_mods=switch_mods_default, label='', overwrite=False
         description='Run ReEDS in PCM mode',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('casepath', type=str, help='ReEDS-2.0/runs/{case} directory')
+    parser.add_argument('casepath', type=str, help='ReEDS/runs/{case} directory')
     parser.add_argument(
         '--year',
         '-t',
diff --git a/scripts/run_r2x.py b/postprocessing/run_r2x.py
similarity index 100%
rename from scripts/run_r2x.py
rename to postprocessing/run_r2x.py
diff --git a/postprocessing/run_reeds2pras.py b/postprocessing/run_reeds2pras.py
index 129a6fe56..b5f6d87bc 100644
--- a/postprocessing/run_reeds2pras.py
+++ b/postprocessing/run_reeds2pras.py
@@ -37,7 +37,7 @@ def submit_job(
     jobname = f'PRAS-{os.path.basename(case)}-{samples}'
     ## Get the SLURM boilerplate
     commands_header, commands_sbatch, commands_other = [], [], []
-    with open(os.path.join(reeds_path,'srun_template.sh'), 'r') as f:
+    with open(os.path.join(reeds_path,'reeds','hpc','srun_template.sh'), 'r') as f:
         for line in f:
             if line.strip().startswith('#!'):
                 commands_header.append(line.strip())
@@ -99,14 +99,11 @@ def main(
     Run prep_data, ReEDS2PRAS, and PRAS as necessary.
     If running PRAS, append the number of samples to the filename.
     """
-    ### Import Augur scripts
     if repo:
         site.addsitedir(reeds_path)
     else:
         site.addsitedir(case)
-    import Augur
     import reeds
-    import ReEDS_Augur.prep_data as prep_data
 
     ### Get the switches, overwriting values as necessary
     sw = reeds.io.get_switches(case)
@@ -134,7 +131,7 @@ def main(
     print(f'Running PRAS for {t}i{iteration}')
 
     ### Check if prep_data.py outputs exist; if not, run it
-    augur_data = os.path.join(case,'ReEDS_Augur','augur_data')
+    reeds_data = os.path.join(case,'handoff','reeds_data')
     files_expected = [
         f'cap_converter_{t}.csv',
         f'energy_cap_{t}.csv',
@@ -144,13 +141,13 @@ def main(
         f'pras_vre_gen_{t}.h5',
     ]
     if (
-        any([not os.path.isfile(os.path.join(augur_data,f)) for f in files_expected])
+        any([not os.path.isfile(os.path.join(reeds_data,f)) for f in files_expected])
         or overwrite
     ):
-        augur_csv, augur_h5 = prep_data.main(t, case)
+        reeds_csv, reeds_h5 = reeds.resource_adequacy.prep_data.main(t, case)
 
     ### Run ReEDS2PRAS
-    Augur.run_pras(
+    reeds.resource_adequacy.ra_calcs.run_pras(
         case,
         t,
         iteration=iteration,
@@ -173,7 +170,7 @@ def main(
     import argparse
     description = """Run prep_data, ReEDS2PRAS, and PRAS as necessary.
     Example usage on the HPC:
-    `case=/projects/reedsweto/github/ReEDS-2.0/runs/v20230524_ntpH0_Pacific`
+    `case=/projects/reedsweto/github/ReEDS/runs/v20230524_ntpH0_Pacific`
     `for s in 100 1000; do python postprocessing/run_reeds2pras.py $case -s $s -r; done`
     """
     parser = argparse.ArgumentParser(
@@ -189,7 +186,7 @@ def main(
     parser.add_argument('--samples', '-s', type=int, default=0,
                         help='PRAS samples to run')
     parser.add_argument('--repo', '-r', action='store_true',
-                        help=('Import Augur scripts from local repo '
+                        help=('Import RA scripts from local repo '
                               '(instead of from the case being rerun)'))
     parser.add_argument('--local', '-l', action='store_true',
                         help='Run locally (not as SLURM job)')
@@ -207,7 +204,7 @@ def main(
                         help="Write hourly unit availability by sample from PRAS")
     parser.add_argument('--switch_mods', '-m', type=json.loads, default=json.dumps({}),
                         help=('Dictionary-formated string of switch arguments for '
-                        'Augur.run_pras(). Use single quotes outside the dictionary and '
+                        'ra_calcs.run_pras(). Use single quotes outside the dictionary and '
                         'double quotes for keys, as in:\n'
                         '`-s \'{"pras_seed":0}\'`'))
 
@@ -229,7 +226,7 @@ def main(
     switch_mods = args.switch_mods
 
     # #%% Inputs for testing
-    # case = '/Users/pbrown/github2/ReEDS-2.0/runs/v20230605_ntpM1_Pacific'
+    # case = '/Users/pbrown/github2/ReEDS/runs/v20230605_ntpM1_Pacific'
     # year = 2026
     # iteration = None
     # samples = 0
diff --git a/postprocessing/single_case_plots.py b/postprocessing/single_case_plots.py
index 79a310005..5acf8dfa1 100644
--- a/postprocessing/single_case_plots.py
+++ b/postprocessing/single_case_plots.py
@@ -105,12 +105,27 @@
     os.path.join(case,'inputs_case','modeledyears.csv')
 ).columns.astype(int).values
 yearstep = years[-1] - years[-2]
-val_r = pd.read_csv(
-    os.path.join(case, 'inputs_case', 'val_r.csv'), header=None).squeeze(1).tolist()
+val_r = reeds.io.read_input(case, 'r').squeeze(1).tolist()
 ## If year not provided, use final solve year
 year = year if year > 0 else max(years)
 
 
+#%% Validation plots
+### Existing capacity vs last historical capacity
+try:
+    mapmethod = 'FIPS'
+    f, ax, df = reedsplots.validate_regional_capacity(case, mapmethod=mapmethod)
+    savename = f'validate_cap_regional-{mapmethod}.png'
+    if write:
+        plt.savefig(os.path.join(savepath, savename))
+    if interactive:
+        plt.show()
+    plt.close()
+    print(savename)
+except Exception:
+    print(traceback.format_exc())
+
+
 #%% Transmission line map with disaggregated transmission types
 ### Plot both total capacity (subtract_baseyear=None) and new (subtract_baseyear=2020)
 for subtract_baseyear in [None, 2020]:
@@ -367,9 +382,7 @@
 ### Specify BAs to plot (None = aggregate all together)
 bas = [None]
 if int(sw['plot_ba_level']):
-    bas += pd.read_csv(
-        os.path.join(case, 'inputs_case', 'val_r.csv'), header=None,
-    ).squeeze(1).tolist()
+    bas += reeds.io.read_input(case, 'r').squeeze(1).tolist()
     savepath_ba = os.path.join(savepath, 'ba')
     os.makedirs(savepath_ba, exist_ok=True)
 else:
@@ -383,21 +396,24 @@
             figpath = savepath_ba if ba else savepath
             for plottype in plottypes:
                 for v in ([1] if ba else [0, 1]):
-                    plt.close()
-                    f, ax, df = reedsplots.plot_dispatch_yearbymonth(
+                    plot_generator = reedsplots.plot_dispatch_yearbymonth(
                         case=case, t=year, plottype=plottype,
                         region=(None if ba is None else f"r/{ba}"),
                         techs=plottechs, highlight_rep_periods=v,
                     )
-                    savename = (
-                        f"plot_{plottype}{'_'+label if len(label) else ''}-yearbymonth"
-                        + f"{'-'+ba if ba else ''}-{v}-{year}.png")
-                    if write and (df is not None):
-                        plt.savefig(os.path.join(figpath, savename))
-                        print(savename)
-                    if interactive and (df is not None):
-                        plt.show()
-                    plt.close()
+                    while True:
+                        try:
+                            f, ax, df, weatheryear = next(plot_generator)
+                            savename = (
+                                f"plot_{plottype}{'_'+label if len(label) else ''}-yearbymonth"
+                                + f"{'-'+ba if ba else ''}-{v}-{year}-w{weatheryear}.png")
+                            if write and (df is not None):
+                                plt.savefig(os.path.join(figpath, savename))
+                                print(savename)
+                            if interactive and (df is not None):
+                                plt.show()
+                        except StopIteration:
+                            break
     except Exception:
         print('plot_dispatch-yearbymonth failed:')
         print(traceback.format_exc())
diff --git a/postprocessing/tableau/readme.md b/postprocessing/tableau/readme.md
index a0a0ce012..c231bd15e 100644
--- a/postprocessing/tableau/readme.md
+++ b/postprocessing/tableau/readme.md
@@ -25,11 +25,11 @@ This folder contains files that facilitate the postprocessing and publication of
 ### Step 2: Run tableau_viz_suite.py
 
 1. Confirm your inputs: 
-- Scenario name and filepaths, as shown in `ReEDS-2.0\postprocessing\example.csv`. `tableau_viz_suite.py` will point to this csv to know which scenarios to include.
-    - Note: the "casename" column in `ReEDS-2.0\postprocessing\example.csv` will become the name shown in the CSV files that you may ultimately publish. It is recommended to choose decipherable scenario names for a general audience not familiar with your scenario matrix. For example, instead of "Scenario1" you may call it "MidDemand_RefTransmission_DefaultPolicy". Each value in the "casename" column must be unique.
-- Description of your suite of scenarios, to be used in the Tableau workbook: `ReEDS-2.0\postprocessing\tableau\scenarios.csv`. This should correspond to the scenarios used in `ReEDS-2.0\postprocessing\example.csv` in the "casename" column. If you do not populate `ReEDS-2.0\postprocessing\tableau\scenarios.csv`, the script will attempt to automatically assign metadata of your scenarios based on the cases.csv switches they used. This is imperfect for publication ready datasets but works well for quick analyses and interrim results. If the script cannot populate `ReEDS-2.0\postprocessing\tableau\scenarios.csv` automatically, the script will break.
+- Scenario name and filepaths, as shown in `ReEDS\postprocessing\example.csv`. `tableau_viz_suite.py` will point to this csv to know which scenarios to include.
+    - Note: the "casename" column in `ReEDS\postprocessing\example.csv` will become the name shown in the CSV files that you may ultimately publish. It is recommended to choose decipherable scenario names for a general audience not familiar with your scenario matrix. For example, instead of "Scenario1" you may call it "MidDemand_RefTransmission_DefaultPolicy". Each value in the "casename" column must be unique.
+- Description of your suite of scenarios, to be used in the Tableau workbook: `ReEDS\postprocessing\tableau\scenarios.csv`. This should correspond to the scenarios used in `ReEDS\postprocessing\example.csv` in the "casename" column. If you do not populate `ReEDS\postprocessing\tableau\scenarios.csv`, the script will attempt to automatically assign metadata of your scenarios based on the cases.csv switches they used. This is imperfect for publication ready datasets but works well for quick analyses and interrim results. If the script cannot populate `ReEDS\postprocessing\tableau\scenarios.csv` automatically, the script will break.
     - Note: Each value in the "Scenario Name" column must be unique. This is what is visible in the Tableau workbooks.
-- Technology mapping and cost mapping are pulled from the bokehpivot csvs (see `ReEDS-2.0\postprocessing\bokehpivot\in\reeds2`). To change those settings, alter the mappings in the bokehpivot csvs.
+- Technology mapping and cost mapping are pulled from the bokehpivot csvs (see `ReEDS\postprocessing\bokehpivot\in\reeds2`). To change those settings, alter the mappings in the bokehpivot csvs.
 - No additional packages are needed beyond the default ReEDS environment.
 2. Run tableau_viz_suite.py 
 
@@ -44,7 +44,7 @@ python postprocessing/tableau/tableau_viz_suite.py ../example.csv --years [2025,
 However, it has has not been configured to submit jobs on Kestrel because even with suites of 100+ runs, it only takes a couple minutes to process. 
 ```
 
-3. Expected outputs: expected output is a directory of csvs and shapefiles `ReEDS-2.0\postprocessing\tableau\out\report-{datetime}`.
+3. Expected outputs: expected output is a directory of csvs and shapefiles `ReEDS\postprocessing\tableau\out\report-{datetime}`.
 
 
 ### Step 3: Load your data into template Tableau workbook
@@ -53,7 +53,7 @@ However, it has has not been configured to submit jobs on Kestrel because even w
 3. You may also be asked to "Allow Extension" in a pop-up box. Click "OK". That extension is needed for the workbook to function properly.
 4. On the bottom left hand corner, click on "Data Source". 
 Under "Connections", in the top-left hand corner, click on the down arrow next to "Scenarios". Click "Edit Connection". 
-Change the directory to the location of the csv files you produced with `tableau_viz_suite.py`. Default location is `ReEDS-2.0\postprocessing\tableau\out\report-{datetime}`. Do this for each of the "Connections" in the workbook. For the `ReEDS_Study_Results_Charts.twbx` workbook, this is "Scenarios", "CSV Files" and "Hierarchy".  For the `ReEDS_Study_Results_Maps.twbx` workbook, this is "Scenarios", "CSV Files", "Transmission Endpoints", "US_PCA" and "Hierarchy".
+Change the directory to the location of the csv files you produced with `tableau_viz_suite.py`. Default location is `ReEDS\postprocessing\tableau\out\report-{datetime}`. Do this for each of the "Connections" in the workbook. For the `ReEDS_Study_Results_Charts.twbx` workbook, this is "Scenarios", "CSV Files" and "Hierarchy".  For the `ReEDS_Study_Results_Maps.twbx` workbook, this is "Scenarios", "CSV Files", "Transmission Endpoints", "US_PCA" and "Hierarchy".
 - Note: When altering the path for the `hierarchy.csv` file, point the path to the Tableau script outputs (ex. postprocessing/tableau/out/report-*), not `inputs/hierarchy.csv`.
 5. Clicking into this folder will automatically update the data. You should now see the new data populated throughout the notebook if you click to the other tabs, which are referred to as "Dashboards" in Tableau.
 6. If you do not see your data populating the "Dashboards", there are a couple common culprits.
@@ -80,7 +80,7 @@ Change the directory to the location of the csv files you produced with `tableau
 
 ## Troubleshooting
 
-`tableau_viz_suite.py` relies on the `ReEDS-2.0/reeds` functions and functions in bokehpivot. Errors may be derived from broken functionality there.  
+`tableau_viz_suite.py` relies on the `ReEDS/reeds` functions and functions in bokehpivot. Errors may be derived from broken functionality there.  
 
 ## Known Incompatibilities
 - Mixed resolution scenarios - this postprocessing pipeline is currently only configured
diff --git a/postprocessing/tableau/tableau_viz_suite.py b/postprocessing/tableau/tableau_viz_suite.py
index 5e0e679f7..36ce1b86f 100644
--- a/postprocessing/tableau/tableau_viz_suite.py
+++ b/postprocessing/tableau/tableau_viz_suite.py
@@ -417,7 +417,7 @@ def calc_peakload(
     ).rename(columns=int)
 
     dictout = {}
-    level_map = reeds.output_calc.get_level_map(case)
+    level_map = reeds.results.get_level_map(case)
     for level in levels:
         df_level = df.loc[level,years].stack().rename_axis(['r','t']).rename('Value').reset_index().astype({'t':int}).set_index(['r','t']).squeeze().reset_index()
         df_level['Spatial Resolution'] = level_map[level]
@@ -426,7 +426,7 @@ def calc_peakload(
     df = pd.concat(dictout.values(), axis=0, ignore_index=True) 
 
     # # convert MW to GW for national data
-    df = reeds.output_calc.scale_column(df,**{'scale_factor': 1e-3, 'column':'Value'})
+    df = reeds.results.scale_column(df,**{'scale_factor': 1e-3, 'column':'Value'})
 
     return df
 
@@ -477,7 +477,7 @@ def calc_transmission_map(case,level='transgrp'):
     trans_total_new = pd.concat([trans_total_new, tran_new], axis=0)
 
     # convert MW to GW
-    trans_total_new = reeds.output_calc.scale_column(trans_total_new,**{'scale_factor': 1e-3, 'column':'Value'})   
+    trans_total_new = reeds.results.scale_column(trans_total_new,**{'scale_factor': 1e-3, 'column':'Value'})   
 
     return trans_total_new
     
@@ -510,10 +510,10 @@ def export(dictin,output_dir):
         description='Create the necessary csv files for Tableau to ingest and visualize from ReEDS outputs')
     parser.add_argument(
         '--reeds_path', default=os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')), 
-        help='ReEDS-2.0 directory')
+        help='ReEDS directory')
     parser.add_argument(
         '--tableau_path', default=os.path.expanduser('~\Documents\My Tableau Repository'), 
-        help='ReEDS-2.0 directory')    
+        help='ReEDS directory')    
     parser.add_argument(
         'caselist', type=str, nargs='+',
         help=('space-delimited list of cases to plot, OR shared casename prefix, '
@@ -561,9 +561,9 @@ def export(dictin,output_dir):
 
     #%%### Inputs for debugging
     # reeds_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
-    # caselist = r'C:\Users\ahamilto\Documents\GitHub\ReEDS-2.0\postprocessing\example.csv'
+    # caselist = r'C:\Users\ahamilto\Documents\GitHub\ReEDS\postprocessing\example.csv'
     # years = list(range(2025-2050))
-    # python tableau_viz_suite.py C:\Users\ahamilto\Documents\GitHub\ReEDS-2.0\postprocessing\example.csv --reeds_path C:\Users\ahamilto\Documents\GitHub\ReEDS-2.0
+    # python tableau_viz_suite.py C:\Users\ahamilto\Documents\GitHub\ReEDS\postprocessing\example.csv --reeds_path C:\Users\ahamilto\Documents\GitHub\ReEDS
     
     #%%###os globals
     this_dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -602,7 +602,7 @@ def export(dictin,output_dir):
     create_scenarios_csv(output_dir,cases)
 
     # Grab clean display names for the levels
-    level_map = reeds.output_calc.get_level_map(cases[basecase])
+    level_map = reeds.results.get_level_map(cases[basecase])
 
     # import some key inputs from ReEDS
     dictin_sw = {case: reeds.io.get_switches(cases[case]) for case in cases}
@@ -630,7 +630,7 @@ def export(dictin,output_dir):
     dictin_cap = {}
     metric = 'Capacity (GW)'
     for case in tqdm(cases, desc=metric):
-        dictin_cap[case] = reeds.output_calc.calc_cap(cases[case])
+        dictin_cap[case] = reeds.results.calc_cap(cases[case])
         dictin_cap[case] = reformat(dictin_cap[case],case,metric,years[case])
     dictin_cap = set_zero_values(dictin_cap)
     dictin[metric] = dictin_cap
@@ -639,7 +639,7 @@ def export(dictin,output_dir):
     dictin_gen = {}
     metric = 'Generation (TWh)'
     for case in tqdm(cases, desc=metric):
-        dictin_gen[case] = reeds.output_calc.calc_gen(cases[case])
+        dictin_gen[case] = reeds.results.calc_gen(cases[case])
         dictin_gen[case] = reformat(dictin_gen[case],case,metric,years[case])
     dictin_gen = set_zero_values(dictin_gen)
     dictin[metric] = dictin_gen
@@ -648,7 +648,7 @@ def export(dictin,output_dir):
     dictin_emissions = {}
     metric = 'Emissions (million metric tonnes)'
     for case in tqdm(cases, desc=metric):
-        dictin_emissions[case] = reeds.output_calc.calc_emissions(cases[case])
+        dictin_emissions[case] = reeds.results.calc_emissions(cases[case])
         dictin_emissions[case] = dictin_emissions[case].loc[dictin_emissions[case]['e'] != 'H2'] # remove hydrogen emissions from the dataframe
         dictin_emissions[case] = reformat(dictin_emissions[case],case,metric,years[case])
     dictin[metric] = dictin_emissions
@@ -665,7 +665,7 @@ def export(dictin,output_dir):
     dictin_annualload = {}
     metric = 'Annual End-Use Electricity Demand (TWh)'
     for case in tqdm(cases, desc=metric):
-        dictin_annualload[case] = reeds.output_calc.calc_annualload(cases[case],dictin_scalars[case]) 
+        dictin_annualload[case] = reeds.results.calc_annualload(cases[case],dictin_scalars[case]) 
         dictin_annualload[case] = reformat(dictin_annualload[case],case,metric,years[case])
     dictin[metric] = dictin_annualload
 
@@ -673,7 +673,7 @@ def export(dictin,output_dir):
     dictin_systemcost = {}
     metric = 'System Cost (billion $)'
     for case in tqdm(cases, desc=metric):
-        dictin_systemcost[case] = reeds.output_calc.calc_systemcost(cases[case],group_r=False,drop_zeros=False).rename(columns={'year':'t','Discounted Cost (Bil $)':'Value'})
+        dictin_systemcost[case] = reeds.results.calc_systemcost(cases[case],group_r=False,drop_zeros=False).rename(columns={'year':'t','Discounted Cost (Bil $)':'Value'})
         del dictin_systemcost[case]['Cost (Bil $)']
         dictin_systemcost[case].cost_cat = dictin_systemcost[case].cost_cat.map(lambda x: output_formatting['cost_cat_map'].get(x,x))
         dictin_systemcost[case] = reformat(dictin_systemcost[case],case,metric,years[case])
@@ -703,7 +703,7 @@ def export(dictin,output_dir):
     metric = 'Transmission Capacity (TW-miles)'
     for case in tqdm(cases, desc=metric):
         # pull the total installed transmission capacity from ReEDS outputs
-        dictin_trans_total[case], _ = reeds.output_calc.calc_transmission_capacity(cases[case],levels=['transgrp'])
+        dictin_trans_total[case], _ = reeds.results.calc_transmission_capacity(cases[case],levels=['transgrp'])
         tran_total = dictin_trans_total[case]
         tran_total['Measure'] = 'Total installed'
         # make one df with the new capacity 
@@ -726,7 +726,7 @@ def export(dictin,output_dir):
     dictin_h2prod = {}
     metric = 'Hydrogen Production (million metric tonnes)'
     for case in tqdm(cases, desc=metric):
-        dictin_h2prod[case] = reeds.output_calc.calc_h2prod(cases[case])
+        dictin_h2prod[case] = reeds.results.calc_h2prod(cases[case])
         dictin_h2prod[case] = reformat(dictin_h2prod[case],case,metric,years[case])
     dictin[metric] = dictin_h2prod
 
@@ -734,7 +734,7 @@ def export(dictin,output_dir):
     dictin_sited_load = {}
     metric = 'Load Site Capacity (MW)'
     for case in tqdm(cases, desc=metric):
-        dictin_sited_load[case] = reeds.output_calc.calc_sited_load(cases[case])
+        dictin_sited_load[case] = reeds.results.calc_sited_load(cases[case])
         dictin_sited_load[case] = reformat(dictin_sited_load[case],case,metric,years[case])
     dictin[metric] = dictin_sited_load
 
diff --git a/postprocessing/uncertainty_plots.py b/postprocessing/uncertainty_plots.py
index d8e391d77..84f4a0f4b 100644
--- a/postprocessing/uncertainty_plots.py
+++ b/postprocessing/uncertainty_plots.py
@@ -21,7 +21,6 @@
 import matplotlib.pyplot as plt
 from matplotlib.lines import Line2D
 from matplotlib.ticker import FuncFormatter
-import pptx
 from pptx.util import Inches
 import io
 import seaborn as sns
@@ -486,7 +485,7 @@ def __init__(self, cases_dict: dict):
 
         # --- Fetch methods for output data ---
         self.OUT_FETCH_METHODS = {
-            # Naming as in e_report_params.csv
+            # Naming as in report_params.csv
             "cap_out": self._fetch_cap_out,
             "gen_ann": self._fetch_gen_ann,
             "tran_mi_out_detail": self._fetch_tran_mi_out_detail,
@@ -626,7 +625,7 @@ def _fetch_tran_mi_out_detail(self, case: str) -> pd.DataFrame:
         )
 
         df_tech_trans = (
-            reeds.output_calc.calc_reinforcement_spur_capacity_miles(self.cases_dict[case])
+            reeds.results.calc_reinforcement_spur_capacity_miles(self.cases_dict[case])
         )
         df_tech_trans['rr'] = df_tech_trans['r']  # Duplicate region data
         # Convert "Trans (GW-mi)" to "Trans (TW-mi)"
@@ -681,7 +680,7 @@ def _fetch_npv_r(self, case: str) -> pd.DataFrame:
         Returns:
             pd.DataFrame: Columns ['year', 'r', 'cost_cat', 'Cost (Bil $)', 'Discounted Cost (Bil $)']
         """
-        return reeds.output_calc.calc_systemcost(self.cases_dict[case])
+        return reeds.results.calc_systemcost(self.cases_dict[case])
 
 
     ### ===========================================================================
diff --git a/valuestreams.py b/postprocessing/valuestreams.py
similarity index 100%
rename from valuestreams.py
rename to postprocessing/valuestreams.py
diff --git a/preprocessing/atb_updates_processing/ReEDS_ATB_InputDataUpdate.py b/preprocessing/atb_updates_processing/ReEDS_ATB_InputDataUpdate.py
index 123ac95bd..fbd173af5 100644
--- a/preprocessing/atb_updates_processing/ReEDS_ATB_InputDataUpdate.py
+++ b/preprocessing/atb_updates_processing/ReEDS_ATB_InputDataUpdate.py
@@ -5,11 +5,11 @@
 """
 How to use this script.
 
-1. Copy the latest ATB csv (flattened version)- currently located here //nrelnas01/reeds/ReEDS_ATB_InputDataUpdate into your folder ~/ReEDS-2.0/input_processing/atb_updates_processing/input_files
+1. Copy the latest ATB csv (flattened version)- currently located here //nrelnas01/reeds/ReEDS_ATB_InputDataUpdate into your folder ~/ReEDS/input_processing/atb_updates_processing/input_files
 
-2. Copy the latest 20YY_CSP_occ_vs_tes_duration_formatted csv (flattened version) currently located here //nrelnas01/reeds/ReEDS_ATB_InputDataUpdate into your folder ~/ReEDS-2.0/input_processing/atb_updates_processing/input_files
+2. Copy the latest 20YY_CSP_occ_vs_tes_duration_formatted csv (flattened version) currently located here //nrelnas01/reeds/ReEDS_ATB_InputDataUpdate into your folder ~/ReEDS/input_processing/atb_updates_processing/input_files
 
-3. Copy the historic data csvs (previous year) from ~ReEDS-2.0/inputs/plant_characteristics into the ~/ReEDS-2.0/input_processing/atb_updates_processing/input_files
+3. Copy the historic data csvs (previous year) from ~ReEDS/inputs/plant_characteristics into the ~/ReEDS/input_processing/atb_updates_processing/input_files
     For example if this is ATB 2022 updates to ReEDS the previous year historic data would the 2021 historic data csvs, and the necessary files would be (year will change!)
     1. battery_ATB_2021_moderate.csv
     2. conv_ATB_2021.csv
@@ -29,9 +29,9 @@
    uncomment the final `#run_updates()` call in the script and then run the entire script.  
    If it is successful, you will get a successful completion message as an output. 
 
-7. Copy the formatted ReEDS input csvs to the *~/ReEDS-2.0/inputs/plant_characteristics* folder.  
+7. Copy the formatted ReEDS input csvs to the *~/ReEDS/inputs/plant_characteristics* folder.  
 
-8. Add appropriate formatted ReEDS input csv file names to the *~/ReEDS-2.0/inputs/plant_characteristics/dollaryear.csv* file.  
+8. Add appropriate formatted ReEDS input csv file names to the *~/ReEDS/inputs/plant_characteristics/dollaryear.csv* file.  
 """
 
 import pandas as pd
@@ -945,7 +945,7 @@ def run_updates():
         None
     """
 
-    successful_completion_message = "ReEDS ATB Input Updates complete! \n Be sure to 1) copy the formatted csvs to the ~/ReEDS-2.0/inputs/plant_characteristics folder, \n and 2) Add appropriate formatted ReEDS input csv file names to the ~/ReEDS-2.0/inputs/plant_characteristics/dollaryear.csv file."
+    successful_completion_message = "ReEDS ATB Input Updates complete! \n Be sure to 1) copy the formatted csvs to the ~/ReEDS/inputs/plant_characteristics folder, \n and 2) Add appropriate formatted ReEDS input csv file names to the ~/ReEDS/inputs/plant_characteristics/dollaryear.csv file."
 
     #run necessary setup and retrieve variables needed for for updates
     input_dir, output_dir, df_atb, atb_year, atb_first_year, atb_last_year, dollar_yr_conv, crpyears, case, atb_filters, atb_scenarios = updates_setup()
diff --git a/preprocessing/casemaker.py b/preprocessing/casemaker.py
index c70b8d729..a793a1525 100644
--- a/preprocessing/casemaker.py
+++ b/preprocessing/casemaker.py
@@ -191,9 +191,9 @@ def main(casematrix_path=None, batchname=None):
     ### Write it
     dfcases.to_csv(os.path.join(reeds_path,f'cases_{_batchname}.csv'))
 
-    ### Make sure the switch names and values pass the checks in runbatch.py
+    ### Make sure the switch names and values pass the checks in runreeds.py
     sep = ';' if os.name == 'posix' else '&&'
-    cmd = f"cd {reeds_path} {sep} python runbatch.py -b test -c {_batchname} -r 4 -p 1 --dryrun"
+    cmd = f"cd {reeds_path} {sep} python runreeds.py -b test -c {_batchname} -r 4 -p 1 --dryrun"
     result = subprocess.run(cmd, shell=True, capture_output=True)
     err = result.stderr.decode()
     if len(err):
diff --git a/preprocessing/casematrix_example.yaml b/preprocessing/casematrix_example.yaml
index 664f8639d..bf676e10d 100644
--- a/preprocessing/casematrix_example.yaml
+++ b/preprocessing/casematrix_example.yaml
@@ -4,7 +4,7 @@
 ### Shared settings are applied to all cases.
 ### Any settings not specified use the defaults from cases.csv.
 shared:
-  yearset: 2010_2015_2020_2025_2030_2035_2040_2045_2050
+  yearset: 2010..2050..5
 
 ##################
 ### Dimensions ###
diff --git a/reeds/__init__.py b/reeds/__init__.py
index e6aaa6e10..ff723dfd1 100644
--- a/reeds/__init__.py
+++ b/reeds/__init__.py
@@ -1,15 +1,19 @@
+from . import input_processing as input_processing
+from . import core as core
+from . import hpc as hpc
+from . import resource_adequacy as resource_adequacy
+
 from . import checks as checks
 from . import financials as financials
 from . import inputs as inputs
 from . import io as io
 from . import log as log
-from . import output_calc as output_calc
 from . import plots as plots
 from . import prasplots as prasplots
-from . import ra as ra
 from . import reedsplots as reedsplots
 from . import remote as remote
 from . import report_utils as report_utils
+from . import results as results
 from . import spatial as spatial
 from . import techs as techs
 from . import timeseries as timeseries
diff --git a/reeds/core/setup/a_createmodel.gms b/reeds/core/setup/a_createmodel.gms
new file mode 100644
index 000000000..9250f30ea
--- /dev/null
+++ b/reeds/core/setup/a_createmodel.gms
@@ -0,0 +1,10 @@
+$setglobal ds \
+$ifthen.unix %system.filesys% == UNIX
+$setglobal ds /
+$endif.unix
+
+$include reeds%ds%core%ds%setup%ds%b_inputs.gms
+$include reeds%ds%core%ds%setup%ds%c_model.gms
+$include reeds%ds%core%ds%setup%ds%d_objective.gms
+$include reeds%ds%core%ds%setup%ds%d_mga.gms
+$include reeds%ds%core%ds%setup%ds%e_solveprep.gms
diff --git a/b_inputs.gms b/reeds/core/setup/b_inputs.gms
similarity index 93%
rename from b_inputs.gms
rename to reeds/core/setup/b_inputs.gms
index f1b09e549..90d45c9d6 100644
--- a/b_inputs.gms
+++ b/reeds/core/setup/b_inputs.gms
@@ -14,29 +14,6 @@ $ifthen.unix %system.filesys% == UNIX
 $setglobal ds /
 $endif.unix
 
-*need to convert the 'unit' numhintage to some large value
-$ifthen.unithintage %numhintage%=="unit"
-$eval numhintage 300
-$endif.unithintage
-
-* Under the Clean Air Act, all coal plants are regulated individually. Therefore, we need a large value of hintages to represent these plants
-$include inputs_case%ds%max_hintage_number.txt
-
-$ifthen.unithintage %GSw_Clean_Air_Act%==1
-$eval numhintage max_hintage_number
-$endif.unithintage
-
-*need to convert the 'group' numhintage to some large value
-$ifthen.unithintage %numhintage%=="group"
-$eval numhintage 300
-$endif.unithintage
-
-* there are numeraire hintages on either sides of the outer breaks
-* when using calcmethod = 1, here adding two for safety
-* NB this will not increase model size given conditions
-* dictating valcap and valgen for initial classes
-$eval numhintage %numhintage% + 2
-
 *$ontext
 * --- print timing profile ---
 option profile = 3
@@ -53,9 +30,6 @@ option solprint = off ;
 option sysout = off ;
 *$offtext
 
-set dummy "set used for initialization of numerical sets" / 0*10000 / ;
-alias(dummy,adummy) ;
-
 
 *======================
 * -- Local Switches --
@@ -63,7 +37,7 @@ alias(dummy,adummy) ;
 
 * Following are scalars used to turn on or off various components of the model.
 * For binary switches, [0] is off and [1] is on.
-* These switches are generated from the cases file in runbatch.py.
+* These switches are generated from the cases file in runreeds.py.
 $include inputs_case%ds%gswitches.txt
 
 * Extra switches that are defined based on other switches
@@ -77,7 +51,7 @@ parameter Sw_Timetype(timetype) "Switch that specifies the type of time method u
 
 Sw_Timetype("%timetype%") = 1 ;
 
-* Sw_PCM is always 0 except when running d_solvepcm.gms, where it's set to 1
+* Sw_PCM is always 0 except when running solve_pcm.gms, where it's set to 1
 scalar Sw_PCM "Internal switch used when running PCM mode" / 0 / ;
 * Sw_MGA is always 0 except when running the optimization a second time for MGA, where it's set to 1
 scalar Sw_MGA "Internal switch used when running MGA mode" / 0 / ;
@@ -91,36 +65,13 @@ scalar retireyear  "first year to allow capacity to start retiring" /%GSw_Retire
        upgradeyear "first year to allow capacity to upgrade"        /%GSw_Upgradeyear%/
        climateyear "first year to apply climate impacts"            /%GSw_ClimateStartYear%/ ;
 
-*** Scalars: copied from inputs/scalars.csv to inputs_case/scalars.txt in runbatch.py
+*** Scalars: copied from inputs/scalars.csv to inputs_case/scalars.txt in runreeds.py
 $include inputs_case%ds%scalars.txt
 
 *==========================
 * --- Set Declarations ---
 *==========================
 
-*## Spatial sets (define first so case stays consistent)
-* written by copy_files.py
-$onOrder
-set r "regions"
-/
-$offlisting
-$include inputs_case%ds%val_r.csv
-$onlisting
-/ ;
-$offOrder
-
-$onempty
-set offshore(r) "offshore zones"
-/
-$offlisting
-$include inputs_case%ds%offshore.csv
-$onlisting
-/ ;
-$offempty
-
-set land(r) "land-based (not offshore) zones" ;
-land(r)$[not offshore(r)] = yes ;
-
 * written by copy_files.py
 $onempty
 set cs(*) "carbon storage sites"
@@ -131,109 +82,16 @@ $onlisting
 / ;
 $offempty
 
-* created in and mapped to hierarchy in recf.py
-set ccreg "capacity credit regions"
-/
-$offlisting
-$include inputs_case%ds%ccreg.csv
-$onlisting
-/ ;
-
-set eall "emission categories used in reporting"
-/
-$offlisting
-$include inputs_case%ds%eall.csv
-$onlisting
-/ ;
-
-set e(eall) "emission categories used in model"
-/
-$offlisting
-$include inputs_case%ds%e.csv
-$onlisting
-/ ;
-
-set etype "emission types used in model (upstream and process)"
-/
-$offlisting
-$include inputs_case%ds%etype.csv
-$onlisting
-/ ;
-
-Sets
-nercr "NERC regions"
-* https://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/NERC_LTRA_2021.pdf
-/
-* written by copy_files.py
-$include inputs_case%ds%val_nercr.csv
-/
-
-transreg "Transmission Planning Regions from FERC order 1000"
-* (https://www.ferc.gov/sites/default/files/industries/electric/indus-act/trans-plan/trans-plan-map.pdf)
-/
-* written by copy_files.py
-$include inputs_case%ds%val_transreg.csv
-/,
-
-transgrp "sub-FERC-1000 regions"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_transgrp.csv
-/,
-
-itlgrp "ReEDS zones for additional ITL constraints when doing a run that includes county resolution"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_itlgrp.csv
-/,
-
-cendiv "census divisions"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_cendiv.csv
-/,
-
-interconnect "interconnection regions"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_interconnect.csv
-/,
-
-country "country regions"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_country.csv
-/,
-
-st "US, Mexico, and/or Canadian States/Provinces"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_st.csv
-/,
-
-* biomass supply curves defined by USDA region
-usda_region "Biomass supply curve regions"
-/
-* written by copy_files.py
-$include inputs_case%ds%val_usda_region.csv
-/,
-
-h2ptcreg "Regions which enforce the H2 production incentive regulations, for the US these are the National Transmission Needs Study regions"
-* https://www.energy.gov/sites/default/files/2023-12/National%20Transmission%20Needs%20Study%20Supplemental%20Material%20-%20Final_2023.12.1.pdf
-/
-* written by copy_files.py
-$include inputs_case%ds%val_h2ptcreg.csv
-/
-
-* Hurdle rate regions
-hurdlereg "Hurdle regions"
-/
-$include inputs_case%ds%val_hurdlereg.csv
-/
-;
+* Written by h5_to_gdx.py
+$include autocode%ds%b_declare_sets.gms
+$include autocode%ds%b_declare_parameters.gms
+$gdxin inputs_case%ds%inputs_0.gdx
+$include autocode%ds%b_load_sets.gms
+$include autocode%ds%b_load_parameters.gms
+$gdxin
 
-* Written by copy_files.py
-$include b_sets.gms
+set land(r) "land-based (not offshore) zones" ;
+land(r)$[not offshore(r)] = yes ;
 
 sets
 *The following two sets:
@@ -299,17 +157,6 @@ $endif.hydup2
     ss
   / ;
 
-alias(i,ii,iii) ;
-
-set i_water_nocooling(i) "technologies that use water, but are not differentiated by cooling tech and water source"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%i_water_nocooling.csv
-$offdelim
-$onlisting
-/ ;
-
 set i_water_cooling(i) "derived technologies from original technologies with cooling technologies other than just none",
 *Hereafter numeraire techs in cooling-water context mean original technologies,
 *like gas-CC, and non-numeraire techs mean techs that are derived from numeraire techs
@@ -390,22 +237,6 @@ $offdelim
 $onlisting
 ;
 
-set allt "all potential years"
-/
-$offlisting
-$include inputs_case%ds%allt.csv
-$onlisting
-/ ;
-
-set i_geotech(i,geotech) "crosswalk between an individual geothermal technology and its category"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%i_geotech.csv
-$offdelim
-$onlisting
-/ ;
-
 set
 *technology-specific subsets
   battery(i)           "battery storage technologies",
@@ -428,6 +259,7 @@ set
   combustion_turbine(i)"combustion turbine technologies",
   consume(i)           "technologies that consume electricity and add to load",
   conv(i)              "conventional generation technologies",
+  temp_derate(i)       "generation technologies derated based on ambient temperature",
   csp_storage(i)       "csp generation technologies with thermal storage",
   csp(i)               "csp generation technologies",
   csp1(i)              "csp-tes generation technologies 1",
@@ -489,65 +321,16 @@ set
   vre_no_csp(i)        "variable renewable energy technologies that are not csp",
   vre_utility(i)       "utility scale wind and PV technologies",
   vre(i)               "variable renewable energy technologies",
-  wind(i)              "wind generation technologies",
-
-t(allt) "full set of years" /%startyear%*%endyear%/,
-
-* Each generation technology is broken out by class:
-* 1. initial capacity: init-1, init-2, ..., init-n
-* 2. prescribed capacity: prescribed
-* 3. new capacity: new
-* This allows us to distinguish between existing, prescribed, and model-chosen builds
-* The number of classes is set by numhintage for initial capacity and numclass for new capacity
-v "technology class"
-    /
-      init-1*init-%numhintage%,
-      new1*new%numclass%
-    /,
-
-initv(v) "initial technologies" /init-1*init-%numhintage%/,
-
-newv(v) "new tech set" /new1*new%numclass%/
-
+  wind(i)              "wind generation technologies"
 ;
 
-* DAC == direct air capture
-* H2 == hydrogen
-* Note: no longer tracking H2 by color. This means ReEDS internalizes
-* emissions for any H2 produced for non-power sector demands
-set p "products produced"
-/
-$offlisting
-$include inputs_case%ds%p.csv
-$onlisting
-/ ;
-
-
 hyd_add_pump('hydED_pumped-hydro') = yes ;
 hyd_add_pump('hydED_pumped-hydro-flex') = yes ;
 
 * Sets involved with resource supply curve definitions
-set sc_cat "supply curve categories (capacity and cost)"
-/
-$offlisting
-$include inputs_case%ds%sc_cat.csv
-$onlisting
-/ ;
-
 set rscbin "Resource supply curves bins" /bin1*bin%numbins%/,
     rscfeas(i,r,rscbin) "feasibility set for technologies that have resource supply curves" ;
 
-alias(r,rr,n,nn) ;
-alias(v,vv) ;
-alias(t,tt,ttt) ;
-alias(st,ast) ;
-alias(allt,alltt) ;
-alias(cendiv,cendiv2) ;
-alias(rscbin,arscbin) ;
-alias(nercr,nercrr) ;
-alias(transgrp,transgrpp) ;
-alias(itlgrp,itlgrpp) ;
-
 parameter yeart(t) "numeric value for year",
           year(allt) "numeric year value for allt" ;
 
@@ -577,16 +360,6 @@ $offdelim
 $onlisting
 / ;
 
-*various parameters needed for Present Value Factor (PVF) calculations before solving
-*specifically these are used in the aggregating of the PVF of
-*onm and capital when years are skipped
-set yearafter "set to loop over for the final year calculation"
-/
-$offlisting
-$include inputs_case%ds%yearafter.csv
-$onlisting
-/ ;
-
 * --- Upgrade link definitions ---
 Set upgrade_to(i,ii)         "mapping set that allows for i to be upgraded to ii"
     upgrade_from(i,ii)       "mapping set that allows for i to be upgraded from ii"
@@ -606,15 +379,6 @@ upgrade(i)$[sum{(ii,iii), upgrade_link(i,ii,iii) }] = yes ;
 upgrade_to(i,ii)$[sum{iii, upgrade_link(i,iii,ii) }] = yes ;
 upgrade_from(i,ii)$[sum{iii, upgrade_link(i,ii,iii) }] = yes ;
 
-set unitspec_upgrades(i) "upgraded technologies that get unit-specific characteristics"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%unitspec_upgrades.csv
-$offdelim
-$onlisting
-/ ;
-
 unitspec_upgrades(i)$[sum{ii$ctt_i_ii(i,ii), unitspec_upgrades(ii) }$Sw_WaterMain] =
   sum{ii$ctt_i_ii(i,ii), unitspec_upgrades(ii) } ;
 
@@ -873,13 +637,6 @@ $ifthen.naris %GSw_Region% == "naris"
   ban(i)$i_subsets(i,'canada') = yes ;
 $endif.naris
 
-* Ban DUPV, CSP, and Geothermal resources that do not remain after aggregation
-set resourceclass "renewable resource classes"
-/
-$offlisting
-$include inputs_case%ds%resourceclass.csv
-$onlisting
-/ ;
 parameter resourceclassnum(resourceclass) "numeric value for resource class" ;
 resourceclassnum(resourceclass) = resourceclass.val ;
 set tech_resourceclass(i,resourceclass) "map from CSP/DUPV techs to resource classes"
@@ -961,6 +718,7 @@ combined_cycle(i)$(not ban(i))      = yes$i_subsets(i,'combined_cycle') ;
 combustion_turbine(i)$(not ban(i))  = yes$i_subsets(i,'combustion_turbine') ;
 consume(i)$(not ban(i))             = yes$i_subsets(i,'consume') ;
 conv(i)$(not ban(i))                = yes$i_subsets(i,'conv') ;
+temp_derate(i)$(not ban(i))         = yes$i_subsets(i,'temp_derate') ;
 csp_storage(i)$(not ban(i))         = yes$i_subsets(i,'csp_storage') ;
 csp(i)$(not ban(i))                 = yes$i_subsets(i,'csp') ;
 csp1(i)$(not ban(i))                = yes$i_subsets(i,'csp1') ;
@@ -1042,48 +800,9 @@ tg_i('biomass',i)$bio(i) = yes ;
 tg_i('pumped-hydro',i)$psh(i) = yes ;
 tg_i('dr_shed',i)$dr_shed(i) = yes ;
 
-*Hybrid pv+battery (PVB) configurations are defined by:
-*  (1) inverter loading ratio (DC/AC) and
-*  (2) battery capacity ratio (Battery/PV Array)
-*Each configuration has ten resource classes
-*The PV portion refers to "UPV", but not "DUPV"
-*The battery portion refers to "battery_li"
-set pvb_config "set of hybrid pv+battery configurations"
-/
-$offlisting
-$include inputs_case%ds%pvb_config.csv
-$onlisting
-/ ;
-
-set pvb_agg(pvb_config,i) "crosswalk between hybrid pv+battery configurations and technology options"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%pvb_agg.csv
-$offdelim
-$onlisting
-/ ;
-
 *add non-numeraire CSPs in index i of already defined set tg_i(tg,i)
 tg_i("csp",i)$[(csp1(i) or csp2(i) or csp3(i) or csp4(i))$Sw_WaterMain] = yes ;
 
-*Offhsore wind turbine types
-set ofstype "offshore types used in offshore requirement constraint (eq_RPS_OFSWind)"
-/
-$offlisting
-$include inputs_case%ds%ofstype.csv
-$onlisting
-/ ;
-
-set ofstype_i(ofstype,i) "crosswalk between ofstype and i"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%ofstype_i.csv
-$offdelim
-$onlisting
-/ ;
-
 storage_interday(i)$(Sw_InterDayLinkage = 0) = no ;
 
 $onempty
@@ -1176,15 +895,6 @@ $onlisting
  / ;
 
 
-set prescriptivelink0(pcat,ii) "initial set of prescribed categories and their technologies - used in assigning prescribed builds"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%prescriptivelink0.csv
-$offdelim
-$onlisting
-/ ;
-
 *include non-numeraire CSPs and then exclude numeraire CSPs in ii dimension of
 *prescriptivelink0(pcat,ii) set when Sw_WaterMain is ON
 prescriptivelink0("csp-ws",ii)$[(csp1(ii) or csp2(ii) or csp3(ii) or csp4(ii))$Sw_WaterMain] = yes ;
@@ -1194,8 +904,6 @@ set prescriptivelink(pcat,i) "final set of prescribed categories and their techn
 
 prescriptivelink(pcat,i)$prescriptivelink0(pcat,i) = yes ;
 
-alias(pcat,ppcat) ;
-
 * active prescriptivelink for all techs not included in the table above
 * but restrict out csp techs in this calculation - since they
 * are indexed by a separate pcat (csp-ws) and have special considerations
@@ -1209,15 +917,6 @@ prescriptivelink(pcat,i)$[upgrade(i)] = no ;
 
 set rsc_agg(i,ii)   "rsc technologies that belong to the same class" ;
 
-set tg_rsc_cspagg(i,ii) "csp technologies that belong to the same class"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%tg_rsc_cspagg.csv
-$offdelim
-$onlisting
-/ ;
-
 set tg_rsc_cspagg_tmp(i,ii) "expanded tg_rsc_cspagg(i,ii) to include new non-numeraire CSP techs" ;
 
 *input parameters for linking set only when Sw_WaterMain is ON and start with a blank slate
@@ -1247,15 +946,6 @@ Replicating the construct for CSP to link Hybrid PV+battery and UPV for the reso
   Because the first index of rsc_agg is only a UPV technology the above constraint will never be generated when "i" is a pvb(i).
 $offtext
 
-set tg_rsc_upvagg(i,ii) "pv and pvb technologies that belong to the same class"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%tg_rsc_upvagg.csv
-$offdelim
-$onlisting
-/ ;
-
 *initialize rsc aggregation set for 'i'='ii'
 *rsc_agg(i,ii)$[sameas(i,ii)$(not csp(i))$(not csp(ii))$rsc_i(i)$rsc_i(ii)] = yes ;
 rsc_agg(i,ii)$[sameas(i,ii)$rsc_i(i)$rsc_i(ii)] = yes ;
@@ -1267,19 +957,8 @@ rsc_agg(i,ii)$tg_rsc_upvagg(i,ii) = yes ;
 rsc_agg('pumped-hydro',ii)$psh(ii) = yes ;
 rsc_agg(i,ii)$[ban(i) or ban(ii)] = no ;
 
-*============================
-* -- Demand flexibility setup --
-*============================
-
-set flex_type "set of demand flexibility types: daily, previous, next, adjacent"
-/
-$offlisting
-$include inputs_case%ds%flex_type.csv
-$onlisting
-/ ;
-
 *======================================
-*     --- Begin hierarchy ---
+*     --- Region hierarchy ---
 *======================================
 
 set hierarchy(r,nercr,transreg,transgrp,cendiv,st,interconnect,country,usda_region,h2ptcreg,hurdlereg,ccreg) "hierarchy of various regional definitions"
@@ -1360,16 +1039,6 @@ tsolved(t) = no ;
 * Year specification
 *==============================
 
-* declared over allt to allow for external data files that extend beyond end_year
-set tmodel_new(allt) "years to run the model"
-/
-$offlisting
-$include inputs_case%ds%modeledyears.csv
-$onlisting
-/ ;
-
-tmodel_new(allt)$[year(allt) > %endyear%]= no ;
-
 *reset the first and last year indices of the model
 tfirst(t)$[ord(t) = smin{tt$tmodel_new(tt), ord(tt) }] = yes ;
 tlast(t)$[ord(t) = smax{tt$tmodel_new(tt), ord(tt) }] = yes ;
@@ -1425,7 +1094,7 @@ ivt(i,newv,t)$[ord(newv) = ivt_num(i,t)] = yes ;
 ivt(i,v,t)$[i_water_cooling(i)$Sw_WaterMain] =
   sum{ii$ctt_i_ii(i,ii), ivt(ii,v,t) } ;
 
-*Also expand ivt_num to water techs for use in Augur
+*Also expand ivt_num to water techs for use in resource adequacy calculations
 ivt_num(i,t)$[i_water_cooling(i)$Sw_WaterMain] =
   sum{ii$ctt_i_ii(i,ii), ivt_num(ii,t) } ;
 
@@ -1481,7 +1150,7 @@ $onlisting
 /,
 
 * pvf_capital and pvf_onm here are for intertemporal mode. These parameters
-* are overwritten for sequential mode in d_solveprep.gms.
+* are overwritten for sequential mode in e_solveprep.gms.
           pvf_capital(t) "--unitless-- present value factor for overnight capital costs"
 /
 $offlisting
@@ -1512,7 +1181,7 @@ $onlisting
 /,
 
           h2_ptc(i,v,r,allt)        "--2004$/kg h2 produced -- incentive on hydrogen production by electrolyzers which purchase Energy Attribute Credits"
-          h2_ptc_in(i,v,allt)       "--2004$/kg h2 produced -- incentive on hydrogen production by electrolyzers which purchase Energy Attribute Credits, this parameter is used to build h2_ptc and is produced in input_processing/calc_financial_inputs.py"
+          h2_ptc_in(i,v,allt)       "--2004$/kg h2 produced -- incentive on hydrogen production by electrolyzers which purchase Energy Attribute Credits, this parameter is used to build h2_ptc"
 /
 $offlisting
 $ondelim
@@ -1611,8 +1280,8 @@ $offdelim
 $onlisting
 / ;
 
-*created by /input_processing/writecapdat.py
-table capnonrsc(i,r,*) "--MW-- raw power capacity data for non-RSC tech created by .\input_processing\writecapdat.py"
+*created by reeds/input_processing/writecapdat.py
+table capnonrsc(i,r,*) "--MW-- raw power capacity data for non-RSC tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%capnonrsc.csv
@@ -1620,9 +1289,9 @@ $offdelim
 $onlisting
 ;
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 $onempty
-table capnonrsc_energy(i,r,*) "--MWh-- raw energy capacity data for battery tech created by .\input_processing\writecapdat.py"
+table capnonrsc_energy(i,r,*) "--MWh-- raw energy capacity data for battery tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%capnonrsc_energy.csv
@@ -1631,9 +1300,9 @@ $onlisting
 ;
 $offempty
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 $onempty
-table caprsc(pcat,r,*) "--MW-- raw RSC capacity data, created by .\writecapdat.py"
+table caprsc(pcat,r,*) "--MW-- raw RSC capacity data"
 $offlisting
 $ondelim
 $include inputs_case%ds%caprsc.csv
@@ -1642,10 +1311,10 @@ $onlisting
 ;
 $offempty
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 * declared over allt to allow for external data files that extend beyond end_year
 $onempty
-table prescribednonrsc(allt,pcat,r,*) "--MW-- raw prescribed capacity data for non-RSC tech created by writecapdat.py"
+table prescribednonrsc(allt,pcat,r,*) "--MW-- raw prescribed capacity data for non-RSC tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%prescribed_nonRSC.csv
@@ -1655,7 +1324,7 @@ $onlisting
 $offempty
 
 $onempty
-table prescribednonrsc_energy(allt,pcat,r,*) "--MWh-- raw prescribed energy capacity data for non-RSC tech created by writecapdat.py"
+table prescribednonrsc_energy(allt,pcat,r,*) "--MWh-- raw prescribed energy capacity data for non-RSC tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%prescribed_nonRSC_energy.csv
@@ -1664,9 +1333,9 @@ $onlisting
 ;
 $offempty
 
-*Created using input_processing\writecapdat.py
+*Created using reeds/input_processing/writecapdat.py
 $onempty
-table prescribedrsc(allt,pcat,r,*) "--MW-- raw prescribed capacity data for RSC tech created by .\input_processing\writecapdat.py"
+table prescribedrsc(allt,pcat,r,*) "--MW-- raw prescribed capacity data for RSC tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%prescribed_rsc.csv
@@ -1700,11 +1369,11 @@ $offempty
 
 prescribedrsc(allt,"wind-ofs",r,"value") = prescribed_wind_ofs(r,allt,"capacity") ;
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 *following does not include wind
 *Retirements for techs binned by heatrates are handled in hintage_data.csv
 $onempty
-table prescribedretirements(allt,r,i,*) "--MW-- raw prescribed power capacity retirement data for non-RSC, non-heatrate binned tech created by /input_processing/writecapdat.py"
+table prescribedretirements(allt,r,i,*) "--MW-- raw prescribed power capacity retirement data for non-RSC, non-heatrate binned tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%retirements.csv
@@ -1713,10 +1382,10 @@ $onlisting
 ;
 $offempty
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 *Retirements for techs binned by heatrates are handled in hintage_data.csv
 $onempty
-table prescribedretirements_energy(allt,r,i,*) "--MWh-- raw prescribed energy capacity retirement data for battery tech created by /input_processing/writecapdat.py"
+table prescribedretirements_energy(allt,r,i,*) "--MWh-- raw prescribed energy capacity retirement data for battery tech"
 $offlisting
 $ondelim
 $include inputs_case%ds%retirements_energy.csv
@@ -1743,15 +1412,8 @@ forced_retire(i,r,t)$[sum{st$r_st(r,st), (yeart(t)>=forced_retirements(i,st))$fo
 * upgrade tech in forced_retire
 forced_retire(i,r,t)$[upgrade(i)$(sum{ii$upgrade_to(i,ii), forced_retire(ii,r,t) })] = yes ;
 
-set hintage_char "characteristics available in hintage_data"
-/
-$offlisting
-$include inputs_case%ds%hintage_char.csv
-$onlisting
-/ ;
-
-*created by /input_processing/writehintage.py
-table hintage_data(i,v,r,allt,hintage_char) "table of existing unit characteristics written by writehintage.py"
+*created by reeds/input_processing/writehintage.py
+table hintage_data(i,v,r,allt,hintage_char) "table of existing unit characteristics"
 $offlisting
 $ondelim
 $include inputs_case%ds%hintage_data.csv
@@ -1765,15 +1427,6 @@ if((not Sw_UpgradeHeatRateAdj),
           = hintage_data(i,initv,r,t,"wHR") ;
 ) ;
 
-set upgrade_hintage_char(hintage_char) "sets to operate over in extension of hintage_data characteristics when sw_upgrades = 1"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%upgrade_hintage_char.csv
-$offdelim
-$onlisting
-/ ;
-
 * need to extend characteristics for years where a tech could still exist if it was upgraded in a previous year
 * - ie a hintages characteristics would need to persist if it is upgraded and has a lifetime extension
 if(Sw_Upgrades = 1,
@@ -1791,7 +1444,7 @@ if(Sw_Upgrades = 1,
   ) ;
 ) ;
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 parameter binned_capacity(i,v,r,allt) "existing capacity (that is not rsc, but including distpv) binned by heat rates" ;
 
 binned_capacity(i,v,r,allt) = hintage_data(i,v,r,allt,"cap") ;
@@ -2351,8 +2004,6 @@ m_rscfeas(r,i,"bin1")$[sum{(pcat,t)$[sameas(pcat,i)$tmodel_new(t)], noncumulativ
 *==========================================================
 *--- Interconnection queues (Capacity deployment limit) ---
 *==========================================================
-alias(tg,tgg) ;
-
 $onempty
 table cap_limit(tg,r,allt) "--MW-- capacity deployment limit by region and technology based on interconnection queues"
 $offlisting
@@ -2612,7 +2263,6 @@ m_capacity_exog(i,v,r,t)$[forced_retire(i,r,t)$(not Sw_Clean_Air_Act)$coal_noccs
 
 * If, in the last year in which coal must either retire or upgrade, coal is upgraded, we can continue to 
 * use that m_capacity_exog beyond caa_coal_retire_year. But unabated coal plants must not have capacity after caa_coal_retire_year.
-* This is expanded in d_solveoneyear.gms.
 m_capacity_exog(i,v,r,t)$[forced_retire(i,r,t)$coal_noccs(i)
                          $(t.val > caa_coal_retire_year)
                          $(sum{ii$(not forced_retire(ii,r,t)), upgrade_from(ii,i) }) ] = 0 ;
@@ -2754,7 +2404,7 @@ co2_captured_incentive(i,v,r,t)$[initv(v)$upgrade(i)
                                     $(yeart(t)>=Sw_UpgradeYear)
                                     $(yeart(t) <= co2_capture_incentive_last_year_)] =
 * note we populate the incentive for all years and then trim the incentive for later years
-* when the upgrade occurs - this is after the solve statement in d_solveoneyear
+* when the upgrade occurs - this is after the solve statement
 * we also cast this forward based on whether or not a plant was built in that year
 * but remove the last year for consideration of that below via co2_capture_incentive_last_year_
   sum{(ii,vv,tt)$[upgrade_to(i,ii)$newv(vv)
@@ -2785,19 +2435,10 @@ h2_ptc_years(t) = tmodel_new(t)$[sum{(i,v,r),h2_ptc(i,v,r,t)}];
 * --- Parameters for water constraints ---
 *==========================================
 
-set sw(wst) "surface water types where access is based on consumption not withdrawal"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%sw.csv
-$offdelim
-$onlisting
-/ ;
-
 set i_water_surf(i) "subset of technologies that uses surface water",
   i_w(i,w) "linking set between technology and water use type used in constraining water availability" ;
 
-i_water_surf(i)$[sum{(sw,ctt,ii)$i_ii_ctt_wst(i,ii,ctt,sw), 1}] = yes ;
+i_water_surf(i)$[sum{(wst_surface,ctt,ii)$i_ii_ctt_wst(i,ii,ctt,wst_surface), 1}] = yes ;
 i_w(i,"cons")$[i_water(i)$i_water_surf(i)] = yes ;
 i_w(i,"with")$[i_water(i)$(not i_water_surf(i))] = yes ;
 
@@ -2892,13 +2533,6 @@ $onlisting
 / ;
 $offempty
 
-set climate_param "parameters defined in climate_heuristics_finalyear"
-/
-$offlisting
-$include inputs_case%ds%climate_param.csv
-$onlisting
-/ ;
-
 parameter climate_heuristics_finalyear(climate_param) "--fraction-- climate heuristic adjustment in final year"
 $onempty
 /
@@ -2921,13 +2555,6 @@ hydro_capcredit_delta(i,t)$hydro_d(i) =
 *         --- RPS data ---
 *====================================
 
-set RPSCat "RPS constraint categories, including clean energy standards"
-/
-$offlisting
-$include inputs_case%ds%RPSCat.csv
-$onlisting
-/ ;
-
 set RPSCat_i(RPSCat,i,st)     "mapping between rps category and technologies for each state",
     RecMap(i,RPSCat,st,ast,t) "Mapping set for technologies to RPS categories and indicates if credits can be sent from st to ast",
     RecStates(RPSCat,st,t)    "states that can generate RECS for their own or other states' requirements",
@@ -3222,23 +2849,6 @@ REC_unbundled_limit("CES",st,t) = CES_unbundled_limit_in(st,t) ;
 
 st_unbundled_limit(RPSCat,st)$sum{t, REC_unbundled_limit(RPSCat,st,t) } = yes ;
 
-parameter national_gen_frac(allt) "--%-- national fraction of load + losses that must be met by RE"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%gen_mandate_trajectory.csv
-$offdelim
-$onlisting
-/ ;
-
-parameter nat_gen_tech_frac(i) "--fraction-- fraction of each tech generation that may be counted toward eq_national_gen"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%gen_mandate_tech_list.csv
-$offdelim
-$onlisting
-/ ;
 nat_gen_tech_frac(i)$[i_water_cooling(i)$Sw_WaterMain] = sum{ii$ctt_i_ii(i,ii), nat_gen_tech_frac(ii) } ;
 
 *====================
@@ -3247,21 +2857,7 @@ nat_gen_tech_frac(i)$[i_water_cooling(i)$Sw_WaterMain] = sum{ii$ctt_i_ii(i,ii),
 
 * a CSAPR budget indicates the cap for trading whereas
 * assurance indicates the maximum amount a state can emit regardless of trading
-set csapr_cat "CSAPR regulation categories"
-/
-$offlisting
-$include inputs_case%ds%csapr_cat.csv
-$onlisting
-/ ;
-
 *trading rules dictate there are two groups of states that can trade with each other
-set csapr_group "CSAPR trading group"
-/
-$offlisting
-$include inputs_case%ds%csapr_group.csv
-$onlisting
-/ ;
-
 $onempty
 table csapr_cap(st,csapr_cat,allt) "--metric tons-- maximum amount of NOX emissions during the ozone season (May-September)"
 $offlisting
@@ -3310,55 +2906,12 @@ parameter quarter_weight_csapr(quarter) "quarter weights for CSAPR ozone season
 *==============================
 
 * --- transmission sets ---
-set trtype "transmission capacity type"
-/
-$offlisting
-$include inputs_case%ds%trtype.csv
-$onlisting
-/ ;
-
-set aclike(trtype) "AC transmission capacity types"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%aclike.csv
-$offdelim
-$onlisting
-/ ;
-
-set notvsc(trtype) "transmission capacity types that are not VSC"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%notvsc.csv
-$offdelim
-$onlisting
-/ ;
-
-set lcclike(trtype) "transmission capacity types where lines are bundled with AC/DC converters"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%lcclike.csv
-$offdelim
-$onlisting
-/ ;
-
-set trancap_fut_cat "categories of near-term transmission projects that describe the likelihood of being completed"
-/
-$offlisting
-$include inputs_case%ds%trancap_fut_cat.csv
-$onlisting
-/ ;
-
 set routes(r,rr,trtype,t)     "final conditional on transmission feasibility"
     routes_inv(r,rr,trtype,t) "routes where new transmission investment is allowed"
     routes_prm(r,rr)          "routes where PRM trading is allowed"
     opres_routes(r,rr,t)      "final conditional on operating reserve flow feasibility"
 ;
 
-alias(trtype,intype,outtype) ;
-
 * Specify the transmission types that are limited by Sw_TransCapMax and Sw_TransCapMaxTotal
 set trtypemax(trtype) "trtypes to limit" ;
 trtypemax(trtype)$[(Sw_TransCapMaxTypes=0)] = no ;
@@ -3448,10 +3001,10 @@ routes_inv(r,rr,trtype,t)$[notvsc(trtype)$routes(r,rr,trtype,t)] = yes ;
 routes_inv(r,rr,trtype,t)$[yeart(t)<firstyear_trans_nearterm] = no ;
 * If not allowing near-term transmission, turn those off until firstyear_trans_longterm
 routes_inv(r,rr,trtype,t)$[(not Sw_TransInvNearTerm)$(yeart(t)<firstyear_trans_longterm)] = no ;
-* Do allow "possible" interfaces to be expanded
+* Allow interfaces with planned expansions to be expanded
 routes_inv(r,rr,trtype,t)
-    $[sum{tt$[(yeart(tt)<=yeart(t))],
-          trancap_fut(r,rr,"possible",trtype,tt) + trancap_fut(rr,r,"possible",trtype,tt) }
+    $[sum{(tt,trancap_fut_cat)$[yeart(tt)<=yeart(t)],
+          trancap_fut(r,rr,trancap_fut_cat,trtype,tt) + trancap_fut(rr,r,trancap_fut_cat,trtype,tt) }
     $routes(r,rr,trtype,t)] = yes ;
 routes_inv(rr,r,trtype,t)$[not routes_inv(r,rr,trtype,t)] = no ;
 
@@ -3773,22 +3326,6 @@ trans_inv_max(t)$[
 *============================
 *Note - NG supply curve has its own section
 
-set f "fuel types"
-/
-$offlisting
-$include inputs_case%ds%f.csv
-$onlisting
-/ ;
-
-set fuel2tech(f,i) "mapping between fuel types and generations"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%fuel2tech.csv
-$offdelim
-$onlisting
-/ ;
-
 *double check in case any sets have been changed.
 fuel2tech("coal",i)$coal(i) = yes ;
 fuel2tech("naturalgas",i)$gas(i) = yes ;
@@ -3801,13 +3338,6 @@ fuel2tech(f,i)$upgrade(i) = sum{ii$upgrade_to(i,ii), fuel2tech(f,ii) } ;
 *   Generator Characteristics
 *===============================
 
-set plantcat "categories for plant characteristics"
-/
-$offlisting
-$include inputs_case%ds%plantcat.csv
-$onlisting
-/ ;
-
 * declared over allt to allow for external data files that extend beyond end_year
 parameter plant_char0(i,allt,plantcat) "--units vary-- input plant characteristics"
 /
@@ -3919,7 +3449,7 @@ parameter cost_prod(i,v,r,t)                  "--$/metric ton/hr-- cost or benef
 scalar    h2_combustion_intensity              "--metric tons/MMBtu-- amount of hydrogen consumed per MMBtu of H2-Combustion fuel consumption" ;
 
 parameter pipeline_distance(r,rr) "--miles-- distance between all adjacent BA centroids for pipeline investments" ;
-pipeline_distance(r,rr) = distance(r,rr,"AC") ;
+pipeline_distance(r,rr) = distance(r,rr,"VSC") ;
 
 * For smr consume_char0 has capital costs in $/(kg/day) and "ele_efficiency" of kWh/kg
 * convert to $/MW by [$/(kg/day)] / [kwh/kg] * [1000 kWh/MWh] * [24 hr/day]
@@ -3938,13 +3468,6 @@ plant_char0(i,t,"fom")$smr(i) = deflator("2018") * consume_char0(i,t,"fom") /
 plant_char0("electrolyzer",t,"capcost") = deflator("2022") * consume_char0("electrolyzer",t,"cost_cap") * 1000 ;
 plant_char0("electrolyzer",t,"fom") = deflator("2022") * consume_char0("electrolyzer",t,"fom") * 1000 ;
 
-set consumecat "categories for consuming facility characteristics"
-/
-$offlisting
-$include inputs_case%ds%consumecat.csv
-$onlisting
-/ ;
-
 * capcost        - $/(metric ton CO2/hr)
 * fom            - $/(metric ton CO2/hr)/yr
 * vom            - $/metric ton CO2
@@ -4008,15 +3531,6 @@ plant_char(i,v,t,plantcat) = plant_char0(i,t,plantcat) ;
 
 * -- Consuming Technologies costs and demands --
 
-set i_p(i,p) "mapping from technologies to the products they produce"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%i_p.csv
-$offdelim
-$onlisting
-/ ;
-
 * see note from earlier... converting from MT / MWh from kg / kWh does not require adjustment..
 * but we still need to convert from MWh / MT to MT / MWh <- could choose either units
 * just need to make sure we change signs throughout
@@ -4040,22 +3554,6 @@ h2_combustion_intensity = (1/h2_energy_intensity) * (1/lb_per_tonne) * 1e6 ;
 
 * -- H2 Transport network  --
 
-set h2_st "defines investments needed to store and transport H2"
-/
-$offlisting
-$include inputs_case%ds%h2_st.csv
-$onlisting
-/ ;
-
-set h2_stor(h2_st) "H2 storage options"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%h2_stor.csv
-$offdelim
-$onlisting
-/ ;
-
 * Units for H2 transport and storage
 * See ReEDS_2.0_Input_Processing for formatting
 *Pipelines
@@ -4172,13 +3670,6 @@ h2_network_load(h2_st,allt) = h2_cost_inputs(h2_st,allt,"electric_load") ;
 * --- Flexible CCS Parameters ---
 *==================================
 
-set ccsflex_cat "flexible ccs performance parameter categories"
-/
-$offlisting
-$include inputs_case%ds%ccsflex_cat.csv
-$onlisting
-/ ;
-
 table ccsflex_perf(i,ccsflex_cat)  "--varies-- flexible ccs performance characteristics"
 $offlisting
 $ondelim
@@ -4257,7 +3748,7 @@ $onlisting
 / ;
 
 parameter ilr(i) "--unitless-- inverter loading ratio - used to convert DC capacity to AC capacity for PV" ;
-* assign a default value of 1.0 for every technology (used in e_report.gms)
+* assign a default value of 1.0 for every technology
 ilr(i)$[valcap_i(i)] = 1 ;
 ilr(i)$[upv(i)] = ilr_utility ;
 ilr(i)$distpv(i) = ilr_dist ;
@@ -4553,13 +4044,6 @@ heat_rate(i,v,r,t)$[valcap(i,v,r,t)$sum{allt$att(allt,t), binned_heatrates(i,v,r
                     sum{allt$att(allt,t), binned_heatrates(i,v,r,allt) } / 1000 ;
 
 
-set prepost "set defining pre-2010 values versus post-2010 values"
-/
-$offlisting
-$include inputs_case%ds%prepost.csv
-$onlisting
-/ ;
-
 *part load heatrate adjust based on historical EIA generation and fuel use data
 *this reflects the indescrepancy from the partial-loaded heat rate
 *and the fully-loaded heat rate
@@ -4606,7 +4090,7 @@ heat_rate(i,v,r,t)$[heat_rate_adj(i,'post2010')$newv(v)] = heat_rate_adj(i,'post
 parameter fuel_price(i,r,t) "$/MMBtu - fuel prices by technology" ;
 
 
-* Written by input_processing\fuelcostprep.py
+* Written by reeds/input_processing/fuelcostprep.py
 * declared over allt to allow for external data files that extend beyond end_year
 table fprice(allt,r,f) "--2004$/MMBtu-- fuel prices by fuel type"
 $offlisting
@@ -4674,7 +4158,7 @@ $endif.climatewater
 * -- Capacity Factor Adjustments over Time -- 
 *=============================================
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 parameter cap_hyd_ccseason_adj(i,ccseason,r) "--fraction-- ccseason max capacity adjustment for dispatchable hydro"
 /
 $offlisting
@@ -4721,22 +4205,8 @@ cf_adj_t(i,newv,t)$[(pv(i) or pvb(i))$countnc(i,newv)$sum{r, valcap(i,newv,r,t)
 *      --- OPERATING RESERVES ---
 *========================================
 
-set ortype "types of operating reserve constraints"
-/
-$offlisting
-$include inputs_case%ds%ortype.csv
-$onlisting
-/ ;
-
 set opres_model(ortype)       "operating reserve types modeled" ;
 
-set orcat "operating reserve category for RHS calculations"
-/
-$offlisting
-$include inputs_case%ds%orcat.csv
-$onlisting
-/ ;
-
 * define elements in opres_model based on sw_opres
 opres_model(ortype)$[not Sw_Opres] = no ;
 opres_model(ortype)$[(Sw_Opres = 1)$(not sameas(ortype,"combo"))] = yes ;
@@ -5289,14 +4759,6 @@ emit_rate("process",e,i,v,r,t)
 * set upgraded H2 tech emissions
 emit_rate("process","H2",i,v,r,t)$[upgrade(i)] = sum{ii$upgrade_to(i,ii), emit_rate("process","H2",ii,v,r,t) } ;
 
-* Global warming potential of different pollutants
-parameter gwp(e)   "--metric ton CO2-equivalents --global warming potential"
-/
-$ondelim
-$include inputs_case%ds%gwp.csv
-$offdelim
-/ ;
-
 * CO2(e) emissions rate (used in postprocessing only)
 emit_rate(etype,"CO2e",i,v,r,t)$[Sw_AnnualCap=2]
   = round(sum{e, emit_rate(etype,e,i,v,r,t) * gwp(e)$[(not sameas(e, "H2"))]},10) ;
@@ -5310,7 +4772,7 @@ capture_rate(e,i,v,r,t)$[capture_rate_fuel(i,e)$valcap(i,v,r,t)]
 
 capture_rate(e,i,v,r,t)$[upgrade(i)$capture_rate_fuel(i,e)] = round(heat_rate(i,v,r,t) * capture_rate_fuel(i,e),10) ;
 
-* Declare regional emissions rate (used in c_supplymodel, defined in d_solveoneyear)
+* Regional emissions rate
 parameter
     co2_emit_rate_r(r,t) "--metric tons per MWh-- CO2 emissions rate by ReEDS region, for use in state carbon caps"
     co2_emit_rate_regional(%GSw_StateCO2ImportLevel%,t) "--metric tons per MWh-- CO2 regional emissions rate, for use in state carbon caps"
@@ -5332,13 +4794,6 @@ emit_rate_limit(e,r,t) = 0 ;
 * Growth limits and penalties
 *============================
 
-set gbin "growth bins"
-/
-$offlisting
-$include inputs_case%ds%gbin.csv
-$onlisting
-/ ;
-        
 *absolute growth penalties based on greatest annual change of capacity for each tech group from 1990-2016
 parameter growth_limit_absolute(tg) "--MW-- growth limit for technology groups in absolute terms"
 /
@@ -5390,15 +4845,6 @@ cost_growth(i,st,t) = 0 ;
 * --- CES Gas supply curve setup ---
 *====================================
 
-set gb "gas price bin must be an odd number of bins, e.g. gb1*gb15"
-/
-$offlisting
-$include inputs_case%ds%gb.csv
-$onlisting
-/ ;
-
-alias(gb,gbb) ;
-
 * gassupply scale determines how far the bins reference quantity should deviate from its reference price
 * with gassupply scale = -0.5, the center of the reference price bin will be the reference quantity
 * with gassupplyscale = 0, the end of the reference gas price's bin will the limit for that reference bin
@@ -5577,15 +5023,6 @@ ng_crf_penalty_nat(i,t)$[gas(i)$ccs(i)$sum{r, valcap_irt(i,r,t) }] = 1 ;
 * --- Regional Gas supply curve ---
 *===========================================
 
-set fuelbin "gas usage bracket"
-/
-$offlisting
-$include inputs_case%ds%fuelbin.csv
-$onlisting
-/ ;
-
-alias(fuelbin,afuelbin) ;
-
 Scalar numfuelbins       "number of fuel bins",
        normfuelbinwidth  "typical fuel bin width",
        botfuelbinwidth   "bottom fuel bin width"
@@ -5639,7 +5076,7 @@ nat_beta(t)$(not tfirst(t)) =  nat_beta_nonenergy ;
 
 $endif.gassector
 
-* Written by input_processing\fuelcostprep.py
+* Written by reeds/input_processing/fuelcostprep.py
 * declared over allt to allow for external data files that extend beyond end_year
 table cd_alpha(allt,cendiv) "--$/MMBtu-- alpha value for natural gas supply curves"
 $offlisting
@@ -6064,14 +5501,6 @@ if((not Sw_UpgradeDerate),
 * --- BIOMASS SUPPLY CURVES ---
 *==============================
 
-* supply curves defined by 21 price increments
-set bioclass
-/
-$offlisting
-$include inputs_case%ds%bioclass.csv
-$onlisting
-/ ;
-
 set biofeas(r) "regions with biomass supply and biopower";
 
 * supply curve derived from 2016 ORNL Billion Ton study
@@ -6112,7 +5541,7 @@ scalar bio_transport_cost ;
 * biomass transport cost enter in $ per ton, convert to $ per MMBtu
 bio_transport_cost = Sw_BioTransportCost / bio_energy_content ;
 
-* get price of cheapest supply curve bin that has resources (needed for Augur)
+* get price of cheapest supply curve bin that has resources (needed for resource adequacy calculations)
 * price includes any transport costs for biomass
 parameter rep_bio_price_unused(r) "--2004$/MWh-- marginal price of lowest cost available supply curve bin for biofuel" ;
 rep_bio_price_unused(r)$[sum{usda_region, 1$r_usda(r,usda_region) }] =
@@ -6128,7 +5557,7 @@ cost_curt(t)$[yeart(t)>=model_builds_start_yr] = Sw_CurtMarket ;
 *======================
 
 parameter emit_cap(eall,t)    "--metric tons per year-- annual CO2 emissions cap",
-          yearweight(t)       "--unitless-- weights applied to each solve year for the banking and borrowing cap - updated in d_solveprep.gms",
+          yearweight(t)       "--unitless-- weights applied to each solve year for the banking and borrowing cap - updated in e_solveprep.gms",
           emit_tax(e,r,t)     "--$ per metric ton-- tax applied to emissions" ;
 
 emit_cap(e,t) = 0 ;
@@ -6138,15 +5567,6 @@ yearweight(t) = 0 ;
 yearweight(t)$tmodel_new(t) = sum{tt$tprev(tt,t), yeart(tt) } - yeart(t) ;
 yearweight(t)$tlast(t) = 1 + smax{yearafter, yearafter.val } ;
 
-* declared over allt to allow for external data files that extend beyond end_year
-parameter co2_cap(allt)      "--metric tons-- CO2 emissions cap used when Sw_AnnualCap is on"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%co2_cap.csv
-$offdelim
-$onlisting
-/ ;
 if(Sw_AnnualCap = 1,
     emit_cap("CO2",t) = co2_cap(t) ;
 ) ;
@@ -6155,16 +5575,6 @@ if(Sw_AnnualCap > 1,
     emit_cap("CO2e",t) = co2_cap(t) ;
 ) ;
 
-parameter co2_tax(allt)      "--$/metric ton-- CO2 tax used when Sw_CarbTax is on"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%co2_tax.csv
-$offdelim
-$onlisting
-/ ;
-
-
 * set the carbon tax based on switch arguments
 if(Sw_CarbTax = 1,
 emit_tax("CO2",r,t) = co2_tax(t) ;
@@ -6216,15 +5626,6 @@ emit_capped(e)$[gwp(e)$(Sw_AnnualCap=3)] = yes ;
 
 set valret(i,v) "technologies and classes that can be retired" ;
 
-set noretire(i) "technologies that will never be retired"
-/
-$offlisting
-$ondelim
-$include inputs_case%ds%noretire.csv
-$offdelim
-$onlisting
-/ ;
-
 * storage technologies are not appropriately attributing capacity value to CAP variable
 * therefore not allowing them to endogenously retire
 noretire(i)$[(storage_standalone(i) or hyd_add_pump(i))] = yes ;
@@ -6504,7 +5905,7 @@ allow_cap_up(i,v,r,rscbin,t)$[valcap(i,v,r,t)$cap_cap_up(i,v,r,rscbin,t)$(t.val>
 allow_ener_up(i,v,r,rscbin,t)$[valcap(i,v,r,t)$cap_ener_up(i,v,r,rscbin,t)$(t.val>=Sw_UpgradeYear)] = yes ;
 
 
-* Track the initial amount of m_rsc_dat capacity to compare in e_report
+* Track the initial amount of m_rsc_dat capacity to compare in report.gms
 * We adjust upwards by small amounts given potential for infeasibilities
 * in very tiny amounts and thus track the extent of the adjustments
 parameter m_rsc_dat_init(r,i,rscbin) "--MW-- Initial amount of resource supply curve capacity to compare with final amounts after adjustments" ;
@@ -6542,15 +5943,10 @@ $include inputs_case%ds%r_cs_distance_mi.csv
 $offdelim
 $ondigit
 $onlisting
-/ ,
-          min_co2_spurline_distance     "--mi-- minimum distance for a spur line (used to provide a floor for pipeline distances in r_cs_distance)"
+/
 ;
 $offempty
 
-* Wherever BA centroids fall within formation boundaries, assume some average spur line distance to connect a CCS or DAC plant with an injection site
-min_co2_spurline_distance = 20 ;
-r_cs_distance(r,cs)$[r_cs_distance(r,cs) < min_co2_spurline_distance] = min_co2_spurline_distance ;
-
 * Assign spurline costs
 cost_co2_spurline_cap(r,cs,t)$[r_cs(r,cs)$tmodel_new(t)] = Sw_CO2_spurline_cost * r_cs_distance(r,cs) ;
 
@@ -6558,7 +5954,7 @@ cost_co2_spurline_cap(r,cs,t)$[r_cs(r,cs)$tmodel_new(t)] = Sw_CO2_spurline_cost
 cost_co2_pipeline_cap(r,rr,t)$[routes_adjacent(r,rr)$tmodel_new(t)] = Sw_CO2_pipeline_cost * pipeline_distance(r,rr) ;
 cost_co2_pipeline_fom(r,rr,t)$[routes_adjacent(r,rr)$tmodel_new(t)] = Sw_CO2_pipeline_fom * pipeline_distance(r,rr) ;
 
-co2_routes(r,rr)$routes_adjacent(r,rr) = yes ;
+co2_routes(r,rr)$[routes_adjacent(r,rr)$pipeline_distance(r,rr)] = yes ;
 
 $onempty
 table co2_char(cs,*) "co2 site characteristics including injection rate limit, total storage limit, and break even cost"
@@ -6589,7 +5985,7 @@ cost_co2_spurline_cap(r,cs,t) =  %GSw_CO2_CostAdj% * cost_co2_spurline_cap(r,cs,
 
 * Parameter tracking for sequential solve
 parameter
-    m_capacity_exog0(i,v,r,t) "--MW-- original value of m_capacity_exog used in d_solveoneyear to make sure upgraded capacity isnt forced into retirement"
+    m_capacity_exog0(i,v,r,t) "--MW-- original value of m_capacity_exog used to make sure upgraded capacity isnt forced into retirement"
     z_rep(t)      "--$-- objective function value by year"
     z_rep_inv(t)  "--$-- investment component of objective function by year"
     z_rep_op(t)   "--$-- operation component of objective function by year"
@@ -6599,24 +5995,9 @@ z_rep_op(t) = 0 ;
 
 
 *================================================================================================
-*== h- and szn-dependent sets and parameters (declared here, populated in d1_temporal_params) ===
+*== h- and szn-dependent sets and parameters (declared here, populated in 2_temporal_params) ===
 *================================================================================================
 
-* allh and allszn need to be populated here so they can be used in c_supplymodel and c_supplyobjective
-Set allh "all potentially modeled hours"
-/
-$offlisting
-$include inputs_case%ds%set_allh.csv
-$onlisting
-/ ;
-
-Set allszn "all potentially modeled seasons (used as representative days/weks for hourly resolution)"
-/
-$offlisting
-$include inputs_case%ds%set_allszn.csv
-$onlisting
-/ ;
-
 Set
 * Timeslices
     h(allh)                                "representative and stress timeslices"
@@ -6645,7 +6026,7 @@ Set
     starting_hour_nowrap(allh)             "Flag for whether allh is the first chronological hour by day type"
     final_hour(allh)                       "Flag for whether allh is the last chronological hour in a day type" 
     final_hour_nowrap(allh)                "Flag for whether allh is the last chronological hour in a day type"
-    nextszn(allszn,allszn)                 "Mapping between one actual period (allszn) and the next"
+    nextszn(actualszn,actualszn)           "Mapping between one actual period (actualszn) and the next"
     nextpartition(allszn,allszn)           "Mapping between one partition (allszn) and the next"
 * Peak demand
     maxload_szn(r,allh,t,allszn)           "hour with highest load within each szn"
@@ -6661,6 +6042,10 @@ Set
     hour_szn_group(allh,allh)              "h and hh in the same season - used in minloading constraint"
 ;
 
+alias(h,hh,hhh) ;
+alias(szn,sznn) ;
+alias(actualszn,actualsznn,actualsznnn) ;
+
 Parameter
 * Hour/period weighting
     hours(allh)                            "--hours-- number of hours in each time block"
@@ -6730,7 +6115,8 @@ Parameter
     cc_excess(i,r,ccseason,t)              "--MW-- this is the excess capacity credit when assuming marginal capacity credit in intertemporal solve"
     vre_gen_last_year(r,allh,t)            "--MW-- generation from VRE generators in the prior solve year"
     hybrid_cc_derate(i,r,ccseason,sdbin,t) "--fraction-- derate factor for hybrid PV+battery storage capacity credit"
-    m_cc_mar(i,r,ccseason,t)               "--fraction-- marginal capacity credit",
+    m_cc_mar(i,r,ccseason,t)               "--fraction-- marginal capacity credit"
+    mean_forced_outage_rate(i,r,ccseason,t)"--fraction-- mean forced outage rate for each technology, region, and ccseason - used to derate thermal generator capacity"
 * Heuristic climate impacts
     trans_cap_delta(allh,allt)             "--fraction-- fractional adjustment to transmission capacity from climate heuristics"
 * Emissions and policies
@@ -6745,12 +6131,6 @@ Parameter
     szn_adj_gas(allh)                      "--fraction-- seasonal adjustment for gas prices"
 ;
 
-alias(allh,allhh,allhhh) ;
-alias(h,hh,hhh) ;
-alias(allszn,allsznn) ;
-alias(actualszn,actualsznn,actualsznnn) ;
-alias(szn,sznn) ;
-
 * Initialize some parameters
 sdbin_size(ccreg,ccseason,sdbin,"%startyear%") = 1000 ;
 cc_int(i,v,r,ccseason,t) = 0 ;
@@ -6758,6 +6138,7 @@ cc_excess(i,r,ccseason,t) = 0 ;
 cc_old(i,r,ccseason,t) = 0 ;
 m_cc_mar(i,r,ccseason,t) = 0 ;
 hybrid_cc_derate(i,r,ccseason,sdbin,t)$[pvb(i)$valcap_irt(i,r,t)] = 1 ;
+mean_forced_outage_rate(i,r,ccseason,t) = 0 ;
 
 * Trim some of the largest matrices to reduce file sizes
 cost_vom(i,v,r,t)$[not valgen(i,v,r,t)] = 0 ;
diff --git a/c_supplymodel.gms b/reeds/core/setup/c_model.gms
similarity index 99%
rename from c_supplymodel.gms
rename to reeds/core/setup/c_model.gms
index 029d53f10..f8bca2101 100644
--- a/c_supplymodel.gms
+++ b/reeds/core/setup/c_model.gms
@@ -465,7 +465,7 @@ eq_loadsite_siting(loadsitereg,t)
 * ---------------------------------------------------------------------------
 
 *The following 3 equations apply to the flexibility of load in ReEDS, originally developed
-*as part of the EFS study in ReEDS heritage and adapted for ReEDS-2.0 here.
+*as part of the EFS study in ReEDS heritage and adapted for ReEDS here.
 
 * Additional work has been done to represent flexible load as a generator + storage
 * with boundaries on how many timeslices this generator may shift. See equations
@@ -1718,6 +1718,7 @@ eq_reserve_margin(r,ccseason,t)
     + sum{(i,v)$[valcap(i,v,r,t)$(not vre(i))$(not hydro(i))$(not storage(i))$(not consume(i))$(not forced_retire(i,r,t))],
           CAP(i,v,r,t)
           * (1 + ccseason_cap_frac_delta(i,v,r,ccseason,t))
+          * (1 - mean_forced_outage_rate(i,r,ccseason,t))
          }
 
 *[plus] firm capacity from existing VRE or CSP
@@ -2017,7 +2018,7 @@ eq_POI_cap(r,t)
 * to make sure we account for the existing spur line capacity already included in poi_cap_init)...
     + sum{x$[xfeas(x)$x_r(x,r)$Sw_SpurScen], CAP_SPUR(x,t) }
 * and AC/DC converter capacity for VSC...
-    + CAP_CONVERTER(r,t)
+    + CAP_CONVERTER(r,t)$val_converter(r,t)
 * and LCC (INVTRAN(r,rr) == INVTRAN(rr,r) for DC so only need to add one direction)
     + sum{(rr,tt)$[routes_inv(r,rr,"LCC",t)$(yeart(tt)<=yeart(t))$(tmodel(tt) or tfix(tt))],
           INVTRAN(r,rr,"LCC",tt)
@@ -2029,9 +2030,9 @@ eq_POI_cap(r,t)
 * flows cannot exceed the total transmission capacity
 eq_transmission_limit(r,rr,h,t,trtype)$[tmodel(t)$routes(r,rr,trtype,t)]..
 
-* Representative periods use the energy (n-0) capacity.
+* Representative periods use the energy capacity
     (CAPTRAN_ENERGY(r,rr,trtype,t) * (1 + trans_cap_delta(h,t)))$h_rep(h)
-* Stress periods use the PRM (n-1) capacity.
+* Stress periods use the PRM capacity
 * Because these periods are assumed to be mutually exclusive, each timeslice should only
 * have a single capacity applied.
     + (CAPTRAN_PRM(r,rr,trtype,t) * (1 + trans_cap_delta(h,t)))$[h_stress(h)$routes_prm(r,rr)]
@@ -2381,7 +2382,7 @@ eq_offshore_no_backflow(r,rr,trtype,h,t)
 * ---------------------------------------------------------------------------
 
 * Because EMIT is only evaluated for emit_modeled, the full emissions need to be
-* calculated in the e_report rather than relying on the EMIT variable
+* calculated in report.gms rather than relying on the EMIT variable
 eq_emit_accounting(etype,e,r,t)$[emit_modeled(e,r,t)$tmodel(t)]..
 
     EMIT(etype,e,r,t)
diff --git a/c_mga.gms b/reeds/core/setup/d_mga.gms
similarity index 100%
rename from c_mga.gms
rename to reeds/core/setup/d_mga.gms
diff --git a/c_supplyobjective.gms b/reeds/core/setup/d_objective.gms
similarity index 99%
rename from c_supplyobjective.gms
rename to reeds/core/setup/d_objective.gms
index 38a5b0b36..fae5e5cae 100644
--- a/c_supplyobjective.gms
+++ b/reeds/core/setup/d_objective.gms
@@ -109,7 +109,7 @@ eq_ObjFn_inv(t)$tmodel(t)..
                         trans_cost_cap_fin_mult(t) * cost_acdc_lcc * INVTRAN(r,rr,trtype,t) }
 
 * VSC AC/DC converter stations
-                  + sum{r,
+                  + sum{r$val_converter(r,t),
                         trans_cost_cap_fin_mult(t) * cost_acdc_vsc * INV_CONVERTER(r,t) }
 
 * --- storage capacity credit---
@@ -184,7 +184,7 @@ eq_Objfn_op(t)$tmodel(t)..
                     cost_acdc_lcc * 2 * trans_fom_frac * CAPTRAN_ENERGY(r,rr,trtype,t) }
 
 * VSC AC/DC converter stations
-              + sum{r,
+              + sum{r$val_converter(r,t),
                     cost_acdc_vsc * trans_fom_frac * CAP_CONVERTER(r,t) }
 
 * spur lines modeled as part of supply curve
diff --git a/d_solveprep.gms b/reeds/core/setup/e_solveprep.gms
similarity index 98%
rename from d_solveprep.gms
rename to reeds/core/setup/e_solveprep.gms
index dbaa6df9b..326d0d629 100644
--- a/d_solveprep.gms
+++ b/reeds/core/setup/e_solveprep.gms
@@ -40,6 +40,7 @@ parameter
     sdbin_size_load(ccreg,ccseason,sdbin,t)  "--MW-- bin_size power loading in from the cc_out gdx file"
     cc_mar_load(i,r,ccreg,ccseason,t)        "--fraction-- cc_mar loading in from the cc_out gdx file"
     cc_evmc_load(i,r,ccseason,t)               "--fraction--  cc_evmc loading in from the cc_out gdx file"
+    mean_forced_outage_rate_load(i,r,ccseason,t)  "--fraction-- mean_forced_outage_rate loading in from the cc_out gdx file"
 ;
 
 
@@ -135,7 +136,7 @@ $endif.seq
 $ifthen.intwin ((%timetype%=="int") or (%timetype%=="win"))
 
 set
-    loadset "set used for loading in merged gdx files" / ReEDS_Augur_%startyear%*ReEDS_Augur_%endyear% /
+    loadset "set used for loading in merged gdx files" / ccdata_%startyear%*ccdata_%endyear% /
 ;
 
 parameter
@@ -197,7 +198,6 @@ cc_old(i,r,ccseason,t) = 0 ;
 *following sets are needed for linear interpolation of price
 *that determine the year before the non-solved year and the year after
 set t_before, t_after ;
-alias(t,ttt) ;
 t_before(t,tt)$[tprev(t,tt)$(ord(tt) = smax{ttt, ord(ttt)$tprev(t,ttt) })] = yes ;
 t_after(t,tt)$tprev(tt,t) = yes ;
 
diff --git a/tc_phaseout.py b/reeds/core/solve/1_tc_phaseout.py
similarity index 96%
rename from tc_phaseout.py
rename to reeds/core/solve/1_tc_phaseout.py
index b89feeff2..66454f473 100644
--- a/tc_phaseout.py
+++ b/reeds/core/solve/1_tc_phaseout.py
@@ -14,10 +14,13 @@
 ###########
 #%% IMPORTS
 import argparse
+import gdxpds
 import pandas as pd
 import numpy as np
-import gdxpds
 import os
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent.parent))
 import reeds
 
 ##########
@@ -39,7 +42,7 @@ def calc_tc_phaseout_mult(year, case, use_historical=use_historical):
     '''
     # #%% Debugging
     # year = 2035
-    # case = os.path.expanduser('~/github2/ReEDS-2.0/runs/v20230305_reccM0_ref_seq')
+    # case = os.path.expanduser('~/github2/ReEDS/runs/v20230305_reccM0_ref_seq')
 
     #%% Get switches
     sw = reeds.io.get_switches(case)
@@ -208,7 +211,7 @@ def calc_tc_phaseout_mult(year, case, use_historical=use_historical):
 #%% PROCEDURE
 if __name__ == '__main__':
 
-    parser = argparse.ArgumentParser(description="""Running tc_phaseout.py""")
+    parser = argparse.ArgumentParser(description="Running 1_tc_phaseout.py")
     parser.add_argument("year", help="ReEDS solve year", type=int)
     parser.add_argument("case", help="filepath for ReEDS case")
     args = parser.parse_args()
@@ -221,6 +224,6 @@ def calc_tc_phaseout_mult(year, case, use_historical=use_historical):
         logpath=os.path.join(case,'gamslog.txt'),
     )
 
-    print(f'starting tc_phaseout.py for {year}')
+    print(f'starting 1_tc_phaseout.py for {year}')
     calc_tc_phaseout_mult(year, case, use_historical=use_historical)
-    print(f'finished tc_phaseout.py for {year}')
+    print(f'finished 1_tc_phaseout.py for {year}')
diff --git a/d1_financials.gms b/reeds/core/solve/2_financials.gms
similarity index 100%
rename from d1_financials.gms
rename to reeds/core/solve/2_financials.gms
diff --git a/d1_temporal_params.gms b/reeds/core/solve/2_temporal_params.gms
similarity index 97%
rename from d1_temporal_params.gms
rename to reeds/core/solve/2_temporal_params.gms
index 444c7fc5e..5c0c7ab5d 100644
--- a/d1_temporal_params.gms
+++ b/reeds/core/solve/2_temporal_params.gms
@@ -134,6 +134,16 @@ $include inputs_case%ds%stress%stress_year%%ds%nexth.csv
 $offdelim
 $onlisting
 /
+
+nextszn(actualszn,actualszn) "Mapping between one actual period (actualszn) and the next"
+/
+$offlisting
+$ondelim
+$include inputs_case%ds%nextszn.csv
+$offdelim
+$onlisting
+/
+
 $ONEMPTY
 nextpartition(allszn,allszn) "Mapping between one partition (allszn) and the next"
 /
@@ -181,13 +191,6 @@ starting_hour_nowrap(h)$[sum{szn, h_szn_start(szn,h) }$(not Sw_HourlyWrap)] = ye
 final_hour_nowrap(allh) = no ;
 final_hour_nowrap(h)$[sum{szn, h_szn_end(szn,h) }$(not Sw_HourlyWrap)] = yes ;
 
-* Get the order of actual periods
-nextszn(actualszn,actualsznn)$[(ord(actualsznn) = ord(actualszn) + 1)] = yes ;
-nextszn(actualszn,actualsznn)
-    $[(ord(actualszn) = smax(actualsznnn, ord(actualsznnn)))
-    $(ord(actualsznn) = smin(actualsznnn, ord(actualsznnn)))]
-    = yes ;
-
 $onOrder
 
 
@@ -223,7 +226,7 @@ $offdelim
 $onlisting
 / ;
 
-* Written by input_processing/hourly_writetimeseries.py
+* Written by reeds/input_processing/hourly_writetimeseries.py
 parameter frac_h_quarter_weights(allh,quarter) "--unitless-- fraction of timeslice associated with each quarter"
 /
 $offlisting
@@ -456,7 +459,7 @@ cf_in(i,r,h)$[i_water_cooling(i)$Sw_WaterMain] =
 cf_rsc(i,v,r,allh,t) = 0 ;
 cf_rsc(i,v,r,h,t)$[cf_in(i,r,h)$cf_tech(i)$valcap(i,v,r,t)] = cf_in(i,r,h) ;
 
-* Written by input_processing/hourly_writetimeseries.py
+* Written by reeds/input_processing/hourly_writetimeseries.py
 $onempty
 parameter cf_hyd(i,allszn,r,allt) "--fraction-- hydro capacity factors by season and year"
 /
@@ -492,7 +495,7 @@ cf_hyd(i,szn,r,t)$[hydro_d(i)$(yeart(t)>=Sw_ClimateStartYear)]  =
 
 $endif.climatehydro
 
-*created by /input_processing/writecapdat.py
+*created by reeds/input_processing/writecapdat.py
 parameter cap_hyd_szn_adj(i,allszn,r) "--fraction-- seasonal max capacity adjustment for dispatchable hydro"
 /
 $offlisting
@@ -894,10 +897,14 @@ szn_adj_gas(h)$frac_h_quarter_weights(h,"wint") =
 avail(i,r,h)$avail(i,r,h) = round(avail(i,r,h),3) ;
 can_imports_szn(r,szn,t)$can_imports_szn(r,szn,t) = round(can_imports_szn(r,szn,t),3) ;
 can_exports_h(r,h,t)$can_exports_h(r,h,t) = round(can_exports_h(r,h,t),3) ;
+frac_h_ccseason_weights(h,ccseason)$frac_h_ccseason_weights(h,ccseason) = round(frac_h_ccseason_weights(h,ccseason),3) ;
+gasadder_cd(cendiv,t,h)$gasadder_cd(cendiv,t,h) = round(gasadder_cd(cendiv,t,h),3) ;
 h_weight_csapr(h)$h_weight_csapr(h) = round(h_weight_csapr(h),3) ;
+hours(h)$hours(h) = round(hours(h),3) ;
 load_exog(r,h,t)$load_exog(r,h,t) = round(load_exog(r,h,t),3) ;
 load_exog_static(r,h,t)$load_exog_static(r,h,t) = round(load_exog_static(r,h,t),3) ;
 minloadfrac(r,i,h)$minloadfrac(r,i,h) = round(minloadfrac(r,i,h),3) ;
+numdays(szn)$numdays(szn) = round(numdays(szn),3) ;
 szn_adj_gas(h)$szn_adj_gas(h) = round(szn_adj_gas(h), 3) ;
 cap_hyd_szn_adj(i,szn,r)$cap_hyd_szn_adj(i,szn,r) = round(cap_hyd_szn_adj(i,szn,r),3) ;
 peakdem_static_ccseason(r,ccseason,t)$peakdem_static_ccseason(r,ccseason,t) = round(peakdem_static_ccseason(r,ccseason,t),2) ;
diff --git a/d_solveallyears.gms b/reeds/core/solve/3_solve_allyears.gms
similarity index 96%
rename from d_solveallyears.gms
rename to reeds/core/solve/3_solve_allyears.gms
index 204d1bc54..e06d360b2 100644
--- a/d_solveallyears.gms
+++ b/reeds/core/solve/3_solve_allyears.gms
@@ -37,7 +37,7 @@ $endif.loadref
 *indicate we're loading data
 tload(t)$tmodel(t) = yes ;
 
-$gdxin ReEDS_Augur%ds%augur_data%ds%ReEDS_Augur_merged_%niter%.gdx
+$gdxin handoff%ds%reeds_data%ds%ccdata_merged_%niter%.gdx
 $loaddcr cc_old_load2 = cc_old
 $loaddcr cc_mar_load2 = cc_mar
 $loaddcr cc_evmc_load2 = cc_evmc
@@ -120,7 +120,7 @@ cc_int(i,v,r,szn,t)$[cc_int(i,v,r,szn,t) < 0.001] = 0 ;
 
 cc_iter(i,v,r,szn,t,"%niter%")$cc_int(i,v,r,szn,t) = cc_int(i,v,r,szn,t) ;
 
-execute_unload 'ReEDS_Augur%ds%augur_data%ds%curtout_%case%_%niter%.gdx' cc_int ;
+execute_unload 'handoff%ds%reeds_data%ds%curtout_%case%_%niter%.gdx' cc_int ;
 
 *following line will load in the level values if the switch is enabled
 *note that this is still within the conditional that we are now past the first iteration
@@ -162,4 +162,4 @@ gen_iter(i,v,r,t,"%niter%")$valcap(i,v,r,t) = sum{h, GEN.l(i,v,r,h,t) * hours(h)
 gen_iter(i,v,r,t,"%niter%")$[vre(i)$valcap(i,v,r,t)] = sum{h, m_cf(i,v,r,h,t) * CAP.l(i,v,r,t) * hours(h) } ;
 cap_firm_iter(i,v,r,szn,t,"%niter%")$cc_int(i,v,r,szn,t) = cc_int(i,v,r,szn,t) * CAP.l(i,v,r,t) ;
 cap_firm_iter(i,v,r,szn,t,"%niter%")$storage(i) = sum{sdbin, CAP_SDBIN.l(i,v,r,szn,sdbin,t) * cc_storage(i,sdbin) } ;
-cap_energy_firm_iter(i,v,r,szn,t,"%niter%")$storage(i) = sum{sdbin, CAP_SDBIN_ENERGY.l(i,v,r,szn,sdbin,t) } ;
\ No newline at end of file
+cap_energy_firm_iter(i,v,r,szn,t,"%niter%")$storage(i) = sum{sdbin, CAP_SDBIN_ENERGY.l(i,v,r,szn,sdbin,t) } ;
diff --git a/d_solveoneyear.gms b/reeds/core/solve/3_solve_oneyear.gms
similarity index 93%
rename from d_solveoneyear.gms
rename to reeds/core/solve/3_solve_oneyear.gms
index b3484a805..36550d5b6 100644
--- a/d_solveoneyear.gms
+++ b/reeds/core/solve/3_solve_oneyear.gms
@@ -1,10 +1,10 @@
 * Includes these scripts, in order:
-* - d1_temporal_params.gms
-* - d1_financials.gms
+* - 2_temporal_params.gms
+* - 2_financials.gms
 * - * solves the model *
-* - d2_post_solve_adjustments.gms
-* - d2_varfix.gms
-* - d3_data_dump.gms
+* - 4_post_solve_adjustments.gms
+* - 5_varfix.gms
+* - 6_data_dump.gms
 
 $setglobal ds \
 
@@ -30,7 +30,7 @@ $log '  Year: %cur_year%'
 
 *** Define the h- and szn-dependent parameters
 $onMultiR
-$include d1_temporal_params.gms
+$include reeds%ds%core%ds%solve%ds%2_temporal_params.gms
 $offMulti
 
 
@@ -81,7 +81,7 @@ if(Sw_Upgrades = 1,
     m_capacity_exog(i,v,r,t)$[valcap(i,v,r,t)$sameas(t,"%cur_year%")
                          $(sum{(ii,tt)$[(tt.val <= t.val)$(t.val - tt.val <= Sw_UpgradeLifespan)
                                        $valcap(ii,v,r,tt)$upgrade_from(ii,i)], UPGRADES.l(ii,v,r,tt) } ) ] =
-* [maximum of] initial capacity recorded in d_solveprep
+* [maximum of] initial capacity recorded in e_solveprep
                     max( m_capacity_exog0(i,v,r,t),
 * -or- capacity of upgrades that have occurred from this i v r t combination
                     sum{(ii,tt)$[(tt.val <= t.val)$(t.val - tt.val <= Sw_UpgradeLifespan)
@@ -136,18 +136,18 @@ if(Sw_GrowthPenalties > 0,
 $endif.post_startyear
 
 * Load capacity credit results
-$ifthene.tcheck %cur_year%>%GSw_SkipAugurYear%
+$ifthene.tcheck %cur_year%>%GSw_SkipRAyear%
 
 *indicate we're loading data
 tload("%cur_year%") = yes ;
 
-*file written by ReEDS_Augur.py
 * loaddcr = domain check (dc) + overwrite values storage previously (r)
-$gdxin ReEDS_Augur%ds%augur_data%ds%ReEDS_Augur_%prev_year%.gdx
+$gdxin handoff%ds%reeds_data%ds%ccdata_%prev_year%.gdx
 $loaddcr cc_old_load = cc_old
 $loaddcr cc_mar_load = cc_mar
 $loaddcr cc_evmc_load = cc_evmc
 $loaddcr sdbin_size_load = sdbin_size
+$loaddcr mean_forced_outage_rate_load = mean_forced_outage_rate
 $gdxin
 
 *Note: these values are rounded before they are written to the gdx file, so no need to round them here
@@ -182,13 +182,14 @@ cc_old(i,r,ccseason,t)$[tload(t)$pvb(i)$sum{(v,tt)$tprev(t,tt), CAP.l(i,v,r,tt)}
     min{ cc_old(i,r,ccseason,t) / sum{(v,tt)$tprev(t,tt), CAP.l(i,v,r,tt)}, 1 / ilr(i) - bcr(i) }
     * sum{(v,tt)$tprev(t,tt), CAP.l(i,v,r,tt)};
 
+mean_forced_outage_rate(i,r,ccseason,t)$tload(t) = mean_forced_outage_rate_load(i,r,ccseason,t) ;
 $endif.tcheck
 
 
 *** Calculate financial multipliers
 * These are calculated here because the ITC phaseout can influence these parameters,
 * and the timing of the phaseout is not known beforehand.
-$include d1_financials.gms
+$include reeds%ds%core%ds%solve%ds%2_financials.gms
 
 
 $ifthene %cur_year%==%startyear%
@@ -219,7 +220,7 @@ if(Sw_GrowthPenalties > 0,
 
 * Write the inputs for debugging and error checks:
 * Always write data for the first solve year (currently always 2010).
-* Overwrites the versions written by d_solveprep.gms and d1_temporal_params.gms.
+* Overwrites the versions written by e_solveprep.gms and 2_temporal_params.gms.
 $ifthene.write %cur_year%=%startyear%
 execute_unload 'inputs_case%ds%inputs.gdx' ;
 $endif.write
@@ -275,14 +276,14 @@ $endif.mga
 
 
 *** Adjust some parameters based on the solution for this solve year
-$include d2_post_solve_adjustments.gms
+$include reeds%ds%core%ds%solve%ds%4_post_solve_adjustments.gms
 
 *** Fix decision variables to their optimized levels for this solve year
 tfix("%cur_year%") = yes ;
-$include d2_varfix.gms
+$include reeds%ds%core%ds%solve%ds%5_varfix.gms
 
 *** Dump data used in calculations between solve years
-$include d3_data_dump.gms
+$include reeds%ds%core%ds%solve%ds%6_data_dump.gms
 
 *** Abort if the solver returns an error
 if (ReEDSmodel.modelStat > 1,
diff --git a/d_solvewindow.gms b/reeds/core/solve/3_solve_window.gms
similarity index 96%
rename from d_solvewindow.gms
rename to reeds/core/solve/3_solve_window.gms
index 3fa679bfa..325ada4fa 100644
--- a/d_solvewindow.gms
+++ b/reeds/core/solve/3_solve_window.gms
@@ -44,7 +44,7 @@ $ifthene.notfirstiter %niter%>0
 *indicate we're loading data
 tload(t)$tmodel(t) = yes ;
 
-$gdxin ReEDS_Augur%ds%augur_data%ds%ReEDS_Augur_merged_%niter%.gdx
+$gdxin handoff%ds%reeds_data%ds%ccdata_merged_%niter%.gdx
 $loaddcr loadset = merged_set_1
 $loaddcr cc_old_load2 = cc_old
 $loaddcr cc_mar_load2 = cc_mar
@@ -126,7 +126,7 @@ cc_int(i,v,r,szn,t)$[cc_int(i,v,r,szn,t) < 0.001] = 0 ;
 
 cc_iter(i,v,r,szn,t,"%niter%")$cc_int(i,v,r,szn,t) = cc_int(i,v,r,szn,t) ;
 
-execute_unload 'ReEDS_Augur%ds%augur_data%ds%curtout_%case%_%niter%.gdx' cc_int ;
+execute_unload 'handoff%ds%reeds_data%ds%curtout_%case%_%niter%.gdx' cc_int ;
 
 *following line will load in the level values if the switch is enabled
 *note that this is still within the conditional that we are now past the first iteration
@@ -149,6 +149,6 @@ $ifthene.lastiter %niter%=%maxiter%
 
 $eval nextwindow %window% + 1
 tfix(t)$(tmodel(t)$(yeart(t)<solvewindows("%nextwindow%","start"))) = yes ;
-$include d2_varfix.gms
+$include reeds%ds%core%ds%solve%ds%5_varfix.gms
 
 $endif.lastiter
diff --git a/d2_post_solve_adjustments.gms b/reeds/core/solve/4_post_solve_adjustments.gms
similarity index 100%
rename from d2_post_solve_adjustments.gms
rename to reeds/core/solve/4_post_solve_adjustments.gms
diff --git a/d2_varfix.gms b/reeds/core/solve/5_varfix.gms
similarity index 98%
rename from d2_varfix.gms
rename to reeds/core/solve/5_varfix.gms
index 85db24429..9b133772c 100644
--- a/d2_varfix.gms
+++ b/reeds/core/solve/5_varfix.gms
@@ -92,8 +92,8 @@ CAPTRAN_PRM.fx(r,rr,trtype,tfix)$[routes(r,rr,trtype,tfix)$routes_prm(r,rr)] = C
 CAPTRAN_GRP.fx(transgrp,transgrpp,tfix)$trancap_init_transgroup(transgrp,transgrpp,"AC") = CAPTRAN_GRP.l(transgrp,transgrpp,tfix) ;
 INVTRAN.fx(r,rr,trtype,tfix)$routes_inv(r,rr,trtype,tfix) = INVTRAN.l(r,rr,trtype,tfix) ;
 INVTRAN_AC.fx(r,rr,tscbin,tfix)$routes_inv(r,rr,"AC",tfix) = INVTRAN_AC.l(r,rr,tscbin,tfix) ;
-INV_CONVERTER.fx(r,tfix)$Sw_VSC = INV_CONVERTER.l(r,tfix) ;
-CAP_CONVERTER.fx(r,tfix)$Sw_VSC = CAP_CONVERTER.l(r,tfix) ;
+INV_CONVERTER.fx(r,tfix)$val_converter(r,tfix) = INV_CONVERTER.l(r,tfix) ;
+CAP_CONVERTER.fx(r,tfix)$val_converter(r,tfix) = CAP_CONVERTER.l(r,tfix) ;
 CONVERSION.fx(r,h,intype,outtype,tfix)$Sw_VSC = CONVERSION.l(r,h,intype,outtype,tfix) ;
 CONVERSION_PRM.fx(r,ccseason,intype,outtype,tfix)$Sw_VSC = CONVERSION_PRM.l(r,ccseason,intype,outtype,tfix) ;
 CAP_SPUR.fx(x,tfix)$[Sw_SpurScen$xfeas(x)] = CAP_SPUR.l(x,tfix) ;
diff --git a/d3_data_dump.gms b/reeds/core/solve/6_data_dump.gms
similarity index 98%
rename from d3_data_dump.gms
rename to reeds/core/solve/6_data_dump.gms
index 29c97036e..5e804c1d9 100644
--- a/d3_data_dump.gms
+++ b/reeds/core/solve/6_data_dump.gms
@@ -1,5 +1,5 @@
 $ontext
-This file creates a gdx file with all of the data necessary for the Augur module to solve. This includes:
+This file creates a gdx file with all of the data necessary for the resource adequacy calculations:
     - Generator capacities
     - Exogenous retirments (sequential solves only)
     - Wind capacity by build year (because wind CFs change by build year)
@@ -16,7 +16,7 @@ $if not set start_year $setglobal start_year %startyear%
 * Set and parameter definitions
 *===============================
 
-set rfeas(r)                 "list of feasible r regions - for use in Augur only"
+set rfeas(r)                 "list of feasible r regions"
     trange(t)                "range from first year to current year"
     tcur(t)                  "current year"
     tnext(t)                 "next year"
@@ -280,7 +280,7 @@ cap_trans_prm(r,rr,trtype) = sum{t$tcur(t), CAPTRAN_PRM.l(r,rr,trtype,t) } ;
 
 cap_converter_filt(r) = sum{t$tcur(t), CAP_CONVERTER.l(r,t) } ;
 
-* In Augur, trtype="AC" includes everything except for VSC
+* In resource adequacy calculations, trtype="AC" includes everything except for VSC
 routes_filt(r,rr,trtype) = sum{t$tcur(t), routes(r,rr,trtype,t) } ;
 
 *============================
@@ -334,7 +334,7 @@ energy_price(r,h)$hours(h) =
 * Unload all relevant data to a gdx file
 *=======================================
 
-execute_unload 'ReEDS_Augur%ds%augur_data%ds%reeds_data_%cur_year%.gdx'
+execute_unload 'handoff%ds%reeds_data%ds%reeds_data_%cur_year%.gdx'
     avail_filt
     bcr
     bir_pvb_config
@@ -421,4 +421,4 @@ execute_unload 'ReEDS_Augur%ds%augur_data%ds%reeds_data_%cur_year%.gdx'
 *** dump data for tax credit phaseout calculations
 execute_unload "outputs%ds%tc_phaseout_data%ds%emit_for_tc_phaseout_calc_%cur_year%.gdx"
   emit_nat_tc, emit_r_tc
-;
\ No newline at end of file
+;
diff --git a/d_solve_iterate.py b/reeds/core/solve/solve.py
similarity index 83%
rename from d_solve_iterate.py
rename to reeds/core/solve/solve.py
index d22675fe7..1f90a0646 100644
--- a/d_solve_iterate.py
+++ b/reeds/core/solve/solve.py
@@ -1,29 +1,27 @@
 #%% Imports
 import os
-import site
+import sys
 import argparse
 import pandas as pd
 import subprocess
 from glob import glob
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent.parent))
 import reeds
-import Augur
 
 
 #%% Main function
-def run_reeds(casepath, t, onlygams=False, iteration=0):
+def run_reeds(casepath, t, iteration=0, onlygams=False, onlyra=False):
     """
     """
     # #%% Arguments for testing
-    # casepath = os.path.expanduser('~/github/ReEDS-2.0/runs/v20230512_prasM0_ERCOT')
+    # casepath = os.path.expanduser('~/github/ReEDS/runs/v20230512_prasM0_ERCOT')
     # t = 2020
-    # onlygams = 0
     # iteration = 0
+    # onlygams = 0
+    # onlyra = 0
     # os.chdir(casepath)
 
-    #%% Inferred inputs
-    site.addsitedir(casepath)
-    import runbatch
-
     #%% Get the run settings
     sw = reeds.io.get_switches(casepath)
     years = pd.read_csv(
@@ -33,7 +31,7 @@ def run_reeds(casepath, t, onlygams=False, iteration=0):
     tnext = {**dict(zip(years, years[1:])), **{years[-1]:years[-1]}}
 
     #%%### Run GAMS LP
-    if not onlyaugur:
+    if not onlyra:
         #%% Get the command to run GAMS for this solve year
         batch_case = os.path.basename(casepath)
         stress_year = f"{t}i{iteration}"
@@ -45,7 +43,7 @@ def run_reeds(casepath, t, onlygams=False, iteration=0):
                 glob(os.path.join(casepath,'g00files',f"{batch_case}_{tprev[t]}i*"))
             )[-1]
 
-        cmd_gams = runbatch.solvestring_sequential(
+        cmd_gams = reeds.inputs.solvestring_sequential(
             batch_case=batch_case,
             caseSwitches=sw,
             cur_year=t,
@@ -61,7 +59,7 @@ def run_reeds(casepath, t, onlygams=False, iteration=0):
         ### Run GAMS LP
         result = subprocess.run(cmd_gams, shell=True)
         if result.returncode:
-            raise Exception(f'd_solveoneyear.gms failed with return code {result.returncode}')
+            raise Exception(f'3_solve_oneyear.gms failed with return code {result.returncode}')
 
         #%% Add solve time to run metadata
         try:
@@ -79,9 +77,14 @@ def run_reeds(casepath, t, onlygams=False, iteration=0):
             raise Exception(f"Missing {savefile}.g00")
 
 
-    #%%### Run Augur
-    if (not onlygams) and (tnext[t] > int(sw.GSw_SkipAugurYear)):
-        Augur.main(t=t, tnext=tnext[t], casedir=casepath, iteration=iteration)
+    #%%### Run resource adequacy calculations
+    if (not onlygams) and (tnext[t] > int(sw.GSw_SkipRAyear)):
+        reeds.resource_adequacy.ra_calcs.main(
+            t=t,
+            tnext=tnext[t],
+            casedir=casepath,
+            iteration=iteration,
+        )
 
 
 #%% Driver function
@@ -105,15 +108,15 @@ def main(casepath, t, overwrite=False):
             and os.path.isfile(
                 os.path.join(
                     sw.casedir, 'inputs_case', f'stress{t}i{iteration+1}', 'cf_vre.csv'))
-            ## Check if Augur finished
+            ## Check if resource adequacy calculations finished
             and os.path.isfile(
                 os.path.join(
-                    sw.casedir, 'ReEDS_Augur', 'augur_data', f'ReEDS_Augur_{t}.gdx'))
+                    sw.casedir, 'handoff', 'reeds_data', f'ccdata_{t}.gdx'))
         ):
             print(f'Already ran {t}i{iteration} so continuing to next iteration')
             continue
 
-        #%% Run ReEDS and Augur
+        #%% Run ReEDS and RA calculations
         run_reeds(casepath, t, iteration=iteration)
 
         #%% Stop here if there's no stress period data for the next iteration
@@ -151,10 +154,6 @@ def main(casepath, t, overwrite=False):
                         help='year to run')
     parser.add_argument('--iteration', '-i', type=int, default=0,
                         help='iteration counter for this run')
-    parser.add_argument('--onlygams', '-g', action='store_true',
-                        help='Only run GAMS (skip Augur)')
-    parser.add_argument('--onlyaugur', '-a', action='store_true',
-                        help='Only run Augur (skip GAMS)')
     parser.add_argument('--overwrite', '-o', action='store_true',
                         help='Overwrite iterations that have already finished')
 
@@ -162,8 +161,6 @@ def main(casepath, t, overwrite=False):
     casepath = args.casepath
     t = args.t
     iteration = args.iteration
-    onlygams = args.onlygams
-    onlyaugur = args.onlyaugur
     overwrite = args.overwrite
 
     #%% Switch to run folder
diff --git a/d_solvepcm.gms b/reeds/core/solve_pcm/solve_pcm.gms
similarity index 82%
rename from d_solvepcm.gms
rename to reeds/core/solve_pcm/solve_pcm.gms
index 711b58c79..7742a76e3 100644
--- a/d_solvepcm.gms
+++ b/reeds/core/solve_pcm/solve_pcm.gms
@@ -9,11 +9,11 @@ Sw_PCM = 1 ;
 Sw_MinCF = 0 ;
 
 *** Unfix the operational variables
-$include d2_unfix_op.gms
+$include reeds%ds%core%ds%solve_pcm%ds%unfix_op.gms
 
 *** Define the h- and szn-dependent parameters
 $onMultiR
-$include d1_temporal_params.gms
+$include reeds%ds%core%ds%solve%ds%2_temporal_params.gms
 $offMulti
 
 *** Solve it
diff --git a/d2_unfix_op.gms b/reeds/core/solve_pcm/unfix_op.gms
similarity index 100%
rename from d2_unfix_op.gms
rename to reeds/core/solve_pcm/unfix_op.gms
diff --git a/dump_alldata.gms b/reeds/core/terminus/dump_alldata.gms
similarity index 100%
rename from dump_alldata.gms
rename to reeds/core/terminus/dump_alldata.gms
diff --git a/reeds/core/terminus/get_last_iter.py b/reeds/core/terminus/get_last_iter.py
new file mode 100644
index 000000000..f6b6c92cd
--- /dev/null
+++ b/reeds/core/terminus/get_last_iter.py
@@ -0,0 +1,42 @@
+"""
+This script contains the function to get the last iteration g00 file for a given case. 
+This is used in the batch call scripts to identify the last iteration of a solved year 
+for output processing.
+"""
+
+
+#%% ===========================================================================
+### --- IMPORTS ---
+### ===========================================================================
+import sys
+import argparse
+from pathlib import Path
+
+#%% ===========================================================================
+### --- Function ---
+### ===========================================================================
+
+def get_last_g00(batch_case, max_year):
+    pattern = f'{batch_case}_{max_year}i*.g00'
+    matches = list(Path('g00files').glob(pattern))
+    
+    if not matches:
+        print(f"ERROR: The run {batch_case} has not solved last modeled year {max_year}.", file=sys.stderr)
+        sys.exit(1)
+    
+    last_file = max(
+        matches,
+        key=lambda f: int(f.stem[f.stem.rfind('i')+1:])
+    )
+    
+    # Use forward slashes universally, works on both OS
+    print((Path('g00files') / last_file.name).as_posix())
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Get the last iteration g00 file for a given batch case.')
+    parser.add_argument('batch_case', type=str, help='The batch case name (e.g., v20260504_Pacific)')
+    parser.add_argument('max_year', type=int, help='The last modeled year to check for (e.g., 2032)')
+    args = parser.parse_args()
+    
+    
+    get_last_g00(args.batch_case, args.max_year)
\ No newline at end of file
diff --git a/e_powfrac_calc.gms b/reeds/core/terminus/powfrac_calc.gms
similarity index 100%
rename from e_powfrac_calc.gms
rename to reeds/core/terminus/powfrac_calc.gms
diff --git a/e_report.gms b/reeds/core/terminus/report.gms
similarity index 99%
rename from e_report.gms
rename to reeds/core/terminus/report.gms
index ebd14d10e..33ff1c967 100644
--- a/e_report.gms
+++ b/reeds/core/terminus/report.gms
@@ -113,10 +113,10 @@ h2_demand_type / "electricity", "cross-sector"/
 
 ;
 
-* Parameter definitions in the following file are read from e_report_params.csv
+* Parameter definitions in the following file are read from report_params.csv
 * and parsed in copy_files.py.
-* All output parameters should be defined in e_report_params.csv.
-$include e_report_params.gms
+* All output parameters should be defined in report_params.csv.
+$include autocode%ds%report_params.gms
 
 * Restrict operational outputs to representative timeslices and seasons
 h(h)$[not h_rep(h)] = no ;
@@ -934,7 +934,10 @@ cc_new(i,r,ccseason,t)$[valcap_irt(i,r,t)$cap_new_cc(i,r,ccseason,t)] = sum{v$iv
 
 cap_firm(i,r,ccseason,t)$[valcap_irt(i,r,t)$[not consume(i)]$tmodel_new(t)$Sw_PRM_CapCredit] =
       sum{v$[(not vre(i))$(not hydro(i))$(not storage(i))$(not storage_hybrid(i)$(not csp(i)))$valcap(i,v,r,t)],
-          CAP.l(i,v,r,t) * (1 + ccseason_cap_frac_delta(i,v,r,ccseason,t)) }
+          CAP.l(i,v,r,t) 
+          * (1 + ccseason_cap_frac_delta(i,v,r,ccseason,t)) 
+          * (1 - mean_forced_outage_rate(i,r,ccseason,t))
+          }
     + cc_old(i,r,ccseason,t)
     + sum{v$[(vre(i) or csp(i) or storage_hybrid(i)$(not csp(i)))$valinv(i,v,r,t)],
          m_cc_mar(i,r,ccseason,t) * (INV.l(i,v,r,t) + INV_REFURB.l(i,v,r,t)$[refurbtech(i)$Sw_Refurb]) }
@@ -2077,15 +2080,15 @@ h2_usage(r,h,t)$tmodel_new(t) =
 *========================================
 
 $ifthene.powerfrac %GSw_calc_powfrac% == 1
-$include e_powfrac_calc.gms
+$include reeds%ds%core%ds%terminus%ds%powfrac_calc.gms
 $endif.powerfrac
 
 *========================================
 * Dump results
 *========================================
 
-* The parameter list in the following file is read from e_report_params.csv
+* The parameter list in the following file is read from report_params.csv
 * and parsed in copy_files.py
 execute_unload "outputs%ds%rep_%fname%.gdx"
-$include e_report_paramlist.txt
+$include autocode%ds%report_paramlist.txt
 ;
diff --git a/e_report_dump.py b/reeds/core/terminus/report_dump.py
similarity index 95%
rename from e_report_dump.py
rename to reeds/core/terminus/report_dump.py
index faa3a20ab..7e3ef7477 100644
--- a/e_report_dump.py
+++ b/reeds/core/terminus/report_dump.py
@@ -7,11 +7,10 @@
 import os
 import traceback
 import sys
-
-# Third-party packages
-import pandas as pd
 import gdxpds
-
+import pandas as pd
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent.parent))
 import reeds
 
 
@@ -201,13 +200,13 @@ def postprocess_outputs(case, outputs_path=None, verbose=0):
     _outputs_path = os.path.join(case, 'outputs') if outputs_path is None else outputs_path
 
     ## System cost
-    reeds.output_calc.calc_systemcost(case).to_csv(
+    reeds.results.calc_systemcost(case).to_csv(
         os.path.join(_outputs_path, 'post_systemcost_annualized.csv'),
         index=False,
     )
 
     ## Reinforcement and spur-line 
-    reeds.output_calc.calc_reinforcement_spur_capacity_miles(case).to_csv(
+    reeds.results.calc_reinforcement_spur_capacity_miles(case).to_csv(
         os.path.join(_outputs_path, 'post_tech_transmission.csv'),
         index=False,
     )
@@ -249,7 +248,7 @@ def postprocess_outputs(case, outputs_path=None, verbose=0):
     reeds_path = os.path.abspath(os.path.dirname(__file__))
     log = reeds.log.makelog(scriptname=__file__, logpath=os.path.join(case, "gamslog.txt"))
 
-    print("Starting e_report_dump.py")
+    print("Starting report_dump.py")
 
     # %%### Parse inputs and get switches
     outputs_path = os.path.join(case, "outputs")
@@ -259,7 +258,7 @@ def postprocess_outputs(case, outputs_path=None, verbose=0):
 
     ### Get new file names if applicable
     dfparams = pd.read_csv(
-        os.path.join(case, "e_report_params.csv"),
+        os.path.join(case, 'reeds', 'core', 'terminus', 'report_params.csv'),
         comment="#",
         index_col="param",
     )
@@ -304,8 +303,8 @@ def postprocess_outputs(case, outputs_path=None, verbose=0):
 
 
     #%% All done
-    print("Completed e_report_dump.py")
+    print("Completed report_dump.py")
     try:
-        toc(tic=tic, year=0, path=case, process="e_report_dump.py")
+        toc(tic=tic, year=0, path=case, process="report_dump.py")
     except NameError:
         print("reeds/log.py not found, so not logging output")
diff --git a/e_report_params.csv b/reeds/core/terminus/report_params.csv
similarity index 99%
rename from e_report_params.csv
rename to reeds/core/terminus/report_params.csv
index 70b370208..92d20d84f 100644
--- a/e_report_params.csv
+++ b/reeds/core/terminus/report_params.csv
@@ -235,7 +235,7 @@ tax_expenditure_ptc(t),2004$,PTC tax expenditure,,,
 "watret_ivrt(i,v,r,t)",Mgal,retired water capacity by region and vintage,,,
 "watret_out(i,r,t)",Mgal,retired water capacity by region,,,
 "watret_ann_out(i,v,r,t)",Mgal/yr,annual retired water capacity by region,,,
-# Parameters defined before e_report.gms - some are used in r2x,,,,,
+# Parameters defined earlier in model - some are used in r2x,,,,,
 bcr,,,,,1
 biosupply,,,,,1
 cap_hyd_szn_adj,,,,,1
diff --git a/aws_setup.sh b/reeds/hpc/aws_setup.sh
similarity index 98%
rename from aws_setup.sh
rename to reeds/hpc/aws_setup.sh
index 285eec2a1..74944cb99 100644
--- a/aws_setup.sh
+++ b/reeds/hpc/aws_setup.sh
@@ -95,7 +95,7 @@ git clone git@github.com:ReEDS-Model/ReEDS.git
 
 # Run ReEDS! 
 # (using nohup to keep the process from dying when you end your ssh session)
-#nohup python runbatch_aws.py -c weekendcentroid -r 4 -b centwknd > myout.txt &
+#nohup python runreeds.py -c weekendcentroid -r 4 -b centwknd > myout.txt &
 
 
 #========================================
@@ -130,4 +130,3 @@ git clone git@github.com:ReEDS-Model/ReEDS.git
 #move to your git directory
 #!!! could be different for different users
 #cd ~/r2/r2_aws
-
diff --git a/srun_template.sh b/reeds/hpc/srun_template.sh
similarity index 100%
rename from srun_template.sh
rename to reeds/hpc/srun_template.sh
diff --git a/input_processing/WriteHintage.py b/reeds/input_processing/WriteHintage.py
similarity index 94%
rename from input_processing/WriteHintage.py
rename to reeds/input_processing/WriteHintage.py
index 777f31e10..755993d10 100644
--- a/input_processing/WriteHintage.py
+++ b/reeds/input_processing/WriteHintage.py
@@ -49,13 +49,16 @@
 import datetime
 from sklearn.cluster import KMeans
 from sklearn.exceptions import ConvergenceWarning
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 # Time the operation of this script
 tic = datetime.datetime.now()
 
-# ignore ConvergenceWarnings that occur in this file from the kmeans function 
+# ignore ConvergenceWarnings and Windows+MKL memory leak warnings that
+# occur in this file from the kmeans function
 warnings.filterwarnings("ignore", category=ConvergenceWarning)
+warnings.filterwarnings("ignore", message="KMeans is known to have a memory leak")
 
 
 #%% ===========================================================================
@@ -294,7 +297,7 @@ def main(reeds_path, inputs_case):
     print('Starting WriteHintage.py')
 
     # #%% Settings for testing
-    # reeds_path = os.path.expanduser('~/github/ReEDS-2.0')
+    # reeds_path = os.path.expanduser('~/github/ReEDS')
     # inputs_case = os.path.join(
     #     reeds_path,'runs','v20231027_yamM0_Z45_h_d_365_transreg_z69_core','inputs_case')
 
@@ -464,12 +467,6 @@ def main(reeds_path, inputs_case):
 
     tdat = pd.concat([tdat_non_coal, tdat_coal], ignore_index=True, axis=0)
     
-    # calculate the maximum hintage number, to be used in b_inputs.gms, and export it
-    max_hintage_number = tdat['bin'].max()
-    max_hintage_number_text = f'scalar max_hintage_number "--number-- the maximum number of bins used in this ReEDS run" /{max_hintage_number}/ ;'
-    with open(os.path.join(inputs_case,'max_hintage_number.txt'), 'w') as file:
-        file.write(f'{max_hintage_number_text}')
-
    # calculate the capacity-weighted average heat rate for each bin 
     # by taking the product of the sum of the capacity and the centroid of the bin
     tdat['wHR'] = tdat.HR * tdat['Summer.capacity']
@@ -597,7 +594,48 @@ def main(reeds_path, inputs_case):
     zout.wCCS_Retro_HR = zout.wCCS_Retro_HR.round(decimals=1)
     zout.wCCS_Retro_CapAdjust = zout.wCCS_Retro_CapAdjust.round(decimals=3)
     zout.wCCS_Retro_LocFactor = zout.wCCS_Retro_LocFactor.round(decimals=3)
-    
+
+    #%% Get vintages
+    # Each generation technology is broken out by class:
+    # 1. initial capacity: init-1, init-2, ..., init-n
+    # 2. prescribed capacity: prescribed
+    # 3. new capacity: new
+    # This allows us to distinguish between existing, prescribed, and model-chosen builds
+    # The number of classes is set by numhintage for initial capacity and numclass for new capacity
+    # Under the Clean Air Act, all coal plants are regulated individually.
+    # Therefore, we need a large value of hintages to represent these plants
+    scalars = reeds.io.get_scalars(inputs_case)
+    max_hintage_number = tdat['bin'].max()
+    if sw.numhintage in ['unit', 'group']:
+        numhintage = int(scalars.hintage_unit_number)
+    else:
+        if int(sw.GSw_Clean_Air_Act) == 1:
+            numhintage = max_hintage_number
+        else:
+            numhintage = int(sw.numhintage)
+    # there are numeraire hintages on either sides of the outer breaks
+    # when using calcmethod = 1, here adding two for safety
+    # NB this will not increase model size given conditions
+    # dictating valcap and valgen for initial classes
+    numhintage += 2
+
+    initv = [f'init-{i}' for i in range(1, numhintage+1)]
+    newv = [f'new{i}' for i in range(1, int(sw.numclass)+1)]
+    v = initv + newv
+
+    reeds.io.write_to_inputs_h5(
+        pd.Series(v), 'v', inputs_case, gamstype='set',
+        comment='technology class',
+    )
+    for (name, ds, comment) in [
+        ('initv', initv, 'initial technologies'),
+        ('newv', newv, 'new technologies'),
+    ]:
+        reeds.io.write_to_inputs_h5(
+            pd.Series(ds, name='v'), name, inputs_case, gamstype='set',
+            comment=comment,
+        )
+
     #%% Save output dataframe in inputs_case folder
     cols = ['TECH', 'bin', 'r', 'yr', 'cap', 'wintercap', 'wHR',
             'wFOM', 'wVOM', 'wOnlineYear',
diff --git a/input_processing/__init__.py b/reeds/input_processing/__init__.py
similarity index 100%
rename from input_processing/__init__.py
rename to reeds/input_processing/__init__.py
diff --git a/input_processing/calc_financial_inputs.py b/reeds/input_processing/calc_financial_inputs.py
similarity index 98%
rename from input_processing/calc_financial_inputs.py
rename to reeds/input_processing/calc_financial_inputs.py
index c0053265f..f2611a52d 100644
--- a/input_processing/calc_financial_inputs.py
+++ b/reeds/input_processing/calc_financial_inputs.py
@@ -9,7 +9,8 @@
 import sys
 import datetime
 # Time the operation of this script
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -41,7 +42,7 @@ def calc_financial_inputs(inputs_case):
     print('Starting calculation of financial parameters')
 
     # #%% Settings for testing
-    # reeds_path = '/Users/pbrown/github/ReEDS-2.0/'
+    # reeds_path = '/Users/pbrown/github/ReEDS/'
     # inputs_case = os.path.join(reeds_path,'runs','v20220621_NTPm0_ercot_seq_test','inputs_case')
 
     #%% Inputs from switches
@@ -502,8 +503,8 @@ def calc_financial_inputs(inputs_case):
 
     #%% Parse arguments
     parser = argparse.ArgumentParser(description="calc_financial_inputs.py")
-    parser.add_argument("reeds_path", help="ReEDS-2.0 directory")
-    parser.add_argument("inputs_case", help="ReEDS-2.0/runs/{case}/inputs_case directory")
+    parser.add_argument("reeds_path", help="ReEDS directory")
+    parser.add_argument("inputs_case", help="ReEDS/runs/{case}/inputs_case directory")
     
     args = parser.parse_args()
     reeds_path = args.reeds_path
diff --git a/input_processing/check_inputs.py b/reeds/input_processing/check_inputs.py
similarity index 97%
rename from input_processing/check_inputs.py
rename to reeds/input_processing/check_inputs.py
index 46dd9738f..d94fff118 100644
--- a/input_processing/check_inputs.py
+++ b/reeds/input_processing/check_inputs.py
@@ -5,7 +5,8 @@
 import argparse
 import datetime
 import yaml
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 tic = datetime.datetime.now()
 
@@ -14,7 +15,7 @@
     description='Check inputs.gdx parameters against objective_function_params.yaml',
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
-parser.add_argument('casepath', help='ReEDS-2.0/runs/{case} directory')
+parser.add_argument('casepath', help='ReEDS/runs/{case} directory')
 args = parser.parse_args()
 casepath = args.casepath
 
diff --git a/input_processing/climateprep.py b/reeds/input_processing/climateprep.py
similarity index 97%
rename from input_processing/climateprep.py
rename to reeds/input_processing/climateprep.py
index d8c179af7..e7c339702 100644
--- a/input_processing/climateprep.py
+++ b/reeds/input_processing/climateprep.py
@@ -73,8 +73,8 @@
 import os
 import sys
 import pandas as pd
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 # Time the operation of this script
 tic = datetime.datetime.now()
@@ -89,9 +89,9 @@
 inputs_case = args.inputs_case
 
 # #%% Settings for testing ###
-# reeds_path = os.path.expanduser('~/github/ReEDS-2.0/')
+# reeds_path = os.path.expanduser('~/github/ReEDS/')
 # inputs_case = os.path.expanduser(
-#     '~/github/ReEDS-2.0/runs/v20201208_beyond2050_Climate2100step5/inputs_case/')
+#     '~/github/ReEDS/runs/v20201208_beyond2050_Climate2100step5/inputs_case/')
 
 #%%#################
 ### FIXED INPUTS ###
diff --git a/input_processing/copy_files.py b/reeds/input_processing/copy_files.py
similarity index 81%
rename from input_processing/copy_files.py
rename to reeds/input_processing/copy_files.py
index 4059eef79..5a74bdfaf 100644
--- a/input_processing/copy_files.py
+++ b/reeds/input_processing/copy_files.py
@@ -13,7 +13,7 @@
 import h5py
 from pathlib import Path
 # Local Imports
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -48,7 +48,7 @@ def read_runfiles(reeds_path, inputs_case, sw, agglevel_variables):
     """
     runfiles = (
         pd.read_csv(
-            os.path.join(reeds_path, 'runfiles.csv'),
+            os.path.join(reeds_path, 'reeds', 'input_processing', 'runfiles.csv'),
             dtype={'fix_cols':str,
                    'depends_on_switch':str,
                    'depends_on_switch_value': str},
@@ -154,7 +154,7 @@ def get_source_deflator_map(reeds_path):
     """
     # Inflation-adjusted inputs
     sources_dollaryear = pd.read_csv(
-        os.path.join(reeds_path,'sources.csv'),
+        os.path.join(reeds_path,'docs','sources.csv'),
         usecols=["RelativeFilePath", "DollarYear"]
     )
     deflator = pd.read_csv(
@@ -181,6 +181,7 @@ def get_regions_and_agglevel(
     reeds_path,
     inputs_case,
     save_regions_and_agglevel=True,
+    overwrite=False,
 ):
     """
     Create a regional mapping to help filter for specific regions and aggregation levels.
@@ -308,7 +309,7 @@ def get_regions_and_agglevel(
 
         # Write out val_county and val_ba before collapsing to unique regions
         hier_sub['county'].dropna().to_csv(
-            os.path.join(inputs_case, 'val_county.csv'), header=False, index=False)
+            os.path.join(inputs_case, 'county.csv'), header=False, index=False)
         hier_sub['ba'].drop_duplicates().to_csv(
             os.path.join(inputs_case, 'val_ba.csv'), header=False, index=False)
 
@@ -355,9 +356,12 @@ def get_regions_and_agglevel(
     hier_sub[['r','itlgrp']].rename(columns={'r':'*r'}).to_csv(
         os.path.join(inputs_case, 'hierarchy_itlgrp.csv'), index=False)
 
-    # save the itlgrp values
-    hier_sub[['itlgrp']].drop_duplicates().to_csv(
-        os.path.join(inputs_case, 'val_itlgrp.csv'), header=False, index=False)
+    itlgrp = hier_sub['itlgrp'].drop_duplicates().rename()
+    reeds.io.write_to_inputs_h5(
+        itlgrp, 'itlgrp', inputs_case, gamstype='set',
+        comment='zone for additional interface transfer limit constraint',
+        overwrite=overwrite,
+    )
 
     # Drop any substate region columns as these will no longer be needed
     hier_sub = hier_sub.drop(['county', 'ba', 'itlgrp'], axis=1)
@@ -369,13 +373,31 @@ def get_regions_and_agglevel(
     # Write out the unique values of each column in hier_sub to val_[column name].csv
     # Note the conversion to a pd Series is necessary to leverage the to_csv function
     if save_regions_and_agglevel:
-        for i in hier_sub.columns.drop('offshore', errors='ignore'):
-            pd.Series(hier_sub[i].unique()).to_csv(
-                os.path.join(inputs_case,'val_' + i + '.csv'),index=False,header=False)
+        comments = {
+            'aggreg': 'aggregated region',
+            'cendiv': 'census division',
+            'country': 'nation',
+            'h2ptcreg': 'H2 production tax credit region',
+            'hurdlereg': 'hurdle rate region (for extra costs on interregional flows)',
+            'interconnect': 'synchronous interconnection',
+            'nercr': 'NERC region',
+            'transgrp': 'sub-FERC-1000 region',
+            'transreg': 'Transmission Planning Regions from FERC Order 1000',
+            'usda_region': 'biomass supply curve region',
+        }
+        for level, comment in comments.items():
+            df = pd.Series(hier_sub[level].unique())
+            reeds.io.write_to_inputs_h5(
+                df, level, inputs_case, gamstype='set', comment=comment,
+                overwrite=overwrite,
+            )
 
-        # Overwrite val_st with the val_st used here (which includes 'voluntary')
-        pd.Series(val_st).to_csv(
-            os.path.join(inputs_case, 'val_st.csv'), header=False, index=False)
+        # Use a modified version of val_st that includes 'voluntary'
+        reeds.io.write_to_inputs_h5(
+            pd.Series(val_st), 'st', inputs_case, gamstype='set',
+            comment="state (or special 'voluntary' entry for corporate procurements)",
+            overwrite=overwrite,
+        )
 
         # Rename columns and save as hierarchy.csv
         (
@@ -385,8 +407,10 @@ def get_regions_and_agglevel(
         ).to_csv(os.path.join(inputs_case, 'hierarchy.csv'), index=False)
 
         # Write offshore zones
-        hier_sub.loc[hier_sub.offshore == 1, 'r'].to_csv(
-            os.path.join(inputs_case, 'offshore.csv'), index=False, header=False,
+        offshore = hier_sub.loc[hier_sub.offshore == 1, 'r']
+        reeds.io.write_to_inputs_h5(
+            offshore, 'offshore', inputs_case, gamstype='set', comment='offshore zones',
+            overwrite=overwrite,
         )
 
     levels = [i for i in hier_sub if i != 'offshore']
@@ -396,8 +420,10 @@ def get_regions_and_agglevel(
 
     # Export region files
     if save_regions_and_agglevel:
-        pd.Series(val_r).to_csv(
-            os.path.join(inputs_case, 'val_r.csv'), header=False, index=False)
+        reeds.io.write_to_inputs_h5(
+            pd.Series(val_r), 'r', inputs_case, gamstype='set',
+            comment='regions', overwrite=overwrite,
+        )
 
     regions_and_agglevel = {
         "valid_regions": valid_regions,
@@ -478,29 +504,6 @@ def read_banned_tech_file(full_path, filepath, inputs_case, r_county):
     return df, nuclear_ban_regions
 
 
-def read_special_h5file(full_path):
-    """
-    Read .h5 file and make special-case adjustments if necessary:
-    - recf_distpv: drop 'distpv|' from column titles
-    - transmission_cost_ac: reset index and decode strings
-    - transmission_distance: stack from wide into long and decode strings
-    """
-    filename = os.path.basename(full_path)
-    df = reeds.io.read_file(full_path, parse_timestamps=True)
-
-    if filename.startswith('recf_distpv'):
-        df.columns = df.columns.str.replace('distpv|','')
-    elif filename.startswith('transmission_cost_ac'):
-        df = df.reset_index()
-        for col in ['r', 'rr', 'tscbin']:
-            df[col] = df[col].str.decode('utf-8')
-    elif filename.startswith('transmission_distance'):
-        df = df.stack().rename('miles').reset_index()
-        df['r'] = df['r'].str.decode('utf-8')
-
-    return df
-
-
 def subset_to_valid_regions(
     sw,
     region_file_entry,
@@ -542,8 +545,8 @@ def subset_to_valid_regions(
         full_path_county = full_path.replace('{lvl}', 'county')
         match filetype_in:
             case 'h5':
-                df_ba = read_special_h5file(full_path_ba)
-                df_county = read_special_h5file(full_path_county)
+                df_ba = reeds.io.read_file(full_path_ba, parse_timestamps=True)
+                df_county = reeds.io.read_file(full_path_county, parse_timestamps=True)
             case 'csv':
                 df_ba = pd.read_csv(
                     full_path_ba,
@@ -590,7 +593,7 @@ def subset_to_valid_regions(
             )
         ## Filetype conditions
         elif filetype_in == 'h5':
-            df = read_special_h5file(full_path)
+            df = reeds.io.read_file(full_path, parse_timestamps=True)
         elif filetype_in == 'csv':
             df = pd.read_csv(full_path, dtype={'FIPS':str, 'fips':str, 'cnty_fips':str}, comment='#')
         else:
@@ -619,89 +622,27 @@ def subset_to_valid_regions(
             val_st=val_st_ba,
             filename=filename
         )
+        # Filter function parameters to only include county resolution regions
+        valid_regions_county = {level: (hier[hier['r']
+                                .isin(agglevel_variables['county_regions'])][level].unique()) for level in levels}
+        val_st_county = valid_regions_county['st']
+        val_r_all_county = []
+        for value in valid_regions_county.values():
+            val_r_all_county.extend(value)
+        val_r_all_county = list(dict.fromkeys(val_r_all_county))
+
+        df_county = filter_data(
+            df_county,
+            region_col,
+            fix_cols,
+            levels,
+            val_r_all=val_r_all_county,
+            valid_regions=valid_regions_county,
+            val_st=val_st_county,
+            filename=filename
+        )
 
-
-        # Transmission files need to be filtered differently to allow interfaces between BA and county resolution regions
-        transmission_files = [
-            'transmission_cost_ac.csv',
-            'transmission_cost_dc.csv',
-            'transmission_distance.csv',
-        ]
-
-        if filename in transmission_files:
-            # Filter county data to include regions being solved at both BA and county resolution
-            df_county = filter_data(
-                df_county,
-                region_col,
-                fix_cols,
-                levels,
-                val_r_all,
-                valid_regions,
-                val_st,
-                filename=filename
-            )
-            # Assign the counties that are not being solved at county resolution to the correct BA
-            tx_region_col = '*r' if '*r' in df_county.columns.values else 'r'
-            for idx, region in df_county[tx_region_col].items():
-                if region not in agglevel_variables['county_regions']:
-                    df_county.loc[idx, tx_region_col] = agglevel_variables['BA_2_county'][df_county.loc[idx,tx_region_col]]
-
-            for idx,region in df_county['rr'].items():
-                if region not in agglevel_variables['county_regions']:
-                    df_county.loc[idx, 'rr'] = agglevel_variables['BA_2_county'][df_county.loc[idx, 'rr']]
-            # Drop if *r and rr are same region
-            df_county = df_county.drop(df_county[df_county[tx_region_col]==df_county['rr']].index)
-            # Drop if line is BA-to-BA
-            drop_list = []
-            for idx,region in df_county.iterrows():
-                if (
-                    (region[tx_region_col] in agglevel_variables['ba_regions'])
-                    and (region['rr'] in agglevel_variables['ba_regions'])
-                ):
-                    drop_list.append(idx)
-
-            df_county = df_county.drop(drop_list)
-            # Group lines going between same BA and county
-            if 'distance' in filename:
-                df_county = df_county.groupby([tx_region_col,'rr'] + fix_cols).mean().reset_index()
-            elif 'cost_ac' in filename:
-                df_county = df_county.groupby([tx_region_col,'rr'] + fix_cols).sum().reset_index()
-                # Drop reverse interfaces
-                # Keep only the interface where the first region is alphabetically first
-                df_county['region_pair'] = df_county.apply(
-                    lambda x: '||'.join(sorted([x[tx_region_col], x['rr']])), axis=1)
-                df_county = df_county.sort_values(by=['region_pair','tscbin'])
-                df_county = df_county.drop_duplicates(subset=['region_pair','tscbin'], keep='first')
-                df_county = df_county.drop(columns=['region_pair'])
-            else:
-                df_county = df_county.groupby([tx_region_col,'rr'] + fix_cols).sum().reset_index()
-
-            df_county['interface'] = df_county[tx_region_col] + '||'+df_county['rr']
-            df_county.reset_index(drop=True,inplace=True)
-
-        else:
-            # Filter function parameters to only include county resolution regions
-            valid_regions_county = {level: (hier[hier['r']
-                                    .isin(agglevel_variables['county_regions'])][level].unique()) for level in levels}
-            val_st_county = valid_regions_county['st']
-            val_r_all_county = []
-            for value in valid_regions_county.values():
-                val_r_all_county.extend(value)
-            val_r_all_county = list(dict.fromkeys(val_r_all_county))
-
-            df_county = filter_data(
-                df_county,
-                region_col,
-                fix_cols,
-                levels,
-                val_r_all=val_r_all_county,
-                valid_regions=valid_regions_county,
-                val_st=val_st_county,
-                filename=filename
-            )
-
-         # Combine BA and county data
-
+        # Combine BA and county data
         # The filter data function returns a dataframe with NAN values to prevent empty H5 files
         # If either the BA data or county data are populated we can drop the nan data
         if df_county.isna().all().all() and not df_ba.isna().all().all():
@@ -769,14 +710,14 @@ def scalar_csv_to_txt(path_to_scalar_csv):
     return dfscalar
 
 
-def param_csv_to_txt(path_to_param_csv, writelist=True):
+def param_csv_to_txt(infilepath, outdirpath, writelist=True):
     """
     Write a parameter csv to GAMS-readable text
     Format of csv should be: parameter(indices),units,comment
     """
     # Load the csv
     dfparams = pd.read_csv(
-        path_to_param_csv,
+        infilepath,
         index_col='param', comment='#',
     )
     # Create the GAMS-readable param definition string (comments must be ≤255 characters)
@@ -786,15 +727,17 @@ def param_csv_to_txt(path_to_param_csv, writelist=True):
         for i, row in dfparams.loc[dfparams.input != 1].iterrows()
     ])
     # Write it to a file, replacing .csv with .gms in the filename
-    param_gms_path = path_to_param_csv.replace('.csv','.gms')
+    param_gms_path = Path(outdirpath, Path(infilepath).stem + '.gms')
     with open(param_gms_path, 'w') as w:
         w.write(paramtext)
     # Write the list of parameters if desired
     if writelist:
         # Create the GAMS-readable list of parameters (without indices)
         paramlist = '\n'.join(dfparams.index.map(lambda x: x.split('(')[0]).tolist())
-        param_list_path = (
-            path_to_param_csv.replace('params','paramlist').replace('.csv','.txt'))
+        param_list_path = Path(
+            outdirpath,
+            Path(infilepath).stem.replace('params','paramlist') + '.txt'
+        )
         with open(param_list_path, 'w') as w:
             w.write(paramlist)
 
@@ -846,12 +789,6 @@ def filter_data(
             except Exception:
                 df = pd.DataFrame()
 
-    # If there is a region-to-region mapping set
-    elif region_col.strip('*') in ['r,rr','transgrp,transgrpp']:
-        # make sure both the r and rr regions are in val_r
-        r,rr = region_col.split(',')
-        df = df.loc[df[r].isin(val_r_all) & df[rr].isin(val_r_all)]
-
     # Subset on the valid regions except for r regions
     # (r regions might also include s regions, which complicates things...)
     elif ((region_col.strip('*') in levels) & (region_col.strip('*') != 'r')):
@@ -905,59 +842,38 @@ def write_scalars(scalars, inputs_case):
     scalar_csv_to_txt(os.path.join(inputs_case,'scalars.csv'))
 
 
-def write_GAMS_sets(runfiles, reeds_path, inputs_case):
-    """
-    Write GAMS-readable sets to the inputs_case directory
-    """
-    casedir = os.path.dirname(inputs_case)
-
-    # Create Sets folder
-    shutil.copytree(
-        os.path.join(reeds_path,'inputs','sets'),
-        os.path.join(inputs_case,'sets'),
-        dirs_exist_ok=True,
-        ignore=shutil.ignore_patterns('README*','readme*'),
-    )
-
-    # Write commands to load sets
-    sets = runfiles.loc[runfiles.GAMStype=='set'].copy()
-    settext = '$offlisting\n' + '\n\n'.join([
-        f"set {row.GAMSname} '{row.comment:.255}'"
-        '\n/'
-        f"\n$include inputs_case%ds%{row.filename}"
-        '\n/ ;'
-        for i, row in sets.iterrows()
-    ]) + '\n$onlisting\n'
-    # Write to file
-    with open(os.path.join(casedir,'b_sets.gms'), 'w') as f:
-        f.write(settext)
-
-
-def write_non_region_file(filename, filepath, src_file, dir_dst, sw, regions_and_agglevel, source_deflator_map):
+def write_non_region_file(
+    row,
+    case,
+    dir_dst,
+    sw,
+    regions_and_agglevel,
+    source_deflator_map,
+):
     """
     Copy a non-region specific file (filename) from src_file to dir_dst
     """
     # Check if source file exists and is not rev_paths.csv
-    if (os.path.exists(src_file)) and (filename!='rev_paths.csv'):
+    if (os.path.exists(row.full_filepath)) and (row.filename != 'rev_paths.csv'):
 
         # Special Case: Values in load_multiplier.csv need to be rounded prior to copy
-        if filename == 'load_multiplier.csv':
-            df_load_multiplier = pd.read_csv(src_file).round(6)
+        if row.filename == 'load_multiplier.csv':
+            df_load_multiplier = pd.read_csv(row.full_filepath).round(6)
             df_load_multiplier.to_csv(os.path.join(dir_dst,'load_multiplier.csv'),index=False)
 
-        elif filename == 'h2_exogenous_demand.csv':
+        elif row.filename == 'h2_exogenous_demand.csv':
             # h2_exogenous_demand.csv has a path in runfiles.csv (considered a non-region file)
-            df=pd.read_csv(src_file,index_col=['p','t'])
+            df=pd.read_csv(row.full_filepath, index_col=['p','t'])
             df[sw['GSw_H2_Demand_Case']].round(3).rename_axis(['*p','t']).to_csv(
                 os.path.join(dir_dst,'h2_exogenous_demand.csv')
             )
 
-        elif filename in ['energy_communities.csv', 'nuclear_energy_communities.csv']:
+        elif row.filename in ['energy_communities.csv', 'nuclear_energy_communities.csv']:
             # Map energy communities to regions and compute the percentage of energy communities
             # within each region to assign a weighted bonus.
 
             # Rename column in energy_communities.csv and map county to r, save as energy_communities.csv
-            energy_communities = pd.read_csv(src_file)
+            energy_communities = pd.read_csv(row.full_filepath)
             energy_communities.rename(columns={'County Region': 'county'}, inplace=True)
 
             r_county = regions_and_agglevel ['r_county']
@@ -976,22 +892,27 @@ def write_non_region_file(filename, filepath, src_file, dir_dst, sw, regions_and
             # Rename columns from ['r','county'] to ['r','percentage_energy_communities']
             e_df.columns = ['r', 'percentage_energy_communities']
 
-            e_df.to_csv(os.path.join(dir_dst,filename),index=False)
+            e_df.to_csv(os.path.join(dir_dst, row.filename),index=False)
         
-        elif filename == 'co2_site_char.csv':
+        elif row.filename == 'co2_site_char.csv':
             # Adjust for inflation
-            df = pd.read_csv(src_file)
-            df[f"bec_{sw['GSw_CO2_BEC']}"] *= source_deflator_map[filepath]
+            df = pd.read_csv(row.full_filepath)
+            df[f"bec_{sw['GSw_CO2_BEC']}"] *= source_deflator_map[row.filepath]
             df.to_csv(os.path.join(dir_dst, 'co2_site_char.csv'), index=False)
 
         else:
-            shutil.copy(src_file, os.path.join(dir_dst,filename))
-
-            if filename == 'e_report_params.csv':
-                # Rewrite e_report_params as GAMS-readable definition
-                param_csv_to_txt(os.path.join(dir_dst,'e_report_params.csv'))
+            if str(row.GAMStype).lower() == 'set':
+                reeds.io.write_csv_to_inputs_h5(
+                    filepath=row.full_filepath,
+                    case=case,
+                    gamstype=row.GAMStype.lower(),
+                    comment=(row.comment if isinstance(row.comment, str) else ''),
+                    overwrite=True,
+                )
+            else:
+                shutil.copy(row.full_filepath, os.path.join(dir_dst, row.filename))
 
-            if filename == 'scalars.csv':
+            if row.filename == 'scalars.csv':
                 # Rewrite scalars.csv as GAMS-readable definition
                 scalars = reeds.io.get_scalars(full=True)
                 write_scalars(scalars, dir_dst)
@@ -1002,7 +923,7 @@ def write_non_region_files(non_region_files, sw, inputs_case, regions_and_agglev
     Copy non-region specific files to the input case directory.
     """
     print('Copying non-region-indexed files')
-
+    case = reeds.io.standardize_case(inputs_case)
     for _, row in non_region_files.iterrows():
         if row['filepath'].split('/')[0] in ['inputs','postprocessing','tests']:
             dir_dst = inputs_case
@@ -1016,10 +937,14 @@ def write_non_region_files(non_region_files, sw, inputs_case, regions_and_agglev
             write_empty_file(os.path.join(dir_dst,row['filename']))
         else:
             print(f'...copying {row.filename}')
-            filename = row['filename']
-            filepath = row['filepath']
-            src_file = row['full_filepath']
-            write_non_region_file(filename, filepath, src_file, dir_dst, sw, regions_and_agglevel, source_deflator_map)
+            write_non_region_file(
+                row,
+                case,
+                dir_dst,
+                sw,
+                regions_and_agglevel,
+                source_deflator_map,
+            )
 
     
 def calculate_county_fractions(df, county2zone):
@@ -1139,50 +1064,6 @@ def map_and_aggregate(
 
     return df
 
-def upscale_from_county_to_ba(
-    df,
-    region_file_entry,
-    agglevel_variables,
-    regions_and_agglevel,
-    aggfunc=None
-):
-    """
-    Changes the resolution of the provided region_col from county to BA
-    or mixed resolution and aggregates according to the provided aggfunc.
-    """
-    original_cols = df.columns
-    region_col = region_file_entry['region_col']
-
-    # Exception for cendiv
-    if region_col.strip('*') == 'r_cendiv':
-        val_cendiv = regions_and_agglevel['valid_regions']['cendiv']
-        df = df.loc[df['r'].isin(regions_and_agglevel['r_county']['county'])]
-        df = df.loc[:, df.columns.isin(["r"] + val_cendiv)].reset_index(drop=True)
-        region_col = 'r'
-
-    # Aggregate values to ba resolution if not running county-level resolution
-    # and if county level is not a desired resolution in mixed resolution runs
-    if 'county' not in agglevel_variables['agglevel']:
-        df = map_and_aggregate(df,regions_and_agglevel,region_file_entry,region_col,aggfunc)
-
-
-    # Mixed resolution procedure
-    elif agglevel_variables['lvl'] == 'mult':
-        df_ba = df[df[region_col].isin(agglevel_variables['BA_county_list'])]
-        df_ba = map_and_aggregate(df_ba,regions_and_agglevel,region_file_entry,region_col,aggfunc)
-
-        # Filter out county regions
-        df_county = df[df[region_col].isin(agglevel_variables['county_regions'])]
-        # Combine county and BA
-        df = pd.concat([df_ba, df_county])
-
-    else:
-         pass
-
-    df = df[original_cols]
-
-    return df
-
 
 def write_region_indexed_file(
     df,
@@ -1190,8 +1071,7 @@ def write_region_indexed_file(
     source_deflator_map,
     sw,
     region_file_entry,
-    regions_and_agglevel,
-    agglevel_variables
+    regions_and_agglevel
 ):
     """
     Write a single region-indexed file to the dir_dst directory
@@ -1200,6 +1080,27 @@ def write_region_indexed_file(
     # Get the filetype of the output file from the filename string
     filetype_out = os.path.splitext(filename)[1].strip('.')
 
+    region_col = region_file_entry['region_col']
+    fix_cols = region_file_entry['fix_cols'].split(',')
+
+    if region_file_entry['disaggfunc'] != 'ignore':
+        df = reeds.spatial.downscale_from_legacy_zone_to_county(
+            df=df,
+            region_col=region_col,
+            fix_cols=fix_cols,
+            inputs_case=inputs_case,
+            disaggfunc=region_file_entry['disaggfunc']
+        )
+
+    if region_file_entry['aggfunc'] != 'ignore':
+        df = reeds.spatial.upscale_from_county_to_zone(
+            df=df,
+            region_col=region_col,
+            fix_cols=fix_cols,
+            inputs_case=inputs_case,
+            aggfunc=region_file_entry['aggfunc']
+        )
+
     #---- Write data to dir_dst (inputs_case) folder ----
     if filetype_out == 'h5':
         reeds.io.write_profile_to_h5(df, filename, dir_dst)
@@ -1210,27 +1111,6 @@ def write_region_indexed_file(
             case 'bio_supplycurve.csv':
                 # Adjust for inflation
                 df['price'] = df['price'].astype(float) * source_deflator_map[filepath]
-            case (
-                'can_exports.csv'
-                | 'can_imports.csv'
-                | 'demonstration_plants.csv'
-                | 'distpvcap.csv'
-                | 'h2_ba_share.csv'
-                | 'regional_cap_cost_diff.csv'
-                | 'cendivweights.csv'
-                | 'cap_existing_psh.csv'
-            ):
-                # The upscale_from_county_to_ba function correctly handles the different spatial resolution options
-                # This sections just needs to check if the run is at pure county resolution
-                # The above listed data need to be upscaled if the run includes anything coarser than county resolution
-                if agglevel_variables['lvl'] != 'county':
-                        df = upscale_from_county_to_ba(
-                            df=df,
-                            region_file_entry=region_file_entry,
-                            agglevel_variables=agglevel_variables,
-                            regions_and_agglevel=regions_and_agglevel,
-                            aggfunc=region_file_entry.aggfunc
-                        )
             case 'unitdata.csv':
                 fips_ba_map = regions_and_agglevel['ba_county'].dropna().set_index('county')['ba']
                 df['reeds_ba'] = df['FIPS'].map(fips_ba_map)
@@ -1285,8 +1165,7 @@ def write_region_indexed_files(
                 source_deflator_map,
                 sw,
                 region_file_entry,
-                regions_and_agglevel,
-                agglevel_variables
+                regions_and_agglevel
             )
 
 
@@ -1302,7 +1181,12 @@ def write_miscellaneous_files(
     Many of these files are not in the non_region_files and region_files set
     (runfiles.csv - from function read_runfiles).
     """
-    # ---- Miscellaneous files not in non_region_files or region_files ----
+    ### Solver file
+    case = Path(inputs_case).parent
+    optfile = reeds.io.get_optfile(case)
+    shutil.copy(Path(reeds_path, 'reeds', 'solver', optfile), case)
+
+    ### Parsed switches
     pd.DataFrame(
         {'*pvb_type': [f'pvb{i}' for i in sw['GSw_PVB_Types'].split('_')],
         'ilr': [np.around(float(c) / 100, 2) for c in sw['GSw_PVB_ILR'].split('_')
@@ -1367,7 +1251,10 @@ def write_miscellaneous_files(
 
     gwp_write['H2'] = scalars.loc['h2_gwp','value'].copy()
 
-    gwp_write.to_csv(os.path.join(inputs_case,'gwp.csv'), header=False)
+    reeds.io.write_to_inputs_h5(
+        gwp_write, 'gwp', inputs_case, gamstype='parameter',
+        comment='--metric ton CO2-equivalents-- global warming potential',
+    )
 
     # Calculate CO2 cap based on GSw_Region chosen (national or sub-national regions)
     # Read in national co2 cap
@@ -1377,7 +1264,7 @@ def write_miscellaneous_files(
             index_col='t',
         )
         .loc[sw['GSw_AnnualCapScen']]
-        .rename_axis('*t')
+        .rename_axis('allt')
         .rename('tonne_per_year')
     )
 
@@ -1388,7 +1275,7 @@ def write_miscellaneous_files(
         index_col=0)
 
     # Filter the counties that are in chosen GSw_Region
-    val_county = pd.read_csv(os.path.join(inputs_case,'val_county.csv'),names=['r'])
+    val_county = pd.read_csv(os.path.join(inputs_case,'county.csv'),names=['r'])
 
     # Merge emission share by county with the counties in GSw_Region and calculate emission share of GSw_Region
     region_em_share = val_county.merge(em_share, on='r', how='left').fillna(0)
@@ -1396,13 +1283,19 @@ def write_miscellaneous_files(
 
     # Apply the emission share to national cap to get the emission cap trajectory of GSw_Region
     co2_cap *= region_em_share
-    co2_cap.round(0).to_csv(os.path.join(inputs_case, 'co2_cap.csv'))
+
+    reeds.io.write_to_inputs_h5(
+        co2_cap, 'co2_cap', inputs_case, gamstype='parameter',
+        comment='--metric tons-- CO2 emissions cap used when Sw_AnnualCap is on',
+    )
 
     # CO2 tax
-    pd.read_csv(
+    co2_tax = pd.read_csv(
         os.path.join(reeds_path,'inputs','emission_constraints','co2_tax.csv'), index_col='t',
-    )[sw['GSw_CarbTaxOption']].rename_axis('*t').round(2).to_csv(
-        os.path.join(inputs_case,'co2_tax.csv')
+    )[sw['GSw_CarbTaxOption']].rename_axis('allt')
+    reeds.io.write_to_inputs_h5(
+        co2_tax, 'co2_tax', inputs_case, gamstype='parameter',
+        comment='--$/metric ton-- CO2 tax used when Sw_CarbTax is on',
     )
 
     solveyears = reeds.inputs.parse_yearset(sw['yearset'])
@@ -1412,19 +1305,33 @@ def write_miscellaneous_files(
     solveyears = [y for y in solveyears if (y >= int(sw['startyear'])) and (y <= int(sw['endyear']))]
     pd.DataFrame(columns=solveyears).to_csv(
         os.path.join(inputs_case,'modeledyears.csv'), index=False)
+    reeds.io.write_to_inputs_h5(
+        pd.Series(solveyears, name='allt'), 'tmodel_new', inputs_case, gamstype='set',
+        comment='years to run the model',
+    )
 
-    pd.read_csv(
+    t = pd.Series(range(int(sw.startyear), int(sw.endyear)+1), name='allt')
+    reeds.io.write_to_inputs_h5(
+        pd.Series(t, name='allt'), 't', inputs_case, gamstype='set',
+        comment='full set of years',
+    )
+
+    gen_mandate_trajectory = pd.read_csv(
         os.path.join(reeds_path,'inputs','national_generation','gen_mandate_trajectory.csv'),
         index_col='GSw_GenMandateScen'
-    ).loc[sw['GSw_GenMandateScen']].rename_axis('*t').round(5).to_csv(
-        os.path.join(inputs_case,'gen_mandate_trajectory.csv')
+    ).loc[sw['GSw_GenMandateScen']].rename_axis('allt')
+    reeds.io.write_to_inputs_h5(
+        gen_mandate_trajectory, 'national_gen_frac', inputs_case, gamstype='parameter',
+        comment='--fraction-- national fraction of load + losses that must be met by RE',
     )
 
-    pd.read_csv(
-        os.path.join(reeds_path,'inputs','national_generation','gen_mandate_tech_list.csv'),
+    nat_gen_tech_frac = pd.read_csv(
+        os.path.join(reeds_path,'inputs','national_generation','nat_gen_tech_frac.csv'),
         index_col='*i',
-    )[sw['GSw_GenMandateList']].to_csv(
-        os.path.join(inputs_case,'gen_mandate_tech_list.csv')
+    )[sw['GSw_GenMandateList']].rename_axis('i')
+    reeds.io.write_to_inputs_h5(
+        nat_gen_tech_frac, 'nat_gen_tech_frac', inputs_case, gamstype='parameter',
+        comment='--fraction-- fraction of each tech generation that may be counted toward eq_national_gen',
     )
 
     pd.read_csv(
@@ -1460,9 +1367,12 @@ def write_miscellaneous_files(
         index_col=['month', 'day'],
     )[sw['GSw_PRM_CapCreditSeasons']].rename('ccseason')
     ccseason_dates.to_csv(os.path.join(inputs_case, 'ccseason_dates.csv'))
-    ccseason_dates.drop_duplicates().to_csv(
-        os.path.join(inputs_case, 'ccseason.csv'),
-        index=False, header=False,
+    reeds.io.write_to_inputs_h5(
+        df=ccseason_dates.drop_duplicates().rename().reset_index(drop=True),
+        key='ccseason',
+        case=inputs_case,
+        gamstype='set',
+        comment='seasons used for capacity credit',
     )
 
     prm_profiles = pd.read_csv(
@@ -1524,17 +1434,19 @@ def write_miscellaneous_files(
     # ----  Miscelanous files in non_region_files or region_files (in this case we are overwriting them)
     # Expand i (technologies) set if modeling water use. Overwrite originals.
     if int(sw['GSw_WaterMain']):
-        pd.concat([
-            pd.read_csv(
-                os.path.join(inputs_case,'i.csv'),
-                comment='*', header=None).squeeze(1),
+        techs = pd.concat([
+            reeds.io.read_input(inputs_case, 'i').squeeze(1),
             pd.read_csv(
                 os.path.join(inputs_case,'i_coolingtech_watersource.csv'),
                 comment='*', header=None).squeeze(1),
             pd.read_csv(
                 os.path.join(inputs_case,'i_coolingtech_watersource_upgrades.csv'),
                 comment='*', header=None).squeeze(1),
-        ]).to_csv(os.path.join(inputs_case,'i.csv'), header=False, index=False)
+        ])
+        reeds.io.write_to_inputs_h5(
+            techs, 'i', inputs_case, gamstype='set', comment='generation technologies',
+            overwrite=True,
+        )
 
     ## Unit sizes for ReEDS2PRAS
     fpath_out = os.path.join(inputs_case, 'unitsize.csv')
@@ -1554,6 +1466,12 @@ def write_miscellaneous_files(
         ).rename_axis('tech').dropna().astype(int)
         unitsize.to_csv(fpath_out)
 
+    # Rewrite report_params as GAMS-readable definitions
+    param_csv_to_txt(
+        infilepath=Path(reeds_path, 'reeds', 'core', 'terminus', 'report_params.csv'),
+        outdirpath=Path(inputs_case, '..', 'autocode'),
+    )
+
 
 def generate_maps_gpkg(inputs_case):
     """
@@ -1604,18 +1522,17 @@ def main(reeds_path, inputs_case):
         agglevel_variables
     )
 
-    # Write general GAMS files
-    # Write GAMS-readable sets to the inputs_case directory
-    write_GAMS_sets(runfiles, reeds_path, inputs_case)
-
     # Rewrite the switches tables as GAMS-readable definition
-    # (gswitches.csv is first written at runbatch.py)
+    # (gswitches.csv is first written at runreeds.py)
     scalar_csv_to_txt(os.path.join(inputs_case,'gswitches.csv'))
     
     source_deflator_map = get_source_deflator_map(reeds_path)
 
     # Copy non-region files
     write_non_region_files(non_region_files, sw, inputs_case, regions_and_agglevel, source_deflator_map)
+    
+    # Write files used for disaggregation
+    write_disagg_data_files(runfiles, inputs_case)
 
     # Copy region files
     write_region_indexed_files(
@@ -1627,9 +1544,6 @@ def main(reeds_path, inputs_case):
         source_deflator_map
     )
 
-    # Write files used for disaggregation
-    write_disagg_data_files(runfiles, inputs_case)
-
     # Create a maps.gpkg for this run
     # Skip if using region dis/aggregation, maps will be written in aggregation_regions.py.
     # Run if using mixed resolution aggreg-county combination 
@@ -1664,7 +1578,7 @@ def main(reeds_path, inputs_case):
 
     # #%% Settings for testing ###
     # reeds_path = reeds.io.reeds_path
-    # inputs_case = os.path.join(reeds_path,'runs','v20260305_itlM0_USA_defaults','inputs_case')
+    # inputs_case = os.path.join(reeds_path,'runs','v20260522_transcostM0_OR_water','inputs_case')
 
 
     # ---- Set up logger ----
diff --git a/input_processing/forecast.py b/reeds/input_processing/forecast.py
similarity index 99%
rename from input_processing/forecast.py
rename to reeds/input_processing/forecast.py
index d9cfaa1ed..f1442c469 100644
--- a/input_processing/forecast.py
+++ b/reeds/input_processing/forecast.py
@@ -16,16 +16,17 @@
 ### ===========================================================================
 
 import argparse
+import gdxpds
 import pandas as pd
 import numpy as np
-import gdxpds
 import os
 import sys
 import shutil
 from glob import glob
 from warnings import warn
 import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 # Time the operation of this script
 tic = datetime.datetime.now()
@@ -166,7 +167,7 @@ def forecast(
     inputs_case = os.path.join(args.inputs_case, '')
 
     # #%% Settings for testing
-    # reeds_path = os.path.expanduser('~/github/ReEDS-2.0')
+    # reeds_path = os.path.expanduser('~/github/ReEDS')
     # inputs_case = os.path.join(reeds_path,'runs','v20220411_prmM0_USA2060','inputs_case')
 
     #%% Settings for debugging
@@ -200,7 +201,7 @@ def forecast(
     #%%###################
     ### Derived inputs ###
 
-    ### Get the case name (ReEDS-2.0/runs/{casename}/inputscase/)
+    ### Get the case name (ReEDS/runs/{casename}/inputscase/)
     casename = inputs_case.split(os.sep)[-3]
 
     ### DEBUG: Make the outputs directory
@@ -522,4 +523,4 @@ def forecast(
 ## infiles.to_csv(
 ##     os.path.join(reeds_path, 'inputs', 'userinput', 'futurefiles.csv'),
 ##     index=False
-## )
\ No newline at end of file
+## )
diff --git a/reeds/input_processing/fuelcostprep.py b/reeds/input_processing/fuelcostprep.py
new file mode 100644
index 000000000..d72a17418
--- /dev/null
+++ b/reeds/input_processing/fuelcostprep.py
@@ -0,0 +1,252 @@
+'''
+The purpose of this script is to write out fuel costs for the following fuels at 
+census division level:
+    - coal
+    - uranium
+    - H2 (for H2-CT/CC tech)
+    - natural gas
+Additionally, this script also writes out natural gas demand (total NG demand as 
+well as NG demand for electricity generation) and natural gas alphas
+'''
+#%% ===========================================================================
+### --- IMPORTS ---
+### ===========================================================================
+
+import pandas as pd
+import os
+import sys
+import argparse
+import datetime
+import numpy as np
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+import reeds
+# Time the operation of this script
+tic = datetime.datetime.now()
+
+
+#%% Functions
+def smear(dfzones, dfgroups, decay_km:float=150, decay_func=np.exp) -> pd.DataFrame:
+    """
+    Calculate a weighted-average parameter between dfzones and dfgroups, where the weighting
+    between a zone in dfzones and all groups in dfgroups is determined by decay_func (default
+    is exponential decay) and decay_km.
+    """
+    weights = {}
+
+    for r, row in dfzones.iterrows():
+        ## Get distance from centroid to edge of all other zones
+        distances_km = dfgroups.distance(row.geometry.centroid) / 1000
+        ## Weight decays with distance from centroid
+        if decay_km != 0:
+            weight = decay_func(-distances_km / decay_km)
+        else:
+            ## 1 if zero distance, 0 otherwise
+            weight = (~distances_km.astype(bool)).astype(int)
+        weights[r] = weight
+
+    weight_df = pd.DataFrame(weights)
+    weight_norm = weight_df / weight_df.sum()
+    weight_norm = weight_norm.T
+
+    return weight_norm
+
+
+def plot_cendivweights(inputs_case, dfmap, cendivweights):
+    import cmocean
+    import matplotlib.pyplot as plt
+    cmap = cmocean.cm.rain
+    cendivs = dfmap['cendiv'].bounds.minx.sort_values().index
+    nrows, ncols, coords = reeds.plots.get_coordinates(cendivs, aspect=1)
+    plt.close()
+    f,ax = plt.subplots(
+        nrows, ncols, sharex=True, sharey=True, figsize=(3*ncols, 2.5*nrows),
+        gridspec_kw={'hspace':0, 'wspace':0},
+    )
+    for cendiv in cendivs:
+        _ax = ax[coords[cendiv]] if len(cendivs) > 1 else ax
+        _ax.axis('off')
+        dfmap['cendiv'].plot(ax=_ax, facecolor='none', edgecolor='k', lw=0.5, zorder=1e6)
+        dfplot = dfmap['r'].copy()
+        dfplot['value'] = dfplot.index.map(cendivweights[cendiv])
+        dfplot = dfplot[['value','geometry']].replace(0,np.nan).dropna()
+        dfplot.plot(ax=_ax, column='value', vmin=0, vmax=1, cmap=cmap)
+        _ax.set_title(cendiv, fontsize=12, weight='bold', y=0.92)
+    cax = ax if len(cendivs) == 1 else ax[-1, 1]
+    reeds.plots.addcolorbarhist(
+        f, cax, dfplot.value,
+        cmap=cmap, vmin=0, vmax=1,
+        orientation='horizontal', cbarbottom=-0.1, cbarheight=2, cbarwidth=0.1,
+        histratio=0.1, histcolor='w', title='Weight [fraction]',
+        labelpad=1.3, title_fontsize=12, ticklabel_fontsize=12,
+    )
+    figpath = Path(inputs_case, '..', 'outputs', 'figures', 'inputs', 'cendivweights.png')
+    figpath.parent.mkdir(parents=True, exist_ok=True)
+    plt.savefig(figpath)
+    return f, ax
+
+
+#%% Procedure
+if __name__ == '__main__':
+    #%% Parse arguments
+    parser = argparse.ArgumentParser(description="""This file organizes fuel cost data by techonology""")
+
+    parser.add_argument("reeds_path", help='ReEDS directory')
+    parser.add_argument("inputs_case", help='ReEDS/runs/{case}/inputs_case directory')
+
+    args = parser.parse_args()
+    reeds_path = args.reeds_path
+    inputs_case = args.inputs_case
+
+    # #%% Settings for testing
+    # reeds_path = reeds.io.reeds_path
+    # inputs_case = os.path.join(reeds_path,'runs','v20260609_cendivM0_Pacific','inputs_case')
+
+    #%% Set up logger
+    log = reeds.log.makelog(
+        scriptname=__file__,
+        logpath=os.path.join(inputs_case,'..','gamslog.txt'),
+    )
+    print("Starting fuelcostprep.py")
+
+    #%% Inputs from switches
+    sw = reeds.io.get_switches(inputs_case)
+
+    # Load valid regions
+    val_r = reeds.io.read_input(inputs_case, 'r').squeeze(1).tolist()
+    val_cendiv = reeds.io.read_input(inputs_case, 'cendiv').squeeze(1).tolist()
+
+    r_cendiv = pd.read_csv(os.path.join(inputs_case,"r_cendiv.csv"))
+
+    dollaryear = pd.read_csv(os.path.join(inputs_case, "dollaryear_fuel.csv"))
+    deflator = pd.read_csv(os.path.join(inputs_case,'deflator.csv'))
+    deflator.columns = ["Dollar.Year","Deflator"]
+    dollaryear = dollaryear.merge(deflator,on="Dollar.Year",how="left")
+
+    #%% ===========================================================================
+    ### --- PROCEDURE: FUEL PRICE CALCULATIONS ---
+    ### ===========================================================================
+
+    ####################
+    #    -- Coal --    #
+    ####################
+    coal = pd.read_csv(os.path.join(inputs_case, 'coal_price.csv'))
+    coal = coal.melt(id_vars = ['year']).rename(columns={'variable':'cendiv'})
+    coal = coal.loc[coal['cendiv'].isin(val_cendiv)]
+
+    # Adjust prices to 2004$
+    deflate = dollaryear.loc[dollaryear['Scenario'] == sw.coalscen,'Deflator'].values[0]
+    coal.loc[:,'value'] = coal['value'] * deflate
+
+    coal = coal.merge(r_cendiv,on='cendiv',how='left')
+    coal = coal.drop('cendiv', axis=1)
+    coal = coal[['year','r','value']].rename(columns={'year':'t','value':'coal'})
+    coal.coal = coal.coal.round(6)
+
+    #######################
+    #    -- Uranium --    #
+    #######################
+    uranium = pd.read_csv(os.path.join(inputs_case, 'uranium_price.csv'))
+
+    # Adjust prices to 2004$
+    deflate = dollaryear.loc[dollaryear['Scenario'] == sw.uraniumscen,'Deflator'].values[0]
+    uranium.loc[:,'cost'] = uranium['cost'] * deflate
+    uranium = pd.concat([uranium.assign(r=i) for i in val_r], ignore_index=True)
+    uranium = uranium[['year','r','cost']].rename(columns={'year':'t','cost':'uranium'})
+    uranium.uranium = uranium.uranium.round(6)
+
+    #############################
+    #    -- H2-Combustion --    #
+    #############################
+    # note that these fuel inputs are not used when H2 production is run endogenously in ReEDS (GSw_H2 > 0)
+    h2fuel = pd.read_csv(os.path.join(inputs_case, 'hydrogen_price.csv'), index_col='year')
+
+    #Adjust prices to 2004$
+    deflate = dollaryear.loc[dollaryear['Scenario'] == sw.h2combustionfuelscen,'Deflator'].squeeze()
+    h2fuel['cost'] = h2fuel['cost'] * deflate
+    # Reshape from [:,[t,cost]] to [:,[t,r,cost]]
+    h2fuel = (
+        pd.concat({r:h2fuel for r in val_r}, axis=0, names=['r'])
+        .reset_index().rename(columns={'year':'t','cost':'h2fuel'})
+        [['t','r','h2fuel']]
+        .round(6)
+    )
+
+    ###########################
+    #    -- Natural Gas --    #
+    ###########################
+
+    ngprice = pd.read_csv(os.path.join(inputs_case,'natgas_price_cendiv.csv'))
+    ngprice = ngprice.melt(id_vars=['year']).rename(columns={'variable':'cendiv'})
+    ngprice = ngprice.loc[ngprice['cendiv'].isin(val_cendiv)]
+
+    # Adjust prices to 2004$
+    deflate = dollaryear.loc[dollaryear['Scenario'] == sw.ngscen,'Deflator'].values[0]
+    ngprice.loc[:,'value'] = ngprice['value'] * deflate
+
+    # Save Natural Gas prices by census region
+    ngprice_cendiv = ngprice.copy()
+    ngprice_cendiv = ngprice_cendiv.pivot_table(index='cendiv',columns='year',values='value')
+    ngprice_cendiv = ngprice_cendiv.round(6)
+
+    # Map census regions to model regions
+    ngprice = ngprice.merge(r_cendiv,on='cendiv',how='left')
+    ngprice = ngprice.drop('cendiv', axis=1)
+    ngprice = ngprice[['year','r','value']].rename(columns={'year':'t','value':'naturalgas'})
+    ngprice.naturalgas = ngprice.naturalgas.round(6)
+
+    # Census division weights
+    dfmap = reeds.io.get_dfmap(reeds.io.standardize_case(inputs_case))
+    cendivweights = smear(
+        dfzones=dfmap['r'],
+        dfgroups=dfmap['cendiv'],
+        decay_km=float(sw.GSw_GasRegionSmooth),
+    ).round(3)
+    if int(sw.debug):
+        try:
+            plot_cendivweights(inputs_case, dfmap, cendivweights)
+        except Exception as err:
+            print(err)
+
+    # Combine all fuel data
+    fuel = coal.merge(uranium,on=['t','r'],how='left')
+    fuel = fuel.merge(ngprice,on=['t','r'],how='left')
+    fuel = fuel.merge(h2fuel,on=['t','r'],how='left')
+    fuel = fuel.sort_values(['t','r'])
+
+    #%%#################################### 
+    ### Natural Gas Demand Calculations ###
+
+    # Natural Gas demand
+    ngdemand = pd.read_csv(os.path.join(inputs_case,'ng_demand_elec.csv'), index_col='year')
+    ngdemand = ngdemand[ngdemand.columns[ngdemand.columns.isin(val_cendiv)]]
+    ngdemand = ngdemand.transpose()
+    ngdemand = ngdemand.round(6)
+
+    # Total Natural Gas demand
+    ngtotdemand = pd.read_csv(os.path.join(inputs_case, 'ng_demand_tot.csv'), index_col='year')
+    ngtotdemand = ngtotdemand[ngtotdemand.columns[ngtotdemand.columns.isin(val_cendiv)]]
+    ngtotdemand = ngtotdemand.transpose()
+    ngtotdemand = ngtotdemand.round(6)
+
+    ### Natural Gas Alphas (already in 2004$)
+    alpha = pd.read_csv(os.path.join(inputs_case, 'alpha.csv'), index_col='t')
+    alpha = alpha[alpha.columns[alpha.columns.isin(val_cendiv)]]
+    alpha = alpha.round(6)
+
+    #%%###################
+    ### Data Write-Out ###
+    ######################
+
+    fuel.to_csv(os.path.join(inputs_case,'fprice.csv'),index=False)
+    ngprice_cendiv.to_csv(os.path.join(inputs_case,'gasprice_ref.csv'))
+
+    ngdemand.to_csv(os.path.join(inputs_case,'ng_demand_elec.csv'))
+    ngtotdemand.to_csv(os.path.join(inputs_case,'ng_demand_tot.csv'))
+    alpha.to_csv(os.path.join(inputs_case,'alpha.csv'))
+    cendivweights.to_csv(os.path.join(inputs_case,'cendivweights.csv'))
+
+    reeds.log.toc(tic=tic, year=0, process='input_processing/fuelcostprep.py', 
+        path=os.path.join(inputs_case,'..'))
+
+    print('Finished fuelcostprep.py')
diff --git a/input_processing/h2_storage.py b/reeds/input_processing/h2_storage.py
similarity index 93%
rename from input_processing/h2_storage.py
rename to reeds/input_processing/h2_storage.py
index 69e29df59..0a8ba75c5 100644
--- a/input_processing/h2_storage.py
+++ b/reeds/input_processing/h2_storage.py
@@ -9,7 +9,8 @@
 import os
 import sys
 import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -119,8 +120,8 @@ def main(reeds_path, inputs_case):
         description='Process H2 storage inputs',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
@@ -138,4 +139,4 @@ def main(reeds_path, inputs_case):
     reeds.log.toc(tic=tic, year=0, process='input_processing/h2_storage.py',
         path=os.path.join(inputs_case,'..'))
     
-    print('Finished h2_storage.py')
\ No newline at end of file
+    print('Finished h2_storage.py')
diff --git a/reeds/input_processing/h5_to_gdx.py b/reeds/input_processing/h5_to_gdx.py
new file mode 100644
index 000000000..bed3e258c
--- /dev/null
+++ b/reeds/input_processing/h5_to_gdx.py
@@ -0,0 +1,203 @@
+#%% Imports
+import sys
+import h5py
+import gdxpds
+import argparse
+import datetime
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from typing import Literal
+sys.path.append(str(Path(__file__).parent.parent.parent))
+import reeds
+
+
+#%% Functions
+def read_inputs(case:str|Path) -> tuple:
+    """
+    Read a ReEDS-formatted inputs.h5 file
+
+    Args:
+        case: Path to a ReEDS case
+    
+    Returns:
+        tuple of 3 dictionaries, where the keys of each are the names of the elements
+        read from the inputs.h5 file:
+        - dictin (first element of tuple): pd.DataFrame of contents
+        - gamstypes (second element of tuple): 'set' or 'parameter'
+        - comments (third element of tuple): string to be used as comment in GAMS
+    """
+    ## Allow either a ReEDS case or a .h5 path to be provided
+    if Path(case).suffix == '.h5':
+        h5path = case
+    else:
+        h5path = Path(case, 'inputs_case', 'inputs.h5')
+    dictin = {}
+    gamstypes = {}
+    comments = {}
+    with h5py.File(h5path, 'r') as f:
+        keys = list(f)
+        for key in keys:
+            gamstypes[key] = f[key].attrs['gamstype']
+            if 'comment' in f[key].attrs:
+                comments[key] = f[key].attrs['comment']
+                if isinstance(comments[key], np.float64):
+                    comments[key] = ''
+            columns = [i.decode() for i in list(f[key]['columns'])]
+            try:
+                df = pd.DataFrame({col: f[key][col] for col in columns})
+            except KeyError:
+                df = pd.DataFrame(columns=columns)
+            for col in df:
+                if df[col].dtype == 'O':
+                    df[col] = df[col].str.decode('utf-8')
+            dictin[key] = df
+    return dictin, gamstypes, comments
+
+
+def get_declaration(df):
+    """
+    For everything except primary sets, return the domain as "(dim1,dim2,...)"
+    """
+    columns = [i for i in df.columns if i != 'Value']
+    if (len(columns) == 1) and (columns[0] == '*'):
+        out = ''
+    else:
+        out = '(' + ','.join(columns) + ')'
+    return out
+
+
+def sort_primary_first(declarations:list):
+    """Put primary sets before subsetes"""
+    writelist = (
+        [i for i in declarations if '(' not in i]
+        + [i for i in declarations if '(' in i]
+    )
+    return writelist
+
+
+def write_declaration(
+    case:str|Path,
+    declarations:list,
+    gamstype:Literal['set','parameter'],
+):
+    """
+    Write GAMS code to declare sets/parameters before loading them from a .gdx file
+    """
+    ## Get aliases so we can define them after the parent is defined
+    aliases = pd.read_csv(
+        Path(reeds.io.reeds_path, 'inputs', 'sets', '_aliases.csv'),
+        header=0, index_col=0,
+    ).squeeze()
+    ## Need to write primary sets before subsets
+    if gamstype == 'set':
+        writelist = sort_primary_first(declarations)
+    else:
+        writelist = declarations
+    fpath = Path(case, 'autocode', f'b_declare_{gamstype}s.gms')
+    with open(fpath, 'w') as f:
+        if len(writelist):
+            for line in writelist:
+                f.write(f'{gamstype} {line} ;\n')
+                name = line.split('(')[0]
+                for alias in aliases.get([name], []):
+                    f.write(f'alias({name},{alias}) ;\n')
+    print(f'Wrote {fpath}')
+
+
+def write_gdxread(
+    case:str|Path,
+    declarations:list,
+    gamstype:Literal['set','parameter'],
+):
+    """
+    Write GAMS code to read sets/parameters from .gdx file
+    """
+    ## Need to write primary sets before subsets
+    if gamstype == 'set':
+        writelist = sort_primary_first(declarations)
+    else:
+        writelist = declarations
+    fpath = Path(case, 'autocode', f'b_load_{gamstype}s.gms')
+    with open(fpath, 'w') as f:
+        for line in writelist:
+            key = line.split('(')[0]
+            f.write(f'$loadDCR {key} = {key}\n')
+    print(f'Wrote {fpath}')
+
+
+def main(case, overwrite=True, verbose=1):
+    dictin, gamstypes, comments = read_inputs(case)
+    gdxpath = Path(reeds.io.standardize_case(case), 'inputs_case', 'inputs_0.gdx')
+    ## Some sets need to be defined first to conserve ordering
+    keys_in = list(dictin.keys())
+    special_keys = ['r']
+    keys = special_keys + [i for i in keys_in if i not in special_keys]
+    ## Load each h5 key and write it to gdx
+    declare_sets = []
+    declare_parameters = []
+    if gdxpath.is_file():
+        if overwrite:
+            gdxpath.unlink()
+        else:
+            raise FileExistsError(gdxpath)
+    with gdxpds.gdx.GdxFile() as gdx:
+        for key in keys:
+            df = dictin[key]
+            if gamstypes[key] == 'set':
+                gdxpds.gdx.append_set(
+                    gdx_file=gdx,
+                    set_name=key,
+                    df=df,
+                    description=comments.get(key, None),
+                )
+                declare_sets.append(f'{key}{get_declaration(df)}')
+            elif gamstypes[key] == 'parameter':
+                gdxpds.gdx.append_parameter(
+                    gdx_file=gdx,
+                    param_name=key,
+                    df=df,
+                    description=comments.get(key, None),
+                )
+                declare_parameters.append(f'{key}{get_declaration(df)}')
+            else:
+                raise NotImplementedError(gamstypes[key])
+        gdx.write(gdxpath)
+        print(f'Wrote inputs.h5 to {gdxpath}')
+    ## Write GAMS code to declare and load the sets/parameters
+    write_declaration(case, declare_sets, 'set')
+    write_declaration(case, declare_parameters, 'parameter')
+    write_gdxread(case, declare_sets, 'set')
+    write_gdxread(case, declare_parameters, 'parameter')
+
+
+#%% Procedure
+if __name__ == '__main__':
+    #%% Time the operation of this script
+    tic = datetime.datetime.now()
+
+    #%% Parse arguments
+    parser = argparse.ArgumentParser(
+        description='Convert a ReEDS-formatted .h5 file to .gdx',
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
+
+    args = parser.parse_args()
+    case = reeds.io.standardize_case(Path(args.inputs_case))
+
+    # #%% Inputs for testing
+    # case = Path(reeds.io.reeds_path, 'runs', 'v20260427_inputsM0_github_Everything')
+
+    #%% Set up logger
+    log = reeds.log.makelog(
+        scriptname=__file__,
+        logpath=Path(case, 'gamslog.txt'),
+    )
+
+    #%% Run it
+    main(case)
+
+    #%% Record the runtime
+    reeds.log.toc(tic=tic, year=0, process='input_processing/h5_to_gdx.py', path=case)
diff --git a/input_processing/hourly_load.py b/reeds/input_processing/hourly_load.py
similarity index 99%
rename from input_processing/hourly_load.py
rename to reeds/input_processing/hourly_load.py
index 8b2114fb9..ce4b8e0a7 100644
--- a/input_processing/hourly_load.py
+++ b/reeds/input_processing/hourly_load.py
@@ -33,7 +33,7 @@
 import pandas as pd
 from pathlib import Path
 import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -761,8 +761,8 @@ def main(reeds_path, inputs_case):
         description='Create run-specific hourly profiles',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
diff --git a/input_processing/hourly_plots.py b/reeds/input_processing/hourly_plots.py
similarity index 97%
rename from input_processing/hourly_plots.py
rename to reeds/input_processing/hourly_plots.py
index a11987aa6..ff05d0d00 100644
--- a/input_processing/hourly_plots.py
+++ b/reeds/input_processing/hourly_plots.py
@@ -11,9 +11,8 @@
 import matplotlib.pyplot as plt
 from matplotlib import patheffects as pe
 import cmocean
-
-import hourly_repperiods
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 from reeds import plots
 plots.plotparams()
@@ -135,9 +134,9 @@ def plot_ldc(
     ### Get clustered load, repeating representative periods based on how many
     ### periods they represent
     numperiods = period_szn.value_counts().rename('numperiods').to_frame()
-    numperiods['yearperiod'] = numperiods.index.map(hourly_repperiods.szn2yearperiod).values
-    numperiods['year'] = numperiods.index.map(hourly_repperiods.szn2yearperiod).map(lambda x: x[0])
-    numperiods['yperiod'] = numperiods.index.map(hourly_repperiods.szn2period)
+    numperiods['yearperiod'] = numperiods.index.map(reeds.timeseries.szn2yearperiod).values
+    numperiods['year'] = numperiods.index.map(reeds.timeseries.szn2yearperiod).map(lambda x: x[0])
+    numperiods['yperiod'] = numperiods.index.map(reeds.timeseries.szn2period)
     periods = [[row.yearperiod] * row.numperiods for (i,row) in numperiods.iterrows()]
     periods = [item for sublist in periods for item in sublist]
 
@@ -150,11 +149,11 @@ def plot_ldc(
     hourly_out = hourly_in.unstack('h_of_period').loc[periods].stack('h_of_period')
 
     #### Daily
-    periodly_in = hourly_in.groupby('yperiod').mean()
+    periodly_in = hourly_in.groupby(['year','yperiod']).mean()
     ## Index doesn't matter; replace it so we can take daily mean
     periodly_out = hourly_out.copy()
     hourly_out.index = hourly_in.index.copy()
-    periodly_out = hourly_out.groupby('yperiod').mean()
+    periodly_out = hourly_out.groupby(['year','yperiod']).mean()
 
     ### Get axis coordinates: properties = rows, regions = columns
     properties = periodly_out.columns.get_level_values('property').unique().values
@@ -563,7 +562,7 @@ def get_profiles(regions, year):
         dforig = pd.concat(dforig, axis=1)
 
         ### Representative profiles
-        periodmap = period_szn.map(hourly_repperiods.szn2yearperiod).to_frame()
+        periodmap = period_szn.map(reeds.timeseries.szn2yearperiod).to_frame()
         periodmap['year'] = periodmap.szn.map(lambda x: x[0])
         periodmap['yperiod'] = periodmap.szn.map(lambda x: x[1])
         periodmap = periodmap.loc[periodmap.year==year].yperiod
@@ -657,7 +656,7 @@ def plot_load_days(profiles, rep_periods, period_szn, sw, reeds_path, figpath):
     """
     """
     ### Input processing
-    idx_reedsyr = period_szn.map(hourly_repperiods.szn2yearperiod)
+    idx_reedsyr = period_szn.map(reeds.timeseries.szn2yearperiod)
     medoid_profiles = profiles.loc[rep_periods]
     centroids = profiles.loc[rep_periods]
     centroid_profiles = centroids * profiles.stack('h_of_period').max()
diff --git a/input_processing/hourly_repperiods.py b/reeds/input_processing/hourly_repperiods.py
similarity index 70%
rename from input_processing/hourly_repperiods.py
rename to reeds/input_processing/hourly_repperiods.py
index 2c57bcc8d..d45142b8b 100644
--- a/input_processing/hourly_repperiods.py
+++ b/reeds/input_processing/hourly_repperiods.py
@@ -23,19 +23,19 @@
 ### ===========================================================================
 import argparse
 import json
-import numpy as np
 import os
 import sys
 import datetime
+import numpy as np
 import pandas as pd
 import scipy
-import sklearn.cluster
 import sklearn.neighbors
 import traceback
-import hourly_writetimeseries
-import hourly_plots
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
+from reeds.input_processing import hourly_writetimeseries
+from reeds.input_processing import hourly_plots
 ## Time the operation of this script
 tic = datetime.datetime.now()
 
@@ -49,32 +49,7 @@
 ### VRE techs considered for GSw_PRM_StressSeedMinRElevel and GSw_HourlyMinRElevel
 techs_min_vre = ['upv', 'wind-ons']
 
-#%% ===========================================================================
-### --- FUNCTIONS ---
-### ===========================================================================
-
-def szn2yearperiod(szn):
-    """
-    szn's are formatted as 'y{20xx}{d or w}{day of year or wek of year}'
-    where a 'wek' is a 5-day period (5*73 = 365)
-    """
-    year, period = szn.split('d') if 'd' in szn else szn.split('w')
-    return int(year.strip('y')), int(period)
-
-
-def szn2period(szn):
-    """
-    szn's are formatted as 'y{20xx}{d or w}{day of year or wek of year}'
-    where a 'wek' is a 5-day period (5*73 = 365)
-    """
-    year, period = szn.split('d') if 'd' in szn else szn.split('w')
-    return int(period)
-
-
-###############################
-#    -- Load Processing --    #
-###############################
-
+#%%### Functions
 def get_load(inputs_case, keep_modelyear=None, keep_weatheryears=[2012]):
     """
     """
@@ -94,143 +69,6 @@ def get_load(inputs_case, keep_modelyear=None, keep_weatheryears=[2012]):
     return load
 
 
-def optimize_period_weights(profiles_fitperiods, numclusters=100):
-    """
-    The optimization approach (minimizing sum of absolute errors) is described at
-    https://optimization.mccormick.northwestern.edu/index.php/Optimization_with_absolute_values
-    The general idea of optimizing period weights to reproduce regional variability is similar
-    to the method used in the EPRI US-REGEN model, described at
-    https://www.epri.com/research/products/000000003002016601
-    """
-    ### Imports
-    import pulp
-
-    ### Input processing
-    profiles_day = (
-        profiles_fitperiods.groupby(['property','region'], axis=1).mean())
-    profiles_mean = profiles_day.mean()
-    numdays = len(profiles_day)
-    days = profiles_day.index.values
-
-    ### Optimization: minimize sum of absolute errors
-    m = pulp.LpProblem('LinearDaySelection', pulp.LpMinimize)
-    ###### Variables
-    ### day weights
-    WEIGHT = pulp.LpVariable.dicts('WEIGHT', (d for d in days), lowBound=0, cat='Continuous')
-    ### errors
-    ERROR_POS = pulp.LpVariable.dicts(
-        'ERROR_POS', (c for c in profiles_day.columns), lowBound=0, cat='Continuous')
-    ERROR_NEG = pulp.LpVariable.dicts(
-        'ERROR_NEG', (c for c in profiles_day.columns), lowBound=0, cat='Continuous')
-    ###### Constraints
-    ### weights must sum to 1
-    m += pulp.lpSum([WEIGHT[d] for d in days]) == 1
-    ### definition of errors
-    for c in profiles_day.columns:
-        m += (
-            ### Full error for column (given by positive component minus negative component)...
-            ERROR_POS[c] - ERROR_NEG[c]
-            ### ...plus sum of values for weighted representative days...
-            + pulp.lpSum([WEIGHT[d] * profiles_day[c][d] for d in days])
-            ### ...equals the mean for that column
-            == profiles_mean[c])
-    ###### Objective: minimize the sum of absolute values of errors across all columns
-    m += pulp.lpSum([
-        ERROR_POS[c] + ERROR_NEG[c]
-        for c in profiles_day.columns
-    ])
-
-    ### Solve it
-    m.solve(solver=pulp.PULP_CBC_CMD(msg=True))
-
-    ### Collect weights, scaled by total number of days
-    weights = pd.Series({d:WEIGHT[d].varValue for d in days}) * numdays
-
-    ### Truncate based on numclusters, scale appropriately, and convert to integers
-    ### Keep the the 'numclusters' highest-weighted days
-    rweights = (weights.sort_values(ascending=False)[:numclusters])
-    ### Scale so that the weights sum to numdays (have to do if numclusters is small)
-    rweights *= numdays / rweights.sum()
-    ### Convert to integers
-    iweights = rweights.round(0).astype(int)
-    ### Scale all weights little by little until they sum to number of actual days
-    sumweights = iweights.sum()
-    diffweights = sumweights - numdays
-    increment = 0.00001 * (1 if diffweights < 0 else -1)
-    for i in range(1000000):
-        iweights = (rweights * (1 + increment*i)).round(0).astype(int)
-        if iweights.sum() == numdays:
-            break
-
-    iweights = iweights.replace(0,np.nan).dropna().astype(int)
-    ### Make sure it worked
-    if iweights.sum() != numdays:
-        raise ValueError(f'Sum of rounded weights = {iweights.sum()} != {numdays}')
-
-    return profiles_day, iweights, weights
-
-
-def assign_representative_days(profiles_day, rweights):
-    """
-    """
-    ### Imports
-    import pulp
-
-    ### Input processing
-    actualdays = profiles_day.index.values
-    repdays = list(rweights.index)
-
-    ### Optimization: minimize sum of absolute errors
-    m = pulp.LpProblem('RepDayAssignment', pulp.LpMinimize)
-    ###### Variables
-    ### Weighting of rep days (r) for each actual day (a).
-    ### Can only use whole days, so it's a binary variable.
-    WEIGHT = pulp.LpVariable.dicts(
-        'WEIGHT', ((a,r) for a in actualdays for r in repdays),
-        lowBound=0, upBound=1, cat=pulp.LpInteger)
-    ### Errors. These are defined for features (c) and for actual days (a).
-    ERROR_POS = pulp.LpVariable.dicts(
-        'ERROR_POS', ((a,c) for a in actualdays for c in profiles_day.columns),
-        lowBound=0, cat='Continuous')
-    ERROR_NEG = pulp.LpVariable.dicts(
-        'ERROR_NEG', ((a,c) for a in actualdays for c in profiles_day.columns),
-        lowBound=0, cat='Continuous')
-    ###### Constraints
-    ### Each actual day can only be assigned to one representative day
-    for a in actualdays:
-        m += pulp.lpSum([WEIGHT[a,r] for r in repdays]) == 1
-    ### Each representative day must be used a number of times equal to its weight
-    for r in repdays:
-        m += pulp.lpSum([WEIGHT[a,r] for a in actualdays]) == rweights[r]
-    ### Define the error variables
-    for a in actualdays:
-        for c in profiles_day.columns:
-            m += (
-                ### Full error for column on actual day (given by positive
-                ### component minus negative component)...
-                ERROR_POS[a,c] - ERROR_NEG[a,c]
-                ### ...plus value for its representative day (since WEIGHT is binary)...
-                + pulp.lpSum([WEIGHT[a,r] * profiles_day[c][r] for r in repdays])
-                ### ...equals the actual value for that column and day
-                == profiles_day[c][a])
-    ###### Objective: minimize the sum of absolute values of errors
-    m += pulp.lpSum([
-        ERROR_POS[a,c] + ERROR_NEG[a,c]
-        for a in actualdays for c in profiles_day.columns
-    ])
-
-    ### Solve it
-    m.solve(solver=pulp.PULP_CBC_CMD(msg=True))
-
-    ### Collect assignments
-    assignments = pd.Series(
-        {(a,r):WEIGHT[a,r].varValue for a in actualdays for r in repdays}).astype(int)
-    assignments.index = assignments.index.rename(['act','rep'])
-    a2r = assignments.replace(0,np.nan).dropna().reset_index(level='rep').rep
-
-    return a2r
-
-
 def identify_peak_containing_periods(df, hierarchy, level):
     """
     Identify the period containing the peak value.
@@ -273,11 +111,6 @@ def identify_min_periods(df, hierarchy, level, prefix=''):
     return forceperiods
 
 
-
-###########################
-#    -- Clustering --     #
-###########################
-
 def cluster_profiles(profiles_fitperiods, sw, forceperiods_yearperiod):
     """
     Cluster the load and (optionally) RE profiles to find representative days for dispatch in ReEDS.
@@ -300,47 +133,21 @@ def cluster_profiles(profiles_fitperiods, sw, forceperiods_yearperiod):
         sw['GSw_HourlyClusterAlgorithm'].startswith('hierarchical')
         or sw['GSw_HourlyClusterAlgorithm'].lower().startswith('kme')
     ):
-        if sw['GSw_HourlyClusterAlgorithm'].startswith('hierarchical'):
-            args = sw['GSw_HourlyClusterAlgorithm'].split('_')
-            if len(args) > 1:
-                metric = args[1]
-                linkage = args[2]
-            else:
-                metric = 'euclidean'
-                linkage = 'ward'
-            clusters = sklearn.cluster.AgglomerativeClustering(
-                n_clusters=int(sw['GSw_HourlyNumClusters']),
-                metric=metric, linkage=linkage,
-            )
-        elif sw['GSw_HourlyClusterAlgorithm'].lower().startswith('kmeans'):
-            clusters = sklearn.cluster.KMeans(
-                n_clusters=int(sw['GSw_HourlyNumClusters']),
-                random_state=0, n_init='auto', max_iter=1000,
-            )
-        elif sw['GSw_HourlyClusterAlgorithm'].lower().startswith('kmedoids'):
-            import sklearn_extra.cluster
-            args = sw['GSw_HourlyClusterAlgorithm'].split('_')
-            if len(args) > 1:
-                metric = args[1]
-                init = args[2]
-            else:
-                metric = 'euclidean'
-                init = 'heuristic'
-            clusters = sklearn_extra.cluster.KMedoids(
-                n_clusters=int(sw['GSw_HourlyNumClusters']),
-                metric=metric, init=init, method='pam',
-                max_iter=1000, random_state=0,
-            )
         ### Generate the fits
-        idx = clusters.fit_predict(profiles_fitperiods)
+        cluster_assignment = reeds.timeseries.get_clusters(
+            profiles_fitperiods,
+            GSw_HourlyClusterAlgorithm=sw.GSw_HourlyClusterAlgorithm,
+            GSw_HourlyNumClusters=int(sw.GSw_HourlyNumClusters),
+        )
         ### Get nearest period to each centroid
         centroids = pd.DataFrame(
-            sklearn.neighbors.NearestCentroid().fit(profiles_fitperiods, idx).centroids_,
+            sklearn.neighbors.NearestCentroid()
+            .fit(profiles_fitperiods, cluster_assignment).centroids_,
             columns=profiles_fitperiods.columns,
         )
         nearest_period = {
             i:
-            profiles_fitperiods.loc[:,idx==i,:].apply(
+            profiles_fitperiods.loc[:,cluster_assignment==i,:].apply(
                 lambda row: scipy.spatial.distance.euclidean(row, centroids.loc[i]),
                 axis=1
             ).nsmallest(1).index[0]
@@ -350,7 +157,7 @@ def cluster_profiles(profiles_fitperiods, sw, forceperiods_yearperiod):
         period_szn = pd.DataFrame({
             'period': profiles_fitperiods.index.values,
             'szn': [f"y{i[0]}{sw['GSw_HourlyType'][0]}{i[1]:>03}"
-                       for i in pd.Series(idx).map(nearest_period)]
+                       for i in pd.Series(cluster_assignment).map(nearest_period)]
         ### Add the force-include periods to the end of the list of seasons
         })
         period_szn = pd.concat([
@@ -363,16 +170,29 @@ def cluster_profiles(profiles_fitperiods, sw, forceperiods_yearperiod):
         ]).sort_values('period').set_index('period').szn
 
     elif sw['GSw_HourlyClusterAlgorithm'] in ['opt','optimized','optimize']:
+        profiles_period_mean = (
+            profiles_fitperiods.groupby(['property','region'], axis=1)
+            .mean()
+        )
         ### Optimize the weights of representative days
-        profiles_day, rweights, weights = optimize_period_weights(
-            profiles_fitperiods=profiles_fitperiods, numclusters=int(sw['GSw_HourlyNumClusters']))
+        iweights, weights = reeds.timeseries.optimize_period_weights(
+            profiles_period_mean=profiles_period_mean,
+            GSw_HourlyNumClusters=int(sw['GSw_HourlyNumClusters']),
+        )
         ### Optimize the assignment of actual days to representative days
-        a2r = assign_representative_days(profiles_day=profiles_day.round(4), rweights=rweights)
+        mapfunc = {
+            'milp': reeds.timeseries.match_act2rep_milp,
+            'bestfirst': reeds.timeseries.match_act2rep_bestfirst,
+        }[sw.get('GSw_HourlyClusterMapMethod', 'milp')]
+        a2r = mapfunc(
+            profiles_period_mean=profiles_period_mean.round(4),
+            iweights=iweights,
+        )
 
-        if len(rweights) < int(sw['GSw_HourlyNumClusters']):
+        if len(iweights) < int(sw['GSw_HourlyNumClusters']):
             print(
                 'Asked for {} representative periods but only needed {}'.format(
-                    sw['GSw_HourlyNumClusters'], len(rweights)))
+                    sw['GSw_HourlyNumClusters'], len(iweights)))
 
         period_szn = pd.concat([
             a2r.reset_index().rename(columns={'act':'period','rep':'szn'}),
@@ -387,75 +207,16 @@ def cluster_profiles(profiles_fitperiods, sw, forceperiods_yearperiod):
         period_szn = pd.read_csv(
             os.path.join(inputs_case,'period_szn_user.csv')
         ).set_index('actual_period').rep_period.rename('szn')
-        period_szn.index = period_szn.index.map(szn2yearperiod).values
+        period_szn.index = period_szn.index.map(reeds.timeseries.szn2yearperiod).values
         period_szn.index = period_szn.index.rename('period')
 
 
     ### Get the list of representative periods for convenience
-    rep_periods = sorted(period_szn.map(szn2yearperiod).unique())
+    rep_periods = sorted(period_szn.map(reeds.timeseries.szn2yearperiod).unique())
 
     return rep_periods, period_szn
 
 
-def make_timestamps(sw):
-    ### Get some useful constants
-    hoursperperiod = {'day':24, 'wek':120, 'year':24}
-    periodsperyear = {'day':365, 'wek':73, 'year':365}
-    weather_years = sw.resource_adequacy_years_list
-
-    ### Get map from yperiod, hour, and h_of_period to timestamp
-    timestamps = pd.DataFrame({
-        'year': np.ravel([[y]*8760 for y in weather_years]),
-        'h_of_year': np.ravel([list(range(1,8761)) * len(weather_years)]),
-        'h_of_period': np.ravel(
-            [f'{h+1:>03}' for h in range(hoursperperiod[sw['GSw_HourlyType']])]
-            * periodsperyear[sw['GSw_HourlyType']] * len(weather_years)),
-        'yperiod': np.ravel(
-            [p+1 for p in range(periodsperyear[sw['GSw_HourlyType']])
-             for h in range(hoursperperiod[sw['GSw_HourlyType']])]
-            * len(weather_years)),
-        'h_of_day': np.ravel(
-            [f'{h+1:>03}' for h in range(hoursperperiod['day'])]
-            * periodsperyear['day'] * len(weather_years)),
-        'yday': np.ravel(
-            [p+1 for p in range(periodsperyear['day'])
-             for h in range(hoursperperiod['day'])]
-            * len(weather_years)),
-        'h_of_wek': np.ravel(
-            [f'{h+1:>03}' for h in range(hoursperperiod['wek'])]
-            * periodsperyear['wek'] * len(weather_years)),
-        'ywek': np.ravel(
-            [p+1 for p in range(periodsperyear['wek'])
-             for h in range(hoursperperiod['wek'])]
-            * len(weather_years)),
-    })
-    timestamps['timestamp'] = (
-        'y' + timestamps.year.astype(str)
-        ## d for day and w for wek 
-        + ('w' if sw.GSw_HourlyType == 'wek' else 'd')
-        + timestamps.yperiod.astype(str).map('{:>03}'.format)
-        + 'h' + timestamps.h_of_period
-    )
-    timestamps['period'] = timestamps['timestamp'].map(lambda x: x.split('h')[0])
-    timestamps['day'] = (
-        'y' + timestamps.year.astype(str)
-        + 'd' + timestamps.yday.astype(str).map('{:>03}'.format)
-    )
-    timestamps['wek'] = (
-        'y' + timestamps.year.astype(str)
-        + 'w' + timestamps.ywek.astype(str).map('{:>03}'.format)
-    )
-    timestamps.index = np.ravel([
-        pd.date_range(
-            f'{y}-01-01', f'{y+1}-01-01',
-            freq='H', inclusive='left', tz='Etc/GMT+6',
-        )[:8760]
-        for y in weather_years
-    ])
-
-    return timestamps
-
-
 #%% ===========================================================================
 ### --- MAIN FUNCTION ---
 ### ===========================================================================
@@ -501,7 +262,7 @@ def main(
     agglevel_variables  = reeds.spatial.get_agglevel_variables(reeds_path, inputs_case)
 
     #%% Get map from yperiod, hour, and h_of_period to timestamp
-    timestamps = make_timestamps(sw)
+    timestamps = reeds.timeseries.make_timestamps(sw)
     timestamps_myr = timestamps.loc[timestamps.year.isin(sw['GSw_HourlyWeatherYears'])].copy()
 
     ### Get region hierarchy for use with GSw_HourlyClusterRegionLevel
@@ -790,13 +551,10 @@ def main(
 
 
     #%% Get some other convenience sets
-    timestamps_day = make_timestamps(sw=pd.Series({**sw, **{'GSw_HourlyType':'day'}}))
-    timestamps_wek = make_timestamps(sw=pd.Series({**sw, **{'GSw_HourlyType':'wek'}}))
+    timestamps_day = reeds.timeseries.make_timestamps(sw=pd.Series({**sw, **{'GSw_HourlyType':'day'}}))
+    timestamps_wek = reeds.timeseries.make_timestamps(sw=pd.Series({**sw, **{'GSw_HourlyType':'wek'}}))
     ## Include all possible seasons so dispatch mode can be rerun with any of them
-    quarters = pd.read_csv(
-        os.path.join(inputs_case, 'sets', 'quarter.csv'),
-        header=None,
-    ).squeeze(1).tolist()
+    quarters = reeds.io.read_input(inputs_case, 'quarter').squeeze(1).tolist()
     set_allszn = pd.Series(
         list(timestamps_day.period.unique())
         + list(timestamps_wek.period.unique())
@@ -816,6 +574,10 @@ def main(
         period_szn_write['season'].drop_duplicates() if sw['GSw_HourlyType'] == 'year'
         else period_szn_write['actual_period'])
 
+    nextszn = pd.Series(
+        index=set_actualszn, data=np.roll(set_actualszn.values, -1), name='actualszn',
+    ).rename_axis('*actualszn')
+
     stress_period_szn = (
         stressperiods_write.assign(rep_period=stressperiods_write.szn)
         [['rep_period','year','yperiod','szn']].rename(columns={'szn':'actual_period'})
@@ -870,11 +632,16 @@ def main(
         return period_szn_write
 
     #%% Write the sets over all possible periods (representative and stress)
-    set_allszn.to_csv(
-        os.path.join(inputs_case, 'set_allszn.csv'), header=False, index=False)
+    reeds.io.write_to_inputs_h5(
+        set_allszn.rename(), 'allszn', inputs_case, gamstype='set',
+        comment='all potentially modeled time periods (days/weks)',
+    )
+    reeds.io.write_to_inputs_h5(
+        set_allh.rename(), 'allh', inputs_case, gamstype='set',
+        comment='all potentially modeled time chunks (hour groupings)',
+    )
 
-    set_allh.to_csv(
-        os.path.join(inputs_case, 'set_allh.csv'), header=False, index=False)
+    nextszn.to_csv(os.path.join(inputs_case, 'nextszn.csv'))
 
     #%% Write the seed stress periods to use for the PRM constraint
     if 'user' in sw.GSw_PRM_StressModel:
@@ -923,18 +690,18 @@ def main(
     #%% Parse arguments
     parser = argparse.ArgumentParser(
         description='Create the necessary 8760 and capacity factor data for hourly resolution')
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
     inputs_case = args.inputs_case
 
     # #%% Settings for testing
-    # reeds_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    # reeds_path = reeds.io.reeds_path
     # inputs_case = os.path.join(
     #     reeds_path,'runs',
-    #     'v20260411_itlM0_USA_faster','inputs_case','')
+    #     'v20260525_repM0_USA_fast','inputs_case','')
     # interactive = True
 
     #%% Set up logger
diff --git a/input_processing/hourly_writetimeseries.py b/reeds/input_processing/hourly_writetimeseries.py
similarity index 99%
rename from input_processing/hourly_writetimeseries.py
rename to reeds/input_processing/hourly_writetimeseries.py
index 5fa326089..8ac35efbc 100644
--- a/input_processing/hourly_writetimeseries.py
+++ b/reeds/input_processing/hourly_writetimeseries.py
@@ -8,7 +8,8 @@
 import datetime
 import pandas as pd
 import numpy as np
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 ##% Time the operation of this script
 tic = datetime.datetime.now()
@@ -789,7 +790,7 @@ def main(sw, reeds_path, inputs_case, periodtype='rep', make_plots=1, logging=Tr
         np.ravel([[c]*GSw_HourlyChunkLength for c in outchunks_allyrs])
     ))
 
-    # %%### h_dt_szn for Augur
+    # %%### h_dt_szn for resource adequacy calculations
     if not len(hmap_myr) % 8760:
         ## Important: When modeling a single weather year, rep periods in the
         ## h_dt_szn table are just the single-year periods concatenated n times.
@@ -1330,7 +1331,7 @@ def main(sw, reeds_path, inputs_case, periodtype='rep', make_plots=1, logging=Tr
             False,
             False,
         ],
-        ## 8760 hour linkage set for Augur (h,szn,year,hour)
+        ## 8760 hour linkage set for resource adequacy (h,szn,year,hour)
         "h_dt_szn": [
             h_dt_szn[["h", "season", "ccseason", "year", "hour"]].assign(
                 h=h_dt_szn.h.map(chunkmap)
@@ -1574,8 +1575,12 @@ def main(sw, reeds_path, inputs_case, periodtype='rep', make_plots=1, logging=Tr
             ## Capacity factor and load
             hourly_plots.plot_maps(sw, inputs_case, reeds_path, figpath)
             ## Representative days
-            f, ax, _ = reeds.reedsplots.plot_repdays(os.path.dirname(os.path.abspath(inputs_case)))
-            plt.savefig(os.path.join(figpath, 'inputs_repdays.png'))
+            for year in sw.GSw_HourlyWeatherYears:
+                f, ax, _ = reeds.reedsplots.plot_repdays(
+                    case=os.path.dirname(os.path.abspath(inputs_case)),
+                    year=int(year),
+                )
+                plt.savefig(os.path.join(figpath, f'inputs_repdays-{year}.png'))
         except Exception:
             import traceback
             print(traceback.format_exc())
diff --git a/input_processing/hydcf.py b/reeds/input_processing/hydcf.py
similarity index 98%
rename from input_processing/hydcf.py
rename to reeds/input_processing/hydcf.py
index 0b22d6db7..c62e3b202 100644
--- a/input_processing/hydcf.py
+++ b/reeds/input_processing/hydcf.py
@@ -19,7 +19,8 @@
 import os
 import sys
 import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 def get_monthly_plant_generation(inputs_case: str) -> (
@@ -447,8 +448,8 @@ def main(reeds_path, inputs_case):
         description='Process hydro capacity factors',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
@@ -466,4 +467,4 @@ def main(reeds_path, inputs_case):
     reeds.log.toc(tic=tic, year=0, process='input_processing/hydcf.py',
         path=os.path.join(inputs_case,'..'))
     
-    print('Finished hydcf.py')
\ No newline at end of file
+    print('Finished hydcf.py')
diff --git a/input_processing/mcs_sampler.py b/reeds/input_processing/mcs_sampler.py
similarity index 57%
rename from input_processing/mcs_sampler.py
rename to reeds/input_processing/mcs_sampler.py
index 8dd3e5144..605b2bfc7 100644
--- a/input_processing/mcs_sampler.py
+++ b/reeds/input_processing/mcs_sampler.py
@@ -6,21 +6,23 @@
 #%% ===========================================================================
 ### --- IMPORTS ---
 ### ===========================================================================
-import os
-import sys
+import argparse
+import copy
+import datetime
 import numpy as np
+import os
 import pandas as pd
-import copy
-import argparse
+import scipy.stats
+import sys
 import yaml
-import datetime
 from typing import Tuple, List
 from collections import defaultdict
 
 # Local Imports
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
-from input_processing import copy_files
+from reeds.input_processing import copy_files
 
 
 #%% ===========================================================================
@@ -61,8 +63,7 @@ class MCSConstants:
 
     SITING_SWITCHES = ["GSw_SitingUPV", "GSw_SitingWindOfs", "GSw_SitingWindOns"]
 
-    ### --- Valid distributions
-    VALID_DISTRIBUTIONS = ["dirichlet", "discrete", "uniform_multiplier", "triangular_multiplier"]
+    ### --- Distribution categories (used outside validation, e.g. WeightCalculator)
     MULTIPLICATIVE_DISTRIBUTIONS = ["uniform_multiplier", "triangular_multiplier"]
 
 
@@ -159,7 +160,7 @@ def read_csv_h5_file(sw_runfiles_csv, aux_files, reeds_path, inputs_case) -> pd.
     df = read_exception_file(sw_runfiles_csv['sw_assignment'], file_name, file_path)
     if df is not None:
         return df
-
+    
     if file_name in region_files['filename'].values:
         # Regional file (works for both csv and h5)
         df = copy_files.subset_to_valid_regions(
@@ -174,7 +175,7 @@ def read_csv_h5_file(sw_runfiles_csv, aux_files, reeds_path, inputs_case) -> pd.
     elif file_name in nonregion_files['filename'].values:
         files_not_supported = ['scalars.csv']
         if file_path.endswith('.csv') and file_name not in files_not_supported:
-            #Read the csv file
+            # read the csv file
             df = pd.read_csv(file_path)
         else:
             #File not implemented yet
@@ -184,6 +185,13 @@ def read_csv_h5_file(sw_runfiles_csv, aux_files, reeds_path, inputs_case) -> pd.
     elif file_name in ['switches.csv']:
         df = pd.read_csv(os.path.join(inputs_case, file_name),
             header = None, index_col=0, dtype=str)
+    
+    elif file_name == 'load.h5':
+        # special handling for load file since it is processed downstream of copyfiles.py
+        sw_mc = sw.copy()
+        sw_mc.GSw_LoadProfiles = sw_runfiles_csv.sw_assignment
+        # read load.h5 file directly from the repo
+        df = reeds.io.get_load_hourly(GSw_LoadProfiles=sw_runfiles_csv.sw_assignment)
 
     else:
         error_message = (
@@ -226,6 +234,21 @@ def get_hierarchy_file(inputs_case: str, ReEDS_resolution: str) -> pd.DataFrame:
 
     return filtered_hierarchy
 
+def check_lhs_param_order(lower, upper):
+    """Ensure lower bounds are less than upper bounds, swapping where necessary.
+
+    Args:
+        lower (np.ndarray or float): Lower bound value(s).
+        upper (np.ndarray or float): Upper bound value(s).
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: Corrected (lower, upper) arrays.
+    """
+    # ensure that lower (loc) < upper (loc + scale)
+    lower_new = np.where(lower > upper, upper, lower)
+    upper_new = np.where(lower > upper, lower, upper)
+
+    return lower_new, upper_new
 
 #%% ===========================================================================
 ### --- FILE PATHS & DISTRIBUTION INSTRUCTIONS ---
@@ -236,7 +259,6 @@ def mcs_find_copy_paths(
     runfiles: pd.DataFrame,
     reeds_path: str,
     inputs_case: str,
-    run_ReEDS: bool = True
 ) -> Tuple[list, pd.DataFrame]:
     """
     Find the paths where the MCS samples should be copied to and the associated runfiles.csv rows.
@@ -247,7 +269,6 @@ def mcs_find_copy_paths(
         runfiles (pd.DataFrame): The runfiles.csv DataFrame.
         reeds_path (str): The path to the ReEDS directory.
         inputs_case (str): The path to the inputs case directory.
-        run_ReEDS (bool): Whether to run the ReEDS model or not.
 
     Returns:
         save_path_list: A list of destination paths for the MCS samples.
@@ -257,8 +278,13 @@ def mcs_find_copy_paths(
     rf_contains_sw = runfiles['filepath'].fillna('').str.contains('{' + sw_name + '}')
     if any(rf_contains_sw):
         runfile_instructions = runfiles[rf_contains_sw].reset_index(drop=True)
+    # load files are processed downstream by hourly_load.py and do not have a path 
+    # in runfiles so they need special treatment here
+    elif sw_name == 'GSw_LoadProfiles':
+        runfile_instructions = runfiles[runfiles.filename == 'load.h5'].copy()
+        runfile_instructions['filepath'] = 'inputs/profiles_demand/demand_{GSw_LoadProfiles}.h5'
     elif sw_name in MCSConstants.HARD_CODED_SWITCH_TO_FILE_READ:
-        # If the switch name is found in the hardcoded exceptions, fid the rows 
+        # If the switch name is found in the hardcoded exceptions, find the rows 
         # in runfiles.csv that contain all the files associated with the switch.
         runfile_instructions = runfiles[
             runfiles['filename'].isin(MCSConstants.HARD_CODED_SWITCH_TO_FILE_READ[sw_name])
@@ -266,6 +292,9 @@ def mcs_find_copy_paths(
     else:
         # If the switch name is not found in runfiles.csv, or in the hardcoded exceptions, 
         # assume it is only part of switches.csv
+        print(f'Path to file specified by {sw_name} was not found in runfiles;' 
+              ' treating this as a value in switches.csv'
+        )
         runfile_instructions = runfiles[runfiles['filename'] == 'switches.csv'].reset_index(drop=True)
 
     # Reorder rows: if any filename has "supply_curve", place those rows first.
@@ -281,14 +310,7 @@ def mcs_find_copy_paths(
     save_path_list = []
     for _, row in runfile_instructions.iterrows():
         file_name = row['filename']
-
-        if run_ReEDS:
-            dest_path = os.path.join(inputs_case, file_name)
-        else:
-            # Save samples at runs/Sample_ for later use. Useful for checking the samples.
-            dest_path = os.path.join(
-                reeds_path, 'runs', 'Sample_{sample_n}', file_name
-            )
+        dest_path = os.path.join(inputs_case, file_name)
         save_path_list.append(dest_path)
     
     # Supply curve files are used in other distributions, so they need to be first in the list.
@@ -305,101 +327,212 @@ def mcs_find_copy_paths(
 
     return save_path_list, runfile_instructions
 
-
 def general_mcs_dist_validation(reeds_path: str, mcs_dist_path: str, sw: pd.Series) -> None:
     """
     Validate the contents of mcs_distributions_{MCS_dist}.yaml used for Monte Carlo sampling.
+    Distribution validation rules are defined in mcs_distribution_rules.yaml.  
+    All violations are collected and raised together in a single ``ValueError`` at the end.
 
     Args:
         reeds_path (str): Path to the ReEDS directory.
         mcs_dist_path (str): Path to the input .yaml file.
-        sw (pd.Series): Case switches 
+        sw (pd.Series): Case switches.
 
     Raises:
         ValueError: If any structure or content in the .yaml file is invalid.
     """
     print('Validating the input distribution information for Monte Carlo sampling...')
 
+    # load distribution rules
+    rules_path = os.path.join(reeds_path, 'inputs', 'userinput', 'mcs_distribution_rules.yaml')
+    with open(rules_path, 'r') as f:
+        data = yaml.safe_load(f)
+        dist_rules = pd.DataFrame(data).T
+    
+    # load distribution settings file 
     with open(mcs_dist_path, 'r') as f:
         data = yaml.safe_load(f)
         df_input_dist = pd.DataFrame(data)
 
+    # get relevant switch settings
     mcs_dist_groups = sw['MCS_dist_groups'].split('.')
+    sampling_method = 'lhs' if int(sw.MCS_lhs) else 'random'
 
-    # Read cases.csv to get the list of valid switches.
-    cases_default = pd.read_csv(os.path.join(reeds_path, 'cases.csv'))
-    valid_switches = cases_default.iloc[:, 0].values
+    ## Verify that all dist group names are unique
+    if df_input_dist['name'].nunique() != len(df_input_dist):
+        raise ValueError(
+            'The distribution names in mcs_distributions.yaml are not unique.'
+            'Please correct the file.'
+        )
+
+    ## Ensure all MCS_dist_groups options are present, and if so subset
+    missing = set(mcs_dist_groups) - set(df_input_dist['name'].unique())
+    if missing:
+        raise ValueError(
+            f"The following MCS_dist_groups switch options are missing in mcs_distributions.yaml {missing}"
+        )        
+    # subset to groups selected in mcs_dist_groups
+    df_input_dist = df_input_dist[df_input_dist['name'].isin(mcs_dist_groups)].reset_index(drop=True)
 
-    # Validate mandatory keys in df_input_dist
+    ## Validate mandatory keys in df_input_dist
     required_keys = {'name', 'assignments_list', 'dist', 'dist_params', 'weight_r'}
     missing_keys = required_keys - set(df_input_dist.columns)
     if missing_keys:
         raise ValueError(f"Missing mandatory keys in mcs_distributions.yaml object: {missing_keys}")
 
-    # Make sure that dist_params is a list
-    if not all(isinstance(df_input_dist.at[i, 'dist_params'], list) for i in range(len(df_input_dist))):
-        raise ValueError('The dist_params field must be a list')
+    ## Make sure that dist_params is a list if required
+    bad_params = [
+        df_input_dist.at[i, 'name']
+        for i in range(len(df_input_dist))
+        if 'dist_params' in dist_rules.loc[df_input_dist.at[i, 'dist']]['required_keys']
+            and not isinstance(df_input_dist.at[i, 'dist_params'], list)
+    ]
+    if bad_params:
+        raise ValueError(
+            f"The dist_params field must be a list for: {bad_params}"
+        )
 
-    # Verify that all dist group names in mcs_distributions.yaml are unique.
-    if df_input_dist['name'].nunique() != len(df_input_dist):
-        raise ValueError('The distribution names in mcs_distributions.yaml are not unique. Please correct the file')
+    ## Check for missing data in required columns
+    df_supplied = ~df_input_dist.isnull()
+    df_required = df_input_dist['dist'].apply(
+        lambda d: pd.Series({
+            col: col in dist_rules.loc[d, 'required_keys']
+            for col in df_input_dist.columns
+        })
+    )
+    missing_data = ~(df_supplied == df_required)
+    if missing_data.any().any():
+        raise ValueError(
+            f"The following dist names have missing data: "
+            f"{df_input_dist.loc[missing_data.sum(axis=1) > 0, 'name'].values}. "
+            "Make sure you have all mandatory fields in the input distribution file."
+        )
+    
+    ## Extract derived columns for batch checks
+    df_input_dist['switch_names'] = df_input_dist['assignments_list'].apply(
+        lambda al: [next(iter(d)) for d in al]
+    )
+    df_input_dist['sw_assignments'] = df_input_dist['assignments_list'].apply(
+        lambda al: [next(iter(d.values())) for d in al]
+    )
+    df_input_dist['num_sw_assignments'] = df_input_dist['sw_assignments'].apply(
+        lambda sa: [len(c) for c in sa]
+    )
 
-    # Ensure that we are not missing data for each row of the input distribution file.
-    missing_data = df_input_dist.isnull().sum(axis=1)
-    if missing_data.any():
-        raise ValueError(f"The following dist names have missing data: {df_input_dist.loc[missing_data > 0, 'name'].values}. "
-                        "Make sure you have all mandatory fields in the input distribution file")
+    ## check that all distributions are valid
+    valid_distributions = list(dist_rules.index)
+    invalid_dists = set(df_input_dist['dist']) - set(valid_distributions)
+    if invalid_dists:
+        raise ValueError(
+            f"The following distributions are not supported: {invalid_dists}. "
+            f"Please choose from: {valid_distributions}"
+        )
 
-    # Ignore all cases not in mcs_dist_groups
-    df_input_dist = df_input_dist[df_input_dist['name'].isin(mcs_dist_groups)].reset_index(drop=True)
-    
-    # Ensure all MCS_dist_groups options are present in the input distribution names.
-    missing = set(mcs_dist_groups) - set(df_input_dist['name'].unique())
-    if missing:
-        raise ValueError(f"The following MCS_dist_groups switch options are missing in mcs_distributions.yaml {missing}")
+    ## check that all switches are valid  
+    cases_default = pd.read_csv(os.path.join(reeds_path, 'cases.csv'))
+    valid_switches = set(cases_default.iloc[:, 0].values)
+    all_switch_names = {
+        sw_name
+        for switch_list in df_input_dist['switch_names']
+        for sw_name in switch_list
+    }
+    invalid_switches = all_switch_names - valid_switches
+    if invalid_switches:
+        raise ValueError(
+            f"The following switches are not valid (check cases.csv): {invalid_switches}"
+        )
 
-    for i, sample_group in df_input_dist.iterrows():
-        distribution = sample_group['dist']
+    ## sampling using siting switches is currently disabled
+    siting_set = set(MCSConstants.SITING_SWITCHES)
+    used_siting = all_switch_names & siting_set
+    if used_siting:
+        raise ValueError(
+            f"Sampling using siting switches {MCSConstants.SITING_SWITCHES} is "
+            "currently disabled. For details see "
+            "https://github.com/ReEDS-Model/ReEDS/issues/41."
+        )
 
-        switch_names = [next(iter(s)) for s in sample_group["assignments_list"]]
-        sw_assignments = [next(iter(s.values())) for s in sample_group["assignments_list"]]
+    ## siting switches can only use specific distributions
+    for _, row in df_input_dist.iterrows():
+        if set(row['switch_names']) & siting_set:
+            if row['dist'] not in ['dirichlet', 'discrete']:
+                raise ValueError(
+                    f"[{row['name']}] Siting switches can only be sampled "
+                    "using a dirichlet or discrete distribution."
+                )
 
-        for d in sample_group["assignments_list"]:
-            if not (isinstance(d, dict) and len(d) == 1):
-                raise ValueError("Each item in assignments_list must be a single-key dictionary")
+    ## check that all distributions are compatible with selected sampling method
+    unsupported_sampling = dist_rules.loc[
+        df_input_dist.dist.unique(), 'sampling_methods'].apply(lambda x: sampling_method not in x)
+    if unsupported_sampling.any():
+        unsupported_dists = list(unsupported_sampling[unsupported_sampling].index)
+        unsupported_sampling_entries = list(df_input_dist.loc[df_input_dist.dist.isin(unsupported_dists), 'name'])
+        raise ValueError(
+            f"{sampling_method} not support with the following distributions: "
+            f"{unsupported_dists}. Adjust the distribution choices for {unsupported_sampling_entries} "
+            "or change the sampling method set by 'MCS_lhs'."
+        )
 
+    ## regional sampling currently only supported with random sampling method
+    regional_rows = df_input_dist.loc[df_input_dist.weight_r != 'country']
+    if sampling_method == 'lhs' and len(regional_rows) > 0:
+        raise ValueError(
+            "Latin hypercube sampling not supported for regional-level sampling. "
+            f"Adjust the sampling choice for {df_input_dist['name'].to_list()} or set 'MCS_lhs=0'."
+        )
+
+    ## validate assignments_list structure (each item: single-key dict -> list)
+    for _, row in df_input_dist.iterrows():
+        for d in row['assignments_list']:
+            if not (isinstance(d, dict) and len(d) == 1):
+                raise ValueError(
+                    f"[{row['name']}] Each item in assignments_list must be a "
+                    "single-key dictionary."
+                )
             val = next(iter(d.values()))
             if not isinstance(val, list):
-                raise ValueError("The value in each dictionary must be a list")
-
-        if distribution not in ["dirichlet", "discrete"] and any(
-            switch in MCSConstants.SITING_SWITCHES for switch in switch_names
-        ):
-            raise ValueError(
-                "The siting related switches can only be sampled "
-                "using a dirichlet or discrete distribution"
+                raise ValueError(
+                    f"[{row['name']}] The value in each assignments_list "
+                    "dictionary must be a list."
                 )
 
-        if distribution not in MCSConstants.VALID_DISTRIBUTIONS:
-            raise ValueError(
-                f"The distribution {distribution} is not supported."
-                f"Please choose one of the following: {MCSConstants.VALID_DISTRIBUTIONS}")
+    ## check switch assignment and number of distribution parameters
+    for i, sample_group in df_input_dist.iterrows():
+        assignment_rule = dist_rules.loc[sample_group['dist'],'num_assignments']
+        n_params = len(sample_group['dist_params']) if isinstance(sample_group['dist_params'], list) else None
+        n_sw_assignments = [len(c) for c in sample_group['sw_assignments']]
 
-        if distribution in MCSConstants.MULTIPLICATIVE_DISTRIBUTIONS:
-            num_files = np.max([len(c) for c in sw_assignments])
-            if num_files > 1:
+        if assignment_rule == 'match_dist_params':
+            # rules for distributions that match switch and dist_params
+            if len(set(n_sw_assignments)) != 1 or n_sw_assignments[0] != n_params:
                 raise ValueError(
-                    f"The distribution {distribution} can only have a single reference file/value per switch."
+                        f"{sample_group['dist']} for {sample_group['name']} requires the same "
+                        f"number of switch assignments and distribution parameters "
                 )
-
-        # Iterate over each switch in the instruction.
-        for sw_name in switch_names:
-            # Check if the switch is valid.
-            if sw_name not in valid_switches:
-                raise ValueError(f'The switch {sw_name} is not a valid switch. Please check cases.csv')
-
-
-def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = True) -> Tuple[pd.DataFrame, dict]:
+        else:
+            # multiplicative distribution rules
+            if dist_rules.loc[sample_group['dist'],'multiplicative']:
+                if n_params != assignment_rule:
+                    raise ValueError(
+                        f"{sample_group['dist']} for {sample_group['name']} requires "
+                        f"{assignment_rule} entries for 'dist_params'."
+                    )
+                num_files = np.max(n_sw_assignments) 
+                if num_files > 1:
+                    raise ValueError(
+                        f"{sample_group['dist']} for {sample_group['name']} " 
+                        "can only have a single reference file/value per switch."
+                    )
+            else: 
+                # other distributions
+                if len(set(n_sw_assignments)) != 1 or n_sw_assignments[0] != assignment_rule:
+                    raise ValueError(
+                        f"{sample_group['dist']} for {sample_group['name']} requires "
+                        f"{assignment_rule} entries for each switch assignment."
+                    )
+    
+            
+def get_dist_instructions(reeds_path: str, inputs_case: str) -> Tuple[pd.DataFrame, dict]:
     """
     Obtain the instructions to sample the distributions for each switch 
     and organize information to facilitate the Monte Carlo sampling process.
@@ -407,7 +540,6 @@ def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = T
     Args:
         reeds_path (str): The path to the ReEDS directory.
         inputs_case (str): The path to the inputs case directory.
-        run_ReEDS (bool): Whether to run the ReEDS model or not.
 
     Returns:
         df_input_dist_ex: A DataFrame with the distribution instructions for each switch.
@@ -424,10 +556,6 @@ def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = T
     sw = reeds.io.get_switches(inputs_case)
     mcs_dist_groups = sw['MCS_dist_groups'].split('.')
 
-    if not run_ReEDS:
-        # Since you did not run using runbatch.py - check inputs here
-        general_mcs_dist_validation(reeds_path, mcs_dist_path, sw)
-
     # Ignore all cases not in mcs_dist_groups
     df_input_dist = df_input_dist[df_input_dist['name'].isin(mcs_dist_groups)].reset_index(drop=True)
 
@@ -461,7 +589,7 @@ def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = T
             sw_name, sw_assignments = next(iter(assignments_list.items()))
 
             filepaths, runfiles_csv = mcs_find_copy_paths(
-                sw_name, sw_assignments, runfiles, reeds_path, inputs_case, run_ReEDS=run_ReEDS
+                sw_name, sw_assignments, runfiles, reeds_path, inputs_case
             )
 
             # handle cases where a single switch assignment is associated with multiple files and cases
@@ -481,7 +609,7 @@ def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = T
 
     # Obtain the data used by copy_files.py to filter regions and create tailored dataframes.
     regions_and_agglevel = copy_files.get_regions_and_agglevel(
-        reeds_path, inputs_case, save_regions_and_agglevel=False)
+        reeds_path, inputs_case, save_regions_and_agglevel=False, overwrite=True)
 
     source_deflator_map = copy_files.get_source_deflator_map(reeds_path)
 
@@ -504,36 +632,35 @@ def get_dist_instructions(reeds_path: str, inputs_case: str, run_ReEDS: bool = T
 #%% ===========================================================================
 ### --- WEIGHT CALCULATION ---
 ### ===========================================================================
-def get_region_weights(distribution: str, dist_params: list, n_samples: int = 1) -> np.ndarray:
+def get_region_weights(distribution: str, dist_params: list) -> np.ndarray:
     """
     Generate weights for a single region based on the assigned distribution.
 
     Args:
         distribution (str): The distribution to use for sampling.
         dist_params (list): The parameters for the distribution.
-        n_samples (int): The number of samples to generate.
 
     Returns:
         np.ndarray: The weights for the region-based sample ([n_samples, n_ref_files|values]).
     """
-    if distribution == "dirichlet":
-        r_weights = np.random.dirichlet(dist_params, n_samples)
+    # since we're only sample for a single ReEDS run, we only need to sample once
+    n_samples_weight = 1
 
+    if distribution == "dirichlet":
+        r_weights = np.random.dirichlet(dist_params, n_samples_weight)
     elif distribution == "discrete":
         prob = np.array(dist_params) / np.sum(dist_params)
-        sampled_indices = np.random.choice(len(dist_params), n_samples, p=prob)
-        r_weights = np.zeros((n_samples, len(dist_params)), dtype=int)
-        r_weights[np.arange(n_samples), sampled_indices] = 1
-
+        sampled_index = np.random.choice(len(dist_params), n_samples_weight, p=prob)[0]
+        r_weights = np.zeros(len(dist_params), dtype=int)
+        r_weights[sampled_index] = 1
     elif distribution == "uniform_multiplier":
-        r_weights = np.random.uniform(dist_params[0], dist_params[1], n_samples)
-
+        r_weights = np.random.uniform(dist_params[0], dist_params[1], n_samples_weight)
     elif distribution == "triangular_multiplier":
-        r_weights = np.random.triangular(dist_params[0], dist_params[1], dist_params[2], n_samples)
+        r_weights = np.random.triangular(dist_params[0], dist_params[1], dist_params[2], n_samples_weight)
 
-    # Make sure r_weights is a 2D array
-    if r_weights.ndim == 1:
-        r_weights = r_weights[:, np.newaxis]
+    # convert to 1D array 
+    if r_weights.ndim > 1:
+        r_weights = r_weights[0,:]
 
     return r_weights
 
@@ -575,11 +702,16 @@ def get_all_region_weights(
         for ba in bas:
             all_r_weights[ba] = r_weights
 
-        # Only save the cendiv weights if the sample_hierarchy_lvl is 'country' or 'cendiv'
+        # Natural gas fuel prices are at the census division level, so save the cendiv weights 
+        # if the sample_hierarchy_lvl is 'country' or 'cendiv'
         if sample_hierarchy_lvl in ["country", "cendiv"]:
             cendivs = hierarchy_file.loc[hierarchy_file[sample_hierarchy_lvl] == region, "cendiv"].unique()
             for cendiv in cendivs:
                 all_r_weights[cendiv] = r_weights
+        # Load data is at the state level, so save the state weights if
+        # the sample_hierarchy_lvl is 'st'
+        elif sample_hierarchy_lvl == "st":
+            all_r_weights[region] = r_weights
 
     return all_r_weights
 
@@ -593,13 +725,11 @@ class WeightCalculator:
             This contains the distribution, dist_params, switch_names, sw_assignments, file_names, save_paths, ...
             It is a row of the df_input_dist_ex DataFrame.
         aux_files (dict): Dictionary with auxiliary information - from get_dist_instructions (.)
-        n_samples (int): The number of samples
     """
     def __init__(
         self,
         sample_group: pd.Series,
         aux_files: dict,
-        n_samples: int = 1,
     ):
         self.sample_group = sample_group
         self.aux_files = aux_files
@@ -607,7 +737,6 @@ def __init__(
         self.dist_params = sample_group['dist_params']
         self.sample_hierarchy_lvl = sample_group['weight_r'].lower()
         self.hierarchy_file = aux_files['hierarchy_file']
-        self.n_samples = n_samples
 
         # Get all general region weights
         self.r_weights = get_all_region_weights(
@@ -626,7 +755,7 @@ def __init__(
         # Those are computed during the the supply curve file sampling 
         self.recf_weights_map = {}
 
-        # Flag to validade that recf_weights_map was normalized
+        # Flag to validate that recf_weights_map was normalized
         self.flag_recf_normalization = defaultdict(lambda: False)
 
     def _validate_inputs(self, dist_files: list, sw_name: str, file_name: str) -> None:
@@ -703,7 +832,8 @@ def get_df_weights(
             file_name (str): Name of the file we are getting the weights for.
 
         Returns:
-            dict: Dictionary with the weights for each reference file and sample.
+            Dict[int, pd.DataFrame | float]: Dictionary mapping reference file index to
+                the weight values for that file.
         """
 
         self._validate_inputs(dist_files, sw_name, file_name)
@@ -736,56 +866,67 @@ def _get_weights_general(
             file_name (str): Name of the file we are getting the weights for.
 
         Returns:
-            dict: Dictionary with the weights for each reference file and sample.
+            Dict[int, pd.DataFrame | float]: Dictionary mapping reference file index to
+                the weight values for that file.
         """
-        # Number of reference files/values. Sicne all sw_assignments
+        # Number of reference files/values. Since all sw_assignments
         # have the same number of files, we can use the first one.
         n_files = len(self.sample_group["sw_assignments"][0]) 
 
         # Identify relevant columns that exist in the hierarchy
-        #Examples: p1, p2, New_England, ...(covers NG and LOAD)
+        #Examples: p1, p2, New_England, ME ...(covers NG and LOAD)
         columns_in_hierarchy = [col for col in dist_files[0].keys() if col in set(self.r_weights.keys())]
 
+        # load files are subsetted to the relevant regions later in hourly_load.py, 
+        # so here we add placeholder weights for any states not being modeled
+        if file_name == 'load.h5':
+            columns_other_states = [col for col in dist_files[0].keys() if col not in columns_in_hierarchy]
+            if len(columns_other_states) > 0:
+                # get the first entry of the regional weights as a placeholder
+                first_region = list(self.r_weights)[0]
+                generic_weight_matrix = self.r_weights[first_region]
+                generic_weights = dict.fromkeys(columns_other_states, generic_weight_matrix)
+                self.r_weights.update(generic_weights)
+                columns_in_hierarchy = columns_in_hierarchy + columns_other_states
+
+
         # We have as many unique weights as len(unique_sample_levels)
         unique_sample_levels = self.hierarchy_file[self.sample_hierarchy_lvl].unique()
         single_r_weight = len(unique_sample_levels) == 1
 
         # Dictionary to store computed weights for the modifiable columns
-        # (sample, file) -> pd.DataFrame
+        # file -> pd.DataFrame
         dict_df_weights = {}
 
         # Handle the simple case where there is only one weight for all regions 
         # (or no regions). 
         if single_r_weight:
             # Get the first region key
-            first_region = next(iter(self.r_weights)) 
+            first_region = list(self.r_weights)[0]
             weight_matrix = self.r_weights[first_region]
 
             if file_name == "switches.csv":
-                for s in range(self.n_samples):
-                    for f in range(n_files):
-                        dict_df_weights[(s, f)] = weight_matrix[s, f]
+                for f in range(n_files):
+                    dict_df_weights[f] = weight_matrix[f]
             else:
-                for s in range(self.n_samples):
-                    for f in range(n_files):
-                        dict_df_weights[(s, f)] = pd.DataFrame(
-                                data=weight_matrix[s, f],
-                                columns=modifiable_columns,
-                                index=dist_files[0].index,
-                        )
+                for f in range(n_files):
+                    dict_df_weights[f] = pd.DataFrame(
+                            data=weight_matrix[f],
+                            columns=modifiable_columns,
+                            index=dist_files[0].index,
+                    )
 
         # Cases that have regional columns from columns_in_hierarchy
         # and the weights are not the same for all regions
         elif not single_r_weight and len(columns_in_hierarchy) and file_name != "switches.csv" :
-            for s in range(self.n_samples):
-                for f in range(n_files):
+            for f in range(n_files):
 
-                    w_df_tmp = pd.DataFrame(
-                        {col: self.r_weights[col][s, f] for col in columns_in_hierarchy},  
-                        index=dist_files[0].index 
-                    )
+                w_df_tmp = pd.DataFrame(
+                    {col: self.r_weights[col][f] for col in columns_in_hierarchy},  
+                    index=dist_files[0].index 
+                )
 
-                    dict_df_weights[(s, f)] = w_df_tmp
+                dict_df_weights[f] = w_df_tmp
 
         return dict_df_weights
 
@@ -805,14 +946,15 @@ def _get_weights_supply_curve(
             sw_name (str): Name of the switch we are getting the weights for.
 
         Returns:
-            dict: Dictionary with the weights for each reference file and sample.
+            Dict[int, pd.DataFrame]: Dictionary mapping reference file index to
+                the weight DataFrame for that file.
         """
         # Dictionary to store computed weights for the modifiable columns
-        # (sample, file) -> pd.DataFrame
+        # file index -> pd.DataFrame
         dict_df_weights = {}
 
         # Store weights to use later in the recf files (CF files)
-        self.recf_weights_map [sw_name] = {}
+        self.recf_weights_map[sw_name] = {}
 
         # Create a new column with the class|region combination (like in the CF file)
         dist_files_copy = [copy.deepcopy(df) for df in dist_files] 
@@ -822,43 +964,42 @@ def _get_weights_supply_curve(
                 df["class"].astype(int).astype(str) + "|" + df["region"].astype(str)
             )
 
-        for s in range(self.n_samples):
-            for f, df in enumerate(dist_files_copy):
-                # Initial skeleton of the weights DataFrame
-                w_df_tmp = df[["region", "sc_point_gid", "old c|r"]]
+        for f, df in enumerate(dist_files_copy):
+            # Initial skeleton of the weights DataFrame
+            w_df_tmp = df[["region", "sc_point_gid", "old c|r"]]
 
-                # Create a mapping from each unique region to its corresponding weight
-                region_to_weight = {
-                    r: self.r_weights[r][s, f]
-                    for r in w_df_tmp["region"].unique()
-                }
+            # Create a mapping from each unique region to its corresponding weight
+            region_to_weight = {
+                r: self.r_weights[r][f]
+                for r in w_df_tmp["region"].unique()
+            }
 
-                # Compute the region weights for each row
-                region_weights = w_df_tmp["region"].map(region_to_weight).values
+            # Compute the region weights for each row
+            region_weights = w_df_tmp["region"].map(region_to_weight).values
 
-                # Build a new DataFrame for the modifiable columns using a dict comprehension.
-                # For "capacity", we assign the raw region weight; for others, multiply by capacity.
-                modifiable_df = pd.DataFrame({
-                    col: (region_weights if col == "capacity" else region_weights * df["capacity"])
-                    for col in modifiable_columns
-                }, index=w_df_tmp.index)
+            # Build a new DataFrame for the modifiable columns using a dict comprehension.
+            # For "capacity", we assign the raw region weight; for others, multiply by capacity.
+            modifiable_df = pd.DataFrame({
+                col: (region_weights if col == "capacity" else region_weights * df["capacity"])
+                for col in modifiable_columns
+            }, index=w_df_tmp.index)
 
-                # Join the modifiable columns back into the original DataFrame
-                w_df_tmp = w_df_tmp.join(modifiable_df)
+            # Join the modifiable columns back into the original DataFrame
+            w_df_tmp = w_df_tmp.join(modifiable_df)
 
-                # Save the intermediate weights for the recf files (These are weights multiplied by capacity)
-                self.recf_weights_map [sw_name][(s, f)] = w_df_tmp[["old c|r","class"]].rename(columns={"class": "weight"})
+            # Save the intermediate weights for the recf files (These are weights multiplied by capacity)
+            self.recf_weights_map[sw_name][f] = w_df_tmp[["old c|r","class"]].rename(columns={"class": "weight"})
 
-                # Store in dictionary
-                dict_df_weights[(s, f)] = w_df_tmp.drop(columns=["old c|r"])
+            # Store in dictionary
+            dict_df_weights[f] = w_df_tmp.drop(columns=["old c|r"])
 
             # Normalize the weights to sum to 1
             # Divide the weights by the sum of the weights across all files
-            sum_weights = sum(dict_df_weights[(s, f)][modifiable_columns] for f in range(len(dist_files)))
+            sum_weights = sum(dict_df_weights[f][modifiable_columns] for f in range(len(dist_files)))
             sum_weights[sum_weights == 0] = 1
 
             for f in range(len(dist_files)):
-                dict_df_weights[(s, f)][modifiable_columns] /= sum_weights
+                dict_df_weights[f][modifiable_columns] /= sum_weights
                 # recf_weights_map is not normalized here because it will be normalized later
                 # values need to be aggregated according to the new c|r column from supply curves
 
@@ -872,22 +1013,22 @@ def _get_weights_exog_prescribed(self, dist_files: list) -> dict:
             dist_files (list of pd.DataFrame): List of reference dataframes (ajusted to have the same # of rows)
 
         Returns:
-            dict: Dictionary with the weights for each reference file and sample.
+            Dict[int, pd.DataFrame]: Dictionary mapping reference file index to
+                the weight DataFrame for that file.
         """
         dict_df_weights = {}
-        for s in range(self.n_samples):
-            for f, df in enumerate(dist_files):
-
-                region_to_weight = {
-                    r: self.r_weights[r][s, f]
-                    for r in df["region"].unique()
-                }
-
-                dict_df_weights[(s, f)] = pd.DataFrame(
-                    data=df["region"].map(region_to_weight).values,
-                    columns=["capacity"],
-                    index=df.index,
-                )
+        for f, df in enumerate(dist_files):
+
+            region_to_weight = {
+                r: self.r_weights[r][f]
+                for r in df["region"].unique()
+            }
+
+            dict_df_weights[f] = pd.DataFrame(
+                data=df["region"].map(region_to_weight).values,
+                columns=["capacity"],
+                index=df.index,
+            )
 
         return dict_df_weights
 
@@ -916,58 +1057,57 @@ def _get_weights_recf(self, sw_name: str) -> dict:
 
         return self.recf_weights_map[sw_name]
 
-    def normalize_recf_weights_map(self, samples_sw: list, sw_name: str) -> None:
+    def normalize_recf_weights_map(self, samples_sw: pd.DataFrame, sw_name: str) -> None:
         """
         The recf map is responsible for informing how the old class/region data files
         need to be put together (weights) to form the new class/region data.
         After creating the new supply curve sample, we normalize the weights to sum to 1.
 
         Args:
-            samples_sw (list of pd.DataFrame): List of samples for the supply curve files.
+            samples_sw (pd.DataFrame): The sampled supply curve DataFrame.
             sw_name (str): Name of the switch being sampled.
 
         Updates:
             self.recf_weights_map (dict): Dictionary with the normalized weights for the recf files.
-                Each element of this dictionary is a pd.DataFrame (for the sample s and reference file f)
+                Each element of this dictionary is a pd.DataFrame (for the reference file f)
                 with the normalized weights, indexed by new and old class|region (c|r).
         """
         n_files = len({key[1] for key in self.recf_weights_map[sw_name].keys()})
 
-        for s in range(self.n_samples):
-            # The normalization can change depending on the sample #
-            for f in range(n_files):
-                # Add a new column with the new class|region combination
-                self.recf_weights_map[sw_name][(s, f)]["new c|r"] = (
-                    samples_sw[s]["class"].astype(str) + "|" +
-                    samples_sw[s]["region"].astype(str)
-                )
+        # The normalization can change depending on the sample #
+        for f in range(n_files):
+            # Add a new column with the new class|region combination
+            self.recf_weights_map[sw_name][f]["new c|r"] = (
+                samples_sw["class"].astype(str) + "|" +
+                samples_sw["region"].astype(str)
+            )
 
-                # Sum the weights for each new class|region combination
-                self.recf_weights_map[sw_name][(s, f)] = self.recf_weights_map[sw_name][(s, f)].groupby(
-                    ["new c|r","old c|r"], as_index=False).sum()
+            # Sum the weights for each new class|region combination
+            self.recf_weights_map[sw_name][f] = self.recf_weights_map[sw_name][f].groupby(
+                ["new c|r","old c|r"], as_index=False).sum()
 
-                # Remove cases with 0 weight (e.g  old c|r had no capacity -> class 0)
-                self.recf_weights_map[sw_name][(s, f)] = self.recf_weights_map[sw_name][(s, f)][
-                    self.recf_weights_map[sw_name][(s, f)]["weight"] > 0
-                ]
+            # Remove cases with 0 weight (e.g  old c|r had no capacity -> class 0)
+            self.recf_weights_map[sw_name][f] = self.recf_weights_map[sw_name][f][
+                self.recf_weights_map[sw_name][f]["weight"] > 0
+            ]
 
-            # Go over all files and obtain the total sum of weights for each new class|region
-            sum_weights_recf_map = (
-                pd.concat(
-                    [self.recf_weights_map[sw_name][(s, f)] for f in range(n_files)]
-                )
-                .groupby("new c|r")["weight"]
-                .sum()
-                .to_dict()
+        # Go over all files and obtain the total sum of weights for each new class|region
+        sum_weights_recf_map = (
+            pd.concat(
+                [self.recf_weights_map[sw_name][f] for f in range(n_files)]
             )
+            .groupby("new c|r")["weight"]
+            .sum()
+            .to_dict()
+        )
 
-            for f in range(n_files):
-                # Get current DataFrame
-                df = self.recf_weights_map[sw_name][(s, f)]
-                # Perform division using "new c|r" as the reference
-                df["weight"] = df["weight"] / df["new c|r"].map(sum_weights_recf_map)
-                # Assign back to original structure
-                self.recf_weights_map[sw_name][(s, f)] = df.set_index(["new c|r", "old c|r"])   
+        for f in range(n_files):
+            # Get current DataFrame
+            df = self.recf_weights_map[sw_name][f]
+            # Perform division using "new c|r" as the reference
+            df["weight"] = df["weight"] / df["new c|r"].map(sum_weights_recf_map)
+            # Assign back to original structure
+            self.recf_weights_map[sw_name][f] = df.set_index(["new c|r", "old c|r"])   
 
         # Flag to validade that recf_weights_map was normalized
         self.flag_recf_normalization[sw_name] = True 
@@ -981,29 +1121,25 @@ class MCS_Sampler:
     Monte Carlo Sampling Distribution Manager for ReEDS.
 
     This class allows enforcing sampling variability at different ReEDS regions
-    (st, ba, ...) and enforcing correlation between samples from differen switches.
-
-    The following sampling strategies have been implemented:
-
-    1. Dirichlet Sampling:
-       - Generates a Dirichlet sample (h1, ..., hn) ~ Dir(alpha1, ..., alphan).
-       - Uses these weights to compute a weighted average of reference files:
-           sample = h1*f1 + ... + hn*fn
+    (st, ba, ...) and enforcing correlation between samples from different switches.
 
-    2. Discrete Sampling:
-        - Chooses a single reference file based on a discrete probability distribution.
-
-    3. Multiplicative Sampling:
-       - Applies a random multiplier to a single reference file or switch.
-       - The multiplier is drawn from a uniform or triangular distribution:
-           sample = multiplier * f
+    See distribution options listed in /inputs/userinput/mcs_distribution_rules.yaml
 
     """
-    def __init__(self, sample_group, aux_files, n_samples=1):
+    def __init__(self, sample_group, aux_files, n_samples, mcs_run_number, lhs_samples=None):
         self.sample_group = sample_group
         self.aux_files = aux_files
         self.n_samples = n_samples
 
+        # sampling method (random or latin hypercube)
+        if lhs_samples is None:
+            self.sampling_method = 'random'
+        else:
+            self.sampling_method = 'latin hypercube'
+            self.lhs_samples = lhs_samples
+            # for each ReEDS run the sample id corresponds to the run number (-1 for 0 index)
+            self.sample_num = mcs_run_number - 1
+
         # Derive parameters from inputs
         self.reeds_path = sample_group['reeds_path']
         self.inputs_case = sample_group['inputs_case']
@@ -1022,9 +1158,6 @@ def __init__(self, sample_group, aux_files, n_samples=1):
         # why we refer to the switch by its adjusted name)
         self.samples = {sw_name: [] for sw_name in self.sample_group['Sample_ID']}
 
-        # Instantiate WeightCalculator.
-        self.weight_calc = WeightCalculator(sample_group, aux_files, n_samples)
-
     @staticmethod
     def prepare_ref_data(
         dist_files: list,
@@ -1160,7 +1293,7 @@ def load_ref_files(self, sample_idx: int) -> List[pd.DataFrame]:
         return dist_files
 
     # ----------------------- Weight Application Helpers -----------------------
-    def _adjust_supply_curve_sample(self, samples_sw: list, sw_name: str, sample_idx: int) -> list:
+    def _adjust_supply_curve_sample(self, samples_sw: pd.DataFrame, sw_name: str, sample_idx: int) -> pd.DataFrame:
         """
         Adjust samples for supply curve files:
           - Convert the 'class' column to integers.
@@ -1168,39 +1301,41 @@ def _adjust_supply_curve_sample(self, samples_sw: list, sw_name: str, sample_idx
           - Remove rows with no capacity.
 
         Args:
-            samples_sw (list of pd.DataFrame): List of samples for the supply curve files.
+            samples_sw (pd.DataFrame): The sampled supply curve DataFrame.
             sw_name (str): Name of the switch being sampled.
             sample_idx (int): Index of the Sample_ID in sample_group.
 
         Returns:
-            list of pd.DataFrame: List of adjusted samples for the supply curve files.
+            pd.DataFrame: Adjusted supply curve sample.
         """
 
-        for s in range(self.n_samples):
-            # Convert class to integer
-            samples_sw[s]["class"] = samples_sw[s]["class"].astype(int)
+        # Convert class to integer
+        samples_sw["class"] = samples_sw["class"].astype(int)
 
         # Update the recf weights map (weight_calc.recf_weights_map)
         self.weight_calc.normalize_recf_weights_map(samples_sw, sw_name)
 
         # Remove samples with no capacity. 
         # Need to do this after normalizing the recf weights
-        samples_sw = [df[df["capacity"] > 0].copy() for df in samples_sw]
+        samples_sw = samples_sw[samples_sw["capacity"] > 0]
         
         return samples_sw
 
-    def _adjust_exog_cap_samples(self, samples_sw: list, file_name: str) -> list:
+    def _adjust_exog_cap_samples(self, samples_sw: pd.DataFrame, file_name: str) -> pd.DataFrame:
         """
         Adjust samples for exogenous capacity files:
           - Remove rows with no capacity.
           - Adjust the tech classes based on available classes per sc_point_gid.
 
         Args:
-            samples_sw (list of pd.DataFrame): List of samples for the exogenous capacity files.
+            samples_sw (pd.DataFrame): The sampled exogenous capacity DataFrame.
             file_name (str): Name of the file being sampled.
+
+        Returns:
+            pd.DataFrame: Adjusted exogenous capacity sample.
         """
         # Remove samples with no capacity
-        samples_sw = [df[df["capacity"] > 0].copy() for df in samples_sw]
+        samples_sw = samples_sw[samples_sw["capacity"] > 0].copy()
 
         tech_mapping = {
             "exog_cap_upv.csv": ("upv", "supplycurve_upv.csv"),
@@ -1208,21 +1343,20 @@ def _adjust_exog_cap_samples(self, samples_sw: list, file_name: str) -> list:
         }
         tech_name, Sample_ID = tech_mapping[file_name]
 
-        for s in range(self.n_samples):
-            # Get the class available for each sc_point_gid
-            class_sc_point_map = self.samples[Sample_ID][s][["sc_point_gid", "class"]]
-            class_sc_point_map = class_sc_point_map.set_index("sc_point_gid").to_dict()["class"]
+        # Get the class available for each sc_point_gid
+        class_sc_point_map = self.samples[Sample_ID][["sc_point_gid", "class"]]
+        class_sc_point_map = class_sc_point_map.set_index("sc_point_gid").to_dict()["class"]
 
-            # Remove any rows from samples_sw[s] that cannot be mapped
-            # These are cases with zero supply in the region
-            valid_sc_point_gids = samples_sw[s]["sc_point_gid"].isin(class_sc_point_map.keys())
-            samples_sw[s] = samples_sw[s][valid_sc_point_gids].copy()
+        # Remove any rows from samples_sw that cannot be mapped
+        # These are cases with zero supply in the region
+        valid_sc_point_gids = samples_sw["sc_point_gid"].isin(class_sc_point_map.keys())
+        samples_sw = samples_sw[valid_sc_point_gids].copy()
 
-            # Create a new tech name for each sc_point_gid
-            new_tech_name = [tech_name + "_" + str(int(c)) for c in 
-                samples_sw[s]["sc_point_gid"].map(class_sc_point_map).values]
+        # Create a new tech name for each sc_point_gid
+        new_tech_name = [tech_name + "_" + str(int(c)) for c in 
+            samples_sw["sc_point_gid"].map(class_sc_point_map).values]
 
-            samples_sw[s]["*tech"] = new_tech_name
+        samples_sw["*tech"] = new_tech_name
 
         return samples_sw
 
@@ -1242,11 +1376,12 @@ def _apply_weights_general(
             dist_files (List[pd.DataFrame]): List of input DataFrames for sampling.
             modifiable_columns (List[str]): List of columns that can be directly multiplied by the weights.
             n_decimals (Dict[str, int]): Dictionary with the number of decimal places for each column.
-            dict_df_weights (Dict[Tuple[int, int], pd.DataFrame]): Dictionary with the weights for each reference file and sample.
+            dict_df_weights (Dict[int, pd.DataFrame]): Dictionary mapping reference file index to
+                the weight DataFrame for that file.
             sample_idx (int): Index of the Sample_ID in sample_group.
 
         Update:
-            self.samples (Dict[str, List[pd.DataFrame]]): Dictionary with the samples for each switch/file_name.
+            self.samples (Dict[str, pd.DataFrame]): Dictionary with the samples for each switch/file_name.
         """
 
         Sample_ID = self.sample_group['Sample_ID'][sample_idx]
@@ -1254,17 +1389,13 @@ def _apply_weights_general(
         file_name = self.sample_group["runfiles_csv"][sample_idx]["filename"]
 
         # Initialize samples with zero values
-        samples_sw = [dist_files[0].copy() for n in range(self.n_samples)]   
+        samples_sw = dist_files[0].copy()
+        # Initialize with zeros
+        samples_sw[modifiable_columns] = 0  
 
-        for s in range(self.n_samples):
-            # Initialize with zeros
-            samples_sw[s][modifiable_columns] = 0  
-
-            for f, df in enumerate(dist_files):
-                samples_sw[s][modifiable_columns] += df[modifiable_columns] * dict_df_weights[s, f][modifiable_columns] 
-
-            for col in modifiable_columns:
-                samples_sw[s][col] = samples_sw[s][col].round(n_decimals[col]+1)
+        for f, df in enumerate(dist_files):
+            samples_sw[modifiable_columns] += df[modifiable_columns] * dict_df_weights[f][modifiable_columns] 
+        samples_sw = samples_sw.round(n_decimals)
 
         if file_name in MCSConstants.SUPPLY_CURVE_FILES:
             adjusted_samples = self._adjust_supply_curve_sample(samples_sw, sw_name, sample_idx)
@@ -1274,7 +1405,7 @@ def _apply_weights_general(
 
         elif file_name in MCSConstants.PRESCRIBED_BUILDS_FILES:
             # Remove samples with no capacity
-            adjusted_samples = [df[df["capacity"] > 0].copy() for df in samples_sw]
+            adjusted_samples = samples_sw[samples_sw["capacity"] > 0]
 
         else:
             # For all other files we can directly apply the weights
@@ -1297,7 +1428,7 @@ def _apply_weights_recf(
             sample_idx (int): Index of the Sample_ID in sample_group.
 
         Update:
-            self.samples (Dict[str, List[pd.DataFrame]]): Dictionary with the samples for each switch/file_name.
+            self.samples (Dict[str, pd.DataFrame]): Dictionary with the samples for each switch/file_name.
         """
 
         Sample_ID = self.sample_group['Sample_ID'][sample_idx]
@@ -1308,23 +1439,19 @@ def _apply_weights_recf(
         # Index is the same for all files (time)
         indexes = dist_files[0].index 
 
-        samples_sw = []  
+        # get initial switch values
+        sample_sw = defaultdict(int)
 
-        for s in range(self.n_samples):
-            sample_sw = defaultdict(int)
+        for f, df in enumerate(dist_files):
+            # Get the old and new class|region combinations from weights[(s, f)]
+            for (new_c_r, old_c_r) in weights[f].index:
+                sample_sw[new_c_r] += df[old_c_r] * weights[f].loc[(new_c_r,old_c_r)].values[0]
 
-            for f, df in enumerate(dist_files):
-                # Get the old and new class|region combinations from weights[(s, f)]
-                for (new_c_r, old_c_r) in weights[(s, f)].index:
-                    sample_sw[new_c_r] += df[old_c_r] * weights[(s, f)].loc[(new_c_r,old_c_r)].values[0]
+        # Round numbers to 9 decimal places and allow min/max values of 0 and 1
+        for new_c_r in sample_sw.keys():
+            sample_sw[new_c_r] = sample_sw[new_c_r].round(9).clip(0,1)
 
-            # Round numbers to 9 decimal places and allow a maxium values of 1
-            for new_c_r in sample_sw.keys():
-                sample_sw[new_c_r] = sample_sw[new_c_r].round(9).clip(0,1)
-            
-            samples_sw.append(pd.DataFrame(sample_sw, index=indexes))
-
-        self.samples[Sample_ID] = samples_sw
+        self.samples[Sample_ID] = pd.DataFrame(sample_sw, index=indexes)
 
     def _apply_weights_switches_csv(
         self,
@@ -1339,121 +1466,301 @@ def _apply_weights_switches_csv(
         Args:
             dist_files (List[pd.DataFrame]): List of input DataFrames for sampling.
             n_decimals (Dict[str, int]): Dictionary with the number of decimal places for each column.
-            dict_df_weights (Dict[Tuple[int, int], pd.DataFrame]): Dictionary with the weights for each reference file and sample.
+            dict_df_weights (Dict[int, float]): Dictionary mapping reference file/assignment index
+                to the scalar weight for that assignment.
             sample_idx (int): Index of the Sample_ID in sample_group.
 
         Update:
-            self.samples (Dict[str, List[pd.DataFrame]]): Dictionary with the samples for each switch/file_name.
+            self.samples (Dict[str, str]): Dictionary with the sampled switch value.
         """
         # Switches are saved only for the rows changed because this allow 
         # multiple json objects changing different switches using different distributions
 
         sw_name = self.sample_group['switch_names'][sample_idx]
-
         samples_sw = [None for n in range(self.n_samples)]
         sw_assignments = self.sample_group['sw_assignments'][sample_idx]
 
-        for s in range(self.n_samples):
-            for assingment_idx, sw_assignment in enumerate(sw_assignments):
-
-                # The switch assignments case can be a int, a float or a string
-                # If it is a str or int it must be used in a discrete distribution
-                if isinstance(sw_assignment, (str, int)):
-                    # Check if we have a discrete distribution
-                    if self.distribution != "discrete":
-                        raise ValueError(
-                            f"You specified a str/int assignment for switch '{sw_name}', "
-                            "but the distribution is not set to 'discrete'. "
-                            "This file is likely hard-coded in `copy_files.py`.\n\n"
-                            "To fix this, you can try to:\n"
-                            "  - Change the distribution to 'discrete'\n"
-                            "  - Use a float assignment instead, or\n"
-                            "  - Add support for this switch's files\n\n"
-                            "A good place to start is the `read_exception_file()` function."
-                        )
-
-                    if isinstance(sw_assignment, str) and dict_df_weights[s, assingment_idx]:
-                        # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
-                        samples_sw[s] = sw_assignment
-
-                    elif isinstance(sw_assignment, int) and dict_df_weights[s, assingment_idx]:
-                        # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
-                        samples_sw[s] = str(sw_assignment)
-
-                elif isinstance(sw_assignment, float):
-                    if self.distribution == "discrete":
-                        # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
-                        samples_sw[s] = str(sw_assignment)
-
-                    elif self.distribution in MCSConstants.MULTIPLICATIVE_DISTRIBUTIONS:
-                        # We have a validation process that makes sure that we only have one file
-                        samples_sw[s] = (
-                            str(np.round(sw_assignment * dict_df_weights[s, 0], n_decimals+1))
-                        )
-                    else:
-                        raise ValueError(
-                            f"Float assignments can only be used with a discrete or multiplicative distribution. "
-                            f"Check the distribution for switch '{sw_name}'."
-                        )
+        for assingment_idx, sw_assignment in enumerate(sw_assignments):
+
+            # The switch assignments case can be a int, a float or a string
+            # If it is a str or int it must be used in a discrete distribution
+            if isinstance(sw_assignment, (str, int)):
+                # Check if we have a discrete distribution
+                if self.distribution != "discrete":
+                    raise ValueError(
+                        f"You specified a str/int assignment for switch '{sw_name}', "
+                        "but the distribution is not set to 'discrete'. "
+                        "This file is likely hard-coded in `copy_files.py`.\n\n"
+                        "To fix this, you can try to:\n"
+                        "  - Change the distribution to 'discrete'\n"
+                        "  - Use a float assignment instead, or\n"
+                        "  - Add support for this switch's files\n\n"
+                        "A good place to start is the `read_exception_file()` function."
+                    )
+
+                if isinstance(sw_assignment, str) and dict_df_weights[assingment_idx]:
+                    # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
+                    samples_sw = sw_assignment
+
+                elif isinstance(sw_assignment, int) and dict_df_weights[assingment_idx]:
+                    # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
+                    samples_sw = str(sw_assignment)
+
+            elif isinstance(sw_assignment, float):
+                if self.distribution == "discrete":
+                    # dict_df_weights[s,f] is a one hot encoding of the sw_assignment options
+                    samples_sw = str(sw_assignment)
+
+                elif self.distribution in MCSConstants.MULTIPLICATIVE_DISTRIBUTIONS:
+                    # We have a validation process that makes sure that we only have one file
+                    samples_sw = (
+                        str(np.round(sw_assignment * dict_df_weights[0], n_decimals+1))
+                    )
+                else:
+                    raise ValueError(
+                        f"Float assignments can only be used with a discrete or multiplicative distribution. "
+                        f"Check the distribution for switch '{sw_name}'."
+                    )
 
         self.samples[sw_name] = samples_sw
 
-    def record_group_weights(self, log_folder: str) -> None:
+    def assign_weight_calculator(self):
+        self.weight_calc = WeightCalculator(self.sample_group, self.aux_files)
+
+    def record_group_weights(self, inputs_case: str) -> None:
         """
         Record the weights for each distribution group in 
-        lstfiles/mcs_group_weights.csv. Appends to the file if it exists.
+        inputs_case/mcs_group_weights.csv. Appends to the file if it exists.
 
         Args:
             mcs_sampler (MCS): The MCS object containing the distribution groups and weights.
-            log_folder (str): Directory where the weights file will be stored.
+            inputs_case (str): Directory where the weights file will be stored.
         """
-        os.makedirs(log_folder, exist_ok=True)
-        save_path = os.path.join(log_folder, 'mcs_group_weights.csv')
+        save_path = os.path.join(inputs_case, 'mcs_group_weights.csv')
         r_weights = self.weight_calc.r_weights
         group_name = self.sample_group["name"]
         assignments_list = self.sample_group["assignments_list"]
 
-        # Get shape of any region’s weight array
-        any_region = next(iter(r_weights))
-        n_samples, n_assignments = r_weights[any_region].shape
-
         # Build column names
-        columns = (
-            ['group_name', 'switch_name', 'sw_assignment', 'r'] +
-            [f"Weight Sample {i}" for i in range(1, n_samples + 1)]
-        )
-
+        columns = ['group_name', 'switch_name', 'sw_assignment', 'r', 'weight']
         data = []
 
         for switch_idx, switch_dict in enumerate(assignments_list):
             sw_name, sw_assignment = next(iter(switch_dict.items()))
             for r in r_weights.keys():
                 for assignment_idx, assignment_value in enumerate(sw_assignment):
-                    weights = list(r_weights[r][:, assignment_idx])  # column for this switch
-                    row = [group_name, sw_name, assignment_value, r] + weights
+                    weight = r_weights[r][assignment_idx]  # column for this switch
+                    row = [group_name, sw_name, assignment_value, r, weight]
                     data.append(row)
 
         weight_record_df = pd.DataFrame(data, columns=columns)
 
+        # drop regionality levels that aren't used
+        print(f"recording weights for {sw_name}")
+        unique_sample_levels = self.hierarchy_file[self.sample_hierarchy_lvl].unique()
+        if self.sample_group.weight_r == 'country':
+            weight_record_df['r'] = 'country'
+            weight_record_df = weight_record_df.drop_duplicates()
+        else:  
+            weight_record_df = weight_record_df.loc[weight_record_df.r.isin(unique_sample_levels)]
+
         # Append if file exists, else write with header
         if os.path.exists(save_path):
             weight_record_df.to_csv(save_path, mode='a', index=False, header=False)
         else:
             weight_record_df.to_csv(save_path, mode='w', index=False, header=True)
+
     # ----------------------- End of Weight Application Helpers -----------------------
 
-    def get_samples(self, aux_files):
+    def sample_lhs_uniform(self, sample_num, dim_num, lower, upper):
+        """Draw a sample from a uniform distribution using the LHS quantile matrix.
+
+        Args:
+            sample_num (int): Row index into self.lhs_samples for this run.
+            dim_num (int): Column index (dimension) into self.lhs_samples for this sample group.
+            lower (np.ndarray or float): Lower bound(s) of the uniform distribution.
+            upper (np.ndarray or float): Upper bound(s) of the uniform distribution.
+
+        Returns:
+            np.ndarray or float: Sampled value(s) from the uniform distribution.
+        """
+        # check order
+        lower_new, upper_new = check_lhs_param_order(lower, upper)
+        # set uniform distribution parameters: location (loc) and scale
+        unif_loc = lower_new
+        unif_scale = upper_new - lower_new
+        # extract sample
+        lhs_vals = scipy.stats.uniform.ppf(self.lhs_samples[sample_num, dim_num], loc=unif_loc, scale=unif_scale)
+        
+        return lhs_vals
+
+    def sample_lhs_triangular(self, sample_num, dim_num, lower, mode, upper):
+        """Draw a sample from a triangular distribution using the LHS quantile matrix.
+
+        Args:
+            sample_num (int): Row index into self.lhs_samples for this run.
+            dim_num (int): Column index (dimension) into self.lhs_samples for this sample group.
+            lower (np.ndarray or float): Lower bound(s) of the triangular distribution.
+            mode (np.ndarray or float): Mode (peak) value(s) of the triangular distribution.
+            upper (np.ndarray or float): Upper bound(s) of the triangular distribution.
+
+        Returns:
+            np.ndarray or float: Sampled value(s) from the triangular distribution.
+        """
+        # check order
+        lower_new, upper_new = check_lhs_param_order(lower, upper)
+        # set triangular distribution parametesr: location (loc), scale, and center (c)
+        tri_loc = lower_new
+        tri_scale = upper_new - lower_new
+        tri_c = (mode - tri_loc) / tri_scale
+
+        # draw lhs samples; 'sample_num' matches the sample row for the relevant ReEDS run and
+        # 'dim_num' matches the relevant column for the dimension
+        lhs_vals = scipy.stats.triang.ppf(self.lhs_samples[sample_num, dim_num], c=tri_c, loc=tri_loc, scale=tri_scale)
+        
+        return lhs_vals
+
+    def sample_lhs_discrete(self, sample_num, dim_num):
+        """Select a discrete option index using the LHS quantile matrix.
+
+        Uses the distribution parameters as unnormalized probabilities to
+        define CDF bins, then maps the LHS quantile to a discrete index.
+
+        Args:
+            sample_num (int): Row index into self.lhs_samples for this run.
+            dim_num (int): Column index (dimension) into self.lhs_samples for this sample group.
+
+        Returns:
+            int: Index of the selected discrete option.
+        """
+        # normalize probabilities
+        probs = np.array(self.sample_group.dist_params) / np.sum(self.sample_group.dist_params)
+        # get CDF bins
+        bins = np.cumsum(probs)
+        # get index of discrete option selected
+        lhs_vals = np.array(np.arange(0, len(bins)))[np.digitize(self.lhs_samples[sample_num, dim_num], bins)]
+
+        return lhs_vals
+
+    def apply_lhs_switches_csv(self, sample_group_num, sample_idx, dist_files, n_decimals):
+        """Apply LHS sampling to a switches.csv entry.
+
+        Draws a sample for the switch value based on the configured distribution
+        and stores the result as a string in self.samples.
+
+        Args:
+            sample_group_num (int): Index of the sample group (LHS dimension).
+            sample_idx (int): Index of the Sample_ID in sample_group.
+            dist_files (List[pd.DataFrame]): Reference DataFrames for the switch.
+            n_decimals (int): Number of decimal places for rounding the result.
+        """
+        sw_name = self.sample_group['switch_names'][sample_idx]
+        sw_assignments = self.sample_group['sw_assignments'][sample_idx]
+
+        if self.distribution == "triangular":
+            lower, mode, upper = sw_assignments
+            # get sample values
+            lhs_sw_val = self.sample_lhs_triangular(self.sample_num, sample_group_num, lower, mode, upper)
+        elif self.distribution == "triangular_multiplier":
+            lower, mode, upper = self.sample_group.dist_params
+            # get multipler and apply to switch
+            lhs_sw_mult = self.sample_lhs_triangular(self.sample_num, sample_group_num, lower, mode, upper)
+            lhs_sw_val = lhs_sw_mult * sw_assignments[0]
+        elif self.distribution == "uniform":
+            lower, upper = sw_assignments
+            # get sample values
+            lhs_sw_val = self.sample_lhs_uniform(self.sample_num, sample_group_num, lower, upper)
+        elif self.distribution == "uniform_multiplier":
+            lower, upper = self.sample_group.dist_params
+            # get multipler and apply to switch
+            lhs_sw_mult = self.sample_lhs_uniform(self.sample_num, sample_group_num, lower, upper)
+            lhs_sw_val = lhs_sw_mult * sw_assignments[0]
+        elif self.distribution == "discrete":
+            lhs_sw_index = self.sample_lhs_discrete(self.sample_num, sample_group_num)
+            lhs_sw_val = sw_assignments[lhs_sw_index]
+
+        # format as a string
+        self.samples[sw_name] = str(np.round(lhs_sw_val, n_decimals+1))
+
+
+    def apply_lhs_general(self, sample_group_num, Sample_ID, dist_files, aux_files, modifiable_columns):
+        """Apply LHS sampling to a general (non-switch, non-RECF) file.
+
+        For each modifiable column, draws values from the configured distribution
+        using the LHS quantile matrix and stores the resulting DataFrame in self.samples.
+
+        Args:
+            sample_group_num (int): Index of the sample group (LHS dimension).
+            Sample_ID (str): Identifier for this sample in self.samples.
+            dist_files (List[pd.DataFrame]): Reference DataFrames providing distribution bounds.
+            aux_files (dict): Auxiliary files dictionary.
+            modifiable_columns (List[str]): Columns to sample new values for.
+        """
+        # set up final data
+        samples_sw = dist_files[0].copy()
+        # iterate over columns to update with lhs 
+        for mod_col in modifiable_columns:
+      
+            if self.distribution == "triangular":
+                # get triangular distribution parameters (ordering checked in sample_lhs_triangular function)
+                lower = np.array(dist_files[0][mod_col])
+                mode = np.array(dist_files[1][mod_col])
+                upper = np.array(dist_files[2][mod_col])                
+                # get new values
+                lhs_vals = self.sample_lhs_triangular(self.sample_num, sample_group_num, lower, mode, upper)
+                # replace file data with with lhs sample values
+                # for NA values from sampling (occurs if lower == upper), use existing values file values
+                samples_sw[mod_col] = np.where(np.isnan(lhs_vals), samples_sw[mod_col], lhs_vals)
+            
+            elif self.distribution == "uniform":
+                lower = np.array(dist_files[0][mod_col])
+                upper = np.array(dist_files[1][mod_col])
+                # get new values
+                lhs_vals = self.sample_lhs_uniform(self.sample_num, sample_group_num, lower, upper)
+                samples_sw[mod_col] = np.where(np.isnan(lhs_vals), samples_sw[mod_col], lhs_vals)
+
+            elif self.distribution == "discrete":
+                lhs_index = self.sample_lhs_discrete(self.sample_num, sample_group_num)
+                samples_sw[mod_col] = dist_files[lhs_index][mod_col]
+
+            elif self.distribution == "uniform_multiplier":
+                lower = np.array(dist_files[0][mod_col]) * self.dist_params[0]
+                upper = np.array(dist_files[0][mod_col]) * self.dist_params[1]
+                # get new values
+                lhs_vals = self.sample_lhs_uniform(self.sample_num, sample_group_num, lower, upper)
+                samples_sw[mod_col] = np.where(np.isnan(lhs_vals), samples_sw[mod_col], lhs_vals)
+
+            elif self.distribution == "triangular_multiplier":
+                lower = np.array(dist_files[0][mod_col]) * self.dist_params[0]
+                mode = np.array(dist_files[0][mod_col]) * self.dist_params[1]
+                upper = np.array(dist_files[0][mod_col]) * self.dist_params[2]                
+                # get new values
+                lhs_vals = self.sample_lhs_triangular(self.sample_num, sample_group_num, lower, mode, upper)
+                # replace file data with with lhs sample values
+                # for NA values from sampling (occurs if lower == upper), use existing values file values
+                samples_sw[mod_col] = np.where(np.isnan(lhs_vals), samples_sw[mod_col], lhs_vals)
+
+
+        self.samples[Sample_ID] = samples_sw
+        # output: dictionary of sample values by filename (see samples_dict)
+
+    def get_samples(self, aux_files, sample_group_num):
         """
         Generates Monte Carlo samples for each switch and applies the appropriate weight assignment.
 
         Returns:
             Dict[str, List[pd.DataFrame]]: Dictionary with the samples for each switch/file_name.
         """
+
+        # assign Weight Calculator to sample group when using random sampling approach
+        if self.sampling_method == 'random':
+            self.assign_weight_calculator()
+
         # Iterate over each switch file and apply the appropriate weight assignment method
         for sample_idx, sample_ID in enumerate(self.sample_group['Sample_ID']):
-
             sw_name = self.sample_group['switch_names'][sample_idx]
             file_name = self.sample_group["file_names"][sample_idx]
+            Sample_ID = self.sample_group['Sample_ID'][sample_idx]
             dist_files = self.load_ref_files(sample_idx)
 
             #In some small cases all dist_files are empty
@@ -1467,15 +1774,23 @@ def get_samples(self, aux_files):
             )
 
             # Get weights we will apply to the reference files
-            dict_df_weights = self.weight_calc.get_df_weights(dist_files, modifiable_columns, sw_name, file_name)
-
-            # Dispatch weight application based on file type.
-            if file_name == "switches.csv":
-                self._apply_weights_switches_csv(dist_files, n_decimals, dict_df_weights, sample_idx)
-            elif file_name in MCSConstants.RECF_FILES:
-                self._apply_weights_recf(dist_files, sample_idx)
+            if self.sampling_method == 'latin hypercube':
+                # apply latin hybercube sampling based on file type
+                if file_name == "switches.csv":
+                    self.apply_lhs_switches_csv(sample_group_num, sample_idx, dist_files, n_decimals)
+                elif file_name in MCSConstants.RECF_FILES:
+                    self._apply_weights_recf(dist_files, sample_idx)
+                else:
+                    self.apply_lhs_general(sample_group_num, Sample_ID, dist_files, aux_files, modifiable_columns)
             else:
-                self._apply_weights_general(dist_files, modifiable_columns, n_decimals, dict_df_weights, sample_idx)
+                dict_df_weights = self.weight_calc.get_df_weights(dist_files, modifiable_columns, sw_name, file_name)
+                # Dispatch weight application based on file type
+                if file_name == "switches.csv":
+                    self._apply_weights_switches_csv(dist_files, n_decimals, dict_df_weights, sample_idx)
+                elif file_name in MCSConstants.RECF_FILES:
+                    self._apply_weights_recf(dist_files, sample_idx)
+                else:
+                    self._apply_weights_general(dist_files, modifiable_columns, n_decimals, dict_df_weights, sample_idx)
 
         return self.samples
 
@@ -1487,7 +1802,6 @@ def write_samples(
     sample_group: pd.Series,
     samples_dict: dict,
     aux_files: dict,
-    run_ReEDS: bool = True,
 ):
     """
     Write the samples to the appropriate locations
@@ -1496,57 +1810,51 @@ def write_samples(
         sample_group (pd.Series): Row of the input file with the sampling instructions.
         samples_dict (dict): Dictionary with the samples for each switch/file_name.
         aux_files (dict): Dictionary with the auxiliary files needed for sampling.
-        run_ReEDS (bool): If True, the script is being used to run ReEDS. 
-            If False, the script is being used to test the samples before running ReEDS.
     """
 
     inputs_case = sample_group['inputs_case']
-
     for sample_idx, sample_ID in enumerate(samples_dict.keys()):
-        samples = samples_dict[sample_ID]
+        sample_values = samples_dict[sample_ID]
         sw_name = sample_group['switch_names'][sample_idx]
-        save_path_structure = sample_group['save_paths'][sample_idx]  # Where the samples will be copied to
+        save_path = sample_group['save_paths'][sample_idx]  # Where the samples will be copied to
+        
         file_name = sample_group["file_names"][sample_idx]
-
-        for n, sample in enumerate(samples):
-            save_path = save_path_structure.replace('{sample_n}', str(n))
-            folder_path = os.path.dirname(save_path)  # Folder path without the file name
-            file_termination = os.path.splitext(save_path)[-1]  # File termination (.csv, .h5, etc.)
-
-            # For run_ReEDS==False, create folder if it does not exist
-            if (not run_ReEDS):
-                os.makedirs(folder_path, exist_ok=True)
-
-            # If we have a region-indexed file
-            if file_name in aux_files['region_files']['filename'].values:
-                # Get destination directory instead of save_path
-                dir_dst = os.path.dirname(save_path)
-                # Get the row of the region-indexed file
-                region_files_row = aux_files['region_files'].query('filename == @file_name').iloc[0]
-                copy_files.write_region_indexed_file(sample, dir_dst, aux_files['source_deflator_map'],
-                                                        aux_files['sw'], region_files_row,
-                                                        aux_files['regions_and_agglevel'],
-                                                        aux_files['agglevel_variables'])
-
-            elif file_termination == '.csv':
-                # Not a region-indexed file but it is a CSV file
-                if file_name != 'switches.csv':
-                    sample.to_csv(save_path, index=False)
-                else:
-                    # Read the original switches.csv file
-                    original_switches = pd.read_csv(save_path, header=None, index_col=0)
-                    # Update the original switches.csv file with the new samples
-                    original_switches.loc[sw_name] = sample
-                    original_switches.to_csv(save_path, header=False)
-                    if run_ReEDS:
-                        # Create gswitches.csv and .txt files 
-                        gswitches_path = reeds.io.write_gswitches(original_switches, inputs_case)
-                        copy_files.scalar_csv_to_txt(gswitches_path)
-                        
-                  
-            if run_ReEDS:  # Only print if running ReEDS optimization
-                reduced_path = os.sep.join(save_path.strip(os.sep).split(os.sep)[-3:])
-                print(f"...Sample related to switch {sw_name} was copied to {reduced_path}")
+        file_termination = os.path.splitext(save_path)[-1]  # File termination (.csv, .h5, etc.)
+
+        ## save file method varies depending on...
+        # ...if we have a region-indexed file
+        if file_name in aux_files['region_files']['filename'].values:
+            # Get destination directory instead of save_path
+            dir_dst = os.path.dirname(save_path)
+            # Get the row of the region-indexed file
+            region_files_row = aux_files['region_files'].query('filename == @file_name').iloc[0]
+            copy_files.write_region_indexed_file(sample_values, dir_dst, aux_files['source_deflator_map'],
+                                                    aux_files['sw'], region_files_row,
+                                                    aux_files['regions_and_agglevel'])
+        # ...if we have a csv file that isn't region-indexed (including switches.csv)
+        elif file_termination == '.csv':
+            if file_name == 'switches.csv':
+                # Read the original switches.csv file
+                original_switches = pd.read_csv(save_path, header=None, index_col=0)
+                # Update the original switches.csv file with the new samples
+                original_switches.loc[sw_name] = sample_values
+                original_switches.to_csv(save_path, header=False)
+                # Create gswitches.csv and .txt files 
+                gswitches_path = reeds.io.write_gswitches(original_switches, inputs_case)
+                copy_files.scalar_csv_to_txt(gswitches_path)
+            else:
+                sample_values.to_csv(save_path, index=False)
+        # ...if we are saving load
+        elif file_name == 'load.h5':
+            reeds.io.write_profile_to_h5(sample_values, 'load.h5', inputs_case)
+        # ...not one of these file methods then raise an exception
+        else:
+            raise NotImplementedError(
+                f"Writing samples for '{file_name}' with extension '{file_termination}' is not supported"
+            )
+                            
+        reduced_path = os.sep.join(save_path.strip(os.sep).split(os.sep)[-3:])
+        print(f"...Sample related to switch {sw_name} was copied to {reduced_path}")
 
 
 #%% ===========================================================================
@@ -1556,8 +1864,8 @@ def main(
     reeds_path: str,
     inputs_case: str,
     n_samples: int = 1,
+    lhs_sampling: int = 1,
     seed: int = 0,
-    run_ReEDS: bool = True,
 ):
     """
     Create samples for the Monte Carlo Simulation (MCS).
@@ -1566,76 +1874,95 @@ def main(
         reeds_path (str): Path to the ReEDS directory.
         inputs_case (str): Path to the inputs_case directory.
         n_samples (int): Number of samples to generate.
-        seed (int): Seed for the random number generator.
-        run_ReEDS (bool): If True, the script is being used to run ReEDS. 
-            If False, the script is being used to test the samples before running ReEDS.
+        lhs_sampling (int): whether to use random (0) or latin hypercube (1) sampling.
+        seed (int): global seed value for the random number generator.
 
-    Notes:
-        If run_ReEDS=False inputs_case only needs to be a folder containing switches.csv so 
-        we can perform any spatial filtering needed to get the samples
     """
 
-    if run_ReEDS:
-        # Set random seed as the (MCS run number + seed) to allow reproducibility without having
-        # the same sample for each MCS-ReEDS call
-        runs_folder_name = os.path.basename(os.path.dirname(inputs_case.rstrip(os.path.sep)))
-        mcs_run_number = int((runs_folder_name.split('_')[-1]).replace('MC', ''))
-        seed += mcs_run_number
-        np.random.seed(seed)
-    else:
-        # The script is being used to test the samples before running ReEDS
-        np.random.seed(seed)
+    # get sample number associated with this ReEDS run
+    runs_folder_name = os.path.basename(os.path.dirname(inputs_case.rstrip(os.path.sep)))
+    mcs_run_number = int((runs_folder_name.split('_')[-1]).replace('MC', ''))
 
     # Obtain instructions to sample the distributions for each switch
-    df_input_dist_instructions, aux_files = get_dist_instructions(reeds_path, inputs_case, run_ReEDS=run_ReEDS)
+    df_input_dist_instructions, aux_files = get_dist_instructions(reeds_path, inputs_case)
+
+    # if using latin hypercube sampling (lhs), set up the sampler and draw samples from cdf
+    lhs_samples = None
+    if lhs_sampling:
+        # number of dimensions for lhs = number of sample groups 
+        lhs_dim = df_input_dist_instructions.shape[0]
+        # lhs requires drawing all samples simultaneously, so rather than using
+        # a run-specific seed we draw for all runs at once using the global seed value 
+        lhs_sampler = scipy.stats.qmc.LatinHypercube(d=lhs_dim, seed=seed)
+        # lhs_samples are arranged n x d (n = samples, d = dimensions)
+        lhs_samples = lhs_sampler.random(n=n_samples)
+        # record the lhs sampling matrix in each run folder
+        lhs_samples_out = pd.DataFrame({'run': [f"MC{i:0>4}" for i in range(1, n_samples + 1)]})
+        lhs_samples_out = pd.concat(
+            [lhs_samples_out, pd.DataFrame(lhs_samples.round(6), columns=df_input_dist_instructions.name.values)], axis=1
+        )
+        lhs_samples_out.to_csv(os.path.join(inputs_case, "mcs_latin_hypercube_samples.csv"), index=False)
+
+    # if using random sampling, set random seed using the global seed + MCS run number 
+    # to allow reproducibility without having the same sample for each MCS-ReEDS call
+    else:
+        np.random.seed(seed + mcs_run_number)
 
     print('Sampling...')
-    for _, sample_group in df_input_dist_instructions.iterrows():
+    for sample_group_num, sample_group in df_input_dist_instructions.iterrows():
         dist_switches = sample_group['switch_names']
         unique_switches = set(dist_switches)
 
         print(f"Sampling for switch(es): {unique_switches}")
-        # Create Samples
-        mcs_sampler = MCS_Sampler(sample_group, aux_files, n_samples)
-        samples_dict = mcs_sampler.get_samples(aux_files)
-
+        # create sampling object and draw samples
+        mcs_sampler = MCS_Sampler(sample_group, aux_files, n_samples, mcs_run_number, lhs_samples)
+        samples_dict = mcs_sampler.get_samples(aux_files, sample_group_num)     
+        
         # Record the weights of each sample group
-        mcs_sampler.record_group_weights(
-            os.path.join(os.path.dirname(inputs_case), 'lstfiles')
-        )
+        if not lhs_sampling:
+            mcs_sampler.record_group_weights(inputs_case)
+
         # Write Samples
-        write_samples(sample_group, samples_dict, aux_files, run_ReEDS=run_ReEDS)
+        write_samples(sample_group, samples_dict, aux_files)
 
 
 if __name__ == '__main__' and not hasattr(sys, 'ps1'):
     parser = argparse.ArgumentParser(description='Copy files needed for this run')
     parser.add_argument('reeds_path', help='ReEDS directory')
     parser.add_argument('inputs_case', help='Output directory')
-
+    parser.add_argument('--nolog', '-n', default=False, action='store_true', help='turn off logging for debugging')
+                    
     args = parser.parse_args()
     reeds_path = os.path.abspath(args.reeds_path)
     inputs_case = os.path.abspath(args.inputs_case)
+    nolog = args.nolog
 
     # ---- Settings for testing ----
     # reeds_path = reeds.io.reeds_path
     # inputs_case = os.path.join(reeds_path,'runs','v20250825_revM2_MonteCarlo_MC1','inputs_case')
     # n_samples = 1
     # seed = 0
-    # run_ReEDS = True
 
     # Set up logger
     tic = datetime.datetime.now()
-    log = reeds.log.makelog(
-        scriptname=__file__,
-        logpath=os.path.join(os.path.dirname(inputs_case), 'gamslog.txt'),
-    )
-
+    if not nolog:
+        log = reeds.log.makelog(
+            scriptname=__file__,
+            logpath=os.path.join(os.path.dirname(inputs_case), 'gamslog.txt'),
+        )
+    
     # Read switches and check if MCS_runs is enabled.
     sw = reeds.io.get_switches(inputs_case)
-    MCS_Runs = int(sw.get('MCS_runs', 0))
-    if MCS_Runs >= 1:
+    MCS_runs = int(sw.get('MCS_runs', 0))
+    MCS_lhs = int(sw.get('MCS_lhs', 0))
+
+    # get global seed from scalars (used to set the seed for a batch of runs)
+    scalars = reeds.io.get_scalars()
+    seed = int(scalars['MCS_seed'])
+
+    if MCS_runs >= 1:
         print('Starting mcs_sampler.py')
-        main(reeds_path, inputs_case, n_samples=1)
+        main(reeds_path, inputs_case, n_samples=MCS_runs, lhs_sampling=MCS_lhs, seed=seed)
     else:
         print('MCS_runs switch is set to 0 or not found. No Monte Carlo sampling will be performed')
 
diff --git a/input_processing/outage_rates.py b/reeds/input_processing/outage_rates.py
similarity index 97%
rename from input_processing/outage_rates.py
rename to reeds/input_processing/outage_rates.py
index 53ce64fa5..7ccadf228 100644
--- a/input_processing/outage_rates.py
+++ b/reeds/input_processing/outage_rates.py
@@ -6,12 +6,13 @@
 import datetime
 import argparse
 import h5py
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
+reeds_path = reeds.io.reeds_path
 ## Time the operation of this script
 tic = datetime.datetime.now()
 
-reeds_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
 
 #%%### Fixed inputs
 tz_in = 'UTC'
@@ -97,7 +98,7 @@ def pm_to_tech(df, inputs_case):
         index = timeseries
         top column level = prime movers
     inputs_case: str
-        path to ReEDS-2.0/runs/{case}/inputs_case
+        path to ReEDS/runs/{case}/inputs_case
 
     Returns
     -------
@@ -131,10 +132,10 @@ def fill_empty_techs(df_prefill, inputs_case, fillvalues_tech=None, during_quart
     -------
     """
     ### Parse inputs
-    quarters = pd.read_csv(
-        os.path.join(inputs_case, 'sets', 'quarter.csv'),
-        header=None,
-    ).squeeze(1).map(lambda x: x[:4]).tolist()
+    quarters = (
+        reeds.io.read_input(inputs_case, 'quarter')
+        .squeeze(1).map(lambda x: x[:4]).tolist()
+    )
     if isinstance(during_quarters, str):
         assert during_quarters == 'all'
     elif isinstance(during_quarters, list):
@@ -476,8 +477,8 @@ def main(reeds_path, inputs_case, debug=0, interactive=False):
         description='Calculate temperature-dependent forced-outage rates',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
diff --git a/input_processing/plantcostprep.py b/reeds/input_processing/plantcostprep.py
similarity index 98%
rename from input_processing/plantcostprep.py
rename to reeds/input_processing/plantcostprep.py
index 0026ccfae..2956af204 100644
--- a/input_processing/plantcostprep.py
+++ b/reeds/input_processing/plantcostprep.py
@@ -8,7 +8,8 @@
 import sys
 import argparse
 import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 # Time the operation of this script
 tic = datetime.datetime.now()
@@ -23,10 +24,10 @@
 inputs_case = args.inputs_case
 
 # #%% Settings for testing
-#reeds_path = os.path.expanduser('~/github2/ReEDS-2.0/')
+#reeds_path = os.path.expanduser('~/github2/ReEDS/')
 #inputs_case = os.path.join(reeds_path,'runs','v20220421_prmM0_ercot_seq','inputs_case')
-#reeds_path = '/Users/apham/Documents/GitHub/ReEDS/ReEDS-2.0/'
-#inputs_case = '/Users/apham/Documents/GitHub/ReEDS/ReEDS-2.0/runs/test_Pacific/inputs_case/'
+#reeds_path = '/Users/apham/Documents/GitHub/ReEDS/ReEDS/'
+#inputs_case = '/Users/apham/Documents/GitHub/ReEDS/ReEDS/runs/test_Pacific/inputs_case/'
 
 #%% Set up logger
 log = reeds.log.makelog(
diff --git a/input_processing/recf.py b/reeds/input_processing/recf.py
similarity index 96%
rename from input_processing/recf.py
rename to reeds/input_processing/recf.py
index fc16d170f..40204f8d9 100644
--- a/input_processing/recf.py
+++ b/reeds/input_processing/recf.py
@@ -5,7 +5,7 @@
 
 Resources:
         - Creates a resource-to-(i,r,ccreg) lookup table for use in hourly_writesupplycurves.py 
-          and Augur
+          and resource adequacy calculations
         - Add the distributed PV resources
 RECF:
         - Add the distributed PV recf profiles
@@ -23,7 +23,8 @@
 import os
 import pandas as pd
 import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -220,16 +221,15 @@ def calculate_class_region_cf_hourly(
     # Concatenate all CF data
     class_region_cf_hourly = pd.concat(df_list)
 
-    # Shift timezone from UTC to tz_out
+    # Shift timezone from UTC to tz_out and trim to modeled years
+    timeindex = reeds.timeseries.get_timeindex(weather_years)
     utc_offset = -1 * int(tz_out.split('Etc/GMT')[1])
     class_region_cf_hourly = (
         class_region_cf_hourly.shift(utc_offset)
         .tz_localize(None)
         .tz_localize(tz_out)
+        .loc[timeindex]
     )
-    class_region_cf_hourly = class_region_cf_hourly.loc[(
-        class_region_cf_hourly.index.year.isin(weather_years)
-    )]
     class_region_cf_hourly.index.names = ['datetime']
 
     return class_region_cf_hourly
@@ -281,9 +281,9 @@ def main(reeds_path, inputs_case):
     print('Starting recf.py')
     
     # #%% Settings for testing
-    # reeds_path = os.path.realpath(os.path.join(os.path.dirname(__file__),'..'))
+    # reeds_path = reeds.io.reeds_path
     # inputs_case = os.path.join(
-    #     reeds_path,'runs','v20260416_mainM0_Pacific','inputs_case')
+    #     reeds_path,'runs','v20260601_repM0_USA_H20_ramp3_yr12_ys1623','inputs_case')
 
     #%% Inputs from switches
     sw = reeds.io.get_switches(inputs_case)
@@ -453,7 +453,7 @@ def main(reeds_path, inputs_case):
         :, resources.loc[resources.tech.isin(vre_dist),'resource'].values
     ] /= (1 - distloss)
 
-    # Set the column names for resources to match ReEDS-2.0
+    # Set the column names for resources to match ReEDS
     resources['ccreg'] = resources.area.map(r2ccreg)
     resources.rename(columns={'area':'r','tech':'i'}, inplace=True)
     resources = resources[['r','i','ccreg','resource']]
@@ -522,8 +522,11 @@ def main(reeds_path, inputs_case):
     ### Overwrite the original hierarchy.csv based on capcredit_hierarchy_level
     hierarchy.rename_axis('*r').to_csv(
         os.path.join(inputs_case, 'hierarchy.csv'), index=True, header=True)
-    pd.Series(hierarchy.ccreg.unique()).to_csv(
-        os.path.join(inputs_case,'ccreg.csv'), index=False, header=False)
+    ccreg = pd.Series(hierarchy.ccreg.unique())
+    reeds.io.write_to_inputs_h5(
+        ccreg, 'ccreg', inputs_case, gamstype='set', comment='capacity credit region',
+    )
+
 
 
 #%% ===========================================================================
@@ -539,8 +542,8 @@ def main(reeds_path, inputs_case):
         description='Create run-specific hourly profiles',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
-    parser.add_argument('reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('inputs_case', help='ReEDS-2.0/runs/{case}/inputs_case directory')
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='ReEDS/runs/{case}/inputs_case directory')
 
     args = parser.parse_args()
     reeds_path = args.reeds_path
diff --git a/runfiles.csv b/reeds/input_processing/runfiles.csv
similarity index 56%
rename from runfiles.csv
rename to reeds/input_processing/runfiles.csv
index 1a60f823d..6ca5b1998 100644
--- a/runfiles.csv
+++ b/reeds/input_processing/runfiles.csv
@@ -9,96 +9,49 @@ i_subtech.csv,inputs/sets/i_subtech.csv,1,ignore,ignore,,,,,,,1,set,i_subtech,te
 i_h2_ptc_gen.csv,inputs/sets/i_h2_ptc_gen.csv,1,ignore,ignore,,,,,,,1,set,i_h2_ptc_gen,technology subset category for clean generators which qualify for the hydrogen production tax credit,
 sdbin.csv,inputs/sets/sdbin.csv,1,ignore,ignore,,,,,,,1,set,sdbin,storage duration bins,
 tg.csv,inputs/sets/tg.csv,1,ignore,ignore,,,,,,,1,set,tg,tech groups for growth constraints,
-pcat.csv,inputs/sets/pcat.csv,1,ignore,ignore,,,,,,,1,set,pcat,prescribed capacity categories,
-ccseason.csv,,1,ignore,ignore,,,,,,,1,set,ccseason,seasons used for capacity credit; cold is Oct 15-April 14 and hot is April 15-Oct 14,
 quarter.csv,inputs/sets/quarter.csv,1,ignore,ignore,,,,,,,1,set,quarter,original h17 seasons (four per year),
 month.csv,inputs/sets/month.csv,1,ignore,ignore,,,,,,,1,set,month,calendar months in a year,
-RPSCat.csv,inputs/sets/RPSCat.csv,1,ignore,ignore,,,,,,,,,RPSCat,,
-aclike.csv,inputs/sets/aclike.csv,1,ignore,ignore,,,,,0,,,,aclike,,
+RPSCat.csv,inputs/sets/RPSCat.csv,1,ignore,ignore,,,,,,,,set,RPSCat,RPS constraint categories including clean energy standards,
+aclike.csv,inputs/sets/aclike.csv,1,ignore,ignore,,,,,0,,,set,aclike,AC transmission capacity types,
 acp_disallowed.csv,inputs/state_policies/acp_disallowed.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,*st,"RPSCat,val",,,0,,,,,,
 acp_prices.csv,inputs/state_policies/acp_prices.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,st,,1,0,,,,,,
-tscbin.csv,,1,ignore,ignore,,,,,,,1,set,tscbin,transmission upgrade supply curve bins,
-numpartitions.csv,,1,ignore,ignore,,,,,0,,,,,,
-agglevels.csv,,1,ignore,ignore,ignore,,,,0,,,,,,
-aggreg.csv,,1,ignore,ignore,ignore,,,0,0,,,,,,
-aggreg2anchorreg.csv,,1,ignore,ignore,ignore,,,0,0,,,,,,
-anchorreg2aggreg.csv,,1,ignore,ignore,ignore,,,0,0,,,,,,
-allt.csv,inputs/sets/allt.csv,1,ignore,ignore,,,,,,,,,allt,,
+allt.csv,inputs/sets/allt.csv,1,ignore,ignore,,,,,,,,set,allt,all potential years,
 alpha.csv,inputs/fuelprices/alpha_{ngscen}.csv,1,ignore,ignore,wide_cendiv,t,,1,0,,,,,,
 bio_supplycurve.csv,inputs/supply_curve/bio_supplycurve.csv,1,ignore,ignore,usda_region,,,,0,,,,,,
-bioclass.csv,inputs/sets/bioclass.csv,1,ignore,ignore,,,,,,,,,bioclass,,
+bioclass.csv,inputs/sets/bioclass.csv,1,ignore,ignore,,,,,,,,set,bioclass,,
 can_exports.csv,inputs/canada_imports/can_exports.csv,int(sw.GSw_Canada) != 0,sum,ignore,r,wide,,1,0,,,,,,
-can_exports_h_frac.csv,,1,ignore,ignore,,,,,0,,,,,,
 can_exports_szn_frac.csv,inputs/canada_imports/can_exports_szn_frac.csv,int(sw.GSw_Canada) != 0,ignore,ignore,,,,,0,,,,,,
 can_imports.csv,inputs/canada_imports/can_imports.csv,int(sw.GSw_Canada) != 0,sum,ignore,r,wide,,1,0,,,,,,
-can_imports_capacity.csv,,1,sum,ignore,*r,t,,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
 can_imports_quarter_frac.csv,inputs/canada_imports/can_imports_quarter_frac.csv,int(sw.GSw_Canada) != 0,ignore,ignore,,,,,0,,,,,,
-can_imports_szn_frac.csv,,1,ignore,ignore,,,,,0,,1,,,,
 cangrowth.csv,inputs/load/cangrowth.csv,int(sw.GSw_Canada) != 0,ignore,ignore,st,,,1,0,,,,,,
-canmexload.csv,,1,sum,ignore,*r,h,,0,0,,1,,,,
-cap_cspns.csv,,1,sum,ignore,*r,t,,0,0,,1,,,,disaggfunc set to ignore because data is pulled from the county-indexed generator database
 cap_existing_hydro.csv,inputs/hydro/cap_existing_hydro.csv,1,ignore,ignore,,t,,0,0,,,,,,
 cap_existing_psh.csv,inputs/storage/cap_existing_psh.csv,(int(sw.GSw_Storage) != 0) and ((int(sw.GSw_HydroPSHDurData) == 1) or (sw.GSw_HydroStorInMaxFrac == 'data')),sum,ignore,r,"*i,v",,0,0,,,,,,
-cap_hyd_ccseason_adj.csv,,1,mean,uniform,r,"*i,ccseason",,0,0,,1,,,,
-cap_hyd_szn_adj.csv,,1,mean,uniform,r,"*i,szn",,0,0,,1,,,,
-cap_limit.csv,,1,ignore,ignore,,,,1,0,,,,,,
 cap_penalty.csv,inputs/financials/cap_penalty.csv,1,ignore,ignore,,tg,,,0,,,,,,
-capnonrsc.csv,,1,sum,ignore,r,i,,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-capnonrsc_energy.csv,,1,sum,ignore,r,i,,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
 cappayments_ba.csv,inputs/capacity_exogenous/cappayments_ba.csv,1,ignore,ignore,,*r,,0,0,,,,,,not done but not used
-caprsc.csv,,1,sum,ignore,r,i,,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-ccreg.csv,,1,ignore,ignore,,,,,0,,,,,,
 ccs_link.csv,inputs/emission_constraints/ccs_link.csv,1,ignore,ignore,,,,,0,,,,,,
 ccs_link_water.csv,inputs/emission_constraints/ccs_link_water.csv,1,ignore,ignore,,,,,0,,,,,,
-ccseason_dates.csv,,1,ignore,ignore,,,,,0,,,,,,
-ccsflex_cat.csv,inputs/sets/ccsflex_cat.csv,1,ignore,ignore,,,,,,,,,ccsflex_cat,,
-ccsflex_perf.csv,,1,ignore,ignore,,,,,0,,,,,,
+ccsflex_cat.csv,inputs/sets/ccsflex_cat.csv,1,ignore,ignore,,,,,,,,set,ccsflex_cat,flexible ccs performance parameter categories,
 cd_beta0.csv,inputs/fuelprices/cd_beta0.csv,1,ignore,ignore,*cendiv,,,,0,,,,,,
 cd_beta0_allsector.csv,inputs/fuelprices/cd_beta0_allsector.csv,1,ignore,ignore,*cendiv,,,,0,,,,,,
-cendivweights.csv,inputs/fuelprices/cendivweights.csv,1,mean,ignore,r_cendiv,wide,,1,0,,,,,,Includes two region definitions; disaggfunc set to ignore because data is already at county resolution in inputs folder
 ces_fraction.csv,inputs/state_policies/ces_fraction.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,st,,1,0,,,,,,
-cf_hyd.csv,,1,mean,uniform,r,"*i,szn,t",,0,0,,1,,,,
-cf_vre.csv,,1,mean_cap,ignore,r,"*i,h",*i,0,0,,1,,,,disaggfunc set to ignore because data will be written in correct spatial resolution by hourly_writetimeseries in hourly_repperiods.py
-climate_UnappWaterMult.csv,,1,ignore,ignore,,,,,0,,1,,,,created by hourly_writetimeseries.py from temp_UnappWaterMult.csv data
-climate_UnappWaterMultAnn.csv,,1,ignore,ignore,,,,,0,,1,,,,created by climateprep.py
-climate_UnappWaterSeaAnnDistr.csv,,1,ignore,ignore,,,,,0,,1,,,,created by hourly_writetimeseries.py from temp_UnappWaterSeaAnnDistr.csv data
-climate_heuristics_finalyear.csv,,1,ignore,ignore,,,,,0,,,,,,
-climate_heuristics_yearfrac.csv,,1,ignore,ignore,,,,,0,,,,,,
-climate_hydadjann.csv,,1,ignore,ignore,,,,,0,,1,,,,created by climateprep.py
-climate_hydadjsea.csv,,1,ignore,ignore,,,,,0,,1,,,,created by hourly_writetimeseries.py from temp_hydadjsea.csv data
-climate_loaddelta_timeslice.csv,,1,ignore,ignore,,"r,h",,1,0,,,,,,not done but rarely used; ignore for now
-climate_param.csv,inputs/sets/climate_param.csv,1,ignore,ignore,,,,,,,,,climate_param,,
-co2_cap.csv,,int(sw.GSw_AnnualCap) != 0,ignore,ignore,,,,0,0,,,,,,
-co2_capture_incentive.csv,,1,ignore,ignore,,,,,0,,,,,,
+climate_param.csv,inputs/sets/climate_param.csv,1,ignore,ignore,,,,,,,,set,climate_param,parameters defined in climate_heuristics_finalyear,
 co2_site_char.csv,inputs/ctus/co2_site_char.csv,1,ignore,ignore,,,,0,,,,,,,
-co2_tax.csv,,int(sw.GSw_CarbTax) != 0,ignore,ignore,,,,0,,,,,,,
 coal_price.csv,inputs/fuelprices/coal_{coalscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
 construction_schedules.csv,inputs/financials/construction_schedules_{construction_schedules_suffix}.csv,1,ignore,ignore,,,,1,0,,,,,,
 construction_times.csv,inputs/financials/construction_times_{construction_times_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
 consume_char.csv,inputs/consume/consume_char_{GSw_H2_Inputs}.csv,int(sw.GSw_H2) != 0,ignore,ignore,,"*i,t,parameter",,0,0,,,,,,
-consumecat.csv,inputs/sets/consumecat.csv,1,ignore,ignore,,,,,,,,,consumecat,,
-consumechardac.csv,,1,ignore,ignore,,"*i,t,variable",,0,0,,,,,,
+consumecat.csv,inputs/sets/consumecat.csv,1,ignore,ignore,,,,,,,,set,consumecat,categories for consuming facility characteristics,
 cost_cap_mult.csv,inputs/waterclimate/cost_cap_mult.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
 cost_hurdle_country.csv,inputs/transmission/cost_hurdle_country.csv,1,ignore,ignore,*country,,,,0,,,,,,
 cost_hurdle_intra.csv,inputs/transmission/cost_hurdle_intra.csv,1,ignore,ignore,,t,,,0,,,,,,
-cost_hurdle_rate1.csv,,1,ignore,ignore,,t,,0,0,,1,,,,
-cost_hurdle_rate2.csv,,1,ignore,ignore,,t,,0,0,,1,,,,
-cost_opres.csv,,1,ignore,ignore,,,,,0,,,,,,
 cost_opres_default.csv,inputs/plant_characteristics/cost_opres_default.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
 cost_opres_market.csv,inputs/plant_characteristics/cost_opres_market.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
-cost_vom.csv,,1,mean,ignore,r,"i,v,t",,0,0,,1,,,,ReEDS-to-PLEXOS output
 cost_vom_mult.csv,inputs/waterclimate/cost_vom_mult.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
-county2zone.csv,,1,ignore,ignore,,,,,,,1,,,,
-county2zone_original.csv,,1,ignore,ignore,,,,,,,1,,,,
-crf.csv,,1,ignore,ignore,,,,0,,,,,,,
-crf_co2_incentive.csv,,1,ignore,ignore,,,,,0,,,,,,
-crf_h2_incentive.csv,,1,ignore,ignore,,,,,0,,,,,,
-csapr_cat.csv,inputs/sets/csapr_cat.csv,1,ignore,ignore,,,,,,,,,csapr_cat,,
-csapr_group.csv,inputs/sets/csapr_group.csv,1,ignore,ignore,,,,,,,,,csapr_group,,
+csapr_cat.csv,inputs/sets/csapr_cat.csv,1,ignore,ignore,,,,,,,,set,csapr_cat,CSAPR regulation categories,
+csapr_group.csv,inputs/sets/csapr_group.csv,1,ignore,ignore,,,,,,,,set,csapr_group,CSAPR trading group,
 csapr_group1_ex.csv,inputs/emission_constraints/csapr_group1_ex.csv,int(sw.GSw_CSAPR) != 0,ignore,ignore,*st,,,,0,,,,,,
 csapr_group2_ex.csv,inputs/emission_constraints/csapr_group2_ex.csv,int(sw.GSw_CSAPR) != 0,ignore,ignore,*st,,,,0,,,,,,
 csapr_ozone_season.csv,inputs/emission_constraints/csapr_ozone_season.csv,int(sw.GSw_CSAPR) != 0,ignore,ignore,st,,,,0,,,,,,
-ctus_r_cs_spurlines_200mi.csv,,1,ignore,ignore,,,,,,,1,,,,
 currency_incentives.csv,inputs/financials/currency_incentives.csv,1,ignore,ignore,,,,,0,,,,,,
 dac_elec.csv,inputs/consume/dac_elec_{dacscen}.csv,int(sw.GSw_DAC) != 0,ignore,ignore,,,,1,0,,,,,,
 dac_gas.csv,inputs/consume/dac_gas_{GSw_DAC_Gas_Case}.csv,int(sw.GSw_DAC) != 0,ignore,ignore,,,,1,0,,1,,,,
@@ -107,12 +60,9 @@ degradation_annual.csv,inputs/degradation/degradation_annual_{degrade_suffix}.cs
 demonstration_plants.csv,inputs/capacity_exogenous/demonstration_plants.csv,int(sw.GSw_NuclearDemo) != 0,sum,ignore,r,"t,i,coolingwatertech,ctt,wst,notes",i,0,0,,,,,,
 depreciation_schedules.csv,inputs/financials/depreciation_schedules_{depreciation_schedules_suffix}.csv,1,ignore,ignore,,,,1,0,,,,,,
 diagnose.gms,postprocessing/diagnose/diagnose.gms,1,ignore,ignore,,,,,,,,,,,
-disagg_geosize.csv,,1,ignore,ignore,,,,,0,,,,,,
 disagg_hydroexist.csv,inputs/disaggregation/disagg_hydroexist.csv,1,ignore,ignore,,,,,0,,,,,,
 disagg_population.csv,inputs/disaggregation/county_population.csv,1,ignore,ignore,FIPS,,,,0,,1,,,,
 disagg_state_lpf.csv,inputs/disaggregation/county_state_lpf.csv,1,ignore,ignore,FIPS,,,,0,,1,,,,
-distance_reinforcement.csv,,1,ignore,ignore,r,"*i,rscbin",*i,0,0,,1,,,,
-distance_spur.csv,,1,ignore,ignore,r,"*i,rscbin",*i,0,0,,1,,,,
 distpvcap.csv,inputs/dgen_model_inputs/{distpvscen}/distpvcap_{distpvscen}.csv,1,sum,ignore,r,wide,,1,0,,,,,,
 dollaryear_consume.csv,inputs/consume/dollaryear.csv,int(sw.GSw_DAC) != 0,ignore,ignore,,,,,0,,,,,,Do we really need 3 separate instances of dollaryear?
 dollaryear_plant.csv,inputs/plant_characteristics/dollaryear.csv,1,ignore,ignore,,,,,0,,,,,,Do we really need 3 separate instances of dollaryear?
@@ -123,184 +73,105 @@ dr_shed_cap.csv,inputs/supply_curve/dr_shed_cap_{dr_shedscen}.csv,int(sw.GSw_DRS
 dr_shed_cost.csv,inputs/supply_curve/dr_shed_cost_{dr_shedscen}.csv,int(sw.GSw_DRShed) != 0,ignore,ignore,wide,tech,,1,0,,,,,,done
 dr_shed_capacity_scalar.csv,inputs/demand_response/dr_shed_capacity_scalar_{dr_shedscen}.csv,int(sw.GSw_DRShed) != 0,ignore,ignore,r,"tech,wide",,1,0,,,,,,done
 dr_shed_hourly.h5,inputs/profiles_dr/dr_shed_hourly_{dr_shedscen}.h5,int(sw.GSw_DRShed) != 0,ignore,ignore,wide,"datetime,year",,1,keepindex,,,,,,Agg/Disagg handled in hourly_load
-e.csv,inputs/sets/e.csv,1,ignore,ignore,,,,,0,,,,e,,
-eall.csv,inputs/sets/eall.csv,1,ignore,ignore,,,,,,,,,eall,,
-emit_rate.csv,,1,ignore,ignore,,"etype,e,i,v,r",,0,0,,,,,,ReEDS-to-PLEXOS output
+e.csv,inputs/sets/e.csv,1,ignore,ignore,,,,,0,,,set,e,emission categories used in model,
+eall.csv,inputs/sets/eall.csv,1,ignore,ignore,,,,,,,,set,eall,emission categories used in reporting,
 emitrate.csv,inputs/emission_constraints/emitrate.csv,1,ignore,ignore,,,,,0,,,,,,
 energy_communities.csv,inputs/financials/energy_communities.csv,1,ignore,ignore,,,,,0,,,,,,region aggregation and filtering is handled in copy_files
-etype.csv,inputs/sets/etype.csv,1,ignore,ignore,,,,,,,,,etype,,
-eval_period_adj_mult.csv,,1,ignore,ignore,,,,,0,,,,,,
+etype.csv,inputs/sets/etype.csv,1,ignore,ignore,,,,,,,,set,etype,emission types used in model (upstream and process),
 exog_cap_geohydro.csv,inputs/capacity_exogenous/exog_cap_geohydro_{GSw_SitingGeo}.csv,int(sw.GSw_Geothermal) != 0,ignore,ignore,region,"*tech,sc_point_gid,year",,0,0,,,,,,
 exog_cap_upv.csv,inputs/capacity_exogenous/exog_cap_upv_{GSw_SitingUPV}.csv,1,ignore,ignore,region,"*tech,sc_point_gid,year",,0,0,,,,,,
 exog_cap_wind-ons.csv,inputs/capacity_exogenous/exog_cap_wind-ons_{GSw_SitingWindOns}.csv,1,ignore,ignore,region,"*tech,sc_point_gid,year",,0,0,,,,,,
-f.csv,inputs/sets/f.csv,1,ignore,ignore,,,,,,,,,f,,
+f.csv,inputs/sets/f.csv,1,ignore,ignore,,,,,,,,set,f,fuel types,
 financials_hydrogen.csv,inputs/financials/financials_hydrogen.csv,int(sw.GSw_H2) != 0,ignore,ignore,,,,,0,,,,,,
 financials_sys.csv,inputs/financials/financials_sys_{financials_sys_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
 financials_tech.csv,inputs/financials/financials_tech_{financials_tech_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
 financials_transmission.csv,inputs/financials/financials_transmission_{financials_trans_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
-financing_risk_mult.csv,,1,ignore,ignore,,,,,0,,,,,,
-firm_import_limit.csv,,1,ignore,ignore,,,,,0,,1,,,,
-firstyear.csv,,1,ignore,ignore,,,,,0,,1,,,,
 years_until_endogenous.csv,inputs/plant_characteristics/years_until_endogenous.csv,1,ignore,ignore,,,,,0,,,,,,
-flex_frac_all.csv,,1,mean,population,r,"*flextype,h,wide",,1,0,,1,,,,
-flex_type.csv,inputs/sets/flex_type.csv,1,ignore,ignore,,,,,,,,,flex_type,,
+flex_type.csv,inputs/sets/flex_type.csv,1,ignore,ignore,,,,,,,,set,flex_type,demand flexibility types,
 forced_retirements.csv,inputs/state_policies/forced_retirements.csv,1,ignore,ignore,st,"*i,t",,0,0,,,,,,
-forceperiods.csv,,1,ignore,ignore,,,,,,,,,,,
-frac_h_ccseason_weights.csv,,1,ignore,ignore,,,,,,,,,,,
-frac_h_month_weights.csv,,1,ignore,ignore,,,,,,,,,,,
-frac_h_quarter_weights.csv,,1,ignore,ignore,,,,,,,,,,,
-fuel2tech.csv,inputs/sets/fuel2tech.csv,1,ignore,ignore,,,,,0,,,,fuel2tech,,
-fuel_price.csv,,1,ignore,ignore,,"i,r",,0,0,,,,,,ReEDS-to-PLEXOS output
-fuelbin.csv,inputs/sets/fuelbin.csv,1,ignore,ignore,,,,,,,,,fuelbin,,
+fuel2tech.csv,inputs/sets/fuel2tech.csv,1,ignore,ignore,,,,,0,,,set,fuel2tech,mapping between fuel types and generations,
+fuelbin.csv,inputs/sets/fuelbin.csv,1,ignore,ignore,,,,,,,,set,fuelbin,gas usage bracket,
 futurefiles.csv,inputs/userinput/futurefiles.csv,1,ignore,ignore,,,,,0,,,,,,
-gb.csv,inputs/sets/gb.csv,1,ignore,ignore,,,,,,,,,gb,,
-gbin.csv,inputs/sets/gbin.csv,1,ignore,ignore,,,,,,,,,gbin,,
+gb.csv,inputs/sets/gb.csv,1,ignore,ignore,,,,,,,,set,gb,gas price bins; must have an odd number of bins; e.g. gb1*gb15,
+gbin.csv,inputs/sets/gbin.csv,1,ignore,ignore,,,,,,,,set,gbin,growth bins,
 gbin_min.csv,inputs/growth_constraints/gbin_min.csv,1,ignore,ignore,,,,,0,,,,,,
-gen_mandate_tech_list.csv,,1,ignore,ignore,,,,,0,,,,,,
-gen_mandate_trajectory.csv,,1,ignore,ignore,,,,,0,,,,,,
 geo_discovery_factor.csv,inputs/geothermal/geo_discovery_factor_{geohydrosupplycurve}.csv,int(sw.GSw_Geothermal) != 0,mean,uniform,r,*i,*i,0,0,,,,,,
 geo_discovery_rate.csv,inputs/geothermal/geo_discovery_{geodiscov}.csv,int(sw.GSw_Geothermal) != 0,ignore,ignore,,,,0,,,,,,,
-geo_fom.csv,,1,mean,uniform,r,*i,,0,0,,1,,,,
-geo_fom_mult.csv,,1,ignore,ignore,,0,,0,,,,,,,
-geo_retirements.csv,,1,sum,ignore,r,"i,v,wide",,1,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
 geo_rsc.csv,inputs/geothermal/geo_rsc_ATB_2023.csv,int(sw.GSw_Geothermal) != 0,sc_cat,geosize,r,*i,,0,0,value,,,,,
-geocapcostmult.csv,,1,ignore,ignore,,,,1,0,,,,,,
-geoexist.csv,,1,ignore,ignore,r,*i,,0,0,,1,,,,written to proper spatial aggregation in writecapdat.py
 growth_bin_size_mult.csv,inputs/growth_constraints/growth_bin_size_mult.csv,1,ignore,ignore,,,,,0,,,,,,
 growth_limit_absolute.csv,inputs/growth_constraints/growth_limit_absolute.csv,1,ignore,ignore,,,,,0,,,,,,
 growth_penalty.csv,inputs/growth_constraints/growth_penalty.csv,1,ignore,ignore,,,,,0,,,,,,
-gswitches.csv,,1,ignore,ignore,,,,,0,,,,,,
 h2_ba_share.csv,inputs/consume/h2_demand_county_share.csv,int(sw.GSw_H2) != 0,sum,ignore,*r,t,,0,0,,,,,,
-gwp.csv,inputs/emission_constraints/gwp.csv,1,ignore,ignore,,,,,0,,,,gwp,,
-h2_existing_smr_cap.csv,,1,sum,population,*r,t,,0,0,,1,,,,
-h_preh.csv,,1,ignore,ignore,,,,,0,,,,,,
+gwp.csv,inputs/emission_constraints/gwp.csv,1,ignore,ignore,,,,,0,,1,parameter,gwp,,
 h2_leakage_rate.csv,inputs/emission_constraints/h2_leakage_rate.csv,1,ignore,ignore,,,,,0,,,,,,
 h2_exogenous_demand.csv,inputs/consume/h2_exogenous_demand.csv,int(sw.GSw_H2) != 0,ignore,ignore,,p,,1,0,,,,,,
-h2_pipeline_cap_cost_mult.csv,,1,ignore,ignore,,,,,,,1,,,,
-h2_ptc.csv,,1,ignore,ignore,,,*i,,0,,,,,,
-h2_st.csv,inputs/sets/h2_st.csv,1,ignore,ignore,,,,,,,,,h2_st,,
-h2_stor.csv,inputs/sets/h2_stor.csv,1,ignore,ignore,,,,,0,,,,h2_stor,,
-h2_storage_rb.csv,,int(sw.GSw_H2) != 0,ignore,ignore,rb,,,0,0,,1,,,,
+h2_st.csv,inputs/sets/h2_st.csv,1,ignore,ignore,,,,,,,,set,h2_st,investments needed to store and transport H2,
+h2_stor.csv,inputs/sets/h2_stor.csv,1,ignore,ignore,,,,,0,,,set,h2_stor,H2 storage options,
 h2_transport_and_storage_costs.csv,inputs/consume/h2_transport_and_storage_costs.csv,int(sw.GSw_H2) != 0,ignore,ignore,,,,,,,,,,,
-h_actualszn.csv,,1,ignore,ignore,,,,,0,,,,,,
-h_ccseason_prm.csv,,1,ignore,ignore,,,,,0,,,,,,
-h_dt_szn.csv,,1,ignore,ignore,,,,,0,,1,,,,
-h_szn.csv,,1,ignore,ignore,,,,0,0,,,,,,
-h_szn_end.csv,,1,ignore,ignore,,,,,0,,,,,,
-h_szn_start.csv,,1,ignore,ignore,,,,,0,,,,,,
-heat_rate.csv,,1,ignore,ignore,,"i,v,r",,0,0,,,,,,ReEDS-to-PLEXOS output
 heat_rate_adj.csv,inputs/plant_characteristics/heat_rate_adj.csv,1,ignore,ignore,,,,,0,,,,,,
 heat_rate_mult.csv,inputs/waterclimate/heat_rate_mult.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
 heat_rate_penalty_spin.csv,inputs/plant_characteristics/heat_rate_penalty_spin.csv,1,ignore,ignore,,,,,0,,,,,,
-hierarchy.csv,,1,first,ignore,*r,"nercr,transreg,transgrp,cendiv,st,interconnect,country,usda_region,h2ptcreg",,0,0,,1,,,,post_copy column set to 1 since copy_files filters this file separately
-hierarchy_itlgrp.csv,,1,ignore,ignore,,,,,0,,1,,,,post_copy column set to 1 since copy_files copies this file separately
-hierarchy_original.csv,,1,ignore,ignore,,,,,0,,1,,,,post_copy column set to 1 since copy_files copies this file separately
-hierarchy_with_res.csv,,1,ignore,ignore,,,,,0,,1,,,,post_copy column set to 1 since copy_files copies this file separately
-hintage_char.csv,inputs/sets/hintage_char.csv,1,ignore,ignore,,,,,,,,,hintage_char,,
-hintage_data.csv,,1,ignore,ignore,,,,,0,,,,,,handled separately in WriteHintage.py
-hmap_allyrs.csv,,1,ignore,ignore,,,,,0,,,,,,
-hmap_myr.csv,,1,ignore,ignore,,,,,0,,,,,,
-hour_szn_group.csv,,1,ignore,ignore,,,,,,,,,,,
-hourly_szn_end.csv,,1,ignore,ignore,,,,,,,,,,,
-hourly_szn_start.csv,,1,ignore,ignore,,,,,,,,,,,
-hours_hourly.csv,,1,ignore,ignore,,,,,,,,,,,
-hset_hourly.csv,,1,ignore,ignore,,,,,,,,,,,
+hintage_char.csv,inputs/sets/hintage_char.csv,1,ignore,ignore,,,,,,,,set,hintage_char,characteristics available in hintage_data,
 hyd_add_upg_cap.csv,inputs/supply_curve/hyd_add_upg_cap.csv,int(sw.GSw_HydroCapEnerUpgradeType) == 2,sum,hydroexist,r,"i,rscbin,wide",,1,0,,,,,,
 hyd_fom.csv,inputs/hydro/hyd_fom.csv,1,mean,uniform,wide,i,,1,0,,,,,,
-hydadjann.csv,inputs/climate/{climatescen}/hydadjann.csv,int(sw.GSw_ClimateHydro) != 0,mean,uniform,r,t,,0,0,,,,,,
-hydadjsea.csv,inputs/climate/{climatescen}/hydadjsea.csv,int(sw.GSw_ClimateHydro) != 0,mean,uniform,r,"month,t",,0,0,,,,,,
 hydcap.csv,inputs/supply_curve/hydcap.csv,1,sum,geosize,wide,"tech,class",,1,0,,,,,,
 hydcapadj.csv,inputs/hydro/SeaCapAdj_hy.csv,1,mean,uniform,r,"*i,month",,0,0,,,,,,
-hydcf.csv,,1,ignore,ignore,r,"t,*i,month",,0,0,,1,,,,
 hydcf_fixed.csv,inputs/hydro/hydcf_fixed.csv,1,mean,uniform,r,"*i,month",,0,0,,,,,,
 hydcost.csv,inputs/supply_curve/hydcost.csv,1,mean,uniform,wide,"tech,class",,1,0,,,,,,
 hydro_mingen.csv,inputs/hydro/hydro_mingen.csv,1,mean,uniform,r,"*i,quarter",,0,0,,,,,,might be better to do something capacity-weighted
-hydrocapcostmult.csv,,1,ignore,ignore,,,,1,0,,,,,,
 hydrofrac_policy.csv,inputs/state_policies/hydrofrac_policy.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,"RPS_All,CES",,,0,,,,,,
 hydrogen_price.csv,inputs/fuelprices/h2-combustion_{h2combustionfuelscen}.csv,int(sw.GSw_H2Combustion) != 0,ignore,ignore,,,,0,0,,,,,,
 i_coolingtech_watersource.csv,inputs/waterclimate/i_coolingtech_watersource.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
 i_coolingtech_watersource_link.csv,inputs/waterclimate/i_coolingtech_watersource_link.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
 i_coolingtech_watersource_upgrades.csv,inputs/upgrades/i_coolingtech_watersource_upgrades.csv,1,ignore,ignore,,,,,0,,,,,,
 i_coolingtech_watersource_upgrades_link.csv,inputs/upgrades/i_coolingtech_watersource_upgrades_link.csv,1,ignore,ignore,,,,,0,,,,,,
-i_geotech.csv,inputs/sets/i_geotech.csv,1,ignore,ignore,,,,,0,,,,i_geotech,,
-i_p.csv,inputs/sets/i_p.csv,1,ignore,ignore,,,,,0,,,,i_p,,
-i_water_nocooling.csv,inputs/sets/i_water_nocooling.csv,1,ignore,ignore,,,,,0,,,,i_water_nocooling,,
+i_geotech.csv,inputs/sets/i_geotech.csv,1,ignore,ignore,,,,,0,,,set,i_geotech,crosswalk between an individual geothermal technology and its category,
+i_p.csv,inputs/sets/i_p.csv,1,ignore,ignore,,,,,0,,,set,i_p,mapping from technologies to the products they produce,
+i_water_nocooling.csv,inputs/sets/i_water_nocooling.csv,1,ignore,ignore,,,,,0,,,set,i_water_nocooling,technologies that use water but are not differentiated by cooling tech and water source,
 incentives.csv,inputs/financials/incentives_{incentives_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
 inflation.csv,inputs/financials/inflation_{inflation_suffix}.csv,1,ignore,ignore,,,,0,0,,,,,,
 interconnection_queues.csv,inputs/capacity_exogenous/interconnection_queues.csv,1,ignore,ignore,r,"tg,r",,1,0,,,,,,
-itc_energy_comm_bonus.csv,,1,mean,ignore,r,*i,,0,0,,1,,,,
-itc_frac_monetized.csv,,1,ignore,ignore,,,,,0,,,,,,
-itc_fractions.csv,,1,ignore,ignore,,"i,country,t",,0,0,,,,,,
-ivt.csv,,1,ignore,ignore,,,,,0,,,,,,created in runbatch.py
-ivt_step.csv,,1,ignore,ignore,,,,,0,,,,,,
-lcclike.csv,inputs/sets/lcclike.csv,1,ignore,ignore,,,,,0,,,,lcclike,,
-load_2010.csv,,1,sum,ignore,r,wide,,1,0,,1,,,,disaggfunc set to ignore because load will already be in correct spatial resolution
-load_allyear.csv,,1,sum,ignore,*r,"h,t",,0,0,,1,,,,disaggfunc set to ignore because load will already be in correct spatial resolution
+lcclike.csv,inputs/sets/lcclike.csv,1,ignore,ignore,,,,,0,,,set,lcclike,transmission capacity types where lines are bundled with AC/DC converters,
 load_multiplier.csv,inputs/load/demand_{demandscen}.csv,1,ignore,ignore,,,,,0,,,,,,
-load_multiplier_r.csv,,1,ignore,ignore,,,,1,0,,,,,,
 loadsite_annual.csv,inputs/load/loadsite_{GSw_LoadSiteTrajectory}.csv,float(sw.GSw_LoadSiteCF) > 0,ignore,ignore,*loadsitereg,t,,,0,,,,,,
-maps.gpkg,,1,ignore,ignore,,,,,,,1,,,,
 maxage.csv,inputs/plant_characteristics/maxage.csv,1,ignore,ignore,,,,,0,,,,,,
 maxdailycf.csv,inputs/plant_characteristics/maxdailycf.csv,int(sw.GSw_MaxDailyCF) != 0,ignore,ignore,,,,,0,,,,,,
 mcs_distributions.yaml,inputs/userinput/mcs_distributions_{MCS_dist}.yaml,int(sw.MCS_runs) != 0,ignore,ignore,,,,,,,,,,,
-mcs_group_weights.csv,,1,ignore,ignore,,,,,,,,,,,
-methane_leakage_rate.csv,,1,ignore,ignore,,,,0,0,,,,,,
 mex_growth_rate.csv,inputs/load/mex_growth_rate.csv,1,ignore,ignore,,,,0,,,,,,,
 minCF.csv,inputs/plant_characteristics/minCF.csv,int(sw.GSw_MinCF) != 0,ignore,ignore,,,,,0,,,,,,
 min_retire_age.csv,inputs/plant_characteristics/min_retire_age.csv,1,ignore,ignore,,,,,0,,,,,,
 mingen_fixed.csv,inputs/plant_characteristics/mingen_fixed.csv,int(sw.GSw_MingenFixed) != 0,ignore,ignore,,,*i,,0,,,,,,
 minloadfrac0.csv,inputs/plant_characteristics/minloadfrac0.csv,(int(sw.GSw_Mingen) != 0) or (int(sw.GSw_MinLoading) != 0),ignore,ignore,,,,,0,,,,,,
 modeled_regions.csv,inputs/userinput/modeled_regions.csv,1,ignore,ignore,,,,,,,,,,,
-modeledyears.csv,,1,ignore,ignore,,,,,0,,,,,,
 month2quarter.csv,inputs/temporal/month2quarter.csv,1,ignore,ignore,,,,,0,,,,,,
 mttr.csv,inputs/plant_characteristics/mttr.csv,1,ignore,ignore,,,tech,,0,,,,,,
 natgas_price_cendiv.csv,inputs/fuelprices/ng_{ngscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
 national_rps_frac_allScen.csv,inputs/national_generation/national_rps_frac_allScen.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,,,,1,0,,,,,,
 net_gen_existing_hydro.csv,inputs/hydro/net_gen_existing_hydro.csv,1,ignore,ignore,,"t,month",,0,0,,,,,,
 peak_net_imports.csv,inputs/reserves/peak_net_imports.csv,1,ignore,ignore,nercr,t,,0,0,,,,,,
-nexth.csv,,1,ignore,ignore,,,,,0,,,,,,
-nexth_actualszn.csv,,1,ignore,ignore,,,,,0,,,,,,
-nextpartition.csv,,1,ignore,ignore,,,,,0,,,,,,
-ng_crf_penalty.csv,,1,ignore,ignore,,,,0,0,,,,,,
 ng_crf_penalty_st.csv,inputs/state_policies/ng_crf_penalty_st.csv,1,ignore,ignore,st,*t,,0,0,,,,,,
 ng_demand_elec.csv,inputs/fuelprices/ng_demand_{ngscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
 ng_demand_tot.csv,inputs/fuelprices/ng_tot_demand_{ngscen}.csv,1,ignore,ignore,wide_cendiv,year,,1,0,,,,,,
-noretire.csv,inputs/sets/noretire.csv,1,ignore,ignore,,,,,0,,,,noretire,,
-notvsc.csv,inputs/sets/notvsc.csv,1,ignore,ignore,,,,,0,,,,notvsc,,
-nuclear_ba_ban_list.csv,,int(sw.GSw_NukeStateBan) != 0,ignore,ignore,,,,,,,,,,,
+noretire.csv,inputs/sets/noretire.csv,1,ignore,ignore,,,,,0,,,set,noretire,technologies that will never be retired,
+notvsc.csv,inputs/sets/notvsc.csv,1,ignore,ignore,,,,,0,,,set,notvsc,transmission capacity types that are not VSC,
 nuclear_energy_communities.csv,inputs/financials/nuclear_energy_communities.csv,1,ignore,ignore,,,,,0,,,,,,region aggregation and filtering is handled in copy_files
 nuclear_subsidies.csv,inputs/state_policies/nuclear_subsidies.csv,1,ignore,ignore,*st,year,,0,0,,,,,,
-numhours.csv,,1,ignore,ignore,,,,,0,,,,,,
-numhours_nexth.csv,,1,ignore,ignore,,,,,0,,1,,,,
 objective_function_params.yaml,tests/objective_function_params.yaml,1,ignore,ignore,,,,,,,,,,,
 offshore_req.csv,inputs/state_policies/offshore_req_{GSw_OfsWindForceScen}.csv,(int(sw.GSw_StateRPS) != 0) and (int(sw.GSw_OfsWind) != 0),ignore,ignore,st,,,1,0,,,,,,
-offshore.csv,,1,ignore,ignore,,,,0,none,,,,,,
-ofstype.csv,inputs/sets/ofstype.csv,1,ignore,ignore,,,,,,,,,ofstype,,
-ofstype_i.csv,inputs/sets/ofstype_i.csv,1,ignore,ignore,,,,,0,,,,ofstype_i,,
-ofswind_rsc_mult.csv,,1,ignore,ignore,,,,1,0,,,,,,
+ofstype.csv,inputs/sets/ofstype.csv,1,ignore,ignore,,,,,,,,set,ofstype,offshore types used in offshore requirement constraint (eq_RPS_OFSWind),
+ofstype_i.csv,inputs/sets/ofstype_i.csv,1,ignore,ignore,,,,,0,,,set,ofstype_i,crosswalk between ofstype and i,
 oosfrac.csv,inputs/state_policies/oosfrac.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,*st,,,,0,,,,,,
 opres_periods.csv,inputs/reserves/opres_periods.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
-orcat.csv,inputs/sets/orcat.csv,1,ignore,ignore,,,,,,,,,orcat,,
+orcat.csv,inputs/sets/orcat.csv,1,ignore,ignore,,,,,,,,set,orcat,operating reserve category for RHS calculations,
 orperc.csv,inputs/reserves/orperc.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
-ortype.csv,inputs/sets/ortype.csv,1,ignore,ignore,,,,,,,,,ortype,,
-outage_forced_h.csv,,1,ignore,ignore,r,"*i,h",,0,0,,1,,,,handled in outage_rates.py
-outage_forced_hourly.h5,,1,ignore,ignore,wide,index,index,1,keepindex,,1,,,,handled in outage_rates.py
+ortype.csv,inputs/sets/ortype.csv,1,ignore,ignore,,,,,,,,set,ortype,types of operating reserve constraints,
 outage_forced_static.csv,inputs/plant_characteristics/outage_forced_static.csv,1,ignore,ignore,,,,,none,,,,,,
 outage_forced_temperature.csv,inputs/plant_characteristics/outage_forced_temperature_{GSw_OutageScen}.csv,sw.GSw_OutageScen != 'static',ignore,ignore,,,,,0,,,,,,
 outage_scheduled_static.csv,inputs/plant_characteristics/outage_scheduled_static.csv,1,ignore,ignore,,,,,0,,,,,,
 outage_scheduled_monthly.csv,inputs/plant_characteristics/outage_scheduled_monthly.csv,1,ignore,ignore,,,,,0,,,,,,
-p.csv,inputs/sets/p.csv,1,ignore,ignore,,,,,,,,,p,,
-peak_ccseason.csv,,1,sum,ignore,*r,"ccseason,t",,0,0,,1,,,,ok because it's load during peak NERC hour
-peak_h.csv,,1,sum,ignore,r,"h,wide",,1,0,,1,,,,
-peakload.csv,,1,ignore,ignore,,,,,0,,1,,,,
-peakload_nercr.csv,,1,ignore,ignore,,,,,0,,1,,,,
-period_szn.csv,,1,ignore,ignore,,,,,0,,,,,,
+p.csv,inputs/sets/p.csv,1,ignore,ignore,,,,,,,,set,p,products produced,
 period_szn_user.csv,inputs/temporal/period_szn_{GSw_HourlyClusterAlgorithm}.csv,sw.GSw_HourlyClusterAlgorithm == 'user*',ignore,ignore,,,,,,,,,,,
-period_weights.csv,,1,ignore,ignore,,,,,0,,,,,,
-periodmap_1yr.csv,,1,ignore,ignore,,,,0,0,,,,,,
-pipeline_cost_mult.csv,,int(sw.GSw_H2) != 0,trans_lookup,uniform,"*r,rr",,,0,0,drop_dup_r,1,,,,
-plantcat.csv,inputs/sets/plantcat.csv,1,ignore,ignore,,,,,,,,,plantcat,,
-plantcharout.csv,,1,ignore,ignore,,"0,2",,0,,,,,,,
+plantcat.csv,inputs/sets/plantcat.csv,1,ignore,ignore,,,,,,,,set,plantcat,categories for plant characteristics,
 plantchar_beccs.csv,inputs/plant_characteristics/{plantchar_beccs}.csv,int(sw.GSw_BECCS) != 0,ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
 plantchar_biopower.csv,inputs/plant_characteristics/{plantchar_biopower}.csv,1,ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
 plantchar_ccsflex_cost.csv,inputs/plant_characteristics/{ccsflexscen}_cost.csv,(int(sw.GSw_CCSFLEX_BYP) != 0) or (int(sw.GSw_CCSFLEX_DAC) != 0) or (int(sw.GSw_CCSFLEX_STO) != 0),ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
@@ -328,80 +199,39 @@ plantchar_other.csv,inputs/plant_characteristics/{plantchar_other}.csv,1,ignore,
 plantchar_pvb.csv,inputs/plant_characteristics/pvb_{pvbscen}.csv,int(sw.GSw_PVB) != 0,ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
 plantchar_upgrades.csv,inputs/upgrades/{upgradescen}.csv,sw.upgradescen != 'default',ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
 plantchar_upv.csv,inputs/plant_characteristics/{plantchar_upv}.csv,1,ignore,ignore,,,,,0,,,,,,used to define plantcharout.csv
-poi_cap_init.csv,,1,sum,ignore,*r,,,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-prepost.csv,inputs/sets/prepost.csv,1,ignore,ignore,,,,,,,,,prepost,,
-prescribed_builds_wind-ofs.csv,inputs/capacity_exogenous/prescribed_builds_wind-ofs_{GSw_OffshoreFiles}_{GSw_SitingWindOfs}.csv,int(sw.GSw_OfsWind) != 0,sum,ignore,region,year,,0,0,,,,,,disaggfunc set to ignore because data will be read in at the correct spatial resolution
-prescribed_builds_wind-ons.csv,inputs/capacity_exogenous/prescribed_builds_wind-ons_{GSw_SitingWindOns}.csv,1,sum,ignore,region,year,,0,0,,,,,,disaggfunc set to ignore because data will be read in at the correct spatial resolution
-prescribed_nonRSC.csv,,1,sum,ignore,r,"t,i",,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-prescribed_rsc.csv,,1,sum,ignore,r,"t,i",,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-prescriptivelink0.csv,inputs/sets/prescriptivelink0.csv,1,ignore,ignore,,,,,0,,,,prescriptivelink0,,
-prm_initial.csv,,1,ignore,ignore,*r,,,,0,,1,,,,
-prm.csv,,1,ignore,ignore,*r,,,,0,,1,,,,
-psh_sc_duration.csv,,1,ignore,ignore,,,,,0,,1,,,,Delete once aggregate_regions.py is moved up
+prepost.csv,inputs/sets/prepost.csv,1,ignore,ignore,,,,,,,,set,prepost,defines pre-2010 versus post-2010 years,
+prescribed_builds_wind-ofs.csv,inputs/capacity_exogenous/prescribed_builds_wind-ofs_{GSw_OffshoreFiles}_{GSw_SitingWindOfs}.csv,int(sw.GSw_OfsWind) != 0,ignore,ignore,region,year,,0,0,,,,,,aggfunc and disaggfunc set to ignore because data will be read in at the correct spatial resolution
+prescribed_builds_wind-ons.csv,inputs/capacity_exogenous/prescribed_builds_wind-ons_{GSw_SitingWindOns}.csv,1,ignore,ignore,region,year,,0,0,,,,,,aggfunc and disaggfunc set to ignore because data will be read in at the correct spatial resolution
+prescriptivelink0.csv,inputs/sets/prescriptivelink0.csv,1,ignore,ignore,,,,,0,,,set,prescriptivelink0,initial set of prescribed categories and their technologies - used in assigning prescribed builds,
 psh_supply_curves_duration.csv,inputs/storage/PSH_supply_curves_durations.csv,int(sw.GSw_Storage) != 0,ignore,ignore,,,,,0,,1,,,,
 psh_supply_curves_capacity.csv,inputs/supply_curve/PSH_supply_curves_capacity_{pshsupplycurve}.csv,int(sw.GSw_Storage) != 0,sum,geosize,r,wide,,1,0,,,,,,
 psh_supply_curves_cost.csv,inputs/supply_curve/PSH_supply_curves_cost_{pshsupplycurve}.csv,int(sw.GSw_Storage) != 0,mean,uniform,r,wide,,1,0,,,,,,
-pv_cf_improve.csv,,1,ignore,ignore,,,,0,,,,,,,
-pvb_agg.csv,inputs/sets/pvb_agg.csv,1,ignore,ignore,,,,,0,,,,pvb_agg,,
-pvb_bir.csv,,1,ignore,ignore,,,,,0,,,,,,
-pvb_config.csv,inputs/sets/pvb_config.csv,1,ignore,ignore,,,,,,,,,pvb_config,,
-pvb_ilr.csv,,1,ignore,ignore,,,,,0,,,,,,
-pvbcapcostmult.csv,,1,ignore,ignore,,,,0,0,,,,,,
-r.csv,,1,first,ignore,0,,,0,,,1,,,,disaggfunc set to ignore because this file is dynamic to the user-defined spatial aggregation level
-r_ba.csv,,1,ignore,ignore,,,,,,,,,,,
-r_cendiv.csv,,1,ignore,ignore,,,,,,,,,,,
-r_county.csv,,1,ignore,ignore,,,,,,,,,,,
-r_cs.csv,,1,first,ignore,*r,cs,,0,0,,1,,,,disaggfunc set to ignore because the spatial aggregation level of this file is controlled by the agglevel switch
-r_cs_distance_mi.csv,,1,mean,ignore,*r,cs,,0,0,,1,,,,disaggfunc set to ignore because the spatial aggregation level of this file is controlled by the agglevel switch
-routes_adjacent.csv,,1,trans_lookup,ignore,"*r,rr",,,0,0,,1,,,,disaggfunc set to ignore because the spatial aggregation level of this file is controlled by the agglevel switch
+pvb_agg.csv,inputs/sets/pvb_agg.csv,1,ignore,ignore,,,,,0,,,set,pvb_agg,crosswalk between hybrid pv+battery configurations and technology options,
+pvb_config.csv,inputs/sets/pvb_config.csv,1,ignore,ignore,,,,,,,,set,pvb_config,set of hybrid pv+battery configurations,
 ramprate.csv,inputs/plant_characteristics/ramprate.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
 ramptime.csv,inputs/reserves/ramptime.csv,int(sw.GSw_OpRes) != 0,ignore,ignore,,,,,0,,,,,,
-rb.csv,,1,first,ignore,0,,,0,,,1,,,,disaggfunc set to ignore because this file is specifically a collection of all valid BA regions
-rb_aggreg.csv,,1,ignore,ignore,,,,,,,,,,,
 recstyle.csv,inputs/state_policies/recstyle.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,*st,"RPSCat,style",,,0,,,,,,
 rectable.csv,inputs/state_policies/rectable.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st_st,st,,,0,,,,,,
 regional_cap_cost_diff.csv,inputs/financials/reg_cap_cost_diff_{reg_cap_cost_diff_suffix}.csv,1,mean,ignore,r,wide,,1,0,,,,,,precursor data to reg_cap_cost_diff.csv
-regions.csv,,1,ignore,ignore,,,,,0,,,,,,not done but only for retail
-resourceclass.csv,inputs/sets/resourceclass.csv,1,ignore,ignore,,,,,,,,,resourceclass,,
-resources.csv,,1,resources,ignore,r,"i,ccreg",i,0,0,,1,,,,disaggfunc set to ignore because this file contains all spatial resolutions valid to the model run
+resourceclass.csv,inputs/sets/resourceclass.csv,1,ignore,ignore,,,,,,,,set,resourceclass,renewable resource classes,
 retire_penalty.csv,inputs/financials/retire_penalty.csv,1,ignore,ignore,,,,,0,,,,,,
-retirements.csv,,1,sum,ignore,r,"t,i",,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-retirements_energy.csv,,1,sum,ignore,r,"t,i",,0,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
 rev_paths.csv,inputs/supply_curve/rev_paths.csv,1,ignore,ignore,,,,,0,,,,,,
-rev_transmission_basecost.csv,inputs/transmission/rev_transmission_basecost.csv,1,ignore,ignore,,,,,0,,,,,,
 rggi_states.csv,inputs/emission_constraints/rggi_states.csv,int(sw.GSw_RGGI) != 0,ignore,ignore,*st,,,,0,,,,,,
 rggicon.csv,inputs/emission_constraints/rggicon.csv,int(sw.GSw_RGGI) != 0,ignore,ignore,,,,0,,,,,,,
 rps_fraction.csv,inputs/state_policies/rps_fraction.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,st,,1,0,,,,,,
-rsc_combined.csv,,1,sc_cat,ignore,r,"*i,rscbin",*i,0,0,value,1,,,,done for upv/csp/wind; disaggfunc set to ignore because supply curve data is already at county level
-rsc_wsc.csv,,1,ignore,ignore,,,,,0,,,,,,
-runfiles.csv,runfiles.csv,1,ignore,ignore,ignore,,,0,0,,,,,,so meta
-sc_cat.csv,inputs/sets/sc_cat.csv,1,ignore,ignore,,,,,0,,,,sc_cat,,
+sc_cat.csv,inputs/sets/sc_cat.csv,1,ignore,ignore,,,,,0,,,set,sc_cat,supply curve categories (capacity and cost),
 scalars.csv,inputs/scalars.csv,1,ignore,ignore,,,,,0,,1,,,,
-set_actualszn.csv,,1,ignore,ignore,,,,,0,,,,,,
-set_allh.csv,,1,ignore,ignore,,,,,0,,,,,,
-set_allszn.csv,,1,ignore,ignore,,,,,0,,,,,,
-set_h.csv,,1,ignore,ignore,,,,,,,,,,,
-set_szn.csv,,1,ignore,ignore,,,,,,,,,,,
-site_bin_map.csv,,1,ignore,ignore,,,,,0,,,,,,
-spur_parameters.csv,,1,ignore,ignore,,,,,0,,,,,,"TODO (only used for plotting so ignoring for now, but should be fixed)"
-spurline_cost.csv,,1,ignore,ignore,,,,,0,,1,,,,Delete once aggregate_regions.py is moved up
-spurline_sitemap.csv,,1,ignore,ignore,,,,,0,,,,,,handled in writesupplycurves.py
 startcost.csv,inputs/plant_characteristics/startcost.csv,int(sw.GSw_StartCost) != 0,ignore,ignore,,,*i,,0,,,,,,
 state_cap.csv,inputs/emission_constraints/state_cap.csv,int(sw.GSw_StateCap) != 0,ignore,ignore,*st,t,,0,0,,,,,,
 storage_duration.csv,inputs/storage/storage_duration.csv,int(sw.GSw_Storage) != 0,ignore,ignore,,,,,0,,,,,,
-storage_duration_pshdata.csv,,1,ignore,ignore,r,"*i,v",,0,0,,1,,,,
 storage_mandates.csv,inputs/state_policies/storage_mandates.csv,int(sw.GSw_BatteryMandate) != 0,ignore,ignore,*st,t,,0,0,,,,,,
-storinmaxfrac.csv,,1,ignore,ignore,r,"*i,v",,0,0,,1,,,,
 stressperiods_user.csv,inputs/temporal/stressperiods_{GSw_PRM_StressModel}.csv,sw.GSw_PRM_StressModel == 'user*',ignore,ignore,,,,,,,,,,,
-stressperiods_seed.csv,,1,ignore,ignore,,,,,0,,,,,,
 supply_chain_adjust.csv,inputs/financials/supply_chain_adjust.csv,1,ignore,ignore,,,,,0,,,,,,
 supplycurve_egs.csv,inputs/supply_curve/supplycurve_egs-reference.csv,int(sw.GSw_Geothermal) != 0,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file
 supplycurve_upv.csv,inputs/supply_curve/supplycurve_upv-{GSw_SitingUPV}.csv,1,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file
 supplycurve_wind-ofs.csv,inputs/supply_curve/supplycurve_wind-ofs-{GSw_SitingWindOfs}.csv,int(sw.GSw_OfsWind) != 0,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file
 supplycurve_wind-ons.csv,inputs/supply_curve/supplycurve_wind-ons-{GSw_SitingWindOns}.csv,1,ignore,ignore,region,,,,0,,,,,,No fix_col (only region_col) because this is an intermediate input file
-sw.csv,inputs/sets/sw.csv,1,ignore,ignore,,,,,0,,,,sw,,
-switches.csv,,1,ignore,ignore,,,,,0,,,,,,
-szn_actualszn.csv,,1,ignore,ignore,,,,,0,,,,,,
+wst_surface.csv,inputs/sets/wst_surface.csv,1,ignore,ignore,,,,,0,,,set,wst_surface,surface water types where access is based on consumption not withdrawal,
 tc_phaseout_schedule.csv,inputs/financials/tc_phaseout_schedule_{GSw_TCPhaseout_schedule}.csv,int(sw.GSw_TCPhaseout) != 0,ignore,ignore,,,,,0,,,,,,
 tech-subset-table.csv,inputs/tech-subset-table.csv,1,ignore,ignore,,,,,0,,,,,,
 tech_resourceclass.csv,inputs/techs/tech_resourceclass.csv,1,ignore,ignore,,,,,0,,,,,,
@@ -410,116 +240,33 @@ techs_banned.csv,inputs/state_policies/techs_banned.yaml,1,ignore,ignore,wide,i,
 techs_banned_ces.csv,inputs/state_policies/techs_banned_ces.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,wide_st,i,,,0,,,,,,
 techs_banned_imports_rps.csv,inputs/state_policies/techs_banned_imports_rps.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,wide_st,i,,,0,,,,,,
 techs_banned_rps.csv,inputs/state_policies/techs_banned_rps.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,wide_st,i,,,0,,,,,,
-temp_UnappWaterMult.csv,,1,ignore,ignore,,,,,0,,1,,,,intermediate file created in climateprep.py to create climate_UnappWaterMult.csv
-temp_UnappWaterSeaAnnDistr.csv,,1,ignore,ignore,,,,,0,,1,,,,intermediate file created in climateprep.py to create climate_UnappWaterSeaAnnDistr.csv
-temp_hydadjsea.csv,,1,ignore,ignore,,,,,0,,1,,,,intermediate file created in climateprep.py to create climate_hydadjsea.csv
-tg_rsc_cspagg.csv,inputs/sets/tg_rsc_cspagg.csv,1,ignore,ignore,,,,,0,,,,tg_rsc_cspagg,,
+tg_rsc_cspagg.csv,inputs/sets/tg_rsc_cspagg.csv,1,ignore,ignore,,,,,0,,,set,tg_rsc_cspagg,csp technologies that belong to the same class,
 tg_rsc_cspagg_tmp.csv,inputs/waterclimate/tg_rsc_cspagg_tmp.csv,(int(sw.GSw_CSP) != 0) and (int(sw.GSw_WaterMain) != 0),ignore,ignore,,,,,0,,,,,,
-tg_rsc_upvagg.csv,inputs/sets/tg_rsc_upvagg.csv,1,ignore,ignore,,,,,0,,,,tg_rsc_upvagg,,
-timestamps.csv,,1,ignore,ignore,,,,,0,,,,,,
-trancap_fut.csv,,1,sum,ignore,"*r,rr","status,trtype,t",,0,0,drop_dup_r,1,,,,disaggfunc set to ignore because all transmisison data will be read into model at the correct spatial resolution
-trancap_fut_cat.csv,inputs/sets/trancap_fut_cat.csv,1,ignore,ignore,,,,,,,,,trancap_fut_cat,,
-trancap_init_energy.csv,,1,ignore,ignore,"*r,rr",trtype,,0,0,drop_dup_r,1,,,,
-trancap_init_prm.csv,,1,ignore,ignore,"*r,rr",trtype,,0,0,drop_dup_r,1,,,,
-trancap_init_transgroup.csv,,1,ignore,ignore,,,,0,0,,,,,,
-trancap_init_itlgrp.csv,,1,ignore,ignore,,,,0,0,,,,,,
-tranloss.csv,,1,trans_lookup,ignore,"*r,rr",trtype,,0,0,drop_dup_r,1,,,,disaggfunc set to ignore because all transmisison data will be read into model at the correct spatial resolution
-trans_itc_fractions.csv,,1,ignore,ignore,,,,,0,,,,,,
-transmission_capacity_future.csv,inputs/transmission/transmission_capacity_future_{lvl}_{GSw_TransScen}.csv,1,sum,ignore,"r,rr","status,trtype,t",,0,0,drop_dup_r,,,,,'ignore’ in disaggfunc because all transmisison data will be read into model at appropriate spatial resolution
-transmission_capacity_future_baseline.csv,inputs/transmission/transmission_capacity_future_{lvl}_baseline.csv,1,sum,ignore,"r,rr","status,trtype,t",,0,0,drop_dup_r,,,,,'ignore’ in disaggfunc because all transmisison data will be read into model at appropriate spatial resolution
-transmission_cost_ac.csv,inputs/transmission/transmission_cost_ac_{GSw_TransUpgradeMethod}_{lvl}.h5,1,trans_lookup,ignore,"r,rr",tscbin,,0,0,drop_dup_r,,,,,
-transmission_cost_dc.csv,inputs/transmission/transmission_cost_dc_{lvl}.csv,1,trans_lookup,ignore,"r,rr",,,0,0,drop_dup_r,,,,,
-transmission_distance.csv,inputs/transmission/transmission_distance_{lvl}.h5,1,trans_lookup,ignore,"r,rr",,,0,0,drop_dup_r,,,,,Stored in wide-format h5 to reduce county filesize but converted to long in copy_files.py
-transmission_line_fom.csv,,1,trans_lookup,ignore,"*r,rr",trtype,,0,0,drop_dup_r,1,,,,disaggfunc set to ignore because all transmisison data will be read into model at the correct spatial resolution
-trtype.csv,inputs/sets/trtype.csv,1,ignore,ignore,,,,,,,,,trtype,,
-unapp_water_sea_distr.csv,inputs/waterclimate/unapp_water_sea_distr.csv,int(sw.GSw_WaterMain) != 0,mean,geosize,r,"wst,wide",,1,0,,,,,,
+tg_rsc_upvagg.csv,inputs/sets/tg_rsc_upvagg.csv,1,ignore,ignore,,,,,0,,,set,tg_rsc_upvagg,pv and pvb technologies that belong to the same class,
+trancap_fut_cat.csv,inputs/sets/trancap_fut_cat.csv,1,ignore,ignore,,,,,,,,set,trancap_fut_cat,categories of near-term transmission projects that describe the likelihood of being completed,
+trtype.csv,inputs/sets/trtype.csv,1,ignore,ignore,,,,,,,,set,trtype,transmission capacity type,
+unapp_water_sea_distr.csv,inputs/waterclimate/unapp_water_sea_distr.csv,int(sw.GSw_WaterMain) != 0,mean,uniform,r,"wst,wide",,1,0,,,,,,
 UnappWaterMult.csv,inputs/climate/{climatescen}/UnappWaterMult.csv,int(sw.GSw_ClimateWater) != 0,mean,uniform,r,"wst,month,t",,0,0,,,,,,
 UnappWaterMultAnn.csv,inputs/climate/{climatescen}/UnappWaterMultAnn.csv,int(sw.GSw_ClimateWater) != 0,mean,uniform,r,"wst,t",,0,0,,,,,,
 UnappWaterSeaAnnDistr.csv,inputs/climate/{climatescen}/UnappWaterSeaAnnDistr.csv,int(sw.GSw_ClimateWater) != 0,mean,uniform,r,"wst,month,t",,0,0,,,,,,
 unbundled_limit_ces.csv,inputs/state_policies/unbundled_limit_ces.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,,,,0,,,,,,
 unbundled_limit_rps.csv,inputs/state_policies/unbundled_limit_rps.csv,int(sw.GSw_StateRPS) != 0,ignore,ignore,st,,,,0,,,,,,
 unitdata.csv,inputs/capacity_exogenous/ReEDS_generator_database_final_{unitdata}.csv,1,ignore,ignore,FIPS,,,,0,,,,,,
-unitsize.csv,,1,ignore,ignore,,,,,0,,1,,,,
-unitspec_upgrades.csv,inputs/sets/unitspec_upgrades.csv,1,ignore,ignore,,,,,0,,,,unitspec_upgrades,,
-upgrade_costs_ccs_coal.csv,,1,ignore,ignore,,,,,0,,,,,,
-upgrade_costs_ccs_gas.csv,,1,ignore,ignore,,,,,0,,,,,,
-upgrade_hintage_char.csv,inputs/sets/upgrade_hintage_char.csv,1,ignore,ignore,,,,,0,,,,upgrade_hintage_char,,
+unitspec_upgrades.csv,inputs/sets/unitspec_upgrades.csv,1,ignore,ignore,,,,,0,,,set,unitspec_upgrades,upgraded technologies that get unit-specific characteristics,
+upgrade_hintage_char.csv,inputs/sets/upgrade_hintage_char.csv,1,ignore,ignore,,,,,0,,,set,upgrade_hintage_char,sets to operate over in extension of hintage_data characteristics when sw_upgrades = 1,
 upgrade_link.csv,inputs/upgrades/upgrade_link.csv,1,ignore,ignore,,,,,0,,,,,,
 upgrade_mult_advanced.csv,inputs/upgrades/upgrade_mult_atb23_ccs_mid.csv,int(sw.GSw_UpgradeCost_Mult) != 0,ignore,ignore,,,,,0,,,,,,
 upgrade_mult_conservative.csv,inputs/upgrades/upgrade_mult_atb23_ccs_con.csv,int(sw.GSw_UpgradeCost_Mult) == 2,ignore,ignore,,,,,0,,,,,,
-upgrade_mult_final.csv,,1,ignore,ignore,,,,,0,,,,,,
 upgrade_mult_mid.csv,inputs/upgrades/upgrade_mult_atb23_ccs_mid.csv,"int(sw.GSw_UpgradeCost_Mult) in [0, 4]",ignore,ignore,,,,,0,,,,,,
 upgradelink_water.csv,inputs/upgrades/upgradelink_water.csv,1,ignore,ignore,,,,,0,,,,,,
 uranium_price.csv,inputs/fuelprices/uranium_{uraniumscen}.csv,1,ignore,ignore,,,,0,0,,,,,,
-va_ng_crf_penalty.csv,,1,ignore,ignore,,,,0,0,,,,,,
-val_aggreg.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_ba.csv,,1,ignore,ignore,,,,,,,1,,,,
-val_itlgrp.csv,,1,ignore,ignore,,,,,,,1,,,,
-val_cendiv.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_country.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_county.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_cs.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_hurdlereg.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_interconnect.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_nercr.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_h2ptcreg.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_r.csv,,1,ignore,ignore,0,,,0,none,,1,,,,
-val_r_all.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_st.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_transgrp.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_transreg.csv,,1,ignore,ignore,,,,0,none,,,,,,
-val_usda_region.csv,,1,ignore,ignore,,,,0,none,,,,,,
 var_map.csv,inputs/valuestreams/var_map.csv,1,ignore,ignore,,,,,0,,,,,,
 wat_access_cap_cost.csv,inputs/waterclimate/wat_access_cap_cost.csv,int(sw.GSw_WaterMain) != 0,sc_cat,geosize,r,*wst,,0,0,value,,,,,
-water_req_psh_10h_1_51.csv,inputs/waterclimate/water_req_psh_10h_1_51.csv,(int(sw.GSw_PSHwatercon) != 0) and (int(sw.GSw_WaterMain) != 0),mean,geosize,r,wide,,1,0,,,,,,not yet ready for county-level disaggregation
+water_req_psh_10h_1_51.csv,inputs/waterclimate/water_req_psh_10h_1_51.csv,(int(sw.GSw_PSHwatercon) != 0) and (int(sw.GSw_WaterMain) != 0),sum,geosize,r,wide,,1,0,,,,,,not yet ready for county-level disaggregation
 water_with_cons_rate.csv,inputs/waterclimate/water_with_cons_rate.csv,int(sw.GSw_WaterMain) != 0,ignore,ignore,,,,,0,,,,,,
-wind_retirements.csv,,1,sum,ignore,r,"i,v,wide",,1,0,,1,,,,disaggfunc set to ignore because data will be written at county resolution by writecapdat.py
-windcfmult.csv,,1,ignore,ignore,,,,1,0,,,,,,
-windcfout.csv,,1,ignore,ignore,,,,1,0,,,,,,
 windows.csv,inputs/userinput/windows_{windows_suffix}.csv,1,ignore,ignore,,,,,0,,,,,,
-wst_climate.csv,inputs/sets/wst_climate.csv,1,ignore,ignore,,,,,0,,,,wst_climate,,
-x.csv,,1,ignore,ignore,,,,,0,,,,,,
-x_r.csv,,1,first,ignore,"*x,r",,,0,0,,1,,,,handled in writesupplycurves.py
-yearafter.csv,inputs/sets/yearafter.csv,1,ignore,ignore,,,,,,,,,yearafter,,
-inputs.gdx,,1,ignore,ignore,,,,,0,,,,,,
-plexos_inputs.gdx,,1,ignore,ignore,,,,,0,,,,,,
-load.h5,,1,sum,ignore,wide,"year,hour",,1,keepindex,,1,,,,Disaggregation handled in hourly_load.py
-recf.h5,,1,recf,ignore,wide,datetime,,1,keepindex,,1,,,,
-csp.h5,,1,csp,ignore,wide,datetime,,1,keepindex,,1,,,,
-gswitches.txt,,1,ignore,ignore,,,,,0,,,,,,
-scalars.txt,,1,ignore,ignore,,,,,0,,,,,,
-Augur.py,Augur.py,1,ignore,ignore,,,,,,,,,,,
+wst_climate.csv,inputs/sets/wst_climate.csv,1,ignore,ignore,,,,,0,,,set,wst_climate,,
+yearafter.csv,inputs/sets/yearafter.csv,1,ignore,ignore,,,,,,,,set,yearafter,set to loop over for the final year calculation,
 Project.toml,Project.toml,1,ignore,ignore,,,,,,,,,,,
-b_inputs.gms,b_inputs.gms,1,ignore,ignore,,,,,,,,,,,
-c_mga.gms,c_mga.gms,1,ignore,ignore,,,,,,,,,,,
-c_supplymodel.gms,c_supplymodel.gms,1,ignore,ignore,,,,,,,,,,,
-c_supplyobjective.gms,c_supplyobjective.gms,1,ignore,ignore,,,,,,,,,,,
-cbc.opt,cbc.opt,1,ignore,ignore,,,,,,,,,,,
-cplex.op2,cplex.op2,1,ignore,ignore,,,,,,,,,,,
-cplex.opt,cplex.opt,1,ignore,ignore,,,,,,,,,,,
-createmodel.gms,createmodel.gms,1,ignore,ignore,,,,,,,,,,,
-d_solveallyears.gms,d_solveallyears.gms,1,ignore,ignore,,,,,,,,,,,
-d_solveoneyear.gms,d_solveoneyear.gms,1,ignore,ignore,,,,,,,,,,,
-d_solve_iterate.py,d_solve_iterate.py,1,ignore,ignore,,,,,,,,,,,
-d_solvepcm.gms,d_solvepcm.gms,1,ignore,ignore,,,,,,,,,,,
-d_solveprep.gms,d_solveprep.gms,1,ignore,ignore,,,,,,,,,,,
-d_solvewindow.gms,d_solvewindow.gms,1,ignore,ignore,,,,,,,,,,,
-d1_temporal_params.gms,d1_temporal_params.gms,1,ignore,ignore,,,,,,,,,,,
-d1_financials.gms,d1_financials.gms,1,ignore,ignore,,,,,,,,,,,
-d2_post_solve_adjustments.gms,d2_post_solve_adjustments.gms,1,ignore,ignore,,,,,,,,,,,
-d2_unfix_op.gms,d2_unfix_op.gms,1,ignore,ignore,,,,,,,,,,,
-d2_varfix.gms,d2_varfix.gms,1,ignore,ignore,,,,,,,,,,,
-d3_data_dump.gms,d3_data_dump.gms,1,ignore,ignore,,,,,,,,,,,
-dump_alldata.gms,dump_alldata.gms,1,ignore,ignore,,,,,,,,,,,
-e_powfrac_calc.gms,e_powfrac_calc.gms,1,ignore,ignore,,,,,,,,,,,
-e_report.gms,e_report.gms,1,ignore,ignore,,,,,,,,,,,
-e_report_dump.py,e_report_dump.py,1,ignore,ignore,,,,,,,,,,,
-e_report_params.csv,e_report_params.csv,1,ignore,ignore,,,,,,,,,,,
 gamslice.txt,gamslice.txt,0,ignore,ignore,,,,,,,,,,,
-gurobi.opt,gurobi.opt,1,ignore,ignore,,,,,,,,,,,
-interim_report.py,interim_report.py,1,ignore,ignore,,,,,,,,,,,
-max_hintage_number.txt,,1,ignore,ignore,,,,,0,,,,,,
-raw_value_streams.py,raw_value_streams.py,1,ignore,ignore,,,,,,,,,,,
-runbatch.py,runbatch.py,1,ignore,ignore,,,,,,,,,,,
-tc_phaseout.py,tc_phaseout.py,1,ignore,ignore,,,,,,,,,,,
-valuestreams.py,valuestreams.py,1,ignore,ignore,,,,,,,,,,,
+runreeds.py,runreeds.py,1,ignore,ignore,,,,,,,,,,,
diff --git a/reeds/input_processing/transmission.py b/reeds/input_processing/transmission.py
new file mode 100644
index 000000000..02d4d9e38
--- /dev/null
+++ b/reeds/input_processing/transmission.py
@@ -0,0 +1,793 @@
+#%% Imports
+import argparse
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+import sys
+import shapely
+import datetime
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+import reeds
+tic = datetime.datetime.now()
+
+
+#%% Functions and constants
+METERS_PER_MILE = 1609.34
+
+def sort_regions(df:pd.DataFrame) -> pd.DataFrame:
+    """Sort r and rr columns alphabetically"""
+    dfout = df.copy()
+    dfout.r, dfout.rr = dfout[['r','rr']].min(axis=1), dfout[['r','rr']].max(axis=1)
+    return dfout
+
+
+def keep_longer_entry(df:pd.DataFrame) -> pd.DataFrame:
+    """For duplicate entries, keep the version with the longer route"""
+    dfout = (
+        df.sort_values('length_miles', ascending=False)
+        .drop_duplicates(['r','rr','polarity','voltage'], keep='first')
+    )
+    return dfout
+
+
+def include_reverse_direction(df:pd.Series, indices=['r', 'rr', 'trtype']):
+    """Add duplicate values for (rr,r) direction"""
+    assert (('r' in indices) and ('rr' in indices))
+    reverse = (
+        df.reset_index().rename(columns={'r':'rr', 'rr':'r'})
+        .set_index(indices).squeeze(1)
+    )
+    dfout = pd.concat([df, reverse])
+    return dfout
+
+
+def get_individual_lines(case, **kwargs):
+    """Get individual lines existing, prescribed, or considered in this run"""
+    sw = reeds.io.get_switches(case, **kwargs)
+    fpaths = [
+        Path(reeds.io.reeds_path, 'inputs', 'transmission', 'hvdc_existing.csv'),
+        Path(reeds.io.reeds_path, 'inputs', 'transmission', 'hvdc_planned-baseline.csv'),
+    ]
+    if sw.GSw_TransScen != 'none':
+        fpaths.append(
+            Path(
+                reeds.io.reeds_path, 'inputs', 'transmission',
+                f'hvdc_planned-{sw.GSw_TransScen}.csv'
+            )
+        )
+    lines = pd.concat([pd.read_csv(fpath, index_col='name') for fpath in fpaths])
+    return lines
+
+
+def _make_line(row):
+    """Turn a lat/lon pair into a LineString geometry"""
+    return shapely.LineString([[row.start_lon, row.start_lat], [row.end_lon, row.end_lat]])
+
+
+def get_interface_params(case, **kwargs):
+    """Get cost and distance for every interface that might be used in this run"""
+    sw = reeds.io.get_switches(case, **kwargs)
+    scalars = reeds.io.get_scalars(case)
+
+    interface_params = reeds.inputs.get_distances(case)
+    ## Apply the minimum-squiggliness factor
+    interface_params['geometry'] = interface_params.apply(_make_line, axis=1)
+    interface_params = gpd.GeoDataFrame(interface_params, crs='EPSG:4326')
+    interface_params['straight_miles'] = (
+        interface_params.geometry.to_crs('EPSG:5070').length
+        / METERS_PER_MILE
+    )
+    interface_params['squiggliness'] = interface_params.length_miles / interface_params.straight_miles
+    interface_params['multiplier'] = (
+        float(sw['GSw_TransSquigglinessMin']) / interface_params['squiggliness']
+    ).clip(lower=1)
+    interface_params.loc[:, ['cost_MUSD', 'length_miles']] = (
+        interface_params[['cost_MUSD', 'length_miles']]
+        .multiply(interface_params['multiplier'], axis=0)
+    )
+    ## Swapping the start/end points sometimes gives slightly different routes,
+    ## resulting in duplicate values. So drop duplicates, keeping the longer route.
+    interface_params = keep_longer_entry(interface_params)
+    ## Include info for individual lines
+    lines = get_individual_lines(case, **kwargs)
+    individual_lines = reeds.inputs.map_hvdc_lines_to_interfaces(
+        case=case, filename='transmission_cost_distance_lines.csv',
+        dtype='cost',
+    )
+    individual_lines = individual_lines.loc[individual_lines.name.isin(lines.index)].copy()
+    individual_lines = keep_longer_entry(sort_regions(individual_lines))
+    keepcols = ['r', 'rr', 'polarity', 'voltage', 'cost_MUSD', 'length_miles']
+    ## Only keep the individual-line values that are not already included.
+    ## (We want to use the zone-geometry-specific endpoints wherever possible;
+    ## the individual-line values are just a fallback.)
+    interface_params = (
+        pd.concat([interface_params[keepcols], individual_lines[keepcols]])
+        .drop_duplicates(['r','rr','polarity','voltage'], keep='first')
+    )
+    ## Make sure there are no duplicates
+    if interface_params[['r','rr','polarity']].duplicated().sum():
+        dup = interface_params.loc[
+            interface_params[['r','rr','polarity']].duplicated(keep=False)
+        ].sort_values(['r','rr','polarity'])
+        print(dup)
+        raise Exception(f'{len(dup)} duplicate entries in interface_params')
+
+    ## Get representative line capacity [MW] and calculate $/MW
+    _line_params = {}
+    for polarity in ['ac', 'dc']:
+        if polarity == 'ac':
+            fpath = Path(
+                reeds.io.reeds_path, 'inputs', 'transmission',
+                f'conductor_{polarity}_{sw.GSw_TransConductor}.csv',
+            )
+            df = pd.read_csv(fpath)
+            df['MVA'] = df.voltage_kv * df.amp * np.sqrt(3) / 1e3
+            df['MW'] = df['MVA'] * scalars['power_factor_ac']
+        else:
+            fpath = Path(reeds.io.reeds_path, 'inputs', 'transmission', f'conductor_{polarity}.csv')
+            df = pd.read_csv(fpath)
+        _line_params[polarity] = df
+    line_params = pd.concat(
+        {key: df.set_index('voltage_kv') for key, df in _line_params.items()},
+        names=('polarity','voltage'),
+    ).MW
+    interface_params = interface_params.merge(line_params, on=['polarity','voltage'], how='left')
+
+    ## Convert to output dollaryear
+    inflatable = reeds.io.get_inflatable()
+    input_dollar_year = pd.read_csv(
+        Path(reeds.io.reeds_path, 'inputs', 'transmission', 'dollaryear.csv'), index_col=0,
+    ).squeeze(1)
+
+    deflator = inflatable[input_dollar_year['transmission_cost_distance.csv'], int(sw.dollar_year)]
+    interface_params[f'USD{sw.dollar_year}perMW'] = (
+        interface_params['cost_MUSD'] * 1e6
+        / interface_params['MW']
+        * deflator
+    )
+    interface_params[f'USD{sw.dollar_year}perMWmile'] = (
+        interface_params[f'USD{sw.dollar_year}perMW']
+        / interface_params['length_miles']
+    )
+    interface_params[f'USD{sw.dollar_year}perMile'] = (
+        interface_params['cost_MUSD'] * 1e6 / interface_params['length_miles']
+    )
+
+    ### Calculate losses
+    tranloss_fixed = {
+        'AC': 1 - scalars['converter_efficiency_ac'],
+        'B2B': 1 - scalars['converter_efficiency_lcc'],
+        'LCC': 1 - scalars['converter_efficiency_lcc'],
+        'VSC': 1 - scalars['converter_efficiency_vsc'],
+    }
+    ## B2B converters are AC-AC/DC-DC/AC-AC, so use AC per-mile losses
+    trtypes = {'AC':'ac', 'B2B':'ac', 'LCC':'dc', 'VSC':'dc'}
+    tranloss_permile = {
+        trtype: scalars[f'tranloss_permile_{polarity}'] for trtype, polarity in trtypes.items()
+    }
+    def _getloss(row):
+        """
+        Fixed losses are entered as per-endpoint values (e.g. for each AC/DC converter station
+        on a LCC DC line). There are two endpoints per line, so multiply fixed losses by 2.
+        Note that this approach only applies for LCC DC lines; VSC AC/DC losses are applied later.
+        """
+        return row.length_miles * tranloss_permile[row.trtype] + tranloss_fixed[row.trtype] * 2
+
+    interface_params = pd.concat({
+        trtype: interface_params.loc[interface_params.polarity == polarity]
+        for trtype, polarity in trtypes.items()
+    }, names=('trtype', 'drop')).reset_index().drop(columns=['drop'])
+    interface_params['loss'] = interface_params.apply(lambda row: _getloss(row), axis=1)
+
+    return interface_params.rename(columns={'length_miles':'miles'})
+
+
+def get_trancap_fut(case):
+    """Get certain and possible transmission additions"""
+    sw = reeds.io.get_switches(case)
+    scalars = reeds.io.get_scalars(case)
+    ## Always-included lines
+    planned_capacity = reeds.inputs.map_hvdc_lines_to_interfaces(
+        case=case, filename='hvdc_planned-baseline.csv',
+    )
+    ## Scenario-specific lines
+    if sw.GSw_TransScen != 'none':
+        planned_capacity = pd.concat([
+            planned_capacity,
+            reeds.inputs.map_hvdc_lines_to_interfaces(
+                case=case, filename=f'hvdc_planned-{sw.GSw_TransScen}.csv',
+            )
+        ])
+    ## Offshore lines
+    if int(sw.GSw_OffshoreZones):
+        zonepath = Path(reeds.io.reeds_path, 'inputs', 'zones', sw.GSw_ZoneSet)
+        ## Land-to-offshore links (these are only built for offshore wind delivery
+        ## so they won't be built early unless there's exogenous offshore wind)
+        fpath_land = Path(zonepath, 'newlinks_offshore_radial.csv')
+        offshore_land = (
+            pd.read_csv(fpath_land)
+            .assign(year_online=int(sw.startyear))
+            .assign(name='radial')
+        )
+        ## Offshore-to-offshore backbone links
+        fpath_offshore = Path(
+            reeds.io.reeds_path, 'inputs', 'transmission', 'newlinks_offshore_backbone.csv',
+        )
+        offshore_offshore = (
+            pd.read_csv(fpath_offshore)
+            .assign(year_online=0)
+            .assign(name='backbone')
+        )
+        offshore_links = (
+            pd.concat([offshore_land, offshore_offshore])
+            .assign(trtype='VSC')
+            .assign(certain=0)
+            .assign(MW=100000)
+        ).set_index(['r','rr','trtype','year_online','certain'])
+        planned_capacity = pd.concat([planned_capacity, offshore_links])
+    trancap_fut = (
+        planned_capacity.reset_index()
+        .rename(columns={'year_online':'t', 'certain':'status'})
+        .astype({'t':int})
+        ## '0' is used as a filler value in the t column for firstyear_trans,
+        ## so we replace it whenever we load a transmission_capacity_future file.
+        .replace({
+            't': {0: int(scalars['firstyear_trans_longterm'])},
+            'status': {0:'possible', 1:'certain'}
+        })
+        [['r', 'rr', 'status', 'trtype', 't', 'MW']]
+        .astype({'t':int}).round(3)
+    )
+
+    return trancap_fut
+
+
+def get_firm_import_limit(case):
+    """Limits on PRMTRADE across nercr boundaries"""
+    sw = reeds.io.get_switches(case)
+    if not int(sw.GSw_PRM_NetImportLimit):
+        ## No limit
+        firm_import_limit = pd.DataFrame(columns=['nercr','t','fraction']).set_index(['nercr','t'])
+    else:
+        limits = pd.Series(
+            {int(i.split('_')[0]): i.split('_')[1] for i in sw.GSw_PRM_NetImportLimitScen.split('/')}
+        )
+
+        solveyears = pd.read_csv(
+            Path(case, 'inputs_case', 'modeledyears.csv')
+        ).columns.astype(int).tolist()
+        startyear = min(solveyears)
+        endyear = max(solveyears)
+        allyears = range(startyear, max(endyear, limits.index.max())+1)
+
+        ## calculate the historical net_firm_import fraction for each region and drop negative values
+        peak_net_imports = pd.read_csv(
+            Path(case, 'inputs_case', 'peak_net_imports.csv'),
+            index_col=['nercr']
+        )
+        net_firm_import_frac = (
+            peak_net_imports.MW / peak_net_imports.MW_TotalDemand
+        ).clip(lower=0)
+        nercrs = net_firm_import_frac.index
+
+        _dfout = {}
+        for key, val in limits.items():
+            ## If 'hist' is in GSw_PRM_NetImportLimitScen,
+            ## all years up until that year use the historical regional max
+            if val == 'hist':
+                for y in range(startyear, key+1):
+                    _dfout[y] = net_firm_import_frac
+            ## If 'histmax', all prior years use the historical max across all regions
+            elif val == 'histmax':
+                for y in range(startyear, key+1):
+                    _dfout[y] = net_firm_import_frac.clip(lower=net_firm_import_frac.max())
+            else:
+                ## Input values are percentages so convert to fractions
+                _dfout[key] = pd.Series(index=nercrs, data=float(val) / 100)
+
+        firm_import_limit = (
+            pd.concat(_dfout, names=('t',)).unstack('nercr')
+            ## Linear interpolation between values; flat projections before and after
+            .reindex(allyears).interpolate('linear').bfill().ffill()
+            .loc[solveyears]
+            .unstack('t').rename('fraction')
+        )
+
+    return firm_import_limit.reset_index()
+
+
+def get_trancap_init(case, interface_params, level='r'):
+    """
+    AC capacity is defined for each direction and calculated using the scripts at
+    https://github.nrel.gov/ReEDS/TSC
+    """
+    sw = reeds.io.get_switches(case)
+    trancap_init_ac = (
+        reeds.inputs.get_itls(case, level=level, GSw_ZoneSet=sw.GSw_ZoneSet)
+        [['r', 'rr', 'MW_forward', 'MW_reverse']]
+        .assign(trtype='AC')
+    )
+    valid_regions = {}
+    for i in ['r', 'itlgrp', 'transgrp']:
+        valid_regions[i] = reeds.io.read_input(case, i).squeeze(1).tolist()
+
+    ### DC
+    if level == 'r':
+        ## transgrp capacity is only defined for AC
+        hvdc = (
+            reeds.inputs.map_hvdc_lines_to_interfaces(case, filename='hvdc_existing.csv')
+            .reset_index()
+        )
+        b2b = reeds.inputs.get_b2b(case).assign(trtype='B2B')
+        ## DC capacity is only defined in one direction,
+        ## so duplicate it for the opposite direction
+        trancap_init_nonac_undup = pd.concat([hvdc, b2b])[['r', 'rr', 'trtype', 'MW']]
+        trancap_init_nonac = pd.concat([
+            trancap_init_nonac_undup,
+            trancap_init_nonac_undup.rename(columns={'r':'rr', 'rr':'r'})
+        ], axis=0)
+    else:
+        trancap_init_nonac = pd.DataFrame(columns=['r', 'rr', 'trtype', 'MW'])
+
+    ### Initial trading limit, using contingency levels specified by contingency level
+    ### (but assuming full capacity of DC is available for both energy and capcity)
+    dfout = (
+        pd.concat(
+            [
+                ## AC
+                pd.concat([
+                    ## Forward direction
+                    (trancap_init_ac[['r', 'rr', 'trtype', 'MW_forward']]
+                    .rename(columns={'MW_forward':'MW'})),
+                    ## Reverse direction
+                    (trancap_init_ac[['r', 'rr', 'trtype', 'MW_reverse']]
+                    .rename(columns={'r':'rr', 'rr':'r', 'MW_reverse':'MW'}))
+                ], axis=0),
+                ## DC
+                trancap_init_nonac[['r', 'rr', 'trtype', 'MW']]
+            ],
+            axis=0
+        )
+        ## Drop entries with zero capacity
+        .replace(0.,np.nan).dropna()
+        .groupby(['r', 'rr', 'trtype']).sum().reset_index()
+    )
+    dfout = dfout.loc[
+        dfout['r'].isin(valid_regions[level])
+        & dfout['rr'].isin(valid_regions[level])
+    ].copy()
+
+    ### TEMPORARY 20260402: Drop county interfaces with no distance/cost
+    if (level == 'r') and (sw.GSw_RegionResolution in ['county', 'mixed']):
+        transmission_line_fom = get_transmission_fom(case, interface_params)
+        indices = ['r', 'rr', 'trtype']
+        drop = (
+            dfout
+            .merge(transmission_line_fom.reset_index(), on=indices, how='left')
+        )
+        drop = list(drop.loc[drop.USDperMWyear.isnull(), indices].itertuples(index=False))
+        dfout = dfout.set_index(indices).drop(drop).reset_index()
+
+    ## Get alias for level (e.g. rr, transgrpp)
+    levell = level + level[-1]
+    dfout = (
+        dfout
+        .rename(columns={'r':level, 'rr':levell})
+        .round(3)
+    )
+    return dfout
+
+
+def get_transmission_fom(case, interface_params):
+    """Get transmission fixed operations & maintenance (FOM) costs"""
+    sw = reeds.io.get_switches(case)
+    scalars = reeds.io.get_scalars(case)
+
+    ### Get the line-specific transmission FOM costs [$/MW/year]
+    trans_fom_frac = scalars['trans_fom_frac']
+
+    ### For simplicity we just take the unweighted average greenfield $/MWmile cost
+    ### across all interfaces.
+    ### Future work should identify a better assumption.
+    transfom_USDperMWmileyear = (
+        interface_params.groupby('trtype')[f'USD{sw.dollar_year}perMWmile'].mean()
+        * trans_fom_frac
+    )
+
+    ### Multiply $/MW/mile/year by distance [miles] to get $/MW/year for ALL lines
+    transmission_line_fom = interface_params.copy()
+    transmission_line_fom['USDperMWyear'] = transmission_line_fom.apply(
+        lambda row: transfom_USDperMWmileyear[row.trtype] * row.miles,
+        axis=1
+    )
+    transmission_line_fom = (
+        transmission_line_fom
+        .set_index(['r','rr','trtype'])
+        .USDperMWyear
+        .round(2)
+    )
+    ## Include both directions
+    transmission_line_fom = include_reverse_direction(transmission_line_fom)
+    dups = transmission_line_fom.index.duplicated()
+    if dups.sum():
+        print(transmission_line_fom.loc[dups])
+        raise ValueError(f'{len(dups)} duplicate values in transmission_line_fom')
+    return transmission_line_fom
+
+
+def convert_to_tsc(interface_params, dollar_year=2004):
+    """Convert greenfield costs into single-bin transmission upgrade supply curves (TSC)"""
+    transmission_cost_ac = interface_params.loc[
+        interface_params.trtype=='AC',
+        ['r', 'rr', f'USD{dollar_year}perMW']
+    ].copy()
+    transmission_cost_ac = (
+        transmission_cost_ac
+        .rename(columns={f'USD{dollar_year}perMW':f'USD{dollar_year}perMW_forward'})
+        .assign(**{f'USD{dollar_year}perMW_reverse':transmission_cost_ac[f'USD{dollar_year}perMW']})
+        .assign(tscbin='t0')
+        .assign(**{f'binwidth_USD{dollar_year}':1e12})
+        [[
+            'r', 'rr', 'tscbin', f'binwidth_USD{dollar_year}',
+            f'USD{dollar_year}perMW_forward', f'USD{dollar_year}perMW_reverse',
+        ]]
+    )
+    _test = transmission_cost_ac.r < transmission_cost_ac.rr
+    if not _test.all():
+        print(transmission_cost_ac.loc[~_test])
+        raise ValueError('Region must be lexicographically sorted so r < rr')
+    return transmission_cost_ac
+
+
+def get_transmission_cost(case, interface_params):
+    """Get ReEDS-formatted AC transmission upgrade cost curves"""
+    sw = reeds.io.get_switches(case)
+    if sw.GSw_TransUpgradeMethod == 'greenfield':
+        transmission_cost_ac = convert_to_tsc(interface_params, sw.dollar_year)
+    else:
+        fpath = Path(
+            reeds.io.reeds_path, 'inputs', 'transmission',
+            f'transmission_cost_ac_{sw.GSw_TransUpgradeMethod}_{sw.GSw_ZoneSet}.h5'
+        )
+        transmission_cost_ac = reeds.io.read_file(fpath).reset_index()
+        for col in ['r', 'rr', 'tscbin']:
+            transmission_cost_ac[col] = transmission_cost_ac[col].str.decode('utf-8')
+    ### Interfaces are always defined with the zones sorted in lexicographic order
+    reverse_interfaces = transmission_cost_ac.loc[
+        transmission_cost_ac.apply(lambda row: row.r > row.rr, axis=1)
+    ]
+    for i, row in reverse_interfaces.iterrows():
+        transmission_cost_ac.loc[i, ['r', 'rr']] = transmission_cost_ac.loc[i, ['rr', 'r']].values
+        transmission_cost_ac.loc[i, ['USD2004perMW_forward', 'USD2004perMW_reverse']] = (
+            transmission_cost_ac.loc[i, ['USD2004perMW_reverse', 'USD2004perMW_forward']].values
+        )
+
+    _test = transmission_cost_ac.apply(lambda row: row.r < row.rr, axis=1)
+    if not _test.all():
+        print(transmission_cost_ac.loc[~_test])
+        err = (
+            "Must have r < rr in AC transmission cost inputs but the interfaces "
+            "listed above are out of order"
+        )
+        raise ValueError(err)
+    return transmission_cost_ac
+
+
+def get_transmission_cost_nonac(case, interface_params):
+    """Get HVDC and B2B costs"""
+    transmission_cost_nonac = interface_params.loc[
+        interface_params.trtype != 'AC',
+        ['r', 'rr', 'trtype', f'USD{reeds.io.get_switches(case).dollar_year}perMW']
+    ].round(2).set_index(['r','rr','trtype']).squeeze(1)
+    transmission_cost_nonac = include_reverse_direction(transmission_cost_nonac).reset_index()
+    return transmission_cost_nonac
+
+
+def get_hurdle_rates(case, hurdle_level=1):
+    """Get hurdle rates on transmission flows (wheeling charges) used in this run"""
+    sw = reeds.io.get_switches(case)
+    cost_hurdle_intra = (
+        pd.read_csv(Path(reeds.io.reeds_path, 'inputs', 'transmission', 'cost_hurdle_intra.csv'))
+        .set_index('t').round(3)
+    )
+    cost_hurdle_rate = (
+        cost_hurdle_intra[sw[f'GSw_TransHurdleLevel{hurdle_level}']].rename('USDperMWh')
+        if int(sw.GSw_TransHurdleRate)
+        else pd.Series(name='USDperMWh').rename_axis('t')
+    )
+    return cost_hurdle_rate.reset_index()
+
+
+def calculate_adjacent_routes(case):
+    """Determine which pairs of model regions are adjacent to each other"""
+    dfzones = reeds.io.get_dfmap(case, levels=['r'], exclude_water_areas=True)['r']
+    routes_adjacent = dfzones.copy()
+    routes_adjacent['r_adj'] = routes_adjacent.apply(
+        axis=1,
+        func=lambda x: (
+            routes_adjacent.loc[(
+                routes_adjacent.touches(x['geometry'])
+                | routes_adjacent.overlaps(x['geometry'])
+            )]
+            .index
+            .values
+            .tolist()
+        )
+    )
+    # Reformat so that each row represents a pair of regions
+    routes_adjacent = (
+        routes_adjacent.drop(columns='geometry')
+        .explode('r_adj')
+        .reset_index(names=['r'])
+        .rename(columns={'r_adj': 'rr'})
+        [['r', 'rr']]
+        .dropna()
+    )
+
+    return routes_adjacent
+
+
+def get_pipeline_cost_mult(case, interface_params, transmission_cost_nonac):
+    """
+    Calculate H2 pipeline cost multipliers by dividing the [$/mile] cost of DC transmission
+    between each pair of regions by the minimum interface [$/mile] cost for DC transmission
+    and subtracting 1 to get a fractional adder (which is then added to 1 in b_inputs.gms)
+    """
+    sw = reeds.io.get_switches(case)
+    if len(transmission_cost_nonac):
+        dc_cost_permile = (
+            interface_params.loc[interface_params.trtype=='VSC']
+            .set_index(['r','rr'])[f'USD{sw.dollar_year}perMile']
+        )
+        pipeline_cost_mult = (
+            (dc_cost_permile.rename('multiplier') / dc_cost_permile.min() - 1)
+            .reset_index().round(3)
+        )
+    else:
+        pipeline_cost_mult = pd.DataFrame(columns=['r','rr','multiplier'])
+    return pipeline_cost_mult
+
+
+def calculate_co2_storage_routes(dfzones, max_miles=200):
+    """
+    Determine spurline routes from model regions to carbon storage sites.
+    Keep storage sites that are within {max_miles} miles
+    of each region's transmission endpoint.
+    """
+    scalars = reeds.io.get_scalars(case)
+    co2_storage_sites = reeds.io.get_co2_storage_sites()
+    dfzones = reeds.io.get_dfmap(case, levels=['r'], exclude_water_areas=True)['r']
+    region_centroids = (
+        gpd.GeoDataFrame(
+            dfzones[['x', 'y']],
+            geometry=gpd.points_from_xy(dfzones.x, dfzones.y),
+            crs=dfzones.crs
+        )
+        [['geometry']]
+        .rename_axis(index='r')
+        .reset_index()
+    )
+    region_centroids['cs'] = region_centroids.apply(
+        axis=1,
+        func=lambda x: (
+            co2_storage_sites.loc[(
+                co2_storage_sites.distance(x['geometry']) / METERS_PER_MILE <= max_miles
+            )]
+            ['cs']
+            .tolist()
+        )
+    )
+
+    # Calculate the lengths of the spurlines between regions and storage sites,
+    # excluding routes not completely within the U.S.
+    routes_cs = (
+        region_centroids.explode('cs')
+        .merge(
+            co2_storage_sites[['cs', 'geometry']],
+            on='cs',
+            suffixes=('_region', '_site')
+        )
+        .assign(
+            geometry=lambda x: (
+                gpd.GeoSeries(x['geometry_region'])
+                .shortest_line(gpd.GeoSeries(x['geometry_site']))
+            )
+        )
+        [['r', 'cs', 'geometry']]
+    )
+    routes_cs = gpd.GeoDataFrame(routes_cs, geometry='geometry', crs=dfzones.crs)
+    routes_cs = routes_cs.loc[(
+        routes_cs.within(
+            reeds.io.get_dfmap(levels=['country'])['country'].loc['USA','geometry']
+        )
+        | (routes_cs.length == 0)
+    )]
+    routes_cs['distance_m'] = routes_cs.length
+    ## Wherever BA centroids fall within formation boundaries, assume some minimal
+    # spur line distance to connect a CCS or DAC plant with an injection site
+    routes_cs['miles'] = (
+        (routes_cs['distance_m'] / METERS_PER_MILE)
+        .clip(lower=scalars.min_co2_spurline_miles)
+        .round(2)
+    )
+
+    return routes_cs
+
+
+def check_nonac_costs(trancap_fut, trancap_init_energy, transmission_cost_nonac):
+    """Make sure every non-AC transmission route has a cost"""
+    routecols = ['r', 'rr', 'trtype']
+    rename = {'r':'rr', 'rr':'r'}
+    ## Add both directions and sort them
+    routes = pd.concat([
+        trancap_fut[routecols],
+        trancap_fut.rename(columns=rename)[routecols],
+        trancap_init_energy[routecols],
+        trancap_init_energy.rename(columns=rename)[routecols],
+    ])
+    routes.r, routes.rr = routes[['r','rr']].min(axis=1), routes[['r','rr']].max(axis=1)
+    routes = routes.loc[routes.trtype != 'AC'].drop_duplicates()
+    ## Make sure each has a cost
+    dfcost = transmission_cost_nonac.copy()
+    dfcost.r, dfcost.rr = dfcost[['r','rr']].min(axis=1), dfcost[['r','rr']].max(axis=1)
+    dftest = routes.merge(dfcost, on=routecols, how='left').set_index(routecols).squeeze(1)
+    missing = dftest.loc[dftest.isnull()]
+    if len(missing):
+        print(missing)
+        raise ValueError(f'Missing non-AC transmission costs for {len(missing)} routes')
+
+
+#%% Main function
+def main(case):
+    #%% Calculate parameters
+    outputs = {}
+    outputs['firm_import_limit'] = get_firm_import_limit(case)
+
+    interface_params = get_interface_params(case)
+
+    outputs['transmission_miles'] = interface_params[['r','rr','trtype','miles']].round(3)
+    outputs['tranloss'] = interface_params[['r','rr','trtype','loss']].round(5)
+    outputs['transmission_line_fom'] = get_transmission_fom(case, interface_params).reset_index()
+    outputs['trancap_fut'] = get_trancap_fut(case)
+    outputs['transmission_cost_nonac'] = get_transmission_cost_nonac(case, interface_params)
+    ## A few downstream processes expect a single distance for each zone pair;
+    ## keep the longest
+    outputs['transmission_distance'] = (
+        outputs['transmission_miles'].drop(columns='trtype')
+        .sort_values('miles', ascending=False).drop_duplicates(['r','rr'], keep='first')
+        .sort_values(['r','rr'])
+    )
+
+    for hurdle_level in [1, 2]:
+        outputs[f'cost_hurdle_rate{hurdle_level}'] = get_hurdle_rates(case, hurdle_level)
+
+    for captype, level in [
+        ('energy', 'r'),
+        ('transgroup', 'transgrp'),
+    ]:
+        outputs[f'trancap_init_{captype}'] = get_trancap_init(
+            case=case, interface_params=interface_params, level=level,
+        )
+    outputs['trancap_init_prm'] = outputs['trancap_init_energy']
+
+    check_nonac_costs(
+        trancap_fut=outputs['trancap_fut'],
+        trancap_init_energy=outputs['trancap_init_energy'],
+        transmission_cost_nonac=outputs['transmission_cost_nonac'],
+    )
+
+    ### TEMPORARY 20260402: Skip itlgrp functionality until we fix it
+    # ### Also write itlgrp capacity
+    # trancap_itlgrp = trancap_init['energy'].copy()
+    # ## Map counties to itlgrp's
+    # hierarchy_itlgrp = pd.read_csv(Path(inputs_case, 'hierarchy_itlgrp.csv'))
+    # itl_d = dict(zip(hierarchy_itlgrp['r'], hierarchy_itlgrp['itlgrp']))
+    # for r in ['r', 'rr']:
+    #     trancap_itlgrp[r] = trancap_itlgrp[r].map(lambda x: itl_d.get(x,x))
+    # outputs['trancap_itlgrp'] = (
+    #     trancap_itlgrp
+    #     .rename(columns={'r':'itlgrp', 'rr':'itlgrpp'}).round(3)
+    # )
+
+    ### Transmission upgrade supply curve
+    transmission_cost_ac = get_transmission_cost(case, interface_params)
+    labels = {
+        'binwidth_USD2004': 'binwidth',
+        'USD2004perMW_forward': 'forward',
+        'USD2004perMW_reverse': 'reverse',
+    }
+    for col, label in labels.items():
+        outputs[f'tsc_{label}'] = (
+            transmission_cost_ac[['r','rr','tscbin',col]]
+            .round(2)
+        )
+    outputs['tscbin'] = transmission_cost_ac.tscbin.drop_duplicates().rename()
+    ## Write transmission_cost_ac for R2X
+    outputs['transmission_cost_ac'] = transmission_cost_ac
+
+    ### Pipelines
+    outputs['pipeline_cost_mult'] = get_pipeline_cost_mult(
+        case,
+        interface_params,
+        outputs['transmission_cost_nonac'],
+    )
+    outputs['routes_adjacent'] = calculate_adjacent_routes(case)
+
+    ### CO2 storage sites
+    routes_cs = calculate_co2_storage_routes(case)
+    outputs['r_cs'] = routes_cs[['r', 'cs']]
+    outputs['r_cs_distance_mi'] = routes_cs[['r', 'cs', 'miles']]
+
+    # Determine sites that have valid routes to model regions
+    val_cs = pd.Series(routes_cs['cs'].unique())
+    outputs['val_cs'] = val_cs
+
+    # Subset CO2 site characteristics data to valid sites
+    co2_site_char = pd.read_csv(Path(reeds.io.reeds_path, 'inputs', 'ctus', 'co2_site_char.csv'))
+    outputs['co2_site_char'] = co2_site_char.loc[co2_site_char['cs'].isin(val_cs)]
+
+    #%% Downselect to active regions
+    table = {'co2_site_char': True}
+    hierarchy = reeds.io.get_hierarchy(case).reset_index()
+    for key, df in outputs.items():
+        columns = df.columns if isinstance(df, pd.DataFrame) else []
+        for level in hierarchy:
+            levell = level + level[-1]
+            if level in columns:
+                df = df.loc[df[level].isin(hierarchy[level])]
+            if levell in columns:
+                df = df.loc[df[levell].isin(hierarchy[level])]
+        ### Add '*' to the beginning so GAMS reads the header as a comment
+        if not table.get(key, False):
+            if isinstance(df, pd.DataFrame):
+                df = df.rename(columns={df.columns[0]: '*'+str(df.columns[0])})
+            else:
+                df = df.rename('*'+df.name) if df.name else df
+        outputs[key] = df
+
+    #%% Write the outputs
+    header = {
+        'val_cs': False,
+        'tscbin': False,
+    }
+    inputs_h5 = {
+        'tscbin': ('set', 'transmission upgrade supply curve bins'),
+    }
+    for key, df in outputs.items():
+        if key in inputs_h5:
+            gamstype, comment = inputs_h5[key]
+            reeds.io.write_to_inputs_h5(df, key, case, gamstype=gamstype, comment=comment)
+        else:
+            df.to_csv(
+                Path(case, 'inputs_case', f'{key}.csv'),
+                index=False, header=header.get(key, True),
+            )
+
+    #%% Done
+    return outputs
+
+
+#%% Procedure
+if __name__ == '__main__':
+    #%% Parse arguments
+    parser = argparse.ArgumentParser(description="Format and write climate inputs")
+    parser.add_argument('reeds_path', help='ReEDS directory')
+    parser.add_argument('inputs_case', help='output directory (inputs_case)')
+
+    args = parser.parse_args()
+    case = Path(args.inputs_case).parent
+
+    # #%% Settings for testing ###
+    # case = str(Path(reeds.io.reeds_path, 'runs', 'v20260601_transcostM1_USA_faster'))
+
+    #%% Set up logger
+    log = reeds.log.makelog(scriptname=__file__, logpath=Path(case, 'gamslog.txt'))
+    print('Starting transmission.py', flush=True)
+
+    #%% Run it
+    main(case)
+
+    #%% Finish the timer
+    reeds.log.toc(tic=tic, year=0, process='input_processing/transmission.py', path=case)
+    print('Finished transmission.py', flush=True)
diff --git a/input_processing/writecapdat.py b/reeds/input_processing/writecapdat.py
similarity index 95%
rename from input_processing/writecapdat.py
rename to reeds/input_processing/writecapdat.py
index aa5dde86e..fd49d5941 100644
--- a/input_processing/writecapdat.py
+++ b/reeds/input_processing/writecapdat.py
@@ -30,7 +30,8 @@
 import os
 import sys
 import pandas as pd
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 
 
@@ -65,11 +66,6 @@ def create_rsc_wsc(gendb,TECH,scalars,startyear):
 
 #########################
 ### STATIC DICTIONARY ###
-'''
-This dictionary must be placed at the module level of this script to be used with the 
-create_rsc_wsc() function in aggregate_regions
-'''
-
 TECH = {
     'capnonrsc': [
         'coaloldscr', 'coalolduns', 'biopower', 'coal-igcc',
@@ -127,7 +123,7 @@ def create_rsc_wsc(gendb,TECH,scalars,startyear):
 def main(reeds_path, inputs_case, agglevel, regions):
     
     # #%% Settings for testing
-    #reeds_path = "/Users/apham/Documents/GitHub/ReEDS/ReEDS-2.0/"
+    #reeds_path = "/Users/apham/Documents/GitHub/ReEDS/ReEDS/"
     #inputs_case = os.path.join(reeds_path,'runs','test_newNEMS_OR_water','inputs_case')
 
 
@@ -762,6 +758,12 @@ def main(reeds_path, inputs_case, agglevel, regions):
         .stack().rename_axis(['*r','t']).rename('MW').round(3)
     )
 
+    #%% Prescribed capacity categories
+    fpath = Path(reeds.io.reeds_path, 'inputs', 'sets', '_pcat.csv')
+    _pcat = pd.read_csv(fpath, header=None).squeeze(1).tolist()
+    i = reeds.io.read_input(inputs_case, 'i').squeeze(1).tolist()
+    pcat = pd.Series(i + _pcat)
+
 
     #%%----------------------------------------------------------------------------
     ##############################
@@ -795,10 +797,14 @@ def main(reeds_path, inputs_case, agglevel, regions):
                 'hydcapadj_ccszn' : hydcapadj_ccszn[['*i','ccseason','r','value']],
                 'can_imports_capacity' : can_imports_capacity,
                 'geoexist' : geoexist,
-                'h2_ba_share': h2_ba_share_out
+                'h2_ba_share': h2_ba_share_out,
+                'pcat': pcat,
                 }
+    comments = {
+        'pcat': 'prescribed capacity categories',
+    }
 
-    return files_out 
+    return files_out, comments
 
 #%% ===========================================================================
 ### --- PROCEDURE ---
@@ -830,17 +836,27 @@ def main(reeds_path, inputs_case, agglevel, regions):
 
     # For mixed resolution runs the main function of writecapdat needs to be executed separately for each desired resolution 
     # Then the data from each resolution are combined and written to the inputs_case folder 
+    comments = {}
     if agglevel_variables['lvl'] == 'mult':
         for resolution in agglevel_variables['agglevel']:
             if resolution == 'aggreg':
-                aggreg_data  = main(reeds_path, inputs_case, agglevel=resolution, 
-                                     regions=agglevel_variables['ba_regions'] )
+                aggreg_data, _comments = main(
+                    reeds_path, inputs_case, agglevel=resolution, 
+                    regions=agglevel_variables['ba_regions'],
+                )
+                comments = {**comments, **_comments}
             if resolution == 'ba':
-                ba_data = main(reeds_path, inputs_case, agglevel=resolution, 
-                                regions=agglevel_variables['ba_regions'])
+                ba_data, _comments = main(
+                    reeds_path, inputs_case, agglevel=resolution, 
+                    regions=agglevel_variables['ba_regions'],
+                )
+                comments = {**comments, **_comments}
             if resolution == 'county':
-                county_data = main(reeds_path, inputs_case, agglevel=resolution,
-                                     regions=agglevel_variables['county_regions'],)
+                county_data, _comments = main(
+                    reeds_path, inputs_case, agglevel=resolution,
+                    regions=agglevel_variables['county_regions'],
+                )
+                comments = {**comments, **_comments}
         
         # Combine and write mixed resolution data
         # ReEDS only supports county-BA, county-aggreg combinations 
@@ -868,8 +884,8 @@ def main(reeds_path, inputs_case, agglevel, regions):
     # Single Resolution Procedure
     else: 
         agglevel = agglevel_variables['agglevel']
-        regions = pd.read_csv(os.path.join(inputs_case,f'val_{agglevel}.csv'),header=None).squeeze(1).values
-        data = main(reeds_path, inputs_case,agglevel, regions)
+        regions = reeds.io.read_input(inputs_case, agglevel).squeeze(1).values
+        data, comments = main(reeds_path, inputs_case,agglevel, regions)
 
     # Write it
     print('Writing out capacity data')
@@ -884,11 +900,20 @@ def main(reeds_path, inputs_case, agglevel, regions):
         'cap_cspns': True,
         'can_imports_capacity': True,
     }
+    gamstype = {
+        'pcat': 'set',
+    }
     for key, df in data.items():
-        df.to_csv(
-            os.path.join(inputs_case, f'{outname.get(key, key)}.csv'),
-            index=keep_index.get(key, False),
-        )
+        if gamstype.get(key, False):
+            reeds.io.write_to_inputs_h5(
+                df=df, key=outname.get(key, key), case=inputs_case, gamstype=gamstype[key],
+                comment=comments.get(key, ''),
+            )
+        else:
+            df.to_csv(
+                os.path.join(inputs_case, f'{outname.get(key, key)}.csv'),
+                index=keep_index.get(key, False),
+            )
 
     reeds.log.toc(tic=tic, year=0, process='input_processing/writecapdat.py',
         path=os.path.join(inputs_case,'..'))
diff --git a/input_processing/writedrshift.py b/reeds/input_processing/writedrshift.py
similarity index 92%
rename from input_processing/writedrshift.py
rename to reeds/input_processing/writedrshift.py
index 106528f7c..a8bc631c6 100644
--- a/input_processing/writedrshift.py
+++ b/reeds/input_processing/writedrshift.py
@@ -18,7 +18,8 @@
 import argparse
 import pandas as pd
 import datetime
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 # Time the operation of this script
 tic = datetime.datetime.now()
@@ -58,12 +59,7 @@
 
     #%% Inputs from switches
     sw = reeds.io.get_switches(inputs_case)
-
-    val_r = (
-        pd.read_csv(os.path.join(inputs_case, "val_r.csv"), header=None)
-        .squeeze(1)
-        .tolist()
-    )
+    val_r = reeds.io.read_input(inputs_case, 'r').squeeze(1).tolist()
 
     ### Create empty EVMC data files if GSw_EVMC == 0:
     evmc_files = [
diff --git a/input_processing/writesupplycurves.py b/reeds/input_processing/writesupplycurves.py
similarity index 97%
rename from input_processing/writesupplycurves.py
rename to reeds/input_processing/writesupplycurves.py
index 52b80a44b..610d6fbc6 100644
--- a/input_processing/writesupplycurves.py
+++ b/reeds/input_processing/writesupplycurves.py
@@ -18,7 +18,8 @@
 import h5py
 import datetime
 import pandas as pd
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 reeds_path = reeds.io.reeds_path
 
@@ -71,7 +72,6 @@ def agg_supplycurve(
     inputs_case,
     numbins_tech,
     agglevel,
-    AggregateRegions,
     bin_method='equal_cap_cut',
     bin_col='supply_curve_cost_per_mw',
     spur_cutoff=1e7, 
@@ -86,9 +86,6 @@ def agg_supplycurve(
     dfin = reeds.io.assemble_supplycurve(
         scfile=scpath,
         case=os.path.dirname(os.path.normpath(inputs_case)),
-        agg=AggregateRegions,
-        ## TEMPORARY 20260402
-        **({'GSw_ZoneSet': 'z134'} if not AggregateRegions else {}),
     ).reset_index().drop(columns=['FIPS','cf'], errors='ignore')
     ## Convert dollar year and recalculate total cost
     transcost_cols = [c for c in dfin if 'cost' in c]
@@ -132,13 +129,11 @@ def agg_supplycurve(
 
 
 def main(
-    reeds_path, inputs_case, AggregateRegions=1, rsc_wsc_dat=None, write=True, **kwargs
+    reeds_path, inputs_case, write=True, **kwargs
 ):
     # #%% Settings for testing
     # reeds_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
     # inputs_case = os.path.join(reeds_path,'runs','v20251209_scM0_Pacific','inputs_case')
-    # AggregateRegions = 1
-    # rsc_wsc_dat = None
     # write = True
     # kwargs = {}
 
@@ -189,14 +184,9 @@ def main(
     ).squeeze(1)
     deflate = dollaryear.map(deflator).rename('Deflator')
 
-    #%% Load the existing RSC capacity (PV plants, wind, and CSP) if not provided in main function call
-    if rsc_wsc_dat is None:
-        # writesupplycurves.py is being run as a main input processing script
-        rsc_wsc = pd.read_csv(os.path.join(inputs_case, "rsc_wsc.csv"))
-    else:
-        # writesupplycurves.py is being passed rsc_wsc data from an aggregate_regions.py call
-        rsc_wsc = rsc_wsc_dat.copy()
-    
+    #%% Load the existing RSC capacity (PV plants, wind, and CSP)
+    rsc_wsc = pd.read_csv(os.path.join(inputs_case, "rsc_wsc.csv"))
+
     # Group CSP tech
     rsc_wsc.loc[rsc_wsc['i']=='csp-ws', 'i'] = 'csp'
     rsc_wsc = rsc_wsc.groupby(["r", "i"]).sum().reset_index()
@@ -233,7 +223,7 @@ def main(
         windin[s], wind[s] = agg_supplycurve(
             scpath=os.path.join(inputs_case,f'supplycurve_wind-{s}.csv'),
             inputs_case=inputs_case, 
-            agglevel=agglevel, AggregateRegions=AggregateRegions, 
+            agglevel=agglevel,
             numbins_tech=numbins[f'wind-{s}'], spur_cutoff=spur_cutoff,
             agglevel_variables=agglevel_variables, deflate=deflate,
             sw=sw, write=write
@@ -330,7 +320,7 @@ def main(
     upvin, upv = agg_supplycurve(
         scpath=os.path.join(inputs_case, 'supplycurve_upv.csv'),
         inputs_case=inputs_case,
-        agglevel=agglevel, AggregateRegions=AggregateRegions,
+        agglevel=agglevel,
         numbins_tech=numbins['upv'], spur_cutoff=spur_cutoff,
         agglevel_variables=agglevel_variables, deflate=deflate,
         sw=sw, write=write
@@ -411,7 +401,7 @@ def main(
         cspin, csp = agg_supplycurve(
             scpath=os.path.join(inputs_case, 'supplycurve_csp.csv'),
             inputs_case=inputs_case,
-            agglevel=agglevel, AggregateRegions=AggregateRegions, 
+            agglevel=agglevel,
             numbins_tech=numbins['csp'], spur_cutoff=spur_cutoff,
             agglevel_variables=agglevel_variables, deflate=deflate,
             sw=sw, write=False
@@ -492,7 +482,7 @@ def main(
                     inputs_case,
                     f'supplycurve_{s}.csv'),
                 numbins_tech=numbins[s], inputs_case=inputs_case,
-                agglevel=agglevel, AggregateRegions=AggregateRegions,
+                agglevel=agglevel,
                 spur_cutoff=spur_cutoff,agglevel_variables=agglevel_variables, deflate=deflate,
                 sw=sw, write=False
             )
@@ -986,10 +976,7 @@ def main(
     sitemap = reeds.io.get_sitemap()
     county2zone = reeds.io.get_county2zone(os.path.dirname(os.path.normpath(inputs_case)))
     interconnection_cost['r'] = interconnection_cost.index.map(sitemap.FIPS).map(county2zone)
-    val_r = pd.read_csv(
-        os.path.join(inputs_case, 'val_r.csv'),
-        header=None,
-    ).squeeze(1).values
+    val_r = reeds.io.read_input(inputs_case, 'r').squeeze(1).values
     spursites = interconnection_cost.loc[interconnection_cost.r.isin(val_r)].copy()
     spursites['x'] = 'i' + spursites.index.astype(str)
     if write:
diff --git a/reeds/inputs.py b/reeds/inputs.py
index 45dfc002b..e598dc261 100644
--- a/reeds/inputs.py
+++ b/reeds/inputs.py
@@ -11,52 +11,41 @@
 import geopandas as gpd
 from pathlib import Path
 from warnings import warn
+from typing import Literal
 sys.path.append(str(Path(__file__).parent.parent))
 import reeds
-from input_processing import mcs_sampler
+from reeds.input_processing import mcs_sampler
 
 ### Functions
-def parse_regions(case_or_string, case=None):
+def parse_regions(case:str|Path|None=None, **kwargs) -> list:
     """
+    Get the list of active model zones.
+
     Inputs
     ------
-    case_or_string: path to a ReEDS case or a parseable string in the format of GSw_Region
-    case: path to a ReEDS case. Only used if case_or_string is not a ReEDS case. Should be
-        used if you want to select a subset of model zones from a ReEDS case that used
-        region aggregation.
+    case: Filepath to a ReEDS case or None. If None, the defaults from cases.csv are used.
+    **kwargs: key/value pairs from cases.csv that override the values in the provided case.
+        The only relevant switches here are GSw_ZoneSet and GSw_Region.
 
     Returns
     -------
-    np.array of zone names
-        - If case_or_string is a case, return the regions modeled in the run
-        - If case_or_string is a parseable string in the format of GSw_Region, return
-          the regions that obey that string
+    list of zone names
 
     Examples
     --------
-    parse_regions('transreg/NYISO') -> ['p127', 'p128']
-    parse_regions('st/PA') -> ['p115', 'p119', 'p120', 'p122']
-    parse_regions('st/PA', 'path/to/case/using/region/aggregation') -> ['p115', 'p120', 'z122']
+    parse_regions(GSw_ZoneSet='z134', GSw_Region='transreg/NYISO') -> ['p127', 'p128']
+    parse_regions(GSw_ZoneSet='z134', GSw_Region='st/PA') -> ['p115', 'p119', 'p120', 'p122']
+    parse_regions(GSw_ZoneSet='z132', GSw_Region='st/PA') -> ['p115', 'p120', 'z122']
     """
-    if os.path.exists(case_or_string):
-        sw = reeds.io.get_switches(case_or_string)
-        hierarchy = reeds.io.get_hierarchy(case_or_string)
-        GSw_Region = sw['GSw_Region']
-    ## Provide case argument if using aggregated regions
-    elif os.path.exists(str(case)):
-        hierarchy = reeds.io.get_hierarchy(case)
-        GSw_Region = case_or_string
-    else:
-        hierarchy = reeds.io.get_hierarchy()
-        GSw_Region = case_or_string
-
-    level, regions = GSw_Region.split('/')
-    regions = regions.split('.')
-    if level in ['r', 'ba']:
-        rs = [r for r in hierarchy.index if r in regions]
-    else:
-        rs = hierarchy.loc[hierarchy[level].isin(regions)].index
-    return rs
+    sw = reeds.io.get_switches(case, **kwargs)
+    hierarchy = reeds.io.assemble_hierarchy(case, **kwargs)
+    region_groups = sw.GSw_Region.split('//')
+    zones = []
+    for region_group in region_groups:
+        level, _regions = region_group.split('/')
+        regions = _regions.split('.')
+        zones.extend(hierarchy.loc[hierarchy[level].isin(regions), 'r'].tolist())
+    return zones
 
 
 def parse_yearset(yearset:str) -> list:
@@ -338,6 +327,62 @@ def parse_cases(
     return dfcases_out
 
 
+def solvestring_sequential(
+        batch_case, caseSwitches,
+        cur_year, next_year, prev_year, restartfile,
+        toLogGamsString=' logOption=4 logFile=gamslog.txt appendLog=1 ',
+        hpc=0, iteration=0, stress_year=None,
+        temporal_inputs='rep',
+    ):
+    """
+    Typical inputs:
+    * restartfile: batch_case if first solve year else {batch_case}_{prev_year}
+    * caseSwitches: loaded from {batch_case}/inputs_case/switches.csv
+    """
+    savefile = f"{batch_case}_{cur_year}i{iteration}"
+    _stress_year = f"{cur_year}i0" if stress_year is None else stress_year
+    out = (
+        f"gams {Path('reeds','core','solve','3_solve_oneyear.gms')}"
+        + (" license=gamslice.txt" if hpc else '')
+        + " o=" + os.path.join("lstfiles", f"{savefile}.lst")
+        + " r=" + os.path.join("g00files", restartfile)
+        + " gdxcompress=1"
+        + " xs=" + os.path.join("g00files", savefile)
+        + toLogGamsString
+        + f" --case={batch_case}"
+        + f" --cur_year={cur_year}"
+        + f" --next_year={next_year}"
+        + f" --prev_year={prev_year}"
+        + f" --stress_year={_stress_year}"
+        + f" --temporal_inputs={temporal_inputs}"
+        + ''.join([f" --{s}={caseSwitches[s]}" for s in [
+            'GSw_Canada',
+            'GSw_ClimateHydro',
+            'GSw_ClimateWater',
+            'GSw_gopt',
+            'GSw_HourlyChunkLengthRep',
+            'GSw_HourlyChunkLengthStress',
+            'GSw_HourlyType',
+            'GSw_HourlyWrapLevel',
+            'GSw_MGA_CostDelta',
+            'GSw_MGA_Direction',
+            'GSw_PVB_Dur',
+            'GSw_SkipRAyear',
+            'GSw_StateCO2ImportLevel',
+            'GSw_StartMarkets',
+            'GSw_ValStr',
+            'solver',
+            'debug',
+            'startyear',
+            'diagnose',
+            'diagnose_year'
+        ]])
+        + '\n'
+    )
+
+    return out
+
+
 def get_bin(
     df_in,
     bin_num,
@@ -483,46 +528,58 @@ def get_itl(r, rr, case=None, errors='raise', **kwargs) -> dict:
     return itl
 
 
-def get_itls(case=None, level:str='r', errors='raise', **kwargs) -> pd.DataFrame:
+def get_interface_data(
+    case=None,
+    datafile:Literal['itl_NARIS.csv', 'transmission_cost_distance.csv']='itl_NARIS.csv',
+    level:str='r',
+    interfaces:bool=True,
+    errors='raise',
+    **kwargs,
+) -> pd.DataFrame:
     """
-    Get all the ITLs for the specified resolution. The resolution can be specified by:
+    Get all the transmission interface data for the specified resolution.
+    The resolution can be specified by:
         - Providing a path to a ReEDS run via `case`
         - Providing `GSw_ZoneSet` as a keyword argument
     If neither `case` nor `GSw_ZoneSet` are provided, the default resolution from
     `cases.csv` is used.
 
     Args:
+        datafile (str): 'itl_NARIS.csv' or 'transmission_cost_distance.csv'
         level (str): 'r' or 'transgrp'
-
-    Inputs for testing:
-        case = None
-        level = 'r'
-        kwargs = {}
-        errors = 'raise'
+        interfaces (bool): If True, return values for
+            inputs/zones/{GSw_ZoneSet}/interfaces_{level}.csv;
+            if False, return values for all start/end zone pairs in GSw_ZoneSet
     """
+    ## Derivative inputs
+    datacols = {
+        'itl_NARIS.csv': ['MW_forward', 'MW_reverse'],
+        'transmission_cost_distance.csv': ['polarity', 'voltage', 'cost_MUSD', 'length_miles'],
+    }[datafile]
+    ## Get some settings
     sw = reeds.io.get_switches(case, **kwargs)
     inputs = Path(reeds.io.reeds_path, 'inputs')
-    ## Get the ITL config settings
     config = get_itl_config()
     hashfunc = config['hashfunc']
-    ## Get the ITLs for all interfaces
-    fpath = Path(inputs, 'transmission', 'itl_NARIS.csv')
-    itls = (
+    ## Get the data for all interfaces
+    fpath = Path(inputs, 'transmission', datafile)
+    dfin = (
         pd.read_csv(fpath)
         .rename(columns={
-            f'{hashfunc}_from':f'{hashfunc}_r',
-            f'{hashfunc}_to':f'{hashfunc}_rr',
+            f'{hashfunc}_from': 'start',
+            f'{hashfunc}_to': 'end',
         })
     )
-    if itls.index.duplicated().sum():
-        raise ValueError('Duplicate entries in ITL database')
+    if dfin.index.duplicated().sum():
+        raise ValueError(f'Duplicate entries in {datafile}')
     ### Get the zone hashes
+    zone_node = pd.read_csv(
+        Path(inputs, 'zones', sw.GSw_ZoneSet, 'zonehash.csv'),
+        index_col='r',
+    )
     if level == 'r':
         ## We save the zonehash for level == 'r' directly for peace of mind
-        zonehash = pd.read_csv(
-            Path(inputs, 'zones', sw.GSw_ZoneSet, 'zonehash.csv'),
-            index_col='r',
-        )[hashfunc]
+        zonehash = zone_node[hashfunc]
     else:
         ## For other levels, we calculate the zonehash from the hierarchy
         hierarchy = reeds.io.assemble_hierarchy(case, **kwargs).set_index('r')
@@ -536,18 +593,43 @@ def get_itls(case=None, level:str='r', errors='raise', **kwargs) -> pd.DataFrame
             )
             raise ValueError(err)
         zonehash = county2level.reset_index().groupby(level).FIPS.agg(hash_counties)
-    ### Get the ITLs
-    interfacepath = Path(inputs, 'zones', sw.GSw_ZoneSet, f'interfaces_{level}.csv')
-    dfout = pd.read_csv(interfacepath)
-    for i, r in enumerate(['r', 'rr']):
-        dfout[r] = dfout.interface.str.split(config['idelim']).str[i]
-        dfout[f'{hashfunc}_{r}'] = dfout[r].map(zonehash)
-    dfout = dfout.merge(itls, on=[f'{hashfunc}_r', f'{hashfunc}_rr'], how='left')
+    ### Get the data for the defined interfaces
+    if interfaces:
+        interfacepath = Path(inputs, 'zones', sw.GSw_ZoneSet, f'interfaces_{level}.csv')
+        dfout = pd.read_csv(interfacepath)
+        for i, (r, side) in enumerate([('r', 'start'), ('rr', 'end')]):
+            dfout[r] = dfout.interface.str.split(config['idelim']).str[i]
+            dfout[side] = dfout[r].map(zonehash)
+        dfout = dfout.merge(dfin, on=['start', 'end'], how='left')
+    else:
+        ## Include any connections to offshore zones for now
+        dfoffshore = reeds.io.get_offshore_zones()
+        offshore_zones = dfoffshore.index.values.tolist()
+        dfout = dfin.loc[
+            dfin.start.isin(zonehash.tolist()+offshore_zones)
+            & dfin.end.isin(zonehash.tolist()+offshore_zones)
+        ].copy()
+        hash2zone = pd.Series(
+            data=zonehash.index.tolist()+offshore_zones,
+            index=zonehash.values.tolist()+offshore_zones,
+        )
+        for r, side in [('r', 'start'), ('rr', 'end')]:
+            dfout[r] = dfout[side].map(hash2zone)
+        ## Keep them sorted
+        dfout.r, dfout.rr = dfout[['r','rr']].min(axis=1), dfout[['r','rr']].max(axis=1)
+        ## Include the lat/lon for plotting and straight-line distance
+        zone2latlon = pd.concat([
+            dfoffshore[['node_lat','node_lon']],
+            zone_node.set_index(hashfunc)[['node_lat','node_lon']],
+        ])
+        for side in ['start', 'end']:
+            for datum in ['lat', 'lon']:
+                dfout[f'{side}_{datum}'] = dfout[side].map(zone2latlon[f'node_{datum}'])
     ### Make sure it worked
-    missing = dfout.loc[dfout.MW_forward.isnull() | dfout.MW_reverse.isnull()]
+    missing = dfout.loc[dfout[datacols].isnull().any(axis=1)]
     if len(missing):
         print(missing)
-        err = f'Missing ITL for {len(missing)} interfaces'
+        err = f'Missing data from {datafile} for {len(missing)} interfaces'
         if len(missing) <= 10:
             err += ': ' + (' '.join(missing.interface))
         if errors == 'raise':
@@ -557,34 +639,55 @@ def get_itls(case=None, level:str='r', errors='raise', **kwargs) -> pd.DataFrame
     return dfout.dropna()
 
 
-def get_zones(case=None, crs='ESRI:102008', **kwargs) -> gpd.GeoDataFrame:
+def get_itls(case=None, level:str='r', errors='raise', **kwargs) -> pd.DataFrame:
     """
+    Get all the ITLs for the specified resolution using get_interface_data().
+    The resolution can be specified by:
+        - Providing a path to a ReEDS run via `case`
+        - Providing `GSw_ZoneSet` as a keyword argument
+    If neither `case` nore `GSw_ZoneSet` are provided, the default resolution from
+    `cases.csv` is used.
+
     Args:
-        case (str, Path, or None): Path to a ReEDS case.
-            If None, uses the default GSw_ZoneSet from cases.csv.
-        crs (str): Coordinate reference system
-        **kwargs: ReEDS switch:value pairs (overrides case argument)
-    """
-    dfcounty = reeds.spatial.get_map('county', source='tiger', crs=crs)
-    dfstates = reeds.spatial.get_map('states', source='census', crs=crs)
-    country = dfstates.dissolve().geometry[0]
-    county2zone = reeds.io.get_county2zone(case, **kwargs)
+        level (str): 'r' or 'transgrp'
 
-    dfcounty['r'] = county2zone
+    Inputs for testing:
+        case = None
+        level = 'r'
+        kwargs = {}
+        errors = 'raise'
+    """
+    return get_interface_data(
+        case=case,
+        datafile='itl_NARIS.csv',
+        level=level,
+        interfaces=True,
+        errors=errors,
+        **kwargs,
+    )
 
-    dfzones = dfcounty.dissolve('r')
-    dfzones.geometry = dfzones.intersection(country).buffer(0)
 
-    return dfzones[['geometry']]
+def get_distances(case=None, errors='raise', **kwargs) -> pd.DataFrame:
+    """
+    """
+    distances = get_interface_data(
+        case=case,
+        datafile='transmission_cost_distance.csv',
+        level='r',
+        interfaces=False,
+        errors=errors,
+        **kwargs,
+    )
+    return distances
 
 
 def _make_line(row):
     return shapely.LineString([[row.from_lon, row.from_lat], [row.to_lon, row.to_lat]])
 
 
-def get_hvdc_lines():
+def get_hvdc_lines(filename='hvdc_existing.csv'):
     """Load data for individual HVDC lines"""
-    datapath = Path(reeds.io.reeds_path, 'inputs', 'transmission', 'hvdc_lines.csv')
+    datapath = Path(reeds.io.reeds_path, 'inputs', 'transmission', filename)
     dfdc = pd.read_csv(datapath)
     dfdc['geometry'] = dfdc.apply(_make_line, axis=1)
     dfdc = gpd.GeoDataFrame(dfdc, crs='EPSG:4326')
@@ -593,16 +696,28 @@ def get_hvdc_lines():
     return dfdc
 
 
-def map_hvdc_lines_to_interfaces(case=None, **kwargs) -> pd.DataFrame:
+def map_hvdc_lines_to_interfaces(
+    case=None,
+    filename='hvdc_existing.csv',
+    dtype:Literal['capacity', 'cost']='capacity',
+    **kwargs,
+) -> pd.DataFrame:
     """
     Assign HVDC line capacity to interfaces by mapping start/end points to zones
 
     Inputs for testing:
         case = None
-        kwargs = {'GSw_ZoneSet': 'z90'}
+        kwargs = {'GSw_ZoneSet': 'z132'}
+        for filename in [
+            'hvdc_existing.csv',
+            'hvdc_planned-baseline.csv',
+            'hvdc_planned-NTP_MT.csv',
+            'hvdc_planned-NTP_P2P.csv',
+        ]:
+            map_hvdc_lines_to_interfaces(case=case, filename=filename, kwargs=kwargs)
     """
-    dfzones = get_zones(case, **kwargs)
-    dfdc = get_hvdc_lines().to_crs(dfzones.crs)
+    dfzones = reeds.io.get_zones(reeds.io.standardize_case(case), **kwargs)
+    dfdc = get_hvdc_lines(filename=filename).to_crs(dfzones.crs)
     for i, side in enumerate(['from', 'to']):
         dfdc[f'zone_{side}'] = gpd.sjoin(
             dfdc.set_geometry(f'{side}_latlon').set_crs('EPSG:4326').to_crs(dfzones.crs),
@@ -614,11 +729,19 @@ def map_hvdc_lines_to_interfaces(case=None, **kwargs) -> pd.DataFrame:
         dfdc.loc[dfdc.zone_from != dfdc.zone_to].dropna()
         .rename(columns={'zone_from':'r', 'zone_to':'rr'})
     ).copy()
-    ## Normalize from/to order and sum capacity for each interface
+    ## Normalize from/to order
     for index, row in dfcap.iterrows():
         for side, r in enumerate(['r', 'rr']):
             dfcap.loc[index, r] = sorted(row[['r','rr']])[side]
-    dfout = dfcap.groupby(['r','rr'])[['name','MW']].agg({'MW':sum, 'name':list})
+    if dtype == 'capacity':
+        ## Sum capacity for each interface
+        dfout = (
+            dfcap
+            .groupby(['r', 'rr', 'trtype', 'year_online', 'certain'])
+            [['name', 'MW']].agg({'MW':sum, 'name':list})
+        )
+    else:
+        dfout = dfcap.copy()
     return dfout
 
 
@@ -755,9 +878,16 @@ def validate_zoneset(GSw_ZoneSet):
             f"match for {len(wrong)} zones: {', '.join(wrong.index)}"
         )
     ## Do all the zone interfaces have ITLs?
-    get_itls(GSw_ZoneSet=GSw_ZoneSet, errors='raise')
+    dfitl = get_itls(GSw_ZoneSet=GSw_ZoneSet, errors='raise')
     ## Do all the transgrp interfaces have ITLs?
     get_itls(GSw_ZoneSet=GSw_ZoneSet, level='transgrp', errors='raise')
+    ## Do all interfaces with ITLs have distances and costs?
+    dfdistance = get_distances(GSw_ZoneSet=GSw_ZoneSet)
+    dftest = dfitl.merge(dfdistance.loc[dfdistance.polarity=='ac'], on=['r','rr'], how='left')
+    nulls = dftest.loc[dftest[['cost_MUSD','length_miles']].isnull().sum(axis=1) > 0]
+    if len(nulls):
+        print(nulls)
+        raise ValueError(f'Missing transmission distance/cost for {len(nulls)} interfaces')
     ## Do the hierarchy files have all the required columns?
     required_levels = ['st', 'transreg', 'transgrp', 'nercr', 'interconnect']
     hierarchy = reeds.io.assemble_hierarchy(GSw_ZoneSet=GSw_ZoneSet).set_index('r')
diff --git a/reeds/io.py b/reeds/io.py
index 57d7dec6c..4c13b5212 100644
--- a/reeds/io.py
+++ b/reeds/io.py
@@ -5,11 +5,12 @@
 import datetime
 import h5py
 import ctypes
+import inspect
 import numpy as np
 import pandas as pd
 import geopandas as gpd
-from glob import glob
 from pathlib import Path
+from typing import Literal
 from pandas.api.types import is_float_dtype
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -80,6 +81,15 @@ def assemble_hierarchy(case=None, fpath=None, extra=True, **kwargs) -> pd.DataFr
     if fpath is None:
         fpath = Path(fpath_base, 'hierarchy.csv')
     dfin = pd.read_csv(fpath)
+    ## Add offshore zones if necessary
+    if int(sw.GSw_OffshoreZones):
+        fpath_offshore = Path(reeds.io.reeds_path, 'inputs', 'zones', 'hierarchy_offshore.csv')
+        hierarchy_offshore = (
+            pd.read_csv(fpath_offshore)
+            .rename(columns={'ba':'r'})
+            .drop(columns='aggreg', errors='ignore')
+        )
+        dfin = pd.concat([dfin, hierarchy_offshore])
     ## Add hierarchy levels defined by groups of states
     fpath_state = Path(reeds.io.reeds_path, 'inputs', 'zones', 'state_groups.csv')
     state_groups = pd.read_csv(fpath_state, index_col='st')
@@ -92,6 +102,17 @@ def assemble_hierarchy(case=None, fpath=None, extra=True, **kwargs) -> pd.DataFr
     if extra:
         fpath_zonehash = Path(fpath_base, 'zonehash.csv')
         zonehash = pd.read_csv(fpath_zonehash)
+        if int(sw.GSw_OffshoreZones):
+            hashfunc = reeds.inputs.get_itl_config()['hashfunc']
+            offshore_zones = (
+                get_offshore_zones().reset_index()
+                .rename(columns={'zone':'r'})
+                .drop(columns='geometry', errors='ignore')
+            )
+            ## Offshore zones are not groups of counties and are not user-adjustable,
+            ## so we use their hardcoded name as their zonehash
+            offshore_zones[hashfunc] = offshore_zones['r'].copy()
+            zonehash = pd.concat([zonehash, offshore_zones])
         dfout = dfout.merge(zonehash, on='r', how='left')
         if any(dfout.isnull().sum()):
             print(dfout.loc[dfout.isnull().sum(axis=1) > 0])
@@ -103,9 +124,9 @@ def get_hierarchy(case=None, original=False, **kwargs):
     """Get hierarchy for ReEDs case if provided, or for country if case not provided"""
     if case:
         if original:
-            filepath = Path(case, 'inputs_case', 'hierarchy_original.csv')
+            filepath = Path(standardize_case(case), 'inputs_case', 'hierarchy_original.csv')
         else:
-            filepath = Path(case, 'inputs_case', 'hierarchy.csv')
+            filepath = Path(standardize_case(case), 'inputs_case', 'hierarchy.csv')
     else:
         ## TEMPORARY 20260402: Use deprecated hierarchy inputs.
         ## Use the line below once we make the switch:
@@ -165,6 +186,48 @@ def get_county2zone(
     return dfout
 
 
+
+def get_zone_nodes(case=None, crs='ESRI:102008', **kwargs):
+    """Get the transmission node for each model zone"""
+    sw = get_switches(case, **kwargs)
+    zonepath = Path(reeds_path, 'inputs', 'zones', sw.GSw_ZoneSet)
+    zonehash = pd.read_csv(Path(zonepath, 'zonehash.csv'), index_col='r')
+    ## Convert lat/lon to x/y
+    xy = reeds.plots.df2gdf(zonehash, lat='node_lat', lon='node_lon', crs=crs)
+    zonehash['x'] = xy.geometry.x
+    zonehash['y'] = xy.geometry.y
+    ## Drop zone hash
+    hashfunc = reeds.inputs.get_itl_config()['hashfunc']
+    zonehash = zonehash.drop(columns=hashfunc, errors='ignore')
+    return zonehash
+
+
+def get_zones(
+    case=None,
+    crs='ESRI:102008',
+    exclude_water_areas=True,
+    **kwargs,
+) -> gpd.GeoDataFrame:
+    """
+    Args:
+        case (str, Path, or None): Path to a ReEDS case.
+            If None, uses the default GSw_ZoneSet from cases.csv.
+        crs (str): Coordinate reference system
+        **kwargs: ReEDS switch:value pairs (overrides case argument)
+    """
+    dfcounty = reeds.spatial.get_map('county', source='tiger', crs=crs)
+    county2zone = reeds.io.get_county2zone(case, **kwargs)
+    dfcounty['r'] = county2zone
+    dfzones = dfcounty.dissolve('r')
+
+    if exclude_water_areas:
+        dfstates = reeds.spatial.get_map('states', source='census', crs=crs)
+        country = dfstates.dissolve().geometry[0]
+        dfzones.geometry = dfzones.intersection(country).buffer(0)
+
+    return dfzones[['geometry']]
+
+
 def get_countymap(select_counties=None, exclude_water_areas=False):
     """Get geodataframe of US counties"""
     dfcounty = reeds.spatial.get_map('county', source='tiger')
@@ -190,193 +253,52 @@ def get_countymap(select_counties=None, exclude_water_areas=False):
     return dfcounty
 
 
-def get_zonemap(case=None, exclude_water_areas=False, crs='ESRI:102008'):
+def get_offshore_zones(crs='ESRI:102008'):
+    offshore_zones = (
+        gpd.read_file(
+            os.path.join(reeds_path, 'inputs', 'shapefiles', 'offshore_zones.gpkg')
+        ).set_index('zone').to_crs(crs).drop(columns=['zone_old'], errors='ignore')
+        .rename(columns={'node_latitude':'node_lat', 'node_longitude':'node_lon'})
+    )
+    return offshore_zones
+
+
+def get_zonemap(case=None, exclude_water_areas=False, crs='ESRI:102008', **kwargs):
     """
-    Get geodataframe of model zones, applying aggregation if necessary
+    Get geodataframe of model zones, node locations, and hierarchy levels
     """
+    zone_nodes = get_zone_nodes(case=case, **kwargs)
+    dfzones = get_zones(case=case, exclude_water_areas=exclude_water_areas, crs=crs, kwargs=kwargs)
+    dfzones = dfzones.merge(zone_nodes, left_index=True, right_index=True, how='left')
+    ## Add offshore zones if necessary
     sw = get_switches(case)
-    ## Backwards compatibility
-    if 'GSw_RegionResolution' not in sw:
-        sw['GSw_RegionResolution'] = 'ba'
-
-    if case:
-        agglevel_variables = reeds.spatial.get_agglevel_variables(
-            reeds_path,
-            os.path.join(case, 'inputs_case'),
-        )
-    else:
-        agglevel_variables = {'lvl': 'ba',
-                              'agglevel': 'ba',
-                              }
-
-    # Mixed resolution procedure
-    if agglevel_variables['lvl'] == 'mult':
-        ### Model zones
-        dfba = gpd.read_file(os.path.join(reeds_path, 'inputs', 'shapefiles', 'US_PCA'))
-        ### Use transmission endpoints from reV
-        endpoints = gpd.read_file(
-            os.path.join(reeds_path, 'inputs', 'shapefiles', 'transmission_endpoints')
-        ).set_index('ba_str')
-        endpoints['x'] = endpoints.centroid.x
-        endpoints['y'] = endpoints.centroid.y
-
-        dfba['x'] = dfba['rb'].map(endpoints.x)
-        dfba['y'] = dfba['rb'].map(endpoints.y)
-        dfba['centroid_x'] = dfba.geometry.centroid.x
-        dfba['centroid_y'] = dfba.geometry.centroid.y
-
-        # Filter to regions being solved at BA resolution
-        dfba = dfba[dfba['rb'].isin(agglevel_variables['ba_regions'])].set_index('rb')
-
-        if 'aggreg' in agglevel_variables['agglevel']:
-            r2aggreg = (
-                pd.read_csv(os.path.join(case, 'inputs_case', 'hierarchy_original.csv'))
-                .rename(columns={'ba': 'r'})
-                .set_index('r')
-                .aggreg
-            )
-            ### Take the "anchor" zone as the zone with the largest area [km2]
-            dfba['km2'] = dfba.area / 1e6
-            ## Add column for new regions
-            dfba['aggreg'] = dfba.index.map(r2aggreg)
-            ## Take the original zone with largest area
-            aggreg2anchorreg = dfba.groupby('aggreg').km2.idxmax().rename('rb')
-            ## Save it for plotting
-            aggreg2anchorreg.to_csv(os.path.join(case,'inputs_case', 'aggreg2anchorreg.csv'))
-
-            aggreg2anchorreg = aggreg2anchorreg.reset_index()
-            aggreg2anchorreg = aggreg2anchorreg[aggreg2anchorreg
-                ['aggreg'].isin(agglevel_variables['ba_regions'])
-            ]
-            dfba = dfba.reset_index()
-            dfba.rb = dfba.rb.map(r2aggreg)
-            dfba = dfba.dissolve('rb').loc[aggreg2anchorreg.aggreg].copy()
-
-        ### Get the county map
-        dfcounty = get_countymap(
-            agglevel_variables['county_regions'], exclude_water_areas
-        )
-        dfcounty = dfcounty[['rb', 'NAMELSAD', 'STATE', 'geometry']]
-
-        ## Use the centroid for both the transmission endpoint and centroid
-        for prefix in ['', 'centroid_']:
-            dfcounty[prefix + 'x'] = dfcounty.geometry.centroid.x
-            dfcounty[prefix + 'y'] = dfcounty.geometry.centroid.y
-
-        dfcounty = (
-            dfcounty.rename(columns={'NAMELSAD': 'county', 'STCODE': 'st'})
-            .set_index('rb')
-            .drop(columns=['county'])
+    if int(sw.GSw_OffshoreZones):
+        offshore_zones = get_offshore_zones(crs=crs)
+        regions = reeds.inputs.parse_regions(case=case, **kwargs)
+        regions_offshore = [i for i in regions if i in offshore_zones.index]
+        offshore_zones = offshore_zones.loc[regions_offshore]
+        ## Get node x/y for consistency with land-based zones
+        xy = reeds.plots.df2gdf(
+            offshore_zones.drop(columns='geometry'),
+            lat='node_lat',
+            lon='node_lon',
+            crs=crs,
         )
-
-        # Combine BA and County
-        dfcounty = dfcounty.to_crs(dfba.crs)
-        dfba = pd.concat([dfba, dfcounty])
-
-        ### Include all hierarchy levels
-        hierarchy = get_hierarchy(case)
-
-        for col in hierarchy:
-            dfba[col] = dfba.index.map(hierarchy[col])
-
-    ######## Single Resolution Procedure ########
-    else:
-        ### Check if resolution is at county level
-        if sw.GSw_RegionResolution != 'county':
-            hierarchy = get_hierarchy(case, original=True)
-            ### Model zones
-            dfba = gpd.read_file(
-                os.path.join(reeds_path, 'inputs', 'shapefiles', 'US_PCA')
-            ).set_index('rb').to_crs(crs)[['geometry']].copy()
-            ## Add transmission endpoints
-            endpoints = (
-                gpd.read_file(
-                    os.path.join(reeds_path, 'inputs', 'shapefiles', 'transmission_endpoints')
-                )
-                .set_index('ba_str')
-                .rename(columns={'lon':'node_longitude','lat':'node_latitude'})
-                [['node_longitude','node_latitude','geometry']]
-            )
-            endpoints['x'] = endpoints.centroid.x
-            endpoints['y'] = endpoints.centroid.y
-            dfba = dfba.merge(endpoints.drop(columns='geometry'), left_index=True, right_index=True)
-            ## Add offshore zones (transmission endpoints already included)
-            if int(sw.GSw_OffshoreZones):
-                offshore_zones = gpd.read_file(
-                    os.path.join(reeds_path, 'inputs', 'shapefiles', 'offshore_zones.gpkg')
-                ).set_index('zone').to_crs(crs).drop(columns=['zone_old'], errors='ignore')
-                ## Get node x/y for consistency with land-based zones
-                xy = reeds.plots.df2gdf(
-                    offshore_zones.drop(columns='geometry'),
-                    lat='node_latitude',
-                    lon='node_longitude',
-                    crs=crs,
-                )
-                offshore_zones['x'] = xy.geometry.x
-                offshore_zones['y'] = xy.geometry.y
-                ## Combine
-                dfba = pd.concat([dfba.assign(offshore=0), offshore_zones.assign(offshore=1)])
-            ## Filter to regions used in this run
-            if 'ba_regions' in agglevel_variables:
-                dfba = dfba.loc[(
-                    dfba.index.intersection(agglevel_variables['ba_regions'])
-                )]
-            ## Record centroid locations for plot labels
-            dfba['centroid_x'] = dfba.geometry.centroid.x
-            dfba['centroid_y'] = dfba.geometry.centroid.y
-
-            if 'aggreg' in agglevel_variables['agglevel']:
-                r2aggreg = (
-                    pd.read_csv(os.path.join(case, 'inputs_case', 'hierarchy_original.csv'))
-                    .rename(columns={'ba': 'r'})
-                    .set_index('r')
-                    .aggreg
-                    )
-                ### Take the "anchor" zone as the zone with the largest area [km2]
-                dfba['km2'] = dfba.area / 1e6
-                ## Add column for new regions
-                dfba['aggreg'] = dfba.index.map(r2aggreg)
-                ## Take the original zone with largest area
-                aggreg2anchorreg = dfba.groupby('aggreg').km2.idxmax().rename('rb')
-                ## Save it for plotting
-                aggreg2anchorreg.to_csv(os.path.join(case,'inputs_case', 'aggreg2anchorreg.csv'))
-
-        else:
-            hierarchy = (
-                pd.read_csv(os.path.join(case, 'inputs_case', 'hierarchy.csv'))
-                .rename(columns={'*r': 'r', 'ba': 'r'})
-                .set_index('r')
-            )
-            ### Get the county map
-            select_counties = agglevel_variables.get('county_regions')
-            dfba = get_countymap(select_counties, exclude_water_areas)
-
-            ### Add US state code and drop states outside of CONUS
-            state_fips = pd.read_csv(
-                os.path.join(reeds_path, 'inputs', 'shapefiles', "state_fips_codes.csv"),
-                names=["STATE", "STCODE", "STATEFP", "CONUS"],
-                dtype={"STATEFP": "string"},
-                header=0,
-            )
-            state_fips = state_fips.loc[state_fips['CONUS'], :]
-            dfba = dfba.merge(state_fips, on="STATEFP")
-            dfba = dfba[['rb', 'NAMELSAD', 'STATE_x', 'geometry']].set_index('rb')
-
-            ## Use the centroid for both the transmission endpoint and centroid
-            for prefix in ['', 'centroid_']:
-                dfba[prefix + 'x'] = dfba.geometry.centroid.x
-                dfba[prefix + 'y'] = dfba.geometry.centroid.y
-
-            dfba.rename(columns={'NAMELSAD': 'county', 'STATE_x': 'st'}, inplace=True)
-
-        ### Include all hierarchy levels
-        for col in hierarchy:
-            dfba[col] = dfba.index.map(hierarchy[col])
+        offshore_zones['x'] = xy.geometry.x
+        offshore_zones['y'] = xy.geometry.y
+        ## Combine
+        dfzones = pd.concat([dfzones.assign(offshore=0), offshore_zones.assign(offshore=1)])
+    ## Add spatial hierarchy levels
+    hierarchy = assemble_hierarchy(case=case, extra=False, kwargs=kwargs)
+    dfba = dfzones.merge(hierarchy, left_index=True, right_on='r').set_index('r')
+    ## Record centroid locations for plot labels
+    dfba['centroid_x'] = dfba.geometry.centroid.x
+    dfba['centroid_y'] = dfba.geometry.centroid.y
 
     return dfba
 
 
-def get_dfmap(case=None, levels=None, exclude_water_areas=False):
+def get_dfmap(case=None, levels=None, exclude_water_areas=True):
     """Get dictionary of maps at different hierarchy levels"""
     hierarchy = (
         get_hierarchy(case, original=True)
@@ -412,10 +334,23 @@ def get_dfmap(case=None, levels=None, exclude_water_areas=False):
 
     return dfmap
 
-def get_disagg_data(case, disagg_variable='population'):
+def get_disagg_data(
+    case: str | Path,
+    disagg_variable: Literal['hydroexist', 'geosize', 'population', 'state_lpf']
+):
     """
     Get state/region-to-county disaggregation factors for the given variable.
     """
+    if disagg_variable not in [
+        'hydroexist',
+        'geosize',
+        'population',
+        'state_lpf'
+    ]:
+        raise NotImplementedError(
+            f"'{disagg_variable}' is not a valid disagg variable."
+        )
+
     return pd.read_csv(
         os.path.join(case, 'inputs_case', f'disagg_{disagg_variable}.csv')
     )
@@ -451,6 +386,60 @@ def get_h2_storage_sites(h2_storage_type="salt"):
 
 
 ### Read files from a ReEDS case
+def read_h5(h5path:str|Path, key:str) -> pd.DataFrame:
+    """Read a key from a ReEDS-formatted .h5 file"""
+    if not Path(h5path).is_file():
+        raise FileNotFoundError(h5path)
+    with h5py.File(h5path, 'r') as f:
+        columns = [i.decode() for i in list(f[key]['columns'])]
+        try:
+            df = pd.DataFrame({col: f[key][col] for col in columns})
+        except KeyError:
+            df = pd.DataFrame(columns=columns)
+    for col in df:
+        if df[col].dtype == 'O':
+            df[col] = df[col].str.decode('utf-8')
+    return df
+
+
+def read_input(
+    case:str|Path,
+    name:str,
+    **kwargs,
+) -> pd.DataFrame:
+    """
+    Read a ReEDS input set or parameter (name) from {case}/inputs_case.
+    If {case}/inputs_case/inputs.h5 exists, the named parameter is read from there;
+    otherwise, it is read from {case}/inputs_case/{name}.csv;
+    if that doesn't exist, an error is raised.
+
+    Args:
+        case (str or Path): Absolute path to a ReEDS case
+        name (str): Name of the ReEDS parameter or {case}/inputs_case/{name}.csv
+
+    Returns:
+        pd.DataFrame
+    """
+    key = Path(name).stem
+    h5path = Path(reeds.io.standardize_case(case), 'inputs_case', 'inputs.h5')
+    csvpath = Path(h5path.parent, f'{key}.csv')
+    if h5path.is_file():
+        try:
+            df = read_h5(h5path, key)
+        ## Fall back to csv if the requested dataset is not yet in inputs.h5
+        except KeyError:
+            try:
+                df = pd.read_csv(csvpath, **kwargs)
+            except FileNotFoundError:
+                err = f"{h5path} has no '{name}' key and {csvpath} does not exist"
+                raise FileNotFoundError(err)
+    elif csvpath.is_file():
+        df = pd.read_csv(csvpath, **kwargs)
+    else:
+        raise FileNotFoundError(f'Neither {h5path} nor {csvpath} exist')
+    return df
+
+
 def read_output(
     case: str,
     filename: str,
@@ -482,20 +471,16 @@ def read_output(
     if os.path.exists(h5path) and not filename.endswith('.csv'):
         key = os.path.basename(filename)
         try:
-            with h5py.File(h5path, 'r') as f:
-                columns = [i.decode() for i in list(f[key]['columns'])]
-                df = pd.DataFrame({col: f[key][col] for col in columns})
-            for col in df:
-                if df[col].dtype == 'O':
-                    df[col] = df[col].str.decode('utf-8')
+            df = read_h5(h5path, key)
         except KeyError:
             ## Empty dataframes aren't written to h5 file, so make one ourselves
-            e_report_params = pd.read_csv(
-                os.path.join(case, 'e_report_params.csv'),
-                comment='#',
-            )
-            _index = e_report_params.loc[
-                e_report_params.param.map(lambda x: x.split('(')[0]) == key, 'param'
+            fpath = Path(case, 'reeds', 'core', 'terminus', 'report_params.csv')
+            ## Fall back to older params list if necessary for backwards compatibility
+            if not fpath.is_file():
+                fpath = Path(case, 'e_report_params.csv')
+            report_params = pd.read_csv(fpath, comment='#')
+            _index = report_params.loc[
+                report_params.param.map(lambda x: x.split('(')[0]) == key, 'param'
             ].squeeze()
             if not len(_index):
                 raise KeyError(f"{filename} is not in {h5path}")
@@ -619,10 +604,9 @@ def standardize_case(case=None):
     return case
 
 
-def get_switches(case=None, **kwargs):
+def get_switches_base(case=None, **kwargs):
     """
-    Get pd.Series of switch values from switches.csv, augur_switches.csv,
-    and CPLEX opt file.
+    Get pd.Series of switch values from switches.csv.
     Accepts either {case} or {case}/inputs_case as input.
 
     If {case} is None, the default switch values listed in cases.csv are retrieved.
@@ -646,11 +630,49 @@ def get_switches(case=None, **kwargs):
             index_col=0,
             header=None,
         ).squeeze(1)
-    ### Augur-specific switches
+    return sw
+
+
+def get_optfile(case=None, **kwargs):
+    """
+    Get the name of the optfile used by GAMS, formatted as described by
+    https://gams.com/49/docs/UG_GamsCall.html#GAMSAOoptfile
+    """
+    sw = get_switches_base(case, **kwargs)
+    GSw_gopt = int(sw.GSw_gopt)
+    if GSw_gopt == 1:
+        suffix = 'opt'
+    elif len(str(GSw_gopt)) == 1:
+        suffix = f'op{GSw_gopt}'
+    elif len(str(GSw_gopt)) == 2:
+        suffix = f'o{GSw_gopt}'
+    else:
+        suffix = str(GSw_gopt)
+    optfile = f'{sw.solver}.{suffix}'.lower()
+    return optfile
+
+
+def get_switches(case=None, **kwargs):
+    """
+    Get pd.Series of switch values from switches.csv, ra_switches.csv,
+    and solver settings file.
+    Accepts either {case} or {case}/inputs_case as input.
+
+    If {case} is None, the default switch values listed in cases.csv are retrieved.
+
+    If additional keyword arguments are provided, they replace the values specified
+    in {case}. This behavior can be used to read all the switches for a case (or all
+    the default settings) but change a single switch to a different value (when
+    making plots for different input settings, for example). If a key is provided
+    that is not a valid switch name, it is ignored.
+    """
+    case = standardize_case(case)
+    sw = get_switches_base(case)
+    ### Resource-adequacy-specific switches
     try:
         fpath_asw = os.path.join(
             (case if case is not None else reeds_path),
-            'ReEDS_Augur', 'augur_switches.csv',
+            'reeds', 'resource_adequacy', 'ra_switches.csv',
         )
         asw = pd.read_csv(fpath_asw, index_col='key')
         for i, row in asw.iterrows():
@@ -670,7 +692,7 @@ def get_switches(case=None, **kwargs):
                 row.value = float(row.value)
         sw = pd.concat([sw, asw.value])
     except FileNotFoundError:
-        print(f"{fpath_asw} not found so leaving out Augur switches")
+        print(f"{fpath_asw} not found so leaving out resource adequacy switches")
     ### Add derivative switches
     sw['resource_adequacy_years_list'] = [int(y) for y in sw['resource_adequacy_years'].split('_')]
     sw['num_resource_adequacy_years'] = len(sw['resource_adequacy_years_list'])
@@ -679,22 +701,27 @@ def get_switches(case=None, **kwargs):
     sw['future_hydcf_rep_years_list'] = [
         int(y) for y in sw.get('GSw_FutureHydCF_RepYears', _fallback).split('_')
     ]
-    ### Get number of threads to use in PRAS
-    opt_file = 'cplex.opt' if int(sw.GSw_gopt) == 1 else f'cplex.op{sw.GSw_gopt}'
-    try:
-        threads = get_param_value(os.path.join(case, opt_file), "threads", dtype=int)
-    except (FileNotFoundError, TypeError):
-        threads = get_param_value(os.path.join(reeds_path, opt_file), "threads", dtype=int)
+    ## Get number of threads to use in PRAS
+    ## (read from case folder; fall back to repo if case folder doesn't exist yet)
+    opt_file = get_optfile(case)
+    fpath_repo = Path(reeds_path, 'reeds', 'solver', opt_file)
+    if case is None:
+        fpath_opt = fpath_repo
+    else:
+        fpath_opt = Path(case, opt_file)
+        if not fpath_opt.is_file():
+            fpath_opt = fpath_repo
+    threads = get_param_value(fpath_opt, "threads", dtype=int)
     sw['threads'] = threads
-    ### Determine whether run is on HPC
+    ## Determine whether run is on HPC
     sw['hpc'] = True if int(os.environ.get('REEDS_USE_SLURM', 0)) else False
-    ### Add the run location
+    ## Add the run location
     sw['casedir'] = case
     sw['reeds_path'] = reeds_path if case is None else os.path.dirname(os.path.dirname(case))
-    ### Get the number of hours per period to use in plots
+    ## Get the number of hours per period to use in plots
     sw['hoursperperiod'] = {'day': 24, 'wek': 120, 'year': 24}[sw['GSw_HourlyType']]
     sw['periodsperyear'] = {'day': 365, 'wek': 73, 'year': 365}[sw['GSw_HourlyType']]
-
+    ### Overwrite values with keyword arguments if provided
     for key, value in kwargs.items():
         if key in sw.keys():
             sw[key] = value
@@ -973,10 +1000,7 @@ def get_temperatures(case, tz_in='UTC', tz_out='Etc/GMT+6', subset_years=True):
     ## Add one more year on either end of weather years to allow for timezone conversion
     weather_years = sw.resource_adequacy_years_list
     read_years = range(min(weather_years)-1, max(weather_years)+2)
-    val_st = (
-        pd.read_csv(os.path.join(inputs_case, 'val_st.csv'), header=None)
-        .squeeze(1).values
-    )
+    val_st = reeds.io.read_input(inputs_case, 'st').squeeze(1).values
     ### Load temperatures
     _temperatures = {}
     with h5py.File(h5path, 'r') as f:
@@ -1228,15 +1252,21 @@ def get_last_iteration(case, year=2050, datum=None, samples=None):
     """Get the last iteration of PRAS for a given case/year"""
     if datum not in [None,'flow','energy']:
         raise ValueError(f"datum must be in [None,'flow','energy'] but is {datum}")
-    infile = sorted(glob(
-        os.path.join(
-            case, 'ReEDS_Augur', 'PRAS',
-            f"PRAS_{year}i*"
-            + (f'-{samples}' if samples is not None else '')
-            + (f'-{datum}' if datum is not None else '')
-            + '.h5'
-        )
-    ))[-1]
+    pattern = (
+        f"PRAS_{year}i*"
+        + (f'-{samples}' if samples is not None else '')
+        + (f'-{datum}' if datum is not None else '')
+        + '.h5'
+    )
+    matches = list(Path(case, 'handoff', 'PRAS').glob(pattern))
+    if not matches:
+        raise ValueError(f"{case} has not solved year {year}")
+    infile = max(
+        matches,
+        ## File names are formatted as 'PRAS_{year}i{iteration}.h5' or
+        ## 'PRAS_{year}i{iteration}-{other_identifiers}.h5'; keep the largest iteration
+        key=lambda f: int(f.stem[f.stem.rfind('i')+1:].split('-')[0])
+    )
     iteration = int(
         os.path.splitext(os.path.basename(infile))[0]
         .split('-')[0].split('_')[1].split('i')[1]
@@ -1254,11 +1284,11 @@ def get_pras_system(case, year=None, iteration='last', verbose=0):
         get_last_iteration(case, t)[1] if iteration in [None, 'last']
         else iteration
     )
-    infile = os.path.join(case, 'ReEDS_Augur', 'PRAS', f"PRAS_{t}i{_iteration}.pras")
+    infile = os.path.join(case, 'handoff', 'PRAS', f"PRAS_{t}i{_iteration}.pras")
     if not os.path.exists(infile):
         raise FileNotFoundError(
             f'{infile} does not exist; run postprocessing/run_reeds2pras.py or rerun '
-            'the ReEDS case with keep_augur_files=1'
+            'the ReEDS case with keep_resource_adequacy_files=1'
         )
     pras = {}
     with h5py.File(infile,'r') as f:
@@ -1512,6 +1542,8 @@ def assemble_supplycurve(
                 'trans_type',
                 'node_latitude',
                 'node_longitude',
+                'node_lat',
+                'node_lon',
                 'always_radial',
                 'ba',
             ],
@@ -1676,6 +1708,126 @@ def write_to_h5(
                 )
 
 
+def write_to_inputs_h5(
+    df:pd.DataFrame|pd.Series,
+    key:str,
+    case:str|Path,
+    gamstype:Literal['set','parameter'],
+    comment:str='',
+    units:str='',
+    verbose:int|bool=1,
+    **kwargs,
+):
+    """
+    Write a Series or DataFrame (long format) to a ReEDS-formatted inputs.h5 file
+
+    Args:
+        df (pd.Series or pd.DataFrame): Long-formatted series/dataframe (where "long
+            format" means there is a single data column; all the other columns are indices).
+            If an unnamed pd.Series is provided, it is assumed to be a primary set and
+            is renamed to '*'. Otherwise, the name of the pd.Series (or the column names of
+            a pd.DataFrame) should match the indices used by the corresponding set/parameter
+            in GAMS.
+        key (str): Name of the key to be written to inputs.h5
+        case (str or Path): Absolute path to a ReEDS case OR inputs_case OR inputs.h5.
+            That is, any of the following work as inputs:
+            - {absolute_casepath}
+            - {absolute_casepath}/inputs_case
+            - {absolute_casepath}/inputs_case/inputs.h5
+        gamstype (str): 'set' or 'parameter', indicating the kind of data to write
+        comment (str): Comment assigned to the data in GAMS.
+            If the `units` input is provided, the comment is written as,
+            "[{units}] {comment} (written by {script that called this function})".
+            If the `units` input is not provided, the comment is written as,
+            "{comment} (written by {script that called this function})".
+        units (str): Physical units, like MW or MMBtu
+        verbose (bool or int): If true, prints a message to the log after successful write
+    """
+    ### Parse inputs
+    if Path(case).name == 'inputs_case':
+        h5path = os.path.join(case, 'inputs.h5')
+    elif Path(case).suffix == '.h5':
+        h5path = case
+    else:
+        h5path = Path(case, 'inputs_case', 'inputs.h5')
+
+    dfwrite = df.copy()
+    ## We write all the info in the dataframe but ignore the index, so if sets are used
+    ## as the index, move them into the dataframe
+    if isinstance(dfwrite, pd.Series):
+        if dfwrite.name is None:
+            dfwrite.name = '*'
+        dfwrite = dfwrite.to_frame()
+    if isinstance(dfwrite.index, pd.MultiIndex) or dfwrite.index.name:
+        dfwrite = dfwrite.reset_index()
+    ## Format for GAMS: The final column of a parameter should be named 'Value' and
+    ## should contain the data as floats; all the other columns are treated as indices
+    if gamstype == 'parameter':
+        dfwrite.columns = dfwrite.columns.tolist()[:-1] + ['Value']
+    ### Write record to h5 file
+    calling_file = Path(inspect.stack()[-1][1]).name
+    attrs = {'gamstype': gamstype.lower(), 'written_by': calling_file}
+    if len(units):
+        attrs['comment'] = f'[{units}] {comment} (written by {calling_file})'
+    else:
+        attrs['comment'] = f'{comment} (written by {calling_file})'
+    write_to_h5(
+        dfwrite,
+        key,
+        h5path,
+        attrs=attrs,
+        **kwargs,
+    )
+    if verbose:
+        print(f'{Path(h5path).name}: Wrote {key} from {calling_file}')
+
+
+def write_csv_to_inputs_h5(
+    filepath:str|Path,
+    case:str|Path,
+    gamstype:Literal['set','parameter'],
+    comment:str='',
+    **kwargs,
+):
+    """
+    Read a csv file (formatted as described in inputs/sets/README.md)
+    and write it to inputs.h5
+    """
+    df = pd.read_csv(filepath, dtype=str, header=None)
+    key = Path(filepath).stem
+    if df.shape[1] == 1:
+        ## Subsets have a header column beginning with '*';
+        ## primary sets do not have a header
+        primary = False if df.loc[0,0].startswith('*') else True
+    else:
+        ## Multidimensional sets must be subsets so must have a header
+        primary = False
+    ## For primary sets we use the set name as the output header;
+    ## for subsets we read the header from the file
+    if primary:
+        df.columns = ['*']
+    else:
+        df.columns = df.loc[0].str.replace('*','').values
+        df = df.drop(0)
+    ## No other *'s are allowed
+    if df.applymap(lambda x: '*' in x).any().any():
+        err = (
+            "'*' characters are only allowed in subset headers.\n"
+            f"{filepath} has at least one disallowed '*' character."
+        )
+        raise ValueError(err)
+    ## Write it
+    reeds.io.write_to_inputs_h5(
+        df=df,
+        key=key,
+        case=case,
+        comment=comment,
+        gamstype=gamstype,
+        **kwargs,
+    )
+    return df
+
+
 def write_output_to_h5(
     df,
     key,
diff --git a/reeds/log.py b/reeds/log.py
index 2dee9a4ab..ea9a9130a 100755
--- a/reeds/log.py
+++ b/reeds/log.py
@@ -68,7 +68,7 @@ def toc(tic, year, process, path=''):
 def get_solve_times(path=''):
     """Get all solve times, disaggregated by GAMS/barrier/crossover/remainder.
     Disaggregation only works when using CPLEX as the solver."""
-    # path = '/Users/pbrown/github/ReEDS-2.0/runs/v20240111_stressM0_stress_WECC_crossover'
+    # path = '/Users/pbrown/github/ReEDS/runs/v20240111_stressM0_stress_WECC_crossover'
     lengths = {
         'gams': {},
         'barrier': {},
@@ -103,7 +103,7 @@ def get_solve_times(path=''):
                 process = line[len('--- Job ') : line.index(' Stop ')]
                 x = f'--- Job {process} Stop '
                 y = ' elapsed '
-                label = process if process != 'd_solveoneyear.gms' else stress_year
+                label = process if process != '3_solve_oneyear.gms' else stress_year
                 lengths['total'][label] = pd.Timedelta(line[line.index(y) + len(y) :])
                 times['stop'][label] = pd.Timestamp(line[len(x) : line.index(y)])
                 times['start'][label] = times['stop'][label] - lengths['total'][label]
@@ -164,7 +164,7 @@ def write_last_solve_time(path=''):
         scriptname = lasttime.name
         year = 0
     else:
-        scriptname = 'd_solveoneyear.gms'
+        scriptname = '3_solve_oneyear.gms'
         year = int(lasttime.name.split('i')[0])
     towrite = {
         'gams': scriptname,
@@ -173,7 +173,7 @@ def write_last_solve_time(path=''):
         'remainder': 'solver/remainder',
     }
     with open(os.path.join(path, 'meta.csv'), 'a') as METAFILE:
-        if (scriptname == 'd_solveoneyear.gms') and all([i in lasttime for i in towrite]):
+        if (scriptname == '3_solve_oneyear.gms') and all([i in lasttime for i in towrite]):
             for i, process in enumerate(towrite):
                 METAFILE.writelines(
                     '{},{},{},{},{}\n'.format(
diff --git a/reeds/prasplots.py b/reeds/prasplots.py
index 9722ff6e1..d7152a449 100644
--- a/reeds/prasplots.py
+++ b/reeds/prasplots.py
@@ -42,7 +42,7 @@ def plot_interface_flows(
             case=case, year=year, datum='flow', samples=samples)
         dfflow = reeds.io.read_pras_results(infile).set_index(timeindex)
         ## Filter out AC/DC converters from scenarios with VSC
-        dfflow = dfflow[[c for c in dfflow if '"DC_' not in c]].copy()
+        dfflow = dfflow[[c for c in dfflow if '"DC|' not in c]].copy()
         ## Normalize the interface names
         renamer = {i: '→'.join(i.replace('"','').split(' => ')) for i in dfflow}
     else:
@@ -252,7 +252,7 @@ def plot_pras_eue_timeseries_full(
     else:
         _iteration = iteration
     infile = os.path.join(
-        case, 'ReEDS_Augur', 'PRAS',
+        case, 'handoff', 'PRAS',
         f"PRAS_{year}i{_iteration}" + (f'-{samples}' if samples is not None else '') + '.h5'
     )
     dfpras = reeds.io.read_pras_results(infile)
@@ -317,7 +317,6 @@ def plot_pras_samples(
     tend='2012-08-07',
     plottype='outage',
     drawload=False,
-    region='country/USA',
     year=2050,
     iteration='last',
     samples=None,
@@ -339,7 +338,7 @@ def plot_pras_samples(
         reeds.io.get_last_iteration(case, t) if iteration in [None, 'last']
         else iteration
     )
-    rs = reeds.inputs.parse_regions(region, case)
+    rs = reeds.inputs.parse_regions(case)
 
     bokehcolors, plotorder = reeds.reedsplots.get_tech_colors_order(order='fuel_storage_vre')
 
@@ -363,7 +362,7 @@ def plot_pras_samples(
 
     ### Get unit availability
     filebase = os.path.join(
-        case, 'ReEDS_Augur', 'PRAS',
+        case, 'handoff', 'PRAS',
         f"PRAS_{t}i{_iteration}"
         f"{f'-{samples}' if isinstance(samples, int) else ''}"
     )
diff --git a/reeds/ra.py b/reeds/ra.py
deleted file mode 100644
index 22d8ee353..000000000
--- a/reeds/ra.py
+++ /dev/null
@@ -1,125 +0,0 @@
-### Imports
-import os
-import sys
-import numpy as np
-import pandas as pd
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import reeds
-
-
-### Functions
-def get_pras_eue(case, t, iteration=0):
-    """
-    """
-    ### Get PRAS outputs
-    dfpras = reeds.io.read_pras_results(
-        os.path.join(case, 'ReEDS_Augur', 'PRAS', f"PRAS_{t}i{iteration}.h5")
-    )
-    ### Create the time index
-    sw = reeds.io.get_switches(case)
-    dfpras.index = reeds.timeseries.get_timeindex(sw['resource_adequacy_years'])
-
-    ### Keep the EUE columns by zone
-    eue_tail = '_EUE'
-    dfeue = dfpras[[
-        c for c in dfpras
-        if (c.endswith(eue_tail) and not c.startswith('USA'))
-    ]].copy()
-    ## Drop the tailing _EUE
-    dfeue = dfeue.rename(
-        columns=dict(zip(dfeue.columns, [c[:-len(eue_tail)] for c in dfeue])))
-
-    return dfeue
-
-
-def get_eue_periods(
-        case, t, iteration=0,
-        hierarchy_level='transgrp',
-        stress_metric='EUE',
-        period_agg_method='sum',
-    ):
-    """_summary_
-
-    Args:
-        sw (pd.series): ReEDS switches for this run.
-        t (int): Model solve year.
-        iteration (int, optional): Iteration number of this solve year. Defaults to 0.
-        hierarchy_level (str, optional): column of hierarchy.csv specifying the spatial
-            level over which to calculate stress_metric. Defaults to 'country'.
-        stress_metric (str, optional): 'EUE' or 'NEUE'. Defaults to 'EUE'.
-        period_agg_method (str, optional): 'sum' or 'max', indicating how to aggregate
-            over the hours in each period. Defaults to 'sum'.
-
-    Raises:
-        NotImplementedError: if invalid value for stress_metric or GSw_PRM_StressModel
-
-    Returns:
-        pd.DataFrame: Table of periods sorted in descending order by stress metric.
-    """
-    sw = reeds.io.get_switches(case)
-    ### Get the region aggregator
-    rmap = reeds.io.get_rmap(case=case, hierarchy_level=hierarchy_level)
-
-    ### Get EUE from PRAS
-    dfeue = get_pras_eue(case=case, t=t, iteration=iteration)
-    ## Aggregate to hierarchy_level
-    dfeue = (
-        dfeue
-        .rename_axis('r', axis=1).rename_axis('h', axis=0)
-        .rename(columns=rmap).groupby(axis=1, level=0).sum()
-    )
-
-    ###### Calculate the stress metric by period
-    if stress_metric.upper() == 'EUE':
-        ### Aggregate according to period_agg_method
-        dfmetric_period = (
-            dfeue
-            .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
-            .agg(period_agg_method)
-            .rename_axis(['y','m','d'])
-        )
-    elif stress_metric.upper() == 'NEUE':
-        ### Get load at hierarchy_level
-        dfload = reeds.io.read_h5py_file(
-            os.path.join(
-                case,'ReEDS_Augur','augur_data',f'pras_load_{t}.h5')
-        ).rename(columns=rmap).groupby(level=0, axis=1).sum()
-        dfload.index = dfeue.index
-
-        ### Recalculate NEUE [ppm] and aggregate appropriately
-        if period_agg_method == 'sum':
-            dfmetric_period = (
-                dfeue
-                .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
-                .agg(period_agg_method)
-                .rename_axis(['y','m','d'])
-            ) / (
-                dfload
-                .groupby([dfload.index.year, dfload.index.month, dfload.index.day])
-                .agg(period_agg_method)
-                .rename_axis(['y','m','d'])
-            ) * 1e6
-        elif period_agg_method == 'max':
-            dfmetric_period = (
-                (dfeue / dfload)
-                .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
-                .agg(period_agg_method)
-                .rename_axis(['y','m','d'])
-            ) * 1e6
-
-    ### Sort and drop zeros and duplicates
-    dfmetric_top = (
-        dfmetric_period.stack('r')
-        .sort_values(ascending=False)
-        .replace(0,np.nan).dropna()
-        .reset_index().drop_duplicates(['y','m','d'], keep='first')
-        .set_index(['y','m','d','r']).squeeze(1).rename(stress_metric)
-        .reset_index('r')
-    )
-    ## Convert to timestamp, then to ReEDS period
-    dfmetric_top['actual_period'] = [
-        reeds.timeseries.timestamp2h(pd.Timestamp(*d), sw['GSw_HourlyType']).split('h')[0]
-        for d in dfmetric_top.index.values
-    ]
-
-    return dfmetric_top
diff --git a/reeds/reedsplots.py b/reeds/reedsplots.py
index da1013b1f..45c585184 100644
--- a/reeds/reedsplots.py
+++ b/reeds/reedsplots.py
@@ -1,7 +1,9 @@
 ### Imports
 import numpy as np
 import pandas as pd
+from pathlib import Path
 import matplotlib as mpl
+from typing import Literal
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from matplotlib import patheffects as pe
@@ -230,25 +232,6 @@ def plot_diff(
         'Runtime by year (hours)': 1,
         'NEUE (ppm)': 1,
     }
-    outputs_unit_converstion = {
-        'Error Check': 1,
-        'Generation (TWh)': 1,
-        'Capacity (GW)': 1,
-        'New Annual Capacity (GW)': 1,
-        'Annual Retirements (GW)': 1,
-        'Final Gen by timeslice (GW)': 1,
-        'Firm Capacity (GW)': 1,
-        'Curtailment Rate': 1,
-        'Transmission (GW-mi)': 0.001,
-        'Transmission (PRM) (GW-mi)': 0.001,
-        'Bulk System Electricity Pric': 1,
-        'National Average Electricity': 1,
-        '2022-2050 Present Value of S': 1,
-        'Present Value of System Cost': 1,
-        'Runtime (hours)': 1,
-        'Runtime by year (hours)': 1,
-        'NEUE (ppm)': 1,
-    }
 
     output_formatting = reeds.io.get_plot_formatting()
 
@@ -272,7 +255,6 @@ def plot_diff(
 
     ### Load the data
     dfbase = reeds.io.read_report(casebase, sheet, val2sheet).rename(columns={'trtype':'type','i':'tech'})
-    dfbase[ycol[val]]*=outputs_unit_converstion[val]
     if 'tech' in dfbase.columns:
         dfbase.tech = simplify_techs(dfbase.tech, display_level = simple_techs)
         dfbase = (
@@ -291,7 +273,6 @@ def plot_diff(
         dfbase = dfbase.loc[dfbase[col] == fixval].copy()
 
     dfcomp = reeds.io.read_report(casecomp, sheet, val2sheet).rename(columns={'trtype':'type','i':'tech'})
-    dfcomp[ycol[val]]*=outputs_unit_converstion[val]
     if 'tech' in dfcomp.columns:
         dfcomp.tech = simplify_techs(dfcomp.tech, display_level = simple_techs)
         dfcomp = (
@@ -1239,9 +1220,7 @@ def plot_transmission_utilization(
             dftrans['trans_flow_power'].loc[dftrans['trans_flow_power'].Value < 0, ['rr','r']].values
         )
         dftrans['trans_flow_power'].Value = dftrans['trans_flow_power'].Value.abs()
-    val_r = pd.read_csv(
-        os.path.join(case,'inputs_case','val_r.csv'), header=None,
-    ).squeeze(1).values.tolist()
+    val_r = reeds.io.read_input(case, 'r').squeeze(1).tolist()
     ### Combine all transmission types
     for data in ['trans_flow_power','trans_cap']:
         dftrans[data]['trtype'] = 'all'
@@ -1736,19 +1715,16 @@ def plot_prmtrade(
     dfba = dfmap['r']
     dfstates = dfmap['st']
     ## Downselect to modeled regions
-    val_r = pd.read_csv(
-        os.path.join(case,'inputs_case','val_r.csv'), header=None,
-    ).squeeze(1).values.tolist()
+    val_r = reeds.io.read_input(case, 'r').squeeze(1).tolist()
     dfba = dfba.loc[val_r].copy()
 
     if sw.get('GSw_RegionResolution', 'ba') != 'county':
-        endpoints = (
-            gpd.read_file(os.path.join(reeds_path,'inputs','shapefiles','transmission_endpoints'))
-            .set_index('ba_str'))
-        endpoints['x'] = endpoints.centroid.x
-        endpoints['y'] = endpoints.centroid.y
-        dfba['x'] = dfba.index.map(endpoints.x)
-        dfba['y'] = dfba.index.map(endpoints.y)
+        endpoints = reeds.plots.df2gdf(
+            reeds.io.assemble_hierarchy(case).set_index('r'),
+            lat='node_lat', lon='node_lon',
+        )
+        dfba['x'] = dfba.index.map(endpoints.centroid.x)
+        dfba['y'] = dfba.index.map(endpoints.centroid.y)
 
     ### Get scaling and layout
     _vmax = dfplot.MW.abs().max() if vmax in [None, 0, 0.] else vmax
@@ -1825,9 +1801,7 @@ def plot_average_flow(
     dfmap = reeds.io.get_dfmap(case)
     dfba = dfmap['r']
     dfstates = dfmap['st']
-    val_r = pd.read_csv(
-        os.path.join(case,'inputs_case','val_r.csv'), header=None,
-    ).squeeze(1).values.tolist()
+    val_r = reeds.io.read_input(case, 'r').squeeze(1).tolist()
     if extent.lower() not in ['usa','full','nation','us','country','all']:
         dfba = dfba.loc[val_r]
 
@@ -2034,7 +2008,7 @@ def plot_interreg_transfer_cap_ratio(
     """Plot interregional transfer capability / peak demand over time"""
     ### Inputs for debugging
     # case = (
-    #     '/Users/pbrown/github2/ReEDS-2.0/runs/'
+    #     '/Users/pbrown/github2/ReEDS/runs/'
     #     'v20240212_transopM0_WECC_CPNP_GP1_TFY2035_PTL2035_TRc_MITCg0p3')
     # casenames = 'base'
     # level = 'transreg'; tstart=2020;
@@ -3574,10 +3548,21 @@ def map_hybrid_pv_wind(
 def plot_dispatch_yearbymonth(
         case, t=2050, plottype='gen', periodtype='rep',
         techs=None, region=None,
-        f=None, ax=None, figsize=(12,6), highlight_rep_periods=1,
+        figsize=(12,6), highlight_rep_periods=1,
     ):
     """
     Full year dispatch for final year with rep days mapped to actual days
+
+    Usage:
+        ```python
+        plot_generator = plot_dispatch_yearbymonth(case)
+        while True:
+            try:
+                f, ax, df = next(plot_generator)
+            except StopIteration:
+                break
+        ```
+
     Inputs
     ------
     techs: None to plot all techs, or list of subset techs, or single tech string
@@ -3689,46 +3674,51 @@ def plot_dispatch_yearbymonth(
         period_szn['repnum'] = period_szn.rep_period.map(repnum)
 
     ### Plot it
-    plt.close()
-    f, ax = plots.plotyearbymonth(
-        dfplot,
-        colors=[
-            tech_color[i.replace('_pos','').replace('_neg','').replace('_off','')]
-            for i in dfplot],
-        lwforline=0, f=f, ax=ax, figsize=figsize)
+    weatheryears = [int(i) for i in sw.GSw_HourlyWeatherYears.split('_')]
+    for weatheryear in weatheryears:
+        _dfplot = dfplot.loc[str(weatheryear)]
 
-    if highlight_rep_periods:
-        width = pd.Timedelta('5D') if sw['GSw_HourlyType'] == 'wek' else pd.Timedelta('1D')
-        ylim = ax[0].get_ylim()
-        for i, row in period_szn.iterrows():
-            plottime = pd.Timestamp(2001, 1, row.timestamp.day)
-            if row.rep:
-                ## Draw an outline
-                box = mpl.patches.Rectangle(
-                    xy=(plottime, ylim[0]),
-                    width=width, height=(ylim[1]-ylim[0]),
-                    lw=0.75, edgecolor='k', facecolor='none', ls=':',
-                    clip_on=False, zorder=2e6
-                )
-            else:
-                ## Wash out the dispatch
-                box = mpl.patches.Rectangle(
-                    xy=(plottime, ylim[0]),
-                    width=width, height=(ylim[1]-ylim[0]),
-                    lw=0.75, edgecolor='none', facecolor='w', alpha=0.4,
-                    clip_on=False, zorder=1e6
+        plt.close()
+        f, ax = plots.plotyearbymonth(
+            _dfplot,
+            colors=[
+                tech_color[i.replace('_pos','').replace('_neg','').replace('_off','')]
+                for i in _dfplot],
+            lwforline=0, figsize=figsize)
+
+        if highlight_rep_periods:
+            _period_szn = period_szn.loc[period_szn.year==weatheryear]
+            width = pd.Timedelta('5D') if sw['GSw_HourlyType'] == 'wek' else pd.Timedelta('1D')
+            ylim = ax[0].get_ylim()
+            for i, row in _period_szn.iterrows():
+                plottime = pd.Timestamp(2001, 1, row.timestamp.day)
+                if row.rep:
+                    ## Draw an outline
+                    box = mpl.patches.Rectangle(
+                        xy=(plottime, ylim[0]),
+                        width=width, height=(ylim[1]-ylim[0]),
+                        lw=0.75, edgecolor='k', facecolor='none', ls=':',
+                        clip_on=False, zorder=2e6
+                    )
+                else:
+                    ## Wash out the dispatch
+                    box = mpl.patches.Rectangle(
+                        xy=(plottime, ylim[0]),
+                        width=width, height=(ylim[1]-ylim[0]),
+                        lw=0.75, edgecolor='none', facecolor='w', alpha=0.4,
+                        clip_on=False, zorder=1e6
+                    )
+                ax[row.timestamp.month-1].add_patch(box)
+                ## Note the rep period
+                ax[row.timestamp.month-1].annotate(
+                    row.repnum,
+                    (plottime+pd.Timedelta('30m'), ylim[1]*0.95),
+                    va='top', size=5, zorder=1e7,
+                    color=('k' if row.rep else 'C7'),
+                    weight=('normal' if row.rep else 'normal'),
                 )
-            ax[row.timestamp.month-1].add_patch(box)
-            ## Note the rep period
-            ax[row.timestamp.month-1].annotate(
-                row.repnum,
-                (plottime+pd.Timedelta('30m'), ylim[1]*0.95),
-                va='top', size=5, zorder=1e7,
-                color=('k' if row.rep else 'C7'),
-                weight=('normal' if row.rep else 'normal'),
-            )
 
-    return f, ax, dfplot
+        yield f, ax, _dfplot, weatheryear
 
 
 def plot_dispatch_weightwidth(
@@ -4986,14 +4976,27 @@ def map_period_dispatch(
     return f, ax, out
 
 
+def make_dayofyear_colormap(startfrom=200, fmt='%-m/%-d'):
+    """
+    Map day of year to a value in [0-1] that can be used to look up a color from a cyclic colormap
+    """
+    days_of_year = pd.date_range('2004-01-01','2004-12-31',freq='D')
+    monthdays = days_of_year.strftime(fmt)
+    colorvals = (
+        list(np.linspace(0,1,len(monthdays))[startfrom:])
+        + list(np.linspace(0,1,len(monthdays))[:startfrom])
+    )
+    monthday2val = dict(zip(monthdays, colorvals))
+    return monthday2val
+
+
 def plot_seed_stressperiods(
-    case, cmap=cmocean.cm.phase, startfrom=200,
+    case, cmap=cmocean.cm.phase,
     alpha=0.7, fontsize=5, pealpha=0.8, pelinewidth=1.5,
 ):
     """
     """
-    sys.path.append(os.path.join(reeds.io.reeds_path, 'input_processing'))
-    import hourly_repperiods
+    from reeds.input_processing import hourly_repperiods
 
     sw = reeds.io.get_switches(case)
     hierarchy = reeds.io.get_hierarchy(case)
@@ -5015,7 +5018,9 @@ def plot_seed_stressperiods(
         dictin_seed[year]['monthday'] = dictin_seed[year].timestamp.map(lambda x: x.strftime('%m-%d'))
 
     days_of_year = pd.date_range('2004-01-01','2004-12-31',freq='D')
-    monthdays = days_of_year.strftime('%m-%d')
+    fmt = '%m-%d'
+    monthdays = days_of_year.strftime(fmt)
+    monthday2val = make_dayofyear_colormap(fmt=fmt)
 
     ### Recalculate peak load days since we dropped duplicates above
     load_allyears = hourly_repperiods.get_load(
@@ -5038,26 +5043,6 @@ def plot_seed_stressperiods(
         for y in years
     }
 
-    ### Put cold colors in winter
-    colorvals = (
-        list(np.linspace(0,1,len(monthdays))[startfrom:])
-        + list(np.linspace(0,1,len(monthdays))[:startfrom])
-    )
-    monthday2val = dict(zip(monthdays, colorvals))
-
-    # ### Test it
-    # step = 5
-    # plt.close()
-    # f,ax = plt.subplots(figsize=(12,3))
-    # for x, monthday in enumerate(monthdays[::step]):
-    #     color = cmap(monthday2val[monthday])
-    #     ax.plot([x], [0], color=color, marker='o')
-    #     ax.annotate(monthday, (x, 0.015), rotation=90, ha='center', va='center', color=color)
-    # ax.set_title(startfrom)
-    # plots.despine(ax)
-    # plt.show()
-
-
     ### Plot it
     ncols = 3
     nrows = len(years) // ncols + bool(len(years) % ncols) + 1
@@ -5153,36 +5138,41 @@ def plot_seed_stressperiods(
     return f, ax
 
 
-def plot_repdays(case, cmap=cmocean.cm.phase, alpha=0.7, startfrom=200):
+def plot_repdays(case=None, year=None, actualday2repday=None, cmap=cmocean.cm.phase, alpha=0.7):
     """Plot representative days in (12month)x(monthdays) format"""
     ### Setup
     months = [
         'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
         'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec',
     ]
-    ### Get color map
-    days_of_year = pd.date_range('2004-01-01','2004-12-31',freq='D')
-    monthdays = days_of_year.strftime('%m/%d')
-    colorvals = (
-        list(np.linspace(0,1,len(monthdays))[startfrom:])
-        + list(np.linspace(0,1,len(monthdays))[:startfrom])
-    )
-    monthday2val = dict(zip(monthdays, colorvals))
 
     ### Data
-    sw = pd.read_csv(
-        os.path.join(case, 'inputs_case', 'switches.csv'), header=None, index_col=0
-    ).squeeze(1)
-
-    hmap_myr = pd.read_csv(
-        os.path.join(case, 'inputs_case', 'rep', 'hmap_myr.csv'),
-        index_col='*timestamp', parse_dates=True,
-    )
+    sw = reeds.io.get_switches(case)
+    stylestring = '%-m/%-d' if os.name == 'posix' else '%#m/%#d'
+    monthday2val = make_dayofyear_colormap(fmt=stylestring)
+    va = 'center'
+    ytext = 0.5
+    if len(sw.GSw_HourlyWeatherYears.split('_')) > 1:
+        stylestring += '/\n%Y'
+        va = 'top'
+        ytext = 0.9
+
+    if actualday2repday is None:
+        hmap_myr = pd.read_csv(
+            os.path.join(case, 'inputs_case', 'rep', 'hmap_myr.csv'),
+            index_col='*timestamp', parse_dates=True,
+        )
+        hmap_myr['timestamp_rep'] = hmap_myr.h.map(reeds.timeseries.h2timestamp)
+        hmap_myr['repday'] = hmap_myr.season.map(reeds.timeseries.h2timestamp)
 
-    hmap_myr['timestamp_rep'] = hmap_myr.h.map(reeds.timeseries.h2timestamp)
-    hmap_myr['repday'] = hmap_myr.season.map(reeds.timeseries.h2timestamp)
+        if year is None:
+            year = int(sw.GSw_HourlyWeatherYears.split('_')[0])
 
-    actualday2repday = hmap_myr.drop_duplicates('yearperiod', keep='first').timestamp_rep
+        actualday2repday = (
+            hmap_myr.loc[str(year)]
+            .drop_duplicates(['year','yearperiod'], keep='first')
+            .timestamp_rep
+        )
     repdaycounts = actualday2repday.value_counts()
 
     ### Plot it
@@ -5202,17 +5192,17 @@ def plot_repdays(case, cmap=cmocean.cm.phase, alpha=0.7, startfrom=200):
         row = actualday.month - 1
         xstart = actualday.day - 1
         xend = xstart + (5 if sw.GSw_HourlyType == 'wek' else 1)
-        monthday = repday.strftime('%m/%d')
+        monthday = repday.strftime(stylestring).split('/\n')[0]
         ## Background color
         ax[row].axvspan(xstart, xend, color=cmap(monthday2val[monthday]), alpha=alpha, lw=0)
         ## Date
         ax[row].annotate(
-            repday.strftime('%-m/%-d' if os.name == 'posix' else '%#m/%#d'),
-            (xstart+0.5, 0.5), ha='center', va='center', fontsize=8,
-            path_effects=[pe.withStroke(linewidth=2.1, foreground='w', alpha=1)],
+            repday.strftime(stylestring),
+            (xstart+0.5, ytext), ha='center', va=va, fontsize=8,
+            path_effects=[pe.withStroke(linewidth=1.5, foreground='w', alpha=0.8)],
         )
         ## Box
-        if (actualday.month == repday.month) and (actualday.day == repday.day):
+        if (actualday.year, actualday.month, actualday.day) == (repday.year, repday.month, repday.day):
             ax[row].axvspan(xstart, xend, facecolor='none', edgecolor='k', zorder=1e6, clip_on=False)
             ax[row].annotate(
                 f'×{repdaycounts[repday]}', (xend-0.03, 0.05), ha='right', fontsize=7,
@@ -6207,21 +6197,20 @@ def map_stressors(
     dflevel = dfmap[level].copy()
 
     ### Get the data
-    augur_files = {
-        'vre_gen': os.path.join(case, 'ReEDS_Augur', 'augur_data', f'pras_vre_gen_{t}.h5'),
-        'load': os.path.join(case, 'ReEDS_Augur', 'augur_data', f'pras_load_{t}.h5'),
+    ra_files = {
+        'vre_gen': os.path.join(case, 'handoff', 'reeds_data', f'pras_vre_gen_{t}.h5'),
+        'load': os.path.join(case, 'handoff', 'reeds_data', f'pras_load_{t}.h5'),
     }
-    if any([not os.path.exists(fpath) for fpath in augur_files.values()]):
-        import ReEDS_Augur.prep_data as prep_data
-        prep_data.main(t, case, iteration)
+    if any([not os.path.exists(fpath) for fpath in ra_files.values()]):
+        reeds.resource_adequacy.prep_data.main(t, case, iteration)
 
-    vre_gen = reeds.io.read_file(augur_files['vre_gen'], parse_timestamps=True)
+    vre_gen = reeds.io.read_file(ra_files['vre_gen'], parse_timestamps=True)
     vre_gen.columns = pd.MultiIndex.from_tuples(
         vre_gen.columns.map(lambda x: tuple(x.split('|'))),
         names=['i','r'],
     )
 
-    load = reeds.io.read_file(augur_files['load'], parse_timestamps=True)
+    load = reeds.io.read_file(ra_files['load'], parse_timestamps=True)
 
     recf = reeds.io.read_file(
         os.path.join(case, 'inputs_case', 'recf.h5'),
@@ -6693,3 +6682,89 @@ def map_prm(case, tmin=2023, cmap=cmocean.cm.rain, scale=3, fontsize=7, vmax=Non
     plots.trim_subplots(ax, nrows, ncols, len(coords))
 
     return f, ax, prm_final
+
+
+def validate_regional_capacity(
+    case,
+    mapmethod:Literal['FIPS','latlon']='county',
+    scale:float=1.2,
+    sharey=False,
+):
+    """Compare regional capacity from EIA-NEMS to ReEDS results for last historical year"""
+    ### Settings
+    plot_settings = reeds.io.get_plot_formatting()
+    tech_color = plot_settings['tech_color'].squeeze(1)
+    unitspath = Path(
+        reeds.io.reeds_path, 'inputs', 'capacity_exogenous',
+        'ReEDS_generator_database_final_EIA-NEMS.csv',
+    )
+    ### Get run capacity and last historical year
+    hierarchy = reeds.io.get_hierarchy(case)
+    dfmap = reeds.io.get_dfmap(case)
+    crs = dfmap['r'].crs
+    county2zone = reeds.io.get_county2zone(case)
+    sw = reeds.io.get_switches(case)
+    years = reeds.inputs.parse_yearset(sw.yearset)
+    scalars = reeds.io.get_scalars(case)
+    plotyear = max([y for y in years if y <= int(scalars.this_year)])
+    cap = reeds.io.read_output(case, 'cap')
+    cap = cap.loc[~cap.i.isin(['can-imports'])].copy()
+    cap.i = simplify_techs(cap.i)
+    cap_year = cap.loc[cap.t==plotyear].groupby(['i','r']).Value.sum().unstack('r')
+    ### Get EIA-NEMS capacity and formatting
+    dfunits = reeds.plots.df2gdf(pd.read_csv(unitspath), lat='T_LAT', lon='T_LONG').to_crs(crs)
+    ## Clean up old techs
+    dfunits.tech = dfunits.tech.replace({'dupv':'upv'})
+    dfunits.tech = simplify_techs(dfunits.tech)
+    dfunits.FIPS = dfunits.FIPS.str.strip('p')
+    ## Map to zones
+    if mapmethod == 'FIPS':
+        dfunits['r'] = dfunits.FIPS.map(county2zone)
+    elif mapmethod == 'latlon':
+        dfunits = (
+            dfunits.sjoin(dfmap['r'][['geometry']], how='left')
+            .rename(columns={'index_right':'r'})
+        )
+    dfunits_year = dfunits.loc[
+        (dfunits.StartYear <= plotyear)
+        & (dfunits.RetireYear > plotyear)
+    ].copy()
+    ### Plot setup
+    rs = sorted(hierarchy.index)
+    nrows, ncols, coords = reeds.plots.get_coordinates(rs, aspect=1.3)
+    order = tech_color.index.tolist()
+    dictout = {}
+    ### Plot it
+    plt.close()
+    f,ax = plt.subplots(
+        nrows, ncols, figsize=(scale*ncols, scale*nrows), sharex=True, sharey=sharey,
+        gridspec_kw={'wspace':0.5, 'hspace':0.5},
+    )
+    for r in rs:
+        _ax = ax[coords[r]]
+        _ax.set_title(r.replace('_','.'), y=0.95, fontsize=11)
+        dfplot = pd.concat({
+            'NEMS': dfunits_year.loc[dfunits_year.r==r].groupby('tech').summer_power_capacity_MW.sum(),
+            'ReEDS': cap_year[r],
+        ## Convert from MW to GW
+        }, axis=1).dropna(how='all').T / 1e3
+        keep = [i for i in order if i in dfplot]
+        if len(keep) != dfplot.shape[1]:
+            _missing = [i for i in dfplot if i not in keep]
+            print(dfplot)
+            raise ValueError(f"Dropped these techs: {_missing}")
+        dfplot = dfplot[keep]
+        reeds.plots.stackbar(
+            df=dfplot, ax=_ax, colors=tech_color, net=False, width=0.8,
+        )
+        dictout[r] = dfplot
+        ## Formatting
+        row, col = coords[r]
+        if row == nrows - 1:
+            ax[row,col].set_xticks(range(len(dfplot)))
+            ax[row,col].set_xticklabels(dfplot.index, rotation=90)
+    ## Formatting
+    ax[-1,0].set_ylabel(f'{plotyear} capacity [GW]', y=0, ha='left')
+    reeds.plots.trim_subplots(ax, nrows, ncols, len(rs))
+    reeds.plots.despine(ax)
+    return f, ax, dictout
diff --git a/reeds/remote.py b/reeds/remote.py
index 11cf28793..7b6c4e357 100644
--- a/reeds/remote.py
+++ b/reeds/remote.py
@@ -98,6 +98,8 @@ def download_remote_files(only=None, force=False, access_token=None):
                     "of inputs/remote_files.csv."
                 )
                 raise ValueError(err)
+        else:
+            print(f'local version ok: {filename}')
         ## Always update the link
         linkpath = Path(row.linkpath)
         linkpath.unlink(missing_ok=True)
diff --git a/reeds/resource_adequacy/__init__.py b/reeds/resource_adequacy/__init__.py
new file mode 100644
index 000000000..63c5372ad
--- /dev/null
+++ b/reeds/resource_adequacy/__init__.py
@@ -0,0 +1,4 @@
+from . import capacity_credit as capacity_credit
+from . import prep_data as prep_data
+from . import ra_calcs as ra_calcs
+from . import stress_periods as stress_periods
diff --git a/ReEDS_Augur/capacity_credit.py b/reeds/resource_adequacy/capacity_credit.py
similarity index 93%
rename from ReEDS_Augur/capacity_credit.py
rename to reeds/resource_adequacy/capacity_credit.py
index d778d6daf..7bfd2a6bb 100644
--- a/ReEDS_Augur/capacity_credit.py
+++ b/reeds/resource_adequacy/capacity_credit.py
@@ -1,8 +1,8 @@
 #%% IMPORTS
 import os
+import gdxpds
 import numpy as np
 import pandas as pd
-import gdxpds
 import reeds
 
 
@@ -34,8 +34,8 @@ def set_marg_vre_step_size(t, sw, gdx, hierarchy):
     Inputs
     * marg_vre_steps [int]: Number of previous solve years to consider when
       evaluating the marginal VRE step size (default: 2). Must be at least 1;
-      a value of 2 can help reduce oscillations. Augur will automatically drop
-      from consideration solves that are more than 5 years from the previous solve.
+      a value of 2 can help reduce oscillations. Solves that are more than
+      5 years from the previous solve are automatically dropped.
     '''
     # load yearset for getting various previous steps
     yearset = gdx['tmodel_new'].allt.astype(int).tolist()
@@ -45,8 +45,7 @@ def set_marg_vre_step_size(t, sw, gdx, hierarchy):
     step_sizes = []
     for step in range(int(sw['marg_vre_steps'])):
 
-        # try-except to handle cases where there aren't multiple
-        # steps to go back to (e.g. running Augur after 1st solve)
+        # try-except to handle cases where there aren't multiple steps to go back to
         try:
             target_last_step = yearset[yearset.index(t)-step]
 
@@ -56,7 +55,7 @@ def set_marg_vre_step_size(t, sw, gdx, hierarchy):
                 step_sizes.append(get_relative_step_sizes(t, yearset, target_last_step))
                 prev_year_list.append(target_last_step)
         except Exception:
-            print('First Augur year so no previous steps')
+            print('First resource adequacy year so no previous steps')
 
     relative_step_sizes = pd.DataFrame(list(zip(prev_year_list, step_sizes)),
                                             columns=['t', 'step'])
@@ -121,10 +120,10 @@ def reeds_cc(t, tnext, casedir):
     hierarchy = reeds.io.get_hierarchy(casedir).reset_index()
     resources = pd.read_csv(os.path.join(inputs_case, 'resources.csv'))
     
-    augur_data = os.path.join(casedir,'ReEDS_Augur','augur_data')
-    cap = pd.read_csv(os.path.join(augur_data, f'max_cap_{t}.csv'))
+    reeds_data = os.path.join(casedir, 'handoff', 'reeds_data')
+    cap = pd.read_csv(os.path.join(reeds_data, f'max_cap_{t}.csv'))
 
-    gdx = gdxpds.to_dataframes(os.path.join(augur_data,f'reeds_data_{t}.gdx'))
+    gdx = gdxpds.to_dataframes(os.path.join(reeds_data, f'reeds_data_{t}.gdx'))
     techs = gdx['i_subsets'].pivot(columns='i_subtech',index='i',values='Value')
     techs.columns = techs.columns.str.lower()
     r = gdx['rfeas']
@@ -153,6 +152,20 @@ def reeds_cc(t, tnext, casedir):
     ### Get the non-duplicated profiles
     resource_profiles = resources.drop_duplicates('resource')
 
+    ### Get forced outage rates for thermal technologies
+    forced_outage_rate = reeds.io.get_outage_hourly(inputs_case,'forced')
+    
+    tech_subset_table = reeds.techs.expand_GAMS_tech_groups(
+        reeds.techs.get_tech_subset_table(casedir).reset_index()
+    ).set_index('tech_group').i
+    techs_to_keep = tech_subset_table.loc['TEMP_DERATE'].tolist()
+    techs_to_drop = [
+        col for col in forced_outage_rate.columns.get_level_values('i').unique()
+        if col not in techs_to_keep
+    ]
+    
+    forced_outage_rate = forced_outage_rate.drop(columns=techs_to_drop, level=0, errors='ignore')
+    
     # Remove the "8760" safety valve bin
     safety_bin = max(sdb['bin'].values)
     sdb = sdb[sdb['bin'] != max(sdb['bin'])]
@@ -160,7 +173,6 @@ def reeds_cc(t, tnext, casedir):
 
     # Temporal definitions
     h_dt_szn = pd.read_csv(os.path.join('inputs_case', 'rep', 'h_dt_szn.csv'))
-
     ccseasons = []
     if sw['cc_calc_annual']:
         ccseasons += ['year']
@@ -170,9 +182,9 @@ def reeds_cc(t, tnext, casedir):
     ### Prepare the seasonal profiles
     ## vre_gen needs to have tech_class_r columns
     ## last version has (ccseason,year,h,hour) index
-    vre_gen = pd.read_hdf(os.path.join(augur_data,f'vre_gen_exist_{t}.h5'))
+    vre_gen = pd.read_hdf(os.path.join(reeds_data,f'vre_gen_exist_{t}.h5'))
     ## vre_cf_marg has same columns and index as vre_gen
-    vre_cf_marg = pd.read_hdf(os.path.join(augur_data,f'vre_cf_marg_{t}.h5'))
+    vre_cf_marg = pd.read_hdf(os.path.join(reeds_data,f'vre_cf_marg_{t}.h5'))
 
     if int(sw['GSw_PRM_CapCreditMulti']) == 0:
         # Restrict capacity credit evaluation to use 2012 only (rather than multi-year)
@@ -191,7 +203,7 @@ def reeds_cc(t, tnext, casedir):
 
     load_profiles = (
         # HOURLY_PROFILES['load'].profiles
-        pd.read_hdf(os.path.join(augur_data,f'load_{t}.h5'))
+        pd.read_hdf(os.path.join(reeds_data,f'load_{t}.h5'))
         ### Map BA regions to ccreg's and sum over them
         .rename(columns=hierarchy.set_index('r').ccreg)
         .groupby(axis=1, level=0).sum()
@@ -465,6 +477,27 @@ def pivot_melt_data(df):
     ### Reorder to match ReEDS convention
     net_load_2012 = net_load_2012.reindex(['ccreg','ccseason','year','h','hour','t','value'], axis=1)
 
+    # export mean forced outage rate for top load hours in each region and ccseason
+    top_net_load_hours = net_load.groupby(['ccreg','ccseason']).head(int(sw['GSw_PRM_CapCreditHours']))
+    # map back to regions from ccreg for ReEDS input
+    top_net_load_hours = top_net_load_hours.merge(
+        hierarchy[['r','ccreg']], on='ccreg', how='left'
+    ).drop(['ccreg','year','hour','t','value'], axis=1)
+    # map h to timestamp to match forced outage rate
+    top_net_load_hours['*timestamp'] = top_net_load_hours['h'].apply(reeds.timeseries.h2timestamp)
+    hours = top_net_load_hours.groupby(['ccseason', 'r'])['*timestamp'].agg(list)
+
+    mean_forced_outage_rate = (
+        pd.concat({  
+            (ccseason, r): forced_outage_rate.loc[h].xs(r, 1, 'r').mean()  
+            for (ccseason, r), h in hours.items()  
+        }, names=('ccseason', 'r'))  
+        .reset_index()
+        .rename(columns={0:'value'})
+        .assign(t=str(tnext))
+        .reindex(['i','r','ccseason','t','value'], axis=1)
+    )
+
     if int(sw['GSw_EVMC']):
         cc_evmc = (
             pd.concat(dict_cc_evmc, axis=0)
@@ -485,6 +518,7 @@ def pivot_melt_data(df):
         'sdbin_size': sdbin_size,
         'net_load': net_load,
         'net_load_2012': net_load_2012,
+        'mean_forced_outage_rate' : mean_forced_outage_rate,
     }
 
     return cc_results
diff --git a/ReEDS_Augur/diagnostic_plots.py b/reeds/resource_adequacy/diagnostic_plots.py
similarity index 94%
rename from ReEDS_Augur/diagnostic_plots.py
rename to reeds/resource_adequacy/diagnostic_plots.py
index d0f0bbec6..361456645 100644
--- a/ReEDS_Augur/diagnostic_plots.py
+++ b/reeds/resource_adequacy/diagnostic_plots.py
@@ -1,6 +1,7 @@
 #%%### Imports
 import os
 import sys
+import gdxpds
 import pandas as pd
 import numpy as np
 import matplotlib as mpl
@@ -8,10 +9,10 @@
 from matplotlib import patheffects as pe
 from glob import glob
 import traceback
-import gdxpds
 import cmocean
 ### Local imports
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
 from reeds import plots
 
@@ -30,10 +31,10 @@ def delete_temporary_files(sw):
     Delete temporary csv, pkl, and h5 files
     """
     dropfiles = (
-        glob(os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f"*_{sw['t']}.pkl"))
-        + glob(os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f"*_{sw['t']}.h5"))
-        + glob(os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f"*_{sw['t']}.csv"))
-        + glob(os.path.join(sw['casedir'],'ReEDS_Augur','PRAS',f"PRAS_{sw['t']}*.pras"))
+        glob(os.path.join(sw['casedir'],'handoff','reeds_data',f"*_{sw['t']}.pkl"))
+        + glob(os.path.join(sw['casedir'],'handoff','reeds_data',f"*_{sw['t']}.h5"))
+        + glob(os.path.join(sw['casedir'],'handoff','reeds_data',f"*_{sw['t']}.csv"))
+        + glob(os.path.join(sw['casedir'],'handoff','PRAS',f"PRAS_{sw['t']}*.pras"))
     )
 
     for keyword in sw['keepfiles']:
@@ -45,7 +46,7 @@ def delete_temporary_files(sw):
 #%% Input-loading function
 def get_inputs(sw):
     ### Make savepath
-    sw['savepath'] = os.path.join(sw['casedir'], 'outputs', 'Augur_plots')
+    sw['savepath'] = os.path.join(sw['casedir'], 'outputs', 'figures', 'resource_adequacy')
     os.makedirs(sw['savepath'], exist_ok=True)
 
     ##### Load shared parameters
@@ -60,7 +61,7 @@ def get_inputs(sw):
     h_dt_szn['d'] = h_dt_szn.datetime.dt.strftime('sy%Yd%j')
 
     gdxreeds = gdxpds.to_dataframes(
-        os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f'reeds_data_{sw["t"]}.gdx'))
+        os.path.join(sw['casedir'],'handoff','reeds_data',f'reeds_data_{sw["t"]}.gdx'))
 
     techs = gdxreeds['i_subsets'].pivot(columns='i_subtech',index='i',values='Value')
     h2dac = techs['CONSUME'].dropna().index
@@ -88,7 +89,7 @@ def get_inputs(sw):
     ### Load and aggregate the VRE generation profiles by tech group
     try:
         vre_gen = reeds.io.read_file(
-            os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f'pras_vre_gen_{sw.t}.h5'),
+            os.path.join(sw['casedir'],'handoff','reeds_data',f'pras_vre_gen_{sw.t}.h5'),
             parse_timestamps=True,
         )
     except FileNotFoundError:
@@ -117,7 +118,7 @@ def get_inputs(sw):
     try:
         load_r = pd.read_hdf(
             os.path.join(
-                sw['casedir'],'ReEDS_Augur','augur_data',f'load_{sw.t}.h5')
+                sw['casedir'],'handoff','reeds_data',f'load_{sw.t}.h5')
         )
         load_r.index = fulltimeindex
     except FileNotFoundError:
@@ -126,7 +127,7 @@ def get_inputs(sw):
     ### Load PRAS load
     try:
         pras_load = reeds.io.read_file(
-            os.path.join(sw['casedir'],'ReEDS_Augur','augur_data',f'pras_load_{sw.t}.h5'),
+            os.path.join(sw['casedir'],'handoff','reeds_data',f'pras_load_{sw.t}.h5'),
             parse_timestamps=True,
         )
     except FileNotFoundError:
@@ -135,7 +136,7 @@ def get_inputs(sw):
     try:
         pras_h2dac_load = reeds.io.read_file(
             os.path.join(
-                sw['casedir'],'ReEDS_Augur','augur_data',
+                sw['casedir'],'handoff','reeds_data',
                 f"pras_h2dac_load_{sw['t']}.h5"),
             parse_timestamps=True,
         )
@@ -147,7 +148,7 @@ def get_inputs(sw):
     try:
         max_cap = pd.read_csv(
             os.path.join(
-                sw['casedir'],'ReEDS_Augur','augur_data',f"max_cap_{sw['t']}.csv"))
+                sw['casedir'],'handoff','reeds_data',f"max_cap_{sw['t']}.csv"))
         max_cap.i = reeds.reedsplots.simplify_techs(max_cap.i, display_level = 'diagnostics')
     except FileNotFoundError:
         max_cap = pd.DataFrame(columns=['i','v','r','MW'])
@@ -155,7 +156,7 @@ def get_inputs(sw):
     ### Load LOLE/EUE/NEUE from PRAS
     try:
         pras = reeds.io.read_pras_results(
-            os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS',
+            os.path.join(sw['casedir'], 'handoff', 'PRAS',
                          f"PRAS_{sw.t}i{sw.iteration}.h5")
         )
         pras.index = fulltimeindex
@@ -498,14 +499,14 @@ def plot_pras_ICAP(sw, dfs):
         plt.close()
 
 
-def plot_augur_pras_capacity(sw, dfs):
+def plot_reeds_pras_capacity(sw, dfs):
     """
-    Plot the nameplate capacity from Augur and PRAS to check consistency
+    Plot the nameplate capacity from ReEDS and PRAS to check consistency
     """
     if not len(dfs['pras_system']):
         print('PRAS system was not loaded')
         return
-    savename = f"PRAS-Augur-capacity-{sw['t']}.png"
+    savename = f"PRAS-ReEDS-capacity-{sw['t']}.png"
     ### Get the colors
     tech_style = dfs['tech_style']['color'].squeeze()
     ### Collect the PRAS system capacities
@@ -522,12 +523,12 @@ def plot_augur_pras_capacity(sw, dfs):
         .groupby(axis=1, level=[1,0]).sum().max().rename('MW')
     )
 
-    ### Collect the Augur capacities
-    cap['augur'] = dfs['max_cap'].groupby(['i','r'], as_index=False).MW.sum()
+    ### Collect the ReEDS capacities
+    cap['reeds'] = dfs['max_cap'].groupby(['i','r'], as_index=False).MW.sum()
     ## Convert from s to p regions
-    cap['augur'].r = cap['augur'].r
+    cap['reeds'].r = cap['reeds'].r
     ## Aggregate by type
-    cap['augur'] = (cap['augur']
+    cap['reeds'] = (cap['reeds']
         .replace({'i':{'Hydropower Existing':'Hydropower', 'Hydropower New':'Hydropower'}})
         .groupby(['r','i']).MW.sum() / 1e3
     )
@@ -555,7 +556,7 @@ def plot_augur_pras_capacity(sw, dfs):
     )
     alltechs = set()
     for r in zones:
-        df = pd.concat({'A':cap['augur'].get(r,pd.Series()), 'P':cap['pras'].get(r,pd.Series())}, axis=1).T
+        df = pd.concat({'A':cap['reeds'].get(r,pd.Series()), 'P':cap['pras'].get(r,pd.Series())}, axis=1).T
         order = [c for c in tech_style.index if c in df]
         missing = [c for c in df if c not in order]
         if len(missing):
@@ -905,17 +906,17 @@ def plot_pras_load_units(sw, dfs):
     plt.close()
 
 
-def plot_pras_augur_load(sw, dfs):
-    """PRAS load against Augur load"""
+def plot_pras_reeds_load(sw, dfs):
+    """PRAS load against ReEDS load"""
     dfpras = dfs['pras_system']['load'].sum(axis=1).rename('PRAS')
-    dfaugur = dfs['load_r'].set_axis(dfpras.index).sum(axis=1).rename('Augur')
+    dfreeds = dfs['load_r'].set_axis(dfpras.index).sum(axis=1).rename('ReEDS')
     years = dfpras.index.year.unique()
-    linecolors = {'Augur':'C0', 'PRAS':'C3'}
+    linecolors = {'ReEDS':'C0', 'PRAS':'C3'}
     for year in years:
-        savename = f"demand_USA-Augur-PRAS-w{year}-{sw['t']}.png"
+        savename = f"demand_USA-ReEDS-PRAS-w{year}-{sw['t']}.png"
         plt.close()
         f,ax = plots.plotyearbymonth(
-            dfaugur.loc[str(year)], style='line', colors=linecolors['Augur'])
+            dfreeds.loc[str(year)], style='line', colors=linecolors['ReEDS'])
         plots.plotyearbymonth(
             dfpras.loc[str(year)], style='line', colors=linecolors['PRAS'], f=f, ax=ax)
         ## Legend
@@ -1032,8 +1033,7 @@ def plot_cc_mar(sw, dfs):
     if not int(sw['GSw_PRM_CapCredit']):
         raise KeyError('No capacity credit values to plot')
     cc_results = gdxpds.to_dataframes(os.path.join(
-        sw['casedir'],'ReEDS_Augur','augur_data',
-        'ReEDS_Augur_{}.gdx'.format(sw['t'])
+        sw['casedir'], 'handoff', 'reeds_data', f"ccdata_{sw['t']}.gdx"
     ))
 
     dfplot = cc_results[param].drop('t',axis=1).copy()
@@ -1176,7 +1176,7 @@ def plot_netloadhours_histogram(sw, dfs):
 
 def plot_stressors(sw, dfs):
     """
-    Map demand/CF/FOR (organized differently to allow use outside of Augur)
+    Map demand/CF/FOR (organized differently to allow for independent use)
     """
     for iteration in range(sw['iteration']):
         plot_generator = reeds.reedsplots.map_stressors(
@@ -1230,9 +1230,9 @@ def main(sw, debug=False):
             print('plot_pras_unitnumber() failed:', traceback.format_exc())
 
         try:
-            plot_augur_pras_capacity(sw, dfs)
+            plot_reeds_pras_capacity(sw, dfs)
         except Exception:
-            print('plot_augur_pras_capacity() failed:', traceback.format_exc())
+            print('plot_reeds_pras_capacity() failed:', traceback.format_exc())
 
         try:
             plot_pras_load(sw, dfs)
@@ -1278,9 +1278,9 @@ def main(sw, debug=False):
 
     if debug:
         try:
-            plot_pras_augur_load(sw, dfs)
+            plot_pras_reeds_load(sw, dfs)
         except Exception:
-            print('plot_pras_augur_load() failed:', traceback.format_exc())
+            print('plot_pras_reeds_load() failed:', traceback.format_exc())
 
         try:
             plot_pras_ICAP(sw, dfs)
@@ -1304,8 +1304,8 @@ def main(sw, debug=False):
     import argparse
     parser = argparse.ArgumentParser(
         description='Create the necessary 8760 and capacity factor data for hourly resolution')
-    parser.add_argument('--reeds_path', help='ReEDS-2.0 directory')
-    parser.add_argument('--casedir', help='ReEDS-2.0/runs/{case} directory')
+    parser.add_argument('--reeds_path', help='ReEDS directory')
+    parser.add_argument('--casedir', help='ReEDS/runs/{case} directory')
     parser.add_argument('--t', type=int, default=2050, help='solve year to plot')
     parser.add_argument('--iteration', '-i', type=int, default=-1,
                         help='iteration to plot (default of -1 means latest iteration)')
@@ -1346,7 +1346,7 @@ def main(sw, debug=False):
         sw['iteration'] = iteration
 
     ### Make the plots
-    print('plotting intermediate Augur results...')
+    print('plotting intermediate resource adequacy results...')
     try:
         main(sw, debug)
     except Exception as _err:
@@ -1354,5 +1354,5 @@ def main(sw, debug=False):
         print(traceback.format_exc())
 
     ### Remove intermediate csv files to save drive space
-    if (not int(sw['keep_augur_files'])) and (not int(sw['debug'])):
+    if (not int(sw['keep_resource_adequacy_files'])) and (not int(sw['debug'])):
         delete_temporary_files(sw)
diff --git a/ReEDS_Augur/prep_data.py b/reeds/resource_adequacy/prep_data.py
similarity index 97%
rename from ReEDS_Augur/prep_data.py
rename to reeds/resource_adequacy/prep_data.py
index 8f4febfcc..c27637ad8 100644
--- a/ReEDS_Augur/prep_data.py
+++ b/reeds/resource_adequacy/prep_data.py
@@ -5,7 +5,7 @@
 * run_pras.jl -> ReEDS2PRAS.jl -> PRAS.jl (probabilistic resource adequacy)
 
 The files used by PRAS are:
-* In {case}/ReEDS_Augur/augur_data:
+* In {case}/handoff/reeds_data:
     * cap_converter_{year}.csv
     * energy_cap_{year}.csv
     * max_cap_{year}.csv
@@ -25,9 +25,9 @@
 #%% General imports
 import os
 import re
+import gdxpds
 import pandas as pd
 import numpy as np
-import gdxpds
 ### Local imports
 import reeds
 
@@ -78,7 +78,7 @@ def errorcheck_reeds2pras(casedir, csvout, h5out):
     ### Make sure there are no missing values in data sent to ReEDS2PRAS
     for key in ['pras_load', 'pras_vre_gen']:
         if h5out[key].isnull().sum().sum() > 0:
-            missing_data_cols = [i for i in h5out[key] if h5out[key].isnull().sum(axis=0) > 0]
+            missing_data_cols = [i for i in h5out[key] if h5out[key][i].isnull().sum(axis=0) > 0]
             print(missing_data_cols)
             raise ValueError(f'{key} has NaN values in {len(missing_data_cols)} columns')
 
@@ -105,11 +105,11 @@ def errorcheck_reeds2pras(casedir, csvout, h5out):
 def main(t, casedir, iteration=0):
     #%%### DEBUGGING: Inputs
     # t = 2020
-    # reeds_path = os.path.expanduser('~/github2/ReEDS-2.0')
-    # casedir = os.path.join(reeds_path,'runs','v20230214_PRMaugurM0_Pacific_d7fIrh4_CC_y2012')
+    # reeds_path = os.path.expanduser('~/github/ReEDS')
+    # casedir = os.path.join(reeds_path,'runs','v20260417_reorgM0_Pacific')
 
     #%%### Get inputs from ReEDS
-    gdx_file = os.path.join(casedir,'ReEDS_Augur','augur_data',f'reeds_data_{t}.gdx')
+    gdx_file = os.path.join(casedir,'handoff','reeds_data',f'reeds_data_{t}.gdx')
     gdxreeds = gdxpds.to_dataframes(gdx_file)
     ### Use indices as multiindex
     for key in gdxreeds:
@@ -174,10 +174,7 @@ def main(t, casedir, iteration=0):
         ))
 
     try:
-        offshore = pd.read_csv(
-            os.path.join(casedir, 'inputs_case', 'offshore.csv'),
-            header=None,
-        ).squeeze(1).tolist()
+        offshore = reeds.io.read_input(casedir, 'offshore').squeeze(1).tolist()
     except pd.errors.EmptyDataError:
         offshore = []
 
@@ -567,7 +564,7 @@ def intify(v):
     #%% .csv files
     for key in csvout:
         csvout[key].round(int(sw['decimals'])).to_csv(
-            os.path.join(casedir,'ReEDS_Augur','augur_data',f'{key}_{t}.csv'),
+            os.path.join(casedir,'handoff','reeds_data',f'{key}_{t}.csv'),
         )
 
     #%% .h5 files
@@ -576,11 +573,11 @@ def intify(v):
             reeds.io.write_profile_to_h5(
                 df=h5out[key].astype(np.float32),
                 filename=f'{key}_{t}.h5',
-                outfolder=os.path.join(casedir,'ReEDS_Augur','augur_data'),
+                outfolder=os.path.join(casedir,'handoff','reeds_data'),
             )
         else:
             h5out[key].astype(np.float32).to_hdf(
-                os.path.join(casedir,'ReEDS_Augur','augur_data',f'{key}_{t}.h5'),
+                os.path.join(casedir,'handoff','reeds_data',f'{key}_{t}.h5'),
                 key='data', complevel=4, mode='w',
             )
 
diff --git a/Augur.py b/reeds/resource_adequacy/ra_calcs.py
similarity index 85%
rename from Augur.py
rename to reeds/resource_adequacy/ra_calcs.py
index 11c856eb4..214f26ab7 100644
--- a/Augur.py
+++ b/reeds/resource_adequacy/ra_calcs.py
@@ -4,13 +4,11 @@
 import sys
 import subprocess
 import datetime
-import pandas as pd
 import gdxpds
-
+import pandas as pd
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 import reeds
-import ReEDS_Augur.prep_data as prep_data
-import ReEDS_Augur.capacity_credit as capacity_credit
-import ReEDS_Augur.stress_periods as stress_periods
 
 
 #%% Functions
@@ -59,7 +57,7 @@ def run_pras(
             '--threads=1' if (sys.platform == 'darwin') or int(sw.get('pras_singlethread', 0))
             else f"--threads={sw['threads'] if sw['threads'] > 0 else 'auto'}"
         ),
-        f"{os.path.join(scriptpath, 'ReEDS_Augur','run_pras.jl')}",
+        f"{os.path.join(scriptpath, 'reeds', 'resource_adequacy', 'run_pras.jl')}",
         f"--reeds_path={sw['reeds_path']}",
         f"--reedscase={casedir}",
         f"--solve_year={t}",
@@ -101,20 +99,20 @@ def run_pras(
 def main(t, tnext, casedir, iteration=0):
 
     # #%% To debug, uncomment these lines and update the run path
-    # t = 2026
-    # tnext = 2029
+    # t = 2020
+    # tnext = 2023
     # reeds_path = reeds.io.reeds_path
     # casedir = os.path.join(
-    #     reeds_path,'runs','v20250521_prasM0_Pacific')
+    #     reeds_path,'runs','v20260417_reorgM1_Pacific')
     # iteration = 0
     # assert tnext >= t
     # os.chdir(casedir)
     # ## Copy reeds2pras from repo to run folder
     # import shutil
-    # shutil.rmtree(os.path.join(casedir, 'reeds2pras'))
+    # shutil.rmtree(os.path.join(casedir, 'reeds', 'resource_adequacy', 'reeds2pras'))
     # shutil.copytree(
-    #     os.path.join(reeds_path, 'reeds2pras'),
-    #     os.path.join(casedir, 'reeds2pras'),
+    #     os.path.join(reeds_path, 'reeds', 'resource_adequacy', 'reeds2pras'),
+    #     os.path.join(casedir, 'reeds', 'resource_adequacy', 'reeds2pras'),
     #     ignore=shutil.ignore_patterns('test'),
     # )
 
@@ -125,24 +123,25 @@ def main(t, tnext, casedir, iteration=0):
     #%% Prep data for resource adequacy
     print('Preparing data for resource adequacy calculations')
     tic = datetime.datetime.now()
-    augur_csv, augur_h5 = prep_data.main(t, casedir, iteration)
-    reeds.log.toc(tic=tic, year=t, process='ReEDS_Augur/prep_data.py')
+    reeds_csv, reeds_h5 = reeds.resource_adequacy.prep_data.main(t, casedir, iteration)
+    reeds.log.toc(tic=tic, year=t, process='ra/prep_data.py')
 
     #%% Calculate capacity credit if necessary; otherwise bypass
     print('calculating capacity credit...')
     tic = datetime.datetime.now()
 
     if int(sw['GSw_PRM_CapCredit']):
-        cc_results = capacity_credit.reeds_cc(t, tnext, casedir)
+        cc_results = reeds.resource_adequacy.capacity_credit.reeds_cc(t, tnext, casedir)
     else:
         cc_results = {
             'cc_mar': pd.DataFrame(columns=['i','r','ccreg','szn','t','Value']),
             'cc_old': pd.DataFrame(columns=['i','r','ccreg','szn','t','Value']),
             'cc_evmc': pd.DataFrame(columns=['i','r','szn','t','Value']),
             'sdbin_size': pd.DataFrame(columns=['ccreg','szn','bin','t','Value']),
+            'mean_forced_outage_rate': pd.DataFrame(columns=['i','r','ccseason','t','Value']),
         }
 
-    reeds.log.toc(tic=tic, year=t, process='ReEDS_Augur/capacity_credit.py')
+    reeds.log.toc(tic=tic, year=t, process='ra/capacity_credit.py')
 
     #%% Run PRAS if necessary
     solveyears = pd.read_csv(
@@ -167,14 +166,13 @@ def main(t, tnext, casedir, iteration=0):
             )
 
     #%% Identify stress periods
-    print('identifying new stress periods...')
     tic = datetime.datetime.now()
     if (
         ('user' not in sw['GSw_PRM_StressModel'].lower())
         or ((int(sw.GSw_PRM_StressIterateMax)) and int(sw['GSw_PRM_CapCredit']))
     ):
-        stress_periods.main(sw=sw, t=t, iteration=iteration)
-    reeds.log.toc(tic=tic, year=t, process='ReEDS_Augur/stress_periods.py')
+        reeds.resource_adequacy.stress_periods.main(sw=sw, t=t, iteration=iteration)
+    reeds.log.toc(tic=tic, year=t, process='ra/stress_periods.py')
 
     #%% Write gdx file explicitly to ensure that all entries
     ### (even empty dataframes) are written as parameters, not sets
@@ -188,14 +186,13 @@ def main(t, tnext, casedir, iteration=0):
             )
             gdx[-1].dataframe = cc_results[key]
         gdx.write(
-            os.path.join('ReEDS_Augur', 'augur_data', f'ReEDS_Augur_{t}.gdx')
+            os.path.join('handoff', 'reeds_data', f'ccdata_{t}.gdx')
         )
 
     # #%% Uncomment to run diagnostic_plots
     # ### (typically run from call_{}.sh script for parallelization)
     # try:
-    #     import ReEDS_Augur.diagnostic_plots as diagnostic_plots
-    #     diagnostic_plots.main(sw)
+    #     reeds.resource_adequacy.diagnostic_plots.main(sw)
     # except Exception as err:
     #     print('diagnostic_plots.py failed with the following exception:')
     #     print(err)
@@ -204,7 +201,7 @@ def main(t, tnext, casedir, iteration=0):
 #%% Procedure
 if __name__ == '__main__':
 
-    parser = argparse.ArgumentParser(description="""Running ReEDS Augur""")
+    parser = argparse.ArgumentParser(description="Resource adequacy calculations")
 
     parser.add_argument("tnext", help="Next ReEDS solve year", type=int)
     parser.add_argument("t", help="Previous ReEDS solve year", type=int)
diff --git a/ReEDS_Augur/augur_switches.csv b/reeds/resource_adequacy/ra_switches.csv
similarity index 89%
rename from ReEDS_Augur/augur_switches.csv
rename to reeds/resource_adequacy/ra_switches.csv
index bff65dbe7..092ca9cd9 100644
--- a/ReEDS_Augur/augur_switches.csv
+++ b/reeds/resource_adequacy/ra_switches.csv
@@ -12,5 +12,5 @@ cc_stor_stepsize,100,int,step size (in MW) used when determining the peaking cap
 decimals,3,int,number of decimals to round results to for ReEDS
 flex_consume_techs,"dac,electrolyzer",list,list of consume techs that are flexible
 keepfiles,"dropped_load,cf",list,list of temporary files to keep
-marg_vre_steps,2,int,Number of previous solve years to consider when evaluating the marginal VRE step size (default: 2). Must be at least 1; a value of 2 can help reduce oscillations. Augur will automatically drop from consideration solves that are more than 5 years from the previous solve.
+marg_vre_steps,2,int,Number of previous solve years to consider when evaluating the marginal VRE step size (default: 2). Must be at least 1; a value of 2 can help reduce oscillations. Solves that are more than 5 years from the previous solve are automatically dropped from consideration.
 storcap_cutoff,1,float,[MW and MWh] Minimum storage capacity to send to ReEDS2PRAS (applies to both power and energy capacity)
diff --git a/reeds2pras/R2P_Test_Summary.png b/reeds/resource_adequacy/reeds2pras/R2P_Test_Summary.png
similarity index 100%
rename from reeds2pras/R2P_Test_Summary.png
rename to reeds/resource_adequacy/reeds2pras/R2P_Test_Summary.png
diff --git a/reeds2pras/README.md b/reeds/resource_adequacy/reeds2pras/README.md
similarity index 76%
rename from reeds2pras/README.md
rename to reeds/resource_adequacy/reeds2pras/README.md
index daa5ddd53..4662cb6f2 100644
--- a/reeds2pras/README.md
+++ b/reeds/resource_adequacy/reeds2pras/README.md
@@ -23,7 +23,7 @@ curl -fsSL https://install.julialang.org | sh
 winget install --name Julia --id 9NJNWW8PVKMN -e -s msstore
 ```
 
-Then, from the ReEDS-2.0 directory, run `julia --project=. instantiate.jl` to ensure proper installation of Julia and the ReEDS2PRAS environment.
+Then, from the ReEDS directory, run `julia --project=. instantiate.jl` to ensure proper installation of Julia and the ReEDS2PRAS environment.
 
 ## Basic Usage
 
@@ -31,15 +31,14 @@ If you have a completed ReEDS run and a REPL with ReEDS2PRAS (`using ReEDS2PRAS`
 
 ```julia
 using ReEDS2PRAS
-
-reedscase = "/projects/ntps/llavin/ReEDS-2.0/runs/ntpsrerun_Xlim_DemHi_90by2035EarlyPhaseout__core" # path to completed ReEDS run
-solve_year = 2035 #need ReEDS Augur data for the input solve year
-weather_year = 2012 # must be 2007-2013 or 2016-2023
+# path to completed ReEDS run
+reedscase = "/path/to/ReEDS/runs/v20260511_main0_USA_defaults"
+solve_year = 2035
+weather_year = 2012
 timesteps = 8760
-user_descriptors = "your_user_descriptors_json_location_here" # Optional - if not passed uses default values
 
 # returns a parameterized PRAS system
-pras_system = ReEDS2PRAS.reeds_to_pras(reedscase, solve_year, timesteps, weather_year, user_descriptors = user_descriptors)
+pras_system = ReEDS2PRAS.reeds_to_pras(reedscase, solve_year, timesteps, weather_year)
 ```
 
 This will save out a pras system to the variable `pras_system` from the ReEDS2PRAS run. The user can also save a PRAS system to a specific location using `PRAS.savemodel(pras_system, joinpath("MYPATH"*".pras")`. The saved PRAS system may then be read in by other tools like PRAS Analytics (`https://github.nrel.gov/PRAS/PRAS-Analytics`) for further analysis, post-processing, and plotting.
@@ -52,14 +51,13 @@ ReEDS2PRAS can be run for multiple weather years of a completed ReEDS run by pas
 using ReEDS2PRAS
 
 # path to completed ReEDS run
-reedscase = "/projects/ntps/llavin/ReEDS-2.0/runs/ntpsrerun_Xlim_DemHi_90by2035EarlyPhaseout__core"
-solve_year = 2035 #need ReEDS Augur data for the input solve year
-weather_year = 2007 # must be 2007-2013 or 2016-2023
+reedscase = "/path/to/ReEDS/runs/v20260511_main0_USA_defaults"
+solve_year = 2035
+weather_year = 2007
 timesteps = 61320
-user_descriptors = "your_user_descriptors_json_location_here" # Optional - if not passed uses default values
 
 # returns a parameterized PRAS system
-pras_system = ReEDS2PRAS.reeds_to_pras(reedscase, solve_year, timesteps, weather_year, user_descriptors = user_descriptors)
+pras_system = ReEDS2PRAS.reeds_to_pras(reedscase, solve_year, timesteps, weather_year)
 ```
 
 Importantly, the timesteps count from the first hour of the first `weather_year`, so the user must input `2007` as the `weather_year` to run all 61320 hourly timesteps.
@@ -71,14 +69,14 @@ It is always good practice to include new tests if the current tests don't cover
 You can run ReEDS2PRAS tests by running:
 
 ```shell
-cd ReEDS-2.0/reeds2pras/test
+cd ReEDS/reeds2pras/test
 julia --project runtests.jl
 ```
 
 or
 
 ```shell
-cd ReEDS-2.0/reeds2pras/test
+cd ReEDS/reeds2pras/test
 julia --project
 ```
 
diff --git a/reeds2pras/src/ReEDS2PRAS.jl b/reeds/resource_adequacy/reeds2pras/src/ReEDS2PRAS.jl
similarity index 100%
rename from reeds2pras/src/ReEDS2PRAS.jl
rename to reeds/resource_adequacy/reeds2pras/src/ReEDS2PRAS.jl
diff --git a/reeds2pras/src/main/create_pras_system.jl b/reeds/resource_adequacy/reeds2pras/src/main/create_pras_system.jl
similarity index 100%
rename from reeds2pras/src/main/create_pras_system.jl
rename to reeds/resource_adequacy/reeds2pras/src/main/create_pras_system.jl
diff --git a/reeds2pras/src/main/parse_reeds_data.jl b/reeds/resource_adequacy/reeds2pras/src/main/parse_reeds_data.jl
similarity index 100%
rename from reeds2pras/src/main/parse_reeds_data.jl
rename to reeds/resource_adequacy/reeds2pras/src/main/parse_reeds_data.jl
diff --git a/reeds2pras/src/models/Battery.jl b/reeds/resource_adequacy/reeds2pras/src/models/Battery.jl
similarity index 100%
rename from reeds2pras/src/models/Battery.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Battery.jl
diff --git a/reeds2pras/src/models/Gen_Storage.jl b/reeds/resource_adequacy/reeds2pras/src/models/Gen_Storage.jl
similarity index 100%
rename from reeds2pras/src/models/Gen_Storage.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Gen_Storage.jl
diff --git a/reeds2pras/src/models/Generator.jl b/reeds/resource_adequacy/reeds2pras/src/models/Generator.jl
similarity index 100%
rename from reeds2pras/src/models/Generator.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Generator.jl
diff --git a/reeds2pras/src/models/Interface.jl b/reeds/resource_adequacy/reeds2pras/src/models/Interface.jl
similarity index 100%
rename from reeds2pras/src/models/Interface.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Interface.jl
diff --git a/reeds2pras/src/models/Line.jl b/reeds/resource_adequacy/reeds2pras/src/models/Line.jl
similarity index 100%
rename from reeds2pras/src/models/Line.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Line.jl
diff --git a/reeds2pras/src/models/Region.jl b/reeds/resource_adequacy/reeds2pras/src/models/Region.jl
similarity index 100%
rename from reeds2pras/src/models/Region.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Region.jl
diff --git a/reeds2pras/src/models/Storage.jl b/reeds/resource_adequacy/reeds2pras/src/models/Storage.jl
similarity index 100%
rename from reeds2pras/src/models/Storage.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Storage.jl
diff --git a/reeds2pras/src/models/Thermal_Gen.jl b/reeds/resource_adequacy/reeds2pras/src/models/Thermal_Gen.jl
similarity index 100%
rename from reeds2pras/src/models/Thermal_Gen.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Thermal_Gen.jl
diff --git a/reeds2pras/src/models/Variable_Gen.jl b/reeds/resource_adequacy/reeds2pras/src/models/Variable_Gen.jl
similarity index 100%
rename from reeds2pras/src/models/Variable_Gen.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/Variable_Gen.jl
diff --git a/reeds2pras/src/models/utils.jl b/reeds/resource_adequacy/reeds2pras/src/models/utils.jl
similarity index 100%
rename from reeds2pras/src/models/utils.jl
rename to reeds/resource_adequacy/reeds2pras/src/models/utils.jl
diff --git a/reeds2pras/src/reeds_to_pras.jl b/reeds/resource_adequacy/reeds2pras/src/reeds_to_pras.jl
similarity index 100%
rename from reeds2pras/src/reeds_to_pras.jl
rename to reeds/resource_adequacy/reeds2pras/src/reeds_to_pras.jl
diff --git a/reeds2pras/src/utils/reeds_data_parsing.jl b/reeds/resource_adequacy/reeds2pras/src/utils/reeds_data_parsing.jl
similarity index 98%
rename from reeds2pras/src/utils/reeds_data_parsing.jl
rename to reeds/resource_adequacy/reeds2pras/src/utils/reeds_data_parsing.jl
index ec1acc2de..23597d12b 100644
--- a/reeds2pras/src/utils/reeds_data_parsing.jl
+++ b/reeds/resource_adequacy/reeds2pras/src/utils/reeds_data_parsing.jl
@@ -66,16 +66,6 @@ function process_lines(
             converter_capacity_data[!, "MW"],
     )
 
-    #add 0 converter capacity for regions that lack a converter
-    if length(keys(converter_capacity_dict)) > 0
-        for reg in regions
-            if !(reg in keys(converter_capacity_dict))
-                @info("$reg does not have VSC converter capacity, so adding" * " a 0")
-                converter_capacity_dict[reg] = 0.0
-            end
-        end
-    end
-
     function keep_line(from_pca, to_pca)
         from_idx = findfirst(x -> x == from_pca, regions)
         to_idx = findfirst(x -> x == to_pca, regions)
@@ -143,8 +133,8 @@ function process_lines(
                     MTTR = 24,
                     VSC = true,
                     converter_capacity = Dict(
-                        row.r => converter_capacity_dict[string(row.r)],
-                        row.rr => converter_capacity_dict[string(row.rr)],
+                        row.r => get(converter_capacity_dict, string(row.r), 0.0),
+                        row.rr => get(converter_capacity_dict, string(row.rr), 0.0),
                     ),
                 ),
             )
@@ -173,7 +163,7 @@ function split_generator_types(ReEDS_data::ReEDSdatapaths)
     ## Read {case}/inputs_case/tech-subset-table.csv
     tech_subset_table = get_technology_types(ReEDS_data)
     @debug "tech_subset_table is $(tech_subset_table)"
-    ## Read {case}/ReEDS_Augur/augur_data/max_cap_{year}.csv
+    ## Read {case}/handoff/reeds_data/max_cap_{year}.csv
     capacity_data = get_ICAP_data(ReEDS_data)
     ## Read {case}/inputs_case/resources.csv
     resources = get_valid_resources(ReEDS_data)
@@ -738,8 +728,8 @@ function process_storages(
     efficiency_in = Dict(
         polarity => DataFrames.DataFrame(CSV.File(joinpath(
             ReEDS_data.ReEDSfilepath,
-            "ReEDS_Augur",
-            "augur_data",
+            "handoff",
+            "reeds_data",
             "$(polarity)_eff_$(ReEDS_data.year).csv"
         )))
         for polarity in ["charge", "discharge"]
diff --git a/reeds2pras/src/utils/reeds_input_parsing.jl b/reeds/resource_adequacy/reeds2pras/src/utils/reeds_input_parsing.jl
similarity index 94%
rename from reeds2pras/src/utils/reeds_input_parsing.jl
rename to reeds/resource_adequacy/reeds2pras/src/utils/reeds_input_parsing.jl
index 985936bc7..446792f8d 100644
--- a/reeds2pras/src/utils/reeds_input_parsing.jl
+++ b/reeds/resource_adequacy/reeds2pras/src/utils/reeds_input_parsing.jl
@@ -5,7 +5,7 @@
     Parameters
     ----------
     x : String
-        Path to ReEDS case (e.g. "~/github/ReEDS-2.0/runs/name_of_reeds_case")
+        Path to ReEDS case (e.g. "~/github/ReEDS/runs/name_of_reeds_case")
     y : Int
         ReEDS model year
 
@@ -75,15 +75,15 @@ end
     Returns
     -------
     HDF5.h5read(filepath, "data")
-        A readout of the Augur load h5 file associated with the given ReEDS
+        A readout of the load h5 file associated with the given ReEDS
         filepath and year.
 
 """
 function get_load_file(data::ReEDSdatapaths)
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "pras_load_$(string(data.year)).h5",
     )
     columns = HDF5.h5read(filepath, "columns")
@@ -93,7 +93,7 @@ function get_load_file(data::ReEDSdatapaths)
 end
 
 """
-    This function reads a hdf5 file from the ReEDS Augur directory, based on
+    This function reads a hdf5 file from the ReEDS directory, based on
     the year provided in the ReEDSdatapaths struct.
 
     Parameters
@@ -109,8 +109,8 @@ end
 function get_vg_cf_data(data::ReEDSdatapaths)
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "pras_vre_gen_$(string(data.year)).h5",
     )
     columns = HDF5.h5read(filepath, "columns")
@@ -145,7 +145,7 @@ end
 """
 function get_max_unitsize(data::ReEDSdatapaths)
     filepath = joinpath(
-        data.ReEDSfilepath, "ReEDS_Augur", "augur_data",
+        data.ReEDSfilepath, "handoff", "reeds_data",
         "max_unitsize_$(string(data.year)).csv"
     )
     df = DataFrames.DataFrame(CSV.File(filepath))
@@ -154,7 +154,7 @@ end
 
 
 """
-    Get the forced outage data from the augur files.
+    Get the forced outage data.
 
     Parameters
     ----------
@@ -227,8 +227,8 @@ function get_line_capacity_data(data::ReEDSdatapaths)
     #assumes this file has been formatted by ReEDS to be PRM line capacity data
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "tran_cap_$(string(data.year)).csv",
     )
     return DataFrames.DataFrame(CSV.File(filepath))
@@ -251,8 +251,8 @@ end
 function get_converter_capacity_data(data::ReEDSdatapaths)
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "cap_converter_$(string(data.year)).csv",
     )
     return DataFrames.DataFrame(CSV.File(filepath))
@@ -285,7 +285,7 @@ end
 
 """
     Returns a DataFrame containing the installed capacity of generators for a
-    given year, read from {case}/ReEDS_Augur/augur_data/max_cap_{year}.csv.
+    given year, read from {case}/handoff/reeds_data/max_cap_{year}.csv.
 
     Parameters
     ----------
@@ -305,8 +305,8 @@ end
 function get_ICAP_data(data::ReEDSdatapaths)
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "max_cap_$(string(data.year)).csv",
     )
     return DataFrames.DataFrame(CSV.File(filepath))
@@ -380,8 +380,8 @@ end
 function get_storage_energy_capacity_data(data::ReEDSdatapaths)
     filepath = joinpath(
         data.ReEDSfilepath,
-        "ReEDS_Augur",
-        "augur_data",
+        "handoff",
+        "reeds_data",
         "energy_cap_$(string(data.year)).csv",
     )
     return DataFrames.DataFrame(CSV.File(filepath))
@@ -420,7 +420,7 @@ function get_hourly_scheduled_outage_data(data::ReEDSdatapaths)
 end
 
 """
-    Get the hourly forced outage data from the augur files.
+    Get the hourly forced outage data.
 
     Parameters
     ----------
diff --git a/reeds2pras/src/utils/runchecks.jl b/reeds/resource_adequacy/reeds2pras/src/utils/runchecks.jl
similarity index 67%
rename from reeds2pras/src/utils/runchecks.jl
rename to reeds/resource_adequacy/reeds2pras/src/utils/runchecks.jl
index 9a5a88a65..31f4d72ca 100644
--- a/reeds2pras/src/utils/runchecks.jl
+++ b/reeds/resource_adequacy/reeds2pras/src/utils/runchecks.jl
@@ -14,17 +14,17 @@ function check_file(loc::String)
 end
 
 function run_checks(data::ReEDSdatapaths)
-    augur_data_path = joinpath(data.ReEDSfilepath, "ReEDS_Augur", "augur_data")
+    reeds_data_path = joinpath(data.ReEDSfilepath, "handoff", "reeds_data")
     filepaths = [
-        joinpath(augur_data_path, "cap_converter_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "charge_eff_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "discharge_eff_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "energy_cap_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "max_cap_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "max_unitsize_$(string(data.year)).csv"),
-        joinpath(augur_data_path, "pras_load_$(string(data.year)).h5"),
-        joinpath(augur_data_path, "pras_vre_gen_$(string(data.year)).h5"),
-        joinpath(augur_data_path, "tran_cap_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "cap_converter_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "charge_eff_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "discharge_eff_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "energy_cap_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "max_cap_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "max_unitsize_$(string(data.year)).csv"),
+        joinpath(reeds_data_path, "pras_load_$(string(data.year)).h5"),
+        joinpath(reeds_data_path, "pras_vre_gen_$(string(data.year)).h5"),
+        joinpath(reeds_data_path, "tran_cap_$(string(data.year)).csv"),
         joinpath(data.ReEDSfilepath, "inputs_case", "hydcapadj.csv"),
         joinpath(data.ReEDSfilepath, "inputs_case", "hydcf.csv"),
         joinpath(data.ReEDSfilepath, "inputs_case", "mttr.csv"),
diff --git a/reeds2pras/test/Project.toml b/reeds/resource_adequacy/reeds2pras/test/Project.toml
similarity index 100%
rename from reeds2pras/test/Project.toml
rename to reeds/resource_adequacy/reeds2pras/test/Project.toml
diff --git a/reeds2pras/test/runtests.jl b/reeds/resource_adequacy/reeds2pras/test/runtests.jl
similarity index 100%
rename from reeds2pras/test/runtests.jl
rename to reeds/resource_adequacy/reeds2pras/test/runtests.jl
diff --git a/reeds2pras/test/test-ReEDS2PRAS.jl b/reeds/resource_adequacy/reeds2pras/test/test-ReEDS2PRAS.jl
similarity index 100%
rename from reeds2pras/test/test-ReEDS2PRAS.jl
rename to reeds/resource_adequacy/reeds2pras/test/test-ReEDS2PRAS.jl
diff --git a/reeds2pras/test/test-benchmark.jl b/reeds/resource_adequacy/reeds2pras/test/test-benchmark.jl
similarity index 100%
rename from reeds2pras/test/test-benchmark.jl
rename to reeds/resource_adequacy/reeds2pras/test/test-benchmark.jl
diff --git a/reeds2pras/test/utils.jl b/reeds/resource_adequacy/reeds2pras/test/utils.jl
similarity index 100%
rename from reeds2pras/test/utils.jl
rename to reeds/resource_adequacy/reeds2pras/test/utils.jl
diff --git a/ReEDS_Augur/run_pras.jl b/reeds/resource_adequacy/run_pras.jl
similarity index 97%
rename from ReEDS_Augur/run_pras.jl
rename to reeds/resource_adequacy/run_pras.jl
index a44e86f6f..10f59e0c0 100644
--- a/ReEDS_Augur/run_pras.jl
+++ b/reeds/resource_adequacy/run_pras.jl
@@ -18,11 +18,11 @@ function parse_commandline()
 
     @ArgParse.add_arg_table s begin
         "--reeds_path"
-            help = "Path to ReEDS-2.0 folder"
+            help = "Path to ReEDS folder"
             arg_type = String
             required = true
         "--reedscase"
-            help = "Path to ReEDS run (usually .../ReEDS-2.0/runs/{casename})"
+            help = "Path to ReEDS run (usually .../ReEDS/runs/{casename})"
             arg_type = String
             required = true
         "--solve_year"
@@ -391,7 +391,7 @@ end
 function main(args::Dict)
     #%% Define some intermediate filenames
     pras_system_path = joinpath(
-        args["reedscase"],"ReEDS_Augur","PRAS",
+        args["reedscase"], "handoff", "PRAS",
         "PRAS_$(args["solve_year"])i$(args["iteration"]).pras"
     )
 
@@ -441,11 +441,11 @@ end
 #%% Procedure
 if abspath(PROGRAM_FILE) == @__FILE__
     #%% Inputs for debugging
-    # julia --project=/path/to/ReEDS-2.0 --threads=1
+    # julia --project=/path/to/ReEDS --threads=1
     # args = Dict(
-    #     "reeds_path" => "/path/to/ReEDS-2.0",
+    #     "reeds_path" => "/path/to/ReEDS",
     #     "reedscase" => (
-    #         "/path/to/ReEDS-2.0/runs/"
+    #         "/path/to/ReEDS/runs/"
     #         *"runname"),
     #     "solve_year" => 2035,
     #     "weather_year" => 2007,
@@ -477,7 +477,9 @@ if abspath(PROGRAM_FILE) == @__FILE__
     args = parse_commandline()
 
     #%% Include ReEDS2PRAS
-    include(joinpath(args["reedscase"], "reeds2pras", "src", "ReEDS2PRAS.jl"))
+    include(joinpath(
+        args["reedscase"], "reeds", "resource_adequacy", "reeds2pras", "src", "ReEDS2PRAS.jl"
+    ))
 
     #%% Run it
     main(args)
diff --git a/ReEDS_Augur/stress_periods.py b/reeds/resource_adequacy/stress_periods.py
similarity index 83%
rename from ReEDS_Augur/stress_periods.py
rename to reeds/resource_adequacy/stress_periods.py
index 905a710b4..8d159deb3 100644
--- a/ReEDS_Augur/stress_periods.py
+++ b/reeds/resource_adequacy/stress_periods.py
@@ -1,29 +1,140 @@
 #%%### General imports
 import os
-import site
 import traceback
 import pandas as pd
 import numpy as np
 from glob import glob
 import re
 import matplotlib.pyplot as plt
-### Local imports
-
-## use this to import reeds when running locally for debugging
-# import site
-# this_dir_path = os.path.dirname(os.path.realpath(__file__))
-# site.addsitedir(os.path.join(this_dir_path, ".."))
 
 import reeds
+from reeds.input_processing import hourly_writetimeseries
 
 # #%% Debugging
-# sw['reeds_path'] = os.path.expanduser('~/github/ReEDS-2.0/')
+# sw['reeds_path'] = os.path.expanduser('~/github/ReEDS/')
 # sw['casedir'] = os.path.join(sw['reeds_path'],'runs','v20230123_prmM3_Pacific_d7sIrh4sh2_y2')
 # import importlib
 # importlib.reload(functions)
 
 
 #%%### Functions
+def get_pras_eue(case, t, iteration=0):
+    """
+    """
+    ### Get PRAS outputs
+    dfpras = reeds.io.read_pras_results(
+        os.path.join(case, 'handoff', 'PRAS', f"PRAS_{t}i{iteration}.h5")
+    )
+    ### Create the time index
+    sw = reeds.io.get_switches(case)
+    dfpras.index = reeds.timeseries.get_timeindex(sw['resource_adequacy_years'])
+
+    ### Keep the EUE columns by zone
+    eue_tail = '_EUE'
+    dfeue = dfpras[[
+        c for c in dfpras
+        if (c.endswith(eue_tail) and not c.startswith('USA'))
+    ]].copy()
+    ## Drop the tailing _EUE
+    dfeue = dfeue.rename(
+        columns=dict(zip(dfeue.columns, [c[:-len(eue_tail)] for c in dfeue])))
+
+    return dfeue
+
+
+def get_eue_periods(
+        case, t, iteration=0,
+        hierarchy_level='transgrp',
+        stress_metric='EUE',
+        period_agg_method='sum',
+    ):
+    """_summary_
+
+    Args:
+        sw (pd.series): ReEDS switches for this run.
+        t (int): Model solve year.
+        iteration (int, optional): Iteration number of this solve year. Defaults to 0.
+        hierarchy_level (str, optional): column of hierarchy.csv specifying the spatial
+            level over which to calculate stress_metric. Defaults to 'country'.
+        stress_metric (str, optional): 'EUE' or 'NEUE'. Defaults to 'EUE'.
+        period_agg_method (str, optional): 'sum' or 'max', indicating how to aggregate
+            over the hours in each period. Defaults to 'sum'.
+
+    Raises:
+        NotImplementedError: if invalid value for stress_metric or GSw_PRM_StressModel
+
+    Returns:
+        pd.DataFrame: Table of periods sorted in descending order by stress metric.
+    """
+    sw = reeds.io.get_switches(case)
+    ### Get the region aggregator
+    rmap = reeds.io.get_rmap(case=case, hierarchy_level=hierarchy_level)
+
+    ### Get EUE from PRAS
+    dfeue = get_pras_eue(case=case, t=t, iteration=iteration)
+    ## Aggregate to hierarchy_level
+    dfeue = (
+        dfeue
+        .rename_axis('r', axis=1).rename_axis('h', axis=0)
+        .rename(columns=rmap).groupby(axis=1, level=0).sum()
+    )
+
+    ###### Calculate the stress metric by period
+    if stress_metric.upper() == 'EUE':
+        ### Aggregate according to period_agg_method
+        dfmetric_period = (
+            dfeue
+            .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
+            .agg(period_agg_method)
+            .rename_axis(['y','m','d'])
+        )
+    elif stress_metric.upper() == 'NEUE':
+        ### Get load at hierarchy_level
+        dfload = reeds.io.read_h5py_file(
+            os.path.join(
+                case,'handoff','reeds_data',f'pras_load_{t}.h5')
+        ).rename(columns=rmap).groupby(level=0, axis=1).sum()
+        dfload.index = dfeue.index
+
+        ### Recalculate NEUE [ppm] and aggregate appropriately
+        if period_agg_method == 'sum':
+            dfmetric_period = (
+                dfeue
+                .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
+                .agg(period_agg_method)
+                .rename_axis(['y','m','d'])
+            ) / (
+                dfload
+                .groupby([dfload.index.year, dfload.index.month, dfload.index.day])
+                .agg(period_agg_method)
+                .rename_axis(['y','m','d'])
+            ) * 1e6
+        elif period_agg_method == 'max':
+            dfmetric_period = (
+                (dfeue / dfload)
+                .groupby([dfeue.index.year, dfeue.index.month, dfeue.index.day])
+                .agg(period_agg_method)
+                .rename_axis(['y','m','d'])
+            ) * 1e6
+
+    ### Sort and drop zeros and duplicates
+    dfmetric_top = (
+        dfmetric_period.stack('r')
+        .sort_values(ascending=False)
+        .replace(0,np.nan).dropna()
+        .reset_index().drop_duplicates(['y','m','d'], keep='first')
+        .set_index(['y','m','d','r']).squeeze(1).rename(stress_metric)
+        .reset_index('r')
+    )
+    ## Convert to timestamp, then to ReEDS period
+    dfmetric_top['actual_period'] = [
+        reeds.timeseries.timestamp2h(pd.Timestamp(*d), sw['GSw_HourlyType']).split('h')[0]
+        for d in dfmetric_top.index.values
+    ]
+
+    return dfmetric_top
+
+
 def plot_eue_diagnostics(sw, t, iteration, high_eue_periods):
     try:
         dates = (
@@ -45,7 +156,7 @@ def plot_eue_diagnostics(sw, t, iteration, high_eue_periods):
                 vmax=vmax[outage_type],
             )
             plt.savefig(
-                os.path.join(sw.casedir, 'outputs', 'Augur_plots', savename)
+                os.path.join(sw.casedir, 'outputs', 'figures', 'resource_adequacy', savename)
             )
             plt.close()
     except Exception as err:
@@ -64,7 +175,7 @@ def get_and_write_neue(sw, write=True):
     """
     infiles = [
         i for i in sorted(glob(
-            os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS', 'PRAS_*.h5')))
+            os.path.join(sw['casedir'], 'handoff', 'PRAS', 'PRAS_*.h5')))
         if re.match(r"PRAS_[0-9]+i[0-9]+.h5", os.path.basename(i))
     ]
     eue = {}
@@ -94,12 +205,12 @@ def get_annual_neue(case, t, iteration=0):
     """
     """
     ### Get EUE from PRAS
-    dfeue = reeds.ra.get_pras_eue(case=case, t=t, iteration=iteration)
+    dfeue = get_pras_eue(case=case, t=t, iteration=iteration)
 
     ### Get load (for calculating NEUE)
     dfload = reeds.io.read_h5py_file(
         os.path.join(
-            case,'ReEDS_Augur','augur_data',f'pras_load_{t}.h5')
+            case,'handoff','reeds_data',f'pras_load_{t}.h5')
     )
     dfload.index = dfeue.index
 
@@ -204,7 +315,7 @@ def get_shoulder_periods(sw, criterion, dfenergy_r, high_eue_periods):
 def get_eue_sorted_periods(sw, t, iteration):
     ### Get storage state of charge (SOC) to use in selection of "shoulder" stress periods
     dfenergy = reeds.io.read_pras_results(
-        os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS', f"PRAS_{t}i{iteration}-energy.h5")
+        os.path.join(sw['casedir'], 'handoff', 'PRAS', f"PRAS_{t}i{iteration}-energy.h5")
     )
     timeindex = reeds.timeseries.get_timeindex(sw['resource_adequacy_years'])
     dfenergy.index = timeindex
@@ -236,7 +347,7 @@ def get_eue_sorted_periods(sw, t, iteration):
         ## Example: criterion = 'transgrp_10_EUE_sum'
         (hierarchy_level, ppm, stress_metric, period_agg_method) = criterion.split('_')
 
-        eue_periods = reeds.ra.get_eue_periods(
+        eue_periods = get_eue_periods(
             case=sw.casedir, t=t, iteration=iteration,
             hierarchy_level=hierarchy_level,
             stress_metric=stress_metric,
@@ -364,7 +475,7 @@ def prm_increment_pras(sw, t, iteration, combined_periods_write, failed_regions)
     ## shortfall data
     # read the net shortfall (positive) and net surplus (negative) results
     # by sample from PRAS run (MWh)
-    filepath = os.path.join(sw['casedir'], 'ReEDS_Augur', 'PRAS',
+    filepath = os.path.join(sw['casedir'], 'handoff', 'PRAS',
                             f'PRAS_{sw["t"]}i{iteration}-shortfall_samples.h5')
     net_short = reeds.io.read_pras_results(filepath)
     # get number of samples
@@ -387,7 +498,7 @@ def prm_increment_pras(sw, t, iteration, combined_periods_write, failed_regions)
     ## get load data
     dfload = reeds.io.read_file(
         os.path.join(
-        sw['casedir'],'ReEDS_Augur','augur_data',f'pras_load_{t}.h5'),
+        sw['casedir'],'handoff','reeds_data',f'pras_load_{t}.h5'),
         parse_timestamps=True
     )
 
@@ -536,11 +647,6 @@ def update_prm(sw, t, iteration, failed, combined_periods_write):
 def main(sw, t, iteration=0, logging=True):
     """
     """
-    #%% More imports and settings
-    site.addsitedir(os.path.join(sw['casedir'],'input_processing'))
-    import hourly_writetimeseries
-    newstresspath = f'stress{t}i{iteration+1}'
-
     #%% Write consolidated NEUE so far
     try:
         _neue_simple = get_and_write_neue(sw, write=True)
@@ -573,6 +679,7 @@ def main(sw, t, iteration=0, logging=True):
         return
 
     #%% Write timeseries data for stress periods for the next iteration of ReEDS
+    newstresspath = f'stress{t}i{iteration+1}'
     hourly_writetimeseries.main(
         sw=sw, reeds_path=sw['reeds_path'],
         inputs_case=os.path.join(sw['casedir'], 'inputs_case'),
@@ -604,7 +711,7 @@ def main(sw, t, iteration=0, logging=True):
 
 # if __name__ == '__main__':
 #     #%%###  option to run script directly for debugging
-#     casedir =  "/path/to/ReEDS-2.0/runs/runname"
+#     casedir =  "/path/to/ReEDS/runs/runname"
 #     t = 2030 # previous solve year
 #     iteration = 0
 #     # load switches
diff --git a/reeds/output_calc.py b/reeds/results.py
similarity index 98%
rename from reeds/output_calc.py
rename to reeds/results.py
index a641bfe53..a475cfe55 100644
--- a/reeds/output_calc.py
+++ b/reeds/results.py
@@ -10,7 +10,6 @@
 import os
 import sys
 import pandas as pd
-import numpy as np
 from itertools import product
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))  
@@ -319,10 +318,7 @@ def calc_systemcost(
     scalars = reeds.io.get_scalars(case)  
 
     # Valid regions
-    val_r = pd.read_csv(
-        os.path.join(inputs_case, 'val_r.csv'),
-        header=None,
-    ).squeeze(1).values
+    val_r = reeds.io.read_input(case, 'r').squeeze(1).values
 
     # Rename columns to match the expected format
     systemcost.rename(columns={'t':'year', 'sys_costs':'cost_cat'}, inplace=True)
@@ -531,10 +527,7 @@ def calc_reinforcement_spur_capacity_miles(case):
     scalars = reeds.io.get_scalars(case)  
 
     # Valid regions
-    val_r = pd.read_csv(
-        os.path.join(inputs_case, 'val_r.csv'),
-        header=None,
-    ).squeeze(1).values
+    val_r = reeds.io.read_input(case, 'r').squeeze(1).values
 
     # Get the spur/reinforcement distance for each i/r/rscbin  
     spur_parameters = pd.read_csv(os.path.join(inputs_case, 'spur_parameters.csv'))
@@ -660,7 +653,7 @@ def calc_transmission_capacity(case,levels):
 
     # pull intra and interregional transmission data into dictionaries for processing
     dict_inter = {}
-    level_map = reeds.output_calc.get_level_map(case)
+    level_map = get_level_map(case)
     hierarchy = reeds.io.get_hierarchy(case)
     for level in levels:
         
@@ -679,7 +672,7 @@ def calc_transmission_capacity(case,levels):
             dict_inter[spatial_resolution] = pd.concat({
                 r: (
                     trans_r.loc[
-                        (trans_r[f'inter_transreg'] == 1)
+                        (trans_r['inter_transreg'] == 1)
                     ].groupby(['trtype','t']).MW.sum()
                     .rename('GW')
                     / 1e3
@@ -719,7 +712,7 @@ def calc_transmission_capacity(case,levels):
     return intraregional, interregional
 
 def get_level_map(case):
-    # for all regions available in ReEDS-2.0/inputs/hierarchy.csv, map them to a clean display name
+    # for all regions available in ReEDS/inputs/hierarchy.csv, map them to a clean display name
     level_map = pd.read_csv(
         os.path.join(reeds_path, 'postprocessing', 'plots', 'level_map.csv'), 
         index_col='raw').squeeze(1)
diff --git a/cbc.opt b/reeds/solver/cbc.opt
similarity index 100%
rename from cbc.opt
rename to reeds/solver/cbc.opt
diff --git a/cplex.op2 b/reeds/solver/cplex.op2
similarity index 100%
rename from cplex.op2
rename to reeds/solver/cplex.op2
diff --git a/cplex.opt b/reeds/solver/cplex.opt
similarity index 100%
rename from cplex.opt
rename to reeds/solver/cplex.opt
diff --git a/gurobi.opt b/reeds/solver/gurobi.opt
similarity index 100%
rename from gurobi.opt
rename to reeds/solver/gurobi.opt
diff --git a/reeds/spatial.py b/reeds/spatial.py
index 74f0392c1..cbaa0a9b7 100644
--- a/reeds/spatial.py
+++ b/reeds/spatial.py
@@ -3,6 +3,7 @@
 import pandas as pd
 import geopandas as gpd
 from pathlib import Path
+from typing import Literal
 sys.path.append(str(Path(__file__).parent.parent))
 import reeds
 
@@ -215,3 +216,355 @@ def get_map(resolution='county', source='tiger', crs='ESRI:102008'):
         df = df.set_index(index[resolution]).copy()
 
     return df
+
+
+def apply_uniform_disaggregation(
+    df: pd.DataFrame,
+    region_col: str
+):
+    """
+    Disaggregate a dataframe whose regional scope is the 134 legacy
+    zones (as specified in the dataframe's 'region_col' column) to
+    the county level by copying the zonal values to their constituent counties.
+    """
+    county2zone = reeds.io.get_county2zone(GSw_ZoneSet='z134', as_map=False)
+    county2zone['FIPS'] = 'p' + county2zone.FIPS
+    df = (
+        df
+        .merge(county2zone[['r', 'FIPS']], left_on=region_col, right_on='r')
+        .drop(columns=[region_col, 'r'])
+        .rename(columns={'FIPS': region_col})
+        [df.columns]
+    )
+
+    return df
+
+
+def apply_variable_disaggregation(
+    df: pd.DataFrame,
+    region_col: str,
+    fix_cols: list[str],
+    inputs_case: str,
+    disagg_variable: Literal['hydroexist', 'geosize', 'population', 'state_lpf']
+):
+    """
+    Disaggregate a dataframe whose regional scope is the 134 legacy zones
+    (as specified in the dataframe's 'region_col' column) to the county level
+    by allocating zonal values to their corresponding counties according to
+    each county's share of 'disagg_variable'.
+
+    The county shares of 'disagg_variable' are read from the provided
+    'inputs_case' folder. The 'fix_cols' argument lists columns that
+    should be considered part of the dataframe's index.
+    """
+    # Save the dataframe's original columns
+    # (used later to put the output in the correct format)
+    original_columns = df.columns
+
+    # Get legacy zone-to-county allocation factors for disagg_variable
+    disagg_data = reeds.io.get_disagg_data(
+        os.path.dirname(inputs_case),
+        disagg_variable
+    )
+
+    # Append the allocation factors to the dataframe
+    if disagg_variable == 'hydroexist':
+        df = df.merge(
+            disagg_data,
+            left_on=[region_col, 'i'],
+            right_on=['PCA_REG', 'i']
+        )
+    else:
+        df = df.merge(
+            disagg_data[['PCA_REG', 'FIPS', 'fracdata']],
+            left_on=region_col,
+            right_on='PCA_REG'
+        )
+
+    # Replace legacy zones in region_col with the county FIPS codes
+    df = (
+        df.drop(columns=[region_col, 'PCA_REG'])
+        .rename(columns={'FIPS': region_col})
+    )
+
+    # If the dataframe values are 'wide', set the dataframe index
+    # and then multiply all values by their allocation factor.
+    # Otherwise, multiply the 'value' and allocation factor columns.
+    if 'wide' in fix_cols:
+        index_cols = (
+            [col for col in fix_cols if col in original_columns]
+            + [region_col]
+        )
+        df = df.set_index(index_cols)
+        df = (
+            df.mul(df['fracdata'], axis='index')
+            .reset_index()
+            [original_columns]
+        )
+    else:
+        df = (
+            df.assign(value=lambda x: x['value'] * x['fracdata'])
+            [original_columns]
+        )
+
+    return df
+
+
+def apply_supply_curve_disaggregation(
+    df: pd.DataFrame,
+    region_col: str,
+    fix_cols: list[str],
+    inputs_case: str,
+    disagg_variable: Literal['hydroexist', 'geosize', 'population', 'state_lpf']
+):
+    """
+    Disaggregate a supply curve dataframe whose regional scope is the 134
+    legacy zones (as specified in the dataframe's 'region_col' column) to
+    the county level by allocating zonal capacities to their corresponding
+    counties according to each county's share of 'disagg_variable' and
+    assigning zonal costs to counties uniformly.
+
+    The county shares of 'disagg_variable' are read from the provided
+    'inputs_case' folder. The 'fix_cols' argument lists columns that
+    should be considered part of the dataframe's index.
+    """
+    # Disaggregate zonal capacities to counties according to disagg_variable
+    df_cap = df.loc[df['sc_cat'] == 'cap'].drop(columns='sc_cat')
+    df_cap = apply_variable_disaggregation(
+        df_cap,
+        region_col,
+        fix_cols,
+        inputs_case,
+        disagg_variable
+    )
+
+    # Disaggregate zonal costs to counties uniformly
+    df_cost = df.loc[df['sc_cat'] == 'cost'].drop(columns='sc_cat')
+    df_cost = apply_uniform_disaggregation(df_cost, region_col)
+
+    # Combine capacities and costs and return to original format
+    df = (
+        pd.concat(
+            [df_cap.assign(sc_cat='cap'), df_cost.assign(sc_cat='cost')],
+            ignore_index=True
+        )
+        [df.columns]
+    )
+
+    return df
+
+
+def downscale_from_legacy_zone_to_county(
+    df: pd.DataFrame,
+    region_col: str,
+    fix_cols: list[str],
+    inputs_case: str,
+    disaggfunc: str
+):
+    """
+    Disaggregate a dataframe whose regional scope is the 134 legacy zones
+    (as specified in the dataframe's 'region_col' column) to the county level
+    according to the specified disaggregation function ('disaggfunc').
+
+    If 'disaggfunc' is a variable by which zonal values are allocated to
+    counties (e.g., population), county shares of the variable are read from
+    the provided 'inputs_case' folder. The 'fix_cols' argument lists columns
+    that should be considered part of the dataframe's index.
+    """
+    # If 'region_col' is 'wide' (zones are contained in the column headers
+    # rather than one of the columns), temporarily reformat the dataframe
+    # to 'long' format, specifying zones in a new 'r' column
+    if region_col == 'wide':
+        df = pd.melt(df, id_vars=fix_cols, var_name='r')
+        region_col = 'r'
+        # Indicate that the dataframe should be reformatted
+        # to its original (wide) format after disaggregation
+        reformat_to_wide = True
+    else:
+        reformat_to_wide = False
+
+    # Apply disaggregation according to 'disaggfunc'
+    match disaggfunc:
+        case 'uniform':
+            df = apply_uniform_disaggregation(df, region_col)
+        case 'geosize' | 'hydroexist' | 'population' | 'state_lpf':
+            if 'sc_cat' in df.columns:
+                df = apply_supply_curve_disaggregation(
+                    df,
+                    region_col,
+                    fix_cols,
+                    inputs_case,
+                    disaggfunc
+                )
+            else:
+                df = apply_variable_disaggregation(
+                    df,
+                    region_col,
+                    fix_cols,
+                    inputs_case,
+                    disaggfunc
+                )
+        case 'ignore':
+            pass
+        case _:
+            raise NotImplementedError(
+                f"Disaggfunc '{disaggfunc}' has not been implemented."
+            )
+
+    # If applicable, restore original (wide) format
+    if reformat_to_wide:        
+        df = (
+            pd.pivot_table(df, values='value', index=fix_cols, columns=['r'])
+            .reset_index()
+            .rename_axis('', axis=1)
+        )
+
+    return df
+
+
+def apply_supply_curve_aggregation(
+    df: pd.DataFrame,
+    region_col: str,
+    groupby_cols: list[str],
+    county_r_map: dict[str, str],
+):
+    """
+    Aggregate a supply curve dataframe with county-level regional scope
+    (as specified in the dataframe's 'region_col' column) to the zone level
+    (using zones corresponding to 'county_r_map') by combining capacities
+    via sum and combining costs via capacity-weighted average. The
+    'groupby_cols' argument lists columns that should be grouped before
+    aggregation.
+    """
+    # Save the dataframe's original columns
+    # (used later to put the output in the correct format)
+    original_columns = df.columns
+
+    # Reformat dataframe so that capacities and costs are listed
+    # side-by-side for each group (based on 'groupby_cols')
+    df_cap = (
+        df.loc[df.sc_cat == 'cap']
+        .drop(columns='sc_cat')
+        .set_index(groupby_cols)
+        ['value']
+        .rename('cap')
+    )
+    df_cost = (
+        df.loc[df.sc_cat == 'cost']
+        .drop(columns='sc_cat')
+        .set_index(groupby_cols)
+        ['value']
+        .rename('cost')
+    )
+    df = (
+        pd.concat([df_cap, df_cost], axis=1)
+        .reset_index()
+    )
+
+    # Calculate products of weights (capacities) and costs, which will be
+    # used to calculate capacity-weighted costs. Where capacities are
+    # null or 0, we use 1 MW as the weight.
+    df['cap_weight'] = df['cap'].fillna(1).replace(0, 1)
+    df['cap_weight_times_cost'] = df['cap_weight'] * df['cost']
+
+    # Map counties to zones
+    df[region_col] = df[region_col].map(county_r_map)
+
+    # Calculate capacity/cost/weight totals for each group
+    # and then divide total weight*cost by total weight to derive
+    # capacity-weighted cost for each group.
+    df = (
+        df.groupby(groupby_cols)
+        .sum()
+        .assign(cost=lambda x: x['cap_weight_times_cost'] / x['cap_weight'])
+        .reset_index()
+        .drop(columns=['cap_weight', 'cap_weight_times_cost'])
+    )
+
+    # Restore original format
+    df = pd.melt(
+        df,
+        id_vars=groupby_cols,
+        value_vars=['cap', 'cost'],
+        var_name='sc_cat'
+    )
+    df = df[original_columns]
+
+    return df
+
+
+def upscale_from_county_to_zone(
+    df: pd.DataFrame,
+    region_col: str,
+    fix_cols: list[str],
+    inputs_case: str,
+    aggfunc: str
+):
+    """
+    Aggregate a dataframe with county-level regional scope (as specified in
+    the dataframe's 'region_col' column) to the zone level (using zones
+    corresponding to 'inputs_case') according to the specified aggregation
+    function ('aggfunc'). The 'fix_cols' argument lists columns that should
+    be considered part of the dataframe's index.
+    """
+    # If 'region_col' is 'r_cendiv', counties are stored in the 'r' column
+    # and dataframe values are stored in columns whose headers are cendivs,
+    # so for this function, 'r' can be treated as the region column.
+    if region_col == 'r_cendiv':
+        region_col = 'r'
+    
+    # If 'region_col' is 'wide' (zones are contained in the column headers
+    # rather than one of the columns), temporarily reformat the dataframe
+    # to 'long' format, specifying zones in a new 'r' column
+    if region_col == 'wide':
+        df = pd.melt(df, id_vars=fix_cols, var_name='r')
+        region_col = 'r'
+        # Indicate that the dataframe should be reformatted
+        # to its original (wide) format after aggregation
+        reformat_to_wide = True
+    else:
+        reformat_to_wide = False
+
+    # Identify columns that should be grouped before aggregation
+    groupby_cols = (
+        [col for col in fix_cols if col in df.columns]
+        + [region_col]
+    )
+
+    # Get county-to-zone map
+    county_r_map = reeds.io.get_county2zone(os.path.dirname(inputs_case))
+    county_r_map.index = 'p' + county_r_map.index.str.zfill(5)
+
+    # Apply aggregation according to 'aggfunc'
+    match aggfunc:
+        case 'sc_cat':
+            df = apply_supply_curve_aggregation(
+                df,
+                region_col,
+                groupby_cols,
+                county_r_map
+            )
+        case 'sum' | 'mean':
+            df[region_col] = df[region_col].map(county_r_map)
+            df = (
+                df.groupby(groupby_cols, as_index=False)
+                .agg(aggfunc)
+                [df.columns]
+            )
+        case 'ignore':
+            pass
+        case _:
+            raise NotImplementedError(
+                f"Aggfunc '{aggfunc}' has not been implemented."
+            )
+
+
+    # If applicable, restore original (wide) format
+    if reformat_to_wide:
+        df = (
+            pd.pivot_table(df, values='value', index=fix_cols, columns=['r'])
+            .reset_index()
+            .rename_axis('', axis=1)
+        )
+
+    return df
\ No newline at end of file
diff --git a/reeds/timeseries.py b/reeds/timeseries.py
index 3dee720f7..56de44047 100644
--- a/reeds/timeseries.py
+++ b/reeds/timeseries.py
@@ -63,6 +63,24 @@ def timestamp2h(ts, GSw_HourlyType='day'):
     return out
 
 
+def szn2yearperiod(szn):
+    """
+    szn's are formatted as 'y{20xx}{d or w}{day of year or wek of year}'
+    where a 'wek' is a 5-day period (5*73 = 365)
+    """
+    year, period = szn.split('d') if 'd' in szn else szn.split('w')
+    return int(year.strip('y')), int(period)
+
+
+def szn2period(szn):
+    """
+    szn's are formatted as 'y{20xx}{d or w}{day of year or wek of year}'
+    where a 'wek' is a 5-day period (5*73 = 365)
+    """
+    year, period = szn.split('d') if 'd' in szn else szn.split('w')
+    return int(period)
+
+
 def timeslice_to_timestamp(case, param):
     ### Load the timestamps and other ReEDS settings
     h_dt_szn = pd.read_csv(os.path.join(case, 'inputs_case', 'rep', 'h_dt_szn.csv'))
@@ -105,3 +123,405 @@ def timeslice_to_timestamp(case, param):
         .set_index(hs.index)
     )
     return dfout_h
+
+
+def make_timestamps(sw):
+    ### Get some useful constants
+    hoursperperiod = {'day':24, 'wek':120, 'year':24}
+    periodsperyear = {'day':365, 'wek':73, 'year':365}
+    weather_years = sw.resource_adequacy_years_list
+
+    ### Get map from yperiod, hour, and h_of_period to timestamp
+    timestamps = pd.DataFrame({
+        'year': np.ravel([[y]*8760 for y in weather_years]),
+        'h_of_year': np.ravel([list(range(1,8761)) * len(weather_years)]),
+        'h_of_period': np.ravel(
+            [f'{h+1:>03}' for h in range(hoursperperiod[sw['GSw_HourlyType']])]
+            * periodsperyear[sw['GSw_HourlyType']] * len(weather_years)),
+        'yperiod': np.ravel(
+            [p+1 for p in range(periodsperyear[sw['GSw_HourlyType']])
+             for h in range(hoursperperiod[sw['GSw_HourlyType']])]
+            * len(weather_years)),
+        'h_of_day': np.ravel(
+            [f'{h+1:>03}' for h in range(hoursperperiod['day'])]
+            * periodsperyear['day'] * len(weather_years)),
+        'yday': np.ravel(
+            [p+1 for p in range(periodsperyear['day'])
+             for h in range(hoursperperiod['day'])]
+            * len(weather_years)),
+        'h_of_wek': np.ravel(
+            [f'{h+1:>03}' for h in range(hoursperperiod['wek'])]
+            * periodsperyear['wek'] * len(weather_years)),
+        'ywek': np.ravel(
+            [p+1 for p in range(periodsperyear['wek'])
+             for h in range(hoursperperiod['wek'])]
+            * len(weather_years)),
+    })
+    timestamps['timestamp'] = (
+        'y' + timestamps.year.astype(str)
+        ## d for day and w for wek 
+        + ('w' if sw.GSw_HourlyType == 'wek' else 'd')
+        + timestamps.yperiod.astype(str).map('{:>03}'.format)
+        + 'h' + timestamps.h_of_period
+    )
+    timestamps['period'] = timestamps['timestamp'].map(lambda x: x.split('h')[0])
+    timestamps['day'] = (
+        'y' + timestamps.year.astype(str)
+        + 'd' + timestamps.yday.astype(str).map('{:>03}'.format)
+    )
+    timestamps['wek'] = (
+        'y' + timestamps.year.astype(str)
+        + 'w' + timestamps.ywek.astype(str).map('{:>03}'.format)
+    )
+    timestamps.index = np.ravel([
+        pd.date_range(
+            f'{y}-01-01', f'{y+1}-01-01',
+            freq='H', inclusive='left', tz='Etc/GMT+6',
+        )[:8760]
+        for y in weather_years
+    ])
+
+    return timestamps
+
+
+### Timeseries reduction
+def get_clusters(
+    profiles_fitperiods,
+    GSw_HourlyClusterAlgorithm='hierarchical',
+    GSw_HourlyNumClusters:int=35,
+):
+    """
+    Hierarchical, k-means, or k-medoids clustering.
+    Returns an array of the same length as profiles_fitperiods,
+    with values ranging from 0 to GSw_HourlyNumClusters indicating the assignment
+    of each period to a cluster.
+    """
+    import sklearn.cluster
+    if GSw_HourlyClusterAlgorithm.startswith('hierarchical'):
+        args = GSw_HourlyClusterAlgorithm.split('_')
+        if len(args) > 1:
+            metric = args[1]
+            linkage = args[2]
+        else:
+            metric = 'euclidean'
+            linkage = 'ward'
+        clusters = sklearn.cluster.AgglomerativeClustering(
+            n_clusters=GSw_HourlyNumClusters,
+            metric=metric, linkage=linkage,
+        )
+    elif GSw_HourlyClusterAlgorithm.lower().startswith('kmeans'):
+        clusters = sklearn.cluster.KMeans(
+            n_clusters=GSw_HourlyNumClusters,
+            random_state=0, n_init='auto', max_iter=1000,
+        )
+    elif GSw_HourlyClusterAlgorithm.lower().startswith('kmedoids'):
+        import sklearn_extra.cluster
+        args = GSw_HourlyClusterAlgorithm.split('_')
+        if len(args) > 1:
+            metric = args[1]
+            init = args[2]
+        else:
+            metric = 'euclidean'
+            init = 'heuristic'
+        clusters = sklearn_extra.cluster.KMedoids(
+            n_clusters=GSw_HourlyNumClusters,
+            metric=metric, init=init, method='pam',
+            max_iter=1000, random_state=0,
+        )
+    ### Fit it
+    cluster_assignment = clusters.fit_predict(profiles_fitperiods)
+    return cluster_assignment
+
+
+def round_to_integers(rweights, num_actual_periods, increment=1e-6):
+    ### Convert to integers
+    iweights = rweights.round(0).astype(int)
+    ### Scale all weights little by little until they sum to number of actual days
+    sumweights = iweights.sum()
+    diffweights = sumweights - num_actual_periods
+    _increment = increment * (1 if diffweights < 0 else -1)
+    for i in range(int(10/increment)):
+        iweights = (rweights * (1 + _increment*i)).round(0).astype(int)
+        if iweights.sum() == num_actual_periods:
+            break
+
+    iweights = iweights.replace(0,np.nan).dropna().astype(int)
+    ### Make sure it worked
+    if iweights.sum() != num_actual_periods:
+        raise ValueError(f'Sum of rounded weights = {iweights.sum()} != {num_actual_periods}')
+    return iweights
+
+
+def minimize_abs_error_in_means(basis_periods, target_feature_mean):
+    """
+    Weight days from basis_periods to minimize the sum of absolute error vs
+    target_feature_mean values by feature & region.
+    The method is described at https://doi.org/10.1016/j.energy.2025.135830.
+    """
+    import pulp
+    ### Input processing
+    assert (target_feature_mean.index == basis_periods.columns).all()
+
+    days = basis_periods.index.values
+
+    ### Optimization: minimize sum of absolute errors
+    m = pulp.LpProblem('LinearDaySelection', pulp.LpMinimize)
+    ###### Variables
+    ### day weights
+    WEIGHT = pulp.LpVariable.dicts('WEIGHT', (d for d in days), lowBound=0, cat='Continuous')
+    ### errors
+    ERROR_POS = pulp.LpVariable.dicts(
+        'ERROR_POS', (c for c in basis_periods.columns), lowBound=0, cat='Continuous')
+    ERROR_NEG = pulp.LpVariable.dicts(
+        'ERROR_NEG', (c for c in basis_periods.columns), lowBound=0, cat='Continuous')
+    ###### Constraints
+    ### weights must sum to 1
+    m += pulp.lpSum([WEIGHT[d] for d in days]) == 1
+    ### definition of errors
+    for c in basis_periods.columns:
+        m += (
+            ### Full error for column (given by positive component minus negative component)...
+            ERROR_POS[c] - ERROR_NEG[c]
+            ### ...plus sum of values for weighted representative days...
+            + pulp.lpSum([WEIGHT[d] * basis_periods[c][d] for d in days])
+            ### ...equals the mean for that column
+            == target_feature_mean[c])
+    ###### Objective: minimize the sum of absolute values of errors across all columns
+    m += pulp.lpSum([
+        ERROR_POS[c] + ERROR_NEG[c]
+        for c in basis_periods.columns
+    ])
+
+    ### Solve it
+    m.solve(solver=pulp.PULP_CBC_CMD(msg=True))
+
+    ### Collect weights
+    weights = pd.Series({d:WEIGHT[d].varValue for d in days})
+    return weights
+
+
+def optimize_period_weights(
+    profiles_period_mean,
+    target_feature_mean=None,
+    GSw_HourlyNumClusters=35,
+):
+    """
+    """
+    ### Input processing
+    if target_feature_mean is None:
+        target_feature_mean = profiles_period_mean.mean()
+    else:
+        assert (target_feature_mean.index == profiles_period_mean.mean().index).all()
+        assert target_feature_mean.isnull().sum() == 0
+
+    ### Get weights and scale by number of days
+    weights = minimize_abs_error_in_means(
+        basis_periods=profiles_period_mean,
+        target_feature_mean=target_feature_mean,
+    )
+
+    ### Truncate based on GSw_HourlyNumClusters, scale appropriately, and convert to integers
+    ### Keep the the 'GSw_HourlyNumClusters' highest-weighted days
+    rweights = (weights.sort_values(ascending=False)[:GSw_HourlyNumClusters])
+    ### Scale so that the weights sum to numperiods (have to do if GSw_HourlyNumClusters is small)
+    num_actual_periods = len(profiles_period_mean)
+    rweights *= num_actual_periods / rweights.sum()
+    iweights = round_to_integers(rweights, num_actual_periods)
+
+    return iweights, weights
+
+
+def match_act2rep_milp(profiles_period_mean, iweights):
+    """
+    Assign representative periods to actual periods to minimize sum of errors in
+    feature values by period (MILP optimization)
+    """
+    ### Imports
+    import pulp
+
+    ### Input processing
+    actualdays = profiles_period_mean.index.values
+    repdays = list(iweights.index)
+
+    ### Optimization: minimize sum of absolute errors
+    m = pulp.LpProblem('RepDayAssignment', pulp.LpMinimize)
+    ###### Variables
+    ### Weighting of rep days (r) for each actual day (a).
+    ### Can only use whole days, so it's a binary variable.
+    WEIGHT = pulp.LpVariable.dicts(
+        'WEIGHT', ((a,r) for a in actualdays for r in repdays),
+        lowBound=0, upBound=1, cat=pulp.LpInteger)
+    ### Errors. These are defined for features (c) and for actual days (a).
+    ERROR_POS = pulp.LpVariable.dicts(
+        'ERROR_POS', ((a,c) for a in actualdays for c in profiles_period_mean.columns),
+        lowBound=0, cat='Continuous')
+    ERROR_NEG = pulp.LpVariable.dicts(
+        'ERROR_NEG', ((a,c) for a in actualdays for c in profiles_period_mean.columns),
+        lowBound=0, cat='Continuous')
+    ###### Constraints
+    ### Each actual day can only be assigned to one representative day
+    for a in actualdays:
+        m += pulp.lpSum([WEIGHT[a,r] for r in repdays]) == 1
+    ### Each representative day must be used a number of times equal to its weight
+    for r in repdays:
+        m += pulp.lpSum([WEIGHT[a,r] for a in actualdays]) == iweights[r]
+    ### Define the error variables
+    for a in actualdays:
+        for c in profiles_period_mean.columns:
+            m += (
+                ### Full error for column on actual day (given by positive
+                ### component minus negative component)...
+                ERROR_POS[a,c] - ERROR_NEG[a,c]
+                ### ...plus value for its representative day (since WEIGHT is binary)...
+                + pulp.lpSum([WEIGHT[a,r] * profiles_period_mean[c][r] for r in repdays])
+                ### ...equals the actual value for that column and day
+                == profiles_period_mean[c][a])
+    ###### Objective: minimize the sum of absolute values of errors
+    m += pulp.lpSum([
+        ERROR_POS[a,c] + ERROR_NEG[a,c]
+        for a in actualdays for c in profiles_period_mean.columns
+    ])
+
+    ### Solve it
+    m.solve(solver=pulp.PULP_CBC_CMD(msg=True))
+
+    ### Collect assignments
+    assignments = pd.Series(
+        {(a,r):WEIGHT[a,r].varValue for a in actualdays for r in repdays}).astype(int)
+    assignments.index = assignments.index.rename(['act','rep'])
+    a2r = assignments.replace(0,np.nan).dropna().reset_index(level='rep').rep
+
+    return a2r
+
+
+def _plot_weights(iweights, remaining_weights, i):
+    import cmocean
+    from pathlib import Path
+    from datetime import datetime
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+    today = datetime.now().strftime("%Y%m%d")
+
+    plotweights = remaining_weights.copy()
+    plotweights.index = plotweights.index.map(
+        lambda x: pd.to_datetime(f'y{x[0]}d{x[1]}h0', format='y%Yd%jh%H').strftime('%-m/%-d')
+    )
+    monthday2val = reeds.reedsplots.make_dayofyear_colormap()
+
+    plt.close()
+    f,ax = plt.subplots(figsize=(3,7))
+    ax.barh(
+        plotweights.index, plotweights.values,
+        color=[cmocean.cm.phase(monthday2val[i]) for i in plotweights.index],
+    )
+    ax.plot(
+        [1]*len(plotweights), range(len(plotweights)),
+        marker='|', color='k', lw=0, markersize=9,
+    )
+    ax.set_ylim(-0.5,len(iweights)-0.5)
+    ax.set_xlim(0, iweights.max())
+    ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1))
+    reeds.plots.despine(ax)
+    fpath = Path(f'~/scratch/{today}/weights_{i}.png').expanduser()
+    fpath.parent.mkdir(exist_ok=True, parents=True)
+    plt.savefig(fpath)
+
+
+def _plot_act2repday(a2r, i):
+    from pathlib import Path
+    from datetime import datetime
+    import matplotlib.pyplot as plt
+
+    today = datetime.now().strftime("%Y%m%d")
+    actualday2repday = pd.Series(a2r).map(
+        lambda x: pd.to_datetime(f'y{x[0]}d{x[1]}h0', format='y%Yd%jh%H')
+    )
+    actualday2repday.index = actualday2repday.index.map(
+        lambda x: pd.to_datetime(f'y{x[0]}d{x[1]}h0', format='y%Yd%jh%H')
+    )
+
+    plt.close()
+    f, ax, df = reeds.reedsplots.plot_repdays(actualday2repday=actualday2repday)
+    fpath = Path(f'~/scratch/{today}/a2r_{i}.png').expanduser()
+    plt.savefig(fpath)
+
+
+def match_act2rep_bestfirst(
+    profiles_period_mean,
+    iweights,
+    metric:str='seuclidean',
+    verbose:int=0,
+):
+    """
+    Assign actual periods to representative periods using a simple heuristic algorithm:
+
+    1. Begin with a table of weights for each representative (rep) period
+    2. Calculate the distance in (feature × region)-dimensional space between each actual
+       and rep period.
+    3. While there is still weight remaining in the rep-period weight table:
+        a. Match each rep period to the single remaining actual period that is closest to it,
+           recording its match in the actual-to-rep-period table
+        b. Decrement the remaining weight for each rep period. If any weights reach zero,
+           remove them from future consideration for matches. (So the lowest-weighted rep
+           periods will fill up and drop out of consideration first.)
+        c. Remove the actual periods that were matched from the table of distances
+        d. Return to 3.a. and repeat until there are no remaining weights in the rep-period
+           weight table (and no remaining unmatched actual periods)
+
+    Args:
+        profiles_period_mean (pd.DataFrame): (year, yperiod) x (feature, region)
+        iweights (pd.Series): Integer weights for rep periods; (year, yperiod) index
+        metric (string): Passed to scipy.spatial.distance.cdist()
+        verbose (int): How much extra information to print for debugging.
+            If ≥3, saves plots to your ~/scratch/{today} folder.
+
+    """
+    import scipy.spatial
+    ### Get the distance
+    keep_basis = profiles_period_mean.loc[iweights.index]
+    distance = pd.DataFrame(
+        data=scipy.spatial.distance.cdist(
+            keep_basis,
+            profiles_period_mean,
+            metric=metric,
+        ),
+        index=keep_basis.index,
+        columns=profiles_period_mean.index,
+    )
+
+    a2r = {}
+    num_actual_periods = len(profiles_period_mean)
+    remaining_weights = iweights.copy()
+    remaining_distance = distance.copy()
+    if verbose:
+        print('Original weights:')
+        print(remaining_weights)
+    for i in range(num_actual_periods):
+        if verbose:
+            print(f'Iteration {i}:')
+        if verbose > 2:
+            _plot_weights(iweights, remaining_weights, i)
+        ## Loop over the remaining weights
+        for repday in remaining_weights.index:
+            ## Keep the best match
+            actday = remaining_distance.columns[np.argmin(remaining_distance.loc[repday])]
+            a2r[actday] = repday
+            if verbose > 1:
+                print(f'{actday} → {repday}')
+            ## Decrement the remaining weight
+            remaining_distance.drop(columns=actday, inplace=True)
+            remaining_weights.loc[repday] -= 1
+        if verbose:
+            print(remaining_weights)
+        if verbose > 2:
+            _plot_act2repday(a2r, i)
+        ## If any remaining rep days have zero weight, drop them
+        if (remaining_weights == 0).any():
+            remaining_weights = remaining_weights.loc[remaining_weights > 0]
+        if not len(remaining_weights):
+            break
+
+    out = pd.Series(a2r, name='rep')
+    out.index = out.index.tolist()
+    out.index = out.index.rename('act')
+    return out
diff --git a/runbatch.py b/runreeds.py
similarity index 90%
rename from runbatch.py
rename to runreeds.py
index 2f8af4dc9..e06286228 100644
--- a/runbatch.py
+++ b/runreeds.py
@@ -2,6 +2,7 @@
 ### --- IMPORTS ---
 ### ===========================================================================
 
+import reeds
 import os
 import git
 import queue
@@ -17,7 +18,6 @@
 from datetime import datetime
 import argparse
 from pathlib import Path
-import reeds
 
 # Assert core programs are accessible
 CORE_PROGRAMS = ["gams"]
@@ -293,8 +293,6 @@ def check_compatibility(sw):
 
     if sw['GSw_RegionResolution'] in ['county','mixed']:
         err_switch_configs = []
-        if int(sw['GSw_OffshoreZones']):
-            err_switch_configs.append('GSw_OffshoreZones=1')
         if sw['GSw_LoadAllocationMethod'] == 'state_lpf':
             err_switch_configs.append('GSw_LoadAllocationMethod=state_lpf')
 
@@ -386,6 +384,16 @@ def check_compatibility(sw):
             except ValueError:
                 raise ValueError(err)
 
+    if int(sw['GSw_PRM_UpdateMethod']) == 0 and int(sw['GSw_PRM_CapCredit']) == 1 and int(sw['GSw_PRM_StressIterateMax']) > 0:
+        raise ValueError(
+            "The combination of GSw_PRM_UpdateMethod=0, GSw_PRM_CapCredit=1, "
+            "and GSw_PRM_StressIterateMax>0 is not supported.\n"
+            "To iteratively update the PRM, set GSw_PRM_UpdateMethod to an integer between 1-3:"
+            "\n1: static update set by GSw_PRM_UpdateFraction; "
+            "\n2: dynamic update informed by PRAS; "
+            "\n3: dynamic update but only after all new stress periods have been added"
+        )
+
     for bir in sw['GSw_PVB_BIR'].split('_'):
         if not (float(bir) >= 0):
             raise ValueError("Fix GSw_PVB_BIR")
@@ -486,6 +494,15 @@ def check_compatibility(sw):
         err = f"GSw_LoadProfiles={sw['GSw_LoadProfiles']} but the specified file does not exist"
         raise FileNotFoundError(err)
 
+    if sw['GSw_LoadProfiles'].startswith('EER2023'):
+        allowed_ra_years = range(2007,2014)
+        if not all([y in allowed_ra_years for y in resource_adequacy_years]):
+            err = (
+                f"GSw_LoadProfiles={sw['GSw_LoadProfiles']} only supports resource_adequacy_years="
+                f"{list(allowed_ra_years)} but {resource_adequacy_years} was supplied"
+            )
+            raise ValueError(err)
+
     ### Dependent model availability
     if (
         ((int(sw['pras']) == 2) or int(sw['GSw_PRM_StressIterateMax']))
@@ -531,62 +548,14 @@ def check_compatibility(sw):
             )
             raise ModuleNotFoundError(err)
 
-
-def solvestring_sequential(
-        batch_case, caseSwitches,
-        cur_year, next_year, prev_year, restartfile,
-        toLogGamsString=' logOption=4 logFile=gamslog.txt appendLog=1 ',
-        hpc=0, iteration=0, stress_year=None,
-        temporal_inputs='rep',
-    ):
-    """
-    Typical inputs:
-    * restartfile: batch_case if first solve year else {batch_case}_{prev_year}
-    * caseSwitches: loaded from {batch_case}/inputs_case/switches.csv
-    """
-    savefile = f"{batch_case}_{cur_year}i{iteration}"
-    _stress_year = f"{cur_year}i0" if stress_year is None else stress_year
-    out = (
-        "gams d_solveoneyear.gms"
-        + (" license=gamslice.txt" if hpc else '')
-        + " o=" + os.path.join("lstfiles", f"{savefile}.lst")
-        + " r=" + os.path.join("g00files", restartfile)
-        + " gdxcompress=1"
-        + " xs=" + os.path.join("g00files", savefile)
-        + toLogGamsString
-        + f" --case={batch_case}"
-        + f" --cur_year={cur_year}"
-        + f" --next_year={next_year}"
-        + f" --prev_year={prev_year}"
-        + f" --stress_year={_stress_year}"
-        + f" --temporal_inputs={temporal_inputs}"
-        + ''.join([f" --{s}={caseSwitches[s]}" for s in [
-            'GSw_Canada',
-            'GSw_ClimateHydro',
-            'GSw_ClimateWater',
-            'GSw_gopt',
-            'GSw_HourlyChunkLengthRep',
-            'GSw_HourlyChunkLengthStress',
-            'GSw_HourlyType',
-            'GSw_HourlyWrapLevel',
-            'GSw_MGA_CostDelta',
-            'GSw_MGA_Direction',
-            'GSw_PVB_Dur',
-            'GSw_SkipAugurYear',
-            'GSw_StateCO2ImportLevel',
-            'GSw_StartMarkets',
-            'GSw_ValStr',
-            'solver',
-            'debug',
-            'startyear',
-            'diagnose',
-            'diagnose_year'
-        ]])
-        + '\n'
+# function to stop the model after input processing
+def stop_after_input_processing(OPATH, reeds_path, casedir, caseSwitches):
+    comment('Exit after input_processing', OPATH)
+    OPATH.writelines(
+        f"python {os.path.join(reeds_path, 'postprocessing', 'cleanup_files.py')} "
+        f"{casedir} --force --quiet --level {caseSwitches['cleanup_level']}\n"
     )
-
-    return out
-
+    OPATH.writelines('\n' + ('exit' if LINUXORMAC else 'goto:eof') + '\n\n')
 
 def setup_sequential_year(
         cur_year, prev_year, next_year,
@@ -601,12 +570,12 @@ def setup_sequential_year(
     if (cur_year >= min(solveyears)):
         ## solve one year
         OPATH.writelines(
-            solvestring_sequential(
+            reeds.inputs.solvestring_sequential(
                 batch_case, caseSwitches,
                 cur_year, next_year, prev_year, restartfile,
                 toLogGamsString, hpc,
             ))
-        OPATH.writelines(writescripterrorcheck(f"d_solveoneyear.gms_{cur_year}"))
+        OPATH.writelines(writescripterrorcheck(f"3_solve_oneyear.gms_{cur_year}"))
         OPATH.writelines(f'python {logger} --year={cur_year}\n')
 
         if int(caseSwitches['GSw_ValStr']):
@@ -615,17 +584,17 @@ def setup_sequential_year(
         ## check to see if the restart file exists
         OPATH.writelines(writeerrorcheck(os.path.join("g00files", savefile + ".g*")))
 
-    ## Run Augur if it not the final solve year and if not skipping Augur
+    ## Run resource adequacy (RA) calculations if it not the final solve year and if not skipping RA
     if ((
         (cur_year < max(solveyears))
-        and (next_year > int(caseSwitches['GSw_SkipAugurYear']))
+        and (next_year > int(caseSwitches['GSw_SkipRAyear']))
     ) or (cur_year == max(solveyears))):
         OPATH.writelines(
-            f"\npython Augur.py {next_year} {cur_year} {casedir}\n")
-        ## Check to make sure Augur ran successfully; quit otherwise
+            f"\npython {Path('reeds', 'resource_adequacy', 'ra_calcs.py')} {next_year} {cur_year} {casedir}\n")
+        ## Check to make sure RA ran successfully; quit otherwise
         OPATH.writelines(
             writeerrorcheck(os.path.join(
-                "ReEDS_Augur", "augur_data", f"ReEDS_Augur_{cur_year}.gdx")))
+                "handoff", "reeds_data", f"ccdata_{cur_year}.gdx")))
 
     ## delete the previous restart file unless we're keeping them
     if (cur_year > min(solveyears)) and (not int(caseSwitches['keep_g00_files'])):
@@ -658,14 +627,14 @@ def setup_sequential(
         big_comment(f'Year: {cur_year}', OPATH)
 
         ### Write the tax credit phaseout call
-        OPATH.writelines(f"python tc_phaseout.py {cur_year} {casedir}\n\n")
+        OPATH.writelines(f"python {Path('reeds','core','solve','1_tc_phaseout.py')} {cur_year} {casedir}\n\n")
 
-        ### Write the GAMS LP and Augur calls
+        ### Write the GAMS LP and resource adequacy calls
         if int(caseSwitches['GSw_PRM_StressIterateMax']):
             OPATH.writelines(
-                f"python d_solve_iterate.py {casedir} {cur_year}\n"
+                f"python {Path('reeds','core','solve','solve.py')} {casedir} {cur_year}\n"
             )
-            OPATH.writelines(writescripterrorcheck(f"d_solve_iterate.py_{cur_year}"))
+            OPATH.writelines(writescripterrorcheck(f"solve.py_{cur_year}"))
         else:
             setup_sequential_year(
                 cur_year, prev_year, next_year,
@@ -678,24 +647,22 @@ def setup_sequential(
             ### multipliers aren't created until the first solve year is run)
             if cur_year == min(solveyears):
                 OPATH.writelines(
-                    f"\npython {os.path.join(casedir, 'input_processing', 'check_inputs.py')} "
+                    f"\npython {os.path.join(casedir, 'reeds', 'input_processing', 'check_inputs.py')} "
                     f"{casedir}\n"
                 )
                 OPATH.writelines(writescripterrorcheck('check_inputs.py')+'\n')
 
-        ### Run Augur plots in background
+        ### Run resource adequacy plots in background
         OPATH.writelines(
-            f"python {os.path.join('ReEDS_Augur','diagnostic_plots.py')} "
+            f"python {Path('reeds','resource_adequacy','diagnostic_plots.py')} "
             f"--reeds_path={reeds_path} --casedir={casedir} --t={cur_year} &\n")
 
 
 def setup_intertemporal(
         caseSwitches, startiter, niter, ccworkers,
-        solveyears, endyear, batch_case, toLogGamsString, yearset_augur, OPATH,
+        solveyears, endyear, batch_case, toLogGamsString, modeledyears, OPATH,
     ):
-    ### beginning year is passed to augurbatch
-    begyear = min(solveyears)
-    ### first save file from d_solveprep is just the case name
+    ### first save file from e_solveprep is just the case name
     savefile = batch_case
     ### if this is the first iteration
     if startiter == 0:
@@ -724,7 +691,8 @@ def setup_intertemporal(
             OPATH.writelines(writeerrorcheck(os.path.join("g00files", restartfile + ".g*")))
 
         OPATH.writelines(
-            "gams d_solveallyears.gms o="+os.path.join("lstfiles",batch_case + "_" + str(i) + ".lst")
+            f"gams {Path('reeds','core','3_solve_allyears.gms')} o="
+            +os.path.join("lstfiles",batch_case + "_" + str(i) + ".lst")
             +" r="+os.path.join("g00files", restartfile)
             + " gdxcompress=1 xs="+os.path.join("g00files", savefile) + toLogGamsString
             + " --niter=" + str(i) + " --case=" + batch_case  + ' \n')
@@ -732,29 +700,19 @@ def setup_intertemporal(
         ## check to see if the save file exists
         OPATH.writelines(writeerrorcheck(os.path.join("g00files",savefile + ".g*")))
 
-        ## start threads for cc/curt
-        ## no need to run cc curt scripts for final iteration
+        ## Run resource adequacy calculations (no need to run for final iteration)
         if i < niter-1:
-            ## batch out calls to augurbatch
-            OPATH.writelines(
-                "python augurbatch.py " + batch_case + " " + str(ccworkers) + " "
-                + yearset_augur + " " + savefile + " " + str(begyear) + " "
-                + str(endyear) + " " + caseSwitches['distpvscen'] + " "
-                + str(caseSwitches['calc_csp_cc']) + " "
-                + str(caseSwitches['timetype']) + " "
-                + str(caseSwitches['GSw_WaterMain']) + " " + str(i) + " "
-                + str(caseSwitches['marg_vre_mw']) + " "
-                + str(caseSwitches['marg_stor_mw']) + " "
-                + str(caseSwitches['marg_evmc_mw']) + " "
-                + '\n')
+            ## TODO: Run the RA calculations via ra_calcs.py for each solve year
+            ## (needs to be reimplemented)
+
             ## merge all the resulting gdx files
             ## the output file will be for the next iteration
             nextiter = i+1
             gdxmergedfile = os.path.join(
-                "ReEDS_Augur","augur_data","ReEDS_Augur_merged_" + str(nextiter))
+                'handoff', 'reeds_data', f'ccdata_merged_{nextiter}')
             OPATH.writelines(
-                "gdxmerge "+os.path.join("ReEDS_Augur","augur_data","ReEDS_Augur*")
-                + " output=" + gdxmergedfile  + ' \n')
+                'gdxmerge ' + os.path.join('handoff', 'reeds_data', 'ccdata*')
+                + f' output={gdxmergedfile} \n')
             ## check to make sure previous calls were successful
             OPATH.writelines(writeerrorcheck(gdxmergedfile+".gdx"))
 
@@ -767,7 +725,7 @@ def setup_intertemporal(
 
 def setup_window(
         caseSwitches, startiter, niter, ccworkers, reeds_path,
-        batch_case, toLogGamsString, yearset_augur, OPATH,
+        batch_case, toLogGamsString, modeledyears, OPATH,
     ):
     ### load the windows
     win_in = list(csv.reader(open(
@@ -780,12 +738,6 @@ def setup_window(
 
     ### for windows indicated in the csv file
     for win in win_in[1:]:
-
-        ## beginning year is the first column (start)
-        begyear = win[1]
-        ## end year is the second column (end)
-        endyear = win[2]
-        ## for the number of iterations we have...
         for i in range(startiter,niter):
             big_comment(f'Window: {win}', OPATH)
             comment(f'Iteration: {i}', OPATH)
@@ -796,32 +748,25 @@ def setup_window(
             OPATH.writelines(writeerrorcheck(os.path.join("g00files", restartfile + ".g*")))
             ## solve via the window solve file
             OPATH.writelines(
-                "gams d_solvewindow.gms o=" + os.path.join("lstfiles", batch_case + "_" + str(i) + ".lst")
+                f"gams {Path('reeds','core','3_solvewindow.gms')} o="
+                + os.path.join("lstfiles", batch_case + "_" + str(i) + ".lst")
                 +" r=" + os.path.join("g00files", restartfile)
                 + " gdxcompress=1 xs=g00files\\"+savefile + toLogGamsString + " --niter=" + str(i)
                 + " --maxiter=" + str(niter-1) + " --case=" + batch_case + " --window=" + win[0] + ' \n')
             ## start threads for cc/curt
             OPATH.writelines(writeerrorcheck(os.path.join("g00files",savefile + ".g*")))
-            OPATH.writelines(
-                "python augurbatch.py " + batch_case + " " + str(ccworkers) + " "
-                + yearset_augur + " " + savefile + " " + str(begyear) + " "
-                + str(endyear) + " " + caseSwitches['distpvscen'] + " "
-                + str(caseSwitches['calc_csp_cc']) + " "
-                + str(caseSwitches['timetype']) + " "
-                + str(caseSwitches['GSw_WaterMain']) + " " + str(i) + " "
-                + str(caseSwitches['marg_vre_mw']) + " "
-                + str(caseSwitches['marg_stor_mw']) + " "
-                + str(caseSwitches['marg_evmc_mw']) + " "
-                + '\n')
+            ## TODO: Run the RA calculations via ra_calcs.py for each solve year
+            ## in window (needs to be reimplemented)
+
             ## merge all the resulting r2_in gdx files
             ## the output file will be for the next iteration
             nextiter = i+1
             ## create names for then merge the curt and cc gdx files
             gdxmergedfile = os.path.join(
-                "ReEDS_Augur","augur_data","ReEDS_Augur_merged_" + str(nextiter))
+                'handoff', 'reeds_data', f'ccdata_merged_{nextiter}')
             OPATH.writelines(
-                "gdxmerge " + os.path.join("ReEDS_Augur","augur_data","ReEDS_Augur*")
-                + " output=" + gdxmergedfile  + ' \n')
+                'gdxmerge ' + os.path.join('handoff', 'reeds_data', 'ccdata*')
+                + f' output={gdxmergedfile} \n')
             ## check to make sure previous calls were successful
             OPATH.writelines(writeerrorcheck(gdxmergedfile+".gdx"))
             restartfile = savefile
@@ -988,6 +933,9 @@ def setupEnvironment(
                 shutil.rmtree(outpath)
         else:
             keep = [i for (i,c) in enumerate(outpaths) if c not in existing_outpaths]
+            if len(keep) == 0:
+                print('All output directories already exist and were not overwritten. Exiting without starting runs.')
+                quit()
             caseList = [caseList[i] for i in keep]
             casenames = [casenames[i] for i in keep]
             caseSwitches = [caseSwitches[i] for i in keep]
@@ -1001,6 +949,12 @@ def setupEnvironment(
             if skip.lower() not in ['y','yes']:
                 raise IsADirectoryError('\n'+'\n'.join(existing_outpaths))
 
+    # Exit early if no runnable cases remain after filtering
+    all_case_names = list(df_cases.columns)
+    if len(caseList) == 0:
+        print(f"All {len(all_case_names)} scenario(s) in {cases_filename} have ignore=1. Exiting without starting runs.")
+        quit()
+
     #%% User warnings
     if (df_cases.loc['cleanup_level'].astype(int) > 0).any() and not skip_checks:
         print(
@@ -1030,7 +984,9 @@ def setupEnvironment(
     elif simult_runs > 0:
         WORKERS = simult_runs
     else:
-        WORKERS = int(input('Number of simultaneous runs [integer]: '))
+        WORKERS = int(input('Number of simultaneous runs [positive integer]: '))
+        if WORKERS <= 0:
+            raise ValueError(f'Provided {WORKERS} but must be a positive integer')
 
     if 'int' in df_cases.loc['timetype'].tolist() or 'win' in df_cases.loc['timetype'].tolist():
         ccworkers = int(input('Number of simultaneous CC/Curt runs [integer]: '))
@@ -1168,6 +1124,7 @@ def write_batch_script(
 
     #%% Set up case-specific directory structure
     os.makedirs(inputs_case, exist_ok=True)
+    os.makedirs(os.path.join(casedir, 'autocode'), exist_ok=True)
     os.makedirs(os.path.join(casedir, 'g00files'), exist_ok=True)
     os.makedirs(os.path.join(casedir, 'lstfiles'), exist_ok=True)
     os.makedirs(os.path.join(casedir, 'outputs', 'figures'), exist_ok=True)
@@ -1258,7 +1215,7 @@ def write_batch_script(
     shutil.copy2(os.path.join(reeds_path, cases_filename), casedir)
 
     ### Switches with values derived from other switches
-    ## Get hpc setting (used in Augur)
+    ## Determine whether we're running on the HPC
     caseSwitches['hpc'] = int(hpc)
     ## Get numclass from the max value in ivt
     caseSwitches['numclass'] = get_ivt_numclass(
@@ -1285,20 +1242,19 @@ def write_batch_script(
 
     solveyears = [y for y in solveyears if (y <= endyear and y >= startyear)]
 
-    yearset_augur = os.path.join('inputs_case','modeledyears.csv')
+    modeledyears = os.path.join('inputs_case','modeledyears.csv')
     toLogGamsString = ' logOption=4 logFile=gamslog.txt appendLog=1 '
 
-    ## Copy code folders
-    for dirname in ['reeds', 'ReEDS_Augur', 'input_processing', 'reeds2pras']:
-        shutil.copytree(
-            os.path.join(reeds_path, dirname),
-            os.path.join(casedir, dirname),
-            ignore=shutil.ignore_patterns('test'),
-        )
+    ## Copy code folder
+    shutil.copytree(
+        os.path.join(reeds_path, 'reeds'),
+        os.path.join(casedir, 'reeds'),
+        ignore=shutil.ignore_patterns('test'),
+    )
 
-    #make the augur_data folder
-    os.makedirs(os.path.join(casedir,'ReEDS_Augur','augur_data'), exist_ok=True)
-    os.makedirs(os.path.join(casedir,'ReEDS_Augur','PRAS'), exist_ok=True)
+    #make the reeds_data folder
+    os.makedirs(os.path.join(casedir,'handoff','reeds_data'), exist_ok=True)
+    os.makedirs(os.path.join(casedir,'handoff','PRAS'), exist_ok=True)
 
     ###### Replace files according to 'file_replacements' in cases. Ignore quotes in input text.
     # << is used to separate the file that is to be replaced from the file that is used
@@ -1346,7 +1302,6 @@ def write_batch_script(
         for s in [
             'copy_files',
             'mcs_sampler',
-            'aggregate_regions',
             'hydcf',
             'h2_storage',
             'calc_financial_inputs',
@@ -1363,35 +1318,38 @@ def write_batch_script(
             'transmission',
             'outage_rates',
             'hourly_repperiods',
+            'h5_to_gdx',
         ]:
             OPATH.writelines(f"echo {'-'*12+'-'*len(s)}\n")
             OPATH.writelines(f"echo 'starting {s}.py'\n")
             OPATH.writelines(f"echo {'-'*12+'-'*len(s)}\n")
             OPATH.writelines(
-                f"python {os.path.join(casedir,'input_processing',s)}.py {reeds_path} {inputs_case}\n")
+                f"python {os.path.join(casedir,'reeds','input_processing',s)}.py {reeds_path} {inputs_case}\n")
             OPATH.writelines(writescripterrorcheck(s)+'\n')
 
+            # option to stop input processing after Monte Carlo sampler
+            if s == 'mcs_sampler' and int(caseSwitches['input_processing_only']) == 2:
+                stop_after_input_processing(OPATH, reeds_path, casedir, caseSwitches)
+
         OPATH.writelines(
             f"python {os.path.join(reeds_path, 'postprocessing', 'cleanup_files.py')} "
             f"{casedir} --force --quiet\n"
         )
 
-        if int(caseSwitches['input_processing_only']):
-            OPATH.writelines(
-                f"python {os.path.join(reeds_path, 'postprocessing', 'cleanup_files.py')} "
-                f"{casedir} --force --quiet --level {caseSwitches['cleanup_level']}\n"
-            )
-            OPATH.writelines('\n' + ('exit' if LINUXORMAC else 'goto:eof') + '\n\n')
+        if int(caseSwitches['input_processing_only']) == 1:
+            stop_after_input_processing(OPATH, reeds_path, casedir, caseSwitches)
 
         big_comment('Compile model', OPATH)
 
         OPATH.writelines(
-            "\ngams createmodel.gms gdxcompress=1 xs="+os.path.join("g00files",batch_case)
+            f"\ngams {Path('reeds','core','setup','a_createmodel.gms')} "
+            + "gdxcompress=1 xs="+os.path.join("g00files",batch_case)
             + (' license=gamslice.txt' if hpc else '')
             + " o="+os.path.join("lstfiles","1_Inputs.lst") + options + " " + toLogGamsString + '\n')
         OPATH.writelines(f'python {logger}\n')
         restartfile = batch_case
         OPATH.writelines(writeerrorcheck(os.path.join('g00files', restartfile + '.g*')))
+        OPATH.writelines(writescripterrorcheck(s)+'\n')
 
         ################################
         #    -- CORE MODEL SETUP --    #
@@ -1404,12 +1362,12 @@ def write_batch_script(
         elif caseSwitches['timetype'] == 'int':
             setup_intertemporal(
                 caseSwitches, startiter, niter, ccworkers,
-                solveyears, endyear, batch_case, toLogGamsString, yearset_augur, OPATH,
+                solveyears, endyear, batch_case, toLogGamsString, modeledyears, OPATH,
             )
         elif caseSwitches['timetype'] == 'win':
             setup_window(
                 caseSwitches, startiter, niter, ccworkers, reeds_path,
-                batch_case, toLogGamsString, yearset_augur, OPATH,
+                batch_case, toLogGamsString, modeledyears, OPATH,
             )
 
         #################################
@@ -1426,15 +1384,18 @@ def write_batch_script(
                 if LINUXORMAC else
                 f'set "r={os.path.join("g00files", f"{batch_case}_{max(solveyears)}i0")}"\n'
             )
-        ### Otherwise, run for the last iteration (lexicographically sorted)
+        ### Otherwise, run for the last iteration (selected numerically)
         else:
+            fpath = os.path.join(casedir, "reeds", "core", "terminus", "get_last_iter.py")
             OPATH.writelines(
-                f"for r in g00files/{batch_case}_*.g00; do true; done\n"
+                f'r=$(python {fpath} {batch_case} {max(solveyears)})\n'
                 if LINUXORMAC else
-                f'for %%i in (g00files\{batch_case}_*.g00) do (set "r=%%i")\n'
+                f'for /f "delims=" %%i in '
+                f'(\'python {fpath} {batch_case} {max(solveyears)}\')'
+                f' do set "r=%%i"\n'
             )
         OPATH.writelines(
-            "gams e_report.gms"
+            f"gams {Path('reeds','core','terminus','report.gms')}"
             + f" o={os.path.join('lstfiles',f'report_{batch_case}.lst')}"
             + (' license=gamslice.txt' if hpc else '')
             + (' r=$r' if LINUXORMAC else ' r=!r!')
@@ -1443,11 +1404,11 @@ def write_batch_script(
             + f"--fname={batch_case}"
             + f" --GSw_calc_powfrac={caseSwitches['GSw_calc_powfrac']} \n"
         )
-        OPATH.writelines(writescripterrorcheck("e_report.gms"))
+        OPATH.writelines(writescripterrorcheck("report.gms"))
         if not LINUXORMAC:
             OPATH.writelines("endlocal\n")
         OPATH.writelines(f'python {logger}\n')
-        OPATH.writelines(f'python e_report_dump.py {casedir} -c\n\n')
+        OPATH.writelines(f"python {Path('reeds','core','terminus','report_dump.py')} {casedir} -c\n\n")
         if int(caseSwitches['diagnose']):
              OPATH.writelines(
                 "python"
@@ -1471,7 +1432,7 @@ def write_batch_script(
 
         ### Make script to unload all data to .gdx file
         command = (
-            'gams dump_alldata.gms'
+            f"gams {Path('reeds','core','terminus','dump_alldata.gms')}"
             + ' o='+os.path.join('lstfiles','dump_alldata_{}_{}.lst'.format(BatchName,case))
         )
         command_write = (
@@ -1562,7 +1523,9 @@ def write_batch_script(
 
         ### Run dispatch mode if desired
         if int(caseSwitches['pcm']):
-            OPATH.writelines(f'\npython run_pcm.py {casedir} -b\n\n')
+            OPATH.writelines(
+                f"\npython {Path('reeds','postprocessing','run_pcm.py')} {casedir} -b\n\n"
+            )
 
 
 def submit_slurm_parallel_jobs(
@@ -1587,7 +1550,7 @@ def submit_slurm_parallel_jobs(
         stop_case_index = min(start_case_index + cases_per_node, num_cases)
         casenames_print = casenames[start_case_index:stop_case_index]
         run_script_fpath = os.path.join(batch_folder, f"run_{'-'.join(casenames_print)}.sh")
-        shutil.copy("srun_template.sh", run_script_fpath)
+        shutil.copy(Path(reeds_path,'reeds','hpc','srun_template.sh'), run_script_fpath)
         job_name = f"{BatchName}_({','.join(casenames_print)})"
 
         writelines = []
@@ -1657,7 +1620,7 @@ def write_case_submission_script(
     """
     # Create a copy of the SLURM template
     slurm_script_path = os.path.join(casedir, batch_case + ".sh")
-    shutil.copy("srun_template.sh", slurm_script_path)
+    shutil.copy(Path(reeds.io.reeds_path,'reeds','hpc','srun_template.sh'), slurm_script_path)
 
     # If using debug node, comment out time and replace with short time
     if debugnode:
diff --git a/tests/objective_function_params.yaml b/tests/objective_function_params.yaml
index 1764036c7..8ffddb180 100644
--- a/tests/objective_function_params.yaml
+++ b/tests/objective_function_params.yaml
@@ -1,6 +1,6 @@
 ### Notes
 # This file holds parameters used in the objective function. These parameters
-# are checked using input_processing/check_inputs.py, which will identify missing
+# are checked using reeds/input_processing/check_inputs.py, which will identify missing
 # values in the parameters if there are any.
 
 # Instructions:
@@ -29,12 +29,12 @@
 
 # Note that in many cases the specific configuration here does not match the GAMS
 # code. For example, the check for cost_cap excludes hydro and psh, even though
-# those are not explicitly excluded in c_supplyobjective.gms. When values are
+# those are not explicitly excluded in d_objective.gms. When values are
 # known to be zero they must be excluded otherwise they will be flagged as missing
 # (GAMS does not hold zero values, so values explicitly set to zero will appear
 # the same as missing values).
 
-# The ordering of parameters here should follow the ordering in c_supplyobjective.gms.
+# The ordering of parameters here should follow the ordering in d_objective.gms.
 # Most general-purpose parameters used in the objective function should be added to
 # this file. Parameters that are primarily user-defined or that define their own scope
 # without the use of separate index sets are not tested here. Examples of such skipped
diff --git a/tests/test_outputs.py b/tests/test_outputs.py
index cdd56086f..f2da27f2f 100644
--- a/tests/test_outputs.py
+++ b/tests/test_outputs.py
@@ -6,30 +6,30 @@
 
 def test_output_files(casepath):
     outputs_folder = os.path.join(casepath, 'outputs')
-    e_report_params_path = os.path.join(casepath, 'e_report_params.csv')
+    report_params_path = os.path.join(casepath, 'reeds', 'core', 'terminus', 'report_params.csv')
     lastyear = reeds.io.get_years(casepath)[-1]
 
-    # each parameter in e_report_params.csv should be associated with an output csv file
-    e_report_params = pd.read_csv(e_report_params_path, comment='#')
-    e_report_params['fpath'] = e_report_params.param.map(lambda x: x.split('(')[0])
+    # each parameter in report_params.csv should be associated with an output csv file
+    report_params = pd.read_csv(report_params_path, comment='#')
+    report_params['fpath'] = report_params.param.map(lambda x: x.split('(')[0])
     # rename outputs as specified by output_rename column
-    rename = e_report_params.loc[
-        ~e_report_params.output_rename.isnull()
+    rename = report_params.loc[
+        ~report_params.output_rename.isnull()
     ].set_index('fpath').output_rename.to_dict()
-    e_report_params['fpath'] = e_report_params['fpath'].replace(rename) + '.csv'
+    report_params['fpath'] = report_params['fpath'].replace(rename) + '.csv'
 
     # Include additional files in outputs folder that should be generated for each run
     expected_plots = [
         # Static plots (single_case_plots.py)
         os.path.join('figures', i) + '.png' for i in [
             'bars_retirements_additions',
-            'inputs_repdays',
+            'inputs_cfmap-upv',
             f'plot_dispatch-weightwidth-{lastyear}',
         ]
     ]
 
     expected_files = (
-        e_report_params.fpath.tolist()
+        report_params.fpath.tolist()
         + [
             # Standard bokeh outputs (postprocessing/bokehpivot)
             os.path.join('reeds-report', 'report.html'),