diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aba330a..69054f9 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -162,7 +162,7 @@ jobs: - name: Publish distribution to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: - repository-url: https://pypi.org/legacy/ verbose: true skip-existing: true - + # fix error 404 with default https://pypi.org/legacy/ + repository-url: https://upload.pypi.org/legacy/ diff --git a/.gitignore b/.gitignore index fa02029..13610a9 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ __pycache__/ # C extensions *.so +*.mod *.pyd *.dll @@ -72,6 +73,7 @@ instance/ # Sphinx documentation docs/_build/ +generated # PyBuilder .pybuilder/ diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index d7c81ff..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,139 +0,0 @@ -# Gitlab CI config - Courtesy of T. Janvier (BRGM) -default: # linux image on linux runnner - image: nexus-docker.brgm.fr/brgm/miniconda3:latest - tags: - - brgm - -.windows: # windows image on windows runnner - image: nexus-docker.brgm.fr/brgm/miniconda3:windows - tags: - - windows-2022-docker - - -stages: - - build - - test - - deploy - - -.version_tag: - only: # see https://peps.python.org/pep-0440/ - variables: - - $CI_COMMIT_TAG =~ /^(?P\D+)?(?P(?P\d+(?:\.\d+)*)(?P
(?:a|b|rc)\d+?)?((?P\.post\d+))?(?P\.dev\d+)?(?P\+\S+)?)$/
-  except:
-    - branches
-
-
-# ----- Build ----- #
-
-.build:
-  stage: build
-  extends: .version_tag
-  when: manual
-  script:
-    # build multiple versions using cibuildwheel
-    # - python -m cibuildwheel --output-dir ./dist 
-    # build standalone environment for testing (current package + all dependencies)
-    # - python -m pip wheel -w ./dist/wheelhouse .[test]
-    - python -m pip wheel -w ./dist/ .
-  artifacts:
-    paths:
-    - dist/
-    expose_as: wheel
-    expire_in: 12 hours
-    
-
-build-linux:
-  when: manual
-  extends: .build
-  before_script:
-    - echo "gridmarthe ci/cd pipeline"
-    - python --version
-    - mamba install gfortran
-    - gfortran --version
-    - python -m pip install --upgrade pip setuptools wheel
-    - python -m pip install cibuildwheel build
-    # uncomment next line if you need internal dependencies
-    # - python -m pip config set global.index-url ${REGISTRY_NEXUS}/pypi-all/simple
-
-build-windows:
-  when: manual
-  extends: 
-    - .windows
-    - .build
-  before_script:
-    - echo "gridmarthe ci/cd pipeline"
-    - python --version
-    - gfortran --version
-    - pip install --upgrade pip setuptools wheel
-    - pip install cibuildwheel build
-
-
-# ----- Test config ----- #
-
-.test:
-  stage: test
-  extends: .version_tag
-  when: manual
-  script:
-    - python -c 'import gridmarthe'
-  # allow_failure: true
-
-
-test-linux:
-  needs: ["build-linux"]
-  extends: .test
-  before_script:
-    - echo "gridmarthe ci/cd pipeline"
-    - python --version
-    - mamba install gfortran
-    - gfortran --version
-    - python -m pip install --no-index --no-deps dist/*none*.whl # no-index: avoid search pypi index ; no-deps, wheels already build
-    - python -m pip install --no-index --no-deps dist/*linux*.whl
-
-
-test-windows:
-  needs: ["build-windows"]
-  extends: 
-    - .windows
-    - .test
-  before_script:
-    - echo "gridmarthe ci/cd pipeline"
-    - python --version
-    - gfortran --version
-    - pip install --no-index --no-deps dist/*none*.whl
-    - pip install --no-index --no-deps dist/*win*.whl
-
-
-
-# ----- Publish ----- #
-
-.deploy:
-  extends: .version_tag
-  stage: deploy
-  except:
-    - branches
-
-
-deploy-nexus_snapshot:
-  needs: ["build-linux", "build-windows"]
-  extends: .deploy
-  before_script:
-    - echo ${NEXUS_URL}
-    - echo ${REGISTRY_NEXUS_USER}
-    - echo ${REGISTRY_NEXUS}
-    - pip install twine
-  script:
-    - twine upload -u ${REGISTRY_NEXUS_USER} -p ${REGISTRY_NEXUS_PASSWORD} --repository-url ${REGISTRY_NEXUS}/pypi-snapshots/ dist/*.whl
-  when: manual
-
-
-deploy-pip:
-  needs: ["build-linux", "build-windows"]
-  extends: .deploy
-  before_script:
-    - pip install twine
-  script:
-    - twine upload --skip-existing --verbose dist/*.whl
-  when: manual
-
diff --git a/COPYING b/COPYING
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/COPYING
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. 
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
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+  The licenses for most software and other practical works are designed
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+
+  Nothing in this License shall be construed as excluding or limiting
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+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+the Program, the only way you could satisfy both those terms and this
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+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
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+
+  14. Revised Versions of this License.
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+  The Free Software Foundation may publish revised and/or new versions of
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+be similar in spirit to the present version, but may differ in detail to
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+  Each version is given a distinguishing version number.  If the
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+  If the Program specifies that a proxy can decide which future
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
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+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
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+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
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+
+    
+    Copyright (C)   
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+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
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+
+      Copyright (C)   
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+.
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 23f589c..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,619 +0,0 @@
-GNU General Public License
-
-Version 3, 29 June 2007
-Copyright © 2007 Free Software Foundation, Inc. 
-
-Everyone is permitted to copy and distribute verbatim copies of this license
-document, but changing it is not allowed.
-
-Preamble
---------
-
-The GNU General Public License is a free, copyleft license for software and other
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-The licenses for most software and other practical works are designed to take away
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-Terms and Conditions
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-primarily for and in connection with specific products or compilations that contain
-the covered work, unless you entered into that arrangement, or that patent license
-was granted, prior to 28 March 2007.
-
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-license or other defenses to infringement that may otherwise be available to you
-under applicable patent law.
-
-12. No Surrender of Others' Freedom
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If conditions are imposed on you (whether by court order, agreement or otherwise)
-that contradict the conditions of this License, they do not excuse you from the
-conditions of this License. If you cannot convey a covered work so as to satisfy
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-agree to terms that obligate you to collect a royalty for further conveying from
-those to whom you convey the Program, the only way you could satisfy both those terms
-and this License would be to refrain entirely from conveying the Program.
-
-13. Use with the GNU Affero General Public License
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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-13, concerning interaction through a network will apply to the combination as such.
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-14. Revised Versions of this License
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-to the present version, but may differ in detail to address new problems or concerns.
-
-Each version is given a distinguishing version number. If the Program specifies that
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-If the Program specifies that a proxy can decide which future versions of the GNU
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-15. Disclaimer of Warranty
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
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-EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
-PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER
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-QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
-DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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-16. Limitation of Liability
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
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-IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY
-COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS
-PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
-INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
-PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE
-OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE
-WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGES.
-
-17. Interpretation of Sections 15 and 16
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If the disclaimer of warranty and limitation of liability provided above cannot be
-given local legal effect according to their terms, reviewing courts shall apply local
-law that most closely approximates an absolute waiver of all civil liability in
-connection with the Program, unless a warranty or assumption of liability accompanies
-a copy of the Program in return for a fee.
-
-END OF TERMS AND CONDITIONS
-
-How to Apply These Terms to Your New Programs
----------------------------------------------
-
-If you develop a new program, and you want it to be of the greatest possible use to
-the public, the best way to achieve this is to make it free software which everyone
-can redistribute and change under these terms.
-
-To do so, attach the following notices to the program. It is safest to attach them
-to the start of each source file to most effectively state the exclusion of warranty;
-and each file should have at least the “copyright” line and a pointer to
-where the full notice is found.
-
-.. pull-quote::
-
-   
-   Copyright (C)   
-
-   This program is free software: you can redistribute it and/or modify
-   it under the terms of the GNU General Public License as published by
-   the Free Software Foundation, either version 3 of the License, or
-   (at your option) any later version.
-
-   This program is distributed in the hope that it will be useful,
-   but WITHOUT ANY WARRANTY; without even the implied warranty of
-   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-   GNU General Public License for more details.
-
-   You should have received a copy of the GNU General Public License
-   along with this program.  If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
-If the program does terminal interaction, make it output a short notice like this
-when it starts in an interactive mode:
-
-.. pull-quote::
-
-     Copyright (C)   
-   This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
-   This is free software, and you are welcome to redistribute it
-   under certain conditions; type 'show c' for details.
-
-The hypothetical commands `show w` and `show c` should show the appropriate parts of
-the General Public License. Of course, your program's commands might be different;
-for a GUI interface, you would use an “about box”.
-
-You should also get your employer (if you work as a programmer) or school, if any, to
-sign a “copyright disclaimer” for the program, if necessary. For more
-information on this, and how to apply and follow the GNU GPL, see
-.
-
-The GNU General Public License does not permit incorporating your program into
-proprietary programs. If your program is a subroutine library, you may consider it
-more useful to permit linking proprietary applications with the library. If this is
-what you want to do, use the GNU Lesser General Public License instead of this
-License. But first, please read
-.
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 16d2e99..31fb39f 100644
--- a/Makefile
+++ b/Makefile
@@ -31,47 +31,45 @@ PIPFLAGS ?= -e
 #Flags are primarily useful for initial check that code compiles correctly
 F2PYOPT =--backend=meson --lower
 
-FFLAGS = 
-# FFLAGS +=-fdefault-real-8 # no
-# already O3 in f2py ? does not seem to force it.
-FFLAGS +=-O2 
+FFLAGS = -fdefault-real-8
+# already O3 in f2py, change it here
+FFLAGS += -O2 
 # Position-Independent Code, si shared library, utile
 # FFLAGS +=-fPIC -shared
-FFLAGS +=-ffree-line-length-none
+FFLAGS += -ffree-line-length-none
 # FFLAGS +=-fallow-argument-mismatch # only gfortran > 12.0
-FFLAGS +=-std=legacy
-# FFLAGS +=-static
+# FFLAGS += -std=legacy  # not really necessary
+# FFLAGS +=-static # only windows // and not for dev
 
 COMPILE = CC=$(CC) FC=$(FC) FFLAGS="$(FFLAGS)" $(F2PY) -c $(F90FILES) -m lecsem $(F2PYOPT)
-# COMPILE = CC=$(CC) FC=$(FC) $(F2PY) -c $(F90FILES) -m lecsem $(F2PYOPT)
 
 
-# ---- Rules ---- #
+# ------------- Rules ------------- #
 
-.PHONY: all docs clean requirements
-all: clean install bakup_pyproj
+.PHONY: all docs clean requirements wheel
+# all: clean install bakup_pyproj
+all: clean editable
+# only compile with f2py for develop purpose
+lib: lecsem.so
 
 
 doc:
 	cd docs; $(MAKE) html
 
-setuptools: pyproject.toml
-	cp pyproject.toml pyproject.bak ; sed -i '16s/# //' pyproject.toml ; sed -i '17s/^/# /' pyproject.toml
-    # change to setuptools for editable version
-
-bakup_pyproj: pyproject.toml
-	mv pyproject.bak pyproject.toml
-    # format pyproject.{toml,bak} non accepté par make ?
+# deprecated
+# setuptools: pyproject.toml
+# 	cp pyproject.toml pyproject.bak ; sed -i '16s/# //' pyproject.toml ; sed -i '17s/^/# /' pyproject.toml
+#     # change to setuptools for editable version
+# 
+# bakup_pyproj: pyproject.toml
+# 	mv pyproject.bak pyproject.toml
+#     # format pyproject.{toml,bak} non accepté par make ?
 
 requirements:
 	$(PY) -m pip install charset_normalizer numpy meson meson-python
 
-# only compile with f2py for develop purpose
-lib: lecsem.so
-
-# install: requirements lecsem.pyf lecsem.so
-install: requirements lecsem.so setuptools
-	$(PY) -m pip install $(PIPFLAGS) .
+#install: requirements lecsem.so setuptools bakup_pyproj
+#	$(PY) -m pip install $(PIPFLAGS) . -vvv
 
 lecsem.pyf:
 	cd $(F90SRCDIR); echo "******** Generating signature ********"; \
@@ -85,6 +83,19 @@ lecsem.so:
     # because meson/f2py does not allow path separator in files.
 	# FC="$(FC)" FFLAGS="$(FFLAGS)" python -m numpy.f2py -c lecsem.pyf lecsem.f90 edsemigl.f90 scan_grid.f90 -m lecsem --backend=meson --lower
 
+# meson editable for test
+editable: requirements
+	$(PY) -m pip install --no-build-isolation --config-settings=editable-verbose=true --editable . -vvv
+    # meson version of editable - for future replacement of setuptools
+
+wheel:
+	$(PY) -m pip install build
+	$(PY) -m build -w
+
+sdist:
+	$(PY) -m pip install build
+	$(PY) -m build -s
+
 clean:
 	cd $(F90SRCDIR); \
 	rm -f *.so *.o *.mod *.c *pywrappers* *.dll *.pyd ; \
diff --git a/README.md b/README.md
index 7c21c1f..9844c58 100644
--- a/README.md
+++ b/README.md
@@ -1,25 +1,29 @@
-# Gridmarthe
+[![PyPI - Version](https://img.shields.io/pypi/v/gridmarthe)](https://pypi.org/project/gridmarthe/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/gridmarthe.svg)](https://anaconda.org/conda-forge/gridmarthe)
+[![GitLab License](https://img.shields.io/gitlab/license/brgm%2Fhydrogeological-modelling%2Fmarthe-tools%2Fgridmarthe?color=blue)](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/blob/dev/LICENSE?ref_type=heads)
 
-Python project for (fast) Marthe grid operations.
-MARTHE is a hydrogeological modelling code developped at BRGM, French Geological Survey [[1]](#1),
-and is available at https://www.brgm.fr/en/software/marthe-modelling-software-groundwater-flows
+# Gridmarthe
 
+`gridmarthe` is a Python project for (fast) Marthe grid operations.
 
-**THIS IS A BETA VERSION, under development.**
+MARTHE is a hydrogeological modelling code developped at BRGM, French Geological
+Survey [(Thiery, 2020)](#references), and is available at
+https://www.brgm.fr/en/software/marthe-modelling-software-groundwater-flows
 
 
 ## gridmarthe in a nutshell
 
 `gridmarthe` allow users to read/write efficiently Marthe Grids (v9, v8, constant_data, etc.)
-for any MARTHE variable.
+with python, for any MARTHE variable.
 
 With the `gridmarthe` API, data are stored in a `xarray` dataset, and can be manage with
-`xarray` (or `numpy`) functions as with "utils" functions provided by `gridmarthe`.
+`xarray` functions (or `numpy`). Specific treatments/functions are also provided by `gridmarthe`.
 
-The package also install a command line tool, `ncmart` to convert Marthe Grid to netCDF format.
-Help can be found with `ncmart -h`.
+The package also install different command line tools: `ncmart` to convert Marthe Grid to netCDF format,
+`martshp` to convert in shapefile/geopackage, `cleanmgrid` to fix marthe grid format.
+
+Full documentation and tutorials can be founded at https://gridmarthe.readthedocs.io
 
-Full documentation can be founded at https://gridmarthe.readthedocs.io
 
 ## Installation
 
@@ -29,13 +33,14 @@ Full documentation can be founded at https://gridmarthe.readthedocs.io
 On pip, `gridmarthe` is available for GNU/Linux, macOS and Windows for python >=3.10.
 Users can install it with:
 
-```
+```bash
 pip install gridmarthe
 ```
 
 For GNU/Linux and MacOS, the package needs gfotran/gcc to run.
 
 Linux, example with debian/ubuntu:
+
 ```bash
 sudo apt install gcc gfortran
 ```
@@ -47,6 +52,16 @@ MacOS:
 brew install gcc gfortran
 ```
 
+### From conda-forge
+
+`gridmarthe` is also available in the `conda-forge` channel. Conda users can install it with:
+
+```bash
+conda install gridmarthe
+# or, with mamba
+mamba install gridmarthe
+```
+
 
 ### From sources
 
@@ -112,18 +127,17 @@ Simple examples can be found in the
 
 ## License
 
-This software is open-source and released under the GNU General Public License (v3+) [GNU/GPL-V3 Licensed](LICENSE).
+This program is free software and released under the terms of the
+[GNU General Public License (version 3 or later)](LICENSE).
 
 
 ## Authors and acknowledgment
-J.P. Vergnes and A. Manlay
+
+A. Manlay and J.P. Vergnes, (c) BRGM
 
 
 ## References
 
-[1] 
 Thiery, D. (2020). Guidelines for MARTHE v7.8 computer code for
 hydro-systems modelling (English version) (Report BRGM/RP-69660-FR; p. 246 p.)
  
-
-
diff --git a/add_copyleft.sh b/add_copyleft.sh
new file mode 100644
index 0000000..13672c3
--- /dev/null
+++ b/add_copyleft.sh
@@ -0,0 +1,43 @@
+#!/bin/bash
+
+# get all possible args in var
+FILES=("$@")  # all args in an array
+
+# check if any
+if [ "$#" -eq 0 ]; then
+  echo "Usage: $0 "
+  exit 1
+fi
+
+# loop over files in args
+for filename in "${FILES[@]}";do
+    # adapt header with original filename
+    header="! SPDX-License-Identifier: GPL-3.0-or-later
+! Copyright 2025, BRGM
+! 
+! This file is part of gridmarthe.
+! 
+! Gridmarthe is free software: you can redistribute it and/or modify it under the
+! terms of the GNU General Public License as published by the Free Software
+! Foundation, either version 3 of the License, or (at your option) any later
+! version.
+! 
+! Gridmarthe is distributed in the hope that it will be useful, but WITHOUT ANY
+! WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+! PARTICULAR PURPOSE. See the GNU General Public License for more details.
+! 
+! You should have received a copy of the GNU General Public License along with
+! Gridmarthe. If not, see .
+!
+!
+! (from MARTHE, file : ${filename}, convert utf-8 )
+!
+! MARTHE, Copyright (c) 1990-2025 BRGM
+!
+"
+    # echo $filename
+    # echo $header
+    echo -e "$header$(cat $filename)" > "$filename.tmp" && mv "$filename.tmp" "$filename"
+done
+
+exit 0
diff --git a/changelog.md b/changelog.md
new file mode 100644
index 0000000..adc960d
--- /dev/null
+++ b/changelog.md
@@ -0,0 +1,46 @@
+
+
+v0.1.3
+------
+
+Released on 2025-09-02
+
+* add geometry functions (get_mask_array, compute_geometry)
+* add reprojection functions (still experimental)
+* add `martshp` script to easily convert a marthe grid file into a shp/gpkg file in command line
+* code refactoring (lecsem module and global organization)
+* add automatic tests
+* minor fixes in plot method
+* change: rename function to write raster: `to_raster()` instead of `write_raster_from_da()` 
+* change: zone index is reset when the `drop_nan` option is used in `load_grid_marthe()`. A new
+  variable 'zone_all' store the old index for the `reset_geometry()` method
+
+
+v0.1.2
+------
+
+Released on 2025-01-09
+
+* Change build to meson
+* fix scripts 'ncmart' and 'cleanmgrid' paths
+* update write method
+
+
+v0.1.1
+------
+
+Released on 2024-11-03.
+
+* first release as a stand alone package
+
+
+v0.0.1
+------
+
+Unreleased - 2021-2024
+
+* Use fortran sources from Marthe with f2py to read grids, single layer, homogeneous grids (1st version from JP Vergnes)
+* Extend to multilayer, nested grid (A Manlay)
+* Python wrappers for easy read operations (AM)
+* Add write method in Fortran sources, scan var and wrappers (AM)
+* Add command line scripts, and some derived treatments (surface layer, rescale, etc.)
diff --git a/changelog.rst b/changelog.rst
deleted file mode 100644
index 2e81b82..0000000
--- a/changelog.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-v0.1.2
-------
-
-Released on 2025-01-09
-
-* Change build to meson
-* fix scripts 'ncmart' and 'cleanmgrid' paths
-* update write method
-
-v0.1.1
-------
-
-Released on 2024-11-03.
-
-* first release
diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css
new file mode 100644
index 0000000..a3a122a
--- /dev/null
+++ b/docs/source/_static/custom.css
@@ -0,0 +1,87 @@
+/* Main index page overview cards */
+.sd-card-img-top {
+  width: 33% !important;
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  margin-top: 10px;
+}
+
+/* customise nbsphinx ----------------------------------------------- */
+div.nbinput.container div.prompt pre,
+div.nbinput.container div.prompt pre > code {
+  color: var(--pst-color-primary);
+}
+
+div.nboutput.container div.prompt pre,
+div.nboutput.container div.prompt pre > code {
+  color: var(--pst-color-secondary);
+}
+
+div.nbinput.container div.input_area {
+    border: 1px solid var(--pst-color-border);
+}
+
+div.rendered_html table {
+    color: var(--pst-color-text-base);
+}
+
+div.rendered_html thead {
+    border-bottom: 1px solid var(--pst-color-border);
+}
+
+div.rendered_html tbody tr:nth-child(odd) {
+    background: var(--pst-color-surface);
+}
+
+div.rendered_html tbody tr:hover {
+    background: var(--pst-color-accent-bg);
+}
+
+html[data-theme="dark"] .bd-content .nboutput .output_area.rendered_html {
+    background-color: transparent;
+}
+
+/* customise footer ------------------------------------------------- */
+.bd-footer .footer-items__end, .bd-footer .footer-items__start {
+    gap: 0;
+}
+
+.navbar-corporate img {
+    margin-bottom: 0;
+    height: 77px;
+}
+
+.navbar-corporate img {
+    margin-bottom: 0;
+    height: 77px;
+}
+a.navbar-corporate, a.navbar-corporate:hover {
+    border-bottom: hidden;
+}
+
+/* customise header ------------------------------------------------- */
+/* Change font size in header */
+.navbar-brand {
+    font-size: 20px;
+}
+
+/* Change elements size (eg switcher) */
+.navbar-nav .nav-item {
+    font-size: 14px;
+}
+
+/* Change link size in navbar */
+.navbar-nav .nav-link {
+    font-size: 16px;
+}
+
+/* Force navbar to use full width */
+.navbar {
+    width: 100%;
+}
+
+/* reduce margin */
+.navbar-nav {
+    margin: 0;
+}
\ No newline at end of file
diff --git a/docs/source/_static/switcher.json b/docs/source/_static/switcher.json
new file mode 100644
index 0000000..8908852
--- /dev/null
+++ b/docs/source/_static/switcher.json
@@ -0,0 +1,12 @@
+[
+    {
+        "name": "dev",
+        "version": "latest",
+        "url": "https://gridmarthe.readthedocs.io/en/dev/"
+    },
+    {
+        "name": "0.1.3 (stable)",
+        "version": "stable",
+        "url": "https://gridmarthe.readthedocs.io/en/stable/"
+    }
+]
diff --git a/docs/source/_templates/autosummary/module.rst b/docs/source/_templates/autosummary/module.rst
new file mode 100644
index 0000000..18460ed
--- /dev/null
+++ b/docs/source/_templates/autosummary/module.rst
@@ -0,0 +1,6 @@
+{{ fullname | escape | underline }}
+
+.. automodule:: {{ fullname }}
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/_templates/brgm-logo-en.html b/docs/source/_templates/brgm-logo-en.html
new file mode 100644
index 0000000..556e039
--- /dev/null
+++ b/docs/source/_templates/brgm-logo-en.html
@@ -0,0 +1,4 @@
+
+    Marianne+BRGM logo
+    Marianne+BRGM logo
+
diff --git a/docs/source/api/_autosummary/gridmarthe.load_marthe_grid.rst b/docs/source/api/_autosummary/gridmarthe.load_marthe_grid.rst
deleted file mode 100644
index 2ef3385..0000000
--- a/docs/source/api/_autosummary/gridmarthe.load_marthe_grid.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-gridmarthe.load\_marthe\_grid
-=============================
-
-.. currentmodule:: gridmarthe
-
-.. autofunction:: load_marthe_grid
\ No newline at end of file
diff --git a/docs/source/api/_autosummary/gridmarthe.operasem.rst b/docs/source/api/_autosummary/gridmarthe.operasem.rst
deleted file mode 100644
index cef1799..0000000
--- a/docs/source/api/_autosummary/gridmarthe.operasem.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-gridmarthe.operasem
-===================
-
-.. automodule:: gridmarthe.operasem
-
-   
-   .. rubric:: Functions
-
-   .. autosummary::
-   
-      coarse_nested_grid
-      get_mask
-      get_min_layer
-      get_new_coords
-      interp_grid
-      rescale
-      search_zone
-      subset_with_coords
-      to_geodataframe
-      write_raster_from_da
-   
\ No newline at end of file
diff --git a/docs/source/api/_autosummary/gridmarthe.plot.rst b/docs/source/api/_autosummary/gridmarthe.plot.rst
deleted file mode 100644
index 2644e4e..0000000
--- a/docs/source/api/_autosummary/gridmarthe.plot.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-gridmarthe.plot
-===============
-
-.. automodule:: gridmarthe.plot
-
-   
-   .. rubric:: Functions
-
-   .. autosummary::
-   
-      plot_mesh_time_serie
-      plot_nested_grid
-      plot_outcrop
-   
\ No newline at end of file
diff --git a/docs/source/api/_autosummary/gridmarthe.write_marthe_grid.rst b/docs/source/api/_autosummary/gridmarthe.write_marthe_grid.rst
deleted file mode 100644
index 65ed37c..0000000
--- a/docs/source/api/_autosummary/gridmarthe.write_marthe_grid.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-gridmarthe.write\_marthe\_grid
-==============================
-
-.. currentmodule:: gridmarthe
-
-.. autofunction:: write_marthe_grid
\ No newline at end of file
diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst
index c97005e..516421c 100644
--- a/docs/source/api/index.rst
+++ b/docs/source/api/index.rst
@@ -1,44 +1,16 @@
+=============
 API Reference
 =============
 
-.. toctree::
-    :maxdepth: 3
-
-
-Summary
--------
-
-.. autosummary::
-    :toctree: _autosummary
-
-    gridmarthe.load_marthe_grid
-    gridmarthe.write_marthe_grid
-    gridmarthe.plot
-    gridmarthe.operasem
-
-
-
-Full Guide
-----------
-
-IO Operations
-~~~~~~~~~~~~~
-
-.. automodule:: gridmarthe.gridmarthe
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Visualisation
-~~~~~~~~~~~~~
-
-.. automodule:: gridmarthe.plot
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-
+This section contains the Documentation of the Application Programming
+Interface (API) of gridmarthe. The information in this section is automatically
+created from the documentation strings in original Python code. In the
+left-hand menu you will find the different categories of the API documentation.
 
 
+.. toctree::
+    :maxdepth: 2
+    
+    io_operations
+    operasem
+    plotting
diff --git a/docs/source/api/io_operations.rst b/docs/source/api/io_operations.rst
new file mode 100644
index 0000000..b148108
--- /dev/null
+++ b/docs/source/api/io_operations.rst
@@ -0,0 +1,12 @@
+.. currentmodule:: gridmarthe
+.. default-role:: obj
+
+IO operations
+-------------
+
+.. autosummary::
+    :toctree: ./generated
+    
+    load_marthe_grid
+    
+    write_marthe_grid
diff --git a/docs/source/api/operasem.rst b/docs/source/api/operasem.rst
new file mode 100644
index 0000000..1c7bebc
--- /dev/null
+++ b/docs/source/api/operasem.rst
@@ -0,0 +1,13 @@
+.. currentmodule:: gridmarthe
+.. default-role:: obj
+
+Grid operations
+---------------
+
+.. autosummary::
+    :toctree: ./generated
+    
+    reset_geometry
+    operasem.geometry
+    operasem.gis
+
diff --git a/docs/source/api/plotting.rst b/docs/source/api/plotting.rst
new file mode 100644
index 0000000..bbc1141
--- /dev/null
+++ b/docs/source/api/plotting.rst
@@ -0,0 +1,13 @@
+.. currentmodule:: gridmarthe
+.. default-role:: obj
+
+Plotting
+--------
+
+.. autosummary::
+    :toctree: ./generated
+    
+    
+    plot_nested_grid
+    
+    plot_outcrop
diff --git a/docs/source/conf.py b/docs/source/conf.py
index b17d7fe..cb17421 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,5 +1,30 @@
-# Configuration file for the Sphinx documentation builder.
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2025  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
 #
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+# Configuration file for the Sphinx documentation builder.
+# Disclaimer: this doc configuration is heavily inspired by rameau's documentation,
+# and pandas, pastas, scikit-learn. Huge thanks are due to all their contributors.
+
 # For the full list of built-in configuration values, see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
@@ -7,18 +32,25 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 import os, sys
-# sys.path.insert(0, '../../src')
+
+sys.path.insert(0, os.path.abspath("."))
+sys.path.insert(0, os.path.abspath("sphinxext"))
+
+from sphinxext.git_link import make_linkcode_resolve
 from gridmarthe import __version__
 
 project = 'gridmarthe'
-copyright = '2024, Adrien Manlay, Jean-Pierre Vergnes - BRGM'
-author = 'Jean-Pierre Vergnes, Adrien Manlay'
+authors = 'Adrien Manlay, Jean-Pierre Vergnes'
+# copyright = '2024,  BRGM.\nAuthors: {}'.format(authors)
+copyright = '2024-2025,  BRGM'
 
 release = __version__
+language = 'en'
 
 html_short_title = project
 html_title = project
 
+git_repo_url = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/"
 html_baseurl = 'https://gridmarthe.readthedocs.io/'
 
 # -- General configuration ---------------------------------------------------
@@ -33,7 +65,8 @@
     'sphinx.ext.autosummary',       # Create neat summary tables, by importing code. autoapi just parse the code
     'sphinx.ext.mathjax',
     'sphinx.ext.doctest',
-    'sphinx.ext.viewcode',
+    # 'sphinx.ext.viewcode',        # view source code in doc
+    'sphinx.ext.linkcode',          # link source code (file:Line in git repo)
     'sphinx.ext.napoleon',          # allow different style of docstrings
     'sphinx.ext.intersphinx',
     'sphinx_design',                # grid layout
@@ -55,36 +88,54 @@
 
 # html_theme = 'furo'
 html_theme = 'pydata_sphinx_theme'
-# html_static_path = ['_static']
-# html_css_files = ['custom.css']
-
-# adapted from gardenia/ramo doc:
+html_static_path = ['_static']
+html_css_files = ['custom.css']
+
+# version switcher
+json_url = html_baseurl + language + '/latest/_static/switcher.json'
+version_match = os.environ.get("READTHEDOCS_VERSION")
+
+# If READTHEDOCS_VERSION doesn't exist, we're not on RTD
+# If it is an integer, we're in a PR build and the version isn't correct.
+# If it's "latest" → change to "dev" (that's what we want the switcher to call it)
+if not version_match or version_match.isdigit() or version_match in ["latest", "dev"]:
+    # For local development, infer the version to match from the package.
+    version_match = "dev"
+    json_url = "_static/switcher.json"
+    # -------------------------------- #
+    # *** TMP for dev/faster build *** #
+    # comment before release/CI
+    # nbsphinx_execute = 'never'
+    # -------------------------------- #
+elif version_match == "stable":
+    version_match = f"v{release}"
+
+# adapted from gardenia/rameau doc:
 html_permalinks_icon = ''
 html_theme_options = {
-    # "logo": {
-    #     "text": 'aquida'
-    # },
     "icon_links": [
         {
             "name": "GitLab",
-            "url": f"https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe.git",
+            "url" : "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe.git",
             "icon": "fa-brands fa-gitlab",
             "type": "fontawesome",
         }
     ],
-    "show_prev_next": False,
-    "show_toc_level": 1,
-    "show_nav_level": 1,
     "navbar_align": "content",
     "navbar_start": ["navbar-logo"],
-    "navbar_end": ["version-switcher","theme-switcher", "navbar-icon-links"],
+    "navbar_end": ["version-switcher", "theme-switcher", "navbar-icon-links"],
     "footer_start": ["copyright", "sphinx-version", "last-updated"],
-    # "footer_end": ["corporate-logo"],
+    "footer_end": ["brgm-logo-{}".format(language)],
+    "show_toc_level": 1,
+    "show_nav_level": 1,
     "header_links_before_dropdown": 6,
-    "switcher": {
-        "json_url": f"{html_baseurl}/en/latest/_static/switcher.json",
-        "version_match": release
+    "show_prev_next": False,
+     "switcher": {
+        "json_url": json_url,
+        "version_match": version_match,
     },
+    "show_version_warning_banner": True,
+    "announcement": None,
 }
 
 html_last_updated_fmt = '%b %d, %Y'
@@ -97,6 +148,7 @@
 # https://www.sphinx-doc.org/en/master/usage/extensions/autosummary.html
 
 autosummary_generate = True
+autosummary_template = 'autosummary/module.rst'  # add template to always show doc in module
 
 
 # ----------------------------------------------------------------------
@@ -105,7 +157,7 @@
 # https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
 
 autodoc_member_order = 'bysource'
-#autodoc_default_options = {"members":True}#, "show-inheritance":True}
+#autodoc_default_options = {"members":True}#, "show-inheritance":True}  # could be enought instead of tpl ?
 autodoc_typehints = 'none' # signature, description, none, both
 autoclass_content = 'class'
 
@@ -125,6 +177,19 @@
 bibtex_bibfiles = ['refs.bib']
 bibtex_reference_style = 'author_year'
 
+# ----------------------------------------------------------------------
+# Options for *linkcode* extension - from scikit-learn doc
+# ----------------------------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/extensions/linkcode.html#module-sphinx.ext.linkcode
+# CREDITS: https://github.com/scikit-learn/scikit-learn/blob/c5497b7f7eacfaff061cf68e09bcd48aa93d4d6b/doc/sphinxext/github_link.py
+linkcode_resolve = make_linkcode_resolve(
+    "gridmarthe",
+    (
+        git_repo_url +
+        "-/blob/{revision}/"
+        "src/{package}/{path}#L{lineno}"
+    ),
+)
 
 # ----------------------------------------------------------------------
 # Options for *sphinx.ext.intersphinx* extension
diff --git a/docs/source/getting_started/install.rst b/docs/source/getting_started/install.rst
index 141c637..c489ee8 100644
--- a/docs/source/getting_started/install.rst
+++ b/docs/source/getting_started/install.rst
@@ -4,7 +4,8 @@ Installation
 From pip
 ~~~~~~~~
 
-`gridmarthe` is available on PyPi and can be installed with:
+``gridmarthe`` is available on `PyPi `_
+and can be installed with:
 
 .. code-block:: bash
     
@@ -14,7 +15,21 @@ From pip
 From conda-forge (with conda or mamba)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Not yet available.
+``gridmarthe`` is also available in the `conda-forge `_ channel.
+Conda users can install it with:
+
+.. code-block:: bash
+    
+    conda install gridmarthe
+    # or, with mamba
+    mamba install gridmarthe
+
+It is also possible to search for available versions:
+
+.. code-block:: bash
+
+    mamba search gridmarthe --channel conda-forge
+
 
 
 From the source
@@ -28,15 +43,16 @@ Get the sources:
     cd gridmarthe
 
 Gridmarthe uses `Fortran` sources for efficient I/O operations on Marthe grid files.
-These sources need to be compiled in order to use the `lecsem` module in `gridmarthe`.
-The F90 sources are compiled with `f2py` (so make sure to have a compiler [`gfortran` 
-for example] and `make` installed on your system/python environment).
+These sources need to be compiled in order to use the `lecsem` module in ``gridmarthe``.
+The F90 sources are compiled with ``f2py`` (so make sure to have a compiler [``gfortran`` 
+for example] and ``make`` installed on your system/python environment).
 
 
 With pip
 ^^^^^^^^
 
-Then, for **developper mode** (ie *with editable flag*) just use the Makefile provided to do the compilation process and python installation.
+Then, for **developper mode** (ie *with editable flag*) just use the Makefile provided to do
+the compilation process and python installation.
 
 .. code-block:: bash
     
@@ -61,20 +77,16 @@ Alternatively, you can use a conda environment. For example with miniforge/mamba
     mamba activate gm
     pip install --no-deps .
 
-For the editable/developper mode, one can add the `-e` flag in pip command, or use `conda-build`:
+For the editable/developper mode, one can add the `-e` flag in pip command, or use ``conda-build``:
 
 .. code-block:: bash
 
     mamba env create -n gm -f environment.yml
     mamba activate gm
     mamba install conda-build
-    ## Compile lecsem lib
-    ## manual compilation
-    # cd src/gridmarthe/lecsem
-    # f2py -c lecsem.f90 edsemigl.f90 scan_grid.f90 -m lecsem --backend=meson --lower
-    # cd ../../../
-    ## or just use the makefile
+    # Compile lecsem lib
     make lib
+    # add develop path
     conda develop src/
 
 
diff --git a/docs/source/getting_started/nutshell.rst b/docs/source/getting_started/nutshell.rst
index 9b7cb45..68bc997 100644
--- a/docs/source/getting_started/nutshell.rst
+++ b/docs/source/getting_started/nutshell.rst
@@ -4,12 +4,14 @@ Gridmarthe in a nutshell
 .. warning::
     This section is under construction !
 
-``gridmarthe`` is a python library which allows users to read/write efficiently Marthe Grids (v9, v8, constant_data, etc.)
-for any MARTHE variable.
+``gridmarthe`` allow users to read/write efficiently Marthe Grids (v9, v8, constant_data, etc.)
+with python, for any MARTHE variable.
 
 With the ``gridmarthe`` API, data are stored in a `xarray dataset `_,
-and can be manage with `xarray `_ (or `numpy `_)
-functions as with "utils" functions provided by ``gridmarthe``.
+and can be manage with `xarray `_ functions (or `numpy `_).
+Specific treatments/functions are also provided by `gridmarthe`.
 
-The package also install a command line tool, ``ncmart`` to convert Marthe Grid to netCDF format.
-Help can be found with ``ncmart -h``. A tutorial is available in the :ref:`userguide` section.
\ No newline at end of file
+The package also install different command line tools: `ncmart` to convert Marthe Grid to netCDF format,
+`martshp` to convert in shapefile/geopackage, `cleanmgrid` to fix marthe grid format.
+
+Tutorials are available in the :ref:`userguide` section.
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c0cd1cc..51c5b94 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,3 +1,5 @@
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
 .. gridmarthe documentation master file, created by
    sphinx-quickstart on Mon Dec  2 15:02:14 2024.
    You can adapt this file completely to your liking, but it should at least
@@ -7,13 +9,34 @@
    `reStructuredText `_
    documentation for details.
 
+
+   ..  This file is part of gridmarthe.
+
+   ..  gridmarthe is a python library to manage grid files for 
+   ..  MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+   ..  Copyright (C) 2024  BRGM
+
+   ..  This program is free software: you can redistribute it and/or modify
+   ..  it under the terms of the GNU General Public License as published by
+   ..  the Free Software Foundation, either version 3 of the License, or
+   ..  (at your option) any later version.
+
+   ..  This program is distributed in the hope that it will be useful,
+   ..  but WITHOUT ANY WARRANTY; without even the implied warranty of
+   ..  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+   ..  GNU General Public License for more details.
+
+   ..  You should have received a copy of the GNU General Public License
+   ..  along with this program.  If not, see .
+
+
 ==========
-GridMarthe
+gridmarthe
 ==========
 **Date** : |today| **Version**: |release|
 
 
-Gridmarthe is a Python project for (fast) operations on Marthe grid files.
+`gridmarthe` is a Python project for (fast) operations on Marthe grid files.
 
 `Marthe `_
 is a hydrogeological modelling code developped at BRGM, French Geological Survey
@@ -27,10 +50,9 @@ This library is open-source and released under the GNU General Public License (v
    
  ..  to add image => :img-top: _static/index_getting_started.svg
 
-.. warning::
-   This documentation and the library itself are under heavy developpement
 
 .. grid:: 1 1 2 2 
+   :gutter: 1
    
    .. grid-item-card:: Getting started
       :link: getting_started/index
diff --git a/docs/source/references.rst b/docs/source/references.rst
index caeb74a..bd72a60 100644
--- a/docs/source/references.rst
+++ b/docs/source/references.rst
@@ -7,6 +7,11 @@ About
 
 The MARTHE software is available at 
 
+``gridmarthe`` was developped with the hope it will be useful, but without any warranty
+(see the term of GNU Public Licence v3).
+For any bug, please report it on the project repository
+.
+
 
 Bibliography
 ------------
diff --git a/docs/source/sphinxext/COPYING b/docs/source/sphinxext/COPYING
new file mode 100644
index 0000000..e1cd01d
--- /dev/null
+++ b/docs/source/sphinxext/COPYING
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2007-2024 The scikit-learn developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/docs/source/sphinxext/git_link.py b/docs/source/sphinxext/git_link.py
new file mode 100644
index 0000000..7b0b663
--- /dev/null
+++ b/docs/source/sphinxext/git_link.py
@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# DISCLAIMER: This module is taken from scikit-learn sources
+# all credits to the sklearn authors and contributors.
+# and huge thanks to them.
+# source: https://github.com/scikit-learn/scikit-learn/blob/c5497b7f7eacfaff061cf68e09bcd48aa93d4d6b/doc/sphinxext/github_link.py
+# License: BSD-3 Clause
+# See the COPYING file from this directory: ./COPYING
+
+
+import inspect
+import os
+import subprocess
+import sys
+from functools import partial
+from operator import attrgetter
+
+REVISION_CMD = "git rev-parse --short HEAD"
+
+
+def _get_git_revision():
+    try:
+        revision = subprocess.check_output(REVISION_CMD.split()).strip()
+    except (subprocess.CalledProcessError, OSError):
+        print("Failed to execute git to get revision")
+        return None
+    return revision.decode("utf-8")
+
+
+def _linkcode_resolve(domain, info, package, url_fmt, revision):
+    """Determine a link to online source for a class/method/function
+
+    This is called by sphinx.ext.linkcode
+
+    An example with a long-untouched module that everyone has
+    >>> _linkcode_resolve('py', {'module': 'tty',
+    ...                          'fullname': 'setraw'},
+    ...                   package='tty',
+    ...                   url_fmt='https://hg.python.org/cpython/file/'
+    ...                           '{revision}/Lib/{package}/{path}#L{lineno}',
+    ...                   revision='xxxx')
+    'https://hg.python.org/cpython/file/xxxx/Lib/tty/tty.py#L18'
+    """
+
+    if revision is None:
+        return
+    if domain not in ("py", "pyx"):
+        return
+    if not info.get("module") or not info.get("fullname"):
+        return
+
+    class_name = info["fullname"].split(".")[0]
+    module = __import__(info["module"], fromlist=[class_name])
+    obj = attrgetter(info["fullname"])(module)
+
+    # Unwrap the object to get the correct source
+    # file in case that is wrapped by a decorator
+    obj = inspect.unwrap(obj)
+
+    try:
+        fn = inspect.getsourcefile(obj)
+    except Exception:
+        fn = None
+    if not fn:
+        try:
+            fn = inspect.getsourcefile(sys.modules[obj.__module__])
+        except Exception:
+            fn = None
+    if not fn:
+        return
+
+    fn = os.path.relpath(fn, start=os.path.dirname(__import__(package).__file__))
+    try:
+        lineno = inspect.getsourcelines(obj)[1]
+    except Exception:
+        lineno = ""
+    return url_fmt.format(revision=revision, package=package, path=fn, lineno=lineno)
+
+
+def make_linkcode_resolve(package, url_fmt):
+    """Returns a linkcode_resolve function for the given URL format
+
+    revision is a git commit reference (hash or name)
+
+    package is the name of the root module of the package
+
+    url_fmt is along the lines of ('https://github.com/USER/PROJECT/'
+                                   'blob/{revision}/{package}/'
+                                   '{path}#L{lineno}')
+    """
+    revision = _get_git_revision()
+    return partial(
+        _linkcode_resolve, revision=revision, package=package, url_fmt=url_fmt
+    )
diff --git a/docs/source/switcher.json b/docs/source/switcher.json
deleted file mode 100644
index 5e5f814..0000000
--- a/docs/source/switcher.json
+++ /dev/null
@@ -1,7 +0,0 @@
-[
-    {
-        "name": "v0.1.1 (dev)",
-        "version": "0.1.1",
-        "url": "https://gridmarthe.readthedocs.io/en/latest/"
-    }
-]
diff --git a/docs/source/user_guide/example/cli.ipynb b/docs/source/user_guide/example/cli.ipynb
index 207fe61..304a365 100644
--- a/docs/source/user_guide/example/cli.ipynb
+++ b/docs/source/user_guide/example/cli.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {
     "vscode": {
      "languageId": "shellscript"
@@ -27,27 +27,32 @@
      "output_type": "stream",
      "text": [
       "usage: ncmart [-h] [--output OUTPUT] [--variable VARIABLE] [--as2d]\n",
-      "              [--xyfactor XYFACTOR]\n",
-      "              chasim pastp [chasim pastp ...]\n",
+      "              [--xyfactor XYFACTOR] [--version]\n",
+      "              [grid timesteps ...]\n",
       "\n",
       "Convert a Marthe GridFile to netCDF format.\n",
       "\n",
       "positional arguments:\n",
-      "  chasim pastp          Paths to chasim and pastp files are expected\n",
+      "  grid timesteps        Paths to grid and timesteps files are expected\n",
       "\n",
       "options:\n",
       "  -h, --help            show this help message and exit\n",
       "  --output OUTPUT, -o OUTPUT\n",
       "                        Output filename. Default is input.nc\n",
       "  --variable VARIABLE, -v VARIABLE\n",
-      "                        Variable to read, default is None; i.e variable will\n",
-      "                        be parse from file and ONLY the first variable will be\n",
-      "                        read.\n",
+      "                        Variable (field) to read, default is None; i.e\n",
+      "                        variable will be parsed from file and ONLY the first\n",
+      "                        variable will be read.\n",
       "  --as2d, -d            Store grid as 2D (or more), default is 1D for space\n",
       "                        dimension\n",
       "  --xyfactor XYFACTOR, -x XYFACTOR\n",
       "                        Transformation factor for coordinates. Optionnal,\n",
-      "                        default is 1 (no transformation).\n"
+      "                        default is 1 (no transformation).\n",
+      "  --version, -V         Show version and exit\n",
+      "\n",
+      "gridmarthe  Copyright (C) 2024  BRGM.\n",
+      "This program comes with ABSOLUTELY NO WARRANTY.\n",
+      "This is free software, see GNU General Public Lisence v3 for copying.\n"
      ]
     }
    ],
@@ -58,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "shellscript"
@@ -67,12 +72,13 @@
    "outputs": [],
    "source": [
     "# minimal setup: call ncmart with a grid file and a timestep file\n",
-    "!ncmart chasim_hallue.out hallue.pastp"
+    "# this will write the output with input filename and 'nc' extension\n",
+    "!ncmart ./data/chasim_hallue.out ./data/hallue.pastp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "vscode": {
      "languageId": "shellscript"
@@ -88,8 +94,8 @@
       "\ttime = 205 ;\n",
       "\tzone = 927 ;\n",
       "variables:\n",
-      "\tfloat charge(time, zone) ;\n",
-      "\t\tcharge:_FillValue = NaNf ;\n",
+      "\tdouble charge(time, zone) ;\n",
+      "\t\tcharge:_FillValue = NaN ;\n",
       "\t\tstring charge:varname = \"CHARGE\" ;\n",
       "\t\tstring charge:units = \"m\" ;\n",
       "\t\tcharge:missing_value = 9999. ;\n",
@@ -112,7 +118,7 @@
       "\tint64 time(time) ;\n",
       "\t\tstring time:units = \"days since 1995-07-31 00:00:00\" ;\n",
       "\t\tstring time:calendar = \"proleptic_gregorian\" ;\n",
-      "\tint64 zone(zone) ;\n",
+      "\tint zone(zone) ;\n",
       "\n",
       "// global attributes:\n",
       "\t\tstring :conventions = \"CF-1.10\" ;\n",
@@ -124,11 +130,11 @@
       "\t\t:scale_factor = 1. ;\n",
       "\t\tstring :nested_grid = \"False\" ;\n",
       "\t\tstring :extend = \"xymin : 596.75 2542.25; xymax: 622.75 2568.75\" ;\n",
-      "\t\tstring :frequency = \"30 day(s)\" ;\n",
       "\t\tstring :period = \"1995-2012\" ;\n",
-      "\t\tstring :creation_date = \"Created on 2025-01-07T15:54:32Z UTC\" ;\n",
+      "\t\tstring :frequency = \"30 day(s)\" ;\n",
+      "\t\tstring :creation_date = \"Created on 2025-03-26T13:28:35Z UTC\" ;\n",
       "\t\tstring :institution = \"BRGM, French Geological Survey, Orléans, France\" ;\n",
-      "\t\tstring :comment = \"Hydrogeological model created with MARTHE code (Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code for hydro-systems modelling. report BRGM/RP-69660-FR).\" ;\n",
+      "\t\tstring :comment = \"Hydrogeological model created with MARTHE code (Thiery, D. 2020. Guidelines for MARTHE v7.8 computer codefor hydro-systems modelling. report BRGM/RP-69660-FR).\" ;\n",
       "\t\tstring :resolution_units = \"m\" ;\n",
       "\t\tstring :projection = \"epsg:27572\" ;\n",
       "\t\tstring :domain = \"FR-France\" ;\n",
@@ -137,14 +143,14 @@
     }
    ],
    "source": [
-    "# print variables headers and dimensions with nco tools\n",
-    "!ncdump -h chasim_hallue.nc"
+    "# print variables headers and dimensions with netcdf/nco tools\n",
+    "!ncdump -h ./data/chasim_hallue.nc"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "am",
+   "display_name": "gm",
    "language": "python",
    "name": "python3"
   },
@@ -158,7 +164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/user_guide/example/data/chasim_hallue.nc b/docs/source/user_guide/example/data/chasim_hallue.nc
new file mode 100644
index 0000000..efebfe3
Binary files /dev/null and b/docs/source/user_guide/example/data/chasim_hallue.nc differ
diff --git a/docs/source/user_guide/example/chasim_hallue.out b/docs/source/user_guide/example/data/chasim_hallue.out
similarity index 100%
rename from docs/source/user_guide/example/chasim_hallue.out
rename to docs/source/user_guide/example/data/chasim_hallue.out
diff --git a/docs/source/user_guide/example/data/craie_npc.permh b/docs/source/user_guide/example/data/craie_npc.permh
new file mode 100644
index 0000000..36d4be6
--- /dev/null
+++ b/docs/source/user_guide/example/data/craie_npc.permh
@@ -0,0 +1,6564 @@
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU   RPERMEAB        1
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=0
+Max_NestG=1
+[Structure]
+X_Left_Corner=543500
+Y_Lower_Corner=2526100
+Ncolumn=387
+Nrows=304
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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+Title=Craie NPC                                                      H_SU   RPERMEAB        2
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+Title=Craie NPC                                                      H_SU   RPERMEAB        3
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+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU   RPERMEAB        4
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=4
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+Ncolumn=387
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+[Data_Descript]
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+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=Craie NPC                                                      H_SU   RPERMEAB        6
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+Title=Craie NPC                                                      H_SU   RPERMEAB        7
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+Title=Craie NPC                                                      H_SU   RPERMEAB        9
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+Title=Craie NPC                                                      H_SU   RPERMEAB       10
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        1
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=1
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+[Structure]
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+Ncolumn=307
+Nrows=442
+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        2
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=2
+Max_Layer=10
+Nest_grid=1
+Max_NestG=1
+[Structure]
+X_Left_Corner=637500
+Y_Lower_Corner=2586600
+Ncolumn=307
+Nrows=442
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=Craie NPC                                                      H_SU  1RPERMEAB        3
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        4
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=4
+Max_Layer=10
+Nest_grid=1
+Max_NestG=1
+[Structure]
+X_Left_Corner=637500
+Y_Lower_Corner=2586600
+Ncolumn=307
+Nrows=442
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        5
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=5
+Max_Layer=10
+Nest_grid=1
+Max_NestG=1
+[Structure]
+X_Left_Corner=637500
+Y_Lower_Corner=2586600
+Ncolumn=307
+Nrows=442
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        6
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=6
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+[Structure]
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+Ncolumn=307
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+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        7
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=7
+Max_Layer=10
+Nest_grid=1
+Max_NestG=1
+[Structure]
+X_Left_Corner=637500
+Y_Lower_Corner=2586600
+Ncolumn=307
+Nrows=442
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        8
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=8
+Max_Layer=10
+Nest_grid=1
+Max_NestG=1
+[Structure]
+X_Left_Corner=637500
+Y_Lower_Corner=2586600
+Ncolumn=307
+Nrows=442
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU  1RPERMEAB        9
+[Infos]
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+Type=
+Elem_Number=0
+Name=
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+[Structure]
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+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
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+Title=Craie NPC                                                      H_SU  1RPERMEAB       10
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+[Num_Rows_/_y_/_dy]
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+[End_Grid]
diff --git a/docs/source/user_guide/example/data/example.hsubs b/docs/source/user_guide/example/data/example.hsubs
new file mode 100644
index 0000000..8cf0cb6
--- /dev/null
+++ b/docs/source/user_guide/example/data/example.hsubs
@@ -0,0 +1,2730 @@
+Marthe_Grid Version=9.0
+Title=                                                                       H_SUBSTRAT     1
+[Infos]
+Field=H_SUBSTRAT
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=543500
+Y_Lower_Corner=2526100
+Ncolumn=387
+Nrows=304
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=                                                                       H_SUBSTRAT     5
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+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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+[End_Grid]
diff --git a/docs/source/user_guide/example/data/example.topog b/docs/source/user_guide/example/data/example.topog
new file mode 100644
index 0000000..18d9ccf
--- /dev/null
+++ b/docs/source/user_guide/example/data/example.topog
@@ -0,0 +1,595 @@
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       1
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=543500
+Y_Lower_Corner=2526100
+Ncolumn=387
+Nrows=304
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       3
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       4
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       5
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       6
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=6
+Max_Layer=10
+Nest_grid=0
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       7
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       8
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       9
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR      10
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+Ncolumn=387
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+[Constant_Data]
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+[End_Grid]
diff --git a/docs/source/user_guide/example/data/grid_clean.hsubs b/docs/source/user_guide/example/data/grid_clean.hsubs
new file mode 100644
index 0000000..696ea7e
--- /dev/null
+++ b/docs/source/user_guide/example/data/grid_clean.hsubs
@@ -0,0 +1,905 @@
+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT     1
+[Infos]
+Field=H_SUBSTRAT
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=1
+Nest_grid=0
+Max_NestG=3
+[Structure]
+X_Left_Corner=528
+Y_Lower_Corner=2503
+Ncolumn=320
+Nrows=189
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >
+[Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Bidon                                                                1 H_SUBSTRAT     1
+[Infos]
+Field=H_SUBSTRAT
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=1
+Nest_grid=1
+Max_NestG=3
+[Structure]
+X_Left_Corner=550.5
+Y_Lower_Corner=2552
+Ncolumn=322
+Nrows=237
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >
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+6	2551.05	14.94	14.59	14.59	14.59	14.59	14.59	14.9	14.9	14.9	14.9	14.9	16.06	16.06	16.06	16.06	16.06	18.41	18.41	18.41	18.41	18.41	20.02	20.02	20.02	20.02	20.02	21.47	21.47	21.47	21.47	21.47	21.84	21.84	21.84	21.84	21.84	22.04	22.04	22.04	22.04	22.04	22.58	22.58	22.58	22.58	22.58	25.14	25.14	25.14	25.14	25.14	27.29	27.29	27.29	27.29	27.29	30.52	30.52	30.52	30.52	30.52	31.31	31.31	31.31	31.31	31.31	30.86	30.86	30.86	30.86	30.86	30.28	30.28	30.28	30.28	30.28	29.57	29.57	29.57	29.57	29.57	29.39	29.39	29.39	29.39	29.39	29.73	29.73	29.73	29.73	29.73	30.3	30.3	30.3	30.3	30.3	31.22	31.22	31.22	31.22	31.22	31.93	31.93	31.93	31.93	31.93	33.16	33.16	33.16	33.16	33.16	34.14	34.14	34.14	34.14	34.14	35.66	35.66	35.66	35.66	35.66	36.49	36.49	36.49	36.49	36.49	37.48	37.48	37.48	37.48	37.48	37.92	37.92	37.92	37.92	37.92	38.3	38.3	38.3	38.3	38.3	38.56	38.56	38.56	38.56	38.56	38.92	38.92	38.92	38.92	38.92	39.08	39.08	39.08	39.08	39.08	39.28	39.28	39.28	39.28	39.28	39.61	39.61	39.61	39.61	39.61	40.35	40.35	40.35	40.35	40.35	40.73	40.73	40.73	40.73	40.73	41.04	41.04	41.04	41.04	41.04	41.2	41.2	41.2	41.2	41.2	42.11	42.11	42.11	42.11	42.11	43.24	43.24	43.24	43.24	43.24	44.89	44.89	44.89	44.89	44.89	45.79	45.79	45.79	45.79	45.79	47.06	47.06	47.06	47.06	47.06	47.91	47.91	47.91	47.91	47.91	48.79	.1000977	
+7	2550.95	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+8	2550.85	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+9	2550.75	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+10	2550.65	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+11	2550.55	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+12	2550.45	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+14	2550.25	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+28	2548.85	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+29	2548.75	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+30	2548.65	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
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+50	2546.65	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
+51	2546.55	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
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+91	2542.55	25.91	24.95	24.95	24.95	24.95	24.95	23.46	23.46	23.46	23.46	23.46	22.41	22.41	22.41	22.41	22.41	20.89	20.89	20.89	20.89	20.89	20.1	20.1	20.1	20.1	20.1	18.05	18.05	18.05	18.05	18.05	15.44	15.44	15.44	15.44	15.44	11.84	11.84	11.84	11.84	11.84	10.93	10.93	10.93	10.93	10.93	12.91	12.91	12.91	12.91	12.91	16.21	16.21	16.21	16.21	16.21	20.11	20.11	20.11	20.11	20.11	21.33	21.33	21.33	21.33	21.33	21.85	21.85	21.85	21.85	21.85	22.36	22.36	22.36	22.36	22.36	23.99	23.99	23.99	23.99	23.99	25.18	25.18	25.18	25.18	25.18	27.13	27.13	27.13	27.13	27.13	28.18	28.18	28.18	28.18	28.18	29.49	29.49	29.49	29.49	29.49	30.2	30.2	30.2	30.2	30.2	31.49	31.49	31.49	31.49	31.49	32.35	32.35	32.35	32.35	32.35	33.38	33.38	33.38	33.38	33.38	33.94	33.94	33.94	33.94	33.94	34.51	34.51	34.51	34.51	34.51	35.07	35.07	35.07	35.07	35.07	36.03	36.03	36.03	36.03	36.03	36.65	36.65	36.65	36.65	36.65	37.52	37.52	37.52	37.52	37.52	38.11	38.11	38.11	38.11	38.11	38.77	38.77	38.77	38.77	38.77	38.93	38.93	38.93	38.93	38.93	38.92	38.92	38.92	38.92	38.92	38.97	38.97	38.97	38.97	38.97	39.26	39.26	39.26	39.26	39.26	39.52	39.52	39.52	39.52	39.52	40.01	40.01	40.01	40.01	40.01	40.15	40.15	40.15	40.15	40.15	42	42	42	42	42	40.55	40.55	40.55	40.55	40.55	41.12	41.12	41.12	41.12	41.12	41.99	41.99	41.99	41.99	41.99	42.98	.1000977	
+92	2542.45	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+93	2542.35	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+94	2542.25	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+95	2542.15	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+96	2542.05	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+97	2541.95	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+98	2541.85	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+99	2541.75	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+100	2541.65	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+101	2541.55	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+102	2541.45	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+103	2541.35	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+104	2541.25	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+105	2541.15	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+106	2541.05	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+107	2540.95	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+108	2540.85	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+109	2540.75	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+110	2540.65	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
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+112	2540.45	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+113	2540.35	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+114	2540.25	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+115	2540.15	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+116	2540.05	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+117	2539.95	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+118	2539.85	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+120	2539.65	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+121	2539.55	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+192	2532.45	36.37	35.59	35.59	35.59	35.59	35.59	34.26	34.26	34.26	34.26	34.26	33.14	33.14	33.14	33.14	33.14	31.63	31.63	31.63	31.63	31.63	30.63	30.63	30.63	30.63	30.63	29.28	29.28	29.28	29.28	29.28	28.24	28.24	28.24	28.24	28.24	27.07	27.07	27.07	27.07	27.07	26.57	26.57	26.57	26.57	26.57	26.17	26.17	26.17	26.17	26.17	25.86	25.86	25.86	25.86	25.86	25.33	25.33	25.33	25.33	25.33	25.09	25.09	25.09	25.09	25.09	27	27	27	27	27	25.52	25.52	25.52	25.52	25.52	26.69	26.69	26.69	26.69	26.69	27.22	27.22	27.22	27.22	27.22	28.37	28.37	28.37	28.37	28.37	29.25	29.25	29.25	29.25	29.25	30.65	30.65	30.65	30.65	30.65	31.33	31.33	31.33	31.33	31.33	31.35	31.35	31.35	31.35	31.35	31.01	31.01	31.01	31.01	31.01	30.48	30.48	30.48	30.48	30.48	30.32	30.32	30.32	30.32	30.32	30.64	30.64	30.64	30.64	30.64	31.18	31.18	31.18	31.18	31.18	32.12	32.12	32.12	32.12	32.12	32.58	32.58	32.58	32.58	32.58	33.04	33.04	33.04	33.04	33.04	33.76	33.76	33.76	33.76	33.76	34.91	34.91	34.91	34.91	34.91	35.72	35.72	35.72	35.72	35.72	36.57	36.57	36.57	36.57	36.57	36.85	36.85	36.85	36.85	36.85	37.71	37.71	37.71	37.71	37.71	38.56	38.56	38.56	38.56	38.56	39.72	39.72	39.72	39.72	39.72	40.33	40.33	40.33	40.33	40.33	40.8	40.8	40.8	40.8	40.8	40.82	40.82	40.82	40.82	40.82	40.6	40.6	40.6	40.6	40.6	40.32	40.32	40.32	40.32	40.32	39.7	.1000977	
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+199	2531.75	35.99	35.17	35.17	35.17	35.17	35.17	33.81	33.81	33.81	33.81	33.81	32.67	32.67	32.67	32.67	32.67	31.33	31.33	31.33	31.33	31.33	30.4	30.4	30.4	30.4	30.4	29.21	29.21	29.21	29.21	29.21	28.32	28.32	28.32	28.32	28.32	27.27	27.27	27.27	27.27	27.27	26.82	26.82	26.82	26.82	26.82	26.27	26.27	26.27	26.27	26.27	26.12	26.12	26.12	26.12	26.12	25.51	25.51	25.51	25.51	25.51	25.23	25.23	25.23	25.23	25.23	27	27	27	27	27	25.11	25.11	25.11	25.11	25.11	25.8	25.8	25.8	25.8	25.8	26.08	26.08	26.08	26.08	26.08	27.04	27.04	27.04	27.04	27.04	28.04	28.04	28.04	28.04	28.04	29.62	29.62	29.62	29.62	29.62	30.45	30.45	30.45	30.45	30.45	30.3	30.3	30.3	30.3	30.3	29.85	29.85	29.85	29.85	29.85	29.39	29.39	29.39	29.39	29.39	29.47	29.47	29.47	29.47	29.47	30.18	30.18	30.18	30.18	30.18	30.79	30.79	30.79	30.79	30.79	31.77	31.77	31.77	31.77	31.77	32.29	32.29	32.29	32.29	32.29	32.9	32.9	32.9	32.9	32.9	33.63	33.63	33.63	33.63	33.63	34.75	34.75	34.75	34.75	34.75	35.36	35.36	35.36	35.36	35.36	35.85	35.85	35.85	35.85	35.85	36	36	36	36	36	36.65	36.65	36.65	36.65	36.65	37.4	37.4	37.4	37.4	37.4	38.52	38.52	38.52	38.52	38.52	39.12	39.12	39.12	39.12	39.12	39.71	39.71	39.71	39.71	39.71	39.86	39.86	39.86	39.86	39.86	39.9	39.9	39.9	39.9	39.9	39.78	39.78	39.78	39.78	39.78	39.36	.1000977	
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+202	2531.45	35.3	34.49	34.49	34.49	34.49	34.49	33.24	33.24	33.24	33.24	33.24	32.21	32.21	32.21	32.21	32.21	30.97	30.97	30.97	30.97	30.97	30.12	30.12	30.12	30.12	30.12	29.04	29.04	29.04	29.04	29.04	28.46	28.46	28.46	28.46	28.46	27.6	27.6	27.6	27.6	27.6	27.08	27.08	27.08	27.08	27.08	26.78	26.78	26.78	26.78	26.78	26.75	26.75	26.75	26.75	26.75	26.27	26.27	26.27	26.27	26.27	28	28	28	28	28	24.83	24.83	24.83	24.83	24.83	27	27	27	27	27	27	27	27	27	27	24.49	24.49	24.49	24.49	24.49	25.34	25.34	25.34	25.34	25.34	26.4	26.4	26.4	26.4	26.4	28.09	28.09	28.09	28.09	28.09	28.89	28.89	28.89	28.89	28.89	28.48	28.48	28.48	28.48	28.48	28.21	28.21	28.21	28.21	28.21	28.03	28.03	28.03	28.03	28.03	28.54	28.54	28.54	28.54	28.54	30.09	30.09	30.09	30.09	30.09	30.82	30.82	30.82	30.82	30.82	31.69	31.69	31.69	31.69	31.69	32.04	32.04	32.04	32.04	32.04	32.53	32.53	32.53	32.53	32.53	33.06	33.06	33.06	33.06	33.06	33.87	33.87	33.87	33.87	33.87	34.24	34.24	34.24	34.24	34.24	34.44	34.44	34.44	34.44	34.44	34.58	34.58	34.58	34.58	34.58	35.19	35.19	35.19	35.19	35.19	35.82	35.82	35.82	35.82	35.82	37.01	37.01	37.01	37.01	37.01	37.65	37.65	37.65	37.65	37.65	38.32	38.32	38.32	38.32	38.32	38.59	38.59	38.59	38.59	38.59	38.74	38.74	38.74	38.74	38.74	38.78	38.78	38.78	38.78	38.78	38.61	.1000977	
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+[End_Grid]
diff --git a/docs/source/user_guide/example/data/grid_wrong_attrs.hsubs b/docs/source/user_guide/example/data/grid_wrong_attrs.hsubs
new file mode 100644
index 0000000..9bb7058
--- /dev/null
+++ b/docs/source/user_guide/example/data/grid_wrong_attrs.hsubs
@@ -0,0 +1,905 @@
+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT     1
+[Infos]
+Field=
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=0
+Max_Layer=0
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=528
+Y_Lower_Corner=2503
+Ncolumn=320
+Nrows=189
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >
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+Title=Bidon                                                                2 H_SUBSTRAT     1
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+6	2551.05	14.94	14.59	14.59	14.59	14.59	14.59	14.9	14.9	14.9	14.9	14.9	16.06	16.06	16.06	16.06	16.06	18.41	18.41	18.41	18.41	18.41	20.02	20.02	20.02	20.02	20.02	21.47	21.47	21.47	21.47	21.47	21.84	21.84	21.84	21.84	21.84	22.04	22.04	22.04	22.04	22.04	22.58	22.58	22.58	22.58	22.58	25.14	25.14	25.14	25.14	25.14	27.29	27.29	27.29	27.29	27.29	30.52	30.52	30.52	30.52	30.52	31.31	31.31	31.31	31.31	31.31	30.86	30.86	30.86	30.86	30.86	30.28	30.28	30.28	30.28	30.28	29.57	29.57	29.57	29.57	29.57	29.39	29.39	29.39	29.39	29.39	29.73	29.73	29.73	29.73	29.73	30.3	30.3	30.3	30.3	30.3	31.22	31.22	31.22	31.22	31.22	31.93	31.93	31.93	31.93	31.93	33.16	33.16	33.16	33.16	33.16	34.14	34.14	34.14	34.14	34.14	35.66	35.66	35.66	35.66	35.66	36.49	36.49	36.49	36.49	36.49	37.48	37.48	37.48	37.48	37.48	37.92	37.92	37.92	37.92	37.92	38.3	38.3	38.3	38.3	38.3	38.56	38.56	38.56	38.56	38.56	38.92	38.92	38.92	38.92	38.92	39.08	39.08	39.08	39.08	39.08	39.28	39.28	39.28	39.28	39.28	39.61	39.61	39.61	39.61	39.61	40.35	40.35	40.35	40.35	40.35	40.73	40.73	40.73	40.73	40.73	41.04	41.04	41.04	41.04	41.04	41.2	41.2	41.2	41.2	41.2	42.11	42.11	42.11	42.11	42.11	43.24	43.24	43.24	43.24	43.24	44.89	44.89	44.89	44.89	44.89	45.79	45.79	45.79	45.79	45.79	47.06	47.06	47.06	47.06	47.06	47.91	47.91	47.91	47.91	47.91	48.79	.1000977	
+7	2550.95	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+8	2550.85	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+9	2550.75	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+10	2550.65	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+11	2550.55	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+12	2550.45	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+14	2550.25	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+28	2548.85	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+29	2548.75	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+30	2548.65	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
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+50	2546.65	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
+51	2546.55	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
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+91	2542.55	25.91	24.95	24.95	24.95	24.95	24.95	23.46	23.46	23.46	23.46	23.46	22.41	22.41	22.41	22.41	22.41	20.89	20.89	20.89	20.89	20.89	20.1	20.1	20.1	20.1	20.1	18.05	18.05	18.05	18.05	18.05	15.44	15.44	15.44	15.44	15.44	11.84	11.84	11.84	11.84	11.84	10.93	10.93	10.93	10.93	10.93	12.91	12.91	12.91	12.91	12.91	16.21	16.21	16.21	16.21	16.21	20.11	20.11	20.11	20.11	20.11	21.33	21.33	21.33	21.33	21.33	21.85	21.85	21.85	21.85	21.85	22.36	22.36	22.36	22.36	22.36	23.99	23.99	23.99	23.99	23.99	25.18	25.18	25.18	25.18	25.18	27.13	27.13	27.13	27.13	27.13	28.18	28.18	28.18	28.18	28.18	29.49	29.49	29.49	29.49	29.49	30.2	30.2	30.2	30.2	30.2	31.49	31.49	31.49	31.49	31.49	32.35	32.35	32.35	32.35	32.35	33.38	33.38	33.38	33.38	33.38	33.94	33.94	33.94	33.94	33.94	34.51	34.51	34.51	34.51	34.51	35.07	35.07	35.07	35.07	35.07	36.03	36.03	36.03	36.03	36.03	36.65	36.65	36.65	36.65	36.65	37.52	37.52	37.52	37.52	37.52	38.11	38.11	38.11	38.11	38.11	38.77	38.77	38.77	38.77	38.77	38.93	38.93	38.93	38.93	38.93	38.92	38.92	38.92	38.92	38.92	38.97	38.97	38.97	38.97	38.97	39.26	39.26	39.26	39.26	39.26	39.52	39.52	39.52	39.52	39.52	40.01	40.01	40.01	40.01	40.01	40.15	40.15	40.15	40.15	40.15	42	42	42	42	42	40.55	40.55	40.55	40.55	40.55	41.12	41.12	41.12	41.12	41.12	41.99	41.99	41.99	41.99	41.99	42.98	.1000977	
+92	2542.45	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+93	2542.35	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+94	2542.25	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+95	2542.15	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+96	2542.05	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+97	2541.95	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+98	2541.85	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+99	2541.75	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+100	2541.65	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+101	2541.55	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+102	2541.45	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+103	2541.35	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+104	2541.25	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+105	2541.15	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+106	2541.05	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+107	2540.95	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+108	2540.85	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+109	2540.75	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+110	2540.65	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
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+112	2540.45	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+113	2540.35	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+114	2540.25	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+115	2540.15	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+116	2540.05	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+117	2539.95	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+118	2539.85	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+120	2539.65	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+121	2539.55	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+192	2532.45	36.37	35.59	35.59	35.59	35.59	35.59	34.26	34.26	34.26	34.26	34.26	33.14	33.14	33.14	33.14	33.14	31.63	31.63	31.63	31.63	31.63	30.63	30.63	30.63	30.63	30.63	29.28	29.28	29.28	29.28	29.28	28.24	28.24	28.24	28.24	28.24	27.07	27.07	27.07	27.07	27.07	26.57	26.57	26.57	26.57	26.57	26.17	26.17	26.17	26.17	26.17	25.86	25.86	25.86	25.86	25.86	25.33	25.33	25.33	25.33	25.33	25.09	25.09	25.09	25.09	25.09	27	27	27	27	27	25.52	25.52	25.52	25.52	25.52	26.69	26.69	26.69	26.69	26.69	27.22	27.22	27.22	27.22	27.22	28.37	28.37	28.37	28.37	28.37	29.25	29.25	29.25	29.25	29.25	30.65	30.65	30.65	30.65	30.65	31.33	31.33	31.33	31.33	31.33	31.35	31.35	31.35	31.35	31.35	31.01	31.01	31.01	31.01	31.01	30.48	30.48	30.48	30.48	30.48	30.32	30.32	30.32	30.32	30.32	30.64	30.64	30.64	30.64	30.64	31.18	31.18	31.18	31.18	31.18	32.12	32.12	32.12	32.12	32.12	32.58	32.58	32.58	32.58	32.58	33.04	33.04	33.04	33.04	33.04	33.76	33.76	33.76	33.76	33.76	34.91	34.91	34.91	34.91	34.91	35.72	35.72	35.72	35.72	35.72	36.57	36.57	36.57	36.57	36.57	36.85	36.85	36.85	36.85	36.85	37.71	37.71	37.71	37.71	37.71	38.56	38.56	38.56	38.56	38.56	39.72	39.72	39.72	39.72	39.72	40.33	40.33	40.33	40.33	40.33	40.8	40.8	40.8	40.8	40.8	40.82	40.82	40.82	40.82	40.82	40.6	40.6	40.6	40.6	40.6	40.32	40.32	40.32	40.32	40.32	39.7	.1000977	
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+199	2531.75	35.99	35.17	35.17	35.17	35.17	35.17	33.81	33.81	33.81	33.81	33.81	32.67	32.67	32.67	32.67	32.67	31.33	31.33	31.33	31.33	31.33	30.4	30.4	30.4	30.4	30.4	29.21	29.21	29.21	29.21	29.21	28.32	28.32	28.32	28.32	28.32	27.27	27.27	27.27	27.27	27.27	26.82	26.82	26.82	26.82	26.82	26.27	26.27	26.27	26.27	26.27	26.12	26.12	26.12	26.12	26.12	25.51	25.51	25.51	25.51	25.51	25.23	25.23	25.23	25.23	25.23	27	27	27	27	27	25.11	25.11	25.11	25.11	25.11	25.8	25.8	25.8	25.8	25.8	26.08	26.08	26.08	26.08	26.08	27.04	27.04	27.04	27.04	27.04	28.04	28.04	28.04	28.04	28.04	29.62	29.62	29.62	29.62	29.62	30.45	30.45	30.45	30.45	30.45	30.3	30.3	30.3	30.3	30.3	29.85	29.85	29.85	29.85	29.85	29.39	29.39	29.39	29.39	29.39	29.47	29.47	29.47	29.47	29.47	30.18	30.18	30.18	30.18	30.18	30.79	30.79	30.79	30.79	30.79	31.77	31.77	31.77	31.77	31.77	32.29	32.29	32.29	32.29	32.29	32.9	32.9	32.9	32.9	32.9	33.63	33.63	33.63	33.63	33.63	34.75	34.75	34.75	34.75	34.75	35.36	35.36	35.36	35.36	35.36	35.85	35.85	35.85	35.85	35.85	36	36	36	36	36	36.65	36.65	36.65	36.65	36.65	37.4	37.4	37.4	37.4	37.4	38.52	38.52	38.52	38.52	38.52	39.12	39.12	39.12	39.12	39.12	39.71	39.71	39.71	39.71	39.71	39.86	39.86	39.86	39.86	39.86	39.9	39.9	39.9	39.9	39.9	39.78	39.78	39.78	39.78	39.78	39.36	.1000977	
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+202	2531.45	35.3	34.49	34.49	34.49	34.49	34.49	33.24	33.24	33.24	33.24	33.24	32.21	32.21	32.21	32.21	32.21	30.97	30.97	30.97	30.97	30.97	30.12	30.12	30.12	30.12	30.12	29.04	29.04	29.04	29.04	29.04	28.46	28.46	28.46	28.46	28.46	27.6	27.6	27.6	27.6	27.6	27.08	27.08	27.08	27.08	27.08	26.78	26.78	26.78	26.78	26.78	26.75	26.75	26.75	26.75	26.75	26.27	26.27	26.27	26.27	26.27	28	28	28	28	28	24.83	24.83	24.83	24.83	24.83	27	27	27	27	27	27	27	27	27	27	24.49	24.49	24.49	24.49	24.49	25.34	25.34	25.34	25.34	25.34	26.4	26.4	26.4	26.4	26.4	28.09	28.09	28.09	28.09	28.09	28.89	28.89	28.89	28.89	28.89	28.48	28.48	28.48	28.48	28.48	28.21	28.21	28.21	28.21	28.21	28.03	28.03	28.03	28.03	28.03	28.54	28.54	28.54	28.54	28.54	30.09	30.09	30.09	30.09	30.09	30.82	30.82	30.82	30.82	30.82	31.69	31.69	31.69	31.69	31.69	32.04	32.04	32.04	32.04	32.04	32.53	32.53	32.53	32.53	32.53	33.06	33.06	33.06	33.06	33.06	33.87	33.87	33.87	33.87	33.87	34.24	34.24	34.24	34.24	34.24	34.44	34.44	34.44	34.44	34.44	34.58	34.58	34.58	34.58	34.58	35.19	35.19	35.19	35.19	35.19	35.82	35.82	35.82	35.82	35.82	37.01	37.01	37.01	37.01	37.01	37.65	37.65	37.65	37.65	37.65	38.32	38.32	38.32	38.32	38.32	38.59	38.59	38.59	38.59	38.59	38.74	38.74	38.74	38.74	38.74	38.78	38.78	38.78	38.78	38.78	38.61	.1000977	
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+0	0	.5	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	9.99756E-02	.5	
+[End_Grid]
diff --git a/docs/source/user_guide/example/hallue.pastp b/docs/source/user_guide/example/data/hallue.pastp
similarity index 100%
rename from docs/source/user_guide/example/hallue.pastp
rename to docs/source/user_guide/example/data/hallue.pastp
diff --git a/docs/source/user_guide/example/nested.permh b/docs/source/user_guide/example/data/nested.permh
similarity index 100%
rename from docs/source/user_guide/example/nested.permh
rename to docs/source/user_guide/example/data/nested.permh
diff --git a/docs/source/user_guide/example/gigogne.ipynb b/docs/source/user_guide/example/gigogne.ipynb
index 25c34e8..50b563b 100644
--- a/docs/source/user_guide/example/gigogne.ipynb
+++ b/docs/source/user_guide/example/gigogne.ipynb
@@ -21,12 +21,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import gridmarthe as gm\n",
-    "ds = gm.load_marthe_grid('nested.permh', drop_nan=True, nanval=[0., -9999.])"
+    "ds = gm.load_marthe_grid('./data/nested.permh', drop_nan=True, nanval=[0., -9999.])"
    ]
   },
   {
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
     }
    ],
    "source": [
-    "gm.plot_nested_grid(ds2d, var='permeab')\n",
+    "gm.plot_nested_grid(ds2d, varname='permeab')\n",
     "plt.show()"
    ]
   }
diff --git a/docs/source/user_guide/example/gis.ipynb b/docs/source/user_guide/example/gis.ipynb
new file mode 100644
index 0000000..133f706
--- /dev/null
+++ b/docs/source/user_guide/example/gis.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GIS interoperability and export (shapefile, raster)\n",
+    "This notebook shows how to export gridmarthe features to common GIS format.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Script API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import modules\n",
+    "import numpy as np\n",
+    "import gridmarthe as gm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shapefile, step 1: conversion\n",
+    "\n",
+    "``gridmarthe`` use the ``geopandas`` API to convert grids to a geodataframe object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/d/home/gridmarthe/src/gridmarthe/operasem/gis.py:65: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  if 'time' in ds.dims.keys():\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
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+       "    \n",
+       "  \n",
+       "
chargexydxdytimegeometry
zone
19999.0596.752568.750.50.52012-07-01POLYGON ((596.5 2568.5, 597 2568.5, 597 2569, ...
29999.0597.252568.750.50.52012-07-01POLYGON ((597 2568.5, 597.5 2568.5, 597.5 2569...
39999.0597.752568.750.50.52012-07-01POLYGON ((597.5 2568.5, 598 2568.5, 598 2569, ...
49999.0598.252568.750.50.52012-07-01POLYGON ((598 2568.5, 598.5 2568.5, 598.5 2569...
59999.0598.752568.750.50.52012-07-01POLYGON ((598.5 2568.5, 599 2568.5, 599 2569, ...
\n",
+       "
"
+      ],
+      "text/plain": [
+       "      charge       x        y   dx   dy       time  \\\n",
+       "zone                                                 \n",
+       "1     9999.0  596.75  2568.75  0.5  0.5 2012-07-01   \n",
+       "2     9999.0  597.25  2568.75  0.5  0.5 2012-07-01   \n",
+       "3     9999.0  597.75  2568.75  0.5  0.5 2012-07-01   \n",
+       "4     9999.0  598.25  2568.75  0.5  0.5 2012-07-01   \n",
+       "5     9999.0  598.75  2568.75  0.5  0.5 2012-07-01   \n",
+       "\n",
+       "                                               geometry  \n",
+       "zone                                                     \n",
+       "1     POLYGON ((596.5 2568.5, 597 2568.5, 597 2569, ...  \n",
+       "2     POLYGON ((597 2568.5, 597.5 2568.5, 597.5 2569...  \n",
+       "3     POLYGON ((597.5 2568.5, 598 2568.5, 598 2569, ...  \n",
+       "4     POLYGON ((598 2568.5, 598.5 2568.5, 598.5 2569...  \n",
+       "5     POLYGON ((598.5 2568.5, 599 2568.5, 599 2569, ...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_to_data = './data'\n",
+    "ds = gm.load_marthe_grid(f'{path_to_data}/chasim_hallue.out', 'CHARGE', fpastp=f'{path_to_data}/hallue.pastp').isel(time=-1)\n",
+    "gdf = ds.mart.to_geodataframe()  # equivalent to: gm.to_geodataframe(ds)\n",
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shapefile, step 2: export"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gdf.to_file('chasim.shp')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Export to raster\n",
+    "\n",
+    "For one time step, user can export a regular grid as a TIFF file with function ``glm.to_raster``\n",
+    "For all time steps at once, use ``ds.mart.to_raster(filename_tpl='chasim_hallue')``, the filename template\n",
+    "argument must not contain any extension.\n",
+    "\n",
+    "**CAUTION**: this is only for regular grids. For irregular/nested grids,\n",
+    "the QGIS plugin PostMARTHE is preferable to save chasim as .tiff\n",
+    "It will save the results in raster file with the smallest resolution\n",
+    "available in all nested grids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function to_raster in module gridmarthe.operasem.gis:\n",
+      "\n",
+      "to_raster(da, x_dim='x', y_dim='y', epsg=27572, fout='raster.tiff')\n",
+      "    Write a xr.DataArray to a raster file\n",
+      "    \n",
+      "    need xarray with rioxarray installed.\n",
+      "    Only for regular grids.\n",
+      "    TODO: add support for irregular grids, using PostMARTHE QGIS plugin code.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    da : xarray.DataArray\n",
+      "        The DataArray to write to a raster file (only 2D, i.e. no time dimension, select\n",
+      "        a variable and timestep before).\n",
+      "    x_dim : str, optional\n",
+      "        The name of the x dimension, by default 'x'.\n",
+      "    y_dim : str, optional\n",
+      "        The name of the y dimension, by default 'y'.\n",
+      "    epsg : int, optional\n",
+      "        The EPSG code for the coordinate reference system, by default 27572.\n",
+      "    fout : str, optional\n",
+      "        The output file path for the raster file, by default 'raster.tiff'.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    None\n",
+      "        The function writes the raster file and returns None.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(gm.to_raster)  # function doc to write a raster file\n",
+    "# wrapper with time dimension : ds.mart.to_raster('chasim_hallue.tif')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CLI for gis transformation\n",
+    "\n",
+    "A command line script is also provided to transform a marthe grid file to shapefile or geopackage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "usage: martshp [-h] [--output OUTPUT] [--variable VARIABLE]\n",
+      "               [--xyfactor XYFACTOR] [--gpkg] [--mask] [--version]\n",
+      "               [--wide_fmt]\n",
+      "               [grid timesteps ...]\n",
+      "\n",
+      "Convert a Marthe GridFile to shapefile format.\n",
+      "\n",
+      "positional arguments:\n",
+      "  grid timesteps        Paths to grid [and timesteps if result] files are\n",
+      "                        expected\n",
+      "\n",
+      "options:\n",
+      "  -h, --help            show this help message and exit\n",
+      "  --output OUTPUT, -o OUTPUT\n",
+      "                        Output filename. Default is input.nc\n",
+      "  --variable VARIABLE, -v VARIABLE\n",
+      "                        Variable (field) to read, default is None: i.e\n",
+      "                        variable will be parsed from file and ONLY the first\n",
+      "                        variable will be read. Pass 'all' to get all\n",
+      "                        variables.\n",
+      "  --xyfactor XYFACTOR, -x XYFACTOR\n",
+      "                        Transformation factor for coordinates. Optionnal,\n",
+      "                        default is 1 (no transformation).\n",
+      "  --gpkg, -g            Use GPKG format instead of shapefile\n",
+      "  --mask, -m            Only get a mask of active domain\n",
+      "  --version, -V         Show version and exit\n",
+      "  --wide_fmt, -w        Use wide format (columns) for time\n",
+      "\n",
+      "gridmarthe  Copyright (C) 2024  BRGM.\n",
+      "This program comes with ABSOLUTELY NO WARRANTY.\n",
+      "This is free software, see GNU General Public Lisence v3 for copying.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!martshp -h"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/user_guide/example/gm_example.ipynb b/docs/source/user_guide/example/gm_example.ipynb
index 1c98d28..be27f47 100644
--- a/docs/source/user_guide/example/gm_example.ipynb
+++ b/docs/source/user_guide/example/gm_example.ipynb
@@ -93,12 +93,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# lecture d'une grille marthe et stockage dans un Dataset (librairie xarray)\n",
-    "ds = gm.load_marthe_grid('./chasim_hallue.out', 'CHARGE', fpastp='./hallue.pastp', drop_nan=True) # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur"
+    "ds = gm.load_marthe_grid('./data/chasim_hallue.out', 'CHARGE', fpastp='./data/hallue.pastp', drop_nan=True) # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur"
    ]
   },
   {
diff --git a/docs/source/user_guide/example/preproc.ipynb b/docs/source/user_guide/example/preproc.ipynb
new file mode 100644
index 0000000..ee00d12
--- /dev/null
+++ b/docs/source/user_guide/example/preproc.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Command line executable to fix marthe fmt\n",
+    "\n",
+    "In some older versions of MARTHE, grid attributes were miswritten.\n",
+    "A fix script is included in `gridmarthe` if users need to read such file.\n",
+    "This example show how to use this script, called `cleanmgrid`. All kind of gridfiles may not be included. Please make an issue if you appear to lack a treatment.\n",
+    "\n",
+    "\n",
+    "*nb: the next commands are shell commands, the '!' mark is only here for the python notebook version.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Marthe_Grid Version=9.0\n",
+      "Title=Bidon                                                                  H_SUBSTRAT     1\n",
+      "[Infos]\n",
+      "Field=\n",
+      "Type=\n",
+      "Elem_Number=0\n",
+      "Name=\n",
+      "Time_Step=-9999\n",
+      "Time=0\n",
+      "Layer=0\n",
+      "Max_Layer=0\n",
+      "Nest_grid=0\n",
+      "Max_NestG=0\n",
+      "[Structure]\n",
+      "X_Left_Corner=528\n",
+      "Y_Lower_Corner=2503\n",
+      "Ncolumn=320\n",
+      "Nrows=189\n",
+      "[Data_Descript]\n",
+      "! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >\n",
+      "! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >\n",
+      "! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1\n",
+      "! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2\n",
+      "! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2\n",
+      "! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >\n",
+      "[Data]\n",
+      "0\t0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11\t12\t13\t14\t15\t16\t17\t18\t19\t20\t21\t22\t23\t24\t25\t26\t27\t28\t29\t30\t31\t32\t33\t34\t35\t36\t37\t38\t39\t40\t41\t42\t43\t44\t45\t46\t47\t48\t49\t50\t51\t52\t53\t54\t55\t56\t57\t58\t59\t60\t61\t62\t63\t64\t65\t66\t67\t68\t69\t70\t71\t72\t73\t74\t75\t76\t77\t78\t79\t80\t81\t82\t83\t84\t85\t86\t87\t88\t89\t90\t91\t92\t93\t94\t95\t96\t97\t98\t99\t100\t101\t102\t103\t104\t105\t106\t107\t108\t109\t110\t111\t112\t113\t114\t115\t116\t117\t118\t119\t120\t121\t122\t123\t124\t125\t126\t127\t128\t129\t130\t131\t132\t133\t134\t135\t136\t137\t138\t139\t140\t141\t142\t143\t144\t145\t146\t147\t148\t149\t150\t151\t152\t153\t154\t155\t156\t157\t158\t159\t160\t161\t162\t163\t164\t165\t166\t167\t168\t169\t170\t171\t172\t173\t174\t175\t176\t177\t178\t179\t180\t181\t182\t183\t184\t185\t186\t187\t188\t189\t190\t191\t192\t193\t194\t195\t196\t197\t198\t199\t200\t201\t202\t203\t204\t205\t206\t207\t208\t209\t210\t211\t212\t213\t214\t215\t216\t217\t218\t219\t220\t221\t222\t223\t224\t225\t226\t227\t228\t229\t230\t231\t232\t233\t234\t235\t236\t237\t238\t239\t240\t241\t242\t243\t244\t245\t246\t247\t248\t249\t250\t251\t252\t253\t254\t255\t256\t257\t258\t259\t260\t261\t262\t263\t264\t265\t266\t267\t268\t269\t270\t271\t272\t273\t274\t275\t276\t277\t278\t279\t280\t281\t282\t283\t284\t285\t286\t287\t288\t289\t290\t291\t292\t293\t294\t295\t296\t297\t298\t299\t300\t301\t302\t303\t304\t305\t306\t307\t308\t309\t310\t311\t312\t313\t314\t315\t316\t317\t318\t319\t320\t\n",
+      "0\t0\t528.25\t528.75\t529.25\t529.75\t530.25\t530.75\t531.25\t531.75\t532.25\t532.75\t533.25\t533.75\t534.25\t534.75\t535.25\t535.75\t536.25\t536.75\t537.25\t537.75\t538.25\t538.75\t539.25\t539.75\t540.25\t540.75\t541.25\t541.75\t542.25\t542.75\t543.25\t543.75\t544.25\t544.75\t545.25\t545.75\t546.25\t546.75\t547.25\t547.75\t548.25\t548.75\t549.25\t549.75\t550.25\t550.75\t551.25\t551.75\t552.25\t552.75\t553.25\t553.75\t554.25\t554.75\t555.25\t555.75\t556.25\t556.75\t557.25\t557.75\t558.25\t558.75\t559.25\t559.75\t560.25\t560.75\t561.25\t561.75\t562.25\t562.75\t563.25\t563.75\t564.25\t564.75\t565.25\t565.75\t566.25\t566.75\t567.25\t567.75\t568.25\t568.75\t569.25\t569.75\t570.25\t570.75\t571.25\t571.75\t572.25\t572.75\t573.25\t573.75\t574.25\t574.75\t575.25\t575.75\t576.25\t576.75\t577.25\t577.75\t578.25\t578.75\t579.25\t579.75\t580.25\t580.75\t581.25\t581.75\t582.25\t582.75\t583.25\t583.75\t584.25\t584.75\t585.25\t585.75\t586.25\t586.75\t587.25\t587.75\t588.25\t588.75\t589.25\t589.75\t590.25\t590.75\t591.25\t591.75\t592.25\t592.75\t593.25\t593.75\t594.25\t594.75\t595.25\t595.75\t596.25\t596.75\t597.25\t597.75\t598.25\t598.75\t599.25\t599.75\t600.25\t600.75\t601.25\t601.75\t602.25\t602.75\t603.25\t603.75\t604.25\t604.75\t605.25\t605.75\t606.25\t606.75\t607.25\t607.75\t608.25\t608.75\t609.25\t609.75\t610.25\t610.75\t611.25\t611.75\t612.25\t612.75\t613.25\t613.75\t614.25\t614.75\t615.25\t615.75\t616.25\t616.75\t617.25\t617.75\t618.25\t618.75\t619.25\t619.75\t620.25\t620.75\t621.25\t621.75\t622.25\t622.75\t623.25\t623.75\t624.25\t624.75\t625.25\t625.75\t626.25\t626.75\t627.25\t627.75\t628.25\t628.75\t629.25\t629.75\t630.25\t630.75\t631.25\t631.75\t632.25\t632.75\t633.25\t633.75\t634.25\t634.75\t635.25\t635.75\t636.25\t636.75\t637.25\t637.75\t638.25\t638.75\t639.25\t639.75\t640.25\t640.75\t641.25\t641.75\t642.25\t642.75\t643.25\t643.75\t644.25\t644.75\t645.25\t645.75\t646.25\t646.75\t647.25\t647.75\t648.25\t648.75\t649.25\t649.75\t650.25\t650.75\t651.25\t651.75\t652.25\t652.75\t653.25\t653.75\t654.25\t654.75\t655.25\t655.75\t656.25\t656.75\t657.25\t657.75\t658.25\t658.75\t659.25\t659.75\t660.25\t660.75\t661.25\t661.75\t662.25\t662.75\t663.25\t663.75\t664.25\t664.75\t665.25\t665.75\t666.25\t666.75\t667.25\t667.75\t668.25\t668.75\t669.25\t669.75\t670.25\t670.75\t671.25\t671.75\t672.25\t672.75\t673.25\t673.75\t674.25\t674.75\t675.25\t675.75\t676.25\t676.75\t677.25\t677.75\t678.25\t678.75\t679.25\t679.75\t680.25\t680.75\t681.25\t681.75\t682.25\t682.75\t683.25\t683.75\t684.25\t684.75\t685.25\t685.75\t686.25\t686.75\t687.25\t687.75\t\n",
+      "1\t2597.25\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t.5\t\n",
+      "2\t2596.75\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t-24.81\t-24.93\t-23\t-25.13\t-23\t-24.04\t-20\t-21.98\t-20.87\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t.5\t\n"
+     ]
+    }
+   ],
+   "source": [
+    "# how to use: print help message\n",
+    "!head -30 ./data/grid_wrong_attrs.hsubs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above results, we can see that `Layer`, `Max_Layer`, `Nest_grid`, `Max_Nestg` are all set to 0.\n",
+    "This is an error which prevent successfull Marthe IO operations with `gridmarthe` or `pymarthe`.\n",
+    "The script `cleanmgrid` is going to fix it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "usage: cleanmgrid [-h] [--layer LAYER] [--grid GRID] [--output OUTPUT]\n",
+      "                  [--no_overwrite]\n",
+      "                  PATH_TO_FILE\n",
+      "\n",
+      "Clean marthe grid file for miswritten attributes. Only works for marthe grid v9.0 (a check is performed and \n",
+      "! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >\n",
+      "! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1\n",
+      "! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2\n",
+      "! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2\n",
+      "! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >\n",
+      "[Data]\n",
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+      "2\t2596.75\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t-24.81\t-24.93\t-23\t-25.13\t-23\t-24.04\t-20\t-21.98\t-20.87\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t9999\t.5\t\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print variables headers and dimensions with nco tools\n",
+    "!head -30 ./data/grid_clean.hsubs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cleanmgrid` also work if you specified a `*.rma` file (Marthe Project). In such case, the program will try to fix all grid files of the project !"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/user_guide/example/treatments.ipynb b/docs/source/user_guide/example/treatments.ipynb
new file mode 100644
index 0000000..251ae47
--- /dev/null
+++ b/docs/source/user_guide/example/treatments.ipynb
@@ -0,0 +1,1097 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of treatments available\n",
+    "\n",
+    "This notebook shows how to use some extra features for your gridmarthe treatments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import modules\n",
+    "import numpy as np\n",
+    "import gridmarthe as gm\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:  (time: 1, zone: 101209)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 1850-01-01\n",
+       "  * zone     (zone) int32 405kB 47402 47403 47763 ... 2381150 2381151 2381457\n",
+       "Data variables:\n",
+       "    permeab  (time, zone) float64 810kB 1e-05 1e-05 1e-05 ... 0.0001 0.0001\n",
+       "    x        (zone) float32 405kB 6.372e+05 6.378e+05 ... 6.382e+05 6.38e+05\n",
+       "    y        (zone) float32 405kB 2.617e+06 2.617e+06 ... 2.592e+06 2.592e+06\n",
+       "    dx       (zone) float32 405kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    dy       (zone) float32 405kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    z        (zone) int32 405kB 1 1 1 1 1 1 1 1 1 1 1 ... 9 9 9 9 9 9 9 9 9 9 9\n",
+       "Attributes: (12/17)\n",
+       "    conventions:          CF-1.10\n",
+       "    title:                \n",
+       "    marthe_grid_version:  9.0\n",
+       "    original_dimensions:  x,y,z [grids]: 387 304 10; 307 442 10\n",
+       "    lon_resolution:       100.0, 500.0\n",
+       "    lat_resolution:       100.0, 500.0\n",
+       "    ...                   ...\n",
+       "    creation_date:        Created on 2025-03-26T13:40:44Z UTC\n",
+       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
+       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
+       "    resolution_units:     m\n",
+       "    projection:           epsg:27572\n",
+       "    domain:               FR-France
"
+      ],
+      "text/plain": [
+       " Size: 3MB\n",
+       "Dimensions:  (time: 1, zone: 101209)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 1850-01-01\n",
+       "  * zone     (zone) int32 405kB 47402 47403 47763 ... 2381150 2381151 2381457\n",
+       "Data variables:\n",
+       "    permeab  (time, zone) float64 810kB 1e-05 1e-05 1e-05 ... 0.0001 0.0001\n",
+       "    x        (zone) float32 405kB 6.372e+05 6.378e+05 ... 6.382e+05 6.38e+05\n",
+       "    y        (zone) float32 405kB 2.617e+06 2.617e+06 ... 2.592e+06 2.592e+06\n",
+       "    dx       (zone) float32 405kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    dy       (zone) float32 405kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    z        (zone) int32 405kB 1 1 1 1 1 1 1 1 1 1 1 ... 9 9 9 9 9 9 9 9 9 9 9\n",
+       "Attributes: (12/17)\n",
+       "    conventions:          CF-1.10\n",
+       "    title:                \n",
+       "    marthe_grid_version:  9.0\n",
+       "    original_dimensions:  x,y,z [grids]: 387 304 10; 307 442 10\n",
+       "    lon_resolution:       100.0, 500.0\n",
+       "    lat_resolution:       100.0, 500.0\n",
+       "    ...                   ...\n",
+       "    creation_date:        Created on 2025-03-26T13:40:44Z UTC\n",
+       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
+       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
+       "    resolution_units:     m\n",
+       "    projection:           epsg:27572\n",
+       "    domain:               FR-France"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid = gm.load_marthe_grid('./data/craie_npc.permh', drop_nan=True)  # NaN needs to be drop or set (change 0 to nan for permh for example)\n",
+    "grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get surface mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
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+       "Dimensions:  (zone: 21025)\n",
+       "Coordinates:\n",
+       "  * zone     (zone) int32 84kB 178171 177784 647989 ... 1389346 1660427 1660120\n",
+       "Data variables:\n",
+       "    time     (zone) datetime64[ns] 168kB 1850-01-01 1850-01-01 ... 1850-01-01\n",
+       "    permeab  (zone) float64 168kB 1e-06 1e-06 0.003068 ... 1e-06 2e-05 2e-05\n",
+       "    x        (zone) float32 84kB 6.188e+05 6.188e+05 ... 6.494e+05 6.494e+05\n",
+       "    y        (zone) float32 84kB 2.6e+06 2.6e+06 ... 2.606e+06 2.606e+06\n",
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+       "    dy       (zone) float32 84kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    z        (zone) int32 84kB 2 2 6 6 8 8 8 8 5 2 6 6 ... 2 2 2 2 2 2 2 2 2 4 4
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+      ],
+      "text/plain": [
+       " Size: 841kB\n",
+       "Dimensions:  (zone: 21025)\n",
+       "Coordinates:\n",
+       "  * zone     (zone) int32 84kB 178171 177784 647989 ... 1389346 1660427 1660120\n",
+       "Data variables:\n",
+       "    time     (zone) datetime64[ns] 168kB 1850-01-01 1850-01-01 ... 1850-01-01\n",
+       "    permeab  (zone) float64 168kB 1e-06 1e-06 0.003068 ... 1e-06 2e-05 2e-05\n",
+       "    x        (zone) float32 84kB 6.188e+05 6.188e+05 ... 6.494e+05 6.494e+05\n",
+       "    y        (zone) float32 84kB 2.6e+06 2.6e+06 ... 2.606e+06 2.606e+06\n",
+       "    dx       (zone) float32 84kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    dy       (zone) float32 84kB 500.0 500.0 500.0 500.0 ... 100.0 100.0 100.0\n",
+       "    z        (zone) int32 84kB 2 2 6 6 8 8 8 8 5 2 6 6 ... 2 2 2 2 2 2 2 2 2 4 4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "surf = gm.get_surface_layer(grid)\n",
+    "surf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# xarray version\n",
+    "toto = gm.assign_coords(surf, add_lay=False) # add lay false because it needs to be a variable to plot, not a dimension\n",
+    "gm.plot_outcrop(toto)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function also allow user to get the value of a variable in the surface layer,\n",
+    "or a subset of aquifer layers.\n",
+    "\n",
+    "For example, to get the value of your variable (groundwater head for example) in\n",
+    "the first layer encountered between 3 layers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_surf = gm.get_surface_layer(grid, aquif_layers=[6,8,9])  # get the values in the first layer between layers 6, 8 and 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get mask of active domain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/manlaya/envs/gm/lib/python3.11/site-packages/gridmarthe/operasem/gis.py:58: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
+      "  if 'time' in ds.dims.keys():\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask = gm.get_active_mask(grid) # return a geopandas geodataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask.boundary.plot(color='k')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute depths and thickness\n",
+    "\n",
+    "Users can get geometry attributes (depth, thickness, upper/lower altitudes) in a dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'gm' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m topo \u001b[38;5;241m=\u001b[39m \u001b[43mgm\u001b[49m\u001b[38;5;241m.\u001b[39mload_marthe_grid(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/example.topog\u001b[39m\u001b[38;5;124m'\u001b[39m, drop_nan\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, nanval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9999.\u001b[39m)\n\u001b[1;32m      2\u001b[0m hsubs \u001b[38;5;241m=\u001b[39m gm\u001b[38;5;241m.\u001b[39mload_marthe_grid(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/example.hsubs\u001b[39m\u001b[38;5;124m'\u001b[39m, drop_nan\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, nanval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9999\u001b[39m)\n\u001b[1;32m      3\u001b[0m geom \u001b[38;5;241m=\u001b[39m gm\u001b[38;5;241m.\u001b[39mcompute_geometry(topo, hsubs)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'gm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "topo = gm.load_marthe_grid('./data/example.topog')   # be careful with nan here, topography can be set outside of active area\n",
+    "hsubs = gm.load_marthe_grid('./data/example.hsubs')  # same here, hsubs can be set outside of active area\n",
+    "geom = gm.compute_geometry(topo, hsubs)\n",
+    "# print(geom.where(~geom['depth'].isnull(), drop=True))\n",
+    "geom\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolation, regridding, transformations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regrid (coarse/refine resolution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tutorial in construction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Projection transformation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst
index 1401b21..4859250 100644
--- a/docs/source/user_guide/index.rst
+++ b/docs/source/user_guide/index.rst
@@ -8,5 +8,8 @@ User Guide
     :maxdepth: 1
 
     example/gm_example.ipynb
+    example/gigogne.ipynb
+    example/treatments.ipynb
+    example/gis.ipynb
     example/cli.ipynb
-    example/gigogne.ipynb
\ No newline at end of file
+    example/preproc.ipynb
\ No newline at end of file
diff --git a/meson.build b/meson.build
index 04c8472..66c4432 100644
--- a/meson.build
+++ b/meson.build
@@ -7,7 +7,7 @@
 project('gridmarthe', ['fortran', 'c'],
   version : run_command('meson_get_version.py').stdout().strip(),
   license: 'GNU/GPL-V3',
-  meson_version: '>=0.64.0',
+  meson_version: '>=1.1.0',
   default_options : [
     'warning_level=1',
     'buildtype=release'
@@ -15,16 +15,15 @@ project('gridmarthe', ['fortran', 'c'],
 )
 
 # add_languages('fortran')
-# subdir('src/gridmarthe')
-fortran_sources = files([
+lecsem_sources = files([
     'src/gridmarthe/lecsem/lecsem.f90',
     'src/gridmarthe/lecsem/edsemigl.f90',
     'src/gridmarthe/lecsem/scan_grid.f90'
 ])
-lecsem_install_dir = 'gridmarthe/lecsem/' # in python_install_dir
+lecsem_install_dir = 'gridmarthe/lecsem/'  # in python_install_dir
 
 
-# ----- Get compiler and build options ------ #
+# ------ Get compiler and build options ------ #
 fc = meson.get_compiler('fortran')
 cc = meson.get_compiler('c')
 deps = []
@@ -32,10 +31,13 @@ inc_dir = []
 
 # Options de compilation Fortran
 fargs = []  # compile args
-largs = []  # arg de link
+largs = []  # link args
 
 if fc.get_id() == 'gcc'
   
+  fargs += ['-fdefault-real-8']
+  fargs += ['-O2', '-ffree-line-length-none']
+  
   if cc.version().version_compare('>=13.0')
     fargs += ['-fallow-argument-mismatch', '-std=legacy']
   endif
@@ -51,26 +53,25 @@ if fc.get_id() == 'gcc'
     quadmath = declare_dependency(link_args : ['-lquadmath'])
     deps += [quadmath_dep,quadmath]
   else
-      fargs += ['-O2', '-ffree-line-length-none']
       # fargs += ['-fPIC', '-shared'] # not really necessary here
-      # fargs += ['-fdefault-real-8'] # EDIT, no, in args: kind=4 / single precision
   endif
 
 elif fc.get_id() in ['llvm-flang', 'flang', 'flang-new']
-  
   # TODO add options for llvm-flang for conda-forge
   fargs += ['-fdefault-real-8', '-ffree-form']
   
-# elif cc.get_id() in ['msvc', 'intel-llvm', 'intel', 'intel-llvm-cl', 'intel-cl']
+elif cc.get_id() in ['intel-llvm', 'intel', 'intel-llvm-cl', 'intel-cl']
+  fargs += ['-real-size 64', '-stand f95']
 
 else
-  
+  # 'msvc' ?
   error('Build is recommended with GCC/gfortran')
 
 endif
 
+
 fortran_args = fc.get_supported_arguments(fargs)
-link_args = fc.get_supported_arguments(largs)
+link_args = fc.get_supported_link_arguments(largs)
 add_project_arguments(fortran_args, language: 'fortran')
 add_project_link_arguments(link_args, language: 'fortran')
 
@@ -81,18 +82,12 @@ python_install_dir = py.get_install_dir()
 
 deps += [py_dep]
 
-
 incdir_numpy = run_command(py,
+  # ['-c', 'import os; import numpy; print(os.path.relpath(numpy.get_include()))'],
   ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'],
   check : true
 ).stdout().strip()
 
-# incdir_f2py = run_command(py,
-#     ['-c', 'import os; os.chdir(".."); import numpy.f2py; print(numpy.f2py.get_include())'],
-#     check : true
-# ).stdout().strip()
-
-# inc_np = include_directories(incdir_numpy, incdir_f2py)
 inc_np = include_directories(incdir_numpy)
 np_dep = declare_dependency(include_directories: inc_np)
 
@@ -100,19 +95,19 @@ incdir_f2py = incdir_numpy / '..' / '..' / 'f2py' / 'src'
 inc_f2py = include_directories(incdir_f2py)
 fortranobject_c = incdir_f2py / 'fortranobject.c'
 
-# -------- Building interface ----------- #
+inc_dir += [inc_np,inc_f2py]
+
+# ---- Building interface for LECSEM ---- #
+# subdir('src/gridmarthe/lecsem') # not functionnal yet, keep it here for now
 lecsem_interface = custom_target('lecsemmodule.c',
-  input : fortran_sources,
+  input : lecsem_sources,
   output : ['lecsemmodule.c', 'lecsem-f2pywrappers.f', 'lecsem-f2pywrappers2.f90'],
   command : [py, '-m', 'numpy.f2py', '@INPUT@', '-m', 'lecsem', '--lower']
 )
 
-
-inc_dir += [inc_np,inc_f2py]
-
 py.extension_module('lecsem',
   [
-    fortran_sources,
+    lecsem_sources,
     lecsem_interface,
     fortranobject_c
   ],
@@ -120,8 +115,7 @@ py.extension_module('lecsem',
   dependencies: deps,
   install: true,
   install_dir: python_install_dir / lecsem_install_dir,
-  # fortran_args : fortran_args
 )
 
-# -------- Install pypackage ----------- #
+# --------- Install pypackage ----------- #
 install_subdir('src/gridmarthe', install_dir : python_install_dir)
diff --git a/pyproject.toml b/pyproject.toml
index e1d1b0b..6effedb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,17 +26,23 @@ authors = [
 
 description = "python package to read/write/manage Marthe Grids"
 readme = {file = "README.md", content-type = "text/markdown"}   
-license = {file = "LICENSE"}
+license = {file = "COPYING"}
 
 classifiers = [
     "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: Implementation :: CPython",
     "Programming Language :: Fortran",
+    "Operating System :: Unix",
     "Operating System :: POSIX :: Linux",
     "Operating System :: MacOS",
     "Operating System :: Microsoft :: Windows",
+    "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Hydrology",
+    "Topic :: Software Development",
+    "Intended Audience :: Science/Research",
+    "Intended Audience :: Developers",
     "Development Status :: 4 - Beta",
 ]
 
@@ -48,6 +54,7 @@ dependencies = [
     "pandas",
     "h5netcdf",
     "xarray",
+    "pyproj",
     "shapely",
     "matplotlib",
     "geopandas",
@@ -61,6 +68,7 @@ doc = ['sphinx', 'pydata_sphinx_theme', 'sphinx-copybutton', 'sphinx_design', 's
 [project.scripts]
 ncmart = "gridmarthe:scripts.ncmart.main"
 cleanmgrid = "gridmarthe:scripts.cleanmgrid.main"
+martshp = "gridmarthe:scripts.martshp.main"
 
 
 [project.urls]
@@ -87,13 +95,14 @@ include-package-data = true
 # https://mesonbuild.com/meson-python/reference/pyproject-settings.html
 # https://mesonbuild.com/meson-python/how-to-guides/meson-args.html
 # setup = ['--default-library=static']
-# setup = ['--build-dir=src']
+# setup = ['--build-dir=build']
 
 
 [tool.cibuildwheel]
-# skip = "cp36-* cp37-* cp-38* pp37-* *-manylinux_i686 *_ppc64le *_s390x *_universal2"
 skip = "cp36-* cp37-* cp-38* pp* *musl* *_i686 *_ppc64le *_s390x *_universal2"
 # skip = ["pp*", "*musl*"]
+# test-requires = "pytest"
+# test-command = "pytest {project}/tests"
 
 [tool.cibuildwheel.linux]
 archs = ["x86_64"]
@@ -112,5 +121,5 @@ archs = ["arm64"]
 package = 'gridmarthe'
 [tool.spin.commands]
 Build = [
-    'spin.cmds.meson.build',
+    'spin.cmds.pip.install',
 ]
diff --git a/src/gridmarthe/__init__.py b/src/gridmarthe/__init__.py
index 16b1d96..464a67b 100644
--- a/src/gridmarthe/__init__.py
+++ b/src/gridmarthe/__init__.py
@@ -1,15 +1,35 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
 
+import xarray as xr
+from typing import Union
 
+from .__version__ import __version__
 from .gridmarthe import *
 from .operasem import *
 from .plot import plot_nested_grid, plot_outcrop
 
-from typing import Union
-
-from .__version__ import __version__
-
 
 @xr.register_dataset_accessor('mart')
 class MartheGrid(object):
@@ -40,23 +60,20 @@ def to_recarray(self):
         df = self.obj.to_dataframe()
         return df.to_records()
     
-    def to_raster(self, x_dim='x', y_dim='y', time=None, epsg=27572, fout_template='raster'):
+    def to_raster(self, x_dim='x', y_dim='y', time=None, epsg=27572, filename_tpl='raster'):
         """ Write raster file for a specific timestep (or all) from dataset 
         Warning, only functionnal for regular grids
         """
         if time is None:
-            time = self.obj.times # if not defined, get all available times
-        if isinstance(time, str): # make sure to get a iterable for slicing
+            time = self.obj.times  # if not defined, get all available times
+        if isinstance(time, str):  # make sure to get a iterable for slicing
             time = [time]
         
         for t in time:
-            write_raster_from_da(self.obj.sel(time=slice(t)), x_dim, y_dim, epsg, "{}_{}.tiff".format(fout_template, t))
+            to_raster(self.obj.sel(time=slice(t)), x_dim, y_dim, epsg, "{}_{}.tiff".format(filename_tpl, t))
         
         return None
     
-    def get_outcrop(self, subset_layers=None):
-        return get_min_layer(self.obj, subset_layers)
-    
     def subset_coords(self, dims=['x', 'y'], gdf=None, xmin=None, ymin=None, xmax=None, ymax=None):
         return subset_with_coords(self.obj, dims, gdf, xmin, ymin, xmax, ymax)
     
@@ -64,9 +81,3 @@ def dropna(self, varname='charge', nanval=9999.):
         ds = self.obj.copy()
         masque = ds[varname].where(ds[varname] != nanval).dropna(dim='zone') # drop nanval
         return ds.sel(zone=masque['zone'])
-    
-    # def write(self, fmt='mart'):
-        # if fmt.lower() in ['mart', 'marthe']
-            # write_marthe_grid()
-        # else:
-            # self.obj.to_netcdf()
diff --git a/src/gridmarthe/__version__.py b/src/gridmarthe/__version__.py
index 58bdced..c41139c 100644
--- a/src/gridmarthe/__version__.py
+++ b/src/gridmarthe/__version__.py
@@ -1,4 +1,41 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
 
-__version__ = '0.1.2'
+__version__ = '0.1.3'
+
+_copyleft = """
+    {program}  Copyright (C) {year}  {name_of_author}.
+    This program comes with ABSOLUTELY NO WARRANTY.
+    This is free software, see GNU General Public Lisence v3 for copying.
+""".format(program='gridmarthe', year=2025, name_of_author='BRGM')
+
+
+# def _show_c():
+    # print('c')
+    # return
+
+
+# def _show_w():
+    # print('w')
+    # return
\ No newline at end of file
diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py
index 07276d9..11f87e9 100644
--- a/src/gridmarthe/gridmarthe.py
+++ b/src/gridmarthe/gridmarthe.py
@@ -1,15 +1,56 @@
 #! /usr/bin/env python3
 # -*- coding: utf-8 -*-
-
-import os, re
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+
+import os
 from datetime import datetime
 
 import pandas as pd
 import numpy as np
-import xarray as xr #needs netcdf4
-
-from . import lecsem
-from .utils import _datetime64_to_float, _is_sorted
+import xarray as xr
+
+from .lecsem import (
+    modgridmarthe,
+    _read_marthe_grid,
+    _transform_xcoords,
+    _transform_ycoords,
+    _set_layers,
+    _get_id_grid,
+    _get_col_and_lig,
+    _decode_title,
+    _parse_dims_from_xr_attrs,
+    _extract_zvar_from_ds,
+    scan_var,
+)
+
+
+from .utils import (
+    read_dates_from_pastp,
+    assign_coords,
+    stack_coords,
+    dropna,
+    fillna
+)
 
 from typing import Union
 
@@ -30,7 +71,7 @@
         'varname': 'CHARGE',
         'units': 'm',
         'missing_value': 9999.,
-        'standard_name': 'water_table_level',#depth
+        'standard_name': 'water_table_level',
         'long_name':
         'groundwater head'
     },
@@ -44,7 +85,7 @@
     'debit'  : {
         'varname': 'DEBIT',
         'units': 'm3/s',
-        'missing_value': 9999.,
+        'missing_value': 0.,
         'standard_name': '',
         'long_name': 'flow'
     },
@@ -65,198 +106,69 @@
 }
 
 
-def read_dates_from_pastp(fpastp, encoding='ISO-8859-1'):
-    """Read simulation timesteps from a .pastp file
+
+def _parse_attrs(
+    title=None,
+    dims=None,
+    xyfactor=1.,
+    dates=None,
+    is_nested=False,
+    dxlus=None,
+    dylus=None,
+    xcols=None,
+    yligs=None
+):
+    """ Parse attributes for xarray.Dataset
+    nb: attrs must be string, int, float
     """
-    # reading file as raw df - not str ; faster with pandas func
-    pastp = pd.read_csv(
-        fpastp,
-        header=None,
-        encoding=encoding
-    ).squeeze('columns')
-
-    # First, get steady state time
-    idx_0  = pastp.loc[pastp.str.contains(r' \*\*\* D.*but de la simulation.*', regex=True)].index.values[0]
-    date_0 = re.findall(r'[0-9]+', pastp.iloc[idx_0] )
-    
-    # convert as DF
-    timesteps = pd.DataFrame([{
-        'timestep': 0, 
-        'date': datetime(int(date_0[2]), int(date_0[1]), int(date_0[0]) ) 
-    }])
-
-    # Then, get all ending times for transient state
-    idx  = pastp.loc[pastp.str.contains(r'^ \*\*\* Le pas.*\d+: se termine.*', regex=True)]
-    # extract dates from strings
-    dates= pd.DataFrame(
-        idx.str.findall(r'[0-9]+').to_list(),
-        columns=['timestep', 'day', 'month', 'year'],
-        #dtype={'timestep':int, 'day':int, 'month':int, 'year':int}
-    )
-    
-    # assign dtype
-    dates['timestep'] = pd.to_numeric(dates['timestep'])
+    prologue = {
+        'conventions'         :'CF-1.10', # check https://cfconventions.org/
+        'title'               : title if title is not None else '',
+        'marthe_grid_version' : 9.0,
+        'original_dimensions' : 'x,y,z [grids]: ' + '; '.join(
+            [ ' '.join(map(str, x)) for x in dims]
+        ),
+    }
 
-    # convert data as datetime object
-    dates['date'] = pd.to_datetime(dates[['day', 'month', 'year']])
-    dates = dates.drop(['month','year', 'day'], axis=1)
+    grid_attrs = {
+        'lon_resolution': ', '.join(map(str, np.unique(dxlus))),
+        'lat_resolution': ', '.join(map(str, np.unique(dylus))),
+        'scale_factor'  : xyfactor,
+        'nested_grid'   : str(is_nested),
+        'extend'        : "xymin : {} {}; xymax: {} {}".format(
+            np.min(xcols), np.min(yligs), np.max(xcols), np.max(yligs)
+        ),
+    }
+    dates_attrs = {}
+    if isinstance(dates, pd.DatetimeIndex):
+        dates_attrs = {
+            'period'    : '{}-{}'.format(
+                pd.to_datetime(dates.min()).year, pd.to_datetime(dates.max()).year
+            ), # force pd.date_time, case of pastp => numpydatetime64 / # np.datetime_as_string(i, unit='M')
+            'frequency' : '{} day(s)'.format(str(dates.to_series().diff().mean().days)),
+        }
     
-    return pd.concat([timesteps, dates], axis=0)
-
-
-def scan_var(xfile):
-    """ List all variables stored in a Marthe grid file """
-    var = lecsem.modgridmarthe.scan_typevar(xfile) # get a list of unique type_var that are in xfile
-    var = np.char.strip(np.char.decode(var, 'ISO-8859-1')) # decode byte array provided by f2py
-    var = var[var != ''] # get rid of empty element provided by fortran code
-    return var
+    epilogue = {
+        'creation_date' : 'Created on {}'.format(datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ UTC')),
+        'institution'   : 'BRGM, French Geological Survey, Orléans, France',  # TODO: make it optional
+        'comment'       : 'Hydrogeological model created with MARTHE code '\
+                          '(Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code'\
+                          'for hydro-systems modelling. report BRGM/RP-69660-FR).'
+    }
 
-def _read_marthe_grid(xfile, varname='CHARGE', shallow_only=False):
-    """ Read a Marthe grid file
-    using fortran wrapper, for a specific variable
-    
-    Parameters
-    ----------
-    xfile: str
-        Filename to read
-    varname : str
-        string of variable in xfile to get values.
-        Default is CHARGE (groundwater head)
-    
-    Returns
-    -------
-    zvar  : np.array
-        variable read from marthe grid file as numpy ndarray (one vector)
-    zdates: np.array
-        array of dates (from start))
-    isteps: np.array
-        array of indexes of timesteps
-    zxcol : np.array
-        array of x coordinates
-    zylig : np.array
-        array of y coordinates
-    zdxlu : np.array
-        array of dx (equals np.diff(x))
-    zdylu : np.array
-        array of dy (equals np.diff(y))
-    ztitle: np.array
-        title of marthe grid file read
-    dims  : np.array
-        list of dimensions of grid [maingrid[x, y, z], nestedgrid1[...], ...]
-    """
-    nu_zoomx = lecsem.modgridmarthe.scan_nu_zoomx(xfile) # scan nb of nested grids (gig)
-    dims, nbsteps = lecsem.modgridmarthe.scan_dim(xfile, varname, nu_zoomx)
-    nbtot = np.prod(dims, axis=1).sum() # product deprecated => prod // DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
-    if nbtot == 0:
-        raise ValueError(f'Varname ({varname}) not found in xfile. No data to parse.')
-    
-    if shallow_only:
-        res = list(lecsem.modgridmarthe.read_grid_shallow( xfile, varname, nbsteps, dims[0][-1] ,nbtot, nu_zoomx ))
-    else:
-        res = list(lecsem.modgridmarthe.read_grid( xfile, varname, nbsteps, nbtot, nu_zoomx))
-    
-    res.append(dims)
-    return res
-
-
-def _transform_xcoords(zxcol, zylig, zdxlu, nlayer=1, factor=1):
-    xcols, dxlus = [], []
-    for igig in range(zxcol.shape[0]):
-        nb = np.extract(zylig[igig] != 1e+20, zylig[igig]).shape[0]
-        xcols2 = np.tile(zxcol[igig], nb)
-        xcols2 = np.extract(xcols2 != 1e+20, xcols2)
-        dxlus2 = np.tile(zdxlu[igig], nb)
-        dxlus2 = np.extract(dxlus2 != 1e+20, dxlus2)
-        if nlayer > 1:
-            # need to paste coords for multilayer to match dims of zvar
-            xcols2 = np.tile(xcols2, nlayer)
-            dxlus2 = np.tile(dxlus2, nlayer)
-        xcols.append(xcols2)
-        dxlus.append(dxlus2)
-    xcols = np.hstack(xcols)*factor
-    dxlus = np.hstack(dxlus)*factor
-    return xcols, dxlus
-
-def _transform_ycoords(zxcol, zylig, zdylu, nlayer=1, factor=1):
-    yligs, dylus = [], []
-    for igig in range(zylig.shape[0]):
-        yligs2, dylus2 = [], []
-        for i in range(len(zxcol[igig][zxcol[igig] != 1e+20])):
-            tmp1 = zylig[igig][zylig[igig] != 1e+20]
-            tmp2 = zdylu[igig][zdylu[igig] != 1e+20]
-            if nlayer > 1:
-                # need to paste coords for multilayer to match dims of zvar
-                tmp1 = np.tile(tmp1, nlayer)
-                tmp2 = np.tile(tmp2, nlayer)
-            yligs2.append(tmp1)
-            dylus2.append(tmp2)
-        yligs2 = np.vstack(yligs2).transpose().flatten()
-        dylus2 = np.vstack(dylus2).transpose().flatten()
-        yligs.append(yligs2)
-        dylus.append(dylus2)
-    yligs = np.hstack(yligs)*factor
-    dylus = np.hstack(dylus)*factor
-    return yligs, dylus
-
-def _set_layers(dims):
-    # add layer to match dim of zvar
-    zlay = []
-    for igig in range(len(dims)):
-        for z in range(dims[igig][-1]):
-            zlay.append(np.tile(z+1, dims[igig][0] * dims[igig][1]))
-    zlay = np.hstack(zlay)
-    return zlay
-
-def _decode_title(title, encoding='ISO-8859-1'):
-    title = title.decode(encoding)
-    title = re.search('(\D+)\s+Pas\s+\d+;', title)
-    if title is not None:
-        return title.group(1).strip()
-    else:
-        return None
-
-def _get_col_and_lig(dims):
-    """ Add col/lig indexes (i,j) """
-    # cols are xcoords index (x are sorted asc)
-    # ligs are ycoords index (y are sorted dsc)
-    # dims = [maingrid[x, y, z], gig[x, y, z], ...]
-    # cols and lig indexes are a range from 1 to len(x), for each grid. Same index col/lig, for every layer (z dim).
-    cols, ligs = [], []
-    for grid in dims:
-        zcols = np.arange(1, grid[0]+1)
-        zligs = np.arange(1, grid[1]+1) # lig 0 is max y ; max lig is min y.
-        # add res tiled on y and z dims, for xcols
-        cols = np.append(cols, np.tile(zcols, grid[-1] * grid[1]))
-        # for ylig, it's a bit different, we need to map ylig on shape of xcol for each ylig, then tile on z dim
-        # e.g. we need:
-        # [xcol] 1, 2, 3, 4, 5
-        # [ylig]   [ value ]
-        # 1,     1  1  1  1  1
-        # 2,     2  2  2  2  2
-        # 3,     ...
-        # but flattened, so: repeat ylig value on xcol size, then tile on z_dim size
-        ligs = np.append( ligs, np.tile( np.repeat(zligs, zcols.shape[0]), grid[-1] ) )
-    return cols.astype(np.int32), ligs.astype(np.int32)
-
-def _get_id_grid(dims):
-    # add id grid : 0 = main grid, >0 = nested grid(s)
-    id_grids = []
-    for igig in range(len(dims)):
-        id_grids.append(np.tile(igig, np.prod(dims[igig])))
-    id_grids = np.hstack(id_grids)
-    return id_grids
+    return {**prologue, **grid_attrs, **dates_attrs, **epilogue}
 
 
 
 def load_marthe_grid(
     filename: str,
     varname: Union[str, None] = None,
-    dates=None,
     fpastp: Union[str, None] = None,
+    dates=None,
     nanval: Union[int, float, None] = None,
     drop_nan: bool = False,
     xyfactor: Union[int, float] = 1.,
-    # shallow_only=False,
+    shallow_only=False,
     keepligcol: bool = False,
     add_id_grid: bool = False,
     title: Union[str, None] = None,
@@ -266,6 +178,7 @@ def load_marthe_grid(
         'projection'      : 'epsg:27572',
         'domain'          : 'FR-France',
     },
+    engine: str = 'xarray',
     verbose: bool=False,
 ):
     """ Read Marthe Grid File as xarray.Dataset 
@@ -273,34 +186,32 @@ def load_marthe_grid(
     The gridfile is read as a sequence: the variable for all layer
     for main grid, then all layer for nested grids, is stored in
     a 1D vector for every timestep. A single spatial identifier
-    `zone` is used to map spatial coordinates.
+    ``zone`` is used to map spatial coordinates.
 
     Before plot operations, user can assign coordinates (set x,y
     as dimension coordinates and drop zone) to get 2-D arrays (or
     3D arrays if multilayer) for every timesteps.
 
-    
     Parameters
     ----------
-    
         filename: str
-            A path to marthegrid file (*.permh, *.out, etc.)
+            A path to marthegrid file (.permh, .out, etc.)
         
         varname : str, Optionnal
-            variable to access in martgrid file, e.g `CHARGE` for groundwater head. See marthegrid file content.
+            variable to access in martgrid file, e.g ``CHARGE`` for groundwater head. See marthegrid file content.
             if None  is passed (default), function will scan all varnames in filename and keep first only
             if 'all' is passed,  function will scan all varnames in filename and keep all. 
             All datavars are added to dataset, using recursive call to func
             if wrong variable name is passed, empty data will be returned.
         
+        fpastp: str, Optionnal
+            A pastp file to read for dates
+        
         dates: sequence, Optionnal
             Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of datetime/np.datetime objects.
             If no dates (or no fpastp) is provided, a fake sequence of dates from 1850 to 1900 will
             be used for xarray object
         
-        fpastp: str, Optionnal
-            A pastp file to read for dates
-        
         nanval: float, Optionnal
             A code value for nan values. Default is 9999.
         
@@ -312,6 +223,9 @@ def load_marthe_grid(
             factor to transform X and Y values. e.g.: 1000 to convert km XY to meters.
             Default is 1.
         
+        shallow_only: bool, Optionnal
+            Boolean to read only the first layer. Default is False.
+        
         keepligcol: bool, Optionnal
             Add columns (col) and rows (lig) index (from 1 to n), Default is False.
         
@@ -328,10 +242,18 @@ def load_marthe_grid(
         model_attrs: dict, Optionnal
             Dictionnary of attributes to add to Dataset.
             by default, gis attrs are added and can be modified
-            >>>    'resolution_units': 'm',
-            >>>    'projection'      : 'epsg:27572',
-            >>>    'domain'          : 'FR-France',
+            
+            >>> {
+            ...    'resolution_units': 'm',
+            ...    'projection'      : 'epsg:27572',
+            ...    'domain'          : 'FR-France'
+            ... }
         
+        engine: str, Optionnal
+            Engine to use for returned object. Default is 'xarray', which return xarray.Dataset object.
+            Another option is 'numpy', which return a list of numpy arrays :
+            [zvar, zdates, isteps, zxcol, zylig, zdxlu, zdylu, ztitle, dims]
+
         verbose: bool, Optionnal
             Print some information about execution in stdout.
             Default is False.
@@ -340,7 +262,6 @@ def load_marthe_grid(
     -------
         ds: xr.Dataset
             A xarray.Dataset object containing values and attributes read from Marthe grid file.
-    
     """
     
     # Fortran error cause sys exit. To avoid this, we add a test on file first
@@ -359,14 +280,14 @@ def load_marthe_grid(
             # if no varname read from scan, it can be a bug (some version of marthe did not write field name in metadata)
             raise ValueError('No variable founded in file, please consider check file or clean it (cleanmgrid util or winmarthe)')
 
-    elif varname == 'all':
+    elif varname.lower() == 'all':
         varname  = scan_var(filename)
         # -- recursive call
         arrays = []
         for var in varname:
             arrays.append( load_marthe_grid(
-                filename, var, dates, fpastp, nanval, dropna, xyfactor,
-                keepligcol, add_id_grid, title, var_attrs, model_attrs, verbose
+                filename, var, fpastp, dates, nanval, drop_nan, xyfactor, shallow_only,
+                keepligcol, add_id_grid, title, var_attrs, model_attrs, engine, verbose
             ) )
         return xr.merge(arrays)
         
@@ -377,9 +298,16 @@ def load_marthe_grid(
     (
         zvar, zdates, isteps, zxcol,
         zylig, zdxlu, zdylu, ztitle, dims
-    ) = _read_marthe_grid(filename, varname) #, shallow_only=shallow_only)
+    ) = _read_marthe_grid(filename, varname, shallow_only=shallow_only)
+
+    if engine == 'numpy':
+        return [zvar, zdates, isteps, zxcol, zylig, zdxlu, zdylu, ztitle, dims]
     
     # --- transform data and parse into xarray.Dataset
+    if shallow_only:
+        print('NOT YET AVAILABLE')
+        # TODO shape (time, gig, values) -> (time, values)
+    
     if title is None:
         title = _decode_title(ztitle)
     
@@ -393,7 +321,7 @@ def load_marthe_grid(
     xcols, dxlus = _transform_xcoords(zxcol, zylig, zdxlu, nlayer=dims[0][-1], factor=xyfactor)
     yligs, dylus = _transform_ycoords(zxcol, zylig, zdylu, nlayer=dims[0][-1], factor=xyfactor)
     
-    if varname == '': varname = 'variable' # security if force mode
+    if varname == '': varname = 'variable'  # security if force mode
     vattrs = VARS_ATTRS.get(varname.lower(), {})
     vattrs.update(var_attrs)
     dic_data = {
@@ -404,6 +332,10 @@ def load_marthe_grid(
         'dy' : ("zone", dylus)
     }
     
+    # dic_coords = {
+    #
+    # }
+    
     if keepligcol:
         if is_nested:
             add_id_grid = True # force to add id_grid if nested grid
@@ -419,7 +351,7 @@ def load_marthe_grid(
     if dims[0][-1] > 1:
         zlus = _set_layers(dims)
         zattrs = {'units': '-', 'axis': 'Z', 'positive': 'down', 'standard_name': 'depth', 'long_name': 'aquifer_layer'} # not if full 3D ! TODO better
-        dic_data['z'] = ("zone", zlus, zattrs) # ajout lay
+        dic_data['z'] = ("zone", zlus, zattrs) # add lay
         
     if fpastp is not None:
         # add dates from a pastp file, case of non-uniform timesteps or edition not set every timestep
@@ -430,154 +362,68 @@ def load_marthe_grid(
         if verbose:
             print('Warning: No dates or fpastp provided, using default (fake) dates to constructed xarray object.')
         dates = pd.date_range('1850', '1900', len(isteps))
-    
-    
+        # dates = np.arange(1, len(isteps)+1) # TODO use integers for time is no dates provided ?
+
+    # --- Create xarray.Dataset object
     ds = xr.Dataset(
         data_vars=dic_data,
         coords={
             'time': dates,
-            'zone': range(1, zvar.shape[1] + 1),
-            # 'x': (['zone'], xcols),
-            # 'y': (['zone'], yligs),
-            # 'domain_size': dims, # add non dimension coordinate for info
-            # 'domain_origin': [(x0, y0) for igig in grids], # add non dimension coordinate for info
+            'zone': np.arange(1, zvar.shape[1] + 1, dtype=np.int32),
         },
         attrs={
-            # attrs must be string, int, float
-            'conventions'         :'CF-1.10', # check https://cfconventions.org/
-            'title'               : title if title is not None else '',
-            'marthe_grid_version' : 9.0,
-            'original_dimensions' : 'x,y,z [grids]: ' + '; '.join([ ' '.join(map(str, x)) for x in dims]),
-            'lon_resolution'      : ', '.join(map(str, np.unique(dxlus))),
-            'lat_resolution'      : ', '.join(map(str, np.unique(dylus))),
-            'scale_factor'        : xyfactor,
-            'nested_grid'         : str(is_nested),
-            'extend'              : "xymin : {} {}; xymax: {} {}".format(np.min(xcols), np.min(yligs), np.max(xcols), np.max(yligs)),
-            'frequency'           : '{} day(s)'.format(str(dates.to_series().diff().mean().days)),
-            'period'              : '{}-{}'.format(pd.to_datetime(dates.min()).year, pd.to_datetime(dates.max()).year), # force pd.date_time, case of pastp => numpydatetime64 / # np.datetime_as_string(i, unit='M')
-            'creation_date'       : 'Created on {}'.format(datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ UTC')),
-            'institution'         : 'BRGM, French Geological Survey, Orléans, France',
-            'comment'             : 'Hydrogeological model created with MARTHE code (Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code for hydro-systems modelling. report BRGM/RP-69660-FR).',
-            **model_attrs,
+            **_parse_attrs(title, dims, xyfactor, dates, is_nested, dxlus, dylus, xcols, yligs),
+            **model_attrs
         }
     )
     
+    # add non-dimensionnal coordinates
+    # 'xc': (['zone'], xcols), # TODO coordinates directly as coords depending on dims ?
+    # 'yc': (['zone'], yligs),
+    # 'domain_size': dims, # add non dimension coordinate for info
+    # 'domain_origin': [(x0, y0) for igig in grids], # add non dimension coordinate for info
+    # ds = ds.assign_coords(  # or toto.set_coords(['time', 'zone', 'x', 'y', 'z', 'dx', 'dy'])
+        # dic_coords
+    # )
+    
     if drop_nan:
         if nanval is None:
-            nanval = vattrs.get('missing_value', 9999.) # if no  user defined nanval, try to get corresponding val in dict then 9999. if not present
-        if varname == 'permeab' and is_nested:
-            nanval = [nanval, -9999.]
+            nanval = vattrs.get('missing_value', 9999.)  # if no  user defined nanval, try to get corresponding val in dict then 9999. if not present
+        
+        if not isinstance(nanval, (list, tuple)):
+            nanval = [nanval]
+        elif isinstance(nanval, tuple):
+            nanval = list(nanval)
+        
+        if (varname.lower() == 'permeab' or filename.endswith("permh")) and is_nested:
+            if -9999. not in nanval:
+                nanval += [-9999.]
+        
         ds = dropna(ds, varname, nanval)
+        ds['zone_all'] = ('zone', ds['zone'].data)  # keep old zone as variable for write method // memo: remove tuple to keep as dim
+        ds['zone'] = np.arange(1, np.size(ds['zone'].data) + 1, dtype=np.int32)  # rearange zone
         
     # FIXME better, prevent bug at write : https://github.com/pydata/xarray/issues/7722 // https://stackoverflow.com/questions/65019301/variable-has-conflicting-fillvalue-and-missing-value-cannot-encode-data-when
     # del ds[varname.lower()].encoding['missing_value']
     return ds
 
 
-def dropna(ds, varname: str, nanval: Union[list, float]):
-    """ Drop values corrresponding to NaN (marthe convention, eg. code 9999.) 
-    for 1D (or 2D (time, zone)) array
-    zone must me a coordinate dimension.
-    
-    Returns
-    -------
-    dataset where variable != nanval
-    """
-    if isinstance(nanval, (float, int, str)):
-        nanval = [nanval]
-    # mask = ds[varname.lower()].where(ds[varname.lower()] != nanval).dropna(dim='zone') # drop nanval
-    mask = ds[varname.lower()].where(~ds[varname.lower()].isin(nanval)).dropna(dim='zone') # drop nanval
-    ds_no_nan = ds.sel(zone=mask['zone'])
-    return ds_no_nan
-
-def subset(ds, varname: str, value: Union[list, float]):
-    """ Subset dataset based on variable name and value.
-    --> inverse of :py:func:`dropna`
-    
-    Returns
-    -------
-    dataset where variable = value
-    """
-    if isinstance(value, (float, int, str)):
-        value = [value]
-    mask = ds[varname.lower()].where(ds[varname.lower()].isin(value)).dropna(dim='zone')
-    ds_filter = ds.sel(zone=mask['zone'])
-    return ds_filter
-
-def replace(ds, varname: str, value: float, replace: float):
-    """ Replace a value in xr.Dataset for a variable
-    """
-    ds[varname].data = np.where(ds[varname].data == value, replace, ds[varname].data)
-    return ds
-    
-def fillna(ds, varname, value):
-    """ Replace real nan (np.nan) value in dataset[varname], edge case of :py:func:`replace()`
-    """
-    ds[varname].data = np.where(np.isnan(ds[varname].data), value, ds[varname].data)
-    return ds
-
-
-def assign_coords(da_in, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zone_label='zone'):
-    """ assign coords to transform a 1D or 2D (time, zone) array to 3D or 4D
-    """
-    if len(coords) == 3:
-        z_coords = da_in.get(coords[2], None) # assert z is here, or bypass
-    else:
-        z_coords = None
-    
-    if add_lay is False:
-        # in some case, even if z is included it should not be treated as coord (ex. plot outcrop)
-        z_coords = None
-    
-    da = da_in.assign_coords(
-        #x=(zone_label, np.around(da_in[coords[0]].data, 1) ),
-        x=(zone_label, da_in[coords[0]].data ),
-        y=(zone_label, da_in[coords[1]].data ),
-    )
-    dims = ['y', 'x']
-    
-    if z_coords is not None:
-        da = da.assign_coords(z=(zone_label, da_in[coords[2]].data))
-        dims.insert(0, 'z')
-    
-    da = da.set_index(zone=dims)
-    if not keep_zone:
-        da = da.drop_duplicates(zone_label).unstack(zone_label) # drop duplicates is a security for nested grids, if dropnan was not performed
-    return da.sortby(dims)
-
+def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False):
+    """ Reset a Marthe grid geometry based on permh dataset
 
-def stack_coords(ds, coords=['z', 'y', 'x'], dropna=False):
-    """ Transform a 3 or 4D aray into 1 or 2D array 
-    inverse of  :py:func:`assign_coords`
-    """
-    # create zone index
-    coords = [d for d in coords if d in ds.coords.keys()] # make sure to drop coords that are not present
-    dims = np.prod( [len(ds[d]) for d in coords] ) # create new zone dim
-    zone = np.arange(dims)
-    
-    # stack coords
-    ds2 = ds.copy().stack(zone=coords) # multiindex zone grouping coords key
-    
-    # keep only zone as dim
-    ds3 = ds2.drop_vars(['zone'] + coords).assign_coords(zone=('zone', zone))
+    This function is useful/used, to reconstruct the geometry of the dataset
+    (if NaN were dropped for example), before writting marthe grid, where the full
+    domain is needed (including non active cells).
     
-    # get back xy[z] as var
-    for c in coords:
-        ds3[c] = ('zone', ds2[c].data)
+    Note
+    ----
     
-    if dropna:
-        ds3 = ds3.dropna(dim='zone')
-    return ds3
-
-
-
-def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False):
-    """ Reset a Marthe grid geometry based on permh dataset
-    All values (nan, nested grid margins) should be included in
-    permh dataset.
     Join is performed with xy[z] (if xy are present in coords) or zone
-    to get zone back in full domain (if dropped, or nan were dropped, etc.)
-    Useful before writting marthe grid (full domain is needed)
+    to get zone back in full domain (if dropped, or nan were dropped, etc.).
+
+    If nan were dropped during :py:func:`gridmarthe.load_grid_marthe()`, 'zone_all'
+    was added and will be used (this variable store the zone index before the reindexing
+    during :py:func:`gridmarthe.dropna`).
     
     Parameters
     ----------
@@ -599,11 +445,17 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False):
         xr.Dataset containing original variables and geometry read from permh file
     """
     da = ds.copy()
+    # All values (nan, nested grid margins) should be included in permh dataset.
     permh = load_marthe_grid(path_to_permh, drop_nan=False, add_id_grid=True, keepligcol=True, verbose=False)
-
+    
+    
     if 'x' in da.coords.keys():
         da = stack_coords(da, dropna=True)
         coords = [x for x in da.coords.keys() if x in ['x', 'y', 'z']] # if xy assert only existing coords in xyz
+    elif 'zone_all' in da.keys():
+        da['zone'] = da['zone_all'].data  # restore zone_all (zone before reorder after dropnan) for merge
+        da = da.drop('zone_all')
+        coords = ['zone']
     else:
         coords = ['zone']
 
@@ -633,45 +485,12 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False):
 
 
 
-def _parse_dims(str_dims):
-    if str_dims is None:
-        return None
-    else:
-        return [list(map(int, x.split(' '))) for x in str_dims.strip('x, y, z [grids]: ').split('; ')]
-
-
-# def sort_data(ds):
-    # TODO:
-    # s'assurer de l'ordre si ds a été retravaillé :
-        # order by z, y, x, dx
-    # extraire les x, y, dx, dy selon dims = pas de doublons
-    # print('not yet available')
-
-def _extract_zvar(ds, varname):
-    
-    zvar    = ds[varname].data
-    zdates  = ds.time.data
-    zxcol   = ds.x.data
-    zylig   = ds.y.data
-    zdxlu   = ds.dx.data
-    zdylu   = ds.dy.data
-    # from pymarthe : dx, dy = map(abs, map(np.gradient, [xcc,ycc])) # Using the absolute gradient TODO
-    ztitle  = ds.attrs.get('title')
-    izdates = _datetime64_to_float(zdates)
-
-    return (
-        zvar, zdates,
-        zxcol, zylig, zdxlu, zdylu,
-        ztitle, izdates
-    )
-
-
 def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str = None, title=None, dims=None, debug=False):
     """ Write Dataset as MartheGrid v9 file
     
     ds should contain x, y, dx, dy, attrs[['title', 'original_dimensions']]
-    in case of error, please use :py:func:`gm.reset_geometry` first.
-    When providing a path to `file_permh` argument, :py:func:`gm.reset_geometry` is called automatically.
+    in case of error, please use :py:func:`gridmarthe.reset_geometry` first.
+    When providing a path to ``file_permh`` argument, :py:func:`gridmarthe.reset_geometry` is called automatically.
     
     A good pratice is to provide the permh file when writing dataset to marthegrid format.
     
@@ -711,7 +530,7 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str
         if only main grid : `[[x,y,z]]`
         if None (default, dims will be parsed from ds.attrs['original_dimensions'] which is added
         when read with :py:func:`gridmarthe.load_marthe_grid`. If not present (lost in some computation for example),
-        please use py:func:`gridmarthe.reset_geometry(ds)` or provide list of dims manually.
+        please use py:func:`gridmarthe.reset_geometry` or provide list of dims manually.
     
     debug: bool, Optionnal (default is False).
     
@@ -725,7 +544,7 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str
     ds2 = ds.copy()
     
     if dims is None:
-        dims = _parse_dims(ds2.attrs.get('original_dimensions'))
+        dims = _parse_dims_from_xr_attrs(ds2.attrs.get('original_dimensions'))
     
     if dims is None:
         raise ValueError("""Original dimensions cannot be None.\
@@ -753,14 +572,14 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str
         zvar, zdates,
         zxcol, zylig, zdxlu, zdylu,
         ztitle, izdates
-    ) = _extract_zvar(ds2, varname)
+    ) = _extract_zvar_from_ds(ds2, varname)
     
     if title is None and ztitle == '':
         title = 'Marthe Grid ' # dummy arg to set type as string
     
     # call fortran module to write marthe grid
-    status = lecsem.modgridmarthe.write_grid(
-        xvar=zvar,
+    status = modgridmarthe.write_grid(
+        zvar=zvar,
         xcol=zxcol,
         ylig=zylig,
         dxlu=zdxlu,
@@ -784,10 +603,3 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str
     
     return status 
 
-
-
-if __name__ == '__main__':
-    
-    print(lecsem.__doc__)
-    print(lecsem.modgridmarthe.__doc__)
-    
diff --git a/src/gridmarthe/lecsem/__init__.py b/src/gridmarthe/lecsem/__init__.py
index 5712458..8549712 100644
--- a/src/gridmarthe/lecsem/__init__.py
+++ b/src/gridmarthe/lecsem/__init__.py
@@ -1 +1,14 @@
-from .lecsem import *
+from ._parse_gridmarthe import (
+    modgridmarthe,
+    _read_marthe_grid,
+    _transform_xcoords,
+    _transform_ycoords,
+    _set_layers,
+    _get_id_grid,
+    _get_col_and_lig,
+    _decode_title,
+    _extract_zvar_from_ds,
+    _parse_dims_from_xr_attrs,
+    scan_var,
+)
+
diff --git a/src/gridmarthe/lecsem/_parse_gridmarthe.py b/src/gridmarthe/lecsem/_parse_gridmarthe.py
new file mode 100644
index 0000000..55dfe50
--- /dev/null
+++ b/src/gridmarthe/lecsem/_parse_gridmarthe.py
@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+import re
+import numpy as np
+
+from .lecsem import modgridmarthe
+from ..utils import _datetime64_to_float
+
+
+def scan_var(xfile):
+    """ List all variables stored in a Marthe grid file """
+    var = modgridmarthe.scan_typevar(xfile)  # get a list of unique type_var that are in xfile
+    var = np.char.strip(np.char.decode(var, 'ISO-8859-1'))  # decode byte array provided by f2py
+    var = var[var != '']  # get rid of empty element provided by fortran code
+    return var
+
+
+def _read_marthe_grid(xfile, varname='CHARGE', shallow_only=False):
+    """ Read a Marthe grid file
+    using fortran wrapper, for a specific variable
+    
+    Parameters
+    ----------
+    xfile: str
+        Filename to read
+    varname : str
+        string of variable in xfile to get values.
+        Default is CHARGE (groundwater head)
+    
+    Returns
+    -------
+    zvar  : np.array
+        variable read from marthe grid file as numpy ndarray (one vector)
+    zdates: np.array
+        array of dates (from start))
+    isteps: np.array
+        array of indexes of timesteps
+    zxcol : np.array
+        array of x coordinates
+    zylig : np.array
+        array of y coordinates
+    zdxlu : np.array
+        array of dx (equals np.diff(x))
+    zdylu : np.array
+        array of dy (equals np.diff(y))
+    ztitle: np.array
+        title of marthe grid file read
+    dims  : np.array
+        list of dimensions of grid [maingrid[x, y, z], nestedgrid1[...], ...]
+    """
+    nu_zoomx = modgridmarthe.scan_nu_zoomx(xfile) # scan nb of nested grids (gig)
+    dims, nbsteps = modgridmarthe.scan_dim(xfile, varname, nu_zoomx)
+    nbtot = np.prod(dims, axis=1).sum() # product deprecated => prod // DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
+    if nbtot == 0:
+        raise ValueError(f'Varname ({varname}) not found in xfile. No data to parse.')
+    
+    if shallow_only:
+        res = list(modgridmarthe.read_grid_shallow( xfile, varname, nbsteps, dims[0][-1] ,nbtot, nu_zoomx ))
+    else:
+        res = list(modgridmarthe.read_grid( xfile, varname, nbsteps, nbtot, nu_zoomx))
+    
+    res.append(dims)
+    return res
+
+
+def _transform_xcoords(zxcol, zylig, zdxlu, nlayer=1, factor=1):
+    xcols, dxlus = [], []
+    for igig in range(zxcol.shape[0]):
+        nb = np.extract(zylig[igig] != 1e+20, zylig[igig]).shape[0]
+        xcols2 = np.tile(zxcol[igig], nb)
+        xcols2 = np.extract(xcols2 != 1e+20, xcols2)
+        dxlus2 = np.tile(zdxlu[igig], nb)
+        dxlus2 = np.extract(dxlus2 != 1e+20, dxlus2)
+        if nlayer > 1:
+            # need to paste coords for multilayer to match dims of zvar
+            xcols2 = np.tile(xcols2, nlayer)
+            dxlus2 = np.tile(dxlus2, nlayer)
+        xcols.append(xcols2)
+        dxlus.append(dxlus2)
+    xcols = np.hstack(xcols)*factor
+    dxlus = np.hstack(dxlus)*factor
+    return xcols, dxlus
+
+
+def _transform_ycoords(zxcol, zylig, zdylu, nlayer=1, factor=1):
+    yligs, dylus = [], []
+    for igig in range(zylig.shape[0]):
+        yligs2, dylus2 = [], []
+        for i in range(len(zxcol[igig][zxcol[igig] != 1e+20])):
+            tmp1 = zylig[igig][zylig[igig] != 1e+20]
+            tmp2 = zdylu[igig][zdylu[igig] != 1e+20]
+            if nlayer > 1:
+                # need to paste coords for multilayer to match dims of zvar
+                tmp1 = np.tile(tmp1, nlayer)
+                tmp2 = np.tile(tmp2, nlayer)
+            yligs2.append(tmp1)
+            dylus2.append(tmp2)
+        yligs2 = np.vstack(yligs2).transpose().flatten()
+        dylus2 = np.vstack(dylus2).transpose().flatten()
+        yligs.append(yligs2)
+        dylus.append(dylus2)
+    yligs = np.hstack(yligs)*factor
+    dylus = np.hstack(dylus)*factor
+    return yligs, dylus
+
+
+def _set_layers(dims):
+    # add layer to match dim of zvar
+    zlay = []
+    for igig in range(len(dims)):
+        for z in range(dims[igig][-1]):
+            zlay.append(np.tile(z+1, dims[igig][0] * dims[igig][1]))
+    zlay = np.hstack(zlay)
+    return zlay.astype(np.int32)
+
+
+def _decode_title(title, encoding='ISO-8859-1'):
+    title = title.decode(encoding)
+    title = re.search(r'(\D+)\s+Pas\s+\d+;', title)
+    if title is not None:
+        return title.group(1).strip()
+    else:
+        return None
+
+
+def _get_col_and_lig(dims):
+    """ Add col/lig indexes (i,j) """
+    # cols are xcoords index (x are sorted asc)
+    # ligs are ycoords index (y are sorted dsc)
+    # dims = [maingrid[x, y, z], gig[x, y, z], ...]
+    # cols and lig indexes are a range from 1 to len(x), for each grid. Same index col/lig, for every layer (z dim).
+    cols, ligs = [], []
+    for grid in dims:
+        zcols = np.arange(1, grid[0]+1)
+        zligs = np.arange(1, grid[1]+1) # lig 0 is max y ; max lig is min y.
+        # add res tiled on y and z dims, for xcols
+        cols = np.append(cols, np.tile(zcols, grid[-1] * grid[1]))
+        # for ylig, it's a bit different, we need to map ylig on shape of xcol for each ylig, then tile on z dim
+        # e.g. we need:
+        # [xcol] 1, 2, 3, 4, 5
+        # [ylig]   [ value ]
+        # 1,     1  1  1  1  1
+        # 2,     2  2  2  2  2
+        # 3,     ...
+        # but flattened, so: repeat ylig value on xcol size, then tile on z_dim size
+        ligs = np.append( ligs, np.tile( np.repeat(zligs, zcols.shape[0]), grid[-1] ) )
+    return cols.astype(np.int32), ligs.astype(np.int32)
+
+
+def _get_id_grid(dims):
+    # add id grid : 0 = main grid, >0 = nested grid(s)
+    id_grids = []
+    for igig in range(len(dims)):
+        id_grids.append(np.tile(igig, np.prod(dims[igig])))
+    id_grids = np.hstack(id_grids)
+    return id_grids
+
+
+def _parse_dims_from_xr_attrs(str_dims):
+    if str_dims is None:
+        return None
+    else:
+        return [list(map(int, x.split(' '))) for x in str_dims.strip('x, y, z [grids]: ').split('; ')]
+
+
+# def sort_data(ds):
+    # TODO:
+    # s'assurer de l'ordre si ds a été retravaillé :
+        # order by z, y, x, dx
+    # extraire les x, y, dx, dy selon dims = pas de doublons
+    # print('not yet available')
+
+
+def _extract_zvar_from_ds(ds, varname):
+    
+    zvar    = ds[varname].data
+    zdates  = ds.time.data
+    zxcol   = ds.x.data
+    zylig   = ds.y.data
+    zdxlu   = ds.dx.data
+    zdylu   = ds.dy.data
+    ztitle  = ds.attrs.get('title')
+    izdates = _datetime64_to_float(zdates)
+
+    return (
+        zvar, zdates,
+        zxcol, zylig, zdxlu, zdylu,
+        ztitle, izdates
+    )
+
+
+if __name__ == '__main__':
+    
+    # print(_lecsem.__doc__)
+    print(modgridmarthe.__doc__)
+    
+
diff --git a/src/gridmarthe/lecsem/edsemigl.f90 b/src/gridmarthe/lecsem/edsemigl.f90
index d798511..4f87236 100644
--- a/src/gridmarthe/lecsem/edsemigl.f90
+++ b/src/gridmarthe/lecsem/edsemigl.f90
@@ -1,4 +1,25 @@
-! (Provenance MARTHE, Fichier : edsemi.f90, convert utf-8 )
+! SPDX-License-Identifier: GPL-3.0-or-later
+! Copyright 2024, BRGM
+! 
+! This file is part of gridmarthe.
+! 
+! Gridmarthe is free software: you can redistribute it and/or modify it under the
+! terms of the GNU General Public License as published by the Free Software
+! Foundation, either version 3 of the License, or (at your option) any later
+! version.
+! 
+! Gridmarthe is distributed in the hope that it will be useful, but WITHOUT ANY
+! WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+! PARTICULAR PURPOSE. See the GNU General Public License for more details.
+! 
+! You should have received a copy of the GNU General Public License along with
+! Gridmarthe. If not, see .
+!
+!
+! (from MARTHE, file : edsemi.f90, convert utf-8 )
+!
+! MARTHE, Copyright (c) 1990-2024 BRGM
+!
       SUBROUTINE EDSEMI7_0(FONC, NKOL, NLIG, XCOL, YLIG, X0, Y0, INVY &
        ,TITSEM, IEREDI, IOUMAI, DXLU, DYLU)
 !=================================================================================
@@ -193,6 +214,7 @@ END SUBROUTINE Ecrit_Opti_12Car
       ENDDO
       IF ((NLIG <= 0).OR.(NKOL <= 0)) THEN
          IEREDI = 2
+         print *, 'nlig or nkol <= 0'
          GO TO 999
       ENDIF
 !     =========================================================
@@ -210,11 +232,14 @@ END SUBROUTINE Ecrit_Opti_12Car
          ENDIF
          XP = AUX
       ENDDO
+      if (IEREDI == 2) then
+         print *, 'Writing marthe grid Fortran error: X not sorted'
+         ! GO TO 999
+      endif
 !     ===================================================================
 !      Dans cette version : On continue si X = XP ou Y = YP (IEREDI = 1)
 !                       Mais on sort si si X < XP ou Y < YP (IEREDI = 2)
 !     ===================================================================
-      IF (IEREDI == 2) GO TO 999
       DO I=1,NLIG
          IND = MERGE( I , NLIG-I+1 , (INVY >= 1) )
          AUX = YLIG(IND)
@@ -224,7 +249,10 @@ END SUBROUTINE Ecrit_Opti_12Car
          ENDIF
          YP = AUX
       ENDDO
-      IF (IEREDI == 2) GO TO 999
+      IF (IEREDI == 2) then
+         print *, 'Writing marthe grid Fortran error: Y not sorted'
+         GO TO 999
+      endif
       NTOT = NLIG * NKOL
 !     =========================================
 !      Vérification si la Grille est constante
diff --git a/src/gridmarthe/lecsem/lecsem.f90 b/src/gridmarthe/lecsem/lecsem.f90
index dab9fe5..88c01b5 100644
--- a/src/gridmarthe/lecsem/lecsem.f90
+++ b/src/gridmarthe/lecsem/lecsem.f90
@@ -1,4 +1,25 @@
+! SPDX-License-Identifier: GPL-3.0-or-later
+! Copyright 2024, BRGM
+! 
+! This file is part of gridmarthe.
+! 
+! Gridmarthe is free software: you can redistribute it and/or modify it under the
+! terms of the GNU General Public License as published by the Free Software
+! Foundation, either version 3 of the License, or (at your option) any later
+! version.
+! 
+! Gridmarthe is distributed in the hope that it will be useful, but WITHOUT ANY
+! WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+! PARTICULAR PURPOSE. See the GNU General Public License for more details.
+! 
+! You should have received a copy of the GNU General Public License along with
+! Gridmarthe. If not, see .
+!
+!
 ! (Provenance MARTHE, Fichier : lecsem.f90, convert utf-8 )
+!
+! MARTHE, Copyright (c) 1990-2024 BRGM
+!
       SUBROUTINE LECSEM8_0(X0, Y0, FONC, XCOL, YLIG, NLIG, NKOL, INVERS, TITSEM &
        , IOUCON, LEC, IERLEC, NUMERR, NTOT &
        , LIRE_DXDY, LU_DXDY, LU_XY, DXLU, DYLU)
diff --git a/src/gridmarthe/lecsem/scan_grid.f90 b/src/gridmarthe/lecsem/scan_grid.f90
index 95b4bcc..7eb77c9 100644
--- a/src/gridmarthe/lecsem/scan_grid.f90
+++ b/src/gridmarthe/lecsem/scan_grid.f90
@@ -1,3 +1,20 @@
+! SPDX-License-Identifier: GPL-3.0-or-later
+! Copyright 2024, BRGM
+! 
+! This file is part of gridmarthe.
+! 
+! Gridmarthe is free software: you can redistribute it and/or modify it under the
+! terms of the GNU General Public License as published by the Free Software
+! Foundation, either version 3 of the License, or (at your option) any later
+! version.
+! 
+! Gridmarthe is distributed in the hope that it will be useful, but WITHOUT ANY
+! WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+! PARTICULAR PURPOSE. See the GNU General Public License for more details.
+! 
+! You should have received a copy of the GNU General Public License along with
+! Gridmarthe. If not, see .
+!
 MODULE MODGRIDMARTHE
 !   MODGRIDMARTHE
 !   -------------
@@ -30,8 +47,9 @@ MODULE MODGRIDMARTHE
     REAL, DIMENSION(999) :: YLIG        ! YLIG  = Tableau des ordonnées (si LU_XY > 0)
     REAL, DIMENSION(999) :: DXLU        ! DXLU  = DX lus si LU_DXDY > 0
     REAL, DIMENSION(999) :: DYLU        ! DYLU  = DY lus si LU_DXDY > 0
-    REAL    :: X0                       ! X0    = Abscisse du cote ouest de la colonne n°1    (si LU_XY > 0)
-    REAL    :: Y0                       ! Y0    = Ordonnée du    bas     de la ligne   n°NLIG (si LU_XY > 0)
+    REAL  :: X0                         ! X0    = Abscisse du cote ouest de la colonne n°1    (si LU_XY > 0)
+    REAL  :: Y0                         ! Y0    = Ordonnée du    bas     de la ligne   n°NLIG (si LU_XY > 0)
+    REAL  :: DATE                       ! DATE     = Date associée à la Grille
     REAL, DIMENSION(999*999) :: FONC    ! FONC  = Tableau des valeurs lues
     INTEGER :: IERLEC                   ! IERLEC =  0 Si normal
                                         ! IERLEC = -1 Si erreur dans les nombres de Ligne, Colonne ou Panneau
@@ -52,7 +70,6 @@ MODULE MODGRIDMARTHE
     INTEGER    :: NUMERR                ! Maille NUMERR
     INTEGER    :: NCOUC_MX              ! NCOUC_MX = Nombre maxi de Couches
     INTEGER    :: NU_ZOOMX              ! NU_ZOOMX = Nombre maxi de Gigognes
-    REAL       :: DATE                  ! DATE     = Date associée à la Grille
 
 CONTAINS
 
@@ -141,7 +158,8 @@ END SUBROUTINE SCAN_DIM
     !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     ! =============================================================================!
     !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-    SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES, KSTEPS, PXCOL, PYLIG, PDXLU, PDYLU, TITSEM)
+    SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES, &
+        KSTEPS, PXCOL, PYLIG, PDXLU, PDYLU, TITSEM)
         !
         IMPLICIT NONE
         !
@@ -151,8 +169,8 @@ SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES,
         INTEGER, INTENT(IN)                           :: KNU_ZOOMX
         INTEGER, DIMENSION(KNBSTEP), INTENT(OUT)      :: KSTEPS
         REAL(KIND=4), DIMENSION(KNBSTEP), INTENT(OUT) :: PDATES
-        REAL(KIND=4), DIMENSION(KNBSTEP, KNBTOT), INTENT(OUT) :: PVAR
         REAL(KIND=4), DIMENSION(KNU_ZOOMX + 1, 999), INTENT(OUT) :: PXCOL, PYLIG, PDXLU, PDYLU
+        REAL(KIND=8), DIMENSION(KNBSTEP, KNBTOT), INTENT(OUT) :: PVAR
         CHARACTER (LEN=132), INTENT(OUT)              :: TITSEM             ! Modifs AM: ajout TITSEM dans les sorties de la subroutine (+ ajout en `dummy argument` càd réf dans la list d'arg de la procedure)
         !
         INTEGER    :: ISTEPINC, ISTEP_TEMP, INTOT_TEMP
@@ -200,13 +218,13 @@ SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES,
                     ISTEP_TEMP = ISTEP
                     INTOT_TEMP = 1
                     KSTEPS(ISTEPINC) = ISTEP
-                    PDATES(ISTEPINC) = DATE
+                    PDATES(ISTEPINC) = real(DATE, 4)
                 ENDIF
                 IF (ISTEPINC == 1 .AND. N_COUCH == 1) THEN
-                    PXCOL(NU_ZOO + 1, :NKOL) = XCOL(:NKOL)
-                    PYLIG(NU_ZOO + 1, :NLIG) = YLIG(:NLIG)
-                    PDXLU(NU_ZOO + 1, :NKOL) = DXLU(:NKOL)
-                    PDYLU(NU_ZOO + 1, :NLIG) = DYLU(:NLIG)
+                    PXCOL(NU_ZOO + 1, :NKOL) = real(XCOL(:NKOL), 4)
+                    PYLIG(NU_ZOO + 1, :NLIG) = real(YLIG(:NLIG), 4)
+                    PDXLU(NU_ZOO + 1, :NKOL) = real(DXLU(:NKOL), 4)
+                    PDYLU(NU_ZOO + 1, :NLIG) = real(DYLU(:NLIG), 4)
                 ENDIF
                 PVAR(ISTEPINC, INTOT_TEMP:INTOT_TEMP + NTOT -1) = FONC(:NTOT)
                 INTOT_TEMP = INTOT_TEMP + NTOT
@@ -221,7 +239,7 @@ END SUBROUTINE READ_GRID
     ! =============================================================================!
     !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     SUBROUTINE READ_GRID_SHALLOW(XFILE, XTYP_DON, KNBSTEP, KN_COUCHMX, KNBTOT, &
-        KNU_ZOOMX, PVAR, PDATES, KSTEPS, PXCOL, PYLIG, PDXLU, PDYLU)
+        KNU_ZOOMX, PVAR, PDATES, KSTEPS, PXCOL, PYLIG, PDXLU, PDYLU, TITSEM)
         !
         IMPLICIT NONE
         !
@@ -232,13 +250,14 @@ SUBROUTINE READ_GRID_SHALLOW(XFILE, XTYP_DON, KNBSTEP, KN_COUCHMX, KNBTOT, &
         INTEGER, INTENT(IN)                           :: KNU_ZOOMX
         INTEGER, DIMENSION(KNBSTEP), INTENT(OUT)      :: KSTEPS
         REAL(KIND=4), DIMENSION(KNBSTEP), INTENT(OUT) :: PDATES
-        REAL(KIND=4), DIMENSION(KNBSTEP, KNU_ZOOMX + 1, KNBTOT), INTENT(OUT) :: PVAR
         REAL(KIND=4), DIMENSION(KNU_ZOOMX + 1, 999), INTENT(OUT) :: PXCOL, PYLIG, PDXLU, PDYLU
+        REAL(KIND=8), DIMENSION(KNBSTEP, KNU_ZOOMX + 1, KNBTOT), INTENT(OUT) :: PVAR
+        CHARACTER (LEN=132), INTENT(OUT)              :: TITSEM
         !
         !
         INTEGER    :: ISTEPINC, ISTEP_TEMP, INTOT_TEMP, N_COUCH2
-        REAL(KIND=4), DIMENSION(KNBSTEP, KNU_ZOOMX + 1, KN_COUCHMX, KNBTOT) :: ZTEMP
         INTEGER, DIMENSION(KNU_ZOOMX + 1, 3) :: KDIMEN
+        REAL(KIND=8), DIMENSION(KNBSTEP, KNU_ZOOMX + 1, KN_COUCHMX, KNBTOT) :: ZTEMP
         !
         !
         LIRE_DXDY =  1
@@ -285,13 +304,13 @@ SUBROUTINE READ_GRID_SHALLOW(XFILE, XTYP_DON, KNBSTEP, KN_COUCHMX, KNBTOT, &
                     ISTEP_TEMP = ISTEP
                     INTOT_TEMP = 1
                     KSTEPS(ISTEPINC) = ISTEP
-                    PDATES(ISTEPINC) = DATE
+                    PDATES(ISTEPINC) = real(DATE, 4)
                 ENDIF
                 IF (ISTEPINC == 1 .AND. N_COUCH == 1) THEN
-                    PXCOL(NU_ZOO + 1, :NKOL) = XCOL(:NKOL)
-                    PYLIG(NU_ZOO + 1, :NLIG) = YLIG(:NLIG)
-                    PDXLU(NU_ZOO + 1, :NKOL) = DXLU(:NKOL)
-                    PDYLU(NU_ZOO + 1, :NLIG) = DYLU(:NLIG)
+                    PXCOL(NU_ZOO + 1, :NKOL) = real(XCOL(:NKOL), 4)
+                    PYLIG(NU_ZOO + 1, :NLIG) = real(YLIG(:NLIG), 4)
+                    PDXLU(NU_ZOO + 1, :NKOL) = real(DXLU(:NKOL), 4)
+                    PDYLU(NU_ZOO + 1, :NLIG) = real(DYLU(:NLIG), 4)
                 ENDIF
                 ZTEMP(ISTEPINC, NU_ZOO + 1, N_COUCH, :NTOT) = FONC(:NTOT)
             ENDIF
@@ -382,20 +401,21 @@ end function is_in_array_strings
     !
     !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     !
-    SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
+    SUBROUTINE WRITE_GRID(ZVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
                           N_DIMS, NVAL, NGRID, NSTEPS, DATES, XFILE, DEBUG, IEREDI)
         ! --- Write array to Marthe Grid format (v9.0) ---
-        ! XVAR should not have missing value (if nan, set 9999. before writing !)
-        ! BUT, XVAR should contain all possible value (9999. if nan but do NOT drop nan before)
-        ! XVAR shoult be sorted according to sorted indexes (in this order) : Time(asc), GRID(main/gig, asc) LAYER(asc), YCOL (dsc), XCOL (asc)
+        ! ZVAR should not have missing value (if nan, set 9999. before writing !)
+        ! BUT, ZVAR should contain all possible value (9999. if nan but do NOT drop nan before)
+        ! ZVAR shoult be sorted according to sorted indexes (in this order) : 
+        !       Time(asc), GRID(main/gig, asc) LAYER(asc), YCOL (dsc), XCOL (asc)
         IMPLICIT NONE
         !
         !inputs
         integer, intent(in)                          :: NVAL, NGRID, NSTEPS
         integer, intent(in), dimension(NGRID, 3)     :: N_DIMS
-        real(KIND=4), intent(in), dimension(NSTEPS, NVAL) :: XVAR
-        real(KIND=4), intent(in), dimension(NVAL)         :: DXLU, DYLU, XCOL, YLIG
-        real(KIND=4), intent(in), dimension(NSTEPS)       :: DATES
+        real(kind=8), intent(in), dimension(NSTEPS, NVAL) :: ZVAR
+        real(kind=4), intent(in), dimension(NVAL)         :: DXLU, DYLU, XCOL, YLIG
+        real(kind=4), intent(in), dimension(NSTEPS)       :: DATES
         character(len=13)  , intent(in)              :: TYP_DON
         character(len=132) , intent(in)              :: TITSEM, XFILE
         logical, optional                            :: DEBUG
@@ -405,8 +425,9 @@ SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
         !
         ! local
         integer :: ISTEP, TMP_ISTEP, NU_ZOO, NU_GRID, N_COUCH, LEC, INVY, NKOL, NLIG, NTOT, NLAY, NGIG, start_idx, end_idx, shift, i
-        real(KIND=4) :: X0, Y0
-        real(KIND=4), dimension(:), allocatable :: XTEMPVAR, XTEMPCOL, YTEMPLIG, DXTEMP, DYTEMP
+        real :: X0, Y0
+        real, dimension(:), allocatable :: XTEMPCOL, YTEMPLIG, DXTEMP, DYTEMP
+        real, dimension(:), allocatable :: ZTEMPVAR
         logical :: DEBUGG
         
         INVY   = 0
@@ -433,7 +454,7 @@ SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
                 NKOL = N_DIMS(NU_GRID, 1)
                 NLIG = N_DIMS(NU_GRID, 2)
                 
-                ! To navigate through XVAR, before computing start_idx, end_idx based on NLAY, NLIG, NKOL,
+                ! To navigate through ZVAR, before computing start_idx, end_idx based on NLAY, NLIG, NKOL,
                 ! we compute a 'shift' index, to jump over previous grids, if id_grid > 0
                 ! number of value to skip depends on nlay and nkol, nlig of every previous grid (main, and each gig)
                 ! wich might not be equals
@@ -451,20 +472,16 @@ SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
                     start_idx = ((N_COUCH - 1) * NTOT ) + shift + 1     ! +1 to get 0-based (n-couch -1) to 1-based starting index. if 1st layer return 1 (cause n-couch-1 would be 0)
                     end_idx   = start_idx + NTOT - 1                    ! here minus 1 cause upper bound is included in fortran, would cause error (idx > len)
                     
-                    ! if (DEBUG) then
-                    ! print *, 'start_idx=', start_idx, 'end_idx=', end_idx
-                    ! endif
-                    
-                    XTEMPVAR = XVAR(ISTEP, start_idx:end_idx)
-                    XTEMPCOL = XCOL(start_idx:end_idx)      ! x is repeated for every different y, but EDSEMI_3 will subset to NKOL
-                    YTEMPLIG = YLIG(start_idx:end_idx:NKOL) ! here, same y for each x, so we sample y every nkol (n_x) to get every unique y values, EDSEMI_3 will subset to NLIG
-                    DXTEMP   = DXLU(start_idx:end_idx)
-                    DYTEMP   = DYLU(start_idx:end_idx:NKOL) ! same here
+                    ! Get current var and coords + transf to real 8 for edsemigl
+                    ZTEMPVAR = ZVAR(ISTEP, start_idx:end_idx)
+                    XTEMPCOL = XCOL(start_idx:start_idx + NKOL - 1)  ! x is repeated for every different y, but EDSEMI_3 will subset to NKOL
+                    YTEMPLIG = YLIG(start_idx:end_idx:NKOL)          ! here, same y for each x, so we sample y every nkol (n_x) to get every unique y values, EDSEMI_3 will subset to NLIG
+                    DXTEMP   = DXLU(start_idx:start_idx + NKOL - 1)
+                    DYTEMP   = DYLU(start_idx:end_idx:NKOL)
                     !
                     X0 = minval(XTEMPCOL) - (DXTEMP(1)/2)                   ! x0 is lowerleft coordinate, xy read are center of cells
                     Y0 = minval(YTEMPLIG) - (DYTEMP(Ubound(DYTEMP, 1))/2)   ! y0 is lowerleft coordinate, xy read are center of cells
                     !
-                    !
                     ! ISTEP = -9999 is code value for no timestep (eg. used in parameters field)
                     IF (DATE == 0.) THEN
                         TMP_ISTEP = -9999
@@ -473,7 +490,7 @@ SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
                     ENDIF
                     
                     CALL EDSEMI_3(&
-                        XTEMPVAR, NKOL, NLIG, XTEMPCOL, YTEMPLIG, X0, Y0, INVY &
+                        ZTEMPVAR, NKOL, NLIG, XTEMPCOL, YTEMPLIG, X0, Y0, INVY &
                         ,TITSEM, IEREDI, LEC, TYP_DON &
                         ! //////// Not used now
                         ,TYP_DON3, N_ELEMCH, &
@@ -488,9 +505,16 @@ SUBROUTINE WRITE_GRID(XVAR, XCOL, YLIG, DXLU, DYLU, TYP_DON, TITSEM, &
                     if (IEREDI /= 0) then
                         if (DEBUGG) then
                             ! errors might come from non-sorted XY or negatives XY
-                            write (LEC, *),"Writing error, status ", IEREDI, "Lay=", N_COUCH, "Grid=", NU_ZOO, "X0=", X0, "Y0=", Y0
-                            write (LEC, *), XTEMPCOL, ''
-                            write (LEC, *), YTEMPLIG, ''
+                            write (LEC, *)"Writing error, status ", IEREDI
+                            write (LEC, *) "Lay=", N_COUCH, "Grid=", NU_ZOO, "X0=", X0, "Y0=", Y0
+                            write (LEC, *) "NCOL=", NKOL, "NLIG=", NLIG, 'NTOT=', NTOT
+                            write (LEC, *) "len(X)=", size(XTEMPCOL), "len(Y)=", size(YTEMPLIG), "len(v)", size(ZTEMPVAR)
+                            write (LEC, *) 'start_idx=', start_idx, 'end_idx=', end_idx, 'shift_idx=', shift
+                            write (LEC, *) 'X: ', XTEMPCOL, ''
+                            write (LEC, *) 'Y: ', YTEMPLIG, ''
+                            write (LEC, *) 'dx:', DXTEMP, ''
+                            write (LEC, *) 'dy:', DYTEMP, ''
+                            write (LEC, *) 'v: ', ZTEMPVAR, ''
                         endif
                     endif
                     !
diff --git a/src/gridmarthe/operasem.py b/src/gridmarthe/operasem.py
deleted file mode 100644
index a543b26..0000000
--- a/src/gridmarthe/operasem.py
+++ /dev/null
@@ -1,261 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import numpy as np
-import xarray as xr #needs netcdf4, rioxarray
-
-from shapely.geometry import Polygon
-import geopandas as gpd
-
-from .utils import _get_scale, _nearest_node
-
-"""
-Some useful functions to manage MartheGrid in python
-"""
-# TODO:   add functions available in winmarthe or operasem.
-#         eg. get_layer_depths(), get_layer_thickness(),
-#         get_runoff_direction(), remove_layer(), remove_nested(), etc.
-
-# -------------------------------------------------------- #
-#  Operation on Marthe Grids (semis, marthe easter eggs).  #
-# -------------------------------------------------------- #
-
-def get_new_coords(ds, res=1000):
-    """ Reset xy with a range from min to max, with res as step"""
-    xmin, xmax = np.min(ds.x).values, np.max(ds.x).values
-    ymin, ymax = np.min(ds.y).values, np.max(ds.y).values
-    
-    new_x = np.arange(xmin, xmax+res, res)
-    new_y = np.arange(ymin, ymax+res, res)
-    
-    return new_x, new_y
-
-def coarse_nested_grid(da, varname='charge', dx=None, dy=None):
-    """ Coarse nested grid to res of main grid
-    only realy valid if nested grid resolution is a multiple of maingrid resolution
-    coords needs to be assign first
-    """
-    if dx is None or dy is None:
-        dx, dy = _get_scale(da)
-    dx1, dy1 = dx.pop(0), dy.pop(0)
-    grid = da.where(da['dx'] == dx1, drop=True) # & da['dy'] == dy1
-    for dx2, dy2 in zip(dx, dy):
-        gig = da.where(da['dx'] == dx2, drop=True)
-        gig = gig[varname].coarsen(x=int(dx1/dx2), y=int(dy1/dy2), boundary='trim').mean()
-        grid = xr.combine_by_coords([grid, gig])
-    return grid
-
-def interp_grid(da, new_x=None, new_y=None, method='nearest', **kwargs):
-    """ Interpolate on a new grid using `xarray.Dataset.interp`
-
-    Parameters
-    ----------
-    da: xr.Dataset
-        the input dataset to interpolate on new coordinates.
-    new_x: array-like
-        the new x-axis coordinate to use
-    new_y: array-like
-        the new y-axis coordinate to use
-    method: str, Optionnal (default='nearest')
-        `xr.Dataset.interp` method to use. Default is 'nearest'
-    **kwargs: dict, Optionnal.
-        Any keywords argument to pass to `xr.Dataset.interp`.
-    
-    Returns
-    -------
-        interpolated xr.Dataset
-    """
-    # https://docs.xarray.dev/en/stable/user-guide/interpolation.html
-    # https://earth-env-data-science.github.io/lectures/xarray/xarray-part2.html
-    if new_x is None or new_y is None:
-        new_x = np.linspace(da['x'][0], da['x'][-1], int(da['x'].size / 2) ) # da.dims["lat"]
-        new_y = np.linspace(da['y'][0], da['y'][-1], int(da['y'].size / 2) )
-    return da.interp(x=new_x, y=new_y, method=method, **kwargs)
-
-def rescale(da, res=1000, **kwargs):
-    """ Wrapper function that uses `get_new_coords()` and `interp_grid()` together
-    
-    See also
-    --------
-    `get_new_coords`
-    `interp_grid`
-    """
-    new_x, new_y = get_new_coords(da, res)
-    new_da = interp_grid(da, new_x, new_y, **kwargs) # here da with assign coords
-    return new_da
-
-def get_min_layer(ds, aquif_layers=None):
-    """ Compute surface mask of marthe domain
-    
-    This function return min layer for every zone of a grimarthe dataset with z coords
-    A subset on specific (aquifers) layers can be performed with `aquif_layers`.
-    if set, aquif_layers must be a sequence (list, tuple, array) of layer (list of int).
-    
-    This should be used to get a surface mask, ie get zone to filter a dataset.
-    
-    Examples
-    --------
-    >>>    mask = get_min_layer(ds, [6,8,9])
-    >>>    ds_surf = ds.sel(zone=mask.zone.data)
-        
-    Parameters
-    ----------
-        ds: xr.Dataset
-        aquif_layers: sequence (list, tuple, array) of int
-            representing layers to subset ds
-    Returns
-    -------
-        surface_mask: xr.Dataset
-    """
-    df = ds.to_dataframe()
-    df = df.reset_index()
-    
-    if aquif_layers is not None:
-        df = df[df['z'].isin(aquif_layers)]
-    
-    idx_z_min = df.groupby(['x', 'y', 'time']).z.idxmin() # get index of min z ("layer") for each x,y,t groups
-    first_aquif_lay = df.loc[idx_z_min].reset_index().set_index('zone').drop('index', axis=1)
-    # time not needed here, zone are independant from time coords
-    return first_aquif_lay.to_xarray()
-
-
-def get_mask(ds, varname: str='permeab', nanval: list=[-9999., 0.], fileout: str='mask.shp'):
-    """ Filter dataset on non-nan values, and dissolve results to get a mask shape 
-    input ds should be the permh dataset (read from permh file, ie Horizontal hydraulic conductivity)
-    """
-    mask = ds.where(~ds[varname].isin(nanval), drop=True)
-    mask = ds.sel(zone=mask['zone'])
-    gdf  = to_geodataframe(mask)
-    gdf  = gdf.dissolve()
-    gdf.to_file(fileout)
-    return gdf
-
-
-def search_zone(ds, i=None, j=None, x=None, y=None, z=None):
-    """ search zone number in marthe grid,
-    based on xy or ij (col, lig)
-    
-    if ds is multilayered, you need to provide the layer you want (int)
-    ds should contains dx and dy
-    ds should not have assigned coords (x and y are variables, zone is the dimension coordinates (with time))
-    """
-    ds_search = ds.copy()
-    
-    if z is not None:
-        ds_search = ds_search.where(ds_search.z == z, drop=True)
-
-    if x is not None:
-        assert y is not None, 'if x is provided, y cannot be None'
-        ## mask = ds.sel(x=x, y=y, method='nearest') # possible uniquement si x,y sont des coordonnées/dim
-        nearest = _nearest_node(np.array([(x, y)]), np.array(list(zip(ds_search['x'].data, ds_search['y'].data))))
-        nearest_zone = ds_search.isel(zone=nearest)
-        
-        # check if xy is in a cell == dx and dy are not greater than grid resolution
-        dx = np.abs(nearest_zone.x.data - x)
-        dy = np.abs(nearest_zone.y.data - y)
-        mask = ds_search['zone'] == nearest_zone.zone if (dx <= nearest_zone.dx.data) & (dy <= nearest_zone.dy.data) else ds_search['zone'].isnull()
-
-    if i is not None:
-        assert j is not None, 'if i is provided, j cannot be None'
-        mask = (ds_search['col'] == i) & (ds_search['lig'] == j)
-
-    # zone = ds.where(mask, drop=True)['zone'].data
-    zone = ds_search.where(mask, drop=True)
-    return zone
-
-# -------------------------------------------------------- #
-#                      GIS Functions                       #
-# -------------------------------------------------------- #
-
-def subset_with_coords(da, dims=['x', 'y'], gdf=None, xmin=None, ymin=None, xmax=None, ymax=None):
-    """
-    subset DataArray or Dataset with gpd.GeoDataFrame or bounds
-    TODO: real shp clip
-    """
-    if gdf is not None:
-        # edit, one line with total_bounds attribute instead of bounds
-        # xmin, ymin, xmax, ymax = gdf.bounds.T.values # or .T.to_numpy(), in any case return np.array // total_bounds instead of bounds
-        # xmin, ymin, xmax, ymax = xmin[0], ymin[0], xmax[0], ymax[0]
-        xmin, ymin, xmax, ymax = gdf.total_bounds #gdf.bounds.T.values # or .T.to_numpy(), in any case return np.array // total_bounds instead of bounds
-    else:
-        assert xmin is not None, "When using manual bounds, all must be set"
-        assert xmax is not None, "When using manual bounds, all must be set"
-        assert ymin is not None, "When using manual bounds, all must be set"
-        assert ymax is not None, "When using manual bounds, all must be set"
-    
-    mask_lon = ( da[dims[0]] >= xmin) & ( da[dims[0]] <= xmax) #da.xc
-    mask_lat = ( da[dims[1]] >= ymin) & ( da[dims[1]] <= ymax)
-    
-    # imin, imax = np.where(da[var[0]].values==xmin)[0], np.where(da[var[0]].values==xmax)[0]
-    # jmin, jmax = np.where(da[var[1]].values==ymin)[0], np.where(da[var[1]].values==ymax)[0]
-
-    # sub_da = da.isel(i=slice(int(imin), int(imax)+1), j=slice(int(jmax), int(jmin)+1)) # j in reverse order / +1 on imax, jmin because upper is exclude in py slicing
-    
-    return da.where(mask_lon & mask_lat, drop=True)
-
-
-def _mk_cell_polygon(xleft, ylower, xright, yupper):
-    return Polygon(
-        (
-            (xleft , ylower),
-            (xright, ylower),
-            (xright, yupper),
-            (xleft , yupper),
-            (xleft , ylower)
-        )
-    )
-
-_polygonize = np.vectorize(_mk_cell_polygon)
-
-
-def _build_polyg(ds):
-    """ build a (rectangular) polygon shape from marthegrid dataset """
-    
-    x0 = ds.x.values - (ds.dx.values / 2.)
-    y0 = ds.y.values - (ds.dy.values / 2.)
-    x1 = ds.x.values + (ds.dx.values / 2.)
-    y1 = ds.y.values + (ds.dy.values / 2.)
-    
-    return _polygonize(x0, y0, x1, y1)
-
-
-def to_geodataframe(ds, epsg='EPSG:27572', fmt='long'):
-    """ Convert marthegrid.Dataset to a geodataframe 
-    fmt must be long or wide, default is long
-    """
-    
-    polygons = _build_polyg(ds.isel(time=0))
-    df = ds.to_dataframe() #.to_pandas() # only for 1 dim
-    
-    if 'time' in ds.dims.keys():
-        polygons = np.tile(polygons.flatten(), len(np.unique(df.index.get_level_values('time')))) # ad geom for every timestep
-    
-    gdf = gpd.GeoDataFrame(
-        df,
-        geometry=polygons,
-        crs=epsg
-    )
-    
-    if fmt == "wide":
-        # here no wide fmt if no time, so no if 'time' in ds.dims.keys():
-        gdf = gdf.unstack('time')
-        gdf.columns = [
-            '{}_{}'.format(x, y.strftime('%Y%m%d'))\
-            if x not in ['x', 'y', 'dx', 'dy', 'z', 'geometry'] else x\
-            for x, y in gdf.columns
-        ]
-        gdf = gdf.loc[:,~gdf.columns.duplicated()].copy() # drop duplicated cols
-        gdf = gdf.set_geometry('geometry') # need to make again geom after drop dupl
-    
-    return gdf
-
-def write_raster_from_da(da, x_dim='x', y_dim='y', epsg=27572, fout='raster.tiff'):
-    """ Write a xr.DataArray to a raster file
-    need xarray with rioxarray installed
-    """
-    da = da.copy().rio.write_crs('epsg:{}'.format(epsg))
-    da = da.rio.set_spatial_dims(x_dim=x_dim, y_dim=y_dim)
-    da.rio.to_raster(fout)
-    return None
-
diff --git a/src/gridmarthe/operasem/__init__.py b/src/gridmarthe/operasem/__init__.py
new file mode 100644
index 0000000..ff5b8df
--- /dev/null
+++ b/src/gridmarthe/operasem/__init__.py
@@ -0,0 +1,14 @@
+# -------------------------------------------------------- #
+#  Operation on Marthe Grids (semis, marthe easter eggs).  #
+# -------------------------------------------------------- #
+
+"""
+Some useful functions to manage MartheGrid in python
+"""
+# TODO:   add functions available in winmarthe or operasem.
+#         get_runoff_direction(), remove_layer(), remove_nested(), etc.
+
+
+from .geometry import *
+from .interp import *
+from .gis import *
diff --git a/src/gridmarthe/operasem/geometry.py b/src/gridmarthe/operasem/geometry.py
new file mode 100644
index 0000000..353377a
--- /dev/null
+++ b/src/gridmarthe/operasem/geometry.py
@@ -0,0 +1,280 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+
+""" Module to manage geometry attributes of Marthe grids/domain
+"""
+
+import numpy as np
+import xarray as xr
+
+from .gis import to_geodataframe
+from ..utils import _nearest_node, subset
+
+
+def _get_mask_array(ds, varname: str='permeab', nanval: list=[-9999., 0.]):
+    """ Get mask array of active domain, from permh file (hydraulic conductivity)
+    """
+    return ds.where(~ds[varname].isin(nanval), drop=True)
+
+
+def get_active_mask(ds, varname: str='permeab', nanval: list=[-9999., 0.], as_array=False, shp_file=None):
+    """ Filter dataset on non-nan values, and dissolve results to get a mask shape 
+    input ds should be the permh dataset (read from permh file, ie Horizontal hydraulic conductivity)
+    
+    Parameters
+    ----------
+    
+    ds : xr.Dataset
+    
+    varname: str, optional
+        default is 'permeab'
+    
+    nanval: float or list, optional.
+        default are 'permeab' nan values : 0,-9999.
+    
+    as_array: bool, optional.
+        default is False. Option to get result as a xr.Dataset and not geodataframe.
+        
+    shp_file: str, optional.
+        if set (and not `as_array`), used to stored result in a file.
+    
+    Returns
+    -------
+    either a xr.Dataset filter to mask array or a
+    gpd.GeoDataFrame with active domain.
+    """
+    mask = _get_mask_array(ds, varname, nanval)
+    mask = ds.sel(zone=mask['zone'])
+    if not as_array:
+        gdf  = to_geodataframe(mask)
+        gdf  = gdf.dissolve()
+        if shp_file is not None:
+            gdf.to_file(shp_file)
+        return gdf
+    return mask
+
+
+def _get_true_topo(topo, key='h_topogr'):
+    # in Marthe, topography is stored in the first layer,
+    # for the whole domain, avoiding duplicates data.
+    # Here, this function tile topography to all layers
+    # to allow vectorized operations.
+    ds = topo.copy()
+    if 'z' not in list(topo.dims) + list(topo.keys()):
+        zdim = 1
+    else:
+        zdim = len(np.unique(topo.z.data))
+    true_topo = subset(topo, 'z', 1)[key].data  # true topo is only 1st layer
+    true_topo = np.tile(true_topo, zdim)  # set topo for all layers
+    ds[key] = (('time', 'zone'), true_topo)
+    return ds
+
+
+def _get_upper_alt(topo, hsubs):
+    """ Compute upper altitude of cells by layer
+    Topo should contains the same values in all layers, see :py_func:`_get_true_topo`
+
+    TODO: make valid version with time (if topo change with times)
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    dataset with only (time,zone), (h_topogr, h_substr, h_upper)
+    """
+    ds = xr.combine_by_coords([topo, hsubs], combine_attrs='override')
+    
+    df = ds.isel(time=0).to_dataframe().reset_index()  # time is constant! TODO change this/ check if working
+    df = df.sort_values(by=['x', 'y', 'z']).copy()  # assure data are sort in this way
+    # set nans for topo and hsubs
+    # this is constant in Marthe / should not be changed by user
+    for x in ['h_substrat', 'h_topogr']:
+        df[x] = df[x].replace(9999., np.nan)  # avoid doing this on full df, zone might be impacted
+    
+    # Compute z top of layers
+    df['h_topogr'] = df.groupby(['x', 'y'])['h_topogr'].transform('first')  # topo is always first of group
+    df['tmp']      = df.groupby(['x', 'y'])['h_substrat'].ffill()  # ffill z down for each group
+    df['h_upper']  = df.groupby(['x', 'y'])['tmp'].shift(1)  # then shift to initiate z top
+    # For the first layer (or if h_upper is NaN but h_substrat is valid), then h_topogr is z upper (first layer)
+    mask = (df['h_upper'].isna()) & (df['h_substrat'].notna())
+    df.loc[mask, 'h_upper'] = df.loc[mask, 'h_topogr']
+
+    # debug
+    # df.to_csv('toto.csv')
+
+    # # switch back to xarray backend
+    ds = df.set_index(['time','zone'])[['h_topogr', 'h_substrat', 'h_upper']].to_xarray()
+    return ds
+
+
+def _get_thickness(h_upper, hsubs):
+    """ Compute layer thickness from upper altitudes
+    and substratums.
+
+    See, :py_func:`_get_upper_alt`
+    """
+    return h_upper - hsubs
+
+
+def _get_depth(topo, h_upper):
+    return topo - h_upper
+
+
+def compute_geometry(topo, hsubs, mask=None):
+    """ Compute geometry attributes of Marthe domain
+
+    Parameters
+    ----------
+    topo : xr.Dataset
+        Topgraphy of the domain (stored in the first layer, in Marthe Conventions).
+    hsubs : xr.Dataset
+        altitude of all the lower boundary in the domain
+    mask : numpy.array
+        list of indices (`zone`) to keep, if None (default) not used.
+
+    Returns
+    -------
+    xr.Dataset
+        A new dataset with layer, depth, thickness, upper/lower altitude.
+    """
+
+    # compute elements of geometry
+    xtopo = _get_true_topo(topo)  # map topo values to all layers
+    xhsubs = hsubs.copy()
+    
+    if mask is not None:
+        xtopo  = xtopo.sel(zone=mask)
+        xhsubs = xhsubs.sel(zone=mask)
+    
+    tmp   = _get_upper_alt(xtopo, xhsubs)
+    thick = _get_thickness(tmp['h_upper'].data, tmp['h_substrat'].data)
+    depth = _get_depth(tmp['h_topogr'].data, tmp['h_upper'].data)
+    
+    # put values in xr.Dataset
+    # ds = xr.combine_by_coords([ds, tmp])
+    ds = xtopo.copy()
+    ds['z_lower']    = (('time', 'zone'), tmp['h_substrat'].data)
+    ds['z_upper']    = (('time', 'zone'), tmp['h_upper'].data)
+    ds['thickness']  = (('time', 'zone'), thick)
+    ds['depth']      = (('time', 'zone'), depth)
+    return ds
+
+
+def get_surface_layer(ds, aquif_layers=None):
+    """ Compute surface mask of marthe domain
+    
+    This function return min layer for every zone of a grimarthe dataset with z coords
+    A subset on specific (aquifers) layers can be performed with `aquif_layers`.
+    if set, aquif_layers must be a sequence (list, tuple, array) of layer (list of int).
+    
+    This should be used to get a surface mask, ie get zone to filter a dataset.
+    
+    Examples
+    --------
+    >>>    mask = get_surface_layer(ds, [6,8,9])
+    >>>    ds_surf = ds.sel(zone=mask.zone.data)
+        
+    Parameters
+    ----------
+        ds: xr.Dataset
+        aquif_layers: sequence (list, tuple, array) of int
+            representing layers to subset ds. Only active domain must 
+            be passed to function (ie drop nan first)
+            
+    Returns
+    -------
+        surface_mask: xr.Dataset
+    """
+    df = ds.to_dataframe()
+    df = df.reset_index()
+    
+    if aquif_layers is not None:
+        df = df[df['z'].isin(aquif_layers)]
+    
+    idx_z_min = df.groupby(['x', 'y', 'time']).z.idxmin() # get index of min z ("layer") for each x,y,t groups
+    first_aquif_lay = df.loc[idx_z_min].reset_index().set_index('zone').drop('index', axis=1)
+    # time not needed here, zone are independant from time coords
+    return first_aquif_lay.to_xarray()
+
+
+
+def search_zone(ds, i=None, j=None, x=None, y=None, z=None):
+    """ search zone number in marthe grid,
+    based on xy or ij (col, lig)
+
+    This function can be used to search zone number from coordinates or indices.
+    You must provide either (i,j) or (x,y).
+
+    Note
+    ----
+    if ds is multilayered, you need to provide the layer you want (int)
+    ds should contains dx and dy
+    ds should not have assigned coords (x and y are variables, zone is the dimension coordinates (with time))
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+        dataset with zone, x, y, dx, dy variables.
+    i : int, optional
+        column index to search zone.
+    j : int, optional
+        row index to search zone.
+    x : float, optional
+        x coordinate to search zone.
+    y : float, optional
+        y coordinate to search zone.
+    z : int, optional
+        layer index to search zone. If not provided, all layers are considered.
+    
+    Returns
+    -------
+    zone : xr.Dataset
+        dataset with zone variable, containing the zone number(s) corresponding to the provided coordinates.
+        If no zone is found, an empty dataset is returned.
+    If multiple zones are found, all of them are returned.
+    """
+    ds_search = ds.copy()
+    
+    if z is not None:
+        ds_search = ds_search.where(ds_search.z == z, drop=True)
+
+    if x is not None:
+        assert y is not None, 'if x is provided, y cannot be None'
+        ## mask = ds.sel(x=x, y=y, method='nearest') # possible uniquement si x,y sont des coordonnées/dim
+        nearest = _nearest_node(np.array([(x, y)]), np.array(list(zip(ds_search['x'].data, ds_search['y'].data))))
+        nearest_zone = ds_search.isel(zone=nearest)
+        
+        # check if xy is in a cell == dx and dy are not greater than grid resolution
+        dx = np.abs(nearest_zone.x.data - x)
+        dy = np.abs(nearest_zone.y.data - y)
+        mask = ds_search['zone'] == nearest_zone.zone if (dx <= nearest_zone.dx.data) & (dy <= nearest_zone.dy.data) else ds_search['zone'].isnull()
+
+    if i is not None:
+        assert j is not None, 'if i is provided, j cannot be None'
+        mask = (ds_search['col'] == i) & (ds_search['lig'] == j)
+
+    # zone = ds.where(mask, drop=True)['zone'].data
+    zone = ds_search.where(mask, drop=True)
+    return zone
diff --git a/src/gridmarthe/operasem/gis.py b/src/gridmarthe/operasem/gis.py
new file mode 100644
index 0000000..390b85a
--- /dev/null
+++ b/src/gridmarthe/operasem/gis.py
@@ -0,0 +1,283 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024-2025  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+""" GIS utility for marthe grids
+"""
+
+
+import numpy as np
+
+from shapely.geometry import Polygon
+from pyproj import Transformer
+import geopandas as gpd
+import xarray as xr
+
+from ..utils import assign_coords
+
+
+def _mk_cell_polygon(xleft, ylower, xright, yupper):
+    return Polygon(
+        (
+            (xleft , ylower),
+            (xright, ylower),
+            (xright, yupper),
+            (xleft , yupper),
+            (xleft , ylower)
+        )
+    )
+
+
+_polygonize = np.vectorize(_mk_cell_polygon)
+
+
+def _build_polyg(ds):
+    """ build a (rectangular) polygon shape from marthegrid dataset """
+    
+    x0 = ds.x.values - (ds.dx.values / 2.)
+    y0 = ds.y.values - (ds.dy.values / 2.)
+    x1 = ds.x.values + (ds.dx.values / 2.)
+    y1 = ds.y.values + (ds.dy.values / 2.)
+    
+    return _polygonize(x0, y0, x1, y1)
+
+
+def to_geodataframe(ds, epsg='EPSG:27572', fmt='long'):
+    """ Convert marthegrid.Dataset to a geodataframe
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The dataset to convert.
+    epsg : str, optional
+        The EPSG code for the coordinate reference system, by default 'EPSG:27572'.
+    fmt : str, optional
+        The format of the output GeoDataFrame, either 'long' or 'wide', by default 'long'.
+    
+    Returns
+    -------
+    geopandas.GeoDataFrame
+        The converted GeoDataFrame.
+    """
+    
+    polygons = _build_polyg(ds) # .isel(time=0) # x,y does not vary in time
+    df = ds.to_dataframe() #.to_pandas() # only for 1 dim
+    
+    if 'time' in ds.dims.keys():
+        polygons = np.tile(polygons.flatten(), len(np.unique(df.index.get_level_values('time')))) # ad geom for every timestep
+    
+    gdf = gpd.GeoDataFrame(
+        df,
+        geometry=polygons,
+        crs=epsg
+    )
+    
+    if fmt == "wide" and 'time' in ds.dims.keys():
+        # here no wide fmt if no time, so no if 'time' in ds.dims.keys():
+        gdf = gdf.unstack('time')
+        gdf.columns = [
+            '{}_{}'.format(x, y.strftime('%Y%m%d'))\
+            if x not in ['x', 'y', 'dx', 'dy', 'z', 'geometry'] else x\
+            for x, y in gdf.columns
+        ]
+        gdf = gdf.loc[:,~gdf.columns.duplicated()].copy()  # drop duplicated cols
+        gdf = gdf.set_geometry('geometry')  # need to make again geom after drop dupl
+    
+    return gdf
+
+
+def _check_rioxarray():
+    try:
+        import rioxarray
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError(
+            'rioxarray is not Found in python env.' + \
+            'Please install it or reinstall gridmarthe with optionnal dependancies: pip install gridmarthe[opt]'
+        )
+    return
+
+
+def clip_dataset(ds, gdf, crs=27572, engine='gdf'):
+    """ Clip a xarray Dataset with a gpd.GeoDataFrame
+
+    Needs rioxarray. If not installed, raise ModuleNotFoundError
+    please install it or reinstall gridmarthe with optionnal dependancies: pip install gridmarthe[opt]
+
+    See: https://corteva.github.io/rioxarray/html/examples/clip_geom.html
+    Todo: shapely version
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The dataset to clip.
+    gdf : geopandas.GeoDataFrame
+        The GeoDataFrame containing the geometry to clip with.
+    crs : int, optional
+        The coordinate reference system to use for the clipping, by default 27572.
+    engine : str, optional
+        The engine to use for clipping, by default 'gdf'.
+    
+    Returns
+    -------
+    xarray.DataArray or xarray.Dataset
+        The clipped dataset.
+    """
+    _check_rioxarray()
+    shp = gdf.to_crs(crs)
+    da  = assign_coords(ds.rio.write_crs("EPSG:{}".format(crs)))
+    clipped_da = da.rio.clip(shp.geometry.values, shp.crs, drop=True)
+    return clipped_da
+
+
+def subset_with_coords(da, dims=['x', 'y'], gdf=None, xmin=None, ymin=None, xmax=None, ymax=None):
+    """ subset DataArray or Dataset on rectangular shape, with gpd.GeoDataFrame or bounds
+
+    Parameters
+    ----------
+    da : xarray.DataArray or xarray.Dataset
+        The data to subset.
+    dims : list of str, optional
+        The dimensions to use for subsetting, by default ['x', 'y'].
+    gdf : geopandas.GeoDataFrame, optional
+        A GeoDataFrame containing the geometry to use for subsetting, by default None.
+    xmin, ymin, xmax, ymax : float, optional
+        The manual bounds to use for subsetting, by default None.
+    
+    Returns
+    -------
+    xarray.DataArray or xarray.Dataset
+        The subsetted data.
+    """
+    if gdf is not None:
+        # edit, one line with total_bounds attribute instead of bounds
+        # xmin, ymin, xmax, ymax = gdf.bounds.T.values # or .T.to_numpy(), in any case return np.array // total_bounds instead of bounds
+        # xmin, ymin, xmax, ymax = xmin[0], ymin[0], xmax[0], ymax[0]
+        xmin, ymin, xmax, ymax = gdf.total_bounds #gdf.bounds.T.values # or .T.to_numpy(), in any case return np.array // total_bounds instead of bounds
+    else:
+        assert xmin is not None, "When using manual bounds, all must be set"
+        assert xmax is not None, "When using manual bounds, all must be set"
+        assert ymin is not None, "When using manual bounds, all must be set"
+        assert ymax is not None, "When using manual bounds, all must be set"
+    
+    mask_lon = ( da[dims[0]] >= xmin) & ( da[dims[0]] <= xmax) #da.xc
+    mask_lat = ( da[dims[1]] >= ymin) & ( da[dims[1]] <= ymax)
+    
+    # imin, imax = np.where(da[var[0]].values==xmin)[0], np.where(da[var[0]].values==xmax)[0]
+    # jmin, jmax = np.where(da[var[1]].values==ymin)[0], np.where(da[var[1]].values==ymax)[0]
+
+    # sub_da = da.isel(i=slice(int(imin), int(imax)+1), j=slice(int(jmax), int(jmin)+1)) # j in reverse order / +1 on imax, jmin because upper is exclude in py slicing
+    
+    return da.where(mask_lon & mask_lat, drop=True)
+
+
+def _transf_proj_xy(ds, from_epsg="EPSG:27572", to_epsg="EPSG:2154"):
+    """ Transform coordinates of a dataset using pyproj.
+
+    **!** return more unique points than initial due to projection deformation
+    """
+    transformer = Transformer.from_crs(from_epsg, to_epsg, always_xy=True)
+    x_source, y_source = ds.x.data, ds.y.data
+    x_target, y_target = transformer.transform(x_source, y_source)
+    ds = ds.copy()
+    ds['x'].data, ds['y'].data = np.astype(x_target, np.float32), np.astype(y_target, np.float32)
+    ds.attrs['projection'] = to_epsg
+    return ds
+
+# def _transf_proj_regrid(ds, from_epsg="EPSG:27572", to_epsg="EPSG:2154"):
+    # """ Recreate a transformed grid based on dx,dy and x0,y0"
+    # transformer = Transformer.from_crs(from_epsg, to_epsg, always_xy=True)
+    # nx, ny = len(x), len(y)
+    # x0, y0 = np.nanmin(x), np.nanmin(y)
+    # x1, y1 = np.nanmax(x), np.nanmax(y)
+    # dx, dy = ds['dx'].data, ds['dy'].data
+    # return
+
+def _transf_proj_meshgrid(ds, from_epsg="EPSG:27572", to_epsg="EPSG:2154"):
+    """ Transform grid of a dataset using pyproj,
+    using meshgrid
+    """
+    x, y = np.unique(ds['x'].data), np.unique(ds['y'].data)
+    transformer = Transformer.from_crs(from_epsg, to_epsg, always_xy=True)
+    
+    xx, yy = np.meshgrid(x, y)
+    xx_transformed, yy_transformed = transformer.transform(xx, yy)
+    data_transformed = xr.DataArray(
+        assign_coords(ds)['permeab'].data,
+        dims=["y", "x"],
+        coords={"y": yy_transformed[:, 0], "x": xx_transformed[0, :]}
+    )
+    return data_transformed
+
+
+def transf_proj(ds, from_epsg="EPSG:27572", to_epsg="EPSG:2154", engine='rioxarray'):
+    """  Transform grid of a dataset using pyproj
+
+    engine: rioxarray, meshgrid, xy
+
+    Data (ds) needs to be a 2D array with xy as coords dimensions and time sliced.
+    If needed, use :py:func:`gridmarthe.assign_coords` first.
+
+    Warning: IN DEVELOPMENT, not tested yet // USE WITH CAUTION
+    """ 
+    if engine == 'rioxarray':
+        _check_rioxarray()
+        ds_transf = ds.rio.write_crs(from_epsg).rio.reproject(to_epsg)
+    elif engine == 'meshgrid':
+        ds_transf = _transf_proj_meshgrid(ds, from_epsg, to_epsg)
+    elif engine == 'xy':
+        ds_transf = _transf_proj_xy(ds, from_epsg, to_epsg)
+    return ds_transf
+
+
+def to_raster(da, x_dim='x', y_dim='y', epsg=27572, fout='raster.tiff'):
+    """ Write a xr.DataArray to a raster file
+    
+    need xarray with rioxarray installed.
+    Only for regular grids.
+    
+    TODO: add support for irregular grids, using PostMARTHE QGIS plugin code.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        The DataArray to write to a raster file (only 2D, i.e. no time dimension, select
+        a variable and timestep before).
+    x_dim : str, optional
+        The name of the x dimension, by default 'x'.
+    y_dim : str, optional
+        The name of the y dimension, by default 'y'.
+    epsg : int, optional
+        The EPSG code for the coordinate reference system, by default 27572.
+    fout : str, optional
+        The output file path for the raster file, by default 'raster.tiff'.
+    
+    Returns
+    -------
+    None
+        The function writes the raster file and returns None.
+    """
+    _check_rioxarray()
+    da = da.copy().rio.write_crs('epsg:{}'.format(epsg))
+    da = da.rio.set_spatial_dims(x_dim=x_dim, y_dim=y_dim)
+    da.rio.to_raster(fout)
+    return None
+
diff --git a/src/gridmarthe/operasem/interp.py b/src/gridmarthe/operasem/interp.py
new file mode 100644
index 0000000..0c928f1
--- /dev/null
+++ b/src/gridmarthe/operasem/interp.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+""" Interpolation and regridding module
+"""
+
+
+import numpy as np
+import xarray as xr #needs netcdf4, rioxarray
+
+from ..utils import _get_scale
+
+
+def get_new_coords(ds, res=1000):
+    """ Reset xy with a range from min to max, with res as step"""
+    xmin, xmax = np.min(ds.x).values, np.max(ds.x).values
+    ymin, ymax = np.min(ds.y).values, np.max(ds.y).values
+    
+    new_x = np.arange(xmin, xmax+res, res)
+    new_y = np.arange(ymin, ymax+res, res)
+    
+    return new_x, new_y
+
+
+def interp_grid(da, new_x=None, new_y=None, method='nearest', **kwargs):
+    """ Interpolate on a new grid using `xarray.Dataset.interp`
+
+    Parameters
+    ----------
+    da: xr.Dataset
+        the input dataset to interpolate on new coordinates.
+    new_x: array-like
+        the new x-axis coordinate to use
+    new_y: array-like
+        the new y-axis coordinate to use
+    method: str, Optionnal (default='nearest')
+        `xr.Dataset.interp` method to use. Default is 'nearest'
+    **kwargs: dict, Optionnal.
+        Any keywords argument to pass to `xr.Dataset.interp`.
+    
+    Returns
+    -------
+        interpolated xr.Dataset
+    """
+    # https://docs.xarray.dev/en/stable/user-guide/interpolation.html
+    # https://earth-env-data-science.github.io/lectures/xarray/xarray-part2.html
+    if new_x is None or new_y is None:
+        new_x = np.linspace(da['x'][0], da['x'][-1], int(da['x'].size / 2) ) # da.dims["lat"]
+        new_y = np.linspace(da['y'][0], da['y'][-1], int(da['y'].size / 2) )
+    return da.interp(x=new_x, y=new_y, method=method, **kwargs)
+
+
+def rescale_grid(da, res=1000, **kwargs):
+    """ Wrapper function that uses `get_new_coords()` and `interp_grid()` together
+    
+    See also
+    --------
+    `get_new_coords`
+    `interp_grid`
+    """
+    new_x, new_y = get_new_coords(da, res)
+    new_da = interp_grid(da, new_x, new_y, **kwargs) # here da with assign coords
+    return new_da
+
+
+def coarse_nested_grid(da, varname='charge', dx=None, dy=None):
+    """ Coarse nested grid to res of main grid
+    only realy valid if nested grid resolution is a multiple of maingrid resolution
+    coords needs to be assign first
+    """
+    if dx is None or dy is None:
+        dx, dy = _get_scale(da)
+    dx1, dy1 = dx.pop(0), dy.pop(0)
+    grid = da.where(da['dx'] == dx1, drop=True) # & da['dy'] == dy1
+    for dx2, dy2 in zip(dx, dy):
+        gig = da.where(da['dx'] == dx2, drop=True)
+        gig = gig[varname].coarsen(x=int(dx1/dx2), y=int(dy1/dy2), boundary='trim').mean()
+        grid = xr.combine_by_coords([grid, gig])
+    return grid
+
+
+def aggregate_to_grid(value_grid, target_grid):
+    # Définir un facteur d'agrégation basé sur les résolutions
+    factor_x = int(round((value_grid.x.size / target_grid.x.size)))
+    factor_y = int(round((value_grid.y.size / target_grid.y.size)))
+
+    # Vérifier que le facteur est valide
+    if factor_x > 1 and factor_y > 1:
+        value_grid_coarse = value_grid.coarsen(x=factor_x, y=factor_y, boundary="trim").mean()
+    else:
+        raise ValueError("Les tailles des grilles ne permettent pas une agrégation nette.")
+    return value_grid_coarse
diff --git a/src/gridmarthe/plot.py b/src/gridmarthe/plot.py
index 8cd19ac..d3abdd0 100644
--- a/src/gridmarthe/plot.py
+++ b/src/gridmarthe/plot.py
@@ -1,3 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+
 from matplotlib import pyplot as plt
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from matplotlib import (colors, cm)
@@ -13,19 +37,29 @@
 """ Module for visualisation of gridmarthe files
 """
 
-def plot_nested_grid(da, ax=None, var='charge', **kwargs):
+
+def _set_map_lims(ax, xmin, ymin, xmax, ymax, perc=.05):
+    x_range = xmax - xmin
+    y_range = ymax - ymin
+    # and add 5% margin around bounds (2.5% on each side)
+    ax.set_xlim([xmin - (perc * x_range)/2, xmax + (perc * x_range)/2])
+    ax.set_ylim([ymin - (perc * y_range)/2, ymax + (perc * y_range)/2])
+    return None
+
+
+def plot_nested_grid(ds, ax=None, varname='charge', **kwargs):
     """ Usefull function to plot nested grids, keeping heterogeneous resolution
     
     Parameters
     ----------
-    da: xr.Dataset
+    ds: xr.Dataset
         the dataset MUST be a 2D array, with dims = x,y.
         In other words, you may need to sel z and time before plot, and you need to apply `assign_coord()`
     
     ax: matplotlib axe, Optionnal.
         if provided, data are plotted on this axis, otherwise fig, ax instances will be created.
     
-    var: str, Optionnal
+    varname: str, Optionnal
         the variable to plot in dataset. Default is 'charge'.
     
     kwargs: optionnal.
@@ -35,9 +69,15 @@ def plot_nested_grid(da, ax=None, var='charge', **kwargs):
     -------
     ax: matplotlib axis.
     """
+    da = ds.copy()
+    if 'var' in kwargs.keys():
+        # legacy, previous arg name was only var, harmonize between functions/methods
+        varname = kwargs.pop('var')
     
-    vmin, vmax = da[var].min(), da[var].max()
+    vmin, vmax = da[varname].min(), da[varname].max()
     vmin, vmax = kwargs.pop('vmin', vmin), kwargs.pop('vmax', vmax) # replace with user defined, if defined
+    if kwargs.get('norm') is not None:
+        vmin, vmax = None, None  # if user set a norm, vmin and vmax are not allowed
     cbar_kwargs = kwargs.pop('cbar_kwargs', {})
     
     # split grids
@@ -51,11 +91,20 @@ def plot_nested_grid(da, ax=None, var='charge', **kwargs):
     # plots nested then main
     for dx2, dy2 in zip(dx, dy):
         gig = da.where(da['dx'] == dx2, drop=True)
-        gig[var].plot.pcolormesh(x='x', y='y', ax=ax, vmin=vmin, vmax=vmax, add_colorbar=False, **{k:v for k,v in kwargs.items() if k != 'add_colorbar'})
-    grid[var].plot.pcolormesh(x='x', y='y', ax=ax, vmin=vmin, vmax=vmax, cbar_kwargs=cbar_kwargs, **kwargs)
+        gig[varname].plot.pcolormesh(
+            x='x', y='y',
+            ax=ax, vmin=vmin, vmax=vmax,
+            add_colorbar=False,
+            **{k:v for k,v in kwargs.items() if k != 'add_colorbar'}
+        )
+    
+    grid[varname].plot.pcolormesh(x='x', y='y', ax=ax, vmin=vmin, vmax=vmax, cbar_kwargs=cbar_kwargs, **kwargs)
+    
+    _set_map_lims(ax, da.x.min().data, da.y.min().data, da.x.max().data, da.y.max().data)
     
     return ax
 
+
 def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize=(12,4), **kwargs):
     """ Usefull function to plot time serie from any dataset, by extracting a specific cell timeserie
     
@@ -92,37 +141,53 @@ def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize=
         plt.show(block=False)
     return ax
 
-def plot_outcrop(ds_outcrop, fout=None, engine='xr', show=False):
+
+def plot_outcrop(ds_outcrop, file_out=None, engine='xr', show=False):
     
     """ Usefull function to plot outcrop layers of a marthe (multilayer) grid
     
-    The Marthe grid must have a `z` dimension.
-    
-    There is two mode implementend yet, using xr.plot or gpd.plot (useful for nested grid)
-    TODO xr version for nested with `plot_nested_grid()`, see func below in src
+    There is two mode implementend yet, using xr.plot or
+    gpd.plot (useful for nested grid)
 
-    if engine == 'xr' => ds_outcrop need to get coords before (use `gm.assign_coords(ds_outcrop, add_lay=False)`)
-    if engine == 'gpd => ds_outcrop need to be a gpd.GeoDataframe (use `gm.to_geodataframe(ds_outcrop)`)
+    if engine == 'xr' => ds_outcrop need to get coords before
+        (use `gm.assign_coords(ds_outcrop, add_lay=False)`)
+    if engine == 'gpd => ds_outcrop need to be a gpd.GeoDataframe
+        (use `gm.to_geodataframe(ds_outcrop)`)
+    
+    nb: The Marthe grid must have a `z` dimension.
     
     Parameters
     ----------
-    TODO docstring
+    ds_outcrop: xr.Dataset
+        output of `gm.get_surface_mask()`
+    
+    file_out: str, Optionnal.
+        If not None (default), file name to write plot
+    
+    engine: str, Optionnal.
+        Engine to use for plotting (default is `xr`).
+    
+    show: bool, Optionnal.
+        Show result (`plt.show()`), default is False.
     
     Returns
     -------
+    fig, ax, ax_cbar if not `show`, otherwise return None
     """
     if isinstance(ds_outcrop, xr.Dataset):
         assert 'z' in ds_outcrop.keys(), "No `z` dimension. Outcrop plot is not possible."
-        maxn = np.max(ds_outcrop['z'].data)
-    elif isinstance(ds_outcrop, gpd.GeoDataframe):
+        maxn = np.nanmax(ds_outcrop['z'].data)
+    elif isinstance(ds_outcrop, gpd.geodataframe.GeoDataFrame):
         assert 'z' in ds_outcrop.columns, "No `z` dimension. Outcrop plot is not possible."
-        maxn = np.max(ds_outcrop['z'].to_numpy())
+        maxn = np.nanmax(ds_outcrop['z'].to_numpy())
 
     # custom cbar to force categories
     cmap = cm.tab10 if maxn <= 10 else cm.tab20
-    cmap = colors.ListedColormap(cmap.colors[:maxn]) # subset on number of colors, if not wrong legend
+    cmap = colors.ListedColormap(cmap.colors[:int(maxn)]) # subset on number of colors, if not wrong legend
     bounds = np.arange(1, maxn+1)
-    norm = colors.BoundaryNorm(bounds, cmap.N,)  # set bins to custom values
+    if len(bounds) == 1:
+        bounds = np.append(bounds, [maxn+1])
+    norm = colors.BoundaryNorm(bounds, cmap.N+1,)  # set bins to custom values
     cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
     
     fig, ax = plt.subplots(1, 1)
@@ -130,8 +195,8 @@ def plot_outcrop(ds_outcrop, fout=None, engine='xr', show=False):
     cax = divider.append_axes("right", size="5%", pad=0.1)
     
     if engine == 'xr':
-        the_plot = ds_outcrop['z'].plot.pcolormesh(
-            x='x', y='y',
+        the_plot = plot_nested_grid(
+            ds_outcrop, var='z',
             ax=ax,
             cmap=cmap,
             levels=bounds,
@@ -156,10 +221,10 @@ def plot_outcrop(ds_outcrop, fout=None, engine='xr', show=False):
         label='Layers',
     )
     ax_cbar.ax.tick_params(size=0)
-    ax_cbar.set_ticklabels(bounds)
+    ax_cbar.set_ticklabels(['{:.0f}'.format(x) for x in bounds])
     
-    if fout is not None:
-        plt.savefig(fout, dpi=300)
+    if file_out is not None:
+        plt.savefig(file_out, dpi=300)
     if show:
         plt.show()
         plt.close()
diff --git a/src/gridmarthe/scripts/__init__.py b/src/gridmarthe/scripts/__init__.py
index 72081ec..de72657 100644
--- a/src/gridmarthe/scripts/__init__.py
+++ b/src/gridmarthe/scripts/__init__.py
@@ -1 +1 @@
-from . import ncmart, cleanmgrid
+from . import ncmart, cleanmgrid, martshp
diff --git a/src/gridmarthe/scripts/cleanmgrid.py b/src/gridmarthe/scripts/cleanmgrid.py
index 06a646b..2a2e1d3 100644
--- a/src/gridmarthe/scripts/cleanmgrid.py
+++ b/src/gridmarthe/scripts/cleanmgrid.py
@@ -1,58 +1,127 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
 
 # objectif, lire l'ensemble des fichiers maillés et les corriger.
 # sinon, a minima le permh
 
-import os, sys, re, shutil
+import os, sys, re, shutil, textwrap
 from pathlib import Path
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
 
+from gridmarthe.__version__ import _copyleft
 
+
+# ext: VARIABLE_NAME in FRench
 MARTGRID_FILES = {
-    # ext: VARIABLE_NAME
-    'topog' : 'H_TOPOGR',
-    'hsubs' : 'H_SUBSTRAT',
     'permh' : 'PERMEAB',
-    'perm_r': 'PERM_LIT_RIVI',
-    'anisv' : 'ANISO_VERTI',
-    'anish' : 'ANISO_HORIZ',
-    'emmli' : 'EMMAG_LIBR',
+    'debit' : 'DEBIT',
+    'charg' : 'CHARGE',
     'emmca' : 'EMMAG_CAPT',
+    'emmli' : 'EMMAG_LIBR',
+    'zgeom' : 'ZONE_GEOM',
+    'hsubs' : 'H_SUBSTRAT',
+    'equip' : 'Z_EQUIPOT',
+    'topog' : 'H_TOPOGR',
+    'zonep' : 'ZONE_SOL',
+    'zonei' : 'ZONE_IRRIG',
     'idebo' : 'IND_DEBORD',
-    'charg' : 'CHARGE',
-    'debit' : 'DEBIT', 
+    'sepon' : 'SUBS_EPONTE',
+    'salini': 'SALINITE',
+    'poros' : 'POROSITE',
+    'satur' : 'SATURAT',
+    'salext': 'SALIN_EXT',
+    'conce' : 'CONCENTR',
+    'conex' : 'CONCEN_EXT',
+    'tempe' : 'TEMPERAT',
+    'temex' : 'TEMPER_EXT',
+    'mconc' : 'MASS_CONCEN',
+    'porfx' : 'PORO_FIX',
     'aff_r' : 'AFFLU_RIVI',
-    'flo_r' : 'DEBIT_RIVI',
-    'fon_r' : 'FOND_RIVI',
-    'hau_r' : 'HAUTEU_RIVI',
-    'lar_r' : 'LARG_RIVI',
-    'lon_r' : 'LONG_RIVI',
     'trc_r' : 'TRONC_RIVI',
+    'lon_r' : 'LONG_RIVI',
+    'lar_r' : 'LARG_RIVI',
+    'hau_r' : 'HAUTEU_RIVI',
+    'epai_r': 'EPAI_LIT_RIV',
+    'perm_r': 'PERM_LIT_RIVI',
     'qext_r': 'Q_EXTER_RIVI',
-    'd_ava' : 'DIRECT_AVAL',# Q_AMONT_RIVI, EPAI_LIT_RIV, RUGOS_RIVI, PENTE_RIVI
+    'flo_r' : 'DEBIT_RIVI',
+    'qamon' : 'Q_AMONT_RIVI',
+    'rug_r' : 'RUGOS_RIVI',
+    'pnt_r' : 'PENTE_RIVI',
+    'fon_r' : 'FOND_RIVI',
+    'conc_r': 'CONCEN_RIVI',
+    'cext_r': 'CONC_EXT_RIVI',
+    'qmas_r': 'QMASS_RIVI',
+    'mass_r': 'MASSE_RIVI',
+    'aff_d' : 'AFFLU_DRAIN',
+    'trc_d' : 'TRONC_DRAIN',
+    'lon_d' : 'LONG_DRAIN',
+    'z_dra' : 'ALTIT_DRAIN',
     'meteo' : 'ZONE_METEO',
-    'zonep' : 'ZONE_SOL',
-    'zgeom' : 'ZONE_GEOM',
-    'zoneg2': 'ZONE_2',
-    'zoneg3': 'ZONE_3',
-    # 'ZONE_CULTUR'
+    'd_ava' : 'DIRECT_AVAL',
+    'v_rui' : 'VITESS_RUISS',
+    'ruiss' : 'RUISSEL',
+    'recha' : 'RECHARGE',
+    's_bvext' : 'SUPERF_BV_EXT',
+    'z_bvext' : 'NZON_RIV_EXT',
 }
 
-def print_help():
-    msg = """ Usage: 
-    `cleanmgrid PATH_TO_RMA`
+
+def parse_args():
+    """ CLI program """
+    parser = ArgumentParser(
+        prog='cleanmgrid',
+        formatter_class=RawDescriptionHelpFormatter,
+        description="Clean marthe grid file for miswritten attributes. Only works for marthe grid v9.0 (a check is performed and  0]
     return files
 
+
 def read_files_from_rma(frma):
     
     # root = os.path.dirname(frma)
@@ -96,14 +168,17 @@ def read_files_from_rma(frma):
     layers, ngrid = parse_geom(layer)
     return res, layers, ngrid
 
+
 def insert_str(string, index, insert_str):
     return string[:index] + insert_str + string[index:]
 
+
 def search_index(string, pattern):
     # get research start, match end, research pattern and result
     idx = [(m.start(0), m.end(1), m.group(0).replace(m.group(1), ''), m.group(1)) for m in re.finditer(pattern, string)]
     return idx
 
+
 def clean_grid_str(string: str, pattern: str, fillvalue: str|list, test=lambda x: len(x) < 1):
     """ Search for a pattern and replace with a unique or varying value
     if match satisfy a custom test
@@ -132,34 +207,51 @@ def clean_grid_str(string: str, pattern: str, fillvalue: str|list, test=lambda x
             new_str = re.sub(pattern, '{}{}'.format(matches[0][2], fillvalue), new_str)
     return new_str
 
+
 def write_res(string, fname):
     with open(f'{str(fname)}', 'w', encoding='ISO-8859-1') as f:
         f.write(string)
     return 0
 
-def parse_args():
-    if len(sys.argv) < 2:
-        print('cleanmgrid NO argument were passed')
-        print_help()
-        sys.exit(1)
-    frma  = sys.argv[1]
-    if frma in ['h', '-h', '--help'] or not frma.endswith('rma'):
-        print_help()
-        sys.exit(1)
-    return frma
 
 def main():
-    frma = parse_args()
-    # root = os.path.dirname(frma) # edit no, if not ./MONMODEL.rma but MONMODEL.rma, dirname is '' so /bakup => not allowed in linux non root
-    root = os.getcwd()
-    os.makedirs('{}/bakup'.format(root), exist_ok=True)
-    files, layers, ngrid = read_files_from_rma(frma)
+    """ CLEANMGRID """
+    
+    args = parse_args()
+    finputs = args.opt[0]
+    
+    root = os.path.dirname(finputs) # edit no, if not ./MONMODEL.rma but MONMODEL.rma, dirname is '' so /bakup => not allowed in linux non root
+    if root == '' or root is None:
+        root = os.getcwd()
+    if args.output is None and not args.no_overwrite:
+        os.makedirs('{}/bakup'.format(root), exist_ok=True)
+    filename = os.path.split(finputs)[-1]
+    
+    # --- get geom
+    if finputs.endswith('rma'):
+        files, layers, ngrid = read_files_from_rma(finputs)
+    else:
+        ext = os.path.splitext(finputs)[-1].replace('.', '')
+        key = MARTGRID_FILES.get(ext)
+        if key is None:
+            print('Unkwnown key field for grid kind {} (file: {})'.format(ext, finputs))
+            sys.exit(1)
+        files, layers, ngrid = [(ext, filename, key)], list(range(1, args.layer + 1)), str(args.grid)
+    
+    
     for ext, file, key in files:
+        
+        # --- treatment of each grid file --- #
+        # --- read and scan var
         fpath = Path(root, file)
-        grid = fread(fpath)
+        grid  = fread(fpath)
         version = scan_vers_semi(grid)
+        fileout = fpath
+        
         if version != 9.0:
             continue
+        
+        # --- clean attributes:
         grid = clean_grid_str(grid, r'\nField=(.?)', key, test=lambda x: len(x) < 1 )
         grid = clean_grid_str(grid, pattern=r'\nLayer=([0-9]*)'  , fillvalue=layers * (int(ngrid)+1)   , test=lambda x: int(x) == 0)
         grid = clean_grid_str(grid, pattern=r'Max_Layer=([0-9]*)', fillvalue=str(max(map(int, layers))), test=lambda x: int(x) == 0)
@@ -168,8 +260,17 @@ def main():
         grid = clean_grid_str(grid, pattern=r'Nest_grid=([0-9]*)', fillvalue=x, test=lambda x: int(x) == 0)
         grid = clean_grid_str(grid, pattern=r'Max_NestG=([0-9]*)', fillvalue=ngrid                     , test=lambda x: int(x) == 0)
         grid = clean_grid_str(grid, pattern=r'Time=([0-9]*\.[0-9]*E[|\-|\+][0-9]*)', fillvalue='0', test=lambda x: float(x) != 0)
-        shutil.copy(fpath, Path(root, 'bakup', file))
-        write_res(grid, fpath)
+        
+        # --- save results
+        if args.no_overwrite:
+            fpath = '{}.fix'.format(str(fpath))
+        elif args.output is not None:
+            fileout = Path(root, args.output)
+        else:
+            shutil.copy(fpath, Path(root, 'bakup', file))
+        
+        write_res(grid, fileout)
+    
     return 0
 
 
diff --git a/src/gridmarthe/scripts/martshp.py b/src/gridmarthe/scripts/martshp.py
new file mode 100644
index 0000000..9a91805
--- /dev/null
+++ b/src/gridmarthe/scripts/martshp.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2025  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+import os, sys, textwrap
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
+import gridmarthe as gm
+from gridmarthe.__version__ import _copyleft
+
+
+def parse_args():
+    """ CLI program """
+    parser = ArgumentParser(
+        prog='martshp',
+        formatter_class=RawDescriptionHelpFormatter,
+        description="Convert a Marthe GridFile to shapefile format.",
+        epilog=textwrap.dedent(_copyleft)
+    )
+    
+    parser.add_argument('opt', metavar='grid timesteps', type=str, nargs='*', help='Paths to grid [and timesteps if result] files are expected') # nargs='+'
+    parser.add_argument('--output'  , '-o', type=str, default=None, help='Output filename. Default is input.nc')
+    parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.')
+    parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optionnal, default is 1 (no transformation).')
+    parser.add_argument('--gpkg', '-g', action="store_const", const=True, default=False, help='Use GPKG format instead of shapefile')
+    parser.add_argument('--mask', '-m', action="store_const", const=True, default=False, help='Only get a mask of active domain')
+    parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit')
+    parser.add_argument('--wide_fmt', '-w', action="store_const", const=True, default=False, help='Use wide format (columns) for time')
+    
+    args = parser.parse_args()
+    
+    if args.version:
+        print('gridmarthe {}'.format(gm.__version__))
+        print(_copyleft)
+        sys.exit(0)
+    
+    if args.output is not None:
+        dirout = os.path.dirname(args.output)
+        if dirout != '':
+            os.makedirs(dirout, exist_ok=True)
+    else:
+        fname, ext = os.path.splitext(args.opt[0])
+        args.output = '{}.shp'.format(fname)
+    
+    if args.gpkg:
+        fname, ext = os.path.splitext(args.output)
+        args.output = '{}.gpkg'.format(fname)
+    
+    if os.path.exists(args.output):
+        os.remove(args.output)
+
+    if args.variable is not None:
+        args.variable = args.variable.upper()
+
+    return args
+
+
+def main():
+    """
+    Convert a Marthe Grid file to NetCDF format, using gridmarthe pymodule
+    """
+    args   = parse_args()
+    fpastp = args.opt[1] if len(args.opt) > 1 else None
+    
+    ds = gm.load_marthe_grid(
+        args.opt[0],
+        fpastp=fpastp,
+        drop_nan=True,
+        varname=args.variable,
+        xyfactor=args.xyfactor
+    )
+    
+    if args.mask:
+        mask = gm.get_active_mask(ds)
+        mask.to_file(args.output)
+        sys.exit(0)
+
+    #ds = gm.assign_coords(ds)
+    gdf = gm.to_geodataframe(ds, fmt='long' if not args.wide_fmt else 'wide')
+    gdf.to_file(args.output)
+    return 0
+    
+    
+if __name__ == "__main__":
+    
+    status = main()
+    sys.exit(status)
+   
diff --git a/src/gridmarthe/scripts/ncmart.py b/src/gridmarthe/scripts/ncmart.py
index 545f12d..c7b40fd 100644
--- a/src/gridmarthe/scripts/ncmart.py
+++ b/src/gridmarthe/scripts/ncmart.py
@@ -1,10 +1,30 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import os, sys
-from argparse import ArgumentParser
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+import os, sys, textwrap
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
 import gridmarthe as gm
-
+from gridmarthe.__version__ import _copyleft #, _show_c, _show_w
 
 # Usage: `ncmart PATH_CHASIM PATH_PASTP [-o output] [-v varname]` 
 
@@ -12,16 +32,33 @@ def parse_args():
     """ CLI program """
     parser = ArgumentParser(
         prog='ncmart',
-        description="Convert a Marthe GridFile to netCDF format."
+        formatter_class=RawDescriptionHelpFormatter,
+        description="Convert a Marthe GridFile to netCDF format.",
+        epilog=textwrap.dedent(_copyleft)
     )
     
-    parser.add_argument('opt', metavar='chasim pastp', type=str, nargs='+', help='Paths to chasim and pastp files are expected')
-    parser.add_argument('--output'  , '-o'  , type=str, default=None, help='Output filename. Default is input.nc')
-    parser.add_argument('--variable', '-v'  , type=str, default=None, help='Variable to read, default is None; i.e variable will be parse from file and ONLY the first variable will be read.')
-    parser.add_argument('--as2d', '-d', action="store_const", const=True, default=False, help='Store grid as 2D (or more), default is 1D for space dimension') #choices=('True','False'), dest='monnomdevariable'
+    parser.add_argument('opt', metavar='grid timesteps', type=str, nargs='*', help='Paths to grid and timesteps files are expected') # nargs='+'
+    parser.add_argument('--output'  , '-o', type=str, default=None, help='Output filename. Default is input.nc')
+    parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.')
+    parser.add_argument('--as2d'    , '-d', action="store_const", const=True, default=False, help='Store grid as 2D (or more), default is 1D for space dimension') #choices=('True','False'), dest='monnomdevariable'
     parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optionnal, default is 1 (no transformation).')
+    # parser.add_argument('--show', type=str, default='', choices=('c', 'w'), help='Print licensing for warranty (w) and redistribution conditions (c)')
+    parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit')
+    
     args = parser.parse_args()
     
+    # if args.show != '':
+        # if args.show == 'c':
+            # _show_c()
+        # if args.show == 'w':
+            # _show_w()
+        # sys.exit(0)
+    if args.version:
+        print('gridmarthe {}'.format(gm.__version__))
+        print(_copyleft)
+        sys.exit(0)
+    
+    
     if args.output is not None:
         dirout = os.path.dirname(args.output)
         if dirout != '':
diff --git a/src/gridmarthe/utils.py b/src/gridmarthe/utils.py
index fba3e21..e8986bd 100644
--- a/src/gridmarthe/utils.py
+++ b/src/gridmarthe/utils.py
@@ -1,11 +1,42 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+#    This file is part of gridmarthe.
+#
+#    gridmarthe is a python library to manage grid files for 
+#    MARTHE hydrogeological computer code from French Geological Survey (BRGM).
+#    Copyright (C) 2024  BRGM
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see .
+#
+
+import re
+from datetime import datetime
+import pandas as pd
 import numpy as np
 
+from typing import Union
+
+
 def _datetime64_to_float(zdates, origin='1970-01-01T00:00:00'):
     # Memo: here, origin should be defined from pastp (time since timestep 0)
     idate = (zdates - np.datetime64(origin)) / np.timedelta64(1, 's')
     idate = np.where(idate < 0., 0., idate) # fake dates from load_marthe_grid will be set to 0, meaning timestep -9999. (eg used in parameters grids)
     return idate
 
+
 def _is_sorted(a):
     return np.all(a[:-1] <= a[1:])
 
@@ -31,3 +62,142 @@ def _nearest_node(node, nodes):
     dist_2 = np.sum((nodes - node)**2, axis=1)
     return np.argmin(dist_2)
 
+
+def read_dates_from_pastp(fpastp, encoding='ISO-8859-1'):
+    """Read simulation timesteps from a .pastp file
+    """
+    # reading file as raw df - not str ; faster with pandas func
+    pastp = pd.read_csv(
+        fpastp,
+        header=None,
+        encoding=encoding
+    ).squeeze('columns')
+
+    # First, get steady state time
+    idx_0  = pastp.loc[pastp.str.contains(r' \*\*\* D.*but de la simulation.*', regex=True)].index.values[0]
+    date_0 = re.findall(r'[0-9]+', pastp.iloc[idx_0] )
+    
+    # convert as DF
+    timesteps = pd.DataFrame([{
+        'timestep': 0, 
+        'date': datetime(int(date_0[2]), int(date_0[1]), int(date_0[0]) ) 
+    }])
+
+    # Then, get all ending times for transient state
+    idx  = pastp.loc[pastp.str.contains(r'^ \*\*\* Le pas.*\d+: se termine.*', regex=True)]
+    # extract dates from strings
+    dates= pd.DataFrame(
+        idx.str.findall(r'[0-9]+').to_list(),
+        columns=['timestep', 'day', 'month', 'year'],
+        #dtype={'timestep':int, 'day':int, 'month':int, 'year':int}
+    )
+    
+    # assign dtype
+    dates['timestep'] = pd.to_numeric(dates['timestep'])
+
+    # convert data as datetime object
+    dates['date'] = pd.to_datetime(dates[['day', 'month', 'year']])
+    dates = dates.drop(['month','year', 'day'], axis=1)
+    
+    return pd.concat([timesteps, dates], axis=0)
+
+
+def dropna(ds, varname: str, nanval: Union[list, float]):
+    """ Drop values corrresponding to NaN (marthe convention, eg. code 9999.) 
+    for 1D (or 2D (time, zone)) array
+    zone must me a coordinate dimension.
+    
+    Returns
+    -------
+    dataset where variable != nanval
+    """
+    if isinstance(nanval, (float, int, str)):
+        nanval = [nanval]
+    elif isinstance(nanval, tuple):
+        nanval = list(nanval)  # convert to list to be mutated
+    nanval += [1.e+20]
+    mask = ds[varname.lower()].where(~ds[varname.lower()].isin(nanval)).dropna(dim='zone') # drop nanval
+    ds_no_nan = ds.sel(zone=mask['zone'])
+    return ds_no_nan
+
+
+def subset(ds, varname: str, value: Union[list, float]):
+    """ Subset dataset based on variable name and value.
+    --> inverse of :py:func:`dropna`
+    
+    Returns
+    -------
+    dataset where variable = value
+    """
+    if isinstance(value, (float, int, str)):
+        value = [value]
+    mask = ds[varname.lower()].where(ds[varname.lower()].isin(value)).dropna(dim='zone')
+    ds_filter = ds.sel(zone=mask['zone'])
+    return ds_filter
+
+
+def replace(ds, varname: str, value: float, replace: float):
+    """ Replace a value in xr.Dataset for a variable
+    """
+    ds[varname].data = np.where(ds[varname].data == value, replace, ds[varname].data)
+    return ds
+
+
+def fillna(ds, varname, value):
+    """ Replace real nan (np.nan) value in dataset[varname], edge case of :py:func:`replace()`
+    """
+    ds[varname].data = np.where(np.isnan(ds[varname].data), value, ds[varname].data)
+    return ds
+
+
+def assign_coords(da_in, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zone_label='zone'):
+    """ assign coords to transform a 1D or 2D (time, zone) array to 3D or 4D
+    """
+    if len(coords) == 3:
+        z_coords = da_in.get(coords[2], None) # assert z is here, or bypass
+    else:
+        z_coords = None
+    
+    if add_lay is False:
+        # in some case, even if z is included it should not be treated as coord (ex. plot outcrop)
+        z_coords = None
+    
+    da = da_in.assign_coords(
+        #x=(zone_label, np.around(da_in[coords[0]].data, 1) ),
+        x=(zone_label, da_in[coords[0]].data ),
+        y=(zone_label, da_in[coords[1]].data ),
+    )
+    dims = ['y', 'x']
+    
+    if z_coords is not None:
+        da = da.assign_coords(z=(zone_label, da_in[coords[2]].data))
+        dims.insert(0, 'z')
+    
+    da = da.set_index(zone=dims)
+    if not keep_zone:
+        da = da.drop_duplicates(zone_label).unstack(zone_label)  # drop duplicates is a security for nested grids, if dropnan was not performed
+    return da.sortby(dims)
+
+
+def stack_coords(ds, coords=['z', 'y', 'x'], dropna=False):
+    """ Transform a 3 or 4D aray into 1 or 2D array 
+    inverse of  :py:func:`assign_coords`
+    """
+    # create zone index
+    coords = [d for d in coords if d in ds.coords.keys()] # make sure to drop coords that are not present
+    dims = np.prod( [len(ds[d]) for d in coords] ) # create new zone dim
+    zone = np.arange(dims)
+    
+    # stack coords
+    ds2 = ds.copy().stack(zone=coords) # multiindex zone grouping coords key
+    
+    # keep only zone as dim
+    ds3 = ds2.drop_vars(['zone'] + coords).assign_coords(zone=('zone', zone))
+    
+    # get back xy[z] as var
+    for c in coords:
+        ds3[c] = ('zone', ds2[c].data)
+    
+    if dropna:
+        ds3 = ds3.dropna(dim='zone')
+    return ds3
diff --git a/tests/chasim2nc.py b/tests/chasim2nc.py
deleted file mode 100644
index 2c1c399..0000000
--- a/tests/chasim2nc.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import gridmarthe as gm
-
-# needs, xarray, netcdf4, h5netcdf
-
-def main(fchasim, fpastp, fnc):
-    ds = gm.load_marthe_grid(fchasim, fpastp=fpastp, dropna=True, varname='CHARGE')
-    encode = {'charge': {'zlib': True, 'complevel': 7}}
-    ds.to_netcdf(fnc, engine='h5netcdf', encoding=encode)
-    return 0
-    
-    
-if __name__ == "__main__":
-    
-    import os, sys
-    from pathlib import Path
-    
-    os.makedirs('nc', exist_ok=True)
-    
-    # chasim = sys.argv[1]
-    # pastp = sys.argv[2]
-
-    chasim='./data/chasim_hallue.out'
-    pastp='./data/hallue.pastp'
-    nc = Path('nc', os.path.basename(chasim.replace("out", "nc")))
-
-    status = main(chasim, pastp, nc)
-    sys.exit(status)
-   
diff --git a/docs/source/user_guide/example/chasim_hallue.nc b/tests/data/chasim_hallue.nc
similarity index 99%
rename from docs/source/user_guide/example/chasim_hallue.nc
rename to tests/data/chasim_hallue.nc
index bc8940e..417ba3d 100644
Binary files a/docs/source/user_guide/example/chasim_hallue.nc and b/tests/data/chasim_hallue.nc differ
diff --git a/tests/data/chasim_hallue_2var.out b/tests/data/chasim_hallue_2var.out
new file mode 100644
index 0000000..fc6953c
--- /dev/null
+++ b/tests/data/chasim_hallue_2var.out
@@ -0,0 +1,168 @@
+Marthe_Grid Version=9.0
+Title=Modlisation du bassin de la SOMME Nappe_Libre         Pas       4;    CHARGE  1                    <> CHARGE 1
+[Infos]
+Field=CHARGE
+Type=
+Elem_Number=0
+Name=
+Time_Step=4
+Time=216
+Layer=1
+Max_Layer=1
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=596.5
+Y_Lower_Corner=2542
+Ncolumn=53
+Nrows=54
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
+0	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	
+0	0	596.75	597.25	597.75	598.25	598.75	599.25	599.75	600.25	600.75	601.25	601.75	602.25	602.75	603.25	603.75	604.25	604.75	605.25	605.75	606.25	606.75	607.25	607.75	608.25	608.75	609.25	609.75	610.25	610.75	611.25	611.75	612.25	612.75	613.25	613.75	614.25	614.75	615.25	615.75	616.25	616.75	617.25	617.75	618.25	618.75	619.25	619.75	620.25	620.75	621.25	621.75	622.25	622.75	
+1	2568.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+2	2568.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+3	2567.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+4	2567.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+5	2566.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	100.0473	100.6405	101.1256	101.6138	102.6527	103.3882	103.5225	9999	9999	9999	9999	0.5	
+6	2566.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	96.99962	97.5959	98.07607	98.84633	99.31061	100.1469	9999	9999	9999	9999	9999	0.5	
+7	2565.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	94.18056	94.62288	95.05609	95.75846	96.5719	96.67666	97.31791	9999	9999	9999	9999	9999	0.5	
+8	2565.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	92.61234	93.01282	93.28211	94.13884	94.57119	94.71706	95.01626	94.39854	9999	9999	9999	9999	0.5	
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+[End_Grid]
diff --git a/tests/data/craie_npc.hsubs b/tests/data/craie_npc.hsubs
new file mode 100644
index 0000000..8cf0cb6
--- /dev/null
+++ b/tests/data/craie_npc.hsubs
@@ -0,0 +1,2730 @@
+Marthe_Grid Version=9.0
+Title=                                                                       H_SUBSTRAT     1
+[Infos]
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+Elem_Number=0
+Name=
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+Title=                                                                       H_SUBSTRAT     5
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+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
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diff --git a/tests/data/craie_npc.topog b/tests/data/craie_npc.topog
new file mode 100644
index 0000000..18d9ccf
--- /dev/null
+++ b/tests/data/craie_npc.topog
@@ -0,0 +1,595 @@
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       1
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=543500
+Y_Lower_Corner=2526100
+Ncolumn=387
+Nrows=304
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       3
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       4
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+Title=Craie NPC                                                      H_TO   RH_TOPOGR       5
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       6
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=6
+Max_Layer=10
+Nest_grid=0
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       7
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       8
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR       9
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+[Constant_Data]
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_TO   RH_TOPOGR      10
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
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+Ncolumn=387
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+[Constant_Data]
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+[End_Grid]
diff --git a/tests/data/craie_npc_nogig.permh b/tests/data/craie_npc_nogig.permh
new file mode 100644
index 0000000..2d38f45
--- /dev/null
+++ b/tests/data/craie_npc_nogig.permh
@@ -0,0 +1,3340 @@
+Marthe_Grid Version=9.0
+Title=Craie NPC                                                      H_SU   RPERMEAB        1
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=10
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=543500
+Y_Lower_Corner=2526100
+Ncolumn=387
+Nrows=304
+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=Craie NPC                                                      H_SU   RPERMEAB        2
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+Title=Craie NPC                                                      H_SU   RPERMEAB        3
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+Title=Craie NPC                                                      H_SU   RPERMEAB        4
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+Title=Craie NPC                                                      H_SU   RPERMEAB        5
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+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+Title=Craie NPC                                                      H_SU   RPERMEAB        6
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+Title=Craie NPC                                                      H_SU   RPERMEAB        7
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+Title=Craie NPC                                                      H_SU   RPERMEAB        8
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+[End_Grid]
diff --git a/tests/data/grid_wrong_attrs.hsubs b/tests/data/grid_wrong_attrs.hsubs
new file mode 100644
index 0000000..9bb7058
--- /dev/null
+++ b/tests/data/grid_wrong_attrs.hsubs
@@ -0,0 +1,905 @@
+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT     1
+[Infos]
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+Type=
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+Nrows=189
+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
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+Title=Bidon                                                                2 H_SUBSTRAT     1
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+6	2551.05	14.94	14.59	14.59	14.59	14.59	14.59	14.9	14.9	14.9	14.9	14.9	16.06	16.06	16.06	16.06	16.06	18.41	18.41	18.41	18.41	18.41	20.02	20.02	20.02	20.02	20.02	21.47	21.47	21.47	21.47	21.47	21.84	21.84	21.84	21.84	21.84	22.04	22.04	22.04	22.04	22.04	22.58	22.58	22.58	22.58	22.58	25.14	25.14	25.14	25.14	25.14	27.29	27.29	27.29	27.29	27.29	30.52	30.52	30.52	30.52	30.52	31.31	31.31	31.31	31.31	31.31	30.86	30.86	30.86	30.86	30.86	30.28	30.28	30.28	30.28	30.28	29.57	29.57	29.57	29.57	29.57	29.39	29.39	29.39	29.39	29.39	29.73	29.73	29.73	29.73	29.73	30.3	30.3	30.3	30.3	30.3	31.22	31.22	31.22	31.22	31.22	31.93	31.93	31.93	31.93	31.93	33.16	33.16	33.16	33.16	33.16	34.14	34.14	34.14	34.14	34.14	35.66	35.66	35.66	35.66	35.66	36.49	36.49	36.49	36.49	36.49	37.48	37.48	37.48	37.48	37.48	37.92	37.92	37.92	37.92	37.92	38.3	38.3	38.3	38.3	38.3	38.56	38.56	38.56	38.56	38.56	38.92	38.92	38.92	38.92	38.92	39.08	39.08	39.08	39.08	39.08	39.28	39.28	39.28	39.28	39.28	39.61	39.61	39.61	39.61	39.61	40.35	40.35	40.35	40.35	40.35	40.73	40.73	40.73	40.73	40.73	41.04	41.04	41.04	41.04	41.04	41.2	41.2	41.2	41.2	41.2	42.11	42.11	42.11	42.11	42.11	43.24	43.24	43.24	43.24	43.24	44.89	44.89	44.89	44.89	44.89	45.79	45.79	45.79	45.79	45.79	47.06	47.06	47.06	47.06	47.06	47.91	47.91	47.91	47.91	47.91	48.79	.1000977	
+7	2550.95	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+8	2550.85	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+9	2550.75	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+10	2550.65	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+11	2550.55	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+12	2550.45	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+14	2550.25	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+28	2548.85	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+29	2548.75	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+30	2548.65	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
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+50	2546.65	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
+51	2546.55	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
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+91	2542.55	25.91	24.95	24.95	24.95	24.95	24.95	23.46	23.46	23.46	23.46	23.46	22.41	22.41	22.41	22.41	22.41	20.89	20.89	20.89	20.89	20.89	20.1	20.1	20.1	20.1	20.1	18.05	18.05	18.05	18.05	18.05	15.44	15.44	15.44	15.44	15.44	11.84	11.84	11.84	11.84	11.84	10.93	10.93	10.93	10.93	10.93	12.91	12.91	12.91	12.91	12.91	16.21	16.21	16.21	16.21	16.21	20.11	20.11	20.11	20.11	20.11	21.33	21.33	21.33	21.33	21.33	21.85	21.85	21.85	21.85	21.85	22.36	22.36	22.36	22.36	22.36	23.99	23.99	23.99	23.99	23.99	25.18	25.18	25.18	25.18	25.18	27.13	27.13	27.13	27.13	27.13	28.18	28.18	28.18	28.18	28.18	29.49	29.49	29.49	29.49	29.49	30.2	30.2	30.2	30.2	30.2	31.49	31.49	31.49	31.49	31.49	32.35	32.35	32.35	32.35	32.35	33.38	33.38	33.38	33.38	33.38	33.94	33.94	33.94	33.94	33.94	34.51	34.51	34.51	34.51	34.51	35.07	35.07	35.07	35.07	35.07	36.03	36.03	36.03	36.03	36.03	36.65	36.65	36.65	36.65	36.65	37.52	37.52	37.52	37.52	37.52	38.11	38.11	38.11	38.11	38.11	38.77	38.77	38.77	38.77	38.77	38.93	38.93	38.93	38.93	38.93	38.92	38.92	38.92	38.92	38.92	38.97	38.97	38.97	38.97	38.97	39.26	39.26	39.26	39.26	39.26	39.52	39.52	39.52	39.52	39.52	40.01	40.01	40.01	40.01	40.01	40.15	40.15	40.15	40.15	40.15	42	42	42	42	42	40.55	40.55	40.55	40.55	40.55	41.12	41.12	41.12	41.12	41.12	41.99	41.99	41.99	41.99	41.99	42.98	.1000977	
+92	2542.45	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+93	2542.35	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+94	2542.25	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+95	2542.15	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+96	2542.05	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+97	2541.95	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+98	2541.85	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+99	2541.75	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+100	2541.65	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+101	2541.55	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+102	2541.45	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+103	2541.35	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+104	2541.25	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+105	2541.15	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+106	2541.05	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+107	2540.95	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+108	2540.85	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+109	2540.75	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+110	2540.65	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
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+112	2540.45	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+113	2540.35	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+114	2540.25	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+115	2540.15	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+116	2540.05	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+117	2539.95	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+118	2539.85	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+120	2539.65	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+121	2539.55	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+192	2532.45	36.37	35.59	35.59	35.59	35.59	35.59	34.26	34.26	34.26	34.26	34.26	33.14	33.14	33.14	33.14	33.14	31.63	31.63	31.63	31.63	31.63	30.63	30.63	30.63	30.63	30.63	29.28	29.28	29.28	29.28	29.28	28.24	28.24	28.24	28.24	28.24	27.07	27.07	27.07	27.07	27.07	26.57	26.57	26.57	26.57	26.57	26.17	26.17	26.17	26.17	26.17	25.86	25.86	25.86	25.86	25.86	25.33	25.33	25.33	25.33	25.33	25.09	25.09	25.09	25.09	25.09	27	27	27	27	27	25.52	25.52	25.52	25.52	25.52	26.69	26.69	26.69	26.69	26.69	27.22	27.22	27.22	27.22	27.22	28.37	28.37	28.37	28.37	28.37	29.25	29.25	29.25	29.25	29.25	30.65	30.65	30.65	30.65	30.65	31.33	31.33	31.33	31.33	31.33	31.35	31.35	31.35	31.35	31.35	31.01	31.01	31.01	31.01	31.01	30.48	30.48	30.48	30.48	30.48	30.32	30.32	30.32	30.32	30.32	30.64	30.64	30.64	30.64	30.64	31.18	31.18	31.18	31.18	31.18	32.12	32.12	32.12	32.12	32.12	32.58	32.58	32.58	32.58	32.58	33.04	33.04	33.04	33.04	33.04	33.76	33.76	33.76	33.76	33.76	34.91	34.91	34.91	34.91	34.91	35.72	35.72	35.72	35.72	35.72	36.57	36.57	36.57	36.57	36.57	36.85	36.85	36.85	36.85	36.85	37.71	37.71	37.71	37.71	37.71	38.56	38.56	38.56	38.56	38.56	39.72	39.72	39.72	39.72	39.72	40.33	40.33	40.33	40.33	40.33	40.8	40.8	40.8	40.8	40.8	40.82	40.82	40.82	40.82	40.82	40.6	40.6	40.6	40.6	40.6	40.32	40.32	40.32	40.32	40.32	39.7	.1000977	
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+199	2531.75	35.99	35.17	35.17	35.17	35.17	35.17	33.81	33.81	33.81	33.81	33.81	32.67	32.67	32.67	32.67	32.67	31.33	31.33	31.33	31.33	31.33	30.4	30.4	30.4	30.4	30.4	29.21	29.21	29.21	29.21	29.21	28.32	28.32	28.32	28.32	28.32	27.27	27.27	27.27	27.27	27.27	26.82	26.82	26.82	26.82	26.82	26.27	26.27	26.27	26.27	26.27	26.12	26.12	26.12	26.12	26.12	25.51	25.51	25.51	25.51	25.51	25.23	25.23	25.23	25.23	25.23	27	27	27	27	27	25.11	25.11	25.11	25.11	25.11	25.8	25.8	25.8	25.8	25.8	26.08	26.08	26.08	26.08	26.08	27.04	27.04	27.04	27.04	27.04	28.04	28.04	28.04	28.04	28.04	29.62	29.62	29.62	29.62	29.62	30.45	30.45	30.45	30.45	30.45	30.3	30.3	30.3	30.3	30.3	29.85	29.85	29.85	29.85	29.85	29.39	29.39	29.39	29.39	29.39	29.47	29.47	29.47	29.47	29.47	30.18	30.18	30.18	30.18	30.18	30.79	30.79	30.79	30.79	30.79	31.77	31.77	31.77	31.77	31.77	32.29	32.29	32.29	32.29	32.29	32.9	32.9	32.9	32.9	32.9	33.63	33.63	33.63	33.63	33.63	34.75	34.75	34.75	34.75	34.75	35.36	35.36	35.36	35.36	35.36	35.85	35.85	35.85	35.85	35.85	36	36	36	36	36	36.65	36.65	36.65	36.65	36.65	37.4	37.4	37.4	37.4	37.4	38.52	38.52	38.52	38.52	38.52	39.12	39.12	39.12	39.12	39.12	39.71	39.71	39.71	39.71	39.71	39.86	39.86	39.86	39.86	39.86	39.9	39.9	39.9	39.9	39.9	39.78	39.78	39.78	39.78	39.78	39.36	.1000977	
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+202	2531.45	35.3	34.49	34.49	34.49	34.49	34.49	33.24	33.24	33.24	33.24	33.24	32.21	32.21	32.21	32.21	32.21	30.97	30.97	30.97	30.97	30.97	30.12	30.12	30.12	30.12	30.12	29.04	29.04	29.04	29.04	29.04	28.46	28.46	28.46	28.46	28.46	27.6	27.6	27.6	27.6	27.6	27.08	27.08	27.08	27.08	27.08	26.78	26.78	26.78	26.78	26.78	26.75	26.75	26.75	26.75	26.75	26.27	26.27	26.27	26.27	26.27	28	28	28	28	28	24.83	24.83	24.83	24.83	24.83	27	27	27	27	27	27	27	27	27	27	24.49	24.49	24.49	24.49	24.49	25.34	25.34	25.34	25.34	25.34	26.4	26.4	26.4	26.4	26.4	28.09	28.09	28.09	28.09	28.09	28.89	28.89	28.89	28.89	28.89	28.48	28.48	28.48	28.48	28.48	28.21	28.21	28.21	28.21	28.21	28.03	28.03	28.03	28.03	28.03	28.54	28.54	28.54	28.54	28.54	30.09	30.09	30.09	30.09	30.09	30.82	30.82	30.82	30.82	30.82	31.69	31.69	31.69	31.69	31.69	32.04	32.04	32.04	32.04	32.04	32.53	32.53	32.53	32.53	32.53	33.06	33.06	33.06	33.06	33.06	33.87	33.87	33.87	33.87	33.87	34.24	34.24	34.24	34.24	34.24	34.44	34.44	34.44	34.44	34.44	34.58	34.58	34.58	34.58	34.58	35.19	35.19	35.19	35.19	35.19	35.82	35.82	35.82	35.82	35.82	37.01	37.01	37.01	37.01	37.01	37.65	37.65	37.65	37.65	37.65	38.32	38.32	38.32	38.32	38.32	38.59	38.59	38.59	38.59	38.59	38.74	38.74	38.74	38.74	38.74	38.78	38.78	38.78	38.78	38.78	38.61	.1000977	
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+[End_Grid]
diff --git a/tests/data/grid_wrong_attrs.hsubs.fix b/tests/data/grid_wrong_attrs.hsubs.fix
new file mode 100644
index 0000000..696ea7e
--- /dev/null
+++ b/tests/data/grid_wrong_attrs.hsubs.fix
@@ -0,0 +1,905 @@
+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT     1
+[Infos]
+Field=H_SUBSTRAT
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=1
+Nest_grid=0
+Max_NestG=3
+[Structure]
+X_Left_Corner=528
+Y_Lower_Corner=2503
+Ncolumn=320
+Nrows=189
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Colums    >
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+6	2551.05	14.94	14.59	14.59	14.59	14.59	14.59	14.9	14.9	14.9	14.9	14.9	16.06	16.06	16.06	16.06	16.06	18.41	18.41	18.41	18.41	18.41	20.02	20.02	20.02	20.02	20.02	21.47	21.47	21.47	21.47	21.47	21.84	21.84	21.84	21.84	21.84	22.04	22.04	22.04	22.04	22.04	22.58	22.58	22.58	22.58	22.58	25.14	25.14	25.14	25.14	25.14	27.29	27.29	27.29	27.29	27.29	30.52	30.52	30.52	30.52	30.52	31.31	31.31	31.31	31.31	31.31	30.86	30.86	30.86	30.86	30.86	30.28	30.28	30.28	30.28	30.28	29.57	29.57	29.57	29.57	29.57	29.39	29.39	29.39	29.39	29.39	29.73	29.73	29.73	29.73	29.73	30.3	30.3	30.3	30.3	30.3	31.22	31.22	31.22	31.22	31.22	31.93	31.93	31.93	31.93	31.93	33.16	33.16	33.16	33.16	33.16	34.14	34.14	34.14	34.14	34.14	35.66	35.66	35.66	35.66	35.66	36.49	36.49	36.49	36.49	36.49	37.48	37.48	37.48	37.48	37.48	37.92	37.92	37.92	37.92	37.92	38.3	38.3	38.3	38.3	38.3	38.56	38.56	38.56	38.56	38.56	38.92	38.92	38.92	38.92	38.92	39.08	39.08	39.08	39.08	39.08	39.28	39.28	39.28	39.28	39.28	39.61	39.61	39.61	39.61	39.61	40.35	40.35	40.35	40.35	40.35	40.73	40.73	40.73	40.73	40.73	41.04	41.04	41.04	41.04	41.04	41.2	41.2	41.2	41.2	41.2	42.11	42.11	42.11	42.11	42.11	43.24	43.24	43.24	43.24	43.24	44.89	44.89	44.89	44.89	44.89	45.79	45.79	45.79	45.79	45.79	47.06	47.06	47.06	47.06	47.06	47.91	47.91	47.91	47.91	47.91	48.79	.1000977	
+7	2550.95	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+8	2550.85	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+9	2550.75	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+10	2550.65	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+11	2550.55	15.81	15.59	15.59	15.59	15.59	15.59	15.87	15.87	15.87	15.87	15.87	16.82	16.82	16.82	16.82	16.82	18.7	18.7	18.7	18.7	18.7	20.08	20.08	20.08	20.08	20.08	21.29	21.29	21.29	21.29	21.29	21.7	21.7	21.7	21.7	21.7	21.97	21.97	21.97	21.97	21.97	22.61	22.61	22.61	22.61	22.61	25.38	25.38	25.38	25.38	25.38	27.55	27.55	27.55	27.55	27.55	30.62	30.62	30.62	30.62	30.62	30.86	30.86	30.86	30.86	30.86	29.97	29.97	29.97	29.97	29.97	29.22	29.22	29.22	29.22	29.22	28.45	28.45	28.45	28.45	28.45	28.41	28.41	28.41	28.41	28.41	28.97	28.97	28.97	28.97	28.97	29.51	29.51	29.51	29.51	29.51	30.43	30.43	30.43	30.43	30.43	31.27	31.27	31.27	31.27	31.27	32.86	32.86	32.86	32.86	32.86	33.88	33.88	33.88	33.88	33.88	35.27	35.27	35.27	35.27	35.27	35.91	35.91	35.91	35.91	35.91	36.55	36.55	36.55	36.55	36.55	37.01	37.01	37.01	37.01	37.01	37.61	37.61	37.61	37.61	37.61	38.07	38.07	38.07	38.07	38.07	38.66	38.66	38.66	38.66	38.66	38.91	38.91	38.91	38.91	38.91	39.13	39.13	39.13	39.13	39.13	39.42	39.42	39.42	39.42	39.42	40.37	40.37	40.37	40.37	40.37	40.78	40.78	40.78	40.78	40.78	41.05	41.05	41.05	41.05	41.05	41.3	41.3	41.3	41.3	41.3	41.94	41.94	41.94	41.94	41.94	42.92	42.92	42.92	42.92	42.92	44.39	44.39	44.39	44.39	44.39	45.27	45.27	45.27	45.27	45.27	46.69	46.69	46.69	46.69	46.69	47.61	47.61	47.61	47.61	47.61	48.53	.1000977	
+12	2550.45	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+14	2550.25	17.11	17.05	17.05	17.05	17.05	17.05	17.43	17.43	17.43	17.43	17.43	18.03	18.03	18.03	18.03	18.03	19.19	19.19	19.19	19.19	19.19	22	22	22	22	22	21.01	21.01	21.01	21.01	21.01	21.46	21.46	21.46	21.46	21.46	21.83	21.83	21.83	21.83	21.83	22.2	22.2	22.2	22.2	22.2	24.56	24.56	24.56	24.56	24.56	26.55	26.55	26.55	26.55	26.55	29.1	29.1	29.1	29.1	29.1	29.55	29.55	29.55	29.55	29.55	28.69	28.69	28.69	28.69	28.69	27.84	27.84	27.84	27.84	27.84	27.13	27.13	27.13	27.13	27.13	27.28	27.28	27.28	27.28	27.28	28.18	28.18	28.18	28.18	28.18	29.03	29.03	29.03	29.03	29.03	30.21	30.21	30.21	30.21	30.21	31.16	31.16	31.16	31.16	31.16	32.8	32.8	32.8	32.8	32.8	33.68	33.68	33.68	33.68	33.68	34.73	34.73	34.73	34.73	34.73	35.14	35.14	35.14	35.14	35.14	35.64	35.64	35.64	35.64	35.64	36.18	36.18	36.18	36.18	36.18	36.95	36.95	36.95	36.95	36.95	37.54	37.54	37.54	37.54	37.54	38.36	38.36	38.36	38.36	38.36	38.77	38.77	38.77	38.77	38.77	39.17	39.17	39.17	39.17	39.17	39.4	39.4	39.4	39.4	39.4	40.08	40.08	40.08	40.08	40.08	40.35	40.35	40.35	40.35	40.35	40.74	40.74	40.74	40.74	40.74	41.14	41.14	41.14	41.14	41.14	41.67	41.67	41.67	41.67	41.67	42.48	42.48	42.48	42.48	42.48	43.72	43.72	43.72	43.72	43.72	44.59	44.59	44.59	44.59	44.59	46.15	46.15	46.15	46.15	46.15	47.17	47.17	47.17	47.17	47.17	48.39	.1000977	
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+28	2548.85	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+29	2548.75	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
+30	2548.65	19.15	18.84	18.84	18.84	18.84	18.84	18.55	18.55	18.55	18.55	18.55	18.61	18.61	18.61	18.61	18.61	22	22	22	22	22	19.4	19.4	19.4	19.4	19.4	20.05	20.05	20.05	20.05	20.05	20.22	20.22	20.22	20.22	20.22	20.33	20.33	20.33	20.33	20.33	20.41	20.41	20.41	20.41	20.41	20.9	20.9	20.9	20.9	20.9	21.73	21.73	21.73	21.73	21.73	23.13	23.13	23.13	23.13	23.13	24.09	24.09	24.09	24.09	24.09	24.92	24.92	24.92	24.92	24.92	25.37	25.37	25.37	25.37	25.37	26.15	26.15	26.15	26.15	26.15	26.97	26.97	26.97	26.97	26.97	28.72	28.72	28.72	28.72	28.72	29.91	29.91	29.91	29.91	29.91	31.15	31.15	31.15	31.15	31.15	31.62	31.62	31.62	31.62	31.62	32.13	32.13	32.13	32.13	32.13	32.33	32.33	32.33	32.33	32.33	35	35	35	35	35	33.33	33.33	33.33	33.33	33.33	34.52	34.52	34.52	34.52	34.52	35.47	35.47	35.47	35.47	35.47	36.93	36.93	36.93	36.93	36.93	37.83	37.83	37.83	37.83	37.83	38.97	38.97	38.97	38.97	38.97	39.57	39.57	39.57	39.57	39.57	40.07	40.07	40.07	40.07	40.07	40.14	40.14	40.14	40.14	40.14	39.98	39.98	39.98	39.98	39.98	40.05	40.05	40.05	40.05	40.05	40.69	40.69	40.69	40.69	40.69	41.44	41.44	41.44	41.44	41.44	42.52	42.52	42.52	42.52	42.52	42.95	42.95	42.95	42.95	42.95	43.15	43.15	43.15	43.15	43.15	43.2	43.2	43.2	43.2	43.2	43.67	43.67	43.67	43.67	43.67	44.29	44.29	44.29	44.29	44.29	45.75	.1000977	
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+50	2546.65	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
+51	2546.55	21.67	20.63	20.63	20.63	20.63	20.63	17.85	17.85	17.85	17.85	17.85	15.82	15.82	15.82	15.82	15.82	13.84	13.84	13.84	13.84	13.84	13.51	13.51	13.51	13.51	13.51	14.17	14.17	14.17	14.17	14.17	14.78	14.78	14.78	14.78	14.78	15.29	15.29	15.29	15.29	15.29	15.5	15.5	15.5	15.5	15.5	16.02	16.02	16.02	16.02	16.02	16.93	16.93	16.93	16.93	16.93	19.24	19.24	19.24	19.24	19.24	20.94	20.94	20.94	20.94	20.94	23.19	23.19	23.19	23.19	23.19	24.3	24.3	24.3	24.3	24.3	25.26	25.26	25.26	25.26	25.26	25.92	25.92	25.92	25.92	25.92	27.01	27.01	27.01	27.01	27.01	27.77	27.77	27.77	27.77	27.77	29.24	29.24	29.24	29.24	29.24	30.23	30.23	30.23	30.23	30.23	31.62	31.62	31.62	31.62	31.62	32.4	32.4	32.4	32.4	32.4	33.28	33.28	33.28	33.28	33.28	33.73	33.73	33.73	33.73	33.73	34.42	34.42	34.42	34.42	34.42	35.4	35.4	35.4	35.4	35.4	37.47	37.47	37.47	37.47	37.47	39.06	39.06	39.06	39.06	39.06	41.07	41.07	41.07	41.07	41.07	41.58	41.58	41.58	41.58	41.58	41.79	41.79	41.79	41.79	41.79	41.75	41.75	41.75	41.75	41.75	41.79	41.79	41.79	41.79	41.79	41.85	41.85	41.85	41.85	41.85	41.96	41.96	41.96	41.96	41.96	42.07	42.07	42.07	42.07	42.07	42.23	42.23	42.23	42.23	42.23	42.29	42.29	42.29	42.29	42.29	42.31	42.31	42.31	42.31	42.31	42.29	42.29	42.29	42.29	42.29	42.22	42.22	42.22	42.22	42.22	42.17	42.17	42.17	42.17	42.17	42.13	.1000977	
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+91	2542.55	25.91	24.95	24.95	24.95	24.95	24.95	23.46	23.46	23.46	23.46	23.46	22.41	22.41	22.41	22.41	22.41	20.89	20.89	20.89	20.89	20.89	20.1	20.1	20.1	20.1	20.1	18.05	18.05	18.05	18.05	18.05	15.44	15.44	15.44	15.44	15.44	11.84	11.84	11.84	11.84	11.84	10.93	10.93	10.93	10.93	10.93	12.91	12.91	12.91	12.91	12.91	16.21	16.21	16.21	16.21	16.21	20.11	20.11	20.11	20.11	20.11	21.33	21.33	21.33	21.33	21.33	21.85	21.85	21.85	21.85	21.85	22.36	22.36	22.36	22.36	22.36	23.99	23.99	23.99	23.99	23.99	25.18	25.18	25.18	25.18	25.18	27.13	27.13	27.13	27.13	27.13	28.18	28.18	28.18	28.18	28.18	29.49	29.49	29.49	29.49	29.49	30.2	30.2	30.2	30.2	30.2	31.49	31.49	31.49	31.49	31.49	32.35	32.35	32.35	32.35	32.35	33.38	33.38	33.38	33.38	33.38	33.94	33.94	33.94	33.94	33.94	34.51	34.51	34.51	34.51	34.51	35.07	35.07	35.07	35.07	35.07	36.03	36.03	36.03	36.03	36.03	36.65	36.65	36.65	36.65	36.65	37.52	37.52	37.52	37.52	37.52	38.11	38.11	38.11	38.11	38.11	38.77	38.77	38.77	38.77	38.77	38.93	38.93	38.93	38.93	38.93	38.92	38.92	38.92	38.92	38.92	38.97	38.97	38.97	38.97	38.97	39.26	39.26	39.26	39.26	39.26	39.52	39.52	39.52	39.52	39.52	40.01	40.01	40.01	40.01	40.01	40.15	40.15	40.15	40.15	40.15	42	42	42	42	42	40.55	40.55	40.55	40.55	40.55	41.12	41.12	41.12	41.12	41.12	41.99	41.99	41.99	41.99	41.99	42.98	.1000977	
+92	2542.45	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+93	2542.35	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+94	2542.25	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+95	2542.15	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+96	2542.05	26.53	25.48	25.48	25.48	25.48	25.48	23.77	23.77	23.77	23.77	23.77	22.6	22.6	22.6	22.6	22.6	21.06	21.06	21.06	21.06	21.06	20.34	20.34	20.34	20.34	20.34	18.71	18.71	18.71	18.71	18.71	16.99	16.99	16.99	16.99	16.99	14.39	14.39	14.39	14.39	14.39	14	14	14	14	14	16.17	16.17	16.17	16.17	16.17	18.46	18.46	18.46	18.46	18.46	20.44	20.44	20.44	20.44	20.44	20.78	20.78	20.78	20.78	20.78	20.94	20.94	20.94	20.94	20.94	22.01	22.01	22.01	22.01	22.01	24.74	24.74	24.74	24.74	24.74	26.37	26.37	26.37	26.37	26.37	28.55	28.55	28.55	28.55	28.55	29.38	29.38	29.38	29.38	29.38	29.99	29.99	29.99	29.99	29.99	30.25	30.25	30.25	30.25	30.25	30.95	30.95	30.95	30.95	30.95	31.54	31.54	31.54	31.54	31.54	32.28	32.28	32.28	32.28	32.28	32.69	32.69	32.69	32.69	32.69	33	33	33	33	33	33.29	33.29	33.29	33.29	33.29	33.92	33.92	33.92	33.92	33.92	34.46	34.46	34.46	34.46	34.46	35.69	35.69	35.69	35.69	35.69	36.69	36.69	36.69	36.69	36.69	38.05	38.05	38.05	38.05	38.05	38.73	38.73	38.73	38.73	38.73	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.12	39.03	39.03	39.03	39.03	39.03	38.96	38.96	38.96	38.96	38.96	39.07	39.07	39.07	39.07	39.07	39.1	39.1	39.1	39.1	39.1	42	42	42	42	42	39.5	39.5	39.5	39.5	39.5	40.73	40.73	40.73	40.73	40.73	41.81	41.81	41.81	41.81	41.81	43.18	.1000977	
+97	2541.95	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+98	2541.85	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+99	2541.75	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+100	2541.65	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+101	2541.55	26.9	25.86	25.86	25.86	25.86	25.86	24.03	24.03	24.03	24.03	24.03	22.88	22.88	22.88	22.88	22.88	21.51	21.51	21.51	21.51	21.51	20.76	20.76	20.76	20.76	20.76	19.16	19.16	19.16	19.16	19.16	18.09	18.09	18.09	18.09	18.09	16.69	16.69	16.69	16.69	16.69	16.62	16.62	16.62	16.62	16.62	18.45	18.45	18.45	18.45	18.45	19.7	19.7	19.7	19.7	19.7	20.24	20.24	20.24	20.24	20.24	20.25	20.25	20.25	20.25	20.25	20.48	20.48	20.48	20.48	20.48	21.74	21.74	21.74	21.74	21.74	24.89	24.89	24.89	24.89	24.89	26.68	26.68	26.68	26.68	26.68	28.78	28.78	28.78	28.78	28.78	29.44	29.44	29.44	29.44	29.44	29.9	29.9	29.9	29.9	29.9	30.04	30.04	30.04	30.04	30.04	30.38	30.38	30.38	30.38	30.38	30.75	30.75	30.75	30.75	30.75	31.21	31.21	31.21	31.21	31.21	31.52	31.52	31.52	31.52	31.52	32.03	32.03	32.03	32.03	32.03	32.44	32.44	32.44	32.44	32.44	32.97	32.97	32.97	32.97	32.97	33.42	33.42	33.42	33.42	33.42	34.72	34.72	34.72	34.72	34.72	35.77	35.77	35.77	35.77	35.77	37.49	37.49	37.49	37.49	37.49	38.34	38.34	38.34	38.34	38.34	39.27	39.27	39.27	39.27	39.27	39.25	39.25	39.25	39.25	39.25	38.89	38.89	38.89	38.89	38.89	38.62	38.62	38.62	38.62	38.62	38.52	38.52	38.52	38.52	38.52	38.46	38.46	38.46	38.46	38.46	38.57	38.57	38.57	38.57	38.57	39.07	39.07	39.07	39.07	39.07	40.46	40.46	40.46	40.46	40.46	41.82	41.82	41.82	41.82	41.82	43.47	.1000977	
+102	2541.45	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+103	2541.35	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+104	2541.25	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+105	2541.15	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+106	2541.05	27.41	26.42	26.42	26.42	26.42	26.42	24.66	24.66	24.66	24.66	24.66	23.69	23.69	23.69	23.69	23.69	22.54	22.54	22.54	22.54	22.54	21.7	21.7	21.7	21.7	21.7	23	23	23	23	23	22	22	22	22	22	18.62	18.62	18.62	18.62	18.62	18.87	18.87	18.87	18.87	18.87	20.07	20.07	20.07	20.07	20.07	20.53	20.53	20.53	20.53	20.53	20.17	20.17	20.17	20.17	20.17	20.1	20.1	20.1	20.1	20.1	20.75	20.75	20.75	20.75	20.75	22.06	22.06	22.06	22.06	22.06	24.95	24.95	24.95	24.95	24.95	26.5	26.5	26.5	26.5	26.5	28.15	28.15	28.15	28.15	28.15	28.81	28.81	28.81	28.81	28.81	29.38	29.38	29.38	29.38	29.38	29.56	29.56	29.56	29.56	29.56	29.68	29.68	29.68	29.68	29.68	29.61	29.61	29.61	29.61	29.61	29.58	29.58	29.58	29.58	29.58	30.04	30.04	30.04	30.04	30.04	31.05	31.05	31.05	31.05	31.05	31.92	31.92	31.92	31.92	31.92	32.69	32.69	32.69	32.69	32.69	33.1	33.1	33.1	33.1	33.1	37	37	37	37	37	38	38	38	38	38	38	38	38	38	38	38.08	38.08	38.08	38.08	38.08	38.85	38.85	38.85	38.85	38.85	39.14	39.14	39.14	39.14	39.14	38.7	38.7	38.7	38.7	38.7	38.54	38.54	38.54	38.54	38.54	38.35	38.35	38.35	38.35	38.35	38.22	38.22	38.22	38.22	38.22	38.71	38.71	38.71	38.71	38.71	39.48	39.48	39.48	39.48	39.48	41.11	41.11	41.11	41.11	41.11	42.59	42.59	42.59	42.59	42.59	44.42	.1000977	
+107	2540.95	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+108	2540.85	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+109	2540.75	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
+110	2540.65	27.68	26.7	26.7	26.7	26.7	26.7	25.04	25.04	25.04	25.04	25.04	24.16	24.16	24.16	24.16	24.16	23.16	23.16	23.16	23.16	23.16	22.39	22.39	22.39	22.39	22.39	20.64	20.64	20.64	20.64	20.64	22	22	22	22	22	19.24	19.24	19.24	19.24	19.24	19.59	19.59	19.59	19.59	19.59	20.35	20.35	20.35	20.35	20.35	20.6	20.6	20.6	20.6	20.6	20.5	20.5	20.5	20.5	20.5	20.58	20.58	20.58	20.58	20.58	21.5	21.5	21.5	21.5	21.5	22.77	22.77	22.77	22.77	22.77	25.08	25.08	25.08	25.08	25.08	26.31	26.31	26.31	26.31	26.31	27.68	27.68	27.68	27.68	27.68	28.35	28.35	28.35	28.35	28.35	29.01	29.01	29.01	29.01	29.01	29.27	29.27	29.27	29.27	29.27	29.41	29.41	29.41	29.41	29.41	29.15	29.15	29.15	29.15	29.15	29.11	29.11	29.11	29.11	29.11	29.61	29.61	29.61	29.61	29.61	30.87	30.87	30.87	30.87	30.87	31.92	31.92	31.92	31.92	31.92	32.94	32.94	32.94	32.94	32.94	33.46	33.46	33.46	33.46	33.46	34.99	34.99	34.99	34.99	34.99	37	37	37	37	37	37	37	37	37	37	33	33	33	33	33	40	40	40	40	40	39.43	39.43	39.43	39.43	39.43	39.12	39.12	39.12	39.12	39.12	38.91	38.91	38.91	38.91	38.91	38.81	38.81	38.81	38.81	38.81	38.79	38.79	38.79	38.79	38.79	39.47	39.47	39.47	39.47	39.47	40.33	40.33	40.33	40.33	40.33	41.99	41.99	41.99	41.99	41.99	43.5	43.5	43.5	43.5	43.5	45.39	.1000977	
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+112	2540.45	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+113	2540.35	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+114	2540.25	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+115	2540.15	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+116	2540.05	28.25	27.2	27.2	27.2	27.2	27.2	25.56	25.56	25.56	25.56	25.56	24.75	24.75	24.75	24.75	24.75	23.91	23.91	23.91	23.91	23.91	23.17	23.17	23.17	23.17	23.17	21.6	21.6	21.6	21.6	21.6	22	22	22	22	22	23	23	23	23	23	22	22	22	22	22	23	23	23	23	23	21.39	21.39	21.39	21.39	21.39	21.74	21.74	21.74	21.74	21.74	22.26	22.26	22.26	22.26	22.26	23.24	23.24	23.24	23.24	23.24	24.04	24.04	24.04	24.04	24.04	25.24	25.24	25.24	25.24	25.24	26.08	26.08	26.08	26.08	26.08	27.36	27.36	27.36	27.36	27.36	28.15	28.15	28.15	28.15	28.15	29.08	29.08	29.08	29.08	29.08	32	32	32	32	32	29.64	29.64	29.64	29.64	29.64	29.59	29.59	29.59	29.59	29.59	32	32	32	32	32	32	32	32	32	32	33	33	33	33	33	36	36	36	36	36	33.92	33.92	33.92	33.92	33.92	34.6	34.6	34.6	34.6	34.6	36.44	36.44	36.44	36.44	36.44	37.74	37.74	37.74	37.74	37.74	39.16	39.16	39.16	39.16	39.16	39.84	39.84	39.84	39.84	39.84	40.21	40.21	40.21	40.21	40.21	40.43	40.43	40.43	40.43	40.43	42	42	42	42	42	40.07	40.07	40.07	40.07	40.07	40.04	40.04	40.04	40.04	40.04	40.29	40.29	40.29	40.29	40.29	41.22	41.22	41.22	41.22	41.22	42.15	42.15	42.15	42.15	42.15	43.92	43.92	43.92	43.92	43.92	45.3	45.3	45.3	45.3	45.3	47.18	.1000977	
+117	2539.95	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+118	2539.85	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+120	2539.65	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
+121	2539.55	28.78	27.63	27.63	27.63	27.63	27.63	26.03	26.03	26.03	26.03	26.03	25.14	25.14	25.14	25.14	25.14	24.17	24.17	24.17	24.17	24.17	23.43	23.43	23.43	23.43	23.43	21.92	21.92	21.92	21.92	21.92	21.4	21.4	21.4	21.4	21.4	21.28	21.28	21.28	21.28	21.28	21.46	21.46	21.46	21.46	21.46	21.92	21.92	21.92	21.92	21.92	22	22	22	22	22	22.43	22.43	22.43	22.43	22.43	22.97	22.97	22.97	22.97	22.97	23.86	23.86	23.86	23.86	23.86	24.35	24.35	24.35	24.35	24.35	28	28	28	28	28	25.69	25.69	25.69	25.69	25.69	27.14	27.14	27.14	27.14	27.14	28.13	28.13	28.13	28.13	28.13	29.46	29.46	29.46	29.46	29.46	29.98	29.98	29.98	29.98	29.98	30.49	30.49	30.49	30.49	30.49	32	32	32	32	32	31.23	31.23	31.23	31.23	31.23	32.16	32.16	32.16	32.16	32.16	33.65	33.65	33.65	33.65	33.65	34.37	34.37	34.37	34.37	34.37	34.89	34.89	34.89	34.89	34.89	35.49	35.49	35.49	35.49	35.49	37.58	37.58	37.58	37.58	37.58	39.28	39.28	39.28	39.28	39.28	40.85	40.85	40.85	40.85	40.85	41.24	41.24	41.24	41.24	41.24	41.17	41.17	41.17	41.17	41.17	41.26	41.26	41.26	41.26	41.26	41.09	41.09	41.09	41.09	41.09	40.95	40.95	40.95	40.95	40.95	41.12	41.12	41.12	41.12	41.12	41.42	41.42	41.42	41.42	41.42	42.45	42.45	42.45	42.45	42.45	43.46	43.46	43.46	43.46	43.46	45.32	45.32	45.32	45.32	45.32	46.65	46.65	46.65	46.65	46.65	48.47	.1000977	
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+192	2532.45	36.37	35.59	35.59	35.59	35.59	35.59	34.26	34.26	34.26	34.26	34.26	33.14	33.14	33.14	33.14	33.14	31.63	31.63	31.63	31.63	31.63	30.63	30.63	30.63	30.63	30.63	29.28	29.28	29.28	29.28	29.28	28.24	28.24	28.24	28.24	28.24	27.07	27.07	27.07	27.07	27.07	26.57	26.57	26.57	26.57	26.57	26.17	26.17	26.17	26.17	26.17	25.86	25.86	25.86	25.86	25.86	25.33	25.33	25.33	25.33	25.33	25.09	25.09	25.09	25.09	25.09	27	27	27	27	27	25.52	25.52	25.52	25.52	25.52	26.69	26.69	26.69	26.69	26.69	27.22	27.22	27.22	27.22	27.22	28.37	28.37	28.37	28.37	28.37	29.25	29.25	29.25	29.25	29.25	30.65	30.65	30.65	30.65	30.65	31.33	31.33	31.33	31.33	31.33	31.35	31.35	31.35	31.35	31.35	31.01	31.01	31.01	31.01	31.01	30.48	30.48	30.48	30.48	30.48	30.32	30.32	30.32	30.32	30.32	30.64	30.64	30.64	30.64	30.64	31.18	31.18	31.18	31.18	31.18	32.12	32.12	32.12	32.12	32.12	32.58	32.58	32.58	32.58	32.58	33.04	33.04	33.04	33.04	33.04	33.76	33.76	33.76	33.76	33.76	34.91	34.91	34.91	34.91	34.91	35.72	35.72	35.72	35.72	35.72	36.57	36.57	36.57	36.57	36.57	36.85	36.85	36.85	36.85	36.85	37.71	37.71	37.71	37.71	37.71	38.56	38.56	38.56	38.56	38.56	39.72	39.72	39.72	39.72	39.72	40.33	40.33	40.33	40.33	40.33	40.8	40.8	40.8	40.8	40.8	40.82	40.82	40.82	40.82	40.82	40.6	40.6	40.6	40.6	40.6	40.32	40.32	40.32	40.32	40.32	39.7	.1000977	
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+199	2531.75	35.99	35.17	35.17	35.17	35.17	35.17	33.81	33.81	33.81	33.81	33.81	32.67	32.67	32.67	32.67	32.67	31.33	31.33	31.33	31.33	31.33	30.4	30.4	30.4	30.4	30.4	29.21	29.21	29.21	29.21	29.21	28.32	28.32	28.32	28.32	28.32	27.27	27.27	27.27	27.27	27.27	26.82	26.82	26.82	26.82	26.82	26.27	26.27	26.27	26.27	26.27	26.12	26.12	26.12	26.12	26.12	25.51	25.51	25.51	25.51	25.51	25.23	25.23	25.23	25.23	25.23	27	27	27	27	27	25.11	25.11	25.11	25.11	25.11	25.8	25.8	25.8	25.8	25.8	26.08	26.08	26.08	26.08	26.08	27.04	27.04	27.04	27.04	27.04	28.04	28.04	28.04	28.04	28.04	29.62	29.62	29.62	29.62	29.62	30.45	30.45	30.45	30.45	30.45	30.3	30.3	30.3	30.3	30.3	29.85	29.85	29.85	29.85	29.85	29.39	29.39	29.39	29.39	29.39	29.47	29.47	29.47	29.47	29.47	30.18	30.18	30.18	30.18	30.18	30.79	30.79	30.79	30.79	30.79	31.77	31.77	31.77	31.77	31.77	32.29	32.29	32.29	32.29	32.29	32.9	32.9	32.9	32.9	32.9	33.63	33.63	33.63	33.63	33.63	34.75	34.75	34.75	34.75	34.75	35.36	35.36	35.36	35.36	35.36	35.85	35.85	35.85	35.85	35.85	36	36	36	36	36	36.65	36.65	36.65	36.65	36.65	37.4	37.4	37.4	37.4	37.4	38.52	38.52	38.52	38.52	38.52	39.12	39.12	39.12	39.12	39.12	39.71	39.71	39.71	39.71	39.71	39.86	39.86	39.86	39.86	39.86	39.9	39.9	39.9	39.9	39.9	39.78	39.78	39.78	39.78	39.78	39.36	.1000977	
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+202	2531.45	35.3	34.49	34.49	34.49	34.49	34.49	33.24	33.24	33.24	33.24	33.24	32.21	32.21	32.21	32.21	32.21	30.97	30.97	30.97	30.97	30.97	30.12	30.12	30.12	30.12	30.12	29.04	29.04	29.04	29.04	29.04	28.46	28.46	28.46	28.46	28.46	27.6	27.6	27.6	27.6	27.6	27.08	27.08	27.08	27.08	27.08	26.78	26.78	26.78	26.78	26.78	26.75	26.75	26.75	26.75	26.75	26.27	26.27	26.27	26.27	26.27	28	28	28	28	28	24.83	24.83	24.83	24.83	24.83	27	27	27	27	27	27	27	27	27	27	24.49	24.49	24.49	24.49	24.49	25.34	25.34	25.34	25.34	25.34	26.4	26.4	26.4	26.4	26.4	28.09	28.09	28.09	28.09	28.09	28.89	28.89	28.89	28.89	28.89	28.48	28.48	28.48	28.48	28.48	28.21	28.21	28.21	28.21	28.21	28.03	28.03	28.03	28.03	28.03	28.54	28.54	28.54	28.54	28.54	30.09	30.09	30.09	30.09	30.09	30.82	30.82	30.82	30.82	30.82	31.69	31.69	31.69	31.69	31.69	32.04	32.04	32.04	32.04	32.04	32.53	32.53	32.53	32.53	32.53	33.06	33.06	33.06	33.06	33.06	33.87	33.87	33.87	33.87	33.87	34.24	34.24	34.24	34.24	34.24	34.44	34.44	34.44	34.44	34.44	34.58	34.58	34.58	34.58	34.58	35.19	35.19	35.19	35.19	35.19	35.82	35.82	35.82	35.82	35.82	37.01	37.01	37.01	37.01	37.01	37.65	37.65	37.65	37.65	37.65	38.32	38.32	38.32	38.32	38.32	38.59	38.59	38.59	38.59	38.59	38.74	38.74	38.74	38.74	38.74	38.78	38.78	38.78	38.78	38.78	38.61	.1000977	
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+[End_Grid]
diff --git a/tests/data/hallue.gpkg b/tests/data/hallue.gpkg
new file mode 100644
index 0000000..e5b372a
Binary files /dev/null and b/tests/data/hallue.gpkg differ
diff --git a/tests/data/hallue.permh b/tests/data/hallue.permh
new file mode 100644
index 0000000..4eccfb0
--- /dev/null
+++ b/tests/data/hallue.permh
@@ -0,0 +1,84 @@
+Marthe_Grid Version=9.0
+Title=Champ : Origine 'Travail'                                              PERMEAB        1
+[Infos]
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+52	2543.25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2.99160E-03	2.12400E-03	1.40400E-03	1.91880E-03	3.06720E-03	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.5	
+53	2542.75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.5	
+54	2542.25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.5	
+0	0	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	
+[End_Grid]
diff --git a/tests/data/hallue_multilayer.hsubs b/tests/data/hallue_multilayer.hsubs
new file mode 100644
index 0000000..42a1421
--- /dev/null
+++ b/tests/data/hallue_multilayer.hsubs
@@ -0,0 +1,252 @@
+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT  1
+[Infos]
+Field=H_SUBSTRAT
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=3
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=596.5
+Y_Lower_Corner=2542
+Ncolumn=53
+Nrows=54
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
+[Data]
+0	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	
+0	0	596.75	597.25	597.75	598.25	598.75	599.25	599.75	600.25	600.75	601.25	601.75	602.25	602.75	603.25	603.75	604.25	604.75	605.25	605.75	606.25	606.75	607.25	607.75	608.25	608.75	609.25	609.75	610.25	610.75	611.25	611.75	612.25	612.75	613.25	613.75	614.25	614.75	615.25	615.75	616.25	616.75	617.25	617.75	618.25	618.75	619.25	619.75	620.25	620.75	621.25	621.75	622.25	622.75	
+1	2568.75	51.58	48.94	45.8	44.4	43.14	42.65	42.05	41.88	40.95	40.07	39.1	39.18	40.37	41.48	42.47	42.64	42.34	42.09	42	41.95	41.95	41.96	42.17	42.97	46.16	48.97	53.44	55.89	58.2	59.33	60.21	60.89	62.05	63.14	65.53	67.51	70.84	72.77	75.04	76.43	78.3	79.46	80.9	81.59	82.35	82.6	82.38	81.81	80.39	79.12	76.76	75.09	0	0.5	
+2	2568.25	58.03	54.75	50.89	48.84	46.35	45.07	43.25	42.46	42.02	41.93	42.14	43.04	44.68	45.23	44.85	43.71	42.4	42.09	42	42	42	42.07	42.53	43.67	46.81	49.82	54.46	56.85	59.16	60.15	60.92	61.22	61.8	62.52	64.72	66.99	70.62	72.87	75.5	76.99	79.22	80.33	80.87	80.73	80.29	80.18	79.9	79.34	77.88	76.57	74.51	73.4	72.3	0.5	
+3	2567.75	62.17	58.71	54.12	51.87	48.69	47.2	44.97	43.73	42.48	42.33	43.04	44.28	46.01	46.33	45.41	43.93	42.4	42.09	42	42	42.08	42.31	43.32	44.69	47.95	50.82	54.96	57.13	59.47	60.61	61.62	61.85	62.02	62.41	64.25	66.36	69.78	72.18	75.15	76.75	79.28	80.37	80.5	79.98	78.98	78.57	78.09	77.58	76.33	75.16	73.46	72.62	71.77	0.5	
+4	2567.25	66.1	62.9	58.12	55.81	52.42	50.41	48.05	46.58	44.48	43.46	42.98	43.88	45.63	46.06	45.24	43.91	42.5	42.16	42.14	42.26	42.67	43.37	45.07	46.74	50.07	52.43	55.59	57.43	60.09	61.7	63.05	63.17	62.63	62.41	63.32	65	68.1	70.67	73.98	75.72	78.1	79.01	79.07	78.32	76.8	76.19	75.81	75.64	74.95	74	72.36	71.48	70.39	0.5	
+5	2566.75	67.37	64.49	59.76	57.36	54.5	52.69	50.04	48.64	46.42	45.06	43.59	43.62	45	45.63	45.18	44.15	42.88	42.51	42.63	43.01	43.71	44.57	46.44	48.17	51.33	53.29	56.02	57.69	60.37	62.12	63.57	63.69	62.87	62.35	62.64	63.86	66.72	69.24	72.38	74.17	76.24	76.86	77.15	76.51	75.25	74.75	74.67	74.9	74.56	73.76	71.81	70.57	69.1	0.5	
+6	2566.25	69.16	66.87	62.44	59.81	57.24	55.83	53.82	52.49	50.14	48.37	46.08	45.47	46.13	47.04	47.28	46.46	44.82	44.11	44.46	45.07	46.15	47.13	48.85	50.43	53.08	54.47	56.72	58.08	60.53	62.27	63.66	63.68	62.8	62.25	62.13	62.69	64.6	66.54	69.07	70.49	72.35	73.03	73.65	73.51	72.98	72.75	73.16	73.75	73.6	72.81	70.55	68.87	66.91	0.5	
+7	2565.75	70.22	68.41	64.44	61.69	59.15	57.89	56.35	55.47	53.59	51.83	49.38	48.56	49.04	49.71	50.05	49.18	47.27	46.36	46.29	46.79	48.05	49.01	50.4	51.75	54.14	55.54	57.45	58.67	60.72	62.15	63.29	63.24	62.58	62.26	62.24	62.56	63.71	65.05	66.92	67.92	69.63	70.48	71.54	71.64	71.45	71.42	72.06	72.75	72.59	71.69	69.29	67.54	65.3	0.5	
+8	2565.25	69.48	68.65	65.93	64.06	62.63	62.02	61.01	60.12	58.59	57.27	55.33	54.6	54.51	54.62	54.11	52.99	50.87	49.68	48.95	49.26	50.52	51.25	52.25	53.31	55.71	57.36	59.09	60.06	61.37	62.08	62.58	62.56	62.39	62.48	62.81	62.95	63.09	63.24	63.83	64.48	66.13	67.36	68.87	69.28	69.3	69.63	70.22	70.55	70.3	68.96	66.37	64.57	62.04	0.5	
+9	2564.75	67.88	67.63	66.23	65.23	65.29	65.31	64.9	64.17	62.5	61.12	59.06	58.17	57.71	57.28	55.97	54.59	52.46	51.09	49.76	49.77	50.64	51.26	52.54	53.77	56.37	58.2	60.14	61.03	62.07	62.42	62.4	62.34	62.39	62.68	63.19	63.25	63.04	62.74	62.68	63.15	64.77	65.97	67.47	67.96	68.14	68.55	68.86	68.7	67.72	66.12	63.62	61.81	59.11	0.5	
+10	2564.25	64.35	64.8	65.38	66.13	67.98	69.04	69.02	68.58	67.4	66.16	63.99	62.5	60.38	58.74	56.15	54.6	52.85	51.48	49.46	48.71	48.84	49.38	50.94	52.59	55.74	57.76	60.22	61.37	62.31	62.5	61.96	61.79	62.14	62.69	63.41	63.47	62.98	62.41	61.94	62.08	62.89	63.56	64.47	65.03	65.41	65.6	65.36	64.64	62.79	61.04	58.56	56.57	53.9	0.5	
+11	2563.75	61.86	62.86	64.71	66.43	69.33	70.95	71.19	70.81	69.67	68.47	66.04	64.25	60.94	58.48	55.57	53.93	52.27	50.93	48.53	47.24	46.88	47.33	49.01	51.17	54.98	56.93	58.96	59.85	60.74	61.06	60.85	60.83	61.51	62.14	62.91	63.04	62.5	61.9	61.33	61.11	60.93	61.08	61.59	62.04	62.53	62.49	61.61	60.68	58.85	57.4	55.13	53.27	50.87	0.5	
+12	2563.25	59.25	60.84	63.5	65.72	69.12	71.07	71.99	72	71.78	71.08	68.41	65.75	60.89	57.58	54.51	53.06	51.81	50.51	47.59	45.62	44.13	44.28	46.42	49.24	53.08	54.54	55.11	55.08	55.77	56.53	57.72	58.55	59.78	60.3	60.57	60.63	60.39	60.04	59.24	58.4	57.16	56.45	55.75	55.56	55.9	55.99	55.31	54.53	53.29	52.61	51.28	49.86	47.64	0.5	
+13	2562.75	57.56	59.31	62.03	64.5	67.94	70.09	71.72	71.91	71.99	71.79	69.17	66.07	60.39	56.78	53.68	52.57	51.92	50.65	47.66	45.42	43.3	43.19	45.25	47.85	50.79	51.59	51.25	50.68	51.22	52.23	54.42	55.96	57.58	57.78	57.54	57.53	57.32	56.93	56.13	55.33	53.93	52.76	51.47	51.07	50.99	51.06	50.81	50.54	50	49.89	49.1	48	45.93	0.5	
+14	2562.25	54.79	56.7	59.43	61.97	65.7	67.9	70.37	71.24	71.89	71.78	69.19	65.31	58.88	55.01	52.08	51.59	51.49	50.41	47.47	45.27	42.72	42.27	43.38	45	46.47	46.28	44.76	43.79	44.29	45.76	48.92	50.91	52.38	52.28	51.95	51.87	51.84	51.44	50.51	50.01	48.87	47.62	46.09	45.27	44.63	44.74	45.14	45.58	46.38	46.73	46.22	45.57	43.94	0.5	
+15	2561.75	52.94	54.99	57.99	60.34	63.88	66.09	68.82	70.15	71.45	71.53	68.6	64.4	57.38	53.31	50.56	50.25	50.28	49.36	46.49	44.3	41.82	41.32	41.62	42.22	42.78	42.18	40.52	39.8	40.37	41.92	45.15	46.91	47.87	47.55	47.21	46.93	47.11	47.19	46.78	46.74	46.3	45.4	43.59	42.17	40.91	41.04	41.8	42.62	43.94	44.51	44.06	43.45	42.21	0.5	
+16	2561.25	49.82	52.06	55.55	57.81	60.78	62.62	65.4	67.26	69.74	70.39	67.45	62.95	54.87	50.36	47.58	47.25	47.05	46.18	43.22	41.1	39.05	38.76	38.74	38.55	37.93	37.12	36.03	35.9	36.59	37.7	39.81	40.78	40.78	40.34	40.21	40.14	40.58	41.07	41.9	42.91	43.55	42.95	40.81	39.04	37.19	36.92	37.92	38.97	40.37	40.92	40.72	40.07	39.17	0.5	
+17	2560.75	47.77	50.06	53.75	56.1	58.57	60.05	62.54	64.63	67.6	68.46	65.49	61	52.94	48.49	45.61	45.13	44.67	43.59	40.65	38.81	37.01	36.79	36.97	36.8	36.09	35.3	34.4	34.29	34.96	35.87	37.09	37.48	37.1	36.51	36.4	36.63	37.22	37.93	39.7	41.22	42.3	41.83	39.61	37.76	35.66	35.24	36.23	37.39	38.77	39.11	38.68	37.94	36.99	0.5	
+18	2560.25	44.36	46.82	50.73	53.14	55.42	56.39	58.33	60.06	62.74	63.61	61.37	57.64	50.65	46.63	43.46	42.67	41.71	40.26	37.28	35.61	34.26	34.23	34.74	34.73	34.34	33.89	33.17	33.09	33.54	33.96	34.34	34.25	33.59	33.14	32.79	32.84	33.38	34.39	37.01	38.85	40.37	40.18	38.24	36.57	34.66	34.29	35.03	35.82	36.58	36.4	35.54	34.59	33.18	0.5	
+19	2559.75	42.64	44.88	48.73	51.12	53.34	54.21	55.71	57.1	59.08	59.82	58.66	55.88	49.99	46.37	42.94	41.84	40.13	38.48	35.52	33.99	32.84	32.83	33.24	33.29	33.16	32.91	32.41	32.43	32.63	32.65	32.76	32.73	32.35	32.22	32.1	32.12	32.5	33.42	35.81	37.61	39.22	39.17	37.64	36.31	34.73	34.35	34.75	35.18	35.3	34.83	33.81	32.81	31.21	0.5	
+20	2559.25	41.38	43.32	46.74	48.72	50.79	51.65	52.67	53.46	54.52	54.81	54.48	52.77	49	46.25	42.59	40.98	38.43	36.58	33.68	32.16	30.75	30.04	29.46	29.22	29.56	29.99	30.19	30.31	30.23	30.19	30.34	30.53	31.06	31.44	31.76	31.93	32.29	32.99	34.81	36.19	37.5	37.64	36.79	35.89	34.64	34.14	34	33.95	33.4	32.61	31.32	30.42	29.1	0.5	
+21	2558.75	40.72	42.56	45.77	47.48	49.35	50.17	50.95	51.44	52.02	52.17	52.07	50.84	47.96	45.48	42.08	40.45	37.72	35.75	32.63	30.94	29.03	27.68	25.44	24.94	25.84	27.17	28.6	28.69	28.47	28.5	28.81	29.18	30.2	30.79	31.37	31.7	32.25	32.9	34.45	35.57	36.6	36.7	35.86	34.98	33.82	33.35	33.06	32.77	31.91	30.99	29.73	29.04	28.11	0.5	
+22	2558.25	39.16	41.17	44.37	45.85	47.32	47.85	48.36	48.56	48.9	49.32	49.43	48.48	45.29	42.97	40.47	39.34	37.03	35	31.63	29.7	26.98	24.58	20.57	19.89	21.03	22.92	26.03	26.53	26.39	26.49	26.93	27.51	28.8	29.66	30.74	31.33	32.07	32.65	33.78	34.45	34.81	34.58	33.4	32.41	31.14	30.62	30.31	30.08	29.26	28.44	27.26	26.7	26.26	0.5	
+23	2557.75	38.04	40.17	43.42	44.8	46.14	46.53	46.76	46.69	47.02	47.57	47.22	46.17	42.87	40.74	39.05	38.16	36.29	34.37	31.05	29.03	25.93	23.39	19.2	18.42	19.61	21.6	24.92	25.5	25.68	25.89	26.32	26.84	28.13	29.11	30.66	31.39	32.11	32.51	33.19	33.57	33.48	32.89	31.25	30.03	28.72	28.23	28.07	27.93	27.33	26.7	25.71	25.14	24.69	0.5	
+24	2557.25	36.61	38.85	42.05	43.23	44.19	44.41	44.25	43.97	44.18	44.58	43.61	42.29	38.83	36.95	35.86	35.46	34.42	32.87	29.99	28	24.99	23.13	20	19.07	20.29	21.95	24.21	24.63	25.14	25.37	25.92	26.56	28.23	29.46	31.16	31.81	31.99	32.09	32.41	32.42	31.63	30.38	27.85	26.35	25.02	24.73	24.83	24.89	24.75	24.44	23.69	23.11	22.53	0.5	
+25	2556.75	36.18	38.27	41.05	42.07	42.86	43.06	42.93	42.6	42.34	42.41	41.08	39.38	35.92	34.33	33.87	33.94	33.09	31.68	29.04	27.04	24.27	22.91	21.05	20.53	21.5	22.37	23.78	24.33	25.01	25.49	26.81	28.03	30.38	31.56	32.46	32.46	31.64	31.46	31.56	31.39	30.28	28.77	26.13	24.67	23.38	23.12	23.24	23.39	23.46	23.18	22.4	21.77	21.22	0.5	
+26	2556.25	35.95	37.61	39.54	40.33	41.17	41.57	41.74	41.28	39.71	38.61	36.12	34.02	31.33	30.62	31.02	31.42	31.26	29.81	27.1	25.08	22.46	21.32	20.47	20.55	21.18	21.69	25	26	25.18	29	32	35.77	39.84	40.04	36.85	33.91	30.12	28.97	28.64	28.51	27.58	26.12	24	23.03	22.22	22.17	22.31	22.46	22.3	21.78	20.77	20.13	19.78	0.5	
+27	2555.75	35.39	37.03	38.75	39.52	40.83	41.58	41.65	40.79	38.31	36.44	33.37	31.29	29.04	28.73	29.25	29.77	29.85	28.49	25.46	23.31	20.7	19.72	19.24	19.45	20.14	20.84	24	23.11	25.33	28.46	37.16	43.72	49.61	48.69	40.46	33.96	27.35	25.41	24.9	24.89	24.37	23.44	22.45	22.18	22.01	22.01	22.06	22.1	21.76	21.05	19.86	19.17	18.92	0.5	
+28	2555.25	33.4	35.26	37.42	38.97	43.36	45.62	44.21	41.06	35.6	33.48	30.75	29.07	27.29	26.72	27.32	27.61	27.26	25.55	22.25	20.11	18.12	17.64	17.56	17.68	18.4	19.47	21.21	22.25	25.14	29.45	41.92	51.72	59.24	56.17	42.18	31.51	21.72	19.17	18.46	18.55	18.58	18.76	19.64	20.55	21.68	21.88	21.69	21.4	20.57	19.63	18.25	17.56	17.38	0.5	
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+Marthe_Grid Version=9.0
+Title=Bidon                                                                  H_SUBSTRAT  2
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+3	2567.75	52.17	48.71	44.12	41.87	38.69	37.2	34.97	33.73	32.48	32.33	33.04	34.28	36.01	36.33	35.41	33.93	32.4	32.09	32	32	32.08	32.31	33.32	34.69	37.95	40.82	44.96	47.13	49.47	50.61	51.62	51.85	52.02	52.41	54.25	56.36	59.78	62.18	65.15	66.75	69.28	70.37	70.5	69.98	68.98	68.57	68.09	67.58	66.33	65.16	63.46	62.62	61.77	0.5	
+4	2567.25	56.1	52.9	48.12	45.81	42.42	40.41	38.05	36.58	34.48	33.46	32.98	33.88	35.63	36.06	35.24	33.91	32.5	32.16	32.14	32.26	32.67	33.37	35.07	36.74	40.07	42.43	45.59	47.43	50.09	51.7	53.05	53.17	52.63	52.41	53.32	55	58.1	60.67	63.98	65.72	68.1	69.01	69.07	68.32	66.8	66.19	65.81	65.64	64.95	64	62.36	61.48	60.39	0.5	
+5	2566.75	57.37	54.49	49.76	47.36	44.5	42.69	40.04	38.64	36.42	35.06	33.59	33.62	35	35.63	35.18	34.15	32.88	32.51	32.63	33.01	33.71	34.57	36.44	38.17	41.33	43.29	46.02	47.69	50.37	52.12	53.57	53.69	52.87	52.35	52.64	53.86	56.72	59.24	62.38	64.17	66.24	66.86	67.15	66.51	65.25	64.75	64.67	64.9	64.56	63.76	61.81	60.57	59.1	0.5	
+6	2566.25	59.16	56.87	52.44	49.81	47.24	45.83	43.82	42.49	40.14	38.37	36.08	35.47	36.13	37.04	37.28	36.46	34.82	34.11	34.46	35.07	36.15	37.13	38.85	40.43	43.08	44.47	46.72	48.08	50.53	52.27	53.66	53.68	52.8	52.25	52.13	52.69	54.6	56.54	59.07	60.49	62.35	63.03	63.65	63.51	62.98	62.75	63.16	63.75	63.6	62.81	60.55	58.87	56.91	0.5	
+7	2565.75	60.22	58.41	54.44	51.69	49.15	47.89	46.35	45.47	43.59	41.83	39.38	38.56	39.04	39.71	40.05	39.18	37.27	36.36	36.29	36.79	38.05	39.01	40.4	41.75	44.14	45.54	47.45	48.67	50.72	52.15	53.29	53.24	52.58	52.26	52.24	52.56	53.71	55.05	56.92	57.92	59.63	60.48	61.54	61.64	61.45	61.42	62.06	62.75	62.59	61.69	59.29	57.54	55.3	0.5	
+8	2565.25	59.48	58.65	55.93	54.06	52.63	52.02	51.01	50.12	48.59	47.27	45.33	44.6	44.51	44.62	44.11	42.99	40.87	39.68	38.95	39.26	40.52	41.25	42.25	43.31	45.71	47.36	49.09	50.06	51.37	52.08	52.58	52.56	52.39	52.48	52.81	52.95	53.09	53.24	53.83	54.48	56.13	57.36	58.87	59.28	59.3	59.63	60.22	60.55	60.3	58.96	56.37	54.57	52.04	0.5	
+9	2564.75	57.88	57.63	56.23	55.23	55.29	55.31	54.9	54.17	52.5	51.12	49.06	48.17	47.71	47.28	45.97	44.59	42.46	41.09	39.76	39.77	40.64	41.26	42.54	43.77	46.37	48.2	50.14	51.03	52.07	52.42	52.4	52.34	52.39	52.68	53.19	53.25	53.04	52.74	52.68	53.15	54.77	55.97	57.47	57.96	58.14	58.55	58.86	58.7	57.72	56.12	53.62	51.81	49.11	0.5	
+10	2564.25	54.35	54.8	55.38	56.13	57.98	59.04	59.02	58.58	57.4	56.16	53.99	52.5	50.38	48.74	46.15	44.6	42.85	41.48	39.46	38.71	38.84	39.38	40.94	42.59	45.74	47.76	50.22	51.37	52.31	52.5	51.96	51.79	52.14	52.69	53.41	53.47	52.98	52.41	51.94	52.08	52.89	53.56	54.47	55.03	55.41	55.6	55.36	54.64	52.79	51.04	48.56	46.57	43.9	0.5	
+11	2563.75	51.86	52.86	54.71	56.43	59.33	60.95	61.19	60.81	59.67	58.47	56.04	54.25	50.94	48.48	45.57	43.93	42.27	40.93	38.53	37.24	36.88	37.33	39.01	41.17	44.98	46.93	48.96	49.85	50.74	51.06	50.85	50.83	51.51	52.14	52.91	53.04	52.5	51.9	51.33	51.11	50.93	51.08	51.59	52.04	52.53	52.49	51.61	50.68	48.85	47.4	45.13	43.27	40.87	0.5	
+12	2563.25	49.25	50.84	53.5	55.72	59.12	61.07	61.99	62	61.78	61.08	58.41	55.75	50.89	47.58	44.51	43.06	41.81	40.51	37.59	35.62	34.13	34.28	36.42	39.24	43.08	44.54	45.11	45.08	45.77	46.53	47.72	48.55	49.78	50.3	50.57	50.63	50.39	50.04	49.24	48.4	47.16	46.45	45.75	45.56	45.9	45.99	45.31	44.53	43.29	42.61	41.28	39.86	37.64	0.5	
+13	2562.75	47.56	49.31	52.03	54.5	57.94	60.09	61.72	61.91	61.99	61.79	59.17	56.07	50.39	46.78	43.68	42.57	41.92	40.65	37.66	35.42	33.3	33.19	35.25	37.85	40.79	41.59	41.25	40.68	41.22	42.23	44.42	45.96	47.58	47.78	47.54	47.53	47.32	46.93	46.13	45.33	43.93	42.76	41.47	41.07	40.99	41.06	40.81	40.54	40	39.89	39.1	38	35.93	0.5	
+14	2562.25	44.79	46.7	49.43	51.97	55.7	57.9	60.37	61.24	61.89	61.78	59.19	55.31	48.88	45.01	42.08	41.59	41.49	40.41	37.47	35.27	32.72	32.27	33.38	35	36.47	36.28	34.76	33.79	34.29	35.76	38.92	40.91	42.38	42.28	41.95	41.87	41.84	41.44	40.51	40.01	38.87	37.62	36.09	35.27	34.63	34.74	35.14	35.58	36.38	36.73	36.22	35.57	33.94	0.5	
+15	2561.75	42.94	44.99	47.99	50.34	53.88	56.09	58.82	60.15	61.45	61.53	58.6	54.4	47.38	43.31	40.56	40.25	40.28	39.36	36.49	34.3	31.82	31.32	31.62	32.22	32.78	32.18	30.52	29.8	30.37	31.92	35.15	36.91	37.87	37.55	37.21	36.93	37.11	37.19	36.78	36.74	36.3	35.4	33.59	32.17	30.91	31.04	31.8	32.62	33.94	34.51	34.06	33.45	32.21	0.5	
+16	2561.25	39.82	42.06	45.55	47.81	50.78	52.62	55.4	57.26	59.74	60.39	57.45	52.95	44.87	40.36	37.58	37.25	37.05	36.18	33.22	31.1	29.05	28.76	28.74	28.55	27.93	27.12	26.03	25.9	26.59	27.7	29.81	30.78	30.78	30.34	30.21	30.14	30.58	31.07	31.9	32.91	33.55	32.95	30.81	29.04	27.19	26.92	27.92	28.97	30.37	30.92	30.72	30.07	29.17	0.5	
+17	2560.75	37.77	40.06	43.75	46.1	48.57	50.05	52.54	54.63	57.6	58.46	55.49	51	42.94	38.49	35.61	35.13	34.67	33.59	30.65	28.81	27.01	26.79	26.97	26.8	26.09	25.3	24.4	24.29	24.96	25.87	27.09	27.48	27.1	26.51	26.4	26.63	27.22	27.93	29.7	31.22	32.3	31.83	29.61	27.76	25.66	25.24	26.23	27.39	28.77	29.11	28.68	27.94	26.99	0.5	
+18	2560.25	34.36	36.82	40.73	43.14	45.42	46.39	48.33	50.06	52.74	53.61	51.37	47.64	40.65	36.63	33.46	32.67	31.71	30.26	27.28	25.61	24.26	24.23	24.74	24.73	24.34	23.89	23.17	23.09	23.54	23.96	24.34	24.25	23.59	23.14	22.79	22.84	23.38	24.39	27.01	28.85	30.37	30.18	28.24	26.57	24.66	24.29	25.03	25.82	26.58	26.4	25.54	24.59	23.18	0.5	
+19	2559.75	32.64	34.88	38.73	41.12	43.34	44.21	45.71	47.1	49.08	49.82	48.66	45.88	39.99	36.37	32.94	31.84	30.13	28.48	25.52	23.99	22.84	22.83	23.24	23.29	23.16	22.91	22.41	22.43	22.63	22.65	22.76	22.73	22.35	22.22	22.1	22.12	22.5	23.42	25.81	27.61	29.22	29.17	27.64	26.31	24.73	24.35	24.75	25.18	25.3	24.83	23.81	22.81	21.21	0.5	
+20	2559.25	31.38	33.32	36.74	38.72	40.79	41.65	42.67	43.46	44.52	44.81	44.48	42.77	39	36.25	32.59	30.98	28.43	26.58	23.68	22.16	20.75	20.04	19.46	19.22	19.56	19.99	20.19	20.31	20.23	20.19	20.34	20.53	21.06	21.44	21.76	21.93	22.29	22.99	24.81	26.19	27.5	27.64	26.79	25.89	24.64	24.14	24	23.95	23.4	22.61	21.32	20.42	19.1	0.5	
+21	2558.75	30.72	32.56	35.77	37.48	39.35	40.17	40.95	41.44	42.02	42.17	42.07	40.84	37.96	35.48	32.08	30.45	27.72	25.75	22.63	20.94	19.03	17.68	15.44	14.94	15.84	17.17	18.6	18.69	18.47	18.5	18.81	19.18	20.2	20.79	21.37	21.7	22.25	22.9	24.45	25.57	26.6	26.7	25.86	24.98	23.82	23.35	23.06	22.77	21.91	20.99	19.73	19.04	18.11	0.5	
+22	2558.25	29.16	31.17	34.37	35.85	37.32	37.85	38.36	38.56	38.9	39.32	39.43	38.48	35.29	32.97	30.47	29.34	27.03	25	21.63	19.7	16.98	14.58	10.57	9.89	11.03	12.92	16.03	16.53	16.39	16.49	16.93	17.51	18.8	19.66	20.74	21.33	22.07	22.65	23.78	24.45	24.81	24.58	23.4	22.41	21.14	20.62	20.31	20.08	19.26	18.44	17.26	16.7	16.26	0.5	
+23	2557.75	28.04	30.17	33.42	34.8	36.14	36.53	36.76	36.69	37.02	37.57	37.22	36.17	32.87	30.74	29.05	28.16	26.29	24.37	21.05	19.03	15.93	13.39	9.2	8.42	9.61	11.6	14.92	15.5	15.68	15.89	16.32	16.84	18.13	19.11	20.66	21.39	22.11	22.51	23.19	23.57	23.48	22.89	21.25	20.03	18.72	18.23	18.07	17.93	17.33	16.7	15.71	15.14	14.69	0.5	
+24	2557.25	26.61	28.85	32.05	33.23	34.19	34.41	34.25	33.97	34.18	34.58	33.61	32.29	28.83	26.95	25.86	25.46	24.42	22.87	19.99	18	14.99	13.13	10	9.07	10.29	11.95	14.21	14.63	15.14	15.37	15.92	16.56	18.23	19.46	21.16	21.81	21.99	22.09	22.41	22.42	21.63	20.38	17.85	16.35	15.02	14.73	14.83	14.89	14.75	14.44	13.69	13.11	12.53	0.5	
+25	2556.75	26.18	28.27	31.05	32.07	32.86	33.06	32.93	32.6	32.34	32.41	31.08	29.38	25.92	24.33	23.87	23.94	23.09	21.68	19.04	17.04	14.27	12.91	11.05	10.53	11.5	12.37	13.78	14.33	15.01	15.49	16.81	18.03	20.38	21.56	22.46	22.46	21.64	21.46	21.56	21.39	20.28	18.77	16.13	14.67	13.38	13.12	13.24	13.39	13.46	13.18	12.4	11.77	11.22	0.5	
+26	2556.25	25.95	27.61	29.54	30.33	31.17	31.57	31.74	31.28	29.71	28.61	26.12	24.02	21.33	20.62	21.02	21.42	21.26	19.81	17.1	15.08	12.46	11.32	10.47	10.55	11.18	11.69	15	16	15.18	19	22	25.77	29.84	30.04	26.85	23.91	20.12	18.97	18.64	18.51	17.58	16.12	14	13.03	12.22	12.17	12.31	12.46	12.3	11.78	10.77	10.13	9.78	0.5	
+27	2555.75	25.39	27.03	28.75	29.52	30.83	31.58	31.65	30.79	28.31	26.44	23.37	21.29	19.04	18.73	19.25	19.77	19.85	18.49	15.46	13.31	10.7	9.72	9.24	9.45	10.14	10.84	14	13.11	15.33	18.46	27.16	33.72	39.61	38.69	30.46	23.96	17.35	15.41	14.9	14.89	14.37	13.44	12.45	12.18	12.01	12.01	12.06	12.1	11.76	11.05	9.86	9.17	8.92	0.5	
+28	2555.25	23.4	25.26	27.42	28.97	33.36	35.62	34.21	31.06	25.6	23.48	20.75	19.07	17.29	16.72	17.32	17.61	17.26	15.55	12.25	10.11	8.12	7.64	7.56	7.68	8.4	9.47	11.21	12.25	15.14	19.45	31.92	41.72	49.24	46.17	32.18	21.51	11.72	9.17	8.46	8.55	8.58	8.76	9.64	10.55	11.68	11.88	11.69	11.4	10.57	9.63	8.25	7.56	7.38	0.5	
+29	2554.75	21.17	23.09	25.91	28.54	35.99	37.27	33.39	29.35	22.25	21.28	19.48	18.22	16.55	15.71	15.65	15.81	15.04	13.35	10.14	8.21	6.71	6.49	6.54	6.72	7.56	8.77	10.75	11.89	14.21	18.48	31.02	40.78	47.39	43.16	28.12	17.46	7.7	5.32	4.9	5	5.27	5.86	7.65	9.12	10.98	11.43	11.08	10.42	9.24	8.3	10	9	6.46	0.5	
+30	2554.25	16.92	18.6	21.78	25.53	33.99	33.45	29.43	25.03	17.34	17.93	17.99	17.23	15.86	14.76	13.76	13.27	11.74	9.96	7.14	5.7	5.14	5.27	5.36	8	6.44	7.85	10.25	11.5	12.54	15.18	23.31	29.42	33.39	29.27	17.24	9.24	3.26	2.23	2.19	2.25	2.49	3.08	5.08	6.76	8.98	9.78	9.27	8.38	6.96	6.17	8	5.21	5.19	0.5	
+31	2553.75	13.51	15.03	17.98	21.51	29.26	29.19	25.54	21.21	14.84	16.48	17.21	16.76	15.26	13.96	12.37	11.4	9.37	7.59	5.25	4.23	4.17	4.41	7	4.54	5.66	7.33	10.01	11.44	11.95	12.95	16.92	19.72	20.9	17.87	9.68	5.02	2.16	1.92	1.91	1.93	2.04	2.35	3.73	5.1	7.25	8.09	7.75	6.93	5.52	4.8	4.45	7	4.49	0.5	
+32	2553.25	9.02	10.31	12.41	14.62	18.86	19.49	17.44	15.43	15.64	16.67	16.46	15.5	13.44	11.95	9.61	8.2	5.95	4.51	3.02	2.64	2.77	2.86	3.15	3.3	4.71	6.55	9.55	11.11	11.41	10.63	9.81	9.4	8.01	6.28	3.6	2.3	1.57	1.45	1.44	1.56	1.74	1.81	2.23	2.94	4.4	5.08	5.02	4.45	3.54	3.2	3.26	3.34	3.61	0.5	
+33	2552.75	6.69	7.68	9.1	10.5	13.24	14.55	15.08	15.58	16.45	16.64	15.79	14.28	11.7	9.89	7.3	5.82	3.93	2.88	1.98	1.82	1.93	2.02	2.28	2.56	3.96	5.86	8.83	10.33	10.41	9.03	6.47	5.17	3.5	2.69	1.87	1.42	1.02	0.93	0.95	1.11	1.38	1.45	1.55	1.86	2.63	3.11	3.32	3.11	2.73	2.6	2.66	2.72	3.02	0.5	
+34	2552.25	3.54	4.27	4.91	5.77	7.61	9.23	11.86	13.73	15	14.91	13.69	11.63	8.58	6.56	3.97	2.9	1.82	1.36	1.11	1.16	1	1.05	1.33	1.57	2.83	4.39	6.66	7.92	7.99	6.51	3.37	1.36	-0.44	-0.66	-0.68	-0.72	-0.7	-0.57	-0.3	-7.0000E-02	0.34	0.43	0.42	0.39	0.6	3	2	2	1.94	1.97	2.1	2.14	2.34	0.5	
+35	2551.75	1.83	2.49	2.91	3.48	4.92	6.69	9.78	11.76	13.33	13.07	11.26	9.4	6.52	4.61	2.45	1.67	1.06	0.85	0.87	0.97	0.64	0.65	0.8	1.03	1.88	2.88	4.4	5.38	5.66	4.44	1.59	-0.37	-2.26	-2.55	-2.69	-2.6	-2.3	-2.05	-1.59	-1.13	2	2	2	2	2	2	0.74	1.13	1.65	1.81	1.97	1.98	2.11	0.5	
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+11	2563.75	41.86	42.86	44.71	46.43	49.33	50.95	51.19	50.81	49.67	48.47	46.04	44.25	40.94	38.48	35.57	33.93	32.27	30.93	28.53	27.24	26.88	27.33	29.01	31.17	34.98	36.93	38.96	39.85	40.74	41.06	40.85	40.83	41.51	42.14	42.91	43.04	42.5	41.9	41.33	41.11	40.93	41.08	41.59	42.04	42.53	42.49	41.61	40.68	38.85	37.4	35.13	33.27	30.87	0.5	
+12	2563.25	39.25	40.84	43.5	45.72	49.12	51.07	51.99	52	51.78	51.08	48.41	45.75	40.89	37.58	34.51	33.06	31.81	30.51	27.59	25.62	24.13	24.28	26.42	29.24	33.08	34.54	35.11	35.08	35.77	36.53	37.72	38.55	39.78	40.3	40.57	40.63	40.39	40.04	39.24	38.4	37.16	36.45	35.75	35.56	35.9	35.99	35.31	34.53	33.29	32.61	31.28	29.86	27.64	0.5	
+13	2562.75	37.56	39.31	42.03	44.5	47.94	50.09	51.72	51.91	51.99	51.79	49.17	46.07	40.39	36.78	33.68	32.57	31.92	30.65	27.66	25.42	23.3	23.19	25.25	27.85	30.79	31.59	31.25	30.68	31.22	32.23	34.42	35.96	37.58	37.78	37.54	37.53	37.32	36.93	36.13	35.33	33.93	32.76	31.47	31.07	30.99	31.06	30.81	30.54	30	29.89	29.1	28	25.93	0.5	
+14	2562.25	34.79	36.7	39.43	41.97	45.7	47.9	50.37	51.24	51.89	51.78	49.19	45.31	38.88	35.01	32.08	31.59	31.49	30.41	27.47	25.27	22.72	22.27	23.38	25	26.47	26.28	24.76	23.79	24.29	25.76	28.92	30.91	32.38	32.28	31.95	31.87	31.84	31.44	30.51	30.01	28.87	27.62	26.09	25.27	24.63	24.74	25.14	25.58	26.38	26.73	26.22	25.57	23.94	0.5	
+15	2561.75	32.94	34.99	37.99	40.34	43.88	46.09	48.82	50.15	51.45	51.53	48.6	44.4	37.38	33.31	30.56	30.25	30.28	29.36	26.49	24.3	21.82	21.32	21.62	22.22	22.78	22.18	20.52	19.8	20.37	21.92	25.15	26.91	27.87	27.55	27.21	26.93	27.11	27.19	26.78	26.74	26.3	25.4	23.59	22.17	20.91	21.04	21.8	22.62	23.94	24.51	24.06	23.45	22.21	0.5	
+16	2561.25	29.82	32.06	35.55	37.81	40.78	42.62	45.4	47.26	49.74	50.39	47.45	42.95	34.87	30.36	27.58	27.25	27.05	26.18	23.22	21.1	19.05	18.76	18.74	18.55	17.93	17.12	16.03	15.9	16.59	17.7	19.81	20.78	20.78	20.34	20.21	20.14	20.58	21.07	21.9	22.91	23.55	22.95	20.81	19.04	17.19	16.92	17.92	18.97	20.37	20.92	20.72	20.07	19.17	0.5	
+17	2560.75	27.77	30.06	33.75	36.1	38.57	40.05	42.54	44.63	47.6	48.46	45.49	41	32.94	28.49	25.61	25.13	24.67	23.59	20.65	18.81	17.01	16.79	16.97	16.8	16.09	15.3	14.4	14.29	14.96	15.87	17.09	17.48	17.1	16.51	16.4	16.63	17.22	17.93	19.7	21.22	22.3	21.83	19.61	17.76	15.66	15.24	16.23	17.39	18.77	19.11	18.68	17.94	16.99	0.5	
+18	2560.25	24.36	26.82	30.73	33.14	35.42	36.39	38.33	40.06	42.74	43.61	41.37	37.64	30.65	26.63	23.46	22.67	21.71	20.26	17.28	15.61	14.26	14.23	14.74	14.73	14.34	13.89	13.17	13.09	13.54	13.96	14.34	14.25	13.59	13.14	12.79	12.84	13.38	14.39	17.01	18.85	20.37	20.18	18.24	16.57	14.66	14.29	15.03	15.82	16.58	16.4	15.54	14.59	13.18	0.5	
+19	2559.75	22.64	24.88	28.73	31.12	33.34	34.21	35.71	37.1	39.08	39.82	38.66	35.88	29.99	26.37	22.94	21.84	20.13	18.48	15.52	13.99	12.84	12.83	13.24	13.29	13.16	12.91	12.41	12.43	12.63	12.65	12.76	12.73	12.35	12.22	12.1	12.12	12.5	13.42	15.81	17.61	19.22	19.17	17.64	16.31	14.73	14.35	14.75	15.18	15.3	14.83	13.81	12.81	11.21	0.5	
+20	2559.25	21.38	23.32	26.74	28.72	30.79	31.65	32.67	33.46	34.52	34.81	34.48	32.77	29	26.25	22.59	20.98	18.43	16.58	13.68	12.16	10.75	10.04	9.46	9.22	9.56	9.99	10.19	10.31	10.23	10.19	10.34	10.53	11.06	11.44	11.76	11.93	12.29	12.99	14.81	16.19	17.5	17.64	16.79	15.89	14.64	14.14	14	13.95	13.4	12.61	11.32	10.42	9.1	0.5	
+21	2558.75	20.72	22.56	25.77	27.48	29.35	30.17	30.95	31.44	32.02	32.17	32.07	30.84	27.96	25.48	22.08	20.45	17.72	15.75	12.63	10.94	9.03	7.68	5.44	4.94	5.84	7.17	8.6	8.69	8.47	8.5	8.81	9.18	10.2	10.79	11.37	11.7	12.25	12.9	14.45	15.57	16.6	16.7	15.86	14.98	13.82	13.35	13.06	12.77	11.91	10.99	9.73	9.04	8.11	0.5	
+22	2558.25	19.16	21.17	24.37	25.85	27.32	27.85	28.36	28.56	28.9	29.32	29.43	28.48	25.29	22.97	20.47	19.34	17.03	15	11.63	9.7	6.98	4.58	0.57	-0.11	1.03	2.92	6.03	6.53	6.39	6.49	6.93	7.51	8.8	9.66	10.74	11.33	12.07	12.65	13.78	14.45	14.81	14.58	13.4	12.41	11.14	10.62	10.31	10.08	9.26	8.44	7.26	6.7	6.26	0.5	
+23	2557.75	18.04	20.17	23.42	24.8	26.14	26.53	26.76	26.69	27.02	27.57	27.22	26.17	22.87	20.74	19.05	18.16	16.29	14.37	11.05	9.03	5.93	3.39	-0.8	-1.58	-0.39	1.6	4.92	5.5	5.68	5.89	6.32	6.84	8.13	9.11	10.66	11.39	12.11	12.51	13.19	13.57	13.48	12.89	11.25	10.03	8.72	8.23	8.07	7.93	7.33	6.7	5.71	5.14	4.69	0.5	
+24	2557.25	16.61	18.85	22.05	23.23	24.19	24.41	24.25	23.97	24.18	24.58	23.61	22.29	18.83	16.95	15.86	15.46	14.42	12.87	9.99	8	4.99	3.13	0	-0.93	0.29	1.95	4.21	4.63	5.14	5.37	5.92	6.56	8.23	9.46	11.16	11.81	11.99	12.09	12.41	12.42	11.63	10.38	7.85	6.35	5.02	4.73	4.83	4.89	4.75	4.44	3.69	3.11	2.53	0.5	
+25	2556.75	16.18	18.27	21.05	22.07	22.86	23.06	22.93	22.6	22.34	22.41	21.08	19.38	15.92	14.33	13.87	13.94	13.09	11.68	9.04	7.04	4.27	2.91	1.05	0.53	1.5	2.37	3.78	4.33	5.01	5.49	6.81	8.03	10.38	11.56	12.46	12.46	11.64	11.46	11.56	11.39	10.28	8.77	6.13	4.67	3.38	3.12	3.24	3.39	3.46	3.18	2.4	1.77	1.22	0.5	
+26	2556.25	15.95	17.61	19.54	20.33	21.17	21.57	21.74	21.28	19.71	18.61	16.12	14.02	11.33	10.62	11.02	11.42	11.26	9.81	7.1	5.08	2.46	1.32	0.47	0.55	1.18	1.69	5	6	5.18	9	12	15.77	19.84	20.04	16.85	13.91	10.12	8.97	8.64	8.51	7.58	6.12	4	3.03	2.22	2.17	2.31	2.46	2.3	1.78	0.77	0.13	-0.22	0.5	
+27	2555.75	15.39	17.03	18.75	19.52	20.83	21.58	21.65	20.79	18.31	16.44	13.37	11.29	9.04	8.73	9.25	9.77	9.85	8.49	5.46	3.31	0.7	-0.28	-0.76	-0.55	0.14	0.84	4	3.11	5.33	8.46	17.16	23.72	29.61	28.69	20.46	13.96	7.35	5.41	4.9	4.89	4.37	3.44	2.45	2.18	2.01	2.01	2.06	2.1	1.76	1.05	-0.14	-0.83	-1.08	0.5	
+28	2555.25	13.4	15.26	17.42	18.97	23.36	25.62	24.21	21.06	15.6	13.48	10.75	9.07	7.29	6.72	7.32	7.61	7.26	5.55	2.25	0.11	-1.88	-2.36	-2.44	-2.32	-1.6	-0.53	1.21	2.25	5.14	9.45	21.92	31.72	39.24	36.17	22.18	11.51	1.72	-0.83	-1.54	-1.45	-1.42	-1.24	-0.36	0.55	1.68	1.88	1.69	1.4	0.57	-0.37	-1.75	-2.44	-2.62	0.5	
+29	2554.75	11.17	13.09	15.91	18.54	25.99	27.27	23.39	19.35	12.25	11.28	9.48	8.22	6.55	5.71	5.65	5.81	5.04	3.35	0.14	-1.79	-3.29	-3.51	-3.46	-3.28	-2.44	-1.23	0.75	1.89	4.21	8.48	21.02	30.78	37.39	33.16	18.12	7.46	-2.3	-4.68	-5.1	-5	-4.73	-4.14	-2.35	-0.88	0.98	1.43	1.08	0.42	-0.76	-1.7	0	-1	-3.54	0.5	
+30	2554.25	6.92	8.6	11.78	15.53	23.99	23.45	19.43	15.03	7.34	7.93	7.99	7.23	5.86	4.76	3.76	3.27	1.74	-4.0000E-02	-2.86	-4.3	-4.86	-4.73	-4.64	-2	-3.56	-2.15	0.25	1.5	2.54	5.18	13.31	19.42	23.39	19.27	7.24	-0.76	-6.74	-7.77	-7.81	-7.75	-7.51	-6.92	-4.92	-3.24	-1.02	-0.22	-0.73	-1.62	-3.04	-3.83	-2	-4.79	-4.81	0.5	
+31	2553.75	3.51	5.03	7.98	11.51	19.26	19.19	15.54	11.21	4.84	6.48	7.21	6.76	5.26	3.96	2.37	1.4	-0.63	-2.41	-4.75	-5.77	-5.83	-5.59	-3	-5.46	-4.34	-2.67	1.00000E-02	1.44	1.95	2.95	6.92	9.72	10.9	7.87	-0.32	-4.98	-7.84	-8.08	-8.09	-8.07	-7.96	-7.65	-6.27	-4.9	-2.75	-1.91	-2.25	-3.07	-4.48	-5.2	-5.55	-3	-5.51	0.5	
+32	2553.25	-0.98	0.31	2.41	4.62	8.86	9.49	7.44	5.43	5.64	6.67	6.46	5.5	3.44	1.95	-0.39	-1.8	-4.05	-5.49	-6.98	-7.36	-7.23	-7.14	-6.85	-6.7	-5.29	-3.45	-0.45	1.11	1.41	0.63	-0.19	-0.6	-1.99	-3.72	-6.4	-7.7	-8.43	-8.55	-8.56	-8.44	-8.26	-8.19	-7.77	-7.06	-5.6	-4.92	-4.98	-5.55	-6.46	-6.8	-6.74	-6.66	-6.39	0.5	
+33	2552.75	-3.31	-2.32	-0.9	0.5	3.24	4.55	5.08	5.58	6.45	6.64	5.79	4.28	1.7	-0.11	-2.7	-4.18	-6.07	-7.12	-8.02	-8.18	-8.07	-7.98	-7.72	-7.44	-6.04	-4.14	-1.17	0.33	0.41	-0.97	-3.53	-4.83	-6.5	-7.31	-8.13	-8.58	-8.98	-9.07	-9.05	-8.89	-8.62	-8.55	-8.45	-8.14	-7.37	-6.89	-6.68	-6.89	-7.27	-7.4	-7.34	-7.28	-6.98	0.5	
+34	2552.25	-6.46	-5.73	-5.09	-4.23	-2.39	-0.77	1.86	3.73	5	4.91	3.69	1.63	-1.42	-3.44	-6.03	-7.1	-8.18	-8.64	-8.89	-8.84	-9	-8.95	-8.67	-8.43	-7.17	-5.61	-3.34	-2.08	-2.01	-3.49	-6.63	-8.64	-10.44	-10.66	-10.68	-10.72	-10.7	-10.57	-10.3	-10.07	-9.66	-9.57	-9.58	-9.61	-9.4	-7	-8	-8	-8.06	-8.03	-7.9	-7.86	-7.66	0.5	
+35	2551.75	-8.17	-7.51	-7.09	-6.52	-5.08	-3.31	-0.22	1.76	3.33	3.07	1.26	-0.6	-3.48	-5.39	-7.55	-8.33	-8.94	-9.15	-9.13	-9.03	-9.36	-9.35	-9.2	-8.97	-8.12	-7.12	-5.6	-4.62	-4.34	-5.56	-8.41	-10.37	-12.26	-12.55	-12.69	-12.6	-12.3	-12.05	-11.59	-11.13	-8	-8	-8	-8	-8	-8	-9.26	-8.87	-8.35	-8.19	-8.03	-8.02	-7.89	0.5	
+36	2551.25	-10.65	-10.08	-9.68	-9.1	-7.63	-5.99	-3.13	-1.95	-0.73	-0.95	-2.35	-3.5	-5.68	-7.16	-8.78	-9.34	-9.71	-9.78	-9.71	-9.65	-9.84	-8	-9.98	-9.88	-9.81	-9.65	-9.22	-8.64	-8.35	-8.94	-10.86	-12.54	-14.43	-15.14	-15.56	-15.43	-14.83	-14.27	-11	-11	-11.01	-8	-10.61	-10.89	-10.86	-10.44	-9.47	-8.9	-8.47	-8.37	-8.25	-8.19	-8.06	0.5	
+37	2550.75	-11.88	-11.29	-10.88	-10.25	-8.68	-7.25	-4.64	-3.73	-2.9	-3	-4.03	-4.8	-6.43	-7.68	-9.2	-9.76	-10.18	-10.26	-10.31	-10.31	-10.45	-8	-10.58	-10.59	-10.86	-11.1	-11.22	-11	-10.93	-11.25	-12.65	-14.06	-15.75	-16.43	-16.91	-16.84	-16.13	-13	-13	-11	-9	-10.24	-10.44	-10.68	-10.68	-10.31	-9.32	-8.9	-8.72	-8.63	-8.47	-8.31	-8.12	0.5	
+38	2550.25	-13.64	-12.92	-12.06	-11.37	-9.92	-8.92	-6.69	-5.75	-5.03	-4.99	-5.56	-6.06	-7.32	-8.44	-9.9	-10.53	-10.97	-11.14	-11.33	-11.34	-11.39	-11.4	-11.52	-11.62	-12.08	-12.56	-13.24	-13.49	-13.77	-14.06	-15.11	-16.02	-17.06	-17.51	-17.8	-17.75	-17.13	-16.21	-14.03	-12.31	-10	-9.28	-9.52	-9.84	-9.93	-9.68	-9.01	-8.84	-8.77	-8.72	-8.59	-8.39	-8.11	0.5	
+39	2549.75	-15	-14.22	-12.96	-12.12	-10.62	-9.76	-8.01	-7.15	-6.26	-5.98	-6.23	-6.68	-7.95	-8.99	-10.41	-11.06	-11.54	-11.74	-11.97	-12.12	-12.35	-12.3	-12.41	-12.42	-12.78	-13.23	-13.96	-14.38	-14.93	-15.32	-16.07	-16.63	-17.31	-17.65	-17.91	-17.91	-17.4	-16.51	-14.15	-12.19	-9.69	-8.8	-8.92	-9.17	-9.24	-9.04	-8.66	-8.65	-8.72	-8.7	-8.55	-8.31	-8.07	0.5	
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+[End_Grid]
diff --git a/tests/data/hallue_multilayer.permh b/tests/data/hallue_multilayer.permh
new file mode 100644
index 0000000..6202b89
--- /dev/null
+++ b/tests/data/hallue_multilayer.permh
@@ -0,0 +1,252 @@
+Marthe_Grid Version=9.0
+Title=Champ : Origine 'Travail'                                              PERMEAB  1
+[Infos]
+Field=PERMEAB
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=3
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=596.5
+Y_Lower_Corner=2542
+Ncolumn=53
+Nrows=54
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
diff --git a/tests/data/hallue_multilayer.topog b/tests/data/hallue_multilayer.topog
new file mode 100644
index 0000000..5ff7bd0
--- /dev/null
+++ b/tests/data/hallue_multilayer.topog
@@ -0,0 +1,142 @@
+Marthe_Grid Version=9.0
+Title=modelisation du bassin de la somme                                     H_TOPOGR  1
+[Infos]
+Field=H_TOPOGR
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=1
+Max_Layer=3
+Nest_grid=0
+Max_NestG=0
+[Structure]
+X_Left_Corner=596.5
+Y_Lower_Corner=2542
+Ncolumn=53
+Nrows=54
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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diff --git a/tests/data/hallue_multilayer_depth.sem b/tests/data/hallue_multilayer_depth.sem
new file mode 100644
index 0000000..9c92ae3
--- /dev/null
+++ b/tests/data/hallue_multilayer_depth.sem
@@ -0,0 +1,252 @@
+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          1
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+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          2
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+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          3
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+[End_Grid]
diff --git a/tests/data/hallue_multilayer_thick.sem b/tests/data/hallue_multilayer_thick.sem
new file mode 100644
index 0000000..9025e64
--- /dev/null
+++ b/tests/data/hallue_multilayer_thick.sem
@@ -0,0 +1,252 @@
+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          1
+[Infos]
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+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
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+Y_Lower_Corner=2542
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+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          2
+[Infos]
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+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
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+[Data_Descript]
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+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+[End_Grid]
+Marthe_Grid Version=9.0
+Title=Travail                                                                TRAVA          3
+[Infos]
+Field=TRAVA
+Type=
+Elem_Number=0
+Name=
+Time_Step=-9999
+Time=0
+Layer=3
+Max_Layer=3
+Nest_grid=0
+Max_NestG=0
+[Structure]
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+Y_Lower_Corner=2542
+Ncolumn=53
+Nrows=54
+[Data_Descript]
+! Line 1       :   0   ,     0          , <   1 , 2 , 3 , Ncolumn   >
+! Line 2       :   0   ,     0          , < X_Center_of_all_Columns >
+! Line 2+1     :   1   , Y_of_Row_1     , < Field_Values_of_all_Columns > , Dy_of_Row_1
+! Line 2+2     :   2   , Y_of_Row_2     , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2
+! Line 3+Nrows :   0   ,     0          , <     Dx_of_all_Columns   >
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+42	2548.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	10	10	10	10	9.999999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+43	2547.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	10	10	10	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+44	2547.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	10	10	10	10	10	10	10	9.999999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+45	2546.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	10	10	9.999999	10	10	9.999999	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+46	2546.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	9.999999	10	10	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+47	2545.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	10	10	10	10	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+48	2545.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	9.999999	10	10	9.999999	10	10	10	10	9.999999	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+49	2544.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9.999999	10	9.999999	10	10	10	10	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+50	2544.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9.999999	10	10	9.999999	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+51	2543.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	9.999999	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+52	2543.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	10	10	10	10	10	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+53	2542.75	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+54	2542.25	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	9999	0.5	
+0	0	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	
+[End_Grid]
diff --git a/tests/res/.gitignore b/tests/res/.gitignore
new file mode 100644
index 0000000..5e7d273
--- /dev/null
+++ b/tests/res/.gitignore
@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore
diff --git a/tests/test_cleanm.sh b/tests/test_cleanm.sh
new file mode 100644
index 0000000..9c7c384
--- /dev/null
+++ b/tests/test_cleanm.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+cleanmgrid -h
+cleanmgrid data/grid_wrong_attrs.hsubs -l 1 -g 3
+if [[ $? -ne 0 ]]; then
+    echo "Test for cleanmgrid script failed"
+    exit 1
+fi
diff --git a/tests/test_convert.sh b/tests/test_convert.sh
index c84392c..a7d7ec7 100644
--- a/tests/test_convert.sh
+++ b/tests/test_convert.sh
@@ -3,7 +3,12 @@
 # CLI interface to convert MartheGrid to netCDF
 # Usage: `ncmart PATH_CHASIM PATH_PASTP [-o output] [-v varname]`
 
+mkdir -p nc
 # ncmart -h
-# ncmart ./data/chasim_hallue.out ./data/hallue.pastp
-# ncmart ./data/chasim_hallue.out ./data/hallue.pastp -o hallue.nc
-ncmart ./data/chasim_hallue.out ./data/hallue.pastp -o nc/hallue.nc
+# ncmart ./data/chasim_hallue.out ./data/hallue.pastp -o nc/hallue.nc
+ncmart ./data/chasim_hallue.out ./data/hallue.pastp -o nc/hallue.nc -x 1000
+# ncmart ./data/chasim_hallue.out ./data/hallue.pastp -o nc/hallue.nc -x 1000 -v all
+if [[ $? -ne 0 ]]; then
+    echo "Test for ncmart script failed"
+    exit 1
+fi
\ No newline at end of file
diff --git a/tests/test_edsemi.py b/tests/test_edsemi.py
index 2eeef61..21ef8ff 100644
--- a/tests/test_edsemi.py
+++ b/tests/test_edsemi.py
@@ -1,46 +1,62 @@
 
-import gridmarthe as gm
-from gridmarthe.gridmarthe import _extract_zvar, _parse_dims
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
 import numpy as np
+import gridmarthe as gm
+# from gridmarthe.lecsem import _extract_zvar_from_ds, _parse_dims_from_xr_attrs, modgridmarthe
 
 
-if __name__ == "__main__":
-    
-    import os;os.chdir('./tests')
+def test_write_marthe_grid():
     
-    martfile = './data/craie_npc.permh'
-    martfile = './data/Somme_V3_Surfex.permh'
+    # martfile = './tests/data/craie_npc.permh'   # multilayer
+    # martfile = './tests/data/hallue.permh'      # simple, single layer
+    martfile = './tests/data/Somme_V3_Surfex.permh'  # nested
     varname  = 'PERMEAB'
+    fout = './tests/res/grid.out'
     
+    # ds = gm.load_marthe_grid(martfile, varname)
+    # dims = _parse_dims_from_xr_attrs(ds.attrs.get('original_dimensions'))
+    # (
+    #     zvar, zdates,
+    #     zxcol, zylig, zdxlu, zdylu,
+    #     ztitle, izdates
+    # ) = _extract_zvar_from_ds(ds, varname.lower())
     
-    ds = gm.load_marthe_grid(martfile, varname)
-
-    dims = _parse_dims(ds.attrs.get('original_dimensions'))
-    (
-        zvar, zdates,
-        zxcol, zylig, zdxlu, zdylu,
-        ztitle, izdates
-    ) = _extract_zvar(ds, varname.lower())
-    
-    np.prod(np.array(dims), axis=1).sum() == np.size(zvar)
+    # np.prod(np.array(dims), axis=1).sum() == np.size(zvar)
     
     # help(gm.lecsem.modgridmarthe.write_grid)
-    gm.lecsem.modgridmarthe.write_grid(
-        xvar=zvar,
-        xcol=zxcol,
-        ylig=zylig,
-        dxlu=zdxlu.round(2),
-        dylu=zdylu.round(2),
-        typ_don=varname.upper(),
-        titsem='ztitle',
-        n_dims=dims,
-        nval=len(zvar[0]),
-        ngrid=len(dims),
-        nsteps=len(zdates),
-        dates=izdates,
-        debug=True,
-        xfile='test.out'
-    )
+    # modgridmarthe.write_grid(
+    #     zvar=zvar,
+    #     xcol=zxcol,
+    #     ylig=zylig,
+    #     dxlu=zdxlu.round(2),
+    #     dylu=zdylu.round(2),
+    #     typ_don=varname.upper(),
+    #     titsem='ztitle',
+    #     n_dims=dims,
+    #     nval=len(zvar[0]),
+    #     ngrid=len(dims),
+    #     nsteps=len(zdates),
+    #     dates=izdates,
+    #     debug=True,
+    #     xfile='test.out'
+    # )
+
+    # t = np.arange(1, 2862)
+    # t2 = np.arange(1, 2862, dtype=np.int32)
+    # t.dtype
+    # t2.dtype
+    # np.allclose(t, t2)
 
     ds2 = gm.load_marthe_grid(martfile, varname, drop_nan=True, nanval=[0.,-9999.])
-    gm.write_marthe_grid(ds2, varname='permeab', file_permh=martfile)
+    status = gm.write_marthe_grid(ds2, fout, varname='permeab', file_permh=martfile)
+    assert status == 0, "write_marthe_grid test failed"
+
+    # also test reading the written file
+    ds3 = gm.load_marthe_grid(fout, varname, drop_nan=True, nanval=[0.,-9999.])
+    assert 'permeab' in ds3.data_vars, "re-read written file failed"
+    assert ds3.permeab.shape == ds2.permeab.shape, "re-read written file shape mismatch"
+    assert np.allclose(ds3.permeab.values, ds2.permeab.values, equal_nan=True), "re-read written file values mismatch"
+    print("write_marthe_grid test passed")
+
diff --git a/tests/test_geom.py b/tests/test_geom.py
new file mode 100644
index 0000000..fb3ab36
--- /dev/null
+++ b/tests/test_geom.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import gridmarthe as gm
+
+
+# np.set_printoptions(threshold=np.inf)
+
+def test_compute_geometry():
+    
+    model_name = 'hallue_multilayer'
+    hsubs = gm.load_marthe_grid('./tests/data/{}.hsubs'.format(model_name))
+    topo  = gm.load_marthe_grid('./tests/data/{}.topog'.format(model_name))
+    permh = gm.load_marthe_grid('./tests/data/{}.permh'.format(model_name), varname='PERMEAB')
+
+    mask = gm.get_active_mask(permh, as_array=True)['zone'].data
+
+    # geom = gm.compute_geometry(topo, hsubs, mask)  # ok
+    geom = gm.compute_geometry(topo, hsubs)  # ok
+
+    # add tests
+    # pour ça il faut un vrai topo et hsubs, (réalisé avec Winmarthe)
+    # puis lire le résultat et comparer aux résultats de compute_geometry
+    true_depth = gm.load_marthe_grid('./tests/data/{}_depth.sem'.format(model_name), varname='TRAVA')
+    true_thick = gm.load_marthe_grid('./tests/data/{}_thick.sem'.format(model_name), varname='TRAVA')
+
+    assert np.allclose(
+        geom['depth'].sel(zone=mask),
+        true_depth['trava'].sel(zone=mask)
+    ), "depth does not match"
+
+    assert np.allclose(
+        geom['thickness'].sel(zone=mask), 
+        true_thick['trava'].sel(zone=mask)
+    ), "thickness does not match"
+
+    print("test_compute_geometry passed")
+
diff --git a/tests/test_gridmarthe.py b/tests/test_gridmarthe.py
index d5ff0ad..e0eabde 100644
--- a/tests/test_gridmarthe.py
+++ b/tests/test_gridmarthe.py
@@ -1,30 +1,98 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+
+import numpy as np
+import xarray as xr
+import pytest
 
 import gridmarthe as gm
 
-inputs = './data/craie_npc.permh', "PERMEAB" # ajouter tests/data ici pour conda-forge ?
 
+DATA_PATH = './tests/data/chasim_hallue_2var.out'
+VAR = "CHARGE"
 
-toto = gm.load_marthe_grid(*inputs)
-# toto = gm.load_marthe_grid(*inputs, dropna=True)
-# toto = gm.load_marthe_grid(*inputs, dropna=True, nanval=0.)
-# toto = gm.load_marthe_grid(inputs[0], varname=None)
-# toto = gm.load_marthe_grid(inputs[0], varname='all')
-# toto = gm.load_marthe_grid(*inputs, keepligcol=True)
-# toto = gm.load_marthe_grid(*inputs, add_id_grid=True)
 
-print(toto.attrs)
+def test_load_valid_grid_returns_xarray():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR)
+    assert isinstance(ds, xr.Dataset)
+    assert VAR.lower() in ds.data_vars
+
+
+def test_load_with_varname_none_picks_first():
+    ds = gm.load_marthe_grid(DATA_PATH, varname=None)
+    assert isinstance(ds, xr.Dataset)
+    # assert len(ds.data_vars) > 0
+    assert VAR.lower() in ds.data_vars  # here we know the first var is CHARGE
+
+
+def test_load_with_varname_all_returns_multiple():
+    ds = gm.load_marthe_grid(DATA_PATH, varname='all')
+    assert isinstance(ds, xr.Dataset)
+    data_vars = [ x for x in list(ds.data_vars) if not x in ['x', 'y', 'z', 'dx', 'dy']]
+    assert len(data_vars) >= 1
+
+
+def test_load_with_drop_nan_removes_nan():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR, drop_nan=True)
+    arr = ds[VAR.lower()].values
+    assert not np.any(np.isnan(arr))
+    assert not np.any(arr == 9999.)  # here 9999. is the default nanval
 
-# for pymarthe compat'
-# to recarray
-# df=  toto.to_dataframe()
-# df.to_records()
 
-toto2 = toto.mart.assign_coords()
-toto3 = toto.mart.to_recarray()
+def test_load_with_custom_nanval():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR, drop_nan=True, nanval=0.)
+    arr = ds[VAR.lower()].values
+    assert not np.any(arr == 0.)
 
 
-# import matplotlib.pyplot as plt
-# toto2.permeab.sel(z=6).isel(time=0).plot.pcolormesh()
-# plt.show()
+def test_load_with_keepligcol_adds_col_lig():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR, keepligcol=True)
+    assert 'col' in ds.data_vars
+    assert 'lig' in ds.data_vars
 
 
+def test_load_with_add_id_grid_adds_id_grid():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR, add_id_grid=True)
+    assert 'id_grid' in ds.data_vars
+
+
+def test_load_nonexistent_file_raises():
+    with pytest.raises(FileNotFoundError):
+        gm.load_marthe_grid('not_a_file.permh', VAR)
+
+
+def test_load_invalid_varname_raises():
+    with pytest.raises(ValueError):
+        gm.load_marthe_grid(DATA_PATH, varname='INVALIDVAR')
+
+
+def test_load_grid_attrs_present():
+    ds = gm.load_marthe_grid(DATA_PATH, VAR)
+    assert 'title' in ds.attrs
+    assert 'marthe_grid_version' in ds.attrs
+    assert 'original_dimensions' in ds.attrs
+
+
+# MY TESTS
+# inputs = './tests/data/craie_npc.permh', "PERMEAB" # ajouter tests/data ici pour conda-forge ?
+# 
+# toto = gm.load_marthe_grid(*inputs, drop_nan=True)
+# # toto = gm.load_marthe_grid(*inputs, drop_nan=True, nanval=0.)
+# # toto = gm.load_marthe_grid(inputs[0], varname=None)
+# # toto = gm.load_marthe_grid(inputs[0], varname='all')
+# # toto = gm.load_marthe_grid(*inputs, keepligcol=True)
+# # toto = gm.load_marthe_grid(*inputs, add_id_grid=True)
+# 
+# test = toto.set_coords(['time', 'zone', 'x', 'y', 'z', 'dx', 'dy'])
+# print(toto.attrs)
+# test.mart.assign_coords()
+# 
+# # for pymarthe compat'
+# # to recarray
+# df = toto.to_dataframe()
+# df.to_records()
+# 
+# toto2 = toto.mart.assign_coords()
+# toto3 = toto.mart.to_recarray()
+# 
\ No newline at end of file
diff --git a/tests/test_lecsem.py b/tests/test_lecsem.py
index 693d22a..ce377a4 100644
--- a/tests/test_lecsem.py
+++ b/tests/test_lecsem.py
@@ -1,59 +1,56 @@
-#! /usr/bin/env python3
+#!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-import pandas as pd
-import gridmarthe as gm
+import numpy as np
+from gridmarthe import lecsem
 
 
-if __name__ == '__main__':
-    import matplotlib.pyplot as plt
-    
-    print(gm.lecsem.__doc__)
-    print(gm.lecsem.modgridmarthe.__doc__)
+XFILE, VAR = './tests/data/chasim_hallue_2var.out', "CHARGE"
+
+
+def test_lecsem_scan_dim():
+
+    nu_zoomx = lecsem.modgridmarthe.scan_nu_zoomx(XFILE) # scan nb of nested grids (gig)
+    dims, nbsteps = lecsem.modgridmarthe.scan_dim(XFILE, VAR, nu_zoomx)
     
-    xfiles = [
-        ('hallue', './data/chasim_hallue.out', 'CHARGE'),
-        # ('hallue', './data/chasim_hallue_multilay.out', 'CHARGE'),
-        # ('hallue', './data/chasim_hallue_2fields.out', 'CHARGE'),
-        # ('mart-npc', './data/chasim_npc_gig_1_layer.out', 'CHARGE'),
-        # ('mart-npc', './data/chasim_npc_gig.out', 'CHARGE'),
-        ('mart-npc2', './data/craie_npc.permh', 'PERMEAB')
-                                        
-    ]
+    assert isinstance(dims, np.ndarray)
+    assert dims.shape[0] == nu_zoomx + 1
+    assert dims.shape[1] == 3  # x, y, z
+    assert isinstance(nbsteps, int)
+    assert nbsteps > 0
+    print("test_lecsem_scan_dim passed")
+
+
+def test_lecsem_scan_typevar():
+
+    var = lecsem.modgridmarthe.scan_typevar(XFILE)
+    var = np.char.strip(np.char.decode(var, 'utf-8'))
+    var = var[var != '']
+
+    assert isinstance(var, np.ndarray)
+    assert var.size > 0
+    assert all(isinstance(v, str) for v in var)
+    print("test_lecsem_scan_typevar passed")
+
+
+def test_lecsem_read_marthe_grid():
+
+    (
+        zvar, zdates, isteps, zxcol,
+        zylig, zdxlu, zdylu, ztitle, dims
+    ) = lecsem._read_marthe_grid(XFILE, VAR, shallow_only=False)
     
-    date_range = {
-        'hallue'  : pd.DatetimeIndex(gm.read_dates_from_pastp('./data/hallue.pastp')['Date']),
-        'mart-npc': pd.date_range('2020-12-01', '2020-12-31', freq='M'),# fake dates for testing
-        'mart-npc2': None,# fake dates for testing
-    }
-
-    for modele, filename, varname in xfiles:
-        # modele, filename = xfiles[-1] # testing
-        # modele, filename = xfiles[1] # testing
-        dates = date_range[modele]
-        
-        ds = gm.load_marthe_grid(
-            filename,
-            varname,
-            dates,
-            # xyfactor=1000,
-            # ajouter les unités si besoin
-            # idealement, ajouter la réf du model, la projection
-            model_attrs={'projection': 'epsg:27572', "references": "Manlay et al., 2023"}
-        )
-        
-        masque = ds[varname.lower()].where(ds[varname.lower()] != 9999.).dropna(dim='zone') # drop nan_val
-        ds = ds.sel(zone=masque['zone'])
-
-        if "z" in ds.keys():
-            first_lay = gm.get_min_layer(ds.isel(time=0))
-            gm.plot_outcrop(ds, engine='xr')
-            # plot_outcrop(ds, varname='permeab', nanval=0., engine='xr')
-
-            # dsbis = gm.assign_coords(ds)
-            # dsbis['charge'].plot.pcolormesh(x='x', y='y', row='z', col='time')
-            # Pour les gigognes, la grille irrégulière créée rend discontinu le graphique
-            # soit interpol, soit geopandas --> polyg.
-            # dsbis['charge'].sel(z=[6,9]).plot.pcolormesh(x='x', y='y', row='z', col='time')
-            plt.show()
-            plt.close()
+    assert isinstance(zvar, np.ndarray)
+    assert np.prod(dims, axis=1).sum() == np.size(zvar)
+
+    assert np.size(zxcol[zxcol != 1e+20]) == dims[:,0].sum()
+    assert np.size(zylig[zylig != 1e+20]) == dims[:,1].sum()
+    assert np.size(zdxlu[zdxlu != 1e+20]) == np.size(zxcol[zxcol != 1e+20])
+    assert np.size(zdylu[zdylu != 1e+20]) == np.size(zylig[zylig != 1e+20])
+
+    print("test_lecsem_read_marthe_grid passed")
+
+
+def test_lecsem_transform_coords():
+    # TODO
+    pass
diff --git a/tests/test_scan.py b/tests/test_scan.py
deleted file mode 100644
index fd80bfa..0000000
--- a/tests/test_scan.py
+++ /dev/null
@@ -1,20 +0,0 @@
-
-import numpy as np
-from gridmarthe import lecsem
-# inputs = './data/craie_npc.permh', "PERMEAB"
-inputs = './data/craie_npc.permh', "KKKK"
-xfile = inputs[0]
-varname = inputs[1]
-
-var = lecsem.modgridmarthe.scan_typevar(inputs[0])
-var = np.char.strip(np.char.decode(var, 'utf-8'))
-var = var[var != '']
-# var[0]
-print(var)
-len(var)
-
-varname = var[0]
-nu_zoomx = lecsem.modgridmarthe.scan_nu_zoomx(xfile) # scan nb of nested grids (gig)
-dims, nbsteps = lecsem.modgridmarthe.scan_dim(xfile, varname, nu_zoomx)
-np.prod(dims, axis=1).sum()
-
diff --git a/tests/test_surface_mask.py b/tests/test_surface_mask.py
new file mode 100644
index 0000000..4bad8d7
--- /dev/null
+++ b/tests/test_surface_mask.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# import os;os.chdir('tests')
+import matplotlib.pyplot as plt
+
+import gridmarthe as gm
+
+
+grid = gm.load_marthe_grid('./tests/data/craie_npc.permh', drop_nan=True)
+surf = gm.get_surface_layer(ds=grid)
+
+# Plot with geopandas
+gdf = gm.to_geodataframe(surf)
+gm.plot_outcrop(gdf, engine='gpd')
+plt.show()
+
+# Plot with xarray
+toto = gm.assign_coords(surf, add_lay=False)
+# gm.plot_nested_grid(toto, var='z')
+gm.plot_outcrop(toto)
+plt.show()
+# plt.close('all')
\ No newline at end of file