jpdeleon · jpdeleon · Jul 10, 2026 · May 25, 2026 · May 25, 2026 · Jun 9, 2026
diff --git a/CLAUDE.md b/CLAUDE.md
@@ -0,0 +1,55 @@
+# Python Package Management with uv
+
+Use uv exclusively for Python package management in this project.
+
+## Package Management Commands
+
+- All Python dependencies **must be installed, synchronized, and locked** using uv
+- Never use pip, pip-tools, poetry, or conda directly for dependency management
+
+Use these commands:
+
+- Install dependencies: `uv add <package>`
+- Remove dependencies: `uv remove <package>`
+- Sync environment: `uv sync`
+- Lock dependencies: `uv lock`
+
+## Running Python Code
+
+- Run a Python script with `uv run <script-name>.py`
+- Run Python tools with `uv run <tool>` (e.g. `uv run pytest`, `uv run ruff`, `uv run mypy`, `uv run pre-commit`)
+- Launch a Python REPL with `uv run python`
+
+
+## Linting and formatting
+
+This project uses Ruff for both linting and formatting. Do not call Black,
+flake8, isort, or pylint.
+
+- Lint: `uv run ruff check .`
+- Lint and auto-fix: `uv run ruff check --fix .`
+- Format: `uv run ruff format .`
+- Check formatting without writing: `uv run ruff format --check .`
+- Always invoke Ruff through `uv run` so it resolves to the project's
+  virtual environment.
+
+Ruff configuration lives in `pyproject.toml` under `[tool.ruff]`. Do not
+add a separate `ruff.toml` or `.ruff.toml`. Do not add inline `# noqa`
+comments without a rule code.
+
+## Create test, Update readme, and Git commit for each new feature
+Create a unit tests. Add tests as new features are added. Organize the tests accordingly.
+Update readme.md.
+Create a detailed git commit after accomplishing a unique feature.
+
+## Screenshot
+Take a screenshot by running scripts/screenshot_gui.py whenever the GUI is changed.
+Then use the new figure in readme.md
+
+## gitignore
+Do not track large files locally and in git history. Update .gitignore if needed.
+
+## Session limit
+When the prompt starts `session limit`, it means we hit a session limit and restarted.
+The goal is marked with `goal`. Based on git status, the modified files are marked with
+`file1`. Review these local files, check the current implementation, and tell me what the immediate next step should be.
diff --git a/LIGHTKURVE_TGLC_BUG_REPORT.txt b/LIGHTKURVE_TGLC_BUG_REPORT.txt
@@ -0,0 +1,202 @@
+# OverflowError reading TGLC light curves with `quality_bitmask='hardest'` under NumPy 2
+
+## Summary
+
+Calling `LightCurve.read(path, format="tglc", quality_bitmask="hardest")` —
+or the equivalent via `search.download(quality_bitmask="hardest")` — fails
+with:
+
+```
+OverflowError: Python integer 65535 out of bounds for int16
+```
+
+which `lightkurve` surfaces to the user as:
+
+```
+LightkurveError: Error in reading Data product <path> of type TGLC.
+This file may be corrupt due to an interrupted download. Please remove it
+from your disk and try again.
+```
+
+The diagnostic is misleading: **the file is not corrupt** — `astropy.io.fits.verify('exception')` passes on it. The failure is in the TGLC reader's quality-mask step, and it is triggered purely by the choice of `quality_bitmask`. `"none"`, `"default"`, and `"hard"` work; `"hardest"` does not.
+
+## Reproducer (synthetic, no network)
+
+```python
+import numpy as np
+from lightkurve.utils import TessQualityFlags
+
+# TGLC HLSP stores ``tess_flags`` as signed int16 (FITS format='I').
+quality = np.zeros(100, dtype=np.int16)
+
+TessQualityFlags.create_quality_mask(
+    quality_array=quality,
+    bitmask=TessQualityFlags.HARDEST_BITMASK,   # 65535
+)
+# -> OverflowError: Python integer 65535 out of bounds for int16
+```
+
+## Reproducer (real data)
+
+```python
+import lightkurve as lk
+
+search = lk.search_lightcurve("TIC 360906004", author="TGLC")
+lc = search[0].download(quality_bitmask="hardest")
+# -> LightkurveError: Error in reading Data product ... of type TGLC.
+```
+
+(Any TGLC HLSP file reproduces this; TIC 360906004 sector 11 is a small one.)
+
+## Full traceback
+
+```
+Traceback (most recent call last):
+  File ".../lightkurve/io/read.py", line 134, in read
+    out = self.registry.read(cls, *args, **kwargs)
+  File ".../astropy/io/registry/core.py", line 221, in read
+    data = reader(*args, **kwargs)
+  File ".../lightkurve/io/tglc.py", line 55, in read_tglc_lightcurve
+    quality_mask = TessQualityFlags.create_quality_mask(
+  File ".../lightkurve/utils.py", line 114, in create_quality_mask
+    quality_mask = (quality_array & bitmask) == 0
+OverflowError: Python integer 65535 out of bounds for int16
+
+The above exception was the direct cause of the following exception:
+
+LightkurveError: Error in reading Data product
+  <cache>/hlsp_tglc_tess_ffi_gaiaid-5842130724965127040-s0011-cam3-ccd1_tess_v1_llc.fits
+  of type TGLC.
+This file may be corrupt due to an interrupted download. Please remove it
+from your disk and try again.
+```
+
+## Root cause
+
+The TGLC HLSP stores `tess_flags` as **signed 16-bit int** (FITS column
+`format='I'`). `read_tglc_lightcurve` feeds that column straight into
+`TessQualityFlags.create_quality_mask`, which performs:
+
+```python
+quality_mask = (quality_array & bitmask) == 0
+```
+
+When `bitmask` is a Python `int` larger than the int16 max (32767), NumPy
+2.0+ refuses the bitwise AND and raises `OverflowError`. NumPy 1.x silently
+truncated this. The relevant `TessQualityFlags` constants:
+
+| Bitmask name      | Value | Fits int16? |
+|-------------------|-------|-------------|
+| `DEFAULT_BITMASK` | 175   | yes         |
+| `HARD_BITMASK`    | 24319 | yes         |
+| `HARDEST_BITMASK` | 65535 | **no**      |
+
+So the bug is exposed for any reader/caller that combines a TGLC quality
+column with `quality_bitmask="hardest"` (or any integer > 32767).
+
+Two notes:
+
+* The reader's user-facing error blames a "corrupt download", which is
+  incorrect and sends users on a futile redownload loop. The cause is an
+  adapter-level dtype mismatch, not a transport issue.
+* Other lightkurve readers that route int32 quality columns through the
+  same helper are unaffected — this is specifically the int16 column the
+  TGLC HLSP exposes.
+
+## Proposed fix
+
+Cast the quality column to int32 before the bitwise AND. One-line change
+in `src/lightkurve/io/tglc.py`:
+
+```diff
+--- a/src/lightkurve/io/tglc.py
++++ b/src/lightkurve/io/tglc.py
+@@
+-    quality_mask = TessQualityFlags.create_quality_mask(
+-        quality_array=lc["quality"], bitmask=quality_bitmask
+-    )
++    # TGLC stores ``tess_flags`` as signed int16. Cast to int32 before the
++    # bitwise AND in create_quality_mask so masks > 32767 (e.g. HARDEST =
++    # 65535) don't raise ``OverflowError`` under NumPy 2.
++    quality_mask = TessQualityFlags.create_quality_mask(
++        quality_array=np.asarray(lc["quality"], dtype=np.int32),
++        bitmask=quality_bitmask,
++    )
+```
+
+`numpy` is already imported in this module (`import numpy as np` at the top).
+The cast is local to the mask computation; the stored `lc["quality"]` column
+keeps its original dtype.
+
+A slightly broader fix would normalize the dtype inside
+`TessQualityFlags.create_quality_mask` itself (e.g. with
+`np.asarray(quality_array, dtype=np.int32)`), which would also protect any
+other reader that ever passes a sub-int32 quality column. Either fix
+resolves the symptom; the reader-local cast is the minimal change.
+
+## Suggested regression test
+
+```python
+def test_tglc_hardest_bitmask_no_int16_overflow():
+    """TGLC reader must not overflow int16 when HARDEST_BITMASK is used.
+
+    The TGLC HLSP stores ``tess_flags`` as int16 (FITS format='I').
+    Under NumPy 2, ``int16_array & 65535`` raises OverflowError; the
+    reader must cast to a wider dtype before the bitwise AND.
+    """
+    import lightkurve as lk
+
+    res = lk.search_lightcurve("TIC 360906004", author="TGLC")
+    assert len(res) > 0, "TIC 360906004 should have at least one TGLC product"
+    lc = res[0].download(quality_bitmask="hardest")
+    assert lc is not None
+    assert len(lc) > 0
+```
+
+A network-free variant using only `lightkurve.utils.TessQualityFlags`:
+
+```python
+def test_create_quality_mask_accepts_int16_quality_array():
+    import numpy as np
+    from lightkurve.utils import TessQualityFlags
+
+    quality = np.zeros(8, dtype=np.int16)
+    mask = TessQualityFlags.create_quality_mask(
+        quality_array=quality,
+        bitmask=TessQualityFlags.HARDEST_BITMASK,
+    )
+    assert mask.all()
+```
+
+## Environment
+
+* Python 3.10
+* lightkurve 2.6.0
+* numpy 2.2.6
+* astropy 6.1.7
+
+The bug is **dtype-driven** (int16 column + Python int > 32767) and the
+behavior change is **NumPy 2** (which rejects out-of-bounds Python ints in
+bitwise ops with smaller-dtype arrays). It will reproduce on any combination
+of lightkurve >= the TGLC reader's introduction with NumPy >= 2.0.
+
+## Impact
+
+Any pipeline that requests `quality_bitmask="hardest"` (or any custom
+integer > 32767) when reading TGLC HLSP light curves fails immediately on
+download/read. The misleading "file may be corrupt" message wastes user time
+on redownload attempts that cannot succeed. Downstream tools that wrap
+lightkurve (e.g. quicklook pipelines that use TGLC for FFI targets) inherit
+the failure.
+
+## Verified locally
+
+Applied the one-line patch to a venv copy of
+`lightkurve/io/tglc.py` and re-ran with `quality_bitmask="hardest"`:
+
+```
+sector: 11   cadences: 1180   finite cal_psf_flux: 1180/1180
+```
+
+i.e. the read now succeeds and produces the same light curve as
+`quality_bitmask="default"`, with the user's selected mask applied.
diff --git a/README.md b/README.md
@@ -11,12 +11,12 @@ Although `quicklook` is optimized to find transiting exoplanets, it can also det
 
 ## Features
 
-- **Multi-pipeline support** -- SPOC, TESS-SPOC, QLP, CDIPS, PATHOS, TGLC, TASOC
+- **Multi-pipeline support** -- SPOC, TESS-SPOC, QLP, CDIPS, PATHOS, TGLC, TASOC, T16
 - **Flux / light-curve type** -- PDCSAP or SAP for SPOC; aperture or PSF photometry for TGLC, with an automatic best-quality default
 - **Automated detrending** -- biweight, cosine, median, GP, and other [wotan](https://github.com/hippke/wotan) methods
 - **Stellar rotation** -- Generalized Lomb-Scargle (GLS) periodogram
 - **Transit detection** -- Transit Least Squares (TLS) periodogram
-- **Neighbor check** -- Gaia source overlay on archival sky images
+- **Neighbor check** -- Gaia source overlay on cached archival sky images, with optional nearby SIMBAD object labels
 - **Batch processing** -- `--each-sector` mode, GNU parallel support, and candidate ranking tools
 - **Web GUI** -- Flask-based interface with live progress, job queue, and gallery
 - **HDF5 output** -- full TLS results saved for downstream filtering
@@ -70,6 +70,12 @@ ql --name TOI-125.01 --each-sector -save
 
 # Run all sectors with 4 parallel workers
 ql --name TOI-125.01 --each-sector -j 4 -save
+
+# Run every available pipeline on the latest sector
+ql --name TOI-125.01 --each-pipeline -save
+
+# TGLC PSF photometry with nearby SIMBAD objects overlaid
+ql --name TOI-125.01 --pipeline tglc --fluxtype psf -show_simbad -save
 ```
 
 ### Python API
@@ -126,7 +132,7 @@ The 9-panel figure shows:
 | 3 | Phase-folded light curve at rotation period |
 | 4 | Flattened light curve + detected transits |
 | 5 | TLS periodogram (orbital period) |
-| 6 | TESS aperture + Gaia sources on archival image |
+| 6 | TESS aperture + Gaia sources (and optional SIMBAD objects) on archival image |
 | 7 | Phase-folded transit (odd/even comparison) |
 | 8 | Secondary eclipse check at phase 0.5 |
 | 9 | Summary of stellar and companion parameters |

diff --git a/docs/cli.md b/docs/cli.md
@@ -19,8 +19,8 @@ ql --name TARGET [options]
 | Flag | Default | Description |
 |------|---------|-------------|
 | `--sector SECTOR` | `-1` (latest) | TESS sector number |
-| `--pipeline PIPELINE` | `spoc` | Light curve pipeline: `spoc`, `tess-spoc`, `qlp`, `cdips`, `pathos`, `tglc`, `tasoc` |
-| `--fluxtype TYPE` | `pdcsap` | Flux column: `pdcsap` or `sap` |
+| `--pipeline PIPELINE` | `spoc` | Light curve pipeline: `spoc`, `tess-spoc`, `qlp`, `cdips`, `pathos`, `tglc`, `tasoc`, `t16` |
+| `--fluxtype TYPE` | `pdcsap` | Light-curve type. SPOC: `pdcsap` or `sap`. TGLC: `aperture`, `psf`, or `auto` (best-quality default) |
 | `--exptime SECONDS` | auto | Exposure time in seconds |
 | `--quality_bitmask MASK` | `default` | Quality mask: `none`, `default`, `hard`, `hardest` |
 
@@ -55,16 +55,20 @@ ql --name TARGET [options]
 | `--outdir DIR` | `.` | Output directory |
 | `--suffix TEXT` | none | Suffix appended to output filenames |
 | `--survey NAME` | `dss1` | Archival image survey for overlay |
+| `-show_simbad` | off | Overplot nearby non-stellar SIMBAD objects on the archival image |
 | `-save` | off | Save figure (.png) and TLS results (.h5) |
 | `-verbose` | off | Print detailed progress |
 | `-overwrite` | off | Overwrite existing output files |
 
-### Multi-sector mode
+### Multi-sector / multi-pipeline mode
 
 | Flag | Default | Description |
 |------|---------|-------------|
 | `--each-sector` | off | Run on every available sector individually |
+| `--each-pipeline` | off | Run on every available pipeline for the latest sector (mutually exclusive with `--each-sector`) |
 | `-j, --jobs N` | `1` | Number of parallel jobs for `--each-sector` |
+| `--nice INC` | unchanged | Lower CPU priority by this increment (POSIX; e.g. `19` = lowest) |
+| `--cores N` | auto | CPU cores used by TLS per run (default: `cpu_count // 2` single run, `cpu_count // jobs` for `--each-sector`) |
 
 ### Examples
 
@@ -87,6 +91,12 @@ ql --name TOI-125.01 --each-sector -save
 # Run all sectors with 4 parallel workers
 ql --name TOI-125.01 --each-sector -j 4 -save
 
+# Run every available pipeline on the latest sector
+ql --name TOI-125.01 --each-pipeline -save
+
+# TGLC PSF photometry with nearby SIMBAD objects overlaid
+ql --name TOI-125.01 --pipeline tglc --fluxtype psf -show_simbad -save
+
 # Pipe output to a log file
 ql --name TIC-52368076 -verbose -save | tee output.log
 ```

diff --git a/docs/gui.md b/docs/gui.md
@@ -22,8 +22,9 @@ This starts a local server at [http://127.0.0.1:5000](http://127.0.0.1:5000).
 
 1. Enter a target name (TOI, TIC, or common name)
 2. Adjust pipeline parameters (sector, pipeline, flux type, etc.)
-3. Click **Run QuickLook**
-4. Watch live progress with step-by-step updates and ETA
+3. Optionally toggle **Show SIMBAD** to overlay nearby non-stellar SIMBAD objects on the archival image (on by default in the GUI)
+4. Click **Run QuickLook**
+5. Watch live progress with step-by-step updates and ETA
 
 ### Each-sector mode
 
@@ -51,7 +52,9 @@ Completed jobs display the output figure inline. Click to view full size. Result
 
 ### Gallery
 
-The `/gallery` page shows all previously generated output figures with search, sorting, and pagination.
+The `/gallery` page shows all previously generated output figures with search, sorting, and pagination. Click a thumbnail to open it in a zoomable modal -- pan by dragging, and use the **left/right arrow keys** to step between figures without closing the viewer.
+
+![QuickLook Gallery](img/ql-gui-gallery.png)
 
 ### Cancelling jobs
 

diff --git a/docs/img/ql-gui-gallery.png b/docs/img/ql-gui-gallery.png
diff --git a/docs/img/ql-gui.png b/docs/img/ql-gui.png
diff --git a/docs/pipeline.md b/docs/pipeline.md
@@ -63,7 +63,7 @@ The [Transit Least Squares](https://ui.adsabs.harvard.edu/abs/2019A%26A...623A..
 
 **Panel 6: Archival image + Gaia overlay**
 
-The TESS aperture (blue polygon) overlaid on an archival sky survey image (DSS by default). Orange and red circles show nearby Gaia sources, scaled by brightness. This panel helps identify:
+The TESS aperture (blue polygon) overlaid on an archival sky survey image (DSS by default). Orange and red circles show nearby Gaia sources, scaled by brightness. With `-show_simbad` (CLI) or the GUI checkbox, nearby non-stellar SIMBAD objects within the field of view are also labelled by their condensed object type (e.g. `EclBin`), which helps flag known eclipsing binaries or other variables near the target. DSS cutouts are cached under `~/.astropy/cache` so repeated runs of the same field do not re-download the image. This panel helps identify:
 
 - Blended neighbors that could be the true source of the signal
 - Background eclipsing binaries contaminating the aperture
@@ -116,4 +116,6 @@ When `-save` is used, QuickLook produces:
 
 Example: `WASP-21_s56_pdcsap_sc.png` and `WASP-21_s56_pdcsap_sc_tls.h5`
 
+For TGLC runs, the flux token encodes the photometry method: `--fluxtype aperture` writes `tglc_aper` and `--fluxtype psf` writes `tglc_psf` (e.g. `TIC123_s11_tglc_psf_lc.png`). This keeps aperture and PSF runs of the same target/sector from overwriting each other on disk. SPOC stems and default (auto) TGLC stems are unchanged.
+
 The HDF5 file contains the TLS periodogram, best-fit parameters, stellar parameters, and metadata. Use `read_tls` to extract a summary CSV from a directory of these files.