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Add some documentation on the calibration process - #141

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calib_doc
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Add some documentation on the calibration process#141
lucabaldini wants to merge 10 commits into
mainfrom
calib_doc

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Copilot AI review requested due to automatic review settings July 22, 2026 12:23

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Pull request overview

This PR expands the Sphinx documentation by adding a new calibration guide and introducing a set of API reference pages for many hexsample modules, along with a few documentation-path fixes to make included assets resolve correctly.

Changes:

  • Added a new Calibration documentation page and linked it from the main docs index under a new “Technical info” section.
  • Added initial API reference .rst pages (autodoc stubs and a few narrative pages) for multiple hexsample.* modules.
  • Updated some documentation asset paths (e.g., figure:: / literalinclude::) to use correct relative locations.

Reviewed changes

Copilot reviewed 10 out of 25 changed files in this pull request and generated 5 comments.

Show a summary per file
File Description
docs/index.rst Adds a “Technical info” toctree entry for the new calibration docs (also where module toctree wiring needs alignment with docs/api/).
docs/calibration.rst New narrative documentation describing the calibration process (noise/pedestal + gain).
docs/api/analysis.rst New API reference stub for hexsample.analysis.
docs/api/base.rst New API page with narrative + autodoc for hexsample.base.
docs/api/calibration.rst New API reference stub for hexsample.calibration.
docs/api/cli.rst New API page for hexsample.cli (intro + autodoc).
docs/api/clustering.rst New API reference stub for hexsample.clustering.
docs/api/digi.rst Updates/introduces the hexsample.digi API page and fixes literalinclude path.
docs/api/display.rst New API reference stub for hexsample.display.
docs/api/fileio.rst Fixes literalinclude paths and includes autodoc for hexsample.fileio.
docs/api/hexagon.rst Fixes the figure path for the hexagonal layouts image.
docs/api/legacy.rst New API reference stub for hexsample.legacy.
docs/api/mc.rst New API reference stub for hexsample.mc.
docs/api/pipeline.rst New API page for hexsample.pipeline (narrative + autodoc).
docs/api/pretty.rst New API page for hexsample.pretty (narrative + autodoc).
docs/api/readout.rst New API reference stub for hexsample.readout.
docs/api/recon.rst New API reference stub for hexsample.recon.
docs/api/resolution.rst New API reference stub for hexsample.resolution.
docs/api/rng.rst New API page for hexsample.rng (narrative + autodoc).
docs/api/roi.rst New API page for hexsample.roi (narrative + autodoc).
docs/api/sensor.rst New API reference stub for hexsample.sensor.
docs/api/shell.rst New API reference stub for hexsample.shell.
docs/api/source.rst New API page for hexsample.source (narrative, examples + autodoc).
docs/api/tasks.rst New API page for hexsample.tasks (intro + autodoc).
docs/api/xpol.rst New API reference stub for hexsample.xpol.

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Comment thread docs/index.rst Outdated
Comment on lines 33 to 51
Comment thread docs/calibration.rst Outdated
Comment on lines +27 to +29
Basic algorithm: take the pixel the highest value, remove it along with the 8
pixels around (in a rectangular sense, which makes the things easier to
implement and reason about with numpy arrays).
Comment thread docs/calibration.rst Outdated
we are clipping the negative part with the zero suppression. The algorithm
is accumulating the sum of the squares, which works out of the box.

-> Issue for the new algorithm for pedestal and noise.
Comment thread docs/calibration.rst Outdated
Comment on lines +41 to +45
This will produce a HDF5 file with two matrices, one with the noise values,
and one with the number of hits per pixels.

If there are no hits for a specific pixel, we just use the average of the pixels
with hits.
Comment thread docs/calibration.rst Outdated
Comment on lines +51 to +55
Two-pass processes. We need a monochromatic source, so that we know the
average number of electrons/holes produced.

The process is based on a chisquare that can be minimized with respect to the
gain. The effect of the threshold introduces a bias, which can be corrected with
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