Chore canon

CITATION.cff

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+cff-version: 1.2.0
+title: Anscombe Transform Codec
+message: If you use this software, please cite it.
+version: 1.0.0
+repository-code: https://github.com/datajoint/anscombe-transform
+abstract: >
+  Codec implementation of the Anscombe transform for compressing photon-limited
+  microscopy, radiography, and astronomy image recordings, published by DataJoint.
+authors:
+  - family-names: Yatsenko
+    given-names: Dimitri
+    email: [email protected]
+  - family-names: Lecoq
+    given-names: Jerome
+    email: [email protected]
+  - family-names: Bennett
+    given-names: Davis
+    email: [email protected]
+contact:
+  - family-names: Yatsenko
+    given-names: Dimitri
+    email: [email protected]
+license: MIT
+preferred-citation:
+  type: article
+  title: Standardized measurements for monitoring and comparing multiphoton microscope systems
+  authors:
+    - family-names: Lees
+      given-names: Robert M.
+    - family-names: Bianco
+      given-names: Isaac H.
+    - family-names: Campbell
+      given-names: Robert A. A.
+    - family-names: Orlova
+      given-names: Natalia
+    - family-names: Peterka
+      given-names: Darcy S.
+    - family-names: Pichler
+      given-names: Bruno
+    - family-names: Smith
+      given-names: Spencer LaVere
+    - family-names: Yatsenko
+      given-names: Dimitri
+    - family-names: Yu
+      given-names: Che-Hang
+    - family-names: Packer
+      given-names: Adam M.
+  journal: Nature Protocols
+  volume: '20'
+  pages: 1-38
+  date-published: 2025-03-17
+  doi: 10.1038/s41596-024-01120-w
+  url: https://doi.org/10.1038/s41596-024-01120-w

README.md

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-[![PyPI version](https://badge.fury.io/py/anscombe-transform.svg)](https://badge.fury.io/py/anscombe-transform) ![tests](https://github.com/datajoint/anscombe-transform/actions/workflows/tests.yaml/badge.svg)
+[![PyPI version](https://badge.fury.io/py/anscombe-transform.svg)](https://badge.fury.io/py/anscombe-transform) ![tests](https://github.com/datajoint/anscombe-transform/actions/workflows/test.yml/badge.svg)
 
 # Anscombe transform
 
-This codec is designed for compressing image recordings with Poisson noise, which are produced by photon-limited modalities such multiphoton microscopy, radiography, and astronomy.
+This codec is designed for compressing image recordings with Poisson noise such as in microscopy, radiography, and astronomy.
 
-The codec assumes that the video is linearly encoded with a potential offset (`zero_level`) and that the `photon_sensitivity` (the average increase in intensity per photon) is either already known or can be accurately estimated from the data.
+**Status:** This is the official and actively maintained Anscombe transform codec for Zarr/Numcodecs, maintained by DataJoint.
+It originated as a fork of [AllenNeuralDynamics/poisson-numcodecs](https://github.com/AllenNeuralDynamics/poisson-numcodecs) and earlier developments at https://github.com/datajoint/compress-multiphoton. It has since diverged significantly. New users should rely on this repository as the canonical source.
+
+The codec assumes that the video is linearly encoded with a potential offset (`zero_level`) and that the `conversion_gain` (also called `photon_sensitivity`)—the average increase in intensity per photon—is either already known or can be accurately estimated from the data.
 
 The codec re-quantizes the grayscale efficiently with a square-root-like transformation to equalize the noise variance across the grayscale levels: the [Anscombe Transform](https://en.wikipedia.org/wiki/Anscombe_transform).
 This results in a smaller number of unique grayscale levels and significant improvements in the compressibility of the data without sacrificing signal accuracy.
 
-To use the codec, one must supply two pieces of information: `zero_level` (the input value corresponding to the absence of light) and `photon_sensitivity` (levels/photon).
+To use the codec, one must supply two pieces of information: `zero_level` (the input value corresponding to the absence of light) and `conversion_gain` (levels/photon).
 
 The codec is used in Zarr as a filter prior to compression.