separate repo from the earlier Allen Insitute repo. Make this repo canonical.

Author: dimitri-yatsenko

README.md

@@ -1,15 +1,18 @@
-[![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 develpoments 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.