Chore canon

CITATION.cff

@@ -0,0 +1,53 @@
+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

@@ -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 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.
 

docs/examples/workbook.md

@@ -29,17 +29,19 @@ First, let's create synthetic data that mimics photon-limited imaging:
 
 ```python
 # Parameters for synthetic data
-n_frames = 100
-height, width = 512, 512
-mean_photons = 50  # Average photons per pixel
-zero_level = 100   # Camera baseline
-conversion_gain = 2.5  # ADU per photon
+n_frames = 30
+height, width = 120, 120
+mean_photon_rate = 5.0  # Average photons per pixel (exponential distribution of rates)
+zero_level = -10.0   # camera baseline
+conversion_gain = 2.5  # levels per photon
 
 # Generate Poisson-distributed photon counts
-photon_counts = np.random.poisson(lam=mean_photons, size=(n_frames, height, width))
+photon_rate = np.random.exponential(scale=5, size=(1, height, width))
+photon_counts = np.random.poisson(lam=np.tile(photon_rate, (n_frames, 1, 1)))
+measured_signal = photon_counts + np.random.randn(*size) * 0.2
 
-# Convert to camera signal (ADU)
-camera_signal = (photon_counts * conversion_gain + zero_level).astype('int16')
+# Convert to camera signal
+camera_signal = (measured_signal * conversion_gain + zero_level).astype('int16')
 
 print(f"Data shape: {camera_signal.shape}")
 print(f"Data range: [{camera_signal.min()}, {camera_signal.max()}]")
@@ -58,16 +60,16 @@ estimated_gain = result['sensitivity']
 estimated_zero = result['zero_level']
 
 print(f"\nTrue parameters:")
-print(f"  Conversion gain: {conversion_gain:.3f} ADU/photon")
-print(f"  Zero level: {zero_level:.1f} ADU")
+print(f"  Conversion gain: {conversion_gain:.3f} units/photon")
+print(f"  Zero level: {zero_level:.1f} ")
 
 print(f"\nEstimated parameters:")
-print(f"  Conversion gain: {estimated_gain:.3f} ADU/photon")
-print(f"  Zero level: {estimated_zero:.1f} ADU")
+print(f"  Conversion gain: {estimated_gain:.3f} units/photon")
+print(f"  Zero level: {estimated_zero:.1f}")
 
 print(f"\nEstimation error:")
-print(f"  Gain error: {abs(estimated_gain - conversion_gain):.3f} ADU/photon")
-print(f"  Zero level error: {abs(estimated_zero - zero_level):.1f} ADU")
+print(f"  Gain error: {abs(estimated_gain - conversion_gain):.3f} units/photon")
+print(f"  Zero level error: {abs(estimated_zero - zero_level):.1f} units")
 ```
 
 ## Visualize Noise Model

docs/getting-started/quick-start.md

@@ -9,18 +9,25 @@ import zarr
 import numpy as np
 from anscombe_transform import AnscombeTransformV3
 
-# Generate sample data with Poisson noise
-# Simulating photon-limited imaging data
-data = np.random.poisson(lam=50, size=(100, 512, 512)).astype('int16')
+# Synthesize data with Poisson noise - simulating photon-limited microscopy data
+n_frames, height, width = 50, 256, 256
+mean_photon_rate = 5.0  # Average photons per pixel (exponential distribution of rates)
+zero_level = 20.0   # camera baseline
+conversion_gain = 30.0  # levels per photon
+photon_rate = np.random.exponential(scale=mean_photon_rate, size=(1, height, width))
+photon_counts = np.random.poisson(np.tile(photon_rate, (n_frames, 1, 1)))
+measured_signal = photon_counts + np.random.randn(*size) * 0.2
+
+data = (zero_level + conversion_gain * measured_signal).astype('int16')
 
 # Create a Zarr array with the Anscombe codec
 store = zarr.storage.MemoryStore()
 arr = zarr.create(
     store=store,
     shape=data.shape,
-    chunks=(10, 512, 512),
+    chunks=(12, 160, 80),
     dtype='int16',
-    filters=[AnscombeTransformV3(zero_level=100, conversion_gain=2.5)],
+    filters=[AnscombeTransformV3(zero_level=zero_level, conversion_gain=conversion_gain)],
     zarr_format=3
 )
 
@@ -44,10 +51,10 @@ import zarr
 arr = zarr.open_array(
     'data.zarr',
     mode='w',
-    shape=(100, 512, 512),
-    chunks=(10, 512, 512),
+    shape=data.shape,
+    chunks=(12, 160, 80),
     dtype='int16',
-    compressor=AnscombeTransformV2(zero_level=100, conversion_gain=2.5)
+    compressor=AnscombeTransformV2(zero_level=zero_level, conversion_gain=conversion_gain)
 )
 
 # Write and read data
@@ -64,10 +71,10 @@ from anscombe_transform import compute_conversion_gain
 import numpy as np
 
 # Load your movie data as (time, height, width)
-movie = np.random.poisson(lam=50, size=(100, 512, 512))
+movie = data
 
 # Estimate parameters
-result = compute_conversion_gain(movie)
+result = compute_conversion_gain(data)
 
 print(f"Estimated conversion gain: {result['sensitivity']:.3f}")
 print(f"Estimated zero level: {result['zero_level']:.3f}")
@@ -90,10 +97,10 @@ from anscombe_transform import AnscombeTransformV3
 
 # For Zarr V3, use filters + codecs
 arr = zarr.create(
-    shape=(100, 512, 512),
+    shape=data.shape,
     chunks=(10, 512, 512),
     dtype='int16',
-    filters=[AnscombeTransformV3(zero_level=100, conversion_gain=2.5)],
+    filters=[AnscombeTransformV3(zero_level=zero_level, conversion_gain=conversion_gain)],
     compressor={'id': 'blosc', 'cname': 'zstd', 'clevel': 5},
     zarr_format=3
 )
@@ -102,7 +109,7 @@ arr = zarr.create(
 ## Key Parameters
 
 - **`zero_level`**: The signal value when no photons are detected. This is the baseline offset in your camera sensor.
-- **`conversion_gain`** (also called `photon_sensitivity`): How many signal units correspond to one photon. For example, if your camera reports 2.5 ADU per photon, use `conversion_gain=2.5`.
+- **`conversion_gain`** (also called `photon_sensitivity`): How many signal units correspond to one photon. For example, if your camera reports 2.5 levels increase in signal per photon, use `conversion_gain=2.5`.
 - **`encoded_dtype`**: The data type for encoded values (default: `uint8`). Use `uint8` for maximum compression.
 - **`decoded_dtype`**: The data type for decoded values (default: inferred from data).
 

docs/user-guide/overview.md

@@ -74,7 +74,7 @@ These tables are computed once during codec initialization and reused for all en
 ### Compression Ratios
 
 Typical compression ratios (Anscombe + Blosc/Zstd):
-- **4-8x** for typical multiphoton microscopy data
+- **3-8x** for typical multiphoton microscopy data
 - **6-10x** for astronomy data
 - **3-6x** for radiography data
 

pyproject.toml

@@ -7,8 +7,8 @@ name = "anscombe_transform"
 version = "1.0.0"
 
 authors = [
-  { name = "Jerome Lecoq", email = "[email protected]" },
   { name = "Dimitri Yatsenko", email = "[email protected]" },
+  { name = "Jerome Lecoq", email = "[email protected]" },
   { name = "Davis Bennett", email = "[email protected]" },
 ]
 maintainers = [

tests/test_zarr.py

@@ -10,35 +10,57 @@
 def test_zarr_v2_roundtrip() -> None:
     decoded_dtype = "int16"
     encoded_dtype = "uint8"
-    data = np.random.poisson(100, size=(20, 20)).astype(decoded_dtype)
+
+    # generate fake data
+    np.random.seed(42)
+    size = (20, 20)
     sensitivity = 100.0
+    zero_level = -5.0
+    true_rate = np.random.exponential(scale=5, size=size)
+    data = (
+        zero_level
+        + sensitivity * (np.random.poisson(true_rate) + np.random.randn(*size) * 0.25)
+    ).astype(decoded_dtype)
+
+    # construct codec
     codec = AnscombeTransformV2(
         conversion_gain=sensitivity,
-        zero_level=0,
+        zero_level=zero_level,
         encoded_dtype=encoded_dtype,
         decoded_dtype=decoded_dtype,
     )
     data_encoded = codec.encode(data)
     data_rt = codec.decode(data_encoded).reshape(data.shape)
 
-    store = {}
-
     # write data
+    store = {}
     _ = create_array(store=store, data=data, zarr_format=2, compressors=codec)
+
     # read data
     z_arr_r = open_array(store=store)
     assert z_arr_r.dtype == decoded_dtype
-    assert nearly_equal(z_arr_r, data_rt, sensitivity / 2)
+    breakpoint()
+    assert nearly_equal(z_arr_r, data_rt, sensitivity * 0.5)
 
 
 def test_zarr_v3_roundtrip() -> None:
     decoded_dtype = "int16"
     encoded_dtype = "uint8"
-    data = np.random.poisson(100, size=(20, 20)).astype(decoded_dtype)
+
+    # generate fake data
+    np.random.seed(42)
+    size = (20, 20)
     sensitivity = 100.0
+    zero_level = -5.0
+    true_rate = np.random.exponential(scale=5, size=size)
+    data = (
+        zero_level
+        + sensitivity * (np.random.poisson(true_rate) + np.random.randn(*size) * 0.25)
+    ).astype(decoded_dtype)
+
     codec = AnscombeTransformV3(
         conversion_gain=sensitivity,
-        zero_level=0,
+        zero_level=zero_level,
         encoded_dtype=encoded_dtype,
         decoded_dtype=decoded_dtype,
     )
@@ -47,9 +69,8 @@ def test_zarr_v3_roundtrip() -> None:
     data_encoded = codec._encode(data)
     data_rt = codec._decode(data_encoded).reshape(data.shape)
 
-    store = {}
-
     # write data
+    store = {}
     _ = create_array(store=store, data=data, zarr_format=3, filters=[codec])
     # read data
     z_arr_r = open_array(store=store)