correct tests and tutorial example.

Author: dimitri-yatsenko

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

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)