Add test_unit_ROI_indices.py (closes #116)

Author: tcompa

tests/test_unit_ROI_indices.py

@@ -0,0 +1,171 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from fractal.tasks.lib_regions_of_interest import convert_FOV_ROIs_3D_to_2D
+from fractal.tasks.lib_regions_of_interest import convert_ROI_table_to_indices
+from fractal.tasks.lib_regions_of_interest import prepare_FOV_ROI_table
+from fractal.tasks.lib_regions_of_interest import (
+    split_3D_indices_into_z_layers,
+)
+
+
+PIXEL_SIZE_X = 0.1625
+PIXEL_SIZE_Y = 0.1625
+PIXEL_SIZE_Z = 1.0
+
+IMG_SIZE_X = 2560
+IMG_SIZE_Y = 2160
+NUM_Z_PLANES = 4
+
+
+def get_metadata_dataframe():
+    """
+    Create artificial metadata dataframe
+    """
+    df = pd.DataFrame(np.zeros((4, 10)), dtype=int)
+    df.index = ["FOV1", "FOV2", "FOV3", "FOV4"]
+    df.columns = [
+        "x_micrometer",
+        "y_micrometer",
+        "z_micrometer",
+        "x_pixel",
+        "y_pixel",
+        "z_pixel",
+        "pixel_size_x",
+        "pixel_size_y",
+        "pixel_size_z",
+        "bit_depth",
+    ]
+    img_size_x_micrometer = IMG_SIZE_X * PIXEL_SIZE_X
+    img_size_y_micrometer = IMG_SIZE_Y * PIXEL_SIZE_Y
+    df["x_micrometer"] = [
+        0.0,
+        img_size_x_micrometer,
+        0.0,
+        img_size_x_micrometer,
+    ]
+    df["y_micrometer"] = [
+        0.0,
+        0.0,
+        img_size_y_micrometer,
+        img_size_y_micrometer,
+    ]
+    df["z_micrometer"] = [0.0, 0.0, 0.0, 0.0]
+    df["x_pixel"] = [IMG_SIZE_X] * 4
+    df["y_pixel"] = [IMG_SIZE_Y] * 4
+    df["z_pixel"] = [NUM_Z_PLANES] * 4
+    df["pixel_size_x"] = [PIXEL_SIZE_X] * 4
+    df["pixel_size_y"] = [PIXEL_SIZE_Y] * 4
+    df["pixel_size_z"] = [PIXEL_SIZE_Z] * 4
+    df["bit_depth"] = [16.0] * 4
+
+    return df
+
+
+list_params = [
+    (0, 2),
+    (1, 2),
+    (2, 2),
+    (3, 2),
+    (0, 3),
+    (1, 3),
+    (2, 3),
+    (3, 3),
+    (0, 7),
+    (1, 7),
+    (2, 7),
+]
+
+
+@pytest.mark.parametrize("level,coarsening_xy", list_params)
+def test_ROI_indices_3D(level, coarsening_xy):
+
+    metadata_dataframe = get_metadata_dataframe()
+    adata = prepare_FOV_ROI_table(metadata_dataframe)
+
+    full_res_pxl_sizes_zyx = [PIXEL_SIZE_Z, PIXEL_SIZE_Y, PIXEL_SIZE_X]
+    list_indices = convert_ROI_table_to_indices(
+        adata,
+        level=level,
+        coarsening_xy=coarsening_xy,
+        full_res_pxl_sizes_zyx=full_res_pxl_sizes_zyx,
+    )
+    print()
+    original_shape = (
+        NUM_Z_PLANES,
+        2 * IMG_SIZE_Y,
+        2 * IMG_SIZE_X // coarsening_xy**level,
+    )
+    expected_shape = (
+        NUM_Z_PLANES,
+        2 * IMG_SIZE_Y // coarsening_xy**level,
+        2 * IMG_SIZE_X // coarsening_xy**level,
+    )
+    print(f"Original shaep: {original_shape}")
+    print(f"coarsening_xy={coarsening_xy}, level={level}")
+    print(f"Expected shape: {expected_shape}")
+    print("FOV-ROI indices:")
+    for indices in list_indices:
+        print(indices)
+    print()
+
+    assert list_indices[0][5] == list_indices[1][4]
+    assert list_indices[0][3] == list_indices[2][2]
+    assert (
+        abs(
+            (list_indices[0][5] - list_indices[0][4])
+            - (list_indices[1][5] - list_indices[1][4])
+        )
+        < coarsening_xy
+    )
+    assert (
+        abs(
+            (list_indices[0][3] - list_indices[0][2])
+            - (list_indices[1][3] - list_indices[1][2])
+        )
+        < coarsening_xy
+    )
+    assert abs(list_indices[1][5] - expected_shape[2]) < coarsening_xy
+    assert abs(list_indices[2][3] - expected_shape[1]) < coarsening_xy
+    for indices in list_indices:
+        assert indices[0] == 0
+        assert indices[1] == NUM_Z_PLANES
+
+
+@pytest.mark.parametrize("level,coarsening_xy", list_params)
+def test_ROI_indices_split(level, coarsening_xy):
+
+    metadata_dataframe = get_metadata_dataframe()
+    adata = prepare_FOV_ROI_table(metadata_dataframe)
+
+    full_res_pxl_sizes_zyx = [PIXEL_SIZE_Z, PIXEL_SIZE_Y, PIXEL_SIZE_X]
+    list_indices = convert_ROI_table_to_indices(
+        adata,
+        level=level,
+        coarsening_xy=coarsening_xy,
+        full_res_pxl_sizes_zyx=full_res_pxl_sizes_zyx,
+    )
+
+    list_indices = split_3D_indices_into_z_layers(list_indices)
+    assert len(list_indices) == NUM_Z_PLANES * 2 * 2
+
+
+@pytest.mark.parametrize("level,coarsening_xy", list_params)
+def test_ROI_indices_2D(level, coarsening_xy):
+
+    metadata_dataframe = get_metadata_dataframe()
+    adata = prepare_FOV_ROI_table(metadata_dataframe)
+    adata = convert_FOV_ROIs_3D_to_2D(adata, PIXEL_SIZE_Z)
+
+    full_res_pxl_sizes_zyx = [PIXEL_SIZE_Z, PIXEL_SIZE_Y, PIXEL_SIZE_X]
+    list_indices = convert_ROI_table_to_indices(
+        adata,
+        level=level,
+        coarsening_xy=coarsening_xy,
+        full_res_pxl_sizes_zyx=full_res_pxl_sizes_zyx,
+    )
+
+    for indices in list_indices:
+        assert indices[0] == 0
+        assert indices[1] == 1