feat: init

2025-12-04 16:07:30 +08:00
commit 262583a57f
681 changed files with 117578 additions and 0 deletions
--- a/test/model/data_sources/test_tiff_data_source.py
+++ b/test/model/data_sources/test_tiff_data_source.py
@@ -0,0 +1,108 @@
+import os
+
+import pytest
+
+from navigate.tools.file_functions import delete_folder
+
+
+@pytest.mark.parametrize("is_ome", [True, False])
+@pytest.mark.parametrize("multiposition", [True, False])
+@pytest.mark.parametrize("per_stack", [True, False])
+@pytest.mark.parametrize("z_stack", [True, False])
+@pytest.mark.parametrize("stop_early", [True, False])
+def test_tiff_write_read(is_ome, multiposition, per_stack, z_stack, stop_early):
+    import numpy as np
+
+    from test.model.dummy import DummyModel
+    from navigate.model.data_sources.tiff_data_source import TiffDataSource
+
+    print(
+        f"Conditions are is_ome: {is_ome} multiposition: {multiposition} "
+        f"per_stack: {per_stack} z_stack: {z_stack} stop_early: {stop_early}"
+    )
+
+    # Set up model with a random number of z-steps to modulate the shape
+    model = DummyModel()
+    z_steps = np.random.randint(1, 3)
+    timepoints = np.random.randint(1, 3)
+    model.configuration["experiment"]["MicroscopeState"]["image_mode"] = (
+        "z-stack" if z_stack else "single"
+    )
+    model.configuration["experiment"]["MicroscopeState"]["number_z_steps"] = z_steps
+    model.configuration["experiment"]["MicroscopeState"][
+        "is_multiposition"
+    ] = multiposition
+    model.configuration["experiment"]["MicroscopeState"]["timepoints"] = timepoints
+    if per_stack:
+        model.configuration["experiment"]["MicroscopeState"][
+            "stack_cycling_mode"
+        ] == "per_stack"
+    else:
+        model.configuration["experiment"]["MicroscopeState"][
+            "stack_cycling_mode"
+        ] == "per_slice"
+
+    if not os.path.exists("test_save_dir"):
+        os.mkdir("test_save_dir")
+
+    # Establish a TIFF data source
+    if is_ome:
+        fn = "./test_save_dir/test.ome.tif"
+    else:
+        fn = "./test_save_dir/test.tif"
+    ds = TiffDataSource(fn)
+    ds.set_metadata_from_configuration_experiment(model.configuration)
+
+    # Populate one image per channel per timepoint per position
+    n_images = ds.shape_c * ds.shape_z * ds.shape_t * ds.positions
+    data = (np.random.rand(n_images, ds.shape_y, ds.shape_x) * 2**16).astype(
+        np.uint16
+    )
+    file_names_raw = []
+    for i in range(n_images):
+        ds.write(data[i, ...].squeeze())
+        file_names_raw.extend(ds.file_name)
+        if stop_early and np.random.rand() > 0.5:
+            break
+    ds.close()
+
+    # Cannot use list(set()) trick here because ordering is important
+    file_names = []
+    for fn in file_names_raw:
+        if fn not in file_names:
+            file_names.append(fn)
+    # print(file_names)
+
+    try:
+        # For each file...
+        for i, fn in enumerate(file_names):
+            ds2 = TiffDataSource(fn, "r")
+            # Make sure XYZ size is correct (and C and T are each of size 1)
+            assert (
+                (ds2.shape_x == ds.shape_x)
+                and (ds2.shape_y == ds.shape_y)
+                and (ds2.shape_c == 1)
+                and (ds2.shape_t == 1)
+                and (ds2.shape_z == ds.shape_z)
+            )
+            # Make sure the data copied properly
+            np.testing.assert_equal(
+                ds2.data, data[i * ds.shape_z : (i + 1) * ds.shape_z, ...].squeeze()
+            )
+            ds2.close()
+    except IndexError as e:
+        if stop_early:
+            # This file was not written
+            pass
+        else:
+            raise e
+    except AssertionError as e:
+        if stop_early:
+            # This file has an underfilled axes
+            pass
+        else:
+            raise e
+    except Exception as e:
+        raise e
+    finally:
+        delete_folder("test_save_dir")