Merge branch 'main' into solve_gin_test_config_tracking

.github/workflows/testing-linux.yml

@@ -73,6 +73,24 @@ jobs:
           git config --global user.name "CI Almighty"
           datalad install -rg https://gin.g-node.org/CatalystNeuro/ophys_testing_data
 
+      - name: Get behavior_testing_data current head hash
+        id: behavior
+        run: echo "::set-output name=HASH_BEHAVIOR_DATASET::$(git ls-remote https://gin.g-node.org/CatalystNeuro/behavior_testing_data.git HEAD | cut -f1)"
+      - name: Cache behavior dataset - ${{ steps.behavior.outputs.HASH_BEHAVIOR_DATASET }}
+        uses: actions/cache@v2
+        id: cache-behavior-datasets
+        with:
+          path: /home/runner/work/nwb-conversion-tools/nwb-conversion-tools/behavior_testing_data
+          key: behavior-datasets-8-${{ steps.behavior.outputs.HASH_behavior_DATASET }}
+          restore-keys: behavior-datasets-8-${{ steps.behavior.outputs.HASH_behavior_DATASET }}
+      - name: Force GIN behavior download
+        if: steps.cache-behavior-datasets.outputs.cache-hit == false
+        run: |
+          conda install -c conda-forge datalad==0.14.5
+          git config --global user.email "[email protected]"
+          git config --global user.name "CI Almighty"
+          datalad install -rg https://gin.g-node.org/CatalystNeuro/behavior_testing_data
+
       - name: Run full pytest with coverage
         run: pytest --cov=./ --cov-report xml:/home/runner/work/nwb-conversion-tools/nwb-conversion-tools/coverage.xml
       - if: ${{ matrix.python-version == '3.9' }}

.github/workflows/update-testing-data.yml

@@ -66,4 +66,22 @@ jobs:
           cd ophys_testing_data
           datalad get -r ./imaging_datasets/
           datalad get -r ./segmentation_datasets/
-          cd ..
+          cd ..
+
+      - name: Get behavior_testing_data current head hash
+        id: behavior
+        run: echo "::set-output name=HASH_BEHAVIOR_DATASET::$(git ls-remote https://gin.g-node.org/CatalystNeuro/behavior_testing_data.git HEAD | cut -f1)"
+      - name: Cache behavior dataset - ${{ steps.behavior.outputs.HASH_BEHAVIOR_DATASET }}
+        uses: actions/cache@v2
+        id: cache-behavior-datasets
+        with:
+          path: /home/runner/work/nwb-conversion-tools/nwb-conversion-tools/behavior_testing_data
+          key: behavior-datasets-8-${{ steps.behavior.outputs.HASH_behavior_DATASET }}
+          restore-keys: behavior-datasets-8-${{ steps.behavior.outputs.HASH_behavior_DATASET }}
+      - name: Force GIN behavior download
+        if: steps.cache-behavior-datasets.outputs.cache-hit == false
+        run: |
+          conda install -c conda-forge datalad==0.14.5
+          git config --global user.email "[email protected]"
+          git config --global user.name "CI Almighty"
+          datalad install -rg https://gin.g-node.org/CatalystNeuro/behavior_testing_data

tests/test_on_data/test_gin_behavior.py

@@ -0,0 +1,218 @@
+import tempfile
+import unittest
+import os
+from pathlib import Path
+from datetime import datetime
+
+import pytest
+import numpy as np
+from pynwb import NWBHDF5IO
+from nwb_conversion_tools import NWBConverter, MovieInterface
+from nwb_conversion_tools.utils import load_dict_from_file
+
+
+# Load the configuration for the data tests
+test_config_dict = load_dict_from_file(Path(__file__).parent / "gin_test_config.json")
+
+#  GIN dataset: https://gin.g-node.org/CatalystNeuro/behavior_testing_data
+if os.getenv("CI"):
+    LOCAL_PATH = Path(".")  # Must be set to "." for CI
+    print("Running GIN tests on Github CI!")
+else:
+    # Override LOCAL_PATH in the `gin_test_config.json` file to a point on your system that contains the dataset folder
+    # Use DANDIHub at hub.dandiarchive.org for open, free use of data found in the /shared/catalystneuro/ directory
+    LOCAL_PATH = Path(test_config_dict["LOCAL_PATH"])
+    print("Running GIN tests locally!")
+BEHAVIOR_DATA_PATH = LOCAL_PATH / "behavior_testing_data"
+HAVE_BEHAVIOR_DATA = BEHAVIOR_DATA_PATH.exists()
+
+if test_config_dict["SAVE_OUTPUTS"]:
+    OUTPUT_PATH = LOCAL_PATH / "example_nwb_output"
+    OUTPUT_PATH.mkdir(exist_ok=True)
+else:
+    OUTPUT_PATH = Path(tempfile.mkdtemp())
+
+if not HAVE_BEHAVIOR_DATA:
+    pytest.fail(f"No oephys_testing_data folder found in location: {BEHAVIOR_DATA_PATH}!")
+
+
+class TestMovieDataNwbConversions(unittest.TestCase):
+    savedir = OUTPUT_PATH
+
+    def setUp(self) -> None:
+        self.movie_files = list((BEHAVIOR_DATA_PATH / "videos" / "CFR").iterdir())
+        self.nwb_converter = self.create_movie_converter()
+        self.nwbfile_path = os.path.join(self.savedir, "movie_test.nwb")
+
+    def create_movie_converter(self):
+        class MovieTestNWBConverter(NWBConverter):
+            data_interface_classes = dict(Movie=MovieInterface)
+
+        source_data = dict(Movie=dict(file_paths=self.movie_files))
+        return MovieTestNWBConverter(source_data)
+
+    def get_metadata(self):
+        metadata = self.nwb_converter.get_metadata()
+        metadata["NWBFile"].update(session_start_time=datetime.now().astimezone().strftime("%Y-%m-%dT%H:%M:%S"))
+        return metadata
+
+    def test_movie_starting_times(self):
+        starting_times = [np.float(np.random.randint(200)) for i in range(len(self.movie_files))]
+        conversion_opts = dict(Movie=dict(starting_times=starting_times, external_mode=False))
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=self.get_metadata(),
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            metadata = self.nwb_converter.get_metadata()
+            for no in range(len(metadata["Behavior"]["Movies"])):
+                movie_interface_name = metadata["Behavior"]["Movies"][no]["name"]
+                assert movie_interface_name in mod
+                if mod[movie_interface_name].starting_time is not None:
+                    assert starting_times[no] == mod[movie_interface_name].starting_time
+                else:
+                    assert starting_times[no] == mod[movie_interface_name].timestamps[0]
+
+    def test_movie_starting_times_none(self):
+        """For multiple ImageSeries containers, starting times must be provided with len(movie_files)"""
+        conversion_opts = dict(Movie=dict(external_mode=False))
+        with self.assertRaises(ValueError):
+            self.nwb_converter.run_conversion(
+                nwbfile_path=self.nwbfile_path,
+                overwrite=True,
+                conversion_options=conversion_opts,
+                metadata=self.get_metadata(),
+            )
+
+    def test_movie_starting_times_none_duplicate(self):
+        """When all movies go in one ImageSeries container, starting times should be assumed 0.0"""
+        conversion_opts = dict(Movie=dict(external_mode=True))
+        metadata = self.get_metadata()
+        movie_interface_name = metadata["Behavior"]["Movies"][0]["name"]
+        for no in range(1, len(self.movie_files)):
+            metadata["Behavior"]["Movies"][no]["name"] = movie_interface_name
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=metadata,
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            assert movie_interface_name in mod
+            assert mod[movie_interface_name].starting_time == 0.0
+
+    def test_movie_custom_module(self):
+        starting_times = [np.float(np.random.randint(200)) for i in range(len(self.movie_files))]
+        module_name = "TestModule"
+        module_description = "This is a test module."
+        conversion_opts = dict(
+            Movie=dict(
+                starting_times=starting_times,
+                external_mode=False,
+                module_name=module_name,
+                module_description=module_description,
+            )
+        )
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=self.get_metadata(),
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            assert module_name in nwbfile.processing
+            assert module_description == nwbfile.processing[module_name].description
+
+    def test_movie_chunking(self):
+        starting_times = [np.float(np.random.randint(200)) for i in range(len(self.movie_files))]
+        conv_ops = dict(
+            Movie=dict(external_mode=False, stub_test=True, starting_times=starting_times, chunk_data=False)
+        )
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path, overwrite=True, conversion_options=conv_ops, metadata=self.get_metadata()
+        )
+
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            metadata = self.nwb_converter.get_metadata()
+            for no in range(len(metadata["Behavior"]["Movies"])):
+                movie_interface_name = metadata["Behavior"]["Movies"][no]["name"]
+                assert mod[movie_interface_name].data.chunks is not None  # TODO retrive storage_layout of hdf5 dataset
+
+    def test_movie_external_mode(self):
+        starting_times = [np.float(np.random.randint(200)) for i in range(len(self.movie_files))]
+        conversion_opts = dict(Movie=dict(starting_times=starting_times, external_mode=True))
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=self.get_metadata(),
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            metadata = self.nwb_converter.get_metadata()
+            for no in range(len(metadata["Behavior"]["Movies"])):
+                movie_interface_name = metadata["Behavior"]["Movies"][no]["name"]
+                assert mod[movie_interface_name].external_file[0] == str(self.movie_files[no])
+
+    def test_movie_duplicate_kwargs_external(self):
+        conversion_opts = dict(Movie=dict(external_mode=True))
+        metadata = self.get_metadata()
+        movie_interface_name = metadata["Behavior"]["Movies"][0]["name"]
+        for no in range(1, len(self.movie_files)):
+            metadata["Behavior"]["Movies"][no]["name"] = movie_interface_name
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=metadata,
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            assert len(mod) == 1
+            assert movie_interface_name in mod
+            assert len(mod[movie_interface_name].external_file) == len(self.movie_files)
+
+    def test_movie_duplicate_kwargs(self):
+        conversion_opts = dict(Movie=dict(external_mode=False))
+        metadata = self.get_metadata()
+        movie_interface_name = metadata["Behavior"]["Movies"][0]["name"]
+        metadata["Behavior"]["Movies"][1]["name"] = movie_interface_name
+        with self.assertRaises(AssertionError):
+            self.nwb_converter.run_conversion(
+                nwbfile_path=self.nwbfile_path,
+                overwrite=True,
+                conversion_options=conversion_opts,
+                metadata=metadata,
+            )
+
+    def test_movie_stub(self):
+        starting_times = [np.float(np.random.randint(200)) for i in range(len(self.movie_files))]
+        conversion_opts = dict(Movie=dict(starting_times=starting_times, external_mode=False, stub_test=True))
+        self.nwb_converter.run_conversion(
+            nwbfile_path=self.nwbfile_path,
+            overwrite=True,
+            conversion_options=conversion_opts,
+            metadata=self.get_metadata(),
+        )
+        with NWBHDF5IO(path=self.nwbfile_path, mode="r") as io:
+            nwbfile = io.read()
+            mod = nwbfile.acquisition
+            metadata = self.nwb_converter.get_metadata()
+            for no in range(len(metadata["Behavior"]["Movies"])):
+                movie_interface_name = metadata["Behavior"]["Movies"][no]["name"]
+                assert mod[movie_interface_name].data.shape[0] == 10
+
+
+if __name__ == "__main__":
+    unittest.main()