Update tests to fix validation errors and warning (#1839)

.readthedocs.yaml

@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-20.04
   tools:
-    python: '3.8'
+    python: '3.11'
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

CHANGELOG.md

@@ -11,6 +11,7 @@
 - Expose `starting_time` in `mock_ElectricalSeries`. @h-mayorquin [#1805](https://github.com/NeurodataWithoutBorders/pynwb/pull/1805)
 - Enhance `get_data_in_units()` to work with objects that have a `channel_conversion` attribute like the `ElectricalSeries`. @h-mayorquin [#1806](https://github.com/NeurodataWithoutBorders/pynwb/pull/1806)
 - Refactor validation CLI tests to use `{sys.executable} -m coverage` to use the same Python version and run correctly on Debian systems. @yarikoptic [#1811](https://github.com/NeurodataWithoutBorders/pynwb/pull/1811)
+- Fixed tests to address newly caught validation errors. @rly [#1839](https://github.com/NeurodataWithoutBorders/pynwb/pull/1839)
 
 ### Bug fixes
 - Fix bug where namespaces were loaded in "w-" mode. @h-mayorquin [#1795](https://github.com/NeurodataWithoutBorders/pynwb/pull/1795)

docs/source/overview_citing.rst

@@ -35,7 +35,7 @@ If you use PyNWB in your research, please use the following citation:
 Using RRID
 ----------
 
-* ResourceID: `SCR_017452 <https://scicrunch.org/browse/resources/SCR_017452>`_
+* ResourceID: `SCR_017452 <https://scicrunch.org/resolver/SCR_017452>`_
 * Proper Citation: **(PyNWB, RRID:SCR_017452)**
 
 

requirements-min.txt

@@ -1,6 +1,6 @@
 # minimum versions of package dependencies for installing PyNWB
 h5py==2.10  # support for selection of datasets with list of indices added in 2.10
-hdmf==3.9.0
+hdmf==3.12.0
 numpy==1.18
 pandas==1.1.5
 python-dateutil==2.7.3

requirements.txt

@@ -1,7 +1,7 @@
 # pinned dependencies to reproduce an entire development environment to use PyNWB
-h5py==3.8.0
-hdmf==3.9.0
-numpy==1.24.2
-pandas==2.0.0
+h5py==3.10.0
+hdmf==3.12.0
+numpy==1.26.1
+pandas==2.1.2
 python-dateutil==2.8.2
-setuptools==65.5.1
+setuptools==65.5.1

setup.py

@@ -20,7 +20,7 @@
 
 reqs = [
     'h5py>=2.10',
-    'hdmf>=3.9.0',
+    'hdmf>=3.12.0',
     'numpy>=1.16',
     'pandas>=1.1.5',
     'python-dateutil>=2.7.3',

src/pynwb/validate.py

@@ -120,7 +120,11 @@ def _get_cached_namespaces_to_validate(
     is_method=False,
 )
 def validate(**kwargs):
-    """Validate NWB file(s) against a namespace or its cached namespaces."""
+    """Validate NWB file(s) against a namespace or its cached namespaces.
+
+    NOTE: If an io object is provided and no namespace name is specified, then the file will be validated
+    against the core namespace, even if use_cached_namespaces is True.
+    """
     from . import NWBHDF5IO  # TODO: modularize to avoid circular import
 
     io, paths, use_cached_namespaces, namespace, verbose, driver = getargs(

tests/back_compat/test_read.py

@@ -29,6 +29,12 @@ class TestReadOldVersions(TestCase):
                                "- expected an array of shape '[None]', got non-array data 'one publication'")],
         '1.0.3_str_pub.nwb': [("root/general/related_publications (general/related_publications): incorrect shape "
                                "- expected an array of shape '[None]', got non-array data 'one publication'")],
+        '1.5.1_timeseries_no_data.nwb': [("TimeSeries/data/data (acquisition/test_timeseries/data): argument missing")],
+        '1.5.1_timeseries_no_unit.nwb': [("TimeSeries/data/unit (acquisition/test_timeseries/data): argument missing")],
+        '1.5.1_imageseries_no_data.nwb': [("ImageSeries/data/data (acquisition/test_imageseries/data): "
+                                           "argument missing")],
+        '1.5.1_imageseries_no_unit.nwb': [("ImageSeries/data/unit (acquisition/test_imageseries/data): "
+                                           "argument missing")],
     }
 
     def get_io(self, path):

tests/integration/hdf5/test_misc.py

@@ -109,20 +109,36 @@ class TestDecompositionSeriesIO(NWBH5IOMixin, TestCase):
 
     def setUpContainer(self):
         """ Return the test DecompositionSeries to read/write """
-        self.timeseries = TimeSeries(name='dummy timeseries', description='desc',
-                                     data=np.ones((3, 3)), unit='flibs',
-                                     timestamps=np.ones((3,)))
-        bands = DynamicTable(name='bands', description='band info for LFPSpectralAnalysis', columns=[
-            VectorData(name='band_name', description='name of bands', data=['alpha', 'beta', 'gamma']),
-            VectorData(name='band_limits', description='low and high cutoffs in Hz', data=np.ones((3, 2)))
-        ])
-        spec_anal = DecompositionSeries(name='LFPSpectralAnalysis',
-                                        description='my description',
-                                        data=np.ones((3, 3, 3)),
-                                        timestamps=np.ones((3,)),
-                                        source_timeseries=self.timeseries,
-                                        metric='amplitude',
-                                        bands=bands)
+        self.timeseries = TimeSeries(
+            name='dummy timeseries',
+            description='desc',
+            data=np.ones((3, 3)),
+            unit='flibs',
+            timestamps=np.ones((3,)),
+        )
+        bands = DynamicTable(
+            name='bands',
+            description='band info for LFPSpectralAnalysis',
+            columns=[
+                VectorData(name='band_name', description='name of bands', data=['alpha', 'beta', 'gamma']),
+                VectorData(name='band_limits', description='low and high cutoffs in Hz', data=np.ones((3, 2))),
+                VectorData(name='band_mean', description='mean gaussian filters in Hz', data=np.ones((3,))),
+                VectorData(
+                    name='band_stdev',
+                    description='standard deviation of gaussian filters in Hz',
+                    data=np.ones((3,))
+                ),
+            ],
+        )
+        spec_anal = DecompositionSeries(
+            name='LFPSpectralAnalysis',
+            description='my description',
+            data=np.ones((3, 3, 3)),
+            timestamps=np.ones((3,)),
+            source_timeseries=self.timeseries,
+            metric='amplitude',
+            bands=bands,
+        )
 
         return spec_anal
 
@@ -144,27 +160,48 @@ def make_electrode_table(self):
         """ Make an electrode table, electrode group, and device """
         self.table = get_electrode_table()
         self.dev1 = Device(name='dev1')
-        self.group = ElectrodeGroup(name='tetrode1',
-                                    description='tetrode description',
-                                    location='tetrode location',
-                                    device=self.dev1)
-        for i in range(4):
+        self.group = ElectrodeGroup(
+            name='tetrode1',
+            description='tetrode description',
+            location='tetrode location',
+            device=self.dev1
+        )
+        for _ in range(4):
             self.table.add_row(location='CA1', group=self.group, group_name='tetrode1')
 
     def setUpContainer(self):
         """ Return the test ElectricalSeries to read/write """
         self.make_electrode_table(self)
-        region = DynamicTableRegion(name='source_channels',
-                                    data=[0, 2],
-                                    description='the first and third electrodes',
-                                    table=self.table)
+        region = DynamicTableRegion(
+            name='source_channels',
+            data=[0, 2],
+            description='the first and third electrodes',
+            table=self.table
+        )
         data = np.random.randn(100, 2, 30)
         timestamps = np.arange(100)/100
-        ds = DecompositionSeries(name='test_DS',
-                                 data=data,
-                                 source_channels=region,
-                                 timestamps=timestamps,
-                                 metric='amplitude')
+        bands = DynamicTable(
+            name='bands',
+            description='band info for LFPSpectralAnalysis',
+            columns=[
+                VectorData(name='band_name', description='name of bands', data=['alpha', 'beta', 'gamma']),
+                VectorData(name='band_limits', description='low and high cutoffs in Hz', data=np.ones((3, 2))),
+                VectorData(name='band_mean', description='mean gaussian filters in Hz', data=np.ones((3,))),
+                VectorData(
+                    name='band_stdev',
+                    description='standard deviation of gaussian filters in Hz',
+                    data=np.ones((3,))
+                ),
+            ],
+        )
+        ds = DecompositionSeries(
+            name='test_DS',
+            data=data,
+            source_channels=region,
+            timestamps=timestamps,
+            metric='amplitude',
+            bands=bands,
+        )
         return ds
 
     def addContainer(self, nwbfile):

tests/integration/ros3/test_ros3.py

@@ -4,6 +4,7 @@
 from pynwb.testing import TestCase
 import urllib.request
 import h5py
+import warnings
 
 
 class TestRos3Streaming(TestCase):
@@ -28,16 +29,28 @@ def setUp(self):
 
     def test_read(self):
         s3_path = 'https://dandiarchive.s3.amazonaws.com/ros3test.nwb'
-        with NWBHDF5IO(s3_path, mode='r', driver='ros3') as io:
-            nwbfile = io.read()
-            test_data = nwbfile.acquisition['ts_name'].data[:]
-            self.assertEqual(len(test_data), 3)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message=r"Ignoring cached namespace .*",
+                category=UserWarning,
+            )
+            with NWBHDF5IO(s3_path, mode='r', driver='ros3') as io:
+                nwbfile = io.read()
+                test_data = nwbfile.acquisition['ts_name'].data[:]
+                self.assertEqual(len(test_data), 3)
 
     def test_dandi_read(self):
-        with NWBHDF5IO(path=self.s3_test_path, mode='r', driver='ros3') as io:
-            nwbfile = io.read()
-            test_data = nwbfile.acquisition['TestData'].data[:]
-            self.assertEqual(len(test_data), 3)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message=r"Ignoring cached namespace .*",
+                category=UserWarning,
+            )
+            with NWBHDF5IO(path=self.s3_test_path, mode='r', driver='ros3') as io:
+                nwbfile = io.read()
+                test_data = nwbfile.acquisition['TestData'].data[:]
+                self.assertEqual(len(test_data), 3)
 
     def test_dandi_get_cached_namespaces(self):
         expected_namespaces = ["core"]

tests/unit/test_misc.py

@@ -33,7 +33,13 @@ def test_init(self):
                                 timestamps=[1., 2., 3.])
         bands = DynamicTable(name='bands', description='band info for LFPSpectralAnalysis', columns=[
             VectorData(name='band_name', description='name of bands', data=['alpha', 'beta', 'gamma']),
-            VectorData(name='band_limits', description='low and high cutoffs in Hz', data=np.ones((3, 2)))
+            VectorData(name='band_limits', description='low and high cutoffs in Hz', data=np.ones((3, 2))),
+            VectorData(name='band_mean', description='mean gaussian filters in Hz', data=np.ones((3,))),
+            VectorData(
+                name='band_stdev',
+                description='standard deviation of gaussian filters in Hz',
+                data=np.ones((3,))
+            ),
         ])
         spec_anal = DecompositionSeries(name='LFPSpectralAnalysis',
                                         description='my description',
@@ -49,6 +55,8 @@ def test_init(self):
         np.testing.assert_equal(spec_anal.timestamps, [1., 2., 3.])
         self.assertEqual(spec_anal.bands['band_name'].data, ['alpha', 'beta', 'gamma'])
         np.testing.assert_equal(spec_anal.bands['band_limits'].data, np.ones((3, 2)))
+        np.testing.assert_equal(spec_anal.bands['band_mean'].data, np.ones((3,)))
+        np.testing.assert_equal(spec_anal.bands['band_stdev'].data, np.ones((3,)))
         self.assertEqual(spec_anal.source_timeseries, timeseries)
         self.assertEqual(spec_anal.metric, 'amplitude')