Stimuli can be filtered by boolean masks

.github/workflows/test-package-conda.yml

@@ -61,7 +61,7 @@ jobs:
       run: |
         conda install pytest hypothesis
         python setup.py build_ext --inplace
-        python -m pytest --nomatlab --notheano tests
+        python -m pytest --nomatlab --notheano --nodownload tests
     - name: test build and install
       shell: bash -el {0}
       run: |

pysaliency/datasets.py

@@ -1170,7 +1170,13 @@ def __getitem__(self, index):
         if isinstance(index, slice):
             attributes = self._get_attribute_for_stimulus_subset(index)
             return ObjectStimuli([self.stimulus_objects[i] for i in range(len(self))[index]], attributes=attributes)
-        elif isinstance(index, list):
+        elif isinstance(index, (list, np.ndarray)):
+            index = np.asarray(index)
+            if index.dtype == bool:
+                if not len(index) == len(self.stimuli):
+                    raise ValueError(f"Boolean index has to have the same length as the stimuli list but got {len(index)} and {len(self.stimuli)}")
+                index = np.nonzero(index)[0]
+
             attributes = self._get_attribute_for_stimulus_subset(index)
             return ObjectStimuli([self.stimulus_objects[i] for i in index], attributes=attributes)
         else:
@@ -1345,6 +1351,12 @@ def __getitem__(self, index):
             index = list(range(len(self)))[index]
 
         if isinstance(index, (list, np.ndarray)):
+            index = np.asarray(index)
+            if index.dtype == bool:
+                if not len(index) == len(self.stimuli):
+                    raise ValueError(f"Boolean index has to have the same length as the stimuli list but got {len(index)} and {len(self.stimuli)}")
+                index = np.nonzero(index)[0]
+
             filenames = [self.filenames[i] for i in index]
             shapes = [self.shapes[i] for i in index]
             attributes = self._get_attribute_for_stimulus_subset(index)

tests/conftest.py

@@ -14,17 +14,20 @@ def pytest_addoption(parser):
     parser.addoption("--nomatlab", action="store_true", default=False, help="don't run matlab tests")
     parser.addoption("--nooctave", action="store_true", default=False, help="don't run octave tests")
     parser.addoption("--notheano", action="store_true", default=False, help="don't run slow theano tests")
+    parser.addoption("--nodownload", action="store_true", default=False, help="don't download external data")
 
 
 def pytest_collection_modifyitems(config, items):
     run_slow = config.getoption("--runslow")
     run_nonfree = config.getoption('--run-nonfree')
     no_matlab = config.getoption("--nomatlab")
     no_theano = config.getoption("--notheano")
+    no_download = config.getoption("--nodownload")
     skip_slow = pytest.mark.skip(reason="need --runslow option to run")
     skip_nonfree = pytest.mark.skip(reason="need --run-nonfree option to run")
     skip_matlab = pytest.mark.skip(reason="skipped because of --nomatlab")
     skip_theano = pytest.mark.skip(reason="skipped because of --notheano")
+    skip_download = pytest.mark.skip(reason="skipped because of --nodownload")
     for item in items:
         if "slow" in item.keywords and not run_slow:
             item.add_marker(skip_slow)
@@ -34,6 +37,8 @@ def pytest_collection_modifyitems(config, items):
             item.add_marker(skip_matlab)
         if "theano" in item.keywords and no_theano:
             item.add_marker(skip_theano)
+        if "download" in item.keywords and no_download:
+            item.add_marker(skip_download)
 
 
 @pytest.fixture(params=["matlab", "octave"])

tests/external_models/test_deepgaze.py

@@ -33,14 +33,15 @@ def fixations():
     )
 
 
+@pytest.mark.download
 def test_deepgaze1(stimuli, fixations):
     model = DeepGazeI(centerbias_model=pysaliency.UniformModel(), device='cpu')
 
     ig = model.information_gain(stimuli, fixations)
 
     np.testing.assert_allclose(ig, 0.9455161648442227, rtol=5e-6)
 
-
+@pytest.mark.download
 def test_deepgaze2e(stimuli, fixations):
     model = DeepGazeIIE(centerbias_model=pysaliency.UniformModel(), device='cpu')
 

tests/test_datasets.py

@@ -566,6 +566,17 @@ def test_stimuli_attributes(stimuli_with_attributes, tmp_path):
     assert list(np.array(stimuli_with_attributes.attributes['dva'])[[1, 2, 6]]) == partial_stimuli.attributes['dva']
     assert list(np.array(stimuli_with_attributes.attributes['some_strings'])[[1, 2, 6]]) == partial_stimuli.attributes['some_strings']
 
+    mask = np.array([True, False, True, False, True, False, True, False, True, False, True, False])
+    with pytest.raises(ValueError):
+        partial_stimuli = stimuli_with_attributes[mask]
+
+    mask = np.array([True, False, True, False, True, False, True, False, True, False])
+    partial_stimuli = stimuli_with_attributes[mask]
+    assert stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys()
+    assert list(np.array(stimuli_with_attributes.attributes['dva'])[mask]) == partial_stimuli.attributes['dva']
+    assert list(np.array(stimuli_with_attributes.attributes['some_strings'])[mask]) == partial_stimuli.attributes['some_strings']
+
+
 
 @pytest.fixture
 def file_stimuli_with_attributes(tmpdir):
@@ -611,6 +622,17 @@ def test_file_stimuli_attributes(file_stimuli_with_attributes, tmp_path):
     assert list(np.array(file_stimuli_with_attributes.attributes['dva'])[[1, 2, 6]]) == partial_stimuli.attributes['dva']
     assert list(np.array(file_stimuli_with_attributes.attributes['some_strings'])[[1, 2, 6]]) == partial_stimuli.attributes['some_strings']
 
+    mask = np.array([True, False, True, False, True, False, True, False, True, False])
+    with pytest.raises(ValueError):
+        partial_stimuli = file_stimuli_with_attributes[mask]
+
+    mask = np.array([True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False])
+    partial_stimuli = file_stimuli_with_attributes[mask]
+
+    assert file_stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys()
+    assert list(np.array(file_stimuli_with_attributes.attributes['dva'])[mask]) == partial_stimuli.attributes['dva']
+    assert list(np.array(file_stimuli_with_attributes.attributes['some_strings'])[mask]) == partial_stimuli.attributes['some_strings']
+
 
 def test_concatenate_stimuli_with_attributes(stimuli_with_attributes, file_stimuli_with_attributes):
     concatenated_stimuli = pysaliency.datasets.concatenate_stimuli([stimuli_with_attributes, file_stimuli_with_attributes])