Bugfix: MIT1003 test failed since dataset refactoring

Signed-off-by: Matthias Kümmerer <[email protected]>

tests/external_datasets/test_DUT_OMRON.py

@@ -0,0 +1,54 @@
+import numpy as np
+import pytest
+from pathlib import Path
+from pytest import approx
+from scipy.stats import kurtosis, skew
+
+import pysaliency
+import pysaliency.external_datasets
+from pysaliency.utils import remove_trailing_nans
+
+from tests.test_external_datasets import _location, entropy
+
+@pytest.mark.slow
+@pytest.mark.download
+def test_DUT_OMRON(location, tmpdir):
+    real_location = _location(location)
+
+    stimuli, fixations = pysaliency.external_datasets.get_DUT_OMRON(location=real_location)
+    if location is None:
+        assert isinstance(stimuli, pysaliency.Stimuli)
+        assert not isinstance(stimuli, pysaliency.FileStimuli)
+    else:
+        assert isinstance(stimuli, pysaliency.FileStimuli)
+        assert location.join('DUT-OMRON/stimuli.hdf5').check()
+        assert location.join('DUT-OMRON/fixations.hdf5').check()
+
+    assert len(stimuli.stimuli) == 5168
+
+    assert len(fixations.x) == 797542
+
+    assert np.mean(fixations.x) == approx(182.16198519952553)
+    assert np.mean(fixations.y) == approx(147.622566585835)
+    assert np.mean(fixations.t) == approx(21.965026293286122)
+    assert np.mean(fixations.scanpath_history_length) == approx(21.965026293286122)
+
+    assert np.std(fixations.x) == approx(64.01040053828082)
+    assert np.std(fixations.y) == approx(58.292098903584176)
+    assert np.std(fixations.t) == approx(17.469479262739807)
+    assert np.std(fixations.scanpath_history_length) == approx(17.469479262739807)
+
+    assert kurtosis(fixations.x) == approx(-0.0689271960358524)
+    assert kurtosis(fixations.y) == approx(0.637871926687533)
+    assert kurtosis(fixations.t) == approx(2.914601085582113)
+    assert kurtosis(fixations.scanpath_history_length) == approx(2.914601085582113)
+
+    assert skew(fixations.x) == approx(0.23776167825897998)
+    assert skew(fixations.y) == approx(0.6328497077003701)
+    assert skew(fixations.t) == approx(1.2911168563657345)
+    assert skew(fixations.scanpath_history_length) == approx(1.2911168563657345)
+
+    assert entropy(fixations.n) == approx(12.20642017670851)
+    assert (fixations.n == 0).sum() == 209
+
+    assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations))

tests/external_datasets/test_MIT.py

@@ -0,0 +1,95 @@
+import numpy as np
+import pytest
+from pathlib import Path
+from pytest import approx
+from scipy.stats import kurtosis, skew
+
+import pysaliency
+import pysaliency.external_datasets
+from pysaliency.utils import remove_trailing_nans
+
+
+from tests.test_external_datasets import _location, entropy
+
+
+@pytest.mark.slow
+@pytest.mark.download
+@pytest.mark.skip_octave
+@pytest.mark.matlab
+def test_mit1003(location, matlab):
+    real_location = _location(location)
+
+    stimuli, fixations = pysaliency.external_datasets.get_mit1003(location=real_location)
+
+    if location is None:
+        assert isinstance(stimuli, pysaliency.Stimuli)
+        assert not isinstance(stimuli, pysaliency.FileStimuli)
+    else:
+        assert isinstance(stimuli, pysaliency.FileStimuli)
+        assert location.join('MIT1003/stimuli.hdf5').check()
+        assert location.join('MIT1003/fixations.hdf5').check()
+
+    assert len(stimuli.stimuli) == 1003
+    for n in range(len(stimuli.stimuli)):
+        assert max(stimuli.sizes[n]) == 1024
+
+    assert len(fixations.x) == 104171
+
+    assert np.mean(fixations.x) == approx(487.13683496521253)
+    assert np.mean(fixations.y) == approx(392.72728829760155)
+    assert np.mean(fixations.t) == approx(1.5039892740461995)
+    assert np.mean(fixations.scanpath_history_length) == approx(3.3973754691804823)
+
+    assert np.std(fixations.x) == approx(190.0203102093757)
+    assert np.std(fixations.y) == approx(159.99210430350126)
+    assert np.std(fixations.t) == approx(0.816414737693668)
+    assert np.std(fixations.scanpath_history_length) == approx(2.5433689996843354)
+
+    assert kurtosis(fixations.x) == approx(-0.39272472247196033)
+    assert kurtosis(fixations.y) == approx(0.6983793465837596)
+    assert kurtosis(fixations.t) == approx(-1.2178525798721818)
+    assert kurtosis(fixations.scanpath_history_length) == approx(-0.45897225172578704)
+
+    assert skew(fixations.x) == approx(0.2204976032609953)
+    assert skew(fixations.y) == approx(0.6445191904777621)
+    assert skew(fixations.t) == approx(0.08125182887100482)
+    assert skew(fixations.scanpath_history_length) == approx(0.5047182860999948)
+
+    assert entropy(fixations.n) == approx(9.954348058662386)
+    assert (fixations.n == 0).sum() == 121
+
+    assert 'duration_hist' in fixations.__attributes__
+    assert 'duration' in fixations.__attributes__
+    assert len(fixations.duration_hist) == len(fixations.x)
+    assert len(fixations.duration) == len(fixations.x)
+    for i in range(len(fixations.x)):
+        assert len(remove_trailing_nans(fixations.duration_hist[i])) == len(remove_trailing_nans(fixations.x_hist[i]))
+
+
+    assert 'durations' in fixations.scanpaths.fixation_attributes
+
+    assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations))
+
+@pytest.mark.slow
+@pytest.mark.download
+@pytest.mark.skip_octave
+@pytest.mark.matlab
+def test_mit1003_onesize(location, matlab):
+    real_location = _location(location)
+
+    stimuli, fixations = pysaliency.external_datasets.get_mit1003_onesize(location=real_location)
+
+    if location is None:
+        assert isinstance(stimuli, pysaliency.Stimuli)
+        assert not isinstance(stimuli, pysaliency.FileStimuli)
+    else:
+        assert isinstance(stimuli, pysaliency.FileStimuli)
+        assert location.join('MIT1003_onesize/stimuli.hdf5').check()
+        assert location.join('MIT1003_onesize/fixations.hdf5').check()
+
+    assert len(stimuli.stimuli) == 463
+    for n in range(len(stimuli.stimuli)):
+        assert stimuli.sizes[n] == (768, 1024)
+
+    assert len(fixations.x) == 48771
+    assert (fixations.n == 0).sum() == 121

tests/test_external_datasets.py

@@ -199,91 +199,6 @@ def test_cat2000_test(location):
     assert np.all(np.array(stimuli.attributes['category'][100:200]) == 1)
 
 
-@pytest.mark.slow
-@pytest.mark.download
-@pytest.mark.skip_octave
-@pytest.mark.matlab
-def test_mit1003(location, matlab):
-    real_location = _location(location)
-
-    stimuli, fixations = pysaliency.external_datasets.get_mit1003(location=real_location)
-
-    if location is None:
-        assert isinstance(stimuli, pysaliency.Stimuli)
-        assert not isinstance(stimuli, pysaliency.FileStimuli)
-    else:
-        assert isinstance(stimuli, pysaliency.FileStimuli)
-        assert location.join('MIT1003/stimuli.hdf5').check()
-        assert location.join('MIT1003/fixations.hdf5').check()
-
-    assert len(stimuli.stimuli) == 1003
-    for n in range(len(stimuli.stimuli)):
-        assert max(stimuli.sizes[n]) == 1024
-
-    assert len(fixations.x) == 104171
-
-    assert np.mean(fixations.x) == approx(487.13683496521253)
-    assert np.mean(fixations.y) == approx(392.72728829760155)
-    assert np.mean(fixations.t) == approx(1.5039892740461995)
-    assert np.mean(fixations.scanpath_history_length) == approx(3.3973754691804823)
-
-    assert np.std(fixations.x) == approx(190.0203102093757)
-    assert np.std(fixations.y) == approx(159.99210430350126)
-    assert np.std(fixations.t) == approx(0.816414737693668)
-    assert np.std(fixations.scanpath_history_length) == approx(2.5433689996843354)
-
-    assert kurtosis(fixations.x) == approx(-0.39272472247196033)
-    assert kurtosis(fixations.y) == approx(0.6983793465837596)
-    assert kurtosis(fixations.t) == approx(-1.2178525798721818)
-    assert kurtosis(fixations.scanpath_history_length) == approx(-0.45897225172578704)
-
-    assert skew(fixations.x) == approx(0.2204976032609953)
-    assert skew(fixations.y) == approx(0.6445191904777621)
-    assert skew(fixations.t) == approx(0.08125182887100482)
-    assert skew(fixations.scanpath_history_length) == approx(0.5047182860999948)
-
-    assert entropy(fixations.n) == approx(9.954348058662386)
-    assert (fixations.n == 0).sum() == 121
-
-    assert 'duration_hist' in fixations.__attributes__
-    assert 'duration' in fixations.__attributes__
-    assert len(fixations.duration_hist) == len(fixations.x)
-    assert len(fixations.duration) == len(fixations.x)
-    for i in range(len(fixations.x)):
-        assert len(remove_trailing_nans(fixations.duration_hist[i])) == len(remove_trailing_nans(fixations.x_hist[i]))
-
-    assert 'durations' in fixations.scanpath_fixation_attributes
-    assert len(fixations.scanpath_fixation_attributes['durations']) == len(fixations.train_xs)
-    for i in range(len(fixations.train_xs)):
-        assert len(remove_trailing_nans(fixations.scanpath_fixation_attributes['durations'][i])) == len(remove_trailing_nans(fixations.train_xs[i]))
-
-    assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations))
-
-@pytest.mark.slow
-@pytest.mark.download
-@pytest.mark.skip_octave
-@pytest.mark.matlab
-def test_mit1003_onesize(location, matlab):
-    real_location = _location(location)
-
-    stimuli, fixations = pysaliency.external_datasets.get_mit1003_onesize(location=real_location)
-
-    if location is None:
-        assert isinstance(stimuli, pysaliency.Stimuli)
-        assert not isinstance(stimuli, pysaliency.FileStimuli)
-    else:
-        assert isinstance(stimuli, pysaliency.FileStimuli)
-        assert location.join('MIT1003_onesize/stimuli.hdf5').check()
-        assert location.join('MIT1003_onesize/fixations.hdf5').check()
-
-    assert len(stimuli.stimuli) == 463
-    for n in range(len(stimuli.stimuli)):
-        assert stimuli.sizes[n] == (768, 1024)
-
-    assert len(fixations.x) == 48771
-    assert (fixations.n == 0).sum() == 121
-
-
 @pytest.mark.slow
 @pytest.mark.nonfree
 @pytest.mark.skip_octave