def test_get_stimulus_templates(behavior_stimuli_data_fixture, remove_stimuli,
expected_templates):
for stimuli in remove_stimuli:
del (behavior_stimuli_data_fixture['items']['behavior']['stimuli']
[stimuli])
templates = get_stimulus_templates(behavior_stimuli_data_fixture)
assert templates == expected_templates
def test_get_stimulus_templates(behavior_stimuli_data_fixture):
templates = get_stimulus_templates(behavior_stimuli_data_fixture,
grating_images_dict={})
assert templates.image_set_name == 'test_image_set'
assert len(templates) == 1
assert list(templates.keys()) == ['im065']
for img in templates.values():
assert isinstance(img, StimulusImage)
expected_path = os.path.join(get_resources_dir(), 'stimulus_template',
'expected')
expected_unwarped_path = os.path.join(
expected_path, 'im065_unwarped.pkl')
expected_unwarped = pd.read_pickle(expected_unwarped_path)
expected_warped_path = os.path.join(
expected_path, 'im065_warped.pkl')
expected_warped = pd.read_pickle(expected_warped_path)
for img_name in templates:
img = templates[img_name]
assert np.allclose(a=expected_unwarped,
b=img.unwarped, equal_nan=True)
assert np.allclose(a=expected_warped,
b=img.warped, equal_nan=True)
for img_name, img in templates.items():
img = templates[img_name]
assert np.allclose(a=expected_unwarped,
b=img.unwarped, equal_nan=True)
assert np.allclose(a=expected_warped,
b=img.warped, equal_nan=True)
def test_get_stimulus_templates_for_gratings(behavior_stimuli_data_fixture,
grating_images_dict, expected):
templates = get_stimulus_templates(behavior_stimuli_data_fixture,
grating_images_dict=grating_images_dict)
assert templates.image_set_name == 'grating'
assert list(templates.keys()) == ['gratings_90.0']
assert np.allclose(templates['gratings_90.0'].warped,
np.array([[1, 1], [1, 1]]))
assert np.allclose(templates['gratings_90.0'].unwarped,
np.array([[2, 2], [2, 2]]))
def get_stimulus_templates(self) -> Dict[str, np.ndarray]:
"""Get stimulus templates (movies, scenes) for behavior session.
Returns
-------
Dict[str, np.ndarray]
A dictionary containing the stimulus images presented during the
session. Keys are data set names, and values are 3D numpy arrays.
"""
data = self._behavior_stimulus_file()
return get_stimulus_templates(data)
def from_stimulus_file(
cls, stimulus_file: StimulusFile,
limit_to_images: Optional[List] = None) -> "Templates":
"""Get stimulus templates (movies, scenes) for behavior session."""
# TODO: Eventually the `grating_images_dict` should be provided by the
# BehaviorLimsExtractor/BehaviorJsonExtractor classes.
# - NJM 2021/2/23
gratings_dir = "/allen/programs/braintv/production/visualbehavior"
gratings_dir = os.path.join(gratings_dir,
"prod5/project_VisualBehavior")
grating_images_dict = {
"gratings_0.0": {
"warped": np.asarray(imageio.imread(
safe_system_path(os.path.join(gratings_dir,
"warped_grating_0.png")))),
"unwarped": np.asarray(imageio.imread(
safe_system_path(os.path.join(
gratings_dir, "masked_unwarped_grating_0.png"))))
},
"gratings_90.0": {
"warped": np.asarray(imageio.imread(
safe_system_path(os.path.join(gratings_dir,
"warped_grating_90.png")))),
"unwarped": np.asarray(imageio.imread(
safe_system_path(os.path.join(
gratings_dir, "masked_unwarped_grating_90.png"))))
},
"gratings_180.0": {
"warped": np.asarray(imageio.imread(
safe_system_path(os.path.join(gratings_dir,
"warped_grating_180.png")))),
"unwarped": np.asarray(imageio.imread(
safe_system_path(os.path.join(
gratings_dir, "masked_unwarped_grating_180.png"))))
},
"gratings_270.0": {
"warped": np.asarray(imageio.imread(
safe_system_path(os.path.join(gratings_dir,
"warped_grating_270.png")))),
"unwarped": np.asarray(imageio.imread(
safe_system_path(os.path.join(
gratings_dir, "masked_unwarped_grating_270.png"))))
}
}
pkl = stimulus_file.data
t = get_stimulus_templates(pkl=pkl,
grating_images_dict=grating_images_dict,
limit_to_images=limit_to_images)
return Templates(templates=t)
def test_add_stimulus_templates(nwbfile, behavior_stimuli_data_fixture,
roundtrip, roundtripper):
stimulus_templates = get_stimulus_templates(behavior_stimuli_data_fixture,
grating_images_dict={})
nwb.add_stimulus_template(nwbfile, stimulus_templates)
if roundtrip:
obt = roundtripper(nwbfile, BehaviorNwbApi)
else:
obt = BehaviorNwbApi.from_nwbfile(nwbfile)
stimulus_templates_obt = obt.get_stimulus_templates()
assert stimulus_templates_obt == stimulus_templates
def get_stimulus_templates(self):
behavior_stimulus_file = self.get_behavior_stimulus_file()
data = pd.read_pickle(behavior_stimulus_file)
return get_stimulus_templates(data)
def get_stimulus_templates(self) -> Optional[StimulusTemplate]:
"""Get stimulus templates (movies, scenes) for behavior session.
Returns
-------
StimulusTemplate or None if there are no images for the experiment
"""
# TODO: Eventually the `grating_images_dict` should be provided by the
# BehaviorLimsExtractor/BehaviorJsonExtractor classes.
# - NJM 2021/2/23
gratings_dir = "/allen/programs/braintv/production/visualbehavior"
gratings_dir = os.path.join(gratings_dir,
"prod5/project_VisualBehavior")
grating_images_dict = {
"gratings_0.0": {
"warped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"warped_grating_0.png")))),
"unwarped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"masked_unwarped_grating_0.png"))))
},
"gratings_90.0": {
"warped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"warped_grating_90.png")))),
"unwarped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"masked_unwarped_grating_90.png"))))
},
"gratings_180.0": {
"warped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"warped_grating_180.png")))),
"unwarped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"masked_unwarped_grating_180.png"))))
},
"gratings_270.0": {
"warped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"warped_grating_270.png")))),
"unwarped":
np.asarray(
imageio.imread(
safe_system_path(
os.path.join(gratings_dir,
"masked_unwarped_grating_270.png"))))
}
}
pkl = self._behavior_stimulus_file()
return get_stimulus_templates(pkl=pkl,
grating_images_dict=grating_images_dict)
