def from_nwb(cls, nwbfile: NWBFile) -> "Presentations":
# Note: using NwbApi class because ecephys uses this method
# TODO figure out how behavior and ecephys can share this method
nwbapi = NwbApi.from_nwbfile(nwbfile=nwbfile)
df = nwbapi.get_stimulus_presentations()
df['is_change'] = is_change_event(stimulus_presentations=df)
df = cls._postprocess(presentations=df, fill_omitted_values=False)
return Presentations(presentations=df)
def test_is_change_mult_omission():
"""Test case for multiple omission"""
stimulus_presentations = pd.DataFrame({
'image_name': ['A', 'B', 'C', 'D'],
'omitted': [False, True, True, False]
})
obtained = is_change_event(stimulus_presentations=stimulus_presentations)
expected = pd.Series([False, False, False, True], name='is_change')
pd.testing.assert_series_equal(obtained, expected)
def test_is_change_event_no_change():
"""Test case for no change"""
stimulus_presentations = pd.DataFrame({
'image_name': ['A', 'A', 'A'],
'omitted': [False, False, False]
})
obtained = is_change_event(stimulus_presentations=stimulus_presentations)
expected = pd.Series([False, False, False], name='is_change')
pd.testing.assert_series_equal(obtained, expected)
def get_stimulus_presentations(self) -> pd.DataFrame:
"""Get stimulus presentation data.
NOTE: Uses timestamps that do not account for monitor delay.
:returns: pd.DataFrame --
Table whose rows are stimulus presentations
(i.e. a given image, for a given duration, typically 250 ms)
and whose columns are presentation characteristics.
"""
stimulus_timestamps = self.get_stimulus_timestamps()
data = self._behavior_stimulus_file()
raw_stim_pres_df = get_stimulus_presentations(data,
stimulus_timestamps)
# Fill in nulls for image_name
# This makes two assumptions:
# 1. Nulls in `image_name` should be "gratings_<orientation>"
# 2. Gratings are only present (or need to be fixed) when all
# values for `image_name` are null.
if pd.isnull(raw_stim_pres_df["image_name"]).all():
if ~pd.isnull(raw_stim_pres_df["orientation"]).all():
raw_stim_pres_df["image_name"] = (
raw_stim_pres_df["orientation"].apply(
lambda x: f"gratings_{x}"))
else:
raise ValueError("All values for 'orentation' and 'image_name'"
" are null.")
stimulus_metadata_df = get_stimulus_metadata(data)
idx_name = raw_stim_pres_df.index.name
stimulus_index_df = (raw_stim_pres_df.reset_index().merge(
stimulus_metadata_df.reset_index(),
on=["image_name"]).set_index(idx_name))
stimulus_index_df = (stimulus_index_df[[
"image_set", "image_index", "start_time", "phase",
"spatial_frequency"
]].rename(columns={
"start_time": "timestamps"
}).sort_index().set_index("timestamps", drop=True))
stim_pres_df = raw_stim_pres_df.merge(stimulus_index_df,
left_on="start_time",
right_index=True,
how="left")
if len(raw_stim_pres_df) != len(stim_pres_df):
raise ValueError("Length of `stim_pres_df` should not change after"
f" merge; was {len(raw_stim_pres_df)}, now "
f" {len(stim_pres_df)}.")
stim_pres_df['is_change'] = is_change_event(
stimulus_presentations=stim_pres_df)
# Sort columns then drop columns which contain only all NaN values
return stim_pres_df[sorted(stim_pres_df)].dropna(axis=1, how='all')
def from_stimulus_file(
cls,
stimulus_file: StimulusFile,
stimulus_timestamps: StimulusTimestamps,
limit_to_images: Optional[List] = None) -> "Presentations":
"""Get stimulus presentation data.
:param stimulus_file
:param limit_to_images
Only return images given by these image names
:param stimulus_timestamps
:returns: pd.DataFrame --
Table whose rows are stimulus presentations
(i.e. a given image, for a given duration, typically 250 ms)
and whose columns are presentation characteristics.
"""
stimulus_timestamps = stimulus_timestamps.value
data = stimulus_file.data
raw_stim_pres_df = get_stimulus_presentations(data,
stimulus_timestamps)
# Fill in nulls for image_name
# This makes two assumptions:
# 1. Nulls in `image_name` should be "gratings_<orientation>"
# 2. Gratings are only present (or need to be fixed) when all
# values for `image_name` are null.
if pd.isnull(raw_stim_pres_df["image_name"]).all():
if ~pd.isnull(raw_stim_pres_df["orientation"]).all():
raw_stim_pres_df["image_name"] = (
raw_stim_pres_df["orientation"].apply(
lambda x: f"gratings_{x}"))
else:
raise ValueError("All values for 'orentation' and 'image_name'"
" are null.")
stimulus_metadata_df = get_stimulus_metadata(data)
idx_name = raw_stim_pres_df.index.name
stimulus_index_df = (raw_stim_pres_df.reset_index().merge(
stimulus_metadata_df.reset_index(),
on=["image_name"]).set_index(idx_name))
stimulus_index_df = (stimulus_index_df[[
"image_set", "image_index", "start_time", "phase",
"spatial_frequency"
]].rename(columns={
"start_time": "timestamps"
}).sort_index().set_index("timestamps", drop=True))
stim_pres_df = raw_stim_pres_df.merge(stimulus_index_df,
left_on="start_time",
right_index=True,
how="left")
if len(raw_stim_pres_df) != len(stim_pres_df):
raise ValueError("Length of `stim_pres_df` should not change after"
f" merge; was {len(raw_stim_pres_df)}, now "
f" {len(stim_pres_df)}.")
stim_pres_df['is_change'] = is_change_event(
stimulus_presentations=stim_pres_df)
# Sort columns then drop columns which contain only all NaN values
stim_pres_df = \
stim_pres_df[sorted(stim_pres_df)].dropna(axis=1, how='all')
if limit_to_images is not None:
stim_pres_df = \
stim_pres_df[stim_pres_df['image_name'].isin(limit_to_images)]
stim_pres_df.index = pd.Int64Index(range(stim_pres_df.shape[0]),
name=stim_pres_df.index.name)
stim_pres_df = cls._postprocess(presentations=stim_pres_df)
return Presentations(presentations=stim_pres_df)
def get_stimulus_presentations(self) -> pd.DataFrame:
df = super().get_stimulus_presentations()
df['is_change'] = is_change_event(stimulus_presentations=df)
return df