def get_stimulus_presentations(self):
stimulus_timestamps = self.get_stimulus_timestamps()
behavior_stimulus_file = self.get_behavior_stimulus_file()
data = pd.read_pickle(behavior_stimulus_file)
stimulus_presentations_df_pre = get_stimulus_presentations(
data, stimulus_timestamps)
stimulus_metadata_df = get_stimulus_metadata(data)
idx_name = stimulus_presentations_df_pre.index.name
stimulus_index_df = stimulus_presentations_df_pre.reset_index().merge(
stimulus_metadata_df.reset_index(),
on=['image_name']).set_index(idx_name)
stimulus_index_df.sort_index(inplace=True)
stimulus_index_df = stimulus_index_df[[
'image_set', 'image_index', 'start_time'
]].rename(columns={'start_time': 'timestamps'})
stimulus_index_df.set_index('timestamps', inplace=True, drop=True)
stimulus_presentations_df = stimulus_presentations_df_pre.merge(
stimulus_index_df,
left_on='start_time',
right_index=True,
how='left')
assert len(stimulus_presentations_df_pre) == len(
stimulus_presentations_df)
return stimulus_presentations_df[sorted(
stimulus_presentations_df.columns)]
def test_get_stimulus_presentations(behavior_stimuli_time_fixture,
behavior_stimuli_data_fixture, expected):
presentations_df = get_stimulus_presentations(
behavior_stimuli_data_fixture, behavior_stimuli_time_fixture)
expected_df = pd.DataFrame.from_dict(expected)
assert presentations_df.equals(expected_df)
def generate_behavior_stim_table(pkl_data,
sync_dataset,
frame_offset=0,
block_offset=0):
p = pkl_data
image_set = p['items']['behavior']['params']['stimulus']['params'][
'image_set']
image_set = image_set.split('/')[-1].split('.')[0]
num_frames = p['items']['behavior']['intervalsms'].size + 1
frame_timestamps = get_vsyncs(sync_dataset)
epoch_timestamps = frame_timestamps[frame_offset:frame_offset + num_frames]
stim_table = get_stimulus_presentations(p, epoch_timestamps)
stim_table['stimulus_block'] = block_offset
stim_table['stimulus_name'] = image_set
stim_table = stim_table.rename(columns={
'frame': 'start_frame',
'start_time': 'Start',
'stop_time': 'End'
})
# add columns for change and flashes since change
change = np.zeros(len(stim_table))
repeat_number = np.zeros(len(stim_table))
current_image = stim_table.iloc[0]['stimulus_name']
for index, row in stim_table.iterrows():
if (row['image_name'] == 'omitted') or (row['omitted']):
repeat_number[index] = repeat_number[index - 1]
else:
if row['image_name'] != current_image:
change[index] = 1
repeat_number[index] = 0
current_image = row['image_name']
else:
repeat_number[index] = repeat_number[index - 1] + 1
#don't call first change a change
change[np.where(change)[0][0]] = 0
stim_table['change'] = change.astype(int)
#stim_table.loc[0, 'change'] = 0 # MAKE BETTER SOMETIMES THIS IS THE SECOND ROW SINCE THE FIRST CAN BE OMITTED
stim_table['flashes_since_change'] = repeat_number.astype(int)
stim_table['active'] = True
# Fill in 'end frame' and 'End' for omitted stimuli
median_stim_frame_duration = np.nanmedian(stim_table['end_frame'] -
stim_table['start_frame'])
stim_table.loc[stim_table['omitted'], 'end_frame'] = stim_table[
stim_table['omitted']]['start_frame'] + median_stim_frame_duration
stim_table.loc[stim_table['omitted'], 'End'] = epoch_timestamps[stim_table[
stim_table['omitted']]['end_frame'].astype(int)]
stim_table['common_name'] = 'behavior'
return stim_table
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)
