def load_time_meta():
data_dir = neural_data_dir / 'StoriesData_Dec2018' / 'stories_textgridsbyJeanne'
files = data_dir.glob("*TextGrid*")
time_to_words = []
for file in files:
textgrid = TextGrid.load(file)
words = [tier for tier in textgrid.tiers if tier.nameid == 'words'][0]
rows = defaultdict(list)
for (time_start, time_end, word) in words.simple_transcript:
rows['time_start'].append(float(time_start))
rows['time_end'].append(float(time_end))
rows['word'].append(word)
story_index = int(file.stem)
story = stories_meta.sel(number=story_index).values
story = next(iter(set(story))) # Boar was read twice
rows = DataArray(rows['word'],
coords={
'filepath':
('time_bin', [file.name] * len(rows['word'])),
'story':
('time_bin', [story] * len(rows['word'])),
'time_start': ('time_bin', rows['time_start']),
'time_end': ('time_bin', rows['time_end']),
},
dims=['time_bin'])
gather_indexes(rows)
time_to_words.append(rows)
time_to_words = merge_data_arrays(time_to_words)
return time_to_words
def listen_to(candidate,
stimulus_set,
reset_column='story',
average_sentence=True):
"""
Pass a `stimulus_set` through a model `candidate`.
Operates on a sentence-based `stimulus_set`.
"""
activations = []
for story in ordered_set(stimulus_set[reset_column].values):
story_stimuli = stimulus_set[stimulus_set[reset_column] == story]
story_stimuli.name = f"{stimulus_set.name}-{story}"
story_activations = candidate(stimuli=story_stimuli,
average_sentence=average_sentence)
activations.append(story_activations)
model_activations = merge_data_arrays(activations)
# merging does not maintain stimulus order. the following orders again
idx = [
model_activations['stimulus_id'].values.tolist().index(stimulus_id)
for stimulus_id in itertools.chain.from_iterable(
s['stimulus_id'].values for s in activations)
]
assert len(
set(idx)) == len(idx), "Found duplicate indices to order activations"
model_activations = model_activations[{'presentation': idx}]
return model_activations
def from_paths(self, *args, **kwargs):
raw_activations = super(TemporalExtractor, self).from_paths(*args, **kwargs)
# introduce time dimension
regions = defaultdict(list)
for layer in set(raw_activations['layer'].values):
match = re.match(r'(([^-]*)\..*|logits|avgpool)-t([0-9]+)', layer)
region, timestep = match.group(2) if match.group(2) else match.group(1), match.group(3)
stripped_layer = match.group(1)
regions[region].append((layer, stripped_layer, timestep))
activations = {}
for region, time_layers in regions.items():
for (full_layer, stripped_layer, timestep) in time_layers:
region_time_activations = raw_activations.sel(layer=full_layer)
region_time_activations['layer'] = 'neuroid', [stripped_layer] * len(region_time_activations['neuroid'])
activations[(region, timestep)] = region_time_activations
for key, key_activations in activations.items():
region, timestep = key
key_activations['region'] = 'neuroid', [region] * len(key_activations['neuroid'])
activations[key] = NeuroidAssembly([key_activations.values], coords={
**{coord: (dims, values) for coord, dims, values in walk_coords(activations[key])
if coord != 'neuroid_id'}, # otherwise, neuroid dim will be as large as before with nans
**{'time_step': [int(timestep)]}
}, dims=['time_step'] + list(key_activations.dims))
activations = list(activations.values())
activations = merge_data_arrays(activations)
# rebuild neuroid_id without timestep
neuroid_id = [".".join([f"{value}" for value in values]) for values in zip(*[
activations[coord].values for coord in ['model', 'region', 'neuroid_num']])]
activations['neuroid_id'] = 'neuroid', neuroid_id
return activations
def get_activations_for_sentence(model_name, layers, sentences):
model = load_model(model_name)
activations = []
for sentence in sentences:
sentence_activations = model.get_activations([sentence], layers=layers)
activations.append(sentence_activations)
activations = merge_data_arrays(activations)
return activations
def extrapolate_neuroid(self, ceilings):
# figure out how many extrapolation x points we have. E.g. for Pereira, not all combinations are possible
subject_subsamples = list(sorted(set(ceilings['num_subjects'].values)))
rng = RandomState(0)
bootstrap_params = []
for bootstrap in range(self.num_bootstraps):
bootstrapped_scores = []
for num_subjects in subject_subsamples:
num_scores = ceilings.sel(num_subjects=num_subjects)
# the sub_subjects dimension creates nans, get rid of those
num_scores = num_scores.dropna(f'sub_{self.subject_column}')
assert set(num_scores.dims) == {f'sub_{self.subject_column}', 'split'} or \
set(num_scores.dims) == {f'sub_{self.subject_column}'}
# choose from subject subsets and the splits therein, with replacement for variance
choices = num_scores.values.flatten()
bootstrapped_score = rng.choice(choices,
size=len(choices),
replace=True)
bootstrapped_scores.append(np.mean(bootstrapped_score))
try:
params = self.fit(subject_subsamples, bootstrapped_scores)
except RuntimeError: # optimal parameters not found
params = [np.nan, np.nan]
params = DataAssembly([params],
coords={
'bootstrap': [bootstrap],
'param': ['v0', 'tau0']
},
dims=['bootstrap', 'param'])
bootstrap_params.append(params)
bootstrap_params = merge_data_arrays(bootstrap_params)
# find endpoint and error
asymptote_threshold = .0005
interpolation_xs = np.arange(1000)
ys = np.array([
v(interpolation_xs, *params) for params in bootstrap_params.values
if not np.isnan(params).any()
])
median_ys = np.median(ys, axis=0)
diffs = np.diff(median_ys)
end_x = np.where(diffs < asymptote_threshold)[0].min(
) # first x where increase smaller than threshold
# put together
center = np.median(np.array(bootstrap_params)[:, 0])
error = ci_error(ys[:, end_x], center=center)
score = Score(
[center] + list(error),
coords={'aggregation': ['center', 'error_low', 'error_high']},
dims=['aggregation'])
score.attrs['raw'] = ceilings
score.attrs['bootstrapped_params'] = bootstrap_params
score.attrs['endpoint_x'] = DataAssembly(end_x)
return score
def predict(self, source):
predictions = []
for time_bin_start, time_bin_end in source['time_bin'].values:
time_source = source.sel(time_bin=(time_bin_start, time_bin_end))
prediction = self._regression.predict(time_source)
prediction = prediction.expand_dims('time_bin_start').expand_dims(
'time_bin_end')
prediction['time_bin_start'] = [time_bin_start]
prediction['time_bin_end'] = [time_bin_end]
prediction = prediction.stack(
time_bin=['time_bin_start', 'time_bin_end'])
predictions.append(prediction)
return merge_data_arrays(predictions)
def cross_correlation(prediction, target, cross, correlation):
assert (prediction[cross] == target[cross]).all()
scores = []
coords = [coord for coord, dims, values in walk_coords(target[cross])]
for cross_value in target[cross].values:
_prediction = prediction.sel(**{cross: cross_value})
_target = target.sel(**{cross: cross_value})
score = correlation(_prediction, _target)
for coord, coord_value in zip(coords, cross_value):
score = score.expand_dims(coord)
score[coord] = [coord_value]
score = score.stack(**{cross: coords})
scores.append(score)
score = merge_data_arrays(scores)
score = apply_aggregate(lambda score: score.mean(cross), score)
return score
def look_at(self, stimuli, number_of_trials=1):
responses = self._layer_model.look_at(stimuli, number_of_trials=number_of_trials)
time_responses = []
self._logger.debug(f'Repeating single assembly across time bins {self._time_bins}')
for time_bin in self._time_bins:
time_bin = time_bin if not isinstance(time_bin, np.ndarray) else time_bin.tolist()
time_bin_start, time_bin_end = time_bin
bin_responses = responses.expand_dims('time_bin_start').expand_dims('time_bin_end')
bin_responses['time_bin_start'] = [time_bin_start]
bin_responses['time_bin_end'] = [time_bin_end]
bin_responses = bin_responses.stack(time_bin=['time_bin_start', 'time_bin_end'])
time_responses.append(bin_responses)
responses = merge_data_arrays(time_responses)
if len(self._time_bins) == 1:
responses = responses.squeeze('time_bin')
responses = fix_timebin_naming(responses)
return responses
def merge(cls, *scores, ignore_exceptions=False):
"""
Merges the raw values in addition to the score assemblies.
"""
try:
result = merge_data_arrays(scores)
raws = [
score.attrs[cls.RAW_VALUES_KEY] for score in scores
if cls.RAW_VALUES_KEY in score.attrs
]
if len(raws) > 0:
raw = Score.merge(*raws, ignore_exceptions=True)
result.attrs[cls.RAW_VALUES_KEY] = raw
except Exception as e:
if ignore_exceptions:
warnings.warn("failed to merge raw values: " + str(e))
return None
else:
raise e
return result
def read_words(candidate,
stimulus_set,
reset_column='sentence_id',
copy_columns=(),
average_sentence=False):
"""
Pass a `stimulus_set` through a model `candidate`.
In contrast to the `listen_to` function, this function operates on a word-based `stimulus_set`.
"""
# Input: stimulus_set = pandas df, col 1 with sentence ID and 2nd col as word.
activations = []
for i, reset_id in enumerate(ordered_set(
stimulus_set[reset_column].values)):
part_stimuli = stimulus_set[stimulus_set[reset_column] == reset_id]
# stimulus_ids = part_stimuli['stimulus_id']
sentence_stimuli = StimulusSet({
'sentence':
' '.join(part_stimuli['word']),
reset_column:
list(set(part_stimuli[reset_column]))
})
sentence_stimuli.name = f"{stimulus_set.name}-{reset_id}"
sentence_activations = candidate(stimuli=sentence_stimuli,
average_sentence=average_sentence)
for column in copy_columns:
sentence_activations[column] = ('presentation',
part_stimuli[column])
activations.append(sentence_activations)
model_activations = merge_data_arrays(activations)
# merging does not maintain stimulus order. the following orders again
idx = [
model_activations['stimulus_id'].values.tolist().index(stimulus_id)
for stimulus_id in itertools.chain.from_iterable(
s['stimulus_id'].values for s in activations)
]
assert len(
set(idx)) == len(idx), "Found duplicate indices to order activations"
model_activations = model_activations[{'presentation': idx}]
return model_activations
def _merge_voxel_meta(data, meta, bold_shift_seconds):
data_missing = set(meta['story'].values) - set(data['story'].values)
if data_missing:
warnings.warn(f"Stories missing from the data: {data_missing}")
meta_missing = set(data['story'].values) - set(meta['story'].values)
if meta_missing:
warnings.warn(f"Stories missing from the meta: {meta_missing}")
ignored_words = [None, '', '<s>', '</s>', '<s']
annotated_data = []
for story in tqdm(ordered_set(data['story'].values), desc='merge meta'):
if story not in meta['story'].values:
continue
story_meta = meta.sel(story=story)
story_meta = story_meta.sortby('time_end')
story_data = data.sel(story=story).stack(timepoint=['timepoint_value'])
story_data = story_data.sortby('timepoint_value')
timepoints = story_data['timepoint_value'].values.tolist()
assert is_sorted(timepoints)
timepoints = [
timepoint - bold_shift_seconds for timepoint in timepoints
]
sentences = []
last_timepoint = -np.inf
for timepoint in timepoints:
if last_timepoint >= max(story_meta['time_end'].values):
break
if timepoint <= 0:
sentences.append(None)
continue # ignore fixation period
timebin_meta = [
last_timepoint < end <= timepoint
for end in story_meta['time_end'].values
]
timebin_meta = story_meta[{'time_bin': timebin_meta}]
sentence = ' '.join(word.strip() for word in timebin_meta.values
if word not in ignored_words)
sentence = sentence.lower().strip()
# quick-fixes
if story == 'Boar' and sentence == 'interactions the the': # Boar duplicate
sentence = 'interactions the'
if story == 'KingOfBirds' and sentence == 'the fact that the larger': # missing word in TextGrid
sentence = 'earth ' + sentence
if story == 'MrSticky' and sentence == 'worry don\'t worry i went extra slowly since it\'s':
sentence = 'don\'t worry i went extra slowly since it\'s'
sentences.append(sentence)
last_timepoint = timebin_meta['time_end'].values[-1]
sentence_index = [
i for i, sentence in enumerate(sentences) if sentence
]
sentences = np.array(sentences)[sentence_index]
if story not in ['Boar', 'KingOfBirds',
'MrSticky']: # ignore quick-fixes
annotated_sentence = ' '.join(sentences)
meta_sentence = ' '.join(word.strip() for word in story_meta.values if word not in ignored_words) \
.lower().strip()
assert annotated_sentence == meta_sentence
# re-interpret timepoints as stimuli
coords = {}
for coord_name, dims, coord_value in walk_coords(story_data):
dims = [
dim if not dim.startswith('timepoint') else 'presentation'
for dim in dims
]
# discard the timepoints for which the stimulus did not change (empty word)
coord_value = coord_value if not array_is_element(
dims, 'presentation') else coord_value[sentence_index]
coords[coord_name] = dims, coord_value
coords = {
**coords,
**{
'stimulus_sentence': ('presentation', sentences)
}
}
story_data = story_data[{
dim: slice(None) if dim != 'timepoint' else sentence_index
for dim in story_data.dims
}]
dims = [
dim if not dim.startswith('timepoint') else 'presentation'
for dim in story_data.dims
]
story_data = xr.DataArray(story_data.values, coords=coords, dims=dims)
story_data['story'] = 'presentation', [story] * len(
story_data['presentation'])
gather_indexes(story_data)
annotated_data.append(story_data)
annotated_data = merge_data_arrays(annotated_data)
return annotated_data
def load_Pereira2018_Blank_languageresiduals():
# hijack the corresponding encoding benchmark to regress, but then store residuals instead of correlate
from neural_nlp.benchmarks.neural import PereiraEncoding
benchmark = PereiraEncoding()
assembly, cross = benchmark._target_assembly, benchmark._cross
residuals = []
def store_residuals(nonlanguage_prediction, language_target):
residual = language_target - nonlanguage_prediction
residuals.append(residual)
return Score([0],
coords={'neuroid_id': ('neuroid', [0])},
dims=['neuroid']) # dummy score
pseudo_metric = CrossRegressedCorrelation(regression=linear_regression(
xarray_kwargs=dict(stimulus_coord='stimulus_id')),
correlation=store_residuals,
crossvalidation_kwargs=dict(
splits=5,
kfold=True,
split_coord='stimulus_id',
stratification_coord=None))
# separate language from non-language networks
language_assembly = assembly[{
'neuroid': [
atlas in ['DMN', 'MD', 'language']
for atlas in assembly['atlas'].values
]
}]
nonlanguage_assembly = assembly[{
'neuroid': [
atlas in ['visual', 'auditory']
for atlas in assembly['atlas'].values
]
}]
# run
def apply_cross(source_assembly, target_assembly):
# filter experiment
source_assembly = source_assembly[{
'presentation': [
stimulus_id in target_assembly['stimulus_id'].values
for stimulus_id in source_assembly['stimulus_id'].values
]
}]
assert all(source_assembly['stimulus_id'].values ==
target_assembly['stimulus_id'].values)
# filter subjects that have not done this experiment
source_assembly = source_assembly.dropna('neuroid')
# for the target assembly, it's going to become awkward if we just drop those neuroids.
# instead, we set them to zero which makes for simple zero regression weights.
target_assembly = target_assembly.fillna(0)
# this will regress from joint visual+auditory neural space to one of the language networks
return pseudo_metric(source_assembly, target_assembly)
cross(language_assembly,
apply=lambda cross_assembly: apply_cross(nonlanguage_assembly,
cross_assembly))
# combine residuals
assert len(
residuals
) == 5 * 2 * 3 # 5-fold CV, 2 experiments, 3 language brain networks
# ensure uniqueness
neuroid_ids, stimulus_ids = [], []
for residual in residuals:
neuroid_ids += residual['neuroid_id'].values.tolist()
stimulus_ids += residual['stimulus_id'].values.tolist()
assert len(neuroid_ids) == len(language_assembly['neuroid']) * 5 * 2
assert len(set(neuroid_ids)) == len(
set(language_assembly['neuroid_id'].values))
assert len(stimulus_ids) == len(language_assembly['presentation']) * 3
assert len(set(stimulus_ids)) == len(
set(language_assembly['stimulus_id'].values))
residuals = merge_data_arrays(residuals)
residuals = type(language_assembly)(residuals)
residuals.attrs['stimulus_set'] = assembly.stimulus_set
return residuals
def load_Pereira2018():
data_dir = neural_data_dir / "Pereira2018"
experiment2, experiment3 = "243sentences.mat", "384sentences.mat"
stimuli = {} # experiment -> stimuli
assemblies = []
subject_directories = [d for d in data_dir.iterdir() if d.is_dir()]
for subject_directory in tqdm(subject_directories, desc="subjects"):
for experiment_filename in [experiment2, experiment3]:
data_file = subject_directory / f"examples_{experiment_filename}"
if not data_file.is_file():
_logger.debug(
f"{subject_directory} does not contain {experiment_filename}"
)
continue
data = scipy.io.loadmat(str(data_file))
# assembly
assembly = data['examples']
meta = data['meta']
assembly = NeuroidAssembly(
assembly,
coords={
'experiment': ('presentation',
[os.path.splitext(experiment_filename)[0]] *
assembly.shape[0]),
'stimulus_num':
('presentation', list(range(assembly.shape[0]))),
'passage_index':
('presentation', data['labelsPassageForEachSentence'][:,
0]),
'passage_label': ('presentation', [
data['keyPassages'][index - 1, 0][0]
for index in data['labelsPassageForEachSentence'][:, 0]
]),
'passage_category': ('presentation', [
data['keyPassageCategory']
[0,
data['labelsPassageCategory'][index - 1, 0] - 1][0][0]
for index in data['labelsPassageForEachSentence']
]),
'subject':
('neuroid', [subject_directory.name] * assembly.shape[1]),
'voxel_num': ('neuroid', list(range(assembly.shape[1]))),
'AAL_roi_index':
('neuroid', meta[0][0]['roiMultimaskAAL'][:, 0]),
},
dims=['presentation', 'neuroid'])
stimulus_id = _build_id(assembly, ['experiment', 'stimulus_num'])
assembly['stimulus_id'] = 'presentation', stimulus_id
# set story for compatibility
assembly['story'] = 'presentation', _build_id(
assembly, ['experiment', 'passage_category'])
assembly['neuroid_id'] = 'neuroid', _build_id(
assembly, ['subject', 'voxel_num'])
assemblies.append(assembly)
# stimuli
if experiment_filename not in stimuli:
sentences = data['keySentences']
sentences = [sentence[0] for sentence in sentences[:, 0]]
stimuli[experiment_filename] = {
'sentence': sentences,
'sentence_num': list(range(len(sentences))),
'stimulus_id': stimulus_id,
'experiment': assembly['experiment'].values,
'story': assembly['story'].values,
}
for copy_coord in [
'experiment', 'story', 'passage_index',
'passage_label', 'passage_category'
]:
stimuli[experiment_filename][copy_coord] = assembly[
copy_coord].values
_logger.debug(f"Merging {len(assemblies)} assemblies")
assembly = merge_data_arrays(assemblies)
_logger.debug("Creating StimulusSet")
combined_stimuli = {}
for key in stimuli[experiment2]:
combined_stimuli[key] = np.concatenate(
(stimuli[experiment2][key], stimuli[experiment3][key]))
stimuli = StimulusSet(combined_stimuli)
stimuli.name = "Pereira2018"
assembly.attrs['stimulus_set'] = stimuli
return assembly
def load_Pereira2018_Blank(version='base'):
reference_data = load_Pereira2018()
data_dir = neural_data_dir / ("Pereira2018_Blank" +
("_langonly" if version != 'base' else ""))
experiments = {'n72': "243sentences", 'n96': "384sentences"}
assemblies = []
subjects = [
'018', '199', '215', '288', '289', '296', '343', '366', '407', '426'
]
for subject in tqdm(subjects, desc="subjects"):
subject_assemblies = []
for experiment_filepart, experiment_name in experiments.items():
filepath = data_dir / f"{'ICA_' if version != 'base' else ''}" \
f"{subject}_complang_passages_{experiment_filepart}_persent.mat"
if not filepath.is_file():
_logger.debug(
f"Subject {subject} did not run {experiment_name}: {filepath} does not exist"
)
continue
data = scipy.io.loadmat(str(filepath))
if version != 'base':
data = data['x'][0, 0]
# construct assembly
assembly = data['data' if version == 'base' else f'data{version}']
neuroid_meta = data['meta']
expanded_assembly = []
voxel_nums, atlases, filter_strategies, atlas_selections, atlas_filter_lower, rois = [], [], [], [], [], []
for voxel_num in range(assembly.shape[1]):
for atlas_iter, atlas_selection in enumerate(
neuroid_meta['atlases'][0, 0][:, 0]):
multimask = neuroid_meta['roiMultimask'][0,
0][atlas_iter,
0][voxel_num,
0]
if np.isnan(multimask):
continue
atlas_selection = atlas_selection[0].split('_')
filter_strategy = None if len(
atlas_selection) != 3 else atlas_selection[1]
filter_lower = re.match(r'from([0-9]{2})to100prcnt',
atlas_selection[-1])
atlas_filter_lower.append(int(filter_lower.group(1)))
atlas, selection = atlas_selection[0], atlas_selection[-1]
atlases.append(atlas)
filter_strategies.append(filter_strategy)
atlas_selections.append(selection)
multimask = int(
multimask
) - 1 # Matlab 1-based to Python 0-based indexing
rois.append(neuroid_meta['rois'][0, 0][atlas_iter,
0][multimask, 0][0])
voxel_nums.append(voxel_num)
expanded_assembly.append(assembly[:, voxel_num])
# ensure all are mapped
assert set(voxel_nums) == set(range(
assembly.shape[1])), "not all voxels mapped"
# add indices
assembly = np.stack(expanded_assembly).T
assert assembly.shape[1] == len(atlases) == len(
atlas_selections) == len(rois)
indices_in_3d = neuroid_meta['indicesIn3D'][0, 0][:, 0]
indices_in_3d = [
indices_in_3d[voxel_num] for voxel_num in voxel_nums
]
# add coords
col_to_coords = np.array([
neuroid_meta['colToCoord'][0, 0][voxel_num]
for voxel_num in voxel_nums
])
# put it all together
assembly = NeuroidAssembly(
assembly,
coords={
**{
coord: (dims, value)
for coord, dims, value in walk_coords(
reference_data.sel(experiment=experiment_name)['presentation'])
},
**{
'experiment': ('presentation', [experiment_name] * assembly.shape[0]),
'subject': ('neuroid', [subject] * assembly.shape[1]),
'voxel_num': ('neuroid', voxel_nums),
'atlas': ('neuroid', atlases),
'filter_strategy': ('neuroid', filter_strategies),
'atlas_selection': ('neuroid', atlas_selections),
'atlas_selection_lower': ('neuroid', atlas_filter_lower),
'roi': ('neuroid', rois),
'indices_in_3d': ('neuroid', indices_in_3d),
'col_to_coord_1': ('neuroid', col_to_coords[:, 0]),
'col_to_coord_2': ('neuroid', col_to_coords[:, 1]),
'col_to_coord_3': ('neuroid', col_to_coords[:, 2]),
}
},
dims=['presentation', 'neuroid'])
assembly['neuroid_id'] = 'neuroid', _build_id(
assembly, ['subject', 'voxel_num'])
subject_assemblies.append(assembly)
assembly = merge_data_arrays(subject_assemblies)
assemblies.append(assembly)
_logger.debug(f"Merging {len(assemblies)} assemblies")
assembly = merge_data_arrays(assemblies)
assembly.attrs['version'] = version
_logger.debug("Creating StimulusSet")
assembly.attrs['stimulus_set'] = reference_data.stimulus_set
return assembly
def look_at_temporal(self, stimuli):
responses = self.activations_model(stimuli,
layers=self.recording_layers)
# map time
if hasattr(self, 'recording_target'):
regions = set([self.recording_target])
else:
regions = set(responses['region'].values)
if len(regions) > 1:
raise NotImplementedError(
"cannot handle more than one simultaneous region")
region = list(regions)[0]
time_bins = [
self.time_mapping[region][timestep]
if timestep in self.time_mapping[region] else (None, None)
for timestep in responses['time_step'].values
]
responses['time_bin_start'] = 'time_step', [
time_bin[0] for time_bin in time_bins
]
responses['time_bin_end'] = 'time_step', [
time_bin[1] for time_bin in time_bins
]
responses = NeuroidAssembly(responses.rename({'time_step':
'time_bin'}))
responses = responses[{
'time_bin': [
not np.isnan(time_start)
for time_start in responses['time_bin_start']
]
}]
# select time
time_responses = []
for time_bin in tqdm(self.recording_time_bins,
desc='CORnet-time to recording time'):
time_bin = time_bin if not isinstance(
time_bin, np.ndarray) else time_bin.tolist()
time_bin_start, time_bin_end = time_bin
nearest_start = find_nearest(responses['time_bin_start'].values,
time_bin_start)
bin_responses = responses.sel(time_bin_start=nearest_start)
bin_responses = NeuroidAssembly(
bin_responses.values,
coords={
**{
coord: (dims, values)
for coord, dims, values in walk_coords(bin_responses) if coord not in [
'time_bin_level_0', 'time_bin_end'
]
},
**{
'time_bin_start': ('time_bin', [time_bin_start]),
'time_bin_end': ('time_bin', [time_bin_end])
}
},
dims=bin_responses.dims)
time_responses.append(bin_responses)
responses = merge_data_arrays(time_responses)
responses = fix_timebin_naming(
responses) # work around xarray merge bug introduced in 0.16.2
return responses