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import functools
import dateutil
import re
import os
import six
import itertools
import logging
from pkg_resources import parse_version
import h5py
import pandas as pd
import numpy as np
import allensdk.brain_observatory.roi_masks as roi
from allensdk.brain_observatory.locally_sparse_noise import LocallySparseNoise
import allensdk.brain_observatory.stimulus_info as si
from allensdk.brain_observatory.brain_observatory_exceptions import (MissingStimulusException,
NoEyeTrackingException)
from allensdk.api.warehouse_cache.cache import memoize
from allensdk.core import h5_utilities
from allensdk.brain_observatory.stimulus_info import mask_stimulus_template as si_mask_stimulus_template
from allensdk.brain_observatory.brain_observatory_exceptions import EpochSeparationException
_STIMULUS_PRESENTATION_PATH = 'stimulus/presentation'
_STIMULUS_PRESENTATION_PATTERNS = ('{}', '{}_stimulus',)
[docs]def get_epoch_mask_list(st, threshold, max_cuts=2):
'''Convenience function to cut a stim table into multiple epochs
:param st: input stimtable
:param threshold: threshold on the max duration of a subepoch
:param max_cuts: maximum number of allowed epochs to cut into
:return: epoch_mask_list, a list of indices that define the start and end of sub-epochs
'''
if threshold is None:
raise NotImplementedError('threshold not set for this type of session')
delta = (st.start.values[1:] - st.end.values[:-1])
cut_inds = np.where(delta > threshold)[0] + 1
epoch_mask_list = []
if len(cut_inds) > max_cuts:
# See: https://gist.github.com/nicain/bce66cd073e422f07cf337b476c63be7
# https://github.com/AllenInstitute/AllenSDK/issues/66
raise EpochSeparationException('more than 2 epochs cut', delta=delta)
for ii in range(len(cut_inds)+1):
if ii == 0:
first_ind = st.iloc[0].start
else:
first_ind = st.iloc[cut_inds[ii-1]].start
if ii == len(cut_inds):
last_ind_inclusive = st.iloc[-1].end
else:
last_ind_inclusive = st.iloc[cut_inds[ii]-1].end
epoch_mask_list.append((first_ind,last_ind_inclusive))
return epoch_mask_list
[docs]class BrainObservatoryNwbDataSet(object):
PIPELINE_DATASET = 'brain_observatory_pipeline'
SUPPORTED_PIPELINE_VERSION = "3.0"
FILE_METADATA_MAPPING = {
'age': 'general/subject/age',
'sex': 'general/subject/sex',
'imaging_depth': 'general/optophysiology/imaging_plane_1/imaging depth',
'targeted_structure': 'general/optophysiology/imaging_plane_1/location',
'ophys_experiment_id': 'general/session_id',
'experiment_container_id': 'general/experiment_container_id',
'device_string': 'general/devices/2-photon microscope',
'excitation_lambda': 'general/optophysiology/imaging_plane_1/excitation_lambda',
'indicator': 'general/optophysiology/imaging_plane_1/indicator',
'fov': 'general/fov',
'genotype': 'general/subject/genotype',
'session_start_time': 'session_start_time',
'session_type': 'general/session_type',
'specimen_name': 'general/specimen_name',
'generated_by': 'general/generated_by'
}
STIMULUS_TABLE_TYPES = {
'abstract_feature_series': [si.DRIFTING_GRATINGS, si.STATIC_GRATINGS],
'indexed_time_series': [si.NATURAL_SCENES, si.LOCALLY_SPARSE_NOISE,
si.LOCALLY_SPARSE_NOISE_4DEG, si.LOCALLY_SPARSE_NOISE_8DEG],
'repeated_indexed_time_series':[si.NATURAL_MOVIE_ONE, si.NATURAL_MOVIE_TWO, si.NATURAL_MOVIE_THREE]
}
# this array was moved before file versioning was in place
MOTION_CORRECTION_DATASETS = [ "MotionCorrection/2p_image_series/xy_translations",
"MotionCorrection/2p_image_series/xy_translation" ]
def __init__(self, nwb_file):
self.nwb_file = nwb_file
self.pipeline_version = None
if os.path.exists(self.nwb_file):
meta = self.get_metadata()
if meta and 'pipeline_version' in meta:
pipeline_version_str = meta['pipeline_version']
self.pipeline_version = parse_version(pipeline_version_str)
if self.pipeline_version > parse_version(self.SUPPORTED_PIPELINE_VERSION):
logging.warning("File %s has a pipeline version newer than the version supported by this class (%s vs %s)."
" Please update your AllenSDK." % (nwb_file, pipeline_version_str, self.SUPPORTED_PIPELINE_VERSION))
self._stimulus_search = None
[docs] def get_stimulus_epoch_table(self):
'''Returns a pandas dataframe that summarizes the stimulus epoch duration for each acquisition time index in
the experiment
Parameters
----------
None
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
traces: 2D numpy array
Fluorescence traces for each cell
'''
# These are thresholds used by get_epoch_mask_list to set a maximum limit on the delta aqusistion frames to
# count as different trials (rows in the stim table). This helps account for dropped frames, so that they dont
# cause the cutting of an entire experiment into too many stimulus epochs. If these thresholds are too low,
# the assert statment in get_epoch_mask_list will halt execution. In that case, make a bug report!.
threshold_dict = {si.THREE_SESSION_A:32+7,
si.THREE_SESSION_B:15,
si.THREE_SESSION_C:7,
si.THREE_SESSION_C2:7}
stimulus_table_dict = {}
for stimulus in self.list_stimuli():
stimulus_table_dict[stimulus] = self.get_stimulus_table(stimulus)
if stimulus == si.SPONTANEOUS_ACTIVITY:
stimulus_table_dict[stimulus]['frame'] = 0
interval_list = []
interval_stimulus_dict = {}
for stimulus in self.list_stimuli():
stimulus_interval_list = get_epoch_mask_list(stimulus_table_dict[stimulus], threshold=threshold_dict.get(self.get_session_type(), None))
for stimulus_interval in stimulus_interval_list:
interval_stimulus_dict[stimulus_interval] = stimulus
interval_list += stimulus_interval_list
interval_list.sort(key=lambda x: x[0])
stimulus_signature_list = ['gap']
duration_signature_list = [int(interval_list[0][0])]
interval_signature_list = [(0,int(interval_list[0][0]))]
for ii, interval in enumerate(interval_list):
stimulus_signature_list.append(interval_stimulus_dict[interval])
duration_signature_list.append(int(interval[1] - interval[0]))
interval_signature_list.append((int(interval[0]), int(interval[1])))
if ii != len(interval_list)-1:
stimulus_signature_list.append('gap')
duration_signature_list.append((int(interval_list[ii+1][0] - interval_list[ii][1])))
interval_signature_list.append((int(interval_list[ii][1]), int(interval_list[ii+1][0])))
stimulus_signature_list.append('gap')
interval_signature_list.append((int(interval_list[-1][1]), len(self.get_fluorescence_timestamps())))
duration_signature_list.append(interval_signature_list[-1][1]-interval_signature_list[-1][0])
interval_df = pd.DataFrame({'stimulus':stimulus_signature_list,
'duration':duration_signature_list,
'interval':interval_signature_list})
# Gaps are uninformative; remove them:
interval_df = interval_df[interval_df.stimulus != 'gap']
interval_df['start'] = [x[0] for x in interval_df['interval'].values]
interval_df['end'] = [x[1] for x in interval_df['interval'].values]
interval_df.reset_index(inplace=True, drop=True)
interval_df.drop(['interval', 'duration'], axis=1, inplace=True)
return interval_df
[docs] def get_fluorescence_traces(self, cell_specimen_ids=None):
''' Returns an array of fluorescence traces for all ROI and
the timestamps for each datapoint
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
traces: 2D numpy array
Fluorescence traces for each cell
'''
timestamps = self.get_fluorescence_timestamps()
with h5py.File(self.nwb_file, 'r') as f:
ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1']['data']
if cell_specimen_ids is None:
cell_traces = ds[()]
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
cell_traces = ds[inds, :]
return timestamps, cell_traces
[docs] def get_fluorescence_timestamps(self):
''' Returns an array of timestamps in seconds for the fluorescence traces '''
with h5py.File(self.nwb_file, 'r') as f:
timestamps = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1']['timestamps'][()]
return timestamps
[docs] def get_neuropil_traces(self, cell_specimen_ids=None):
''' Returns an array of neuropil fluorescence traces for all ROIs
and the timestamps for each datapoint
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
traces: 2D numpy array
Neuropil fluorescence traces for each cell
'''
timestamps = self.get_fluorescence_timestamps()
with h5py.File(self.nwb_file, 'r') as f:
if self.pipeline_version >= parse_version("2.0"):
ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1_neuropil_response']['data']
else:
ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1']['neuropil_traces']
if cell_specimen_ids is None:
np_traces = ds[()]
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
np_traces = ds[inds, :]
return timestamps, np_traces
[docs] def get_neuropil_r(self, cell_specimen_ids=None):
''' Returns a scalar value of r for neuropil correction of flourescence traces
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then results for all are returned
Returns
-------
r: 1D numpy array, len(r)=len(cell_specimen_ids)
Scalar for neuropil subtraction for each cell
'''
with h5py.File(self.nwb_file, 'r') as f:
if self.pipeline_version >= parse_version("2.0"):
r_ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1_neuropil_response']['r']
else:
r_ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1']['r']
if cell_specimen_ids is None:
r = r_ds[()]
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
r = r_ds[inds]
return r
[docs] def get_demixed_traces(self, cell_specimen_ids=None):
''' Returns an array of demixed fluorescence traces for all ROIs
and the timestamps for each datapoint
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
traces: 2D numpy array
Demixed fluorescence traces for each cell
'''
timestamps = self.get_fluorescence_timestamps()
with h5py.File(self.nwb_file, 'r') as f:
ds = f['processing'][self.PIPELINE_DATASET][
'Fluorescence']['imaging_plane_1_demixed_signal']['data']
if cell_specimen_ids is None:
traces = ds[()]
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
traces = ds[inds, :]
return timestamps, traces
[docs] def get_corrected_fluorescence_traces(self, cell_specimen_ids=None):
''' Returns an array of demixed and neuropil-corrected fluorescence traces
for all ROIs and the timestamps for each datapoint
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
traces: 2D numpy array
Corrected fluorescence traces for each cell
'''
# starting in version 2.0, neuropil correction follows trace demixing
if self.pipeline_version >= parse_version("2.0"):
timestamps, cell_traces = self.get_demixed_traces(cell_specimen_ids)
else:
timestamps, cell_traces = self.get_fluorescence_traces(cell_specimen_ids)
r = self.get_neuropil_r(cell_specimen_ids)
_, neuropil_traces = self.get_neuropil_traces(cell_specimen_ids)
fc = cell_traces - neuropil_traces * r[:, np.newaxis]
return timestamps, fc
[docs] def get_cell_specimen_indices(self, cell_specimen_ids):
''' Given a list of cell specimen ids, return their index based on their order in this file.
Parameters
----------
cell_specimen_ids: list of cell specimen ids
'''
all_cell_specimen_ids = list(self.get_cell_specimen_ids())
try:
inds = [list(all_cell_specimen_ids).index(i)
for i in cell_specimen_ids]
except ValueError as e:
raise ValueError("Cell specimen not found (%s)" % str(e))
return inds
[docs] def get_dff_traces(self, cell_specimen_ids=None):
''' Returns an array of dF/F traces for all ROIs and
the timestamps for each datapoint
Parameters
----------
cell_specimen_ids: list or array (optional)
List of cell IDs to return data for. If this is None (default)
then all are returned
Returns
-------
timestamps: 2D numpy array
Timestamp for each fluorescence sample
dF/F: 2D numpy array
dF/F values for each cell
'''
with h5py.File(self.nwb_file, 'r') as f:
dff_ds = f['processing'][self.PIPELINE_DATASET][
'DfOverF']['imaging_plane_1']
timestamps = dff_ds['timestamps'][()]
if cell_specimen_ids is None:
cell_traces = dff_ds['data'][()]
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
cell_traces = dff_ds['data'][inds, :]
return timestamps, cell_traces
[docs] def get_roi_ids(self):
''' Returns an array of IDs for all ROIs in the file
Returns
-------
ROI IDs: list
'''
with h5py.File(self.nwb_file, 'r') as f:
roi_id = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['roi_ids'][()]
return roi_id
[docs] def get_cell_specimen_ids(self):
''' Returns an array of cell IDs for all cells in the file
Returns
-------
cell specimen IDs: list
'''
with h5py.File(self.nwb_file, 'r') as f:
cell_id = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['cell_specimen_ids'][()]
return cell_id
[docs] def get_session_type(self):
''' Returns the type of experimental session, presently one of the
following: three_session_A, three_session_B, three_session_C
Returns
-------
session type: string
'''
with h5py.File(self.nwb_file, 'r') as f:
session_type = f['general/session_type'][()]
return session_type.decode('utf-8')
[docs] def get_max_projection(self):
'''Returns the maximum projection image for the 2P movie.
Returns
-------
max projection: np.ndarray
'''
with h5py.File(self.nwb_file, 'r') as f:
max_projection = f['processing'][self.PIPELINE_DATASET]['ImageSegmentation'][
'imaging_plane_1']['reference_images']['maximum_intensity_projection_image']['data'][()]
return max_projection
[docs] def list_stimuli(self):
''' Return a list of the stimuli presented in the experiment.
Returns
-------
stimuli: list of strings
'''
with h5py.File(self.nwb_file, 'r') as f:
keys = list(f["stimulus/presentation/"].keys())
return [ k.replace('_stimulus', '') for k in keys ]
def _get_master_stimulus_table(self):
''' Builds a table for all stimuli by concatenating (vertically) the
sub-tables describing presentation of each stimulus
'''
epoch_table = self.get_stimulus_epoch_table()
stimulus_table_dict = {}
for stimulus in self.list_stimuli():
stimulus_table_dict[stimulus] = self.get_stimulus_table(stimulus)
table_list = []
for stimulus in self.list_stimuli():
curr_stimtable = stimulus_table_dict[stimulus]
for _, row in epoch_table[epoch_table['stimulus'] == stimulus].iterrows():
epoch_start_ind, epoch_end_ind = row['start'], row['end']
curr_subtable = curr_stimtable[(epoch_start_ind <= curr_stimtable['start']) &
(curr_stimtable['end'] <= epoch_end_ind)].copy()
curr_subtable['stimulus'] = stimulus
table_list.append(curr_subtable)
new_table = pd.concat(table_list, sort=True)
new_table.reset_index(drop=True, inplace=True)
return new_table
[docs] def get_stimulus_table(self, stimulus_name):
''' Return a stimulus table given a stimulus name
Notes
-----
For more information, see:
http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf
'''
if stimulus_name == 'master':
return self._get_master_stimulus_table()
with h5py.File(self.nwb_file, 'r') as nwb_file:
stimulus_group = _find_stimulus_presentation_group(nwb_file, stimulus_name)
if stimulus_name in self.STIMULUS_TABLE_TYPES['abstract_feature_series']:
datasets = h5_utilities.load_datasets_by_relnames(
['data', 'features', 'frame_duration'], nwb_file, stimulus_group)
return _make_abstract_feature_series_stimulus_table(
datasets['data'], h5_utilities.decode_bytes(datasets['features']), datasets['frame_duration'])
if stimulus_name in self.STIMULUS_TABLE_TYPES['indexed_time_series']:
datasets = h5_utilities.load_datasets_by_relnames(['data', 'frame_duration'], nwb_file, stimulus_group)
return _make_indexed_time_series_stimulus_table(datasets['data'], datasets['frame_duration'])
if stimulus_name in self.STIMULUS_TABLE_TYPES['repeated_indexed_time_series']:
datasets = h5_utilities.load_datasets_by_relnames(['data', 'frame_duration'], nwb_file, stimulus_group)
return _make_repeated_indexed_time_series_stimulus_table(datasets['data'], datasets['frame_duration'])
if stimulus_name == 'spontaneous':
datasets = h5_utilities.load_datasets_by_relnames(['data', 'frame_duration'], nwb_file, stimulus_group)
return _make_spontaneous_activity_stimulus_table(datasets['data'], datasets['frame_duration'])
raise IOError("Could not find a stimulus table named '%s'" % stimulus_name)
[docs] @memoize
def get_stimulus_template(self, stimulus_name):
''' Return an array of the stimulus template for the specified stimulus.
Parameters
----------
stimulus_name: string
Must be one of the strings returned by list_stimuli().
Returns
-------
stimulus table: pd.DataFrame
'''
stim_name = stimulus_name + "_image_stack"
with h5py.File(self.nwb_file, 'r') as f:
image_stack = f['stimulus']['templates'][stim_name]['data'][()]
return image_stack
[docs] def get_locally_sparse_noise_stimulus_template(self,
stimulus,
mask_off_screen=True):
''' Return an array of the stimulus template for the specified stimulus.
Parameters
----------
stimulus: string
Which locally sparse noise stimulus to retrieve. Must be one of:
stimulus_info.LOCALLY_SPARSE_NOISE
stimulus_info.LOCALLY_SPARSE_NOISE_4DEG
stimulus_info.LOCALLY_SPARSE_NOISE_8DEG
mask_off_screen: boolean
Set off-screen regions of the stimulus to LocallySparseNoise.LSN_OFF_SCREEN.
Returns
-------
tuple: (template, off-screen mask)
'''
if stimulus not in si.LOCALLY_SPARSE_NOISE_DIMENSIONS:
raise KeyError("%s is not a known locally sparse noise stimulus" % stimulus)
template = self.get_stimulus_template(stimulus)
# build mapping from template coordinates to display coordinates
template_shape = si.LOCALLY_SPARSE_NOISE_DIMENSIONS[stimulus]
template_shape = [ template_shape[1], template_shape[0] ]
template_display_shape = (1260, 720)
display_shape = (1920, 1200)
scale = [
float(template_shape[0]) / float(template_display_shape[0]),
float(template_shape[1]) / float(template_display_shape[1])
]
offset = [
-(display_shape[0] - template_display_shape[0]) * 0.5,
-(display_shape[1] - template_display_shape[1]) * 0.5
]
x, y = np.meshgrid(np.arange(display_shape[0]), np.arange(
display_shape[1]), indexing='ij')
template_display_coords = np.array([(x + offset[0]) * scale[0] - 0.5,
(y + offset[1]) * scale[1] - 0.5],
dtype=float)
template_display_coords = np.rint(template_display_coords).astype(int)
# build mask
template_mask, template_frac = si_mask_stimulus_template(
template_display_coords, template_shape)
if mask_off_screen:
template[:, ~template_mask.T] = LocallySparseNoise.LSN_OFF_SCREEN
return template, template_mask.T
[docs] def get_roi_mask_array(self, cell_specimen_ids=None):
''' Return a numpy array containing all of the ROI masks for requested cells.
If cell_specimen_ids is omitted, return all masks.
Parameters
----------
cell_specimen_ids: list
List of cell specimen ids. Default None.
Returns
-------
np.ndarray: NxWxH array, where N is number of cells
'''
roi_masks = self.get_roi_mask(cell_specimen_ids)
if len(roi_masks) == 0:
raise IOError("no masks found for given cell specimen ids")
roi_arr = roi.create_roi_mask_array(roi_masks)
return roi_arr
[docs] def get_roi_mask(self, cell_specimen_ids=None):
''' Returns an array of all the ROI masks
Parameters
----------
cell specimen IDs: list or array (optional)
List of cell IDs to return traces for. If this is None (default)
then all are returned
Returns
-------
List of ROI_Mask objects
'''
with h5py.File(self.nwb_file, 'r') as f:
mask_loc = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']
roi_list = f['processing'][self.PIPELINE_DATASET][
'ImageSegmentation']['imaging_plane_1']['roi_list'][()]
inds = None
if cell_specimen_ids is None:
inds = range(self.number_of_cells)
else:
inds = self.get_cell_specimen_indices(cell_specimen_ids)
roi_array = []
for i in inds:
v = roi_list[i]
roi_mask = mask_loc[v]["img_mask"][()]
m = roi.create_roi_mask(roi_mask.shape[1], roi_mask.shape[0],
[0, 0, 0, 0], roi_mask=roi_mask, label=v)
roi_array.append(m)
return roi_array
@property
def number_of_cells(self):
'''Number of cells in the experiment'''
# Replace here is there is a better way to get this info:
return len(self.get_cell_specimen_ids())
[docs] def get_running_speed(self):
''' Returns the mouse running speed in cm/s
'''
with h5py.File(self.nwb_file, 'r') as f:
dx_ds = f['processing'][self.PIPELINE_DATASET][
'BehavioralTimeSeries']['running_speed']
dxcm = dx_ds['data'][()]
dxtime = dx_ds['timestamps'][()]
timestamps = self.get_fluorescence_timestamps()
# v0.9 stored this as an Nx1 array instead of a flat 1-d array
if len(dxcm.shape) == 2:
dxcm = dxcm[:, 0]
dxcm, dxtime = align_running_speed(dxcm, dxtime, timestamps)
return dxcm, dxtime
[docs] def get_pupil_location(self, as_spherical=True):
'''Returns the x, y pupil location.
Parameters
----------
as_spherical : bool
Whether to return the location as spherical (default) or
not. If true, the result is altitude and azimuth in
degrees, otherwise it is x, y in centimeters. (0,0) is
the center of the monitor.
Returns
-------
(timestamps, location)
Timestamps is an (Nx1) array of timestamps in seconds.
Location is an (Nx2) array of spatial location.
'''
if as_spherical:
location_key = "pupil_location_spherical"
else:
location_key = "pupil_location"
try:
with h5py.File(self.nwb_file, 'r') as f:
eye_tracking = f['processing'][self.PIPELINE_DATASET][
'EyeTracking'][location_key]
pupil_location = eye_tracking['data'][()]
pupil_times = eye_tracking['timestamps'][()]
except KeyError:
raise NoEyeTrackingException("No eye tracking for this experiment.")
return pupil_times, pupil_location
[docs] def get_pupil_size(self):
'''Returns the pupil area in pixels.
Returns
-------
(timestamps, areas)
Timestamps is an (Nx1) array of timestamps in seconds.
Areas is an (Nx1) array of pupil areas in pixels.
'''
try:
with h5py.File(self.nwb_file, 'r') as f:
pupil_tracking = f['processing'][self.PIPELINE_DATASET][
'PupilTracking']['pupil_size']
pupil_size = pupil_tracking['data'][()]
pupil_times = pupil_tracking['timestamps'][()]
except KeyError:
raise NoEyeTrackingException("No pupil tracking for this experiment.")
return pupil_times, pupil_size
[docs] def get_motion_correction(self):
''' Returns a Panda DataFrame containing the x- and y- translation of each image used for image alignment
'''
motion_correction = None
with h5py.File(self.nwb_file, 'r') as f:
pipeline_ds = f['processing'][self.PIPELINE_DATASET]
# pipeline 0.9 stores this in xy_translations
# pipeline 1.0 stores this in xy_translation
for mc_ds_name in self.MOTION_CORRECTION_DATASETS:
try:
mc_ds = pipeline_ds[mc_ds_name]
motion_log = mc_ds['data'][()]
motion_time = mc_ds['timestamps'][()]
motion_names = mc_ds['feature_description'][()]
motion_correction = pd.DataFrame(motion_log, columns=motion_names)
motion_correction['timestamp'] = motion_time
# break out if we found it
break
except KeyError as e:
pass
if motion_correction is None:
raise KeyError("Could not find motion correction data.")
# Python3 compatibility:
rename_dict = {}
for c in motion_correction.columns:
if not isinstance(c, str):
rename_dict[c] = c.decode("utf-8")
motion_correction.rename(columns=rename_dict, inplace=True)
return motion_correction
[docs] def save_analysis_dataframes(self, *tables):
store = pd.HDFStore(self.nwb_file, mode='a')
for k, v in tables:
store.put('analysis/%s' % (k), v)
store.close()
[docs] def save_analysis_arrays(self, *datasets):
with h5py.File(self.nwb_file, 'a') as f:
for k, v in datasets:
if k in f['analysis']:
del f['analysis'][k]
f.create_dataset('analysis/%s' % k, data=v)
@property
def stimulus_search(self):
if self._stimulus_search is None:
self._stimulus_search = si.StimulusSearch(self)
return self._stimulus_search
[docs] def get_stimulus(self, frame_ind):
search_result = self.stimulus_search.search(frame_ind)
if search_result is None or search_result[2]['stimulus'] == si.SPONTANEOUS_ACTIVITY:
return None, None
else:
curr_stimulus = search_result[2]['stimulus']
if curr_stimulus in si.LOCALLY_SPARSE_NOISE_STIMULUS_TYPES + si.NATURAL_MOVIE_STIMULUS_TYPES + [si.NATURAL_SCENES]:
curr_frame = search_result[2]['frame']
return search_result, self.get_stimulus_template(curr_stimulus)[int(curr_frame), :, :]
elif curr_stimulus == si.STATIC_GRATINGS or curr_stimulus == si.DRIFTING_GRATINGS:
return search_result, None
def _find_stimulus_presentation_group(nwb_file,
stimulus_name,
base_path=_STIMULUS_PRESENTATION_PATH,
group_patterns=_STIMULUS_PRESENTATION_PATTERNS):
''' Searches an NWB file for a stimulus presentation group.
Parameters
----------
nwb_file : h5py.File
File to search
stimulus_name : str
Identifier for this stimulus. Corresponds to the relative name of its h5
group.
base_path : str, optional
Begin the search from here. Defaults to 'stimulus/presentation'
group_patterns : array-like of str, optional
Patterns for the relative name of the stimulus' h5 group. Defaults to
the name, and the name suffixed by '_stimulus'
Returns
-------
h5py.Group, h5py.Dataset :
h5 object found
'''
group_candidates = [ pattern.format(stimulus_name) for pattern in group_patterns ]
matcher = functools.partial(h5_utilities.h5_object_matcher_relname_in, group_candidates)
matches = h5_utilities.locate_h5_objects(matcher, nwb_file, base_path)
if len(matches) == 0:
raise MissingStimulusException(
'Unable to locate stimulus: {}. '
'Looked for this stimulus under the names: {} '.format(stimulus_name, group_candidates)
)
if len(matches) > 1:
raise MissingStimulusException(
'Unable to locate stimulus: {}. '
'Found multiple matching stimuli: {}'.format(stimulus_name, [match.name for match in matches])
)
return matches[0]
[docs]def align_running_speed(dxcm, dxtime, timestamps):
''' If running speed timestamps differ from fluorescence
timestamps, adjust by inserting NaNs to running speed.
Returns
-------
tuple: dxcm, dxtime
'''
if dxtime[0] != timestamps[0]:
adjust = np.where(timestamps == dxtime[0])[0][0]
dxtime = np.insert(dxtime, 0, timestamps[:adjust])
dxcm = np.insert(dxcm, 0, np.repeat(np.NaN, adjust))
adjust = len(timestamps) - len(dxtime)
if adjust > 0:
dxtime = np.append(dxtime, timestamps[(-1 * adjust):])
dxcm = np.append(dxcm, np.repeat(np.NaN, adjust))
return dxcm, dxtime
def _make_abstract_feature_series_stimulus_table(stim_data, features, frame_dur):
''' Return the a stimulus table for an abstract feature series.
Parameters
----------
stim_data : array-like
Stimulus feature values at each interval
features : array-like of str
Stimulus feature labels
frame_dur : array-like
Start and end times of presentation intervals
Returns
-------
stimulus table : pd.DataFrame
Describes the intervals of presentation of the stimulus
Notes
-----
For more information, see:
http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf
'''
stimulus_table = pd.DataFrame(stim_data, columns=features)
stimulus_table.loc[:, 'start'] = frame_dur[:, 0].astype(int)
stimulus_table.loc[:, 'end'] = frame_dur[:, 1].astype(int)
stimulus_table = stimulus_table.sort_values(['start', 'end'])
return stimulus_table
def _make_indexed_time_series_stimulus_table(inds, frame_dur):
''' Return the a stimulus table for an indexed time series.
Parameters
----------
inds :
frame_durations : np.ndarray
start and stop times (s) of frames
Returns
-------
stimulus table : pd.DataFrame
Describes the intervals of presentation of the stimulus
Notes
-----
For more information, see:
http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf
'''
stimulus_table = pd.DataFrame(inds, columns=['frame'])
stimulus_table.loc[:, 'start'] = frame_dur[:, 0].astype(int)
stimulus_table.loc[:, 'end'] = frame_dur[:, 1].astype(int)
stimulus_table = stimulus_table.sort_values(['start', 'end'])
return stimulus_table
def _make_repeated_indexed_time_series_stimulus_table(inds, frame_dur):
stimulus_table = _make_indexed_time_series_stimulus_table(inds, frame_dur)
a = stimulus_table.groupby(by='frame')
# If this ever occurs, the repeat counter cant be trusted!
assert np.floor(len(stimulus_table))/len(a) == int(len(stimulus_table))/len(a)
stimulus_table['repeat'] = np.repeat(range(len(stimulus_table)//len(a)), len(a))
return stimulus_table
def _make_spontaneous_activity_stimulus_table(events, frame_durations):
''' Builds a table describing the start and end times of the spontaneous viewing
intervals.
Parameters
----------
events : np.ndarray
events data
frame_durations : np.ndarray
start and stop times (s) of frames
Returns
-------
pd.DataFrame :
Each row describes an interval of spontaneous viewing. Columns are start and end times.
Notes
-----
For more information, see:
http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf
'''
start_inds = np.where(events == 1)
stop_inds = np.where(events == -1)
if len(start_inds) != len(stop_inds):
raise Exception(
"inconsistent start and time times in spontaneous activity stimulus table")
stim_data = np.column_stack([
frame_durations[start_inds, 0].T,
frame_durations[stop_inds, 0].T]
).astype(int)
stimulus_table = pd.DataFrame(stim_data, columns=['start', 'end'])
stimulus_table = stimulus_table.sort_values(['start', 'end'])
return stimulus_table