def test_cell_type_subsamples(session='1', animal='MD0ST5', lab='dipoppa', expt='full_trial',
sample='sst85_sample_0', cell_id=3):
data_dir = get_user_dir('data')
path = os.path.join(data_dir, lab, expt, animal, session, 'data.hdf5')
if not os.path.exists(path):
print(path, ' does not exist.')
return
# Get cell types (Need to change this for different mice)
info_cells = loadmat('neural_dir/info_cells_MD0ST5_2018-04-04.mat')['info_cells']
cell_type_idxs = info_cells[0][0][0][0] # 0 = excitatory, 3 = inhibitory
good_cell_idxs = info_cells[0][0][1][0]
# Label all "bad" cells as -1
cell_type_idxs = [cell_type_idxs[i] if good_cell_idxs[i] == 1 else -1 for i in range(len(good_cell_idxs))]
def select_idxs(arr, key):
return [i for i in range(len(arr)) if arr[i] == key]
idxs = select_idxs(cell_type_idxs, cell_id)
data = h5py.File(path, 'r', libver='latest', swmr=True)
cell_types = data['samples']['cell_types']
session = cell_types[sample]
# Correct number of indexes
assert len(session[:]) == len(idxs)
# No repeats
assert len(set(session[:])) == len(idxs)
data.close()
print('Cell Type Subsample: Success')
def get_all_params(search_type='grid_search', args=None):
# Raise error if user has other command line arguments specified (as could override configs in
# confusing ways)
if args is not None and len(args) != 8:
raise ValueError(
'No command line arguments allowed other than config file names')
elif args is None and len(sys.argv[1:]) != 8:
raise ValueError(
'No command line arguments allowed other than config file names')
# Create parser
parser = HyperOptArgumentParser(strategy=search_type)
parser.add_argument('--data_config', type=str)
parser.add_argument('--model_config', type=str)
parser.add_argument('--training_config', type=str)
parser.add_argument('--compute_config', type=str)
namespace, extra = parser.parse_known_args(args)
# Add arguments from all configs
configs = [
namespace.data_config, namespace.model_config,
namespace.training_config, namespace.compute_config
]
for config in configs:
config_json = commentjson.load(open(config, 'r'))
for (key, value) in config_json.items():
add_to_parser(parser, key, value)
# Add save/user dirs
parser.add_argument('--save_dir', default=get_user_dir('save'), type=str)
parser.add_argument('--data_dir', default=get_user_dir('data'), type=str)
# Add parameters dependent on previous inputs
namespace, extra = parser.parse_known_args(args)
add_dependent_params(parser, namespace)
return parser.parse_args(args)
def main():
# Get hdf5 file
fs = []
data_dir = get_user_dir('data')
for i in [1, 3, 4, 5, 6]:
path = data_dir + '/dipoppa/MSP_pupil/SB028/{}/data.hdf5'.format(i)
fs.append(h5py.File(path, 'a', libver='latest', swmr=True))
print(fs)
# Get cell types
info_cells = loadmat(
'neural_dir/SB028/2019-11-08/info_cells_SB028_2019-11-08.mat'
)['info_cells']
cell_type_idxs = info_cells[0][0][0][0] # 0 = excitatory, 3 = inhibitory
good_cell_idxs = info_cells[0][0][1][0]
# Label all "bad" cells as -1
cell_type_idxs = [
cell_type_idxs[i] if good_cell_idxs[i] == 1 else -1
for i in range(len(good_cell_idxs))
]
def select_idxs(arr, key):
return [i for i in range(len(arr)) if arr[i] == key]
inh_id = [i for i in cell_type_idxs if i != 0 and i != -1][0]
print('Inhibitory Cell Type: ' + str(inh_id))
inh_idxs = select_idxs(cell_type_idxs, inh_id)
exc_idxs = select_idxs(cell_type_idxs, 0)
num_cells = len(cell_type_idxs)
print(num_cells, 'NUM CELLS')
print('inh: ', len(inh_idxs))
print('exc: ', len(exc_idxs))
'''
for f in fs:
if f.get('samples'):
del f['samples']
del f['samples']
'''
print('Adding subsamples')
add_subsamples(fs, num_cells)
# print('Adding Histogram Trials')
add_cell_type(fs, inh_idxs, exc_idxs)
print('Adding powerlaw samples')
cell_types_powerlaw(fs, inh_idxs, exc_idxs)
print('...done')
close(fs)
def sample_n_neurons(session_params, sample_sizes, n_samples, group_class='samples', group_name='subsamples', prefix=''):
'''
Create N samples of randomly selected neurons in behavenet's HDF5 files. Supports multiple sessions for a given animal,
but not multiple animals/experiements/labs.
Params:
session_params:dict: Each entry in session params dictionary should
contain another dictionary specifying the following fields which define a dataset:
- lab (str)
- experiment_name (str)
- animal (str)
- sessions (list)
sample_sizes:list: list specifying the number of neurons in each sample
n_samples:int: how many repeats to take of each sample size (ie; n=10 would add 10 samples for each sample size)
group_class:str: HDF5 upper level group name
group_name:str: name of second level group to add these samples under
prefix:str: identifying prefix for datasets
'''
lab = session_params.get('lab')
experiment_name = session_params.get('experiment_name')
animal = session_params.get('animal')
sessions = session_params.get('sessions')
for session in sessions:
data_path = os.path.join(behavenet.get_user_dir('data'), lab, experiment_name, animal, str(session), 'data.hdf5')
if not os.path.isfile(data_path):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), data_path)
logging.info('HDF5 File: {}'.format(data_path))
data = h5py.File(data_path, 'a', libver='latest', swmr=True)
ex_trial = list(data['neural'].keys())[0]
n_neural = data['neural'][ex_trial][:].shape[1]
# Delete old data
if group_name in data[group_class]:
logging.info('Deleting old group: {}'.format(group_name))
del data[group_class][group_name]
group = data[group_class].require_group(group_name)
for size in sample_sizes:
for i in range(n_samples):
dset = np.random.choice(n_neural, size, replace=False)
dset_name = prefix + 'n{}_t{}'.format(size, i)
logging.info('Adding dataset: {}'.format(dset_name))
group.create_dataset(dset_name, data=dset)
data.close()
logging.info('Added {} total datasets'.format(n_samples * len(sample_sizes)))
def add_cell_type_samples(session_params, inh_idxs, exc_idxs, n_samples, group_class='samples', group_name='cell_types', prefix=''):
'''
Adds n_samples of inhibitory and excitatory neurons. Uses the maximum number of neurons in the smaller
dataset as default number of neurons
params:
session_params:dict: Dictionary containing information to extract data.hdf5 file
inh_idxs:list: list of inhibitory cell indexes
exc_idxs:list: excitatory cell indexes
n_samples:int: how many random samples to take of each
group_class:str: higher level group to add data to
group_name:str: lower level group to add data to
prefix:str: unique naming prefix (otherwise will be labelled exc_t{trial_number}, inh_t{trial_number})
'''
lab = session_params.get('lab')
experiment_name = session_params.get('experiment_name')
animal = session_params.get('animal')
sessions = session_params.get('sessions')
for session_number in sessions:
data_path = os.path.join(behavenet.get_user_dir('data'), lab, experiment_name, animal, str(session_number), 'data.hdf5')
if not os.path.isfile(data_path):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), data_path)
logging.info('HDF5 File: {}'.format(data_path))
data = h5py.File(data_path, 'a', libver='latest', swmr=True)
data.require_group(group_class)
# Delete old data
if group_name in data[group_class]:
logging.info('Deleting old group: {}'.format(group_name))
#del data[group_class][group_name]
group = data[group_class].require_group(group_name)
n_neurons = min(len(inh_idxs), len(exc_idxs))
# Assumes that there are fewer inhibitory neurons than excitatory (so we only need one inhibitory sample)
# TODO: Probably a fair assumption, but correct this
inh_dataset = np.random.choice(inh_idxs, n_neurons, replace=False)
group.create_dataset(prefix + 'inh_all', data=inh_dataset)
logging.info('Added Inhibitory dataset: {}'.format(inh_dataset.shape))
for i in range(n_samples):
exc_dataset = np.random.choice(exc_idxs, n_neurons, replace=False)
group.create_dataset(prefix + 'exc_t{}'.format(i), data=exc_dataset)
logging.info('Added excitatory dataset {}: {}'.format(i, exc_dataset.shape))
data.close()
def test_subsamples(session='1', animal='MD0ST5', lab='dipoppa', expt='full_trial',
sample='sample_100_t0', cell_id=3):
data_dir = get_user_dir('data')
path = os.path.join(data_dir, lab, expt, animal, session, 'data.hdf5')
if not os.path.exists(path):
print(path, ' does not exist.')
return
data = h5py.File(path, 'r', libver='latest', swmr=True)
subsamples = data['samples']['subsamples']
session = subsamples[sample]
# No duplicates
assert len(set(session[:])) == len(session[:])
print('Subsample Test: Success')
data.close()
def get_cell_idxs(session_params, inh_key=3):
'''
Return indexes of all the inhibitory, excitatory, and overall good cells
Requires info_cells file in same session level directory of behavenet's data directory
(ie; if my data.hdf5 files are in "lab/experiment/animal/session_1/data.hdf5", the info_cells file
should be in the same folder as "session_1")
Params:
session_params:dict: Dictionary specifying relevant details for session
inh_key:int: key indicitating inhibitory cell type (2: PV, 3: SST, 5: GAD)
Returns:
inhibitory indexes, excitatory indexes, all good cell indexes
'''
lab = session_params.get('lab')
experiment_name = session_params.get('experiment_name')
animal = session_params.get('animal')
date = session_params.get('date')
# Load file and separate contents
key = 'info_cells_' + animal + '_' + date + '.mat'
cell_data_path = os.path.join(behavenet.get_user_dir('data'), lab, experiment_name, animal, key)
if not os.path.exists(cell_data_path):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), cell_data_path)
info_cells = loadmat(cell_data_path)['info_cells']
good_cells = info_cells[0][0][1][0]
cell_types = info_cells[0][0][0][0]
good_cells = info_cells[0][0][1][0]
cell_types = info_cells[0][0][0][0]
# Remove bad cells from neural activity and labelled cell data
bad_cells = [i for i in range(len(good_cells)) if good_cells[i] == 0]
cell_types = np.delete(cell_types, bad_cells, axis=0)
# Collect indexes of inhibitory and excitatory neurons
inh_idxs = [idx for idx,key in enumerate(cell_types) if key == inh_key]
exc_idxs = [idx for idx,key in enumerate(cell_types) if key == 0]
return inh_idxs, exc_idxs, list(range(len(cell_types)))
def process(sess_id,
lab='dipoppa',
expt='SSSVAE',
animal='MD0ST5',
date='2018-04-04',
neural_data_root='/home/yoni/behavenet/neural_dir'):
vid_id = '{}_{}_{}'.format(date, sess_id, animal)
neural_dir = os.path.join(neural_data_root, animal, date)
# data will be stored in data_dir/lab/expt/animal/session/data.hdf5
lab = lab
expt = expt
animal = animal
date = date
session = sess_id
mp4_file = os.path.join(
neural_dir, session,
vid_id + '_eye.mj2') # (can be any format loadable by open cv)
# video frames will be resized to these dimensions
# downsampling is preferrable as it speeds up autoencoder fitting time
# we typically use images with the largest dimension <= 256
xpix = 256 # choose a number (e.g. 256)
ypix = 128 # choose a number (e.g. 128)
# processed data in behavenet format will be stored here
data_dir = get_user_dir('data')
proc_data_filepath = os.path.join(data_dir, lab, expt, animal, session)
print('Writing to: ', proc_data_filepath, 'Video from: ', mp4_file)
###########
# Load data
###########
# set up hdf5 file
hdf5_file = os.path.join(proc_data_filepath, 'data.hdf5')
if False: # os.path.exists(hdf5_file):
raise IOError('data.hdf5 file already exists; skipping')
else:
hdf5_dir = os.path.dirname(hdf5_file)
if not os.path.exists(hdf5_dir):
os.makedirs(hdf5_dir)
# read video file and check
cap = cv2.VideoCapture(mp4_file)
if not cap.isOpened():
raise IOError('error opening video file at %s' % mp4_file)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print('Loading and processing data...')
# Load trial info to sync video/neural data, and neural activity
vvi = loadmat(
os.path.join(neural_dir, sess_id,
'batch_video_id' + vid_id + '.mat'))['batch_video_id']
nni = loadmat(
os.path.join(neural_dir, sess_id,
'batch_neural_id' + vid_id + '.mat'))['batch_neural_id']
nna = loadmat(
os.path.join(neural_dir, sess_id,
'neural_activity' + vid_id + '.mat'))['neural_activity']
# Load cell labels
cell_data_path = os.path.join(neural_dir,
'info_cells_{}_{}.mat'.format(animal, date))
info_cells = loadmat(cell_data_path)['info_cells']
good_cells = info_cells[0][0][1][0]
cell_types = info_cells[0][0][0][0]
# Remove bad cells from neural activity and labelled cell data
bad_cells = [i for i in range(len(good_cells)) if good_cells[i] == 0]
cell_types = np.delete(cell_types, bad_cells, axis=0)
nna = np.delete(nna, bad_cells, axis=1)
nna = ZScore(nna)
# Collect indexes of inhibitory and excitatory neurons
inh_idxs = [i for i in range(len(cell_types)) if cell_types[i] == 3]
exc_idxs = [i for i in range(len(cell_types)) if cell_types[i] == 0]
# Load facemap data and create labels
facemap_data = np.load(os.path.join(neural_dir, sess_id, 'facemap',
vid_id + '_eye_proc.npy'),
allow_pickle=True).item()
pupil_area = facemap_data['pupil'][0]['area_smooth'].reshape(-1, 1)
pupil_com = facemap_data['pupil'][0]['com_smooth']
print('Neural data: ', nna.shape, '# Inhibitory neurons: ', len(inh_idxs),
'# Excitatory neurons: ', len(exc_idxs))
# Labels_sc are the un-normalized versions of the labels
labels_sc = np.concatenate([pupil_area, pupil_com], axis=1)
# Labels are a collection of ZScored pupil area and center of mass
p_area_norm = ZScore(pupil_area, axis=0)
p_com_norm = ZScore(pupil_com, axis=0)
labels = np.concatenate([p_area_norm, p_com_norm], axis=1)
print('Label shape (scaled+unscaled): ', labels.shape, labels_sc.shape)
n_trials = vvi.shape[1]
print(hdf5_file)
print('Creating hdf5 file')
t_beg = time.time()
f = h5py.File(hdf5_file, 'a', libver='latest', swmr=True)
with f as f:
# single write multi-read
f.swmr_mode = True
# create image group
group_i = f.require_group('images')
# create neural group
group_n = f.require_group('neural')
# Create group for behavioral variables
group_labels = f.require_group('labels')
group_labels_sc = f.require_group('labels_sc')
# create a dataset for each trial within groups
t = 0
for trial in range(n_trials):
if trial % 10 == 0:
print('processing trial %03i' % trial)
# find video indices during this trial
trial_beg = vvi[0, trial]
trial_end = vvi[1, trial]
ts_idxs = np.arange(trial_beg - 1, trial_end)
# load and process corresponding frames
frames = get_frames_from_idxs(cap, ts_idxs)
sh = frames.shape
frames_proc = np.zeros((sh[0], sh[1], ypix, xpix), dtype='uint8')
for i in range(sh[0]):
frames_proc[i, 0, :, :] = cv2.resize(frames[i, 0],
(xpix, ypix))
# save image data
group_i.create_dataset('trial_%04i' % t,
data=frames_proc,
dtype='uint8')
label_frames = labels[ts_idxs]
label_sc_frames = labels_sc[ts_idxs]
#print('Image: ', frames_proc.shape, 'Labels: ', label_frames.shape, label_sc_frames.shape)
group_labels.create_dataset('trial_%04i' % t,
data=label_frames,
dtype='float32')
group_labels_sc.create_dataset('trial_%04i' % t,
data=label_sc_frames,
dtype='float32')
# find neural indices during this trial
trial_beg = nni[0, trial]
trial_end = nni[1, trial]
# pick out corresponding neural activity
neural = nna[trial_beg - 1:trial_end, :]
# save neural data
group_n.create_dataset('trial_%04i' % t,
data=neural,
dtype='float32')
t += 1
# Add variable size subsamples
samples = f.create_group('samples')
subsamples = samples.create_group('subsamples')
subsample_sizes = [
20, 40, 60, 80, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,
1100, 1200, 1300
]
n_cells = range(nna.shape[1])
for size in subsample_sizes:
for trial in range(10):
# Choose random sample w/out replacement from all neural indexes
subsample = np.random.choice(n_cells, size, replace=False)
subsamples.create_dataset('n{}_t{}'.format(size, trial),
data=subsample)
# Add Inh/Exc subsamples
cell_types = samples.create_group('cell_types')
cell_types.create_dataset('inh_t0', data=inh_idxs, dtype='uint8')
for i in range(50):
exc_sample = np.random.choice(exc_idxs,
len(inh_idxs),
replace=False)
cell_types.create_dataset('exc_t{}'.format(i),
data=exc_sample,
dtype='uint8')
# print out timing info
t_end = time.time()
t_tot = t_end - t_beg
print('Processed {} frames in total'.format(t))
print('total processing time: %f sec' % t_tot)
print('time per trial: %f sec' % (t_tot / n_trials))
import os
import h5py
from tqdm import tqdm
import behavenet
import numpy as np
mouse = 'MD0ST5'
expt = 'full_trial'
sessions = ['1', '2', '3', '4']
for session in sessions:
data_path = os.path.join(behavenet.get_user_dir('data'), 'dipoppa', expt, mouse, session, 'data.hdf5')
f = h5py.File(data_path, 'a', swmr=True, libver='latest')
# Concatenate all trials together
data = np.array([])
neural = f['neural']
print('Loading data for session: ', session)
for trial_name in tqdm(neural):
trial = neural[trial_name][:]
if data.size == 0:
data = trial
else:
data = np.concatenate([data, trial], axis=0)
print('Computing mean, std...')
