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spikedata.py
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spikedata.py
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import sys, math, copy
from collections import defaultdict
import numpy as np
from neuroh5.io import scatter_read_cell_attributes, read_cell_attributes, read_population_names, read_population_ranges, write_cell_attributes
import ca1
from ca1.utils import get_module_logger, viewitems, zip, Struct, get_trial_time_ranges, baks
## This logger will inherit its setting from its root logger
## which is created in module env
logger = get_module_logger(__name__)
default_baks_analysis_options = Struct(**{'BAKS Alpha': 4.77,
'BAKS Beta': None})
def get_env_spike_dict(env, include_artificial=True):
"""
Constructs a dictionary with per-gid per-trial spike times from the output vectors with spike times and gids contained in env.
"""
equilibration_duration = float(env.stimulus_config['Equilibration Duration'])
n_trials = env.n_trials
t_vec = np.array(env.t_vec, dtype=np.float32)
id_vec = np.array(env.id_vec, dtype=np.uint32)
trial_time_ranges = get_trial_time_ranges(env.t_rec.to_python(), env.n_trials)
trial_time_bins = [ t_trial_start for t_trial_start, t_trial_end in trial_time_ranges ]
trial_dur = np.asarray([env.tstop + equilibration_duration] * n_trials, dtype=np.float32)
binlst = []
typelst = sorted(env.celltypes.keys())
binvect = np.asarray([env.celltypes[k]['start'] for k in typelst ])
sort_idx = np.argsort(binvect, axis=0)
pop_names = [typelst[i] for i in sort_idx]
bins = binvect[sort_idx][1:]
inds = np.digitize(id_vec, bins)
pop_spkdict = {}
for i, pop_name in enumerate(pop_names):
spkdict = {}
sinds = np.where(inds == i)
if len(sinds) > 0:
ids = id_vec[sinds]
ts = t_vec[sinds]
for j in range(0, len(ids)):
gid = ids[j]
t = ts[j]
if (not include_artificial) and (gid in env.artificial_cells[pop_name]):
continue
if gid in spkdict:
spkdict[gid].append(t)
else:
spkdict[gid] = [t]
for gid in spkdict:
spiketrain = np.array(spkdict[gid], dtype=np.float32)
if gid in env.spike_onset_delay:
spiketrain -= env.spike_onset_delay[gid]
trial_bins = np.digitize(spiketrain, trial_time_bins) - 1
trial_spikes = [np.copy(spiketrain[np.where(trial_bins == trial_i)[0]])
for trial_i in range(env.n_trials)]
for trial_i, trial_spiketrain in enumerate(trial_spikes):
trial_spiketrain -= np.sum(trial_dur[:(trial_i)]) + equilibration_duration
spkdict[gid] = trial_spikes
pop_spkdict[pop_name] = spkdict
return pop_spkdict
def read_spike_events(input_file, population_names, namespace_id, spike_train_attr_name='t', time_range=None,
max_spikes=None, n_trials=-1, merge_trials=False, comm=None, io_size=0, include_artificial=True):
"""
Reads spike trains from a NeuroH5 file, and returns a dictionary with spike times and cell indices.
:param input_file: str (path to file)
:param population_names: list of str
:param namespace_id: str
:param spike_train_attr_name: str
:param time_range: list of float
:param max_spikes: float
:param n_trials: int
:param merge_trials: bool
:return: dict
"""
assert((n_trials >= 1) | (n_trials == -1))
trial_index_attr = 'Trial Index'
trial_dur_attr = 'Trial Duration'
artificial_attr = 'artificial'
spkpoplst = []
spkindlst = []
spktlst = []
spktrials = []
num_cell_spks = {}
pop_active_cells = {}
tmin = float('inf')
tmax = 0.
for pop_name in population_names:
if time_range is None or time_range[1] is None:
logger.info('Reading spike data for population %s...' % pop_name)
else:
logger.info('Reading spike data for population %s in time range %s...' % (pop_name, str(time_range)))
spike_train_attr_set = set([spike_train_attr_name, trial_index_attr, trial_dur_attr, artificial_attr])
spkiter_dict = scatter_read_cell_attributes(input_file, pop_name, namespaces=[namespace_id],
mask=spike_train_attr_set, comm=comm, io_size=io_size)
spkiter = spkiter_dict[namespace_id]
this_num_cell_spks = 0
active_set = set([])
pop_spkindlst = []
pop_spktlst = []
pop_spktriallst = []
logger.info('Read spike cell attributes for population %s...' % pop_name)
# Time Range
if time_range is not None:
if time_range[0] is None:
time_range[0] = 0.0
for spkind, spkattrs in spkiter:
is_artificial_flag = spkattrs.get(artificial_attr, None)
is_artificial = (is_artificial_flag[0] > 0) if is_artificial_flag is not None else None
if is_artificial is not None:
if is_artificial and (not include_artificial):
continue
slen = len(spkattrs[spike_train_attr_name])
trial_dur = spkattrs.get(trial_dur_attr, np.asarray([0.]))
trial_ind = spkattrs.get(trial_index_attr, np.zeros((slen,),dtype=np.uint8))
if n_trials == -1:
n_trials = len(set(trial_ind))
filtered_spk_idxs_by_trial = np.argwhere(trial_ind <= n_trials).ravel()
filtered_spkts = spkattrs[spike_train_attr_name][filtered_spk_idxs_by_trial]
filtered_trial_ind = trial_ind[filtered_spk_idxs_by_trial]
if time_range is not None:
filtered_spk_idxs_by_time = np.argwhere(np.logical_and(filtered_spkts >= time_range[0],
filtered_spkts <= time_range[1])).ravel()
filtered_spkts = filtered_spkts[filtered_spk_idxs_by_time]
filtered_trial_ind = filtered_trial_ind[filtered_spk_idxs_by_time]
pop_spkindlst.append(np.repeat([spkind], len(filtered_spkts)).astype(np.uint32))
pop_spktriallst.append(filtered_trial_ind)
this_num_cell_spks += len(filtered_spkts)
active_set.add(spkind)
for i, spkt in enumerate(filtered_spkts):
trial_i = filtered_trial_ind[i]
if merge_trials:
spkt += np.sum(trial_dur[:trial_i])
pop_spktlst.append(spkt)
tmin = min(tmin, spkt)
tmax = max(tmax, spkt)
pop_active_cells[pop_name] = active_set
num_cell_spks[pop_name] = this_num_cell_spks
if not active_set:
continue
pop_spkts = np.asarray(pop_spktlst, dtype=np.float32)
del (pop_spktlst)
pop_spkinds = np.concatenate(pop_spkindlst, dtype=np.uint32)
del (pop_spkindlst)
pop_spktrials = np.concatenate(pop_spktriallst, dtype=np.uint32)
del (pop_spktriallst)
# Limit to max_spikes
if (max_spikes is not None) and (len(pop_spkts) > max_spikes):
logger.warn(' Reading only randomly sampled %i out of %i spikes for population %s' %
(max_spikes, len(pop_spkts), pop_name))
sample_inds = np.random.randint(0, len(pop_spkinds) - 1, size=int(max_spikes))
pop_spkts = pop_spkts[sample_inds]
pop_spkinds = pop_spkinds[sample_inds]
pop_spktrials = pop_spkinds[sample_inds]
tmax = max(tmax, max(pop_spkts))
spkpoplst.append(pop_name)
pop_trial_spkindlst = []
pop_trial_spktlst = []
for trial_i in range(n_trials):
trial_idxs = np.where(pop_spktrials == trial_i)[0]
sorted_trial_idxs = np.argsort(pop_spkts[trial_idxs])
pop_trial_spktlst.append(np.take(pop_spkts[trial_idxs], sorted_trial_idxs))
pop_trial_spkindlst.append(np.take(pop_spkinds[trial_idxs], sorted_trial_idxs))
del pop_spkts
del pop_spkinds
del pop_spktrials
if merge_trials:
pop_spkinds = np.concatenate(pop_trial_spkindlst)
pop_spktlst = np.concatenate(pop_trial_spktlst)
spkindlst.append(pop_spkinds)
spktlst.append(pop_spktlst)
else:
spkindlst.append(pop_trial_spkindlst)
spktlst.append(pop_trial_spktlst)
logger.info(' Read %i spikes and %i trials for population %s' % (this_num_cell_spks, n_trials, pop_name))
return {'spkpoplst': spkpoplst, 'spktlst': spktlst, 'spkindlst': spkindlst,
'tmin': tmin, 'tmax': tmax,
'pop_active_cells': pop_active_cells, 'num_cell_spks': num_cell_spks,
'n_trials': n_trials}
def make_spike_dict(spkinds, spkts):
"""
Given arrays with cell indices and spike times, returns a dictionary with per-cell spike times.
"""
spk_dict = defaultdict(list)
for spkind, spkt in zip(np.nditer(spkinds), np.nditer(spkts)):
spk_dict[int(spkind)].append(float(spkt))
return spk_dict
def spike_density_estimate(population, spkdict, time_bins, arena_id=None, trajectory_id=None, output_file_path=None,
progress=False, inferred_rate_attr_name='Inferred Rate Map', **kwargs):
"""
Calculates spike density function for the given spike trains.
:param population:
:param spkdict:
:param time_bins:
:param arena_id: str
:param trajectory_id: str
:param output_file_path:
:param progress:
:param inferred_rate_attr_name: str
:param kwargs: dict
:return: dict
"""
if progress:
from tqdm import tqdm
analysis_options = copy.copy(default_baks_analysis_options)
analysis_options.update(kwargs)
def make_spktrain(lst, t_start, t_stop):
spkts = np.asarray(lst, dtype=np.float32)
return spkts[(spkts >= t_start) & (spkts <= t_stop)]
t_start = time_bins[0]
t_stop = time_bins[-1]
spktrains = {ind: make_spktrain(lst, t_start, t_stop) for (ind, lst) in viewitems(spkdict)}
baks_args = dict()
baks_args['a'] = analysis_options['BAKS Alpha']
baks_args['b'] = analysis_options['BAKS Beta']
if progress:
seq = tqdm(viewitems(spktrains))
else:
seq = viewitems(spktrains)
spk_rate_dict = {ind: baks(spkts / 1000., time_bins / 1000., **baks_args)[0].reshape((-1,))
if len(spkts) > 1 else np.zeros(time_bins.shape)
for ind, spkts in seq}
if output_file_path is not None:
if arena_id is None or trajectory_id is None:
raise RuntimeError('spike_density_estimate: arena_id and trajectory_id required to write Spike Density'
'Function namespace')
namespace = 'Spike Density Function %s %s' % (arena_id, trajectory_id)
attr_dict = {ind: {inferred_rate_attr_name: np.asarray(spk_rate_dict[ind], dtype='float32')}
for ind in spk_rate_dict}
write_cell_attributes(output_file_path, population, attr_dict, namespace=namespace)
result = {ind: {'rate': rate, 'time': time_bins} for ind, rate in viewitems(spk_rate_dict)}
result = { ind: { 'rate': rate, 'time': time_bins }
for ind, rate in viewitems(spk_rate_dict) }
return result
def spike_bin_counts(spkdict, time_bins):
bin_dict = {}
for (ind, lst) in viewitems(spkdict):
if len(lst) > 0:
spkts = np.asarray(lst, dtype=np.float32)
bins, bin_edges = np.histogram(spkts, bins=time_bins)
bin_dict[ind] = bins
return bin_dict