# Copyright 2017. Allen Institute. All rights reserved
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import pandas as pd
import numpy as np
try:
from distutils.version import LooseVersion
use_sort_values = LooseVersion(pd.__version__) >= LooseVersion('0.19.0')
except:
use_sort_values = False
[docs]def spikes2dict(spikes_file):
spikes_df = pd.read_csv(spikes_file, sep=' ', names=['time', 'gid'])
if use_sort_values:
spikes_sorted = spikes_df.sort_values(['gid', 'time'])
else:
spikes_sorted = spikes_df.sort(['gid', 'time'])
spike_dict = {}
for gid, spike_train in spikes_sorted.groupby('gid'):
spike_dict[gid] = np.array(spike_train['time'])
return spike_dict
[docs]def spike_files_equal(spikes_txt_1, spikes_txt_2, err=0.0001):
trial_1 = spikes2dict(spikes_txt_1)
trial_2 = spikes2dict(spikes_txt_2)
if set(trial_1.keys()) != set(trial_2.keys()):
return False
for gid, spike_train1 in trial_1.items():
spike_train2 = trial_2[gid]
if len(spike_train1) != len(spike_train2):
return False
for s1, s2 in zip(spike_train1, spike_train2):
if abs(s1 - s2) > err:
return False
return True
[docs]def get_mean_firing_rates(spike_gids, node_ids, tstop_msec):
"""
Compute mean firing rate over the duration of the simulation
:param spike_gids: gids of cells which spiked
:param node_ids: np.array of node_ids
:return mean_firing_rate: np.array mean firing rates
"""
min_gid = np.min(node_ids)
max_gid = np.max(node_ids)
gid_bins = np.arange(min_gid-0.5,max_gid+1.5,1)
hist,bins = np.histogram(spike_gids, bins=gid_bins)
tstop_sec = tstop_msec*1E-3
mean_firing_rates = hist/tstop_sec
return mean_firing_rates
[docs]def spikes_equal_in_window(spikes1,spikes2,twindow):
"""
Compare spikes within a time window
:param spikes1: dict with "time" and "gid" arrays for raster 1
:param spikes2: dict with "time" and "gid" arrays for raster 2
:param twindow: [tstart,tend] time window
:return boolean: True if equal, False if different
"""
ix1_window0=np.where(spikes1["time"]>twindow[0])
ix1_window1=np.where(spikes1["time"]<twindow[1])
ix1_window = np.intersect1d(ix1_window0,ix1_window1)
ix2_window0=np.where(spikes2["time"]>twindow[0])
ix2_window1=np.where(spikes2["time"]<twindow[1])
ix2_window = np.intersect1d(ix2_window0,ix2_window1)
print(len(spikes1["time"][ix1_window]),len(spikes2["time"][ix2_window]))
if len(spikes1["time"][ix1_window]) != len(spikes2["time"][ix2_window]):
print("There is a DIFFERENT number of spikes in each file within the window")
print("No point to compare individual spikes")
return
else:
print("number of spikes are the same, checking details...")
ix1_sort = np.argsort(spikes1["time"][ix1_window],kind="mergesort")
ix2_sort = np.argsort(spikes2["time"][ix2_window],kind="mergesort")
if (np.array_equal(spikes1["gid"][ix1_window[ix1_sort]],spikes2["gid"][ix2_window[ix2_sort]])) and (np.array_equal(spikes1["time"][ix1_window[ix1_sort]],spikes2["time"][ix2_window[ix2_sort]])):
print("spikes are IDENTICAL!")
return True
else:
print("spikes are DIFFERENT")
return False
