def test__issue_285(self):
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch([0, 10, 20], [2, 2, 2], ["a", "b", "c"], units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
def test__issue_285(self):
# Spiketrain
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch(np.array([0, 10, 20]),
np.array([2, 2, 2]),
np.array(["a", "b", "c"]),
units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
os.remove('blk.pkl')
# Epoch
epoch = Epoch(times=np.arange(0, 30, 10) * pq.s,
durations=[10, 5, 7] * pq.ms,
labels=np.array(['btn0', 'btn1', 'btn2'], dtype='U'))
epoch.segment = Segment()
blk = Block()
seg = Segment()
seg.epochs.append(epoch)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.epochs[0].segment, Segment)
os.remove('blk.pkl')
# Event
event = Event(np.arange(0, 30, 10) * pq.s,
labels=np.array(['trig0', 'trig1', 'trig2'], dtype='U'))
event.segment = Segment()
blk = Block()
seg = Segment()
seg.events.append(event)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.events[0].segment, Segment)
os.remove('blk.pkl')
# IrregularlySampledSignal
signal = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],
units='mV',
time_units='ms')
signal.segment = Segment()
blk = Block()
seg = Segment()
seg.irregularlysampledsignals.append(signal)
blk.segments.append(seg)
blk.segments[0].block = blk
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.irregularlysampledsignals[0].segment,
Segment)
os.remove('blk.pkl')
def test__issue_285(self):
##Spiketrain
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch([0, 10, 20], [2, 2, 2], ["a", "b", "c"], units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
os.remove('blk.pkl')
##Epoch
train = Epoch(times=np.arange(0, 30, 10)*pq.s,durations=[10, 5, 7]*pq.ms,labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##Event
train = Event(np.arange(0, 30, 10)*pq.s,labels=np.array(['trig0', 'trig1', 'trig2'],dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##IrregularlySampledSignal
train = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],units='mV', time_units='ms')
train.segment = Segment()
unit = Unit()
train.channel_index = ChannelIndex(1)
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
blk.segments[0].block = blk
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
self.assertIsInstance(r_seg.spiketrains[0].channel_index, ChannelIndex)
os.remove('blk.pkl')
from spikeAnalysis import *
from bayes_analyses import *
from neo_utils import *
from mechanics import *
from neo.io import PickleIO as PIO
import os
import glob
p = r'C:\Users\guru\Box Sync\__VG3D\deflection_trials\data'
p_save = r'C:\Users\guru\Box Sync\__VG3D\deflection_trials\figs'
files = glob.glob(os.path.join(p, '*.pkl'))
for f in files:
print os.path.basename(f)
fid = PIO(f)
blk = fid.read_block()
for unit in blk.channel_indexes[-1].units:
cell_num = int(unit.name[-1])
root = get_root(blk, cell_num)
M = get_var(blk)[0]
M = replace_NaNs(M)
sp = concatenate_sp(blk)
st = sp[unit.name]
kernel = elephant.kernels.GaussianKernel(5 * pq.ms)
r = np.array(
instantaneous_rate(st, sampling_period=pq.ms,
kernel=kernel)).ravel()
Mdot = get_deriv(M)
fig = plt.figure()
axy = fig.add_subplot(121)
axz = fig.add_subplot(122)
def main(p,file,save_path):
pre = 10*pq.ms
post = 10*pq.ms
fid = PIO(file)
blk = fid.read_block()
FR,ISI,contact_trains = get_contact_sliced_trains(blk,pre=pre,post=post)
binsize = 2*pq.ms
for unit in blk.channel_indexes[-1].units:
root = blk.annotations['ratnum'] + blk.annotations['whisker'] + 'c{}'.format(unit.name[-1])
trains = contact_trains[unit.name]
all_isi = np.array([])
CV_array = np.array([])
LV_array = np.array([])
for interval in ISI[unit.name]:
all_isi = np.concatenate([all_isi,interval])
if np.all(np.isfinite(interval)):
CV_array = np.concatenate([CV_array,[cv(interval)]])
LV_array = np.concatenate([LV_array,[lv(interval)]])
all_isi = all_isi * interval.units
CV_array = CV_array
CV = np.mean(CV_array)
LV = np.mean(LV_array)
## calculate data for PSTH
b,durations = get_binary_trains(contact_trains[unit.name])
b_times = np.where(b)[1] * pq.ms#interval.units
b_times-=pre
PSTH,t_edges = np.histogram(b_times,bins=np.arange(-np.array(pre),np.max(durations)+np.array(post),float(binsize)))
plt.bar(t_edges[:-1],
PSTH.astype('f8')/len(durations)/binsize*1000,
width=float(binsize),
align='edge',
alpha=0.8
)
ax = plt.gca()
thresh = 500 * pq.ms
ax.set_xlim(-15, thresh.__int__())
ax.set_xlabel('Time after contact (ms)')
ax.set_ylabel('Spikes per second')
ax.set_title('PSTH for: {}'.format(root))
plt.savefig(os.path.join(save_path,root+'_PSTH.svg'))
plt.close('all')
# ============================================
# PLOT ISIs
plt.figure()
thresh = 100 * pq.ms
if len(all_isi[np.logical_and(np.isfinite(all_isi), all_isi < thresh)])==0:
return
ax = sns.distplot(all_isi[np.logical_and(np.isfinite(all_isi), all_isi < thresh)],
bins=np.arange(0,100,1),
kde_kws={'color':'k','lw':3,'alpha':0.5,'label':'KDE'})
ax.set_xlabel('ISI '+all_isi.dimensionality.latex)
ax.set_ylabel('Percentage of all ISIs')
a_inset = plt.axes([.55, .5, .2, .2], facecolor='w')
a_inset.grid(color='k',linestyle=':',alpha=0.4)
a_inset.axvline(CV,color='k',lw=0.5)
a_inset.set_title('CV = {:0.2f}\nLV = {:0.2f}'.format(CV,LV))
a_inset.set_xlabel('CV')
a_inset.set_ylabel('# of Contacts')
sns.distplot(CV_array,color='g',kde=False)
ax.set_title('ISI distribution for {}'.format(root))
plt.savefig(os.path.join(save_path, root + '_ISI.svg'))
plt.close('all')