def test_invhash_base_not_two(self):
N = 3
h = np.array([1, 4, 13])
base = 3
expected = np.array([[0, 0, 1], [0, 1, 1], [1, 1, 1]])
m = ue.inverse_hash_from_pattern(h, N, base)
self.assertTrue(np.all(expected == m))
def test_invhash_shape_mat(self):
N = 8
h = np.array([178, 212, 232])
expected = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1],
[1, 1, 0], [1, 0, 1], [0, 1, 1], [1, 1, 1]])
m = ue.inverse_hash_from_pattern(h, N)
self.assertTrue(np.shape(m)[0] == N)
def test_invhash_base_not_two(self):
N = 3
h = np.array([1,4,13])
base = 3
expected = np.array([[0,0,1],[0,1,1],[1,1,1]])
m = ue.inverse_hash_from_pattern(h, N, base)
self.assertTrue(np.all(expected == m))
def test_invhash_default_base(self):
N = 3
h = np.array([0, 4, 2, 1, 6, 5, 3, 7])
expected = np.array([[0, 1, 0, 0, 1, 1, 0,
1], [0, 0, 1, 0, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1]])
m = ue.inverse_hash_from_pattern(h, N)
self.assertTrue(np.all(expected == m))
def test_hash_inverse_longpattern(self):
n_patterns = 100
m = np.random.randint(low=0, high=2, size=(n_patterns, 2))
h = ue.hash_from_pattern(m)
m_inv = ue.inverse_hash_from_pattern(h, N=n_patterns)
assert_array_equal(m, m_inv)
def test_hash_invhash_consistency(self):
m = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0],
[1, 0, 1], [0, 1, 1], [1, 1, 1]])
inv_h = ue.hash_from_pattern(m)
m1 = ue.inverse_hash_from_pattern(inv_h, N=8)
self.assertTrue(np.all(m == m1))
def _plot_UE(data,
Js_dict,
sig_level,
binsize,
winsize,
winstep,
pattern_hash,
N,
args,
add_epochs=[]):
"""
Examples:
---------
dict_args = {'events':{'SO':[100*pq.ms]},
'save_fig': True,
'path_filename_format':'UE1.pdf',
'showfig':True,
'suptitle':True,
'figsize':(12,10),
'unit_ids':[10, 19, 20],
'ch_ids':[1,3,4],
'fontsize':15,
'linewidth':2,
'set_xticks' :False'}
'marker_size':8,
"""
import matplotlib.pylab as plt
t_start = data[0][0].t_start
t_stop = data[0][0].t_stop
t_winpos = ue._winpos(t_start, t_stop, winsize, winstep)
Js_sig = ue.jointJ(sig_level)
num_tr = len(data)
pat = ue.inverse_hash_from_pattern(pattern_hash, N)
events = args['events']
# figure format
figsize = args['figsize']
if 'top' in args.keys():
top = args['top']
else:
top = .90
if 'bottom' in args.keys():
bottom = args['bottom']
else:
bottom = .05
if 'right' in args.keys():
right = args['right']
else:
right = .95
if 'left' in args.keys():
left = args['left']
else:
left = .1
if 'hspace' in args.keys():
hspace = args['hspace']
else:
hspace = .5
if 'wspace' in args.keys():
wspace = args['wspace']
else:
wspace = .5
if 'fontsize' in args.keys():
fsize = args['fontsize']
else:
fsize = 20
if 'unit_ids' in args.keys():
unit_real_ids = args['unit_ids']
if len(unit_real_ids) != N:
raise ValueError(
'length of unit_ids should be equal to number of neurons!')
else:
unit_real_ids = numpy.arange(1, N + 1, 1)
if 'ch_ids' in args.keys():
ch_real_ids = args['ch_ids']
if len(ch_real_ids) != N:
raise ValueError(
'length of ch_ids should be equal to number of neurons!')
else:
ch_real_ids = []
if 'showfig' in args.keys():
showfig = args['showfig']
else:
showfig = False
if 'linewidth' in args.keys():
lw = args['linewidth']
else:
lw = 2
if 'S_ylim' in args.keys():
S_ylim = args['S_ylim']
else:
S_ylim = [-3, 3]
if 'marker_size' in args.keys():
ms = args['marker_size']
else:
ms = 8
if add_epochs != []:
coincrate = add_epochs['coincrate']
backgroundrate = add_epochs['backgroundrate']
num_row = 6
else:
num_row = 5
num_col = 1
ls = '-'
alpha = 0.5
plt.figure(1, figsize=figsize)
if args['suptitle'] == True:
plt.suptitle("Spike Pattern:" + str((pat.T)[0]), fontsize=20)
print 'plotting UEs ...'
plt.subplots_adjust(top=top,
right=right,
left=left,
bottom=bottom,
hspace=hspace,
wspace=wspace)
ax = plt.subplot(num_row, 1, 1)
ax.set_title('Unitary Events', fontsize=20, color='r')
for n in range(N):
for tr, data_tr in enumerate(data):
plt.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=0.5,
color='k')
sig_idx_win = numpy.where(Js_dict['Js'] >= Js_sig)[0]
if len(sig_idx_win) > 0:
x = numpy.unique(Js_dict['indices']['trial' + str(tr)])
if len(x) > 0:
xx = []
for j in sig_idx_win:
xx = numpy.append(
xx, x[numpy.where((x * binsize >= t_winpos[j]) & (
x * binsize < t_winpos[j] + winsize))])
plt.plot(numpy.unique(xx) * binsize,
numpy.ones_like(numpy.unique(xx)) * tr + n *
(num_tr + 1) + 1,
ms=ms,
marker='s',
ls='',
mfc='none',
mec='r')
plt.axhline((tr + 2) * (n + 1), lw=2, color='k')
y_ticks_pos = numpy.arange(num_tr / 2 + 1, N * (num_tr + 1), num_tr + 1)
plt.yticks(y_ticks_pos)
plt.gca().set_yticklabels(unit_real_ids, fontsize=fsize)
for ch_cnt, ch_id in enumerate(ch_real_ids):
print ch_id
plt.gca().text((max(t_winpos) + winsize).rescale('ms').magnitude,
y_ticks_pos[ch_cnt],
'CH-' + str(ch_id),
fontsize=fsize)
plt.ylim(0, (tr + 2) * (n + 1) + 1)
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
plt.xticks([])
plt.ylabel('Unit ID', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
plt.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
if 'set_xticks' in args.keys() and args['set_xticks'] == False:
plt.xticks([])
print 'plotting Raw Coincidences ...'
ax1 = plt.subplot(num_row, 1, 2, sharex=ax)
ax1.set_title('Raw Coincidences', fontsize=20, color='c')
for n in range(N):
for tr, data_tr in enumerate(data):
plt.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=0.5,
color='k')
plt.plot(
numpy.unique(Js_dict['indices']['trial' + str(tr)]) * binsize,
numpy.ones_like(
numpy.unique(Js_dict['indices']['trial' + str(tr)])) * tr +
n * (num_tr + 1) + 1,
ls='',
ms=ms,
marker='s',
markerfacecolor='none',
markeredgecolor='c')
plt.axhline((tr + 2) * (n + 1), lw=2, color='k')
plt.ylim(0, (tr + 2) * (n + 1) + 1)
plt.yticks(numpy.arange(num_tr / 2 + 1, N * (num_tr + 1), num_tr + 1))
plt.gca().set_yticklabels(unit_real_ids, fontsize=fsize)
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
plt.xticks([])
plt.ylabel('Unit ID', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
plt.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
print 'plotting PSTH ...'
plt.subplot(num_row, 1, 3, sharex=ax)
#max_val_psth = 0.*pq.Hz
for n in range(N):
#data_psth = []
#for tr,data_tr in enumerate(data):
# data_psth.append(data_tr[p])
#psth = ss.peth(data_psth, w = psth_width)
#plt.plot(psth.times,psth.base/float(num_tr)/psth_width.rescale('s'), label = 'unit '+str(unit_real_ids[p]))
#max_val_psth = max(max_val_psth, max((psth.base/float(num_tr)/psth_width.rescale('s')).magnitude))
plt.plot(t_winpos + winsize / 2.,
Js_dict['rate_avg'][:, n].rescale('Hz'),
label='unit ' + str(unit_real_ids[n]),
lw=lw)
#max_val_psth = max(max_val_psth, max(Js_dict['rate_avg'][:,n].rescale('Hz')))
plt.ylabel('Rate [Hz]', fontsize=fsize)
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
max_val_psth = plt.gca().get_ylim()[1]
plt.ylim(0, max_val_psth)
plt.yticks([0, int(max_val_psth / 2), int(max_val_psth)], fontsize=fsize)
plt.legend(bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
for key in events.keys():
for e_val in events[key]:
plt.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
if 'set_xticks' in args.keys() and args['set_xticks'] == False:
plt.xticks([])
print 'plotting emp. and exp. coincidences rate ...'
plt.subplot(num_row, 1, 4, sharex=ax)
plt.plot(t_winpos + winsize / 2.,
Js_dict['n_emp'],
label='empirical',
lw=lw,
color='c')
plt.plot(t_winpos + winsize / 2.,
Js_dict['n_exp'],
label='expected',
lw=lw,
color='m')
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
plt.ylabel('# Coinc.', fontsize=fsize)
plt.legend(bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
YTicks = plt.ylim(0, int(max(max(Js_dict['n_emp']),
max(Js_dict['n_exp']))))
plt.yticks([0, YTicks[1]], fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
plt.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
if 'set_xticks' in args.keys() and args['set_xticks'] == False:
plt.xticks([])
print 'plotting Surprise ...'
plt.subplot(num_row, 1, 5, sharex=ax)
plt.plot(t_winpos + winsize / 2., Js_dict['Js'], lw=lw, color='k')
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
plt.axhline(Js_sig, ls='-', color='gray')
plt.axhline(-Js_sig, ls='-', color='gray')
plt.gca().text(10,
Js_sig + 0.2,
str(int(sig_level * 100)) + '%',
fontsize=fsize - 2,
color='gray')
plt.xticks(t_winpos.magnitude[::len(t_winpos) / 10])
plt.yticks([-2, 0, 2], fontsize=fsize)
plt.ylabel('S', fontsize=fsize)
plt.xlabel('Time [ms]', fontsize=fsize)
plt.ylim(S_ylim)
for key in events.keys():
for e_val in events[key]:
plt.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
plt.gca().text(e_val - 10 * pq.ms,
2 * S_ylim[0],
key,
fontsize=fsize,
color='r')
if 'set_xticks' in args.keys() and args['set_xticks'] == False:
plt.xticks([])
if add_epochs != []:
plt.subplot(num_row, 1, 6, sharex=ax)
plt.plot(coincrate, lw=lw, color='c')
plt.plot(backgroundrate, lw=lw, color='m')
plt.xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
plt.ylim(plt.gca().get_ylim()[0] - 2, plt.gca().get_ylim()[1] + 2)
if args['save_fig'] == True:
plt.savefig(args['path_filename_format'])
if showfig == False:
plt.cla()
plt.close()
# plt.xticks(t_winpos.magnitude)
if showfig == True:
plt.show()
def plot_figure3(data_list, Js_dict_lst_lst, sig_level, binsize, winsize,
winstep, patterns, N, N_comb, plot_params_user):
"""plots Figure 3 of the manuscript"""
import matplotlib.pylab as plt
# figure format
plot_params = plot_params_default
plot_params.update(plot_params_user)
globals().update(plot_params)
if len(unit_real_ids) != N:
raise ValueError(
'length of unit_ids should be equal to number of neurons!')
plt.rcParams.update({'font.size': fsize})
plt.rc('legend', fontsize=fsize)
ms1 = 0.5
plt.figure(1, figsize=figsize)
plt.subplots_adjust(top=top,
right=right,
left=left,
bottom=bottom,
hspace=hspace,
wspace=wspace)
events = [events1, events2]
ls = '-'
alpha = 1.
num_row, num_col = 3, 5
for data_cnt, data in enumerate(data_list):
Js_dict_lst = Js_dict_lst_lst[data_cnt]
t_start = data[0][0].t_start
t_stop = data[0][0].t_stop
t_winpos = ue._winpos(t_start, t_stop, winsize, winstep)
Js_sig = ue.jointJ(sig_level)
num_tr = len(data)
ax0 = plt.subplot2grid((num_row, num_col), (0, data_cnt * 3),
rowspan=1,
colspan=3 - data_cnt)
ax0.set_xticks([])
if data_cnt == 0:
ax0.set_title('Spike Events', fontsize=fsize)
ax0.set_yticks([num_tr / 2 + 1, num_tr * 3 / 2., num_tr * 5 / 2])
ax0.set_yticklabels(['5', '4', '3'], fontsize=fsize)
ax0.spines['right'].set_visible(False)
ax0.yaxis.set_ticks_position('left')
# ax0.text(-110, 190, 'Neuron #', rotation=90, fontsize=fsize)
ax0.set_ylabel('Neuron #', fontsize=fsize)
else:
ax0.spines['left'].set_visible(False)
ax0.yaxis.set_ticks_position('right')
ax0.set_yticks([])
for n in range(N):
for tr, data_tr in enumerate(data):
ax0.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=ms1,
color='k')
if n < N - 1:
ax0.axhline((tr + 2) * (n + 1), lw=0.5, color='k')
ax0.set_ylim(0, (tr + 2) * (n + 1) + 1)
for ev in events[data_cnt].keys():
ax0.axvline(events[data_cnt][ev], color='k')
ax1 = plt.subplot2grid((num_row, num_col), (1, data_cnt * 3),
rowspan=1,
colspan=3 - data_cnt)
ax1.set_xticks([])
if data_cnt == 0:
ax1.set_title('Coincident Events', fontsize=fsize)
ax1.text(-110, 190, 'Neuron #', rotation=90, fontsize=fsize)
ax1.set_yticks([num_tr / 2 + 1, num_tr * 3 / 2., num_tr * 5 / 2])
ax1.set_yticklabels(['5', '4', '3'], fontsize=fsize)
ax1.spines['right'].set_visible(False)
ax1.yaxis.set_ticks_position('left')
else:
ax1.spines['left'].set_visible(False)
ax1.yaxis.set_ticks_position('right')
ax1.set_yticks([])
for N_cnt, N_comb_sel in enumerate(N_comb):
patt_tmp = ue.inverse_hash_from_pattern([patterns[N_cnt]],
len(N_comb_sel))
for n_cnt, n in enumerate(N_comb_sel):
for tr, data_tr in enumerate(data):
if N_cnt == 0:
ax1.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr +
n * (num_tr + 1) + 1,
'.',
markersize=ms1,
color='k')
if patt_tmp[n_cnt][0] == 1:
ax1.plot(numpy.unique(
Js_dict_lst[N_cnt]['indices']['trial' + str(tr)]) *
binsize,
numpy.ones_like(
numpy.unique(Js_dict_lst[N_cnt]['indices']
['trial' + str(tr)])) * tr +
n * (num_tr + 1) + 1,
ls='',
ms=ms,
marker='s',
markerfacecolor='none',
markeredgecolor='c')
if N_comb_sel == [0, 1, 2] and n < len(N_comb_sel) - 1:
ax1.axhline((tr + 2) * (n + 1), lw=0.5, color='k')
for ev in events[data_cnt].keys():
ax1.axvline(events[data_cnt][ev], color='k')
ax2 = plt.subplot2grid((num_row, num_col), (2, data_cnt * 3),
rowspan=1,
colspan=3 - data_cnt)
ax2.set_xticks([])
if data_cnt == 0:
ax2.set_title('Unitary Events', fontsize=fsize)
ax2.text(-110, 190, 'Neuron #', rotation=90, fontsize=fsize)
ax2.set_yticks([num_tr / 2 + 1, num_tr * 3 / 2., num_tr * 5 / 2])
ax2.set_yticklabels(['5', '4', '3'], fontsize=fsize)
ax2.spines['right'].set_visible(False)
ax2.yaxis.set_ticks_position('left')
else:
ax2.spines['left'].set_visible(False)
ax2.yaxis.set_ticks_position('right')
ax2.set_yticks([])
for N_cnt, N_comb_sel in enumerate(N_comb):
patt_tmp = ue.inverse_hash_from_pattern([patterns[N_cnt]],
len(N_comb_sel))
for n_cnt, n in enumerate(N_comb_sel):
for tr, data_tr in enumerate(data):
if N_cnt == 0:
ax2.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr +
n * (num_tr + 1) + 1,
'.',
markersize=ms1,
color='k')
if patt_tmp[n_cnt][0] == 1:
sig_idx_win = numpy.where(
Js_dict_lst[N_cnt]['Js'] >= Js_sig)[0]
if len(sig_idx_win) > 0:
x = numpy.unique(
Js_dict_lst[N_cnt]['indices']['trial' +
str(tr)])
if len(x) > 0:
xx = []
for j in sig_idx_win:
xx = numpy.append(
xx, x[numpy.where(
(x * binsize >= t_winpos[j])
& (x * binsize < t_winpos[j] +
winsize))])
ax2.plot(
numpy.unique(xx) * binsize,
numpy.ones_like(numpy.unique(xx)) * tr +
n * (num_tr + 1) + 1,
ms=ms,
marker='s',
ls='',
mfc='none',
mec='r')
if N_comb_sel == [0, 1, 2] and n < len(N_comb_sel) - 1:
plt.axhline((tr + 2) * (n + 1), lw=0.5, color='k')
for ev in events[data_cnt].keys():
ax2.axvline(events[data_cnt][ev], color='k')
for key in events[data_cnt].keys():
for e_val in events[data_cnt][key]:
ax2.text(e_val - 10 * pq.ms,
plt.gca().get_ylim()[0] - 80,
key,
fontsize=fsize,
color='k')
if save_fig:
plt.savefig(path_filename_format)
if not showfig:
plt.cla()
plt.close()
if showfig:
plt.show()
def plot_figure1_2(data, Js_dict, sig_level, binsize, winsize, winstep,
pattern_hash, N, plot_params_user):
"""plots Figure 1 and Figure 2 of the manuscript"""
import matplotlib.pylab as plt
t_start = data[0][0].t_start
t_stop = data[0][0].t_stop
t_winpos = ue._winpos(t_start, t_stop, winsize, winstep)
Js_sig = ue.jointJ(sig_level)
num_tr = len(data)
pat = ue.inverse_hash_from_pattern(pattern_hash, N)
# figure format
plot_params = plot_params_default
plot_params.update(plot_params_user)
globals().update(plot_params)
if len(unit_real_ids) != N:
raise ValueError('length of unit_ids should be' +
'equal to number of neurons!')
plt.rcParams.update({'font.size': fsize})
plt.rc('legend', fontsize=fsize)
num_row, num_col = 6, 1
ls = '-'
alpha = 0.5
plt.figure(1, figsize=figsize)
if 'suptitle' in plot_params.keys():
plt.suptitle("Trial aligned on " + plot_params['suptitle'],
fontsize=20)
plt.subplots_adjust(top=top,
right=right,
left=left,
bottom=bottom,
hspace=hspace,
wspace=wspace)
print('plotting raster plot ...')
ax0 = plt.subplot(num_row, 1, 1)
ax0.set_title('Spike Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax0.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=0.5,
color='k')
if n < N - 1:
ax0.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax0.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax0.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax0.set_yticklabels([1, 15, 30], fontsize=fsize)
ax0.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax0.set_xticks([])
ax0.set_ylabel('Trial', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax0.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
Xlim = ax0.get_xlim()
ax0.text(Xlim[1] - 200, num_tr * 2 + 7, 'Neuron 2')
ax0.text(Xlim[1] - 200, -12, 'Neuron 3')
print('plotting Spike Rates ...')
ax1 = plt.subplot(num_row, 1, 2, sharex=ax0)
ax1.set_title('Spike Rates')
for n in range(N):
ax1.plot(t_winpos + winsize / 2.,
Js_dict['rate_avg'][:, n].rescale('Hz'),
label='Neuron ' + str(unit_real_ids[n]),
lw=lw)
ax1.set_ylabel('(1/s)', fontsize=fsize)
ax1.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
max_val_psth = 40
ax1.set_ylim(0, max_val_psth)
ax1.set_yticks([0, int(max_val_psth / 2), int(max_val_psth)])
ax1.legend(bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
for key in events.keys():
for e_val in events[key]:
ax1.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
ax1.set_xticks([])
print('plotting Raw Coincidences ...')
ax2 = plt.subplot(num_row, 1, 3, sharex=ax0)
ax2.set_title('Coincident Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax2.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=0.5,
color='k')
ax2.plot(
numpy.unique(Js_dict['indices']['trial' + str(tr)]) * binsize,
numpy.ones_like(
numpy.unique(Js_dict['indices']['trial' + str(tr)])) * tr +
n * (num_tr + 1) + 1,
ls='',
ms=ms,
marker='s',
markerfacecolor='none',
markeredgecolor='c')
if n < N - 1:
ax2.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax2.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax2.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax2.set_yticklabels([1, 15, 30], fontsize=fsize)
ax2.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax2.set_xticks([])
ax2.set_ylabel('Trial', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax2.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
print('plotting emp. and exp. coincidences rate ...')
ax3 = plt.subplot(num_row, 1, 4, sharex=ax0)
ax3.set_title('Coincidence Rates')
ax3.plot(t_winpos + winsize / 2.,
Js_dict['n_emp'] / (winsize.rescale('s').magnitude * num_tr),
label='empirical',
lw=lw,
color='c')
ax3.plot(t_winpos + winsize / 2.,
Js_dict['n_exp'] / (winsize.rescale('s').magnitude * num_tr),
label='expected',
lw=lw,
color='m')
ax3.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax3.set_ylabel('(1/s)', fontsize=fsize)
ax3.legend(bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
YTicks = ax3.get_ylim()
ax3.set_yticks([0, YTicks[1] / 2, YTicks[1]])
for key in events.keys():
for e_val in events[key]:
ax3.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
ax3.set_xticks([])
print('plotting Surprise ...')
ax4 = plt.subplot(num_row, 1, 5, sharex=ax0)
ax4.set_title('Statistical Significance')
ax4.plot(t_winpos + winsize / 2., Js_dict['Js'], lw=lw, color='k')
ax4.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax4.axhline(Js_sig, ls='-', color='r')
ax4.axhline(-Js_sig, ls='-', color='g')
ax4.text(t_winpos[30], Js_sig + 0.3, '$\\alpha +$', color='r')
ax4.text(t_winpos[30], -Js_sig - 0.5, '$\\alpha -$', color='g')
ax4.set_xticks(t_winpos.magnitude[::int(len(t_winpos) / 10)])
ax4.set_yticks([ue.jointJ(0.99), ue.jointJ(0.5), ue.jointJ(0.01)])
ax4.set_yticklabels([0.99, 0.5, 0.01])
ax4.set_ylim(S_ylim)
for key in events.keys():
for e_val in events[key]:
ax4.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
ax4.set_xticks([])
print('plotting UEs ...')
ax5 = plt.subplot(num_row, 1, 6, sharex=ax0)
ax5.set_title('Unitary Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax5.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) * tr + n *
(num_tr + 1) + 1,
'.',
markersize=0.5,
color='k')
sig_idx_win = numpy.where(Js_dict['Js'] >= Js_sig)[0]
if len(sig_idx_win) > 0:
x = numpy.unique(Js_dict['indices']['trial' + str(tr)])
if len(x) > 0:
xx = []
for j in sig_idx_win:
xx = numpy.append(
xx, x[numpy.where((x * binsize >= t_winpos[j]) & (
x * binsize < t_winpos[j] + winsize))])
ax5.plot(numpy.unique(xx) * binsize,
numpy.ones_like(numpy.unique(xx)) * tr + n *
(num_tr + 1) + 1,
ms=ms,
marker='s',
ls='',
mfc='none',
mec='r')
if n < N - 1:
ax5.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax5.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax5.set_yticklabels([1, 15, 30], fontsize=fsize)
ax5.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax5.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax5.set_xticks([])
ax5.set_ylabel('Trial', fontsize=fsize)
ax5.set_xlabel('Time [ms]', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax5.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
ax5.text(e_val - 10 * pq.ms,
S_ylim[0] - 35,
key,
fontsize=fsize,
color='r')
ax5.set_xticks([])
for i in range(num_row):
ax = locals()['ax' + str(i)]
ax.text(-0.05,
1.1,
string.ascii_uppercase[i],
transform=ax.transAxes,
size=fsize + 5,
weight='bold')
if plot_params['save_fig']:
plt.savefig(path_filename_format)
if not showfig:
plt.cla()
plt.close()
if showfig:
plt.show()
def test_hash_invhash_consistency(self):
m = np.array([[0, 0, 0],[1, 0, 0],[0, 1, 0],[0, 0, 1],[1, 1, 0],[1, 0, 1],[0, 1, 1],[1, 1, 1]])
inv_h = ue.hash_from_pattern(m, N=8)
m1 = ue.inverse_hash_from_pattern(inv_h, N = 8)
self.assertTrue(np.all(m == m1))
def test_invhash_shape_mat(self):
N = 8
h = np.array([178, 212, 232])
expected = np.array([[0,0,0], [1,0,0], [0,1,0], [0,0,1], [1,1,0],[1,0,1],[0,1,1],[1,1,1]])
m = ue.inverse_hash_from_pattern(h, N)
self.assertTrue(np.shape(m)[0] == N)
def test_invhash_default_base(self):
N = 3
h = np.array([0, 4, 2, 1, 6, 5, 3, 7])
expected = np.array([[0, 1, 0, 0, 1, 1, 0, 1],[0, 0, 1, 0, 1, 0, 1, 1],[0, 0, 0, 1, 0, 1, 1, 1]])
m = ue.inverse_hash_from_pattern(h, N)
self.assertTrue(np.all(expected == m))
def plot_UE(data, Js_dict, sig_level, binsize, winsize, winstep,
pattern_hash, N, plot_params_user):
"""plots Figure 1 and Figure 2 of the manuscript"""
t_start = data[0][0].t_start
t_stop = data[0][0].t_stop
t_winpos = ue._winpos(t_start, t_stop, winsize, winstep)
Js_sig = ue.jointJ(sig_level)
num_tr = len(data)
pat = ue.inverse_hash_from_pattern(pattern_hash, N)
# figure format
plot_params = plot_params_default
plot_params.update(plot_params_user)
globals().update(plot_params)
if len(unit_real_ids) != N:
raise ValueError('length of unit_ids should be' +
'equal to number of neurons!')
plt.rcParams.update({'font.size': fsize})
plt.rc('legend', fontsize=fsize)
num_row, num_col = 6, 1
ls = '-'
alpha = 0.5
plt.figure(1, figsize=figsize)
if 'suptitle' in plot_params.keys():
plt.suptitle("Trial aligned on " +
plot_params['suptitle'], fontsize=20)
plt.subplots_adjust(top=top, right=right, left=left,
bottom=bottom, hspace=hspace, wspace=wspace)
print('plotting raster plot ...')
ax0 = plt.subplot(num_row, 1, 1)
ax0.set_title('Spike Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax0.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) *
tr + n * (num_tr + 1) + 1,
'.', markersize=0.5, color='k')
if n < N - 1:
ax0.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax0.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax0.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax0.set_yticklabels([1, 15, 30], fontsize=fsize)
ax0.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax0.set_xticks([])
ax0.set_ylabel('Trial', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax0.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
Xlim = ax0.get_xlim()
ax0.text(Xlim[1] - 200, num_tr * 2 + 7, 'Neuron 2')
ax0.text(Xlim[1] - 200, -12, 'Neuron 3')
print('plotting Spike Rates ...')
ax1 = plt.subplot(num_row, 1, 2, sharex=ax0)
ax1.set_title('Spike Rates')
for n in range(N):
ax1.plot(t_winpos + winsize / 2.,
Js_dict['rate_avg'][:, n].rescale('Hz'),
label='Neuron ' + str(unit_real_ids[n]), lw=lw)
ax1.set_ylabel('(1/s)', fontsize=fsize)
ax1.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
max_val_psth = 40
ax1.set_ylim(0, max_val_psth)
ax1.set_yticks([0, int(max_val_psth / 2), int(max_val_psth)])
ax1.legend(
bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
for key in events.keys():
for e_val in events[key]:
ax1.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
ax1.set_xticks([])
print('plotting Raw Coincidences ...')
ax2 = plt.subplot(num_row, 1, 3, sharex=ax0)
ax2.set_title('Coincident Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax2.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) *
tr + n * (num_tr + 1) + 1,
'.', markersize=0.5, color='k')
ax2.plot(
numpy.unique(Js_dict['indices']['trial' + str(tr)]) *
binsize,
numpy.ones_like(numpy.unique(Js_dict['indices'][
'trial' + str(tr)])) * tr + n * (num_tr + 1) + 1,
ls='', ms=ms, marker='s', markerfacecolor='none',
markeredgecolor='c')
if n < N - 1:
ax2.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax2.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax2.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax2.set_yticklabels([1, 15, 30], fontsize=fsize)
ax2.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax2.set_xticks([])
ax2.set_ylabel('Trial', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax2.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
print('plotting emp. and exp. coincidences rate ...')
ax3 = plt.subplot(num_row, 1, 4, sharex=ax0)
ax3.set_title('Coincidence Rates')
ax3.plot(t_winpos + winsize / 2.,
Js_dict['n_emp'] / (winsize.rescale('s').magnitude * num_tr),
label='empirical', lw=lw, color='c')
ax3.plot(t_winpos + winsize / 2.,
Js_dict['n_exp'] / (winsize.rescale('s').magnitude * num_tr),
label='expected', lw=lw, color='m')
ax3.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax3.set_ylabel('(1/s)', fontsize=fsize)
ax3.legend(bbox_to_anchor=(1.12, 1.05), fancybox=True, shadow=True)
YTicks = ax3.get_ylim()
ax3.set_yticks([0, YTicks[1] / 2, YTicks[1]])
for key in events.keys():
for e_val in events[key]:
ax3.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
ax3.set_xticks([])
print('plotting Surprise ...')
ax4 = plt.subplot(num_row, 1, 5, sharex=ax0)
ax4.set_title('Statistical Significance')
ax4.plot(t_winpos + winsize / 2., Js_dict['Js'], lw=lw, color='k')
ax4.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax4.axhline(Js_sig, ls='-', color='r')
ax4.axhline(-Js_sig, ls='-', color='g')
ax4.text(t_winpos[30], Js_sig + 0.3, '$\\alpha +$', color='r')
ax4.text(t_winpos[30], -Js_sig - 0.5, '$\\alpha -$', color='g')
ax4.set_xticks(t_winpos.magnitude[::int(len(t_winpos) / 10)])
ax4.set_yticks([ue.jointJ(0.99), ue.jointJ(0.5), ue.jointJ(0.01)])
ax4.set_yticklabels([0.99, 0.5, 0.01])
ax4.set_ylim(S_ylim)
for key in events.keys():
for e_val in events[key]:
ax4.axvline(e_val, ls=ls, color='r', lw=lw, alpha=alpha)
ax4.set_xticks([])
print('plotting UEs ...')
ax5 = plt.subplot(num_row, 1, 6, sharex=ax0)
ax5.set_title('Unitary Events')
for n in range(N):
for tr, data_tr in enumerate(data):
ax5.plot(data_tr[n].rescale('ms').magnitude,
numpy.ones_like(data_tr[n].magnitude) *
tr + n * (num_tr + 1) + 1, '.',
markersize=0.5, color='k')
sig_idx_win = numpy.where(Js_dict['Js'] >= Js_sig)[0]
if len(sig_idx_win) > 0:
x = numpy.unique(Js_dict['indices']['trial' + str(tr)])
if len(x) > 0:
xx = []
for j in sig_idx_win:
xx = numpy.append(xx, x[numpy.where(
(x * binsize >= t_winpos[j]) &
(x * binsize < t_winpos[j] + winsize))])
ax5.plot(
numpy.unique(
xx) * binsize,
numpy.ones_like(numpy.unique(xx)) *
tr + n * (num_tr + 1) + 1,
ms=ms, marker='s', ls='', mfc='none', mec='r')
if n < N - 1:
ax5.axhline((tr + 2) * (n + 1), lw=2, color='k')
ax5.set_yticks([num_tr + 1, num_tr + 16, num_tr + 31])
ax5.set_yticklabels([1, 15, 30], fontsize=fsize)
ax5.set_ylim(0, (tr + 2) * (n + 1) + 1)
ax5.set_xlim(0, (max(t_winpos) + winsize).rescale('ms').magnitude)
ax5.set_xticks([])
ax5.set_ylabel('Trial', fontsize=fsize)
ax5.set_xlabel('Time [ms]', fontsize=fsize)
for key in events.keys():
for e_val in events[key]:
ax5.axvline(e_val, ls=ls, color='r', lw=2, alpha=alpha)
ax5.text(e_val - 10 * pq.ms,
S_ylim[0] - 35, key, fontsize=fsize, color='r')
ax5.set_xticks([])
for i in range(num_row):
ax = locals()['ax' + str(i)]
ax.text(-0.05, 1.1, string.ascii_uppercase[i],
transform=ax.transAxes, size=fsize + 5,
weight='bold')
if plot_params['save_fig']:
plt.savefig(path_filename_format)
if not showfig:
plt.cla()
plt.close()
if showfig:
plt.show()
return None
