sum_vals.append(vals)
fig = plt.figure()
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
xvals = []
xlbls = []
offset = 0
divider = 2
for i in range(len(groups)):
base_ind = i * divider
print base_ind
dt = np.array(csv_vals[base_ind: base_ind + divider]).T
offsets = np.arange(dt.shape[1]) + offset
mean_adjust = (i != 1)
do_point_line_plot(dt, offsets, mean_adjust=mean_adjust)
# for j in range(dt.shape[1]):
# p_offset = -0.2
# if dt[j].sum() > 0:
# for ind in range(j + 1, dt.shape[1]):
# if dt[ind].sum() == 0:
# continue
# stat, p = scipy.stats.ttest_ind(dt[j], dt[ind])
# print 'p', p, j, ind
# if p < 0.05:
# plt.scatter(offset + j + p_offset,
# 0.95, c=colors[ind],
# edgecolor=colors[ind],
# marker='*')
# p_offset *= -1
fig8 = plt.figure(figsize=(4.5, 3.5))
fig8.set_facecolor('white')
plt.hold(True)
offset = 1
xvals = []
xlbls = []
ax = plt.subplot(111)
adjust_spines(ax, ['left', 'bottom'])
for exp_type in exp_types:
dt = []
offsets = offset + np.arange(len(stim_types))
plt.text(offsets.mean(), 1.02, exp_type, ha='center')
for i, k in enumerate(stim_types):
dt.append(cell_max_time[exp_type][k][:, 1])
dt = np.array(dt).T
do_point_line_plot(dt, offsets, mean_adjust=True, width=0.7)
xvals += offsets.tolist()
xlbls += stim_types_short
offset = offsets.max() + 2
print xvals
plt.ylabel('Corr of Cells to Prediction')
#plt.ylim(-0.01, 1)
plt.xticks(xvals, xlbls, rotation='vertical')
plt.subplots_adjust(left=0.12,
bottom=0.2,
right=0.97,
top=0.95,
wspace=0.23,
hspace=0.23)
fig_path = startup.fig_path + 'Sparseness/summary/'
fig8.savefig(fig_path + '%s_pred_summary.eps' % (str(filt)))
fig8 = plt.figure(figsize=(4.5, 3.5))
fig8.set_facecolor('white')
plt.hold(True)
offset = 1
xvals = []
xlbls = []
ax = plt.subplot(111)
adjust_spines(ax, ['left', 'bottom'])
for exp_type in exp_types:
dt = []
offsets = offset + np.arange(len(stim_types))
plt.text(offsets.mean(), 1.02, exp_type, ha='center')
for i, k in enumerate(stim_types):
dt.append(cell_max_time[exp_type][k][:, 1])
dt = np.array(dt).T
do_point_line_plot(dt, offsets, mean_adjust=True, width=0.7)
xvals += offsets.tolist()
xlbls += stim_types_short
offset = offsets.max() + 2
print xvals
plt.ylabel('Corr of Cells to Prediction')
#plt.ylim(-0.01, 1)
plt.xticks(xvals, xlbls, rotation='vertical')
plt.subplots_adjust(left=0.12, bottom=0.2, right=0.97, top=0.95,
wspace=0.23, hspace=0.23)
fig_path = startup.fig_path + 'Sparseness/summary/'
fig8.savefig(fig_path + '%s_pred_summary.eps' % (str(filt)))
fig8.savefig(fig_path + '%s_pred_summary.png' % (str(filt)))
#plt.show()
plt.close(fig8)
sum_vals.append(vals)
fig = plt.figure()
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
xvals = []
xlbls = []
offset = 0
divider = 2
for i in range(len(groups)):
base_ind = i * divider
print base_ind
dt = np.array(csv_vals[base_ind:base_ind + divider]).T
offsets = np.arange(dt.shape[1]) + offset
mean_adjust = (i != 1)
do_point_line_plot(dt, offsets, mean_adjust=mean_adjust)
# for j in range(dt.shape[1]):
# p_offset = -0.2
# if dt[j].sum() > 0:
# for ind in range(j + 1, dt.shape[1]):
# if dt[ind].sum() == 0:
# continue
# stat, p = scipy.stats.ttest_ind(dt[j], dt[ind])
# print 'p', p, j, ind
# if p < 0.05:
# plt.scatter(offset + j + p_offset,
# 0.95, c=colors[ind],
# edgecolor=colors[ind],
# marker='*')
# p_offset *= -1
adjust_spines(ax, ['bottom', 'left'])
xvals = []
xlbls = []
offset = 1
if exp_type == 'PYR':
divider = 2
else:
divider = 3
for i in range(len(groups)):
base_ind = i * divider
mean_adjust = (i != 1)
dt = csv_vals[:, base_ind: base_ind + divider]
offsets = np.arange(dt.shape[1]) + offset
plt.text(offsets.mean(), 1, groups[i], ha='center')
do_point_line_plot(dt, offsets, width=0.7,
mean_adjust=mean_adjust,
alpha=0.5,
c=colors)
# do_spot_scatter_plot(csv_vals[:, i], offset, col,
# width=0.7, mean_adjust=mean_adjust)
xvals += offsets.tolist()
xlbls += headers[1 + base_ind: base_ind + divider + 1]
offset += divider + 1
plt.xticks(xvals, xlbls, rotation='vertical')
plt.ylim(0, 1.15)
plt.subplots_adjust(left=0.05, bottom=0.25, right=0.98, top=0.98,
wspace=0.3, hspace=0.34)
xvals = []
xlbls = []
offset = 1
if exp_type == 'PYR':
divider = 2
else:
divider = 3
for i in range(len(groups)):
base_ind = i * divider
mean_adjust = (i != 1)
dt = csv_vals[:, base_ind:base_ind + divider]
offsets = np.arange(dt.shape[1]) + offset
plt.text(offsets.mean(), 1, groups[i], ha='center')
do_point_line_plot(dt,
offsets,
width=0.7,
mean_adjust=mean_adjust,
alpha=0.5,
c=colors)
# do_spot_scatter_plot(csv_vals[:, i], offset, col,
# width=0.7, mean_adjust=mean_adjust)
xvals += offsets.tolist()
xlbls += headers[1 + base_ind:base_ind + divider + 1]
offset += divider + 1
plt.xticks(xvals, xlbls, rotation='vertical')
plt.ylim(0, 1.15)
plt.subplots_adjust(left=0.05,
bottom=0.25,
