def plot_summary(cmb_corrs, fig_path, extras=''):
""" plot a summary showing the mean and std for all attempted
combinations"""
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
colors = ['r', 'b', 'g', 'y', 'c', 'm']
_ = plt.subplot(111)
xaxis = []
xvals = []
boxes = []
lbls = []
#targets.append('Overall')
offset = -1
for i, k in enumerate(sorted(cmb_corrs.keys())):
offset += 2
min_x = offset
for j, cmb in enumerate(sorted(cmb_corrs[k].keys())):
xaxis.append(cmb)
xvals.append(offset)
dat = cmb_corrs[k][cmb]
if len(dat) == 0:
continue
col = colors[i]
# do_box_plot(np.array(dat), np.array([i + offset]),
# col, widths=[0.2])
do_spot_scatter_plot(np.array(dat), offset, col)
offset += 1
# if j == 0:
# boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
# lbls.append(k)
max_x = offset
plt.text(min_x + (max_x - min_x) / 2., 1., k, ha='center')
plt.title('N=%d' % len(dat))
plt.xticks(xvals, xaxis, rotation='vertical')
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.xlim(0, offset)
plt.ylim(-0.05, 1)
plt.subplots_adjust(left=0.05,
bottom=0.5,
right=0.97,
top=0.95,
wspace=0.3,
hspace=0.34)
plt.legend(boxes, lbls, frameon=False, loc=4)
plt.ylabel('Correlation between prediction and Experimental Mean')
fname = '%s%s_pred_%s' % (fig_path, 'summary', extras)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
def plot_summary(cmb_corrs, fig_path, extras=''):
""" plot a summary showing the mean and std for all attempted
combinations"""
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
colors = ['r', 'b', 'g', 'y', 'c', 'm']
_ = plt.subplot(111)
xaxis = []
xvals = []
boxes = []
lbls = []
#targets.append('Overall')
offset = -1
for i, k in enumerate(sorted(cmb_corrs.keys())):
offset += 2
min_x = offset
for j, cmb in enumerate(sorted(cmb_corrs[k].keys())):
xaxis.append(cmb)
xvals.append(offset)
dat = cmb_corrs[k][cmb]
if len(dat) == 0:
continue
col = colors[i]
# do_box_plot(np.array(dat), np.array([i + offset]),
# col, widths=[0.2])
do_spot_scatter_plot(np.array(dat), offset, col)
offset += 1
# if j == 0:
# boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
# lbls.append(k)
max_x = offset
plt.text(min_x + (max_x - min_x) / 2., 1., k, ha='center')
plt.title('N=%d' % len(dat))
plt.xticks(xvals, xaxis, rotation='vertical')
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.xlim(0, offset)
plt.ylim(-0.05, 1)
plt.subplots_adjust(left=0.05, bottom=0.5, right=0.97, top=0.95,
wspace=0.3, hspace=0.34)
plt.legend(boxes, lbls, frameon=False, loc=4)
plt.ylabel('Correlation between prediction and Experimental Mean')
fname = '%s%s_pred_%s' % (fig_path, 'summary', extras)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
cnt_lims = [
np.minimum(cnt_lims[0], cnts.min()),
np.maximum(cnt_lims[1], cnts.max())
]
# crr_lims = [np.minimum(crr_lims[0], crrs[:, 0].min()),
# np.maximum(crr_lims[1], crrs[:, 0].max() + crrs[:, 1].max())]
cnt_axs.append(plt.subplot(2, 2, cnt))
cnt += 2
plt.title(k)
plt.plot(shifts, cnts, '-o')
if i == 0:
plt.ylabel('# correlated responses')
crr_axs.append(plt.subplot(2, 2, cnt))
for cr, shift in zip(crrs, shifts):
do_spot_scatter_plot(cr, shift, 'k', 0.4, False, mean_adjust=True)
#do_box_plot(crrs, shifts, 'k', np.ones_like(shifts) * 0.5)
cnt -= 1
if i == 0:
plt.ylabel('mean corr of responders')
plt.xlabel('Shift in Frames')
adjuster = np.array([-0.5, 0.5])
for ax in cnt_axs:
ax.set_ylim(cnt_lims + adjuster)
ax.set_xlim(np.array([shifts.min(), shifts.max()]) + adjuster)
# for ax in crr_axs:
# ax.set_ylim(crr_lims + adjuster * 0.05)
# ax.set_xlim(np.array([shifts.min(), shifts.max()]) + adjuster)
fig3.savefig(fig_path + '%s_%s_shift_avg_count.eps' %
(str(filt), exp_type))
fig3.savefig(fig_path + '%s_%s_shift_avg_count.png' %
# adjust_spines(ax, ['left', 'bottom'])
# elif ax.is_last_row():
# plt.setp(ax.get_xticklabels(), rotation='vertical')
# adjust_spines(ax, ['bottom'])
# elif ax.is_first_col():
# adjust_spines(ax, ['left'])
# ax.text(-0.5, 0.5, label, transform=ax.transAxes,
# rotation='vertical', va='center', ha='center')
# else:
# adjust_spines(ax, [])
ttl = '%s %s' % (exp_type, stim_type)
plt.title(ttl)
plt.hold(True)
for s, shift in enumerate(shifts):
vals = np.array(new_res[rbm_type][act_type][exp_type][stim_type][shift])
vals = np.array([vals[vals > 0.].mean()])
if vals.max() > mx:
mx = vals.max()
do_spot_scatter_plot(vals, shift, 'k', 0.4, False, True)
cnt += 1
for ax in axes:
ax.set_ylim(-0.01, mx + (mx * 0.1))
ax.set_xlim(shifts.min() - 0.5, shifts.max() + 0.5)
plt.subplots_adjust(left=0.05, bottom=0.05, right=0.99, top=0.93, wspace=0.25, hspace=0.25)
fig.savefig(fig_path + 'scatter_%s_%s.eps' % (rbm_type, act_type))
fig.savefig(fig_path + 'scatter_%s_%s.png' % (rbm_type, act_type))
plt.close(fig)
print 'CHECK WHAT TYPE OF STIMULUS::: WHOLE FIELD OR CENTRE: DO THE PEAKS OF SHIFT DIFFER BETWEEN THE TWO, WHAT DO THE RECEPTIVE FIELDS LOOK LIKE?'
print 'DO THE SAME PLOT AS THE PRED WITH SCATTER'
xvals = []
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
offset = -1
for k in cell_results[cell].keys():
offset += 2
min_x = offset
for cmb in cell_results[cell][k].keys():
xaxis.append(cmb)
xvals.append(offset)
dat = []
for s in cell_results[cell][k][cmb].keys():
#dat.append([s, cell_results[cell][k][cmb][s]['crr_pred']])
dat.append(cell_results[cell][k][cmb][s]['crr_pred'])
do_spot_scatter_plot(np.array(dat, dtype=np.float), offset,
width=0.4)
offset += 1
max_x = offset
plt.text(min_x + (max_x - min_x) / 2., 1., k, ha='center')
plt.xticks(xvals, xaxis, rotation='vertical')
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.xlim(0, offset)
plt.ylim(-0.05, 1)
plt.subplots_adjust(left=0.05, bottom=0.2, right=0.97, top=0.95,
wspace=0.3, hspace=0.34)
plt.ylabel('Correlation between prediction and Experimental Mean')
fname = '%s%s_pred' % (fig_path, cell)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
crrs = np.array(crrs)
cnts = np.array(cnts)
cnt_lims = [np.minimum(cnt_lims[0], cnts.min()),
np.maximum(cnt_lims[1], cnts.max())]
# crr_lims = [np.minimum(crr_lims[0], crrs[:, 0].min()),
# np.maximum(crr_lims[1], crrs[:, 0].max() + crrs[:, 1].max())]
cnt_axs.append(plt.subplot(2, 2, cnt))
cnt += 2
plt.title(k)
plt.plot(shifts, cnts, '-o')
if i == 0:
plt.ylabel('# correlated responses')
crr_axs.append(plt.subplot(2, 2, cnt))
for cr, shift in zip(crrs, shifts):
do_spot_scatter_plot(cr, shift, 'k', 0.4, False, mean_adjust=True)
#do_box_plot(crrs, shifts, 'k', np.ones_like(shifts) * 0.5)
cnt -= 1
if i == 0:
plt.ylabel('mean corr of responders')
plt.xlabel('Shift in Frames')
adjuster = np.array([-0.5, 0.5])
for ax in cnt_axs:
ax.set_ylim(cnt_lims + adjuster)
ax.set_xlim(np.array([shifts.min(), shifts.max()]) + adjuster)
# for ax in crr_axs:
# ax.set_ylim(crr_lims + adjuster * 0.05)
# ax.set_xlim(np.array([shifts.min(), shifts.max()]) + adjuster)
fig3.savefig(fig_path + '%s_%s_shift_avg_count.eps' % (str(filt), exp_type))
fig3.savefig(fig_path + '%s_%s_shift_avg_count.png' % (str(filt), exp_type))
plt.close(fig3)
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
adjust_spines(ax, ['bottom', 'left'])
offset = -1
for k in cell_results[cell].keys():
offset += 2
min_x = offset
for cmb in cell_results[cell][k].keys():
xaxis.append(cmb)
xvals.append(offset)
dat = []
for s in cell_results[cell][k][cmb].keys():
#dat.append([s, cell_results[cell][k][cmb][s]['crr_pred']])
dat.append(cell_results[cell][k][cmb][s]['crr_pred'])
do_spot_scatter_plot(np.array(dat, dtype=np.float),
offset,
width=0.4)
offset += 1
max_x = offset
plt.text(min_x + (max_x - min_x) / 2., 1., k, ha='center')
plt.xticks(xvals, xaxis, rotation='vertical')
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.xlim(0, offset)
plt.ylim(-0.05, 1)
plt.subplots_adjust(left=0.05,
bottom=0.2,
right=0.97,
top=0.95,
wspace=0.3,
hspace=0.34)
# elif ax.is_first_col():
# adjust_spines(ax, ['left'])
# ax.text(-0.5, 0.5, label, transform=ax.transAxes,
# rotation='vertical', va='center', ha='center')
# else:
# adjust_spines(ax, [])
ttl = '%s %s' % (exp_type, stim_type)
plt.title(ttl)
plt.hold(True)
for s, shift in enumerate(shifts):
vals = np.array(new_res[rbm_type][act_type][exp_type]
[stim_type][shift])
vals = np.array([vals[vals > 0.].mean()])
if vals.max() > mx:
mx = vals.max()
do_spot_scatter_plot(vals, shift, 'k', 0.4, False, True)
cnt += 1
for ax in axes:
ax.set_ylim(-0.01, mx + (mx * 0.1))
ax.set_xlim(shifts.min() - 0.5, shifts.max() + 0.5)
plt.subplots_adjust(left=0.05,
bottom=0.05,
right=0.99,
top=0.93,
wspace=0.25,
hspace=0.25)
fig.savefig(fig_path + 'scatter_%s_%s.eps' % (rbm_type, act_type))
fig.savefig(fig_path + 'scatter_%s_%s.png' % (rbm_type, act_type))
plt.close(fig)