def plot_weight_dist(weights, plt_num=[1, 1, 1]):
""" plot a histogram of the learnt weight distribution """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.hist(weights, 40)
adjust_spines(ax, ['bottom', 'left'])
plt.title('Weight Distribution')
def plot_preds_orient(weights, plt_num=[1, 1, 1]):
orients = ['Mean Horiz', 'Mean Vert', 'Mean Diag', 'Mean Diag 2']
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
x = 7
mid = x / 2
vals_hor = np.zeros([x, x])
vals_ver = np.zeros([x, x])
vals_diag = np.zeros([x, x])
delta = (mid / 2)
vals_hor[mid, [mid - delta, mid + delta]] = 1
vals_ver[[mid - delta, mid + delta], mid] = 1
x, y = np.diag_indices_from(vals_diag)
vals_diag[x[mid - delta], y[mid - delta]] = 1
vals_diag[x[mid + delta], y[mid + delta]] = 1
vals_diag2 = np.array(zip(*vals_diag[::-1]))
# delta = mid - mid / 2
# vals_hor[mid, [mid - delta, mid + delta]] = 1
# vals_ver[:, mid] = 1
# np.fill_diagonal(vals_diag, 1)
# np.fill_diagonal(vals_diag2, 1)
# vals_diag2 = np.array(zip(*vals_diag2[::-1]))
dat = (vals_hor * weights[0] + vals_ver * weights[1] + vals_diag
* weights[2] + vals_diag2 * weights[3])
dat = reverse_fft(dat)
ext = mid
_ = plt.imshow(dat, cmap=colors.CoolWarm,
interpolation='bilinear',
extent=(-ext, ext, -ext, ext))
ylims = np.array([0, np.abs(dat).max()])
plt.clim(ylims)
plt.colorbar()
adjust_spines(ax, [])
def plot_weight_dist(weights, plt_num=[1, 1, 1]):
""" plot a histogram of the learnt weight distribution """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.hist(weights, 40)
adjust_spines(ax, ['bottom', 'left'])
plt.title('Weight Distribution')
def plot_mean_std(mn, std, title, plt_num=[1, 1, 1]):
"""plot the mean +/- std as timeline """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.fill_between(range(len(std)), mn - std, mn + std, facecolor=clr2)
plt.plot(range(len(std)), mn, color=clr1, label='Mean Center', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1), frameon=False, ncol=2)
adjust_spines(ax, ['bottom', 'left'])
plt.title(title)
def plot_mean_std(mn, std, title, plt_num=[1, 1, 1]):
"""plot the mean +/- std as timeline """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.fill_between(range(len(std)), mn - std, mn + std, facecolor=clr2)
plt.plot(range(len(std)), mn, color=clr1,
label='Mean Center', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1), frameon=False, ncol=2)
adjust_spines(ax, ['bottom', 'left'])
plt.title(title)
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_bar_weights(weights, labels, ylims, plt_num=[1, 1, 1]):
""" plot some weights (luminance, contrast, flow strength)
on a bar chart """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.bar(range(len(weights)), weights)
ax.set_xticks(np.arange(len(weights)) + 0.5)
ax.set_xticklabels(labels)
plt.ylim(ylims)
adjust_spines(ax, ['bottom', 'left'])
plt.title('Other Weights')
def plot_bar_weights(weights, labels, ylims, plt_num=[1, 1, 1]):
""" plot some weights (luminance, contrast, flow strength)
on a bar chart """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.bar(range(len(weights)), weights)
ax.set_xticks(np.arange(len(weights)) + 0.5)
ax.set_xticklabels(labels)
plt.ylim(ylims)
adjust_spines(ax, ['bottom', 'left'])
plt.title('Other Weights')
def plot_summary(comb_corrs, targets, 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 = []
boxes = []
lbls = []
#targets.append('Overall')
for i, [comb, vals] in enumerate(comb_corrs):
xaxis.append(comb)
offset = 0
for j, targ in enumerate(targets):
dat = vals[targ]
if len(dat) == 0:
continue
if targ == 'Overall':
col = '0.3'
else:
col = colors[j]
do_box_plot(np.array(dat),
np.array([i + offset]),
col,
widths=[0.2])
#do_spot_scatter_plot(np.array(dat), np.array([i + offset]), col)
offset += 0.3
if i == 0:
boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
lbls.append(targ)
if j == 0:
plt.title('N=%d' % len(dat))
plt.xticks(range(len(xaxis)), xaxis, rotation='vertical')
plt.xlim(-1, len(xaxis) + 1)
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.ylim(0, 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_prediction(pred, actual, title, plt_num=[1, 1, 1]):
""" plot the prediction of the model vs the mean of the actual
experiment """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
#plt.fill_between(range(len(pred)), actual, pred, facecolor=clr3)
plt.plot(range(len(pred)), pred, color=clr4, linewidth=2,
label='Predicted')
plt.plot(range(len(pred)), actual, color=clr1,
label='Mean Experimental', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1), frameon=False)
adjust_spines(ax, ['bottom', 'left'])
plt.title(title)
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_four(four_weights, ylims, plt_num=[1, 1, 1], title=None,
ax_off=False, interpolation='None'):
"""plot the weights of the spatial fourier """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
ext = four_weights.shape[0] / 2.
_ = plt.imshow(four_weights.T, cmap=plt.cm.gray,
interpolation=interpolation,
extent=(-ext, ext, -ext, ext))
plt.clim(ylims)
if ax_off:
adjust_spines(ax, [])
else:
adjust_spines(ax, ['bottom', 'left'])
if title is None:
title = 'Filter'
plt.title(title)
def plot_summary(comb_corrs, targets, 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 = []
boxes = []
lbls = []
#targets.append('Overall')
for i, [comb, vals] in enumerate(comb_corrs):
xaxis.append(comb)
offset = 0
for j, targ in enumerate(targets):
dat = vals[targ]
if len(dat) == 0:
continue
if targ == 'Overall':
col = '0.3'
else:
col = colors[j]
do_box_plot(np.array(dat), np.array([i + offset]), col, widths=[0.2])
#do_spot_scatter_plot(np.array(dat), np.array([i + offset]), col)
offset += 0.3
if i == 0:
boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
lbls.append(targ)
if j == 0:
plt.title('N=%d' % len(dat))
plt.xticks(range(len(xaxis)), xaxis, rotation='vertical')
plt.xlim(-1, len(xaxis) + 1)
plt.plot([-1, len(xaxis) + 1], [0, 0], '--')
plt.ylim(0, 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_four(four_weights, ylims, plt_num=[1, 1, 1], title=None,
ax_off=False, interpolation='None'):
"""plot the weights of the spatial fourier """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
ext = four_weights.shape[0] / 2.
im = plt.imshow(four_weights.T, cmap=plt.cm.gray,
interpolation=interpolation,
extent=(-ext, ext, -ext, ext))
plt.clim(ylims)
if ax_off:
adjust_spines(ax, [])
else:
adjust_spines(ax, ['bottom', 'left'])
plt.xlabel('Cycles/Patch', fontsize='large')
plt.ylabel('Cycles/Patch', fontsize='large')
if title is None:
title = 'Spatial Fourier Weights'
plt.title(title)
def plot_prediction(pred, actual, title, plt_num=[1, 1, 1]):
""" plot the prediction of the model vs the mean of the actual
experiment """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
#plt.fill_between(range(len(pred)), actual, pred, facecolor=clr3)
plt.plot(range(len(pred)),
pred,
color=clr4,
linewidth=2,
label='Predicted')
plt.plot(range(len(pred)),
actual,
color=clr1,
label='Mean Experimental',
linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1), frameon=False)
adjust_spines(ax, ['bottom', 'left'])
plt.title(title)
def plot_four(four_weights,
ylims,
plt_num=[1, 1, 1],
title=None,
ax_off=False,
interpolation='None'):
"""plot the weights of the spatial fourier """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
ext = four_weights.shape[0] / 2.
_ = plt.imshow(four_weights.T,
cmap=plt.cm.gray,
interpolation=interpolation,
extent=(-ext, ext, -ext, ext))
plt.clim(ylims)
if ax_off:
adjust_spines(ax, [])
else:
adjust_spines(ax, ['bottom', 'left'])
if title is None:
title = 'Filter'
plt.title(title)
def plot_four(four_weights,
ylims,
plt_num=[1, 1, 1],
title=None,
ax_off=False,
interpolation='None'):
"""plot the weights of the spatial fourier """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
ext = four_weights.shape[0] / 2.
im = plt.imshow(four_weights.T,
cmap=plt.cm.gray,
interpolation=interpolation,
extent=(-ext, ext, -ext, ext))
plt.clim(ylims)
if ax_off:
adjust_spines(ax, [])
else:
adjust_spines(ax, ['bottom', 'left'])
plt.xlabel('Cycles/Patch', fontsize='large')
plt.ylabel('Cycles/Patch', fontsize='large')
if title is None:
title = 'Spatial Fourier Weights'
plt.title(title)
def plot_movie(lum, con, flow, four, movie, fname):
num_frames = lum.shape[1]
fig = plt.figure(figsize=(16, 7))
fig.set_facecolor('white')
ax = plt.subplot(231)
plt.plot(lum[0])
plt.xlim(0, num_frames)
plt.title('Luminence')
adjust_spines(ax, ['left', 'bottom'])
ax = plt.subplot(234)
plt.plot(con[0])
plt.xlim(0, num_frames)
plt.title('Contrast')
adjust_spines(ax, ['left', 'bottom'])
total_plots = 4
frames = np.linspace(0, num_frames - 1, total_plots).astype(np.int)
print 'plotting frames ', frames
four = np.abs(four) ** 2
lims = [four[0, frames].min(), four[0, frames].max()]
for i, _ in enumerate(frames):
ax = plt.subplot(3, 6, 3 + i, aspect='equal')
plt.imshow(movie[0, i].T, cmap=plt.cm.gray)
adjust_spines(ax, [])
if i == 0:
plt.title('Movie')
ax = plt.subplot(3, 6, 9 + i, aspect='equal')
plt.imshow(four[0, i].T, cmap=plt.cm.gray, interpolation='None')
plt.clim(lims)
if i == 0:
adjust_spines(ax, ['left', 'bottom'])
plt.title('Spatial Fourier')
plt.xticks(np.linspace(0, four[0, i].shape[1], 4).astype(np.int))
plt.yticks(np.linspace(0, four[0, i].shape[0], 4).astype(np.int))
plt.colorbar()
else:
adjust_spines(ax, [])
if len(flow) > 0:
ax = plt.subplot(3, 6, 15 + i, polar=True)
plt.plot([0, flow[0, i, 0]], [0, flow[0, i, 1]], '-o', color='0.3',
linewidth=2)
plt.ylim(0, flow[0, :, 1].max())
for _, spine in ax.spines.iteritems():
spine.set_color('none') # don't draw spine
#plt.adjust_spines(ax,[])
if i != 0:
plt.gca().set_yticklabels([])
plt.gca().set_xticklabels([])
else:
plt.title('Flow')
plt.xticks([0, np.pi / 2, np.pi, np.pi * 3. / 2])
plt.yticks(np.linspace(0, flow[0, :, 1].max(), 4).astype(np.int))
plt.subplots_adjust(left=0.04, bottom=0.06, right=0.96, top=0.95,
wspace=0.20, hspace=0.35)
#adjust_spines(ax,[])
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
plt.close(fig)
fig.set_facecolor('white')
clr_c = '0.3'
clr_w = np.array([79, 79, 217]) / 255.
ax = plt.subplot(421)
plt.hold(True)
plt.fill_between(range(len(std_c)), mn_c - std_c, mn_c + std_c,
facecolor='0.9')
plt.plot(range(len(std_c)), mn_c, color=clr_c,
label='Center', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False, ncol=2)
plt.text(5, plt.ylim()[1] * 0.9,
'corr trl-trl: %.2f, #cells: : %d, #trials: %d' %
(xcorr_c, dat_c.shape[0], dat_c.shape[2]))
adjust_spines(ax, ['bottom', 'left'])
ax = plt.subplot(422)
plt.hold(True)
plt.fill_between(range(len(std_w)), mn_w - std_w, mn_w + std_w,
facecolor='0.9')
plt.plot(range(len(std_w)), mn_w, color=clr_w,
label='Whole Field', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False, ncol=2)
plt.text(5, plt.ylim()[1] * 0.9,
'corr trl-trl: %.2f, #cells: : %d, #trials: %d' %
(xcorr_w, dat_w.shape[0], dat_w.shape[2]))
adjust_spines(ax, ['bottom', 'left'])
plt.title('Mean & STD')
def plot_surround(lum, con, flow, four, movie, fname):
num_frames = lum.shape[1]
fig = plt.figure(figsize=(16, 9))
fig.set_facecolor('white')
four = np.abs(four)**2
parts = lum.shape[0]
time = np.arange(0, num_frames, 20)
for s in range(parts):
ax = plt.subplot(5, parts, s + 1)
plt.plot(lum[s])
plt.xlim(0, num_frames)
if s == 0:
plt.title('Luminence')
plt.xticks(time)
adjust_spines(ax, ['left', 'bottom'])
ax = plt.subplot(5, parts, parts + (s + 1))
plt.plot(con[s])
plt.xlim(0, num_frames)
if s == 0:
plt.title('Contrast')
plt.xticks(time)
adjust_spines(ax, ['left', 'bottom'])
frame = num_frames / 2
lims = [four[s, frame].min(), four[s, frame].max()]
ax = plt.subplot(5, parts, 2 * parts + (s + 1), aspect='equal')
plt.imshow(movie[s, frame].T, cmap=plt.cm.gray)
adjust_spines(ax, [])
if s == 0:
plt.title('Movie')
ax = plt.subplot(5, parts, 3 * parts + (s + 1), aspect='equal')
plt.imshow(four[s, frame].T, cmap=plt.cm.gray, interpolation='None')
plt.clim(lims)
if s == 0:
adjust_spines(ax, ['left', 'bottom'])
plt.title('Spatial Fourier')
tix = np.array(plt.xticks()[0], dtype=np.float)
plt.xticks(np.linspace(tix.min(), tix.max(), 4).astype(np.int))
tix = np.array(plt.yticks()[0])
plt.yticks(np.linspace(tix.min(), tix.max(), 4).astype(np.int))
#plt.colorbar()
else:
adjust_spines(ax, [])
if len(flow) > 0:
ax = plt.subplot(5, parts, 4 * parts + (s + 1), polar=True)
plt.plot([0, flow[s, frame, 0]], [0, flow[s, frame, 1]],
'-o',
color='0.3',
linewidth=2)
for _, spine in ax.spines.iteritems():
spine.set_color('none') # don't draw spine
if s != 0:
plt.gca().set_yticklabels([])
plt.gca().set_xticklabels([])
else:
plt.title('Flow')
plt.xticks([0, np.pi / 2, np.pi, np.pi * 3. / 2])
plt.yticks(
np.linspace(0, flow[s, frame, 1].max(), 4).astype(np.int))
plt.subplots_adjust(left=0.04,
bottom=0.06,
right=0.96,
top=0.95,
wspace=0.50,
hspace=0.5)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
plt.close(fig)
def plot_movie(lum, con, flow, four, movie, fname):
num_frames = lum.shape[1]
fig = plt.figure(figsize=(16, 7))
fig.set_facecolor('white')
ax = plt.subplot(231)
plt.plot(lum[0])
plt.xlim(0, num_frames)
plt.title('Luminence')
adjust_spines(ax, ['left', 'bottom'])
ax = plt.subplot(234)
plt.plot(con[0])
plt.xlim(0, num_frames)
plt.title('Contrast')
adjust_spines(ax, ['left', 'bottom'])
total_plots = 4
frames = np.linspace(0, num_frames - 1, total_plots).astype(np.int)
print 'plotting frames ', frames
four = np.abs(four)**2
lims = [four[0, frames].min(), four[0, frames].max()]
for i, _ in enumerate(frames):
ax = plt.subplot(3, 6, 3 + i, aspect='equal')
plt.imshow(movie[0, i].T, cmap=plt.cm.gray)
adjust_spines(ax, [])
if i == 0:
plt.title('Movie')
ax = plt.subplot(3, 6, 9 + i, aspect='equal')
plt.imshow(four[0, i].T, cmap=plt.cm.gray, interpolation='None')
plt.clim(lims)
if i == 0:
adjust_spines(ax, ['left', 'bottom'])
plt.title('Spatial Fourier')
plt.xticks(np.linspace(0, four[0, i].shape[1], 4).astype(np.int))
plt.yticks(np.linspace(0, four[0, i].shape[0], 4).astype(np.int))
plt.colorbar()
else:
adjust_spines(ax, [])
if len(flow) > 0:
ax = plt.subplot(3, 6, 15 + i, polar=True)
plt.plot([0, flow[0, i, 0]], [0, flow[0, i, 1]],
'-o',
color='0.3',
linewidth=2)
plt.ylim(0, flow[0, :, 1].max())
for _, spine in ax.spines.iteritems():
spine.set_color('none') # don't draw spine
#plt.adjust_spines(ax,[])
if i != 0:
plt.gca().set_yticklabels([])
plt.gca().set_xticklabels([])
else:
plt.title('Flow')
plt.xticks([0, np.pi / 2, np.pi, np.pi * 3. / 2])
plt.yticks(np.linspace(0, flow[0, :, 1].max(), 4).astype(np.int))
plt.subplots_adjust(left=0.04,
bottom=0.06,
right=0.96,
top=0.95,
wspace=0.20,
hspace=0.35)
#adjust_spines(ax,[])
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
plt.close(fig)
fig5 = plt.figure(figsize=(14, 8))
plt.hold(True)
cnt = 1
for exp_type in exp_types:
for k in stim_types:
ax = plt.subplot(3, 2, cnt)
plt.scatter(cell_max_time[exp_type][k][:, 0],
cell_max_time[exp_type][k][:, 1],
c='r',
marker='x')
# c=colors[i], marker=style[i])
plt.ylabel('corr of responders')
if ax.is_last_row():
plt.xlabel('Shift in Frames')
adjust_spines(ax, ['bottom', 'left'])
else:
adjust_spines(ax, ['left'])
if ax.is_first_row():
plt.title(k)
if ax.is_first_col():
ax.text(-0.12,
0.5,
exp_type,
transform=ax.transAxes,
rotation='vertical',
va='center',
ha='center')
adjuster = np.array([-0.5, 0.5])
ax.set_ylim(-0.05, 1)
ax.set_xlim(np.array([shifts.min(), shifts.max()]) + adjuster)
def plot_corrs(c_vals, w_vals, mn_c_crr, mn_w_crr, n_cells, header, fname):
fig = plt.figure(figsize=(14, 8))
fig.set_facecolor('white')
c_vals = average_corrs(c_vals)
w_vals = average_corrs(w_vals)
c_vals_mn = average_corrs(c_vals)
w_vals_mn = average_corrs(w_vals)
mn_c_vals_mn = average_corrs(mn_c_crr)
mn_w_vals_mn = average_corrs(mn_w_crr)
print mn_c_crr.min(), mn_c_crr.max()
# ax = plt.subplot(411)
# plt.hold(True)
# plt.plot(c_vals.T, '0.8')
# plt.plot(c_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Masked')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(412)
# plt.hold(True)
# plt.plot(w_vals.T, '0.8')
# plt.plot(w_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, w_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Whole Field')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(413)
# plt.hold(True)
# plt.plot(w_vals_mn, 'g', linewidth=2, label='Whole')
# plt.plot(c_vals_mn, 'k', linewidth=2, label='Centre')
# crr = do_thresh_corr(w_vals_mn, c_vals_mn)
# leg = plt.legend(ncol=2)
# leg.draw_frame(False)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.ylabel('Mean R')
# plt.xlabel('Sample')
# plt.title('Whole vs Masked: Crr: {0:.2f}'.format(crr))
ax = plt.subplot(211)
plt.hold(True)
plt.plot(mn_w_vals_mn, 'g', linewidth=2, label='Whole')
plt.plot(mn_c_vals_mn, 'k', linewidth=2, label='Centre')
crr = do_thresh_corr(mn_w_vals_mn, mn_c_vals_mn)
leg = plt.legend(ncol=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Mean Whole vs Masked: R^2: {0:.2f}'.format(crr))
plt.ylabel('Mean R')
plt.xlabel('Sample')
ax = plt.subplot(212)
plt.hold(True)
plt.plot(mn_c_vals_mn - mn_w_vals_mn, '0.3', linewidth=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Difference - Mean Whole vs Masked')
plt.ylabel('Mean R')
plt.xlabel('Sample')
plt.suptitle('%s - Intercell R^2 over Trials - #Cells: %d' %
(header, n_cells))
plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.9,
wspace=0.2, hspace=0.25)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
print fname
print n_cells
print '\tMean\tMax'
print 'Centre:\t%.3f\t%.3f' % (c_vals_mn.mean(), c_vals.max())
print 'Whole:\t%.3f\t%.3f' % (w_vals.mean(), w_vals.max())
print
def plot_cell_summary(res, fig_path, extras=''):
colors = ['0.5', '0.05', 'k', 'r', 'g', 'y', 'c', 'm']
summary = []
for cnum, cell in enumerate(sorted(res.keys())):
vals = []
maxes = {}
x = 0
lbls = []
xtcks = []
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
plt.hold(True)
for cmb in sorted(res[cell].keys()):
x += 1
plt.text(x, 0.95, cmb, rotation=60, verticalalignment='bottom')
for i, k in enumerate(sorted(res[cell][cmb])):
val = res[cell][cmb][k]['crr_pred']
lbls.append(k)
if k in maxes:
if val > maxes[k][1]:
maxes[k] = [cmb, val]
else:
maxes[k] = [cmb, val]
xtcks.append(x)
plt.plot([x, x], [0, val], ':', color='0.3')
plt.scatter(x, val, c=colors[i], s=200)
x += 1
if val > 0:
vals.append([cmb, '%s' % (k), '%.2f' % val])
x += 2
plt.plot([0, x], [0, 0], '--')
plt.xlim(0, x)
plt.ylim(0, 1)
plt.xticks(xtcks, lbls, rotation='vertical')
adjust_spines(ax, ['bottom', 'left'])
plt.ylabel('Prediction Correlation to Experimental Mean')
plt.subplots_adjust(left=0.05,
bottom=0.15,
right=0.97,
top=0.92,
wspace=0.3,
hspace=0.34)
fig.savefig(fig_path + 'summary_' + cell + '.eps')
fig.savefig(fig_path + 'summary_' + cell + '.png')
plt.close(fig)
f = open(fig_path + 'summary_' + cell + '.csv', 'w')
if len(vals) == 0:
vals.append(['', '', ''])
for v in vals:
f.write("\t".join(v))
f.write('\n')
f.close()
line2 = '%s\t' % cell
line1 = '\t'
for mm in sorted(maxes.keys()):
line1 += '%s\t\t' % mm
if maxes[mm][1] > 0:
line2 += '%.2f\t%s\t' % (maxes[mm][1], maxes[mm][0])
else:
line2 += '\t\t'
if cnum == 0:
summary.append(line1 + '\n')
summary.append(line2 + '\n')
f = open(fig_path + 'summary_all.csv', 'w')
for l in summary:
f.write(l)
f.close()
mn_c + std_c,
facecolor='0.9')
plt.plot(range(len(std_c)),
mn_c,
color=clr_c,
label='Center',
linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False,
ncol=2)
plt.text(
5,
plt.ylim()[1] * 0.9,
'corr trl-trl: %.2f, #cells: : %d, #trials: %d' %
(xcorr_c, dat_c.shape[0], dat_c.shape[2]))
adjust_spines(ax, ['bottom', 'left'])
ax = plt.subplot(422)
plt.hold(True)
plt.fill_between(range(len(std_w)),
mn_w - std_w,
mn_w + std_w,
facecolor='0.9')
plt.plot(range(len(std_w)),
mn_w,
color=clr_w,
label='Whole Field',
linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False,
ncol=2)
def plot_corrs(c_vals, w_vals, mn_c_crr, mn_w_crr, n_cells, header, fname):
fig = plt.figure(figsize=(14, 8))
fig.set_facecolor('white')
c_vals = average_corrs(c_vals)
w_vals = average_corrs(w_vals)
c_vals_mn = average_corrs(c_vals)
w_vals_mn = average_corrs(w_vals)
mn_c_vals_mn = average_corrs(mn_c_crr)
mn_w_vals_mn = average_corrs(mn_w_crr)
print mn_c_crr.min(), mn_c_crr.max()
# ax = plt.subplot(411)
# plt.hold(True)
# plt.plot(c_vals.T, '0.8')
# plt.plot(c_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Masked')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(412)
# plt.hold(True)
# plt.plot(w_vals.T, '0.8')
# plt.plot(w_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, w_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Whole Field')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(413)
# plt.hold(True)
# plt.plot(w_vals_mn, 'g', linewidth=2, label='Whole')
# plt.plot(c_vals_mn, 'k', linewidth=2, label='Centre')
# crr = do_thresh_corr(w_vals_mn, c_vals_mn)
# leg = plt.legend(ncol=2)
# leg.draw_frame(False)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.ylabel('Mean R')
# plt.xlabel('Sample')
# plt.title('Whole vs Masked: Crr: {0:.2f}'.format(crr))
ax = plt.subplot(211)
plt.hold(True)
plt.plot(mn_w_vals_mn, 'g', linewidth=2, label='Whole')
plt.plot(mn_c_vals_mn, 'k', linewidth=2, label='Centre')
crr = do_thresh_corr(mn_w_vals_mn, mn_c_vals_mn)
leg = plt.legend(ncol=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Mean Whole vs Masked: R^2: {0:.2f}'.format(crr))
plt.ylabel('Mean R')
plt.xlabel('Sample')
ax = plt.subplot(212)
plt.hold(True)
plt.plot(mn_c_vals_mn - mn_w_vals_mn, '0.3', linewidth=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Difference - Mean Whole vs Masked')
plt.ylabel('Mean R')
plt.xlabel('Sample')
plt.suptitle('%s - Intercell R^2 over Trials - #Cells: %d' %
(header, n_cells))
plt.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.9,
wspace=0.2,
hspace=0.25)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
print fname
print n_cells
print '\tMean\tMax'
print 'Centre:\t%.3f\t%.3f' % (c_vals_mn.mean(), c_vals.max())
print 'Whole:\t%.3f\t%.3f' % (w_vals.mean(), w_vals.max())
print
def plot_cell_summary(res, fig_path, extras=''):
colors = ['0.5', '0.05', 'k', 'r', 'g', 'y', 'c', 'm']
summary = []
for cnum, cell in enumerate(sorted(res.keys())):
vals = []
maxes = {}
x = 0
lbls = []
xtcks = []
fig = plt.figure(figsize=(14, 12))
ax = plt.subplot(111)
plt.hold(True)
for comb in sorted(res[cell].keys()):
x += 1
plt.text(x, 0.95, comb, rotation=60, verticalalignment='bottom')
for i, targ in enumerate(sorted(res[cell][comb].keys())):
for src in sorted(res[cell][comb][targ].keys()):
val = res[cell][comb][targ][src]
targ_src = '%s_%s' % (targ, src)
lbls.append(targ_src)
if targ_src in maxes:
if val > maxes[targ_src][1]:
maxes[targ_src] = [comb, val]
else:
maxes[targ_src] = [comb, val]
xtcks.append(x)
plt.plot([x, x], [0, val], ':', color='0.3')
plt.scatter(x, val, c=colors[i], s=200)
x += 1
vals.append([comb, '%s_%s' % (targ, src), '%.2f' % val])
x += 2
plt.plot([0, x], [0, 0], '--')
plt.xlim(0, x)
plt.ylim(0, 1)
plt.xticks(xtcks, lbls, rotation='vertical')
adjust_spines(ax, ['bottom', 'left'])
plt.ylabel('Prediction Correlation to Experimental Mean')
plt.subplots_adjust(left=0.05, bottom=0.2, right=0.97, top=0.7,
wspace=0.3, hspace=0.34)
fig.savefig(fig_path + 'summary_' + cell + '.eps')
fig.savefig(fig_path + 'summary_' + cell + '.png')
plt.close(fig)
f = open(fig_path + 'summary_' + cell + '.csv', 'w')
for v in vals:
f.write("\t".join(v))
f.write('\n')
f.close()
line2 = '%s\t' % cell
line1 = '\t'
for mm in sorted(maxes.keys()):
line1 += '%s\t\t' % mm
line2 += '%.2f\t%s\t' % (maxes[mm][1], maxes[mm][0])
if cnum == 0:
summary.append(line1 + '\n')
summary.append(line2 + '\n')
f = open(fig_path + 'summary_all.csv', 'w')
for l in summary:
f.write(l)
f.close()
def plot_surround(lum, con, flow, four, movie, fname):
num_frames = lum.shape[1]
fig = plt.figure(figsize=(16, 9))
fig.set_facecolor('white')
four = np.abs(four) ** 2
parts = lum.shape[0]
time = np.arange(0, num_frames, 20)
for s in range(parts):
ax = plt.subplot(5, parts, s + 1)
plt.plot(lum[s])
plt.xlim(0, num_frames)
if s == 0:
plt.title('Luminence')
plt.xticks(time)
adjust_spines(ax, ['left', 'bottom'])
ax = plt.subplot(5, parts, parts + (s + 1))
plt.plot(con[s])
plt.xlim(0, num_frames)
if s == 0:
plt.title('Contrast')
plt.xticks(time)
adjust_spines(ax, ['left', 'bottom'])
frame = num_frames / 2
lims = [four[s, frame].min(), four[s, frame].max()]
ax = plt.subplot(5, parts, 2 * parts + (s + 1), aspect='equal')
plt.imshow(movie[s, frame].T, cmap=plt.cm.gray)
adjust_spines(ax, [])
if s == 0:
plt.title('Movie')
ax = plt.subplot(5, parts, 3 * parts + (s + 1), aspect='equal')
plt.imshow(four[s, frame].T, cmap=plt.cm.gray, interpolation='None')
plt.clim(lims)
if s == 0:
adjust_spines(ax, ['left', 'bottom'])
plt.title('Spatial Fourier')
tix = np.array(plt.xticks()[0], dtype=np.float)
plt.xticks(np.linspace(tix.min(), tix.max(), 4).astype(np.int))
tix = np.array(plt.yticks()[0])
plt.yticks(np.linspace(tix.min(), tix.max(), 4).astype(np.int))
#plt.colorbar()
else:
adjust_spines(ax, [])
if len(flow) > 0:
ax = plt.subplot(5, parts, 4 * parts + (s + 1), polar=True)
plt.plot([0, flow[s, frame, 0]], [0, flow[s, frame, 1]], '-o',
color='0.3', linewidth=2)
for _, spine in ax.spines.iteritems():
spine.set_color('none') # don't draw spine
if s != 0:
plt.gca().set_yticklabels([])
plt.gca().set_xticklabels([])
else:
plt.title('Flow')
plt.xticks([0, np.pi / 2, np.pi, np.pi * 3. / 2])
plt.yticks(np.linspace(0, flow[s, frame, 1].max(), 4).astype(np.int))
plt.subplots_adjust(left=0.04, bottom=0.06, right=0.96, top=0.95,
wspace=0.50, hspace=0.5)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
plt.close(fig)
fig = plt.figure(figsize=(7, 4))
fig.set_facecolor('white')
ax = plt.subplot(111)
plt.suptitle(label)
plt.hold(True)
#plt.plot(dt.T, '0.7')
mn_cell = dt.mean(0)
mn_cell = (mn_cell - mn_cell.mean()) / np.std(mn_cell)
mn_pred = (pred[cell] - pred[cell].mean()) / np.std(pred[cell])
plt.plot(mn_cell, 'k', lw=2, label='Ephys Cell (Mean)')
plt.plot(mn_pred, 'r', label='aTRBM Cell')
plt.ylabel('Normalised Activation')
plt.xlabel('Sample')
plt.legend(bbox_to_anchor=(0.1, 0, 0.15, 0.91), bbox_transform=plt.gcf().transFigure, frameon=False, prop={'size':10})
plt.subplots_adjust(left=0.28, bottom=0.11, right=0.96, top=0.88, wspace=0.25, hspace=0.25)
adjust_spines(ax, ['left', 'bottom'])
fig.savefig(fig_path + '/cells/%s.eps' % (fname))
fig.savefig(fig_path + '/cells/%s.png' % (fname))
#plt.show()
plt.close(fig)
print mx
bins = np.linspace(0, mx + 0.05, 10)
print bins
for rbm_type in new_res:
fig = plt.figure(figsize=(20, 14))
fig.set_facecolor('white')
plt.suptitle('%s' % (rbm_type))
rows = len(exp_type) * len(new_res[rbm_type])
cols = len(shifts)
def plot_preds_freq(weights, plt_num=[1, 1, 1]):
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.scatter(range(len(weights)), weights)
adjust_spines(ax, ['bottom', 'left'])