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_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_summary(comb_corrs, fig_path, extras=''):
""" plot a summary showing the mean and std for all attempted
combinations"""
fig = plt.figure(figsize=(14, 12))
_ = plt.subplot(111)
xaxis = []
boxes = []
lbls = []
for i, [comb, vals] in enumerate(comb_corrs):
xaxis.append(comb)
offset = 0
for [name, dat] in vals:
if len(dat) == 0:
continue
if name == 'mask':
col = 'r'
elif name == 'surround':
col = 'b'
elif name == 'whole':
col = 'g'
elif name == 'overall':
col = '0.3'
do_box_plot(np.array(dat), np.array([i + offset]), col)
offset += 0.15
if i == 0:
boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
lbls.append(name)
plt.xticks(range(len(xaxis)), xaxis, rotation='vertical')
plt.xlim(-1, len(xaxis) + 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(comb_corrs, fig_path, extras=''):
""" plot a summary showing the mean and std for all attempted
combinations"""
fig = plt.figure(figsize=(14, 12))
_ = plt.subplot(111)
xaxis = []
boxes = []
lbls = []
for i, [comb, vals] in enumerate(comb_corrs):
xaxis.append(comb)
offset = 0
for [name, dat] in vals:
if len(dat) == 0:
continue
if name == 'mask':
col = 'r'
elif name == 'surround':
col = 'b'
elif name == 'whole':
col = 'g'
elif name == 'overall':
col = '0.3'
do_box_plot(np.array(dat), np.array([i + offset]), col)
offset += 0.15
if i == 0:
boxes.append(plt.Rectangle((0, 0), 1, 1, fc=col))
lbls.append(name)
plt.xticks(range(len(xaxis)), xaxis, rotation='vertical')
plt.xlim(-1, len(xaxis) + 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)
plt.ylim(-0.3, 1)
plt.xlim(-1, len(x))
adjust_spines(ax, ['bottom', 'left'])
plt.title('Whole vs Surround')
plt.ylabel('Correlation Coef')
plt.xlabel('Neuron #')
ax = plt.subplot(414)
plt.hold(True)
plt.plot([-1, x[-1]], [0, 0], 'k--')
xval = np.array([1])
lbls = []
tks = []
for i, exp_type in enumerate(exp_types):
idx = cells[:, 0] == exp_type
do_box_plot(corrs[idx, 0], xval, colors[i])
tks.append(xval[0])
lbls.append('Centre Vs Whole')
if exp_type != 'PYR':
xval += 1
plt.text(xval[0] - 0.25, -0.15, exp_type)
do_box_plot(corrs[idx, 1], xval, colors[i])
tks.append(xval[0])
lbls.append('Centre Vs Surround')
xval += 1
do_box_plot(corrs[idx, 2], xval, colors[i])
tks.append(xval[0])
lbls.append('Surround Vs Whole')
xval += 2
else:
plt.text(xval[0] - 0.25, -0.15, exp_type)
plt.ylim(-0.3, 1)
plt.xlim(-1, len(x))
adjust_spines(ax, ['bottom', 'left'])
plt.title('Whole vs Surround')
plt.ylabel('Correlation Coef')
plt.xlabel('Neuron #')
ax = plt.subplot(414)
plt.hold(True)
plt.plot([-1, x[-1]], [0, 0], 'k--')
xval = np.array([1])
lbls = []
tks = []
for i, exp_type in enumerate(exp_types):
idx = cells[:, 0] == exp_type
do_box_plot(corrs[idx, 0], xval, colors[i])
tks.append(xval[0])
lbls.append('Centre Vs Whole')
if exp_type != 'PYR':
xval += 1
plt.text(xval[0] - 0.25, -0.15, exp_type)
do_box_plot(corrs[idx, 1], xval, colors[i])
tks.append(xval[0])
lbls.append('Centre Vs Surround')
xval += 1
do_box_plot(corrs[idx, 2], xval, colors[i])
tks.append(xval[0])
lbls.append('Surround Vs Whole')
xval += 2
else:
plt.text(xval[0] - 0.25, -0.15, exp_type)
