stimdur = expdata['stim_duration']
clusterids = plf.clusters_to_ids(clusters)
index = [i for i, cl in enumerate(clusterids)
if cl == clustertoplot][0]
fr = expdata['all_frs'][index]
t = expdata['t']
baselines = expdata['baselines'][index]
plotind = [1, 3, 5, 7][i + 2 * j]
ax = plt.subplot(4, 2, plotind)
ax.plot(t, fr, 'k', linewidth=.5)
plf.spineless(ax)
if cond == 'M':
plf.drawonoff(ax, preframedur, stimdur, h=.1)
plf.subplottext(['A', 'B'][j], ax, x=-0.1)
elif cond != 'M' and j == 0:
scalebars.add_scalebar(
ax,
matchx=False,
sizex=.5,
labelx='500 ms',
matchy=False,
sizey=30,
labely='30 Hz',
# labely=fr'{dist_set} $\upmu$m',
hidey=False,
barwidth=1.2,
loc='upper right',
sep=2,
pad=0)
groups = []
bins = np.arange(-.2, .35+0.05, 0.05)
for i, color in enumerate(colorcategories):
group = [index for index, c in enumerate(colors) if c == color]
ax = axes[i]
change = csichange[group]
groups.append(group)
changes.append(change)
if i == 0:
csichange_on = np.copy(change)
elif i == 1:
csichange_off = np.copy(change)
ax.hist(change, color=color , bins=bins)
ax.set_ylim([0, 10])
ax.set_yticks(np.linspace(0, 8, 3))
ax.plot([0, 0], [0, 12], 'r--', alpha=.3)
plf.subplottext(['A', 'B', 'C', 'D', 'E'][i], ax, x=-.08)
plf.spineless(ax)
print(i, stats.wilcoxon(group)[1])
plt.subplots_adjust(hspace=.3)
ax.set_xlabel(r'CSI$_{photopic}$ - CSI$_{mesopic}$')
texplot.savefig('csihistogram_mp')
plt.show()
manwhitu = stats.mannwhitneyu(csichange_on, csichange_off)
print('Mann-Whitney U test for ON and OFF populations '
f'being different: {manwhitu[1]:5.4f}')
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*divide by zero*.', RuntimeWarning)
pars = gfit.gaussfit(fit_frame * onoroff)
f = gfit.twodgaussian(pars)
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z - pars[0]) / pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm * -2)
ax = plt.subplot(1, 2, 1)
plf.subplottext('A', ax, x=0, y=1.3)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax)
ax.set_aspect('equal')
plf.spineless(ax)
ax.set_axis_off()
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax.contour(Y,
X,
Zm, [inner_b, outer_b],
cmap=plf.RFcolormap(('C0', 'C1')))
warnings.filterwarnings('ignore',
'.*divide by zero*.', RuntimeWarning)
pars = gfit.gaussfit(fit_frame*onoroff)
f = gfit.twodgaussian(pars)
Z = f(X, Y)
# Correcting for Mahalonobis dist.
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
'.*divide by zero*.', RuntimeWarning)
Zm = np.log((Z-pars[0])/pars[1])
Zm[np.isinf(Zm)] = np.nan
Zm = np.sqrt(Zm*-2)
ax1 = plt.subplot(rows, columns, 1)
plf.subplottext('A', ax1)
vmax = np.abs(fit_frame).max()
vmin = -vmax
im = plf.stashow(fit_frame, ax1)
ax1.set_aspect('equal')
plf.spineless(ax1)
ax1.set_xticks([])
ax1.set_yticks([])
checkercolors = ['black', 'orange']
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', '.*invalid value encountered*.')
ax1.contour(Y, X, Zm, [inner_b, outer_b], linewidths=.5,
fig = texplot.texfig(.85, aspect=1.85)
ax = plt.subplot2grid((4, 3), (0, 0), colspan=3, rowspan=2)
# Create an array for all the colors to use with plt.legend()
patches = []
for color, label in zip(colorcategories, colorlabels):
patches.append(mpatches.Patch(color=color, label=label))
ax.legend(handles=patches, fontsize='xx-small')
for group, color in zip(groups, colorcategories):
ax.scatter(csichange[group], biaschange[group], c=color, **scatterkwargs)
ax.axhline(0, **linekwargs)
ax.axvline(0, **linekwargs)
plf.subplottext('A', ax, x=-0.05)
ax.set_xlabel(r'CSI$_{photopic}$ - CSI$_{mesopic}$')
ax.set_ylabel(r'PI$_{photopic}$ - PI$_{mesopic}$')
for i, (group, color) in enumerate(zip(groups, colorcategories)):
ax = plt.subplot2grid((4, 3), (2 + int((np.round((i - 1) / 3))), i % 3))
ax.scatter(csichange, biaschange, c='grey')
ax.scatter(csichange[group], biaschange[group], c=color, **scatterkwargs)
ax.axhline(0, **linekwargs)
ax.axvline(0, **linekwargs)
plf.spineless(ax, 'tr')
plf.subplottext(['B', 'C', 'D', 'E', 'F'][i], ax, x=-.25)
#axes[-1].set_axis_off()
plt.subplots_adjust(hspace=.45, wspace=.45)
#texplot.savefig('csichangevsbiaschange')
popt = all_parameters[index]
cut_time = int(100/(frame_duration*1000)/2)
# Changed width from 700 micrometer to 400 to zoom in on the
# region of interest. This shifts where the fit is drawn,
# it's fixed when plotting.
fsize_original = int(700/(stx_w*px_size))
fsize = int(400/(stx_w*px_size))
fsize_diff = fsize_original - fsize
t = np.arange(filter_length)*frame_duration*1000
vscale = fsize * stx_w*px_size
sta, max_i = msc.cutstripe(sta, max_i, fsize*2)
ax1 = axes[2*j]
plf.subplottext(['A', 'C'][j], ax1, x=-.4)
plf.subplottext(['Mesopic', 'Photopic'][j],
ax1, x=-.5, y=.5, rotation=90, va='center')
plf.stashow(sta, ax1, extent=[0, t[-1], -vscale, vscale])
ax1.set_xlabel('Time [ms]')
# ax1.set_ylabel(r'Distance [$\upmu$m]')
ax1.set_ylabel(r'Distance [μm]')
fitv = np.mean(sta[:, max_i[1]-cut_time:max_i[1]+cut_time+1],
axis=1)
s = np.arange(fitv.shape[0])
ax2 = axes[2*j+1]
plf.subplottext(['B', 'D'][j], ax2, x=-.1)
plf.subplottext(f'Center-Surround Index: {csi:4.2f}',