def make_chart(self, img=None):
self.fig.clear()
self.axes = self.fig.add_subplot(111)
if img is None:
img = np.zeros((32,32)) + 0.5
img = self.sta_data.float_to_rgb(img,cmap=self.cmap)
self.img_dim = img.shape
self.im = self.axes.imshow(img,interpolation=self.interpolation)
if self.showing_colorbar:
self.cbar = self.fig.colorbar(self.im, shrink=1.0, fraction=0.045, pad=0.05, ticks=[])
adjust_spines(self.axes,spines=['left','bottom'],spine_outward=[],
xticks='bottom',yticks='left',tick_label=['x','y'])
#self.axes.autoscale_view(scalex=True, scaley=True)
self.fig.canvas.draw()
def make_chart(self,data=np.zeros(1),bins=np.arange(10)+1,polar=False):
self.polar_chart = polar
self.hist_bins = []
self.hist_patches = []
self.x = np.arange(17)
self.means = np.zeros(self.x.size)
self.stds = np.zeros(self.x.size)
self.fitting_x = np.linspace(self.x[0], self.x[-1], 100, endpoint=True)
self.fitting_y = np.zeros(self.fitting_x.size)
self.fig.clear()
grid = 18
height = grid // 9
gs = gridspec.GridSpec(grid, grid)
# make tuning curve plot
axes = self.fig.add_subplot(gs[:-height*2,height//2:-height//2],polar=polar)
if polar:
self.curve_data = axes.plot(self.x, self.means, 'ko-')[0]
else:
adjust_spines(axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2)
axes.set_ylabel('Response(spikes/sec)',fontsize=12)
self.curve_data = axes.plot(self.x, self.means, self.data_point_styles[0])[0]
self.errbars = axes.errorbar(self.x, self.means, yerr=self.stds, fmt='k.') if self.showing_errbar else None
self.curve_axes = axes
self.fitting_data = axes.plot(self.fitting_x, self.fitting_y, self.fitting_curve_styles[0])[0]
axes.set_ylim(0,100)
axes.relim()
axes.autoscale_view(scalex=True, scaley=False)
axes.grid(b=True, which='major',axis='both',linestyle='-.')
# make histgrams plot
rows,cols = (grid-height,grid)
for row in range(rows,cols)[::height]:
for col in range(cols)[::height]:
axes = self.fig.add_subplot(gs[row:row+height,col:col+height])
axes.set_axis_bgcolor('white')
#axes.set_title('PSTH', size=8)
axes.set_ylim(0,100)
if col == 0:
adjust_spines(axes,spines=['left','bottom'],xticks='bottom',yticks='left',tick_label=['y'],xaxis_loc=4,yaxis_loc=3)
axes.set_ylabel('Spikes',fontsize=11)
elif col == cols-height:
adjust_spines(axes,spines=['right','bottom'],xticks='bottom',yticks='right',tick_label=['y'],xaxis_loc=4,yaxis_loc=3)
else:
adjust_spines(axes,spines=['bottom'],xticks='bottom',yticks='none',tick_label=[],xaxis_loc=4,yaxis_loc=3)
pylab.setp(axes.get_xticklabels(), fontsize=8)
pylab.setp(axes.get_yticklabels(), fontsize=8)
_n, bins, patches = axes.hist(data, bins, facecolor='black', alpha=1.0)
self.hist_bins.append(bins)
self.hist_patches.append(patches)
self.fig.canvas.draw()
def make_chart(self, bins=np.arange(150), bin_data=np.zeros(150)):
self.bins = bins
self.bin_data = bin_data
self.fig.clear()
# make Average curve plot
axes = self.fig.add_subplot(111)
adjust_spines(axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=0,youtward=0,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=7,xminor_auto_loc=2,yminor_auto_loc=2)
axes.set_xlabel('Time(ms)',fontsize=12)
axes.set_ylabel('Response(spikes/sec)',fontsize=12)
self.curve_data = axes.plot(self.bins, self.bin_data, self.data_point_styles[0])[0]
self.curve_axes = axes
axes.set_ylim(0,100)
axes.relim()
axes.autoscale_view(scalex=False, scaley=False)
#axes.grid(b=True, which='major',axis='both',linestyle='-.')
self.fig.canvas.draw()
def make_chart(self, img=None):
self.fig.clear()
self.axes = self.fig.add_subplot(111)
if img is None:
img = np.zeros((32, 32)) + 0.5
img = self.sta_data.float_to_rgb(img, cmap=self.cmap)
self.img_dim = img.shape
self.im = self.axes.imshow(img, interpolation=self.interpolation)
if self.showing_colorbar:
self.cbar = self.fig.colorbar(self.im,
shrink=1.0,
fraction=0.045,
pad=0.05,
ticks=[])
adjust_spines(self.axes,
spines=['left', 'bottom'],
spine_outward=[],
xticks='bottom',
yticks='left',
tick_label=['x', 'y'])
#self.axes.autoscale_view(scalex=True, scaley=True)
self.fig.canvas.draw()
def update_chart(self, data=None):
if data is None and self.data is None:
return
elif data is None and self.data is not None:
data = self.data
selected_unit = wx.FindWindowByName('unit_choice').get_selected_unit()
if selected_unit is not None:
channel, unit = selected_unit
zeroth_psth_data = None
polar_dict = {
'orientation': True,
'spatial_frequency': False,
'phase': False,
'disparity': False
}
self.parameter = self.psth_data.parameter
if self.parameter in polar_dict:
polar_chart = polar_dict[self.parameter]
else:
polar_chart = self.polar_chart
# histogram
for index in [
i for i in data[channel][unit].iterkeys()
if (not i & 1 and i < 16)
]:
patch_index = index // 2
spike_times = data[channel][unit][index]['spikes']
bins = data[channel][unit][index]['bins']
psth_data = data[channel][unit][index]['psth_data']
if index == 0:
zeroth_psth_data = psth_data
_trials = data[channel][unit][index]['trials']
self.show_fitting_changed = False
if len(bins) != len(
self.hist_bins[patch_index]
) or self.show_errbar_changed or polar_chart != self.polar_chart:
self.make_chart(spike_times, bins, polar_chart)
self.show_errbar_changed = False
self.show_fitting_changed = False
#else:
for rect, h in zip(self.hist_patches[patch_index], psth_data):
rect.set_height(h)
for index in data[channel][unit].iterkeys():
mean = data[channel][unit][index]['mean']
std = data[channel][unit][index]['std']
if index == -1:
self.bg_noise_mean = mean
self.bg_noise_std = std
elif index <= 15:
self.means[index] = mean
self.stds[index] = std
elif index == 16:
self.mono_left_mean = mean
self.mono_left_std = std
elif index == 17:
self.mono_right_mean = mean
self.mono_right_std = std
self.curve_axes.set_xscale('linear')
if self.parameter == 'orientation':
self.log_fitting = False
self.x = np.linspace(0.0, 360.0, 17) / 180 * np.pi
self.curve_axes.set_title('Orientation Tuning Curve',
fontsize=12)
if zeroth_psth_data is not None:
for rect, h in zip(self.hist_patches[-1],
zeroth_psth_data):
rect.set_height(h)
self.means[-1] = self.means[0]
self.stds[-1] = self.stds[0]
if self.parameter == 'spatial_frequency':
self.log_fitting = True
self.x = np.logspace(-1.0, 0.5, 16)
self.curve_axes.set_title('Spatial Frequency Tuning Curve',
fontsize=12)
self.curve_axes.set_xscale('log')
self.means = self.means[:len(self.x)]
self.stds = self.stds[:len(self.x)]
adjust_spines(self.curve_axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2,xmajor_loc=[0.1,0.5,1.0,2.0])
if self.parameter in ('disparity', 'phase'):
self.log_fitting = False
self.x = np.linspace(0.0, 360.0, 17)
if self.parameter == 'disparity':
self.curve_axes.set_title('Disparity Tuning Curve',
fontsize=12)
if self.parameter == 'phase':
self.curve_axes.set_title('Phase Tuning Curve',
fontsize=12)
if zeroth_psth_data is not None:
for rect, h in zip(self.hist_patches[-1],
zeroth_psth_data):
rect.set_height(h)
self.means[-1] = self.means[0]
self.stds[-1] = self.stds[0]
if self.mono_left_mean is not None and self.mono_right_mean is not None:
#annotate dominant eye activity
self.mono_dom_mean = max(self.mono_left_mean,
self.mono_right_mean)
self.curve_axes.annotate('',
xy=(360, self.mono_dom_mean),
xytext=(370, self.mono_dom_mean),
arrowprops=dict(facecolor='black',
frac=1.0,
headwidth=10,
shrink=0.05))
#annotate non-dominant eye activity
self.mono_nod_mean = min(self.mono_left_mean,
self.mono_right_mean)
self.curve_axes.annotate('',
xy=(360, self.mono_nod_mean),
xytext=(370, self.mono_nod_mean),
arrowprops=dict(facecolor='gray',
frac=1.0,
headwidth=10,
shrink=0.05))
if self.bg_noise_mean is not None:
#annotate background activity
self.curve_axes.annotate('',
xy=(360, self.bg_noise_mean),
xytext=(370, self.bg_noise_mean),
arrowprops=dict(facecolor='white',
frac=1.0,
headwidth=10,
shrink=0.05))
adjust_spines(self.curve_axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2)
if self.append_data_curve:
self.curve_axes.plot(
self.x, self.means,
self.data_point_styles[self.data_curves - 1])
else:
self.curve_data.set_xdata(self.x)
self.curve_data.set_ydata(self.means)
if self.errbars is not None:
self._update_errbars(self.errbars, self.x, self.means,
self.stds)
##################################################################
##### Curve Fitting
##################################################################
if self.log_fitting:
self.fitting_x = np.logspace(np.log10(self.x[0]),
np.log10(self.x[-1]),
self.fitting_x.size,
endpoint=True)
else:
self.fitting_x = np.linspace(self.x[0],
self.x[-1],
self.fitting_x.size,
endpoint=True)
model_fitting = np.zeros(self.fitting_x.size)
model_xdata = np.zeros(self.x.size)
nonzero = np.nonzero(self.means)[0]
if self.curve_fitting == 'gauss':
if self.log_fitting:
model_xdata, model_fitting = self.curve_fitter.loggaussfit1d(
self.x[nonzero], self.means[nonzero], self.fitting_x)
else:
model_xdata, model_fitting = self.curve_fitter.gaussfit1d(
self.x[nonzero], self.means[nonzero], self.fitting_x)
elif self.curve_fitting == 'sin':
model_xdata, model_fitting = self.curve_fitter.sinusoid1d(
self.x[nonzero], self.means[nonzero], self.fitting_x)
elif self.curve_fitting == 'gabor':
model_xdata, model_fitting = self.curve_fitter.gaborfit1d(
self.x[nonzero], self.means[nonzero], self.fitting_x)
if self.append_data_curve:
self.curve_axes.plot(
self.fitting_x, model_fitting,
self.fitting_curve_styles[self.data_curves - 1])
else:
self.fitting_data.set_xdata(self.fitting_x)
self.fitting_data.set_ydata(model_fitting)
label = [self.parameter, 'rate', 'std']
self.data_form.gen_curve_data(self.x, self.means, self.stds,
self.bg_noise_mean,
self.mono_dom_mean,
self.mono_nod_mean, self.fitting_x,
model_fitting, model_xdata, label)
if self.parameter == 'orientation':
self.data_form.gen_psth_data(data[channel][unit])
self.curve_axes.set_xlim(min(self.x), max(self.x))
self.curve_axes.set_ylim(min(0, min(self.means)),
(max(self.means) * 1.2) // 10 * 10)
#self.curve_axes.set_ylim(auto=True)
self.curve_axes.relim()
self.curve_axes.autoscale_view(scalex=False, scaley=False)
self.fig.canvas.draw()
def make_chart(self,
data=np.zeros(1),
bins=np.arange(10) + 1,
polar=False):
self.polar_chart = polar
self.hist_bins = []
self.hist_patches = []
self.x = np.arange(17)
self.means = np.zeros(self.x.size)
self.stds = np.zeros(self.x.size)
self.fitting_x = np.linspace(self.x[0], self.x[-1], 100, endpoint=True)
self.fitting_y = np.zeros(self.fitting_x.size)
self.fig.clear()
grid = 18
height = grid // 9
gs = gridspec.GridSpec(grid, grid)
# make tuning curve plot
axes = self.fig.add_subplot(gs[:-height * 2, height // 2:-height // 2],
polar=polar)
if polar:
self.curve_data = axes.plot(self.x, self.means, 'ko-')[0]
else:
adjust_spines(axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2)
axes.set_ylabel('Response(spikes/sec)', fontsize=12)
self.curve_data = axes.plot(self.x, self.means,
self.data_point_styles[0])[0]
self.errbars = axes.errorbar(
self.x, self.means, yerr=self.stds,
fmt='k.') if self.showing_errbar else None
self.curve_axes = axes
self.fitting_data = axes.plot(self.fitting_x, self.fitting_y,
self.fitting_curve_styles[0])[0]
axes.set_ylim(0, 100)
axes.relim()
axes.autoscale_view(scalex=True, scaley=False)
axes.grid(b=True, which='major', axis='both', linestyle='-.')
# make histgrams plot
rows, cols = (grid - height, grid)
for row in range(rows, cols)[::height]:
for col in range(cols)[::height]:
axes = self.fig.add_subplot(gs[row:row + height,
col:col + height])
axes.set_axis_bgcolor('white')
#axes.set_title('PSTH', size=8)
axes.set_ylim(0, 100)
if col == 0:
adjust_spines(axes,
spines=['left', 'bottom'],
xticks='bottom',
yticks='left',
tick_label=['y'],
xaxis_loc=4,
yaxis_loc=3)
axes.set_ylabel('Spikes', fontsize=11)
elif col == cols - height:
adjust_spines(axes,
spines=['right', 'bottom'],
xticks='bottom',
yticks='right',
tick_label=['y'],
xaxis_loc=4,
yaxis_loc=3)
else:
adjust_spines(axes,
spines=['bottom'],
xticks='bottom',
yticks='none',
tick_label=[],
xaxis_loc=4,
yaxis_loc=3)
pylab.setp(axes.get_xticklabels(), fontsize=8)
pylab.setp(axes.get_yticklabels(), fontsize=8)
_n, bins, patches = axes.hist(data,
bins,
facecolor='black',
alpha=1.0)
self.hist_bins.append(bins)
self.hist_patches.append(patches)
self.fig.canvas.draw()
def update_chart(self, data=None):
if data is None and self.data is None:
return
elif data is None and self.data is not None:
data = self.data
selected_unit = wx.FindWindowByName('unit_choice').get_selected_unit()
if selected_unit is not None:
channel, unit = selected_unit
zeroth_psth_data = None
polar_dict = {'orientation':True, 'spatial_frequency':False, 'phase':False, 'disparity':False}
self.parameter = self.psth_data.parameter
if self.parameter in polar_dict:
polar_chart = polar_dict[self.parameter]
else:
polar_chart = self.polar_chart
# histogram
for index in [i for i in data[channel][unit].iterkeys() if (not i&1 and i<16)]:
patch_index = index // 2
spike_times = data[channel][unit][index]['spikes']
bins = data[channel][unit][index]['bins']
psth_data = data[channel][unit][index]['psth_data']
if index == 0:
zeroth_psth_data = psth_data
_trials = data[channel][unit][index]['trials']
self.show_fitting_changed = False
if len(bins) != len(self.hist_bins[patch_index]) or self.show_errbar_changed or polar_chart != self.polar_chart:
self.make_chart(spike_times, bins, polar_chart)
self.show_errbar_changed = False
self.show_fitting_changed = False
#else:
for rect,h in zip(self.hist_patches[patch_index],psth_data):
rect.set_height(h)
for index in data[channel][unit].iterkeys():
mean = data[channel][unit][index]['mean']
std = data[channel][unit][index]['std']
if index == -1:
self.bg_noise_mean = mean
self.bg_noise_std = std
elif index <= 15:
self.means[index] = mean
self.stds[index] = std
elif index == 16:
self.mono_left_mean = mean
self.mono_left_std = std
elif index == 17:
self.mono_right_mean = mean
self.mono_right_std = std
self.curve_axes.set_xscale('linear')
if self.parameter == 'orientation':
self.log_fitting = False
self.x = np.linspace(0.0, 360.0, 17)/180*np.pi
self.curve_axes.set_title('Orientation Tuning Curve',fontsize=12)
if zeroth_psth_data is not None:
for rect,h in zip(self.hist_patches[-1],zeroth_psth_data):
rect.set_height(h)
self.means[-1] = self.means[0]
self.stds[-1] = self.stds[0]
if self.parameter == 'spatial_frequency':
self.log_fitting = True
self.x = np.logspace(-1.0,0.5,16)
self.curve_axes.set_title('Spatial Frequency Tuning Curve',fontsize=12)
self.curve_axes.set_xscale('log')
self.means = self.means[:len(self.x)]
self.stds = self.stds[:len(self.x)]
adjust_spines(self.curve_axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2,xmajor_loc=[0.1,0.5,1.0,2.0])
if self.parameter in ('disparity','phase'):
self.log_fitting = False
self.x = np.linspace(0.0, 360.0, 17)
if self.parameter == 'disparity':
self.curve_axes.set_title('Disparity Tuning Curve',fontsize=12)
if self.parameter == 'phase':
self.curve_axes.set_title('Phase Tuning Curve',fontsize=12)
if zeroth_psth_data is not None:
for rect,h in zip(self.hist_patches[-1],zeroth_psth_data):
rect.set_height(h)
self.means[-1] = self.means[0]
self.stds[-1] = self.stds[0]
if self.mono_left_mean is not None and self.mono_right_mean is not None:
#annotate dominant eye activity
self.mono_dom_mean = max(self.mono_left_mean, self.mono_right_mean)
self.curve_axes.annotate('', xy=(360, self.mono_dom_mean), xytext=(370, self.mono_dom_mean),
arrowprops=dict(facecolor='black', frac=1.0, headwidth=10, shrink=0.05))
#annotate non-dominant eye activity
self.mono_nod_mean = min(self.mono_left_mean, self.mono_right_mean)
self.curve_axes.annotate('', xy=(360, self.mono_nod_mean), xytext=(370, self.mono_nod_mean),
arrowprops=dict(facecolor='gray', frac=1.0, headwidth=10, shrink=0.05))
if self.bg_noise_mean is not None:
#annotate background activity
self.curve_axes.annotate('', xy=(360, self.bg_noise_mean), xytext=(370, self.bg_noise_mean),
arrowprops=dict(facecolor='white', frac=1.0, headwidth=10, shrink=0.05))
adjust_spines(self.curve_axes,spines=['left','bottom','right'],spine_outward=['left','right','bottom'],xoutward=10,youtward=30,\
xticks='bottom',yticks='both',tick_label=['x','y'],xaxis_loc=5,xminor_auto_loc=2,yminor_auto_loc=2)
if self.append_data_curve:
self.curve_axes.plot(self.x, self.means, self.data_point_styles[self.data_curves-1])
else:
self.curve_data.set_xdata(self.x)
self.curve_data.set_ydata(self.means)
if self.errbars is not None:
self._update_errbars(self.errbars,self.x,self.means,self.stds)
##################################################################
##### Curve Fitting
##################################################################
if self.log_fitting:
self.fitting_x = np.logspace(np.log10(self.x[0]), np.log10(self.x[-1]), self.fitting_x.size, endpoint=True)
else:
self.fitting_x = np.linspace(self.x[0], self.x[-1], self.fitting_x.size, endpoint=True)
model_fitting = np.zeros(self.fitting_x.size)
model_xdata = np.zeros(self.x.size)
nonzero = np.nonzero(self.means)[0]
if self.curve_fitting == 'gauss':
if self.log_fitting:
model_xdata,model_fitting = self.curve_fitter.loggaussfit1d(self.x[nonzero], self.means[nonzero], self.fitting_x)
else:
model_xdata,model_fitting = self.curve_fitter.gaussfit1d(self.x[nonzero], self.means[nonzero], self.fitting_x)
elif self.curve_fitting == 'sin':
model_xdata,model_fitting = self.curve_fitter.sinusoid1d(self.x[nonzero], self.means[nonzero], self.fitting_x)
elif self.curve_fitting == 'gabor':
model_xdata,model_fitting = self.curve_fitter.gaborfit1d(self.x[nonzero], self.means[nonzero], self.fitting_x)
if self.append_data_curve:
self.curve_axes.plot(self.fitting_x, model_fitting, self.fitting_curve_styles[self.data_curves-1])
else:
self.fitting_data.set_xdata(self.fitting_x)
self.fitting_data.set_ydata(model_fitting)
label = [self.parameter, 'rate', 'std']
self.data_form.gen_curve_data(self.x, self.means, self.stds,
self.bg_noise_mean, self.mono_dom_mean, self.mono_nod_mean,
self.fitting_x, model_fitting, model_xdata, label)
if self.parameter == 'orientation':
self.data_form.gen_psth_data(data[channel][unit])
self.curve_axes.set_xlim(min(self.x),max(self.x))
self.curve_axes.set_ylim(min(0, min(self.means)), (max(self.means)*1.2)//10*10)
#self.curve_axes.set_ylim(auto=True)
self.curve_axes.relim()
self.curve_axes.autoscale_view(scalex=False, scaley=False)
self.fig.canvas.draw()