def plot_GoRTs(self):
fig = plt.gcf()
fig.subplots_adjust(bottom=0.2)
base = np.array([np.mean(subj) for subj in self.GoRT['intact']])
tags = self.tags[1:]
for t,tag in enumerate(tags):
diff_scores = [np.mean(subj) for subj in self.GoRT[tag]]-base
plt.bar(t-.4, np.mean(diff_scores)*self.ms,
yerr=sem(diff_scores)*self.ms, color='.7', label=tag, ecolor='k')
plt.xticks(range(len(self.names)), self.names) #np.linspace(0.5,len(self.tags),len(self.tags)-.5), self.tags)
plt.ylabel('GoRT relative to intact (ms)')
plt.ylim(-20*self.ms,20*self.ms)
#plt.tick_params(labelsize='medium')
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
ax = plt.gca()
fontsize = 13
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
def calc_cond_mean_std(data, cond, col):
cond_idx = np.where(cond)[0]
cond_data = data[cond_idx]
cond_data_mean = np.mean(cond_data[col], axis=0)
cond_data_median = np.median(cond_data[col], axis=0)
cond_data_sem = sem(cond_data[col], axis=0)
return (cond_data, cond_data_mean, cond_data_median, cond_data_sem)
def plot_GoRTs(self):
fig = plt.gcf()
fig.subplots_adjust(bottom=0.2)
base = np.array([np.mean(subj) for subj in self.GoRT['intact']])
tags = self.tags[1:]
for t,tag in enumerate(tags):
diff_scores = [np.mean(subj) for subj in self.GoRT[tag]]-base
plt.bar(t-.4, np.mean(diff_scores)*self.ms,
yerr=sem(diff_scores)*self.ms, color='.7', label=tag, ecolor='k')
plt.xticks(range(len(self.names)), self.names) #np.linspace(0.5,len(self.tags),len(self.tags)-.5), self.tags)
plt.ylabel('GoRT relative to intact (ms)')
plt.ylim(-20*self.ms,20*self.ms)
#plt.tick_params(labelsize='medium')
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
ax = plt.gca()
fontsize = 13
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
def calc_cond_mean_std(data, cond, col):
cond_idx = np.where(cond)[0]
cond_data = data[cond_idx]
cond_data_mean = np.mean(cond_data[col], axis=0)
cond_data_median = np.median(cond_data[col], axis=0)
cond_data_sem = sem(cond_data[col], axis=0)
return (cond_data, cond_data_mean, cond_data_median, cond_data_sem)
def plot_SSDs(self):
fig = plt.gcf()
fig.subplots_adjust(bottom=0.2)
base = np.array([np.mean(subj) for subj in self.SSD['intact']])
tags = self.tags[1:]
for t, tag in enumerate(tags):
plt.bar(t - .4,
np.mean([np.mean(subj)
for subj in self.SSD[tag]] - base) * self.ms,
yerr=sem([np.mean(subj)
for subj in self.SSD[tag]]) * self.ms,
color='.7',
label=tag,
ecolor='k')
plt.xticks(
range(len(self.names)), self.names
) #np.linspace(0.5,len(self.tags),len(self.tags)-.5), self.tags)
plt.ylabel('SSD relative to intact (ms)')
#plt.tick_params(labelsize='medium')
plt.ylim(-20 * self.ms, 20 * self.ms)
ax = plt.gca()
fontsize = 13
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
def plot_SSDs(self):
fig = plt.gcf()
fig.subplots_adjust(bottom=0.2)
base = np.array([np.mean(subj) for subj in self.SSD['intact']])
tags = self.tags[1:]
for t,tag in enumerate(tags):
plt.bar(t-.4, np.mean([np.mean(subj) for subj in self.SSD[tag]]-base)*self.ms,
yerr=sem([np.mean(subj) for subj in self.SSD[tag]])*self.ms, color='.7', label=tag, ecolor='k')
plt.xticks(range(len(self.names)), self.names) #np.linspace(0.5,len(self.tags),len(self.tags)-.5), self.tags)
plt.ylabel('SSD relative to intact (ms)')
#plt.tick_params(labelsize='medium')
plt.ylim(-20*self.ms,20*self.ms)
ax = plt.gca()
fontsize = 13
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
