# plot.set_axis_labels('Popen', 'Amp (pA)')
# plot.savefig(os.path.join('/Volumes/c-floor/William/data','{}_scatter.png'.format(concentration)),dpi=300)
# plt.close()
#
# plot = sns.distplot(result['popen'], bins = np.arange(0,1.05,0.05), kde=False, rug=True);
# plt.title('Popen distribution ({}mM glycine)'.format(concentration))
# plt.savefig(os.path.join('/Volumes/c-floor/William/data','{}_Popen_original.png'.format(concentration)),dpi=300)
# plt.close()
#
amp.append(result['mean_amp'])
result = cluster_summary.compute_cluster_summary(patchname = '2015_07_24_0011.csv')
result = cluster_summary.compute_cluster_summary(mean_amp = [0,100])
result = cluster_summary.get_summary(output = 'dict')
popen.append(result['popen']['value'])
errorbar.append(result['popen']['se'])
string = cluster_summary.get_summary(output = 'string')
string = '{}mM: \n'.format(concentration) + string
total += string + '\n'
print(string)
#file = codecs.open('./summary.txt', 'w', "utf-8")
#file.write(total)
#file.close()
#
#amp = np.hstack(amp)
#sns.distplot(amp, kde= False, rug=True);
#plt.xlabel('Amplitude (pA)')
#plt.savefig(os.path.join('/Volumes/c-floor/William/data','AMP_dist.png'.format(concentration)),dpi=300)
