def test_batch_analysis(self):
'''
test the batch analysis.
'''
cluster_list = []
for patch in self._name_list:
test_patch = Patch(os.path.join(self._dir, patch))
test_patch.scan()
for cluster in test_patch:
cluster.compute_mode()
cluster.compute_mode_detail()
cluster_list.append(cluster)
# Test BatchAnalysis class
batch_analysis = BatchAnalysis(list(cluster_list))
test_dict = batch_analysis.compute_cluster_summary()
for patchname in self._name_list:
for cluster_no in range(1, self.cluster_num+1):
test_dict = batch_analysis.compute_cluster_summary(
patchname = patchname, cluster_no = cluster_no)
temp_amp = np.mean(self._patch_list[patchname][cluster_no-1]['open_amp']
-
self._patch_list[patchname][cluster_no-1]['shut_amp'])
if abs(test_dict['mean_amp'] - temp_amp) > 0.0001:
print('Amplitude error')
temp_duration = (sum(self._patch_list[patchname][cluster_no-1]['open_period'])
+
sum(self._patch_list[patchname][cluster_no-1]['shut_period']))
if abs(test_dict['duration'] - temp_duration) > 0.0001:
print('Duration error')
temp_popen = sum(self._patch_list[patchname][cluster_no-1]['open_period']) / temp_duration
if abs(test_dict['popen'] - temp_popen) > 0.0001:
print('Popen error')
# Test StretchSummary class
batch_analysis.compute_stretch_summary()
"""
import os
import codecs
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
from batch_query import Batch
from batch_analysis import BatchAnalysis, PatchExamination
sns.set(color_codes=True)
batch = Batch('./data/')
batch.scan_orded_folder()
concentation_list = ['0.3', '1', '3', '10', '100']
for concentration in concentation_list:
cluster_list = batch.query(mutation = 'S270T', concentration = concentration)
cluster_summary = BatchAnalysis(cluster_list)
summary = cluster_summary.compute_cluster_summary()
np.savetxt(concentration+'.csv', summary,
fmt= ['%s', '%i', '%.2f', '%.2f','%.2f','%i','%.2f','%.2f'],
delimiter = ',')
import numpy as np
from batch_query import Batch
from batch_analysis import BatchAnalysis
batch = Batch('/Users/zhiyiwu/GitHub/sc_py/data')
batch.scan_orded_folder()
list_100 = batch.query(mutation = 'wt', agonist = 'taurine', concentration = '100')
list_30 = batch.query(mutation = 'wt', agonist = 'taurine', concentration = '30')
combined_list = list_100 + list_30
for cluster in combined_list:
cluster.compute_mode()
cluster.compute_mode_detail(output = True)
batch_100 = BatchAnalysis(list_100)
cluster_100 = batch_100.compute_cluster_summary()
stretch_100 = batch_100.compute_stretch_summary()
popen_100 = plt.figure()
ax = popen_100.add_subplot(111)
ax.hist(stretch_100['popen'], bins = np.arange(0,1.05,0.05), color = 'blue')
ax.hist(cluster_100['popen'], bins = np.arange(0,1.05,0.05), color = 'red')
ax.set_title('Popen distribution for 100mM taurine')
popen_100.savefig(os.path.join('/Users/zhiyiwu/GitHub/sc_py/data','taurine_100_Popen.png'),dpi=300)
plt.close(popen_100)
open_shut_100 = plt.figure()
ax = open_shut_100.add_subplot(111)
ax.scatter(stretch_100['mean_open'], stretch_100['mean_shut'], s=1, color = 'blue')
ax.scatter(cluster_100['mean_open'], cluster_100['mean_shut'], s=1, color = 'red')
concentrations = [1,3,10,100]
for concentration in concentrations:
for patch in data:
if float(patch['concentration']) == concentration:
filename = "./ekdist_traces/{}.scn".format(patch['filename'].decode('utf8'))
#
# filename = "./raw trace/{}/{}.scn".format(patch['concentration'].decode('utf8'),
# patch['filename'].decode('utf8'))
patch = Patch(filename)
patch.read_scn(tres=patch['res']*1e-6, tcrit=patch['tcrit']*1e-6)
cluster_list.extend(patch.get_cluster_list())
batch_analysis = BatchAnalysis(cluster_list)
summary = batch_analysis.compute_cluster_summary()
summary['start'] /= 1000
summary['end'] /= 1000
np.savetxt('temp.csv', summary, delimiter=',', fmt = '%s,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e,%.18e')
col_name = str(summary.dtype.names)
col_name = col_name[1:-1] + '\n'
f = open('temp.csv','r')
string = f.read()
string = col_name + string
f.close()
f = open('scan_summary_{}.csv'.format(concentration),'w')
f.write(string)
f.close()
total_summary = pd.DataFrame()
for concentration in concentation_list:
cluster_list = batch.query(mutation = 'S270T', concentration = concentration)
patch_list = batch.query(mutation = 'S270T', concentration = concentration,
output = 'patch')
patch_summary = PatchExamination(patch_list)
summary = patch_summary.compute_summary_table()
summary = pd.DataFrame(summary)
length = len(summary)
# summary.loc[:,'concentration'] = float(concentration)
total_summary = total_summary.append(summary, ignore_index=True)
if cluster_list:
cluster_summary = BatchAnalysis(cluster_list)
result = cluster_summary.compute_cluster_summary()
# plot = sns.jointplot(result['popen'],result['mean_amp'], stat_func= None)
# 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')