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()
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')
ax.set_xscale('log')
ax.set_yscale('log')
