def test_batch_query(self):
'''
test the batch_query module.
'''
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_list.append(cluster)
# Test search orded folder scan
choice_dict = OrderedDict([('receptor', ['NMDA', 'AMPA']),
('mutation', ['S219P', 'S200T']),
('composition', ['a1','ab']),
('agonist', ['glycine', 'taurine']),
('concentration', ['100', '0.1'])])
organisation = {keys:{key: [] for key in choice_dict[keys]} for keys in choice_dict}
for index in range(len(self._name_list)):
temp_filepath = self._dir
for key in choice_dict:
choice = random.choice(choice_dict[key])
temp_filepath = os.path.join(temp_filepath, choice)
organisation[key][choice].extend([index]*self.cluster_num)
try:
os.makedirs(temp_filepath)
except FileExistsError:
pass
shutil.copy2(os.path.join(self._dir, self._name_list[index]),
os.path.join(temp_filepath, self._name_list[index]))
test_batch = Batch(folder_list = self._dir)
if len(test_batch.scan_folder()) != len(cluster_list):
print('scan_folder error')
test_cluster_list = test_batch.scan_orded_folder(clear = True,export = True)
copy_organisation = copy.deepcopy(organisation)
for cluster in test_cluster_list:
index = self._name_list.index(cluster.patchname)
for key in organisation:
if not index in organisation[key][getattr(cluster, key)]:
print('scan_orded_folder error')
else:
copy_organisation[key][getattr(cluster, key)].remove(index)
for i in copy_organisation:
for j in copy_organisation[i]:
if copy_organisation[i][j]:
print('scan_orded_folder error')
# Test query
copy_organisation = copy.deepcopy(organisation)
for i in organisation:
for j in organisation[i]:
if organisation[i][j]:
query_list = test_batch.query(**{i: j})
for cluster in query_list:
index = self._name_list.index(cluster.patchname)
try:
copy_organisation[i][j].remove(index)
except ValueError:
print('query error')
for i in copy_organisation:
for j in copy_organisation[i]:
if copy_organisation[i][j]:
print('query error')
"""
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 = ',')
"""
Created on Mon Jul 20 09:21:42 2015
@author: zhiyiwu
"""
import os
import matplotlib.pyplot as plt
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')
from batch_query import Batch
from batch_analysis import BatchAnalysis, PatchExamination
#sns.set(color_codes=True)
batch = Batch('/Users/zhiyiwu/Documents/pharmfit/data/')
batch.scan_orded_folder()
concentation_list = ['100',]
total = ''
amp = []
popen = []
errorbar = []
total_summary = pd.DataFrame()
for concentration in concentation_list:
cluster_list = batch.query(mutation = 'wt', composition = 'alpha1beta', concentration = concentration)
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('cfit_summary_wt100ab.csv','w')
f.write(string)
f.close()
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 = ['10',]
total = ''
amp = []
popen = []
errorbar = []
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()