def test_scn_write():
intervals = np.hstack((np.arange(1,501),np.arange(1,501)))
amplitudes = np.ones(1000)*-1
amplitudes[::2] = 0
amplitudes = amplitudes.astype('int')
flags = np.zeros(1000)
flags = flags.astype('int')
dcio.scn_write(intervals, amplitudes, flags, filename='./test.SCN')
new_patch = Patch('./test.SCN')
new_patch.read_scn(tres=0, tcrit=500)
new_patch.write_scn()
new_patch = Patch('./modified_test.SCN')
new_patch.read_scn(tres=0, tcrit=np.inf)
period = new_patch[1].period
print(sum(intervals[3:-1]-period))
data = np.loadtxt('./ekdist_paramteters.csv',
delimiter=',', dtype={
'names': ('filename', 'concentration', 'res', 'tcrit'),
'formats': ('S8', 'S3', 'f4', 'f4')})
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
# -*- coding: utf-8 -*-
"""
Created on Fri May 20 11:26:23 2016
@author: zhiyiwu
"""
from info import Cluster,Patch
from dcpyps import dataset
from PlotAnalysis import PlotSingle
from batch_analysis import BatchAnalysis
import matplotlib.pyplot as plt
patch_F = Patch('/Users/zhiyiwu/Documents/pharmfit/raw trace/1/15071710.SCN')
patch_F.read_scn(tres=30, tcrit=100)
plt.plot(patch_F.mean_open, patch_F.mean_shut,'o', alpha=0.5, label = 'F')
patch_G = Patch('/Users/zhiyiwu/Documents/pharmfit/raw trace/1/15080500.SCN')
patch_G.read_scn(tres=20, tcrit=300)
plt.plot(patch_G.mean_open, patch_G.mean_shut,'o', alpha=0.5, label = 'G')
patch_H = Patch('/Users/zhiyiwu/Documents/pharmfit/raw trace/1/15080705.SCN')
patch_H.read_scn(tres=20, tcrit=300)
plt.plot(patch_H.mean_open, patch_H.mean_shut,'o', alpha=0.5, label = 'H')
plt.legend()
'15080408'
]
tres = [
20,
30,
20
]
tcrit = [
100,
70,
100
]
popen =[]
for index, name in enumerate(name_list):
patch = Patch('/Users/zhiyiwu/Documents/pharmfit/raw trace/3/{}.SCN'.format(name))
patch.read_scn(tres=tres[index], tcrit=tcrit[index])
maxopen = 1
maxshut = float('inf')
def rule(cluster, maxopen, maxshut):
if (cluster.mean_open > maxopen) or (cluster.mean_shut > maxshut):
return False
else:
return True
for i in range(3):
patch = Patch('/Users/zhiyiwu/Documents/pharmfit/raw trace/3/{}.SCN'.format(name_list[i]))
patch.read_scn(tres=tres[i], tcrit=tcrit[i])
patch.filter_cluster(rule, maxopen, maxshut)
patch.write_scn()
def change(name, tres, tcrit, minPopen = 0, maxPopen = 1.1):
new_patch = Patch('./set/{}.SCN'.format(name))
new_patch.read_scn(tres=tres, tcrit=tcrit)
new_patch.filter_cluster(rule, minPopen, maxPopen)
new_patch.write_scn()
