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mosaicConverter.py
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mosaicConverter.py
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import numpy.random.common
import numpy.random.bounded_integers
import numpy.random.entropy
import glob
import os
import numpy as np
import sqlite3
import mosaic.mdio.sqlite3MDIO as sql
import tkinter as tk
import pandas as pd
import pandasql as sqldf
import tkinter.filedialog
from progress.bar import ChargingBar, Bar
db=sql.sqlite3MDIO()
pd.options.mode.chained_assignment = None
def do_Stuff(file_path_string,events_directory_path,directory_path,file_name):
db.openDB(glob.glob(file_path_string)[-1])
q = "SELECT * from metadata WHERE ProcessingStatus='normal'"
column_list = ['recIDX',
'ProcessingStatus',
'OpenChCurrent',
'NStates',
'CurrentStep',
'BlockDepth',
'EventStart',
'EventEnd',
'EventDelay',
'StateResTime',
'ResTime',
'RCConstant',
'AbsEventStart',
'ReducedChiSquared',
'ProcessTime',
'TimeSeries']
column_list2 = ['id',
'type',
'start_time_s',
'event_delay_s',
'duration_us',
'baseline_before_pA',
'baseline_after_pA',
'effective_baseline_pA',
'area_pC',
'average_blockage_pA',
'relative_average_blockage',
'max_blockage_pA',
'relative_max_blockage',
'max_blockage_duration_us',
'n_levels',
'rc_const1_us',
'residual_pA',
'max_deviation_pA',
'min_blockage_pA',
'relative_min_blockage',
'min_blockage_duration_us',
'level_current_pA',
'level_duration_us',
'blockages_pA',
'stdev_pA']
eventsdb = pd.DataFrame(db.queryDB(q), columns=column_list, dtype=object)
eventsdb_converted = pd.DataFrame(columns=column_list2, dtype=object)
bar = ChargingBar('Processing Events', max=len(eventsdb))
for index in range(len(eventsdb)):
eventid = int(eventsdb['recIDX'][index])
eventtype = int()
start_time_s = eventsdb['AbsEventStart'][index] / 1000.
if index == 0:
event_delay_s = start_time_s
else:
event_delay_s = (eventsdb['AbsEventStart'][index] - eventsdb['AbsEventStart'][index-1]) /1000.
duration_us = eventsdb['ResTime'][index] * 1000.
effective_baseline_pA = eventsdb['OpenChCurrent'][index]
n_levels = eventsdb['NStates'][index] + 1
rc_const1_us = eventsdb['RCConstant'][index][0] * 1000.
max_blockage_pA = (1 - min(eventsdb['BlockDepth'][index])) * effective_baseline_pA
relative_max_blockage = (1 - min(eventsdb['BlockDepth'][index]))
max_blockage_duration_us = eventsdb['StateResTime'][index][eventsdb['BlockDepth'][index].index(min(eventsdb['BlockDepth'][index]))] * 1000.
min_blockage_pA = (1 - max(eventsdb['BlockDepth'][index])) * effective_baseline_pA
relative_min_blockage = (1 - max(eventsdb['BlockDepth'][index]))
min_blockage_duration_us = eventsdb['StateResTime'][index][eventsdb['BlockDepth'][index].index(max(eventsdb['BlockDepth'][index]))] * 1000.
timeseries = np.array(eventsdb['TimeSeries'][index])
eventdelay = np.array(eventsdb['EventDelay'][index]) * 1000.
currentstep = np.array(eventsdb['CurrentStep'][index])
timescale = np.arange(len(timeseries)) * 1/4.16666666667
eventfit = np.zeros_like(timeseries)
for i in range(len(eventfit)):
n = 1
eventfit[i] = -effective_baseline_pA
for j in range(len(eventdelay) - 1):
if (timescale[i] > eventdelay[j]) and (timescale[i] < eventdelay[j+1]):
for k in range(n):
eventfit[i] = eventfit[i] - currentstep[k]
break
else:
n += 1
residual_pA = np.std(timeseries - eventfit)
max_deviation_pA = max(abs(timeseries + effective_baseline_pA))
baseline_before = timescale < eventdelay[0]
baseline_after = timescale > eventdelay[-1]
baseline_before_series = timeseries[baseline_before]
baseline_after_series = timeseries[baseline_after]
baseline_before_pA = -np.mean(baseline_before_series)
baseline_after_pA = -np.mean(baseline_after_series)
event = timeseries[~baseline_before & ~baseline_after]
average_blockage_pA = effective_baseline_pA - np.mean(event)
relative_average_blockage = average_blockage_pA / effective_baseline_pA
stdev = []
level_current = []
level_duration = []
level_current = np.append(level_current, baseline_before_pA)
stdev = np.append(stdev, np.std(baseline_before_series))
level_duration = np.append(level_duration, len(baseline_before)/4.16666666667)
for level_index in range(len(eventdelay)-1):
before_level = timescale < eventdelay[level_index]
after_level = timescale > eventdelay[level_index+1]
level = timeseries[~before_level & ~after_level]
level_stdev = np.std(level)
stdev = np.append(stdev, level_stdev)
level_current = np.append(level_current, -np.mean(level))
level_duration = np.append(level_duration, len(level)/4.16666666667)
level_current = np.append(level_current, baseline_after_pA)
stdev = np.append(stdev, np.std(baseline_after_series))
blockages = [(1 - eventsdb['BlockDepth'][index][i]) * effective_baseline_pA for i in range(len(eventsdb['BlockDepth'][index]))]
blockages = [baseline_before_pA - effective_baseline_pA] + blockages + [(baseline_after_pA - effective_baseline_pA)]
level_duration = np.append(level_duration, len(baseline_after)/4.16666666667)
level_current_pA = ''
stdev_pA = ''
blockages_pA =''
level_duration_us = ''
for i in range(len(level_current)):
level_current_pA = level_current_pA + '%.16g;' %level_current[i]
level_duration_us = level_duration_us + '%.16g;' %level_duration[i]
blockages_pA = blockages_pA + '%.16g;' %blockages[i]
stdev_pA = stdev_pA + '%.16g;' %stdev[i]
level_current_pA = level_current_pA[:-1]
level_duration_us = level_duration_us[:-1]
blockages_pA = blockages_pA[:-1]
stdev_pA = stdev_pA[:-1]
area_pC = 0.
for I in event:
delta_t = 1/4.16666666667
delta_A = I * delta_t
area_pC = area_pC + delta_A
column_dict_converted = {'id' : eventid,
'type' : eventtype,
'start_time_s' : start_time_s,
'event_delay_s' : event_delay_s,
'duration_us' : duration_us,
'baseline_before_pA' : baseline_before_pA,
'baseline_after_pA' : baseline_after_pA,
'effective_baseline_pA' : effective_baseline_pA,
'area_pC' : area_pC,
'average_blockage_pA' : average_blockage_pA,
'relative_average_blockage' : relative_average_blockage,
'max_blockage_pA' : max_blockage_pA,
'relative_max_blockage' : relative_max_blockage,
'max_blockage_duration_us' : max_blockage_duration_us,
'n_levels' : n_levels,
'rc_const1_us' : rc_const1_us,
'residual_pA' : residual_pA,
'max_deviation_pA' : max_deviation_pA,
'min_blockage_pA' : min_blockage_pA,
'relative_min_blockage' : relative_min_blockage,
'min_blockage_duration_us' : min_blockage_duration_us,
'level_current_pA' : level_current_pA,
'level_duration_us' : level_duration_us,
'blockages_pA' : blockages_pA,
'stdev_pA' : stdev_pA}
eventtrace = pd.DataFrame(data=np.array([timescale, timeseries, eventfit]).T)
eventfile = 'event_%08d.csv' %(eventid)
event_file_name = events_directory_path + eventfile
with open(event_file_name, 'wb'):
eventtrace.to_csv(event_file_name, index=False, header=False)
eventsdb_converted = eventsdb_converted.append(column_dict_converted, ignore_index=True)
next(bar)
bar.finish()
filename = directory_path + file_name[:-7] + '_converted.csv'
with open(filename, 'wb'):
eventsdb_converted.to_csv(filename, index=False)
def main():
root=tk.Tk()
root.withdraw()
#file_path_string = 'F:\\Chimera Data\\20161222 - PK079-1\\eventMD-20170110-115345.sqlite'
file_path_string = tkinter.filedialog.askopenfilename(initialdir='F:\\Chimera Data\\')
file_name = os.path.basename(file_path_string)
directory_path = os.path.dirname(os.path.abspath(file_path_string))
directory_path = directory_path + '\\' + file_name[:-7] + '\\'
events_directory_path = directory_path + 'events\\'
do_Stuff(file_path_string,events_directory_path,directory_path,file_name)
if __name__=="__main__":
main()