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POTATO_ForceRamp.py
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"""Copyright 2021 Helmholtz-Zentrum für Infektionsforschung GmbH"""
import pandas as pd
import h5py
import numpy as np
from pathlib import Path
import lumicks.pylake as lk
# relative imports
from POTATO_fitting import fitting_ds, fitting_ss, plot_fit
from POTATO_preprocessing import preprocess_RAW, trim_data, create_derivative
from POTATO_find_steps import find_steps_F, find_steps_PD, find_common_steps, calc_integral, save_figure
from POTATO_processMultiH5 import split_H5
"""define the functions of the subprocess processing the data"""
def show_h5_structure(file_path):
file_h5 = lk.File(file_path)
return file_h5
def read_in_data(file_num, Files, input_settings, input_format):
if input_format['CSV'] == 1:
df = pd.read_csv(Files[file_num])
directory_i = Path(Files[file_num])
filename_i = directory_i.name[:-4]
# access the raw data
Force = df.to_numpy()[:, 0]
if input_format['length_measure'] == 1:
Distance = df.to_numpy()[:, 1]
else:
Distance = df.to_numpy()[:, 1] / 1000
# accessing the data frequency from user input
Frequency_value = input_settings['data_frequency']
Force_Distance, Force_Distance_um = preprocess_RAW(Force, Distance, input_settings, input_format)
else:
with h5py.File(Files[file_num], "r") as f:
directory_i = Path(Files[file_num])
filename_i = directory_i.name[:-3]
# access the raw data
if input_format['HF'] == 1:
if input_format['Trap'] == 1:
Force = f.get("Force HF/Force 1x")
elif input_format['Trap'] == 0:
Force = f.get("Force HF/Force 2x")
Distance = f.get("Distance/Piezo Distance")
# accessing the data frequency from the h5 file
Frequency_value = Force.attrs['Sample rate (Hz)']
Force_Distance, Force_Distance_um = preprocess_RAW(Force, Distance, input_settings, input_format)
elif input_format['LF'] == 1:
if input_format['Trap'] == 1:
load_force = f.get("Force LF/Force 1x")
Force = load_force[:]['Value'][:]
try:
load_distance = f.get("Distance/Distance 1x")[:]
except:
load_distance = f.get("Distance/Distance 2")[:]
Distance = load_distance['Value'][:]
elif input_format['Trap'] == 0:
load_force = f.get("Force LF/Force 2x")
Force = load_force[:]['Value'][:]
try:
load_distance = f.get("Distance/Distance 2x")[:]
except:
load_distance = f.get("Distance/Distance 1")[:]
Distance = load_distance['Value'][:]
Force_Distance, Force_Distance_um = preprocess_RAW(Force, Distance, input_settings, input_format)
# calculating the data frequency based on start- and end-time of the measurement
size_F_LF = len(Force)
stop_time_F_LF = load_force.attrs['Stop time (ns)']
timestamp_F_LF = load_force.attrs['Start time (ns)']
Frequency_value = size_F_LF / ((stop_time_F_LF - timestamp_F_LF) / 10**9)
return Force_Distance, Force_Distance_um, Frequency_value, filename_i
# open a folder containing raw data and lead through the analysis process
def start_subprocess(analysis_folder, timestamp, Files, input_settings, input_format, export_data, input_fitting, output_q):
# create file to store total results
if export_data['export_TOTAL'] == 1:
filename_total_results = analysis_folder + '/total_results_' + timestamp + '.csv'
with open(filename_total_results, 'w') as f:
f.write('>Common steps from all curves of the folder:\n')
head = (
'filename',
'orientation',
'Derivative of',
'step number',
'F1',
'F2',
'Fc',
'step start',
'step end',
'step length',
'filename',
'model',
'log_likelihood',
'Lc_ds',
'Lp_ds',
'Lp_ds_stderr',
'St_ds',
'Lc_ss',
'Lc_ss_stderr',
'Lp_ss',
'St_ss',
'f_offset',
'd_offset',
'Work_(pN*nm)',
'Work_(kB*T)'
)
f.write(','.join(head))
f.write('\n')
# iterate through the files in the selected folder
file_num = 0
while file_num < len(Files):
if file_num == 0:
print('\nHard work ahead!\n')
output_q.put('Hard work ahead!')
# proceed differently with h5 and csv files
Force_Distance, Force_Distance_um, Frequency_value, filename = read_in_data(file_num, Files, input_settings, input_format)
num_curves = 1
###### Detect MultiFiles ######
if input_format['MultiH5'] == 1:
try:
fw_curves, rv_curves = split_H5(Force_Distance, input_settings, Frequency_value)
num_fw = len(fw_curves)
fw_curves.extend(rv_curves)
curves = fw_curves
except:
print('No Multi-File detected!')
curves = [Force_Distance]
else:
curves = [Force_Distance]
num_curves = len(curves)
for x in range(num_curves):
# empty dataframe to store all step results of all curves in the folder
total_results_steps = pd.DataFrame()
# create dataframe to store all fitting parameters of all curves in the folder
header_fit = [
"filename",
"model",
"log_likelihood",
"Lc_ds",
"Lp_ds",
"Lp_ds_stderr",
"St_ds",
"Lc_ss",
"Lc_ss_stderr",
"Lp_ss",
"St_ss",
"f_offset",
"d_offset",
"Work_(pN*nm)",
"Work_(kB*T)"
]
total_results_fit = pd.DataFrame(columns=header_fit)
if num_curves == 1:
filename_i = filename
else:
if x < num_fw:
suffix = 'fw_curve{num}'.format(num=x + 1)
filename_i = filename + '_' + suffix
else:
suffix = 'rv_curve{num}'.format(num=x + 1 - num_fw)
filename_i = filename + '_' + suffix
Force_Distance = curves[x][:, :2]
print('################ FD', len(Force_Distance))
Force_Distance_um = np.copy(Force_Distance)
Force_Distance_um[:, 1] = Force_Distance_um[:, 1] / 1000
###### Detect MultiFiles end ######
orientation = "forward"
if Force_Distance[0, 1] > Force_Distance[-1, 1]: # reverse
orientation = "reverse"
Force_Distance = np.flipud(Force_Distance)
Force_Distance_um = np.flipud(Force_Distance_um)
# Export down sampled and smoothened FD values
if export_data['export_SMOOTH'] == 1:
save_to = analysis_folder + "/" + filename_i + "_smooth_" + timestamp + ".csv"
np.savetxt(save_to, Force_Distance_um, delimiter=",")
else:
pass
# trim data below specified force thresholds
F_trimmed, PD_trimmed, F_low = trim_data(Force_Distance, input_settings['F_min'])
print('#################### Trimmmed', len(F_trimmed))
if not F_trimmed.size == 0:
# create force and distance derivative of the pre-processed data to be able to identify steps
derivative_array = create_derivative(input_settings, Frequency_value, F_trimmed, PD_trimmed, F_low)
print('################### der array', len(derivative_array))
"""find steps based on force derivative"""
filename_results = analysis_folder + "/" + filename_i + "_results_" + timestamp + ".csv"
# try:
results_F, PD_start_F = find_steps_F(
input_settings,
filename_i,
Force_Distance,
derivative_array,
orientation
)
results_F_list = list(results_F)
if export_data['export_STEPS'] == 1:
steps_results_F = pd.DataFrame(results_F_list)
with open(filename_results, 'a+') as f:
f.write('\nSteps found by force derivative:\n')
steps_results_F.to_csv(filename_results, mode='a', index=False, header=True)
else:
pass
# except:
# results_F = []
# PD_start_F = []
# print("Error in finding steps for file " + str(filename_i) + '\n' 'There was an error in finding Force steps')
# pass
"""find steps based on distance derivative"""
try:
results_PD, PD_start_PD = find_steps_PD(
input_settings,
filename_i,
Force_Distance,
derivative_array,
orientation
)
results_PD_list = list(results_PD)
if export_data['export_STEPS'] == 1:
steps_results_PD = pd.DataFrame(results_PD_list)
with open(filename_results, 'a+') as f:
f.write('\nSteps found by distance derivative:\n')
steps_results_PD.to_csv(filename_results, mode='a', index=False, header=True)
except:
results_PD = []
PD_start_PD = []
err_PD = str("Error in finding steps for file " + str(filename_i) + '\n' 'There was an error in finding Distance steps')
print(err_PD)
pass
# save plot with FD-curve, derivatives and found steps
save_figure(
export_data['export_PLOT'],
timestamp,
filename_i,
analysis_folder,
Force_Distance,
derivative_array,
F_trimmed,
PD_trimmed,
PD_start_F,
PD_start_PD
)
# when steps are found by force AND distance derivative, they are considered common steps
common_steps = []
try:
common_steps = find_common_steps(results_F_list, results_PD_list)
# to match with the fitting rows (always one more than steps) put a 'step 0' as first line
common_steps_results = [{'filename': filename_i, 'orientation': orientation, 'Derivative of': '', 'step #': 0, 'F1': '', 'F2': '', 'Fc': '', 'step start': '', 'step end': '', 'step length': ''}]
except:
err_FCS = str("Error in finding common steps" + str(filename_i) + '\n' 'There was an error in finding common steps')
output_q.put(err_FCS)
pass
# append common steps to the 'step 0'
if common_steps:
for x in range(len(common_steps)):
common_steps_results.append(common_steps[x])
# convert common steps to dataframe for export
common_steps_results = pd.DataFrame(common_steps_results)
# export the steps into the results for ONLY this file
with open(filename_results, 'a+') as f:
f.write('\nCommon steps:\n')
common_steps_results.to_csv(filename_results, mode='a', index=False, header=True)
# put common steps into a total_results dataframe so all steps from all files of the analysed folder can be exported together
total_results_steps = pd.concat([total_results_steps, common_steps_results], ignore_index=True, sort=False)
else:
common_steps_results = pd.DataFrame({'filename': filename_i, 'orientation': orientation, 'Derivative of': '', 'step #': 0, 'F1': '', 'F2': '', 'Fc': '', 'step start': '', 'step end': '', 'step length': ''}, index=[0])
total_results_steps = pd.concat([total_results_steps, common_steps_results], ignore_index=True, sort=False)
'''if common steps were found, try to fit FD-Curve'''
empty = {
'filename': filename_i,
'model': 'None',
'log_likelihood': 'None',
'Lc_ds': 'None',
'Lp_ds': 'None',
'Lp_ds_stderr': 'None',
'St_ds': 'None',
'f_offset': 'None',
'd_offset': 'None'
}
if export_data['export_FIT'] == 1:
try:
export_fit = []
fit = []
start_force_ss = []
start_distance_ss = []
integral_ss_fit_start = []
integral_ss_fit_end = []
# try to fit all parts of curve based on the common steps
try:
# fit part between start of the FD-cure up to the first common step
export_fit_ds, area_ds, step_start = fitting_ds(
filename_i,
input_settings,
export_data,
input_fitting,
float(common_steps[0]['step start']),
Force_Distance,
derivative_array,
F_low,
0
)
export_fit.append(export_fit_ds)
# fit parts after steps, when more than one common step was found, there are multiple parts to fit
if len(common_steps) > 1:
for n in range(0, len(common_steps) - 1):
# try to fit each part of the curve, if one of the parts can not be fitted, still try to fit the others
try:
fit_ss, f_fitting_region_ss, d_fitting_region_ss, export_fit_ss, area_ss_fit_start, area_ss_fit_end = fitting_ss(
filename_i,
input_settings,
export_data,
input_fitting,
float(common_steps[n]['step end']),
float(common_steps[n + 1]['step start']),
Force_Distance,
1,
1,
derivative_array,
F_low,
0,
n
)
fit.append(fit_ss)
start_force_ss.append(f_fitting_region_ss)
start_distance_ss.append(d_fitting_region_ss)
export_fit.append(export_fit_ss)
integral_ss_fit_start.append(area_ss_fit_start)
integral_ss_fit_end.append(area_ss_fit_end)
except:
export_fit.append(empty)
pass
# fit the last part of the curve
try:
fit_ss, f_fitting_region_ss, d_fitting_region_ss, export_fit_ss, area_ss_fit_start, area_ss_fit_end = fitting_ss(
filename_i,
input_settings,
export_data,
input_fitting,
float(common_steps[len(common_steps) - 1]['step end']),
max(derivative_array[:, 1]),
Force_Distance,
1,
1,
derivative_array,
F_low,
0,
len(common_steps)
)
fit.append(fit_ss)
start_force_ss.append(f_fitting_region_ss)
start_distance_ss.append(d_fitting_region_ss)
export_fit.append(export_fit_ss)
integral_ss_fit_start.append(area_ss_fit_start)
integral_ss_fit_end.append(area_ss_fit_end)
except:
export_fit.append(empty)
pass
'''from the fits, work put into the system is calculated'''
if common_steps:
work_per_step = [0] # in pN*nm
kT_per_step = [0] # in kT
work_first_step, kT_1 = calc_integral(
area_ds,
integral_ss_fit_start[0],
common_steps[0]['step start'],
common_steps[0]['step end'],
common_steps[0]['F1'],
common_steps[0]['F2']
)
print("Work of first step: " + str(work_first_step))
work_per_step.append(work_first_step)
kT_per_step.append(kT_1)
if len(common_steps) > 1:
for n in range(0, len(common_steps) - 1):
work_step_n, kT_n = calc_integral(
integral_ss_fit_end[n],
integral_ss_fit_start[n + 1],
common_steps[n + 1]['step start'],
common_steps[n + 1]['step end'],
common_steps[n + 1]['F1'],
common_steps[n + 1]['F2']
)
work_per_step.append(work_step_n)
kT_per_step.append(kT_n)
j = 0
for dict in export_fit:
dict["Work_(pN*nm)"] = work_per_step[j]
dict["Work_(kB*T)"] = kT_per_step[j]
j += 1
# if no step was found, the common step index 0 is not available and will raise an IndexError.
# So in this case the fit will be performed for the whole curve from beginning to end.
except IndexError:
if not common_steps:
export_fit_ds, area_ds, step_start = fitting_ds(
filename_i,
input_settings,
export_data,
input_fitting,
derivative_array[-1, 1],
Force_Distance,
derivative_array,
F_low,
0
)
export_fit.append(export_fit_ds)
total_results_fit = total_results_fit.append(export_fit, ignore_index=True, sort=False)
# create a plot for the fitted curve
plot_fit(fit, start_force_ss, start_distance_ss, Force_Distance, analysis_folder, filename_i, timestamp)
except:
print('Something went wrong with fitting')
pass
results_total_total = pd.concat([total_results_steps, total_results_fit], axis=1)
results_total_total.to_csv((filename_total_results), mode='a', index=False, header=False)
print('done', x + 1, 'curves from', len(curves))
out_progress = str('File ' + str(file_num + 1) + ': Done ' + str(x + 1) + ' curves from ' + str(len(curves)))
output_q.put(out_progress)
print(filename_i)
output_q.put(filename_i)
else:
print('This curve was below the Force threshold and could not be processed!\nPlease check if the correct trap was selected.')
output_q.put('This curve was below the Force threshold and could not be processed!\nPlease check if the correct trap was selected.')
if file_num == int(len(Files) / 2):
print('\nHalf way there!\n')
output_q.put('Half way there!')
print()
elif file_num == len(Files) - 1:
print('\nAlmost there!\n')
output_q.put('Almost there!')
file_num = file_num + 1
print('done', file_num, 'from', len(Files))
out_progress = str('Done ' + str(file_num) + ' files from ' + str(len(Files)))
output_q.put(out_progress)
print(filename_i)
output_q.put(filename_i)
print('Analysis finished! \nProgram can be closed.')
output_q.put('Analysis finished! \nProgram can be closed.')