""" ------------------------------------------------------------------------ Program 9 ------------------------------------------------------------------------ Author: Anshul Khatri ID: 193313680 Email: [email protected] __updated__ = "2019-10-30" ------------------------------------------------------------------------ """ from functions import get_int val = get_int(0, 100) print() print('Value: {}'.format(val))
for n, file in enumerate(trackbin_files):
# get reference-frequency
A.append(file[10:13])
B.append(file[15:18])
# files
datfile = trackbin_path + file
# read dataframe
df = pd.read_csv(datfile, sep="\t")
headers = list(df)
# get number of beads
beads = headers[len(headers) - 1]
beads = func.get_int(beads)
# correct global drift
time = np.array(df['Time (s)'])
freq = 1 / np.median(np.diff(time))
drift = []
for i in range(beads):
z_drift = np.array(df['Z' + str(i) + ' (um)'])
amplitude_drift = np.array(df['Amp' + str(i) + ' (a.u.)'])
rupt = func.rupture(time, amplitude_drift)
if rupt == False:
drift.append(func.drift_self(z_drift, time))
drift = float(np.median(drift))
import argparse
import glob
import functions as fn
## Init
# arguments
parser = argparse.ArgumentParser(description='Simple password generator')
parser.add_argument('--count',
type=int,
default='5',
help='number of password(s) to return')
args = parser.parse_args()
# var
wordlists = glob.glob('wordlists//*.txt')
## Main
for i in range(0, args.count):
got_word = fn.get_word(wordlists)
got_int = fn.get_int()
print(got_word + '#' + got_int)
plt.figure(figsize=(12, 6))
for n, measurement in enumerate(measurements):
file = measurement[1] + "_" + measurement[2] + "_" + measurement[3] + ".fit"
df = pd.read_csv(folder + file, sep="\t")
# columns
time = np.array(df['t (s)'])
force = np.array(df['F (pN)'])
z = np.array(df['z (um)'])
z_fit = np.array(df['z fit (um)'])
# get rupture force
df_pars = pd.read_csv(pars_file, sep="\t")
f_rupt = df_pars.loc[(df_pars['date'] == int(measurement[1])) &
(df_pars['data'] == func.get_int((measurement[2]))) &
(df_pars['bead'] == float(measurement[3])),
"f_rupt_pN"]
# split in pull & release
dx = np.diff(time)
dy = np.diff(force)
diff_force = np.append([0], np.divide(dy,
dx)) # add a zero as first element
factor = max(diff_force / 1000)
trajectory = "T1"
if measurement[0] == "175x16" or measurement[0] == "177x16":
trajectory = "T2"
import pandas as pd
import matplotlib.cm as cm
data_path = r"C:\Users\tbrouwer\Desktop\TrackImages\post-processing\data_002"
save_path = data_path
dat_files = []
os.chdir(data_path)
for file in glob.glob("*.dat"):
dat_files.append(file)
for n, file in enumerate(dat_files):
freqs = str(func.get_int((file[15:])))[:-2]
# files
datfile = data_path + "\\" + file
# read dataframe
df = pd.read_csv(datfile, sep="\t")
headers = list(df)
# get number of beads
beads = headers[len(headers) - 2]
beads = func.get_int(beads) + 1
# correct global drift
time = np.array(df['Time (s)'])
freq = 1 / np.median(np.diff(time))
def read_analyze(measurement, pars, data_path):
try:
p = pars
except:
print('Error: no parameters')
return
global_drift = True
# constants from parameters
L_bp = p['L_bp'] # contour length (bp)
S_pN = p['S_pN'] # stretch modulus (pN)
P_nm = p['P_nm'] # persistence length (nm)
# open data
file_location = data_path
file_name = "data_" + str(measurement[1])
file_extension = ".dat"
file_all = file_location + file_name + file_extension
bead = int(measurement[2])
# title
title = int(func.get_num(file_location))
# read DataFrame
df = pd.read_csv(file_all, sep="\t")
magnet = np.array(df['Stepper shift (mm)'])
time = np.array(df['Time (s)'])
force = func.calc_force(magnet)
Z = np.array(df['Z' + str(bead) + ' (um)'])
# number of beads
file_log = file_location + file_name + ".log"
f = open(file_log, 'r')
try:
beads = f.readlines()[9]
except:
headers = list(df)
# get number of beads
beads = headers[len(headers) - 1]
f.close()
try:
beads = int(func.get_num(beads))
except:
beads = func.get_int(beads)
drift = []
for i in range(beads):
z_drift = np.array(df['Z' + str(i) + ' (um)'])
try:
amplitude_drift = np.array(df['Amp' + str(i) + ' (a.u.)'])
except:
amplitude_drift = np.array(df['Amp a.u.'])
# does the tether rupture?
rupt, _ = func.rupture(time, amplitude_drift)
if rupt == False:
drift.append(func.drift_self(z_drift, time))
if not drift:
p['drift'] = 'uncorrected'
drift_med = 0
else:
drift_med = float(np.median(drift))
Z = Z - (drift_med / 1000) * time
p['drift'] = str(round(drift_med, 3)) + " nm/s (global drift)"
# calculating the first derivative of magnet
dx = np.diff(time)
dy = np.diff(magnet)
diff_magnet = np.append([0], np.divide(dy, dx)) # add a zero as first element
# split in pull & release
factor = max(diff_magnet / 1000)
time_pull = time[diff_magnet < factor]
f_pull = force[diff_magnet < factor]
f_release = force[diff_magnet > factor]
z_pull = Z[diff_magnet < factor]
z_release = Z[diff_magnet > factor]
# select high-force data
select_f = f_pull[np.where((f_pull > 50) & (f_pull < 55) & (time_pull > 60) & (time_pull < 140))]
select_z = z_pull[np.where((f_pull > 50) & (f_pull < 55) & (time_pull > 60) & (time_pull < 140))]
if select_f.size != 0:
# fit the WLC in fashion (x,y) - only fit offset, fix everything else
popt, pcov = curve_fit(lambda f, z0: func.WLC_fit(f, P_nm, L_bp * 0.34, S_pN, z0), select_f, select_z, p0=1)
z_fit = popt[0]
# subtract fitted offset from data
z_pull -= z_fit
z_release -= z_fit
select_z -= z_fit
else:
z_fit = 0
title = str(title) + '_' + str(measurement[1]) + '_' + str(measurement[2]) + '_' + str(measurement[3])
return f_pull, z_pull, f_release, z_release, title, drift_med, z_fit
