# 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))
print("Processing file: " + str(file) + " (drift: " + str(round(drift, 2)) + " nm/s)")
print("Reference Frequencies: A = "+str(file[10:13])+", B = "+str(file[15:18]))
for bead in range(beads):
# print("Processing bead " + str(bead))
Z_meas = "Z" + str(bead) + " (um)"
Z = np.array(df[Z_meas])
# corrections
if correct_global_drift == True:
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
def read_analyze_DNA(measurement, pars):
# import parameters
try:
p = pars
except:
print('Error: no parameters')
return
# 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)
# working on laptop or work PC?
folder = "C:\\Users\\tbrouwer\\Desktop\\Data\\"
bool = os.path.isdir(folder)
if bool == False:
folder = "C:\\Users\\brouw\\Desktop\\Data\\"
# open data
sub = measurement
file_location = folder + str(sub[0]) + "\\"
file_name = "data_" + str(sub[1])
file_extension = ".dat"
file_all = file_location + file_name + file_extension
bead = int(sub[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')
beads = f.readlines()[9]
f.close()
beads = int(func.get_num(beads))
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.)'])
# does the tether rupture?
rupt = rupture(time, amplitude_drift)
if rupt == False:
drift.append(func.drift_self(z_drift, time))
drift_med = float(np.median(drift))
Z = Z - (drift_med / 1000) * time
# 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)
# f_pull = force[diff_magnet > factor]
# f_release = force[diff_magnet < factor]
# z_pull = Z[diff_magnet > factor]
# z_release = Z[diff_magnet < factor]
# 1st pull
# f_pull = force[np.where((diff_magnet > factor) & (time < 25))]
# f_release = force[np.where((diff_magnet < factor) & (time < 25))]
# z_pull = Z[np.where((diff_magnet > factor) & (time < 25))]
# z_release = Z[np.where((diff_magnet < factor) & (time < 25))]
# 2nd pull
f_pull = force[np.where((diff_magnet > factor) & (time > 25))]
f_release = force[np.where((diff_magnet < factor) & (time > 25))]
z_pull = Z[np.where((diff_magnet > factor) & (time > 25))]
z_release = Z[np.where((diff_magnet < factor) & (time > 25))]
# select high-force data
select_f = f_pull[np.where((f_pull > 14) & (f_pull < 16))]
select_z = z_pull[np.where((f_pull > 14) & (f_pull < 16))]
# 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
title = str(title) + '_' + str(sub[1]) + '_' + str(sub[2]) + '_' + str(
sub[3])
return f_pull, z_pull, f_release, z_release, title
def read_analyze_rot(measurement, pars, data_path):
try:
p = pars
except:
print('Error: no parameters')
return
# 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("{0:0=3d}".format(int(measurement[1]) - 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")
try:
rotation = np.array(df['Stepper rot (turns)'])
except:
print("No rotation measurement")
p['drift'] = 'uncorrected'
return [], [], [], [], [], []
time = np.array(df['Time (s)'])
X = np.array(df['X' + str(bead) + ' (um)'])
Y = np.array(df['Y' + str(bead) + ' (um)'])
Z = np.array(df['Z' + str(bead) + ' (um)'])
# number of beads
file_log = file_location + file_name + ".log"
f = open(file_log, 'r')
beads = f.readlines()[9]
f.close()
beads = int(func.get_num(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'
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(rotation)
diff_rot = np.append([0], np.divide(dy, dx)) # add a zero as first element
# offset the Z
Z -= np.mean(Z)
# calculate LND
LND = rotation / (p['L_bp'] / 10.4)
# split in pull & release
factor = max(diff_rot / 1000)
twist_pos = rotation[diff_rot < factor]
twist_neg = rotation[diff_rot > factor]
x_pos = X[diff_rot < factor]
x_neg = X[diff_rot > factor]
y_pos = Y[diff_rot < factor]
y_neg = Y[diff_rot > factor]
z_pos = Z[diff_rot < factor]
z_neg = Z[diff_rot > factor]
lnd_pos = LND[diff_rot < factor]
lnd_neg = LND[diff_rot > factor]
x_sel = np.concatenate((x_pos,x_neg))
y_sel = np.concatenate((y_pos,y_neg))
# fit circle to extract radius
x_m, y_m, R = fit_circle(x_sel,y_sel)
# alternative method (currently works better)
R2 = (abs(np.percentile(x_sel,1)-np.percentile(x_sel,99))/2+abs(np.percentile(y_sel,1)-np.percentile(y_sel,99))/2)/2
x_m_2 = np.percentile(x_sel,1)+R2
y_m_2 = np.percentile(y_sel,1)+R2
p['radius_um'] = R2
# plt.close()
# plt.scatter(x_sel,y_sel)
# plt.scatter(x_m, y_m)
# plt.scatter(x_m_2, y_m_2, color='r')
# plt.plot(x_m+R,y_m)
# plt.show()
return twist_pos, twist_neg, z_pos, z_neg, lnd_pos, lnd_neg