def main_no_rot():
plt.close("all")
table_path = "C:\\Users\\brouw\\Desktop\\Data\\"
table_file = "180509"
measurements = ba.build_measurements(table_path + table_file + ".xlsx", p)
for measurement in measurements:
print("Processing measurement... " + str(measurement))
f_pull, z_pull, f_release, z_release, title = ba.read_analyze(
measurement, p)
f_wlc = np.logspace(np.log10(0.15), np.log10(int(np.max(f_pull))),
1000)
wlc, _ = func.WLC(f_wlc,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.tick_params(direction='in', top=True, right=True)
plt.ylim(-1, 65)
plt.xlim(500, 2300)
plt.scatter(1000 * z_pull,
f_pull,
color='darkgreen',
label="Pull",
s=10,
zorder=25,
facecolors='none')
plt.scatter(1000 * z_release,
f_release,
color='lightgrey',
s=10,
zorder=15,
label='Release',
facecolors='none')
plt.plot(wlc, f_wlc, '--', color="black", label="WLC", zorder=10000)
plt.plot([], [], ' ',
label="Drift: " + str(p['drift'])) # quick and very dirty
plt.legend(loc=2, frameon=False)
plt.title(title)
if p['save'] == True:
new_path = table_path + table_file + "\\Selected figures (no rot)\\"
if not os.path.exists(new_path):
os.makedirs(new_path)
plt.savefig(new_path + title)
# plt.show()
plt.close()
return
def main_assemble():
plt.close("all")
plt.rcParams.update({'font.size': 28})
measurements = meas.measurements()
for measurement in measurements:
f_pull, z_pull, f_release, z_release, title = hmf.read_analyze_compare(
measurement, p)
wlc, _ = func.WLC(f_pull,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
plt.scatter(1000 * z_pull, f_pull, label=title, s=10, zorder=25)
# plt.scatter(1000 * z_release, f_release, color='lightgrey', s=10, zorder=15, facecolors='none')
plt.plot(wlc, f_pull, '--', color="black", label="WLC")
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.ylim(-1, 52)
plt.xlim(0, 1350)
# plt.yscale('log')
# plt.ylim(10 ** -1, 10 ** 2)
plt.legend(loc=2, frameon=False)
plt.tick_params(direction='in',
axis="both",
bottom="on",
top="on",
left="on",
right="on")
# placement of figure
mngr = plt.get_current_fig_manager()
mngr.window.setGeometry(0, 56, 3840, 2004) # laptop
# mngr.window.setGeometry(0, -1024, 1920, 984) # work PC
# HMfB - reference
# f_pull, z_pull, f_release, z_release, title = hmf.read_analyze_compare(['171201', '012', ' 5', 'HMfB'], p)
# f_pull, z_pull, f_release, z_release, title = hmf.read_analyze_compare(['171201', '007', '18', 'HMfB'], p)
# plt.scatter(1000 * z_pull, f_pull, label=title, s=20, zorder=100, color='black')
plt.title(title[-4:] + " assembly")
plt.show()
return
import numpy as np
import matplotlib.pyplot as plt
import functions as func
L_bp = 1000
P_nm = 50
S_pN = 1000
z0_nm = 0
k_pN_nm = 0.01
L_nm = L_bp * 0.34
f = np.linspace(0.1, 10, 100)
z_wlc, w_wlc = func.WLC(f, L_bp=L_bp, P_nm=P_nm, S_pN=S_pN, z0_nm=z0_nm)
z_fjc, w_fjc = func.FJC(f, b=2 * P_nm, k_pN_nm=k_pN_nm, L_nm=L_nm, z0_nm=0)
f_z_wlc = np.full((len(f)), max(f))
f_z_fjc = np.full((len(f)), max(f))
# initialize plots
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True)
''' W = f dz'''
# # WLC -- W = f dz
# ax1.plot(f, z_wlc, color='black')
# ax1.fill_between(f, 0, z_wlc, color='red')
# ax1.set_title('WLC -- W = f dz')
# ax1.set_ylabel('Extension (nm)')
#
# # FJC -- W = f dz
# ax2.plot(f, z_fjc, color='black')
def main_fit_no_constant():
plt.close("all")
measurements = meas.measurements()
f_pull, z_pull, f_release, z_release, title = hmf.read_analyze(
measurements, p)
wlc, _ = func.WLC(f_pull, L_bp=p['L_bp'], P_nm=p['P_nm'], S_pN=p['S_pN'])
Fmax_pN = np.max(f_pull)
f = np.logspace(np.log10(0.02), np.log10(int(Fmax_pN)), 1000)
n_prot = (p['L_bp'] // p['L1_bp'])
# plot the states
Z, Zi, Gi = hmf.hmf_WLC(f, pars=p)
barrier = hmf.barrier(p)
plt.rcParams.update({'font.size': 14})
# select high-force data
select_f = f_pull[np.where((f_pull > 5) & (f_pull < 50))]
select_z = z_pull[np.where((f_pull > 5) & (f_pull < 50))]
select_z *= 1000
if p['two_transitions']:
if p['fit_G1']:
# fit the Boltzmann distribution to find 'G2' and 'constant'
popt, pcov = curve_fit(
lambda f, G1, G2: hmf.hmf_boltzmann_fit(f, p, G1, G2, 0),
select_f,
select_z,
p0=[p['G1_kT'], p['G2_kT']],
bounds=(0.01, [10, 100]))
print("G1 = " + str(popt[0]) + " kT")
print("G2 = " + str(popt[1]) + " kT")
print("constant = 0")
p['G1_kT'] = popt[0]
p['G2_kT'] = popt[1]
p['constant'] = 0
else:
# fit the Boltzmann distribution to find 'G2' and 'constant'
popt, pcov = curve_fit(
lambda f, G2: hmf.hmf_boltzmann_fit(f, p, p['G1_kT'], G2, 0),
select_f,
select_z,
p0=p['G2_kT'],
bounds=(0.01, 100))
print("G1 = " + str(p['G1_kT']) + " kT")
print("G2 = " + str(popt[0]) + " kT")
print("constant = 0")
p['G2_kT'] = popt[0]
p['constant'] = 0
# plot the Boltzmann distribution
Z = hmf.hmf_boltzmann_fit(f,
p,
G1=p['G1_kT'],
G2=p['G2_kT'],
constant=p['constant'])
plt.plot(Z,
f,
color='k',
linewidth=3,
zorder=120,
label="Stat.Mech.Model fit")
else:
# fit the Boltzmann distribution to find 'G2', 'G3' and 'constant'
if p['fit_G1']:
print("Starting fit procedure...")
popt, pcov = curve_fit(
lambda f, G1, G2, G3: hmf.hmf_boltzmann_fit_three(
f, p, G1, G2, G3, 0),
select_f,
select_z,
p0=[p['G1_kT'], p['G2_kT'], p['G3_kT']],
bounds=(0.01, [10, 100, 100]))
print("Done!")
print("G1 = " + str(popt[0]) + " kT")
print("G2 = " + str(popt[1]) + " kT")
print("G3 = " + str(popt[2]) + " kT")
print("constant = 0")
p['G1_kT'] = popt[0]
p['G2_kT'] = popt[1]
p['G3_kT'] = popt[2]
p['constant'] = 0
else:
popt, pcov = curve_fit(
lambda f, G2, G3: hmf.hmf_boltzmann_fit_three(
f, p, p['G1_kT'], G2, G3, 0),
select_f,
select_z,
p0=[p['G2_kT'], p['G3_kT']],
bounds=(0.01, [100, 100]))
print("G1 = " + str(p['G1_kT']) + " kT")
print("G2 = " + str(popt[0]) + " kT")
print("G3 = " + str(popt[1]) + " kT")
print("constant = 0")
p['G2_kT'] = popt[0]
p['G3_kT'] = popt[1]
p['constant'] = 0
# plot the Boltzmann distribution
Z = hmf.hmf_boltzmann_fit_three(f,
p,
G1=p['G1_kT'],
G2=p['G2_kT'],
G3=p['G3_kT'],
constant=p['constant'])
plt.plot(Z,
f,
color='k',
linewidth=3,
zorder=120,
label="Stat.Mech.Model fit")
# plot the grid
colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
for n, z in enumerate(Zi):
plt.plot(z, f, color=next(colors), linewidth=2, zorder=int(-n))
plt.plot(Zi[0],
f,
'--',
color='lightgrey',
linewidth=4,
zorder=1000,
label='state 0')
plt.plot(Zi[barrier],
f,
'--',
color='grey',
linewidth=4,
zorder=1000,
label='state 1')
plt.plot(wlc,
f_pull,
'--',
color="black",
linewidth=4,
zorder=10000,
label='state 2')
if p['two_transitions'] == False:
plt.plot(Zi[barrier + n_prot - p['dimer_beads']],
f,
'--',
color='dimgrey',
linewidth=4,
zorder=1000,
label='state 1.5')
# plot the data
if title[-4:] == "HMfA":
pull_color = 'navy'
release_color = 'lightblue'
else:
pull_color = 'darkgreen'
release_color = 'lightgrey'
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.ylim(-0.5, 5)
plt.xlim(0, 1000)
plt.ylim(-1, 52)
plt.xlim(0, 1350)
plt.yscale('log')
plt.ylim(10**-1, 10**2)
plt.scatter(1000 * z_pull,
f_pull,
color=pull_color,
label="Pull",
s=200,
zorder=25,
facecolors='none')
plt.scatter(1000 * z_release,
f_release,
color=release_color,
s=200,
zorder=15,
label='Release',
facecolors='none')
plt.legend(loc=2, frameon=False)
plt.tick_params(direction='in',
axis="both",
bottom="on",
top="on",
left="on",
right="on")
plt.title(title[-4:])
# placement of figure
mngr = plt.get_current_fig_manager()
mngr.window.setGeometry(0, 56, 3840, 2004) # laptop
mngr.window.setGeometry(0, -1024, 1920, 984) # work PC
plt.show()
return
def main():
plt.close("all")
measurements = meas.measurements()
f_pull, z_pull, f_release, z_release, title = hmf.read_analyze(
measurements, p)
wlc, _ = func.WLC(f_pull, L_bp=p['L_bp'], P_nm=p['P_nm'], S_pN=p['S_pN'])
Fmax_pN = np.max(f_pull)
f = np.logspace(np.log10(0.02), np.log10(int(Fmax_pN)), 1000)
n_prot = (p['L_bp'] // p['L1_bp'])
# plot the states
Z, Zi, Gi = hmf.hmf_WLC(f, pars=p)
barrier = hmf.barrier(p)
skip = 1
Gi = Gi[::skip]
Zi = Zi[::skip]
plt.rcParams.update({'font.size': 14})
''' Force Extension plot '''
# plot the grid
colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
for n, z in enumerate(Zi):
plt.plot(z, f, color=next(colors), linewidth=2, zorder=int(-n))
plt.plot(Zi[0],
f,
'--',
color='lightgrey',
linewidth=2,
zorder=1000,
label='state 0')
plt.plot(Zi[barrier],
f,
'--',
color='grey',
linewidth=2,
zorder=1000,
label='state 1')
# if p['two_transitions'] == False:
# plt.plot(Zi[barrier + n_prot - p['dimer_beads']], f, '--', color='dimgrey', linewidth=4, zorder=1000,
# label='state 1.5')
plt.plot(wlc,
f_pull,
'--',
color="black",
linewidth=2,
zorder=10000,
label='state 2')
# plt.plot(Zi[121-45], f, '--', color='red', linewidth=2, zorder=1000, label = "Kulic") # Kulic Requirement
# plot the Boltzmann distribution
# Z_b = hmf.hmf_boltzmann_fit_three_fix(f, p, p['G1_kT'], p['G2_kT'], p['constant'], p['constant_wlc'])
# plt.plot(Z_b, f, color='k', linewidth=3, zorder=120, label="Stat.Mech.Model fit")
# plot the data
if title[-4:] == "HMfA":
pull_color = 'navy'
release_color = 'lightblue'
else:
pull_color = 'darkgreen'
release_color = 'lightgrey'
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.tick_params(direction='in', top=True, right=True)
plt.ylim(-0.5, 5)
plt.xlim(0, 1000)
plt.ylim(-1, 52)
plt.xlim(0, 1350)
plt.yscale('log')
plt.ylim(10**-1, 10**2)
plt.scatter(1000 * z_pull,
f_pull,
color=pull_color,
label="Pull",
s=100,
zorder=25,
facecolors='none')
plt.scatter(1000 * z_release,
f_release,
color=release_color,
s=100,
zorder=15,
label='Release',
facecolors='none')
# plt.plot(wlc, f_pull, '--', color="black", label="WLC", zorder=10000)
plt.legend(loc=2, frameon=False)
plt.tick_params(direction='in',
axis="both",
bottom="on",
top="on",
left="on",
right="on")
# plt.title(title[-4:] + ', G2 = ' + str(p['G2_kT']) + ' kT, G3 = ' + str(p['G3_kT']) + ' kT')
# plt.title(title[-4:])
save = p['save']
# placement of figure
mngr = plt.get_current_fig_manager()
mngr.window.setGeometry(0, 56, 3840, 2004) # laptop
# mngr.window.setGeometry(0, -1024, 1920, 984) # work PC
# if save:
# # save figure
# plt.rcParams.update({'font.size': 14})
# plt.savefig('C:\\Brouwer\\HMf analysis\\Fits\\' + str(title) + '__' + time.strftime("%H%M%S") + ".png", dpi=300)
# # save parameters
# with open('C:\\Brouwer\\HMf analysis\\Fits\\' + str(title) + '__' + time.strftime("%H%M%S") + '.json',
# 'w') as ff:
# ff.write(json.dumps(p))
plt.show()
# plt.close()
''' Energy Plot '''
sequence = False
if sequence:
for F_ref in range(0, 151):
F_ref /= 10
plt.title(' F = ' + str(F_ref) + ' pN', ha=u'left')
colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
for g, z in zip(Gi, Zi):
g_tot = g - F_ref * z / kT
plt.plot(z, g_tot, color=next(colors), linewidth=2)
# ax[1].plot(z_ref, w_ref, color = 'k', linewidth = 2)
plt.ylim(-3000, 3000)
plt.xlim(0, 1350)
plt.ylabel('G (kT)')
plt.xlabel('z (nm)')
plt.tick_params(direction='in', top=True, right=True)
plt.savefig("C:\\Users\\brouw\\Desktop\\Figs\\Free Energy @ " +
str(F_ref) + " pN.png")
plt.tight_layout()
plt.close()
# plt.show()
else:
F_ref = 30
plt.title(' F = ' + str(F_ref) + ' pN', ha=u'left')
colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
for g, z in zip(Gi, Zi):
g_tot = g - F_ref * z / kT
plt.plot(z, g_tot, color=next(colors), linewidth=2)
plt.ylim(-7000, -6000)
plt.xlim(0, 1350)
plt.ylabel('G (kT)')
plt.xlabel('z (nm)')
plt.tick_params(direction='in', top=True, right=True)
# plt.tight_layout()
# plt.show()
plt.close()
'''
# plotting the difference between states
diff_FJC = []
diff_WLC = []
diff_tot = []
for z_fjc, z_wlc, z_tot in zip(Z_FJC, Z_WLC, Zi):
diff_FJC.append(max(z_fjc))
diff_WLC.append(max(z_wlc))
diff_tot.append(max(z_tot))
diff_FJC = [j - i for i, j in zip(diff_FJC[:-1], diff_FJC[1:])]
diff_WLC = [j - i for i, j in zip(diff_WLC[:-1], diff_WLC[1:])]
diff_tot = [j - i for i, j in zip(diff_tot[:-1], diff_tot[1:])]
x = np.arange(len(diff_WLC))
plt.scatter(x, diff_FJC, label = "FJC")
plt.scatter(x, diff_WLC, label = "WLC")
plt.scatter(x, diff_tot, marker='x', s = 115, label = "Total")
plt.ylabel("Max distance between states (nm)")
plt.xlabel("States")
plt.legend()
plt.show()
'''
return
def main_compare():
plt.close("all")
plt.rcParams.update({'font.size': 45})
plt.rc('axes', linewidth=4)
plt.figure(figsize=(17.5, 22))
plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 4
plt.rcParams['xtick.minor.size'] = 10
plt.rcParams['xtick.minor.width'] = 2
plt.rcParams['ytick.major.size'] = 20
plt.rcParams['ytick.major.width'] = 4
plt.rcParams['ytick.minor.size'] = 10
plt.rcParams['ytick.minor.width'] = 2
plt.tick_params(direction='in', top=True, right=True)
p = default_pars()
measurements = meas.measurements()
for n, measurement in enumerate(measurements):
if p['load_pars'] == True:
par_dir = "C:\\Brouwer\\HMf analysis\\Fits\\"
par_file = str(measurement[0]) + "_" + str(
measurement[1]) + "_" + str(measurement[2]) + "_" + str(
measurement[3]) + ".json"
# open parameters
with open(par_dir + par_file, 'r') as read_file:
loaded_parameters = json.loads(read_file.read())
p = loaded_parameters
print(measurement)
print(p)
f_pull, z_pull, f_release, z_release, title = hmf.read_analyze_compare(
measurement, p)
wlc, _ = func.WLC(f_pull,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
if title[-4:] == "HMfA":
pull_color = 'red'
release_color = 'grey'
else:
pull_color = 'forestgreen'
release_color = 'grey'
plt.scatter(1000 * z_pull,
f_pull,
color=pull_color,
label=title[-4:],
s=500,
linewidth=2,
zorder=25,
alpha=0.7,
facecolors='none')
plt.scatter(1000 * z_release,
f_release,
color=release_color,
s=500,
linewidth=2,
zorder=15,
alpha=0.7,
facecolors='none')
Fmax_pN = np.max(f_pull)
f = np.logspace(np.log10(0.02), np.log10(int(Fmax_pN)), 1000)
# plot the Boltzmann distribution
Z_b = hmf.hmf_boltzmann_fit(f, p, p['G1_kT'], p['G2_kT'],
p['constant'])
# if n == 0:
# plt.plot(Z_b, f, color='black', linewidth=6, zorder=120, label="Stat.Mech.Model fit")
# else:
# plt.plot(Z_b, f, color='black', linewidth=6, zorder=120)
plt.plot(Z_b, f, color='black', linewidth=10, zorder=10000)
# plot the states
Z, Zi, Gi = hmf.hmf_WLC(f, pars=p)
barrier = hmf.barrier(p)
# plot the grid
# colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
# for n, z in enumerate(Zi):
# plt.plot(z, f, color=next(colors), linewidth=2, zorder=int(-n))
# if p['two_transitions'] == False:
# plt.plot(Zi[barrier + n_prot - p['dimer_beads']], f, '--', color='dimgrey', linewidth=4, zorder=1000,
# label='state 1.5')
# plt.plot(Zi[0], f, '--', color='lightgrey', linewidth=5, zorder=1000, label='state 0')
# plt.plot(Zi[barrier], f, '--', color='grey', linewidth=5, zorder=1000, label='state 1')
plt.plot(wlc,
f_pull,
'--',
color="black",
linewidth=5,
zorder=10000,
label='WLC')
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.ylim(-1, 52)
plt.xlim(0, 1500)
plt.yscale('log')
plt.ylim(10**-1, 10**2)
plt.xticks(range(0, 1501, 500))
plt.legend(loc=2, frameon=False)
plt.tick_params(direction='in',
axis="both",
bottom="on",
top="on",
left="on",
right="on")
# placement of figure
# mngr = plt.get_current_fig_manager()
# mngr.window.setGeometry(0, 56, 3840, 2004) # laptop
# mngr.window.setGeometry(0, -1024, 1920, 984) # work PC
# plt.title("HMfA / HMfB comparison")
plt.savefig('C:\\Brouwer\\HMf analysis\\Figure 2A - HMfA-HMfB comparison',
dpi=600)
plt.savefig(
'C:\\Brouwer\\HMf analysis\\Figure 2A - HMfA-HMfB comparison.pdf')
plt.show()
return
def main_fit_three():
plt.close("all")
measurements = meas.measurements()
f_pull, z_pull, f_release, z_release, title = hmf.read_analyze(
measurements, p)
wlc, _ = func.WLC(f_pull, L_bp=p['L_bp'], P_nm=p['P_nm'], S_pN=p['S_pN'])
Fmax_pN = np.max(f_pull)
f = np.logspace(np.log10(0.02), np.log10(int(Fmax_pN)), 1000)
n_prot = (p['L_bp'] // p['L1_bp'])
# plot the states
Z, Zi, Gi = hmf.hmf_WLC(f, pars=p)
barrier = hmf.barrier(p)
plt.rcParams.update({'font.size': 28})
# select high-force data
lower_bound = 0.5
higher_bound = 50
select_f = f_pull[np.where((f_pull > lower_bound)
& (f_pull < higher_bound))]
select_z = z_pull[np.where((f_pull > lower_bound)
& (f_pull < higher_bound))]
select_z *= 1000
if p['two_transitions']:
if p['fit_G1']:
if p['fit_G1_only']:
# fit the Boltzmann distribution to find 'G2' and 'constant'
popt, pcov = curve_fit(lambda f, G1: hmf.hmf_boltzmann_fit(
f, p, G1, p['G2_kT'], p['constant']),
select_f,
select_z,
p0=p['G1_kT'],
bounds=(0.01, 10))
p['G1_kT'] = popt[0]
p['fitted_G1_var'] = pcov[0][0]
print("G1 = " + str(round(p['G1_kT'], 2)) + " +/- " +
str(round(p['fitted_G1_var'], 2)) + " kT")
print("G2 = " + str(p['G2_kT']) + " kT")
print("constant = " + str(p['constant']))
else:
# fit the Boltzmann distribution to find 'G2' and 'constant'
popt, pcov = curve_fit(
lambda f, G1, G2, constant: hmf.hmf_boltzmann_fit(
f, p, G1, G2, constant),
select_f,
select_z,
p0=[p['G1_kT'], p['G2_kT'], p['constant']],
bounds=(0.01, [10, 100, 1]))
p['G1_kT'] = popt[0]
p['G2_kT'] = popt[1]
p['constant'] = popt[2]
p['fitted_G1_var'] = pcov[0][0]
p['fitted_G2_var'] = pcov[1][1]
p['fitted_constant_var'] = pcov[2][2]
print("G1 = " + str(round(p['G1_kT'], 2)) + " +/- " +
str(round(p['fitted_G1_var'], 2)) + " kT")
print("G2 = " + str(round(p['G2_kT'], 2)) + " +/- " +
str(round(p['fitted_G2_var'], 2)) + " kT")
print("constant = " + str(round(p['constant'], 2)) + " +/- " +
str(round(p['fitted_constant_var'], 2)))
else:
# fit the Boltzmann distribution to find 'G2' and 'constant'
popt, pcov = curve_fit(
lambda f, G2, constant: hmf.hmf_boltzmann_fit(
f, p, p['G1_kT'], G2, constant),
select_f,
select_z,
p0=[p['G2_kT'], p['constant']],
bounds=(0.01, [100, 1]))
p['G2_kT'] = popt[0]
p['constant'] = popt[1]
p['fitted_G2_var'] = pcov[0][0]
p['fitted_constant_var'] = pcov[1][1]
print("G1 = " + str(p['G1_kT']) + " kT")
print("G2 = " + str(round(p['G2_kT'], 2)) + " +/- " +
str(round(p['fitted_G2_var'], 2)) + " kT")
print("constant = " + str(round(p['constant'], 2)) + " +/- " +
str(round(p['fitted_constant_var'], 2)))
# plot the Boltzmann distribution
Z = hmf.hmf_boltzmann_fit(f,
p,
G1=p['G1_kT'],
G2=p['G2_kT'],
constant=p['constant'])
plt.plot(Z,
f,
color='k',
linewidth=3,
zorder=120,
label="Stat.Mech.Model fit")
else:
# fit the Boltzmann distribution to find 'G2', 'G3' and 'constant'
if p['fit_G1']:
print("Starting fit procedure...")
t1 = time.time()
popt, pcov = curve_fit(
lambda f, G1, G2, constant,
constant_wlc: hmf.hmf_boltzmann_fit_three_fix(
f, p, G1, G2, constant, constant_wlc),
select_f,
select_z,
p0=[p['G1_kT'], p['G2_kT'], p['constant'], p['constant_wlc']],
bounds=(0.01, [10, 100, 1, 1]))
t2 = time.time()
t_tot = t2 - t1
print("Done! Elapsed time: " + str(round(t_tot, 2)) + " seconds")
p['G1_kT'] = popt[0]
p['G2_kT'] = popt[1]
p['constant'] = popt[2]
p['constant_wlc'] = popt[3]
p['fitted_G1_var'] = pcov[0][0]
p['fitted_G2_var'] = pcov[1][1]
p['fitted_constant_var'] = pcov[2][2]
p['fitted_constant_wlc_var'] = pcov[3][3]
print("G1 = " + str(round(p['G1_kT'], 2)) + " +/- " +
str(round(p['fitted_G1_var'], 2)) + " kT")
print("G2 = " + str(round(p['G2_kT'], 2)) + " +/- " +
str(round(p['fitted_G2_var'], 2)) + " kT")
print("constant = " + str(round(p['constant'], 2)) + " +/- " +
str(round(p['fitted_constant_var'], 2)))
print("constant_wlc = " + str(round(p['constant_wlc'], 2)) +
" +/- " + str(round(p['fitted_constant_wlc_var'], 2)))
else:
# '''
print("Starting fit procedure...")
t1 = time.time()
popt, pcov = curve_fit(
lambda f, G2, constant_wlc: hmf.hmf_boltzmann_fit_three_fix(
f, p, p['G1_kT'], G2, p['constant'], constant_wlc),
select_f,
select_z,
p0=[p['G2_kT'], p['constant_wlc']],
bounds=(0.001, [100, 1]))
t2 = time.time()
t_tot = t2 - t1
print("Done! Elapsed time: " + str(round(t_tot, 2)) + " seconds")
p['G2_kT'] = popt[0]
p['constant_wlc'] = popt[1]
p['fitted_G2_var'] = pcov[0][0]
p['fitted_constant_wlc_var'] = pcov[1][1]
print("G1 = " + str(p['G1_kT']) + " kT")
print("G2 = " + str(round(p['G2_kT'], 2)) + " +/- " +
str(round(p['fitted_G2_var'], 2)) + " kT")
print("constant = " + str(round(p['constant'], 2)))
print("constant_wlc = " + str(round(p['constant_wlc'], 2)) +
" +/- " + str(round(p['fitted_constant_wlc_var'], 2)))
'''
print("Starting fit procedure...")
t1 = time.time()
popt, pcov = curve_fit(
lambda f, G2 : hmf.hmf_boltzmann_fit_three_fix(f, p, p['G1_kT'], G2, p['constant'], p['constant_wlc']),
select_f, select_z, p0=p['G2_kT'], bounds=(0.001, 100))
t2 = time.time()
t_tot = t2 - t1
print("Done! Elapsed time: " + str(round(t_tot,2)) + " seconds")
p['G2_kT'] = popt[0]
p['fitted_G2_var'] = pcov[0][0]
print("G1 = " + str(p['G1_kT']) + " kT")
print("G2 = " + str(round(p['G2_kT'], 2)) + " +/- " + str(round(p['fitted_G2_var'], 2)) + " kT")
print("constant = " + str(round(p['constant'], 2)))
print("constant_wlc = " + str(round(p['constant_wlc'], 2)))
'''
# plot the Boltzmann distribution
Z = hmf.hmf_boltzmann_fit_three_fix(f,
p,
G1=p['G1_kT'],
G2=p['G2_kT'],
constant_fjc=p['constant'],
constant_wlc=p['constant_wlc'])
plt.plot(Z,
f,
color='k',
linewidth=3,
zorder=120,
label="Stat.Mech.Model fit")
# plot the grid
colors = iter(cm.rainbow(np.linspace(0, 1, len(Zi))))
for n, z in enumerate(Zi):
plt.plot(z, f, color=next(colors), linewidth=2, zorder=int(-n))
plt.plot(Zi[0],
f,
'--',
color='lightgrey',
linewidth=4,
zorder=1000,
label='state 0')
plt.plot(Zi[barrier],
f,
'--',
color='grey',
linewidth=4,
zorder=1000,
label='state 1')
plt.plot(wlc,
f_pull,
'--',
color="black",
linewidth=4,
zorder=10000,
label='state 2')
if p['two_transitions'] == False:
plt.plot(Zi[barrier + n_prot - p['dimer_beads']],
f,
'--',
color='dimgrey',
linewidth=4,
zorder=1000,
label='state 1.5')
# plot the data
if title[-4:] == "HMfA":
pull_color = 'navy'
release_color = 'lightblue'
else:
pull_color = 'darkgreen'
release_color = 'lightgrey'
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.ylim(-0.5, 5)
plt.xlim(0, 1000)
plt.ylim(-1, 52)
plt.xlim(0, 1350)
plt.yscale('log')
plt.ylim(10**-1, 10**2)
plt.scatter(1000 * z_pull,
f_pull,
color=pull_color,
label="Pull",
s=200,
zorder=25,
facecolors='none')
plt.scatter(1000 * z_release,
f_release,
color=release_color,
s=200,
zorder=15,
label='Release',
facecolors='none')
plt.legend(loc=2, frameon=False)
plt.tick_params(direction='in',
axis="both",
bottom="on",
top="on",
left="on",
right="on")
plt.title(title[-4:])
save = p['save']
if save:
# save figure
plt.savefig('C:\\Brouwer\\HMf analysis\\Fits\\' + str(title) + ".png")
# save parameters
with open('C:\\Brouwer\\HMf analysis\\Fits\\' + str(title) + '.json',
'w') as ff:
ff.write(json.dumps(p))
# placement of figure
# mngr = plt.get_current_fig_manager()
# mngr.window.setGeometry(0, 56, 3840, 2004) # laptop
# mngr.window.setGeometry(0, -1024, 1920, 984) # work PC
plt.show()
return
def batch_analysis(beads, data_lines, headers, time, diff_magnet, force, title,
file_name, analysis_path):
# constants
kBT = 4.114 # (pN nm) - Boltzmann factor
Lc = 1500 # contour length (bp)
p = 50 # persistence length (nm)
S = 1000 # stretch modulus (pN)
L = Lc * 0.34 # contour length (nm)
x0 = 0 # offset (nm)
for bead in range(0, beads):
print("Processing bead " + str(bead) + " of " + str(beads))
# load the data
Z = []
for x in data_lines:
Z.append(float(x.split()[headers.index('Z' + str(bead) +
' (um)')]))
# calculate drift for the individual bead
slope = func.calc_drift_self(data_lines, headers, time, bead)
# correcting drift
Z_drift = []
for n, t in enumerate(time):
Z_drift.append(Z[n] - (slope / 1000) * t)
Z = np.array(Z_drift)
# split the data in pull/release-curve
f_pull = []
f_release = []
z_pull = []
z_release = []
time_pull = []
time_release = []
trigger = [] # from what data point does the pulling trace start
# if the differential of the magnet is positive -> pull, else -> release ('factor' since 0 does not work)
for n, i in enumerate(diff_magnet):
factor = max(diff_magnet / 1000)
if i < -factor:
trigger.append(n)
f_pull.append(force[n])
z_pull.append(Z[n])
time_pull.append(time[n])
if i > factor:
f_release.append(force[n])
z_release.append(Z[n])
time_release.append(time[n])
# wlc for reference
wlc = []
for f in f_pull:
wlc.append(func.WLC(f, p, L, S, x0))
# select data
select_f = []
select_z = []
for n, f in enumerate(f_pull):
if 20 < f < 30:
select_f.append(f)
select_z.append(Z[n + min(trigger)])
# initial guesses
x_init = 1
# fit the WLC in fashion (x,y) - only fit offset, fix everything else
popt, pcov = curve_fit(lambda f, x0: func.WLC(f, p, L, S, x0),
select_f,
select_z,
p0=(x_init))
std = np.sqrt(np.diag(pcov)) # returns the standard deviation
x_fit = popt[0]
z_pull -= x_fit # subtract fitted offset from data
z_release -= x_fit # subtract fitted offset from data
select_z -= x_fit
a = np.percentile(z_pull, 1)
dZ = "{0:.3f}".format(a - np.percentile(z_pull, 99))
# plotting + saving
# marker_size = 10
#
# plt.subplot(2, 1, 1)
#
# plt.title(str(title) + " / " + str(file_name) + " / bead " + str(bead) + " (dZ = " + str(dZ) + " nm)")
# plt.scatter(time_pull, z_pull-a, facecolor='None', edgecolors="darkgreen", s=marker_size)
# plt.scatter(time_release, z_release-a, facecolor='None', edgecolors="darkgrey", s=marker_size)
# plt.ylim(0, 0.75)
# plt.xlabel("Time (s)")
# plt.ylabel("Extension ($\mu$m)")
#
# plt.subplot(2, 2, 3)
#
# plt.plot(wlc, f_pull, color='black', zorder=100)
# plt.scatter(z_pull, f_pull, facecolor='None', edgecolors="darkgreen", s = marker_size)
# plt.ylabel("Force (pN)")
# plt.xlabel("Extension ($\mu$m)")
# plt.xlim(0, 0.75)
#
# plt.subplot(2, 2, 4)
#
# # plt.plot(wlc, f_pull, color='black', zorder=100)
# plt.scatter(z_release, f_release, facecolor='None', edgecolors="darkgrey", s = marker_size)
# plt.ylabel("Force (pN)")
# plt.xlabel("Extension ($\mu$m)")
# plt.xlim(0, 0.75)
#
# plt.savefig(analysis_path + "dZ_" + str(dZ) + "_" + str(title) + "_" + str(file_name) + "_bead" + str(bead) + '_subplot.png', dpi=300, bbox_inches='tight')
#
# plt.close()
return
z_pull = z[np.where((diff_force > factor))]
z_release = z[np.where((diff_force < factor))]
time_pull = time[np.where(diff_force > factor)]
time_release = time[np.where((diff_force < factor))]
z_fit_pull = z_fit[np.where((diff_force > factor))]
T1 = T1[np.where((diff_force > factor))]
T2 = T2[np.where((diff_force > factor))]
T3 = T3[np.where((diff_force > factor))]
transitions = np.stack((T1, T2, T3)) # all transitions in a 3D array
if measurement[1] == "197x15":
# wlc
f_wlc = np.linspace(0.1,35, 1000)
z_wlc, _ = func.WLC(f_wlc, L_bp=4985, S_pN=900)
ax1 = fig.add_subplot(1, 2, 2)
ax1.scatter(z_pull, f_pull, label=measurement[1], color='blue', s=100, zorder=25, facecolors='none', alpha=0.5)
for t in range(len(np.transpose(transitions[0]))):
plt.plot(np.transpose(transitions[2])[t], f_pull, '--', color='lightgrey') # transition 3
# ax1.scatter(z_release, f_release, color='lightgrey', s=30, zorder=15, facecolors='none', alpha=0.5)
ax1.plot(z_fit_pull, f_pull, color='black', linewidth=3, zorder=1000)
ax1.plot(z_wlc/1000, f_wlc, color='black', linewidth=3, zorder=10)
# ax1.legend(loc=2, frameon=False)
ax1.set_xlim(-0.1, 1.75)
ax1.set_ylim(-1, 35)
ax1.tick_params(direction='in', length=6, width=3, top=True, right=True)
ax1.xaxis.set_ticks(np.arange(0, 2, 0.5))
ax1.yaxis.set_ticks(np.arange(0, 40, 10))
def main_fitfiles_single():
fitfile_path = "C:\\Users\\brouw\\Desktop\\Data\\"
fitfiles = []
os.chdir(fitfile_path)
for file in glob.glob("*.fit"):
fitfiles.append(file)
ass_fit_pars = []
ass_fit_errors = []
for fitfile in fitfiles:
print("Processing fitfile... " + str(fitfile))
title = fitfile[:6]
f_pull, f_release, z_pull, z_release, z_fit_pull, transitions = ba.read_fitfiles(
fitfile_path, fitfile, p)
f_wlc = np.logspace(np.log10(0.15), np.log10(int(np.max(f_pull))),
1000)
wlc, _ = func.WLC(f_wlc,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
# read pars from logfile
logfile = fitfile[:-3] + "log"
fit_pars, fit_errors, table = ba.read_logfile(fitfile_path, logfile)
ass_fit_pars.append(fit_pars)
ass_fit_errors.append(fit_errors)
# print pars in figure
report = str(table[0]) + '\n' + str(table[1]) + '\n' + str(
table[2]) + '\n' + str(table[3]) + '\n' + str(
table[4]) + '\n' + str(table[5])
plt.annotate(report,
xy=(0, 0.75),
xytext=(12, -12),
va='top',
xycoords='axes fraction',
textcoords='offset points')
# plot transitions
for t in range(len(np.transpose(transitions[0]))):
# plt.plot(np.transpose(transitions[0])[t],f_trans,'--',color='lightgrey') # transition 1
# plt.plot(np.transpose(transitions[1])[t],f_trans,'--',color='lightgrey') # transition 2
plt.plot(np.transpose(transitions[2])[t],
f_pull,
'--',
color='lightgrey') # transition 3
plt.ylabel('F (pN)')
plt.xlabel('z (nm)')
plt.tick_params(direction='in', top=True, right=True)
plt.ylim(-1, 60)
plt.xlim(0, 1.8)
plt.scatter(z_pull,
f_pull,
color='darkgreen',
label="Pull",
s=10,
zorder=25,
facecolors='none')
plt.scatter(z_release,
f_release,
color='lightgrey',
s=10,
zorder=15,
label='Release',
facecolors='none')
plt.plot(wlc / 1000,
f_wlc,
'--',
color="black",
label="WLC",
zorder=100)
plt.plot(z_fit_pull,
f_pull,
color='black',
linewidth=2,
label="Stat. Mech. Model fit",
zorder=1000)
plt.legend(loc=2, frameon=False)
plt.title(fitfile[:-4] + " - " + p['NRL_str'])
# TODO fix the offset in title
if p['save'] == True:
new_path = fitfile_path + "\\" + fitfile[:6] + "\\Fitfile figures (single)\\"
if not os.path.exists(new_path):
os.makedirs(new_path)
plt.savefig(new_path + fitfile[:-4])
# plt.show()
plt.close()
# assemble parameters into histogram
if p['save'] == True:
ba.plot_hist(ass_fit_pars,
ass_fit_errors,
title,
new_path,
p,
show_plot=False)
return
def main():
plt.close("all")
table_path = "C:\\Users\\brouw\\Desktop\\Data\\"
table_file = "180612_168"
measurements = ba.build_measurements(table_path, table_file + ".txt", p)
drift_arr = []
z_offset_arr = []
for measurement in measurements:
print("Processing measurement... " + str(measurement))
f_pull, z_pull, f_release, z_release, title, drift, z_offset = ba.read_analyze(
measurement, p)
f_wlc = np.logspace(np.log10(0.15), np.log10(int(np.max(f_pull))),
1000)
wlc, _ = func.WLC(f_wlc,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
drift_FE = p['drift']
drift_arr.append(drift)
z_offset_arr.append(z_offset)
twist_pos, twist_neg, z_pos, z_neg, lnd_pos, lnd_neg = ba.read_analyze_rot(
measurement, p)
drift_rot = p['drift']
fig = plt.figure(figsize=(15, 5))
# rotation
ax1 = fig.add_subplot(1, 2, 1)
ax1.set_ylabel('z (nm)')
ax1.set_xlabel('$\sigma$')
ax1.tick_params(direction='in', top=True, right=True)
ax1.set_xlim(-0.025, 0.025)
ax1.set_ylim(-500, 500)
# twist on x-axis
# ax1.scatter(twist_pos, 1000*z_pos, color='darkgreen', label="Forward twisting", s=10, zorder=25, facecolors='none')
# ax1.scatter(twist_neg, 1000*z_neg, color='lightgrey', label='Backward twisting', s=10, zorder=15, facecolors='none')
# LND on x-axis
ax1.scatter(lnd_pos,
1000 * z_pos,
color='darkgreen',
label="Forward twisting",
s=10,
zorder=25,
facecolors='none')
ax1.scatter(lnd_neg,
1000 * z_neg,
color='lightgrey',
label='Backward twisting',
s=10,
zorder=15,
facecolors='none')
ax1.plot([], [], ' ',
label="Drift: " + str(drift_rot)) # quick and very dirty
ax1.legend(loc=2, frameon=False)
ax1.set_title("Twist @ 1 pN")
# force-extension
ax2 = fig.add_subplot(1, 2, 2)
ax2.set_ylabel('F (pN)')
ax2.set_xlabel('z (nm)')
ax2.tick_params(direction='in', top=True, right=True)
ax2.set_ylim(-1, 65)
ax2.set_xlim(0, 2000)
ax2.plot([], [], ' ',
label="Drift: " + str(drift_FE)) # quick and very dirty
ax2.scatter(1000 * z_pull,
f_pull,
color='darkgreen',
label="Pull",
s=10,
zorder=25,
facecolors='none')
ax2.scatter(1000 * z_release,
f_release,
color='lightgrey',
s=10,
zorder=15,
label='Release',
facecolors='none')
ax2.plot(wlc, f_wlc, '--', color="black", label="WLC", zorder=10000)
ax2.legend(loc=2, frameon=False)
ax2.set_title("Force Extension")
fig.suptitle(title)
if p['save'] == True:
new_path = table_path + table_file[:
6] + "\\Selected figures" + table_file[
6:] + "\\"
if not os.path.exists(new_path):
os.makedirs(new_path)
fig.savefig(new_path + title)
# fig.show()
plt.close()
if p['save'] == True:
measurements = np.transpose(measurements)
measurements = np.append(measurements,
np.full((len(z_offset_arr)),
p['L_bp'])) # append length
measurements = np.append(measurements,
np.full((len(z_offset_arr)),
p['NRL'])) # append NRL
measurements = np.append(measurements,
np.full((len(z_offset_arr)),
p['repeats'])) # append repeats
measurements = np.append(np.transpose(measurements),
z_offset_arr) # append z-offset
measurements = np.transpose(
np.append(measurements, drift_arr).reshape(
(9, -1))) # append drift
headers = [
"date", "data_", "bead", "type", "length (bp)", "NRL", "repeats",
"z-offset", "drift"
]
measurements = np.append(headers, measurements).reshape((-1, 9))
with open(new_path + table_file + "_list_of_measurements.txt",
"w+") as my_csv:
csvWriter = csv.writer(my_csv, delimiter='\t')
csvWriter.writerows(measurements)
# TODO fix why pycharm closes figures
return
def main_fitfiles():
fitfile_path = "C:\\Users\\brouw\\Desktop\\Data\\"
fitfiles = []
os.chdir(fitfile_path)
for file in glob.glob("*.fit"):
fitfiles.append(file)
ass_fit_pars = []
ass_fit_errors = []
for fitfile in fitfiles:
print("Processing fitfile... " + str(fitfile))
f_pull, f_release, z_pull, z_release, z_fit_pull, transitions = ba.read_fitfiles(
fitfile_path, fitfile, p)
f_wlc = np.logspace(np.log10(0.15), np.log10(int(np.max(f_pull))),
1000)
wlc, _ = func.WLC(f_wlc,
L_bp=p['L_bp'],
P_nm=p['P_nm'],
S_pN=p['S_pN'])
# read pars from logfile
logfile = fitfile[:-3] + "log"
fit_pars, fit_errors, table = ba.read_logfile(fitfile_path, logfile)
ass_fit_pars.append(fit_pars)
ass_fit_errors.append(fit_errors)
fig = plt.figure(figsize=(30, 10))
plt.rcParams.update({'font.size': 20})
plt.rc('axes', linewidth=3)
# number of nucleosomes
ax0 = fig.add_subplot(1, 2, 1)
ax0.set_ylabel('F (pN)')
ax0.set_xlabel('z (nm)')
ax0.tick_params(direction='in', top=True, right=True)
ax0.set_ylim(0, 6)
ax0.set_xlim(0.25, 1.25)
ax0.set_title("Zoom in")
ax0.scatter(z_pull,
f_pull,
color='darkgreen',
label="Pull",
s=30,
zorder=25,
facecolors='none')
ax0.scatter(z_release,
f_release,
color='lightgrey',
s=30,
zorder=15,
label='Release',
facecolors='none')
ax0.plot(wlc / 1000,
f_wlc,
'--',
color="black",
label="WLC",
zorder=100)
ax0.plot(z_fit_pull,
f_pull,
color='black',
linewidth=3,
label="Stat. Mech. Model fit",
zorder=1000)
ax0.legend(loc=2, frameon=False)
# number of tetrasomes
ax1 = fig.add_subplot(1, 2, 2)
ax1.set_ylabel('F (pN)')
ax1.set_xlabel('z (nm)')
ax1.tick_params(direction='in', top=True, right=True)
ax1.set_ylim(-1, 60)
ax1.set_xlim(0, 1.8)
ax1.set_title("Zoom out")
# print pars in figure
report = str(table[0]) + '\n' + str(table[1]) + '\n' + str(
table[2]) + '\n' + str(table[3]) + '\n' + str(
table[4]) + '\n' + str(table[5])
ax1.annotate(report,
xy=(0, 0.75),
xytext=(12, -12),
va='top',
xycoords='axes fraction',
textcoords='offset points')
# plot transitions
for t in range(len(np.transpose(transitions[0]))):
# ax1.plot(np.transpose(transitions[0])[t],f_trans,'--',color='lightgrey') # transition 1
# ax1.plot(np.transpose(transitions[1])[t],f_trans,'--',color='lightgrey') # transition 2
ax1.plot(np.transpose(transitions[2])[t],
f_pull,
'--',
color='lightgrey') # transition 3
ax1.scatter(z_pull,
f_pull,
color='darkgreen',
label="Pull",
s=30,
zorder=25,
facecolors='none')
ax1.scatter(z_release,
f_release,
color='lightgrey',
s=30,
zorder=15,
label='Release',
facecolors='none')
ax1.plot(wlc / 1000,
f_wlc,
'--',
color="black",
label="WLC",
zorder=100)
ax1.plot(z_fit_pull,
f_pull,
color='black',
linewidth=3,
label="Stat. Mech. Model fit",
zorder=1000)
ax1.legend(loc=2, frameon=False)
fig.suptitle(fitfile[:-4] + " - " + p['NRL_str'])
if p['save'] == True:
new_path = fitfile_path + "\\Fitfile figures\\"
if not os.path.exists(new_path):
os.makedirs(new_path)
plt.savefig(new_path + fitfile[:-4])
# plt.show()
plt.close()
# assemble parameters into histogram
if p['save'] == True:
ba.plot_hist(ass_fit_pars,
ass_fit_errors,
new_path,
p,
show_plot=False)
return
plt.close("all")
data_path = "C:\\Users\\tbrouwer\\Desktop\\Data\\181115\\"
datx_file = "181115_197_WT"
measurements = ba.build_measurements(data_path, datx_file + ".datx", p)
drift_arr = []
z_offset_arr = []
print("n = " + str(len(measurements)))
for measurement in measurements:
print("Processing measurement... " + str(measurement))
f_pull, z_pull, f_release, z_release, title, drift, z_offset = ba.read_analyze(
measurement, p, data_path)
f_wlc = np.logspace(np.log10(0.15), np.log10(int(np.max(f_pull))), 1000)
wlc, _ = func.WLC(f_wlc, L_bp=p['L_bp'], P_nm=p['P_nm'], S_pN=p['S_pN'])
drift_FE = p['drift']
drift_arr.append(drift)
z_offset_arr.append(z_offset)
twist_pos, twist_neg, z_pos, z_neg, lnd_pos, lnd_neg = ba.read_analyze_rot(
measurement, p, data_path)
drift_rot = p['drift']
fig = plt.figure(figsize=(15, 5))
# rotation
ax1 = fig.add_subplot(1, 2, 1)
ax1.set_ylabel('z (nm)')
ax1.set_xlabel('$\sigma$')
