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
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