def main_pars():
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))
# 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)
ass_fit_pars = np.transpose(ass_fit_pars)
plt.scatter(ass_fit_pars[0], ass_fit_pars[3])
plt.xlabel("Number of Nucleosomes")
plt.ylabel("G1 (kT)")
plt.show()
return
def main_assemble_pars():
logfile_path = "C:\\Users\\brouw\\Desktop\\Data\\"
logfiles = []
os.chdir(logfile_path)
for file in glob.glob("*.log"):
logfiles.append(file)
for logfile in logfiles:
print("Processing logfile... " + str(logfile))
fit_pars, errors, table = ba.read_logfile(logfile_path, logfile)
# do do stuff with parameters
return
os.chdir(fitfile_path)
for file in glob.glob("*.fit"):
fitfiles.append(file)
colors = iter(cm.rainbow(np.linspace(0, 1, len(fitfiles))))
plt.figure(figsize=(30, 20))
for n, fitfile in enumerate(fitfiles):
p = {}
print("Processing fitfile... " + str(fitfile))
# read pars from logfile
logfile = fitfile[:-3] + "log"
fit_pars, fit_errors, table = ba.read_logfile(fitfile_path, logfile, p)
# open data
f_pull, f_release, z_pull, z_release, z_fit_pull, transitions = ba.read_fitfiles_plain(fitfile_path, fitfile, p, standard_trajectory=standard_trajectory, evaluate_ruptures=evaluate_ruptures)
wlc = np.transpose(transitions[2])[-1]
# offset all curves to L_c 177
offset = (177 - p['NRL']) * 16 * 0.33/1000
# offset all curves to z[f=10]
for n1,z in enumerate(z_fit_pull):
if f_pull[n1] > 11:
offset = -(z - 1.2)
break
# no offset
# colors = iter(cm.rainbow(np.linspace(0, 1, len(measurements))))
plt.figure(figsize=(15, 10))
for n, measurement in enumerate(measurements):
fitfile = measurement[3] + "\\" + measurement[0] + "_data_" + measurement[1] + "_" + measurement[2] + ".fit"
fitfile_ID = measurement[3] + "_" + measurement[0] + "_data_" + measurement[1] + "_" + measurement[2]
p = {}
print("Processing fitfile... " + str(measurement))
# read pars from logfile
logfile = fitfile[:-3] + "log"
fit_pars, fit_errors, table = ba.read_logfile(folder, logfile, p)
# open data
f_pull, f_release, z_pull, z_release, z_fit_pull, transitions = ba.read_fitfiles_plain(folder, fitfile, p, standard_trajectory=True, evaluate_ruptures=evaluate_ruptures)
wlc = np.transpose(transitions[2])[-1]
offset = 0
# offset all curves to z[f=10]
for n1, z in enumerate(z_fit_pull):
if f_pull[n1] > 10:
offset = -(z - 1.2)
break
# # correcting the offset
z_pull += offset
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_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