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CD_master.py
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#-------------------------------------------------------------------------------
# Name: CDtools
# Purpose:
#
# Author: Andrey Romanyuk
#
# Created: 02/11/2021
# Copyright: (c) Andrey Romanyuk 2021
# Licence: <your licence>
#-------------------------------------------------------------------------------
import math
import numpy as np
import re
import matplotlib.pyplot as plt
import seaborn as sns
import colorcet as cc
import itertools
class cdExp(object):
"""
"""
def __init__(self):
# self.mode =
self.samples = {} # let's store all the data for the samples of interest as a dict
self.blanks = {} # let's store blanks separately
self.plot_order = []
self.blanks_order = []
self.title = ''
self.mode = 'single_scans'
def get_samples(self):
return self.samples
def sample_param(self, sample, par_key, par_val):
self.get_samples()[sample][par_key] = par_val
def all_sample_param(self, par_key, par_vals):
for s, v in zip(self.get_samples().values(), par_vals):
s[par_key] = v
def get_blanks(self):
return self.blanks
def get_mode(self):
return self.mode
def set_mode(self, mode):
self.mode = mode
def get_title(self):
return self.title
def set_title(self, title):
self.title = title
def set_peptide_bonds(self, peptide_bonds=[]):
# Depending on the mode, the number of peptide bonds can either be entered manually or
# calculated for the mixtures based on the ratios of the components
if not peptide_bonds:
peptide_bonds = [int(i) for i in input('Specify the number of peptide bonds for each sample:\t').split(',')]
if len(peptide_bonds) == 1 and len(peptide_bonds) < len(self.get_samples()):
peptide_bonds = peptide_bonds * len(self.get_samples())
for v, p in zip(self.get_samples().values(), peptide_bonds):
v['Pep_bonds'] = p
def get_labels(self):
for val in self.get_samples().values():
if 'Label' not in val.keys():
self.set_labels(
[f"{val['Title']}_{val['Conc_uM']} uM_{val['Pathlength_mm']} mm_{val['Temp_C']}C" for val in
self.get_samples().values()])
labs = [val['Label'] for val in self.get_samples().values()]
return labs
def set_labels(self, labels):
# if self.get_labels():
for val, lab in zip(self.get_samples().values(), labels):
val['Label'] = lab
def get_plot_order(self):
return self.plot_order
def set_plot_order(self, _list=[]):
if not _list:
_list = list(self.get_samples().keys())
self.plot_order = _list
def set_precise_conc(self, conc, sample_number):
self.samples[sample_number]['Conc_uM'] = conc
self.calculate_MRE()
def get_n_samples(self):
return len(self.get_samples())
def calc_pep_bonds(self, fractions=[], individual_pb=[]):
assert len(individual_pb) == len(fractions), "The number of components and provided fractions don't match up"
fractions = np.asarray(fractions)
individual_pb = np.asarray(individual_pb)
prod = [pb * fr for pb, fr in zip(individual_pb, fractions)]
peptide_bonds = np.sum(prod, axis=0)
self.set_peptide_bonds(peptide_bonds)
def calc_helical_fraction(self, MRE222, temp, n_pep):
return 100*(MRE222 - 640+45*temp) / (-42500 * (1 - 3/n_pep) - 640+45*temp)
def calc_all_helical_fractions(self):
for val in self.get_samples().values():
val['Hel_fr'] = self.calc_helical_fraction(MRE222=val['MRE'][np.where(val['WL'] == 222)][0], temp=val['Temp_C'], n_pep=val['Pep_bonds'])
def print_off_dict(self, _dict):
for k, v in _dict.items():
if isinstance(v, dict):
print(k)
self.print_off_dict(v)
else:
print(f'{k}\t{v}')
def set_blanks_order(self, blanks):
if len(self.get_blanks()) == 1:
self.blanks_order = [1] * len(self.get_samples())
elif blanks:
self.blanks_order = blanks
else:
blanks_order = [int(i) for i in input(
'Please, list the numbers of blanks in the order corresponding to the samples they should be used with:\t').split(
',')]
def get_blanks_order(self):
return self.blanks_order
def input_datasets(self, files, blanks, bl_cor=1):
count_blanks = 1
count_samples = 1
for f in files:
# if bl_cor==1:
if 'blank'.casefold() in f.lower():
self.blanks[count_blanks] = {'Name': f}
count_blanks += 1
else:
self.samples[count_samples] = {'Name': f}
count_samples += 1
print('\nSamples:\n')
for k, v in self.get_samples().items():
print(k, v['Name'])
if bl_cor == 1:
print('\nBlanks:\n')
for k, v in self.get_blanks().items():
print(k, v['Name'])
self.set_blanks_order(blanks)
# if len(self.get_blanks()) == 1:
# blanks_order = [1] * len(self.get_samples())
# else:
#
# blanks_order = [int(i) for i in input(
# 'Please, list the numbers of blanks in the order corresponding to the samples they should be used with:\t').split(',')]
for v, b in zip(self.get_samples().values(), self.get_blanks_order()):
v['Blank'] = b
def process_filenames(self, mode='single_scans'):
if mode:
self.set_mode(mode)
for val in self.get_samples().values():
params = {'Info': []}
filename = val['Name'].split('_')
for el in filename:
if filename.index(el) == 0:
params['Date'] = el
if filename.index(el) == 1:
params['Title'] = el
if filename.index(el) == 2:
if el.endswith('mM'):
conc_uM = float(el[:-2]) * 1000
elif el.endswith('uM'):
conc_uM = float(el[:-2])
elif el.endswith('M'):
conc_uM = float(el[:-2]) * 1e6
params['Conc_uM'] = conc_uM
elif el.endswith('mm'):
if re.match('0+\d+', el):
pathlength_mm = float(el[:-2]) / 10 ** (len(el[:-2]) - 1)
else:
pathlength_mm = float(el[:-2])
params['Pathlength_mm'] = pathlength_mm
elif el.endswith('C'):
temp_C = np.array([float(te) for te in re.split('-', el[:-1])])
if self.get_mode() == 'single_scans' or self.get_mode() == 'kinet_scans':
params['Temp_C'] = temp_C[0]
elif self.get_mode() == 'var_temp' or self.get_mode() == 'var_temp_scans':
params['Temp_C_range'] = temp_C
if temp_C[-1] > temp_C[0]:
params['melt'] = 'melt'
else:
params['melt'] = 'cool'
elif el.endswith('nm') and (self.get_mode() == 'var_temp' or self.get_mode() == 'var_temp_scans') :
params['WL'] = float(el[:-2])
elif (el.endswith('min') or el.endswith('s') or el.endswith('h') ) and\
(self.get_mode() == 'kin' or self.get_mode() == 'kinet_scans'):
if el.endswith('min'):
params['Time_min'] = float(el[:-3])
elif el.endswith('s'):
params['Time_min'] = float(el[:-1])/60
elif el.endswith('h'):
params['Time_min'] = float(el[:-1]) / 3600
elif el.startswith('pH'):
pH = float(el[2:])
if pH > 14:
pH /= 10
params['pH'] = pH
else:
params['Info'].append(el)
params['Info'] = '_'.join(params['Info'])
if self.get_mode() == 'single_scans':
par_checklist = ['Date', 'Title', 'Conc_uM', 'Pathlength_mm', 'Temp_C', 'pH']
elif self.get_mode() == 'var_temp':
par_checklist = ['Date', 'Title', 'Conc_uM', 'Pathlength_mm', 'Temp_C_range', 'pH', 'WL']
elif self.get_mode() == 'var_temp_scans':
par_checklist = ['Date', 'Title', 'Conc_uM', 'Pathlength_mm', 'Temp_C_range', 'pH']
elif self.get_mode() == 'kinet_scans':
par_checklist = ['Date', 'Title', 'Conc_uM', 'Pathlength_mm', 'Temp_C', 'pH', 'Time_min']
for par in par_checklist:
assert par in params.keys(), f'Error: some parameters are missing in the file name:\t {par}'
for k, v in params.items():
val[k] = v
def read_jasco_datafiles(self, mode='single_scans'):
if mode:
self.set_mode(mode)
read_set = { 'single_scans':
{'main_var': '',
'samp_col_name': ['WL', "mdeg", "HT"],
'blank_col_name': ['WL', "mdeg", "HT"],
'n_head_lines': 19,
'n_foot_lines': 0 },
'var_temp':
{'main_var': '',
'samp_col_name': ['t', "mdeg", "HT"],
'blank_col_name': ['WL', "mdeg", "HT"],
'n_head_lines': 19,
'n_foot_lines': 0},
'var_temp_scans':
{'main_var': 'Time_s',
'samp_col_name': ['t', "mdeg", "HT"],
'blank_col_name': ['WL', "mdeg", "HT"],
'n_head_lines': 20,
'n_foot_lines': 0},
'kinet_scans':
{'main_var': 'Temp_C',
'samp_col_name': ['t', "mdeg", "HT"],
'blank_col_name': ['WL', "mdeg", "HT"],
'n_head_lines': 20,
'n_foot_lines': 0}
}
blank_n_head_lines = 19
for kk, val in self.get_samples().items():
with open(val['Name'] + '.txt', 'r') as f:
val['Metadata'] = f.readlines()[:20]
for line in val['Metadata']:
line = line.split('\t')
if line[0] == 'DELTAX':
val['Step_x'] = abs(float(line[1]))
elif line[0] == 'FIRSTX':
val['x0'] = float(line[1])
elif line[0] == 'LASTX':
val['xe'] = float(line[1])
elif line[0] == 'NPOINTS':
val['n_points'] = int(line[1])
elif line[0] == 'NPOINTST': # t is either temperature or time
val['tn'] = int(line[1])
elif line[0] == 'TUNITS': # t is either temperature or time
val['t_units'] = (line[1])
# if 'sec' in line[1]:
# self.set_mode('kinet')
elif line[0] == 'FIRSTT':
val['t0'] = int(line[1])
elif line[0] == 'LASTT':
val['te'] = int(line[1])
if self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
val['t'] = (np.linspace(val['t0'], val['te'], val['tn'], endpoint=1, dtype=int))
if 'sec' in line[1]:
print('True')
val['t'] = val['t'] / 60
read_set[self.get_mode()]['n_foot_lines'] = val['n_points'] + 1
read_set[self.get_mode()]['samp_col_name'] = ["WL"] + [str(t) for t in val['t']]
sub_samples = {}
for key in read_set[self.get_mode()]['samp_col_name'][1:]:
sub_samples[key] = {}
# generate numpy arrays from data
array = np.genfromtxt(val['Name'] + '.txt', skip_header=read_set[self.get_mode()]['n_head_lines'],
skip_footer=read_set[self.get_mode()]['n_foot_lines'],
names=read_set[self.get_mode()]['samp_col_name'], delimiter=''
)
if self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
array_HT = np.genfromtxt(val['Name'] + '.txt',
skip_header=read_set[self.get_mode()]['n_head_lines']+val['n_points']+1,
names=read_set[self.get_mode()]['samp_col_name'], delimiter='')
for count, key in enumerate(read_set[self.get_mode()]['samp_col_name'][:]):
# sub_samples[key] = {}
if self.get_mode() == 'single_scans' or self.get_mode() == 'var_temp':
val[key] = array[key]
elif self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
if count != 0:
sub_samples[key]['WL'] = array['WL']
sub_samples[key]['mdeg'] = array[key]
sub_samples[key]['HT'] = array_HT[key]
for k in val:
sub_samples[key][k] = val[k]
if self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
delete_keys = list(self.get_samples().keys())[:]
for key, val in sub_samples.items():
self.samples[key] = val
if kk in delete_keys:
self.samples.pop(kk)
for val in self.get_blanks().values():
# generate numpy arrays from data
array = np.genfromtxt(val['Name'] + '.txt', skip_header=blank_n_head_lines,
names=read_set[self.get_mode()]['blank_col_name'], delimiter='')
# val['Raw_data'] = array
for key in read_set[self.get_mode()]['blank_col_name']:
val[key] = array[key]
# self.print_off_dict(self.get_blanks())
def calculate_MRE(self):
for val in self.get_samples().values():
Divide_by = val['Pathlength_mm'] * val['Conc_uM'] * val['Pep_bonds'] * 10 ** -6
if self.get_mode() == 'single_scans' or self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
# subtract blank array from data array
blank = self.get_blanks()[val['Blank']]
if len(val["mdeg"]) == len(blank["mdeg"]):
scan_blanked = np.subtract(val["mdeg"], blank["mdeg"])
else:
# print(val['Conc_uM'])
blank_mdeg_sele = []
for wl in blank['WL']:
if wl in val['WL']:
# print(np.where(blank['WL'] == wl)[0])
blank_mdeg_sele.append(blank['mdeg'][np.where(blank['WL'] == wl)[0]])
# print(blank_mdeg_sele)
scan_blanked = np.subtract(val["mdeg"], np.array(blank_mdeg_sele).flatten())
# convert mdeg to MRE
MRE = scan_blanked[:] / Divide_by
val['MRE'] = MRE
# print(len(MRE))
if self.get_mode() == 'var_temp':
condition = self.get_blanks()[val['Blank']]["WL"] == val['WL']
blank_mdeg_at_WL = np.extract(condition, self.get_blanks()[val['Blank']]["mdeg"])
melt_blanked = np.subtract(val["mdeg"], blank_mdeg_at_WL)
MRE_melt = melt_blanked / Divide_by
val['MRE'] = MRE_melt
# def fit_to_GLF(self, x, y, pars={}):
# from lmfit import Parameters, minimize, report_fit, Model
# def GLF(x,K, A, Q, B, M, v):
# return A + ((K-A) / (1 + Q * np.exp(-B*(x-M)))**(1/v))
# glf_model = Model(GLF)
# print(f'parameter names: {glf_model.param_names}')
# print(f'independent variables: {glf_model.independent_vars}')
# if pars:
# params = glf_model.make_params(A=pars['A'], K=pars['K'], B=pars['B'], Q=pars['Q'], v=pars['v'], M=pars['M'])
# else:
# params = glf_model.make_params(A=np.min(y), K=np.max(y), B=3, Q=1, ν=1, M=0)
#
# print(params)
# result = glf_model.fit(y, params, x=x)
# print(result.fit_report())
# return result.best_fit
def fit_to_GLF(self, x, y, pars={}):
from lmfit import Parameters, minimize, report_fit, Model
def GLF(x, y,K, A, Q, B, M, v):
return A + ((K-A) / (1 + Q * np.exp(-B*(x-M)))**(1/v))
glf_model = Model(GLF)
print(f'parameter names: {glf_model.param_names}')
print(f'independent variables: {glf_model.independent_vars}')
if pars:
params = glf_model.make_params(A=pars['A'], K=pars['K'], B=pars['B'], Q=pars['Q'], v=pars['v'], M=pars['M'])
else:
params = glf_model.make_params(A=np.min(y), K=np.max(y), B=3, Q=1, ν=1, M=0)
print(params)
result = glf_model.fit(y, params, x=x)
print(result.fit_report())
# return result.best_fit
return result
def fit_all_to_sigmoid(self):
for val in self.get_samples().values():
x, y = val['t'].tolist(), val['MRE'].tolist()
fit = self.fit_to_sigmoid(x, y)
val['fit'] = fit
val['fit_best'] = fit.best_fit
val['params'] = fit.params
#print(fit)
# val['abs_folded'] = fit
def fit_to_sigmoid(self, x, y, pars={}):
from lmfit import Parameters, minimize, report_fit, Model
T = 273.15
R = 0.0083144598
def sigmoid(x, alphaN, betaN, alphaD, betaD, Tm, DHm):
return (alphaN + betaN * (T + x) + (alphaD + betaD * (T + x)) *
np.exp(-DHm * (1 - ((T + x) / (T + Tm))) / (R * (T + x)))) /\
(1 + np.exp(-DHm * (1 - ((T + x) / (T + Tm))) / (R * (T + x))))
sigmoid_model = Model(sigmoid)
print(f'parameter names: {sigmoid_model.param_names}')
print(f'independent variables: {sigmoid_model.independent_vars}')
# params = sigmoid_model.make_params()
if pars:
params = sigmoid_model.make_params(alphaN=pars['alphaN'], betaN=pars['betaN'],
alphaD=pars['alphaD'], betaD=pars['betaD'], Tm=pars['Tm'], DHm=pars['DHm'])
else:
params = sigmoid_model.make_params(alphaN=np.max(y), betaN=0, alphaD=np.min(y), betaD=0, Tm=30, DHm=150)
print(params)
result = sigmoid_model.fit(y, params, x=x)
print(result.fit_report())
# return result.best_fit
return result
def folded_fraction(self):
for val in self.get_samples().values():
val['max_folded'] = val['max_folded']
val['fr_folded'] = val['unfolded']
# TODO: add graying out parts of the graph where HT exceeds 700
def spec_plotter(self, fig_title='', legend_pos='best', xlim=(190, 260, 10), every_nth = 1,
ylim=((-50, 90, 20), (150, 1050, 200)), palette="Set2", title_size=16, leg_size=16, axlab_size=16, ticklab_size=14, lw=1,
legend_bool=True, figsize=(9,9), dpi=300, spines=2, zero_line_bool=1):
if fig_title:
self.set_title(fig_title)
self.get_labels() # makes sure Labels are in place
# plot the data
sns.set_theme()
sns.set_style("ticks")
# my_palette = sns.color_palette(cc.glasbey, n_colors=24)
#my_palette = sns.color_palette(palette)
my_palette = sns.color_palette(palette)
# ax1 = plt.subplot2grid((4, 4), (0, 0), colspan=3, rowspan=3)
fig, axs = plt.subplots(2,1,
figsize=figsize,
dpi=dpi,
sharex=True,
gridspec_kw=dict(height_ratios=[2, 0.5]))
palette = itertools.cycle(sns.color_palette(my_palette))
coef = 1e-3 #
for count, sample_n in enumerate(self.get_plot_order()):
# print(sample_n)
# print(self.get_samples()[sample_n]['Conc_uM'])
# if count % 2 == 0:
# color = next(palette)
if count % every_nth == 0:
color = next(palette)
data = self.get_samples()[sample_n]
# if self.get_mode() == 'var_temp_scans' or self.get_mode() == 'kinet_scans':
sns.lineplot(x=data['WL'], y=data['MRE'] * coef, linewidth=lw,
legend=legend_bool, ax=axs[0], label=data['Label'], color=color)
sns.lineplot(x=data['WL'], y=data['HT'], linewidth=lw, label=data['Label'],
legend=False, ax=axs[1], color=color)
if zero_line_bool == 1:
zero_line_x = np.linspace((min(data['WL'])), (max(data['WL'])), len(data['WL']))
sns.lineplot(x=zero_line_x, y=[0] * len(zero_line_x), color='k', legend=False, ax=axs[0], linewidth=lw)
axs[0].lines[-1].set_linestyle("--")
axs[0].set_ylabel('$\mathrm{MRE}$$_{222}$\n$\mathrm{(deg\ cm^{2}\ dmol^{-1}\ res^{-1} × 10^{3}}$)', size=axlab_size)
# axs[0].legend(loc=legend_pos, prop={'size': leg_size})
if fig_title:
axs[0].set_title(self.get_title(), size=title_size)
axs[1].set_ylabel('HT (V)', size=axlab_size)
axs[1].set_xlabel('Wavelength ($\mathrm{nm}$)\n\n', size=axlab_size)
for i, ax in enumerate(axs):
ax.set_ylim(ylim[i][:2])
ax.set_xlim(xlim[:2])
major_yticks = np.arange(ylim[i][0], ylim[i][1]+0.001, ylim[i][2])
major_xticks = np.arange(xlim[0], xlim[1]+0.001, xlim[2])
ax.set_xticks(major_xticks)
ax.set_yticks(major_yticks)
ax.tick_params(axis='x', labelsize=ticklab_size)
ax.tick_params(axis='y', labelsize=ticklab_size)
# change all spines
for axis in ['top', 'bottom', 'left', 'right']:
for ax in [axs[0], axs[1]]:
ax.spines[axis].set_linewidth(spines)
box = axs[0].get_position()
axs[0].set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
box = axs[1].get_position()
axs[1].set_position([box.x0, box.y0 + box.height * 0.5, box.width, box.height * 0.9])
# fig.legend(bbox_to_anchor=(0.05,0.0), loc="lower left", fancybox=True, shadow=True, prop={'size': 14},
# bbox_transform=fig.transFigure, ncol=1)
plt.show()
def t_plotter(self, labels=[], every_nth = 1, fig_title='', legend_pos='best', xlim=(5, 95, 10),
ylim=((-50, 90, 20), (150, 1050, 200)), palette="Set2", title_size=16, leg_size=16, axlab_size=16,
ticklab_size=14, lw=1, HT_bool = 1, zero_line_bool=1):
if fig_title:
self.set_title(fig_title)
if labels:
self.set_labels(labels)
# plot the data
sns.set_theme()
sns.set_style("ticks")
# my_palette = sns.color_palette(cc.glasbey, n_colors=24)
print(len(self.get_plot_order()))
my_palette = sns.color_palette(palette)
if HT_bool == 1:
fig, axs = plt.subplots(2, 1,
figsize=(9, 9),
sharex=True,
gridspec_kw=dict(height_ratios=[2, 0.5]))
palette = itertools.cycle(sns.color_palette(my_palette))
coef = 1e-3 #
for count, sample_n in enumerate(self.get_plot_order()):
print(f'plotting sample {sample_n}')
if count % every_nth == 0:
color = next(palette)
# color = next(palette)
data = self.get_samples()[sample_n]
# if data['melt'] == 'cool':
# color = next(palette)
ax = sns.lineplot(x=data['t'], y=data['MRE'] * coef, color=color,
legend=True, ax=axs[0], label=data['Label'], lw=lw)
if data['melt'] == 'cool':
ax.lines[-1].set_linestyle("--")
sns.lineplot(x=data['t'], y=data['HT'], color=color, linewidth=3, label=data['Label'],
legend=False, ax=axs[1])
#axs[0].set_ylabel('$\mathrm{MRE}$$_{222}$${*10^{3}}$\n $\mathrm{(deg\ cm^{2}\ dmol^{-1}\ res^{-1}}$)', size=axlab_size)
axs[0].set_ylabel('$\mathrm{MRE}$$_{222}$\n$\mathrm{(deg\ cm^{2}\ dmol^{-1}\ res^{-1} × 10^{3}}$)', size=axlab_size)
axs[0].legend(loc=legend_pos, prop={'size': leg_size})
axs[0].set_title(self.get_title(), size=title_size)
axs[1].set_ylabel('HT (V)', size=axlab_size)
axs[1].set_xlabel('Temperature (\u00B0C)\n', size=axlab_size)
for i, ax in enumerate(axs):
ax.set_ylim(ylim[i][:2])
ax.set_xlim(xlim[:2])
major_yticks = np.arange(ylim[i][0], ylim[i][1] + 0.001, ylim[i][2])
major_xticks = np.arange(xlim[0], xlim[1] + 0.001, xlim[2])
ax.set_xticks(major_xticks)
ax.set_yticks(major_yticks)
ax.tick_params(axis='x', labelsize=ticklab_size)
ax.tick_params(axis='y', labelsize=ticklab_size)
box = axs[0].get_position()
axs[0].set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
box = axs[1].get_position()
axs[1].set_position([box.x0, box.y0 + box.height * 0.5, box.width, box.height * 0.9])
# plt.show()
else:
fig, axs = plt.subplots(1, 1,
figsize=(9, 9),
)
palette = itertools.cycle(sns.color_palette(my_palette))
coef = 1e-3 #
for count, sample_n in enumerate(self.get_plot_order()):
if count % every_nth == 0:
color = next(palette)
data = self.get_samples()[sample_n]
# if data['melt'] == 'cool':
# color = next(palette)
ax = sns.lineplot(x=data['t'], y=data['MRE'] * coef, color=color,
legend=True, ax=axs, label=data['Label'], lw=lw)
if data['melt'] == 'cool':
ax.lines[-1].set_linestyle("--")
zero_line_x = np.linspace(min(data['t']), max(data['t']), len(data['t']))
sns.lineplot(x=zero_line_x, y=[0] * len(zero_line_x), color='k', legend=False, ax=axs, linewidth=lw)
axs.lines[-1].set_linestyle("--")
axs.set_ylabel('$\mathrm{MRE}$$_{222}$\n$\mathrm{(deg\ cm^{2}\ dmol^{-1}\ res^{-1} × 10^{3}}$)', size=axlab_size)
axs.legend(loc=legend_pos, prop={'size': leg_size})
axs.set_title(self.get_title(), size=title_size)
ax.set_ylim(ylim[:2])
ax.set_xlim(xlim[:2])
major_yticks = np.arange(ylim[0], ylim[1] + 0.001, ylim[2])
major_xticks = np.arange(xlim[0], xlim[1] + 0.001, xlim[2])
ax.set_xticks(major_xticks)
ax.set_yticks(major_yticks)
ax.tick_params(axis='x', labelsize=ticklab_size)
ax.tick_params(axis='y', labelsize=ticklab_size)
box = axs.get_position()
axs.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
if zero_line_bool == 1:
zero_line_x = np.linspace(min(data['t']), max(data['t']), len(data['t']))
sns.lineplot(x=zero_line_x, y=[0] * len(zero_line_x), color='k', legend=False, ax=axs[0], linewidth=lw)
axs[0].lines[-1].set_linestyle("--")
plt.show()
# def flexi_plotter(self, X, Y, fig_title='', legend_pos='best', xlim=(190, 260, 10), every_nth=1,
# ylim=((-50, 90, 20), (150, 1050, 200)), palette="Set2", title_size=16, leg_size=16, axlab_size=16,
# ticklab_size=14):
def export(self, datasets, filename='output.txt'):
with open(filename, 'w+') as f:
lines = ''
for i in range(len(datasets[0])):
for ds in datasets:
lines += str(ds[i]) + '\t'
lines += '\n'
f.write(lines)
def export_all_MRE(self):
for count, val in self.get_samples().items():
self.export([val['WL'], val['MRE'], val['HT']], filename=f"{val['Name']}_MRE-{count}.txt")
def process_experiment(self, files, mode, blanks, peptide_bonds):
self.input_datasets(files, blanks=blanks)
self.set_mode(mode=mode)
self.process_filenames(mode=mode)
self.set_peptide_bonds(peptide_bonds=peptide_bonds)
self.read_jasco_datafiles(mode=mode)
self.calculate_MRE()
self.set_plot_order()
def read_MRE_datafiles(self, files, mode='single_scans'):
self.input_datasets(files, bl_cor=0)
self.set_mode(mode=mode)
self.process_filenames(mode=mode)
for kk, val in self.get_samples().items():
# with open(val['Name'] + '.txt', 'r') as f:
array = np.genfromtxt(val['Name'] + '.txt', names=['WL', 'MRE', 'HT'], delimiter='')
for k in ['WL', 'MRE', 'HT']:
val[k] = array[k]
self.set_plot_order()
def main():
pass
if __name__ == '__main__':
main()