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dypole.sync-conflict-20230217-144405-WS6A44C.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
''' Collection of functions to process multidimensional NMR datasets.
Created on Wed Jan 22 14:31:16 2020
@author: Henrik Bradtmüller - mail@bradtmueller.net - https://hbrmn.github.io/
ToDo: Include parabolic approximation for WRESPDOR curves:
\Delta S / S_0 = 2/(3*\pi^2) M_2 (nTr)^2 - Anuraag Gaddam 2022
Functions
---------
process1d
Does something
process2d
Does another thing
'''
import csv
import numpy as np
import scipy.optimize as opt
import scipy.special as ss
import nmrglue as ng
import matplotlib.pyplot as plt
import palettable
from cycler import cycler
class Dataset:
''' Docstring
'''
##### Initializations #####
def __init__(self, path, name, vendor,
ls=False, trim=False, zf=False, lb=False):
self.path = path
self.name = name
self.vendor = vendor
self.procpar = np.array([ls, trim, zf, lb])
self.ls = ls
self.trim = trim
self.zf = zf
self.lb = lb
self.get_data()
# Figure options
fig_width_pt = 336.0 # From LaTeX: \showthe\columnwidth
inches_per_pt = 1.0 / 72.27 # pt to inch
golden_mean = ((5)**(0.5) - 1.0) / 2.0 # Golden ratio
self.fig_width = fig_width_pt * inches_per_pt # width in inch
self.fig_height = self.fig_width * golden_mean # height in inch
plt.rc('lines', linewidth=1)
plt.rc('axes', prop_cycle=(
cycler('color',
palettable.cmocean.sequential.Thermal_20.mpl_colors)),
# plt.rc('axes', prop_cycle=(cycler('color', 'k')),
titlesize=11, labelsize=11)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
plt.rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
def reset(self):
del self.spec
del self.area
def get_data(self):
''' Fetches the dictionary and NMR data from Bruker binary data.
The data is then prepared by removing the group delay
artifact if data was acquired digitally and attempting to repair
2D data from possibly aborted 2D experiments.
'''
if self.vendor == 'bruker':
self.dic, rawdata = ng.bruker.read(self.path)
if 'acqu2s' in self.dic:
# Check if rawdata has right shape
if len(rawdata) != self.dic['acqu2s']['TD']:
# If not, try to repair the data array
aux = ng.bruker.guess_shape(self.dic)
self.rawdata = np.reshape(
rawdata,(int((aux[0][1]+aux[0][0])*2),
int(aux[0][2]/2)))[0:self.dic['acqu2s']['TD'],:]
else:
self.rawdata = rawdata
if self.dic['acqus']['DIGMOD'] != 0:
# Removes the group delay artifact when recording digitally
self.rawdata = ng.bruker.remove_digital_filter(self.dic,
self.rawdata)
# Zero fills the number of omitted initial points to value 2^n
self.rawdata = ng.proc_base.zf(
self.rawdata, (self.rawdata.shape[self.rawdata.ndim - 1]
- self.rawdata.shape[self.rawdata.ndim - 1]))
self.car_freq = self.dic['acqus']['SFO1']
self.spec_width = self.dic['acqus']['SW_h']
self.rel_off_freq = (self.dic['acqus']['O1']
- (self.dic['procs']['SF']
- self.dic['acqus']['BF1']) * 1e6)
if self.vendor == 'varian':
# Fetches the dictionary and NMR data from VNMR (Varian)
# binary data.
self.dic, rawdata = ng.varian.read(self.path)
self.rawdata = rawdata[0]
self.car_freq = np.float64(
self.dic['procpar']['reffrq1']['values'][0])
self.spec_width = np.float64(
self.dic['procpar']['sw']['values'][0])
self.rel_off_freq = (
np.float64(self.dic['procpar']['sfrq']['values'][0])
- self.car_freq) * 1e6
def process(self):
'''Returns frequency and ppm scales of input dataset as well as
the data array, data dictionary and transmitter offset value
(SFO) in points
'''
def input_wrapper(func):
def inner(self, data):
if self.procpar.any():
proc_data = func(self, data)
else:
done = 0
while done != 1:
plt.plot(np.real(data[self.index][0]))
plt.show()
proc_data = func(self, data)
done = int(input('Selection OK? 1 - yes, 2 - no: '))
return proc_data
return inner
@input_wrapper
def left_shift(self, data):
#Left-shift to FID max for integrative SED evaluation
if self.experiment == 'SED':
left_shift = [data[n,:].argmax() for n
in range(len(data))]
data = [ng.proc_base.ls(data[n], left_shift[n]) for n
in range(len(data))]
data = np.asarray(data)
else:
if self.ls:
left_shift = self.ls
else:
left_shift = int(
input('Enter number of points to left shift: '))
if data.ndim == 1:
data = data[left_shift:]
else:
data = data[:, left_shift:]
plt.plot(np.real(data[self.index][0][0:25]))
plt.show()
return data
@input_wrapper
def trim(self, data):
if self.trim is not False:
cutoff = self.trim
else:
cutoff = float(
input('Enter fraction (e.g. 0.5) of FID to be used: '))
# Calculates the number of points after which FID is set to zeros
trim = int(cutoff * self.rawdata.shape[self.rawdata.ndim - 1])
# if data.ndim == 1:
# data[trim:] = np.zeros(len(data[trim:]))
# else:
# data[ :, trim:] = np.zeros(len(data[0, trim:]))
if data.ndim == 1:
data = data[0:trim]
else:
data = data[:, 0:trim]
plt.plot(np.real(data[self.index][0]))
plt.show()
return data
@input_wrapper
def zero_fill(self, data):
if self.zf is not False:
zero_fill = self.zf
if self.zf==0:
zero_fill = int(
input('Multiplier cannot be zero, enter multiplier of number of points: '))
else:
zero_fill = int(
input('Enter multiplier of number of points: '))
data = ng.proc_base.zf_double(data, zero_fill - 1)
plt.plot(np.real(data[self.index][0]))
plt.show()
return data
@input_wrapper
def apodize(self, data):
if self.lb is not False:
exp_lb = self.lb
else:
exp_lb = int(input('Enter exponential line broadeing in Hz: '))
# Calculates Line broadening (Topspin style) and
# divides value by sw in Hz
data = (
ng.proc_base.em(data, lb=(exp_lb/(self.spec_width))))
plt.plot(np.real(data[self.index][0]))
plt.show()
return data
@input_wrapper
def manual_phase(self, spec):
self.zero_order = int(input('Enter zero order phasing: '))
self.first_order = int(input('Enter first order phasing: '))
spec = ng.proc_base.ps(spec, p0=self.zero_order,
p1=self.first_order)
plt.plot(spec[self.index][0])
plt.show()
return spec
def auto_phase(self, spec):
spec = ng.proc_autophase.autops(spec, "acme", p0=0, p1=0)
plt.plot(np.real(spec[self.index][0]))
plt.show()
return spec
@input_wrapper
def backcorr(self, spec):
poly_degree = int(input('Enter degree of polynomial: '))
threshold = float(input('Enter threshold (e.g., 0.02): '))
spec_corr = [(spec[i] - bg_corr(self.freq_scale, spec[i],
poly_degree, threshold))
for i in range(len(spec))]
spec = np.asarray(spec_corr)
plt.plot(spec[1,:])
plt.show()
return spec
# def phase(self, spec):
# self.zero_order = 0
# self.first_order = 0
# done = 0
# while done != 1:
# spec = zero_phase(self, spec)
# spec = first_phase(self, spec)
# done = int(input('Selection OK? 1 - yes, 2 - no: '))
# return spec
# def auto_phase(self, spec):
# Begin data processing
try:
self.proc_data
except:
proc = 1
else:
proc = int(input('Re-process data?: 1 - yes, 2 - no: '))
if proc == 1:
data = self.rawdata
#find index of FID with maximum value among array
self.index = np.where(data**2 == np.max(data**2))[0]
plt.show()
data = left_shift(self, data)
data = trim(self, data)
data = zero_fill(self, data)
length = data.shape[data.ndim - 1]
# Create frequency axis
self.freq_scale = (
(np.arange((self.spec_width/2) + self.rel_off_freq,
- self.spec_width/2 + self.rel_off_freq,
- self.spec_width/length)))
# Create ppm axis
self.ppm_scale = self.freq_scale/self.car_freq
# Transmitter offset frequency in points
# Selecting data size out of array
self.sfo_point = (int(np.round(length/2)
+ (self.rel_off_freq
/ ((self.spec_width)/length)
)))
data = apodize(self, data)
self.proc_data = data
# Fourier transform
spec = ng.proc_base.fft(self.proc_data)
# Normalize data - autophase works faster
spec = spec/np.max(spec)
phasing = int(input('Automatic Phasing?: 1 - yes, 2 - no, 3 - Abs: '))
if phasing == 1:
spec = auto_phase(self, spec)
elif phasing == 3:
spec = np.abs(spec)
else:
spec = manual_phase(self,spec)
spec = ng.proc_base.di(spec) # Discard the imaginaries
if self.vendor == 'bruker':
spec = ng.proc_base.rev(spec) # Reverse the spectrum
correction = int(input('Baseline Correction?: 1 - yes, 2 - no: '))
if correction == 1:
spec = backcorr(self, spec)
self.spec = spec
def integrate(self, back_corr=False):
'''Returns the sum of the datapoints in the specified intervals
Parameters
----------
data : numpy array of float
Possible 2D dataset containing spectral intensities
Returns
-------
area : numpy array of float
Array of integrated intensities. Array has one column for SED data
input and two columns for REDOR and RESPDOR, S and S0 respectively.
'''
self.process()
try:
self.area
except:
integrate = 1
else:
integrate = int(input('Re-integrate data?: 1 - yes, 2 - no: '))
if integrate == 1:
ppm_scale = self.ppm_scale
spec = self.spec
# ppm per point
ppm_per_pt = ((np.abs(ppm_scale[- 1])
+ np.abs(ppm_scale[0])) / len(ppm_scale))
ylim = np.max(spec)
plt.plot(ppm_scale, spec[self.index[0]])
plt.xlim(ppm_scale[int(len(ppm_scale) * 0.33)],
ppm_scale[int(len(ppm_scale) * 0.66)])
plt.ylim(-ylim * 0.05, ylim * 1.05)
plt.show()
done = 0
while done != 1:
x_low = int(input('Enter left bound for integration in ppm: '))
x_high = int(
input('Enter right bound for integration in ppm: '))
min_int = int(self.sfo_point - x_low / ppm_per_pt)
max_int = int(self.sfo_point - x_high / ppm_per_pt)
plt.plot(ppm_scale, spec[self.index[0]])
plt.hlines(0, np.max(ppm_scale), np.min(ppm_scale))
plt.xlim(ppm_scale[int(min_int) - 10],
ppm_scale[int(max_int) + 12])
plt.ylim(-0.05, ylim)
plt.vlines(x_low, 0, ylim)
plt.vlines(x_high, 0, ylim)
plt.show()
done = int(input('Selection OK? 1 - yes, 2 - no: '))
# Number of points in F2 dimension
#numberDirectPoints = self.dic['acqus']['TD']
# Number of points in F1 dimension
num_points = spec.shape[0]
# Initialize area parameter
# area = np.ones(num_points)
# Background correction and integration
area = np.array(
[np.sum(spec[i, min_int:max_int]) for i in range(num_points)])
if self.experiment == 'REDOR' or self.experiment == 'RESPDOR':
area = area.reshape(int(num_points / 2), 2)
self.area = area
##### Exporting #####
def export(self, xaxis, yaxis, xlab, ylab, data_type, result=None):
exp_var = zip(xaxis, yaxis)
with open(self.path + '\\' + self.name + data_type
+ '.csv', 'w') as file:
writer = csv.writer(file, delimiter='\t', lineterminator='\n')
writer.writerow((xlab, ylab, result))
for word in exp_var:
writer.writerows([word])
##### Experiment evaluation #####
def sed_eval(self, fid=False, export=False):
'''Returns the homonuclear dipole-dipole second moment of a spin-echo
decay experiment based in the FID intensities.
Parameters
----------
export : string
'full', 'zoom', exports SED curve showing either all data points
or the ones selected for the linear fit + an additional 10.
Todo:
Works only if rawdata contains FIDs - make sure if input is from
Bruker, that the FID will be used.
Returns
-------
second_moment
Homonuclear dipole-dipole second moment in units 1e6 rad²/s²
'''
self.experiment = 'SED'
if self.vendor == 'varian':
vdlist = (np.array(
self.dic['procpar']['t1Xecho']['values']).astype(float))
if self.vendor == 'bruker':
# vdlist = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
# 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280])
vdlist = np.linspace(5, 100, 20)
# vdlist = vdlist
# time scale (2tau) in ms²
time = (((vdlist*1e-3)*2)**2)
# Calculates log(I/I0)
if fid:
sed_int = (np.abs(self.rawdata)).max(axis=1).reshape(len(time))
else:
self.integrate()
sed_int = self.area
sed_int = np.log(sed_int/sed_int[0])
# Removes missing experiments, yielding all zero FID's
if np.any(np.isinf(sed_int)):
max_index = np.where(np.isinf(sed_int))[0][0]
time = time[0:max_index]
sed_int = sed_int[0:max_index]
# Plot data
plt.scatter(time, sed_int, color='w', edgecolors='k')
plt.show()
# User input loop to select fit range
done = 0
while done != 1:
x_low = int(input('Enter first point in linear fit: '))
x_high = int(input('Enter last point in linear fit: '))
x = time[x_low:x_high]
y = sed_int[x_low:x_high]
plt.scatter(x, y, color='w', edgecolors='k')
plt.show()
done = int(input('Selection OK? 1 - yes, 2 - no: '))
# Linear regression analysis
def fit_func_norm(xaxis, slope, yintercept):
return slope*xaxis+yintercept # linear function with y-intercept
def fit_func(xaxis, slope):
return slope*xaxis # linear function w/o y-intercept
# Initial guess.
# x0 = -100 # Initial guess of curvature value
# sigma = np.ones(fit_max[0][0]) # Std. deviation of y-data
[popt, _] = (opt.curve_fit(fit_func_norm, x, y))
# Normalization of data by y-intercept of first linear fit
sed_int = (np.log(np.exp(sed_int) /
np.exp(fit_func_norm(0, popt[0], popt[1]))))
y = sed_int[x_low:x_high]
# Corrected linear fitting
[popt2, _] = (opt.curve_fit(fit_func, x, y))
# Since time is in ms², the M2 unit is 1e6 rad²s-²
second_moment = -2*popt2[0]
# Exporting data
if export:
# Experimental data
self.export(time, sed_int, '2tau^2 / ms^2',
'ln(I/I0)', 'SED')
# Fit
scale = np.round(np.linspace(time[0], time[-1], 1000),
decimals=4)
fit = fit_func(scale, popt2[0])
result = ('M2 = ' + str(np.round(second_moment, decimals=4))
+ ' e6 rad^2s^-2')
self.export(scale, fit, '2tau^2 / ms^2', 'ln(I/I0)', 'Fit',
result)
# Creating and saving SED plot
fig = plt.figure(figsize=(self.fig_width, self.fig_height))
plt.scatter(time, sed_int, color='w', edgecolors='k')
plt.plot(scale, fit_func(scale, popt2[0]), color='k')
if export == 'full':
plt.xlim(-0.005, 0.38)
# plt.xlim(-0.005, time[-4]+0.025)
plt.ylim(-3, 0.025)
# plt.ylim(sed_int[-4]-0.25, 0.025)
elif export == 'zoom':
# plt.xlim(0, time[x_high+10])
plt.xlim(-0.001, 0.041)
# plt.ylim(sed_int[x_high]*2, 0.025)
plt.ylim(-1.5, 0.025)
plt.xlabel(r'(2$\tau$)$^2$ / ms$^2$')
plt.ylabel(r'ln(I / I$_{0}$)')
fig.savefig(self.path + '\\' + self.name + '_SED.png',
format='png', dpi=300, bbox_inches='tight')
return second_moment
def t1_eval(self, fid=False, fit_lim=False):
'''
Returns
-------
None.
'''
self.experiment = 'T1'
if fid:
t1_int = np.abs(self.rawdata).max(axis=1)
else:
self.integrate()
t1_int = self.area
if self.vendor == 'varian':
vdlist = (np.array(
self.dic['procpar']['d2']['values']).astype(float))
if fit_lim == False:
fit_lim = 0
# time scale in s
time = vdlist[0:len(t1_int)-fit_lim]
# normalized intensities
t1_int = self.area/np.max(self.area)
t1_int = t1_int[0:len(t1_int)-fit_lim]
# Plot data
plt.plot(time, t1_int)
plt.show()
def mono_exp_fit(time, amp, T1, beta):
return amp * (1 - np.exp(-(time/T1)**beta)) # saturation recovery
def biexp_fit(time, amp1, T1a, beta1, amp2, T1b, beta2):
return (amp1 * (1 - np.exp(-(time/T1a)**beta1))
+ amp2 * (1 - np.exp(-(time/T1b)**beta2)))
fit_type = int(input('Mono (1) or Bi (2) exponential fit?: '))
if fit_type == 1:
[popt, _] = (opt.curve_fit(mono_exp_fit, time, t1_int,
bounds=(np.array([0, 0, 0.1]),
np.array([np.inf, np.inf, 1]
))))
amp, T1, beta = popt[0], popt[1], popt[2]
else:
[popt, _] = (opt.curve_fit(biexp_fit, time, t1_int,
bounds=(np.array([0, 0, 0.99999, 0, 0, 0.99999]),
np.array([np.inf, np.inf, 1,
0.1, np.inf, 1]
))))
amp1, T1a, beta1, amp2, T1b, beta2 = popt[0], popt[1], popt[2], popt[3], popt[4], popt[5]
# Exporting data
# Experimental data
self.export(time, t1_int, 'tau / s', 'I', 'T1-Satrec')
# Fit
scale = np.round(np.linspace(time[0], time[-1], 1000),
decimals=4)
if fit_type == 1:
fit = mono_exp_fit(scale, amp, T1, beta)
result = ('T1 = ' + str(np.round(T1, decimals=4)) + ' s, '
+ 'beta = ' + str(np.round(beta, decimals=4)))
else:
fit = biexp_fit(scale, amp1, T1a, beta1, amp2, T1b, beta2)
result = ('amp1 = ' + str(np.round(amp1, decimals=4))
+ ', T1a = ' + str(np.round(T1a, decimals=4)) + ' s, '
+ 'beta1 = ' + str(np.round(beta1, decimals=4))
+ ', amp2 = ' + str(np.round(amp2, decimals=4))
+ ', T1b = ' + str(np.round(T1b, decimals=4)) + ' s, '
+ 'beta2 = ' + str(np.round(beta2, decimals=4)))
self.export(scale, fit, 'tau / s', 'I', 'Fit', result)
# Creating and saving T1 plot
fig = plt.figure(figsize=(self.fig_width, self.fig_height))
plt.scatter(time, t1_int, color='w', edgecolors='k')
plt.plot(scale, fit,
'--', color='k')
if fit_type == 2:
plt.plot(scale, mono_exp_fit(scale, amp1, T1a, beta1), '--', color='b')
plt.plot(scale, mono_exp_fit(scale, amp2, T1b, beta2), '--', color='g')
plt.xlabel(r'$t$ / s')
plt.ylabel(r'I/I$_0$')
# plt.axhline(y=1.0, color='k', linestyle='--')
fig.savefig(self.path + '\\' + self.name + '_T1.png',
format='png', dpi=300, bbox_inches='tight')
return result
def t1rho_eval(self, fid=False, fit_lim=False):
'''
Returns
-------
None.
'''
self.experiment = 'T1rho'
if fid:
t1rho_int = np.abs(self.rawdata).max(axis=1)
else:
self.integrate()
t1rho_int = self.area
if self.vendor == 'varian':
vdlist = (np.array(
self.dic['procpar']['d2']['values']).astype(float))
elif self.vendor == 'bruker':
vdlist = np.array([2000, 2700, 3700, 5000, 6800, 9300, 12600, 17200, 23000,
31700, 43100, 58600, 79600, 108200, 147100, 200000]).astype(float)
if fit_lim == False:
fit_lim = 0
# time scale in s
time = vdlist[0:len(t1rho_int)-fit_lim] * 1e-6
# normalized intensities
t1rho_int = t1rho_int/np.max(t1rho_int)
t1rho_int = t1rho_int[0:len(t1rho_int)-fit_lim]
def mono_exp_fit(time, amp, T1, beta):
return amp * (np.exp(-(time*T1)**(beta))) # saturation recovery
def biexp_fit(time, amp1, T1a, beta1, amp2, T1b, beta2):
return (amp1 * (1 - np.exp(-(time/T1a)**beta1))
+ amp2 * (1 - np.exp(-(time/T1b)**beta2)))
[popt, _] = (opt.curve_fit(mono_exp_fit, time, t1rho_int,
bounds=(np.array([0, 0, 0.1]),
np.array([np.inf, np.inf, 1]
))))
amp, T1, beta = popt[0], popt[1], popt[2]
# Exporting data
# Experimental data
self.export(time, t1rho_int, 'tau / s^-1', 'I', 'T1-Satrec')
# Fit
scale = np.round(np.linspace(time[0], time[-1], 1000),
decimals=4)
fit = mono_exp_fit(scale, amp, T1, beta)
result = ('T1rho = ' + str(np.round(T1, decimals=4)) + ' s^-1, '
+ 'beta = ' + str(np.round(beta, decimals=4)))
self.export(scale, fit, 'tau / s', 'I', 'Fit', result)
# Creating and saving T1 plot
fig = plt.figure(figsize=(self.fig_width, self.fig_height))
plt.scatter(time, t1rho_int, color='w', edgecolors='k')
plt.plot(scale, fit,
'--', color='k')
plt.xlabel(r'$t$ / ms')
plt.ylabel(r'I/I$_0$')
plt.ylim(-0.05, 1.05)
# plt.axhline(y=1.0, color='k', linestyle='--')
fig.savefig(self.path + '\\' + self.name + '_T1rho.png',
format='png', dpi=300, bbox_inches='tight')
return result
def sae_eval(self, fid=False, fit_lim=False):
'''
Returns
-------
None.
'''
self.experiment = 'SAE'
if fid:
sae_int = np.abs(self.rawdata).max(axis=1)
else:
self.integrate()
sae_int = self.area
if self.vendor == 'varian':
vdlist = (np.array(
self.dic['procpar']['tau2']['values']).astype(float))
# time scale in s
time = vdlist*1e-6
if fit_lim == False:
fit_lim = len(time)
fit_time = time[0:fit_lim]
# normalized intensities
sae_int = sae_int/sae_int[0]
fit_int = sae_int[0:fit_lim]
# Plot data
plt.scatter(time, sae_int, color = 'w', edgecolor = 'k')
plt.semilogx(time, sae_int, '--')
plt.show()
def mono_exp_fit(time, Tsae, beta):
return (np.exp(-(time*Tsae)**beta)) # saturation recovery
def Tsae_exp(time, A, Tsae, beta1, B):
return (A * np.exp(-(time*Tsae))**beta1 + B)
def T1sae_exp(time, T1sae, beta2):
return (np.exp(-(time*T1sae)**beta2))
def biexp_fit(time, A, Tsae, beta1, B, T1sae, beta2):
return (Tsae_exp(time, A, Tsae, beta1, B) * T1sae_exp(time, T1sae, beta2))
fit_type = int(input('Mono (1) or Bi (KWW) (2) exponential fit?: '))
if fit_type == 1:
[popt, _] = (opt.curve_fit(mono_exp_fit, fit_time, fit_int,
bounds=(np.array([0.1, 0.1]),
np.array([np.inf, 1]
))))
Tsae, beta = popt[0], popt[1]
elif fit_type == 2:
[popt, _] = (opt.curve_fit(biexp_fit, fit_time, fit_int,
p0=[0.8, 466, 0.99, 0.2, 80, 0.17],
bounds=(np.array([ 0, 0, 0, 0, 0, 0]),
np.array([1, np.inf, 1, 1, np.inf, 1]
))))
A, Tsae, beta1, B, T1sae, beta2 = popt[0], popt[1], popt[2], popt[3], popt[4], popt[5]
S_inf = B/(A+B)
# elif fit_type == 3:
# Exporting data
# Experimental data
self.export(time, sae_int, 'tau / s', 'I', 'SAE')
# Fit
scale = np.geomspace(time[0], time[-1], num=1000)
if fit_type == 1:
fit = mono_exp_fit(scale, Tsae, beta)
result = ('Tsae-1 = ' + str(np.round(Tsae, decimals=4)) + ' Hz, '
+ 'beta = ' + str(np.round(beta, decimals=4)))
else:
fit = biexp_fit(scale, A, Tsae, beta1, B, T1sae, beta2)
result = ('S_inf = ' + str(np.round(S_inf, decimals=4))
+ ', T1sae-1 = ' + str(np.round(Tsae, decimals=4)) + ' Hz, '
+ 'beta1 = ' + str(np.round(beta1, decimals=4))
+ ', T1sae-1 = ' + str(np.round(T1sae, decimals=4)) + ' Hz, '
+ 'beta2 = ' + str(np.round(beta2, decimals=4)))
self.export(scale, fit, 'tau / s', 'I', 'Fit', result)
# Creating and saving T1 plot
fig = plt.figure(figsize=(self.fig_width, self.fig_height))
plt.scatter(time, sae_int, color = 'w', edgecolor = 'k')
plt.semilogx(scale, fit, '-', color='k')
y_min = sae_int[-1]*0.5
if fit_type == 2:
plt.loglog(scale, (Tsae_exp(scale, A, Tsae, beta1, 0)
* T1sae_exp(scale, T1sae, beta2)) + y_min, '--', color='g')
plt.loglog(scale, (T1sae_exp(scale, T1sae, beta2) * B), '--', color='m')
if fit_type == 1:
plt.ylim(y_min*0.9, 1.1)
else:
plt.ylim(y_min*0.9, 1.2)
# plt.xlim(1e-6, 100)
plt.xlabel(r'mixing time $t_m$ / s')
plt.ylabel(r'$S_2$ ($t_p$, $t_m$) / a.u.')
fig.savefig(self.path + '\\' + self.name + '_SAE.png',
format='png', dpi=300, bbox_inches='tight')
return result
def respdor_eval(self, spin=False, fit_lim=False):
"""Returns the heterodipolar second moment value of a RESPDOR experiment
and exports the deltaS/S0 data set together with a fitted bessel function.
Parameters
----------
Returns
-------
TYPE
DESCRIPTION.
"""
self.experiment = 'RESPDOR'
self.fit_lim = fit_lim
self.integrate()
if self.vendor == 'bruker':
spin_rate = self.dic['acqus']['CNST'][31]
elif self.vendor == 'varian':
spin_rate = self.dic['procpar']['srate']['values'][0]
# Number of points in F1 dimension
number_indirect_points = self.dic['acqu2s']['TD']
respdor_int = np.round((self.area[:, 1]-self.area[:, 0])
/ self.area[:, 1], decimals=4) # calc DS
respdor_int = np.insert(respdor_int, 0, 1e-10)
loop_increment = (2*self.dic['acqus']['L'][1])
# Builds the time scale in ms
respdor_ntr = (np.round(np.arange(2/spin_rate,
((number_indirect_points/2)+0.1) *
(loop_increment/spin_rate),
((loop_increment/spin_rate))) * 1e3,
decimals=2))
respdor_ntr = np.insert(respdor_ntr, 0, 1e-10)
# fit_max = np.where(respdor_int > fit_lim)
fit_max = fit_lim
# Bessel function
def sat_rec(xaxis, dip_const):
"""Saturation-Based recoupling curve
Todo: make curve between integration regions -> 0
----------
xaxis : numpy array of float
Time data as array
dip_const : float
Dipolar coupling constant in Hz.
nat_abund : float
Natural abundance of nonobserved nucleus.
Returns
-------
float
Bessel function value for given values of time and dipolar coupling.
"""
spin_fact = 1 / (2 * spin + 1)
def bessel_func(k):
return ((4 * spin - 2 * (k - 1)) *
ss.jv(0.25, k * np.sqrt(2) * dip_const * xaxis) *
ss.jv(-0.25, k * np.sqrt(2) * dip_const * xaxis))
return nat_abund * spin_fact * (
2 * spin - (spin_fact * np.pi * np.sqrt(2) / 4) *
sum([bessel_func(k) for k in range(1, int(2*spin+1))]))
# RESPDOR analysis via Bessel function approach
# after Goldbourt et al. doi: 10.1016/j.ssnmr.2018.04.001
# x0 = 500 # Initial guess of dipolar coupling constant in Hertz
nat_abund = 1 # Placeholder make abfragbar ##############
# lower nat abundancy bound as ugly work around
[dipole_const, res2] = (
opt.curve_fit(
sat_rec,
respdor_ntr[0:fit_max]/1000,
respdor_int[0:fit_max],
bounds=(1, np.inf),
p0=300))
# Exporting data
self.export(respdor_ntr, respdor_int, 'nTr / ms', 'DS/S0',
'RESPDOR')
scale = np.round(np.linspace(respdor_ntr[0], respdor_ntr[-1], 1001),
decimals=4)
fit = sat_rec(scale / 1000, dipole_const)
result = ('dip_const = ' + str(np.round(dipole_const, decimals=4)) + ' Hz')
self.export(scale, fit, 'NTr / ms', 'DS/S0', 'Fit', result)
# Creating and saving RESPDOR plot
fig = plt.figure(figsize=(self.fig_width, self.fig_height))
plt.scatter(respdor_ntr, respdor_int)
plt.plot(scale, sat_rec(scale / 1e3, dipole_const),
'--', color='b')
plt.xlabel(r'$nTr$ / ms')
plt.ylabel(r'$\Delta$ S / S$_0$')
plt.ylim(-0.05, 1.2)
fig.savefig(self.path + '\\' + self.name + '_RESPDOR.png',
format='png', dpi=300, bbox_inches='tight')
return dipole_const[0]
def redor_eval(self, spin=False, fit_lim=False):
"""Returns the heterodipolar second moment value of a REDOR experiment
and exports the deltaS/S0 data set together with a quadratic fit.
Parameters
----------
Returns
-------
TYPE
DESCRIPTION.
"""
self.experiment = 'REDOR'
self.fit_lim = fit_lim
self.integrate()
if self.vendor == 'bruker':
spin_rate = self.dic['acqus']['CNST'][31]
elif self.vendor == 'varian':
spin_rate = self.dic['procpar']['srate']['values'][0]
# Number of points in F1 dimension
number_indirect_points = self.dic['acqu2s']['TD']
redor_int = np.round((self.area[:, 1]-self.area[:, 0])
/ self.area[:, 1], decimals=4) # calc DS
redor_int = np.insert(redor_int, 0, 0)
loop_increment = (self.dic['acqus']['L'][1])
# Builds the time scale in ms
redor_ntr = (np.round(np.arange(2/spin_rate,
((number_indirect_points/2)+0.1) *
(loop_increment/spin_rate),
((loop_increment/spin_rate))) * 1e3,
decimals=3))
redor_ntr = np.insert(redor_ntr, 0, 0)
fit_max = np.where(redor_int > fit_lim)
# REDOR analysis via quadratic function,
# typically within dS/S0 regime < 0.2 for glasses
def quad_func(xaxis, const):
return const*xaxis**2
# Initial guess of curvature value
x0 = 10000
# sigma = np.ones(fit_max[0][0]) # Std. deviation of y-data
[res1, res2] = (opt.curve_fit(quad_func,
redor_ntr[0:fit_max[0][0]],
redor_int[0:fit_max[0][0]], x0))
# Since redor_ntr is in ms, the M2 unit is 1e6 rad²s-²
second_moment = (res1*(spin*(spin+1)*(np.pi**2)))
# Exporting data
self.export(redor_ntr, redor_int, 'nTr / ms', 'DS/S0', 'REDOR')