FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-

"""
Batch fitting of multiple spectra - evaluation of spectroscopic response of InGaN/GaN quantum-well sensors to the 
presence of surface charges induced by gases or liquids.

Invocation:
    ./multifit.py   <filename>.dat
where

FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-


"""
Parses one or more XLS files, each containing arbitrary number of sheets, and filters all rows for a 
pattern as determined by filter_lines=... below. Prints matching names.

Typical invocation 
    python3 ./xlsfilter.py *201{6,7,8}.{1,2}.xlsx | vi -

FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-

import re, sys
with open(sys.argv[1]) as inputf: 
    c = 1
    ls = inputf.readlines()
    fromline = 1

    for splitter in sys.argv[2:]:

FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-


import numpy as np
import scipy.constants as sc

import matplotlib.pyplot as plt
import matplotlib

FilipDominec

#!/usr/bin/python3  
#-*- coding: utf-8 -*-

## Import common moduli
import matplotlib, sys, os, time
import matplotlib.pyplot as plt
import numpy as np
from scipy.constants import c, hbar, pi
from scipy.misc import imread

FilipDominec

fw=int(len(xs[0])/30) # filter width
fl=int(len(xs[0])/20) # filter limit: minimum position of the Fabry-Pérot peak in correlation function
skippoints = 0 # sometimes the first point is some header

def nGaN(energies):
    ## returns index of refraction in GaN according to [Tisch et al., JAP 89 (2001)]
    eV = [1.503,      1.655,      1.918,      2.300, 2.668, 2.757, 2.872, 3.006, 3.136, 3.229, 3.315,     3.395,      3.422] 
    n  = [2.359+0.08, 2.366+0.04, 2.383+0.02, 2.419, 2.470, 2.486, 2.511, 2.549, 2.596, 2.643, 2.711-.01, 2.818-.045, 2.893-.100]     
    return np.interp(energies, eV, n)

FilipDominec

3.003903829999999857e+02 1.640232643851876337e-01
3.006813710000000128e+02 1.663268589412743859e-01
3.009723399999999742e+02 1.676764695268916916e-01
3.012632909999999811e+02 1.669212216194967402e-01
3.015542229999999790e+02 1.670782231063905887e-01
3.018451360000000250e+02 1.743939035195906928e-01
3.021360310000000027e+02 1.665717034735433866e-01
3.024269070000000283e+02 1.757442043184819225e-01
3.027177649999999858e+02 1.777328050331506148e-01
3.030086029999999937e+02 1.730466534811957724e-01

FilipDominec

matplotlib.rc('font', size=12)
ys = np.array(ys)[:, :1024]
ys /= 5782000/.1517*0.02301 # np.max(ys)    # fixed normalization for all samples
x = np.poly1d([4.64708212e-15, -1.65806129e-05, 5.20397778e-01, 3.53568373e+02])(range(1024))



for yy in ys:
    yy[:] = np.convolve(yy, 2**(-np.linspace(-2,2,15)**2),mode='same')

FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-

import liborigin
opj = liborigin.parseOriginFile('../test.opj')

coloured = True
if coloured:
    normal = "\033[1;0m"
    bold   = "\033[1;1m"

FilipDominec

#!/usr/bin/python3
#-*- coding: utf-8 -*-
"""
Do you have some "outlier" noise in your experimental data, e.g. due to "hot pixels" in spectrometer?

Run

    python3 rm_outliers.py my_file.dat

and the new "my_file.dat_corrected.dat" will be free of these errors.