Filip Dominec FilipDominec

## RPSDF.py
#!/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

## calibration_curve.dat
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

## cathodoluminescence_contours.py
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')

## explore-object-tree.py
#!/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"

## filter_outliers.py
#!/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.

## pixel-covariance.py
#!/usr/bin/env python
#-*- coding: utf-8 -*-

"""

Program accepts two parameters: image1 image2

Both files have to have the same dimension. Any format accepted by scipy is possible.
They may be grayscale or RGB, in the latter case the R+G+B value is taken.

## num
#!/usr/bin/python3
try:
	with open('num.txt') as ff:
		num = int(ff.read())+1
except FileNotFoundError:
	num=1
numstr = '{:02d}'.format(num)
print(numstr)

with open('num.txt', 'w') as ff:

## SVD2Dmap_for_plotcommander.py
#plotstyle = "basis"
plotstyle = "amplitude"
decimatex = 1
ncomponents = 6
a = ys[:, ::decimatex]
xs = xs[:, ::decimatex]
U, s, V = np.linalg.svd(a, full_matrices=False)

if plotstyle=="basis":
  for x, y, label in         zip(xs[:ncomponents], V[:ncomponents], range(ncomponents)):

## SVD_for_plotcommander.py
plotstyle = "basis"
#plotstyle = "amplitude"
decimatex = 20
ncomponents = 5
a = ys[:, ::decimatex]
xs = xs[:, ::decimatex]
U, s, V = np.linalg.svd(a, full_matrices=True)

if plotstyle=="basis":
  for x, y, label in         zip(xs[:ncomponents], V[:ncomponents], range(ncomponents)):

## manualdetox.py
#!/usr/bin/env python3
#-*- coding: utf-8 -*-

import os, sys

#import fileinput
AAA, BBB = u'¬ì¡ ýØçæ§£§ççå¬Øý', u'éčůčýíářěšŠžÚčžššňČěřň'

filelist = [os.path.join(dp, f) for dp, dn, fn in os.walk('.') for f in fn]
filelist.sort()
	#!/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
	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
	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')
	#!/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"
	#!/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.
	#!/usr/bin/env python
	#-- coding: utf-8 --

	"""

	Program accepts two parameters: image1 image2

	Both files have to have the same dimension. Any format accepted by scipy is possible.
	They may be grayscale or RGB, in the latter case the R+G+B value is taken.
	#!/usr/bin/python3
	try:
	with open('num.txt') as ff:
	num = int(ff.read())+1
	except FileNotFoundError:
	num=1
	numstr = '{:02d}'.format(num)
	print(numstr)

	with open('num.txt', 'w') as ff:
	#plotstyle = "basis"
	plotstyle = "amplitude"
	decimatex = 1
	ncomponents = 6
	a = ys[:, ::decimatex]
	xs = xs[:, ::decimatex]
	U, s, V = np.linalg.svd(a, full_matrices=False)

	if plotstyle=="basis":
	for x, y, label in zip(xs[:ncomponents], V[:ncomponents], range(ncomponents)):
	plotstyle = "basis"
	#plotstyle = "amplitude"
	decimatex = 20
	ncomponents = 5
	a = ys[:, ::decimatex]
	xs = xs[:, ::decimatex]
	U, s, V = np.linalg.svd(a, full_matrices=True)

	if plotstyle=="basis":
	for x, y, label in zip(xs[:ncomponents], V[:ncomponents], range(ncomponents)):
	#!/usr/bin/env python3
	#-- coding: utf-8 --

	import os, sys

	#import fileinput
	AAA, BBB = u'¬ì¡ ýØçæ§£§ççå¬Øý', u'éčůčýíářěšŠžÚčžššňČěřň'

	filelist = [os.path.join(dp, f) for dp, dn, fn in os.walk('.') for f in fn]
	filelist.sort()