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process_igor_pro_fiel_class.py
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process_igor_pro_fiel_class.py
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#!/usr/bin/python3
import numpy as np
import traceback
import sys
from matplotlib.widgets import Button
import pandas as pd
from pandas import read_pickle
from scipy import constants as const
from scipy.interpolate import interp1d
import dataToPickle as dtp
#import workingFunctions as wf
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, FigureCanvasAgg
import tkinter as Tk
#import matplotlib.backends.tkagg as tkagg
import matplotlib._pylab_helpers
import pprint
import lmfit
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.io import output_notebook
from bokeh.models import HoverTool
from collections import OrderedDict
from scipy.optimize import curve_fit
import PySimpleGUI as sg
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
# from PyQt5 import QtGui
# from PyQt5 import QtCore
# from PyQt5.QtCore import Qt
# plt.switch_backend('Qt5Agg') #### macht segfault hmmmmmmm
sg.SetOptions(auto_size_text = False)
# root = Tk.Tk()
# screen_width = root.winfo_screenwidth()
# screen_height = root.winfo_screenheight()
class Uphos:
def __init__(self, path = None):
if path:
try:
self.path = path
self.name = self.path.split('/')[-1][:-4]
self.workingPath = path.split('/')[-2]+'/'+path.split('/')[-1]
if path.endswith('.txt'):
print('Processing: %s' % self.workingPath)
self.info, self.data = dtp.readIgorTxt(path)
else:
print('Processing: %s' % self.workingPath)
self.info, self.data = read_pickle(path)
except Exception as err:
print ('Can not read file: %s' % path )
traceback.print_tb(err.__traceback__)
pass
else:
self.path = ''
self.info = ''
self.data = None
pass
# def __init__(self, path = None):
# if path:
# try:
# self.path = path
# self.name = self.path.split('/')[-1][:-4]
# self.workingPath = path.split('/')[-2]+'/'+path.split('/')[-1]
# if path.endswith('.txt'):
# print('Processing: %s' % self.workingPath)
# self.info, self.data = dtp.readIgorTxt(path)
# else:
# print('Processing: %s' % self.workingPath)
# self.info, self.data = read_pickle(path)
# except Exception as err:
# print ('Can not read file: %s' % path )
# traceback.print_tb(err.__traceback__)
# pass
# else:
# self.path = ''
# self.info = ''
# self.data = None
# pass
def get_data(self):
return self.data
def get_INFO(self):
return self.info
def exportCSV(self, path, data = None):
# if path.endswith('/'):
# path = path + self.path.split('/')[-1]
# else:
# path = path + '/' + self.path.split('/')[-1]
f = open(path, 'a')
info = grab_dic(self.info)
for i in info:
f.write('# '+i)
if data:
data.to_csv(f)
else:
self.data.to_csv(f)
f.close()
# def plotAll(self):
# for i in self.data[1]:
# plotData
def plotData(self, xy_label = (True,True), axExtern = None, title = None, interactive = True):
'''
Plots data of an object into an Image.
Data format ist (name,pandas.DataFrame)
Parameters:
xy_label: Boolean : Display or not x or/and y Labels from pandas Dataframe Column/Index names
axExtern: Matplotlib axes: for External ploting
Title: String: Title of the subplot
interactive: Bollean: Display or not the buttons for data Proccecing
Returns:
imshow Object
'''
# cid = fig.canvas.mpl_connect('resize_event', onresize)
for current_data in self.data:
if not axExtern:
fig = plt.figure()
global ax
ax = fig.add_subplot(111)
if title:
fig.canvas.set_window_title(title)
else:
fig.canvas.set_window_title(current_data[0])
y,x = current_data[1].index.values, current_data[1].columns.values
extent = np.min(x), np.max(x), np.min(y), np.max(y)
if axExtern:
fig = axExtern
im = axExtern.imshow(curent_data[1],extent=extent, origin = 'lower', cmap='hot', aspect = 'auto')
plt.colorbar(im, ax=axExtern)
# if xy_label[0] == True:
# axExtern.set_xlabel(current_data.index.name)
# if xy_label[1] == True:
# axExtern.set_ylabel(current_data.columns.name)
else:
im = plt.imshow(current_data[1],extent=extent, origin = 'lower', cmap='hot', aspect = 'auto')
plt.colorbar()
# if xy_label[0] == True:
# plt.xlabel(current_data.index.name)
# if xy_label[1] == True:
# plt.ylabel(current_data.columns.name)
plt.tight_layout()
if interactive:
button1pos= plt.axes([0.79, 0.0, 0.1, 0.075]) #posx, posy, width, height in %
button2pos = plt.axes([0.9, 0.0, 0.1, 0.075])
button3pos = plt.axes([0.9, 0.1, 0.1, 0.075])
bcut1 = Button(button1pos, 'Int. X', color=buttoncolor)
bcut2 = Button(button2pos, 'Int. Y', color=buttoncolor)
bcut3 = Button(button3pos, 'Info', color=buttoncolor)
bcut1.on_clicked(lambda event: self.on_click(event, current_data[1], ax = ax))
bcut2.on_clicked(lambda event: self.on_click(event, current_data[1], ax = ax,axes ='y'))
bcut3.on_clicked(lambda event: self.on_clickInfo(event))
button1pos._button = bcut1 #otherwise the butten will be killed by carbagcollector
button2pos._button = bcut2
button3pos._button = bcut3
return im
def sumAllDataFrames(self, data =None):
'''
Sum all panda.dataFrames element wise
'''
if data: data = data
else: data = self.data
cnt = 0
for i in data:
if cnt == 0:
data_frame_sum = i[1]
else:
data_frame_sum = data_frame_sum.add(i[1], axis='index', fill_value=0.0)
#else: data_frame_sum = data_frame_sum.merge(i[1])
cnt +=1
return data_frame_sum
def plotOverview(self, data = None):
'''
Plots DataFrame and Integratet Plots alon x an y axes
'''
if data is not None:
tmp_data = data
else:
tmp_data = self.data
fig = plt.figure(figsize=(10, 10))
grid = plt.GridSpec(4, 4, hspace=0.01, wspace=0.01)
main_ax = fig.add_subplot(grid[1:4, :-1])
x,y = tmp_data.index.values, tmp_data.columns.values
extent = np.min(x), np.max(x), np.min(y), np.max(y)
im = plt.imshow(tmp_data, extent = extent, origin = 'lower', cmap='hot', aspect = 'auto')
main_xlim = plt.xlim()
main_ylim = plt.ylim()
plt.xlabel(tmp_data.index.name)
plt.ylabel(tmp_data.columns.name)
cbar_ax = fig.add_axes([0.01, 0.95, 0.9, 0.05])
plt.colorbar(im, cax = cbar_ax, orientation='horizontal')
y_int = fig.add_subplot(grid[1:4, -1])
y_int.get_yaxis().set_ticks([])
#plt.ticklabel_format(axis='x', style='sci',scilimits=(0,0), useMathText = True)
#plt.ylim(main_ylim)
yred = self.reduceY(tmp_data)
plt.xticks(rotation='vertical')
y_int.plot(yred.values, yred.index.values,'o')
#x_int = fig.add_subplot(grid[-1, 1:])
x_int = fig.add_subplot(grid[0, :-1])
x_int.set_title(self.workingPath)
x_int.get_xaxis().set_ticks([])
#plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useMathText = True)
#plt.xlim(main_xlim)
xred = self.reduceX(tmp_data)
x_int.plot(xred,'o')
fig.tight_layout()
return fig
def reduceX(self, data = None):
'''Integriere Daten entlang einzelnen Energiewerten '''
if data is not None:
self.XredData = data.apply(np.sum, axis = 0)
new_name ='summed over ('+str(data.columns.min()) +' : '+str(data.columns.max()) +') ' + data.columns.name
else:
self.XredData = self.data.apply(np.sum, axis = 0)
new_name ='summed over ('+str(self.data.columns.min()) +' : '+str(self.data.columns.max()) +') ' + self.data.columns.name
self.XredData.name = self.name +'_'+new_name
return self.XredData
def reduceY(self, data = None):
'''Integritmp_dataere Entlang Y.'''
#return np.add.reduce(data)
if data is not None:
self.YredData = data.apply(np.sum, axis = 1)
else:
self.YredData = self.data.apply(np.sum, axis = 1)
return self.YredData
def fermiFct(self, x, E_f, b, s, T):
k_b = const.value(u'Boltzmann constant in eV/K')
return b + s*(1./(np.exp((x-E_f)/(k_b*T))+1))
def fitFermi(self, a=16.9,b = 1.,c = 1. ,d =70.):
x = self.XredData.index
y = self.XredData.values
#mod = lmfit.models.ExponentialModel()
mod = lmfit.Model(self.fermiFct)
#pars = mod.guess(y, x=x) ###(x,E_f,b,s,T):
out = mod.fit(y,E_f = a, b = b, s = c , T = d, x=x)
print(out.fit_report())
#plt.plot(x, out.best_fit, 'k-')
#values = {'E_f':popt[0],'B':popt[1],'S':popt[2],'T':popt[3]}
print(out.best_fit)
return (x,out.best_fit)
def fitFermi_old(self, p0 =[0.0001,0.00001,0.0001,0.0001], x_lim = None , y_lim = None):
if x_lim == None:
x_lim = (self.XredData.index.values.min(), self.XredData.index.values.max())
if y_lim == None:
y_lim = (self.XredData.values.min(), self.XredData.values.max())
if x_lim is not None: mask = (self.XredData .index > x_lim[0]) & (self.XredData.index <= x_lim[1])
try:
p0=[float(x) for x in p0]
except:
print("Error:", sys.exc_info()[0])
# raise
try:
popt, pcov = curve_fit(self.fermiFct, self.XredData.index[mask], self.XredData.values[mask], p0=p0)
except:
print("Error:", sys.exc_info()[0])
raise
#fitPlot = ax.plot(data.index[mask], fermiFct(data.index[mask], *popt), 'r-', label='fit: E_f=%5.3f, T=%5.3f,b=%5.3f,c=%5.3f ' % tuple(popt))
#ax.set_xlim(x_lim)
#ax.set_ylim(y_lim)
#plt.show()
values = {'E_f':popt[0],'B':popt[1],'S':popt[2],'T':popt[3]}
return values
def reduceByX(self, data):
'''Integriere Daten entlang einzelnen Energiewerten '''
#return np.add.reduce(data.T
return self.data.apply(np.sum, axis = 1)
def reduceByY(self, data):
'''Integriere Entlang Y.'''
#return np.add.reduce(data)
return self.data.apply(np.sum, axis = 0)
def sliceData(self, xlim = None, ylim = None):
# print(len(self.data))
# print(self.data[0])
# if xlim and ylim:
# self.data = self.data.iloc[x1:x2,y1:y2]
# elif xlim:
# self.data = self.data.iloc[x1:x2,:]
# elif ylim:
# self.data = self.data.iloc[:,y1:y2]
return self.data
def on_click(self, event, data, ax = None, axes = 'x'):
print('Start to Integrate X')
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
if ax:
x_lim = ax.get_xlim()
y_lim = ax.get_ylim()
slicedData = self.sliceData(xlim = x_lim, ylim = y_lim)
print(slicedData)
else:
slicedData = data
digis = 3
ax2.set_title('x:%s y:%s' %((round(x_lim[0],digis),round(x_lim[1],digis)), (round(y_lim[0],digis),round(y_lim[1],digis))))
if axes == 'x':
#reducedData = self.reduceX(slicedData)
reducedData = self.reduceX(data)
else:
#reducedData = self.reduceY(slicedData)
reducedData = self.reduceY(data)
#print(reducedData.values, type(reducedData), len(reducedData))
ax2.plot(reducedData, 'bo')
#ax2.plot(data, 'bo')
button3pos = plt.axes([0.9, 0.0, 0.1, 0.075])
bcut3 = Button(button3pos, 'Save', color=buttoncolor)
buttonFitpos = plt.axes([0.9, 0.1, 0.1, 0.075])
buttonFit = Button(buttonFitpos, 'Fit-Panel', color=buttoncolor)
buttonEXPpos = plt.axes([0.9, 0.2, 0.1, 0.075])
buttonEXP = Button(buttonEXPpos, 'Export as csv', color=buttoncolor)
buttonEXP.on_clicked(lambda event:self.exportCSVtrigger(event, reducedData))
bcut3.on_clicked(lambda event:self.saveReduceData(event, reducedData))
buttonFit.on_clicked(lambda event:fitPanel(event, ax2, reducedData))
button3pos._button = bcut3 #without this the garbage collector destroyes the button
buttonFitpos._button = buttonFit
buttonEXPpos._button = buttonEXP
figures=[manager.canvas.figure
for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
for i in figures:
try:
axies= i.get_axes()
for j in axies:
print(j.get_title())
except:
pass
plt.draw()
def exportCSVtrigger(self, event, reddata):
event, (filename,) = sg.Window('Export to csv'). Layout([[sg.Text('Filename')], [sg.Input(), sg.SaveAs()], [sg.OK(), sg.Cancel()]]).Read()
self.exportCSV(filename, reddata)
return event
def saveReduceData(self, event, reddata):
event, (filename,) = sg.Window('Save data'). Layout([[sg.Text('Filename')], [sg.Input(), sg.SaveAs()], [sg.OK(), sg.Cancel()]]).Read()
if filename.endswith('.pkl'):reddata.to_pickle(filename)
elif filename.endswith('.csv'):
f = open(filename, 'a')
f.write(pprint.pformat(self.info))
reddata.to_csv(f, header = True , sep = ' ')
f.close()
else: print('Only .pkl or .csv are implemented.')
return event
def on_clickInfo(self, event):
event = sg.Window('Info',auto_size_text=True,font=("Helvetica", 18)). Layout([[sg.Multiline(pprint.pformat(self.info),size=(80, 10))],[sg.Cancel()]]).Read()
return event
def plotRed(dataSet,info, currentPlot = False):
if currentPlot:
p = currentPlot
else:
p = figure(plot_width=1000, plot_height=600,
tools="pan,box_zoom,reset,save,crosshair,hover,wheel_zoom",
title="",
x_axis_label=dataSet.index.name,
y_axis_label='Counts',
toolbar_location="left"
)
df = dataSet.reset_index()
df.columns = [dataSet.index.name,'Counts']
source = ColumnDataSource.from_df(df)
hover = p.select(dict(type=HoverTool))
hover.tooltips = OrderedDict(info['[Info 1]'].items())
p.line(x='index', y='Counts', source=source, legend=info['[Info 1]']['Spectrum Name'])
return p
buttoncolor = 'lightskyblue'#'lightgoldenrodyellow'
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
The Savitzky-Golay filter removes high frequency noise from data.
It has the advantage of preserving the original shape and
features of the signal better than other types of filtering
approaches, such as moving averages techniques.
Parameters
----------
y : array_like, shape (N,)
the values of the time history of the signal.
window_size : int
the length of the window. Must be an odd integer number.
order : int
the order of the polynomial used in the filtering.
Must be less then `window_size` - 1.
deriv: int
the order of the derivative to compute (default = 0 means only smoothing)
Returns
-------
ys : ndarray, shape (N)
the smoothed signal (or it's n-th derivative).
Notes
-----
The Savitzky-Golay is a type of low-pass filter, particularly
suited for smoothing noisy data. The main idea behind this
approach is to make for each point a least-square fit with a
polynomial of high order over a odd-sized window centered at
the point.
Examples
--------
t = np.linspace(-4, 4, 500)
y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.show()
References
----------
.. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
Data by Simplified Least Squares Procedures. Analytical
Chemistry, 1964, 36 (8), pp 1627-1639.
.. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
Cambridge University Press ISBN-13: 9780521880688
"""
from math import factorial
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError as msg:
raise ValueError("window_size and order have to be of type int")
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window_size size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomials order")
order_range = range(order+1)
half_window = (window_size -1) // 2
# precompute coefficients
b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
y = np.concatenate((firstvals, y, lastvals))
return np.convolve( m[::-1], y, mode='valid')
def grab_dic(data):
info_list = []
for ele in data.values():
if isinstance(ele,dict):
for k, v in ele.items():
tmp_list = []
tmp_list = [k+' : '+v.replace(';', '\n#\t\t')+'\n']
info_list.append(' '.join(tmp_list))
return info_list
def exportCSV(path, data, info = None):
# if path.endswith('/'):
# path = path + self.path.split('/')[-1]
# else:
# path = path + '/' + self.path.split('/')[-1]
f = open(path, 'a')
if info:
info = grab_dic(self.info)
for i in info:
f.write('# '+i)
data.to_csv(f)
f.close()
def fitPanel(event, ax, data):
x_lim = ax.get_xlim()
y_lim = ax.get_ylim()
x_abstand = abs(x_lim[1]-x_lim[0])/len(data)
leftbound, rightbound = x_lim[0], x_lim[1]
leftboundStep = x_lim[0]+abs(x_lim[1]-x_lim[0])*0.05
rightboundStep = x_lim[1]-abs(x_lim[1]-x_lim[0])*0.05
faktor = 1e5 # da es in PySimpleGUI der slider nur die int Werte zurueck gibt
layout = [# ll, lr steht fuer LeftLeft, LeftRight, ...
[sg.Text(r'Left'), \
sg.Slider(key = 'll_slider', change_submits = True, background_color = 'red',\
range=(x_lim[0]*faktor,x_lim[1]*faktor), resolution = 1, orientation='h', size=(34, 20), default_value=leftbound),
sg.Slider(key = 'lr_slider', change_submits = True, background_color = 'red', \
range=(x_lim[0]*faktor,x_lim[1]*faktor),resolution = 1, orientation='h', size=(34, 20), default_value=leftboundStep),\
sg.Spin(data.index,key='ll_spin',size=(10, 20), auto_size_text = True),\
sg.Spin(data.index,key='lr_spin',size=(10, 20), auto_size_text = True)],
[sg.Text(r'Right'), sg.Slider(key = 'rl_slider', change_submits = True, background_color = 'green',\
range=(x_lim[0]*faktor,x_lim[1]*faktor), resolution = x_abstand, orientation='h', size=(34, 20), default_value=rightboundStep),
sg.Slider(key = 'rr_slider', change_submits = True, background_color = 'green',\
range=(x_lim[0]*faktor,x_lim[1]*faktor),resolution = x_abstand, orientation='h', size=(34, 20), default_value=rightbound),
sg.Spin(data.index,key='rl_spin',size=(10, 20), auto_size_text = True),
sg.Spin(data.index,key='rr_spin',size=(10, 20), auto_size_text = True)],
[sg.ReadButton('Fit')],
[sg.ReadButton('Finde Fermi Edge'), sg.Text(r'Fermi edge [eV]'), sg.InputText(size =(10,10), key='fermi_edge'), sg.Text('16%-84% width [eV]'), sg.InputText(size =(10,10), key = 'resolution')] ,
[sg.ReadButton('Fit Fermi Function'), sg.Text(r'E_f:'),sg.InputText(size =(10,20), key = 'E_f', default_text= '16.9'), sg.Text(r'b:'), sg.InputText(size =(10,20), default_text = '20000', key = 'b'),sg.Text(r's:'),sg.InputText(size =(10,20),default_text = '100', key = 's'),sg.Text(r'T:'),sg.InputText(size =(10,20),default_text = '300', key = 'T'),],
[sg.Cancel()],
]
window = sg.Window('Fit Parameter for figure ' + str(plt.gcf().number), grab_anywhere=False, auto_size_text=True)
window.Layout(layout)
window.Finalize()
line1, = ax.plot((leftbound,leftbound),y_lim, color = 'r', marker = '>', alpha=0.5)
line2, = ax.plot((leftboundStep, leftboundStep),y_lim, color = 'r', marker = '<', alpha=0.5)
line3, = ax.plot((rightboundStep, rightboundStep),y_lim, color = 'g', marker = '>', alpha=0.5)
line4, = ax.plot((rightbound, rightbound),y_lim, color = 'g', marker = '<', alpha=0.5)
leftFit = None # Initiate some elements, important for Canceling of fit-panel
rightFit = None
inter_line = None
inter_dot = None
fermi_edge_plot = None
sexteen_plot = None
eigthy4_plot = None
while True:
event, values = window.Read()
line1.set_xdata((values['ll_slider']/faktor,values['ll_slider']/faktor))
line2.set_xdata((values['lr_slider']/faktor,values['lr_slider']/faktor))
line3.set_xdata((values['rl_slider']/faktor,values['rl_slider']/faktor))
line4.set_xdata((values['rr_slider']/faktor,values['rr_slider']/faktor))
window.FindElement('ll_spin').Update(values['ll_slider']/faktor)
window.FindElement('lr_spin').Update(values['lr_slider']/faktor)
window.FindElement('rl_spin').Update(values['rl_slider']/faktor)
window.FindElement('rr_spin').Update(values['rr_slider']/faktor)
if event == 'Fit':
try:
leftFitPara = fitLinear(event, (values['ll_slider']/faktor,values['lr_slider']/faktor), data, ax, 'red')
rightFitPara = fitLinear(event, (values['rl_slider']/faktor,values['rr_slider']/faktor), data, ax, 'green')
if leftFit:
leftFit.set_ydata(LinearFit(data.index,*leftFitPara))
rightFit.set_ydata(LinearFit(data.index,*rightFitPara))
if inter_line: inter_line.remove()
if inter_dot: inter_dot.remove()
if fermi_edge_plot: fermi_edge_plot.remove()
if sexteen_plot: sexteen_plot.remove()
if eigthy4_plot: eigthy4_plot.remove()
else:
leftFit, = ax.plot(data.index, LinearFit(data.index, *leftFitPara), color = 'red', label='fit: a=%5.3f, b=%5.3f ' % tuple(leftFitPara))
rightFit, = ax.plot(data.index, LinearFit(data.index, *rightFitPara), color = 'green', label='fit: a=%5.3f, b=%5.3f ' % tuple(rightFitPara))
inter = interpolate(data, ax)
inter_line, = ax.plot(inter[0], inter[1])
inter_dot = ax.scatter(inter[0], inter[1])
except TypeError as error:
if str(error) == 'Improper input: N=2 must not exceed M=0':
print('Please select the appropriate limits for the fit')
pass
else:
print("Error:", sys.exc_info()[0])
raise
if event == 'Fit Fermi Function':
try:
xFit, yFit, out = experiment.fitFermi(float(values['E_f']), float(values['b']), float(values['s']), float(values['T']))
fitParam = out.params.valuesdict()
print(fitParam)
window.FindElement('E_f').Update(str(fitParam['E_f']))
window.FindElement('b').Update(str(fitParam['b']))
window.FindElement('s').Update(str(fitParam['s']))
window.FindElement('T').Update(str(fitParam['T']))
ax.plot(xFit,yFit)
except:
print("Error:", sys.exc_info()[0])
raise
plt.draw()
if event == 'Finde Fermi Edge':
try:
fermi_edge_plot, sexteen_plot, eigthy4_plot = finde_edge(inter,leftFitPara,rightFitPara,ax,window)
except:
print("Error:", sys.exc_info()[0])
raise
plt.draw()
if event == 'Cancel' or event is None: # be nice to your user, always have an exit from your form
line1.remove()
line2.remove()
line3.remove()
line4.remove()
if inter_line: inter_line.remove()
if inter_dot: inter_dot.remove()
if fermi_edge_plot: fermi_edge_plot.remove()
if sexteen_plot: sexteen_plot.remove()
if eigthy4_plot: eigthy4_plot.remove()
if leftFit: leftFit.remove()
if rightFit: rightFit.remove()
break
window.Close()
return event, values
def finde_edge(interPolData, fit1Para, fit2Para, ax, window):
for i in range(0, len(interPolData[0])):
diff = 0.5*(abs(LinearFit(interPolData[0][i],*fit1Para)-LinearFit(interPolData[0][i],*fit2Para)))
fermi_edge = interPolData[1][i] - LinearFit(interPolData[0][i],*fit2Para)
if fermi_edge <= diff:
fermi_edge_plot = ax.axvline(x=interPolData[0][i], color = 'k', dashes = (5, 1))
fermi_edge_x = interPolData[0][i]
plt.draw()
window.FindElement('fermi_edge').Update(str(fermi_edge_x))
break
for i in range(0, len(interPolData[0])):
diff = 0.16*(abs(LinearFit(interPolData[0][i],*fit1Para)-LinearFit(interPolData[0][i],*fit2Para)))
sexteen = interPolData[1][i] - LinearFit(interPolData[0][i],*fit2Para)
if sexteen <= diff:
sexteen_x = interPolData[0][i]
sexteen_plot = ax.axvline(x=interPolData[0][i], color = 'k', dashes = (5, 1))
plt.draw()
break
for i in range(0, len(interPolData[0])):
diff = 0.84*(abs(LinearFit(interPolData[0][i],*fit1Para)-LinearFit(interPolData[0][i],*fit2Para)))
eigthy4 = interPolData[1][i] - LinearFit(interPolData[0][i],*fit2Para)
if eigthy4 <= diff:
eigthy4_plot = ax.axvline(x=interPolData[0][i], color = 'k', dashes = (5, 1))
eigthy4_x = interPolData[0][i]
plt.draw()
window.FindElement('resolution').Update(str(abs(eigthy4_x-sexteen_x)))
break
return fermi_edge_plot, sexteen_plot, eigthy4_plot
def fermiFct(x,E_f,b,s,T):
k_b = const.value(u'Boltzmann constant in eV/K')
return b + s*(1./(np.exp((x-E_f)/(k_b*T))))
def LinearFit(x,a,b):
return a*x+b
def fitLinear(event, x_range, data, ax, color):
mask = (data.index > x_range[0]) & (data.index <= x_range[1])
try:
popt, pcov = curve_fit(LinearFit, data.index.values[mask], data.values[mask])
except:
print("Error:", sys.exc_info()[0])
#raise
return popt
def interpolate(data, ax, xstep = None):
'''
xstep: int faktor of the interpolated points. So if xstep = 2, two times more points would be created. Default 10
'''
x_lim = ax.get_xlim()
y_lim = ax.get_ylim()
if x_lim[0]<data.index.min():
x_lim = data.index.min(), x_lim[1]
if x_lim[1]>data.index.max():
x_lim = x_lim[0], data.index.max()
mask = (data.index > x_lim[0]) & (data.index <= x_lim[1])
f = interp1d(data.index[mask], data.values[mask], fill_value="extrapolate")
if xstep == None:
xstep = 10
newx = np.linspace(x_lim[0], x_lim[1], num=xstep*len(data.index[mask]), endpoint=True)
return newx, f(newx)
def allMethodsOf(object):
return [method_name for method_name in dir(object)
if callable(getattr(object, method_name))]
def main():
"""
Proceding of ARPES data sets from OMICON SES Software.
"""
__author__ = "Alexander Kononov"
__copyright__ = "Royalty-free"
__credits__ = ""
__license__ = ""
__version__ = "2.0"
__maintainer__ = "Alexander Kononov"
__email__ = "[email protected]"
__status__ = "Production"
# ------ Menu Definition ------ #
menu_def = [['File', ['Open', 'Exit' ]],
['Help', 'About...'], ]
# ------ GUI Defintion ------ #
layout = [
[sg.Menu(menu_def, )],
[sg.Output(size=(60, 20))]
]
window = sg.Window("UPhoS", default_element_size=(15, 1), auto_size_text=False, auto_size_buttons=False, location=(250, 250), default_button_element_size=(15, 1)).Layout(layout)
win = window.Finalize()
# ------ Loop & Process button menu choices ------ #
while True:
event, values = window.Read()
if event == None or event == 'Exit':
break
# ------ Process menu choices ------ #
if event == 'About...':
sg.Popup(main.__doc__+'\n Author: '+__author__+'\n E-mail: '+__email__+'\n Copyright: '+\
__copyright__+'\n License: '+__license__+'\n Version: '+\
__version__+'\n Status: '+__status__)
elif event == 'Open':
filename = sg.PopupGetFile('file to open', no_window=True, keep_on_top =True, default_extension='txt', default_path='../../Data/')
try:
if filename: print(filename)
plt.ion()
global experiment
experiment = Uphos(filename)
name = filename.split('/')
#experiment.plotData(title = name[-2]+'/'+name[-1][:-4])
#for i in experiment.data:
experiment.plotData()
plt.show()
# data = read_pickle(filename)
# plotData(data, title = filename.split('/')[-1:])#, title = filename.split('/')[:-2])
except AttributeError:
print('Open file function was aborted.')
raise
#pass
if __name__ == '__main__':
main()