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ICoFit 1.1.0.py
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ICoFit 1.1.0.py
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""" ver. 1.1.0
> power-low fitting is newly added """
# Inport of necessary modules
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
import wx
class FileDropTarget(wx.FileDropTarget):
""" Drag & Drop Class """
def __init__(self, window):
wx.FileDropTarget.__init__(self)
self.window = window
def OnDropFiles(self, x, y, files):
self.window.text_entry.SetLabel(files[0])
return 0
class App(wx.Frame):
""" GUI """
def __init__(self, parent, id, title):
wx.Frame.__init__(self, parent, id, title, size=(500, 500), style=wx.DEFAULT_FRAME_STYLE)
# Panel
p = wx.Panel(self, wx.ID_ANY)
label = wx.StaticText(p, wx.ID_ANY, 'Drop the DLS data file here\n or input the path of the file below.\nExcel (.xlsx) is only allowed.', style=wx.SIMPLE_BORDER | wx.TE_CENTER)
font = wx.Font(20, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL)
label.SetFont(font)
label.SetBackgroundColour("#e0ffe0")
# Set the drop target
label.SetDropTarget(FileDropTarget(self))
# Textbox
self.text_entry = wx.TextCtrl(p, wx.ID_ANY)
# Button to select fitting type
self.btntxt = wx.StaticText(p, wx.ID_ANY, 'Fit by:')
self.btn_1 = wx.RadioButton(p, wx.ID_ANY, 'Stretched exponential')
self.btn_2 = wx.RadioButton(p, wx.ID_ANY, 'Power-law')
self.btn_1.SetValue(True)
# Button to start fitting
fitbtn = wx.Button(p, wx.ID_ANY, 'Fit')
fitbtn.Bind(wx.EVT_BUTTON, self.clicked)
# Space to show fixed file path
self.pathfixed = wx.StaticText(p, wx.ID_ANY,'')
# Space to show process situation
self.situ = wx.StaticText(p, wx.ID_ANY,'\n')
# Layout
layout = wx.BoxSizer(wx.VERTICAL)
layout.Add(label, flag=wx.EXPAND | wx.ALL, border=10, proportion=1)
layout.Add(self.text_entry, flag=wx.EXPAND | wx.ALL, border=10)
layout.Add(self.btntxt, flag=wx.EXPAND | wx.ALL, border=10)
layout.Add(self.btn_1, flag=wx.EXPAND | wx.ALL, border=10)
layout.Add(self.btn_2, flag=wx.EXPAND | wx.ALL, border=10)
layout.Add(fitbtn, flag=wx.EXPAND |wx.ALL, border=10)
layout.Add(self.pathfixed, flag=wx.EXPAND | wx.ALL, border=10)
layout.Add(self.situ, flag=wx.EXPAND | wx.ALL, border=10)
p.SetSizer(layout)
self.Show()
def clicked(self, event):
# Extract the file path
filepath = self.text_entry.GetValue()
self.text_entry.Clear()
self.pathfixed.SetLabel(filepath)
self.situ.SetLabel('Calculating...\nThis might take minutes to be finished (Do not drop another file now.)')
if self.btn_1.GetValue():
ICFfit1(filepath)
else:
pass
if self.btn_2.GetValue():
ICFfit2(filepath)
else:
pass
self.situ.SetLabel('Omedetou! All calculations finished.\nYou can drop another file.')
class ICFfit1():
''' Fitting Class'''
def __init__(self,filepath):
# Inport excel
raw_data = pd.read_excel(filepath)
# names of each parameter corresponding to the paper
def func_ICF(CDT, sigma2, A, tauf, taus, beta):
CD = sigma2 * (A*np.e**(-CDT/tauf)+(1-A)*np.e**(-(CDT/taus)**beta))**2
return CD
# Initial value of fitting parameters
initial=[1,1,10**(-4),10**(-3),0.5] #sigma2, A,tauf,taus,beta
popt=initial
# Fitted parameters and variances are collected in:
popts=pd.DataFrame()
sd=pd.DataFrame()
# time as sec. (not micro sec.)
CDT = pd.DataFrame(raw_data.loc[1,'Correlation Delay Times[1] (µs)':'Correlation Delay Times[192] (µs)'])*10**(-6)
# Fit
for r in np.arange(0,len(raw_data)):
try:
# Make DataFrame containning CDT and CD
CD = pd.DataFrame(raw_data.loc[r, 'Correlation Data[1]':'Correlation Data[192]'])
CDT.index=CD.index
CDT_CD = pd.merge(CDT,CD,left_index=True,right_index=True).astype(float)
CDT_CD.columns=np.arange(2)
CDT_CD.index=np.arange(len(CDT_CD))
# Use the last fitted parameters as initial value for fitting
initial=popt #sigma2, A,tauf,taus,beta
# Fit
popt, pcov = curve_fit(func_ICF,CDT_CD[0],CDT_CD[1],
p0=initial,bounds=(0, [1,1, 10, 10,1]))
popts[r]=popt
sd[r]=np.sqrt(np.diag(pcov))
# Report if fitting successed
except RuntimeError: # Process fitting error
nans=np.zeros(5)
nans[:]=np.nan
popts[r]=nans
sd[r]=nans
print('Fitting Error at row ',r,end='\t')
popts.index=['sigma2', 'A', 'tauf', 'taus', 'beta']
sd.index=['sd_sigma2', 'sd_A', 'sd_tauf', 'sd_taus', 'sd_beta']
sd=sd.T
popts=popts.T
popts_sd = pd.merge(popts,sd,left_index=True,right_index=True)
# Export parameters as csv
exportname = filepath[:-5]+'_ICoFited_StretchedExponential.xlsx'
popts_sd.to_excel(exportname,index=False)
class ICFfit2():
''' Fitting Class
Power-law'''
def __init__(self,filepath):
# Inport excel
raw_data = pd.read_excel(filepath)
# names of each parameter corresponding to the paper
def func_ICF(CDT, sigma2, A, tauf, taux, n):
CD = sigma2 * (A*np.e**(-CDT/tauf)+(1-A)*(1+(CDT/taux))**((n-1)/2))**2
return CD
# Initial value of fitting parameters
initial=[1,1,10**(-4),10**(-3),0.5] #sigma2, A,tauf,taux,n
popt=initial
# Fitted parameters and variances are collected in:
popts=pd.DataFrame()
sd=pd.DataFrame()
# time as sec. (not micro sec.)
CDT = pd.DataFrame(raw_data.loc[1,'Correlation Delay Times[1] (µs)':'Correlation Delay Times[192] (µs)'])*10**(-6)
# Fit
for r in np.arange(0,len(raw_data)):
try:
# Make DataFrame containning CDT and CD
CD = pd.DataFrame(raw_data.loc[r, 'Correlation Data[1]':'Correlation Data[192]'])
CDT.index=CD.index
CDT_CD = pd.merge(CDT,CD,left_index=True,right_index=True).astype(float)
CDT_CD.columns=np.arange(2)
CDT_CD.index=np.arange(len(CDT_CD))
# Use the last fitted parameters as initial value for fitting
initial=popt #sigma2, A,tauf,taux,n
# Fit
popt, pcov = curve_fit(func_ICF,CDT_CD[0],CDT_CD[1],
p0=initial,bounds=(0, [1,1, 10, 10,1]))
popts[r]=popt
sd[r]=np.sqrt(np.diag(pcov))
# Report if fitting successed
except RuntimeError: # Process fitting error
nans=np.zeros(5)
nans[:]=np.nan
popts[r]=nans
sd[r]=nans
print('Fitting Error at row ',r,end='\t')
popts.index=['sigma2', 'A', 'tauf', 'taux', 'n']
sd.index=['sd_sigma2', 'sd_A', 'sd_tauf', 'sd_taux', 'sd_n']
sd=sd.T
popts=popts.T
popts_sd = pd.merge(popts,sd,left_index=True,right_index=True)
# Export parameters as csv
exportname = filepath[:-5]+'_ICoFited_PowerLaw.xlsx'
popts_sd.to_excel(exportname,index=False)
app = wx.App()
App(None, -1, 'ICoFit 1.1.0')
app.MainLoop()