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pppr_gui.py
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pppr_gui.py
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"""
Functions and classes used to build the pppr GUI
Authors/modifications:
----------------------
Frank Heberling ([email protected])
"""
###############################################################################
import wx
import wx.grid as gridlib
import os
import time
import random
import numpy as Num
from phreeqpy.iphreeqc.phreeqc_dll import IPhreeqc
from simplex import simplex, LM_fit
from pppr_functions import read_parameters, eval_datastring, write_par
from plotframe import createplotframe
###############################################################################
class wxPPPRFrame(wx.Frame):
def __init__(self, parent, title, size):
wx.Frame.__init__(self, parent, title= title, size=size)
# A status bar
self.dirname = ''
self.statusbar = self.CreateStatusBar(2)
# Setting up the ReadFilesmenu.
filemenu= wx.Menu()
menuReadModel = filemenu.Append(wx.ID_ANY,"&Read Modelfile"," Read in a Model file")
menuReadData = filemenu.Append(wx.ID_ANY,"&Read Datafile"," Read in a Data file")
menuReadDatabase = filemenu.Append(wx.ID_ANY,"&Load Database"," Load Database from a file")
menuReadParameter = filemenu.Append(wx.ID_ANY,"&Read Parameterfile"," Read in a Parameter file")
filemenu.AppendSeparator()
menuExit = filemenu.Append(wx.ID_EXIT,"E&xit"," Terminate the program")
#Setting up the WriteFiles
writemenu = wx.Menu()
menuWriteModel = writemenu.Append(wx.ID_ANY, "&Write .mod file", " Write PhreeqC Model with param. labels to a .mod file")
menuWriteModel2 = writemenu.Append(wx.ID_ANY, "&Write .phrq file", " Write PhreeqC Model with best fit params to a .phrq file")
menuWritePar = writemenu.Append(wx.ID_ANY, "&Write Parameter file", " Write parameters to a .par file")
menuWriteData = writemenu.Append(wx.ID_ANY, "&Write Data input file", " Write data to a .dat file")
menuWriteData2 = writemenu.Append(wx.ID_ANY, "&Write Results file", " Write data and fit results to a .dat file")
# Creating the menubar.
menuBar = wx.MenuBar()
menuBar.Append(filemenu,"&Read Files") # Adding the "filemenu" to the MenuBar
menuBar.Append(writemenu,"&Write Files")
self.SetMenuBar(menuBar) # Adding the MenuBar to the Frame content.
# define events
self.Bind(wx.EVT_MENU, self.OnReadData, menuReadData)
self.Bind(wx.EVT_MENU, self.OnReadDatabase, menuReadDatabase)
self.Bind(wx.EVT_MENU, self.OnReadModel, menuReadModel)
self.Bind(wx.EVT_MENU, self.OnReadParameter, menuReadParameter)
self.Bind(wx.EVT_MENU, self.OnExit, menuExit)
self.Bind(wx.EVT_MENU, self.OnWriteModel, menuWriteModel)
self.Bind(wx.EVT_MENU, self.OnWriteModel2, menuWriteModel2)
self.Bind(wx.EVT_MENU, self.OnWritePar, menuWritePar)
self.Bind(wx.EVT_MENU, self.OnWriteData, menuWriteData)
self.Bind(wx.EVT_MENU, self.OnWriteData2, menuWriteData2)
self.nb = PPPRNotebook(self)
sizer = wx.BoxSizer()
sizer.Add(self.nb, 1, wx.EXPAND)
self.SetSizer(sizer)
self.Show(True)
def OnReadData(self,e):
dlg = wx.FileDialog(self, "Choose a data file ", self.dirname, ".dat", "*.dat", wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
f = open(self.dirname+'/'+filename, 'r')
data = f.readlines()
f.close()
model = ''
for line in data:
model = model + line
self.nb.datastring = model
self.nb.DataPage.control.SetValue(self.nb.datastring)
self.nb.data, self.nb.datasets = eval_datastring(self.nb.datastring)
self.nb.MainControlPage.fill_grid()
self.nb.SetSelection(2)
dlg.Destroy()
def OnReadDatabase(self,e):
dlg = wx.FileDialog(self, "Choose a database ", self.dirname, ".dat", "*.dat", wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
dirname = dlg.GetDirectory()
self.nb.database = dirname+'/'+filename
self.nb.database_loaded = True
dlg.Destroy()
def OnReadModel(self,e):
""" Read in a Model file"""
dlg = wx.FileDialog(self, "Choose a Model file", self.dirname, ".mod", "*.mod", wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
f = open(self.dirname+'/'+filename, 'r')
data = f.readlines()
f.close()
model = ''
for line in data:
model = model + line
self.nb.batch_model = model
self.nb.ModelPage.control.SetValue(self.nb.batch_model)
self.nb.SetSelection(1)
dlg.Destroy()
def OnReadParameter(self,e):
""" Read in parameter file"""
dlg = wx.FileDialog(self, "Choose a parameter file", self.dirname, ".par", "*.par", wx.FD_OPEN)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
self.nb.parameter, self.nb.param_labels = read_parameters(self.dirname+'/'+filename)
self.nb.DeletePage(3)
self.nb.ParameterPage = ParameterPanel(self.nb)
self.nb.AddPage(self.nb.ParameterPage, " Parameters ")
for i in range(len(self.nb.param_labels)):
control0_tmp = wx.Button(self.nb.ParameterPage, 20000+i+7*len(self.nb.param_labels), label = self.nb.param_labels[i], pos=(20, 23*(i+1)+20), size=(120, 20))
self.nb.ParameterPage.control0.append(control0_tmp)
self.Bind(wx.EVT_BUTTON, self.nb.ParameterPage.clicklabel , self.nb.ParameterPage.control0[i])
control1_tmp = wx.TextCtrl(self.nb.ParameterPage,20000+i, pos=(150, 23*(i+1)+20), size=(120,20))
self.nb.ParameterPage.control1.append(control1_tmp)
self.nb.ParameterPage.control1[i].SetValue(str(round(self.nb.parameter[self.nb.param_labels[i]][0], 12)))
self.Bind(wx.EVT_TEXT, self.nb.ParameterPage.editparvalue, self.nb.ParameterPage.control1[i])
control2_tmp = (wx.TextCtrl(self.nb.ParameterPage,20000+i+len(self.nb.param_labels), pos=(370, 23*(i+1)+20), size=(80,20)))
self.nb.ParameterPage.control2.append(control2_tmp)
self.nb.ParameterPage.control2[i].SetValue(str(self.nb.parameter[self.nb.param_labels[i]][1]))
self.Bind(wx.EVT_TEXT, self.nb.ParameterPage.editparmin, self.nb.ParameterPage.control2[i])
control3_tmp = (wx.TextCtrl(self.nb.ParameterPage,20000+i+2*len(self.nb.param_labels), pos=(460, 23*(i+1)+20), size=(80,20)))
self.nb.ParameterPage.control3.append(control3_tmp)
self.nb.ParameterPage.control3[i].SetValue(str(self.nb.parameter[self.nb.param_labels[i]][2]))
self.Bind(wx.EVT_TEXT, self.nb.ParameterPage.editparmax, self.nb.ParameterPage.control3[i])
control4_tmp = (wx.CheckBox(self.nb.ParameterPage,20000+i+3*len(self.nb.param_labels), label = '', pos = (550, 23*(i+1)+25)))
self.nb.ParameterPage.control4.append(control4_tmp)
self.nb.ParameterPage.control4[i].SetValue(self.nb.parameter[self.nb.param_labels[i]][3])
self.Bind(wx.EVT_CHECKBOX, self.nb.ParameterPage.editparstate)
control5_tmp = wx.Button(self.nb.ParameterPage, 20000+i+4*len(self.nb.param_labels), label = '<', pos = (610, 23*(i+1)+20), size = (20,20))
self.nb.ParameterPage.control5.append(control5_tmp)
self.Bind(wx.EVT_BUTTON, self.nb.ParameterPage.toggleminus , self.nb.ParameterPage.control5[i])
control6_tmp = (wx.TextCtrl(self.nb.ParameterPage,20000+i+5*len(self.nb.param_labels), pos=(640, 23*(i+1)+20), size=(50,20)))
self.nb.ParameterPage.control6.append(control6_tmp)
self.nb.ParameterPage.control6[i].SetValue('0')
self.nb.ParameterPage.togglesteps.append(0)
self.Bind(wx.EVT_TEXT, self.nb.ParameterPage.togglestep, self.nb.ParameterPage.control6[i])
control7_tmp = wx.Button(self.nb.ParameterPage,20000+i+6*len(self.nb.param_labels), label = '>', pos = (700, 23*(i+1)+20), size = (20,20))
self.nb.ParameterPage.control7.append(control7_tmp)
self.Bind(wx.EVT_BUTTON, self.nb.ParameterPage.toggleplus , self.nb.ParameterPage.control7[i])
control8_tmp = wx.TextCtrl(self.nb.ParameterPage,20000+i+8*len(self.nb.param_labels), pos=(280, 23*(i+1)+20), size=(80,20))
self.nb.ParameterPage.control8.append(control8_tmp)
self.nb.ParameterPage.control8[i].SetValue(str(round(self.nb.parameter[self.nb.param_labels[i]][4], 8)))
self.Bind(wx.EVT_TEXT, self.nb.ParameterPage.editparstddev, self.nb.ParameterPage.control8[i])
self.nb.ParameterPage.SetScrollbars(0, 10, 0, int((len(self.nb.param_labels)+4)*2.3)+1)
self.nb.SetSelection(3)
dlg.Destroy()
def OnExit(self,e):
self.Close(True) # Close the frame.
def OnWriteModel(self,e): #write the model with parameter labels to a .mod file
dlg = wx.FileDialog(self, "Write model to .mod file", self.dirname, ".mod", "*.mod", wx.FD_SAVE)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
f = file(self.dirname+'/'+filename, 'w')
f.write(self.nb.batch_model)
f.close()
dlg.Destroy()
def OnWriteModel2(self,e): #write the model with best fit parameters inserted to a .phrq file - the output should be a vaild PhreeqC input file
dlg = wx.FileDialog(self, "Write model to .phrq file", self.dirname,".phrq","*.phrq", wx.FD_SAVE)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
self.nb.update_model()
f = file(self.dirname+'/'+filename, 'w')
f.write(self.nb.modelstring)
f.close()
dlg.Destroy()
def OnWritePar(self,e):
dirname = ''
dlg = wx.FileDialog(self, "Write parameters to .par file", dirname, ".par", "*.par", wx.FD_SAVE)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
dirname = dlg.GetDirectory()
os.chdir(dirname)
write_par(self.nb.parameter, self.nb.param_labels, filename)
dlg.Destroy()
def OnWriteData(self,e):
dlg = wx.FileDialog(self, "Write Data .dat file", self.dirname, ".dat", "*.dat", wx.FD_SAVE)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
f = file(self.dirname+'/'+filename, 'w')
f.write(self.nb.datastring)
f.close()
dlg.Destroy()
def OnWriteData2(self,e):
dlg = wx.FileDialog(self, "Write Data and Fit Results to .dat file", self.dirname, ".dat", "*.dat", wx.FD_SAVE)
if dlg.ShowModal() == wx.ID_OK:
filename = dlg.GetFilename()
self.dirname = dlg.GetDirectory()
f = file(self.dirname+'/'+filename, 'w')
f.write(' x_value y_value y_err y_model residual chi**2 lower_bound upper_bound \n')
dat = self.nb.data
for i in range(len(dat[0])):
line = " %12.6e %12.6e %12.6e %12.6e %12.6e %12.6e %12.6e %12.6e\n" %\
(dat[0][i],dat[1][i],dat[2][i],dat[3][i],dat[4][i],dat[5][i], (dat[3][i]-dat[6][i]), (dat[3][i]+dat[6][i]))
f.write(line)
f.close()
dlg.Destroy()
############################################################################################################
class PPPRNotebook(wx.Notebook):
def __init__(self, parent):
wx.Notebook.__init__(self,parent)
self.Phreeq = None
self.database_loaded = False
self.database = ''
self.Phreeq_age = 0
self.batch_model = ''
self.modelstring = ''
self.datastring = ''
self.data= []
self.datasets = []
self.chi2 = 0
self.sensitivities = Num.array([])
self.correl_matrix = Num.array([])
self.cov_matrix = Num.array([])
self.fpc = 0.0001
self.used_params = ['None']
self.mcn = 50
self.mcmode = 0
self.plotmode = 0
self.parameter = {}
self.param_labels = []
self.frame = self.GetParent()
self.FigureFrame = None
self.Figure1 = None
self.MainControlPage = MainControlPanel(self)
self.ParameterPage = ParameterPanel(self)
self.DataPage = DataPanel(self)
self.ModelPage = ModelPanel(self)
self.AddPage(self.MainControlPage, " Main Controls " )
self.AddPage(self.ModelPage, " Model ")
self.AddPage(self.DataPage, " Data ")
self.AddPage(self.ParameterPage, " Parameters ")
def phreeq_check(self):
if self.Phreeq == None or self.Phreeq_age >= 20:
self.Phreeq = None
self.Phreeq = IPhreeqc()
if self.database_loaded:
self.Phreeq.load_database(self.database)
self.Phreeq.set_selected_output_file_off()
self.Phreeq_age = 0
else:
self.Phreeq_age += 1
def plot(self):
if self.data != []:
plot_dims = self.MainControlPage.plotdims
if self.FigureFrame == None:
self.FigureFrame = createplotframe(self, "PPPR Data and Model plot", (1200,900))
self.FigureFrame.Show(True)
self.Figure1 = self.FigureFrame.figure
b=0
for bl in self.datasets:
if bl.plot > -1: b+=1
if b > (plot_dims[0] * plot_dims[1]):
print("\nPlot Dimensions are too small \nfor the number of Datasets to be displayed!")
pass
else:
self.Figure1.clear()
self.Figure1.suptitle('chi**2 = '+str(round(self.chi2,12)), fontsize=20)
subplots = []
other = []
j = 0
for i in range(len(self.datasets)):
if self.datasets[i].plot > -1:
subplots.append(self.Figure1.add_subplot(plot_dims[0],plot_dims[1],j+1))
tmp = self.datasets[i]
x = self.data[0][tmp.lower:tmp.upper]
subplots[j].plot(x,self.data[3][tmp.lower:tmp.upper],'k')
subplots[j].errorbar(x,self.data[1][tmp.lower:tmp.upper]\
,self.data[2][tmp.lower:tmp.upper], fmt = 'bo')
subplots[j].set_xlabel(tmp.x_label)
subplots[j].set_ylabel(tmp.y_label)
if self.plotmode == 1:
other.append(subplots[j].twinx())
other[-1].bar(x,self.data[4][tmp.lower:tmp.upper], width = 0.03, bottom = 0,\
color = 'c', edgecolor = 'c', alpha = 0.25)
elif self.plotmode == 2:
y1 = self.data[3][tmp.lower:tmp.upper]+self.data[6][tmp.lower:tmp.upper]
y2 = self.data[3][tmp.lower:tmp.upper]-self.data[6][tmp.lower:tmp.upper]
subplots[j].plot(x, y1, 'g')
subplots[j].plot(x, y2, 'g')
subplots[j].fill_between(x, y1, y2, facecolor = 'green', alpha = 0.25)
if tmp.plot == 0:
subplots[j].set_yscale('linear')
subplots[j].set_xscale('linear')
elif tmp.plot == 1:
subplots[j].set_yscale('log')
subplots[j].set_xscale('linear')
elif tmp.plot == 2:
subplots[j].set_yscale('linear')
subplots[j].set_xscale('log')
elif tmp.plot == 3:
subplots[j].set_yscale('log')
subplots[j].set_xscale('log')
j +=1
self.Figure1.canvas.draw()
def plot_sens(self,dp,sens,param):
if self.data != []:
plot_dims = self.MainControlPage.plotdims
if self.FigureFrame == None:
self.FigureFrame = createplotframe(self, "PPPR Data and Model plot", (1200,900))
self.FigureFrame.Show(True)
self.Figure1 = self.FigureFrame.figure
b=0
for bl in self.datasets:
if bl.plot > -1: b+=1
if b > (plot_dims[0] * plot_dims[1]):
print("\nPlot Dimensions are too small \nfor the number of Datasets to be displayed!")
pass
else:
self.Figure1.clear()
self.Figure1.suptitle('scaled sensitivities for parameter ' +param+ ', standard deviation = '+str(round(dp,12)), fontsize=16)
subplots = []
sensplots = []
j = 0
for i in range(len(self.datasets)):
if self.datasets[i].plot > -1:
subplots.append(self.Figure1.add_subplot(plot_dims[0],plot_dims[1],j+1))
sensplots.append(subplots[j].twinx())
tmp = self.datasets[i]
subplots[j].plot(self.data[0][tmp.lower:tmp.upper],self.data[3][tmp.lower:tmp.upper],'k')
subplots[j].errorbar(self.data[0][tmp.lower:tmp.upper],self.data[1][tmp.lower:tmp.upper]\
,self.data[2][tmp.lower:tmp.upper], fmt = 'bo')
subplots[j].set_xlabel(tmp.x_label)
subplots[j].set_ylabel(tmp.y_label)
sensplots[j].bar(self.data[0][tmp.lower:tmp.upper],sens[tmp.lower:tmp.upper], width = 0.03,\
bottom = 0, color = 'r', edgecolor = 'r', alpha = 0.25)
if tmp.plot == 0:
subplots[j].set_yscale('linear')
subplots[j].set_xscale('linear')
elif tmp.plot == 1:
subplots[j].set_yscale('log')
subplots[j].set_xscale('linear')
elif tmp.plot == 2:
subplots[j].set_yscale('linear')
subplots[j].set_xscale('log')
elif tmp.plot == 3:
subplots[j].set_yscale('log')
subplots[j].set_xscale('log')
j +=1
self.Figure1.canvas.draw()
def update_model(self):
keys = self.parameter.keys()
for key in keys:
if self.parameter[key][5] in keys:
self.parameter[key][0] = self.parameter[self.parameter[key][5]][0]
self.modelstring = self.batch_model[:]
for key in keys:
self.modelstring = self.modelstring.replace(key, str(self.parameter[key][0]))
def get_used_params(self):
self.used_params = []
for i in self.parameter.keys():
if self.parameter[i][3]:
self.used_params.append(i)
def model(self):
self.phreeq_check()
self.get_used_params()
self.update_model()
if self.modelstring != '':
if self.database_loaded:
self.Phreeq.run_string(self.modelstring)
output = self.Phreeq.get_selected_output_array()
output = output[(len(output)-len(self.data[0])):len(output)]
if len(output) != len(self.data[0]):
print('model output structure does not match data structure')
print(output)
print(len(output), len(self.data[0]))
pass
else:
output = Num.transpose(output)
for i in range(len(output[0])):
if round(self.data[0][i],1) != round(output[0][i],1):
print('Warning model conditions do not match data')
print(round(self.data[0][i],2), round(output[0][i],2))
self.data[3][i] = output[1][i]
self.data[4] = self.data[1]-self.data[3]
self.data[5] = (self.data[4]/self.data[2])**2
self.chi2 = 0
n = 0
for dat in self.datasets:
self.chi2 = self.chi2 + Num.sum(self.data[5][dat.lower:dat.upper])*dat.weight
n = n+ dat.n
self.chi2 = self.chi2/(n-len(self.used_params))
else:
print('need to load a database before you can start modelling')
pass
else:
pass
def model_modified(self,param, h):
self.phreeq_check()
self.parameter[param][0] += h
self.update_model()
if self.modelstring != '':
if self.database_loaded:
self.Phreeq.run_string(self.modelstring)
output = self.Phreeq.get_selected_output_array()
output = output[(len(output)-len(self.data[0])):len(output)]
output = Num.transpose(output)
self.parameter[param][0] += -h
self.update_model()
return output[1]
def statistics(self):
#setup w matrix
n = len(self.data[0])
w = Num.zeros((n,n))
for i in range(n):
w[i][i] = (1/self.data[2][i])**2
#setup Sensitivity matrix X
self.get_used_params()
b = len(self.used_params)
X = Num.zeros((b,n))
for i in range(b):
h = self.parameter[self.used_params[i]][0] * self.fpc
if h == 0:
h = self.fpc
if self.parameter[self.used_params[i]][0] -0.5*h >= self.parameter[self.used_params[i]][1] \
and self.parameter[self.used_params[i]][0] +0.5*h <= self.parameter[self.used_params[i]][2]:
y1 = self.model_modified(self.used_params[i],-0.5*h)
y2 = self.model_modified(self.used_params[i], 0.5*h)
y1 = (y2 - y1)/h
elif self.parameter[self.used_params[i]][0] -0.5*h < self.parameter[self.used_params[i]][1]:
y1 = self.model_modified(self.used_params[i],0)
y2 = self.model_modified(self.used_params[i],h)
y1 = (y2 - y1)/h
elif self.parameter[self.used_params[i]][0] +0.5*h > self.parameter[self.used_params[i]][2]:
y1 = self.model_modified(self.used_params[i],-h)
y2 = self.model_modified(self.used_params[i],0)
y1 = (y2 - y1)/h
for j in range(n):
X[i][j] = y1[j] * self.parameter[self.used_params[i]][0] * Num.sqrt(w[j][j])
self.model()
V = Num.dot(X, Num.dot(w, Num.transpose(X)))
C = Num.zeros((b,b))
try:
V = self.chi2 * Num.linalg.inv(V)
for i in range(b):
for j in range(b):
if i == j:
C[i][j] = Num.sqrt(V[i][j])
self.parameter[self.used_params[i]][4] = C[i][j]
else:
C[i][j] = V[i][j]/Num.sqrt(V[i][i]*V[j][j])
keys = self.parameter.keys()
for key in keys:
if self.parameter[key][5] in keys:
self.parameter[key][4] = self.parameter[parameter[key][5]][4]
self.sensitivities = X
self.correl_matrix = C
self.cov_matrix = V
except:
print("There's a hole in the matrix !!!\nOmit unused or insignificant parameters in the calculation.\n")
return
def single_param_sensitivity(self, param):
#setup w matrix
n = len(self.data[0])
w = Num.zeros((n,n))
for i in range(n):
w[i][i] = (1/self.data[2][i])**2
#setup Sensitivity matrix X
X = Num.zeros((n))
h = self.parameter[param][0] * self.fpc
if h == 0:
h = self.fpc
if self.parameter[param][0] -0.5*h >= self.parameter[param][1] \
and self.parameter[param][0] +0.5*h <= self.parameter[param][2]:
y1 = self.model_modified(param,-0.5*h)
y2 = self.model_modified(param, 0.5*h)
y1 = (y2 - y1)/h
elif self.parameter[param][0] -0.5*h < self.parameter[param][1]:
y1 = self.model_modified(param,0)
y2 = self.model_modified(param,h)
y1 = (y2 - y1)/h
elif self.parameter[param][0] +0.5*h > self.parameter[param][2]:
y1 = self.model_modified(param,-h)
y2 = self.model_modified(param,0)
y1 = (y2 - y1)/h
for j in range(n):
X[j] = y1[j] * self.parameter[param][0] * Num.sqrt(w[j][j])
self.model()
V = Num.dot(X, Num.dot(w, Num.transpose(X)))
if V > 0:
dp = Num.sqrt(self.chi2/V)
else:
dp = 0.
return X, dp
############################################################################################################
class MainControlPanel(wx.Panel):
def __init__(self,parent):
wx.Panel.__init__(self,parent)
self.fitalgo = 0
self.plotdims = [3,3]
self.simplex_params = [1.0,0.5,2.0,0.2,1e-6,10000, False]
self.nb = self.GetParent()
self.StopFit = False
self.auto_fit = False
self.auto_fit_n = 3
self.FigureFrame = None
self.Figure2 = None
#Panel Headings
wx.StaticText(self, label = 'Data Information: ', pos=(20, 12), size=(100, 20))
wx.StaticText(self, label = 'Fit options: ', pos=(520, 12), size=(100, 20))
wx.StaticLine(self, pos = (500,0), size = (5,650), style = wx.LI_VERTICAL)
wx.StaticLine(self, pos = (500,115), size = (285,5), style = wx.LI_HORIZONTAL)
wx.StaticLine(self, pos = (500,235), size = (285,5), style = wx.LI_HORIZONTAL)
wx.StaticLine(self, pos = (500,345), size = (285,5), style = wx.LI_HORIZONTAL)
wx.StaticLine(self, pos = (500,455), size = (285,5), style = wx.LI_HORIZONTAL)
wx.StaticLine(self, pos = (500,565), size = (285,5), style = wx.LI_HORIZONTAL)
#data set display in grid structure
self.datagrid = gridlib.Grid(self, id = wx.ID_ANY, pos = (20,40), size = (460,600))
self.datagrid.CreateGrid(25,5)
for i in range(25):
self.datagrid.SetRowSize(i,25)
for j in range(5):
self.datagrid.SetCellAlignment(i,j,wx.ALIGN_CENTRE,wx.ALIGN_CENTRE)
if i == 0:
self.datagrid.SetColSize(j,70)
if j == 0:
self.datagrid.SetColLabelValue(j,'x label')
elif j == 1:
self.datagrid.SetColLabelValue(j,'y label')
elif j == 2:
self.datagrid.SetColLabelValue(j,'n')
elif j == 3:
self.datagrid.SetColLabelValue(j,'weight')
elif j == 4:
self.datagrid.SetColLabelValue(j,'plot type')
if j < 3:
self.datagrid.SetReadOnly(i,j)
elif j == 3:
self.datagrid.SetCellEditor(i,j,gridlib.GridCellFloatEditor())
elif j == 4:
self.datagrid.SetCellEditor(i,j,gridlib.GridCellNumberEditor(-1,3))
self.Bind(gridlib.EVT_GRID_CELL_CHANGED, self.OnCellChange)
#Fitting option use random start parameters
self.getfitalgo = wx.ComboBox(self,-1, value="Levenberg-Marquardt",\
pos=(520, 35), size=(240,20),\
choices=["Levenberg-Marquardt","Downhill-Simplex"],style=wx.CB_READONLY)
self.Bind(wx.EVT_COMBOBOX, self.setfitalgo, self.getfitalgo)
self.random_pars = wx.CheckBox(self, label = ' start Fit with random parameter values', pos = (520, 65))
self.random_pars.SetValue(False)
self.Bind(wx.EVT_CHECKBOX, self.setrandom_pars)
#Fitting option auto_fit
self.auto_fit_box = wx.CheckBox(self, label = ' auto fit, number of params:', pos = (520, 90))
self.auto_fit_box.SetValue(False)
self.Bind(wx.EVT_CHECKBOX, self.setauto_fit)
self.auto_fit_n_box = wx.TextCtrl(self, pos=(700,90), size=(60,20))
self.auto_fit_n_box.SetValue(str(self.auto_fit_n))
self.Bind(wx.EVT_TEXT, self.setauto_fit_n, self.auto_fit_n_box)
#### Downhill Simplex parameters and options ####################################
wx.StaticText(self, label = 'Simplex Parameters: ', pos=(520, 130), size=(200, 20))
wx.StaticText(self, label = 'alpha: ', pos=(520, 157), size=(40, 20))
self.alpha = wx.TextCtrl(self, pos=(570,155), size=(60,20))
self.alpha.SetValue(str(self.simplex_params[0]))
self.Bind(wx.EVT_TEXT, self.setalpha, self.alpha)
wx.StaticText(self, label = 'beta: ', pos=(640, 157), size=(50, 20))
self.beta = wx.TextCtrl(self, pos=(700,155), size=(60,20))
self.beta.SetValue(str(self.simplex_params[1]))
self.Bind(wx.EVT_TEXT, self.setbeta, self.beta)
wx.StaticText(self, label = 'gamma: ', pos=(520, 182), size=(40, 20))
self.gamma = wx.TextCtrl(self, pos=(570,180), size=(60,20))
self.gamma.SetValue(str(self.simplex_params[2]))
self.Bind(wx.EVT_TEXT, self.setgamma, self.gamma)
wx.StaticText(self, label = 'delta: ', pos=(640, 182), size=(50, 20))
self.delta = wx.TextCtrl(self, pos=(700,180), size=(60,20))
self.delta.SetValue(str(self.simplex_params[3]))
self.Bind(wx.EVT_TEXT, self.setdelta, self.delta)
wx.StaticText(self, label = 'Ftol: ', pos=(520, 207), size=(40, 20))
self.ftol = wx.TextCtrl(self, pos=(570,205), size=(60,20))
self.ftol.SetValue(str(self.simplex_params[4]))
self.Bind(wx.EVT_TEXT, self.setftol, self.ftol)
wx.StaticText(self, label = 'maxiter: ', pos=(640, 207), size=(50, 20))
self.maxiter = wx.TextCtrl(self, pos=(700,205), size=(60,20))
self.maxiter.SetValue(str(self.simplex_params[5]))
self.Bind(wx.EVT_TEXT, self.setmaxiter, self.maxiter)
################### statistics input ###################################################
wx.StaticText(self, label = 'Parameter Statistics: ', pos=(520, 247), size=(200, 20))
wx.StaticText(self, label = 'fract. param. change: ', pos=(520, 272), size=(110, 20))
self.fpc_control = wx.TextCtrl(self, pos=(700,270), size=(60,20))
self.fpc_control.SetValue(str(self.nb.fpc))
self.Bind(wx.EVT_TEXT, self.setfpc, self.fpc_control)
self.statisticsbutton = wx.Button(self, label = 'calculate parameter\nstatistics', pos =(640,295), size=(120,40))
self.Bind(wx.EVT_BUTTON, self.OnClickStatistics, self.statisticsbutton)
self.getparam = wx.ComboBox(self,-1, value=self.nb.used_params[-1],\
pos=(520, 295), size=(110,20),\
choices=self.nb.used_params,style=wx.CB_READONLY)
self.Bind(wx.EVT_COMBOBOX, self.setparam, self.getparam)
#Data plotting
wx.StaticText(self, label = 'Plotting Options (Fig. 1): ', pos=(520, 357), size=(200, 20))
wx.StaticText(self, label = 'Plot dimensions', pos=(520, 382), size=(110, 20))
self.getplotdims = wx.TextCtrl(self, pos=(700, 380), size=(60,20))
self.getplotdims.SetValue(str(self.plotdims[0])+' '+str(self.plotdims[1]))
self.Bind(wx.EVT_TEXT, self.setplotdims, self.getplotdims)
self.getplotmode = wx.ComboBox(self,-1, value="Data+Model",\
pos=(520, 405), size=(110,20),\
choices=["Data+Model","Residuals","Monte-Carlo-range"],style=wx.CB_READONLY)
self.Bind(wx.EVT_COMBOBOX, self.setplotmode, self.getplotmode)
self.button = wx.Button(self, label = 'Plot', pos =(640,405), size = (120,40))
self.Bind(wx.EVT_BUTTON, self.OnClick, self.button)
#Monte Carlo Perturbation
wx.StaticText(self, label = 'Monte Carlo Parameter Perturbation: ', pos=(520, 467), size=(200, 20))
wx.StaticText(self, label = 'number of samples', pos=(520, 492), size=(110, 20))
self.getmcn = wx.TextCtrl(self, pos=(700, 490), size=(60,20))
self.getmcn.SetValue(str(self.nb.mcn))
self.Bind(wx.EVT_TEXT, self.setmcn, self.getmcn)
self.getmcmode = wx.ComboBox(self,-1, value="Normal",\
pos=(520, 515), size=(110,20),\
choices=["Normal","Multivar.-Normal"],style=wx.CB_READONLY)
self.Bind(wx.EVT_COMBOBOX, self.setmcmode, self.getmcmode)
self.mcbutton = wx.Button(self, label = 'Run Monte Carlo', pos =(640,515), size = (120,40))
self.Bind(wx.EVT_BUTTON, self.OnClickMC, self.mcbutton)
# Start and Stop Fit ###################################################################
self.Startfitbutton = wx.Button(self, label = 'Start Fit', pos =(520,580), size=(170,55))
self.Bind(wx.EVT_BUTTON, self.OnClickStartFit, self.Startfitbutton)
self.Stopfitbutton = wx.Button(self, label = 'Stop Fit', pos =(700,580), size=(60,55))
self.Bind(wx.EVT_BUTTON, self.OnClickStopFit, self.Stopfitbutton)
#################### data grid functions ####################################################
def fill_grid(self):
for i in range(25):
for j in range(5):
self.datagrid.SetCellValue(i,j,'')
for i in range(len(self.nb.datasets)):
dat = self.nb.datasets[i]
self.datagrid.SetCellValue(i,0,dat.x_label)
self.datagrid.SetCellValue(i,1,dat.y_label)
self.datagrid.SetCellValue(i,2,str(dat.n))
self.datagrid.SetCellValue(i,3,str(dat.weight))
if i < 9:
self.datagrid.SetCellValue(i,4,str(0))
else:
self.datagrid.SetCellValue(i,4,str(-1))
def OnCellChange(self, event):
x = event.GetRow()
y = event.GetCol()
value = self.nb.MainControlPage.datagrid.GetCellValue(x,y)
if y == 3:
self.nb.datasets[x].weight = float(value)
elif y == 4:
self.nb.datasets[x].plot = int(value)
self.nb.plot()
################################# Plotting options event functions #########################################
def setplotdims(self, event):
if event.GetString() == '':
None
else:
dims = str.rsplit(str(event.GetString()))
if len(dims) != 2:
print('need two integer numbers')
else:
self.plotdims = []
for i in dims:
self.plotdims.append(int(i))
def setplotmode(self, event):
self.nb.plotmode = event.GetSelection()
self.nb.plot()
def OnClick(self,e):
self.nb.model()
self.nb.plot()
################################# Fitting options event functions #########################################
def setfitalgo(self,event):
self.fitalgo = event.GetSelection()
def setrandom_pars(self,e):
cb = e.GetEventObject()
self.simplex_params[6] = cb.GetValue()
def setauto_fit(self, e):
cb = e.GetEventObject()
self.auto_fit = cb.GetValue()
def setauto_fit_n(self, event):
try:
a = int(event.GetString())
self.auto_fit_n = a
except ValueError:
pass
################################# Simplex options event functions #########################################
def setalpha(self,event):
try:
a = float(event.GetString())
if a <= 0 or a > 1:
print('alpha must be > 0 and <= 1')
else:
self.simplex_params[0] = a
except ValueError:
pass
def setbeta(self,event):
try:
a = float(event.GetString())
if a <= 0 or a > 1:
print('beta must be > 0 and <= 1')
else:
self.simplex_params[1] = a
except ValueError:
pass
def setgamma(self,event):
try:
a = float(event.GetString())
if a <= 0:
print('gamma must be > 0')
else:
self.simplex_params[2] = a
except ValueError:
pass
def setdelta(self,event):
try:
a = float(event.GetString())
if a <= 0 or a > 1:
print('delta must be > 0 and <= 1')
else:
self.simplex_params[3] = a
except ValueError:
pass
def setftol(self,event):
try:
a = float(event.GetString())
if a <= 0:
print('ftol must be > 0')
else:
self.simplex_params[4] = a
except ValueError:
pass
def setmaxiter(self,event):
try:
a = int(event.GetString())
if a <= 0:
print('maxiter must be > 0')
else:
self.simplex_params[5] = a
except ValueError:
pass
##############################################################################
def setparam(self, event):
param = event.GetSelection()
sens = self.nb.sensitivities[param]
parameter = self.nb.used_params[param]
dp = self.nb.parameter[parameter][4]
self.nb.plot_sens(dp, sens, parameter)
pass
def setfpc(self, event):
try:
a = float(event.GetString())
if a <= 0 or a > 0.1:
print('fpc must be > 0 and <= 0.1')
else:
self.nb.fpc = a
except ValueError:
pass
################################################################################################################################
def OnClickStatistics(self,e):
self.nb.get_used_params()
if len(self.nb.used_params) == 0:
print("NO PARAMETERS SELECTED!")
pass
else:
self.nb.frame.SetStatusText(' computing parameter statistics ', 0)
self.nb.statistics()
for i in range(len(self.nb.param_labels)):
self.nb.ParameterPage.control8[i].SetValue(str(round(self.nb.parameter[self.nb.param_labels[i]][4], 12)))
self.getparam.Clear()
self.getparam.AppendItems(self.nb.used_params)
self.getparam.SetSelection(0)
if len(self.nb.correl_matrix) > 0:
print('\n\n COVARIANCE MATRIX for: \n')
print(self.nb.used_params)
print('\n')
print(self.nb.cov_matrix)
print('\n\n CORRELATION MATRIX for: \n (numbers on the diagonal are std-deviations of the parameters)\n')
print(self.nb.used_params)
print('\n')
print(self.nb.correl_matrix)
b = len(self.nb.correl_matrix)
print('\n\nParameter Correlations: \n')
print('\nParameter Correlations between 0.95 and 1.00: \n')
n = 0
for i in range(b):
for j in range(b):
if j > i:
if (self.nb.correl_matrix[i][j] >= 0.95 and self.nb.correl_matrix[i][j] <= 1.0) or (self.nb.correl_matrix[i][j] <= -0.95 and self.nb.correl_matrix[i][j] >= -1.0):
n = n+1
print(self.nb.used_params[i] + ' & ' + self.nb.used_params[j] + ': ' + str(round(self.nb.correl_matrix[i][j],5)))
if n == 0: print('None \n')
else: print('\n')
n = 0
print('\nParameter Correlations between 0.8 and 0.95: \n')
for i in range(b):
for j in range(b):
if j > i:
if (self.nb.correl_matrix[i][j] >= 0.8 and self.nb.correl_matrix[i][j] < 0.95) or (self.nb.correl_matrix[i][j] <= -0.8 and self.nb.correl_matrix[i][j] > -0.95):
n = n+1
print(self.nb.used_params[i] + ' & ' + self.nb.used_params[j] + ': ' + str(round(self.nb.correl_matrix[i][j],5)))
if n == 0: print('None \n')
else: print('\n')
n = 0
print('\nParameter Correlations between 0.5 and 0.8: \n')
for i in range(b):
for j in range(b):
if j > i:
if (self.nb.correl_matrix[i][j] >= 0.5 and self.nb.correl_matrix[i][j] < 0.8) or (self.nb.correl_matrix[i][j] <= -0.5 and self.nb.correl_matrix[i][j] > -0.8):
n = n+1
print(self.nb.used_params[i] + ' & ' + self.nb.used_params[j] + ': ' + str(round(self.nb.correl_matrix[i][j],5)))
if n == 0: print('None \n')
else: print('\n')
print('statistics calculation finished, chi**2 = '+str(round(self.nb.chi2,12))+'\n')
print 'number of used variables = '+str(b)
self.nb.frame.SetStatusText(' statistics calculation finished, chi**2 = '+str(round(self.nb.chi2,12)), 0)
self.nb.plot()
pass
#########################################################################################################################
def setmcn(self, event):
try:
self.nb.mcn = int(event.GetString())
except ValueError:
pass
def setmcmode(self, event):
self.nb.mcmode = event.GetSelection()
def OnClickMC(self, event):
flag = True
self.nb.get_used_params()
for param in self.nb.used_params:
if param not in self.nb.batch_model:
flag = False
print('Parameter "'+param+'" shell be perturbed by the Monte Carlo method,\n but is not used in the model!!')
if self.nb.parameter[param][4] == 0:
flag = False
print('Parameter "'+param+'" shell be perturbed by the Monte Carlo method,\n but has a Std-dev of zero!!')
if flag:
results = []
means = []
for param in self.nb.used_params:
means.append(self.nb.parameter[param][0])
if self.nb.mcmode == 0:
self.nb.frame.SetStatusText('Performing Monte Carlo Parameter Perturbation',0)
for i in range(self.nb.mcn):
wx.Yield()
self.nb.frame.SetStatusText(str(i),1)
for j in range(len(self.nb.used_params)):
self.nb.parameter[self.nb.used_params[j]][0] = Num.random.normal(means[j],self.nb.parameter[self.nb.used_params[j]][4])
model = self.nb.model_modified(self.nb.used_params[0], 0)
results.append(model)
results = Num.transpose(results)
for i in range(len(self.nb.data[0])):
self.nb.data[6][i] = Num.std(results[i])
for i in range(len(self.nb.used_params)):
self.nb.parameter[self.nb.used_params[i]][0] = means[i]
elif self.nb.mcmode == 1:
self.nb.frame.SetStatusText('Updating Parameter Statistics',0)
self.nb.statistics()
self.nb.frame.SetStatusText('Performing Monte Carlo Parameter Perturbation',0)
for i in range(self.nb.mcn):
wx.Yield()
self.nb.frame.SetStatusText(str(i),1)
values = Num.random.multivariate_normal(means,self.nb.cov_matrix)
for j in range(len(self.nb.used_params)):
self.nb.parameter[self.nb.used_params[j]][0] = values[j]
model = self.nb.model_modified(self.nb.used_params[0], 0)
results.append(model)
results = Num.transpose(results)
for i in range(len(self.nb.data[0])):
self.nb.data[6][i] = Num.std(results[i])
for i in range(len(self.nb.used_params)):
self.nb.parameter[self.nb.used_params[i]][0] = means[i]
self.nb.model()
self.nb.frame.SetStatusText('Monte Carlo finished',0)
self.nb.frame.SetStatusText('',1)
self.nb.plotmode = 2
self.getplotmode.SetStringSelection("Monte-Carlo-range")
self.nb.plot()
#########################################################################################################################
def OnClickStartFit(self,e):
flag = True
self.nb.get_used_params()
for param in self.nb.used_params:
if param not in self.nb.batch_model:
flag = False
print('Parameter "'+param+'" shell be adjusted in the fit,\n but is not used in the model!!')
if flag == True and self.auto_fit == False:
self.chi2 = -1
self.StopFit = False