import wx
from plottables import Theory1D
import math
import numpy as np
import fittings
import transform
import sys
# Linear fit panel size
if sys.platform.count("win32") > 0:
FONT_VARIANT = 0
PNL_WIDTH = 450
PNL_HEIGHT = 500
else:
FONT_VARIANT = 1
PNL_WIDTH = 500
PNL_HEIGHT = 500
RG_ON = True
[docs]class LinearFit(wx.Dialog):
def __init__(self, parent, plottable, push_data, transform, title):
"""
Dialog window pops- up when select Linear fit on Context menu
Displays fitting parameters. This class handles the linearized
fitting and derives and displays specialized output parameters based
on the scale choice of the plot calling it.
:note1: The fitting is currently a bit convoluted as besides using
plottools.transform.py to handle all the conversions, it uses
LineModel to define a linear model and calculate a number of
things like residuals etc as well as the function itself given an x
value. It also uses fittings.py to set up the defined LineModel for
fitting and then send it to the SciPy NLLSQ method. As these are by
definition "linear nodels" it would make more sense to just call
a linear solver such as scipy.stats.linregress or bumps.wsolve directly.
This would considerably simplify the code and remove the need I think
for LineModel.py and possibly fittins.py altogether. -PDB 7/10/16
:note2: The linearized fits do not take resolution into account. This
means that for poor resolution such as slit smearing the answers will
be completely wrong --- Rg would be OK but I0 would be orders of
magnitude off. Eventually we should fix this to account properly for
resolution. -PDB 7/10/16
"""
wx.Dialog.__init__(self, parent, title=title,
size=(PNL_WIDTH, 350))
self.parent = parent
self.transform = transform
# Font
self.SetWindowVariant(variant=FONT_VARIANT)
# Registered owner for close event
self._registered_close = None
# dialog panel self call function to plot the fitting function
# calls the calling PlotPanel method onFitDisplay
self.push_data = push_data
# dialog self plottable - basically the plot we are working with
# passed in by the caller
self.plottable = plottable
# is this a Guinier fit
self.rg_on = False
# Receive transformations of x and y - basically transform is passed
# as caller method that returns its current value for these
self.xLabel, self.yLabel, self.Avalue, self.Bvalue, \
self.ErrAvalue, self.ErrBvalue, self.Chivalue = self.transform()
# Now set up the dialog interface
self.layout()
# Receives the type of model for the fitting
from LineModel import LineModel
self.model = LineModel()
# Display the fittings values
self.default_A = self.model.getParam('A')
self.default_B = self.model.getParam('B')
self.cstA = fittings.Parameter(self.model, 'A', self.default_A)
self.cstB = fittings.Parameter(self.model, 'B', self.default_B)
# Set default value of parameter in the dialog panel
if self.Avalue is None:
self.tcA.SetValue(format_number(self.default_A))
else:
self.tcA.SetLabel(format_number(self.Avalue))
if self.Bvalue is None:
self.tcB.SetValue(format_number(self.default_B))
else:
self.tcB.SetLabel(format_number(self.Bvalue))
if self.ErrAvalue is None:
self.tcErrA.SetLabel(format_number(0.0))
else:
self.tcErrA.SetLabel(format_number(self.ErrAvalue))
if self.ErrBvalue is None:
self.tcErrB.SetLabel(format_number(0.0))
else:
self.tcErrB.SetLabel(format_number(self.ErrBvalue))
if self.Chivalue is None:
self.tcChi.SetLabel(format_number(0.0))
else:
self.tcChi.SetLabel(format_number(self.Chivalue))
if self.plottable.x != []:
# store the values of View in self.x,self.y,self.dx,self.dy
self.x, self.y, self.dx, \
self.dy = self.plottable.returnValuesOfView()
try:
self.mini = self.floatForwardTransform(min(self.x))
except:
self.mini = "Invalid"
try:
self.maxi = self.floatForwardTransform(max(self.x))
except:
self.maxi = "Invalid"
self.initXmin.SetValue(format_number(min(self.plottable.x)))
self.initXmax.SetValue(format_number(max(self.plottable.x)))
self.mini = min(self.x)
self.maxi = max(self.x)
self.xminFit.SetValue(format_number(self.mini))
self.xmaxFit.SetValue(format_number(self.maxi))
[docs] def layout(self):
"""
Sets up the panel layout for the linear fit including all the
labels, text entry boxes, and buttons.
"""
# set up sizers first.
# vbox is the panel sizer and is a vertical sizer
# The first element of the panel is sizer which is a gridbagsizer
# and contains most of the text fields
# this is followed by a line separator added to vbox
# and finally the sizer_button (a horizontal sizer) adds the buttons
vbox = wx.BoxSizer(wx.VERTICAL)
sizer = wx.GridBagSizer(5, 5)
sizer_button = wx.BoxSizer(wx.HORIZONTAL)
#size of string boxes in pixels
_BOX_WIDTH = 100
_BOX_HEIGHT = 20
#now set up all the text fields
self.tcA = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
self.tcA.SetToolTipString("Fit value for the slope parameter.")
self.tcErrA = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
self.tcErrA.SetToolTipString("Error on the slope parameter.")
self.tcB = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
self.tcA.SetToolTipString("Fit value for the constant parameter.")
self.tcErrB = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
self.tcErrB.SetToolTipString("Error on the constant parameter.")
self.tcChi = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
self.tcChi.SetToolTipString("Chi^2 over degrees of freedom.")
self.xminFit = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
msg = "Enter the minimum value on "
msg += "the x-axis to be included in the fit."
self.xminFit.SetToolTipString(msg)
self.xmaxFit = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
msg = "Enter the maximum value on "
msg += " the x-axis to be included in the fit."
self.xmaxFit.SetToolTipString(msg)
self.initXmin = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
msg = "Minimum value on the x-axis for the plotted data."
self.initXmin.SetToolTipString(msg)
self.initXmax = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, _BOX_HEIGHT))
msg = "Maximum value on the x-axis for the plotted data."
self.initXmax.SetToolTipString(msg)
# Make the info box not editable
# _BACKGROUND_COLOR = '#ffdf85'
_BACKGROUND_COLOR = self.GetBackgroundColour()
self.initXmin.SetEditable(False)
self.initXmin.SetBackgroundColour(_BACKGROUND_COLOR)
self.initXmax.SetEditable(False)
self.initXmax.SetBackgroundColour(_BACKGROUND_COLOR)
#set some flags for specific types of fits like Guinier (Rg) and
#Porod (bg) -- this will determine WHAT boxes show up in the
#sizer layout and depends on the active axis transform
self.bg_on = False
if RG_ON:
if (self.yLabel == "ln(y)" or self.yLabel == "ln(y*x)") and \
(self.xLabel == "x^(2)"):
self.rg_on = True
if (self.xLabel == "x^(4)") and (self.yLabel == "y*x^(4)"):
self.bg_on = True
# Finally set up static text strings
warning = "WARNING! Resolution is NOT accounted for. \n"
warning += "Thus slit smeared data will give very wrong answers!"
self.textwarn = wx.StaticText(self, -1, warning)
self.textwarn.SetForegroundColour(wx.RED)
explanation = "Perform fit for y(x) = ax + b \n"
if self.bg_on:
param_a = 'Background (= Parameter a)'
else:
param_a = 'Parameter a'
#Now set this all up in the GridBagSizer sizer
ix = 0
iy = 0
sizer.Add(self.textwarn, (iy, ix),
(2, 3), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
iy += 2
sizer.Add(wx.StaticText(self, -1, explanation), (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
iy += 1
sizer.Add(wx.StaticText(self, -1, param_a), (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.tcA, (iy, ix), (1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(wx.StaticText(self, -1, '+/-'),
(iy, ix), (1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.tcErrA, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(wx.StaticText(self, -1, 'Parameter b'), (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.tcB, (iy, ix), (1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(wx.StaticText(self, -1, '+/-'), (iy, ix),
(1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.tcErrB, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(wx.StaticText(self, -1, 'Chi2/dof'), (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.tcChi, (iy, ix), (1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 2
ix = 1
sizer.Add(wx.StaticText(self, -1, 'Min'), (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 2
sizer.Add(wx.StaticText(self, -1, 'Max'), (iy, ix),
(1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(wx.StaticText(self, -1, 'Maximum range (linear scale)'),
(iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.initXmin, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 2
sizer.Add(self.initXmax, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(wx.StaticText(self, -1, 'Fit range of ' + self.xLabel),
(iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.xminFit, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 2
sizer.Add(self.xmaxFit, (iy, ix), (1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
if self.rg_on:
self.SetSize((PNL_WIDTH, PNL_HEIGHT))
I0_stxt = wx.StaticText(self, -1, 'I(q=0)')
self.I0_tctr = wx.TextCtrl(self, -1, '')
self.I0_tctr.SetEditable(False)
self.I0_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
self.I0err_tctr = wx.TextCtrl(self, -1, '')
self.I0err_tctr.SetEditable(False)
self.I0err_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
Rg_stxt = wx.StaticText(self, -1, 'Rg [A]')
Rg_stxt.Show(self.yLabel == "ln(y)")
self.Rg_tctr = wx.TextCtrl(self, -1, '')
self.Rg_tctr.SetEditable(False)
self.Rg_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
self.Rg_tctr.Show(self.yLabel == "ln(y)")
self.Rgerr_tctr = wx.TextCtrl(self, -1, '')
self.Rgerr_tctr.SetEditable(False)
self.Rgerr_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
self.Rgerr_tctr.Show(self.yLabel == "ln(y)")
self.Rgerr_pm = wx.StaticText(self, -1, '+/-')
self.Rgerr_pm.Show(self.yLabel == "ln(y)")
Diameter_stxt = wx.StaticText(self, -1, 'Rod Diameter [A]')
Diameter_stxt.Show(self.yLabel == "ln(y*x)")
self.Diameter_tctr = wx.TextCtrl(self, -1, '')
self.Diameter_tctr.SetEditable(False)
self.Diameter_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
self.Diameter_tctr.Show(self.yLabel == "ln(y*x)")
self.Diameter_pm = wx.StaticText(self, -1, '+/-')
self.Diameter_pm.Show(self.yLabel == "ln(y*x)")
self.Diametererr_tctr = wx.TextCtrl(self, -1, '')
self.Diametererr_tctr.SetEditable(False)
self.Diametererr_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
self.Diametererr_tctr.Show(self.yLabel == "ln(y*x)")
RgQmin_stxt = wx.StaticText(self, -1, 'Rg*Qmin')
self.RgQmin_tctr = wx.TextCtrl(self, -1, '')
self.RgQmin_tctr.SetEditable(False)
self.RgQmin_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
RgQmax_stxt = wx.StaticText(self, -1, 'Rg*Qmax')
self.RgQmax_tctr = wx.TextCtrl(self, -1, '')
self.RgQmax_tctr.SetEditable(False)
self.RgQmax_tctr.SetBackgroundColour(_BACKGROUND_COLOR)
iy += 2
ix = 0
sizer.Add(I0_stxt, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.I0_tctr, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(wx.StaticText(self, -1, '+/-'), (iy, ix),
(1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.I0err_tctr, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(Rg_stxt, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.Rg_tctr, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.Rgerr_pm, (iy, ix),
(1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.Rgerr_tctr, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(Diameter_stxt, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.Diameter_tctr, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.Diameter_pm, (iy, ix),
(1, 1), wx.EXPAND | wx.ADJUST_MINSIZE, 0)
ix += 1
sizer.Add(self.Diametererr_tctr, (iy, ix), (1, 1),
wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(RgQmin_stxt, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.RgQmin_tctr, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
iy += 1
ix = 0
sizer.Add(RgQmax_stxt, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
ix += 1
sizer.Add(self.RgQmax_tctr, (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
#Now add some space before the separation line
iy += 1
ix = 0
sizer.Add((20,20), (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 0)
# Buttons on the bottom
self.btFit = wx.Button(self, -1, 'Fit')
self.btFit.Bind(wx.EVT_BUTTON, self._onFit)
self.btFit.SetToolTipString("Perform fit.")
self.btClose = wx.Button(self, wx.ID_CANCEL, 'Close')
self.btClose.Bind(wx.EVT_BUTTON, self._on_close)
sizer_button.Add((20, 20), 1, wx.EXPAND | wx.ADJUST_MINSIZE, 0)
sizer_button.Add(self.btFit, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
sizer_button.Add(self.btClose, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
vbox.Add(sizer)
self.static_line_1 = wx.StaticLine(self, -1)
vbox.Add(self.static_line_1, 0, wx.EXPAND, 0)
vbox.Add(sizer_button, 0, wx.EXPAND | wx.BOTTOM | wx.TOP, 10)
# panel.SetSizer(sizer)
self.SetSizer(vbox)
self.Centre()
[docs] def register_close(self, owner):
"""
Method to register the close event to a parent
window that needs notification when the dialog
is closed
:param owner: parent window
"""
self._registered_close = owner
def _on_close(self, event):
"""
Close event.
Notify registered owner if available.
"""
event.Skip()
if self._registered_close is not None:
self._registered_close()
def _onFit(self, event):
"""
Performs the fit. Receive an event when clicking on
the button Fit.Computes chisqr ,
A and B parameters of the best linear fit y=Ax +B
Push a plottable to the caller
"""
tempx = []
tempy = []
tempdy = []
# Check if View contains a x array .we online fit when x exits
# makes transformation for y as a line to fit
if self.x != []:
if self.checkFitValues(self.xminFit):
# Check if the field of Fit Dialog contain values
# and use the x max and min of the user
if not self._checkVal(self.xminFit, self.xmaxFit):
return
xminView = float(self.xminFit.GetValue())
xmaxView = float(self.xmaxFit.GetValue())
xmin = xminView
xmax = xmaxView
# Set the qmin and qmax in the panel that matches the
# transformed min and max
self.initXmin.SetValue(format_number(self.floatInvTransform(xmin)))
self.initXmax.SetValue(format_number(self.floatInvTransform(xmax)))
# Store the transformed values of view x, y,dy
# in variables before the fit
if self.yLabel.lower() == "log10(y)":
if self.xLabel.lower() == "log10(x)":
for i in range(len(self.x)):
if self.x[i] >= math.log10(xmin):
tempy.append(math.log10(self.y[i]))
tempdy.append(transform.errToLogX(self.y[i], 0, self.dy[i], 0))
else:
for i in range(len(self.y)):
tempy.append(math.log10(self.y[i]))
tempdy.append(transform.errToLogX(self.y[i], 0, self.dy[i], 0))
else:
tempy = self.y
tempdy = self.dy
if self.xLabel.lower() == "log10(x)":
for x_i in self.x:
if x_i >= math.log10(xmin):
tempx.append(math.log10(x_i))
else:
tempx = self.x
# Find the fitting parameters
# Always use the same defaults, so that fit history
# doesn't play a role!
self.cstA = fittings.Parameter(self.model, 'A', self.default_A)
self.cstB = fittings.Parameter(self.model, 'B', self.default_B)
if self.xLabel.lower() == "log10(x)":
tempdy = np.asarray(tempdy)
tempdy[tempdy == 0] = 1
chisqr, out, cov = fittings.sasfit(self.model,
[self.cstA, self.cstB],
tempx, tempy,
tempdy,
math.log10(xmin),
math.log10(xmax))
else:
tempdy = np.asarray(tempdy)
tempdy[tempdy == 0] = 1
chisqr, out, cov = fittings.sasfit(self.model,
[self.cstA, self.cstB],
tempx, tempy, tempdy,
xminView, xmaxView)
# Use chi2/dof
if len(tempx) > 0:
chisqr = chisqr / len(tempx)
# Check that cov and out are iterable before displaying them
if cov is None:
errA = 0.0
errB = 0.0
else:
errA = math.sqrt(cov[0][0])
errB = math.sqrt(cov[1][1])
if out is None:
cstA = 0.0
cstB = 0.0
else:
cstA = out[0]
cstB = out[1]
# Reset model with the right values of A and B
self.model.setParam('A', float(cstA))
self.model.setParam('B', float(cstB))
tempx = []
tempy = []
y_model = 0.0
# load tempy with the minimum transformation
if self.xLabel == "log10(x)":
y_model = self.model.run(math.log10(xmin))
tempx.append(xmin)
else:
y_model = self.model.run(xminView)
tempx.append(xminView)
if self.yLabel == "log10(y)":
tempy.append(math.pow(10, y_model))
else:
tempy.append(y_model)
# load tempy with the maximum transformation
if self.xLabel == "log10(x)":
y_model = self.model.run(math.log10(xmax))
tempx.append(xmax)
else:
y_model = self.model.run(xmaxView)
tempx.append(xmaxView)
if self.yLabel == "log10(y)":
tempy.append(math.pow(10, y_model))
else:
tempy.append(y_model)
# Set the fit parameter display when FitDialog is opened again
self.Avalue = cstA
self.Bvalue = cstB
self.ErrAvalue = errA
self.ErrBvalue = errB
self.Chivalue = chisqr
self.push_data(tempx, tempy, xminView, xmaxView,
xmin, xmax, self._ongetValues())
# Display the fitting value on the Fit Dialog
self._onsetValues(cstA, cstB, errA, errB, chisqr)
def _onsetValues(self, cstA, cstB, errA, errB, Chi):
"""
Display the value on fit Dialog
"""
rg = None
_diam = None
self.tcA.SetValue(format_number(cstA))
self.tcB.SetValue(format_number(cstB))
self.tcErrA.SetValue(format_number(errA))
self.tcErrB.SetValue(format_number(errB))
self.tcChi.SetValue(format_number(Chi))
if self.rg_on:
if self.Rg_tctr.IsShown():
rg = np.sqrt(-3 * float(cstA))
value = format_number(rg)
self.Rg_tctr.SetValue(value)
if self.I0_tctr.IsShown():
val = np.exp(cstB)
self.I0_tctr.SetValue(format_number(val))
if self.Rgerr_tctr.IsShown():
if rg is not None and rg != 0:
value = format_number(3 * float(errA) / (2 * rg))
else:
value = ''
self.Rgerr_tctr.SetValue(value)
if self.I0err_tctr.IsShown():
val = np.abs(np.exp(cstB) * errB)
self.I0err_tctr.SetValue(format_number(val))
if self.Diameter_tctr.IsShown():
rg = np.sqrt(-2 * float(cstA))
_diam = 4 * np.sqrt(-float(cstA))
value = format_number(_diam)
self.Diameter_tctr.SetValue(value)
if self.Diametererr_tctr.IsShown():
if rg is not None and rg != 0:
value = format_number(8 * float(errA) / _diam)
else:
value = ''
self.Diametererr_tctr.SetValue(value)
if self.RgQmin_tctr.IsShown():
value = format_number(rg * self.floatInvTransform(self.mini))
self.RgQmin_tctr.SetValue(value)
if self.RgQmax_tctr.IsShown():
value = format_number(rg * self.floatInvTransform(self.maxi))
self.RgQmax_tctr.SetValue(value)
def _ongetValues(self):
"""
Display the value on fit Dialog
"""
return self.Avalue, self.Bvalue, self.ErrAvalue, \
self.ErrBvalue, self.Chivalue
def _checkVal(self, usermin, usermax):
"""
Ensure that fields parameter contains a min and a max value
within x min and x max range
"""
self.mini = float(self.xminFit.GetValue())
self.maxi = float(self.xmaxFit.GetValue())
flag = True
try:
mini = float(usermin.GetValue())
maxi = float(usermax.GetValue())
if mini < maxi:
usermin.SetBackgroundColour(wx.WHITE)
usermin.Refresh()
else:
flag = False
usermin.SetBackgroundColour("pink")
usermin.Refresh()
except:
# Check for possible values entered
flag = False
usermin.SetBackgroundColour("pink")
usermin.Refresh()
return flag
[docs] def checkFitValues(self, item):
"""
Check the validity of input values
"""
flag = True
value = item.GetValue()
# Check for possible values entered
if self.xLabel == "log10(x)":
if float(value) > 0:
item.SetBackgroundColour(wx.WHITE)
item.Refresh()
else:
flag = False
item.SetBackgroundColour("pink")
item.Refresh()
return flag
[docs] def setFitRange(self, xmin, xmax, xminTrans, xmaxTrans):
"""
Set fit parameters
"""
self.xminFit.SetValue(format_number(xmin))
self.xmaxFit.SetValue(format_number(xmax))
[docs] def set_fit_region(self, xmin, xmax):
"""
Set the fit region
:param xmin: minimum x-value to be included in fit
:param xmax: maximum x-value to be included in fit
"""
# Check values
try:
float(xmin)
float(xmax)
except:
msg = "LinearFit.set_fit_region: fit range must be floats"
raise ValueError, msg
self.xminFit.SetValue(format_number(xmin))
self.xmaxFit.SetValue(format_number(xmax))
[docs]class MyApp(wx.App):
"""
Test application
"""
[docs] def OnInit(self):
"""
Test application initialization
"""
wx.InitAllImageHandlers()
plot = Theory1D([], [])
dialog = LinearFit(parent=None, plottable=plot,
push_data=self.onFitDisplay,
transform=self.returnTrans,
title='Linear Fit')
if dialog.ShowModal() == wx.ID_OK:
pass
dialog.Destroy()
return 1
[docs] def onFitDisplay(self, tempx, tempy, xminView, xmaxView, xmin, xmax, func):
"""
Test application dummy method
"""
pass
[docs] def returnTrans(self):
"""
Test application dummy method
"""
return '', '', 0, 0, 0, 0, 0