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))
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) == True:
# 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 __init__(self, parent, plottable, push_data, transform, title):
"""
Dialog window pops- up when select Linear fit on Context menu
Displays fitting parameters
"""
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
self.push_data = push_data
# dialog self plottable
self.plottable = plottable
self.rg_on = False
# Receive transformations of x and y
self.xLabel, self.yLabel, self.Avalue, self.Bvalue, \
self.ErrAvalue, self.ErrBvalue, self.Chivalue = self.transform()
# Dialog interface
vbox = wx.BoxSizer(wx.VERTICAL)
sizer = wx.GridBagSizer(5, 5)
_BOX_WIDTH = 100
self.tcA = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
self.tcA.SetToolTipString("Fit value for the slope parameter.")
self.tcErrA = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
self.tcErrA.SetToolTipString("Error on the slope parameter.")
self.tcB = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
self.tcA.SetToolTipString("Fit value for the constant parameter.")
self.tcErrB = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
self.tcErrB.SetToolTipString("Error on the constant parameter.")
self.tcChi = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
self.tcChi.SetToolTipString("Chi^2 over degrees of freedom.")
self.xminFit = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
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, 20))
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, 20))
msg = "Minimum value on the x-axis for the plotted data."
self.initXmin.SetToolTipString(msg)
self.initXmax = wx.TextCtrl(self, -1, size=(_BOX_WIDTH, 20))
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)
# Buttons on the bottom
self.bg_on = False
self.static_line_1 = wx.StaticLine(self, -1)
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)
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
# Intro
explanation = "Perform fit for y(x) = ax + b"
if self.bg_on:
param_a = 'Background (= Parameter a)'
else:
param_a = 'Parameter a'
vbox.Add(sizer)
ix = 0
iy = 1
sizer.Add(wx.StaticText(self, -1, explanation), (iy, ix), (1, 1),
wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
iy += 2
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)
iy += 1
ix = 1
vbox.Add(self.static_line_1, 0, wx.EXPAND, 0)
sizer_button = wx.BoxSizer(wx.HORIZONTAL)
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_button, 0, wx.EXPAND | wx.BOTTOM | wx.TOP, 10)
sizer.Add(self.btFit, (iy, ix), (1, 1), wx.LEFT | wx.ADJUST_MINSIZE, 0)
# panel.SetSizer(sizer)
self.SetSizer(vbox)
self.Centre()
# 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 fit dialog
if self.Avalue == None:
self.tcA.SetValue(format_number(self.default_A))
else:
self.tcA.SetLabel(format_number(self.Avalue))
if self.Bvalue == None:
self.tcB.SetValue(format_number(self.default_B))
else:
self.tcB.SetLabel(format_number(self.Bvalue))
if self.ErrAvalue == None:
self.tcErrA.SetLabel(format_number(0.0))
else:
self.tcErrA.SetLabel(format_number(self.ErrAvalue))
if self.ErrBvalue == None:
self.tcErrB.SetLabel(format_number(0.0))
else:
self.tcErrB.SetLabel(format_number(self.ErrBvalue))
if self.Chivalue == 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))