class FittingPanel(wx.Panel):
"""
Those are the Panels that show the fitting dialogs with the Plots.
"""
def __init__(self, parent, counter, modelid, active_parms, tau=None):
""" Initialize with given parameters. """
wx.Panel.__init__(self, parent=parent, id=wx.ID_ANY)
self.parent = parent
self.corr = Correlation(fit_model=modelid)
if tau is not None:
self.corr.lag_time = tau
# active_parameters:
# [0] labels
# [1] values
# [2] bool values to fit
self.corr.fit_parameters = active_parms[1]
self.corr.fit_parameters_variable = active_parms[2]
self._bgselected = None
self._bg2selected = None
self.FitKnots = 5 # number of knots for spline fit or similiars
self.weighted_fittype_id = 0 # integer (drop down item)
self.weighted_nuvar = 3 # bins for std-dev. (left and rigth)
# The weights that are plotted in the page
# This is set by the PlotAll function
self.weights_plot_fill_area = None
# A list containing page numbers that share parameters with this page.
# This parameter is defined by the global fitting tool and is saved in
# sessions.
self.GlobalParameterShare = []
# Counts number of Pages already created:
self.counter = counter
# Has inital plot been performed?
# Call PlotAll("init") to set this to true. If it is true, then
# nothing will be plotted if called with "init"
self.InitialPlot = False
# Model we are using
# Tool statistics uses this list:
self.StatisticsCheckboxes = None
# Splitter window
# Sizes
size = parent.notebook.GetSize()
tabsize = 33
size[1] = size[1] - tabsize
self.sizepanelx = 270
canvasx = size[0] - self.sizepanelx + 5
sizepanel = (self.sizepanelx, size[1])
sizecanvas = (canvasx, size[1])
self.sp = wx.SplitterWindow(self, size=size, style=wx.SP_3DSASH)
# This is necessary to prevent "Unsplit" of the SplitterWindow:
self.sp.SetMinimumPaneSize(1)
# Settings Section (left side)
#self.panelsettings = wx.Panel(self.sp, size=sizepanel)
self.panelsettings = scrolled.ScrolledPanel(self.sp, size=sizepanel)
self.panelsettings.SetupScrolling(scroll_x=False)
# Setting up Plot (correlation + chi**2)
self.spcanvas = wx.SplitterWindow(self.sp,
size=sizecanvas,
style=wx.SP_3DSASH)
# This is necessary to prevent "Unsplit" of the SplitterWindow:
self.spcanvas.SetMinimumPaneSize(1)
# y difference in pixels between Auocorrelation and Residuals
cupsizey = size[1] * 4 / 5
# Calculate initial data
self.calculate_corr()
# Draw the settings section
self.settings()
# Load default values
self.apply_parameters_reverse()
# Upper Plot for plotting of Correlation Function
self.canvascorr = plot.PlotCanvas(self.spcanvas)
self.canvascorr.logScale = (True, False)
self.canvascorr.enableZoom = True
self.PlotAll(event="init", trigger="tab_init")
self.canvascorr.SetSize((canvasx, cupsizey))
# Lower Plot for plotting of the residuals
self.canvaserr = plot.PlotCanvas(self.spcanvas)
self.canvaserr.logScale = (True, False)
self.canvaserr.enableZoom = True
self.canvaserr.SetSize((canvasx, size[1] - cupsizey))
self.spcanvas.SplitHorizontally(self.canvascorr, self.canvaserr,
cupsizey)
self.sp.SplitVertically(self.panelsettings, self.spcanvas,
self.sizepanelx)
# Bind resizing to resizing function.
wx.EVT_SIZE(self, self.OnSize)
@property
def active_parms(self):
names = self.corr.fit_model.parameters[0]
parms = self.corr.fit_parameters
bools = self.corr.fit_parameters_variable
return [names, parms, bools]
@property
def IsCrossCorrelation(self):
return self.corr.is_cc
@property
def modelid(self):
return self.corr.fit_model.id
@property
def prevent_batch_modification(self):
return self.wxCBPreventBatchParms.GetValue()
@prevent_batch_modification.setter
def prevent_batch_modification(self, value):
self.wxCBPreventBatchParms.SetValue(value)
@property
def title(self):
return self.tabtitle.GetValue()
@title.setter
def title(self, title):
self.tabtitle.SetValue(title.strip())
self.corr.title = title.strip()
@property
def traceavg(self):
warnings.warn("Trace average always set to none!")
return None
@property
def tracecc(self):
if self.corr.is_cc and len(self.corr.traces) != 0:
return self.corr.traces
else:
return None
@property
def bgselected(self):
return self._bgselected
@bgselected.setter
def bgselected(self, value):
if value is None:
self.corr.backgrounds = []
return
# check paren.Background and get id
background = self.parent.Background[value]
self.corr.background_replace(0, background)
self._bgselected = value
@property
def bg2selected(self):
return self._bg2selected
@bg2selected.setter
def bg2selected(self, value):
if value is None:
if self.corr.is_cc:
self.corr.backgrounds = []
return
# check paren.Background and get id
background = self.parent.Background[value]
self.corr.background_replace(1, background)
self._bg2selected = value
def apply_parameters(self, event=None):
""" Read the values from the GUI form and write it to the
pages parameters / correlation class.
This function is called when the "Apply" button is hit.
"""
modelid = self.corr.fit_model.id
parameters = list()
parameters_variable = list()
# Read parameters from form and update self.active_parms[1]
for i in np.arange(len(self.spincontrol)):
parameters.append(1 * self.spincontrol[i].GetValue())
parameters_variable.append(self.checkboxes[i].GetValue())
self.corr.fit_parameters_variable = np.array(parameters_variable,
dtype=bool)
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
# Here: Convert human readable units to program internal
# units
parmsconv = mdls.GetInternalFromHumanReadableParm(
modelid, np.array(parameters))[1]
self.corr.fit_parameters = parmsconv
# Fitting parameters
self.weighted_nuvar = self.Fitbox[5].GetValue()
self.weighted_fittype_id = self.Fitbox[1].GetSelection()
fitbox_value = self.Fitbox[1].GetValue()
if self.weighted_fittype_id == -1:
# User edited knot number
Knots = fitbox_value
Knots = "".join(filter(lambda x: x.isdigit(), Knots))
if Knots == "":
Knots = "5"
self.weighted_fittype_id = 1
self.FitKnots = str(Knots)
fit_weight_type = "spline{}".format(self.FitKnots)
fit_weight_data = self.weighted_nuvar
elif self.weighted_fittype_id == 1:
Knots = fitbox_value
Knots = "".join(filter(lambda x: x.isdigit(), Knots))
self.FitKnots = int(Knots)
fit_weight_type = "spline{}".format(self.FitKnots)
fit_weight_data = self.weighted_nuvar
elif self.weighted_fittype_id == 0:
fit_weight_type = "none"
fit_weight_data = None
elif self.weighted_fittype_id == 2:
fit_weight_type = "model function"
fit_weight_data = self.weighted_nuvar
else: # fitbox_selection > 2:
fit_weight_type = fitbox_value
self.corr.fit_weight_type = fitbox_value
fit_weight_data = self.corr.fit_weight_data
# Fitting algorithm
keys = fit.GetAlgorithmStringList()[0]
idalg = self.AlgorithmDropdown.GetSelection()
self.corr.fit_algorithm = keys[idalg]
self.corr.fit_weight_type = fit_weight_type
self.corr.fit_weight_data = fit_weight_data
# If parameters have been changed because of the check_parms
# function, write them back.
self.apply_parameters_reverse()
def apply_parameters_reverse(self, event=None):
""" Read the values from the pages parameters and write
it to the GUI form.
"""
modelid = self.corr.fit_model.id
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
# Here: Convert program internal units to
# human readable units
parameters = mdls.GetHumanReadableParms(modelid,
self.corr.fit_parameters)[1]
parameters_variable = self.corr.fit_parameters_variable
# Write parameters to the form on the Page
for i in np.arange(len(self.active_parms[1])):
self.spincontrol[i].SetValue(parameters[i])
self.checkboxes[i].SetValue(parameters_variable[i])
# Fitting parameters
self.Fitbox[5].SetValue(self.weighted_nuvar)
idf = self.weighted_fittype_id
List = self.Fitbox[1].GetItems()
List[1] = "spline (" + str(self.FitKnots) + " knots)"
self.Fitbox[1].SetItems(List)
self.Fitbox[1].SetSelection(idf)
# Normalization
if self.corr.normparm is None:
normsel = 0
else:
normsel = self.corr.normparm + 1
self.AmplitudeInfo[2].SetSelection(normsel)
# Fitting algorithm
keys = fit.GetAlgorithmStringList()[0]
idalg = keys.index(self.corr.fit_algorithm)
self.AlgorithmDropdown.SetSelection(idalg)
self.updateChi2()
def calculate_corr(self):
"""
Calculate model correlation function
"""
return self.corr.modeled
def Fit_enable_fitting(self):
""" Enable the fitting button and the weighted fit control"""
# self.Fitbox = [ fitbox, weightedfitdrop, fittext, fittext2,
# fittextvar, fitspin, buttonfit, textalg,
# self.AlgorithmDropdown]
self.Fitbox[0].Enable()
self.Fitbox[1].Enable()
self.Fitbox[6].Enable()
self.Fitbox[7].Enable()
self.Fitbox[8].Enable()
def Fit_function(self, event=None, noplots=False, trigger=None):
""" Calls the fit function.
`noplots=True` prevents plotting of spline fits
`trigger` is passed to page.PlotAll.
If trigger is "fit_batch", then `noplots` is set
to `True`.
"""
tools.batchcontrol.FitProgressDlg(self, self)
def Fit_finalize(self, trigger):
""" Things that need be done after fitting
"""
# Reset list of globally shared parameters, because we are only
# fitting this single page now.
# TODO:
# - also remove this page from the GlobalParameterShare list of
# the other pages
self.GlobalParameterShare = []
# Update spin-control values
self.apply_parameters_reverse()
# Plot everything
try:
self.PlotAll(trigger=trigger)
except OverflowError:
# Sometimes parameters are just bad and
# we still want the user to use the
# program.
warnings.warn("Could not plot canvas.")
# update displayed chi2
self.updateChi2()
def Fit_WeightedFitCheck(self, event=None):
""" Enable Or disable variance calculation, dependent on
"Weighted Fit" checkbox
"""
# self.Fitbox=[ fitbox, weightedfitdrop, fittext, fittext2, fittextvar,
# fitspin, buttonfit ]
weighted = (self.Fitbox[1].GetSelection() != 0)
# In the case of "Average" we do not enable the
# "Calculation of variance" part.
if weighted is True and self.Fitbox[1].GetValue() != "Average":
self.Fitbox[2].Enable()
self.Fitbox[3].Enable()
self.Fitbox[4].Enable()
self.Fitbox[5].Enable()
else:
self.Fitbox[2].Disable()
self.Fitbox[3].Disable()
self.Fitbox[4].Disable()
self.Fitbox[5].Disable()
def MakeStaticBoxSizer(self, boxlabel):
""" Create a Box with check boxes (fit yes/no) and possibilities to
change initial values for fitting.
Parameters:
*boxlabel*: The name of the box (is being displayed)
*self.active_parms[0]*: A list of things to put into the box
Returns:
*sizer*: The static Box
*check*: The (un)set checkboxes
*spin*: The spin text fields
"""
modelid = self.corr.fit_model.id
box = wx.StaticBox(self.panelsettings, label=boxlabel)
sizer = wx.StaticBoxSizer(box, wx.VERTICAL)
check = list()
spin = list()
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
labels = mdls.GetHumanReadableParms(modelid,
self.corr.fit_parameters)[0]
sizerh = wx.GridSizer(len(labels), 2, 2, 2)
for label in labels:
checkbox = wx.CheckBox(self.panelsettings, label=label)
# We needed to "from wx.lib.agw import floatspin" to get this:
spinctrl = wxutils.PCFFloatTextCtrl(self.panelsettings)
sizerh.Add(spinctrl)
sizerh.Add(checkbox)
# Put everything into lists to be able to refer to it later
check.append(checkbox)
spin.append(spinctrl)
sizer.Add(sizerh)
return sizer, check, spin
def OnAmplitudeCheck(self, event=None):
""" Enable/Disable BG rate text line.
New feature introduced in 0.7.8
"""
modelid = self.corr.fit_model.id
# Normalization to a certain parameter in plots
# Find all parameters that start with an "N"
# ? and "C" ?
# Create List
normlist = list()
normlist.append("None")
# Add parameters
parameterlist = list()
for i in np.arange(len(self.active_parms[0])):
label = self.active_parms[0][i]
if label[0].lower() == "n":
normlist.append("*" + label)
parameterlist.append(i)
# Add supplementary parameters
# Get them from models
supplement = mdls.GetMoreInfo(modelid, self)
if supplement is not None:
for i in np.arange(len(supplement)):
label = supplement[i][0]
if label[0].lower() == "n":
normlist.append("*" + label)
# Add the id of the supplement starting at the
# number of fitting parameters of current page.
parameterlist.append(i + len(self.active_parms[0]))
normsel = self.AmplitudeInfo[2].GetSelection()
if normsel in [0, -1]:
# init or no normalization selected
self.corr.normparm = None
normsel = 0
else:
self.corr.normparm = parameterlist[normsel - 1]
if len(parameterlist) > 0:
self.AmplitudeInfo[2].Enable()
self.AmplitudeInfo[3].Enable()
else:
self.AmplitudeInfo[2].Disable()
self.AmplitudeInfo[3].Disable()
# Set dropdown values
self.AmplitudeInfo[2].SetItems(normlist)
self.AmplitudeInfo[2].SetSelection(normsel)
# Plot intensities
# Quick reminder:
# self.AmplitudeInfo = [ [intlabel1, intlabel2],
# [bgspin1, bgspin2],
# normtoNDropdown, textnor]
# Signal
self.AmplitudeInfo[0][0].SetValue("{:.4f}".format(0))
self.AmplitudeInfo[0][1].SetValue("{:.4f}".format(0))
for i in range(len(self.corr.traces)):
S = self.corr.traces[i].countrate
self.AmplitudeInfo[0][i].SetValue("{:.4f}".format(S))
if self.corr.is_cc:
self.AmplitudeInfo[0][1].Enable()
else:
self.AmplitudeInfo[0][1].Disable()
# Background
# self.parent.Background[self.bgselected][i]
# [0] average signal [kHz]
# [1] signal name (edited by user)
# [2] signal trace (tuple) ([ms], [kHz])
if len(self.corr.backgrounds) >= 1:
self.AmplitudeInfo[1][0].SetValue(
self.corr.backgrounds[0].countrate)
else:
self.AmplitudeInfo[1][0].SetValue(0)
self.AmplitudeInfo[1][1].SetValue(0)
if len(self.corr.backgrounds) == 2:
self.AmplitudeInfo[1][1].SetValue(
self.corr.backgrounds[1].countrate)
else:
self.AmplitudeInfo[1][1].SetValue(0)
# Disable the second line in amplitude correction, if we have
# autocorrelation only.
boolval = self.corr.is_cc
for item in self.WXAmplitudeCCOnlyStuff:
item.Enable(boolval)
def OnBGSpinChanged(self, e):
""" Calls tools.background.ApplyAutomaticBackground
to update background information
"""
# Quick reminder:
# self.AmplitudeInfo = [ [intlabel1, intlabel2],
# [bgspin1, bgspin2],
# normtoNDropdown, textnor]
if self.corr.is_cc:
# update both self.bgselected and self.bg2selected
bg = [
float(self.AmplitudeInfo[1][0].GetValue()),
float(self.AmplitudeInfo[1][1].GetValue())
]
sig = [
float(self.AmplitudeInfo[0][0].GetValue()),
float(self.AmplitudeInfo[0][1].GetValue())
]
# Make sure bg < sig
for ii in range(len(bg)):
if sig[ii] != 0:
if bg[ii] > .99 * sig[ii]:
bg[ii] = .99 * sig[ii]
self.AmplitudeInfo[1][ii].SetValue(bg[ii])
else:
# Only update self.bgselected
bg = float(self.AmplitudeInfo[1][0].GetValue())
sig = float(self.AmplitudeInfo[0][0].GetValue())
# Make sure bg < sig
if sig != 0:
if bg > .99 * sig:
bg = .99 * sig
self.AmplitudeInfo[1][0].SetValue(bg)
tools.background.ApplyAutomaticBackground(self, bg, self.parent)
e.Skip()
def OnTitleChanged(self, e):
modelid = self.corr.fit_model.id
pid = self.parent.notebook.GetPageIndex(self)
if self.tabtitle.GetValue() == "":
text = self.counter + mdls.modeldict[modelid][1]
else:
# How many characters of the the page title should be displayed
# in the tab? We choose 9: AC1-012 plus 2 whitespaces
text = self.counter + self.tabtitle.GetValue()[-9:]
self.parent.notebook.SetPageText(pid, text)
def OnSetRange(self, e):
""" Open a new Frame where the parameter range can be set.
Rewrites self.parameter_range
Parameter ranges are treated like parameters: They are saved in
sessions and applied in batch mode.
"""
# TODO:
# - make range selector work with new class
# We write a separate tool for that.
# This tool does not show up in the Tools menu.
if self.parent.RangeSelector is None:
self.parent.RangeSelector = tools.RangeSelector(self)
self.parent.RangeSelector.Bind(wx.EVT_CLOSE,
self.parent.RangeSelector.OnClose)
else:
try:
self.parent.RangeSelector.OnClose()
except:
pass
self.parent.RangeSelector = None
def OnSize(self, event):
""" Resize the fitting Panel, when Window is resized. """
size = self.parent.notebook.GetSize()
tabsize = 33
size[1] = size[1] - tabsize
self.sp.SetSize(size)
def PlotAll(self, event=None, trigger=None):
"""
This function plots the whole correlation and residuals canvas.
We do:
- Channel selection
- Background correction
- Apply Parameters (separate function)
- Drawing of plots
The `event` is usually just an event from buttons or similar
wx objects. It can be "init", then some initial plotting is
done before the data is handled.
The `trigger` is passed to `self.parent.OnFNBPageChanged` so
that tools can update their content accordingly. For more
information on triggers, have a look at the doctring of the
`tools` submodule.
"""
if event == "init":
# We use this to have the page plotted at least once before
# readout of parameters (e.g. startcrop, endcrop)
# This is a performence tweak.
if self.InitialPlot:
return
else:
self.InitialPlot = True
# Enable/Disable, set values frontend normalization
self.OnAmplitudeCheck()
# Apply parameters
self.apply_parameters()
# Calculate correlation function from parameters
# Drawing of correlation plot
# Plots corr.correlation_fit and the calcualted correlation function
# self.datacorr into the upper canvas.
# Create a line @ y=zero:
zerostart = self.corr.lag_time_fit[0]
zeroend = self.corr.lag_time_fit[-1]
datazero = [[zerostart, 0], [zeroend, 0]]
# Set plot colors
width = 1
colexp = "grey"
colfit = "blue"
colweight = "cyan"
lines = list()
linezero = plot.PolyLine(datazero, colour='orange', width=width)
lines.append(linezero)
if self.corr.correlation is not None:
if self.corr.is_weighted_fit and \
self.parent.MenuShowWeights.IsChecked():
try:
weights = self.corr.fit_results["fit weights"]
except:
weights = self.corr.fit_weight_data
if isinstance(weights, np.ndarray):
# user might have selected a new weight type and
# presses apply, do not try to display weights
# if weights are from average or other, make sure that the
# dimensions are correct
if weights.shape[0] == self.corr.correlation.shape[0]:
weights = weights[self.corr.fit_ival[0]:self.corr.
fit_ival[1]]
# perform some checks
if np.allclose(weights, np.ones_like(weights)):
weights = 0
elif weights.shape[0] != self.corr.modeled_fit.shape[0]:
# non-matching weigths
warnings.warn(
"Unmatching weights found. Probably from previous data set."
)
weights = 0
# Add the weights to the graph.
# This is done by drawing two lines.
w = 1 * self.corr.modeled_fit
w1 = 1 * w
w2 = 1 * w
w1[:, 1] = w[:, 1] + weights
w2[:, 1] = w[:, 1] - weights
# crop w1 and w2 if corr.correlation_fit does not include all
# data points.
if np.all(w[:, 0] == self.corr.correlation_fit[:, 0]):
pass
else:
raise ValueError(
"This should not have happened: size of weights is wrong."
)
# Normalization with self.normfactor
w1[:, 1] *= self.corr.normalize_factor
w2[:, 1] *= self.corr.normalize_factor
self.weights_plot_fill_area = [w1, w2]
lineweight1 = plot.PolyLine(w1,
legend='',
colour=colweight,
width=width)
lines.append(lineweight1)
lineweight2 = plot.PolyLine(w2,
legend='',
colour=colweight,
width=width)
lines.append(lineweight2)
else:
self.weights_plot_fill_area = None
# Plot Correlation curves
# Plot both, experimental and calculated data
# Normalization with self.normfactor, new feature in 0.7.8
datacorr_norm = self.corr.modeled_plot
linecorr = plot.PolyLine(datacorr_norm,
legend='',
colour=colfit,
width=width)
dataexp_norm = self.corr.correlation_plot
lineexp = plot.PolyLine(dataexp_norm,
legend='',
colour=colexp,
width=width)
# Draw linezero first, so it is in the background
lines.append(lineexp)
lines.append(linecorr)
PlotCorr = plot.PlotGraphics(lines,
xLabel=u'lag time τ [ms]',
yLabel=u'G(τ)')
self.canvascorr.Draw(PlotCorr)
# Calculate residuals
resid_norm = self.corr.residuals_plot
lineres = plot.PolyLine(resid_norm,
legend='',
colour=colfit,
width=width)
# residuals or weighted residuals?
if self.corr.is_weighted_fit:
yLabelRes = "weighted \nresiduals"
else:
yLabelRes = "residuals"
PlotRes = plot.PlotGraphics([linezero, lineres],
xLabel=u'lag time τ [ms]',
yLabel=yLabelRes)
self.canvaserr.Draw(PlotRes)
else:
# Amplitude normalization, new feature in 0.7.8
datacorr_norm = self.corr.modeled_plot
linecorr = plot.PolyLine(datacorr_norm,
legend='',
colour='blue',
width=1)
PlotCorr = plot.PlotGraphics([linezero, linecorr],
xLabel=u'Lag time τ [ms]',
yLabel=u'G(τ)')
self.canvascorr.Draw(PlotCorr)
self.Refresh()
self.parent.OnFNBPageChanged(trigger=trigger)
def settings(self):
""" Here we define, what should be displayed at the left side
of the fitting page/tab.
Parameters:
"""
modelid = self.corr.fit_model.id
horizontalsize = self.sizepanelx - 10
# Title
# Create empty tab title
mddat = mdls.modeldict[modelid]
modelshort = mdls.GetModelType(modelid)
titlelabel = u"Data set {} ({} {})".format(self.counter.strip(" :"),
modelshort, mddat[1])
boxti = wx.StaticBox(self.panelsettings, label=titlelabel)
sizerti = wx.StaticBoxSizer(boxti, wx.VERTICAL)
sizerti.SetMinSize((horizontalsize, -1))
self.tabtitle = wx.TextCtrl(self.panelsettings,
value="",
size=(horizontalsize - 20, -1))
self.Bind(wx.EVT_TEXT, self.OnTitleChanged, self.tabtitle)
sizerti.Add(self.tabtitle, 0, wx.EXPAND, 0)
# Create StaticBoxSizer
box1, check, spin = self.MakeStaticBoxSizer("Model parameters")
# Make the check boxes and spin-controls available everywhere
self.checkboxes = check
self.spincontrol = spin
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
labels, parameters = mdls.GetHumanReadableParms(
modelid, self.active_parms[1])
parameterstofit = self.active_parms[2]
# Set initial values given by user/programmer for Diffusion Model
for i in np.arange(len(labels)):
self.checkboxes[i].SetValue(parameterstofit[i])
self.spincontrol[i].SetValue(parameters[i])
# Put everything together
self.panelsettings.sizer = wx.BoxSizer(wx.VERTICAL)
self.panelsettings.sizer.Add(sizerti, 0, wx.EXPAND, 0)
self.panelsettings.sizer.Add(box1, 0, wx.EXPAND, 0)
# checkbox "Protect from batch control"
self.wxCBPreventBatchParms = wx.CheckBox(self.panelsettings, -1,
"prevent batch modification")
box1.Add(self.wxCBPreventBatchParms)
# buttons "Apply" and "Set range"
horzs = wx.BoxSizer(wx.HORIZONTAL)
buttonapply = wx.Button(self.panelsettings, label="Apply")
self.Bind(wx.EVT_BUTTON, self.PlotAll, buttonapply)
horzs.Add(buttonapply)
buttonrange = wx.Button(self.panelsettings, label="Set range")
self.Bind(wx.EVT_BUTTON, self.OnSetRange, buttonrange)
horzs.Add(buttonrange)
box1.Add(horzs)
# Set horizontal size
box1.SetMinSize((horizontalsize, -1))
# More info
normbox = wx.StaticBox(self.panelsettings,
label="Amplitude corrections")
miscsizer = wx.StaticBoxSizer(normbox, wx.VERTICAL)
miscsizer.SetMinSize((horizontalsize, -1))
# Intensities and Background
sizeint = wx.FlexGridSizer(rows=3, cols=3, vgap=5, hgap=5)
sizeint.Add(wx.StaticText(self.panelsettings, label="[kHz]"))
sizeint.Add(wx.StaticText(self.panelsettings, label="Intensity"))
sizeint.Add(wx.StaticText(self.panelsettings, label="Background"))
sizeint.Add(wx.StaticText(self.panelsettings, label="Ch1"))
intlabel1 = wx.TextCtrl(self.panelsettings)
bgspin1 = wxutils.PCFFloatTextCtrl(self.panelsettings)
bgspin1.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged)
bgspin1.Bind(wx.EVT_TEXT_ENTER, self.OnBGSpinChanged)
sizeint.Add(intlabel1)
intlabel1.SetEditable(False)
sizeint.Add(bgspin1)
chtext2 = wx.StaticText(self.panelsettings, label="Ch2")
sizeint.Add(chtext2)
intlabel2 = wx.TextCtrl(self.panelsettings)
intlabel2.SetEditable(False)
bgspin2 = wxutils.PCFFloatTextCtrl(self.panelsettings)
bgspin2.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged)
sizeint.Add(intlabel2)
sizeint.Add(bgspin2)
miscsizer.Add(sizeint)
# Normalize to n?
textnor = wx.StaticText(self.panelsettings, label="Plot normalization")
miscsizer.Add(textnor)
normtoNDropdown = wx.ComboBox(self.panelsettings)
self.Bind(wx.EVT_COMBOBOX, self.PlotAll, normtoNDropdown)
miscsizer.Add(normtoNDropdown)
self.AmplitudeInfo = [[intlabel1, intlabel2], [bgspin1, bgspin2],
normtoNDropdown, textnor]
self.WXAmplitudeCCOnlyStuff = [chtext2, intlabel2, bgspin2]
self.panelsettings.sizer.Add(miscsizer, 0, wx.EXPAND, 0)
# Add fitting Box
fitbox = wx.StaticBox(self.panelsettings, label="Fitting options")
fitsizer = wx.StaticBoxSizer(fitbox, wx.VERTICAL)
fitsizer.SetMinSize((horizontalsize, -1))
# Add a checkbox for weighted fitting
weightedfitdrop = wx.ComboBox(self.panelsettings)
self.weightlist = ["no weights", "spline (5 knots)", "model function"]
weightedfitdrop.SetItems(self.weightlist)
weightedfitdrop.SetSelection(0)
fitsizer.Add(weightedfitdrop)
# WeightedFitCheck() Enables or Disables the variance part
weightedfitdrop.Bind(wx.EVT_COMBOBOX, self.Fit_WeightedFitCheck)
# Add the variance part.
# In order to do a weighted fit, we need to calculate the variance
# at each point of the experimental data array.
# In order to do that, we need to know how many data points from left
# and right of the interesting data point we want to include in that
# calculation.
fittext = wx.StaticText(self.panelsettings,
label="Calculation of the variance")
fitsizer.Add(fittext)
fittext2 = wx.StaticText(self.panelsettings,
label="from 2j+1 data points")
fitsizer.Add(fittext2)
fitsizerspin = wx.BoxSizer(wx.HORIZONTAL)
fittextvar = wx.StaticText(self.panelsettings, label="j = ")
fitspin = wx.SpinCtrl(self.panelsettings,
-1,
initial=3,
min=1,
max=100)
fitsizerspin.Add(fittextvar)
fitsizerspin.Add(fitspin)
fitsizer.Add(fitsizerspin)
# Add algorithm selection
textalg = wx.StaticText(self.panelsettings, label="Algorithm")
fitsizer.Add(textalg)
self.AlgorithmDropdown = wx.ComboBox(self.panelsettings)
items = fit.GetAlgorithmStringList()[1]
self.AlgorithmDropdown.SetItems(items)
self.Bind(wx.EVT_COMBOBOX, self.apply_parameters,
self.AlgorithmDropdown)
fitsizer.Add(self.AlgorithmDropdown)
self.AlgorithmDropdown.SetMaxSize(weightedfitdrop.GetSize())
# Add button "Fit" and chi2 display
fitbuttonsizer = wx.BoxSizer(wx.HORIZONTAL)
buttonfit = wx.Button(self.panelsettings, label="Fit")
self.Bind(wx.EVT_BUTTON, self.Fit_function, buttonfit)
fitbuttonsizer.Add(buttonfit)
# add shortcut
acctbl = wx.AcceleratorTable([(wx.ACCEL_CTRL, ord('F'),
buttonfit.GetId())])
self.SetAcceleratorTable(acctbl)
##
self.WXTextChi2 = wx.StaticText(self.panelsettings)
# this StaticText is updated by `self.updateChi2()`
fitbuttonsizer.Add(self.WXTextChi2, flag=wx.ALIGN_CENTER)
fitsizer.Add(fitbuttonsizer)
self.panelsettings.sizer.Add(fitsizer, 0, wx.EXPAND, 0)
# Squeeze everything into the sizer
self.panelsettings.SetSizer(self.panelsettings.sizer)
# This is also necessary in Windows
self.panelsettings.Layout()
self.panelsettings.Show()
# Make all the stuff available for everyone
self.Fitbox = [
fitbox, weightedfitdrop, fittext, fittext2, fittextvar, fitspin,
buttonfit, textalg, self.AlgorithmDropdown
]
# Disable Fitting since no data has been loaded yet
for element in self.Fitbox:
element.Disable()
self.panelsettings.sizer.Fit(self.panelsettings)
self.parent.Layout()
def updateChi2(self):
"""
updates the self.WXTextChi2 text control
"""
label = u""
if hasattr(self.corr, "fit_results"):
if "chi2" in self.corr.fit_results:
chi2 = self.corr.fit_results["chi2"]
chi2str = wxutils.float2string_nsf(chi2, n=3)
chi2str = wxutils.nice_string(chi2str)
label = u" χ²={}".format(chi2str)
# This does not work with wxPython 2.8.12:
# self.WXTextChi2.SetLabelMarkup(u"<b>{}</b>".format(label))
self.WXTextChi2.SetLabel(u"{}".format(label))
class FittingPanel(wx.Panel):
"""
Those are the Panels that show the fitting dialogs with the Plots.
"""
def __init__(self, parent, counter, modelid, active_parms, tau=None):
""" Initialize with given parameters. """
wx.Panel.__init__(self, parent=parent, id=wx.ID_ANY)
self.parent = parent
self.corr = Correlation(fit_model=modelid)
if tau is not None:
self.corr.lag_time = tau
# active_parameters:
# [0] labels
# [1] values
# [2] bool values to fit
self.corr.fit_parameters = active_parms[1]
self.corr.fit_parameters_variable = active_parms[2]
self._bgselected = None
self._bg2selected = None
self.FitKnots = 5 # number of knots for spline fit or similiars
self.weighted_fittype_id = 0 # integer (drop down item)
self.weighted_nuvar = 3 # bins for std-dev. (left and rigth)
# The weights that are plotted in the page
# This is set by the PlotAll function
self.weights_plot_fill_area = None
# A list containing page numbers that share parameters with this page.
# This parameter is defined by the global fitting tool and is saved in
# sessions.
self.GlobalParameterShare = []
# Counts number of Pages already created:
self.counter = counter
# Has inital plot been performed?
# Call PlotAll("init") to set this to true. If it is true, then
# nothing will be plotted if called with "init"
self.InitialPlot = False
# Model we are using
# Tool statistics uses this list:
self.StatisticsCheckboxes = None
### Splitter window
# Sizes
size = parent.notebook.GetSize()
tabsize = 33
size[1] = size[1] - tabsize
self.sizepanelx = 270
canvasx = size[0]-self.sizepanelx+5
sizepanel = (self.sizepanelx, size[1])
sizecanvas = (canvasx, size[1])
self.sp = wx.SplitterWindow(self, size=size, style=wx.SP_3DSASH)
# This is necessary to prevent "Unsplit" of the SplitterWindow:
self.sp.SetMinimumPaneSize(1)
## Settings Section (left side)
#self.panelsettings = wx.Panel(self.sp, size=sizepanel)
self.panelsettings = scrolled.ScrolledPanel(self.sp, size=sizepanel)
self.panelsettings.SetupScrolling(scroll_x=False)
## Setting up Plot (correlation + chi**2)
self.spcanvas = wx.SplitterWindow(self.sp, size=sizecanvas,
style=wx.SP_3DSASH)
# This is necessary to prevent "Unsplit" of the SplitterWindow:
self.spcanvas.SetMinimumPaneSize(1)
# y difference in pixels between Auocorrelation and Residuals
cupsizey = size[1]*4/5
# Calculate initial data
self.calculate_corr()
# Draw the settings section
self.settings()
# Load default values
self.apply_parameters_reverse()
# Upper Plot for plotting of Correlation Function
self.canvascorr = plot.PlotCanvas(self.spcanvas)
self.canvascorr.setLogScale((True, False))
self.canvascorr.SetEnableZoom(True)
self.PlotAll(event="init", trigger="tab_init")
self.canvascorr.SetSize((canvasx, cupsizey))
# Lower Plot for plotting of the residuals
self.canvaserr = plot.PlotCanvas(self.spcanvas)
self.canvaserr.setLogScale((True, False))
self.canvaserr.SetEnableZoom(True)
self.canvaserr.SetSize((canvasx, size[1]-cupsizey))
self.spcanvas.SplitHorizontally(self.canvascorr, self.canvaserr,
cupsizey)
self.sp.SplitVertically(self.panelsettings, self.spcanvas,
self.sizepanelx)
# Bind resizing to resizing function.
wx.EVT_SIZE(self, self.OnSize)
@property
def active_parms(self):
names = self.corr.fit_model.parameters[0]
parms = self.corr.fit_parameters
bools = self.corr.fit_parameters_variable
return [names, parms, bools]
@property
def IsCrossCorrelation(self):
return self.corr.is_cc
@property
def modelid(self):
return self.corr.fit_model.id
@property
def prevent_batch_modification(self):
return self.wxCBPreventBatchParms.GetValue()
@prevent_batch_modification.setter
def prevent_batch_modification(self, value):
self.wxCBPreventBatchParms.SetValue(value)
@property
def title(self):
return self.tabtitle.GetValue()
@title.setter
def title(self, title):
self.tabtitle.SetValue(title.strip())
self.corr.title = title.strip()
@property
def traceavg(self):
warnings.warn("Trace average always set to none!")
return None
@property
def tracecc(self):
if self.corr.is_cc and len(self.corr.traces) != 0:
return self.corr.traces
else:
return None
@property
def bgselected(self):
return self._bgselected
@bgselected.setter
def bgselected(self, value):
if value is None:
self.corr.backgrounds=[]
return
# check paren.Background and get id
background = self.parent.Background[value]
self.corr.background_replace(0, background)
self._bgselected = value
@property
def bg2selected(self):
return self._bg2selected
@bg2selected.setter
def bg2selected(self, value):
if value is None:
if self.corr.is_cc:
self.corr.backgrounds=[]
return
# check paren.Background and get id
background = self.parent.Background[value]
self.corr.background_replace(1, background)
self._bg2selected = value
def apply_parameters(self, event=None):
""" Read the values from the GUI form and write it to the
pages parameters / correlation class.
This function is called when the "Apply" button is hit.
"""
modelid = self.corr.fit_model.id
parameters = list()
parameters_variable = list()
# Read parameters from form and update self.active_parms[1]
for i in np.arange(len(self.spincontrol)):
parameters.append(1*self.spincontrol[i].GetValue())
parameters_variable.append(self.checkboxes[i].GetValue())
self.corr.fit_parameters_variable = np.array(parameters_variable,
dtype=bool)
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
# Here: Convert human readable units to program internal
# units
parmsconv = mdls.GetInternalFromHumanReadableParm(
modelid, np.array(parameters))[1]
self.corr.fit_parameters = parmsconv
# Fitting parameters
self.weighted_nuvar = self.Fitbox[5].GetValue()
self.weighted_fittype_id = self.Fitbox[1].GetSelection()
fitbox_value = self.Fitbox[1].GetValue()
if self.weighted_fittype_id == -1:
# User edited knot number
Knots = fitbox_value
Knots = filter(lambda x: x.isdigit(), Knots)
if Knots == "":
Knots = "5"
self.weighted_fittype_id = 1
self.FitKnots = str(Knots)
fit_weight_type = "spline{}".format(self.FitKnots)
fit_weight_data = self.weighted_nuvar
elif self.weighted_fittype_id == 1:
Knots = fitbox_value
Knots = filter(lambda x: x.isdigit(), Knots)
self.FitKnots = int(Knots)
fit_weight_type = "spline{}".format(self.FitKnots)
fit_weight_data = self.weighted_nuvar
elif self.weighted_fittype_id == 0:
fit_weight_type = "none"
fit_weight_data = None
elif self.weighted_fittype_id == 2:
fit_weight_type = "model function"
fit_weight_data = self.weighted_nuvar
else: # fitbox_selection > 2:
fit_weight_type = fitbox_value
self.corr.fit_weight_type = fitbox_value
fit_weight_data = self.corr.fit_weight_data
# Fitting algorithm
keys = fit.GetAlgorithmStringList()[0]
idalg = self.AlgorithmDropdown.GetSelection()
self.corr.fit_algorithm = keys[idalg]
self.corr.fit_weight_type = fit_weight_type
self.corr.fit_weight_data = fit_weight_data
# If parameters have been changed because of the check_parms
# function, write them back.
self.apply_parameters_reverse()
def apply_parameters_reverse(self, event=None):
""" Read the values from the pages parameters and write
it to the GUI form.
"""
modelid = self.corr.fit_model.id
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
# Here: Convert program internal units to
# human readable units
parameters = mdls.GetHumanReadableParms(modelid,
self.corr.fit_parameters)[1]
parameters_variable = self.corr.fit_parameters_variable
# Write parameters to the form on the Page
for i in np.arange(len(self.active_parms[1])):
self.spincontrol[i].SetValue(parameters[i])
self.checkboxes[i].SetValue(parameters_variable[i])
# Fitting parameters
self.Fitbox[5].SetValue(self.weighted_nuvar)
idf = self.weighted_fittype_id
List = self.Fitbox[1].GetItems()
List[1] = "spline ("+str(self.FitKnots)+" knots)"
self.Fitbox[1].SetItems(List)
self.Fitbox[1].SetSelection(idf)
# Normalization
if self.corr.normparm is None:
normsel = 0
else:
normsel = self.corr.normparm + 1
self.AmplitudeInfo[2].SetSelection(normsel)
# Fitting algorithm
keys = fit.GetAlgorithmStringList()[0]
idalg = keys.index(self.corr.fit_algorithm)
self.AlgorithmDropdown.SetSelection(idalg)
self.updateChi2()
def calculate_corr(self):
"""
Calculate model correlation function
"""
return self.corr.modeled
def Fit_enable_fitting(self):
""" Enable the fitting button and the weighted fit control"""
#self.Fitbox = [ fitbox, weightedfitdrop, fittext, fittext2,
# fittextvar, fitspin, buttonfit, textalg,
# self.AlgorithmDropdown]
self.Fitbox[0].Enable()
self.Fitbox[1].Enable()
self.Fitbox[6].Enable()
self.Fitbox[7].Enable()
self.Fitbox[8].Enable()
def Fit_function(self, event=None, noplots=False, trigger=None):
""" Calls the fit function.
`noplots=True` prevents plotting of spline fits
`trigger` is passed to page.PlotAll.
If trigger is "fit_batch", then `noplots` is set
to `True`.
"""
tools.batchcontrol.FitProgressDlg(self, self)
def Fit_finalize(self, trigger):
""" Things that need be done after fitting
"""
# Reset list of globally shared parameters, because we are only
# fitting this single page now.
# TODO:
# - also remove this page from the GlobalParameterShare list of
# the other pages
self.GlobalParameterShare = []
# Update spin-control values
self.apply_parameters_reverse()
# Plot everything
try:
self.PlotAll(trigger=trigger)
except OverflowError:
# Sometimes parameters are just bad and
# we still want the user to use the
# program.
warnings.warn("Could not plot canvas.")
# update displayed chi2
self.updateChi2()
def Fit_WeightedFitCheck(self, event=None):
""" Enable Or disable variance calculation, dependent on
"Weighted Fit" checkbox
"""
#self.Fitbox=[ fitbox, weightedfitdrop, fittext, fittext2, fittextvar,
# fitspin, buttonfit ]
weighted = (self.Fitbox[1].GetSelection() != 0)
# In the case of "Average" we do not enable the
# "Calculation of variance" part.
if weighted is True and self.Fitbox[1].GetValue() != "Average":
self.Fitbox[2].Enable()
self.Fitbox[3].Enable()
self.Fitbox[4].Enable()
self.Fitbox[5].Enable()
else:
self.Fitbox[2].Disable()
self.Fitbox[3].Disable()
self.Fitbox[4].Disable()
self.Fitbox[5].Disable()
def MakeStaticBoxSizer(self, boxlabel):
""" Create a Box with check boxes (fit yes/no) and possibilities to
change initial values for fitting.
Parameters:
*boxlabel*: The name of the box (is being displayed)
*self.active_parms[0]*: A list of things to put into the box
Returns:
*sizer*: The static Box
*check*: The (un)set checkboxes
*spin*: The spin text fields
"""
modelid = self.corr.fit_model.id
box = wx.StaticBox(self.panelsettings, label=boxlabel)
sizer = wx.StaticBoxSizer(box, wx.VERTICAL)
check = list()
spin = list()
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
labels = mdls.GetHumanReadableParms(modelid,
self.corr.fit_parameters)[0]
for label in labels:
sizerh = wx.BoxSizer(wx.HORIZONTAL)
checkbox = wx.CheckBox(self.panelsettings, label=label)
# We needed to "from wx.lib.agw import floatspin" to get this:
spinctrl = wxutils.PCFFloatTextCtrl(self.panelsettings)
sizerh.Add(spinctrl)
sizerh.Add(checkbox)
sizer.Add(sizerh)
# Put everything into lists to be able to refer to it later
check.append(checkbox)
spin.append(spinctrl)
return sizer, check, spin
def OnAmplitudeCheck(self, event=None):
""" Enable/Disable BG rate text line.
New feature introduced in 0.7.8
"""
modelid = self.corr.fit_model.id
## Normalization to a certain parameter in plots
# Find all parameters that start with an "N"
# ? and "C" ?
# Create List
normlist = list()
normlist.append("None")
## Add parameters
parameterlist = list()
for i in np.arange(len(self.active_parms[0])):
label = self.active_parms[0][i]
if label[0].lower() == "n":
normlist.append("*"+label)
parameterlist.append(i)
## Add supplementary parameters
# Get them from models
supplement = mdls.GetMoreInfo(modelid, self)
if supplement is not None:
for i in np.arange(len(supplement)):
label = supplement[i][0]
if label[0].lower() == "n":
normlist.append("*"+label)
# Add the id of the supplement starting at the
# number of fitting parameters of current page.
parameterlist.append(i+len(self.active_parms[0]))
normsel = self.AmplitudeInfo[2].GetSelection()
if normsel in [0, -1]:
# init or no normalization selected
self.corr.normparm = None
normsel = 0
else:
self.corr.normparm = parameterlist[normsel-1]
if len(parameterlist) > 0:
self.AmplitudeInfo[2].Enable()
self.AmplitudeInfo[3].Enable()
else:
self.AmplitudeInfo[2].Disable()
self.AmplitudeInfo[3].Disable()
# Set dropdown values
self.AmplitudeInfo[2].SetItems(normlist)
self.AmplitudeInfo[2].SetSelection(normsel)
## Plot intensities
# Quick reminder:
#self.AmplitudeInfo = [ [intlabel1, intlabel2],
# [bgspin1, bgspin2],
# normtoNDropdown, textnor]
# Signal
self.AmplitudeInfo[0][0].SetValue("{:.4f}".format(0))
self.AmplitudeInfo[0][1].SetValue("{:.4f}".format(0))
for i in range(len(self.corr.traces)):
S = self.corr.traces[i].countrate
self.AmplitudeInfo[0][i].SetValue("{:.4f}".format(S))
if self.corr.is_cc:
self.AmplitudeInfo[0][1].Enable()
else:
self.AmplitudeInfo[0][1].Disable()
# Background
## self.parent.Background[self.bgselected][i]
## [0] average signal [kHz]
## [1] signal name (edited by user)
## [2] signal trace (tuple) ([ms], [kHz])
if len(self.corr.backgrounds) >= 1:
self.AmplitudeInfo[1][0].SetValue(
self.corr.backgrounds[0].countrate)
else:
self.AmplitudeInfo[1][0].SetValue(0)
self.AmplitudeInfo[1][1].SetValue(0)
if len(self.corr.backgrounds) == 2:
self.AmplitudeInfo[1][1].SetValue(
self.corr.backgrounds[1].countrate)
else:
self.AmplitudeInfo[1][1].SetValue(0)
# Disable the second line in amplitude correction, if we have
# autocorrelation only.
boolval = self.corr.is_cc
for item in self.WXAmplitudeCCOnlyStuff:
item.Enable(boolval)
def OnBGSpinChanged(self, e):
""" Calls tools.background.ApplyAutomaticBackground
to update background information
"""
# Quick reminder:
#self.AmplitudeInfo = [ [intlabel1, intlabel2],
# [bgspin1, bgspin2],
# normtoNDropdown, textnor]
if self.corr.is_cc:
# update both self.bgselected and self.bg2selected
bg = [float(self.AmplitudeInfo[1][0].GetValue()),
float(self.AmplitudeInfo[1][1].GetValue())]
sig = [float(self.AmplitudeInfo[0][0].GetValue()),
float(self.AmplitudeInfo[0][1].GetValue())]
# Make sure bg < sig
for ii in range(len(bg)):
if sig[ii] != 0:
if bg[ii] > .99*sig[ii]:
bg[ii] = .99*sig[ii]
self.AmplitudeInfo[1][ii].SetValue(bg[ii])
else:
# Only update self.bgselected
bg = float(self.AmplitudeInfo[1][0].GetValue())
sig = float(self.AmplitudeInfo[0][0].GetValue())
# Make sure bg < sig
if sig != 0:
if bg > .99*sig:
bg = .99*sig
self.AmplitudeInfo[1][0].SetValue(bg)
tools.background.ApplyAutomaticBackground(self, bg,
self.parent)
e.Skip()
def OnTitleChanged(self, e):
modelid = self.corr.fit_model.id
pid = self.parent.notebook.GetPageIndex(self)
if self.tabtitle.GetValue() == "":
text = self.counter + mdls.modeldict[modelid][1]
else:
# How many characters of the the page title should be displayed
# in the tab? We choose 9: AC1-012 plus 2 whitespaces
text = self.counter + self.tabtitle.GetValue()[-9:]
self.parent.notebook.SetPageText(pid,text)
def OnSetRange(self, e):
""" Open a new Frame where the parameter range can be set.
Rewrites self.parameter_range
Parameter ranges are treated like parameters: They are saved in
sessions and applied in batch mode.
"""
# TODO:
# - make range selector work with new class
# We write a separate tool for that.
# This tool does not show up in the Tools menu.
if self.parent.RangeSelector is None:
self.parent.RangeSelector = tools.RangeSelector(self)
self.parent.RangeSelector.Bind(wx.EVT_CLOSE,
self.parent.RangeSelector.OnClose)
else:
try:
self.parent.RangeSelector.OnClose()
except:
pass
self.parent.RangeSelector = None
def OnSize(self, event):
""" Resize the fitting Panel, when Window is resized. """
size = self.parent.notebook.GetSize()
tabsize = 33
size[1] = size[1] - tabsize
self.sp.SetSize(size)
def PlotAll(self, event=None, trigger=None):
"""
This function plots the whole correlation and residuals canvas.
We do:
- Channel selection
- Background correction
- Apply Parameters (separate function)
- Drawing of plots
The `event` is usually just an event from buttons or similar
wx objects. It can be "init", then some initial plotting is
done before the data is handled.
The `trigger` is passed to `self.parent.OnFNBPageChanged` so
that tools can update their content accordingly. For more
information on triggers, have a look at the doctring of the
`tools` submodule.
"""
if event == "init":
# We use this to have the page plotted at least once before
# readout of parameters (e.g. startcrop, endcrop)
# This is a performence tweak.
if self.InitialPlot:
return
else:
self.InitialPlot = True
## Enable/Disable, set values frontend normalization
self.OnAmplitudeCheck()
## Apply parameters
self.apply_parameters()
# Calculate correlation function from parameters
## Drawing of correlation plot
# Plots corr.correlation_fit and the calcualted correlation function
# self.datacorr into the upper canvas.
# Create a line @ y=zero:
zerostart = self.corr.lag_time_fit[0]
zeroend = self.corr.lag_time_fit[-1]
datazero = [[zerostart, 0], [zeroend,0]]
# Set plot colors
width = 1
colexp = "grey"
colfit = "blue"
colweight = "cyan"
lines = list()
linezero = plot.PolyLine(datazero, colour='orange', width=width)
lines.append(linezero)
if self.corr.correlation is not None:
if self.corr.is_weighted_fit and \
self.parent.MenuShowWeights.IsChecked():
try:
weights = self.corr.fit_results["fit weights"]
except:
weights = self.corr.fit_weight_data
if isinstance(weights, np.ndarray):
# user might have selected a new weight type and
# presses apply, do not try to display weights
# if weights are from average or other, make sure that the
# dimensions are correct
if weights.shape[0] == self.corr.correlation.shape[0]:
weights = weights[self.corr.fit_ival[0]:self.corr.fit_ival[1]]
# perform some checks
if np.allclose(weights, np.ones_like(weights)):
weights = 0
elif weights.shape[0] != self.corr.modeled_fit.shape[0]:
# non-matching weigths
warnings.warn("Unmatching weights found. Probably from previous data set.")
weights = 0
# Add the weights to the graph.
# This is done by drawing two lines.
w = 1*self.corr.modeled_fit
w1 = 1*w
w2 = 1*w
w1[:, 1] = w[:, 1] + weights
w2[:, 1] = w[:, 1] - weights
# crop w1 and w2 if corr.correlation_fit does not include all
# data points.
if np.all(w[:,0] == self.corr.correlation_fit[:,0]):
pass
else:
raise ValueError("This should not have happened: size of weights is wrong.")
## Normalization with self.normfactor
w1[:,1] *= self.corr.normalize_factor
w2[:,1] *= self.corr.normalize_factor
self.weights_plot_fill_area = [w1,w2]
lineweight1 = plot.PolyLine(w1, legend='',
colour=colweight, width=width)
lines.append(lineweight1)
lineweight2 = plot.PolyLine(w2, legend='',
colour=colweight, width=width)
lines.append(lineweight2)
else:
self.weights_plot_fill_area = None
## Plot Correlation curves
# Plot both, experimental and calculated data
# Normalization with self.normfactor, new feature in 0.7.8
datacorr_norm = self.corr.modeled_plot
linecorr = plot.PolyLine(datacorr_norm, legend='', colour=colfit,
width=width)
dataexp_norm = self.corr.correlation_plot
lineexp = plot.PolyLine(dataexp_norm, legend='', colour=colexp,
width=width)
# Draw linezero first, so it is in the background
lines.append(lineexp)
lines.append(linecorr)
PlotCorr = plot.PlotGraphics(lines,
xLabel=u'lag time τ [ms]', yLabel=u'G(τ)')
self.canvascorr.Draw(PlotCorr)
## Calculate residuals
resid_norm = self.corr.residuals_plot
lineres = plot.PolyLine(resid_norm, legend='', colour=colfit,
width=width)
# residuals or weighted residuals?
if self.corr.is_weighted_fit:
yLabelRes = "weighted \nresiduals"
else:
yLabelRes = "residuals"
PlotRes = plot.PlotGraphics([linezero, lineres],
xLabel=u'lag time τ [ms]',
yLabel=yLabelRes)
self.canvaserr.Draw(PlotRes)
else:
# Amplitude normalization, new feature in 0.7.8
datacorr_norm = self.corr.modeled_plot
linecorr = plot.PolyLine(datacorr_norm, legend='', colour='blue',
width=1)
PlotCorr = plot.PlotGraphics([linezero, linecorr],
xLabel=u'Lag time τ [ms]', yLabel=u'G(τ)')
self.canvascorr.Draw(PlotCorr)
self.parent.OnFNBPageChanged(trigger=trigger)
def settings(self):
""" Here we define, what should be displayed at the left side
of the fitting page/tab.
Parameters:
"""
modelid = self.corr.fit_model.id
horizontalsize = self.sizepanelx-10
# Title
# Create empty tab title
mddat = mdls.modeldict[modelid]
modelshort = mdls.GetModelType(modelid)
titlelabel = u"Data set {} ({} {})".format(
self.counter.strip(" :"), modelshort, mddat[1])
boxti = wx.StaticBox(self.panelsettings, label=titlelabel)
sizerti = wx.StaticBoxSizer(boxti, wx.VERTICAL)
sizerti.SetMinSize((horizontalsize, -1))
self.tabtitle = wx.TextCtrl(self.panelsettings, value="",
size=(horizontalsize-20, -1))
self.Bind(wx.EVT_TEXT, self.OnTitleChanged, self.tabtitle)
sizerti.Add(self.tabtitle)
# Create StaticBoxSizer
box1, check, spin = self.MakeStaticBoxSizer("Model parameters")
# Make the check boxes and spin-controls available everywhere
self.checkboxes = check
self.spincontrol = spin
#
# As of version 0.7.5: we want the units to be displayed
# human readable - the way they are displayed
# in the Page info tool.
#
labels, parameters = mdls.GetHumanReadableParms(modelid,
self.active_parms[1])
parameterstofit = self.active_parms[2]
# Set initial values given by user/programmer for Diffusion Model
for i in np.arange(len(labels)):
self.checkboxes[i].SetValue(parameterstofit[i])
self.spincontrol[i].SetValue(parameters[i])
# Put everything together
self.panelsettings.sizer = wx.BoxSizer(wx.VERTICAL)
self.panelsettings.sizer.Add(sizerti)
self.panelsettings.sizer.Add(box1)
# checkbox "Protect from batch control"
self.wxCBPreventBatchParms = wx.CheckBox(self.panelsettings,
-1,
"prevent batch modification")
box1.Add(self.wxCBPreventBatchParms)
# buttons "Apply" and "Set range"
horzs = wx.BoxSizer(wx.HORIZONTAL)
buttonapply = wx.Button(self.panelsettings, label="Apply")
self.Bind(wx.EVT_BUTTON, self.PlotAll, buttonapply)
horzs.Add(buttonapply)
buttonrange = wx.Button(self.panelsettings, label="Set range")
self.Bind(wx.EVT_BUTTON, self.OnSetRange, buttonrange)
horzs.Add(buttonrange)
box1.Add(horzs)
# Set horizontal size
box1.SetMinSize((horizontalsize, -1))
## More info
normbox = wx.StaticBox(self.panelsettings, label="Amplitude corrections")
miscsizer = wx.StaticBoxSizer(normbox, wx.VERTICAL)
miscsizer.SetMinSize((horizontalsize, -1))
# Intensities and Background
sizeint = wx.FlexGridSizer(rows=3, cols=3, vgap=5, hgap=5)
sizeint.Add(wx.StaticText(self.panelsettings, label="[kHz]"))
sizeint.Add(wx.StaticText(self.panelsettings,
label="Intensity"))
sizeint.Add(wx.StaticText(self.panelsettings,
label="Background"))
sizeint.Add(wx.StaticText(self.panelsettings, label="Ch1"))
intlabel1 = wx.TextCtrl(self.panelsettings)
bgspin1 = wxutils.PCFFloatTextCtrl(self.panelsettings)
bgspin1.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged)
bgspin1.Bind(wx.EVT_TEXT_ENTER, self.OnBGSpinChanged)
sizeint.Add(intlabel1)
intlabel1.SetEditable(False)
sizeint.Add(bgspin1)
chtext2 = wx.StaticText(self.panelsettings, label="Ch2")
sizeint.Add(chtext2)
intlabel2 = wx.TextCtrl(self.panelsettings)
intlabel2.SetEditable(False)
bgspin2 = wxutils.PCFFloatTextCtrl(self.panelsettings)
bgspin2.Bind(wx.EVT_KILL_FOCUS, self.OnBGSpinChanged)
sizeint.Add(intlabel2)
sizeint.Add(bgspin2)
miscsizer.Add(sizeint)
## Normalize to n?
textnor = wx.StaticText(self.panelsettings, label="Plot normalization")
miscsizer.Add(textnor)
normtoNDropdown = wx.ComboBox(self.panelsettings)
self.Bind(wx.EVT_COMBOBOX, self.PlotAll, normtoNDropdown)
miscsizer.Add(normtoNDropdown)
self.AmplitudeInfo = [ [intlabel1, intlabel2],
[bgspin1, bgspin2],
normtoNDropdown, textnor]
self.WXAmplitudeCCOnlyStuff = [chtext2, intlabel2, bgspin2]
self.panelsettings.sizer.Add(miscsizer)
## Add fitting Box
fitbox = wx.StaticBox(self.panelsettings, label="Fitting options")
fitsizer = wx.StaticBoxSizer(fitbox, wx.VERTICAL)
fitsizer.SetMinSize((horizontalsize, -1))
# Add a checkbox for weighted fitting
weightedfitdrop = wx.ComboBox(self.panelsettings)
self.weightlist = ["no weights", "spline (5 knots)", "model function"]
weightedfitdrop.SetItems(self.weightlist)
weightedfitdrop.SetSelection(0)
fitsizer.Add(weightedfitdrop)
# WeightedFitCheck() Enables or Disables the variance part
weightedfitdrop.Bind(wx.EVT_COMBOBOX, self.Fit_WeightedFitCheck)
# Add the variance part.
# In order to do a weighted fit, we need to calculate the variance
# at each point of the experimental data array.
# In order to do that, we need to know how many data points from left
# and right of the interesting data point we want to include in that
# calculation.
fittext = wx.StaticText(self.panelsettings,
label="Calculation of the variance")
fitsizer.Add(fittext)
fittext2 = wx.StaticText(self.panelsettings,
label="from 2j+1 data points")
fitsizer.Add(fittext2)
fitsizerspin = wx.BoxSizer(wx.HORIZONTAL)
fittextvar = wx.StaticText(self.panelsettings, label="j = ")
fitspin = wx.SpinCtrl(self.panelsettings, -1, initial=3, min=1, max=100)
fitsizerspin.Add(fittextvar)
fitsizerspin.Add(fitspin)
fitsizer.Add(fitsizerspin)
# Add algorithm selection
textalg = wx.StaticText(self.panelsettings, label="Algorithm")
fitsizer.Add(textalg)
self.AlgorithmDropdown = wx.ComboBox(self.panelsettings)
items = fit.GetAlgorithmStringList()[1]
self.AlgorithmDropdown.SetItems(items)
self.Bind(wx.EVT_COMBOBOX, self.apply_parameters,
self.AlgorithmDropdown)
fitsizer.Add(self.AlgorithmDropdown)
self.AlgorithmDropdown.SetMaxSize(weightedfitdrop.GetSize())
# Add button "Fit" and chi2 display
fitbuttonsizer = wx.BoxSizer(wx.HORIZONTAL)
buttonfit = wx.Button(self.panelsettings, label="Fit")
self.Bind(wx.EVT_BUTTON, self.Fit_function, buttonfit)
fitbuttonsizer.Add(buttonfit)
self.WXTextChi2 = wx.StaticText(self.panelsettings)
# this StaticText is updated by `self.updateChi2()`
fitbuttonsizer.Add(self.WXTextChi2, flag=wx.ALIGN_CENTER)
fitsizer.Add(fitbuttonsizer)
self.panelsettings.sizer.Add(fitsizer)
# Squeeze everything into the sizer
self.panelsettings.SetSizer(self.panelsettings.sizer)
# This is also necessary in Windows
self.panelsettings.Layout()
self.panelsettings.Show()
# Make all the stuff available for everyone
self.Fitbox = [ fitbox, weightedfitdrop, fittext, fittext2,
fittextvar, fitspin, buttonfit, textalg,
self.AlgorithmDropdown]
# Disable Fitting since no data has been loaded yet
for element in self.Fitbox:
element.Disable()
self.panelsettings.sizer.Fit(self.panelsettings)
self.parent.Layout()
def updateChi2(self):
"""
updates the self.WXTextChi2 text control
"""
label = u""
if hasattr(self.corr, "fit_results"):
if "chi2" in self.corr.fit_results:
chi2 = self.corr.fit_results["chi2"]
chi2str = wxutils.float2string_nsf(chi2, n=3)
chi2str = wxutils.nice_string(chi2str)
label = u" χ²={}".format(chi2str)
# This does not work with wxPython 2.8.12:
#self.WXTextChi2.SetLabelMarkup(u"<b>{}</b>".format(label))
self.WXTextChi2.SetLabel(u"{}".format(label))