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 GetPageInfo(self, Page):
""" Needs a Page and gets all information from it """
Page.PlotAll("init")
# A dictionary with headings as keys and lists of singletts/tuples as
# values. If it is a tuple, it might me interesting for a table.
InfoDict = dict()
# Get Correlation
corr = Page.corr
# Get model information
model = corr.fit_model
parms = corr.fit_parameters
fct = corr.fit_model.function.__name__
InfoDict["version"] = [Page.parent.version]
Title = list()
# The tool statistics relys on the string "filename/title".
# Do not change it!
if len(model[1]) == 0:
# Prevent saving no title
model[1] = "NoName"
Title.append(["filename/title", Page.title])
Title.append(["Model species", model.components])
Title.append(["Model name", model.name])
Title.append(["Model ID", str(model.id)])
Title.append(["Model function", fct])
Title.append(["Page number", Page.counter[1:-2]])
## Parameters
Parameters = list()
# Use this function to determine human readable parameters, if possible
Units, Newparameters = mdls.GetHumanReadableParms(model.id, parms)
# Add Parameters
for i in np.arange(len(parms)):
Parameters.append([Units[i], Newparameters[i]])
InfoDict["parameters"] = Parameters
# Add some more information if available
# Info is a dictionary or None
MoreInfo = mdls.GetMoreInfo(model.id, Page)
if MoreInfo is not None:
InfoDict["supplement"] = MoreInfo
# Try to get the dictionary entry of a model
try:
# This function should return all important information
# that can be calculated from the given parameters.
func_info = mdls.supplement[model.id]
except KeyError:
# No information available
pass
else:
InfoDict["modelsupdoc"] = [func_info.func_doc]
## Fitting
if hasattr(corr, "fit_results"):
Fitting = list()
weightedfit = corr.fit_results["weighted fit"]
if corr.correlation is not None:
# Mode AC vs CC
if corr.is_cc:
Title.append(["Type AC/CC", "Cross-correlation"])
else:
Title.append(["Type AC/CC", "Autocorrelation"])
if "chi2" in corr.fit_results:
Fitting.append([u"χ²", corr.fit_results["chi2"]])
if weightedfit:
try:
Fitting.append([
"Weighted fit",
corr.fit_results["weighted fit type"]
])
except KeyError:
Fitting.append(
["Weighted fit", u"" + Page.Fitbox[1].GetValue()])
if corr.fit_results.has_key("chi2 type"):
ChiSqType = corr.fit_results["chi2 type"]
else:
ChiSqType = "unknown"
Fitting.append([u"χ²-type", ChiSqType])
Fitting.append(
["Algorithm", fit.Algorithms[corr.fit_algorithm][1]])
if len(Page.GlobalParameterShare) != 0:
shared = str(Page.GlobalParameterShare[0])
for item in Page.GlobalParameterShare[1:]:
shared += ", " + str(item)
Fitting.append(["Shared parameters with Pages", shared])
if corr.fit_results.has_key("weighted fit bins"):
Fitting.append([
"Std. channels",
2 * corr.fit_results["weighted fit bins"] + 1
])
# Fitting range:
t1 = 1. * corr.lag_time[corr.fit_ival[0]]
t2 = 1. * corr.lag_time[corr.fit_ival[1] - 1]
Fitting.append(["Ival start [ms]", "%.4e" % t1])
Fitting.append(["Ival end [ms]", "%.4e" % t2])
# Fittet parameters
try:
fitparmsid = corr.fit_results["fit parameters"]
except:
fitparmsid = corr.fit_parameters_variable
fitparms = np.array(corr.fit_model.parameters[0])[fitparmsid]
fitparms_short = [f.split()[0] for f in fitparms]
fitparms_short = u", ".join(fitparms_short)
Fitting.append(["Fit parm.", fitparms_short])
# global fitting
for key in corr.fit_results.keys():
if key.startswith("global"):
Fitting.append(
[key.capitalize(), corr.fit_results[key]])
# Fit errors
if corr.fit_results.has_key("fit error estimation"):
errors = corr.fit_results["fit error estimation"]
for err, par in zip(errors, fitparms):
nam, val = mdls.GetHumanReadableParameterDict(
model.id, [par], [err])
Fitting.append(["Err " + nam[0], val[0]])
InfoDict["fitting"] = Fitting
## Normalization parameter id to name
if corr.normparm is None:
normparmtext = "None"
elif corr.normparm < len(corr.fit_parameters):
normparmtext = corr.fit_model.parameters[0][corr.normparm]
else:
# supplementary parameters
supnum = corr.normparm - len(corr.fit_parameters)
normparmtext = MoreInfo[supnum][0]
Title.append(["Normalization", normparmtext])
## Background
Background = list()
if corr.is_cc:
if len(corr.backgrounds) == 2:
# Channel 1
Background.append(["bg name Ch1", corr.backgrounds[0].name])
Background.append(
["bg rate Ch1 [kHz]", corr.backgrounds[0].countrate])
# Channel 2
Background.append(["bg name Ch2", corr.backgrounds[1].name])
Background.append(
["bg rate Ch2 [kHz]", corr.backgrounds[1].countrate])
InfoDict["background"] = Background
else:
if len(corr.backgrounds) == 1:
Background.append(["bg name", corr.backgrounds[0].name])
Background.append(
["bg rate [kHz]", corr.backgrounds[0].countrate])
InfoDict["background"] = Background
## Function doc string
InfoDict["modeldoc"] = [corr.fit_model.description_long]
InfoDict["title"] = Title
return InfoDict