import wx from plottables import Theory1D import math import numpy 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 def format_number(value, high=False): """ Return a float in a standardized, human-readable formatted string. This is used to output readable (e.g. x.xxxe-y) values to the panel. """ try: value = float(value) except: output = "NaN" return output.lstrip().rstrip() if high: output = "%-6.4g" % value else: output = "%-5.3g" % value return output.lstrip().rstrip() 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 == 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)) 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() 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) == 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 = numpy.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 = numpy.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 == None: errA = 0.0 errB = 0.0 else: errA = math.sqrt(cov[0][0]) errB = math.sqrt(cov[1][1]) if out == 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 = numpy.sqrt(-3 * float(cstA)) value = format_number(rg) self.Rg_tctr.SetValue(value) if self.I0_tctr.IsShown(): val = numpy.exp(cstB) self.I0_tctr.SetValue(format_number(val)) if self.Rgerr_tctr.IsShown(): if rg != 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 = numpy.abs(numpy.exp(cstB) * errB) self.I0err_tctr.SetValue(format_number(val)) if self.Diameter_tctr.IsShown(): rg = numpy.sqrt(-2 * float(cstA)) _diam = 4 * numpy.sqrt(-float(cstA)) value = format_number(_diam) self.Diameter_tctr.SetValue(value) if self.Diametererr_tctr.IsShown(): if rg != 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 def floatForwardTransform(self, x): """ transform a float. """ # TODO: refactor this with proper object-oriented design # This code stinks. if self.xLabel == "x": return transform.toX(x) if self.xLabel == "x^(2)": return transform.toX2(x) if self.xLabel == "ln(x)": return transform.toLogX(x) if self.xLabel == "log10(x)": return math.log10(x) def floatTransform(self, x): """ transform a float.It is use to determine the x. View min and x.View max for values not in x """ # TODO: refactor this with proper object-oriented design # This code stinks. if self.xLabel == "x": return transform.toX(x) if self.xLabel == "x^(2)": return transform.toX2(x) if self.xLabel == "ln(x)": return transform.toLogX(x) if self.xLabel == "log10(x)": if x > 0: return x else: raise ValueError, "cannot compute log of a negative number" def floatInvTransform(self, x): """ transform a float.It is used to determine the x.View min and x.View max for values not in x. Also used to properly calculate RgQmin, RgQmax and to update qmin and qmax in the linear range boxes on the panel. """ # TODO: refactor this. This is just a hack to make the # functionality work without rewritting the whole code # with good design (which really should be done...). if self.xLabel == "x": return x elif self.xLabel == "x^(2)": return math.sqrt(x) elif self.xLabel == "x^(4)": return math.sqrt(math.sqrt(x)) elif self.xLabel == "log10(x)": return math.pow(10, x) elif self.xLabel == "ln(x)": return math.exp(x) elif self.xLabel == "log10(x^(4))": return math.sqrt(math.sqrt(math.pow(10, x))) return x 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 def setFitRange(self, xmin, xmax, xminTrans, xmaxTrans): """ Set fit parameters """ self.xminFit.SetValue(format_number(xmin)) self.xmaxFit.SetValue(format_number(xmax)) 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)) class MyApp(wx.App): """ Test application """ 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 def onFitDisplay(self, tempx, tempy, xminView, xmaxView, xmin, xmax, func): """ Test application dummy method """ pass def returnTrans(self): """ Test application dummy method """ return '', '', 0, 0, 0, 0, 0