-                      r79492222
+                      rc1c14ba
+"""
+    P(r) perspective for SasView
+"""
 ################################################################################
 #This software was developed by the University of Tennessee as part of the
 #Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
 #project funded by the US National Science Foundation.
+#project funded by the US National Science Foundation.
+#
 #See the license text in license.txt
 …
 # Make sure the option of saving each curve is available
+# Make sure the option of saving each curve is available
 # Use the I(q) curve as input and compare the output to P(r)
-import os
 import sys
 import wx
 …
 from sas.guiframe.dataFitting import Data1D
 from sas.guiframe.events import NewPlotEvent
 from sas.guiframe.events import StatusEvent
 from sas.guiframe.gui_style import GUIFRAME_ID
+from sas.guiframe.events import StatusEvent
+from sas.guiframe.gui_style import GUIFRAME_ID
 from sas.pr.invertor import Invertor
 from sas.dataloader.loader import Loader
 import sas.dataloader
 from pr_widgets import load_error
+from pr_widgets import load_error
 from sas.guiframe.plugin_base import PluginBase
 PR_FIT_LABEL       = r"$P_{fit}(r)$"
 PR_LOADED_LABEL    = r"$P_{loaded}(r)$"
 IQ_DATA_LABEL      = r"$I_{obs}(q)$"
 IQ_FIT_LABEL       = r"$I_{fit}(q)$"
 IQ_SMEARED_LABEL   = r"$I_{smeared}(q)$"
+PR_FIT_LABEL = r"$P_{fit}(r)$"
+PR_LOADED_LABEL = r"$P_{loaded}(r)$"
+IQ_DATA_LABEL = r"$I_{obs}(q)$"
+IQ_FIT_LABEL = r"$I_{fit}(q)$"
+IQ_SMEARED_LABEL = r"$I_{smeared}(q)$"
 GROUP_ID_IQ_DATA = r"$I_{obs}(q)$"
 GROUP_ID_PR_FIT = r"$P_{fit}(r)$"
 …
     DEFAULT_ALPHA = 0.0001
     DEFAULT_NFUNC = 10
     DEFAULT_DMAX  = 140.0
+    DEFAULT_DMAX = 140.0
     def __init__(self, standalone=True):
         PluginBase.__init__(self, name="Pr Inversion", standalone=standalone)
         ## Simulation window manager
         self.simview = None
         ## State data
         self.alpha      = self.DEFAULT_ALPHA
         self.nfunc      = self.DEFAULT_NFUNC
+        self.alpha = self.DEFAULT_ALPHA
+        self.nfunc = self.DEFAULT_NFUNC
         self.max_length = self.DEFAULT_DMAX
         self.q_min      = None
         self.q_max      = None
         self.has_bck    = False
+        self.q_min = None
+        self.q_max = None
+        self.has_bck = False
         self.slit_height = 0
         self.slit_width  = 0
+        self.slit_width = 0
         ## Remember last plottable processed
         self.last_data  = "sphere_60_q0_2.txt"
+        self.last_data = ""
         self._current_file_data = None
         ## Time elapsed for last computation [sec]
 …
         self.elapsed = 0.022
         self.iq_data_shown = False
         ## Current invertor
         self.invertor    = None
         self.pr          = None
         self.data_id = IQ_DATA_LABEL
+        self.invertor = None
+        self.pr = None
+        self.data_id = IQ_DATA_LABEL
         # Copy of the last result in case we need to display it.
         self._last_pr    = None
         self._last_out   = None
         self._last_cov   = None
+        self._last_pr = None
+        self._last_out = None
+        self._last_cov = None
         ## Calculation thread
         self.calc_thread = None
 …
         self._normalize_output = False
         self._scale_output_unity = False
         ## List of added P(r) plots
         self._added_plots = {}
         self._default_Iq  = {}
+        self._default_Iq = {}
         self.list_plot_id = []
         # Associate the inversion state reader with .prv files
         from inversion_state import Reader
         # Create a CanSAS/Pr reader
         self.state_reader = Reader(self.set_state)
 …
         l.associate_file_reader('.prv', self.state_reader)
         #l.associate_file_reader(".svs", self.state_reader)
         # Log startup
         logging.info("Pr(r) plug-in started")
     def delete_data(self, data_id):
         """
 …
         """
         self.control_panel.clear_panel()
     def get_data(self):
         """
 …
         """
         return self.current_plottable
     def set_state(self, state=None, datainfo=None):
         """
         Call-back method for the inversion state reader.
         This method is called when a .prv file is loaded.
         :param state: InversionState object
         :param datainfo: Data1D object [optional]
         """
         try:
 …
                 data = datainfo
             if data is None:
                 msg =  "Pr.set_state: datainfo parameter cannot "
+                msg = "Pr.set_state: datainfo parameter cannot "
                 msg += "be None in standalone mode"
                 raise RuntimeError, msg
             # Ensuring that plots are coordinated correctly
             t = time.localtime(data.meta_data['prstate'].timestamp)
             time_str = time.strftime("%b %d %H:%M", t)
             # Check that no time stamp is already appended
             max_char = data.meta_data['prstate'].file.find("[")
             if max_char < 0:
                 max_char = len(data.meta_data['prstate'].file)
             datainfo.meta_data['prstate'].file =\
+            datainfo.meta_data['prstate'].file = \
                 data.meta_data['prstate'].file[0:max_char]\
                 + ' [' + time_str + ']'
             data.filename = data.meta_data['prstate'].file
             # TODO:
 …
             self.current_plottable = self.parent.create_gui_data(data, None)
             self.current_plottable.group_id = data.meta_data['prstate'].file
             # Make sure the user sees the P(r) panel after loading
             #self.parent.set_perspective(self.perspective)
             self.on_perspective(event=None)
+            #self.parent.set_perspective(self.perspective)
+            self.on_perspective(event=None)
             # Load the P(r) results
             #state = self.state_reader.get_state()
 …
             self.parent.add_data(data_list=data_dict)
             wx.PostEvent(self.parent, NewPlotEvent(plot=self.current_plottable,
                                         title=self.current_plottable.title))
+                                                   title=self.current_plottable.title))
             self.control_panel.set_state(state)
         except:
             logging.error("prview.set_state: %s" % sys.exc_value)
     def help(self, evt):
         """
         Show a general help dialog.
+        Show a general help dialog.
         :TODO: replace the text with a nice image
         """
         from inversion_panel import HelpDialog
 …
         else:
             dialog.Destroy()
     def _fit_pr(self, evt):
         """
 …
         # Generate P(r) for sphere
         radius = 60.0
         d_max  = 2*radius
         r = pylab.arange(0.01, d_max, d_max/51.0)
+        d_max = 2 * radius
+        r = pylab.arange(0.01, d_max, d_max / 51.0)
         M = len(r)
         y = numpy.zeros(M)
         pr_err = numpy.zeros(M)
         sum = 0.0
+        total = 0.0
         for j in range(M):
             value = self.pr_theory(r[j], radius)
             sum += value
+            total += value
             y[j] = value
             pr_err[j] = math.sqrt(y[j])
         y = y/sum*d_max/len(r)
+        y = y / total * d_max / len(r)
         # Perform fit
 …
         for i in range(len(out)):
             print "%g +- %g" % (out[i], math.sqrt(cov[i][i]))
         # Show input P(r)
         title = "Pr"
 …
         new_plot.name = "P_{obs}(r)"
         new_plot.xaxis("\\rm{r}", 'A')
         new_plot.yaxis("\\rm{P(r)} ","cm^{-3}")
+        new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
         new_plot.group_id = "P_{obs}(r)"
         new_plot.id = "P_{obs}(r)"
         new_plot.title = title
         self.parent.update_theory(data_id=self.data_id,  theory=new_plot)
+        self.parent.update_theory(data_id=self.data_id, theory=new_plot)
         wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
         # Show P(r) fit
         self.show_pr(out, pr)
         # Show I(q) fit
         q = pylab.arange(0.001, 0.1, 0.01/51.0)
+        q = pylab.arange(0.001, 0.1, 0.01 / 51.0)
         self.show_iq(out, pr, q)
     def show_shpere(self, x, radius=70.0, x_range=70.0):
         """
 …
         sum_true = 0.0
         for i in range(len(x)):
             y_true[i] = self.pr_theory(x[i], radius)
+            y_true[i] = self.pr_theory(x[i], radius)
             sum_true += y_true[i]
         y_true = y_true/sum_true*x_range/len(x)
+        y_true = y_true / sum_true * x_range / len(x)
         # Show the theory P(r)
         new_plot = Data1D(x, y_true)
 …
         new_plot.name = "P_{true}(r)"
         new_plot.xaxis("\\rm{r}", 'A')
         new_plot.yaxis("\\rm{P(r)} ","cm^{-3}")
+        new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
         new_plot.id = "P_{true}(r)"
         new_plot.group_id = "P_{true}(r)"
         self.parent.update_theory(data_id=self.data_id, theory=new_plot)
         #Put this call in plottables/guitools
+        #Put this call in plottables/guitools
         wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot,
+                                                title="Sphere P(r)"))
+                                               title="Sphere P(r)"))
     def get_npts(self):
         """
 …
         except:
             return 0
     def show_iq(self, out, pr, q=None):
         """
+        """
+            Display computed I(q)
+        """
         qtemp = pr.x
         if not q == None:
 …
         maxq = -1
         for q_i in qtemp:
             if q_i>maxq:
                 maxq=q_i
+            if q_i > maxq:
+                maxq = q_i
         minq = 0.001
         # Check for user min/max
         if not pr.q_min == None:
 …
         if not pr.q_max == None:
             maxq = pr.q_max
         x = pylab.arange(minq, maxq, maxq/301.0)
+        x = pylab.arange(minq, maxq, maxq / 301.0)
         y = numpy.zeros(len(x))
         err = numpy.zeros(len(x))
 …
                 err[i] = 1.0
                 print "Error getting error", value, x[i]
         new_plot = Data1D(x, y)
         new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
         new_plot.name = IQ_FIT_LABEL
         new_plot.xaxis("\\rm{Q}", 'A^{-1}')
         new_plot.yaxis("\\rm{Intensity} ","cm^{-1}")
+        new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
         title = "I(q)"
         new_plot.title = title
         # If we have a group ID, use it
         if pr.info.has_key("plot_group_id"):
 …
         new_plot.id = IQ_FIT_LABEL
         self.parent.update_theory(data_id=self.data_id, theory=new_plot)
         wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
         # If we have used slit smearing, plot the smeared I(q) too
         if pr.slit_width > 0 or pr.slit_height > 0:
             x = pylab.arange(minq, maxq, maxq/301.0)
+            x = pylab.arange(minq, maxq, maxq / 301.0)
             y = numpy.zeros(len(x))
             err = numpy.zeros(len(x))
 …
                     err[i] = 1.0
                     print "Error getting error", value, x[i]
             new_plot = Data1D(x, y)
             new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
             new_plot.name = IQ_SMEARED_LABEL
             new_plot.xaxis("\\rm{Q}", 'A^{-1}')
             new_plot.yaxis("\\rm{Intensity} ","cm^{-1}")
+            new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
             # If we have a group ID, use it
             if pr.info.has_key("plot_group_id"):
               new_plot.group_id = pr.info["plot_group_id"]
+                new_plot.group_id = pr.info["plot_group_id"]
             new_plot.id = IQ_SMEARED_LABEL
             new_plot.title = title
             self.parent.update_theory(data_id=self.data_id, theory=new_plot)
             wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title=title))
     def _on_pr_npts(self, evt):
         """
         Redisplay P(r) with a different number of points
         """
+        """
         from inversion_panel import PrDistDialog
         dialog = PrDistDialog(None, -1)
         dialog.set_content(self._pr_npts)
         if dialog.ShowModal() == wx.ID_OK:
             self._pr_npts= dialog.get_content()
+            self._pr_npts = dialog.get_content()
             dialog.Destroy()
             self.show_pr(self._last_out, self._last_pr, self._last_cov)
         else:
             dialog.Destroy()
     def show_pr(self, out, pr, cov=None):
         """
         """
+        """
         # Show P(r)
         x = pylab.arange(0.0, pr.d_max, pr.d_max/self._pr_npts)
+        x = pylab.arange(0.0, pr.d_max, pr.d_max / self._pr_npts)
         y = numpy.zeros(len(x))
         dy = numpy.zeros(len(x))
         y_true = numpy.zeros(len(x))
         sum = 0.0
+        total = 0.0
         pmax = 0.0
         cov2 = numpy.ascontiguousarray(cov)
         for i in range(len(x)):
             if cov2==None:
+            if cov2 == None:
                 value = pr.pr(out, x[i])
             else:
                 (value, dy[i]) = pr.pr_err(out, cov2, x[i])
             sum += value*pr.d_max/len(x)
+            total += value * pr.d_max / len(x)
             # keep track of the maximum P(r) value
             if value>pmax:
+            if value > pmax:
                 pmax = value
             y[i] = value
         if self._normalize_output==True:
             y = y/sum
             dy = dy/sum
         elif self._scale_output_unity==True:
             y = y/pmax
             dy = dy/pmax
         if cov2==None:
+        if self._normalize_output == True:
+            y = y / total
+            dy = dy / total
+        elif self._scale_output_unity == True:
+            y = y / pmax
+            dy = dy / pmax
+        if cov2 == None:
             new_plot = Data1D(x, y)
             new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
 …
         new_plot.name = PR_FIT_LABEL
         new_plot.xaxis("\\rm{r}", 'A')
         new_plot.yaxis("\\rm{P(r)} ","cm^{-3}")
+        new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
         new_plot.title = "P(r) fit"
         new_plot.id = PR_FIT_LABEL
         # Make sure that the plot is linear
         new_plot.xtransform = "x"
         new_plot.ytransform = "y"
+        new_plot.ytransform = "y"
         new_plot.group_id = GROUP_ID_PR_FIT
         self.parent.update_theory(data_id=self.data_id, theory=new_plot)
+        self.parent.update_theory(data_id=self.data_id, theory=new_plot)
         wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="P(r) fit"))
         return x, pr.d_max
     def load(self, data):
         """
 …
         by our standard DataLoader class.
         """
         class FileData:
+        class FileData(object):
             x = None
             y = None
             err = None
             path = None
             def __init__(self, path):
                 self.path = path
         self._current_file_data = FileData(data.path)
         # Use data loader to load file
-        #dataread = Loader().load(path)
         dataread = data
         # Notify the user if we could not read the file
         if dataread is None:
             raise RuntimeError, "Invalid data"
         x = None
         y = None
 …
             err = dataread.dy
         else:
             if isinstance(dataread, list) and len(dataread)>0:
+            if isinstance(dataread, list) and len(dataread) > 0:
                 x = dataread[0].x
                 y = dataread[0].y
                 err = dataread[0].dy
                 msg = "PrView only allows a single data set at a time. "
                 msg += "Only the first data set was loaded."
+                msg += "Only the first data set was loaded."
                 wx.PostEvent(self.parent, StatusEvent(status=msg))
             else:
 …
                     return x, y, err
                 raise RuntimeError, "This tool can only read 1D data"
         self._current_file_data.x = x
         self._current_file_data.y = y
         self._current_file_data.err = err
         return x, y, err
     def load_columns(self, path = "sphere_60_q0_2.txt"):
+    def load_columns(self, path="sphere_60_q0_2.txt"):
         """
         Load 2- or 3- column ascii
         """
         # Read the data from the data file
         data_x   = numpy.zeros(0)
         data_y   = numpy.zeros(0)
+        data_x = numpy.zeros(0)
+        data_y = numpy.zeros(0)
         data_err = numpy.zeros(0)
         scale    = None
         min_err  = 0.0
+        scale = None
+        min_err = 0.0
         if not path == None:
             input_f = open(path,'r')
             buff    = input_f.read()
             lines   = buff.split('\n')
+            input_f = open(path, 'r')
+            buff = input_f.read()
+            lines = buff.split('\n')
             for line in lines:
                 try:
 …
                     x = float(toks[0])
                     y = float(toks[1])
                     if len(toks)>2:
+                    if len(toks) > 2:
                         err = float(toks[2])
                     else:
                         if scale==None:
                             scale = 0.05*math.sqrt(y)
+                        if scale == None:
+                            scale = 0.05 * math.sqrt(y)
                             #scale = 0.05/math.sqrt(y)
                             min_err = 0.01*y
                         err = scale*math.sqrt(y)+min_err
+                            min_err = 0.01 * y
+                        err = scale * math.sqrt(y) + min_err
                         #err = 0
                     data_x = numpy.append(data_x, x)
                     data_y = numpy.append(data_y, y)
                     data_err = numpy.append(data_err, err)
                 except:
                     pass
         if not scale==None:
+                    logging.error(sys.exc_value)
+        if not scale == None:
             message = "The loaded file had no error bars, statistical errors are assumed."
             wx.PostEvent(self.parent, StatusEvent(status=message))
         else:
             wx.PostEvent(self.parent, StatusEvent(status=''))
         return data_x, data_y, data_err
+        return data_x, data_y, data_err
     def load_abs(self, path):
         """
         Load an IGOR .ABS reduced file
         :param path: file path
         :return: x, y, err vectors
         """
         # Read the data from the data file
         data_x   = numpy.zeros(0)
         data_y   = numpy.zeros(0)
+        data_x = numpy.zeros(0)
+        data_y = numpy.zeros(0)
         data_err = numpy.zeros(0)
         scale    = None
         min_err  = 0.0
+        scale = None
+        min_err = 0.0
         data_started = False
         if not path == None:
             input_f = open(path,'r')
             buff    = input_f.read()
             lines   = buff.split('\n')
+            input_f = open(path, 'r')
+            buff = input_f.read()
+            lines = buff.split('\n')
             for line in lines:
                 if data_started == True:
 …
                         x = float(toks[0])
                         y = float(toks[1])
                         if len(toks)>2:
+                        if len(toks) > 2:
                             err = float(toks[2])
                         else:
                             if scale == None:
                                 scale = 0.05*math.sqrt(y)
+                                scale = 0.05 * math.sqrt(y)
                                 #scale = 0.05/math.sqrt(y)
                                 min_err = 0.01*y
                             err = scale*math.sqrt(y)+min_err
+                                min_err = 0.01 * y
+                            err = scale * math.sqrt(y) + min_err
                             #err = 0
                         data_x = numpy.append(data_x, x)
                         data_y = numpy.append(data_y, y)
                         data_err = numpy.append(data_err, err)
                     except:
                         pass
                 elif line.find("The 6 columns")>=0:
                     data_started = True
+                        logging.error(sys.exc_value)
+                elif line.find("The 6 columns") >= 0:
+                    data_started = True
         if not scale == None:
             message = "The loaded file had no error bars, statistical errors are assumed."
 …
         else:
             wx.PostEvent(self.parent, StatusEvent(status=''))
         return data_x, data_y, data_err
+        return data_x, data_y, data_err
     def pr_theory(self, r, R):
         """
             Return P(r) of a sphere for a given R
+        """
+            Return P(r) of a sphere for a given R
             For test purposes
         """
         if r <= 2*R:
             return 12.0* ((0.5*r/R)**2) * ((1.0-0.5*r/R)**2) * ( 2.0 + 0.5*r/R )
+        if r <= 2 * R:
+            return 12.0 * ((0.5 * r / R) ** 2) * ((1.0 - 0.5 * r / R) ** 2) * (2.0 + 0.5 * r / R)
         else:
             return 0.0
 …
         """
         Get the context menu items available for P(r)
         :param graph: the Graph object to which we attach the context menu
         :return: a list of menu items with call-back function
         """
         graph = plotpanel.graph
 …
             change_n_hint += " points on the P(r) output"
             change_n_label = "Change number of P(r) points"
             m_list = [[change_n_label, change_n_hint , self._on_pr_npts]]
+            m_list = [[change_n_label, change_n_hint, self._on_pr_npts]]
             if self._scale_output_unity or self._normalize_output:
                 hint = "Let the output P(r) keep the scale of the data"
                 m_list.append(["Disable P(r) scaling", hint,
+                m_list.append(["Disable P(r) scaling", hint,
                                self._on_disable_scaling])
             if not self._scale_output_unity:
                 m_list.append(["Scale P_max(r) to unity",
                                "Scale P(r) so that its maximum is 1",
+                m_list.append(["Scale P_max(r) to unity",
+                               "Scale P(r) so that its maximum is 1",
                                self._on_scale_unity])
             if not self._normalize_output:
                 m_list.append(["Normalize P(r) to unity",
                                "Normalize the integral of P(r) to 1",
+                m_list.append(["Normalize P(r) to unity",
+                               "Normalize the integral of P(r) to 1",
                                self._on_normalize])
             return m_list
+        elif item.id in [PR_LOADED_LABEL, IQ_DATA_LABEL, IQ_FIT_LABEL,
+                          IQ_SMEARED_LABEL]:
+        elif item.id in [PR_LOADED_LABEL, IQ_DATA_LABEL, IQ_FIT_LABEL, IQ_SMEARED_LABEL]:
             return []
         elif item.id == graph.selected_plottable:
+           if not self.standalone and issubclass(item.__class__, Data1D):
+                return [["Compute P(r)",
+                             "Compute P(r) from distribution",
+                             self._on_context_inversion]]
+            if not self.standalone and issubclass(item.__class__, Data1D):
+                return [["Compute P(r)",
+                         "Compute P(r) from distribution",
+                         self._on_context_inversion]]
         return []
 …
         """
         Disable P(r) scaling
         :param evt: Menu event
         """
         self._normalize_output = False
         self._scale_output_unity = False
         self.show_pr(self._last_out, self._last_pr, self._last_cov)
         # Now replot the original added data
         for plot in self._added_plots:
             self._added_plots[plot].y = numpy.copy(self._default_Iq[plot])
             wx.PostEvent(self.parent,
                          NewPlotEvent(plot=self._added_plots[plot],
+            wx.PostEvent(self.parent,
+                         NewPlotEvent(plot=self._added_plots[plot],
                                       title=self._added_plots[plot].name,
                                                    update=True))
+                                      update=True))
         # Need the update flag in the NewPlotEvent to protect against
         # the plot no longer being there...
     def _on_normalize(self, evt):
         """
         Normalize the area under the P(r) curve to 1.
         This operation is done for all displayed plots.
         :param evt: Menu event
         """
         self._normalize_output = True
         self._scale_output_unity = False
         self.show_pr(self._last_out, self._last_pr, self._last_cov)
         # Now scale the added plots too
         for plot in self._added_plots:
             sum = numpy.sum(self._added_plots[plot].y)
+            total = numpy.sum(self._added_plots[plot].y)
             npts = len(self._added_plots[plot].x)
             sum *= self._added_plots[plot].x[npts-1]/npts
             y = self._added_plots[plot].y/sum
+            total *= self._added_plots[plot].x[npts - 1] / npts
+            y = self._added_plots[plot].y / total
             new_plot = Data1D(self._added_plots[plot].x, y)
             new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
 …
             new_plot.name = self._added_plots[plot].name
             new_plot.xaxis("\\rm{r}", 'A')
             new_plot.yaxis("\\rm{P(r)} ","cm^{-3}")
             self.parent.update_theory(data_id=self.data_id, theory=new_plot)
             wx.PostEvent(self.parent,
+            new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
+            self.parent.update_theory(data_id=self.data_id, theory=new_plot)
+            wx.PostEvent(self.parent,
                          NewPlotEvent(plot=new_plot, update=True,
                                          title=self._added_plots[plot].name))
+                                      title=self._added_plots[plot].name))
     def _on_scale_unity(self, evt):
         """
         Scale the maximum P(r) value on each displayed plot to 1.
         :param evt: Menu event
         """
         self._scale_output_unity = True
         self._normalize_output = False
         self.show_pr(self._last_out, self._last_pr, self._last_cov)
         # Now scale the added plots too
         for plot in self._added_plots:
             _max = 0
             for y in self._added_plots[plot].y:
                 if y>_max:
+                if y > _max:
                     _max = y
             y = self._added_plots[plot].y/_max
+            y = self._added_plots[plot].y / _max
             new_plot = Data1D(self._added_plots[plot].x, y)
             new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
             new_plot.name = self._added_plots[plot].name
             new_plot.xaxis("\\rm{r}", 'A')
             new_plot.yaxis("\\rm{P(r)} ","cm^{-3}")
             self.parent.update_theory(data_id=self.data_id,theory=new_plot)
             wx.PostEvent(self.parent,
+            new_plot.yaxis("\\rm{P(r)} ", "cm^{-3}")
+            self.parent.update_theory(data_id=self.data_id, theory=new_plot)
+            wx.PostEvent(self.parent,
                          NewPlotEvent(plot=new_plot, update=True,
                                 title=self._added_plots[plot].name))
+                                      title=self._added_plots[plot].name))
     def start_thread(self):
         """
+            Start a calculation thread
         """
         from pr_thread import CalcPr
         # If a thread is already started, stop it
         if self.calc_thread != None and self.calc_thread.isrunning():
             self.calc_thread.stop()
             ## stop just raises the flag -- the thread is supposed to
+            ## stop just raises the flag -- the thread is supposed to
             ## then kill itself. In August 2014 it was shown that this is
             ## incorrectly handled by fitting.py and a fix implemented.
+            ## incorrectly handled by fitting.py and a fix implemented.
             ## It is not clear whether it is properly used here, but the
             ## "fix" of waiting for the previous thread to end breaks the
 …
             ## are being properly handled.
             ##
             ##    -PDB January 25, 2015
+            ##    -PDB January 25, 2015
         pr = self.pr.clone()
         self.calc_thread = CalcPr(pr, self.nfunc,
                                    error_func=self._thread_error,
                                    completefn=self._completed, updatefn=None)
+                                  error_func=self._thread_error,
+                                  completefn=self._completed, updatefn=None)
         self.calc_thread.queue()
         self.calc_thread.ready(2.5)
     def _thread_error(self, error):
         """
+            Call-back method for calculation errors
         """
         wx.PostEvent(self.parent, StatusEvent(status=error))
     def _estimate_completed(self, alpha, message, elapsed):
         """
         Parameter estimation completed,
+        Parameter estimation completed,
         display the results to the user
         :param alpha: estimated best alpha
         :param elapsed: computation time
         """
         # Save useful info
         self.elapsed = elapsed
         self.control_panel.alpha_estimate = alpha
         if not message==None:
+        if not message == None:
             wx.PostEvent(self.parent, StatusEvent(status=str(message)))
         self.perform_estimateNT()
     def _estimateNT_completed(self, nterms, alpha, message, elapsed):
         """
         Parameter estimation completed,
+        Parameter estimation completed,
         display the results to the user
         :param alpha: estimated best alpha
         :param nterms: estimated number of terms
         :param elapsed: computation time
         """
         # Save useful info
 …
         self.control_panel.nterms_estimate = nterms
         self.control_panel.alpha_estimate = alpha
         if not message==None:
+        if not message == None:
             wx.PostEvent(self.parent, StatusEvent(status=str(message)))
     def _completed(self, out, cov, pr, elapsed):
         """
         wxCallAfter Method called with the results when the inversion
         is done
         :param out: output coefficient for the base functions
         :param cov: covariance matrix
 …
         # Without CallAfter, it will freeze with wx >= 2.9.
         wx.CallAfter(self._completed_call, out, cov, pr, elapsed)
     def _completed_call(self, out, cov, pr, elapsed):
         """
         Method called with the results when the inversion
         is done
         :param out: output coefficient for the base functions
         :param cov: covariance matrix
         :param pr: Invertor instance
         :param elapsed: time spent computing
         """
         # Save useful info
         self.elapsed = elapsed
         # Keep a copy of the last result
         self._last_pr  = pr.clone()
+        self._last_pr = pr.clone()
         self._last_out = out
         self._last_cov = cov
         # Save Pr invertor
         self.pr = pr
-        #message = "Computation completed in"
-        #message +=  %g seconds [chi2=%g]" % (elapsed, pr.chi2)
-        #wx.PostEvent(self.parent, StatusEvent(status=message))
         cov = numpy.ascontiguousarray(cov)
 …
         self.control_panel.oscillation = pr.oscillations(out)
         self.control_panel.positive = pr.get_positive(out)
         self.control_panel.pos_err  = pr.get_pos_err(out, cov)
+        self.control_panel.pos_err = pr.get_pos_err(out, cov)
         self.control_panel.rg = pr.rg(out)
         self.control_panel.iq0 = pr.iq0(out)
         self.control_panel.bck = pr.background
         # Show I(q) fit
         self.show_iq(out, self.pr)
         # Show P(r) fit
         x_values, x_range = self.show_pr(out, self.pr, cov)
+        self.show_pr(out, self.pr, cov)
     def show_data(self, path=None, data=None, reset=False):
         """
         Show data read from a file
         :param path: file path
         :param reset: if True all other plottables will be cleared
         """
         #if path is not None:
 …
                 wx.PostEvent(self.parent, StatusEvent(status=status))
                 raise RuntimeError, status
             # If the file contains nothing, just return
             if pr is None:
                 raise RuntimeError, "Loaded data is invalid"
             self.pr = pr
         # Make a plot of I(q) data
         if self.pr.err == None:
 …
         new_plot.name = IQ_DATA_LABEL
         new_plot.xaxis("\\rm{Q}", 'A^{-1}')
         new_plot.yaxis("\\rm{Intensity} ","cm^{-1}")
+        new_plot.yaxis("\\rm{Intensity} ", "cm^{-1}")
         new_plot.interactive = True
         new_plot.group_id = GROUP_ID_IQ_DATA
         new_plot.id = self.data_id
         new_plot.title = "I(q)"
         wx.PostEvent(self.parent,
+        new_plot.title = "I(q)"
+        wx.PostEvent(self.parent,
                      NewPlotEvent(plot=new_plot, title="I(q)", reset=reset))
         self.current_plottable = new_plot
         # Get Q range
         self.control_panel.q_min = min(self.pr.x)
         self.control_panel.q_max = max(self.pr.x)
     def save_data(self, filepath, prstate=None):
         """
         Save data in provided state object.
         :TODO: move the state code away from inversion_panel and move it here.
+        :TODO: move the state code away from inversion_panel and move it here.
                 Then remove the "prstate" input and make this method private.
         :param filepath: path of file to write to
         :param prstate: P(r) inversion state
+        :param prstate: P(r) inversion state
         """
         #TODO: do we need this or can we use DataLoader.loader.save directly?
         # Add output data and coefficients to state
         prstate.coefficients = self._last_out
         prstate.covariance = self._last_cov
         # Write the output to file
         # First, check that the data is of the right type
         if issubclass(self.current_plottable.__class__,
                        sas.dataloader.data_info.Data1D):
+                      sas.dataloader.data_info.Data1D):
             self.state_reader.write(filepath, self.current_plottable, prstate)
         else:
             msg = "pr.save_data: the data being saved is not a"
             msg += " sas.data_info.Data1D object"
+            msg += " sas.data_info.Data1D object"
             raise RuntimeError, msg
+    def setup_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None,
+    def setup_plot_inversion(self, alpha, nfunc, d_max, q_min=None, q_max=None,
                              bck=False, height=0, width=0):
         """
+            Set up inversion from plotted data
         """
         self.alpha = alpha
 …
         self.has_bck = bck
         self.slit_height = height
         self.slit_width  = width
+        self.slit_width = width
         try:
             pr = self._create_plot_pr()
             if not pr==None:
+            if not pr == None:
                 self.pr = pr
                 self.perform_inversion()
 …
             wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
     def estimate_plot_inversion(self, alpha, nfunc, d_max,
                                 q_min=None, q_max=None,
+    def estimate_plot_inversion(self, alpha, nfunc, d_max,
+                                q_min=None, q_max=None,
                                 bck=False, height=0, width=0):
         """
+            Estimate parameters from plotted data
         """
         self.alpha = alpha
 …
         self.has_bck = bck
         self.slit_height = height
         self.slit_width  = width
+        self.slit_width = width
         try:
             pr = self._create_plot_pr()
             if not pr==None:
+            if not pr == None:
                 self.pr = pr
                 self.perform_estimate()
         except:
             wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
+            wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
     def _create_plot_pr(self, estimate=False):
 …
         Create and prepare invertor instance from
         a plottable data set.
         :param path: path of the file to read in
+        :param path: path of the file to read in
         """
         # Sanity check
         if self.current_plottable is None:
             msg = "Please load a valid data set before proceeding."
             wx.PostEvent(self.parent, StatusEvent(status=msg))
             return None
+            wx.PostEvent(self.parent, StatusEvent(status=msg))
+            return None
         # Get the data from the chosen data set and perform inversion
         pr = Invertor()
 …
         pr.slit_height = self.slit_height
         pr.slit_width = self.slit_width
         # Keep track of the plot window title to ensure that
         # we can overlay the plots
         pr.info["plot_group_id"] = self.current_plottable.group_id
         # Fill in errors if none were provided
         err = self.current_plottable.dy
         all_zeros = True
         if err == None:
             err = numpy.zeros(len(pr.y))
         else:
+            err = numpy.zeros(len(pr.y))
+        else:
             for i in range(len(err)):
                 if err[i]>0:
+                if err[i] > 0:
                     all_zeros = False
         if all_zeros:
+        if all_zeros:
             scale = None
             min_err = 0.0
             for i in range(len(pr.y)):
                 # Scale the error so that we can fit over several decades of Q
                 if scale==None:
                     scale = 0.05*math.sqrt(pr.y[i])
                     min_err = 0.01*pr.y[i]
                 err[i] = scale*math.sqrt( math.fabs(pr.y[i]) ) + min_err
+                if scale == None:
+                    scale = 0.05 * math.sqrt(pr.y[i])
+                    min_err = 0.01 * pr.y[i]
+                err[i] = scale * math.sqrt(math.fabs(pr.y[i])) + min_err
             message = "The loaded file had no error bars, "
             message += "statistical errors are assumed."
 …
         pr.err = err
         return pr
     def setup_file_inversion(self, alpha, nfunc, d_max, data,
                              path=None, q_min=None, q_max=None,
+                             path=None, q_min=None, q_max=None,
                              bck=False, height=0, width=0):
         """
+            Set up inversion
         """
         self.alpha = alpha
 …
         self.has_bck = bck
         self.slit_height = height
         self.slit_width  = width
+        self.slit_width = width
         try:
-            #pr = self._create_file_pr(path)
             pr = self._create_file_pr(data)
             if not pr==None:
+            if not pr == None:
                 self.pr = pr
                 self.perform_inversion()
         except:
             wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
     def estimate_file_inversion(self, alpha, nfunc, d_max, data,
                                 path=None, q_min=None, q_max=None,
+                                path=None, q_min=None, q_max=None,
                                 bck=False, height=0, width=0):
         """
+            Estimate parameters for inversion
         """
         self.alpha = alpha
 …
         self.has_bck = bck
         self.slit_height = height
         self.slit_width  = width
+        self.slit_width = width
         try:
             pr = self._create_file_pr(data)
-            #pr = self._create_file_pr(path)
             if not pr is None:
                 self.pr = pr
 …
         except:
             wx.PostEvent(self.parent, StatusEvent(status=sys.exc_value))
     def _create_file_pr(self, data):
         """
         Create and prepare invertor instance from
         a file data set.
+        :param path: path of the file to read in
+        """
+        # Load data
+        #if os.path.isfile(path):
+        """
+        if self._current_file_data is not None \
+            and self._current_file_data.path==path:
+            # Protect against corrupted data from
+            # previous failed load attempt
+            if self._current_file_data.x is None:
+                return None
+            x = self._current_file_data.x
+            y = self._current_file_data.y
+            err = self._current_file_data.err
+            message = "The data from this file has already been loaded."
+            wx.PostEvent(self.parent, StatusEvent(status=message))
+        else:
+        :param path: path of the file to read in
         """
         # Reset the status bar so that we don't get mixed up
         # with old messages.
+        # with old messages.
         #TODO: refactor this into a proper status handling
         wx.PostEvent(self.parent, StatusEvent(status=''))
         try:
             class FileData:
+            class FileData(object):
                 x = None
                 y = None
 …
                 def __init__(self, path):
                     self.path = path
             self._current_file_data = FileData(data.path)
             self._current_file_data.x = data.x
 …
             load_error(sys.exc_value)
             return None
         # If the file contains no data, just return
         if x is None or len(x) == 0:
 …
                 message += "statistical errors are assumed."
                 wx.PostEvent(self.parent, StatusEvent(status=message))
         try:
             # Get the data from the chosen data set and perform inversion
 …
             load_error(sys.exc_value)
         return None
     def perform_estimate(self):
         """
+            Perform parameter estimation
         """
         from pr_thread import EstimatePr
+        from copy import deepcopy
         # If a thread is already started, stop it
         if self.estimation_thread != None and \
             self.estimation_thread.isrunning():
             self.estimation_thread.stop()
             ## stop just raises the flag -- the thread is supposed to
+            ## stop just raises the flag -- the thread is supposed to
             ## then kill itself. In August 2014 it was shown that this is
             ## incorrectly handled by fitting.py and a fix implemented.
+            ## incorrectly handled by fitting.py and a fix implemented.
             ## It is not clear whether it is properly used here, but the
             ## "fix" of waiting for the previous thread to end breaks the
 …
             ## are being properly handled.
             ##
+            ##    -PDB January 25, 2015
+            ##    -PDB January 25, 2015
         pr = self.pr.clone()
         self.estimation_thread = EstimatePr(pr, self.nfunc,
                                              error_func=self._thread_error,
                                          completefn = self._estimate_completed,
                                             updatefn   = None)
+                                            error_func=self._thread_error,
+                                            completefn=self._estimate_completed,
+                                            updatefn=None)
         self.estimation_thread.queue()
         self.estimation_thread.ready(2.5)
     def perform_estimateNT(self):
         """
+            Perform parameter estimation
         """
         from pr_thread import EstimateNT
+        from copy import deepcopy
         # If a thread is already started, stop it
         if self.estimation_thread != None and self.estimation_thread.isrunning():
             self.estimation_thread.stop()
             ## stop just raises the flag -- the thread is supposed to
+            ## stop just raises the flag -- the thread is supposed to
             ## then kill itself. In August 2014 it was shown that this is
             ## incorrectly handled by fitting.py and a fix implemented.
+            ## incorrectly handled by fitting.py and a fix implemented.
             ## It is not clear whether it is properly used here, but the
             ## "fix" of waiting for the previous thread to end breaks the
 …
             ## are being properly handled.
             ##
+            ##    -PDB January 25, 2015
+            ##    -PDB January 25, 2015
         pr = self.pr.clone()
         # Skip the slit settings for the estimation
         # It slows down the application and it doesn't change the estimates
         pr.slit_height = 0.0
         pr.slit_width  = 0.0
         self.estimation_thread = EstimateNT(pr, self.nfunc,
                                             error_func=self._thread_error,
                                         completefn = self._estimateNT_completed,
                                             updatefn   = None)
+        pr.slit_width = 0.0
+        self.estimation_thread = EstimateNT(pr, self.nfunc,
+                                            error_func=self._thread_error,
+                                            completefn=self._estimateNT_completed,
+                                            updatefn=None)
         self.estimation_thread.queue()
         self.estimation_thread.ready(2.5)
     def perform_inversion(self):
         """
+        """
+        # Time estimate
+        #estimated = self.elapsed*self.nfunc**2
+        #message = "Computation time may take up to %g seconds" % self.elapsed
+        #wx.PostEvent(self.parent, StatusEvent(status=message))
+        # Start inversion thread
+            Perform inversion
+        """
         self.start_thread()
+        return
+        out, cov = self.pr.lstsq(self.nfunc)
+        # Save useful info
+        self.elapsed = self.pr.elapsed
+        for i in range(len(out)):
+            try:
+                print "%d: %g +- %g" % (i, out[i],
+                                         math.sqrt(math.fabs(cov[i][i])))
+            except:
+                print "%d: %g +- ?" % (i, out[i])
+        # Make a plot of I(q) data
+        new_plot = Data1D(self.pr.x, self.pr.y, dy=self.pr.err)
+        new_plot.name = "I_{obs}(q)"
+        new_plot.xaxis("\\rm{Q}", 'A^{-1}')
+        new_plot.yaxis("\\rm{Intensity} ","cm^{-1}")
+        wx.PostEvent(self.parent, NewPlotEvent(plot=new_plot, title="Iq"))
+        # Show I(q) fit
+        self.show_iq(out, self.pr)
+        # Show P(r) fit
+        x_values, x_range = self.show_pr(out, self.pr, cov=cov)
     def _on_context_inversion(self, event):
         """
+            Call-back method for plot context menu
         """
         panel = event.GetEventObject()
 …
             logging.info("Prview Error: No data is available")
             return
         # Store a reference to the current plottable
         # If we have a suggested value, use it.
 …
             self.control_panel.alpha = self.alpha
             logging.info("Prview :Alpha Not estimate yet")
-            pass
         try:
             estimate = int(self.control_panel.nterms_estimate)
 …
             self.control_panel.nfunc = self.nfunc
             logging.info("Prview : ntemrs Not estimate yet")
+            pass
         self.current_plottable = panel.plots[panel.graph.selected_plottable]
         self.set_data([self.current_plottable])
 …
         #self.parent.set_perspective(self.perspective)
         self.control_panel._on_invert(None)
     def get_panels(self, parent):
         """
 …
         """
         from inversion_panel import InversionControl
         self.parent = parent
         self.frame = MDIFrame(self.parent, None, 'None', (100, 200))
         self.control_panel = InversionControl(self.frame, -1,
+        self.frame = MDIFrame(self.parent, None, 'None', (100, 200))
+        self.control_panel = InversionControl(self.frame, -1,
                                               style=wx.RAISED_BORDER,
                                               standalone=self.standalone)
 …
         self.perspective = []
         self.perspective.append(self.control_panel.window_name)
         return [self.control_panel]
     def set_data(self, data_list=None):
         """
 …
                             data_1d_list.append(data)
                         else:
                             error_msg += " %s  type %s \n" % (str(data.name),
                                              str(data.__class__.__name__))
+                            error_msg += " %s type %s \n" % (str(data.name),
+                                                             str(data.__class__.__name__))
                             data_2d_list.append(data)
                 if len(data_2d_list) > 0:
 …
                     msg += error_msg
                 if len(data_1d_list) == 0:
+                    wx.PostEvent(self.parent,
+                    StatusEvent(status=msg, info='error'))
+                    wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
                     return
                 msg += "Prview does not allow multiple data!\n"
 …
             if data is None:
                 msg += "PrView receives no data. \n"
+                wx.PostEvent(self.parent,
+                     StatusEvent(status=msg, info='error'))
+                wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
                 return
             if issubclass(data.__class__, Data1D):
                 try:
                     wx.PostEvent(self.parent, NewPlotEvent(action='remove',
                                                 group_id=GROUP_ID_IQ_DATA,
                                                 id=self.data_id))
+                    wx.PostEvent(self.parent,
+                                 NewPlotEvent(action='remove',
+                                              group_id=GROUP_ID_IQ_DATA,
+                                              id=self.data_id))
                     self.data_id = data.id
                     self.control_panel._change_file(evt=None, data=data)
                 except:
                     msg = "Prview Set_data: " + str(sys.exc_value)
+                    wx.PostEvent(self.parent, StatusEvent(status=msg,
+                                                            info="error"))
+            else:
+                    wx.PostEvent(self.parent, StatusEvent(status=msg, info="error"))
+            else:
                 msg = "Pr cannot be computed for data of "
                 msg += "type %s" % (data_list[0].__class__.__name__)
+                wx.PostEvent(self.parent,
+                         StatusEvent(status=msg, info='error'))
+                wx.PostEvent(self.parent, StatusEvent(status=msg, info='error'))
         else:
             msg = "Pr contain no data"
             wx.PostEvent(self.parent, StatusEvent(status=msg, info='warning'))
     def post_init(self):
         """
 …
         if self.standalone:
             self.parent.set_perspective(self.perspective)
-if __name__ == "__main__":
-    i = Plugin()
-    print i.perform_estimateNT()

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SasView

Changeset c1c14ba in sasview for src/sas/perspectives/pr

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src/sas/perspectives/pr/pr.py

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