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perspectives

Timestamp:

Sep 18, 2017 10:20:29 AM (7 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

60a7820

Parents:

460d3a1 (diff), cfd27dd (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Message:

Merge branch 'master' into ticket-887-reorg

Location:

src/sas/sasgui/perspectives

Files:

: 3 added
: 7 edited

calculator/model_editor.py (modified) (6 diffs)
fitting/fitting.py (modified) (2 diffs)
fitting/media/combine_batch_grid.png (added)
fitting/media/combine_batch_page.png (added)
fitting/media/combine_batch_plot.png (added)
fitting/media/fitting_help.rst (modified) (4 diffs)
fitting/media/plugin.rst (modified) (1 diff)
fitting/model_thread.py (modified) (3 diffs)
fitting/simfitpage.py (modified) (3 diffs)
fitting/models.py (modified) (2 diffs)

Legend:

: Unmodified
: Added
: Removed

src/sas/sasgui/perspectives/calculator/model_editor.py

-                      r07ec714
+                      r23359ccb
         self.model2_string = "cylinder"
         self.name = 'Sum' + M_NAME
-        self.factor = 'scale_factor'
         self._notes = ''
         self._operator = '+'
 …
         self.model2_name = str(self.model2.GetValue())
         self.good_name = True
         self.fill_oprator_combox()
+        self.fill_operator_combox()
     def _layout_name(self):
 …
         a sum or multiply model then create the appropriate string
         """
         name = ''
         if operator == '*':
             name = 'Multi'
+            factor = 'BackGround'
+            f_oper = '+'
+            factor = 'background'
         else:
             name = 'Sum'
             factor = 'scale_factor'
+            f_oper = '*'
+        self.factor = factor
         self._operator = operator
         self.explanation = "  Plugin Model = %s %s (model1 %s model2)\n" % \
                            (self.factor, f_oper, self._operator)
+        self.explanation = ("  Plugin_model = scale_factor * (model_1 {} "
+            "model_2) + background").format(operator)
         self.explanationctr.SetLabel(self.explanation)
         self.name = name + M_NAME
     def fill_oprator_combox(self):
+    def fill_operator_combox(self):
         """
         fill the current combobox with the operator
 …
         return [self.model1_name, self.model2_name]
     def write_string(self, fname, name1, name2):
+    def write_string(self, fname, model1_name, model2_name):
         """
         Write and Save file
 …
         self.fname = fname
         description = self.desc_tcl.GetValue().lstrip().rstrip()
+        if description == '':
+            description = name1 + self._operator + name2
+        text = self._operator_choice.GetValue()
+        if text.count('+') > 0:
+            factor = 'scale_factor'
+            f_oper = '*'
+            default_val = '1.0'
+        else:
+            factor = 'BackGround'
+            f_oper = '+'
+            default_val = '0.0'
+        path = self.fname
+        try:
+            out_f = open(path, 'w')
+        except:
+            raise
+        lines = SUM_TEMPLATE.split('\n')
+        for line in lines:
+            try:
+                if line.count("scale_factor"):
+                    line = line.replace('scale_factor', factor)
+                    #print "scale_factor", line
+                if line.count("= %s"):
+                    out_f.write(line % (default_val) + "\n")
+                elif line.count("import Model as P1"):
+                    if self.is_p1_custom:
+                        line = line.replace('#', '')
+                        out_f.write(line % name1 + "\n")
+                    else:
+                        out_f.write(line + "\n")
+                elif line.count("import %s as P1"):
+                    if not self.is_p1_custom:
+                        line = line.replace('#', '')
+                        out_f.write(line % (name1) + "\n")
+                    else:
+                        out_f.write(line + "\n")
+                elif line.count("import Model as P2"):
+                    if self.is_p2_custom:
+                        line = line.replace('#', '')
+                        out_f.write(line % name2 + "\n")
+                    else:
+                        out_f.write(line + "\n")
+                elif line.count("import %s as P2"):
+                    if not self.is_p2_custom:
+                        line = line.replace('#', '')
+                        out_f.write(line % (name2) + "\n")
+                    else:
+                        out_f.write(line + "\n")
+                elif line.count("P1 = find_model"):
+                    out_f.write(line % (name1) + "\n")
+                elif line.count("P2 = find_model"):
+                    out_f.write(line % (name2) + "\n")
+                elif line.count("self.description = '%s'"):
+                    out_f.write(line % description + "\n")
+                #elif line.count("run") and line.count("%s"):
+                #    out_f.write(line % self._operator + "\n")
+                #elif line.count("evalDistribution") and line.count("%s"):
+                #    out_f.write(line % self._operator + "\n")
+                elif line.count("return") and line.count("%s") == 2:
+                    #print "line return", line
+                    out_f.write(line % (f_oper, self._operator) + "\n")
+                elif line.count("out2")and line.count("%s"):
+                    out_f.write(line % self._operator + "\n")
+                else:
+                    out_f.write(line + "\n")
+            except:
+                raise
+        out_f.close()
+        #else:
+        #    msg = "Name exists already."
+        desc_line = ''
+        if description.strip() != '':
+            # Sasmodels generates a description for us. If the user provides
+            # their own description, add a line to overwrite the sasmodels one
+            desc_line = "\nmodel_info.description = '{}'".format(description)
+        name = os.path.splitext(os.path.basename(self.fname))[0]
+        output = SUM_TEMPLATE.format(name=name, model1=model1_name,
+            model2=model2_name, operator=self._operator, desc_line=desc_line)
+        with open(self.fname, 'w') as out_f:
+            out_f.write(output)
     def compile_file(self, path):
 …
 """
 SUM_TEMPLATE = """
+# A sample of an experimental model function for Sum/Multiply(Pmodel1,Pmodel2)
+import os
+import sys
+import copy
+import collections
+import numpy
+from sas.sascalc.fit.pluginmodel import Model1DPlugin
+from sasmodels.sasview_model import find_model
+class Model(Model1DPlugin):
+    name = os.path.splitext(os.path.basename(__file__))[0]
+    is_multiplicity_model = False
+    def __init__(self, multiplicity=1):
+        Model1DPlugin.__init__(self, name='')
+        P1 = find_model('%s')
+        P2 = find_model('%s')
+        p_model1 = P1()
+        p_model2 = P2()
+        ## Setting  model name model description
+        self.description = '%s'
+        if self.name.rstrip().lstrip() == '':
+            self.name = self._get_name(p_model1.name, p_model2.name)
+        if self.description.rstrip().lstrip() == '':
+            self.description = p_model1.name
+            self.description += p_model2.name
+            self.fill_description(p_model1, p_model2)
+        ## Define parameters
+        self.params = collections.OrderedDict()
+        ## Parameter details [units, min, max]
+        self.details = {}
+        ## Magnetic Panrameters
+        self.magnetic_params = []
+        # non-fittable parameters
+        self.non_fittable = p_model1.non_fittable
+        self.non_fittable += p_model2.non_fittable
+        ##models
+        self.p_model1= p_model1
+        self.p_model2= p_model2
+        ## dispersion
+        self._set_dispersion()
+        ## Define parameters
+        self._set_params()
+        ## New parameter:scaling_factor
+        self.params['scale_factor'] = %s
+        ## Parameter details [units, min, max]
+        self._set_details()
+        self.details['scale_factor'] = ['', 0.0, numpy.inf]
+        #list of parameter that can be fitted
+        self._set_fixed_params()
+        ## parameters with orientation
+        self.orientation_params = []
+        for item in self.p_model1.orientation_params:
+            new_item = "p1_" + item
+            if not new_item in self.orientation_params:
+                self.orientation_params.append(new_item)
+        for item in self.p_model2.orientation_params:
+            new_item = "p2_" + item
+            if not new_item in self.orientation_params:
+                self.orientation_params.append(new_item)
+        ## magnetic params
+        self.magnetic_params = []
+        for item in self.p_model1.magnetic_params:
+            new_item = "p1_" + item
+            if not new_item in self.magnetic_params:
+                self.magnetic_params.append(new_item)
+        for item in self.p_model2.magnetic_params:
+            new_item = "p2_" + item
+            if not new_item in self.magnetic_params:
+                self.magnetic_params.append(new_item)
+        # get multiplicity if model provide it, else 1.
+        try:
+            multiplicity1 = p_model1.multiplicity
+            try:
+                multiplicity2 = p_model2.multiplicity
+            except:
+                multiplicity2 = 1
+        except:
+            multiplicity1 = 1
+            multiplicity2 = 1
+        ## functional multiplicity of the model
+        self.multiplicity1 = multiplicity1
+        self.multiplicity2 = multiplicity2
+        self.multiplicity_info = []
+    def _clone(self, obj):
+        import copy
+        obj.params     = copy.deepcopy(self.params)
+        obj.description     = copy.deepcopy(self.description)
+        obj.details    = copy.deepcopy(self.details)
+        obj.dispersion = copy.deepcopy(self.dispersion)
+        obj.p_model1  = self.p_model1.clone()
+        obj.p_model2  = self.p_model2.clone()
+        #obj = copy.deepcopy(self)
+        return obj
+    def _get_name(self, name1, name2):
+        p1_name = self._get_upper_name(name1)
+        if not p1_name:
+            p1_name = name1
+        name = p1_name
+        name += "_and_"
+        p2_name = self._get_upper_name(name2)
+        if not p2_name:
+            p2_name = name2
+        name += p2_name
+        return name
+    def _get_upper_name(self, name=None):
+        if name is None:
+            return ""
+        upper_name = ""
+        str_name = str(name)
+        for index in range(len(str_name)):
+            if str_name[index].isupper():
+                upper_name += str_name[index]
+        return upper_name
+    def _set_dispersion(self):
+        self.dispersion = collections.OrderedDict()
+        ##set dispersion only from p_model
+        for name , value in self.p_model1.dispersion.iteritems():
+            #if name.lower() not in self.p_model1.orientation_params:
+            new_name = "p1_" + name
+            self.dispersion[new_name]= value
+        for name , value in self.p_model2.dispersion.iteritems():
+            #if name.lower() not in self.p_model2.orientation_params:
+            new_name = "p2_" + name
+            self.dispersion[new_name]= value
+    def function(self, x=0.0):
+        return 0
+    def getProfile(self):
+        try:
+            x,y = self.p_model1.getProfile()
+        except:
+            x = None
+            y = None
+        return x, y
+    def _set_params(self):
+        for name , value in self.p_model1.params.iteritems():
+            # No 2D-supported
+            #if name not in self.p_model1.orientation_params:
+            new_name = "p1_" + name
+            self.params[new_name]= value
+        for name , value in self.p_model2.params.iteritems():
+            # No 2D-supported
+            #if name not in self.p_model2.orientation_params:
+            new_name = "p2_" + name
+            self.params[new_name]= value
+        # Set "scale" as initializing
+        self._set_scale_factor()
+    def _set_details(self):
+        for name ,detail in self.p_model1.details.iteritems():
+            new_name = "p1_" + name
+            #if new_name not in self.orientation_params:
+            self.details[new_name]= detail
+        for name ,detail in self.p_model2.details.iteritems():
+            new_name = "p2_" + name
+            #if new_name not in self.orientation_params:
+            self.details[new_name]= detail
+    def _set_scale_factor(self):
+        pass
+    def setParam(self, name, value):
+        # set param to this (p1, p2) model
+        self._setParamHelper(name, value)
+        ## setParam to p model
+        model_pre = ''
+        new_name = ''
+        name_split = name.split('_', 1)
+        if len(name_split) == 2:
+            model_pre = name.split('_', 1)[0]
+            new_name = name.split('_', 1)[1]
+        if model_pre == "p1":
+            if new_name in self.p_model1.getParamList():
+                self.p_model1.setParam(new_name, value)
+        elif model_pre == "p2":
+             if new_name in self.p_model2.getParamList():
+                self.p_model2.setParam(new_name, value)
+        elif name == 'scale_factor':
+            self.params['scale_factor'] = value
+        else:
+            raise ValueError, "Model does not contain parameter %s" % name
+    def getParam(self, name):
+        # Look for dispersion parameters
+        toks = name.split('.')
+        if len(toks)==2:
+            for item in self.dispersion.keys():
+                # 2D not supported
+                if item.lower()==toks[0].lower():
+                    for par in self.dispersion[item]:
+                        if par.lower() == toks[1].lower():
+                            return self.dispersion[item][par]
+        else:
+            # Look for standard parameter
+            for item in self.params.keys():
+                if item.lower()==name.lower():
+                    return self.params[item]
+        return
+        #raise ValueError, "Model does not contain parameter %s" % name
+    def _setParamHelper(self, name, value):
+        # Look for dispersion parameters
+        toks = name.split('.')
+        if len(toks)== 2:
+            for item in self.dispersion.keys():
+                if item.lower()== toks[0].lower():
+                    for par in self.dispersion[item]:
+                        if par.lower() == toks[1].lower():
+                            self.dispersion[item][par] = value
+                            return
+        else:
+            # Look for standard parameter
+            for item in self.params.keys():
+                if item.lower()== name.lower():
+                    self.params[item] = value
+                    return
+        raise ValueError, "Model does not contain parameter %s" % name
+    def _set_fixed_params(self):
+        self.fixed = []
+        for item in self.p_model1.fixed:
+            new_item = "p1" + item
+            self.fixed.append(new_item)
+        for item in self.p_model2.fixed:
+            new_item = "p2" + item
+            self.fixed.append(new_item)
+        self.fixed.sort()
+    def run(self, x = 0.0):
+        self._set_scale_factor()
+        return self.params['scale_factor'] %s \
+(self.p_model1.run(x) %s self.p_model2.run(x))
+    def runXY(self, x = 0.0):
+        self._set_scale_factor()
+        return self.params['scale_factor'] %s \
+(self.p_model1.runXY(x) %s self.p_model2.runXY(x))
+    ## Now (May27,10) directly uses the model eval function
+    ## instead of the for-loop in Base Component.
+    def evalDistribution(self, x = []):
+        self._set_scale_factor()
+        return self.params['scale_factor'] %s \
+(self.p_model1.evalDistribution(x) %s \
+self.p_model2.evalDistribution(x))
+    def set_dispersion(self, parameter, dispersion):
+        value= None
+        new_pre = parameter.split("_", 1)[0]
+        new_parameter = parameter.split("_", 1)[1]
+        try:
+            if new_pre == 'p1' and \
+new_parameter in self.p_model1.dispersion.keys():
+                value= self.p_model1.set_dispersion(new_parameter, dispersion)
+            if new_pre == 'p2' and \
+new_parameter in self.p_model2.dispersion.keys():
+                value= self.p_model2.set_dispersion(new_parameter, dispersion)
+            self._set_dispersion()
+            return value
+        except:
+            raise
+    def fill_description(self, p_model1, p_model2):
+        description = ""
+        description += "This model gives the summation or multiplication of"
+        description += "%s and %s. "% ( p_model1.name, p_model2.name )
+        self.description += description
+if __name__ == "__main__":
+    m1= Model()
+    #m1.setParam("p1_scale", 25)
+    #m1.setParam("p1_length", 1000)
+    #m1.setParam("p2_scale", 100)
+    #m1.setParam("p2_rg", 100)
+    out1 = m1.runXY(0.01)
+    m2= Model()
+    #m2.p_model1.setParam("scale", 25)
+    #m2.p_model1.setParam("length", 1000)
+    #m2.p_model2.setParam("scale", 100)
+    #m2.p_model2.setParam("rg", 100)
+    out2 = m2.p_model1.runXY(0.01) %s m2.p_model2.runXY(0.01)\n
+    print "My name is %s."% m1.name
+    print out1, " = ", out2
+    if out1 == out2:
+        print "===> Simple Test: Passed!"
+    else:
+        print "===> Simple Test: Failed!"
+from sasmodels.core import load_model_info
+from sasmodels.sasview_model import make_model_from_info
+model_info = load_model_info('{model1}{operator}{model2}')
+model_info.name = '{name}'{desc_line}
+Model = make_model_from_info(model_info)
 """
 if __name__ == "__main__":
 #    app = wx.PySimpleApp()

src/sas/sasgui/perspectives/fitting/fitting.py

-                      r489f53a
+                      r2d9526d
             @param unsmeared_error: data error, rescaled to unsmeared model
         """
         number_finite = np.count_nonzero(np.isfinite(y))
         np.nan_to_num(y)
 …
                                          data_description=model.name,
                                          data_id=str(page_id) + " " + data.name)
+        plots_to_update = [] # List of plottables that have changed since last calculation
+        # Create the new theories
         if unsmeared_model is not None:
+            self.create_theory_1D(x, unsmeared_model, page_id, model, data, state,
+            unsmeared_model_plot = self.create_theory_1D(x, unsmeared_model,
+                                  page_id, model, data, state,
                                   data_description=model.name + " unsmeared",
                                   data_id=str(page_id) + " " + data.name + " unsmeared")
+            plots_to_update.append(unsmeared_model_plot)
             if unsmeared_data is not None and unsmeared_error is not None:
+                self.create_theory_1D(x, unsmeared_data, page_id, model, data, state,
+                unsmeared_data_plot = self.create_theory_1D(x, unsmeared_data,
+                                      page_id, model, data, state,
                                       data_description="Data unsmeared",
                                       data_id="Data  " + data.name + " unsmeared",
                                       dy=unsmeared_error)
+        # Comment this out until we can get P*S models with correctly populated parameters
+        #if sq_model is not None and pq_model is not None:
+        #    self.create_theory_1D(x, sq_model, page_id, model, data, state,
+        #                          data_description=model.name + " S(q)",
+        #                          data_id=str(page_id) + " " + data.name + " S(q)")
+        #    self.create_theory_1D(x, pq_model, page_id, model, data, state,
+        #                          data_description=model.name + " P(q)",
+        #                          data_id=str(page_id) + " " + data.name + " P(q)")
+                plots_to_update.append(unsmeared_data_plot)
+        if sq_model is not None and pq_model is not None:
+            sq_id = str(page_id) + " " + data.name + " S(q)"
+            sq_plot = self.create_theory_1D(x, sq_model, page_id, model, data, state,
+                                  data_description=model.name + " S(q)",
+                                  data_id=sq_id)
+            plots_to_update.append(sq_plot)
+            pq_id = str(page_id) + " " + data.name + " P(q)"
+            pq_plot = self.create_theory_1D(x, pq_model, page_id, model, data, state,
+                                  data_description=model.name + " P(q)",
+                                  data_id=pq_id)
+            plots_to_update.append(pq_plot)
+        # Update the P(Q), S(Q) and unsmeared theory plots if they exist
+        wx.PostEvent(self.parent, NewPlotEvent(plots=plots_to_update,
+                                              action='update'))
         current_pg = self.fit_panel.get_page_by_id(page_id)

src/sas/sasgui/perspectives/fitting/media/fitting_help.rst

-                      r5295cf5
+                      rca383a0
 the :ref:`Advanced_Plugin_Editor` .
+**SasView version 4.2** made it possible to specify whether a plugin created with
+the *New Plugin Model* dialog is actually a form factor P(Q) or a structure factor
+S(Q). To do this, simply add one or other of the following lines under the *import*
+statements.
+For a form factor::
+     form_factor = True
+or for a structure factor::
+     structure_factor = True
+If the plugin is a structure factor it is *also* necessary to add two variables to
+the parameter list::
+     parameters = [
+                     ['radius_effective', '', 1, [0.0, numpy.inf], 'volume', ''],
+                     ['volfraction', '', 1, [0.0, 1.0], '', ''],
+                     [...],
+and to the declarations of the functions Iq and Iqxy:::
+     def Iq(x , radius_effective, volfraction, ...):
+     def Iqxy(x, y, radius_effective, volfraction, ...):
+Such a plugin should then be available in the S(Q) drop-down box on a FitPage (once
+a P(Q) model has been selected).
 Sum|Multi(p1,p2)
 ^^^^^^^^^^^^^^^^
 …
 or::
      Plugin Model = scale_factor * model_1 /* model_2 + background
+     Plugin Model = scale_factor * (model1 * model2) + background
 In the *Easy Sum/Multi Editor* give the new model a function name and brief
 description (to appear under the *Details* button on the *FitPage*). Then select
 two existing models, as p1 and p2, and the required operator, '+' or '*' between
+them. Finally, click the *Apply* button to generate the model and then click *Close*.
+Any changes to a plugin model generated in this way only become effective *after* it is re-selected from the model drop-down menu on the FitPage.
+them. Finally, click the *Apply* button to generate and test the model and then click *Close*.
+Any changes to a plugin model generated in this way only become effective *after* it is re-selected
+from the plugin models drop-down menu on the FitPage. If the model is not listed you can force a
+recompilation of the plugins by selecting *Fitting* > *Plugin Model Operations* > *Load Plugin Models*.
+**SasView version 4.2** introduced a much simplified and more extensible structure for plugin models
+generated through the Easy Sum/Multi Editor. For example, the code for a combination of a sphere model
+with a power law model now looks like this::
+     from sasmodels.core import load_model_info
+     from sasmodels.sasview_model import make_model_from_info
+     model_info = load_model_info('sphere+power_law')
+     model_info.name = 'MyPluginModel'
+     model_info.description = 'sphere + power_law'
+     Model = make_model_from_info(model_info)
+To change the models or operators contributing to this plugin it is only necessary to edit the string
+in the brackets after *load_model_info*, though it would also be a good idea to update the model name
+and description too!!!
+The model specification string can handle multiple models and combinations of operators (+ or *) which
+are processed according to normal conventions. Thus 'model1+model2*model3' would be valid and would
+multiply model2 by model3 before adding model1. In this example, parameters in the *FitPage* would be
+prefixed A (for model2), B (for model3) and C (for model1). Whilst this might appear a little
+confusing, unless you were creating a plugin model from multiple instances of the same model the parameter
+assignments ought to be obvious when you load the plugin.
+If you need to include another plugin model in the model specification string, just prefix the name of
+that model with *custom*. For instance::
+     sphere+custom.MyPluginModel
+To create a P(Q)*\S(Q) model use the @ symbol instead of * like this::
+     sphere@hardsphere
+This streamlined approach to building complex plugin models from existing library models, or models
+available on the *Model Marketplace*, also permits the creation of P(Q)*\S(Q) plugin models, something
+that was not possible in earlier versions of SasView.
 .. _Advanced_Plugin_Editor:
 …
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+.. _Batch_Fit_Mode:
 Batch Fit Mode
 --------------
 …
      Example: radius [2 : 5] , radius [10 : 25]
+.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+.. note::  This help document was last changed by Steve King, 10Oct2016
+.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+Combined Batch Fit Mode
+-----------------------
+The purpose of the Combined Batch Fit is to allow running two or more batch
+fits in sequence without overwriting the output table of results.  This may be
+of interest for example if one is fitting a series of data sets where there is
+a shape change occurring in the series that requires changing the model part
+way through the series; for example a sphere to rod transition.  Indeed the
+regular batch mode does not allow for multiple models and requires all the
+files in the series to be fit with single model and set of parameters.  While
+it is of course possible to just run part of the series as a batch fit using
+model one followed by running another batch fit on the rest of the series with
+model two (and/or model three etc), doing so will overwrite the table of
+outputs from the previous batch fit(s).  This may not be desirable if one is
+interested in comparing the parameters: for example the sphere radius of set
+one and the cylinder radius of set two.
+Method
+^^^^^^
+In order to use the *Combined Batch Fit*, first load all the data needed as
+described in :ref:`Loading_data`. Next start up two or more *BatchPage* fits
+following the instructions in :ref:`Batch_Fit_Mode` but **DO NOT PRESS FIT**.
+At this point the *Combine Batch Fit* menu item under the *Fitting menu* should
+be active (if there is one or no *BatchPage* the menu item will be greyed out
+and inactive).  Clicking on *Combine Batch Fit* will bring up a new panel,
+similar to the *Const & Simult Fit* panel. In this case there will be a
+checkbox for each *BatchPage* instead of each *FitPage* that should be included
+in the fit.  Once all are selected, click the Fit button on
+the *BatchPage* to run each batch fit in *sequence*
+.. image:: combine_batch_page.png
+The batch table will then pop up at the end as for the case of the simple Batch
+Fitting with the following caveats:
+.. note::
+   The order matters.  The parameters in the table will be taken from the model
+   used in the first *BatchPage* of the list.  Any parameters from the
+   second and later *BatchPage* s that have the same name as a parameter in the
+   first will show up allowing for plotting of that parameter across the
+   models. The other parameters will not be available in the grid.
+.. note::
+   a corralary of the above is that currently models created as a sum|multiply
+   model will not work as desired because the generated model parameters have a
+   p#_ appended to the beginning and thus radius and p1_radius will not be
+   recognized as the same parameter.
+.. image:: combine_batch_grid.png
+In the example shown above the data is a time series with a shifting peak.
+The first part of the series was fitted using the *broad_peak* model, while
+the rest of the data were fit using the *gaussian_peak* model. Unfortunately the
+time is not listed in the file but the file name contains the information. As
+described in :ref:`Grid_Window`, a column can be added manually, in this case
+called time, and the peak position plotted against time.
+.. image:: combine_batch_plot.png
+Note the discontinuity in the peak position.  This reflects the fact that the
+Gaussian fit is a rather poor model for the data and is not actually
+finding the peak.
+.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+.. note::  This help document was last changed by Paul Butler, 10 September

src/sas/sasgui/perspectives/fitting/media/plugin.rst

-                      r72100ee
+                      re081946
 * By writing a model from scratch outside of SasView (only recommended for
   code monkeys!)
+**What follows below is quite technical. If you just want a helping hand to get
+started creating your own models see** :ref:`Adding_your_own_models`.
 Overview

src/sas/sasgui/perspectives/fitting/model_thread.py

-                      r7432acb
+                      r0f9ea1c
                     (self.data.qy_data * self.data.qy_data))
         # For theory, qmax is based on 1d qmax
+        # For theory, qmax is based on 1d qmax
         # so that must be mulitified by sqrt(2) to get actual max for 2d
         index_model = (self.qmin <= radius) & (radius <= self.qmax)
 …
                 self.data.qy_data[index_model]
             ])
+        output = np.zeros(len(self.data.qx_data))
+        # Initialize output to NaN so masked elements do not get plotted.
+        output = np.empty_like(self.data.qx_data)
         # output default is None
         # This method is to distinguish between masked
         #point(nan) and data point = 0.
         output = output / output
+        output[:] = np.NaN
         # set value for self.mask==True, else still None to Plottools
         output[index_model] = value
 …
             output[index] = self.model.evalDistribution(self.data.x[index])
+        x=self.data.x[index]
+        y=output[index]
         sq_values = None
         pq_values = None
-        s_model = None
-        p_model = None
         if isinstance(self.model, MultiplicationModel):
             s_model = self.model.s_model
             p_model = self.model.p_model
+        elif hasattr(self.model, "get_composition_models"):
+            p_model, s_model = self.model.get_composition_models()
+        if p_model is not None and s_model is not None:
+            sq_values = np.zeros((len(self.data.x)))
+            pq_values = np.zeros((len(self.data.x)))
+            sq_values[index] = s_model.evalDistribution(self.data.x[index])
+            pq_values[index] = p_model.evalDistribution(self.data.x[index])
+            sq_values = s_model.evalDistribution(x)
+            pq_values = p_model.evalDistribution(x)
+        elif hasattr(self.model, "calc_composition_models"):
+            results = self.model.calc_composition_models(x)
+            if results is not None:
+                pq_values, sq_values = results
         elapsed = time.time() - self.starttime
         self.complete(x=self.data.x[index], y=output[index],
+        self.complete(x=x, y=y,
                       page_id=self.page_id,
                       state=self.state,

src/sas/sasgui/perspectives/fitting/simfitpage.py

-                      r959eb01
+                      ra9f9ca4
 """
     Simultaneous fit page
+    Simultaneous or Batch fit page
 """
+# Note that this is used for both Simultaneous/Constrained fit AND for
+# combined batch fit.  This is done through setting of the batch_on parameter.
+# There are the a half dozen or so places where an if statement is used as in
+# if not batch_on:
+#     xxxx
+# else:
+#     xxxx
+# This is just wrong but dont have time to fix this go. Proper approach would be
+# to strip all parts of the code that depend on batch_on and create the top
+# level class from which a contrained/simultaneous fit page and a combined
+# batch page inherit.
+#
+#            04/09/2017   --PDB
 import sys
 from collections import namedtuple
 …
         # General Help button
         self.btHelp = wx.Button(self, wx.ID_HELP, 'HELP')
+        self.btHelp.SetToolTipString("Simultaneous/Constrained Fitting help.")
+        if self.batch_on:
+            self.btHelp.SetToolTipString("Combined Batch Fitting help.")
+        else:
+            self.btHelp.SetToolTipString("Simultaneous/Constrained Fitting help.")
         self.btHelp.Bind(wx.EVT_BUTTON, self._on_help)
 …
     """
         _TreeLocation = "user/sasgui/perspectives/fitting/fitting_help.html"
+        _PageAnchor = "#simultaneous-fit-mode"
+        _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation,
+        if not self.batch_on:
+            _PageAnchor = "#simultaneous-fit-mode"
+            _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation,
                                           _PageAnchor,
                                           "Simultaneous/Constrained Fitting Help")
+        else:
+            _PageAnchor = "#combined-batch-fit-mode"
+            _doc_viewer = DocumentationWindow(self, self.ID_DOC, _TreeLocation,
+                                          _PageAnchor,
+                                          "Combined Batch Fit Help")
     def set_manager(self, manager):

src/sas/sasgui/perspectives/fitting/models.py

-                      rb1c2011
+                      r12f7f24
 import py_compile
 import shutil
+from sasmodels.sasview_model import load_custom_model, load_standard_models
 # Explicitly import from the pluginmodel module so that py2exe
 # places it in the distribution. The Model1DPlugin class is used
 # as the base class of plug-in models.
+from sas.sasgui import get_user_dir
 from sas.sascalc.fit.pluginmodel import Model1DPlugin
 from sas.sasgui.guiframe.CategoryInstaller import CategoryInstaller
-from sasmodels.sasview_model import load_custom_model, load_standard_models
 logger = logging.getLogger(__name__)
 …
 PLUGIN_DIR = 'plugin_models'
+PLUGIN_LOG = os.path.join(os.path.expanduser("~"), '.sasview', PLUGIN_DIR,
+                          "plugins.log")
+PLUGIN_LOG = os.path.join(get_user_dir(), PLUGIN_DIR, "plugins.log")
 PLUGIN_NAME_BASE = '[plug-in] '

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