-                      r9d80623
+                      rc351bf1
         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
+        operator_text = self._operator_choice.GetValue()
+        f_oper = '*' if '+' in operator_text else '+'
+        path = self.fname
+        output = SUM_TEMPLATE.format(model1=name1, model2=name2,
+            scale_factor_default=1.0, background_default=0.001,
+            factor_operator=f_oper, operator=self._operator,
+            description=description)
+        if self._operator == '*':
+            # Multiplication models only have 1 overall scale factor. Don't use
+            # sub-models' individual scales as fitting params
+            output = output.replace("if name == 'background'",
+                "if name == 'background' or name == 'scale'")
+        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 + "\n")
+            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('{model1}')
+        P2 = find_model('{model2}')
+        p_model1 = P1()
+        p_model2 = P2()
+        ## Setting  model name model description
+        self.description = '{description}'
+        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'] = {scale_factor_default}
+        # Set each model's background to 0, and define our own background param
+        if 'background' in self.p_model1.params:
+            self.p_model1.setParam('background', 0.0)
+        if 'background' in self.p_model2.params:
+            self.p_model2.setParam('background', 0.0)
+        self.params['background'] = {background_default}
+        ## Parameter details [units, min, max]
+        self._set_details()
+        self.details['scale_factor'] = ['', 0.0, numpy.inf]
+        self.details['background'] = ['1/cm', 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():
+            # Don't use the model's background param - we've defined our own
+            if name == 'background':
+                continue
+            new_name = "p1_" + name
+            self.params[new_name] = value
+        for name , value in self.p_model2.params.iteritems():
+            # Don't use the model's background param - we've defined our own
+            if name == 'background':
+                continue
+            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():
+            if name == 'background':
+                continue
+            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():
+            if name == 'background':
+                continue
+            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' or name == 'background':
+            self.params[name] = 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'] {factor_operator} \
+(self.p_model1.run(x) {operator} self.p_model2.run(x)) + self.params['background']
+    def runXY(self, x = 0.0):
+        self._set_scale_factor()
+        return self.params['scale_factor'] {factor_operator} \
+(self.p_model1.runXY(x) {operator} self.p_model2.runXY(x)) + self.params['background']
+    ## 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'] {factor_operator} \
+(self.p_model1.evalDistribution(x) {operator} \
+self.p_model2.evalDistribution(x)) + self.params['background']
+    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) {operator} 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}', force_mixture=True)
+model_info.name = '{name}'{desc_line}
+Model = make_model_from_info(model_info)
 """
 if __name__ == "__main__":
 #    app = wx.PySimpleApp()

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SasView

Changeset c351bf1 in sasview for src/sas/sasgui/perspectives

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

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