Changes in example/multiscatfit.py [2c4a190:49d1f8b8] in sasmodels

File:

• example/multiscatfit.py (modified) (3 diffs)

example/multiscatfit.py

@@ -15 +15 @@
 
     # Show the model without fitting
-    PYTHONPATH=..:../../bumps:../../sasview/src python multiscatfit.py
+    PYTHONPATH=..:../explore:../../bumps:../../sasview/src python multiscatfit.py
 
     # Run the fit
-    PYTHONPATH=..:../../bumps:../../sasview/src ../../bumps/run.py \
+    PYTHONPATH=..:../explore:../../bumps:../../sasview/src ../../bumps/run.py \
     multiscatfit.py --store=/tmp/t1
 
@@ -55 +55 @@
     )
 
-# Tie the model to the data
-M = Experiment(data=data, model=model)
-
-# Stack mulitple scattering on top of the existing resolution function.
-M.resolution = MultipleScattering(resolution=M.resolution, probability=0.)
-
 # SET THE FITTING PARAMETERS
 model.radius_polar.range(15, 3000)
@@ -71 +65 @@
 model.scale.range(0, 0.1)
 
-# The multiple scattering probability parameter is in the resolution function
-# instead of the scattering function, so access it through M.resolution
-M.scattering_probability.range(0.0, 0.9)
+# Mulitple scattering probability parameter
+# HACK: the probability is stuffed in as an extra parameter to the experiment.
+probability = Parameter(name="probability", value=0.0)
+probability.range(0.0, 0.9)
 
-# Let bumps know that we are fitting this experiment
+M = Experiment(data=data, model=model, extra_pars={'probability': probability})
+
+# Stack mulitple scattering on top of the existing resolution function.
+# Because resolution functions in sasview don't have fitting parameters,
+# we instead allow the multiple scattering calculator to take a function
+# instead of a probability.  This function returns the current value of
+# the parameter. ** THIS IS TEMPORARY ** when multiple scattering is
+# properly integrated into sasmodels and sasview, its fittable parameter
+# will be treated like the model parameters.
+M.resolution = MultipleScattering(resolution=M.resolution,
+                                  probability=lambda: probability.value,
+                                  )
+M._kernel_inputs = M.resolution.q_calc
 problem = FitProblem(M)