Fix Python WeightedProfileFitterChi.compute_score

Fix the wrapper for compute_score so that it can be
used from Python. In Python, the determined weights
will be returned as a second return value, rather
than modifying a passed-in vector. Add a simple test.

modules/saxs/include/WeightedProfileFitter.h

@@ -46,6 +46,18 @@ class WeightedProfileFitter : public ProfileFitter<ScoringFunctionT> {
     Wb_ = W_.asDiagonal() * Wb_;
   }
 
+// When used from Python, compute_score will return the weights as a second
+// return value, rather than modifying a passed-in vector
+#ifdef SWIG
+%typemap(in, numinputs=0) Vector<double>& weights (Vector<double> temp) {
+  $1 = &temp;
+}
+%typemap(argout) Vector<double>& weights {
+  PyObject *obj = ConvertSequence<Vector<double>, Convert<double>>::create_python_object(ValueOrObject<Vector<double>>::get(*$1), $descriptor(double*), SWIG_POINTER_OWN);
+  $result = SWIG_AppendOutput($result, obj);
+}
+#endif
+
   //! compute a weighted score that minimizes chi
   /**
      it is assumed that the q values of the profiles are the same as
@@ -56,6 +68,10 @@ class WeightedProfileFitter : public ProfileFitter<ScoringFunctionT> {
   double compute_score(const ProfilesTemp& profiles, Vector<double>& weights,
                        bool use_offset = false, bool NNLS = true) const;
 
+#ifdef SWIG
+%clear Vector<double>& weights;
+#endif
+
   //! fit profiles by optimization of c1/c2 and weights
   /**
      it is assumed that the q values of the profiles are the same as

modules/saxs/test/test_weighted_profile.py

@@ -58,6 +58,19 @@ def test_weighted_profile(self):
         saxs_score = IMP.saxs.WeightedProfileFitterChi(weighted_profile)
 
         profile_list = [resampled_profile1, resampled_profile2]
+        # Get score for single profile
+        score, weights = saxs_score.compute_score(profile_list[:1],
+                                                  False, False)
+        self.assertAlmostEqual(score, 81.765, delta=0.001)
+        self.assertEqual(len(weights), 1)
+        self.assertAlmostEqual(weights[0], 1.0, delta=0.001)
+
+        # Get score for both profiles
+        score, weights = saxs_score.compute_score(profile_list, False, False)
+        self.assertAlmostEqual(score, 3.031, delta=0.001)
+        self.assertEqual(len(weights), 2)
+        self.assertAlmostEqual(weights[0], 0.297, delta=0.001)
+        self.assertAlmostEqual(weights[1], 0.703, delta=0.001)
 
         wfp = saxs_score.fit_profile(profile_list, 0.95, 1.05, -2.0, 4.0)
         chi = wfp.get_chi_square()