Doc update: renamed MC test script to fit into new module.

doc/tests/CMakeLists.txt

@@ -28,7 +28,6 @@ set (DOC_TEST_SCRIPTS
 
   scripts/loop_main.py
   scripts/loop_backbone.py
-  scripts/loop_monte_carlo.py
   scripts/loop_structure_db.py
   scripts/loop_frag_db.py
   scripts/loop_fragger.py
@@ -43,6 +42,7 @@ set (DOC_TEST_SCRIPTS
   scripts/modelling_fill_loops_by_database.py
   scripts/modelling_fill_loops_by_monte_carlo.py
   scripts/modelling_model_termini.py
+  scripts/modelling_monte_carlo.py
   scripts/modelling_loop_candidates.py
 
   scripts/sidechain_reconstruct.py

doc/tests/test_doctests.py

@@ -203,14 +203,6 @@ class DocTests(unittest.TestCase):
         # clean up
         os.remove('randomized_fragment.pdb')
 
-    def testLoopMonteCarlo(self):
-        # run it
-        self.checkPMRun('loop_monte_carlo.py', [], 0)
-        # check that result exists and is readable
-        io.LoadPDB('sampled_frag.pdb')
-        # clean up
-        os.remove('sampled_frag.pdb')
-
     def testLoopStructureDB(self):
         # check binaries
         binaries_found = True
@@ -333,6 +325,14 @@ class DocTests(unittest.TestCase):
                         'Number of gaps before: 2\n' +
                         'Number of gaps after: 0')
 
+    def testModellingMonteCarlo(self):
+        # run it
+        self.checkPMRun('modelling_monte_carlo.py', [], 0)
+        # check that result exists and is readable
+        io.LoadPDB('sampled_frag.pdb')
+        # clean up
+        os.remove('sampled_frag.pdb')
+
     def testModellingLoopCandidates(self):
         # run it
         self.checkPMRun('modelling_loop_candidates.py', [], 0)

modelling/doc/monte_carlo.rst

@@ -19,7 +19,7 @@ pipelines. This combination either happens manually or by using the convenient
 :func:`SampleMonteCarlo` function. For example, here we show how to apply Monte
 Carlo sampling to the N-terminal part of crambin:
 
-.. literalinclude:: ../../../tests/doc/scripts/loop_monte_carlo.py
+.. literalinclude:: ../../../tests/doc/scripts/modelling_monte_carlo.py
 
 .. method:: SampleMonteCarlo(sampler, closer, scorer, cooler, steps,\
                              bb_list, initialize=true, seed=0,\