add option max_reflections_per_experiment to combine_experiments (#1369)

* add option max_reflections_per_experiment to combine_experiments

* test min_ and max_reflections_per_experiment

AUTHORS

@@ -4,6 +4,7 @@ Aaron Brewster
 Asmit Bhowmick
 Benjamin Williams
 Billy Poon
+Daniel Paley
 David Waterman
 Derek Mendez
 Graeme Winter

command_line/combine_experiments.py

@@ -178,6 +178,12 @@
       .help = "If not None, throw out any experiment with fewer than this"
               "many reflections"
 
+    max_reflections_per_experiment = None
+      .type = int
+      .expert_level = 2
+      .help = "If not None, throw out any experiment with more than this"
+              "many reflections"
+
     include scope dials.algorithms.integration.stills_significance_filter.phil_scope
   }
 """,
@@ -501,7 +507,8 @@ def average_detectors(target, panelgroups, depth):
         # set up global experiments and reflections lists
         reflections = flex.reflection_table()
         global_id = 0
-        skipped_expts = 0
+        skipped_expts_min_refl = 0
+        skipped_expts_max_refl = 0
         experiments = ExperimentList()
 
         # loop through the input, building up the global lists
@@ -523,7 +530,13 @@ def average_detectors(target, panelgroups, depth):
                     params.output.min_reflections_per_experiment is not None
                     and n_sub_ref < params.output.min_reflections_per_experiment
                 ):
-                    skipped_expts += 1
+                    skipped_expts_min_refl += 1
+                    continue
+                if (
+                    params.output.max_reflections_per_experiment is not None
+                    and n_sub_ref > params.output.max_reflections_per_experiment
+                ):
+                    skipped_expts_max_refl += 1
                     continue
 
                 nrefs_per_exp.append(n_sub_ref)
@@ -550,11 +563,20 @@ def average_detectors(target, panelgroups, depth):
 
         if (
             params.output.min_reflections_per_experiment is not None
-            and skipped_expts > 0
+            and skipped_expts_min_refl > 0
         ):
             print(
                 "Removed {0} experiments with fewer than {1} reflections".format(
-                    skipped_expts, params.output.min_reflections_per_experiment
+                    skipped_expts_min_refl, params.output.min_reflections_per_experiment
+                )
+            )
+        if (
+            params.output.max_reflections_per_experiment is not None
+            and skipped_expts_max_refl > 0
+        ):
+            print(
+                "Removed {0} experiments with more than {1} reflections".format(
+                    skipped_expts_max_refl, params.output.max_reflections_per_experiment
                 )
             )
 

newsfragments/1369.feature

@@ -0,0 +1 @@
+dials.combine_experiments: add an option output.max_reflections_per_experiment

test/command_line/test_combine_experiments.py

@@ -299,6 +299,38 @@ def narrow_wedge_input_with_identifiers(dials_regression, tmpdir):
     return phil_input
 
 
+@pytest.mark.parametrize("min_refl", ["None", "100"])
+@pytest.mark.parametrize("max_refl", ["None", "150"])
+def test_min_max_reflections_per_experiment(
+    dials_regression, run_in_tmpdir, min_refl, max_refl
+):
+
+    expected_results = {
+        ("None", "None"): 10,
+        ("None", "150"): 9,
+        ("100", "None"): 6,
+        ("100", "150"): 5,
+    }
+
+    data_dir = os.path.join(dials_regression, "refinement_test_data", "multi_stills")
+    input_phil = (
+        " input.experiments={0}/combined_experiments.json\n"
+        + " input.reflections={0}/combined_reflections.pickle\n"
+        + " output.min_reflections_per_experiment={1}\n"
+        + " output.max_reflections_per_experiment={2}\n"
+    ).format(data_dir, min_refl, max_refl)
+    with open("input.phil", "w") as phil_file:
+        phil_file.writelines(input_phil)
+
+    result = procrunner.run(["dials.combine_experiments", "input.phil"])
+    assert not result.returncode and not result.stderr
+
+    # load results
+    exp = ExperimentListFactory.from_json_file("combined.expt", check_format=False)
+
+    assert len(exp) == expected_results[(min_refl, max_refl)]
+
+
 @pytest.mark.parametrize("with_identifiers", ["True", "False"])
 @pytest.mark.parametrize("method", ["random", "n_refl", "significance_filter"])
 def test_combine_nsubset(