Remove unnecessary use of comprehension

ema_workbench/analysis/dimensional_stacking.py

@@ -61,7 +61,7 @@ def discretize(data, nbins=3, with_labels=False):
             n_unique = column_data.unique().shape[0]
             n = n_unique
             column_data = column_data.cat.rename_categories(
-                [x for x in range(1, n + 1)]
+                list(range(1, n + 1))
             )
             indices = column_data
 
@@ -414,8 +414,8 @@ def create_pivot_plot(
     n = nr_levels * 2
 
     scores = scores.index.tolist()
-    rows = [entry for entry in scores[0:n:2]]
-    columns = [entry for entry in scores[1:n:2]]
+    rows = list(scores[0:n:2])
+    columns = list(scores[1:n:2])
 
     discretized_x = discretize(x, nbins=nbins, with_labels=bin_labels)
 

ema_workbench/analysis/plotting_util.py

@@ -222,7 +222,7 @@ def plot_violinplot(ax, values, log, group_labels=None):
     if not group_labels:
         group_labels = [""]
 
-    data = pd.DataFrame.from_records({k: v for k, v in zip(group_labels, values)})
+    data = pd.DataFrame.from_records(dict(zip(group_labels, values)))
     data = pd.melt(data)
 
     sns.violinplot(x="variable", y="value", data=data, order=group_labels, ax=ax)
@@ -246,7 +246,7 @@ def plot_boxenplot(ax, values, log, group_labels=None):
     if not group_labels:
         group_labels = [""]
 
-    data = pd.DataFrame.from_records({k: v for k, v in zip(group_labels, values)})
+    data = pd.DataFrame.from_records(dict(zip(group_labels, values)))
     data = pd.melt(data)
 
     sns.boxenplot(x="variable", y="value", data=data, order=group_labels, ax=ax)
@@ -771,7 +771,7 @@ def prepare_data(
     if filter_scalar:
         outcomes = filter_scalar_outcomes(outcomes)
     if not outcomes_to_show:
-        outcomes_to_show = [o for o in outcomes.keys()]
+        outcomes_to_show = list(outcomes.keys())
 
     # group the data if desired
     if group_by:

ema_workbench/analysis/scenario_discovery_util.py

@@ -68,7 +68,7 @@ def _get_sorted_box_lims(boxes, box_init):
     # sort the uncertainties based on the normalized size of the
     # restricted dimensions
     uncs = uncs[np.argsort(box_size)]
-    box_lims = [box for box in boxes]
+    box_lims = list(boxes)
 
     return box_lims, uncs.tolist()
 
@@ -454,7 +454,7 @@ def _setup_figure(uncs):
     ax.add_patch(rect)
     ax.set_xlim(left=-0.2, right=1.2)
     ax.set_ylim(top=-0.5, bottom=nr_unc - 0.5)
-    ax.yaxis.set_ticks([y for y in range(nr_unc)])
+    ax.yaxis.set_ticks(list(range(nr_unc)))
     ax.xaxis.set_ticks([0, 0.25, 0.5, 0.75, 1])
     ax.set_yticklabels(uncs[::-1])
     return fig, ax

ema_workbench/em_framework/parameters.py

@@ -384,7 +384,7 @@ def __init__(
         self._categories = NamedObjectMap(Category)
 
         self.categories = cats
-        self.resolution = [i for i in range(len(self.categories))]
+        self.resolution = list(range(len(self.categories)))
         self.multivalue = multivalue
 
     def index_for_cat(self, category):
@@ -488,7 +488,7 @@ def parameters_to_csv(parameters, file_name):
         else:
             values = param.lower_bound, param.upper_bound
 
-        dict_repr = {j: value for j, value in enumerate(values)}
+        dict_repr = dict(enumerate(values))
         dict_repr["name"] = param.name
 
         params[i] = dict_repr

ema_workbench/em_framework/points.py

@@ -163,7 +163,7 @@ def combine_cases_sampling(*point_collection):
 
     # figure out the longest
     def exhaust_cases(cases):
-        return [case for case in cases]
+        return list(cases)
 
     point_collection = [exhaust_cases(case) for case in point_collection]
     longest_cases = max(point_collection, key=len)

ema_workbench/examples/eijgenraam_example.py

@@ -112,7 +112,7 @@
     52: (49.2200, 1.6075, 0.0047, 0.036173, 0.304, 0.001716, 4025.6, 0.00171, 1 / 1250),
     53: (69.4565, 1.1625, 0.0028, 0.031651, 0.336, 0.002700, 9819.5, 0.00171, 1 / 1250),
 }
-data = {i: {k: v for k, v in zip(params, raw_data[i])} for i in raw_data.keys()}
+data = {i: dict(zip(params, raw_data[i])) for i in raw_data.keys()}
 
 # Set the ring we are analyzing
 ring = 15

test/test_em_framework/test_callback.py

@@ -87,7 +87,7 @@ def test_store_results(self):
 
         _, out = callback.get_results()
 
-        self.assertIn(outcomes[0].name, {entry for entry in out.keys()})
+        self.assertIn(outcomes[0].name, set(out.keys()))
         self.assertEqual(out[outcomes[0].name].shape, (3,))
 
         # case 2 time series shape = (1, nr_time_steps)

test/test_em_framework/test_points.py

@@ -13,15 +13,15 @@ def test_experiment_gemerator(self):
         experiments = points.experiment_generator(
             scenarios, model_structures, policies, combine="factorial"
         )
-        experiments = [e for e in experiments]
+        experiments = list(experiments)
         self.assertEqual(
             len(experiments), 6, ("wrong number of experiments " "for factorial")
         )
 
         experiments = points.experiment_generator(
             scenarios, model_structures, policies, combine="sample"
         )
-        experiments = [e for e in experiments]
+        experiments = list(experiments)
         self.assertEqual(
             len(experiments), 3, ("wrong number of experiments " "for zipover")
         )
@@ -30,7 +30,7 @@ def test_experiment_gemerator(self):
             experiments = points.experiment_generator(
                 scenarios, model_structures, policies, combine="adf"
             )
-            _ = [e for e in experiments]
+            _ = list(experiments)
 
     # def test_experiment_generator(self):
     #     sampler = LHSSampler()