Formatting: Remove trailing commas to combine lines

Remove trailing commas to combine multiple lines to a single line. A few edge-cases in which this degraded readability were not included (not visible in this commit). This should make code more compact while retaining or improving readability.

Command used: black --line-length 100 -C

See https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html#trailing-commas

docs/source/indepth_tutorial/directed-search.ipynb

@@ -111,14 +111,7 @@
     "ema_logging.log_to_stderr(ema_logging.INFO)\n",
     "\n",
     "with MultiprocessingEvaluator(model) as evaluator:\n",
-    "    results = evaluator.optimize(\n",
-    "        nfe=250,\n",
-    "        searchover=\"levers\",\n",
-    "        epsilons=[\n",
-    "            0.1,\n",
-    "        ]\n",
-    "        * len(model.outcomes),\n",
-    "    )"
+    "    results = evaluator.optimize(nfe=250, searchover=\"levers\", epsilons=[0.1] * len(model.outcomes))"
    ]
   },
   {
@@ -314,13 +307,7 @@
     "\n",
     "with MultiprocessingEvaluator(model) as evaluator:\n",
     "    results = evaluator.optimize(\n",
-    "        nfe=250,\n",
-    "        searchover=\"levers\",\n",
-    "        epsilons=[\n",
-    "            0.1,\n",
-    "        ]\n",
-    "        * len(model.outcomes),\n",
-    "        constraints=constraints,\n",
+    "        nfe=250, searchover=\"levers\", epsilons=[0.1] * len(model.outcomes), constraints=constraints\n",
     "    )"
    ]
   },
@@ -469,10 +456,7 @@
     "    results, convergence = evaluator.optimize(\n",
     "        nfe=10000,\n",
     "        searchover=\"levers\",\n",
-    "        epsilons=[\n",
-    "            0.05,\n",
-    "        ]\n",
-    "        * len(model.outcomes),\n",
+    "        epsilons=[0.05] * len(model.outcomes),\n",
     "        convergence=convergence_metrics,\n",
     "        constraints=constraints,\n",
     "    )\n",
@@ -548,12 +532,7 @@
     "\n",
     "with MultiprocessingEvaluator(model) as evaluator:\n",
     "    results = evaluator.optimize(\n",
-    "        nfe=1000,\n",
-    "        searchover=\"uncertainties\",\n",
-    "        epsilons=[\n",
-    "            0.1,\n",
-    "        ]\n",
-    "        * len(model.outcomes),\n",
+    "        nfe=1000, searchover=\"uncertainties\", epsilons=[0.1] * len(model.outcomes)\n",
     "    )"
    ]
   },
@@ -626,10 +605,7 @@
     "MINIMIZE = ScalarOutcome.MINIMIZE\n",
     "robustnes_functions = [\n",
     "    ScalarOutcome(\n",
-    "        \"90th percentile max_p\",\n",
-    "        kind=MINIMIZE,\n",
-    "        variable_name=\"max_P\",\n",
-    "        function=percentile90,\n",
+    "        \"90th percentile max_p\", kind=MINIMIZE, variable_name=\"max_P\", function=percentile90\n",
     "    ),\n",
     "    ScalarOutcome(\n",
     "        \"10th percentile reliability\",\n",
@@ -638,16 +614,10 @@
     "        function=percentile10,\n",
     "    ),\n",
     "    ScalarOutcome(\n",
-    "        \"10th percentile inertia\",\n",
-    "        kind=MAXIMIZE,\n",
-    "        variable_name=\"inertia\",\n",
-    "        function=percentile10,\n",
+    "        \"10th percentile inertia\", kind=MAXIMIZE, variable_name=\"inertia\", function=percentile10\n",
     "    ),\n",
     "    ScalarOutcome(\n",
-    "        \"10th percentile utility\",\n",
-    "        kind=MAXIMIZE,\n",
-    "        variable_name=\"utility\",\n",
-    "        function=percentile10,\n",
+    "        \"10th percentile utility\", kind=MAXIMIZE, variable_name=\"utility\", function=percentile10\n",
     "    ),\n",
     "]"
    ]

docs/source/indepth_tutorial/general-introduction.ipynb

@@ -123,9 +123,7 @@
     "        X[0] = 0.0\n",
     "        decision = 0.1\n",
     "\n",
-    "        decisions = np.zeros(\n",
-    "            myears,\n",
-    "        )\n",
+    "        decisions = np.zeros(myears)\n",
     "        decisions[0] = decision\n",
     "\n",
     "        natural_inflows = np.random.lognormal(\n",

ema_workbench/__init__.py

@@ -21,13 +21,7 @@
     ArrayOutcome,
     Samplers,
 )
-from .util import (
-    save_results,
-    load_results,
-    ema_logging,
-    EMAError,
-    process_replications,
-)
+from .util import save_results, load_results, ema_logging, EMAError, process_replications
 
 # from . import analysis
 

ema_workbench/analysis/dimensional_stacking.py

@@ -276,12 +276,7 @@ def plot_pivot_table(
         height_ratios = dim_ratios(figsize=figsize, axis=0)
 
         gs = mpl.gridspec.GridSpec(
-            3,
-            3,
-            wspace=0.01,
-            hspace=0.01,
-            width_ratios=width_ratios,
-            height_ratios=height_ratios,
+            3, 3, wspace=0.01, hspace=0.01, width_ratios=width_ratios, height_ratios=height_ratios
         )
 
         ax_plot = fig.add_subplot(gs[2, 2])

ema_workbench/analysis/pairs_plotting.py

@@ -118,13 +118,7 @@ def pairs_lines(
 
         for ax in figure.axes:
             gs2 = ax._subplotspec
-            if all(
-                (
-                    gs1._gridspec == gs2._gridspec,
-                    gs1.num1 == gs2.num1,
-                    gs1.num2 == gs2.num2,
-                )
-            ):
+            if all((gs1._gridspec == gs2._gridspec, gs1.num1 == gs2.num1, gs1.num2 == gs2.num2)):
                 break
 
         make_legend(grouping_labels, ax, legend_type=LegendEnum.LINE)
@@ -312,14 +306,7 @@ def determine_extents(outcomes, outcomes_to_show):
 
 
 def simple_pairs_density(
-    outcomes,
-    outcomes_to_show,
-    log,
-    colormap,
-    gridsize,
-    ylabels,
-    extents=None,
-    title=None,
+    outcomes, outcomes_to_show, log, colormap, gridsize, ylabels, extents=None, title=None
 ):
     """
 
@@ -509,13 +496,7 @@ def pairs_scatter(
 
         for ax in figure.axes:
             gs2 = ax._subplotspec
-            if all(
-                (
-                    gs1._gridspec == gs2._gridspec,
-                    gs1.num1 == gs2.num1,
-                    gs1.num2 == gs2.num2,
-                )
-            ):
+            if all((gs1._gridspec == gs2._gridspec, gs1.num1 == gs2.num1, gs1.num2 == gs2.num2)):
                 break
 
         make_legend(grouping_labels, ax, legend_type=LegendEnum.SCATTER)

ema_workbench/analysis/plotting.py

@@ -145,15 +145,7 @@ def envelopes(
 
         if group_by:
             group_by_envelopes(
-                outcomes,
-                outcome_to_plot,
-                time,
-                density,
-                ax,
-                ax_d,
-                fill,
-                grouping_labels,
-                log,
+                outcomes, outcome_to_plot, time, density, ax, ax_d, fill, grouping_labels, log
             )
         else:
             single_envelope(outcomes, outcome_to_plot, time, density, ax, ax_d, fill, log)
@@ -171,13 +163,7 @@ def envelopes(
 
         for ax in figure.axes:
             gs2 = ax._subplotspec
-            if all(
-                (
-                    gs1._gridspec == gs2._gridspec,
-                    gs1.num1 == gs2.num1,
-                    gs1.num2 == gs2.num2,
-                )
-            ):
+            if all((gs1._gridspec == gs2._gridspec, gs1.num1 == gs2.num1, gs1.num2 == gs2.num2)):
                 break
         if fill:
             make_legend(grouping_labels, ax, alpha=0.3, legend_type=LegendEnum.PATCH)
@@ -354,12 +340,7 @@ def lines(
         )
 
     data = prepare_data(
-        experiments,
-        experiments_to_show,
-        outcomes,
-        outcomes_to_show,
-        group_by,
-        grouping_specifiers,
+        experiments, experiments_to_show, outcomes, outcomes_to_show, group_by, grouping_specifiers
     )
     experiments, outcomes, outcomes_to_show, time, grouping_labels = data
 
@@ -392,13 +373,7 @@ def lines(
 
         for ax in figure.axes:
             gs2 = ax._subplotspec
-            if all(
-                (
-                    gs1._gridspec == gs2._gridspec,
-                    gs1.num1 == gs2.num1,
-                    gs1.num2 == gs2.num2,
-                )
-            ):
+            if all((gs1._gridspec == gs2._gridspec, gs1.num1 == gs2.num1, gs1.num2 == gs2.num2)):
                 break
 
         make_legend(grouping_labels, ax)
@@ -472,12 +447,7 @@ def plot_lines_with_envelopes(
         experiments, None, outcomes, outcomes_to_show, group_by, grouping_specifiers
     )[1]
     data = prepare_data(
-        experiments,
-        experiments_to_show,
-        outcomes,
-        outcomes_to_show,
-        group_by,
-        grouping_specifiers,
+        experiments, experiments_to_show, outcomes, outcomes_to_show, group_by, grouping_specifiers
     )
     experiments, outcomes, outcomes_to_show, time, grouping_labels = data
 
@@ -510,8 +480,7 @@ def plot_lines_with_envelopes(
                 group_density(ax_d, density, full_outcomes, outcome_to_plot, grouping_labels, log)
 
                 ax_d.get_yaxis().set_view_interval(
-                    ax.get_yaxis().get_view_interval()[0],
-                    ax.get_yaxis().get_view_interval()[1],
+                    ax.get_yaxis().get_view_interval()[0], ax.get_yaxis().get_view_interval()[1]
                 )
 
         else:
@@ -533,13 +502,7 @@ def plot_lines_with_envelopes(
 
         for ax in figure.axes:
             gs2 = ax._subplotspec
-            if all(
-                (
-                    gs1._gridspec == gs2._gridspec,
-                    gs1.num1 == gs2.num1,
-                    gs1.num2 == gs2.num2,
-                )
-            ):
+            if all((gs1._gridspec == gs2._gridspec, gs1.num1 == gs2.num1, gs1.num2 == gs2.num2)):
                 break
         make_legend(grouping_labels, ax)
 
@@ -823,14 +786,7 @@ def multiple_densities(
             ax4 = plt.subplot2grid((2, 6), (1, 3), sharex=ax1, sharey=ax_env)
             ax5 = plt.subplot2grid((2, 6), (1, 4), sharex=ax1, sharey=ax_env)
             ax6 = plt.subplot2grid((2, 6), (1, 5), sharex=ax1, sharey=ax_env)
-            kde_axes = [
-                ax1,
-                ax2,
-                ax3,
-                ax4,
-                ax5,
-                ax6,
-            ]
+            kde_axes = [ax1, ax2, ax3, ax4, ax5, ax6]
         else:
             raise EMAError("too many points in time provided")
 
@@ -875,13 +831,7 @@ def multiple_densities(
             # TODO grouping labels, boxplots, and sharex
             # create a problem
             group_density(
-                ax,
-                density,
-                outcomes,
-                outcome_to_show,
-                grouping_labels,
-                index=index,
-                log=log,
+                ax, density, outcomes, outcome_to_show, grouping_labels, index=index, log=log
             )
 
         min_y, max_y = ax_env.get_ylim()

ema_workbench/analysis/plotting_util.py

@@ -322,8 +322,7 @@ def simple_density(density, value, ax_d, ax, log):
         ax.get_yaxis().get_view_interval()[0], ax.get_yaxis().get_view_interval()[1]
     )
     ax_d.set_ylim(
-        bottom=ax.get_yaxis().get_view_interval()[0],
-        top=ax.get_yaxis().get_view_interval()[1],
+        bottom=ax.get_yaxis().get_view_interval()[0], top=ax.get_yaxis().get_view_interval()[1]
     )
 
     ax_d.set_xlabel("")
@@ -676,13 +675,7 @@ def prepare_pairs_data(
         raise EMAError("for pair wise plotting, more than one outcome needs to be provided")
 
     experiments, outcomes, outcomes_to_show, time, grouping_labels = prepare_data(
-        experiments,
-        None,
-        outcomes,
-        outcomes_to_show,
-        group_by,
-        grouping_specifiers,
-        filter_scalar,
+        experiments, None, outcomes, outcomes_to_show, group_by, grouping_specifiers, filter_scalar
     )
 
     def filter_outcomes(outcomes, point_in_time):

ema_workbench/analysis/prim.py

@@ -368,16 +368,7 @@ def __init__(self, prim, box_lims, indices):
         self._resampled = []
         self.yi_initial = indices[:]
 
-        columns = [
-            "name",
-            "lower",
-            "upper",
-            "minimum",
-            "maximum",
-            "qp_lower",
-            "qp_upper",
-            "id",
-        ]
+        columns = ["name", "lower", "upper", "minimum", "maximum", "qp_lower", "qp_upper", "id"]
         self.boxes_quantitative = pd.DataFrame(columns=columns)
 
         columns = ["item", "name", "n_items", "x", "id"]
@@ -559,11 +550,7 @@ def inspect_tradeoff(self):
                     # unless we can force a selection?
                     name = f"{dim}, {qp.loc[qp.index[0], dim]: .2g}"
                     entry = dict(
-                        name=name,
-                        n_items=len(items) + 1,
-                        item=item,
-                        id=int(i),
-                        x=j / len(items),
+                        name=name, n_items=len(items) + 1, item=item, id=int(i), x=j / len(items)
                     )
                     nominal_vars.append(entry)
 
@@ -988,10 +975,7 @@ def __init__(
         mode=sdutil.RuleInductionType.BINARY,
         update_function="default",
     ):
-        assert mode in {
-            sdutil.RuleInductionType.BINARY,
-            sdutil.RuleInductionType.REGRESSION,
-        }
+        assert mode in {sdutil.RuleInductionType.BINARY, sdutil.RuleInductionType.REGRESSION}
         assert self._assert_mode(y, mode, update_function)
         # preprocess x
         try:
@@ -1207,7 +1191,7 @@ def _peel(self, box):
         # identify all possible peels
         possible_peels = []
 
-        for x, columns, dtype, in [
+        for x, columns, dtype in [
             (x_float, self.x_float_colums, "float"),
             (x_int, self.x_int_columns, "int"),
             (x_nominal, self.x_nominal_columns, "object"),
@@ -1421,7 +1405,7 @@ def _paste(self, box):
 
         # identify all possible pastes
         possible_pastes = []
-        for columns, dtype, in [
+        for columns, dtype in [
             (self.x_float_colums, "float"),
             (self.x_int_columns, "int"),
             (self.x_nominal_columns, "object"),