Formatting: Format with Black, increase max line length to 100, combine multi-line blocks

docs/source/conf.py

@@ -130,15 +130,15 @@ def establish_version():
 # further.  For a list of options available for each theme, see the
 # documentation.
 html_theme_options = dict(
-    github_user='quaquel',
-    github_repo='EMAworkbench',
-    github_version='/tree/master/docs/',
+    github_user="quaquel",
+    github_repo="EMAworkbench",
+    github_version="/tree/master/docs/",
     # On master branch and new branch still in
     # pre-release status: true; else: false.
-    in_progress='true',
+    in_progress="true",
     # On branches previous to "latest": true; else: false.
-    outdated='false',
-    )
+    outdated="false",
+)
 
 # Add any paths that contain custom themes here, relative to this directory.
 # html_theme_path = []
@@ -257,12 +257,10 @@ def establish_version():
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    ("index", "emaworkbench", "EMA workbench Documentation", ["J.H. Kwakkel"], 1)
-]
+man_pages = [("index", "emaworkbench", "EMA workbench Documentation", ["J.H. Kwakkel"], 1)]
 
 
 def setup(app):
     # copy changelog into source folder for documentation
     dest = osp.join(HERE, "changelog.md")
-    shutil.copy(osp.join(HERE, "..", "..", "CHANGELOG.md"), dest)
+    shutil.copy(osp.join(HERE, "..", "..", "CHANGELOG.md"), dest)

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))"
    ]
   },
   {
@@ -287,9 +280,7 @@
    "source": [
     "from ema_workbench import Constraint\n",
     "\n",
-    "constraints = [\n",
-    "    Constraint(\"max pollution\", outcome_names=\"max_P\", function=lambda x: max(0, x - 1))\n",
-    "]"
+    "constraints = [Constraint(\"max pollution\", outcome_names=\"max_P\", function=lambda x: max(0, x - 1))]"
    ]
   },
   {
@@ -316,13 +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",
     "    )"
    ]
   },
@@ -471,10 +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",
@@ -550,12 +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",
     "    )"
    ]
   },
@@ -628,10 +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",
@@ -640,16 +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/dps_lake_model.py

@@ -86,7 +86,7 @@ def lake_model(
 
     """
     np.random.seed(seed)
-    Pcrit = brentq(lambda x: x ** q / (1 + x ** q) - b * x, 0.01, 1.5)
+    Pcrit = brentq(lambda x: x**q / (1 + x**q) - b * x, 0.01, 1.5)
 
     X = np.zeros((myears,))
     average_daily_P = np.zeros((myears,))
@@ -98,12 +98,12 @@ def lake_model(
         X[0] = 0.0
         decision = 0.1
 
-        decisions = np.zeros(myears,)
+        decisions = np.zeros(myears)
         decisions[0] = decision
 
         natural_inflows = np.random.lognormal(
-            math.log(mean ** 2 / math.sqrt(stdev ** 2 + mean ** 2)),
-            math.sqrt(math.log(1.0 + stdev ** 2 / mean ** 2)),
+            math.log(mean**2 / math.sqrt(stdev**2 + mean**2)),
+            math.sqrt(math.log(1.0 + stdev**2 / mean**2)),
             size=myears,
         )
 
@@ -123,8 +123,6 @@ def lake_model(
 
         reliability += np.sum(X < Pcrit) / (nsamples * myears)
         inertia += np.sum(np.absolute(np.diff(decisions) < 0.02)) / (nsamples * myears)
-        utility += (
-            np.sum(alpha * decisions * np.power(delta, np.arange(myears))) / nsamples
-        )
+        utility += np.sum(alpha * decisions * np.power(delta, np.arange(myears))) / nsamples
     max_P = np.max(average_daily_P)
     return max_P, utility, inertia, reliability

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",
@@ -150,9 +148,7 @@
     "\n",
     "        reliability += np.sum(X < Pcrit) / (nsamples * myears)\n",
     "        inertia += np.sum(np.absolute(np.diff(decisions) < 0.02)) / (nsamples * myears)\n",
-    "        utility += (\n",
-    "            np.sum(alpha * decisions * np.power(delta, np.arange(myears))) / nsamples\n",
-    "        )\n",
+    "        utility += np.sum(alpha * decisions * np.power(delta, np.arange(myears))) / nsamples\n",
     "    max_P = np.max(average_daily_P)\n",
     "    return max_P, utility, inertia, reliability"
    ]

docs/source/indepth_tutorial/open-exploration.ipynb

@@ -132,9 +132,7 @@
    "source": [
     "from ema_workbench.analysis import pairs_plotting\n",
     "\n",
-    "fig, axes = pairs_plotting.pairs_scatter(\n",
-    "    experiments, outcomes, group_by=\"policy\", legend=False\n",
-    ")\n",
+    "fig, axes = pairs_plotting.pairs_scatter(experiments, outcomes, group_by=\"policy\", legend=False)\n",
     "fig.set_size_inches(8, 8)\n",
     "plt.show()"
    ]
@@ -458,9 +456,7 @@
     "x = experiments\n",
     "y = outcomes[\"max_P\"] < 0.8\n",
     "\n",
-    "fs, alg = feature_scoring.get_ex_feature_scores(\n",
-    "    x, y, mode=RuleInductionType.CLASSIFICATION\n",
-    ")\n",
+    "fs, alg = feature_scoring.get_ex_feature_scores(x, y, mode=RuleInductionType.CLASSIFICATION)\n",
     "fs.sort_values(ascending=False, by=1)"
    ]
   },
@@ -599,16 +595,12 @@
     "from ema_workbench.em_framework.salib_samplers import get_SALib_problem\n",
     "\n",
     "with MultiprocessingEvaluator(model) as evaluator:\n",
-    "    sa_results = evaluator.perform_experiments(\n",
-    "        scenarios=1000, uncertainty_sampling=Samplers.SOBOL\n",
-    "    )\n",
+    "    sa_results = evaluator.perform_experiments(scenarios=1000, uncertainty_sampling=Samplers.SOBOL)\n",
     "\n",
     "experiments, outcomes = sa_results\n",
     "\n",
     "problem = get_SALib_problem(model.uncertainties)\n",
-    "Si = sobol.analyze(\n",
-    "    problem, outcomes[\"max_P\"], calc_second_order=True, print_to_console=False\n",
-    ")"
+    "Si = sobol.analyze(problem, outcomes[\"max_P\"], calc_second_order=True, print_to_console=False)"
    ]
   },
   {

docs/source/pyplots/basicEnvelope2.py

@@ -9,7 +9,5 @@
 from analysis.plotting import envelopes
 
 data = load_results(r"../../../src/analysis/1000 flu cases.cPickle", zipped=False)
-fig = envelopes(
-    data, group_by="policy", grouping_specifiers=["static policy", "adaptive policy"]
-)
+fig = envelopes(data, group_by="policy", grouping_specifiers=["static policy", "adaptive policy"])
 plt.show()

docs/source/pyplots/basicMultiplotDensity.py

@@ -5,9 +5,7 @@
 from expWorkbench.util import load_results
 
 # load the data
-experiments, results = load_results(
-    r"../../../src/analysis/1000 flu cases.cPickle", zipped=False
-)
+experiments, results = load_results(r"../../../src/analysis/1000 flu cases.cPickle", zipped=False)
 
 # transform the results to the required format
 newResults = {}

docs/source/pyplots/primExample.py

@@ -37,9 +37,7 @@ def classify(data):
 results = (newExperiments, newResults)
 
 # perform prim on modified results tuple
-prims, uncertainties, x = prim.perform_prim(
-    results, classify, threshold=0.8, threshold_type=1
-)
+prims, uncertainties, x = prim.perform_prim(results, classify, threshold=0.8, threshold_type=1)
 
 # visualize
 

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/b_and_w_plotting.py

@@ -194,9 +194,7 @@ def _set_ax_polycollection_to_bw(collection, ax, style, colormap):
             collection.update({"alpha": 1})
 
             for path in collection.get_paths():
-                p1 = mpl.patches.PathPatch(
-                    path, fc="none", hatch=colormap[orig_color]["hatch"]
-                )
+                p1 = mpl.patches.PathPatch(path, fc="none", hatch=colormap[orig_color]["hatch"])
                 ax.add_patch(p1)
                 p1.set_zorder(collection.get_zorder() - 0.1)
 
@@ -335,9 +333,7 @@ def set_fig_to_bw(fig, style=HATCHING, line_style="continuous"):
 
     if len(all_colors) > len(bw_mapping):
         mapping_cycle = itertools.cycle(bw_mapping)
-        _logger.warning(
-            "more colors used than provided in B&W mapping, cycling over mapping"
-        )
+        _logger.warning("more colors used than provided in B&W mapping, cycling over mapping")
     else:
         mapping_cycle = bw_mapping
     colormap = dict(zip(all_colors, mapping_cycle))

ema_workbench/analysis/cart.py

@@ -304,7 +304,7 @@ def build_tree(self):
 
     def show_tree(self, mplfig=True, format="png"):
         """return a png (defaults) or svg of the tree
-        
+
         On Windows, graphviz needs to be installed with conda.
 
         Parameters
@@ -320,9 +320,7 @@ def show_tree(self, mplfig=True, format="png"):
         import pydot  # dirty hack for read the docs
 
         dot_data = StringIO()
-        tree.export_graphviz(
-            self.clf, out_file=dot_data, feature_names=self.feature_names
-        )
+        tree.export_graphviz(self.clf, out_file=dot_data, feature_names=self.feature_names)
         dot_data = dot_data.getvalue()  # .encode('ascii') # @UndefinedVariable
         graphs = pydot.graph_from_dot_data(dot_data)