Merge pull request #119 from EwoutH/static-analysis

Improve code quality with static analysis

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:
@@ -783,9 +783,7 @@ def prepare_data(
                 )
             else:
                 column_to_group_by = experiments[group_by]
-                if (column_to_group_by.dtype == object) or (
-                    column_to_group_by.dtype == "category"
-                ):
+                if column_to_group_by.dtype in (object, "category"):
                     grouping_specifiers = set(column_to_group_by)
                 else:
                     grouping_specifiers = make_continuous_grouping_specifiers(

ema_workbench/analysis/prim_util.py

@@ -224,7 +224,7 @@ def __call__(self, box):
 
 def is_significant(box, i, alpha=0.05):
     qp = box.qp[i]
-    return not any([value > alpha for values in qp.values() for value in values])
+    return not any(value > alpha for values in qp.values() for value in values)
 
 
 def is_pareto_efficient(data):

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/connectors/vensim.py

@@ -390,17 +390,6 @@ def check_data(result):
 
         return results
 
-    def cleanup(self):
-        super().cleanup()
-
-    def reset_model(self):
-        """
-        Method for reseting the model to its initial state before runModel
-        was called
-        """
-
-        super().reset_model()
-
     def _delete_lookup_uncertainties(self):
         """
         deleting lookup uncertainties from the uncertainty list
@@ -552,7 +541,7 @@ def _get_initial_lookup(self, name):
         list2 = []
         number = []
         for c in b:
-            if (c != "(") and (c != ")"):
+            if c not in ("(", ")"):
                 list1.append(c)
 
         list1.append(",")

ema_workbench/em_framework/evaluators.py

@@ -274,8 +274,6 @@ def robust_optimize(
 
 
 class SequentialEvaluator(BaseEvaluator):
-    def __init__(self, models, **kwargs):
-        super().__init__(models, **kwargs)
 
     def initialize(self):
         pass

ema_workbench/em_framework/optimization.py

@@ -844,7 +844,7 @@ def _optimize(
                 "number of epsilon values does not match number " "of outcomes"
             )
 
-    if all([isinstance(t, klass) for t in problem.types]):
+    if all(isinstance(t, klass) for t in problem.types):
         variator = None
     else:
         variator = CombinedVariator()

ema_workbench/em_framework/outcomes.py

@@ -197,7 +197,7 @@ def __eq__(self, other):
             all(
                 hasattr(self, key) == hasattr(other, key)
                 and getattr(self, key) == getattr(other, key)
-                for key in self.__dict__.keys()
+                for key in self.__dict__
             ),
             self.__class__ == other.__class__,
         ]

ema_workbench/em_framework/parameters.py

@@ -79,8 +79,7 @@ def __repr__(self, *args, **kwargs):
 
 
 class Category(Constant):
-    def __init__(self, name, value):
-        super().__init__(name, value)
+    pass
 
 
 def create_category(cat):
@@ -384,7 +383,7 @@ 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 +487,7 @@ 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/em_framework/samplers.py

@@ -49,9 +49,6 @@ class AbstractSampler(metaclass=abc.ABCMeta):
 
     """
 
-    def __init__(self):
-        super().__init__()
-
     def sample(self, distribution, size):
         """
         method for sampling a number of samples from a particular distribution.
@@ -132,9 +129,6 @@ class LHSSampler(AbstractSampler):
     generates a Latin Hypercube sample for each of the parameters
     """
 
-    def __init__(self):
-        super().__init__()
-
     def sample(self, distribution, size):
         """
         generate a Latin Hypercube Sample.
@@ -253,9 +247,6 @@ class MonteCarloSampler(AbstractSampler):
 
     """
 
-    def __init__(self):
-        super().__init__()
-
     def sample(self, distribution, size):
         """
         generate a Monte Carlo Sample.
@@ -286,9 +277,6 @@ class FullFactorialSampler(AbstractSampler):
 
     """
 
-    def __init__(self):
-        super().__init__()
-
     def generate_samples(self, parameters, size):
         """
         The main method of :class: `~sampler.Sampler` and its

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}
 
 # Set the ring we are analyzing
 ring = 15

test/models/Sales_Agent_Market_Building_Dynamics.py

@@ -319,7 +319,7 @@ def income():
         tier_1_income()
         + tier_2_income()
         + if_then_else(
-            time() < startup_subsidy_length(), lambda: startup_subsidy(), lambda: 0
+            time() < startup_subsidy_length(), startup_subsidy, lambda: 0
         )
     )
 
@@ -1051,16 +1051,16 @@ def time_step():
     return 0.0625
 
 
-_integ_total_cumulative_sales = Integ(lambda: accumulating_sales(), lambda: 0)
+_integ_total_cumulative_sales = Integ(accumulating_sales, lambda: 0)
 
 
-_integ_tenure = Integ(lambda: accumulating_tenure(), lambda: 0)
+_integ_tenure = Integ(accumulating_tenure, lambda: 0)
 
 
-_integ_total_cumulative_income = Integ(lambda: accumulating_income(), lambda: 0)
+_integ_total_cumulative_income = Integ(accumulating_income, lambda: 0)
 
 
-_integ_months_of_buffer = Integ(lambda: income() - expenses(), lambda: initial_buffer())
+_integ_months_of_buffer = Integ(lambda: income() - expenses(), initial_buffer)
 
 
 _integ_tier_2_clients = Integ(

test/models/Sales_Agent_Motivation_Dynamics.py

@@ -297,7 +297,7 @@ def income():
     Technically in units of months of expenses earned per month
     """
     return months_of_expenses_per_sale() * sales() + if_then_else(
-        time() < startup_subsidy_length(), lambda: startup_subsidy(), lambda: 0
+        time() < startup_subsidy_length(), startup_subsidy, lambda: 0
     )
 
 
@@ -481,13 +481,13 @@ def time_step():
     return 0.0625
 
 
-_integ_total_cumulative_income = Integ(lambda: accumulating_income(), lambda: 0)
+_integ_total_cumulative_income = Integ(accumulating_income, lambda: 0)
 
 
-_integ_total_cumulative_sales = Integ(lambda: accumulating_sales(), lambda: 0)
+_integ_total_cumulative_sales = Integ(accumulating_sales, lambda: 0)
 
 
-_integ_tenure = Integ(lambda: accumulating_tenure(), lambda: 0)
+_integ_tenure = Integ(accumulating_tenure, lambda: 0)
 
 
-_integ_motivation = Integ(lambda: motivation_adjustment(), lambda: 1)
+_integ_motivation = Integ(motivation_adjustment, lambda: 1)

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_ema_ipyparallel.py

@@ -87,7 +87,7 @@ def add_engines(n=1, profile="iptest", total=False):
         eps.append(ep)
     tic = time.time()
     while len(rc) < base + n:
-        if any([ep.poll() is not None for ep in eps]):
+        if any(ep.poll() is not None for ep in eps):
             raise RuntimeError("A test engine failed to start.")
         elif time.time() - tic > 15:
             raise RuntimeError("Timeout waiting for engines to connect.")

test/test_em_framework/test_model.py

@@ -20,8 +20,7 @@
 
 
 class FileModelTest(FileModel):
-    def run_model(self, scenario, policy):
-        super().run_model(scenario, policy)
+    pass
 
 
 class TestFileModel(unittest.TestCase):

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()

test/test_em_framework/test_util.py

@@ -44,7 +44,7 @@ def test_namedict(self):
         self.assertEqual(2, len(nd), "length not correct")
 
         # test in
-        for entry in kwargs.keys():
+        for entry in kwargs:
             self.assertIn(entry, nd, f"{entry} not in NamedDict")
 
         # test addition

test/test_util/test_utilities.py

@@ -46,8 +46,7 @@ def test_save_results(self):
         )
         outcome_q = np.random.rand(nr_experiments, 1)
 
-        outcomes = {}
-        outcomes[ScalarOutcome("q").name] = outcome_q
+        outcomes = {ScalarOutcome("q").name: outcome_q}
         results = (experiments, outcomes)
 
         # test for 2d
@@ -61,8 +60,7 @@ def test_save_results(self):
         )
         outcome_r = np.zeros((nr_experiments, nr_timesteps))
 
-        outcomes = {}
-        outcomes[ArrayOutcome("r").name] = outcome_r
+        outcomes = {ArrayOutcome("r").name: outcome_r}
         results = (experiments, outcomes)
 
         save_results(results, fn)
@@ -78,8 +76,7 @@ def test_save_results(self):
         )
         outcome_s = np.zeros((nr_experiments, nr_timesteps, nr_replications))
 
-        outcomes = {}
-        outcomes[ArrayOutcome("s").name] = outcome_s
+        outcomes = {ArrayOutcome("s").name: outcome_s}
         results = (experiments, outcomes)
 
         save_results(results, fn)
@@ -104,8 +101,7 @@ def test_load_results(self):
 
         outcome_a = np.zeros((nr_experiments, 1))
 
-        outcomes = {}
-        outcomes[ArrayOutcome("a").name] = outcome_a
+        outcomes = {ArrayOutcome("a").name: outcome_a}
         results = (experiments, outcomes)
 
         save_results(results, "../data/test.tar.gz")
@@ -129,8 +125,7 @@ def test_load_results(self):
 
         outcome_b = np.zeros((nr_experiments, nr_timesteps, nr_replications))
 
-        outcomes = {}
-        outcomes[ArrayOutcome("b").name] = outcome_b
+        outcomes = {ArrayOutcome("b").name: outcome_b}
         results = (experiments, outcomes)
 
         save_results(results, "../data/test.tar.gz")