Merge pull request #971 from rl-institut/fix/black-vulnerability

Fix/black vulnerability

docs/conf.py

@@ -75,15 +75,24 @@ def generate_parameter_description(input_csv_file, output_rst_file):
             see_also = []
         lines = (
             lines
-            + [f".. _{props.ref}:", "", props.label, "^" * len(props.label), "",]
+            + [
+                f".. _{props.ref}:",
+                "",
+                props.label,
+                "^" * len(props.label),
+                "",
+            ]
             + [f"{p} {props[p]}" for p in parameter_properties]
             + [""]
             + [
                 "This parameter is used within the following categories: "
                 + ", ".join([f":ref:`{cat}`" for cat in props.category.split(";")])
             ]
             + see_also
-            + ["", "",]
+            + [
+                "",
+                "",
+            ]
         )
 
     with open(output_rst_file, "w") as ofs:
@@ -139,11 +148,22 @@ def generate_parameter_categories(
 
         lines = (
             lines
-            + [f".. _{props.ref}:", "", cat_label, "^" * len(cat_label), "",]
+            + [
+                f".. _{props.ref}:",
+                "",
+                cat_label,
+                "^" * len(cat_label),
+                "",
+            ]
             + props.description.split("\\n")
-            + ["",]
+            + [
+                "",
+            ]
             + [f"* :ref:`{p}`" for p in parameter_per_cat]
-            + ["", "",]
+            + [
+                "",
+                "",
+            ]
         )
 
     with open(output_rst_file, "w") as ofs:
@@ -194,10 +214,17 @@ def generate_kpi_categories(input_param_csv_file, input_cat_csv_file, output_rst
 
         lines = (
             lines
-            + [f"{props.description} These are the calculated {props.category} KPI:",]
-            + ["",]
+            + [
+                f"{props.description} These are the calculated {props.category} KPI:",
+            ]
+            + [
+                "",
+            ]
             + [f"* :ref:`{parameters[p]} <{p}>`" for p in parameter_per_cat]
-            + ["", "",]
+            + [
+                "",
+                "",
+            ]
         )
 
     with open(output_rst_file, "w") as ofs:
@@ -290,10 +317,19 @@ def generate_kpi_description(input_csv_file, output_path):
 
         # Write lines based on definitions to an *.inc file
         lines = (
-            [f".. _{props.ref}:", "", title, "^" * len(title), "",]
+            [
+                f".. _{props.ref}:",
+                "",
+                title,
+                "^" * len(title),
+                "",
+            ]
             + [f"{p} {props[p]}" for p in parameter_properties]
             + [f":Related indicators: {see_also}"]
-            + ["", "",]
+            + [
+                "",
+                "",
+            ]
         )
 
         with open(os.path.join(output_path, props.ref + ".inc"), "w") as ofs:

requirements/test.txt

@@ -1,5 +1,5 @@
 pytest>=5.3.1
-black==19.10b0
+black==24.3.0
 coverage>=4.5
 coveralls>=3.0.1
 mock>=3.0.5

setup.py

@@ -138,7 +138,10 @@ def parse_requirements_file(filename):
     # called `my_module.py` to exist:
     #
     #   py_modules=["my_module"],
-    packages=["multi_vector_simulator", "multi_vector_simulator.utils",],  # Required
+    packages=[
+        "multi_vector_simulator",
+        "multi_vector_simulator.utils",
+    ],  # Required
     # Specify which Python versions you support. In contrast to the
     # 'Programming Language' classifiers above, 'pip install' will check this
     # and refuse to install the project if the version does not match. If you

src/multi_vector_simulator/A0_initialization.py

@@ -245,7 +245,8 @@ def report_arg_parser():
     :return: parser
     """
     parser = argparse.ArgumentParser(
-        prog="mvs_report", description="Display the report of a MVS simulation",
+        prog="mvs_report",
+        description="Display the report of a MVS simulation",
     )
     parser.add_argument(
         "-pdf",

src/multi_vector_simulator/A1_csv_to_json.py

@@ -97,7 +97,8 @@
 
 
 def create_input_json(
-    input_directory, pass_back=True,
+    input_directory,
+    pass_back=True,
 ):
     """Convert csv files to json file as input for the simulation.
 
@@ -515,7 +516,9 @@ def create_json_from_csv(
                     df.loc[STORAGE_FILENAME][column]
                 )
                 storage_dict = add_storage_components(
-                    storage_file_name, input_directory, single_dict[column][LABEL],
+                    storage_file_name,
+                    input_directory,
+                    single_dict[column][LABEL],
                 )
                 single_dict[column].update(storage_dict)
 

src/multi_vector_simulator/B0_data_input_json.py

@@ -2,6 +2,7 @@
 Module B0 - Data input json
 ===========================
 """
+
 import logging
 import copy
 import json
@@ -321,7 +322,8 @@ def load_json(
         os.replace(
             path_input_file,
             os.path.join(
-                dict_values[SIMULATION_SETTINGS][PATH_OUTPUT_FOLDER_INPUTS], CSV_FNAME,
+                dict_values[SIMULATION_SETTINGS][PATH_OUTPUT_FOLDER_INPUTS],
+                CSV_FNAME,
             ),
         )
 

src/multi_vector_simulator/C0_data_processing.py

@@ -334,7 +334,9 @@ def energyConversion(dict_values, group):
             and HEADER in dict_values[group][asset][EFFICIENCY]
         ):
             receive_timeseries_from_csv(
-                dict_values[SIMULATION_SETTINGS], dict_values[group][asset], EFFICIENCY,
+                dict_values[SIMULATION_SETTINGS],
+                dict_values[group][asset],
+                EFFICIENCY,
             )
         # in case there is more than one parameter provided (either (A) n input busses and 1 output bus or (B) 1 input bus and n output busses)
         # dictionaries with filenames and headers will be replaced by timeseries, scalars will be mantained
@@ -398,7 +400,8 @@ def energyStorage(dict_values, group):
     for asset in dict_values[group]:
         for subasset in [STORAGE_CAPACITY, INPUT_POWER, OUTPUT_POWER]:
             define_missing_cost_data(
-                dict_values, dict_values[group][asset][subasset],
+                dict_values,
+                dict_values[group][asset][subasset],
             )
             evaluate_lifetime_costs(
                 dict_values[SIMULATION_SETTINGS],
@@ -504,7 +507,9 @@ def define_missing_cost_data(dict_values, dict_asset):
     if DISPATCH_PRICE in dict_asset:
         if isinstance(dict_asset[DISPATCH_PRICE][VALUE], dict):
             receive_timeseries_from_csv(
-                dict_values[SIMULATION_SETTINGS], dict_asset, DISPATCH_PRICE,
+                dict_values[SIMULATION_SETTINGS],
+                dict_asset,
+                DISPATCH_PRICE,
             )
         elif isinstance(dict_asset[DISPATCH_PRICE][VALUE], list):
             treat_multiple_flows(dict_asset, dict_values, DISPATCH_PRICE)
@@ -519,8 +524,14 @@ def define_missing_cost_data(dict_values, dict_asset):
         SPECIFIC_COSTS: {VALUE: 0, UNIT: CURR + "/" + UNIT},
         SPECIFIC_COSTS_OM: {VALUE: 0, UNIT: CURR + "/" + UNIT_YEAR},
         DISPATCH_PRICE: {VALUE: 0, UNIT: CURR + "/" + UNIT + "/" + UNIT_YEAR},
-        LIFETIME: {VALUE: economic_data[PROJECT_DURATION][VALUE], UNIT: UNIT_YEAR,},
-        AGE_INSTALLED: {VALUE: 0, UNIT: UNIT_YEAR,},
+        LIFETIME: {
+            VALUE: economic_data[PROJECT_DURATION][VALUE],
+            UNIT: UNIT_YEAR,
+        },
+        AGE_INSTALLED: {
+            VALUE: 0,
+            UNIT: UNIT_YEAR,
+        },
     }
 
     # checks that an asset has all cost parameters needed for evaluation.
@@ -722,11 +733,17 @@ def define_auxiliary_assets_of_energy_providers(dict_values, dso_name):
     )
 
     dict_availability_timeseries = define_availability_of_peak_demand_pricing_assets(
-        dict_values, number_of_pricing_periods, months_in_a_period,
+        dict_values,
+        number_of_pricing_periods,
+        months_in_a_period,
     )
 
-    list_of_dso_energyConversion_assets = add_a_transformer_for_each_peak_demand_pricing_period(
-        dict_values, dso_dict, dict_availability_timeseries,
+    list_of_dso_energyConversion_assets = (
+        add_a_transformer_for_each_peak_demand_pricing_period(
+            dict_values,
+            dso_dict,
+            dict_availability_timeseries,
+        )
     )
 
     define_source(
@@ -1025,7 +1042,10 @@ def define_transformer_for_peak_demand_pricing(
         AVAILABILITY_DISPATCH: timeseries_availability,
         EFFICIENCY: {VALUE: 1, UNIT: "factor"},
         DEVELOPMENT_COSTS: {VALUE: 0, UNIT: CURR},
-        SPECIFIC_COSTS: {VALUE: 0, UNIT: CURR + "/" + dict_dso[UNIT],},
+        SPECIFIC_COSTS: {
+            VALUE: 0,
+            UNIT: CURR + "/" + dict_dso[UNIT],
+        },
         # the demand pricing is only applied to consumption
         SPECIFIC_COSTS_OM: {
             VALUE: dict_dso[PEAK_DEMAND_PRICING][VALUE],
@@ -1058,7 +1078,10 @@ def define_transformer_for_peak_demand_pricing(
         AVAILABILITY_DISPATCH: timeseries_availability,
         EFFICIENCY: {VALUE: 1, UNIT: "factor"},
         DEVELOPMENT_COSTS: {VALUE: 0, UNIT: CURR},
-        SPECIFIC_COSTS: {VALUE: 0, UNIT: CURR + "/" + dict_dso[UNIT],},
+        SPECIFIC_COSTS: {
+            VALUE: 0,
+            UNIT: CURR + "/" + dict_dso[UNIT],
+        },
         # the demand pricing is only applied to consumption
         SPECIFIC_COSTS_OM: {
             VALUE: 0,
@@ -1068,7 +1091,7 @@ def define_transformer_for_peak_demand_pricing(
         OEMOF_ASSET_TYPE: OEMOF_TRANSFORMER,
         ENERGY_VECTOR: dict_dso[ENERGY_VECTOR],
         AGE_INSTALLED: {VALUE: 0, UNIT: UNIT_YEAR},
-        TYPE_ASSET: dict_dso.get(TYPE_ASSET)
+        TYPE_ASSET: dict_dso.get(TYPE_ASSET),
         # LIFETIME: {VALUE: 100, UNIT: UNIT_YEAR},
     }
     if dict_dso.get(DSO_FEEDIN_CAP, None) is not None:
@@ -1150,7 +1173,10 @@ def define_source(
         },
         OPTIMIZE_CAP: {VALUE: True, UNIT: TYPE_BOOL},
         MAXIMUM_CAP: {VALUE: None, UNIT: "?"},
-        AGE_INSTALLED: {VALUE: 0, UNIT: UNIT_YEAR,},
+        AGE_INSTALLED: {
+            VALUE: 0,
+            UNIT: UNIT_YEAR,
+        },
         ENERGY_VECTOR: energy_vector,
         EMISSION_FACTOR: emission_factor,
         TYPE_ASSET: asset_type,
@@ -1334,7 +1360,10 @@ def define_sink(
             VALUE: dict_values[ECONOMIC_DATA][PROJECT_DURATION][VALUE],
             UNIT: UNIT_YEAR,
         },
-        AGE_INSTALLED: {VALUE: 0, UNIT: UNIT_YEAR,},
+        AGE_INSTALLED: {
+            VALUE: 0,
+            UNIT: UNIT_YEAR,
+        },
         ENERGY_VECTOR: energy_vector,
         OPTIMIZE_CAP: {VALUE: True, UNIT: TYPE_BOOL},
         DISPATCHABILITY: {VALUE: True, UNIT: TYPE_BOOL},
@@ -1408,7 +1437,9 @@ def define_sink(
     for item in [SPECIFIC_COSTS, SPECIFIC_COSTS_OM]:
         if item in kwargs:
             sink.update(
-                {item: kwargs[item],}
+                {
+                    item: kwargs[item],
+                }
             )
 
     # update dictionary
@@ -1751,8 +1782,14 @@ def compute_timeseries_properties(dict_asset):
 
         dict_asset.update(
             {
-                TIMESERIES_PEAK: {VALUE: max(timeseries), UNIT: unit,},
-                TIMESERIES_TOTAL: {VALUE: sum(timeseries), UNIT: unit,},
+                TIMESERIES_PEAK: {
+                    VALUE: max(timeseries),
+                    UNIT: unit,
+                },
+                TIMESERIES_TOTAL: {
+                    VALUE: sum(timeseries),
+                    UNIT: unit,
+                },
                 TIMESERIES_AVERAGE: {
                     VALUE: sum(timeseries) / len(timeseries),
                     UNIT: unit,

src/multi_vector_simulator/C2_economic_functions.py

@@ -13,6 +13,7 @@
 - calculate present costs based on annuity
 - calculate effective fuel price cost, in case there is a annual fuel price change (this functionality still has to be checked in this module)
 """
+
 import logging
 import pandas as pd
 
@@ -26,6 +27,7 @@
     LIFETIME_PRICE_DISPATCH,
 )
 
+
 # annuity factor to calculate present value of cash flows
 def annuity_factor(project_life, discount_factor):
     r"""
@@ -258,9 +260,9 @@ def get_replacement_costs(
         # Subtraction of component value at end of life with last replacement (= number_of_investments - 1)
         replacement_costs -= value_at_project_end
         # Update cash flow projection (specific)
-        present_value_of_capital_expenditures.loc[
-            project_lifetime
-        ] = -value_at_project_end
+        present_value_of_capital_expenditures.loc[project_lifetime] = (
+            -value_at_project_end
+        )
 
     return replacement_costs
 

src/multi_vector_simulator/D0_modelling_and_optimization.py

@@ -313,7 +313,8 @@ def store_lp_file(dict_values, local_energy_system):
             )
             logging.debug("Saving to lp-file.")
             local_energy_system.write(
-                path_lp_file, io_options={"symbolic_solver_labels": True},
+                path_lp_file,
+                io_options={"symbolic_solver_labels": True},
             )
 
     def simulating(dict_values, model, local_energy_system):