Reformat all with Black

examples/3zone_toy_stochastic_PySP/PySPInputGenerator.py

@@ -48,6 +48,7 @@
 
 """
 from __future__ import print_function
+
 # Inputs directory relative to the location of this script.
 inputs_dir = "inputs"
 # ScenarioStructure.dat and RootNode.dat will be saved to a
@@ -58,11 +59,10 @@
 stage_list = ["Investment", "Operation"]
 stage_vars = {
     "Investment": ["BuildGen", "BuildLocalTD", "BuildTx"],
-    "Operation": ["DispatchGen", "GenFuelUseRate"]
+    "Operation": ["DispatchGen", "GenFuelUseRate"],
 }
 # List of scenario names
-scenario_list = [
-    "LowFuelCosts", "MediumFuelCosts", "HighFuelCosts"]
+scenario_list = ["LowFuelCosts", "MediumFuelCosts", "HighFuelCosts"]
 
 ###########################################################
 
@@ -82,6 +82,7 @@
 instance = model.load_inputs(inputs_dir=inputs_dir)
 print("inputs successfully loaded...")
 
+
 def save_dat_files():
 
     if not os.path.exists(os.path.join(inputs_dir, pysp_subdir)):
@@ -92,8 +93,9 @@ def save_dat_files():
 
     dat_file = os.path.join(inputs_dir, pysp_subdir, "RootNode.dat")
     print("creating and saving {}...".format(dat_file))
-    utilities.save_inputs_as_dat(model, instance, save_path=dat_file,
-        sorted_output=model.options.sorted_output)
+    utilities.save_inputs_as_dat(
+        model, instance, save_path=dat_file, sorted_output=model.options.sorted_output
+    )
 
     #######################
     # ScenarioStructure.dat
@@ -117,7 +119,7 @@ def save_dat_files():
 
         f.write("param NodeStage := RootNode {}\n".format(stage_list[0]))
         for s in scenario_list:
-            f.write("    {scen} {st}\n".format(scen=s,st=stage_list[1]))
+            f.write("    {scen} {st}\n".format(scen=s, st=stage_list[1]))
         f.write(";\n\n")
 
         f.write("set Children[RootNode] := ")
@@ -127,7 +129,7 @@ def save_dat_files():
 
         f.write("param ConditionalProbability := RootNode 1.0")
         # All scenarios have the same probability in this example
-        probs = [1.0/len(scenario_list)] * (len(scenario_list) - 1)
+        probs = [1.0 / len(scenario_list)] * (len(scenario_list) - 1)
         # The remaining probability is lumped in the last scenario to avoid rounding issues
         probs.append(1.0 - sum(probs))
         for (s, p) in zip(scenario_list, probs):
@@ -150,14 +152,16 @@ def write_var_name(f, cname):
             if hasattr(instance, cname):
                 dimen = getattr(instance, cname).index_set().dimen
                 if dimen == 0:
-                   f.write("    {cn}\n".format(cn=cname))
+                    f.write("    {cn}\n".format(cn=cname))
                 else:
-                    indexing = (",".join(["*"]*dimen))
+                    indexing = ",".join(["*"] * dimen)
                     f.write("    {cn}[{dim}]\n".format(cn=cname, dim=indexing))
             else:
                 raise ValueError(
-                    "Variable '{}' is not a component of the model. Did you make a typo?".
-                    format(cname))
+                    "Variable '{}' is not a component of the model. Did you make a typo?".format(
+                        cname
+                    )
+                )
 
         for st in stage_list:
             f.write("set StageVariables[{}] := \n".format(st))
@@ -171,8 +175,9 @@ def write_var_name(f, cname):
         f.write("    Operation OperationCost\n")
         f.write(";")
 
+
 ####################
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # If the script is executed on the command line, then the .dat files are created.
     save_dat_files()

examples/3zone_toy_stochastic_PySP/ReferenceModel.py

@@ -37,7 +37,7 @@
 
 # Ideally, we would use the main codebase to generate the model, but the
 # mandatory switch argument parser is interferring with pysp's command line tools
-#model = switch_model.solve.main(return_model=True)
+# model = switch_model.solve.main(return_model=True)
 
 module_list = utilities.get_module_list(args=None)
 model = utilities.create_model(module_list, args=[])
@@ -53,14 +53,19 @@
 # are nested inside another function in the financials module, they can't
 # be called from this script.
 
+
 def calc_tp_costs_in_period(m, t):
-	return sum(
-		getattr(m, tp_cost)[t] * m.tp_weight_in_year[t]
-		for tp_cost in m.Cost_Components_Per_TP)
+    return sum(
+        getattr(m, tp_cost)[t] * m.tp_weight_in_year[t]
+        for tp_cost in m.Cost_Components_Per_TP
+    )
+
+
 def calc_annual_costs_in_period(m, p):
-	return sum(
-		getattr(m, annual_cost)[p]
-		for annual_cost in m.Cost_Components_Per_Period)
+    return sum(
+        getattr(m, annual_cost)[p] for annual_cost in m.Cost_Components_Per_Period
+    )
+
 
 # In the current version of Switch, all annual costs are defined
 # by First Stage decision variables, such as fixed O&M and capital
@@ -73,14 +78,19 @@ def calc_annual_costs_in_period(m, p):
 # decisions in this example.
 # Further comments on this are written in the Readme file.
 
-model.InvestmentCost = Expression(rule=lambda m: sum(
-                calc_annual_costs_in_period(m, p) * m.bring_annual_costs_to_base_year[p]
-                for p in m.PERIODS))
-
-model.OperationCost = Expression(rule=lambda m:
-	sum(
-		sum(calc_tp_costs_in_period(m, t) for t in m.TPS_IN_PERIOD[p]
-		   ) * m.bring_annual_costs_to_base_year[p]
-	for p in m.PERIODS))
+model.InvestmentCost = Expression(
+    rule=lambda m: sum(
+        calc_annual_costs_in_period(m, p) * m.bring_annual_costs_to_base_year[p]
+        for p in m.PERIODS
+    )
+)
+
+model.OperationCost = Expression(
+    rule=lambda m: sum(
+        sum(calc_tp_costs_in_period(m, t) for t in m.TPS_IN_PERIOD[p])
+        * m.bring_annual_costs_to_base_year[p]
+        for p in m.PERIODS
+    )
+)
 
 print("model successfully loaded...")

examples/3zone_toy_stochastic_PySP/pha_bounds_cfg.py

@@ -4,20 +4,24 @@
 # Use this by adding terms like the following to the runph command:
 # --linearize-nonbinary-penalty-terms=5 --bounds-cfgfile=pha_bounds_cfg.py
 
+
 def pysp_boundsetter_callback(self, scenario_tree, scenario):
-    m = scenario._instance 	# see pyomo/pysp/scenariotree/tree_structure.py
+    m = scenario._instance  # see pyomo/pysp/scenariotree/tree_structure.py
 
     # BuildLocalTD
     for p in m.PERIODS:
         for lz in m.LOAD_ZONES:
-            m.BuildLocalTD[lz, p].setub(2 * m.zone_expected_coincident_peak_demand[lz, p])
+            m.BuildLocalTD[lz, p].setub(
+                2 * m.zone_expected_coincident_peak_demand[lz, p]
+            )
 
     # Estimate an upper bound of system peak demand for limiting generation unit
     # & transmission line builds
     system_wide_peak = {}
     for p in m.PERIODS:
         system_wide_peak[p] = sum(
-            m.zone_expected_coincident_peak_demand[lz, p] for lz in m.LOAD_ZONES)
+            m.zone_expected_coincident_peak_demand[lz, p] for lz in m.LOAD_ZONES
+        )
 
     # BuildGen
     for g, bld_yr in m.NEW_GEN_BLD_YRS:
@@ -27,6 +31,7 @@ def pysp_boundsetter_callback(self, scenario_tree, scenario):
     for tx, bld_yr in m.TRANS_BLD_YRS:
         m.BuildTx[tx, bld_yr].setub(5 * system_wide_peak[bld_yr])
 
+
 # For some reason runph looks for pysp_boundsetter_callback when run in
 # single-thread mode and ph_boundsetter_callback when called from mpirun with
 # remote execution via pyro. so we map both names to the same function.

examples/3zone_toy_stochastic_PySP/rhosetter.py

@@ -17,17 +17,22 @@
 from pyomo.environ import Objective
 
 try:
-    from pyomo.repn import generate_standard_repn    # Pyomo >=5.6
+    from pyomo.repn import generate_standard_repn  # Pyomo >=5.6
+
     newPyomo = True
 except ImportError:
-    from pyomo.repn import generate_canonical_repn   # Pyomo <=5.6
+    from pyomo.repn import generate_canonical_repn  # Pyomo <=5.6
+
     newPyomo = False
 
+
 def ph_rhosetter_callback(ph, scenario_tree, scenario):
-    # Derive coefficients from active objective    
-    cost_expr = next(scenario._instance.component_data_objects(
-        Objective, active=True, descend_into=True
-    ))
+    # Derive coefficients from active objective
+    cost_expr = next(
+        scenario._instance.component_data_objects(
+            Objective, active=True, descend_into=True
+        )
+    )
     set_rho_values(ph, scenario_tree, scenario, cost_expr)
 
 
@@ -56,8 +61,7 @@ def set_rho_values(ph, scenario_tree, scenario, cost_expr):
 
     cost_coefficients = {}
     var_names = {}
-    for (variable, coef) in \
-            zip(standard_repn.linear_vars, standard_repn.linear_coefs):
+    for (variable, coef) in zip(standard_repn.linear_vars, standard_repn.linear_coefs):
         variable_id = symbol_map.getSymbol(variable)
         cost_coefficients[variable_id] = coef
         var_names[variable_id] = variable.name
@@ -71,11 +75,13 @@ def set_rho_values(ph, scenario_tree, scenario, cost_expr):
                     tree_node,
                     scenario,
                     variable_id,
-                    cost_coefficients[variable_id] * rho_coefficient)
+                    cost_coefficients[variable_id] * rho_coefficient,
+                )
                 set_rho = True
                 break
         if not set_rho:
             print(
-                "Warning! Could not find tree node for variable {}; rho not set."
-                .format(var_names[variable_id])
+                "Warning! Could not find tree node for variable {}; rho not set.".format(
+                    var_names[variable_id]
+                )
             )

examples/3zone_toy_stochastic_PySP/rhosetter_FS_only.py

@@ -25,6 +25,7 @@
 # The rhosetter module should be in the same directory as this file.
 from rhosetter import set_rho_values
 
+
 def ph_rhosetter_callback(ph, scenario_tree, scenario):
     # This component name must match the expression used for first stage
     # costs defined in the ReferenceModel.

examples/custom_extension/sunk_costs.py

@@ -27,7 +27,5 @@
 
 
 def define_components(mod):
-    mod.administration_fees = Param(
-        mod.PERIODS,
-        initialize=lambda m, p: 1000000)
-    mod.Cost_Components_Per_Period.append('administration_fees')
+    mod.administration_fees = Param(mod.PERIODS, initialize=lambda m, p: 1000000)
+    mod.Cost_Components_Per_Period.append("administration_fees")

run_tests.py

@@ -20,11 +20,12 @@ class TestLoader(unittest.TestLoader):
     # effects when imported.
     def discover(self, start_dir, pattern, top_level_dir):
         test_suite = unittest.TestSuite()
-        for subdir in ('switch_model', 'tests'):
+        for subdir in ("switch_model", "tests"):
             test_suite.addTests(
                 super(TestLoader, self).discover(
-                    os.path.join(top_level_dir, subdir),
-                    pattern, top_level_dir))
+                    os.path.join(top_level_dir, subdir), pattern, top_level_dir
+                )
+            )
         return test_suite
 
     # The unittest module does not have built-in support for finding
@@ -37,7 +38,7 @@ def loadTestsFromModule(self, module, **kwargs):
         if not docstring:
             # Work around a misfeature whereby doctest complains if a
             # module contains no docstrings.
-            module.__doc__ = 'Placeholder docstring'
+            module.__doc__ = "Placeholder docstring"
         test_suite.addTests(doctest.DocTestSuite(module))
         if not docstring:
             # Restore the original, in case this matters.
@@ -48,12 +49,16 @@ def loadTestsFromModule(self, module, **kwargs):
 def main():
     script_dir = os.path.join(os.getcwd(), os.path.dirname(__file__))
     # print('old argv: {}'.format(sys.argv))
-    argv = [sys.argv[0],
-            'discover',
-            '--top-level-directory', script_dir,
-            '--pattern', '*.py'] + sys.argv[1:]
+    argv = [
+        sys.argv[0],
+        "discover",
+        "--top-level-directory",
+        script_dir,
+        "--pattern",
+        "*.py",
+    ] + sys.argv[1:]
     unittest.TestProgram(testLoader=TestLoader(), argv=argv, module=None)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

setup.py

@@ -67,7 +67,7 @@ def read(*rnames):
         "planning",
         "optimization",
     ],
-    python_requires='>=3.7',
+    python_requires=">=3.7",
     install_requires=[
         "Pyomo>=6.1,<6.4.1",  # 6.1 Has all the bug fixes we need
         "pint",  # needed by Pyomo when we run our tests, but not included
@@ -96,7 +96,7 @@ def read(*rnames):
         "dev": ["ipdb", "black", "psycopg2-binary"],
         # On Windows at least, installing these will only work via conda.
         # Run conda install -c conda-forge geopandas shapely [... all the other packages]
-        "maps_INSTALL_WITH_CONDA": ["geopandas", "shapely", "cartopy", "plotnine"]
+        "maps_INSTALL_WITH_CONDA": ["geopandas", "shapely", "cartopy", "plotnine"],
     },
     entry_points={
         "console_scripts": [

switch_model/__init__.py

@@ -18,10 +18,12 @@
 transmission, local_td, reserves, etc.
 """
 from .version import __version__
+
 core_modules = [
-    'switch_model.timescales',
-    'switch_model.financials',
-    'switch_model.balancing.load_zones',
-    'switch_model.energy_sources.properties',
-    'switch_model.generators.core',
-    'switch_model.reporting']
+    "switch_model.timescales",
+    "switch_model.financials",
+    "switch_model.balancing.load_zones",
+    "switch_model.energy_sources.properties",
+    "switch_model.generators.core",
+    "switch_model.reporting",
+]