Test automatic fixing of stickler and black

Author: uvchik

example/modelchain_example.py

@@ -19,7 +19,7 @@
 try:
     from matplotlib import pyplot as plt
 except ImportError:
-    plt = None
+    plt=None
 
 from windpowerlib import ModelChain
 from windpowerlib import WindTurbine
@@ -65,23 +65,23 @@ def get_weather_data(filename='weather.csv', **kwargs):
     """
 
     if 'datapath' not in kwargs:
-        kwargs['datapath'] = os.path.join(os.path.split(
+        kwargs['datapath']=os.path.join(os.path.split(
             os.path.dirname(__file__))[0], 'example')
-    file = os.path.join(kwargs['datapath'], filename)
+    file=os.path.join(kwargs['datapath'], filename)
 
     # read csv file
-    weather_df = pd.read_csv(
+    weather_df=pd.read_csv(
         file, index_col=0, header=[0, 1],
         date_parser=lambda idx: pd.to_datetime(idx, utc=True))
 
     # change type of index to datetime and set time zone
-    weather_df.index = pd.to_datetime(weather_df.index).tz_convert(
+    weather_df.index=pd.to_datetime(weather_df.index).tz_convert(
         'Europe/Berlin')
 
     # change type of height from str to int by resetting columns
-    l0 = [_[0] for _ in weather_df.columns]
-    l1 = [int(_[1]) for _ in weather_df.columns]
-    weather_df.columns = [l0, l1]
+    l0=[_[0] for _ in weather_df.columns]
+    l1=[int(_[1]) for _ in weather_df.columns]
+    weather_df.columns=[l0, l1]
 
     return weather_df
 
@@ -111,16 +111,16 @@ def initialize_wind_turbines():
 
     # specification of wind turbine where data is provided in the oedb
     # turbine library
-    enercon_e126 = {
+    enercon_e126={
         'turbine_type': 'E-126/4200',  # turbine type as in register
         'hub_height': 135  # in m
     }
     # initialize WindTurbine object
-    e126 = WindTurbine(**enercon_e126)
+    e126=WindTurbine(**enercon_e126)
 
     # specification of own wind turbine (Note: power values and nominal power
     # have to be in Watt)
-    my_turbine = {
+    my_turbine={
         'nominal_power': 3e6,  # in W
         'hub_height': 105,  # in m
         'power_curve': pd.DataFrame(
@@ -129,19 +129,19 @@ def initialize_wind_turbines():
                   'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]})  # in m/s
     }
     # initialize WindTurbine object
-    my_turbine = WindTurbine(**my_turbine)
+    my_turbine=WindTurbine(**my_turbine)
 
     # specification of wind turbine where power coefficient curve and nominal
     # power is provided in an own csv file
-    csv_path = os.path.join(os.path.dirname(__file__), 'data')
-    dummy_turbine = {
+    csv_path=os.path.join(os.path.dirname(__file__), 'data')
+    dummy_turbine={
         'turbine_type': "DUMMY 1",
         'hub_height': 100,  # in m
         'rotor_diameter': 70,  # in m
         'path': csv_path
     }
     # initialize WindTurbine object
-    dummy_turbine = WindTurbine(**dummy_turbine)
+    dummy_turbine=WindTurbine(**dummy_turbine)
 
     return my_turbine, e126, dummy_turbine
 
@@ -175,13 +175,13 @@ def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
     # power output calculation for my_turbine
     # initialize ModelChain with default parameters and use run_model method
     # to calculate power output
-    mc_my_turbine = ModelChain(my_turbine).run_model(weather)
+    mc_my_turbine=ModelChain(my_turbine).run_model(weather)
     # write power output time series to WindTurbine object
-    my_turbine.power_output = mc_my_turbine.power_output
+    my_turbine.power_output=mc_my_turbine.power_output
 
     # power output calculation for e126
     # own specifications for ModelChain setup
-    modelchain_data = {
+    modelchain_data={
         'wind_speed_model': 'logarithmic',  # 'logarithmic' (default),
                                             # 'hellman' or
                                             # 'interpolation_extrapolation'
@@ -196,16 +196,16 @@ def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
         'hellman_exp': None}  # None (default) or None
     # initialize ModelChain with own specifications and use run_model method
     # to calculate power output
-    mc_e126 = ModelChain(e126, **modelchain_data).run_model(weather)
+    mc_e126=ModelChain(e126, **modelchain_data).run_model(weather)
     # write power output time series to WindTurbine object
-    e126.power_output = mc_e126.power_output
+    e126.power_output=mc_e126.power_output
 
     # power output calculation for example_turbine
     # own specification for 'power_output_model'
-    mc_example_turbine = ModelChain(
+    mc_example_turbine=ModelChain(
         dummy_turbine,
         power_output_model='power_coefficient_curve').run_model(weather)
-    dummy_turbine.power_output = mc_example_turbine.power_output
+    dummy_turbine.power_output=mc_example_turbine.power_output
 
     return
 
@@ -272,8 +272,8 @@ def run_example():
     Runs the basic example.
 
     """
-    weather = get_weather_data('weather.csv')
-    my_turbine, e126, dummy_turbine = initialize_wind_turbines()
+    weather=get_weather_data('weather.csv')
+    my_turbine, e126, dummy_turbine=initialize_wind_turbines()
     calculate_power_output(weather, my_turbine, e126, dummy_turbine)
     plot_or_print(my_turbine, e126, dummy_turbine)
 

example/test_examples.py

@@ -18,8 +18,8 @@ class TestExamples:
 
     def test_modelchain_example_flh(self):
         # tests full load hours
-        weather = mc_e.get_weather_data('weather.csv')
-        my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
+        weather=mc_e.get_weather_data('weather.csv')
+        my_turbine, e126, dummy_turbine=mc_e.initialize_wind_turbines()
         mc_e.calculate_power_output(weather, my_turbine, e126, dummy_turbine)
 
         assert_allclose(2764.194772, (e126.power_output.sum() /
@@ -29,11 +29,11 @@ def test_modelchain_example_flh(self):
 
     def test_turbine_cluster_modelchain_example_flh(self):
         # tests full load hours
-        weather = mc_e.get_weather_data('weather.csv')
-        my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
-        example_farm, example_farm_2 = tc_mc_e.initialize_wind_farms(
+        weather=mc_e.get_weather_data('weather.csv')
+        my_turbine, e126, dummy_turbine=mc_e.initialize_wind_turbines()
+        example_farm, example_farm_2=tc_mc_e.initialize_wind_farms(
             my_turbine, e126)
-        example_cluster = tc_mc_e.initialize_wind_turbine_cluster(
+        example_cluster=tc_mc_e.initialize_wind_turbine_cluster(
             example_farm, example_farm_2)
         tc_mc_e.calculate_power_output(weather, example_farm, example_cluster)
         assert_allclose(1956.164053, (example_farm.power_output.sum() /
@@ -46,33 +46,33 @@ def _notebook_run(self, path):
         Execute a notebook via nbconvert and collect output.
         Returns (parsed nb object, execution errors)
         """
-        dirname, __ = os.path.split(path)
+        dirname, __=os.path.split(path)
         os.chdir(dirname)
         with tempfile.NamedTemporaryFile(suffix=".ipynb") as fout:
-            args = ["jupyter", "nbconvert", "--to", "notebook", "--execute",
+            args=["jupyter", "nbconvert", "--to", "notebook", "--execute",
                     "--ExecutePreprocessor.timeout=60",
                     "--output", fout.name, path]
             subprocess.check_call(args)
 
             fout.seek(0)
-            nb = nbformat.read(fout, nbformat.current_nbformat)
+            nb=nbformat.read(fout, nbformat.current_nbformat)
 
-        errors = [output for cell in nb.cells if "outputs" in cell
+        errors=[output for cell in nb.cells if "outputs" in cell
                   for output in cell["outputs"]
                   if output.output_type == "error"]
 
         return nb, errors
 
     @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires python3.6")
     def test_modelchain_example_ipynb(self):
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        nb, errors = self._notebook_run(
+        dir_path=os.path.dirname(os.path.realpath(__file__))
+        nb, errors=self._notebook_run(
             os.path.join(dir_path, 'modelchain_example.ipynb'))
         assert errors == []
 
     @pytest.mark.skipif(sys.version_info < (3, 6), reason="requires python3.6")
     def test_turbine_cluster_modelchain_example_ipynb(self):
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        nb, errors = self._notebook_run(
+        dir_path=os.path.dirname(os.path.realpath(__file__))
+        nb, errors=self._notebook_run(
             os.path.join(dir_path, 'turbine_cluster_modelchain_example.ipynb'))
         assert errors == []

example/turbine_cluster_modelchain_example.py

@@ -15,7 +15,7 @@
 try:
     from matplotlib import pyplot as plt
 except ImportError:
-    plt = None
+    plt=None
 
 from example import modelchain_example as mc_e
 from windpowerlib import WindFarm
@@ -58,24 +58,24 @@ def initialize_wind_farms(my_turbine, e126):
     # for each turbine type you can either specify the number of turbines of
     # that type in the wind farm (float values are possible as well) or the
     # total installed capacity of that turbine type in W
-    wind_turbine_fleet = pd.DataFrame(
+    wind_turbine_fleet=pd.DataFrame(
         {'wind_turbine': [my_turbine, e126],  # as windpowerlib.WindTurbine
          'number_of_turbines': [6, None],
          'total_capacity': [None, 12.6e6]}
     )
     # initialize WindFarm object
-    example_farm = WindFarm(name='example_farm',
+    example_farm=WindFarm(name='example_farm',
                             wind_turbine_fleet=wind_turbine_fleet)
 
     # specification of wind farm data (2) containing a wind farm efficiency
     # wind turbine fleet is provided using the to_group function
-    example_farm_2_data = {
+    example_farm_2_data={
         'name': 'example_farm_2',
         'wind_turbine_fleet': [my_turbine.to_group(6),
                                e126.to_group(total_capacity=12.6e6)],
         'efficiency': 0.9}
     # initialize WindFarm object
-    example_farm_2 = WindFarm(**example_farm_2_data)
+    example_farm_2=WindFarm(**example_farm_2_data)
 
     return example_farm, example_farm_2
 
@@ -103,11 +103,11 @@ def initialize_wind_turbine_cluster(example_farm, example_farm_2):
     """
 
     # specification of cluster data
-    example_cluster_data = {
+    example_cluster_data={
         'name': 'example_cluster',
         'wind_farms': [example_farm, example_farm_2]}
     # initialize WindTurbineCluster object
-    example_cluster = WindTurbineCluster(**example_cluster_data)
+    example_cluster=WindTurbineCluster(**example_cluster_data)
 
     return example_cluster
 
@@ -133,17 +133,17 @@ class that provides all necessary steps to calculate the power output of a
         WindTurbineCluster object.
 
     """
-    example_farm.efficiency = 0.9
+    example_farm.efficiency=0.9
     # power output calculation for example_farm
     # initialize TurbineClusterModelChain with default parameters and use
     # run_model method to calculate power output
-    mc_example_farm = TurbineClusterModelChain(example_farm).run_model(weather)
+    mc_example_farm=TurbineClusterModelChain(example_farm).run_model(weather)
     # write power output time series to WindFarm object
-    example_farm.power_output = mc_example_farm.power_output
+    example_farm.power_output=mc_example_farm.power_output
 
     # power output calculation for turbine_cluster
     # own specifications for TurbineClusterModelChain setup
-    modelchain_data = {
+    modelchain_data={
         'wake_losses_model':
             'wind_farm_efficiency',  # 'dena_mean' (default), None,
                                      # 'wind_farm_efficiency' or name
@@ -171,10 +171,10 @@ class that provides all necessary steps to calculate the power output of a
         'hellman_exp': None}  # None (default) or None
     # initialize TurbineClusterModelChain with own specifications and use
     # run_model method to calculate power output
-    mc_example_cluster = TurbineClusterModelChain(
+    mc_example_cluster=TurbineClusterModelChain(
             example_cluster, **modelchain_data).run_model(weather)
     # write power output time series to WindTurbineCluster object
-    example_cluster.power_output = mc_example_cluster.power_output
+    example_cluster.power_output=mc_example_cluster.power_output
 
     return
 
@@ -209,10 +209,10 @@ def run_example():
     Runs the example.
 
     """
-    weather = mc_e.get_weather_data('weather.csv')
-    my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
-    example_farm, example_farm_2 = initialize_wind_farms(my_turbine, e126)
-    example_cluster = initialize_wind_turbine_cluster(example_farm,
+    weather=mc_e.get_weather_data('weather.csv')
+    my_turbine, e126, dummy_turbine=mc_e.initialize_wind_turbines()
+    example_farm, example_farm_2=initialize_wind_farms(my_turbine, e126)
+    example_cluster=initialize_wind_turbine_cluster(example_farm,
                                                       example_farm_2)
     calculate_power_output(weather, example_farm, example_cluster)
     plot_or_print(example_farm, example_cluster)

tests/test_density.py

@@ -14,55 +14,55 @@
 class TestDensity:
 
     def test_barometric(self):
-        parameters = {'pressure': pd.Series(data=[101125, 101000]),
+        parameters={'pressure': pd.Series(data=[101125, 101000]),
                       'pressure_height': 0,
                       'hub_height': 100,
                       'temperature_hub_height': pd.Series(data=[267, 268])}
 
         # Test pressure as pd.Series and temperature_hub_height as pd.Series
         # and np.array
-        rho_exp = pd.Series(data=[1.30305336, 1.29656645])
+        rho_exp=pd.Series(data=[1.30305336, 1.29656645])
         assert_series_equal(barometric(**parameters), rho_exp)
-        parameters['temperature_hub_height'] = np.array(
+        parameters['temperature_hub_height']=np.array(
             parameters['temperature_hub_height'])
         assert_series_equal(barometric(**parameters), rho_exp)
 
         # Test pressure as np.array and temperature_hub_height as pd.Series
-        parameters['pressure'] = np.array(parameters['pressure'])
-        parameters['temperature_hub_height'] = pd.Series(
+        parameters['pressure']=np.array(parameters['pressure'])
+        parameters['temperature_hub_height']=pd.Series(
             data=parameters['temperature_hub_height'])
         assert_series_equal(barometric(**parameters), rho_exp)
 
         # Test pressure as np.array and temperature_hub_height as np.array
-        rho_exp = np.array([1.30305336, 1.29656645])
-        parameters['temperature_hub_height'] = np.array(
+        rho_exp=np.array([1.30305336, 1.29656645])
+        parameters['temperature_hub_height']=np.array(
             parameters['temperature_hub_height'])
         assert_allclose(barometric(**parameters), rho_exp)
         assert isinstance(barometric(**parameters), np.ndarray)
 
     def test_ideal_gas(self):
-        parameters = {'pressure': pd.Series(data=[101125, 101000]),
+        parameters={'pressure': pd.Series(data=[101125, 101000]),
                       'pressure_height': 0,
                       'hub_height': 100,
                       'temperature_hub_height': pd.Series(data=[267, 268])}
 
         # Test pressure as pd.Series and temperature_hub_height as pd.Series
         # and np.array
-        rho_exp = pd.Series(data=[1.30309439, 1.29660728])
+        rho_exp=pd.Series(data=[1.30309439, 1.29660728])
         assert_series_equal(ideal_gas(**parameters), rho_exp)
-        parameters['temperature_hub_height'] = np.array(
+        parameters['temperature_hub_height']=np.array(
             parameters['temperature_hub_height'])
         assert_series_equal(ideal_gas(**parameters), rho_exp)
 
         # Test pressure as np.array and temperature_hub_height as pd.Series
-        parameters['pressure'] = np.array(parameters['pressure'])
-        parameters['temperature_hub_height'] = pd.Series(
+        parameters['pressure']=np.array(parameters['pressure'])
+        parameters['temperature_hub_height']=pd.Series(
             data=parameters['temperature_hub_height'])
         assert_allclose(ideal_gas(**parameters), rho_exp)
 
         # Test pressure as np.array and temperature_hub_height as np.array
-        rho_exp = np.array([1.30309439, 1.29660728])
-        parameters['temperature_hub_height'] = np.array(
+        rho_exp=np.array([1.30309439, 1.29660728])
+        parameters['temperature_hub_height']=np.array(
             parameters['temperature_hub_height'])
         assert_allclose(ideal_gas(**parameters), rho_exp)
         assert isinstance(ideal_gas(**parameters), np.ndarray)