Add stickler and black config files

.stickler.yml

@@ -0,0 +1,12 @@
+linters:
+    flake8:
+      python: 3
+      max-line-length: 79
+      select: C,E,F,W,B,B950
+      ignore: E203, E501, W503
+    black:
+      config: ./pyproject.toml
+      fixer: true
+
+fixers:
+    enable: true

.travis.yml

@@ -5,8 +5,7 @@ matrix:
     - python: 3.5
     - python: 3.6
     - python: 3.7
-      dist: xenial
-      sudo: true
+    - python: 3.8
 
 # command to install dependencies
 #before_install:

example/modelchain_example.py

@@ -64,19 +64,24 @@ def get_weather_data(filename='weather.csv', **kwargs):
 
     """
 
-    if 'datapath' not in kwargs:
-        kwargs['datapath'] = os.path.join(os.path.split(
-            os.path.dirname(__file__))[0], 'example')
-    file = os.path.join(kwargs['datapath'], filename)
+    if "datapath" not in kwargs:
+        kwargs["datapath"] = os.path.join(
+            os.path.split(os.path.dirname(__file__))[0], "example"
+        )
+    file = os.path.join(kwargs["datapath"], filename)
 
     # read csv file
     weather_df = pd.read_csv(
-        file, index_col=0, header=[0, 1],
-        date_parser=lambda idx: pd.to_datetime(idx, utc=True))
+        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(
-        'Europe/Berlin')
+        "Europe/Berlin"
+    )
 
     # change type of height from str to int by resetting columns
     l0 = [_[0] for _ in weather_df.columns]
@@ -112,33 +117,38 @@ def initialize_wind_turbines():
     # specification of wind turbine where data is provided in the oedb
     # turbine library
     enercon_e126 = {
-        'turbine_type': 'E-126/4200',  # turbine type as in register
-        'hub_height': 135  # in m
+        "turbine_type": "E-126/4200",  # turbine type as in register
+        "hub_height": 135,  # in m
     }
     # initialize WindTurbine object
     e126 = WindTurbine(**enercon_e126)
 
     # specification of own wind turbine (Note: power values and nominal power
     # have to be in Watt)
     my_turbine = {
-        'nominal_power': 3e6,  # in W
-        'hub_height': 105,  # in m
-        'power_curve': pd.DataFrame(
-            data={'value': [p * 1000 for p in [
-                      0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],  # in W
-                  'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]})  # in m/s
+        "nominal_power": 3e6,  # in W
+        "hub_height": 105,  # in m
+        "power_curve": pd.DataFrame(
+            data={
+                "value": [
+                    p * 1000
+                    for p in [0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]
+                ],  # in W
+                "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)
 
     # 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')
+    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
+        "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)
@@ -182,18 +192,19 @@ def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
     # power output calculation for e126
     # own specifications for ModelChain setup
     modelchain_data = {
-        'wind_speed_model': 'logarithmic',  # 'logarithmic' (default),
-                                            # 'hellman' or
-                                            # 'interpolation_extrapolation'
-        'density_model': 'ideal_gas',  # 'barometric' (default), 'ideal_gas' or
-                                       # 'interpolation_extrapolation'
-        'temperature_model': 'linear_gradient',  # 'linear_gradient' (def.) or
-                                                 # 'interpolation_extrapolation'
-        'power_output_model': 'power_curve',  # 'power_curve' (default) or
-                                              # 'power_coefficient_curve'
-        'density_correction': True,  # False (default) or True
-        'obstacle_height': 0,  # default: 0
-        'hellman_exp': None}  # None (default) or None
+        "wind_speed_model": "logarithmic",  # 'logarithmic' (default),
+        # 'hellman' or
+        # 'interpolation_extrapolation'
+        "density_model": "ideal_gas",  # 'barometric' (default), 'ideal_gas' or
+        # 'interpolation_extrapolation'
+        "temperature_model": "linear_gradient",  # 'linear_gradient' (def.) or
+        # 'interpolation_extrapolation'
+        "power_output_model": "power_curve",  # 'power_curve' (default) or
+        # 'power_coefficient_curve'
+        "density_correction": True,  # False (default) or True
+        "obstacle_height": 0,  # default: 0
+        "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)
@@ -203,8 +214,8 @@ def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
     # power output calculation for example_turbine
     # own specification for 'power_output_model'
     mc_example_turbine = ModelChain(
-        dummy_turbine,
-        power_output_model='power_coefficient_curve').run_model(weather)
+        dummy_turbine, power_output_model="power_coefficient_curve"
+    ).run_model(weather)
     dummy_turbine.power_output = mc_example_turbine.power_output
 
     return
@@ -272,7 +283,7 @@ def run_example():
     Runs the basic example.
 
     """
-    weather = get_weather_data('weather.csv')
+    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

@@ -15,31 +15,45 @@
 
 
 class TestExamples:
-
     def test_modelchain_example_flh(self):
         # tests full load hours
-        weather = mc_e.get_weather_data('weather.csv')
+        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() /
-                                      e126.nominal_power), 0.01)
-        assert_allclose(1882.7567, (my_turbine.power_output.sum() /
-                                    my_turbine.nominal_power), 0.01)
+        assert_allclose(
+            2764.194772, (e126.power_output.sum() / e126.nominal_power), 0.01
+        )
+        assert_allclose(
+            1882.7567,
+            (my_turbine.power_output.sum() / my_turbine.nominal_power),
+            0.01,
+        )
 
     def test_turbine_cluster_modelchain_example_flh(self):
         # tests full load hours
-        weather = mc_e.get_weather_data('weather.csv')
+        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)
+            my_turbine, e126
+        )
         example_cluster = tc_mc_e.initialize_wind_turbine_cluster(
-            example_farm, example_farm_2)
+            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() /
-                                      example_farm.nominal_power), 0.01)
-        assert_allclose(2156.794154, (example_cluster.power_output.sum() /
-                                      example_cluster.nominal_power), 0.01)
+        assert_allclose(
+            1956.164053,
+            (example_farm.power_output.sum() / example_farm.nominal_power),
+            0.01,
+        )
+        assert_allclose(
+            2156.794154,
+            (
+                example_cluster.power_output.sum()
+                / example_cluster.nominal_power
+            ),
+            0.01,
+        )
 
     def _notebook_run(self, path):
         """
@@ -49,30 +63,44 @@ def _notebook_run(self, path):
         dirname, __ = os.path.split(path)
         os.chdir(dirname)
         with tempfile.NamedTemporaryFile(suffix=".ipynb") as fout:
-            args = ["jupyter", "nbconvert", "--to", "notebook", "--execute",
-                    "--ExecutePreprocessor.timeout=60",
-                    "--output", fout.name, path]
+            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)
 
-        errors = [output for cell in nb.cells if "outputs" in cell
-                  for output in cell["outputs"]
-                  if output.output_type == "error"]
+        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(
-            os.path.join(dir_path, 'modelchain_example.ipynb'))
+            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(
-            os.path.join(dir_path, 'turbine_cluster_modelchain_example.ipynb'))
+            os.path.join(dir_path, "turbine_cluster_modelchain_example.ipynb")
+        )
         assert errors == []

example/turbine_cluster_modelchain_example.py

@@ -59,21 +59,27 @@ def initialize_wind_farms(my_turbine, e126):
     # 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': [my_turbine, e126],  # as windpowerlib.WindTurbine
-         'number_of_turbines': [6, None],
-         'total_capacity': [None, 12.6e6]}
+        {
+            "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',
-                            wind_turbine_fleet=wind_turbine_fleet)
+    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 = {
-        'name': 'example_farm_2',
-        'wind_turbine_fleet': [my_turbine.to_group(6),
-                               e126.to_group(total_capacity=12.6e6)],
-        'efficiency': 0.9}
+        "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)
 
@@ -104,8 +110,9 @@ def initialize_wind_turbine_cluster(example_farm, example_farm_2):
 
     # specification of cluster data
     example_cluster_data = {
-        'name': 'example_cluster',
-        'wind_farms': [example_farm, example_farm_2]}
+        "name": "example_cluster",
+        "wind_farms": [example_farm, example_farm_2],
+    }
     # initialize WindTurbineCluster object
     example_cluster = WindTurbineCluster(**example_cluster_data)
 
@@ -144,35 +151,36 @@ class that provides all necessary steps to calculate the power output of a
     # power output calculation for turbine_cluster
     # own specifications for TurbineClusterModelChain setup
     modelchain_data = {
-        'wake_losses_model':
-            'wind_farm_efficiency',  # 'dena_mean' (default), None,
-                                     # 'wind_farm_efficiency' or name
-                                     #  of another wind efficiency curve
-                #  see :py:func:`~.wake_losses.get_wind_efficiency_curve`
-        'smoothing': True,  # False (default) or True
-        'block_width': 0.5,  # default: 0.5
-        'standard_deviation_method': 'Staffell_Pfenninger',  #
-                                            # 'turbulence_intensity' (default)
-                                            # or 'Staffell_Pfenninger'
-        'smoothing_order': 'wind_farm_power_curves',  #
-                                        # 'wind_farm_power_curves' (default) or
-                                        # 'turbine_power_curves'
-        'wind_speed_model': 'logarithmic',  # 'logarithmic' (default),
-                                            # 'hellman' or
-                                            # 'interpolation_extrapolation'
-        'density_model': 'ideal_gas',  # 'barometric' (default), 'ideal_gas' or
-                                       # 'interpolation_extrapolation'
-        'temperature_model': 'linear_gradient',  # 'linear_gradient' (def.) or
-                                                 # 'interpolation_extrapolation'
-        'power_output_model': 'power_curve',  # 'power_curve' (default) or
-                                              # 'power_coefficient_curve'
-        'density_correction': True,  # False (default) or True
-        'obstacle_height': 0,  # default: 0
-        'hellman_exp': None}  # None (default) or None
+        "wake_losses_model": "wind_farm_efficiency",  # 'dena_mean' (default), None,
+        # 'wind_farm_efficiency' or name
+        #  of another wind efficiency curve
+        #  see :py:func:`~.wake_losses.get_wind_efficiency_curve`
+        "smoothing": True,  # False (default) or True
+        "block_width": 0.5,  # default: 0.5
+        "standard_deviation_method": "Staffell_Pfenninger",  #
+        # 'turbulence_intensity' (default)
+        # or 'Staffell_Pfenninger'
+        "smoothing_order": "wind_farm_power_curves",  #
+        # 'wind_farm_power_curves' (default) or
+        # 'turbine_power_curves'
+        "wind_speed_model": "logarithmic",  # 'logarithmic' (default),
+        # 'hellman' or
+        # 'interpolation_extrapolation'
+        "density_model": "ideal_gas",  # 'barometric' (default), 'ideal_gas' or
+        # 'interpolation_extrapolation'
+        "temperature_model": "linear_gradient",  # 'linear_gradient' (def.) or
+        # 'interpolation_extrapolation'
+        "power_output_model": "power_curve",  # 'power_curve' (default) or
+        # 'power_coefficient_curve'
+        "density_correction": True,  # False (default) or True
+        "obstacle_height": 0,  # default: 0
+        "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(
-            example_cluster, **modelchain_data).run_model(weather)
+        example_cluster, **modelchain_data
+    ).run_model(weather)
     # write power output time series to WindTurbineCluster object
     example_cluster.power_output = mc_example_cluster.power_output
 
@@ -209,11 +217,12 @@ def run_example():
     Runs the example.
 
     """
-    weather = mc_e.get_weather_data('weather.csv')
+    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)
+    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)