Merge c12f224 into 1273dea

example/modelchain_example.ipynb

@@ -41,7 +41,7 @@
     "import requests\n",
     "\n",
     "from windpowerlib import ModelChain, WindTurbine, create_power_curve\n",
-    "from windpowerlib import wind_turbine as wt"
+    "from windpowerlib import data as wt"
    ]
   },
   {

tests/test_data_handling.py

@@ -0,0 +1,10 @@
+"""
+SPDX-FileCopyrightText: 2019 oemof developer group <contact@oemof.org>
+SPDX-License-Identifier: MIT
+"""
+
+from windpowerlib import data
+
+
+def test_integretiy_check():
+    pass

tests/test_wind_turbine.py

@@ -12,11 +12,11 @@
 from windpowerlib.wind_turbine import (
     get_turbine_data_from_file,
     WindTurbine,
-    get_turbine_types,
     WindTurbineGroup,
-    load_turbine_data_from_oedb,
 )
 
+from windpowerlib.data import store_turbine_data_from_oedb, get_turbine_types
+
 
 class TestWindTurbine:
     @classmethod
@@ -54,7 +54,7 @@ def test_wrong_url_load_turbine_data(self):
         with pytest.raises(
             ConnectionError, match="Database connection not successful"
         ):
-            load_turbine_data_from_oedb("wrong_schema")
+            store_turbine_data_from_oedb("wrong_schema")
 
     @pytest.mark.filterwarnings("ignore:The WindTurbine")
     def test_string_representation_of_wind_turbine(self):

windpowerlib/__init__.py

@@ -3,8 +3,8 @@
 __version__ = "0.2.1dev"
 
 from .wind_turbine import WindTurbine  # noqa: F401
-from .wind_turbine import get_turbine_types  # noqa: F401
-from .wind_turbine import create_power_curve  # noqa: F401
+from .data import get_turbine_types  # noqa: F401
+from .power_curves import create_power_curve  # noqa: F401
 from .wind_farm import WindFarm  # noqa: F401
 from .wind_turbine_cluster import WindTurbineCluster  # noqa: F401
 from .modelchain import ModelChain  # noqa: F401

windpowerlib/data.py

@@ -0,0 +1,326 @@
+"""
+The ``data`` module contains functions to handle the needed data.
+
+SPDX-FileCopyrightText: 2019 oemof developer group <contact@oemof.org>
+SPDX-License-Identifier: MIT
+"""
+
+import os
+import warnings
+import logging
+from datetime import datetime
+from shutil import copyfile
+
+import pandas as pd
+import requests
+
+from windpowerlib.wind_turbine import WindTurbine
+
+
+def get_turbine_types(turbine_library="local", print_out=True, filter_=True):
+    r"""
+    Get all provided wind turbine types provided.
+
+    Choose by `turbine_library` whether to get wind turbine types provided by
+    the OpenEnergy Database ('oedb') or wind turbine types provided in your
+    local file(s) ('local').
+    By default only turbine types for which a power coefficient curve or power
+    curve is provided are returned. Set `filter_=False` to see all turbine
+    types for which any data (e.g. hub height, rotor diameter, ...) is
+    provided.
+
+    Parameters
+    ----------
+    turbine_library : str
+        Specifies if the oedb turbine library ('oedb') or your local turbine
+        data file ('local') is evaluated. Default: 'local'.
+    print_out : bool
+        Directly prints a tabular containing the turbine types in column
+        'turbine_type', the manufacturer in column 'manufacturer' and
+        information about whether a power (coefficient) curve exists (True) or
+        not (False) in columns 'has_power_curve' and 'has_cp_curve'.
+        Default: True.
+    filter_ : bool
+        If True only turbine types for which a power coefficient curve or
+        power curve is provided in the oedb turbine library are
+        returned. Default: True.
+
+    Returns
+    -------
+    :pandas:`pandas.DataFrame<frame>`
+        Contains turbine types in column 'turbine_type', the manufacturer in
+        column 'manufacturer' and information about whether a power
+        (coefficient) curve exists (True) or not (False) in columns
+        'has_power_curve' and 'has_cp_curve'.
+
+    Notes
+    -----
+    If the power (coefficient) curve of the desired turbine type (or the
+    turbine type itself) is missing you can contact us via github or
+    windpowerlib@rl-institut.de. You can help us by providing data in the
+    format as shown in
+    `the data base <https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library>`_.
+
+    Examples
+    --------
+    >>> from windpowerlib import get_turbine_types
+    >>> df=get_turbine_types(print_out=False)
+    >>> print(df[df["turbine_type"].str.contains("E-126")].iloc[0])
+    manufacturer          Enercon
+    turbine_type       E-126/4200
+    has_power_curve          True
+    has_cp_curve             True
+    Name: 5, dtype: object
+    >>> print(df[df["manufacturer"].str.contains("Enercon")].iloc[0])
+    manufacturer          Enercon
+    turbine_type       E-101/3050
+    has_power_curve          True
+    has_cp_curve             True
+    Name: 1, dtype: object
+
+    """
+    if turbine_library == "local":
+        filename = os.path.join(
+            os.path.dirname(__file__), "oedb", "turbine_data.csv"
+        )
+        df = pd.read_csv(filename, index_col=0).reset_index()
+    elif turbine_library == "oedb":
+        df = fetch_turbine_data_from_oedb()
+
+    else:
+        raise ValueError(
+            "`turbine_library` is '{}' ".format(turbine_library)
+            + "but must be 'local' or 'oedb'."
+        )
+    if filter_:
+        cp_curves_df = df.loc[df["has_cp_curve"]][
+            ["manufacturer", "turbine_type", "has_cp_curve"]
+        ]
+        p_curves_df = df.loc[df["has_power_curve"]][
+            ["manufacturer", "turbine_type", "has_power_curve"]
+        ]
+        curves_df = pd.merge(
+            p_curves_df, cp_curves_df, how="outer", sort=True
+        ).fillna(False)
+    else:
+        curves_df = df[
+            ["manufacturer", "turbine_type", "has_power_curve", "has_cp_curve"]
+        ]
+    if print_out:
+        pd.set_option("display.max_rows", len(curves_df))
+        print(curves_df)
+        pd.reset_option("display.max_rows")
+    return curves_df
+
+
+def fetch_turbine_data_from_oedb(
+    schema="supply", table="wind_turbine_library"
+):
+    r"""
+    Fetches turbine library from the OpenEnergy database (oedb).
+
+    Parameters
+    ----------
+    schema : str
+        Database schema of the turbine library.
+    table : str
+        Table name of the turbine library.
+
+    Returns
+    -------
+    :pandas:`pandas.DataFrame<frame>`
+        Turbine data of different turbines such as 'manufacturer',
+        'turbine_type', 'nominal_power'.
+
+    """
+    # url of OpenEnergy Platform that contains the oedb
+    oep_url = "http://oep.iks.cs.ovgu.de/"
+
+    # load data
+    result = requests.get(
+        oep_url + "/api/v0/schema/{}/tables/{}/rows/?".format(schema, table),
+    )
+    if not result.status_code == 200:
+        raise ConnectionError(
+            "Database connection not successful. "
+            "Response: [{}]".format(result.status_code)
+        )
+    # extract data to dataframe
+    return pd.DataFrame(result.json())
+
+
+def load_turbine_data_from_oedb(schema="supply", table="wind_turbine_library"):
+    msg = (
+        "\nUse >>store_turbine_data_from_oedb<< and not"
+        " >>load_turbine_data_from_oedb<<"
+    )
+    warnings.warn(msg, FutureWarning)
+    return store_turbine_data_from_oedb(schema=schema, table=table)
+
+
+def store_turbine_data_from_oedb(
+    schema="supply", table="wind_turbine_library"
+):
+    r"""
+    Loads turbine library from the OpenEnergy database (oedb).
+
+    Turbine data is saved to csv files ('oedb_power_curves.csv',
+    'oedb_power_coefficient_curves.csv' and 'oedb_nominal_power') for offline
+    usage of the windpowerlib. If the files already exist they are overwritten.
+
+    Parameters
+    ----------
+    schema : str
+        Database schema of the turbine library.
+    table : str
+        Table name of the turbine library.
+
+    Returns
+    -------
+    :pandas:`pandas.DataFrame<frame>`
+        Turbine data of different turbines such as 'manufacturer',
+        'turbine_type', 'nominal_power'.
+
+    """
+    turbine_data = fetch_turbine_data_from_oedb(schema=schema, table=table)
+    # standard file name for saving data
+    filename = os.path.join(os.path.dirname(__file__), "oedb", "{}.csv")
+
+    time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
+    # get all power (coefficient) curves and save to file
+    # for curve_type in ['power_curve', 'power_coefficient_curve']:
+    for curve_type in ["power_curve", "power_coefficient_curve"]:
+        curves_df = pd.DataFrame(columns=["wind_speed"])
+        for index in turbine_data.index:
+            if (
+                turbine_data["{}_wind_speeds".format(curve_type)][index]
+                and turbine_data["{}_values".format(curve_type)][index]
+            ):
+                df = (
+                    pd.DataFrame(
+                        data=[
+                            eval(
+                                turbine_data[
+                                    "{}_wind_speeds".format(curve_type)
+                                ][index]
+                            ),
+                            eval(
+                                turbine_data["{}_values".format(curve_type)][
+                                    index
+                                ]
+                            ),
+                        ]
+                    )
+                    .transpose()
+                    .rename(
+                        columns={
+                            0: "wind_speed",
+                            1: turbine_data["turbine_type"][index],
+                        }
+                    )
+                )
+                curves_df = pd.merge(
+                    left=curves_df, right=df, how="outer", on="wind_speed"
+                )
+        curves_df = curves_df.set_index("wind_speed").sort_index().transpose()
+        # power curve values in W
+        if curve_type == "power_curve":
+            curves_df *= 1000
+        curves_df.index.name = "turbine_type"
+        copyfile(
+            filename.format("{}s".format(curve_type)),
+            filename.format("{0}s_{1}".format(curve_type, time_stamp)),
+        )
+        curves_df.to_csv(filename.format("{}s".format(curve_type)))
+
+    # get turbine data and save to file (excl. curves)
+    turbine_data_df = turbine_data.drop(
+        [
+            "power_curve_wind_speeds",
+            "power_curve_values",
+            "power_coefficient_curve_wind_speeds",
+            "power_coefficient_curve_values",
+            "thrust_coefficient_curve_wind_speeds",
+            "thrust_coefficient_curve_values",
+        ],
+        axis=1,
+    ).set_index("turbine_type")
+    # nominal power in W
+    turbine_data_df["nominal_power"] *= 1000
+    copyfile(
+        filename.format("turbine_data"),
+        filename.format("turbine_data_{0}".format(time_stamp)),
+    )
+    check_imported_data(turbine_data_df, filename, time_stamp)
+    turbine_data_df.to_csv(filename.format("turbine_data"))
+    remove_tmp_file(filename, time_stamp)
+    return turbine_data
+
+
+def remove_tmp_file(filename, time_stamp):
+    os.remove(filename.format("turbine_data_{0}".format(time_stamp)))
+    for curve_type in ["power_curve", "power_coefficient_curve"]:
+        copyfile(
+            filename.format("{0}s_{1}".format(curve_type, time_stamp)),
+            filename.format("{}s".format(curve_type)),
+        )
+        os.remove(filename.format("{0}s_{1}".format(curve_type, time_stamp)))
+
+
+def check_imported_data(data, filename, time_stamp):
+    data.to_csv(filename.format("turbine_data"))
+    try:
+        data = check_data_integretiy(data)
+    except Exception as e:
+        copyfile(
+            filename.format("turbine_data"),
+            filename.format("turbine_data_error{0}".format(time_stamp)),
+        )
+        copyfile(
+            filename.format("turbine_data_{0}".format(time_stamp)),
+            filename.format("turbine_data"),
+        )
+        for curve_type in ["power_curve", "power_coefficient_curve"]:
+            copyfile(
+                filename.format("{}s".format(curve_type)),
+                filename.format(
+                    "{0}s_error_{1}".format(curve_type, time_stamp)
+                ),
+            )
+            copyfile(
+                filename.format("{0}s_{1}".format(curve_type, time_stamp)),
+                filename.format("{}s".format(curve_type)),
+            )
+        remove_tmp_file(filename, time_stamp)
+        raise e
+    return data
+
+
+def check_data_integretiy(data):
+    for dataset in data.iterrows():
+        ttype = dataset[0]
+        enercon_e126 = {"turbine_type": "{0}".format(ttype), "hub_height": 135}
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            wt = WindTurbine(**enercon_e126)
+            if wt.power_curve is None and dataset[1].has_power_curve is True:
+                logging.warning(
+                    "{0}: No power curve but has_power_curve=True.".format(
+                        ttype
+                    )
+                )
+            if (
+                wt.power_coefficient_curve is None
+                and dataset[1].has_cp_curve is True
+            ):
+                logging.warning(
+                    "{0}: No cp-curve but has_cp_curve=True.".format(ttype)
+                )
+            if dataset[1].has_power_curve is True:
+                if len(wt.power_curve) < 22:
+                    logging.warning(
+                        "{0}: power_curve is to short ({1} values),".format(
+                            ttype, len(wt.power_curve)
+                        )
+                    )
+    return data