Move data check to data module

Author: uvchik

windpowerlib/data.py

@@ -15,6 +15,7 @@
 import requests
 
 from windpowerlib.wind_turbine import WindTurbine
+from windpowerlib.tools import WindpowerlibUserWarning
 
 
 def get_turbine_types(turbine_library="local", print_out=True, filter_=True):
@@ -322,3 +323,39 @@ def check_data_integrity(data, min_pc_length=5):
                         )
                     )
     return data
+
+
+def check_weather_data(weather_data):
+    """
+    Check weather Data Frame.
+
+    - Raise warning if there are nan values.
+    - Convert columns if heights are string and not numeric.
+
+    Parameters
+    ----------
+    weather_data : pandas.DataFrame
+        A weather table with MultiIndex columns (name, data height)
+
+    Returns
+    -------
+    pandas.DataFrame : A valid weather table.
+
+    """
+    # Convert data heights to integer. In some case they are strings.
+    weather_data.columns = pd.MultiIndex.from_arrays(
+        [
+            weather_data.columns.get_level_values(0),
+            pd.to_numeric(weather_data.columns.get_level_values(1)),
+        ]
+    )
+
+    # check for nan values
+    if weather_data.isnull().any().any():
+        nan_columns = list(weather_data.columns[weather_data.isnull().any()])
+        msg = (
+            "The following columns of the weather data contain invalid "
+            "values like 'nan': {0}"
+        )
+        warnings.warn(msg.format(nan_columns), WindpowerlibUserWarning)
+    return weather_data

windpowerlib/tools.py

@@ -227,30 +227,3 @@ def estimate_turbulence_intensity(height, roughness_length):
 
     """
     return 1 / (np.log(height / roughness_length))
-
-
-def check_weather_data(weather_df):
-    """
-    Check weather Data Frame.
-
-    - Raise warning if there are nan values.
-    - Convert columns if heights are string and not numeric.
-
-    """
-    # Convert data heights to integer. In some case they are strings.
-    weather_df.columns = pd.MultiIndex.from_arrays(
-        [
-            weather_df.columns.get_level_values(0),
-            pd.to_numeric(weather_df.columns.get_level_values(1)),
-        ]
-    )
-
-    # check for nan values
-    if weather_df.isnull().any().any():
-        nan_columns = list(weather_df.columns[weather_df.isnull().any()])
-        msg = (
-            "The following columns of the weather data contain invalid "
-            "values like 'nan': {0}"
-        )
-        warnings.warn(msg.format(nan_columns), WindpowerlibUserWarning)
-    return weather_df