Meteostat 1.6.1 (#84)

* Meteostat 1.6.1

* Linting

* Change Monthly tests + examples

* Clean DF based on met cols only

examples/monthly/aggregate.py

@@ -17,9 +17,10 @@
 end = datetime(2018, 12, 31)
 
 # Get monthly data
-data = Monthly('10637', start, end)
+# Then, aggregate annually
+data = Monthly('72202', start, end)
 data = data.normalize().aggregate(freq='1Y').fetch()
 
 # Plot chart
-data.plot(y=['tavg', 'tmin', 'tmax'])
+data.plot(y='tavg')
 plt.show()

meteostat/__init__.py

@@ -15,7 +15,7 @@
 __version__ = '1.6.0'
 
 from .interface.base import Base
-from .interface.timeseries import Timeseries
+from .interface.timeseries import TimeSeries
 from .interface.stations import Stations
 from .interface.point import Point
 from .interface.hourly import Hourly

meteostat/interface/daily.py

@@ -13,11 +13,11 @@
 import pandas as pd
 from meteostat.enumerations.granularity import Granularity
 from meteostat.utilities.aggregations import degree_mean
-from meteostat.interface.timeseries import Timeseries
+from meteostat.interface.timeseries import TimeSeries
 from meteostat.interface.point import Point
 
 
-class Daily(Timeseries):
+class Daily(TimeSeries):
 
     """
     Retrieve daily weather observations for one or multiple weather stations or
@@ -33,6 +33,9 @@ class Daily(Timeseries):
     # Default frequency
     _freq: str = '1D'
 
+    # Flag which represents model data
+    _model_flag = 'G'
+
     # Columns
     _columns: list = [
         'date',
@@ -94,7 +97,7 @@ def __init__(
     ) -> None:
 
         # Initialize time series
-        self._init_timeseries(loc, start, end, model, flags)
+        self._init_time_series(loc, start, end, model, flags)
 
     def expected_rows(self) -> int:
         """

meteostat/interface/hourly.py

@@ -15,11 +15,11 @@
 import pandas as pd
 from meteostat.enumerations.granularity import Granularity
 from meteostat.utilities.aggregations import degree_mean
-from meteostat.interface.timeseries import Timeseries
+from meteostat.interface.timeseries import TimeSeries
 from meteostat.interface.point import Point
 
 
-class Hourly(Timeseries):
+class Hourly(TimeSeries):
 
     """
     Retrieve hourly weather observations for one or multiple weather stations or
@@ -41,6 +41,9 @@ class Hourly(Timeseries):
     # Default frequency
     _freq: str = '1H'
 
+    # Flag which represents model data
+    _model_flag = 'E'
+
     # Raw data columns
     _columns: list = [
         'date',
@@ -155,7 +158,7 @@ def __init__(
         self._set_time(start, end, timezone)
 
         # Initialize time series
-        self._init_timeseries(loc, start, end, model, flags)
+        self._init_time_series(loc, start, end, model, flags)
 
     def expected_rows(self) -> int:
         """

meteostat/interface/meteo.py → meteostat/interface/meteodata.py

@@ -11,20 +11,19 @@
 The code is licensed under the MIT license.
 """
 
-from datetime import datetime
 from typing import Union
-import numpy as np
 import pandas as pd
 from meteostat.enumerations.granularity import Granularity
 from meteostat.core.cache import get_local_file_path, file_in_cache
 from meteostat.core.loader import processing_handler, load_handler
+from meteostat.utilities.mutations import localize, filter_time, adjust_temp
 from meteostat.utilities.validations import validate_series
 from meteostat.utilities.aggregations import weighted_average
 from meteostat.utilities.endpoint import generate_endpoint_path
 from meteostat.interface.base import Base
 
 
-class Meteo(Base):
+class MeteoData(Base):
 
     """
     A parent class for both time series and
@@ -37,33 +36,6 @@ class Meteo(Base):
     # The data frame
     _data: pd.DataFrame = pd.DataFrame()
 
-    @staticmethod
-    def _localize(df: pd.DataFrame, timezone: str) -> pd.DataFrame:
-        """
-        Convert time data to any time zone
-        """
-
-        return df.tz_localize(
-            'UTC',
-            level='time'
-        ).tz_convert(
-            timezone,
-            level='time'
-        )
-
-    @staticmethod
-    def _filter_time(df: pd.DataFrame, start: datetime,
-                     end: datetime) -> pd.DataFrame:
-        """
-        Filter time series data based on start and end date
-        """
-
-        # Get time index
-        time = df.index.get_level_values('time')
-
-        # Filter & return
-        return df.loc[(time >= start) & (time <= end)]
-
     def _load_data(
         self,
         station: str,
@@ -118,7 +90,7 @@ def _load_data(
         # Localize time column
         if self.granularity == Granularity.HOURLY and self._timezone is not None and len(
                 df.index) > 0:
-            df = Meteo._localize(df, self._timezone)
+            df = localize(df, self._timezone)
 
         # Filter time period and append to DataFrame
         # pylint: disable=no-else-return
@@ -128,7 +100,7 @@ def _load_data(
             # Filter & return
             return df.loc[end == self._end]
         elif not self.granularity == Granularity.NORMALS:
-            df = Meteo._filter_time(df, self._start, self._end)
+            df = filter_time(df, self._start, self._end)
 
         # Return
         return df
@@ -167,23 +139,6 @@ def _get_data(self) -> None:
         # Empty DataFrame
         return pd.DataFrame(columns=[*self._types])
 
-    @staticmethod
-    def adjust_temp(df: pd.DataFrame, alt: int):
-        """
-        Adjust temperature-like data based on altitude
-        """
-
-        # Temperature-like columns
-        temp_like = ('temp', 'dwpt', 'tavg', 'tmin', 'tmax')
-
-        # Adjust values for all temperature-like data
-        for col_name in temp_like:
-            if col_name in df.columns:
-                df.loc[df[col_name] != np.NaN, col_name] = df[col_name] + \
-                    ((2 / 3) * ((df['elevation'] - alt) / 100))
-
-        return df
-
     # pylint: disable=too-many-branches
     def _resolve_point(
         self,
@@ -208,7 +163,7 @@ def _resolve_point(
                     stations['elevation'], on='station')
 
                 # Adapt temperature-like data based on altitude
-                data = Meteo.adjust_temp(data, alt)
+                data = adjust_temp(data, alt)
 
                 # Drop elevation & round
                 data = data.drop('elevation', axis=1).round(1)
@@ -236,7 +191,7 @@ def _resolve_point(
 
             # Adapt temperature-like data based on altitude
             if adapt_temp:
-                data = Meteo.adjust_temp(data, alt)
+                data = adjust_temp(data, alt)
 
             # Exclude non-mean data & perform aggregation
             if not self.granularity == Granularity.NORMALS:

meteostat/interface/monthly.py

@@ -11,13 +11,12 @@
 from datetime import datetime
 from typing import Union
 import pandas as pd
-from meteostat.utilities.aggregations import degree_mean
 from meteostat.enumerations.granularity import Granularity
-from meteostat.interface.timeseries import Timeseries
+from meteostat.interface.timeseries import TimeSeries
 from meteostat.interface.point import Point
 
 
-class Monthly(Timeseries):
+class Monthly(TimeSeries):
 
     """
     Retrieve monthly weather data for one or multiple weather stations or
@@ -33,6 +32,9 @@ class Monthly(Timeseries):
     # Default frequency
     _freq: str = '1MS'
 
+    # Flag which represents model data
+    _model_flag = 'I'
+
     # Columns
     _columns: list = [
         'year',
@@ -41,10 +43,7 @@ class Monthly(Timeseries):
         'tmin',
         'tmax',
         'prcp',
-        'snow',
-        'wdir',
         'wspd',
-        'wpgt',
         'pres',
         'tsun'
     ]
@@ -58,10 +57,7 @@ class Monthly(Timeseries):
         'tmin': 'float64',
         'tmax': 'float64',
         'prcp': 'float64',
-        'snow': 'float64',
-        'wdir': 'float64',
         'wspd': 'float64',
-        'wpgt': 'float64',
         'pres': 'float64',
         'tsun': 'float64'
     }
@@ -77,10 +73,7 @@ class Monthly(Timeseries):
         'tmin': 'mean',
         'tmax': 'mean',
         'prcp': 'sum',
-        'snow': 'max',
-        'wdir': degree_mean,
         'wspd': 'mean',
-        'wpgt': 'max',
         'pres': 'mean',
         'tsun': 'sum'
     }
@@ -99,7 +92,7 @@ def __init__(
             start = start.replace(day=1)
 
         # Initialize time series
-        self._init_timeseries(loc, start, end, model, flags)
+        self._init_time_series(loc, start, end, model, flags)
 
     def expected_rows(self) -> int:
         """

meteostat/interface/normals.py

@@ -15,11 +15,11 @@
 import pandas as pd
 from meteostat.enumerations.granularity import Granularity
 from meteostat.core.warn import warn
-from meteostat.interface.meteo import Meteo
+from meteostat.interface.meteodata import MeteoData
 from meteostat.interface.point import Point
 
 
-class Normals(Meteo):
+class Normals(MeteoData):
 
     """
     Retrieve climate normals for one or multiple weather stations or

meteostat/interface/timeseries.py

@@ -1,5 +1,5 @@
 """
-Timeseries Class
+TimeSeries Class
 
 Meteorological data provided by Meteostat (https://dev.meteostat.net)
 under the terms of the Creative Commons Attribution-NonCommercial
@@ -15,16 +15,17 @@
 from meteostat.enumerations.granularity import Granularity
 from meteostat.core.cache import get_local_file_path, file_in_cache
 from meteostat.core.loader import processing_handler, load_handler
+from meteostat.utilities.mutations import localize, filter_time
 from meteostat.utilities.validations import validate_series
 from meteostat.utilities.endpoint import generate_endpoint_path
 from meteostat.interface.point import Point
-from meteostat.interface.meteo import Meteo
+from meteostat.interface.meteodata import MeteoData
 
 
-class Timeseries(Meteo):
+class TimeSeries(MeteoData):
 
     """
-    Timeseries class which provides features which are
+    TimeSeries class which provides features which are
     used across all time series classes
     """
 
@@ -76,7 +77,9 @@ def _load_flags(
                 self.endpoint,
                 file,
                 self._columns,
-                None,
+                {
+                    key: 'string' for key in self._columns[self._first_met_col:]
+                },
                 self._parse_dates)
 
             # Validate Series
@@ -89,11 +92,11 @@ def _load_flags(
         # Localize time column
         if self.granularity == Granularity.HOURLY and self._timezone is not None and len(
                 df.index) > 0:
-            df = Timeseries._localize(df, self._timezone)
+            df = localize(df, self._timezone)
 
         # Filter time period and append to DataFrame
         if self._start and self._end:
-            df = Timeseries._filter_time(df, self._start, self._end)
+            df = filter_time(df, self._start, self._end)
 
         return df
 
@@ -126,8 +129,11 @@ def _filter_model(self) -> None:
         columns = self._columns[self._first_met_col:]
 
         for col_name in columns:
-            self._data.loc[self._data[f'{col_name}_flag']
-                           == 'M', col_name] = np.NaN
+            self._data.loc[
+                (pd.isna(self._data[f'{col_name}_flag'])) |
+                (self._data[f'{col_name}_flag'].str.contains(self._model_flag)),
+                col_name
+            ] = np.NaN
 
         # Conditionally, remove flags from DataFrame
         if not self._flags:
@@ -137,9 +143,9 @@ def _filter_model(self) -> None:
                 inplace=True)
 
         # Drop NaN-only rows
-        self._data.dropna(how='all', inplace=True)
+        self._data.dropna(how='all', subset=columns, inplace=True)
 
-    def _init_timeseries(
+    def _init_time_series(
         self,
         loc: Union[pd.DataFrame, Point, list, str],  # Station(s) or geo point
         start: datetime = None,