Meteostat 1.5.1 (#54)

clampr · web-flow · commit 7cc44592eb0c · 2021-06-29T00:08:59.000+02:00
* Meteostat 1.5.1

* Update README

* Update README
diff --git a/README.md b/README.md
@@ -4,6 +4,8 @@ The Meteostat Python library provides a simple API for accessing open weather an
 
 Among the data sources are national weather services like the National Oceanic and Atmospheric Administration (NOAA) and Germany's national meteorological service (DWD).
 
+Are you looking for a **hosted solution**? Try our [JSON API](https://rapidapi.com/meteostat/api/meteostat/).
+
 ## Installation
 
 The Meteostat Python package is available through [PyPI](https://pypi.org/project/meteostat/):
diff --git a/examples/daily/compare.py b/examples/daily/compare.py
@@ -10,28 +10,19 @@
 
 from datetime import datetime
 import matplotlib.pyplot as plt
-from meteostat import Stations, Daily
-
-# Get weather stations by WMO ID
-stations = Stations()
-stations = stations.id('wmo', ('71624', '72295', '68816', '94767'))
-stations = stations.fetch()
-
-# Get names of weather stations
-names = stations['name'].to_list()
+from meteostat import Daily
 
 # Time period
 start = datetime(2019, 1, 1)
 end = datetime(2019, 12, 31)
 
 # Get daily data
-data = Daily(stations, start, end)
+data = Daily(['71624', '72295', '68816', '94767'], start, end)
 data = data.fetch()
 
 # Plot chart
 data.unstack('station')['tavg'].plot(
     legend=True,
     ylabel='Avg. Daily Temperature °C',
     title='Average Temperature Report for 2019')
-plt.legend(names)
 plt.show()
diff --git a/meteostat/__init__.py b/meteostat/__init__.py
@@ -12,7 +12,7 @@
 """
 
 __appname__ = 'meteostat'
-__version__ = '1.5.0'
+__version__ = '1.5.1'
 
 from .interface.base import Base
 from .interface.timeseries import Timeseries
diff --git a/meteostat/interface/daily.py b/meteostat/interface/daily.py
@@ -170,9 +170,39 @@ def _resolve_point(
         if self._stations.size == 0 or self._data.size == 0:
             return None
 
+        def adjust_temp(data: pd.DataFrame):
+            """
+            Adjust temperature-like data based on altitude
+            """
+
+            data.loc[data['tavg'] != np.NaN, 'tavg'] = data['tavg'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+            data.loc[data['tmin'] != np.NaN, 'tmin'] = data['tmin'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+            data.loc[data['tmax'] != np.NaN, 'tmax'] = data['tmax'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+
+            return data
+
         if method == 'nearest':
 
-            self._data = self._data.groupby(
+            if adapt_temp:
+
+                # Join elevation of involved weather stations
+                data = self._data.join(
+                    stations['elevation'], on='station')
+
+                # Adapt temperature-like data based on altitude
+                data = adjust_temp(data)
+
+                # Drop elevation & round
+                data = data.drop('elevation', axis=1).round(1)
+
+            else:
+
+                data = self._data
+
+            self._data = data.groupby(
                 pd.Grouper(level='time', freq=self._freq)).agg('first')
 
         else:
@@ -183,12 +213,7 @@ def _resolve_point(
 
             # Adapt temperature-like data based on altitude
             if adapt_temp:
-                data.loc[data['tavg'] != np.NaN, 'tavg'] = data['tavg'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
-                data.loc[data['tmin'] != np.NaN, 'tmin'] = data['tmin'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
-                data.loc[data['tmax'] != np.NaN, 'tmax'] = data['tmax'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
+                data = adjust_temp(data)
 
             # Exclude non-mean data & perform aggregation
             excluded = data['wdir']
diff --git a/meteostat/interface/hourly.py b/meteostat/interface/hourly.py
@@ -238,9 +238,37 @@ def _resolve_point(
         if self._stations.size == 0 or self._data.size == 0:
             return None
 
+        def adjust_temp(data: pd.DataFrame):
+            """
+            Adjust temperature-like data based on altitude
+            """
+
+            data.loc[data['temp'] != np.NaN, 'temp'] = data['temp'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+            data.loc[data['dwpt'] != np.NaN, 'dwpt'] = data['dwpt'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+
+            return data
+
         if method == 'nearest':
 
-            self._data = self._data.groupby(pd.Grouper(
+            if adapt_temp:
+
+                # Join elevation of involved weather stations
+                data = self._data.join(
+                    stations['elevation'], on='station')
+
+                # Adapt temperature-like data based on altitude
+                data = adjust_temp(data)
+
+                # Drop elevation & round
+                data = data.drop('elevation', axis=1).round(1)
+
+            else:
+
+                data = self._data
+
+            self._data = data.groupby(pd.Grouper(
                 level='time', freq=self._freq)).agg('first')
 
         else:
@@ -251,10 +279,7 @@ def _resolve_point(
 
             # Adapt temperature-like data based on altitude
             if adapt_temp:
-                data.loc[data['temp'] != np.NaN, 'temp'] = data['temp'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
-                data.loc[data['dwpt'] != np.NaN, 'dwpt'] = data['dwpt'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
+                data = adjust_temp(data)
 
             # Exclude non-mean data & perform aggregation
             excluded = data[['wdir', 'coco']]
diff --git a/meteostat/interface/monthly.py b/meteostat/interface/monthly.py
@@ -170,8 +170,38 @@ def _resolve_point(
         if self._stations.size == 0 or self._data.size == 0:
             return None
 
+        def adjust_temp(data: pd.DataFrame):
+            """
+            Adjust temperature-like data based on altitude
+            """
+
+            data.loc[data['tavg'] != np.NaN, 'tavg'] = data['tavg'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+            data.loc[data['tmin'] != np.NaN, 'tmin'] = data['tmin'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+            data.loc[data['tmax'] != np.NaN, 'tmax'] = data['tmax'] + \
+                ((2 / 3) * ((data['elevation'] - alt) / 100))
+
+            return data
+
         if method == 'nearest':
 
+            if adapt_temp:
+
+                # Join elevation of involved weather stations
+                data = self._data.join(
+                    stations['elevation'], on='station')
+
+                # Adapt temperature-like data based on altitude
+                data = adjust_temp(data)
+
+                # Drop elevation & round
+                data = data.drop('elevation', axis=1).round(1)
+
+            else:
+
+                data = self._data
+
             self._data = self._data.groupby(
                 pd.Grouper(level='time', freq=self._freq)).agg('first')
 
@@ -183,12 +213,7 @@ def _resolve_point(
 
             # Adapt temperature-like data based on altitude
             if adapt_temp:
-                data.loc[data['tavg'] != np.NaN, 'tavg'] = data['tavg'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
-                data.loc[data['tmin'] != np.NaN, 'tmin'] = data['tmin'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
-                data.loc[data['tmax'] != np.NaN, 'tmax'] = data['tmax'] + \
-                    ((2 / 3) * ((data['elevation'] - alt) / 100))
+                data = adjust_temp(data)
 
             # Exclude non-mean data & perform aggregation
             excluded = data['wdir']
diff --git a/meteostat/interface/point.py b/meteostat/interface/point.py
@@ -20,7 +20,7 @@ class Point:
     """
 
     # The interpolation method (weighted or nearest)
-    method: str = 'weighted'
+    method: str = 'nearest'
 
     # Maximum radius for nearby stations
     radius: int = 35000
@@ -41,7 +41,7 @@ class Point:
     weight_alt: float = 0.4
 
     # The list of weather stations
-    stations: pd.Index = None
+    _stations: pd.Index = None
 
     # The latitude
     _lat: float = None
@@ -83,17 +83,19 @@ def get_stations(self, freq: str = None, start: datetime = None,
 
         # Captue unfiltered weather stations
         unfiltered = stations.fetch()
-        unfiltered = unfiltered[abs(self._alt -
-                                    unfiltered['elevation']) <= self.alt_range]
+        if self.alt_range:
+            unfiltered = unfiltered[abs(self._alt -
+                                        unfiltered['elevation']) <= self.alt_range]
 
         # Apply inventory filter
         if freq and start and end:
             stations = stations.inventory(freq, (start, end))
 
         # Apply altitude filter
         stations = stations.fetch()
-        stations = stations[abs(self._alt -
-                                stations['elevation']) <= self.alt_range]
+        if self.alt_range:
+            stations = stations[abs(self._alt -
+                                    stations['elevation']) <= self.alt_range]
 
         # Fill up stations
         selected: int = len(stations.index)
@@ -102,15 +104,18 @@ def get_stations(self, freq: str = None, start: datetime = None,
                 unfiltered.head(
                     self.max_count - selected))
 
-        # Calculate score values
-        stations['score'] = ((1 - (stations['distance'] / self.radius)) * self.weight_dist) + (
-            (1 - (abs(self._alt - stations['elevation']) / self.alt_range)) * self.weight_alt)
+        # Score values
+        if self.radius:
 
-        # Sort by score (descending)
-        stations = stations.sort_values('score', ascending=False)
+            # Calculate score values
+            stations['score'] = ((1 - (stations['distance'] / self.radius)) * self.weight_dist) + (
+                (1 - (abs(self._alt - stations['elevation']) / self.alt_range)) * self.weight_alt)
+
+            # Sort by score (descending)
+            stations = stations.sort_values('score', ascending=False)
 
         # Capture result
-        self.stations = stations.index[:self.max_count]
+        self._stations = stations.index[:self.max_count]
 
         return stations.head(self.max_count)
 
@@ -122,3 +127,12 @@ def alt(self) -> int:
 
         # Return altitude
         return self._alt
+
+    @property
+    def stations(self) -> pd.Index:
+        """
+        Returns the point's weather stations
+        """
+
+        # Return weather stations
+        return self._stations
diff --git a/meteostat/interface/stations.py b/meteostat/interface/stations.py
@@ -151,7 +151,7 @@ def distance(lat1, lon1, lat2, lon2):
             lat, lon, temp._data['latitude'], temp._data['longitude'])
 
         # Filter by radius
-        if radius is not None:
+        if radius:
             temp._data = temp._data[temp._data['distance'] <= radius]
 
         # Sort stations by distance
diff --git a/setup.py b/setup.py
@@ -15,7 +15,7 @@
 # Setup
 setup(
     name='meteostat',
-    version='1.5.0',
+    version='1.5.1',
     author='Meteostat',
     author_email='info@meteostat.net',
     description='Access and analyze historical weather and climate data with Python.',
