Meteostat 1.5.1 (#54)

* Meteostat 1.5.1

* Update README

* Update README

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/):

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()

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

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']

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']]

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']

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

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

setup.py

@@ -15,7 +15,7 @@
 # Setup
 setup(
  name='meteostat',
- version='1.5.0',
+ version='1.5.1',
  author='Meteostat',
  author_email='[email protected]',
  description='Access and analyze historical weather and climate data with Python.',