Merge pull request #147 from jrleeman/Upperair_Data

Move upper air data capability into Siphon

environment.yml

@@ -31,3 +31,4 @@ dependencies:
   - matplotlib
   - xarray
   - cartopy
+  - beautifulsoup4

setup.py

@@ -14,7 +14,7 @@
 ver = versioneer.get_version()
 
 # Need to conditionally add enum support for older Python
-dependencies = ['numpy>=1.8', 'protobuf>=3.0.0a3', 'requests>=1.2']
+dependencies = ['numpy>=1.8', 'protobuf>=3.0.0a3', 'requests>=1.2', 'beautifulsoup4>=4.6']
 if sys.version_info < (3, 4):
     dependencies.append('enum34')
 

siphon/__init__.py

@@ -8,4 +8,4 @@
 __version__ = get_versions()['version']
 del get_versions
 
-__all__ = ['catalog', 'testing', 'http_util']
+__all__ = ['catalog', 'testing', 'http_util', 'upperair']

siphon/_tools.py

@@ -0,0 +1,29 @@
+# Copyright (c) 2013-2015 University Corporation for Atmospheric Research/Unidata.
+# Distributed under the terms of the MIT License.
+# SPDX-License-Identifier: MIT
+"""Tools for data conversion and presentation."""
+
+import numpy as np
+
+
+def get_wind_components(speed, wdir):
+    r"""Calculate the U, V wind vector components from the speed and direction.
+
+    Parameters
+    ----------
+    speed : array_like
+        The wind speed (magnitude)
+    wdir : array_like
+        The wind direction, specified as the direction from which the wind is
+        blowing, with 0 being North.
+
+    Returns
+    -------
+    u, v : tuple of array_like
+        The wind components in the X (East-West) and Y (North-South)
+        directions, respectively.
+
+    """
+    u = -speed * np.sin(wdir)
+    v = -speed * np.cos(wdir)
+    return u, v

siphon/simplewebservice/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) 2013-2015 University Corporation for Atmospheric Research/Unidata.
+# Distributed under the terms of the MIT License.
+# SPDX-License-Identifier: MIT
+"""Contains  functionality for retreiving simple web sevice data."""

siphon/simplewebservice/wyoming.py

@@ -0,0 +1,118 @@
+# Copyright (c) 2013-2015 University Corporation for Atmospheric Research/Unidata.
+# Distributed under the terms of the MIT License.
+# SPDX-License-Identifier: MIT
+"""Read upper air data from the Wyoming archives."""
+
+from io import StringIO
+
+from bs4 import BeautifulSoup
+import numpy as np
+import pandas as pd
+from .._tools import get_wind_components
+from ..http_util import HTTPEndPoint
+
+
+class WyomingUpperAir(HTTPEndPoint):
+    """Download and parse data from the University of Wyoming's upper air archive."""
+
+    def __init__(self):
+        """Set up endpoint."""
+        super(WyomingUpperAir, self).__init__('http://weather.uwyo.edu/cgi-bin/sounding')
+
+    @classmethod
+    def request_data(cls, time, site_id, **kwargs):
+        r"""Retrieve upper air observations from the Wyoming archive.
+
+        Parameters
+        ----------
+        time : datetime
+            The date and time of the desired observation.
+
+        site_id : str
+            The three letter ICAO identifier of the station for which data should be
+            downloaded.
+
+        kwargs
+            Arbitrary keyword arguments to use to initialize source
+
+        Returns
+        -------
+            :class:`pandas.DataFrame` containing the data
+
+        """
+        endpoint = cls()
+        df = endpoint._get_data(time, site_id, **kwargs)
+        return df
+
+    def _get_data(self, time, site_id, region='naconf'):
+        r"""Download and parse upper air observations from an online archive.
+
+        Parameters
+        ----------
+        time : datetime
+            The date and time of the desired observation.
+
+        site_id : str
+            The three letter ICAO identifier of the station for which data should be
+            downloaded.
+
+        region
+            Region to request data from
+
+        Returns
+        -------
+            :class:`pandas.DataFrame` containing the data
+
+        """
+        raw_data = self._get_data_raw(time, site_id, region)
+        col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
+        df = pd.read_fwf(raw_data, skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
+        df['u_wind'], df['v_wind'] = get_wind_components(df['speed'],
+                                                         np.deg2rad(df['direction']))
+
+        # Drop any rows with all NaN values for T, Td, winds
+        df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
+                               'u_wind', 'v_wind'), how='all').reset_index(drop=True)
+
+        # Add unit dictionary
+        df.units = {'pressure': 'hPa',
+                    'height': 'meter',
+                    'temperature': 'degC',
+                    'dewpoint': 'degC',
+                    'direction': 'degrees',
+                    'speed': 'knot',
+                    'u_wind': 'knot',
+                    'v_wind': 'knot'}
+        return df
+
+    def _get_data_raw(self, time, site_id, region='naconf'):
+        """Download data from the University of Wyoming's upper air archive.
+
+        Parameters
+        ----------
+        time : datetime
+            Date and time for which data should be downloaded
+        site_id : str
+            Site id for which data should be downloaded
+        region : str
+            The region in which the station resides. Defaults to `naconf`.
+
+        Returns
+        -------
+        a file-like object from which to read the data
+
+        """
+        path = ('?region={region}&TYPE=TEXT%3ALIST'
+                '&YEAR={time:%Y}&MONTH={time:%m}&FROM={time:%d%H}&TO={time:%d%H}'
+                '&STNM={stid}').format(region=region, time=time, stid=site_id)
+
+        resp = self.get_path(path)
+        # See if the return is valid, but has no data
+        if resp.text.find('Can\'t') != -1:
+            raise ValueError(
+                'No data available for {time:%Y-%m-%d %HZ} from region {region} '
+                'for station {stid}.'.format(time=time, region=region,
+                                             stid=site_id))
+
+        soup = BeautifulSoup(resp.text, 'html.parser')
+        return StringIO(soup.find_all('pre')[0].contents[0])