gridded data example cleanup

.gitignore

@@ -1,3 +1,2 @@
 .ipynb_checkpoints
 docs/build/
-docs/source/examples/generated

docs/source/conf.py

@@ -37,8 +37,8 @@ sys.path.insert(0, os.path.abspath('../..'))
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.intersphinx',
-    'sphinx.ext.viewcode'
+    'sphinx.ext.viewcode',
-    #'notebook_gen_sphinxext'
+    'notebook_gen_sphinxext'
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docs/source/examples/generated/Gridded_Data.rst

@@ -31,6 +31,7 @@ EDEX Grid Inventory
 
 .. parsed-literal::
 
+    AUTOSPE
     AVN211
     AVN225
     DGEX
@@ -47,15 +48,57 @@ EDEX Grid Inventory
     ECMF7
     ECMF8
     ECMF9
+    ESTOFS
     ETA
+    FFG-ALR
+    FFG-FWR
+    FFG-KRF
+    FFG-MSR
+    FFG-ORN
+    FFG-RHA
+    FFG-RSA
+    FFG-TAR
+    FFG-TIR
+    FFG-TUA
     GFS
     GFS40
     GFSGuide
     GFSLAMP5
+    GribModel:9:151:172
+    HFR-EAST_6KM
+    HFR-EAST_PR_6KM
+    HFR-US_EAST_DELAWARE_1KM
+    HFR-US_EAST_FLORIDA_2KM
+    HFR-US_EAST_NORTH_2KM
+    HFR-US_EAST_SOUTH_2KM
+    HFR-US_EAST_VIRGINIA_1KM
+    HFR-US_HAWAII_1KM
+    HFR-US_HAWAII_2KM
+    HFR-US_HAWAII_6KM
+    HFR-US_WEST_500M
+    HFR-US_WEST_CENCAL_2KM
+    HFR-US_WEST_LOSANGELES_1KM
+    HFR-US_WEST_LOSOSOS_1KM
+    HFR-US_WEST_NORTH_2KM
+    HFR-US_WEST_SANFRAN_1KM
+    HFR-US_WEST_SOCAL_2KM
+    HFR-US_WEST_WASHINGTON_1KM
+    HFR-WEST_6KM
     HPCGuide
+    HPCqpf
     HPCqpfNDFD
     HRRR
     LAMP2p5
+    MPE-Local-ORN
+    MPE-Local-RHA
+    MPE-Local-RSA
+    MPE-Local-TAR
+    MPE-Local-TIR
+    MPE-Mosaic-MSR
+    MPE-Mosaic-ORN
+    MPE-Mosaic-RHA
+    MPE-Mosaic-TAR
+    MPE-Mosaic-TIR
     MRMS_1000
     NAM12
     NAM40
@@ -63,14 +106,42 @@ EDEX Grid Inventory
     NOHRSC-SNOW
     NamDNG
     NamDNG5
+    QPE-ALR
+    QPE-Auto-TUA
+    QPE-FWR
+    QPE-KRF
     QPE-MSR
+    QPE-RFC-RSA
+    QPE-RFC-STR
+    QPE-TIR
+    QPE-TUA
+    QPE-XNAV-ALR
+    QPE-XNAV-FWR
+    QPE-XNAV-KRF
+    QPE-XNAV-MSR
+    QPE-XNAV-RHA
+    QPE-XNAV-SJU
+    QPE-XNAV-TAR
+    QPE-XNAV-TIR
+    QPE-XNAV-TUA
     RAP13
     RAP40
+    RCM
+    RFCqpf
     RTMA
     RTMA5
+    UKMET-Global
+    UKMET37
+    UKMET38
+    UKMET39
+    UKMET40
+    UKMET41
+    UKMET42
+    UKMET43
+    UKMET44
     URMA25
     estofsPR
-    estofsUS
+    fnmocWave
 
 
 **LocationNames** is different for different plugins - radar is icao -
@@ -262,134 +333,3 @@ Plotting a Grid with Cartopy
 .. image:: Gridded_Data_files/Gridded_Data_9_0.png
 
 
-.. code:: python
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    
-    from metpy.calc import get_wind_components
-    from metpy.cbook import get_test_data
-    from metpy.plots import StationPlot, StationPlotLayout, simple_layout
-    from metpy.units import units
-    
-    # Initialize
-    data,latitude,longitude,stationName,temperature,dewpoint,seaLevelPress,windDir,windSpeed = [],[],[],[],[],[],[],[],[]
-    request = DataAccessLayer.newDataRequest()
-    request.setDatatype("obs")
-    
-    #
-    # we need to set one station to query latest time.  this is hack-y and should be fixed
-    # because when you DON'T set a location name, you tend to get a single observation
-    # that came in a second ago, so your "latest data for the last time for all stations"
-    # data array consists of one village in Peru and time-matching is suspect right now.
-    #
-    # So here take a known US station (OKC) and hope/assume that a lot of other stations 
-    # are also reporting (and that this is a 00/20/40 ob). 
-    #
-    request.setLocationNames("KOKC")
-    datatimes = DataAccessLayer.getAvailableTimes(request)
-    
-    # Get most recent time for location
-    time = datatimes[-1].validPeriod
-    
-    # "presWeather","skyCover","skyLayerBase"
-    # are multi-dimensional(??) and returned seperately (not sure why yet)... deal with those later
-    request.setParameters("presWeather","skyCover", "skyLayerBase","stationName","temperature","dewpoint","windDir","windSpeed",
-                          "seaLevelPress","longitude","latitude")
-    request.setLocationNames()
-    response = DataAccessLayer.getGeometryData(request,times=time)
-    print time
-    PRES_PARAMS = set(["presWeather"])
-    SKY_PARAMS = set(["skyCover", "skyLayerBase"])
-    # Build ordered arrays
-    wx,cvr,bas=[],[],[]
-    for ob in response:
-        #print ob.getParameters()
-        if set(ob.getParameters()) & PRES_PARAMS :
-            wx.append(ob.getString("presWeather"))
-            continue
-        if set(ob.getParameters()) & SKY_PARAMS :
-            cvr.append(ob.getString("skyCover"))
-            bas.append(ob.getNumber("skyLayerBase"))
-            continue
-        latitude.append(float(ob.getString("latitude")))
-        longitude.append(float(ob.getString("longitude")))
-        #stationName.append(ob.getString("stationName"))
-        temperature.append(float(ob.getString("temperature")))
-        dewpoint.append(float(ob.getString("dewpoint")))
-        seaLevelPress.append(float(ob.getString("seaLevelPress")))
-        windDir.append(float(ob.getString("windDir")))
-        windSpeed.append(float(ob.getString("windSpeed")))
-        
-        
-    print len(wx)
-    print len(temperature)
-    
-    
-    # Convert
-    data = dict()
-    data['latitude']  = np.array(latitude)
-    data['longitude'] = np.array(longitude)
-    data['air_temperature'] = np.array(temperature)* units.degC
-    data['dew_point_temperature'] = np.array(dewpoint)* units.degC
-    #data['air_pressure_at_sea_level'] = np.array(seaLevelPress)* units('mbar')
-    u, v = get_wind_components(np.array(windSpeed) * units('knots'),
-                               np.array(windDir) * units.degree)
-    data['eastward_wind'], data['northward_wind'] = u, v
-    
-    # Convert the fraction value into a code of 0-8, which can be used to pull out
-    # the appropriate symbol
-    #data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).astype(int)
-    
-    # Map weather strings to WMO codes, which we can use to convert to symbols
-    # Only use the first symbol if there are multiple
-    #wx_text = make_string_list(data_arr['weather'])
-    #wx_codes = {'':0, 'HZ':5, 'BR':10, '-DZ':51, 'DZ':53, '+DZ':55,
-    #            '-RA':61, 'RA':63, '+RA':65, '-SN':71, 'SN':73, '+SN':75}
-    #data['present_weather'] = [wx_codes[s.split()[0] if ' ' in s else s] for s in wx]
-    
-    # Set up the map projection
-    import cartopy.crs as ccrs
-    import cartopy.feature as feat
-    from matplotlib import rcParams
-    rcParams['savefig.dpi'] = 255
-    proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
-                                 standard_parallels=[35])
-    state_boundaries = feat.NaturalEarthFeature(category='cultural',
-                                                name='admin_1_states_provinces_lines',
-                                                scale='110m', facecolor='none')
-    # Create the figure
-    fig = plt.figure(figsize=(20, 10))
-    ax = fig.add_subplot(1, 1, 1, projection=proj)
-    
-    # Add map elements 
-    ax.add_feature(feat.LAND, zorder=-1)
-    ax.add_feature(feat.OCEAN, zorder=-1)
-    ax.add_feature(feat.LAKES, zorder=-1)
-    ax.coastlines(resolution='110m', zorder=2, color='black')
-    ax.add_feature(state_boundaries)
-    ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
-    ax.set_extent((-118, -73, 23, 50))
-    
-    # Start the station plot by specifying the axes to draw on, as well as the
-    # lon/lat of the stations (with transform). We also the fontsize to 12 pt.
-    stationplot = StationPlot(ax, data['longitude'], data['latitude'],
-                              transform=ccrs.PlateCarree(), fontsize=12)
-    
-    # The layout knows where everything should go, and things are standardized using
-    # the names of variables. So the layout pulls arrays out of `data` and plots them
-    # using `stationplot`.
-    simple_layout.plot(stationplot, data)
-
-
-.. parsed-literal::
-
-    (Mar 15 16 22:52:00 , Mar 15 16 22:52:00 )
-    430
-    86
-
-
-
-.. image:: Gridded_Data_files/Gridded_Data_10_1.png
-
-