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gridded data example cleanup
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mjames-upc
parent
6728a7bffb
commit
4491987304
5 changed files with 74 additions and 135 deletions
1
.gitignore
vendored
1
.gitignore
vendored
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@ -1,3 +1,2 @@
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.ipynb_checkpoints
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docs/build/
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docs/source/examples/generated
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@ -37,8 +37,8 @@ sys.path.insert(0, os.path.abspath('../..'))
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.intersphinx',
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'sphinx.ext.viewcode'
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#'notebook_gen_sphinxext'
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'sphinx.ext.viewcode',
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'notebook_gen_sphinxext'
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]
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# Add any paths that contain templates here, relative to this directory.
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@ -31,6 +31,7 @@ EDEX Grid Inventory
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.. parsed-literal::
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AUTOSPE
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AVN211
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AVN225
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DGEX
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@ -47,15 +48,57 @@ EDEX Grid Inventory
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ECMF7
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ECMF8
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ECMF9
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ESTOFS
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ETA
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FFG-ALR
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FFG-FWR
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FFG-KRF
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FFG-MSR
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FFG-ORN
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FFG-RHA
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FFG-RSA
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FFG-TAR
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FFG-TIR
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FFG-TUA
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GFS
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GFS40
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GFSGuide
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GFSLAMP5
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GribModel:9:151:172
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HFR-EAST_6KM
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HFR-EAST_PR_6KM
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HFR-US_EAST_DELAWARE_1KM
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HFR-US_EAST_FLORIDA_2KM
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HFR-US_EAST_NORTH_2KM
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HFR-US_EAST_SOUTH_2KM
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HFR-US_EAST_VIRGINIA_1KM
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HFR-US_HAWAII_1KM
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HFR-US_HAWAII_2KM
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HFR-US_HAWAII_6KM
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HFR-US_WEST_500M
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HFR-US_WEST_CENCAL_2KM
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HFR-US_WEST_LOSANGELES_1KM
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HFR-US_WEST_LOSOSOS_1KM
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HFR-US_WEST_NORTH_2KM
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HFR-US_WEST_SANFRAN_1KM
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HFR-US_WEST_SOCAL_2KM
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HFR-US_WEST_WASHINGTON_1KM
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HFR-WEST_6KM
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HPCGuide
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HPCqpf
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HPCqpfNDFD
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HRRR
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LAMP2p5
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MPE-Local-ORN
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MPE-Local-RHA
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MPE-Local-RSA
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MPE-Local-TAR
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MPE-Local-TIR
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MPE-Mosaic-MSR
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MPE-Mosaic-ORN
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MPE-Mosaic-RHA
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MPE-Mosaic-TAR
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MPE-Mosaic-TIR
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MRMS_1000
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NAM12
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NAM40
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@ -63,14 +106,42 @@ EDEX Grid Inventory
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NOHRSC-SNOW
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NamDNG
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NamDNG5
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QPE-ALR
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QPE-Auto-TUA
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QPE-FWR
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QPE-KRF
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QPE-MSR
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QPE-RFC-RSA
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QPE-RFC-STR
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QPE-TIR
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QPE-TUA
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QPE-XNAV-ALR
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QPE-XNAV-FWR
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QPE-XNAV-KRF
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QPE-XNAV-MSR
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QPE-XNAV-RHA
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QPE-XNAV-SJU
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QPE-XNAV-TAR
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QPE-XNAV-TIR
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QPE-XNAV-TUA
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RAP13
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RAP40
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RCM
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RFCqpf
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RTMA
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RTMA5
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UKMET-Global
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UKMET37
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UKMET38
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UKMET39
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UKMET40
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UKMET41
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UKMET42
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UKMET43
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UKMET44
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URMA25
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estofsPR
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estofsUS
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fnmocWave
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**LocationNames** is different for different plugins - radar is icao -
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@ -262,134 +333,3 @@ Plotting a Grid with Cartopy
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.. image:: Gridded_Data_files/Gridded_Data_9_0.png
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.. code:: python
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import matplotlib.pyplot as plt
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import numpy as np
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from metpy.calc import get_wind_components
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from metpy.cbook import get_test_data
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from metpy.plots import StationPlot, StationPlotLayout, simple_layout
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from metpy.units import units
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# Initialize
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data,latitude,longitude,stationName,temperature,dewpoint,seaLevelPress,windDir,windSpeed = [],[],[],[],[],[],[],[],[]
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request = DataAccessLayer.newDataRequest()
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request.setDatatype("obs")
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#
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# we need to set one station to query latest time. this is hack-y and should be fixed
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# because when you DON'T set a location name, you tend to get a single observation
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# that came in a second ago, so your "latest data for the last time for all stations"
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# data array consists of one village in Peru and time-matching is suspect right now.
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#
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# So here take a known US station (OKC) and hope/assume that a lot of other stations
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# are also reporting (and that this is a 00/20/40 ob).
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#
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request.setLocationNames("KOKC")
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datatimes = DataAccessLayer.getAvailableTimes(request)
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# Get most recent time for location
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time = datatimes[-1].validPeriod
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# "presWeather","skyCover","skyLayerBase"
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# are multi-dimensional(??) and returned seperately (not sure why yet)... deal with those later
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request.setParameters("presWeather","skyCover", "skyLayerBase","stationName","temperature","dewpoint","windDir","windSpeed",
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"seaLevelPress","longitude","latitude")
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request.setLocationNames()
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response = DataAccessLayer.getGeometryData(request,times=time)
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print time
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PRES_PARAMS = set(["presWeather"])
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SKY_PARAMS = set(["skyCover", "skyLayerBase"])
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# Build ordered arrays
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wx,cvr,bas=[],[],[]
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for ob in response:
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#print ob.getParameters()
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if set(ob.getParameters()) & PRES_PARAMS :
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wx.append(ob.getString("presWeather"))
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continue
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if set(ob.getParameters()) & SKY_PARAMS :
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cvr.append(ob.getString("skyCover"))
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bas.append(ob.getNumber("skyLayerBase"))
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continue
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latitude.append(float(ob.getString("latitude")))
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longitude.append(float(ob.getString("longitude")))
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#stationName.append(ob.getString("stationName"))
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temperature.append(float(ob.getString("temperature")))
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dewpoint.append(float(ob.getString("dewpoint")))
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seaLevelPress.append(float(ob.getString("seaLevelPress")))
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windDir.append(float(ob.getString("windDir")))
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windSpeed.append(float(ob.getString("windSpeed")))
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print len(wx)
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print len(temperature)
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# Convert
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data = dict()
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data['latitude'] = np.array(latitude)
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data['longitude'] = np.array(longitude)
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data['air_temperature'] = np.array(temperature)* units.degC
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data['dew_point_temperature'] = np.array(dewpoint)* units.degC
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#data['air_pressure_at_sea_level'] = np.array(seaLevelPress)* units('mbar')
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u, v = get_wind_components(np.array(windSpeed) * units('knots'),
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np.array(windDir) * units.degree)
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data['eastward_wind'], data['northward_wind'] = u, v
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# Convert the fraction value into a code of 0-8, which can be used to pull out
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# the appropriate symbol
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#data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).astype(int)
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# Map weather strings to WMO codes, which we can use to convert to symbols
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# Only use the first symbol if there are multiple
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#wx_text = make_string_list(data_arr['weather'])
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#wx_codes = {'':0, 'HZ':5, 'BR':10, '-DZ':51, 'DZ':53, '+DZ':55,
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# '-RA':61, 'RA':63, '+RA':65, '-SN':71, 'SN':73, '+SN':75}
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#data['present_weather'] = [wx_codes[s.split()[0] if ' ' in s else s] for s in wx]
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# Set up the map projection
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import cartopy.crs as ccrs
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import cartopy.feature as feat
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from matplotlib import rcParams
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rcParams['savefig.dpi'] = 255
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proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
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standard_parallels=[35])
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state_boundaries = feat.NaturalEarthFeature(category='cultural',
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name='admin_1_states_provinces_lines',
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scale='110m', facecolor='none')
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# Create the figure
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fig = plt.figure(figsize=(20, 10))
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ax = fig.add_subplot(1, 1, 1, projection=proj)
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# Add map elements
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ax.add_feature(feat.LAND, zorder=-1)
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ax.add_feature(feat.OCEAN, zorder=-1)
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ax.add_feature(feat.LAKES, zorder=-1)
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ax.coastlines(resolution='110m', zorder=2, color='black')
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ax.add_feature(state_boundaries)
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ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
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ax.set_extent((-118, -73, 23, 50))
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# Start the station plot by specifying the axes to draw on, as well as the
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# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
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stationplot = StationPlot(ax, data['longitude'], data['latitude'],
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transform=ccrs.PlateCarree(), fontsize=12)
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# The layout knows where everything should go, and things are standardized using
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# the names of variables. So the layout pulls arrays out of `data` and plots them
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# using `stationplot`.
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simple_layout.plot(stationplot, data)
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.. parsed-literal::
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(Mar 15 16 22:52:00 , Mar 15 16 22:52:00 )
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430
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86
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.. image:: Gridded_Data_files/Gridded_Data_10_1.png
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