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underscore needed in file names
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mjames-upc
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23 changed files with 1211 additions and 1 deletions
3
docs/source/examples/_gen/Dev_Guide.rst
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3
docs/source/examples/_gen/Dev_Guide.rst
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=========
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Dev Guide
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=========
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@ -11,7 +11,7 @@ List Available Parameters for a Grid Name
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from awips.dataaccess import DataAccessLayer
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from awips.dataaccess import DataAccessLayer
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# Select HRRR
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# Select HRRR
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
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DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
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request = DataAccessLayer.newDataRequest()
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request = DataAccessLayer.newDataRequest()
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request.setDatatype("grid")
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request.setDatatype("grid")
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request.setLocationNames("GFS40")
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request.setLocationNames("GFS40")
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Before Width: | Height: | Size: 112 KiB After Width: | Height: | Size: 112 KiB |
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Before Width: | Height: | Size: 84 KiB After Width: | Height: | Size: 84 KiB |
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Before Width: | Height: | Size: 105 KiB After Width: | Height: | Size: 105 KiB |
335
docs/source/examples/_gen/Gridded_Data.rst
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docs/source/examples/_gen/Gridded_Data.rst
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============
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Gridded Data
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============
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Gridded_Data.ipynb>`_
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EDEX Grid Inventory
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-------------------
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.. code:: python
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from awips.dataaccess import DataAccessLayer
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# Set host
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
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# Init data request
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request = DataAccessLayer.newDataRequest()
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# Set datatype
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request.setDatatype("grid")
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# Get a list of all available models
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available_grids = DataAccessLayer.getAvailableLocationNames(request)
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# Sort
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available_grids.sort()
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for grid in available_grids:
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print grid
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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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ECMF-Global
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ECMF1
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ECMF10
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ECMF11
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ECMF12
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ECMF2
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ECMF3
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ECMF4
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ECMF5
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ECMF6
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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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NCWF
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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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fnmocWave
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**LocationNames** is different for different plugins - radar is icao -
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satellite is sector
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Requesting a Grid
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-----------------
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.. code:: python
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# Grid request
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request.setLocationNames('RAP40')
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request.setParameters("RH")
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request.setLevels("850MB")
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# Get available times
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t = DataAccessLayer.getAvailableTimes(request)
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# Select last available time [-1]
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response = DataAccessLayer.getGridData(request, [t[0]])
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data = response[0]
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lon,lat = data.getLatLonCoords()
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# Print info
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print 'Time :', t[-1]
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print 'Model:', data.getLocationName()
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print 'Unit :', data.getUnit()
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print 'Parm :', data.getParameter()
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# Print data array
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print data.getRawData().shape
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print data.getRawData()
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print "lat array =", lat
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print "lon array =", lon
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.. parsed-literal::
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Time : 2016-02-23 15:00:00 (12)
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Model: RAP40
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Unit : %
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Parm : RH
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(151, 113)
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[[ 93.05456543 93.05456543 87.05456543 ..., 73.05456543 72.05456543
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71.05456543]
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[ 70.05456543 70.05456543 67.05456543 ..., 69.05456543 46.05456924
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37.05456924]
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[ 40.05456924 56.05456924 68.05456543 ..., 51.05456924 73.05456543
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74.05456543]
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...,
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[ 65.05456543 62.05456924 63.05456924 ..., 67.05456543 65.05456543
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46.05456924]
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[ 48.05456924 59.05456924 62.05456924 ..., 4.05456877 5.05456877
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5.05456877]
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[ 7.05456877 8.05456829 10.05456829 ..., 91.05456543 95.05456543
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95.05456543]]
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lat array = [[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193
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57.88272858]
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[ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542
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58.09931183]
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[ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973
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56.51980972]
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...,
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[ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152
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20.07156372]
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[ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174
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18.67854691]
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[ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557
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17.36001205]]
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lon array = [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486
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-78.70166016 -78.14326477]
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[ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623
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-100.13801575 -99.57427216]
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[ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039
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-121.15060425 -120.60871887]
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...,
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[ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049
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-98.13802338 -97.75105286]
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[ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487
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-112.69831085 -112.31866455]
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[-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197
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-69.48160553 -69.10926819]]
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Plotting a Grid with Basemap
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----------------------------
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Using **matplotlib**, **numpy**, and **basemap**:
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.. code:: python
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import matplotlib.tri as mtri
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import matplotlib.pyplot as plt
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from matplotlib.transforms import offset_copy
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from mpl_toolkits.basemap import Basemap, cm
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import numpy as np
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from numpy import linspace, transpose
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from numpy import meshgrid
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plt.figure(figsize=(12, 12), dpi=100)
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lons,lats = data.getLatLonCoords()
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map = Basemap(projection='cyl',
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resolution = 'c',
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llcrnrlon = lons.min(), llcrnrlat = lats.min(),
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urcrnrlon =lons.max(), urcrnrlat = lats.max()
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)
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map.drawcoastlines()
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map.drawstates()
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map.drawcountries()
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#
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# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m
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#
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x = linspace(0, map.urcrnrx, data.getRawData().shape[1])
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y = linspace(0, map.urcrnry, data.getRawData().shape[0])
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xx, yy = meshgrid(x, y)
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ngrid = len(x)
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rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),
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ngrid).reshape(ngrid, ngrid)
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rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),
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ngrid).reshape(ngrid, ngrid).T
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tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),
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lats.flatten()), data.getRawData().flatten())
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rdata = tli(rlons, rlats)
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cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
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# add colorbar.
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cbar = map.colorbar(cs,location='bottom',pad="5%")
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cbar.set_label(data.getParameter() + data.getUnit() )
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# Show plot
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plt.show()
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.. image:: Gridded_Data_files/Gridded_Data_5_0.png
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or use **pcolormesh** rather than **contourf**
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.. code:: python
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plt.figure(figsize=(12, 12), dpi=100)
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map = Basemap(projection='cyl',
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resolution = 'c',
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llcrnrlon = lons.min(), llcrnrlat = lats.min(),
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urcrnrlon =lons.max(), urcrnrlat = lats.max()
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)
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map.drawcoastlines()
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map.drawstates()
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map.drawcountries()
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cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
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.. image:: Gridded_Data_files/Gridded_Data_7_0.png
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Plotting a Grid with Cartopy
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----------------------------
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.. code:: python
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import iris
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import cartopy.crs as ccrs
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from cartopy import config
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lon,lat = data.getLatLonCoords()
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plt.figure(figsize=(12, 12), dpi=100)
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ax = plt.axes(projection=ccrs.PlateCarree())
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cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')
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ax.coastlines()
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ax.gridlines()
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# add colorbar
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cbar = plt.colorbar(orientation='horizontal')
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cbar.set_label(data.getParameter() + data.getUnit() )
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plt.show()
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.. image:: Gridded_Data_files/Gridded_Data_9_0.png
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After Width: | Height: | Size: 446 KiB |
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After Width: | Height: | Size: 395 KiB |
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After Width: | Height: | Size: 634 KiB |
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@ -0,0 +1,112 @@
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===================================
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NEXRAD Level 3 Plot with Matplotlib
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||||||
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===================================
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb>`_
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NEXRAD Level 3 Plot with Matplotlib
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||||||
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===================================
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||||||
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.. code:: python
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|
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%matplotlib inline
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from awips.dataaccess import DataAccessLayer
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||||||
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from awips import ThriftClient, RadarCommon
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from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange
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from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.radar.request import GetRadarDataRecordRequest
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||||||
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from datetime import datetime
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from datetime import timedelta
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||||||
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||||||
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import matplotlib.pyplot as plt
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||||||
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import numpy as np
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from numpy import ma
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||||||
|
|
||||||
|
# use metpy for color table
|
||||||
|
from metpy.plots import ctables
|
||||||
|
|
||||||
|
# Set EDEX server and radar site
|
||||||
|
DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
request.setDatatype("radar")
|
||||||
|
request.setLocationNames("klzk")
|
||||||
|
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
|
||||||
|
# Get last available time
|
||||||
|
timerange = datatimes[-1].validPeriod
|
||||||
|
dateTimeStr = str(datatimes[-1])
|
||||||
|
|
||||||
|
# Buffer length in seconds
|
||||||
|
buffer = 60
|
||||||
|
dateTime = datetime.strptime(dateTimeStr, "%Y-%m-%d %H:%M:%S")
|
||||||
|
beginRange = dateTime - timedelta(0, buffer)
|
||||||
|
endRange = dateTime + timedelta(0, buffer)
|
||||||
|
timerange = TimeRange(beginRange, endRange)
|
||||||
|
|
||||||
|
print "using time",dateTimeStr
|
||||||
|
print "buffer by",buffer
|
||||||
|
print "using range",timerange
|
||||||
|
|
||||||
|
client = ThriftClient.ThriftClient(edex)
|
||||||
|
request = GetRadarDataRecordRequest()
|
||||||
|
request.setRadarId(site)
|
||||||
|
request.setPrimaryElevationAngle("0.5")
|
||||||
|
request.setTimeRange(timerange)
|
||||||
|
|
||||||
|
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
|
||||||
|
for v, ctable, ax in zip((94, 99), ('NWSReflectivity', 'NWSVelocity'), axes):
|
||||||
|
request.setProductCode(v)
|
||||||
|
response = client.sendRequest(request)
|
||||||
|
if response.getData():
|
||||||
|
for record in response.getData():
|
||||||
|
idra = record.getHdf5Data()
|
||||||
|
rdat,azdat,depVals,threshVals = RadarCommon.get_hdf5_data(idra)
|
||||||
|
dim = rdat.getDimension()
|
||||||
|
yLen,xLen = rdat.getSizes()
|
||||||
|
array = rdat.getByteData()
|
||||||
|
|
||||||
|
# get data for azimuth angles if we have them.
|
||||||
|
if azdat :
|
||||||
|
azVals = azdat.getFloatData()
|
||||||
|
az = np.array(RadarCommon.encode_radial(azVals))
|
||||||
|
dattyp = RadarCommon.get_data_type(azdat)
|
||||||
|
az = np.append(az,az[-1])
|
||||||
|
|
||||||
|
print "found",v,record.getDataTime()
|
||||||
|
|
||||||
|
header = RadarCommon.get_header(record, format, xLen, yLen, azdat, "description")
|
||||||
|
rng = np.linspace(0, xLen, xLen + 1)
|
||||||
|
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
|
||||||
|
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))
|
||||||
|
multiArray = np.reshape(array, (-1, xLen))
|
||||||
|
data = ma.array(multiArray)
|
||||||
|
data[data==0] = ma.masked
|
||||||
|
|
||||||
|
# Plot the data
|
||||||
|
norm, cmap = ctables.registry.get_with_steps(ctable, 16, 16)
|
||||||
|
ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
|
||||||
|
ax.set_aspect('equal', 'datalim')
|
||||||
|
|
||||||
|
multp = 100*(2*xLen/460)
|
||||||
|
ax.set_xlim(-multp,multp)
|
||||||
|
ax.set_ylim(-multp,multp)
|
||||||
|
# This is setting x/ylim on gate/pixel and not km
|
||||||
|
|
||||||
|
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
|
||||||
|
.. parsed-literal::
|
||||||
|
|
||||||
|
using time 2016-04-11 23:02:22
|
||||||
|
buffer by 60
|
||||||
|
using range (Apr 11 16 23:01:22 , Apr 11 16 23:03:22 )
|
||||||
|
found 94 2016-04-11 23:02:22
|
||||||
|
found 99 2016-04-11 23:02:22
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: NEXRAD_Level_3_Plot_with_Matplotlib_files/NEXRAD_Level_3_Plot_with_Matplotlib_1_1.png
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 47 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 55 KiB |
134
docs/source/examples/_gen/Plotting_a_Sounding_with_MetPy.rst
Normal file
134
docs/source/examples/_gen/Plotting_a_Sounding_with_MetPy.rst
Normal file
|
|
@ -0,0 +1,134 @@
|
||||||
|
==============================
|
||||||
|
Plotting a Sounding with MetPy
|
||||||
|
==============================
|
||||||
|
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Plotting_a_Sounding_with_MetPy.ipynb>`_
|
||||||
|
|
||||||
|
Plotting a Sounding with MetPy
|
||||||
|
==============================
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
%matplotlib inline
|
||||||
|
from awips.dataaccess import DataAccessLayer
|
||||||
|
|
||||||
|
import matplotlib.tri as mtri
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile
|
||||||
|
from metpy.plots import SkewT, Hodograph
|
||||||
|
from metpy.units import units, concatenate
|
||||||
|
|
||||||
|
plt.rcParams['figure.figsize'] = (12, 14)
|
||||||
|
|
||||||
|
# Set EDEX host
|
||||||
|
DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
|
||||||
|
# Data type bufrua
|
||||||
|
request.setDatatype("bufrua")
|
||||||
|
# Parameters
|
||||||
|
request.setParameters("tpMan","tdMan","prMan","htMan","wdMan","wsMan")
|
||||||
|
# Station ID (name doesn't work yet)
|
||||||
|
request.setLocationNames("72469")
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
|
||||||
|
# Get most recent record
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)
|
||||||
|
|
||||||
|
# Initialize data arrays
|
||||||
|
tpMan,tdMan,prMan,htMan,wdMan,wsMan = [],[],[],[],[],[]
|
||||||
|
|
||||||
|
# Build ordered arrays
|
||||||
|
for ob in response:
|
||||||
|
print float(ob.getString("prMan")), float(ob.getString("wsMan"))
|
||||||
|
tpMan.append(float(ob.getString("tpMan")))
|
||||||
|
tdMan.append(float(ob.getString("tdMan")))
|
||||||
|
prMan.append(float(ob.getString("prMan")))
|
||||||
|
htMan.append(float(ob.getString("htMan")))
|
||||||
|
wdMan.append(float(ob.getString("wdMan")))
|
||||||
|
wsMan.append(float(ob.getString("wsMan")))
|
||||||
|
|
||||||
|
# we can use units.* here...
|
||||||
|
T = np.array(tpMan)-273.15
|
||||||
|
Td = np.array(tdMan)-273.15
|
||||||
|
p = np.array(prMan)/100
|
||||||
|
height = np.array(htMan)
|
||||||
|
direc = np.array(wdMan)
|
||||||
|
spd = np.array(wsMan)
|
||||||
|
u, v = get_wind_components(spd, np.deg2rad(direc))
|
||||||
|
|
||||||
|
p = p * units.mbar
|
||||||
|
T = T * units.degC
|
||||||
|
Td = Td * units.degC
|
||||||
|
spd = spd * units.knot
|
||||||
|
direc = direc * units.deg
|
||||||
|
|
||||||
|
# Create a skewT plot
|
||||||
|
skew = SkewT()
|
||||||
|
|
||||||
|
# Plot the data using normal plotting functions, in this case using
|
||||||
|
# log scaling in Y, as dictated by the typical meteorological plot
|
||||||
|
skew.plot(p, T, 'r')
|
||||||
|
skew.plot(p, Td, 'g')
|
||||||
|
skew.plot_barbs(p, u, v)
|
||||||
|
skew.ax.set_ylim(1000, 100)
|
||||||
|
skew.ax.set_xlim(-40, 60)
|
||||||
|
|
||||||
|
# Calculate LCL height and plot as black dot
|
||||||
|
l = lcl(p[0], T[0], Td[0])
|
||||||
|
lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')
|
||||||
|
skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')
|
||||||
|
|
||||||
|
# Calculate full parcel profile and add to plot as black line
|
||||||
|
prof = parcel_profile(p, T[0], Td[0]).to('degC')
|
||||||
|
skew.plot(p, prof, 'k', linewidth=2)
|
||||||
|
|
||||||
|
# Example of coloring area between profiles
|
||||||
|
skew.ax.fill_betweenx(p, T, prof, where=T>=prof, facecolor='blue', alpha=0.4)
|
||||||
|
skew.ax.fill_betweenx(p, T, prof, where=T<prof, facecolor='red', alpha=0.4)
|
||||||
|
|
||||||
|
# An example of a slanted line at constant T -- in this case the 0 isotherm
|
||||||
|
l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)
|
||||||
|
|
||||||
|
# Draw hodograph
|
||||||
|
ax_hod = inset_axes(skew.ax, '40%', '40%', loc=3)
|
||||||
|
h = Hodograph(ax_hod, component_range=80.)
|
||||||
|
h.add_grid(increment=20)
|
||||||
|
h.plot_colormapped(u, v, spd)
|
||||||
|
|
||||||
|
# Show the plot
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
|
||||||
|
.. parsed-literal::
|
||||||
|
|
||||||
|
83900.0 1.5
|
||||||
|
100000.0 -9999998.0
|
||||||
|
92500.0 -9999998.0
|
||||||
|
85000.0 -9999998.0
|
||||||
|
70000.0 0.5
|
||||||
|
50000.0 6.09999990463
|
||||||
|
40000.0 3.0
|
||||||
|
30000.0 7.69999980927
|
||||||
|
25000.0 16.8999996185
|
||||||
|
20000.0 7.19999980927
|
||||||
|
15000.0 10.1999998093
|
||||||
|
10000.0 13.8000001907
|
||||||
|
7000.0 9.19999980927
|
||||||
|
5000.0 7.69999980927
|
||||||
|
3000.0 5.59999990463
|
||||||
|
2000.0 6.59999990463
|
||||||
|
1000.0 10.8000001907
|
||||||
|
700.0 5.09999990463
|
||||||
|
500.0 -9999.0
|
||||||
|
300.0 -9999.0
|
||||||
|
200.0 -9999.0
|
||||||
|
100.0 -9999.0
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: Plotting_a_Sounding_with_MetPy_files/Plotting_a_Sounding_with_MetPy_1_1.png
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 104 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 110 KiB |
213
docs/source/examples/_gen/Surface_Obs.rst
Normal file
213
docs/source/examples/_gen/Surface_Obs.rst
Normal file
|
|
@ -0,0 +1,213 @@
|
||||||
|
===========
|
||||||
|
Surface Obs
|
||||||
|
===========
|
||||||
|
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Surface_Obs.ipynb>`_
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
from awips.dataaccess import DataAccessLayer
|
||||||
|
|
||||||
|
# Set host
|
||||||
|
DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
|
||||||
|
# Init data request
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
request.setDatatype("obs")
|
||||||
|
request.setLocationNames("KBJC")
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
# "presWeather","skyCover","skyLayerBase"
|
||||||
|
# are multi-dimensional... deal with these later
|
||||||
|
request.setParameters(
|
||||||
|
"stationName",
|
||||||
|
"timeObs",
|
||||||
|
"wmoId",
|
||||||
|
"autoStationType",
|
||||||
|
"elevation",
|
||||||
|
"reportType",
|
||||||
|
"temperature",
|
||||||
|
"tempFromTenths",
|
||||||
|
"dewpoint",
|
||||||
|
"dpFromTenths",
|
||||||
|
"windDir",
|
||||||
|
"windSpeed",
|
||||||
|
"windGust",
|
||||||
|
"visibility",
|
||||||
|
"altimeter",
|
||||||
|
"seaLevelPress",
|
||||||
|
"pressChange3Hour",
|
||||||
|
"pressChangeChar",
|
||||||
|
"maxTemp24Hour",
|
||||||
|
"minTemp24Hour",
|
||||||
|
"precip1Hour",
|
||||||
|
"precip3Hour",
|
||||||
|
"precip6Hour",
|
||||||
|
"precip24Hour"
|
||||||
|
)
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
#getParameters
|
||||||
|
print ob.getString("stationName"), "from", ob.getDataTime().getRefTime()
|
||||||
|
print "stationName is",ob.getString("stationName")
|
||||||
|
print "timeObs is",ob.getString("timeObs")
|
||||||
|
print "wmoId is",ob.getString("wmoId")
|
||||||
|
print "autoStationType is",ob.getString("autoStationType")
|
||||||
|
print "elevation is",ob.getString("elevation")
|
||||||
|
print "reportType is",ob.getString("reportType")
|
||||||
|
print "temperature is",ob.getString("temperature")
|
||||||
|
print "tempFromTenths is",ob.getString("tempFromTenths")
|
||||||
|
print "dewpoint is",ob.getString("dewpoint")
|
||||||
|
print "dpFromTenths is",ob.getString("dpFromTenths")
|
||||||
|
print "windDir is",ob.getString("windDir")
|
||||||
|
print "windSpeed is",ob.getString("windSpeed")
|
||||||
|
print "windGust is",ob.getString("windGust")
|
||||||
|
print "visibility is",ob.getString("visibility")
|
||||||
|
print "altimeter is",ob.getString("altimeter")
|
||||||
|
print "seaLevelPress is",ob.getString("seaLevelPress")
|
||||||
|
print "pressChange3Hour is",ob.getString("pressChange3Hour")
|
||||||
|
print "pressChangeChar is",ob.getString("pressChangeChar")
|
||||||
|
print "maxTemp24Hour is",ob.getString("maxTemp24Hour")
|
||||||
|
print "minTemp24Hour is",ob.getString("minTemp24Hour")
|
||||||
|
print "precip1Hour is",ob.getString("precip1Hour")
|
||||||
|
print "precip3Hour is",ob.getString("precip3Hour")
|
||||||
|
print "precip6Hour is",ob.getString("precip6Hour")
|
||||||
|
print "precip24Hour is",ob.getString("precip24Hour")
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
# multi-dimensional present WX
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
request.setDatatype("obs")
|
||||||
|
request.setLocationNames("KBJC")
|
||||||
|
request.setParameters("presWeather")
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print ob.getString("presWeather")
|
||||||
|
|
||||||
|
|
||||||
|
# multi-dimensional Sky Condition
|
||||||
|
request.setParameters("skyCover", "skyLayerBase")
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
for ob in response:
|
||||||
|
print ob.getString("skyCover")
|
||||||
|
print ob.getString("skyLayerBase")
|
||||||
|
|
||||||
|
Synop/Marine
|
||||||
|
------------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
from awips.dataaccess import DataAccessLayer
|
||||||
|
|
||||||
|
DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
request.setDatatype("sfcobs")
|
||||||
|
request.setLocationNames("72421") # Covington, Kentucky (KCVG)
|
||||||
|
|
||||||
|
request.setParameters("stationId","timeObs","elevation","reportType",
|
||||||
|
"wx_present","visibility","seaLevelPress","stationPress",
|
||||||
|
"pressChange3Hour","pressChangeChar","temperature",
|
||||||
|
"dewpoint","seaSurfaceTemp","wetBulb","windDir",
|
||||||
|
"windSpeed","equivWindSpeed10m","windGust","precip1Hour",
|
||||||
|
"precip6Hour","precip24Hour" )
|
||||||
|
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
print response
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
|
||||||
|
|
||||||
|
Profiler
|
||||||
|
--------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
MULTI_DIM_PARAMS = set(['vComponent', 'uComponent', 'peakPower',
|
||||||
|
'levelMode', 'uvQualityCode', 'consensusNum',
|
||||||
|
'HorizSpStdDev', 'wComponent', 'height',
|
||||||
|
'VertSpStdDev'])
|
||||||
|
|
||||||
|
request = DataAccessLayer.newDataRequest("profiler")
|
||||||
|
request.setParameters('numProfLvls', 'elevation', 'windDirSfc', 'validTime',
|
||||||
|
'windSpeedSfc', 'pressure', 'submode', 'relHumidity',
|
||||||
|
'profilerId', 'rainRate', 'temperature')
|
||||||
|
request.getParameters().extend(MULTI_DIM_PARAMS)
|
||||||
|
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
print response
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
|
||||||
|
ACARS
|
||||||
|
-----
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
request = DataAccessLayer.newDataRequest("acars")
|
||||||
|
request.setParameters("tailNumber", "receiver", "pressure", "flightPhase",
|
||||||
|
"rollAngleQuality", "temp", "windDirection", "windSpeed",
|
||||||
|
"humidity", "mixingRatio", "icing")
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
print response
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
|
||||||
|
AIREP
|
||||||
|
-----
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
request = DataAccessLayer.newDataRequest("airep")
|
||||||
|
request.setParameters("id", "flightLevel", "temp", "windDirection", "windSpeed",
|
||||||
|
"flightWeather", "flightHazard", "flightConditions")
|
||||||
|
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
print response
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
|
||||||
|
PIREP
|
||||||
|
-----
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
MULTI_DIM_PARAMS = set(["hazardType",
|
||||||
|
"turbType", "turbBaseHeight", "turbTopHeight",
|
||||||
|
"iceType", "iceBaseHeight", "iceTopHeight",
|
||||||
|
"skyCover1", "skyCover2", "skyBaseHeight", "skyTopHeight"
|
||||||
|
])
|
||||||
|
|
||||||
|
request = DataAccessLayer.newDataRequest("pirep")
|
||||||
|
request.setParameters('id', 'flightLevel', 'temp', 'windDirection', 'windSpeed',
|
||||||
|
'horzVisibility', 'aircraftType', 'weatherGroup')
|
||||||
|
request.getParameters().extend(MULTI_DIM_PARAMS)
|
||||||
|
|
||||||
|
datatimes = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
time = datatimes[-1].validPeriod
|
||||||
|
|
||||||
|
response = DataAccessLayer.getGeometryData(request,times=time)
|
||||||
|
print response
|
||||||
|
for ob in response:
|
||||||
|
print "getParameters is",ob.getParameters()
|
||||||
|
print len(ob.getParameters())
|
||||||
|
|
||||||
144
docs/source/examples/_gen/Surface_Obs_Plot_with_MetPy.rst
Normal file
144
docs/source/examples/_gen/Surface_Obs_Plot_with_MetPy.rst
Normal file
|
|
@ -0,0 +1,144 @@
|
||||||
|
===========================
|
||||||
|
Surface Obs Plot with MetPy
|
||||||
|
===========================
|
||||||
|
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Surface_Obs_Plot_with_MetPy.ipynb>`_
|
||||||
|
|
||||||
|
Based on the MetPy example `"Station Plot with
|
||||||
|
Layout" <http://metpy.readthedocs.org/en/latest/examples/generated/Station_Plot_with_Layout.html>`_
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
%matplotlib inline
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
from awips.dataaccess import DataAccessLayer
|
||||||
|
|
||||||
|
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
|
||||||
|
DataAccessLayer.changeEDEXHost("edex.unidata.ucar.edu")
|
||||||
|
|
||||||
|
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[0].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::
|
||||||
|
|
||||||
|
(Apr 10 16 12:52:00 , Apr 10 16 12:52:00 )
|
||||||
|
425
|
||||||
|
85
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: Surface_Obs_Plot_with_MetPy_files/Surface_Obs_Plot_with_MetPy_1_1.png
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 753 KiB |
269
docs/source/examples/generated/Grid_Levels_and_Parameters.rst
Normal file
269
docs/source/examples/generated/Grid_Levels_and_Parameters.rst
Normal file
|
|
@ -0,0 +1,269 @@
|
||||||
|
==========================
|
||||||
|
Grid Levels and Parameters
|
||||||
|
==========================
|
||||||
|
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Grid_Levels_and_Parameters.ipynb>`_
|
||||||
|
|
||||||
|
List Available Parameters for a Grid Name
|
||||||
|
-----------------------------------------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
from awips.dataaccess import DataAccessLayer
|
||||||
|
|
||||||
|
# Select HRRR
|
||||||
|
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
|
||||||
|
request = DataAccessLayer.newDataRequest()
|
||||||
|
request.setDatatype("grid")
|
||||||
|
request.setLocationNames("GFS40")
|
||||||
|
|
||||||
|
# Print parm list
|
||||||
|
available_parms = DataAccessLayer.getAvailableParameters(request)
|
||||||
|
available_parms.sort()
|
||||||
|
for parm in available_parms:
|
||||||
|
print parm
|
||||||
|
|
||||||
|
|
||||||
|
.. parsed-literal::
|
||||||
|
|
||||||
|
AV
|
||||||
|
BLI
|
||||||
|
CAPE
|
||||||
|
CFRZR6hr
|
||||||
|
CICEP6hr
|
||||||
|
CIn
|
||||||
|
CP6hr
|
||||||
|
CRAIN6hr
|
||||||
|
CSNOW6hr
|
||||||
|
GH
|
||||||
|
P
|
||||||
|
P6hr
|
||||||
|
PMSL
|
||||||
|
PVV
|
||||||
|
PW
|
||||||
|
RH
|
||||||
|
SLI
|
||||||
|
T
|
||||||
|
TP6hr
|
||||||
|
VSS
|
||||||
|
WEASD
|
||||||
|
WGH
|
||||||
|
uW
|
||||||
|
vW
|
||||||
|
|
||||||
|
|
||||||
|
List Available Levels for Parameter
|
||||||
|
-----------------------------------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
# Set parm to u-wind
|
||||||
|
request.setParameters("uW")
|
||||||
|
|
||||||
|
# Print level list
|
||||||
|
available_levels = DataAccessLayer.getAvailableLevels(request)
|
||||||
|
available_levels.sort()
|
||||||
|
for level in available_levels:
|
||||||
|
print level
|
||||||
|
|
||||||
|
|
||||||
|
.. parsed-literal::
|
||||||
|
|
||||||
|
1000.0MB
|
||||||
|
950.0MB
|
||||||
|
925.0MB
|
||||||
|
900.0MB
|
||||||
|
875.0MB
|
||||||
|
850.0MB
|
||||||
|
825.0MB
|
||||||
|
800.0MB
|
||||||
|
775.0MB
|
||||||
|
725.0MB
|
||||||
|
600.0MB
|
||||||
|
575.0MB
|
||||||
|
0.0_30.0BL
|
||||||
|
60.0_90.0BL
|
||||||
|
90.0_120.0BL
|
||||||
|
0.5PV
|
||||||
|
2.0PV
|
||||||
|
30.0_60.0BL
|
||||||
|
1.0PV
|
||||||
|
750.0MB
|
||||||
|
120.0_150.0BL
|
||||||
|
975.0MB
|
||||||
|
700.0MB
|
||||||
|
675.0MB
|
||||||
|
650.0MB
|
||||||
|
625.0MB
|
||||||
|
550.0MB
|
||||||
|
525.0MB
|
||||||
|
500.0MB
|
||||||
|
450.0MB
|
||||||
|
400.0MB
|
||||||
|
300.0MB
|
||||||
|
250.0MB
|
||||||
|
200.0MB
|
||||||
|
150.0MB
|
||||||
|
100.0MB
|
||||||
|
0.0TROP
|
||||||
|
1.5PV
|
||||||
|
150.0_180.0BL
|
||||||
|
350.0MB
|
||||||
|
10.0FHAG
|
||||||
|
0.0MAXW
|
||||||
|
|
||||||
|
|
||||||
|
Construct Wind Field from U and V Components
|
||||||
|
--------------------------------------------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
import numpy
|
||||||
|
from metpy.units import units
|
||||||
|
|
||||||
|
# Set level for u-wind
|
||||||
|
request.setLevels("10.0FHAG")
|
||||||
|
t = DataAccessLayer.getAvailableTimes(request)
|
||||||
|
# Select last time for u-wind
|
||||||
|
response = DataAccessLayer.getGridData(request, [t[-1]])
|
||||||
|
data_uw = response[-1]
|
||||||
|
lons,lats = data_uw.getLatLonCoords()
|
||||||
|
|
||||||
|
# Select v-wind
|
||||||
|
request.setParameters("vW")
|
||||||
|
# Select last time for v-wind
|
||||||
|
response = DataAccessLayer.getGridData(request, [t[-1]])
|
||||||
|
data_uv = response[-1]
|
||||||
|
|
||||||
|
# Print
|
||||||
|
print 'Time :', t[-1]
|
||||||
|
print 'Model:', data_uv.getLocationName()
|
||||||
|
print 'Unit :', data_uv.getUnit()
|
||||||
|
print 'Parms :', data_uw.getParameter(), data_uv.getParameter()
|
||||||
|
print data_uv.getRawData().shape
|
||||||
|
|
||||||
|
# Calculate total wind speed
|
||||||
|
spd = numpy.sqrt( data_uw.getRawData()**2 + data_uv.getRawData()**2 )
|
||||||
|
spd = spd * units.knot
|
||||||
|
print "windArray =", spd
|
||||||
|
|
||||||
|
data = data_uw
|
||||||
|
|
||||||
|
|
||||||
|
.. parsed-literal::
|
||||||
|
|
||||||
|
Time : 2016-04-20 18:00:00 (240)
|
||||||
|
Model: GFS40
|
||||||
|
Unit : m*sec^-1
|
||||||
|
Parms : vW vW
|
||||||
|
(185, 129)
|
||||||
|
windArray = [[ 1.47078204 1.69705617 0.69296461 ..., 9.390378 9.14996147 8.55599213] [ 8.23072243 8.20243835 8.31557465 ..., 1.48492408 0.56568539 0.39597979] [ 0.49497473 0.52325904 0.1979899 ..., 2.67286372 2.63043714 2.65872145] ..., [ 2.17788887 2.20617294 2.13546252 ..., 1.01823378 0.62225395 0.39597979] [ 0.02828427 0.8768124 1.51320839 ..., 6.47709799 6.68922997 6.84479332] [ 6.92964649 7.02864122 6.98621511 ..., 0.91923875 1.24450791 1.28693426]] knot
|
||||||
|
|
||||||
|
|
||||||
|
Plotting a Grid with Basemap
|
||||||
|
----------------------------
|
||||||
|
|
||||||
|
Using **matplotlib**, **numpy**, and **basemap**:
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
%matplotlib inline
|
||||||
|
import matplotlib.tri as mtri
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from matplotlib.transforms import offset_copy
|
||||||
|
from mpl_toolkits.basemap import Basemap, cm
|
||||||
|
import numpy as np
|
||||||
|
from numpy import linspace, transpose
|
||||||
|
from numpy import meshgrid
|
||||||
|
|
||||||
|
plt.figure(figsize=(12, 12), dpi=100)
|
||||||
|
|
||||||
|
map = Basemap(projection='cyl',
|
||||||
|
resolution = 'c',
|
||||||
|
llcrnrlon = lons.min(), llcrnrlat = lats.min(),
|
||||||
|
urcrnrlon =lons.max(), urcrnrlat = lats.max()
|
||||||
|
)
|
||||||
|
map.drawcoastlines()
|
||||||
|
map.drawstates()
|
||||||
|
map.drawcountries()
|
||||||
|
|
||||||
|
#
|
||||||
|
# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m
|
||||||
|
#
|
||||||
|
x = linspace(0, map.urcrnrx, data.getRawData().shape[1])
|
||||||
|
y = linspace(0, map.urcrnry, data.getRawData().shape[0])
|
||||||
|
xx, yy = meshgrid(x, y)
|
||||||
|
ngrid = len(x)
|
||||||
|
rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),
|
||||||
|
ngrid).reshape(ngrid, ngrid)
|
||||||
|
rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),
|
||||||
|
ngrid).reshape(ngrid, ngrid).T
|
||||||
|
tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),
|
||||||
|
lats.flatten()), spd.flatten())
|
||||||
|
rdata = tli(rlons, rlats)
|
||||||
|
#cs = map.contourf(rlons, rlats, rdata, latlon=True)
|
||||||
|
cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=20, cmap='BuPu')
|
||||||
|
|
||||||
|
# Add colorbar
|
||||||
|
cbar = map.colorbar(cs,location='bottom',pad="5%")
|
||||||
|
|
||||||
|
cbar.set_label("Wind Speed (Knots)")
|
||||||
|
|
||||||
|
# Show plot
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: Grid_Levels_and_Parameters_files/Grid_Levels_and_Parameters_7_0.png
|
||||||
|
|
||||||
|
|
||||||
|
or use **pcolormesh** rather than **contourf**
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
plt.figure(figsize=(12, 12), dpi=100)
|
||||||
|
map = Basemap(projection='cyl',
|
||||||
|
resolution = 'c',
|
||||||
|
llcrnrlon = lons.min(), llcrnrlat = lats.min(),
|
||||||
|
urcrnrlon =lons.max(), urcrnrlat = lats.max()
|
||||||
|
)
|
||||||
|
map.drawcoastlines()
|
||||||
|
map.drawstates()
|
||||||
|
map.drawcountries()
|
||||||
|
cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=20, cmap='BuPu')
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: Grid_Levels_and_Parameters_files/Grid_Levels_and_Parameters_9_0.png
|
||||||
|
|
||||||
|
|
||||||
|
Plotting a Grid with Cartopy
|
||||||
|
----------------------------
|
||||||
|
|
||||||
|
.. code:: python
|
||||||
|
|
||||||
|
import os
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
import iris
|
||||||
|
import cartopy.crs as ccrs
|
||||||
|
from cartopy import config
|
||||||
|
|
||||||
|
lon,lat = data.getLatLonCoords()
|
||||||
|
plt.figure(figsize=(12, 12), dpi=100)
|
||||||
|
ax = plt.axes(projection=ccrs.PlateCarree())
|
||||||
|
cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=20, cmap='BuPu')
|
||||||
|
ax.coastlines()
|
||||||
|
ax.gridlines()
|
||||||
|
|
||||||
|
# add colorbar
|
||||||
|
cbar = plt.colorbar(orientation='horizontal')
|
||||||
|
cbar.set_label("Wind Speed (Knots)")
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
.. image:: Grid_Levels_and_Parameters_files/Grid_Levels_and_Parameters_11_0.png
|
||||||
|
|
||||||
|
|
||||||
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Reference in a new issue