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[GRIB] GRIB data analysis api

CodeMath Dev2DHs 2017.02.08 22:57

Python 2.7 using lib pygrib
(https://github.com/jswhit/pygrib)


Example usage

  • from the python interpreter prompt, import the package:
  >>> import pygrib
  • open a GRIB file, create a grib message iterator:
  >>> grbs = pygrib.open('sampledata/flux.grb')  
  • pygrib open instances behave like regular python file objects, with seek, tell, read, readline and close methods, except that offsets are measured in grib messages instead of bytes:
  >>> grbs.seek(2)
>>> grbs.tell()
2
>>> grb = grbs.read(1)[0] # read returns a list with the next N (N=1 in this case) messages.
>>> grb # printing a grib message object displays summary info
3:Maximum temperature:K (instant):regular_gg:heightAboveGround:level 2 m:fcst time 108-120 hrs:from 200402291200
>>> grbs.tell()
3
  • print an inventory of the file:
  >>> grbs.seek(0)
>>> for grb in grbs:
>>> grb
1:Precipitation rate:kg m**-2 s**-1 (avg):regular_gg:surface:level 0:fcst time 108-120 hrs (avg):from 200402291200
2:Surface pressure:Pa (instant):regular_gg:surface:level 0:fcst time 120 hrs:from 200402291200
3:Maximum temperature:K (instant):regular_gg:heightAboveGround:level 2 m:fcst time 108-120 hrs:from 200402291200
4:Minimum temperature:K (instant):regular_gg:heightAboveGround:level 2 m:fcst time 108-120 hrs:from 200402291200
  • find the first grib message with a matching name:
  >>> grb = grbs.select(name='Maximum temperature')[0]
  • extract the data values using the 'values' key (grb.keys() will return a list of the available keys):
  # The data is returned as a numpy array, or if missing values or a bitmap
# are present, a numpy masked array. Reduced lat/lon or gaussian grid
# data is automatically expanded to a regular grid. Details of the internal
# representation of the grib data (such as the scanning mode) are handled
# automatically.
>>> maxt = grb.values # same as grb['values']
>>> maxt.shape, maxt.min(), maxt.max()
(94, 192) 223.7 319.9
  • get the latitudes and longitudes of the grid:
  >>> lats, lons = grb.latlons()
>>> lats.shape, lats.min(), lats.max(), lons.shape, lons.min(), lons.max()
(94, 192) -88.5419501373 88.5419501373 0.0 358.125
  • get the second grib message:
  >>> grb = grbs.message(2) # same as grbs.seek(1); grb=grbs.readline()
>>> grb
2:Surface pressure:Pa (instant):regular_gg:surface:level 0:fcst time 120 hrs:from 200402291200
  • extract data and get lat/lon values for a subset over North America:
  >>> data, lats, lons = grb.data(lat1=20,lat2=70,lon1=220,lon2=320)
>>> data.shape, lats.min(), lats.max(), lons.min(), lons.max()
(26, 53) 21.904439458 69.5216630593 221.25 318.75
  • modify the values associated with existing keys (either via attribute or dictionary access):
  >>> grb['forecastTime'] = 240
>>> grb.dataDate = 20100101
  • get the binary string associated with the coded message:
  >>> msg = grb.tostring()
>>> grbs.close() # close the grib file.
  • write the modified message to a new GRIB file:

  >>> grbout = open('test.grb','wb')
>>> grbout.write(msg)
>>> grbout.close()
>>> pygrib.open('test.grb').readline()
1:Surface pressure:Pa (instant):regular_gg:surface:level 0:fcst time 240 hrs:from 20


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