[GRIB] GRIB data analysis api

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