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GSFC-GRASP-Python-Interface

A set of Python 3 tools for interfacing with the GRASP retrieval code

The core of the code is in runGRASP.py which contains several classes:

graspDB – This takes many pixel or graspRun objects and runs them all though a series of individual calls to GRASP. The class achieves two ends: (1) distribute large batches of pixels into chunks that are manageable from a memory perspective and (2) allow the individual calls to the GRASP binary to occur asynchronously in accordance with user specified parameters (e.g., max number of CPUs to use at a given time).

graspRun – This represents a collection of pixels, generally designed to be called in a single run. This class handles input/output to GRASP through the SDATA and output text files, respectively. The output data is stored in a list of rslt dicts, which are defined below.

pixel – This is class holds most of the information that would be in a typical row (entry for a single pixel) of an SDATA file.

graspYAML – This class has a variety of methods for changing settings in the YAML files.

rslt dict keys and values

for key,val in simBase.rsltFwd[0].items(): 
  print("%23s: %27s %11s" % (("rslt['%s']" % key), str(type(val)), (str(val.shape) if 'numpy' in str(type(val)) else '()')))

       rslt['datetime']: <class 'datetime.datetime'>          ()
      rslt['longitude']:     <class 'numpy.float64'>          ()
       rslt['latitude']:     <class 'numpy.float64'>          ()
              rslt['r']:     <class 'numpy.ndarray'>     (N_modes, N_radii)
         rslt['dVdlnr']:     <class 'numpy.ndarray'>     (N_modes, N_radii)
             rslt['rv']:     <class 'numpy.ndarray'>        (N_modes,)
          rslt['sigma']:     <class 'numpy.ndarray'>        (N_modes,)
            rslt['vol']:     <class 'numpy.ndarray'>        (N_modes,)
            rslt['sph']:     <class 'numpy.ndarray'>        (N_modes,)
         rslt['height']:     <class 'numpy.ndarray'>        (N_modes,)
      rslt['heightStd']:     <class 'numpy.ndarray'>        (N_modes,)
         rslt['lambda']:     <class 'numpy.ndarray'>        (N_lambda,)
            rslt['aod']:     <class 'numpy.ndarray'>        (N_lambda,)
        rslt['aodMode']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
            rslt['ssa']:     <class 'numpy.ndarray'>        (N_lambda,)
        rslt['ssaMode']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
              rslt['n']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
              rslt['k']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
           rslt['rEff']:     <class 'numpy.float64'>          ()
       rslt['rEffCalc']:     <class 'numpy.float64'>          ()
         rslt['albedo']:     <class 'numpy.ndarray'>        (N_lambda,)
        rslt['wtrSurf']:     <class 'numpy.ndarray'>      (3, N_lambda)
           rslt['brdf']:     <class 'numpy.ndarray'>      (3, N_lambda)
           rslt['bpdf']:     <class 'numpy.ndarray'>        (N_lambda,)
        rslt['costVal']:             <class 'float'>          ()
            rslt['sza']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
            rslt['vis']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
            rslt['fis']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
        rslt['sca_ang']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
         rslt['meas_I']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
          rslt['fit_I']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
         rslt['meas_Q']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
          rslt['fit_Q']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
         rslt['meas_U']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
          rslt['fit_U']:     <class 'numpy.ndarray'>     (N_views, N_lambda)
          rslt['angle']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p11']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p12']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p22']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p33']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p34']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
            rslt['p44']:     <class 'numpy.ndarray'> (N_ang, N_modes, N_lambda)
              rslt['g']:     <class 'numpy.ndarray'>        (N_lambda,)
     rslt['LidarRatio']:     <class 'numpy.ndarray'>        (N_lambda,)
     rslt['LidarDepol']:     <class 'numpy.ndarray'>        (N_lambda,)
          rslt['gMode']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
 rslt['LidarRatioMode']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)
 rslt['LidarDepolMode']:     <class 'numpy.ndarray'>      (N_modes, N_lambda)