from netCDF4 import Dataset from argparse import ArgumentParser import numpy as np import sys # # Basic iceberg trajectory post-processing python script. # This script collates iceberg trajectories from the distributed datasets written # out by each processing region and rearranges the ragged arrays into contiguous # streams for each unique iceberg. The output arrays are 2D (ntraj, ntimes) arrays. # Note that some icebergs may only exist for a subset of the possible times. In these # cases the missing instances are filled with invalid (NaN) values. # parser = ArgumentParser(description='produce collated trajectory file from distributed output\ files, e.g. \n python ./icb_pp.py \ -t trajectory_icebergs_004248_ -n 296 -o trajsout.nc' ) parser.add_argument('-t',dest='froot',help='fileroot_of_distrbuted_data; root name of \ distributed trajectory output (usually completed with XXXX.nc, where \ XXXX is the 4 digit processor number)', default='trajectory_icebergs_004248_') parser.add_argument('-n',dest='fnum',help='number of distributed files to process', type=int, default=None) parser.add_argument('-o',dest='fout',help='collated_output_file; file name to receive the \ collated trajectory data', default='trajsout.nc') args = parser.parse_args() default_used = 0 if args.froot is None: pathstart = 'trajectory_icebergs_004248_' default_used = 1 else: pathstart = args.froot if args.fnum is None: procnum = 0 default_used = 1 else: procnum = args.fnum if args.fout is None: pathout = 'trajsout.nc' default_used = 1 else: pathout = args.fout if default_used == 1: print('At least one default value will be used; command executing is:') print('icb_pp.py -t ',pathstart,' -n ',procnum,' -o ',pathout) if procnum < 1: print('Need some files to collate! procnum = ',procnum) sys.exit(11) icu = [] times = [] # # Loop through all distributed datasets to obtain the complete list # of iceberg identification numbers and timesteps # for n in range(procnum): nn = '%4.4d' % n fw = Dataset(pathstart+nn+'.nc') if len(fw.dimensions['n']) > 0: print pathstart+nn+'.nc' ic = fw.variables['iceberg_number'][:,0] ts = fw.variables['timestep'][:] icv = np.unique(ic) ts = np.unique(ts) print('Min Max ts: ',ts.min(), ts.max()) print('Number unique icebergs= ',icv.shape[0]) icu.append(icv) times.append(ts) fw.close() # # Now flatten the lists and reduce to the unique spanning set # icu = np.concatenate(icu) icu = np.unique(icu) times = np.concatenate(times) times = np.unique(times) ntraj = icu.shape[0] print(ntraj, ' unique icebergs found across all datasets') print('Icebergs ids range from: ',icu.min(), 'to: ',icu.max()) print('times range from: ',times.min(), 'to: ', times.max()) # # Declare 2-D arrays to receive the data from all files # nt = times.shape[0] lons = np.zeros((ntraj, nt)) lats = np.zeros((ntraj, nt)) tims = np.zeros((ntraj, nt)) xis = np.zeros((ntraj, nt)) yjs = np.zeros((ntraj, nt)) # # initially fill with invalid data # lons.fill(np.nan) lats.fill(np.nan) xis.fill(np.nan) yjs.fill(np.nan) tims.fill(np.nan) # # loop through distributed datasets again, this time # checking indices against icu and times lists and # inserting data into the correct locations in the # 2-D collated sets. # for n in range(procnum): nn = '%4.4d' % n fw = Dataset(pathstart+nn+'.nc') # Note many distributed datafiles will contain no iceberg data # so skip quickly over these m = len(fw.dimensions['n']) if m > 0: inx = np.zeros(m, dtype=int) tsx = np.zeros(m, dtype=int) print pathstart+nn+'.nc' ic = fw.variables['iceberg_number'][:,0] ts = fw.variables['timestep'][:] lns = fw.variables['lon'][:] lts = fw.variables['lat'][:] xxs = fw.variables['xi'][:] yys = fw.variables['yj'][:] for k in range(m): inxx = np.where(icu == ic[k]) inx[k] = inxx[0] for k in range(m): inxx = np.where(times == ts[k]) tsx[k] = inxx[0] lons[inx[:],tsx[:]] = lns[:] lats[inx[:],tsx[:]] = lts[:] tims[inx[:],tsx[:]] = ts[:] xis[inx[:],tsx[:]] = xxs[:] yjs[inx[:],tsx[:]] = yys[:] fw.close() # Finally create the output file and write out the collated sets # fo = Dataset(pathout, 'w', format='NETCDF4') ntrj = fo.createDimension('ntraj', ntraj) nti = fo.createDimension('ntime', None) olon = fo.createVariable('lon', 'f4',('ntraj','ntime')) olat = fo.createVariable('lat', 'f4',('ntraj','ntime')) otim = fo.createVariable('ttim', 'f4',('ntraj','ntime')) oxis = fo.createVariable('xis', 'f4',('ntraj','ntime')) oyjs = fo.createVariable('yjs', 'f4',('ntraj','ntime')) icbn = fo.createVariable('icbn', 'f4',('ntraj')) olon[:,:] = lons olat[:,:] = lats otim[:,:] = tims oxis[:,:] = xis oyjs[:,:] = yjs icbn[:] = icu fo.close()