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Re-introduction of LMR_convertNPZtoNETCDF_gmt.py, improved error hand…
…ling in other convertNPZtoNETCDF modules and py3 tested.
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Module: LMR_convertNPZtoNETCDF_gmt.py | ||
| Purpose: Converts LMR output from .npz files to netcdf files. | ||
| Now restricted to gmt reconstruction variables, for every | ||
| Monte-Carlo realization included in the experiment. | ||
| Originator: Robert Tardif | Univ. of Washington, Dept. of Atmospheric Sciences | ||
| Michael Erb | University of Southern California | ||
| | June 2017 | ||
| Revisions: None | ||
| """ | ||
| import glob | ||
| import numpy as np | ||
| from netCDF4 import Dataset | ||
|
|
||
| # LMR-specific import | ||
| from LMR_utils import natural_sort | ||
|
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| # --- Begin section of user-defined parameters --- | ||
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| # name of directory where the output of LMR experiments are located | ||
| #datadir = '/home/disk/ekman4/rtardif/LMR/output' | ||
| datadir = '/home/disk/kalman3/rtardif/LMR/output' | ||
| #datadir = '/home/scec-00/lmr/erbm/LMR/archive_output/older_experiments' | ||
|
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| # name of the experiment | ||
| nexp = 'test' | ||
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| # --- End section of user-defined parameters --- | ||
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| expdir = datadir + '/'+nexp | ||
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| # where the netcdf files are created | ||
| outdir = expdir | ||
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| print('\n Getting information on Monte-Carlo realizations...\n') | ||
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| dirs = glob.glob(expdir+"/r*") | ||
| # keep names of MC directories (i.r. "r...") only | ||
| mcdirs = [item.split('/')[-1] for item in dirs] | ||
| # Make sure list is properly sorted | ||
| mcdirs = natural_sort(mcdirs) | ||
| # number of MC realizations found | ||
| niters = len(mcdirs) | ||
|
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| print('mcdirs:' + str(mcdirs)) | ||
| print('niters = ' + str(niters)) | ||
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| print('\n Getting information on reconstructed variables...\n') | ||
|
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| # look in first "mcdirs" only. It should be the same for all. | ||
| workdir = expdir+'/'+mcdirs[0] | ||
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| print('\n Global-mean temperature file.') | ||
| # Loop over realizations | ||
| r = 0 | ||
| for dir in mcdirs: | ||
| fname = expdir+'/'+dir+'/gmt_ensemble.npz' | ||
| npzfile = np.load(fname) | ||
|
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| # Get the reconstructed field | ||
| gmt_values = npzfile['gmt_ensemble'] | ||
| nhmt_values = npzfile['nhmt_ensemble'] | ||
| shmt_values = npzfile['shmt_ensemble'] | ||
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| if r == 0: # first realization | ||
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| npzcontent = npzfile.files | ||
| print(' file contents:', npzcontent) | ||
|
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| # get the years in the reconstruction | ||
| years = npzfile['recon_times'] | ||
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| # no spatial coordinate, must be a scalar (time series) | ||
| field_type='1D:time_series' | ||
| print(' field type:', field_type) | ||
|
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| # declare master array that will contain data from all the M-C realizations | ||
| # (i.e. the "Monte-Carlo ensemble") | ||
| dims = gmt_values.shape | ||
| print(' gmt field dimensions', dims) | ||
| tmp_gmt = np.expand_dims(gmt_values, axis=0) | ||
| tmp_nhmt = np.expand_dims(nhmt_values, axis=0) | ||
| tmp_shmt = np.expand_dims(shmt_values, axis=0) | ||
| # Form the array with the right total dimensions | ||
| mc_ens_gmt = np.repeat(tmp_gmt,niters,axis=0) | ||
| mc_ens_nhmt = np.repeat(tmp_nhmt,niters,axis=0) | ||
| mc_ens_shmt = np.repeat(tmp_shmt,niters,axis=0) | ||
|
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||
| else: | ||
| mc_ens_gmt[r,:,:] = gmt_values | ||
| mc_ens_nhmt[r,:,:] = nhmt_values | ||
| mc_ens_shmt[r,:,:] = shmt_values | ||
|
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| r = r + 1 | ||
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| # Roll array to get dims as [time, niters, nens] | ||
| mc_ens_outarr_gmt = np.swapaxes(mc_ens_gmt,0,1) | ||
| mc_ens_outarr_nhmt = np.swapaxes(mc_ens_nhmt,0,1) | ||
| mc_ens_outarr_shmt = np.swapaxes(mc_ens_shmt,0,1) | ||
|
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| # Create the netcdf file for the current variable | ||
| outfile_nc = outdir+'/gmt_ensemble_MCiters.nc' | ||
| outfile = Dataset(outfile_nc, 'w', format='NETCDF4') | ||
| outfile.description = 'LMR climate reconstruction for global-mean, NH-mean, and SH-mean surface air temperature (K).' | ||
| outfile.experiment = nexp | ||
| outfile.comment = 'File contains all ensemble values (ensemble_member) for each Monte-Carlo realization (iteration_member)' | ||
|
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| # define dimensions | ||
| ntime = years.shape[0] | ||
| nensemble = mc_ens_outarr_gmt.shape[2] | ||
|
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| outfile.createDimension('time', ntime) | ||
| outfile.createDimension('iteration_member', niters) | ||
| outfile.createDimension('ensemble_member', nensemble) | ||
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| # define variables & upload the data to file | ||
|
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| # time | ||
| time = outfile.createVariable('time', 'i', ('time',)) | ||
| time.description = 'time' | ||
| time.long_name = 'year CE' | ||
|
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| # reconstructed fields | ||
| varout_gmt = outfile.createVariable('gmt', 'f', ('time','iteration_member','ensemble_member')) | ||
| varout_gmt.description = 'gmt' | ||
| varout_gmt.long_name = 'Global-mean surface air temperature anomaly' | ||
| varout_gmt.units = 'K' | ||
|
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| varout_nhmt = outfile.createVariable('nhmt', 'f', ('time','iteration_member','ensemble_member')) | ||
| varout_nhmt.description = 'nhmt' | ||
| varout_nhmt.long_name = 'Northern Hemisphere-mean surface air temperature anomaly' | ||
| varout_nhmt.units = 'K' | ||
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| varout_shmt = outfile.createVariable('shmt', 'f', ('time','iteration_member','ensemble_member')) | ||
| varout_shmt.description = 'shmt' | ||
| varout_shmt.long_name = 'Southern Hemisphere-mean surface air temperature anomaly' | ||
| varout_shmt.units = 'K' | ||
|
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| # upload the data to file | ||
| time[:] = years | ||
| varout_gmt[:] = mc_ens_outarr_gmt | ||
| varout_nhmt[:] = mc_ens_outarr_nhmt | ||
| varout_shmt[:] = mc_ens_outarr_shmt | ||
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| # Closing the file | ||
| outfile.close() |