def run(self):
if self.netcdf3: io.netcdf3()
else: io.netcdf4(level=self.netcdf4level)
cdms2.setAutoBounds(0)
self.notice('Masking %s to %s', self.inpfile, self.outfile)
inpfile = cdms2.open(self.inpfile)
if os.path.isfile(self.outfile):
if os.path.samefile(self.inpfile, self.outfile):
raise Exception('Cannot use same input and output file')
if self.overwrite:
os.remove(self.outfile)
else:
raise Exception('Output file already exists and overwriting is not requested')
outfile = cdms2.open(self.outfile, 'w')
# Copy global attributes
for a,v in inpfile.attributes.items():
setattr(outfile, a, v)
# Keep grid masks in memory to improve performances (TODO: use a file cache as for basemaps ?)
mask_cache = dict()
stats = True # self.is_verbose()
# Iterate over input variables
for varid,filevar in inpfile.variables.items():
# Process only gridded/specified variables
grid = filevar.getGrid()
maskit = grid is not None
if maskit and self.included_variables and varid not in self.included_variables: maskit = False
if maskit and self.excluded_variables and varid in self.excluded_variables: maskit = False
if not maskit and self.masked_only:
self.verbose('Ignoring %s: variable has no grid and masked_only was specified')
continue
self.logger.info(bases.psinfo())
self.notice('Processing variable: %s, grid: %s', varid, grid.shape if grid else None)
# NOTE: With scalar variables, memvar could be numpy.<type> instead of cdms2.tvariable.TransientVariable
memvar = filevar()
self.info(bases.describe(memvar, stats=stats))
self.logger.info(bases.psinfo())
if maskit:
# Build the mask, check if it is already cached ?
cache_id = id(grid)
self.info('Get mask for grid %s', cache_id)
mask = mask_cache.get(cache_id, None)
if mask is None:
self.notice('Loading mask: %s', dict(resolution=self.resolution, mode=self.mode, thresholds=self.thresholds, reverse=self.reverse))
mask = masking.polygon_mask(grid, self.resolution, mode=self.mode, thresholds=self.thresholds)
if self.reverse: mask = ~mask
mask_cache[cache_id] = mask
self.info(bases.describe(mask, stats=stats))
self.logger.info(bases.psinfo())
# Mask variable
# TODO: check/handle dimensions count and order
self.notice('Masking variable: %s, mask: %s', memvar.shape, mask.shape)
mask = MV2.resize(mask, filevar.shape)
memvar[:] = MV2.masked_where(mask, memvar)
self.info(bases.describe(memvar, stats=stats))
# Special scalar case which could fail if directly written (because fill_value is None)
if not filevar.shape:
fill_value = filevar.getMissing()
if fill_value is None:
fill_value = -memvar
memvar = cdms2.createVariable(
memvar, id=filevar.id, shape=(), typecode=filevar.typecode(),
fill_value=fill_value, attributes=filevar.attributes)
# Write masked variable to output file
self.notice('Writing variable to file')
outfile.write(memvar)
self.logger.info(bases.psinfo())
outfile.close()
inpfile.close()
# Creation de l'axe des profondeurs cibles
depth_out = create_depth(depths)
# Interpolation
xmap = (0, 2, 3) # la profondeur varie en T/Y/X
xmapper = np.rollaxis(depths_in, 1, 4) # profondeur = dernier axe
data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1)
# Plot
kw = dict(show=False, vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0)
section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', **kw)
s = section2(data_out[0, :, 10], subplot=212, title='Z', savefigs=__file__,
close=True, **kw)
# Sauvegarde
outfile = __file__[:-2]+'nc'
if os.path.isfile(outfile):
os.remove(outfile)
netcdf3()
f2 = cdms2.open(outfile,'w')
f2.write(data_out)
f2.close()
print 'Saved to', outfile
# Temps de calcul
print "Whole computation took %.2f s" % (time() - t0)
print "End : " + strftime(print_time_format)
f.close() # Creation de l'axe des profondeurs cibles depth_out = create_depth(depths) # Interpolation xmap = (0, 2, 3) # la profondeur varie en T/Y/X xmapper = np.rollaxis(depths_in, 1, 4) # profondeur = dernier axe data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1) # Plot kw = dict(show=False, vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0) section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', **kw) s = section2(data_out[0, :, 10], subplot=212, title='Z', savefigs=__file__, **kw) # Sauvegarde outfile = __file__[:-2]+'nc' if os.path.isfile(outfile): os.remove(outfile) netcdf3() f2 = cdms2.open(outfile,'w') f2.write(data_out) f2.close() print 'Saved to', outfile # Temps de calcul print "Whole computation took %.2f s" % (time() - t0) print "End : " + strftime(print_time_format)
