- Timestamp:
- Oct 26, 2020 1:03:52 PM (4 years ago)
- Location:
- palm/trunk/SCRIPTS
- Files:
-
- 3 edited
-
.palm.config.aurora (modified) (2 diffs)
-
.palm.config.aurora_debug (modified) (2 diffs)
-
palm_cvd (modified) (6 diffs)
Legend:
- Unmodified
- Added
- Removed
-
palm/trunk/SCRIPTS/.palm.config.aurora
r4370 r4758 5 5 %base_data ~/palm/current_version/JOBS 6 6 %base_directory $HOME/palm/current_version 7 %fast_io_catalog /scratch/ <replace by LUIS username>7 %fast_io_catalog /scratch/nhbksueh 8 8 %source_path $HOME/palm/current_version/trunk/SOURCE 9 %user_source_path $base_directory/ JOBS/$run_identifier/USER_CODE10 %local_jobcatalog $HOME/job_ queue9 %user_source_path $base_directory/USER_CODE/$run_identifier 10 %local_jobcatalog $HOME/job_logfiles 11 11 # 12 12 %local_ip 130.75.7.130 13 %local_username <replace by LUIS username>13 %local_username nhbksueh 14 14 %compiler_name mpinfort 15 15 %compiler_name_ser mpinfort … … 26 26 # 27 27 #---------------------------------------------------------------------------- 28 # INPUT-commands, executed before running PALM - lines must start with "IC:" 28 # INPUT-commands, executed before running PALM - lines must start with "IC:" 29 29 #---------------------------------------------------------------------------- 30 30 #IC:ulimit -s unlimited -
palm/trunk/SCRIPTS/.palm.config.aurora_debug
r4371 r4758 5 5 %base_data ~/palm/current_version/JOBS 6 6 %base_directory $HOME/palm/current_version 7 %fast_io_catalog /scratch/ <replace by LUIS username>7 %fast_io_catalog /scratch/nhbksueh 8 8 %source_path $HOME/palm/current_version/trunk/SOURCE 9 %user_source_path $base_directory/ JOBS/$run_identifier/USER_CODE10 %local_jobcatalog $HOME/job_ queue9 %user_source_path $base_directory/USER_CODE/$run_identifier 10 %local_jobcatalog $HOME/job_logfiles 11 11 # 12 12 %local_ip 130.75.7.130 13 %local_username <replace by LUIS username>13 %local_username nhbksueh 14 14 %compiler_name mpinfort 15 15 %compiler_name_ser mpinfort 16 16 %cpp_options -fpp -D__parallel -D__nec -D__netcdf -D__netcdf4 -D__netcdf4_parallel -D__fftw -DMPI_REAL=MPI_DOUBLE_PRECISION -DMPI_2REAL=MPI_2DOUBLE_PRECISION 17 17 %make_options -j 4 18 %compiler_options -O 0 -fcheck=all -traceback -ftrace -proginf -report-all -g -Werror -fdiag-vector=0-I /home/nhbkklau/opt/NetCDF_parallel_nec/include -I /home/nhbkklau/opt/fftw3_nec/include19 %linker_options - ftrace -L/home/nhbkklau/opt/NetCDF_parallel_nec/lib -lnetcdff -lnetcdf -Wl,-rpath=/home/nhbkklau/opt/NetCDF_parallel_nec/lib -L/home/nhbkklau/opt/hdf5_parallel_nec/lib -lhdf5 -lhdf5_hl -Wl,-rpath=/home/nhbkklau/opt/hdf5_parallel_nec/lib -L/home/nhbkklau/opt/fftw3_nec/lib -lfftw3 -lmpi18 %compiler_options -O3 -traceback -finline-functions -g -report-all -Werror -I /home/nhbkklau/opt/NetCDF_parallel_nec/include -I /home/nhbkklau/opt/fftw3_nec/include 19 %linker_options -L/home/nhbkklau/opt/NetCDF_parallel_nec/lib -lnetcdff -lnetcdf -Wl,-rpath=/home/nhbkklau/opt/NetCDF_parallel_nec/lib -L/home/nhbkklau/opt/hdf5_parallel_nec/lib -lhdf5 -lhdf5_hl -Wl,-rpath=/home/nhbkklau/opt/hdf5_parallel_nec/lib -L/home/nhbkklau/opt/fftw3_nec/lib -lfftw3 -lmpi 20 20 #%hostfile auto 21 21 %defaultqueue all … … 23 23 %memory 2000 24 24 %login_init_cmd source /opt/nec/ve/mpi/2.2.0/bin/necmpivars.sh 25 %execute_command mpirun -v -ve 0 -np {{mpi_tasks}} ./palm25 %execute_command mpirun -v -ve 0-3 -np {{mpi_tasks}} ./palm 26 26 # 27 27 #---------------------------------------------------------------------------- 28 # INPUT-commands, executed before running PALM - lines must start with "IC:" 28 # INPUT-commands, executed before running PALM - lines must start with "IC:" 29 29 #---------------------------------------------------------------------------- 30 30 #IC:ulimit -s unlimited 31 IC:export VE_ERRCTL_DEALLOCATE=ABORT32 IC:export VE_FPE_ENABLE=DIV,FOF,INV33 IC:export VE_TRACEBACK=ALL34 31 # 35 32 #---------------------------------------------------------------------------- -
palm/trunk/SCRIPTS/palm_cvd
r4663 r4758 26 26 # ----------------- 27 27 # $Id$ 28 # In order to do not omit observations that are on the same site but have different 29 # coordinates or feature-types, process all files rather than only one and omit 30 # the rest. 31 # 32 # 4663 2020-09-02 14:54:09Z gronemeier 28 33 # bugfix in non_measurable_vars; ignore station_h if featureType is trajectory 29 34 # … … 198 203 name_fill = "_FillValue" 199 204 name_site = "site" 205 name_acro = "acronym" 206 name_content = "data_content" 200 207 name_orig_x = "origin_x" 201 208 name_orig_y = "origin_y" … … 268 275 # virtual measurement has an overhead and consumes memory. 269 276 sites = [] 277 input_files = [] 270 278 for dirname in list_input_data: 271 279 data_file = data_path + dirname … … 284 292 input_file = data_file + "/" + latest_file 285 293 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii') 286 287 # Read global attributes and write them immediately into the output file 288 for att in ncfile_in.ncattrs(): 289 if ( att == name_site ): 290 site = ncfile_in.getncattr(att) 291 292 if ( site not in sites ): 293 sites.append(site) 294 295 # Define a flag array that is used to identify whether site dimensions are already 296 # defined or not. 297 create_metadata_for_site = [None] * len(sites) 294 input_files.append(input_file) 298 295 299 296 # Define a nested list of default variables that shall be measured. Based on this list, 300 297 # the final number of measured variables is determined. 301 measured_variables_all_sites = [ ['u', 'v', 'w', 'theta', 'hus'] for var in range(0, len( sites))]298 measured_variables_all_sites = [ ['u', 'v', 'w', 'theta', 'hus'] for var in range(0, len(input_files))] 302 299 303 300 # Run loop over all subdirectories that contain observational data 304 for dirname in list_input_data: 305 data_file = data_path + dirname 306 307 # Directory may contain various file versions. 308 # Take the one with highest cycle number. 309 highest_cycle_nr = 0 310 for filename in os.listdir(data_file): 311 start_seq = len( filename ) - 6 312 end_seq = len( filename ) - 3 313 if int( filename[start_seq:end_seq] ) > highest_cycle_nr: 314 highest_cycle_nr = int(filename[start_seq:end_seq]) 315 latest_file = filename 301 for counter, file in enumerate(input_files): 316 302 317 303 # Open the NetCDF input file 318 input_file = data_file + "/" + latest_file304 input_file = input_files[counter] 319 305 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) 320 306 321 # Read site attribue first 307 # Determine index for the treated site 308 num_vmeas = input_files.index( input_file ) + 1 309 print( counter, num_vmeas ) 310 311 # Read global attributes and write them immediately into the output file 322 312 for att in ncfile_in.ncattrs(): 313 if ( att == name_featuretype ): 314 feature = ncfile_in.getncattr(att) 315 if ( att == name_datacontent ): 316 content = ncfile_in.getncattr(att) 323 317 if ( att == name_site ): 324 318 site = ncfile_in.getncattr(att) 325 319 326 # Determine index for the treated site 327 num_vmeas = sites.index( site ) + 1 328 329 # Check whether metadata for this site has been already created 330 if ( create_metadata_for_site[sites.index( site )] != "Done" ): 331 332 # Read global attributes and write them immediately into the output file 333 for att in ncfile_in.ncattrs(): 334 if ( att == name_featuretype ): 335 feature = ncfile_in.getncattr(att) 336 if ( att == name_datacontent ): 337 content = ncfile_in.getncattr(att) 338 if ( att == name_site ): 339 site = ncfile_in.getncattr(att) 340 341 if ( att in atts_float ): 342 ncfile_out.setncattr( att + str(num_vmeas), np.double(ncfile_in.getncattr(att)) ) 320 if ( att in atts_float ): 321 ncfile_out.setncattr( att + str(num_vmeas), np.double(ncfile_in.getncattr(att)) ) 322 else: 323 ncfile_out.setncattr( att + str(num_vmeas), ncfile_in.getncattr(att) ) 324 325 #timeSeries 326 if ( feature == name_ts ): 327 ntime = len( ncfile_in.dimensions[name_ntime] ) 328 nstat = len( ncfile_in.dimensions[name_station] ) 329 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 330 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 331 332 #trajectory 333 elif ( feature == name_traj ): 334 ntime = len( ncfile_in.dimensions[name_ntime] ) 335 ntraj = len( ncfile_in.dimensions[name_traj_dim] ) 336 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 337 ncfile_out.createDimension( name_traj_dim + str(num_vmeas), ntraj ) 338 339 #timeseriesProfile 340 else: 341 ntime = len( ncfile_in.dimensions[name_ntime] ) 342 nstat = len( ncfile_in.dimensions[name_station] ) 343 nz = len( ncfile_in.dimensions[name_nz] ) 344 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 345 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 346 ncfile_out.createDimension( name_nz + str(num_vmeas), nz ) 347 348 for var in ncfile_in.variables.keys(): 349 if ( var in dims_out ): 350 # Create a variable and write it to file after it is read. In order to 351 # avoid fill values in the dimensions, these are converted to zero 352 # before written to file. Depending on the featureType of the measurement, 353 # the array shape is different. For more informations, please see 354 # [UC]2 data standard. 355 # Timeseries 356 if ( feature == name_ts ): 357 temp_ts = ncfile_out.createVariable( var + str(num_vmeas), float, \ 358 name_station + str(num_vmeas)) 359 temp_ts[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 360 361 # Trajectories 362 elif ( feature == name_traj ): 363 # @note: If there are files where station_h is present although featureType is 364 # trajectory, station_h must not be read. 365 if var != name_station_h: 366 temp_traj = ncfile_out.createVariable( var + str(num_vmeas), float, \ 367 ( name_traj_dim + str(num_vmeas), \ 368 name_ntime + str(num_vmeas) ) ) 369 temp_traj[:,:] = np.nan_to_num( ncfile_in.variables[var][:,:] ) 370 371 # TimeseriesProfiles 343 372 else: 344 ncfile_out.setncattr( att + str(num_vmeas), ncfile_in.getncattr(att) ) 345 346 #timeSeries 347 if ( feature == name_ts ): 348 ntime = len( ncfile_in.dimensions[name_ntime] ) 349 nstat = len( ncfile_in.dimensions[name_station] ) 350 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 351 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 352 353 #trajectory 354 elif ( feature == name_traj ): 355 ntime = len( ncfile_in.dimensions[name_ntime] ) 356 ntraj = len( ncfile_in.dimensions[name_traj_dim] ) 357 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 358 ncfile_out.createDimension( name_traj_dim + str(num_vmeas), ntraj ) 359 360 #timeseriesProfile 361 else: 362 ntime = len( ncfile_in.dimensions[name_ntime] ) 363 nstat = len( ncfile_in.dimensions[name_station] ) 364 nz = len( ncfile_in.dimensions[name_nz] ) 365 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 366 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 367 ncfile_out.createDimension( name_nz + str(num_vmeas), nz ) 368 369 for var in ncfile_in.variables.keys(): 370 if ( var in dims_out ): 371 # Create a variable and write it to file after it is read. In order to 372 # avoid fill values in the dimensions, these are converted to zero 373 # before written to file. Depending on the featureType of the measurement, 374 # the array shape is different. For more informations, please see 375 # [UC]2 data standard. 376 # Timeseries 377 if ( feature == name_ts ): 378 temp_ts = ncfile_out.createVariable( var + str(num_vmeas), float, \ 373 if ( var == 'z' ): 374 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 375 ( name_station + str(num_vmeas), \ 376 name_nz + str(num_vmeas) ) ) 377 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:,0,:] ) 378 else: 379 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 379 380 name_station + str(num_vmeas)) 380 temp_ts[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 381 382 # Trajectories 383 elif ( feature == name_traj ): 384 # @note: If there are files where station_h is present although featureType is 385 # trajectory, station_h must not be read. 386 if var != name_station_h: 387 temp_traj = ncfile_out.createVariable( var + str(num_vmeas), float, \ 388 ( name_traj_dim + str(num_vmeas), \ 389 name_ntime + str(num_vmeas) ) ) 390 temp_traj[:,:] = np.nan_to_num( ncfile_in.variables[var][:,:] ) 391 392 # TimeseriesProfiles 393 else: 394 if ( var == 'z' ): 395 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 396 ( name_station + str(num_vmeas), \ 397 name_nz + str(num_vmeas) ) ) 398 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:,0,:] ) 399 else: 400 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 401 name_station + str(num_vmeas)) 402 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 381 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 403 382 404 383 # Search for variables to be measured. In case the variable isn't already defined, … … 407 386 if ( var not in non_measurable_vars and \ 408 387 var not in vars_default and \ 409 var not in measured_variables_all_sites[ sites.index( site )] ):410 411 measured_variables_all_sites[ sites.index( site )].append(var)388 var not in measured_variables_all_sites[input_files.index( input_file )] ): 389 390 measured_variables_all_sites[input_files.index( input_file )].append(var) 412 391 413 392 # Close the NetCDF input file 414 393 ncfile_in.close() 415 394 416 # Set flag to indicate that for this specific site dimensions have been417 # already created and attributes are already set.418 if ( create_metadata_for_site[sites.index( site )] != "Done" ):419 create_metadata_for_site[sites.index( site )] = "Done"420 421 395 # After variables are gathered and dimensions / attributes are already written to file, 422 396 # the list of measured variables is written to file. 423 for site in sites:424 425 num_vmeas = sites.index( site ) + 1397 for site in input_files: 398 399 num_vmeas = input_files.index( site ) + 1 426 400 427 401 ncfile_out.createDimension( "nvar"+ str(num_vmeas), \ 428 len( measured_variables_all_sites[ sites.index( site )] ) )402 len( measured_variables_all_sites[input_files.index( site )] ) ) 429 403 430 404 measured = ncfile_out.createVariable( 'measured_variables' + str(num_vmeas), 'S1', \ 431 405 ("nvar" + str(num_vmeas), "string_len")) # must be NC_CHAR 432 406 433 for counter, meas in enumerate( measured_variables_all_sites[ sites.index( site )] ):407 for counter, meas in enumerate( measured_variables_all_sites[input_files.index( site )] ): 434 408 measured[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) 435 409 436 410 # Check if any of the measured variables is a soil variable. Set flag accordingly. 437 411 soil = False 438 for var in measured_variables_all_sites[ sites.index( site )]:412 for var in measured_variables_all_sites[input_files.index( site )]: 439 413 if ( var in soil_vars ): 440 414 soil = True … … 446 420 447 421 # Store the number of observational sites 448 num_sites += len( sites )422 num_sites += len( input_files ) 449 423 450 424
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