| [4400] | 1 | #!/usr/bin/env python3 |
|---|
| 2 | # -*- coding: utf-8 -*- |
|---|
| 3 | # |
|---|
| 4 | #--------------------------------------------------------------------------------# |
|---|
| 5 | # This file is part of the PALM model system. |
|---|
| 6 | # |
|---|
| 7 | # PALM is free software: you can redistribute it and/or modify it under the terms |
|---|
| 8 | # of the GNU General Public License as published by the Free Software Foundation, |
|---|
| 9 | # either version 3 of the License, or (at your option) any later version. |
|---|
| 10 | # |
|---|
| 11 | # PALM is distributed in the hope that it will be useful, but WITHOUT ANY |
|---|
| 12 | # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR |
|---|
| 13 | # A PARTICULAR PURPOSE. See the GNU General Public License for more details. |
|---|
| 14 | # |
|---|
| 15 | # You should have received a copy of the GNU General Public License along with |
|---|
| 16 | # PALM. If not, see <http://www.gnu.org/licenses/>. |
|---|
| 17 | # |
|---|
| 18 | # Copyright 1997-2020 Leibniz Universitaet Hannover |
|---|
| 19 | #--------------------------------------------------------------------------------# |
|---|
| 20 | # |
|---|
| 21 | # Current revisions: |
|---|
| 22 | # ----------------- |
|---|
| [4663] | 23 | # |
|---|
| 24 | # |
|---|
| [4400] | 25 | # Former revisions: |
|---|
| 26 | # ----------------- |
|---|
| 27 | # $Id$ |
|---|
| [4663] | 28 | # bugfix in non_measurable_vars; ignore station_h if featureType is trajectory |
|---|
| 29 | # |
|---|
| 30 | # 4400 2020-02-10 20:32:41Z suehring |
|---|
| [4400] | 31 | # Initial revision |
|---|
| 32 | # |
|---|
| 33 | # Description: |
|---|
| 34 | # ------------ |
|---|
| 35 | # Processing tool for creating PIDS conform virtual measurement setup file |
|---|
| 36 | # from UC2 data-standard conform observational data or from prescribed input |
|---|
| 37 | # coordinates. |
|---|
| 38 | # |
|---|
| 39 | # @Authors Matthias Suehring (suehring@muk.uni-hannover.de) |
|---|
| 40 | # Tobias Gronemeier (gronemeier@muk.uni-hannover.de) |
|---|
| 41 | # |
|---|
| 42 | # @todo Add further feature tpyes for customized observations. At the moment only |
|---|
| 43 | # timeSeries is possible. |
|---|
| 44 | #--------------------------------------------------------------------------------# |
|---|
| 45 | |
|---|
| 46 | |
|---|
| 47 | import netCDF4 |
|---|
| 48 | from netCDF4 import Dataset, stringtochar |
|---|
| 49 | import os |
|---|
| 50 | import numpy as np |
|---|
| 51 | |
|---|
| 52 | |
|---|
| 53 | # Function to read the config file |
|---|
| 54 | def read_config_file(): |
|---|
| 55 | |
|---|
| 56 | import configparser |
|---|
| 57 | import os |
|---|
| 58 | import sys |
|---|
| 59 | import json |
|---|
| 60 | |
|---|
| 61 | # Definition of global configuration parameters |
|---|
| [4663] | 62 | global global_acronym |
|---|
| 63 | global global_author |
|---|
| 64 | global global_campaign |
|---|
| 65 | global global_comment |
|---|
| 66 | global global_contact |
|---|
| [4400] | 67 | global global_data_content |
|---|
| 68 | global global_dependencies |
|---|
| [4663] | 69 | global global_institution |
|---|
| 70 | global global_keywords |
|---|
| 71 | global global_location |
|---|
| 72 | global global_references |
|---|
| 73 | global global_site |
|---|
| 74 | global global_source |
|---|
| [4400] | 75 | global global_palm_version |
|---|
| [4663] | 76 | global data_path |
|---|
| 77 | global output_path |
|---|
| 78 | global output_filename |
|---|
| [4400] | 79 | global number_positions |
|---|
| 80 | global input_from_observations |
|---|
| 81 | global coordinates |
|---|
| 82 | global vars_to_be_measured |
|---|
| 83 | global custom_coordinates |
|---|
| 84 | |
|---|
| 85 | global_acronym = " " |
|---|
| 86 | global_author = " " |
|---|
| 87 | global_campaign = " " |
|---|
| 88 | global_comment = " " |
|---|
| 89 | global_contact = " " |
|---|
| 90 | global_data_content = " " |
|---|
| 91 | global_dependencies = " " |
|---|
| 92 | global_institution = " " |
|---|
| 93 | global_keywords = " " |
|---|
| 94 | global_location = " " |
|---|
| 95 | global_references = " " |
|---|
| 96 | global_site = " " |
|---|
| 97 | global_source = " " |
|---|
| 98 | global_palm_version = 6.0 |
|---|
| 99 | data_path = " " |
|---|
| 100 | output_path = " " |
|---|
| 101 | output_filename = "none" |
|---|
| 102 | number_positions = -999 |
|---|
| 103 | input_from_observations = False |
|---|
| 104 | coordinates = [] |
|---|
| 105 | vars_to_be_measured = [] |
|---|
| 106 | custom_coordinates = False |
|---|
| 107 | |
|---|
| 108 | # Check if configuration files exists and quit otherwise |
|---|
| 109 | input_config = ".cvd.config.default" |
|---|
| [4663] | 110 | for i in range(1,len(sys.argv)): |
|---|
| [4400] | 111 | input_config = str(sys.argv[i]) |
|---|
| 112 | |
|---|
| 113 | # Allow empty settings |
|---|
| 114 | config = configparser.RawConfigParser(allow_no_value=True) |
|---|
| 115 | |
|---|
| 116 | # Check if a config file exists. |
|---|
| 117 | if ( os.path.isfile(input_config) == False ): |
|---|
| 118 | print ("Error. No configuration file " + input_config + " found.") |
|---|
| 119 | quit() |
|---|
| 120 | |
|---|
| 121 | config.read(input_config) |
|---|
| [4663] | 122 | |
|---|
| [4400] | 123 | for section in range( 0, len( config.sections() ) ): |
|---|
| 124 | |
|---|
| 125 | current_section = config.sections()[section] |
|---|
| 126 | |
|---|
| 127 | # read global attributes which are written into the output file header |
|---|
| 128 | if ( current_section == 'global' ): |
|---|
| 129 | |
|---|
| 130 | global_acronym = config.get( current_section, 'acronym' ) |
|---|
| 131 | global_author = config.get( current_section, 'author' ) |
|---|
| 132 | global_campaign = config.get( current_section, 'campaign' ) |
|---|
| 133 | global_comment = config.get( current_section, 'comment' ) |
|---|
| 134 | global_contact = config.get( current_section, 'contact_person' ) |
|---|
| 135 | global_data_content = config.get( current_section, 'data_content' ) |
|---|
| 136 | global_dependencies = config.get( current_section, 'dependencies' ) |
|---|
| 137 | global_institution = config.get( current_section, 'institution' ) |
|---|
| 138 | global_keywords = config.get( current_section, 'keywords' ) |
|---|
| 139 | global_location = config.get( current_section, 'location' ) |
|---|
| 140 | global_references = config.get( current_section, 'references' ) |
|---|
| 141 | global_site = config.get( current_section, 'site' ) |
|---|
| 142 | global_source = config.get( current_section, 'source' ) |
|---|
| 143 | global_palm_version = float( config.get( current_section, 'palm_version' ) ) |
|---|
| 144 | |
|---|
| 145 | # Read data input path for observational data |
|---|
| 146 | elif ( current_section == 'input' ): |
|---|
| 147 | |
|---|
| 148 | data_path = config.get( current_section, 'data_path' ) |
|---|
| 149 | input_from_observations = True |
|---|
| 150 | |
|---|
| 151 | # Read output path and filename for the VM driver |
|---|
| 152 | elif ( current_section == 'output' ): |
|---|
| 153 | |
|---|
| 154 | output_path = config.get( current_section, 'output_path' ) |
|---|
| 155 | output_filename = config.get( current_section, 'output_filename' ) |
|---|
| 156 | |
|---|
| [4663] | 157 | # Read customized coordinates where virtual measurements shall be taken, |
|---|
| [4400] | 158 | # as well as the variables that should be sampled. |
|---|
| 159 | elif ( current_section == 'custom_positions' ): |
|---|
| 160 | |
|---|
| 161 | number_positions = config.get( current_section, 'number_positions' ) |
|---|
| 162 | |
|---|
| 163 | for count in range( 0, int( number_positions ) ): |
|---|
| 164 | coordinates.append( json.loads( config.get( current_section, \ |
|---|
| 165 | "coordinates" + str( count + 1 ) ) ) ) |
|---|
| 166 | # If coordinates are given, set a global flag. |
|---|
| 167 | custom_coordinates = True |
|---|
| 168 | |
|---|
| 169 | for count in range( 0, int( number_positions ) ): |
|---|
| 170 | vars_to_be_measured.append( json.loads( config.get( current_section, \ |
|---|
| 171 | "vars_to_be_measured" + str( count + 1 ) ) ) ) |
|---|
| 172 | |
|---|
| 173 | |
|---|
| 174 | return 0 |
|---|
| 175 | |
|---|
| 176 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
|---|
| 177 | # Main program: |
|---|
| 178 | #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ |
|---|
| 179 | |
|---|
| 180 | |
|---|
| 181 | # Define strings |
|---|
| 182 | name_featuretype = "featureType" |
|---|
| 183 | name_ts = "timeSeries" |
|---|
| 184 | name_traj = "trajectory" |
|---|
| 185 | name_ntime = "ntime" |
|---|
| 186 | name_time = "time" |
|---|
| 187 | name_station = "station" |
|---|
| 188 | name_traj_dim = "traj" |
|---|
| 189 | name_nz = "nz" |
|---|
| 190 | name_datacontent = "data_content" |
|---|
| 191 | name_eutm = "E_UTM" |
|---|
| 192 | name_nutm = "N_UTM" |
|---|
| 193 | name_hao = "height_above_origin" |
|---|
| 194 | name_station_h = "station_h" |
|---|
| 195 | name_z = "z" |
|---|
| 196 | name_soil_sampling = "soil_sample" |
|---|
| 197 | name_num_stat = "number_of_stations" |
|---|
| 198 | name_fill = "_FillValue" |
|---|
| 199 | name_site = "site" |
|---|
| 200 | name_orig_x = "origin_x" |
|---|
| 201 | name_orig_y = "origin_y" |
|---|
| 202 | name_orig_z = "origin_z" |
|---|
| 203 | |
|---|
| 204 | max_string_len = 50 |
|---|
| 205 | |
|---|
| 206 | name_measvars = "measured_variables" |
|---|
| 207 | |
|---|
| 208 | non_measurable_vars = ['station_name', 'time', 'time_bounds', 'crs', \ |
|---|
| 209 | 'vrs', 'x', 'y', 'z', 'lon', 'lat', 'ntime', 'station', 'traj', \ |
|---|
| 210 | 'E_UTM', 'N_UTM', 'height_above_origin', 'station_h', \ |
|---|
| [4663] | 211 | 'traj_name', 'height', 'band_pm_size', 'bands_pm', 'bands_pm_size_bounds', \ |
|---|
| [4400] | 212 | 'bands_pm_size', 'ancillary_detected_layer' ] |
|---|
| 213 | |
|---|
| 214 | soil_vars = [ 't_soil', 'm_soil', 'lwc', 'lwcs', 'smp' ] |
|---|
| 215 | |
|---|
| 216 | dims_out = [ name_eutm, name_nutm, name_hao, name_z, name_station_h ] |
|---|
| 217 | |
|---|
| [4663] | 218 | # Define list of attributes which need to be of type float. In the data set this is not |
|---|
| [4400] | 219 | # necessarily guranteed. |
|---|
| 220 | atts_float = [ 'origin_x', 'origin_y', 'origin_z', 'origin_lon', 'origin_lat', 'rotation_angle' ] |
|---|
| 221 | |
|---|
| 222 | # Define list of default variables that shall be measured at each site |
|---|
| 223 | vars_default = [ 'u', 'v', 'w', 'theta', 'hus' ] |
|---|
| 224 | |
|---|
| 225 | |
|---|
| 226 | #Read config file |
|---|
| 227 | read_config_file() |
|---|
| 228 | |
|---|
| 229 | # Initialize counter variable for the number of sites |
|---|
| 230 | num_sites = 0 |
|---|
| 231 | |
|---|
| 232 | # Set the output path for the data |
|---|
| 233 | output_filename = output_path + output_filename |
|---|
| 234 | |
|---|
| 235 | # Open output file |
|---|
| 236 | ncfile_out = Dataset( output_filename, "w", format="NETCDF4" ) |
|---|
| 237 | |
|---|
| 238 | # First, add global attributes |
|---|
| 239 | ncfile_out.setncattr( 'acronym', global_acronym ) |
|---|
| 240 | ncfile_out.setncattr( 'author', global_author ) |
|---|
| 241 | ncfile_out.setncattr( 'campaign', global_campaign ) |
|---|
| 242 | ncfile_out.setncattr( 'comment', global_comment ) |
|---|
| 243 | ncfile_out.setncattr( 'contact_person', global_contact ) |
|---|
| 244 | ncfile_out.setncattr( 'data_content', global_data_content ) |
|---|
| 245 | ncfile_out.setncattr( 'dependencies', global_dependencies ) |
|---|
| 246 | ncfile_out.setncattr( 'institution', global_institution ) |
|---|
| 247 | ncfile_out.setncattr( 'keywords', global_keywords ) |
|---|
| 248 | ncfile_out.setncattr( 'location', global_location ) |
|---|
| 249 | ncfile_out.setncattr( 'references', global_references ) |
|---|
| 250 | ncfile_out.setncattr( 'site', global_site ) |
|---|
| 251 | ncfile_out.setncattr( 'source', global_source ) |
|---|
| 252 | ncfile_out.setncattr( 'palm_version', global_palm_version ) |
|---|
| 253 | |
|---|
| 254 | # Create universal dimension for the string length. |
|---|
| 255 | ncfile_out.createDimension("string_len", max_string_len) |
|---|
| 256 | |
|---|
| 257 | |
|---|
| [4663] | 258 | # Check if observational data is available. This case, |
|---|
| 259 | # obtain an alphabetically sorted list of input data. List is sorted |
|---|
| [4400] | 260 | # just for the sake of clarity in the resulting setup file. |
|---|
| 261 | if ( input_from_observations == True ): |
|---|
| 262 | list_input_data = sorted( os.listdir( data_path ) ) |
|---|
| 263 | |
|---|
| 264 | if ( input_from_observations ): |
|---|
| 265 | |
|---|
| 266 | # Run loop over all subdirectories, detect the files and extract a list of sites. |
|---|
| 267 | # This is done to reduce the number of virtual measurements in the model. Each |
|---|
| 268 | # virtual measurement has an overhead and consumes memory. |
|---|
| 269 | sites = [] |
|---|
| 270 | for dirname in list_input_data: |
|---|
| 271 | data_file = data_path + dirname |
|---|
| 272 | |
|---|
| 273 | # Directory may contain various file versions. |
|---|
| 274 | # Take the one with highest cycle number. |
|---|
| 275 | highest_cycle_nr = 0 |
|---|
| 276 | for filename in os.listdir(data_file): |
|---|
| 277 | start_seq = len( filename ) - 6 |
|---|
| 278 | end_seq = len( filename ) - 3 |
|---|
| 279 | if int( filename[start_seq:end_seq] ) > highest_cycle_nr: |
|---|
| 280 | highest_cycle_nr = int(filename[start_seq:end_seq]) |
|---|
| 281 | latest_file = filename |
|---|
| 282 | |
|---|
| 283 | # Open the NetCDF file |
|---|
| 284 | input_file = data_file + "/" + latest_file |
|---|
| 285 | 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) |
|---|
| 298 | |
|---|
| 299 | # Define a nested list of default variables that shall be measured. Based on this list, |
|---|
| 300 | # 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))] |
|---|
| 302 | |
|---|
| 303 | # 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 |
|---|
| 316 | |
|---|
| 317 | # Open the NetCDF input file |
|---|
| 318 | input_file = data_file + "/" + latest_file |
|---|
| 319 | ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) |
|---|
| 320 | |
|---|
| 321 | # Read site attribue first |
|---|
| 322 | for att in ncfile_in.ncattrs(): |
|---|
| 323 | if ( att == name_site ): |
|---|
| 324 | site = ncfile_in.getncattr(att) |
|---|
| 325 | |
|---|
| 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)) ) |
|---|
| 343 | 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 ): |
|---|
| [4663] | 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 |
|---|
| [4400] | 373 | # before written to file. Depending on the featureType of the measurement, |
|---|
| [4663] | 374 | # the array shape is different. For more informations, please see |
|---|
| [4400] | 375 | # [UC]2 data standard. |
|---|
| 376 | # Timeseries |
|---|
| [4663] | 377 | if ( feature == name_ts ): |
|---|
| [4400] | 378 | temp_ts = ncfile_out.createVariable( var + str(num_vmeas), float, \ |
|---|
| 379 | name_station + str(num_vmeas)) |
|---|
| 380 | temp_ts[:] = np.nan_to_num( ncfile_in.variables[var][:] ) |
|---|
| 381 | |
|---|
| 382 | # Trajectories |
|---|
| 383 | elif ( feature == name_traj ): |
|---|
| [4663] | 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][:,:] ) |
|---|
| [4400] | 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][:] ) |
|---|
| 403 | |
|---|
| 404 | # Search for variables to be measured. In case the variable isn't already defined, |
|---|
| 405 | # append the variable to the list. |
|---|
| 406 | for var in ncfile_in.variables.keys(): |
|---|
| 407 | if ( var not in non_measurable_vars and \ |
|---|
| 408 | 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) |
|---|
| 412 | |
|---|
| 413 | # Close the NetCDF input file |
|---|
| 414 | ncfile_in.close() |
|---|
| 415 | |
|---|
| [4663] | 416 | # Set flag to indicate that for this specific site dimensions have been |
|---|
| [4400] | 417 | # 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 | # After variables are gathered and dimensions / attributes are already written to file, |
|---|
| 422 | # the list of measured variables is written to file. |
|---|
| 423 | for site in sites: |
|---|
| 424 | |
|---|
| 425 | num_vmeas = sites.index( site ) + 1 |
|---|
| 426 | |
|---|
| 427 | ncfile_out.createDimension( "nvar"+ str(num_vmeas), \ |
|---|
| 428 | len( measured_variables_all_sites[sites.index( site )] ) ) |
|---|
| 429 | |
|---|
| 430 | measured = ncfile_out.createVariable( 'measured_variables' + str(num_vmeas), 'S1', \ |
|---|
| 431 | ("nvar" + str(num_vmeas), "string_len")) # must be NC_CHAR |
|---|
| 432 | |
|---|
| 433 | for counter, meas in enumerate( measured_variables_all_sites[sites.index( site )] ): |
|---|
| 434 | measured[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| 435 | |
|---|
| 436 | # Check if any of the measured variables is a soil variable. Set flag accordingly. |
|---|
| 437 | soil = False |
|---|
| 438 | for var in measured_variables_all_sites[sites.index( site )]: |
|---|
| 439 | if ( var in soil_vars ): |
|---|
| 440 | soil = True |
|---|
| 441 | |
|---|
| 442 | # Write soil flag |
|---|
| 443 | ncfile_out.setncattr( name_soil_sampling + str( num_vmeas), np.int8(soil) ) |
|---|
| 444 | |
|---|
| 445 | |
|---|
| 446 | |
|---|
| 447 | # Store the number of observational sites |
|---|
| 448 | num_sites += len( sites ) |
|---|
| 449 | |
|---|
| 450 | |
|---|
| 451 | # Now process the customized input data. Please note, at the moment only timeseries are |
|---|
| 452 | # are possible. |
|---|
| 453 | if ( custom_coordinates ): |
|---|
| 454 | |
|---|
| 455 | count_site = num_sites + 1 |
|---|
| 456 | for coord in coordinates: |
|---|
| 457 | # Define mandatory attributes |
|---|
| 458 | ncfile_out.setncattr( name_featuretype + str(count_site), \ |
|---|
| 459 | name_ts ) |
|---|
| 460 | ncfile_out.setncattr( name_site + str(count_site), \ |
|---|
| 461 | "custom" + str(count_site - num_sites) ) |
|---|
| 462 | ncfile_out.setncattr( name_orig_x + str(count_site), \ |
|---|
| 463 | coord[0] ) |
|---|
| 464 | ncfile_out.setncattr( name_orig_y + str(count_site), \ |
|---|
| 465 | coord[1] ) |
|---|
| 466 | ncfile_out.setncattr( name_orig_z + str(count_site), \ |
|---|
| 467 | 0.0 ) |
|---|
| 468 | |
|---|
| 469 | # Define dimensions |
|---|
| 470 | ntime = 1 |
|---|
| 471 | nstat = 1 |
|---|
| 472 | ncfile_out.createDimension( name_ntime + str(count_site), ntime ) |
|---|
| 473 | ncfile_out.createDimension( name_station + str(count_site), nstat ) |
|---|
| 474 | |
|---|
| 475 | # Define coordinate variables |
|---|
| 476 | temp_ts = ncfile_out.createVariable( name_eutm + str(count_site), \ |
|---|
| 477 | float, \ |
|---|
| 478 | name_station + str(count_site) ) |
|---|
| 479 | temp_ts[:] = np.array( coord[0] ) |
|---|
| 480 | |
|---|
| 481 | temp_ts = ncfile_out.createVariable( name_nutm + str(count_site), \ |
|---|
| 482 | float, \ |
|---|
| 483 | name_station + str(count_site) ) |
|---|
| 484 | temp_ts[:] = np.array( coord[1] ) |
|---|
| 485 | |
|---|
| 486 | temp_ts = ncfile_out.createVariable( name_z + str(count_site), \ |
|---|
| 487 | float, \ |
|---|
| 488 | name_station + str(count_site) ) |
|---|
| 489 | temp_ts[:] = np.array( coord[2] ) |
|---|
| 490 | |
|---|
| 491 | temp_ts = ncfile_out.createVariable( name_station_h + str(count_site), \ |
|---|
| 492 | float, \ |
|---|
| 493 | name_station + str(count_site) ) |
|---|
| 494 | temp_ts[:] = np.array( 0.0 ) |
|---|
| 495 | |
|---|
| 496 | |
|---|
| 497 | count_site += 1 |
|---|
| 498 | |
|---|
| 499 | # Reset counter variable |
|---|
| 500 | count_site = num_sites + 1 |
|---|
| 501 | |
|---|
| 502 | # check if variables are prescribed. If so, prepare final output string |
|---|
| 503 | # stored in measured_variables. |
|---|
| 504 | if ( vars_to_be_measured ): |
|---|
| 505 | |
|---|
| 506 | for custom_vars in vars_to_be_measured: |
|---|
| 507 | |
|---|
| 508 | measured_variables = [] |
|---|
| 509 | for var in vars_default: |
|---|
| 510 | measured_variables.append(var) |
|---|
| 511 | |
|---|
| [4663] | 512 | # Check if given variables are already in the default variables. |
|---|
| [4400] | 513 | # If not, extend. |
|---|
| 514 | for var in custom_vars: |
|---|
| 515 | if ( var not in measured_variables ): |
|---|
| 516 | |
|---|
| 517 | measured_variables.append(var) |
|---|
| 518 | |
|---|
| 519 | ncfile_out.createDimension( "nvar"+ str(count_site), \ |
|---|
| 520 | len( measured_variables ) ) |
|---|
| 521 | |
|---|
| 522 | measured_var = ncfile_out.createVariable( 'measured_variables' + str(count_site), 'S1', \ |
|---|
| 523 | ("nvar" + str(count_site), "string_len") ) # must be NC_CHAR |
|---|
| 524 | |
|---|
| 525 | # Write the variables to the file |
|---|
| 526 | for counter, meas in enumerate( measured_variables ): |
|---|
| 527 | measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) |
|---|
| 528 | |
|---|
| 529 | # Add soil attribute for the current measurement. |
|---|
| 530 | soil = False |
|---|
| 531 | if ( any( var == soil_vars for var in measured_variables) ): |
|---|
| 532 | soil = True |
|---|
| 533 | |
|---|
| 534 | # Write soil flag |
|---|
| 535 | ncfile_out.setncattr( name_soil_sampling + str( count_site), np.int8(soil) ) |
|---|
| 536 | |
|---|
| 537 | # Increment counter variable |
|---|
| 538 | count_site += 1 |
|---|
| 539 | |
|---|
| 540 | del ( measured_variables[:] ) |
|---|
| 541 | |
|---|
| 542 | # Add the number of customized sites. |
|---|
| 543 | num_sites += int( number_positions ) |
|---|
| 544 | |
|---|
| 545 | |
|---|
| 546 | # Finally, write the total number of sites to the output file |
|---|
| 547 | ncfile_out.setncattr( name_num_stat, num_sites ) |
|---|
| 548 | |
|---|
| 549 | |
|---|
| 550 | print( "*** palm_cvd has been finished. You can find the output file under: " ) |
|---|
| 551 | print( " " + output_filename ) |
|---|
| 552 | |
|---|
| 553 | quit() |
|---|
