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Changeset 26871
- Timestamp:
- 02/10/22 00:48:20 (3 years ago)
- Location:
- issm/trunk-jpl/src/m
- Files:
-
- 4 edited
-
contrib/defleurian/netCDF/export_netCDF.m (modified) (5 diffs)
-
contrib/defleurian/netCDF/export_netCDF.py (modified) (10 diffs)
-
contrib/defleurian/netCDF/restable.m (modified) (1 diff)
-
io/loadvars.py (modified) (4 diffs)
Legend:
- Unmodified
- Added
- Removed
-
issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.m
r26858 r26871 137 137 end 138 138 Var = md.(groups{i}).(fields{j}){k}.(subfields{l}); 139 if sum(numel(Var) == size(Var)) == 0, %this is a 2D array or more (and not a vector with dimension 2 = 1) 140 Var = Var'; 141 end 139 142 [DimSize,DimValue,varid]=CreateVar(ncid,Var,subgroupID,subfields{l},DimSize,DimValue); 140 143 if ~isempty(varid), … … 165 168 166 169 elseif isa(Var,'struct') % structures need special treatment 167 168 170 if strcmp(groups{i}, 'results'), 169 171 klasstring='results.results'; … … 182 184 if ~ismember(subfields{k}, {'errlog', 'outlog'}) 183 185 StackedVar=restable(); 184 for l=1:Listsize 186 for l=1:Listsize, 185 187 Var = md.(groups{i}).(fields{j})(l).(subfields{k}); 186 188 if length(Var) == 0, … … 192 194 end 193 195 if verbose > 4, 194 disp(sprintf("=@@=creating var for %s.%s.%s",groups{i}, fields{j}, subfields{k})); 196 disp(sprintf("=$$=creating var for %s.%s.%s",groups{i}, fields{j}, subfields{k})); 197 disp(sprintf("last index on the list is %i",lastindex)); 195 198 end 196 199 StackedVar=StackedVar.finalize(lastindex); … … 218 221 disp(sprintf("WARNING, md.%s.%s is not treated as it does not fall in one of the existing cases with class '%s'.",groups{i}, fields{j}, class(md.(groups{i}).(fields{j})))) 219 222 end 220 % elseif strcmp(fields{j},'bamg')221 % klass=class(md.(groups{i}));222 % % if strcmp(klass,'struct')223 % % klass=groups{i};224 % % end225 % netcdf.putAtt(groupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass);226 % if length(Var)>1227 % listsize=length(Var);228 % subgroupID=netcdf.defGrp(groupID,fields{j});229 % klass=class(md.(groups{i}).(fields{j}));230 % netcdf.putAtt(subgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass);231 % for k=1:listsize232 % if isfield(Var(k),'step')233 % lname=[int2str(Var(k).step)];234 % else235 % lname=[class(Var(k)) int2str(k)];236 % end237 238 % listgroupID=netcdf.defGrp(subgroupID,lname);239 % klass=class(md.(groups{i}).(fields{j}){k});240 % netcdf.putAtt(listgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass);241 % subfields=fieldnames(Var(k));242 % for l=1:length(subfields)243 % if ~strcmp(subfields{l},'outlog')244 % if verbose > 4,245 % disp(sprintf("=[]=creating var for %s.%s[%i].%s", groups{i}, fields{j}, k, subfields{l}))246 % end247 % Var=md.(groups{i}).(fields{j}){k}.(subfields{l});248 % [DimSize,DimValue,varid]=CreateVar(ncid,Var,listgroupID,subfields{l},DimSize,DimValue);249 % if ~isempty(varid),250 % FillVar(Var,listgroupID,varid);251 % end252 % end253 % end254 % end255 % else256 % subgroupID=netcdf.defGrp(groupID,fields{j});257 % klass=class(md.(groups{i}));258 % netcdf.putAtt(subgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass);259 % subfields=fieldnames(Var);260 % for k=1:length(subfields)261 % if ~strcmp(subfields{k},'outlog')262 % if verbose > 4,263 % disp(sprintf("====creating var for %s.%s.%s", groups{i}, fields{j}, subfields{k}))264 % end265 % Var=md.(groups{i}).(fields{j}).(subfields{k});266 % [DimSize,DimValue,varid]=CreateVar(ncid,Var,subgroupID,subfields{k},DimSize,DimValue);267 % if ~isempty(varid),268 % FillVar(Var,subgroupID,varid);269 % end270 % end271 % end272 % end273 223 elseif sum(strcmp(class(Var), issmclasses)) == 1, % that is an issm class 274 224 if strcmp(class(Var), 'solution'), -
issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py
r26858 r26871 58 58 #as much scalars as stpes (or less) so just one value per step 59 59 else: 60 return np. asarray(self.data)60 return np.squeeze(np.asarray(self.data)) 61 61 62 62 … … 158 158 subfields = dict.keys(md.__dict__[group].__dict__[field].__getitem__(0)) 159 159 for subfield in subfields: 160 if subfield not in ['errlog', 'outlog']:160 if subfield not in ['errlog', 'outlog']: 161 161 StackedVar = ResTable() 162 162 #first loop over the field (result type) to find the index of the last subfield (variable) … … 241 241 subfields = dict.keys(md.__dict__[group].__dict__[field].__getitem__(0)) 242 242 for subfield in subfields: 243 if subfield not in ['errlog', 'outlog']:243 if subfield not in ['errlog', 'outlog']: 244 244 StackedVar = ResTable() 245 245 for listindex in range(0, Listsize): … … 248 248 lastindex = listindex + 1 249 249 except AttributeError: 250 Var = md.__dict__[group].__dict__[field].__getitem__(listindex)[subfield] 250 Var = md.__dict__[group].__dict__[field].__dict__[subfield] 251 lastindex = listindex 251 252 except KeyError: 252 253 #Some fields only exist for the first step 253 254 lastindex = listindex 254 continue255 break 255 256 Var = SqueezeVar(Var) 256 257 StackedVar.update(Var) … … 308 309 if val_type.startswith('<U'): 309 310 val_type = 'stringarray' 311 print(var) 310 312 except AttributeError: 311 313 val_type = type(var) … … 332 334 if field == 'name': # it looks like netCDF does not like attributes that are called "name" 333 335 field = 'varname' 334 #Group.__setattr__(str(field).swapcase(), str(var))335 336 Group.__setattr__(str(field), str(var)) 336 337 ncvar = None … … 338 339 elif val_type == "stringarray": 339 340 #if all strings are the same set it as an attribute 340 if all(var == var[0]): 341 try: 342 samestring = all(var == var[0]) 343 except IndexError: 344 #Only one string 345 samestring = True 346 if samestring: 341 347 if field == 'name': 342 348 field = 'varname' 343 #Group.__setattr__(str(field).swapcase(), str(var[0])) 344 Group.__setattr__(str(field), str(var[0])) 349 try: 350 Group.__setattr__(str(field), str(var[0])) 351 except IndexError: 352 Group.__setattr__(str(field), str(var)) 345 353 ncvar = None 346 354 else: … … 360 368 dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) 361 369 ncvar = Group.createVariable(str(field), nctype, dimensions=dimensions, zlib=True) 362 # treating bool tables anddict as string tables363 elif val_type in [collections.OrderedDict, dict , 'bool']:370 # treating dict as string tables 371 elif val_type in [collections.OrderedDict, dict]: 364 372 if val_shape in [(), (0,), 0]: 365 373 ncvar = Group.createVariable(str(field), str, zlib=True) … … 367 375 dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) 368 376 ncvar = Group.createVariable(str(field), str, dimensions=dimensions, zlib=True) 369 # Now dealing with numeric variables 370 elif val_type in [float, 'float64', np.float64, int, 'int64']: 377 # treating bool as integers 378 elif val_type == 'bool': 379 if val_shape in [(), (0,), 0]: 380 ncvar = Group.createVariable(str(field), int, zlib=True) 381 else: 382 dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) 383 ncvar = Group.createVariable(str(field), int, dimensions=dimensions, zlib=True) 384 # Now dealing with doubles, we convert them to int if possible 385 elif val_type in [float, 'float64', np.float64]: 386 try: 387 #check if we are integer and under C long overflow also skip empty arrays 388 IsInt = np.sum(np.mod(var, 1)) == 0 and np.all(abs(var) < 2147483647) and len(var) > 0 389 except TypeError: 390 #check if we are integer and under C long overflow 391 IsInt = np.mod(var, 1) == 0 and abs(var) < 2147483647 392 if IsInt: 393 val_type = 'int64' 394 if val_shape in [(), (0,), 0] and not SupDim: 395 ncvar = Group.createVariable(str(field), TypeDict[val_type], zlib=True) 396 else: 397 dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) 398 if SupDim: 399 dimensions = SupDim + dimensions 400 ncvar = Group.createVariable(str(field), TypeDict[val_type], dimensions=dimensions, zlib=True) 401 elif val_type in [int, 'int64']: 371 402 if val_shape in [(), (0,), 0] and not SupDim: 372 403 ncvar = Group.createVariable(str(field), TypeDict[val_type], zlib=True) … … 415 446 elif val_type == 'bool': 416 447 for elt in range(0, val_shape[0]): 417 ncvar[elt] = str(invar[elt])448 ncvar[elt] = int(invar[elt]) #str(invar[elt]) 418 449 # treating dictionaries as tables of strings 419 450 elif val_type in [collections.OrderedDict, dict]: -
issm/trunk-jpl/src/m/contrib/defleurian/netCDF/restable.m
r26858 r26871 21 21 function outdat = finalize(self, rows) 22 22 if length(self.data)>rows, 23 %we have more scalars than steps, so we have an array 24 maxsize=[]; 25 for d=1:size(self.sizes,2) 26 maxsize=[maxsize,max(self.sizes(:,d))]; 27 end 28 findim=[maxsize, rows]; 29 %first check if all steps are the same size 30 SameSize = sum(self.sizes - self.sizes(1))==0; 31 if SameSize, 32 %same size for all steps, just reshape 33 outdat=reshape(self.data, findim); 23 if size(self.sizes, 1)==1, 24 %just one step, data don't need treatment 25 outdat=self.data; 34 26 else, 35 %different sizes at each steps, first create a table big enough for the biggest step 36 startpoint=1; 37 datadim=ndims(self.data); 38 outdat=NaN*ones(findim); 39 for r=1:rows 40 curlen = prod(self.sizes(r, :)); 41 outdat(1:self.sizes(r,1), 1:self.sizes(r,2), r) = reshape(self.data(startpoint:startpoint+curlen-1),self.sizes(r,:)); 42 startpoint = startpoint+curlen; 27 %we have more scalars than steps, so we have an array 28 maxsize=[]; 29 for d=1:size(self.sizes,2) 30 maxsize=[maxsize,max(self.sizes(:,d))]; 43 31 end 32 findim=[maxsize, rows]; 33 %first check if all steps are the same size 34 SameSize = sum(self.sizes - self.sizes(1))==0; 35 if SameSize, 36 %same size for all steps, just reshape 37 outdat=reshape(self.data, findim); 38 else, 39 %different sizes at each steps, first create a table big enough for the biggest step 40 startpoint=1; 41 datadim=ndims(self.data); 42 outdat=NaN*ones(findim); 43 for r=1:rows 44 curlen = prod(self.sizes(r, :)); 45 outdat(1:self.sizes(r,1), 1:self.sizes(r,2), r) = reshape(self.data(startpoint:startpoint+curlen-1),self.sizes(r,:)); 46 startpoint = startpoint+curlen; 47 end 44 48 45 end 49 end 50 end, 46 51 else, 47 52 %as much scalars as steps (or less) so just one value per step -
issm/trunk-jpl/src/m/io/loadvars.py
r26857 r26871 132 132 Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] 133 133 else: 134 #nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = [getattr(classtype[mod][1], listtype)() for i in range(max(1, len(NCFile.dimensions['Time'])))]135 #Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:]136 134 setattr(nvdict['md'].__dict__[classtree[mod][0]], classtree[mod][1], getattr(classtype[mod][1], 'solution')([])) 137 135 for i in range(max(1, stepnum)): … … 139 137 Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] 140 138 #}}} 141 elif classtype[mod][0] == 'massfluxatgate.massfluxatgate': #this is for output definitions {{{ 139 #elif classtype[mod][0] == 'massfluxatgate.massfluxatgate': #this is for output definitions {{{ 140 elif mod.startswith('outputdefinition'): #this is for output definitions {{{ 142 141 defname = split('Output|[0-9]+', classtree[mod][1])[1] + 's' 143 142 defindex = int(findall('[0-9]+', classtree[mod][1])[0]) 144 nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname].append(getattr(classtype[mod][1], 'massfluxatgate')()) 143 outdeftype = split(r'\.', classtype[mod][0])[0] 144 nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname].append(getattr(classtype[mod][1], outdeftype)()) 145 145 Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname][defindex - 1] 146 146 #}}} … … 212 212 else: 213 213 if NewFormat: 214 print(varval.dimensions)215 214 incomplete = 'Time' not in varval.dimensions 216 215 if incomplete: 217 chosendim = varval.dimensions[0] 218 timelist = np.arange(0, len(NCFile.dimensions[chosendim])) 219 print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) 216 try: 217 chosendim = varval.dimensions[0] 218 timelist = np.arange(0, len(NCFile.dimensions[chosendim])) 219 print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) 220 except IndexError: 221 #just one step, so no dimension, we just put it on the first solutionstep 222 timelist = [0] 220 223 else: 221 224 timelist = np.arange(0, len(NCFile.dimensions['Time'])) … … 239 242 else: 240 243 if vardim == 0: #that is a scalar 241 if str(varval[0]) in ['', '--', 'emptycell']: #no value244 if str(varval[0]) in ['', '--', 'emptycell']: #no value 242 245 Tree.__dict__[str(var)] = [] 243 246 elif varval[0] == 'True': #treatin bool
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