Index: /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.m =================================================================== --- /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.m (revision 26870) +++ /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.m (revision 26871) @@ -137,4 +137,7 @@ end Var = md.(groups{i}).(fields{j}){k}.(subfields{l}); + if sum(numel(Var) == size(Var)) == 0, %this is a 2D array or more (and not a vector with dimension 2 = 1) + Var = Var'; + end [DimSize,DimValue,varid]=CreateVar(ncid,Var,subgroupID,subfields{l},DimSize,DimValue); if ~isempty(varid), @@ -165,5 +168,4 @@ elseif isa(Var,'struct') % structures need special treatment - if strcmp(groups{i}, 'results'), klasstring='results.results'; @@ -182,5 +184,5 @@ if ~ismember(subfields{k}, {'errlog', 'outlog'}) StackedVar=restable(); - for l=1:Listsize + for l=1:Listsize, Var = md.(groups{i}).(fields{j})(l).(subfields{k}); if length(Var) == 0, @@ -192,5 +194,6 @@ end if verbose > 4, - disp(sprintf("=@@=creating var for %s.%s.%s",groups{i}, fields{j}, subfields{k})); + disp(sprintf("=$$=creating var for %s.%s.%s",groups{i}, fields{j}, subfields{k})); + disp(sprintf("last index on the list is %i",lastindex)); end StackedVar=StackedVar.finalize(lastindex); @@ -218,57 +221,4 @@ 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})))) end - % elseif strcmp(fields{j},'bamg') - % klass=class(md.(groups{i})); - % % if strcmp(klass,'struct') - % % klass=groups{i}; - % % end - % netcdf.putAtt(groupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass); - % if length(Var)>1 - % listsize=length(Var); - % subgroupID=netcdf.defGrp(groupID,fields{j}); - % klass=class(md.(groups{i}).(fields{j})); - % netcdf.putAtt(subgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass); - % for k=1:listsize - % if isfield(Var(k),'step') - % lname=[int2str(Var(k).step)]; - % else - % lname=[class(Var(k)) int2str(k)]; - % end - - % listgroupID=netcdf.defGrp(subgroupID,lname); - % klass=class(md.(groups{i}).(fields{j}){k}); - % netcdf.putAtt(listgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass); - % subfields=fieldnames(Var(k)); - % for l=1:length(subfields) - % if ~strcmp(subfields{l},'outlog') - % if verbose > 4, - % disp(sprintf("=[]=creating var for %s.%s[%i].%s", groups{i}, fields{j}, k, subfields{l})) - % end - % Var=md.(groups{i}).(fields{j}){k}.(subfields{l}); - % [DimSize,DimValue,varid]=CreateVar(ncid,Var,listgroupID,subfields{l},DimSize,DimValue); - % if ~isempty(varid), - % FillVar(Var,listgroupID,varid); - % end - % end - % end - % end - % else - % subgroupID=netcdf.defGrp(groupID,fields{j}); - % klass=class(md.(groups{i})); - % netcdf.putAtt(subgroupID,netcdf.getConstant('NC_GLOBAL'),'classtype',klass); - % subfields=fieldnames(Var); - % for k=1:length(subfields) - % if ~strcmp(subfields{k},'outlog') - % if verbose > 4, - % disp(sprintf("====creating var for %s.%s.%s", groups{i}, fields{j}, subfields{k})) - % end - % Var=md.(groups{i}).(fields{j}).(subfields{k}); - % [DimSize,DimValue,varid]=CreateVar(ncid,Var,subgroupID,subfields{k},DimSize,DimValue); - % if ~isempty(varid), - % FillVar(Var,subgroupID,varid); - % end - % end - % end - % end elseif sum(strcmp(class(Var), issmclasses)) == 1, % that is an issm class if strcmp(class(Var), 'solution'), Index: /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py =================================================================== --- /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py (revision 26870) +++ /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py (revision 26871) @@ -58,5 +58,5 @@ #as much scalars as stpes (or less) so just one value per step else: - return np.asarray(self.data) + return np.squeeze(np.asarray(self.data)) @@ -158,5 +158,5 @@ subfields = dict.keys(md.__dict__[group].__dict__[field].__getitem__(0)) for subfield in subfields: - if subfield not in['errlog', 'outlog']: + if subfield not in ['errlog', 'outlog']: StackedVar = ResTable() #first loop over the field (result type) to find the index of the last subfield (variable) @@ -241,5 +241,5 @@ subfields = dict.keys(md.__dict__[group].__dict__[field].__getitem__(0)) for subfield in subfields: - if subfield not in['errlog', 'outlog']: + if subfield not in ['errlog', 'outlog']: StackedVar = ResTable() for listindex in range(0, Listsize): @@ -248,9 +248,10 @@ lastindex = listindex + 1 except AttributeError: - Var = md.__dict__[group].__dict__[field].__getitem__(listindex)[subfield] + Var = md.__dict__[group].__dict__[field].__dict__[subfield] + lastindex = listindex except KeyError: #Some fields only exist for the first step lastindex = listindex - continue + break Var = SqueezeVar(Var) StackedVar.update(Var) @@ -308,4 +309,5 @@ if val_type.startswith(' 0 + except TypeError: + #check if we are integer and under C long overflow + IsInt = np.mod(var, 1) == 0 and abs(var) < 2147483647 + if IsInt: + val_type = 'int64' + if val_shape in [(), (0,), 0] and not SupDim: + ncvar = Group.createVariable(str(field), TypeDict[val_type], zlib=True) + else: + dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) + if SupDim: + dimensions = SupDim + dimensions + ncvar = Group.createVariable(str(field), TypeDict[val_type], dimensions=dimensions, zlib=True) + elif val_type in [int, 'int64']: if val_shape in [(), (0,), 0] and not SupDim: ncvar = Group.createVariable(str(field), TypeDict[val_type], zlib=True) @@ -415,5 +446,5 @@ elif val_type == 'bool': for elt in range(0, val_shape[0]): - ncvar[elt] = str(invar[elt]) + ncvar[elt] = int(invar[elt]) #str(invar[elt]) # treating dictionaries as tables of strings elif val_type in [collections.OrderedDict, dict]: Index: /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/restable.m =================================================================== --- /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/restable.m (revision 26870) +++ /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/restable.m (revision 26871) @@ -21,27 +21,32 @@ function outdat = finalize(self, rows) if length(self.data)>rows, - %we have more scalars than steps, so we have an array - maxsize=[]; - for d=1:size(self.sizes,2) - maxsize=[maxsize,max(self.sizes(:,d))]; - end - findim=[maxsize, rows]; - %first check if all steps are the same size - SameSize = sum(self.sizes - self.sizes(1))==0; - if SameSize, - %same size for all steps, just reshape - outdat=reshape(self.data, findim); + if size(self.sizes, 1)==1, + %just one step, data don't need treatment + outdat=self.data; else, - %different sizes at each steps, first create a table big enough for the biggest step - startpoint=1; - datadim=ndims(self.data); - outdat=NaN*ones(findim); - for r=1:rows - curlen = prod(self.sizes(r, :)); - outdat(1:self.sizes(r,1), 1:self.sizes(r,2), r) = reshape(self.data(startpoint:startpoint+curlen-1),self.sizes(r,:)); - startpoint = startpoint+curlen; + %we have more scalars than steps, so we have an array + maxsize=[]; + for d=1:size(self.sizes,2) + maxsize=[maxsize,max(self.sizes(:,d))]; end + findim=[maxsize, rows]; + %first check if all steps are the same size + SameSize = sum(self.sizes - self.sizes(1))==0; + if SameSize, + %same size for all steps, just reshape + outdat=reshape(self.data, findim); + else, + %different sizes at each steps, first create a table big enough for the biggest step + startpoint=1; + datadim=ndims(self.data); + outdat=NaN*ones(findim); + for r=1:rows + curlen = prod(self.sizes(r, :)); + outdat(1:self.sizes(r,1), 1:self.sizes(r,2), r) = reshape(self.data(startpoint:startpoint+curlen-1),self.sizes(r,:)); + startpoint = startpoint+curlen; + end - end + end + end, else, %as much scalars as steps (or less) so just one value per step Index: /issm/trunk-jpl/src/m/io/loadvars.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadvars.py (revision 26870) +++ /issm/trunk-jpl/src/m/io/loadvars.py (revision 26871) @@ -132,6 +132,4 @@ Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] else: - #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'])))] - #Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] setattr(nvdict['md'].__dict__[classtree[mod][0]], classtree[mod][1], getattr(classtype[mod][1], 'solution')([])) for i in range(max(1, stepnum)): @@ -139,8 +137,10 @@ Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] #}}} - elif classtype[mod][0] == 'massfluxatgate.massfluxatgate': #this is for output definitions {{{ + #elif classtype[mod][0] == 'massfluxatgate.massfluxatgate': #this is for output definitions {{{ + elif mod.startswith('outputdefinition'): #this is for output definitions {{{ defname = split('Output|[0-9]+', classtree[mod][1])[1] + 's' defindex = int(findall('[0-9]+', classtree[mod][1])[0]) - nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname].append(getattr(classtype[mod][1], 'massfluxatgate')()) + outdeftype = split(r'\.', classtype[mod][0])[0] + nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname].append(getattr(classtype[mod][1], outdeftype)()) Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[defname][defindex - 1] #}}} @@ -212,10 +212,13 @@ else: if NewFormat: - print(varval.dimensions) incomplete = 'Time' not in varval.dimensions if incomplete: - chosendim = varval.dimensions[0] - timelist = np.arange(0, len(NCFile.dimensions[chosendim])) - print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) + try: + chosendim = varval.dimensions[0] + timelist = np.arange(0, len(NCFile.dimensions[chosendim])) + print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) + except IndexError: + #just one step, so no dimension, we just put it on the first solutionstep + timelist = [0] else: timelist = np.arange(0, len(NCFile.dimensions['Time'])) @@ -239,5 +242,5 @@ else: if vardim == 0: #that is a scalar - if str(varval[0]) in['', '--', 'emptycell']: #no value + if str(varval[0]) in ['', '--', 'emptycell']: #no value Tree.__dict__[str(var)] = [] elif varval[0] == 'True': #treatin bool