Index: /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py =================================================================== --- /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py (revision 26062) +++ /issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py (revision 26063) @@ -3,5 +3,5 @@ import time import collections -from inspect import isclass, getmodule +from inspect import isclass from os import path, remove @@ -47,4 +47,6 @@ except AttributeError: #no transient so just one timestep StepNum = 1 + except TypeError: #this isnot a result so no results in there + StepNum = 0 TimeDim = NCData.createDimension('Time', StepNum) # time is first DimDict = {len(TimeDim): 'Time'} @@ -80,5 +82,9 @@ NCgroup = NCData.createGroup(str(group)) # In each group gather the fields of the class - fields = dict.keys(md.__dict__[group].__dict__) + try: + fields = dict.keys(md.__dict__[group].__dict__) + except AttributeError: + print("WARNING: md.{} as no fields, we skip it.".format(group)) + continue # looping on fields in each group for field in fields: @@ -220,4 +226,16 @@ if ncvar is not None: FillVar(ncvar, StackedVar) + elif type(md.__dict__[group].__dict__[field]).__name__ == 'dict': + # designed for a dict in dummy but might be used elsewhere + # there is no subgroup + klass = type(md.__dict__[group]).__module__ + '.' + type(md.__dict__[group]).__name__ + NCgroup.__setattr__('classtype', klass) + Var = md.__dict__[group].__dict__[field] + Var = SqueezeVar(Var) + if verbose > 4: + print("=WW=creating var for {}.{}".format(group, field)) + DimDict, ncvar = CreateVar(NCData, Var, field, NCgroup, DimDict) + if ncvar is not None: + FillVar(ncvar, Var) else: klass = type(md.__dict__[group]).__module__ + '.' + type(md.__dict__[group]).__name__ @@ -258,5 +276,5 @@ # grab dimension if val_type in [collections.OrderedDict, dict]: - val_shape = len(var) + val_shape = len(var.keys()) val_dim = 2 else: @@ -304,5 +322,5 @@ ncvar = Group.createVariable(str(field), nctype, dimensions=dimensions, zlib=True) # treating bool tables and dict as string tables - elif val_type in ['bool', collections.OrderedDict, dict]: + elif val_type in [collections.OrderedDict, dict, 'bool']: if val_shape in [(), (0,), 0]: ncvar = Group.createVariable(str(field), str, zlib=True) @@ -310,5 +328,5 @@ dimensions, DimDict = GetDim(NCData, val_shape, val_type, DimDict, val_dim) ncvar = Group.createVariable(str(field), str, dimensions=dimensions, zlib=True) - # Now dealing with numeric variables + # Now dealing with numeric variables elif val_type in [float, 'float64', np.float64, int, 'int64']: if val_shape in [(), (0,), 0] and not SupDim: Index: /issm/trunk-jpl/src/m/io/loadvars.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadvars.py (revision 26062) +++ /issm/trunk-jpl/src/m/io/loadvars.py (revision 26063) @@ -88,10 +88,10 @@ #==== First we create the model structure {{{ if debug: - print(' - Now treating classtype {}'.format(mod)) + print(' ==== Now treating classtype {}'.format(mod)) if np.size(classtree[mod]) > 1: # this points to a subclass (results.TransientSolution for example) curclass = NCFile.groups[classtree[mod][0]].groups[classtree[mod][1]] if debug: - print("===> {} is of class {}".format(mod, classtype[mod])) + print(" ==> {} is of class {}".format(mod, classtype[mod])) if classtype[mod][0] == 'results.solutionstep': #Treating results {{{ keylist = [key for key in curclass.groups] @@ -100,5 +100,4 @@ #here we have a more NC approach with time being a dimension listtype = split(r'\.', classtype[mod][0])[0] - print("listtype is {}".format(listtype)) if len(NCFile.dimensions['Time']) == 1: nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = getattr(classtype[mod][1], listtype)() @@ -126,9 +125,9 @@ else: if debug: - print("Using the default for md.{}.{}, is that right??".format(classtree[mod][0], classtree[mod][1])) + print(" Using the default for md.{}.{}, is that right??".format(classtree[mod][0], classtree[mod][1])) try: modulename = split(r'\.', classtype[mod][0])[0] if debug: - print("trying to import {} from {}".format(classtype[mod][0], modulename)) + print(" trying to import {} from {}".format(classtype[mod][0], modulename)) nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = getattr(classtype[mod][1], modulename)() except AttributeError: @@ -141,5 +140,5 @@ Tree = nvdict['md'].__dict__[classtree[mod][0]] if debug: - print("for {} Tree is a {}".format(mod, Tree.__class__.__name__)) + print(" for {} Tree is a {}".format(mod, Tree.__class__.__name__)) # }}} #==== Then we populate it {{{ @@ -152,5 +151,5 @@ for var in listclass.variables: if debug: - print("treating var {}".format(var)) + print(" ==> treating var {}".format(var)) if var not in ['errlog', 'outlog']: varval = listclass.variables[str(var)] @@ -234,5 +233,5 @@ elif vardim == 2: #dealling with dict - if varval.dtype == str: #that is for toolkits wich needs to be ordered + if varval.dtype == str: #that is for dictionaries if any(varval[:, 0] == 'toolkit'): #toolkit definition have to be first Tree.__dict__[str(var)] = OrderedDict([('toolkit', str(varval[np.where(varval[:, 0] == 'toolkit')[0][0], 1]))]) @@ -240,4 +239,8 @@ strings2 = [str(arg[1]) for arg in varval if arg[0] != 'toolkits'] Tree.__dict__[str(var)].update(list(zip(strings1, strings2))) + else: + strings1 = [str(arg[0]) for arg in varval] + strings2 = [str(arg[1]) for arg in varval] + Tree.__dict__[str(var)] = OrderedDict(list(zip(strings1, strings2))) else: if type(Tree) == list: @@ -264,5 +267,5 @@ for attr in listclass.ncattrs(): if debug: - print("treating attribute {}".format(attr)) + print(" ==> treating attribute {}".format(attr)) if attr != 'classtype': #classtype is for treatment, don't get it back attribute = str(attr).swapcase() #there is a reason for swapcase, no sure what it isanymore @@ -271,5 +274,5 @@ if type(Tree) == list and NewFormat: if debug: - print("printing with index 0") + print(" printing with index 0") if listtype == 'dict': Tree[0][attribute] = str(listclass.getncattr(attr)) @@ -279,5 +282,5 @@ t = int(indexlist[i]) if debug: - print("printing with index {]".format(t)) + print(" printing with index {}".format(t)) if listtype == 'dict': Tree[t][attribute] = str(listclass.getncattr(attr))