Index: /issm/trunk-jpl/src/m/io/loadvars.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadvars.py (revision 26126) +++ /issm/trunk-jpl/src/m/io/loadvars.py (revision 26127) @@ -98,5 +98,4 @@ if classtype[mod][0] == 'results.solutionstep': #Treating results {{{ keylist = [key for key in curclass.groups] - # this is related to the old structure of NC files where every steps of results had its own group #that is the current treatment #here we have a more NC approach with time being a dimension @@ -111,6 +110,4 @@ 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, len(NCFile.dimensions['Time']))): @@ -118,4 +115,26 @@ Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] # }}} + elif "results" in mod and classtype[mod][0] == 'list': #this is the old style of results where every step has a group{{{ + keylist = [key for key in curclass.groups] + #one group per step so use that in place of time + stepnum = len(NCFile.groups[classtree[mod][0]].groups[classtree[mod][1]].groups) + #we need to redefine classtype from list to result + listtype = 'results' + classtype[mod].append(__import__(listtype)) + if stepnum == 1: + nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = getattr(classtype[mod][1], listtype)() + Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] + else: + if onlylast: #we load only the last result to save on time and memory + nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = [getattr(classtype[mod][1], listtype)()] + 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)): + nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]].steps.append(getattr(classtype[mod][1], 'solutionstep')()) + Tree = nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] + #}}} elif classtype[mod][0] == 'massfluxatgate': #this is for output definitions {{{ defname = split('Output|[0-9]+', classtree[mod][1])[1] + 's' @@ -141,24 +160,28 @@ Tree = nvdict['md'].__dict__[classtree[mod][0]] if debug: - print(" for {} Tree is a {}".format(mod, Tree.__class__.__name__)) + print(" for {} Tree is a {} with len {}".format(mod, Tree.__class__.__name__, len(curclass.groups))) # }}} #==== Then we populate it {{{ - for i in range(0, max(1, len(curclass.groups))): - if len(curclass.groups) > 0: - listclass = curclass.groups[keylist[i]] + #for i in range(0, max(1, len(curclass.groups))): + if len(curclass.groups) > 0: #that is presumably only for old style NC where each result step had its own group + if onlylast: + listclass = curclass.groups[keylist[len(curclass.groups) - 1]] else: - listclass = curclass - #==== We deal with Variables {{{ - for var in listclass.variables: - if debug: - print(" ==> treating var {}".format(var)) - if var not in ['errlog', 'outlog']: - varval = listclass.variables[str(var)] - vardim = varval.ndim - #There is a special treatment for results to account for its specific structure - #that is the new export version where time is a named dimension - NewFormat = 'Time' in NCFile.dimensions - if type(Tree) == list and NewFormat: - if onlylast: + listclass = curclass.groups[keylist[0]] + else: + listclass = curclass + #==== We deal with Variables {{{ + for var in listclass.variables: + if debug: + print(" ==> treating var {}".format(var)) + if var not in ['errlog', 'outlog']: + varval = listclass.variables[str(var)] + vardim = varval.ndim + #There is a special treatment for results to account for its specific structure + #that is the new export version where time is a named dimension + NewFormat = 'Time' in NCFile.dimensions + if type(Tree) == list: # and NewFormat: + if onlylast: + if NewFormat: if vardim == 1: Tree[0].__dict__[str(var)] = varval[-1].data @@ -169,130 +192,96 @@ else: print('table dimension greater than 3 not implemented yet') + else: #old format had step sorted in difeerent group so last group is last time + Tree[0].__dict__[str(var)] = varval[:].data + else: + incomplete = 'Time' not in varval.dimensions and NewFormat + 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]))) + elif not NewFormat: + timelist = len(curclass.groups) else: - 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]))) - else: - timelist = np.arange(0, len(NCFile.dimensions['Time'])) - for t in timelist: - print("filing step {} for {}".format(t, var)) - if vardim == 0: - Tree[t].__dict__[str(var)] = varval[:].data - if vardim == 1: - Tree[t].__dict__[str(var)] = varval[t].data - elif vardim == 2: - Tree[t].__dict__[str(var)] = varval[t, :].data - elif vardim == 3: - Tree[t].__dict__[str(var)] = varval[t, :, :].data - else: - print('table dimension greater than 3 not implemented yet') - else: - if vardim == 0: #that is a scalar - if type(Tree) == list: - t = indexlist[i] - if listtype == 'dict': - Tree[t][str(var)] = varval[0].data - else: - Tree[t].__dict__[str(var)] = varval[0].data - else: - if str(varval[0]) == '': #no value - Tree.__dict__[str(var)] = [] - elif varval[0] == 'True': #treatin bool - Tree.__dict__[str(var)] = True - elif varval[0] == 'False': #treatin bool - Tree.__dict__[str(var)] = False - else: - Tree.__dict__[str(var)] = varval[0].item() - - elif vardim == 1: #that is a vector - if varval.dtype == str: - if varval.shape[0] == 1: - Tree.__dict__[str(var)] = [str(varval[0]), ] - elif 'True' in varval[:] or 'False' in varval[:]: - Tree.__dict__[str(var)] = np.asarray([V == 'True' for V in varval[:]], dtype=bool) - else: - Tree.__dict__[str(var)] = [str(vallue) for vallue in varval[:]] - else: - if type(Tree) == list: - t = indexlist[i] - if listtype == 'dict': - Tree[t][str(var)] = varval[:].data - else: - Tree[t].__dict__[str(var)] = varval[:].data - else: - try: - #some thing specifically require a list - mdtype = type(Tree.__dict__[str(var)]) - except KeyError: - mdtype = float - if mdtype == list: - Tree.__dict__[str(var)] = [mdval for mdval in varval[:]] - else: - Tree.__dict__[str(var)] = varval[:].data - - elif vardim == 2: - #dealling with dict - 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]))]) - strings1 = [str(arg[0]) for arg in varval if arg[0] != 'toolkits'] - 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: - t = indexlist[i] - if listtype == 'dict': - Tree[t][str(var)] = varval[:, :].data - else: - Tree[t].__dict__[str(var)] = varval[:, :].data - else: - Tree.__dict__[str(var)] = varval[:, :].data - elif vardim == 3: - if type(Tree) == list: - t = int(indexlist[i]) - if listtype == 'dict': - Tree[t][str(var)] = varval[:, :, :].data - else: - Tree[t].__dict__[str(var)] = varval[:, :, :] - else: - Tree.__dict__[str(var)] = varval[:, :, :].data + timelist = np.arange(0, len(NCFile.dimensions['Time'])) + for t in timelist: + print("filing step {} for {}".format(t, var)) + if vardim == 0: + Tree[t].__dict__[str(var)] = varval[:].data + if vardim == 1: + Tree[t].__dict__[str(var)] = varval[t].data + elif vardim == 2: + Tree[t].__dict__[str(var)] = varval[t, :].data + elif vardim == 3: + Tree[t].__dict__[str(var)] = varval[t, :, :].data + else: + print('table dimension greater than 3 not implemented yet') + else: + if vardim == 0: #that is a scalar + if str(varval[0]) == '': #no value + Tree.__dict__[str(var)] = [] + elif varval[0] == 'True': #treatin bool + Tree.__dict__[str(var)] = True + elif varval[0] == 'False': #treatin bool + Tree.__dict__[str(var)] = False else: - print('table dimension greater than 3 not implemented yet') - # }}} - #==== And with atribute {{{ - for attr in listclass.ncattrs(): - if debug: - 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 - if attr == 'VARNAME': - attribute = 'name' - if type(Tree) == list and NewFormat: - if debug: - print(" printing with index 0") - if listtype == 'dict': - Tree[0][attribute] = str(listclass.getncattr(attr)) + Tree.__dict__[str(var)] = varval[0].item() + + elif vardim == 1: #that is a vector + if varval.dtype == str: + if varval.shape[0] == 1: + Tree.__dict__[str(var)] = [str(varval[0]), ] + elif 'True' in varval[:] or 'False' in varval[:]: + Tree.__dict__[str(var)] = np.asarray([V == 'True' for V in varval[:]], dtype=bool) + else: + Tree.__dict__[str(var)] = [str(vallue) for vallue in varval[:]] else: - Tree[0].__dict__[attribute] = str(listclass.getncattr(attr)) - elif type(Tree) == list: - t = int(indexlist[i]) - if debug: - print(" printing with index {}".format(t)) - if listtype == 'dict': - Tree[t][attribute] = str(listclass.getncattr(attr)) - else: - Tree[t].__dict__[attribute] = str(listclass.getncattr(attr)) - else: - Tree.__dict__[attribute] = str(listclass.getncattr(attr)) - if listclass.getncattr(attr) == 'True': - Tree.__dict__[attribute] = True - elif listclass.getncattr(attr) == 'False': - Tree.__dict__[attribute] = False + try: + #some thing specifically require a list + mdtype = type(Tree.__dict__[str(var)]) + except KeyError: + mdtype = float + if mdtype == list: + Tree.__dict__[str(var)] = [mdval for mdval in varval[:]] + else: + Tree.__dict__[str(var)] = varval[:].data + + elif vardim == 2: + #dealling with dict + 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]))]) + strings1 = [str(arg[0]) for arg in varval if arg[0] != 'toolkits'] + 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))) + elif vardim == 3: + Tree.__dict__[str(var)] = varval[:, :, :].data + else: + print('table dimension greater than 3 not implemented yet') + # }}} + #==== And with atribute {{{ + for attr in listclass.ncattrs(): + if debug: + 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 + if attr == 'VARNAME': + attribute = 'name' + if type(Tree) == list: + if debug: + print(" printing with index 0") + if listtype == 'dict': + Tree[0][attribute] = str(listclass.getncattr(attr)) + else: + Tree[0].__dict__[attribute] = str(listclass.getncattr(attr)) + else: + Tree.__dict__[attribute] = str(listclass.getncattr(attr)) + if listclass.getncattr(attr) == 'True': + Tree.__dict__[attribute] = True + elif listclass.getncattr(attr) == 'False': + Tree.__dict__[attribute] = False # }}} # }}} @@ -322,8 +311,8 @@ grpclass = str(getattr(NCData.groups[group].groups[subgroup], 'classtype')) class_dict[classe] = [grpclass, ] + print(class_dict[classe][0]) if class_dict[classe][0] not in ['dict', 'list', 'cell']: try: modulename = split(r'\.', class_dict[classe][0])[0] - #class_dict[classe].append(__import__(class_dict[classe][0])) class_dict[classe].append(__import__(modulename)) except ModuleNotFoundError: