Index: /issm/trunk-jpl/src/m/io/loadvars.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadvars.py (revision 26127) +++ /issm/trunk-jpl/src/m/io/loadvars.py (revision 26128) @@ -166,122 +166,127 @@ 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]] + groupclass = [curclass.groups[keylist[len(curclass.groups) - 1]]] else: - listclass = curclass.groups[keylist[0]] + groupclass = [curclass.groups[key] for key in keylist] else: - listclass = curclass + groupclass = [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 - elif vardim == 2: - Tree[0].__dict__[str(var)] = varval[-1, :].data - elif vardim == 3: - Tree[0].__dict__[str(var)] = varval[-1, :, :].data + for groupindex, listclass in enumerate(groupclass): + 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 + elif vardim == 2: + Tree[0].__dict__[str(var)] = varval[-1, :].data + elif vardim == 3: + Tree[0].__dict__[str(var)] = varval[-1, :, :].data + 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: + if NewFormat: + 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]))) + else: + timelist = np.arange(0, len(NCFile.dimensions['Time'])) + for t in timelist: + if debug: + print("filing step {} for {}".format(t, var)) + if vardim == 0: + Tree[t].__dict__[str(var)] = varval[:].data + elif 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: - 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 + if debug: + print("filing step {} for {}".format(groupindex, var)) + Tree[groupindex].__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) + 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: + 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: + 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: - 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 + 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.__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: - 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 + Tree[0].__dict__[attribute] = str(listclass.getncattr(attr)) 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 + 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 # }}} # }}}