Index: /issm/trunk-jpl/src/m/io/loadmodel.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadmodel.py (revision 26871) +++ /issm/trunk-jpl/src/m/io/loadmodel.py (revision 26872) @@ -9,5 +9,5 @@ -def loadmodel(path, singletime=None): +def loadmodel(path, singletime=None, singleres=None): """LOADMODEL - load a model @@ -31,5 +31,5 @@ # try: #recover model on file and name it md - struc = loadvars(path, singletime=singletime) + struc = loadvars(path, singletime=singletime, singleres=singleres) name = [key for key in list(struc.keys())] if len(name) > 1: Index: /issm/trunk-jpl/src/m/io/loadvars.py =================================================================== --- /issm/trunk-jpl/src/m/io/loadvars.py (revision 26871) +++ /issm/trunk-jpl/src/m/io/loadvars.py (revision 26872) @@ -31,5 +31,5 @@ filename = '' nvdict = {} - debug = False # print messages if true + debug = True # print messages if true if len(args) >= 1 and isinstance(args[0], str): @@ -58,4 +58,6 @@ if key == 'singletime': timeindex = value + if key == 'singleres': + resname = value if whichdb(filename): #We used python pickle for the save @@ -184,126 +186,126 @@ for groupindex, listclass in enumerate(groupclass): for var in listclass.variables: - if var not in ['errlog', 'outlog']: - varval = listclass.variables[str(var)] - vardim = varval.ndim - if debug: - print(" ==> treating var {} of dimension {}".format(var, vardim)) - #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 timeindex: - if NewFormat: - if vardim == 1: + if not resname or var == resname: + if var not in ['errlog', 'outlog']: + varval = listclass.variables[str(var)] + vardim = varval.ndim + if debug: + print(" ==> treating var {} of dimension {}".format(var, vardim)) + #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 timeindex: + if NewFormat: + if vardim == 1: + try: + Tree[0].__dict__[str(var)] = varval[timeindex].data + except IndexError: + print('WARNING: No data on index {} for {} reverting to last time.'.format(timeindex, str(var))) + Tree[0].__dict__[str(var)] = varval[-1].data + elif vardim == 2: + Tree[0].__dict__[str(var)] = varval[timeindex, :].data + elif vardim == 3: + Tree[0].__dict__[str(var)] = varval[timeindex, :, :].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 + if incomplete: + 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'])) + 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: + if debug: + print("filing step {} for {}".format(groupindex, var)) + Tree[groupindex].__dict__[str(var)] = varval[:].data + else: + if vardim == 0: #that is a scalar + if str(varval[0]) in ['', '--', 'emptycell']: #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 debug: + print(" for variable {} type is {}".format(str(var), varval.dtype)) + 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[:]] + elif varval.dtype == "|S1": #that is for matlab chararcter arrays + stringlist = chartostring(varval[:]) + Tree.__dict__[str(var)] = [stringlist.tolist(), ] + else: try: - Tree[0].__dict__[str(var)] = varval[timeindex].data - except IndexError: - print('WARNING: No data on index {} for {} reverting to last time.'.format(timeindex, str(var))) - Tree[0].__dict__[str(var)] = varval[-1].data - - elif vardim == 2: - Tree[0].__dict__[str(var)] = varval[timeindex, :].data - elif vardim == 3: - Tree[0].__dict__[str(var)] = varval[timeindex, :, :].data + #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 debug: + print(" for variable {} type is {}".format(str(var), varval.dtype)) + 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 varval.dtype == "|S1": #that is for matlab chararcter arrays + stringlist = chartostring(varval[:, :]) + stringlist = [string.strip() for string in stringlist] + Tree.__dict__[str(var)] = stringlist 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 - if incomplete: - 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'])) - 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') + 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: + Tree.__dict__[str(var)] = varval[:, :, :].data else: - if debug: - print("filing step {} for {}".format(groupindex, var)) - Tree[groupindex].__dict__[str(var)] = varval[:].data - else: - if vardim == 0: #that is a scalar - if str(varval[0]) in ['', '--', 'emptycell']: #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 debug: - print(" for variable {} type is {}".format(str(var), varval.dtype)) - 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[:]] - elif varval.dtype == "|S1": #that is for matlab chararcter arrays - stringlist = chartostring(varval[:]) - Tree.__dict__[str(var)] = [stringlist.tolist(), ] - 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 debug: - print(" for variable {} type is {}".format(str(var), varval.dtype)) - 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 varval.dtype == "|S1": #that is for matlab chararcter arrays - stringlist = chartostring(varval[:, :]) - stringlist = [string.strip() for string in stringlist] - Tree.__dict__[str(var)] = stringlist - 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: - Tree.__dict__[str(var)] = varval[:, :, :].data - else: - print('table dimension greater than 3 not implemented yet') + print('table dimension greater than 3 not implemented yet') # }}} #==== And with atribute {{{