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Changeset 26128
- Timestamp:
- 03/23/21 05:38:37 (4 years ago)
- File:
-
- 1 edited
-
issm/trunk-jpl/src/m/io/loadvars.py (modified) (1 diff)
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TabularUnified issm/trunk-jpl/src/m/io/loadvars.py ¶
r26127 r26128 166 166 if len(curclass.groups) > 0: #that is presumably only for old style NC where each result step had its own group 167 167 if onlylast: 168 listclass = curclass.groups[keylist[len(curclass.groups) - 1]]168 groupclass = [curclass.groups[keylist[len(curclass.groups) - 1]]] 169 169 else: 170 listclass = curclass.groups[keylist[0]]170 groupclass = [curclass.groups[key] for key in keylist] 171 171 else: 172 listclass = curclass172 groupclass = [curclass] 173 173 #==== We deal with Variables {{{ 174 for var in listclass.variables: 175 if debug: 176 print(" ==> treating var {}".format(var)) 177 if var not in ['errlog', 'outlog']: 178 varval = listclass.variables[str(var)] 179 vardim = varval.ndim 180 #There is a special treatment for results to account for its specific structure 181 #that is the new export version where time is a named dimension 182 NewFormat = 'Time' in NCFile.dimensions 183 if type(Tree) == list: # and NewFormat: 184 if onlylast: 185 if NewFormat: 186 if vardim == 1: 187 Tree[0].__dict__[str(var)] = varval[-1].data 188 elif vardim == 2: 189 Tree[0].__dict__[str(var)] = varval[-1, :].data 190 elif vardim == 3: 191 Tree[0].__dict__[str(var)] = varval[-1, :, :].data 174 for groupindex, listclass in enumerate(groupclass): 175 for var in listclass.variables: 176 if debug: 177 print(" ==> treating var {}".format(var)) 178 if var not in ['errlog', 'outlog']: 179 varval = listclass.variables[str(var)] 180 vardim = varval.ndim 181 #There is a special treatment for results to account for its specific structure 182 #that is the new export version where time is a named dimension 183 NewFormat = 'Time' in NCFile.dimensions 184 if type(Tree) == list: # and NewFormat: 185 if onlylast: 186 if NewFormat: 187 if vardim == 1: 188 Tree[0].__dict__[str(var)] = varval[-1].data 189 elif vardim == 2: 190 Tree[0].__dict__[str(var)] = varval[-1, :].data 191 elif vardim == 3: 192 Tree[0].__dict__[str(var)] = varval[-1, :, :].data 193 else: 194 print('table dimension greater than 3 not implemented yet') 195 else: #old format had step sorted in difeerent group so last group is last time 196 Tree[0].__dict__[str(var)] = varval[:].data 197 else: 198 if NewFormat: 199 incomplete = 'Time' not in varval.dimensions and NewFormat 200 if incomplete: 201 chosendim = varval.dimensions[0] 202 timelist = np.arange(0, len(NCFile.dimensions[chosendim])) 203 print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) 204 else: 205 timelist = np.arange(0, len(NCFile.dimensions['Time'])) 206 for t in timelist: 207 if debug: 208 print("filing step {} for {}".format(t, var)) 209 if vardim == 0: 210 Tree[t].__dict__[str(var)] = varval[:].data 211 elif vardim == 1: 212 Tree[t].__dict__[str(var)] = varval[t].data 213 elif vardim == 2: 214 Tree[t].__dict__[str(var)] = varval[t, :].data 215 elif vardim == 3: 216 Tree[t].__dict__[str(var)] = varval[t, :, :].data 217 else: 218 print('table dimension greater than 3 not implemented yet') 192 219 else: 193 print('table dimension greater than 3 not implemented yet')194 else: #old format had step sorted in difeerent group so last group is last time195 Tree[0].__dict__[str(var)] = varval[:].data220 if debug: 221 print("filing step {} for {}".format(groupindex, var)) 222 Tree[groupindex].__dict__[str(var)] = varval[:].data 196 223 else: 197 incomplete = 'Time' not in varval.dimensions and NewFormat 198 if incomplete: 199 chosendim = varval.dimensions[0] 200 timelist = np.arange(0, len(NCFile.dimensions[chosendim])) 201 print('WARNING, {} is not present on every times, we chose {}({}) as the dimension to write it with'.format(var, chosendim, len(NCFile.dimensions[chosendim]))) 202 elif not NewFormat: 203 timelist = len(curclass.groups) 224 if vardim == 0: #that is a scalar 225 if str(varval[0]) == '': #no value 226 Tree.__dict__[str(var)] = [] 227 elif varval[0] == 'True': #treatin bool 228 Tree.__dict__[str(var)] = True 229 elif varval[0] == 'False': #treatin bool 230 Tree.__dict__[str(var)] = False 231 else: 232 Tree.__dict__[str(var)] = varval[0].item() 233 234 elif vardim == 1: #that is a vector 235 if varval.dtype == str: 236 if varval.shape[0] == 1: 237 Tree.__dict__[str(var)] = [str(varval[0]), ] 238 elif 'True' in varval[:] or 'False' in varval[:]: 239 Tree.__dict__[str(var)] = np.asarray([V == 'True' for V in varval[:]], dtype=bool) 240 else: 241 Tree.__dict__[str(var)] = [str(vallue) for vallue in varval[:]] 242 else: 243 try: 244 #some thing specifically require a list 245 mdtype = type(Tree.__dict__[str(var)]) 246 except KeyError: 247 mdtype = float 248 if mdtype == list: 249 Tree.__dict__[str(var)] = [mdval for mdval in varval[:]] 250 else: 251 Tree.__dict__[str(var)] = varval[:].data 252 253 elif vardim == 2: 254 #dealling with dict 255 if varval.dtype == str: #that is for dictionaries 256 if any(varval[:, 0] == 'toolkit'): #toolkit definition have to be first 257 Tree.__dict__[str(var)] = OrderedDict([('toolkit', str(varval[np.where(varval[:, 0] == 'toolkit')[0][0], 1]))]) 258 strings1 = [str(arg[0]) for arg in varval if arg[0] != 'toolkits'] 259 strings2 = [str(arg[1]) for arg in varval if arg[0] != 'toolkits'] 260 Tree.__dict__[str(var)].update(list(zip(strings1, strings2))) 261 else: 262 strings1 = [str(arg[0]) for arg in varval] 263 strings2 = [str(arg[1]) for arg in varval] 264 Tree.__dict__[str(var)] = OrderedDict(list(zip(strings1, strings2))) 265 elif vardim == 3: 266 Tree.__dict__[str(var)] = varval[:, :, :].data 204 267 else: 205 timelist = np.arange(0, len(NCFile.dimensions['Time'])) 206 for t in timelist: 207 print("filing step {} for {}".format(t, var)) 208 if vardim == 0: 209 Tree[t].__dict__[str(var)] = varval[:].data 210 if vardim == 1: 211 Tree[t].__dict__[str(var)] = varval[t].data 212 elif vardim == 2: 213 Tree[t].__dict__[str(var)] = varval[t, :].data 214 elif vardim == 3: 215 Tree[t].__dict__[str(var)] = varval[t, :, :].data 216 else: 217 print('table dimension greater than 3 not implemented yet') 218 else: 219 if vardim == 0: #that is a scalar 220 if str(varval[0]) == '': #no value 221 Tree.__dict__[str(var)] = [] 222 elif varval[0] == 'True': #treatin bool 223 Tree.__dict__[str(var)] = True 224 elif varval[0] == 'False': #treatin bool 225 Tree.__dict__[str(var)] = False 268 print('table dimension greater than 3 not implemented yet') 269 # }}} 270 #==== And with atribute {{{ 271 for attr in listclass.ncattrs(): 272 if debug: 273 print(" ==> treating attribute {}".format(attr)) 274 if attr != 'classtype': #classtype is for treatment, don't get it back 275 attribute = str(attr).swapcase() #there is a reason for swapcase, no sure what it isanymore 276 if attr == 'VARNAME': 277 attribute = 'name' 278 if type(Tree) == list: 279 if debug: 280 print(" printing with index 0") 281 if listtype == 'dict': 282 Tree[0][attribute] = str(listclass.getncattr(attr)) 226 283 else: 227 Tree.__dict__[str(var)] = varval[0].item() 228 229 elif vardim == 1: #that is a vector 230 if varval.dtype == str: 231 if varval.shape[0] == 1: 232 Tree.__dict__[str(var)] = [str(varval[0]), ] 233 elif 'True' in varval[:] or 'False' in varval[:]: 234 Tree.__dict__[str(var)] = np.asarray([V == 'True' for V in varval[:]], dtype=bool) 235 else: 236 Tree.__dict__[str(var)] = [str(vallue) for vallue in varval[:]] 237 else: 238 try: 239 #some thing specifically require a list 240 mdtype = type(Tree.__dict__[str(var)]) 241 except KeyError: 242 mdtype = float 243 if mdtype == list: 244 Tree.__dict__[str(var)] = [mdval for mdval in varval[:]] 245 else: 246 Tree.__dict__[str(var)] = varval[:].data 247 248 elif vardim == 2: 249 #dealling with dict 250 if varval.dtype == str: #that is for dictionaries 251 if any(varval[:, 0] == 'toolkit'): #toolkit definition have to be first 252 Tree.__dict__[str(var)] = OrderedDict([('toolkit', str(varval[np.where(varval[:, 0] == 'toolkit')[0][0], 1]))]) 253 strings1 = [str(arg[0]) for arg in varval if arg[0] != 'toolkits'] 254 strings2 = [str(arg[1]) for arg in varval if arg[0] != 'toolkits'] 255 Tree.__dict__[str(var)].update(list(zip(strings1, strings2))) 256 else: 257 strings1 = [str(arg[0]) for arg in varval] 258 strings2 = [str(arg[1]) for arg in varval] 259 Tree.__dict__[str(var)] = OrderedDict(list(zip(strings1, strings2))) 260 elif vardim == 3: 261 Tree.__dict__[str(var)] = varval[:, :, :].data 284 Tree[0].__dict__[attribute] = str(listclass.getncattr(attr)) 262 285 else: 263 print('table dimension greater than 3 not implemented yet') 264 # }}} 265 #==== And with atribute {{{ 266 for attr in listclass.ncattrs(): 267 if debug: 268 print(" ==> treating attribute {}".format(attr)) 269 if attr != 'classtype': #classtype is for treatment, don't get it back 270 attribute = str(attr).swapcase() #there is a reason for swapcase, no sure what it isanymore 271 if attr == 'VARNAME': 272 attribute = 'name' 273 if type(Tree) == list: 274 if debug: 275 print(" printing with index 0") 276 if listtype == 'dict': 277 Tree[0][attribute] = str(listclass.getncattr(attr)) 278 else: 279 Tree[0].__dict__[attribute] = str(listclass.getncattr(attr)) 280 else: 281 Tree.__dict__[attribute] = str(listclass.getncattr(attr)) 282 if listclass.getncattr(attr) == 'True': 283 Tree.__dict__[attribute] = True 284 elif listclass.getncattr(attr) == 'False': 285 Tree.__dict__[attribute] = False 286 Tree.__dict__[attribute] = str(listclass.getncattr(attr)) 287 if listclass.getncattr(attr) == 'True': 288 Tree.__dict__[attribute] = True 289 elif listclass.getncattr(attr) == 'False': 290 Tree.__dict__[attribute] = False 286 291 # }}} 287 292 # }}}
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