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Changeset 21247
- Timestamp:
- 10/06/16 09:22:31 (8 years ago)
- Location:
- issm/trunk-jpl/src/m
- Files:
-
- 2 edited
-
contrib/defleurian/netCDF/export_netCDF.py (modified) (6 diffs)
-
io/loadvars.py (modified) (2 diffs)
Legend:
- Unmodified
- Added
- Removed
-
issm/trunk-jpl/src/m/contrib/defleurian/netCDF/export_netCDF.py
r21235 r21247 24 24 25 25 #gather geometry and timestepping as dimensions 26 Duration=md.timestepping.final_time-md.timestepping.start_time 27 if Duration>0 and md.timestepping.time_step*md.settings.output_frequency>0: 28 StepNum=int(Duration/(md.timestepping.time_step*md.settings.output_frequency)) 29 else: 26 try: 27 StepNum=np.shape(dict.values(md.results.__dict__))[1] 28 except IndexError: 30 29 StepNum=1 31 32 Dimension1=NCData.createDimension('Dimension1',md.mesh.numberofelements) 33 Dimension2=NCData.createDimension('Dimension2',md.mesh.numberofvertices) 34 Dimension3=NCData.createDimension('Dimension3',np.shape(md.mesh.elements)[1]) 35 Dimension4=NCData.createDimension('Dimension4',StepNum) 36 Dimension5=NCData.createDimension('Dimension5',2) 37 38 DimDict = {len(Dimension1):'Dimension1', 39 len(Dimension2):'Dimension2', 40 len(Dimension3):'Dimension3', 41 len(Dimension4):'Dimension4', 42 len(Dimension5):'Dimension5'} 30 Dimension1=NCData.createDimension('DimNum1',StepNum)#time is first 31 DimDict={len(Dimension1):'DimNum1'} 32 dimindex=1 33 34 dimlist=[2,md.mesh.numberofelements,md.mesh.numberofvertices,np.shape(md.mesh.elements)[1]] 35 for i in range(0,4): 36 if dimlist[i] not in DimDict.keys(): 37 dimindex+=1 38 NewDim=NCData.createDimension('DimNum'+str(dimindex),dimlist[i]) 39 DimDict[len(NewDim)]='DimNum'+str(dimindex) 43 40 44 41 #get all model classes and create respective groups 42 typelist=[bool,str,unicode,int,float,complex,numpy.ndarray,collections.OrderedDict] 45 43 groups=dict.keys(md.__dict__) 46 44 for group in groups: … … 49 47 fields=dict.keys(md.__dict__[group].__dict__) 50 48 51 #Special treatment for the results 52 if str(group)=='results': 53 for supfield in fields:#looping on the different solutions 54 NCgroup.__setattr__('classtype', "results") 55 Subgroup=NCgroup.createGroup(str(supfield)) 56 #Subgroup.__setattr__('classtype',md.results.__dict__[supfield].__class__.__name__) 57 Subgroup.__setattr__('classtype',md.results.__class__.__name__) 58 if type(md.results.__dict__[supfield])==list:#the solution have several timestep 59 #get last timesteps and output frequency 60 last_step = np.size(md.results.__dict__[supfield]) 61 step_freq = md.settings.output_frequency 62 #grab first time step 63 subfields=dict.keys(md.results.__dict__[supfield].__getitem__(0).__dict__) 64 for field in subfields: 65 if str(field)!='errlog' and str(field)!='outlog' and str(field)!='SolutionType': 66 Var=md.results.__dict__[supfield].__getitem__(0).__dict__[field] 67 DimDict=CreateVar(NCData,Var,field,Subgroup,DimDict,True,last_step,step_freq,md,supfield) 68 69 elif type(md.results.__dict__[supfield])==results:#only one timestep 70 subfields=dict.keys(md.results.__dict__[supfield].__dict__) 71 for field in subfields: 72 if str(field)!='errlog' and str(field)!='outlog' and str(field)!='SolutionType': 73 Var=md.results.__dict__[supfield].__dict__[field] 74 DimDict=CreateVar(NCData,Var,field,NCgroup,DimDict,False) 49 #looping on fields 50 for field in fields: 51 #Special treatment for list fields 52 if type(md.__dict__[group].__dict__[field])==list: 53 StdList=False 54 if len(md.__dict__[group].__dict__[field])==0: 55 StdList=True 75 56 else: 76 print ('Result format not suported') 77 else: 78 79 for field in fields: 57 StdList=type(md.__dict__[group].__dict__[field][0]) in typelist 58 59 if StdList: #this is a standard or empty list just proceed 60 NCgroup.__setattr__('classtype', md.__dict__[group].__class__.__name__) 61 Var=md.__dict__[group].__dict__[field] 62 DimDict=CreateVar(NCData,Var,field,NCgroup,DimDict,False) 63 else: #this is a list of fields, specific treatment needed 64 Listsize=len(md.__dict__[group].__dict__[field]) 65 NCgroup.__setattr__('classtype', md.__dict__[group].__class__.__name__) 66 Subgroup=NCgroup.createGroup(str(field)) 67 Subgroup.__setattr__('classtype',md.__dict__[group].__dict__[field].__class__.__name__) 68 for listindex in range(0,Listsize): 69 try: 70 Listgroup=Subgroup.createGroup(str(md.__dict__[group].__dict__[field].__getitem__(listindex).__dict__['name'])) 71 except KeyError: 72 for naming in ['step']: 73 Listgroup=Subgroup.createGroup(str(md.__dict__[group].__dict__[field].__getitem__(listindex).__dict__[naming])) 74 except AttributeError: 75 Listgroup=Subgroup.createGroup(str(md.__dict__[group].__dict__[field].__class__.__name__)+str(listindex)) 76 Listgroup.__setattr__('classtype',md.__dict__[group].__dict__[field].__getitem__(listindex).__class__.__name__) 77 try: 78 subfields=dict.keys(md.__dict__[group].__dict__[field].__getitem__(listindex).__dict__) 79 except AttributeError: 80 subfields=dict.keys(md.__dict__[group].__dict__[field].__getitem__(listindex)) 81 for subfield in subfields: 82 try: 83 Var=md.__dict__[group].__dict__[field].__getitem__(listindex).__dict__[subfield] 84 except AttributeError: 85 Var=md.__dict__[group].__dict__[field].__getitem__(listindex)[subfield] 86 DimDict=CreateVar(NCData,Var,subfield,Listgroup,DimDict,False,md.__dict__[group],field,listindex) 87 #No subgroup, we directly treat the variable 88 elif type(md.__dict__[group].__dict__[field]) in typelist: 80 89 NCgroup.__setattr__('classtype', md.__dict__[group].__class__.__name__) 81 90 Var=md.__dict__[group].__dict__[field] 82 91 DimDict=CreateVar(NCData,Var,field,NCgroup,DimDict,False) 92 else: 93 NCgroup.__setattr__('classtype', str(group)) 94 Subgroup=NCgroup.createGroup(str(field)) 95 Subgroup.__setattr__('classtype',md.__dict__[group].__class__.__name__) 96 subfields=dict.keys(md.__dict__[group].__dict__[field].__dict__) 97 for subfield in subfields: 98 if str(subfield)!='errlog' and str(subfield)!='outlog' and str(subfield)!='SolutionType': 99 Var=md.__dict__[group].__dict__[field].__dict__[subfield] 100 DimDict=CreateVar(NCData,Var,subfield,Subgroup,DimDict,False) 101 83 102 NCData.close() 84 103 … … 97 116 val_shape=np.shape(var) 98 117 99 100 118 TypeDict = {float:'f8', 101 119 'float64':'f8', 102 120 int:'i8', 103 'int64':'i8'} 121 'int64':'i8', 122 str:'str', 123 dict:'str'} 104 124 105 125 val_dim=np.shape(val_shape)[0] … … 111 131 elif val_type==list: 112 132 dimensions,DimDict=GetDim(NCData,var,val_shape,DimDict,val_dim,istime) 113 ncvar = Group.createVariable(str(field),'str',dimensions,zlib=True) 114 for elt in range(0,val_dim): 115 try: 133 #try to get the type from the first element 134 try: 135 nctype=TypeDict[type(var[0])] 136 except IndexError: 137 nctype='str' #most probably an empty list take str for that 138 ncvar = Group.createVariable(str(field),nctype,dimensions,zlib=True) 139 if val_shape==0: 140 ncvar= [] 141 else: 142 for elt in range(0,val_shape[0]): 116 143 ncvar[elt] = var[elt] 117 except IndexError:118 ncvar= []119 144 #treating bool tables as string tables 120 145 elif val_type=='bool': … … 135 160 ncvar = Group.createVariable(str(field),TypeDict[val_type],dimensions,zlib=True) 136 161 if istime: 137 last=step_args[0]138 md=step_args[2]139 supfield=step_args[3]162 md=step_args[0] 163 supfield=step_args[1] 164 index=step_args[2] 140 165 vartab=var 141 for time in range(0,last,1): 142 if time!=0: 143 timevar=md.results.__dict__[supfield].__getitem__(time).__dict__[field] 144 vartab=np.column_stack((vartab,timevar)) 145 try: 146 ncvar[:,:]=vartab[:,:] 166 timevar=md.__dict__[supfield].__getitem__(index).__dict__[field] 167 try: 168 ncvar[:,:]=timevar[:,:] 147 169 except ValueError: 148 ncvar[:]= vartab.T[:]170 ncvar[:]=timevar.T[:] 149 171 else: 150 172 try: … … 170 192 if (shape[dim])>0: 171 193 index=len(DimDict)+1 172 NewDim=NCData.createDimension('Dim ension'+str(index),(shape[dim]))173 DimDict[len(NewDim)]='Dim ension'+str(index)194 NewDim=NCData.createDimension('DimNum'+str(index),(shape[dim])) 195 DimDict[len(NewDim)]='DimNum'+str(index) 174 196 output=output+[str(DimDict[shape[dim]])] 175 197 elif type(shape[0])==str:#dealling with a dictionnary 176 198 try: 177 199 #dimension5 is 2 to treat with dict 178 output=[str(DimDict[np.shape(shape)[0]])]+[ 'Dimension5']200 output=[str(DimDict[np.shape(shape)[0]])]+[DimDict[2]] 179 201 except KeyError: 180 202 index=len(DimDict)+1 181 NewDim=NCData.createDimension('Dim ension'+str(index),np.shape(shape)[0])182 DimDict[len(NewDim)]='Dim ension'+str(index)183 output=[str(DimDict[np.shape(dict.keys(var))[0]])]+[ 'Dimension5']203 NewDim=NCData.createDimension('DimNum'+str(index),np.shape(shape)[0]) 204 DimDict[len(NewDim)]='DimNum'+str(index) 205 output=[str(DimDict[np.shape(dict.keys(var))[0]])]+[DimDict[2]] 184 206 break 185 207 if istime: 186 output=output+['Dim ension4']208 output=output+['DimNum1'] 187 209 return tuple(output), DimDict -
issm/trunk-jpl/src/m/io/loadvars.py
r21236 r21247 78 78 NCFile=Dataset(filename,mode='r') 79 79 NCFile.close() 80 classtype,classtree=netCDFread(filename)81 nvdict['md']=model()82 module=map(__import__,dict.values(classtype))83 for i,mod in enumerate(dict.keys(classtype)):84 # print('treating md.{}'.format(mod))85 if np.size(classtree[mod])>1:86 if classtree[mod][0]=='results':87 #treating results (Dimension4 is time)88 resdim=len(NCFile.dimensions['Dimension4'])89 curclass=NCFile.groups[classtree[mod][0]].groups[classtree[mod][1]]90 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = [getattr(module[i],classtype[mod])()]91 if resdim>1:92 for t in range(1,resdim):93 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]].append(getattr(module[i],classtype[mod])())94 Tree=nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:]95 else:96 curclass=NCFile.groups[classtree[mod][0]].groups[classtree[mod][1]]97 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = getattr(module[i],classtype[mod])()98 Tree=nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]]99 else:100 curclass=NCFile.groups[classtree[mod][0]]101 nvdict['md'].__dict__[mod] = getattr(module[i],classtype[mod])()102 Tree=nvdict['md'].__dict__[classtree[mod][0]]103 for var in curclass.variables:104 #print(' treating {}'.format(var))105 varval=curclass.variables[str(var)]106 vardim=varval.ndim107 try:108 val_type=str(varval.dtype)109 except AttributeError:110 val_type=type(varval)111 if vardim==0:112 try:113 Tree.__dict__[str(var)]=varval.getValue()114 if varval.getValue()=='True':115 Tree.__dict__[str(var)]=True116 elif varval.getValue()=='False':117 Tree.__dict__[str(var)]=False118 except IndexError:119 Tree.__dict__[str(var)]=[]120 elif vardim==1:121 if varval.dtype==str:122 if varval.shape==1:123 Tree.__dict__[str(var)]=str(varval[0])124 if 'True' in varval[:] or 'False' in varval[:]:125 Tree.__dict__[str(var)]=np.asarray(varval[:],dtype=bool)126 else:127 if classtree[mod][0]=='results' and resdim>1:128 for t in range(0,resdim):129 Tree[t].__dict__[str(var)]=varval[t]130 else:131 Tree.__dict__[str(var)]=varval[:]132 elif vardim==2:133 #dealling with dict134 if varval.dtype==str:135 Tree.__dict__[str(var)]=dict(zip(varval[:,0], varval[:,1]))136 else:137 if classtree[mod][0]=='results' and resdim>1:138 for t in range(0,resdim):139 Tree[t].__dict__[str(var)]=varval[:,t]140 else:141 Tree.__dict__[str(var)]=varval[:,:]142 elif vardim==3:143 if classtree[mod][0]=='results' and resdim>1:144 for t in range(0,resdim):145 Tree[t].__dict__[str(var)]=varval[:,:,t]146 else:147 Tree.__dict__[str(var)]=varval[:,:,:]148 else:149 print 'table dimension greater than 3 not implemented yet'150 for attr in curclass.ncattrs():151 #print(' treating {}'.format(attr))152 if classtree[mod][0]!='results' and attr!='classtype': #no attributes in results153 Tree.__dict__[str(attr)]=str(curclass.getncattr(attr))154 if curclass.getncattr(attr)=='True':155 Tree.__dict__[str(attr)]=True156 elif curclass.getncattr(attr)=='False':157 Tree.__dict__[str(attr)]=False158 159 80 except RuntimeError: 160 81 raise IOError("File '%s' not found." % filename) 161 82 83 classtype,classtree=netCDFread(filename) 84 nvdict['md']=model() 85 NCFile=Dataset(filename,mode='r') 86 for mod in dict.keys(classtype): 87 if np.size(classtree[mod])>1: 88 curclass=NCFile.groups[classtree[mod][0]].groups[classtree[mod][1]] 89 if classtype[mod][0]=='list': 90 keylist=[key for key in curclass.groups] 91 listtype=curclass.groups[keylist[0]].classtype 92 if listtype=='dict': 93 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = [{} for i in range(max(1,len(curclass.groups)))] 94 else: 95 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = [getattr(__import__(listtype),listtype)() for i in range(max(1,len(curclass.groups)))] 96 Tree=nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]][:] 97 else: 98 nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] = getattr(classtype[mod][1],classtype[mod][0])() 99 Tree=nvdict['md'].__dict__[classtree[mod][0]].__dict__[classtree[mod][1]] 100 else: 101 curclass=NCFile.groups[classtree[mod][0]] 102 nvdict['md'].__dict__[mod] = getattr(classtype[mod][1],classtype[mod][0])() 103 Tree=nvdict['md'].__dict__[classtree[mod][0]] 104 #treating groups that are lists 105 for i in range(0,max(1,len(curclass.groups))): 106 if len(curclass.groups)>0: 107 listclass=curclass.groups[keylist[i]] 108 else: 109 listclass=curclass 110 for var in listclass.variables: 111 if var not in ['errlog','outlog']: 112 varval=listclass.variables[str(var)] 113 vardim=varval.ndim 114 try: 115 val_type=str(varval.dtype) 116 except AttributeError: 117 val_type=type(varval) 118 if vardim==0: 119 if type(Tree)==list: 120 t=int(keylist[i][-1])-1 121 if listtype=='dict': 122 Tree[t][str(var)]=varval.getValue() 123 else: 124 Tree[t].__dict__[str(var)]=varval.getValue() 125 else: 126 try: 127 Tree.__dict__[str(var)]=varval.getValue() 128 if varval.getValue()=='True': 129 Tree.__dict__[str(var)]=True 130 elif varval.getValue()=='False': 131 Tree.__dict__[str(var)]=False 132 except IndexError: 133 Tree.__dict__[str(var)]=[] 134 elif vardim==1: 135 if varval.dtype==str: 136 if varval.shape[0]==1: 137 Tree.__dict__[str(var)]=[str(varval[0]),] 138 elif 'True' in varval[:] or 'False' in varval[:]: 139 Tree.__dict__[str(var)]=np.asarray([V=='True' for V in varval[:]],dtype=bool) 140 else: 141 Tree.__dict__[str(var)]=[str(vallue) for vallue in varval[:]] 142 else: 143 if type(Tree)==list: 144 t=int(keylist[i][-1])-1 145 if listtype=='dict': 146 Tree[t][str(var)]=varval[:] 147 else: 148 Tree[t].__dict__[str(var)]=varval[:] 149 else: 150 try: 151 #some thing specifically require a list 152 mdtype=type(Tree.__dict__[str(var)]) 153 except KeyError: 154 print 'not defined yet' 155 mdtype=float 156 if mdtype==list: 157 Tree.__dict__[str(var)]=[mdval for mdval in varval[:]] 158 else: 159 Tree.__dict__[str(var)]=varval[:] 160 elif vardim==2: 161 #dealling with dict 162 if varval.dtype==str: 163 Tree.__dict__[str(var)]=dict(zip(varval[:,0], varval[:,1])) 164 else: 165 if type(Tree)==list: 166 t=int(keylist[i][-1])-1 167 if listtype=='dict': 168 Tree[t][str(var)]=varval[:,:] 169 else: 170 Tree[t].__dict__[str(var)]=varval[:,:] 171 else: 172 Tree.__dict__[str(var)]=varval[:,:] 173 elif vardim==3: 174 if type(Tree)==list: 175 t=int(keylist[i][-1])-1 176 if listtype=='dict': 177 Tree[t][str(var)]=varval[:,:,:] 178 else: 179 Tree[t].__dict__[str(var)]=varval[:,:,:] 180 else: 181 Tree.__dict__[str(var)]=varval[:,:,:] 182 else: 183 print 'table dimension greater than 3 not implemented yet' 184 for attr in listclass.ncattrs(): 185 if attr!='classtype': #classtype is for treatment, don't get it back 186 if type(Tree)==list: 187 t=int(keylist[i][-1])-1 188 if listtype=='dict': 189 Tree[t][str(attr)]=str(listclass.getncattr(attr)) 190 else: 191 Tree[t].__dict__[str(attr)]=str(listclass.getncattr(attr)) 192 else: 193 Tree.__dict__[str(attr)]=str(listclass.getncattr(attr)) 194 if listclass.getncattr(attr)=='True': 195 Tree.__dict__[str(attr)]=True 196 elif listclass.getncattr(attr)=='False': 197 Tree.__dict__[str(attr)]=False 198 NCFile.close() 162 199 if len(args) >= 2 and isinstance(args[1],(str,unicode)): # (value) 163 200 value=[nvdict[name] for name in args[1:]] … … 173 210 174 211 def netCDFread(filename): 175 def walktree(data):176 keys = data.groups.keys()177 for key in keys:178 yield [str(key)]179 for children in walktree(data.groups[str(key)]):180 child=[str(key)]181 child.append(str(children[0]))182 yield child183 212 print ('Opening {} for reading '.format(filename)) 184 213 NCData=Dataset(filename, 'r') 185 214 class_dict={} 186 215 class_tree={} 216 217 for group in NCData.groups: 218 if len(NCData.groups[group].groups)>0: 219 for subgroup in NCData.groups[group].groups: 220 classe=str(group)+'.'+str(subgroup) 221 class_dict[classe]=[str(getattr(NCData.groups[group].groups[subgroup],'classtype')),] 222 if class_dict[classe][0] not in ['dict','list']: 223 class_dict[classe].append(__import__(class_dict[classe][0])) 224 class_tree[classe]=[group,subgroup] 225 else: 226 classe=str(group) 227 class_dict[classe]=[str(getattr(NCData.groups[group],'classtype')),] 228 if class_dict[classe][0] not in ['dict','list']: 229 class_dict[classe].append(__import__(class_dict[classe][0])) 230 class_tree[classe]=[group,] 187 231 188 for children in walktree(NCData): 189 classe=str(children[0]) 190 if np.size(children)>1: 191 for name in children[1:]: 192 classe=classe+'.'+name 193 class_dict[classe]=str(getattr(NCData.groups[children[0]].groups[children[1]],'classtype')) 194 else: 195 class_dict[classe]=str(getattr(NCData.groups[classe],'classtype')) 196 class_tree[classe]=children 197 232 NCData.close() 198 233 return class_dict,class_tree
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