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Changeset 19097
- Timestamp:
- 02/11/15 11:47:25 (10 years ago)
- File:
-
- 1 edited
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- Unmodified
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issm/trunk-jpl/src/m/contrib/netCDF/export_netCDF.py
r19096 r19097 8 8 from os import path, remove 9 9 10 def netCDFExp(md,filename): 11 12 #defining some sub-functions 13 #retriev the dimension tuple from a dictionnary 14 def GetDim(var,shape,i,istime): 15 output=[] 16 for dim in range(0,i): #loop on the dimensions 17 if type(shape[0])==int: 18 try: 19 output=output+[str(DimDict[shape[dim]])] #test if the dimension allready exist 20 except KeyError: #if not create it 21 if (shape[dim])>1: 22 index=len(DimDict)+1 23 NewDim=NCData.createDimension('Dimension'+str(index),(shape[dim])) 24 DimDict[len(NewDim)]='Dimension'+str(index) 25 output=output+[str(DimDict[shape[dim]])] 26 print 'Defining dimension ' +str(shape[dim]) +' for '+str(field)+str(dim) 27 elif type(shape[0])==str:#dealling with a dictionnary 28 try: 29 output=[str(DimDict[numpy.shape(shape)[0]])]+['DictDim'] 30 except KeyError: 31 index=len(DimDict)+1 32 NewDim=NCData.createDimension('Dimension'+str(index),numpy.shape(shape)[0]) 33 DimDict[len(NewDim)]='Dimension'+str(index) 34 output=[str(DimDict[numpy.shape(dict.keys(var))[0]])]+['Dimension5'] 35 print 'Defining dimension ' +str(numpy.shape(shape)[0]) +' for '+str(field) 36 break 37 if istime: 38 output=output+['Dimension4'] 39 return tuple(output) 40 #============================================================================ 41 42 #Define the variables 43 def CreateVar(var,Group,istime,*step_args): 44 #grab type 45 try: 46 val_type=str(var.dtype) 47 except AttributeError: 48 val_type=type(var) 49 #grab dimension 50 try: 51 val_shape=dict.keys(var) 52 except TypeError: 53 val_shape=numpy.shape(var) 54 55 val_dim=numpy.shape(val_shape)[0] 56 #Now define and fill up variable 57 58 #treating scalar string or bool as atribute 59 if val_type==str or val_type==bool: 60 Group.__setattr__(str(field), str(var)) 61 62 #treating list as string table 63 elif val_type==list: 64 ncvar = Group.createVariable(str(field),str,GetDim(var,val_shape,val_dim,istime),zlib=True) 65 for elt in range(0,val_dim): 66 try: 67 ncvar[elt] = var[elt] 68 except IndexError: 69 ncvar[0]= " " 70 #treating bool tables as string tables 71 elif val_type=='bool': 72 ncvar = Group.createVariable(str(field),str,GetDim(var,val_shape,val_dim,istime),zlib=True) 73 for elt in range(0,val_shape[0]): 74 ncvar[elt] = str(var[elt]) 75 #treating dictionaries as string tables of dim 2 76 elif val_type==collections.OrderedDict: 77 ncvar = Group.createVariable(str(field),str,GetDim(var,val_shape,val_dim,istime),zlib=True) 78 for elt in range(0,val_dim): 79 ncvar[elt,0]=dict.keys(var)[elt] 80 ncvar[elt,1]=str(dict.values(var)[elt]) #converting to str to avoid potential problems 81 #Now dealing with numeric variables 82 else: 83 ncvar = Group.createVariable(str(field),TypeDict[val_type],GetDim(var,val_shape,val_dim,istime),zlib=True) 84 85 if istime: 86 last=step_args[0] 87 freq=step_args[1] 88 vartab=var 89 for time in range(freq-1,last,freq): 90 if time!=0: 91 timevar=md.results.__dict__[supfield].__getitem__(time).__dict__[field] 92 print 'Treating '+str(group)+'.'+str(supfield)+'.'+str(field)+' for time '+str(time) 93 vartab=numpy.column_stack((vartab,timevar)) 94 print numpy.shape(vartab) 95 try: 96 ncvar[:,:]=vartab[:,:] 97 except ValueError: 98 ncvar[:]=vartab.T[:] 99 else: 100 try: 101 nan_val=numpy.isnan(var) 102 if nan_val.all(): 103 ncvar [:] = 'NaN' 104 else: 105 ncvar[:] = var 106 except TypeError: #type does not accept nan, get vallue of the variable 107 ncvar[:] = var 108 #============================================================================ 109 10 def export_netCDF(md,filename): 110 11 #Now going on Real treatment 111 12 if path.exists(filename): … … 141 42 len(Dimension5):'Dimension5'} 142 43 143 TypeDict = {float:'f8',144 'float64':'f8',145 int:'i8',146 'int64':'i8'}147 148 44 #get all model classes and create respective groups 149 45 groups=dict.keys(md.__dict__) … … 168 64 if str(field)!='errlog' and str(field)!='outlog' and str(field)!='SolutionType': 169 65 Var=md.results.__dict__[supfield].__getitem__(0).__dict__[field] 170 CreateVar(Var,Subgroup,True,last_step,step_freq)66 DimDict=CreateVar(NCData,Var,field,Subgroup,DimDict,True,last_step,step_freq,md,supfield) 171 67 172 68 elif type(md.results.__dict__[supfield])==results:#only one timestep … … 174 70 for field in subfields: 175 71 if str(field)!='errlog' and str(field)!='outlog' and str(field)!='SolutionType': 176 #print 'Treating '+str(group)+'.'+str(supfield)+'.'+str(field)72 print 'Treating '+str(group)+'.'+str(supfield)+'.'+str(field) 177 73 Var=md.results.__dict__[supfield].__dict__[field] 178 CreateVar(Var,Subgroup,False)74 DimDict=CreateVar(NCData,Var,field,NCgroup,DimDict,False) 179 75 else: 180 76 print 'Result format not suported' … … 182 78 183 79 for field in fields: 184 #print 'Treating ' +str(group)+'.'+str(field)80 print 'Treating ' +str(group)+'.'+str(field) 185 81 NCgroup.__setattr__('classtype', md.__dict__[group].__class__.__name__) 186 82 Var=md.__dict__[group].__dict__[field] 187 CreateVar(Var,NCgroup,False)83 DimDict=CreateVar(NCData,Var,field,NCgroup,DimDict,False) 188 84 NCData.close() 189 85 86 #============================================================================ 87 #Define the variables 88 def CreateVar(NCData,var,field,Group,DimDict,istime,*step_args): 89 #grab type 90 try: 91 val_type=str(var.dtype) 92 except AttributeError: 93 val_type=type(var) 94 #grab dimension 95 try: 96 val_shape=dict.keys(var) 97 except TypeError: 98 val_shape=numpy.shape(var) 190 99 100 101 TypeDict = {float:'f8', 102 'float64':'f8', 103 int:'i8', 104 'int64':'i8'} 105 106 val_dim=numpy.shape(val_shape)[0] 107 #Now define and fill up variable 108 #treating scalar string or bool as atribute 109 if val_type==str or val_type==bool: 110 Group.__setattr__(str(field), str(var)) 111 112 #treating list as string table 113 #matlab does not recognise strings so we have to settle down with char arrays 114 elif val_type==list: 115 dimensions,DimDict=GetDim(NCData,var,val_shape,DimDict,val_dim,istime) 116 ncvar = Group.createVariable(str(field),str,dimensions,zlib=True) 117 for elt in range(0,val_dim): 118 try: 119 ncvar[elt] = var[elt] 120 except IndexError: 121 ncvar[0]= " " 122 #treating bool tables as string tables 123 elif val_type=='bool': 124 dimensions,DimDict=GetDim(NCData,var,val_shape,DimDict,val_dim,istime) 125 ncvar = Group.createVariable(str(field),str,dimensions,zlib=True) 126 for elt in range(0,val_shape[0]): 127 ncvar[elt] = str(var[elt]) 128 #treating dictionaries as string tables of dim 2 129 elif val_type==collections.OrderedDict: 130 dimensions,DimDict=GetDim(NCData,var,val_shape,DimDict,val_dim,istime) 131 ncvar = Group.createVariable(str(field),str,dimensions,zlib=True) 132 for elt in range(0,val_dim): 133 ncvar[elt,0]=dict.keys(var)[elt] 134 ncvar[elt,1]=str(dict.values(var)[elt]) #converting to str to avoid potential problems 135 #Now dealing with numeric variables 136 else: 137 dimensions,DimDict=GetDim(NCData,var,val_shape,DimDict,val_dim,istime) 138 ncvar = Group.createVariable(str(field),TypeDict[val_type],dimensions,zlib=True) 139 140 if istime: 141 last=step_args[0] 142 freq=step_args[1] 143 md=step_args[2] 144 supfield=step_args[3] 145 vartab=var 146 for time in range(freq-1,last,freq): 147 if time!=0: 148 timevar=md.results.__dict__[supfield].__getitem__(time).__dict__[field] 149 print 'Treating results.'+str(supfield)+'.'+str(field)+' for time '+str(time) 150 vartab=numpy.column_stack((vartab,timevar)) 151 print numpy.shape(vartab) 152 try: 153 ncvar[:,:]=vartab[:,:] 154 except ValueError: 155 ncvar[:]=vartab.T[:] 156 else: 157 try: 158 nan_val=numpy.isnan(var) 159 if nan_val.all(): 160 ncvar [:] = 'NaN' 161 else: 162 ncvar[:] = var 163 except TypeError: #type does not accept nan, get vallue of the variable 164 ncvar[:] = var 165 return DimDict 166 167 #============================================================================ 168 #retriev the dimension tuple from a dictionnary 169 def GetDim(NCData,var,shape,DimDict,i,istime): 170 output=[] 171 for dim in range(0,i): #loop on the dimensions 172 if type(shape[0])==int: 173 try: 174 output=output+[str(DimDict[shape[dim]])] #test if the dimension allready exist 175 except KeyError: #if not create it 176 if (shape[dim])>1: 177 index=len(DimDict)+1 178 NewDim=NCData.createDimension('Dimension'+str(index),(shape[dim])) 179 DimDict[len(NewDim)]='Dimension'+str(index) 180 output=output+[str(DimDict[shape[dim]])] 181 print 'Defining dimension ' +'Dimension'+str(index) 182 elif type(shape[0])==str:#dealling with a dictionnary 183 try: 184 output=[str(DimDict[numpy.shape(shape)[0]])]+['DictDim'] 185 except KeyError: 186 index=len(DimDict)+1 187 NewDim=NCData.createDimension('Dimension'+str(index),numpy.shape(shape)[0]) 188 DimDict[len(NewDim)]='Dimension'+str(index) 189 output=[str(DimDict[numpy.shape(dict.keys(var))[0]])]+['Dimension5'] 190 print 'Defining dimension ' +'Dimension'+str(index) 191 break 192 if istime: 193 output=output+['Dimension4'] 194 return tuple(output), DimDict
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