def read1DVariableFromDataset( dimStr, varStr, ds, iLOff=0, iROff=0, cl=1):
# iLOff: left offset
# iROff: right offset
# cl : coarsening level
if(varStr in ds.variables.keys()):
vs = ds.variables[varStr]
dimList = vs.dimensions # return a list of variable dimensions ('time', 'x', 'y', etc.)
print(' dimList = {} '.format( dimList ))
vdim = partialMatchFromList( dimStr, dimList )
try:
print(' Reading variable {} ... '.format(vdim))
if(iROff == 0 or (iROff is None) ):
var = ds.variables[vdim][(0 + iLOff):]
else:
var = ds.variables[vdim][(0 + iLOff):-abs(iROff)]
print(' ... done.')
except:
print(' Cannot read the array of variable: {}.'.format(varStr))
sys.exit(1)
else:
print(' Variable {} not in list {}.'.format(varStr, ds.variables.keys()))
sys.exit(1)
return var[::cl], np.shape(var[::cl])
def read3DVariableFromDataset(varStr, ds, iTOff=0, iLOff=0, iROff=0, cl=1, meanOn=False):
# iLOff: left offset
# iROff: right offset
# cl : coarsening level
varStr = partialMatchFromList( varStr , ds.variables.keys() )
print(' Reading variable {} ... '.format(varStr))
var, dDict = readVariableFromDataset(varStr, ds, cl=1 )
print(' ... done.')
iL = 0 + int(iLOff/cl)
iR = int(abs(iROff/cl))
iT = 0 + int(iTOff)
if(iR == 0):
# Param list (time, z, y, x )
if(meanOn):
vo = var[iL:, iL:, iL:]
else:
vo = var[iT:, iL:, iL:, iL:]
else:
if(meanOn):
vo = var[iL:-iR, iL:-iR, iL:-iR]
else:
vo = var[iT:, iL:-iR, iL:-iR, iL:-iR]
var = None
return vo, np.array(vo.shape)
def read3dDataFromNetCDF( fname, varStr, cl=1 ):
'''
Establish two boolean variables which indicate whether the created variable is an
independent or dependent variable in function createNetcdfVariable().
'''
parameter = True; variable = False
'''
Create a NETCDF input dataset (ds), and its associated lists of dependent (varList)
and independent (dimList) variables.
'''
ds, varList, paramList = netcdfDataset(fname)
varStr = partialMatchFromList( varStr , varList )
print(' Extracting {} from dataset in {} ... '.format( varStr, fname ))
var, dDict = readVariableFromDataset(varStr, ds, cl )
print(' {}_dims = {}\n Done!'.format(varStr, var.shape ))
# Rename the keys in dDict to simplify the future postprocessing
for dn in dDict.keys():
idNan = np.isnan(dDict[dn]); dDict[dn][idNan] = 0.
if( 'time' in dn and 'time' != dn ):
dDict['time'] = dDict.pop( dn )
elif( 'x' == dn[0] and 'x' != dn ):
dDict['x'] = dDict.pop( dn )
elif( 'y' == dn[0] and 'y' != dn ):
dDict['y'] = dDict.pop( dn )
elif( 'z' == dn[0] and 'z' != dn ):
dDict['z'] = dDict.pop( dn )
else: pass
# Append the variable into the dict.
dDict['v'] = var
return dDict
and independent (dimList) variables.
'''
ds, varList, paramList = netcdfDataset(filename)
'''
Read cell center coordinates and time.
Create the output independent variables right away and empty memory.
'''
if (meanOn):
time = np.array([
0,
])
time_dims = np.shape(time)
else:
time, time_dims = read1DVariableFromDataset('time', ds, paramList, 0, 0, 1)
uStr = partialMatchFromList('u', varList)
x, x_dims = read1DVariableFromDataset('x', uStr, ds, paramList, 0, 0, cl)
y, y_dims = read1DVariableFromDataset('y', uStr, ds, paramList, 0, 0, cl)
z, z_dims = read1DVariableFromDataset('z', uStr, ds, paramList, 0, 0, cl)
# - - - - velocity components - - - - - - - - - - - -
sa = ''
if (meanOn): sa = 'm'
u, u_dims = read3DVariableFromDataset('u', ds, varList, 0, 0, cl, meanOn)
v, v_dims = read3DVariableFromDataset('v', ds, varList, 0, 0, cl, meanOn)
w, w_dims = read3DVariableFromDataset('w', ds, varList, 0, 0, cl, meanOn)
infoStr = '''
time_dims = {}
x_dims = {}