def __variables(self,k):
if k in ['TFLAG','ETFLAG','NSTKS']:
return self.variables[k]
elif k in ['XSTK','YSTK','HSTK','DSTK','TSTK','VSTK']:
v=PseudoNetCDFVariable(self,k,'f',('NSTK',),values=self.__stk_props[k].ravel())
v.units={'XSTK':'m','YSTK':'m','HSTK':'m','DSTK':'m','TSTK':'K','VSTK':'m/h'}[k]
v.long_name=k.ljust(16)
v.var_desc=k.ljust(16)
return v
elif k in ['IONE', 'ITWO', 'KCELL','FLOW','PLMHT']:
data_type={'IONE':'i', 'ITWO':'i', 'KCELL':'i','FLOW':'f','PLMHT':'f'}[k]
v=self.createVariable(k,data_type,('TSTEP','NSTK'))
v.units={'IONE':'#', 'ITWO':'#', 'KCELL':'#', 'FLOW':'m**3/hr', 'PLMHT':'m'}[k]
v.long_name=k.ljust(16)
v.var_desc=k.ljust(16)
vals = self.__hourly_stk_props[:,:,['IONE','ITWO','KCELL','FLOW','PLMHT'].index(k)]
v[:] = vals.view('>' + data_type)
return v
elif k in self.__spc_names:
v=PseudoNetCDFVariable(self,k,'f',('TSTEP','NSTK'),values=self.__emiss_data[:,self.__getspcidx(k),:])
v.units='mole/hr'.ljust(16)
v.long_name=k.ljust(16)
v.var_desc=k.ljust(16)
return v
else:
raise KeyError("Unknown key %s" % k)
def __DEPTH__(self):
val=CAMxHeightToDepth(self.variables['HGHT'])
var=PseudoNetCDFVariable(self,'DEPTH','f',('TSTEP','LAY','ROW','COL'),values=val)
var.units='m'
var.long_name='RATE'.ljust(16)
var.var_desc='RATE'.ljust(16)
return var
def __FCLOUD__(self):
val = self.variables["CLOUD"] >= 5
var = PseudoNetCDFVariable(self, "FCLOUD", "i", ("TSTEP", "LAY", "ROW", "COL"), values=array(val, dtype="i"))
var.units = "None"
var.long_name = "FCLOUD".ljust(16)
var.var_desc = "FCLOUD".ljust(16)
return var
def __set_var(self, key, vals_idx):
times = len(self.dimensions['TSTEP'])
lays = len(self.dimensions['LAY'])
rows = len(self.dimensions['ROW'])
cols = len(self.dimensions['COL'])
v = PseudoNetCDFVariable(self, key, 'f', ('TSTEP', 'LAY', 'ROW', 'COL'), values = self.__memmap[vals_idx].reshape(times, lays, rows, cols))
v.units = {'COD':'None'}.get(key, 'g/m**3')
v.long_name = key
v.var_desc = key
self.variables[key] = v
def __PRECIP_RATE__(self):
if "PRECIP" in self.variables.keys():
val = self.variables["PRECIP"]
else:
val = self.variables["RAIN"] + self.variables["SNOW"] + self.variables["GRAUPEL"]
var = PseudoNetCDFVariable(self, "PRECIP_RATE", "f", ("TSTEP", "LAY", "ROW", "COL"), values=(val * 10) ** 1.27)
var.units = "mm/h"
var.long_name = "PRECIP_RATE".ljust(16)
var.var_desc = "PRECIP_RATE".ljust(16)
return var
def __FCLOUD__(self):
val = self.variables['CLOUD'] >= 5
var = PseudoNetCDFVariable(self,
'FCLOUD',
'i', ('TSTEP', 'LAY', 'ROW', 'COL'),
values=array(val, dtype='i'))
var.units = 'None'
var.long_name = 'FCLOUD'.ljust(16)
var.var_desc = 'FCLOUD'.ljust(16)
return var
def __PRECIP_RATE__(self):
if 'PRECIP' in self.variables.keys():
val = self.variables['PRECIP']
else:
val = self.variables['RAIN'] + \
self.variables['SNOW'] + self.variables['GRAUPEL']
var = PseudoNetCDFVariable(self,
'PRECIP_RATE',
'f', ('TSTEP', 'LAY', 'ROW', 'COL'),
values=(val * 10)**1.27)
var.units = 'mm/h'
var.long_name = 'PRECIP_RATE'.ljust(16)
var.var_desc = 'PRECIP_RATE'.ljust(16)
return var
def __set_var(self, key, vals_idx):
times = len(self.dimensions['TSTEP'])
lays = len(self.dimensions['LAY'])
rows = len(self.dimensions['ROW'])
cols = len(self.dimensions['COL'])
v = PseudoNetCDFVariable(self,
key,
'f', ('TSTEP', 'LAY', 'ROW', 'COL'),
values=self.__memmap[vals_idx].reshape(
times, lays, rows, cols))
v.units = {'COD': 'None'}.get(key, 'g/m**3')
v.long_name = key
v.var_desc = key
self.variables[key] = v
def childvariables(self, k):
for f in self.__files:
if k in f.variables.keys():
v = f.variables[k]
if k=='TFLAG':
v = PseudoNetCDFVariable(self, 'TFLAG', 'i', v.dimensions, values = v[:][:, [0], :].repeat(len(self.dimensions['VAR']), 1))
v.long_name = 'TFLAG'.ljust(16)
v.var_desc = 'TFLAG'.ljust(16)
v.units = 'DATE-TIME'
if k=='LAY' and k in self.dimensions.keys() and len(k.shape) > 1:
if v.shape[1]==1:
dims = list(v.dimensions)
dims[1] = 'SURFLAY'
v.dimensions = tuple(dims)
return v
def ConvertCAMxTime(date,time,nvars):
class temp:
pass
f = temp()
f.dimensions = {'TSTEP': date.shape[0], 'VAR': nvars, 'DATE-TIME': 2}
a=array([date,time],dtype='i').swapaxes(0,1)
if len(a.shape)==2:
a=a[:,newaxis,:]
date=a[:,:,0]
if (date<70000).any():
date+=2000000
else:
date+=1900000
time=a[:,:,1]
while not (time==0).all() and time.max()<10000:
time*=100
a=PseudoNetCDFVariable(f,'TFLAG','i',('TSTEP','VAR','DATE-TIME'),values=a[:,[0],:].repeat(nvars,1))
a.units='DATE-TIME'.ljust(16)
a.long_name='TFLAG'.ljust(16)
a.var_desc=a.long_name
return a
def __add_variables(self):
v=self.createVariable('TFLAG','i',('TSTEP','VAR','DATE-TIME'),keep=True)
v[:] = self.__windfile.variables['TFLAG'][self.__timeslice]
v.long_name='Time flag'
v.units='DATE-TIME'
if self.__force_stagger and self.__windfile.LSTAGGER==0:
warn('Cell centered values are being averaged as though staggered'+ \
'Could just be pre v4.3 file that was actually staggered')
for k in ['U','V']:
if self.__force_stagger or self.__windfile.LSTAGGER!=0:
if k=='U':
preproc=CenterCAMxU
elif k=='V':
preproc=CenterCAMxV
else:
preproc=CenterTime
var=self.__windfile.variables[k]
v=PseudoNetCDFVariable(self,k,'f',('TSTEP','LAY','ROW','COL'),values=preproc(var))
v.units=var.units
v.long_name=k.ljust(16)
v.var_desc=(k+' at center').ljust(16)
self.variables[k]=PseudoNetCDFVariableConvertUnit(v,self.__outunit)
