def __init__(self, fn, convert=True):
fn=os.path.abspath(fn)
try:
self.f=nc.Dataset(fn)
except RuntimeError:
raise IOError, "no such file or dir %s"%fn
if self.f.project_id!='CMIP5':
print 'projet_id is "%s" not "CMIP5"'%self.f.project_id
# определяем основную переменную в массиве, это так которая зависит от трёх других
dtList=[v for v in self.f.variables if self.f.variables[v].ndim==3]
if len(dtList)>1:
dtList=[tmpDt for tmpDt in dtList if tmpDt in elSynom]
if len(dtList)>1: raise TypeError, "Unknown data type in nc file"
self.dt=dtList[0]
if self.dt=='tas' and convert is True:
from tempConvert import kelvin2celsius
self.convertValue=lambda val,year,month: kelvin2celsius(val)
self.convertArray=lambda arr: np.subtract(arr,273.15)
elif self.dt=='pr' and convert is True:
if self.f.frequency=='day':
from precConvert import si2mmPerDay
self.convertValue=si2mmPerDay
elif self.f.frequency=='mon':
from precConvert import si2mmPerMonth
self.convertValue=si2mmPerMonth
self.convertArray=np.vectorize(lambda val: val*86400*30.5, otypes=[np.float])
else:
self.convertValue=lambda val,year,month: val
self.convertArray=lambda arr: arr
# print "Warning! There is no converter for data type = '%s'"%self.dt
self.var=self.f.variables[self.dt]
self.lat=self.f.variables['lat']
self.latvals=[self.lat[l] for l in range(self.lat.size)]
self.lon=self.f.variables['lon']
self.lonvals=[cLon(self.lon[l]) for l in range(self.lon.size)]
if self.f.variables['time'].units=='days since 1-01-01 00:00:00':
self.timeUnits='days since 0001-01-01 00:00:00'
elif self.f.variables['time'].units=='days since 850-1-1 00:00:00':
self.timeUnits='days since 0850-1-1 00:00:00'
else:
self.timeUnits=self.f.variables['time'].units
try:
self.startDate=nc.num2date(self.f.variables['time'][0], self.timeUnits,
self.f.variables['time'].calendar)
except ValueError:
raise
print 'Warning! failed to parse date string -%s. Model id - %s. startDate set to 0850-1-1 assuming model is MPI'%(self.f.variables['time'].units,self.f.model_id)
# if self.f.variables['time'].units=='days since 1-01-01 00:00:00':
# self.startDate=datetime(1,1,1)
# else:
# print 'Warning! failed to parse date string -%s. Model id - %s. startDate '\
# 'set to 0850-1-1 assuming model is MPI'%(self.f.variables['time'].units,self.f.model_id)
# self.startDate=datetime(850,1,1)
self.startYear = int(self.startDate.year)
self.startMonth = int(self.startDate.month)
self.warningShown = False
self.cliSetMeta = {'modelId':self.f.model_id, 'calendar':self.f.variables['time'].calendar, 'source':fn,
'scenario':self.f.experiment_id, 'dt':self.dt}
def setConverter(self, convert=False): """ define function for conversion to standard units @return: """ if self.dt=='temp' and convert is True: from tempConvert import kelvin2celsius self.convertValue=lambda val,year,month: kelvin2celsius(val) self.convertArray=lambda arr: np.subtract(arr,273.15) elif self.dt=='prec' and convert is True: if self.var.units.lower()=='kg/m^2/s' and self.f.frequency=='day': from precConvert import si2mmPerDay self.convertValue=si2mmPerDay elif self.var.units.lower()=='kg/m^2/s' and self.f.frequency=='mon': from precConvert import si2mmPerMonth self.convertValue=si2mmPerMonth self.convertArray=np.vectorize(lambda val: val*86400*30.5, otypes=[np.float]) elif self.var.units.lower()=='cm': from precConvert import cm2mm self.convertValue=cm2mm self.convertArray=np.vectorize(lambda val: val/10., otypes=[np.float]) else: self.convertValue=lambda val,year,month: val self.convertArray=lambda arr: arr
