In [10]:

    

from pylab import *
import netCDF4

# open NetCDF input files

f = netCDF4.MFDataset('/usgs/data2/rsignell/models/ncep/narr/air.2m.19??.nc')
# print variables
f.variables.keys()


    

    
Out[10]:





[u'lat',
 u'lon',
 u'x',
 u'y',
 u'Lambert_Conformal',
 u'time',
 u'time_bnds',
 u'air']

In [11]:

    

print f.variables['time']


    

    




<class 'netCDF4._Variable'>
float64 time('time',)
    units: hours since 1800-1-1 00:00:0.0
    long_name: Time
    axis: T
    standard_name: time
    coordinate_defines: start
    delta_t: 0000-00-01 00:00:00
    actual_range: [ 1569072.  1577808.]
    avg_period: 0000-00-01 00:00:00
unlimited dimensions = ('time',)
current size = (731,)

In [12]:

    

atemp = f.variables['air']
print atemp

ntimes, ny, nx = shape(atemp)
cold_days = zeros((ny,nx),dtype=int)

# create output NetCDF file

nco = netCDF4.Dataset('/usgs/data2/notebook/cold_days3.nc','w',clobber=True)
nco.createDimension('x',nx)
nco.createDimension('y',ny)
nco.createDimension('time',ntimes)

cold_days_v = nco.createVariable('cold_days', 'i4',  ( 'time', 'y', 'x'))
cold_days_v.units='days'
cold_days_v.long_name='total number of days below 0 degC'
cold_days_v.grid_mapping = 'Lambert_Conformal'

timeo = nco.createVariable('time','f8',('time'))
lono = nco.createVariable('lon','f4',('y','x'))
lato = nco.createVariable('lat','f4',('y','x'))
xo = nco.createVariable('x','f4',('x'))
yo = nco.createVariable('y','f4',('y'))
lco = nco.createVariable('Lambert_Conformal','i4')

# copy all the variable attributes from original file
for var in ['lon','lat','x','y','Lambert_Conformal','time']:
    for att in f.variables[var].ncattrs():
        setattr(nco.variables[var],att,getattr(f.variables[var],att))

# copy variable data for lon,lat,x and y
lono[:]=f.variables['lon'][:]
lato[:]=f.variables['lat'][:]
xo[:]=f.variables['x'][:]
yo[:]=f.variables['y'][:]
timeo[:]=f.variables['time'][:]

for i in xrange(ntimes):
    cold_days += atemp[i,:,:].data-273.15 < 0
    #  write the cold_days data
    cold_days_v[i,:,:]=cold_days


# copy Global attributes from original file
for att in f.ncattrs():
    setattr(nco,att,getattr(f,att))

nco.Conventions='CF-1.6'
nco.close()
f.close()


    

    




<class 'netCDF4._Variable'>
int16 air('time', 'y', 'x')
    units: K
    long_name: Daily Air Temperature at 2 m
    unpacked_valid_range: [ 151.  400.]
    precision: 0.00379979
    actual_range: [ 219.79893494  312.32373047]
    missing_value: 32767
    valid_range: [-32765  32765]
    _FillValue: -32767
    GRIB_name: TMP
    GRIB_id: 11
    var_desc: Air temperature
    standard_name: air_temperature
    level_desc: 2 m
    dataset: NARR Daily Averages
    statistic: Mean
    parent_stat: Individual Obs
    grid_mapping: Lambert_Conformal
    coordinates: lat lon
    add_offset: 275.5
    scale_factor: 0.00379979
    cell_methods: time: mean (of 8 3-hourly values in one day)
unlimited dimensions = ('time',)
current size = (731, 277, 349)

In [15]:

    

f2v = netCDF4.Dataset('/usgs/data2/notebook/cold_days3.nc').variables
print f2v['cold_days']


    

    




<type 'netCDF4.Variable'>
int32 cold_days(u'time', u'y', u'x')
    units: days
    long_name: total number of days below 0 degC
    grid_mapping: Lambert_Conformal
unlimited dimensions = ()
current size = (731, 277, 349)

In [17]:

    

pcolor(f2v['cold_days'][100,:,:])
colorbar()


    

    













    
Out[17]:





<matplotlib.colorbar.Colorbar instance at 0x2bf08c0>






    

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