In this example we work through the following steps:

  1. Import the cf package.
  2. Read a field from disk and find a summary of its contents.
  3. Read in a second field and find a summary of its contents.
  4. Regrid the first field to the grid of the second bilinearly and find a summary of the resulting field's contents.
  5. Import the cf-plot package.
  6. Plot the results.

1. Import the cf package.

In [1]:
import cf

2. Read a field from disk and find a summary of its contents.

In [2]:
f = cf.read('EURO4M-APGD_DJF_precip_means.nc')
f
Out[2]:
<CF Field: long_name:PRECIPITATION(long_name:time(1), projection y coordinate(138), projection x coordinate(243)) mm.d-1>
In [3]:
print f

long_name:PRECIPITATION field summary
-------------------------------------
Data           : long_name:PRECIPITATION(long_name:time(1), projection y coordinate(138), projection x coordinate(243)) mm.d-1
Cell methods   : long_name:time: mean
Axes           : long_name:time(1) = [1996-07-15 12:00:00] standard
               : projection y coordinate(138) = [2210000.0, ..., 2895000.0] meters
               : projection x coordinate(243) = [3675000.0, ..., 4885000.0] meters
Aux coords     : longitude(projection y coordinate(138), projection x coordinate(243)) = [[2.12521505356, ..., 17.7066478729]] degrees_east
               : latitude(projection y coordinate(138), projection x coordinate(243)) = [[42.6916656494, ..., 48.9023704529]] degrees_north
Coord refs     : <CF CoordinateReference: lambert_azimuthal_equal_area>

3. Read in a second field and find a summary of its contents.

In [4]:
g = cf.read('E-OBS_DJF_precip_means.nc')
g
Out[4]:
<CF Field: thickness_of_rainfall_amount(time(1), latitude(201), longitude(464)) mm>
In [5]:
print g

thickness_of_rainfall_amount field summary
------------------------------------------
Data           : thickness_of_rainfall_amount(time(1), latitude(201), longitude(464)) mm
Cell methods   : time: mean
Axes           : time(1) = [1996-07-15 12:00:00]
               : latitude(201) = [25.375, ..., 75.375] degrees_north
               : longitude(464) = [-40.375, ..., 75.375] degrees_east

4. Regrid the first field to the grid of the second bilinearly and find a summary of the resulting field's contents.

In [6]:
f.dim('projection x coordinate').axis = 'X'
f.dim('projection y coordinate').axis = 'Y'
h = f.regrids(g, method='bilinear')
h
Out[6]:
<CF Field: long_name:PRECIPITATION(long_name:time(1), latitude(201), longitude(464)) mm.d-1>
In [7]:
print h

long_name:PRECIPITATION field summary
-------------------------------------
Data           : long_name:PRECIPITATION(long_name:time(1), latitude(201), longitude(464)) mm.d-1
Cell methods   : long_name:time: mean
Axes           : long_name:time(1) = [1996-07-15 12:00:00] standard
               : latitude(201) = [25.375, ..., 75.375] degrees_north
               : longitude(464) = [-40.375, ..., 75.375] degrees_east

5. Import the cfplot package.

In [8]:
%matplotlib inline

import cfplot as cfp

6. Plot the results.

In [9]:
cfp.con(h)

/home/charles/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:650: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors_original != str('face'):
/home/charles/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):
/home/charles/anaconda/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if rotation in ('horizontal', None):
/home/charles/anaconda/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  elif rotation == 'vertical':