Graduation exercise

1. Load 'A1B_north_america.nc' from the iris sample data



In [2]:

    
import iris
filename = iris.sample_data_path("A1B_north_america.nc")
cube = iris.load_cube(filename)
print cube









    



air_temperature / (K)               (time: 240; latitude: 37; longitude: 49)
     Dimension coordinates:
          time                           x              -              -
          latitude                       -              x              -
          longitude                      -              -              x
     Auxiliary coordinates:
          forecast_period                x              -              -
     Scalar coordinates:
          forecast_reference_time: 1859-09-01 06:00:00
          height: 1.5 m
     Attributes:
          Conventions: CF-1.5
          Model scenario: A1B
          STASH: m01s03i236
          source: Data from Met Office Unified Model 6.05
     Cell methods:
          mean: time (6 hour)

2. Extract just data from the year 1980 and beyond from the loaded cube



In [3]:

    
tcoord = cube.coord('time')
def since_1980(cell):
    return tcoord.units.num2date(cell.point).year >= 1980

tcon = iris.Constraint(time=since_1980)
cube = cube.extract(tcon)



In [4]:

    
tcoord = cube.coord('time')

print tcoord.units.num2date(tcoord.points.min())
print tcoord.units.num2date(tcoord.points.max())









    



1980-06-01 00:00:00
2099-06-01 00:00:00

3. Define a function which takes a coordinate and a single time point as arguments, and returns the decade. For example, your function should return 2010 for the following:

   time = iris.coords.DimCoord([10], 'time', units='days since 2018-01-01')
   print your_decade_function(time, time.points[0])



In [5]:

    
def get_decade(coord, point):
    year = coord.units.num2date(point).year
    return (year/10)*10
time = iris.coords.DimCoord([10], 'time', units='days since 2018-01-01')
print get_decade(time, time.points[0])

4. Add a "decade" coordinate to the loaded cube using your function and the coord categorisation module



In [6]:

    
import iris.coord_categorisation as coord_cat
coord_cat.add_categorised_coord(cube, 'decade', 'time', get_decade)
print cube.coord('decade')









    



AuxCoord(array([1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1990,
       1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 2000, 2000,
       2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2010, 2010, 2010,
       2010, 2010, 2010, 2010, 2010, 2010, 2010, 2020, 2020, 2020, 2020,
       2020, 2020, 2020, 2020, 2020, 2020, 2030, 2030, 2030, 2030, 2030,
       2030, 2030, 2030, 2030, 2030, 2040, 2040, 2040, 2040, 2040, 2040,
       2040, 2040, 2040, 2040, 2050, 2050, 2050, 2050, 2050, 2050, 2050,
       2050, 2050, 2050, 2060, 2060, 2060, 2060, 2060, 2060, 2060, 2060,
       2060, 2060, 2070, 2070, 2070, 2070, 2070, 2070, 2070, 2070, 2070,
       2070, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080,
       2090, 2090, 2090, 2090, 2090, 2090, 2090, 2090, 2090, 2090]), standard_name=None, units=Unit('1'), long_name=u'decade')

5. Calculate the decadal means cube for this scenario



In [7]:

    
import iris.analysis
cube = cube.aggregated_by('decade', iris.analysis.MEAN)
print cube









    



air_temperature / (K)               (time: 12; latitude: 37; longitude: 49)
     Dimension coordinates:
          time                           x             -              -
          latitude                       -             x              -
          longitude                      -             -              x
     Auxiliary coordinates:
          decade                         x             -              -
          forecast_period                x             -              -
     Scalar coordinates:
          forecast_reference_time: 1859-09-01 06:00:00
          height: 1.5 m
     Attributes:
          Conventions: CF-1.5
          Model scenario: A1B
          STASH: m01s03i236
          history: Mean of air_temperature aggregated over decade
          source: Data from Met Office Unified Model 6.05
     Cell methods:
          mean: time (6 hour)
          mean: decade

6. Create a figure with 3 rows and 4 columns displaying the decadal means, with the decade displayed prominently in each axes' title



In [16]:

    
import matplotlib.pyplot as plt
import iris.plot as iplt

plt.figure(figsize=(12, 6))

plt.suptitle('Decadal means for the A1B scenario')
for i, decade_cube in enumerate(cube.slices(['latitude', 'longitude'])):
    plt.subplot(3, 4, i+1)
    iplt.contourf(decade_cube, 20)
    plt.title('{}'.format(decade_cube.coord('decade').points[0]))
    plt.gca().coastlines()
plt.show()