Cheat Sheet¶
Version 3.16.2 for version 1.11 of the CF conventions.
This cheat sheet provides a summary of some key functions and methods in
cfpython (also available as a printable PDF for
pdf
).
Operation 
How to? 


Install cfpython, including any required dependencies, and the cfplot visualisation package using pip: $ pip install cfpython cfplot

Install cfpython with all of its required and optional dependencies, and the cfplot visualisation package using conda: $ conda install c condaforge cfpython cfplot udunits2 esmpy>=8.0.0



Import cfpython and cfplot: >>> import cf
>>> import cfplot as cfp


Read the field constructs from a single file: >>> fl_1 = cf.read("file.nc")
>>> fl_2 = cf.read("umfile.pp")

Read the field constructs from two or more files: >>> fl = cf.read(['temperature.nc', 'precipitation_flux.nc'])


Read the field constructs using the * wildcard character (which reads all files in the directory that match the specified pattern): >>> fl = cf.read('~/file*.nc')


Return field constructs by selecting the elements of the resulting * wildcard file list: >>> fl = cf.read('~/file*.nc')[0:3]
>>> f = cf.read('~/file*nc')[1]


Return field constructs whose identities match the given values: >>> fl = cf.read('~/file*.nc', select='units=hPa')
>>> fl = cf.read('~/file*.nc', select='air_temperature')
>>> fl = cf.read('~/cfpython/docs/source/sample_files/file*.nc', select='ncvar%q')


Selecting field: Select a field directly by index while reading the file: >>> temp = cf.read("file.nc")[0]
Select a field from a field list by identity: >>> temp = fl.select_field('air_temperature')
Select more than one field from a field list: >>> data = fl.select('long_name=precipitation', 'ncvar%stn')



Write one or more field constructs to a netCDF file: >>> cf.write(tas, 'temperature.nc')

Convert PP and UM fields files to netCDF files: >>> cf.write(pp, 'umfile1.nc')



Basic collapses: >>> min = f.collapse('minimum')
>>> v = f.collapse('variance')
>>> i = f.collapse('integral', measure=True)

Weighted collapses: Weighted time average: >>> avg = f.collapse('T: mean', weights=True)
Mean over the time and latitude axes, with weights only applied to the latitude axis: >>> mean_latitude = f.collapse('time: latitude: mean', weights='latitude')
Weighted area means: >>> mean = f.collapse('area: mean', weights=True)


Specifying Axes: Temporal maxima: >>> max = f.collapse('maximum', axes='T') # or equivalently:
>>> max = f.collapse('T: maximum')
Horizontal maximum: >>> max = f.collapse('maximum', axes=['X', 'Y']) # or equivalently:
>>> max = f.collapse('X: Y: maximum')


Grouped collapses: Annual maxima starting 1st December: >>> y_max = f.collapse('T: maximum', cf.Y(month=12))
December, January, February maxima: >>> djf_max = f.collapse('T: maximum', group=cf.djf())
Maxima for each 3month season (DJF, MAM, JJA, SON): >>> seas_max_3 = f.collapse('T: maximum', group=cf.seasons())
Maxima for each 4month season (JJAS, ONDJ, FMAM): >>> seas_max_4 = f.collapse('T: maximum', group=cf.seasons(3, 6))
Multiannual average of the seasonal means: >>> seas_avg = f.collapse('T: mean within years T: mean over years',
... within_years=cf.seasons(), weights=True)



Subspacing by index: >>> new = temp[:, :, 0]
>>> new = temp[0, [2, 3, 9], [4, 8]]
>>> new = temp[..., [True, False, True, True, False]]

Subspacing by metadata: Subspace to a new field construct whose ‘longitude’ coordinate spans only 45 degrees east, with the other domain axes remaining unchanged: >>> new = temp.subspace(longitude=45) # or equivalently:
>>> new = temp.subspace(X=45)
Subspace to a new field construct whose longitude coordinate is 45 degrees east and latitude coordinate is 60 degrees north, with the other domain axes remaining unchanged: >>> new = temp.subspace(X=45, Y=60)
Subspace to a new field construct whose domain spans 34 to 72 degrees north and 25 to 45 degrees east: >>> new = temp.subspace(latitude=cf.wi(34, 72), longitude=cf.wi(25, 45))
Subspaces based on the time dimension: >>> new = temp.subspace(T=cf.dt('19960120'))
>>> jan_2023 = temp.subspace(T=cf.year(2023) & cf.month(1))
>>> annual_avg_61_90 = annual_global_avg.subspace(T=cf.year(cf.wi(1961, 1990)))



Select constructs by identity: >>> x = f.dimension_coordinate('latitude')
>>> x = f.auxiliary_coordinate('altitude')
Select constructs by property: >>> d = f.dimension_coordinate(units='degrees_east')
>>> d = f.dimension_coordinate(long_name='height', units='m')
Select constructs by cell method: >>> d = f.cell_method('mean')
>>> d = f.cell_method('mean', 'maximum')
Select constructs by axis: >>> d = f.constructs.filter_by_axis('domainaxis1')
Select constructs by type: >>> d = f.dimension_coordinates()
>>> d = f.constructs.filter_by_type('dimension_coordinate', 'field_ancillary')
Select constructs by netCDF variable name: >>> d = f.constructs.filter_by_ncvar('time')
>>> d = f.constructs.filter_by_ncvar('time', 'lat')


Regridding in spherical polar coordinates: Regrid observational data to that of the model data using bilinear interpolation: >>> obs_regrid = obs.regrids(model, method='linear')
Regrid observational data to that of the model data conservatively: >>> obs_regrid = obs.regrids(model, method='conservative')
Regrid observational data onto twodimensional dimension coordinates latitude and longitude using bilinear interpolation: >>> import numpy
>>> domain = cf.Domain.create_regular((0, 360, 5.0), (90, 90, 2.5))
>>> obs_regrid = obs.regrids(domain, method='linear')

Regridding in cartesian coordinates: Regrid the time axis ‘T’ of observational data with the linear method onto the grid specified in the dimension coordinate time: >>> time = cf.DimensionCoordinate.create_regular(
... (0.5, 60.5, 1),
... units=cf.Units("days since 18600101", calendar="360_day"),
... standard_name="time",
... )
>>> obs_regrid = obs.regridc([time], axes='T', method='linear')
