Passing data to cf-plot

Using cf-python - cf compliant data

Data is generally passed to cf-plot via cf-python as a 2-dimensional field.

import cf, cfplot as cfp'cfplot_data/')

f is now an list of 4 fields.

[<CF Field: air_temperature(time(1), pressure(23), latitude(160), longitude(320)) K>,
 <CF Field: eastward_wind(time(1), pressure(23), latitude(160), longitude(320)) m s**-1>,
 <CF Field: geopotential(time(1), pressure(23), latitude(160), longitude(320)) m**2 s**-2>,
 <CF Field: northward_wind(time(1), pressure(23), latitude(160), longitude(320)) m s**-1>]

To contour data a 2 dimensional field is needed. In the case below we select the 500mb temperature with the cf subspace method.

<CF Field: air_temperature(time(1), pressure(1), latitude(160), longitude(320)) K>

To mean the field use the cf collapse function:

f[0].collapse( 'mean','longitude')
<CF Field: air_temperature(time(1), pressure(23), latitude(160), longitude(1)) K>
cfp.con(f[2].collapse( 'mean','longitude'))

Using cf-plot with non-cf compliant data

Although newer model and reanalyis products are generally cf compliant there is a considerable amount of data being used that is of varying degrees of cf compliance.

Example 1

In this locally processed ERA40 reanalysis field there is a distinct lack of standard names. Even the long names are somewhat terse - p for pressure for example.'cfplot_data/')[2]
<CF Field: geopotential(time(1), pressure(23), latitude(160), longitude(320)) m**2 s**-2>

We can still slice and plot the data by inspecting the dimensions of the field:

{'dim2': <CF DimensionCoordinate: latitude(160) degrees_north>,
 'dim3': <CF DimensionCoordinate: longitude(320) degrees_east>,
 'dim0': <CF DimensionCoordinate: time(1) days since 1964-01-21 00:00:00>,
 'dim1': <CF DimensionCoordinate: pressure(23) mbar>}

We see that dim1 is the pressure coordinate and can select the 700mb level and contour that.

array([ 1000.,   925.,   850.,   775.,   700.,   600.,   500.,   400.,
        300.,   250.,   200.,   150.,   100.,    70.,    50.,    30.,
        20.,    10.,     7.,     5.,     3.,     2.,     1.], dtype=float32)

<CF Field: geopotential(time(1), pressure(1), latitude(160), longitude(320)) m**2 s**-2>


Example 2

In this dataset there are neither standard or long names.'cfplot_data/')[0]
<CF Field: ncvar:temp(ncvar:p(22), ncvar:lat(73), ncvar:lon(145)) >

We can still slice and plot the data as below.

{'dim2': <CF DimensionCoordinate: ncvar:lon(145)>,
'dim0': <CF DimensionCoordinate: ncvar:p(22)>,
'dim1': <CF DimensionCoordinate: ncvar:lat(73)>}

array([  1.00000000e+03,   7.00000000e+02,   5.00000000e+02,
      3.20000000e+02,   2.15000000e+02,   1.50000000e+02,
      1.00000000e+02,   7.00000000e+01,   5.00000000e+01,
      3.20000000e+01,   2.00000000e+01,   1.50000000e+01,
      1.00000000e+01,   7.00000000e+00,   5.00000000e+00,
      3.20000005e+00,   2.00000000e+00,   1.50000000e+00,
      1.00000000e+00,   6.99999988e-01,   5.00000000e-01,
      3.00000012e-01], dtype=float32)

So to plot the 1000mb temperature we would use:


Passing data via arrays

cf-plot can also make contour and vector plots by passing data arrays.

import cfplot as cfp
from netCDF4 import Dataset as ncfile
nc = ncfile('cfplot_data/')[0]
cfp.con(f=temp, x=lons, y=lats)

The contouring routine doesn’t know that the data passed is a map plot. This can be explicitly set with the ptype flag to con.

cfp.con(f=temp, x=lons, y=lats, ptype=1)

Other types of plot are:

ptype=2 - latitude - pressure/height plot
ptype=3 - longitude - pressure/height plot
ptype=4 - longitude - time plot
ptype=5 - latitude - time plot
ptype=6 - rotated pole plot
ptype=7 - time - pressure/height plot