How to convert auxiliary coordinates to dimension coordinates in a cube in iris?

[maczik]

I am pretty new to using NetCDF files. I am trying to write a routine to create multiple plots visualising the output from my simulations.

After reading in the cubes I have the following format:

filename = '/home/users/---------.nc'
cubelist = iris.load(filename)

cons = iris.Constraint(cube_func=lambda x: x.var_name == 'gpp_gb')
cube = cubelist.extract_cube(cons)

print (cube)
type(cube)
print (cube.shape)
print (cube.ndim)

first_timestep = cube[0,...]
print (first_timestep)

output:

Gridbox gross primary productivity / (kg m-2 s-1) (time: 12; -- : 1; -- : 7247)
    Dimension coordinates:
        time                                           x        -       -
    Auxiliary coordinates:
        latitude                                       -        x       x
        longitude                                      -        x       x
    Cell methods:
        mean                                      time
(12, 1, 7247)
3
Gridbox gross primary productivity / (kg m-2 s-1) (-- : 1; -- : 7247)
    Auxiliary coordinates:
        latitude                                      x       x
        longitude                                     x       x
    Scalar coordinates:
        time                                      2009-02-01 00:00:00, bound=(2009-01-01 00:00:00, 2009-02-01 00:00:00)
    Cell methods:
        mean                                      time

However when i try to map the data with:

qplt.contourf(first_timestep, 50)

I get the following error message:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_2386/1836137079.py in <module>
     16 
     17 
---> 18 qplt.contourf(first_timestep, 50)

/opt/jaspy/lib/python3.8/site-packages/iris/quickplot.py in contourf(cube, *args, **kwargs)
    206     coords = kwargs.get("coords")
    207     axes = kwargs.get("axes")
--> 208     result = iplt.contourf(cube, *args, **kwargs)
    209     _label_with_points(cube, result, coords=coords, axes=axes)
    210     return result

/opt/jaspy/lib/python3.8/site-packages/iris/plot.py in contourf(cube, *args, **kwargs)
   1057     coords = kwargs.get("coords")
   1058     kwargs.setdefault("antialiased", True)
-> 1059     result = _draw_2d_from_points("contourf", None, cube, *args, **kwargs)
   1060 
   1061     # Matplotlib produces visible seams between anti-aliased polygons.

/opt/jaspy/lib/python3.8/site-packages/iris/plot.py in _draw_2d_from_points(draw_method_name, arg_func, cube, *args, **kwargs)
    495 
    496     if _can_draw_map(plot_defn.coords):
--> 497         result = _map_common(
    498             draw_method_name,
    499             arg_func,

/opt/jaspy/lib/python3.8/site-packages/iris/plot.py in _map_common(draw_method_name, arg_func, mode, cube, plot_defn, *args, **kwargs)
   1007     axes = kwargs.pop("axes", None)
   1008     plotfn = getattr(axes if axes else plt, draw_method_name)
-> 1009     return plotfn(*new_args, **kwargs)
   1010 
   1011 

/opt/jaspy/lib/python3.8/site-packages/matplotlib/pyplot.py in contourf(data, *args, **kwargs)
   2743 @_copy_docstring_and_deprecators(Axes.contourf)
   2744 def contourf(*args, data=None, **kwargs):
-> 2745     __ret = gca().contourf(
   2746         *args, **({"data": data} if data is not None else {}),
   2747         **kwargs)

/opt/jaspy/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py in wrapper(self, *args, **kwargs)
    308 
    309         kwargs['transform'] = transform
--> 310         return func(self, *args, **kwargs)
    311     return wrapper
    312 

/opt/jaspy/lib/python3.8/site-packages/cartopy/mpl/geoaxes.py in contourf(self, *args, **kwargs)
   1506                         sub_trans.force_path_ccw = True
   1507 
-> 1508         result = matplotlib.axes.Axes.contourf(self, *args, **kwargs)
   1509 
   1510         # We need to compute the dataLim correctly for contours.

/opt/jaspy/lib/python3.8/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
   1359     def inner(ax, *args, data=None, **kwargs):
   1360         if data is None:
-> 1361             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1362 
   1363         bound = new_sig.bind(ax, *args, **kwargs)

/opt/jaspy/lib/python3.8/site-packages/matplotlib/axes/_axes.py in contourf(self, *args, **kwargs)
   6432     def contourf(self, *args, **kwargs):
   6433         kwargs['filled'] = True
-> 6434         contours = mcontour.QuadContourSet(self, *args, **kwargs)
   6435         self._request_autoscale_view()
   6436         return contours

/opt/jaspy/lib/python3.8/site-packages/matplotlib/contour.py in __init__(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, *args, **kwargs)
    775         self._transform = transform
    776 
--> 777         kwargs = self._process_args(*args, **kwargs)
    778         self._process_levels()
    779 

/opt/jaspy/lib/python3.8/site-packages/matplotlib/contour.py in _process_args(self, corner_mask, *args, **kwargs)
   1364             self._corner_mask = corner_mask
   1365 
-> 1366             x, y, z = self._contour_args(args, kwargs)
   1367 
   1368             _mask = ma.getmask(z)

/opt/jaspy/lib/python3.8/site-packages/matplotlib/contour.py in _contour_args(self, args, kwargs)
   1422             args = args[1:]
   1423         elif Nargs <= 4:
-> 1424             x, y, z = self._check_xyz(args[:3], kwargs)
   1425             args = args[3:]
   1426         else:

/opt/jaspy/lib/python3.8/site-packages/matplotlib/contour.py in _check_xyz(self, args, kwargs)
   1452             raise TypeError(f"Input z must be 2D, not {z.ndim}D")
   1453         if z.shape[0] < 2 or z.shape[1] < 2:
-> 1454             raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
   1455                             f"but has shape {z.shape}")
   1456         Ny, Nx = z.shape

TypeError: Input z must be at least a (2, 2) shaped array, but has shape (1, 7247)

I think it could be because my coordinate data is not in a format the iris package can understand as it is in the auxiliary fields when i print the cube. I think the code should work if I could put the latitude and longitude data into the dimension coordinates. Any help or ideas would be much appreciated.

Thank you for considering the problem.

[trexfeathers]

Hi @maczik, thanks for getting in touch.

cubelist = iris.load(filename)

cons = iris.Constraint(cube_func=lambda x: x.var_name == 'gpp_gb')
cube = cubelist.extract_cube(cons)

These lines can be simplified:

cube = iris.load_cube('gpp_gb')

I think the code should work if I could put the latitude and longitude data into the dimension coordinates. Any help or ideas would be much appreciated.

A dimension coordinate is identified as a NetCDF variable that has the same shape and name as a dimension. This doesn't look possible for longitude and latitude in this case, as your data appears to be one dimensional with 7247 individual geographic points. I don't know what the extra 1-long dimension might be.

Are you able to run ncdump -h on your file so we can see what is being loaded?

[rcomer]

These look like JULES output files. If they are, and you have access to the Met Office Science Repository Service, then you should be able to make use of this module:
https://code.metoffice.gov.uk/trac/utils/browser/smstress_jpeg/trunk/jules.py

[maczik]

Hi thanks for the tip! it worked with a script I have modified from the link you have provided

[pp-mo]

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