Plotting functions

class aeolus.plot.CloudPlotter(cubelist)[source]

Bases: object

Factory to create a composite plot of low, medium, high cloud area fractions.

__init__(cubelist)[source]

Initialise CloudPlotter from iris.cube.CubeList containing cloud fractions.

scale_data(threshold=0.1)[source]

Scale cloud levels and add them together to make a composite cloud field.

Values below threshold are set to zero.

get_all(nsteps=10, cmap=None)[source]

Get cloud types cube and kwargs for external plotting.

pcolormesh(ax, xy=(), nsteps=10, cmap=None, cb_kwargs={}, **pc_kwargs)[source]

Display composite cloud plot using matplotlib.pyplot.pcolormesh.

Parameters

  • ax (matplotlib axes object) – Axes where to create the plot in

  • xy (tuple, optional) – Sequence of X and Y coordinate arrays (passed to pcolormesh)

  • nsteps (integer, optional) – Number of colour steps for each of cloud category

  • cmap (matplotlib.colors.ListedColormap, optional) – Colormap instance. If not given, custom cloud colormap is used.

  • cb_kwags (dict, optional) – Dictionary of kwargs passed to colorbar

  • **pc_kwargs (optional) – additional pcolormesh arguments

Returns

  • h (matplotlib.collections.QuadMesh) – pcolormesh instance

  • cb (matplotlib.colorbar.Colorbar) – colorbar

class aeolus.plot.GeoAxesGrid(fig, rect, nrows_ncols, projection, **axesgrid_kw)[source]

Bases: mpl_toolkits.axes_grid1.axes_grid.ImageGrid

Grid of cartopy axes.

A subclass of mpl_toolkits.axes_grid1.AxesGrid representing a grid of maps with the same projection Projection.

  • axes_class is defined automatically

  • The AxesGrid built-in labelling is always switched off, and instead a standard procedure of creating grid lines and labels should be used.

__init__(fig, rect, nrows_ncols, projection, **axesgrid_kw)[source]

Initialise GeoAxesGrid.

Build a GeoAxesGrid instance with a grid nrows*ncols GeoAxes with a projection Projection in Figure fig with rect=[left, bottom, width, height] (in Figure coordinates) or the subplot position code (e.g., “121”).

aeolus.plot.label_global_map_gridlines(fig, ax, xticks=[], yticks=[], xoff=- 10, yoff=- 10, degree=False, **text_kw)[source]

Label gridlines of a global cartopy map.

Parameters

  • fig (matplotlib.figure.Figure) – Figure object.

  • ax (cartopy.mpl.geoaxes.GeoAxesSubplot) – Cartopy axes.

  • xticks (array-like, optional) – Sequence of longitude ticks.

  • yticks (array-like, optional) – Sequence of latitude ticks.

  • xoff (float, optional) – Longitude label offset from the axis (units are points). If negative (by default), the labels are drawn at the east boundary, otherwise at the west boundary.

  • yoff (float, optional) – Latitude label offset from the axis (units are points). If negative (by default), the labels are drawn at the south boundary, otherwise at the north boundary.

  • degree (bool, optional) – Add a degree symbol to tick labels.

  • **text_kw (dict, optional) – Label text properties.

aeolus.plot.add_custom_legend(ax, styles_and_labels, **leg_kw)[source]

Add a custom legend to matplotlib axes.

Parameters

  • ax (matplotlib.axes._subplots.AxesSubplot) – Axes where to put the legend.

  • styles_and_labels (dict) – Dictionary with labels as keys and a dictionary of plot keywords as values.

  • leg_kw (dict, optional) – Keyword arguments passed to legend() function.

Example

>>> import matplotlib.pyplot as plt
>>> ax = plt.axes()
>>> my_dict = dict(foo=dict(color='C0', marker="X"),
                   bar=dict(color='C1', marker="o"))
>>> add_custom_legend(ax, my_dict, loc=2, title="blah")

PyVista interface

aeolus.plot.pv.grid_for_scalar_cube_sph(cube, z_scale=1, z_offset=0, grid=None, label='scalar3d')[source]

Create a pyvista grid for an iris cube (2D or 3D) in spherical coordinates.

Parameters

  • cube (iris.cube.Cube) – 2D or 3D cube with (longitude, latitude, [level_height]) coordinates.

  • z_scale (float, optional) – Scaling factor for the vertical level dimension.

  • z_offset (float, optional) – Scaling offset for the vertical level dimension.

  • grid (pyvista.StructuredGrid, optional) – If given, add data to the existing grid, otherwise create a new one from the input cube’s coordinates.

  • label (str, optional) – Label for the data within the grid.

Returns

PyVista grid with data in cell_arrays.

Return type

pyvista.StructuredGrid

aeolus.plot.pv.grid_for_vector_cubes_sph(u, v, w, vector_scale=1, vertical_wind_scale=1, z_scale=1, z_offset=0, xstride=1, ystride=1, grid=None, label='vector3d')[source]

Take wind vectors in spherical coordinates and create a pyvista grid for them.

Parameters

  • u (iris.cube.Cube) – 2D or 3D cube of x-wind component (zonal wind).

  • v (iris.cube.Cube) – 2D or 3D cube of y-wind component (meridional wind).

  • w (iris.cube.Cube) – 2D or 3D cube of z-wind component (vertical wind).

  • vector_scale (float, optional) – Scaling factor for vectors.

  • vertical_wind_scale (float, optional) – Scaling factor for the vertical wind component (for better visibility).

  • z_scale (float, optional) – Scaling factor for the vertical level dimension.

  • z_offset (float, optional) – Scaling offset for the vertical level dimension.

  • xstride (float, optional) – Stride along the longitude axis.

  • ystride (float, optional) – Stride along the latitude axis.

  • grid (pyvista.StructuredGrid, optional) – If given, add data to the existing grid, otherwise create a new one from the input cube’s coordinates.

  • label (str, optional) – Label for the data within the grid.

Returns

PyVista grid with vector data in point_arrays.

Return type

pyvista.StructuredGrid