PseudoNetCDF.plotutil package

Submodules

PseudoNetCDF.plotutil.colors module

PseudoNetCDF.plotutil.colors.get_cmap(name=None, lut=None)[source]

[Deprecated] Get a colormap instance, defaulting to rc values if name is None.

Parameters:

name (~matplotlib.colors.Colormap or str or None, default: None) – If a .Colormap instance, it will be returned. Otherwise, the name of a colormap known to Matplotlib, which will be resampled by lut. The default, None, means :rc:`image.cmap`.
lut (int or None, default: None) – If name is not already a Colormap instance and lut is not None, the colormap will be resampled to have lut entries in the lookup table.

Return type:

Colormap

Notes

Deprecated since version 3.7: Use matplotlib.colormaps[name] or matplotlib.colormaps.get_cmap() or pyplot.get_cmap() instead.

PseudoNetCDF.plotutil.colors.get_norm(name)[source]

PseudoNetCDF.plotutil.colors.register(cmap, *, name=None, force=False)

Register a new colormap.

The colormap name can then be used as a string argument to any cmap parameter in Matplotlib. It is also available in pyplot.get_cmap.

The colormap registry stores a copy of the given colormap, so that future changes to the original colormap instance do not affect the registered colormap. Think of this as the registry taking a snapshot of the colormap at registration.

Parameters:

cmap (matplotlib.colors.Colormap) – The colormap to register.
name (str, optional) – The name for the colormap. If not given, cmap.name is used.
force (bool, default: False) – If False, a ValueError is raised if trying to overwrite an already registered name. True supports overwriting registered colormaps other than the builtin colormaps.

PseudoNetCDF.plotutil.colors.register_norm(name, norm)[source]

PseudoNetCDF.plotutil.pnc2d module

PseudoNetCDF.plotutil.pnc2d.make2ds(args)[source]

PseudoNetCDF.plotutil.pncmap module

PseudoNetCDF.plotutil.pncmap.makemaps(args)[source]

PseudoNetCDF.plotutil.pncscatter module

PseudoNetCDF.plotutil.pncscatter.pncscatter(args)[source]

PseudoNetCDF.plotutil.pncts module

PseudoNetCDF.plotutil.pncts.plotts(args)[source]

PseudoNetCDF.plotutil.vertprofile module

PseudoNetCDF.plotutil.vertprofile.add_vertprofile_options(vertparser)[source]

PseudoNetCDF.plotutil.vertprofile.maskfilled(v)[source]

PseudoNetCDF.plotutil.vertprofile.matchspace(lons, lats, lon_bnds, lat_bnds)[source]

PseudoNetCDF.plotutil.vertprofile.minmaxmean(ax, vals, vertcrd, **kwds)[source]

PseudoNetCDF.plotutil.vertprofile.plot_omi(ax, lon_bnds, lat_bnds, omipaths, key='O3Profile', airden=None, airdenvert=None)[source]

PseudoNetCDF.plotutil.vertprofile.plot_tes(ax, lon_bnds, lat_bnds, tespaths)[source]

PseudoNetCDF.plotutil.vertprofile.plotprofile(args)[source]

PseudoNetCDF.plotutil.vertprofile.vertprofileplot(ifiles, args)[source]

Module contents

PseudoNetCDF.plotutil.SegmentedLogNorm(vmin, vmax, bins=10, ncolors=256, full_levels=False)[source]

Create BoundaryNorm with (N=bins) log spacing. If full_levels, then colorbar will start on a full log level and end on a full log level.

boundaries = np.logspace(np.log10(vmin), np.log10(vmax), bins + 1) return BoundaryNorm(boundaries, ncolors)

PseudoNetCDF.plotutil.SegmentedNorm(vmin, vmax, bins=10, ncolors=256)[source]

PseudoNetCDF.plotutil.add_vertprofile_options(vertparser)[source]

PseudoNetCDF.plotutil.plot(args, plotfunc=<function plot>)[source]

PseudoNetCDF.plotutil.plot2d(args)[source]

PseudoNetCDF.plotutil.plotcontour(args)[source]

PseudoNetCDF.plotutil.plotcontourf(args)[source]

PseudoNetCDF.plotutil.plotprofile(args)[source]

PseudoNetCDF.plotutil.plotscatter(args)

PseudoNetCDF.plotutil.plotts(args)[source]