healpy.projaxes.HpxGnomonicAxes.hexbin

HpxGnomonicAxes.hexbin(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='none', reduce_C_function=<function mean>, mincnt=None, marginals=False, **kwargs)

Make a hexagonal binning plot.

Call signature:

hexbin(x, y, C = None, gridsize = 100, bins = None,
       xscale = 'linear', yscale = 'linear',
       cmap=None, norm=None, vmin=None, vmax=None,
       alpha=None, linewidths=None, edgecolors='none'
       reduce_C_function = np.mean, mincnt=None, marginals=True
       **kwargs)

Make a hexagonal binning plot of x versus y, where x, y are 1-D sequences of the same length, N. If C is None (the default), this is a histogram of the number of occurences of the observations at (x[i],y[i]).

If C is specified, it specifies values at the coordinate (x[i],y[i]). These values are accumulated for each hexagonal bin and then reduced according to reduce_C_function, which defaults to numpy’s mean function (np.mean). (If C is specified, it must also be a 1-D sequence of the same length as x and y.)

x, y and/or C may be masked arrays, in which case only unmasked points will be plotted.

Optional keyword arguments:

gridsize: [ 100 | integer ]

The number of hexagons in the x-direction, default is 100. The corresponding number of hexagons in the y-direction is chosen such that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the x-direction and the y-direction.

bins: [ None | ‘log’ | integer | sequence ]

If None, no binning is applied; the color of each hexagon directly corresponds to its count value.

If ‘log’, use a logarithmic scale for the color map. Internally, is used to determine the hexagon color.

If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly.

If a sequence of values, the values of the lower bound of the bins to be used.

xscale: [ ‘linear’ | ‘log’ ]

Use a linear or log10 scale on the horizontal axis.

scale: [ ‘linear’ | ‘log’ ]

Use a linear or log10 scale on the vertical axis.

mincnt: [ None | a positive integer ]

If not None, only display cells with more than mincnt number of points in the cell

marginals: [ True | False ]

if marginals is True, plot the marginal density as colormapped rectagles along the bottom of the x-axis and left of the y-axis

extent: [ None | scalars (left, right, bottom, top) ]

The limits of the bins. The default assigns the limits based on gridsize, x, y, xscale and yscale.

Other keyword arguments controlling color mapping and normalization arguments:

cmap: [ None | Colormap ]

a matplotlib.colors.Colormap instance. If None, defaults to rc image.cmap.

norm: [ None | Normalize ]

matplotlib.colors.Normalize instance is used to scale luminance data to 0,1.

vmin / vmax: scalar

vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored.

alpha: scalar between 0 and 1, or None

the alpha value for the patches

linewidths: [ None | scalar ]

If None, defaults to rc lines.linewidth. Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by RegularPolyCollection.

Other keyword arguments controlling the Collection properties:

edgecolors: [ None | 'none' | mpl color | color sequence ]

If 'none', draws the edges in the same color as the fill color. This is the default, as it avoids unsightly unpainted pixels between the hexagons.

If None, draws the outlines in the default color.

If a matplotlib color arg or sequence of rgba tuples, draws the outlines in the specified color.

Here are the standard descriptions of all the Collection kwargs:

agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an Axes instance clim: a length 2 sequence of floats clip_box: a matplotlib.transforms.Bbox instance clip_on: [True | False] clip_path: [ (Path, Transform) | Patch | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a matplotlib.figure.Figure instance gid: an id string hatch: [ ‘/’ | ‘\’ | ‘|’ | ‘-‘ | ‘+’ | ‘x’ | ‘o’ | ‘O’ | ‘.’ | ‘*’ ] label: string or anything printable with ‘%s’ conversion. linestyle or linestyles or dashes: [‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) ] linewidth or lw or linewidths: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: Transform instance url: a url string urls: unknown visible: [True | False] zorder: any number

The return value is a PolyCollection instance; use get_array() on this PolyCollection to get the counts in each hexagon. If marginals is True, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes hbar and vbar.

Example: