Make a 2D hexagonal binning plot of points x, y.

If C is None, the value of the hexagon is determined by the number of points in the hexagon. Otherwise, C specifies values at the coordinate (x[i], y[i]). For each hexagon, these values are reduced using reduce_C_function.

Parameters¶

x, yarray-like

The data positions. x and y must be of the same length.

Carray-like, optional

If given, these values are accumulated in the bins. Otherwise, every point has a value of 1. Must be of the same length as x and y.

gridsizeint or (int, int), default: 100

If a single int, the number of hexagons in the x-direction. The number of hexagons in the y-direction is chosen such that the hexagons are approximately regular.

Alternatively, if a tuple (nx, ny), the number of hexagons in the x-direction and the y-direction. In the y-direction, counting is done along vertically aligned hexagons, not along the zig-zag chains of hexagons; see the following illustration.

To get approximately regular hexagons, choose \(n_x = \sqrt{3}\,n_y\).

bins‘log’ or int or sequence, default: None

Discretization of the hexagon values.

• 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 colormap. Internally, \(log_{10}(i+1)\) is used to determine the hexagon color. This is equivalent to norm=LogNorm().

• 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’}, default: ‘linear’

Use a linear or log10 scale on the horizontal axis.

yscale{‘linear’, ‘log’}, default: ‘linear’

Use a linear or log10 scale on the vertical axis.

mincntint >= 0, default: None

If not None, only display cells with at least mincnt number of points in the cell.

marginalsbool, default: False

If marginals is True, plot the marginal density as colormapped rectangles along the bottom of the x-axis and left of the y-axis.

extent4-tuple of float, default: None

The limits of the bins (xmin, xmax, ymin, ymax). The default assigns the limits based on gridsize, x, y, xscale and yscale.

If xscale or yscale is set to ‘log’, the limits are expected to be the exponent for a power of 10. E.g. for x-limits of 1 and 50 in ‘linear’ scale and y-limits of 10 and 1000 in ‘log’ scale, enter (1, 50, 1, 3).

Returns¶

~matplotlib.collections.PolyCollection

A .PolyCollection defining the hexagonal bins.

• .PolyCollection.get_offsets contains a Mx2 array containing the x, y positions of the M hexagon centers.

• .PolyCollection.get_array contains the values of the M hexagons.

If marginals is True, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes hbar and vbar.

Other Parameters¶

cmapstr or ~matplotlib.colors.Colormap, default: :rc:`image.cmap`

The Colormap instance or registered colormap name used to map scalar data to colors.

normstr or ~matplotlib.colors.Normalize, optional

The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1.

If given, this can be one of the following:

• An instance of .Normalize or one of its subclasses (see colormapnorms).

• A scale name, i.e. one of “linear”, “log”, “symlog”, “logit”, etc. For a list of available scales, call matplotlib.scale.get_scale_names(). In that case, a suitable .Normalize subclass is dynamically generated and instantiated.

vmin, vmaxfloat, optional

When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use vmin/vmax when a norm instance is given (but using a str norm name together with vmin/vmax is acceptable).

alphafloat between 0 and 1, optional

The alpha blending value, between 0 (transparent) and 1 (opaque).

linewidthsfloat, default: None

If None, defaults to :rc:`patch.linewidth`.

edgecolors{‘face’, ‘none’, None} or color, default: ‘face’

The color of the hexagon edges. Possible values are:

• ‘face’: Draw the edges in the same color as the fill color.

• ‘none’: No edges are drawn. This can sometimes lead to unsightly unpainted pixels between the hexagons.

• None: Draw outlines in the default color.

• An explicit color.

reduce_C_functioncallable, default: numpy.mean

The function to aggregate C within the bins. It is ignored if C is not given. This must have the signature:

def reduce_C_function(C: array) -> float

Commonly used functions are:

• numpy.mean: average of the points

• numpy.sum: integral of the point values

• numpy.amax: value taken from the largest point

By default will only reduce cells with at least 1 point because some reduction functions (such as numpy.amax) will error/warn with empty input. Changing mincnt will adjust the cutoff, and if set to 0 will pass empty input to the reduction function.

dataindexable object, optional

If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception):

x, y, C

**kwargs~matplotlib.collections.PolyCollection properties

All other keyword arguments are passed on to .PolyCollection:

Properties: agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: array-like or scalar or None animated: bool antialiased or aa or antialiaseds: bool or list of bools array: array-like or None capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} clim: (vmin: float, vmax: float) clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None cmap: .Colormap or str or None color: :mpltype:`color` or list of RGBA tuples edgecolor or ec or edgecolors: :mpltype:`color` or list of :mpltype:`color` or ‘face’ facecolor or facecolors or fc: :mpltype:`color` or list of :mpltype:`color` figure: ~matplotlib.figure.Figure gid: str hatch: {‘/’, ‘\’, ‘|’, ‘-’, ‘+’, ‘x’, ‘o’, ‘O’, ‘.’, ‘*’} in_layout: bool joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} label: object linestyle or dashes or linestyles or ls: str or tuple or list thereof linewidth or linewidths or lw: float or list of floats mouseover: bool norm: .Normalize or str or None offset_transform or transOffset: .Transform offsets: (N, 2) or (2,) array-like path_effects: list of .AbstractPathEffect paths: list of array-like picker: None or bool or float or callable pickradius: float rasterized: bool sizes: numpy.ndarray or None sketch_params: (scale: float, length: float, randomness: float) snap: bool or None transform: ~matplotlib.transforms.Transform url: str urls: list of str or None verts: list of array-like verts_and_codes: unknown visible: bool zorder: float

See Also¶

hist2d : 2D histogram rectangular bins