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11.1.2.1. astroML.plotting.MultiAxes

class astroML.plotting.MultiAxes(ndim, inner_labels=False, fig=None, left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

Visualize Multiple-dimensional data

This class enables the visualization of multi-dimensional data, using a triangular grid of 2D plots.

Parameters :

ndim : integer

Number of data dimensions

inner_labels : bool

If true, then label the inner axes. If false, then only the outer axes will be labeled

fig : matplotlib.Figure

if specified, draw the plot on this figure. Otherwise, use the current active figure.

left, bottom, right, top, wspace, hspace : floats

these parameters control the layout of the plots. They behave have an identical effect as the arguments to plt.subplots_adjust. If not specified, default values from the rc file will be used.

Examples

A grid of scatter plots can be created as follows:

x = np.random.normal((4, 1000))
R = np.random.random((4, 4))  # projection matrix
x = np.dot(R, x)
ax = MultiAxes(4)
ax.scatter(x)
ax.set_labels(['x1', 'x2', 'x3', 'x4'])

Alternatively, the scatter plot can be visualized as a density:

ax = MultiAxes(4)
ax.density(x, bins=[20, 20, 20, 20])

Methods

density(data[, bins]) Density plot of data
plot(data, *args, **kwargs) Plot data
scatter(data, *args, **kwargs) Scatter plot data
set_formatters(formatters) Set the tick formatters for the outer edge of plots
set_labels(labels) Set the axes labels
set_limits(limits) Set the axes limits
set_locators(locators) Set the tick locators for the plots
__init__(ndim, inner_labels=False, fig=None, left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)