lampe.plots#

Plotting helpers.

Functions#

`nice_rc`	Returns a dictionary of runtime configuration (rc) settings for nicer `matplotlib` plots.
`corner`	Displays each 1 or 2-d projection of multi-dimensional data, as a triangular matrix of histograms, known as corner plot.
`mark_point`	Marks a point on the histograms of a corner plot.
`coverage_plot`	Plots the expected coverage at various credible levels.

Descriptions#

lampe.plots.nice_rc(latex=False)#

Returns a dictionary of runtime configuration (rc) settings for nicer matplotlib plots. The settings include 12pt font size, higher DPI, tight layout, transparent background, etc.

Parameters: latex (bool) – Whether to use LaTeX typesetting or not.

Example

>>> plt.rcParams.update(nice_rc())

lampe.plots.corner(data, weights=None, domain=None, bins=64, creds=(0.6827, 0.9545, 0.9973), color=None, alpha=(0.0, 0.5), legend=None, labels=None, smooth=0, figure=None, **kwargs)#

Displays each 1 or 2-d projection of multi-dimensional data, as a triangular matrix of histograms, known as corner plot. For 2-d histograms, highest density credibility regions are delimited.

Parameters

data (ndarray) – Multi-dimensional data, either as a table or as a matrix of histograms.
weights (Optional[ndarray]) – The importance weights of the data samples.
domain (Optional[Tuple[ndarray, ndarray]]) – A pair of lower and upper domain bounds. If None, inferred from data.
bins (Union[int, Sequence[int]]) – The number(s) of bins per dimension.
creds (Sequence[float]) – The region credibilities (in \([0, 1]\)) to delimit.
color (Optional[Union[str, tuple]]) – The color of histograms. If None, use the color cycler instead.
alpha (Tuple[float, float]) – The transparency range.
legend (Optional[str]) – A legend.
labels (Optional[Sequence[str]]) – The dimension labels.
smooth (float) – The standard deviation of the smoothing kernels.
figure (Optional[Figure]) – A corner plot over which to draw the new one.
kwargs – Keyword arguments passed to matplotlib.pyplot.subplots.

Returns

The figure instance for the corner plot.

Return type

Figure

Example

>>> data = np.random.randn(2**16, 3)
>>> labels = [r'$\alpha$', r'$\beta$', r'$\gamma$']
>>> figure = corner(data, bins=32, labels=labels, figsize=(4.8, 4.8))

lampe.plots.mark_point(figure, point, color='black', linestyle='dashed', marker='s', legend=None)#

Marks a point on the histograms of a corner plot.

Parameters

figure (Figure) – The corner plot figure (see corner).
point (ndarray) – The point to mark.
color (Union[str, tuple]) – The color of the lines and markers.
linestyle (str) – The style of the lines.
marker (str) – The shape of the markers.
legend (Optional[str]) – A legend.

Example

>>> mark_point(figure, [0.5, 0.3, -0.7], color='black')

lampe.plots.coverage_plot(levels, coverages, color=None, legend=None, figure=None, **kwargs)#

Plots the expected coverage at various credible levels.

Parameters

levels (ndarray) – A vector of increasing credible levels.
coverages (ndarray) – A vector of corresponding expected coverages.
legend (Optional[str]) – A legend.
figure (Optional[Figure]) – Another coverage plot over which to draw the new one.
kwargs – Keyword arguments passed to matplotlib.pyplot.subplots.

Returns

The figure instance for the coverage plot.

Return type

Figure

Example

>>> levels = np.linspace(0, 1, 512) ** 2
>>> coverages = np.linspace(0, 1, 512)
>>> figure = coverage_plot(levels, coverages)