getdist.densities

exception getdist.densities.DensitiesError

class getdist.densities.Density1D(x, P=None, view_ranges=None)

Class for 1D marginalized densities, inheriting from GridDensity. You can call it like a InterpolatedUnivariateSpline obect to get interpolated values, or call Prob.

Parameters:

• x – array of x values

• P – array of densities at x values

• view_ranges – optional range for viewing density

Prob(x, derivative=0)

Calculate density at position x by interpolation in the density grid

Parameters:

• x – x value

• derivative – optional order of derivative to calculate (default: no derivative)

Returns:

P(x) density value (or array if x is an array)

bounds()

Get min, max bounds (from view_ranges if set)

getLimits(p, interpGrid=None, accuracy_factor=None)

Get parameter equal-density confidence limits (a credible interval). If the density is bounded, may only have a one-tail limit.

Parameters:

• p – limit to calculate, or list of limits to calculate, e.g. [0.68, 0.95]

• interpGrid – optional pre-computed cache

• accuracy_factor – parameter to boost default accuracy for fine sampling

Returns:

list of (min, max, has_min, has_top) values, or tuple fo single limit, where has_min and has_top are True or False depending on whether lower and upper limit exists

class getdist.densities.Density2D(x, y, P=None, view_ranges=None, mask=None)

Class for 2D marginalized densities, inheriting from GridDensity. You can call it like a RectBivariateSpline object to get interpolated values.

Parameters:

• x – array of x values

• y – array of y values

• P – 2D array of density values at x, y

• view_ranges – optional ranges for viewing density

• mask – optional 2D boolean array for non-trivial mask

Prob(x, y, grid=False)

Evaluate density at x,y using interpolation

Parameters:

• x – x value or array

• y – y value or array

• grid – whether to make a grid, see RectBivariateSpline. Default False.

class getdist.densities.DensityND(xs, P=None, view_ranges=None)

Class for ND marginalized densities, inheriting from GridDensity and LinearNDInterpolator.

This is not well tested recently.

Parameters:

• xs – list of arrays of x values

• P – ND array of density values at xs

• view_ranges – optional ranges for viewing density

Prob(xs)

Evaluate density at x,y,z using interpolation

class getdist.densities.GridDensity

Base class for probability density grids (normalized or not)

Variables:

P – array of density values

bounds()

Get bounds in order x, y, z..

Returns:

list of (min,max) values

getContourLevels(contours=(0.68, 0.95))

Get contour levels

Parameters:

contours – list of confidence limits to get (default [0.68, 0.95])

Returns:

list of contour levels

normalize(by='integral', in_place=False)

Normalize the density grid

Parameters:

• by – ‘integral’ for standard normalization, or ‘max’, to normalize so the maximum value is unity

• in_place – if True, normalize in place, otherwise make copy (in case self.P is used elsewhere)

setP(P=None)

Set the density grid values

Parameters:

P – numpy array of density values

getdist.densities.getContourLevels(inbins, contours=(0.68, 0.95), missing_norm=0, half_edge=True)

Get contour levels enclosing “contours” fraction of the probability, for any dimension bins array

Parameters:

• inbins – binned density.

• contours – list or tuple of confidence contours to calculate, default [0.68, 0.95]

• missing_norm – accounts of any points not included in inbins (e.g. points in far tails that are not in inbins)

• half_edge – If True, edge bins are only half integrated over in each direction.

Returns:

list of density levels