streamobs.samplers module#

Probabilistic samplers.

class streamobs.samplers.CubicSplineInterpolationSampler(nodes, node_values)[source]#: Bases: InterpolationSampler

class streamobs.samplers.FileCubicSplineInterpolationSampler(filename, stream_name=None, spline_type=None)[source]#: Bases: InterpolationSampler

class streamobs.samplers.FileInterpolationSampler(filename, columns=None)[source]#: Bases: InterpolationSampler

class streamobs.samplers.FileLinearDensityCubicSplineInterpolationSampler(filename, stream_name)[source]#: Bases: InterpolationSampler

class streamobs.samplers.GaussianSampler(mu, sigma)[source]#

Bases: ScipySampler

Sample from Gaussian.

class streamobs.samplers.InterpolationSampler(xvals, yvals, **kwargs)[source]#

Bases: Sampler

Sample from interpolated function.

class streamobs.samplers.LinearDensityCubicSplineInterpolationSampler(intensity_nodes, intensity_node_values, spread_nodes, spread_node_values)[source]#: Bases: InterpolationSampler

class streamobs.samplers.SinusoidSampler(**kwargs)[source]#: Bases: InterpolationSampler

class streamobs.samplers.UniformSampler(xmin, xmax)[source]#

Bases: ScipySampler

Sample from uniform distribution.

streamobs.samplers.inverse_transform_sample(vals, pdf, size, rng=None)[source]#

Perform inverse transform sampling

Parameters:

vals (value at which pdf is measured)
pdf (pdf value)
size (number of stars to sample)
rng (numpy.random.Generator, optional) – Random number generator used to draw the underlying uniform variates. A fresh, unseeded generator is created if omitted.

Returns:

samples

Return type:

samples of vals

streamobs.samplers.sampler_factory(type_, **kwargs)[source]#

Create a sampler with given kwargs.

Parameters:

streamobs.samplers module