chippr enables simulation of surveys of photo-z interim posteriors.

The discrete Class

class discrete.discrete(bin_ends, weights)

evaluate(xs)

Function to evaluate the discrete probability distribution at many points

Parameters:xs (ndarray, float) – values at which to evaluate discrete probability distribution Returns:ps – values of discrete probability distribution at xs Return type:ndarray, float

evaluate_one(x)

Function to evaluate the discrete probability distribution at one point

Parameters:x (float) – value at which to evaluate discrete probability distribution Returns:p – value of discrete probability distribution at x Return type:float

pdf(xs)

sample(n_samps)

Function to take samples from discrete probability distribution

Parameters:n_samps (int) – number of samples to take Returns:xs – array of points sampled from the discrete probability distribution Return type:ndarray, float

sample_one()

Function to sample a single value from discrete probability distribution

Returns:x – a single point sampled from the discrete probability distribution Return type:float

The gauss Class

class gauss.gauss(mean, var, bounds=None)

evaluate(xs)

Function to evaluate univariate Gaussian probability distribution at multiple points

Parameters:xs (numpy.ndarray, float) – input values at which to evaluate probability Returns:ps – output probabilities Return type:ndarray, float

evaluate_one(x)

Function to evaluate Gaussian probability distribution once

Parameters:x (float) – value at which to evaluate Gaussian probability distribution Returns:p – probability associated with x Return type:float

invert_var()

Function to invert variance

norm_var()

Function to create standard deviation from variance

pdf(xs)

sample(n_samps)

Function to sample univariate Gaussian probability distribution

Parameters:n_samps (positive int) – number of samples to take Returns:xs – array of n_samps samples from Gaussian probability distribution Return type:ndarray, float

sample_one()

Function to take one sample from univariate Gaussian probability distribution

Returns:x – single sample from Gaussian probability distribution Return type:float

The gmix Class

class gmix.gmix(amps, funcs, limits=(0.001, 3.501))

evaluate(xs)

Function to evaluate the Gaussian mixture probability distribution at many points

Parameters:xs (ndarray, float) – values at which to evaluate Gaussian mixture probability distribution Returns:ps – values of Gaussian mixture probability distribution at xs Return type:ndarray, float

evaluate_one(x)

Function to evaluate Gaussian mixture once

Parameters:x (float) – value at which to evaluate Gaussian mixture Returns:p – probability associated with x Return type:float

pdf(xs)

sample(n_samps)

Function to take samples from Gaussian mixture probability distribution

Parameters:n_samps (int) – number of samples to take Returns:xs – array of points sampled from the Gaussian mixture probability distribution Return type:ndarray, float

sample_one()

Function to sample a single value from Gaussian mixture probability distribution

Returns:x – a single point sampled from the Gaussian mixture probability distribution Return type:float

The mvn Class

class mvn.mvn(mean, var)

evaluate(zs)

Function to evaluate multivariate Gaussian probability distribution at multiple points

Parameters:zs (ndarray, float) – input vectors at which to evaluate probability Returns:ps – output probabilities Return type:ndarray, float

evaluate_one(z)

Function to evaluate multivariate Gaussian probability distribution once

Parameters:z (numpy.ndarray, float) – value at which to evaluate multivariate Gaussian probability distribution Returns:p – probability associated with z Return type:float

invert_var()

Function to invert covariance matrix

Returns:inv – inverse variance Return type:numpy.ndarray, float

norm_var()

Function to normalize covariance matrix

Returns:det – determinant of variance Return type:float

pdf(points)

sample(n_samps)

Function to sample from multivariate Gaussian probability distribution

Parameters:n_samps (positive int) – number of samples to take Returns:zs – array of n_samps samples from multivariate Gaussian probability distribution Return type:ndarray, float

sample_one()

Function to take one sample from multivariate Gaussian probability distribution

Returns:z – single sample from multivariate Gaussian probability distribution Return type:numpy.ndarray, float

The catalog Class

class catalog.catalog(params={}, vb=True, loc='.', prepend='')

coarsify(fine)

Function to bin function evaluated on fine grid

Parameters:fine (numpy.ndarray, float) – matrix of probability values of function on fine grid for N galaxies Returns:coarse – vector of binned values of function Return type:numpy.ndarray, float

create(truth, int_pr, N=4, vb=True)

Function creating a catalog of interim posterior probability distributions, will split this up into helper functions

Parameters:

• truth (chippr.gmix object or chippr.gauss object or chippr.discrete) –
• object – true redshift distribution object
• int_pr (chippr.gmix object or chippr.gauss object or chippr.discrete) –
• object – interim prior distribution object
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

self.cat – dictionary comprising catalog information

Return type:

dict

evaluate_lfs(pspace, vb=True)

Evaluates likelihoods based on observed sample values

Parameters:

• pspace (chippr.gauss or chippr.gmix or chippr.gamma or chippr.multi object) – the probability function to evaluate
• vb (boolean) – print progress to stdout?

Returns:

lfs – array of likelihood values for each item as a function of fine binning

Return type:

numpy.ndarray, float

make_probs(vb=True)

Makes the continuous 2D probability distribution over z_spec, z_phot

Parameters:vb (boolean) – print progress to stdout?

Notes

TO DO: only one outlier population at a time for now, will enable more TO DO: also doesn’t yet include perpendicular features from passing between filter curves, should add that

proc_bins(vb=True)

Function to process binning

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress

read(loc='data', style='.txt')

Function to read in catalog file

Parameters:loc (string, optional) – location of catalog file

sample(N, vb=False)

Samples (z_spec, z_phot) pairs

Parameters:

• N (int) – number of samples to take
• vb (boolean) – print progress to stdout?

Returns:

samps – (z_spec, z_phot) pairs

Return type:

numpy.ndarray, float

write(loc='data', style='.txt')

Function to write newly-created catalog to file

Parameters:

• loc (string, optional) – file name into which to save catalog
• style (string, optional) – file format in which to save the catalog