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

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)

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

invert_var()

Function to invert variance

norm_var()

Function to create standard deviation from variance

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, means, sigmas, limits=(-4503599627370496.0, 4503599627370496.0))

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

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(xs)

Function to evaluate multivariate Gaussian probability distribution at multiple points

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

evaluate_one(x)

Function to evaluate multivariate Gaussian probability distribution once

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

invert_var()

Function to invert covariance matrix

norm_var()

Function to normalize covariance matrix

sample(n_samps)

Function to sample from multivariate Gaussian probability distribution

Parameters:n_samps (positive int) – number of samples to take Returns:xs – 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:x – single sample from multivariate Gaussian probability distribution Return type:numpy.ndarray, float

The catalog Class

Simulation Utilities

sim_utils.choice(weights)

Function sampling discrete distribution

Parameters:weights (numpy.ndarray) – relative probabilities for each category Returns:index – chosen category Return type:int

sim_utils.ingest(in_info)

Function reading in parameter file to define functions necessary for generation of posterior probability distributions

Parameters:in_info (string or dict) – string containing path to plaintext input file or dict containing likelihood input parameters Returns:in_dict – dict containing keys and values necessary for posterior probability distributions Return type:dict