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This Python package enables estimation of cosmological quantities using photometric redshift probability distributions.

Tutorials

See the following IPython Notebook for an example of using chippr:

• Basic Demo

Simulation

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

Inference

chippr currently enables estimation of the redshift density function.

The log_z_dens Class

class log_z_dens.log_z_dens(catalog, hyperprior, truth=None, vb=True)

log_hyper_posterior(log_nz)

Function to evaluate log hyperposterior

Parameters:log_nz (numpy.ndarray, float) – vector of logged redshift density bin values at which to evaluate the full posterior Returns:log_prob – log posterior probability associated with parameters in log_nz Return type:float

mexp()

Calculates the marginalized expected value estimator of the redshift density function

Returns:mexp_dens – array of redshift density function bin values Return type:ndarray

mmap()

Calculates the marginalized maximum a posteriori estimator of the redshift density function

Returns:mmap_dens – array of redshift density function bin values Return type:ndarray

optimize(start, vb=True)

Calculates the marginalized maximum likelihood estimator of the redshift density function

Parameters:

• start (numpy.ndarray) – array of log redshift density function bin values at which to begin optimization
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

mmle_dens – array of redshift density function bin values

Return type:

numpy.ndarray

plot(plot_loc='')

Plots all available estimators of the redshift density function.

sample(n_samps, vb=True)

Calculates samples estimating the redshift density function

Parameters:

• n_samps (int) – number of samples to accept before stopping
• vb (boolean, optional) – True to print progress messages to stdout, False to suppress

Returns:

samp_dens – array of sampled redshift density function bin values

Return type:

ndarray

stack(vb=True)

Calculates the stacked estimator of the redshift density function

Parameters:vb (boolean, optional) – True to print progress messages to stdout, False to suppress Returns:log_stack – array of logged redshift density function bin values Return type:ndarray

Plotting Utilities

plot_utils.plot_h(sub_plot, bin_ends, plot, s='--', c='k', a=1, w=1, d=[(0, (1, 0.0001))], l=None, r=False)

Helper function to plot horizontal lines of a step function

Parameters:

• sub_plot (matplotlib.pyplot subplot object) – subplot into which step function is drawn
• bin_ends (list or ndarray) – list or array of endpoints of bins
• plot (list or ndarray) – list or array of values within each bin
• s (string, optional) – matplotlib.pyplot linestyle
• c (string, optional) – matplotlib.pyplot color
• a (int or float, [0., 1.], optional) – matplotlib.pyplot alpha (transparency)
• w (int or float, optional) – matplotlib.pyplot linewidth
• d (list of tuple, optional) – matplotlib.pyplot dash style, of form [(start_point, (points_on, points_off, ...))]
• l (string, optional) – label for function
• r (boolean, optional) – True for rasterized, False for vectorized

plot_utils.plot_step(sub_plot, bin_ends, plot, s='--', c='k', a=1, w=1, d=[(0, (1, 0.0001))], l=None, r=False)

Plots a step function

Parameters:

• sub_plot (matplotlib.pyplot subplot object) – subplot into which step function is drawn
• bin_ends (list or ndarray) – list or array of endpoints of bins
• plot (list or ndarray) – list or array of values within each bin
• s (string, optional) – matplotlib.pyplot linestyle
• c (string, optional) – matplotlib.pyplot color
• a (int or float, [0., 1.], optional) – matplotlib.pyplot alpha (transparency)
• w (int or float, optional) – matplotlib.pyplot linewidth
• d (list of tuple, optional) – matplotlib.pyplot dash style, of form [(start_point, (points_on, points_off, ...))]
• l (string, optional) – label for function
• r (boolean, optional) – True for rasterized, False for vectorized

plot_utils.plot_v(sub_plot, bin_ends, plot, s='--', c='k', a=1, w=1, d=[(0, (1, 0.0001))], r=False)

Helper function to plot vertical lines of a step function

Parameters:

• sub_plot (matplotlib.pyplot subplot object) – subplot into which step function is drawn
• bin_ends (list or ndarray) – list or array of endpoints of bins
• plot (list or ndarray) – list or array of values within each bin
• s (string, optional) – matplotlib.pyplot linestyle
• c (string, optional) – matplotlib.pyplot color
• a (int or float, [0., 1.], optional) – matplotlib.pyplot alpha (transparency)
• w (int or float, optional) – matplotlib.pyplot linewidth
• d (list of tuple, optional) – matplotlib.pyplot dash style, of form [(start_point, (points_on, points_off, ...))]
• r (boolean, optional) – True for rasterized, False for vectorized