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)[source]
log_hyper_posterior(log_nz)[source]

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()[source]

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()[source]

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)[source]

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='')[source]

Plots all available estimators of the redshift density function.

sample(n_samps, vb=True)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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