fpfs.catalog#

fpfs.catalog.fpfs_m2e(mm, const=1.0, nn=None)[source]#

Estimates FPFS ellipticities from fpfs moments

Parameters:

  • mm (ndarray) – FPFS moments

  • const (float) – the weight constant [default:1]

  • nn (ndarray) – noise covaraince elements [default: None]

Returns:

out (ndarray) – an array of [FPFS ellipticities, FPFS ellipticity response, FPFS flux, size and FPFS selection response]

fpfs.catalog.fpfscov_to_imptcov(data)[source]#

Converts FPFS noise Covariance elements into a covariance matrix of lensPT.

Parameters:

data (ndarray) – FPFS shapelet mode catalog

Returns:

out (ndarray) – Covariance matrix

fpfs.catalog.get_wbias(x, cut, sigma, use_sig, w_sel, rev=None)[source]#

Returns the weight bias due to shear dependence and noise bias [first order in w]

Parameters:

  • x (ndarray) – selection observable

  • cut (float) – the cut on selection observable

  • sigma (float) – width of the selection function

  • use_sig (bool) – whether use sigmoid [True] of truncated sine [False]

  • w_sel (ndarray) – selection weights as function of selection observable

  • rev (ndarray) – selection response array

Returns:

cor (float) – correction for shear response

fpfs.catalog.get_wsel_eff(x, cut, sigma, use_sig, deriv=0)[source]#

Returns the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]

Parameters:

  • x (ndarray) – input selection observable

  • cut (float) – the cut on selection observable

  • sigma (float) – width of the selection function

  • use_sig (bool) – whether use sigmoid [True] of truncated sine [False]

  • deriv (int) – whether do derivative (1) or not (0)

Returns:

out (ndarray) – the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]

fpfs.catalog.imptcov_to_fpfscov(data)[source]#

Converts FPFS noise Covariance elements into a covariance matrix of lensPT.

Parameters:

data (ndarray) – impt covariance matrix

Returns:

out (ndarray) – FPFS covariance elements

fpfs.catalog.sigfunc(x, deriv=0, mu=0.0, sigma=1.5)[source]#

Returns the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]

Parameters:

  • deriv (int) – whether do derivative [deriv=1] or not [deriv=0]

  • x (ndarray) – input data vector

  • mu (float) – center of the cut

  • sigma (float) – width of the selection function

Returns:

out (ndarray) – the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]

class fpfs.catalog.summary_stats(mm, ell, use_sig=False)[source]#

Bases: object

clear_outcomes()[source]#

clears the outcome of the class

update_ellsum()[source]#

Updates the weighted sum of ellipticity and response with the currenct selection weight

update_selection_bias(snms, cuts, cutsigs)[source]#

Updates the selection bias correction term with the current selection weight

update_selection_weight(snms, cuts, cutsigs)[source]#

Updates the selection weight term with the current selection weight

fpfs.catalog.tsfunc1(x, deriv=0, mu=0.0, sigma=1.5)[source]#

Returns the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]. This is for C1 function

Parameters:

  • deriv (int) – whether do derivative [deriv=1] or not [deriv=0]

  • x (ndarray) – input data vector

  • mu (float) – center of the cut

  • sigma (float) – width of the selection function

Returns:

out (ndarray) – the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]

fpfs.catalog.tsfunc2(x, mu=0.0, sigma=1.5, deriv=0)[source]#

Returns the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]. This is for C2 funciton

Parameters:

  • deriv (int) – whether do derivative [deriv=1] or not [deriv=0]

  • x (ndarray) – input data vector

  • mu (float) – center of the cut

  • sigma (float) – width of the selection function

Returns:

out (ndarray) – the weight funciton [deriv=0], or the multiplicative factor to the weight function for first order derivative [deriv=1]