sp_validation.calibration

CALIBRATION.

Name:

calibration.py

Description:

This script contains methods for shear calibration.

Author:

Martin Kilbinger

get_calibrated_quantities(gal_metacal)

Get Calibrated Quantities.

Return catalogue quantities for objects calibrated for multiplicative bias.

Parameters:

gal_metacal (dict) – galaxy metacalibration catalogue

Returns:

• g_corr (array(2, ngal) of float) – shear estimates calibrated for multiplicative bias

• g_uncorr (array(2, ngal) of float) – uncalibrated shear estimates

• w (array of float) – weights

• mask (array of bool) – mask to indicate valid objects in “no-shear” sample

get_calibrated_m_c(gal_metacal, additive_correction=True)

Get Calibrated C.

Return catalogue quantities for objects calibrated for multiplicative and additive bias.

Parameters:

• gal_metacal (dict) – galaxy metacalibration catalogue

• additive_correction (bool, optional, default=True) – if False, do not subtract the additive bias c from the shear estimates; use for constant-shear image sims, where the mean shear is the signal (see issue #226). c and c_err are still computed and returned

Returns:

• numpy.ndarray – shear estimates calibrated for multiplicative and additive bias; array(2, ngal) of float

• numpy.ndarray – uncalibrated shear estimates; array(2, ngal) of float

• numpy.ndarray – weights; array of float

• numpy.ndarray – mask to indicate valid objects in “no-shear” sample; array of bool

• numpy.ndarray – additive bias for both components;

• numpy.ndarray – error on the additive bias for both components

create_bins(x, num_bins, type='log', x_min=None, x_max=None)

Create Bins. Create bins for a given array. The bins are logarithmic by default.

Parameters:

• x (array) – Array to bin

• num_bins (int) – Number of bins

• type (str, optional) – Type of binning. Options are ‘log’ (defaults)

• x_min (float, optional) – Minimum value of the bins. If None, the minimum value of x is used.

• x_max (float, optional) – Maximum value of the bins. If None, the maximum value of x is used.

cut_to_bins(df, key, num_bins, type='log', x_min=None, x_max=None)

Cut To Bins.

Cut a given array into bins. Create a new column in the DataFrame with the binning.

Parameters:

• df (pandas.DataFrame) – DataFrame to cut

• key (str) – Key to cut

• num_bins (int) – Number of bins

• type (str, optional) – Type of binning. Options are ‘log’ (default)

• x_min (float, optional) – Minimum value of the bins. If None, the minimum value of x is used.

• x_max (float, optional) – Maximum value of the bins. If None, the maximum value of x is used.

Returns:

bin edges

Return type:

numpy.ndarray

fill_cat_gal(cat_gal, dat, g_uncorr, gal_metacal, mask1, mask2, purpose='weights')

build_df(cat_gal)

Build DF.

Build pandas dataframe.

Parameters:

cat_gal (dict) – input data

Returns:

collected data

Return type:

pd.DataFrame

get_w_des(cat_gal, num_bins, snr_min=None, snr_max=None, size_ratio_min=None, size_ratio_max=None)

Get DES weights. (Gatti et al. 2021) Return an array of DES weights obtained by binning in SNR and size and computing the ratio between the shear response and the shape noise.

Parameters:

• cat_gal (dict) – A catalog of galaxies containing the response matrix and the uncalibrated ellipticities

• num_bins (int) – Number of bins to use for the binning of the SNR and size.

• snr_min (float, optional) – Minimum SNR, default (None): determined by the data

• snr_max (float, optional) – Maximum SNR, default (None): determined by the data

• size_ratio_min (float, optional) – Minimum size ratio, default (None): determined by the data

• size_ratio_max (float, optional) – Maximum size ratio, default (None): determined by the data

Returns:

w – DES weights

Return type:

array of float

get_alpha_leakage_per_object(cat_gal, num_bins, weight_type='des')

Compute the leakage per object (Li et al. 2024) Return an array of leakage coefficients obtained by binning in SNR and size.

Parameters:

• cat_gal (dict) – A catalog of galaxies containing galaxy ellipticity, PSF ellipticity, SNR and size of the galaxy and the PSF.

• num_bins (int) – Number of bins

Returns:

• alpha_1 (np.array) – Array containing the correction coefficient for the PSF leakage per object for the first component.

• alpha_2 (np.array) – Array containing the correction coefficient for the PSF leakage per object for the second component.

get_quantities_binned(cat_gal, num_bins_x, num_bins_y=None, which=['response', 'number', 'leakage'], verbose=True)

get_calibrate_e_from_cat(path_cat_gal, weight_type='des', verbose=False)

Calibrates ellipticities from a galaxy catalog with a certain weight type.

Parameters:

• path_cat_gal (str) – Path to the galaxy catalog

• weight_type (str, optional, default='des') – Type of weight to use. Options are ‘des’ (DES weight) or ‘iv’ (inverse variance)

• verbose (bool, optional, default=False) – If True, print intermediate results

Returns:

g_cal – Calibrated ellipticities

Return type:

np.array

get_calibrate_no_leakage_e_from_cat(path_cat_gal, weight_type='des', verbose=False)

Calibrate ellipticities and removes leakage from a galaxy catalog with a certain weight type.

Parameters:

• path_cat_gal (str) – Path to the galaxy catalog

• weight_type (str, optional, default='des') – Type of weight to use. Options are ‘des’ (DES weight) or ‘iv’ (inverse variance)

• verbose (bool, optional, default=False) – If True, print intermediate results

Returns:

• e1_noleak (np.array) – Calibrated ellipticities without leakage for the first component

• e2_noleak (np.array) – Calibrated ellipticities without leakage for the second component

class metacal(data, mask, masking_type='gal', step=0.01, prefix='NGMIX', snr_min=10, snr_max=500, rel_size_min=0.5, rel_size_max=3.0, size_corr_ell=True, global_R_weight=None, sigma_eps=0.34, verbose=False)

Bases: object

Metacal.

Metacalibration.

Parameters:

• data – input galaxy catalogue

• mask (array of bool) – mask according to galaxy selection, e.g. spread_model

• masking_type (string, optional, default='gal') – masking type, one in ‘gal’, ‘gal_mom’, ‘star’

• step (float, optional, default=0.01) – step h in finite differences

• prefix (string, optional, default='NGMIX') – to specify columns in input catalogue

• snr_min (float, optional, default=10) – signal-to-noise minimum

• float (snr_max;) – signal-to-noise maximum

• optional – signal-to-noise maximum

• default=500 – signal-to-noise maximum

• rel_size_min (float, optional, default=0.5) – relative size minimum

• rel_size_max (float, optional, default=3.0) – relative size maximum

• size_corr_ell (bool, optional, default=True)

• global_R_weight (str, optional,) – weight column name for global response matrix; default is None (unweighted mean)

• sigma_eps (float, optional) – ellipticity dispersion (one component) for computation of weights; default is 0.34

• verbose (bool, optional, default=False) – verbose output if True

_read_data(data, mask)

Read Data.

Read relevant data columns.

_read_data_ngmix(masked_data, m1, p1, m2, p2, ns)

Read Data Ngmix.

Read data from ngmix catalogue.

static get_variance_ivweights(data, sigma_eps, prefix='NGMIX', mask=None)

Get Variance IVWEIGHTS.

Compute variance and inverse-variance weights.

Parameters:

• data (numpy.ndarray) – input data

• sigma_eps (float) – ellipticity dispersion

• prefix (str, optional) – shape measurement identifier; default is “NGMIX”

• mask (list, optional) – indicates valid objects with True values; default is None = use all objects type has to be bool

Returns:

• float – variance first component

• float – variance second component

• float – weight

_compute_calibration()

Compute Calibration.

Perform masking and compute calibration.

add_cuts(snr_min=10, snr_max=500, rel_size_min=0.5, rel_size_max=3.0)

Add Cuts.

Apply additional cuts to metacal galaxy catalogue.

_masking_gal()

Masking Gal.

Mask metacal catalogue, i.e. apply cuts.

_masking_gal_mom()

Add docstring.

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_masking_star()

Add docstring.

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_shear_response()

Shear Response.

Compute shear response matrix

_shear_response_std(stat_operator=<function metacal.<lambda>>)

Shear Response Std.

Standard deviation of shear response

_selection_response()

Add docstring.

…

_total_response()

Add docstring.

…

mask_gal_size(T, Tpsf, rel_size_min, rel_size_max, size_corr_ell=False, g1=None, g2=None)

mask_gal_SNR(SNR, snr_min, snr_max)