xpipe.xhandle.shearops.StackedProfileContainer

class xpipe.xhandle.shearops.StackedProfileContainer(info, data, labels, ncen, lcname=None, metadata=None, metatags=None, Rs=None, weights=None, shear=False, **kwargs)

Object Oriented interface for stacked shear and calculated via xshear

Features

• Jackknife based covariance estimation

• Calculate shear, DeltaSigma, and other averaged quantities

• Simple arithmetic with shear profiles: +, -, *, /

• Integration with Metacalibration style selection responses

Overview

The lensing profile is calculated in two stages:

• First, for each Jackknife-region defined by the labels (that is for all-except-one region) the lensing profile is calculated, and stored in self.*_sub (These are the JK-subprofiles).

• Using these JK-subprofiles, the mean lensing profile and the corresponding covariance is calculated via blockwise omitt-one Jackknife resampling.

In practice both of these steps are performed when calling self.prof_maker(), and the results are stored in the below varaiables. The default quantity calculated is the DeltaSigma:

self.rr:        radial centers
self.dst:       E-mode lensing signal
self.dst_err:   error for E-mode lensing signal
self.dst_cov:   covariance for E-mode lensing signal
self.dst:       B-mode lensing signal
self.dst_err:   error for B-mode lensing signal
self.dst_cov:   covariance for B-mode  lensing signal

A benefit of this two-step method, is that it allows for the quick calculation of errors on the differences, ratios and other transformations of shear profiles.

In particular, the self.composite function allows four basic operations +, -, *, / between shear profiles, while the self.multiply function allows \*, / with integers or 1-D arrays (in which case each element is a value for a lens)

E.g to calculate , we only need to compute the differences of JK-subprofiles and then re-compute the JK-mean, and JK-covariance.

In practice the above operation is achieved by:

# the operation is performed in-place, hence the deepcopy
prof3 = copy.deepcopy(prof2)
prof3.composite(prof1, operation="-")

Changing the physical quantity computed by self.prof_maker() is possibly by modifying the columns used by the stacking algorithm. The default values used for DeltaSigma are:

# default values for stacked DeltaSigma
self.dst_nom = 4
self.dsx_nom = 5
self.dst_denom = 6
self.dsx_denom = 7

self.e1_nom = 10
self.e2_nom = 11
self.meta_denom = 3
self.meta_prefac = 2

To calculate shear one would need to change the above to:

self.dst_denom = 8
self.dsx_denom = 9

self.meta_denom = 3
self.meta_prefac = 3

Further information on the column positions are found in the xread documentation…

Parameters

• info (np.ndarray) – info table from xread

• data (np.ndarray) – data table from xread

• labels (np.array) – JK-region labels

• ncen (int) – number of JK regions in total

• lcname (str) – tag string

• metadata (list of np.ndarray) – sheared versions of data

• metatags (list of str) – sheared tags

• Rs (float) – metacal selection response (constant) which will be used instead of metadata / metatags

composite(other, operation='-', keep_rr=True)

Calculate the JK estimate on the operation applied to the two profiles

Possible Operations:

• “-”: self - other

• “+”: self + other

• “*”: self * other

• “/”: self / other

The results is updated to self. Use deepcopy to obtain copies of the object for storing the previous state.

Parameters

• other (StackedProfileContainer) – an other profile…

• operation (str) – string specifying what to do…

• keep_rr (bool) – whether to reset radial values to placeholder

drop_data()

resets the info, data and labels containers to None

This reduces the size of the object in memory, but cannot re-stack the lenses anymore. Leaves the profile, JK-subprofiles and ancillary quantities unaffected

classmethod from_sub_dict(sub_dict)

Reconstructs the object from a dictionary

The resulting object does not contain the base data, only the profile, JK-subprofiles and ancillary quantities

multiply(val, keep_rr=True)

Calculate the JK estimate on profile times the specified “val”,

Operation performed in-place. Use deepcopy to obtain copies of the object for storing the previous state.

Parameters

val (float or np.array) – value to multiply by

prof_maker(weights=None)

Calculates the Jackknife estimate of on stacked profile and on the corresponding covariance

Parameters

weights (np.array) – weight for each entry in the lens catalog

to_dict()

Extracts the raw data to a dictionary

Contains the arguments passed to this dataset

to_sub_dict()

Extracts the calculated profile and ancillary information to a dictionary

The resulting dict does not contain the base data, only the profile, the JK-subprofiles and ancillary quantities