fixed bug in calculation of f_bias

CHANGELOG.md

@@ -6,6 +6,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.7.1] - 2023-01-18
+
+### Changed
+
+- `dsigma.jackknife.compress_jackknife_fields` now supresses `numpy` warnings if columns contain NaNs
+
+### Fixed
+
+- bug in the calcaluation of the photo-z dilution correction factor, led to percent-level biases in the total galaxy-galaxy lensing amplitude, did not affect DES and KiDS calculations since those are based on n(z)'s, bug was introduced in version 0.6
+
 ## [0.7.0] - 2023-01-06
 
 ### Changed

dsigma/jackknife.py

@@ -1,5 +1,6 @@
 """Module containing jackknife resampling functions."""
 
+import warnings
 import numpy as np
 import astropy.units as u
 from astropy.coordinates import SkyCoord
@@ -135,8 +136,12 @@ def compress_jackknife_fields(table):
             elif key in ['w_sys', 'sum 1']:
                 table_jk[i][key] = np.sum(table[key][mask], axis=0)
             else:
-                table_jk[i][key] = np.average(
-                    table[key][mask], weights=table['w_sys'][mask], axis=0)
+                with warnings.catch_warnings():
+                    if np.any(np.isnan(table[key][mask])):
+                        warnings.simplefilter(
+                            'ignore', category=RuntimeWarning)
+                    table_jk[i][key] = np.average(
+                        table[key][mask], weights=table['w_sys'][mask], axis=0)
 
     return table_jk
 

dsigma/precompute.py

@@ -80,7 +80,7 @@ def photo_z_dilution_factor(z_l, table_c, cosmology, weighting=-2,
     sigma_crit_true = critical_surface_density(z_l, z_s_true, d_l=d_l,
                                                d_s=d_s_true)
 
-    mask = (z_l_max < z_l) & (z_l > z_s)
+    mask = (z_l_max < z_l) | (z_l > z_s)
 
     if np.any(np.all(mask, axis=-1)):
         warnings.warn('Could not find valid calibration sources for some ' +
@@ -148,7 +148,7 @@ def mean_photo_z_offset(z_l, table_c, cosmology, weighting=-2,
     sigma_crit = critical_surface_density(z_l, z_s, d_l=d_l, d_s=d_s)
     w = w * sigma_crit**weighting
 
-    mask = (z_l_max < z_l) & (z_l > z_s)
+    mask = (z_l_max < z_l) | (z_l > z_s)
 
     return np.sum((z_s - z_s_true) * w * (~mask), axis=-1) / np.sum(
         w * (~mask), axis=-1)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "dsigma"
-version = "0.7.0"
+version = "0.7.1"
 requires-python = ">=3.8"
 authors = [{name = "Johannes U. Lange", email = "julange.astro@pm.me"},
            {name = "Song Huang"}]