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Remove vestigial FHP/MK NOSHEAR←1P Tpsf substitution (guaranteed no-op) #267

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Remove vestigial FHP/MK NOSHEAR←1P Tpsf substitution (guaranteed no-op) #267

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10 changes: 0 additions & 10 deletions scripts/calibration/calibrate_comprehensive_cat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,16 +191,6 @@
for key in add_cols:
add_cols_data[key] = cat.get_col(dat, key, mask_combined._mask, mask_metacal)

# Keep original NOSHEAR column, override with 1P PSF values (FHP/MK hack)
print(
"FHP/MK hack: explicit copying of the metacal no-shear (updated from 1p)"
+ " PSF size"
)
add_cols_data["NGMIX_T_PSF_RECONV_NOSHEAR_orig"] = add_cols_data[
"NGMIX_T_PSF_RECONV_NOSHEAR"
]
add_cols_data["NGMIX_T_PSF_RECONV_NOSHEAR"] = gal_metacal.ns["Tpsf"][mask_metacal]

# %%
# Additional post-processing columns to write to output cat
add_cols_post = [
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8 changes: 0 additions & 8 deletions src/sp_validation/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -789,14 +789,6 @@ def _read_data(self, data, mask):
f"Unsupported shape prefix '{self._prefix}'; only 'NGMIX' is supported"
)

print("FHP/MK hack using p1 PSF for ns in cuts")
indices = np.where(mask)[0]
col_1p = f"{self._prefix}_T_PSF_RECONV_1P"
new_psf = data[col_1p][indices]

# Overwriting incorrect no-shear PSF size to the one from 1p
ns["Tpsf"] = new_psf

self.m1 = m1
self.p1 = p1
self.m2 = m2
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4 changes: 2 additions & 2 deletions src/sp_validation/tests/test_calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,8 +296,8 @@ def test_metacal_R_matrix_recovers_injected_response():
re-runs with slope 5.0 and confirms R11 tracks it (5.0 != 2.0), so a
change that decouples R from the input numbers fails.

NOTE: the estimator prints an 'FHP/MK hack' line and an unweighted /
weighted response line; these are expected stdout, not errors.
NOTE: the estimator prints an unweighted / weighted response line; this
is expected stdout, not an error.
"""
data, n = _build_ngmix_catalog(slope_11=2.0, slope_22=3.0, step=0.01)
mask = np.ones(n, dtype=bool)
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