REF remove pixel weighting for now

CHANGES.md

@@ -4,8 +4,6 @@
 
     - Added `fwhm_smooth` keyword to pre-PSF moments routines to allow for extra
       smoothing of the profile before the moments are measured.
-    - Added `use_pix_weight` keyword to pre-PSF moments routines to enable inverse
-      variance pixel weighting.
     - Added caching of FFTs in metacal and pre-PSF moment rountines.
 
 

ngmix/prepsfmom.py

@@ -41,22 +41,15 @@ class PrePSFMom(object):
         If non-zero, this optional applies additional Gaussian smoothing to the
         object before computing the moments. Typically a non-zero value results
         in less shape noise.
-    use_pix_weight : bool, optional
-        If `True`, this option applies inverse pixel variance weighting in Fourier
-        space when computing the moments. Typically this optional results in poorer
-        performance, but for round PSFs, it results in similar performance as
-        `fwhm_smooth`.
     """
     def __init__(
-        self, fwhm, kernel, pad_factor=4, ap_rad=1.5,
-        fwhm_smooth=0, use_pix_weight=False
+        self, fwhm, kernel, pad_factor=4, ap_rad=1.5, fwhm_smooth=0
     ):
         self.fwhm = fwhm
         self.pad_factor = pad_factor
         self.kernel = kernel
         self.ap_rad = ap_rad
         self.fwhm_smooth = fwhm_smooth
-        self.use_pix_weight = use_pix_weight
         if self.kernel == "ksigma":
             self.kind = "ksigma"
         elif self.kernel in ["gauss", "pgauss"]:
@@ -170,7 +163,6 @@ def _meas(self, obs, psf_obs, return_kernels):
         mom, mom_cov = _measure_moments_fft(
             kim, kpsf_im, tot_var, eff_pad_factor, kernels,
             im_row - psf_im_row, im_col - psf_im_col,
-            use_pix_weight=self.use_pix_weight,
         )
         res = make_mom_result(mom, mom_cov)
         if res['flags'] != 0:
@@ -214,18 +206,13 @@ class KSigmaMom(PrePSFMom):
         If non-zero, this optional applies additional Gaussian smoothing to the
         object before computing the moments. Typically a non-zero value results
         in less shape noise.
-    use_pix_weight : bool, optional
-        If `True`, this option applies inverse pixel variance weighting in Fourier
-        space when computing the moments. Typically this optional results in poorer
-        performance, but for round PSFs, it results in similar performance as
-        `fwhm_smooth`.
     """
     def __init__(
-        self, fwhm, pad_factor=4, ap_rad=1.5, fwhm_smooth=0, use_pix_weight=False,
+        self, fwhm, pad_factor=4, ap_rad=1.5, fwhm_smooth=0
     ):
         super().__init__(
             fwhm, 'ksigma', pad_factor=pad_factor, ap_rad=ap_rad,
-            fwhm_smooth=fwhm_smooth, use_pix_weight=use_pix_weight,
+            fwhm_smooth=fwhm_smooth,
         )
 
 
@@ -252,18 +239,13 @@ class PGaussMom(PrePSFMom):
         If non-zero, this optional applies additional Gaussian smoothing to the
         object before computing the moments. Typically a non-zero value results
         in less shape noise.
-    use_pix_weight : bool, optional
-        If `True`, this option applies inverse pixel variance weighting in Fourier
-        space when computing the moments. Typically this optional results in poorer
-        performance, but for round PSFs, it results in similar performance as
-        `fwhm_smooth`.
     """
     def __init__(
-        self, fwhm, pad_factor=4, ap_rad=1.5, fwhm_smooth=0, use_pix_weight=False,
+        self, fwhm, pad_factor=4, ap_rad=1.5, fwhm_smooth=0,
     ):
         super().__init__(
             fwhm, 'pgauss', pad_factor=pad_factor, ap_rad=ap_rad,
-            fwhm_smooth=fwhm_smooth, use_pix_weight=use_pix_weight,
+            fwhm_smooth=fwhm_smooth,
         )
 
 
@@ -272,7 +254,7 @@ def __init__(
 
 
 def _measure_moments_fft(
-    kim, kpsf_im, tot_var, eff_pad_factor, kernels, drow, dcol, use_pix_weight=False,
+    kim, kpsf_im, tot_var, eff_pad_factor, kernels, drow, dcol,
 ):
     # we only need to do things where the kernel is non-zero
     # this saves a bunch of CPU cycles
@@ -309,15 +291,10 @@ def _measure_moments_fft(
     fkp = kernels["fkp"]
     fkc = kernels["fkc"]
 
-    if use_pix_weight:
-        wgt = (kpsf_im * np.conj(kpsf_im)).real
-    else:
-        wgt = 1.0
-
-    mf = np.sum((kim * fkf * wgt).real) * df2
-    mr = np.sum((kim * fkr * wgt).real) * df2
-    mp = np.sum((kim * fkp * wgt).real) * df2
-    mc = np.sum((kim * fkc * wgt).real) * df2
+    mf = np.sum((kim * fkf).real) * df2
+    mr = np.sum((kim * fkr).real) * df2
+    mp = np.sum((kim * fkp).real) * df2
+    mc = np.sum((kim * fkc).real) * df2
 
     # build a covariance matrix of the moments
     # here we assume each Fourier mode is independent and sum the variances
@@ -331,7 +308,7 @@ def _measure_moments_fft(
     m_cov[1, 1] = 1
     tot_var *= eff_pad_factor**2
     tot_var_df4 = tot_var * df4
-    psf_kerns_fac = wgt / kpsf_im
+    psf_kerns_fac = 1 / kpsf_im
     kerns = [
         fkp * psf_kerns_fac,
         fkc * psf_kerns_fac,

ngmix/tests/test_prepsfmom.py

@@ -394,13 +394,10 @@ def test_prepsfmom_gauss(
 @pytest.mark.parametrize('image_size', [53])
 @pytest.mark.parametrize('pad_factor', [1.5])
 @pytest.mark.parametrize('round', [True, False])
-@pytest.mark.parametrize(
-    'fwhm_smooth,use_pix_weight',
-    [(0, False), (0, True), (1, False)],
-)
+@pytest.mark.parametrize('fwhm_smooth', [0, 1])
 def test_prepsfmom_mn_cov_psf(
     pad_factor, image_size, fwhm, psf_fwhm, pixel_scale, snr, mom_fwhm, cls,
-    use_pix_weight, fwhm_smooth, round,
+    fwhm_smooth, round,
 ):
     """Slower test to make sure means and errors are right
     w/ tons of monte carlo samples.
@@ -461,35 +458,20 @@ def test_prepsfmom_mn_cov_psf(
     fitter = cls(
         fwhm=mom_fwhm,
         pad_factor=pad_factor,
-        use_pix_weight=use_pix_weight,
         fwhm_smooth=fwhm_smooth,
     )
 
     # get true flux
-    if use_pix_weight:
-        im_true = galsim.Convolve([gal, psf]).drawImage(
-            nx=image_size,
-            ny=image_size,
-            wcs=gs_wcs,
-            method='no_pixel').array
-        obs = Observation(
-            image=im_true,
-            weight=wgt,
-            jacobian=jac,
-            psf=Observation(image=psf_im, jacobian=psf_jac),
-        )
-        res = fitter.go(obs=obs)
-    else:
-        im_true = gal.drawImage(
-            nx=image_size,
-            ny=image_size,
-            wcs=gs_wcs,
-            method='no_pixel').array
-        obs = Observation(
-            image=im_true,
-            jacobian=jac,
-        )
-        res = fitter.go(obs=obs, no_psf=True)
+    im_true = gal.drawImage(
+        nx=image_size,
+        ny=image_size,
+        wcs=gs_wcs,
+        method='no_pixel').array
+    obs = Observation(
+        image=im_true,
+        jacobian=jac,
+    )
+    res = fitter.go(obs=obs, no_psf=True)
     flux_true = res["flux"]
     T_true = res["T"]
     g1_true = res["e"][0]