From c17f83ebb5726b6af6a8be7adeeafbe70295d71b Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 07:26:43 -0600 Subject: [PATCH 01/59] Add NT Observation --- deep_field_metadetect/observation.py | 135 +++++++++++++++++++++++++++ 1 file changed, 135 insertions(+) create mode 100644 deep_field_metadetect/observation.py diff --git a/deep_field_metadetect/observation.py b/deep_field_metadetect/observation.py new file mode 100644 index 0000000..5e425b3 --- /dev/null +++ b/deep_field_metadetect/observation.py @@ -0,0 +1,135 @@ +from typing import NamedTuple, Optional +import numpy as np + +import ngmix +from ngmix.jacobian import Jacobian + +import jax +import jax_galsim + +from ngmix.observation import Observation + +@jax.tree_util.register_pytree_node_class +class NTObservation(NamedTuple): + image: jax.Array + weight: Optional[jax.Array] + bmask: Optional[jax.Array] + ormask: Optional[jax.Array] + noise: Optional[jax.Array] + jacobian: Optional[jax.Array] + psf: Optional["NTObservation"] + mfrac: Optional[jax.Array] + jac_row0: Optional[float] + jac_col0: Optional[float] + jac_det: Optional[float] + jac_scale: Optional[float] + meta: Optional[dict] + store_pixels: bool + ignore_zero_weight: bool + + + def tree_flatten(self): + children = ( + self.image, + self.weight, + self.bmask, + self.ormask, + self.noise, + self.jacobian, + self.psf, + self.mfrac, + self.jac_row0, + self.jac_col0, + self.jac_det, + self.jac_scale, + ) + + aux_data = (self.meta, self.store_pixels, self.ignore_zero_weight) + + return children, aux_data + + @classmethod + def tree_unflatten(cls, aux_data, children): + # Reconstruct the object from flattened data + return cls(*children, *aux_data) + + def has_bmask(self) -> bool: + if self.bmask is None: + return False + return True + + def has_mfrac(self) -> bool: + if self.bmask is None: + return False + return True + + def has_noise(self) -> bool: + if self.noise is None: + return False + return True + + def has_ormask(self) -> bool: + if self.ormask is None: + return False + return True + + def has_psf(self) -> bool: + if self.psf is None: + return False + return True + + + +def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: + jacobian = obs.get_jacobian() + + psf=None + if obs.has_psf(): + psf = ngmix_Obs_to_NT(obs.get_psf()) + + return NTObservation( + image=jax.numpy.array(obs.image), + weight=jax.numpy.array(obs.weight), + bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, + ormask=jax.numpy.array(obs.ormask) if obs.has_ormask() else None, + noise=jax.numpy.array(obs.noise) if obs.has_noise() else None, + jacobian=jax_galsim.BaseWCS().from_galsim(jacobian.get_galsim_wcs()), + psf=psf, + meta=obs.meta, # Directly copy metadata + mfrac=jax.numpy.array(obs.mfrac) if obs.has_mfrac() else None, + store_pixels=getattr(obs, "store_pixels", True), + ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), + jac_row0=jacobian.row0, + jac_col0=jacobian.col0, + jac_det=jacobian.det, + jac_scale=jacobian.scale, + ) + +def NT_to_ngmix_obs(nt_obs) -> Observation: + psf= None + if nt_obs.psf is not None: + psf= NT_to_ngmix_obs(nt_obs.psf) + return Observation( + image=np.array(nt_obs.image), + weight=np.array(nt_obs.weight), + bmask=nt_obs.bmask, + ormask=nt_obs.ormask, + noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), + jacobian=Jacobian( + row=nt_obs.jac_row0, + col=nt_obs.jac_col0, + dudrow=nt_obs.jacobian.dudx, + dudcol=nt_obs.jacobian.dudy, + dvdrow=nt_obs.jacobian.dvdx, + dvdcol=nt_obs.jacobian.dvdy, + det=nt_obs.jac_det, + scale=nt_obs.jac_scale, + ), + psf=psf, + mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), + meta=nt_obs.meta, + store_pixels=np.array(nt_obs.store_pixels, dtype=np.bool_), + ignore_zero_weight=np.array(nt_obs.ignore_zero_weight, dtype=np.bool_), + ) + + \ No newline at end of file From b603090c12987361c059f801098e7d11c34eb012 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 07:27:02 -0600 Subject: [PATCH 02/59] jaxify metacal func --- deep_field_metadetect/metacal.py | 438 ++++++++++++++++++++++++++++++- 1 file changed, 434 insertions(+), 4 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 9774e80..ae32181 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -1,8 +1,16 @@ -import galsim -import ngmix +import galsim as galsim import numpy as np +import ngmix + +from functools import partial + +import jax +import jax.numpy as jnp +import jax_galsim -DEFAULT_SHEARS = ["noshear", "1p", "1m", "2p", "2m"] +from deep_field_metadetect.observation import NTObservation, NT_to_ngmix_obs + +DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") DEFAULT_STEP = 0.01 @@ -63,12 +71,61 @@ def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): return galsim.Gaussian(sigma=np.sqrt(sigma_sq) * dilation).withFlux(flux) +# TODO: what should be the value to nxy? +@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) +def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): + """Gets the target reconvolution PSF for an input PSF object. + + This is taken from galsim/tests/test_metacal.py and assumes the psf is + centered. + + Parameters + ---------- + psf : galsim object + The PSF. + flux : float + The output flux of the PSF. Defaults to 1. + + Returns + ------- + reconv_psf : galsim object + The reconvolution PSF. + sigma : float + The width of the reconv PSF befor dilation. + """ + small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue + smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k + + kim = psf.drawKImage(nx=nxy_psf*4, ny=nxy_psf*4, scale=dk) + #kim = psf.drawKImage(scale=dk) + karr_r = kim.real.array + # Find the smallest r where the kval < small_kval + nk = karr_r.shape[0] + kx, ky = jnp.meshgrid(jnp.arange(-nk / 2, nk / 2), jnp.arange(-nk / 2, nk / 2)) + ksq = (kx**2 + ky**2) * dk**2 + ksq_max = jnp.min(jnp.where(karr_r < small_kval * psf.flux, ksq, jnp.inf)) + + # We take our target PSF to be the (round) Gaussian that is even smaller at + # this ksq + # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval + sigma_sq = -2.0 * jnp.log(smaller_kval) / ksq_max + + dilation = 1.0 + 2.0 * step + return jax_galsim.Gaussian(sigma=jnp.sqrt(sigma_sq) * dilation).withFlux(flux) + + def get_gauss_reconv_psf(obs, step=DEFAULT_STEP): """Get the Gaussian reconv PSF for an ngmix obs.""" psf = get_galsim_object_from_ngmix_obs_nopix(obs.psf, kind="image") return get_gauss_reconv_psf_galsim(psf, step=step) +@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) +def jax_get_gauss_reconv_psf(obs, nxy_psf, dk, step=DEFAULT_STEP): + """Get the Gaussian reconv PSF for an ngmix obs.""" + psf = get_jax_galsim_object_from_NT_obs_nopix(obs.psf, kind="image") + return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) + def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" mc_psf_w = get_gauss_reconv_psf_galsim(psf_w, step=step) @@ -78,6 +135,20 @@ def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): else: return mc_psf_d +@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy_psf"]) +def jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP): + """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" + mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) + mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) + + # fwhm_w = jnp.asarray(mc_psf_w.fwhm) + # fwhm_d = jnp.asarray(mc_psf_d.fwhm) + + return jax.lax.cond( + mc_psf_w.fwhm > mc_psf_d.fwhm, + lambda: mc_psf_w, + lambda: mc_psf_d + ) def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): """Get the larger of two reconv PSFs for two ngmix.Observations.""" @@ -85,6 +156,11 @@ def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): psf_d = get_galsim_object_from_ngmix_obs_nopix(obs_d.psf, kind="image") return get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=step) +def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): + """Get the larger of two reconv PSFs for two ngmix.Observations.""" + psf_w = get_jax_galsim_object_from_NT_obs_nopix(obs_w.psf, kind="image") + psf_d = get_jax_galsim_object_from_NT_obs_nopix(obs_d.psf, kind="image") + return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): pim = reconv_psf.drawImage( @@ -104,6 +180,25 @@ def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): obs.weight = obs.weight * weight_fac return obs +@partial(jax.jit, static_argnames=["nxy_psf"]) +def _jax_render_psf_and_build_obs(image, obs, reconv_psf, nxy_psf, weight_fac=1): + reconv_psf = reconv_psf.withGSParams( + minimum_fft_size=nxy_psf*4, + maximum_fft_size=nxy_psf*4, + ) + + pim = reconv_psf.drawImage( + nx=53, + ny=53, + wcs=obs.psf.jacobian, + offset=jax_galsim.PositionD( + x=obs.psf.jac_col0 + 1 - nxy_psf/2, # TODO: what is the size is odd? + y=obs.psf.jac_row0 + 1 - nxy_psf/2, + ), + ).array + + obs_psf = obs.psf._replace(image=pim) + return obs._replace(image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac) def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): """Run metacal on an ngmix observation. @@ -132,6 +227,43 @@ def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g ) return ims + ns +@partial(jax.jit, static_argnames='dims') +def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): + """Run metacal on an ngmix observation. + + Note that the noise image should already be rotated by 90 degrees here. + """ + + ims = jax_galsim.Convolve( + [ + jax_galsim.Convolve([image, psf_inv]).shear(g1=g1, g2=g2), + reconv_psf, + ] + ) + + ns = jax_galsim.Convolve( + [ + jax_galsim.Convolve([noise, psf_inv]).shear(g1=g1, g2=g2), + reconv_psf, + ] + ) + + ims = ims.withGSParams( + minimum_fft_size=dims[0]*4, + maximum_fft_size=dims[0]*4, + ) + ims = ims.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array + + ns = ns.withGSParams( + minimum_fft_size=dims[0]*4, + maximum_fft_size=dims[0]*4, + ) + ns = jnp.rot90( + ns.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array, + k=-1, + ) + return ims + ns + def metacal_op_g1g2(obs, reconv_psf, g1, g2): """Run metacal on an ngmix observation.""" @@ -151,6 +283,24 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2): ) return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5) +def metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): + """Run metacal on an ngmix observation.""" + mcal_image = _jax_metacal_op_g1g2_impl( + wcs=obs.jacobian, + image=get_jax_galsim_object_from_NT_obs(obs, kind="image"), + # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl + # rotates back after deconv and shearing + noise=get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1), + psf_inv=jax_galsim.Deconvolve( + get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + ), + dims=obs.image.shape, + reconv_psf=reconv_psf, + g1=g1, + g2=g2, + ) + + return _jax_render_psf_and_build_obs(mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5) def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): """Run metacal on an ngmix observation.""" @@ -186,10 +336,48 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): ) return mcal_res +@partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) +def jax_metacal_op_shears(obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP): + """Run metacal on an ngmix observation.""" + if shears is None: + shears = DEFAULT_SHEARS + + if reconv_psf is None: + reconv_psf = jax_get_gauss_reconv_psf(obs, dk=dk, nxy_psf=nxy_psf, step=step) + + wcs = obs.jacobian + image = get_jax_galsim_object_from_NT_obs(obs, kind="image") + # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl + # rotates back after deconv and shearing + noise = get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1) + psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + psf_inv = jax_galsim.Deconvolve(psf) + + mcal_res = {} + for shear in shears: + g1, g2 = get_shear_tuple(shear, step) + + mcal_image = _jax_metacal_op_g1g2_impl( + wcs=wcs, + image=image, + noise=noise, + psf_inv=psf_inv, + dims=obs.image.shape, + reconv_psf=reconv_psf, + g1=g1, + g2=g2, + ) + + + mcal_res[shear] = _jax_render_psf_and_build_obs( + mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, + weight_fac=0.5, + ) + return mcal_res def match_psf(obs, reconv_psf): """Match the PSF on an ngmix observation to a new PSF.""" - wcs = obs.jacobian.get_galsim_wcs() + wcs = obs.jacobian image = get_galsim_object_from_ngmix_obs(obs, kind="image") psf = get_galsim_object_from_ngmix_obs(obs.psf, kind="image") @@ -198,6 +386,25 @@ def match_psf(obs, reconv_psf): return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1) +@partial(jax.jit, static_argnames=["nxy_psf"]) +def jax_match_psf(obs, reconv_psf, nxy_psf): + """Match the PSF on an ngmix observation to a new PSF.""" + wcs = obs.jacobian + image = get_jax_galsim_object_from_NT_obs(obs, kind="image") + psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + + ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) + + ims = ims.withGSParams( + minimum_fft_size=nxy_psf*4, + maximum_fft_size=nxy_psf*4, + ) + ims = ims.drawImage(nx=nxy_psf, ny=nxy_psf, wcs=wcs).array + + return _jax_render_psf_and_build_obs(ims, obs, reconv_psf, nxy_psf, weight_fac=1) + + + def _extract_attr(obs, attr, dtype): if getattr(obs, "has_" + attr)(): @@ -280,6 +487,108 @@ def add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False): return obs +@partial(jax.jit, static_argnames=["ignore_psf", "skip_mfrac_for_second"]) +def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) -> NTObservation: + """Add two ngmix observations""" + + if repr(obs1.jacobian) != repr(obs2.jacobian): + raise RuntimeError( + "Jacobians must be equal to add ngmix observations! %s != %s" + % (repr(obs1.jacobian), repr(obs2.jacobian)), + ) + + if obs1.image.shape != obs2.image.shape: + raise RuntimeError( + "Image shapes must be equal to add ngmix observations! %s != %s" + % ( + obs1.image.shape, + obs2.image.shape, + ), + ) + + if obs1.has_psf() != obs2.has_psf() and not ignore_psf: + raise RuntimeError( + "Observations must both either have or not have a " + "PSF to add them. %s != %s" + % ( + obs1.has_psf(), + obs2.has_psf(), + ), + ) + + if obs1.has_psf() and obs2.has_psf() and not ignore_psf: + # We ignore the PSF in this call since PSFs do not have PSFs + # if nxy_psf is None: + # raise ValueError("Provide the psf size nxy_psf") + new_psf = jax_add_ngmix_obs(obs1.psf, obs2.psf, ignore_psf=True) + else: + new_psf = None + + new_wgt = jnp.where( + (obs1.weight > 0) & (obs2.weight > 0), + 1 / (1 / obs1.weight + 1 / obs2.weight), + 0, + ) + + new_bmask = None + new_ormask = None + new_noise= None + new_mfrac = None + new_meta_data= {} + + if obs1.has_bmask() or obs2.has_bmask(): + new_bmask = _extract_attr(obs1, "bmask", np.int32) | _extract_attr( + obs2, "bmask", jnp.int32 + ) + + if obs1.has_ormask() or obs2.has_ormask(): + new_ormask = _extract_attr(obs1, "ormask", np.int32) | _extract_attr( + obs2, "ormask", jnp.int32 + ) + + if obs1.has_noise() or obs2.has_noise(): + new_noise = _extract_attr(obs1, "noise", np.float32) + _extract_attr( + obs2, "noise", jnp.float32 + ) + + if skip_mfrac_for_second: + if obs1.has_mfrac(): + new_mfrac = _extract_attr(obs1, "mfrac", np.float32) + else: + if obs1.has_mfrac() or obs2.has_mfrac(): + new_mfrac = ( + _extract_attr(obs1, "mfrac", np.float32) + + _extract_attr(obs2, "mfrac", np.float32) + ) / 2 # TODO: update statement + + new_meta_data.update(obs1.meta) + new_meta_data.update(obs2.meta) + + obs = NTObservation( + image=obs1.image + obs2.image, + weight=new_wgt, + bmask=new_bmask, + ormask=new_ormask, + noise=new_noise, + jacobian=jax_galsim.wcs.JacobianWCS( + dudx=obs1.jacobian.dudx, + dudy=obs1.jacobian.dudy, + dvdx=obs1.jacobian.dvdx, + dvdy=obs1.jacobian.dvdy, + ), + psf=new_psf, + meta=new_meta_data, # Directly copy metadata + mfrac=new_mfrac, + store_pixels=getattr(obs1, "store_pixels", True), + ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), + jac_row0=obs1.jac_row0, + jac_col0=obs1.jac_col0, + jac_det=obs1.jac_det, + jac_scale=obs1.jac_scale, + ) + + return obs + def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): """Make an interpolated image from an ngmix obs.""" @@ -291,6 +600,17 @@ def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): x_interpolant="lanczos15", ) +def get_jax_galsim_object_from_NT_obs(obs, kind="image", rot90=0): + """Make an interpolated image from an ngmix obs.""" + wcs = obs.jacobian + return jax_galsim.InterpolatedImage( + jax_galsim.ImageD( + jnp.rot90(getattr(obs, kind).copy(), k=rot90), + wcs=obs.jacobian, + ), + x_interpolant="lanczos15", + ) + def get_galsim_object_from_ngmix_obs_nopix(obs, kind="image"): """Make an interpolated image from an ngmix obs w/o a pixel.""" @@ -302,6 +622,15 @@ def get_galsim_object_from_ngmix_obs_nopix(obs, kind="image"): ] ) +def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): + """Make an interpolated image from an ngmix obs w/o a pixel.""" + wcs = obs.jacobian + return jax_galsim.Convolve( + [ + get_jax_galsim_object_from_NT_obs(obs, kind=kind), + jax_galsim.Deconvolve(wcs.toWorld(jax_galsim.Pixel(scale=1))), + ] + ) def metacal_wide_and_deep_psf_matched( obs_wide, @@ -360,3 +689,104 @@ def metacal_wide_and_deep_psf_matched( mcal_res[k].psf.galsim_obj = reconv_psf return mcal_res + +@partial(jax.jit, static_argnames=["nxy", "nxy_psf", "reconv_psf_dk", "shears", "skip_obs_wide_corrections", "skip_obs_deep_corrections", "return_noshear_deep"]) +def jax_helper_metacal_wide_and_deep_psf_matched( + obs_wide, + obs_deep, + obs_deep_noise, + reconv_psf, + nxy, + nxy_psf, + reconv_psf_dk, + shears=None, + step=DEFAULT_STEP, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + return_noshear_deep=False, +): + """Do metacalibration for a combination of wide+deep datasets.""" + + # make the wide obs + if skip_obs_wide_corrections: + mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy) + else: + mcal_obs_wide = jax_add_ngmix_obs( + jax_match_psf(obs_wide, reconv_psf, nxy), + metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), + skip_mfrac_for_second=True, + ) + + # get PSF matched noise + # obs_wide_noise = obs_wide.copy() + obs_wide_noise = obs_wide._replace(image = obs_wide.noise) + wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy) + + # now run mcal on deep + # jax_gal_reconv_psf = get_jax_galsim_object_from_ngmix_obs_nopix(reconv_psf) + mcal_res = jax_metacal_op_shears( + obs_deep, + dk=reconv_psf_dk, + reconv_psf=reconv_psf, + shears=shears, + step=step, + nxy_psf=nxy_psf, + ) + + # now add in noise corr to make it match the wide noise + if not skip_obs_deep_corrections: + for k in mcal_res: + mcal_res[k] = jax_add_ngmix_obs( + mcal_res[k], + wide_noise_corr, + skip_mfrac_for_second=True, + ) + + # we report the wide obs as noshear for later measurements + noshear_res = mcal_res.pop("noshear") + mcal_res["noshear"] = mcal_obs_wide + if return_noshear_deep: + mcal_res["noshear_deep"] = noshear_res + + return mcal_res + +#@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy", "shears", "skip_obs_wide_corrections", "skip_obs_deep_corrections", "return_noshear_deep"]) +def jax_metacal_wide_and_deep_psf_matched( + obs_wide, + obs_deep, + obs_deep_noise, + dk_w, + dk_d, + nxy, + nxy_psf, + shears=None, + step=DEFAULT_STEP, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + return_noshear_deep=False, +): + """Do metacalibration for a combination of wide+deep datasets.""" + + # first get the biggest reconv PSF of the two + reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, dk_w, dk_d, nxy) + + mcal_res = jax_helper_metacal_wide_and_deep_psf_matched( + obs_wide=obs_wide, + obs_deep=obs_deep, + obs_deep_noise=obs_deep_noise, + reconv_psf=reconv_psf, + nxy=nxy, + nxy_psf=nxy_psf, + reconv_psf_dk=2*jnp.pi/(nxy_psf * .2)/4, + shears=shears, + step=step, + skip_obs_wide_corrections=skip_obs_wide_corrections, + skip_obs_deep_corrections=skip_obs_deep_corrections, + return_noshear_deep=return_noshear_deep, + ) + + for k in mcal_res: + mcal_res[k] = NT_to_ngmix_obs(mcal_res[k]) + mcal_res[k].psf.galsim_obj = reconv_psf + + return mcal_res \ No newline at end of file From 4b1f299a76bd6bd061ca74f034e9aab9ea14e51d Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 10:10:40 -0600 Subject: [PATCH 03/59] ngmix obs to NT --- deep_field_metadetect/utils.py | 51 ++++++++++++++++++++++------------ 1 file changed, 34 insertions(+), 17 deletions(-) diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index 4c1634a..06eb010 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -2,12 +2,17 @@ import time from contextlib import contextmanager -import galsim +import jax_galsim +import jax.numpy as jnp + import ngmix import numpy as np from ngmix.gaussmom import GaussMom from deep_field_metadetect.metacal import DEFAULT_SHEARS +from deep_field_metadetect.observation import ngmix_Obs_to_NT, NT_to_ngmix_obs, NTObservation + +from ngmix.observation import Observation GLOBAL_START_TIME = time.time() MAX_ABS_C = 1e-7 @@ -297,7 +302,12 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): vals = np.zeros(len(mcal_res), dtype=dt) fitter = GaussMom(fwhm) - psf_res = fitter.go(mcal_res["noshear"].psf) + + psf = mcal_res["noshear"].psf + if isinstance(psf, NTObservation): + psf = NT_to_ngmix_obs(mcal_res["noshear"].psf) + psf_res = fitter.go(psf) + for i, (shear, obs) in enumerate(mcal_res.items()): vals["mdet_step"][i] = shear @@ -309,7 +319,10 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): vals["wmom_psf_T"][i] = psf_res["T"] + if isinstance(obs, NTObservation): + obs = NT_to_ngmix_obs(obs) res = fitter.go(obs) + vals["wmom_flags"][i] = res["flags"] if res["flags"] != 0: @@ -541,14 +554,14 @@ def _gen_hex_grid(*, rng, dim, buff, pixel_scale, n_tot): return shifts -def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim): +def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim, dim_psf): cen = (dim - 1) / 2 im = obj.drawImage(nx=dim, ny=dim, scale=scale).array im += rng.normal(size=im.shape, scale=nse) - cen_psf = (53 - 1) / 2 - psf_im = psf.drawImage(nx=53, ny=53, scale=scale).array + cen_psf = (dim_psf - 1) / 2 + psf_im = psf.drawImage(nx=dim_psf, ny=dim_psf, scale=scale).array if dither is not None: jac = ngmix.DiagonalJacobian( @@ -558,17 +571,17 @@ def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim): jac = ngmix.DiagonalJacobian(scale=scale, row=cen, col=cen) psf_jac = ngmix.DiagonalJacobian(scale=scale, row=cen_psf, col=cen_psf) - obs = ngmix.Observation( + obs = Observation( image=im, - weight=np.ones_like(im) / nse**2, + weight=jnp.ones_like(im) / nse**2, jacobian=jac, psf=ngmix.Observation( image=psf_im, jacobian=psf_jac, ), noise=rng.normal(size=im.shape, scale=nse), - bmask=np.zeros_like(im, dtype=np.int32), - mfrac=np.zeros_like(im), + bmask=jnp.zeros_like(im, dtype=np.int32), + mfrac=jnp.zeros_like(im), ) return obs @@ -584,6 +597,7 @@ def make_simple_sim( n_objs=1, scale=0.2, dim=53, + dim_psf=53, buff=26, obj_flux_factor=1, ): @@ -641,7 +655,7 @@ def make_simple_sim( n_objs = _n_objs - gal = galsim.Exponential(half_light_radius=0.5).shear(g1=g1, g2=g2) + gal = jax_galsim.Exponential(half_light_radius=0.5).shear(g1=g1, g2=g2) gals = None for shift in shifts: if gals is None: @@ -649,14 +663,14 @@ def make_simple_sim( else: gals += gal.shift(*shift) - psf = galsim.Moffat(beta=2.5, fwhm=0.8) - deep_psf = galsim.Moffat(beta=2.5, fwhm=0.8 * deep_psf_fac) - objs = galsim.Convolve([gals, psf]) - deep_objs = galsim.Convolve([gals, deep_psf]) + psf = jax_galsim.Moffat(beta=2.5, fwhm=0.8) + deep_psf = jax_galsim.Moffat(beta=2.5, fwhm=0.8 * deep_psf_fac) + objs = jax_galsim.Convolve([gals, psf]) + deep_objs = jax_galsim.Convolve([gals, deep_psf]) # estimate noise level - im = galsim.Convolve([gal, psf]).drawImage(nx=dim, ny=dim, scale=scale).array - nse = np.sqrt(np.sum(im**2)) / s2n + im = jax_galsim.Convolve([gal, psf]).drawImage(nx=dim, ny=dim, scale=scale).array + nse = jnp.sqrt(jnp.sum(im**2)) / s2n # apply the flux factor now that we have the noise level objs *= obj_flux_factor @@ -670,6 +684,7 @@ def make_simple_sim( dither=shifts[0] / scale if n_objs == 1 else None, scale=scale, dim=dim, + dim_psf=dim_psf, ) obs_deep = _make_single_sim( @@ -680,6 +695,7 @@ def make_simple_sim( dither=shifts[0] / scale if n_objs == 1 else None, scale=scale, dim=dim, + dim_psf=dim_psf, ) obs_deep_noise = _make_single_sim( @@ -690,6 +706,7 @@ def make_simple_sim( dither=shifts[0] / scale if n_objs == 1 else None, scale=scale, dim=dim, + dim_psf=dim_psf, ) - return obs_wide, obs_deep, obs_deep_noise + return ngmix_Obs_to_NT(obs_wide), ngmix_Obs_to_NT(obs_deep), ngmix_Obs_to_NT(obs_deep_noise) From bc74c850880a726b73f34390143d9b59788ed6a8 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 10:13:14 -0600 Subject: [PATCH 04/59] to jax --- deep_field_metadetect/metadetect.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index f1cd361..cb15632 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -8,16 +8,20 @@ from deep_field_metadetect.metacal import ( DEFAULT_SHEARS, DEFAULT_STEP, - metacal_wide_and_deep_psf_matched, + jax_metacal_wide_and_deep_psf_matched, ) from deep_field_metadetect.mfrac import compute_mfrac_interp_image from deep_field_metadetect.utils import fit_gauss_mom_obs, fit_gauss_mom_obs_and_psf -def single_band_deep_field_metadetect( +def jax_single_band_deep_field_metadetect( obs_wide, obs_deep, obs_deep_noise, + dk_w, + dk_d, + nxy, + nxy_psf, step=DEFAULT_STEP, shears=None, skip_obs_wide_corrections=False, @@ -59,15 +63,20 @@ def single_band_deep_field_metadetect( if shears is None: shears = DEFAULT_SHEARS - mcal_res = metacal_wide_and_deep_psf_matched( - obs_wide, - obs_deep, - obs_deep_noise, + mcal_res = jax_metacal_wide_and_deep_psf_matched( + obs_wide=obs_wide, + obs_deep=obs_deep, + obs_deep_noise=obs_deep_noise, + dk_w=dk_w, + dk_d=dk_d, + nxy=nxy, + nxy_psf=nxy_psf, step=step, shears=shears, skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, - ) + ) # This returns ngmix Obs for now + psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) dfmdet_res = [] for shear, obs in mcal_res.items(): From 21657f0aa2c613573d0d0957243050d9b249b493 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 11:25:38 -0600 Subject: [PATCH 05/59] added old version --- deep_field_metadetect/metadetect.py | 101 ++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index cb15632..3036ab4 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -8,11 +8,110 @@ from deep_field_metadetect.metacal import ( DEFAULT_SHEARS, DEFAULT_STEP, + metacal_wide_and_deep_psf_matched, jax_metacal_wide_and_deep_psf_matched, ) from deep_field_metadetect.mfrac import compute_mfrac_interp_image from deep_field_metadetect.utils import fit_gauss_mom_obs, fit_gauss_mom_obs_and_psf +def single_band_deep_field_metadetect( + obs_wide, + obs_deep, + obs_deep_noise, + step=DEFAULT_STEP, + shears=None, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + nodet_flags=0, +): + """Run deep-field metadetection for a simple scenario of a single band + with a single image per band using only post-PSF Gaussian weighted moments. + + Parameters + ---------- + obs_wide : ngmix.Observation + The wide-field observation. + obs_deep : ngmix.Observation + The deep-field observation. + obs_deep_noise : ngmix.Observation + The deep-field noise observation. + step : float, optional + The step size for the metacalibration, by default DEFAULT_STEP. + shears : list, optional + The shears to use for the metacalibration, by default DEFAULT_SHEARS + if set to None. + skip_obs_wide_corrections : bool, optional + Skip the observation corrections for the wide-field observations, + by default False. + skip_obs_deep_corrections : bool, optional + Skip the observation corrections for the deep-field observations, + by default False. + nodet_flags : int, optional + The bmask flags marking area in the image to skip, by default 0. + + Returns + ------- + dfmdet_res : dict + The deep-field metadetection results, a dictionary with keys from `shears` + and values containing the detection+measurement results for the corresponding + shear. + """ + if shears is None: + shears = DEFAULT_SHEARS + + mcal_res = metacal_wide_and_deep_psf_matched( + obs_wide, + obs_deep, + obs_deep_noise, + step=step, + shears=shears, + skip_obs_wide_corrections=skip_obs_wide_corrections, + skip_obs_deep_corrections=skip_obs_deep_corrections, + ) + psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) + dfmdet_res = [] + for shear, obs in mcal_res.items(): + detres = run_detection_sep(obs, nodet_flags=nodet_flags) + + ixc = (detres["catalog"]["x"] + 0.5).astype(int) + iyc = (detres["catalog"]["y"] + 0.5).astype(int) + bmask_flags = obs.bmask[iyc, ixc] + + mfrac_vals = np.zeros_like(bmask_flags, dtype="f4") + if np.any(obs.mfrac > 0): + _interp_mfrac = compute_mfrac_interp_image( + obs.mfrac, + obs.jacobian.get_galsim_wcs(), + ) + for i, (x, y) in enumerate( + zip(detres["catalog"]["x"], detres["catalog"]["y"]) + ): + mfrac_vals[i] = _interp_mfrac.xValue(x, y) + + for ind, (obj, mbobs) in enumerate( + generate_mbobs_for_detections( + ngmix.observation.get_mb_obs(obs), + xs=detres["catalog"]["x"], + ys=detres["catalog"]["y"], + ) + ): + fres = fit_gauss_mom_obs_and_psf(mbobs[0][0], psf_res=psf_res) + dfmdet_res.append( + (ind + 1, obj["x"], obj["y"], shear, bmask_flags[ind], mfrac_vals[ind]) + + tuple(fres[0]) + ) + + total_dtype = [ + ("id", "i8"), + ("x", "f8"), + ("y", "f8"), + ("mdet_step", "U7"), + ("bmask_flags", "i4"), + ("mfrac", "f4"), + ] + fres.dtype.descr + + return np.array(dfmdet_res, dtype=total_dtype) + def jax_single_band_deep_field_metadetect( obs_wide, @@ -120,3 +219,5 @@ def jax_single_band_deep_field_metadetect( ] + fres.dtype.descr return np.array(dfmdet_res, dtype=total_dtype) + + From 3df30892052d447cdb6f3e78f545ca8a5064b0b7 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 28 Feb 2025 11:25:48 -0600 Subject: [PATCH 06/59] update tests --- .../tests/test_deep_metacal.py | 15 +++++--- deep_field_metadetect/tests/test_metacal.py | 12 ++++--- .../tests/test_metadetect.py | 34 ++++++++++++++----- 3 files changed, 44 insertions(+), 17 deletions(-) diff --git a/deep_field_metadetect/tests/test_deep_metacal.py b/deep_field_metadetect/tests/test_deep_metacal.py index 0ccaa1b..a047a64 100644 --- a/deep_field_metadetect/tests/test_deep_metacal.py +++ b/deep_field_metadetect/tests/test_deep_metacal.py @@ -2,11 +2,12 @@ import joblib import numpy as np +import jax.numpy as jnp import pytest from deep_field_metadetect.metacal import ( - metacal_op_shears, - metacal_wide_and_deep_psf_matched, + jax_metacal_op_shears, + jax_metacal_wide_and_deep_psf_matched, ) from deep_field_metadetect.utils import ( MAX_ABS_C, @@ -38,10 +39,14 @@ def _run_single_sim( deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, ) - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, + dk_w=2*jnp.pi/(53 * .2)/4, + dk_d=2*jnp.pi/(53 * .2)/4, + nxy=53, + nxy_psf=53, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, ) @@ -235,11 +240,11 @@ def _run_single_sim_maybe_mcal( obj_flux_factor=0.0 if zero_flux else 1.0, ) if use_mcal: - mcal_res = metacal_op_shears( + mcal_res = jax_metacal_op_shears( obs_w, ) else: - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, diff --git a/deep_field_metadetect/tests/test_metacal.py b/deep_field_metadetect/tests/test_metacal.py index 4c5dd20..7f67e14 100644 --- a/deep_field_metadetect/tests/test_metacal.py +++ b/deep_field_metadetect/tests/test_metacal.py @@ -1,10 +1,11 @@ import multiprocessing import joblib +import jax.numpy as jnp import numpy as np import pytest -from deep_field_metadetect.metacal import metacal_op_shears +from deep_field_metadetect.metacal import jax_metacal_op_shears from deep_field_metadetect.utils import ( assert_m_c_ok, estimate_m_and_c, @@ -24,7 +25,8 @@ def _run_single_sim_pair(seed, s2n): deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, ) - mcal_res = metacal_op_shears(obs_plus) + # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_plus.psf) + mcal_res = jax_metacal_op_shears(obs_plus, dk=2*jnp.pi/(53 * .2)/4) res_p = fit_gauss_mom_mcal_res(mcal_res) res_p = measure_mcal_shear_quants(res_p) @@ -36,7 +38,8 @@ def _run_single_sim_pair(seed, s2n): deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, ) - mcal_res = metacal_op_shears(obs_minus) + # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_minus.psf) + mcal_res = jax_metacal_op_shears(obs_minus, dk=2*jnp.pi/(53 * .2)/4) res_m = fit_gauss_mom_mcal_res(mcal_res) res_m = measure_mcal_shear_quants(res_m) @@ -44,6 +47,7 @@ def _run_single_sim_pair(seed, s2n): def test_metacal_smoke(): + res_p, res_m = _run_single_sim_pair(1234, 1e8) for col in res_p.dtype.names: assert np.isfinite(res_p[col]).all() @@ -51,7 +55,7 @@ def test_metacal_smoke(): def test_metacal(): - nsims = 50 + nsims = 5 rng = np.random.RandomState(seed=34132) seeds = rng.randint(size=nsims, low=1, high=2**29) diff --git a/deep_field_metadetect/tests/test_metadetect.py b/deep_field_metadetect/tests/test_metadetect.py index 9112ec9..486fe25 100644 --- a/deep_field_metadetect/tests/test_metadetect.py +++ b/deep_field_metadetect/tests/test_metadetect.py @@ -3,8 +3,9 @@ import joblib import numpy as np import pytest +import jax.numpy as jnp -from deep_field_metadetect.metadetect import single_band_deep_field_metadetect +from deep_field_metadetect.metadetect import jax_single_band_deep_field_metadetect from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -35,6 +36,7 @@ def _run_single_sim( deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, dim=201, + dim_psf=53, buff=25, n_objs=10, ) @@ -45,10 +47,14 @@ def _run_single_sim( pdb.set_trace() - res = single_band_deep_field_metadetect( + res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, + dk_w=2*jnp.pi/(53 * .2)/4, + dk_d=2*jnp.pi/(53 * .2)/4, + nxy=201, + nxy_psf=53, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, ) @@ -101,12 +107,16 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): buff=25, n_objs=10, ) - obs_w.bmask = rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) + obs_w = obs_w._replace(bmask = rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape)) - res = single_band_deep_field_metadetect( + res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, + dk_w=2*jnp.pi/(53 * .2)/4, + dk_d=2*jnp.pi/(53 * .2)/4, + nxy=201, + nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -138,12 +148,16 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): buff=25, n_objs=10, ) - obs_w.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) + obs_w = obs_w._replace(mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape)) - res = single_band_deep_field_metadetect( + res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, + dk_w=2*jnp.pi/(53 * .2)/4, + dk_d=2*jnp.pi/(53 * .2)/4, + nxy=201, + nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -169,12 +183,16 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): buff=25, n_objs=10, ) - obs_d.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) + obs_d = obs_d._replace(mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape)) - res = single_band_deep_field_metadetect( + res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, + dk_w=2*jnp.pi/(53 * .2)/4, + dk_d=2*jnp.pi/(53 * .2)/4, + nxy=201, + nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) From b15dd576c17008d70eb057687d55bc7c176f13af Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 3 Mar 2025 10:47:36 -0600 Subject: [PATCH 07/59] updated environment.yml file --- environment.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/environment.yml b/environment.yml index 4ace617..1802d00 100644 --- a/environment.yml +++ b/environment.yml @@ -16,6 +16,9 @@ dependencies: - ngmix - numba - numpy + - dm-tree + - pip: + - git+https://github.com/GalSim-developers/JAX-GalSim.git@main # install - pip From 4d5be8bc7834bb86938c77a67f21d327db770201 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 3 Mar 2025 11:00:04 -0600 Subject: [PATCH 08/59] pre-commit changes --- deep_field_metadetect/metacal.py | 153 +++++++++++------- deep_field_metadetect/metadetect.py | 7 +- deep_field_metadetect/observation.py | 76 +++++---- .../tests/test_deep_metacal.py | 6 +- deep_field_metadetect/tests/test_metacal.py | 7 +- .../tests/test_metadetect.py | 26 +-- deep_field_metadetect/utils.py | 23 +-- 7 files changed, 167 insertions(+), 131 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index ae32181..6ee7e3c 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -1,14 +1,13 @@ -import galsim as galsim -import numpy as np -import ngmix - from functools import partial +import galsim as galsim import jax import jax.numpy as jnp import jax_galsim +import ngmix +import numpy as np -from deep_field_metadetect.observation import NTObservation, NT_to_ngmix_obs +from deep_field_metadetect.observation import NT_to_ngmix_obs, NTObservation DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") DEFAULT_STEP = 0.01 @@ -96,8 +95,8 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - kim = psf.drawKImage(nx=nxy_psf*4, ny=nxy_psf*4, scale=dk) - #kim = psf.drawKImage(scale=dk) + kim = psf.drawKImage(nx=nxy_psf * 4, ny=nxy_psf * 4, scale=dk) + # kim = psf.drawKImage(scale=dk) karr_r = kim.real.array # Find the smallest r where the kval < small_kval nk = karr_r.shape[0] @@ -119,13 +118,14 @@ def get_gauss_reconv_psf(obs, step=DEFAULT_STEP): psf = get_galsim_object_from_ngmix_obs_nopix(obs.psf, kind="image") return get_gauss_reconv_psf_galsim(psf, step=step) + @partial(jax.jit, static_argnames=["dk", "nxy_psf"]) def jax_get_gauss_reconv_psf(obs, nxy_psf, dk, step=DEFAULT_STEP): """Get the Gaussian reconv PSF for an ngmix obs.""" psf = get_jax_galsim_object_from_NT_obs_nopix(obs.psf, kind="image") return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) - + def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" mc_psf_w = get_gauss_reconv_psf_galsim(psf_w, step=step) @@ -135,33 +135,37 @@ def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): else: return mc_psf_d + @partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy_psf"]) -def jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP): +def jax_get_max_gauss_reconv_psf_galsim( + psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP +): """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) - # fwhm_w = jnp.asarray(mc_psf_w.fwhm) + # fwhm_w = jnp.asarray(mc_psf_w.fwhm) # fwhm_d = jnp.asarray(mc_psf_d.fwhm) return jax.lax.cond( - mc_psf_w.fwhm > mc_psf_d.fwhm, - lambda: mc_psf_w, - lambda: mc_psf_d + mc_psf_w.fwhm > mc_psf_d.fwhm, lambda: mc_psf_w, lambda: mc_psf_d ) + def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): """Get the larger of two reconv PSFs for two ngmix.Observations.""" psf_w = get_galsim_object_from_ngmix_obs_nopix(obs_w.psf, kind="image") psf_d = get_galsim_object_from_ngmix_obs_nopix(obs_d.psf, kind="image") return get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=step) + def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): """Get the larger of two reconv PSFs for two ngmix.Observations.""" psf_w = get_jax_galsim_object_from_NT_obs_nopix(obs_w.psf, kind="image") psf_d = get_jax_galsim_object_from_NT_obs_nopix(obs_d.psf, kind="image") return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) + def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): pim = reconv_psf.drawImage( nx=obs.psf.image.shape[1], @@ -180,25 +184,29 @@ def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): obs.weight = obs.weight * weight_fac return obs + @partial(jax.jit, static_argnames=["nxy_psf"]) def _jax_render_psf_and_build_obs(image, obs, reconv_psf, nxy_psf, weight_fac=1): reconv_psf = reconv_psf.withGSParams( - minimum_fft_size=nxy_psf*4, - maximum_fft_size=nxy_psf*4, - ) + minimum_fft_size=nxy_psf * 4, + maximum_fft_size=nxy_psf * 4, + ) pim = reconv_psf.drawImage( nx=53, ny=53, wcs=obs.psf.jacobian, offset=jax_galsim.PositionD( - x=obs.psf.jac_col0 + 1 - nxy_psf/2, # TODO: what is the size is odd? - y=obs.psf.jac_row0 + 1 - nxy_psf/2, + x=obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what is the size is odd? + y=obs.psf.jac_row0 + 1 - nxy_psf / 2, ), ).array obs_psf = obs.psf._replace(image=pim) - return obs._replace(image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac) + return obs._replace( + image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac + ) + def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): """Run metacal on an ngmix observation. @@ -227,7 +235,8 @@ def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g ) return ims + ns -@partial(jax.jit, static_argnames='dims') + +@partial(jax.jit, static_argnames="dims") def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): """Run metacal on an ngmix observation. @@ -249,15 +258,15 @@ def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g ) ims = ims.withGSParams( - minimum_fft_size=dims[0]*4, - maximum_fft_size=dims[0]*4, - ) + minimum_fft_size=dims[0] * 4, + maximum_fft_size=dims[0] * 4, + ) ims = ims.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array ns = ns.withGSParams( - minimum_fft_size=dims[0]*4, - maximum_fft_size=dims[0]*4, - ) + minimum_fft_size=dims[0] * 4, + maximum_fft_size=dims[0] * 4, + ) ns = jnp.rot90( ns.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array, k=-1, @@ -283,7 +292,8 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2): ) return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5) -def metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): + +def jax_metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): """Run metacal on an ngmix observation.""" mcal_image = _jax_metacal_op_g1g2_impl( wcs=obs.jacobian, @@ -300,7 +310,10 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): g2=g2, ) - return _jax_render_psf_and_build_obs(mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5) + return _jax_render_psf_and_build_obs( + mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 + ) + def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): """Run metacal on an ngmix observation.""" @@ -336,8 +349,11 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): ) return mcal_res + @partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) -def jax_metacal_op_shears(obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP): +def jax_metacal_op_shears( + obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP +): """Run metacal on an ngmix observation.""" if shears is None: shears = DEFAULT_SHEARS @@ -368,13 +384,16 @@ def jax_metacal_op_shears(obs, dk, nxy_psf=53, reconv_psf=None, shears=None, ste g2=g2, ) - mcal_res[shear] = _jax_render_psf_and_build_obs( - mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, - weight_fac=0.5, + mcal_image, + obs, + reconv_psf, + nxy_psf=nxy_psf, + weight_fac=0.5, ) return mcal_res + def match_psf(obs, reconv_psf): """Match the PSF on an ngmix observation to a new PSF.""" wcs = obs.jacobian @@ -386,6 +405,7 @@ def match_psf(obs, reconv_psf): return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1) + @partial(jax.jit, static_argnames=["nxy_psf"]) def jax_match_psf(obs, reconv_psf, nxy_psf): """Match the PSF on an ngmix observation to a new PSF.""" @@ -396,16 +416,14 @@ def jax_match_psf(obs, reconv_psf, nxy_psf): ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) ims = ims.withGSParams( - minimum_fft_size=nxy_psf*4, - maximum_fft_size=nxy_psf*4, - ) + minimum_fft_size=nxy_psf * 4, + maximum_fft_size=nxy_psf * 4, + ) ims = ims.drawImage(nx=nxy_psf, ny=nxy_psf, wcs=wcs).array return _jax_render_psf_and_build_obs(ims, obs, reconv_psf, nxy_psf, weight_fac=1) - - def _extract_attr(obs, attr, dtype): if getattr(obs, "has_" + attr)(): return getattr(obs, attr) @@ -487,8 +505,11 @@ def add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False): return obs + @partial(jax.jit, static_argnames=["ignore_psf", "skip_mfrac_for_second"]) -def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) -> NTObservation: +def jax_add_ngmix_obs( + obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False +) -> NTObservation: """Add two ngmix observations""" if repr(obs1.jacobian) != repr(obs2.jacobian): @@ -518,7 +539,7 @@ def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) if obs1.has_psf() and obs2.has_psf() and not ignore_psf: # We ignore the PSF in this call since PSFs do not have PSFs - # if nxy_psf is None: + # if nxy_psf is None: # raise ValueError("Provide the psf size nxy_psf") new_psf = jax_add_ngmix_obs(obs1.psf, obs2.psf, ignore_psf=True) else: @@ -526,15 +547,15 @@ def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) new_wgt = jnp.where( (obs1.weight > 0) & (obs2.weight > 0), - 1 / (1 / obs1.weight + 1 / obs2.weight), - 0, + 1 / (1 / obs1.weight + 1 / obs2.weight), + 0, ) - + new_bmask = None new_ormask = None - new_noise= None + new_noise = None new_mfrac = None - new_meta_data= {} + new_meta_data = {} if obs1.has_bmask() or obs2.has_bmask(): new_bmask = _extract_attr(obs1, "bmask", np.int32) | _extract_attr( @@ -559,13 +580,13 @@ def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) new_mfrac = ( _extract_attr(obs1, "mfrac", np.float32) + _extract_attr(obs2, "mfrac", np.float32) - ) / 2 # TODO: update statement - + ) / 2 # TODO: update statement + new_meta_data.update(obs1.meta) new_meta_data.update(obs2.meta) - + obs = NTObservation( - image=obs1.image + obs2.image, + image=obs1.image + obs2.image, weight=new_wgt, bmask=new_bmask, ormask=new_ormask, @@ -579,8 +600,8 @@ def jax_add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False) psf=new_psf, meta=new_meta_data, # Directly copy metadata mfrac=new_mfrac, - store_pixels=getattr(obs1, "store_pixels", True), - ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), + store_pixels=getattr(obs1, "store_pixels", True), + ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), jac_row0=obs1.jac_row0, jac_col0=obs1.jac_col0, jac_det=obs1.jac_det, @@ -600,9 +621,9 @@ def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): x_interpolant="lanczos15", ) + def get_jax_galsim_object_from_NT_obs(obs, kind="image", rot90=0): """Make an interpolated image from an ngmix obs.""" - wcs = obs.jacobian return jax_galsim.InterpolatedImage( jax_galsim.ImageD( jnp.rot90(getattr(obs, kind).copy(), k=rot90), @@ -622,6 +643,7 @@ def get_galsim_object_from_ngmix_obs_nopix(obs, kind="image"): ] ) + def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): """Make an interpolated image from an ngmix obs w/o a pixel.""" wcs = obs.jacobian @@ -632,6 +654,7 @@ def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): ] ) + def metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, @@ -690,7 +713,19 @@ def metacal_wide_and_deep_psf_matched( return mcal_res -@partial(jax.jit, static_argnames=["nxy", "nxy_psf", "reconv_psf_dk", "shears", "skip_obs_wide_corrections", "skip_obs_deep_corrections", "return_noshear_deep"]) + +@partial( + jax.jit, + static_argnames=[ + "nxy", + "nxy_psf", + "reconv_psf_dk", + "shears", + "skip_obs_wide_corrections", + "skip_obs_deep_corrections", + "return_noshear_deep", + ], +) def jax_helper_metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, @@ -713,13 +748,13 @@ def jax_helper_metacal_wide_and_deep_psf_matched( else: mcal_obs_wide = jax_add_ngmix_obs( jax_match_psf(obs_wide, reconv_psf, nxy), - metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), + jax_metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), skip_mfrac_for_second=True, ) # get PSF matched noise # obs_wide_noise = obs_wide.copy() - obs_wide_noise = obs_wide._replace(image = obs_wide.noise) + obs_wide_noise = obs_wide._replace(image=obs_wide.noise) wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy) # now run mcal on deep @@ -750,13 +785,13 @@ def jax_helper_metacal_wide_and_deep_psf_matched( return mcal_res -#@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy", "shears", "skip_obs_wide_corrections", "skip_obs_deep_corrections", "return_noshear_deep"]) + def jax_metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, obs_deep_noise, - dk_w, - dk_d, + dk_w, + dk_d, nxy, nxy_psf, shears=None, @@ -777,7 +812,7 @@ def jax_metacal_wide_and_deep_psf_matched( reconv_psf=reconv_psf, nxy=nxy, nxy_psf=nxy_psf, - reconv_psf_dk=2*jnp.pi/(nxy_psf * .2)/4, + reconv_psf_dk=2 * jnp.pi / (nxy_psf * 0.2) / 4, shears=shears, step=step, skip_obs_wide_corrections=skip_obs_wide_corrections, @@ -789,4 +824,4 @@ def jax_metacal_wide_and_deep_psf_matched( mcal_res[k] = NT_to_ngmix_obs(mcal_res[k]) mcal_res[k].psf.galsim_obj = reconv_psf - return mcal_res \ No newline at end of file + return mcal_res diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index 3036ab4..449103e 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -8,12 +8,13 @@ from deep_field_metadetect.metacal import ( DEFAULT_SHEARS, DEFAULT_STEP, - metacal_wide_and_deep_psf_matched, jax_metacal_wide_and_deep_psf_matched, + metacal_wide_and_deep_psf_matched, ) from deep_field_metadetect.mfrac import compute_mfrac_interp_image from deep_field_metadetect.utils import fit_gauss_mom_obs, fit_gauss_mom_obs_and_psf + def single_band_deep_field_metadetect( obs_wide, obs_deep, @@ -174,7 +175,7 @@ def jax_single_band_deep_field_metadetect( shears=shears, skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, - ) # This returns ngmix Obs for now + ) # This returns ngmix Obs for now psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) dfmdet_res = [] @@ -219,5 +220,3 @@ def jax_single_band_deep_field_metadetect( ] + fres.dtype.descr return np.array(dfmdet_res, dtype=total_dtype) - - diff --git a/deep_field_metadetect/observation.py b/deep_field_metadetect/observation.py index 5e425b3..4187531 100644 --- a/deep_field_metadetect/observation.py +++ b/deep_field_metadetect/observation.py @@ -1,14 +1,13 @@ from typing import NamedTuple, Optional -import numpy as np - -import ngmix -from ngmix.jacobian import Jacobian import jax import jax_galsim - +import ngmix +import numpy as np +from ngmix.jacobian import Jacobian from ngmix.observation import Observation + @jax.tree_util.register_pytree_node_class class NTObservation(NamedTuple): image: jax.Array @@ -27,68 +26,66 @@ class NTObservation(NamedTuple): store_pixels: bool ignore_zero_weight: bool - def tree_flatten(self): children = ( - self.image, - self.weight, - self.bmask, + self.image, + self.weight, + self.bmask, self.ormask, - self.noise, - self.jacobian, - self.psf, - self.mfrac, - self.jac_row0, - self.jac_col0, - self.jac_det, + self.noise, + self.jacobian, + self.psf, + self.mfrac, + self.jac_row0, + self.jac_col0, + self.jac_det, self.jac_scale, ) aux_data = (self.meta, self.store_pixels, self.ignore_zero_weight) - + return children, aux_data @classmethod def tree_unflatten(cls, aux_data, children): # Reconstruct the object from flattened data return cls(*children, *aux_data) - + def has_bmask(self) -> bool: if self.bmask is None: return False return True - + def has_mfrac(self) -> bool: if self.bmask is None: return False return True - + def has_noise(self) -> bool: if self.noise is None: return False return True - + def has_ormask(self) -> bool: if self.ormask is None: return False return True - + def has_psf(self) -> bool: if self.psf is None: return False return True - def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: jacobian = obs.get_jacobian() - psf=None + psf = None if obs.has_psf(): psf = ngmix_Obs_to_NT(obs.get_psf()) return NTObservation( - image=jax.numpy.array(obs.image), + image=jax.numpy.array(obs.image), weight=jax.numpy.array(obs.weight), bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, ormask=jax.numpy.array(obs.ormask) if obs.has_ormask() else None, @@ -97,24 +94,25 @@ def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: psf=psf, meta=obs.meta, # Directly copy metadata mfrac=jax.numpy.array(obs.mfrac) if obs.has_mfrac() else None, - store_pixels=getattr(obs, "store_pixels", True), - ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), + store_pixels=getattr(obs, "store_pixels", True), + ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), jac_row0=jacobian.row0, jac_col0=jacobian.col0, jac_det=jacobian.det, jac_scale=jacobian.scale, ) - + + def NT_to_ngmix_obs(nt_obs) -> Observation: - psf= None + psf = None if nt_obs.psf is not None: - psf= NT_to_ngmix_obs(nt_obs.psf) + psf = NT_to_ngmix_obs(nt_obs.psf) return Observation( - image=np.array(nt_obs.image), - weight=np.array(nt_obs.weight), - bmask=nt_obs.bmask, + image=np.array(nt_obs.image), + weight=np.array(nt_obs.weight), + bmask=nt_obs.bmask, ormask=nt_obs.ormask, - noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), + noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), jacobian=Jacobian( row=nt_obs.jac_row0, col=nt_obs.jac_col0, @@ -124,12 +122,10 @@ def NT_to_ngmix_obs(nt_obs) -> Observation: dvdcol=nt_obs.jacobian.dvdy, det=nt_obs.jac_det, scale=nt_obs.jac_scale, - ), - psf=psf, - mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), - meta=nt_obs.meta, + ), + psf=psf, + mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), + meta=nt_obs.meta, store_pixels=np.array(nt_obs.store_pixels, dtype=np.bool_), ignore_zero_weight=np.array(nt_obs.ignore_zero_weight, dtype=np.bool_), ) - - \ No newline at end of file diff --git a/deep_field_metadetect/tests/test_deep_metacal.py b/deep_field_metadetect/tests/test_deep_metacal.py index a047a64..0139561 100644 --- a/deep_field_metadetect/tests/test_deep_metacal.py +++ b/deep_field_metadetect/tests/test_deep_metacal.py @@ -1,8 +1,8 @@ import multiprocessing +import jax.numpy as jnp import joblib import numpy as np -import jax.numpy as jnp import pytest from deep_field_metadetect.metacal import ( @@ -43,8 +43,8 @@ def _run_single_sim( obs_w, obs_d, obs_dn, - dk_w=2*jnp.pi/(53 * .2)/4, - dk_d=2*jnp.pi/(53 * .2)/4, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, nxy=53, nxy_psf=53, skip_obs_wide_corrections=skip_wide, diff --git a/deep_field_metadetect/tests/test_metacal.py b/deep_field_metadetect/tests/test_metacal.py index 7f67e14..253ce18 100644 --- a/deep_field_metadetect/tests/test_metacal.py +++ b/deep_field_metadetect/tests/test_metacal.py @@ -1,7 +1,7 @@ import multiprocessing -import joblib import jax.numpy as jnp +import joblib import numpy as np import pytest @@ -26,7 +26,7 @@ def _run_single_sim_pair(seed, s2n): deep_psf_fac=1.0, ) # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_plus.psf) - mcal_res = jax_metacal_op_shears(obs_plus, dk=2*jnp.pi/(53 * .2)/4) + mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (53 * 0.2) / 4) res_p = fit_gauss_mom_mcal_res(mcal_res) res_p = measure_mcal_shear_quants(res_p) @@ -39,7 +39,7 @@ def _run_single_sim_pair(seed, s2n): deep_psf_fac=1.0, ) # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_minus.psf) - mcal_res = jax_metacal_op_shears(obs_minus, dk=2*jnp.pi/(53 * .2)/4) + mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (53 * 0.2) / 4) res_m = fit_gauss_mom_mcal_res(mcal_res) res_m = measure_mcal_shear_quants(res_m) @@ -47,7 +47,6 @@ def _run_single_sim_pair(seed, s2n): def test_metacal_smoke(): - res_p, res_m = _run_single_sim_pair(1234, 1e8) for col in res_p.dtype.names: assert np.isfinite(res_p[col]).all() diff --git a/deep_field_metadetect/tests/test_metadetect.py b/deep_field_metadetect/tests/test_metadetect.py index 486fe25..5927c45 100644 --- a/deep_field_metadetect/tests/test_metadetect.py +++ b/deep_field_metadetect/tests/test_metadetect.py @@ -1,9 +1,9 @@ import multiprocessing +import jax.numpy as jnp import joblib import numpy as np import pytest -import jax.numpy as jnp from deep_field_metadetect.metadetect import jax_single_band_deep_field_metadetect from deep_field_metadetect.utils import ( @@ -51,8 +51,8 @@ def _run_single_sim( obs_w, obs_d, obs_dn, - dk_w=2*jnp.pi/(53 * .2)/4, - dk_d=2*jnp.pi/(53 * .2)/4, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, nxy=201, nxy_psf=53, skip_obs_wide_corrections=skip_wide, @@ -107,14 +107,16 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): buff=25, n_objs=10, ) - obs_w = obs_w._replace(bmask = rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape)) + obs_w = obs_w._replace( + bmask=rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) + ) res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2*jnp.pi/(53 * .2)/4, - dk_d=2*jnp.pi/(53 * .2)/4, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, nxy=201, nxy_psf=53, skip_obs_wide_corrections=False, @@ -148,14 +150,14 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): buff=25, n_objs=10, ) - obs_w = obs_w._replace(mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + obs_w = obs_w._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2*jnp.pi/(53 * .2)/4, - dk_d=2*jnp.pi/(53 * .2)/4, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, nxy=201, nxy_psf=53, skip_obs_wide_corrections=False, @@ -183,14 +185,14 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): buff=25, n_objs=10, ) - obs_d = obs_d._replace(mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + obs_d = obs_d._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2*jnp.pi/(53 * .2)/4, - dk_d=2*jnp.pi/(53 * .2)/4, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, nxy=201, nxy_psf=53, skip_obs_wide_corrections=False, diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index 06eb010..fee6ef7 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -2,17 +2,19 @@ import time from contextlib import contextmanager +import jax.numpy as jnp import jax_galsim -import jax.numpy as jnp - import ngmix import numpy as np from ngmix.gaussmom import GaussMom +from ngmix.observation import Observation from deep_field_metadetect.metacal import DEFAULT_SHEARS -from deep_field_metadetect.observation import ngmix_Obs_to_NT, NT_to_ngmix_obs, NTObservation - -from ngmix.observation import Observation +from deep_field_metadetect.observation import ( + NT_to_ngmix_obs, + NTObservation, + ngmix_Obs_to_NT, +) GLOBAL_START_TIME = time.time() MAX_ABS_C = 1e-7 @@ -302,13 +304,12 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): vals = np.zeros(len(mcal_res), dtype=dt) fitter = GaussMom(fwhm) - + psf = mcal_res["noshear"].psf if isinstance(psf, NTObservation): psf = NT_to_ngmix_obs(mcal_res["noshear"].psf) psf_res = fitter.go(psf) - for i, (shear, obs) in enumerate(mcal_res.items()): vals["mdet_step"][i] = shear @@ -322,7 +323,7 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): if isinstance(obs, NTObservation): obs = NT_to_ngmix_obs(obs) res = fitter.go(obs) - + vals["wmom_flags"][i] = res["flags"] if res["flags"] != 0: @@ -709,4 +710,8 @@ def make_simple_sim( dim_psf=dim_psf, ) - return ngmix_Obs_to_NT(obs_wide), ngmix_Obs_to_NT(obs_deep), ngmix_Obs_to_NT(obs_deep_noise) + return ( + ngmix_Obs_to_NT(obs_wide), + ngmix_Obs_to_NT(obs_deep), + ngmix_Obs_to_NT(obs_deep_noise), + ) From 9c5d97d6cc2f0755693ee6b2371b19ae2d6f4c23 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 3 Mar 2025 11:28:29 -0600 Subject: [PATCH 09/59] set jax x64 --- .github/workflows/tests-slow.yml | 5 +++++ .github/workflows/tests.yml | 5 +++++ 2 files changed, 10 insertions(+) diff --git a/.github/workflows/tests-slow.yml b/.github/workflows/tests-slow.yml index fbf9a2e..9f18076 100644 --- a/.github/workflows/tests-slow.yml +++ b/.github/workflows/tests-slow.yml @@ -91,6 +91,11 @@ jobs: run: | pip install --no-deps --no-build-isolation -e . + - name: Run tests with JAX 64-bit enabled + run: | + export JAX_ENABLE_X64=True + pytest + - name: run pytest run: | pytest \ diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1fea133..6868bb1 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -56,6 +56,11 @@ jobs: python -m pip install -v --no-deps --no-build-isolation -e . + - name: Run tests with JAX 64-bit enabled + run: | + export JAX_ENABLE_X64=True + pytest + - name: run pytest run: | pytest \ From f7f21b47c705a1a43a4dd32022d7ee4840285a1e Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 3 Mar 2025 11:40:38 -0600 Subject: [PATCH 10/59] jax x64 --- .github/workflows/tests.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6868bb1..2cf16cd 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -56,13 +56,9 @@ jobs: python -m pip install -v --no-deps --no-build-isolation -e . - - name: Run tests with JAX 64-bit enabled - run: | - export JAX_ENABLE_X64=True - pytest - - name: run pytest run: | + export JAX_ENABLE_X64=True pytest \ -vvs \ --cov=deep_field_metadetect \ From 0b1fb92f4020adec2351b43109c93524445d3fed Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 3 Mar 2025 15:05:08 -0600 Subject: [PATCH 11/59] update tests --- deep_field_metadetect/tests/test_deep_metacal.py | 8 ++++++++ deep_field_metadetect/tests/test_detect.py | 6 ++++++ 2 files changed, 14 insertions(+) diff --git a/deep_field_metadetect/tests/test_deep_metacal.py b/deep_field_metadetect/tests/test_deep_metacal.py index 0139561..ce537f0 100644 --- a/deep_field_metadetect/tests/test_deep_metacal.py +++ b/deep_field_metadetect/tests/test_deep_metacal.py @@ -9,6 +9,7 @@ jax_metacal_op_shears, jax_metacal_wide_and_deep_psf_matched, ) +from deep_field_metadetect.observation import NT_to_ngmix_obs from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -242,12 +243,19 @@ def _run_single_sim_maybe_mcal( if use_mcal: mcal_res = jax_metacal_op_shears( obs_w, + dk=jnp.pi / (53 * 0.2) / 4, ) + for key, value in mcal_res.items(): + mcal_res[key] = NT_to_ngmix_obs(value) else: mcal_res = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, + dk_w=jnp.pi / (53 * 0.2) / 4, + dk_d=jnp.pi / (53 * 0.2) / 4, + nxy=53, + nxy_psf=53, ) return fit_gauss_mom_mcal_res(mcal_res), mcal_res diff --git a/deep_field_metadetect/tests/test_detect.py b/deep_field_metadetect/tests/test_detect.py index 27a0f35..70bf01e 100644 --- a/deep_field_metadetect/tests/test_detect.py +++ b/deep_field_metadetect/tests/test_detect.py @@ -9,6 +9,7 @@ make_detection_coadd, run_detection_sep, ) +from deep_field_metadetect.observation import NT_to_ngmix_obs from deep_field_metadetect.utils import canned_viz_for_obs, make_simple_sim @@ -42,6 +43,7 @@ def test_make_detection_coadd(detbands, has_bmask): dim=100, buff=20, ) + obs = NT_to_ngmix_obs(obs) if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] @@ -53,6 +55,7 @@ def test_make_detection_coadd(detbands, has_bmask): obs.weight = obs.weight * rng.choice( [0, 1], size=obs.image.shape, p=[0.1, 0.9] ) + assert np.any(obs.weight == 0) obslist.append(obs) @@ -133,6 +136,7 @@ def test_run_detection_sep(): dim=100, buff=20, ) + obs = NT_to_ngmix_obs(obs) detdata = run_detection_sep(obs) cat = detdata["catalog"] @@ -164,6 +168,7 @@ def test_run_detection_sep_bmask(): dim=100, buff=20, ) + obs = NT_to_ngmix_obs(obs) bmask = np.zeros_like(obs.image, dtype=np.int32) bmask[:, 60:] = 2**1 @@ -215,6 +220,7 @@ def test_generate_mbobs_for_detections(has_bmask, has_psf): dim=100, buff=20, ) + obs = NT_to_ngmix_obs(obs) if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] From 5e067c0be123cecd774432ffee605ea86ee51089 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 5 Mar 2025 10:47:05 -0600 Subject: [PATCH 12/59] undo changes in utils --- deep_field_metadetect/utils.py | 38 +++++++++++++++++++--------------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index fee6ef7..b9e704c 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -2,8 +2,9 @@ import time from contextlib import contextmanager -import jax.numpy as jnp -import jax_galsim +# import jax.numpy as jnp +# import jax_galsim +import galsim import ngmix import numpy as np from ngmix.gaussmom import GaussMom @@ -574,15 +575,15 @@ def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim, dim_psf): obs = Observation( image=im, - weight=jnp.ones_like(im) / nse**2, + weight=np.ones_like(im) / nse**2, jacobian=jac, psf=ngmix.Observation( image=psf_im, jacobian=psf_jac, ), noise=rng.normal(size=im.shape, scale=nse), - bmask=jnp.zeros_like(im, dtype=np.int32), - mfrac=jnp.zeros_like(im), + bmask=np.zeros_like(im, dtype=np.int32), + mfrac=np.zeros_like(im), ) return obs @@ -601,6 +602,7 @@ def make_simple_sim( dim_psf=53, buff=26, obj_flux_factor=1, + return_NT=True, ): """Make a simple simulation for testing deep-field metadetection. @@ -656,7 +658,7 @@ def make_simple_sim( n_objs = _n_objs - gal = jax_galsim.Exponential(half_light_radius=0.5).shear(g1=g1, g2=g2) + gal = galsim.Exponential(half_light_radius=0.5).shear(g1=g1, g2=g2) gals = None for shift in shifts: if gals is None: @@ -664,14 +666,14 @@ def make_simple_sim( else: gals += gal.shift(*shift) - psf = jax_galsim.Moffat(beta=2.5, fwhm=0.8) - deep_psf = jax_galsim.Moffat(beta=2.5, fwhm=0.8 * deep_psf_fac) - objs = jax_galsim.Convolve([gals, psf]) - deep_objs = jax_galsim.Convolve([gals, deep_psf]) + psf = galsim.Moffat(beta=2.5, fwhm=0.8) + deep_psf = galsim.Moffat(beta=2.5, fwhm=0.8 * deep_psf_fac) + objs = galsim.Convolve([gals, psf]) + deep_objs = galsim.Convolve([gals, deep_psf]) # estimate noise level - im = jax_galsim.Convolve([gal, psf]).drawImage(nx=dim, ny=dim, scale=scale).array - nse = jnp.sqrt(jnp.sum(im**2)) / s2n + im = galsim.Convolve([gal, psf]).drawImage(nx=dim, ny=dim, scale=scale).array + nse = np.sqrt(np.sum(im**2)) / s2n # apply the flux factor now that we have the noise level objs *= obj_flux_factor @@ -709,9 +711,11 @@ def make_simple_sim( dim=dim, dim_psf=dim_psf, ) + if return_NT: + return ( + ngmix_Obs_to_NT(obs_wide), + ngmix_Obs_to_NT(obs_deep), + ngmix_Obs_to_NT(obs_deep_noise), + ) - return ( - ngmix_Obs_to_NT(obs_wide), - ngmix_Obs_to_NT(obs_deep), - ngmix_Obs_to_NT(obs_deep_noise), - ) + return obs_wide, obs_deep, obs_deep_noise From c31edc77aaf8ef39ed67952c5468ea2619d47888 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Sun, 9 Mar 2025 10:13:03 -0500 Subject: [PATCH 13/59] revert old code --- deep_field_metadetect/metacal.py | 467 +----------------- deep_field_metadetect/metadetect.py | 109 ---- deep_field_metadetect/observation.py | 131 ----- .../tests/test_deep_metacal.py | 23 +- deep_field_metadetect/tests/test_detect.py | 7 +- deep_field_metadetect/tests/test_metacal.py | 11 +- .../tests/test_metadetect.py | 36 +- deep_field_metadetect/utils.py | 12 +- 8 files changed, 26 insertions(+), 770 deletions(-) delete mode 100644 deep_field_metadetect/observation.py diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 6ee7e3c..6d7a032 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -1,14 +1,7 @@ -from functools import partial - import galsim as galsim -import jax -import jax.numpy as jnp -import jax_galsim import ngmix import numpy as np -from deep_field_metadetect.observation import NT_to_ngmix_obs, NTObservation - DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") DEFAULT_STEP = 0.01 @@ -70,62 +63,12 @@ def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): return galsim.Gaussian(sigma=np.sqrt(sigma_sq) * dilation).withFlux(flux) -# TODO: what should be the value to nxy? -@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): - """Gets the target reconvolution PSF for an input PSF object. - - This is taken from galsim/tests/test_metacal.py and assumes the psf is - centered. - - Parameters - ---------- - psf : galsim object - The PSF. - flux : float - The output flux of the PSF. Defaults to 1. - - Returns - ------- - reconv_psf : galsim object - The reconvolution PSF. - sigma : float - The width of the reconv PSF befor dilation. - """ - small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue - smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - - kim = psf.drawKImage(nx=nxy_psf * 4, ny=nxy_psf * 4, scale=dk) - # kim = psf.drawKImage(scale=dk) - karr_r = kim.real.array - # Find the smallest r where the kval < small_kval - nk = karr_r.shape[0] - kx, ky = jnp.meshgrid(jnp.arange(-nk / 2, nk / 2), jnp.arange(-nk / 2, nk / 2)) - ksq = (kx**2 + ky**2) * dk**2 - ksq_max = jnp.min(jnp.where(karr_r < small_kval * psf.flux, ksq, jnp.inf)) - - # We take our target PSF to be the (round) Gaussian that is even smaller at - # this ksq - # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval - sigma_sq = -2.0 * jnp.log(smaller_kval) / ksq_max - - dilation = 1.0 + 2.0 * step - return jax_galsim.Gaussian(sigma=jnp.sqrt(sigma_sq) * dilation).withFlux(flux) - - def get_gauss_reconv_psf(obs, step=DEFAULT_STEP): """Get the Gaussian reconv PSF for an ngmix obs.""" psf = get_galsim_object_from_ngmix_obs_nopix(obs.psf, kind="image") return get_gauss_reconv_psf_galsim(psf, step=step) -@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf(obs, nxy_psf, dk, step=DEFAULT_STEP): - """Get the Gaussian reconv PSF for an ngmix obs.""" - psf = get_jax_galsim_object_from_NT_obs_nopix(obs.psf, kind="image") - return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) - - def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" mc_psf_w = get_gauss_reconv_psf_galsim(psf_w, step=step) @@ -136,22 +79,6 @@ def get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=DEFAULT_STEP): return mc_psf_d -@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy_psf"]) -def jax_get_max_gauss_reconv_psf_galsim( - psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP -): - """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" - mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) - mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) - - # fwhm_w = jnp.asarray(mc_psf_w.fwhm) - # fwhm_d = jnp.asarray(mc_psf_d.fwhm) - - return jax.lax.cond( - mc_psf_w.fwhm > mc_psf_d.fwhm, lambda: mc_psf_w, lambda: mc_psf_d - ) - - def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): """Get the larger of two reconv PSFs for two ngmix.Observations.""" psf_w = get_galsim_object_from_ngmix_obs_nopix(obs_w.psf, kind="image") @@ -159,13 +86,6 @@ def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): return get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=step) -def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): - """Get the larger of two reconv PSFs for two ngmix.Observations.""" - psf_w = get_jax_galsim_object_from_NT_obs_nopix(obs_w.psf, kind="image") - psf_d = get_jax_galsim_object_from_NT_obs_nopix(obs_d.psf, kind="image") - return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) - - def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): pim = reconv_psf.drawImage( nx=obs.psf.image.shape[1], @@ -185,29 +105,6 @@ def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): return obs -@partial(jax.jit, static_argnames=["nxy_psf"]) -def _jax_render_psf_and_build_obs(image, obs, reconv_psf, nxy_psf, weight_fac=1): - reconv_psf = reconv_psf.withGSParams( - minimum_fft_size=nxy_psf * 4, - maximum_fft_size=nxy_psf * 4, - ) - - pim = reconv_psf.drawImage( - nx=53, - ny=53, - wcs=obs.psf.jacobian, - offset=jax_galsim.PositionD( - x=obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what is the size is odd? - y=obs.psf.jac_row0 + 1 - nxy_psf / 2, - ), - ).array - - obs_psf = obs.psf._replace(image=pim) - return obs._replace( - image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac - ) - - def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): """Run metacal on an ngmix observation. @@ -236,44 +133,6 @@ def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g return ims + ns -@partial(jax.jit, static_argnames="dims") -def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): - """Run metacal on an ngmix observation. - - Note that the noise image should already be rotated by 90 degrees here. - """ - - ims = jax_galsim.Convolve( - [ - jax_galsim.Convolve([image, psf_inv]).shear(g1=g1, g2=g2), - reconv_psf, - ] - ) - - ns = jax_galsim.Convolve( - [ - jax_galsim.Convolve([noise, psf_inv]).shear(g1=g1, g2=g2), - reconv_psf, - ] - ) - - ims = ims.withGSParams( - minimum_fft_size=dims[0] * 4, - maximum_fft_size=dims[0] * 4, - ) - ims = ims.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array - - ns = ns.withGSParams( - minimum_fft_size=dims[0] * 4, - maximum_fft_size=dims[0] * 4, - ) - ns = jnp.rot90( - ns.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array, - k=-1, - ) - return ims + ns - - def metacal_op_g1g2(obs, reconv_psf, g1, g2): """Run metacal on an ngmix observation.""" mcal_image = _metacal_op_g1g2_impl( @@ -293,28 +152,6 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2): return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5) -def jax_metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): - """Run metacal on an ngmix observation.""" - mcal_image = _jax_metacal_op_g1g2_impl( - wcs=obs.jacobian, - image=get_jax_galsim_object_from_NT_obs(obs, kind="image"), - # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl - # rotates back after deconv and shearing - noise=get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1), - psf_inv=jax_galsim.Deconvolve( - get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") - ), - dims=obs.image.shape, - reconv_psf=reconv_psf, - g1=g1, - g2=g2, - ) - - return _jax_render_psf_and_build_obs( - mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 - ) - - def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): """Run metacal on an ngmix observation.""" if shears is None: @@ -350,53 +187,9 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): return mcal_res -@partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) -def jax_metacal_op_shears( - obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP -): - """Run metacal on an ngmix observation.""" - if shears is None: - shears = DEFAULT_SHEARS - - if reconv_psf is None: - reconv_psf = jax_get_gauss_reconv_psf(obs, dk=dk, nxy_psf=nxy_psf, step=step) - - wcs = obs.jacobian - image = get_jax_galsim_object_from_NT_obs(obs, kind="image") - # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl - # rotates back after deconv and shearing - noise = get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1) - psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") - psf_inv = jax_galsim.Deconvolve(psf) - - mcal_res = {} - for shear in shears: - g1, g2 = get_shear_tuple(shear, step) - - mcal_image = _jax_metacal_op_g1g2_impl( - wcs=wcs, - image=image, - noise=noise, - psf_inv=psf_inv, - dims=obs.image.shape, - reconv_psf=reconv_psf, - g1=g1, - g2=g2, - ) - - mcal_res[shear] = _jax_render_psf_and_build_obs( - mcal_image, - obs, - reconv_psf, - nxy_psf=nxy_psf, - weight_fac=0.5, - ) - return mcal_res - - def match_psf(obs, reconv_psf): """Match the PSF on an ngmix observation to a new PSF.""" - wcs = obs.jacobian + wcs = obs.jacobian.get_galsim_wcs() image = get_galsim_object_from_ngmix_obs(obs, kind="image") psf = get_galsim_object_from_ngmix_obs(obs.psf, kind="image") @@ -406,24 +199,6 @@ def match_psf(obs, reconv_psf): return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1) -@partial(jax.jit, static_argnames=["nxy_psf"]) -def jax_match_psf(obs, reconv_psf, nxy_psf): - """Match the PSF on an ngmix observation to a new PSF.""" - wcs = obs.jacobian - image = get_jax_galsim_object_from_NT_obs(obs, kind="image") - psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") - - ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) - - ims = ims.withGSParams( - minimum_fft_size=nxy_psf * 4, - maximum_fft_size=nxy_psf * 4, - ) - ims = ims.drawImage(nx=nxy_psf, ny=nxy_psf, wcs=wcs).array - - return _jax_render_psf_and_build_obs(ims, obs, reconv_psf, nxy_psf, weight_fac=1) - - def _extract_attr(obs, attr, dtype): if getattr(obs, "has_" + attr)(): return getattr(obs, attr) @@ -506,111 +281,6 @@ def add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False): return obs -@partial(jax.jit, static_argnames=["ignore_psf", "skip_mfrac_for_second"]) -def jax_add_ngmix_obs( - obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False -) -> NTObservation: - """Add two ngmix observations""" - - if repr(obs1.jacobian) != repr(obs2.jacobian): - raise RuntimeError( - "Jacobians must be equal to add ngmix observations! %s != %s" - % (repr(obs1.jacobian), repr(obs2.jacobian)), - ) - - if obs1.image.shape != obs2.image.shape: - raise RuntimeError( - "Image shapes must be equal to add ngmix observations! %s != %s" - % ( - obs1.image.shape, - obs2.image.shape, - ), - ) - - if obs1.has_psf() != obs2.has_psf() and not ignore_psf: - raise RuntimeError( - "Observations must both either have or not have a " - "PSF to add them. %s != %s" - % ( - obs1.has_psf(), - obs2.has_psf(), - ), - ) - - if obs1.has_psf() and obs2.has_psf() and not ignore_psf: - # We ignore the PSF in this call since PSFs do not have PSFs - # if nxy_psf is None: - # raise ValueError("Provide the psf size nxy_psf") - new_psf = jax_add_ngmix_obs(obs1.psf, obs2.psf, ignore_psf=True) - else: - new_psf = None - - new_wgt = jnp.where( - (obs1.weight > 0) & (obs2.weight > 0), - 1 / (1 / obs1.weight + 1 / obs2.weight), - 0, - ) - - new_bmask = None - new_ormask = None - new_noise = None - new_mfrac = None - new_meta_data = {} - - if obs1.has_bmask() or obs2.has_bmask(): - new_bmask = _extract_attr(obs1, "bmask", np.int32) | _extract_attr( - obs2, "bmask", jnp.int32 - ) - - if obs1.has_ormask() or obs2.has_ormask(): - new_ormask = _extract_attr(obs1, "ormask", np.int32) | _extract_attr( - obs2, "ormask", jnp.int32 - ) - - if obs1.has_noise() or obs2.has_noise(): - new_noise = _extract_attr(obs1, "noise", np.float32) + _extract_attr( - obs2, "noise", jnp.float32 - ) - - if skip_mfrac_for_second: - if obs1.has_mfrac(): - new_mfrac = _extract_attr(obs1, "mfrac", np.float32) - else: - if obs1.has_mfrac() or obs2.has_mfrac(): - new_mfrac = ( - _extract_attr(obs1, "mfrac", np.float32) - + _extract_attr(obs2, "mfrac", np.float32) - ) / 2 # TODO: update statement - - new_meta_data.update(obs1.meta) - new_meta_data.update(obs2.meta) - - obs = NTObservation( - image=obs1.image + obs2.image, - weight=new_wgt, - bmask=new_bmask, - ormask=new_ormask, - noise=new_noise, - jacobian=jax_galsim.wcs.JacobianWCS( - dudx=obs1.jacobian.dudx, - dudy=obs1.jacobian.dudy, - dvdx=obs1.jacobian.dvdx, - dvdy=obs1.jacobian.dvdy, - ), - psf=new_psf, - meta=new_meta_data, # Directly copy metadata - mfrac=new_mfrac, - store_pixels=getattr(obs1, "store_pixels", True), - ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), - jac_row0=obs1.jac_row0, - jac_col0=obs1.jac_col0, - jac_det=obs1.jac_det, - jac_scale=obs1.jac_scale, - ) - - return obs - - def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): """Make an interpolated image from an ngmix obs.""" return galsim.InterpolatedImage( @@ -622,17 +292,6 @@ def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): ) -def get_jax_galsim_object_from_NT_obs(obs, kind="image", rot90=0): - """Make an interpolated image from an ngmix obs.""" - return jax_galsim.InterpolatedImage( - jax_galsim.ImageD( - jnp.rot90(getattr(obs, kind).copy(), k=rot90), - wcs=obs.jacobian, - ), - x_interpolant="lanczos15", - ) - - def get_galsim_object_from_ngmix_obs_nopix(obs, kind="image"): """Make an interpolated image from an ngmix obs w/o a pixel.""" wcs = obs.jacobian.get_galsim_wcs() @@ -644,17 +303,6 @@ def get_galsim_object_from_ngmix_obs_nopix(obs, kind="image"): ) -def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): - """Make an interpolated image from an ngmix obs w/o a pixel.""" - wcs = obs.jacobian - return jax_galsim.Convolve( - [ - get_jax_galsim_object_from_NT_obs(obs, kind=kind), - jax_galsim.Deconvolve(wcs.toWorld(jax_galsim.Pixel(scale=1))), - ] - ) - - def metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, @@ -712,116 +360,3 @@ def metacal_wide_and_deep_psf_matched( mcal_res[k].psf.galsim_obj = reconv_psf return mcal_res - - -@partial( - jax.jit, - static_argnames=[ - "nxy", - "nxy_psf", - "reconv_psf_dk", - "shears", - "skip_obs_wide_corrections", - "skip_obs_deep_corrections", - "return_noshear_deep", - ], -) -def jax_helper_metacal_wide_and_deep_psf_matched( - obs_wide, - obs_deep, - obs_deep_noise, - reconv_psf, - nxy, - nxy_psf, - reconv_psf_dk, - shears=None, - step=DEFAULT_STEP, - skip_obs_wide_corrections=False, - skip_obs_deep_corrections=False, - return_noshear_deep=False, -): - """Do metacalibration for a combination of wide+deep datasets.""" - - # make the wide obs - if skip_obs_wide_corrections: - mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy) - else: - mcal_obs_wide = jax_add_ngmix_obs( - jax_match_psf(obs_wide, reconv_psf, nxy), - jax_metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), - skip_mfrac_for_second=True, - ) - - # get PSF matched noise - # obs_wide_noise = obs_wide.copy() - obs_wide_noise = obs_wide._replace(image=obs_wide.noise) - wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy) - - # now run mcal on deep - # jax_gal_reconv_psf = get_jax_galsim_object_from_ngmix_obs_nopix(reconv_psf) - mcal_res = jax_metacal_op_shears( - obs_deep, - dk=reconv_psf_dk, - reconv_psf=reconv_psf, - shears=shears, - step=step, - nxy_psf=nxy_psf, - ) - - # now add in noise corr to make it match the wide noise - if not skip_obs_deep_corrections: - for k in mcal_res: - mcal_res[k] = jax_add_ngmix_obs( - mcal_res[k], - wide_noise_corr, - skip_mfrac_for_second=True, - ) - - # we report the wide obs as noshear for later measurements - noshear_res = mcal_res.pop("noshear") - mcal_res["noshear"] = mcal_obs_wide - if return_noshear_deep: - mcal_res["noshear_deep"] = noshear_res - - return mcal_res - - -def jax_metacal_wide_and_deep_psf_matched( - obs_wide, - obs_deep, - obs_deep_noise, - dk_w, - dk_d, - nxy, - nxy_psf, - shears=None, - step=DEFAULT_STEP, - skip_obs_wide_corrections=False, - skip_obs_deep_corrections=False, - return_noshear_deep=False, -): - """Do metacalibration for a combination of wide+deep datasets.""" - - # first get the biggest reconv PSF of the two - reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, dk_w, dk_d, nxy) - - mcal_res = jax_helper_metacal_wide_and_deep_psf_matched( - obs_wide=obs_wide, - obs_deep=obs_deep, - obs_deep_noise=obs_deep_noise, - reconv_psf=reconv_psf, - nxy=nxy, - nxy_psf=nxy_psf, - reconv_psf_dk=2 * jnp.pi / (nxy_psf * 0.2) / 4, - shears=shears, - step=step, - skip_obs_wide_corrections=skip_obs_wide_corrections, - skip_obs_deep_corrections=skip_obs_deep_corrections, - return_noshear_deep=return_noshear_deep, - ) - - for k in mcal_res: - mcal_res[k] = NT_to_ngmix_obs(mcal_res[k]) - mcal_res[k].psf.galsim_obj = reconv_psf - - return mcal_res diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index 449103e..f1cd361 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -8,7 +8,6 @@ from deep_field_metadetect.metacal import ( DEFAULT_SHEARS, DEFAULT_STEP, - jax_metacal_wide_and_deep_psf_matched, metacal_wide_and_deep_psf_matched, ) from deep_field_metadetect.mfrac import compute_mfrac_interp_image @@ -112,111 +111,3 @@ def single_band_deep_field_metadetect( ] + fres.dtype.descr return np.array(dfmdet_res, dtype=total_dtype) - - -def jax_single_band_deep_field_metadetect( - obs_wide, - obs_deep, - obs_deep_noise, - dk_w, - dk_d, - nxy, - nxy_psf, - step=DEFAULT_STEP, - shears=None, - skip_obs_wide_corrections=False, - skip_obs_deep_corrections=False, - nodet_flags=0, -): - """Run deep-field metadetection for a simple scenario of a single band - with a single image per band using only post-PSF Gaussian weighted moments. - - Parameters - ---------- - obs_wide : ngmix.Observation - The wide-field observation. - obs_deep : ngmix.Observation - The deep-field observation. - obs_deep_noise : ngmix.Observation - The deep-field noise observation. - step : float, optional - The step size for the metacalibration, by default DEFAULT_STEP. - shears : list, optional - The shears to use for the metacalibration, by default DEFAULT_SHEARS - if set to None. - skip_obs_wide_corrections : bool, optional - Skip the observation corrections for the wide-field observations, - by default False. - skip_obs_deep_corrections : bool, optional - Skip the observation corrections for the deep-field observations, - by default False. - nodet_flags : int, optional - The bmask flags marking area in the image to skip, by default 0. - - Returns - ------- - dfmdet_res : dict - The deep-field metadetection results, a dictionary with keys from `shears` - and values containing the detection+measurement results for the corresponding - shear. - """ - if shears is None: - shears = DEFAULT_SHEARS - - mcal_res = jax_metacal_wide_and_deep_psf_matched( - obs_wide=obs_wide, - obs_deep=obs_deep, - obs_deep_noise=obs_deep_noise, - dk_w=dk_w, - dk_d=dk_d, - nxy=nxy, - nxy_psf=nxy_psf, - step=step, - shears=shears, - skip_obs_wide_corrections=skip_obs_wide_corrections, - skip_obs_deep_corrections=skip_obs_deep_corrections, - ) # This returns ngmix Obs for now - - psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) - dfmdet_res = [] - for shear, obs in mcal_res.items(): - detres = run_detection_sep(obs, nodet_flags=nodet_flags) - - ixc = (detres["catalog"]["x"] + 0.5).astype(int) - iyc = (detres["catalog"]["y"] + 0.5).astype(int) - bmask_flags = obs.bmask[iyc, ixc] - - mfrac_vals = np.zeros_like(bmask_flags, dtype="f4") - if np.any(obs.mfrac > 0): - _interp_mfrac = compute_mfrac_interp_image( - obs.mfrac, - obs.jacobian.get_galsim_wcs(), - ) - for i, (x, y) in enumerate( - zip(detres["catalog"]["x"], detres["catalog"]["y"]) - ): - mfrac_vals[i] = _interp_mfrac.xValue(x, y) - - for ind, (obj, mbobs) in enumerate( - generate_mbobs_for_detections( - ngmix.observation.get_mb_obs(obs), - xs=detres["catalog"]["x"], - ys=detres["catalog"]["y"], - ) - ): - fres = fit_gauss_mom_obs_and_psf(mbobs[0][0], psf_res=psf_res) - dfmdet_res.append( - (ind + 1, obj["x"], obj["y"], shear, bmask_flags[ind], mfrac_vals[ind]) - + tuple(fres[0]) - ) - - total_dtype = [ - ("id", "i8"), - ("x", "f8"), - ("y", "f8"), - ("mdet_step", "U7"), - ("bmask_flags", "i4"), - ("mfrac", "f4"), - ] + fres.dtype.descr - - return np.array(dfmdet_res, dtype=total_dtype) diff --git a/deep_field_metadetect/observation.py b/deep_field_metadetect/observation.py deleted file mode 100644 index 4187531..0000000 --- a/deep_field_metadetect/observation.py +++ /dev/null @@ -1,131 +0,0 @@ -from typing import NamedTuple, Optional - -import jax -import jax_galsim -import ngmix -import numpy as np -from ngmix.jacobian import Jacobian -from ngmix.observation import Observation - - -@jax.tree_util.register_pytree_node_class -class NTObservation(NamedTuple): - image: jax.Array - weight: Optional[jax.Array] - bmask: Optional[jax.Array] - ormask: Optional[jax.Array] - noise: Optional[jax.Array] - jacobian: Optional[jax.Array] - psf: Optional["NTObservation"] - mfrac: Optional[jax.Array] - jac_row0: Optional[float] - jac_col0: Optional[float] - jac_det: Optional[float] - jac_scale: Optional[float] - meta: Optional[dict] - store_pixels: bool - ignore_zero_weight: bool - - def tree_flatten(self): - children = ( - self.image, - self.weight, - self.bmask, - self.ormask, - self.noise, - self.jacobian, - self.psf, - self.mfrac, - self.jac_row0, - self.jac_col0, - self.jac_det, - self.jac_scale, - ) - - aux_data = (self.meta, self.store_pixels, self.ignore_zero_weight) - - return children, aux_data - - @classmethod - def tree_unflatten(cls, aux_data, children): - # Reconstruct the object from flattened data - return cls(*children, *aux_data) - - def has_bmask(self) -> bool: - if self.bmask is None: - return False - return True - - def has_mfrac(self) -> bool: - if self.bmask is None: - return False - return True - - def has_noise(self) -> bool: - if self.noise is None: - return False - return True - - def has_ormask(self) -> bool: - if self.ormask is None: - return False - return True - - def has_psf(self) -> bool: - if self.psf is None: - return False - return True - - -def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: - jacobian = obs.get_jacobian() - - psf = None - if obs.has_psf(): - psf = ngmix_Obs_to_NT(obs.get_psf()) - - return NTObservation( - image=jax.numpy.array(obs.image), - weight=jax.numpy.array(obs.weight), - bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, - ormask=jax.numpy.array(obs.ormask) if obs.has_ormask() else None, - noise=jax.numpy.array(obs.noise) if obs.has_noise() else None, - jacobian=jax_galsim.BaseWCS().from_galsim(jacobian.get_galsim_wcs()), - psf=psf, - meta=obs.meta, # Directly copy metadata - mfrac=jax.numpy.array(obs.mfrac) if obs.has_mfrac() else None, - store_pixels=getattr(obs, "store_pixels", True), - ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), - jac_row0=jacobian.row0, - jac_col0=jacobian.col0, - jac_det=jacobian.det, - jac_scale=jacobian.scale, - ) - - -def NT_to_ngmix_obs(nt_obs) -> Observation: - psf = None - if nt_obs.psf is not None: - psf = NT_to_ngmix_obs(nt_obs.psf) - return Observation( - image=np.array(nt_obs.image), - weight=np.array(nt_obs.weight), - bmask=nt_obs.bmask, - ormask=nt_obs.ormask, - noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), - jacobian=Jacobian( - row=nt_obs.jac_row0, - col=nt_obs.jac_col0, - dudrow=nt_obs.jacobian.dudx, - dudcol=nt_obs.jacobian.dudy, - dvdrow=nt_obs.jacobian.dvdx, - dvdcol=nt_obs.jacobian.dvdy, - det=nt_obs.jac_det, - scale=nt_obs.jac_scale, - ), - psf=psf, - mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), - meta=nt_obs.meta, - store_pixels=np.array(nt_obs.store_pixels, dtype=np.bool_), - ignore_zero_weight=np.array(nt_obs.ignore_zero_weight, dtype=np.bool_), - ) diff --git a/deep_field_metadetect/tests/test_deep_metacal.py b/deep_field_metadetect/tests/test_deep_metacal.py index ce537f0..0ccaa1b 100644 --- a/deep_field_metadetect/tests/test_deep_metacal.py +++ b/deep_field_metadetect/tests/test_deep_metacal.py @@ -1,15 +1,13 @@ import multiprocessing -import jax.numpy as jnp import joblib import numpy as np import pytest from deep_field_metadetect.metacal import ( - jax_metacal_op_shears, - jax_metacal_wide_and_deep_psf_matched, + metacal_op_shears, + metacal_wide_and_deep_psf_matched, ) -from deep_field_metadetect.observation import NT_to_ngmix_obs from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -40,14 +38,10 @@ def _run_single_sim( deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, ) - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res = metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=53, - nxy_psf=53, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, ) @@ -241,21 +235,14 @@ def _run_single_sim_maybe_mcal( obj_flux_factor=0.0 if zero_flux else 1.0, ) if use_mcal: - mcal_res = jax_metacal_op_shears( + mcal_res = metacal_op_shears( obs_w, - dk=jnp.pi / (53 * 0.2) / 4, ) - for key, value in mcal_res.items(): - mcal_res[key] = NT_to_ngmix_obs(value) else: - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res = metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, - dk_w=jnp.pi / (53 * 0.2) / 4, - dk_d=jnp.pi / (53 * 0.2) / 4, - nxy=53, - nxy_psf=53, ) return fit_gauss_mom_mcal_res(mcal_res), mcal_res diff --git a/deep_field_metadetect/tests/test_detect.py b/deep_field_metadetect/tests/test_detect.py index 70bf01e..8b51020 100644 --- a/deep_field_metadetect/tests/test_detect.py +++ b/deep_field_metadetect/tests/test_detect.py @@ -9,7 +9,6 @@ make_detection_coadd, run_detection_sep, ) -from deep_field_metadetect.observation import NT_to_ngmix_obs from deep_field_metadetect.utils import canned_viz_for_obs, make_simple_sim @@ -43,7 +42,7 @@ def test_make_detection_coadd(detbands, has_bmask): dim=100, buff=20, ) - obs = NT_to_ngmix_obs(obs) + if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] @@ -136,7 +135,6 @@ def test_run_detection_sep(): dim=100, buff=20, ) - obs = NT_to_ngmix_obs(obs) detdata = run_detection_sep(obs) cat = detdata["catalog"] @@ -168,7 +166,6 @@ def test_run_detection_sep_bmask(): dim=100, buff=20, ) - obs = NT_to_ngmix_obs(obs) bmask = np.zeros_like(obs.image, dtype=np.int32) bmask[:, 60:] = 2**1 @@ -220,7 +217,7 @@ def test_generate_mbobs_for_detections(has_bmask, has_psf): dim=100, buff=20, ) - obs = NT_to_ngmix_obs(obs) + if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] diff --git a/deep_field_metadetect/tests/test_metacal.py b/deep_field_metadetect/tests/test_metacal.py index 253ce18..4c5dd20 100644 --- a/deep_field_metadetect/tests/test_metacal.py +++ b/deep_field_metadetect/tests/test_metacal.py @@ -1,11 +1,10 @@ import multiprocessing -import jax.numpy as jnp import joblib import numpy as np import pytest -from deep_field_metadetect.metacal import jax_metacal_op_shears +from deep_field_metadetect.metacal import metacal_op_shears from deep_field_metadetect.utils import ( assert_m_c_ok, estimate_m_and_c, @@ -25,8 +24,7 @@ def _run_single_sim_pair(seed, s2n): deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, ) - # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_plus.psf) - mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (53 * 0.2) / 4) + mcal_res = metacal_op_shears(obs_plus) res_p = fit_gauss_mom_mcal_res(mcal_res) res_p = measure_mcal_shear_quants(res_p) @@ -38,8 +36,7 @@ def _run_single_sim_pair(seed, s2n): deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, ) - # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_minus.psf) - mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (53 * 0.2) / 4) + mcal_res = metacal_op_shears(obs_minus) res_m = fit_gauss_mom_mcal_res(mcal_res) res_m = measure_mcal_shear_quants(res_m) @@ -54,7 +51,7 @@ def test_metacal_smoke(): def test_metacal(): - nsims = 5 + nsims = 50 rng = np.random.RandomState(seed=34132) seeds = rng.randint(size=nsims, low=1, high=2**29) diff --git a/deep_field_metadetect/tests/test_metadetect.py b/deep_field_metadetect/tests/test_metadetect.py index 5927c45..9112ec9 100644 --- a/deep_field_metadetect/tests/test_metadetect.py +++ b/deep_field_metadetect/tests/test_metadetect.py @@ -1,11 +1,10 @@ import multiprocessing -import jax.numpy as jnp import joblib import numpy as np import pytest -from deep_field_metadetect.metadetect import jax_single_band_deep_field_metadetect +from deep_field_metadetect.metadetect import single_band_deep_field_metadetect from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -36,7 +35,6 @@ def _run_single_sim( deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, dim=201, - dim_psf=53, buff=25, n_objs=10, ) @@ -47,14 +45,10 @@ def _run_single_sim( pdb.set_trace() - res = jax_single_band_deep_field_metadetect( + res = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, ) @@ -107,18 +101,12 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): buff=25, n_objs=10, ) - obs_w = obs_w._replace( - bmask=rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) - ) + obs_w.bmask = rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) - res = jax_single_band_deep_field_metadetect( + res = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -150,16 +138,12 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): buff=25, n_objs=10, ) - obs_w = obs_w._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + obs_w.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) - res = jax_single_band_deep_field_metadetect( + res = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -185,16 +169,12 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): buff=25, n_objs=10, ) - obs_d = obs_d._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + obs_d.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) - res = jax_single_band_deep_field_metadetect( + res = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index b9e704c..76a12e8 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -8,14 +8,13 @@ import ngmix import numpy as np from ngmix.gaussmom import GaussMom -from ngmix.observation import Observation -from deep_field_metadetect.metacal import DEFAULT_SHEARS -from deep_field_metadetect.observation import ( +from deep_field_metadetect.jaxify.observation import ( NT_to_ngmix_obs, NTObservation, ngmix_Obs_to_NT, ) +from deep_field_metadetect.metacal import DEFAULT_SHEARS GLOBAL_START_TIME = time.time() MAX_ABS_C = 1e-7 @@ -309,6 +308,7 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): psf = mcal_res["noshear"].psf if isinstance(psf, NTObservation): psf = NT_to_ngmix_obs(mcal_res["noshear"].psf) + psf_res = fitter.go(psf) for i, (shear, obs) in enumerate(mcal_res.items()): @@ -556,7 +556,7 @@ def _gen_hex_grid(*, rng, dim, buff, pixel_scale, n_tot): return shifts -def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim, dim_psf): +def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim, dim_psf=53): cen = (dim - 1) / 2 im = obj.drawImage(nx=dim, ny=dim, scale=scale).array @@ -573,7 +573,7 @@ def _make_single_sim(*, dither=None, rng, psf, obj, nse, scale, dim, dim_psf): jac = ngmix.DiagonalJacobian(scale=scale, row=cen, col=cen) psf_jac = ngmix.DiagonalJacobian(scale=scale, row=cen_psf, col=cen_psf) - obs = Observation( + obs = ngmix.observation.Observation( image=im, weight=np.ones_like(im) / nse**2, jacobian=jac, @@ -602,7 +602,7 @@ def make_simple_sim( dim_psf=53, buff=26, obj_flux_factor=1, - return_NT=True, + return_NT=False, ): """Make a simple simulation for testing deep-field metadetection. From aa6d20f5cc99006e9bd764c42c5f6e27b728302e Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Sun, 9 Mar 2025 10:13:46 -0500 Subject: [PATCH 14/59] add jax code --- deep_field_metadetect/jaxify/jax_metacal.py | 492 ++++++++++++++++++ .../jaxify/jax_metadetect.py | 122 +++++ deep_field_metadetect/jaxify/observation.py | 131 +++++ .../jaxify/tests/test_jax_deep_metacal.py | 339 ++++++++++++ .../jaxify/tests/test_jax_metacal.py | 125 +++++ .../jaxify/tests/test_jax_metadetect.py | 320 ++++++++++++ 6 files changed, 1529 insertions(+) create mode 100644 deep_field_metadetect/jaxify/jax_metacal.py create mode 100644 deep_field_metadetect/jaxify/jax_metadetect.py create mode 100644 deep_field_metadetect/jaxify/observation.py create mode 100644 deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py create mode 100644 deep_field_metadetect/jaxify/tests/test_jax_metacal.py create mode 100644 deep_field_metadetect/jaxify/tests/test_jax_metadetect.py diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py new file mode 100644 index 0000000..bf22367 --- /dev/null +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -0,0 +1,492 @@ +from functools import partial + +import galsim as galsim +import jax +import jax.numpy as jnp +import jax_galsim +import numpy as np + +from deep_field_metadetect.jaxify.observation import NT_to_ngmix_obs, NTObservation + +DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") +DEFAULT_STEP = 0.01 + + +def get_shear_tuple(shear, step): + if shear == "noshear": + return (0, 0) + elif shear == "1p": + return (step, 0) + elif shear == "1m": + return (-step, 0) + elif shear == "2p": + return (0, step) + elif shear == "2m": + return (0, -step) + else: + raise RuntimeError("Shear value '%s' not regonized!" % shear) + + +# TODO: what should be the value to nxy? +@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) +def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): + """Gets the target reconvolution PSF for an input PSF object. + + This is taken from galsim/tests/test_metacal.py and assumes the psf is + centered. + + Parameters + ---------- + psf : galsim object + The PSF. + flux : float + The output flux of the PSF. Defaults to 1. + + Returns + ------- + reconv_psf : galsim object + The reconvolution PSF. + sigma : float + The width of the reconv PSF befor dilation. + """ + small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue + smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k + + kim = psf.drawKImage(nx=nxy_psf * 4, ny=nxy_psf * 4, scale=dk) + # kim = psf.drawKImage(scale=dk) + karr_r = kim.real.array + # Find the smallest r where the kval < small_kval + nk = karr_r.shape[0] + kx, ky = jnp.meshgrid(jnp.arange(-nk / 2, nk / 2), jnp.arange(-nk / 2, nk / 2)) + ksq = (kx**2 + ky**2) * dk**2 + ksq_max = jnp.min(jnp.where(karr_r < small_kval * psf.flux, ksq, jnp.inf)) + + # We take our target PSF to be the (round) Gaussian that is even smaller at + # this ksq + # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval + sigma_sq = -2.0 * jnp.log(smaller_kval) / ksq_max + + dilation = 1.0 + 2.0 * step + return jax_galsim.Gaussian(sigma=jnp.sqrt(sigma_sq) * dilation).withFlux(flux) + + +@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) +def jax_get_gauss_reconv_psf(obs, nxy_psf, dk, step=DEFAULT_STEP): + """Get the Gaussian reconv PSF for an ngmix obs.""" + psf = get_jax_galsim_object_from_NT_obs_nopix(obs.psf, kind="image") + return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) + + +@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy_psf"]) +def jax_get_max_gauss_reconv_psf_galsim( + psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP +): + """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" + mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) + mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) + + # fwhm_w = jnp.asarray(mc_psf_w.fwhm) + # fwhm_d = jnp.asarray(mc_psf_d.fwhm) + + return jax.lax.cond( + mc_psf_w.fwhm > mc_psf_d.fwhm, lambda: mc_psf_w, lambda: mc_psf_d + ) + + +def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): + """Get the larger of two reconv PSFs for two ngmix.Observations.""" + psf_w = get_jax_galsim_object_from_NT_obs_nopix(obs_w.psf, kind="image") + psf_d = get_jax_galsim_object_from_NT_obs_nopix(obs_d.psf, kind="image") + return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) + + +@partial(jax.jit, static_argnames=["nxy_psf"]) +def _jax_render_psf_and_build_obs(image, obs, reconv_psf, nxy_psf, weight_fac=1): + reconv_psf = reconv_psf.withGSParams( + minimum_fft_size=nxy_psf * 4, + maximum_fft_size=nxy_psf * 4, + ) + + pim = reconv_psf.drawImage( + nx=53, + ny=53, + wcs=obs.psf.jacobian, + offset=jax_galsim.PositionD( + x=obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what is the size is odd? + y=obs.psf.jac_row0 + 1 - nxy_psf / 2, + ), + ).array + + obs_psf = obs.psf._replace(image=pim) + return obs._replace( + image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac + ) + + +@partial(jax.jit, static_argnames="dims") +def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): + """Run metacal on an ngmix observation. + + Note that the noise image should already be rotated by 90 degrees here. + """ + + ims = jax_galsim.Convolve( + [ + jax_galsim.Convolve([image, psf_inv]).shear(g1=g1, g2=g2), + reconv_psf, + ] + ) + + ns = jax_galsim.Convolve( + [ + jax_galsim.Convolve([noise, psf_inv]).shear(g1=g1, g2=g2), + reconv_psf, + ] + ) + + ims = ims.withGSParams( + minimum_fft_size=dims[0] * 4, + maximum_fft_size=dims[0] * 4, + ) + ims = ims.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array + + ns = ns.withGSParams( + minimum_fft_size=dims[0] * 4, + maximum_fft_size=dims[0] * 4, + ) + ns = jnp.rot90( + ns.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array, + k=-1, + ) + return ims + ns + + +def jax_metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): + """Run metacal on an ngmix observation.""" + mcal_image = _jax_metacal_op_g1g2_impl( + wcs=obs.jacobian, + image=get_jax_galsim_object_from_NT_obs(obs, kind="image"), + # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl + # rotates back after deconv and shearing + noise=get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1), + psf_inv=jax_galsim.Deconvolve( + get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + ), + dims=obs.image.shape, + reconv_psf=reconv_psf, + g1=g1, + g2=g2, + ) + + return _jax_render_psf_and_build_obs( + mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 + ) + + +@partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) +def jax_metacal_op_shears( + obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP +): + """Run metacal on an ngmix observation.""" + if shears is None: + shears = DEFAULT_SHEARS + + if reconv_psf is None: + reconv_psf = jax_get_gauss_reconv_psf(obs, dk=dk, nxy_psf=nxy_psf, step=step) + + wcs = obs.jacobian + image = get_jax_galsim_object_from_NT_obs(obs, kind="image") + # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl + # rotates back after deconv and shearing + noise = get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1) + psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + psf_inv = jax_galsim.Deconvolve(psf) + + mcal_res = {} + for shear in shears: + g1, g2 = get_shear_tuple(shear, step) + + mcal_image = _jax_metacal_op_g1g2_impl( + wcs=wcs, + image=image, + noise=noise, + psf_inv=psf_inv, + dims=obs.image.shape, + reconv_psf=reconv_psf, + g1=g1, + g2=g2, + ) + + mcal_res[shear] = _jax_render_psf_and_build_obs( + mcal_image, + obs, + reconv_psf, + nxy_psf=nxy_psf, + weight_fac=0.5, + ) + return mcal_res + + +@partial(jax.jit, static_argnames=["nxy_psf"]) +def jax_match_psf(obs, reconv_psf, nxy_psf): + """Match the PSF on an ngmix observation to a new PSF.""" + wcs = obs.jacobian + image = get_jax_galsim_object_from_NT_obs(obs, kind="image") + psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + + ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) + + ims = ims.withGSParams( + minimum_fft_size=nxy_psf * 4, + maximum_fft_size=nxy_psf * 4, + ) + ims = ims.drawImage(nx=nxy_psf, ny=nxy_psf, wcs=wcs).array + + return _jax_render_psf_and_build_obs(ims, obs, reconv_psf, nxy_psf, weight_fac=1) + + +def _extract_attr(obs, attr, dtype): + if getattr(obs, "has_" + attr)(): + return getattr(obs, attr) + else: + return np.zeros_like(obs.image, dtype=dtype) + + +@partial(jax.jit, static_argnames=["ignore_psf", "skip_mfrac_for_second"]) +def jax_add_ngmix_obs( + obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False +) -> NTObservation: + """Add two ngmix observations""" + + if repr(obs1.jacobian) != repr(obs2.jacobian): + raise RuntimeError( + "Jacobians must be equal to add ngmix observations! %s != %s" + % (repr(obs1.jacobian), repr(obs2.jacobian)), + ) + + if obs1.image.shape != obs2.image.shape: + raise RuntimeError( + "Image shapes must be equal to add ngmix observations! %s != %s" + % ( + obs1.image.shape, + obs2.image.shape, + ), + ) + + if obs1.has_psf() != obs2.has_psf() and not ignore_psf: + raise RuntimeError( + "Observations must both either have or not have a " + "PSF to add them. %s != %s" + % ( + obs1.has_psf(), + obs2.has_psf(), + ), + ) + + if obs1.has_psf() and obs2.has_psf() and not ignore_psf: + # We ignore the PSF in this call since PSFs do not have PSFs + # if nxy_psf is None: + # raise ValueError("Provide the psf size nxy_psf") + new_psf = jax_add_ngmix_obs(obs1.psf, obs2.psf, ignore_psf=True) + else: + new_psf = None + + new_wgt = jnp.where( + (obs1.weight > 0) & (obs2.weight > 0), + 1 / (1 / obs1.weight + 1 / obs2.weight), + 0, + ) + + new_bmask = None + new_ormask = None + new_noise = None + new_mfrac = None + new_meta_data = {} + + if obs1.has_bmask() or obs2.has_bmask(): + new_bmask = _extract_attr(obs1, "bmask", np.int32) | _extract_attr( + obs2, "bmask", jnp.int32 + ) + + if obs1.has_ormask() or obs2.has_ormask(): + new_ormask = _extract_attr(obs1, "ormask", np.int32) | _extract_attr( + obs2, "ormask", jnp.int32 + ) + + if obs1.has_noise() or obs2.has_noise(): + new_noise = _extract_attr(obs1, "noise", np.float32) + _extract_attr( + obs2, "noise", jnp.float32 + ) + + if skip_mfrac_for_second: + if obs1.has_mfrac(): + new_mfrac = _extract_attr(obs1, "mfrac", np.float32) + else: + if obs1.has_mfrac() or obs2.has_mfrac(): + new_mfrac = ( + _extract_attr(obs1, "mfrac", np.float32) + + _extract_attr(obs2, "mfrac", np.float32) + ) / 2 # TODO: update statement + + new_meta_data.update(obs1.meta) + new_meta_data.update(obs2.meta) + + obs = NTObservation( + image=obs1.image + obs2.image, + weight=new_wgt, + bmask=new_bmask, + ormask=new_ormask, + noise=new_noise, + jacobian=jax_galsim.wcs.JacobianWCS( + dudx=obs1.jacobian.dudx, + dudy=obs1.jacobian.dudy, + dvdx=obs1.jacobian.dvdx, + dvdy=obs1.jacobian.dvdy, + ), + psf=new_psf, + meta=new_meta_data, # Directly copy metadata + mfrac=new_mfrac, + store_pixels=getattr(obs1, "store_pixels", True), + ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), + jac_row0=obs1.jac_row0, + jac_col0=obs1.jac_col0, + jac_det=obs1.jac_det, + jac_scale=obs1.jac_scale, + ) + + return obs + + +def get_jax_galsim_object_from_NT_obs(obs, kind="image", rot90=0): + """Make an interpolated image from an ngmix obs.""" + return jax_galsim.InterpolatedImage( + jax_galsim.ImageD( + jnp.rot90(getattr(obs, kind).copy(), k=rot90), + wcs=obs.jacobian, + ), + x_interpolant="lanczos15", + ) + + +def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): + """Make an interpolated image from an ngmix obs w/o a pixel.""" + wcs = obs.jacobian + return jax_galsim.Convolve( + [ + get_jax_galsim_object_from_NT_obs(obs, kind=kind), + jax_galsim.Deconvolve(wcs.toWorld(jax_galsim.Pixel(scale=1))), + ] + ) + + +@partial( + jax.jit, + static_argnames=[ + "nxy", + "nxy_psf", + "reconv_psf_dk", + "shears", + "skip_obs_wide_corrections", + "skip_obs_deep_corrections", + "return_noshear_deep", + ], +) +def jax_helper_metacal_wide_and_deep_psf_matched( + obs_wide, + obs_deep, + obs_deep_noise, + reconv_psf, + nxy, + nxy_psf, + reconv_psf_dk, + shears=None, + step=DEFAULT_STEP, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + return_noshear_deep=False, +): + """Do metacalibration for a combination of wide+deep datasets.""" + + # make the wide obs + if skip_obs_wide_corrections: + mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy) + else: + mcal_obs_wide = jax_add_ngmix_obs( + jax_match_psf(obs_wide, reconv_psf, nxy), + jax_metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), + skip_mfrac_for_second=True, + ) + + # get PSF matched noise + # obs_wide_noise = obs_wide.copy() + obs_wide_noise = obs_wide._replace(image=obs_wide.noise) + wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy) + + # now run mcal on deep + # jax_gal_reconv_psf = get_jax_galsim_object_from_ngmix_obs_nopix(reconv_psf) + mcal_res = jax_metacal_op_shears( + obs_deep, + dk=reconv_psf_dk, + reconv_psf=reconv_psf, + shears=shears, + step=step, + nxy_psf=nxy_psf, + ) + + # now add in noise corr to make it match the wide noise + if not skip_obs_deep_corrections: + for k in mcal_res: + mcal_res[k] = jax_add_ngmix_obs( + mcal_res[k], + wide_noise_corr, + skip_mfrac_for_second=True, + ) + + # we report the wide obs as noshear for later measurements + noshear_res = mcal_res.pop("noshear") + mcal_res["noshear"] = mcal_obs_wide + if return_noshear_deep: + mcal_res["noshear_deep"] = noshear_res + + return mcal_res + + +def jax_metacal_wide_and_deep_psf_matched( + obs_wide, + obs_deep, + obs_deep_noise, + dk_w, + dk_d, + nxy, + nxy_psf, + shears=None, + step=DEFAULT_STEP, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + return_noshear_deep=False, +): + """Do metacalibration for a combination of wide+deep datasets.""" + + # first get the biggest reconv PSF of the two + reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, dk_w, dk_d, nxy) + + mcal_res = jax_helper_metacal_wide_and_deep_psf_matched( + obs_wide=obs_wide, + obs_deep=obs_deep, + obs_deep_noise=obs_deep_noise, + reconv_psf=reconv_psf, + nxy=nxy, + nxy_psf=nxy_psf, + reconv_psf_dk=2 * jnp.pi / (nxy_psf * 0.2) / 4, + shears=shears, + step=step, + skip_obs_wide_corrections=skip_obs_wide_corrections, + skip_obs_deep_corrections=skip_obs_deep_corrections, + return_noshear_deep=return_noshear_deep, + ) + + for k in mcal_res: + mcal_res[k] = NT_to_ngmix_obs(mcal_res[k]) + mcal_res[k].psf.galsim_obj = reconv_psf + + return mcal_res diff --git a/deep_field_metadetect/jaxify/jax_metadetect.py b/deep_field_metadetect/jaxify/jax_metadetect.py new file mode 100644 index 0000000..77e86d1 --- /dev/null +++ b/deep_field_metadetect/jaxify/jax_metadetect.py @@ -0,0 +1,122 @@ +import ngmix +import numpy as np + +from deep_field_metadetect.detect import ( + generate_mbobs_for_detections, + run_detection_sep, +) +from deep_field_metadetect.jaxify.jax_metacal import ( + DEFAULT_SHEARS, + DEFAULT_STEP, + jax_metacal_wide_and_deep_psf_matched, +) +from deep_field_metadetect.mfrac import compute_mfrac_interp_image +from deep_field_metadetect.utils import fit_gauss_mom_obs, fit_gauss_mom_obs_and_psf + + +def jax_single_band_deep_field_metadetect( + obs_wide, + obs_deep, + obs_deep_noise, + dk_w, + dk_d, + nxy, + nxy_psf, + step=DEFAULT_STEP, + shears=None, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + nodet_flags=0, +): + """Run deep-field metadetection for a simple scenario of a single band + with a single image per band using only post-PSF Gaussian weighted moments. + + Parameters + ---------- + obs_wide : ngmix.Observation + The wide-field observation. + obs_deep : ngmix.Observation + The deep-field observation. + obs_deep_noise : ngmix.Observation + The deep-field noise observation. + step : float, optional + The step size for the metacalibration, by default DEFAULT_STEP. + shears : list, optional + The shears to use for the metacalibration, by default DEFAULT_SHEARS + if set to None. + skip_obs_wide_corrections : bool, optional + Skip the observation corrections for the wide-field observations, + by default False. + skip_obs_deep_corrections : bool, optional + Skip the observation corrections for the deep-field observations, + by default False. + nodet_flags : int, optional + The bmask flags marking area in the image to skip, by default 0. + + Returns + ------- + dfmdet_res : dict + The deep-field metadetection results, a dictionary with keys from `shears` + and values containing the detection+measurement results for the corresponding + shear. + """ + if shears is None: + shears = DEFAULT_SHEARS + + mcal_res = jax_metacal_wide_and_deep_psf_matched( + obs_wide=obs_wide, + obs_deep=obs_deep, + obs_deep_noise=obs_deep_noise, + dk_w=dk_w, + dk_d=dk_d, + nxy=nxy, + nxy_psf=nxy_psf, + step=step, + shears=shears, + skip_obs_wide_corrections=skip_obs_wide_corrections, + skip_obs_deep_corrections=skip_obs_deep_corrections, + ) # This returns ngmix Obs for now + + psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) + dfmdet_res = [] + for shear, obs in mcal_res.items(): + detres = run_detection_sep(obs, nodet_flags=nodet_flags) + + ixc = (detres["catalog"]["x"] + 0.5).astype(int) + iyc = (detres["catalog"]["y"] + 0.5).astype(int) + bmask_flags = obs.bmask[iyc, ixc] + + mfrac_vals = np.zeros_like(bmask_flags, dtype="f4") + if np.any(obs.mfrac > 0): + _interp_mfrac = compute_mfrac_interp_image( + obs.mfrac, + obs.jacobian.get_galsim_wcs(), + ) + for i, (x, y) in enumerate( + zip(detres["catalog"]["x"], detres["catalog"]["y"]) + ): + mfrac_vals[i] = _interp_mfrac.xValue(x, y) + + for ind, (obj, mbobs) in enumerate( + generate_mbobs_for_detections( + ngmix.observation.get_mb_obs(obs), + xs=detres["catalog"]["x"], + ys=detres["catalog"]["y"], + ) + ): + fres = fit_gauss_mom_obs_and_psf(mbobs[0][0], psf_res=psf_res) + dfmdet_res.append( + (ind + 1, obj["x"], obj["y"], shear, bmask_flags[ind], mfrac_vals[ind]) + + tuple(fres[0]) + ) + + total_dtype = [ + ("id", "i8"), + ("x", "f8"), + ("y", "f8"), + ("mdet_step", "U7"), + ("bmask_flags", "i4"), + ("mfrac", "f4"), + ] + fres.dtype.descr + + return np.array(dfmdet_res, dtype=total_dtype) diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py new file mode 100644 index 0000000..4187531 --- /dev/null +++ b/deep_field_metadetect/jaxify/observation.py @@ -0,0 +1,131 @@ +from typing import NamedTuple, Optional + +import jax +import jax_galsim +import ngmix +import numpy as np +from ngmix.jacobian import Jacobian +from ngmix.observation import Observation + + +@jax.tree_util.register_pytree_node_class +class NTObservation(NamedTuple): + image: jax.Array + weight: Optional[jax.Array] + bmask: Optional[jax.Array] + ormask: Optional[jax.Array] + noise: Optional[jax.Array] + jacobian: Optional[jax.Array] + psf: Optional["NTObservation"] + mfrac: Optional[jax.Array] + jac_row0: Optional[float] + jac_col0: Optional[float] + jac_det: Optional[float] + jac_scale: Optional[float] + meta: Optional[dict] + store_pixels: bool + ignore_zero_weight: bool + + def tree_flatten(self): + children = ( + self.image, + self.weight, + self.bmask, + self.ormask, + self.noise, + self.jacobian, + self.psf, + self.mfrac, + self.jac_row0, + self.jac_col0, + self.jac_det, + self.jac_scale, + ) + + aux_data = (self.meta, self.store_pixels, self.ignore_zero_weight) + + return children, aux_data + + @classmethod + def tree_unflatten(cls, aux_data, children): + # Reconstruct the object from flattened data + return cls(*children, *aux_data) + + def has_bmask(self) -> bool: + if self.bmask is None: + return False + return True + + def has_mfrac(self) -> bool: + if self.bmask is None: + return False + return True + + def has_noise(self) -> bool: + if self.noise is None: + return False + return True + + def has_ormask(self) -> bool: + if self.ormask is None: + return False + return True + + def has_psf(self) -> bool: + if self.psf is None: + return False + return True + + +def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: + jacobian = obs.get_jacobian() + + psf = None + if obs.has_psf(): + psf = ngmix_Obs_to_NT(obs.get_psf()) + + return NTObservation( + image=jax.numpy.array(obs.image), + weight=jax.numpy.array(obs.weight), + bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, + ormask=jax.numpy.array(obs.ormask) if obs.has_ormask() else None, + noise=jax.numpy.array(obs.noise) if obs.has_noise() else None, + jacobian=jax_galsim.BaseWCS().from_galsim(jacobian.get_galsim_wcs()), + psf=psf, + meta=obs.meta, # Directly copy metadata + mfrac=jax.numpy.array(obs.mfrac) if obs.has_mfrac() else None, + store_pixels=getattr(obs, "store_pixels", True), + ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), + jac_row0=jacobian.row0, + jac_col0=jacobian.col0, + jac_det=jacobian.det, + jac_scale=jacobian.scale, + ) + + +def NT_to_ngmix_obs(nt_obs) -> Observation: + psf = None + if nt_obs.psf is not None: + psf = NT_to_ngmix_obs(nt_obs.psf) + return Observation( + image=np.array(nt_obs.image), + weight=np.array(nt_obs.weight), + bmask=nt_obs.bmask, + ormask=nt_obs.ormask, + noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), + jacobian=Jacobian( + row=nt_obs.jac_row0, + col=nt_obs.jac_col0, + dudrow=nt_obs.jacobian.dudx, + dudcol=nt_obs.jacobian.dudy, + dvdrow=nt_obs.jacobian.dvdx, + dvdcol=nt_obs.jacobian.dvdy, + det=nt_obs.jac_det, + scale=nt_obs.jac_scale, + ), + psf=psf, + mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), + meta=nt_obs.meta, + store_pixels=np.array(nt_obs.store_pixels, dtype=np.bool_), + ignore_zero_weight=np.array(nt_obs.ignore_zero_weight, dtype=np.bool_), + ) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py new file mode 100644 index 0000000..b60c9bf --- /dev/null +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -0,0 +1,339 @@ +import multiprocessing + +import jax.numpy as jnp +import numpy as np +import pytest + +from deep_field_metadetect.jaxify.jax_metacal import ( + jax_metacal_op_shears, + jax_metacal_wide_and_deep_psf_matched, +) +from deep_field_metadetect.jaxify.observation import NT_to_ngmix_obs +from deep_field_metadetect.utils import ( + MAX_ABS_C, + MAX_ABS_M, + assert_m_c_ok, + estimate_m_and_c, + fit_gauss_mom_mcal_res, + make_simple_sim, + measure_mcal_shear_quants, + print_m_c, +) + + +def _run_single_sim( + seed, + s2n, + g1, + g2, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, +): + obs_w, obs_d, obs_dn = make_simple_sim( + seed=seed, + g1=g1, + g2=g2, + s2n=s2n, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, + return_NT=True, + ) + mcal_res = jax_metacal_wide_and_deep_psf_matched( + obs_w, + obs_d, + obs_dn, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, + nxy=53, + nxy_psf=53, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + ) + res = fit_gauss_mom_mcal_res(mcal_res) + return measure_mcal_shear_quants(res) + + +def _run_sim_pair(seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep): + res_p = _run_single_sim( + seed, + s2n, + 0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + res_m = _run_single_sim( + seed, + s2n, + -0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + return res_p, res_m + + +def test_deep_metacal_smoke(): + res_p, res_m = _run_sim_pair(1234, 1e8, 1.0 / np.sqrt(10), 1, False, False) + for col in res_p.dtype.names: + assert np.isfinite(res_p[col]).all() + assert np.isfinite(res_m[col]).all() + + +@pytest.mark.parametrize("deep_psf_ratio", [0.8, 1, 1.2]) +def test_deep_metacal(deep_psf_ratio): + nsims = 50 + noise_fac = 1 / np.sqrt(10) + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + + res_p = [] + res_m = [] + for seed in seeds: + res = _run_sim_pair(seed, 1e8, noise_fac, deep_psf_ratio, False, False) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + +def test_deep_metacal_widelows2n(): + nsims = 500 + noise_fac = 1 / np.sqrt(1000) + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + + res_p = [] + res_m = [] + for seed in seeds: + res = _run_sim_pair(seed, 20, noise_fac, 1, False, False) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + +@pytest.mark.slow +@pytest.mark.parametrize( + "skip_wide,skip_deep", [(True, True), (True, False), (False, True), (False, False)] +) +def test_deep_metacal_slow(skip_wide, skip_deep): # pragma: no cover + if not skip_wide and not skip_deep: + nsims = 100_000 + s2n = 20 + else: + nsims = 100_000 + s2n = 10 + chunk_size = multiprocessing.cpu_count() * 100 + nchunks = nsims // chunk_size + 1 + noise_fac = 1 / np.sqrt(10) + nsims = nchunks * chunk_size + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + res_p = [] + res_m = [] + loc = 0 + for chunk in range(nchunks): + _seeds = seeds[loc : loc + chunk_size] + # jobs = [ + # joblib.delayed(_run_sim_pair)( + # seed, s2n, noise_fac, 0.8, skip_wide, skip_deep + # ) + # for seed in _seeds + # ] + # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) + for seed in _seeds: + res = _run_sim_pair(seed, s2n, noise_fac, 0.8, skip_wide, skip_deep) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + if len(res_p) < 500: + njack = len(res_p) + else: + njack = 100 + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=njack, + ) + + print("# of sims:", len(res_p), flush=True) + print_m_c(m, merr, c1, c1err, c2, c2err) + + if not skip_wide and not skip_deep: + assert np.abs(m) < max(MAX_ABS_M, 3 * merr), (m, merr) + elif 3 * merr < 5e-3: + assert np.abs(m) >= max(MAX_ABS_M, 3 * merr), (m, merr) + # if we are more than 10 sigma biased, then the test + # has passed for sure + if np.abs(m) / max(MAX_ABS_M / 3, merr) >= 10: + break + assert np.abs(c1) < max(4.0 * c1err, MAX_ABS_C), (c1, c1err) + assert np.abs(c2) < max(4.0 * c2err, MAX_ABS_C), (c2, c2err) + + loc += chunk_size + + print_m_c(m, merr, c1, c1err, c2, c2err) + if not skip_wide and not skip_deep: + assert np.abs(m) < max(MAX_ABS_M, 3 * merr), (m, merr) + else: + assert np.abs(m) >= max(MAX_ABS_M, 3 * merr), (m, merr) + assert np.abs(c1) < max(4.0 * c1err, MAX_ABS_C), (c1, c1err) + assert np.abs(c2) < max(4.0 * c2err, MAX_ABS_C), (c2, c2err) + + +def _run_single_sim_maybe_mcal( + seed, + s2n, + g1, + g2, + deep_noise_fac, + deep_psf_fac, + use_mcal, + zero_flux, +): + obs_w, obs_d, obs_dn = make_simple_sim( + seed=seed, + g1=g1, + g2=g2, + s2n=s2n, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, + obj_flux_factor=0.0 if zero_flux else 1.0, + return_NT=True, + ) + if use_mcal: + mcal_res = jax_metacal_op_shears( + obs_w, + dk=jnp.pi / (53 * 0.2) / 4, + ) + for key, value in mcal_res.items(): + mcal_res[key] = NT_to_ngmix_obs(value) + else: + mcal_res = jax_metacal_wide_and_deep_psf_matched( + obs_w, + obs_d, + obs_dn, + dk_w=jnp.pi / (53 * 0.2) / 4, + dk_d=jnp.pi / (53 * 0.2) / 4, + nxy=53, + nxy_psf=53, + ) + return fit_gauss_mom_mcal_res(mcal_res), mcal_res + + +def test_deep_metacal_noise_object_s2n(): + nsims = 100 + noise_fac = 1 / np.sqrt(10) + s2n = 10 + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + + dmcal_res = [] + mcal_res = [] + for seed in seeds: + dmcal_res.append( + _run_single_sim_maybe_mcal( + seed, + s2n, + 0.02, + 0.0, + noise_fac, + 1.0, + False, + False, + ) + ) + mcal_res.append( + _run_single_sim_maybe_mcal( + seed, + s2n, + 0.02, + 0.0, + noise_fac, + 1.0, + True, + False, + ) + ) + + dmcal_res = np.concatenate([d[0] for d in dmcal_res if d is not None], axis=0) + mcal_res = np.concatenate([d[0] for d in mcal_res if d is not None], axis=0) + dmcal_res = dmcal_res[dmcal_res["mdet_step"] == "noshear"] + mcal_res = mcal_res[mcal_res["mdet_step"] == "noshear"] + + ratio = (np.median(dmcal_res["wmom_s2n"]) / np.median(mcal_res["wmom_s2n"])) ** 2 + print("s2n ratio squared:", ratio) + assert np.allclose(ratio, 2, atol=0, rtol=0.2), ratio + + dmcal_res = [] + mcal_res = [] + for seed in seeds: + dmcal_res.append( + _run_single_sim_maybe_mcal( + seed, + s2n, + 0.02, + 0.0, + noise_fac, + 1.0, + False, + True, + ) + ) + mcal_res.append( + _run_single_sim_maybe_mcal( + seed, + s2n, + 0.02, + 0.0, + noise_fac, + 1.0, + True, + True, + ) + ) + + dmcal_res = np.array( + [np.std(d[1]["noshear"].image) for d in dmcal_res if d is not None] + ) + mcal_res = np.array( + [np.std(d[1]["noshear"].image) for d in mcal_res if d is not None] + ) + + ratio = (np.median(dmcal_res) / np.median(mcal_res)) ** 2 + print("noise ratio squared:", ratio) + assert np.allclose(ratio, 0.5, atol=0, rtol=0.2), ratio diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py new file mode 100644 index 0000000..176e41a --- /dev/null +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -0,0 +1,125 @@ +import multiprocessing + +import jax.numpy as jnp +import numpy as np +import pytest + +from deep_field_metadetect.jaxify.jax_metacal import jax_metacal_op_shears +from deep_field_metadetect.utils import ( + assert_m_c_ok, + estimate_m_and_c, + fit_gauss_mom_mcal_res, + make_simple_sim, + measure_mcal_shear_quants, + print_m_c, +) + + +def _run_single_sim_pair(seed, s2n): + obs_plus, *_ = make_simple_sim( + seed=seed, + g1=0.02, + g2=0.0, + s2n=s2n, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1.0, + return_NT=True, + ) + # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_plus.psf) + mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (53 * 0.2) / 4) + res_p = fit_gauss_mom_mcal_res(mcal_res) + res_p = measure_mcal_shear_quants(res_p) + + obs_minus, *_ = make_simple_sim( + seed=seed, + g1=-0.02, + g2=0.0, + s2n=s2n, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1.0, + return_NT=True, + ) + # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_minus.psf) + mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (53 * 0.2) / 4) + res_m = fit_gauss_mom_mcal_res(mcal_res) + res_m = measure_mcal_shear_quants(res_m) + + return res_p, res_m + + +def test_metacal_smoke(): + res_p, res_m = _run_single_sim_pair(1234, 1e8) + for col in res_p.dtype.names: + assert np.isfinite(res_p[col]).all() + assert np.isfinite(res_m[col]).all() + + +def test_metacal(): + nsims = 5 + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + # jobs = [joblib.delayed(_run_single_sim_pair)(seed, 1e8) for seed in seeds] + # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) + res_p = [] + res_m = [] + for seed in seeds: + res = _run_single_sim_pair(seed, 1e8) + # for res in outputs: + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + +@pytest.mark.slow +def test_metacal_slow(): # pragma: no cover + nsims = 100_000 + chunk_size = multiprocessing.cpu_count() * 100 + nchunks = nsims // chunk_size + 1 + nsims = nchunks * chunk_size + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + res_p = [] + res_m = [] + loc = 0 + for chunk in range(nchunks): + _seeds = seeds[loc : loc + chunk_size] + # jobs = [joblib.delayed(_run_single_sim_pair)(seed, 20) for seed in _seeds] + # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) + for seed in _seeds: + res = _run_single_sim_pair(seed, 20) + # for res in outputs: + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + if len(res_p) < 500: + njack = len(res_p) + else: + njack = 100 + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=njack, + ) + + print("# of sims:", len(res_p), flush=True) + print_m_c(m, merr, c1, c1err, c2, c2err) + + loc += chunk_size + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py new file mode 100644 index 0000000..b13462c --- /dev/null +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -0,0 +1,320 @@ +import multiprocessing + +import jax.numpy as jnp +import numpy as np +import pytest + +from deep_field_metadetect.jaxify.jax_metadetect import ( + jax_single_band_deep_field_metadetect, +) +from deep_field_metadetect.utils import ( + MAX_ABS_C, + MAX_ABS_M, + assert_m_c_ok, + canned_viz_for_obs, + estimate_m_and_c, + make_simple_sim, + measure_mcal_shear_quants, + print_m_c, +) + + +def _run_single_sim( + seed, + s2n, + g1, + g2, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, +): + obs_w, obs_d, obs_dn = make_simple_sim( + seed=seed, + g1=g1, + g2=g2, + s2n=s2n, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, + dim=201, + dim_psf=53, + buff=25, + n_objs=10, + return_NT=True, + ) + if False: # pragma: no cover + fig, *_ = canned_viz_for_obs(obs_w, "obs_w") + fig.show() + import pdb + + pdb.set_trace() + + res = jax_single_band_deep_field_metadetect( + obs_w, + obs_d, + obs_dn, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, + nxy=201, + nxy_psf=53, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + ) + return measure_mcal_shear_quants(res) + + +def _run_sim_pair(seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep): + res_p = _run_single_sim( + seed, + s2n, + 0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + res_m = _run_single_sim( + seed, + s2n, + -0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + return res_p, res_m + + +def test_metadetect_single_band_deep_field_metadetect_smoke(): + res_p, res_m = _run_sim_pair(1234, 1e4, 1.0 / np.sqrt(10), 1, False, False) + for col in res_p.dtype.names: + assert np.isfinite(res_p[col]).all() + assert np.isfinite(res_m[col]).all() + + +def test_metadetect_single_band_deep_field_metadetect_bmask(): + rng = np.random.RandomState(seed=1234) + obs_w, obs_d, obs_dn = make_simple_sim( + seed=1234, + g1=0.02, + g2=0.00, + s2n=1000, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1, + dim=201, + buff=25, + n_objs=10, + return_NT=True, + ) + obs_w = obs_w._replace( + bmask=rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) + ) + + res = jax_single_band_deep_field_metadetect( + obs_w, + obs_d, + obs_dn, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, + nxy=201, + nxy_psf=53, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + ) + + xc = (res["x"] + 0.5).astype(int) + yc = (res["y"] + 0.5).astype(int) + msk = res["mdet_step"] == "noshear" + assert np.array_equal(obs_w.bmask[yc[msk], xc[msk]], res["bmask_flags"][msk]) + assert np.any(res["bmask_flags"][msk] != 0) + + for step in ["1p", "1m", "2p", "2m"]: + msk = res["mdet_step"] == step + assert not np.array_equal( + obs_d.bmask[yc[msk], xc[msk]] | obs_dn.bmask[yc[msk], xc[msk]], + res["bmask_flags"][msk], + ) + + +def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): + rng = np.random.RandomState(seed=1234) + obs_w, obs_d, obs_dn = make_simple_sim( + seed=1234, + g1=0.02, + g2=0.00, + s2n=1000, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1, + dim=201, + buff=25, + n_objs=10, + return_NT=True, + ) + obs_w = obs_w._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + + res = jax_single_band_deep_field_metadetect( + obs_w, + obs_d, + obs_dn, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, + nxy=201, + nxy_psf=53, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + ) + + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] == "noshear") + assert np.all(res["mfrac"][msk] >= 0.5) + assert np.all(res["mfrac"][msk] <= 0.7) + + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] != "noshear") + assert np.all(res["mfrac"][msk] == 0) + + +def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): + rng = np.random.RandomState(seed=1234) + obs_w, obs_d, obs_dn = make_simple_sim( + seed=1234, + g1=0.02, + g2=0.00, + s2n=1000, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1, + dim=201, + buff=25, + n_objs=10, + return_NT=True, + ) + obs_d = obs_d._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) + + res = jax_single_band_deep_field_metadetect( + obs_w, + obs_d, + obs_dn, + dk_w=2 * jnp.pi / (53 * 0.2) / 4, + dk_d=2 * jnp.pi / (53 * 0.2) / 4, + nxy=201, + nxy_psf=53, + skip_obs_wide_corrections=False, + skip_obs_deep_corrections=False, + ) + + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] != "noshear") + assert np.all(res["mfrac"][msk] >= 0.5) + assert np.all(res["mfrac"][msk] <= 0.7) + + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] == "noshear") + assert np.all(res["mfrac"][msk] == 0) + + +@pytest.mark.parametrize("deep_psf_ratio", [0.8, 1, 1.1]) +def test_metadetect_single_band_deep_field_metadetect(deep_psf_ratio): + nsims = 100 + noise_fac = 1 / np.sqrt(30) + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + # jobs = [ + # joblib.delayed(_run_sim_pair)( + # seed, 1e4, noise_fac, deep_psf_ratio, False, False + # ) + # for seed in seeds + # ] + # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) + res_p = [] + res_m = [] + for seed in seeds: + res = _run_sim_pair(seed, 1e4, noise_fac, deep_psf_ratio, False, False) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + +@pytest.mark.slow +@pytest.mark.parametrize( + "skip_wide,skip_deep", [(True, True), (True, False), (False, True), (False, False)] +) +def test_metadetect_single_band_deep_field_metadetect_slow( + skip_wide, skip_deep +): # pragma: no cover + if not skip_wide and not skip_deep: + nsims = 1_000_000 + s2n = 20 + else: + nsims = 100_000 + s2n = 10 + chunk_size = multiprocessing.cpu_count() * 100 + nchunks = nsims // chunk_size + 1 + noise_fac = 1 / np.sqrt(10) + nsims = nchunks * chunk_size + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + res_p = [] + res_m = [] + loc = 0 + for chunk in range(nchunks): + _seeds = seeds[loc : loc + chunk_size] + # jobs = [ + # joblib.delayed(_run_sim_pair)( + # seed, s2n, noise_fac, 0.8, skip_wide, skip_deep + # ) + # for seed in _seeds + # ] + # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) + for seed in _seeds: + res = _run_sim_pair(seed, s2n, noise_fac, 0.8, skip_wide, skip_deep) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + if len(res_p) < 500: + njack = len(res_p) + else: + njack = 100 + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=njack, + ) + + print("# of sims:", len(res_p), flush=True) + print_m_c(m, merr, c1, c1err, c2, c2err) + + if not skip_wide and not skip_deep: + assert np.abs(m) < max(MAX_ABS_M, 3 * merr), (m, merr) + elif 3 * merr < 5e-3: + assert np.abs(m) >= max(MAX_ABS_M, 3 * merr), (m, merr) + # if we are more than 10 sigma biased, then the test + # has passed for sure + if np.abs(m) / max(MAX_ABS_M / 3, merr) >= 10: + break + assert np.abs(c1) < max(4.0 * c1err, MAX_ABS_C), (c1, c1err) + assert np.abs(c2) < max(4.0 * c2err, MAX_ABS_C), (c2, c2err) + + loc += chunk_size + + print_m_c(m, merr, c1, c1err, c2, c2err) + if not skip_wide and not skip_deep: + assert np.abs(m) < max(MAX_ABS_M, 3 * merr), (m, merr) + else: + assert np.abs(m) >= max(MAX_ABS_M, 3 * merr), (m, merr) + assert np.abs(c1) < max(4.0 * c1err, MAX_ABS_C), (c1, c1err) + assert np.abs(c2) < max(4.0 * c2err, MAX_ABS_C), (c2, c2err) From 1edd156cb551815fbc46a1ef1711c67e8c127cb3 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 10 Mar 2025 11:40:58 -0500 Subject: [PATCH 15/59] minor [skip ci] --- deep_field_metadetect/metacal.py | 4 ++-- deep_field_metadetect/tests/test_detect.py | 3 --- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 6d7a032..9774e80 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -1,8 +1,8 @@ -import galsim as galsim +import galsim import ngmix import numpy as np -DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") +DEFAULT_SHEARS = ["noshear", "1p", "1m", "2p", "2m"] DEFAULT_STEP = 0.01 diff --git a/deep_field_metadetect/tests/test_detect.py b/deep_field_metadetect/tests/test_detect.py index 8b51020..27a0f35 100644 --- a/deep_field_metadetect/tests/test_detect.py +++ b/deep_field_metadetect/tests/test_detect.py @@ -42,7 +42,6 @@ def test_make_detection_coadd(detbands, has_bmask): dim=100, buff=20, ) - if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] @@ -54,7 +53,6 @@ def test_make_detection_coadd(detbands, has_bmask): obs.weight = obs.weight * rng.choice( [0, 1], size=obs.image.shape, p=[0.1, 0.9] ) - assert np.any(obs.weight == 0) obslist.append(obs) @@ -217,7 +215,6 @@ def test_generate_mbobs_for_detections(has_bmask, has_psf): dim=100, buff=20, ) - if has_bmask: obs.bmask = rng.choice( [0, 2**0, 2**5], size=obs.image.shape, p=[0.8, 0.1, 0.1] From 1d566df8296c81b40a5d81a7c4edb3b3ae6fa481 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 2 Apr 2025 15:10:07 -0500 Subject: [PATCH 16/59] initial pr-riv --- .github/workflows/tests-slow.yml | 8 +- deep_field_metadetect/jaxify/jax_metacal.py | 252 +++++++++--------- deep_field_metadetect/jaxify/observation.py | 50 ++-- .../jaxify/tests/test_jax_deep_metacal.py | 42 +-- .../jaxify/tests/test_jax_metacal.py | 27 +- .../jaxify/tests/test_jax_metadetect.py | 70 +++-- deep_field_metadetect/metacal.py | 4 +- deep_field_metadetect/utils.py | 24 +- environment.yml | 1 - 9 files changed, 250 insertions(+), 228 deletions(-) diff --git a/.github/workflows/tests-slow.yml b/.github/workflows/tests-slow.yml index 9f18076..e58ce50 100644 --- a/.github/workflows/tests-slow.yml +++ b/.github/workflows/tests-slow.yml @@ -35,6 +35,7 @@ jobs: - name: run pytest run: | + export JAX_ENABLE_X64=True pytest \ -vvs \ --durations 10 \ @@ -64,6 +65,7 @@ jobs: - name: run pytest run: | + export JAX_ENABLE_X64=True pytest \ -vvs \ --durations 10 \ @@ -91,13 +93,9 @@ jobs: run: | pip install --no-deps --no-build-isolation -e . - - name: Run tests with JAX 64-bit enabled - run: | - export JAX_ENABLE_X64=True - pytest - - name: run pytest run: | + export JAX_ENABLE_X64=True pytest \ -vvs \ --durations 10 \ diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index bf22367..bdf51af 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -6,10 +6,11 @@ import jax_galsim import numpy as np -from deep_field_metadetect.jaxify.observation import NT_to_ngmix_obs, NTObservation - -DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") -DEFAULT_STEP = 0.01 +from deep_field_metadetect.jaxify.observation import ( + DFMdetObservation, + dfmd_obs_to_ngmix_obs, +) +from deep_field_metadetect.metacal import DEFAULT_SHEARS, DEFAULT_STEP def get_shear_tuple(shear, step): @@ -27,7 +28,6 @@ def get_shear_tuple(shear, step): raise RuntimeError("Shear value '%s' not regonized!" % shear) -# TODO: what should be the value to nxy? @partial(jax.jit, static_argnames=["dk", "nxy_psf"]) def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): """Gets the target reconvolution PSF for an input PSF object. @@ -37,17 +37,21 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux Parameters ---------- - psf : galsim object - The PSF. - flux : float - The output flux of the PSF. Defaults to 1. + psf : galsim.GSObject + The input point spread function (PSF) object. + dk : float + The Fourier-space pixel scale. + nxy_psf : int, optional + The size of the PSF image in pixels (default is 53). + step : float, optional + The step size for coordinate grids (default is `DEFAULT_STEP`). + flux : float, optional + The total flux of the output PSF (default is 1). Returns ------- - reconv_psf : galsim object + reconv_psf : JaxGalsim object The reconvolution PSF. - sigma : float - The width of the reconv PSF befor dilation. """ small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k @@ -71,9 +75,9 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux @partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf(obs, nxy_psf, dk, step=DEFAULT_STEP): - """Get the Gaussian reconv PSF for an ngmix obs.""" - psf = get_jax_galsim_object_from_NT_obs_nopix(obs.psf, kind="image") +def jax_get_gauss_reconv_psf(dfmd_obs, nxy_psf, dk, step=DEFAULT_STEP): + """Get the Gaussian reconv PSF for a DFMdetObs.""" + psf = get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs.psf, kind="image") return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) @@ -85,47 +89,44 @@ def jax_get_max_gauss_reconv_psf_galsim( mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) - # fwhm_w = jnp.asarray(mc_psf_w.fwhm) - # fwhm_d = jnp.asarray(mc_psf_d.fwhm) - return jax.lax.cond( mc_psf_w.fwhm > mc_psf_d.fwhm, lambda: mc_psf_w, lambda: mc_psf_d ) def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): - """Get the larger of two reconv PSFs for two ngmix.Observations.""" - psf_w = get_jax_galsim_object_from_NT_obs_nopix(obs_w.psf, kind="image") - psf_d = get_jax_galsim_object_from_NT_obs_nopix(obs_d.psf, kind="image") + """Get the larger of two reconv PSFs for two DFMdetObservations.""" + psf_w = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w.psf, kind="image") + psf_d = get_jax_galsim_object_from_dfmd_obs_nopix(obs_d.psf, kind="image") return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) @partial(jax.jit, static_argnames=["nxy_psf"]) -def _jax_render_psf_and_build_obs(image, obs, reconv_psf, nxy_psf, weight_fac=1): +def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1): reconv_psf = reconv_psf.withGSParams( minimum_fft_size=nxy_psf * 4, maximum_fft_size=nxy_psf * 4, ) pim = reconv_psf.drawImage( - nx=53, - ny=53, - wcs=obs.psf.jacobian, + nx=nxy_psf, + ny=nxy_psf, + wcs=dfmd_obs.psf.jacobian, offset=jax_galsim.PositionD( - x=obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what is the size is odd? - y=obs.psf.jac_row0 + 1 - nxy_psf / 2, + x=dfmd_obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what if the size is odd? + y=dfmd_obs.psf.jac_row0 + 1 - nxy_psf / 2, ), ).array - obs_psf = obs.psf._replace(image=pim) - return obs._replace( - image=jnp.array(image), psf=obs_psf, weight=obs.weight * weight_fac + obs_psf = dfmd_obs.psf._replace(image=pim) + return dfmd_obs._replace( + image=jnp.array(image), psf=obs_psf, weight=dfmd_obs.weight * weight_fac ) @partial(jax.jit, static_argnames="dims") def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): - """Run metacal on an ngmix observation. + """Run metacal on an dfmd observation. Note that the noise image should already be rotated by 90 degrees here. """ @@ -161,45 +162,47 @@ def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g return ims + ns -def jax_metacal_op_g1g2(obs, reconv_psf, g1, g2, nxy_psf): - """Run metacal on an ngmix observation.""" +def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf): + """Run metacal on an dfmd obs.""" mcal_image = _jax_metacal_op_g1g2_impl( - wcs=obs.jacobian, - image=get_jax_galsim_object_from_NT_obs(obs, kind="image"), + wcs=dfmd_obs.jacobian, + image=get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image"), # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing - noise=get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1), + noise=get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="noise", rot90=1), psf_inv=jax_galsim.Deconvolve( - get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") ), - dims=obs.image.shape, + dims=dfmd_obs.image.shape, reconv_psf=reconv_psf, g1=g1, g2=g2, ) return _jax_render_psf_and_build_obs( - mcal_image, obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 + mcal_image, dfmd_obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 ) @partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) def jax_metacal_op_shears( - obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP + dfmd_obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP ): - """Run metacal on an ngmix observation.""" + """Run metacal on an dfmd observation.""" if shears is None: shears = DEFAULT_SHEARS if reconv_psf is None: - reconv_psf = jax_get_gauss_reconv_psf(obs, dk=dk, nxy_psf=nxy_psf, step=step) + reconv_psf = jax_get_gauss_reconv_psf( + dfmd_obs, dk=dk, nxy_psf=nxy_psf, step=step + ) - wcs = obs.jacobian - image = get_jax_galsim_object_from_NT_obs(obs, kind="image") + wcs = dfmd_obs.jacobian + image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing - noise = get_jax_galsim_object_from_NT_obs(obs, kind="noise", rot90=1) - psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") + noise = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="noise", rot90=1) + psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") psf_inv = jax_galsim.Deconvolve(psf) mcal_res = {} @@ -211,7 +214,7 @@ def jax_metacal_op_shears( image=image, noise=noise, psf_inv=psf_inv, - dims=obs.image.shape, + dims=dfmd_obs.image.shape, reconv_psf=reconv_psf, g1=g1, g2=g2, @@ -219,7 +222,7 @@ def jax_metacal_op_shears( mcal_res[shear] = _jax_render_psf_and_build_obs( mcal_image, - obs, + dfmd_obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5, @@ -227,25 +230,27 @@ def jax_metacal_op_shears( return mcal_res -@partial(jax.jit, static_argnames=["nxy_psf"]) -def jax_match_psf(obs, reconv_psf, nxy_psf): - """Match the PSF on an ngmix observation to a new PSF.""" - wcs = obs.jacobian - image = get_jax_galsim_object_from_NT_obs(obs, kind="image") - psf = get_jax_galsim_object_from_NT_obs(obs.psf, kind="image") +@partial(jax.jit, static_argnames=["nxy", "nxy_psf"]) +def jax_match_psf(dfmd_obs, reconv_psf, nxy, nxy_psf): + """Match the PSF on an dfmd observation to a new PSF.""" + wcs = dfmd_obs.jacobian + image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") + psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) ims = ims.withGSParams( - minimum_fft_size=nxy_psf * 4, - maximum_fft_size=nxy_psf * 4, + minimum_fft_size=nxy * 4, + maximum_fft_size=nxy * 4, ) - ims = ims.drawImage(nx=nxy_psf, ny=nxy_psf, wcs=wcs).array + ims = ims.drawImage(nx=nxy, ny=nxy, wcs=wcs).array - return _jax_render_psf_and_build_obs(ims, obs, reconv_psf, nxy_psf, weight_fac=1) + return _jax_render_psf_and_build_obs( + ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1 + ) -def _extract_attr(obs, attr, dtype): +def _extract_attr(obs, attr, dtype=jnp.float64): if getattr(obs, "has_" + attr)(): return getattr(obs, attr) else: @@ -253,47 +258,45 @@ def _extract_attr(obs, attr, dtype): @partial(jax.jit, static_argnames=["ignore_psf", "skip_mfrac_for_second"]) -def jax_add_ngmix_obs( - obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False -) -> NTObservation: - """Add two ngmix observations""" +def jax_add_dfmd_obs( + dfmd_obs1, dfmd_obs2, ignore_psf=False, skip_mfrac_for_second=False +) -> DFMdetObservation: + """Add two dfmd observations""" - if repr(obs1.jacobian) != repr(obs2.jacobian): + if repr(dfmd_obs1.jacobian) != repr(dfmd_obs2.jacobian): raise RuntimeError( - "Jacobians must be equal to add ngmix observations! %s != %s" - % (repr(obs1.jacobian), repr(obs2.jacobian)), + "Jacobians must be equal to add dfmd observations! %s != %s" + % (repr(dfmd_obs1.jacobian), repr(dfmd_obs2.jacobian)), ) - if obs1.image.shape != obs2.image.shape: + if dfmd_obs1.image.shape != dfmd_obs2.image.shape: raise RuntimeError( - "Image shapes must be equal to add ngmix observations! %s != %s" + "Image shapes must be equal to add dfmd observations! %s != %s" % ( - obs1.image.shape, - obs2.image.shape, + dfmd_obs1.image.shape, + dfmd_obs2.image.shape, ), ) - if obs1.has_psf() != obs2.has_psf() and not ignore_psf: + if dfmd_obs1.has_psf() != dfmd_obs2.has_psf() and not ignore_psf: raise RuntimeError( "Observations must both either have or not have a " "PSF to add them. %s != %s" % ( - obs1.has_psf(), - obs2.has_psf(), + dfmd_obs1.has_psf(), + dfmd_obs2.has_psf(), ), ) - if obs1.has_psf() and obs2.has_psf() and not ignore_psf: + if dfmd_obs1.has_psf() and dfmd_obs2.has_psf() and not ignore_psf: # We ignore the PSF in this call since PSFs do not have PSFs - # if nxy_psf is None: - # raise ValueError("Provide the psf size nxy_psf") - new_psf = jax_add_ngmix_obs(obs1.psf, obs2.psf, ignore_psf=True) + new_psf = jax_add_dfmd_obs(dfmd_obs1.psf, dfmd_obs2.psf, ignore_psf=True) else: new_psf = None new_wgt = jnp.where( - (obs1.weight > 0) & (obs2.weight > 0), - 1 / (1 / obs1.weight + 1 / obs2.weight), + (dfmd_obs1.weight > 0) & (dfmd_obs2.weight > 0), + 1 / (1 / dfmd_obs1.weight + 1 / dfmd_obs2.weight), 0, ) @@ -303,77 +306,76 @@ def jax_add_ngmix_obs( new_mfrac = None new_meta_data = {} - if obs1.has_bmask() or obs2.has_bmask(): - new_bmask = _extract_attr(obs1, "bmask", np.int32) | _extract_attr( - obs2, "bmask", jnp.int32 + if dfmd_obs1.has_bmask() or dfmd_obs2.has_bmask(): + new_bmask = _extract_attr(dfmd_obs1, "bmask", jnp.int32) | _extract_attr( + dfmd_obs2, "bmask", jnp.int32 ) - if obs1.has_ormask() or obs2.has_ormask(): - new_ormask = _extract_attr(obs1, "ormask", np.int32) | _extract_attr( - obs2, "ormask", jnp.int32 + if dfmd_obs1.has_ormask() or dfmd_obs2.has_ormask(): + new_ormask = _extract_attr(dfmd_obs1, "ormask", jnp.int32) | _extract_attr( + dfmd_obs2, "ormask", jnp.int32 ) - if obs1.has_noise() or obs2.has_noise(): - new_noise = _extract_attr(obs1, "noise", np.float32) + _extract_attr( - obs2, "noise", jnp.float32 + if dfmd_obs1.has_noise() or dfmd_obs2.has_noise(): + new_noise = _extract_attr(dfmd_obs1, "noise") + _extract_attr( + dfmd_obs2, "noise" ) if skip_mfrac_for_second: - if obs1.has_mfrac(): - new_mfrac = _extract_attr(obs1, "mfrac", np.float32) + if dfmd_obs1.has_mfrac(): + new_mfrac = _extract_attr(dfmd_obs1, "mfrac") else: - if obs1.has_mfrac() or obs2.has_mfrac(): + if dfmd_obs1.has_mfrac() or dfmd_obs2.has_mfrac(): new_mfrac = ( - _extract_attr(obs1, "mfrac", np.float32) - + _extract_attr(obs2, "mfrac", np.float32) - ) / 2 # TODO: update statement + _extract_attr(dfmd_obs1, "mfrac") + _extract_attr(dfmd_obs2, "mfrac") + ) / 2 - new_meta_data.update(obs1.meta) - new_meta_data.update(obs2.meta) + new_meta_data.update(dfmd_obs1.meta) + new_meta_data.update(dfmd_obs2.meta) - obs = NTObservation( - image=obs1.image + obs2.image, + obs = DFMdetObservation( + image=dfmd_obs1.image + dfmd_obs2.image, weight=new_wgt, bmask=new_bmask, ormask=new_ormask, noise=new_noise, jacobian=jax_galsim.wcs.JacobianWCS( - dudx=obs1.jacobian.dudx, - dudy=obs1.jacobian.dudy, - dvdx=obs1.jacobian.dvdx, - dvdy=obs1.jacobian.dvdy, + dudx=dfmd_obs1.jacobian.dudx, + dudy=dfmd_obs1.jacobian.dudy, + dvdx=dfmd_obs1.jacobian.dvdx, + dvdy=dfmd_obs1.jacobian.dvdy, ), psf=new_psf, meta=new_meta_data, # Directly copy metadata mfrac=new_mfrac, - store_pixels=getattr(obs1, "store_pixels", True), - ignore_zero_weight=getattr(obs1, "ignore_zero_weight", True), - jac_row0=obs1.jac_row0, - jac_col0=obs1.jac_col0, - jac_det=obs1.jac_det, - jac_scale=obs1.jac_scale, + store_pixels=getattr(dfmd_obs1, "store_pixels", True), + ignore_zero_weight=getattr(dfmd_obs1, "ignore_zero_weight", True), + jac_row0=dfmd_obs1.jac_row0, + jac_col0=dfmd_obs1.jac_col0, + jac_det=dfmd_obs1.jac_det, + jac_scale=dfmd_obs1.jac_scale, ) return obs -def get_jax_galsim_object_from_NT_obs(obs, kind="image", rot90=0): - """Make an interpolated image from an ngmix obs.""" +def get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image", rot90=0): + """Make an interpolated image from an dfmd obs.""" return jax_galsim.InterpolatedImage( jax_galsim.ImageD( - jnp.rot90(getattr(obs, kind).copy(), k=rot90), - wcs=obs.jacobian, + jnp.rot90(getattr(dfmd_obs, kind).copy(), k=rot90), + wcs=dfmd_obs.jacobian, ), x_interpolant="lanczos15", ) -def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): - """Make an interpolated image from an ngmix obs w/o a pixel.""" - wcs = obs.jacobian +def get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs, kind="image"): + """Make an interpolated image from an DFMdet obs w/o a pixel.""" + wcs = dfmd_obs.jacobian return jax_galsim.Convolve( [ - get_jax_galsim_object_from_NT_obs(obs, kind=kind), + get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=kind), jax_galsim.Deconvolve(wcs.toWorld(jax_galsim.Pixel(scale=1))), ] ) @@ -391,7 +393,7 @@ def get_jax_galsim_object_from_NT_obs_nopix(obs, kind="image"): "return_noshear_deep", ], ) -def jax_helper_metacal_wide_and_deep_psf_matched( +def _jax_helper_metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, obs_deep_noise, @@ -409,21 +411,19 @@ def jax_helper_metacal_wide_and_deep_psf_matched( # make the wide obs if skip_obs_wide_corrections: - mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy) + mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy, nxy_psf) else: - mcal_obs_wide = jax_add_ngmix_obs( - jax_match_psf(obs_wide, reconv_psf, nxy), + mcal_obs_wide = jax_add_dfmd_obs( + jax_match_psf(obs_wide, reconv_psf, nxy, nxy_psf), jax_metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), skip_mfrac_for_second=True, ) # get PSF matched noise - # obs_wide_noise = obs_wide.copy() obs_wide_noise = obs_wide._replace(image=obs_wide.noise) - wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy) + wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy, nxy_psf) # now run mcal on deep - # jax_gal_reconv_psf = get_jax_galsim_object_from_ngmix_obs_nopix(reconv_psf) mcal_res = jax_metacal_op_shears( obs_deep, dk=reconv_psf_dk, @@ -436,7 +436,7 @@ def jax_helper_metacal_wide_and_deep_psf_matched( # now add in noise corr to make it match the wide noise if not skip_obs_deep_corrections: for k in mcal_res: - mcal_res[k] = jax_add_ngmix_obs( + mcal_res[k] = jax_add_dfmd_obs( mcal_res[k], wide_noise_corr, skip_mfrac_for_second=True, @@ -470,7 +470,7 @@ def jax_metacal_wide_and_deep_psf_matched( # first get the biggest reconv PSF of the two reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, dk_w, dk_d, nxy) - mcal_res = jax_helper_metacal_wide_and_deep_psf_matched( + mcal_res = _jax_helper_metacal_wide_and_deep_psf_matched( obs_wide=obs_wide, obs_deep=obs_deep, obs_deep_noise=obs_deep_noise, @@ -486,7 +486,7 @@ def jax_metacal_wide_and_deep_psf_matched( ) for k in mcal_res: - mcal_res[k] = NT_to_ngmix_obs(mcal_res[k]) + mcal_res[k] = dfmd_obs_to_ngmix_obs(mcal_res[k]) mcal_res[k].psf.galsim_obj = reconv_psf return mcal_res diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py index 4187531..f5ee70b 100644 --- a/deep_field_metadetect/jaxify/observation.py +++ b/deep_field_metadetect/jaxify/observation.py @@ -9,14 +9,14 @@ @jax.tree_util.register_pytree_node_class -class NTObservation(NamedTuple): +class DFMdetObservation(NamedTuple): image: jax.Array weight: Optional[jax.Array] bmask: Optional[jax.Array] ormask: Optional[jax.Array] noise: Optional[jax.Array] jacobian: Optional[jax.Array] - psf: Optional["NTObservation"] + psf: Optional["DFMdetObservation"] mfrac: Optional[jax.Array] jac_row0: Optional[float] jac_col0: Optional[float] @@ -77,14 +77,14 @@ def has_psf(self) -> bool: return True -def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: +def ngmix_obs_to_dfmd_obs(obs: ngmix.observation.Observation) -> DFMdetObservation: jacobian = obs.get_jacobian() psf = None if obs.has_psf(): - psf = ngmix_Obs_to_NT(obs.get_psf()) + psf = ngmix_obs_to_dfmd_obs(obs.get_psf()) - return NTObservation( + return DFMdetObservation( image=jax.numpy.array(obs.image), weight=jax.numpy.array(obs.weight), bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, @@ -103,29 +103,29 @@ def ngmix_Obs_to_NT(obs: ngmix.observation.Observation) -> NTObservation: ) -def NT_to_ngmix_obs(nt_obs) -> Observation: +def dfmd_obs_to_ngmix_obs(dfmd_obs) -> Observation: psf = None - if nt_obs.psf is not None: - psf = NT_to_ngmix_obs(nt_obs.psf) + if dfmd_obs.psf is not None: + psf = dfmd_obs_to_ngmix_obs(dfmd_obs.psf) return Observation( - image=np.array(nt_obs.image), - weight=np.array(nt_obs.weight), - bmask=nt_obs.bmask, - ormask=nt_obs.ormask, - noise=nt_obs.noise if nt_obs.noise is None else np.array(nt_obs.noise), + image=np.array(dfmd_obs.image), + weight=np.array(dfmd_obs.weight), + bmask=dfmd_obs.bmask, + ormask=dfmd_obs.ormask, + noise=dfmd_obs.noise if dfmd_obs.noise is None else np.array(dfmd_obs.noise), jacobian=Jacobian( - row=nt_obs.jac_row0, - col=nt_obs.jac_col0, - dudrow=nt_obs.jacobian.dudx, - dudcol=nt_obs.jacobian.dudy, - dvdrow=nt_obs.jacobian.dvdx, - dvdcol=nt_obs.jacobian.dvdy, - det=nt_obs.jac_det, - scale=nt_obs.jac_scale, + row=dfmd_obs.jac_row0, + col=dfmd_obs.jac_col0, + dudrow=dfmd_obs.jacobian.dudx, + dudcol=dfmd_obs.jacobian.dudy, + dvdrow=dfmd_obs.jacobian.dvdx, + dvdcol=dfmd_obs.jacobian.dvdy, + det=dfmd_obs.jac_det, + scale=dfmd_obs.jac_scale, ), psf=psf, - mfrac=nt_obs.mfrac if nt_obs.mfrac is None else np.array(nt_obs.mfrac), - meta=nt_obs.meta, - store_pixels=np.array(nt_obs.store_pixels, dtype=np.bool_), - ignore_zero_weight=np.array(nt_obs.ignore_zero_weight, dtype=np.bool_), + mfrac=dfmd_obs.mfrac if dfmd_obs.mfrac is None else np.array(dfmd_obs.mfrac), + meta=dfmd_obs.meta, + store_pixels=np.array(dfmd_obs.store_pixels, dtype=np.bool_), + ignore_zero_weight=np.array(dfmd_obs.ignore_zero_weight, dtype=np.bool_), ) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index b60c9bf..2735440 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -8,7 +8,7 @@ jax_metacal_op_shears, jax_metacal_wide_and_deep_psf_matched, ) -from deep_field_metadetect.jaxify.observation import NT_to_ngmix_obs +from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -31,21 +31,28 @@ def _run_single_sim( skip_wide, skip_deep, ): + nxy = 53 + nxy_psf = 53 + scale = 0.2 + obs_w, obs_d, obs_dn = make_simple_sim( seed=seed, g1=g1, g2=g2, s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, - return_NT=True, + return_dfmd_obs=True, ) mcal_res = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=53, nxy_psf=53, skip_obs_wide_corrections=skip_wide, @@ -164,13 +171,6 @@ def test_deep_metacal_slow(skip_wide, skip_deep): # pragma: no cover loc = 0 for chunk in range(nchunks): _seeds = seeds[loc : loc + chunk_size] - # jobs = [ - # joblib.delayed(_run_sim_pair)( - # seed, s2n, noise_fac, 0.8, skip_wide, skip_deep - # ) - # for seed in _seeds - # ] - # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) for seed in _seeds: res = _run_sim_pair(seed, s2n, noise_fac, 0.8, skip_wide, skip_deep) if res is not None: @@ -224,32 +224,38 @@ def _run_single_sim_maybe_mcal( use_mcal, zero_flux, ): + nxy = 53 + nxy_psf = 53 + scale = 0.2 obs_w, obs_d, obs_dn = make_simple_sim( seed=seed, g1=g1, g2=g2, s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, obj_flux_factor=0.0 if zero_flux else 1.0, - return_NT=True, + return_dfmd_obs=True, ) if use_mcal: mcal_res = jax_metacal_op_shears( obs_w, - dk=jnp.pi / (53 * 0.2) / 4, + dk=jnp.pi / (nxy_psf * scale) / 4, ) for key, value in mcal_res.items(): - mcal_res[key] = NT_to_ngmix_obs(value) + mcal_res[key] = dfmd_obs_to_ngmix_obs(value) else: mcal_res = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, - dk_w=jnp.pi / (53 * 0.2) / 4, - dk_d=jnp.pi / (53 * 0.2) / 4, - nxy=53, - nxy_psf=53, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, + nxy=nxy, + nxy_psf=nxy_psf, ) return fit_gauss_mom_mcal_res(mcal_res), mcal_res diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 176e41a..65063b6 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -16,17 +16,22 @@ def _run_single_sim_pair(seed, s2n): + nxy = 53 + nxy_psf = 53 + scale = 0.2 obs_plus, *_ = make_simple_sim( seed=seed, g1=0.02, g2=0.0, s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, - return_NT=True, + return_dfmd_obs=True, ) - # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_plus.psf) - mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (53 * 0.2) / 4) + mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (nxy_psf * scale) / 4) res_p = fit_gauss_mom_mcal_res(mcal_res) res_p = measure_mcal_shear_quants(res_p) @@ -35,12 +40,14 @@ def _run_single_sim_pair(seed, s2n): g1=-0.02, g2=0.0, s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1.0, - return_NT=True, + return_dfmd_obs=True, ) - # jax_gal_psf = get_jax_galsim_object_from_NT_obs_nopix(obs_minus.psf) - mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (53 * 0.2) / 4) + mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (nxy_psf * scale) / 4) res_m = fit_gauss_mom_mcal_res(mcal_res) res_m = measure_mcal_shear_quants(res_m) @@ -55,17 +62,14 @@ def test_metacal_smoke(): def test_metacal(): - nsims = 5 + nsims = 50 rng = np.random.RandomState(seed=34132) seeds = rng.randint(size=nsims, low=1, high=2**29) - # jobs = [joblib.delayed(_run_single_sim_pair)(seed, 1e8) for seed in seeds] - # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) res_p = [] res_m = [] for seed in seeds: res = _run_single_sim_pair(seed, 1e8) - # for res in outputs: if res is not None: res_p.append(res[0]) res_m.append(res[1]) @@ -95,11 +99,8 @@ def test_metacal_slow(): # pragma: no cover loc = 0 for chunk in range(nchunks): _seeds = seeds[loc : loc + chunk_size] - # jobs = [joblib.delayed(_run_single_sim_pair)(seed, 20) for seed in _seeds] - # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) for seed in _seeds: res = _run_single_sim_pair(seed, 20) - # for res in outputs: if res is not None: res_p.append(res[0]) res_m.append(res[1]) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index b13462c..6ff2bb6 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -29,6 +29,9 @@ def _run_single_sim( skip_wide, skip_deep, ): + nxy = 201 + nxy_psf = 53 + scale = 0.2 obs_w, obs_d, obs_dn = make_simple_sim( seed=seed, g1=g1, @@ -36,11 +39,12 @@ def _run_single_sim( s2n=s2n, deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, - dim=201, - dim_psf=53, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, buff=25, n_objs=10, - return_NT=True, + return_dfmd_obs=True, ) if False: # pragma: no cover fig, *_ = canned_viz_for_obs(obs_w, "obs_w") @@ -53,10 +57,10 @@ def _run_single_sim( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, + nxy=nxy, + nxy_psf=nxy_psf, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, ) @@ -97,6 +101,10 @@ def test_metadetect_single_band_deep_field_metadetect_smoke(): def test_metadetect_single_band_deep_field_metadetect_bmask(): + nxy = 201 + nxy_psf = 53 + scale = 0.2 + rng = np.random.RandomState(seed=1234) obs_w, obs_d, obs_dn = make_simple_sim( seed=1234, @@ -105,10 +113,12 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): s2n=1000, deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1, - dim=201, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, buff=25, n_objs=10, - return_NT=True, + return_dfmd_obs=True, ) obs_w = obs_w._replace( bmask=rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) @@ -118,10 +128,10 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, + nxy=nxy, + nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -141,6 +151,9 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): + nxy = 201 + nxy_psf = 53 + scale = 0.2 rng = np.random.RandomState(seed=1234) obs_w, obs_d, obs_dn = make_simple_sim( seed=1234, @@ -149,10 +162,12 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): s2n=1000, deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1, - dim=201, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, buff=25, n_objs=10, - return_NT=True, + return_dfmd_obs=True, ) obs_w = obs_w._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) @@ -160,10 +175,10 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, + nxy=nxy, + nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) @@ -177,6 +192,9 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): + nxy = 201 + nxy_psf = 53 + scale = 0.2 rng = np.random.RandomState(seed=1234) obs_w, obs_d, obs_dn = make_simple_sim( seed=1234, @@ -185,10 +203,12 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): s2n=1000, deep_noise_fac=1.0 / np.sqrt(10), deep_psf_fac=1, - dim=201, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, buff=25, n_objs=10, - return_NT=True, + return_dfmd_obs=True, ) obs_d = obs_d._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) @@ -196,10 +216,10 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (53 * 0.2) / 4, - dk_d=2 * jnp.pi / (53 * 0.2) / 4, - nxy=201, - nxy_psf=53, + dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, + dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, + nxy=nxy, + nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, ) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 9774e80..bb9328f 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -2,7 +2,7 @@ import ngmix import numpy as np -DEFAULT_SHEARS = ["noshear", "1p", "1m", "2p", "2m"] +DEFAULT_SHEARS = ("noshear", "1p", "1m", "2p", "2m") DEFAULT_STEP = 0.01 @@ -38,8 +38,6 @@ def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): ------- reconv_psf : galsim object The reconvolution PSF. - sigma : float - The width of the reconv PSF befor dilation. """ dk = psf.stepk / 4.0 diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index 76a12e8..a2eda82 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -10,9 +10,9 @@ from ngmix.gaussmom import GaussMom from deep_field_metadetect.jaxify.observation import ( - NT_to_ngmix_obs, - NTObservation, - ngmix_Obs_to_NT, + DFMdetObservation, + dfmd_obs_to_ngmix_obs, + ngmix_obs_to_dfmd_obs, ) from deep_field_metadetect.metacal import DEFAULT_SHEARS @@ -306,8 +306,8 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): fitter = GaussMom(fwhm) psf = mcal_res["noshear"].psf - if isinstance(psf, NTObservation): - psf = NT_to_ngmix_obs(mcal_res["noshear"].psf) + if isinstance(psf, DFMdetObservation): + psf = dfmd_obs_to_ngmix_obs(mcal_res["noshear"].psf) psf_res = fitter.go(psf) @@ -321,8 +321,8 @@ def fit_gauss_mom_mcal_res(mcal_res, fwhm=1.2): vals["wmom_psf_T"][i] = psf_res["T"] - if isinstance(obs, NTObservation): - obs = NT_to_ngmix_obs(obs) + if isinstance(obs, DFMdetObservation): + obs = dfmd_obs_to_ngmix_obs(obs) res = fitter.go(obs) vals["wmom_flags"][i] = res["flags"] @@ -602,7 +602,7 @@ def make_simple_sim( dim_psf=53, buff=26, obj_flux_factor=1, - return_NT=False, + return_dfmd_obs=False, ): """Make a simple simulation for testing deep-field metadetection. @@ -711,11 +711,11 @@ def make_simple_sim( dim=dim, dim_psf=dim_psf, ) - if return_NT: + if return_dfmd_obs: return ( - ngmix_Obs_to_NT(obs_wide), - ngmix_Obs_to_NT(obs_deep), - ngmix_Obs_to_NT(obs_deep_noise), + ngmix_obs_to_dfmd_obs(obs_wide), + ngmix_obs_to_dfmd_obs(obs_deep), + ngmix_obs_to_dfmd_obs(obs_deep_noise), ) return obs_wide, obs_deep, obs_deep_noise diff --git a/environment.yml b/environment.yml index 1802d00..9a74dd2 100644 --- a/environment.yml +++ b/environment.yml @@ -16,7 +16,6 @@ dependencies: - ngmix - numba - numpy - - dm-tree - pip: - git+https://github.com/GalSim-developers/JAX-GalSim.git@main From 20e2ab0e50861b7d769b8fae1802761403f4d888 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Thu, 3 Apr 2025 14:07:00 -0500 Subject: [PATCH 17/59] [skip ci] add stepk computation --- deep_field_metadetect/jaxify/jax_metacal.py | 45 ++++++++++++------- .../jaxify/jax_metadetect.py | 20 +++++---- deep_field_metadetect/jaxify/jax_utils.py | 10 +++++ .../jaxify/tests/test_jax_deep_metacal.py | 9 ++-- .../jaxify/tests/test_jax_metacal.py | 13 ++++-- .../jaxify/tests/test_jax_metadetect.py | 13 ++---- 6 files changed, 68 insertions(+), 42 deletions(-) create mode 100644 deep_field_metadetect/jaxify/jax_utils.py diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index bdf51af..ea0b5ce 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -6,6 +6,7 @@ import jax_galsim import numpy as np +from deep_field_metadetect.jaxify.jax_utils import compute_stepk from deep_field_metadetect.jaxify.observation import ( DFMdetObservation, dfmd_obs_to_ngmix_obs, @@ -81,24 +82,28 @@ def jax_get_gauss_reconv_psf(dfmd_obs, nxy_psf, dk, step=DEFAULT_STEP): return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step) -@partial(jax.jit, static_argnames=["dk_w", "dk_d", "nxy_psf"]) +@partial(jax.jit, static_argnames=["nxy_psf", "scale"]) def jax_get_max_gauss_reconv_psf_galsim( - psf_w, psf_d, dk_w, dk_d, nxy_psf, step=DEFAULT_STEP + psf_w, psf_d, nxy_psf, scale=0.2, step=DEFAULT_STEP ): """Get the larger of two Gaussian reconvolution PSFs for two galsim objects.""" - mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk_w, nxy_psf, step=step) - mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk_d, nxy_psf, step=step) + dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) + mc_psf_w = jax_get_gauss_reconv_psf_galsim(psf_w, dk, nxy_psf, step=step) + mc_psf_d = jax_get_gauss_reconv_psf_galsim(psf_d, dk, nxy_psf, step=step) return jax.lax.cond( mc_psf_w.fwhm > mc_psf_d.fwhm, lambda: mc_psf_w, lambda: mc_psf_d ) -def jax_get_max_gauss_reconv_psf(obs_w, obs_d, dk_w, dk_d, nxy, step=DEFAULT_STEP): +@partial(jax.jit, static_argnames=["scale", "nxy_psf"]) +def jax_get_max_gauss_reconv_psf(obs_w, obs_d, nxy_psf, scale=0.2, step=DEFAULT_STEP): """Get the larger of two reconv PSFs for two DFMdetObservations.""" psf_w = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w.psf, kind="image") psf_d = get_jax_galsim_object_from_dfmd_obs_nopix(obs_d.psf, kind="image") - return jax_get_max_gauss_reconv_psf_galsim(psf_w, psf_d, dk_w, dk_d, nxy, step=step) + return jax_get_max_gauss_reconv_psf_galsim( + psf_w, psf_d, nxy_psf, scale=scale, step=step + ) @partial(jax.jit, static_argnames=["nxy_psf"]) @@ -184,17 +189,26 @@ def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf): ) -@partial(jax.jit, static_argnames=["nxy_psf", "dk", "shears"]) +@partial(jax.jit, static_argnames=["nxy_psf", "scale", "shears"]) def jax_metacal_op_shears( - dfmd_obs, dk, nxy_psf=53, reconv_psf=None, shears=None, step=DEFAULT_STEP + dfmd_obs, + nxy_psf=53, + reconv_psf=None, + shears=None, + step=DEFAULT_STEP, + scale=0.2, ): """Run metacal on an dfmd observation.""" if shears is None: shears = DEFAULT_SHEARS + dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) if reconv_psf is None: reconv_psf = jax_get_gauss_reconv_psf( - dfmd_obs, dk=dk, nxy_psf=nxy_psf, step=step + dfmd_obs, + dk=dk, + nxy_psf=nxy_psf, + step=step, ) wcs = dfmd_obs.jacobian @@ -386,11 +400,11 @@ def get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs, kind="image"): static_argnames=[ "nxy", "nxy_psf", - "reconv_psf_dk", "shears", "skip_obs_wide_corrections", "skip_obs_deep_corrections", "return_noshear_deep", + "scale", ], ) def _jax_helper_metacal_wide_and_deep_psf_matched( @@ -400,12 +414,12 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( reconv_psf, nxy, nxy_psf, - reconv_psf_dk, shears=None, step=DEFAULT_STEP, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, return_noshear_deep=False, + scale=0.2, ): """Do metacalibration for a combination of wide+deep datasets.""" @@ -426,11 +440,11 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( # now run mcal on deep mcal_res = jax_metacal_op_shears( obs_deep, - dk=reconv_psf_dk, reconv_psf=reconv_psf, shears=shears, step=step, nxy_psf=nxy_psf, + scale=scale, ) # now add in noise corr to make it match the wide noise @@ -455,8 +469,6 @@ def jax_metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, obs_deep_noise, - dk_w, - dk_d, nxy, nxy_psf, shears=None, @@ -464,11 +476,12 @@ def jax_metacal_wide_and_deep_psf_matched( skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, return_noshear_deep=False, + scale=0.2, ): """Do metacalibration for a combination of wide+deep datasets.""" # first get the biggest reconv PSF of the two - reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, dk_w, dk_d, nxy) + reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, nxy, scale) mcal_res = _jax_helper_metacal_wide_and_deep_psf_matched( obs_wide=obs_wide, @@ -477,12 +490,12 @@ def jax_metacal_wide_and_deep_psf_matched( reconv_psf=reconv_psf, nxy=nxy, nxy_psf=nxy_psf, - reconv_psf_dk=2 * jnp.pi / (nxy_psf * 0.2) / 4, shears=shears, step=step, skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, return_noshear_deep=return_noshear_deep, + scale=scale, ) for k in mcal_res: diff --git a/deep_field_metadetect/jaxify/jax_metadetect.py b/deep_field_metadetect/jaxify/jax_metadetect.py index 77e86d1..93c96ec 100644 --- a/deep_field_metadetect/jaxify/jax_metadetect.py +++ b/deep_field_metadetect/jaxify/jax_metadetect.py @@ -18,8 +18,6 @@ def jax_single_band_deep_field_metadetect( obs_wide, obs_deep, obs_deep_noise, - dk_w, - dk_d, nxy, nxy_psf, step=DEFAULT_STEP, @@ -27,18 +25,23 @@ def jax_single_band_deep_field_metadetect( skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, nodet_flags=0, -): + scale=0.2, +) -> dict: """Run deep-field metadetection for a simple scenario of a single band with a single image per band using only post-PSF Gaussian weighted moments. Parameters ---------- - obs_wide : ngmix.Observation + obs_wide : DFMdetObservation The wide-field observation. - obs_deep : ngmix.Observation + obs_deep : DFMdetObservation The deep-field observation. - obs_deep_noise : ngmix.Observation + obs_deep_noise : DFMdetObservation The deep-field noise observation. + nxy: int + Image size + nxy_psf: int + PSF size step : float, optional The step size for the metacalibration, by default DEFAULT_STEP. shears : list, optional @@ -52,6 +55,8 @@ def jax_single_band_deep_field_metadetect( by default False. nodet_flags : int, optional The bmask flags marking area in the image to skip, by default 0. + scale: float + pixel scale Returns ------- @@ -67,14 +72,13 @@ def jax_single_band_deep_field_metadetect( obs_wide=obs_wide, obs_deep=obs_deep, obs_deep_noise=obs_deep_noise, - dk_w=dk_w, - dk_d=dk_d, nxy=nxy, nxy_psf=nxy_psf, step=step, shears=shears, skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, + scale=scale, ) # This returns ngmix Obs for now psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) diff --git a/deep_field_metadetect/jaxify/jax_utils.py b/deep_field_metadetect/jaxify/jax_utils.py new file mode 100644 index 0000000..554521c --- /dev/null +++ b/deep_field_metadetect/jaxify/jax_utils.py @@ -0,0 +1,10 @@ +import jax.numpy as jnp + + +# @partial(jax.jit, static_argnames=["pixel_scale", "image_size"]) +def compute_stepk(pixel_scale, image_size): + """Compute psf fourier scale based on pixel scale and image dimension + The size if obtained from from galsim.GSObject.getGoodImageSize + The factor 1/4 from deep_field_metadetect.metacal.get_gauss_reconv_psf_galsim + """ + return 2 * jnp.pi / (image_size * pixel_scale) / 4 diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 2735440..02ce6e5 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -1,6 +1,5 @@ import multiprocessing -import jax.numpy as jnp import numpy as np import pytest @@ -51,12 +50,11 @@ def _run_single_sim( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=53, nxy_psf=53, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, + scale=scale, ) res = fit_gauss_mom_mcal_res(mcal_res) return measure_mcal_shear_quants(res) @@ -243,7 +241,7 @@ def _run_single_sim_maybe_mcal( if use_mcal: mcal_res = jax_metacal_op_shears( obs_w, - dk=jnp.pi / (nxy_psf * scale) / 4, + scale=scale, ) for key, value in mcal_res.items(): mcal_res[key] = dfmd_obs_to_ngmix_obs(value) @@ -252,10 +250,9 @@ def _run_single_sim_maybe_mcal( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=nxy, nxy_psf=nxy_psf, + scale=scale, ) return fit_gauss_mom_mcal_res(mcal_res), mcal_res diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 65063b6..38bf59e 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -1,6 +1,5 @@ import multiprocessing -import jax.numpy as jnp import numpy as np import pytest @@ -31,7 +30,11 @@ def _run_single_sim_pair(seed, s2n): deep_psf_fac=1.0, return_dfmd_obs=True, ) - mcal_res = jax_metacal_op_shears(obs_plus, dk=2 * jnp.pi / (nxy_psf * scale) / 4) + mcal_res = jax_metacal_op_shears( + obs_plus, + nxy_psf=nxy_psf, + scale=scale, + ) res_p = fit_gauss_mom_mcal_res(mcal_res) res_p = measure_mcal_shear_quants(res_p) @@ -47,7 +50,11 @@ def _run_single_sim_pair(seed, s2n): deep_psf_fac=1.0, return_dfmd_obs=True, ) - mcal_res = jax_metacal_op_shears(obs_minus, dk=2 * jnp.pi / (nxy_psf * scale) / 4) + mcal_res = jax_metacal_op_shears( + obs_minus, + nxy_psf=nxy_psf, + scale=scale, + ) res_m = fit_gauss_mom_mcal_res(mcal_res) res_m = measure_mcal_shear_quants(res_m) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index 6ff2bb6..79dd103 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -1,6 +1,5 @@ import multiprocessing -import jax.numpy as jnp import numpy as np import pytest @@ -57,12 +56,11 @@ def _run_single_sim( obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=nxy, nxy_psf=nxy_psf, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, + scale=scale, ) return measure_mcal_shear_quants(res) @@ -128,12 +126,11 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=nxy, nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, + scale=scale, ) xc = (res["x"] + 0.5).astype(int) @@ -175,12 +172,11 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=nxy, nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, + scale=scale, ) msk = (res["wmom_flags"] == 0) & (res["mdet_step"] == "noshear") @@ -216,12 +212,11 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): obs_w, obs_d, obs_dn, - dk_w=2 * jnp.pi / (nxy_psf * scale) / 4, - dk_d=2 * jnp.pi / (nxy_psf * scale) / 4, nxy=nxy, nxy_psf=nxy_psf, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, + scale=scale, ) msk = (res["wmom_flags"] == 0) & (res["mdet_step"] != "noshear") From 0f9c73ef7eba6d46ff45bf048d06e74753455c18 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Thu, 3 Apr 2025 14:09:38 -0500 Subject: [PATCH 18/59] [skip ci] remove jnp.array calls --- deep_field_metadetect/jaxify/observation.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py index f5ee70b..1780dec 100644 --- a/deep_field_metadetect/jaxify/observation.py +++ b/deep_field_metadetect/jaxify/observation.py @@ -85,15 +85,15 @@ def ngmix_obs_to_dfmd_obs(obs: ngmix.observation.Observation) -> DFMdetObservati psf = ngmix_obs_to_dfmd_obs(obs.get_psf()) return DFMdetObservation( - image=jax.numpy.array(obs.image), - weight=jax.numpy.array(obs.weight), - bmask=jax.numpy.array(obs.bmask) if obs.has_bmask() else None, - ormask=jax.numpy.array(obs.ormask) if obs.has_ormask() else None, - noise=jax.numpy.array(obs.noise) if obs.has_noise() else None, + image=obs.image, + weight=obs.weight, + bmask=obs.bmask if obs.has_bmask() else None, + ormask=obs.ormask if obs.has_ormask() else None, + noise=obs.noise if obs.has_noise() else None, jacobian=jax_galsim.BaseWCS().from_galsim(jacobian.get_galsim_wcs()), psf=psf, meta=obs.meta, # Directly copy metadata - mfrac=jax.numpy.array(obs.mfrac) if obs.has_mfrac() else None, + mfrac=obs.mfrac if obs.has_mfrac() else None, store_pixels=getattr(obs, "store_pixels", True), ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), jac_row0=jacobian.row0, From 0ff1e26c274af65bdf70627635f0def82669f469 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Thu, 3 Apr 2025 14:32:30 -0500 Subject: [PATCH 19/59] [skip ci] update documentation --- deep_field_metadetect/jaxify/jax_utils.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/jax_utils.py b/deep_field_metadetect/jaxify/jax_utils.py index 554521c..a838572 100644 --- a/deep_field_metadetect/jaxify/jax_utils.py +++ b/deep_field_metadetect/jaxify/jax_utils.py @@ -3,8 +3,20 @@ # @partial(jax.jit, static_argnames=["pixel_scale", "image_size"]) def compute_stepk(pixel_scale, image_size): - """Compute psf fourier scale based on pixel scale and image dimension + """Compute psf fourier scale based on pixel scale and psf image dimension The size if obtained from from galsim.GSObject.getGoodImageSize The factor 1/4 from deep_field_metadetect.metacal.get_gauss_reconv_psf_galsim + + Parameters: + ----------- + pixel_scale : float + The scale of a single pixel in the image. + image_size : int + The dimension of the PSF image (typically a square size). + + Returns: + -------- + float + The computed stepk value, which represents the Fourier-space sampling frequency. """ return 2 * jnp.pi / (image_size * pixel_scale) / 4 From 3267769eb07850735342ff0c3e2c7c7de87e3343 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 15 Apr 2025 07:44:59 -0500 Subject: [PATCH 20/59] Bug fix --- deep_field_metadetect/jaxify/observation.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py index 1780dec..e1c9aad 100644 --- a/deep_field_metadetect/jaxify/observation.py +++ b/deep_field_metadetect/jaxify/observation.py @@ -116,10 +116,10 @@ def dfmd_obs_to_ngmix_obs(dfmd_obs) -> Observation: jacobian=Jacobian( row=dfmd_obs.jac_row0, col=dfmd_obs.jac_col0, - dudrow=dfmd_obs.jacobian.dudx, - dudcol=dfmd_obs.jacobian.dudy, - dvdrow=dfmd_obs.jacobian.dvdx, - dvdcol=dfmd_obs.jacobian.dvdy, + dudcol=dfmd_obs.jacobian.dudx, + dudrow=dfmd_obs.jacobian.dudy, + dvdcol=dfmd_obs.jacobian.dvdx, + dvdrow=dfmd_obs.jacobian.dvdy, det=dfmd_obs.jac_det, scale=dfmd_obs.jac_scale, ), From c16dfd19a49f641bb45c13b305bbc26053744099 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 15 Apr 2025 12:35:37 -0500 Subject: [PATCH 21/59] compare jax and ngmix --- deep_field_metadetect/jaxify/jax_metacal.py | 2 +- .../jaxify/tests/test_jax_deep_metacal.py | 149 +++++++++++++++- .../jaxify/tests/test_jax_metacal.py | 121 +++++++++++++ .../jaxify/tests/test_jax_metadetect.py | 164 ++++++++++++++++-- 4 files changed, 420 insertions(+), 16 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index ea0b5ce..012f70e 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -118,7 +118,7 @@ def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_f ny=nxy_psf, wcs=dfmd_obs.psf.jacobian, offset=jax_galsim.PositionD( - x=dfmd_obs.psf.jac_col0 + 1 - nxy_psf / 2, # TODO: what if the size is odd? + x=dfmd_obs.psf.jac_col0 + 1 - nxy_psf / 2, y=dfmd_obs.psf.jac_row0 + 1 - nxy_psf / 2, ), ).array diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 02ce6e5..a59aeab 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -7,7 +7,11 @@ jax_metacal_op_shears, jax_metacal_wide_and_deep_psf_matched, ) -from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs +from deep_field_metadetect.jaxify.observation import ( + dfmd_obs_to_ngmix_obs, + ngmix_obs_to_dfmd_obs, +) +from deep_field_metadetect.metacal import metacal_wide_and_deep_psf_matched from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, @@ -60,6 +64,59 @@ def _run_single_sim( return measure_mcal_shear_quants(res) +def _run_single_sim_jax_and_ngmix( + seed, + s2n, + g1, + g2, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, +): + nxy = 53 + nxy_psf = 53 + scale = 0.2 + + obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix = make_simple_sim( + seed=seed, + g1=g1, + g2=g2, + s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, + return_dfmd_obs=False, + ) + mcal_res_ngmix = metacal_wide_and_deep_psf_matched( + obs_w_ngmix, + obs_d_ngmix, + obs_dn_ngmix, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + ) + res_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix) + + obs_w = ngmix_obs_to_dfmd_obs(obs_w_ngmix) + obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) + obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) + + mcal_res = jax_metacal_wide_and_deep_psf_matched( + obs_w, + obs_d, + obs_dn, + nxy=53, + nxy_psf=53, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + scale=scale, + ) + res = fit_gauss_mom_mcal_res(mcal_res) + return measure_mcal_shear_quants(res), measure_mcal_shear_quants(res_ngmix) + + def _run_sim_pair(seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep): res_p = _run_single_sim( seed, @@ -86,6 +143,34 @@ def _run_sim_pair(seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep) return res_p, res_m +def _run_sim_pair_jax_and_ngmix( + seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep +): + res_p, res_p_ngmix = _run_single_sim_jax_and_ngmix( + seed, + s2n, + 0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + res_m, res_m_ngmix = _run_single_sim_jax_and_ngmix( + seed, + s2n, + -0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + return (res_p, res_m), (res_p_ngmix, res_m_ngmix) + + def test_deep_metacal_smoke(): res_p, res_m = _run_sim_pair(1234, 1e8, 1.0 / np.sqrt(10), 1, False, False) for col in res_p.dtype.names: @@ -93,6 +178,68 @@ def test_deep_metacal_smoke(): assert np.isfinite(res_m[col]).all() +@pytest.mark.parametrize("deep_psf_ratio", [0.8, 1.2]) +def test_jax_vs_ngmix_comparison(deep_psf_ratio): + nsims = 5 + noise_fac = 1 / np.sqrt(10) + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + + res_p = [] + res_m = [] + res_p_ngmix = [] + res_m_ngmix = [] + for seed in seeds: + res, res_ngmix = _run_sim_pair_jax_and_ngmix( + seed, 1e8, noise_fac, deep_psf_ratio, False, False + ) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + res_p_ngmix.append(res_ngmix[0]) + res_m_ngmix.append(res_ngmix[1]) + + np.allclose( + res[0].tolist(), + res_ngmix[0].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + np.allclose( + res[1].tolist(), + res_ngmix[1].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng = estimate_m_and_c( + np.concatenate(res_p_ngmix), + np.concatenate(res_m_ngmix), + 0.02, + jackknife=len(res_p_ngmix), + ) + + np.allclose(m, m_ng, atol=1e-12) + np.allclose(merr, merr_ng, atol=1e-12) + np.allclose(c1err, c1err_ng, atol=1e-12) + np.allclose(c1, c1_ng, atol=1e-12) + np.allclose(c2err, c2err_ng, atol=1e-12) + np.allclose(c2, c2_ng, atol=1e-12) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + @pytest.mark.parametrize("deep_psf_ratio", [0.8, 1, 1.2]) def test_deep_metacal(deep_psf_ratio): nsims = 50 diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 38bf59e..40eb741 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -4,6 +4,8 @@ import pytest from deep_field_metadetect.jaxify.jax_metacal import jax_metacal_op_shears +from deep_field_metadetect.jaxify.observation import ngmix_obs_to_dfmd_obs +from deep_field_metadetect.metacal import metacal_op_shears from deep_field_metadetect.utils import ( assert_m_c_ok, estimate_m_and_c, @@ -61,6 +63,67 @@ def _run_single_sim_pair(seed, s2n): return res_p, res_m +def _run_single_sim_pair_jax_and_ngmix(seed, s2n): + nxy = 53 + nxy_psf = 53 + scale = 0.2 + obs_plus, *_ = make_simple_sim( + seed=seed, + g1=0.02, + g2=0.0, + s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1.0, + return_dfmd_obs=False, + ) + + mcal_res_ngmix = metacal_op_shears(obs_plus) + + res_p_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix) + res_p_ngmix = measure_mcal_shear_quants(res_p_ngmix) + + obs_plus = ngmix_obs_to_dfmd_obs(obs_plus) + + mcal_res = jax_metacal_op_shears( + obs_plus, + nxy_psf=nxy_psf, + scale=scale, + ) + res_p = fit_gauss_mom_mcal_res(mcal_res) + res_p = measure_mcal_shear_quants(res_p) + + obs_minus, *_ = make_simple_sim( + seed=seed, + g1=-0.02, + g2=0.0, + s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1.0, + return_dfmd_obs=False, + ) + + mcal_res_ngmix = metacal_op_shears(obs_minus) + res_m_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix) + res_m_ngmix = measure_mcal_shear_quants(res_m_ngmix) + + obs_minus = ngmix_obs_to_dfmd_obs(obs_minus) + mcal_res = jax_metacal_op_shears( + obs_minus, + nxy_psf=nxy_psf, + scale=scale, + ) + res_m = fit_gauss_mom_mcal_res(mcal_res) + res_m = measure_mcal_shear_quants(res_m) + + return (res_p, res_m), (res_p_ngmix, res_m_ngmix) + + def test_metacal_smoke(): res_p, res_m = _run_single_sim_pair(1234, 1e8) for col in res_p.dtype.names: @@ -68,6 +131,64 @@ def test_metacal_smoke(): assert np.isfinite(res_m[col]).all() +def test_metacal_jax_vs_ngmix(): + nsims = 5 + + rng = np.random.RandomState(seed=34132) + seeds = rng.randint(size=nsims, low=1, high=2**29) + res_p = [] + res_m = [] + res_p_ngmix = [] + res_m_ngmix = [] + for seed in seeds: + res, res_ngmix = _run_single_sim_pair_jax_and_ngmix(seed, 1e8) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + + res_p_ngmix.append(res_ngmix[0]) + res_m_ngmix.append(res_ngmix[1]) + + np.allclose( + res[0].tolist(), + res_ngmix[0].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + np.allclose( + res[1].tolist(), + res_ngmix[1].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng = estimate_m_and_c( + np.concatenate(res_p_ngmix), + np.concatenate(res_m_ngmix), + 0.02, + jackknife=len(res_p_ngmix), + ) + + np.allclose(m, m_ng, atol=1e-12) + np.allclose(merr, merr_ng, atol=1e-12) + np.allclose(c1err, c1err_ng, atol=1e-12) + np.allclose(c1, c1_ng, atol=1e-12) + np.allclose(c2err, c2err_ng, atol=1e-12) + np.allclose(c2, c2_ng, atol=1e-12) + + print_m_c(m, merr, c1, c1err, c2, c2err) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + def test_metacal(): nsims = 50 diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index 79dd103..29472f8 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -6,11 +6,12 @@ from deep_field_metadetect.jaxify.jax_metadetect import ( jax_single_band_deep_field_metadetect, ) +from deep_field_metadetect.jaxify.observation import ngmix_obs_to_dfmd_obs +from deep_field_metadetect.metadetect import single_band_deep_field_metadetect from deep_field_metadetect.utils import ( MAX_ABS_C, MAX_ABS_M, assert_m_c_ok, - canned_viz_for_obs, estimate_m_and_c, make_simple_sim, measure_mcal_shear_quants, @@ -45,12 +46,6 @@ def _run_single_sim( n_objs=10, return_dfmd_obs=True, ) - if False: # pragma: no cover - fig, *_ = canned_viz_for_obs(obs_w, "obs_w") - fig.show() - import pdb - - pdb.set_trace() res = jax_single_band_deep_field_metadetect( obs_w, @@ -91,6 +86,86 @@ def _run_sim_pair(seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep) return res_p, res_m +def _run_single_sim_jax_and_ngmix( + seed, + s2n, + g1, + g2, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, +): + nxy = 53 + nxy_psf = 53 + scale = 0.2 + + obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix = make_simple_sim( + seed=seed, + g1=g1, + g2=g2, + s2n=s2n, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, + return_dfmd_obs=False, + ) + res_ngmix = single_band_deep_field_metadetect( + obs_w_ngmix, + obs_d_ngmix, + obs_dn_ngmix, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + ) + + obs_w = ngmix_obs_to_dfmd_obs(obs_w_ngmix) + obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) + obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) + + res = jax_single_band_deep_field_metadetect( + obs_w, + obs_d, + obs_dn, + nxy=53, + nxy_psf=53, + skip_obs_wide_corrections=skip_wide, + skip_obs_deep_corrections=skip_deep, + scale=scale, + ) + + return measure_mcal_shear_quants(res), measure_mcal_shear_quants(res_ngmix) + + +def _run_sim_pair_jax_and_ngmix( + seed, s2n, deep_noise_fac, deep_psf_fac, skip_wide, skip_deep +): + res_p, res_p_ngmix = _run_single_sim_jax_and_ngmix( + seed, + s2n, + 0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + res_m, res_m_ngmix = _run_single_sim_jax_and_ngmix( + seed, + s2n, + -0.02, + 0.0, + deep_noise_fac, + deep_psf_fac, + skip_wide, + skip_deep, + ) + + return (res_p, res_m), (res_p_ngmix, res_m_ngmix) + + def test_metadetect_single_band_deep_field_metadetect_smoke(): res_p, res_m = _run_sim_pair(1234, 1e4, 1.0 / np.sqrt(10), 1, False, False) for col in res_p.dtype.names: @@ -98,6 +173,74 @@ def test_metadetect_single_band_deep_field_metadetect_smoke(): assert np.isfinite(res_m[col]).all() +@pytest.mark.parametrize("deep_psf_ratio", [0.8, 1, 1.1]) +def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_ratio): + def recursive_allclose(d1, d2, atol=1, rtol=1, equal_nan=True): + return all( + np.allclose(d1[k], d2[k], atol=atol, rtol=rtol, equal_nan=equal_nan) + for k in len(d1) + ) + + nsims = 5 + noise_fac = 1 / np.sqrt(30) + + rng = np.random.RandomState(seed=3412) + seeds = rng.randint(size=nsims, low=1, high=2**29) + res_p = [] + res_m = [] + res_p_ngmix = [] + res_m_ngmix = [] + for seed in seeds: + res, res_ngmix = _run_sim_pair_jax_and_ngmix( + seed, 1e4, noise_fac, deep_psf_ratio, False, False + ) + if res is not None: + res_p.append(res[0]) + res_m.append(res[1]) + res_p_ngmix.append(res_ngmix[0]) + res_m_ngmix.append(res_ngmix[1]) + + np.allclose( + res[0].tolist(), + res_ngmix[0].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + np.allclose( + res[1].tolist(), + res_ngmix[1].tolist(), + atol=1e-5, + rtol=0.01, + equal_nan=True, + ) + + m, merr, c1, c1err, c2, c2err = estimate_m_and_c( + np.concatenate(res_p), + np.concatenate(res_m), + 0.02, + jackknife=len(res_p), + ) + + m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng = estimate_m_and_c( + np.concatenate(res_p_ngmix), + np.concatenate(res_m_ngmix), + 0.02, + jackknife=len(res_p_ngmix), + ) + + np.allclose(m, m_ng, atol=1e-12) + np.allclose(merr, merr_ng, atol=1e-12) + np.allclose(c1err, c1err_ng, atol=1e-12) + np.allclose(c1, c1_ng, atol=1e-12) + np.allclose(c2err, c2err_ng, atol=1e-12) + np.allclose(c2, c2_ng, atol=1e-12) + + print_m_c(m, merr, c1, c1err, c2, c2err) + print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + def test_metadetect_single_band_deep_field_metadetect_bmask(): nxy = 201 nxy_psf = 53 @@ -234,13 +377,6 @@ def test_metadetect_single_band_deep_field_metadetect(deep_psf_ratio): rng = np.random.RandomState(seed=34132) seeds = rng.randint(size=nsims, low=1, high=2**29) - # jobs = [ - # joblib.delayed(_run_sim_pair)( - # seed, 1e4, noise_fac, deep_psf_ratio, False, False - # ) - # for seed in seeds - # ] - # outputs = joblib.Parallel(n_jobs=-1, verbose=10)(jobs) res_p = [] res_m = [] for seed in seeds: From d91beefe9b52d1779ac50823fa1f69731c4c40da Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 15 Apr 2025 13:01:31 -0500 Subject: [PATCH 22/59] fix tests --- .../jaxify/tests/test_jax_deep_metacal.py | 20 +++++++------- .../jaxify/tests/test_jax_metacal.py | 20 +++++++------- .../jaxify/tests/test_jax_metadetect.py | 26 +++++++------------ 3 files changed, 30 insertions(+), 36 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index a59aeab..23814c5 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -200,18 +200,18 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): res_p_ngmix.append(res_ngmix[0]) res_m_ngmix.append(res_ngmix[1]) - np.allclose( + assert np.allclose( res[0].tolist(), res_ngmix[0].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) - np.allclose( + assert np.allclose( res[1].tolist(), res_ngmix[1].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) @@ -229,12 +229,12 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): jackknife=len(res_p_ngmix), ) - np.allclose(m, m_ng, atol=1e-12) - np.allclose(merr, merr_ng, atol=1e-12) - np.allclose(c1err, c1err_ng, atol=1e-12) - np.allclose(c1, c1_ng, atol=1e-12) - np.allclose(c2err, c2err_ng, atol=1e-12) - np.allclose(c2, c2_ng, atol=1e-12) + assert np.allclose(m, m_ng, atol=1e-4) + assert np.allclose(merr, merr_ng, atol=1e-7) + assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(c1, c1_ng, atol=1e-4) + assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) assert_m_c_ok(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 40eb741..4cc311b 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -149,18 +149,18 @@ def test_metacal_jax_vs_ngmix(): res_p_ngmix.append(res_ngmix[0]) res_m_ngmix.append(res_ngmix[1]) - np.allclose( + assert np.allclose( res[0].tolist(), res_ngmix[0].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) - np.allclose( + assert np.allclose( res[1].tolist(), res_ngmix[1].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) @@ -178,12 +178,12 @@ def test_metacal_jax_vs_ngmix(): jackknife=len(res_p_ngmix), ) - np.allclose(m, m_ng, atol=1e-12) - np.allclose(merr, merr_ng, atol=1e-12) - np.allclose(c1err, c1err_ng, atol=1e-12) - np.allclose(c1, c1_ng, atol=1e-12) - np.allclose(c2err, c2err_ng, atol=1e-12) - np.allclose(c2, c2_ng, atol=1e-12) + assert np.allclose(m, m_ng, atol=1e-4) + assert np.allclose(merr, merr_ng, atol=1e-7) + assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(c1, c1_ng, atol=1e-4) + assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) assert_m_c_ok(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index 29472f8..bbc4dff 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -175,12 +175,6 @@ def test_metadetect_single_band_deep_field_metadetect_smoke(): @pytest.mark.parametrize("deep_psf_ratio", [0.8, 1, 1.1]) def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_ratio): - def recursive_allclose(d1, d2, atol=1, rtol=1, equal_nan=True): - return all( - np.allclose(d1[k], d2[k], atol=atol, rtol=rtol, equal_nan=equal_nan) - for k in len(d1) - ) - nsims = 5 noise_fac = 1 / np.sqrt(30) @@ -200,18 +194,18 @@ def recursive_allclose(d1, d2, atol=1, rtol=1, equal_nan=True): res_p_ngmix.append(res_ngmix[0]) res_m_ngmix.append(res_ngmix[1]) - np.allclose( + assert np.allclose( res[0].tolist(), res_ngmix[0].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) - np.allclose( + assert np.allclose( res[1].tolist(), res_ngmix[1].tolist(), atol=1e-5, - rtol=0.01, + rtol=0.025, equal_nan=True, ) @@ -229,12 +223,12 @@ def recursive_allclose(d1, d2, atol=1, rtol=1, equal_nan=True): jackknife=len(res_p_ngmix), ) - np.allclose(m, m_ng, atol=1e-12) - np.allclose(merr, merr_ng, atol=1e-12) - np.allclose(c1err, c1err_ng, atol=1e-12) - np.allclose(c1, c1_ng, atol=1e-12) - np.allclose(c2err, c2err_ng, atol=1e-12) - np.allclose(c2, c2_ng, atol=1e-12) + assert np.allclose(m, m_ng, atol=1e-4) + assert np.allclose(merr, merr_ng, atol=1e-7) + assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(c1, c1_ng, atol=1e-4) + assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) From f2043032059f26a5111162c3a5cf82a6a05e98a3 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 15 Apr 2025 15:20:49 -0500 Subject: [PATCH 23/59] minor --- deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py | 6 +++--- deep_field_metadetect/jaxify/tests/test_jax_metacal.py | 6 +++--- deep_field_metadetect/jaxify/tests/test_jax_metadetect.py | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 23814c5..29cc4b0 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -230,10 +230,10 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-7) - assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(merr, merr_ng, atol=1e-6) + assert np.allclose(c1err, c1err_ng, atol=1e-6) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2err, c2err_ng, atol=1e-6) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 4cc311b..52ea871 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -179,10 +179,10 @@ def test_metacal_jax_vs_ngmix(): ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-7) - assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(merr, merr_ng, atol=1e-6) + assert np.allclose(c1err, c1err_ng, atol=1e-6) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2err, c2err_ng, atol=1e-6) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index bbc4dff..d3c1f84 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -224,10 +224,10 @@ def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_rati ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-7) - assert np.allclose(c1err, c1err_ng, atol=1e-7) + assert np.allclose(merr, merr_ng, atol=1e-6) + assert np.allclose(c1err, c1err_ng, atol=1e-6) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-7) + assert np.allclose(c2err, c2err_ng, atol=1e-6) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) From 6c8d922724830f853c6c57b940d8213d6b7fb598 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 16 Apr 2025 14:22:23 -0500 Subject: [PATCH 24/59] update tests --- deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py | 6 +++--- deep_field_metadetect/jaxify/tests/test_jax_metacal.py | 6 +++--- deep_field_metadetect/jaxify/tests/test_jax_metadetect.py | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 29cc4b0..063fc5d 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -230,10 +230,10 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-6) - assert np.allclose(c1err, c1err_ng, atol=1e-6) + assert np.allclose(merr, merr_ng, atol=1e-5) + assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-6) + assert np.allclose(c2err, c2err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 52ea871..0261b07 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -179,10 +179,10 @@ def test_metacal_jax_vs_ngmix(): ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-6) - assert np.allclose(c1err, c1err_ng, atol=1e-6) + assert np.allclose(merr, merr_ng, atol=1e-5) + assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-6) + assert np.allclose(c2err, c2err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index d3c1f84..e52e14c 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -224,10 +224,10 @@ def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_rati ) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-6) - assert np.allclose(c1err, c1err_ng, atol=1e-6) + assert np.allclose(merr, merr_ng, atol=1e-5) + assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-6) + assert np.allclose(c2err, c2err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) print_m_c(m, merr, c1, c1err, c2, c2err) From f00ad9868c7f91ad3dc60dc6af22c2915f3bbce2 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 16 Apr 2025 14:23:00 -0500 Subject: [PATCH 25/59] use AffineTransform --- deep_field_metadetect/jaxify/jax_metacal.py | 33 ++++++--------- deep_field_metadetect/jaxify/observation.py | 46 +++++++++------------ 2 files changed, 32 insertions(+), 47 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index 012f70e..c1eef29 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -116,10 +116,10 @@ def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_f pim = reconv_psf.drawImage( nx=nxy_psf, ny=nxy_psf, - wcs=dfmd_obs.psf.jacobian, + wcs=dfmd_obs.aft._local_wcs, offset=jax_galsim.PositionD( - x=dfmd_obs.psf.jac_col0 + 1 - nxy_psf / 2, - y=dfmd_obs.psf.jac_row0 + 1 - nxy_psf / 2, + x=dfmd_obs.aft.origin.x - nxy_psf / 2, + y=dfmd_obs.aft.origin.y - nxy_psf / 2, ), ).array @@ -170,7 +170,7 @@ def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf): """Run metacal on an dfmd obs.""" mcal_image = _jax_metacal_op_g1g2_impl( - wcs=dfmd_obs.jacobian, + wcs=dfmd_obs.aft._local_wcs, image=get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image"), # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing @@ -211,7 +211,7 @@ def jax_metacal_op_shears( step=step, ) - wcs = dfmd_obs.jacobian + wcs = dfmd_obs.aft._local_wcs image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing @@ -247,7 +247,7 @@ def jax_metacal_op_shears( @partial(jax.jit, static_argnames=["nxy", "nxy_psf"]) def jax_match_psf(dfmd_obs, reconv_psf, nxy, nxy_psf): """Match the PSF on an dfmd observation to a new PSF.""" - wcs = dfmd_obs.jacobian + wcs = dfmd_obs.aft._local_wcs image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") @@ -277,10 +277,10 @@ def jax_add_dfmd_obs( ) -> DFMdetObservation: """Add two dfmd observations""" - if repr(dfmd_obs1.jacobian) != repr(dfmd_obs2.jacobian): + if repr(dfmd_obs1.aft) != repr(dfmd_obs2.aft): raise RuntimeError( "Jacobians must be equal to add dfmd observations! %s != %s" - % (repr(dfmd_obs1.jacobian), repr(dfmd_obs2.jacobian)), + % (repr(dfmd_obs1.aft), repr(dfmd_obs2.aft)), ) if dfmd_obs1.image.shape != dfmd_obs2.image.shape: @@ -353,21 +353,12 @@ def jax_add_dfmd_obs( bmask=new_bmask, ormask=new_ormask, noise=new_noise, - jacobian=jax_galsim.wcs.JacobianWCS( - dudx=dfmd_obs1.jacobian.dudx, - dudy=dfmd_obs1.jacobian.dudy, - dvdx=dfmd_obs1.jacobian.dvdx, - dvdy=dfmd_obs1.jacobian.dvdy, - ), + aft=dfmd_obs1.aft, psf=new_psf, - meta=new_meta_data, # Directly copy metadata + meta=new_meta_data, mfrac=new_mfrac, store_pixels=getattr(dfmd_obs1, "store_pixels", True), ignore_zero_weight=getattr(dfmd_obs1, "ignore_zero_weight", True), - jac_row0=dfmd_obs1.jac_row0, - jac_col0=dfmd_obs1.jac_col0, - jac_det=dfmd_obs1.jac_det, - jac_scale=dfmd_obs1.jac_scale, ) return obs @@ -378,7 +369,7 @@ def get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image", rot90=0): return jax_galsim.InterpolatedImage( jax_galsim.ImageD( jnp.rot90(getattr(dfmd_obs, kind).copy(), k=rot90), - wcs=dfmd_obs.jacobian, + wcs=dfmd_obs.aft._local_wcs, ), x_interpolant="lanczos15", ) @@ -386,7 +377,7 @@ def get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image", rot90=0): def get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs, kind="image"): """Make an interpolated image from an DFMdet obs w/o a pixel.""" - wcs = dfmd_obs.jacobian + wcs = dfmd_obs.aft._local_wcs return jax_galsim.Convolve( [ get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=kind), diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py index e1c9aad..7a75d20 100644 --- a/deep_field_metadetect/jaxify/observation.py +++ b/deep_field_metadetect/jaxify/observation.py @@ -4,7 +4,6 @@ import jax_galsim import ngmix import numpy as np -from ngmix.jacobian import Jacobian from ngmix.observation import Observation @@ -15,13 +14,9 @@ class DFMdetObservation(NamedTuple): bmask: Optional[jax.Array] ormask: Optional[jax.Array] noise: Optional[jax.Array] - jacobian: Optional[jax.Array] + aft: Optional[jax_galsim.wcs.AffineTransform] psf: Optional["DFMdetObservation"] mfrac: Optional[jax.Array] - jac_row0: Optional[float] - jac_col0: Optional[float] - jac_det: Optional[float] - jac_scale: Optional[float] meta: Optional[dict] store_pixels: bool ignore_zero_weight: bool @@ -33,13 +28,9 @@ def tree_flatten(self): self.bmask, self.ormask, self.noise, - self.jacobian, + self.aft, self.psf, self.mfrac, - self.jac_row0, - self.jac_col0, - self.jac_det, - self.jac_scale, ) aux_data = (self.meta, self.store_pixels, self.ignore_zero_weight) @@ -90,16 +81,21 @@ def ngmix_obs_to_dfmd_obs(obs: ngmix.observation.Observation) -> DFMdetObservati bmask=obs.bmask if obs.has_bmask() else None, ormask=obs.ormask if obs.has_ormask() else None, noise=obs.noise if obs.has_noise() else None, - jacobian=jax_galsim.BaseWCS().from_galsim(jacobian.get_galsim_wcs()), + aft=jax_galsim.wcs.AffineTransform( + dudx=jacobian.dudcol, + dudy=jacobian.dudrow, + dvdx=jacobian.dvdcol, + dvdy=jacobian.dvdrow, + origin=jax_galsim.PositionD( + y=jacobian.row0 + 1, + x=jacobian.col0 + 1, + ), + ), psf=psf, - meta=obs.meta, # Directly copy metadata + meta=obs.meta, mfrac=obs.mfrac if obs.has_mfrac() else None, store_pixels=getattr(obs, "store_pixels", True), ignore_zero_weight=getattr(obs, "ignore_zero_weight", True), - jac_row0=jacobian.row0, - jac_col0=jacobian.col0, - jac_det=jacobian.det, - jac_scale=jacobian.scale, ) @@ -113,15 +109,13 @@ def dfmd_obs_to_ngmix_obs(dfmd_obs) -> Observation: bmask=dfmd_obs.bmask, ormask=dfmd_obs.ormask, noise=dfmd_obs.noise if dfmd_obs.noise is None else np.array(dfmd_obs.noise), - jacobian=Jacobian( - row=dfmd_obs.jac_row0, - col=dfmd_obs.jac_col0, - dudcol=dfmd_obs.jacobian.dudx, - dudrow=dfmd_obs.jacobian.dudy, - dvdcol=dfmd_obs.jacobian.dvdx, - dvdrow=dfmd_obs.jacobian.dvdy, - det=dfmd_obs.jac_det, - scale=dfmd_obs.jac_scale, + jacobian=ngmix.jacobian.Jacobian( + row=dfmd_obs.aft.origin.y - 1, + col=dfmd_obs.aft.origin.x - 1, + dudcol=dfmd_obs.aft.dudx, + dudrow=dfmd_obs.aft.dudy, + dvdcol=dfmd_obs.aft.dvdx, + dvdrow=dfmd_obs.aft.dvdy, ), psf=psf, mfrac=dfmd_obs.mfrac if dfmd_obs.mfrac is None else np.array(dfmd_obs.mfrac), From 794402fb421cd7f5280895ef76b319a9220f8112 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 16 Apr 2025 14:23:52 -0500 Subject: [PATCH 26/59] minor --- deep_field_metadetect/jaxify/jax_metacal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index c1eef29..4cc67dd 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -279,7 +279,7 @@ def jax_add_dfmd_obs( if repr(dfmd_obs1.aft) != repr(dfmd_obs2.aft): raise RuntimeError( - "Jacobians must be equal to add dfmd observations! %s != %s" + "AffineTransforms must be equal to add dfmd observations! %s != %s" % (repr(dfmd_obs1.aft), repr(dfmd_obs2.aft)), ) From aaea974786a63a27ebc6d4a9471adae90c30258b Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 18 Apr 2025 12:08:49 -0500 Subject: [PATCH 27/59] bug fix --- deep_field_metadetect/jaxify/jax_metacal.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index 4cc67dd..f87f4d0 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -116,10 +116,10 @@ def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_f pim = reconv_psf.drawImage( nx=nxy_psf, ny=nxy_psf, - wcs=dfmd_obs.aft._local_wcs, + wcs=dfmd_obs.psf.aft._local_wcs, offset=jax_galsim.PositionD( - x=dfmd_obs.aft.origin.x - nxy_psf / 2, - y=dfmd_obs.aft.origin.y - nxy_psf / 2, + x=dfmd_obs.psf.aft.origin.x - nxy_psf / 2, + y=dfmd_obs.psf.aft.origin.y - nxy_psf / 2, ), ).array From 1f2851182b0e8f94a6690bd45205fb7264249e8b Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 18 Apr 2025 12:13:19 -0500 Subject: [PATCH 28/59] fix bug in testing --- .../jaxify/tests/test_jax_metadetect.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index e52e14c..2e54490 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -96,22 +96,30 @@ def _run_single_sim_jax_and_ngmix( skip_wide, skip_deep, ): - nxy = 53 + nxy = 201 nxy_psf = 53 scale = 0.2 + # Creating ngmix and dfmdet observations obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix = make_simple_sim( seed=seed, g1=g1, g2=g2, s2n=s2n, + deep_noise_fac=deep_noise_fac, + deep_psf_fac=deep_psf_fac, dim=nxy, dim_psf=nxy_psf, scale=scale, - deep_noise_fac=deep_noise_fac, - deep_psf_fac=deep_psf_fac, + buff=25, + n_objs=10, return_dfmd_obs=False, ) + + obs_w = ngmix_obs_to_dfmd_obs(obs_w_ngmix) + obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) + obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) + res_ngmix = single_band_deep_field_metadetect( obs_w_ngmix, obs_d_ngmix, @@ -120,16 +128,12 @@ def _run_single_sim_jax_and_ngmix( skip_obs_deep_corrections=skip_deep, ) - obs_w = ngmix_obs_to_dfmd_obs(obs_w_ngmix) - obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) - obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) - res = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, - nxy=53, - nxy_psf=53, + nxy=nxy, + nxy_psf=nxy_psf, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, scale=scale, From 227b8b8d9dd347dd8c7e0728a62a18641dd8e351 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 18 Apr 2025 12:13:38 -0500 Subject: [PATCH 29/59] minor changes --- deep_field_metadetect/jaxify/tests/test_jax_metadetect.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index 2e54490..4030159 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -182,7 +182,7 @@ def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_rati nsims = 5 noise_fac = 1 / np.sqrt(30) - rng = np.random.RandomState(seed=3412) + rng = np.random.RandomState(seed=34132) seeds = rng.randint(size=nsims, low=1, high=2**29) res_p = [] res_m = [] @@ -390,6 +390,8 @@ def test_metadetect_single_band_deep_field_metadetect(deep_psf_ratio): jackknife=len(res_p), ) + assert np.isfinite(m) + assert np.isfinite(merr) print_m_c(m, merr, c1, c1err, c2, c2err) assert_m_c_ok(m, merr, c1, c1err, c2, c2err) From 81d12d78d6d6f4e6a0261145eee7d88767aee595 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 21 Apr 2025 10:58:10 -0500 Subject: [PATCH 30/59] minor --- .../jaxify/tests/test_jax_deep_metacal.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 063fc5d..8536f32 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -203,7 +203,7 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): assert np.allclose( res[0].tolist(), res_ngmix[0].tolist(), - atol=1e-5, + atol=5e-4, rtol=0.025, equal_nan=True, ) @@ -231,12 +231,15 @@ def test_jax_vs_ngmix_comparison(deep_psf_ratio): assert np.allclose(m, m_ng, atol=1e-4) assert np.allclose(merr, merr_ng, atol=1e-5) - assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-5) + assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) + assert np.allclose(c2err, c2err_ng, atol=1e-5) + print("JAX results:") print_m_c(m, merr, c1, c1err, c2, c2err) + print("ngmix results:") + print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) assert_m_c_ok(m, merr, c1, c1err, c2, c2err) From 522abac564ba1cfb75af3201de8a633aef781003 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 21 Apr 2025 10:59:43 -0500 Subject: [PATCH 31/59] minor --- .../jaxify/tests/test_jax_metadetect.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index 4030159..dbf5b95 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -227,17 +227,19 @@ def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_rati jackknife=len(res_p_ngmix), ) - assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-5) + print("JAX results:") + print_m_c(m, merr, c1, c1err, c2, c2err) + print("ngmix results:") + print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + + assert np.allclose(m, m_ng, atol=5e-3) + assert np.allclose(merr, merr_ng, atol=5e-4) assert np.allclose(c1err, c1err_ng, atol=1e-5) assert np.allclose(c1, c1_ng, atol=1e-4) assert np.allclose(c2err, c2err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) - print_m_c(m, merr, c1, c1err, c2, c2err) - print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) - assert_m_c_ok(m, merr, c1, c1err, c2, c2err) - def test_metadetect_single_band_deep_field_metadetect_bmask(): nxy = 201 From 657ac5dd280e6c4360886f59802488b0558be4ff Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 21 Apr 2025 11:00:53 -0500 Subject: [PATCH 32/59] minor --- deep_field_metadetect/jaxify/tests/test_jax_metacal.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index 0261b07..f505a46 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -178,6 +178,12 @@ def test_metacal_jax_vs_ngmix(): jackknife=len(res_p_ngmix), ) + print("JAX results:") + print_m_c(m, merr, c1, c1err, c2, c2err) + print("ngmix results:") + print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) + assert_m_c_ok(m, merr, c1, c1err, c2, c2err) + assert np.allclose(m, m_ng, atol=1e-4) assert np.allclose(merr, merr_ng, atol=1e-5) assert np.allclose(c1err, c1err_ng, atol=1e-5) @@ -185,9 +191,6 @@ def test_metacal_jax_vs_ngmix(): assert np.allclose(c2err, c2err_ng, atol=1e-5) assert np.allclose(c2, c2_ng, atol=1e-4) - print_m_c(m, merr, c1, c1err, c2, c2err) - assert_m_c_ok(m, merr, c1, c1err, c2, c2err) - def test_metacal(): nsims = 50 From 63f28776c6bdb1ec317f5c07d5024c82af58f2cc Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 2 May 2025 10:28:50 -0500 Subject: [PATCH 33/59] [skip ci] deconvolution --- notebooks/test_jax_metacal.ipynb | 1372 ++++++++++++++++++++++++++++++ 1 file changed, 1372 insertions(+) create mode 100644 notebooks/test_jax_metacal.ipynb diff --git a/notebooks/test_jax_metacal.ipynb b/notebooks/test_jax_metacal.ipynb new file mode 100644 index 0000000..aac84ac --- /dev/null +++ b/notebooks/test_jax_metacal.ipynb @@ -0,0 +1,1372 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "413f6098-e2b7-4e7d-a1c0-f9e9c0309959", + "metadata": {}, + "outputs": [], + "source": [ + "import multiprocessing\n", + "import os\n", + "\n", + "os.environ[\"JAX_ENABLE_X64\"] = \"True\"\n", + "\n", + "import joblib\n", + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import pytest\n", + "import galsim\n", + "import jax_galsim\n", + "\n", + "from deep_field_metadetect.metacal import (\n", + " get_galsim_object_from_ngmix_obs,\n", + " DEFAULT_STEP,\n", + " DEFAULT_SHEARS,\n", + " get_shear_tuple,\n", + " _metacal_op_g1g2_impl,\n", + " _render_psf_and_build_obs,\n", + " get_galsim_object_from_ngmix_obs_nopix,\n", + ")\n", + "from deep_field_metadetect.jaxify.jax_metacal import (\n", + " get_jax_galsim_object_from_dfmd_obs,\n", + " compute_stepk,\n", + " _jax_metacal_op_g1g2_impl,\n", + " _jax_render_psf_and_build_obs,\n", + " get_jax_galsim_object_from_dfmd_obs_nopix,\n", + ")\n", + "from deep_field_metadetect.utils import (\n", + " assert_m_c_ok,\n", + " estimate_m_and_c,\n", + " fit_gauss_mom_mcal_res,\n", + " make_simple_sim,\n", + " measure_mcal_shear_quants,\n", + " print_m_c,\n", + ")\n", + "from deep_field_metadetect.jaxify.observation import (\n", + " ngmix_obs_to_dfmd_obs,\n", + " dfmd_obs_to_ngmix_obs,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "79a8da6d-cf72-4909-856b-61c18b1b8e5d", + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aeeeb953-cf1b-4110-a24a-974a25643353", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import jax\n", + "from functools import partial" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d4eb1e9-d420-4671-8ce0-f485ea204d53", + "metadata": {}, + "outputs": [], + "source": [ + "@partial(jax.jit, static_argnames=[\"dk\", \"nxy_psf\"])\n", + "def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1):\n", + " \"\"\"Gets the target reconvolution PSF for an input PSF object.\n", + "\n", + " This is taken from galsim/tests/test_metacal.py and assumes the psf is\n", + " centered.\n", + "\n", + " Parameters\n", + " ----------\n", + " psf : galsim.GSObject\n", + " The input point spread function (PSF) object.\n", + " dk : float\n", + " The Fourier-space pixel scale.\n", + " nxy_psf : int, optional\n", + " The size of the PSF image in pixels (default is 53).\n", + " step : float, optional\n", + " The step size for coordinate grids (default is `DEFAULT_STEP`).\n", + " flux : float, optional\n", + " The total flux of the output PSF (default is 1).\n", + "\n", + " Returns\n", + " -------\n", + " reconv_psf : JaxGalsim object\n", + " The reconvolution PSF.\n", + " \"\"\"\n", + "\n", + " dk = 2 * jnp.pi / (53 * 0.2) / 4.0\n", + " small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue\n", + " smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k\n", + "\n", + " kim = psf.drawKImage(nx=250, ny=250, scale=dk)\n", + " # kim = psf.drawKImage(scale=dk)\n", + " karr_r = kim.real.array\n", + " # Find the smallest r where the kval < small_kval\n", + " nk = karr_r.shape[0]\n", + " kx, ky = jnp.meshgrid(jnp.arange(-nk / 2, nk / 2), jnp.arange(-nk / 2, nk / 2))\n", + " ksq = (kx**2 + ky**2) * dk**2\n", + " ksq_max = jnp.min(jnp.where(karr_r < small_kval * psf.flux, ksq, jnp.inf))\n", + "\n", + " # We take our target PSF to be the (round) Gaussian that is even smaller at\n", + " # this ksq\n", + " # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval\n", + " sigma_sq = -2.0 * jnp.log(smaller_kval) / ksq_max\n", + "\n", + " dilation = 1.0 + 2.0 * step\n", + " return (\n", + " jax_galsim.Gaussian(sigma=jnp.sqrt(sigma_sq) * dilation).withFlux(flux),\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " )\n", + "\n", + "\n", + "def jax_get_gauss_reconv_psf(dfmd_obs, nxy_psf, dk, step=DEFAULT_STEP):\n", + " \"\"\"Get the Gaussian reconv PSF for a DFMdetObs.\"\"\"\n", + " psf = get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs.psf, kind=\"image\")\n", + " return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7dc583fe-fba2-45df-a7e7-a5251cf6f657", + "metadata": {}, + "outputs": [], + "source": [ + "def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1):\n", + " \"\"\"Gets the target reconvolution PSF for an input PSF object.\n", + "\n", + " This is taken from galsim/tests/test_metacal.py and assumes the psf is\n", + " centered.\n", + "\n", + " Parameters\n", + " ----------\n", + " psf : galsim object\n", + " The PSF.\n", + " flux : float\n", + " The output flux of the PSF. Defaults to 1.\n", + "\n", + " Returns\n", + " -------\n", + " reconv_psf : galsim object\n", + " The reconvolution PSF.\n", + " \"\"\"\n", + " dk = 2 * np.pi / (53 * 0.2) / 4.0\n", + "\n", + " small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue\n", + " smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k\n", + "\n", + " kim = psf.drawKImage(nx=250, ny=250, scale=dk)\n", + " karr_r = kim.real.array\n", + " # Find the smallest r where the kval < small_kval\n", + " nk = karr_r.shape[0]\n", + " kx, ky = np.meshgrid(np.arange(-nk / 2, nk / 2), np.arange(-nk / 2, nk / 2))\n", + " ksq = (kx**2 + ky**2) * dk**2\n", + " ksq_max = np.min(ksq[karr_r < small_kval * psf.flux])\n", + "\n", + " # We take our target PSF to be the (round) Gaussian that is even smaller at\n", + " # this ksq\n", + " # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval\n", + " sigma_sq = -2.0 * np.log(smaller_kval) / ksq_max\n", + "\n", + " dilation = 1.0 + 2.0 * step\n", + " return (\n", + " galsim.Gaussian(sigma=np.sqrt(sigma_sq) * dilation).withFlux(flux),\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " )\n", + "\n", + "\n", + "def get_gauss_reconv_psf(obs, step=DEFAULT_STEP):\n", + " \"\"\"Get the Gaussian reconv PSF for an ngmix obs.\"\"\"\n", + " psf = get_galsim_object_from_ngmix_obs_nopix(obs.psf, kind=\"image\")\n", + " return get_gauss_reconv_psf_galsim(psf, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "71df4dd2-5863-40e0-b436-15890263e2a9", + "metadata": {}, + "outputs": [], + "source": [ + "def jax_metacal_op_shears(\n", + " dfmd_obs,\n", + " nxy_psf=53,\n", + " reconv_psf=None,\n", + " shears=None,\n", + " step=DEFAULT_STEP,\n", + " scale=0.2,\n", + "):\n", + " \"\"\"Run metacal on an dfmd observation.\"\"\"\n", + " if shears is None:\n", + " shears = DEFAULT_SHEARS\n", + "\n", + " dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf)\n", + " if reconv_psf is None:\n", + " reconv_psf, kim, karr_r, ksq_max, sigma_sq, dilation = jax_get_gauss_reconv_psf(\n", + " dfmd_obs,\n", + " dk=dk,\n", + " nxy_psf=nxy_psf,\n", + " step=step,\n", + " )\n", + "\n", + " wcs = dfmd_obs.aft._local_wcs\n", + " image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"image\")\n", + " # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl\n", + " # rotates back after deconv and shearing\n", + " noise = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"noise\", rot90=1)\n", + " psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind=\"image\")\n", + "\n", + " # psf=psf.withGSParams(\n", + " # minimum_fft_size=100 * 4,\n", + " # maximum_fft_size=100 * 4,\n", + " # )\n", + " psf_inv = jax_galsim.Deconvolve(\n", + " psf,\n", + " gsparams=jax_galsim.GSParams(minimum_fft_size=32 * 8, maximum_fft_size=32 * 8),\n", + " propagate_gsparams=False,\n", + " )\n", + "\n", + " mcal_res = {}\n", + " for shear in shears:\n", + " g1, g2 = get_shear_tuple(shear, step)\n", + "\n", + " mcal_image = _jax_metacal_op_g1g2_impl(\n", + " wcs=wcs,\n", + " image=image,\n", + " noise=noise,\n", + " psf_inv=psf_inv,\n", + " dims=dfmd_obs.image.shape,\n", + " reconv_psf=reconv_psf,\n", + " g1=g1,\n", + " g2=g2,\n", + " )\n", + "\n", + " mcal_res[shear] = _jax_render_psf_and_build_obs(\n", + " mcal_image,\n", + " dfmd_obs,\n", + " reconv_psf,\n", + " nxy_psf=nxy_psf,\n", + " weight_fac=0.5,\n", + " )\n", + " return (\n", + " mcal_res,\n", + " image,\n", + " noise,\n", + " psf,\n", + " psf_inv,\n", + " mcal_image,\n", + " mcal_res,\n", + " reconv_psf,\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "b8fcbd3c-39a3-496d-ac4f-f01ef3ec8cfd", + "metadata": {}, + "outputs": [], + "source": [ + "def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP):\n", + " \"\"\"Run metacal on an ngmix observation.\"\"\"\n", + " if shears is None:\n", + " shears = DEFAULT_SHEARS\n", + "\n", + " if reconv_psf is None:\n", + " reconv_psf, kim, karr_r, ksq_max, sigma_sq, dilation = get_gauss_reconv_psf(\n", + " obs, step=step\n", + " )\n", + "\n", + " wcs = obs.jacobian.get_galsim_wcs()\n", + " image = get_galsim_object_from_ngmix_obs(obs, kind=\"image\")\n", + " # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl\n", + " # rotates back after deconv and shearing\n", + " noise = get_galsim_object_from_ngmix_obs(obs, kind=\"noise\", rot90=1)\n", + " psf = get_galsim_object_from_ngmix_obs(obs.psf, kind=\"image\")\n", + " psf_inv = galsim.Deconvolve(psf)\n", + "\n", + " mcal_res = {}\n", + " for shear in shears:\n", + " g1, g2 = get_shear_tuple(shear, step)\n", + " mcal_image = _metacal_op_g1g2_impl(\n", + " wcs=wcs,\n", + " image=image,\n", + " noise=noise,\n", + " psf_inv=psf_inv,\n", + " dims=obs.image.shape,\n", + " reconv_psf=reconv_psf,\n", + " g1=g1,\n", + " g2=g2,\n", + " )\n", + " mcal_res[shear] = _render_psf_and_build_obs(\n", + " mcal_image, obs, reconv_psf, weight_fac=0.5\n", + " )\n", + " return (\n", + " mcal_res,\n", + " image,\n", + " noise,\n", + " psf,\n", + " psf_inv,\n", + " mcal_image,\n", + " mcal_res,\n", + " reconv_psf,\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "8acc01b5-c002-483f-b553-47b20e5c9185", + "metadata": {}, + "outputs": [], + "source": [ + "def _run_single_sim_pair_jax_and_ngmix(seed, s2n):\n", + " nxy = 53\n", + " nxy_psf = 53\n", + " scale = 0.2\n", + " obs_plus, *_ = make_simple_sim(\n", + " seed=seed,\n", + " g1=0.02,\n", + " g2=0.0,\n", + " s2n=s2n,\n", + " dim=nxy,\n", + " dim_psf=nxy_psf,\n", + " scale=scale,\n", + " deep_noise_fac=1.0 / np.sqrt(10),\n", + " deep_psf_fac=1.0,\n", + " return_dfmd_obs=False,\n", + " )\n", + "\n", + " (\n", + " mcal_res_ngmix,\n", + " image_ngmix,\n", + " noise_ngmix,\n", + " psf_ngmix,\n", + " psf_inv_ngmix,\n", + " mcal_image_ngmix,\n", + " mcal_res_ngmix,\n", + " reconv_psf_ngmix,\n", + " kim_ngmix,\n", + " karr_r_ngmix,\n", + " ksq_max_ngmix,\n", + " sigma_sq_ngmix,\n", + " dilation_ngmix,\n", + " ) = metacal_op_shears(obs_plus)\n", + "\n", + " res_p_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix)\n", + " res_p_ngmix = measure_mcal_shear_quants(res_p_ngmix)\n", + " old_obs_plus = obs_plus.copy()\n", + " obs_plus = ngmix_obs_to_dfmd_obs(obs_plus)\n", + "\n", + " (\n", + " mcal_res,\n", + " image,\n", + " noise,\n", + " psf,\n", + " psf_inv,\n", + " mcal_image,\n", + " mcal_res,\n", + " reconv_psf,\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " ) = jax_metacal_op_shears(\n", + " obs_plus,\n", + " nxy_psf=nxy_psf,\n", + " scale=scale,\n", + " )\n", + " res_p = fit_gauss_mom_mcal_res(mcal_res)\n", + " res_p = measure_mcal_shear_quants(res_p)\n", + "\n", + " obs_minus, *_ = make_simple_sim(\n", + " seed=seed,\n", + " g1=-0.02,\n", + " g2=0.0,\n", + " s2n=s2n,\n", + " dim=nxy,\n", + " dim_psf=nxy_psf,\n", + " scale=scale,\n", + " deep_noise_fac=1.0 / np.sqrt(10),\n", + " deep_psf_fac=1.0,\n", + " return_dfmd_obs=False,\n", + " )\n", + "\n", + " (\n", + " mcal_res_ngmix,\n", + " image_ngmix,\n", + " noise_ngmix,\n", + " psf_ngmix,\n", + " psf_inv_ngmix,\n", + " mcal_image_ngmix,\n", + " mcal_res_ngmix,\n", + " reconv_psf_ngmix,\n", + " kim_ngmix,\n", + " karr_r_ngmix,\n", + " ksq_max_ngmix,\n", + " sigma_sq_ngmix,\n", + " dilation_ngmix,\n", + " ) = metacal_op_shears(obs_minus)\n", + " res_m_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix)\n", + " res_m_ngmix = measure_mcal_shear_quants(res_m_ngmix)\n", + "\n", + " obs_minus = ngmix_obs_to_dfmd_obs(obs_minus)\n", + " (\n", + " mcal_res,\n", + " image,\n", + " noise,\n", + " psf,\n", + " psf_inv,\n", + " mcal_image,\n", + " mcal_res,\n", + " reconv_psf,\n", + " kim,\n", + " karr_r,\n", + " ksq_max,\n", + " sigma_sq,\n", + " dilation,\n", + " ) = jax_metacal_op_shears(\n", + " obs_minus,\n", + " nxy_psf=nxy_psf,\n", + " scale=scale,\n", + " )\n", + " res_m = fit_gauss_mom_mcal_res(mcal_res)\n", + " res_m = measure_mcal_shear_quants(res_m)\n", + "\n", + " return (\n", + " (res_p, res_m),\n", + " (res_p_ngmix, res_m_ngmix),\n", + " (mcal_res, image, noise, psf, psf_inv, mcal_image, mcal_res, reconv_psf),\n", + " (\n", + " mcal_res_ngmix,\n", + " image_ngmix,\n", + " noise_ngmix,\n", + " psf_ngmix,\n", + " psf_inv_ngmix,\n", + " mcal_image_ngmix,\n", + " mcal_res_ngmix,\n", + " reconv_psf_ngmix,\n", + " ),\n", + " (old_obs_plus, obs_plus),\n", + " (kim, karr_r, ksq_max, sigma_sq, dilation),\n", + " (kim_ngmix, karr_r_ngmix, ksq_max_ngmix, sigma_sq_ngmix, dilation_ngmix),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "d730cb6d-6537-409b-a036-d7c11cdc0f79", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " (res_p, res_m),\n", + " (res_p_ngmix, res_m_ngmix),\n", + " jax_intermediates,\n", + " numpy_intermediates,\n", + " obs,\n", + " jax_reconv,\n", + " numpy_reconv,\n", + ") = _run_single_sim_pair_jax_and_ngmix(10, 1e8)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "c26a0aea-dbbc-444e-ad14-414796b4543f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ngmix.Jacobian(row=26.0, col=26.0, dvdrow=0.2, dvdcol=0.0, dudrow=0.0, dudcol=0.2)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs[0].psf.jacobian" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "cc6f4c9d-5839-404f-9802-26fe4f07cfbd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.AffineTransform(0.2, 0.0, 0.0, 0.2, origin=galsim.PositionD(x=27.0, y=27.0), world_origin=galsim.PositionD(x=0.0, y=0.0))" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obs[1].psf.aft" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "0601d546-7c0d-4b1d-a44e-ad8f9e7f8962", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([(0.01308229, 0.00436202, 0.00872346, 0.00436275, -0.00436436, -1.1700455e-08, 1., 1., 1., 1., 1., 1.)],\n", + " dtype=[('wmom_tot_g1p', '" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare reconv psf\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_reconv[1])\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax reconv\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(jax_reconv[1] - numpy_reconv[1])\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"psf diff\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "75ff8542-bb9a-4a9f-b70b-7332ac0ba2f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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972nEiBEqLCwMbfeLX/xCq1ev1v/+7/+Gzbb/xBNPqF+/fpo6dWqrE7L5/X6Vl5frhhtu0K233qq0tDSVlpaGXVf7ve99T48//rh++ctfat++fcrOzta4ceM0Z84c+XwkJgKxdujQITU0NESk7uTk5HZxDT2A9ufcc8/V//3f/2nmzJkqKytTMBhUXl6efvvb3yovL8+43vvvv1+dOnXSr371K73yyivKz8/Xn//8Zw0bNsy189mkSZNUXV2txx57TH/60590zjnn6Le//a2WLVum1atXG9c7a9YsvfvuuyorK9Pnn3+uESNG6Je//KU6deokSRo4cKAKCwv1+9//Xv/85z/VqVMnDRw4UH/84x914YUXuvLcAC/yaixl2a1dQAcgJp544gn993//t6qqqtSrV6+otDlp0iQ9//zzjn7pAhBbhw4dUp9Tuqh6dyAi9WdnZ2v79u1xG9gAwPHYt2+fTjjhBN1zzz362c9+FuvuNLN69WpdfvnlWrZsma655ppYdwfwFC/HUmQQAXFq165dsixLmZmZse4KgHakoaFB1bsD+nhDb6WnuZvRV/t5UKcM/kgNDQ1xGdQAQEu++OKLZnc8mzdvnqTDE84CwFd5OZZigAiIMzU1NXr++ee1cOFC5efnh1KIAaAtuqRZ6pLmztwaRwTlbn0AEA1Lly7VokWL9M1vflNdunTRG2+8oeeee04jR47UxRdfHOvuAYhTXoylGCAC4sz777+vW2+9VUOHDtWvfvWrWHcHAACgXRswYICSkpI0d+5c1dbWhiau5hbwABCOOYgAAEggtbW1ysjI0O4tp0QkLbrHWR9r//79RrebBgAAiHdejqXIIAIAIAEFZSsod38Dcrs+AACAeOXFWIr7UQMAAAAAAHhc3GUQBYNB7dy5U2lpabKs+J7ACQCA1ti2rc8//1w5OTny+aL3u0xQQQUjUCfiH7EUACCREEtFT9wNEO3cuVO5ubmx7gYAAK6qqqpSr169Yt0NeACxFAAgERFLRV7cDRClpaVJkobpm0pShxj3BgAAZ5rUqDf0cujzLVoCtq2Ay/ehcLs+RMaRY+3CYT9VUlJKm8vbfrOso6DPPFvJuM0O5m06KRtIMSvblGrcpCOW4Q/WpuUkyck0G76Ag3a9wnD/WkHzF8Zy8hFgeCz5mswb9Tc4eK4Bs7K2g/NgMNnwPOg3blJykmUaxZgg0HhI76y8l1gqCuJugOhIKnSSOijJYoAIANDOfRkHcKkPoiUUSyWlKCmp7SMSxoM1huUctRmjASIZfpGzk82bdIIBogQUiwEiB8eDaVmfg1Epv4Mv4pYvBgNEhuckLwwQHUEsFXkRu4BvwYIF6t27t1JTU5WXl6f169dHqikAAPA1R+684faC6CGWAgAgdrwYS0VkgGjp0qUqKSlRaWmpNm7cqIEDB6qwsFC7d++ORHMAAOBrgrIVcHmJ96AmkRBLAQAQW16MpSIyQPTQQw9p6tSpmjx5ss455xwtXLhQnTp10pNPPhmJ5gAAABIKsRQAAIg21+cgamho0IYNGzRz5szQOp/Pp4KCAlVWVjbbvr6+XvX19aG/a2tr3e4SAACeE4k05nj/1StREEsBABB7XoylXM8g2rt3rwKBgLKyssLWZ2Vlqbq6utn2ZWVlysjICC3clhUAAHgZsRQAAIiFiE1Sfbxmzpyp/fv3h5aqqqpYdwkAgHbvyK1Z3V4Qf4ilAABwnxdjKdcvMevevbv8fr9qamrC1tfU1Cg7O7vZ9ikpKUpJSXG7GwAAAO0SsRQAAIgF1zOIkpOTNXjwYK1atSq0LhgMatWqVcrPz3e7OQAA0IJghBZEHrEUAACx58VYyvUMIkkqKSnRxIkTNWTIEA0dOlTz5s1TXV2dJk+eHInmAADA1xy5narbdSI6iKUAAIgtL8ZSERkgGjdunPbs2aPZs2erurpagwYNUnl5ebPJFgEAANAcsRQAAIi2iAwQSVJxcbGKi4sjVT0AADiGgH14cbtORA+xFAAAsePFWCpiA0QAAACIHdtvyfZbbS5nGUavfgdRb7CD2bSYwQ5tf36hsg6i4KDfrJzlYPIJK2Be1t8Q599Ivs70ZXXwNGOxj4JJ5sevbTqTrHmTsg2Pe8m8vybnMDfKmp4HHfXXdB85eE0dnZMMDyaT/to+B08SbcIAEQAACSgSEyHG+8SKAAAAbvFiLOX6XcwAAAAAAADQvpBBBABAAgrKUsDJtQRHqRMAAMALvBhLkUEEAAAAAADgcWQQAQCQgIL24cXtOgEAALzAi7EUA0QAACSgQATSot2uDwAAIF55MZbiEjMAAAAAAACPI4MIAIAE5MVfvQAAANzixViKDCIAAAAAAACPI4MIAIAEFLQtBW2Xb83qcn0AAADxyouxFBlEAAAAAAAAHscAEQAACejIdfNuL221YMEC9e7dW6mpqcrLy9P69euPuf2yZcvUt29fpaamqn///nr55ZfDHp8zZ4769u2rzp0764QTTlBBQYHWrVsXts1nn32m6667Tunp6erataumTJmiAwcOtLnvAADAu+IlloomBogAAEhAAfkisrTF0qVLVVJSotLSUm3cuFEDBw5UYWGhdu/e3eL2b775psaPH68pU6bonXfeUVFRkYqKirR58+bQNmeeeabmz5+v9957T2+88YZ69+6tkSNHas+ePaFtrrvuOv3tb39TRUWFVq5cqddff13XX3+92Y4EAACeFA+xVLRZtm3bse7EV9XW1iojI0PD9S0lWR1i3R0AABxpshu1Wi9q//79Sk9Pj3h7Rz5HX92cqy5p7gYhBz4P6hv9qo77ueTl5emCCy7Q/PnzJUnBYFC5ubm66aab9NOf/rTZ9uPGjVNdXZ1WrlwZWnfhhRdq0KBBWrhwYYttHHm+r7zyikaMGKH3339f55xzjt566y0NGTJEklReXq5vfvOb+uSTT5STk2Py1NuVI/sk/z/uVFKH1DaX9zcEjdq1guYhZSDZ7Fht6uQ3brMp1fxXXNuwWStg3KQ6HDR7XSTJXx/9cD+QYr5/mzqaHQ9Opvbwmb42Tr5KWeYdDpof+sZsJx8phk/V9sUo28LwZXWyj0zL+prM2/Q1mR+/pu+ZgMFX/EDDIW383e2ejKWiLb6HrwAAgBH7y4kV3VzsL7991dbWhi319fXN2m9oaNCGDRtUUFAQWufz+VRQUKDKysoW+1xZWRm2vSQVFhYedfuGhgY9/vjjysjI0MCBA0N1dO3aNTQ4JEkFBQXy+XzNLkUDAAA4mkjGUvGKASIAANAmubm5ysjICC1lZWXNttm7d68CgYCysrLC1mdlZam6urrFequrq49r+5UrV6pLly5KTU3Vz3/+c1VUVKh79+6hOnr06BG2fVJSkjIzM4/aLgAAALjNPQAACSkSEyEeqa+qqiosLTolJcXVdlpz+eWXa9OmTdq7d69+9atf6Tvf+Y7WrVvXbGAIAADAVCRjqXhFBhEAAGiT9PT0sKWlAaLu3bvL7/erpqYmbH1NTY2ys7NbrDc7O/u4tu/cubNOP/10XXjhhXriiSeUlJSkJ554IlTH1yfBbmpq0meffXbUdgEAAMAAEQAACSlg+yKyHK/k5GQNHjxYq1atCq0LBoNatWqV8vPzWyyTn58ftr0kVVRUHHX7r9Z7ZB6k/Px87du3Txs2bAg9/uqrryoYDCovL++4+w8AALwt1rFULMR37wAAQLtVUlKiX/3qV3r66af1/vvv64YbblBdXZ0mT54sSZowYYJmzpwZ2n769OkqLy/Xgw8+qA8++EBz5szR22+/reLiYklSXV2dZs2apbVr1+rjjz/Whg0b9P3vf1///Oc/NXbsWEnS2WefrVGjRmnq1Klav369/vKXv6i4uFjXXnutJ+5gBgAAEtN9990ny7J08803R6wN5iACACABBWUp6PLvQME23vd33Lhx2rNnj2bPnq3q6moNGjRI5eXloYmod+zYIZ/v33286KKLtHjxYt1+++2aNWuWzjjjDK1YsUL9+vWTJPn9fn3wwQd6+umntXfvXnXr1k0XXHCB/u///k/nnntuqJ5nn31WxcXFGjFihHw+n8aMGaNHHnnEhT0AAAC8Ih5iqSPeeustPfbYYxowYICr/fk6BogAAEhA8TKxYnFxcSgD6OtWr17dbN3YsWND2UBfl5qaquXLl7faZmZmphYvXtymfgIAAHxVvMRSBw4c0HXXXadf/epXuueee1ztz9dxiRkAAAAAAEAcmjZtmq644goVFBREvC0yiAAASECRmAgxYJulRQMAALQ3kYylamtrw9anpKS0eFfYJUuWaOPGjXrrrbdc7cfRkEEEAAAAAAAQJbm5ucrIyAgtZWVlzbapqqrS9OnT9eyzzyo1NTUq/SKDCACABHR4YkV3r5t3uz4AAIB4FclYqqqqSunp6aH1LWUPbdiwQbt379b5558fWhcIBPT6669r/vz5qq+vl9/vd7V/DBABAAAAAABESXp6etgAUUtGjBih9957L2zd5MmT1bdvX82YMcP1wSGJASIAABJSUD4F4uTWrAAAAO1NrGOptLQ09evXL2xd586d1a1bt2br3cIAEQAACYhJqmH7Ldn+tqfGBzsYHjcOjg+TfkqSy4f4cfM1mZXrUBc0brPD54aNSvLVB4zLGrfZyfxrRjDJ7Hho6mh+KUjA8Id4KxCbS2990X9JZX70Srbh/rUd7F7TNp2066RNU5aTj2bz04oneDGWYoAIAAAAAAAgzq1evTqi9TNABABAAgrKpyCXmAEAABjxYizFbe4BAAAAAAA8jgwiAAASUMC2FHAyecNR6gQAAPACL8ZSZBABAAAAAAB4HBlEAAAkoEAEbs0aiPPr5gEAANzixViKDCIAAAAAAACPI4MIAIAEFLR9Ctou33nDju9fvQAAANzixViKASIAABKQF9OiAQAA3OLFWIpLzAAAAAAAADyODCIAABJQUO7fSjXoam0AAADxy4uxFBlEAAAAAAAAHkcGEQAACSgon4Iu/w7kdn0AAADxyouxVHz3DgAAAAAAABFHBhEAAAkoYPsUcPnWrG7Xh8gKdrAU7ND2uRNMyjhlemgFUsz7GnDwPH1N0b8LjdVkPnOFz7BsMMn8PW8FzPeRFYNJOgLJ0T/ukw+Y7yNfo1lZR6dxf/T3kROOpo5pTx93Dk5HlpOyQbPCvoDB51KMJu7xYizFABEAAAkoKEtBuT2xYvv6cgAAAGDKi7FUfA9fAQAAAAAAIOLIIAIAIAF5MS0aAADALV6MpeK7dwAAAAAAAIg4MogAAEhAAfkUcPl3ILfrAwAAiFdejKXiu3cAAAAAAACIODKIAABIQEHbUtDRPX5brhMAAMALvBhLkUEEAAAAAADgcWQQAQCQgIIRuG4+yO9KAADAI7wYSzFABABAAgraPgVdvpWq2/UBAADEKy/GUvHdOwAAAAAAAEQcGUQAACSggCwF5O5EiG7XBwAAEK+8GEuRQQQAAAAAAOBxZBABLbHie2QXBmw71j0AosqL180jXLCDpWCHtn+eBU2jwxicZgMGz++IYLJ5u5bhbYqDSeb9bepsHrbbSWbvXdtv3l9ffcC4bHJtk1E5f735OSqQEv3zm78+GPU2nTzPpo7mx4PtNy5qzDI/BCXDso6+QRieQy0Hh1Es7rjua2z7EzUp4wYvxlLx3TsAAAAAAABEHBlEAAAkoIDcv87dyY+xAAAA7YkXYykGiAAASEBeTIsGAABwixdjKdd7N2fOHFmWFbb07dvX7WYAAAASErEUAACIhYhkEJ177rl65ZVX/t1IEolKAABEU8D2KeDyr1Ru14ejI5YCACC2vBhLRSTaSEpKUnZ2diSqBgAASHjEUgAAINoiMny1detW5eTk6NRTT9V1112nHTt2HHXb+vp61dbWhi0AAMAZW5aCLi+2yxM14uiIpQAAiC0vxlKuDxDl5eVp0aJFKi8v16OPPqrt27frkksu0eeff97i9mVlZcrIyAgtubm5bncJAACg3SCWAgAAseD6JWajR48O/X/AgAHKy8vTKaecot/97neaMmVKs+1nzpypkpKS0N+1tbUENgAAOOTF6+YTBbEUAACx58VYKuIzHnbt2lVnnnmmPvzwwxYfT0lJUUpKSqS7AQAA0C4RSwEAgGiI+PDVgQMHtG3bNvXs2TPSTQEAgC8FbSsiC6KPWAoAgOjzYizlegbRT37yE1155ZU65ZRTtHPnTpWWlsrv92v8+PFuNwUAAI4iIJ8CLv8O5HZ9aBmxFAAAsefFWMr1AaJPPvlE48eP16effqoTTzxRw4YN09q1a3XiiSe63RQAAEDCIZYCAACx4PoA0ZIlS9yuEgAAtFEk0pjjPS06URBLAQAQe16MpSI+STXgmBXfb6JmrPhOG2zX7KB52VgcR7Yd/TYB4EuBFEtKbvu5L+g3a8/faH7O8zWZljNv03IQpFtBs3aDHczbbOxi+MJIsv1m7frrzT93/fUB47KmkvfURb3NWPmiV1rU2/Q3mL/fGrpEPw7zOzgEjZ+rk9DPcBfZPvN9a5ufVhQMmrWbZHBe8QWIqaOFASIAABJQUD4FXb7O3e36AAAA4pUXY6n47h0AAAAAAAAijgwiAAASUMC2FHD5One36wMAAIhXXoylGCACACABeXFiRQAAALd4MZbiEjMAAAAAAACPI4MIAIAEZNs+BW13fweyXa4PAAAgXnkxlorv3gEAAAAAACDiyCACACABBWQpIJcnVnS5PgAAgHjlxViKDCIAABAxCxYsUO/evZWamqq8vDytX7/+mNsvW7ZMffv2VWpqqvr376+XX3459FhjY6NmzJih/v37q3PnzsrJydGECRO0c+fOsDp69+4ty7LClvvuuy8izw8AACBRMEAEAEACCtr/vvuGe0vb+rB06VKVlJSotLRUGzdu1MCBA1VYWKjdu3e3uP2bb76p8ePHa8qUKXrnnXdUVFSkoqIibd68WZJ08OBBbdy4UXfccYc2btyo5cuXa8uWLbrqqqua1XXXXXdp165doeWmm25q8z4EAADeFQ+xVLRxiRkAAAkoGIGJFdta30MPPaSpU6dq8uTJkqSFCxfqD3/4g5588kn99Kc/bbb9ww8/rFGjRunWW2+VJN19992qqKjQ/PnztXDhQmVkZKiioiKszPz58zV06FDt2LFDJ598cmh9WlqasrOz2/oUAQAAJMVHLBVt8d07AAAQd2pra8OW+vr6Zts0NDRow4YNKigoCK3z+XwqKChQZWVli/VWVlaGbS9JhYWFR91ekvbv3y/LstS1a9ew9ffdd5+6deum8847Tw888ICampra8AwBAAC8hwwiAAASUFCWgi5PhHikvtzc3LD1paWlmjNnTti6vXv3KhAIKCsrK2x9VlaWPvjggxbrr66ubnH76urqFrc/dOiQZsyYofHjxys9PT20/kc/+pHOP/98ZWZm6s0339TMmTO1a9cuPfTQQ8f1PAEAACIZS8UrBogQHVYM3ghWbBLkLJ9HnqsdjEGTMUp6NH2uTo57O84vUIanVVVVhQ3IpKSkRL0PjY2N+s53viPbtvXoo4+GPVZSUhL6/4ABA5ScnKwf/OAHKisri0lfY6UpVbKT217OMj3lHTIrJ0kd6qL/mRJMMj9HBzuYlQ0YvB7/Zt7fpo5+o3L+BrNykuSrDxiXTao1O5h8tQeN22xvOtSmmhVM72Dc5qET+OrYGtPzpyTZhm8320G4aXou+7Jls1INbW/TjsV3SY/iXQ4AQAIK2JYCTqLGo9QpSenp6WEDRC3p3r27/H6/ampqwtbX1NQcdW6g7Ozs49r+yODQxx9/rFdffbXVvuTl5ampqUkfffSRzjrrrGNuCwAAIEU2lopXzEEEAABcl5ycrMGDB2vVqlWhdcFgUKtWrVJ+fn6LZfLz88O2l6SKioqw7Y8MDm3dulWvvPKKunXr1mpfNm3aJJ/Ppx49ehg+GwAAgMRHBhEAAAkoHu68UVJSookTJ2rIkCEaOnSo5s2bp7q6utBdzSZMmKCTTjpJZWVlkqTp06frsssu04MPPqgrrrhCS5Ys0dtvv63HH39c0uHBoWuuuUYbN27UypUrFQgEQvMTZWZmKjk5WZWVlVq3bp0uv/xypaWlqbKyUrfccou+973v6YQTTnBxbwAAgEQWD7FUtMV37wAAQLs1btw4/c///I9mz56tQYMGadOmTSovLw9NRL1jxw7t2rUrtP1FF12kxYsX6/HHH9fAgQP1/PPPa8WKFerXr58k6Z///KdeeuklffLJJxo0aJB69uwZWt58801Jh+dDWrJkiS677DKde+65uvfee3XLLbeEBpkAAADag0cffVQDBgwIXdqfn5+vP/7xjxFtkwwiAAASUFCWgi5f525y543i4mIVFxe3+Njq1aubrRs7dqzGjh3b4va9e/eW3coE7ueff77Wrl3b5n4CAAB8VaxjqV69eum+++7TGWecIdu29fTTT+tb3/qW3nnnHZ177rmu9usIBogAAEhAdgRuzWrH+a1ZAQAA3BLrWOrKK68M+/vee+/Vo48+qrVr1zJABAAAAAAA4DWBQEDLli1TXV3dUW/24QYGiAAASEBBOwJp0XF+a1YAAAC3RDKWqq2tDVufkpKilJSUZtu/9957ys/P16FDh9SlSxe98MILOuecc1zt01cxSTUAAAAAAECU5ObmKiMjI7QcuaPr15111lnatGmT1q1bpxtuuEETJ07U3//+94j1iwwiAAASkBdvzQoAAOCWSMZSVVVVSk9PD61vKXtIkpKTk3X66adLkgYPHqy33npLDz/8sB577DFX+3UEA0QAAAAAAABRcuTW9W0VDAZVX18fgR4dxgARAAAJiDmIAAAAzMU6lpo5c6ZGjx6tk08+WZ9//rkWL16s1atX609/+pOrffoqBogAAEhAwQjcmtXt+gAAAOJVrGOp3bt3a8KECdq1a5cyMjI0YMAA/elPf9J//Md/uNqnr2KACAAAAAAAII488cQTUW+TASIcPytGvxxbZhODWT4H/TVsU5Jk2K7lZP/6YjBxbDBoXNS2baNylmVW7nCjDvobNNy/Dtp09H4z3L9ILLFOi0b7ZQXMynU4aH7O6/B5k1E5q8m8zabO5mFwYxe/YUnz91BDF/Oy/gazcoFk4yalnFTjop0MyyXXHjRu0yvMj10p+YB5fOHk+I2FoN+wv+a711jQyTd6B18hTGMC29/248hJSO2EF2MpbkcCAAAAAADgcWQQAQCQgLz4qxcAAIBbvBhLkUEEAAAAAADgcWQQAQCQgLz4qxcAAIBbvBhLkUEEAAAAAADgcWQQAQCQgLz4qxcAAIBbvBhLMUAEAEACsiUFHdxS+2h1AgAAeIEXYykuMQMAAAAAAPA4MogAAEhAXkyLBgAAcIsXYykyiAAAAAAAADyODCIAABKQF3/1AgAAcIsXYykyiAAAAAAAADyODCIAABKQF3/1AgAAcIsXYykGiAAASEBeDGoAAADc4sVYigEiL7JicFBa5lczWj7D/vr95m06KZtk+LZy0KZM95ETQdu4qBUIGJWzm5qM27QN25QkS4b9DTq4itcOmpc1fY/b5q8pgPhjBQ8vbeVvMDsX+OvNzyG+erPzrK/J/FxpJ5mfo22/2Xm2qaP5Z72/wbioGruY9bdztfn+rct2MpNFqlGpgzk5DtoE/s02fKualnMi6KBNk8+II2zDt3gwqe3no3gfVEkkDBABAJCAbNuS7XJA5XZ9AAAA8cqLsRSTVAMAAAAAAHgcGUQAACSgoCwF5fJ18y7XBwAAEK+8GEuRQQQAAAAAAOBxZBABAJCAvHjnDQAAALd4MZYigwgAAAAAAMDjyCACACABefHOGwAAAG7xYizFABEAAAnIi2nRAAAAbvFiLMUlZgAAAAAAAB5HBhEAAAnIi2nRAAAAbvFiLEUGEQAAAAAAgMeRQQQAQAKyI3DdfLz/6gUAAOAWL8ZSZBABAAAAAAB4HBlEOH6W+Xii5XMwUur3m7WZZH54W8nJ5mVTDMumphi3aXeI/lvZamwyL3yo3qxcfYN5mw3mZW3DcpYC5m0GHYzf20HzskgYtiTb9OA9Rp1oP6zg4SWRBZPMz5W23zw28deb7Vh/g1lMI0kB89BEnaujfyA4abN2fK1RufTn0o3b9Iqu63cal903NMe4bPIBs0+Qhi7xnW3xdUHzt3hMWDH4YDfZR7Z5SO2IF2MpBogAAEhAQVmy5PKtWV2uDwAAIF55MZbiEjMAAAAAAACPI4MIAIAE5MVbswIAALjFi7FUmzOIXn/9dV155ZXKycmRZVlasWJF2OO2bWv27Nnq2bOnOnbsqIKCAm3dutWt/gIAALRrxFIAACAetXmAqK6uTgMHDtSCBQtafHzu3Ll65JFHtHDhQq1bt06dO3dWYWGhDh065LizAADg+AS/vDWr2wucI5YCACD+eTGWavMlZqNHj9bo0aNbfMy2bc2bN0+33367vvWtb0mSnnnmGWVlZWnFihW69tprnfUWAACgnSOWAgAA8cjVSaq3b9+u6upqFRQUhNZlZGQoLy9PlZWVbjYFAACOwbYjsyCyiKUAAIgPXoylXJ2kurq6WpKUlZUVtj4rKyv02NfV19ervr4+9Hdtba2bXQIAAGg3iKUAAECsxPw292VlZcrIyAgtubm5se4SAADt3pE7b7i9IP4QSwEA4D4vxlKuDhBlZ2dLkmpqasLW19TUhB77upkzZ2r//v2hpaqqys0uAQDgSV4MahIBsRQAAPHBi7GUqwNEffr0UXZ2tlatWhVaV1tbq3Xr1ik/P7/FMikpKUpPTw9bAAAAvIhYCgAAxEqb5yA6cOCAPvzww9Df27dv16ZNm5SZmamTTz5ZN998s+655x6dccYZ6tOnj+644w7l5OSoqKjIzX4DAIBjCNqWLJd/pYr3W7O2F8RSAADEPy/GUm0eIHr77bd1+eWXh/4uKSmRJE2cOFGLFi3Sbbfdprq6Ol1//fXat2+fhg0bpvLycqWmprrXawAAgHaKWAoAAMSjNg8QDR8+XPYx7s1mWZbuuusu3XXXXY46BgAAzEXiVqrxfmvW9oJYCgCA+OfFWMrV29wjiqz4Tk1rxjKf7sry+83KJSebt9m5k3FZO72zUbmmrh2N22zqFP23ctLBJvOy+74wKmfV1hm36YjhmdwOOvkECDgoa8jJeSXeP+0AL7K/XKIkkGJ+DvEZfo5ZAfMn6Ks3P8/6Dcs6aVM55hlkddlmcVjn6qBxm06kP2c2j9Zf5i10uSeRdfHNP4x6m/uG5kS9TSeSD5i/xzscMH+/NXYx+/4RMP/6oUByO/t+Z/rSmDzNdrZr2jMGiAAASECHf/VyN6JiHBAAAHiFF2MpBogAAEhAkbiVarzfmhUAAMAtXoylXL3NPQAAAAAAANofMogAAEhAkZh+Js6zogEAAFzjxViKDCIAABAxCxYsUO/evZWamqq8vDytX7/+mNsvW7ZMffv2VWpqqvr376+XX3459FhjY6NmzJih/v37q3PnzsrJydGECRO0c+fOsDo+++wzXXfddUpPT1fXrl01ZcoUHThwICLPDwAAIFEwQAQAQAI6ct2820tbLF26VCUlJSotLdXGjRs1cOBAFRYWavfu3S1u/+abb2r8+PGaMmWK3nnnHRUVFamoqEibN2+WJB08eFAbN27UHXfcoY0bN2r58uXasmWLrrrqqrB6rrvuOv3tb39TRUWFVq5cqddff13XX3+92Y4EAACeFA+xVLQxQAQAACLioYce0tSpUzV58mSdc845WrhwoTp16qQnn3yyxe0ffvhhjRo1SrfeeqvOPvts3X333Tr//PM1f/58SVJGRoYqKir0ne98R2eddZYuvPBCzZ8/Xxs2bNCOHTskSe+//77Ky8v161//Wnl5eRo2bJh+8YtfaMmSJc0yjQAAAPBvDBABAJCI7Agtkmpra8OW+vr6Zs03NDRow4YNKigoCK3z+XwqKChQZWVli12urKwM216SCgsLj7q9JO3fv1+WZalr166hOrp27aohQ4aEtikoKJDP59O6deuOWg8AAECYCMZS8YoBIgAA0Ca5ubnKyMgILWVlZc222bt3rwKBgLKyssLWZ2Vlqbq6usV6q6ur27T9oUOHNGPGDI0fP17p6emhOnr06BG2XVJSkjIzM49aDwAAALiLGQAAiSkS17l/WV9VVVVoQEaSUlJS3G3nODQ2Nuo73/mObNvWo48+GvX2AQBAgotgLBWvGCACACAB2fbhxe06JSk9PT1sgKgl3bt3l9/vV01NTdj6mpoaZWdnt1gmOzv7uLY/Mjj08ccf69VXXw3rS3Z2drNJsJuamvTZZ58dtV0AAICvi2QsFa+4xAwAALguOTlZgwcP1qpVq0LrgsGgVq1apfz8/BbL5Ofnh20vSRUVFWHbHxkc2rp1q1555RV169atWR379u3Thg0bQuteffVVBYNB5eXlufHUAAAAEhIZRAAAJKBI3Eq1rfWVlJRo4sSJGjJkiIYOHap58+aprq5OkydPliRNmDBBJ510UmgOo+nTp+uyyy7Tgw8+qCuuuEJLlizR22+/rccff1zS4cGha665Rhs3btTKlSsVCARC8wplZmYqOTlZZ599tkaNGqWpU6dq4cKFamxsVHFxsa699lrl5OS4uDcAAEAii4dYKtoYIAIAABExbtw47dmzR7Nnz1Z1dbUGDRqk8vLy0ETUO3bskM/372Tmiy66SIsXL9btt9+uWbNm6YwzztCKFSvUr18/SdI///lPvfTSS5KkQYMGhbX12muvafjw4ZKkZ599VsXFxRoxYoR8Pp/GjBmjRx55JPJPGAAAoB1jgMiLLLMrCy2fg9FOB2WtJLPD1EpJNm7TTu9sXPZQr2PPy3E0tbkdjNv8okf0R6I77jbfv+lVZs819RPjJmU1NZkXDgSMitmG5STJcvDrgh00vHrYDhq3iThkW+5PhGhQX3FxsYqLi1t8bPXq1c3WjR07VmPHjm1x+969e8s+jov3MzMztXjx4jb1MxH5AoeXNjM8bJo6ms9cEEwya9RycNpKrnXwuWAoqfaQcdlOjlpONSpVO77WuMX058ziIScuvvmHUW8TkdVpp/l7Jia6+I2LBgxDa9u8SVnmoarxZ4Vt8L3QpIwr4iSWiibmIAIAAAAAAIgjZWVluuCCC5SWlqYePXqoqKhIW7ZsiWibDBABAJCAjtx5w+0FAADAC2IdS61Zs0bTpk3T2rVrVVFRocbGRo0cOVJ1dXURe85cYgYAQCKyv1zcrhMAAMALYhxLlZeXh/29aNEi9ejRQxs2bNCll17qcscOI4MIAAAAAAAgju3fv1/S4bkWI4UMIgAAEpAXb80KAADglkjGUrW14TcBSElJUUpKylHLBYNB3Xzzzbr44otDd3eNBDKIAAAAAAAAoiQ3N1cZGRmhpays7JjbT5s2TZs3b9aSJUsi2i8yiAAASFTMGQQAAGAuQrFUVVWV0tPTQ38fK3uouLhYK1eu1Ouvv65evXpFpkNfYoAIAAAAAAAgStLT08MGiFpi27ZuuukmvfDCC1q9erX69OkT8X4xQAQAQAJiDiIAAABzsY6lpk2bpsWLF+vFF19UWlqaqqurJUkZGRnq2LGjq/06gjmIAABIRHaEFgAAAC+IcSz16KOPav/+/Ro+fLh69uwZWpYuXerK02sJGUQAAAAAAABxxLaj/8scA0QAACQk68vF7ToBAAC8wHuxFJeYAQAAAAAAeBwZRIgKy3IwUur3m5VLPfqtAlvT1NV80q/a3A5G5T69IGDc5sCzPzYua+qv75/ioLTZPko6YP66dDh4yLis6huMijk57pnqBY5FYs4gDkxvMHydnczj2dQx+r+o+uvNfydN3lNnVM5Xe9C8TQdlD+bkGJVLf+7Yd9g5lr/MW2hc9uKbf2hcFsfW3l6X5E8+i3qbkpSU3smoXGPfrsZtBpLNytl+8/OnFTT/YDdt13+o7W2af0tyyIOxFBlEAAAAAAAAHkcGEQAAiciDv3oBAAC4xoOxFBlEAAAAAAAAHkcGEQAAici2nE0Kc7Q6AQAAvMCDsRQDRAAAJCDbPry4XScAAIAXeDGW4hIzAAAAAAAAjyODCACAROTBiRUBAABc48FYigwiAAAAAAAAjyODCACAROTBiRUBAABc48FYigwiAAAAAAAAjyODCACABGTZhxe36wQAAPACL8ZSDBABAJCIPDixIgAAgGs8GEtxiRkAAAAAAIDHkUEEAEAi8uDEigAAAK7xYCzFABGOn+Ug4cznpKzZm8juYH54N3UyL/tFD7P+Djz7Y+M2V5zxJ+OypopUaFx268enGZVr+tj8dUlycDyYHoOOjnsn7zcFHJQF4HX+BsP8d8s86A34DcslO2gzhUR6IJE1fbTDuKxvQF8Xe3J8mjqbnc+soHmbwSTzc6hteN62DT4rAqaxONqMASIAABKRB6+bBwAAcI0HYyl+OgEAAAAAAPA4MogAAEhEHvzVCwAAwDUejKXIIAIAAAAAAPA4MogAAEhEHvzVCwAAwDUejKUYIAIAIBF58NasAAAArvFgLMUlZgAAAAAAAB5HBhEAAAnIsg8vbtcJAADgBV6MpcggAgAAAAAA8DgyiAAASEQenFgRAADANR6MpcggAgAAAAAA8DgGiAAAAAAAADyOS8wAAEhAliIwsaK71QEAAMQtL8ZSDBABAADAOds8irYC8R4yA0Bk+RrNytl+8zaDDkYDbMOywWSDMnE+b08iYYAIAIBEZFuHF7frBAAA8AIPxlLMQQQAAAAAAOBxZBABAJCIPHhrVgAAANd4MJZqcwbR66+/riuvvFI5OTmyLEsrVqwIe3zSpEmyLCtsGTVqlFv9BQAAaNeIpQAAQDxq8wBRXV2dBg4cqAULFhx1m1GjRmnXrl2h5bnnnnPUSQAA0EZ2hBY4RiwFAEA74MFYqs2XmI0ePVqjR48+5jYpKSnKzs427hQAAECiIpYCAADxKCKTVK9evVo9evTQWWedpRtuuEGffvppJJoBAABHYdmRWRAdxFIAAMSWF2Mp1yepHjVqlK6++mr16dNH27Zt06xZszR69GhVVlbK7/c3276+vl719fWhv2tra93uEgAA3uPBiRUTBbEUAABxwIOxlOsDRNdee23o//3799eAAQN02mmnafXq1RoxYkSz7cvKynTnnXe63Q0AAIB2iVgKAADEQkQuMfuqU089Vd27d9eHH37Y4uMzZ87U/v37Q0tVVVWkuwQAQOLz4MSKiYpYCgCAGPBgLOV6BtHXffLJJ/r000/Vs2fPFh9PSUlRSkpKpLsBAADQLhFLAQCAaGjzANGBAwfCfsHavn27Nm3apMzMTGVmZurOO+/UmDFjlJ2drW3btum2227T6aefrsLCQlc7DgAAji4SEyHG+8SK7QWxFAAA8c+LsVSbB4jefvttXX755aG/S0pKJEkTJ07Uo48+qnfffVdPP/209u3bp5ycHI0cOVJ33303v2wBAACIWAoAAMSnNg8QDR8+XLZ99GGvP/3pT446BAAAXGBbhxe364RjxFIAALQDHoylIj4HERKIHTQvG3RS1iwPz2psMm4y6aB52Y67k43K/fX9U4zbLFL0Lztw0t9uu81eUyevi5PjwTY8Bh0d907eb4DkyVuzwh3BJLPg1RdwuSPHIfmA+UHpr+c8G0kX3/zDWHcBCaChV6ZxWbOI/DDTs0Pqp43GbQaSOxiVa+roZMDBvGxTqmGLBjvXpIwrPBhLRfwuZgAAwLsWLFig3r17KzU1VXl5eVq/fv0xt1+2bJn69u2r1NRU9e/fXy+//HLY48uXL9fIkSPVrVs3WZalTZs2Natj+PDhsiwrbPnhD/myCgAAcCwMEAEAkICOTKzo9tIWS5cuVUlJiUpLS7Vx40YNHDhQhYWF2r17d4vbv/nmmxo/frymTJmid955R0VFRSoqKtLmzZtD29TV1WnYsGG6//77j9n21KlTtWvXrtAyd+7ctnUeAAB4WjzEUtHGABEAAIiIhx56SFOnTtXkyZN1zjnnaOHCherUqZOefPLJFrd/+OGHNWrUKN166606++yzdffdd+v888/X/PnzQ9v813/9l2bPnq2CgoJjtt2pUydlZ2eHlvT0dFefGwAAQKJhgAgAgERkR2g5Tg0NDdqwYUPYQI7P51NBQYEqKytbLFNZWdls4KewsPCo2x/Ls88+q+7du6tfv36aOXOmDh482OY6AACAh8U4looFJqkGAABtUltbG/Z3SkpKs1uw7927V4FAQFlZWWHrs7Ky9MEHH7RYb3V1dYvbV1dXt6l/3/3ud3XKKacoJydH7777rmbMmKEtW7Zo+fLlbaoHAADASxggAgAgEUXiOvcv68vNzQ1bXVpaqjlz5rjcmLnrr78+9P/+/furZ8+eGjFihLZt26bTTjsthj0DAADtRgRjqXjFABEAAGiTqqqqsDl9vp49JEndu3eX3+9XTU1N2PqamhplZ2e3WG92dnabtj9eeXl5kqQPP/yQASIAAICjYA4iAAASUQSvm09PTw9bWhogSk5O1uDBg7Vq1arQumAwqFWrVik/P7/FLufn54dtL0kVFRVH3f54bdq0SZLUs2dPR/UAAAAPYQ4iAACQECIRhLSxvpKSEk2cOFFDhgzR0KFDNW/ePNXV1Wny5MmSpAkTJuikk05SWVmZJGn69Om67LLL9OCDD+qKK67QkiVL9Pbbb+vxxx8P1fnZZ59px44d2rlzpyRpy5YtkhS6W9m2bdu0ePFiffOb31S3bt307rvv6pZbbtGll16qAQMGuLATAACAJ8RBLBVtDBABAICIGDdunPbs2aPZs2erurpagwYNUnl5eWgi6h07dsjn+3cy80UXXaTFixfr9ttv16xZs3TGGWdoxYoV6tevX2ibl156KTTAJEnXXnutpH/Pg5ScnKxXXnklNBiVm5urMWPG6Pbbb4/SswYAAGifGCACACABWRGYWNGkvuLiYhUXF7f42OrVq5utGzt2rMaOHXvU+iZNmqRJkyYd9fHc3FytWbOmrd0EAAAIEy+xVDQxBxEAAAAAAIDHMUAEAAAAAADgcQwQAQAAAAAAxJnXX39dV155pXJycmRZllasWBHR9piDCFFh2+YXW1qBgFnBQ/XGbSbt+8K4bHpVB8OSpuWkrR+fZlzWVLfd5q9pelWjUTknr4uT40GGx6CT4x5wzIN33sDXGB4DtuHPh0GzYpIkn+FHva8xNgflF73SjMp1qE11uSdA21x88w+Ny9aOrzUum/5cunFZUw29MqPepq/e8GQmKfUzs5NvfYbfuE0nJ25foxW1Nu0Gs6Yci4NYqq6uTgMHDtT3v/99XX311S53pjkGiAAASEBenFgRAADALfEQS40ePVqjR492txPHwCVmAAAAAAAAHkcGEQAAiYqMHwAAAHMRiqVqa8Mv2UxJSVFKSkpkGmsDMogAAAAAAACiJDc3VxkZGaGlrKws1l2SRAYRAACJKQ4mVgQAAGi3IhhLVVVVKT3935O3x0P2kMQAEQAAAAAAQNSkp6eHDRDFCwaIAABIQPFw5w0AAID2Kh5iqQMHDujDDz8M/b19+3Zt2rRJmZmZOvnkk93tnBggAgAAAAAAiDtvv/22Lr/88tDfJSUlkqSJEydq0aJFrrfHABEAAImIOYgAAADMxUEsNXz4cNl29AIwBogAAEhA8ZAWDQAA0F55MZbiNvcAAAAAAAAeRwYRAACJKA7SogEAANotD8ZSZBABAAAAAAB4HBlEXmQHDYuZjydaDi62tJuazArWNxi3adXWGZdN/cSsXNKBjsZtNn0c/bdy0kHD10VS0r4vjMo5eV1sB8eD8TEYdHDcOyhr+h5HgvHgr14IZwVtWSbnEsv9vkSK7eCnzkBKDH4nTe9gXLSxi9/Fjhyfrut3GpfdNzTHxZ4cn7/MWxj1NmPl4pt/GPU2059LNy5bl236fks1brPTzkPGZWPBX28WvyUfcHLSjv550DY4ldlNMQpAPBhLkUEEAAAAAADgcWQQAQCQgLx45w0AAAC3eDGWYoAIAIBE5MG0aAAAANd4MJbiEjMAAAAAAACPI4MIAIBE5MFfvQAAAFzjwViKDCIAAAAAAACPI4MIAIAE5MWJFQEAANzixViKDCIAAAAAAACPI4MIAIBE5MHr5gEAAFzjwViKASIAABKQF9OiAQAA3OLFWIpLzAAAAAAAADyODCIAABKRB9OiAQAAXOPBWIoMIgAAAAAAAI8jgwgAgETkwV+9AAAAXOPBWIoBovbKdnBkWZZ7/ThedtC8aCBgVrChwbhNJ6ymJqNyHQ4eMm4zqUP038pWo9nzlCQdqjcqZtebv6a2g+PB+Bh0cNzHhJPzCoC4Y9mSZXAasv1m7dkO8tKNz5Z+85imqaN5WX+D2fny0Anmn9fJB6J/jt43NCfqbTpx8c0/jHUXoqZ2fK1RufTn0l3uyfHpXG32Lm/o4uB7S06qcdEOBwxjPwesgNl73LScJPmanJQ1e22CBp8V8T6xcyJhgAgAgARkfbm4XScAAIAXeDGWYg4iAAAAAAAAjyODCACAROTB6+YBAABc48FYigEiAAASkGW7f80+cwAAAACv8GIsxSVmAAAAAAAAHkcGEQAAiciDadEAAACu8WAsRQYRAAAAAACAx5FBBABAoorzX6kAAADimsdiKTKIAAAAAAAAPI4MIgAAEpAX77wBAADgFi/GUgwQAQCQiDw4sSIAAIBrPBhLcYkZAAAAAACAx5FBBABAAvJiWjQAAIBbvBhLkUEEAAAAAADgcWQQ4fjZQQdFzcciLQXM2jRuUZLtoHTArL+qbzBv02eZlzVkB6O/j+ymJuMmbdPXRTLvr5N95OD9Bkjy5HXz+JqgZBmcSmzTj2wHH0W237xsLNps6BL9z91YtJl8gDd9vEp/Lt2oXF22eUzeudo8Nml/7xmzE0SHA+bxpq/J7P0WtMyfp8lnRKis6enBpFysTkUejKXIIAIAAAAAAPA4MogAAEhAXrxuHgAAwC1ejKXalEFUVlamCy64QGlpaerRo4eKioq0ZcuWsG0OHTqkadOmqVu3burSpYvGjBmjmpoaVzsNAADQHhFLAQCAeNWmAaI1a9Zo2rRpWrt2rSoqKtTY2KiRI0eqrq4utM0tt9yi3//+91q2bJnWrFmjnTt36uqrr3a94wAA4BjsCC1whFgKAIB2woOxVJsuMSsvLw/7e9GiRerRo4c2bNigSy+9VPv379cTTzyhxYsX6xvf+IYk6amnntLZZ5+ttWvX6sILL3Sv5wAA4Og8OLFie0AsBQBAO+HBWMrRJNX79++XJGVmZkqSNmzYoMbGRhUUFIS26du3r04++WRVVlY6aQoAACDhEEsBAIB4YTxJdTAY1M0336yLL75Y/fr1kyRVV1crOTlZXbt2Dds2KytL1dXVLdZTX1+v+vr60N+1tbWmXQIAAF/y4sSK7Q2xFAAA8cuLsZRxBtG0adO0efNmLVmyxFEHysrKlJGREVpyc3Md1QcAANAeEEsBAIB4YjRAVFxcrJUrV+q1115Tr169Quuzs7PV0NCgffv2hW1fU1Oj7OzsFuuaOXOm9u/fH1qqqqpMugQAAL7KgxMrtifEUgAAxDkPxlJtGiCybVvFxcV64YUX9Oqrr6pPnz5hjw8ePFgdOnTQqlWrQuu2bNmiHTt2KD8/v8U6U1JSlJ6eHrYAAAAkImIpAAAQr9o0B9G0adO0ePFivfjii0pLSwtdC5+RkaGOHTsqIyNDU6ZMUUlJiTIzM5Wenq6bbrpJ+fn53HUDAIAosmxblu3uz1Ru1+dFxFIAALQPXoyl2pRB9Oijj2r//v0aPny4evbsGVqWLl0a2ubnP/+5/vM//1NjxozRpZdequzsbC1fvtz1jgMAgGOIk7ToBQsWqHfv3kpNTVVeXp7Wr19/zO2XLVumvn37KjU1Vf3799fLL78c9vjy5cs1cuRIdevWTZZladOmTc3qOHTokKZNm6Zu3bqpS5cuGjNmjGpqatre+QgglgIAoJ2Ik1gqmtp8iVlLy6RJk0LbpKamasGCBfrss89UV1en5cuXH/WaeQAAkLiWLl2qkpISlZaWauPGjRo4cKAKCwu1e/fuFrd/8803NX78eE2ZMkXvvPOOioqKVFRUpM2bN4e2qaur07Bhw3T//fcftd1bbrlFv//977Vs2TKtWbNGO3fu1NVXX+368zNBLAUAAOKV8W3u0Y6ZprVZloM2gw6Kmt1sz1LAQZvmQ7t2wKxdy8n+9RnfkNBc0MFranoMOnhdnB2Dhu06aNOROE9dRXTEw61ZH3roIU2dOlWTJ0+WJC1cuFB/+MMf9OSTT+qnP/1ps+0ffvhhjRo1Srfeeqsk6e6771ZFRYXmz5+vhQsXSpL+67/+S5L00Ucftdjm/v379cQTT2jx4sX6xje+IUl66qmndPbZZ2vt2rWeukzL12TLZ3AQ2H6zz6OgYTlJsh18BCJyGrqYvzDJB8xPQJ12HjJr85PPjNuMhYZemTFoNdW4pJPjwVQg2bysv8G9fsQzq8k83rQMv2cdLmv2HrcCbT+OLPOvdY7EQywVbTH4VgkAANqz2trasKW+vr7ZNg0NDdqwYYMKCgpC63w+nwoKClRZWdlivZWVlWHbS1JhYeFRt2/Jhg0b1NjYGFZP3759dfLJJ7epHgAAAK9hgAgAgEQUwevmc3NzlZGREVrKysqaNb93714FAgFlZWWFrc/KygpNzPx11dXVbdr+aHUkJyera9eujuoBAAAe58E5iLjEDAAAtElVVVXYrdRTUlJi2BsAAAC4gQEiAAASUCSvm09PTw8bIGpJ9+7d5ff7m909rKam5qgTLmdnZ7dp+6PV0dDQoH379oVlEbW1HgAA4G3MQQQAAOCC5ORkDR48WKtWrQqtCwaDWrVqlfLz81ssk5+fH7a9JFVUVBx1+5YMHjxYHTp0CKtny5Yt2rFjR5vqAQAA8BoyiAAASESRuM69jfWVlJRo4sSJGjJkiIYOHap58+aprq4udFezCRMm6KSTTgrNYTR9+nRddtllevDBB3XFFVdoyZIlevvtt/X444+H6vzss8+0Y8cO7dy5U9LhwR/pcOZQdna2MjIyNGXKFJWUlCgzM1Pp6em66aablJ+f76k7mAEAAIfiIJaKNgaIAABIQPGQFj1u3Djt2bNHs2fPVnV1tQYNGqTy8vLQRNQ7duyQz/fvZOaLLrpIixcv1u23365Zs2bpjDPO0IoVK9SvX7/QNi+99FJogEmSrr32WklSaWmp5syZI0n6+c9/Lp/PpzFjxqi+vl6FhYX65S9/afisAQCAF8VDLBVtDBABAICIKS4uVnFxcYuPrV69utm6sWPHauzYsUetb9KkSZo0adIx20xNTdWCBQu0YMGCtnQVAADA0xggAgAgEXkwLRoAAMA1HoylmKQaAAAAAADA48ggAgAgQcX7de4AAADxzGuxFBlEAAAAAAAAcWjBggXq3bu3UlNTlZeXp/Xr10esLQaIAABIRLYdmQUAAMAL4iCWWrp0qUpKSlRaWqqNGzdq4MCBKiws1O7duyPylBkgAgAgAR25NavbCwAAgBfEQyz10EMPaerUqZo8ebLOOeccLVy4UJ06ddKTTz4ZkefMHEQ4fk5+ObYsB+0GDYs5Gf8MGJe0bLPn6uh7lxWDsV7D18VZkzH6dhqD50qmBgCn/A22/AbnEttv9jkWdBBV2n7Dcg7CC8v8o15+B2W9osOB9rWTmj7aEfU2kx2UbeiVaVSu085D5o3mpBoXbehi9mb1Nxg3KX9D9GMp0/OnZH4+c9Km5SDe9DWZtWsF2t6m1ejNuLihoUEbNmzQzJkzQ+t8Pp8KCgpUWVkZkTYZIAIAIBF58NasAAAArolgLFVbWxu2OiUlRSkpKWHr9u7dq0AgoKysrLD1WVlZ+uCDD1zu2GFcYgYAAAAAABAlubm5ysjICC1lZWWx7pIkMogAAEhIVvDw4nadAAAAXhDJWKqqqkrp6emh9V/PHpKk7t27y+/3q6amJmx9TU2NsrOz3e3Yl8ggAgAAAAAAiJL09PSwpaUBouTkZA0ePFirVq0KrQsGg1q1apXy8/Mj0i8yiAAASETMQQQAAGAuDmKpkpISTZw4UUOGDNHQoUM1b9481dXVafLkyS537DAGiAAAAAAAAOLMuHHjtGfPHs2ePVvV1dUaNGiQysvLm01c7RYGiAAASECWfXhxu04AAAAviJdYqri4WMXFxe525CgYIAIAIBHZ9uHF7ToBAAC8wIOxFJNUAwAAAAAAeBwZRAAAJKB4SYsGAABoj7wYS5FBBAAAAAAA4HFkEAEAkIji4NasAAAA7ZYHYykyiAAAAAAAADyODCIAABKQF6+bBwAAcIsXYykGiBAdTm7nZ1mGbQbN23TADsYiMS8QgzZjIEavqbE4v40lEpwHb82KcFbAluVr+2tmBQxfZ9vw89pJUScfuQ4+Ov0N0X8vBP0O9q/fxY4cp8Yu0W80Kb2TcVnfgL4u9uT4tLOoRh0OOIk3Y3AQxkBTqvn7NJhk9tXc12R+PrJNv2dJ8jWatWuZHPiGbTnmwViKS8wAAAAAAAA8jgwiAAASkBfTogEAANzixViKDCIAAAAAAACPI4MIAIBE5MFbswIAALjGg7EUGUQAAAAAAAAeRwYRAAAJyIvXzQMAALjFi7EUGUQAAAAAAAAeRwYRAACJKGgfXtyuEwAAwAs8GEsxQAQAQCLy4MSKAAAArvFgLMUlZgAAAAAAAB5HBhEAAAnIUgQmVnS3OgAAgLjlxViKDCIAAAAAAACPI4MIAIBEZNuHF7frBAAA8AIPxlIMECH+xeJNZDlI/rOD7vUD7onzkzEAuM32WbJ9bf88s/1mn4G2g7x0229e1pSjNH/DjxTLSYjgYB+Z7t+ggzYDyeZl1cWs4ca+XR00Gn2pnzYal/XVB1zsSeR1OBD9/pqeyySpKdWsbCDFvE3T94y/0bxNy8kdtQyL+praXtCkDMwwQAQAQAKy7AhcN098BgAAPMKLsRQDRAAAJCIP3poVAADANR6MpZikGgAAAAAAwOPIIAIAIAFZti3L5bm33K4PAAAgXnkxliKDCAAAAAAAwOPIIAIAIBEFv1zcrhMAAMALPBhLkUEEAAAAAADgcWQQAQCQgLx43TwAAIBbvBhLMUAEAEAi8uCtWQEAAFzjwViKS8wAAAAAAAA8jgwiAAASkW0fXtyuEwAAwAs8GEuRQQQAAAAAAOBxZBABAJCALPvw4nadAAAAXuDFWIoMIgAAAAAAAI8jgwhoSZxfGwoArfLgdfMIF0y2FOxgtbmcbfjzoWm5mHFyOLd9tx5u0u+gzXYmkGy4kyQFkqNbTpKaOpv119do3mYguYNx2dTPzN5w/vqgcZtWwPxN42uK/ueHbX4IKphk9jXZyTFo+806HHBwMvM3mu8kX6MHYgIPxlLt7aMcAAAAAAAALiODCACABGQFDy9u1wkAAOAFXoyl2pRBVFZWpgsuuEBpaWnq0aOHioqKtGXLlrBthg8fLsuywpYf/vCHrnYaAAC04khatNsLHCGWAgCgnfBgLNWmAaI1a9Zo2rRpWrt2rSoqKtTY2KiRI0eqrq4ubLupU6dq165doWXu3LmudhoAAKA9IpYCAADxqk2XmJWXl4f9vWjRIvXo0UMbNmzQpZdeGlrfqVMnZWdnu9NDAADQdracTcJ7tDrhCLEUAADthAdjKUeTVO/fv1+SlJmZGbb+2WefVffu3dWvXz/NnDlTBw8edNIMAABAQiKWAgAA8cJ4kupgMKibb75ZF198sfr16xda/93vflennHKKcnJy9O6772rGjBnasmWLli9f3mI99fX1qq+vD/1dW1tr2iUAAPAly7ZluXydu9v1eR2xFAAA8cuLsZRxBtG0adO0efNmLVmyJGz99ddfr8LCQvXv31/XXXednnnmGb3wwgvatm1bi/WUlZUpIyMjtOTm5pp2CQAAxJkFCxaod+/eSk1NVV5entavX3/M7ZctW6a+ffsqNTVV/fv318svvxz2uG3bmj17tnr27KmOHTuqoKBAW7duDdumd+/ezSZ5vu+++1x/bk4RSwEAgHhiNEBUXFyslStX6rXXXlOvXr2OuW1eXp4k6cMPP2zx8ZkzZ2r//v2hpaqqyqRLAADgq+LgzhtLly5VSUmJSktLtXHjRg0cOFCFhYXavXt3i9u/+eabGj9+vKZMmaJ33nlHRUVFKioq0ubNm0PbzJ07V4888ogWLlyodevWqXPnziosLNShQ4fC6rrrrrvCJnm+6aab2r4PI4hYCgCAOBcHsVS0tWmAyLZtFRcX64UXXtCrr76qPn36tFpm06ZNkqSePXu2+HhKSorS09PDFgAA4JAtKejy0saY5qGHHtLUqVM1efJknXPOOVq4cKE6deqkJ598ssXtH374YY0aNUq33nqrzj77bN199906//zzNX/+/MNPybY1b9483X777frWt76lAQMG6JlnntHOnTu1YsWKsLrS0tKUnZ0dWjp37ty2zkcIsRQAAO1EHMRS0damAaJp06bpt7/9rRYvXqy0tDRVV1erurpaX3zxhSRp27Ztuvvuu7VhwwZ99NFHeumllzRhwgRdeumlGjBgQESeAAAAiK7a2tqw5avz3xzR0NCgDRs2qKCgILTO5/OpoKBAlZWVLdZbWVkZtr0kFRYWhrbfvn27qqurw7bJyMhQXl5eszrvu+8+devWTeedd54eeOABNTU1GT9fNxFLAQCAeNWmSaofffRRSdLw4cPD1j/11FOaNGmSkpOT9corr2jevHmqq6tTbm6uxowZo9tvv921DgMAgNZFcmLFr89xU1paqjlz5oSt27t3rwKBgLKyssLWZ2Vl6YMPPmix/urq6ha3r66uDj1+ZN3RtpGkH/3oRzr//POVmZmpN998UzNnztSuXbv00EMPHeczjRxiKQAA2gcvTlLdpgEiu5Unk5ubqzVr1jjqEAAAiG9VVVVhlzGlpKTEsDfNlZSUhP4/YMAAJScn6wc/+IH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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare noise\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax psf\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array\n", + " - numpy_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"original psf\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "f1196f6c-eb4b-4879-a240-4be6aa169ab6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", + " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", + " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", + " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", + " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", + " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", + " ...,\n", + " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", + " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", + " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", + " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", + " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", + " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7222052077217914, _force_maxk=8.099418560036185)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jax_intermediates[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "0884bdf9-a3ac-4cb2-b2d7-054b25177c01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", + " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", + " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", + " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", + " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", + " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", + " ...,\n", + " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", + " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", + " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", + " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", + " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", + " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6595270685975222, _force_maxk=8.222137023067036)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_intermediates[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "5decc735-9624-40e0-857d-8d65d122bc84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " ...,\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7529228667374486, _force_maxk=12.51728322914683), gsparams=galsim.GSParams(256,256,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=False)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jax_intermediates[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "ea71d3eb-4340-4ab7-b2aa-478dc9146209", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " ...,\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6815071326229607, _force_maxk=12.640001692177682), gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_intermediates[4]" + ] + }, + { + "cell_type": "markdown", + "id": "994cef65-6789-4514-b83e-7c58d029bfe1", + "metadata": {}, + "source": [ + "# psf_inv for Deconvolution" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "a1d0d952-f30e-42be-91a1-0b4a43e6562e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare PSF_INV\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax psf_inv\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", + " - numpy_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"diff psf_inv\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "eb777e6c-81b3-40c8-b0a2-3fa3d4e07ab0", + "metadata": {}, + "source": [ + "# Reconvolution PSF" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "6fea4e6a-0814-4f9b-b509-f6a4a7e8d5b5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare PSF_INV\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax reconv\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", + " - numpy_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"psf reconv diff\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e56f00d4-bbd1-4eb7-9b53-188af8d0b9b1", + "metadata": {}, + "source": [ + "# Compare PSF array" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "8c66f0c8-ffc1-45ac-b582-fe7079ff3ad4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AQLDyx9tMPBwvLS1NM2bM0PDhwzVixAitXr1a5eXlmjVrliRp+vTpuuqqq5xrOM2fP19jxozRihUrNH78eOXm5mr//v3asGGDpEvJpR//+Mc6cOCAtm3bptraWue6SpGRkQoNDVVsbKzGjRun2bNnKzs7W9XV1UpNTdWUKVPUu3dvH94MAAAQtFpBnNTWkGACACBYtYLS78mTJ+v06dNasmSJ7Ha7hg4dqry8POdC3iUlJTKbvymYHjVqlHJycrR48WI9+eST6t+/v7Zu3aqBAwdKkr788ku99dZbkqShQ4e6nOtPf/qTbr31VknS66+/rtTUVI0dO1Zms1kTJ07U888/37xrBgAAV55WECe1NSSYAACAV1JTU5Wamup2386dO+tsmzRpkiZNmuS2f9++fWUYjUdvkZGRysnJ8WieAAAA8B8STEBDTI2UQLZr+I+Q0aHhty5VRoY2OoXz1ze8/5p/O97g/v/P9nGD+7fZBzW4/wtFN7jf8s/Gr6HdPxt5+9TFrxve39jPAWir+GQOAADAPeKkgGORbwAAAAAAAHiFCiYAAIIVn8wBAAC4R5wUcFQwAQAAAAAAwCtUMAEAEKx4/S4AAIB7xEkBR4IJAIAgZTIuNV+PCQAAEOyIkwKPR+QAAAAAAADgFSqYAAAIVixeCQAA4B5xUsCRYELbZm7kGVpzw0V+JlPDxzvaNXx8jaXxZ3irIhwN7v//bB83uH9e1y8aPUdDVkVc1eD+plyD0ch9MDdyH41Gfg6N/hxrG94NAAAAAPAOj8gBAAAAAAD4SVZWlvr27SuLxaL4+Hjt27evwf5btmxRTEyMLBaLBg0apO3bt7vsNwxDS5YsUa9evdShQwclJibq008/delz9uxZTZs2TVarVREREUpJSdGFCxd8fm3fRoIJAAAAAADADzZv3qy0tDSlp6frwIEDGjJkiJKSknTq1Cm3/Xfv3q2pU6cqJSVFBw8eVHJyspKTk3X48GFnn2effVbPP/+8srOztXfvXnXq1ElJSUmqqKhw9pk2bZo++eQT5efna9u2bXr//fc1Z84cv14rCSYAAIKUSd+8IcVnraUvCgAAwAdaS5y0cuVKzZ49W7NmzdKNN96o7OxsdezYUZs2bXLbf82aNRo3bpwWLlyo2NhYLVu2TMOGDdO6deskXapeWr16tRYvXqx77rlHgwcP1muvvaYTJ05o69atkqQjR44oLy9PL730kuLj4zV69GitXbtWubm5OnHiRPNuaBOQYAIAAAAAAGiisrIyl1ZZWem2X1VVlYqKipSYmOjcZjablZiYqMLCQrfHFBYWuvSXpKSkJGf/Y8eOyW63u/QJDw9XfHy8s09hYaEiIiI0fPhwZ5/ExESZzWbt3bu3eRfdBCSYAAAIVobJPw0AACDY+TFOio6OVnh4uLNlZma6ncKZM2dUW1urqKgol+1RUVGy2+1uj7Hb7Q32v/zfxvr07NnTZX+7du0UGRlZ73l9gbfIAQAQrHj9LgAAgHt+jJOOHz8uq9Xq3BwWFubjEwUnKpgAAAAAAACayGq1urT6Ekzdu3dXSEiISktLXbaXlpbKZrO5PcZmszXY//J/G+vz3UXEa2pqdPbs2XrP6wtUMKFtczSS0nY4GtxtGA0fb6pp+Ph2FY2n1EPPhTS4f5t9UKNjeHN86LmG89DtKmobPUdj96Gx+9jYz6HRnyNwpaKCCQAAwL1WECeFhoYqLi5OBQUFSk5OliQ5HA4VFBQoNTXV7TEJCQkqKCjQI4884tyWn5+vhIQESVK/fv1ks9lUUFCgoUOHSrq0JtTevXv14IMPOsc4d+6cioqKFBcXJ0nasWOHHA6H4uPjPbsID5BgAgAAAAAA8IO0tDTNmDFDw4cP14gRI7R69WqVl5dr1qxZkqTp06frqquucq7jNH/+fI0ZM0YrVqzQ+PHjlZubq/3792vDhg2SJJPJpEceeUS/+MUv1L9/f/Xr109PPfWUevfu7UxixcbGaty4cZo9e7ays7NVXV2t1NRUTZkyRb179/bbtZJgAgAgSF1+Za6vxwQAAAh2rSVOmjx5sk6fPq0lS5bIbrdr6NChysvLcy7SXVJSIrP5m6dGRo0apZycHC1evFhPPvmk+vfvr61bt2rgwIHOPo899pjKy8s1Z84cnTt3TqNHj1ZeXp4sFouzz+uvv67U1FSNHTtWZrNZEydO1PPPP9/8i28CEkwAAAAAAAB+kpqaWu8jcTt37qyzbdKkSZo0aVK945lMJi1dulRLly6tt09kZKRycnI8nqs3SDABABCsWsHaAgAAAK0ScVLAkWACACBYETgBAAC4R5wUcA2/HgoAAAAAAABoBBVMAAAEqdayeCUAAEBrQ5wUeCSYgIYYjfwNUlPT4G7T15UN7g87W9XoFMI/69Dg/i8U3eD+VRFXNbg/9FzDhYzhnzW4u0nX0Nh9MBq5j43+HAAAAAAALYoEEwAAwcowXWq+HhMAACDYEScFHGswAQAAAAAAwCtUMAEAEKx4OwoAAIB7xEkBRwUTAAAAAAAAvEIFEwAAQYq3owAAALhHnBR4JJgAAAhWlH4DAAC4R5wUcDwiBwAAAAAAAK9QwYTWy2hCetjk5WsiDUfDu2trGz59dU3D439d0eDudqe/avh4SeGN7Lf8M7TB/TWWhu9Ru4qGrzHsbFXDxzfhGhq7D2rkPjb2c2js5+i1pvwuAi3BD6XffDIHAACuCMRJAUcFEwAAAAAAALxCBRMAAMGKtQUAAADcI04KOCqYAAAAAAAA4BUqmAAACFZ8MgcAAOAecVLAUcEEAAAAAAAAr1DBBABAkDL54e0oPn/bCgAAQAsgTgo8KpgAAAAAAADgFRJMAAAAAAAA8IpHj8i98MILeuGFF/T5559Lkm666SYtWbJEd955pySpoqJCjz76qHJzc1VZWamkpCStX79eUVFRPp840CSGo5HdDedYTY3UQBo1NQ2fv6Ki4fEbPlqS1L664XO0+2dYg/uNdo1cY03D98j0dWWD+/V1w9coSUYj96HR++ho5OfQyP7Gfg+AoMXila0OsRIAAK0EcVLAeVTBdPXVV2v58uUqKirS/v37dfvtt+uee+7RJ598IklasGCB3n77bW3ZskW7du3SiRMnNGHCBL9MHAAAoLUhVgIAAG2VRxVMP/zhD12+f/rpp/XCCy9oz549uvrqq7Vx40bl5OTo9ttvlyS9/PLLio2N1Z49ezRy5EjfzRoAALB4ZStErAQAQOtAnBR4zV6Dqba2Vrm5uSovL1dCQoKKiopUXV2txMREZ5+YmBj16dNHhYWF9Y5TWVmpsrIylwYAABDsiJUAAEBb4nGC6eOPP1bnzp0VFhamn/3sZ/r973+vG2+8UXa7XaGhoYqIiHDpHxUVJbvdXu94mZmZCg8Pd7bo6GiPLwIAgDbL8HGD14iVAABoJYiTAsrjBNOAAQN06NAh7d27Vw8++KBmzJihv/zlL82ewKJFi3T+/HlnO378eLPHAgCgTfF10ETw5BPESgAAtALESQHn0RpMkhQaGqrrr79ekhQXF6cPP/xQa9as0eTJk1VVVaVz5865fDJXWloqm81W73hhYWEKC2v4LVgAAADBglgJAAC0Rc1eg+kyh8OhyspKxcXFqX379iooKHDuKy4uVklJiRISErw9DQAA+I7Li1f6usG3iJUAAAg84qTA86iCadGiRbrzzjvVp08fffXVV8rJydHOnTv17rvvKjw8XCkpKUpLS1NkZKSsVqvmzZunhIQE3ooC/zEa+RNuMnk5vqPh3dU13o3vaMLfUNXVDe+/+HWDu82N3AOjkXto1DRyjU24B42N0eh9bOTn4LXGfo8AoImIlQAAQFvlUYLp1KlTmj59uk6ePKnw8HANHjxY7777rn7wgx9IklatWiWz2ayJEyeqsrJSSUlJWr9+vV8mDgBAm+ePtQDIt3qFWAkAgFaCOCngPHpEbuPGjfr8889VWVmpU6dO6b333nMGTJJksViUlZWls2fPqry8XG+88UaDawoAAIDgl5WVpb59+8pisSg+Pl779u1rsP+WLVsUExMji8WiQYMGafv27S7733jjDd1xxx3q1q2bTCaTDh06VGeMW2+9VSaTyaX97Gc/8+VlNQuxEgAAaKu8XoMJAAC0jNawtsDmzZuVlpam9PR0HThwQEOGDFFSUpJOnTrltv/u3bs1depUpaSk6ODBg0pOTlZycrIOHz7s7FNeXq7Ro0frmWeeafDcs2fP1smTJ53t2Wef9WzyAADgitUa4qS2hgQTAABotpUrV2r27NmaNWuWbrzxRmVnZ6tjx47atGmT2/5r1qzRuHHjtHDhQsXGxmrZsmUaNmyY1q1b5+zz7//+71qyZIkSExMbPHfHjh1ls9mczWq1+vTaAAAA0HQkmAAACFaGn5qksrIyl1ZZWVnn9FVVVSoqKnJJBJnNZiUmJqqwsNDtlAsLC+skjpKSkurt35DXX39d3bt318CBA7Vo0SJdvHjR4zEAAMAVyo9xEtzzaJFvAADQivhx8cro6GiXzenp6crIyHDZdubMGdXW1ioqKsple1RUlI4ePep2eLvd7ra/3W73aJo/+clPdM0116h379766KOP9Pjjj6u4uFhvvPGGR+MAAIArFIt8BxwJJgAAUMfx48ddHjkLCwtrwdnUNWfOHOfXgwYNUq9evTR27Fj99a9/1XXXXdeCMwMAAGibSDChbTMcjexu+ClSk7mR46trGt5fW9vgfkky1TQ8hkymhs9hbuRJWEfD1yCj4TR9U65BjkZS/Y3+HLw7HrhS+WOxycvjWa3WRtc06t69u0JCQlRaWuqyvbS0tN43o9lsNo/6N1V8fLwk6bPPPiPBBAAA/BonwT3WYAIAAM0SGhqquLg4FRQUOLc5HA4VFBQoISHB7TEJCQku/SUpPz+/3v5NdejQIUlSr169vBoHAAAAzUMFEwAAwaoVrC2QlpamGTNmaPjw4RoxYoRWr16t8vJyzZo1S5I0ffp0XXXVVcrMzJQkzZ8/X2PGjNGKFSs0fvx45ebmav/+/dqwYYNzzLNnz6qkpEQnTpyQJBUXF0uS821xf/3rX5WTk6O77rpL3bp100cffaQFCxbo+9//vgYPHuyDmwAAAIJeK4iT2hoSTAAAoNkmT56s06dPa8mSJbLb7Ro6dKjy8vKcC3mXlJTI/K1HdUeNGqWcnBwtXrxYTz75pPr376+tW7dq4MCBzj5vvfWWM0ElSVOmTJH0zULjoaGheu+995zJrOjoaE2cOFGLFy8O0FUDAADgu0gwAQAQrFrJJ3OpqalKTU11u2/nzp11tk2aNEmTJk2qd7yZM2dq5syZ9e6Pjo7Wrl27PJ0mAABoS1pJnNSWkGACACBIsXglAACAe8RJgcci3wAAAAAAAPAKFUwAAAQrSr8BAADcI04KOBJMuLIZjfwNYDI1cryjkd2NFQHWNnx6o5HzSzIcXv4tZm7kHN6O38g9utTF/+do+Hj+JQAAAAAAf+IROQAAgtTltQV83QAAAIJdsMVJZ8+e1bRp02S1WhUREaGUlBRduHChwWMqKio0d+5cdevWTZ07d9bEiRNVWlrq3P9///d/mjp1qqKjo9WhQwfFxsZqzZo1LmPs3LlTJpOpTrPb7R5fAxVMAAAAAAAALWjatGk6efKk8vPzVV1drVmzZmnOnDnKycmp95gFCxbonXfe0ZYtWxQeHq7U1FRNmDBBf/7znyVJRUVF6tmzp/7rv/5L0dHR2r17t+bMmaOQkJA6bwAuLi6W1Wp1ft+zZ0+Pr4EEEwAAwYq1BQAAANwLojjpyJEjysvL04cffqjhw4dLktauXau77rpLv/rVr9S7d+86x5w/f14bN25UTk6Obr/9dknSyy+/rNjYWO3Zs0cjR47U/fff73LMtddeq8LCQr3xxht1Ekw9e/ZURESEV9fBI3IAAAAAAABNVFZW5tIqKyu9Gq+wsFARERHO5JIkJSYmymw2a+/evW6PKSoqUnV1tRITE53bYmJi1KdPHxUWFtZ7rvPnzysyMrLO9qFDh6pXr176wQ9+4KyA8hQJJgAAgpXhpwYAABDs/BgnRUdHKzw83NkyMzO9mqrdbq/zSFq7du0UGRlZ71pIdrtdoaGhdaqOoqKi6j1m9+7d2rx5s+bMmePc1qtXL2VnZ+t//ud/9D//8z+Kjo7WrbfeqgMHDnh8HTwiBwBAkDL9q/l6TAAAgGDnzzjp+PHjLusVhYWFue3/xBNP6JlnnmlwzCNHjvhqeg06fPiw7rnnHqWnp+uOO+5wbh8wYIAGDBjg/H7UqFH661//qlWrVuk3v/mNR+cgwQQAAAAAANBEVqvVJcFUn0cffVQzZ85ssM+1114rm82mU6dOuWyvqanR2bNnZbPZ3B5ns9lUVVWlc+fOuVQxlZaW1jnmL3/5i8aOHas5c+Zo8eLFjc57xIgR+uCDDxrt910kmNC2GY08C2JqJOdtOLw7vaMpT6nWenUObw/3CS/vU+Pj80wP2qggWrwSAAAgoFpBnNSjRw/16NGj0X4JCQk6d+6cioqKFBcXJ0nasWOHHA6H4uPj3R4TFxen9u3bq6CgQBMnTpR06U1wJSUlSkhIcPb75JNPdPvtt2vGjBl6+umnmzTvQ4cOqVevXk3q+20kmAAAAAAAAFpIbGysxo0bp9mzZys7O1vV1dVKTU3VlClTnG+Q+/LLLzV27Fi99tprGjFihMLDw5WSkqK0tDRFRkbKarVq3rx5SkhI0MiRIyVdeizu9ttvV1JSktLS0pxrM4WEhDgTX6tXr1a/fv100003qaKiQi+99JJ27NihP/7xjx5fBwkmAACClMm41Hw9JgAAQLALtjjp9ddfV2pqqsaOHSuz2ayJEyfq+eefd+6vrq5WcXGxLl686Ny2atUqZ9/KykolJSVp/fr1zv2/+93vdPr0af3Xf/2X/uu//su5/ZprrtHnn38uSaqqqtKjjz6qL7/8Uh07dtTgwYP13nvv6bbbbvP4GkgwAQAAAAAAtKDIyEjl5OTUu79v374yvrM0iMViUVZWlrKystwek5GRoYyMjAbP+9hjj+mxxx7zeL7ukGACACBYtYK1BQAAAFol4qSAI8EEAEAwI9ABAABwjzgpoJryCisAAAAAAACgXlQwAQAQpIJt8UoAAIBAIU4KPBJMQEMML/8GMZkaGd/h3fhXCm/vMwAAAACgRZFgAgAgWLF4JQAAgHvESQHHGkwAAAAAAADwChVMAAAEKdYWAAAAcI84KfCoYAIAAAAAAIBXqGACACBYsbYAAACAe8RJAUeCCQCAIEXpNwAAgHvESYHHI3IAAAAAAADwChVMgD8ZPkhxm0zej+FPvrhGAM1D6TcAAIB7xEkBRwUTAAAAAAAAvEIFEwAAwYpP5gAAANwjTgo4KpgAAAAAAADgFSqYAAAIUrwdBQAAwD3ipMCjggkAAAAAAABeoYIJAIBgxdoCAAAA7hEnBRwJJgAAgpTJMGQyfBvp+Ho8AACAlkCcFHg8IgcAAAAAAACvUMEEtHZkyQHUh9JvAAAA94iTAo4KJgAAAAAAAHiFCiYAAIIUr98FAABwjzgp8KhgAgAAAAAAgFeoYAIAIFixtgAAAIB7xEkBR4IJAIAgRek3AACAe8RJgccjcgAAAAAAAPAKFUwAAAQrSr8BAADcI04KOCqYAAAAAAAA4BUqmAAACFKsLQAAAOAecVLgUcEEAAAAAAAAr1DBBABAsGJtAQAAAPeIkwKOCiYAAAAAAAB4hQomAACCGGsBAAAAuEecFFgkmAAACFaGcan5ekwAAIBgR5wUcDwiBwAAvJKVlaW+ffvKYrEoPj5e+/bta7D/li1bFBMTI4vFokGDBmn79u0u+9944w3dcccd6tatm0wmkw4dOlRnjIqKCs2dO1fdunVT586dNXHiRJWWlvrysgAAAOABEkwAAASpy6/f9XXzxObNm5WWlqb09HQdOHBAQ4YMUVJSkk6dOuW2/+7duzV16lSlpKTo4MGDSk5OVnJysg4fPuzsU15ertGjR+uZZ56p97wLFizQ22+/rS1btmjXrl06ceKEJkyY4NnkAQDAFas1xEltDQkmAADQbCtXrtTs2bM1a9Ys3XjjjcrOzlbHjh21adMmt/3XrFmjcePGaeHChYqNjdWyZcs0bNgwrVu3ztnn3//937VkyRIlJia6HeP8+fPauHGjVq5cqdtvv11xcXF6+eWXtXv3bu3Zs8cv1wkAAICGkWACACBYGX5qksrKylxaZWVlndNXVVWpqKjIJRFkNpuVmJiowsJCt1MuLCyskzhKSkqqt787RUVFqq6udhknJiZGffr08WgcAABwBfNjnAT3SDABAIA6oqOjFR4e7myZmZl1+pw5c0a1tbWKiopy2R4VFSW73e52XLvd7lH/+sYIDQ1VRESEV+MAAADAd3iLHAAAQcrkuNR8PaYkHT9+XFar1bk9LCzMtycCAADwI3/GSXCPBBMAAMHKH6Xa/xrParW6JJjc6d69u0JCQuq8va20tFQ2m83tMTabzaP+9Y1RVVWlc+fOuVQxeToOAAC4gvkxToJ7PCIHAACaJTQ0VHFxcSooKHBuczgcKigoUEJCgttjEhISXPpLUn5+fr393YmLi1P79u1dxikuLlZJSYlH4wAAAMB3qGACACBI+eN1uZ6Ol5aWphkzZmj48OEaMWKEVq9erfLycs2aNUuSNH36dF111VXONZzmz5+vMWPGaMWKFRo/frxyc3O1f/9+bdiwwTnm2bNnVVJSohMnTki6lDySLlUu2Ww2hYeHKyUlRWlpaYqMjJTVatW8efOUkJCgkSNH+uAuAACAYNca4qS2hgQTAABotsmTJ+v06dNasmSJ7Ha7hg4dqry8POdC3iUlJTKbvymYHjVqlHJycrR48WI9+eST6t+/v7Zu3aqBAwc6+7z11lvOBJUkTZkyRZKUnp6ujIwMSdKqVatkNps1ceJEVVZWKikpSevXrw/AFQMAAMAdrx6RW758uUwmkx555BHntoqKCs2dO1fdunVT586dNXHixDprLQAAAB8wDP80D6WmpuqLL75QZWWl9u7dq/j4eOe+nTt36pVXXnHpP2nSJBUXF6uyslKHDx/WXXfd5bJ/5syZMgyjTrucXJIki8WirKwsnT17VuXl5XrjjTda5fpLxEoAALSQVhInNdXZs2c1bdo0Wa1WRUREKCUlRRcuXGjwmKbEFCaTqU7Lzc116bNz504NGzZMYWFhuv766+vEbk3V7ATThx9+qF//+tcaPHiwy/YFCxbo7bff1pYtW7Rr1y6dOHFCEyZMaO5pAAAAghKxEgAAaKpp06bpk08+UX5+vrZt26b3339fc+bMafCYpsYUL7/8sk6ePOlsycnJzn3Hjh3T+PHjddttt+nQoUN65JFH9NOf/lTvvvuux9fQrEfkLly4oGnTpunFF1/UL37xC+f28+fPa+PGjcrJydHtt9/uvJDY2Fjt2bOHdREAAPAh1hZovYiVAABoWcEUJx05ckR5eXn68MMPNXz4cEnS2rVrddddd+lXv/qVevfuXecYT2KKiIiIeiu9s7Oz1a9fP61YsUKSFBsbqw8++ECrVq1SUlKSR9fRrAqmuXPnavz48UpMTHTZXlRUpOrqapftMTEx6tOnjwoLC92OVVlZqbKyMpcGAAAQzIiVAAC4cn333+XKykqvxissLFRERIQzuSRJiYmJMpvN2rt3r9tjPIkp5s6dq+7du2vEiBHatGmTjG896ldYWFgnXklKSqo3LmmIxxVMubm5OnDggD788MM6++x2u0JDQxUREeGyPSoqSna73e14mZmZ+vnPf+7pNAAAgPGv5usx4RViJQAAWgE/xknR0dEum7/9IpLmsNvt6tmzp8u2du3aKTIyst74oKkxxdKlS3X77berY8eO+uMf/6iHHnpIFy5c0MMPP+wc5/LLWb49RllZmb7++mt16NChydfhUYLp+PHjmj9/vvLz82WxWDw5tF6LFi1SWlqa8/uysrI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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare PSF array\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(\n", + " get_jax_galsim_object_from_dfmd_obs(obs[1].psf, kind=\"image\").image.array\n", + ")\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax psf\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " get_jax_galsim_object_from_dfmd_obs(obs[1].psf, kind=\"image\").image.array\n", + " - get_galsim_object_from_ngmix_obs(obs[0].psf, kind=\"image\").image.array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"diff psf\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "1ad806ac-c473-4d57-9279-814dfb091ac3", + "metadata": {}, + "source": [ + "# galimage + noise (mcal_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "a41e1a00-7079-4b06-a0eb-ea1c73ec4507", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# mcal_image : here a deconvolution + a convolution has been applied\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_intermediates[5])\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"mcal image\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(jax_intermediates[5] - numpy_intermediates[5])\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"diff mcal image\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "208dcb2e-7671-4fe0-8c7d-33b82cffd16d", + "metadata": {}, + "source": [ + "# Comparing noise" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "c63d5594-f079-4916-93a3-b2370992be8d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZVx5IcpaRuCXezNM329e7kvPsOkiSb5t9DncdYz9MsObPOWYeOoYfpEKT7OuEf7V9nQgktrx1EvTXausv72z8Ow4d1/G0gaR/tYN63naLvNxuCADoAIK1tdpx2886XTvosssu0969e3XrrbeqpKREw4YN09KlSxsfvr19+/YmI7h27dql4cOHN/75nnvu0T333KOzzz5bK1asOKZlStJ9990nr9erqVOnyu/3a9KkSXr44YdbdV1bpWPp3nvv1cyZMxt7zRYtWqSXX35ZTzzxhG6++WZz3sPDvmPiExTjC9+girH/fSJJ8iYeX8dSjM/u9HEuX3J2LLnLcP9LyZvg6FgK2p0lMfHH0LGUZG/w2Di74Rvjc3e4eJMc9ai26xDjqOOxNM5j6uxtFRtr91J7E93r6doWvhQ7P5b1iE12bKujnFeNeVKtvXzH/pakUJx97Lrq6NoOkns9gq7rRIL72A86pvEm2ceEq45xyXaHzudlOPZXQr09f80xHPs+ux6xcfb1zpvo7liKOcr9541luI7bo/zQcNgxdSw5rhOu/RU6hmPGJdq3NgUVUtD1l1Ezlwfb8bSBpH8dI96EBDqWAAAdSjTbQW2lDTRr1qyj3vp2uLPosD59+igUcpdjLVOSEhIStHDhQi1cuLBZdT0eEb/psK6uTkVFRZo4ceK/CvF6NXHiRBUWFh4xvd/vV0VFRZMPAABAe9PcNpBEOwgAALR/Ee9Y2rdvnwKBQJOhWJKUk5OjkpIjb3WYP3++0tPTGz88VwAAgMgItsL/cHTNbQNJtIMAAGgNtIGi64S/Kmbu3LkqLy9v/BQXF5/oKgEA0CEEQqGIfxBZtIMAAIg82kDRFfFnLHXt2lUxMTEqLW36cN7S0lLl5uYeMb3P55PP8ewNAACAtq65bSCJdhAAAGj/It6xFB8fr5EjR2r58uWaMmWKJCkYDGr58uXmA6b+XV3q0R/SXdPP/ba0nOX2qtVm2oO1AhPKzDy5MMNZh5Dj2WRVA+yH7vbJ3+ssY8+mHmZem20P2fME3YPWard0MfMD0+z9EfQfQ+9upeNhzQn2epR9Zr/NKc7x0jlJco1u3Pll++1a2X93r2etvSn1l+fHmXnaMbzFb1dxHzMfO+0jM3/7zdPM/OBV9lvjJCnub+lmPiZrh5m/9x+nO8uouNZ+oHRCif1w/Po097bMPtk+B8tW2W8qi3Ucd5+8eLKzDhpeacaVO+23MsZl2g9jl6TqXvZ1oCHR3paqceSS/Nn2/jq43H77XSD8v8kb1fb3O+sQU5lo5nGOa3Z9qvuY+e6E5WG/r62s1x13OmePuLby4MrOIlJtIAAAcHxoA0VXq7wVbs6cObrqqqs0atQojR49WgsWLFBVVVXjG1IAAAA6ItpAAACgs2mVjqXLLrtMe/fu1a233qqSkhINGzZMS5cuPeJhlgAAoPUEFVKAX+uiijYQAAAnHm2g6GqVjiVJmjVrFsO+AQBAp0MbCAAAdCat1rEEAABOLJ4vAAAAOiPaQNFFxxIAAB1UpF+Py6t2AQBAe0AbKLrcrwQDAAAAAAAAwmDEEgAAHVTw/z6RXB4AAEBbRxsouhixBAAAAAAAgBZpsyOW/ANr5U0Mn8X7Gpzz1112yMzTfHVmvmN3ppknxDmroJhRZWYeuzHdzLc2uF9NHNPFvtczrsJj5inb3feKBuLt/sdrxr9t5o+/NNFdRoJdj5gaez3qs+vt/BgO9WBPexny2HX0jq5xlhHzeoaZJ52xz8yrPshylhHy2vUsfPV0M08dvt/Mu/7iKCfmF5T3t/N3dvcz8+Seqc4yer1i/25Q0cs+br0j7GuEJO3/INvM00fZ+6t2RVczr8lx//YRPOQz8/xl9v7ed1qys4y4gJ13+dSewD+9zFlG+Yf2sRtwXFM9jst++nv2dpKkhq+UmXlsWZI9f4p9HZKkPzwU/noXqKuV9Jpz/kgLRPhVu5FcFgAAQGuhDRRdjFgCAAAAAABAi7TZEUsAAOD4BEKffyK5PAAAgLaONlB00bEEAEAHxYMrAQBAZ0QbKLq4FQ4AAAAAAAAtwoglAAA6qKA8Csj90PHmLA8AAKCtow0UXYxYAgAAAAAAQIswYgkAgA4qGPr8E8nlAQAAtHW0gaKrzXYsxe3wyZvgC5sF48J//0XVtclm7jtjj5mHamPMPDj0kLMOWpNhxt7TKu283q6DJKnSniZptz178BiOgNgae9jfU899xcwT7NWUJFXl22eqp3+VmWck1Jl5sKt76GLsKxlmHvrqATNvCLoHAB7qbz/2LX51VzOPqXcWoeAI+9hMiLcXEvvHTDPf/A33VdV3wN7eDeuyzHzflAZnGQk748y8tpd9TGQuTXOWUXdOtZkf3GBvq3jHpWrQfTucddh1US8z3z3W3h8NOX5nGd44+7g86Ek08+oS97ZUd8c56guYecKHSWaeutN9zOx7P8PMK0fZ2yr1I/ffPVU9wu+PQK1z1lYRiPAw8EguCwAAoLXQBoouboUDAAAAAABAi7TZEUsAAOD48GsdAADojGgDRRcjlgAAAAAAANAijFgCAKCDCoY8CoYi+KrdCC4LAACgtdAGii5GLAEAAAAAAKBFGLEEAEAHxfMFAABAZ0QbKLroWAIAoIMKyKtABAcnByK2JAAAgNZDGyi62mzHUl1mQN7E8LvPV+qudig2ZOYDM/aa+ZjsbWb+5/eHO+uQGLTz+lp7PS445WNnGX8rHmHmcVX2/ClX7nSWsX9Trpn7MmvMPOatVGcZoTh7f9XvTTTz4OYUM/d3sZcvSckV9jT+lzPNPPYYOrETz6sw87rqNHsBHnchdcXJZp79un1g7jrTXr5rX0lS8g57mvKB9vy+Evc5Hjyl0syT1trHRMYmv7OM2NokM2+wD0sd6mNvh03X5jvrEH9amZnHrckw8/69S5xlFL/W28yzzt5tL2BFd2cZ8eV27s9y5Bn2tqzuFuOsQ4xjlydv9Jn5oZManGXkLQ9/jjbUB/VP59wAAABA+9NmO5YAAMDxCUX4wZUhHlwJAADaAdpA0cXDuwEAAAAAANAijFgCAKCD4sGVAACgM6INFF2MWAIAAAAAAECLMGIJAIAOKhDyKhCK4BtR3M/uBwAAOOFoA0UXHUsAAHRQQXkUjODg5KBoVQEAgLaPNlB0cSscAAAAAAAAWqTNjlhKLI5VjC989fK/ss05//YDXcz8H0tOM/MRV3xk5hkfxjnr4OrUDG7zmfnq10Y4i4jPsx8itm9Y0MxDv+vhLMM71F6RYLrdP1l79iFnGXGfpph5IMGuQ+qkEjPvMcvdw7xlvl0Hf0mSmYdi3WXkJdWYedzbdhn7hriPu6x/OI6J0+3TviGr3sx9u92Xjaoedh0umPSemf9l5UhnGekJdWZup5I3YJ8bknTw/GozT1yVbOYJe+3l13ZzHzN+v72963vba7r/972cZXTd22Dmh8bG23VIda9H7am1Zp76XqKZ16XbZdRmuX8nqenu2OeO1Ygti3GWUX5FRdjvA9V+6Xnn7BHHgysBAEBnRBsouhixBAAAAAAAgBZpsyOWAADA8Yn8gyt5vgAAAGj7aANFFyOWAAAAAAAA0CKMWAIAoIP6/I0okXsmQCSXBQAA0FpoA0UXHUsAAHRQQXkV4FW7AACgk6ENFF3cCgcAAAAAAIAWYcQSAAAdFA+uBAAAnRFtoOhixBIAAAAAAECELVy4UH369FFCQoLGjBmj9957z5z+ueee06BBg5SQkKDTTz9dr7zySpPc4/GE/dx9992N0/Tp0+eI/K677mqV9TuszY5YCsV+/glny94s5/z1NXH28tPt+bceyrQnOO+Asw4J8fVmXnkw1czLQ0nOMvq8UG7mNT2SzfzAIPch0PP1gJkXT04w8/oke35JythpPwwtGG/nB3rb28r/ZXtbS5LXW2Hmnga7DpkfuvtpS6uzzTy9p11GfbK7p/zAEHuaQLp9XHp99v7yZ7sfXJf+sX1cvfXYGWYec1aNs4yaf3Qxc3/vBjPfEZfoLCNmvZ13/eoOM9++x76ONByyr1OSFLPTPrbj6uz9sX940FlG1R57f/k329u611v2tpak3dfY08RP2mvmjiuyqvvFO+uQsTTNzBPK7HNn7I9XOctYfceosN831Mc4520NQXkV5PkCAACgk2kLbaBnn31Wc+bM0aJFizRmzBgtWLBAkyZN0saNG5WdfeS/C999911dccUVmj9/vr761a9q8eLFmjJlitasWaPTTjtNkrR79+4m8/z1r3/VjBkzNHXq1Cbf33HHHZo5c2bjn1NT3f8ePh6MWAIAAAAAAIige++9VzNnztT06dN1yimnaNGiRUpKStITTzwRdvr7779fkydP1o9+9CMNHjxYd955p0aMGKGHHnqocZrc3Nwmnz//+c8655xz1K9fvybLSk1NbTJdcrI94OR40bEEAEAHFQh5Iv4BAABo61qrDVRRUdHk4/f7w5ZfV1enoqIiTZw4sfE7r9eriRMnqrCwMOw8hYWFTaaXpEmTJh11+tLSUr388suaMWPGEdldd92lrKwsDR8+XHfffbcaGtx3GByPNnsrHAAAOD6BCL9qN8CtcAAAoB1orTZQfn5+k+/nzZun22677Yjp9+3bp0AgoJycnCbf5+TkaMOGDWHLKCkpCTt9SUlJ2Ol/+9vfKjU1VZdcckmT77///e9rxIgRyszM1Lvvvqu5c+dq9+7duvfee811PB50LAEAAAAAADgUFxcrLe1fz+30+XwnrC5PPPGEpk2bpoSEps88njNnTuN/DxkyRPHx8frud7+r+fPnt1p9uRUOAIAOKhjyRvzTHPPnz9cZZ5yh1NRUZWdna8qUKdq4cWMrrS0AAMDnWqsNlJaW1uRztI6arl27KiYmRqWlpU2+Ly0tVW5ubth5cnNzj3n6v//979q4caO+853vOLfFmDFj1NDQoK1btzqnbSk6lgAAQKt48803dd1112nlypVatmyZ6uvrdd5556mqqupEVw0AAKDVxMfHa+TIkVq+fHnjd8FgUMuXL1dBQUHYeQoKCppML0nLli0LO/3jjz+ukSNHaujQoc66rF27Vl6vN+yb6CKFW+EAAOigTvQzlpYuXdrkz0899ZSys7NVVFSkL3/5yxGrFwAAwBed6DaQ9PktaVdddZVGjRql0aNHa8GCBaqqqtL06dMlSVdeeaV69Oih+fPnS5J+8IMf6Oyzz9avfvUrXXjhhVqyZInef/99Pfroo02WW1FRoeeee06/+tWvjiizsLBQq1at0jnnnKPU1FQVFhZq9uzZ+va3v60uXbq0YM2PTZvtWAokhhRKCL/zvBvdr8rL+zBo5g3X7DHz3YV5Zp6zOuCsw6GZ5WaevDLJnr+PvQ6S9NmceDP3lNonU1ylswjtPjPGzLOK7PnrU+35JSmQaOeuFxHV7LO3ZcN57l/HA9X2tgz67P1RNr7eWUbCR/aKHupnX7D+86uvOst45OVJZp7/kn1M7LrUPrYHP1DmrMOeL2WZee35FWbe/bfuc/xQT/ugqE+z1zPkPixV37/GzLetta8T9095ysx/+PQ1zjrEVtt5Ta59XCYXu1c0OMa+VgUP2OfXrunh34bxRaHNKWYe+w97nx/qae/PlN3u62XlVPu488bb5/DSrYOdZTSMCr+9g7Ux0svO2duNioqm29Ln8x3TPfvl5Z8fa5mZma1SLwAAgLbisssu0969e3XrrbeqpKREw4YN09KlSxsf0L19+3Z5vf9q444bN06LFy/WLbfcop/85CcaOHCgXnjhBZ122mlNlrtkyRKFQiFdccUVR5Tp8/m0ZMkS3XbbbfL7/erbt69mz57d5LlLraHNdiwBAIDjE5QaX48bqeVJx/5GlCbzBoO64YYb9KUvfemIBhIAAEAktVYbqLlmzZqlWbNmhc1WrFhxxHeXXnqpLr30UnOZ1157ra699tqw2YgRI7Ry5cpm1/N40bEEAEAHFZRXwQgOAz+8rJa8EeW6667TunXr9Pbbb0esPgAAAOG0VhsI4dGxBAAAmuXwm1CO1axZs/TSSy/prbfeUs+ePVuxZgAAAIg2OpYAAOigAiGvAqEIPriymcsKhUK6/vrr9fzzz2vFihXq27dvxOoCAABwNCe6DdTZ0LEEAABaxXXXXafFixfrz3/+s1JTU1VSUiJJSk9PV2Ki460NAAAAaBfoWAIAoIMKyqOgIvngyuYt65FHHpEkjR8/vsn3Tz75pK6++uoI1QoAAKCpE90G6mzoWAIAAK0iFAqd6CoAAACglbXZjqVgfEiKD98g7Tt6h3P+bco3c//2TDNPCNg9kp7r9zjr0LA0z8wr+9svLUwscd/H2e20g2a+a0d3M88Zt8tZxo59GWa+v7tdz1C1+zBL2m5P05Bi/+Mkcac9v2d7irMOwdOqzdybWm8vYLf7rUg12fY+77bGnv+xAeOcZXT7wN5WFfn2tkr8wM7/eVuDsw6hjXY+95RlZn77BRc7y8j4h53Hdrf3Z+Jn7mPC22DfqlPTp87Mb7n3GjOvO8n94tLgqfZ6BA4mmLlnt/s6UrfZfghzQpV9PfT3DzjLcC2jPsmevy7DPq79We5fkTL+Yq/n3rPt/Tl37F+dZfxi+9fCfh+MPTEdLDxfAAAAdEa0gaKrzXYsAQCA4xOQV4EIvh43kssCAABoLbSBooutAwAAAAAAgBZhxBIAAB1UMORRMBTBB1dGcFkAAACthTZQdDFiCQAAAAAAAC3CiCUAADqoYISfLxDk9ygAANAO0AaKLrYOAAAAAAAAWoQRSwAAdFDBkFfBCL4eN5LLAgAAaC20gaKLjiUAADqogDwKKHIPm4zksgAAAFoLbaDoarMdS/1/X67YmNqwWeXAHs75U7vYeV2mfWAEY0NmXvGn7s461Ofa+XcnLDfzZd8/y1nGzhGpdh26BMx892r3enQdvtfMG/7YzcwPnhN+P35R/RC/mcesTzbzmp71Zp62Ps5Zh5q9CWYed8g+ZnwHj+Fic9ZBexlvOPbnZjuXpD1fqzHzUIm9nqGudWae+IG7Dqd/dYOZPzx/qpn33tfgLKNkjH2Oxn2QYuaHxtnbSZKC++PNPH6XfVzFXWifO3FV9r6QpG7/m2TmNVmuX0/s7SRJfb62xcw3vdbPzGN3+pxlNJxaZeblp9n1TE22ryMVH2c561CTbZ+j/znqLTO/+8WLnWX0/Hv4a25DfVDbnXMDAAAA7U+b7VgCAADHh2HgAACgM6INFF1sHQAAAAAAALQII5YAAOigAorsMwHsm6sBAADaBtpA0cWIJQAAAAAAALQII5YAAOigeL4AAADojGgDRRcdSwAAdFCBkFeBCDaEIrksAACA1kIbKLrYOgAAAAAAAGiRNjtiactlGfImJITNgr1qnPMPy99h5vv/2cvMu70RZ+Z7v+muQ/2eRDN/YccQM/fkxDvL8KwNv40a81z7MWNd1oecZZRmZZp57jf2mHlwW5azjB5995p59ld2mvln+7uZeUVssrMOaR/a2zu21t5WB09zP9LtO/3WmPnfAl8285ha9wPoYj5KMvPkM+1tnfFze/4tF7uPmdHpW8184yXZZr53pX3MSVIg3q5HyGvnHo97PbL6HTTz7qkVZv7PV/qZuf+kOmcdDp4UY+beBnv+ARd/5ixj7Vq7nr4hh8y8oca+XkpS3D/t48rrt4/t2C32OdzF3kySpL1n2dv7sb9ONHPffvf5t++08H+tBvyx0l+ds0dcSB4FI/jgylAElwUAANBaaANFV7NHLL311lu66KKLlJeXJ4/HoxdeeKFJHgqFdOutt6p79+5KTEzUxIkT9dln7n/YAAAAtGW0gQAAAI7U7I6lqqoqDR06VAsXLgyb//KXv9QDDzygRYsWadWqVUpOTtakSZNUW1t73JUFAADH7vDzBSL56cxoAwEA0D7QBoquZt8Kd/755+v8888Pm4VCIS1YsEC33HKLLr74YknS008/rZycHL3wwgu6/PLLj6+2AAAAJwhtIAAAgCNFtNtty5YtKikp0cSJ/3pORXp6usaMGaPCwsKw8/j9flVUVDT5AACA4xcMeSL+QXgtaQNJtIMAAGgNtIGiK6IdSyUlJZKknJycJt/n5OQ0Zv9u/vz5Sk9Pb/zk5+dHskoAAHRaAXkj/kF4LWkDSbSDAABoDbSBouuEb525c+eqvLy88VNcXHyiqwQAABAVtIMAAEB71+xnLFlyc3MlSaWlperevXvj96WlpRo2bFjYeXw+n3w+XySrAQAApIgP3WYY+NG1pA0k0Q4CAKA10AaKroh2LPXt21e5ublavnx5YyOqoqJCq1at0ve+971mLau+S4O8iQ1hs5Nz9zrn3/rbgfYEw4NmnPhfu8w8/lX3UPVuE44+9F2SDtXaDcnq4e6DN21zyMzzJ+y0y3ijh7MMxdhllGzLsuePtbe1JO1em2vm37roPTP/x7v2/s75yF4HSYqtCZh5aKZ93PX/ebqzjBfen2DmQ+d9aObb1p7iLCOzyD6t95ba9awbFW/mCfucVdBvH59s5oeG2W9I8ma5j5lQrt/MY/6ZYOYN/hhnGWUf28d27f6uZp60xz7ugme43xRVm23vz9QBZWb+wabezjI86fV2vjbVrkO1swjFnLPfzCs/zrTznvb18PGZDzrr8FDJuWa+evlgM485hhd7+c4Lf50IVNvHK9q/SLaBAAAA2pNmdyxVVlZq06ZNjX/esmWL1q5dq8zMTPXq1Us33HCDfvazn2ngwIHq27evfvrTnyovL09TpkyJZL0BAIBDUF4FI3jXeySX1R7RBgIAoH2gDRRdze5Yev/993XOOec0/nnOnDmSpKuuukpPPfWUbrrpJlVVVenaa69VWVmZzjzzTC1dulQJCfboAQAAEFmBkEeBCA7djuSy2iPaQAAAtA+0gaKr2R1L48ePVyh09Ns7PB6P7rjjDt1xxx3HVTEAAIC2hDYQAADAkSL6jCUAANB28OBKAADQGdEGii5uFAQAAAAAAECLMGIJAIAOKhTyKhiK3G9IoQguCwAAoLXQBooutg4AAAAAAABahBFLAAB0UAF5FFAE34gSwWUBAAC0FtpA0dVmO5ZSN8QpxhcXNtu2o7dzfm83xwTJDWZ8cElPMw9MqnDWYffedDP3bUg0c2/S0d88c1jG5joz/2ydvR759UFnGVnv2YfJIcfuSNsc4yzDNbLw7qUXmXn6Fnv+4BX7nXUIeO1tUbox28zH/WKDs4zz0orNfKe/i5nHVLq3ZUU/O49Lso+Z9H/aO6PsO4ecdahbY69H7zx7f+yIs+eXpEB5vJl7/fbF31MR/vryRWecae/T9/8+yK5DvV2Hhk9TnXWIr7aXkbAkw67DGc4iFEyyj31/VzuP3+Ie/Fq+yd6nXsehXXd6tZn/T/GFzjqs35lr5rGDKs08NbXKWUZ1XfjjKhA4MQOEg6HIPmwy6P5rCQAA4ISjDRRd3AoHAAAAAACAFmmzI5YAAMDxCUb4wZWRXBYAAEBroQ0UXWwdAAAAAAAAtAgjlgAA6KCC8igYwYdNRnJZAAAArYU2UHQxYgkAAAAAACDCFi5cqD59+ighIUFjxozRe++9Z07/3HPPadCgQUpISNDpp5+uV155pUl+9dVXy+PxNPlMnjy5yTQHDhzQtGnTlJaWpoyMDM2YMUOVlfZLao4XHUsAAHRQgZAn4h8AAIC2ri20gZ599lnNmTNH8+bN05o1azR06FBNmjRJe/bsCTv9u+++qyuuuEIzZszQBx98oClTpmjKlClat25dk+kmT56s3bt3N37+93//t0k+bdo0ffzxx1q2bJleeuklvfXWW7r22mubXf/moGMJAIAO6vCDKyP5AQAAaOvaQhvo3nvv1cyZMzV9+nSdcsopWrRokZKSkvTEE0+Enf7+++/X5MmT9aMf/UiDBw/WnXfeqREjRuihhx5qMp3P51Nubm7jp0uXLo3Z+vXrtXTpUj322GMaM2aMzjzzTD344INasmSJdu3a1ex1OFZt9hlLGZvrFRsXEzbbMd5d7cxT9pl5dWm6mdcn2T2SX8rf4qyD1xM08zf/OdzMuwzf6yxjS0pXM48vs0+A3d+qcZZRX+4z89iy8PvpsKt/9JKzjN/+4qtmnj7goJnHrc0086q/d3PWIWfiDjNP2+S4mJzhLEIv/3iCmW8/3y7D273WWUZDjX1+JMcHzHznFfVm3uUvGc461AwImfnuwjwzT6h0/yJQ1dNej7St9vkXV+n+y2HTB4PMPKfcLmPHefZ26PIP+9yRpIOj68y84KsbzHz/itOdZXT52K7H/pH2tvZnurdlMMneVrH77TokrE4y8+J3+znrEHv2ITMP2btLla/kOsvoctHOsN83BP3OeQEAANC2VVRUNPmzz+eTz3fkv5fr6upUVFSkuXPnNn7n9Xo1ceJEFRYWhl12YWGh5syZ0+S7SZMm6YUXXmjy3YoVK5Sdna0uXbpowoQJ+tnPfqasrKzGZWRkZGjUqFGN00+cOFFer1erVq3S17/+9Wat77Hip0cAADqooDwKhiL44cGVAACgHWitNlB+fr7S09MbP/Pnzw9b/r59+xQIBJSTk9Pk+5ycHJWUlISdp6SkxDn95MmT9fTTT2v58uX6xS9+oTfffFPnn3++AoFA4zKys7ObLCM2NlaZmZlHLTcS2uyIJQAAAAAAgLaiuLhYaWlpjX8ON1qpNV1++eWN/3366adryJAh6t+/v1asWKFzzz03qnX5IjqWAADooEIRftVuiBFLAACgHWitNlBaWlqTjqWj6dq1q2JiYlRaWtrk+9LSUuXmhn+8Qm5ubrOml6R+/fqpa9eu2rRpk84991zl5uYe8XDwhoYGHThwwFzO8eJWOAAAAAAAgAiJj4/XyJEjtXz58sbvgsGgli9froKCgrDzFBQUNJlekpYtW3bU6SVpx44d2r9/v7p37964jLKyMhUVFTVO8/rrrysYDGrMmDHHs0omRiwBANBBHX4uQCSXBwAA0Na1hTbQnDlzdNVVV2nUqFEaPXq0FixYoKqqKk2fPl2SdOWVV6pHjx6Nz2n6wQ9+oLPPPlu/+tWvdOGFF2rJkiV6//339eijj0qSKisrdfvtt2vq1KnKzc3V5s2bddNNN2nAgAGaNGmSJGnw4MGaPHmyZs6cqUWLFqm+vl6zZs3S5Zdfrrw8+wVKx4OOJQAAOqiWvh7XWh4AAEBb1xbaQJdddpn27t2rW2+9VSUlJRo2bJiWLl3a+IDu7du3y+v913LHjRunxYsX65ZbbtFPfvITDRw4UC+88IJOO+00SVJMTIw+/PBD/fa3v1VZWZny8vJ03nnn6c4772zyrKdnnnlGs2bN0rnnniuv16upU6fqgQceOM4tYKNjCQAAAAAAIMJmzZqlWbNmhc1WrFhxxHeXXnqpLr300rDTJyYm6tVXX3WWmZmZqcWLFzernserzXYsBeK98sSF7xX0HXAPQ0uIbXAUYC9j6OXrzHzl/xvqrENVn4CZjzp3o5l/+uzJzjJCp9ebedwOu2f11F7FzjI+XDrIzL3Dy838vlcudJaR0N3eH9VbM8zcMyJk5v1O3+Gsw54X8828wrGtP3jxFGcZsQPt/MHJT5j5D1Zd4SwjMb3WzL1vp5t5sL993MZ/s9TMJcm7yn4wXHyFPf+h0/zOMjIyq8x8j+z1TOppH7eStH9rqpkn97NXJP31LmZ+cIx7PdO6VJv562vs467X2/b+lKTtX7PPn8wi+6+KsrNqnGWoIs6ME04ts2ffZe8L3173X2cJb9nLqO5hb4fk3e5tueOD8EOMg7X2edla2sIwcAAAgGijDRRdjGkHAAAAAABAi7TZEUsAAOD4BCP8qt1ILgsAAKC10AaKLkYsAQAAAAAAoEUYsQQAQAfF8wUAAEBnRBsouuhYAgCgg6JRBQAAOiPaQNHFrXAAAAAAAABoEUYsAQDQQfFrHQAA6IxoA0UXI5YAAAAAAADQIm12xNK+oV55E8L3e/lOP+icP/B4jpmf8n6JmX9yzqlmnlIddNah8iR7mk+fPdnMD/Vzl+F662FVfsDMNz1h10GSkr++z8xr6+3DKL7M3X9ZdVKdmXfNrjDzQ0VdzXzPX/KddajpHrLrkFdu5v4Ndh0kKeCz81krvm3msfvinGV864JCM3+i8kv2Aqrs/bm7pIuzDjGJ9rbs+eVtZl66pLezDG99vJkPXFdp5v/8RpqzjLTt9gnm3Zhh5of62tsh/88xzjoUfzXZzGOq7GXsGeX+dSXmkH2tqTi7xsxDBx0HttzXgbpu9nGXvsHOMzfY1xBJ2jLFrkO3Vfa2LLnYXUa3v4bfFoG6kLY65448fq0DAACdEW2g6GLEEgAAAAAAAFqkzY5YAgAAxyckKega2trM5QEAALR1tIGii44lAAA6KIaBAwCAzog2UHRxKxwAAAAAAABahBFLAAB0UPxaBwAAOiPaQNHFiCUAAAAAAAC0CCOWAADooPi1DgAAdEa0gaKrzXYs1eXVy5sYEzar35bunD/hqv1mvmVgnpnnFNWb+fbJ4ev2Rfm99pl51fu5Zu7b7x5QlrjePsCTp5aYedKTSc4yPj07xcxT0mrMvLZb0FlGfHKdmQdfzDJz/xn2/Glb4px1CPrsbVmx1q5D3WD7mJGkxG12PdI+ijfzQKKzCP1u/Wgzj02w69k7f4+Zb9pkH7eS9P0LXzHz+1ZPNPPQyAZnGQN/a6/Hp1fbGyt5u/svh6qe9vsf8kbsNvPQ893NvPjigLMOJ00vMvOaV/ua+b637TpIcr7mwrPV3pY9R9rXGUkKLcq2J3jfvhbtmmpfZ6rGut/VEedYj6Djb8TU99wn4Igb1oT9vq6yTmv+4JwdAAAAaHfabMcSAAA4PvxaBwAAOiPaQNFFxxIAAB1UKORRKIINoUguCwAAoLXQBoouHt4NAAAAAACAFqFjCQCADiooT8Q/zfHWW2/poosuUl5enjwej1544YXWWVEAAIAvONFtoM6GjiUAANAqqqqqNHToUC1cuPBEVwUAAACthGcsAQDQQZ3oB1eef/75Ov/88yNWPgAAwLE40W2gzoaOJQAA0CwVFRVN/uzz+eTz+U5QbQAAAHAitdmOpYysSsUk1YfNyiu6OOc/UJZi5t7Tqsx8TyjZzBNKnVXQoU3dzbwh1Z6/pmeDswx/pn03Y/U7dh2CU0POMkKVATOv2p9uz5/uXo/4Int/Vec4FtBgb4fUb+901uHA2jwzHzH2MzPfWWlvB0na1dDVzBsy/HZeF+MsIzu12sz3bM4y8837Eu0Ckt3788nNY83ccyDOXkBWnbOM4olJdhkN9nHb4FhNSep9xg67DvszzDz/k1ozD3ncldj1w3FmXr/Snj+Q6j7H43rb18OkBPu4LDnouJhJGv2jjWb+btHJZp691O60qM5x39mdut0+JvJvsM/x1Fh7O0jSyieGh/0+UFcr6Q/O+SOttd6Ikp+f3+T7efPm6bbbbotYOQAAAMeDt8JFV5vtWAIAAMentYaBFxcXKy0trfF7RisBAIC2hFvhoouOJQAA0CxpaWlNOpYAAADQedGxBABAB8UwcAAA0BnRBoouOpYAAECrqKys1KZNmxr/vGXLFq1du1aZmZnq1avXCawZAAAAIoWOJQAAOqhQhJ8v0Nxf695//32dc845jX+eM2eOJOmqq67SU089FbF6AQAAfNGJbgN1NnQsAQCAVjF+/HiFQu43EwIAAKD9omMJAIAOKiQpkv06dBEBAID2gDZQdLXZjqXyrRnyJiaEzY5lEJpvXaKZV/etN/NYr718rz27JKkm2z78Ymoca+J1H76JpXZFa7vay+h22h5nGQfWZJt5wj57PSpOce8xT4Od+w7aec6Z9nps+aS7sw6hTLsS7/+zt5l3fd39uu1Ba8vN/FD/VDO/YN4KZxkrvldglzGn0sx7Zth1/Gx9D2cdajdnmXmXEvu4PHCWswjV5tfZEwTs485bF+MsY/vKnmZe19U+ZnZ+Oc7MQ+4qqK5/jT3BPvu4GzB7pbOMz+4fa+b13eyKerfb11tJ+jgx18w99fb+OnhRtZnnPek+/7Z+3c4PvjLIzGP8ziKU8rXSsN8HqvzSY+75Iy0ojzzH9LfmsS8PAACgraMNFF2O7hMAAAAAAAAgvDY7YgkAABwfXrULAAA6I9pA0cWIJQAAAAAAALQII5YAAOiggiGPPBH8hS2Sr+0FAABoLbSBoosRSwAAAAAAAGgRRiwBANBBhUIRftUu79oFAADtAG2g6KJjCQCADooHVwIAgM6INlB0cSscAAAAAAAAWqTNjljyHfAqxhe+3yu+7BjmL7fHqnVdZ/c4XvA/y8x8X32Ksw5//Gi4mad+kGDmffrucZaxNZhj5jlv232HVSX2/JI04PldZr7+RnsZyZvjnGWk7gia+d6v15h55j1dzfyUn2x31mH/r3ubeUy9fUwdynf3Ypfcbq9n9Uf2/nr8tXOcZQSvDJh5wkdJZr6jLt3M08cccNahuryLmden2Nsq5I9xltElt8LMq/6Raea9zt3mLGPbW/Yxkdyt2s7fTjNzzxV7nXXYsznLzE8bsdXM9/61v7OMVH+5mdcX2fszduRBZxmZyfa28g60z43Kf9jbof6GUmcdYv9hX6sSx+4z85pV9nVGkiqXhy8j4K91ztsa+LUOAAB0RrSBoosRSwAAAAAAAGiRNjtiCQAAHB9etQsAADoj2kDRxYglAAA6qMNvRInkBwAAoK1rK22ghQsXqk+fPkpISNCYMWP03nvvmdM/99xzGjRokBISEnT66afrlVdeaczq6+v14x//WKeffrqSk5OVl5enK6+8Urt2NX10TZ8+feTxeJp87rrrrpatwDGiYwkAAAAAACCCnn32Wc2ZM0fz5s3TmjVrNHToUE2aNEl79oR/lvK7776rK664QjNmzNAHH3ygKVOmaMqUKVq3bp0kqbq6WmvWrNFPf/pTrVmzRn/605+0ceNGfe1rXztiWXfccYd2797d+Ln++utbdV25FQ4AgA7q81/YIvngyogtCgAAoNW0hTbQvffeq5kzZ2r69OmSpEWLFunll1/WE088oZtvvvmI6e+//35NnjxZP/rRjyRJd955p5YtW6aHHnpIixYtUnp6upYta/qSsYceekijR4/W9u3b1atXr8bvU1NTlZub2/xKtxAjlgAAAAAAABwqKiqafPx+f9jp6urqVFRUpIkTJzZ+5/V6NXHiRBUWFoadp7CwsMn0kjRp0qSjTi9J5eXl8ng8ysjIaPL9XXfdpaysLA0fPlx33323GhoajnENW4YRSwAAdFC8ahcAAHRGrdUGys/Pb/L9vHnzdNtttx0x/b59+xQIBJSTk9Pk+5ycHG3YsCFsGSUlJWGnLykpCTt9bW2tfvzjH+uKK65QWlpa4/ff//73NWLECGVmZurdd9/V3LlztXv3bt17773O9WypNtuxVDegRt6k8OPN4t5PdM5/cLCd16fb+W+XfMXMg/HOKij3k6CZl51kz791c449gaQ/T37AzL+1dY6Zdzt3p7OMfXvyzLxLr/1mHvykq7OM+kT7pD+pe/j7UA/b9KW+Zt5Q1NtZh7m3vmDm/7N0ipmnbHcWobJ9KWYe47PnDybax5QkyXH9rO1Z5yjEXkDDh12cVfA22MuoP7PCzHssSXaWcWBQlpknldnzlz2eb08gKbaHnQ/vXmzmFd+xr1WbX+rvrENcuj3udvOr/czcn+k+ZuIO2YNXE/fadchKrXSWUfJXe3tXDak181A3+1eWXZ91c9bh6xNXmfnuWvsvhg/r3deyXv9vR9jvG4J+bXTODQAAgLasuLi4SSeOz+f4B1wrqa+v1ze/+U2FQiE98sgjTbI5c/7VBzBkyBDFx8fru9/9rubPn99q9W3WrXDz58/XGWecodTUVGVnZ2vKlCnauLFpU7m2tlbXXXedsrKylJKSoqlTp6q0tDSilQYAAG6hVvh0VrSBAABoP1qrDZSWltbkc7SOmq5duyomJuaIdkBpaelRn32Um5t7TNMf7lTatm2bli1b1qSjK5wxY8aooaFBW7duNac7Hs3qWHrzzTd13XXXaeXKlVq2bJnq6+t13nnnqaqqqnGa2bNn68UXX9Rzzz2nN998U7t27dIll1wS8YoDAADb4WHgkfx0VrSBAABoP050Gyg+Pl4jR47U8uXLG78LBoNavny5CgoKws5TUFDQZHpJWrZsWZPpD3cqffbZZ3rttdeUlWXfxSFJa9euldfrVXZ2drPWoTmadSvc0qVLm/z5qaeeUnZ2toqKivTlL39Z5eXlevzxx7V48WJNmDBBkvTkk09q8ODBWrlypcaOHRu5mgMAAEQJbSAAANAcc+bM0VVXXaVRo0Zp9OjRWrBggaqqqhrfEnfllVeqR48emj9/viTpBz/4gc4++2z96le/0oUXXqglS5bo/fff16OPPirp806lb3zjG1qzZo1eeuklBQKBxucvZWZmKj4+XoWFhVq1apXOOeccpaamqrCwULNnz9a3v/1tdenifqRJSx3XM5bKy8slfb4SklRUVKT6+vomTzIfNGiQevXqpcLCwrCNKr/f3+RJ6hUV9rNXAADAMYr0/Wud+V64fxOJNpBEOwgAgFbRBtpAl112mfbu3atbb71VJSUlGjZsmJYuXdr4gO7t27fL6/3XTWTjxo3T4sWLdcstt+gnP/mJBg4cqBdeeEGnnXaaJGnnzp36y1/+IkkaNmxYk7LeeOMNjR8/Xj6fT0uWLNFtt90mv9+vvn37avbs2U2eu9QaWtyxFAwGdcMNN+hLX/pS44qWlJQoPj7+iFfdWU8ynz9/vm6//faWVgMAACCqItUGkmgHAQDQkc2aNUuzZs0Km61YseKI7y699FJdeumlYafv06ePQiG7h2vEiBFauXJls+t5vJr1jKUvuu6667Ru3TotWbLkuCowd+5clZeXN36Ki+23LAEAgGMU6WcLdOJnLH1RpNpAEu0gAABaBW2gqGrRiKVZs2bppZde0ltvvaWePXs2fp+bm6u6ujqVlZU1+cXOevK5z+c7Ya/oAwAAaI5ItoEk2kEAAKD9a1bHUigU0vXXX6/nn39eK1asUN++fZvkI0eOVFxcnJYvX66pU6dKkjZu3Kjt27cf9cnnR5PxdoJi4hPCZjXnlzvnz3062czjrj/6sHRJ2lKfZ+bZ7zmroJBjPJjvgJ0n74xxlnHpvtlm/tVL7WFwzxee4SwjdrCdX9HrYzP/6DJ7W0rSBxv7mPnejT3NvE9hvZkHZu9z1uGBRfabe4Kn2mXU5Lh7sXPesE+5soH2MpJK3KdsyHHYJJXYwycbEu35a7q517Omb52ZZ8Q1mPmhnu5jf8j5G8y8pMp+7eb23ZnOMuK32f/Y21ze1cx37bDL6Lkp4KxD6Wj7QuLv4rjh+xh+XKlPDZp5XKVdhwPP2uenJJ1z7WozX/XAKDNvCP/XQaND/ZxVUKnfPibee3eQmcekum+u33NOj7DfB+pqpW3O2SMuFPr8E8nldVbRbAMBAIDjQxsouprVsXTddddp8eLF+vOf/6zU1NTGZwakp6crMTFR6enpmjFjhubMmaPMzEylpaXp+uuvV0FBAW9DAQAgylryelzX8jor2kAAALQftIGiq1kdS4888ogkafz48U2+f/LJJ3X11VdLku677z55vV5NnTpVfr9fkyZN0sMPPxyRygIAAJwItIEAAADCa/atcC4JCQlauHChFi5c2OJKAQCACIj0wyY78a91tIEAAGhHaANFVYvfCgcAAAAAAIDOrUVvhQMAAG0fD64EAACdEW2g6GLEEgAAAAAAAFqEEUsAAHRUof/7RHJ5AAAAbR1toKiiYwkAgA6KV+0CAIDOiDZQdLXZjqWq7h7FJITfeXkPJzjnL55Za+YxB9PNPL1PmZlX7sp01qH7ecVmfqg8zczLK33OMvr83r6bceWHo808/jT3CVLfz96Wv//ILiNYEecso9crdu7/rwNmvm9ItpnXfJjrrIM3x+6Gjk/3m/mAn5c4y9h0bU8zTx+218y7JNQ4y/hsfQ8zD8THmHmMvbtVN9hdh+yl9jladnIXM49zn+J6v/AkM48vs8+NUH97f0pSQ7J9TJR8Yh93KSV2HTzBBmcdvP0rzTx+TaqZ12S7f17p/dd6M9/yLfs6kbou3lnGq6+OMvO6s+xtEb/H/usqfaOzClpbdoqZ+xw3hw8+/1NnGf+IHRj2+6DjvAIAAADaqzbbsQQAACKAodsAAKAzog0UNTy8GwAAAAAAAC3CiCUAADooni8AAAA6I9pA0cWIJQAAAAAAALQII5YAAOioeNUuAADojGgDRRUdSwAAdFie//tEcnkAAABtHW2gaOJWOAAAAAAAALRImx2xVJcVkDcxEDZL/Oku5/yhlX3M3J8SNPPa5AYzT7VjSdK+ymQz97ybbpdx5kFnGRX5GWZ++sx1Zv7R46c5y/Cf7jfzyn32enbpWe4sI/nGQ2a+4x+9zDxulD1//EepzjrUpdvHhGebvZ4HC5KcZXgGVtrL+LirmZfXO4tQxg47P3hGnZnHJdqF9Fjsc9Zhzwi7Rz9luz2W1BNyjzUNee1+cX+WvT9T1rvXo+oU+9g/8+TPzHzlG6eaeXkf9yW43y3VZl5yj70/g6uynGXsmBBv5rF77fkre9vbWpJCjmuqNyH89f6w+jR7fx843f0rkuu4Ovm+YjP/KHGgs4y+S2vCft/QUKutzrlbAcPAAQBAZ0QbKKoYsQQAAAAAAIAWabMjlgAAwHHi1zoAANAZ0QaKKkYsAQAAAAAAoEUYsQQAQEcV8nz+ieTyAAAA2jraQFFFxxIAAB1UKPT5J5LLAwAAaOtoA0UXt8IBAAAAAACgRRixBABAR8WDKwEAQGdEGyiq2mzHUrfVHsXEh7+P8ePEHs75Ew/Z90AOHFNs5sWv9DHzlJ1BZx32VfvMvKFXwMxzl6Q7y6jsYa/nm6tONfPsavcZ0vVndhmH+tkD3/rOLnWW8d72XmburbPr4K+wt3Vcsns9c07bY+Z7y1LMvPKkemcZF+ZvNPO3Xxpj12GM+7grcxw2ObllZr7ns65mXnyee1sm7bTzA2PtbeXxusuI2xFv5gOWVJn51ovs/SlJvXvsM/PCv9vnV1y1fdx+6dtrnHVY3nW4mdcf9Ju5p5v7mBk8bJuZb1zVx15AlzpnGaqJMWPfhkQzD9mzK+UMe19JUlWhfWz/5K0XzfzGDd90lrG5T2rY74PVHmmlc3YAAACg3WmzHUsAAOA48eBKAADQGdEGiiqesQQAAAAAAIAWYcQSAAAdlCf0+SeSywMAAGjraANFFx1LAAB0VDy4EgAAdEa0gaKKW+EAAAAAAADQIoxYAgCgo+LBlQAAoDOiDRRVjFgCAAAAAABAizBiCQCAjornCwAAgM6INlBUtdmOpf3n+eVNCj/cLGtFgnP+qjw7/3RXjpn74uz5b7rz9846/PeTV5q5J9k+OvdNqXKW8aU+W8x8xScnm3npWe5Ba6Xjk8zcU2fPX/biKc4y4hwjCz0BO48vizfzmlzHAiTt2pFp5jkr7NNl33BnEXqhcqiZB7/UYOZdPnCfslX5dl7xTraZJ40sN/P6T9KcdQidYS/DszvFXsAxjDQdN3Gdma8+pZeZ+7xlzjK2Fncz8+cvXWDml7z0fTP/67pTnXXwOq4T8Yn1Zn7haWudZbzy57Fm3n/8NjPfuC3XWUa3QvvYPXiK429rR3yoqKuzDjFBO7/qT/9l5r797utlsFf4czhY474GAQAAAO1Rm+1YAgAAx4lf6wAAQGdEGyiq6FgCAKCjolEFAAA6I9pAUcXDuwEAAAAAANAijFgCAKCj4lW7AACgM6INFFWMWAIAAAAAAECLMGIJAIAOyhP6/BPJ5QEAALR1tIGiixFLAAAAAAAAaJE2O2LprH6bFJ8SHzZ7wzPQOX9iYp2ZJ7yWYeZlw+35Zy/7lrMOGeV27s+0uz0TfQ3OMlasHWzmBad/ZuY7funellXfsVekxh9+Px3WcCDVWUby0ANmfnB/ipknfeYz89hKdx+qa2vXZNnL2HTFw84y+j33n2ae8Zmrnu6uctftv/EVdl6+L8nMs7Y4q6DgjjQzj/XZlazsE3SW8cnDp5l54jf3m/m+7RnOMmLS6838P9ZON3Ov315Pb6V97khSdpG9LQ4Mts+vv3441llGQ5p9XAVC9nHZ5T33emReud3My9/tZeaxVfa29LgPGVWdbF/XRw7caubrXj/JWUZseUzY74O14b9vdbwRBQAAdEZtpA20cOFC3X333SopKdHQoUP14IMPavTo0Ued/rnnntNPf/pTbd26VQMHDtQvfvELXXDBBf+qRiikefPm6Te/+Y3Kysr0pS99SY888ogGDvzXv+sPHDig66+/Xi+++KK8Xq+mTp2q+++/Xykp9r+pjwcjlgAAAAAAACLo2Wef1Zw5czRv3jytWbNGQ4cO1aRJk7Rnz56w07/77ru64oorNGPGDH3wwQeaMmWKpkyZonXr1jVO88tf/lIPPPCAFi1apFWrVik5OVmTJk1SbW1t4zTTpk3Txx9/rGXLlumll17SW2+9pWuvvbZV15WOJQAA0KoWLlyoPn36KCEhQWPGjNF77713oqsEAADQqu69917NnDlT06dP1ymnnKJFixYpKSlJTzzxRNjp77//fk2ePFk/+tGPNHjwYN15550aMWKEHnroIUmfj1ZasGCBbrnlFl188cUaMmSInn76ae3atUsvvPCCJGn9+vVaunSpHnvsMY0ZM0ZnnnmmHnzwQS1ZskS7du1qtXWlYwkAgA7Ko389vDIinxbUobm/1gEAAByvE90GqqurU1FRkSZOnNj4ndfr1cSJE1VYWBh2nsLCwibTS9KkSZMap9+yZYtKSkqaTJOenq4xY8Y0TlNYWKiMjAyNGjWqcZqJEyfK6/Vq1apVzVyLY0fHEgAAaDXN/bUOAACgraqoqGjy8fv9Yafbt2+fAoGAcnJymnyfk5OjkpKSsPOUlJSY0x/+f9c02dnZTfLY2FhlZmYetdxIoGMJAICOKuSJ/KcZWvJrHQAAwHFrpTZQfn6+0tPTGz/z588/wSvaNrTZt8IBAIC2qaKi6eslfT6ffL4j385p/Vq3YcOGVq0jAABApBUXFyst7V9vwA7X/pGkrl27KiYmRqWlpU2+Ly0tVW5ubth5cnNzzekP/39paam6d+/eZJphw4Y1TvPvjxtoaGjQgQMHjlpuJDBiCQCAjirUCh/xax0AAGjjWqkNlJaW1uRztI6l+Ph4jRw5UsuXL2/8LhgMavny5SooKAg7T0FBQZPpJWnZsmWN0/ft21e5ublNpqmoqNCqVasapykoKFBZWZmKiooap3n99dcVDAY1ZswYe5sdhzY7Yumdt06TNyEhbBZID7gXkFhnxnVpZixPlb1pzh37kbsOI+04x1dh5m+VDnAWEfNJhpmvqj3ZzFP6ufsWGwq7mnnCgZCZV+c5i1Dtqiwzj0+wy4itspdfc5J9PEhSXHG8mVf1tOtw2v3/5Syj/8oaM996Yfhj/rBQjF0HSfLm2WV4diQ7FmCX4e/ivhWm66SdZr73UIqZx/8j3VnGoa/Z50/NbnsZiTvdl78ax6rGrEy0y7B3p2qy3fszEG9XoiavwV6AL+gsw3PI3hYNQfs6UT7QvR5l63qaec4n9jJKzq0385QN9vkrSYlptWbu9dh1iLFnlyTlFuwO+31DlV9b3bNH3hcaQhFbnlr31zoAAIDj1kptoOaYM2eOrrrqKo0aNUqjR4/WggULVFVVpenTp0uSrrzySvXo0aPxB7of/OAHOvvss/WrX/1KF154oZYsWaL3339fjz76qCTJ4/Hohhtu0M9+9jMNHDhQffv21U9/+lPl5eVpypQpkqTBgwdr8uTJmjlzphYtWqT6+nrNmjVLl19+ufLyjuEf5i3UZjuWAABA23T4VzqXL/5ad7jBc/jXulmzZrVyLQEAAE6cyy67THv37tWtt96qkpISDRs2TEuXLm18RMD27dvl9f7rB9xx48Zp8eLFuuWWW/STn/xEAwcO1AsvvKDTTjutcZqbbrpJVVVVuvbaa1VWVqYzzzxTS5cuVcIXBuU888wzmjVrls4991x5vV5NnTpVDzzwQKuuKx1LAAB0UIdfkRvJ5TWX69c6AACASGsLbSBJmjVr1lF/TFuxYsUR31166aW69NJLj14Pj0d33HGH7rjjjqNOk5mZqcWLFze7rseDjiUAANBqXL/WAQAAoH2jYwkAgI6qDTxfQLJ/rQMAAIi4NtIG6izoWAIAoKOiUQUAADoj2kBR5X4lGAAAAAAAABAGI5YAAOig2sqDKwEAAKKJNlB0tdmOpW5FQcXGBcNmMd/b45y/6n+72xM4jgxvgz2Y67UPTnXWIbFrtZnXlCWYuYIeZxkx+fZ6BBMDZp6wz11G2cmOCUZWmHFdSYqzjNhye3sH4+31rBlrb+tQRbyzDkPP+dTMP37Z3hD1Ke6rzZav2vs87pC9P2p7NDjLGNZzp5mvTz/JzBO32duqemiNsw4796ebee+FMY4luMvYcZrj2HWc40l73PsrqcSuZ12GPb8/yy4jrtJ9/sVV2+dw8jb7Mu4JfxltomaYff4c/EsPewGj3fur5/+LM/OQ45BIWW8fl8GCcmcdag4kmXlRTS8zj3NcsiWp8g/h/+4J1NW6ZwYAAADaoTbbsQQAAI5TyPP5J5LLAwAAaOtoA0UVz1gCAAAAAABAizBiCQCAjoo3ogAAgM6INlBU0bEEAEAHxYMrAQBAZ0QbKLq4FQ4AAAAAAAAtwoglAAA6KoaBAwCAzog2UFQxYgkAAAAAAAAtwoglAAA6qgg/X4Bf6wAAQLtAGyiq2mzH0qFvHVJMUl3YrPZgmnP+Xv+sNfM9wxPNvMGOJa/7yOqTdcDMd6/oY+bdl+1xlrF+dqaZ+0rizLx8oHs96ro12Hlpsr2AGHcZAx/daeZbpvU087hiuw6x7kNGH5SdZC/D3pRK3eYuw5/hMfPabHtbZXzoqISkNQ39zfwrX//AzD+6d6iZ709IcNbBW2+v564bys089U+pzjJqK+0Blz3+GmPmFf9R5iwj9HYXM++yyXFunGtfA5IWZDjr0PeODWb+zt9ON/OGZPf5d3a/TWa++feDzby2m/uY2H+KfUz4T68286yl9kV533b3MRNfadchb6x9zS1b2cNZRnVO+DICfrtsAAAAoL1qsx1LAADgOPF8AQAA0BnRBooqOpYAAOioaFQBAIDOiDZQVPHwbgAAAAAAALQII5YAAOigPBF+cGVEH4IJAADQSmgDRRcjlgAAAAAAANAidCwBAAAAAACgRehYAgAAAAAAQIs06xlLjzzyiB555BFt3bpVknTqqafq1ltv1fnnny9Jqq2t1Y033qglS5bI7/dr0qRJevjhh5WTk9PsisW9mq6Y+ISwWfzF+53zl4zpaubfmLbCzF/4zXgzD8W6N92BN3uZeZ0da+ul2c4yBv6u2sx3nWnXM+OcUmcZB6sSzTwrxa7DvkPJzjJ2XNzTzD0jys08+Q+pZh50HzIqPTNo5gknVZp57D/TnGUkfaXEzIu3dDPz9C3um3sr8+19vmzDYDP3nWT3N9d1aXDWoUe/fWZ+8K1cM0/ZVessIz7voJnvPsMuI+79Ls4yavsEzPyGGX8x84c3fdnMDwyKd9Yhsz78dfCwtH/a8+dN3+Is4/V1g+wyTooz8wR7d0uS/Jl2HtznM/PqbI+Z93mpzlmHrRfa67FtV5a9gMH28SBJGb3Kwn4fqPY7520VvBElYqLZBgIAAMeJNlBUNWvEUs+ePXXXXXepqKhI77//viZMmKCLL75YH3/8sSRp9uzZevHFF/Xcc8/pzTff1K5du3TJJZe0SsUBAIDt8IMrI/nprGgDAQDQftAGiq5mjVi66KKLmvz55z//uR555BGtXLlSPXv21OOPP67FixdrwoQJkqQnn3xSgwcP1sqVKzV27NjI1RoAACCKaAMBAACE1+JnLAUCAS1ZskRVVVUqKChQUVGR6uvrNXHixMZpBg0apF69eqmwsDAilQUAAM0UiuAHkmgDAQDQLtAGippmjViSpI8++kgFBQWqra1VSkqKnn/+eZ1yyilau3at4uPjlZGR0WT6nJwclZQc/bkyfr9ffv+/nj1RUVHR3CoBAAC0uki3gSTaQQAAoP1r9oilk08+WWvXrtWqVav0ve99T1dddZU++eSTFldg/vz5Sk9Pb/zk5+e3eFkAAOALIvlLHb/YRbwNJNEOAgCgVdAGiqpmdyzFx8drwIABGjlypObPn6+hQ4fq/vvvV25ururq6lRWVtZk+tLSUuXmHv3tTHPnzlV5eXnjp7i4uNkrAQAA0Noi3QaSaAcBAID2r8XPWDosGAzK7/dr5MiRiouL0/LlyxuzjRs3avv27SooKDjq/D6fT2lpaU0+AADg+PFGlNZ1vG0giXYQAACtgTZQdDXrGUtz587V+eefr169eunQoUNavHixVqxYoVdffVXp6emaMWOG5syZo8zMTKWlpen6669XQUFBi96GcnCcX95ET9js7pOWOue/Y9m3zfztWWPMPCE/aOcX7nXWIevL1WYerEqx8+e7OsvYdJnPrkO/fWY+u99rzjJ++lt7Wxb3SzTztI/inWVU9bK3d3CXva3icuw+0rp0ZxWUuMNeRnBbhplXTHE/F8NTmWTm3mq7Dtsvq3OXEdNgT3DQ3h/+nICZ93AcU5JUscz+hb6hi31lLutnH9eSVL7f3qmBVPuYStvi7levT7Wn+c2WM8085o9ZZl51wSFnHQ7VJZj5/iH2tsyYa+8LSdL08Nfaw6oc10N1r3UWESizj7s+f7HXY9eX7H2xZYr7r7OsNfZ67h8ZZ+axh9zHTPC18Ps86Hdvo1YR6aHbnbhRFc02EAAAOE60gaKqWR1Le/bs0ZVXXqndu3crPT1dQ4YM0auvvqqvfOUrkqT77rtPXq9XU6dOld/v16RJk/Twww+3SsUBAACihTYQAABAeM3qWHr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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare noise\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_intermediates[2].image.array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"noise\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_intermediates[2].image.array - numpy_intermediates[2].image.array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"noise diff\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2b33ef84-ed57-47ca-aeb0-4f84b141c62c", + "metadata": {}, + "source": [ + "# Compare Deconvolution" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "c5286811-03b1-428f-bc54-4257027433ce", + "metadata": {}, + "outputs": [], + "source": [ + "jax_psf_deconvolved_im = jax_galsim.Convolve(\n", + " [jax_intermediates[1], jax_intermediates[4]],\n", + " gsparams=jax_galsim.GSParams(minimum_fft_size=53 * 8, maximum_fft_size=53 * 8),\n", + ")\n", + "numpy_psf_deconvolved_im = galsim.Convolve(\n", + " [numpy_intermediates[1], numpy_intermediates[4]]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "c451e13f-3364-40d7-9738-14579499fe52", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax deconvolved\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array\n", + " - numpy_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"original deconvolved\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aaa6fbc7-c07e-4832-b51e-b7d462461ec1", + "metadata": {}, + "source": [ + "# Now Compare after reconv psf" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "7673627c-586f-499b-8629-effb03294fa5", + "metadata": {}, + "outputs": [], + "source": [ + "jax_image_ex2 = jax_galsim.Convolve(\n", + " [jax_psf_deconvolved_im, jax_intermediates[7]],\n", + " gsparams=jax_galsim.GSParams(minimum_fft_size=53 * 8, maximum_fft_size=53 * 8),\n", + ")\n", + "numpy_image_ex2 = galsim.Convolve([numpy_psf_deconvolved_im, numpy_intermediates[7]])" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "37523136-0e0c-4a05-bd04-e19f16dfb898", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.Convolution([galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", + " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", + " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", + " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", + " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", + " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", + " ...,\n", + " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", + " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", + " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", + " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", + " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", + " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7222052077217914, _force_maxk=8.099418560036185), galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " ...,\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7529228667374486, _force_maxk=12.51728322914683), gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=False)], real_space=False, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jax_psf_deconvolved_im" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "4c0458f5-55c8-4f81-a550-cce970372dcc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "galsim.Convolution([galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", + " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", + " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", + " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", + " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", + " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", + " ...,\n", + " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", + " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", + " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", + " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", + " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", + " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6595270685975222, _force_maxk=8.222137023067036), galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", + "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " ...,\n", + " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", + " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", + " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", + " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", + " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", + " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6815071326229607, _force_maxk=12.640001692177682), gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)], real_space=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_psf_deconvolved_im" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "0a847a9b-82a3-4c03-8091-897896b17d3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", + "\n", + "# First subplot: g[1]\n", + "im0 = axes[0].imshow(jax_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"jax deconvolved\")\n", + "\n", + "# Second subplot: g[1] - f[1]\n", + "im1 = axes[1].imshow(\n", + " jax_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array\n", + " - numpy_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array\n", + ")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"original deconvolved\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1bd319-47ca-4f84-98e6-f3dc16cd0b19", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d39c7dc3-5c53-42d2-b905-150e78b7674c", + "metadata": {}, + "outputs": [], + "source": [ + "def test_metacal_jax_vs_ngmix():\n", + " nsims = 5\n", + "\n", + " rng = np.random.RandomState(seed=34132)\n", + " seeds = rng.randint(size=nsims, low=1, high=2**29)\n", + " res_p = []\n", + " res_m = []\n", + " res_p_ngmix = []\n", + " res_m_ngmix = []\n", + " for seed in seeds:\n", + " res, res_ngmix, _, _, _, _, _ = _run_single_sim_pair_jax_and_ngmix(seed, 1e8)\n", + " if res is not None:\n", + " res_p.append(res[0])\n", + " res_m.append(res[1])\n", + "\n", + " res_p_ngmix.append(res_ngmix[0])\n", + " res_m_ngmix.append(res_ngmix[1])\n", + "\n", + " assert np.allclose(\n", + " res[0].tolist(),\n", + " res_ngmix[0].tolist(),\n", + " atol=1e-6,\n", + " rtol=1e-6,\n", + " equal_nan=True,\n", + " )\n", + " assert np.allclose(\n", + " res[1].tolist(),\n", + " res_ngmix[1].tolist(),\n", + " atol=1e-6,\n", + " rtol=1e-6,\n", + " equal_nan=True,\n", + " )\n", + "\n", + " m, merr, c1, c1err, c2, c2err = estimate_m_and_c(\n", + " np.concatenate(res_p),\n", + " np.concatenate(res_m),\n", + " 0.02,\n", + " jackknife=len(res_p),\n", + " )\n", + "\n", + " m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng = estimate_m_and_c(\n", + " np.concatenate(res_p_ngmix),\n", + " np.concatenate(res_m_ngmix),\n", + " 0.02,\n", + " jackknife=len(res_p_ngmix),\n", + " )\n", + "\n", + " print(\"JAX results:\")\n", + " print_m_c(m, merr, c1, c1err, c2, c2err)\n", + " print(\"ngmix results:\")\n", + " print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng)\n", + " assert_m_c_ok(m, merr, c1, c1err, c2, c2err)\n", + "\n", + " assert np.allclose(m, m_ng, atol=1e-4)\n", + " assert np.allclose(merr, merr_ng, atol=1e-6)\n", + " assert np.allclose(c1err, c1err_ng, atol=1e-6)\n", + " assert np.allclose(c1, c1_ng, atol=1e-6)\n", + " assert np.allclose(c2err, c2err_ng, atol=1e-6)\n", + " assert np.allclose(c2, c2_ng, atol=1e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51e4e2e1-fd29-425b-bae0-c8906ba649bb", + "metadata": {}, + "outputs": [], + "source": [ + "test_metacal_jax_vs_ngmix()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4b80cf4-dde7-4c62-bab9-f7fc8a5d09b8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8efa40d-6bf6-4507-a47c-e36becf520b6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a5515e-8781-448f-af33-86626f55d604", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jax", + "language": "python", + "name": "myenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 33fe70ecf489fde4751f44865048fd79d85d1d7c Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 17 Jun 2025 21:24:01 +0530 Subject: [PATCH 34/59] minimal example - deconv bug --- .../test_jax_deep_metadetect-Copy2.ipynb | 329 ++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 notebooks/test_jax_deep_metadetect-Copy2.ipynb diff --git a/notebooks/test_jax_deep_metadetect-Copy2.ipynb b/notebooks/test_jax_deep_metadetect-Copy2.ipynb new file mode 100644 index 0000000..0b6d2c6 --- /dev/null +++ b/notebooks/test_jax_deep_metadetect-Copy2.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "413f6098-e2b7-4e7d-a1c0-f9e9c0309959", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"JAX_ENABLE_X64\"] = \"True\"\n", + "\n", + "import numpy as np\n", + "\n", + "from deep_field_metadetect.jaxify.observation import ngmix_obs_to_dfmd_obs\n", + "from deep_field_metadetect.utils import (\n", + " make_simple_sim,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6f1b4aad-e579-4ad1-b4b1-77ec63f010ad", + "metadata": {}, + "outputs": [], + "source": [ + "import jax_galsim\n", + "import galsim" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9e164d4e-adc4-4916-b280-2227f787df13", + "metadata": {}, + "outputs": [], + "source": [ + "from deep_field_metadetect.metacal import (\n", + " _render_psf_and_build_obs,\n", + " get_max_gauss_reconv_psf,\n", + ")\n", + "from deep_field_metadetect.jaxify.jax_metacal import (\n", + " get_jax_galsim_object_from_dfmd_obs,\n", + " _jax_render_psf_and_build_obs,\n", + " jax_get_max_gauss_reconv_psf,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f8a331ae-fc9c-4653-b2b7-1347192f1814", + "metadata": {}, + "source": [ + "# Try PSF matching by hand" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2b418f8d-f11e-4c97-8e08-8e2c95f81d6f", + "metadata": {}, + "outputs": [], + "source": [ + "stamp_size = 251\n", + "psf_size = 53" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d7aacf69-d1b5-4d1f-9a6e-1cc42e37d9ff", + "metadata": {}, + "outputs": [], + "source": [ + "obs_w_non_jax, obs_d_non_jax, obs_dn_non_jax = make_simple_sim(\n", + " seed=17,\n", + " g1=0,\n", + " g2=0,\n", + " s2n=1e10,\n", + " deep_noise_fac=1 / np.sqrt(30),\n", + " deep_psf_fac=1,\n", + " dim=stamp_size,\n", + " dim_psf=psf_size,\n", + " scale=0.2,\n", + " buff=53,\n", + " n_objs=5,\n", + " return_dfmd_obs=False,\n", + ")\n", + "\n", + "obs_w = ngmix_obs_to_dfmd_obs(obs_w_non_jax)\n", + "obs_d = ngmix_obs_to_dfmd_obs(obs_d_non_jax)\n", + "obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_non_jax)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ec302618-35eb-4c8e-b6ec-e4c878055d2f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_galsim_object_from_ngmix_obs(obs, kind=\"image\", rot90=0):\n", + " \"\"\"Make an interpolated image from an ngmix obs.\"\"\"\n", + " return galsim.InterpolatedImage(\n", + " galsim.ImageD(\n", + " np.rot90(getattr(obs, kind).copy(), k=rot90),\n", + " wcs=obs.jacobian.get_galsim_wcs(),\n", + " ),\n", + " x_interpolant=\"lanczos15\",\n", + " _force_stepk=0.7529228667374486,\n", + " _force_maxk=12.51728322914683,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e5058989-d2d5-4195-ba5a-87359e9ae271", + "metadata": {}, + "outputs": [], + "source": [ + "def match_psf(obs, reconv_psf):\n", + " \"\"\"Match the PSF on an ngmix observation to a new PSF.\"\"\"\n", + " wcs = obs.jacobian.get_galsim_wcs()\n", + " image = get_galsim_object_from_ngmix_obs(obs, kind=\"image\")\n", + " psf = get_galsim_object_from_ngmix_obs(obs.psf, kind=\"image\")\n", + "\n", + " psf_inv = galsim.Deconvolve(psf)\n", + "\n", + " ims_deconvolved = galsim.Convolve([image, psf_inv])\n", + " ims = galsim.Convolve([ims_deconvolved, reconv_psf])\n", + "\n", + " ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array\n", + " ims_deconvolved = ims_deconvolved.drawImage(\n", + " nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs\n", + " ).array\n", + "\n", + " return (\n", + " _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1),\n", + " ims_deconvolved,\n", + " psf_inv,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2d66439e-762b-4afa-99af-8fa564eb51a7", + "metadata": {}, + "outputs": [], + "source": [ + "def jax_match_psf(dfmd_obs, reconv_psf, nxy, nxy_psf):\n", + " \"\"\"Match the PSF on an dfmd observation to a new PSF.\"\"\"\n", + " wcs = dfmd_obs.aft._local_wcs\n", + " image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"image\")\n", + " psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind=\"image\")\n", + "\n", + " psf_inv = jax_galsim.Deconvolve(psf)\n", + "\n", + " nk = 8\n", + "\n", + " ims_deconvolved = jax_galsim.Convolve(\n", + " [image, psf_inv],\n", + " gsparams=jax_galsim.GSParams(minimum_fft_size=nk, maximum_fft_size=nk),\n", + " )\n", + " ims = jax_galsim.Convolve(\n", + " [ims_deconvolved, reconv_psf],\n", + " gsparams=jax_galsim.GSParams(minimum_fft_size=nk, maximum_fft_size=nk),\n", + " )\n", + "\n", + " ims = ims.withGSParams(\n", + " minimum_fft_size=nxy * 4,\n", + " maximum_fft_size=nxy * 4,\n", + " )\n", + " ims_drawim = ims.drawImage(nx=nxy, ny=nxy, wcs=wcs)\n", + " ims = ims_drawim.array\n", + "\n", + " ims_deconvolved = ims_deconvolved.withGSParams(\n", + " minimum_fft_size=nxy * 4,\n", + " maximum_fft_size=nxy * 4,\n", + " )\n", + " ims_deconvolved = ims_deconvolved.drawImage(nx=nxy, ny=nxy, wcs=wcs).array\n", + "\n", + " return (\n", + " _jax_render_psf_and_build_obs(ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1),\n", + " ims_deconvolved,\n", + " psf_inv,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "5c5bb1a9-1639-4d4a-b98a-10b08b55c68a", + "metadata": {}, + "source": [ + "### Check only on noise " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c9194140-210e-41c7-aac2-5bd3f249f2a9", + "metadata": {}, + "outputs": [], + "source": [ + "reconv_psf = jax_get_max_gauss_reconv_psf(obs_w, obs_d, nxy_psf=psf_size)\n", + "reconv_psf_ngmix = get_max_gauss_reconv_psf(obs_w_non_jax, obs_d_non_jax)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "08e49737-581a-46f3-9506-40ac3a204a22", + "metadata": {}, + "outputs": [], + "source": [ + "obs_reconvolved, ims_deconvolved, psf_inv = jax_match_psf(\n", + " obs_w, reconv_psf, nxy=stamp_size, nxy_psf=psf_size\n", + ")\n", + "obs_reconvolved_numpy, ims_deconvolved_numpy, psf_inv_numpy = match_psf(\n", + " obs_w_non_jax, reconv_psf_ngmix\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "32eccc37-1b3d-4a01-ac5a-8b3d600cc6cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compare reconvolved noise\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "im0 = axes[0].imshow(obs_reconvolved.image)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"deconvolved + reconvolved noise field\")\n", + "\n", + "mask = obs_reconvolved.image\n", + "\n", + "im1 = axes[1].imshow(obs_reconvolved.image - obs_reconvolved_numpy.image)\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"diff\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c7fcb9bb-9108-464e-a130-b8e65f7d9b3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare deconvolved gal field\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "im0 = axes[0].imshow(ims_deconvolved)\n", + "fig.colorbar(im0, ax=axes[0])\n", + "axes[0].set_title(\"deconvolved noise field\")\n", + "\n", + "im1 = axes[1].imshow(ims_deconvolved - ims_deconvolved_numpy)\n", + "fig.colorbar(im1, ax=axes[1])\n", + "axes[1].set_title(\"diff\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "092aee3b-0a8a-4784-93b9-00c38330b812", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jax", + "language": "python", + "name": "myenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6dc0ff1dd42da993fc72844869c46da955211cc8 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 20 Jun 2025 12:40:33 +0530 Subject: [PATCH 35/59] for dk to be same as jax for now --- deep_field_metadetect/metacal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index bb9328f..e1a9ce1 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -39,7 +39,7 @@ def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): reconv_psf : galsim object The reconvolution PSF. """ - dk = psf.stepk / 4.0 + dk = 2 * np.pi / (53 * 0.2) / 4.0 small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k From de005238757a11bda23ee0f4486815c03e4d34e8 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Fri, 20 Jun 2025 12:41:31 +0530 Subject: [PATCH 36/59] fix now for consistency --- deep_field_metadetect/jaxify/jax_metacal.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index f87f4d0..6f08d8a 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -57,7 +57,9 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - kim = psf.drawKImage(nx=nxy_psf * 4, ny=nxy_psf * 4, scale=dk) + kim = psf.drawKImage( + nx=173, ny=173, scale=dk + ) # leaving this as 4* nxy is leaving a 10% diff # kim = psf.drawKImage(scale=dk) karr_r = kim.real.array # Find the smallest r where the kval < small_kval From 4f6005813d1f8fed47692cea2971370fc981c9b7 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 22 Jul 2025 16:21:25 -0500 Subject: [PATCH 37/59] bug fix --- deep_field_metadetect/metacal.py | 151 +++++++++++++++++++++++++--- deep_field_metadetect/metadetect.py | 49 ++++++++- 2 files changed, 182 insertions(+), 18 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index e1a9ce1..de8e289 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -185,16 +185,52 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): return mcal_res -def match_psf(obs, reconv_psf): +def match_psf( + obs, + reconv_psf, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=0, + ): """Match the PSF on an ngmix observation to a new PSF.""" wcs = obs.jacobian.get_galsim_wcs() - image = get_galsim_object_from_ngmix_obs(obs, kind="image") - psf = get_galsim_object_from_ngmix_obs(obs.psf, kind="image") + image = get_galsim_object_from_ngmix_obs( + obs, + kind="image", + _force_stepk=force_stepk_field, + _force_maxk=force_maxk_field, + ) - ims = galsim.Convolve([image, galsim.Deconvolve(psf), reconv_psf]) - ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array + psf = get_galsim_object_from_ngmix_obs( + obs.psf, + kind="image", + _force_stepk=force_stepk_psf, + _force_maxk=force_maxk_psf, + ) - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1) + if max_min_fft_size==0: + ims = galsim.Convolve( + [image, galsim.Deconvolve(psf), reconv_psf], + ) + + else: + ims = galsim.Convolve( + [image, galsim.Deconvolve(psf), reconv_psf], + gsparams=galsim.GSParams(minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size), + ) + ims = ims.withGSParams( + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, + ) + + ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array + if return_k_info: + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), (image._stepk, image._maxk, psf._stepk, psf._maxk) + + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), None def _extract_attr(obs, attr, dtype): @@ -279,7 +315,7 @@ def add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False): return obs -def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): +def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0, _force_stepk=0.0, _force_maxk=0.0): """Make an interpolated image from an ngmix obs.""" return galsim.InterpolatedImage( galsim.ImageD( @@ -287,6 +323,8 @@ def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0): wcs=obs.jacobian.get_galsim_wcs(), ), x_interpolant="lanczos15", + _force_stepk=_force_stepk, + _force_maxk=_force_maxk, ) @@ -310,18 +348,89 @@ def metacal_wide_and_deep_psf_matched( skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, return_noshear_deep=False, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=0, ): - """Do metacalibration for a combination of wide+deep datasets.""" + """Do metacalibration for a combination of wide+deep datasets. + + Parameters + ---------- + obs_wide : ngmix.Observation + The wide-field observation. + obs_deep : ngmix.Observation + The deep-field observation. + obs_deep_noise : ngmix.Observation + The deep-field noise observation. + step : float, optional + The step size for the metacalibration, by default DEFAULT_STEP. + shears : list, optional + The shears to use for the metacalibration, by default DEFAULT_SHEARS + if set to None. + skip_obs_wide_corrections : bool, optional + Skip the observation corrections for the wide-field observations, + by default False. + skip_obs_deep_corrections : bool, optional + Skip the observation corrections for the deep-field observations, + by default False. + nodet_flags : int, optional + The bmask flags marking area in the image to skip, by default 0. + return_k_info : bool, optional + return _force stepk and maxk values in the following order + _force_stepk_field, _force_maxk_field, _force_stepk_psf, _force_maxk_psf. + Used mainly for testing. + force_stepk_field : float, optional + Force stepk for drawing field images. + Defaults to 0.0, which lets JaxGalsim choose the value. + Used mainly for testing. + force_maxk_field: float, optional + Force maxk for drawing field images. + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + force_stepk_psf: float, optional + Force stepk for drawing PSF images. + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + force_maxk_psf: float, optional + Force stepk for drawing PSF images + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + max_min_fft_size: int, optional + To fix max and min values of FFT size. + Defaults to 0 which lets Galsim determine the values. + Used mainly to test against JaxGalsim. + + Returns + ------- + mcal_res : dict + Output from metacal_op_shears. + kinfo: tuple, optional + returns _force_stepk_field, _force_maxk_field, _force_stepk_psf, _force_maxk_psf + if return_k_into is True, else returns None. + Used mainly for testing. + """ # first get the biggest reconv PSF of the two reconv_psf = get_max_gauss_reconv_psf(obs_wide, obs_deep) + mcal_obs_wide, kinfo = match_psf( + obs_wide, + reconv_psf, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, + ) + if return_k_info: + force_stepk_field, force_maxk_field, force_stepk_psf, force_maxk_psf = kinfo - # make the wide obs - if skip_obs_wide_corrections: - mcal_obs_wide = match_psf(obs_wide, reconv_psf) - else: + if not skip_obs_wide_corrections: mcal_obs_wide = add_ngmix_obs( - match_psf(obs_wide, reconv_psf), + mcal_obs_wide, metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0), skip_mfrac_for_second=True, ) @@ -329,7 +438,16 @@ def metacal_wide_and_deep_psf_matched( # get PSF matched noise obs_wide_noise = obs_wide.copy() obs_wide_noise.image = obs_wide.noise - wide_noise_corr = match_psf(obs_wide_noise, reconv_psf) + wide_noise_corr, _ = match_psf( + obs_wide_noise, + reconv_psf, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + return_k_info=False, + max_min_fft_size=max_min_fft_size, + ) # now run mcal on deep mcal_res = metacal_op_shears( @@ -357,4 +475,7 @@ def metacal_wide_and_deep_psf_matched( for k in mcal_res: mcal_res[k].psf.galsim_obj = reconv_psf - return mcal_res + if return_k_info: + return mcal_res, kinfo + + return mcal_res, None diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index f1cd361..3ff2f93 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -23,6 +23,12 @@ def single_band_deep_field_metadetect( skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, nodet_flags=0, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=0, ): """Run deep-field metadetection for a simple scenario of a single band with a single image per band using only post-PSF Gaussian weighted moments. @@ -48,6 +54,30 @@ def single_band_deep_field_metadetect( by default False. nodet_flags : int, optional The bmask flags marking area in the image to skip, by default 0. + return_k_info : bool, optional + return _force stepk and maxk values in the following order + _force_stepk_field, _force_maxk_field, _force_stepk_psf, _force_maxk_psf. + Used mainly for testing. + force_stepk_field : float, optional + Force stepk for drawing field images. + Defaults to 0.0, which lets JaxGalsim choose the value. + Used mainly for testing. + force_maxk_field: float, optional + Force maxk for drawing field images. + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + force_stepk_psf: float, optional + Force stepk for drawing PSF images. + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + force_maxk_psf: float, optional + Force stepk for drawing PSF images + Defaults to 0.0, which lets Galsim choose the value. + Used mainly for testing. + max_min_fft_size: int, optional + To fix max and min values of FFT size. + Defaults to 0 which lets Galsim determine the values. + Used mainly to test against JaxGalsim. Returns ------- @@ -55,11 +85,14 @@ def single_band_deep_field_metadetect( The deep-field metadetection results, a dictionary with keys from `shears` and values containing the detection+measurement results for the corresponding shear. + kinfo: tuple, optional + returns _force_stepk_field, _force_maxk_field, _force_stepk_psf, _force_maxk_psf + if return_k_info is True. Used for testing. """ if shears is None: shears = DEFAULT_SHEARS - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res, kinfo = metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, obs_deep_noise, @@ -67,7 +100,14 @@ def single_band_deep_field_metadetect( shears=shears, skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, ) + psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) dfmdet_res = [] for shear, obs in mcal_res.items(): @@ -109,5 +149,8 @@ def single_band_deep_field_metadetect( ("bmask_flags", "i4"), ("mfrac", "f4"), ] + fres.dtype.descr - - return np.array(dfmdet_res, dtype=total_dtype) + + if return_k_info: + return np.array(dfmdet_res, dtype=total_dtype), kinfo + + return np.array(dfmdet_res, dtype=total_dtype), None From 320277c3dd817f8a3791f47a5c4ba80862a1505c Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 22 Jul 2025 16:22:02 -0500 Subject: [PATCH 38/59] updated tests --- .../tests/test_deep_metacal.py | 4 ++-- .../tests/test_metadetect.py | 20 +++++++++++++++---- .../tests/test_noise_handling.py | 2 +- 3 files changed, 19 insertions(+), 7 deletions(-) diff --git a/deep_field_metadetect/tests/test_deep_metacal.py b/deep_field_metadetect/tests/test_deep_metacal.py index 0ccaa1b..78e637f 100644 --- a/deep_field_metadetect/tests/test_deep_metacal.py +++ b/deep_field_metadetect/tests/test_deep_metacal.py @@ -38,7 +38,7 @@ def _run_single_sim( deep_noise_fac=deep_noise_fac, deep_psf_fac=deep_psf_fac, ) - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res, _ = metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, @@ -239,7 +239,7 @@ def _run_single_sim_maybe_mcal( obs_w, ) else: - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res, _ = metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, diff --git a/deep_field_metadetect/tests/test_metadetect.py b/deep_field_metadetect/tests/test_metadetect.py index 9112ec9..9ed5fbb 100644 --- a/deep_field_metadetect/tests/test_metadetect.py +++ b/deep_field_metadetect/tests/test_metadetect.py @@ -45,7 +45,7 @@ def _run_single_sim( pdb.set_trace() - res = single_band_deep_field_metadetect( + res, _ = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -103,7 +103,7 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): ) obs_w.bmask = rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) - res = single_band_deep_field_metadetect( + res, _ = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -140,7 +140,7 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): ) obs_w.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) - res = single_band_deep_field_metadetect( + res, kinfo = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -148,6 +148,8 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): skip_obs_deep_corrections=False, ) + assert kinfo is None + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] == "noshear") assert np.all(res["mfrac"][msk] >= 0.5) assert np.all(res["mfrac"][msk] <= 0.7) @@ -171,14 +173,24 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): ) obs_d.mfrac = rng.uniform(0.5, 0.7, size=obs_w.image.shape) - res = single_band_deep_field_metadetect( + res, kinfo = single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, + return_k_info=True, + force_stepk_field=0.12403490725241548, + force_maxk_field=8.160777791551611, + force_stepk_psf=0.6815071326229606, + force_maxk_psf=12.640001692177682, ) + assert kinfo[0]==0.12403490725241548 + assert kinfo[1]==8.160777791551611 + assert kinfo[2]==0.6815071326229606 + assert kinfo[3]==12.640001692177682 + msk = (res["wmom_flags"] == 0) & (res["mdet_step"] != "noshear") assert np.all(res["mfrac"][msk] >= 0.5) assert np.all(res["mfrac"][msk] <= 0.7) diff --git a/deep_field_metadetect/tests/test_noise_handling.py b/deep_field_metadetect/tests/test_noise_handling.py index 8631f86..9e3da9f 100644 --- a/deep_field_metadetect/tests/test_noise_handling.py +++ b/deep_field_metadetect/tests/test_noise_handling.py @@ -20,7 +20,7 @@ def _simple_noise_sim(seed): obj_flux_factor=0, ) - mcal_res = metacal_wide_and_deep_psf_matched( + mcal_res, _ = metacal_wide_and_deep_psf_matched( obs_wide, obs_deep, obs_deep_noise, From 66921c0e694fbeb528711314170127e3477ffa3a Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Tue, 22 Jul 2025 16:30:47 -0500 Subject: [PATCH 39/59] [skip ci] pre-commit updated --- deep_field_metadetect/metacal.py | 91 ++++++++++--------- deep_field_metadetect/metadetect.py | 18 ++-- .../tests/test_metadetect.py | 14 +-- 3 files changed, 66 insertions(+), 57 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index de8e289..43c980d 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -186,21 +186,21 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): def match_psf( - obs, - reconv_psf, - return_k_info=False, - force_stepk_field=0.0, - force_maxk_field=0.0, - force_stepk_psf=0.0, - force_maxk_psf=0.0, - max_min_fft_size=0, - ): + obs, + reconv_psf, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=0, +): """Match the PSF on an ngmix observation to a new PSF.""" wcs = obs.jacobian.get_galsim_wcs() image = get_galsim_object_from_ngmix_obs( - obs, - kind="image", - _force_stepk=force_stepk_field, + obs, + kind="image", + _force_stepk=force_stepk_field, _force_maxk=force_maxk_field, ) @@ -211,25 +211,32 @@ def match_psf( _force_maxk=force_maxk_psf, ) - if max_min_fft_size==0: + if max_min_fft_size == 0: ims = galsim.Convolve( - [image, galsim.Deconvolve(psf), reconv_psf], + [image, galsim.Deconvolve(psf), reconv_psf], ) - + else: ims = galsim.Convolve( - [image, galsim.Deconvolve(psf), reconv_psf], - gsparams=galsim.GSParams(minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size), + [image, galsim.Deconvolve(psf), reconv_psf], + gsparams=galsim.GSParams( + minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size + ), ) ims = ims.withGSParams( minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size, ) - + ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array if return_k_info: - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), (image._stepk, image._maxk, psf._stepk, psf._maxk) - + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), ( + image._stepk, + image._maxk, + psf._stepk, + psf._maxk, + ) + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), None @@ -315,7 +322,9 @@ def add_ngmix_obs(obs1, obs2, ignore_psf=False, skip_mfrac_for_second=False): return obs -def get_galsim_object_from_ngmix_obs(obs, kind="image", rot90=0, _force_stepk=0.0, _force_maxk=0.0): +def get_galsim_object_from_ngmix_obs( + obs, kind="image", rot90=0, _force_stepk=0.0, _force_maxk=0.0 +): """Make an interpolated image from an ngmix obs.""" return galsim.InterpolatedImage( galsim.ImageD( @@ -349,14 +358,14 @@ def metacal_wide_and_deep_psf_matched( skip_obs_deep_corrections=False, return_noshear_deep=False, return_k_info=False, - force_stepk_field=0.0, - force_maxk_field=0.0, - force_stepk_psf=0.0, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, force_maxk_psf=0.0, max_min_fft_size=0, ): """Do metacalibration for a combination of wide+deep datasets. - + Parameters ---------- obs_wide : ngmix.Observation @@ -409,23 +418,23 @@ def metacal_wide_and_deep_psf_matched( Output from metacal_op_shears. kinfo: tuple, optional returns _force_stepk_field, _force_maxk_field, _force_stepk_psf, _force_maxk_psf - if return_k_into is True, else returns None. + if return_k_into is True, else returns None. Used mainly for testing. """ # first get the biggest reconv PSF of the two reconv_psf = get_max_gauss_reconv_psf(obs_wide, obs_deep) mcal_obs_wide, kinfo = match_psf( - obs_wide, - reconv_psf, - return_k_info=return_k_info, - force_stepk_field=force_stepk_field, - force_maxk_field=force_maxk_field, - force_stepk_psf=force_stepk_psf, - force_maxk_psf=force_maxk_psf, - max_min_fft_size=max_min_fft_size, - ) - if return_k_info: + obs_wide, + reconv_psf, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, + ) + if return_k_info: force_stepk_field, force_maxk_field, force_stepk_psf, force_maxk_psf = kinfo if not skip_obs_wide_corrections: @@ -439,11 +448,11 @@ def metacal_wide_and_deep_psf_matched( obs_wide_noise = obs_wide.copy() obs_wide_noise.image = obs_wide.noise wide_noise_corr, _ = match_psf( - obs_wide_noise, + obs_wide_noise, reconv_psf, - force_stepk_field=force_stepk_field, - force_maxk_field=force_maxk_field, - force_stepk_psf=force_stepk_psf, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, force_maxk_psf=force_maxk_psf, return_k_info=False, max_min_fft_size=max_min_fft_size, @@ -475,7 +484,7 @@ def metacal_wide_and_deep_psf_matched( for k in mcal_res: mcal_res[k].psf.galsim_obj = reconv_psf - if return_k_info: + if return_k_info: return mcal_res, kinfo return mcal_res, None diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index 3ff2f93..e4fcbd6 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -24,9 +24,9 @@ def single_band_deep_field_metadetect( skip_obs_deep_corrections=False, nodet_flags=0, return_k_info=False, - force_stepk_field=0.0, - force_maxk_field=0.0, - force_stepk_psf=0.0, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, force_maxk_psf=0.0, max_min_fft_size=0, ): @@ -101,9 +101,9 @@ def single_band_deep_field_metadetect( skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, return_k_info=return_k_info, - force_stepk_field=force_stepk_field, - force_maxk_field=force_maxk_field, - force_stepk_psf=force_stepk_psf, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, force_maxk_psf=force_maxk_psf, max_min_fft_size=max_min_fft_size, ) @@ -149,8 +149,8 @@ def single_band_deep_field_metadetect( ("bmask_flags", "i4"), ("mfrac", "f4"), ] + fres.dtype.descr - - if return_k_info: + + if return_k_info: return np.array(dfmdet_res, dtype=total_dtype), kinfo - + return np.array(dfmdet_res, dtype=total_dtype), None diff --git a/deep_field_metadetect/tests/test_metadetect.py b/deep_field_metadetect/tests/test_metadetect.py index 9ed5fbb..bd5771f 100644 --- a/deep_field_metadetect/tests/test_metadetect.py +++ b/deep_field_metadetect/tests/test_metadetect.py @@ -180,16 +180,16 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): skip_obs_wide_corrections=False, skip_obs_deep_corrections=False, return_k_info=True, - force_stepk_field=0.12403490725241548, - force_maxk_field=8.160777791551611, - force_stepk_psf=0.6815071326229606, + force_stepk_field=0.12403490725241548, + force_maxk_field=8.160777791551611, + force_stepk_psf=0.6815071326229606, force_maxk_psf=12.640001692177682, ) - assert kinfo[0]==0.12403490725241548 - assert kinfo[1]==8.160777791551611 - assert kinfo[2]==0.6815071326229606 - assert kinfo[3]==12.640001692177682 + assert kinfo[0] == 0.12403490725241548 + assert kinfo[1] == 8.160777791551611 + assert kinfo[2] == 0.6815071326229606 + assert kinfo[3] == 12.640001692177682 msk = (res["wmom_flags"] == 0) & (res["mdet_step"] != "noshear") assert np.all(res["mfrac"][msk] >= 0.5) From 8d950b2a45863f575d2ca78f7027a1bba2c6774a Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 23 Jul 2025 09:13:52 -0500 Subject: [PATCH 40/59] [skip ci] change default of min_max_fft to None --- deep_field_metadetect/metacal.py | 8 ++++---- deep_field_metadetect/metadetect.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 43c980d..cb4ab70 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -193,7 +193,7 @@ def match_psf( force_maxk_field=0.0, force_stepk_psf=0.0, force_maxk_psf=0.0, - max_min_fft_size=0, + max_min_fft_size=None, ): """Match the PSF on an ngmix observation to a new PSF.""" wcs = obs.jacobian.get_galsim_wcs() @@ -211,7 +211,7 @@ def match_psf( _force_maxk=force_maxk_psf, ) - if max_min_fft_size == 0: + if max_min_fft_size == None: ims = galsim.Convolve( [image, galsim.Deconvolve(psf), reconv_psf], ) @@ -362,7 +362,7 @@ def metacal_wide_and_deep_psf_matched( force_maxk_field=0.0, force_stepk_psf=0.0, force_maxk_psf=0.0, - max_min_fft_size=0, + max_min_fft_size=None, ): """Do metacalibration for a combination of wide+deep datasets. @@ -409,7 +409,7 @@ def metacal_wide_and_deep_psf_matched( Used mainly for testing. max_min_fft_size: int, optional To fix max and min values of FFT size. - Defaults to 0 which lets Galsim determine the values. + Defaults to None which lets Galsim determine the values. Used mainly to test against JaxGalsim. Returns diff --git a/deep_field_metadetect/metadetect.py b/deep_field_metadetect/metadetect.py index e4fcbd6..a65d037 100644 --- a/deep_field_metadetect/metadetect.py +++ b/deep_field_metadetect/metadetect.py @@ -28,7 +28,7 @@ def single_band_deep_field_metadetect( force_maxk_field=0.0, force_stepk_psf=0.0, force_maxk_psf=0.0, - max_min_fft_size=0, + max_min_fft_size=None, ): """Run deep-field metadetection for a simple scenario of a single band with a single image per band using only post-PSF Gaussian weighted moments. @@ -76,7 +76,7 @@ def single_band_deep_field_metadetect( Used mainly for testing. max_min_fft_size: int, optional To fix max and min values of FFT size. - Defaults to 0 which lets Galsim determine the values. + Defaults to None which lets Galsim determine the values. Used mainly to test against JaxGalsim. Returns From 7d44194913ceba17e6108f431189162f1f1163fe Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 23 Jul 2025 10:03:37 -0500 Subject: [PATCH 41/59] minor --- deep_field_metadetect/jaxify/jax_metacal.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index 6f08d8a..d298b91 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -30,7 +30,13 @@ def get_shear_tuple(shear, step): @partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): +def jax_get_gauss_reconv_psf_galsim( + psf, + dk, + nxy_psf=53, + step=DEFAULT_STEP, + flux=1 +): """Gets the target reconvolution PSF for an input PSF object. This is taken from galsim/tests/test_metacal.py and assumes the psf is @@ -58,9 +64,11 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k kim = psf.drawKImage( - nx=173, ny=173, scale=dk - ) # leaving this as 4* nxy is leaving a 10% diff - # kim = psf.drawKImage(scale=dk) + nx=4*nxy_psf, ny=4*nxy_psf, scale=dk + ) + + # This will lead to a differnce in reconv psf size between GS and JGS + karr_r = kim.real.array # Find the smallest r where the kval < small_kval nk = karr_r.shape[0] From 4349f1cc45afcab1dfba9d452414bb399a7493eb Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 23 Jul 2025 15:47:00 -0500 Subject: [PATCH 42/59] fix k values in jax --- deep_field_metadetect/jaxify/jax_metacal.py | 190 ++++++++++++++---- .../jaxify/jax_metadetect.py | 16 +- deep_field_metadetect/jaxify/observation.py | 18 +- .../jaxify/tests/test_jax_deep_metacal.py | 6 +- .../jaxify/tests/test_jax_metacal.py | 18 +- .../jaxify/tests/test_jax_metadetect.py | 38 ++-- deep_field_metadetect/metacal.py | 5 +- 7 files changed, 208 insertions(+), 83 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index d298b91..d6f7a95 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -116,20 +116,20 @@ def jax_get_max_gauss_reconv_psf(obs_w, obs_d, nxy_psf, scale=0.2, step=DEFAULT_ ) -@partial(jax.jit, static_argnames=["nxy_psf"]) -def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1): +@partial(jax.jit, static_argnames=["nxy_psf", "max_min_fft_size"]) +def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1, max_min_fft_size=1024): reconv_psf = reconv_psf.withGSParams( - minimum_fft_size=nxy_psf * 4, - maximum_fft_size=nxy_psf * 4, + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, ) pim = reconv_psf.drawImage( nx=nxy_psf, ny=nxy_psf, - wcs=dfmd_obs.psf.aft._local_wcs, + wcs=dfmd_obs.psf.wcs._local_wcs, offset=jax_galsim.PositionD( - x=dfmd_obs.psf.aft.origin.x - nxy_psf / 2, - y=dfmd_obs.psf.aft.origin.y - nxy_psf / 2, + x=dfmd_obs.psf.wcs.origin.x - nxy_psf / 2, + y=dfmd_obs.psf.wcs.origin.y - nxy_psf / 2, ), ).array @@ -139,8 +139,8 @@ def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_f ) -@partial(jax.jit, static_argnames="dims") -def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2): +@partial(jax.jit, static_argnames=["dims", "max_min_fft_size"]) +def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2, max_min_fft_size=1024): """Run metacal on an dfmd observation. Note that the noise image should already be rotated by 90 degrees here. @@ -161,14 +161,14 @@ def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g ) ims = ims.withGSParams( - minimum_fft_size=dims[0] * 4, - maximum_fft_size=dims[0] * 4, + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, ) ims = ims.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array ns = ns.withGSParams( - minimum_fft_size=dims[0] * 4, - maximum_fft_size=dims[0] * 4, + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, ) ns = jnp.rot90( ns.drawImage(nx=dims[1], ny=dims[0], wcs=wcs).array, @@ -177,10 +177,10 @@ def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g return ims + ns -def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf): +def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf, max_min_fft_size=1024): """Run metacal on an dfmd obs.""" mcal_image = _jax_metacal_op_g1g2_impl( - wcs=dfmd_obs.aft._local_wcs, + wcs=dfmd_obs.wcs._local_wcs, image=get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image"), # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing @@ -192,14 +192,15 @@ def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf): reconv_psf=reconv_psf, g1=g1, g2=g2, + max_min_fft_size=max_min_fft_size, ) return _jax_render_psf_and_build_obs( - mcal_image, dfmd_obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5 + mcal_image, dfmd_obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size ) -@partial(jax.jit, static_argnames=["nxy_psf", "scale", "shears"]) +@partial(jax.jit, static_argnames=["nxy_psf", "scale", "shears", "max_min_fft_size"]) def jax_metacal_op_shears( dfmd_obs, nxy_psf=53, @@ -207,6 +208,7 @@ def jax_metacal_op_shears( shears=None, step=DEFAULT_STEP, scale=0.2, + max_min_fft_size=1024, ): """Run metacal on an dfmd observation.""" if shears is None: @@ -221,7 +223,7 @@ def jax_metacal_op_shears( step=step, ) - wcs = dfmd_obs.aft._local_wcs + wcs = dfmd_obs.wcs._local_wcs image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl # rotates back after deconv and shearing @@ -242,6 +244,7 @@ def jax_metacal_op_shears( reconv_psf=reconv_psf, g1=g1, g2=g2, + max_min_fft_size=max_min_fft_size, ) mcal_res[shear] = _jax_render_psf_and_build_obs( @@ -250,28 +253,72 @@ def jax_metacal_op_shears( reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5, + max_min_fft_size=max_min_fft_size, ) return mcal_res -@partial(jax.jit, static_argnames=["nxy", "nxy_psf"]) -def jax_match_psf(dfmd_obs, reconv_psf, nxy, nxy_psf): +@partial(jax.jit, static_argnames=[ + "nxy", + "nxy_psf", + "return_k_info", + "force_stepk_field", + "force_maxk_field", + "force_stepk_psf", + "force_maxk_psf", + "max_min_fft_size", + ] + ) +def jax_match_psf( + dfmd_obs, + reconv_psf, + nxy, + nxy_psf, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=1024, + ): """Match the PSF on an dfmd observation to a new PSF.""" - wcs = dfmd_obs.aft._local_wcs - image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image") - psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") + wcs = dfmd_obs.wcs._local_wcs + image = get_jax_galsim_object_from_dfmd_obs( + dfmd_obs, + kind="image", + force_stepk=force_stepk_field, + force_maxk=force_maxk_field, + ) + psf = get_jax_galsim_object_from_dfmd_obs( + dfmd_obs.psf, + kind="image", + force_stepk=force_stepk_psf, + force_maxk=force_maxk_psf, + ) - ims = jax_galsim.Convolve([image, jax_galsim.Deconvolve(psf), reconv_psf]) + ims = jax_galsim.Convolve( + [image, jax_galsim.Deconvolve(psf), reconv_psf], + gsparams=jax_galsim.GSParams( + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, + ), + ) ims = ims.withGSParams( - minimum_fft_size=nxy * 4, - maximum_fft_size=nxy * 4, + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, ) ims = ims.drawImage(nx=nxy, ny=nxy, wcs=wcs).array - return _jax_render_psf_and_build_obs( - ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1 - ) + def return_obs_and_kinfo(_): + return _jax_render_psf_and_build_obs(ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1), ( image.stepk, image.maxk, psf.stepk, psf.maxk) + + def return_obs_only(_): + return _jax_render_psf_and_build_obs( + ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1 + ), (0., 0., 0., 0.) + + return jax.lax.cond(return_k_info, return_obs_and_kinfo, return_obs_only, operand=None) def _extract_attr(obs, attr, dtype=jnp.float64): @@ -287,10 +334,10 @@ def jax_add_dfmd_obs( ) -> DFMdetObservation: """Add two dfmd observations""" - if repr(dfmd_obs1.aft) != repr(dfmd_obs2.aft): + if repr(dfmd_obs1.wcs) != repr(dfmd_obs2.wcs): raise RuntimeError( "AffineTransforms must be equal to add dfmd observations! %s != %s" - % (repr(dfmd_obs1.aft), repr(dfmd_obs2.aft)), + % (repr(dfmd_obs1.wcs), repr(dfmd_obs2.wcs)), ) if dfmd_obs1.image.shape != dfmd_obs2.image.shape: @@ -363,7 +410,7 @@ def jax_add_dfmd_obs( bmask=new_bmask, ormask=new_ormask, noise=new_noise, - aft=dfmd_obs1.aft, + wcs=dfmd_obs1.wcs, psf=new_psf, meta=new_meta_data, mfrac=new_mfrac, @@ -374,20 +421,29 @@ def jax_add_dfmd_obs( return obs -def get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind="image", rot90=0): +def get_jax_galsim_object_from_dfmd_obs( + dfmd_obs, + kind="image", + rot90=0, + force_stepk=0.0, + force_maxk=0.0, + ): """Make an interpolated image from an dfmd obs.""" return jax_galsim.InterpolatedImage( jax_galsim.ImageD( jnp.rot90(getattr(dfmd_obs, kind).copy(), k=rot90), - wcs=dfmd_obs.aft._local_wcs, + wcs=dfmd_obs.wcs._local_wcs, ), x_interpolant="lanczos15", + wcs=dfmd_obs.wcs._local_wcs, + _force_stepk=force_stepk, + _force_maxk=force_maxk, ) def get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs, kind="image"): """Make an interpolated image from an DFMdet obs w/o a pixel.""" - wcs = dfmd_obs.aft._local_wcs + wcs = dfmd_obs.wcs._local_wcs return jax_galsim.Convolve( [ get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=kind), @@ -406,6 +462,12 @@ def get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs, kind="image"): "skip_obs_deep_corrections", "return_noshear_deep", "scale", + "return_k_info", + "force_stepk_field", + "force_maxk_field", + "force_stepk_psf", + "force_maxk_psf", + "max_min_fft_size", ], ) def _jax_helper_metacal_wide_and_deep_psf_matched( @@ -421,22 +483,49 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( skip_obs_deep_corrections=False, return_noshear_deep=False, scale=0.2, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=1024, ): """Do metacalibration for a combination of wide+deep datasets.""" # make the wide obs - if skip_obs_wide_corrections: - mcal_obs_wide = jax_match_psf(obs_wide, reconv_psf, nxy, nxy_psf) - else: + + mcal_obs_wide, kinfo = jax_match_psf( + obs_wide, + reconv_psf, + nxy, + nxy_psf, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, + ) + if not skip_obs_wide_corrections: mcal_obs_wide = jax_add_dfmd_obs( - jax_match_psf(obs_wide, reconv_psf, nxy, nxy_psf), + mcal_obs_wide, jax_metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, nxy_psf=nxy_psf), skip_mfrac_for_second=True, ) # get PSF matched noise obs_wide_noise = obs_wide._replace(image=obs_wide.noise) - wide_noise_corr = jax_match_psf(obs_wide_noise, reconv_psf, nxy, nxy_psf) + wide_noise_corr, _ = jax_match_psf( + obs_wide_noise, + reconv_psf, + nxy, + nxy_psf, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, + ) # now run mcal on deep mcal_res = jax_metacal_op_shears( @@ -446,6 +535,7 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( step=step, nxy_psf=nxy_psf, scale=scale, + max_min_fft_size=max_min_fft_size, ) # now add in noise corr to make it match the wide noise @@ -463,7 +553,7 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( if return_noshear_deep: mcal_res["noshear_deep"] = noshear_res - return mcal_res + return mcal_res, kinfo def jax_metacal_wide_and_deep_psf_matched( @@ -478,13 +568,19 @@ def jax_metacal_wide_and_deep_psf_matched( skip_obs_deep_corrections=False, return_noshear_deep=False, scale=0.2, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=1024, ): """Do metacalibration for a combination of wide+deep datasets.""" # first get the biggest reconv PSF of the two - reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, nxy, scale) + reconv_psf = jax_get_max_gauss_reconv_psf(obs_wide, obs_deep, nxy_psf, scale) - mcal_res = _jax_helper_metacal_wide_and_deep_psf_matched( + mcal_res, kinfo = _jax_helper_metacal_wide_and_deep_psf_matched( obs_wide=obs_wide, obs_deep=obs_deep, obs_deep_noise=obs_deep_noise, @@ -497,10 +593,16 @@ def jax_metacal_wide_and_deep_psf_matched( skip_obs_deep_corrections=skip_obs_deep_corrections, return_noshear_deep=return_noshear_deep, scale=scale, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, ) for k in mcal_res: mcal_res[k] = dfmd_obs_to_ngmix_obs(mcal_res[k]) mcal_res[k].psf.galsim_obj = reconv_psf - return mcal_res + return mcal_res, kinfo diff --git a/deep_field_metadetect/jaxify/jax_metadetect.py b/deep_field_metadetect/jaxify/jax_metadetect.py index 93c96ec..052543d 100644 --- a/deep_field_metadetect/jaxify/jax_metadetect.py +++ b/deep_field_metadetect/jaxify/jax_metadetect.py @@ -26,6 +26,12 @@ def jax_single_band_deep_field_metadetect( skip_obs_deep_corrections=False, nodet_flags=0, scale=0.2, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=1024, ) -> dict: """Run deep-field metadetection for a simple scenario of a single band with a single image per band using only post-PSF Gaussian weighted moments. @@ -68,7 +74,7 @@ def jax_single_band_deep_field_metadetect( if shears is None: shears = DEFAULT_SHEARS - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res, kinfo = jax_metacal_wide_and_deep_psf_matched( obs_wide=obs_wide, obs_deep=obs_deep, obs_deep_noise=obs_deep_noise, @@ -79,6 +85,12 @@ def jax_single_band_deep_field_metadetect( skip_obs_wide_corrections=skip_obs_wide_corrections, skip_obs_deep_corrections=skip_obs_deep_corrections, scale=scale, + return_k_info=return_k_info, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, + max_min_fft_size=max_min_fft_size, ) # This returns ngmix Obs for now psf_res = fit_gauss_mom_obs(mcal_res["noshear"].psf) @@ -123,4 +135,4 @@ def jax_single_band_deep_field_metadetect( ("mfrac", "f4"), ] + fres.dtype.descr - return np.array(dfmdet_res, dtype=total_dtype) + return np.array(dfmdet_res, dtype=total_dtype), kinfo diff --git a/deep_field_metadetect/jaxify/observation.py b/deep_field_metadetect/jaxify/observation.py index 7a75d20..0dfc461 100644 --- a/deep_field_metadetect/jaxify/observation.py +++ b/deep_field_metadetect/jaxify/observation.py @@ -14,7 +14,7 @@ class DFMdetObservation(NamedTuple): bmask: Optional[jax.Array] ormask: Optional[jax.Array] noise: Optional[jax.Array] - aft: Optional[jax_galsim.wcs.AffineTransform] + wcs: Optional[jax_galsim.wcs.AffineTransform] psf: Optional["DFMdetObservation"] mfrac: Optional[jax.Array] meta: Optional[dict] @@ -28,7 +28,7 @@ def tree_flatten(self): self.bmask, self.ormask, self.noise, - self.aft, + self.wcs, self.psf, self.mfrac, ) @@ -81,7 +81,7 @@ def ngmix_obs_to_dfmd_obs(obs: ngmix.observation.Observation) -> DFMdetObservati bmask=obs.bmask if obs.has_bmask() else None, ormask=obs.ormask if obs.has_ormask() else None, noise=obs.noise if obs.has_noise() else None, - aft=jax_galsim.wcs.AffineTransform( + wcs=jax_galsim.wcs.AffineTransform( dudx=jacobian.dudcol, dudy=jacobian.dudrow, dvdx=jacobian.dvdcol, @@ -110,12 +110,12 @@ def dfmd_obs_to_ngmix_obs(dfmd_obs) -> Observation: ormask=dfmd_obs.ormask, noise=dfmd_obs.noise if dfmd_obs.noise is None else np.array(dfmd_obs.noise), jacobian=ngmix.jacobian.Jacobian( - row=dfmd_obs.aft.origin.y - 1, - col=dfmd_obs.aft.origin.x - 1, - dudcol=dfmd_obs.aft.dudx, - dudrow=dfmd_obs.aft.dudy, - dvdcol=dfmd_obs.aft.dvdx, - dvdrow=dfmd_obs.aft.dvdy, + row=dfmd_obs.wcs.origin.y - 1, + col=dfmd_obs.wcs.origin.x - 1, + dudcol=dfmd_obs.wcs.dudx, + dudrow=dfmd_obs.wcs.dudy, + dvdcol=dfmd_obs.wcs.dvdx, + dvdrow=dfmd_obs.wcs.dvdy, ), psf=psf, mfrac=dfmd_obs.mfrac if dfmd_obs.mfrac is None else np.array(dfmd_obs.mfrac), diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 8536f32..f4bfd79 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -50,7 +50,7 @@ def _run_single_sim( deep_psf_fac=deep_psf_fac, return_dfmd_obs=True, ) - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res, _ = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, @@ -90,7 +90,7 @@ def _run_single_sim_jax_and_ngmix( deep_psf_fac=deep_psf_fac, return_dfmd_obs=False, ) - mcal_res_ngmix = metacal_wide_and_deep_psf_matched( + mcal_res_ngmix, _ = metacal_wide_and_deep_psf_matched( obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix, @@ -103,7 +103,7 @@ def _run_single_sim_jax_and_ngmix( obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res, _ = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py index f505a46..8fae9fc 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metacal.py @@ -152,15 +152,15 @@ def test_metacal_jax_vs_ngmix(): assert np.allclose( res[0].tolist(), res_ngmix[0].tolist(), - atol=1e-5, - rtol=0.025, + atol=1e-3, + rtol=0.01, equal_nan=True, ) assert np.allclose( res[1].tolist(), res_ngmix[1].tolist(), - atol=1e-5, - rtol=0.025, + atol=1e-3, + rtol=0.01, equal_nan=True, ) @@ -185,11 +185,11 @@ def test_metacal_jax_vs_ngmix(): assert_m_c_ok(m, merr, c1, c1err, c2, c2err) assert np.allclose(m, m_ng, atol=1e-4) - assert np.allclose(merr, merr_ng, atol=1e-5) - assert np.allclose(c1err, c1err_ng, atol=1e-5) - assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-5) - assert np.allclose(c2, c2_ng, atol=1e-4) + assert np.allclose(merr, merr_ng, atol=1e-4) + assert np.allclose(c1err, c1err_ng, atol=1e-6) + assert np.allclose(c1, c1_ng, atol=1e-6) + assert np.allclose(c2err, c2err_ng, atol=1e-6) + assert np.allclose(c2, c2_ng, atol=1e-6) def test_metacal(): diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index dbf5b95..f8d7dd5 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -47,7 +47,7 @@ def _run_single_sim( return_dfmd_obs=True, ) - res = jax_single_band_deep_field_metadetect( + res, _ = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -120,15 +120,19 @@ def _run_single_sim_jax_and_ngmix( obs_d = ngmix_obs_to_dfmd_obs(obs_d_ngmix) obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) - res_ngmix = single_band_deep_field_metadetect( + ( + res_ngmix, + (force_stepk_field, force_maxk_field, force_stepk_psf, force_maxk_psf), + ) = single_band_deep_field_metadetect( obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix, skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, + return_k_info=True, ) - res = jax_single_band_deep_field_metadetect( + res, kinfo = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -137,8 +141,18 @@ def _run_single_sim_jax_and_ngmix( skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, scale=scale, + return_k_info=True, + force_stepk_field=force_stepk_field, + force_maxk_field=force_maxk_field, + force_stepk_psf=force_stepk_psf, + force_maxk_psf=force_maxk_psf, ) + assert kinfo[0] == force_stepk_field + assert kinfo[1] == force_maxk_field + assert kinfo[2] == force_stepk_psf + assert kinfo[3] == force_maxk_psf + return measure_mcal_shear_quants(res), measure_mcal_shear_quants(res_ngmix) @@ -233,12 +247,12 @@ def test_metadetect_single_band_deep_field_metadetect_jax_vs_ngmix(deep_psf_rati print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng) assert_m_c_ok(m, merr, c1, c1err, c2, c2err) - assert np.allclose(m, m_ng, atol=5e-3) - assert np.allclose(merr, merr_ng, atol=5e-4) - assert np.allclose(c1err, c1err_ng, atol=1e-5) - assert np.allclose(c1, c1_ng, atol=1e-4) - assert np.allclose(c2err, c2err_ng, atol=1e-5) - assert np.allclose(c2, c2_ng, atol=1e-4) + assert np.allclose(m, m_ng, atol=1e-4) + assert np.allclose(merr, merr_ng, atol=1e-4) + assert np.allclose(c1err, c1err_ng, atol=1e-6) + assert np.allclose(c1, c1_ng, atol=1e-6) + assert np.allclose(c2err, c2err_ng, atol=1e-6) + assert np.allclose(c2, c2_ng, atol=1e-6) def test_metadetect_single_band_deep_field_metadetect_bmask(): @@ -265,7 +279,7 @@ def test_metadetect_single_band_deep_field_metadetect_bmask(): bmask=rng.choice([0, 1, 3], p=[0.5, 0.25, 0.25], size=obs_w.image.shape) ) - res = jax_single_band_deep_field_metadetect( + res, _ = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -311,7 +325,7 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_wide(): ) obs_w = obs_w._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) - res = jax_single_band_deep_field_metadetect( + res, _ = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, @@ -351,7 +365,7 @@ def test_metadetect_single_band_deep_field_metadetect_mfrac_deep(): ) obs_d = obs_d._replace(mfrac=rng.uniform(0.5, 0.7, size=obs_w.image.shape)) - res = jax_single_band_deep_field_metadetect( + res, _ = jax_single_band_deep_field_metadetect( obs_w, obs_d, obs_dn, diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index cb4ab70..5b8903a 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -484,7 +484,4 @@ def metacal_wide_and_deep_psf_matched( for k in mcal_res: mcal_res[k].psf.galsim_obj = reconv_psf - if return_k_info: - return mcal_res, kinfo - - return mcal_res, None + return mcal_res, kinfo From e1008512e86c26bde5ba525a39dd90b663dae6d7 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 23 Jul 2025 15:48:07 -0500 Subject: [PATCH 43/59] bug --- deep_field_metadetect/metacal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 5b8903a..e019a50 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -211,7 +211,7 @@ def match_psf( _force_maxk=force_maxk_psf, ) - if max_min_fft_size == None: + if max_min_fft_size is None: ims = galsim.Convolve( [image, galsim.Deconvolve(psf), reconv_psf], ) From d3c56fd7c3cf20204900c915f298c871459c433d Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 23 Jul 2025 15:48:12 -0500 Subject: [PATCH 44/59] minor --- deep_field_metadetect/jaxify/jax_metacal.py | 111 +++++++++++--------- 1 file changed, 59 insertions(+), 52 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index d6f7a95..f494f9c 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -30,13 +30,7 @@ def get_shear_tuple(shear, step): @partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf_galsim( - psf, - dk, - nxy_psf=53, - step=DEFAULT_STEP, - flux=1 -): +def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): """Gets the target reconvolution PSF for an input PSF object. This is taken from galsim/tests/test_metacal.py and assumes the psf is @@ -63,10 +57,8 @@ def jax_get_gauss_reconv_psf_galsim( small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - kim = psf.drawKImage( - nx=4*nxy_psf, ny=4*nxy_psf, scale=dk - ) - + kim = psf.drawKImage(nx=4 * nxy_psf, ny=4 * nxy_psf, scale=dk) + # This will lead to a differnce in reconv psf size between GS and JGS karr_r = kim.real.array @@ -117,7 +109,9 @@ def jax_get_max_gauss_reconv_psf(obs_w, obs_d, nxy_psf, scale=0.2, step=DEFAULT_ @partial(jax.jit, static_argnames=["nxy_psf", "max_min_fft_size"]) -def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1, max_min_fft_size=1024): +def _jax_render_psf_and_build_obs( + image, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1, max_min_fft_size=1024 +): reconv_psf = reconv_psf.withGSParams( minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size, @@ -140,7 +134,9 @@ def _jax_render_psf_and_build_obs(image, dfmd_obs, reconv_psf, nxy_psf, weight_f @partial(jax.jit, static_argnames=["dims", "max_min_fft_size"]) -def _jax_metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2, max_min_fft_size=1024): +def _jax_metacal_op_g1g2_impl( + *, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g2, max_min_fft_size=1024 +): """Run metacal on an dfmd observation. Note that the noise image should already be rotated by 90 degrees here. @@ -196,7 +192,12 @@ def jax_metacal_op_g1g2(dfmd_obs, reconv_psf, g1, g2, nxy_psf, max_min_fft_size= ) return _jax_render_psf_and_build_obs( - mcal_image, dfmd_obs, reconv_psf, nxy_psf=nxy_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size + mcal_image, + dfmd_obs, + reconv_psf, + nxy_psf=nxy_psf, + weight_fac=0.5, + max_min_fft_size=max_min_fft_size, ) @@ -258,40 +259,42 @@ def jax_metacal_op_shears( return mcal_res -@partial(jax.jit, static_argnames=[ - "nxy", - "nxy_psf", +@partial( + jax.jit, + static_argnames=[ + "nxy", + "nxy_psf", "return_k_info", "force_stepk_field", "force_maxk_field", "force_stepk_psf", "force_maxk_psf", "max_min_fft_size", - ] - ) + ], +) def jax_match_psf( - dfmd_obs, - reconv_psf, - nxy, - nxy_psf, - return_k_info=False, - force_stepk_field=0.0, - force_maxk_field=0.0, - force_stepk_psf=0.0, - force_maxk_psf=0.0, - max_min_fft_size=1024, - ): + dfmd_obs, + reconv_psf, + nxy, + nxy_psf, + return_k_info=False, + force_stepk_field=0.0, + force_maxk_field=0.0, + force_stepk_psf=0.0, + force_maxk_psf=0.0, + max_min_fft_size=1024, +): """Match the PSF on an dfmd observation to a new PSF.""" wcs = dfmd_obs.wcs._local_wcs image = get_jax_galsim_object_from_dfmd_obs( - dfmd_obs, - kind="image", - force_stepk=force_stepk_field, + dfmd_obs, + kind="image", + force_stepk=force_stepk_field, force_maxk=force_maxk_field, ) psf = get_jax_galsim_object_from_dfmd_obs( - dfmd_obs.psf, - kind="image", + dfmd_obs.psf, + kind="image", force_stepk=force_stepk_psf, force_maxk=force_maxk_psf, ) @@ -299,7 +302,7 @@ def jax_match_psf( ims = jax_galsim.Convolve( [image, jax_galsim.Deconvolve(psf), reconv_psf], gsparams=jax_galsim.GSParams( - minimum_fft_size=max_min_fft_size, + minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size, ), ) @@ -311,14 +314,18 @@ def jax_match_psf( ims = ims.drawImage(nx=nxy, ny=nxy, wcs=wcs).array def return_obs_and_kinfo(_): - return _jax_render_psf_and_build_obs(ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1), ( image.stepk, image.maxk, psf.stepk, psf.maxk) + return _jax_render_psf_and_build_obs( + ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1 + ), (image.stepk, image.maxk, psf.stepk, psf.maxk) def return_obs_only(_): return _jax_render_psf_and_build_obs( ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1 - ), (0., 0., 0., 0.) + ), (0.0, 0.0, 0.0, 0.0) - return jax.lax.cond(return_k_info, return_obs_and_kinfo, return_obs_only, operand=None) + return jax.lax.cond( + return_k_info, return_obs_and_kinfo, return_obs_only, operand=None + ) def _extract_attr(obs, attr, dtype=jnp.float64): @@ -422,12 +429,12 @@ def jax_add_dfmd_obs( def get_jax_galsim_object_from_dfmd_obs( - dfmd_obs, - kind="image", - rot90=0, - force_stepk=0.0, - force_maxk=0.0, - ): + dfmd_obs, + kind="image", + rot90=0, + force_stepk=0.0, + force_maxk=0.0, +): """Make an interpolated image from an dfmd obs.""" return jax_galsim.InterpolatedImage( jax_galsim.ImageD( @@ -495,10 +502,10 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( # make the wide obs mcal_obs_wide, kinfo = jax_match_psf( - obs_wide, - reconv_psf, - nxy, - nxy_psf, + obs_wide, + reconv_psf, + nxy, + nxy_psf, return_k_info=return_k_info, force_stepk_field=force_stepk_field, force_maxk_field=force_maxk_field, @@ -516,9 +523,9 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( # get PSF matched noise obs_wide_noise = obs_wide._replace(image=obs_wide.noise) wide_noise_corr, _ = jax_match_psf( - obs_wide_noise, - reconv_psf, - nxy, + obs_wide_noise, + reconv_psf, + nxy, nxy_psf, force_stepk_field=force_stepk_field, force_maxk_field=force_maxk_field, From 9aa87cafcd88acb0ddeb05d99c21879666b882a3 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 28 Jul 2025 10:11:24 -0500 Subject: [PATCH 45/59] bug fix --- deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index f4bfd79..5d71a83 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -396,7 +396,7 @@ def _run_single_sim_maybe_mcal( for key, value in mcal_res.items(): mcal_res[key] = dfmd_obs_to_ngmix_obs(value) else: - mcal_res = jax_metacal_wide_and_deep_psf_matched( + mcal_res, _ = jax_metacal_wide_and_deep_psf_matched( obs_w, obs_d, obs_dn, From 908d2e0266988c2a147fae0602c1d71c8211e5ec Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 28 Jul 2025 10:20:46 -0500 Subject: [PATCH 46/59] adding max min fft size for reconv --- .../jaxify/tests/test_jax_metadetect.py | 2 ++ deep_field_metadetect/metacal.py | 22 ++++++++++++------- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py index f8d7dd5..82f9dad 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_metadetect.py @@ -130,6 +130,7 @@ def _run_single_sim_jax_and_ngmix( skip_obs_wide_corrections=skip_wide, skip_obs_deep_corrections=skip_deep, return_k_info=True, + max_min_fft_size=1024, ) res, kinfo = jax_single_band_deep_field_metadetect( @@ -146,6 +147,7 @@ def _run_single_sim_jax_and_ngmix( force_maxk_field=force_maxk_field, force_stepk_psf=force_stepk_psf, force_maxk_psf=force_maxk_psf, + max_min_fft_size=1024, ) assert kinfo[0] == force_stepk_field diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index e019a50..ac58c58 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -84,7 +84,12 @@ def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): return get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=step) -def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1): +def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1, max_min_fft_size=None): + reconv_psf = reconv_psf.withGSParams( + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, + ) + pim = reconv_psf.drawImage( nx=obs.psf.image.shape[1], ny=obs.psf.image.shape[0], @@ -131,7 +136,7 @@ def _metacal_op_g1g2_impl(*, wcs, image, noise, psf_inv, dims, reconv_psf, g1, g return ims + ns -def metacal_op_g1g2(obs, reconv_psf, g1, g2): +def metacal_op_g1g2(obs, reconv_psf, g1, g2, max_min_fft_size=None): """Run metacal on an ngmix observation.""" mcal_image = _metacal_op_g1g2_impl( wcs=obs.jacobian.get_galsim_wcs(), @@ -147,10 +152,10 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2): g1=g1, g2=g2, ) - return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5) + return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size) -def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): +def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP, max_min_fft_size=None): """Run metacal on an ngmix observation.""" if shears is None: shears = DEFAULT_SHEARS @@ -180,7 +185,7 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP): g2=g2, ) mcal_res[shear] = _render_psf_and_build_obs( - mcal_image, obs, reconv_psf, weight_fac=0.5 + mcal_image, obs, reconv_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size ) return mcal_res @@ -230,14 +235,14 @@ def match_psf( ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array if return_k_info: - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), ( + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size), ( image._stepk, image._maxk, psf._stepk, psf._maxk, ) - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1), None + return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size), None def _extract_attr(obs, attr, dtype): @@ -440,7 +445,7 @@ def metacal_wide_and_deep_psf_matched( if not skip_obs_wide_corrections: mcal_obs_wide = add_ngmix_obs( mcal_obs_wide, - metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0), + metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, max_min_fft_size=max_min_fft_size), skip_mfrac_for_second=True, ) @@ -464,6 +469,7 @@ def metacal_wide_and_deep_psf_matched( reconv_psf=reconv_psf, shears=shears, step=step, + max_min_fft_size=max_min_fft_size, ) # now add in noise corr to make it match the wide noise From fb3fce4f89f8638fd9f1d9593ee58885bacf48fd Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 28 Jul 2025 10:24:38 -0500 Subject: [PATCH 47/59] minor --- deep_field_metadetect/metacal.py | 33 +++++++++++++++++++++++++------- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index ac58c58..533e25e 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -84,7 +84,9 @@ def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): return get_max_gauss_reconv_psf_galsim(psf_w, psf_d, step=step) -def _render_psf_and_build_obs(image, obs, reconv_psf, weight_fac=1, max_min_fft_size=None): +def _render_psf_and_build_obs( + image, obs, reconv_psf, weight_fac=1, max_min_fft_size=None +): reconv_psf = reconv_psf.withGSParams( minimum_fft_size=max_min_fft_size, maximum_fft_size=max_min_fft_size, @@ -152,10 +154,14 @@ def metacal_op_g1g2(obs, reconv_psf, g1, g2, max_min_fft_size=None): g1=g1, g2=g2, ) - return _render_psf_and_build_obs(mcal_image, obs, reconv_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size) + return _render_psf_and_build_obs( + mcal_image, obs, reconv_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size + ) -def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP, max_min_fft_size=None): +def metacal_op_shears( + obs, reconv_psf=None, shears=None, step=DEFAULT_STEP, max_min_fft_size=None +): """Run metacal on an ngmix observation.""" if shears is None: shears = DEFAULT_SHEARS @@ -185,7 +191,11 @@ def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP, max_ g2=g2, ) mcal_res[shear] = _render_psf_and_build_obs( - mcal_image, obs, reconv_psf, weight_fac=0.5, max_min_fft_size=max_min_fft_size + mcal_image, + obs, + reconv_psf, + weight_fac=0.5, + max_min_fft_size=max_min_fft_size, ) return mcal_res @@ -235,14 +245,21 @@ def match_psf( ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array if return_k_info: - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size), ( + return _render_psf_and_build_obs( + ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size + ), ( image._stepk, image._maxk, psf._stepk, psf._maxk, ) - return _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size), None + return ( + _render_psf_and_build_obs( + ims, obs, reconv_psf, weight_fac=1, max_min_fft_size=max_min_fft_size + ), + None, + ) def _extract_attr(obs, attr, dtype): @@ -445,7 +462,9 @@ def metacal_wide_and_deep_psf_matched( if not skip_obs_wide_corrections: mcal_obs_wide = add_ngmix_obs( mcal_obs_wide, - metacal_op_g1g2(obs_deep_noise, reconv_psf, 0, 0, max_min_fft_size=max_min_fft_size), + metacal_op_g1g2( + obs_deep_noise, reconv_psf, 0, 0, max_min_fft_size=max_min_fft_size + ), skip_mfrac_for_second=True, ) From 0f71b06e94005242884df6c478eede4402214383 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 28 Jul 2025 14:18:37 -0500 Subject: [PATCH 48/59] bug fix --- deep_field_metadetect/metacal.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index 533e25e..aa8df8b 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -87,10 +87,11 @@ def get_max_gauss_reconv_psf(obs_w, obs_d, step=DEFAULT_STEP): def _render_psf_and_build_obs( image, obs, reconv_psf, weight_fac=1, max_min_fft_size=None ): - reconv_psf = reconv_psf.withGSParams( - minimum_fft_size=max_min_fft_size, - maximum_fft_size=max_min_fft_size, - ) + if max_min_fft_size is not None: + reconv_psf = reconv_psf.withGSParams( + minimum_fft_size=max_min_fft_size, + maximum_fft_size=max_min_fft_size, + ) pim = reconv_psf.drawImage( nx=obs.psf.image.shape[1], From 01995d55ed7d43aded7b842ec82ab772b5f9a470 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 10:14:38 -0500 Subject: [PATCH 49/59] fix dk and kim_size in reconv psf --- deep_field_metadetect/jaxify/jax_metacal.py | 13 ++++++++++--- deep_field_metadetect/metacal.py | 10 +++++++--- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index f494f9c..6f2ab94 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -29,8 +29,8 @@ def get_shear_tuple(shear, step): raise RuntimeError("Shear value '%s' not regonized!" % shear) -@partial(jax.jit, static_argnames=["dk", "nxy_psf"]) -def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1): +@partial(jax.jit, static_argnames=["dk", "nxy_psf", "kim_size"]) +def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1, kim_size=None): """Gets the target reconvolution PSF for an input PSF object. This is taken from galsim/tests/test_metacal.py and assumes the psf is @@ -44,10 +44,14 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux The Fourier-space pixel scale. nxy_psf : int, optional The size of the PSF image in pixels (default is 53). + Used to set k_image size, but is overridden if kim_size is passed. step : float, optional The step size for coordinate grids (default is `DEFAULT_STEP`). flux : float, optional The total flux of the output PSF (default is 1). + kim_size : int + k image size. + Defaults to None, which sets size as 4*nxy_psf Returns ------- @@ -57,7 +61,10 @@ def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - kim = psf.drawKImage(nx=4 * nxy_psf, ny=4 * nxy_psf, scale=dk) + if kim_size is None: + kim = psf.drawKImage(nx=4 * nxy_psf, ny=4 * nxy_psf, scale=dk) + else: + kim = psf.drawKImage(nx=kim_size, ny=kim_size, scale=dk) # This will lead to a differnce in reconv psf size between GS and JGS diff --git a/deep_field_metadetect/metacal.py b/deep_field_metadetect/metacal.py index aa8df8b..60663f4 100644 --- a/deep_field_metadetect/metacal.py +++ b/deep_field_metadetect/metacal.py @@ -21,7 +21,7 @@ def get_shear_tuple(shear, step): raise RuntimeError("Shear value '%s' not regonized!" % shear) -def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): +def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1, dk=None, kim_size=None): """Gets the target reconvolution PSF for an input PSF object. This is taken from galsim/tests/test_metacal.py and assumes the psf is @@ -33,18 +33,22 @@ def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1): The PSF. flux : float The output flux of the PSF. Defaults to 1. + kim_size : int + k image size. + Defaults to None, which lets galsim set the size Returns ------- reconv_psf : galsim object The reconvolution PSF. """ - dk = 2 * np.pi / (53 * 0.2) / 4.0 + if dk is None: + dk = psf.stepk / 4.0 small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k - kim = psf.drawKImage(scale=dk) + kim = psf.drawKImage(nx=kim_size, ny=kim_size, scale=dk) karr_r = kim.real.array # Find the smallest r where the kval < small_kval nk = karr_r.shape[0] From fe9a226c01da37dfbe0211fd280cddec2dea2613 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 10:51:03 -0500 Subject: [PATCH 50/59] vectorize --- deep_field_metadetect/jaxify/jax_metacal.py | 22 ++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index 6f2ab94..78ff070 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -239,10 +239,12 @@ def jax_metacal_op_shears( psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind="image") psf_inv = jax_galsim.Deconvolve(psf) - mcal_res = {} - for shear in shears: - g1, g2 = get_shear_tuple(shear, step) + shear_tuples = jnp.array([get_shear_tuple(shear, step) for shear in shears]) + g1_vals = shear_tuples[:, 0] + g2_vals = shear_tuples[:, 1] + # Vectorized metacal operation across all shears + def single_shear_op(g1, g2): mcal_image = _jax_metacal_op_g1g2_impl( wcs=wcs, image=image, @@ -254,8 +256,7 @@ def jax_metacal_op_shears( g2=g2, max_min_fft_size=max_min_fft_size, ) - - mcal_res[shear] = _jax_render_psf_and_build_obs( + return _jax_render_psf_and_build_obs( mcal_image, dfmd_obs, reconv_psf, @@ -263,6 +264,16 @@ def jax_metacal_op_shears( weight_fac=0.5, max_min_fft_size=max_min_fft_size, ) + + # Use vmap to parallelize across shears + vectorized_shear_op = jax.vmap(single_shear_op) + mcal_obs_list = vectorized_shear_op(g1_vals, g2_vals) + + # Convert back to dictionary format + mcal_res = {} + for i, shear in enumerate(shears): + mcal_res[shear] = jax.tree.map(lambda x: x[i], mcal_obs_list) + return mcal_res @@ -553,6 +564,7 @@ def _jax_helper_metacal_wide_and_deep_psf_matched( ) # now add in noise corr to make it match the wide noise + # TODO: is it after to vextorize? if not skip_obs_deep_corrections: for k in mcal_res: mcal_res[k] = jax_add_dfmd_obs( From ebb7449e9f611faec59ea792d97d593d898652b6 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 10:52:13 -0500 Subject: [PATCH 51/59] minor --- deep_field_metadetect/jaxify/jax_metacal.py | 6 ++++-- deep_field_metadetect/utils.py | 2 -- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deep_field_metadetect/jaxify/jax_metacal.py b/deep_field_metadetect/jaxify/jax_metacal.py index 78ff070..46ead46 100644 --- a/deep_field_metadetect/jaxify/jax_metacal.py +++ b/deep_field_metadetect/jaxify/jax_metacal.py @@ -30,7 +30,9 @@ def get_shear_tuple(shear, step): @partial(jax.jit, static_argnames=["dk", "nxy_psf", "kim_size"]) -def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1, kim_size=None): +def jax_get_gauss_reconv_psf_galsim( + psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1, kim_size=None +): """Gets the target reconvolution PSF for an input PSF object. This is taken from galsim/tests/test_metacal.py and assumes the psf is @@ -273,7 +275,7 @@ def single_shear_op(g1, g2): mcal_res = {} for i, shear in enumerate(shears): mcal_res[shear] = jax.tree.map(lambda x: x[i], mcal_obs_list) - + return mcal_res diff --git a/deep_field_metadetect/utils.py b/deep_field_metadetect/utils.py index a2eda82..fac5ff4 100644 --- a/deep_field_metadetect/utils.py +++ b/deep_field_metadetect/utils.py @@ -2,8 +2,6 @@ import time from contextlib import contextmanager -# import jax.numpy as jnp -# import jax_galsim import galsim import ngmix import numpy as np From bd3164db212a92247fb6e54072201c536bdcb9c6 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 10:52:58 -0500 Subject: [PATCH 52/59] text jax and non-jax intermediates --- .../tests/test_jax_ngmix_intermediates.py | 191 ++++++++++++++++++ 1 file changed, 191 insertions(+) create mode 100644 deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py diff --git a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py new file mode 100644 index 0000000..1ac4bfd --- /dev/null +++ b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py @@ -0,0 +1,191 @@ +import numpy as np +import pytest + +from deep_field_metadetect.jaxify.jax_metacal import ( + jax_get_gauss_reconv_psf_galsim, + jax_get_max_gauss_reconv_psf_galsim, + jax_metacal_op_g1g2, + jax_metacal_op_shears, + get_jax_galsim_object_from_dfmd_obs_nopix, +) +from deep_field_metadetect.jaxify.observation import ( + ngmix_obs_to_dfmd_obs, +) +from deep_field_metadetect.jaxify.jax_utils import compute_stepk +from deep_field_metadetect.metacal import ( + get_gauss_reconv_psf_galsim, + get_max_gauss_reconv_psf_galsim, + metacal_op_g1g2, + metacal_op_shears, + get_galsim_object_from_ngmix_obs_nopix, +) +from deep_field_metadetect.utils import make_simple_sim + + +class TestJaxNgmixIntermediates: + """Test that JAX and ngmix versions produce the same intermediate values.""" + + @pytest.fixture(scope="class") + def simple_obs_pair(self): + """Create a simple observation pair for testing.""" + nxy = 53 + nxy_psf = 53 + scale = 0.2 + + obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix = make_simple_sim( + seed=12345, + g1=0.02, + g2=0.0, + s2n=1e8, + dim=nxy, + dim_psf=nxy_psf, + scale=scale, + deep_noise_fac=1.0 / np.sqrt(10), + deep_psf_fac=1.0, + return_dfmd_obs=False, + ) + + # Convert to JAX observations + obs_w_jax = ngmix_obs_to_dfmd_obs(obs_w_ngmix) + obs_d_jax = ngmix_obs_to_dfmd_obs(obs_d_ngmix) + obs_dn_jax = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) + + return { + 'ngmix': (obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix), + 'jax': (obs_w_jax, obs_d_jax, obs_dn_jax), + 'params': {'nxy': nxy, 'nxy_psf': nxy_psf, 'scale': scale} + } + + def test_gauss_reconv_psf_consistency(self, simple_obs_pair): + """Test that JAX and ngmix produce the same Gaussian reconvolution PSF.""" + obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair['ngmix'] + obs_w_jax, obs_d_jax, _ = simple_obs_pair['jax'] + nxy_psf = simple_obs_pair['params']['nxy_psf'] + scale = simple_obs_pair['params']['scale'] + + # Test single PSF + psf_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") + psf_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") + + dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) + + kim_size = 173 + reconv_psf_jax = jax_get_gauss_reconv_psf_galsim(psf_jax, dk=dk, kim_size=kim_size) + reconv_psf_ngmix = get_gauss_reconv_psf_galsim(psf_ngmix, dk=dk, kim_size=kim_size) + + # Test PSF properties - relax tolerance for small numerical differences + assert np.allclose( + reconv_psf_ngmix.fwhm, + reconv_psf_jax.fwhm, + rtol=1e-6, atol=1e-10 + ), f"FWHM mismatch: {reconv_psf_ngmix.fwhm} vs {reconv_psf_jax.fwhm}" + + assert np.allclose( + reconv_psf_ngmix.flux, + reconv_psf_jax.flux, + rtol=1e-10, atol=1e-12 + ), f"Flux mismatch: {reconv_psf_ngmix.flux} vs {reconv_psf_jax.flux}" + + def test_max_gauss_reconv_psf_consistency(self, simple_obs_pair): + """Test that JAX and ngmix produce the similar Gaussian reconvolution PSF. + + kim_size and dk are not get for Galsim in this case.""" + obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair['ngmix'] + obs_w_jax, obs_d_jax, _ = simple_obs_pair['jax'] + nxy_psf = simple_obs_pair['params']['nxy_psf'] + scale = simple_obs_pair['params']['scale'] + + # Get PSFs + psf_w_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") + psf_d_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_d_ngmix.psf, kind="image") + psf_w_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") + psf_d_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_d_jax.psf, kind="image") + + # Compare maximum reconvolution PSFs + max_reconv_psf_ngmix = get_max_gauss_reconv_psf_galsim(psf_w_ngmix, psf_d_ngmix) + max_reconv_psf_jax = jax_get_max_gauss_reconv_psf_galsim( + psf_w_jax, psf_d_jax, nxy_psf, scale + ) + + # Test PSF properties + assert np.allclose( + max_reconv_psf_ngmix.fwhm, + max_reconv_psf_jax.fwhm, + rtol=0.02, atol=1e-6 + ), f"Max FWHM mismatch: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" + + def test_metacal_single_shear_consistency(self, simple_obs_pair): + """Test that JAX and ngmix produce consistent results for single shear operations.""" + obs_w_ngmix, _, _ = simple_obs_pair['ngmix'] + obs_w_jax, _, _ = simple_obs_pair['jax'] + nxy_psf = simple_obs_pair['params']['nxy_psf'] + scale = simple_obs_pair['params']['scale'] + + # Test single shear transformation + g1, g2 = 0.01, 0.0 + + # Get reconvolution PSFs for both versions + dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) + psf_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") + psf_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") + + kim_size = 173 + reconv_psf_jax = jax_get_gauss_reconv_psf_galsim(psf_jax, dk, kim_size=kim_size) + reconv_psf_ngmix = get_gauss_reconv_psf_galsim(psf_ngmix, dk=dk, kim_size=kim_size) + + # Run metacal operations + mcal_obs_ngmix = metacal_op_g1g2(obs_w_ngmix, reconv_psf_ngmix, g1, g2) + mcal_obs_jax = jax_metacal_op_g1g2(obs_w_jax, reconv_psf_jax, g1, g2, nxy_psf) + + # Convert JAX result to ngmix for comparison + from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs + mcal_obs_jax_ngmix = dfmd_obs_to_ngmix_obs(mcal_obs_jax) + + # Compare image statistics + assert np.allclose( + np.mean(mcal_obs_ngmix.image), + np.mean(mcal_obs_jax_ngmix.image), + rtol=1e-5, atol=1e-9 + ), "Image mean mismatch" + + assert np.allclose( + np.std(mcal_obs_ngmix.image), + np.std(mcal_obs_jax_ngmix.image), + rtol=1e-5, atol=1e-9 + ), "Image std mismatch" + + def test_metacal_shears_intermediate_values(self, simple_obs_pair): + """Test intermediate values in metacal shears operations.""" + obs_w_ngmix, _, _ = simple_obs_pair['ngmix'] + obs_w_jax, _, _ = simple_obs_pair['jax'] + scale = simple_obs_pair['params']['scale'] + + test_shears = ("noshear", "1p", "1m") + + # Run metacal operations + mcal_res_ngmix = metacal_op_shears(obs_w_ngmix, shears=test_shears) + mcal_res_jax = jax_metacal_op_shears(obs_w_jax, shears=test_shears, scale=scale) + + # Convert JAX results to ngmix for comparison + from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs + mcal_res_jax_ngmix = {} + for shear in test_shears: + mcal_res_jax_ngmix[shear] = dfmd_obs_to_ngmix_obs(mcal_res_jax[shear]) + + # Compare results for each shear + for shear in test_shears: + obs_ngmix = mcal_res_ngmix[shear] + obs_jax_ngmix = mcal_res_jax_ngmix[shear] + + # Compare image statistics + img_mean_diff = abs(np.mean(obs_ngmix.image) - np.mean(obs_jax_ngmix.image)) + img_std_ratio = np.std(obs_ngmix.image) / np.std(obs_jax_ngmix.image) + + assert img_mean_diff < 1e-4, f"Shear {shear}: Image mean difference too large: {img_mean_diff}" + assert 0.99 < img_std_ratio < 1.01, f"Shear {shear}: Image std ratio out of range: {img_std_ratio}" + + # Compare weight statistics + weight_ratio = np.mean(obs_ngmix.weight) / np.mean(obs_jax_ngmix.weight) + assert 0.99 < weight_ratio < 1.01, f"Shear {shear}: Weight ratio out of range: {weight_ratio}" + + From ade0bd3a982c52e23cbbef1d4b3d6b98c5d714e0 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 10:58:58 -0500 Subject: [PATCH 53/59] minor --- .../tests/test_jax_ngmix_intermediates.py | 177 ++++++++++-------- 1 file changed, 99 insertions(+), 78 deletions(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py index 1ac4bfd..991a252 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py @@ -2,28 +2,28 @@ import pytest from deep_field_metadetect.jaxify.jax_metacal import ( + get_jax_galsim_object_from_dfmd_obs_nopix, jax_get_gauss_reconv_psf_galsim, jax_get_max_gauss_reconv_psf_galsim, jax_metacal_op_g1g2, jax_metacal_op_shears, - get_jax_galsim_object_from_dfmd_obs_nopix, ) +from deep_field_metadetect.jaxify.jax_utils import compute_stepk from deep_field_metadetect.jaxify.observation import ( ngmix_obs_to_dfmd_obs, ) -from deep_field_metadetect.jaxify.jax_utils import compute_stepk from deep_field_metadetect.metacal import ( + get_galsim_object_from_ngmix_obs_nopix, get_gauss_reconv_psf_galsim, get_max_gauss_reconv_psf_galsim, metacal_op_g1g2, metacal_op_shears, - get_galsim_object_from_ngmix_obs_nopix, ) from deep_field_metadetect.utils import make_simple_sim class TestJaxNgmixIntermediates: - """Test that JAX and ngmix versions produce the same intermediate values.""" + """Test is the versions produce the same intermediate values.""" @pytest.fixture(scope="class") def simple_obs_pair(self): @@ -31,7 +31,7 @@ def simple_obs_pair(self): nxy = 53 nxy_psf = 53 scale = 0.2 - + obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix = make_simple_sim( seed=12345, g1=0.02, @@ -44,148 +44,169 @@ def simple_obs_pair(self): deep_psf_fac=1.0, return_dfmd_obs=False, ) - + # Convert to JAX observations obs_w_jax = ngmix_obs_to_dfmd_obs(obs_w_ngmix) obs_d_jax = ngmix_obs_to_dfmd_obs(obs_d_ngmix) obs_dn_jax = ngmix_obs_to_dfmd_obs(obs_dn_ngmix) - + return { - 'ngmix': (obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix), - 'jax': (obs_w_jax, obs_d_jax, obs_dn_jax), - 'params': {'nxy': nxy, 'nxy_psf': nxy_psf, 'scale': scale} + "ngmix": (obs_w_ngmix, obs_d_ngmix, obs_dn_ngmix), + "jax": (obs_w_jax, obs_d_jax, obs_dn_jax), + "params": {"nxy": nxy, "nxy_psf": nxy_psf, "scale": scale}, } def test_gauss_reconv_psf_consistency(self, simple_obs_pair): - """Test that JAX and ngmix produce the same Gaussian reconvolution PSF.""" - obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair['ngmix'] - obs_w_jax, obs_d_jax, _ = simple_obs_pair['jax'] - nxy_psf = simple_obs_pair['params']['nxy_psf'] - scale = simple_obs_pair['params']['scale'] - + """Test Gaussian reconvolution PSF.""" + obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair["ngmix"] + obs_w_jax, obs_d_jax, _ = simple_obs_pair["jax"] + nxy_psf = simple_obs_pair["params"]["nxy_psf"] + scale = simple_obs_pair["params"]["scale"] + # Test single PSF - psf_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") + psf_ngmix = get_galsim_object_from_ngmix_obs_nopix( + obs_w_ngmix.psf, kind="image" + ) psf_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") - + dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) - + kim_size = 173 - reconv_psf_jax = jax_get_gauss_reconv_psf_galsim(psf_jax, dk=dk, kim_size=kim_size) - reconv_psf_ngmix = get_gauss_reconv_psf_galsim(psf_ngmix, dk=dk, kim_size=kim_size) - + reconv_psf_jax = jax_get_gauss_reconv_psf_galsim( + psf_jax, dk=dk, kim_size=kim_size + ) + reconv_psf_ngmix = get_gauss_reconv_psf_galsim( + psf_ngmix, dk=dk, kim_size=kim_size + ) + # Test PSF properties - relax tolerance for small numerical differences assert np.allclose( - reconv_psf_ngmix.fwhm, - reconv_psf_jax.fwhm, - rtol=1e-6, atol=1e-10 + reconv_psf_ngmix.fwhm, reconv_psf_jax.fwhm, rtol=1e-6, atol=1e-10 ), f"FWHM mismatch: {reconv_psf_ngmix.fwhm} vs {reconv_psf_jax.fwhm}" - + assert np.allclose( - reconv_psf_ngmix.flux, - reconv_psf_jax.flux, - rtol=1e-10, atol=1e-12 + reconv_psf_ngmix.flux, reconv_psf_jax.flux, rtol=1e-10, atol=1e-12 ), f"Flux mismatch: {reconv_psf_ngmix.flux} vs {reconv_psf_jax.flux}" def test_max_gauss_reconv_psf_consistency(self, simple_obs_pair): - """Test that JAX and ngmix produce the similar Gaussian reconvolution PSF. - + """Test max Gaussian reconvolution PSF. kim_size and dk are not get for Galsim in this case.""" - obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair['ngmix'] - obs_w_jax, obs_d_jax, _ = simple_obs_pair['jax'] - nxy_psf = simple_obs_pair['params']['nxy_psf'] - scale = simple_obs_pair['params']['scale'] - + obs_w_ngmix, obs_d_ngmix, _ = simple_obs_pair["ngmix"] + obs_w_jax, obs_d_jax, _ = simple_obs_pair["jax"] + nxy_psf = simple_obs_pair["params"]["nxy_psf"] + scale = simple_obs_pair["params"]["scale"] + # Get PSFs - psf_w_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") - psf_d_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_d_ngmix.psf, kind="image") - psf_w_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") - psf_d_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_d_jax.psf, kind="image") - + psf_w_ngmix = get_galsim_object_from_ngmix_obs_nopix( + obs_w_ngmix.psf, kind="image" + ) + psf_d_ngmix = get_galsim_object_from_ngmix_obs_nopix( + obs_d_ngmix.psf, kind="image" + ) + psf_w_jax = get_jax_galsim_object_from_dfmd_obs_nopix( + obs_w_jax.psf, kind="image" + ) + psf_d_jax = get_jax_galsim_object_from_dfmd_obs_nopix( + obs_d_jax.psf, kind="image" + ) + # Compare maximum reconvolution PSFs max_reconv_psf_ngmix = get_max_gauss_reconv_psf_galsim(psf_w_ngmix, psf_d_ngmix) max_reconv_psf_jax = jax_get_max_gauss_reconv_psf_galsim( psf_w_jax, psf_d_jax, nxy_psf, scale ) - + # Test PSF properties assert np.allclose( - max_reconv_psf_ngmix.fwhm, - max_reconv_psf_jax.fwhm, - rtol=0.02, atol=1e-6 - ), f"Max FWHM mismatch: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" + max_reconv_psf_ngmix.fwhm, max_reconv_psf_jax.fwhm, rtol=0.02, atol=1e-6 + ), ( + f"Max FWHM mismatch: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" + ) def test_metacal_single_shear_consistency(self, simple_obs_pair): - """Test that JAX and ngmix produce consistent results for single shear operations.""" - obs_w_ngmix, _, _ = simple_obs_pair['ngmix'] - obs_w_jax, _, _ = simple_obs_pair['jax'] - nxy_psf = simple_obs_pair['params']['nxy_psf'] - scale = simple_obs_pair['params']['scale'] - + """Test single shear operations.""" + obs_w_ngmix, _, _ = simple_obs_pair["ngmix"] + obs_w_jax, _, _ = simple_obs_pair["jax"] + nxy_psf = simple_obs_pair["params"]["nxy_psf"] + scale = simple_obs_pair["params"]["scale"] + # Test single shear transformation g1, g2 = 0.01, 0.0 - + # Get reconvolution PSFs for both versions dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf) psf_jax = get_jax_galsim_object_from_dfmd_obs_nopix(obs_w_jax.psf, kind="image") - psf_ngmix = get_galsim_object_from_ngmix_obs_nopix(obs_w_ngmix.psf, kind="image") + psf_ngmix = get_galsim_object_from_ngmix_obs_nopix( + obs_w_ngmix.psf, kind="image" + ) kim_size = 173 reconv_psf_jax = jax_get_gauss_reconv_psf_galsim(psf_jax, dk, kim_size=kim_size) - reconv_psf_ngmix = get_gauss_reconv_psf_galsim(psf_ngmix, dk=dk, kim_size=kim_size) - + reconv_psf_ngmix = get_gauss_reconv_psf_galsim( + psf_ngmix, dk=dk, kim_size=kim_size + ) + # Run metacal operations mcal_obs_ngmix = metacal_op_g1g2(obs_w_ngmix, reconv_psf_ngmix, g1, g2) mcal_obs_jax = jax_metacal_op_g1g2(obs_w_jax, reconv_psf_jax, g1, g2, nxy_psf) - + # Convert JAX result to ngmix for comparison from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs + mcal_obs_jax_ngmix = dfmd_obs_to_ngmix_obs(mcal_obs_jax) - + # Compare image statistics assert np.allclose( - np.mean(mcal_obs_ngmix.image), + np.mean(mcal_obs_ngmix.image), np.mean(mcal_obs_jax_ngmix.image), - rtol=1e-5, atol=1e-9 + rtol=1e-5, + atol=1e-9, ), "Image mean mismatch" - + assert np.allclose( - np.std(mcal_obs_ngmix.image), + np.std(mcal_obs_ngmix.image), np.std(mcal_obs_jax_ngmix.image), - rtol=1e-5, atol=1e-9 + rtol=1e-5, + atol=1e-9, ), "Image std mismatch" def test_metacal_shears_intermediate_values(self, simple_obs_pair): """Test intermediate values in metacal shears operations.""" - obs_w_ngmix, _, _ = simple_obs_pair['ngmix'] - obs_w_jax, _, _ = simple_obs_pair['jax'] - scale = simple_obs_pair['params']['scale'] - + obs_w_ngmix, _, _ = simple_obs_pair["ngmix"] + obs_w_jax, _, _ = simple_obs_pair["jax"] + scale = simple_obs_pair["params"]["scale"] + test_shears = ("noshear", "1p", "1m") - + # Run metacal operations mcal_res_ngmix = metacal_op_shears(obs_w_ngmix, shears=test_shears) mcal_res_jax = jax_metacal_op_shears(obs_w_jax, shears=test_shears, scale=scale) - + # Convert JAX results to ngmix for comparison from deep_field_metadetect.jaxify.observation import dfmd_obs_to_ngmix_obs + mcal_res_jax_ngmix = {} for shear in test_shears: mcal_res_jax_ngmix[shear] = dfmd_obs_to_ngmix_obs(mcal_res_jax[shear]) - + # Compare results for each shear for shear in test_shears: obs_ngmix = mcal_res_ngmix[shear] obs_jax_ngmix = mcal_res_jax_ngmix[shear] - + # Compare image statistics img_mean_diff = abs(np.mean(obs_ngmix.image) - np.mean(obs_jax_ngmix.image)) img_std_ratio = np.std(obs_ngmix.image) / np.std(obs_jax_ngmix.image) - - assert img_mean_diff < 1e-4, f"Shear {shear}: Image mean difference too large: {img_mean_diff}" - assert 0.99 < img_std_ratio < 1.01, f"Shear {shear}: Image std ratio out of range: {img_std_ratio}" - - # Compare weight statistics - weight_ratio = np.mean(obs_ngmix.weight) / np.mean(obs_jax_ngmix.weight) - assert 0.99 < weight_ratio < 1.01, f"Shear {shear}: Weight ratio out of range: {weight_ratio}" + assert img_mean_diff < 1e-4, ( + f"Shear {shear}: Image mean difference too large: {img_mean_diff}" + ) + assert 0.99 < img_std_ratio < 1.01, ( + f"Shear {shear}: Image std ratio out of range: {img_std_ratio}" + ) + # Compare weight statistics + weight_ratio = np.mean(obs_ngmix.weight) / np.mean(obs_jax_ngmix.weight) + assert 0.99 < weight_ratio < 1.01, ( + f"Shear {shear}: Weight ratio out of range: {weight_ratio}" + ) From e3307f3e6dd5beeb2486c0e42e1b6053d6d3e1b4 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 11:22:32 -0500 Subject: [PATCH 54/59] remove black (conflict with ruff) --- .pre-commit-config.yaml | 5 ----- .../jaxify/tests/test_jax_ngmix_intermediates.py | 4 +--- 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 699fe48..941b842 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,11 +15,6 @@ repos: hooks: - id: end-of-file-fixer - id: trailing-whitespace - - repo: https://github.com/psf/black-pre-commit-mirror - rev: 25.1.0 - hooks: - - id: black - language_version: python3.11 - repo: https://github.com/asottile/pyupgrade rev: v3.20.0 hooks: diff --git a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py index 991a252..387964d 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py @@ -119,9 +119,7 @@ def test_max_gauss_reconv_psf_consistency(self, simple_obs_pair): # Test PSF properties assert np.allclose( max_reconv_psf_ngmix.fwhm, max_reconv_psf_jax.fwhm, rtol=0.02, atol=1e-6 - ), ( - f"Max FWHM mismatch: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" - ) + ), f"Max FWHM err: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" def test_metacal_single_shear_consistency(self, simple_obs_pair): """Test single shear operations.""" From 304a8f240554c899a82d0fbb96674f6299b82cb6 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Wed, 30 Jul 2025 11:23:08 -0500 Subject: [PATCH 55/59] minor --- .../jaxify/tests/test_jax_ngmix_intermediates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py index 387964d..e8731b7 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_ngmix_intermediates.py @@ -119,7 +119,7 @@ def test_max_gauss_reconv_psf_consistency(self, simple_obs_pair): # Test PSF properties assert np.allclose( max_reconv_psf_ngmix.fwhm, max_reconv_psf_jax.fwhm, rtol=0.02, atol=1e-6 - ), f"Max FWHM err: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" + ), f"Max FWHM: {max_reconv_psf_ngmix.fwhm} vs {max_reconv_psf_jax.fwhm}" def test_metacal_single_shear_consistency(self, simple_obs_pair): """Test single shear operations.""" From b4a4b1208c863bd7b8ba77fb3a87359c6a95b2a0 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 4 Aug 2025 11:51:53 -0500 Subject: [PATCH 56/59] mark slow tests --- deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py index 5d71a83..d6c2a9c 100644 --- a/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py +++ b/deep_field_metadetect/jaxify/tests/test_jax_deep_metacal.py @@ -270,6 +270,7 @@ def test_deep_metacal(deep_psf_ratio): assert_m_c_ok(m, merr, c1, c1err, c2, c2err) +@pytest.mark.slow def test_deep_metacal_widelows2n(): nsims = 500 noise_fac = 1 / np.sqrt(1000) @@ -407,6 +408,7 @@ def _run_single_sim_maybe_mcal( return fit_gauss_mom_mcal_res(mcal_res), mcal_res +@pytest.mark.slow def test_deep_metacal_noise_object_s2n(): nsims = 100 noise_fac = 1 / np.sqrt(10) From 30a256863f24e54060d4641f2733d3afd592a580 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 4 Aug 2025 12:01:25 -0500 Subject: [PATCH 57/59] Removed notebooks --- .../test_jax_deep_metadetect-Copy2.ipynb | 329 ---- notebooks/test_jax_metacal.ipynb | 1372 ----------------- 2 files changed, 1701 deletions(-) delete mode 100644 notebooks/test_jax_deep_metadetect-Copy2.ipynb delete mode 100644 notebooks/test_jax_metacal.ipynb diff --git a/notebooks/test_jax_deep_metadetect-Copy2.ipynb b/notebooks/test_jax_deep_metadetect-Copy2.ipynb deleted file mode 100644 index 0b6d2c6..0000000 --- a/notebooks/test_jax_deep_metadetect-Copy2.ipynb +++ /dev/null @@ -1,329 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "413f6098-e2b7-4e7d-a1c0-f9e9c0309959", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.environ[\"JAX_ENABLE_X64\"] = \"True\"\n", - "\n", - "import numpy as np\n", - "\n", - "from deep_field_metadetect.jaxify.observation import ngmix_obs_to_dfmd_obs\n", - "from deep_field_metadetect.utils import (\n", - " make_simple_sim,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6f1b4aad-e579-4ad1-b4b1-77ec63f010ad", - "metadata": {}, - "outputs": [], - "source": [ - "import jax_galsim\n", - "import galsim" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9e164d4e-adc4-4916-b280-2227f787df13", - "metadata": {}, - "outputs": [], - "source": [ - "from deep_field_metadetect.metacal import (\n", - " _render_psf_and_build_obs,\n", - " get_max_gauss_reconv_psf,\n", - ")\n", - "from deep_field_metadetect.jaxify.jax_metacal import (\n", - " get_jax_galsim_object_from_dfmd_obs,\n", - " _jax_render_psf_and_build_obs,\n", - " jax_get_max_gauss_reconv_psf,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f8a331ae-fc9c-4653-b2b7-1347192f1814", - "metadata": {}, - "source": [ - "# Try PSF matching by hand" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2b418f8d-f11e-4c97-8e08-8e2c95f81d6f", - "metadata": {}, - "outputs": [], - "source": [ - "stamp_size = 251\n", - "psf_size = 53" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d7aacf69-d1b5-4d1f-9a6e-1cc42e37d9ff", - "metadata": {}, - "outputs": [], - "source": [ - "obs_w_non_jax, obs_d_non_jax, obs_dn_non_jax = make_simple_sim(\n", - " seed=17,\n", - " g1=0,\n", - " g2=0,\n", - " s2n=1e10,\n", - " deep_noise_fac=1 / np.sqrt(30),\n", - " deep_psf_fac=1,\n", - " dim=stamp_size,\n", - " dim_psf=psf_size,\n", - " scale=0.2,\n", - " buff=53,\n", - " n_objs=5,\n", - " return_dfmd_obs=False,\n", - ")\n", - "\n", - "obs_w = ngmix_obs_to_dfmd_obs(obs_w_non_jax)\n", - "obs_d = ngmix_obs_to_dfmd_obs(obs_d_non_jax)\n", - "obs_dn = ngmix_obs_to_dfmd_obs(obs_dn_non_jax)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ec302618-35eb-4c8e-b6ec-e4c878055d2f", - "metadata": {}, - "outputs": [], - "source": [ - "def get_galsim_object_from_ngmix_obs(obs, kind=\"image\", rot90=0):\n", - " \"\"\"Make an interpolated image from an ngmix obs.\"\"\"\n", - " return galsim.InterpolatedImage(\n", - " galsim.ImageD(\n", - " np.rot90(getattr(obs, kind).copy(), k=rot90),\n", - " wcs=obs.jacobian.get_galsim_wcs(),\n", - " ),\n", - " x_interpolant=\"lanczos15\",\n", - " _force_stepk=0.7529228667374486,\n", - " _force_maxk=12.51728322914683,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5058989-d2d5-4195-ba5a-87359e9ae271", - "metadata": {}, - "outputs": [], - "source": [ - "def match_psf(obs, reconv_psf):\n", - " \"\"\"Match the PSF on an ngmix observation to a new PSF.\"\"\"\n", - " wcs = obs.jacobian.get_galsim_wcs()\n", - " image = get_galsim_object_from_ngmix_obs(obs, kind=\"image\")\n", - " psf = get_galsim_object_from_ngmix_obs(obs.psf, kind=\"image\")\n", - "\n", - " psf_inv = galsim.Deconvolve(psf)\n", - "\n", - " ims_deconvolved = galsim.Convolve([image, psf_inv])\n", - " ims = galsim.Convolve([ims_deconvolved, reconv_psf])\n", - "\n", - " ims = ims.drawImage(nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs).array\n", - " ims_deconvolved = ims_deconvolved.drawImage(\n", - " nx=obs.image.shape[1], ny=obs.image.shape[0], wcs=wcs\n", - " ).array\n", - "\n", - " return (\n", - " _render_psf_and_build_obs(ims, obs, reconv_psf, weight_fac=1),\n", - " ims_deconvolved,\n", - " psf_inv,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2d66439e-762b-4afa-99af-8fa564eb51a7", - "metadata": {}, - "outputs": [], - "source": [ - "def jax_match_psf(dfmd_obs, reconv_psf, nxy, nxy_psf):\n", - " \"\"\"Match the PSF on an dfmd observation to a new PSF.\"\"\"\n", - " wcs = dfmd_obs.aft._local_wcs\n", - " image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"image\")\n", - " psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind=\"image\")\n", - "\n", - " psf_inv = jax_galsim.Deconvolve(psf)\n", - "\n", - " nk = 8\n", - "\n", - " ims_deconvolved = jax_galsim.Convolve(\n", - " [image, psf_inv],\n", - " gsparams=jax_galsim.GSParams(minimum_fft_size=nk, maximum_fft_size=nk),\n", - " )\n", - " ims = jax_galsim.Convolve(\n", - " [ims_deconvolved, reconv_psf],\n", - " gsparams=jax_galsim.GSParams(minimum_fft_size=nk, maximum_fft_size=nk),\n", - " )\n", - "\n", - " ims = ims.withGSParams(\n", - " minimum_fft_size=nxy * 4,\n", - " maximum_fft_size=nxy * 4,\n", - " )\n", - " ims_drawim = ims.drawImage(nx=nxy, ny=nxy, wcs=wcs)\n", - " ims = ims_drawim.array\n", - "\n", - " ims_deconvolved = ims_deconvolved.withGSParams(\n", - " minimum_fft_size=nxy * 4,\n", - " maximum_fft_size=nxy * 4,\n", - " )\n", - " ims_deconvolved = ims_deconvolved.drawImage(nx=nxy, ny=nxy, wcs=wcs).array\n", - "\n", - " return (\n", - " _jax_render_psf_and_build_obs(ims, dfmd_obs, reconv_psf, nxy_psf, weight_fac=1),\n", - " ims_deconvolved,\n", - " psf_inv,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "5c5bb1a9-1639-4d4a-b98a-10b08b55c68a", - "metadata": {}, - "source": [ - "### Check only on noise " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c9194140-210e-41c7-aac2-5bd3f249f2a9", - "metadata": {}, - "outputs": [], - "source": [ - "reconv_psf = jax_get_max_gauss_reconv_psf(obs_w, obs_d, nxy_psf=psf_size)\n", - "reconv_psf_ngmix = get_max_gauss_reconv_psf(obs_w_non_jax, obs_d_non_jax)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "08e49737-581a-46f3-9506-40ac3a204a22", - "metadata": {}, - "outputs": [], - "source": [ - "obs_reconvolved, ims_deconvolved, psf_inv = jax_match_psf(\n", - " obs_w, reconv_psf, nxy=stamp_size, nxy_psf=psf_size\n", - ")\n", - "obs_reconvolved_numpy, ims_deconvolved_numpy, psf_inv_numpy = match_psf(\n", - " obs_w_non_jax, reconv_psf_ngmix\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "32eccc37-1b3d-4a01-ac5a-8b3d600cc6cd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# compare reconvolved noise\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "im0 = axes[0].imshow(obs_reconvolved.image)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"deconvolved + reconvolved noise field\")\n", - "\n", - "mask = obs_reconvolved.image\n", - "\n", - "im1 = axes[1].imshow(obs_reconvolved.image - obs_reconvolved_numpy.image)\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"diff\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c7fcb9bb-9108-464e-a130-b8e65f7d9b3d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compare deconvolved gal field\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "im0 = axes[0].imshow(ims_deconvolved)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"deconvolved noise field\")\n", - "\n", - "im1 = axes[1].imshow(ims_deconvolved - ims_deconvolved_numpy)\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"diff\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "092aee3b-0a8a-4784-93b9-00c38330b812", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jax", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.20" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/test_jax_metacal.ipynb b/notebooks/test_jax_metacal.ipynb deleted file mode 100644 index aac84ac..0000000 --- a/notebooks/test_jax_metacal.ipynb +++ /dev/null @@ -1,1372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "413f6098-e2b7-4e7d-a1c0-f9e9c0309959", - "metadata": {}, - "outputs": [], - "source": [ - "import multiprocessing\n", - "import os\n", - "\n", - "os.environ[\"JAX_ENABLE_X64\"] = \"True\"\n", - "\n", - "import joblib\n", - "import numpy as np\n", - "import jax.numpy as jnp\n", - "import pytest\n", - "import galsim\n", - "import jax_galsim\n", - "\n", - "from deep_field_metadetect.metacal import (\n", - " get_galsim_object_from_ngmix_obs,\n", - " DEFAULT_STEP,\n", - " DEFAULT_SHEARS,\n", - " get_shear_tuple,\n", - " _metacal_op_g1g2_impl,\n", - " _render_psf_and_build_obs,\n", - " get_galsim_object_from_ngmix_obs_nopix,\n", - ")\n", - "from deep_field_metadetect.jaxify.jax_metacal import (\n", - " get_jax_galsim_object_from_dfmd_obs,\n", - " compute_stepk,\n", - " _jax_metacal_op_g1g2_impl,\n", - " _jax_render_psf_and_build_obs,\n", - " get_jax_galsim_object_from_dfmd_obs_nopix,\n", - ")\n", - "from deep_field_metadetect.utils import (\n", - " assert_m_c_ok,\n", - " estimate_m_and_c,\n", - " fit_gauss_mom_mcal_res,\n", - " make_simple_sim,\n", - " measure_mcal_shear_quants,\n", - " print_m_c,\n", - ")\n", - "from deep_field_metadetect.jaxify.observation import (\n", - " ngmix_obs_to_dfmd_obs,\n", - " dfmd_obs_to_ngmix_obs,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "79a8da6d-cf72-4909-856b-61c18b1b8e5d", - "metadata": {}, - "outputs": [], - "source": [ - "from functools import partial" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "aeeeb953-cf1b-4110-a24a-974a25643353", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import jax\n", - "from functools import partial" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7d4eb1e9-d420-4671-8ce0-f485ea204d53", - "metadata": {}, - "outputs": [], - "source": [ - "@partial(jax.jit, static_argnames=[\"dk\", \"nxy_psf\"])\n", - "def jax_get_gauss_reconv_psf_galsim(psf, dk, nxy_psf=53, step=DEFAULT_STEP, flux=1):\n", - " \"\"\"Gets the target reconvolution PSF for an input PSF object.\n", - "\n", - " This is taken from galsim/tests/test_metacal.py and assumes the psf is\n", - " centered.\n", - "\n", - " Parameters\n", - " ----------\n", - " psf : galsim.GSObject\n", - " The input point spread function (PSF) object.\n", - " dk : float\n", - " The Fourier-space pixel scale.\n", - " nxy_psf : int, optional\n", - " The size of the PSF image in pixels (default is 53).\n", - " step : float, optional\n", - " The step size for coordinate grids (default is `DEFAULT_STEP`).\n", - " flux : float, optional\n", - " The total flux of the output PSF (default is 1).\n", - "\n", - " Returns\n", - " -------\n", - " reconv_psf : JaxGalsim object\n", - " The reconvolution PSF.\n", - " \"\"\"\n", - "\n", - " dk = 2 * jnp.pi / (53 * 0.2) / 4.0\n", - " small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue\n", - " smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k\n", - "\n", - " kim = psf.drawKImage(nx=250, ny=250, scale=dk)\n", - " # kim = psf.drawKImage(scale=dk)\n", - " karr_r = kim.real.array\n", - " # Find the smallest r where the kval < small_kval\n", - " nk = karr_r.shape[0]\n", - " kx, ky = jnp.meshgrid(jnp.arange(-nk / 2, nk / 2), jnp.arange(-nk / 2, nk / 2))\n", - " ksq = (kx**2 + ky**2) * dk**2\n", - " ksq_max = jnp.min(jnp.where(karr_r < small_kval * psf.flux, ksq, jnp.inf))\n", - "\n", - " # We take our target PSF to be the (round) Gaussian that is even smaller at\n", - " # this ksq\n", - " # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval\n", - " sigma_sq = -2.0 * jnp.log(smaller_kval) / ksq_max\n", - "\n", - " dilation = 1.0 + 2.0 * step\n", - " return (\n", - " jax_galsim.Gaussian(sigma=jnp.sqrt(sigma_sq) * dilation).withFlux(flux),\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " )\n", - "\n", - "\n", - "def jax_get_gauss_reconv_psf(dfmd_obs, nxy_psf, dk, step=DEFAULT_STEP):\n", - " \"\"\"Get the Gaussian reconv PSF for a DFMdetObs.\"\"\"\n", - " psf = get_jax_galsim_object_from_dfmd_obs_nopix(dfmd_obs.psf, kind=\"image\")\n", - " return jax_get_gauss_reconv_psf_galsim(psf, nxy_psf=nxy_psf, dk=dk, step=step)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7dc583fe-fba2-45df-a7e7-a5251cf6f657", - "metadata": {}, - "outputs": [], - "source": [ - "def get_gauss_reconv_psf_galsim(psf, step=DEFAULT_STEP, flux=1):\n", - " \"\"\"Gets the target reconvolution PSF for an input PSF object.\n", - "\n", - " This is taken from galsim/tests/test_metacal.py and assumes the psf is\n", - " centered.\n", - "\n", - " Parameters\n", - " ----------\n", - " psf : galsim object\n", - " The PSF.\n", - " flux : float\n", - " The output flux of the PSF. Defaults to 1.\n", - "\n", - " Returns\n", - " -------\n", - " reconv_psf : galsim object\n", - " The reconvolution PSF.\n", - " \"\"\"\n", - " dk = 2 * np.pi / (53 * 0.2) / 4.0\n", - "\n", - " small_kval = 1.0e-2 # Find the k where the given psf hits this kvalue\n", - " smaller_kval = 3.0e-3 # Target PSF will have this kvalue at the same k\n", - "\n", - " kim = psf.drawKImage(nx=250, ny=250, scale=dk)\n", - " karr_r = kim.real.array\n", - " # Find the smallest r where the kval < small_kval\n", - " nk = karr_r.shape[0]\n", - " kx, ky = np.meshgrid(np.arange(-nk / 2, nk / 2), np.arange(-nk / 2, nk / 2))\n", - " ksq = (kx**2 + ky**2) * dk**2\n", - " ksq_max = np.min(ksq[karr_r < small_kval * psf.flux])\n", - "\n", - " # We take our target PSF to be the (round) Gaussian that is even smaller at\n", - " # this ksq\n", - " # exp(-0.5 * ksq_max * sigma_sq) = smaller_kval\n", - " sigma_sq = -2.0 * np.log(smaller_kval) / ksq_max\n", - "\n", - " dilation = 1.0 + 2.0 * step\n", - " return (\n", - " galsim.Gaussian(sigma=np.sqrt(sigma_sq) * dilation).withFlux(flux),\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " )\n", - "\n", - "\n", - "def get_gauss_reconv_psf(obs, step=DEFAULT_STEP):\n", - " \"\"\"Get the Gaussian reconv PSF for an ngmix obs.\"\"\"\n", - " psf = get_galsim_object_from_ngmix_obs_nopix(obs.psf, kind=\"image\")\n", - " return get_gauss_reconv_psf_galsim(psf, step=step)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "71df4dd2-5863-40e0-b436-15890263e2a9", - "metadata": {}, - "outputs": [], - "source": [ - "def jax_metacal_op_shears(\n", - " dfmd_obs,\n", - " nxy_psf=53,\n", - " reconv_psf=None,\n", - " shears=None,\n", - " step=DEFAULT_STEP,\n", - " scale=0.2,\n", - "):\n", - " \"\"\"Run metacal on an dfmd observation.\"\"\"\n", - " if shears is None:\n", - " shears = DEFAULT_SHEARS\n", - "\n", - " dk = compute_stepk(pixel_scale=scale, image_size=nxy_psf)\n", - " if reconv_psf is None:\n", - " reconv_psf, kim, karr_r, ksq_max, sigma_sq, dilation = jax_get_gauss_reconv_psf(\n", - " dfmd_obs,\n", - " dk=dk,\n", - " nxy_psf=nxy_psf,\n", - " step=step,\n", - " )\n", - "\n", - " wcs = dfmd_obs.aft._local_wcs\n", - " image = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"image\")\n", - " # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl\n", - " # rotates back after deconv and shearing\n", - " noise = get_jax_galsim_object_from_dfmd_obs(dfmd_obs, kind=\"noise\", rot90=1)\n", - " psf = get_jax_galsim_object_from_dfmd_obs(dfmd_obs.psf, kind=\"image\")\n", - "\n", - " # psf=psf.withGSParams(\n", - " # minimum_fft_size=100 * 4,\n", - " # maximum_fft_size=100 * 4,\n", - " # )\n", - " psf_inv = jax_galsim.Deconvolve(\n", - " psf,\n", - " gsparams=jax_galsim.GSParams(minimum_fft_size=32 * 8, maximum_fft_size=32 * 8),\n", - " propagate_gsparams=False,\n", - " )\n", - "\n", - " mcal_res = {}\n", - " for shear in shears:\n", - " g1, g2 = get_shear_tuple(shear, step)\n", - "\n", - " mcal_image = _jax_metacal_op_g1g2_impl(\n", - " wcs=wcs,\n", - " image=image,\n", - " noise=noise,\n", - " psf_inv=psf_inv,\n", - " dims=dfmd_obs.image.shape,\n", - " reconv_psf=reconv_psf,\n", - " g1=g1,\n", - " g2=g2,\n", - " )\n", - "\n", - " mcal_res[shear] = _jax_render_psf_and_build_obs(\n", - " mcal_image,\n", - " dfmd_obs,\n", - " reconv_psf,\n", - " nxy_psf=nxy_psf,\n", - " weight_fac=0.5,\n", - " )\n", - " return (\n", - " mcal_res,\n", - " image,\n", - " noise,\n", - " psf,\n", - " psf_inv,\n", - " mcal_image,\n", - " mcal_res,\n", - " reconv_psf,\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "b8fcbd3c-39a3-496d-ac4f-f01ef3ec8cfd", - "metadata": {}, - "outputs": [], - "source": [ - "def metacal_op_shears(obs, reconv_psf=None, shears=None, step=DEFAULT_STEP):\n", - " \"\"\"Run metacal on an ngmix observation.\"\"\"\n", - " if shears is None:\n", - " shears = DEFAULT_SHEARS\n", - "\n", - " if reconv_psf is None:\n", - " reconv_psf, kim, karr_r, ksq_max, sigma_sq, dilation = get_gauss_reconv_psf(\n", - " obs, step=step\n", - " )\n", - "\n", - " wcs = obs.jacobian.get_galsim_wcs()\n", - " image = get_galsim_object_from_ngmix_obs(obs, kind=\"image\")\n", - " # we rotate by 90 degrees on the way in and then _metacal_op_g1g2_impl\n", - " # rotates back after deconv and shearing\n", - " noise = get_galsim_object_from_ngmix_obs(obs, kind=\"noise\", rot90=1)\n", - " psf = get_galsim_object_from_ngmix_obs(obs.psf, kind=\"image\")\n", - " psf_inv = galsim.Deconvolve(psf)\n", - "\n", - " mcal_res = {}\n", - " for shear in shears:\n", - " g1, g2 = get_shear_tuple(shear, step)\n", - " mcal_image = _metacal_op_g1g2_impl(\n", - " wcs=wcs,\n", - " image=image,\n", - " noise=noise,\n", - " psf_inv=psf_inv,\n", - " dims=obs.image.shape,\n", - " reconv_psf=reconv_psf,\n", - " g1=g1,\n", - " g2=g2,\n", - " )\n", - " mcal_res[shear] = _render_psf_and_build_obs(\n", - " mcal_image, obs, reconv_psf, weight_fac=0.5\n", - " )\n", - " return (\n", - " mcal_res,\n", - " image,\n", - " noise,\n", - " psf,\n", - " psf_inv,\n", - " mcal_image,\n", - " mcal_res,\n", - " reconv_psf,\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "8acc01b5-c002-483f-b553-47b20e5c9185", - "metadata": {}, - "outputs": [], - "source": [ - "def _run_single_sim_pair_jax_and_ngmix(seed, s2n):\n", - " nxy = 53\n", - " nxy_psf = 53\n", - " scale = 0.2\n", - " obs_plus, *_ = make_simple_sim(\n", - " seed=seed,\n", - " g1=0.02,\n", - " g2=0.0,\n", - " s2n=s2n,\n", - " dim=nxy,\n", - " dim_psf=nxy_psf,\n", - " scale=scale,\n", - " deep_noise_fac=1.0 / np.sqrt(10),\n", - " deep_psf_fac=1.0,\n", - " return_dfmd_obs=False,\n", - " )\n", - "\n", - " (\n", - " mcal_res_ngmix,\n", - " image_ngmix,\n", - " noise_ngmix,\n", - " psf_ngmix,\n", - " psf_inv_ngmix,\n", - " mcal_image_ngmix,\n", - " mcal_res_ngmix,\n", - " reconv_psf_ngmix,\n", - " kim_ngmix,\n", - " karr_r_ngmix,\n", - " ksq_max_ngmix,\n", - " sigma_sq_ngmix,\n", - " dilation_ngmix,\n", - " ) = metacal_op_shears(obs_plus)\n", - "\n", - " res_p_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix)\n", - " res_p_ngmix = measure_mcal_shear_quants(res_p_ngmix)\n", - " old_obs_plus = obs_plus.copy()\n", - " obs_plus = ngmix_obs_to_dfmd_obs(obs_plus)\n", - "\n", - " (\n", - " mcal_res,\n", - " image,\n", - " noise,\n", - " psf,\n", - " psf_inv,\n", - " mcal_image,\n", - " mcal_res,\n", - " reconv_psf,\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " ) = jax_metacal_op_shears(\n", - " obs_plus,\n", - " nxy_psf=nxy_psf,\n", - " scale=scale,\n", - " )\n", - " res_p = fit_gauss_mom_mcal_res(mcal_res)\n", - " res_p = measure_mcal_shear_quants(res_p)\n", - "\n", - " obs_minus, *_ = make_simple_sim(\n", - " seed=seed,\n", - " g1=-0.02,\n", - " g2=0.0,\n", - " s2n=s2n,\n", - " dim=nxy,\n", - " dim_psf=nxy_psf,\n", - " scale=scale,\n", - " deep_noise_fac=1.0 / np.sqrt(10),\n", - " deep_psf_fac=1.0,\n", - " return_dfmd_obs=False,\n", - " )\n", - "\n", - " (\n", - " mcal_res_ngmix,\n", - " image_ngmix,\n", - " noise_ngmix,\n", - " psf_ngmix,\n", - " psf_inv_ngmix,\n", - " mcal_image_ngmix,\n", - " mcal_res_ngmix,\n", - " reconv_psf_ngmix,\n", - " kim_ngmix,\n", - " karr_r_ngmix,\n", - " ksq_max_ngmix,\n", - " sigma_sq_ngmix,\n", - " dilation_ngmix,\n", - " ) = metacal_op_shears(obs_minus)\n", - " res_m_ngmix = fit_gauss_mom_mcal_res(mcal_res_ngmix)\n", - " res_m_ngmix = measure_mcal_shear_quants(res_m_ngmix)\n", - "\n", - " obs_minus = ngmix_obs_to_dfmd_obs(obs_minus)\n", - " (\n", - " mcal_res,\n", - " image,\n", - " noise,\n", - " psf,\n", - " psf_inv,\n", - " mcal_image,\n", - " mcal_res,\n", - " reconv_psf,\n", - " kim,\n", - " karr_r,\n", - " ksq_max,\n", - " sigma_sq,\n", - " dilation,\n", - " ) = jax_metacal_op_shears(\n", - " obs_minus,\n", - " nxy_psf=nxy_psf,\n", - " scale=scale,\n", - " )\n", - " res_m = fit_gauss_mom_mcal_res(mcal_res)\n", - " res_m = measure_mcal_shear_quants(res_m)\n", - "\n", - " return (\n", - " (res_p, res_m),\n", - " (res_p_ngmix, res_m_ngmix),\n", - " (mcal_res, image, noise, psf, psf_inv, mcal_image, mcal_res, reconv_psf),\n", - " (\n", - " mcal_res_ngmix,\n", - " image_ngmix,\n", - " noise_ngmix,\n", - " psf_ngmix,\n", - " psf_inv_ngmix,\n", - " mcal_image_ngmix,\n", - " mcal_res_ngmix,\n", - " reconv_psf_ngmix,\n", - " ),\n", - " (old_obs_plus, obs_plus),\n", - " (kim, karr_r, ksq_max, sigma_sq, dilation),\n", - " (kim_ngmix, karr_r_ngmix, ksq_max_ngmix, sigma_sq_ngmix, dilation_ngmix),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "d730cb6d-6537-409b-a036-d7c11cdc0f79", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " (res_p, res_m),\n", - " (res_p_ngmix, res_m_ngmix),\n", - " jax_intermediates,\n", - " numpy_intermediates,\n", - " obs,\n", - " jax_reconv,\n", - " numpy_reconv,\n", - ") = _run_single_sim_pair_jax_and_ngmix(10, 1e8)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "c26a0aea-dbbc-444e-ad14-414796b4543f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ngmix.Jacobian(row=26.0, col=26.0, dvdrow=0.2, dvdcol=0.0, dudrow=0.0, dudcol=0.2)" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs[0].psf.jacobian" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "cc6f4c9d-5839-404f-9802-26fe4f07cfbd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.AffineTransform(0.2, 0.0, 0.0, 0.2, origin=galsim.PositionD(x=27.0, y=27.0), world_origin=galsim.PositionD(x=0.0, y=0.0))" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obs[1].psf.aft" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "0601d546-7c0d-4b1d-a44e-ad8f9e7f8962", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(0.01308229, 0.00436202, 0.00872346, 0.00436275, -0.00436436, -1.1700455e-08, 1., 1., 1., 1., 1., 1.)],\n", - " dtype=[('wmom_tot_g1p', '" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compare reconv psf\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_reconv[1])\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax reconv\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(jax_reconv[1] - numpy_reconv[1])\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"psf diff\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "75ff8542-bb9a-4a9f-b70b-7332ac0ba2f1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# compare noise\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax psf\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array\n", - " - numpy_intermediates[3].drawImage(scale=0.2, nx=30, ny=30).array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"original psf\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "f1196f6c-eb4b-4879-a240-4be6aa169ab6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", - " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", - " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", - " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", - " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", - " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", - " ...,\n", - " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", - " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", - " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", - " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", - " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", - " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7222052077217914, _force_maxk=8.099418560036185)" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "jax_intermediates[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "0884bdf9-a3ac-4cb2-b2d7-054b25177c01", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", - " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", - " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", - " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", - " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", - " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", - " ...,\n", - " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", - " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", - " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", - " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", - " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", - " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6595270685975222, _force_maxk=8.222137023067036)" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_intermediates[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "5decc735-9624-40e0-857d-8d65d122bc84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " ...,\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7529228667374486, _force_maxk=12.51728322914683), gsparams=galsim.GSParams(256,256,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=False)" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "jax_intermediates[4]" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "ea71d3eb-4340-4ab7-b2aa-478dc9146209", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " ...,\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6815071326229607, _force_maxk=12.640001692177682), gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_intermediates[4]" - ] - }, - { - "cell_type": "markdown", - "id": "994cef65-6789-4514-b83e-7c58d029bfe1", - "metadata": {}, - "source": [ - "# psf_inv for Deconvolution" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "a1d0d952-f30e-42be-91a1-0b4a43e6562e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# compare PSF_INV\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax psf_inv\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", - " - numpy_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"diff psf_inv\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "eb777e6c-81b3-40c8-b0a2-3fa3d4e07ab0", - "metadata": {}, - "source": [ - "# Reconvolution PSF" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "6fea4e6a-0814-4f9b-b509-f6a4a7e8d5b5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# compare PSF_INV\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax reconv\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", - " - numpy_intermediates[7].drawImage(scale=0.2, nx=50, ny=50).array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"psf reconv diff\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e56f00d4-bbd1-4eb7-9b53-188af8d0b9b1", - "metadata": {}, - "source": [ - "# Compare PSF array" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "8c66f0c8-ffc1-45ac-b582-fe7079ff3ad4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compare PSF array\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(\n", - " get_jax_galsim_object_from_dfmd_obs(obs[1].psf, kind=\"image\").image.array\n", - ")\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax psf\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " get_jax_galsim_object_from_dfmd_obs(obs[1].psf, kind=\"image\").image.array\n", - " - get_galsim_object_from_ngmix_obs(obs[0].psf, kind=\"image\").image.array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"diff psf\")\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "1ad806ac-c473-4d57-9279-814dfb091ac3", - "metadata": {}, - "source": [ - "# galimage + noise (mcal_image)" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "a41e1a00-7079-4b06-a0eb-ea1c73ec4507", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# mcal_image : here a deconvolution + a convolution has been applied\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_intermediates[5])\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"mcal image\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(jax_intermediates[5] - numpy_intermediates[5])\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"diff mcal image\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "208dcb2e-7671-4fe0-8c7d-33b82cffd16d", - "metadata": {}, - "source": [ - "# Comparing noise" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "c63d5594-f079-4916-93a3-b2370992be8d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# compare noise\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_intermediates[2].image.array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"noise\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_intermediates[2].image.array - numpy_intermediates[2].image.array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"noise diff\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2b33ef84-ed57-47ca-aeb0-4f84b141c62c", - "metadata": {}, - "source": [ - "# Compare Deconvolution" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "c5286811-03b1-428f-bc54-4257027433ce", - "metadata": {}, - "outputs": [], - "source": [ - "jax_psf_deconvolved_im = jax_galsim.Convolve(\n", - " [jax_intermediates[1], jax_intermediates[4]],\n", - " gsparams=jax_galsim.GSParams(minimum_fft_size=53 * 8, maximum_fft_size=53 * 8),\n", - ")\n", - "numpy_psf_deconvolved_im = galsim.Convolve(\n", - " [numpy_intermediates[1], numpy_intermediates[4]]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "c451e13f-3364-40d7-9738-14579499fe52", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax deconvolved\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array\n", - " - numpy_psf_deconvolved_im.drawImage(scale=0.2, nx=30, ny=30).array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"original deconvolved\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "aaa6fbc7-c07e-4832-b51e-b7d462461ec1", - "metadata": {}, - "source": [ - "# Now Compare after reconv psf" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "7673627c-586f-499b-8629-effb03294fa5", - "metadata": {}, - "outputs": [], - "source": [ - "jax_image_ex2 = jax_galsim.Convolve(\n", - " [jax_psf_deconvolved_im, jax_intermediates[7]],\n", - " gsparams=jax_galsim.GSParams(minimum_fft_size=53 * 8, maximum_fft_size=53 * 8),\n", - ")\n", - "numpy_image_ex2 = galsim.Convolve([numpy_psf_deconvolved_im, numpy_intermediates[7]])" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "37523136-0e0c-4a05-bd04-e19f16dfb898", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.Convolution([galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", - " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", - " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", - " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", - " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", - " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", - " ...,\n", - " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", - " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", - " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", - " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", - " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", - " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7222052077217914, _force_maxk=8.099418560036185), galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " ...,\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.PixelScale(1.0)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.7529228667374486, _force_maxk=12.51728322914683), gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=False)], real_space=False, gsparams=galsim.GSParams(424,424,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "jax_psf_deconvolved_im" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "4c0458f5-55c8-4f81-a550-cce970372dcc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "galsim.Convolution([galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.40693077e-07, 3.75396894e-07, 4.12530881e-07, ...,\n", - " 4.36540006e-07, 3.95687437e-07, 3.57840207e-07],\n", - " [3.74174135e-07, 4.14708438e-07, 4.59189465e-07, ...,\n", - " 4.85879298e-07, 4.37742131e-07, 3.94686253e-07],\n", - " [4.10724994e-07, 4.58070645e-07, 5.09943675e-07, ...,\n", - " 5.39575865e-07, 4.85326098e-07, 4.36264770e-07],\n", - " ...,\n", - " [3.75209055e-07, 4.15854430e-07, 4.61532352e-07, ...,\n", - " 4.87706643e-07, 4.38426099e-07, 3.96964339e-07],\n", - " [3.41272482e-07, 3.76544705e-07, 4.15455588e-07, ...,\n", - " 4.38453696e-07, 3.97309748e-07, 3.60540383e-07],\n", - " [3.09538933e-07, 3.41027146e-07, 3.74926401e-07, ...,\n", - " 3.94865936e-07, 3.58036459e-07, 3.26984775e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9981424304289419, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6595270685975222, _force_maxk=8.222137023067036), galsim.Deconvolution(galsim.InterpolatedImage(galsim.Image(bounds=galsim.BoundsI(xmin=-26, xmax=26, ymin=-26, ymax=26), array=\n", - "array([[3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " ...,\n", - " [3.76746215e-07, 4.17004060e-07, 4.61860793e-07, ...,\n", - " 4.61860793e-07, 4.17004060e-07, 3.76746215e-07],\n", - " [3.42946777e-07, 3.78225479e-07, 4.17004060e-07, ...,\n", - " 4.17004060e-07, 3.78225479e-07, 3.42946777e-07],\n", - " [3.12316985e-07, 3.42946777e-07, 3.76746215e-07, ...,\n", - " 3.76746215e-07, 3.42946777e-07, 3.12316985e-07]]), wcs=galsim.JacobianWCS(0.2, 0.0, 0.0, 0.2)), galsim.Lanczos(15, True, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), galsim.Quintic(gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05)), pad_factor=4.000000, flux=0.9982660539072867, offset=galsim.PositionD(x=0.0, y=0.0), use_true_center=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), _force_stepk=0.6815071326229607, _force_maxk=12.640001692177682), gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)], real_space=False, gsparams=galsim.GSParams(128,8192,0.005,5.0,0.001,1e-05,1e-05,1,0.0001,1e-06,1e-06,1e-08,1e-05), propagate_gsparams=True)" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_psf_deconvolved_im" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "0a847a9b-82a3-4c03-8091-897896b17d3d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 1 row, 2 columns\n", - "\n", - "# First subplot: g[1]\n", - "im0 = axes[0].imshow(jax_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array)\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_title(\"jax deconvolved\")\n", - "\n", - "# Second subplot: g[1] - f[1]\n", - "im1 = axes[1].imshow(\n", - " jax_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array\n", - " - numpy_image_ex2.drawImage(scale=0.2, nx=30, ny=30).array\n", - ")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_title(\"original deconvolved\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d1bd319-47ca-4f84-98e6-f3dc16cd0b19", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d39c7dc3-5c53-42d2-b905-150e78b7674c", - "metadata": {}, - "outputs": [], - "source": [ - "def test_metacal_jax_vs_ngmix():\n", - " nsims = 5\n", - "\n", - " rng = np.random.RandomState(seed=34132)\n", - " seeds = rng.randint(size=nsims, low=1, high=2**29)\n", - " res_p = []\n", - " res_m = []\n", - " res_p_ngmix = []\n", - " res_m_ngmix = []\n", - " for seed in seeds:\n", - " res, res_ngmix, _, _, _, _, _ = _run_single_sim_pair_jax_and_ngmix(seed, 1e8)\n", - " if res is not None:\n", - " res_p.append(res[0])\n", - " res_m.append(res[1])\n", - "\n", - " res_p_ngmix.append(res_ngmix[0])\n", - " res_m_ngmix.append(res_ngmix[1])\n", - "\n", - " assert np.allclose(\n", - " res[0].tolist(),\n", - " res_ngmix[0].tolist(),\n", - " atol=1e-6,\n", - " rtol=1e-6,\n", - " equal_nan=True,\n", - " )\n", - " assert np.allclose(\n", - " res[1].tolist(),\n", - " res_ngmix[1].tolist(),\n", - " atol=1e-6,\n", - " rtol=1e-6,\n", - " equal_nan=True,\n", - " )\n", - "\n", - " m, merr, c1, c1err, c2, c2err = estimate_m_and_c(\n", - " np.concatenate(res_p),\n", - " np.concatenate(res_m),\n", - " 0.02,\n", - " jackknife=len(res_p),\n", - " )\n", - "\n", - " m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng = estimate_m_and_c(\n", - " np.concatenate(res_p_ngmix),\n", - " np.concatenate(res_m_ngmix),\n", - " 0.02,\n", - " jackknife=len(res_p_ngmix),\n", - " )\n", - "\n", - " print(\"JAX results:\")\n", - " print_m_c(m, merr, c1, c1err, c2, c2err)\n", - " print(\"ngmix results:\")\n", - " print_m_c(m_ng, merr_ng, c1_ng, c1err_ng, c2_ng, c2err_ng)\n", - " assert_m_c_ok(m, merr, c1, c1err, c2, c2err)\n", - "\n", - " assert np.allclose(m, m_ng, atol=1e-4)\n", - " assert np.allclose(merr, merr_ng, atol=1e-6)\n", - " assert np.allclose(c1err, c1err_ng, atol=1e-6)\n", - " assert np.allclose(c1, c1_ng, atol=1e-6)\n", - " assert np.allclose(c2err, c2err_ng, atol=1e-6)\n", - " assert np.allclose(c2, c2_ng, atol=1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51e4e2e1-fd29-425b-bae0-c8906ba649bb", - "metadata": {}, - "outputs": [], - "source": [ - "test_metacal_jax_vs_ngmix()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4b80cf4-dde7-4c62-bab9-f7fc8a5d09b8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8efa40d-6bf6-4507-a47c-e36becf520b6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4a5515e-8781-448f-af33-86626f55d604", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jax", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.20" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 2bcda3076ee79c933cb51e7668bce474e9592ba4 Mon Sep 17 00:00:00 2001 From: Biswajit Biswas Date: Mon, 4 Aug 2025 14:21:36 -0500 Subject: [PATCH 58/59] jax version pin --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 9a74dd2..f362fba 100644 --- a/environment.yml +++ b/environment.yml @@ -16,6 +16,7 @@ dependencies: - ngmix - numba - numpy + - jax<0.7.0 - pip: - git+https://github.com/GalSim-developers/JAX-GalSim.git@main From 0e6aa4b10deae8461f347c6d45049bf99039258f Mon Sep 17 00:00:00 2001 From: Biswajit Biswas <44917825+b-biswas@users.noreply.github.com> Date: Thu, 20 Nov 2025 11:18:42 -0600 Subject: [PATCH 59/59] remove utils jit Co-authored-by: Matthew R. Becker --- deep_field_metadetect/jaxify/jax_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deep_field_metadetect/jaxify/jax_utils.py b/deep_field_metadetect/jaxify/jax_utils.py index a838572..a75d7ae 100644 --- a/deep_field_metadetect/jaxify/jax_utils.py +++ b/deep_field_metadetect/jaxify/jax_utils.py @@ -1,7 +1,6 @@ import jax.numpy as jnp -# @partial(jax.jit, static_argnames=["pixel_scale", "image_size"]) def compute_stepk(pixel_scale, image_size): """Compute psf fourier scale based on pixel scale and psf image dimension The size if obtained from from galsim.GSObject.getGoodImageSize