# This file is part of pipe_tasks.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
__all__ = [
"SystematicsMultibandPipeConfig",
"SystematicsMultibandPipe",
"SystematicsMultibandPipeConnections",
]
import logging
import os
from typing import Any
import fitsio
import lsst.afw.image as afwImage
import lsst.pipe.base.connectionTypes as cT
import numpy as np
from lsst.geom import Box2I, Extent2I, Point2D, Point2I
from lsst.meas.base import SkyMapIdGeneratorConfig
from lsst.pex.config import Field, FieldValidationError
from lsst.pipe.base import (
PipelineTask,
PipelineTaskConfig,
PipelineTaskConnections,
Struct,
)
from lsst.skymap import BaseSkyMap
from lsst.utils.logging import LsstLogAdapter
from . import resize_array, subpixel_shift
[docs]
class SystematicsMultibandPipeConnections(
PipelineTaskConnections,
dimensions=("tract", "patch", "band", "skymap"),
defaultTemplates={"coaddName": "deep"},
):
[docs]
skyMap = cT.Input(
doc="SkyMap to use in processing",
name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
storageClass="SkyMap",
dimensions=("skymap",),
)
[docs]
exposure = cT.Input(
doc="Input coadd image",
name="{coaddName}Coadd_calexp",
storageClass="ExposureF",
dimensions=("tract", "patch", "band", "skymap"),
)
[docs]
catalog = cT.Input(
doc=("original measurement catalog"),
name="{coaddName}Coadd_meas",
storageClass="SourceCatalog",
dimensions=("tract", "patch", "band", "skymap"),
)
[docs]
outputNoiseCorr = cT.Output(
doc="noise correlation function",
name="{coaddName}_coadd_systematics_noisecorr",
dimensions=("tract", "patch", "band", "skymap"),
storageClass="ImageF",
)
[docs]
outputPsfCentered = cT.Output(
doc="noise correlation function",
name="{coaddName}_coadd_systematics_psfcentered",
dimensions=("tract", "patch", "band", "skymap"),
storageClass="ImageF",
)
[docs]
outputStarCentered = cT.Output(
doc="noise correlation function",
name="{coaddName}_coadd_systematics_starcentered",
dimensions=("tract", "patch", "band", "skymap"),
storageClass="ImageF",
)
def __init__(self, *, config=None):
super().__init__(config=config)
[docs]
class SystematicsMultibandPipeConfig(
PipelineTaskConfig,
pipelineConnections=SystematicsMultibandPipeConnections,
):
[docs]
psfCache = Field[int](
doc="Size of psfCache",
default=100,
)
[docs]
npix = Field[int](
doc="number of pixels for the length of stamp",
default=49,
)
[docs]
star_snr_min = Field[float](
doc="minimum (aperture) snr threshold of stars",
default=100.0,
)
[docs]
idGenerator = SkyMapIdGeneratorConfig.make_field()
[docs]
def validate(self):
super().validate()
if self.npix % 2 == 0:
raise FieldValidationError(
self.__class__.npix, self, "npix should be odd number"
)
[docs]
class SystematicsMultibandPipe(PipelineTask):
[docs]
_DefaultName = "FpfsTask"
[docs]
ConfigClass = SystematicsMultibandPipeConfig
def __init__(
self,
*,
config: SystematicsMultibandPipeConfig | None = None,
log: logging.Logger | LsstLogAdapter | None = None,
initInputs: dict[str, Any] | None = None,
**kwargs: Any,
):
super().__init__(
config=config, log=log, initInputs=initInputs, **kwargs
)
assert isinstance(self.config, SystematicsMultibandPipeConfig)
[docs]
self.pt_data = fitsio.read(
os.path.join(
"/work/xiangchong.li/work/hsc_s23b_sim/catalogs/",
"tracts_fdfc_v1_trim2_sim.fits",
)
)
return
[docs]
def runQuantum(self, butlerQC, inputRefs, outputRefs):
assert isinstance(self.config, SystematicsMultibandPipeConfig)
# Retrieve the filename of the input exposure
# assert butlerQC.quantum.dataId is not None
# tract = butlerQC.quantum.dataId["tract"]
# patch = butlerQC.quantum.dataId["patch"]
# patch_list = self.pt_data[self.pt_data["tract"] == tract]["patch"]
# patch_y = int(patch) // 9
# patch_x = int(patch) % 9
# patch_db = patch_x * 100 + patch_y
# if patch_db not in patch_list:
# return
inputs = butlerQC.get(inputRefs)
inputs["exposure"].getPsf().setCacheCapacity(self.config.psfCache)
idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
seed = idGenerator.catalog_id
outputs = self.run(seed=seed, **inputs)
butlerQC.put(outputs, outputRefs)
return
[docs]
def run(self, *, exposure, catalog, seed, **kwargs):
noise_corr = self.get_noise_corr(exposure)
psf_image, star_image = self.get_psf_systematics(
exposure,
catalog,
seed,
)
del exposure, catalog
return Struct(
outputNoiseCorr=noise_corr,
outputPsfCentered=psf_image,
outputStarCentered=star_image,
)
[docs]
def get_noise_corr(self, exposure):
assert isinstance(self.config, SystematicsMultibandPipeConfig)
variance_array = exposure.getMaskedImage().variance.array[
1000:3000, 1000:3000
]
window_array = (exposure.mask.array == 0).astype(np.float32)[
1000:3000, 1000:3000
]
noise_array = np.asarray(
exposure.getMaskedImage().image.array,
dtype=np.float32,
)[1000:3000, 1000:3000]
window_array = (
window_array
* (noise_array**2.0 < variance_array * 9)
* (variance_array < 5.0)
* (~np.isnan(variance_array))
)
noise_array[~window_array.astype(bool)] = 0.0
noise_variance = np.average(variance_array[window_array.astype(bool)])
if noise_variance < 1e-20:
raise ValueError(
"the estimated image noise variance should be positive."
)
pad_width = ((10, 10), (10, 10)) # ((top, bottom), (left, right))
window_array = np.pad(
window_array,
pad_width=pad_width,
mode="constant",
constant_values=0.0,
)
noise_array = np.pad(
noise_array,
pad_width=pad_width,
mode="constant",
constant_values=0.0,
)
ny, nx = window_array.shape
npixl = int(self.config.npix // 2)
npixr = int(self.config.npix // 2 + 1)
noise_corr = np.fft.fftshift(
np.fft.ifft2(np.abs(np.fft.fft2(noise_array)) ** 2.0)
).real[
ny // 2 - npixl : ny // 2 + npixr, nx // 2 - npixl : nx // 2 + npixr
]
window_corr = np.fft.fftshift(
np.fft.ifft2(np.abs(np.fft.fft2(window_array)) ** 2.0)
).real[
ny // 2 - npixl : ny // 2 + npixr, nx // 2 - npixl : nx // 2 + npixr
]
noise_corr = noise_corr / window_corr
del window_array, noise_array, window_corr
noise_image = afwImage.ImageF(self.config.npix, self.config.npix)
noise_image.array[:, :] = noise_corr
return noise_image
[docs]
def get_psf_systematics(self, exposure, catalog, seed):
assert isinstance(self.config, SystematicsMultibandPipeConfig)
npixl = int(self.config.npix // 2)
npixr = int(self.config.npix // 2 + 1)
catalog = catalog.asAstropy().as_array()
msk = catalog["calib_psf_reserved"] & catalog["detect_isPrimary"]
catalog = catalog[msk]
snr = (
catalog["base_CircularApertureFlux_3_0_instFlux"]
/ catalog["base_CircularApertureFlux_3_0_instFluxErr"]
)
bbox = exposure.getBBox()
xmin_exp, ymin_exp = bbox.getMinX(), bbox.getMinY()
xmax_exp, ymax_exp = bbox.getMaxX(), bbox.getMaxY()
msk2 = (
(catalog["base_SdssShape_x"] > xmin_exp + npixl)
& (catalog["base_SdssShape_y"] > ymin_exp + npixl)
& (catalog["base_SdssShape_x"] < xmax_exp - npixr)
& (catalog["base_SdssShape_y"] < ymax_exp - npixr)
& (snr > self.config.star_snr_min)
)
catalog = catalog[msk2]
nstars = len(catalog)
if nstars >= 1:
np.random.seed(seed)
ind = np.random.randint(0, nstars)
src = catalog[ind]
# Collect the PSF image
exposure.getPsf().setCacheCapacity(self.config.psfCache)
lsst_psf = exposure.getPsf()
psf_array = lsst_psf.computeImage(
Point2D(
int(src["base_SdssShape_x"]),
int(src["base_SdssShape_y"]),
)
).getArray()
psf_array = resize_array(
psf_array,
(self.config.npix, self.config.npix),
)
psf_image = afwImage.ImageF(self.config.npix, self.config.npix)
psf_image.array[:, :] = psf_array
bbox = Box2I(
Point2I(
int(src["base_SdssShape_x"]) - npixl,
int(src["base_SdssShape_y"]) - npixl,
),
Extent2I(self.config.npix, self.config.npix),
)
# Collect the star image
# Extract the sub-image using the BBox
star_image = exposure.Factory(exposure, bbox).getImage()
# Get the image component and convert to a NumPy array
star_array = star_image.getArray()
offset_x = src["base_SdssShape_x"] - int(src["base_SdssShape_x"])
offset_y = src["base_SdssShape_y"] - int(src["base_SdssShape_y"])
star_array = subpixel_shift(star_array, -offset_x, -offset_y)
star_image.array[:, :] = star_array
else:
psf_image = None
star_image = None
return psf_image, star_image