Coverage for python/lsst/pipe/tasks/healSparseMapping.py: 18%
380 statements
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« prev ^ index » next coverage.py v6.5.0, created at 2022-12-01 21:10 +0000
1#
2# LSST Data Management System
3# Copyright 2008-2021 AURA/LSST.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <http://www.lsstcorp.org/LegalNotices/>.
21#
22from collections import defaultdict
23import warnings
24import numbers
25import numpy as np
26import healpy as hp
27import healsparse as hsp
29import lsst.pex.config as pexConfig
30import lsst.pipe.base as pipeBase
31import lsst.geom
32import lsst.afw.geom as afwGeom
33from lsst.daf.butler import Formatter
34from lsst.skymap import BaseSkyMap
35from .healSparseMappingProperties import (BasePropertyMap, BasePropertyMapConfig,
36 PropertyMapMap, compute_approx_psf_size_and_shape)
39__all__ = ["HealSparseInputMapTask", "HealSparseInputMapConfig",
40 "HealSparseMapFormatter", "HealSparsePropertyMapConnections",
41 "HealSparsePropertyMapConfig", "HealSparsePropertyMapTask",
42 "ConsolidateHealSparsePropertyMapConnections",
43 "ConsolidateHealSparsePropertyMapConfig",
44 "ConsolidateHealSparsePropertyMapTask"]
47class HealSparseMapFormatter(Formatter):
48 """Interface for reading and writing healsparse.HealSparseMap files."""
49 unsupportedParameters = frozenset()
50 supportedExtensions = frozenset({".hsp", ".fit", ".fits"})
51 extension = '.hsp'
53 def read(self, component=None):
54 # Docstring inherited from Formatter.read.
55 path = self.fileDescriptor.location.path
57 if component == 'coverage':
58 try:
59 data = hsp.HealSparseCoverage.read(path)
60 except (OSError, RuntimeError):
61 raise ValueError(f"Unable to read healsparse map with URI {self.fileDescriptor.location.uri}")
63 return data
65 if self.fileDescriptor.parameters is None:
66 pixels = None
67 degrade_nside = None
68 else:
69 pixels = self.fileDescriptor.parameters.get('pixels', None)
70 degrade_nside = self.fileDescriptor.parameters.get('degrade_nside', None)
71 try:
72 data = hsp.HealSparseMap.read(path, pixels=pixels, degrade_nside=degrade_nside)
73 except (OSError, RuntimeError):
74 raise ValueError(f"Unable to read healsparse map with URI {self.fileDescriptor.location.uri}")
76 return data
78 def write(self, inMemoryDataset):
79 # Docstring inherited from Formatter.write.
80 # Update the location with the formatter-preferred file extension
81 self.fileDescriptor.location.updateExtension(self.extension)
82 inMemoryDataset.write(self.fileDescriptor.location.path, clobber=True)
85def _is_power_of_two(value):
86 """Check that value is a power of two.
88 Parameters
89 ----------
90 value : `int`
91 Value to check.
93 Returns
94 -------
95 is_power_of_two : `bool`
96 True if value is a power of two; False otherwise, or
97 if value is not an integer.
98 """
99 if not isinstance(value, numbers.Integral): 99 ↛ 100line 99 didn't jump to line 100, because the condition on line 99 was never true
100 return False
102 # See https://stackoverflow.com/questions/57025836
103 # Every power of 2 has exactly 1 bit set to 1; subtracting
104 # 1 therefore flips every preceding bit. If you and that
105 # together with the original value it must be 0.
106 return (value & (value - 1) == 0) and value != 0
109class HealSparseInputMapConfig(pexConfig.Config):
110 """Configuration parameters for HealSparseInputMapTask"""
111 nside = pexConfig.Field(
112 doc="Mapping healpix nside. Must be power of 2.",
113 dtype=int,
114 default=32768,
115 check=_is_power_of_two,
116 )
117 nside_coverage = pexConfig.Field(
118 doc="HealSparse coverage map nside. Must be power of 2.",
119 dtype=int,
120 default=256,
121 check=_is_power_of_two,
122 )
123 bad_mask_min_coverage = pexConfig.Field(
124 doc=("Minimum area fraction of a map healpixel pixel that must be "
125 "covered by bad pixels to be removed from the input map. "
126 "This is approximate."),
127 dtype=float,
128 default=0.5,
129 )
132class HealSparseInputMapTask(pipeBase.Task):
133 """Task for making a HealSparse input map."""
135 ConfigClass = HealSparseInputMapConfig
136 _DefaultName = "healSparseInputMap"
138 def __init__(self, **kwargs):
139 pipeBase.Task.__init__(self, **kwargs)
141 self.ccd_input_map = None
143 def build_ccd_input_map(self, bbox, wcs, ccds):
144 """Build a map from ccd valid polygons or bounding boxes.
146 Parameters
147 ----------
148 bbox : `lsst.geom.Box2I`
149 Bounding box for region to build input map.
150 wcs : `lsst.afw.geom.SkyWcs`
151 WCS object for region to build input map.
152 ccds : `lsst.afw.table.ExposureCatalog`
153 Exposure catalog with ccd data from coadd inputs.
154 """
155 with warnings.catch_warnings():
156 # Healsparse will emit a warning if nside coverage is greater than
157 # 128. In the case of generating patch input maps, and not global
158 # maps, high nside coverage works fine, so we can suppress this
159 # warning.
160 warnings.simplefilter("ignore")
161 self.ccd_input_map = hsp.HealSparseMap.make_empty(nside_coverage=self.config.nside_coverage,
162 nside_sparse=self.config.nside,
163 dtype=hsp.WIDE_MASK,
164 wide_mask_maxbits=len(ccds))
165 self._wcs = wcs
166 self._bbox = bbox
167 self._ccds = ccds
169 pixel_scale = wcs.getPixelScale().asArcseconds()
170 hpix_area_arcsec2 = hp.nside2pixarea(self.config.nside, degrees=True)*(3600.**2.)
171 self._min_bad = self.config.bad_mask_min_coverage*hpix_area_arcsec2/(pixel_scale**2.)
173 metadata = {}
174 self._bits_per_visit_ccd = {}
175 self._bits_per_visit = defaultdict(list)
176 for bit, ccd_row in enumerate(ccds):
177 metadata[f"B{bit:04d}CCD"] = ccd_row["ccd"]
178 metadata[f"B{bit:04d}VIS"] = ccd_row["visit"]
179 metadata[f"B{bit:04d}WT"] = ccd_row["weight"]
181 self._bits_per_visit_ccd[(ccd_row["visit"], ccd_row["ccd"])] = bit
182 self._bits_per_visit[ccd_row["visit"]].append(bit)
184 ccd_poly = ccd_row.getValidPolygon()
185 if ccd_poly is None:
186 ccd_poly = afwGeom.Polygon(lsst.geom.Box2D(ccd_row.getBBox()))
187 # Detectors need to be rendered with their own wcs.
188 ccd_poly_radec = self._pixels_to_radec(ccd_row.getWcs(), ccd_poly.convexHull().getVertices())
190 # Create a ccd healsparse polygon
191 poly = hsp.Polygon(ra=ccd_poly_radec[: -1, 0],
192 dec=ccd_poly_radec[: -1, 1],
193 value=[bit])
194 self.ccd_input_map.set_bits_pix(poly.get_pixels(nside=self.ccd_input_map.nside_sparse),
195 [bit])
197 # Cut down to the overall bounding box with associated wcs.
198 bbox_afw_poly = afwGeom.Polygon(lsst.geom.Box2D(bbox))
199 bbox_poly_radec = self._pixels_to_radec(self._wcs,
200 bbox_afw_poly.convexHull().getVertices())
201 bbox_poly = hsp.Polygon(ra=bbox_poly_radec[: -1, 0], dec=bbox_poly_radec[: -1, 1],
202 value=np.arange(self.ccd_input_map.wide_mask_maxbits))
203 bbox_poly_map = bbox_poly.get_map_like(self.ccd_input_map)
204 self.ccd_input_map = hsp.and_intersection([self.ccd_input_map, bbox_poly_map])
205 self.ccd_input_map.metadata = metadata
207 # Create a temporary map to hold the count of bad pixels in each healpix pixel
208 self._ccd_input_pixels = self.ccd_input_map.valid_pixels
210 dtype = [(f"v{visit}", "i4") for visit in self._bits_per_visit.keys()]
212 with warnings.catch_warnings():
213 # Healsparse will emit a warning if nside coverage is greater than
214 # 128. In the case of generating patch input maps, and not global
215 # maps, high nside coverage works fine, so we can suppress this
216 # warning.
217 warnings.simplefilter("ignore")
218 self._ccd_input_bad_count_map = hsp.HealSparseMap.make_empty(
219 nside_coverage=self.config.nside_coverage,
220 nside_sparse=self.config.nside,
221 dtype=dtype,
222 primary=dtype[0][0])
224 # Don't set input bad map if there are no ccds which overlap the bbox.
225 if len(self._ccd_input_pixels) > 0:
226 self._ccd_input_bad_count_map[self._ccd_input_pixels] = np.zeros(1, dtype=dtype)
228 def mask_warp_bbox(self, bbox, visit, mask, bit_mask_value):
229 """Mask a subregion from a visit.
230 This must be run after build_ccd_input_map initializes
231 the overall map.
233 Parameters
234 ----------
235 bbox : `lsst.geom.Box2I`
236 Bounding box from region to mask.
237 visit : `int`
238 Visit number corresponding to warp with mask.
239 mask : `lsst.afw.image.MaskX`
240 Mask plane from warp exposure.
241 bit_mask_value : `int`
242 Bit mask to check for bad pixels.
244 Raises
245 ------
246 RuntimeError : Raised if build_ccd_input_map was not run first.
247 """
248 if self.ccd_input_map is None:
249 raise RuntimeError("Must run build_ccd_input_map before mask_warp_bbox")
251 # Find the bad pixels and convert to healpix
252 bad_pixels = np.where(mask.array & bit_mask_value)
253 if len(bad_pixels[0]) == 0:
254 # No bad pixels
255 return
257 # Bad pixels come from warps which use the overall wcs.
258 bad_ra, bad_dec = self._wcs.pixelToSkyArray(bad_pixels[1].astype(np.float64),
259 bad_pixels[0].astype(np.float64),
260 degrees=True)
261 bad_hpix = hp.ang2pix(self.config.nside, bad_ra, bad_dec,
262 lonlat=True, nest=True)
264 # Count the number of bad image pixels in each healpix pixel
265 min_bad_hpix = bad_hpix.min()
266 bad_hpix_count = np.zeros(bad_hpix.max() - min_bad_hpix + 1, dtype=np.int32)
267 np.add.at(bad_hpix_count, bad_hpix - min_bad_hpix, 1)
269 # Add these to the accumulator map.
270 # We need to make sure that the "primary" array has valid values for
271 # this pixel to be registered in the accumulator map.
272 pix_to_add, = np.where(bad_hpix_count > 0)
273 count_map_arr = self._ccd_input_bad_count_map[min_bad_hpix + pix_to_add]
274 primary = self._ccd_input_bad_count_map.primary
275 count_map_arr[primary] = np.clip(count_map_arr[primary], 0, None)
277 count_map_arr[f"v{visit}"] = np.clip(count_map_arr[f"v{visit}"], 0, None)
278 count_map_arr[f"v{visit}"] += bad_hpix_count[pix_to_add]
280 self._ccd_input_bad_count_map[min_bad_hpix + pix_to_add] = count_map_arr
282 def finalize_ccd_input_map_mask(self):
283 """Use accumulated mask information to finalize the masking of
284 ccd_input_map.
286 Raises
287 ------
288 RuntimeError : Raised if build_ccd_input_map was not run first.
289 """
290 if self.ccd_input_map is None:
291 raise RuntimeError("Must run build_ccd_input_map before finalize_ccd_input_map_mask.")
293 count_map_arr = self._ccd_input_bad_count_map[self._ccd_input_pixels]
294 for visit in self._bits_per_visit:
295 to_mask, = np.where(count_map_arr[f"v{visit}"] > self._min_bad)
296 if to_mask.size == 0:
297 continue
298 self.ccd_input_map.clear_bits_pix(self._ccd_input_pixels[to_mask],
299 self._bits_per_visit[visit])
301 # Clear memory
302 self._ccd_input_bad_count_map = None
304 def _pixels_to_radec(self, wcs, pixels):
305 """Convert pixels to ra/dec positions using a wcs.
307 Parameters
308 ----------
309 wcs : `lsst.afw.geom.SkyWcs`
310 WCS object.
311 pixels : `list` [`lsst.geom.Point2D`]
312 List of pixels to convert.
314 Returns
315 -------
316 radec : `numpy.ndarray`
317 Nx2 array of ra/dec positions associated with pixels.
318 """
319 sph_pts = wcs.pixelToSky(pixels)
320 return np.array([(sph.getRa().asDegrees(), sph.getDec().asDegrees())
321 for sph in sph_pts])
324class HealSparsePropertyMapConnections(pipeBase.PipelineTaskConnections,
325 dimensions=("tract", "band", "skymap",),
326 defaultTemplates={"coaddName": "deep"}):
327 input_maps = pipeBase.connectionTypes.Input(
328 doc="Healsparse bit-wise coadd input maps",
329 name="{coaddName}Coadd_inputMap",
330 storageClass="HealSparseMap",
331 dimensions=("tract", "patch", "skymap", "band"),
332 multiple=True,
333 deferLoad=True,
334 )
335 coadd_exposures = pipeBase.connectionTypes.Input(
336 doc="Coadded exposures associated with input_maps",
337 name="{coaddName}Coadd",
338 storageClass="ExposureF",
339 dimensions=("tract", "patch", "skymap", "band"),
340 multiple=True,
341 deferLoad=True,
342 )
343 visit_summaries = pipeBase.connectionTypes.Input(
344 doc="Visit summary tables with aggregated statistics",
345 name="visitSummary",
346 storageClass="ExposureCatalog",
347 dimensions=("instrument", "visit"),
348 multiple=True,
349 deferLoad=True,
350 )
351 sky_map = pipeBase.connectionTypes.Input(
352 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
353 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
354 storageClass="SkyMap",
355 dimensions=("skymap",),
356 )
358 # Create output connections for all possible maps defined in the
359 # registry. The vars() trick used here allows us to set class attributes
360 # programatically. Taken from
361 # https://stackoverflow.com/questions/2519807/
362 # setting-a-class-attribute-with-a-given-name-in-python-while-defining-the-class
363 for name in BasePropertyMap.registry:
364 vars()[f"{name}_map_min"] = pipeBase.connectionTypes.Output(
365 doc=f"Minimum-value map of {name}",
366 name=f"{{coaddName}}Coadd_{name}_map_min",
367 storageClass="HealSparseMap",
368 dimensions=("tract", "skymap", "band"),
369 )
370 vars()[f"{name}_map_max"] = pipeBase.connectionTypes.Output(
371 doc=f"Maximum-value map of {name}",
372 name=f"{{coaddName}}Coadd_{name}_map_max",
373 storageClass="HealSparseMap",
374 dimensions=("tract", "skymap", "band"),
375 )
376 vars()[f"{name}_map_mean"] = pipeBase.connectionTypes.Output(
377 doc=f"Mean-value map of {name}",
378 name=f"{{coaddName}}Coadd_{name}_map_mean",
379 storageClass="HealSparseMap",
380 dimensions=("tract", "skymap", "band"),
381 )
382 vars()[f"{name}_map_weighted_mean"] = pipeBase.connectionTypes.Output(
383 doc=f"Weighted mean-value map of {name}",
384 name=f"{{coaddName}}Coadd_{name}_map_weighted_mean",
385 storageClass="HealSparseMap",
386 dimensions=("tract", "skymap", "band"),
387 )
388 vars()[f"{name}_map_sum"] = pipeBase.connectionTypes.Output(
389 doc=f"Sum-value map of {name}",
390 name=f"{{coaddName}}Coadd_{name}_map_sum",
391 storageClass="HealSparseMap",
392 dimensions=("tract", "skymap", "band"),
393 )
395 def __init__(self, *, config=None):
396 super().__init__(config=config)
398 # Not all possible maps in the registry will be configured to run.
399 # Here we remove the unused connections.
400 for name in BasePropertyMap.registry:
401 if name not in config.property_maps:
402 prop_config = BasePropertyMapConfig()
403 prop_config.do_min = False
404 prop_config.do_max = False
405 prop_config.do_mean = False
406 prop_config.do_weighted_mean = False
407 prop_config.do_sum = False
408 else:
409 prop_config = config.property_maps[name]
411 if not prop_config.do_min:
412 self.outputs.remove(f"{name}_map_min")
413 if not prop_config.do_max:
414 self.outputs.remove(f"{name}_map_max")
415 if not prop_config.do_mean:
416 self.outputs.remove(f"{name}_map_mean")
417 if not prop_config.do_weighted_mean:
418 self.outputs.remove(f"{name}_map_weighted_mean")
419 if not prop_config.do_sum:
420 self.outputs.remove(f"{name}_map_sum")
423class HealSparsePropertyMapConfig(pipeBase.PipelineTaskConfig,
424 pipelineConnections=HealSparsePropertyMapConnections):
425 """Configuration parameters for HealSparsePropertyMapTask"""
426 property_maps = BasePropertyMap.registry.makeField(
427 multi=True,
428 default=["exposure_time",
429 "psf_size",
430 "psf_e1",
431 "psf_e2",
432 "psf_maglim",
433 "sky_noise",
434 "sky_background",
435 "dcr_dra",
436 "dcr_ddec",
437 "dcr_e1",
438 "dcr_e2"],
439 doc="Property map computation objects",
440 )
442 def setDefaults(self):
443 self.property_maps["exposure_time"].do_sum = True
444 self.property_maps["psf_size"].do_weighted_mean = True
445 self.property_maps["psf_e1"].do_weighted_mean = True
446 self.property_maps["psf_e2"].do_weighted_mean = True
447 self.property_maps["psf_maglim"].do_weighted_mean = True
448 self.property_maps["sky_noise"].do_weighted_mean = True
449 self.property_maps["sky_background"].do_weighted_mean = True
450 self.property_maps["dcr_dra"].do_weighted_mean = True
451 self.property_maps["dcr_ddec"].do_weighted_mean = True
452 self.property_maps["dcr_e1"].do_weighted_mean = True
453 self.property_maps["dcr_e2"].do_weighted_mean = True
456class HealSparsePropertyMapTask(pipeBase.PipelineTask):
457 """Task to compute Healsparse property maps.
459 This task will compute individual property maps (per tract, per
460 map type, per band). These maps cover the full coadd tract, and
461 are not truncated to the inner tract region.
462 """
463 ConfigClass = HealSparsePropertyMapConfig
464 _DefaultName = "healSparsePropertyMapTask"
466 def __init__(self, **kwargs):
467 super().__init__(**kwargs)
468 self.property_maps = PropertyMapMap()
469 for name, config, PropertyMapClass in self.config.property_maps.apply():
470 self.property_maps[name] = PropertyMapClass(config, name)
472 @pipeBase.timeMethod
473 def runQuantum(self, butlerQC, inputRefs, outputRefs):
474 inputs = butlerQC.get(inputRefs)
476 sky_map = inputs.pop("sky_map")
478 tract = butlerQC.quantum.dataId["tract"]
479 band = butlerQC.quantum.dataId["band"]
481 input_map_dict = {ref.dataId["patch"]: ref for ref in inputs["input_maps"]}
482 coadd_dict = {ref.dataId["patch"]: ref for ref in inputs["coadd_exposures"]}
484 visit_summary_dict = {ref.dataId["visit"]: ref.get()
485 for ref in inputs["visit_summaries"]}
487 self.run(sky_map, tract, band, coadd_dict, input_map_dict, visit_summary_dict)
489 # Write the outputs
490 for name, property_map in self.property_maps.items():
491 if property_map.config.do_min:
492 butlerQC.put(property_map.min_map,
493 getattr(outputRefs, f"{name}_map_min"))
494 if property_map.config.do_max:
495 butlerQC.put(property_map.max_map,
496 getattr(outputRefs, f"{name}_map_max"))
497 if property_map.config.do_mean:
498 butlerQC.put(property_map.mean_map,
499 getattr(outputRefs, f"{name}_map_mean"))
500 if property_map.config.do_weighted_mean:
501 butlerQC.put(property_map.weighted_mean_map,
502 getattr(outputRefs, f"{name}_map_weighted_mean"))
503 if property_map.config.do_sum:
504 butlerQC.put(property_map.sum_map,
505 getattr(outputRefs, f"{name}_map_sum"))
507 def run(self, sky_map, tract, band, coadd_dict, input_map_dict, visit_summary_dict):
508 """Run the healsparse property task.
510 Parameters
511 ----------
512 sky_map : Sky map object
513 tract : `int`
514 Tract number.
515 band : `str`
516 Band name for logging.
517 coadd_dict : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`]
518 Dictionary of coadd exposure references. Keys are patch numbers.
519 input_map_dict : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`]
520 Dictionary of input map references. Keys are patch numbers.
521 visit_summary_dict : `dict` [`int`: `lsst.afw.table.ExposureCatalog`]
522 Dictionary of visit summary tables. Keys are visit numbers.
524 Raises
525 ------
526 RepeatableQuantumError
527 If visit_summary_dict is missing any visits or detectors found in an
528 input map. This leads to an inconsistency between what is in the coadd
529 (via the input map) and the visit summary tables which contain data
530 to compute the maps.
531 """
532 tract_info = sky_map[tract]
534 tract_maps_initialized = False
536 for patch in input_map_dict.keys():
537 self.log.info("Making maps for band %s, tract %d, patch %d.",
538 band, tract, patch)
540 patch_info = tract_info[patch]
542 input_map = input_map_dict[patch].get()
543 coadd_photo_calib = coadd_dict[patch].get(component="photoCalib")
544 coadd_inputs = coadd_dict[patch].get(component="coaddInputs")
546 coadd_zeropoint = 2.5*np.log10(coadd_photo_calib.getInstFluxAtZeroMagnitude())
548 # Crop input_map to the inner polygon of the patch
549 poly_vertices = patch_info.getInnerSkyPolygon(tract_info.getWcs()).getVertices()
550 patch_radec = self._vertices_to_radec(poly_vertices)
551 patch_poly = hsp.Polygon(ra=patch_radec[:, 0], dec=patch_radec[:, 1],
552 value=np.arange(input_map.wide_mask_maxbits))
553 patch_poly_map = patch_poly.get_map_like(input_map)
554 input_map = hsp.and_intersection([input_map, patch_poly_map])
556 if not tract_maps_initialized:
557 # We use the first input map nside information to initialize
558 # the tract maps
559 nside_coverage = self._compute_nside_coverage_tract(tract_info)
560 nside = input_map.nside_sparse
562 do_compute_approx_psf = False
563 # Initialize the tract maps
564 for property_map in self.property_maps:
565 property_map.initialize_tract_maps(nside_coverage, nside)
566 if property_map.requires_psf:
567 do_compute_approx_psf = True
569 tract_maps_initialized = True
571 valid_pixels, vpix_ra, vpix_dec = input_map.valid_pixels_pos(return_pixels=True)
573 # Check if there are no valid pixels for the inner (unique) patch region
574 if valid_pixels.size == 0:
575 continue
577 # Initialize the value accumulators
578 for property_map in self.property_maps:
579 property_map.initialize_values(valid_pixels.size)
580 property_map.zeropoint = coadd_zeropoint
582 # Initialize the weight and counter accumulators
583 total_weights = np.zeros(valid_pixels.size)
584 total_inputs = np.zeros(valid_pixels.size, dtype=np.int32)
586 for bit, ccd_row in enumerate(coadd_inputs.ccds):
587 # Which pixels in the map are used by this visit/detector
588 inmap, = np.where(input_map.check_bits_pix(valid_pixels, [bit]))
590 # Check if there are any valid pixels in the map from this deteector.
591 if inmap.size == 0:
592 continue
594 # visit, detector_id, weight = input_dict[bit]
595 visit = ccd_row["visit"]
596 detector_id = ccd_row["ccd"]
597 weight = ccd_row["weight"]
599 x, y = ccd_row.getWcs().skyToPixelArray(vpix_ra[inmap], vpix_dec[inmap], degrees=True)
600 scalings = self._compute_calib_scale(ccd_row, x, y)
602 if do_compute_approx_psf:
603 psf_array = compute_approx_psf_size_and_shape(ccd_row, vpix_ra[inmap], vpix_dec[inmap])
604 else:
605 psf_array = None
607 total_weights[inmap] += weight
608 total_inputs[inmap] += 1
610 # Retrieve the correct visitSummary row
611 if visit not in visit_summary_dict:
612 msg = f"Visit {visit} not found in visit_summaries."
613 raise pipeBase.RepeatableQuantumError(msg)
614 row = visit_summary_dict[visit].find(detector_id)
615 if row is None:
616 msg = f"Visit {visit} / detector_id {detector_id} not found in visit_summaries."
617 raise pipeBase.RepeatableQuantumError(msg)
619 # Accumulate the values
620 for property_map in self.property_maps:
621 property_map.accumulate_values(inmap,
622 vpix_ra[inmap],
623 vpix_dec[inmap],
624 weight,
625 scalings,
626 row,
627 psf_array=psf_array)
629 # Finalize the mean values and set the tract maps
630 for property_map in self.property_maps:
631 property_map.finalize_mean_values(total_weights, total_inputs)
632 property_map.set_map_values(valid_pixels)
634 def _compute_calib_scale(self, ccd_row, x, y):
635 """Compute calibration scaling values.
637 Parameters
638 ----------
639 ccd_row : `lsst.afw.table.ExposureRecord`
640 Exposure metadata for a given detector exposure.
641 x : `np.ndarray`
642 Array of x positions.
643 y : `np.ndarray`
644 Array of y positions.
646 Returns
647 -------
648 calib_scale : `np.ndarray`
649 Array of calibration scale values.
650 """
651 photo_calib = ccd_row.getPhotoCalib()
652 bf = photo_calib.computeScaledCalibration()
653 if bf.getBBox() == ccd_row.getBBox():
654 # Track variable calibration over the detector
655 calib_scale = photo_calib.getCalibrationMean()*bf.evaluate(x, y)
656 else:
657 # Spatially constant calibration
658 calib_scale = photo_calib.getCalibrationMean()
660 return calib_scale
662 def _vertices_to_radec(self, vertices):
663 """Convert polygon vertices to ra/dec.
665 Parameters
666 ----------
667 vertices : `list` [ `lsst.sphgeom.UnitVector3d` ]
668 Vertices for bounding polygon.
670 Returns
671 -------
672 radec : `numpy.ndarray`
673 Nx2 array of ra/dec positions (in degrees) associated with vertices.
674 """
675 lonlats = [lsst.sphgeom.LonLat(x) for x in vertices]
676 radec = np.array([(x.getLon().asDegrees(), x.getLat().asDegrees()) for
677 x in lonlats])
678 return radec
680 def _compute_nside_coverage_tract(self, tract_info):
681 """Compute the optimal coverage nside for a tract.
683 Parameters
684 ----------
685 tract_info : `lsst.skymap.tractInfo.ExplicitTractInfo`
686 Tract information object.
688 Returns
689 -------
690 nside_coverage : `int`
691 Optimal coverage nside for a tract map.
692 """
693 num_patches = tract_info.getNumPatches()
695 # Compute approximate patch area
696 patch_info = tract_info.getPatchInfo(0)
697 vertices = patch_info.getInnerSkyPolygon(tract_info.getWcs()).getVertices()
698 radec = self._vertices_to_radec(vertices)
699 delta_ra = np.max(radec[:, 0]) - np.min(radec[:, 0])
700 delta_dec = np.max(radec[:, 1]) - np.min(radec[:, 1])
701 patch_area = delta_ra*delta_dec*np.cos(np.deg2rad(np.mean(radec[:, 1])))
703 tract_area = num_patches[0]*num_patches[1]*patch_area
704 # Start with a fairly low nside and increase until we find the approximate area.
705 nside_coverage_tract = 32
706 while hp.nside2pixarea(nside_coverage_tract, degrees=True) > tract_area:
707 nside_coverage_tract = 2*nside_coverage_tract
708 # Step back one, but don't go bigger pixels than nside=32 or smaller
709 # than 128 (recommended by healsparse).
710 nside_coverage_tract = int(np.clip(nside_coverage_tract/2, 32, 128))
712 return nside_coverage_tract
715class ConsolidateHealSparsePropertyMapConnections(pipeBase.PipelineTaskConnections,
716 dimensions=("band", "skymap",),
717 defaultTemplates={"coaddName": "deep"}):
718 sky_map = pipeBase.connectionTypes.Input(
719 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures",
720 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
721 storageClass="SkyMap",
722 dimensions=("skymap",),
723 )
725 # Create output connections for all possible maps defined in the
726 # registry. The vars() trick used here allows us to set class attributes
727 # programatically. Taken from
728 # https://stackoverflow.com/questions/2519807/
729 # setting-a-class-attribute-with-a-given-name-in-python-while-defining-the-class
730 for name in BasePropertyMap.registry:
731 vars()[f"{name}_map_min"] = pipeBase.connectionTypes.Input(
732 doc=f"Minimum-value map of {name}",
733 name=f"{{coaddName}}Coadd_{name}_map_min",
734 storageClass="HealSparseMap",
735 dimensions=("tract", "skymap", "band"),
736 multiple=True,
737 deferLoad=True,
738 )
739 vars()[f"{name}_consolidated_map_min"] = pipeBase.connectionTypes.Output(
740 doc=f"Minumum-value map of {name}",
741 name=f"{{coaddName}}Coadd_{name}_consolidated_map_min",
742 storageClass="HealSparseMap",
743 dimensions=("skymap", "band"),
744 )
745 vars()[f"{name}_map_max"] = pipeBase.connectionTypes.Input(
746 doc=f"Maximum-value map of {name}",
747 name=f"{{coaddName}}Coadd_{name}_map_max",
748 storageClass="HealSparseMap",
749 dimensions=("tract", "skymap", "band"),
750 multiple=True,
751 deferLoad=True,
752 )
753 vars()[f"{name}_consolidated_map_max"] = pipeBase.connectionTypes.Output(
754 doc=f"Minumum-value map of {name}",
755 name=f"{{coaddName}}Coadd_{name}_consolidated_map_max",
756 storageClass="HealSparseMap",
757 dimensions=("skymap", "band"),
758 )
759 vars()[f"{name}_map_mean"] = pipeBase.connectionTypes.Input(
760 doc=f"Mean-value map of {name}",
761 name=f"{{coaddName}}Coadd_{name}_map_mean",
762 storageClass="HealSparseMap",
763 dimensions=("tract", "skymap", "band"),
764 multiple=True,
765 deferLoad=True,
766 )
767 vars()[f"{name}_consolidated_map_mean"] = pipeBase.connectionTypes.Output(
768 doc=f"Minumum-value map of {name}",
769 name=f"{{coaddName}}Coadd_{name}_consolidated_map_mean",
770 storageClass="HealSparseMap",
771 dimensions=("skymap", "band"),
772 )
773 vars()[f"{name}_map_weighted_mean"] = pipeBase.connectionTypes.Input(
774 doc=f"Weighted mean-value map of {name}",
775 name=f"{{coaddName}}Coadd_{name}_map_weighted_mean",
776 storageClass="HealSparseMap",
777 dimensions=("tract", "skymap", "band"),
778 multiple=True,
779 deferLoad=True,
780 )
781 vars()[f"{name}_consolidated_map_weighted_mean"] = pipeBase.connectionTypes.Output(
782 doc=f"Minumum-value map of {name}",
783 name=f"{{coaddName}}Coadd_{name}_consolidated_map_weighted_mean",
784 storageClass="HealSparseMap",
785 dimensions=("skymap", "band"),
786 )
787 vars()[f"{name}_map_sum"] = pipeBase.connectionTypes.Input(
788 doc=f"Sum-value map of {name}",
789 name=f"{{coaddName}}Coadd_{name}_map_sum",
790 storageClass="HealSparseMap",
791 dimensions=("tract", "skymap", "band"),
792 multiple=True,
793 deferLoad=True,
794 )
795 vars()[f"{name}_consolidated_map_sum"] = pipeBase.connectionTypes.Output(
796 doc=f"Minumum-value map of {name}",
797 name=f"{{coaddName}}Coadd_{name}_consolidated_map_sum",
798 storageClass="HealSparseMap",
799 dimensions=("skymap", "band"),
800 )
802 def __init__(self, *, config=None):
803 super().__init__(config=config)
805 # Not all possible maps in the registry will be configured to run.
806 # Here we remove the unused connections.
807 for name in BasePropertyMap.registry:
808 if name not in config.property_maps:
809 prop_config = BasePropertyMapConfig()
810 prop_config.do_min = False
811 prop_config.do_max = False
812 prop_config.do_mean = False
813 prop_config.do_weighted_mean = False
814 prop_config.do_sum = False
815 else:
816 prop_config = config.property_maps[name]
818 if not prop_config.do_min:
819 self.inputs.remove(f"{name}_map_min")
820 self.outputs.remove(f"{name}_consolidated_map_min")
821 if not prop_config.do_max:
822 self.inputs.remove(f"{name}_map_max")
823 self.outputs.remove(f"{name}_consolidated_map_max")
824 if not prop_config.do_mean:
825 self.inputs.remove(f"{name}_map_mean")
826 self.outputs.remove(f"{name}_consolidated_map_mean")
827 if not prop_config.do_weighted_mean:
828 self.inputs.remove(f"{name}_map_weighted_mean")
829 self.outputs.remove(f"{name}_consolidated_map_weighted_mean")
830 if not prop_config.do_sum:
831 self.inputs.remove(f"{name}_map_sum")
832 self.outputs.remove(f"{name}_consolidated_map_sum")
835class ConsolidateHealSparsePropertyMapConfig(pipeBase.PipelineTaskConfig,
836 pipelineConnections=ConsolidateHealSparsePropertyMapConnections):
837 """Configuration parameters for ConsolidateHealSparsePropertyMapTask"""
838 property_maps = BasePropertyMap.registry.makeField(
839 multi=True,
840 default=["exposure_time",
841 "psf_size",
842 "psf_e1",
843 "psf_e2",
844 "psf_maglim",
845 "sky_noise",
846 "sky_background",
847 "dcr_dra",
848 "dcr_ddec",
849 "dcr_e1",
850 "dcr_e2"],
851 doc="Property map computation objects",
852 )
853 nside_coverage = pexConfig.Field(
854 doc="Consolidated HealSparse coverage map nside. Must be power of 2.",
855 dtype=int,
856 default=32,
857 check=_is_power_of_two,
858 )
860 def setDefaults(self):
861 self.property_maps["exposure_time"].do_sum = True
862 self.property_maps["psf_size"].do_weighted_mean = True
863 self.property_maps["psf_e1"].do_weighted_mean = True
864 self.property_maps["psf_e2"].do_weighted_mean = True
865 self.property_maps["psf_maglim"].do_weighted_mean = True
866 self.property_maps["sky_noise"].do_weighted_mean = True
867 self.property_maps["sky_background"].do_weighted_mean = True
868 self.property_maps["dcr_dra"].do_weighted_mean = True
869 self.property_maps["dcr_ddec"].do_weighted_mean = True
870 self.property_maps["dcr_e1"].do_weighted_mean = True
871 self.property_maps["dcr_e2"].do_weighted_mean = True
874class ConsolidateHealSparsePropertyMapTask(pipeBase.PipelineTask):
875 """Task to consolidate HealSparse property maps.
877 This task will take all the individual tract-based maps (per map type,
878 per band) and consolidate them into one survey-wide map (per map type,
879 per band). Each tract map is truncated to its inner region before
880 consolidation.
881 """
882 ConfigClass = ConsolidateHealSparsePropertyMapConfig
883 _DefaultName = "consolidateHealSparsePropertyMapTask"
885 def __init__(self, **kwargs):
886 super().__init__(**kwargs)
887 self.property_maps = PropertyMapMap()
888 for name, config, PropertyMapClass in self.config.property_maps.apply():
889 self.property_maps[name] = PropertyMapClass(config, name)
891 @pipeBase.timeMethod
892 def runQuantum(self, butlerQC, inputRefs, outputRefs):
893 inputs = butlerQC.get(inputRefs)
895 sky_map = inputs.pop("sky_map")
897 # These need to be consolidated one at a time to conserve memory.
898 for name in self.config.property_maps.names:
899 for type_ in ['min', 'max', 'mean', 'weighted_mean', 'sum']:
900 map_type = f"{name}_map_{type_}"
901 if map_type in inputs:
902 input_refs = {ref.dataId['tract']: ref
903 for ref in inputs[map_type]}
904 consolidated_map = self.consolidate_map(sky_map, input_refs)
905 butlerQC.put(consolidated_map,
906 getattr(outputRefs, f"{name}_consolidated_map_{type_}"))
908 def consolidate_map(self, sky_map, input_refs):
909 """Consolidate the healsparse property maps.
911 Parameters
912 ----------
913 sky_map : Sky map object
914 input_refs : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`]
915 Dictionary of tract_id mapping to dataref.
917 Returns
918 -------
919 consolidated_map : `healsparse.HealSparseMap`
920 Consolidated HealSparse map.
921 """
922 # First, we read in the coverage maps to know how much memory
923 # to allocate
924 cov_mask = None
925 nside_coverage_inputs = None
926 for tract_id in input_refs:
927 cov = input_refs[tract_id].get(component='coverage')
928 if cov_mask is None:
929 cov_mask = cov.coverage_mask
930 nside_coverage_inputs = cov.nside_coverage
931 else:
932 cov_mask |= cov.coverage_mask
934 cov_pix_inputs, = np.where(cov_mask)
936 # Compute the coverage pixels for the desired nside_coverage
937 if nside_coverage_inputs == self.config.nside_coverage:
938 cov_pix = cov_pix_inputs
939 elif nside_coverage_inputs > self.config.nside_coverage:
940 # Converting from higher resolution coverage to lower
941 # resolution coverage.
942 bit_shift = hsp.utils._compute_bitshift(self.config.nside_coverage,
943 nside_coverage_inputs)
944 cov_pix = np.right_shift(cov_pix_inputs, bit_shift)
945 else:
946 # Converting from lower resolution coverage to higher
947 # resolution coverage.
948 bit_shift = hsp.utils._compute_bitshift(nside_coverage_inputs,
949 self.config.nside_coverage)
950 cov_pix = np.left_shift(cov_pix_inputs, bit_shift)
952 # Now read in each tract map and build the consolidated map.
953 consolidated_map = None
954 for tract_id in input_refs:
955 input_map = input_refs[tract_id].get()
956 if consolidated_map is None:
957 consolidated_map = hsp.HealSparseMap.make_empty(
958 self.config.nside_coverage,
959 input_map.nside_sparse,
960 input_map.dtype,
961 sentinel=input_map._sentinel,
962 cov_pixels=cov_pix)
964 # Only use pixels that are properly inside the tract.
965 vpix, ra, dec = input_map.valid_pixels_pos(return_pixels=True)
966 vpix_tract_ids = sky_map.findTractIdArray(ra, dec, degrees=True)
968 in_tract = (vpix_tract_ids == tract_id)
970 consolidated_map[vpix[in_tract]] = input_map[vpix[in_tract]]
972 return consolidated_map