Coverage for python/lsst/analysis/ap/templateInputs.py: 81%
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2#
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4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
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12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
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17# GNU General Public License for more details.
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22"""Find the images that make up the template at the position of a diaSource.
24A template written as an `lsst.images.DifferenceImage` records the coadds it
25was built from in its ``templates`` component. Each coadd in turn records
26its input images and their weights, either as a legacy
27`lsst.afw.image.CoaddInputs` (``coaddInputs`` component) or as an
28`lsst.images.cells.CoaddProvenance` (``provenance`` component). The tools
29here follow that chain from a diaSource back to the observation times and
30fractional weights of the template's input images.
31"""
33__all__ = ["get_template_inputs", "summarize_template_inputs"]
35import logging
37import astropy.table
38import astropy.units as u
39import numpy as np
40import pandas as pd
41from astropy.coordinates import SkyCoord
43import lsst.geom
44from lsst.daf.base import DateTime
45from lsst.images import Box, Interval
47_LOG = logging.getLogger(__name__)
49_INPUT_COLUMNS = ["input_visit", "input_detector", "coadd_weight", "mid_mjd_tai", "exposure_time"]
50"""Columns of the per-coadd input tables returned by the readers
51(`list` [`str`]).
52"""
55def _empty_inputs():
56 """Return a per-coadd input table with no rows."""
57 return astropy.table.Table(
58 names=["point"] + _INPUT_COLUMNS,
59 dtype=[np.int64, np.int64, np.int64, np.float64, np.float64, np.float64],
60 )
63def _nearest_pixel(value):
64 """Return the integer index of the pixel containing a position."""
65 return np.floor(np.asarray(value) + 0.5).astype(np.int64)
68def _clipped_box(ix, iy, bbox):
69 """Return the box enclosing pixel indices, clipped to a bounding box.
71 Parameters
72 ----------
73 ix, iy : `numpy.ndarray`
74 Integer pixel indices to enclose.
75 bbox : `lsst.images.Box`
76 Bounding box of the image to read from.
78 Returns
79 -------
80 box : `lsst.images.Box` or `None`
81 Enclosing box, or `None` if no pixel is inside ``bbox``.
82 """
83 inside = bbox.contains(x=ix, y=iy)
84 if not inside.any(): 84 ↛ 85line 84 didn't jump to line 85 because the condition on line 84 was never true
85 return None
86 return Box(
87 y=Interval(iy[inside].min(), iy[inside].max() + 1),
88 x=Interval(ix[inside].min(), ix[inside].max() + 1),
89 )
92class _LegacyCoaddReader:
93 """Inputs of a coadd whose provenance is a legacy
94 `lsst.afw.image.CoaddInputs`.
96 Parameters
97 ----------
98 ccds : `lsst.afw.table.ExposureCatalog`
99 The ``ccds`` catalog of the coadd's ``coaddInputs``.
100 butler : `lsst.daf.butler.Butler`, optional
101 Butler to read the coadd's variance plane from.
102 ref : `lsst.daf.butler.DatasetRef`, optional
103 Reference to the coadd.
104 """
106 def __init__(self, ccds, butler=None, ref=None):
107 self._ccds = ccds
108 self._butler = butler
109 self._ref = ref
110 self._mid_mjd = np.full(len(ccds), np.nan)
111 self._exposure_time = np.full(len(ccds), np.nan)
112 for i, record in enumerate(ccds):
113 visit_info = record.getVisitInfo()
114 if visit_info is not None: 114 ↛ 112line 114 didn't jump to line 112 because the condition on line 114 was always true
115 self._mid_mjd[i] = visit_info.getDate().get(DateTime.MJD, DateTime.TAI)
116 self._exposure_time[i] = visit_info.getExposureTime()
118 @classmethod
119 def from_butler(cls, butler, ref):
120 """Read the provenance of a coadd.
122 Parameters
123 ----------
124 butler : `lsst.daf.butler.Butler`
125 Butler to read from.
126 ref : `lsst.daf.butler.DatasetRef`
127 Reference to the coadd.
129 Returns
130 -------
131 reader : `_LegacyCoaddReader`
132 Reader for that coadd.
133 """
134 coadd_inputs = butler.get(ref.makeComponentRef("coaddInputs"))
135 return cls(coadd_inputs.ccds, butler=butler, ref=ref)
137 def find_inputs(self, ra, dec):
138 """Find the input images that cover sky positions.
140 Parameters
141 ----------
142 ra, dec : `numpy.ndarray`
143 Sky positions, in degrees.
145 Returns
146 -------
147 inputs : `astropy.table.Table`
148 One row per (position, input image), with ``point`` giving the
149 index of the position.
151 Notes
152 -----
153 An input covers a position if the position is inside both the bounding
154 box and the valid polygon of that input, the same rule
155 `lsst.meas.algorithms.CoaddPsf` uses.
156 """
157 points = [lsst.geom.SpherePoint(r, d, lsst.geom.degrees) for r, d in zip(ra, dec)]
158 rows = []
159 for i, record in enumerate(self._ccds):
160 bbox = lsst.geom.Box2D(record.getBBox())
161 polygon = record.getValidPolygon()
162 for point, pixel in enumerate(record.getWcs().skyToPixel(points)):
163 if bbox.contains(pixel) and (polygon is None or polygon.contains(pixel)):
164 rows.append((point, record["visit"], record["ccd"], record["weight"],
165 self._mid_mjd[i], self._exposure_time[i]))
166 if not rows: 166 ↛ 167line 166 didn't jump to line 167 because the condition on line 166 was never true
167 return _empty_inputs()
168 return astropy.table.Table(rows=rows, names=_empty_inputs().colnames)
170 def variance_at(self, ra, dec):
171 """Read the coadd variance at sky positions.
173 Parameters
174 ----------
175 ra, dec : `numpy.ndarray`
176 Sky positions, in degrees.
178 Returns
179 -------
180 variance : `numpy.ndarray`
181 Variance of the nearest coadd pixel; ``nan`` outside the coadd.
182 """
183 wcs = self._butler.get(self._ref.makeComponentRef("wcs"))
184 points = [lsst.geom.SpherePoint(r, d, lsst.geom.degrees) for r, d in zip(ra, dec)]
185 pixels = wcs.skyToPixel(points)
186 ix = _nearest_pixel([p.getX() for p in pixels])
187 iy = _nearest_pixel([p.getY() for p in pixels])
188 coadd_bbox = Box.from_legacy(self._butler.get(self._ref.makeComponentRef("bbox")))
189 box = _clipped_box(ix, iy, coadd_bbox)
190 result = np.full(len(ix), np.nan)
191 if box is None: 191 ↛ 192line 191 didn't jump to line 192 because the condition on line 191 was never true
192 return result
193 variance = self._butler.get(self._ref.makeComponentRef("variance"),
194 parameters={"bbox": box.to_legacy()})
195 inside = box.contains(x=ix, y=iy)
196 result[inside] = variance.array[iy[inside] - box.y.start, ix[inside] - box.x.start]
197 return result
200class _CellCoaddReader:
201 """Inputs of a coadd whose provenance is an
202 `lsst.images.cells.CoaddProvenance`.
204 Parameters
205 ----------
206 provenance : `lsst.images.cells.CoaddProvenance`
207 Provenance of the coadd.
208 sky_projection : `lsst.images.SkyProjection`
209 Mapping from the coadd's pixels to the sky.
210 grid : `lsst.images.CellGrid`
211 Cell grid of the coadd's patch.
212 times : `dict` [`tuple` [`str`, `int`], `tuple` [`float`, `float`]]
213 Mid-exposure time (MJD, TAI) and exposure time (s), keyed by
214 ``(instrument, visit)``.
215 butler : `lsst.daf.butler.Butler`, optional
216 Butler to read the coadd's variance plane from.
217 ref : `lsst.daf.butler.DatasetRef`, optional
218 Reference to the coadd.
219 """
221 def __init__(self, provenance, sky_projection, grid, times, butler=None, ref=None):
222 self._sky_projection = sky_projection
223 self._grid = grid
224 self._butler = butler
225 self._ref = ref
226 self._polygons = {
227 (str(row["instrument"]), int(row["visit"]), int(row["detector"])): row["polygon"]
228 for row in provenance.inputs
229 }
230 self._contributions = provenance.contributions
231 self._rows_by_cell = {}
232 for n, row in enumerate(self._contributions):
233 self._rows_by_cell.setdefault((int(row["cell_i"]), int(row["cell_j"])), []).append(n)
234 self._times = times
236 @classmethod
237 def from_butler(cls, butler, ref):
238 """Read the provenance of a coadd and the times of its inputs.
240 Parameters
241 ----------
242 butler : `lsst.daf.butler.Butler`
243 Butler to read from.
244 ref : `lsst.daf.butler.DatasetRef`
245 Reference to the coadd.
247 Returns
248 -------
249 reader : `_CellCoaddReader`
250 Reader for that coadd.
251 """
252 parts = butler.get(ref.makeComponentRef("components"),
253 parameters={"components": ["provenance", "sky_projection", "psf"]})
254 provenance = parts["provenance"]
255 times = {}
256 for instrument in np.unique(provenance.inputs["instrument"]):
257 visits = provenance.inputs["visit"][provenance.inputs["instrument"] == instrument]
258 records = butler.query_dimension_records(
259 "visit", instrument=str(instrument), where="visit IN (:visits)",
260 bind={"visits": [int(v) for v in np.unique(visits)]}, limit=None,
261 )
262 for record in records:
263 timespan = record.timespan
264 mid = timespan.begin + (timespan.end - timespan.begin)/2
265 times[str(instrument), record.id] = (mid.tai.mjd, record.exposure_time)
266 return cls(provenance, parts["sky_projection"], parts["psf"].bounds.grid, times,
267 butler=butler, ref=ref)
269 def _pixels(self, ra, dec):
270 """Return the coadd pixel positions of sky positions."""
271 xy = self._sky_projection.sky_to_pixel(SkyCoord(ra*u.deg, dec*u.deg))
272 return np.atleast_1d(xy.x), np.atleast_1d(xy.y)
274 def find_inputs(self, ra, dec):
275 """Find the input images that cover sky positions.
277 Parameters
278 ----------
279 ra, dec : `numpy.ndarray`
280 Sky positions, in degrees.
282 Returns
283 -------
284 inputs : `astropy.table.Table`
285 One row per (position, input image), with ``point`` giving the
286 index of the position.
288 Notes
289 -----
290 An input covers a position if it contributes to the cell containing
291 the position and its overlap polygon contains the position. The
292 weight is the one recorded for that cell.
293 """
294 x, y = self._pixels(ra, dec)
295 rows = []
296 for point, (px, py) in enumerate(zip(x, y)):
297 cell = self._grid.index_of(y=int(_nearest_pixel(py)), x=int(_nearest_pixel(px)))
298 for n in self._rows_by_cell.get((cell.i, cell.j), []):
299 row = self._contributions[n]
300 instrument = str(row["instrument"])
301 key = (instrument, int(row["visit"]), int(row["detector"]))
302 if not self._polygons[key].contains(x=px, y=py):
303 continue
304 mid_mjd, exposure_time = self._times.get(key[:2], (np.nan, np.nan))
305 rows.append((point, key[1], key[2], row["weight"], mid_mjd, exposure_time))
306 if not rows: 306 ↛ 307line 306 didn't jump to line 307 because the condition on line 306 was never true
307 return _empty_inputs()
308 return astropy.table.Table(rows=rows, names=_empty_inputs().colnames)
310 def variance_at(self, ra, dec):
311 """Read the coadd variance at sky positions.
313 Parameters
314 ----------
315 ra, dec : `numpy.ndarray`
316 Sky positions, in degrees.
318 Returns
319 -------
320 variance : `numpy.ndarray`
321 Variance of the nearest coadd pixel; ``nan`` outside the coadd.
322 """
323 x, y = self._pixels(ra, dec)
324 ix = _nearest_pixel(x)
325 iy = _nearest_pixel(y)
326 box = _clipped_box(ix, iy, self._grid.bbox)
327 result = np.full(len(ix), np.nan)
328 if box is None:
329 return result
330 variance = self._butler.get(self._ref.makeComponentRef("variance"), parameters={"bbox": box})
331 inside = box.contains(x=ix, y=iy)
332 result[inside] = variance.array[iy[inside] - box.y.start, ix[inside] - box.x.start]
333 return result
336_READERS = {"coaddInputs": _LegacyCoaddReader, "provenance": _CellCoaddReader}
337"""Coadd reader for each provenance component, in order of preference
338(`dict` [`str`, `type`]).
339"""
342def _get_reader(butler, info, readers):
343 """Return the reader for one template coadd, reading it if needed.
345 Parameters
346 ----------
347 butler : `lsst.daf.butler.Butler`
348 Butler to read from.
349 info : `lsst.images.DifferenceImageTemplateInfo`
350 Record of the coadd.
351 readers : `dict`
352 Readers already made, keyed by dataset ID; updated in place.
354 Returns
355 -------
356 reader : `_LegacyCoaddReader` or `_CellCoaddReader`
357 Reader for the coadd.
359 Raises
360 ------
361 LookupError
362 Raised if the butler has no dataset with the coadd's ID.
363 TypeError
364 Raised if the coadd's storage class has no known provenance
365 component.
366 """
367 if info.dataset_id in readers: 367 ↛ 368line 367 didn't jump to line 368 because the condition on line 367 was never true
368 return readers[info.dataset_id]
369 ref = butler.get_dataset(info.dataset_id)
370 if ref is None: 370 ↛ 371line 370 didn't jump to line 371 because the condition on line 370 was never true
371 raise LookupError(f"Template coadd tract={info.tract}, patch={info.patch} "
372 f"(id={info.dataset_id}, run={info.dataset_run!r}) is not in this butler.")
373 components = ref.datasetType.storageClass.allComponents()
374 for component, reader_type in _READERS.items(): 374 ↛ 378line 374 didn't jump to line 378 because the loop on line 374 didn't complete
375 if component in components: 375 ↛ 374line 375 didn't jump to line 374 because the condition on line 375 was always true
376 readers[info.dataset_id] = reader_type.from_butler(butler, ref)
377 return readers[info.dataset_id]
378 raise TypeError(f"Coadd {ref} has storage class {ref.datasetType.storageClass_name}, which has none "
379 f"of the provenance components {list(_READERS)}.")
382def _combine_weights(coadds, variances):
383 """Compute the fraction of the template flux at one position that came
384 from each input image.
386 Parameters
387 ----------
388 coadds : `list` [`tuple`]
389 One ``(key, tract, inputs)`` entry per coadd whose template bounds
390 contain the position, where ``inputs`` is that coadd's input table
391 for the position.
392 variances : `dict`
393 Coadd variance at the position, keyed by ``key``. Needed only when
394 there is more than one coadd.
396 Returns
397 -------
398 fractions : `dict`
399 Weight to apply to each coadd's ``coadd_weight`` column, keyed by
400 ``key``. Coadds that contribute nothing are omitted.
402 Notes
403 -----
404 This follows `lsst.ip.diffim.GetTemplateTask`: the patches of each tract
405 are averaged with per-pixel weights ``variance**-0.5``, and the tracts
406 are then averaged the same way using the averaged variance of each tract.
407 Within one coadd, the fraction from an input is its coadd weight over the
408 sum of the weights of the inputs covering the position.
409 """
410 coadds = [(key, tract, inputs) for key, tract, inputs in coadds if inputs["coadd_weight"].sum() > 0]
411 if len(coadds) == 1:
412 key, _, inputs = coadds[0]
413 return {key: 1.0/inputs["coadd_weight"].sum()}
414 patch_weights = {}
415 tract_variances = {}
416 for key, tract, inputs in coadds:
417 variance = variances.get(key, np.nan)
418 if np.isfinite(variance) and variance > 0:
419 patch_weights.setdefault(tract, {})[key] = variance**-0.5
420 for tract, weights in patch_weights.items():
421 total = sum(weights.values())
422 for key in weights:
423 weights[key] /= total
424 tract_variances[tract] = sum(weights[key]*variances[key] for key in weights)
425 tract_weights = {tract: variance**-0.5 for tract, variance in tract_variances.items()}
426 tract_total = sum(tract_weights.values())
427 fractions = {}
428 for key, tract, inputs in coadds:
429 if key in patch_weights.get(tract, {}):
430 fractions[key] = (tract_weights[tract]/tract_total*patch_weights[tract][key]
431 / inputs["coadd_weight"].sum())
432 return fractions
435def _template_inputs_for_detector(butler, data_id, sources, template_dataset, readers):
436 """Find the template inputs for the diaSources on one detector.
438 Parameters
439 ----------
440 butler : `lsst.daf.butler.Butler`
441 Butler to read from.
442 data_id : `dict`
443 Data ID of the detector.
444 sources : `astropy.table.Table`
445 DiaSources on the detector.
446 template_dataset : `str`
447 Name of the template dataset type.
448 readers : `dict`
449 Coadd readers already made, keyed by dataset ID; updated in place.
451 Returns
452 -------
453 inputs : `list` [`astropy.table.Table`]
454 Input tables, one per contributing coadd.
455 """
456 templates = butler.get(f"{template_dataset}.templates", data_id)
457 ra = np.asarray(sources["ra"], dtype=float)
458 dec = np.asarray(sources["dec"], dtype=float)
459 x = np.asarray(sources["x"], dtype=float)
460 y = np.asarray(sources["y"], dtype=float)
461 per_coadd = []
462 for info in templates:
463 contains = np.atleast_1d(info.bounds.contains(x=x, y=y))
464 if not contains.any(): 464 ↛ 465line 464 didn't jump to line 465 because the condition on line 464 was never true
465 continue
466 indices = np.flatnonzero(contains)
467 reader = _get_reader(butler, info, readers)
468 inputs = reader.find_inputs(ra[indices], dec[indices])
469 inputs["point"] = indices[inputs["point"]]
470 per_coadd.append((info, reader, indices, inputs))
471 n_coadds = np.zeros(len(sources), dtype=int)
472 for _, _, indices, _ in per_coadd:
473 n_coadds[indices] += 1
474 variances = {}
475 for info, reader, indices, _ in per_coadd:
476 overlap = indices[n_coadds[indices] > 1]
477 if len(overlap): 477 ↛ 475line 477 didn't jump to line 475 because the condition on line 477 was always true
478 for point, variance in zip(overlap, reader.variance_at(ra[overlap], dec[overlap])):
479 variances[point, info.dataset_id] = variance
480 results = []
481 for point in range(len(sources)):
482 coadds = []
483 infos = {}
484 for info, _, _, inputs in per_coadd:
485 mine = inputs[inputs["point"] == point]
486 if len(mine):
487 coadds.append(((point, info.dataset_id), info.tract, mine))
488 infos[point, info.dataset_id] = info
489 fractions = _combine_weights(coadds, variances) if coadds else {}
490 if not fractions: 490 ↛ 491line 490 didn't jump to line 491 because the condition on line 490 was never true
491 _LOG.warning("No template coadd input contributes to diaSource %s.",
492 sources["diaSourceId"][point])
493 continue
494 for key, _, inputs in coadds:
495 if key not in fractions: 495 ↛ 496line 495 didn't jump to line 496 because the condition on line 495 was never true
496 continue
497 table = inputs.copy()
498 table["tract"] = infos[key].tract
499 table["patch"] = infos[key].patch
500 table["weight"] = table["coadd_weight"]*fractions[key]
501 results.append(table)
502 return results
505def get_template_inputs(butler, dia_sources, *, instrument=None, template_dataset="template_detector"):
506 """Find the observation times and weights of the images that make up
507 the template at the position of each diaSource.
509 Parameters
510 ----------
511 butler : `lsst.daf.butler.Butler`
512 Butler with the templates and the coadds they were built from.
513 Coadds are found by dataset ID, so their collections need not be
514 searched.
515 dia_sources : `astropy.table.Table` or `pandas.DataFrame`
516 DiaSources, with columns ``diaSourceId``, ``visit``, ``detector``,
517 ``ra``, ``dec`` (degrees) and ``x``, ``y`` (science image pixels).
518 instrument : `str`, optional
519 Instrument name, if the butler has no default.
520 template_dataset : `str`, optional
521 Template dataset type; must be an `lsst.images.DifferenceImage`.
523 Returns
524 -------
525 inputs : `astropy.table.Table`
526 One row per (diaSource, template coadd, input image), with columns:
528 ``diaSourceId``, ``visit``, ``detector``
529 The diaSource and its science image.
530 ``tract``, ``patch``
531 Template coadd the input contributed through.
532 ``input_visit``, ``input_detector``
533 The input image.
534 ``mid_mjd_tai``
535 Mid-exposure time of the input (MJD, TAI); ``nan`` if unknown.
536 ``exposure_time``
537 Exposure time of the input (s); ``nan`` if unknown.
538 ``coadd_weight``
539 Weight of the input as recorded by the coadd.
540 ``weight``
541 Fraction of the template flux at the diaSource that came from
542 this input through this coadd. These sum to 1 for each
543 diaSource; an input in several overlapping coadds has one row
544 for each.
546 Raises
547 ------
548 TypeError
549 Raised if ``template_dataset`` has no ``templates`` component (a
550 legacy Exposure), or if a coadd has no known provenance component.
551 LookupError
552 Raised if a template coadd is missing from the butler.
554 Notes
555 -----
556 The weights are evaluated at the diaSource position only. Warping the
557 template and PSF matching both mix in nearby template pixels, which can
558 come from other coadds or inputs; that is not modeled. Per-pixel input
559 rejection (masked pixels, or artifacts clipped by
560 `lsst.drp.tasks.assemble_coadd.CompareWarpAssembleCoaddTask`) is not
561 recorded in coadd provenance, so a rejected input is still reported.
562 """
563 dataset_type = butler.get_dataset_type(template_dataset)
564 if "templates" not in dataset_type.storageClass.allComponents():
565 raise TypeError(f"{template_dataset!r} has storage class {dataset_type.storageClass_name}, which "
566 "has no 'templates' component; templates written as legacy Exposures are not "
567 "supported.")
568 if isinstance(dia_sources, pd.DataFrame): 568 ↛ 569line 568 didn't jump to line 569 because the condition on line 568 was never true
569 sources = astropy.table.Table.from_pandas(dia_sources)
570 else:
571 sources = astropy.table.Table(dia_sources)
572 readers = {}
573 results = []
574 pairs = np.unique(np.column_stack([sources["visit"], sources["detector"]]), axis=0)
575 for visit, detector in pairs:
576 selected = sources[(sources["visit"] == visit) & (sources["detector"] == detector)]
577 data_id = {"visit": int(visit), "detector": int(detector)}
578 if instrument is not None: 578 ↛ 579line 578 didn't jump to line 579 because the condition on line 578 was never true
579 data_id["instrument"] = instrument
580 for table in _template_inputs_for_detector(butler, data_id, selected, template_dataset, readers):
581 table["diaSourceId"] = selected["diaSourceId"][table["point"]]
582 table["visit"] = visit
583 table["detector"] = detector
584 results.append(table)
585 columns = ["diaSourceId", "visit", "detector", "tract", "patch"] + _INPUT_COLUMNS + ["weight"]
586 if not results: 586 ↛ 587line 586 didn't jump to line 587 because the condition on line 586 was never true
587 empty = _empty_inputs()
588 for name in ["diaSourceId", "visit", "detector", "tract", "patch"]:
589 empty[name] = np.zeros(0, dtype=np.int64)
590 empty["weight"] = np.zeros(0)
591 return empty[columns]
592 return astropy.table.vstack(results)[columns]
595def summarize_template_inputs(inputs):
596 """Summarize the template inputs of each diaSource.
598 Parameters
599 ----------
600 inputs : `astropy.table.Table`
601 Output of `get_template_inputs`.
603 Returns
604 -------
605 summary : `astropy.table.Table`
606 One row per diaSource, with columns ``diaSourceId``, ``n_inputs``
607 (distinct input images), ``mean_mjd_tai`` (weighted mean
608 mid-exposure time), ``min_mjd_tai`` and ``max_mjd_tai``. Inputs with
609 unknown times are left out of the time columns.
610 """
611 rows = []
612 for group in inputs.group_by("diaSourceId").groups:
613 known = np.isfinite(group["mid_mjd_tai"])
614 times = group["mid_mjd_tai"][known]
615 weights = group["weight"][known]
616 n_inputs = len(np.unique(np.column_stack([group["input_visit"], group["input_detector"]]), axis=0))
617 if len(times): 617 ↛ 621line 617 didn't jump to line 621 because the condition on line 617 was always true
618 row = (np.average(times, weights=weights) if weights.sum() > 0 else np.nan,
619 times.min(), times.max())
620 else:
621 row = (np.nan, np.nan, np.nan)
622 rows.append((group["diaSourceId"][0], n_inputs) + row)
623 return astropy.table.Table(
624 rows=rows,
625 names=["diaSourceId", "n_inputs", "mean_mjd_tai", "min_mjd_tai", "max_mjd_tai"],
626 dtype=[np.int64, np.int64, np.float64, np.float64, np.float64],
627 )