Coverage for python/lsst/images/cells/_provenance.py: 68%
196 statements
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1# This file is part of lsst-images.
2#
3# Developed for the LSST Data Management System.
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# Use of this source code is governed by a 3-clause BSD-style
10# license that can be found in the LICENSE file.
12from __future__ import annotations
14__all__ = ("CoaddProvenance", "CoaddProvenanceSerializationModel")
16from collections.abc import Iterable
17from typing import TYPE_CHECKING, Any, ClassVar
19import astropy.table
20import astropy.units as u
21import numpy as np
22import pydantic
24from .._cell_grid import CellGridBounds, CellIJ
25from .._polygon import Polygon
26from ..describe import DescribableMixin, DescribeOptions, FieldRole, Report, ReportField
27from ..serialization import ArchiveTree, InputArchive, InvalidParameterError, OutputArchive, TableModel
29if TYPE_CHECKING:
30 try:
31 from lsst.afw.geom import Polygon as LegacyPolygon
32 from lsst.cell_coadds import CoaddInputs as LegacyCellCoaddInputs
33 from lsst.cell_coadds import MultipleCellCoadd as LegacyMultipleCellCoadd
34 from lsst.cell_coadds import ObservationIdentifiers as LegacyObservationIdentifiers
35 from lsst.skymap import Index2D as LegacyIndex2D
36 except ImportError:
37 type LegacyIndex2D = Any # type: ignore[no-redef]
38 type LegacyCellCoaddInputs = Any # type: ignore[no-redef]
39 type LegacyPolygon = Any # type: ignore[no-redef]
40 type LegacyMultipleCellCoadd = Any # type: ignore[no-redef]
41 type LegacyObservationIdentifiers = Any # type: ignore[no-redef]
44class CoaddProvenance(DescribableMixin):
45 """A pair of tables that record the inputs to a cell-based coadd.
47 Parameters
48 ----------
49 inputs
50 A table of {visit, detector} combinations that contribute to any cell
51 in the coadd.
52 contributions
53 A table of {visit, detector, cell} combinations that describe how an
54 observation contributed to a cell.
56 Raises
57 ------
58 ValueError
59 Raised if ``inputs`` has no rows; provenance that records no input
60 image is not meaningful.
62 Notes
63 -----
64 This object can represent the provenance of a whole patch, a single cell,
65 or anything in between. In the single-cell case, the ``inputs`` and
66 ``contributions`` tables have the same number of rows (but may not be
67 ordered the same way!).
68 """
70 def __init__(self, inputs: astropy.table.Table, contributions: astropy.table.Table) -> None:
71 if not len(inputs):
72 raise ValueError("Coadd provenance must contain at least one input image.")
73 self._inputs = inputs
74 self._contributions = contributions
76 _INPUT_TABLE_COLUMNS: ClassVar[list[tuple[str, type, str]]] = [
77 ("instrument", np.object_, "Name of the instrument."),
78 ("visit", np.uint64, "ID of the visit."),
79 ("detector", np.uint16, "ID of the detector."),
80 ("physical_filter", np.object_, "Full name of the bandpass filter."),
81 ("day_obs", np.uint32, "Observation night as a YYYYMMDD integer."),
82 (
83 "polygon",
84 np.object_,
85 (
86 "Polygon that approximates the overlap of the observation and the coadd patch, "
87 "in coadd coordinates."
88 ),
89 ),
90 ]
92 _CONTRIBUTION_TABLE_COLUMNS: ClassVar[list[tuple[str, type, str, u.UnitBase | None]]] = [
93 ("cell_i", np.uint16, "Y-axis index of the cell within the patch.", None),
94 ("cell_j", np.uint16, "X-axis index of the cell within the patch.", None),
95 ("instrument", np.object_, "Name of the instrument.", None),
96 ("visit", np.uint64, "ID of the visit.", None),
97 ("detector", np.uint16, "ID of the detector.", None),
98 ("overlaps_center", np.bool_, "Whether a this observation overlaps the center of the cell.", None),
99 ("overlap_fraction", np.float64, "Fraction of the cell that is covered by the overlap region.", None),
100 ("unmasked_fraction", np.float64, "Fraction of the cell propagated to the coadd.", None),
101 ("weight", np.float64, "Weight to be used for this input in this cell.", None),
102 ("psf_shape_xx", np.float64, "Second order moments of the PSF.", u.pix**2),
103 ("psf_shape_yy", np.float64, "Second order moments of the PSF.", u.pix**2),
104 ("psf_shape_xy", np.float64, "Second order moments of the PSF.", u.pix**2),
105 (
106 "psf_shape_flag",
107 np.bool_,
108 "Flag indicating whether the PSF shape measurement was successful.",
109 None,
110 ),
111 ]
113 @classmethod
114 def make_empty_input_table(cls, n_rows: int) -> astropy.table.Table:
115 """Make an empty `inputs` table with a set number of rows.
117 Parameters
118 ----------
119 n_rows
120 Number of rows in the new table.
121 """
122 return astropy.table.Table(
123 [
124 astropy.table.Column(name=name, length=n_rows, dtype=dtype, description=description)
125 for name, dtype, description in cls._INPUT_TABLE_COLUMNS
126 ]
127 )
129 @classmethod
130 def make_empty_contribution_table(cls, n_rows: int) -> astropy.table.Table:
131 """Make an empty `contributions` table with a set number of rows.
133 Parameters
134 ----------
135 n_rows
136 Number of rows in the new table.
137 """
138 return astropy.table.Table(
139 [
140 astropy.table.Column(
141 name=name, length=n_rows, dtype=dtype, description=description, unit=unit
142 )
143 for name, dtype, description, unit in cls._CONTRIBUTION_TABLE_COLUMNS
144 ]
145 )
147 @property
148 def inputs(self) -> astropy.table.Table:
149 """A table of {visit, detector} combinations that contribute to any
150 cell in the coadd.
151 """
152 return self._inputs
154 @property
155 def contributions(self) -> astropy.table.Table:
156 """A table of {visit, detector, cell} combinations that describe how an
157 observation contributed to a cell.
158 """
159 return self._contributions
161 def __getitem__(self, cell: CellIJ) -> CoaddProvenance | None:
162 return self.subset([cell])
164 def subset(self, cells: Iterable[CellIJ]) -> CoaddProvenance | None:
165 """Return a new provenance object with just the given cells.
167 Parameters
168 ----------
169 cells
170 Cells to keep in the returned provenance.
172 Returns
173 -------
174 subset : `CoaddProvenance` or `None`
175 Provenance for ``cells`` only, or `None` if none of them have
176 contributions.
177 """
178 cells_to_keep = astropy.table.Table(
179 rows=[(index.i, index.j) for index in cells],
180 names=["cell_i", "cell_j"],
181 dtype=[np.uint16, np.uint16],
182 )
183 contributions = astropy.table.join(self._contributions, cells_to_keep)
184 assert contributions.columns.keys() == {name for name, _, _, _ in self._CONTRIBUTION_TABLE_COLUMNS}
185 if not len(contributions):
186 # No inputs to derive either, and astropy.table.join rejects an
187 # empty operand outright.
188 return None
189 inputs = astropy.table.join(contributions["instrument", "visit", "detector"], self._inputs)
190 assert inputs.columns.keys() == {name for name, _, _ in self._INPUT_TABLE_COLUMNS}
191 return CoaddProvenance(inputs=inputs, contributions=contributions)
193 def _describe(
194 self,
195 options: DescribeOptions = DescribeOptions(),
196 /,
197 *,
198 bounds: CellGridBounds | None = None,
199 ) -> Report:
200 """Return a `Report` describing this provenance.
202 Parameters
203 ----------
204 options : `DescribeOptions`, optional
205 Rendering options. `DescribeOptions.brief` reports only the
206 number of input images, which no column scan is needed to count.
207 bounds : `CellGridBounds`, optional
208 Cells the image this provenance belongs to has data for. When
209 given, the number of cells with contributions is reported as a
210 fraction of them.
212 Notes
213 -----
214 The report summarizes the tables rather than rendering them. Rich
215 renders an embedded `astropy.table.Table` as plain text and never
216 consults the table's own ``_repr_html_``, so `inputs` and
217 `contributions` describe themselves better than this report could,
218 and a patch has tens of thousands of contribution rows.
219 """
220 n_inputs = len(self._inputs)
221 summary = f"CoaddProvenance({n_inputs} input image{'s' if n_inputs != 1 else ''})"
222 if options.brief:
223 return Report(type_name="CoaddProvenance", summary=summary)
224 fields: list[ReportField] = []
225 for column in ("instrument", "physical_filter"):
226 fields.append(
227 ReportField(
228 label=column,
229 value=", ".join(sorted({str(value) for value in self._inputs[column]})),
230 role=FieldRole.DERIVED,
231 )
232 )
233 n_visits = len(np.unique(self._inputs["visit"]))
234 input_images = f"{n_inputs} from {n_visits} visit{'s' if n_visits != 1 else ''}"
235 fields.append(ReportField(label="input images", value=input_images, role=FieldRole.DERIVED))
236 first_night = int(self._inputs["day_obs"].min())
237 last_night = int(self._inputs["day_obs"].max())
238 fields.append(
239 ReportField(
240 label="day_obs",
241 value=str(first_night) if first_night == last_night else f"{first_night} - {last_night}",
242 role=FieldRole.DERIVED,
243 )
244 )
245 _, counts = np.unique(
246 np.column_stack([self._contributions["cell_i"], self._contributions["cell_j"]]),
247 axis=0,
248 return_counts=True,
249 )
250 if bounds is not None:
251 n_cells_with_data = bounds.subgrid_size.i * bounds.subgrid_size.j - len(bounds.missing)
252 cells = f"{len(counts)} of {n_cells_with_data} with contributions"
253 else:
254 cells = f"{len(counts)} with contributions" if len(counts) else "none"
255 fields.append(ReportField(label="cells", value=cells, role=FieldRole.DERIVED))
256 if len(counts):
257 low = int(counts.min())
258 high = int(counts.max())
259 per_cell = (
260 f"{low} input image{'s' if low != 1 else ''}"
261 if low == high
262 else f"{low} - {high} input images (median {np.median(counts):g})"
263 )
264 fields.append(ReportField(label="per cell", value=per_cell, role=FieldRole.DERIVED))
265 return Report(type_name="CoaddProvenance", summary=summary, fields=fields)
267 def serialize(self, archive: OutputArchive[Any]) -> CoaddProvenanceSerializationModel:
268 """Serialize the provenance to an output archive.
270 Parameters
271 ----------
272 archive
273 Archive to write to.
274 """
275 # The tables are exposed and mutable, so a caller can empty one after
276 # construction. Catch that here rather than deep in the column
277 # rewriting below, where it surfaces as "max() iterable argument is
278 # empty".
279 assert len(self._inputs), "Coadd provenance must contain at least one input image."
280 inputs = self._inputs.copy(copy_data=False)
281 contributions = self._contributions.copy(copy_data=False)
282 instrument = CoaddProvenanceSerializationModel._fix_str_for_serialization(
283 "instrument", inputs, contributions
284 )
285 physical_filter = CoaddProvenanceSerializationModel._fix_str_for_serialization(
286 "physical_filter", inputs
287 )
288 CoaddProvenanceSerializationModel._fix_polygon_for_serialization(inputs)
289 inputs_model = archive.add_table(inputs, name="inputs")
290 contributions_model = archive.add_table(contributions, name="contributions")
291 return CoaddProvenanceSerializationModel(
292 instrument=instrument,
293 physical_filter=physical_filter,
294 inputs=inputs_model,
295 contributions=contributions_model,
296 )
298 @staticmethod
299 def from_legacy(legacy_cell_coadd: LegacyMultipleCellCoadd) -> CoaddProvenance:
300 """Extract provenance from a legacy
301 `lsst.cell_coadds.MultipleCellCoadd` object.
303 Parameters
304 ----------
305 legacy_cell_coadd
306 Legacy cell coadd to extract provenance from.
307 """
308 inputs = CoaddProvenance.make_empty_input_table(len(legacy_cell_coadd.common.visit_polygons))
309 for n, (legacy_identifiers, legacy_polygon) in enumerate(
310 legacy_cell_coadd.common.visit_polygons.items()
311 ):
312 inputs["instrument"][n] = legacy_identifiers.instrument
313 inputs["visit"][n] = legacy_identifiers.visit
314 inputs["detector"][n] = legacy_identifiers.detector
315 inputs["physical_filter"][n] = legacy_identifiers.physical_filter
316 inputs["day_obs"][n] = legacy_identifiers.day_obs
317 inputs["polygon"][n] = Polygon.from_legacy(legacy_polygon)
318 n_contributions = 0
319 for legacy_cell in legacy_cell_coadd.cells.values():
320 n_contributions += len(legacy_cell.inputs)
321 contributions = CoaddProvenance.make_empty_contribution_table(n_contributions)
322 n = 0
323 for legacy_cell in legacy_cell_coadd.cells.values():
324 for legacy_identifiers, legacy_inputs in legacy_cell.inputs.items():
325 contributions["cell_i"][n] = legacy_cell.identifiers.cell.y
326 contributions["cell_j"][n] = legacy_cell.identifiers.cell.x
327 contributions["instrument"][n] = legacy_identifiers.instrument
328 contributions["visit"][n] = legacy_identifiers.visit
329 contributions["detector"][n] = legacy_identifiers.detector
330 contributions["overlaps_center"][n] = legacy_inputs.overlaps_center
331 contributions["overlap_fraction"][n] = legacy_inputs.overlap_fraction
332 contributions["unmasked_fraction"][n] = legacy_inputs.unmasked_overlap_fraction
333 contributions["weight"][n] = legacy_inputs.weight
334 contributions["psf_shape_xx"][n] = legacy_inputs.psf_shape.getIxx()
335 contributions["psf_shape_yy"][n] = legacy_inputs.psf_shape.getIyy()
336 contributions["psf_shape_xy"][n] = legacy_inputs.psf_shape.getIxy()
337 contributions["psf_shape_flag"][n] = legacy_inputs.psf_shape_flag
338 n += 1
339 return CoaddProvenance(inputs=inputs, contributions=contributions)
341 def to_legacy_polygon_map(self) -> dict[LegacyObservationIdentifiers, LegacyPolygon]:
342 """Construct a legacy mapping from
343 `lsst.cell_coadds.ObservationIdentifiers` to `lsst.afw.geom.Polygon`
344 from the `inputs` table.
345 """
346 from lsst.cell_coadds import ObservationIdentifiers as LegacyObservationIdentifiers
348 return {
349 LegacyObservationIdentifiers(
350 instrument=str(row["instrument"]),
351 physical_filter=str(row["physical_filter"]),
352 visit=int(row["visit"]),
353 day_obs=int(row["day_obs"]),
354 detector=int(row["detector"]),
355 ): row["polygon"].to_legacy()
356 for row in self.inputs
357 }
359 def to_legacy_cell_coadd_inputs(
360 self, observations: Iterable[LegacyObservationIdentifiers] | None
361 ) -> dict[LegacyIndex2D, dict[LegacyObservationIdentifiers, LegacyCellCoaddInputs]]:
362 """Construct a mapping from legacy cell index to the list of legacy
363 input structs for that cell.
365 Parameters
366 ----------
367 observations
368 Observations to include, or `None` to include all observations
369 in the `inputs` table.
370 """
371 from lsst.afw.geom.ellipses import Quadrupole
372 from lsst.cell_coadds import CoaddInputs as LegacyCoaddInputs
373 from lsst.skymap import Index2D as LegacyIndex2D
375 if observations is None:
376 observations = self.to_legacy_polygon_map().keys()
377 observations_by_key: dict[tuple[str, int, int], LegacyObservationIdentifiers] = {
378 (obs.instrument, obs.visit, obs.detector): obs for obs in observations
379 }
380 result: dict[LegacyIndex2D, dict[LegacyObservationIdentifiers, LegacyCoaddInputs]] = {}
381 for row in self.contributions:
382 obs_key = (str(row["instrument"]), int(row["visit"]), int(row["detector"]))
383 obs = observations_by_key[obs_key]
384 cell_inputs = result.setdefault(LegacyIndex2D(x=int(row["cell_j"]), y=int(row["cell_i"])), {})
385 cell_inputs[obs] = LegacyCoaddInputs(
386 overlaps_center=bool(row["overlaps_center"]),
387 overlap_fraction=float(row["overlap_fraction"]),
388 unmasked_overlap_fraction=float(row["unmasked_fraction"]),
389 weight=float(row["weight"]),
390 psf_shape=Quadrupole(row["psf_shape_xx"], row["psf_shape_yy"], row["psf_shape_xy"]),
391 psf_shape_flag=bool(row["psf_shape_flag"]),
392 )
393 return result
396class CoaddProvenanceSerializationModel(ArchiveTree):
397 """A Pydantic model used to represent a serialized `CoaddProvenance`.
399 Notes
400 -----
401 We can't rewrite the Astropy tables directly into the archive (e.g. as
402 FITS binary tables for a FITS archive), because:
404 - `str` columns are a huge pain in both Numpy and FITS;
405 - the polygon columns need to be rewritten as array-valued columns.
407 To deal with the string columns (``instrument`` and ``physical_filter``)
408 we do dictionary compression: we map each distinct value of those columns
409 to an integer, and then we save that mapping to the model while saving
410 an integer version of that column in the table. But if there is actually
411 only one value in that column (the most common case by far) we just drop
412 the column and store that value directly in the model.
413 """
415 SCHEMA_NAME: ClassVar[str] = "coadd_provenance"
416 SCHEMA_VERSION: ClassVar[str] = "1.0.0"
417 MIN_READ_VERSION: ClassVar[int] = 1
418 PUBLIC_TYPE: ClassVar[type] = CoaddProvenance
420 instrument: str | dict[str, int] = pydantic.Field(
421 description=(
422 "Instrument name for all inputs to this coadd, or a mapping from "
423 "instrument name to the integer used in its place in the tables."
424 )
425 )
426 physical_filter: str | dict[str, int] = pydantic.Field(
427 description="Physical filter name for all inputs to this coadd."
428 )
429 inputs: TableModel = pydantic.Field(description="Table of all inputs to the coadd.")
430 contributions: TableModel = pydantic.Field(description="Table of per-cell contributions to the coadd.")
432 def deserialize(self, archive: InputArchive[Any], **kwargs: Any) -> CoaddProvenance:
433 """Deserialize a provenance from an input archive.
435 Parameters
436 ----------
437 archive
438 Archive to read from.
439 **kwargs
440 Unsupported keyword arguments are accepted only to provide
441 better error messages (raising
442 `.serialization.InvalidParameterError`).
444 Notes
445 -----
446 While `CoaddProvenance.subset` can be used to filter provenance
447 information down to just certain cells, there is no advantage to be
448 had from doing this during deserialization (the table data is not
449 ordered by cell, and hence there's read-slicing we can do).
450 """
451 if kwargs: 451 ↛ 452line 451 didn't jump to line 452 because the condition on line 451 was never true
452 raise InvalidParameterError(f"Unrecognized parameters for CoaddProvenance: {set(kwargs.keys())}.")
453 inputs = archive.get_table(self.inputs)
454 contributions = archive.get_table(self.contributions)
455 CoaddProvenanceSerializationModel._fix_str_for_deserialization(
456 "instrument", self.instrument, inputs, contributions
457 )
458 CoaddProvenanceSerializationModel._fix_str_for_deserialization(
459 "physical_filter", self.physical_filter, inputs
460 )
461 CoaddProvenanceSerializationModel._fix_polygon_for_deserialization(inputs)
462 for name, _, description in CoaddProvenance._INPUT_TABLE_COLUMNS:
463 inputs.columns[name].description = description
464 for name, _, description, unit in CoaddProvenance._CONTRIBUTION_TABLE_COLUMNS:
465 contributions.columns[name].description = description
466 contributions.columns[name].unit = unit
467 return CoaddProvenance(inputs=inputs, contributions=contributions)
469 @staticmethod
470 def _fix_str_for_serialization(column: str, *tables: astropy.table.Table) -> str | dict[str, int]:
471 """Rewrite a string column as an integer column or drop it.
473 Parameters
474 ----------
475 column
476 Name of the column to rewrite.
477 *tables
478 One or more astropy tables to rewrite. The first table is assumed
479 to have all values for this column that might appear in any other
480 tables.
482 Returns
483 -------
484 `str` | `dict` [`str`, `int`]
485 If there is only one unique value for this column in the first
486 table, that value (and the column will have been dropped from
487 all givne tables). If the tables are empty, the column is
488 dropped and an empty `dict` is returned. In all other cases the
489 given column is replaced with an integer column in all given
490 tables and the mapping from strings to integers is returned.
491 """
492 result: str | dict[str, int] = {name: n for n, name in enumerate(sorted(set(tables[0][column])))}
493 match len(result):
494 case 0: 494 ↛ 495line 494 didn't jump to line 495 because the pattern on line 494 never matched
495 pass
496 case 1: 496 ↛ 498line 496 didn't jump to line 498 because the pattern on line 496 always matched
497 (result,) = result.keys() # type: ignore[union-attr]
498 case _:
499 for table in tables:
500 table.columns[column] = astropy.table.Column(
501 data=[result[k] for k in table.columns[column]],
502 name=column,
503 dtype=np.uint8,
504 description=f"Integer mapped to {column} name.",
505 )
506 return result
507 # If we didn't remap to an integer (case 0 and 1 above), delete the
508 # column.
509 for table in tables:
510 del table.columns[column]
511 return result
513 @staticmethod
514 def _fix_str_for_deserialization(
515 column: str, value: str | dict[str, int], *tables: astropy.table.Table
516 ) -> None:
517 """Rewrite an integer column back to a string one.
519 Parameters
520 ----------
521 column
522 Name of the column to rewrite.
523 value
524 Value or mapping of values returned by
525 `_fix_str_for_serialization`.
526 tables
527 Tables to rewrite this column in.
528 """
529 match value:
530 case str(): 530 ↛ 533line 530 didn't jump to line 533 because the pattern on line 530 always matched
531 for table in tables:
532 table.columns[column] = astropy.table.Column([value] * len(table), dtype=object)
533 case dict():
534 mapping = {v: k for k, v in value.items()}
535 for table in tables:
536 table.columns[column] = astropy.table.Column(
537 [mapping[k] for k in table[column]], dtype=object
538 )
540 @staticmethod
541 def _fix_polygon_for_serialization(inputs: astropy.table.Table) -> None:
542 """Rewrite a polygon `object` column as a pair of array-valued columns
543 and an array-size column.
545 Parameters
546 ----------
547 inputs
548 A copy of the in-memory coadd inputs table to modify in-place into
549 its serialization form.
550 """
551 max_n_vertices = max(p.n_vertices for p in inputs["polygon"])
552 inputs["n_vertices"] = astropy.table.Column(
553 [p.n_vertices for p in inputs["polygon"]],
554 name="n_vertices",
555 dtype=np.uint8,
556 description="Number of polygon vertices.",
557 )
558 inputs["x_vertices"] = astropy.table.Column(
559 name="x_vertices",
560 dtype=np.float64,
561 length=len(inputs),
562 shape=(max_n_vertices,),
563 description="X coordinates of polygon vertices, in tract coordinates.",
564 )
565 inputs["x_vertices"][:, :] = np.nan
566 inputs["y_vertices"] = astropy.table.Column(
567 name="y_vertices",
568 dtype=np.float64,
569 length=len(inputs),
570 shape=(max_n_vertices,),
571 description="Y coordinates of polygon vertices, in tract coordinates.",
572 )
573 inputs["y_vertices"][:, :] = np.nan
574 for i, polygon in enumerate(inputs["polygon"]):
575 inputs["n_vertices"][i] = polygon.n_vertices
576 inputs["x_vertices"][i][: polygon.n_vertices] = polygon.x_vertices
577 inputs["y_vertices"][i][: polygon.n_vertices] = polygon.y_vertices
578 del inputs["polygon"]
580 @staticmethod
581 def _fix_polygon_for_deserialization(inputs: astropy.table.Table) -> None:
582 """Rewrite a a pair of array-valued columns and an array-size column
583 into a polygon `object` column.
585 Parameters
586 ----------
587 inputs
588 The serialized version of the coadd inputs table, to be modified
589 in-place into its in-memory form.
590 """
591 polygons = [
592 Polygon(x_vertices=x_vertices[:n_vertices], y_vertices=y_vertices[:n_vertices])
593 for n_vertices, x_vertices, y_vertices in zip(
594 inputs["n_vertices"], inputs["x_vertices"], inputs["y_vertices"]
595 )
596 ]
597 del inputs["n_vertices"]
598 del inputs["x_vertices"]
599 del inputs["y_vertices"]
600 inputs["polygon"] = astropy.table.Column(polygons, name="polygon", dtype=np.object_)