Coverage for python/lsst/images/cells/_provenance.py: 25%
145 statements
« prev ^ index » next coverage.py v7.14.1, created at 2026-06-03 08:08 +0000
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 CellIJ
25from .._polygon import Polygon
26from ..serialization import ArchiveTree, InputArchive, InvalidParameterError, OutputArchive, TableModel
28if TYPE_CHECKING:
29 try:
30 from lsst.cell_coadds import CoaddInputs as LegacyCoaddInputs
31 from lsst.cell_coadds import MultipleCellCoadd, ObservationIdentifiers
32 from lsst.skymap import Index2D
33 except ImportError:
34 type Index2D = Any # type: ignore[no-redef]
35 type LegacyCoaddInputs = Any # type: ignore[no-redef]
36 type MultipleCellCoadd = Any # type: ignore[no-redef]
37 type ObservationIdentifiers = Any # type: ignore[no-redef]
40class CoaddProvenance:
41 """A pair of tables that record the inputs to a cell-based coadd.
43 Parameters
44 ----------
45 inputs
46 A table of {visit, detector} combinations that contribute to any cell
47 in the coadd.
48 contributions
49 A table of {visit, detector, cell} combinations that describe how an
50 observation contributed to a cell.
52 Notes
53 -----
54 This object can represent the provenance of a whole patch, a single cell,
55 or anything in between. In the single-cell case, the ``inputs`` and
56 ``contributions`` tables have the same number of rows (but may not be
57 ordered the same way!).
58 """
60 def __init__(self, inputs: astropy.table.Table, contributions: astropy.table.Table):
61 self._inputs = inputs
62 self._contributions = contributions
64 _INPUT_TABLE_COLUMNS: ClassVar[list[tuple[str, type, str]]] = [
65 ("instrument", np.object_, "Name of the instrument."),
66 ("visit", np.uint64, "ID of the visit."),
67 ("detector", np.uint16, "ID of the detector."),
68 ("physical_filter", np.object_, "Full name of the bandpass filter."),
69 ("day_obs", np.uint32, "Observation night as a YYYYMMDD integer."),
70 (
71 "polygon",
72 np.object_,
73 (
74 "Polygon that approximates the overlap of the observation and the coadd patch, "
75 "in coadd coordinates."
76 ),
77 ),
78 ]
80 _CONTRIBUTION_TABLE_COLUMNS: ClassVar[list[tuple[str, type, str, u.UnitBase | None]]] = [
81 ("cell_i", np.uint16, "Y-axis index of the cell within the patch.", None),
82 ("cell_j", np.uint16, "X-axis index of the cell within the patch.", None),
83 ("instrument", np.object_, "Name of the instrument.", None),
84 ("visit", np.uint64, "ID of the visit.", None),
85 ("detector", np.uint16, "ID of the detector.", None),
86 ("overlaps_center", np.bool_, "Whether a this observation overlaps the center of the cell.", None),
87 ("overlap_fraction", np.float64, "Fraction of the cell that is covered by the overlap region.", None),
88 ("weight", np.float64, "Weight to be used for this input in this cell.", None),
89 ("psf_shape_xx", np.float64, "Second order moments of the PSF.", u.pix**2),
90 ("psf_shape_yy", np.float64, "Second order moments of the PSF.", u.pix**2),
91 ("psf_shape_xy", np.float64, "Second order moments of the PSF.", u.pix**2),
92 (
93 "psf_shape_flag",
94 np.bool_,
95 "Flag indicating whether the PSF shape measurement was successful.",
96 None,
97 ),
98 ]
100 @classmethod
101 def make_empty_input_table(cls, n_rows: int) -> astropy.table.Table:
102 """Make an empty `inputs` table with a set number of rows."""
103 return astropy.table.Table(
104 [
105 astropy.table.Column(name=name, length=n_rows, dtype=dtype, description=description)
106 for name, dtype, description in cls._INPUT_TABLE_COLUMNS
107 ]
108 )
110 @classmethod
111 def make_empty_contribution_table(cls, n_rows: int) -> astropy.table.Table:
112 """Make an empty `contributions` table with a set number of rows."""
113 return astropy.table.Table(
114 [
115 astropy.table.Column(
116 name=name, length=n_rows, dtype=dtype, description=description, unit=unit
117 )
118 for name, dtype, description, unit in cls._CONTRIBUTION_TABLE_COLUMNS
119 ]
120 )
122 @property
123 def inputs(self) -> astropy.table.Table:
124 """A table of {visit, detector} combinations that contribute to any
125 cell in the coadd.
126 """
127 return self._inputs
129 @property
130 def contributions(self) -> astropy.table.Table:
131 """A table of {visit, detector, cell} combinations that describe how an
132 observation contributed to a cell.
133 """
134 return self._contributions
136 def __getitem__(self, cell: CellIJ) -> CoaddProvenance:
137 return self.subset([cell])
139 def subset(self, cells: Iterable[CellIJ]) -> CoaddProvenance:
140 """Return a new provenance object with just the given cells."""
141 cells_to_keep = astropy.table.Table(
142 rows=[(index.i, index.j) for index in cells],
143 names=["cell_i", "cell_j"],
144 dtype=[np.uint16, np.uint16],
145 )
146 contributions = astropy.table.join(self._contributions, cells_to_keep)
147 assert contributions.columns.keys() == {name for name, _, _, _ in self._CONTRIBUTION_TABLE_COLUMNS}
148 inputs = astropy.table.join(contributions["instrument", "visit", "detector"], self._inputs)
149 assert inputs.columns.keys() == {name for name, _, _ in self._INPUT_TABLE_COLUMNS}
150 return CoaddProvenance(inputs=inputs, contributions=contributions)
152 def serialize(self, archive: OutputArchive[Any]) -> CoaddProvenanceSerializationModel:
153 """Serialize the provenance to an output archive.
155 Parameters
156 ----------
157 archive
158 Archive to write to.
159 """
160 inputs = self._inputs.copy(copy_data=False)
161 contributions = self._contributions.copy(copy_data=False)
162 instrument = CoaddProvenanceSerializationModel._fix_str_for_serialization(
163 "instrument", inputs, contributions
164 )
165 physical_filter = CoaddProvenanceSerializationModel._fix_str_for_serialization(
166 "physical_filter", inputs
167 )
168 CoaddProvenanceSerializationModel._fix_polygon_for_serialization(inputs)
169 inputs_model = archive.add_table(inputs, name="inputs")
170 contributions_model = archive.add_table(contributions, name="contributions")
171 return CoaddProvenanceSerializationModel(
172 instrument=instrument,
173 physical_filter=physical_filter,
174 inputs=inputs_model,
175 contributions=contributions_model,
176 )
178 @staticmethod
179 def from_legacy(legacy_cell_coadd: MultipleCellCoadd) -> CoaddProvenance:
180 """Extract provenance from a legacy
181 `lsst.cell_coadds.MultipleCellCoadd` object.
182 """
183 inputs = CoaddProvenance.make_empty_input_table(len(legacy_cell_coadd.common.visit_polygons))
184 for n, (legacy_identifiers, legacy_polygon) in enumerate(
185 legacy_cell_coadd.common.visit_polygons.items()
186 ):
187 inputs["instrument"][n] = legacy_identifiers.instrument
188 inputs["visit"][n] = legacy_identifiers.visit
189 inputs["detector"][n] = legacy_identifiers.detector
190 inputs["physical_filter"][n] = legacy_identifiers.physical_filter
191 inputs["day_obs"][n] = legacy_identifiers.day_obs
192 inputs["polygon"][n] = Polygon.from_legacy(legacy_polygon)
193 n_contributions = 0
194 for legacy_cell in legacy_cell_coadd.cells.values():
195 n_contributions += len(legacy_cell.inputs)
196 contributions = CoaddProvenance.make_empty_contribution_table(n_contributions)
197 n = 0
198 for legacy_cell in legacy_cell_coadd.cells.values():
199 for legacy_identifiers, legacy_inputs in legacy_cell.inputs.items():
200 contributions["cell_i"][n] = legacy_cell.identifiers.cell.y
201 contributions["cell_j"][n] = legacy_cell.identifiers.cell.x
202 contributions["instrument"][n] = legacy_identifiers.instrument
203 contributions["visit"][n] = legacy_identifiers.visit
204 contributions["detector"][n] = legacy_identifiers.detector
205 contributions["overlaps_center"][n] = legacy_inputs.overlaps_center
206 contributions["overlap_fraction"][n] = legacy_inputs.overlap_fraction
207 contributions["weight"][n] = legacy_inputs.weight
208 contributions["psf_shape_xx"][n] = legacy_inputs.psf_shape.getIxx()
209 contributions["psf_shape_yy"][n] = legacy_inputs.psf_shape.getIyy()
210 contributions["psf_shape_xy"][n] = legacy_inputs.psf_shape.getIxy()
211 contributions["psf_shape_flag"][n] = legacy_inputs.psf_shape_flag
212 n += 1
213 return CoaddProvenance(inputs=inputs, contributions=contributions)
216class CoaddProvenanceSerializationModel(ArchiveTree):
217 """A Pydantic model used to represent a serialized `CoaddProvenance`.
219 Notes
220 -----
221 We can't rewrite the Astropy tables directly into the archive (e.g. as
222 FITS binary tables for a FITS archive), because:
224 - `str` columns are a huge pain in both Numpy and FITS;
225 - the polygon columns need to be rewritten as array-valued columns.
227 To deal with the string columns (``instrument`` and ``physical_filter``)
228 we do dictionary compression: we map each distinct value of those columns
229 to an integer, and then we save that mapping to the model while saving
230 an integer version of that column in the table. But if there is actually
231 only one value in that column (the most common case by far) we just drop
232 the column and store that value directly in the model.
233 """
235 SCHEMA_NAME: ClassVar[str] = "coadd_provenance"
236 SCHEMA_VERSION: ClassVar[str] = "1.0.0"
237 MIN_READ_VERSION: ClassVar[int] = 1
239 instrument: str | dict[str, int] = pydantic.Field(
240 description=(
241 "Instrument name for all inputs to this coadd, or a mapping from "
242 "instrument name to the integer used in its place in the tables."
243 )
244 )
245 physical_filter: str | dict[str, int] = pydantic.Field(
246 description="Physical filter name for all inputs to this coadd."
247 )
248 inputs: TableModel = pydantic.Field(description="Table of all inputs to the coadd.")
249 contributions: TableModel = pydantic.Field(description="Table of per-cell contributions to the coadd.")
251 def deserialize(self, archive: InputArchive[Any], **kwargs: Any) -> CoaddProvenance:
252 """Deserialize a provenance from an input archive.
254 Parameters
255 ----------
256 archive
257 Archive to read from.
259 Notes
260 -----
261 While `CoaddProvenance.subset` can be used to filter provenance
262 information down to just certain cells, there is no advantage to be
263 had from doing this during deserialization (the table data is not
264 ordered by cell, and hence there's read-slicing we can do).
265 """
266 if kwargs:
267 raise InvalidParameterError(f"Unrecognized parameters for CoaddProvenance: {set(kwargs.keys())}.")
268 inputs = archive.get_table(self.inputs)
269 contributions = archive.get_table(self.contributions)
270 CoaddProvenanceSerializationModel._fix_str_for_deserialization(
271 "instrument", self.instrument, inputs, contributions
272 )
273 CoaddProvenanceSerializationModel._fix_str_for_deserialization(
274 "physical_filter", self.physical_filter, inputs
275 )
276 CoaddProvenanceSerializationModel._fix_polygon_for_deserialization(inputs)
277 for name, _, description in CoaddProvenance._INPUT_TABLE_COLUMNS:
278 inputs.columns[name].description = description
279 for name, _, description, unit in CoaddProvenance._CONTRIBUTION_TABLE_COLUMNS:
280 contributions.columns[name].description = description
281 contributions.columns[name].unit = unit
282 return CoaddProvenance(inputs=inputs, contributions=contributions)
284 @staticmethod
285 def _fix_str_for_serialization(column: str, *tables: astropy.table.Table) -> str | dict[str, int]:
286 """Rewrite a string column as an integer column or drop it.
288 Parameters
289 ----------
290 column
291 Name of the column to rewrite.
292 *tables
293 One or more astropy tables to rewrite. The first table is assumed
294 to have all values for this column that might appear in any other
295 tables.
297 Returns
298 -------
299 `str` | `dict` [`str`, `int`]
300 If there is only one unique value for this column in the first
301 table, that value (and the column will have been dropped from
302 all givne tables). If the tables are empty, the column is
303 dropped and an empty `dict` is returned. In all other cases the
304 given column is replaced with an integer column in all given
305 tables and the mapping from strings to integers is returned.
306 """
307 result: str | dict[str, int] = {name: n for n, name in enumerate(sorted(set(tables[0][column])))}
308 match len(result):
309 case 0:
310 pass
311 case 1:
312 (result,) = result.keys() # type: ignore[union-attr]
313 case _:
314 for table in tables:
315 table.columns[column] = astropy.table.Column(
316 data=[result[k] for k in table.columns[column]],
317 name=column,
318 dtype=np.uint8,
319 description=f"Integer mapped to {column} name.",
320 )
321 return result
322 # If we didn't remap to an integer (case 0 and 1 above), delete the
323 # column.
324 for table in tables:
325 del table.columns[column]
326 return result
328 @staticmethod
329 def _fix_str_for_deserialization(
330 column: str, value: str | dict[str, int], *tables: astropy.table.Table
331 ) -> None:
332 """Rewrite an integer column back to a string one.
334 Parameters
335 ----------
336 column
337 Name of the column to rewrite.
338 value
339 Value or mapping of values returned by
340 `_fix_str_for_serialization`.
341 tables
342 Tables to rewrite this column in.
343 """
344 match value:
345 case str():
346 for table in tables:
347 table.columns[column] = astropy.table.Column([value] * len(table), dtype=object)
348 case dict():
349 mapping = {v: k for k, v in value.items()}
350 for table in tables:
351 table.columns[column] = astropy.table.Column(
352 [mapping[k] for k in table[column]], dtype=object
353 )
355 @staticmethod
356 def _fix_polygon_for_serialization(inputs: astropy.table.Table) -> None:
357 """Rewrite a polygon `object` column as a pair of array-valued columns
358 and an array-size column.
360 Parameters
361 ----------
362 inputs
363 A copy of the in-memory coadd inputs table to modify in-place into
364 its serialization form.
365 """
366 max_n_vertices = max(p.n_vertices for p in inputs["polygon"])
367 inputs["n_vertices"] = astropy.table.Column(
368 [p.n_vertices for p in inputs["polygon"]],
369 name="n_vertices",
370 dtype=np.uint8,
371 description="Number of polygon vertices.",
372 )
373 inputs["x_vertices"] = astropy.table.Column(
374 name="x_vertices",
375 dtype=np.float64,
376 length=len(inputs),
377 shape=(max_n_vertices,),
378 description="X coordinates of polygon vertices, in tract coordinates.",
379 )
380 inputs["x_vertices"][:, :] = np.nan
381 inputs["y_vertices"] = astropy.table.Column(
382 name="y_vertices",
383 dtype=np.float64,
384 length=len(inputs),
385 shape=(max_n_vertices,),
386 description="Y coordinates of polygon vertices, in tract coordinates.",
387 )
388 inputs["y_vertices"][:, :] = np.nan
389 for i, polygon in enumerate(inputs["polygon"]):
390 inputs["n_vertices"][i] = polygon.n_vertices
391 inputs["x_vertices"][i][: polygon.n_vertices] = polygon.x_vertices
392 inputs["y_vertices"][i][: polygon.n_vertices] = polygon.y_vertices
393 del inputs["polygon"]
395 @staticmethod
396 def _fix_polygon_for_deserialization(inputs: astropy.table.Table) -> None:
397 """Rewrite a a pair of array-valued columns and an array-size column
398 into a polygon `object` column.
400 Parameters
401 ----------
402 inputs
403 The serialized version of the coadd inputs table, to be modified
404 in-place into its in-memory form.
405 """
406 polygons = [
407 Polygon(x_vertices=x_vertices[:n_vertices], y_vertices=y_vertices[:n_vertices])
408 for n_vertices, x_vertices, y_vertices in zip(
409 inputs["n_vertices"], inputs["x_vertices"], inputs["y_vertices"]
410 )
411 ]
412 del inputs["n_vertices"]
413 del inputs["x_vertices"]
414 del inputs["y_vertices"]
415 inputs["polygon"] = astropy.table.Column(polygons, name="polygon", dtype=np.object_)