def test_envelope_and_interior(self):
pixelization = HtmPixelization(3)
c = Circle(UnitVector3d(1, 1, 1), Angle.fromDegrees(0.1))
rs = pixelization.envelope(c)
self.assertTrue(rs == RangeSet(0x3ff))
rs = pixelization.envelope(c, 1)
self.assertTrue(rs == RangeSet(0x3ff))
self.assertTrue(rs.isWithin(pixelization.universe()))
rs = pixelization.interior(c)
self.assertTrue(rs.empty())
def test_envelope_and_interior(self):
pixelization = Mq3cPixelization(1)
c = Circle(UnitVector3d(1.0, -0.5, -0.5), Angle.fromDegrees(0.1))
rs = pixelization.envelope(c)
self.assertTrue(rs == RangeSet(44))
rs = pixelization.envelope(c, 1)
self.assertTrue(rs == RangeSet(44))
self.assertTrue(rs.isWithin(pixelization.universe()))
rs = pixelization.interior(c)
self.assertTrue(rs.empty())
def testSetOperators(self):
a = RangeSet(1)
b = ~a
self.assertTrue((a | b).full())
self.assertTrue((a & b).empty())
self.assertEqual(a - b, a)
self.assertEqual(b - a, b)
a &= a
b &= b
c = (a ^ b) - RangeSet(2, 4)
self.assertEqual(c, RangeSet(4, 2))
c |= b
self.assertTrue(c.full())
c ^= c
self.assertTrue(c.empty())
def testConstruction(self):
s1 = RangeSet(1)
s2 = RangeSet()
s3 = RangeSet(2, 1)
s4 = RangeSet(s3)
self.assertTrue(s2.empty())
self.assertEqual(s3, s4)
self.assertEqual(s1, s3.complement())
def testRanges(self):
s = RangeSet()
s.insert(0, 1)
s.insert(2, 3)
self.assertEqual(s.ranges(), [(0, 1), (2, 3)])
s = RangeSet(4, 2)
self.assertEqual(list(s), [(0, 2), (4, 0)])
def testString(self):
s = RangeSet(1, 10)
if sys.version_info[0] >= 3:
self.assertEqual(str(s), '[(1, 10)]')
self.assertEqual(repr(s), 'RangeSet([(1, 10)])')
else:
# pybind11 maps C++ integers to Python long instances in Python 2.
self.assertEqual(str(s), '[(1L, 10L)]')
self.assertEqual(repr(s), 'RangeSet([(1L, 10L)])')
self.assertEqual(s, eval(repr(s), dict(RangeSet=RangeSet)))
def addSkyPix(self, registry: Registry, dimension: SkyPixDimension):
"""Populate the included skypix IDs for the given dimension from those
that overlap the visits the `ConversionSubset` was initialized with.
Parameters
----------
registry : `lsst.daf.butler.Registry`
Registry that can be queried for visit regions.
name : `str`
SkyMap name used in Gen3 data IDs.
"""
if self.regions is None:
self.regions = []
for visit in self.visits:
dataId = registry.expandDataId(instrument=self.instrument,
visit=visit)
self.regions.append(dataId.region)
ranges = RangeSet()
for region in self.regions:
ranges = ranges.union(dimension.pixelization.envelope(region))
self.skypix[dimension] = ranges
def testPickle(self):
r = RangeSet([2, 3, 5, 7, 11, 13, 17, 19])
s = pickle.loads(pickle.dumps(r))
self.assertEqual(r, s)
def testComparisonOperators(self):
s1 = RangeSet(1)
s2 = RangeSet(2)
self.assertNotEqual(s1, s2)
s1.insert(2)
s2.insert(1)
self.assertEqual(s1, s2)
self.assertTrue(RangeSet(2, 1).contains(RangeSet(3, 4)))
self.assertTrue(RangeSet(2, 1).contains(3, 4))
self.assertTrue(RangeSet(2, 1).contains(3))
self.assertTrue(RangeSet(2, 4).isWithin(RangeSet(1, 5)))
self.assertTrue(RangeSet(2, 4).isWithin(1, 5))
self.assertFalse(RangeSet(2, 4).isWithin(3))
self.assertTrue(RangeSet(2, 4).intersects(RangeSet(3, 5)))
self.assertTrue(RangeSet(2, 4).intersects(3, 5))
self.assertTrue(RangeSet(2, 4).intersects(3))
self.assertTrue(RangeSet(2, 4).isDisjointFrom(RangeSet(6, 8)))
self.assertTrue(RangeSet(2, 4).isDisjointFrom(6, 8))
self.assertTrue(RangeSet(2, 4).isDisjointFrom(6))
def build_quantum_graph(
cls,
task_def,
registry,
constraint_order,
constraint_ranges,
where=None,
collections=None,
):
"""Generate a `QuantumGraph` for running just this task.
This is a temporary workaround for incomplete butler query support for
HEALPix dimensions.
Parameters
----------
task_def : `lsst.pipe.base.TaskDef`
Task definition.
registry : `lsst.daf.butler.Registry`
Client for the butler database. May be read-only.
constraint_order : `int`
HEALPix order used to contrain which quanta are generated, via
``constraint_indices``. This should be a coarser grid (smaller
order) than the order used for the task's quantum and output data
IDs, and ideally something between the spatial scale of a patch or
the data repository's "common skypix" system (usually ``htm7``).
constraint_ranges : `lsst.sphgeom.RangeSet`
RangeSet which describes constraint pixels (HEALPix NEST, with order
constraint_order) to constrain generated quanta.
where : `str`, optional
A boolean `str` expression of the form accepted by
`Registry.queryDatasets` to constrain input datasets. This may
contain a constraint on tracts, patches, or bands, but not HEALPix
indices. Constraints on tracts and patches should usually be
unnecessary, however - existing coadds that overlap the given
HEALpix indices will be selected without such a constraint, and
providing one may reject some that should normally be included.
collections : `str` or `Iterable` [ `str` ], optional
Collection or collections to search for input datasets, in order.
If not provided, ``registry.defaults.collections`` will be
searched.
"""
config = task_def.config
dataset_types = pipeBase.PipelineDatasetTypes.fromPipeline(
pipeline=[task_def], registry=registry)
# Since we know this is the only task in the pipeline, we know there
# is only one overall input and one overall output.
(input_dataset_type, ) = dataset_types.inputs
# Extract the main output dataset type (which needs multiple
# DatasetRefs, and tells us the output HPX level), and make a set of
# what remains for more mechanical handling later.
output_dataset_type = dataset_types.outputs[
task_def.connections.hips_exposures.name]
incidental_output_dataset_types = dataset_types.outputs.copy()
incidental_output_dataset_types.remove(output_dataset_type)
(hpx_output_dimension, ) = (d for d in output_dataset_type.dimensions
if isinstance(d, SkyPixDimension))
constraint_hpx_pixelization = registry.dimensions[
f"healpix{constraint_order}"].pixelization
common_skypix_name = registry.dimensions.commonSkyPix.name
common_skypix_pixelization = registry.dimensions.commonSkyPix.pixelization
# We will need all the pixels at the quantum resolution as well
task_dimensions = registry.dimensions.extract(
task_def.connections.dimensions)
(hpx_dimension, ) = (d for d in task_dimensions if d.name != "band")
hpx_pixelization = hpx_dimension.pixelization
if hpx_pixelization.level < constraint_order:
raise ValueError(
f"Quantum order {hpx_pixelization.level} must be < {constraint_order}"
)
hpx_ranges = constraint_ranges.scaled(4**(hpx_pixelization.level -
constraint_order))
# We can be generous in looking for pixels here, because we constraint by actual
# patch regions below.
common_skypix_ranges = RangeSet()
for begin, end in constraint_ranges:
for hpx_index in range(begin, end):
constraint_hpx_region = constraint_hpx_pixelization.pixel(
hpx_index)
common_skypix_ranges |= common_skypix_pixelization.envelope(
constraint_hpx_region)
# To keep the query from getting out of hand (and breaking) we simplify until we have fewer
# than 100 ranges which seems to work fine.
for simp in range(1, 10):
if len(common_skypix_ranges) < 100:
break
common_skypix_ranges.simplify(simp)
# Use that RangeSet to assemble a WHERE constraint expression. This
# could definitely get too big if the "constraint healpix" order is too
# fine.
where_terms = []
bind = {}
for n, (begin, end) in enumerate(common_skypix_ranges):
stop = end - 1 # registry range syntax is inclusive
if begin == stop:
where_terms.append(f"{common_skypix_name} = cpx{n}")
bind[f"cpx{n}"] = begin
else:
where_terms.append(
f"({common_skypix_name} >= cpx{n}a AND {common_skypix_name} <= cpx{n}b)"
)
bind[f"cpx{n}a"] = begin
bind[f"cpx{n}b"] = stop
if where is None:
where = " OR ".join(where_terms)
else:
where = f"({where}) AND ({' OR '.join(where_terms)})"
# Query for input datasets with this constraint, and ask for expanded
# data IDs because we want regions. Immediately group this by patch so
# we don't do later geometric stuff n_bands more times than we need to.
input_refs = registry.queryDatasets(input_dataset_type,
where=where,
findFirst=True,
collections=collections,
bind=bind).expanded()
inputs_by_patch = defaultdict(set)
patch_dimensions = registry.dimensions.extract(["patch"])
for input_ref in input_refs:
inputs_by_patch[input_ref.dataId.subset(patch_dimensions)].add(
input_ref)
if not inputs_by_patch:
message_body = '\n'.join(input_refs.explain_no_results())
raise RuntimeError(f"No inputs found:\n{message_body}")
# Iterate over patches and compute the set of output healpix pixels
# that overlap each one. Use that to associate inputs with output
# pixels, but only for the output pixels we've already identified.
inputs_by_hpx = defaultdict(set)
for patch_data_id, input_refs_for_patch in inputs_by_patch.items():
patch_hpx_ranges = hpx_pixelization.envelope(patch_data_id.region)
for begin, end in patch_hpx_ranges & hpx_ranges:
for hpx_index in range(begin, end):
inputs_by_hpx[hpx_index].update(input_refs_for_patch)
# Iterate over the dict we just created and create the actual quanta.
quanta = []
for hpx_index, input_refs_for_hpx_index in inputs_by_hpx.items():
# Group inputs by band.
input_refs_by_band = defaultdict(list)
for input_ref in input_refs_for_hpx_index:
input_refs_by_band[input_ref.dataId["band"]].append(input_ref)
# Iterate over bands to make quanta.
for band, input_refs_for_band in input_refs_by_band.items():
data_id = registry.expandDataId({
hpx_dimension: hpx_index,
"band": band
})
hpx_pixel_ranges = RangeSet(hpx_index)
hpx_output_ranges = hpx_pixel_ranges.scaled(
4**(config.hips_order - hpx_pixelization.level))
output_data_ids = []
for begin, end in hpx_output_ranges:
for hpx_output_index in range(begin, end):
output_data_ids.append(
registry.expandDataId({
hpx_output_dimension: hpx_output_index,
"band": band
}))
outputs = {
dt: [DatasetRef(dt, data_id)]
for dt in incidental_output_dataset_types
}
outputs[output_dataset_type] = [
DatasetRef(output_dataset_type, data_id)
for data_id in output_data_ids
]
quanta.append(
Quantum(
taskName=task_def.taskName,
taskClass=task_def.taskClass,
dataId=data_id,
initInputs={},
inputs={input_dataset_type: input_refs_for_band},
outputs=outputs,
))
if len(quanta) == 0:
raise RuntimeError(
"Given constraints yielded empty quantum graph.")
return pipeBase.QuantumGraph(quanta={task_def: quanta})
def build_quantum_graph_cli(cls, argv):
"""A command-line interface entry point to `build_quantum_graph`.
This method provides the implementation for the
``build-high-resolution-hips-qg`` script.
Parameters
----------
argv : `Sequence` [ `str` ]
Command-line arguments (e.g. ``sys.argv[1:]``).
"""
parser = cls._make_cli_parser()
args = parser.parse_args(argv)
if args.subparser_name is None:
parser.print_help()
sys.exit(1)
pipeline = pipeBase.Pipeline.from_uri(args.pipeline)
expanded_pipeline = list(pipeline.toExpandedPipeline())
if len(expanded_pipeline) != 1:
raise RuntimeError(
f"Pipeline file {args.pipeline} may only contain one task.")
(task_def, ) = expanded_pipeline
butler = Butler(args.butler_config, collections=args.input)
if args.subparser_name == 'segment':
# Do the segmentation
hpix_pixelization = HealpixPixelization(
level=args.hpix_build_order)
dataset = task_def.connections.coadd_exposure_handles.name
data_ids = set(
butler.registry.queryDataIds("tract",
datasets=dataset).expanded())
region_pixels = []
for data_id in data_ids:
region = data_id.region
pixel_range = hpix_pixelization.envelope(region)
for r in pixel_range.ranges():
region_pixels.extend(range(r[0], r[1]))
indices = np.unique(region_pixels)
print(
f"Pixels to run at HEALPix order --hpix_build_order {args.hpix_build_order}:"
)
for pixel in indices:
print(pixel)
elif args.subparser_name == 'build':
# Build the quantum graph.
build_ranges = RangeSet(sorted(args.pixels))
qg = cls.build_quantum_graph(task_def,
butler.registry,
args.hpix_build_order,
build_ranges,
where=args.where,
collections=args.input)
qg.saveUri(args.save_qgraph)
