LSSTApplications  16.0-11-g09ed895+2,16.0-11-g12e47bd,16.0-11-g9bb73b2+6,16.0-12-g5c924a4+6,16.0-14-g9a974b3+1,16.0-15-g1417920+1,16.0-15-gdd5ca33+1,16.0-16-gf0259e2,16.0-17-g31abd91+7,16.0-17-g7d7456e+7,16.0-17-ga3d2e9f+13,16.0-18-ga4d4bcb+1,16.0-18-gd06566c+1,16.0-2-g0febb12+21,16.0-2-g9d5294e+69,16.0-2-ga8830df+6,16.0-20-g21842373+7,16.0-24-g3eae5ec,16.0-28-gfc9ea6c+4,16.0-29-ge8801f9,16.0-3-ge00e371+34,16.0-4-g18f3627+13,16.0-4-g5f3a788+20,16.0-4-ga3eb747+10,16.0-4-gabf74b7+29,16.0-4-gb13d127+6,16.0-49-g42e581f7+6,16.0-5-g27fb78a+7,16.0-5-g6a53317+34,16.0-5-gb3f8a4b+87,16.0-6-g9321be7+4,16.0-6-gcbc7b31+42,16.0-6-gf49912c+29,16.0-7-gd2eeba5+51,16.0-71-ge89f8615e,16.0-8-g21fd5fe+29,16.0-8-g3a9f023+20,16.0-8-g4734f7a+1,16.0-8-g5858431+3,16.0-9-gf5c1f43+8,master-gd73dc1d098+1,w.2019.01
LSSTDataManagementBasePackage
skyObjects.py
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1 
2 __all__ = ["SkyObjectsConfig", "SkyObjectsTask", "generateSkyObjects"]
3 
4 from lsst.pex.config import Config, Field, ListField
5 from lsst.pipe.base import Task
6 
8 import lsst.afw.geom
9 import lsst.afw.math
10 
11 
13  """Configuration for generating sky objects"""
14  avoidMask = ListField(dtype=str, default=["DETECTED", "DETECTED_NEGATIVE", "BAD", "NO_DATA"],
15  doc="Avoid pixels masked with these mask planes")
16  growMask = Field(dtype=int, default=0,
17  doc="Number of pixels to grow the masked pixels when adding sky objects")
18  sourceRadius = Field(dtype=float, default=8, doc="Radius, in pixels, of sky objects")
19  nSources = Field(dtype=int, default=100, doc="Try to add this many sky objects")
20  nTrialSources = Field(dtype=int, default=None, optional=True,
21  doc="Maximum number of trial sky object positions\n"
22  "(default: nSkySources*nTrialSkySourcesMultiplier)")
23  nTrialSourcesMultiplier = Field(dtype=int, default=5,
24  doc="Set nTrialSkySources to\n"
25  " nSkySources*nTrialSkySourcesMultiplier\n"
26  "if nTrialSkySources is None")
27 
28 
29 def generateSkyObjects(mask, seed, config):
30  """Generate a list of Footprints of sky objects
31 
32  Sky objects don't overlap with other objects. This is determined
33  through the provided `mask` (in which objects are typically flagged
34  as `DETECTED`).
35 
36  The algorithm for determining sky objects is random trial and error:
37  we try up to `nTrialSkySources` random positions to find `nSources`
38  sky objects.
39 
40  Parameters
41  ----------
42  mask : `lsst.afw.image.Mask`
43  Input mask plane, which identifies pixels to avoid for the sky
44  objects.
45  seed : `int`
46  Random number generator seed.
47  config : `SkyObjectsConfig`
48  Configuration for finding sky objects.
49 
50  Returns
51  -------
52  skyFootprints : `list` of `lsst.afw.detection.Footprint`
53  Footprints of sky objects. Each will have a peak at the center
54  of the sky object.
55  """
56  if config.nSources <= 0:
57  return []
58 
59  skySourceRadius = config.sourceRadius
60  nSkySources = config.nSources
61  nTrialSkySources = config.nTrialSources
62  if nTrialSkySources is None:
63  nTrialSkySources = config.nTrialSourcesMultiplier*nSkySources
64 
65  box = mask.getBBox()
66  box.grow(-(int(skySourceRadius) + 1)) # Avoid objects partially off the image
67  xMin, yMin = box.getMin()
68  xMax, yMax = box.getMax()
69 
70  avoid = lsst.afw.geom.SpanSet.fromMask(mask, mask.getPlaneBitMask(config.avoidMask))
71  if config.growMask > 0:
72  avoid = avoid.dilated(config.growMask)
73 
74  rng = lsst.afw.math.Random(seed=seed)
75 
76  skyFootprints = []
77  for _ in range(nTrialSkySources):
78  if len(skyFootprints) == nSkySources:
79  break
80 
81  x = int(rng.flat(xMin, xMax))
82  y = int(rng.flat(yMin, yMax))
83  spans = lsst.afw.geom.SpanSet.fromShape(int(skySourceRadius), offset=(x, y))
84  if spans.overlaps(avoid):
85  continue
86 
87  fp = lsst.afw.detection.Footprint(spans, mask.getBBox())
88  fp.addPeak(x, y, 0)
89  skyFootprints.append(fp)
90 
91  return skyFootprints
92 
93 
95  ConfigClass = SkyObjectsConfig
96 
97  def run(self, mask, seed):
98  """Generate a list of Footprints of sky objects
99 
100  Sky objects don't overlap with other objects. This is determined
101  through the provided `mask` (in which objects are typically flagged
102  as `DETECTED`).
103 
104  The algorithm for determining sky objects is random trial and error:
105  we try up to `nTrialSkySources` random positions to find `nSources`
106  sky objects.
107 
108  Parameters
109  ----------
110  mask : `lsst.afw.image.Mask`
111  Input mask plane, which identifies pixels to avoid for the sky
112  objects.
113  seed : `int`
114  Random number generator seed.
115 
116  Returns
117  -------
118  skyFootprints : `list` of `lsst.afw.detection.Footprint`
119  Footprints of sky objects. Each will have a peak at the center
120  of the sky object.
121  """
122  skyFootprints = generateSkyObjects(mask, seed, self.config)
123  self.log.info("Added %d of %d requested sky sources (%.0f%%)", len(skyFootprints),
124  self.config.nSources, 100*len(skyFootprints)/self.config.nSources)
125  return skyFootprints
static std::shared_ptr< geom::SpanSet > fromMask(image::Mask< T > const &mask, UnaryPredicate comparator=details::AnyBitSetFunctor< T >())
Create a SpanSet from a mask.
Definition: SpanSet.h:644
static std::shared_ptr< geom::SpanSet > fromShape(int r, Stencil s=Stencil::CIRCLE, lsst::geom::Point2I offset=lsst::geom::Point2I())
Factory function for creating SpanSets from a Stencil.
Definition: SpanSet.cc:689
def generateSkyObjects(mask, seed, config)
Definition: skyObjects.py:29
Class to describe the properties of a detected object from an image.
Definition: Footprint.h:62
A class that can be used to generate sequences of random numbers according to a number of different a...
Definition: Random.h:57