Coverage for python/lsst/ip/diffim/utils.py: 6%
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1# This file is part of ip_diffim.
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# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
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
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
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22"""Support utilities for Measuring sources"""
24# Export DipoleTestImage to expose fake image generating funcs
25__all__ = ["DipoleTestImage", "evaluateMeanPsfFwhm", "getPsfFwhm"]
27import itertools
28import numpy as np
29import lsst.geom as geom
30import lsst.afw.detection as afwDet
31import lsst.afw.display as afwDisplay
32import lsst.afw.detection as afwDetection
33import lsst.afw.geom as afwGeom
34import lsst.afw.image as afwImage
35import lsst.afw.math as afwMath
36import lsst.afw.table as afwTable
37import lsst.meas.algorithms as measAlg
38import lsst.meas.base as measBase
39from lsst.meas.algorithms.testUtils import plantSources
40from lsst.pex.exceptions import InvalidParameterError
41from lsst.utils.logging import getLogger
42from .dipoleFitTask import DipoleFitAlgorithm
43from . import diffimLib
45afwDisplay.setDefaultMaskTransparency(75)
46keptPlots = False # Have we arranged to keep spatial plots open?
48_LOG = getLogger(__name__)
51def showSourceSet(sSet, xy0=(0, 0), frame=0, ctype=afwDisplay.GREEN, symb="+", size=2):
52 """Draw the (XAstrom, YAstrom) positions of a set of Sources.
54 Image has the given XY0.
55 """
56 disp = afwDisplay.afwDisplay(frame=frame)
57 with disp.Buffering():
58 for s in sSet:
59 xc, yc = s.getXAstrom() - xy0[0], s.getYAstrom() - xy0[1]
61 if symb == "id":
62 disp.dot(str(s.getId()), xc, yc, ctype=ctype, size=size)
63 else:
64 disp.dot(symb, xc, yc, ctype=ctype, size=size)
67# Kernel display utilities
68#
71def showKernelSpatialCells(maskedIm, kernelCellSet, showChi2=False, symb="o",
72 ctype=None, ctypeUnused=None, ctypeBad=None, size=3,
73 frame=None, title="Spatial Cells"):
74 """Show the SpatialCells.
76 If symb is something that display.dot understands (e.g. "o"), the top
77 nMaxPerCell candidates will be indicated with that symbol, using ctype
78 and size.
79 """
80 disp = afwDisplay.Display(frame=frame)
81 disp.mtv(maskedIm, title=title)
82 with disp.Buffering():
83 origin = [-maskedIm.getX0(), -maskedIm.getY0()]
84 for cell in kernelCellSet.getCellList():
85 afwDisplay.utils.drawBBox(cell.getBBox(), origin=origin, display=disp)
87 goodies = ctypeBad is None
88 for cand in cell.begin(goodies):
89 xc, yc = cand.getXCenter() + origin[0], cand.getYCenter() + origin[1]
90 if cand.getStatus() == afwMath.SpatialCellCandidate.BAD:
91 color = ctypeBad
92 elif cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
93 color = ctype
94 elif cand.getStatus() == afwMath.SpatialCellCandidate.UNKNOWN:
95 color = ctypeUnused
96 else:
97 continue
99 if color:
100 disp.dot(symb, xc, yc, ctype=color, size=size)
102 if showChi2:
103 rchi2 = cand.getChi2()
104 if rchi2 > 1e100:
105 rchi2 = np.nan
106 disp.dot("%d %.1f" % (cand.getId(), rchi2),
107 xc - size, yc - size - 4, ctype=color, size=size)
110def showDiaSources(sources, exposure, isFlagged, isDipole, frame=None):
111 """Display Dia Sources.
112 """
113 #
114 # Show us the ccandidates
115 #
116 # Too many mask planes in diffims
117 disp = afwDisplay.Display(frame=frame)
118 for plane in ("BAD", "CR", "EDGE", "INTERPOlATED", "INTRP", "SAT", "SATURATED"):
119 disp.setMaskPlaneColor(plane, color="ignore")
121 mos = afwDisplay.utils.Mosaic()
122 for i in range(len(sources)):
123 source = sources[i]
124 badFlag = isFlagged[i]
125 dipoleFlag = isDipole[i]
126 bbox = source.getFootprint().getBBox()
127 stamp = exposure.Factory(exposure, bbox, True)
128 im = afwDisplay.utils.Mosaic(gutter=1, background=0, mode="x")
129 im.append(stamp.getMaskedImage())
130 lab = "%.1f,%.1f:" % (source.getX(), source.getY())
131 if badFlag:
132 ctype = afwDisplay.RED
133 lab += "BAD"
134 if dipoleFlag:
135 ctype = afwDisplay.YELLOW
136 lab += "DIPOLE"
137 if not badFlag and not dipoleFlag:
138 ctype = afwDisplay.GREEN
139 lab += "OK"
140 mos.append(im.makeMosaic(), lab, ctype)
141 title = "Dia Sources"
142 mosaicImage = mos.makeMosaic(display=disp, title=title)
143 return mosaicImage
146def showKernelCandidates(kernelCellSet, kernel, background, frame=None, showBadCandidates=True,
147 resids=False, kernels=False):
148 """Display the Kernel candidates.
150 If kernel is provided include spatial model and residuals;
151 If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi.
152 """
153 #
154 # Show us the ccandidates
155 #
156 if kernels:
157 mos = afwDisplay.utils.Mosaic(gutter=5, background=0)
158 else:
159 mos = afwDisplay.utils.Mosaic(gutter=5, background=-1)
160 #
161 candidateCenters = []
162 candidateCentersBad = []
163 candidateIndex = 0
164 for cell in kernelCellSet.getCellList():
165 for cand in cell.begin(False): # include bad candidates
166 # Original difference image; if does not exist, skip candidate
167 try:
168 resid = cand.getDifferenceImage(diffimLib.KernelCandidateF.ORIG)
169 except Exception:
170 continue
172 rchi2 = cand.getChi2()
173 if rchi2 > 1e100:
174 rchi2 = np.nan
176 if not showBadCandidates and cand.isBad():
177 continue
179 im_resid = afwDisplay.utils.Mosaic(gutter=1, background=-0.5, mode="x")
181 try:
182 im = cand.getScienceMaskedImage()
183 im = im.Factory(im, True)
184 im.setXY0(cand.getScienceMaskedImage().getXY0())
185 except Exception:
186 continue
187 if (not resids and not kernels):
188 im_resid.append(im.Factory(im, True))
189 try:
190 im = cand.getTemplateMaskedImage()
191 im = im.Factory(im, True)
192 im.setXY0(cand.getTemplateMaskedImage().getXY0())
193 except Exception:
194 continue
195 if (not resids and not kernels):
196 im_resid.append(im.Factory(im, True))
198 # Difference image with original basis
199 if resids:
200 var = resid.variance
201 var = var.Factory(var, True)
202 np.sqrt(var.array, var.array) # inplace sqrt
203 resid = resid.image
204 resid /= var
205 bbox = kernel.shrinkBBox(resid.getBBox())
206 resid = resid.Factory(resid, bbox, deep=True)
207 elif kernels:
208 kim = cand.getKernelImage(diffimLib.KernelCandidateF.ORIG).convertF()
209 resid = kim.Factory(kim, True)
210 im_resid.append(resid)
212 # residuals using spatial model
213 ski = afwImage.ImageD(kernel.getDimensions())
214 kernel.computeImage(ski, False, int(cand.getXCenter()), int(cand.getYCenter()))
215 sk = afwMath.FixedKernel(ski)
216 sbg = 0.0
217 if background:
218 sbg = background(int(cand.getXCenter()), int(cand.getYCenter()))
219 sresid = cand.getDifferenceImage(sk, sbg)
220 resid = sresid
221 if resids:
222 resid = sresid.image
223 resid /= var
224 bbox = kernel.shrinkBBox(resid.getBBox())
225 resid = resid.Factory(resid, bbox, deep=True)
226 elif kernels:
227 kim = ski.convertF()
228 resid = kim.Factory(kim, True)
229 im_resid.append(resid)
231 im = im_resid.makeMosaic()
233 lab = "%d chi^2 %.1f" % (cand.getId(), rchi2)
234 ctype = afwDisplay.RED if cand.isBad() else afwDisplay.GREEN
236 mos.append(im, lab, ctype)
238 if False and np.isnan(rchi2):
239 disp = afwDisplay.Display(frame=1)
240 disp.mtv(cand.getScienceMaskedImage.image, title="candidate")
241 print("rating", cand.getCandidateRating())
243 im = cand.getScienceMaskedImage()
244 center = (candidateIndex, cand.getXCenter() - im.getX0(), cand.getYCenter() - im.getY0())
245 candidateIndex += 1
246 if cand.isBad():
247 candidateCentersBad.append(center)
248 else:
249 candidateCenters.append(center)
251 if resids:
252 title = "chi Diffim"
253 elif kernels:
254 title = "Kernels"
255 else:
256 title = "Candidates & residuals"
258 disp = afwDisplay.Display(frame=frame)
259 mosaicImage = mos.makeMosaic(display=disp, title=title)
261 return mosaicImage
264def showKernelBasis(kernel, frame=None):
265 """Display a Kernel's basis images.
266 """
267 mos = afwDisplay.utils.Mosaic()
269 for k in kernel.getKernelList():
270 im = afwImage.ImageD(k.getDimensions())
271 k.computeImage(im, False)
272 mos.append(im)
274 disp = afwDisplay.Display(frame=frame)
275 mos.makeMosaic(display=disp, title="Kernel Basis Images")
277 return mos
279###############
282def plotKernelSpatialModel(kernel, kernelCellSet, showBadCandidates=True,
283 numSample=128, keepPlots=True, maxCoeff=10):
284 """Plot the Kernel spatial model.
285 """
286 try:
287 import matplotlib.pyplot as plt
288 import matplotlib.colors
289 except ImportError as e:
290 print("Unable to import numpy and matplotlib: %s" % e)
291 return
293 x0 = kernelCellSet.getBBox().getBeginX()
294 y0 = kernelCellSet.getBBox().getBeginY()
296 candPos = list()
297 candFits = list()
298 badPos = list()
299 badFits = list()
300 candAmps = list()
301 badAmps = list()
302 for cell in kernelCellSet.getCellList():
303 for cand in cell.begin(False):
304 if not showBadCandidates and cand.isBad():
305 continue
306 candCenter = geom.PointD(cand.getXCenter(), cand.getYCenter())
307 try:
308 im = cand.getTemplateMaskedImage()
309 except Exception:
310 continue
312 targetFits = badFits if cand.isBad() else candFits
313 targetPos = badPos if cand.isBad() else candPos
314 targetAmps = badAmps if cand.isBad() else candAmps
316 # compare original and spatial kernel coefficients
317 kp0 = np.array(cand.getKernel(diffimLib.KernelCandidateF.ORIG).getKernelParameters())
318 amp = cand.getCandidateRating()
320 targetFits = badFits if cand.isBad() else candFits
321 targetPos = badPos if cand.isBad() else candPos
322 targetAmps = badAmps if cand.isBad() else candAmps
324 targetFits.append(kp0)
325 targetPos.append(candCenter)
326 targetAmps.append(amp)
328 xGood = np.array([pos.getX() for pos in candPos]) - x0
329 yGood = np.array([pos.getY() for pos in candPos]) - y0
330 zGood = np.array(candFits)
332 xBad = np.array([pos.getX() for pos in badPos]) - x0
333 yBad = np.array([pos.getY() for pos in badPos]) - y0
334 zBad = np.array(badFits)
335 numBad = len(badPos)
337 xRange = np.linspace(0, kernelCellSet.getBBox().getWidth(), num=numSample)
338 yRange = np.linspace(0, kernelCellSet.getBBox().getHeight(), num=numSample)
340 if maxCoeff:
341 maxCoeff = min(maxCoeff, kernel.getNKernelParameters())
342 else:
343 maxCoeff = kernel.getNKernelParameters()
345 for k in range(maxCoeff):
346 func = kernel.getSpatialFunction(k)
347 dfGood = zGood[:, k] - np.array([func(pos.getX(), pos.getY()) for pos in candPos])
348 yMin = dfGood.min()
349 yMax = dfGood.max()
350 if numBad > 0:
351 dfBad = zBad[:, k] - np.array([func(pos.getX(), pos.getY()) for pos in badPos])
352 # Can really screw up the range...
353 yMin = min([yMin, dfBad.min()])
354 yMax = max([yMax, dfBad.max()])
355 yMin -= 0.05*(yMax - yMin)
356 yMax += 0.05*(yMax - yMin)
358 fRange = np.ndarray((len(xRange), len(yRange)))
359 for j, yVal in enumerate(yRange):
360 for i, xVal in enumerate(xRange):
361 fRange[j][i] = func(xVal, yVal)
363 fig = plt.figure(k)
365 fig.clf()
366 try:
367 fig.canvas._tkcanvas._root().lift() # == Tk's raise, but raise is a python reserved word
368 except Exception: # protect against API changes
369 pass
371 fig.suptitle('Kernel component %d' % k)
373 # LL
374 ax = fig.add_axes((0.1, 0.05, 0.35, 0.35))
375 vmin = fRange.min() # - 0.05*np.fabs(fRange.min())
376 vmax = fRange.max() # + 0.05*np.fabs(fRange.max())
377 norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
378 im = ax.imshow(fRange, aspect='auto', norm=norm,
379 extent=[0, kernelCellSet.getBBox().getWidth() - 1,
380 0, kernelCellSet.getBBox().getHeight() - 1])
381 ax.set_title('Spatial polynomial')
382 plt.colorbar(im, orientation='horizontal', ticks=[vmin, vmax])
384 # UL
385 ax = fig.add_axes((0.1, 0.55, 0.35, 0.35))
386 ax.plot(-2.5*np.log10(candAmps), zGood[:, k], 'b+')
387 if numBad > 0:
388 ax.plot(-2.5*np.log10(badAmps), zBad[:, k], 'r+')
389 ax.set_title("Basis Coefficients")
390 ax.set_xlabel("Instr mag")
391 ax.set_ylabel("Coeff")
393 # LR
394 ax = fig.add_axes((0.55, 0.05, 0.35, 0.35))
395 ax.set_autoscale_on(False)
396 ax.set_xbound(lower=0, upper=kernelCellSet.getBBox().getHeight())
397 ax.set_ybound(lower=yMin, upper=yMax)
398 ax.plot(yGood, dfGood, 'b+')
399 if numBad > 0:
400 ax.plot(yBad, dfBad, 'r+')
401 ax.axhline(0.0)
402 ax.set_title('dCoeff (indiv-spatial) vs. y')
404 # UR
405 ax = fig.add_axes((0.55, 0.55, 0.35, 0.35))
406 ax.set_autoscale_on(False)
407 ax.set_xbound(lower=0, upper=kernelCellSet.getBBox().getWidth())
408 ax.set_ybound(lower=yMin, upper=yMax)
409 ax.plot(xGood, dfGood, 'b+')
410 if numBad > 0:
411 ax.plot(xBad, dfBad, 'r+')
412 ax.axhline(0.0)
413 ax.set_title('dCoeff (indiv-spatial) vs. x')
415 fig.show()
417 global keptPlots
418 if keepPlots and not keptPlots:
419 # Keep plots open when done
420 def show():
421 print("%s: Please close plots when done." % __name__)
422 try:
423 plt.show()
424 except Exception:
425 pass
426 print("Plots closed, exiting...")
427 import atexit
428 atexit.register(show)
429 keptPlots = True
432def plotKernelCoefficients(spatialKernel, kernelCellSet, showBadCandidates=False, keepPlots=True):
433 """Plot the individual kernel candidate and the spatial kernel solution coefficients.
435 Parameters
436 ----------
438 spatialKernel : `lsst.afw.math.LinearCombinationKernel`
439 The spatial spatialKernel solution model which is a spatially varying linear combination
440 of the spatialKernel basis functions.
441 Typically returned by `lsst.ip.diffim.SpatialKernelSolution.getSolutionPair()`.
443 kernelCellSet : `lsst.afw.math.SpatialCellSet`
444 The spatial cells that was used for solution for the spatialKernel. They contain the
445 local solutions of the AL kernel for the selected sources.
447 showBadCandidates : `bool`, optional
448 If True, plot the coefficient values for kernel candidates where the solution was marked
449 bad by the numerical algorithm. Defaults to False.
451 keepPlots: `bool`, optional
452 If True, sets ``plt.show()`` to be called before the task terminates, so that the plots
453 can be explored interactively. Defaults to True.
455 Notes
456 -----
457 This function produces 3 figures per image subtraction operation.
458 * A grid plot of the local solutions. Each grid cell corresponds to a proportional area in
459 the image. In each cell, local kernel solution coefficients are plotted of kernel candidates (color)
460 that fall into this area as a function of the kernel basis function number.
461 * A grid plot of the spatial solution. Each grid cell corresponds to a proportional area in
462 the image. In each cell, the spatial solution coefficients are evaluated for the center of the cell.
463 * Histogram of the local solution coefficients. Red line marks the spatial solution value at
464 center of the image.
466 This function is called if ``lsst.ip.diffim.psfMatch.plotKernelCoefficients==True`` in lsstDebug. This
467 function was implemented as part of DM-17825.
468 """
469 try:
470 import matplotlib.pyplot as plt
471 except ImportError as e:
472 print("Unable to import matplotlib: %s" % e)
473 return
475 # Image dimensions
476 imgBBox = kernelCellSet.getBBox()
477 x0 = imgBBox.getBeginX()
478 y0 = imgBBox.getBeginY()
479 wImage = imgBBox.getWidth()
480 hImage = imgBBox.getHeight()
481 imgCenterX = imgBBox.getCenterX()
482 imgCenterY = imgBBox.getCenterY()
484 # Plot the local solutions
485 # ----
487 # Grid size
488 nX = 8
489 nY = 8
490 wCell = wImage / nX
491 hCell = hImage / nY
493 fig = plt.figure()
494 fig.suptitle("Kernel candidate parameters on an image grid")
495 arrAx = fig.subplots(nrows=nY, ncols=nX, sharex=True, sharey=True, gridspec_kw=dict(
496 wspace=0, hspace=0))
498 # Bottom left panel is for bottom left part of the image
499 arrAx = arrAx[::-1, :]
501 allParams = []
502 for cell in kernelCellSet.getCellList():
503 cellBBox = geom.Box2D(cell.getBBox())
504 # Determine which panel this spatial cell belongs to
505 iX = int((cellBBox.getCenterX() - x0)//wCell)
506 iY = int((cellBBox.getCenterY() - y0)//hCell)
508 for cand in cell.begin(False):
509 try:
510 kernel = cand.getKernel(cand.ORIG)
511 except Exception:
512 continue
514 if not showBadCandidates and cand.isBad():
515 continue
517 nKernelParams = kernel.getNKernelParameters()
518 kernelParams = np.array(kernel.getKernelParameters())
519 allParams.append(kernelParams)
521 if cand.isBad():
522 color = 'red'
523 else:
524 color = None
525 arrAx[iY, iX].plot(np.arange(nKernelParams), kernelParams, '.-',
526 color=color, drawstyle='steps-mid', linewidth=0.1)
527 for ax in arrAx.ravel():
528 ax.grid(True, axis='y')
530 # Plot histogram of the local parameters and the global solution at the image center
531 # ----
533 spatialFuncs = spatialKernel.getSpatialFunctionList()
534 nKernelParams = spatialKernel.getNKernelParameters()
535 nX = 8
536 fig = plt.figure()
537 fig.suptitle("Hist. of parameters marked with spatial solution at img center")
538 arrAx = fig.subplots(nrows=int(nKernelParams//nX)+1, ncols=nX)
539 arrAx = arrAx[::-1, :]
540 allParams = np.array(allParams)
541 for k in range(nKernelParams):
542 ax = arrAx.ravel()[k]
543 ax.hist(allParams[:, k], bins=20, edgecolor='black')
544 ax.set_xlabel('P{}'.format(k))
545 valueParam = spatialFuncs[k](imgCenterX, imgCenterY)
546 ax.axvline(x=valueParam, color='red')
547 ax.text(0.1, 0.9, '{:.1f}'.format(valueParam),
548 transform=ax.transAxes, backgroundcolor='lightsteelblue')
550 # Plot grid of the spatial solution
551 # ----
553 nX = 8
554 nY = 8
555 wCell = wImage / nX
556 hCell = hImage / nY
557 x0 += wCell / 2
558 y0 += hCell / 2
560 fig = plt.figure()
561 fig.suptitle("Spatial solution of kernel parameters on an image grid")
562 arrAx = fig.subplots(nrows=nY, ncols=nX, sharex=True, sharey=True, gridspec_kw=dict(
563 wspace=0, hspace=0))
564 arrAx = arrAx[::-1, :]
565 kernelParams = np.zeros(nKernelParams, dtype=float)
567 for iX in range(nX):
568 for iY in range(nY):
569 x = x0 + iX * wCell
570 y = y0 + iY * hCell
571 # Evaluate the spatial solution functions for this x,y location
572 kernelParams = [f(x, y) for f in spatialFuncs]
573 arrAx[iY, iX].plot(np.arange(nKernelParams), kernelParams, '.-', drawstyle='steps-mid')
574 arrAx[iY, iX].grid(True, axis='y')
576 global keptPlots
577 if keepPlots and not keptPlots:
578 # Keep plots open when done
579 def show():
580 print("%s: Please close plots when done." % __name__)
581 try:
582 plt.show()
583 except Exception:
584 pass
585 print("Plots closed, exiting...")
586 import atexit
587 atexit.register(show)
588 keptPlots = True
591def showKernelMosaic(bbox, kernel, nx=7, ny=None, frame=None, title=None,
592 showCenter=True, showEllipticity=True):
593 """Show a mosaic of Kernel images.
594 """
595 mos = afwDisplay.utils.Mosaic()
597 x0 = bbox.getBeginX()
598 y0 = bbox.getBeginY()
599 width = bbox.getWidth()
600 height = bbox.getHeight()
602 if not ny:
603 ny = int(nx*float(height)/width + 0.5)
604 if not ny:
605 ny = 1
607 schema = afwTable.SourceTable.makeMinimalSchema()
608 centroidName = "base_SdssCentroid"
609 shapeName = "base_SdssShape"
610 control = measBase.SdssCentroidControl()
611 schema.getAliasMap().set("slot_Centroid", centroidName)
612 schema.getAliasMap().set("slot_Centroid_flag", centroidName + "_flag")
613 centroider = measBase.SdssCentroidAlgorithm(control, centroidName, schema)
614 sdssShape = measBase.SdssShapeControl()
615 shaper = measBase.SdssShapeAlgorithm(sdssShape, shapeName, schema)
616 table = afwTable.SourceTable.make(schema)
617 table.defineCentroid(centroidName)
618 table.defineShape(shapeName)
620 centers = []
621 shapes = []
622 for iy in range(ny):
623 for ix in range(nx):
624 x = int(ix*(width - 1)/(nx - 1)) + x0
625 y = int(iy*(height - 1)/(ny - 1)) + y0
627 im = afwImage.ImageD(kernel.getDimensions())
628 ksum = kernel.computeImage(im, False, x, y)
629 lab = "Kernel(%d,%d)=%.2f" % (x, y, ksum) if False else ""
630 mos.append(im, lab)
632 # SdssCentroidAlgorithm.measure requires an exposure of floats
633 exp = afwImage.makeExposure(afwImage.makeMaskedImage(im.convertF()))
635 w, h = im.getWidth(), im.getHeight()
636 centerX = im.getX0() + w//2
637 centerY = im.getY0() + h//2
638 src = table.makeRecord()
639 spans = afwGeom.SpanSet(exp.getBBox())
640 foot = afwDet.Footprint(spans)
641 foot.addPeak(centerX, centerY, 1)
642 src.setFootprint(foot)
644 try: # The centroider requires a psf, so this will fail if none is attached to exp
645 centroider.measure(src, exp)
646 centers.append((src.getX(), src.getY()))
648 shaper.measure(src, exp)
649 shapes.append((src.getIxx(), src.getIxy(), src.getIyy()))
650 except Exception:
651 pass
653 disp = afwDisplay.Display(frame=frame)
654 mos.makeMosaic(display=disp, title=title if title else "Model Kernel", mode=nx)
656 if centers and frame is not None:
657 disp = afwDisplay.Display(frame=frame)
658 i = 0
659 with disp.Buffering():
660 for cen, shape in zip(centers, shapes):
661 bbox = mos.getBBox(i)
662 i += 1
663 xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
664 if showCenter:
665 disp.dot("+", xc, yc, ctype=afwDisplay.BLUE)
667 if showEllipticity:
668 ixx, ixy, iyy = shape
669 disp.dot("@:%g,%g,%g" % (ixx, ixy, iyy), xc, yc, ctype=afwDisplay.RED)
671 return mos
674def plotWhisker(results, newWcs):
675 """Plot whisker diagram of astromeric offsets between results.matches.
676 """
677 refCoordKey = results.matches[0].first.getTable().getCoordKey()
678 inCentroidKey = results.matches[0].second.getTable().getCentroidSlot().getMeasKey()
679 positions = [m.first.get(refCoordKey) for m in results.matches]
680 residuals = [m.first.get(refCoordKey).getOffsetFrom(
681 newWcs.pixelToSky(m.second.get(inCentroidKey))) for
682 m in results.matches]
683 import matplotlib.pyplot as plt
684 fig = plt.figure()
685 sp = fig.add_subplot(1, 1, 0)
686 xpos = [x[0].asDegrees() for x in positions]
687 ypos = [x[1].asDegrees() for x in positions]
688 xpos.append(0.02*(max(xpos) - min(xpos)) + min(xpos))
689 ypos.append(0.98*(max(ypos) - min(ypos)) + min(ypos))
690 xidxs = np.isfinite(xpos)
691 yidxs = np.isfinite(ypos)
692 X = np.asarray(xpos)[xidxs]
693 Y = np.asarray(ypos)[yidxs]
694 distance = [x[1].asArcseconds() for x in residuals]
695 distance.append(0.2)
696 distance = np.asarray(distance)[xidxs]
697 # NOTE: This assumes that the bearing is measured positive from +RA through North.
698 # From the documentation this is not clear.
699 bearing = [x[0].asRadians() for x in residuals]
700 bearing.append(0)
701 bearing = np.asarray(bearing)[xidxs]
702 U = (distance*np.cos(bearing))
703 V = (distance*np.sin(bearing))
704 sp.quiver(X, Y, U, V)
705 sp.set_title("WCS Residual")
706 plt.show()
709class DipoleTestImage:
710 """Utility class for dipole measurement testing.
712 Generate an image with simulated dipoles and noise; store the original
713 "pre-subtraction" images and catalogs as well.
714 Used to generate test data for DMTN-007 (http://dmtn-007.lsst.io).
715 """
717 def __init__(self, w=101, h=101, xcenPos=[27.], ycenPos=[25.], xcenNeg=[23.], ycenNeg=[25.],
718 psfSigma=2., flux=[30000.], fluxNeg=None, noise=10., gradientParams=None):
719 self.w = w
720 self.h = h
721 self.xcenPos = xcenPos
722 self.ycenPos = ycenPos
723 self.xcenNeg = xcenNeg
724 self.ycenNeg = ycenNeg
725 self.psfSigma = psfSigma
726 self.flux = flux
727 self.fluxNeg = fluxNeg
728 if fluxNeg is None:
729 self.fluxNeg = self.flux
730 self.noise = noise
731 self.gradientParams = gradientParams
732 self._makeDipoleImage()
734 def _makeDipoleImage(self):
735 """Generate an exposure and catalog with the given dipole source(s).
736 """
737 # Must seed the pos/neg images with different values to ensure they get different noise realizations
738 posImage, posCatalog = self._makeStarImage(
739 xc=self.xcenPos, yc=self.ycenPos, flux=self.flux, randomSeed=111)
741 negImage, negCatalog = self._makeStarImage(
742 xc=self.xcenNeg, yc=self.ycenNeg, flux=self.fluxNeg, randomSeed=222)
744 dipole = posImage.clone()
745 di = dipole.getMaskedImage()
746 di -= negImage.getMaskedImage()
748 self.diffim, self.posImage, self.posCatalog, self.negImage, self.negCatalog \
749 = dipole, posImage, posCatalog, negImage, negCatalog
751 def _makeStarImage(self, xc=[15.3], yc=[18.6], flux=[2500], schema=None, randomSeed=None):
752 """Generate an exposure and catalog with the given stellar source(s).
753 """
754 from lsst.meas.base.tests import TestDataset
755 bbox = geom.Box2I(geom.Point2I(0, 0), geom.Point2I(self.w - 1, self.h - 1))
756 dataset = TestDataset(bbox, psfSigma=self.psfSigma, threshold=1.)
758 for i in range(len(xc)):
759 dataset.addSource(instFlux=flux[i], centroid=geom.Point2D(xc[i], yc[i]))
761 if schema is None:
762 schema = TestDataset.makeMinimalSchema()
763 exposure, catalog = dataset.realize(noise=self.noise, schema=schema, randomSeed=randomSeed)
765 if self.gradientParams is not None:
766 y, x = np.mgrid[:self.w, :self.h]
767 gp = self.gradientParams
768 gradient = gp[0] + gp[1]*x + gp[2]*y
769 if len(self.gradientParams) > 3: # it includes a set of 2nd-order polynomial params
770 gradient += gp[3]*x*y + gp[4]*x*x + gp[5]*y*y
771 imgArr = exposure.image.array
772 imgArr += gradient
774 return exposure, catalog
776 def fitDipoleSource(self, source, **kwds):
777 alg = DipoleFitAlgorithm(self.diffim, self.posImage, self.negImage)
778 fitResult = alg.fitDipole(source, **kwds)
779 return fitResult
781 def detectDipoleSources(self, doMerge=True, diffim=None, detectSigma=5.5, grow=3, minBinSize=32):
782 """Utility function for detecting dipoles.
784 Detect pos/neg sources in the diffim, then merge them. A
785 bigger "grow" parameter leads to a larger footprint which
786 helps with dipole measurement for faint dipoles.
788 Parameters
789 ----------
790 doMerge : `bool`
791 Whether to merge the positive and negagive detections into a single
792 source table.
793 diffim : `lsst.afw.image.exposure.exposure.ExposureF`
794 Difference image on which to perform detection.
795 detectSigma : `float`
796 Threshold for object detection.
797 grow : `int`
798 Number of pixels to grow the footprints before merging.
799 minBinSize : `int`
800 Minimum bin size for the background (re)estimation (only applies if
801 the default leads to min(nBinX, nBinY) < fit order so the default
802 config parameter needs to be decreased, but not to a value smaller
803 than ``minBinSize``, in which case the fitting algorithm will take
804 over and decrease the fit order appropriately.)
806 Returns
807 -------
808 sources : `lsst.afw.table.SourceCatalog`
809 If doMerge=True, the merged source catalog is returned OR
810 detectTask : `lsst.meas.algorithms.SourceDetectionTask`
811 schema : `lsst.afw.table.Schema`
812 If doMerge=False, the source detection task and its schema are
813 returned.
814 """
815 if diffim is None:
816 diffim = self.diffim
818 # Start with a minimal schema - only the fields all SourceCatalogs need
819 schema = afwTable.SourceTable.makeMinimalSchema()
821 # Customize the detection task a bit (optional)
822 detectConfig = measAlg.SourceDetectionConfig()
823 detectConfig.returnOriginalFootprints = False # should be the default
825 # code from imageDifference.py:
826 detectConfig.thresholdPolarity = "both"
827 detectConfig.thresholdValue = detectSigma
828 # detectConfig.nSigmaToGrow = psfSigma
829 detectConfig.reEstimateBackground = True # if False, will fail often for faint sources on gradients?
830 detectConfig.thresholdType = "pixel_stdev"
831 detectConfig.excludeMaskPlanes = ["EDGE"]
832 # Test images are often quite small, so may need to adjust background binSize
833 while ((min(diffim.getWidth(), diffim.getHeight()))//detectConfig.background.binSize
834 < detectConfig.background.approxOrderX and detectConfig.background.binSize > minBinSize):
835 detectConfig.background.binSize = max(minBinSize, detectConfig.background.binSize//2)
837 # Create the detection task. We pass the schema so the task can declare a few flag fields
838 detectTask = measAlg.SourceDetectionTask(schema, config=detectConfig)
840 table = afwTable.SourceTable.make(schema)
841 catalog = detectTask.run(table, diffim)
843 # Now do the merge.
844 if doMerge:
845 fpSet = catalog.positive
846 fpSet.merge(catalog.negative, grow, grow, False)
847 sources = afwTable.SourceCatalog(table)
848 fpSet.makeSources(sources)
850 return sources
852 else:
853 return detectTask, schema
856def _sliceWidth(image, threshold, peaks, axis):
857 vec = image.take(peaks[1 - axis], axis=axis)
858 low = np.interp(threshold, vec[:peaks[axis] + 1], np.arange(peaks[axis] + 1))
859 high = np.interp(threshold, vec[:peaks[axis] - 1:-1], np.arange(len(vec) - 1, peaks[axis] - 1, -1))
860 return high - low
863def getPsfFwhm(psf, average=True, position=None):
864 """Directly calculate the horizontal and vertical widths
865 of a PSF at half its maximum value.
867 Parameters
868 ----------
869 psf : `~lsst.afw.detection.Psf`
870 Point spread function (PSF) to evaluate.
871 average : `bool`, optional
872 Set to return the average width over Y and X axes.
873 position : `~lsst.geom.Point2D`, optional
874 The position at which to evaluate the PSF. If `None`, then the
875 average position is used.
877 Returns
878 -------
879 psfSize : `float` | `tuple` [`float`]
880 The FWHM of the PSF computed at its average position.
881 Returns the widths along the Y and X axes,
882 or the average of the two if `average` is set.
884 See Also
885 --------
886 evaluateMeanPsfFwhm
887 """
888 if position is None:
889 position = psf.getAveragePosition()
890 image = psf.computeKernelImage(position).array
891 peak = psf.computePeak(position)
892 peakLocs = np.unravel_index(np.argmax(image), image.shape)
893 width = _sliceWidth(image, peak/2., peakLocs, axis=0), _sliceWidth(image, peak/2., peakLocs, axis=1)
894 return np.nanmean(width) if average else width
897def evaluateMeanPsfFwhm(exposure: afwImage.Exposure,
898 fwhmExposureBuffer: float, fwhmExposureGrid: int) -> float:
899 """Get the mean PSF FWHM by evaluating it on a grid within an exposure.
901 Parameters
902 ----------
903 exposure : `~lsst.afw.image.Exposure`
904 The exposure for which the mean FWHM of the PSF is to be computed.
905 The exposure must contain a `psf` attribute.
906 fwhmExposureBuffer : `float`
907 Fractional buffer margin to be left out of all sides of the image
908 during the construction of the grid to compute mean PSF FWHM in an
909 exposure.
910 fwhmExposureGrid : `int`
911 Grid size to compute the mean FWHM in an exposure.
913 Returns
914 -------
915 meanFwhm : `float`
916 The mean PSF FWHM on the exposure.
918 Raises
919 ------
920 ValueError
921 Raised if the PSF cannot be computed at any of the grid points.
923 See Also
924 --------
925 `getPsfFwhm`
926 `computeAveragePsf`
927 """
929 psf = exposure.psf
931 bbox = exposure.getBBox()
932 xmax, ymax = bbox.getMax()
933 xmin, ymin = bbox.getMin()
935 xbuffer = fwhmExposureBuffer*(xmax-xmin)
936 ybuffer = fwhmExposureBuffer*(ymax-ymin)
938 width = []
939 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
940 np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
941 ):
942 pos = geom.Point2D(x, y)
943 try:
944 fwhm = getPsfFwhm(psf, average=True, position=pos)
945 except InvalidParameterError:
946 _LOG.debug("Unable to compute PSF FWHM at position (%f, %f).", x, y)
947 continue
949 width.append(fwhm)
951 if not width:
952 raise ValueError("Unable to compute PSF FWHM at any position on the exposure.")
954 return np.nanmean(width)
957def computeAveragePsf(exposure: afwImage.Exposure,
958 psfExposureBuffer: float, psfExposureGrid: int) -> afwImage.ImageD:
959 """Get the average PSF by evaluating it on a grid within an exposure.
961 Parameters
962 ----------
963 exposure : `~lsst.afw.image.Exposure`
964 The exposure for which the average PSF is to be computed.
965 The exposure must contain a `psf` attribute.
966 psfExposureBuffer : `float`
967 Fractional buffer margin to be left out of all sides of the image
968 during the construction of the grid to compute average PSF in an
969 exposure.
970 psfExposureGrid : `int`
971 Grid size to compute the average PSF in an exposure.
973 Returns
974 -------
975 psfImage : `~lsst.afw.image.Image`
976 The average PSF across the exposure.
978 Raises
979 ------
980 ValueError
981 Raised if the PSF cannot be computed at any of the grid points.
983 See Also
984 --------
985 `evaluateMeanPsfFwhm`
986 """
988 psf = exposure.psf
990 bbox = exposure.getBBox()
991 xmax, ymax = bbox.getMax()
992 xmin, ymin = bbox.getMin()
994 xbuffer = psfExposureBuffer*(xmax-xmin)
995 ybuffer = psfExposureBuffer*(ymax-ymin)
997 nImg = 0
998 psfArray = None
999 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, psfExposureGrid),
1000 np.linspace(ymin+ybuffer, ymax-ybuffer, psfExposureGrid)
1001 ):
1002 pos = geom.Point2D(x, y)
1003 try:
1004 singleImage = psf.computeKernelImage(pos)
1005 except InvalidParameterError:
1006 _LOG.debug("Unable to compute PSF image at position (%f, %f).", x, y)
1007 continue
1009 if psfArray is None:
1010 psfArray = singleImage.array
1011 else:
1012 psfArray += singleImage.array
1013 nImg += 1
1015 if psfArray is None:
1016 raise ValueError("Unable to compute PSF image at any position on the exposure.")
1018 psfImage = afwImage.ImageD(psfArray/nImg)
1019 return psfImage
1022def detectTestSources(exposure, addMaskPlanes=None):
1023 """Minimal source detection wrapper suitable for unit tests.
1025 Parameters
1026 ----------
1027 exposure : `lsst.afw.image.Exposure`
1028 Exposure on which to run detection/measurement
1029 The exposure is modified in place to set the 'DETECTED' mask plane.
1030 addMaskPlanes : `list` of `str`, optional
1031 Additional mask planes to add to the maskedImage of the exposure.
1033 Returns
1034 -------
1035 selectSources
1036 Source catalog containing candidates
1037 """
1038 if addMaskPlanes is None:
1039 # add empty streak mask plane in lieu of maskStreaksTask
1040 # And add empty INJECTED and INJECTED_TEMPLATE mask planes
1041 addMaskPlanes = ["STREAK", "INJECTED", "INJECTED_TEMPLATE"]
1043 schema = afwTable.SourceTable.makeMinimalSchema()
1044 selectDetection = measAlg.SourceDetectionTask(schema=schema)
1045 selectMeasurement = measBase.SingleFrameMeasurementTask(schema=schema)
1046 table = afwTable.SourceTable.make(schema)
1048 detRet = selectDetection.run(
1049 table=table,
1050 exposure=exposure,
1051 sigma=None, # The appropriate sigma is calculated from the PSF
1052 doSmooth=True
1053 )
1054 for mp in addMaskPlanes:
1055 exposure.mask.addMaskPlane(mp)
1057 selectSources = detRet.sources
1058 selectMeasurement.run(measCat=selectSources, exposure=exposure)
1060 return selectSources
1063def makeFakeWcs():
1064 """Make a fake, affine Wcs.
1065 """
1066 crpix = geom.Point2D(123.45, 678.9)
1067 crval = geom.SpherePoint(0.1, 0.1, geom.degrees)
1068 cdMatrix = np.array([[5.19513851e-05, -2.81124812e-07],
1069 [-3.25186974e-07, -5.19112119e-05]])
1070 return afwGeom.makeSkyWcs(crpix, crval, cdMatrix)
1073def makeTestImage(seed=5, nSrc=20, psfSize=2., noiseLevel=5.,
1074 noiseSeed=6, fluxLevel=500., fluxRange=2.,
1075 kernelSize=32, templateBorderSize=0,
1076 background=None,
1077 xSize=256,
1078 ySize=256,
1079 x0=12345,
1080 y0=67890,
1081 calibration=1.,
1082 doApplyCalibration=False,
1083 xLoc=None,
1084 yLoc=None,
1085 flux=None,
1086 clearEdgeMask=False,
1087 addMaskPlanes=None,
1088 ):
1089 """Make a reproduceable PSF-convolved exposure for testing.
1091 Parameters
1092 ----------
1093 seed : `int`, optional
1094 Seed value to initialize the random number generator for sources.
1095 nSrc : `int`, optional
1096 Number of sources to simulate.
1097 psfSize : `float`, optional
1098 Width of the PSF of the simulated sources, in pixels.
1099 noiseLevel : `float`, optional
1100 Standard deviation of the noise to add to each pixel.
1101 noiseSeed : `int`, optional
1102 Seed value to initialize the random number generator for noise.
1103 fluxLevel : `float`, optional
1104 Reference flux of the simulated sources.
1105 fluxRange : `float`, optional
1106 Range in flux amplitude of the simulated sources.
1107 kernelSize : `int`, optional
1108 Size in pixels of the kernel for simulating sources.
1109 templateBorderSize : `int`, optional
1110 Size in pixels of the image border used to pad the image.
1111 background : `lsst.afw.math.Chebyshev1Function2D`, optional
1112 Optional background to add to the output image.
1113 xSize, ySize : `int`, optional
1114 Size in pixels of the simulated image.
1115 x0, y0 : `int`, optional
1116 Origin of the image.
1117 calibration : `float`, optional
1118 Conversion factor between instFlux and nJy.
1119 doApplyCalibration : `bool`, optional
1120 Apply the photometric calibration and return the image in nJy?
1121 xLoc, yLoc : `list` of `float`, optional
1122 User-specified coordinates of the simulated sources.
1123 If specified, must have length equal to ``nSrc``
1124 flux : `list` of `float`, optional
1125 User-specified fluxes of the simulated sources.
1126 If specified, must have length equal to ``nSrc``
1127 clearEdgeMask : `bool`, optional
1128 Clear the "EDGE" mask plane after source detection.
1129 addMaskPlanes : `list` of `str`, optional
1130 Mask plane names to add to the image.
1132 Returns
1133 -------
1134 modelExposure : `lsst.afw.image.Exposure`
1135 The model image, with the mask and variance planes. The DETECTED
1136 plane is filled in for the injected source footprints.
1137 sourceCat : `lsst.afw.table.SourceCatalog`
1138 Catalog of sources inserted in the model image.
1140 Raises
1141 ------
1142 ValueError
1143 If `xloc`, `yloc`, or `flux` are supplied with inconsistant lengths.
1144 """
1145 # Distance from the inner edge of the bounding box to avoid placing test
1146 # sources in the model images.
1147 bufferSize = kernelSize/2 + templateBorderSize + 1
1149 bbox = geom.Box2I(geom.Point2I(x0, y0), geom.Extent2I(xSize, ySize))
1150 if templateBorderSize > 0:
1151 bbox.grow(templateBorderSize)
1153 rng = np.random.RandomState(seed)
1154 rngNoise = np.random.RandomState(noiseSeed)
1155 x0, y0 = bbox.getBegin()
1156 xSize, ySize = bbox.getDimensions()
1157 if xLoc is None:
1158 xLoc = rng.rand(nSrc)*(xSize - 2*bufferSize) + bufferSize + x0
1159 else:
1160 if len(xLoc) != nSrc:
1161 raise ValueError("xLoc must have length equal to nSrc. %f supplied vs %f", len(xLoc), nSrc)
1162 if yLoc is None:
1163 yLoc = rng.rand(nSrc)*(ySize - 2*bufferSize) + bufferSize + y0
1164 else:
1165 if len(yLoc) != nSrc:
1166 raise ValueError("yLoc must have length equal to nSrc. %f supplied vs %f", len(yLoc), nSrc)
1168 if flux is None:
1169 flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*fluxLevel
1170 else:
1171 if len(flux) != nSrc:
1172 raise ValueError("flux must have length equal to nSrc. %f supplied vs %f", len(flux), nSrc)
1173 sigmas = [psfSize for src in range(nSrc)]
1174 injectList = list(zip(xLoc, yLoc, flux, sigmas))
1175 skyLevel = 0
1176 # Don't use the built in poisson noise: it modifies the global state of numpy random
1177 modelExposure = plantSources(bbox, kernelSize, skyLevel, injectList, addPoissonNoise=False)
1178 modelExposure.setWcs(makeFakeWcs())
1179 noise = rngNoise.randn(ySize, xSize)*noiseLevel
1180 noise -= np.mean(noise)
1181 modelExposure.variance.array = np.sqrt(np.abs(modelExposure.image.array)) + noiseLevel**2
1182 modelExposure.image.array += noise
1184 # Run source detection to set up the mask plane
1185 detectTestSources(modelExposure, addMaskPlanes=addMaskPlanes)
1186 if clearEdgeMask:
1187 modelExposure.mask &= ~modelExposure.mask.getPlaneBitMask("EDGE")
1188 modelExposure.setPhotoCalib(afwImage.PhotoCalib(calibration, 0., bbox))
1189 if background is not None:
1190 modelExposure.image += background
1191 modelExposure.maskedImage /= calibration
1192 modelExposure.info.setId(seed)
1193 if doApplyCalibration:
1194 modelExposure.maskedImage = modelExposure.photoCalib.calibrateImage(modelExposure.maskedImage)
1196 truth = _fillTruthCatalog(injectList)
1198 return modelExposure, truth
1201def _makeTruthSchema():
1202 """Make a schema for the truth catalog produced by `makeTestImage`.
1204 Returns
1205 -------
1206 keys : `dict` [`str`]
1207 Fields added to the catalog, to make it easier to set them.
1208 schema : `lsst.afw.table.Schema`
1209 Schema to use to make a "truth" SourceCatalog.
1210 Calib, Ap, and Psf flux slots all are set to ``truth_instFlux``.
1211 """
1212 schema = afwTable.SourceTable.makeMinimalSchema()
1213 keys = {}
1214 # Don't use a FluxResultKey so we can manage the flux and err separately.
1215 keys["instFlux"] = schema.addField("truth_instFlux", type=np.float64,
1216 doc="true instFlux", units="count")
1217 keys["instFluxErr"] = schema.addField("truth_instFluxErr", type=np.float64,
1218 doc="true instFluxErr", units="count")
1219 keys["centroid"] = afwTable.Point2DKey.addFields(schema, "truth", "true simulated centroid", "pixel")
1220 schema.addField("truth_flag", "Flag", "truth flux failure flag.")
1221 # Add the flag fields a source selector would need.
1222 schema.addField("sky_source", "Flag", "testing flag.")
1223 schema.addField("base_PixelFlags_flag_interpolated", "Flag", "testing flag.")
1224 schema.addField("base_PixelFlags_flag_saturated", "Flag", "testing flag.")
1225 schema.addField("base_PixelFlags_flag_bad", "Flag", "testing flag.")
1226 schema.getAliasMap().set("slot_Centroid", "truth")
1227 schema.getAliasMap().set("slot_CalibFlux", "truth")
1228 schema.getAliasMap().set("slot_ApFlux", "truth")
1229 schema.getAliasMap().set("slot_PsfFlux", "truth")
1230 return keys, schema
1233def _fillTruthCatalog(injectList):
1234 """Add injected sources to the truth catalog.
1236 Parameters
1237 ----------
1238 injectList : `list` [`float`]
1239 Sources that were injected; tuples of (x, y, flux, size).
1241 Returns
1242 -------
1243 catalog : `lsst.afw.table.SourceCatalog`
1244 Catalog with centroids and instFlux/instFluxErr values filled in and
1245 appropriate slots set.
1246 """
1247 keys, schema = _makeTruthSchema()
1248 catalog = afwTable.SourceCatalog(schema)
1249 catalog.reserve(len(injectList))
1250 for x, y, flux, size in injectList:
1251 record = catalog.addNew()
1252 keys["centroid"].set(record, geom.PointD(x, y))
1253 keys["instFlux"].set(record, flux)
1254 # Approximate injected errors
1255 keys["instFluxErr"].set(record, 20)
1256 # 5-sigma effective source width
1257 circle = afwGeom.Ellipse(afwGeom.ellipses.Axes(5*size, 5*size, 0), geom.Point2D(x, y))
1258 footprint = afwDetection.Footprint(afwGeom.SpanSet.fromShape(circle))
1259 footprint.addPeak(x, y, flux)
1260 record.setFootprint(footprint)
1262 return catalog
1265def makeStats(badMaskPlanes=None):
1266 """Create a statistics control for configuring calculations on images.
1268 Parameters
1269 ----------
1270 badMaskPlanes : `list` of `str`, optional
1271 List of mask planes to exclude from calculations.
1273 Returns
1274 -------
1275 statsControl : ` lsst.afw.math.StatisticsControl`
1276 Statistics control object for configuring calculations on images.
1277 """
1278 if badMaskPlanes is None:
1279 badMaskPlanes = ("INTRP", "EDGE", "DETECTED", "SAT", "CR",
1280 "BAD", "NO_DATA", "DETECTED_NEGATIVE")
1281 statsControl = afwMath.StatisticsControl()
1282 statsControl.setNumSigmaClip(3.)
1283 statsControl.setNumIter(3)
1284 statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(badMaskPlanes))
1285 return statsControl
1288def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP):
1289 """Calculate a robust mean of the variance plane of an exposure.
1291 Parameters
1292 ----------
1293 image : `lsst.afw.image.Image`
1294 Image or variance plane of an exposure to evaluate.
1295 mask : `lsst.afw.image.Mask`
1296 Mask plane to use for excluding pixels.
1297 statsCtrl : `lsst.afw.math.StatisticsControl`
1298 Statistics control object for configuring the calculation.
1299 statistic : `lsst.afw.math.Property`, optional
1300 The type of statistic to compute. Typical values are
1301 ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.
1303 Returns
1304 -------
1305 value : `float`
1306 The result of the statistic calculated from the unflagged pixels.
1307 """
1308 statObj = afwMath.makeStatistics(image, mask, statistic, statsCtrl)
1309 return statObj.getValue(statistic)
1312def computePSFNoiseEquivalentArea(psf):
1313 """Compute the noise equivalent area for an image psf
1315 Parameters
1316 ----------
1317 psf : `lsst.afw.detection.Psf`
1319 Returns
1320 -------
1321 nea : `float`
1322 """
1323 psfImg = psf.computeImage(psf.getAveragePosition())
1324 nea = 1./np.sum(psfImg.array**2)
1325 return nea
1328def angleMean(angles):
1329 """Calculate the mean of an array of angles.
1331 Parameters
1332 ----------
1333 angles : `ndarray`
1334 An array of angles, in degrees
1336 Returns
1337 -------
1338 `lsst.geom.Angle`
1339 The mean angle
1340 """
1341 complexArray = [complex(np.cos(np.deg2rad(angle)), np.sin(np.deg2rad(angle))) for angle in angles]
1342 return (geom.Angle(np.angle(np.mean(complexArray))))