Coverage for python/lsst/ip/diffim/utils.py: 5%
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« prev ^ index » next coverage.py v6.5.0, created at 2023-02-07 03:24 -0800
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#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
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.geom as afwGeom
33import lsst.afw.image as afwImage
34import lsst.afw.math as afwMath
35import lsst.afw.table as afwTable
36import lsst.meas.algorithms as measAlg
37import lsst.meas.base as measBase
38from lsst.meas.algorithms.testUtils import plantSources
39from lsst.pex.exceptions import InvalidParameterError
40from lsst.utils.logging import getLogger
41from .dipoleFitTask import DipoleFitAlgorithm
42from . import diffimLib
43from . import diffimTools
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.getVariance()
201 var = var.Factory(var, True)
202 np.sqrt(var.getArray(), var.getArray()) # inplace sqrt
203 resid = resid.getImage()
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.getImage()
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.getImage(), 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 plotPixelResiduals(exposure, warpedTemplateExposure, diffExposure, kernelCellSet,
675 kernel, background, testSources, config,
676 origVariance=False, nptsFull=1e6, keepPlots=True, titleFs=14):
677 """Plot diffim residuals for LOCAL and SPATIAL models.
678 """
679 candidateResids = []
680 spatialResids = []
681 nonfitResids = []
683 for cell in kernelCellSet.getCellList():
684 for cand in cell.begin(True): # only look at good ones
685 # Be sure
686 if not (cand.getStatus() == afwMath.SpatialCellCandidate.GOOD):
687 continue
689 diffim = cand.getDifferenceImage(diffimLib.KernelCandidateF.ORIG)
690 orig = cand.getScienceMaskedImage()
692 ski = afwImage.ImageD(kernel.getDimensions())
693 kernel.computeImage(ski, False, int(cand.getXCenter()), int(cand.getYCenter()))
694 sk = afwMath.FixedKernel(ski)
695 sbg = background(int(cand.getXCenter()), int(cand.getYCenter()))
696 sdiffim = cand.getDifferenceImage(sk, sbg)
698 # trim edgs due to convolution
699 bbox = kernel.shrinkBBox(diffim.getBBox())
700 tdiffim = diffim.Factory(diffim, bbox)
701 torig = orig.Factory(orig, bbox)
702 tsdiffim = sdiffim.Factory(sdiffim, bbox)
704 if origVariance:
705 candidateResids.append(np.ravel(tdiffim.getImage().getArray()
706 / np.sqrt(torig.getVariance().getArray())))
707 spatialResids.append(np.ravel(tsdiffim.getImage().getArray()
708 / np.sqrt(torig.getVariance().getArray())))
709 else:
710 candidateResids.append(np.ravel(tdiffim.getImage().getArray()
711 / np.sqrt(tdiffim.getVariance().getArray())))
712 spatialResids.append(np.ravel(tsdiffim.getImage().getArray()
713 / np.sqrt(tsdiffim.getVariance().getArray())))
715 fullIm = diffExposure.getMaskedImage().getImage().getArray()
716 fullMask = diffExposure.getMaskedImage().getMask().getArray()
717 if origVariance:
718 fullVar = exposure.getMaskedImage().getVariance().getArray()
719 else:
720 fullVar = diffExposure.getMaskedImage().getVariance().getArray()
722 bitmaskBad = 0
723 bitmaskBad |= afwImage.Mask.getPlaneBitMask('NO_DATA')
724 bitmaskBad |= afwImage.Mask.getPlaneBitMask('SAT')
725 idx = np.where((fullMask & bitmaskBad) == 0)
726 stride = int(len(idx[0])//nptsFull)
727 sidx = idx[0][::stride], idx[1][::stride]
728 allResids = fullIm[sidx]/np.sqrt(fullVar[sidx])
730 testFootprints = diffimTools.sourceToFootprintList(testSources, warpedTemplateExposure,
731 exposure, config,
732 _LOG.getChild("plotPixelResiduals"))
733 for fp in testFootprints:
734 subexp = diffExposure.Factory(diffExposure, fp["footprint"].getBBox())
735 subim = subexp.getMaskedImage().getImage()
736 if origVariance:
737 subvar = afwImage.ExposureF(exposure, fp["footprint"].getBBox()).getMaskedImage().getVariance()
738 else:
739 subvar = subexp.getMaskedImage().getVariance()
740 nonfitResids.append(np.ravel(subim.getArray()/np.sqrt(subvar.getArray())))
742 candidateResids = np.ravel(np.array(candidateResids))
743 spatialResids = np.ravel(np.array(spatialResids))
744 nonfitResids = np.ravel(np.array(nonfitResids))
746 try:
747 import pylab
748 from matplotlib.font_manager import FontProperties
749 except ImportError as e:
750 print("Unable to import pylab: %s" % e)
751 return
753 fig = pylab.figure()
754 fig.clf()
755 try:
756 fig.canvas._tkcanvas._root().lift() # == Tk's raise, but raise is a python reserved word
757 except Exception: # protect against API changes
758 pass
759 if origVariance:
760 fig.suptitle("Diffim residuals: Normalized by sqrt(input variance)", fontsize=titleFs)
761 else:
762 fig.suptitle("Diffim residuals: Normalized by sqrt(diffim variance)", fontsize=titleFs)
764 sp1 = pylab.subplot(221)
765 sp2 = pylab.subplot(222, sharex=sp1, sharey=sp1)
766 sp3 = pylab.subplot(223, sharex=sp1, sharey=sp1)
767 sp4 = pylab.subplot(224, sharex=sp1, sharey=sp1)
768 xs = np.arange(-5, 5.05, 0.1)
769 ys = 1./np.sqrt(2*np.pi)*np.exp(-0.5*xs**2)
771 sp1.hist(candidateResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
772 % (np.mean(candidateResids), np.var(candidateResids)))
773 sp1.plot(xs, ys, "r-", lw=2, label="N(0,1)")
774 sp1.set_title("Candidates: basis fit", fontsize=titleFs - 2)
775 sp1.legend(loc=1, fancybox=True, shadow=True, prop=FontProperties(size=titleFs - 6))
777 sp2.hist(spatialResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
778 % (np.mean(spatialResids), np.var(spatialResids)))
779 sp2.plot(xs, ys, "r-", lw=2, label="N(0,1)")
780 sp2.set_title("Candidates: spatial fit", fontsize=titleFs - 2)
781 sp2.legend(loc=1, fancybox=True, shadow=True, prop=FontProperties(size=titleFs - 6))
783 sp3.hist(nonfitResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
784 % (np.mean(nonfitResids), np.var(nonfitResids)))
785 sp3.plot(xs, ys, "r-", lw=2, label="N(0,1)")
786 sp3.set_title("Control sample: spatial fit", fontsize=titleFs - 2)
787 sp3.legend(loc=1, fancybox=True, shadow=True, prop=FontProperties(size=titleFs - 6))
789 sp4.hist(allResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
790 % (np.mean(allResids), np.var(allResids)))
791 sp4.plot(xs, ys, "r-", lw=2, label="N(0,1)")
792 sp4.set_title("Full image (subsampled)", fontsize=titleFs - 2)
793 sp4.legend(loc=1, fancybox=True, shadow=True, prop=FontProperties(size=titleFs - 6))
795 pylab.setp(sp1.get_xticklabels() + sp1.get_yticklabels(), fontsize=titleFs - 4)
796 pylab.setp(sp2.get_xticklabels() + sp2.get_yticklabels(), fontsize=titleFs - 4)
797 pylab.setp(sp3.get_xticklabels() + sp3.get_yticklabels(), fontsize=titleFs - 4)
798 pylab.setp(sp4.get_xticklabels() + sp4.get_yticklabels(), fontsize=titleFs - 4)
800 sp1.set_xlim(-5, 5)
801 sp1.set_ylim(0, 0.5)
802 fig.show()
804 global keptPlots
805 if keepPlots and not keptPlots:
806 # Keep plots open when done
807 def show():
808 print("%s: Please close plots when done." % __name__)
809 try:
810 pylab.show()
811 except Exception:
812 pass
813 print("Plots closed, exiting...")
814 import atexit
815 atexit.register(show)
816 keptPlots = True
819def calcCentroid(arr):
820 """Calculate first moment of a (kernel) image.
821 """
822 y, x = arr.shape
823 sarr = arr*arr
824 xarr = np.asarray([[el for el in range(x)] for el2 in range(y)])
825 yarr = np.asarray([[el2 for el in range(x)] for el2 in range(y)])
826 narr = xarr*sarr
827 sarrSum = sarr.sum()
828 centx = narr.sum()/sarrSum
829 narr = yarr*sarr
830 centy = narr.sum()/sarrSum
831 return centx, centy
834def calcWidth(arr, centx, centy):
835 """Calculate second moment of a (kernel) image.
836 """
837 y, x = arr.shape
838 # Square the flux so we don't have to deal with negatives
839 sarr = arr*arr
840 xarr = np.asarray([[el for el in range(x)] for el2 in range(y)])
841 yarr = np.asarray([[el2 for el in range(x)] for el2 in range(y)])
842 narr = sarr*np.power((xarr - centx), 2.)
843 sarrSum = sarr.sum()
844 xstd = np.sqrt(narr.sum()/sarrSum)
845 narr = sarr*np.power((yarr - centy), 2.)
846 ystd = np.sqrt(narr.sum()/sarrSum)
847 return xstd, ystd
850def printSkyDiffs(sources, wcs):
851 """Print differences in sky coordinates.
853 The difference is that between the source Position and its Centroid mapped
854 through Wcs.
855 """
856 for s in sources:
857 sCentroid = s.getCentroid()
858 sPosition = s.getCoord().getPosition(geom.degrees)
859 dra = 3600*(sPosition.getX() - wcs.pixelToSky(sCentroid).getPosition(geom.degrees).getX())/0.2
860 ddec = 3600*(sPosition.getY() - wcs.pixelToSky(sCentroid).getPosition(geom.degrees).getY())/0.2
861 if np.isfinite(dra) and np.isfinite(ddec):
862 print(dra, ddec)
865def makeRegions(sources, outfilename, wcs=None):
866 """Create regions file for display from input source list.
867 """
868 fh = open(outfilename, "w")
869 fh.write("global color=red font=\"helvetica 10 normal\" "
870 "select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\nfk5\n")
871 for s in sources:
872 if wcs:
873 (ra, dec) = wcs.pixelToSky(s.getCentroid()).getPosition(geom.degrees)
874 else:
875 (ra, dec) = s.getCoord().getPosition(geom.degrees)
876 if np.isfinite(ra) and np.isfinite(dec):
877 fh.write("circle(%f,%f,2\")\n"%(ra, dec))
878 fh.flush()
879 fh.close()
882def showSourceSetSky(sSet, wcs, xy0, frame=0, ctype=afwDisplay.GREEN, symb="+", size=2):
883 """Draw the (RA, Dec) positions of a set of Sources. Image has the XY0.
884 """
885 disp = afwDisplay.Display(frame=frame)
886 with disp.Buffering():
887 for s in sSet:
888 (xc, yc) = wcs.skyToPixel(s.getCoord().getRa(), s.getCoord().getDec())
889 xc -= xy0[0]
890 yc -= xy0[1]
891 disp.dot(symb, xc, yc, ctype=ctype, size=size)
894def plotWhisker(results, newWcs):
895 """Plot whisker diagram of astromeric offsets between results.matches.
896 """
897 refCoordKey = results.matches[0].first.getTable().getCoordKey()
898 inCentroidKey = results.matches[0].second.getTable().getCentroidSlot().getMeasKey()
899 positions = [m.first.get(refCoordKey) for m in results.matches]
900 residuals = [m.first.get(refCoordKey).getOffsetFrom(
901 newWcs.pixelToSky(m.second.get(inCentroidKey))) for
902 m in results.matches]
903 import matplotlib.pyplot as plt
904 fig = plt.figure()
905 sp = fig.add_subplot(1, 1, 0)
906 xpos = [x[0].asDegrees() for x in positions]
907 ypos = [x[1].asDegrees() for x in positions]
908 xpos.append(0.02*(max(xpos) - min(xpos)) + min(xpos))
909 ypos.append(0.98*(max(ypos) - min(ypos)) + min(ypos))
910 xidxs = np.isfinite(xpos)
911 yidxs = np.isfinite(ypos)
912 X = np.asarray(xpos)[xidxs]
913 Y = np.asarray(ypos)[yidxs]
914 distance = [x[1].asArcseconds() for x in residuals]
915 distance.append(0.2)
916 distance = np.asarray(distance)[xidxs]
917 # NOTE: This assumes that the bearing is measured positive from +RA through North.
918 # From the documentation this is not clear.
919 bearing = [x[0].asRadians() for x in residuals]
920 bearing.append(0)
921 bearing = np.asarray(bearing)[xidxs]
922 U = (distance*np.cos(bearing))
923 V = (distance*np.sin(bearing))
924 sp.quiver(X, Y, U, V)
925 sp.set_title("WCS Residual")
926 plt.show()
929class DipoleTestImage(object):
930 """Utility class for dipole measurement testing.
932 Generate an image with simulated dipoles and noise; store the original
933 "pre-subtraction" images and catalogs as well.
934 Used to generate test data for DMTN-007 (http://dmtn-007.lsst.io).
935 """
937 def __init__(self, w=101, h=101, xcenPos=[27.], ycenPos=[25.], xcenNeg=[23.], ycenNeg=[25.],
938 psfSigma=2., flux=[30000.], fluxNeg=None, noise=10., gradientParams=None):
939 self.w = w
940 self.h = h
941 self.xcenPos = xcenPos
942 self.ycenPos = ycenPos
943 self.xcenNeg = xcenNeg
944 self.ycenNeg = ycenNeg
945 self.psfSigma = psfSigma
946 self.flux = flux
947 self.fluxNeg = fluxNeg
948 if fluxNeg is None:
949 self.fluxNeg = self.flux
950 self.noise = noise
951 self.gradientParams = gradientParams
952 self._makeDipoleImage()
954 def _makeDipoleImage(self):
955 """Generate an exposure and catalog with the given dipole source(s).
956 """
957 # Must seed the pos/neg images with different values to ensure they get different noise realizations
958 posImage, posCatalog = self._makeStarImage(
959 xc=self.xcenPos, yc=self.ycenPos, flux=self.flux, randomSeed=111)
961 negImage, negCatalog = self._makeStarImage(
962 xc=self.xcenNeg, yc=self.ycenNeg, flux=self.fluxNeg, randomSeed=222)
964 dipole = posImage.clone()
965 di = dipole.getMaskedImage()
966 di -= negImage.getMaskedImage()
968 # Carry through pos/neg detection masks to new planes in diffim
969 dm = di.getMask()
970 posDetectedBits = posImage.getMaskedImage().getMask().getArray() == dm.getPlaneBitMask("DETECTED")
971 negDetectedBits = negImage.getMaskedImage().getMask().getArray() == dm.getPlaneBitMask("DETECTED")
972 pos_det = dm.addMaskPlane("DETECTED_POS") # new mask plane -- different from "DETECTED"
973 neg_det = dm.addMaskPlane("DETECTED_NEG") # new mask plane -- different from "DETECTED_NEGATIVE"
974 dma = dm.getArray()
975 # set the two custom mask planes to these new masks
976 dma[:, :] = posDetectedBits*pos_det + negDetectedBits*neg_det
977 self.diffim, self.posImage, self.posCatalog, self.negImage, self.negCatalog \
978 = dipole, posImage, posCatalog, negImage, negCatalog
980 def _makeStarImage(self, xc=[15.3], yc=[18.6], flux=[2500], schema=None, randomSeed=None):
981 """Generate an exposure and catalog with the given stellar source(s).
982 """
983 from lsst.meas.base.tests import TestDataset
984 bbox = geom.Box2I(geom.Point2I(0, 0), geom.Point2I(self.w - 1, self.h - 1))
985 dataset = TestDataset(bbox, psfSigma=self.psfSigma, threshold=1.)
987 for i in range(len(xc)):
988 dataset.addSource(instFlux=flux[i], centroid=geom.Point2D(xc[i], yc[i]))
990 if schema is None:
991 schema = TestDataset.makeMinimalSchema()
992 exposure, catalog = dataset.realize(noise=self.noise, schema=schema, randomSeed=randomSeed)
994 if self.gradientParams is not None:
995 y, x = np.mgrid[:self.w, :self.h]
996 gp = self.gradientParams
997 gradient = gp[0] + gp[1]*x + gp[2]*y
998 if len(self.gradientParams) > 3: # it includes a set of 2nd-order polynomial params
999 gradient += gp[3]*x*y + gp[4]*x*x + gp[5]*y*y
1000 imgArr = exposure.getMaskedImage().getArrays()[0]
1001 imgArr += gradient
1003 return exposure, catalog
1005 def fitDipoleSource(self, source, **kwds):
1006 alg = DipoleFitAlgorithm(self.diffim, self.posImage, self.negImage)
1007 fitResult = alg.fitDipole(source, **kwds)
1008 return fitResult
1010 def detectDipoleSources(self, doMerge=True, diffim=None, detectSigma=5.5, grow=3, minBinSize=32):
1011 """Utility function for detecting dipoles.
1013 Detect pos/neg sources in the diffim, then merge them. A
1014 bigger "grow" parameter leads to a larger footprint which
1015 helps with dipole measurement for faint dipoles.
1017 Parameters
1018 ----------
1019 doMerge : `bool`
1020 Whether to merge the positive and negagive detections into a single
1021 source table.
1022 diffim : `lsst.afw.image.exposure.exposure.ExposureF`
1023 Difference image on which to perform detection.
1024 detectSigma : `float`
1025 Threshold for object detection.
1026 grow : `int`
1027 Number of pixels to grow the footprints before merging.
1028 minBinSize : `int`
1029 Minimum bin size for the background (re)estimation (only applies if
1030 the default leads to min(nBinX, nBinY) < fit order so the default
1031 config parameter needs to be decreased, but not to a value smaller
1032 than ``minBinSize``, in which case the fitting algorithm will take
1033 over and decrease the fit order appropriately.)
1035 Returns
1036 -------
1037 sources : `lsst.afw.table.SourceCatalog`
1038 If doMerge=True, the merged source catalog is returned OR
1039 detectTask : `lsst.meas.algorithms.SourceDetectionTask`
1040 schema : `lsst.afw.table.Schema`
1041 If doMerge=False, the source detection task and its schema are
1042 returned.
1043 """
1044 if diffim is None:
1045 diffim = self.diffim
1047 # Start with a minimal schema - only the fields all SourceCatalogs need
1048 schema = afwTable.SourceTable.makeMinimalSchema()
1050 # Customize the detection task a bit (optional)
1051 detectConfig = measAlg.SourceDetectionConfig()
1052 detectConfig.returnOriginalFootprints = False # should be the default
1054 diffimPsf = diffim.getPsf()
1055 psfSigma = diffimPsf.computeShape(diffimPsf.getAveragePosition()).getDeterminantRadius()
1057 # code from imageDifference.py:
1058 detectConfig.thresholdPolarity = "both"
1059 detectConfig.thresholdValue = detectSigma
1060 # detectConfig.nSigmaToGrow = psfSigma
1061 detectConfig.reEstimateBackground = True # if False, will fail often for faint sources on gradients?
1062 detectConfig.thresholdType = "pixel_stdev"
1063 # Test images are often quite small, so may need to adjust background binSize
1064 while ((min(diffim.getWidth(), diffim.getHeight()))//detectConfig.background.binSize
1065 < detectConfig.background.approxOrderX and detectConfig.background.binSize > minBinSize):
1066 detectConfig.background.binSize = max(minBinSize, detectConfig.background.binSize//2)
1068 # Create the detection task. We pass the schema so the task can declare a few flag fields
1069 detectTask = measAlg.SourceDetectionTask(schema, config=detectConfig)
1071 table = afwTable.SourceTable.make(schema)
1072 catalog = detectTask.run(table, diffim, sigma=psfSigma)
1074 # Now do the merge.
1075 if doMerge:
1076 fpSet = catalog.fpSets.positive
1077 fpSet.merge(catalog.fpSets.negative, grow, grow, False)
1078 sources = afwTable.SourceCatalog(table)
1079 fpSet.makeSources(sources)
1081 return sources
1083 else:
1084 return detectTask, schema
1087def _sliceWidth(image, threshold, peaks, axis):
1088 vec = image.take(peaks[1 - axis], axis=axis)
1089 low = np.interp(threshold, vec[:peaks[axis] + 1], np.arange(peaks[axis] + 1))
1090 high = np.interp(threshold, vec[:peaks[axis] - 1:-1], np.arange(len(vec) - 1, peaks[axis] - 1, -1))
1091 return high - low
1094def getPsfFwhm(psf, average=True, position=None):
1095 """Directly calculate the horizontal and vertical widths
1096 of a PSF at half its maximum value.
1098 Parameters
1099 ----------
1100 psf : `~lsst.afw.detection.Psf`
1101 Point spread function (PSF) to evaluate.
1102 average : `bool`, optional
1103 Set to return the average width over Y and X axes.
1104 position : `~lsst.geom.Point2D`, optional
1105 The position at which to evaluate the PSF. If `None`, then the
1106 average position is used.
1108 Returns
1109 -------
1110 psfSize : `float` | `tuple` [`float`]
1111 The FWHM of the PSF computed at its average position.
1112 Returns the widths along the Y and X axes,
1113 or the average of the two if `average` is set.
1115 See Also
1116 --------
1117 evaluateMeanPsfFwhm
1118 """
1119 if position is None:
1120 position = psf.getAveragePosition()
1121 image = psf.computeKernelImage(position).array
1122 peak = psf.computePeak(position)
1123 peakLocs = np.unravel_index(np.argmax(image), image.shape)
1124 width = _sliceWidth(image, peak/2., peakLocs, axis=0), _sliceWidth(image, peak/2., peakLocs, axis=1)
1125 return np.nanmean(width) if average else width
1128def evaluateMeanPsfFwhm(exposure: afwImage.Exposure,
1129 fwhmExposureBuffer: float, fwhmExposureGrid: int) -> float:
1130 """Get the median PSF FWHM by evaluating it on a grid within an exposure.
1132 Parameters
1133 ----------
1134 exposure : `~lsst.afw.image.Exposure`
1135 The exposure for which the mean FWHM of the PSF is to be computed.
1136 The exposure must contain a `psf` attribute.
1137 fwhmExposureBuffer : `float`
1138 Fractional buffer margin to be left out of all sides of the image
1139 during the construction of the grid to compute mean PSF FWHM in an
1140 exposure.
1141 fwhmExposureGrid : `int`
1142 Grid size to compute the mean FWHM in an exposure.
1144 Returns
1145 -------
1146 meanFwhm : `float`
1147 The mean PSF FWHM on the exposure.
1149 Raises
1150 ------
1151 ValueError
1152 Raised if the PSF cannot be computed at any of the grid points.
1154 See Also
1155 --------
1156 getPsfFwhm
1157 """
1159 psf = exposure.psf
1161 bbox = exposure.getBBox()
1162 xmax, ymax = bbox.getMax()
1163 xmin, ymin = bbox.getMin()
1165 xbuffer = fwhmExposureBuffer*(xmax-xmin)
1166 ybuffer = fwhmExposureBuffer*(ymax-ymin)
1168 width = []
1169 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
1170 np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
1171 ):
1172 pos = geom.Point2D(x, y)
1173 try:
1174 fwhm = getPsfFwhm(psf, average=True, position=pos)
1175 except InvalidParameterError:
1176 _LOG.debug("Unable to compute PSF FWHM at position (%f, %f).", x, y)
1177 continue
1179 width.append(fwhm)
1181 if not width:
1182 raise ValueError("Unable to compute PSF FWHM at any position on the exposure.")
1184 return np.nanmean(width)
1187def detectTestSources(exposure):
1188 """Minimal source detection wrapper suitable for unit tests.
1190 Parameters
1191 ----------
1192 exposure : `lsst.afw.image.Exposure`
1193 Exposure on which to run detection/measurement
1194 The exposure is modified in place to set the 'DETECTED' mask plane.
1196 Returns
1197 -------
1198 selectSources :
1199 Source catalog containing candidates
1200 """
1202 schema = afwTable.SourceTable.makeMinimalSchema()
1203 selectDetection = measAlg.SourceDetectionTask(schema=schema)
1204 selectMeasurement = measBase.SingleFrameMeasurementTask(schema=schema)
1205 table = afwTable.SourceTable.make(schema)
1207 detRet = selectDetection.run(
1208 table=table,
1209 exposure=exposure,
1210 sigma=None, # The appropriate sigma is calculated from the PSF
1211 doSmooth=True
1212 )
1213 selectSources = detRet.sources
1214 selectMeasurement.run(measCat=selectSources, exposure=exposure)
1216 return selectSources
1219def makeFakeWcs():
1220 """Make a fake, affine Wcs.
1221 """
1222 crpix = geom.Point2D(123.45, 678.9)
1223 crval = geom.SpherePoint(0.1, 0.1, geom.degrees)
1224 cdMatrix = np.array([[5.19513851e-05, -2.81124812e-07],
1225 [-3.25186974e-07, -5.19112119e-05]])
1226 return afwGeom.makeSkyWcs(crpix, crval, cdMatrix)
1229def makeTestImage(seed=5, nSrc=20, psfSize=2., noiseLevel=5.,
1230 noiseSeed=6, fluxLevel=500., fluxRange=2.,
1231 kernelSize=32, templateBorderSize=0,
1232 background=None,
1233 xSize=256,
1234 ySize=256,
1235 x0=12345,
1236 y0=67890,
1237 calibration=1.,
1238 doApplyCalibration=False,
1239 ):
1240 """Make a reproduceable PSF-convolved exposure for testing.
1242 Parameters
1243 ----------
1244 seed : `int`, optional
1245 Seed value to initialize the random number generator for sources.
1246 nSrc : `int`, optional
1247 Number of sources to simulate.
1248 psfSize : `float`, optional
1249 Width of the PSF of the simulated sources, in pixels.
1250 noiseLevel : `float`, optional
1251 Standard deviation of the noise to add to each pixel.
1252 noiseSeed : `int`, optional
1253 Seed value to initialize the random number generator for noise.
1254 fluxLevel : `float`, optional
1255 Reference flux of the simulated sources.
1256 fluxRange : `float`, optional
1257 Range in flux amplitude of the simulated sources.
1258 kernelSize : `int`, optional
1259 Size in pixels of the kernel for simulating sources.
1260 templateBorderSize : `int`, optional
1261 Size in pixels of the image border used to pad the image.
1262 background : `lsst.afw.math.Chebyshev1Function2D`, optional
1263 Optional background to add to the output image.
1264 xSize, ySize : `int`, optional
1265 Size in pixels of the simulated image.
1266 x0, y0 : `int`, optional
1267 Origin of the image.
1268 calibration : `float`, optional
1269 Conversion factor between instFlux and nJy.
1270 doApplyCalibration : `bool`, optional
1271 Apply the photometric calibration and return the image in nJy?
1273 Returns
1274 -------
1275 modelExposure : `lsst.afw.image.Exposure`
1276 The model image, with the mask and variance planes.
1277 sourceCat : `lsst.afw.table.SourceCatalog`
1278 Catalog of sources detected on the model image.
1279 """
1280 # Distance from the inner edge of the bounding box to avoid placing test
1281 # sources in the model images.
1282 bufferSize = kernelSize/2 + templateBorderSize + 1
1284 bbox = geom.Box2I(geom.Point2I(x0, y0), geom.Extent2I(xSize, ySize))
1285 if templateBorderSize > 0:
1286 bbox.grow(templateBorderSize)
1288 rng = np.random.RandomState(seed)
1289 rngNoise = np.random.RandomState(noiseSeed)
1290 x0, y0 = bbox.getBegin()
1291 xSize, ySize = bbox.getDimensions()
1292 xLoc = rng.rand(nSrc)*(xSize - 2*bufferSize) + bufferSize + x0
1293 yLoc = rng.rand(nSrc)*(ySize - 2*bufferSize) + bufferSize + y0
1295 flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*fluxLevel
1296 sigmas = [psfSize for src in range(nSrc)]
1297 coordList = list(zip(xLoc, yLoc, flux, sigmas))
1298 skyLevel = 0
1299 # Don't use the built in poisson noise: it modifies the global state of numpy random
1300 modelExposure = plantSources(bbox, kernelSize, skyLevel, coordList, addPoissonNoise=False)
1301 modelExposure.setWcs(makeFakeWcs())
1302 noise = rngNoise.randn(ySize, xSize)*noiseLevel
1303 noise -= np.mean(noise)
1304 modelExposure.variance.array = np.sqrt(np.abs(modelExposure.image.array)) + noiseLevel**2
1305 modelExposure.image.array += noise
1307 # Run source detection to set up the mask plane
1308 sourceCat = detectTestSources(modelExposure)
1309 modelExposure.setPhotoCalib(afwImage.PhotoCalib(calibration, 0., bbox))
1310 if background is not None:
1311 modelExposure.image += background
1312 modelExposure.maskedImage /= calibration
1313 modelExposure.info.setId(seed)
1314 if doApplyCalibration:
1315 modelExposure.maskedImage = modelExposure.photoCalib.calibrateImage(modelExposure.maskedImage)
1317 return modelExposure, sourceCat
1320def makeStats(badMaskPlanes=None):
1321 """Create a statistics control for configuring calculations on images.
1323 Parameters
1324 ----------
1325 badMaskPlanes : `list` of `str`, optional
1326 List of mask planes to exclude from calculations.
1328 Returns
1329 -------
1330 statsControl : ` lsst.afw.math.StatisticsControl`
1331 Statistics control object for configuring calculations on images.
1332 """
1333 if badMaskPlanes is None:
1334 badMaskPlanes = ("INTRP", "EDGE", "DETECTED", "SAT", "CR",
1335 "BAD", "NO_DATA", "DETECTED_NEGATIVE")
1336 statsControl = afwMath.StatisticsControl()
1337 statsControl.setNumSigmaClip(3.)
1338 statsControl.setNumIter(3)
1339 statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(badMaskPlanes))
1340 return statsControl
1343def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP):
1344 """Calculate a robust mean of the variance plane of an exposure.
1346 Parameters
1347 ----------
1348 image : `lsst.afw.image.Image`
1349 Image or variance plane of an exposure to evaluate.
1350 mask : `lsst.afw.image.Mask`
1351 Mask plane to use for excluding pixels.
1352 statsCtrl : `lsst.afw.math.StatisticsControl`
1353 Statistics control object for configuring the calculation.
1354 statistic : `lsst.afw.math.Property`, optional
1355 The type of statistic to compute. Typical values are
1356 ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.
1358 Returns
1359 -------
1360 value : `float`
1361 The result of the statistic calculated from the unflagged pixels.
1362 """
1363 statObj = afwMath.makeStatistics(image, mask, statistic, statsCtrl)
1364 return statObj.getValue(statistic)
1367def computePSFNoiseEquivalentArea(psf):
1368 """Compute the noise equivalent area for an image psf
1370 Parameters
1371 ----------
1372 psf : `lsst.afw.detection.Psf`
1374 Returns
1375 -------
1376 nea : `float`
1377 """
1378 psfImg = psf.computeImage(psf.getAveragePosition())
1379 nea = 1./np.sum(psfImg.array**2)
1380 return nea