22"""Support utilities for Measuring sources"""
25__all__ = [
"DipoleTestImage",
"evaluateMeanPsfFwhm",
"getPsfFwhm"]
36import lsst.meas.algorithms
as measAlg
38from lsst.meas.algorithms.testUtils
import plantSources
40from lsst.utils.logging
import getLogger
41from .dipoleFitTask
import DipoleFitAlgorithm
42from .
import diffimLib
43from .
import diffimTools
45afwDisplay.setDefaultMaskTransparency(75)
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.
56 disp = afwDisplay.afwDisplay(frame=frame)
57 with disp.Buffering():
59 xc, yc = s.getXAstrom() - xy0[0], s.getYAstrom() - xy0[1]
62 disp.dot(
str(s.getId()), xc, yc, ctype=ctype, size=size)
64 disp.dot(symb, xc, yc, ctype=ctype, size=size)
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
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:
92 elif cand.getStatus() == afwMath.SpatialCellCandidate.GOOD:
94 elif cand.getStatus() == afwMath.SpatialCellCandidate.UNKNOWN:
100 disp.dot(symb, xc, yc, ctype=color, size=size)
103 rchi2 = cand.getChi2()
106 disp.dot(
"%d %.1f" % (cand.getId(), rchi2),
107 xc - size, yc - size - 4, ctype=color, size=size)
111 """Display Dia Sources.
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)):
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())
132 ctype = afwDisplay.RED
135 ctype = afwDisplay.YELLOW
137 if not badFlag
and not dipoleFlag:
138 ctype = afwDisplay.GREEN
140 mos.append(im.makeMosaic(), lab, ctype)
141 title =
"Dia Sources"
142 mosaicImage = mos.makeMosaic(display=disp, title=title)
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.
157 mos = afwDisplay.utils.Mosaic(gutter=5, background=0)
159 mos = afwDisplay.utils.Mosaic(gutter=5, background=-1)
161 candidateCenters = []
162 candidateCentersBad = []
164 for cell
in kernelCellSet.getCellList():
165 for cand
in cell.begin(
False):
168 resid = cand.getDifferenceImage(diffimLib.KernelCandidateF.ORIG)
172 rchi2 = cand.getChi2()
176 if not showBadCandidates
and cand.isBad():
179 im_resid = afwDisplay.utils.Mosaic(gutter=1, background=-0.5, mode=
"x")
182 im = cand.getScienceMaskedImage()
183 im = im.Factory(im,
True)
184 im.setXY0(cand.getScienceMaskedImage().getXY0())
187 if (
not resids
and not kernels):
188 im_resid.append(im.Factory(im,
True))
190 im = cand.getTemplateMaskedImage()
191 im = im.Factory(im,
True)
192 im.setXY0(cand.getTemplateMaskedImage().getXY0())
195 if (
not resids
and not kernels):
196 im_resid.append(im.Factory(im,
True))
201 var = var.Factory(var,
True)
202 np.sqrt(var.array, var.array)
205 bbox = kernel.shrinkBBox(resid.getBBox())
206 resid = resid.Factory(resid, bbox, deep=
True)
208 kim = cand.getKernelImage(diffimLib.KernelCandidateF.ORIG).convertF()
209 resid = kim.Factory(kim,
True)
210 im_resid.append(resid)
213 ski = afwImage.ImageD(kernel.getDimensions())
214 kernel.computeImage(ski,
False, int(cand.getXCenter()), int(cand.getYCenter()))
218 sbg = background(int(cand.getXCenter()), int(cand.getYCenter()))
219 sresid = cand.getDifferenceImage(sk, sbg)
224 bbox = kernel.shrinkBBox(resid.getBBox())
225 resid = resid.Factory(resid, bbox, deep=
True)
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())
247 candidateCentersBad.append(center)
249 candidateCenters.append(center)
256 title =
"Candidates & residuals"
258 disp = afwDisplay.Display(frame=frame)
259 mosaicImage = mos.makeMosaic(display=disp, title=title)
265 """Display a Kernel's basis images.
267 mos = afwDisplay.utils.Mosaic()
269 for k
in kernel.getKernelList():
270 im = afwImage.ImageD(k.getDimensions())
271 k.computeImage(im,
False)
274 disp = afwDisplay.Display(frame=frame)
275 mos.makeMosaic(display=disp, title=
"Kernel Basis Images")
283 numSample=128, keepPlots=True, maxCoeff=10):
284 """Plot the Kernel spatial model.
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)
293 x0 = kernelCellSet.getBBox().getBeginX()
294 y0 = kernelCellSet.getBBox().getBeginY()
302 for cell
in kernelCellSet.getCellList():
303 for cand
in cell.begin(
False):
304 if not showBadCandidates
and cand.isBad():
306 candCenter =
geom.PointD(cand.getXCenter(), cand.getYCenter())
308 im = cand.getTemplateMaskedImage()
312 targetFits = badFits
if cand.isBad()
else candFits
313 targetPos = badPos
if cand.isBad()
else candPos
314 targetAmps = badAmps
if cand.isBad()
else candAmps
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)
337 xRange = np.linspace(0, kernelCellSet.getBBox().getWidth(), num=numSample)
338 yRange = np.linspace(0, kernelCellSet.getBBox().getHeight(), num=numSample)
341 maxCoeff = min(maxCoeff, kernel.getNKernelParameters())
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])
351 dfBad = zBad[:, k] - np.array([func(pos.getX(), pos.getY())
for pos
in badPos])
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)
367 fig.canvas._tkcanvas._root().lift()
371 fig.suptitle(
'Kernel component %d' % k)
374 ax = fig.add_axes((0.1, 0.05, 0.35, 0.35))
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])
385 ax = fig.add_axes((0.1, 0.55, 0.35, 0.35))
386 ax.plot(-2.5*np.log10(candAmps), zGood[:, k],
'b+')
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")
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+')
400 ax.plot(yBad, dfBad,
'r+')
402 ax.set_title(
'dCoeff (indiv-spatial) vs. y')
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+')
411 ax.plot(xBad, dfBad,
'r+')
413 ax.set_title(
'dCoeff (indiv-spatial) vs. x')
418 if keepPlots
and not keptPlots:
421 print(
"%s: Please close plots when done." % __name__)
426 print(
"Plots closed, exiting...")
428 atexit.register(show)
433 """Plot the individual kernel candidate and the spatial kernel solution coefficients.
439 The spatial spatialKernel solution model which is a spatially varying linear combination
440 of the spatialKernel basis functions.
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.
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
466 This function
is called
if ``lsst.ip.diffim.psfMatch.plotKernelCoefficients==
True``
in lsstDebug. This
467 function was implemented
as part of DM-17825.
470 import matplotlib.pyplot
as plt
471 except ImportError
as e:
472 print(
"Unable to import matplotlib: %s" % e)
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()
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(
499 arrAx = arrAx[::-1, :]
502 for cell
in kernelCellSet.getCellList():
505 iX = int((cellBBox.getCenterX() - x0)//wCell)
506 iY = int((cellBBox.getCenterY() - y0)//hCell)
508 for cand
in cell.begin(
False):
510 kernel = cand.getKernel(cand.ORIG)
514 if not showBadCandidates
and cand.isBad():
517 nKernelParams = kernel.getNKernelParameters()
518 kernelParams = np.array(kernel.getKernelParameters())
519 allParams.append(kernelParams)
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')
533 spatialFuncs = spatialKernel.getSpatialFunctionList()
534 nKernelParams = spatialKernel.getNKernelParameters()
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')
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(
564 arrAx = arrAx[::-1, :]
565 kernelParams = np.zeros(nKernelParams, dtype=float)
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')
577 if keepPlots
and not keptPlots:
580 print(
"%s: Please close plots when done." % __name__)
585 print(
"Plots closed, exiting...")
587 atexit.register(show)
592 showCenter=True, showEllipticity=True):
593 """Show a mosaic of Kernel images.
595 mos = afwDisplay.utils.Mosaic()
597 x0 = bbox.getBeginX()
598 y0 = bbox.getBeginY()
599 width = bbox.getWidth()
600 height = bbox.getHeight()
603 ny = int(nx*float(height)/width + 0.5)
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)
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 ""
635 w, h = im.getWidth(), im.getHeight()
636 centerX = im.getX0() + w//2
637 centerY = im.getY0() + h//2
638 src = table.makeRecord()
641 foot.addPeak(centerX, centerY, 1)
642 src.setFootprint(foot)
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()))
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)
659 with disp.Buffering():
660 for cen, shape
in zip(centers, shapes):
661 bbox = mos.getBBox(i)
663 xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
665 disp.dot(
"+", xc, yc, ctype=afwDisplay.BLUE)
668 ixx, ixy, iyy = shape
669 disp.dot(
"@:%g,%g,%g" % (ixx, ixy, iyy), xc, yc, ctype=afwDisplay.RED)
675 kernel, background, testSources, config,
676 origVariance=False, nptsFull=1e6, keepPlots=True, titleFs=14):
677 """Plot diffim residuals for LOCAL and SPATIAL models.
683 for cell
in kernelCellSet.getCellList():
684 for cand
in cell.begin(
True):
686 if not (cand.getStatus() == afwMath.SpatialCellCandidate.GOOD):
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()))
695 sbg = background(int(cand.getXCenter()), int(cand.getYCenter()))
696 sdiffim = cand.getDifferenceImage(sk, sbg)
699 bbox = kernel.shrinkBBox(diffim.getBBox())
700 tdiffim = diffim.Factory(diffim, bbox)
701 torig = orig.Factory(orig, bbox)
702 tsdiffim = sdiffim.Factory(sdiffim, bbox)
705 candidateResids.append(np.ravel(tdiffim.image.array
706 / np.sqrt(torig.variance.array)))
707 spatialResids.append(np.ravel(tsdiffim.image.array
708 / np.sqrt(torig.variance.array)))
710 candidateResids.append(np.ravel(tdiffim.image.array
711 / np.sqrt(tdiffim.variance.array)))
712 spatialResids.append(np.ravel(tsdiffim.image.array
713 / np.sqrt(tsdiffim.variance.array)))
715 fullIm = diffExposure.image.array
716 fullMask = diffExposure.mask.array
718 fullVar = exposure.variance.array
720 fullVar = diffExposure.variance.array
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,
732 _LOG.getChild(
"plotPixelResiduals"))
733 for fp
in testFootprints:
734 subexp = diffExposure.Factory(diffExposure, fp[
"footprint"].getBBox())
737 subvar = afwImage.ExposureF(exposure, fp[
"footprint"].getBBox()).variance
739 subvar = subexp.variance
740 nonfitResids.append(np.ravel(subim.array/np.sqrt(subvar.array)))
742 candidateResids = np.ravel(np.array(candidateResids))
743 spatialResids = np.ravel(np.array(spatialResids))
744 nonfitResids = np.ravel(np.array(nonfitResids))
748 from matplotlib.font_manager
import FontProperties
749 except ImportError
as e:
750 print(
"Unable to import pylab: %s" % e)
756 fig.canvas._tkcanvas._root().lift()
760 fig.suptitle(
"Diffim residuals: Normalized by sqrt(input variance)", fontsize=titleFs)
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)
805 if keepPlots
and not keptPlots:
808 print(
"%s: Please close plots when done." % __name__)
813 print(
"Plots closed, exiting...")
815 atexit.register(show)
819def calcCentroid(arr):
820 """Calculate first moment of a (kernel) image.
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)])
828 centx = narr.sum()/sarrSum
830 centy = narr.sum()/sarrSum
834def calcWidth(arr, centx, centy):
835 """Calculate second moment of a (kernel) image.
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.)
844 xstd = np.sqrt(narr.sum()/sarrSum)
845 narr = sarr*np.power((yarr - centy), 2.)
846 ystd = np.sqrt(narr.sum()/sarrSum)
851 """Print differences in sky coordinates.
853 The difference is that between the source Position
and its Centroid mapped
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):
866 """Create regions file for display from input source list.
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")
873 (ra, dec) = wcs.pixelToSky(s.getCentroid()).getPosition(geom.degrees)
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))
883 """Draw the (RA, Dec) positions of a set of Sources. Image has the XY0.
885 disp = afwDisplay.Display(frame=frame)
886 with disp.Buffering():
888 (xc, yc) = wcs.skyToPixel(s.getCoord().getRa(), s.getCoord().getDec())
891 disp.dot(symb, xc, yc, ctype=ctype, size=size)
895 """Plot whisker diagram of astromeric offsets between results.matches.
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
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]
916 distance = np.asarray(distance)[xidxs]
919 bearing = [x[0].asRadians()
for x
in residuals]
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")
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).
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):
954 def _makeDipoleImage(self):
955 """Generate an exposure and catalog with the given dipole source(s).
964 dipole = posImage.clone()
965 di = dipole.getMaskedImage()
966 di -= negImage.getMaskedImage()
970 posDetectedBits = posImage.mask.array == dm.getPlaneBitMask(
"DETECTED")
971 negDetectedBits = negImage.mask.array == dm.getPlaneBitMask(
"DETECTED")
972 pos_det = dm.addMaskPlane(
"DETECTED_POS")
973 neg_det = dm.addMaskPlane(
"DETECTED_NEG")
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).
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]))
991 schema = TestDataset.makeMinimalSchema()
992 exposure, catalog = dataset.realize(noise=self.
noise, schema=schema, randomSeed=randomSeed)
995 y, x = np.mgrid[:self.
w, :self.
h]
997 gradient = gp[0] + gp[1]*x + gp[2]*y
999 gradient += gp[3]*x*y + gp[4]*x*x + gp[5]*y*y
1000 imgArr = exposure.image.array
1003 return exposure, catalog
1007 fitResult = alg.fitDipole(source, **kwds)
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.
1020 Whether to merge the positive
and negagive detections into a single
1022 diffim : `lsst.afw.image.exposure.exposure.ExposureF`
1023 Difference image on which to perform detection.
1024 detectSigma : `float`
1025 Threshold
for object detection.
1027 Number of pixels to grow the footprints before merging.
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.)
1038 If doMerge=
True, the merged source catalog
is returned OR
1039 detectTask : `lsst.meas.algorithms.SourceDetectionTask`
1041 If doMerge=
False, the source detection task
and its schema are
1045 diffim = self.diffim
1048 schema = afwTable.SourceTable.makeMinimalSchema()
1051 detectConfig = measAlg.SourceDetectionConfig()
1052 detectConfig.returnOriginalFootprints =
False
1054 diffimPsf = diffim.getPsf()
1055 psfSigma = diffimPsf.computeShape(diffimPsf.getAveragePosition()).getDeterminantRadius()
1058 detectConfig.thresholdPolarity =
"both"
1059 detectConfig.thresholdValue = detectSigma
1061 detectConfig.reEstimateBackground =
True
1062 detectConfig.thresholdType =
"pixel_stdev"
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)
1069 detectTask = measAlg.SourceDetectionTask(schema, config=detectConfig)
1071 table = afwTable.SourceTable.make(schema)
1072 catalog = detectTask.run(table, diffim, sigma=psfSigma)
1076 fpSet = catalog.positive
1077 fpSet.merge(catalog.negative, grow, grow,
False)
1078 sources = afwTable.SourceCatalog(table)
1079 fpSet.makeSources(sources)
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))
1094def getPsfFwhm(psf, average=True, position=None):
1095 """Directly calculate the horizontal and vertical widths
1096 of a PSF at half its maximum value.
1101 Point spread function (PSF) to evaluate.
1102 average : `bool`, optional
1103 Set to return the average width over Y
and X axes.
1105 The position at which to evaluate the PSF. If `
None`, then the
1106 average position
is used.
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.
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.
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
1141 fwhmExposureGrid : `int`
1142 Grid size to compute the mean FWHM
in an exposure.
1147 The mean PSF FWHM on the exposure.
1152 Raised
if the PSF cannot be computed at any of the grid points.
1161 bbox = exposure.getBBox()
1162 xmax, ymax = bbox.getMax()
1163 xmin, ymin = bbox.getMin()
1165 xbuffer = fwhmExposureBuffer*(xmax-xmin)
1166 ybuffer = fwhmExposureBuffer*(ymax-ymin)
1169 for (x, y)
in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
1170 np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
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)
1182 raise ValueError(
"Unable to compute PSF FWHM at any position on the exposure.")
1184 return np.nanmean(width)
1188 """Minimal source detection wrapper suitable for unit tests.
1193 Exposure on which to run detection/measurement
1194 The exposure is modified
in place to set the
'DETECTED' mask plane.
1199 Source catalog containing candidates
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(
1213 selectSources = detRet.sources
1214 selectMeasurement.run(measCat=selectSources, exposure=exposure)
1216 return selectSources
1220 """Make a fake, affine Wcs.
1224 cdMatrix = np.array([[5.19513851e-05, -2.81124812e-07],
1225 [-3.25186974e-07, -5.19112119e-05]])
1230 noiseSeed=6, fluxLevel=500., fluxRange=2.,
1231 kernelSize=32, templateBorderSize=0,
1238 doApplyCalibration=False,
1242 clearEdgeMask=False,
1244 """Make a reproduceable PSF-convolved exposure for testing.
1248 seed : `int`, optional
1249 Seed value to initialize the random number generator for sources.
1250 nSrc : `int`, optional
1251 Number of sources to simulate.
1252 psfSize : `float`, optional
1253 Width of the PSF of the simulated sources,
in pixels.
1254 noiseLevel : `float`, optional
1255 Standard deviation of the noise to add to each pixel.
1256 noiseSeed : `int`, optional
1257 Seed value to initialize the random number generator
for noise.
1258 fluxLevel : `float`, optional
1259 Reference flux of the simulated sources.
1260 fluxRange : `float`, optional
1261 Range
in flux amplitude of the simulated sources.
1262 kernelSize : `int`, optional
1263 Size
in pixels of the kernel
for simulating sources.
1264 templateBorderSize : `int`, optional
1265 Size
in pixels of the image border used to pad the image.
1266 background : `lsst.afw.math.Chebyshev1Function2D`, optional
1267 Optional background to add to the output image.
1268 xSize, ySize : `int`, optional
1269 Size
in pixels of the simulated image.
1270 x0, y0 : `int`, optional
1271 Origin of the image.
1272 calibration : `float`, optional
1273 Conversion factor between instFlux
and nJy.
1274 doApplyCalibration : `bool`, optional
1275 Apply the photometric calibration
and return the image
in nJy?
1276 xLoc, yLoc : `list` of `float`, optional
1277 User-specified coordinates of the simulated sources.
1278 If specified, must have length equal to ``nSrc``
1279 flux : `list` of `float`, optional
1280 User-specified fluxes of the simulated sources.
1281 If specified, must have length equal to ``nSrc``
1282 clearEdgeMask : `bool`, optional
1283 Clear the
"EDGE" mask plane after source detection.
1288 The model image,
with the mask
and variance planes.
1290 Catalog of sources detected on the model image.
1295 If `xloc`, `yloc`,
or `flux` are supplied
with inconsistant lengths.
1299 bufferSize = kernelSize/2 + templateBorderSize + 1
1302 if templateBorderSize > 0:
1303 bbox.grow(templateBorderSize)
1305 rng = np.random.RandomState(seed)
1306 rngNoise = np.random.RandomState(noiseSeed)
1307 x0, y0 = bbox.getBegin()
1308 xSize, ySize = bbox.getDimensions()
1310 xLoc = rng.rand(nSrc)*(xSize - 2*bufferSize) + bufferSize + x0
1312 if len(xLoc) != nSrc:
1313 raise ValueError(
"xLoc must have length equal to nSrc. %f supplied vs %f", len(xLoc), nSrc)
1315 yLoc = rng.rand(nSrc)*(ySize - 2*bufferSize) + bufferSize + y0
1317 if len(yLoc) != nSrc:
1318 raise ValueError(
"yLoc must have length equal to nSrc. %f supplied vs %f", len(yLoc), nSrc)
1321 flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*fluxLevel
1323 if len(flux) != nSrc:
1324 raise ValueError(
"flux must have length equal to nSrc. %f supplied vs %f", len(flux), nSrc)
1325 sigmas = [psfSize
for src
in range(nSrc)]
1326 coordList = list(zip(xLoc, yLoc, flux, sigmas))
1329 modelExposure = plantSources(bbox, kernelSize, skyLevel, coordList, addPoissonNoise=
False)
1331 noise = rngNoise.randn(ySize, xSize)*noiseLevel
1332 noise -= np.mean(noise)
1333 modelExposure.variance.array = np.sqrt(np.abs(modelExposure.image.array)) + noiseLevel**2
1334 modelExposure.image.array += noise
1339 modelExposure.mask &= ~modelExposure.mask.getPlaneBitMask(
"EDGE")
1341 if background
is not None:
1342 modelExposure.image += background
1343 modelExposure.maskedImage /= calibration
1344 modelExposure.info.setId(seed)
1345 if doApplyCalibration:
1346 modelExposure.maskedImage = modelExposure.photoCalib.calibrateImage(modelExposure.maskedImage)
1348 return modelExposure, sourceCat
1352 """Create a statistics control for configuring calculations on images.
1356 badMaskPlanes : `list` of `str`, optional
1357 List of mask planes to exclude from calculations.
1362 Statistics control object
for configuring calculations on images.
1364 if badMaskPlanes
is None:
1365 badMaskPlanes = (
"INTRP",
"EDGE",
"DETECTED",
"SAT",
"CR",
1366 "BAD",
"NO_DATA",
"DETECTED_NEGATIVE")
1368 statsControl.setNumSigmaClip(3.)
1369 statsControl.setNumIter(3)
1370 statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(badMaskPlanes))
1375 """Calculate a robust mean of the variance plane of an exposure.
1380 Image or variance plane of an exposure to evaluate.
1382 Mask plane to use
for excluding pixels.
1384 Statistics control object
for configuring the calculation.
1385 statistic : `lsst.afw.math.Property`, optional
1386 The type of statistic to compute. Typical values are
1387 ``afwMath.MEANCLIP``
or ``afwMath.STDEVCLIP``.
1392 The result of the statistic calculated
from the unflagged pixels.
1395 return statObj.getValue(statistic)
1399 """Compute the noise equivalent area for an image psf
1409 psfImg = psf.computeImage(psf.getAveragePosition())
1410 nea = 1./np.sum(psfImg.array**2)
std::pair< std::shared_ptr< lsst::afw::math::LinearCombinationKernel >, lsst::afw::math::Kernel::SpatialFunctionPtr > getSolutionPair()
def _makeDipoleImage(self)
def __init__(self, w=101, h=101, xcenPos=[27.], ycenPos=[25.], xcenNeg=[23.], ycenNeg=[25.], psfSigma=2., flux=[30000.], fluxNeg=None, noise=10., gradientParams=None)
def fitDipoleSource(self, source, **kwds)
def detectDipoleSources(self, doMerge=True, diffim=None, detectSigma=5.5, grow=3, minBinSize=32)
def _makeStarImage(self, xc=[15.3], yc=[18.6], flux=[2500], schema=None, randomSeed=None)
std::shared_ptr< SkyWcs > makeSkyWcs(daf::base::PropertySet &metadata, bool strip=false)
MaskedImage< ImagePixelT, MaskPixelT, VariancePixelT > * makeMaskedImage(typename std::shared_ptr< Image< ImagePixelT > > image, typename std::shared_ptr< Mask< MaskPixelT > > mask=Mask< MaskPixelT >(), typename std::shared_ptr< Image< VariancePixelT > > variance=Image< VariancePixelT >())
std::shared_ptr< Exposure< ImagePixelT, MaskPixelT, VariancePixelT > > makeExposure(MaskedImage< ImagePixelT, MaskPixelT, VariancePixelT > &mimage, std::shared_ptr< geom::SkyWcs const > wcs=std::shared_ptr< geom::SkyWcs const >())
Statistics makeStatistics(lsst::afw::image::Image< Pixel > const &img, lsst::afw::image::Mask< image::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl=StatisticsControl())
def showSourceSet(sSet, xy0=(0, 0), frame=0, ctype=afwDisplay.GREEN, symb="+", size=2)
def makeTestImage(seed=5, nSrc=20, psfSize=2., noiseLevel=5., noiseSeed=6, fluxLevel=500., fluxRange=2., kernelSize=32, templateBorderSize=0, background=None, xSize=256, ySize=256, x0=12345, y0=67890, calibration=1., doApplyCalibration=False, xLoc=None, yLoc=None, flux=None, clearEdgeMask=False)
def plotWhisker(results, newWcs)
def plotPixelResiduals(exposure, warpedTemplateExposure, diffExposure, kernelCellSet, kernel, background, testSources, config, origVariance=False, nptsFull=1e6, keepPlots=True, titleFs=14)
def showKernelBasis(kernel, frame=None)
def showSourceSetSky(sSet, wcs, xy0, frame=0, ctype=afwDisplay.GREEN, symb="+", size=2)
def showKernelCandidates(kernelCellSet, kernel, background, frame=None, showBadCandidates=True, resids=False, kernels=False)
def printSkyDiffs(sources, wcs)
def computePSFNoiseEquivalentArea(psf)
def makeStats(badMaskPlanes=None)
def showKernelSpatialCells(maskedIm, kernelCellSet, showChi2=False, symb="o", ctype=None, ctypeUnused=None, ctypeBad=None, size=3, frame=None, title="Spatial Cells")
def detectTestSources(exposure)
def makeRegions(sources, outfilename, wcs=None)
def showKernelMosaic(bbox, kernel, nx=7, ny=None, frame=None, title=None, showCenter=True, showEllipticity=True)
def showDiaSources(sources, exposure, isFlagged, isDipole, frame=None)
def plotKernelCoefficients(spatialKernel, kernelCellSet, showBadCandidates=False, keepPlots=True)
def plotKernelSpatialModel(kernel, kernelCellSet, showBadCandidates=True, numSample=128, keepPlots=True, maxCoeff=10)
def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP)