Coverage for python/lsst/ip/diffim/utils.py: 5%
808 statements
« prev ^ index » next coverage.py v7.3.0, created at 2023-08-31 10:51 +0000
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.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 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.image.array
706 / np.sqrt(torig.variance.array)))
707 spatialResids.append(np.ravel(tsdiffim.image.array
708 / np.sqrt(torig.variance.array)))
709 else:
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
717 if origVariance:
718 fullVar = exposure.variance.array
719 else:
720 fullVar = diffExposure.variance.array
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.image
736 if origVariance:
737 subvar = afwImage.ExposureF(exposure, fp["footprint"].getBBox()).variance
738 else:
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))
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 self.diffim, self.posImage, self.posCatalog, self.negImage, self.negCatalog \
969 = dipole, posImage, posCatalog, negImage, negCatalog
971 def _makeStarImage(self, xc=[15.3], yc=[18.6], flux=[2500], schema=None, randomSeed=None):
972 """Generate an exposure and catalog with the given stellar source(s).
973 """
974 from lsst.meas.base.tests import TestDataset
975 bbox = geom.Box2I(geom.Point2I(0, 0), geom.Point2I(self.w - 1, self.h - 1))
976 dataset = TestDataset(bbox, psfSigma=self.psfSigma, threshold=1.)
978 for i in range(len(xc)):
979 dataset.addSource(instFlux=flux[i], centroid=geom.Point2D(xc[i], yc[i]))
981 if schema is None:
982 schema = TestDataset.makeMinimalSchema()
983 exposure, catalog = dataset.realize(noise=self.noise, schema=schema, randomSeed=randomSeed)
985 if self.gradientParams is not None:
986 y, x = np.mgrid[:self.w, :self.h]
987 gp = self.gradientParams
988 gradient = gp[0] + gp[1]*x + gp[2]*y
989 if len(self.gradientParams) > 3: # it includes a set of 2nd-order polynomial params
990 gradient += gp[3]*x*y + gp[4]*x*x + gp[5]*y*y
991 imgArr = exposure.image.array
992 imgArr += gradient
994 return exposure, catalog
996 def fitDipoleSource(self, source, **kwds):
997 alg = DipoleFitAlgorithm(self.diffim, self.posImage, self.negImage)
998 fitResult = alg.fitDipole(source, **kwds)
999 return fitResult
1001 def detectDipoleSources(self, doMerge=True, diffim=None, detectSigma=5.5, grow=3, minBinSize=32):
1002 """Utility function for detecting dipoles.
1004 Detect pos/neg sources in the diffim, then merge them. A
1005 bigger "grow" parameter leads to a larger footprint which
1006 helps with dipole measurement for faint dipoles.
1008 Parameters
1009 ----------
1010 doMerge : `bool`
1011 Whether to merge the positive and negagive detections into a single
1012 source table.
1013 diffim : `lsst.afw.image.exposure.exposure.ExposureF`
1014 Difference image on which to perform detection.
1015 detectSigma : `float`
1016 Threshold for object detection.
1017 grow : `int`
1018 Number of pixels to grow the footprints before merging.
1019 minBinSize : `int`
1020 Minimum bin size for the background (re)estimation (only applies if
1021 the default leads to min(nBinX, nBinY) < fit order so the default
1022 config parameter needs to be decreased, but not to a value smaller
1023 than ``minBinSize``, in which case the fitting algorithm will take
1024 over and decrease the fit order appropriately.)
1026 Returns
1027 -------
1028 sources : `lsst.afw.table.SourceCatalog`
1029 If doMerge=True, the merged source catalog is returned OR
1030 detectTask : `lsst.meas.algorithms.SourceDetectionTask`
1031 schema : `lsst.afw.table.Schema`
1032 If doMerge=False, the source detection task and its schema are
1033 returned.
1034 """
1035 if diffim is None:
1036 diffim = self.diffim
1038 # Start with a minimal schema - only the fields all SourceCatalogs need
1039 schema = afwTable.SourceTable.makeMinimalSchema()
1041 # Customize the detection task a bit (optional)
1042 detectConfig = measAlg.SourceDetectionConfig()
1043 detectConfig.returnOriginalFootprints = False # should be the default
1045 # code from imageDifference.py:
1046 detectConfig.thresholdPolarity = "both"
1047 detectConfig.thresholdValue = detectSigma
1048 # detectConfig.nSigmaToGrow = psfSigma
1049 detectConfig.reEstimateBackground = True # if False, will fail often for faint sources on gradients?
1050 detectConfig.thresholdType = "pixel_stdev"
1051 detectConfig.excludeMaskPlanes = ["EDGE"]
1052 # Test images are often quite small, so may need to adjust background binSize
1053 while ((min(diffim.getWidth(), diffim.getHeight()))//detectConfig.background.binSize
1054 < detectConfig.background.approxOrderX and detectConfig.background.binSize > minBinSize):
1055 detectConfig.background.binSize = max(minBinSize, detectConfig.background.binSize//2)
1057 # Create the detection task. We pass the schema so the task can declare a few flag fields
1058 detectTask = measAlg.SourceDetectionTask(schema, config=detectConfig)
1060 table = afwTable.SourceTable.make(schema)
1061 catalog = detectTask.run(table, diffim)
1063 # Now do the merge.
1064 if doMerge:
1065 fpSet = catalog.positive
1066 fpSet.merge(catalog.negative, grow, grow, False)
1067 sources = afwTable.SourceCatalog(table)
1068 fpSet.makeSources(sources)
1070 return sources
1072 else:
1073 return detectTask, schema
1076def _sliceWidth(image, threshold, peaks, axis):
1077 vec = image.take(peaks[1 - axis], axis=axis)
1078 low = np.interp(threshold, vec[:peaks[axis] + 1], np.arange(peaks[axis] + 1))
1079 high = np.interp(threshold, vec[:peaks[axis] - 1:-1], np.arange(len(vec) - 1, peaks[axis] - 1, -1))
1080 return high - low
1083def getPsfFwhm(psf, average=True, position=None):
1084 """Directly calculate the horizontal and vertical widths
1085 of a PSF at half its maximum value.
1087 Parameters
1088 ----------
1089 psf : `~lsst.afw.detection.Psf`
1090 Point spread function (PSF) to evaluate.
1091 average : `bool`, optional
1092 Set to return the average width over Y and X axes.
1093 position : `~lsst.geom.Point2D`, optional
1094 The position at which to evaluate the PSF. If `None`, then the
1095 average position is used.
1097 Returns
1098 -------
1099 psfSize : `float` | `tuple` [`float`]
1100 The FWHM of the PSF computed at its average position.
1101 Returns the widths along the Y and X axes,
1102 or the average of the two if `average` is set.
1104 See Also
1105 --------
1106 evaluateMeanPsfFwhm
1107 """
1108 if position is None:
1109 position = psf.getAveragePosition()
1110 image = psf.computeKernelImage(position).array
1111 peak = psf.computePeak(position)
1112 peakLocs = np.unravel_index(np.argmax(image), image.shape)
1113 width = _sliceWidth(image, peak/2., peakLocs, axis=0), _sliceWidth(image, peak/2., peakLocs, axis=1)
1114 return np.nanmean(width) if average else width
1117def evaluateMeanPsfFwhm(exposure: afwImage.Exposure,
1118 fwhmExposureBuffer: float, fwhmExposureGrid: int) -> float:
1119 """Get the mean PSF FWHM by evaluating it on a grid within an exposure.
1121 Parameters
1122 ----------
1123 exposure : `~lsst.afw.image.Exposure`
1124 The exposure for which the mean FWHM of the PSF is to be computed.
1125 The exposure must contain a `psf` attribute.
1126 fwhmExposureBuffer : `float`
1127 Fractional buffer margin to be left out of all sides of the image
1128 during the construction of the grid to compute mean PSF FWHM in an
1129 exposure.
1130 fwhmExposureGrid : `int`
1131 Grid size to compute the mean FWHM in an exposure.
1133 Returns
1134 -------
1135 meanFwhm : `float`
1136 The mean PSF FWHM on the exposure.
1138 Raises
1139 ------
1140 ValueError
1141 Raised if the PSF cannot be computed at any of the grid points.
1143 See Also
1144 --------
1145 `getPsfFwhm`
1146 `computeAveragePsf`
1147 """
1149 psf = exposure.psf
1151 bbox = exposure.getBBox()
1152 xmax, ymax = bbox.getMax()
1153 xmin, ymin = bbox.getMin()
1155 xbuffer = fwhmExposureBuffer*(xmax-xmin)
1156 ybuffer = fwhmExposureBuffer*(ymax-ymin)
1158 width = []
1159 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
1160 np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
1161 ):
1162 pos = geom.Point2D(x, y)
1163 try:
1164 fwhm = getPsfFwhm(psf, average=True, position=pos)
1165 except InvalidParameterError:
1166 _LOG.debug("Unable to compute PSF FWHM at position (%f, %f).", x, y)
1167 continue
1169 width.append(fwhm)
1171 if not width:
1172 raise ValueError("Unable to compute PSF FWHM at any position on the exposure.")
1174 return np.nanmean(width)
1177def computeAveragePsf(exposure: afwImage.Exposure,
1178 psfExposureBuffer: float, psfExposureGrid: int) -> afwImage.ImageD:
1179 """Get the average PSF by evaluating it on a grid within an exposure.
1181 Parameters
1182 ----------
1183 exposure : `~lsst.afw.image.Exposure`
1184 The exposure for which the average PSF is to be computed.
1185 The exposure must contain a `psf` attribute.
1186 psfExposureBuffer : `float`
1187 Fractional buffer margin to be left out of all sides of the image
1188 during the construction of the grid to compute average PSF in an
1189 exposure.
1190 psfExposureGrid : `int`
1191 Grid size to compute the average PSF in an exposure.
1193 Returns
1194 -------
1195 psfImage : `~lsst.afw.image.Image`
1196 The average PSF across the exposure.
1198 Raises
1199 ------
1200 ValueError
1201 Raised if the PSF cannot be computed at any of the grid points.
1203 See Also
1204 --------
1205 `evaluateMeanPsfFwhm`
1206 """
1208 psf = exposure.psf
1210 bbox = exposure.getBBox()
1211 xmax, ymax = bbox.getMax()
1212 xmin, ymin = bbox.getMin()
1214 xbuffer = psfExposureBuffer*(xmax-xmin)
1215 ybuffer = psfExposureBuffer*(ymax-ymin)
1217 nImg = 0
1218 psfArray = None
1219 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, psfExposureGrid),
1220 np.linspace(ymin+ybuffer, ymax-ybuffer, psfExposureGrid)
1221 ):
1222 pos = geom.Point2D(x, y)
1223 try:
1224 singleImage = psf.computeKernelImage(pos)
1225 except InvalidParameterError:
1226 _LOG.debug("Unable to compute PSF image at position (%f, %f).", x, y)
1227 continue
1229 if psfArray is None:
1230 psfArray = singleImage.array
1231 else:
1232 psfArray += singleImage.array
1233 nImg += 1
1235 if psfArray is None:
1236 raise ValueError("Unable to compute PSF image at any position on the exposure.")
1238 psfImage = afwImage.ImageD(psfArray/nImg)
1239 return psfImage
1242def detectTestSources(exposure):
1243 """Minimal source detection wrapper suitable for unit tests.
1245 Parameters
1246 ----------
1247 exposure : `lsst.afw.image.Exposure`
1248 Exposure on which to run detection/measurement
1249 The exposure is modified in place to set the 'DETECTED' mask plane.
1251 Returns
1252 -------
1253 selectSources :
1254 Source catalog containing candidates
1255 """
1257 schema = afwTable.SourceTable.makeMinimalSchema()
1258 selectDetection = measAlg.SourceDetectionTask(schema=schema)
1259 selectMeasurement = measBase.SingleFrameMeasurementTask(schema=schema)
1260 table = afwTable.SourceTable.make(schema)
1262 detRet = selectDetection.run(
1263 table=table,
1264 exposure=exposure,
1265 sigma=None, # The appropriate sigma is calculated from the PSF
1266 doSmooth=True
1267 )
1268 selectSources = detRet.sources
1269 selectMeasurement.run(measCat=selectSources, exposure=exposure)
1271 return selectSources
1274def makeFakeWcs():
1275 """Make a fake, affine Wcs.
1276 """
1277 crpix = geom.Point2D(123.45, 678.9)
1278 crval = geom.SpherePoint(0.1, 0.1, geom.degrees)
1279 cdMatrix = np.array([[5.19513851e-05, -2.81124812e-07],
1280 [-3.25186974e-07, -5.19112119e-05]])
1281 return afwGeom.makeSkyWcs(crpix, crval, cdMatrix)
1284def makeTestImage(seed=5, nSrc=20, psfSize=2., noiseLevel=5.,
1285 noiseSeed=6, fluxLevel=500., fluxRange=2.,
1286 kernelSize=32, templateBorderSize=0,
1287 background=None,
1288 xSize=256,
1289 ySize=256,
1290 x0=12345,
1291 y0=67890,
1292 calibration=1.,
1293 doApplyCalibration=False,
1294 xLoc=None,
1295 yLoc=None,
1296 flux=None,
1297 clearEdgeMask=False,
1298 ):
1299 """Make a reproduceable PSF-convolved exposure for testing.
1301 Parameters
1302 ----------
1303 seed : `int`, optional
1304 Seed value to initialize the random number generator for sources.
1305 nSrc : `int`, optional
1306 Number of sources to simulate.
1307 psfSize : `float`, optional
1308 Width of the PSF of the simulated sources, in pixels.
1309 noiseLevel : `float`, optional
1310 Standard deviation of the noise to add to each pixel.
1311 noiseSeed : `int`, optional
1312 Seed value to initialize the random number generator for noise.
1313 fluxLevel : `float`, optional
1314 Reference flux of the simulated sources.
1315 fluxRange : `float`, optional
1316 Range in flux amplitude of the simulated sources.
1317 kernelSize : `int`, optional
1318 Size in pixels of the kernel for simulating sources.
1319 templateBorderSize : `int`, optional
1320 Size in pixels of the image border used to pad the image.
1321 background : `lsst.afw.math.Chebyshev1Function2D`, optional
1322 Optional background to add to the output image.
1323 xSize, ySize : `int`, optional
1324 Size in pixels of the simulated image.
1325 x0, y0 : `int`, optional
1326 Origin of the image.
1327 calibration : `float`, optional
1328 Conversion factor between instFlux and nJy.
1329 doApplyCalibration : `bool`, optional
1330 Apply the photometric calibration and return the image in nJy?
1331 xLoc, yLoc : `list` of `float`, optional
1332 User-specified coordinates of the simulated sources.
1333 If specified, must have length equal to ``nSrc``
1334 flux : `list` of `float`, optional
1335 User-specified fluxes of the simulated sources.
1336 If specified, must have length equal to ``nSrc``
1337 clearEdgeMask : `bool`, optional
1338 Clear the "EDGE" mask plane after source detection.
1340 Returns
1341 -------
1342 modelExposure : `lsst.afw.image.Exposure`
1343 The model image, with the mask and variance planes.
1344 sourceCat : `lsst.afw.table.SourceCatalog`
1345 Catalog of sources detected on the model image.
1347 Raises
1348 ------
1349 ValueError
1350 If `xloc`, `yloc`, or `flux` are supplied with inconsistant lengths.
1351 """
1352 # Distance from the inner edge of the bounding box to avoid placing test
1353 # sources in the model images.
1354 bufferSize = kernelSize/2 + templateBorderSize + 1
1356 bbox = geom.Box2I(geom.Point2I(x0, y0), geom.Extent2I(xSize, ySize))
1357 if templateBorderSize > 0:
1358 bbox.grow(templateBorderSize)
1360 rng = np.random.RandomState(seed)
1361 rngNoise = np.random.RandomState(noiseSeed)
1362 x0, y0 = bbox.getBegin()
1363 xSize, ySize = bbox.getDimensions()
1364 if xLoc is None:
1365 xLoc = rng.rand(nSrc)*(xSize - 2*bufferSize) + bufferSize + x0
1366 else:
1367 if len(xLoc) != nSrc:
1368 raise ValueError("xLoc must have length equal to nSrc. %f supplied vs %f", len(xLoc), nSrc)
1369 if yLoc is None:
1370 yLoc = rng.rand(nSrc)*(ySize - 2*bufferSize) + bufferSize + y0
1371 else:
1372 if len(yLoc) != nSrc:
1373 raise ValueError("yLoc must have length equal to nSrc. %f supplied vs %f", len(yLoc), nSrc)
1375 if flux is None:
1376 flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*fluxLevel
1377 else:
1378 if len(flux) != nSrc:
1379 raise ValueError("flux must have length equal to nSrc. %f supplied vs %f", len(flux), nSrc)
1380 sigmas = [psfSize for src in range(nSrc)]
1381 coordList = list(zip(xLoc, yLoc, flux, sigmas))
1382 skyLevel = 0
1383 # Don't use the built in poisson noise: it modifies the global state of numpy random
1384 modelExposure = plantSources(bbox, kernelSize, skyLevel, coordList, addPoissonNoise=False)
1385 modelExposure.setWcs(makeFakeWcs())
1386 noise = rngNoise.randn(ySize, xSize)*noiseLevel
1387 noise -= np.mean(noise)
1388 modelExposure.variance.array = np.sqrt(np.abs(modelExposure.image.array)) + noiseLevel**2
1389 modelExposure.image.array += noise
1391 # Run source detection to set up the mask plane
1392 sourceCat = detectTestSources(modelExposure)
1393 if clearEdgeMask:
1394 modelExposure.mask &= ~modelExposure.mask.getPlaneBitMask("EDGE")
1395 modelExposure.setPhotoCalib(afwImage.PhotoCalib(calibration, 0., bbox))
1396 if background is not None:
1397 modelExposure.image += background
1398 modelExposure.maskedImage /= calibration
1399 modelExposure.info.setId(seed)
1400 if doApplyCalibration:
1401 modelExposure.maskedImage = modelExposure.photoCalib.calibrateImage(modelExposure.maskedImage)
1403 return modelExposure, sourceCat
1406def makeStats(badMaskPlanes=None):
1407 """Create a statistics control for configuring calculations on images.
1409 Parameters
1410 ----------
1411 badMaskPlanes : `list` of `str`, optional
1412 List of mask planes to exclude from calculations.
1414 Returns
1415 -------
1416 statsControl : ` lsst.afw.math.StatisticsControl`
1417 Statistics control object for configuring calculations on images.
1418 """
1419 if badMaskPlanes is None:
1420 badMaskPlanes = ("INTRP", "EDGE", "DETECTED", "SAT", "CR",
1421 "BAD", "NO_DATA", "DETECTED_NEGATIVE")
1422 statsControl = afwMath.StatisticsControl()
1423 statsControl.setNumSigmaClip(3.)
1424 statsControl.setNumIter(3)
1425 statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(badMaskPlanes))
1426 return statsControl
1429def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP):
1430 """Calculate a robust mean of the variance plane of an exposure.
1432 Parameters
1433 ----------
1434 image : `lsst.afw.image.Image`
1435 Image or variance plane of an exposure to evaluate.
1436 mask : `lsst.afw.image.Mask`
1437 Mask plane to use for excluding pixels.
1438 statsCtrl : `lsst.afw.math.StatisticsControl`
1439 Statistics control object for configuring the calculation.
1440 statistic : `lsst.afw.math.Property`, optional
1441 The type of statistic to compute. Typical values are
1442 ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.
1444 Returns
1445 -------
1446 value : `float`
1447 The result of the statistic calculated from the unflagged pixels.
1448 """
1449 statObj = afwMath.makeStatistics(image, mask, statistic, statsCtrl)
1450 return statObj.getValue(statistic)
1453def computePSFNoiseEquivalentArea(psf):
1454 """Compute the noise equivalent area for an image psf
1456 Parameters
1457 ----------
1458 psf : `lsst.afw.detection.Psf`
1460 Returns
1461 -------
1462 nea : `float`
1463 """
1464 psfImg = psf.computeImage(psf.getAveragePosition())
1465 nea = 1./np.sum(psfImg.array**2)
1466 return nea