Coverage for python/lsst/ip/diffim/utils.py: 5%
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1# This file is part of ip_diffim.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
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 # Test images are often quite small, so may need to adjust background binSize
1052 while ((min(diffim.getWidth(), diffim.getHeight()))//detectConfig.background.binSize
1053 < detectConfig.background.approxOrderX and detectConfig.background.binSize > minBinSize):
1054 detectConfig.background.binSize = max(minBinSize, detectConfig.background.binSize//2)
1056 # Create the detection task. We pass the schema so the task can declare a few flag fields
1057 detectTask = measAlg.SourceDetectionTask(schema, config=detectConfig)
1059 table = afwTable.SourceTable.make(schema)
1060 catalog = detectTask.run(table, diffim)
1062 # Now do the merge.
1063 if doMerge:
1064 fpSet = catalog.positive
1065 fpSet.merge(catalog.negative, grow, grow, False)
1066 sources = afwTable.SourceCatalog(table)
1067 fpSet.makeSources(sources)
1069 return sources
1071 else:
1072 return detectTask, schema
1075def _sliceWidth(image, threshold, peaks, axis):
1076 vec = image.take(peaks[1 - axis], axis=axis)
1077 low = np.interp(threshold, vec[:peaks[axis] + 1], np.arange(peaks[axis] + 1))
1078 high = np.interp(threshold, vec[:peaks[axis] - 1:-1], np.arange(len(vec) - 1, peaks[axis] - 1, -1))
1079 return high - low
1082def getPsfFwhm(psf, average=True, position=None):
1083 """Directly calculate the horizontal and vertical widths
1084 of a PSF at half its maximum value.
1086 Parameters
1087 ----------
1088 psf : `~lsst.afw.detection.Psf`
1089 Point spread function (PSF) to evaluate.
1090 average : `bool`, optional
1091 Set to return the average width over Y and X axes.
1092 position : `~lsst.geom.Point2D`, optional
1093 The position at which to evaluate the PSF. If `None`, then the
1094 average position is used.
1096 Returns
1097 -------
1098 psfSize : `float` | `tuple` [`float`]
1099 The FWHM of the PSF computed at its average position.
1100 Returns the widths along the Y and X axes,
1101 or the average of the two if `average` is set.
1103 See Also
1104 --------
1105 evaluateMeanPsfFwhm
1106 """
1107 if position is None:
1108 position = psf.getAveragePosition()
1109 image = psf.computeKernelImage(position).array
1110 peak = psf.computePeak(position)
1111 peakLocs = np.unravel_index(np.argmax(image), image.shape)
1112 width = _sliceWidth(image, peak/2., peakLocs, axis=0), _sliceWidth(image, peak/2., peakLocs, axis=1)
1113 return np.nanmean(width) if average else width
1116def evaluateMeanPsfFwhm(exposure: afwImage.Exposure,
1117 fwhmExposureBuffer: float, fwhmExposureGrid: int) -> float:
1118 """Get the median PSF FWHM by evaluating it on a grid within an exposure.
1120 Parameters
1121 ----------
1122 exposure : `~lsst.afw.image.Exposure`
1123 The exposure for which the mean FWHM of the PSF is to be computed.
1124 The exposure must contain a `psf` attribute.
1125 fwhmExposureBuffer : `float`
1126 Fractional buffer margin to be left out of all sides of the image
1127 during the construction of the grid to compute mean PSF FWHM in an
1128 exposure.
1129 fwhmExposureGrid : `int`
1130 Grid size to compute the mean FWHM in an exposure.
1132 Returns
1133 -------
1134 meanFwhm : `float`
1135 The mean PSF FWHM on the exposure.
1137 Raises
1138 ------
1139 ValueError
1140 Raised if the PSF cannot be computed at any of the grid points.
1142 See Also
1143 --------
1144 getPsfFwhm
1145 """
1147 psf = exposure.psf
1149 bbox = exposure.getBBox()
1150 xmax, ymax = bbox.getMax()
1151 xmin, ymin = bbox.getMin()
1153 xbuffer = fwhmExposureBuffer*(xmax-xmin)
1154 ybuffer = fwhmExposureBuffer*(ymax-ymin)
1156 width = []
1157 for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
1158 np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
1159 ):
1160 pos = geom.Point2D(x, y)
1161 try:
1162 fwhm = getPsfFwhm(psf, average=True, position=pos)
1163 except InvalidParameterError:
1164 _LOG.debug("Unable to compute PSF FWHM at position (%f, %f).", x, y)
1165 continue
1167 width.append(fwhm)
1169 if not width:
1170 raise ValueError("Unable to compute PSF FWHM at any position on the exposure.")
1172 return np.nanmean(width)
1175def detectTestSources(exposure):
1176 """Minimal source detection wrapper suitable for unit tests.
1178 Parameters
1179 ----------
1180 exposure : `lsst.afw.image.Exposure`
1181 Exposure on which to run detection/measurement
1182 The exposure is modified in place to set the 'DETECTED' mask plane.
1184 Returns
1185 -------
1186 selectSources :
1187 Source catalog containing candidates
1188 """
1190 schema = afwTable.SourceTable.makeMinimalSchema()
1191 selectDetection = measAlg.SourceDetectionTask(schema=schema)
1192 selectMeasurement = measBase.SingleFrameMeasurementTask(schema=schema)
1193 table = afwTable.SourceTable.make(schema)
1195 detRet = selectDetection.run(
1196 table=table,
1197 exposure=exposure,
1198 sigma=None, # The appropriate sigma is calculated from the PSF
1199 doSmooth=True
1200 )
1201 selectSources = detRet.sources
1202 selectMeasurement.run(measCat=selectSources, exposure=exposure)
1204 return selectSources
1207def makeFakeWcs():
1208 """Make a fake, affine Wcs.
1209 """
1210 crpix = geom.Point2D(123.45, 678.9)
1211 crval = geom.SpherePoint(0.1, 0.1, geom.degrees)
1212 cdMatrix = np.array([[5.19513851e-05, -2.81124812e-07],
1213 [-3.25186974e-07, -5.19112119e-05]])
1214 return afwGeom.makeSkyWcs(crpix, crval, cdMatrix)
1217def makeTestImage(seed=5, nSrc=20, psfSize=2., noiseLevel=5.,
1218 noiseSeed=6, fluxLevel=500., fluxRange=2.,
1219 kernelSize=32, templateBorderSize=0,
1220 background=None,
1221 xSize=256,
1222 ySize=256,
1223 x0=12345,
1224 y0=67890,
1225 calibration=1.,
1226 doApplyCalibration=False,
1227 xLoc=None,
1228 yLoc=None,
1229 flux=None,
1230 clearEdgeMask=False,
1231 ):
1232 """Make a reproduceable PSF-convolved exposure for testing.
1234 Parameters
1235 ----------
1236 seed : `int`, optional
1237 Seed value to initialize the random number generator for sources.
1238 nSrc : `int`, optional
1239 Number of sources to simulate.
1240 psfSize : `float`, optional
1241 Width of the PSF of the simulated sources, in pixels.
1242 noiseLevel : `float`, optional
1243 Standard deviation of the noise to add to each pixel.
1244 noiseSeed : `int`, optional
1245 Seed value to initialize the random number generator for noise.
1246 fluxLevel : `float`, optional
1247 Reference flux of the simulated sources.
1248 fluxRange : `float`, optional
1249 Range in flux amplitude of the simulated sources.
1250 kernelSize : `int`, optional
1251 Size in pixels of the kernel for simulating sources.
1252 templateBorderSize : `int`, optional
1253 Size in pixels of the image border used to pad the image.
1254 background : `lsst.afw.math.Chebyshev1Function2D`, optional
1255 Optional background to add to the output image.
1256 xSize, ySize : `int`, optional
1257 Size in pixels of the simulated image.
1258 x0, y0 : `int`, optional
1259 Origin of the image.
1260 calibration : `float`, optional
1261 Conversion factor between instFlux and nJy.
1262 doApplyCalibration : `bool`, optional
1263 Apply the photometric calibration and return the image in nJy?
1264 xLoc, yLoc : `list` of `float`, optional
1265 User-specified coordinates of the simulated sources.
1266 If specified, must have length equal to ``nSrc``
1267 flux : `list` of `float`, optional
1268 User-specified fluxes of the simulated sources.
1269 If specified, must have length equal to ``nSrc``
1270 clearEdgeMask : `bool`, optional
1271 Clear the "EDGE" mask plane after source detection.
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.
1280 Raises
1281 ------
1282 ValueError
1283 If `xloc`, `yloc`, or `flux` are supplied with inconsistant lengths.
1284 """
1285 # Distance from the inner edge of the bounding box to avoid placing test
1286 # sources in the model images.
1287 bufferSize = kernelSize/2 + templateBorderSize + 1
1289 bbox = geom.Box2I(geom.Point2I(x0, y0), geom.Extent2I(xSize, ySize))
1290 if templateBorderSize > 0:
1291 bbox.grow(templateBorderSize)
1293 rng = np.random.RandomState(seed)
1294 rngNoise = np.random.RandomState(noiseSeed)
1295 x0, y0 = bbox.getBegin()
1296 xSize, ySize = bbox.getDimensions()
1297 if xLoc is None:
1298 xLoc = rng.rand(nSrc)*(xSize - 2*bufferSize) + bufferSize + x0
1299 else:
1300 if len(xLoc) != nSrc:
1301 raise ValueError("xLoc must have length equal to nSrc. %f supplied vs %f", len(xLoc), nSrc)
1302 if yLoc is None:
1303 yLoc = rng.rand(nSrc)*(ySize - 2*bufferSize) + bufferSize + y0
1304 else:
1305 if len(yLoc) != nSrc:
1306 raise ValueError("yLoc must have length equal to nSrc. %f supplied vs %f", len(yLoc), nSrc)
1308 if flux is None:
1309 flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*fluxLevel
1310 else:
1311 if len(flux) != nSrc:
1312 raise ValueError("flux must have length equal to nSrc. %f supplied vs %f", len(flux), nSrc)
1313 sigmas = [psfSize for src in range(nSrc)]
1314 coordList = list(zip(xLoc, yLoc, flux, sigmas))
1315 skyLevel = 0
1316 # Don't use the built in poisson noise: it modifies the global state of numpy random
1317 modelExposure = plantSources(bbox, kernelSize, skyLevel, coordList, addPoissonNoise=False)
1318 modelExposure.setWcs(makeFakeWcs())
1319 noise = rngNoise.randn(ySize, xSize)*noiseLevel
1320 noise -= np.mean(noise)
1321 modelExposure.variance.array = np.sqrt(np.abs(modelExposure.image.array)) + noiseLevel**2
1322 modelExposure.image.array += noise
1324 # Run source detection to set up the mask plane
1325 sourceCat = detectTestSources(modelExposure)
1326 if clearEdgeMask:
1327 modelExposure.mask &= ~modelExposure.mask.getPlaneBitMask("EDGE")
1328 modelExposure.setPhotoCalib(afwImage.PhotoCalib(calibration, 0., bbox))
1329 if background is not None:
1330 modelExposure.image += background
1331 modelExposure.maskedImage /= calibration
1332 modelExposure.info.setId(seed)
1333 if doApplyCalibration:
1334 modelExposure.maskedImage = modelExposure.photoCalib.calibrateImage(modelExposure.maskedImage)
1336 return modelExposure, sourceCat
1339def makeStats(badMaskPlanes=None):
1340 """Create a statistics control for configuring calculations on images.
1342 Parameters
1343 ----------
1344 badMaskPlanes : `list` of `str`, optional
1345 List of mask planes to exclude from calculations.
1347 Returns
1348 -------
1349 statsControl : ` lsst.afw.math.StatisticsControl`
1350 Statistics control object for configuring calculations on images.
1351 """
1352 if badMaskPlanes is None:
1353 badMaskPlanes = ("INTRP", "EDGE", "DETECTED", "SAT", "CR",
1354 "BAD", "NO_DATA", "DETECTED_NEGATIVE")
1355 statsControl = afwMath.StatisticsControl()
1356 statsControl.setNumSigmaClip(3.)
1357 statsControl.setNumIter(3)
1358 statsControl.setAndMask(afwImage.Mask.getPlaneBitMask(badMaskPlanes))
1359 return statsControl
1362def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP):
1363 """Calculate a robust mean of the variance plane of an exposure.
1365 Parameters
1366 ----------
1367 image : `lsst.afw.image.Image`
1368 Image or variance plane of an exposure to evaluate.
1369 mask : `lsst.afw.image.Mask`
1370 Mask plane to use for excluding pixels.
1371 statsCtrl : `lsst.afw.math.StatisticsControl`
1372 Statistics control object for configuring the calculation.
1373 statistic : `lsst.afw.math.Property`, optional
1374 The type of statistic to compute. Typical values are
1375 ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.
1377 Returns
1378 -------
1379 value : `float`
1380 The result of the statistic calculated from the unflagged pixels.
1381 """
1382 statObj = afwMath.makeStatistics(image, mask, statistic, statsCtrl)
1383 return statObj.getValue(statistic)
1386def computePSFNoiseEquivalentArea(psf):
1387 """Compute the noise equivalent area for an image psf
1389 Parameters
1390 ----------
1391 psf : `lsst.afw.detection.Psf`
1393 Returns
1394 -------
1395 nea : `float`
1396 """
1397 psfImg = psf.computeImage(psf.getAveragePosition())
1398 nea = 1./np.sum(psfImg.array**2)
1399 return nea