LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
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maskStreaks.py
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1# This file is part of meas_algorithms.
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/>.
21
22__all__ = ["MaskStreaksConfig", "MaskStreaksTask"]
23
24import numpy as np
25import scipy
26import textwrap
27import copy
28from skimage.feature import canny
29from sklearn.cluster import KMeans
30from dataclasses import dataclass
31
32import lsst.pex.config as pexConfig
33import lsst.pipe.base as pipeBase
34import lsst.kht
35from lsst.utils.timer import timeMethod
36
37
38@dataclass
39class Line:
40 """A simple data class to describe a line profile. The parameter `rho`
41 describes the distance from the center of the image, `theta` describes
42 the angle, and `sigma` describes the width of the line.
43 """
44
45 rho: float
46 theta: float
47 sigma: float = 0
48
49
51 """Collection of `Line` objects.
52
53 Parameters
54 ----------
55 rhos : `np.ndarray`
56 Array of `Line` rho parameters.
57 thetas : `np.ndarray`
58 Array of `Line` theta parameters.
59 sigmas : `np.ndarray`, optional
60 Array of `Line` sigma parameters.
61 """
62
63 def __init__(self, rhos, thetas, sigmas=None):
64 if sigmas is None:
65 sigmas = np.zeros(len(rhos))
66
67 self._lines = [Line(rho, theta, sigma) for (rho, theta, sigma) in
68 zip(rhos, thetas, sigmas)]
69
70 def __len__(self):
71 return len(self._lines)
72
73 def __getitem__(self, index):
74 return self._lines[index]
75
76 def __iter__(self):
77 return iter(self._lines)
78
79 def __repr__(self):
80 joinedString = ", ".join(str(line) for line in self._lines)
81 return textwrap.shorten(joinedString, width=160, placeholder="...")
82
83 @property
84 def rhos(self):
85 return np.array([line.rho for line in self._lines])
86
87 @property
88 def thetas(self):
89 return np.array([line.theta for line in self._lines])
90
91 @property
92 def sigmas(self):
93 return np.array([line.sigma for line in self._lines])
94
95 def append(self, newLine):
96 """Add line to current collection of lines.
97
98 Parameters
99 ----------
100 newLine : `Line`
101 `Line` to add to current collection of lines
102 """
103 self._lines.append(copy.copy(newLine))
104
105
107 """Construct and/or fit a model for a linear streak.
108
109 This assumes a simple model for a streak, in which the streak
110 follows a straight line in pixels space, with a Moffat-shaped profile. The
111 model is fit to data using a Newton-Raphson style minimization algorithm.
112 The initial guess for the line parameters is assumed to be fairly accurate,
113 so only a narrow band of pixels around the initial line estimate is used in
114 fitting the model, which provides a significant speed-up over using all the
115 data. The class can also be used just to construct a model for the data with
116 a line following the given coordinates.
117
118 Parameters
119 ----------
120 data : `np.ndarray`
121 2d array of data.
122 weights : `np.ndarray`
123 2d array of weights.
124 line : `Line`, optional
125 Guess for position of line. Data far from line guess is masked out.
126 Defaults to None, in which case only data with `weights` = 0 is masked
127 out.
128 """
129
130 def __init__(self, data, weights, line=None):
131 self.data = data
132 self.weights = weights
133 self._ymax, self._xmax = data.shape
134 self._dtype = data.dtype
135 xrange = np.arange(self._xmax) - self._xmax / 2.
136 yrange = np.arange(self._ymax) - self._ymax / 2.
137 self._rhoMax = ((0.5 * self._ymax)**2 + (0.5 * self._xmax)**2)**0.5
138 self._xmesh, self._ymesh = np.meshgrid(xrange, yrange)
139 self.mask = (weights != 0)
140
141 self._initLine = line
142 self.setLineMask(line)
143
144 def setLineMask(self, line):
145 """Set mask around the image region near the line.
146
147 Parameters
148 ----------
149 line : `Line`
150 Parameters of line in the image.
151 """
152 if line:
153 # Only fit pixels within 5 sigma of the estimated line
154 radtheta = np.deg2rad(line.theta)
155 distance = (np.cos(radtheta) * self._xmesh + np.sin(radtheta) * self._ymesh - line.rho)
156 m = (abs(distance) < 5 * line.sigma)
157 self.lineMask = self.mask & m
158 else:
159 self.lineMask = np.copy(self.mask)
160
161 self.lineMaskSize = self.lineMask.sum()
162 self._maskData = self.data[self.lineMask]
163 self._maskWeights = self.weights[self.lineMask]
164 self._mxmesh = self._xmesh[self.lineMask]
165 self._mymesh = self._ymesh[self.lineMask]
166
167 def _makeMaskedProfile(self, line, fitFlux=True):
168 """Construct the line model in the masked region and calculate its
169 derivatives.
170
171 Parameters
172 ----------
173 line : `Line`
174 Parameters of line profile for which to make profile in the masked
175 region.
176 fitFlux : `bool`
177 Fit the amplitude of the line profile to the data.
178
179 Returns
180 -------
181 model : `np.ndarray`
182 Model in the masked region.
183 dModel : `np.ndarray`
184 Derivative of the model in the masked region.
185 """
186 invSigma = line.sigma**-1
187 # Calculate distance between pixels and line
188 radtheta = np.deg2rad(line.theta)
189 costheta = np.cos(radtheta)
190 sintheta = np.sin(radtheta)
191 distance = (costheta * self._mxmesh + sintheta * self._mymesh - line.rho)
192 distanceSquared = distance**2
193
194 # Calculate partial derivatives of distance
195 drad = np.pi / 180
196 dDistanceSqdRho = 2 * distance * (-np.ones_like(self._mxmesh))
197 dDistanceSqdTheta = (2 * distance * (-sintheta * self._mxmesh + costheta * self._mymesh) * drad)
198
199 # Use pixel-line distances to make Moffat profile
200 profile = (1 + distanceSquared * invSigma**2)**-2.5
201 dProfile = -2.5 * (1 + distanceSquared * invSigma**2)**-3.5
202
203 if fitFlux:
204 # Calculate line flux from profile and data
205 flux = ((self._maskWeights * self._maskData * profile).sum()
206 / (self._maskWeights * profile**2).sum())
207 else:
208 # Approximately normalize the line
209 flux = invSigma**-1
210 if np.isnan(flux):
211 flux = 0
212
213 model = flux * profile
214
215 # Calculate model derivatives
216 fluxdProfile = flux * dProfile
217 fluxdProfileInvSigma = fluxdProfile * invSigma**2
218 dModeldRho = fluxdProfileInvSigma * dDistanceSqdRho
219 dModeldTheta = fluxdProfileInvSigma * dDistanceSqdTheta
220 dModeldInvSigma = fluxdProfile * distanceSquared * 2 * invSigma
221
222 dModel = np.array([dModeldRho, dModeldTheta, dModeldInvSigma])
223 return model, dModel
224
225 def makeProfile(self, line, fitFlux=True):
226 """Construct the line profile model.
227
228 Parameters
229 ----------
230 line : `Line`
231 Parameters of the line profile to model.
232 fitFlux : `bool`, optional
233 Fit the amplitude of the line profile to the data.
234
235 Returns
236 -------
237 finalModel : `np.ndarray`
238 Model for line profile.
239 """
240 model, _ = self._makeMaskedProfile(line, fitFlux=fitFlux)
241 finalModel = np.zeros((self._ymax, self._xmax), dtype=self._dtype)
242 finalModel[self.lineMask] = model
243 return finalModel
244
245 def _lineChi2(self, line, grad=True):
246 """Construct the chi2 between the data and the model.
247
248 Parameters
249 ----------
250 line : `Line`
251 `Line` parameters for which to build model and calculate chi2.
252 grad : `bool`, optional
253 Whether or not to return the gradient and hessian.
254
255 Returns
256 -------
257 reducedChi : `float`
258 Reduced chi2 of the model.
259 reducedDChi : `np.ndarray`
260 Derivative of the chi2 with respect to rho, theta, invSigma.
261 reducedHessianChi : `np.ndarray`
262 Hessian of the chi2 with respect to rho, theta, invSigma.
263 """
264 # Calculate chi2
265 model, dModel = self._makeMaskedProfile(line)
266 chi2 = (self._maskWeights * (self._maskData - model)**2).sum()
267 if not grad:
268 return chi2.sum() / self.lineMaskSize
269
270 # Calculate derivative and Hessian of chi2
271 derivChi2 = ((-2 * self._maskWeights * (self._maskData - model))[None, :] * dModel).sum(axis=1)
272 hessianChi2 = (2 * self._maskWeights * dModel[:, None, :] * dModel[None, :, :]).sum(axis=2)
273
274 reducedChi = chi2 / self.lineMaskSize
275 reducedDChi = derivChi2 / self.lineMaskSize
276 reducedHessianChi = hessianChi2 / self.lineMaskSize
277 return reducedChi, reducedDChi, reducedHessianChi
278
279 def fit(self, dChi2Tol=0.1, maxIter=100, log=None):
280 """Perform Newton-Raphson minimization to find line parameters.
281
282 This method takes advantage of having known derivative and Hessian of
283 the multivariate function to quickly and efficiently find the minimum.
284 This is more efficient than the scipy implementation of the Newton-
285 Raphson method, which doesn't take advantage of the Hessian matrix. The
286 method here also performs a line search in the direction of the steepest
287 derivative at each iteration, which reduces the number of iterations
288 needed.
289
290 Parameters
291 ----------
292 dChi2Tol : `float`, optional
293 Change in Chi2 tolerated for fit convergence.
294 maxIter : `int`, optional
295 Maximum number of fit iterations allowed. The fit should converge in
296 ~10 iterations, depending on the value of dChi2Tol, but this
297 maximum provides a backup.
298 log : `lsst.utils.logging.LsstLogAdapter`, optional
299 Logger to use for reporting more details for fitting failures.
300
301 Returns
302 -------
303 outline : `np.ndarray`
304 Coordinates and inverse width of fit line.
305 chi2 : `float`
306 Reduced Chi2 of model fit to data.
307 fitFailure : `bool`
308 Boolean where `False` corresponds to a successful fit.
309 """
310 # Do minimization on inverse of sigma to simplify derivatives:
311 x = np.array([self._initLine.rho, self._initLine.theta, self._initLine.sigma**-1])
312
313 dChi2 = 1
314 iter = 0
315 oldChi2 = 0
316 fitFailure = False
317
318 def line_search(c, dx):
319 testx = x - c * dx
320 testLine = Line(testx[0], testx[1], testx[2]**-1)
321 return self._lineChi2(testLine, grad=False)
322
323 while abs(dChi2) > dChi2Tol:
324 line = Line(x[0], x[1], x[2]**-1)
325 chi2, b, A = self._lineChi2(line)
326 if chi2 == 0:
327 break
328 if not np.isfinite(A).all():
329 fitFailure = True
330 if log is not None:
331 log.warning("Hessian matrix has non-finite elements.")
332 break
333 dChi2 = oldChi2 - chi2
334 try:
335 cholesky = scipy.linalg.cho_factor(A)
336 except np.linalg.LinAlgError:
337 fitFailure = True
338 if log is not None:
339 log.warning("Hessian matrix is not invertible.")
340 break
341 dx = scipy.linalg.cho_solve(cholesky, b)
342
343 factor, fmin, _, _ = scipy.optimize.brent(line_search, args=(dx,), full_output=True, tol=0.05)
344 x -= factor * dx
345 if (abs(x[0]) > 1.5 * self._rhoMax) or (iter > maxIter):
346 fitFailure = True
347 break
348 oldChi2 = chi2
349 iter += 1
350
351 outline = Line(x[0], x[1], abs(x[2])**-1)
352
353 return outline, chi2, fitFailure
354
355
356class MaskStreaksConfig(pexConfig.Config):
357 """Configuration parameters for `MaskStreaksTask`.
358 """
359
360 minimumKernelHeight = pexConfig.Field(
361 doc="Minimum height of the streak-finding kernel relative to the tallest kernel",
362 dtype=float,
363 default=0.0,
364 )
365 absMinimumKernelHeight = pexConfig.Field(
366 doc="Minimum absolute height of the streak-finding kernel",
367 dtype=float,
368 default=5,
369 )
370 clusterMinimumSize = pexConfig.Field(
371 doc="Minimum size in pixels of detected clusters",
372 dtype=int,
373 default=50,
374 )
375 clusterMinimumDeviation = pexConfig.Field(
376 doc="Allowed deviation (in pixels) from a straight line for a detected "
377 "line",
378 dtype=int,
379 default=2,
380 )
381 delta = pexConfig.Field(
382 doc="Stepsize in angle-radius parameter space",
383 dtype=float,
384 default=0.2,
385 )
386 nSigma = pexConfig.Field(
387 doc="Number of sigmas from center of kernel to include in voting "
388 "procedure",
389 dtype=float,
390 default=2,
391 )
392 rhoBinSize = pexConfig.Field(
393 doc="Binsize in pixels for position parameter rho when finding "
394 "clusters of detected lines",
395 dtype=float,
396 default=30,
397 )
398 thetaBinSize = pexConfig.Field(
399 doc="Binsize in degrees for angle parameter theta when finding "
400 "clusters of detected lines",
401 dtype=float,
402 default=2,
403 )
404 invSigma = pexConfig.Field(
405 doc="Inverse of the Moffat sigma parameter (in units of pixels)"
406 "describing the profile of the streak",
407 dtype=float,
408 default=10.**-1,
409 )
410 footprintThreshold = pexConfig.Field(
411 doc="Threshold at which to determine edge of line, in units of "
412 "nanoJanskys",
413 dtype=float,
414 default=0.01
415 )
416 dChi2Tolerance = pexConfig.Field(
417 doc="Absolute difference in Chi2 between iterations of line profile"
418 "fitting that is acceptable for convergence",
419 dtype=float,
420 default=0.1
421 )
422 detectedMaskPlane = pexConfig.Field(
423 doc="Name of mask with pixels above detection threshold, used for first"
424 "estimate of streak locations",
425 dtype=str,
426 default="DETECTED"
427 )
428 streaksMaskPlane = pexConfig.Field(
429 doc="Name of mask plane holding detected streaks",
430 dtype=str,
431 default="STREAK"
432 )
433
434
435class MaskStreaksTask(pipeBase.Task):
436 """Find streaks or other straight lines in image data.
437
438 Nearby objects passing through the field of view of the telescope leave a
439 bright trail in images. This class uses the Kernel Hough Transform (KHT)
440 (Fernandes and Oliveira, 2007), implemented in `lsst.houghtransform`. The
441 procedure works by taking a binary image, either provided as put or produced
442 from the input data image, using a Canny filter to make an image of the
443 edges in the original image, then running the KHT on the edge image. The KHT
444 identifies clusters of non-zero points, breaks those clusters of points into
445 straight lines, keeps clusters with a size greater than the user-set
446 threshold, then performs a voting procedure to find the best-fit coordinates
447 of any straight lines. Given the results of the KHT algorithm, clusters of
448 lines are identified and grouped (generally these correspond to the two
449 edges of a strea) and a profile is fit to the streak in the original
450 (non-binary) image.
451 """
452
453 ConfigClass = MaskStreaksConfig
454 _DefaultName = "maskStreaks"
455
456 @timeMethod
457 def find(self, maskedImage):
458 """Find streaks in a masked image.
459
460 Parameters
461 ----------
462 maskedImage : `lsst.afw.image.maskedImage`
463 The image in which to search for streaks.
464
465 Returns
466 -------
467 result : `lsst.pipe.base.Struct`
468 Results as a struct with attributes:
469
470 ``originalLines``
471 Lines identified by kernel hough transform.
472 ``lineClusters``
473 Lines grouped into clusters in rho-theta space.
474 ``lines``
475 Final result for lines after line-profile fit.
476 ``mask``
477 2-d boolean mask where detected lines are True.
478 """
479 mask = maskedImage.mask
480 detectionMask = (mask.array & mask.getPlaneBitMask(self.config.detectedMaskPlane))
481
482 self.edges = self._cannyFilter(detectionMask)
483 self.lines = self._runKHT(self.edges)
484
485 if len(self.lines) == 0:
486 lineMask = np.zeros(detectionMask.shape, dtype=bool)
487 fitLines = LineCollection([], [])
488 clusters = LineCollection([], [])
489 else:
490 clusters = self._findClusters(self.lines)
491 fitLines, lineMask = self._fitProfile(clusters, maskedImage)
492
493 # The output mask is the intersection of the fit streaks and the image detections
494 outputMask = lineMask & detectionMask.astype(bool)
495
496 return pipeBase.Struct(
497 lines=fitLines,
498 lineClusters=clusters,
499 originalLines=self.lines,
500 mask=outputMask,
501 )
502
503 @timeMethod
504 def run(self, maskedImage):
505 """Find and mask streaks in a masked image.
506
507 Finds streaks in the image and modifies maskedImage in place by adding a
508 mask plane with any identified streaks.
509
510 Parameters
511 ----------
512 maskedImage : `lsst.afw.image.Exposure` or `lsst.afw.image.maskedImage`
513 The image in which to search for streaks. The mask detection plane
514 corresponding to `config.detectedMaskPlane` must be set with the
515 detected pixels. The mask will have a plane added with any detected
516 streaks, and with the mask plane name set by
517 self.config.streaksMaskPlane.
518
519 Returns
520 -------
521 result : `lsst.pipe.base.Struct`
522 Results as a struct with attributes:
523
524 ``originalLines``
525 Lines identified by kernel hough transform.
526 ``lineClusters``
527 Lines grouped into clusters in rho-theta space.
528 ``lines``
529 Final result for lines after line-profile fit.
530 """
531 streaks = self.find(maskedImage)
532
533 maskedImage.mask.addMaskPlane(self.config.streaksMaskPlane)
534 maskedImage.mask.array[streaks.mask] |= maskedImage.mask.getPlaneBitMask(self.config.streaksMaskPlane)
535
536 return pipeBase.Struct(
537 lines=streaks.lines,
538 lineClusters=streaks.lineClusters,
539 originalLines=streaks.originalLines,
540 )
541
542 def _cannyFilter(self, image):
543 """Apply a canny filter to the data in order to detect edges.
544
545 Parameters
546 ----------
547 image : `np.ndarray`
548 2-d image data on which to run filter.
549
550 Returns
551 -------
552 cannyData : `np.ndarray`
553 2-d image of edges found in input image.
554 """
555 # Ensure that the pixels are zero or one. Change the datatype to
556 # np.float64 to be compatible with the Canny filter routine.
557 filterData = (image > 0).astype(np.float64)
558 return canny(filterData, use_quantiles=True, sigma=0.1)
559
560 def _runKHT(self, image):
561 """Run Kernel Hough Transform on image.
562
563 Parameters
564 ----------
565 image : `np.ndarray`
566 2-d image data on which to detect lines.
567
568 Returns
569 -------
570 result : `LineCollection`
571 Collection of detected lines, with their detected rho and theta
572 coordinates.
573 """
574 lines = lsst.kht.find_lines(image, self.config.clusterMinimumSize,
575 self.config.clusterMinimumDeviation, self.config.delta,
576 self.config.minimumKernelHeight, self.config.nSigma,
577 self.config.absMinimumKernelHeight)
578 self.log.info("The Kernel Hough Transform detected %s line(s)", len(lines))
579
580 return LineCollection(lines.rho, lines.theta)
581
582 def _findClusters(self, lines):
583 """Group lines that are close in parameter space and likely describe
584 the same streak.
585
586 Parameters
587 ----------
588 lines : `LineCollection`
589 Collection of lines to group into clusters.
590
591 Returns
592 -------
593 result : `LineCollection`
594 Average `Line` for each cluster of `Line`s in the input
595 `LineCollection`.
596 """
597 # Scale variables by threshold bin-size variable so that rho and theta
598 # are on the same scale. Since the clustering algorithm below stops when
599 # the standard deviation <= 1, after rescaling each cluster will have a
600 # standard deviation at or below the bin-size.
601 x = lines.rhos / self.config.rhoBinSize
602 y = lines.thetas / self.config.thetaBinSize
603 X = np.array([x, y]).T
604 nClusters = 1
605
606 # Put line parameters in clusters by starting with all in one, then
607 # subdividing until the parameters of each cluster have std dev=1.
608 # If nClusters == len(lines), each line will have its own 'cluster', so
609 # the standard deviations of each cluster must be zero and the loop
610 # is guaranteed to stop.
611 while True:
612 kmeans = KMeans(n_clusters=nClusters, n_init='auto').fit(X)
613 clusterStandardDeviations = np.zeros((nClusters, 2))
614 for c in range(nClusters):
615 inCluster = X[kmeans.labels_ == c]
616 clusterStandardDeviations[c] = np.std(inCluster, axis=0)
617 # Are the rhos and thetas in each cluster all below the threshold?
618 if (clusterStandardDeviations <= 1).all():
619 break
620 nClusters += 1
621
622 # The cluster centers are final line estimates
623 finalClusters = kmeans.cluster_centers_.T
624
625 # Rescale variables:
626 finalRhos = finalClusters[0] * self.config.rhoBinSize
627 finalThetas = finalClusters[1] * self.config.thetaBinSize
628 result = LineCollection(finalRhos, finalThetas)
629 self.log.info("Lines were grouped into %s potential streak(s)", len(finalRhos))
630
631 return result
632
633 def _fitProfile(self, lines, maskedImage):
634 """Fit the profile of the streak.
635
636 Given the initial parameters of detected lines, fit a model for the
637 streak to the original (non-binary image). The assumed model is a
638 straight line with a Moffat profile.
639
640 Parameters
641 ----------
642 lines : `LineCollection`
643 Collection of guesses for `Line`s detected in the image.
644 maskedImage : `lsst.afw.image.maskedImage`
645 Original image to be used to fit profile of streak.
646
647 Returns
648 -------
649 lineFits : `LineCollection`
650 Collection of `Line` profiles fit to the data.
651 finalMask : `np.ndarray`
652 2d mask array with detected streaks=1.
653 """
654 data = maskedImage.image.array
655 weights = maskedImage.variance.array**-1
656 # Mask out any pixels with non-finite weights
657 weights[~np.isfinite(weights) | ~np.isfinite(data)] = 0
658
659 lineFits = LineCollection([], [])
660 finalLineMasks = [np.zeros(data.shape, dtype=bool)]
661 nFinalLines = 0
662 nFitFailures = 0
663 for line in lines:
664 line.sigma = self.config.invSigma**-1
665 lineModel = LineProfile(data, weights, line=line)
666 # Skip any lines that do not cover any data (sometimes happens because of chip gaps)
667 if lineModel.lineMaskSize == 0:
668 continue
669
670 fit, chi2, fitFailure = lineModel.fit(dChi2Tol=self.config.dChi2Tolerance, log=self.log)
671
672 # Initial estimate should be quite close: fit is deemed unsuccessful if rho or theta
673 # change more than the allowed bin in rho or theta:
674 if ((abs(fit.rho - line.rho) > 2 * self.config.rhoBinSize)
675 or (abs(fit.theta - line.theta) > 2 * self.config.thetaBinSize)):
676 fitFailure = True
677 self.log.debug("Streak fit moved too far from initial estimate. Line will be dropped.")
678
679 if fitFailure:
680 nFitFailures += 1
681 continue
682
683 # Make mask
684 lineModel.setLineMask(fit)
685 finalModel = lineModel.makeProfile(fit)
686 # Take absolute value, as streaks are allowed to be negative
687 finalModelMax = abs(finalModel).max()
688 finalLineMask = abs(finalModel) > self.config.footprintThreshold
689 # Drop this line if the model profile is below the footprint threshold
690 if not finalLineMask.any():
691 continue
692 fit.chi2 = chi2
693 fit.finalModelMax = finalModelMax
694 lineFits.append(fit)
695 finalLineMasks.append(finalLineMask)
696 nFinalLines += 1
697
698 if nFitFailures > 0:
699 self.log.info("Streak profile could not be fit for %d out of %d detected lines.", nFitFailures,
700 len(lines))
701 finalMask = np.array(finalLineMasks).any(axis=0)
702
703 return lineFits, finalMask
int max
__init__(self, rhos, thetas, sigmas=None)
fit(self, dChi2Tol=0.1, maxIter=100, log=None)
__init__(self, data, weights, line=None)