Coverage for python / lsst / summit / extras / focusAnalysis.py: 15%
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1# This file is part of summit_extras.
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
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22from dataclasses import dataclass
24import matplotlib
25import matplotlib.cm as cm
26import matplotlib.pyplot as plt
27import numpy as np
28from matplotlib import gridspec
29from matplotlib.colors import LogNorm
30from matplotlib.patches import Arrow, Circle, Rectangle
31from scipy.linalg import norm
32from scipy.optimize import curve_fit
34import lsst.afw.image as afwImage
35import lsst.geom as geom
36from lsst.atmospec.utils import isDispersedExp
37from lsst.pipe.tasks.quickFrameMeasurement import QuickFrameMeasurementTask, QuickFrameMeasurementTaskConfig
38from lsst.summit.utils import ImageExaminer
40# TODO: change these back to local .imports
41from lsst.summit.utils.bestEffort import BestEffortIsr
42from lsst.summit.utils.butlerUtils import getExpRecordFromDataId, makeDefaultLatissButler
43from lsst.summit.utils.utils import FWHMTOSIGMA, SIGMATOFWHM
45__all__ = ["SpectralFocusAnalyzer", "NonSpectralFocusAnalyzer"]
48@dataclass
49class FitResult:
50 amp: float
51 mean: float
52 sigma: float
55def getFocusFromExposure(exp: afwImage.Exposure) -> float:
56 """Get the focus value from an exposure.
58 This was previously accessed via raw metadata but now lives inside the
59 visitInfo.
61 Parameters
62 ----------
63 exp : `lsst.afw.image.Exposure`
64 The exposure.
66 Returns
67 -------
68 focus : `float`
69 The focus value.
71 """
72 return float(exp.visitInfo.focusZ)
75class SpectralFocusAnalyzer:
76 """Analyze a focus sweep taken for spectral data.
78 Take slices across the spectrum for each image, fitting a Gaussian to each
79 slice, and perform a parabolic fit to these widths. The number of slices
80 and their distances can be customized by calling setSpectrumBoxOffsets().
82 Nominal usage is something like:
84 %matplotlib inline
85 dayObs = 20210101
86 seqNums = [100, 101, 102, 103, 104]
87 focusAnalyzer = SpectralFocusAnalyzer()
88 focusAnalyzer.setSpectrumBoxOffsets([500, 750, 1000, 1250])
89 focusAnalyzer.getFocusData(dayObs, seqNums, doDisplay=True)
90 focusAnalyzer.fitDataAndPlot()
92 focusAnalyzer.run() can be used instead of the last two lines separately.
93 """
95 def __init__(self, embargo: bool = False):
96 self.butler = makeDefaultLatissButler(embargo=embargo)
97 self._bestEffort = BestEffortIsr(embargo=embargo)
98 qfmTaskConfig = QuickFrameMeasurementTaskConfig()
99 self._quickMeasure = QuickFrameMeasurementTask(config=qfmTaskConfig)
101 self.spectrumHalfWidth = 100
102 self.spectrumBoxLength = 20
103 self._spectrumBoxOffsets = [882, 1170, 1467]
104 self._setColors(len(self._spectrumBoxOffsets))
106 def setSpectrumBoxOffsets(self, offsets: list[int]) -> None:
107 """Set the current spectrum slice offsets.
109 Parameters
110 ----------
111 offsets : `list` of `int`
112 The distance at which to slice the spectrum, measured in pixels
113 from the main star's location.
114 """
115 self._spectrumBoxOffsets = offsets
116 self._setColors(len(offsets))
118 def getSpectrumBoxOffsets(self) -> list[int]:
119 """Get the current spectrum slice offsets.
121 Returns
122 -------
123 offsets : `list` of `float`
124 The distance at which to slice the spectrum, measured in pixels
125 from the main star's location.
126 """
127 return self._spectrumBoxOffsets
129 def _setColors(self, nPoints: int) -> None:
130 self.COLORS = cm.rainbow(np.linspace(0, 1, nPoints))
132 def _getBboxes(self, centroid: list[float]) -> geom.Box2I:
133 x, y = centroid
134 bboxes = []
136 for offset in self._spectrumBoxOffsets:
137 bbox = geom.Box2I(
138 geom.Point2I(x - self.spectrumHalfWidth, y + offset),
139 geom.Point2I(x + self.spectrumHalfWidth, y + offset + self.spectrumBoxLength),
140 )
141 bboxes.append(bbox)
142 return bboxes
144 def _bboxToMplRectangle(self, bbox: geom.Box2I, colorNum: int) -> matplotlib.patches.Rectangle:
145 xmin = bbox.getBeginX()
146 ymin = bbox.getBeginY()
147 xsize = bbox.getWidth()
148 ysize = bbox.getHeight()
149 rectangle = Rectangle(
150 (xmin, ymin), xsize, ysize, alpha=1, facecolor="none", lw=2, edgecolor=self.COLORS[colorNum]
151 )
152 return rectangle
154 @staticmethod
155 def gauss(x: float, *pars: float) -> float:
156 amp, mean, sigma = pars
157 return amp * np.exp(-((x - mean) ** 2) / (2.0 * sigma**2))
159 def run(
160 self,
161 dayObs: int,
162 seqNums: list[int],
163 doDisplay: bool = False,
164 hideFit: bool = False,
165 hexapodZeroPoint: float = 0,
166 ) -> list[float]:
167 """Perform a focus sweep analysis for spectral data.
169 For each seqNum for the specified dayObs, take a slice through the
170 spectrum at y-offsets as specified by the offsets
171 (see get/setSpectrumBoxOffsets() for setting these) and fit a Gaussian
172 to the spectrum slice to measure its width.
174 For each offset distance, fit a parabola to the fitted spectral widths
175 and return the hexapod position at which the best focus was achieved
176 for each.
178 Parameters
179 ----------
180 dayObs : `int`
181 The dayObs to use.
182 seqNums : `list` of `int`
183 The seqNums for the focus sweep to analyze.
184 doDisplay : `bool`
185 Show the plots? Designed to be used in a notebook with
186 %matplotlib inline.
187 hideFit : `bool`, optional
188 Hide the fit and just return the result?
189 hexapodZeroPoint : `float`, optional
190 Add a zeropoint offset to the hexapod axis?
192 Returns
193 -------
194 bestFits : `list` of `float`
195 A list of the best fit focuses, one for each spectral slice.
196 """
197 self.getFocusData(dayObs, seqNums, doDisplay=doDisplay)
198 bestFits = self.fitDataAndPlot(hideFit=hideFit, hexapodZeroPoint=hexapodZeroPoint)
199 return bestFits
201 def getFocusData(self, dayObs: int, seqNums: list[int], doDisplay: bool = False) -> None:
202 """Perform a focus sweep analysis for spectral data.
204 For each seqNum for the specified dayObs, take a slice through the
205 spectrum at y-offsets as specified by the offsets
206 (see get/setSpectrumBoxOffsets() for setting these) and fit a Gaussian
207 to the spectrum slice to measure its width.
209 Parameters
210 ----------
211 dayObs : `int`
212 The dayObs to use.
213 seqNums : `list` of `int`
214 The seqNums for the focus sweep to analyze.
215 doDisplay : `bool`
216 Show the plots? Designed to be used in a notebook with
217 %matplotlib inline.
219 Notes
220 -----
221 Performs the focus analysis per-image, holding the data in the class.
222 Call fitDataAndPlot() after running this to perform the parabolic fit
223 to the focus data itself.
224 """
225 fitData = {}
226 filters = set()
227 objects = set()
229 for seqNum in seqNums:
230 fitData[seqNum] = {}
231 dataId = {"day_obs": dayObs, "seq_num": seqNum, "detector": 0}
232 exp = self._bestEffort.getExposure(dataId)
234 # sanity checking
235 filt = exp.filter.physicalLabel
236 expRecord = getExpRecordFromDataId(self.butler, dataId)
237 obj = expRecord.target_name
238 objects.add(obj)
239 filters.add(filt)
240 assert isDispersedExp(exp), f"Image is not dispersed! (filter = {filt})"
241 assert len(filters) == 1, "You accidentally mixed filters!"
242 assert len(objects) == 1, "You accidentally mixed objects!"
244 quickMeasResult = self._quickMeasure.run(exp)
245 centroid = quickMeasResult.brightestObjCentroid
246 spectrumSliceBboxes = self._getBboxes(centroid) # inside the loop due to centroid shifts
248 if doDisplay:
249 fig, axes = plt.subplots(1, 2, figsize=(18, 9))
250 exp.image.array[exp.image.array <= 0] = 0.001
251 axes[0].imshow(exp.image.array, norm=LogNorm(), origin="lower", cmap="gray_r")
252 plt.tight_layout()
253 arrowy, arrowx = centroid[0] - 400, centroid[1] # numpy is backwards
254 dx, dy = 0, 300
255 arrow = Arrow(arrowy, arrowx, dy, dx, width=200.0, color="red")
256 circle = Circle(centroid, radius=25, facecolor="none", edgecolor="red")
257 axes[0].add_patch(arrow)
258 axes[0].add_patch(circle)
259 for i, bbox in enumerate(spectrumSliceBboxes):
260 rect = self._bboxToMplRectangle(bbox, i)
261 axes[0].add_patch(rect)
263 for i, bbox in enumerate(spectrumSliceBboxes):
264 data1d = np.mean(exp[bbox].image.array, axis=0) # flatten
265 data1d -= np.median(data1d)
266 xs = np.arange(len(data1d))
268 # get rough estimates for fit
269 # can't use sigma from quickMeasResult due to SDSS shape
270 # failing on saturated starts, and fp.getShape() is weird
271 amp = np.max(data1d)
272 mean = np.argmax(data1d)
273 sigma = 20
274 p0 = amp, mean, sigma
276 try:
277 coeffs, var_matrix = curve_fit(self.gauss, xs, data1d, p0=p0)
278 except RuntimeError:
279 coeffs = (np.nan, np.nan, np.nan)
281 fitData[seqNum][i] = FitResult(amp=abs(coeffs[0]), mean=coeffs[1], sigma=abs(coeffs[2]))
282 if doDisplay:
283 axes[1].plot(xs, data1d, "x", c=self.COLORS[i])
284 highResX = np.linspace(0, len(data1d), 1000)
285 if coeffs[0] is not np.nan:
286 axes[1].plot(highResX, self.gauss(highResX, *coeffs), "k-")
288 if doDisplay: # show all color boxes together
289 plt.title(f"Fits to seqNum {seqNum}")
290 plt.show()
292 focuserPosition = getFocusFromExposure(exp)
293 fitData[seqNum]["focus"] = focuserPosition
295 self.fitData = fitData
296 self.filter = filters.pop()
297 self.object = objects.pop()
299 return
301 def fitDataAndPlot(self, hideFit: bool = False, hexapodZeroPoint: float = 0) -> list[float]:
302 """Fit a parabola to each series of slices and return the best focus.
304 For each offset distance, fit a parabola to the fitted spectral widths
305 and return the hexapod position at which the best focus was achieved
306 for each.
308 Parameters
309 ----------
310 hideFit : `bool`, optional
311 Hide the fit and just return the result?
312 hexapodZeroPoint : `float`, optional
313 Add a zeropoint offset to the hexapod axis?
315 Returns
316 -------
317 bestFits : `list` of `float`
318 A list of the best fit focuses, one for each spectral slice.
319 """
320 data = self.fitData
321 filt = self.filter
322 obj = self.object
324 bestFits = []
326 titleFontSize = 18
327 legendFontSize = 12
328 labelFontSize = 14
330 arcminToPixel = 10
331 sigmaToFwhm = 2.355
333 f, axes = plt.subplots(2, 1, figsize=[10, 12])
334 focusPositions = [data[k]["focus"] - hexapodZeroPoint for k in sorted(data.keys())]
335 fineXs = np.linspace(np.min(focusPositions), np.max(focusPositions), 101)
336 seqNums = sorted(data.keys())
338 nSpectrumSlices = len(data[list(data.keys())[0]]) - 1
339 pointsForLegend = [0.0 for offset in range(nSpectrumSlices)]
340 for spectrumSlice in range(nSpectrumSlices): # the blue/green/red slices through the spectrum
341 # for scatter plots, the color needs to be a single-row 2d array
342 thisColor = np.array([self.COLORS[spectrumSlice]])
344 amps = [data[seqNum][spectrumSlice].amp for seqNum in seqNums]
345 widths = [data[seqNum][spectrumSlice].sigma / arcminToPixel * sigmaToFwhm for seqNum in seqNums]
347 pointsForLegend[spectrumSlice] = axes[0].scatter(focusPositions, amps, c=thisColor)
348 axes[0].set_xlabel("Focus position (mm)", fontsize=labelFontSize)
349 axes[0].set_ylabel("Height (ADU)", fontsize=labelFontSize)
351 axes[1].scatter(focusPositions, widths, c=thisColor)
352 axes[1].set_xlabel("Focus position (mm)", fontsize=labelFontSize)
353 axes[1].set_ylabel("FWHM (arcsec)", fontsize=labelFontSize)
355 quadFitPars = np.polyfit(focusPositions, widths, 2)
356 if not hideFit:
357 axes[1].plot(fineXs, np.poly1d(quadFitPars)(fineXs), c=self.COLORS[spectrumSlice])
358 fitMin = -quadFitPars[1] / (2.0 * quadFitPars[0])
359 bestFits.append(fitMin)
360 axes[1].axvline(fitMin, color=self.COLORS[spectrumSlice])
361 msg = f"Best focus offset = {np.round(fitMin, 2)}"
362 axes[1].text(
363 fitMin,
364 np.mean(widths),
365 msg,
366 horizontalalignment="right",
367 verticalalignment="center",
368 rotation=90,
369 color=self.COLORS[spectrumSlice],
370 fontsize=legendFontSize,
371 )
373 titleText = f"Focus curve for {obj} w/ {filt}"
374 plt.suptitle(titleText, fontsize=titleFontSize)
375 legendText = self._generateLegendText(nSpectrumSlices)
376 axes[0].legend(pointsForLegend, legendText, fontsize=legendFontSize)
377 axes[1].legend(pointsForLegend, legendText, fontsize=legendFontSize)
378 f.tight_layout(rect=(0, 0.03, 1, 0.95))
380 for i, bestFit in enumerate(bestFits):
381 print(f"Best fit for spectrum slice {i} = {bestFit:.4f}mm")
382 return bestFits
384 def _generateLegendText(self, nSpectrumSlices: int) -> str:
385 if nSpectrumSlices == 1:
386 return ["m=+1 spectrum slice"]
387 if nSpectrumSlices == 2:
388 return ["m=+1 blue end", "m=+1 red end"]
390 legendText = []
391 legendText.append("m=+1 blue end")
392 for i in range(nSpectrumSlices - 2):
393 legendText.append("m=+1 redder...")
394 legendText.append("m=+1 red end")
395 return legendText
398class NonSpectralFocusAnalyzer:
399 """Analyze a focus sweep taken for direct imaging data.
401 For each image, measure the FWHM of the main star and the 50/80/90%
402 encircled energy radii, and fit a parabola to get the position of best
403 focus.
405 Nominal usage is something like:
407 %matplotlib inline
408 dayObs = 20210101
409 seqNums = [100, 101, 102, 103, 104]
410 focusAnalyzer = NonSpectralFocusAnalyzer()
411 focusAnalyzer.getFocusData(dayObs, seqNums, doDisplay=True)
412 focusAnalyzer.fitDataAndPlot()
414 focusAnalyzer.run() can be used instead of the last two lines separately.
415 """
417 def __init__(self, embargo: bool = False):
418 self.butler = makeDefaultLatissButler(embargo=embargo)
419 self._bestEffort = BestEffortIsr(embargo=embargo)
421 @staticmethod
422 def gauss(x: float, *pars: float) -> float:
423 amp, mean, sigma = pars
424 return amp * np.exp(-((x - mean) ** 2) / (2.0 * sigma**2))
426 def run(
427 self,
428 dayObs: int,
429 seqNums: list[int],
430 *,
431 manualCentroid: tuple[float, float] | None = None,
432 doCheckDispersed: bool = True,
433 doDisplay: bool = False,
434 doForceCoM: bool = False,
435 ) -> dict:
436 """Perform a focus sweep analysis for direct imaging data.
438 For each seqNum for the specified dayObs, run the image through imExam
439 and collect the widths from the Gaussian fit and the 50/80/90%
440 encircled energy metrics, saving the data in the class for fitting.
442 For each of the [Gaussian fit, 50%, 80%, 90% encircled energy] metrics,
443 fit a parabola and return the focus value at which the minimum is
444 found.
446 Parameters
447 ----------
448 dayObs : `int`
449 The dayObs to use.
450 seqNums : `list` of `int`
451 The seqNums for the focus sweep to analyze.
452 manualCentroid : `tuple` of `float`, optional
453 Use this as the centroid position instead of fitting each image.
454 doCheckDispersed : `bool`, optional
455 Check if any of the seqNums actually refer to dispersed images?
456 doDisplay : `bool`, optional
457 Show the plots? Designed to be used in a notebook with
458 %matplotlib inline.
459 doForceCoM : `bool`, optional
460 Force using centre-of-mass for centroiding?
462 Returns
463 -------
464 result : `dict` of `float`
465 A dict of the fit minima keyed by the metric it is the minimum for.
466 """
467 self.getFocusData(
468 dayObs,
469 seqNums,
470 manualCentroid=manualCentroid,
471 doCheckDispersed=doCheckDispersed,
472 doDisplay=doDisplay,
473 doForceCoM=doForceCoM,
474 )
475 bestFit = self.fitDataAndPlot()
476 return bestFit
478 def getFocusData(
479 self,
480 dayObs: int,
481 seqNums: list[int],
482 *,
483 manualCentroid: tuple[float, float] | None = None,
484 doCheckDispersed: bool = True,
485 doDisplay: bool = False,
486 doForceCoM: bool = False,
487 ) -> None:
488 """Perform a focus sweep analysis for direct imaging data.
490 For each seqNum for the specified dayObs, run the image through imExam
491 and collect the widths from the Gaussian fit and the 50/80/90%
492 encircled energy metrics, saving the data in the class for fitting.
494 Parameters
495 ----------
496 dayObs : `int`
497 The dayObs to use.
498 seqNums : `list` of `int`
499 The seqNums for the focus sweep to analyze.
500 manualCentroid : `tuple` of `float`, optional
501 Use this as the centroid position instead of fitting each image.
502 doCheckDispersed : `bool`, optional
503 Check if any of the seqNums actually refer to dispersed images?
504 doDisplay : `bool`, optional
505 Show the plots? Designed to be used in a notebook with
506 %matplotlib inline.
507 doForceCoM : `bool`, optional
508 Force using centre-of-mass for centroiding?
510 Notes
511 -----
512 Performs the focus analysis per-image, holding the data in the class.
513 Call fitDataAndPlot() after running this to perform the parabolic fit
514 to the focus data itself.
515 """
516 fitData = {}
517 filters = set()
518 objects = set()
520 maxDistance = 200
521 firstCentroid = None
523 for seqNum in seqNums:
524 fitData[seqNum] = {}
525 dataId = {"day_obs": dayObs, "seq_num": seqNum, "detector": 0}
526 exp = self._bestEffort.getExposure(dataId)
528 # sanity/consistency checking
529 filt = exp.filter.physicalLabel
530 expRecord = getExpRecordFromDataId(self.butler, dataId)
531 obj = expRecord.target_name
532 objects.add(obj)
533 filters.add(filt)
534 if doCheckDispersed:
535 assert not isDispersedExp(exp), f"Image is dispersed! (filter = {filt})"
536 assert len(filters) == 1, "You accidentally mixed filters!"
537 assert len(objects) == 1, "You accidentally mixed objects!"
539 imExam = ImageExaminer(
540 exp, centroid=manualCentroid, doTweakCentroid=True, boxHalfSize=105, doForceCoM=doForceCoM
541 )
542 if doDisplay:
543 imExam.plot()
545 fwhm = imExam.imStats.fitFwhm
546 amp = imExam.imStats.fitAmp
547 gausMean = imExam.imStats.fitGausMean
548 centroid = imExam.centroid
550 if seqNum == seqNums[0]:
551 firstCentroid = centroid
553 dist = norm(np.array(centroid) - np.array(firstCentroid))
554 if dist > maxDistance:
555 print(f"Skipping {seqNum} because distance {dist}> maxDistance {maxDistance}")
557 fitData[seqNum]["fitResult"] = FitResult(amp=amp, mean=gausMean, sigma=fwhm * FWHMTOSIGMA)
558 fitData[seqNum]["eeRadius50"] = imExam.imStats.eeRadius50
559 fitData[seqNum]["eeRadius80"] = imExam.imStats.eeRadius80
560 fitData[seqNum]["eeRadius90"] = imExam.imStats.eeRadius90
562 focuserPosition = getFocusFromExposure(exp)
563 fitData[seqNum]["focus"] = focuserPosition
565 self.fitData = fitData
566 self.filter = filters.pop()
567 self.object = objects.pop()
569 return
571 def fitDataAndPlot(self) -> dict:
572 """Fit a parabola to each width metric, returning their best focuses.
574 For each of the [Gaussian fit, 50%, 80%, 90% encircled energy] metrics,
575 fit a parabola and return the focus value at which the minimum is
576 found.
578 Returns
579 -------
580 result : `dict` of `float`
581 A dict of the fit minima keyed by the metric it is the minimum for.
582 """
583 fitData = self.fitData
585 labelFontSize = 14
587 arcminToPixel = 10
589 fig = plt.figure(figsize=(10, 10)) # noqa
590 gs = gridspec.GridSpec(2, 1, height_ratios=[1, 1])
592 seqNums = sorted(fitData.keys())
593 widths = [fitData[seqNum]["fitResult"].sigma * SIGMATOFWHM / arcminToPixel for seqNum in seqNums]
594 focusPositions = [fitData[seqNum]["focus"] for seqNum in seqNums]
595 fineXs = np.linspace(np.min(focusPositions), np.max(focusPositions), 101)
597 fwhmFitPars = np.polyfit(focusPositions, widths, 2)
598 fwhmFitMin = -fwhmFitPars[1] / (2.0 * fwhmFitPars[0])
600 ax0 = plt.subplot(gs[0])
601 ax0.scatter(focusPositions, widths, c="k")
602 ax0.set_ylabel("FWHM (arcsec)", fontsize=labelFontSize)
603 ax0.plot(fineXs, np.poly1d(fwhmFitPars)(fineXs), "b-")
604 ax0.axvline(fwhmFitMin, c="r", ls="--")
606 ee90s = [fitData[seqNum]["eeRadius90"] for seqNum in seqNums]
607 ee80s = [fitData[seqNum]["eeRadius80"] for seqNum in seqNums]
608 ee50s = [fitData[seqNum]["eeRadius50"] for seqNum in seqNums]
609 ax1 = plt.subplot(gs[1], sharex=ax0)
610 ax1.scatter(focusPositions, ee90s, c="r", label="Encircled energy 90%")
611 ax1.scatter(focusPositions, ee80s, c="g", label="Encircled energy 80%")
612 ax1.scatter(focusPositions, ee50s, c="b", label="Encircled energy 50%")
614 ee90FitPars = np.polyfit(focusPositions, ee90s, 2)
615 ee90FitMin = -ee90FitPars[1] / (2.0 * ee90FitPars[0])
616 ee80FitPars = np.polyfit(focusPositions, ee80s, 2)
617 ee80FitMin = -ee80FitPars[1] / (2.0 * ee80FitPars[0])
618 ee50FitPars = np.polyfit(focusPositions, ee50s, 2)
619 ee50FitMin = -ee50FitPars[1] / (2.0 * ee50FitPars[0])
621 ax1.plot(fineXs, np.poly1d(ee90FitPars)(fineXs), "r-")
622 ax1.plot(fineXs, np.poly1d(ee80FitPars)(fineXs), "g-")
623 ax1.plot(fineXs, np.poly1d(ee50FitPars)(fineXs), "b-")
625 ax1.axvline(ee90FitMin, c="r", ls="--")
626 ax1.axvline(ee80FitMin, c="g", ls="--")
627 ax1.axvline(ee50FitMin, c="b", ls="--")
629 ax1.set_xlabel("User-applied focus offset (mm)", fontsize=labelFontSize)
630 ax1.set_ylabel("Radius (pixels)", fontsize=labelFontSize)
632 ax1.legend()
634 plt.subplots_adjust(hspace=0.0)
636 results = {
637 "fwhmFitMin": fwhmFitMin,
638 "ee90FitMin": ee90FitMin,
639 "ee80FitMin": ee80FitMin,
640 "ee50FitMin": ee50FitMin,
641 }
643 return results
646if __name__ == "__main__": 646 ↛ 648line 646 didn't jump to line 648 because the condition on line 646 was never true
647 # TODO: DM-34239 Move this to be a butler-driven test
648 analyzer = SpectralFocusAnalyzer()
649 # dataId = {'dayObs': '2020-02-20', 'seqNum': 485} # direct image
650 dataId = {"day_obs": 20200312}
651 seqNums = [121, 122]
652 analyzer.getFocusData(dataId["day_obs"], seqNums, doDisplay=True)
653 analyzer.fitDataAndPlot()