Coverage for python/lsst/summit/utils/spectrumExaminer.py: 8%
303 statements
« prev ^ index » next coverage.py v6.5.0, created at 2022-10-11 03:15 -0700
« prev ^ index » next coverage.py v6.5.0, created at 2022-10-11 03:15 -0700
1# This file is part of summit_utils.
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__all__ = ['SpectrumExaminer']
24import numpy as np
25import matplotlib.pyplot as plt
26from matplotlib.offsetbox import AnchoredText
27from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
28from scipy.optimize import curve_fit
29from itertools import groupby
30from astropy.stats import sigma_clip
31import warnings
33from lsst.atmospec.processStar import ProcessStarTask
34from lsst.pipe.tasks.quickFrameMeasurement import QuickFrameMeasurementTask, QuickFrameMeasurementTaskConfig
36from lsst.obs.lsst.translators.lsst import FILTER_DELIMITER
37from lsst.summit.utils.utils import getImageStats
40class SpectrumExaminer():
41 """Task for the QUICK spectral extraction of single-star dispersed images.
43 For a full description of how this tasks works, see the run() method.
44 """
46 # ConfigClass = SummarizeImageTaskConfig
47 # _DefaultName = "summarizeImage"
49 def __init__(self, exp, display=None, debug=False, savePlotAs=None, **kwargs):
50 super().__init__(**kwargs)
51 self.exp = exp
52 self.display = display
53 self.debug = debug
54 self.savePlotAs = savePlotAs
56 qfmTaskConfig = QuickFrameMeasurementTaskConfig()
57 self.qfmTask = QuickFrameMeasurementTask(config=qfmTaskConfig)
59 pstConfig = ProcessStarTask.ConfigClass()
60 pstConfig.offsetFromMainStar = 400
61 self.processStarTask = ProcessStarTask(config=pstConfig)
63 self.imStats = getImageStats(exp)
65 self.init()
67 @staticmethod
68 def bboxToAwfDisplayLines(box):
69 """Takes a bbox, returns a list of lines such that they can be plotted:
71 for line in lines:
72 display.line(line, ctype='red')
73 """
74 x0 = box.beginX
75 x1 = box.endX
76 y0 = box.beginY
77 y1 = box.endY
78 return [[(x0, y0), (x1, y0)], [(x0, y0), (x0, y1)], [(x1, y0), (x1, y1)], [(x0, y1), (x1, y1)]]
80 def eraseDisplay(self):
81 if self.display:
82 self.display.erase()
84 def displaySpectrumBbox(self):
85 if self.display:
86 lines = self.bboxToAwfDisplayLines(self.spectrumbbox)
87 for line in lines:
88 self.display.line(line, ctype='red')
89 else:
90 print("No display set")
92 def displayStarLocation(self):
93 if self.display:
94 self.display.dot('x', *self.qfmResult.brightestObjCentroid, size=50)
95 self.display.dot('o', *self.qfmResult.brightestObjCentroid, size=50)
96 else:
97 print("No display set")
99 def calcGoodSpectrumSection(self, threshold=5, windowSize=5):
100 length = len(self.ridgeLineLocations)
101 chunks = length // windowSize
102 stddevs = []
103 for i in range(chunks+1):
104 stddevs.append(np.std(self.ridgeLineLocations[i*windowSize:(i+1)*windowSize]))
106 goodPoints = np.where(np.asarray(stddevs) < threshold)[0]
107 minPoint = (goodPoints[2] - 2) * windowSize
108 maxPoint = (goodPoints[-3] + 3) * windowSize
109 minPoint = max(minPoint, 0)
110 maxPoint = min(maxPoint, length)
111 if self.debug:
112 plt.plot(range(0, length+1, windowSize), stddevs)
113 plt.hlines(threshold, 0, length, colors='r', ls='dashed')
114 plt.vlines(minPoint, 0, max(stddevs)+10, colors='k', ls='dashed')
115 plt.vlines(maxPoint, 0, max(stddevs)+10, colors='k', ls='dashed')
116 plt.title(f'Ridgeline scatter, windowSize={windowSize}')
118 return (minPoint, maxPoint)
120 def fit(self):
121 def gauss(x, a, x0, sigma):
122 return a*np.exp(-(x-x0)**2/(2*sigma**2))
124 data = self.spectrumData[self.goodSlice]
125 nRows, nCols = data.shape
126 # don't subtract the row median or even a percentile - seems bad
127 # fitting a const also seems bad - needs some better thought
129 parameters = np.zeros((nRows, 3))
130 pCovs = []
131 xs = np.arange(nCols)
132 for rowNum, row in enumerate(data):
133 peakPos = self.ridgeLineLocations[rowNum]
134 amplitude = row[peakPos]
135 width = 7
136 try:
137 pars, pCov = curve_fit(gauss, xs, row, [amplitude, peakPos, width], maxfev=100)
138 pCovs.append(pCov)
139 except RuntimeError:
140 pars = [np.nan] * 3
141 if not np.all([p < 1e7 for p in pars]):
142 pars = [np.nan] * 3
143 parameters[rowNum] = pars
145 parameters[:, 0] = np.abs(parameters[:, 0])
146 parameters[:, 2] = np.abs(parameters[:, 2])
147 self.parameters = parameters
149 def plot(self, saveAs=None):
150 fig = plt.figure(figsize=(10, 10))
152 # spectrum
153 ax0 = plt.subplot2grid((4, 4), (0, 0), colspan=3)
154 ax0.tick_params(axis='x', top=True, bottom=False, labeltop=True, labelbottom=False)
155 d = self.spectrumData[self.goodSlice].T
156 vmin = np.percentile(d, 1)
157 vmax = np.percentile(d, 99)
158 pos = ax0.imshow(self.spectrumData[self.goodSlice].T, vmin=vmin, vmax=vmax, origin='lower')
159 div = make_axes_locatable(ax0)
160 cax = div.append_axes("bottom", size="7%", pad="8%")
161 fig.colorbar(pos, cax=cax, orientation="horizontal", label="Counts")
163 # spectrum histogram
164 axHist = plt.subplot2grid((4, 4), (0, 3))
165 data = self.spectrumData
166 histMax = np.nanpercentile(data, 99.99)
167 histMin = np.nanpercentile(data, 0.001)
168 axHist.hist(data[(data >= histMin) & (data <= histMax)].flatten(), bins=100)
169 underflow = len(data[data < histMin])
170 overflow = len(data[data > histMax])
171 axHist.set_yscale('log', nonpositive='clip')
172 axHist.set_title('Spectrum pixel histogram')
173 text = f"Underflow = {underflow}"
174 text += f"\nOverflow = {overflow}"
175 anchored_text = AnchoredText(text, loc=1, pad=0.5)
176 axHist.add_artist(anchored_text)
178 # peak fluxes
179 ax1 = plt.subplot2grid((4, 4), (1, 0), colspan=3)
180 ax1.plot(self.ridgeLineValues[self.goodSlice], label='Raw peak value')
181 ax1.plot(self.parameters[:, 0], label='Fitted amplitude')
182 ax1.axhline(self.continuumFlux98, ls='dashed', color='g')
183 ax1.set_ylabel('Peak amplitude (ADU)')
184 ax1.set_xlabel('Spectrum position (pixels)')
185 ax1.legend(title=f"Continuum flux = {self.continuumFlux98:.0f} ADU",
186 loc="center right", framealpha=0.2, facecolor="black")
187 ax1.set_title('Ridgeline plot')
189 # FWHM
190 ax2 = plt.subplot2grid((4, 4), (2, 0), colspan=3)
191 ax2.plot(self.parameters[:, 2]*2.355, label="FWHM (pix)")
192 fwhmValues = self.parameters[:, 2]*2.355
193 amplitudes = self.parameters[:, 0]
194 minVal, maxVal = self.getStableFwhmRegion(fwhmValues, amplitudes)
195 medianFwhm, bestFwhm = self.getMedianAndBestFwhm(fwhmValues, minVal, maxVal)
197 ax2.axhline(medianFwhm, ls='dashed', color='k',
198 label=f"Median FWHM = {medianFwhm:.1f} pix")
199 ax2.axhline(bestFwhm, ls='dashed', color='r',
200 label=f"Best FWHM = {bestFwhm:.1f} pix")
201 ax2.axvline(minVal, ls='dashed', color='k', alpha=0.2)
202 ax2.axvline(maxVal, ls='dashed', color='k', alpha=0.2)
203 ymin = max(np.nanmin(fwhmValues)-5, 0)
204 if not np.isnan(medianFwhm):
205 ymax = medianFwhm*2
206 else:
207 ymax = 5*ymin
208 ax2.set_ylim(ymin, ymax)
209 ax2.set_ylabel('FWHM (pixels)')
210 ax2.set_xlabel('Spectrum position (pixels)')
211 ax2.legend(loc="upper right", framealpha=0.2, facecolor="black")
212 ax2.set_title('Spectrum FWHM')
214 # row fluxes
215 ax3 = plt.subplot2grid((4, 4), (3, 0), colspan=3)
216 ax3.plot(self.rowSums[self.goodSlice], label="Sum across row")
217 ax3.set_ylabel('Total row flux (ADU)')
218 ax3.set_xlabel('Spectrum position (pixels)')
219 ax3.legend(framealpha=0.2, facecolor="black")
220 ax3.set_title('Row sums')
222 # textbox top
223# ax4 = plt.subplot2grid((4, 4), (1, 3))
224 ax4 = plt.subplot2grid((4, 4), (1, 3), rowspan=2)
225 text = "short text"
226 text = self.generateStatsTextboxContent(0)
227 text += self.generateStatsTextboxContent(1)
228 text += self.generateStatsTextboxContent(2)
229 text += self.generateStatsTextboxContent(3)
230 stats_text = AnchoredText(text, loc="center", pad=0.5,
231 prop=dict(size=10.5, ma="left", backgroundcolor="white",
232 color="black", family='monospace'))
233 ax4.add_artist(stats_text)
234 ax4.axis('off')
236 # textbox middle
237 if self.debug:
238 ax5 = plt.subplot2grid((4, 4), (2, 3))
239 text = self.generateStatsTextboxContent(-1)
240 stats_text = AnchoredText(text, loc="center", pad=0.5,
241 prop=dict(size=10.5, ma="left", backgroundcolor="white",
242 color="black", family='monospace'))
243 ax5.add_artist(stats_text)
244 ax5.axis('off')
246 plt.tight_layout()
247 plt.show()
249 if self.savePlotAs:
250 fig.savefig(self.savePlotAs)
252 def init(self):
253 pass
255 def generateStatsTextboxContent(self, section, doPrint=True):
256 x, y = self.qfmResult.brightestObjCentroid
257 exptime = self.exp.getInfo().getVisitInfo().getExposureTime()
259 info = self.exp.getInfo()
260 vi = info.getVisitInfo()
262 fullFilterString = info.getFilterLabel().physicalLabel
263 filt = fullFilterString.split(FILTER_DELIMITER)[0]
264 grating = fullFilterString.split(FILTER_DELIMITER)[1]
266 airmass = vi.getBoresightAirmass()
267 rotangle = vi.getBoresightRotAngle().asDegrees()
269 azAlt = vi.getBoresightAzAlt()
270 az = azAlt[0].asDegrees()
271 el = azAlt[1].asDegrees()
273 obj = self.exp.visitInfo.object
275 lines = []
277 if section == 0:
278 lines.append("----- Star stats -----")
279 lines.append(f"Star centroid @ {x:.0f}, {y:.0f}")
280 lines.append(f"Star max pixel = {self.starPeakFlux:,.0f} ADU")
281 lines.append(f"Star Ap25 flux = {self.qfmResult.brightestObjApFlux25:,.0f} ADU")
282 lines.extend(["", ""]) # section break
283 return '\n'.join([line for line in lines])
285 if section == 1:
286 lines.append("------ Image stats ---------")
287 imageMedian = np.median(self.exp.image.array)
288 lines.append(f"Image median = {imageMedian:.2f} ADU")
289 lines.append(f"Exposure time = {exptime:.2f} s")
290 lines.extend(["", ""]) # section break
291 return '\n'.join([line for line in lines])
293 if section == 2:
294 lines.append("------- Rate stats ---------")
295 lines.append(f"Star max pixel = {self.starPeakFlux/exptime:,.0f} ADU/s")
296 lines.append(f"Spectrum contiuum = {self.continuumFlux98/exptime:,.1f} ADU/s")
297 lines.extend(["", ""]) # section break
298 return '\n'.join([line for line in lines])
300 if section == 3:
301 lines.append("----- Observation info -----")
302 lines.append(f"object = {obj}")
303 lines.append(f"filter = {filt}")
304 lines.append(f"grating = {grating}")
305 lines.append(f"rotpa = {rotangle:.1f}")
307 lines.append(f"az = {az:.1f}")
308 lines.append(f"el = {el:.1f}")
309 lines.append(f"airmass = {airmass:.3f}")
310 return '\n'.join([line for line in lines])
312 if section == -1: # special -1 for debug
313 lines.append("---------- Debug -----------")
314 lines.append(f"spectrum bbox: {self.spectrumbbox}")
315 lines.append(f"Good range = {self.goodSpectrumMinY},{self.goodSpectrumMaxY}")
316 return '\n'.join([line for line in lines])
318 return
320 def run(self):
321 self.qfmResult = self.qfmTask.run(self.exp)
322 self.intCentroidX = int(np.round(self.qfmResult.brightestObjCentroid)[0])
323 self.intCentroidY = int(np.round(self.qfmResult.brightestObjCentroid)[1])
324 self.starPeakFlux = self.exp.image.array[self.intCentroidY, self.intCentroidX]
326 self.spectrumbbox = self.processStarTask.calcSpectrumBBox(self.exp,
327 self.qfmResult.brightestObjCentroid,
328 200)
329 self.spectrumData = self.exp.image[self.spectrumbbox].array
331 self.ridgeLineLocations = np.argmax(self.spectrumData, axis=1)
332 self.ridgeLineValues = self.spectrumData[range(self.spectrumbbox.getHeight()),
333 self.ridgeLineLocations]
334 self.rowSums = np.sum(self.spectrumData, axis=1)
336 coords = self.calcGoodSpectrumSection()
337 self.goodSpectrumMinY = coords[0]
338 self.goodSpectrumMaxY = coords[1]
339 self.goodSlice = slice(coords[0], coords[1])
341 self.continuumFlux90 = np.percentile(self.ridgeLineValues, 90) # for emission stars
342 self.continuumFlux98 = np.percentile(self.ridgeLineValues, 98) # for most stars
344 self.fit()
345 self.plot()
347 return
349 @staticmethod
350 def getMedianAndBestFwhm(fwhmValues, minIndex, maxIndex):
351 with warnings.catch_warnings(): # to supress nan warnings, which are fine
352 warnings.simplefilter("ignore")
353 clippedValues = sigma_clip(fwhmValues[minIndex:maxIndex])
354 # cast back with asArray needed becase sigma_clip returns
355 # masked array which doesn't play nice with np.nan<med/percentile>
356 clippedValues = np.asarray(clippedValues)
357 medianFwhm = np.nanmedian(clippedValues)
358 bestFocusFwhm = np.nanpercentile(np.asarray(clippedValues), 2)
359 return medianFwhm, bestFocusFwhm
361 def getStableFwhmRegion(self, fwhmValues, amplitudes, smoothing=1, maxDifferential=4):
362 # smooth the fwhmValues values
363 # differentiate
364 # take the longest contiguous region of 1s
365 # check section corresponds to top 25% in ampl to exclude 2nd order
366 # if not, pick next longest run, etc
367 # walk out from ends of that list over bumps smaller than maxDiff
369 smoothFwhm = np.convolve(fwhmValues, np.ones(smoothing)/smoothing, mode='same')
370 diff = np.diff(smoothFwhm, append=smoothFwhm[-1])
372 indices = np.where(1-np.abs(diff) < 1)[0]
373 diffIndices = np.diff(indices)
375 # [list(g) for k, g in groupby('AAAABBBCCD')] -->[['A', 'A', 'A', 'A'],
376 # ... ['B', 'B', 'B'], ['C', 'C'], ['D']]
377 indexLists = [list(g) for k, g in groupby(diffIndices)]
378 listLengths = [len(lst) for lst in indexLists]
380 amplitudeThreshold = np.nanpercentile(amplitudes, 75)
381 sortedListLengths = sorted(listLengths)
383 for listLength in sortedListLengths[::-1]:
384 longestListLength = listLength
385 longestListIndex = listLengths.index(longestListLength)
386 longestListStartTruePosition = int(np.sum(listLengths[0:longestListIndex]))
387 longestListStartTruePosition += int(longestListLength/2) # we want the mid-run value
388 if amplitudes[longestListStartTruePosition] > amplitudeThreshold:
389 break
391 startOfLongList = np.sum(listLengths[0:longestListIndex])
392 endOfLongList = startOfLongList + longestListLength
394 endValue = endOfLongList
395 for lst in indexLists[longestListIndex+1:]:
396 value = lst[0]
397 if value > maxDifferential:
398 break
399 endValue += len(lst)
401 startValue = startOfLongList
402 for lst in indexLists[longestListIndex-1::-1]:
403 value = lst[0]
404 if value > maxDifferential:
405 break
406 startValue -= len(lst)
408 startValue = int(max(0, startValue))
409 endValue = int(min(len(fwhmValues), endValue))
411 if not self.debug:
412 return startValue, endValue
414 medianFwhm, bestFocusFwhm = self.getMedianAndBestFwhm(fwhmValues, startValue, endValue)
415 xlim = (-20, len(fwhmValues))
417 plt.figure(figsize=(10, 6))
418 plt.plot(fwhmValues)
419 plt.vlines(startValue, 0, 50, 'r')
420 plt.vlines(endValue, 0, 50, 'r')
421 plt.hlines(medianFwhm, xlim[0], xlim[1])
422 plt.hlines(bestFocusFwhm, xlim[0], xlim[1], 'r', ls='--')
424 plt.vlines(startOfLongList, 0, 50, 'g')
425 plt.vlines(endOfLongList, 0, 50, 'g')
427 plt.ylim(0, 200)
428 plt.xlim(xlim)
429 plt.show()
431 plt.figure(figsize=(10, 6))
432 plt.plot(diffIndices)
433 plt.vlines(startValue, 0, 50, 'r')
434 plt.vlines(endValue, 0, 50, 'r')
436 plt.vlines(startOfLongList, 0, 50, 'g')
437 plt.vlines(endOfLongList, 0, 50, 'g')
438 plt.ylim(0, 30)
439 plt.xlim(xlim)
440 plt.show()
441 return startValue, endValue