Coverage for python/lsst/summit/utils/spectrumExaminer.py: 8%

1# This file is part of summit_utils.

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.

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]))

105

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}')

117

118 return (minPoint, maxPoint)

119

120 def fit(self):

121 def gauss(x, a, x0, sigma):

122 return a*np.exp(-(x-x0)**2/(2*sigma**2))

123

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

128

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

144

145 parameters[:, 0] = np.abs(parameters[:, 0])

146 parameters[:, 2] = np.abs(parameters[:, 2])

147 self.parameters = parameters

148

149 def plot(self):

150 fig = plt.figure(figsize=(10, 10))

151

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")

162

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)

177

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')

188

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)

196

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')

213

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')

221

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')

235

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')

245

246 plt.tight_layout()

247 plt.show()

248

249 if self.savePlotAs:

250 fig.savefig(self.savePlotAs)

251

252 def init(self):

253 pass

254

255 def generateStatsTextboxContent(self, section):

256 x, y = self.qfmResult.brightestObjCentroid

257

258 vi = self.exp.visitInfo

259 exptime = vi.exposureTime

260

261 fullFilterString = self.exp.filter.physicalLabel

262 filt = fullFilterString.split(FILTER_DELIMITER)[0]

263 grating = fullFilterString.split(FILTER_DELIMITER)[1]

264

265 airmass = vi.getBoresightAirmass()

266 rotangle = vi.getBoresightRotAngle().asDegrees()

267

268 azAlt = vi.getBoresightAzAlt()

269 az = azAlt[0].asDegrees()

270 el = azAlt[1].asDegrees()

271

272 obj = self.exp.visitInfo.object

273

274 lines = []

275

276 if section == 0:

277 lines.append("----- Star stats -----")

278 lines.append(f"Star centroid @ {x:.0f}, {y:.0f}")

279 lines.append(f"Star max pixel = {self.starPeakFlux:,.0f} ADU")

280 lines.append(f"Star Ap25 flux = {self.qfmResult.brightestObjApFlux25:,.0f} ADU")

281 lines.extend(["", ""]) # section break

282 return '\n'.join([line for line in lines])

283

284 if section == 1:

285 lines.append("------ Image stats ---------")

286 imageMedian = np.median(self.exp.image.array)

287 lines.append(f"Image median = {imageMedian:.2f} ADU")

288 lines.append(f"Exposure time = {exptime:.2f} s")

289 lines.extend(["", ""]) # section break

290 return '\n'.join([line for line in lines])

291

292 if section == 2:

293 lines.append("------- Rate stats ---------")

294 lines.append(f"Star max pixel = {self.starPeakFlux/exptime:,.0f} ADU/s")

295 lines.append(f"Spectrum contiuum = {self.continuumFlux98/exptime:,.1f} ADU/s")

296 lines.extend(["", ""]) # section break

297 return '\n'.join([line for line in lines])

298

299 if section == 3:

300 lines.append("----- Observation info -----")

301 lines.append(f"object = {obj}")

302 lines.append(f"filter = {filt}")

303 lines.append(f"grating = {grating}")

304 lines.append(f"rotpa = {rotangle:.1f}")

305

306 lines.append(f"az = {az:.1f}")

307 lines.append(f"el = {el:.1f}")

308 lines.append(f"airmass = {airmass:.3f}")

309 return '\n'.join([line for line in lines])

310

311 if section == -1: # special -1 for debug

312 lines.append("---------- Debug -----------")

313 lines.append(f"spectrum bbox: {self.spectrumbbox}")

314 lines.append(f"Good range = {self.goodSpectrumMinY},{self.goodSpectrumMaxY}")

315 return '\n'.join([line for line in lines])

316

317 return

318

319 def run(self):

320 self.qfmResult = self.qfmTask.run(self.exp)

321 self.intCentroidX = int(np.round(self.qfmResult.brightestObjCentroid)[0])

322 self.intCentroidY = int(np.round(self.qfmResult.brightestObjCentroid)[1])

323 self.starPeakFlux = self.exp.image.array[self.intCentroidY, self.intCentroidX]

324

325 self.spectrumbbox = self.processStarTask.calcSpectrumBBox(self.exp,

326 self.qfmResult.brightestObjCentroid,

327 200)

328 self.spectrumData = self.exp.image[self.spectrumbbox].array

329

330 self.ridgeLineLocations = np.argmax(self.spectrumData, axis=1)

331 self.ridgeLineValues = self.spectrumData[range(self.spectrumbbox.getHeight()),

332 self.ridgeLineLocations]

333 self.rowSums = np.sum(self.spectrumData, axis=1)

334

335 coords = self.calcGoodSpectrumSection()

336 self.goodSpectrumMinY = coords[0]

337 self.goodSpectrumMaxY = coords[1]

338 self.goodSlice = slice(coords[0], coords[1])

339

340 self.continuumFlux90 = np.percentile(self.ridgeLineValues, 90) # for emission stars

341 self.continuumFlux98 = np.percentile(self.ridgeLineValues, 98) # for most stars

342

343 self.fit()

344 self.plot()

345

346 return

347

348 @staticmethod

349 def getMedianAndBestFwhm(fwhmValues, minIndex, maxIndex):

350 with warnings.catch_warnings(): # to supress nan warnings, which are fine

351 warnings.simplefilter("ignore")

352 clippedValues = sigma_clip(fwhmValues[minIndex:maxIndex])

353 # cast back with asArray needed becase sigma_clip returns

354 # masked array which doesn't play nice with np.nan<med/percentile>

355 clippedValues = np.asarray(clippedValues)

356 medianFwhm = np.nanmedian(clippedValues)

357 bestFocusFwhm = np.nanpercentile(np.asarray(clippedValues), 2)

358 return medianFwhm, bestFocusFwhm

359

360 def getStableFwhmRegion(self, fwhmValues, amplitudes, smoothing=1, maxDifferential=4):

361 # smooth the fwhmValues values

362 # differentiate

363 # take the longest contiguous region of 1s

364 # check section corresponds to top 25% in ampl to exclude 2nd order

365 # if not, pick next longest run, etc

366 # walk out from ends of that list over bumps smaller than maxDiff

367

368 smoothFwhm = np.convolve(fwhmValues, np.ones(smoothing)/smoothing, mode='same')

369 diff = np.diff(smoothFwhm, append=smoothFwhm[-1])

370

371 indices = np.where(1-np.abs(diff) < 1)[0]

372 diffIndices = np.diff(indices)

373

374 # [list(g) for k, g in groupby('AAAABBBCCD')] -->[['A', 'A', 'A', 'A'],

375 # ... ['B', 'B', 'B'], ['C', 'C'], ['D']]

376 indexLists = [list(g) for k, g in groupby(diffIndices)]

377 listLengths = [len(lst) for lst in indexLists]

378

379 amplitudeThreshold = np.nanpercentile(amplitudes, 75)

380 sortedListLengths = sorted(listLengths)

381

382 for listLength in sortedListLengths[::-1]:

383 longestListLength = listLength

384 longestListIndex = listLengths.index(longestListLength)

385 longestListStartTruePosition = int(np.sum(listLengths[0:longestListIndex]))

386 longestListStartTruePosition += int(longestListLength/2) # we want the mid-run value

387 if amplitudes[longestListStartTruePosition] > amplitudeThreshold:

388 break

389

390 startOfLongList = np.sum(listLengths[0:longestListIndex])

391 endOfLongList = startOfLongList + longestListLength

392

393 endValue = endOfLongList

394 for lst in indexLists[longestListIndex+1:]:

395 value = lst[0]

396 if value > maxDifferential:

397 break

398 endValue += len(lst)

399

400 startValue = startOfLongList

401 for lst in indexLists[longestListIndex-1::-1]:

402 value = lst[0]

403 if value > maxDifferential:

404 break

405 startValue -= len(lst)

406

407 startValue = int(max(0, startValue))

408 endValue = int(min(len(fwhmValues), endValue))

409

410 if not self.debug:

411 return startValue, endValue

412

413 medianFwhm, bestFocusFwhm = self.getMedianAndBestFwhm(fwhmValues, startValue, endValue)

414 xlim = (-20, len(fwhmValues))

415

416 plt.figure(figsize=(10, 6))

417 plt.plot(fwhmValues)

418 plt.vlines(startValue, 0, 50, 'r')

419 plt.vlines(endValue, 0, 50, 'r')

420 plt.hlines(medianFwhm, xlim[0], xlim[1])

421 plt.hlines(bestFocusFwhm, xlim[0], xlim[1], 'r', ls='--')

422

423 plt.vlines(startOfLongList, 0, 50, 'g')

424 plt.vlines(endOfLongList, 0, 50, 'g')

425

426 plt.ylim(0, 200)

427 plt.xlim(xlim)

428 plt.show()

429

430 plt.figure(figsize=(10, 6))

431 plt.plot(diffIndices)

432 plt.vlines(startValue, 0, 50, 'r')

433 plt.vlines(endValue, 0, 50, 'r')

434

435 plt.vlines(startOfLongList, 0, 50, 'g')

436 plt.vlines(endOfLongList, 0, 50, 'g')

437 plt.ylim(0, 30)

438 plt.xlim(xlim)

439 plt.show()

440 return startValue, endValue