From 0e52afae4e5c4bfe51c58ac24a38f884ddee27b9 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Mon, 16 Sep 2024 14:41:52 -1000 Subject: [PATCH 01/22] still nowhere --- pypeit/core/arc.py | 1 + pypeit/core/flexure.py | 95 +++++++++++++++++++++++++++++++++++------- 2 files changed, 80 insertions(+), 16 deletions(-) diff --git a/pypeit/core/arc.py b/pypeit/core/arc.py index e41fc7fc8b..fa80c439ee 100644 --- a/pypeit/core/arc.py +++ b/pypeit/core/arc.py @@ -1055,6 +1055,7 @@ def detect_lines(censpec, sigdetect=5.0, fwhm=4.0, fit_frac_fwhm=1.25, input_thr tampl_true = tampl_true[ikeep] tampl = tampl[ikeep] tcent = tcent[ikeep] + pixt = pixt[ikeep] twid = twid[ikeep] centerr = centerr[ikeep] ww = np.where(good[ikeep])[0] diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index d4a816993f..2803a70365 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -18,6 +18,7 @@ from astropy import units from astropy.io import ascii from astropy.table import Table +from astropy.stats import sigma_clipped_stats import scipy.signal import scipy.optimize as opt from scipy import interpolate @@ -34,6 +35,7 @@ from pypeit.core import extract from pypeit.core import fitting from pypeit.core import qa +from pypeit.core import trace from pypeit.datamodel import DataContainer from pypeit.images.detector_container import DetectorContainer from pypeit.images.mosaic import Mosaic @@ -68,32 +70,93 @@ def spat_flexure_shift(sciimg, slits, debug=False, maxlag = 20): _sciimg = sciimg if slitmask.shape == sciimg.shape \ else arc.resize_mask2arc(slitmask.shape, sciimg) - onslits = slitmask > -1 - corr_slits = onslits.astype(float).flatten() - - # Compute - mean_sci, med_sci, stddev_sci = stats.sigma_clipped_stats(_sciimg[onslits]) - thresh = med_sci + 5.0*stddev_sci - corr_sci = np.fmin(_sciimg.flatten(), thresh) - lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) + onslits = (slitmask > -1).astype(float) + + # sci_smash_mean, sci_smash_median, sci_smash_sig = sigma_clipped_stats(_sciimg, axis=0, sigma=4.0) + # slit_smash_mean, slit_smash_median, slit_smash_sig = sigma_clipped_stats(onslits, axis=0, sigma=4.0) + # + # lags, xcorr = utils.cross_correlate(sci_smash_median, slit_smash_median, slit_smash_median.size) + # xcorr_denom = np.sqrt(np.sum(sci_smash_median*sci_smash_median)*np.sum(slit_smash_median*slit_smash_median)) + # xcorr_norm = xcorr / xcorr_denom + # + # tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \ + # = arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0, + # cont_frac_fwhm=1.0, cont_samp=30, debug=debug) + + sobelsig, edge_img = trace.detect_slit_edges(_sciimg, sobel_enhance=2) + slit_sobelsig, slit_img = trace.detect_slit_edges(slitmask, sobel_enhance=2) + sci_smash_mean, sci_smash_median, sci_smash_sig = sigma_clipped_stats(edge_img, axis=0, sigma=4.0) + slit_smash_mean, slit_smash_median, slit_smash_sig = sigma_clipped_stats(slit_img, axis=0, sigma=4.0) + corr_sci, corr_slits = sci_smash_mean[1:-1], slit_smash_mean[1:-1] + lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, corr_sci.size) xcorr_denom = np.sqrt(np.sum(corr_sci*corr_sci)*np.sum(corr_slits*corr_slits)) xcorr_norm = xcorr / xcorr_denom - # TODO -- Generate a QA plot - tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \ - = arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0, - cont_frac_fwhm=1.0, cont_samp=30, nfind=1, debug=debug) + tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig = arc.detect_lines(xcorr_norm, + cont_subtract=False, + input_thresh=0.1, debug=debug) + # No peak? -- e.g. data fills the entire detector if len(tampl) == 0: msgs.warn('No peak found in spatial flexure. Assuming there is none...') - + return 0. - + lag0_idx = np.where(lags == 0)[0][0] + pix_max = np.atleast_1d(pix_max) + + peaks = pix_max - lag0_idx + maxpeaks = np.abs(peaks) <= maxlag + lag_max = peaks[maxpeaks] + xcorr_max = tampl_true[maxpeaks] + if not np.any(maxpeaks): + msgs.warn('No peak found within the maximum lag. Assuming there is none...') + return 0. + elif np.sum(maxpeaks) > 1: + msgs.warn('Multiple peaks found within the maximum lag. Using the peak detected with the highest xcross value.') + maxsig = np.argmax(xcorr_max) + lag_max = np.atleast_1d(lag_max[maxsig]) + xcorr_max = np.atleast_1d(tampl_true[maxpeaks][maxsig]) + # Find the peak - xcorr_max = np.interp(pix_max, np.arange(lags.shape[0]), xcorr_norm) - lag_max = np.interp(pix_max, np.arange(lags.shape[0]), lags) msgs.info('Spatial flexure measured: {}'.format(lag_max[0])) + if debug: + xvals = np.arange(corr_slits.size) + plt.clf() + ax = plt.gca() + scale = np.abs(corr_slits).max() / np.abs(corr_sci).max() + ax.plot(xvals, corr_slits, 'k', label='slitmask edge peaks') + ax.plot(xvals, corr_sci*scale, 'g', label='original science edge peaks') + ax.plot(xvals, np.roll(corr_sci*scale, -int(lag_max[0])), '--r', label='shifted science edge peaks') + ax.legend() + plt.show() + + + # corr_slits = onslits.astype(float).flatten() + # + # # Compute + # mean_sci, med_sci, stddev_sci = stats.sigma_clipped_stats(_sciimg[onslits]) + # thresh = med_sci + 5.0*stddev_sci + # corr_sci = np.fmin(_sciimg.flatten(), thresh) + # lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) + # xcorr_denom = np.sqrt(np.sum(corr_sci*corr_sci)*np.sum(corr_slits*corr_slits)) + # xcorr_norm = xcorr / xcorr_denom + # # TODO -- Generate a QA plot + # embed() + # tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \ + # = arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0, + # cont_frac_fwhm=1.0, cont_samp=30, nfind=1, debug=debug) + # # No peak? -- e.g. data fills the entire detector + # if len(tampl) == 0: + # msgs.warn('No peak found in spatial flexure. Assuming there is none...') + # + # return 0. + # + # # Find the peak + # xcorr_max = np.interp(pix_max, np.arange(lags.shape[0]), xcorr_norm) + # lag_max = np.interp(pix_max, np.arange(lags.shape[0]), lags) + # msgs.info('Spatial flexure measured: {}'.format(lag_max[0])) + if debug: plt.figure(figsize=(14, 6)) plt.plot(lags, xcorr_norm, color='black', drawstyle='steps-mid', lw=3, label='x-corr') From a5c44b3cdf1e0805957f069f6864450934b95527 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Mon, 28 Oct 2024 17:08:46 -1000 Subject: [PATCH 02/22] final version? --- pypeit/core/arc.py | 2 +- pypeit/core/flexure.py | 162 ++++++++++++++------------------- pypeit/core/wavecal/wvutils.py | 16 +++- pypeit/images/rawimage.py | 10 +- pypeit/par/pypeitpar.py | 14 ++- 5 files changed, 97 insertions(+), 107 deletions(-) diff --git a/pypeit/core/arc.py b/pypeit/core/arc.py index fa80c439ee..15f4d35acb 100644 --- a/pypeit/core/arc.py +++ b/pypeit/core/arc.py @@ -1001,10 +1001,10 @@ def detect_lines(censpec, sigdetect=5.0, fwhm=4.0, fit_frac_fwhm=1.25, input_thr (mean, med, stddev) = stats.sigma_clipped_stats(arc[cont_mask & np.logical_not(bpm_out)], sigma_lower=3.0, sigma_upper=3.0, cenfunc= np.nanmedian, stdfunc = np.nanstd) - thresh = med + sigdetect*stddev if stddev == 0.0: msgs.warn('stddev = 0.0, so resetting to 1.0') stddev = 1.0 + thresh = med + sigdetect * stddev else: med = 0.0 if isinstance(input_thresh,(float, int)): diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index 2803a70365..3e62743836 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -12,6 +12,7 @@ from matplotlib import pyplot as plt from matplotlib import gridspec +from matplotlib.lines import Line2D import matplotlib from astropy import stats @@ -36,6 +37,7 @@ from pypeit.core import fitting from pypeit.core import qa from pypeit.core import trace +from pypeit.core.wavecal import wvutils from pypeit.datamodel import DataContainer from pypeit.images.detector_container import DetectorContainer from pypeit.images.mosaic import Mosaic @@ -45,7 +47,7 @@ from IPython import embed -def spat_flexure_shift(sciimg, slits, debug=False, maxlag = 20): +def spat_flexure_shift(sciimg, slits, bpm=None, debug=True, maxlag=20, sigdetect=20.): """ Calculate a rigid flexure shift in the spatial dimension between the slitmask and the science image. @@ -57,129 +59,101 @@ def spat_flexure_shift(sciimg, slits, debug=False, maxlag = 20): Args: sciimg (`numpy.ndarray`_): + Science image slits (:class:`pypeit.slittrace.SlitTraceSet`): + Slits object + bpm (`numpy.ndarray`_, optional): + Bad pixel mask (True = Bad) maxlag (:obj:`int`, optional): Maximum flexure searched for + debug (:obj:`bool`, optional): + Run in debug mode Returns: float: The spatial flexure shift relative to the initial slits """ + msgs.info("Measuring spatial flexure") # Mask -- Includes short slits and those excluded by the user (e.g. ['rdx']['slitspatnum']) slitmask = slits.slit_img(initial=True, exclude_flag=slits.bitmask.exclude_for_flexure) _sciimg = sciimg if slitmask.shape == sciimg.shape \ - else arc.resize_mask2arc(slitmask.shape, sciimg) - onslits = (slitmask > -1).astype(float) - - # sci_smash_mean, sci_smash_median, sci_smash_sig = sigma_clipped_stats(_sciimg, axis=0, sigma=4.0) - # slit_smash_mean, slit_smash_median, slit_smash_sig = sigma_clipped_stats(onslits, axis=0, sigma=4.0) - # - # lags, xcorr = utils.cross_correlate(sci_smash_median, slit_smash_median, slit_smash_median.size) - # xcorr_denom = np.sqrt(np.sum(sci_smash_median*sci_smash_median)*np.sum(slit_smash_median*slit_smash_median)) - # xcorr_norm = xcorr / xcorr_denom - # - # tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \ - # = arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0, - # cont_frac_fwhm=1.0, cont_samp=30, debug=debug) - - sobelsig, edge_img = trace.detect_slit_edges(_sciimg, sobel_enhance=2) - slit_sobelsig, slit_img = trace.detect_slit_edges(slitmask, sobel_enhance=2) - sci_smash_mean, sci_smash_median, sci_smash_sig = sigma_clipped_stats(edge_img, axis=0, sigma=4.0) - slit_smash_mean, slit_smash_median, slit_smash_sig = sigma_clipped_stats(slit_img, axis=0, sigma=4.0) - corr_sci, corr_slits = sci_smash_mean[1:-1], slit_smash_mean[1:-1] - lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, corr_sci.size) + else arc.resize_mask2arc(slitmask.shape, sciimg) + + # create sobel images of both slitmask and the science image + sci_sobel, _ = trace.detect_slit_edges(_sciimg, sobel_enhance=2) + slits_sobel, _ = trace.detect_slit_edges(slitmask, sobel_enhance=2) + # collapse both sobel images along the spectral direction + sci_smash_mean, _, _ = sigma_clipped_stats(sci_sobel, axis=0, mask=bpm) + slits_smash_mean, _, _ = sigma_clipped_stats(slits_sobel, axis=0, mask=bpm) + # trim the edges + sci_smash = sci_smash_mean[5:-5] + slits_smash = slits_smash_mean[5:-5] + # invert the negative values + sci_smash[sci_smash < 0] = -sci_smash[sci_smash < 0] + slits_smash[slits_smash < 0] = -slits_smash[slits_smash < 0] + + corr_sci = wvutils. get_xcorr_arc(sci_smash, percent_ceil=50, cont_sub=True, sigdetect=sigdetect, debug=debug) + corr_slits = wvutils.get_xcorr_arc(slits_smash, percent_ceil=50, cont_sub=False, input_thresh=0, debug=debug) + + # run x-cross correlation + lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) xcorr_denom = np.sqrt(np.sum(corr_sci*corr_sci)*np.sum(corr_slits*corr_slits)) xcorr_norm = xcorr / xcorr_denom - tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig = arc.detect_lines(xcorr_norm, - cont_subtract=False, - input_thresh=0.1, debug=debug) - + _, _, pix_max, _, _, _, _, _ = arc.detect_lines(xcorr_norm, cont_subtract=False, input_thresh=0., + nfind=1, debug=debug) # No peak? -- e.g. data fills the entire detector - if len(tampl) == 0: - msgs.warn('No peak found in spatial flexure. Assuming there is none...') + if len(pix_max) == 0: + msgs.warn('No peak found in the x-correlation between the traced slits and the science/calib image.' + ' Assuming there is NO SPATIAL FLEXURE.'+msgs.newline() + 'If a flexure is expected, ' + 'consider either changing the maximum lag for the cross-correlation, ' + 'or the "spat_flexure_sigdetect" parameter, or use the manual flexure correction.') return 0. - lag0_idx = np.where(lags == 0)[0][0] - pix_max = np.atleast_1d(pix_max) - - peaks = pix_max - lag0_idx - maxpeaks = np.abs(peaks) <= maxlag - lag_max = peaks[maxpeaks] - xcorr_max = tampl_true[maxpeaks] - if not np.any(maxpeaks): - msgs.warn('No peak found within the maximum lag. Assuming there is none...') - return 0. - elif np.sum(maxpeaks) > 1: - msgs.warn('Multiple peaks found within the maximum lag. Using the peak detected with the highest xcross value.') - maxsig = np.argmax(xcorr_max) - lag_max = np.atleast_1d(lag_max[maxsig]) - xcorr_max = np.atleast_1d(tampl_true[maxpeaks][maxsig]) - - # Find the peak - msgs.info('Spatial flexure measured: {}'.format(lag_max[0])) + lag0 = np.where(lags == 0)[0][0] + shift = round(pix_max[0] - lag0, 3) + msgs.info('Spatial flexure measured: {}'.format(shift)) if debug: + # 1D plot xvals = np.arange(corr_slits.size) plt.clf() ax = plt.gca() - scale = np.abs(corr_slits).max() / np.abs(corr_sci).max() + scale = np.nanmax(corr_slits) / np.nanmax(corr_sci) ax.plot(xvals, corr_slits, 'k', label='slitmask edge peaks') - ax.plot(xvals, corr_sci*scale, 'g', label='original science edge peaks') - ax.plot(xvals, np.roll(corr_sci*scale, -int(lag_max[0])), '--r', label='shifted science edge peaks') + #ax.plot(xvals, corr_sci*scale, 'g', label='original science edge peaks') + ax.plot(xvals, np.roll(scale*corr_sci, -int(shift)), '--r', label='shifted science edge peaks') ax.legend() plt.show() - - # corr_slits = onslits.astype(float).flatten() - # - # # Compute - # mean_sci, med_sci, stddev_sci = stats.sigma_clipped_stats(_sciimg[onslits]) - # thresh = med_sci + 5.0*stddev_sci - # corr_sci = np.fmin(_sciimg.flatten(), thresh) - # lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) - # xcorr_denom = np.sqrt(np.sum(corr_sci*corr_sci)*np.sum(corr_slits*corr_slits)) - # xcorr_norm = xcorr / xcorr_denom - # # TODO -- Generate a QA plot - # embed() - # tampl_true, tampl, pix_max, twid, centerr, ww, arc_cont, nsig \ - # = arc.detect_lines(xcorr_norm, sigdetect=3.0, fit_frac_fwhm=1.5, fwhm=5.0, - # cont_frac_fwhm=1.0, cont_samp=30, nfind=1, debug=debug) - # # No peak? -- e.g. data fills the entire detector - # if len(tampl) == 0: - # msgs.warn('No peak found in spatial flexure. Assuming there is none...') - # - # return 0. - # - # # Find the peak - # xcorr_max = np.interp(pix_max, np.arange(lags.shape[0]), xcorr_norm) - # lag_max = np.interp(pix_max, np.arange(lags.shape[0]), lags) - # msgs.info('Spatial flexure measured: {}'.format(lag_max[0])) - - if debug: - plt.figure(figsize=(14, 6)) - plt.plot(lags, xcorr_norm, color='black', drawstyle='steps-mid', lw=3, label='x-corr') - plt.plot(lag_max[0], xcorr_max[0], 'g+', markersize=6.0, label='peak') - plt.title('Best shift = {:5.3f}'.format(lag_max[0]) + ', corr_max = {:5.3f}'.format(xcorr_max[0])) - plt.legend() + # 2D plot + left_slits, right_slits, mask_slits = slits.select_edges(initial=True, flexure=None) + left_flex, right_flex, mask = slits.select_edges(initial=True, flexure=shift) + # get vmin and vmax for imshow + m, med, sig = sigma_clipped_stats(sciimg[np.logical_not(bpm)], sigma_lower=5.0, sigma_upper=5.0) + vmin = m - 1.0 * sig + vmax = m + 4.0 * sig + plt.imshow(_sciimg, origin='lower', vmin=vmin, vmax=vmax) + spec = np.tile(np.arange(slits.nspec), (slits.nslits, 1)).T + thin = slits.nspec//50 + for i in range(slits.nslits): + plt.plot(left_slits[::thin, i], spec[::thin, i], color='C3', lw=1, ls='--', zorder=5) + plt.plot(right_slits[::thin, i], spec[::thin, i], color='C1', lw=1, ls='--', zorder=5) + plt.plot(left_flex[::thin, i], spec[::thin, i], color='C3', lw=1, zorder=6) + plt.plot(right_flex[::thin, i], spec[::thin, i], color='C1', lw=1, zorder=6) + + legend_elements = [Line2D([0], [0], color='C3', lw=1, ls='--', label='initial left edges'), + Line2D([0], [0], color='C1', lw=1, ls='--', label='initial right edges'), + Line2D([0], [0], color='C3', lw=1, label='shifted left edges'), + Line2D([0], [0], color='C1', lw=1, label='shifted right edges')] + plt.legend(handles=legend_elements) + plt.tight_layout() plt.show() - #tslits_shift = trace_slits.shift_slits(tslits_dict, lag_max) - # Now translate the tilts - - #slitmask_shift = pixels.tslits2mask(tslits_shift) - #slitmask_shift = slits.slit_img(flexure=lag_max[0]) + embed() - if debug: - # Now translate the slits in the tslits_dict - all_left_flexure, all_right_flexure, mask = slits.select_edges(flexure=lag_max[0]) - gpm = mask == 0 - viewer, ch = display.show_image(_sciimg) - #display.show_slits(viewer, ch, left_flexure[:,gpm], right_flexure)[:,gpm]#, slits.id) #, args.det) - #embed(header='83 of flexure.py') - - return lag_max[0] + return shift def spec_flex_shift(obj_skyspec, sky_file=None, arx_skyspec=None, arx_fwhm_pix=None, diff --git a/pypeit/core/wavecal/wvutils.py b/pypeit/core/wavecal/wvutils.py index 8ede0b92c4..e1213d0521 100644 --- a/pypeit/core/wavecal/wvutils.py +++ b/pypeit/core/wavecal/wvutils.py @@ -455,8 +455,8 @@ def zerolag_shift_stretch(theta, y1, y2, stretch_func = 'quadratic'): return -corr_norm - -def get_xcorr_arc(inspec1, sigdetect=5.0, sig_ceil=10.0, percent_ceil=50.0, use_raw_arc=False, fwhm = 4.0, debug=False): +def get_xcorr_arc(inspec1, sigdetect=5.0, input_thresh=None, sig_ceil=10.0, percent_ceil=50.0, use_raw_arc=False, + fwhm=4.0, cont_sub=True, debug=False): """ Utility routine to create a synthetic arc spectrum for cross-correlation using the location of the peaks in the input spectrum. @@ -465,7 +465,10 @@ def get_xcorr_arc(inspec1, sigdetect=5.0, sig_ceil=10.0, percent_ceil=50.0, use_ inspec1 (`numpy.ndarray`_): Input spectrum, shape = (nspec,) sigdetect (float, optional, default=3.0): - Peak finding threshold for lines that will be used to create the synthetic xcorr_arc + Sigma threshold above fluctuations for finding peaks that will be used to create the synthetic xcorr_arc + input_thresh (float, optional): + Input threshold for finding peaks that will be used to create the synthetic xcorr_arc. If set, sigdetect + will be ignored. sig_ceil (float, optional, default = 10.0): Significance threshold for peaks that will be used to determine the line amplitude clipping threshold. For peaks with significance > sig_ceil, the code will find the amplitude corresponding to @@ -477,6 +480,8 @@ def get_xcorr_arc(inspec1, sigdetect=5.0, sig_ceil=10.0, percent_ceil=50.0, use_ If True, use amplitudes from the raw arc, i.e. do not continuum subtract. Default = False fwhm (float, optional): Fwhm of arc lines. Used for peak finding and to assign a fwhm in the xcorr_arc. + cont_sub (bool, optional): + Perform a simple continuum subtraction when detecting the peaks. Default is True. debug (bool, optional): Show plots for line detection debugging. Default = False @@ -489,7 +494,9 @@ def get_xcorr_arc(inspec1, sigdetect=5.0, sig_ceil=10.0, percent_ceil=50.0, use_ # Run line detection to get the locations and amplitudes of the lines tampl1, tampl1_cont, tcent1, twid1, centerr1, w1, arc1, nsig1 = arc.detect_lines(inspec1, sigdetect=sigdetect, - fwhm=fwhm, debug=debug) + input_thresh=input_thresh, + fwhm=fwhm, cont_subtract=cont_sub, + debug=debug) ampl = tampl1 if use_raw_arc else tampl1_cont @@ -516,7 +523,6 @@ def get_xcorr_arc(inspec1, sigdetect=5.0, sig_ceil=10.0, percent_ceil=50.0, use_ continue xcorr_arc += ampl_clip[ind]*np.exp(-0.5*((spec_vec - tcent1[ind])/sigma)**2) - return xcorr_arc diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py index 8389cc0ac9..9458dc4153 100644 --- a/pypeit/images/rawimage.py +++ b/pypeit/images/rawimage.py @@ -667,7 +667,7 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl # bias and dark subtraction) and before field flattening. Also the # function checks that the slits exist if running the spatial flexure # correction, so no need to do it again here. - self.spat_flexure_shift = self.spatial_flexure_shift(slits, maxlag = self.par['spat_flexure_maxlag']) \ + self.spat_flexure_shift = self.spatial_flexure_shift(slits) \ if self.par['spat_flexure_correct'] else None # - Subtract scattered light... this needs to be done before flatfielding. @@ -761,7 +761,7 @@ def _squeeze(self): return _det, self.image, self.ivar, self.datasec_img, self.det_img, self.rn2img, \ self.base_var, self.img_scale, self.bpm - def spatial_flexure_shift(self, slits, force=False, maxlag = 20): + def spatial_flexure_shift(self, slits, force=False): """ Calculate a spatial shift in the edge traces due to flexure. @@ -774,8 +774,6 @@ def spatial_flexure_shift(self, slits, force=False, maxlag = 20): force (:obj:`bool`, optional): Force the image to be field flattened, even if the step log (:attr:`steps`) indicates that it already has been. - maxlag (:obj:'float', optional): - Maximum range of lag values over which to compute the CCF. Return: float: The calculated flexure correction @@ -789,7 +787,9 @@ def spatial_flexure_shift(self, slits, force=False, maxlag = 20): if self.nimg > 1: msgs.error('CODING ERROR: Must use a single image (single detector or detector ' 'mosaic) to determine spatial flexure.') - self.spat_flexure_shift = flexure.spat_flexure_shift(self.image[0], slits, maxlag = maxlag) + self.spat_flexure_shift = flexure.spat_flexure_shift(self.image[0], slits, bpm=self._bpm[0], + maxlag=self.par['spat_flexure_maxlag'], + sigdetect=self.par['spat_flexure_sigdetect']) self.steps[step] = True # Return return self.spat_flexure_shift diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py index 502f4e026d..f987bf978a 100644 --- a/pypeit/par/pypeitpar.py +++ b/pypeit/par/pypeitpar.py @@ -222,7 +222,8 @@ def __init__(self, trim=None, apply_gain=None, orient=None, empirical_rn=None, shot_noise=None, noise_floor=None, use_pixelflat=None, use_illumflat=None, use_specillum=None, use_pattern=None, subtract_scattlight=None, scattlight=None, subtract_continuum=None, - spat_flexure_correct=None, spat_flexure_maxlag=None): + spat_flexure_correct=None, spat_flexure_maxlag=None, + spat_flexure_sigdetect=None): # Grab the parameter names and values from the function # arguments @@ -364,6 +365,14 @@ def __init__(self, trim=None, apply_gain=None, orient=None, dtypes['spat_flexure_maxlag'] = int descr['spat_flexure_maxlag'] = 'Maximum of possible spatial flexure correction, in pixels' + defaults['spat_flexure_sigdetect'] = 20 + dtypes['spat_flexure_sigdetect'] = [int, float] + descr['spat_flexure_sigdetect'] = 'Sigma threshold above fluctuations for the slit detection in ' \ + 'the spectral image, for which the spatial flexure is computed. ' \ + 'A sobel filter is applied to the image and then collapsed along the ' \ + 'spectral direction. The sigma threshold is used to detect peaks in the '\ + 'collapsed image.' + defaults['combine'] = 'mean' options['combine'] = ProcessImagesPar.valid_combine_methods() dtypes['combine'] = str @@ -461,7 +470,8 @@ def from_dict(cls, cfg): parkeys = ['trim', 'apply_gain', 'orient', 'use_biasimage', 'subtract_continuum', 'subtract_scattlight', 'scattlight', 'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par', 'use_darkimage', 'dark_expscale', - 'spat_flexure_correct', 'spat_flexure_maxlag', 'use_illumflat', 'use_specillum', + 'spat_flexure_correct', 'spat_flexure_maxlag', 'spat_flexure_sigdetect', + 'use_illumflat', 'use_specillum', 'empirical_rn', 'shot_noise', 'noise_floor', 'use_pixelflat', 'combine', 'scale_to_mean', 'correct_nonlinear', 'satpix', #'calib_setup_and_bit', 'n_lohi', 'mask_cr', 'lamaxiter', 'grow', 'clip', 'comb_sigrej', 'rmcompact', From b49a79aeb033da1d8afe0807f625e07254a00501 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Tue, 29 Oct 2024 17:18:32 -1000 Subject: [PATCH 03/22] some stuff for a QA plot --- pypeit/core/flexure.py | 118 +++++++++++++++++++++++++++++++++++++- pypeit/images/rawimage.py | 7 ++- 2 files changed, 120 insertions(+), 5 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index 3e62743836..48efc7b632 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -47,7 +47,7 @@ from IPython import embed -def spat_flexure_shift(sciimg, slits, bpm=None, debug=True, maxlag=20, sigdetect=20.): +def spat_flexure_shift(sciimg, slits, bpm=None, debug=False, maxlag=20, sigdetect=20.): """ Calculate a rigid flexure shift in the spatial dimension between the slitmask and the science image. @@ -93,14 +93,16 @@ def spat_flexure_shift(sciimg, slits, bpm=None, debug=True, maxlag=20, sigdetect sci_smash[sci_smash < 0] = -sci_smash[sci_smash < 0] slits_smash[slits_smash < 0] = -slits_smash[slits_smash < 0] + # create a synthetic "spectrum" of both slitmask and the science image for cross-correlation corr_sci = wvutils. get_xcorr_arc(sci_smash, percent_ceil=50, cont_sub=True, sigdetect=sigdetect, debug=debug) - corr_slits = wvutils.get_xcorr_arc(slits_smash, percent_ceil=50, cont_sub=False, input_thresh=0, debug=debug) + corr_slits = wvutils.get_xcorr_arc(slits_smash, percent_ceil=50, cont_sub=False, input_thresh=1., debug=debug) # run x-cross correlation lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) xcorr_denom = np.sqrt(np.sum(corr_sci*corr_sci)*np.sum(corr_slits*corr_slits)) xcorr_norm = xcorr / xcorr_denom + # detect the highest peak in the cross-correlation _, _, pix_max, _, _, _, _, _ = arc.detect_lines(xcorr_norm, cont_subtract=False, input_thresh=0., nfind=1, debug=debug) # No peak? -- e.g. data fills the entire detector @@ -151,11 +153,121 @@ def spat_flexure_shift(sciimg, slits, bpm=None, debug=True, maxlag=20, sigdetect plt.tight_layout() plt.show() - embed() + # TODO: add QA plot generation here and add parameters to control vranges. return shift +def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, out_dir=None): + """ + Generate QA for the spatial flexure + + Args: + img (`numpy.ndarray`_): + Image of the detector + slits (:class:`pypeit.slittrace.SlitTraceSet`): + Slits object + shift (:obj:`float`): + Shift in pixels + gpm (`numpy.ndarray`_, optional): + Good pixel mask (True = Bad) + vrange (:obj:`tuple`, optional): + Tuple with the min and max values for the imshow plot + out_dir (:obj:`str`, optional): + Path to the output directory for the QA plots. If None, the current + is used. + + + """ + + left_slits, right_slits, mask_slits = slits.select_edges(initial=True, flexure=None) + left_flex, right_flex, mask = slits.select_edges(initial=True, flexure=shift) + if vrange is None: + # get vmin and vmax for imshow + if gpm is None: + gpm = np.ones_like(img, dtype=bool) + m, med, sig = sigma_clipped_stats(img[gpm], sigma_lower=5.0, sigma_upper=5.0) + vmin = m - 1.0 * sig + vmax = m + 4.0 * sig + else: + vmin, vmax = vrange + + # where to start and end the plot in the spatial direction + xstart = np.floor(np.min([left_slits, left_flex]) - 20) + if xstart < 0: + xstart = 0 + xend = np.ceil(np.max([right_slits, right_flex]) + 20) + if xend > img.shape[1]: + xend = img.shape[1] + # where to start and end the plot in the spectral direction for both the upper and lower sections + lower_ystart = 0 + lower_yend = img.shape[0]//6 + upper_ystart = img.shape[0] - img.shape[0]//6 + upper_yend = img.shape[0] + + # how many snippets to plot in the spatial direction + if slits.nslits == 1: + # if longslit plot 2 snippets, one for the left edge and one for the right edge + nxsnip = 2 + snippet = (xend - xstart) // nxsnip + spat_starts = [xstart, xstart + snippet] + spat_ends = [xend - snippet, xend] + elif slits.nslits <= 4: + # if 4 or less slits plot 3 snippets equally spaced + nxsnip = 3 + snippet = (xend - xstart) // nxsnip + spat_starts = [xstart, xstart + snippet, xstart + 2*snippet] + spat_ends = [xend - 2*snippet, xend - snippet, xend] + else: + # if more than 4 slits plot 4 snippets + nxsnip = 4 + # approximately, we want 4 slits in each snippet + snippet = 3 * (xend - xstart)/slits.nslits + # this would give nx many snippets + nx = int((xend - xstart) // snippet) + # but we want to plot only for of those snippets + step = nx / nxsnip + spat_starts = [xstart, xstart + step*snippet, xstart + 2*step*snippet, xstart + 3*step*snippet] + spat_ends = [xend - 3*step*snippet, xend - 2*step*snippet, xend - step*snippet, xend] + + # plot the spatial flexure + fig = plt.figure(figsize=(nxsnip*4, 8)) + gs = gridspec.GridSpec(2, nxsnip, figure=fig) + # spectral vector for plotting the slits + spec = np.tile(np.arange(slits.nspec), (slits.nslits, 1)).T + thin = 10 + # legend elements + legend_elements = [Line2D([0], [0], color='C3', lw=1, ls='--', label='initial left edges'), + Line2D([0], [0], color='C1', lw=1, ls='--', label='initial right edges'), + Line2D([0], [0], color='C3', lw=1, label='shifted left edges'), + Line2D([0], [0], color='C1', lw=1, label='shifted right edges')] + # loop over the 2 rows + for r in range(2): + _ystar, _yend = (lower_ystart, lower_yend) if r == 1 else (upper_ystart, upper_yend) + # loop over the snippets + for s in range(nxsnip): + ax = fig.add_subplot(gs[r, s]) + ax.imshow(img, origin='lower', vmin=vmin, vmax=vmax) + ax.set_ylim(_ystar, _yend) + ax.set_xlim(spat_starts[s], spat_ends[s]) + # plot the slits + for i in range(slits.nslits): + plt.plot(left_slits[::thin, i], spec[::thin, i], color='C3', lw=1, ls='--', zorder=5) + plt.plot(right_slits[::thin, i], spec[::thin, i], color='C1', lw=1, ls='--', zorder=5) + plt.plot(left_flex[::thin, i], spec[::thin, i], color='C3', lw=1, zorder=6) + plt.plot(right_flex[::thin, i], spec[::thin, i], color='C1', lw=1, zorder=6) + ax.tick_params(axis='both', labelsize=6) + if r == 0 and s == 0: + ax.legend(handles=legend_elements, fontsize=7) + ax.set_ylabel('Upper snippets', fontsize=18) + elif r == 1 and s == 0: + ax.set_ylabel('Lower snippets', fontsize=18) + plt.tight_layout() + plt.show() + + + + def spec_flex_shift(obj_skyspec, sky_file=None, arx_skyspec=None, arx_fwhm_pix=None, spec_fwhm_pix=None, mxshft=20, excess_shft="crash", method="boxcar", minwave=None, maxwave=None): diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py index 9458dc4153..943e2f5f96 100644 --- a/pypeit/images/rawimage.py +++ b/pypeit/images/rawimage.py @@ -667,7 +667,7 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl # bias and dark subtraction) and before field flattening. Also the # function checks that the slits exist if running the spatial flexure # correction, so no need to do it again here. - self.spat_flexure_shift = self.spatial_flexure_shift(slits) \ + self.spat_flexure_shift = self.spatial_flexure_shift(slits, debug=debug) \ if self.par['spat_flexure_correct'] else None # - Subtract scattered light... this needs to be done before flatfielding. @@ -761,7 +761,7 @@ def _squeeze(self): return _det, self.image, self.ivar, self.datasec_img, self.det_img, self.rn2img, \ self.base_var, self.img_scale, self.bpm - def spatial_flexure_shift(self, slits, force=False): + def spatial_flexure_shift(self, slits, force=False, debug=False): """ Calculate a spatial shift in the edge traces due to flexure. @@ -774,6 +774,8 @@ def spatial_flexure_shift(self, slits, force=False): force (:obj:`bool`, optional): Force the image to be field flattened, even if the step log (:attr:`steps`) indicates that it already has been. + debug (:obj:`bool`, optional): + Run in debug mode. Return: float: The calculated flexure correction @@ -788,6 +790,7 @@ def spatial_flexure_shift(self, slits, force=False): msgs.error('CODING ERROR: Must use a single image (single detector or detector ' 'mosaic) to determine spatial flexure.') self.spat_flexure_shift = flexure.spat_flexure_shift(self.image[0], slits, bpm=self._bpm[0], + debug=debug, maxlag=self.par['spat_flexure_maxlag'], sigdetect=self.par['spat_flexure_sigdetect']) self.steps[step] = True From 0a029a8c40f76146c2e0755956a9005be7f87a69 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Thu, 7 Nov 2024 15:34:59 -1000 Subject: [PATCH 04/22] finalized QA plot --- pypeit/core/flexure.py | 186 ++++++++++++++++++++------------------ pypeit/core/qa.py | 2 + pypeit/images/rawimage.py | 25 +++-- pypeit/par/pypeitpar.py | 12 ++- 4 files changed, 125 insertions(+), 100 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index c136455193..edb5ae352b 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -47,7 +47,7 @@ from IPython import embed -def spat_flexure_shift(sciimg, slits, bpm=None, debug=False, maxlag=20, sigdetect=20.): +def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug=False, qa_outfile=None): """ Calculate a rigid flexure shift in the spatial dimension between the slitmask and the science image. @@ -66,14 +66,20 @@ def spat_flexure_shift(sciimg, slits, bpm=None, debug=False, maxlag=20, sigdetec Bad pixel mask (True = Bad) maxlag (:obj:`int`, optional): Maximum flexure searched for + sigdetect (:obj:`float`, optional): + Sigma threshold above fluctuations for the slit detection + in the collapsed sobel image debug (:obj:`bool`, optional): Run in debug mode + qa_outfile (:obj:`str`, optional): + Path to the output file where the QA is saved. If None, the QA is not generated. Returns: float: The spatial flexure shift relative to the initial slits """ msgs.info("Measuring spatial flexure") + # TODO: should we use initial or tweaked slits? # Mask -- Includes short slits and those excluded by the user (e.g. ['rdx']['slitspatnum']) slitmask = slits.slit_img(initial=True, exclude_flag=slits.bitmask.exclude_for_flexure) @@ -120,45 +126,25 @@ def spat_flexure_shift(sciimg, slits, bpm=None, debug=False, maxlag=20, sigdetec if debug: # 1D plot xvals = np.arange(corr_slits.size) - plt.clf() - ax = plt.gca() + plt.figure(figsize=(9, 8)) scale = np.nanmax(corr_slits) / np.nanmax(corr_sci) - ax.plot(xvals, corr_slits, 'k', label='slitmask edge peaks') - #ax.plot(xvals, corr_sci*scale, 'g', label='original science edge peaks') - ax.plot(xvals, np.roll(scale*corr_sci, -int(shift)), '--r', label='shifted science edge peaks') - ax.legend() + plt.plot(xvals, corr_slits, 'k', label='slitmask edge peaks') + plt.plot(xvals, np.roll(scale*corr_sci, -int(shift)), '--r', label='shifted science edge peaks') + plt.legend() plt.show() # 2D plot - left_slits, right_slits, mask_slits = slits.select_edges(initial=True, flexure=None) - left_flex, right_flex, mask = slits.select_edges(initial=True, flexure=shift) - # get vmin and vmax for imshow - m, med, sig = sigma_clipped_stats(sciimg[np.logical_not(bpm)], sigma_lower=5.0, sigma_upper=5.0) - vmin = m - 1.0 * sig - vmax = m + 4.0 * sig - plt.imshow(_sciimg, origin='lower', vmin=vmin, vmax=vmax) - spec = np.tile(np.arange(slits.nspec), (slits.nslits, 1)).T - thin = slits.nspec//50 - for i in range(slits.nslits): - plt.plot(left_slits[::thin, i], spec[::thin, i], color='C3', lw=1, ls='--', zorder=5) - plt.plot(right_slits[::thin, i], spec[::thin, i], color='C1', lw=1, ls='--', zorder=5) - plt.plot(left_flex[::thin, i], spec[::thin, i], color='C3', lw=1, zorder=6) - plt.plot(right_flex[::thin, i], spec[::thin, i], color='C1', lw=1, zorder=6) - - legend_elements = [Line2D([0], [0], color='C3', lw=1, ls='--', label='initial left edges'), - Line2D([0], [0], color='C1', lw=1, ls='--', label='initial right edges'), - Line2D([0], [0], color='C3', lw=1, label='shifted left edges'), - Line2D([0], [0], color='C1', lw=1, label='shifted right edges')] - plt.legend(handles=legend_elements) - plt.tight_layout() - plt.show() + spat_flexure_qa(sciimg, slits, shift, gpm=np.logical_not(bpm)) - # TODO: add QA plot generation here and add parameters to control vranges. + if qa_outfile is not None: + # Generate the QA plot + msgs.info("Generating QA plot for spatial flexure") + spat_flexure_qa(sciimg, slits, shift, gpm=np.logical_not(bpm), outfile=qa_outfile) return shift -def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, out_dir=None): +def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, outfile=None): """ Generate QA for the spatial flexure @@ -173,66 +159,72 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, out_dir=None): Good pixel mask (True = Bad) vrange (:obj:`tuple`, optional): Tuple with the min and max values for the imshow plot - out_dir (:obj:`str`, optional): - Path to the output directory for the QA plots. If None, the current + outfile (:obj:`str`, optional): + Path to the output file where the QA is saved. If None, the current is used. """ - + debug = True if outfile is None else False + # TODO: should we use initial or tweaked slits in this plot? left_slits, right_slits, mask_slits = slits.select_edges(initial=True, flexure=None) left_flex, right_flex, mask = slits.select_edges(initial=True, flexure=shift) - if vrange is None: - # get vmin and vmax for imshow - if gpm is None: - gpm = np.ones_like(img, dtype=bool) - m, med, sig = sigma_clipped_stats(img[gpm], sigma_lower=5.0, sigma_upper=5.0) - vmin = m - 1.0 * sig - vmax = m + 4.0 * sig - else: - vmin, vmax = vrange - - # where to start and end the plot in the spatial direction - xstart = np.floor(np.min([left_slits, left_flex]) - 20) - if xstart < 0: - xstart = 0 - xend = np.ceil(np.max([right_slits, right_flex]) + 20) - if xend > img.shape[1]: - xend = img.shape[1] - # where to start and end the plot in the spectral direction for both the upper and lower sections - lower_ystart = 0 - lower_yend = img.shape[0]//6 - upper_ystart = img.shape[0] - img.shape[0]//6 - upper_yend = img.shape[0] - - # how many snippets to plot in the spatial direction - if slits.nslits == 1: - # if longslit plot 2 snippets, one for the left edge and one for the right edge - nxsnip = 2 - snippet = (xend - xstart) // nxsnip - spat_starts = [xstart, xstart + snippet] - spat_ends = [xend - snippet, xend] - elif slits.nslits <= 4: - # if 4 or less slits plot 3 snippets equally spaced - nxsnip = 3 - snippet = (xend - xstart) // nxsnip - spat_starts = [xstart, xstart + snippet, xstart + 2*snippet] - spat_ends = [xend - 2*snippet, xend - snippet, xend] + + if debug: + # where to start and end the plot in the spatial&spectral direction + nxsnip = 1 + spat_starts = [0] + spat_ends = [img.shape[1]] + upper_ystart = 0 + upper_yend = img.shape[0] + else: - # if more than 4 slits plot 4 snippets - nxsnip = 4 - # approximately, we want 4 slits in each snippet - snippet = 3 * (xend - xstart)/slits.nslits - # this would give nx many snippets - nx = int((xend - xstart) // snippet) - # but we want to plot only for of those snippets - step = nx / nxsnip - spat_starts = [xstart, xstart + step*snippet, xstart + 2*step*snippet, xstart + 3*step*snippet] - spat_ends = [xend - 3*step*snippet, xend - 2*step*snippet, xend - step*snippet, xend] + # where to start and end the plot in the spatial direction + xstart = int(np.floor(np.min([left_slits, left_flex]) - 20)) + if xstart < 0: + xstart = 0 + xend = int(np.ceil(np.max([right_slits, right_flex]) + 20)) + if xend > img.shape[1]: + xend = img.shape[1] + + # how many snippets to plot in the spatial direction + if slits.nslits == 1: + # if longslit plot 2 snippets, one for the left edge and one for the right edge + nxsnip = 2 + snippet = int((xend - xstart) // nxsnip) + spat_starts = [xstart, xstart + snippet] + spat_ends = [xend - snippet, xend] + elif slits.nslits <= 12: + # if 12 or less slits plot 3-4 snippets equally spaced + nxsnip = 3 if slits.nslits <= 6 else 4 + snippet = int((xend - xstart) // nxsnip) + spat_starts = [xstart, xstart + snippet, xstart + 2*snippet] + spat_ends = [xend - 2*snippet, xend - snippet, xend] + if slits.nslits > 6: + # add the 4th snippet + spat_starts.append(xstart + 3*snippet) + spat_ends.insert(0, xend - 3*snippet) + else: + # if more than 12 slits plot 4 snippets + nxsnip = 4 + # approximately, we want 3 slits in each snippet + snippet = int(3 * (xend - xstart)/slits.nslits) + # this would give nx many snippets + nx = int((xend - xstart) // snippet) + # but we want to plot only nxsnip of those snippets + spat_starts = [xstart + i * snippet for i in np.linspace(0, nx - 1, nxsnip, dtype=int)] + spat_ends = [xstart + i * snippet for i in np.linspace(1, nx, nxsnip, dtype=int)] + + # where to start and end the plot in the spectral direction for both the upper and lower sections + lower_ystart = 0 + lower_yend = int(snippet) + upper_ystart = int(img.shape[0] - snippet) + upper_yend = img.shape[0] # plot the spatial flexure - fig = plt.figure(figsize=(nxsnip*4, 8)) - gs = gridspec.GridSpec(2, nxsnip, figure=fig) + rows = 1 if debug else 2 + fig = plt.figure(figsize=(9, 8) if debug else (nxsnip*4, 8)) + gs = gridspec.GridSpec(rows, nxsnip, figure=fig) # spectral vector for plotting the slits spec = np.tile(np.arange(slits.nspec), (slits.nslits, 1)).T thin = 10 @@ -241,15 +233,27 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, out_dir=None): Line2D([0], [0], color='C1', lw=1, ls='--', label='initial right edges'), Line2D([0], [0], color='C3', lw=1, label='shifted left edges'), Line2D([0], [0], color='C1', lw=1, label='shifted right edges')] - # loop over the 2 rows - for r in range(2): - _ystar, _yend = (lower_ystart, lower_yend) if r == 1 else (upper_ystart, upper_yend) + # loop over the 2 rows if we save the plot in the output directory, otherwise plot the whole detector + for r in range(rows): + _ystar, _yend = (upper_ystart, upper_yend) if r == 0 else (lower_ystart, lower_yend) # loop over the snippets for s in range(nxsnip): ax = fig.add_subplot(gs[r, s]) + if vrange is None: + # get vmin and vmax for imshow + _img = img[_ystar:_yend, spat_starts[s]:spat_ends[s]] + _gpm = gpm[_ystar:_yend, spat_starts[s]:spat_ends[s]] if gpm is not None else np.ones_like(_img, + dtype=bool) + m, med, sig = sigma_clipped_stats(_img[_gpm], sigma_lower=5.0, sigma_upper=5.0) + vmin = m - 1.0 * sig + vmax = m + 4.0 * sig + else: + vmin, vmax = vrange + # imshow img instead of _img to show the actual pixel values in each snippet ax.imshow(img, origin='lower', vmin=vmin, vmax=vmax) ax.set_ylim(_ystar, _yend) ax.set_xlim(spat_starts[s], spat_ends[s]) + # plot the slits for i in range(slits.nslits): plt.plot(left_slits[::thin, i], spec[::thin, i], color='C3', lw=1, ls='--', zorder=5) @@ -258,14 +262,18 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, out_dir=None): plt.plot(right_flex[::thin, i], spec[::thin, i], color='C1', lw=1, zorder=6) ax.tick_params(axis='both', labelsize=6) if r == 0 and s == 0: + plt.suptitle(f'Shift={shift:.1f} pixels', fontsize=18) ax.legend(handles=legend_elements, fontsize=7) - ax.set_ylabel('Upper snippets', fontsize=18) + if not debug: + ax.set_ylabel('Upper snippets', fontsize=18) elif r == 1 and s == 0: ax.set_ylabel('Lower snippets', fontsize=18) plt.tight_layout() - plt.show() - - + if debug: + plt.show() + else: + fig.savefig(outfile, dpi=200) + plt.close(fig) def spec_flex_shift(obj_skyspec, sky_file=None, arx_skyspec=None, arx_fwhm_pix=None, diff --git a/pypeit/core/qa.py b/pypeit/core/qa.py index 1929f9c5c2..716ab5eb79 100644 --- a/pypeit/core/qa.py +++ b/pypeit/core/qa.py @@ -81,6 +81,8 @@ def set_qa_filename(root, method, det=None, slit=None, prefix=None, out_dir=None outfile = 'QA/PNGs/{:s}_{:s}_obj_trace.png'.format(root, det) elif method == 'obj_profile_qa': outfile = 'PNGs/{:s}_{:s}_S{:04d}_obj_prof.png'.format(root, det, slit) + elif method == 'spat_flexure_qa_corr': + outfile = 'QA/PNGs/{:s}_spat_flex_corr.png'.format(root) elif method == 'spec_flexure_qa_corr': # outfile = 'QA/PNGs/{:s}_D{:02d}_S{:04d}_spec_flex_corr.png'.format(root, det, slit) outfile = 'PNGs/{:s}_S{:04d}_spec_flex_corr.png'.format(root, slit) diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py index 943e2f5f96..44ed602d71 100644 --- a/pypeit/images/rawimage.py +++ b/pypeit/images/rawimage.py @@ -20,11 +20,13 @@ from pypeit.core import flat from pypeit.core import flexure from pypeit.core import scattlight +from pypeit.core import qa from pypeit.core.mosaic import build_image_mosaic from pypeit.images import pypeitimage from pypeit import utils from pypeit.display import display - +from pypeit import io +from pathlib import Path # TODO: I don't understand why we have some of these attributes. E.g., why do # we need both hdu and headarr? @@ -664,11 +666,12 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl # Calculate flexure, if slits are provided and the correction is # requested. NOTE: This step must come after trim, orient (just like - # bias and dark subtraction) and before field flattening. Also the - # function checks that the slits exist if running the spatial flexure - # correction, so no need to do it again here. - self.spat_flexure_shift = self.spatial_flexure_shift(slits, debug=debug) \ - if self.par['spat_flexure_correct'] else None + # bias and dark subtraction) and before field flattening. + if self.par['spat_flexure_correct']: + if slits is not None: + self.spat_flexure_shift = self.spatial_flexure_shift(slits, debug=debug) + else: + msgs.warn('Spatial flexure correction requested but no slits provided.') # - Subtract scattered light... this needs to be done before flatfielding. if self.par['subtract_scattlight']: @@ -789,10 +792,16 @@ def spatial_flexure_shift(self, slits, force=False, debug=False): if self.nimg > 1: msgs.error('CODING ERROR: Must use a single image (single detector or detector ' 'mosaic) to determine spatial flexure.') + + # get filename for QA + basename = f'{io.remove_suffix(self.filename)}_{self.spectrograph.get_det_name(self.det)}' + outdir = str(Path(slits.calib_dir).parent) if slits.calib_dir is not None else None + qa_outfile = qa.set_qa_filename(basename, 'spat_flexure_qa_corr', out_dir=outdir) + self.spat_flexure_shift = flexure.spat_flexure_shift(self.image[0], slits, bpm=self._bpm[0], - debug=debug, maxlag=self.par['spat_flexure_maxlag'], - sigdetect=self.par['spat_flexure_sigdetect']) + sigdetect=self.par['spat_flexure_sigdetect'], + debug=debug, qa_outfile=qa_outfile) self.steps[step] = True # Return return self.spat_flexure_shift diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py index a945b17bb9..a33b098dad 100644 --- a/pypeit/par/pypeitpar.py +++ b/pypeit/par/pypeitpar.py @@ -223,7 +223,7 @@ def __init__(self, trim=None, apply_gain=None, orient=None, use_pixelflat=None, use_illumflat=None, use_specillum=None, use_pattern=None, subtract_scattlight=None, scattlight=None, subtract_continuum=None, spat_flexure_correct=None, spat_flexure_maxlag=None, - spat_flexure_sigdetect=None): + spat_flexure_sigdetect=None, spat_flexure_vrange=None): # Grab the parameter names and values from the function # arguments @@ -365,7 +365,7 @@ def __init__(self, trim=None, apply_gain=None, orient=None, dtypes['spat_flexure_maxlag'] = int descr['spat_flexure_maxlag'] = 'Maximum of possible spatial flexure correction, in pixels' - defaults['spat_flexure_sigdetect'] = 20 + defaults['spat_flexure_sigdetect'] = 10. dtypes['spat_flexure_sigdetect'] = [int, float] descr['spat_flexure_sigdetect'] = 'Sigma threshold above fluctuations for the slit detection in ' \ 'the spectral image, for which the spatial flexure is computed. ' \ @@ -373,6 +373,12 @@ def __init__(self, trim=None, apply_gain=None, orient=None, 'spectral direction. The sigma threshold is used to detect peaks in the '\ 'collapsed image.' + defaults['spat_flexure_vrange'] = None + dtypes['spat_flexure_vrange'] = tuple + descr['spat_flexure_vrange'] = 'This parameter is used when generating the QA plot for the spatial flexure. ' \ + 'It sets the data range (vmin,vmax) used by the colormap when showing the ' \ + 'spectral image. If None, the range is set automatically.' + defaults['combine'] = 'mean' options['combine'] = ProcessImagesPar.valid_combine_methods() dtypes['combine'] = str @@ -471,7 +477,7 @@ def from_dict(cls, cfg): 'subtract_scattlight', 'scattlight', 'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par', 'use_darkimage', 'dark_expscale', 'spat_flexure_correct', 'spat_flexure_maxlag', 'spat_flexure_sigdetect', - 'use_illumflat', 'use_specillum', + 'spat_flexure_vrange', 'use_illumflat', 'use_specillum', 'empirical_rn', 'shot_noise', 'noise_floor', 'use_pixelflat', 'combine', 'scale_to_mean', 'correct_nonlinear', 'satpix', #'calib_setup_and_bit', 'n_lohi', 'mask_cr', 'lamaxiter', 'grow', 'clip', 'comb_sigrej', 'rmcompact', From c84e3e79ac2d09b302aa5aba03fa20a6af141060 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Fri, 8 Nov 2024 11:29:55 -1000 Subject: [PATCH 05/22] small fix --- pypeit/core/flexure.py | 4 ++-- pypeit/images/rawimage.py | 11 +++++------ 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index edb5ae352b..dd35afaa29 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -160,8 +160,8 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, outfile=None): vrange (:obj:`tuple`, optional): Tuple with the min and max values for the imshow plot outfile (:obj:`str`, optional): - Path to the output file where the QA is saved. If None, the current - is used. + Path to the output file where the QA is saved. If None, the QA is + shown on screen and not saved. """ diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py index 44ed602d71..af12ecc0df 100644 --- a/pypeit/images/rawimage.py +++ b/pypeit/images/rawimage.py @@ -666,12 +666,11 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl # Calculate flexure, if slits are provided and the correction is # requested. NOTE: This step must come after trim, orient (just like - # bias and dark subtraction) and before field flattening. - if self.par['spat_flexure_correct']: - if slits is not None: - self.spat_flexure_shift = self.spatial_flexure_shift(slits, debug=debug) - else: - msgs.warn('Spatial flexure correction requested but no slits provided.') + # bias and dark subtraction) and before field flattening. Also the + # function checks that the slits exist if running the spatial flexure + # correction, so no need to do it again here. + self.spat_flexure_shift = self.spatial_flexure_shift(slits, debug=debug) \ + if self.par['spat_flexure_correct'] else None # - Subtract scattered light... this needs to be done before flatfielding. if self.par['subtract_scattlight']: From e526c1e14ca6a50a71cd3d6ca763d5db1fce8634 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Fri, 8 Nov 2024 11:36:09 -1000 Subject: [PATCH 06/22] small fix --- pypeit/core/flexure.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index dd35afaa29..699cb63a40 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -100,7 +100,7 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= slits_smash[slits_smash < 0] = -slits_smash[slits_smash < 0] # create a synthetic "spectrum" of both slitmask and the science image for cross-correlation - corr_sci = wvutils. get_xcorr_arc(sci_smash, percent_ceil=50, cont_sub=True, sigdetect=sigdetect, debug=debug) + corr_sci = wvutils.get_xcorr_arc(sci_smash, percent_ceil=50, cont_sub=True, sigdetect=sigdetect, debug=debug) corr_slits = wvutils.get_xcorr_arc(slits_smash, percent_ceil=50, cont_sub=False, input_thresh=1., debug=debug) # run x-cross correlation From a447a20ec9bbd8a51b99366585737451b81ada1d Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Fri, 8 Nov 2024 13:03:17 -1000 Subject: [PATCH 07/22] improved --- pypeit/core/flexure.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index 699cb63a40..da2eafe85d 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -87,21 +87,21 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= else arc.resize_mask2arc(slitmask.shape, sciimg) # create sobel images of both slitmask and the science image - sci_sobel, _ = trace.detect_slit_edges(_sciimg, sobel_enhance=2) - slits_sobel, _ = trace.detect_slit_edges(slitmask, sobel_enhance=2) + sci_sobel, _ = trace.detect_slit_edges(_sciimg) + slits_sobel, _ = trace.detect_slit_edges(slitmask) # collapse both sobel images along the spectral direction - sci_smash_mean, _, _ = sigma_clipped_stats(sci_sobel, axis=0, mask=bpm) - slits_smash_mean, _, _ = sigma_clipped_stats(slits_sobel, axis=0, mask=bpm) - # trim the edges - sci_smash = sci_smash_mean[5:-5] - slits_smash = slits_smash_mean[5:-5] + sci_smash, _, _ = sigma_clipped_stats(sci_sobel, axis=0, mask=bpm) + slits_smash, _, _ = sigma_clipped_stats(slits_sobel, axis=0, mask=bpm) + # remove nan values + sci_smash[np.isnan(sci_smash)] = 0 + slits_smash[np.isnan(slits_smash)] = 0 # invert the negative values - sci_smash[sci_smash < 0] = -sci_smash[sci_smash < 0] - slits_smash[slits_smash < 0] = -slits_smash[slits_smash < 0] + sci_smash[sci_smash < 0] *= -1 + slits_smash[slits_smash < 0] *= -1 # create a synthetic "spectrum" of both slitmask and the science image for cross-correlation - corr_sci = wvutils.get_xcorr_arc(sci_smash, percent_ceil=50, cont_sub=True, sigdetect=sigdetect, debug=debug) - corr_slits = wvutils.get_xcorr_arc(slits_smash, percent_ceil=50, cont_sub=False, input_thresh=1., debug=debug) + corr_sci = wvutils.get_xcorr_arc(sci_smash, cont_sub=True, percent_ceil=None, sigdetect=sigdetect, debug=debug) + corr_slits = wvutils.get_xcorr_arc(slits_smash, cont_sub=False, percent_ceil=None, input_thresh=1., debug=debug) # run x-cross correlation lags, xcorr = utils.cross_correlate(corr_sci, corr_slits, maxlag) @@ -128,9 +128,11 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= xvals = np.arange(corr_slits.size) plt.figure(figsize=(9, 8)) scale = np.nanmax(corr_slits) / np.nanmax(corr_sci) + plt.suptitle(f'Shift={shift:.1f} pixels', fontsize=18) plt.plot(xvals, corr_slits, 'k', label='slitmask edge peaks') plt.plot(xvals, np.roll(scale*corr_sci, -int(shift)), '--r', label='shifted science edge peaks') plt.legend() + plt.tight_layout() plt.show() # 2D plot From dd06e5c227f9e220fa1d6c7dc87ad4d8205a771c Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Fri, 8 Nov 2024 15:22:37 -1000 Subject: [PATCH 08/22] improved --- pypeit/core/flexure.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index da2eafe85d..07d9c33032 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -87,8 +87,8 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= else arc.resize_mask2arc(slitmask.shape, sciimg) # create sobel images of both slitmask and the science image - sci_sobel, _ = trace.detect_slit_edges(_sciimg) - slits_sobel, _ = trace.detect_slit_edges(slitmask) + sci_sobel, _ = trace.detect_slit_edges(_sciimg, bpm=bpm) + slits_sobel, _ = trace.detect_slit_edges(slitmask, bpm=bpm) # collapse both sobel images along the spectral direction sci_smash, _, _ = sigma_clipped_stats(sci_sobel, axis=0, mask=bpm) slits_smash, _, _ = sigma_clipped_stats(slits_sobel, axis=0, mask=bpm) @@ -100,7 +100,7 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= slits_smash[slits_smash < 0] *= -1 # create a synthetic "spectrum" of both slitmask and the science image for cross-correlation - corr_sci = wvutils.get_xcorr_arc(sci_smash, cont_sub=True, percent_ceil=None, sigdetect=sigdetect, debug=debug) + corr_sci = wvutils.get_xcorr_arc(sci_smash, cont_sub=True, percent_ceil=50, sigdetect=sigdetect, debug=debug) corr_slits = wvutils.get_xcorr_arc(slits_smash, cont_sub=False, percent_ceil=None, input_thresh=1., debug=debug) # run x-cross correlation From d286930b645252bc21e931e75799e2104ba35c69 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Tue, 12 Nov 2024 14:45:26 -1000 Subject: [PATCH 09/22] small fixes --- pypeit/core/flexure.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/pypeit/core/flexure.py b/pypeit/core/flexure.py index 07d9c33032..87f6515f36 100644 --- a/pypeit/core/flexure.py +++ b/pypeit/core/flexure.py @@ -100,7 +100,7 @@ def spat_flexure_shift(sciimg, slits, bpm=None, maxlag=20, sigdetect=10., debug= slits_smash[slits_smash < 0] *= -1 # create a synthetic "spectrum" of both slitmask and the science image for cross-correlation - corr_sci = wvutils.get_xcorr_arc(sci_smash, cont_sub=True, percent_ceil=50, sigdetect=sigdetect, debug=debug) + corr_sci = wvutils.get_xcorr_arc(sci_smash, cont_sub=True, percent_ceil=10., sigdetect=sigdetect, debug=debug) corr_slits = wvutils.get_xcorr_arc(slits_smash, cont_sub=False, percent_ceil=None, input_thresh=1., debug=debug) # run x-cross correlation @@ -183,11 +183,7 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, outfile=None): else: # where to start and end the plot in the spatial direction xstart = int(np.floor(np.min([left_slits, left_flex]) - 20)) - if xstart < 0: - xstart = 0 xend = int(np.ceil(np.max([right_slits, right_flex]) + 20)) - if xend > img.shape[1]: - xend = img.shape[1] # how many snippets to plot in the spatial direction if slits.nslits == 1: @@ -243,9 +239,10 @@ def spat_flexure_qa(img, slits, shift, gpm=None, vrange=None, outfile=None): ax = fig.add_subplot(gs[r, s]) if vrange is None: # get vmin and vmax for imshow - _img = img[_ystar:_yend, spat_starts[s]:spat_ends[s]] - _gpm = gpm[_ystar:_yend, spat_starts[s]:spat_ends[s]] if gpm is not None else np.ones_like(_img, - dtype=bool) + _xstart = spat_starts[s] if spat_starts[s] >= 0 else 0 + _xend = spat_ends[s] if spat_ends[s] <= img.shape[1] else img.shape[1] + _img = img[_ystar:_yend, _xstart:_xend] + _gpm = gpm[_ystar:_yend, _xstart:_xend] if gpm is not None else np.ones_like(_img, dtype=bool) m, med, sig = sigma_clipped_stats(_img[_gpm], sigma_lower=5.0, sigma_upper=5.0) vmin = m - 1.0 * sig vmax = m + 4.0 * sig From 5cc3f3290fc5cb9490b5aa5dab29acc0ea17f553 Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Tue, 12 Nov 2024 17:49:53 -1000 Subject: [PATCH 10/22] docs --- doc/qa.rst | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/doc/qa.rst b/doc/qa.rst index f895fc6003..8cf399fce8 100644 --- a/doc/qa.rst +++ b/doc/qa.rst @@ -112,10 +112,10 @@ For each processed, science exposure there are a series of PNGs generated, per detector and (sometimes) per slit. -Flexure QA ----------- +Spectral Flexure QA +------------------- -If a flexure correction was performed (default), the fit to the +If a spectral flexure correction was performed (default), the fit to the correlation lags per object is shown and the adopted shift is listed. Here is an example: @@ -133,3 +133,14 @@ Here is an example: .. figure:: figures/qa/flex_sky_armlsd.jpg :align: center +Spatial Flexure QA +------------------ + +If a spatial flexure correction was performed, the result of the correction +is shown in a plot like the one below. The plot shows a few snippets of the +science/standard spectral image with overlaid the slit edges as traced in the +``trace`` image (dashed lines) and after applying the spatial flexure correction +(solid lines). The value of the shift is also reported on the top of the plot. + +.. figure:: figures/qa/spat_flex_corr.jpg + :align: center \ No newline at end of file From 3245c8c41aeb846a1a2dd0e6276de29b9fa61d8f Mon Sep 17 00:00:00 2001 From: Debora Pelliccia Date: Wed, 13 Nov 2024 11:50:56 -1000 Subject: [PATCH 11/22] docs --- doc/calibrations/flexure.rst | 28 ++++++++++++++++++ .../r230417_01033_DET01_spat_flex_corr.png | Bin 0 -> 1262646 bytes doc/qa.rst | 25 ++++++++++------ doc/scripts.rst | 24 +++++++++++++++ 4 files changed, 68 insertions(+), 9 deletions(-) create mode 100644 doc/figures/r230417_01033_DET01_spat_flex_corr.png diff --git a/doc/calibrations/flexure.rst b/doc/calibrations/flexure.rst index c75e3c31de..55bac8e218 100644 --- a/doc/calibrations/flexure.rst +++ b/doc/calibrations/flexure.rst @@ -32,6 +32,9 @@ of :ref:`processimagespar`. We have made our own determinations for which instruments to enable this as the default. Inspect the :ref:`instr_par` list to see if your instrument is included (search for the value of ``spat_flexure_correct``). +A QA plot is generated for each frame for which the spatial flexure correction +is applied. We recommend the user to inspect these plots to ensure the flexure +correction is reasonable. See :ref:`qa-spat-flex` for more information. Depending on what frame types you choose to correct, the code will behave somewhat differently. Here we describe @@ -315,4 +318,29 @@ HDU Name HDU Type Data Type Description ``FLEXURE`` `astropy.io.fits.BinTableHDU`_ ... All data from the :class:`~pypeit.core.flexure.MultiSlitFlexure` datamodel ===================== ============================== ========= ========================================================================== +Inspecting +========== + +PypeIt provides the ``pypeit_chk_flexure`` script to inspect both the +spatial and spectral flexure corrections. + +The script usage can be displayed by calling the script with the +``-h`` option: + +.. include:: ../help/pypeit_chk_flexure.rst + +The script takes as input one or multiple `spec2d*.fits` or `spec1d*.fits` files +and print to screen the flexure correction applied to each file. + +Here is a typical call to print the spatial flexure correction: + +.. code-block:: console + + pypeit_chk_flexure Science/spec2d_r230417_01033-frb22022_LRISr_20230417T082242.672.fits --spat + +and to print the spectral flexure correction: + +.. code-block:: console + + pypeit_chk_flexure Science/spec1d_r230417_01033-frb22022_LRISr_20230417T082242.672.fits 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