def bkg_subtraction(time, flux, scope="tpf", sigma=3): """Subtracts background flux from target pixel file. Parameters ---------- scope : string, "tpf" or "postcard" If `tpf`, will use data from the target pixel file only to estimate and remove the background. If `postcard`, will use data from the entire postcard region to estimate and remove the background. sigma : float The standard deviation cut used to determine which pixels are representative of the background in each cadence. """ tpf_flux_bkg = [] sigma_clip = SigmaClip(sigma=sigma) # bkg = MMMBackground(sigma_clip=sigma_clip) bkg = SExtractorBackground(sigma_clip=sigma_clip) for i in range(len(time)): bkg_value = bkg.calc_background(flux[i]) tpf_flux_bkg.append(bkg_value) tpf_flux_bkg = np.array(tpf_flux_bkg) return tpf_flux_bkg
def __init__(self, gid, field, flter, nearby_fitting_region, nearby_gids = None): if not (field == 'S' or field == 'N'): raise Exception('Field must be S or N.') if not type(flter) == str: raise Exception('Filter must be string.') self.gid = gid self.field = field self.flter = flter self.nearby_fitting_region = nearby_fitting_region if self.flter == '850': if self.field == 'N': self.fitsfile = 'GOODS-N_acsz_sci_sub.fits' self.fullfield = 'North' if self.field == 'S': self.fitsfile = 'goodss_3dhst.v4.0.F850LP_orig_sci.fits' self.fullfield = 'South' else: if self.field =='S': self.fitsfile = 'goodss_3dhst.v4.0.F'+flter+'W_orig_sci.fits' self.fullfield = 'South' if self.field =='N': self.fitsfile = 'goodsn_3dhst.v4.0.F'+flter+'W_orig_sci.fits' self.fullfield = 'North' ### CATALOG ### if (field == 'S') or (field == 'South'): catalog = '/Users/rosaliaobrien/research/website/catalogs/goodss_3dhst.v4.4.cat' if (field == 'N') or (field == 'North'): catalog = '/Users/rosaliaobrien/research/website/catalogs/goodsn_3dhst.v4.4.cat' self.cat = ascii.read(catalog) ### GET SKY ESTIMATE ### if not nearby_gids == None: xmin, xmax, ymin, ymax = self.get_boundaries(nearby_gids) path = '/Users/rosaliaobrien/research/GALFIT_CLEAR/running_GALFIT/'+self.fullfield+'/'+self.flter+'/' skydata = fits.open(path+self.fitsfile)[0].data xmid = (xmin+xmax)/2 ymid = (ymin+ymax)/2 cutout = Cutout2D(skydata, (xmid, ymid), (400, 400), copy=True, mode='partial') masked_cutout = make_source_mask(cutout.data, snr = 1.5, npixels=10, dilate_size=11) cutout.data[masked_cutout] = np.nan self.scidata = cutout.data sigma_clip = SigmaClip(sigma=3) bkg = SExtractorBackground(sigma_clip) rms = MADStdBackgroundRMS(sigma_clip) self.sky_value = bkg.calc_background(cutout.data) self.rms_value = rms.calc_background_rms(cutout.data)
def background_rms(data): # Determines the rms of the background. sigmaClipper = SigmaClip(sigma=3, maxiters=5) bck_estimator = SExtractorBackground(data) bkg = Background2D(data,(50,50),filter_size=(3,3),sigma_clip=sigmaClipper,bkg_estimator=bck_estimator) print((bkg.background_median, bkg.background_rms_median)) return bkg.background_rms_median
def sub_bkg(self, img): sigma_clip = SigmaClip(sigma=3.) bkg_estimator = SExtractorBackground() mask_0 = make_source_mask(img, nsigma=3, npixels=5, dilate_size=11) mask_1 = (np.isnan(img)) mask = mask_0 + mask_1 bkg = Background2D(img, (5, 5), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask) back = bkg.background * ~mask_1 return img - back
def bkg_estimation(filename, box=(20, 20), filter_size=(3, 3), bkg_estimator='SExtractor', sigma=3., sigma_lower=None, sigma_upper=None, maxiters=10, outLevel=1): imagelist = np.atleast_1d(filename) for ima in imagelist: print("\nEstimate background in %s" % ima) root = os.path.splitext(ima)[0] hdulist = fits.open(ima) data = hdulist[0].data sigma_clip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower, sigma_upper=sigma_upper, maxiters=maxiters) if bkg_estimator == 'SExtractor': bkg_estimator = SExtractorBackground() elif bkg_estimator == 'MMM': bkg_estimator = MMMBackground() elif bkg_estimator == 'ModeEstimator': bkg_estimator = ModeEstimatorBackground() elif bkg_estimator == 'Median': bkg_estimator = MedianBackground() elif bkg_estimator == 'Mean': bkg_estimator = MeanBackground() bkg = Background2D( data, box, filter_size=filter_size, sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, ) # Create image with background substracted hdulist[0].data = data - bkg.background hdulist.writeto(ima, overwrite=True) if outLevel == 2: # Create image with 2D background bkg_file = root + "_bkg_map.fits" hdulist[0].data = bkg.background hdulist.writeto(bkg_file, overwrite=True)
def reference(self): """ This function ... :return: """ # Inform the user log.info("Estimating background with photutils ...") # Plot total mask if self.config.plot: plotting.plot_mask(self.total_mask, title="total mask") integer_aperture_radius = int(math.ceil(self.aperture_radius)) box_shape = (integer_aperture_radius, integer_aperture_radius) filter_size = (3, 3) # Estimate the background sigma_clip = SigmaClip(sigma=3., iters=10) #bkg_estimator = MedianBackground() bkg_estimator = SExtractorBackground() bkg = Background2D(self.frame.data, box_shape, filter_size=filter_size, sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=self.total_mask.data) # Statistics #print("median background", bkg.background_median) #print("rms background", bkg.background_rms_median) self.statistics.reference = Map() self.statistics.reference.median = bkg.background_median self.statistics.reference.rms = bkg.background_rms_median # Plot if self.config.plot: plotting.plot_box(bkg.background, title="background from photutils") # Set the sky self.reference_sky = Frame(bkg.background) # Set bkg object self.photutils_bkg = bkg # Plot if self.config.plot: plotting.plot_box(self.reference_sky, title="reference sky")
def est_bkg(image, pltshow=1): print("Estimating the background light......") img = image # check the back grounp sigma_clip = SigmaClip(sigma=3., iters=10) bkg_estimator = SExtractorBackground() mask_0 = make_source_mask(img, snr=2, npixels=5, dilate_size=11) mask_1 = (np.isnan(img)) mask = mask_0 + mask_1 bkg = Background2D(img, (50, 50), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask) from matplotlib.colors import LogNorm fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(img, norm=LogNorm(), origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if pltshow == 0: plt.close() else: plt.show() fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(mask, origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if pltshow == 0: plt.close() else: plt.show() back = bkg.background * ~mask_1 fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(back, origin='lower', cmap='Greys_r') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if pltshow == 0: plt.close() else: plt.show() # img -= back # pyfits.PrimaryHDU(img).writeto('sub_coadd.fits',overwrite=True) return back
def sub_bkg(img, plot=True): from astropy.stats import SigmaClip from photutils import Background2D, SExtractorBackground sigma_clip = SigmaClip(sigma=3., iters=10) bkg_estimator = SExtractorBackground() from photutils import make_source_mask mask_0 = make_source_mask(img, snr=2, npixels=5, dilate_size=11) mask_1 = (np.isnan(img)) mask = mask_0 + mask_1 bkg = Background2D(img, (50, 50), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask) from matplotlib.colors import LogNorm fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(img, norm=LogNorm(), origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if plot: plt.show() else: plt.close() fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(mask, origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if plot: plt.show() else: plt.close() back = bkg.background * ~mask_1 fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(back, origin='lower', cmap='Greys_r') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if plot: plt.show() else: plt.close() return img - back, back
def do_photometry(seed): rng = np.random.default_rng(seed=seed) def recipe(): return scopesim_grid(N1d=7, border=100, perturbation=7, magnitude=lambda N: rng.uniform(15, 26, N), custom_subpixel_psf=anisocado_psf, seed=seed) img_noisefloor, tab_noisefloor = read_or_generate_image( f'grid7_pert7_seed{seed}', recipe=recipe, force_generate=False) guess_table = tab_noisefloor.copy() guess_table['x_0'] = guess_table['x'].copy() guess_table['y_0'] = guess_table['y'].copy() guess_table['x_orig'] = guess_table['x'].copy() guess_table['y_orig'] = guess_table['y'].copy() guess_table['flux_0'] = guess_table['f'] guess_table['x_0'] += rng.uniform(-0.1, 0.1, len(guess_table)) guess_table['y_0'] += rng.uniform(-0.1, 0.1, len(guess_table)) fit_stages = [ FitStage(10, 1e-10, 1e-11, np.inf, all_individual), # first stage: get flux approximately right FitStage(60, 0.5, 0.5, 10_000, all_individual), FitStage(130, 0.1, 0.1, 5_000, all_individual), #FitStage(10, 0.1, 0.1, 1, all_individual) #FitStage(10, 0.3, 0.3, 1, all_individual), # optimize position, keep flux constant #FitStage(10, 0.2, 0.2, 5000, all_individual), #FitStage(30, 0.05, 0.05, 100, all_individual) ] photometry = IncrementalFitPhotometry(SExtractorBackground(), anisocado_psf, max_group_size=1, group_extension_radius=50, fit_stages=fit_stages, use_noise=True) result_table = photometry.do_photometry(img_noisefloor, guess_table) result_table['id'] += seed * 10000 return result_table, photometry.residual(result_table)
def grid_photometry_epsf(): fit_stages_grid = [ FitStage(5, 1e-10, 1e-11, np.inf, all_individual), # first stage: get flux approximately right FitStage(5, 0.6, 0.6, 10, all_individual), FitStage(5, 0.3, 0.3, 500_000, all_individual), #FitStage(30, 0.1, 0.1, 5_000, all_simultaneous) ] photometry_grid = IncrementalFitPhotometry(SExtractorBackground(), anisocado_psf, max_group_size=1, group_extension_radius=10, fit_stages=fit_stages_grid, use_noise=True) with open(out_dir / 'processing_params_synthetic_grid_epsf.txt', 'w') as f: f.write(pformat(photometry_grid.__dict__)) return photometry_grid
def fit_background(image, catalog=None, flux_cutoff=8e4): """ Estimate background in Full Frame Image. Parameters: image (ndarray or string): Either the image as 2D ndarray or a path to FITS or NPY file where to load image from. catalog (`astropy.table.Table` object): Catalog of stars in the image. Is not yet being used for anything. flux_cutoff (float): Flux value at which any pixel above will be masked out of the background estimation. Returns: ndarray: Estimated background with the same size as the input image. ndarray: Boolean array specifying which pixels was used to estimate the background (``True`` if pixel was used). .. codeauthor:: Rasmus Handberg <*****@*****.**> """ # Load file: if isinstance(image, np.ndarray): img = image elif isinstance(image, six.string_types): if image.endswith('.npy'): img = np.load(image) else: img = load_ffi_fits(image) else: raise ValueError( "Input image must be either 2D ndarray or path to file.") # Create mask # TODO: Use the known locations of bright stars mask = ~np.isfinite(img) mask |= (img > flux_cutoff) # Estimate the background: sigma_clip = SigmaClip(sigma=3.0, iters=5) bkg_estimator = SExtractorBackground() bkg = Background2D(img, (64, 64), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask, exclude_percentile=50) return bkg.background, mask
def fit_background(ffi): """ Estimate the background of a Full Frame Image (FFI) using the photutils package. This method uses the photoutils Background 2D background estimation using a SExtracktorBackground estimator, a 3-sigma clip to the data, and a masking of all pixels above 3e5 in flux. Parameters: ffi (ndarray): A single TESS Full Frame Image in the form of a 2D array. Returns: ndarray: Estimated background with the same size as the input image. .. codeauthor:: Rasmus Handberg <*****@*****.**> .. codeauthor:: Oliver James Hall <*****@*****.**> """ mask = ~np.isfinite(ffi) mask |= (ffi > 3e5) # Estimate the background: sigma_clip = SigmaClip(sigma=3.0, iters=5) #Sigma clip the data bkg_estimator = SExtractorBackground() #Call background estimator bkg = Background2D(ffi, (64, 64), #Estimate background on sigma clipped data filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask, exclude_percentile=50) bkg_est_unfilt = bkg.background #Smoothing the background using a percentile filter bkg_est = circular_filter(bkg_est_unfilt, diam=15, percentile=50) return bkg_est
def subtract_sky_ccd_2d(ccd, skysub='SKYSUB', skyval='SKYVAL', box_size=(50,50), filter_size=(3,3), bkg_method='sextractor', check_skysub=True, max_val=10000, fitsky=None, sigma=3., iters=10, edge_method='crop'): # Although edge_method='pad' is recommended, it does give errors "total size of new array must be unchanged" for v0.3 if check_skysub and skysub is not None and skysub in ccd.header and ccd.header[skysub]: return ccd if not check_skysub and skysub is not None and skysub in ccd.header and ccd.header[skysub]: print ('WARNING: Image already sky subtracted! Subtracting AGAIN the sky! [set check_skysub = True for checking]') dsky = {'median': MedianBackground(), 'mean': MeanBackground(), 'sextractor': SExtractorBackground(), 'biweight': BiweightLocationBackground(), 'mode': ModeEstimatorBackground(), 'mm': MMMBackground()} if not bkg_method in dsky: print ('WARNING: Background type "%s" NOT available!! Selecting "median" from [%s]' % (bkg_type, ' | '.join(dksy.keys()))) bkg_estimator = dksy['median'] else: bkg_estimator = dsky[bkg_method] sigma_clip = SigmaClip(sigma=sigma, iters=iters) if sigma is not None and iters is not None else None bkg = Background2D(ccd.data, box_size, filter_size=filter_size, sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, edge_method=edge_method) if max_val is not None and bkg.background_median >= max_val: if 'FILENAME' in ccd.header: print ('WARNING: File "%s" has high sky level (%s >= %s). Correction NOT applied' % (ccd.header['FILENAME'], bkg.background_median, max_val)) else: print ('WARNING: File has high sky level (%s >= %s). Correction NOT applied' % (bkg.background_median, max_val)) return ccd ccd.data -= bkg.background ccd.header[skysub] = True ccd.header[skyval] = bkg.background_median ccd.header['SKYTYPE'] = '2D' ccd.header['BKGTYPE'] = bkg_method ccd.bkg = bkg if fitsky is not None: pyfits.writeto(fitsky, bkg.background, clobber=True) return ccd
def remove_image_background_sextractor(data, sigma=3.0, box_size=(50, 50), filter_size=(3, 3), positive=False): sigma_clip = SigmaClip(sigma=sigma) bkg_estimator = SExtractorBackground() bkg = Background2D(data, box_size, filter_size=filter_size, sigma_clip=sigma_clip, bkg_estimator=bkg_estimator) data_wo_bkg = data - bkg.background if positive: data_wo_bkg -= np.min(data_wo_bkg) if parameters.DEBUG: fig, ax = plt.subplots(1, 2, figsize=(11, 5)) plot_image_simple(ax[0], bkg.background, scale="lin") plot_image_simple(ax[1], data_wo_bkg, scale="symlog") fig.tight_layout() plt.show() if parameters.PdfPages: parameters.PdfPages.savefig() return data_wo_bkg
def background_rms(data): """ Computes the RMS from the image stored in the array data. Parameters ---------- data : array Image array. Returns ------- rms_median : List of stacked images """ sigmaClipper = SigmaClip(sigma=3, maxiters=5) bck_estimator = SExtractorBackground(data) bkg = Background2D(data, (50, 50), filter_size=(3, 3), sigma_clip=sigmaClipper, bkg_estimator=bck_estimator) print((bkg.background_median, bkg.background_rms_median)) return bkg.background_rms_median
'gausscluster_N2000_mag22_subpixel') guess_table = prepare_table(tab_gausscluster, perturbation=1) fit_stages = [ FitStage(1, 1e-11, 1e-11, np.inf, all_individual ), # first stage: get flux approximately right FitStage(1, 0.6, 0.6, 200_000, brightest_simultaneous(2)), FitStage(1, 0.6, 0.6, 100_000, not_brightest_individual(2)), FitStage(1, 0.3, 0.3, 100_000, brightest_simultaneous(4)), FitStage(1, 0.3, 0.3, 50_000, not_brightest_individual(4)), FitStage(20, 0.2, 0.2, 20_000, all_individual), FitStage(20, 0.1, 0.1, 5000, all_simultaneous) ] photometry = IncrementalFitPhotometry(SExtractorBackground(), anisocado_psf, max_group_size=12, group_extension_radius=20, fit_stages=fit_stages, use_noise=True) with open(out_dir / 'processing_params_synthetic_cluster.txt', 'w') as f: f.write(pformat(photometry.__dict__)) cluster_result = cached( lambda: photometry.do_photometry(img_gausscluster, guess_table), cache_dir / 'synthetic_gausscluster', rerun=False) [(np.min(np.abs(i)), np.mean(np.abs(i)), np.max(np.abs(i))) for i in calc_devation(cluster_result)]
def _compute_background(image, box_size=50, win_size=3, nsigma=5., threshold_flag=None): """Use Background2D to determine the background of the input image. Parameters ---------- image : ndarray Numpy array of the science extension from the observations FITS file. box_size : int Size of box along each axis win_size : int Size of 2D filter to apply to the background image nsigma : float Number of sigma above background threshold_flag : float or None Value from the image which serves as the limit for determining sources. If None, compute a default value of (background+5*rms(background)). If threshold < 0.0, use absolute value as scaling factor for default value. Returns ------- bkg : 2D ndarray Background image bkg_dao_rms : ndarry Background RMS image threshold : ndarray Numpy array representing the background plus RMS """ # Report configuration values to log log.info("") log.info("Computation of white light image background - Input Parameters") log.info("Box size: {}".format(box_size)) log.info("Window size: {}".format(win_size)) log.info("NSigma: {}".format(nsigma)) # SExtractorBackground ans StdBackgroundRMS are the defaults bkg_estimator = SExtractorBackground() bkgrms_estimator = StdBackgroundRMS() bkg = None bkg_dao_rms = None exclude_percentiles = [10, 25, 50, 75] for percentile in exclude_percentiles: log.info("") log.info("Percentile in use: {}".format(percentile)) try: bkg = Background2D(image, box_size, filter_size=win_size, bkg_estimator=bkg_estimator, bkgrms_estimator=bkgrms_estimator, exclude_percentile=percentile, edge_method="pad") except Exception: bkg = None continue if bkg is not None: # Set the bkg_rms at "nsigma" sigma above background bkg_rms = nsigma * bkg.background_rms default_threshold = bkg.background + bkg_rms bkg_rms_mean = bkg.background.mean() + nsigma * bkg_rms.std() bkg_dao_rms = bkg.background_rms if threshold_flag is None: threshold = default_threshold elif threshold_flag < 0: threshold = -1 * threshold_flag * default_threshold log.info("Background threshold set to {} based on {}".format(threshold.max(), default_threshold.max())) bkg_rms_mean = threshold.max() else: bkg_rms_mean = 3. * threshold_flag threshold = bkg_rms_mean if bkg_rms_mean < 0: bkg_rms_mean = 0. break # If Background2D does not work at all, define default scalar values for # the background to be used in source identification if bkg is None: bkg_rms_mean = max(0.01, image.min()) bkg_rms = nsigma * bkg_rms_mean bkg_dao_rms = bkg_rms_mean threshold = bkg_rms_mean + bkg_rms # *** FIX: Need to do something for bkg if bkg is None *** # Report other useful quantities log.info("") log.info("Mean background: {}".format(bkg.background.mean())) log.info("Mean threshold: {}".format(bkg_rms_mean)) log.info("") log.info("{}".format("=" * 80)) return bkg, bkg_dao_rms, threshold
from photutils import make_source_mask mask = make_source_mask(data, snr=2, npixels=5, dilate_size=11) mean, median, std = sigma_clipped_stats(data, sigma=3.0, mask=mask) print((mean, median, std)) ny, nx = data.shape y, x = np.mgrid[:ny, :nx] gradient = x * y / 5000. data2 = data + gradient plt.imshow(data2, norm=norm, origin='lower', cmap='Greys_r') plt.show() from astropy.stats import SigmaClip from photutils import Background2D, SExtractorBackground sigma_clip = SigmaClip(sigma=3., iters=10) bkg_estimator = SExtractorBackground() bkg = Background2D(data2, (50, 50), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator) plt.imshow(data2, norm=norm, origin='lower', cmap='Greys_r') bkg.plot_meshes(outlines=True, color='#1f77b4') plt.show() print(bkg.background_median) print(bkg.background_rms_median) plt.imshow(bkg.background, origin='lower', cmap='Greys_r') plt.show() #from scipy.ndimage import rotate #data3 = rotate(data2, -45.)
def flux(nddata, tbl, zeroPoint, gain, radius): """ Derive the flux and the magnitude within circular aperture Parameters ---------- nddata: numpy array Numpy array where is saved the data and the sky coordinates wcs. tbl: table Table where the first column is the id, the second the ra coordinate, the third is dec coordinate and the four the skycoordinate. zeroPoint: float Zero point of your image gain: float The gain of your image radius: float The radius of your circle in arcsec to derive the flux Return ------ Table with id, skycoord, flux, flux error, magnitude, magnitude error """ # By convention, sextractor if gain == 0.: gain = 1000000000000000000000000000. result = tbl['id', 'skycoord'] result['flux'] = float(np.size(tbl)) result['flux_err'] = float(np.size(tbl)) for i in range(np.size(tbl)): # Recover the position for each object position = tbl['skycoord'][i] if hdr['NAXIS1'] >= 50 and hdr['NAXIS2'] >= 50: # size of the background map sizeBkg = 50 # cut the mosaic in stamp to the wcs coordinate of your objects cutout = Cutout2D(nddata.data, position, (sizeBkg, sizeBkg), wcs=nddata.wcs) # Recover new data and wcs of the stamp data = cutout.data wcs = cutout.wcs else: # size of the background map sizeBkg = min(hdr['NAXIS1'], hdr['NAXIS2']) # Keep data and wcs of the initial image data = nddata.data wcs = nddata.wcs ######################### ####### Background ###### ######################### # Mask sources mask = make_source_mask(data, snr=1, npixels=3, dilate_size=3) # Derive the background and the rms image bkg = Background2D( data, int(sizeBkg / 10), filter_size=1, sigma_clip=None, bkg_estimator=SExtractorBackground(SigmaClip(sigma=2.5)), bkgrms_estimator=StdBackgroundRMS(SigmaClip(sigma=2.5)), exclude_percentile=60, mask=mask) ########################### ###### Aperture Flux ###### ########################### nddataStamp = NDData(data=data - bkg.background, wcs=wcs) # Calculate the total error error = calc_total_error(cutout.data, bkg.background_rms, gain) # Define a cicularAperture in the wcs position of your objects apertures = SkyCircularAperture(position, r=radius * u.arcsec) # Derive the flux and error flux phot_table = aperture_photometry(nddataStamp, apertures, error=error) phot_table['aperture_sum'].info.format = '%.8g' phot_table['aperture_sum_err'].info.format = '%.8g' # Recover data result['flux'][i] = phot_table['aperture_sum'][0] result['flux_err'][i] = phot_table['aperture_sum_err'][0] ########################### ######## Magnitude ######## ########################### # convert flux into magnitude result['mag'] = -2.5 * np.log10(result['flux']) + zeroPoint # convert flux error into magnitude error result['mag_err'] = 1.0857 * (result['flux_err'] / result['flux']) return result
def extract_spectrogram_background_sextractor(data, err, ws=(20, 30), mask_signal_region=True): """ Use photutils library median filter to estimate background behgin the sources. Parameters ---------- data: array The 2D array image. The transverse profile is fitted on the y direction for all pixels along the x direction. err: array The uncertainties related to the data array. ws: list up/down region extension where the sky background is estimated with format [int, int] (default: [20,30]) mask_signal_region: bool If True, the signal region is masked with np.nan values (default: True) Returns ------- bgd_model_func: callable A 2D function to model the extracted background. bgd_res: array_like The background residuals normalized with their uncertainties. bgd_rms: array_like The background RMS. Examples -------- Build a mock spectrogram with random Poisson noise: >>> from spectractor.extractor.psf import MoffatGauss >>> from spectractor.extractor.chromaticpsf import ChromaticPSF >>> from spectractor import parameters >>> parameters.DEBUG = True >>> psf = MoffatGauss() >>> s0 = ChromaticPSF(psf, Nx=100, Ny=100, saturation=1000) >>> params = s0.generate_test_poly_params() >>> saturation = params[-1] >>> data = s0.build_spectrogram_image(params, mode="1D") >>> bgd = 10*np.ones_like(data) >>> data += bgd >>> data = np.random.poisson(data) >>> data_errors = np.sqrt(data+1) Fit the transverse profile: >>> bgd_model, _, _ = extract_spectrogram_background_sextractor(data, data_errors, ws=[30,50]) """ Ny, Nx = data.shape middle = Ny // 2 # mask sources mask = make_source_mask(data, nsigma=3, npixels=5, dilate_size=11) # Estimate the background in the two lateral bands together sigma_clip = SigmaClip(sigma=3.) bkg_estimator = SExtractorBackground() if mask_signal_region: bgd_bands = np.copy(data).astype(float) bgd_bands[middle - ws[0]:middle + ws[0], :] = np.nan mask += (np.isnan(bgd_bands)) # windows size in x is set to only 6 pixels to be able to estimate rapid variations of the background on real data # filter window size is set to window // 2 so 3 # bkg = Background2D(data, ((ws[1] - ws[0]), (ws[1] - ws[0])), # filter_size=((ws[1] - ws[0]) // 2, (ws[1] - ws[0]) // 2), # sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, # mask=mask) bkg = Background2D( data, (parameters.PIXWIDTH_BOXSIZE, parameters.PIXWIDTH_BOXSIZE), filter_size=(parameters.PIXWIDTH_BOXSIZE // 2, parameters.PIXWIDTH_BOXSIZE // 2), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask) bgd_model_func = interp2d(np.arange(Nx), np.arange(Ny), bkg.background, kind='linear', bounds_error=False, fill_value=None) bgd_res = ((data - bkg.background) / err) bgd_res[mask] = np.nan if parameters.DEBUG: gs = gridspec.GridSpec(3, 2, width_ratios=[4, 1], height_ratios=[1, 1, 1], wspace=0.1, hspace=0.04, right=0.98, left=0.1, top=0.98, bottom=0.1) fig = plt.figure(figsize=(7, 5)) ax0 = plt.subplot(gs[0, 0]) ax1 = plt.subplot(gs[1, 0]) ax2 = plt.subplot(gs[2, 0]) ax3 = plt.subplot(gs[:, 1]) bgd_bands = np.copy(data).astype(float) bgd_bands[middle - ws[0]:middle + ws[0], :] = np.nan bgd_bands[mask] = np.nan mean = np.nanmean(bgd_bands) std = np.nanstd(bgd_bands) cmap = copy.copy(cm.get_cmap()) cmap.set_bad(color='lightgrey') im = ax0.imshow(bgd_bands, origin='lower', aspect="auto", vmin=mean - 3 * std, vmax=mean + 3 * std) v1 = np.linspace(mean - 3 * std, mean + 3 * std, 5, endpoint=True) cb = plt.colorbar(im, ticks=v1, ax=ax0, label=f'Data units') cb.ax.set_yticklabels(["{:2.0f}".format(i) for i in v1]) ax0.text(0.05, 0.95, f'Data background', color="white", horizontalalignment='left', verticalalignment='top', transform=ax0.transAxes ) # : mean={np.mean(b):.3f}, std={np.std(b):.3f}') ax0.set_xlabel(parameters.PLOT_XLABEL) ax0.set_ylabel(parameters.PLOT_YLABEL) ax0.set_xticks([]) ax1.set_xticks([]) bkg.plot_meshes(outlines=True, color='red', axes=ax1, linewidth=0.5) b = bkg.background im = ax1.imshow(b, origin='lower', aspect="auto") ax1.set_xlabel(parameters.PLOT_XLABEL) ax1.set_ylabel(parameters.PLOT_YLABEL) v1 = np.linspace(np.nanmin(b), np.nanmax(b), 5, endpoint=True) cb1 = plt.colorbar(im, ticks=v1, ax=ax1, label=f'Data units') cb1.ax.set_yticklabels(["{:2.0f}".format(i) for i in v1]) ax1.text(0.05, 0.95, f'Fitted background', color="white", horizontalalignment='left', verticalalignment='top', transform=ax1.transAxes ) # : mean={np.mean(b):.3f}, std={np.std(b):.3f}') res = (bgd_bands - b) / err im = ax2.imshow(res, origin='lower', aspect="auto", vmin=-5, vmax=5) ax2.set_xlabel(parameters.PLOT_XLABEL) ax2.set_ylabel(parameters.PLOT_YLABEL) ax2.text(0.05, 0.95, f'Pull: mean={np.nanmean(res):.3f}, std={np.nanstd(res):.3f}', color="white", horizontalalignment='left', verticalalignment='top', transform=ax2.transAxes ) # : mean={np.mean(b):.3f}, std={np.std(b):.3f}') v1 = np.array([-5, -2, 0, 2, 5]) cb2 = plt.colorbar(im, ticks=v1, ax=ax2, label=f'') cb2.ax.set_yticklabels(["{:1.0f}".format(i) for i in v1]) # ax3.set_title(f'Pull') # mean={np.nanmean(res):.3f}, std={np.nanstd(res):.3f}') hist_res = res[~np.isnan(res)].flatten() hist_res = hist_res[hist_res < 5] hist_res = hist_res[hist_res > -5] ax3.hist(hist_res, bins=10) ax3.grid() ax3.set_yticks([]) # ax3.set_xlim((-5, 5)) # ax3.set_xticks(v1) ax3.set_xlabel('Pull distribution') # fig.tight_layout() if parameters.LSST_SAVEFIGPATH: # pragma: no cover fig.savefig( os.path.join(parameters.LSST_SAVEFIGPATH, 'background_extraction.pdf')) if parameters.DISPLAY: # pragma: no cover plt.show() return bgd_model_func, bgd_res, bkg.background_rms
# -*- coding: utf-8 -*- """ Created on Fri Oct 11 15:36:33 2019 @author: dingxu """ from astropy.io import fits from astropy.stats import SigmaClip from photutils import SExtractorBackground fitsname = 'NGC%2011.fts' routename1 = 'E:\\shunbianyuan\\ldf_download\\20190921\\' fitsname1 = routename1+fitsname hduA1 = fits.open(fitsname1) imagedataA1 = hduA1[0].data sigma_clip = SigmaClip(sigma = 3.0) bkg = SExtractorBackground(sigma_clip) bkg_value = bkg.calc_background(imagedataA1) print(bkg_value)
def measure_bkg(img, if_plot=False, nsigma=2, npixels=25, dilate_size=11): """ Estimate the 2D background of a image, based on photutils.Background2D, with SExtractorBackground. Checkout: https://photutils.readthedocs.io/en/stable/background.html Parameter -------- parameters (including nsigma, npixels and dilate_size) will be passed to photutils.make_source_mask() Return -------- A 2D array type of the background light. """ print("Estimating the background light ... ... ...") from astropy.stats import SigmaClip from photutils import Background2D, SExtractorBackground try: sigma_clip = SigmaClip(sigma=3., maxiters=10) except (TypeError): sigma_clip = SigmaClip(sigma=3., iters=10) # if version.parse(astropy.__version__) >= version.parse("0.4"): # sigma_clip = SigmaClip(sigma=3., maxiters=10) # else: # sigma_clip = SigmaClip(sigma=3., iters=10) bkg_estimator = SExtractorBackground() if version.parse(photutils.__version__) > version.parse("0.7"): mask_0 = make_source_mask(img, nsigma=nsigma, npixels=npixels, dilate_size=dilate_size) else: mask_0 = make_source_mask(img, snr=nsigma, npixels=npixels, dilate_size=dilate_size) mask_1 = (np.isnan(img)) mask = mask_0 + mask_1 box_s = int(len(img) / 40) if box_s < 10: box_s = 10 bkg = Background2D(img, (box_s, box_s), filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, mask=mask) from matplotlib.colors import LogNorm fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(img, norm=LogNorm(), origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if if_plot: plt.show() else: plt.close() fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(mask, origin='lower') #bkg.plot_meshes(outlines=True, color='#1f77b4') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if if_plot: plt.show() else: plt.close() bkg_light = bkg.background * ~mask_1 fig = plt.figure(figsize=(15, 15)) ax = fig.add_subplot(1, 1, 1) ax.imshow(bkg_light, origin='lower', cmap='Greys_r') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) if if_plot: plt.show() else: plt.close() return bkg_light
def stack(cutout, radiusStack, gain, zeroPoint, wavelenght): """ Print out pdf with stamp stacking and plot graph magnitude in function of wavelenght Parameters ---------_ cutout: list array of images N filt x N sources radiusStack: float radius in pixel zeroPoint: float Zero point of your image gain: float The gain of your image wavelenght: float wavelenght of each filters """ # define the resulting stacked image as a cutout with the same dimension of the input cutouts o = 0 # Create pdf where are storing stack stamp and graph pdfOut = PdfPages(dirpath + 'stacking.pdf') fig, axs = plt.subplots(1, len(filters), figsize=(5, 5)) # Saving magnitude and error mag = [[0.0 for i in range(len(filters))] for j in range(2)] for i in cutout: # i is a list of galaxies print('Photometry ' + filters[o]) # Assuming that the shape is the same for all galaxies #this is now a tuple, e.g. (25,25), so the code will work also for rectangular stamps sizeStack = i[0].shape #this is now a tuple, e.g. (25,25), so the code will work also for rectangular stamps print(sizeStack) #just for testing stackImg = np.zeros(sizeStack) #LOOP over pixels: (I changed a lot here) # fait tous les pixel x for x in range(sizeStack[0]): # fait tous les pixel y for y in range(sizeStack[1]): # for the given pixels, collect the flux from all stamps into a list pxl = [] #use an empty list, but it can be also np.zeros() for stamp in i: pxl.append(stamp.data[x, y]) # caluclate the median: stackImg[x, y] = np.median(pxl) axs[o].set_title(filters[o], fontsize=5, pad=2.5) axs[o].get_xaxis().set_visible(False) axs[o].get_yaxis().set_visible(False) mappa = axs[o].imshow(stackImg, cmap='afmhot', origin='lower', interpolation='nearest') zrange = zscale.zscale(stackImg) mappa.set_clim(zrange) # Mask sources mask = make_source_mask(stackImg, snr=1, npixels=3, dilate_size=3) # Derive the background and the rms image bkg = Background2D( stackImg, int(sizeStack[0] / 10), filter_size=1, sigma_clip=None, bkg_estimator=SExtractorBackground(SigmaClip(sigma=2.5)), bkgrms_estimator=StdBackgroundRMS(SigmaClip(sigma=2.5)), exclude_percentile=90, mask=mask) if gain[o] == 0.: gain[o] = 1000000000000000000000000000. # Calculate the total error error = calc_total_error(stackImg, bkg.background_rms, gain[o]) # Define a cicularAperture in the wcs position of your objects apertures = CircularAperture( (int(sizeStack[0] / 2), int(sizeStack[1] / 2)), r=radiusStack[o]) # Derive the flux and error flux photometry = aperture_photometry(stackImg - bkg.background, apertures, error=error) # Saving magnitude and error mag[0][o] = -2.5 * np.log10( photometry['aperture_sum'][0]) + zeroPoint[o] mag[1][o] = 1.0857 * (photometry['aperture_sum_err'][0] / photometry['aperture_sum'][0]) o = o + 1 plt.savefig(pdfOut, format='pdf') # Plot plt.clf() y = mag[0] x = wavelenght plt.plot(x, y, 'ro') xlimL = wavelenght[0] - 1000 xlimR = wavelenght[len(wavelenght) - 1] + 1000 plt.xlim(xlimL, xlimR) plt.ylim(32, 22) plt.errorbar(wavelenght, mag[0], yerr=mag[1], fmt='none', capsize=10, ecolor='blue', zorder=1) plt.xlabel('wavelenght(Å)') plt.ylabel('magnitude') plt.title('') plt.savefig(pdfOut, format='pdf') pdfOut.close() np.savetxt(dirpath + 'fluxStack.txt', mag, header='Ligne1: mag, Ligne2: mag_err') return
def write_intro(self, gids, outputfile, sigma_image, use_mask = False): use_mask = True # print('Use mask?', use_mask, gid) # print(gid) outputfilefits = outputfile+'.fits' # if use_mask == False: xmin, xmax, ymin, ymax = self.get_boundaries(gids) # zeropoints taken from https://iopscience.iop.org/article/10.1088/0067-0049/214/2/24/pdf if self.flter == '850': if self.field == 'N': inputpsf = 'goodsn_3dhst.v4.0.F850LP_psf.fits' psffine = 'none' zeropoint_og = 24.871 if self.field == 'S': zeropoint_og = 24.871 inputpsf = 'goodss_3dhst.v4.0.F850LP_psf.fits' psffine = 'none' elif self.flter == '105': if self.field =='S': zeropoint_og = 23.972 inputpsf = 'none' psffine = 'none' else: if self.flter == '160': zeropoint_og = 25.946 if self.flter == '125': zeropoint_og = 26.230 if self.flter == '606': zeropoint_og = 26.511 if self.flter == '775': zeropoint_og = 25.671 if self.field =='S': inputpsf = 'goodss_3dhst.v4.0.F'+self.flter+'W_psf.fits' psffine = 'goodss_3dhst.v4.0.F'+self.flter+'W_orig_wht.fits' if self.field =='N': inputpsf = 'goodsn_3dhst.v4.0.F'+self.flter+'W_psf.fits' psffine = 'goodsn_3dhst.v4.0.F'+self.flter+'W_orig_wht.fits' if use_mask == True: # print('Using mask!', gid) segmap = str(self.gid)+'_segm.fits' # if os.path.exists('/Users/rosaliaobrien/research/GALFIT_CLEAR/running_GALFIT/segm_maps/'+segmap) == False: print('Creating segmentation map...') self.make_segm() # Move segmentation map to working directory os.rename('/Users/rosaliaobrien/research/GALFIT_CLEAR/running_GALFIT/segm_maps/'+segmap, segmap) elif use_mask == False: # print('Not using mask!', gid) segmap = 'none' zeropoint=zeropoint_og if sigma_image == None: sigma_image = 'none' text = '''================================================================================\n\ # IMAGE and GALFIT CONTROL PARAMETERS\n\ A) {0} # Input data image (FITS file)\n\ B) {1} # Output data image block\n\ C) {9} # Sigma image name (made from data if blank or "none") \n\ D) {2} # Input PSF image and (optional) diffusion kernel\n E) none # PSF fine sampling factor relative to data\n F) {10} # Bad pixel mask (FITS image or ASCII coord list)\n\ G) none # File with parameter constraints (ASCII file) \n\ H) {4} {5} {6} {7} # Image region to fit (xmin xmax ymin ymax)\n\ I) 200 200 # Size of the convolution box (x y)\n\ J) {8} # Magnitude photometric zeropoint \n\ K) 0.06 0.06 # Plate scale (dx dy) [arcsec per pixel]\n\ O) regular # Display type (regular, curses, both)\n\ P) 0 # Options: 0=normal run; 1,2=make model/imgblock & quit \n '''.format(self.fitsfile, outputfilefits, inputpsf, psffine, xmin, xmax, ymin, ymax, zeropoint, sigma_image, segmap) path = '/Users/rosaliaobrien/research/GALFIT_CLEAR/running_GALFIT/'+self.fullfield+'/'+self.flter+'/' skydata = fits.open(path+self.fitsfile)[0].data xmid = (xmin+xmax)/2 ymid = (ymin+ymax)/2 cutout = Cutout2D(skydata, (xmid, ymid), (400, 400), copy=True, mode='partial') masked_cutout = make_source_mask(cutout.data, snr = 1.5, npixels=10, dilate_size=11) cutout.data[masked_cutout] = np.nan self.scidata = cutout.data sigma_clip = SigmaClip(sigma=3) bkg = SExtractorBackground(sigma_clip) rms = MADStdBackgroundRMS(sigma_clip) self.sky_value = bkg.calc_background(cutout.data) self.rms_value = rms.calc_background_rms(cutout.data) ###PLOT MASKED IMAGE AND SEGMENTATION MAP### if use_mask == False: #Plot 1 subplot if not using segmentation map num_plots_x = 1 num_plots_y = 1 else: num_plots_x = 2 num_plots_y = 1 fig, ax = plt.subplots(num_plots_y, num_plots_x, figsize = (5,2)) ax_og = ax #Make 'original' axis in case you splot segmentation map if use_mask == True: ax = ax[0] im = ax.imshow(self.scidata, vmin = -100, vmax = 100) plt.colorbar(im, ax = ax) ax.set_title('gid: {}\nSky: {:.2f}\nRMS: {:.2f}'.format(self.gid, self.sky_value, self.rms_value), size=9) ax.set_xticklabels([]) ax.set_yticklabels([]) if use_mask == False: plt.show() ######################## if use_mask == True: ax = ax_og ### PLOT SEGMAP ### segmdata = fits.open(segmap)[0].data xmid = (xmin+xmax)/2 ymid = (ymin+ymax)/2 cutout = Cutout2D(segmdata, (xmid, ymid), (400, 400), copy=True, mode='partial') ax[1].imshow(cutout.data) ax[1].set_title('segmentation map'.format(self.gid, self.sky_value, self.rms_value), size=9) ax[1].set_xticklabels([]) ax[1].set_yticklabels([]) plt.show() ######################## return text
def estimate_background(image, method='1D', sigma=3, diag=False): '''Background estimation. Generate an estimated background image and median background rms from a given input image. Parameters ---------- image: array 2D array containing the image values, no units method: string '2D' or '1D'. 2D is suitable for large images. 1D is more appropriate for small images, especially those with uniform background levels. sigma: float Sigma clip level for background estimation. Default is 3. For 1D background estimation in small frames, sigma should be 2. For 2D background estimation, sigma should usually be 3 or 4. Returns ------- bkg_image: array 2D array the same size as image of the estimated background bkg_rms: float rms median of the background Example ------- >>> np.random.seed(0) >>> im = np.ones((10,10)) + np.random.uniform(size=(10,10)) >>> im *= u.ph >>> im[5,5] += 5 * u.ph >>> bkg_im, bkg_med = estimate_background(im, method='2D', sigma=4) >>> np.allclose([bkg_im[0,0].value, bkg_med.value], ... [1.45771779, 0.288040735]) True >>> bkg_im_1d, bkg_med_1d = estimate_background(im, method='1D', sigma=4) >>> np.allclose([bkg_im_1d[0,0].value, bkg_med_1d.value], ... [1.4686512016477016, 0.28804073501451516]) True ''' if method == '2D': from photutils import Background2D, SExtractorBackground from astropy.stats import SigmaClip # Define estimator (we're using the default SExtractorBackground estimator from photutils) bkg_estimator = SExtractorBackground() # Want the box size to be ~10 pixels or thereabouts boxes0 = np.int(1 if image.shape[0] <= 5 else np.round(image.shape[0] / 10)) boxes1 = np.int(1 if image.shape[1] <= 5 else np.round(image.shape[1] / 10)) bkg = Background2D( image.value, (image.shape[0] // boxes0, image.shape[1] // boxes1), bkg_estimator=bkg_estimator, sigma_clip=SigmaClip(sigma=sigma)) bkg_image = bkg.background * image.unit bkg_rms_median = bkg.background_rms_median * image.unit if diag: print("Image shape:", image.shape) print("Boxes per axis: {}, {}".format(boxes0, boxes1)) print("Box size: {}, {}".format(image.shape[0] // boxes0, image.shape[1] // boxes1)) elif method == '1D': from astropy.stats import sigma_clipped_stats bkg_image = np.zeros(np.shape(image)) * image.unit # Get mean, median and std using a sigmaclip of 1: bkg_mean, bkg_median, bkg_rms_median = sigma_clipped_stats(image, sigma=sigma, maxiters=10) bkg_image[:] = bkg_median return bkg_image, bkg_rms_median
def make_lc(tic, ra=None, dec=None, process=None): print('Calculando Camara y CCD') _, _, _, _, cam, ccd, _, _, _ = ts2p(0, ra, dec, trySector=args.Sector) idx = (cam[0] - 1) * 4 + (ccd[0] - 1) print('Leyendo Header') h5 = h5s[idx] q = h5['data'][3] == 0 ffi = np.array( glob.glob('/raid/TESS/FFI/s%04d/tess*-s%04d-%d-%d-*ffic.fits' % (args.Sector, args.Sector, cam, ccd)))[q][0] hdr = fits.getheader(ffi, 1) w = WCS(hdr) x, y = w.all_world2pix(ra, dec, 0) print('Leyendo hdf5') allhdus = FFICut(h5, y, x, args.size) hdus = allhdus.hdu qual = hdus[1].data['QUALITY'] == 0 time = hdus[1].data['TIME'][q] flux = hdus[1].data['FLUX'][q] errs = hdus[1].data['FLUX_ERR'][q] bkgs = np.zeros(len(flux)) print('Calculando Background') for i, f in enumerate(flux): sigma_clip = SigmaClip(sigma=1) bkg = SExtractorBackground(sigma_clip=sigma_clip) bkgs[i] = bkg.calc_background(f) #DBSCAN Aperture x = x - int(x) + args.size // 2 y = y - int(y) + args.size // 2 if not args.circ: daps = [ generate_aperture(flux - bkgs[:, None, None], n=i) for i in [1, 2, 3, 4, 5] ] dap = np.array([select_aperture(d, x, y) for d in daps]) else: XX, YY = np.ogrid[:args.size, :args.size] dap = [ np.sqrt((XX - y)**2 + (YY - x)**2) < i for i in np.arange(1, 3.1, 0.5) ] #Aperture photometry lcfl = np.einsum('ijk,ljk->li', flux - bkgs[:, None, None], dap) lcer = np.sqrt(np.einsum('ijk,ljk->li', np.square(errs), dap)) #Lightkurves lkf = [ TessLightCurve(time=time, flux=lcfl[i], flux_err=lcer[i]) for i in range(len(lcfl)) ] #Select best cdpp = [lk.estimate_cdpp() for lk in lkf] bidx = np.argmin(cdpp) lkf = lkf[bidx] #Save light curve inst = np.repeat('TESS', len(time)) output = np.transpose([time, lkf.flux, lkf.flux_err, inst]) np.savetxt('TIC%s.dat' % tic, output, fmt='%s') print('LC READY!') #Save JPG preview stamp = flux - bkgs[:, None, None] fig, ax = plt.subplots(figsize=[4, 4]) fig.patch.set_visible(False) stamp = np.log10(np.nanmedian(stamp[::10], axis=0)) stamp[np.isnan(stamp)] = np.nanmedian(stamp) ax.matshow(stamp, cmap='gist_gray', aspect='equal') xm, ym = pixel_border(dap[bidx]) for xi, yi in zip(xm, ym): ax.plot(xi, yi, color='lime', lw=1.25) ax.plot(x, y, '.r') plt.axis('off') fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0) fig.savefig('img/TIC%s.png' % tic, dpi=72) plt.close(fig) del (fig, ax) return 1
w = WCS(hdr) x, y = w.all_world2pix(ra[i], dec[i], 0) allhdus = FFICut(h5, y, x, args.size) hdus = allhdus.hdu qual = hdus[1].data['QUALITY'] == 0 time = hdus[1].data['TIME'][q] flux = hdus[1].data['FLUX'][q] errs = hdus[1].data['FLUX_ERR'][q] bkgs = np.zeros(len(flux)) #print('Calculando Background') for i, f in enumerate(flux): sigma_clip = SigmaClip(sigma=1) bkg = SExtractorBackground(sigma_clip=sigma_clip) bkgs[i] = bkg.calc_background(f) #DBSCAN Aperture x = x - int(x) + flux.shape[1] // 2 y = y - int(y) + flux.shape[2] // 2 if not args.circ: daps = [ generate_aperture(flux - bkgs[:, None, None], n=i) for i in [1, 2, 3, 4, 5] ] dap = np.array([select_aperture(d, x, y) for d in daps]) else: XX, YY = np.ogrid[:flux.shape[1], :flux.shape[2]] dap = [
color_print('CCD Row: ', 'lightcyan', str(row+x), 'default', '\tCCD Column: ', 'lightcyan', str(column+y), 'default') #Background and data ma = hdus[1].data['QUALITY'] == 0 time = hdus[1].data['TIME'][ma] flux = hdus[1].data['FLUX'][ma] errs = hdus[1].data['FLUX_ERR'][ma] bkgs = np.zeros(len(flux)) berr = np.zeros(len(flux)) #Constant background, bks != 0 if not args.backdrop: for i,f in enumerate(flux): sigma_clip = SigmaClip(sigma=1) bkg = SExtractorBackground(sigma_clip=sigma_clip) bkgs[i] = bkg.calc_background(f) if args.manualap is None else bkg.calc_background(f[~theap]) mad_bkg = mad_std(f) berr[i] = (3*1.253 - 2)*mad_bkg/np.sqrt(f.size) #Manual aperture if args.manualap is not None: apix2, apix1 = np.genfromtxt(args.manualap, unpack=True).astype(int) theap = np.zeros(flux[0].shape).astype(bool) theap[apix1, apix2] = True #PRF from lightkurve #if args.prf:
def rm_bkg(source, syntax, xc=0, yc=0): import logging import warnings import numpy as np from astropy.stats import SigmaClip from photutils import Background2D, MedianBackground,MADStdBackgroundRMS, BkgIDWInterpolator from autophot.packages.aperture import ap_phot from astropy.modeling import models, fitting from photutils import CircularAperture from photutils import SExtractorBackground logger = logging.getLogger(__name__) # try: # source_size = syntax['image_radius'] # except: source_size = 2*syntax['fwhm'] try: if syntax['psf_bkg_surface'] and not syntax['psf_bkg_poly']: sigma_clip = SigmaClip(sigma=syntax['lim_SNR']) bkg_estimator = MADStdBackgroundRMS() positions = [source.shape[0]/2,source.shape[1]/2] aperture = CircularAperture(positions, r=source_size) masks = aperture.to_mask(method='center') mask_array = masks.to_image(shape=((source.shape[0], source.shape[1]))) bkg = Background2D(source, box_size = (3, 3), mask = mask_array, filter_size=(3, 3), sigma_clip=sigma_clip, bkg_estimator=SExtractorBackground(), interpolator= BkgIDWInterpolator(), # edge_method = 'pad' ) surface = bkg.background # bkg_median = np.nanmedian(bkg.background_median) source_bkg_free = source - surface elif syntax['psf_bkg_poly']: surface_function_init = models.Polynomial2D(degree=syntax['psf_bkg_poly_degree']) fit_surface = fitting.LevMarLSQFitter() x = np.arange(0,source.shape[0]) y = np.arange(0,source.shape[0]) xx,yy= np.meshgrid(x,y) positions = [source.shape[0]/2,source.shape[1]/2] aperture = CircularAperture(positions, r=source_size) masks = aperture.to_mask(method='center') mask_array = masks.to_image(shape=((source.shape[0], source.shape[1]))) source[mask_array.astype(bool)] == np.nan with warnings.catch_warnings(): # Ignore model linearity warning from the fitter warnings.simplefilter('ignore') surface_fit = fit_surface(surface_function_init, xx, yy, source) surface = surface_fit(xx,yy) # bkg_median = np.nanmedian(surface) source_bkg_free = source - surface elif syntax['psf_bkg_local']: pos = list(zip([xc],[yc])) ap,bkg = ap_phot(pos, source, radius = syntax['ap_size'] * syntax['fwhm'], r_in = syntax['r_in_size'] * syntax['fwhm'], r_out = syntax['r_out_size'] * syntax['fwhm']) surface = np.ones(source.shape) * bkg source_bkg_free = source - surface except Exception as e: logger.exception(e) return source_bkg_free,surface
def GetData(TIC, indir, outdir, cadence='lc', S=None, fits=None, aperture_name=None, max_pixels=75, saturation_tolerance=-0.1, bad_bits=[ 1, 2, 4, 8, 16, 32, 64, 128, 136, 160, 164, 168, 176, 180, 256, 512, 1024, 2048 ], **kwargs): if S is None: raise Exception sector = ast.literal_eval(S) tpf = os.path.join(indir, fits) with pyfits.open(tpf) as f: qdata = f[1].data tpf_aperture = None tpf_big_aperture = None, apertures = {'tpf': tpf_aperture, 'tpf_big': tpf_big_aperture} fitsheader = [ pyfits.getheader(tpf, 0).cards, pyfits.getheader(tpf, 1).cards, pyfits.getheader(tpf, 2).cards ] cadn = np.array(qdata.field('CADENCENO'), dtype='int32') time = np.array(qdata.field('TIME'), dtype='float64') fpix = np.array(qdata.field('FLUX'), dtype='float64') fpix_err = np.array(qdata.field('FLUX_ERR'), dtype='float64') qual = np.array(qdata.field('QUALITY'), dtype=int) naninds = np.where(np.isnan(time)) time = Interpolate(np.arange(0, len(time)), naninds, time) pc1 = np.array(qdata.field('POS_CORR1'), dtype='float64') pc2 = np.array(qdata.field('POS_CORR2'), dtype='float64') if not np.all(np.isnan(pc1)) and not np.all(np.isnan(pc2)): pc1 = Interpolate(time, np.where(np.isnan(pc1)), pc1) pc2 = Interpolate(time, np.where(np.isnan(pc2)), pc2) else: pc1 = None pc2 = None f97 = np.zeros((fpix.shape[1], fpix.shape[2])) for i in range(fpix.shape[1]): for j in range(fpix.shape[2]): tmp = np.delete(fpix[:, i, j], np.where(np.isnan(fpix[:, i, j]))) if len(tmp): f = SavGol(tmp) med = np.nanmedian(f) MAD = 1.4826 * np.nanmedian(np.abs(f - med)) bad = np.where((f > med + 10. * MAD) | (f < med - 10. * MAD))[0] np.delete(tmp, bad) i97 = int(0.975 * len(tmp)) tmp = tmp[np.argsort(tmp)[i97]] f97[i, j] = tmp sav_ap_name = kwargs.get('sav_aper_name', 'aperture') Nmax = np.copy(max_pixels) co = 0 while Nmax >= max_pixels: ticmag = fitsheader[0]['TESSMAG'][1] if ticmag > 6.5: ss = 1.5 elif 6.5 >= ticmag > 9.: ss = 1.3 elif 9. >= ticmag > 12.: ss = 1.2 elif ticmag < 12.: ss = 1.1 aperture, Nmax = A3d(fpix, N_pixels_max=max_pixels - 1, sigma=ss) if np.sum(aperture) == 0.0: sys.exit("WARNING: Aperture can't be created, try another TIC") elif np.sum(aperture) == 1.0: ii = np.where(aperture == 1.) try: aperture[ii[0] + 1, ii[1]] = 1. except: pass try: aperture[ii[0] - 1, ii[1]] = 1. except: pass try: aperture[ii[0], ii[1] + 1] = 1. except: pass try: aperture[ii[0], ii[1] - 1] = 1. except: pass if fpix.shape[1] < 12 and fpix.shape[2] < 12: break if Nmax >= max_pixels: nanind = np.where(np.isnan(fpix_err) == 1) fpix_err[nanind] = 0. bin_row = int(np.ceil(fpix.shape[1] / 12.)) res_row = fpix.shape[1] % bin_row if res_row != 0: add_row = bin_row - res_row fpix_row = np.ndarray(shape=(fpix.shape[0], fpix.shape[1] + add_row, fpix.shape[2])) fpix_err_row = np.ndarray(shape=(fpix.shape[0], fpix.shape[1] + add_row, fpix.shape[2])) for fi, fp in enumerate(fpix): SexBkg = SExtractorBackground() fp[fp <= 0.] = 0.0 try: fp_bkg = np.ones( fpix.shape[2]) * SexBkg.calc_background(fp) except: fp_bkg = np.zeros(fpix.shape[2]) for ad in np.arange(add_row): fp = np.vstack((fp, fp_bkg)) fpix_row[fi] = fp for fi, fe in enumerate(fpix_err): for ad in np.arange(add_row): fe = np.vstack((fe, np.zeros(fpix_err.shape[2]))) fpix_err_row[fi] = fe fpix = np.copy(fpix_row) fpix_err = np.copy(fpix_err_row) bin_col = int(np.ceil(fpix.shape[2] / 12.)) res_col = fpix.shape[2] % bin_col if res_col != 0: add_col = bin_col - res_col fpix_col = np.ndarray(shape=(fpix.shape[0], fpix.shape[1], fpix.shape[2] + add_col)) fpix_err_col = np.ndarray(shape=(fpix.shape[0], fpix.shape[1], fpix.shape[2] + add_col)) for fi, fp in enumerate(fpix): SexBkg = SExtractorBackground() fp[fp <= 0.] = 0.0 try: fp_bkg = np.ones( fpix.shape[1]) * SexBkg.calc_background(fp) except: fp_bkg = np.zeros(fpix.shape[1]) fp_bkg = fp_bkg.reshape(fp_bkg.shape[0], 1) for ad in np.arange(add_col): fp = np.hstack((fp, fp_bkg)) fpix_col[fi] = fp for fi, fe in enumerate(fpix_err): for ad in np.arange(add_col): fe = np.hstack( (fe, np.zeros(shape=(fpix.shape[1], 1)))) fpix = np.copy(fpix_col) fpix_err = np.copy(fpix_err_col) fpix_bin = np.ndarray(shape=(fpix.shape[0], fpix[i].shape[0] // bin_row, fpix[i].shape[1] // bin_col)) for i, f in enumerate(fpix_bin): fpix_bin[i] = fpix[i].reshape(fpix[i].shape[0] // bin_row, bin_row, fpix[i].shape[1] // bin_col, bin_col).sum(3).sum(1) fpix = np.copy(fpix_bin) fpix_err_bin = np.ndarray(shape=(fpix_err.shape[0], fpix_err[0].shape[0] // bin_row, fpix_err[0].shape[1] // bin_col)) for i, f in enumerate(fpix_err_bin): fpix_err_bin[i] = fpix_err[i].reshape( fpix_err[i].shape[0] // bin_row, bin_row, fpix_err[i].shape[1] // bin_col, bin_col).sum(3).sum(1) fpix_err = np.copy(fpix_err_bin) co += 1 apertures.update({'automatic': aperture}) pixel_images = [fpix[0], fpix[len(fpix) // 2], fpix[len(fpix) - 1]] if np.sum(aperture) > max_pixels: keys = list(apertures.keys()) npix = np.array([np.sum(apertures[k]) for k in keys]) aperture_name = keys[np.argmax(npix * (npix <= max_pixels))] aperture = apertures[aperture_name] aperture[np.isnan(fpix[0])] = 0 if np.sum(aperture) > max_pixels: raise Exception( 'No apertures available with fewer than {} pixels. Aborting...' .format(max_pixels)) aperture[np.isnan(fpix[0])] = 0 ap = np.where(aperture == 1) fpix2D = np.array([f[ap] for f in fpix], dtype='float64') fpix_err2D = np.array([p[ap] for p in fpix_err], dtype='float64') binds = np.where(aperture != 1) if len(binds[0]) > 0 and sector not in [9, 10, 11]: bkg = np.nanmedian(np.array([f[binds] for f in fpix], dtype='float64'), axis=1) bkg_err = 1.253 * np.nanmedian(np.array([e[binds] for e in fpix_err], dtype='float64'), axis=1) / np.sqrt(len(binds[0])) bkg = bkg.reshape(-1, 1) bkg_err = bkg_err.reshape(-1, 1) elif len(binds[0]) > 0 and sector in [9, 10, 11]: bkg = np.zeros_like(fpix2D) bkg_err = np.zeros_like(fpix2D) else: bkg = np.zeros_like(fpix2D) bkg_err = np.zeros_like(fpix2D) fpix = fpix2D - bkg fpix_err = np.sqrt(fpix_err2D**2 + bkg_err**2) flux = np.sum(fpix, axis=1) ferr = np.sqrt(np.sum(fpix_err**2, axis=1)) nanmask = np.where(np.isnan(flux) | (flux == 0))[0] badmask = [] for b in bad_bits: badmask += list(np.where(qual == b)[0]) tmpmask = np.array(list(set(np.concatenate([badmask, nanmask])))) t = np.delete(time, tmpmask) f = np.delete(flux, tmpmask) f = SavGol(f) med = np.nanmedian(f) MAD = 1.4826 * np.nanmedian(np.abs(f - med)) bad = np.where((f > med + 10. * MAD) | (f < med - 10. * MAD))[0] outmask = [np.argmax(time == t[i]) for i in bad] fpix = Interpolate(time, nanmask, fpix) fpix_err = Interpolate(time, nanmask, fpix_err) data = dict(ID=TIC, sector=sector, cadn=cadn, time=time, fpix=fpix, fpix_err=fpix_err, nanmask=np.sort(nanmask), badmask=np.sort(badmask), outmask=np.sort(outmask), aperture=aperture, apertures=apertures, pixel_images=pixel_images, bkg=bkg, qual=qual, pc1=pc1, pc2=pc2, fitsheader=fitsheader, tessmag=fitsheader[0]['TESSMAG'][1], radius=fitsheader[0]['RADIUS'][1], ra=fitsheader[0]['RA_OBJ'][1], dec=fitsheader[0]['DEC_OBJ'][1], pmra=fitsheader[0]['PMRA'][1], pmdec=fitsheader[0]['PMDEC'][1], teff=fitsheader[0]['TEFF'][1], pmtotal=fitsheader[0]['PMTOTAL'][1], logg=fitsheader[0]['LOGG'][1], feh=fitsheader[0]['MH'][1]) return data