backup (de19281e) · Commits · Fang Yuedong / injection_pipeline

.gitignore

0 → 100644

+7 −0

Original line number	Diff line number	Diff line
		*.fits
		*.cat
		*.log
		*.list
		*.png
		*.pyc
		*.so
		No newline at end of file

config/config_injection_20231203.yaml

0 → 100644

+99 −0

Original line number	Diff line number	Diff line
		---
		###############################################
		#
		# Configuration file for CSST object injection
		# Last modified: 2022/06/19
		#
		###############################################
		# n_objects: 500
		rotate_objs: NO
		use_mpi: YES
		run_name: "test_20231203"
		project_cycle: 6
		run_counter: 1

		pos_sampling:
		type: "HexGrid"
		# type: "RectGrid"
		# grid_spacing: 18.5 # arcsec (~250 pixels)
		grid_spacing: 15 # arcsec (~500 pixels)
		# type: "uniform"
		# object_density: 37 # arcmin^-2

		output_img_dir: "/share/home/fangyuedong/injection_pipeline/workspace"
		input_img_list: "/share/home/fangyuedong/injection_pipeline/input_L1_IMG_20231203.list"

		###############################################
		# PSF setting
		###############################################
		psf_setting:

		# Which PSF model to use:
		# "Gauss": simple gaussian profile
		# "Interp": Interpolated PSF from sampled ray-tracing data
		psf_model: "Interp"

		# PSF size [arcseconds]
		# radius of 80% energy encircled
		# NOTE: only valid for "Gauss" PSF
		psf_rcont: 0.15

		# path to PSF data
		# NOTE: only valid for "Interp" PSF
		psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"

		###############################################
		# Input path setting
		# (NOTE) Used NGP Catalog for testing
		###############################################
		# Default path settings for NGP footprint simulation
		data_dir: "/share/simudata/CSSOSDataProductsSims/data/"

		input_path:
		cat_dir: "OnOrbitCalibration/CTargets20211231"
		star_cat: "CT-NGP_r1.8_G28.hdf5"
		galaxy_cat: "galaxyCats_r_3.0_healpix_shift_192.859500_27.128300.hdf5"

		SED_templates_path:
		star_SED: "Catalog_20210126/SpecLib.hdf5"
		galaxy_SED: "Templates/Galaxy/"


		###############################################
		# Instrumental effects setting
		# (NOTE) Here only used to construct
		# ObservationSim.Instrument.Chip object
		# (TODO) Should readout from header
		###############################################
		ins_effects:
		# switches
		bright_fatter: ON # Whether to simulate Brighter-Fatter (also diffusion) effect

		# values
		# dark_exptime: 300 # Exposure time for dark current frames [seconds]
		# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
		# readout_time: 40 # The read-out time for each channel [seconds]
		# df_strength: 2.3 # Sillicon sensor diffusion strength
		# bias_level: 500 # bias level [e-/pixel]
		# gain: 1.1 # Gain
		# full_well: 90000 # Full well depth [e-]

		###############################################
		# Random seeds
		###############################################
		random_seeds:
		seed_Av: 121212 # Seed for generating random intrinsic extinction

		###############################################
		# Measurement setting
		###############################################
		measurement_setting:
		input_img_list: "/share/home/fangyuedong/injection_pipeline/L1_INJECTED_20231203.list"
		# input_img_list: "/share/home/fangyuedong/injection_pipeline/input_L1_img_MSC_0000000.list"
		input_wht_list: "/share/home/fangyuedong/injection_pipeline/L1_WHT_20231203.list"
		input_flg_list: "/share/home/fangyuedong/injection_pipeline/L1_FLG_20231203.list"
		# input_psf_list: "/share/home/fangyuedong/injection_pipeline/psf_img_MSC_0000000.list"
		sex_config: "/share/home/fangyuedong/injection_pipeline/config/default.config"
		sex_param: "/share/home/fangyuedong/injection_pipeline/config/default.param"
		n_jobs: 18
		output_dir: "/share/home/fangyuedong/injection_pipeline/workspace"
		No newline at end of file

evaluation/calculate_completeness_fraction.py

+100 −27

Original line number	Diff line number	Diff line
		@@ -8,16 +8,30 @@ from ObservationSim.Instrument import Telescope, Filter, FilterParam

		VC_A = 2.99792458e+18 # speed of light: A/s

		# def define_options():
		# parser = argparse.ArgumentParser()
		# parser.add_argument('--TU_catalog', dest='TU_catalog', type=str, required=True,
		# help='path to the (injected) truth catalog')
		# parser.add_argument('--source_catalog', dest='source_catalog', type=str, required=True,
		# help='path to the (extracted) injected catalog')
		# parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True,
		# help='path to the (extracted) original catalog')
		# parser.add_argument('--image', dest='image', type=str, required=True,
		# help='path to the image, used to get the header info')
		# parser.add_argument('--output_dir', dest='output_dir', type=str, required=False,
		# default='./workspace', help='output path')
		# return parser

		def define_options():
		parser = argparse.ArgumentParser()
		parser.add_argument('--TU_catalog', dest='TU_catalog', type=str, required=True,
		help='path to the (injected) truth catalog')
		parser.add_argument('--source_catalog', dest='source_catalog', type=str, required=True,
		parser.add_argument('--TU_catalog_list', dest='TU_catalog_list', type=str, required=True,
		help='path to the list of (injected) truth catalog')
		parser.add_argument('--source_catalog_list', dest='source_catalog_list', type=str, required=True,
		help='path to the (extracted) injected catalog')
		parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True,
		help='path to the (extracted) original catalog')
		parser.add_argument('--image', dest='image', type=str, required=True,
		help='path to the image, used to get the header info')
		# parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True,
		# help='path to the list of (extracted) original catalog')
		# parser.add_argument('--image', dest='image', type=str, required=True,
		# help='path to the image, used to get the header info')
		parser.add_argument('--output_dir', dest='output_dir', type=str, required=False,
		default='./workspace', help='output path')
		return parser
		@@ -72,13 +86,24 @@ def convert_catalog(catname):
		fits_filename = os.path.join(data_dir, base_name + '.fits')
		text_file.write(fits_filename, overwrite=True)

		def validation_hist(val, idx, name="val", nbins=10, fig_name='detected_counts.png', output_dir='./'):
		def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detected_counts.png', output_dir='./', create_figure=True):
		if bins is None:
		counts, bins = np.histogram(val, bins=nbins)
		else:
		counts, bins = np.histogram(val, bins=bins)
		is_empty = np.full(len(val), False)
		for i in range(len(idx)):
		if idx[i].size == 0:
		is_empty[i] = True
		if bins is None:
		counts_detected, _ = np.histogram(val[~is_empty], bins=nbins)
		else:
		counts_detected, _ = np.histogram(val[~is_empty], bins=bins)
		if create_figure:
		create_hist_figure(counts, counts_detected, bins, name, output_dir, fig_name)
		return counts, counts_detected, bins

		def create_hist_figure(counts, counts_detected, bins, name="val", output_dir='./', fig_name='detected_counts.png'):
		plt.figure()
		plt.stairs(counts, bins, color='r', label='TU objects')
		plt.stairs(counts_detected, bins, color='g', label='Detected')
		@@ -87,22 +112,38 @@ def validation_hist(val, idx, name="val", nbins=10, fig_name='detected_counts.pn
		plt.legend(loc='upper right', fancybox=True)
		fig_name = os.path.join(output_dir, fig_name)
		plt.savefig(fig_name)
		return counts, bins

		def hist_fraction(val, idx, name='val', nbins=10, normed=False, output_dir='./'):
		def hist_fraction(val, idx, name='val', nbins=10, bins=None, output_dir='./', fig_name="completeness_fraction.png"):
		if bins is None:
		counts, bins = np.histogram(val, bins=nbins)
		else:
		counts, bins = np.histogram(val, bins=bins)
		is_empty = np.full(len(val), False)
		for i in range(len(idx)):
		if idx[i].size == 0:
		is_empty[i] = True
		counts_detected, _ = np.histogram(val[~is_empty], bins=nbins, density=normed)
		if bins is None:
		counts_detected, _ = np.histogram(val[~is_empty], bins=nbins)
		else:
		counts_detected, _ = np.histogram(val[~is_empty], bins=bins)
		fraction = counts_detected / counts
		fraction[np.where(np.isnan(fraction))[0]] = 0.
		plt.figure()
		plt.stairs(fraction, bins, color='r', label='completeness fraction')
		plt.xlabel(name, size='x-large')
		plt.title("Completeness Fraction")
		fig_name = os.path.join(output_dir, "completeness_fraction_%s.png"%(name))
		fig_name = os.path.join(output_dir, fig_name)
		plt.savefig(fig_name)
		return fraction

		def create_fraction_figure(counts, counts_detected, bins, name='val', output_dir='./', fig_name="completeness_fraction.png"):
		fraction = counts_detected / counts
		fraction[np.where(np.isnan(fraction))[0]] = 0.
		plt.figure()
		plt.stairs(fraction, bins, color='r', label='completeness fraction')
		plt.xlabel(name, size='x-large')
		plt.title("Completeness Fraction")
		fig_name = os.path.join(output_dir, fig_name)
		plt.savefig(fig_name)
		return fraction

		@@ -116,6 +157,25 @@ def calculate_fraction(TU_catalog, source_catalog, output_dir, nbins=10):
		fraction = hist_fraction(val=mag_TU, idx=idx1, name="mag_injected", nbins=10, output_dir=output_dir)
		return counts, bins, fraction

		def calculate_fraction_multi_cats(TU_catalog_list, source_catalog_list, output_dir, nbins=10):
		counts = np.zeros(nbins)
		counts_detected = np.zeros(nbins)
		bins = np.linspace(18, 26, num=(nbins+1))
		for i in range(len(TU_catalog_list)):
		TU_catalog = TU_catalog_list[i]
		source_catalog = source_catalog_list[i]
		convert_catalog(TU_catalog)
		x_TU_temp, y_TU_temp, col_list = read_catalog(TU_catalog + '.fits', ext_num=1, ra_name="xImage", dec_name="yImage", col_list=["mag"])
		mag_TU_temp = col_list[0]
		x_source_temp, y_source_temp, _ = read_catalog(source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE")
		idx1, _, = match_catalogs_img(x1=x_TU_temp, y1=y_TU_temp, x2=x_source_temp, y2=y_source_temp)
		counts_temp, counts_detected_temp, _ = validation_hist(val=mag_TU_temp, idx=idx1, name="mag_injected", bins=bins, output_dir=output_dir, create_figure=False)
		counts += counts_temp
		counts_detected += counts_detected_temp
		create_hist_figure(counts, counts_detected, bins, "mag_injected", output_dir)
		fraction = create_fraction_figure(counts, counts_detected, bins, 'mag_injected', output_dir)
		return counts, counts_detected, bins, fraction

		def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_high=26.0, image=None, output_dir='./'):
		# Get info from original image
		hdu = fits.open(image)
		@@ -166,19 +226,32 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
		undetected_flux /= (float(nx_pix) * float(ny_pix))
		return undetected_flux

		# if __name__ == "__main__":
		# args = define_options().parse_args()
		# counts, bins, fraction = calculate_fraction(
		# TU_catalog=args.TU_catalog,
		# source_catalog=args.source_catalog,
		# output_dir=args.output_dir,
		# nbins=20
		# )
		# undetected_flux = calculate_undetected_flux(
		# orig_cat=args.orig_catalog,
		# mag_bins=bins,
		# fraction=fraction,
		# image=args.image,
		# output_dir=args.output_dir,
		# )
		# print(undetected_flux)

		if __name__ == "__main__":
		args = define_options().parse_args()
		counts, bins, fraction = calculate_fraction(
		TU_catalog=args.TU_catalog,
		source_catalog=args.source_catalog,
		with open(args.TU_catalog_list) as file:
		TU_catalog_list = [line.rstrip() for line in file]
		with open(args.source_catalog_list) as file:
		source_catalog_list = [line.rstrip() for line in file]
		counts, counts_detected, bins, fraction = calculate_fraction_multi_cats(
		TU_catalog_list=TU_catalog_list,
		source_catalog_list=source_catalog_list,
		output_dir=args.output_dir,
		nbins=20
		)
		No newline at end of file
		undetected_flux = calculate_undetected_flux(
		orig_cat=args.orig_catalog,
		mag_bins=bins,
		fraction=fraction,
		image=args.image,
		output_dir=args.output_dir,
		)
		print(undetected_flux)
		No newline at end of file

evaluation/cross_match_catalogs.py

+5 −19

Original line number	Diff line number	Diff line
		import numpy as np
		import astropy.units as u
		import matplotlib.pyplot as plt
		from astropy.coordinates import SkyCoord
		from astropy.io import fits
		from astropy.io import ascii
		from sklearn.neighbors import BallTree

		# TU_catalog = "test_RectGrid_20220628.cat"
		# source_catalog = "extracted_test_RectGrid_20220628.fits"
		TU_catalog = "injected_bkgsub_img.cat"
		source_catalog = "extracted_injected_bkgsub_img.fits"

		@@ -26,7 +23,6 @@ def read_catalog(catname, ext_num=1, ra_name='ra', dec_name='dec', col_list=[]):
		if len(col_list) > 0:
		for col in col_list:
		col_other.append(data[col])
		# print(ra, dec)
		return ra, dec, col_other

		def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[], thresh=[]):
		@@ -37,33 +33,24 @@ def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[
		# print(idx2)
		# print(np.shape(idx1))
		# print(np.shape(idx2))
		# TODO

		def match_catalogs_img(x1, y1, x2, y2, max_dist=0.5, others1=[], others2=[], thresh=[]):
		def match_catalogs_img(x1, y1, x2, y2, max_dist=2, others1=[], others2=[], thresh=[]):
		cat1 = np.array([(x, y) for x,y in zip(x1, y1)])
		cat2 = np.array([(x, y) for x,y in zip(x2, y2)])
		# print(np.shape(cat1))
		# print(np.shape(cat2))
		tree = BallTree(cat2)
		idx1 = tree.query_radius(cat1, r = max_dist)
		tree = BallTree(cat1)
		idx2 = tree.query_radius(cat2, r = max_dist)
		# print(np.shape(idx1))
		tot = 0
		print(idx1)
		for idx in idx1:
		if len(idx) == 0:
		continue
		if len(idx) > 1: print(len(idx))
		tot += 1
		print(tot)
		print("number of matched sources = ", tot)
		return idx1, idx2

		def validation_hist(val1, idx1, name="val1", nbins=10):
		counts, bins = np.histogram(val1)
		plt.stairs(counts, bins)
		plt.xlabel(name, size='x-large')
		plt.savefig("detection_completeness.png")
		# plt.show()

		if __name__=="__main__":
		convert_catalog(TU_catalog)
		ra_TU, dec_TU, _ = read_catalog('test_ascii_to_fits.fits', ext_num=1, ra_name="ra", dec_name="dec")
		@@ -74,4 +61,3 @@ if __name__=="__main__":
		# match_catalogs_sky(ra1=ra_TU, dec1=dec_TU, ra2=ra_source, dec2=dec_source)
		idx1, idx2, = match_catalogs_img(x1=x_TU, y1=y_TU, x2=x_source, y2=y_source)
		# print(ra_TU, dec_TU)
		No newline at end of file
		validation_hist(mag_TU, idx1, name="mag_injected")
		No newline at end of file

injection_pipeline/Catalog/C6_SimCat.py

0 → 100644

+232 −0

Original line number	Diff line number	Diff line
		import os
		import galsim
		import random
		import numpy as np
		import h5py as h5
		import healpy as hp
		import astropy.constants as cons
		import traceback
		from astropy.coordinates import spherical_to_cartesian
		from astropy.table import Table
		from scipy import interpolate
		from datetime import datetime

		from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar
		from ObservationSim.MockObject._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist
		from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position

		# (TEST)
		from astropy.cosmology import FlatLambdaCDM
		from astropy import constants
		from astropy import units as U

		try:
		import importlib.resources as pkg_resources
		except ImportError:
		# Try backported to PY<37 'importlib_resources'
		import importlib_resources as pkg_resources

		# CONSTANTS
		NSIDE = 128

		def get_bundleIndex(healpixID_ring, bundleOrder=4, healpixOrder=7):
		assert NSIDE == 2**healpixOrder
		shift = healpixOrder - bundleOrder
		shift = 2*shift

		nside_bundle = 2**bundleOrder
		nside_healpix= 2**healpixOrder

		healpixID_nest= hp.ring2nest(nside_healpix, healpixID_ring)
		bundleID_nest = (healpixID_nest >> shift)
		bundleID_ring = hp.nest2ring(nside_bundle, bundleID_nest)

		return bundleID_ring

		class SimCat(CatalogBase):
		def __init__(self, config, chip, nobjects=None):
		super().__init__()
		self.cat_dir = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["cat_dir"])
		self.seed_Av = config["catalog_options"]["seed_Av"]
		self.cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111)

		with pkg_resources.path('Catalog.data', 'SLOAN_SDSS.g.fits') as filter_path:
		self.normF_star = Table.read(str(filter_path))

		self.config = config
		self.chip = chip

		galaxy_dir = config["catalog_options"]["input_path"]["galaxy_cat"]
		self.galaxy_path = os.path.join(self.cat_dir, galaxy_dir)
		self.galaxy_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
		self._load_SED_lib_gals()

		if "rotateEll" in config["catalog_options"]:
		self.rotation = float(int(config["catalog_options"]["rotateEll"]/45.))
		else:
		self.rotation = 0.

		self._get_healpix_list()
		self._load(nobjects=nobjects)

		def _get_healpix_list(self):
		self.sky_coverage = self.chip.getSkyCoverageEnlarged(self.chip.img.wcs, margin=0.2)
		ra_min, ra_max, dec_min, dec_max = self.sky_coverage.xmin, self.sky_coverage.xmax, self.sky_coverage.ymin, self.sky_coverage.ymax
		ra = np.deg2rad(np.array([ra_min, ra_max, ra_max, ra_min]))
		dec = np.deg2rad(np.array([dec_max, dec_max, dec_min, dec_min]))
		self.pix_list = hp.query_polygon(
		NSIDE,
		hp.ang2vec(np.radians(90.) - dec, ra),
		inclusive=True
		)
		if self.logger is not None:
		msg = str(("HEALPix List: ", self.pix_list))
		self.logger.info(msg)
		else:
		print("HEALPix List: ", self.pix_list)

		def load_norm_filt(self, obj):
		if obj.type == "star":
		return self.normF_star
		elif obj.type == "galaxy" or obj.type == "quasar":
		return None
		else:
		return None

		def _load_SED_lib_gals(self):
		pcs = h5.File(os.path.join(self.galaxy_SED_path, "pcs.h5"), "r")
		lamb = h5.File(os.path.join(self.galaxy_SED_path, "lamb.h5"), "r")
		self.lamb_gal = lamb['lamb'][()]
		self.pcs = pcs['pcs'][()]

		def _load_gals(self, gals, pix_id=None, cat_id=0, nobjects=1):
		# Load how mnay objects?
		max_ngals = len(gals['ra'])
		remain = nobjects
		for igals in range(max_ngals):
		if remain == 0:
		break
		param = self.initialize_param()
		param['ra'] = ra_arr[igals]
		param['dec'] = dec_arr[igals]
		param['ra_orig'] = gals['ra'][igals]
		param['dec_orig'] = gals['dec'][igals]
		# [TODO]
		param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals]
		# if self.filt.is_too_dim(mag=param['mag_use_normal'], margin=self.config["obs_setting"]["mag_lim_margin"]):
		# continue

		param['z'] = gals['redshift'][igals]
		param['model_tag'] = 'None'
		param['g1'] = gals['shear'][igals][0]
		param['g2'] = gals['shear'][igals][1]
		param['kappa'] = gals['kappa'][igals]
		param['e1'] = gals['ellipticity_true'][igals][0]
		param['e2'] = gals['ellipticity_true'][igals][1]

		# For shape calculation

		param['ell_total'] = np.sqrt(param['e1']2 + param['e2']2)
		if param['ell_total'] > 0.9:
		continue

		remain -= 1
		param['e1_disk'] = param['e1']
		param['e2_disk'] = param['e2']
		param['e1_bulge'] = param['e1']
		param['e2_bulge'] = param['e2']


		param['delta_ra'] = 0
		param['delta_dec'] = 0

		# Masses
		param['bulgemass'] = gals['bulgemass'][igals]
		param['diskmass'] = gals['diskmass'][igals]

		param['size'] = gals['size'][igals]
		if param['size'] > self.max_size:
		self.max_size = param['size']

		# Sersic index
		param['disk_sersic_idx'] = 1.
		param['bulge_sersic_idx'] = 4.

		# Sizes
		param['bfrac'] = param['bulgemass']/(param['bulgemass'] + param['diskmass'])
		if param['bfrac'] >= 0.6:
		param['hlr_bulge'] = param['size']
		param['hlr_disk'] = param['size'] * (1. - param['bfrac'])
		else:
		param['hlr_disk'] = param['size']
		param['hlr_bulge'] = param['size'] * param['bfrac']

		# SED coefficients
		param['coeff'] = gals['coeff'][igals]
		param['detA'] = gals['detA'][igals]

		# Others
		param['galType'] = gals['type'][igals]
		param['veldisp'] = gals['veldisp'][igals]

		# TEST no redening and no extinction
		param['av'] = 0.0
		param['redden'] = 0

		param['star'] = 0 # Galaxy

		# TEMP
		self.ids += 1
		# param['id'] = self.ids
		param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)

		if param['star'] == 0:
		obj = Galaxy(param, self.rotation, logger=self.logger)

		self.objs.append(obj)
		return remain

		def _load(self, nobjects=1):
		from itertools import cycle
		self.objs = []
		self.ids = 0
		to_be_read_in = nobjects
		pool = cycle(self.pix_list)
		for pix in pool:
		try:
		if to_be_read_in == 0:
		break
		bundleID = get_bundleIndex(pix)
		file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
		gals_cat = h5.File(file_path, 'r')['galaxies']
		gals = gals_cat[str(pix)]
		to_be_read_in = self._load_gals(gals, pix_id=pix, cat_id=bundleID, n_objects=to_be_read_in)
		del gals
		except Exception as e:
		traceback.print_exc()
		print(e)

		def load_sed(self, obj, **kwargs):
		factor = 10*(-.4 self.cosmo.distmod(obj.z).value)
		flux = np.matmul(self.pcs, obj.coeff) * factor
		# if np.any(flux < 0):
		# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
		flux[flux < 0] = 0.
		sedcat = np.vstack((self.lamb_gal, flux)).T
		sed_data = getObservedSED(
		sedCat=sedcat,
		redshift=obj.z,
		av=obj.param["av"],
		redden=obj.param["redden"]
		)
		wave, flux = sed_data[0], sed_data[1]
		speci = interpolate.interp1d(wave, flux)
		lamb = np.arange(2000, 11001+0.5, 0.5)
		y = speci(lamb)
		# erg/s/cm2/A --> photon/s/m2/A
		all_sed = y * lamb / (cons.h.value * cons.c.value) * 1e-13
		sed = Table(np.array([lamb, all_sed]).T, names=('WAVELENGTH', 'FLUX'))

		del wave
		del flux
		return sed
		No newline at end of file