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Commit 38e2c452 authored by Fang Yuedong's avatar Fang Yuedong
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Merge branch 'master' into 'current_stable_for_tests' 

for tests before release

See merge request !23
parents e0f2b9f7 1c35c0e2
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with 257 additions and 206 deletions
+257 -206
ObservationSim/MockObject/Stamp.py → observation_sim/mock_objects/Stamp.py
+ 53
- 46
View file @ 38e2c452
import os, sys
import os
import sys
import numpy as np
import galsim
import astropy.constants as cons
from astropy.table import Table
from scipy import interpolate
from ObservationSim.MockObject.MockObject import MockObject
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
from ObservationSim.MockObject._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG,convolveGaussXorders
from observation_sim.mock_objects.MockObject import MockObject
from observation_sim.mock_objects.SpecDisperser import SpecDisperser
from observation_sim.mock_objects._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, convolveGaussXorders
class Stamp(MockObject):
def __init__(self, param, logger=None):
......@@ -22,17 +24,17 @@ class Stamp(MockObject):
if nphotons_tot == None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
try:
full = integrate_sed_bandpass(sed=self.sed, bandpass=filt.bandpass_full)
full = integrate_sed_bandpass(
sed=self.sed, bandpass=filt.bandpass_full)
except Exception as e:
print(e)
self.logger.error(e)
return False
#nphotons_sum = 0
#photons_list = []
#xmax, ymax = 0, 0
# nphotons_sum = 0
# photons_list = []
# xmax, ymax = 0, 0
if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4
......@@ -72,17 +74,18 @@ class Stamp(MockObject):
nphotons = ratio * nphotons_tot
else:
continue
#nphotons_sum += nphotons
# nphotons_sum += nphotons
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
psf, pos_shear = psf_model.get_PSF(
chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
_gal = self.param['image']
galImg= galsim.ImageF(_gal, scale=self.param['pixScale'])
gal_temp= galsim.InterpolatedImage(galImg)
gal_temp= gal_temp.shear(gal_shear)
gal_temp= gal_temp.withFlux(nphotons)
galImg = galsim.ImageF(_gal, scale=self.param['pixScale'])
gal_temp = galsim.InterpolatedImage(galImg)
gal_temp = gal_temp.shear(gal_shear)
gal_temp = gal_temp.withFlux(nphotons)
gal_temp= galsim.Convolve(psf, gal_temp)
gal_temp = galsim.Convolve(psf, gal_temp)
if i == 0:
gal = gal_temp
......@@ -95,7 +98,8 @@ class Stamp(MockObject):
return 2, pos_shear
stamp.setCenter(x_nominal, y_nominal)
bounds = stamp.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
bounds = stamp.bounds & galsim.BoundsI(
0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() > 0:
chip.img.setOrigin(0, 0)
......@@ -105,21 +109,21 @@ class Stamp(MockObject):
del stamp
if is_updated == 0:
print("fits obj %s missed"%(self.id))
print("fits obj %s missed" % (self.id))
if self.logger:
self.logger.info("fits obj %s missed"%(self.id))
self.logger.info("fits obj %s missed" % (self.id))
return 0, pos_shear
return 1, pos_shear
def drawObj_slitless(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., normFilter=None, grating_split_pos=3685, fd_shear=None):
if normFilter is not None:
norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001
sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed,
norm_thr=normFilter,
sWave=np.floor(normFilter[norm_thr_rang_ids][0][0]),
sWave=np.floor(
normFilter[norm_thr_rang_ids][0][0]),
eWave=np.ceil(normFilter[norm_thr_rang_ids][-1][0]))
if sedNormFactor == 0:
return 2, None
......@@ -140,7 +144,6 @@ class Stamp(MockObject):
chip_wcs_local = self.chip_wcs.local(self.real_pos)
if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4
else:
......@@ -150,7 +153,8 @@ class Stamp(MockObject):
flat_cube = chip.flat_cube
xOrderSigPlus = {'A':1.3909419820029296,'B':1.4760376591236062,'C':4.035447379743442,'D':5.5684364343742825,'E':16.260021029735388}
xOrderSigPlus = {'A': 1.3909419820029296, 'B': 1.4760376591236062,
'C': 4.035447379743442, 'D': 5.5684364343742825, 'E': 16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list), 2])
......@@ -175,7 +179,7 @@ class Stamp(MockObject):
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
_gal = self.param['image']
galImg= galsim.ImageF(_gal, scale=self.param['pixScale'])
galImg = galsim.ImageF(_gal, scale=self.param['pixScale'])
gal = galsim.InterpolatedImage(galImg)
# (TEST) Random knots
......@@ -196,23 +200,25 @@ class Stamp(MockObject):
# # if fd_shear is not None:
# # gal = gal.shear(fd_shear)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset,method = 'real_space')
starImg = gal.drawImage(
wcs=chip_wcs_local, offset=offset, method='real_space')
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0, 0)
gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] - 1, origin_star[1] + starImg.array.shape[1] - 1]
gal_end = [origin_star[0] + starImg.array.shape[0] -
1, origin_star[1] + starImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
## part img disperse
# part img disperse
subImg_p1 = starImg.array[:, 0:subSlitPos]
star_p1 = galsim.Image(subImg_p1)
star_p1.setOrigin(0, 0)
origin_p1 = origin_star
xcenter_p1 = min(x_nominal,grating_split_pos_chip-1) - 0
xcenter_p1 = min(x_nominal, grating_split_pos_chip-1) - 0
ycenter_p1 = y_nominal-0
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
......@@ -227,9 +233,10 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local=[xcenter_p1, ycenter_p1],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
subImg_p2 = starImg.array[:, subSlitPos+1:starImg.array.shape[1]]
subImg_p2 = starImg.array[:,
subSlitPos+1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0)
origin_p2 = [origin_star[0], grating_split_pos_chip]
......@@ -248,11 +255,11 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp_p1
del sdp_p2
elif grating_split_pos_chip<=gal_origin[1]:
elif grating_split_pos_chip <= gal_origin[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
......@@ -264,9 +271,9 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp
elif grating_split_pos_chip>=gal_end[1]:
elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
......@@ -278,7 +285,7 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp
# print(self.y_nominal, starImg.center.y, starImg.ymin)
......
ObservationSim/MockObject/Star.py → observation_sim/mock_objects/Star.py
+ 2
- 2
View file @ 38e2c452
......@@ -6,8 +6,8 @@ import astropy.constants as cons
from astropy.table import Table
from scipy import interpolate
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, tag_sed
from ObservationSim.MockObject.MockObject import MockObject
from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, tag_sed
from observation_sim.mock_objects.MockObject import MockObject
class Star(MockObject):
......
ObservationSim/MockObject/__init__.py → observation_sim/mock_objects/__init__.py
+ 0
- 2
View file @ 38e2c452
......@@ -5,5 +5,3 @@ from .Quasar import Quasar
from .Star import Star
from .Stamp import Stamp
from .FlatLED import FlatLED
# from .SkybackgroundMap import *
# from .CosmicRay import CosmicRay
ObservationSim/MockObject/_util.py → observation_sim/mock_objects/_util.py
+ 187
- 143
View file @ 38e2c452
......@@ -9,14 +9,17 @@ VC_A = 2.99792458e+18 # speed of light: A/s
VC_M = 2.99792458e+8 # speed of light: m/s
H_PLANK = 6.626196e-27 # Plank constant: erg s
def comoving_dist(z, om_m=0.3111, om_L=0.6889, h=0.6766):
# Return comving distance in pc
H0 = h*100. # km / (s Mpc)
def dist_int(z):
return 1./np.sqrt(om_m*(1.+z)**3 + om_L)
res, err = integrate.quad(dist_int, 0., z)
return [res * (VC_M/1e3/H0) * 1e6, err * (VC_M/1e3/H0) * 1e6]
def magToFlux(mag):
"""
flux of a given AB magnitude
......@@ -30,6 +33,7 @@ def magToFlux(mag):
flux = 10**(-0.4*(mag+48.6))
return flux
def extAv(nav, seed=1212123):
"""
Generate random intrinsic extinction Av
......@@ -39,7 +43,7 @@ def extAv(nav, seed=1212123):
tau = 0.4
peak, a = 0.1, 0.5
b = a*(tau-peak)
pav = lambda av: (a*av+b)*np.exp(-av/tau)
def pav(av): return (a*av+b)*np.exp(-av/tau)
avmin, avmax = 0., 3.
avs = np.linspace(avmin, avmax, int((avmax-avmin)/0.001)+1)
norm = np.trapz(pav(avs), avs)
......@@ -66,18 +70,20 @@ def seds(sedlistn, seddir="./", unit="A"):
reds = {}
sedlist = seddir + sedlistn
sedn = open(sedlist).read().splitlines()
sedtype = range(1,len(sedn)+1)
sedtype = range(1, len(sedn)+1)
for i in range(len(sedn)):
xxx = sedn[i].split()
isedn = seddir+xxx[0]
itype = sedtype[i]
ised = np.loadtxt(isedn)
if unit=="nm": ised[:,0] *= 10.0
if unit == "nm":
ised[:, 0] *= 10.0
seds[itype] = ised
reds[itype] = int(xxx[1])
return seds, reds
def sed_assign(phz, btt, rng):
"""
assign SED template to a galaxy.
......@@ -106,6 +112,8 @@ def sed_assign(phz, btt, rng):
return sedtype
###########################################
def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
"""
calculate the theoretical SED for given filter set and template
......@@ -130,7 +138,7 @@ def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
SED in observed frame
"""
z = redshift + 1.0
sw, sf = sed[:,0], sed[:,1]
sw, sf = sed[:, 0], sed[:, 1]
# reddening
sf = reddening(sw, sf, av=av, model=redden)
sw, sf = sw*z, sf*(z**3)
......@@ -144,30 +152,33 @@ def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
sw, sf = sw[::-1], sf[::-1]
sfun = interp1d(sw, sf, kind='linear')
fwave, fresp = filt[:,0], filt[:,1]
fwave, fresp = filt[:, 0], filt[:, 1]
fwave = VC_A/fwave
fwave, fresp = fwave[::-1], fresp[::-1]
tflux = sfun(fwave)
zpflux = 3.631*1.0e-20
tflux = np.trapz(tflux*fresp/fwave,fwave)/np.trapz(zpflux*fresp/fwave,fwave)
#tflux = np.trapz(tflux*fresp,fwave)/np.trapz(zpflux*fresp,fwave)
tflux = np.trapz(tflux*fresp/fwave, fwave) / \
np.trapz(zpflux*fresp/fwave, fwave)
# tflux = np.trapz(tflux*fresp,fwave)/np.trapz(zpflux*fresp,fwave)
return tflux, sedxx
###########################################
def lyman_forest(wavelen, flux, z):
"""
Compute the Lyman forest mean absorption of an input spectrum,
according to D_A and D_B evolution from Madau (1995).
The waveeln and flux are in observed frame
"""
if z<=0:
if z <= 0:
flux0 = flux
else:
nw = 200
istep = np.linspace(0,nw-1,nw)
istep = np.linspace(0, nw-1, nw)
w1a, w2a = 1050.0*(1.0+z), 1170.0*(1.0+z)
w1b, w2b = 920.0*(1.0+z), 1015.0*(1.0+z)
wstepa = (w2a-w1a)/float(nw)
......@@ -177,20 +188,25 @@ def lyman_forest(wavelen, flux, z):
ptaua = np.exp(-3.6e-03*(wtempa/1216.0)**3.46)
wtempb = w1b + istep*wstepb
ptaub = np.exp(-1.7e-3*(wtempb/1026.0)**3.46\
-1.2e-3*(wtempb/972.50)**3.46\
-9.3e-4*(wtempb/950.00)**3.46)
ptaub = np.exp(-1.7e-3*(wtempb/1026.0)**3.46
- 1.2e-3*(wtempb/972.50)**3.46
- 9.3e-4*(wtempb/950.00)**3.46)
da = (1.0/(120.0*(1.0+z)))*np.trapz(ptaua, wtempa)
db = (1.0/(95.0*(1.0+z)))*np.trapz(ptaub, wtempb)
if da>1.0: da=1.0
if db>1.0: db=1.0
if da<0.0: da=0.0
if db<0.0: db=0.0
if da > 1.0:
da = 1.0
if db > 1.0:
db = 1.0
if da < 0.0:
da = 0.0
if db < 0.0:
db = 0.0
flux0 = flux.copy()
id0 = wavelen<=1026.0*(1.0+z)
id1 = np.logical_and(wavelen<1216.0*(1.0+z),wavelen>=1026.0*(1.0+z))
id0 = wavelen <= 1026.0*(1.0+z)
id1 = np.logical_and(wavelen < 1216.0*(1.0+z),
wavelen >= 1026.0*(1.0+z))
flux0[id0] = db*flux[id0]
flux0[id1] = da*flux[id1]
......@@ -220,127 +236,130 @@ def reddening(sw, sf, av=0.0, model=0):
Return:
reddening-corrected flux or observed flux
"""
if model==0 or av==0.0:
flux=sf
elif model==1: # Allen (1976) for the Milky Way
lambda0 = np.array([1000, 1110, 1250, 1430, 1670, \
2000, 2220, 2500, 2850, 3330, \
3650, 4000, 4400, 5000, 5530, \
if model == 0 or av == 0.0:
flux = sf
elif model == 1: # Allen (1976) for the Milky Way
lambda0 = np.array([1000, 1110, 1250, 1430, 1670,
2000, 2220, 2500, 2850, 3330,
3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([4.20, 3.70, 3.30, 3.00, 2.70, \
2.80, 2.90, 2.30, 1.97, 1.69, \
1.58, 1.45, 1.32, 1.13, 1.00, \
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float)
kR = np.array([4.20, 3.70, 3.30, 3.00, 2.70,
2.80, 2.90, 2.30, 1.97, 1.69,
1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw)
A_lambda[A_lambda<0.0] = 0.0
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
elif model==2: # Seaton (1979) fit by Fitzpatrick (1986) for the Milky Way
Rv=3.1
elif model == 2: # Seaton (1979) fit by Fitzpatrick (1986) for the Milky Way
Rv = 3.1
al0, ga, c1, c2, c3, c4 = 4.595, 1.051, -0.38, 0.74, 3.96, 0.26
ff11 = __red(1100.0,al0,ga,c1,c2,c3,c4)
ff12 = __red(1200.0,al0,ga,c1,c2,c3,c4)
slope=(ff12-ff11)/100.0
lambda0 = np.array([3650, 4000, 4400, 5000, 5530, \
ff11 = __red(1100.0, al0, ga, c1, c2, c3, c4)
ff12 = __red(1200.0, al0, ga, c1, c2, c3, c4)
slope = (ff12-ff11)/100.0
lambda0 = np.array([3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([1.58, 1.45, 1.32, 1.13, 1.00, \
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float)
kR = np.array([1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
fun = interp1d(lambda0, kR, kind='linear')
sw0 = sw[sw<1200.0]
sw0 = sw[sw < 1200.0]
A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0
sw1 = sw[np.logical_and(sw>=1200.0, sw<=3650.0)]
ff = __red(sw1,al0,ga,c1,c2,c3,c4)
sw1 = sw[np.logical_and(sw >= 1200.0, sw <= 3650.0)]
ff = __red(sw1, al0, ga, c1, c2, c3, c4)
A_lambda1 = ff/Rv+1.0
sw2 = sw[np.logical_and(sw>3650.0, sw<=100000.0)]
sw2 = sw[np.logical_and(sw > 3650.0, sw <= 100000.0)]
A_lambda2 = fun(sw2)
A_lambda3 = sw[sw>100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0,A_lambda1,A_lambda2,A_lambda3])
A_lambda[A_lambda<0.0] = 0.0
A_lambda3 = sw[sw > 100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0, A_lambda1, A_lambda2, A_lambda3])
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
elif model==3: # Fitzpatrick (1986) for the Large Magellanic Cloud (LMC)
Rv=3.1
elif model == 3: # Fitzpatrick (1986) for the Large Magellanic Cloud (LMC)
Rv = 3.1
al0, ga, c1, c2, c3, c4 = 4.608, 0.994, -0.69, 0.89, 2.55, 0.50
ff11 = __red(1100.0,al0,ga,c1,c2,c3,c4)
ff12 = __red(1200.0,al0,ga,c1,c2,c3,c4)
slope=(ff12-ff11)/100.0
lambda0 = np.array([3330, 3650, 4000, 4400, 5000, 5530, \
ff11 = __red(1100.0, al0, ga, c1, c2, c3, c4)
ff12 = __red(1200.0, al0, ga, c1, c2, c3, c4)
slope = (ff12-ff11)/100.0
lambda0 = np.array([3330, 3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([1.682, 1.58, 1.45, 1.32, 1.13, 1.00, \
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float)
kR = np.array([1.682, 1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
fun = interp1d(lambda0, kR, kind='linear')
sw0 = sw[sw<1200.0]
sw0 = sw[sw < 1200.0]
A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0
sw1 = sw[np.logical_and(sw>=1200.0, sw<=3330.0)]
ff = __red(sw1,al0,ga,c1,c2,c3,c4)
sw1 = sw[np.logical_and(sw >= 1200.0, sw <= 3330.0)]
ff = __red(sw1, al0, ga, c1, c2, c3, c4)
A_lambda1 = ff/Rv+1.0
sw2 = sw[np.logical_and(sw>3330.0, sw<=100000.0)]
sw2 = sw[np.logical_and(sw > 3330.0, sw <= 100000.0)]
A_lambda2 = fun(sw2)
A_lambda3 = sw[sw>100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0,A_lambda1,A_lambda2,A_lambda3])
A_lambda[A_lambda<0.0] = 0.0
A_lambda3 = sw[sw > 100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0, A_lambda1, A_lambda2, A_lambda3])
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
elif model==4: # Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC)
# Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC)
elif model == 4:
Rv = 2.72
lambda0 = np.array([1275, 1330, 1385, 1435, 1490, 1545, \
1595, 1647, 1700, 1755, 1810, 1860, \
1910, 2000, 2115, 2220, 2335, 2445, \
2550, 2665, 2778, 2890, 2995, 3105, \
lambda0 = np.array([1275, 1330, 1385, 1435, 1490, 1545,
1595, 1647, 1700, 1755, 1810, 1860,
1910, 2000, 2115, 2220, 2335, 2445,
2550, 2665, 2778, 2890, 2995, 3105,
3704, 4255, 5291, 12500, 16500, 22000], dtype=float)
kR = np.array([13.54, 12.52, 11.51, 10.80, 9.84, 9.28, \
9.06, 8.49, 8.01, 7.71, 7.17, 6.90, 6.76, \
6.38, 5.85, 5.30, 4.53, 4.24, 3.91, 3.49, \
3.15, 3.00, 2.65, 2.29, 1.81, 1.00, 0.00, \
-2.02, -2.36, -2.47],dtype=float)
kR = np.array([13.54, 12.52, 11.51, 10.80, 9.84, 9.28,
9.06, 8.49, 8.01, 7.71, 7.17, 6.90, 6.76,
6.38, 5.85, 5.30, 4.53, 4.24, 3.91, 3.49,
3.15, 3.00, 2.65, 2.29, 1.81, 1.00, 0.00,
-2.02, -2.36, -2.47], dtype=float)
kR = kR/Rv+1.0
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw)
A_lambda[A_lambda<0.0] = 0.0
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
elif model==5: # Calzetti et al (2000) for starburst galaxies
elif model == 5: # Calzetti et al (2000) for starburst galaxies
Rv = 4.05
sw = sw*1.0e-04 #wavelength in microns
sw = sw*1.0e-04 # wavelength in microns
fun1 = lambda x: 2.659*(-2.156+1.509/x-0.198/x**2+0.011/x**3)+Rv
fun2 = lambda x: 2.659*(-1.857+1.040/x)+Rv
def fun1(x): return 2.659*(-2.156+1.509/x-0.198/x**2+0.011/x**3)+Rv
def fun2(x): return 2.659*(-1.857+1.040/x)+Rv
ff11, ff12 = fun1(0.11), fun1(0.12)
slope1=(ff12-ff11)/0.01
slope1 = (ff12-ff11)/0.01
ff99, ff100 = fun2(2.19), fun2(2.2)
slope2=(ff100-ff99)/0.01
slope2 = (ff100-ff99)/0.01
sw0 = sw[sw<0.12]
sw1 = sw[np.logical_and(sw>=0.12, sw<=0.63)]
sw2 = sw[np.logical_and(sw>0.63, sw<=2.2)]
sw3 = sw[sw>2.2]
sw0 = sw[sw < 0.12]
sw1 = sw[np.logical_and(sw >= 0.12, sw <= 0.63)]
sw2 = sw[np.logical_and(sw > 0.63, sw <= 2.2)]
sw3 = sw[sw > 2.2]
k_lambda0 = ff11+(sw0-0.11)*slope1
k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2)
k_lambda3 = ff99+(sw3-2.19)*slope2
A_lambda = av*np.hstack([k_lambda0,k_lambda1,k_lambda2,k_lambda3])/Rv
A_lambda[A_lambda<0.0] = 0.0
A_lambda = av*np.hstack([k_lambda0, k_lambda1,
k_lambda2, k_lambda3])/Rv
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
elif model==6: # Reddy et al (2015) for satr forming galaxies
elif model == 6: # Reddy et al (2015) for satr forming galaxies
Rv = 2.505
sw = sw*1.0e-04
fun1 = lambda x: -5.726+4.004/x-0.525/x**2+0.029/x**3+Rv
fun2 = lambda x: -2.672-0.010/x+1.532/x**2-0.412/x**3+Rv
def fun1(x): return -5.726+4.004/x-0.525/x**2+0.029/x**3+Rv
def fun2(x): return -2.672-0.010/x+1.532/x**2-0.412/x**3+Rv
ff11, ff12 = fun1(0.14), fun1(0.15)
slope1=(ff12-ff11)/0.01
slope1 = (ff12-ff11)/0.01
ff99, ff100 = fun2(2.84), fun2(2.85)
slope2=(ff100-ff99)/0.01
slope2 = (ff100-ff99)/0.01
sw0 = sw[sw<0.15]
sw1 = sw[np.logical_and(sw>=0.15, sw<0.60)]
sw2 = sw[np.logical_and(sw>=0.60, sw<2.85)]
sw3 = sw[sw>=2.85]
sw0 = sw[sw < 0.15]
sw1 = sw[np.logical_and(sw >= 0.15, sw < 0.60)]
sw2 = sw[np.logical_and(sw >= 0.60, sw < 2.85)]
sw3 = sw[sw >= 2.85]
k_lambda0 = ff11+(sw0-0.14)*slope1
k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2)
k_lambda3 = ff99+(sw3-2.84)*slope2
A_lambda = av*np.hstack([k_lambda0,k_lambda1,k_lambda2,k_lambda3])/Rv
A_lambda[A_lambda<0.0] = 0.0
A_lambda = av*np.hstack([k_lambda0, k_lambda1,
k_lambda2, k_lambda3])/Rv
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
else:
......@@ -349,25 +368,30 @@ def reddening(sw, sf, av=0.0, model=0):
return flux
###########################################
def __red(alan,al0,ga,c1,c2,c3,c4):
fun1 = lambda x: c3/(((x-(al0**2/x))**2)+ga*ga)
fun2 = lambda x,cc: cc*(0.539*((x-5.9)**2)+0.0564*((x-5.9)**3))
fun = lambda x,cc: c1+c2*x+fun1(x)+fun2(x,cc)
ala = alan*1.0e-04 #wavelength in microns
def __red(alan, al0, ga, c1, c2, c3, c4):
def fun1(x): return c3/(((x-(al0**2/x))**2)+ga*ga)
def fun2(x, cc): return cc*(0.539*((x-5.9)**2)+0.0564*((x-5.9)**3))
def fun(x, cc): return c1+c2*x+fun1(x)+fun2(x, cc)
ala = alan*1.0e-04 # wavelength in microns
p = 1.0/ala
if np.size(p)>1:
p1, p2 = p[p>=5.9], p[p<5.9]
ff = np.append(fun(p1,c4), fun(p2,0.0))
elif np.size(p)==1:
if p<5.9: c4 = 0.0
if np.size(p) > 1:
p1, p2 = p[p >= 5.9], p[p < 5.9]
ff = np.append(fun(p1, c4), fun(p2, 0.0))
elif np.size(p) == 1:
if p < 5.9:
c4 = 0.0
ff = fun(p, c4)
else:
return
return ff
###########################################
def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0):
# load the filters
......@@ -378,19 +402,23 @@ def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0):
if filter_list[k].filter_type == 'i':
nid = k
bandpass = filter_list[k].bandpass_full
ktrans = np.transpose(np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)]))
aflux[k], isedObs = tflux(ktrans, sedCat, redshift=redshift, av=av, redden=redden)
ktrans = np.transpose(
np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)]))
aflux[k], isedObs = tflux(
ktrans, sedCat, redshift=redshift, av=av, redden=redden)
# normalize to i-band
aflux = aflux / aflux[nid]
# magnitudes in all filters
amag = -2.5*np.log10(aflux) + mag_i
spec = galsim.LookupTable(x=np.array(isedObs[0]), f=np.array(isedObs[1]), interpolant='nearest')
spec = galsim.LookupTable(x=np.array(isedObs[0]), f=np.array(
isedObs[1]), interpolant='nearest')
isedObs = galsim.SED(spec, wave_type='A', flux_type='1', fast=False)
return amag, isedObs
def eObs(e1,e2,g1,g2):
def eObs(e1, e2, g1, g2):
"""
Calculate the sheared (observed) ellipticity using the
intrinsic ellipticity and cosmic shear components.
......@@ -424,7 +452,7 @@ def eObs(e1,e2,g1,g2):
e = complex(e1[i], e2[i])
g = complex(g1[i], g2[i])
e, gg = abs(e), abs(g)
if gg<=1.0:
if gg <= 1.0:
tt = e + g
bb = 1.0 + e*g.conjugate()
eobs = tt/bb
......@@ -435,24 +463,31 @@ def eObs(e1,e2,g1,g2):
# derive the orientation
dd = 0.5*np.arctan(abs(eobs.imag/eobs.real))*180.0/np.pi
if eobs.imag>0 and eobs.real>0: dd = dd
if eobs.imag>0 and eobs.real<0: dd = 90.0 - dd
if eobs.imag<0 and eobs.real>0: dd = 0.0 - dd
if eobs.imag<0 and eobs.real<0: dd = dd - 90.0
if eobs.imag > 0 and eobs.real > 0:
dd = dd
if eobs.imag > 0 and eobs.real < 0:
dd = 90.0 - dd
if eobs.imag < 0 and eobs.real > 0:
dd = 0.0 - dd
if eobs.imag < 0 and eobs.real < 0:
dd = dd - 90.0
e1obs += [eobs.real]
e2obs += [eobs.imag]
eeobs += [abs(eobs)]
theta += [dd]
e1obs,e2obs,eeobs,theta = np.array(e1obs),np.array(e2obs),np.array(eeobs),np.array(theta)
if nobj == 1: e1obs,e2obs,eeobs,theta = e1obs[0],e2obs[0],eeobs[0],theta[0]
e1obs, e2obs, eeobs, theta = np.array(e1obs), np.array(
e2obs), np.array(eeobs), np.array(theta)
if nobj == 1:
e1obs, e2obs, eeobs, theta = e1obs[0], e2obs[0], eeobs[0], theta[0]
return e1obs, e2obs, eeobs, theta
def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0):
z = redshift + 1.0
sw, sf = sedCat[:,0], sedCat[:,1]
sw, sf = sedCat[:, 0], sedCat[:, 1]
# reddening
sf = reddening(sw, sf, av=av, model=redden)
# sw, sf = sw*z, sf*(z**3)
......@@ -464,15 +499,19 @@ def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0):
isedObs = (sw.copy(), sf.copy())
return isedObs
def integrate_sed_bandpass(sed, bandpass):
wave = np.linspace(bandpass.blue_limit, bandpass.red_limit, 1000) # in nm
flux_normalized = sed(wave)*bandpass(wave)
# print('in integrate_sed_bandpass', bandpass.blue_limit, bandpass.red_limit)
int_flux = np.trapz(y=flux_normalized, x=wave) * 10. # convert to photons s-1 m-2 A-1
int_flux = np.trapz(y=flux_normalized, x=wave) * \
10. # convert to photons s-1 m-2 A-1
return int_flux
def getABMAG(interFlux, bandpass):
throughtput = Table(np.array(np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)])).T, names=(['WAVELENGTH', 'SENSITIVITY']))
throughtput = Table(np.array(np.array([bandpass.wave_list*10.0, bandpass.func(
bandpass.wave_list)])).T, names=(['WAVELENGTH', 'SENSITIVITY']))
sWave = bandpass.blue_limit*10.0
eWave = bandpass.red_limit*10.0
# print('in getABMAG', sWave, eWave)
......@@ -485,7 +524,8 @@ def getABMAG(interFlux, bandpass):
ABMAG_spec = -2.5 * np.log10(flux_ave/ABMAG_zero)
return ABMAG_spec
def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250):
def getABMagAverageVal(ABmag=20., norm_thr=None, sWave=6840, eWave=8250):
"""
norm_thr: astropy.table, 2 colum, 'WAVELENGTH', 'SENSITIVITY'
......@@ -496,15 +536,16 @@ def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250):
inverseLambda = norm_thr['SENSITIVITY']/norm_thr['WAVELENGTH']
norm_thr_i = interpolate.interp1d(norm_thr['WAVELENGTH'], inverseLambda)
x = np.linspace(sWave,eWave, int(eWave)-int(sWave)+1)
x = np.linspace(sWave, eWave, int(eWave)-int(sWave)+1)
y = norm_thr_i(x)
AverageLamdaInverse = np.trapz(y,x)/(eWave-sWave)
AverageLamdaInverse = np.trapz(y, x)/(eWave-sWave)
norm = 54798696332.52474 * pow(10.0, -0.4 * ABmag) * AverageLamdaInverse
# print('AverageLamdaInverse = ', AverageLamdaInverse)
# print('norm = ', norm)
return norm
def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave=6840, eWave=8250):
"""
Use AB magnitude system (zero point, fv = 3631 janskys) in the normal band(norm_thr) normalize the spectrum by inpute ABMag
......@@ -520,17 +561,19 @@ def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave
the normalization factor flux of AB system(fix a band and magnitude ) /the flux of inpute spectrum(fix a band)
"""
spectrumi = interpolate.interp1d(spectrum['WAVELENGTH'], spectrum['FLUX'])
norm_thri = interpolate.interp1d(norm_thr['WAVELENGTH'], norm_thr['SENSITIVITY'])
norm_thri = interpolate.interp1d(
norm_thr['WAVELENGTH'], norm_thr['SENSITIVITY'])
x = np.linspace(sWave,eWave, int(eWave)-int(sWave)+1)
x = np.linspace(sWave, eWave, int(eWave)-int(sWave)+1)
y_spec = spectrumi(x)
y_thr = norm_thri(x)
y = y_spec*y_thr
specAve = np.trapz(y,x)/(eWave-sWave)
norm = getABMagAverageVal(ABmag=ABMag, norm_thr=norm_thr, sWave=sWave, eWave=eWave)
specAve = np.trapz(y, x)/(eWave-sWave)
norm = getABMagAverageVal(
ABmag=ABMag, norm_thr=norm_thr, sWave=sWave, eWave=eWave)
if specAve == 0:
return 0
......@@ -551,12 +594,14 @@ def tag_sed(h5file, model_tag, teff=5000, logg=2, feh=0):
close_feh = 99
else:
close_feh = feh_grid[np.argmin(np.abs(feh_grid - feh))]
path = model_tag_str + f"_teff_{close_teff:.1f}_logg_{close_logg:.2f}_feh_{close_feh:.1f}"
path = model_tag_str + \
f"_teff_{close_teff:.1f}_logg_{close_logg:.2f}_feh_{close_feh:.1f}"
wave = np.array(h5file["wave"][model_tag_str][()]).ravel()
flux = np.array(h5file["sed"][path][()]).ravel()
return path, wave, flux
def convolveGaussXorders(img=None, sigma = 1):
def convolveGaussXorders(img=None, sigma=1):
from astropy.modeling.models import Gaussian2D
from scipy import signal
offset = int(np.ceil(sigma*10))
......@@ -571,14 +616,13 @@ def convolveGaussXorders(img=None, sigma = 1):
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
return convImg, offset
def convolveImg(img=None, psf = None):
def convolveImg(img=None, psf=None):
from astropy.modeling.models import Gaussian2D
from scipy import signal
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
offset_x = int(psf.shape[1]/2. + 0.5) - 1
offset_y = int(psf.shape[0]/2. + 0.5) - 1
offset = [offset_x,offset_y]
offset = [offset_x, offset_y]
return convImg, offset
ObservationSim/Straylight/data/__init__.py → observation_sim/mock_objects/data/led/__init__.py 100755 → 100644
+ 0
- 0
View file @ 38e2c452
File moved
ObservationSim/PSF/FieldDistortion.py → observation_sim/psf/FieldDistortion.py
+ 15
- 13
View file @ 38e2c452
......@@ -2,6 +2,7 @@ import galsim
import numpy as np
import cmath
class FieldDistortion(object):
def __init__(self, chip, fdModel=None, fdModel_path=None, img_rot=0.):
......@@ -13,7 +14,8 @@ class FieldDistortion(object):
with open(fdModel_path, "rb") as f:
self.fdModel = pickle.load(f)
else:
raise ValueError("Error: no field distortion model has been specified!")
raise ValueError(
"Error: no field distortion model has been specified!")
else:
self.fdModel = fdModel
self.img_rot = img_rot
......@@ -21,19 +23,20 @@ class FieldDistortion(object):
self.ixfdModel = self.ifdModel["xImagePos"]
self.iyfdModel = self.ifdModel["yImagePos"]
# first-order derivatives of the global field distortion model
self.ifx_dx = self.ixfdModel.partial_derivative(1,0)
self.ifx_dy = self.ixfdModel.partial_derivative(0,1)
self.ify_dx = self.iyfdModel.partial_derivative(1,0)
self.ify_dy = self.iyfdModel.partial_derivative(0,1)
self.ifx_dx = self.ixfdModel.partial_derivative(1, 0)
self.ifx_dy = self.ixfdModel.partial_derivative(0, 1)
self.ify_dx = self.iyfdModel.partial_derivative(1, 0)
self.ify_dy = self.iyfdModel.partial_derivative(0, 1)
if "residual" in self.fdModel["wave1"]:
self.irsModel = self.fdModel["wave1"]["residual"]["ccd" + chip.getChipLabel(chipID=chip.chipID)]
self.irsModel = self.fdModel["wave1"]["residual"]["ccd" +
chip.getChipLabel(chipID=chip.chipID)]
self.ixrsModel = self.irsModel["xResidual"]
self.iyrsModel = self.irsModel["yResidual"]
# first-order derivatives of the residual field distortion model
self.irx_dx = self.ixrsModel.partial_derivative(1,0)
self.irx_dy = self.ixrsModel.partial_derivative(0,1)
self.iry_dx = self.iyrsModel.partial_derivative(1,0)
self.iry_dy = self.iyrsModel.partial_derivative(0,1)
self.irx_dx = self.ixrsModel.partial_derivative(1, 0)
self.irx_dy = self.ixrsModel.partial_derivative(0, 1)
self.iry_dx = self.iyrsModel.partial_derivative(1, 0)
self.iry_dy = self.iyrsModel.partial_derivative(0, 1)
else:
self.irsModel = None
......@@ -107,4 +110,3 @@ class FieldDistortion(object):
fd_shear = galsim.Shear(g1=g1k_fd, g2=g2k_fd)
return galsim.PositionD(x, y), fd_shear
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