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Commit 29ac5666 authored by Fang Yuedong's avatar Fang Yuedong
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ObservationSim/MockObject/_util.py 0 → 100755
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import numpy as np
import os
from datetime import datetime
from scipy.interpolate import InterpolatedUnivariateSpline, UnivariateSpline, interp1d
from scipy import interpolate
from astropy.table import Table
import galsim
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 magToFlux(mag):
"""
flux of a given AB magnitude
Parameters:
mag: magnitude in unit of AB
Return:
flux: flux in unit of erg/s/cm^2/Hz
"""
flux = 10**(-0.4*(mag+48.6))
return flux
def extAv(nav, seed=1212123):
"""
Generate random intrinsic extinction Av
following the distribution from Holwerda et al, 2015
"""
np.random.seed(seed)
tau = 0.4
peak, a = 0.1, 0.5
b = a*(tau-peak)
pav = lambda av: (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)
pav_base = pav(avs)/np.sum(pav(avs))
rav = np.random.choice(avs, nav, p=pav_base)
return rav
###########################################
def seds(sedlistn, seddir="./", unit="A"):
"""
read SEDs and save into Python dictionary
Parameters:
sedlistn: filename of the sed template list and corresponding intrinsic
extinction model, see tmag_dz.py for detailes
listdir: directory of the list
unit: wavelength unit of the input templates
Return:
SED dictionary, the output wavelength unit is 'A'
"""
seds = {}
reds = {}
sedlist = seddir + sedlistn
sedn = open(sedlist).read().splitlines()
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
seds[itype] = ised
reds[itype] = int(xxx[1])
return seds, reds
def sed_assign(phz, btt, rng):
"""
assign SED template to a galaxy.
"""
sedid = list(range(1, 34))
lzid = sedid[0:13] + sedid[23:28]
hzid = sedid[13:23]
if btt == 1.0:
sedtype = rng.sample(sedid[0:5] + sedid[28:33], 1)[0]
if phz > 2.0 and sedtype in sedid[0:5]:
sedtype = rng.sample(sedid[28:33], 1)[0]
elif btt > 0.3 and btt < 1.0:
sedtype = rng.sample(sedid, 1)[0]
if phz > 2.0 and sedtype in lzid:
sedtype = rng.sample(hzid, 1)[0]
elif btt >= 0.1 and btt < 0.3:
sedtype = rng.sample(sedid[5:28], 1)[0]
if phz > 1.5 and sedtype in lzid:
sedtype = rng.sample(hzid, 1)[0]
elif btt >= 0.0 and btt < 0.1:
sedtype = rng.sample(sedid[5:23], 1)[0]
if phz > 1.5 and sedtype in lzid:
sedtype = rng.sample(hzid, 1)[0]
else:
sedtype = 0
return sedtype
###########################################
def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
"""
calculate the theoretical SED for given filter set and template
Only AB magnitude is support!!!
Parameters:
filt: 2d array
fliter transmissions: lambda(A), T
sed: 2d array
sed templateL lambda (A), flux (erg s-1 cm-2 A-1)
redshift: float
redshift of the corresponding source, default is zero
av: float
extinction value for intrincal extinction
redden: int
reddening model, see Function 'reddening' for details
return:
tflux: float
theoretical flux
sedObs: array
SED in observed frame
"""
z = redshift + 1.0
sw, sf = sed[:,0], sed[:,1]
# reddening
sf = reddening(sw, sf, av=av, model=redden)
sw, sf = sw*z, sf*(z**3)
# lyman forest correction
sf = lyman_forest(sw, sf, redshift)
sedxx = (sw.copy(), sf.copy())
sw = vc_A/sw
sf = sf*(vc_A/sw**2) # convert flux unit to erg/s/cm^s/Hz
sw, sf = sw[::-1], sf[::-1]
sfun = interp1d(sw, sf, kind='linear')
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)
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:
flux0 = flux
else:
nw = 200
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)
wstepb = (w2b-w1b)/float(nw)
wtempa = w1a + istep*wstepa
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)
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
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))
flux0[id0] = db*flux[id0]
flux0[id1] = da*flux[id1]
return flux0
###########################################
def reddening(sw, sf, av=0.0, model=0):
"""
calculate the intrinsic extinction of a given template
Parameters:
sw: array
wavelength
sf: array
flux
av: float or array
model: int
Five models will be used:
1: Allen (1976) for the Milky Way
2: Seaton (1979) fit by Fitzpatrick (1986) for the Milky Way
3: Fitzpatrick (1986) for the Large Magellanic Cloud (LMC)
4: Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC)
5: Calzetti et al (2000) for starburst galaxies
6: Reddy et al (2015) for star forming galaxies
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, \
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)
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw)
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
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, \
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)
fun = interp1d(lambda0, kR, kind='linear')
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)
A_lambda1 = ff/Rv+1.0
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
flux = sf*10**(-0.4*A_lambda)
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, \
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)
fun = interp1d(lambda0, kR, kind='linear')
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)
A_lambda1 = ff/Rv+1.0
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
flux = sf*10**(-0.4*A_lambda)
elif model==4: # Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC)
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, \
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 = kR/Rv+1.0
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw)
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
Rv = 4.05
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
ff11, ff12 = fun1(0.11), fun1(0.12)
slope1=(ff12-ff11)/0.01
ff99, ff100 = fun2(2.19), fun2(2.2)
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]
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
flux = sf*10**(-0.4*A_lambda)
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
ff11, ff12 = fun1(0.14), fun1(0.15)
slope1=(ff12-ff11)/0.01
ff99, ff100 = fun2(2.84), fun2(2.85)
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]
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
flux = sf*10**(-0.4*A_lambda)
else:
print("!!! Please select a proper reddening model")
sys.exit(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
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
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
nfilt = len(filter_list)
aflux = np.zeros(nfilt)
nid = -1
for k in range(nfilt):
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)
# 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')
isedObs = galsim.SED(spec, wave_type='A', flux_type='1', fast=False)
return amag, isedObs
def eObs(e1,e2,g1,g2):
"""
Calculate the sheared (observed) ellipticity using the
intrinsic ellipticity and cosmic shear components.
Parameters:
e1, e2: scalar or numpy array
g1, g2: scalar or numpy array
Return:
Sheared (observed) ellipticity components, absolute value, and orientation
in format of scalar or numpy array
** NOTE: e1, e2, g1, and g2 should have the same dimensions.
"""
if np.isscalar(e1):
e1 = np.array([e1])
e2 = np.array([e2])
g1 = np.array([g1])
g2 = np.array([g2])
else:
e1 = e1.flatten()
e2 = e2.flatten()
g1 = g1.flatten()
g2 = g2.flatten()
# calculate the sheared (observed) ellipticity using complex rule
nobj = len(e1)
e1obs, e2obs = [], []
eeobs, theta = [], []
for i in range(nobj):
e = complex(e1[i], e2[i])
g = complex(g1[i], g2[i])
e, gg = abs(e), abs(g)
if gg<=1.0:
tt = e + g
bb = 1.0 + e*g.conjugate()
eobs = tt/bb
else:
tt = 1.0 + g*e.conjugate()
bb = e.conjugate() + g.conjugate()
eobs = tt/bb
# 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
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]
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]
# reddening
sf = reddening(sw, sf, av=av, model=redden)
sw, sf = sw*z, sf*(z**3)
# lyman forest correction
sf = lyman_forest(sw, sf, redshift)
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
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']))
sWave = bandpass.blue_limit*10.0
eWave = bandpass.red_limit*10.0
# print('in getABMAG', sWave, eWave)
ABMAG_zero = getABMagAverageVal(
ABmag=0,
norm_thr=throughtput,
sWave=sWave,
eWave=eWave)
flux_ave = interFlux / (eWave-sWave)
ABMAG_spec = -2.5 * np.log10(flux_ave/ABMAG_zero)
return ABMAG_spec
def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250):
"""
norm_thr: astropy.table, 2 colum, 'WAVELENGTH', 'SENSITIVITY'
Return:
the integerate flux of AB magnitude in the norm_thr(the throughtput of band), photos s-1 m-2 A-1
"""
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)
y = norm_thr_i(x)
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
Parameters
----------
spectrum: astropy.table, 2 colum, 'WAVELENGTH', 'FLUX'
norm_thr: astropy.table, 2 colum, 'WAVELENGTH', 'SENSITIVITY'
sWave: the start of norm_thr
eWave: the end of norm_thr
Return:
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'])
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)
if specAve == 0:
return 0
# print('specAve = ', specAve)
return norm / specAve
def tag_sed(h5file, model_tag, teff=5000, logg=2, feh=0):
model_tag_str = model_tag.decode("utf-8").strip()
teff_grid = np.unique(h5file["teff"][model_tag_str])
logg_grid = np.unique(h5file["logg"][model_tag_str])
feh_grid = np.unique(h5file["feh"][model_tag_str])
close_teff = teff_grid[np.argmin(np.abs(teff_grid - teff))]
close_logg = logg_grid[np.argmin(np.abs(logg_grid - logg))]
if model_tag_str == "koester" or model_tag_str == "MC":
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}"
wave = np.array(h5file["wave"][model_tag_str][()]).ravel()
flux = np.array(h5file["sed"][path][()]).ravel()
return path, wave, flux
\ No newline at end of file
ObservationSim/ObservationSim.py 0 → 100755
View file @ 29ac5666
from Config import ConfigDir, ReadConfig, ChipOutput
from Config.Header import generatePrimaryHeader, generateExtensionHeader
from Instrument import Telescope, Filter, FilterParam, FocalPlane, Chip
from MockObject import Catalog, MockObject, Star, Galaxy, Quasar, calculateSkyMap_split_g
from PSF import PSFGauss, PSFInterp, FieldDistortion
from _util import makeSubDir, getShearFiled, makeSubDir_PointingList
from astropy.time import Time as asTime
from astropy.io import fits
import numpy as np
import mpi4py.MPI as MPI
import galsim
import os, sys
import logging
import psutil
class Observation(object):
def __init__(self, input_cat_dir=None, work_dir=None, data_dir=None):
self.path_dict = ConfigDir(input_cat_dir, work_dir, data_dir)
self.config = ReadConfig(self.path_dict["config_file"])
self.tel = Telescope(optEffCurve_path=self.path_dict["mirror_file"]) # Currently the default values are hard coded in
self.focal_plane = FocalPlane(survey_type=self.config["survey_type"]) # Currently the default values are hard coded in
self.filter_param = FilterParam(filter_dir=self.path_dict["filter_dir"]) # Currently the default values are hard coded in
self.chip_list = []
self.filter_list = []
# if we want to apply field distortion?
if self.config["field_dist"].lower() == "y":
self.fd_model = FieldDistortion()
else:
self.fd_model = None
# Construct chips & filters:
nchips = self.focal_plane.nchip_x*self.focal_plane.nchip_y
for i in range(nchips):
chipID = i + 1
if self.focal_plane.isIgnored(chipID=chipID):
continue
# Make Chip & Filter lists
chip = Chip(chipID, ccdEffCurve_dir=self.path_dict["ccd_dir"], CRdata_dir=self.path_dict["CRdata_dir"], normalize_dir=self.path_dict["normalize_dir"], sls_dir=self.path_dict["sls_dir"], config=self.config) # currently there is no config file for chips
filter_id, filter_type = chip.getChipFilter()
filt = Filter(filter_id=filter_id, filter_type=filter_type, filter_param=self.filter_param, ccd_bandpass=chip.effCurve)
self.chip_list.append(chip)
self.filter_list.append(filt)
# Read catalog and shear(s)
self.g1_field, self.g2_field, self.nshear = getShearFiled(config=self.config)
def runOneChip(self, chip, filt, chip_output, wcs_fp=None, psf_model=None, pointing_ID=0, ra_cen=None, dec_cen=None, img_rot=None, exptime=150., input_cat_name=None, shear_cat_file=None, cat_dir=None, sed_dir=None):
if (ra_cen is None) or (dec_cen is None):
ra_cen = self.config["ra_center"]
dec_cen = self.config["dec_center"]
if img_rot is None:
img_rot = self.config["image_rot"]
if self.config["psf_model"] == "Gauss":
psf_model = PSFGauss(chip=chip)
elif self.config["psf_model"] == "Interp":
psf_model = PSFInterp(chip=chip)
else:
print("unrecognized PSF model type!!", flush=True)
# Get (extra) shear fields
if shear_cat_file is not None:
self.g1_field, self.g2_field, self.nshear = getShearFiled(config=self.config, shear_cat_file=shear_cat_file)
# Get WCS for the focal plane
if wcs_fp == None:
wcs_fp = self.focal_plane.getTanWCS(ra_cen, dec_cen, img_rot, chip.pix_scale)
# Create chip Image
chip.img = galsim.ImageF(chip.npix_x, chip.npix_y)
chip.img.setOrigin(chip.bound.xmin, chip.bound.ymin)
chip.img.wcs = wcs_fp
if chip.survey_type == "photometric":
sky_map = None
elif chip.survey_type == "spectroscopic":
skyfile = os.path.join(self.path_dict["data_dir"], 'skybackground/sky_emiss_hubble_50_50_A.dat')
sky_map = calculateSkyMap_split_g(xLen=chip.npix_x, yLen=chip.npix_y, blueLimit=filt.blue_limit, redLimit=filt.red_limit, skyfn=skyfile, conf=chip.sls_conf, pixelSize=chip.pix_scale, isAlongY=0)
# Load catalogues and templates
self.cat = Catalog(config=self.config, chip=chip, cat_dir=cat_dir, sed_dir=sed_dir, pRa=ra_cen, pDec=dec_cen, rotation=img_rot, template_dir=self.path_dict["template_dir"])
self.nobj = len(self.cat.objs)
# Loop over objects
missed_obj = 0
bright_obj = 0
dim_obj = 0
for j in range(self.nobj):
# if j >= 20:
# break
obj = self.cat.objs[j]
# Load SED
if obj.type == 'star':
normF = chip.normF_star
try:
obj.load_SED(
survey_type=chip.survey_type,
normFilter=normF,
target_filt=filt,
sed_lib=self.cat.tempSED_star)
except Exception as e:
print(e)
continue
elif obj.type == 'galaxy': # or obj.type == quasar
normF = chip.normF_galaxy
obj.load_SED(
sed_path=sed_dir,
survey_type=chip.survey_type,
sed_templates=self.cat.tempSed_gal,
normFilter=normF,
target_filt=filt)
elif obj.type == 'quasar':
normF = chip.normF_galaxy
obj.load_SED(
sed_path=sed_dir,
survey_type=chip.survey_type,
sed_templates=self.cat.tempSed_gal,
normFilter=normF,
target_filt=filt)
# Exclude very bright/dim objects (for now)
if filt.is_too_bright(mag=obj.getMagFilter(filt)):
# print("obj too birght!!", flush=True)
if obj.type != 'galaxy':
bright_obj += 1
obj.unload_SED()
continue
if filt.is_too_dim(mag=obj.getMagFilter(filt)):
# print("obj too dim!!", flush=True)
dim_obj += 1
obj.unload_SED()
# print(obj.getMagFilter(filt))
continue
if self.config["shear_method"] == "constant":
if obj.type == 'star':
g1, g2 = 0, 0
else:
g1, g2 = self.g1_field, self.g2_field
elif self.config["shear_method"] == "extra":
# TODO: every object with individual shear from input catalog(s)
g1, g2 = self.g1_field[j], self.g2_field[j]
pos_img, offset, local_wcs = obj.getPosImg_Offset_WCS(img=chip.img, fdmodel=self.fd_model, chip=chip, verbose=False)
if pos_img.x == -1 or pos_img.y == -1:
# Exclude object which is outside the chip area (after field distortion)
# print("obj missed!!")
missed_obj += 1
obj.unload_SED()
continue
# Draw object & update output catalog
try:
if chip.survey_type == "photometric":
isUpdated, pos_shear = obj.drawObj_multiband(
tel=self.tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=g1,
g2=g2,
exptime=exptime)
elif chip.survey_type == "spectroscopic":
isUpdated, pos_shear = obj.drawObj_slitless(
tel=self.tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=g1,
g2=g2,
exptime=exptime,
normFilter=normF)
if isUpdated:
# TODO: add up stats
chip_output.cat_add_obj(obj, pos_img, pos_shear, g1, g2)
pass
else:
# print("object omitted", flush=True)
continue
except Exception as e:
print(e)
pass
# Unload SED:
obj.unload_SED()
del obj
del psf_model
del self.cat
print("check running:1: pointing-{:} chip-{:} pid-{:} memory-{:6.2}GB".format(pointing_ID, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ), flush=True)
# Detector Effects
# ===========================================================
chip.img = chip.addNoise(config=self.config, tel=self.tel, filt=filt, img=chip.img, sky_map=sky_map)
chip.img = chip.addEffects(config=self.config, img=chip.img, chip_output=chip_output, filt=filt, pointing_ID=pointing_ID)
h_prim = generatePrimaryHeader(
xlen=chip.npix_x,
ylen=chip.npix_y,
pointNum = str(pointing_ID),
ra=ra_cen,
dec=dec_cen,
psize=chip.pix_scale,
row_num=chip.rowID,
col_num=chip.colID,
date=self.config["date_obs"],
time_obs=self.config["time_obs"])
h_ext = generateExtensionHeader(
xlen=chip.npix_x,
ylen=chip.npix_y,
ra=ra_cen,
dec=dec_cen,
pa=img_rot.deg,
gain=chip.gain,
readout=chip.read_noise,
dark=chip.dark_noise,
saturation=90000,
psize=chip.pix_scale,
row_num=chip.rowID,
col_num=chip.colID)
chip.img = galsim.Image(chip.img.array, dtype=np.uint16)
hdu1 = fits.PrimaryHDU(header=h_prim)
hdu2 = fits.ImageHDU(chip.img.array, header=h_ext)
hdu1 = fits.HDUList([hdu1, hdu2])
fname = os.path.join(chip_output.subdir, h_prim['FILENAME'] + '.fits')
hdu1.writeto(fname, output_verify='ignore', overwrite=True)
del chip.img
print("check running:2: pointing-{:} chip-{:} pid-{:} memory-{:6.2}GB".format(pointing_ID, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ), flush=True)
print("# objects that are too bright %d out of %d"%(bright_obj, self.nobj))
print("# objects that are too dim %d out of %d"%(dim_obj, self.nobj))
print("# objects that are missed %d out of %d"%(missed_obj, self.nobj))
def runExposure(self, ra_cen=None, dec_cen=None, pointing_ID=0, img_rot=None, exptime=150., input_cat_name=None, shear_cat_file=None, oneChip=None):
if (ra_cen == None) or (dec_cen == None):
ra_cen = self.config["ra_center"]
dec_cen = self.config["dec_center"]
if img_rot == None:
img_rot = self.config["image_rot"]
sub_img_dir, prefix = makeSubDir_PointingList(path_dict=self.path_dict, config=self.config, pointing_ID=pointing_ID)
# Loop over chips
for i in range(len(self.chip_list)):
chip = self.chip_list[i]
filt = self.filter_list[i]
# Just run one chip
if oneChip is not None:
if chip.chipID != oneChip:
continue
# Prepare output files
chip_output = ChipOutput(
config=self.config,
focal_plane=self.focal_plane,
chip=chip,
filt=filt,
exptime=exptime,
pointing_ID=pointing_ID,
subdir=sub_img_dir,
prefix=prefix)
self.runOneChip(
chip=chip,
filt=filt,
chip_output=chip_output,
pointing_ID = pointing_ID,
ra_cen=ra_cen,
dec_cen=dec_cen,
img_rot=img_rot,
exptime=exptime,
cat_dir=self.path_dict["cat_dir"],
sed_dir=self.path_dict["SED_dir"])
print("finished running chip#%d..."%(chip.chipID), flush=True)
def runExposure_MPI_PointingList(self, ra_cen=None, dec_cen=None, pRange=None, img_rot=None, exptime=150., input_cat_name=None, shear_cat_file=None):
comm = MPI.COMM_WORLD
ind_thread = comm.Get_rank()
num_thread = comm.Get_size()
nchips_per_fp = len(self.chip_list)
ra_cen = ra_cen[pRange]
dec_cen = dec_cen[pRange]
# The Starting pointing ID
if pRange is not None:
pStart = pRange[0]
else:
pStart = 0
for ipoint in range(len(ra_cen)):
for ichip in range(nchips_per_fp):
i = ipoint*nchips_per_fp + ichip
pointing_ID = pStart + ipoint
if i % num_thread != ind_thread:
continue
pid = os.getpid()
sub_img_dir, prefix = makeSubDir_PointingList(path_dict=self.path_dict, config=self.config, pointing_ID=pointing_ID)
chip = self.chip_list[ichip]
filt = self.filter_list[ichip]
print("running pointing#%d, chip#%d, at PID#%d..."%(pointing_ID, chip.chipID, pid), flush=True)
chip_output = ChipOutput(
config=self.config,
focal_plane=self.focal_plane,
chip=chip,
filt=filt,
exptime=exptime,
pointing_ID=pointing_ID,
subdir=sub_img_dir,
prefix=prefix)
self.runOneChip(
chip=chip,
filt=filt,
chip_output=chip_output,
pointing_ID = pointing_ID,
ra_cen=ra_cen[ipoint],
dec_cen=dec_cen[ipoint],
img_rot=img_rot,
exptime=exptime,
cat_dir=self.path_dict["cat_dir"],
sed_dir=self.path_dict["SED_dir"])
print("finished running chip#%d..."%(chip.chipID), flush=True)
\ No newline at end of file
ObservationSim/PSF/FieldDistortion.py 0 → 100644
View file @ 29ac5666
import galsim
import numpy as np
class FieldDistortion(object):
def __init__(self, fdModel=None, fdModel_path=None):
if fdModel is None:
import pickle
if fdModel_path is not None:
with open(fdModel_path, "rb") as f:
self.fdModel = pickle.load(f)
else:
# with open("/data/simudata/CSSOSDataProductsSims/data/FieldDistModelv1.0.pickle", "rb") as f:
with open("/data/simudata/CSSOSDataProductsSims/data/FieldDistModelv2.0.pickle", "rb") as f:
self.fdModel = pickle.load(f)
else:
self.fdModel = fdModel
def isContainObj_FD(self, chip, pos_img):
ifdModel = self.fdModel["ccd" + chip.getChipLabel(chipID=chip.chipID)]["wave1"]
x, y = pos_img.x, pos_img.y
xLowI, xUpI, yLowI, yUpI = ifdModel["InterpLim"]
if (xLowI - x)*(xUpI - x) > 0 or (yLowI - y) * (yUpI - y) > 0:
return False
return True
def get_Distorted(self, chip, pos_img, bandpass=None):
""" Get the distored position for an undistorted image position
Parameters:
chip: A 'Chip' object representing the
chip we want to extract PSF from.
pos_img: A 'galsim.Position' object representing
the image position.
bandpass: A 'galsim.Bandpass' object representing
the wavelength range.
Returns:
pos_distorted: A 'galsim.Position' object representing
the distored position.
"""
if not self.isContainObj_FD(chip=chip, pos_img=pos_img):
return galsim.PositionD(-1, -1)
ifdModel = self.fdModel["ccd" + chip.getChipLabel(chipID=chip.chipID)]["wave1"]
x, y = pos_img.x, pos_img.y
x = ifdModel["xImagePos"](x, y)[0][0]
y = ifdModel["yImagePos"](x, y)[0][0]
return galsim.PositionD(x, y)
ObservationSim/PSF/PSFGauss.py 0 → 100644
View file @ 29ac5666
import galsim
import sep
import numpy as np
from scipy.interpolate import interp1d
from .PSFModel import PSFModel
import os, sys
class PSFGauss(PSFModel):
def __init__(self, chip, fwhm=0.187, sigSpin=0., psfRa=0.15):
self.pix_size = chip.pix_scale
self.chip = chip
self.fwhm = fwhm
self.sigSpin = sigSpin
self.sigGauss = psfRa # 80% light radius
self.psf = galsim.Gaussian(flux=1.0,fwhm=fwhm)
def perfGauss(self, r, sig):
"""
pseudo-error function, i.e. Cumulative distribution function of Gaussian distribution
Parameter:
r: radius
sig: sigma of the Gaussian distribution
Return:
the value of the pseudo CDF
"""
gaussFun = lambda sigma, r: 1.0/(np.sqrt(2.0*np.pi)*sigma) * np.exp(-r**2/(2.0*sigma**2))
nxx = 1000
rArr = np.linspace(0.0,r,nxx)
gauss = gaussFun(sig,rArr)
erf = 2.0*np.trapz(gauss,rArr)
return erf
def fracGauss(self, sig, r=0.15, pscale=None):
"""
For a given Gaussian PSF with sigma=sig,
derive the flux ratio ar the given radius r
Parameters:
sig: sigma of the Gauss PSF Function in arcsec
r: radius in arcsec
pscale: pixel scale
Return: the flux ratio
"""
if pscale == None:
pscale = self.pix_size
gaussx = galsim.Gaussian(flux=1.0,sigma=sig)
gaussImg = gaussx.drawImage(scale=pscale, method='no_pixel')
gaussImg = gaussImg.array
size = np.size(gaussImg,axis=0)
cxy = 0.5*(size-1)
flux, ferr, flag = sep.sum_circle(gaussImg,cxy,cxy,r/pscale,subpix=0)
frac = flux.tolist()
return frac
def fwhmGauss(self, r=0.15,fr=0.8,pscale=None):
"""
Given a total flux ratio 'fr' within a fixed radius 'r',
estimate the fwhm of the Gaussian function
return the fwhm in arcsec
"""
if pscale == None:
pscale = self.pix_size
err = 1.0e-3
nxx = 100
sig = np.linspace(0.5*pscale,1.0,nxx)
frA = np.zeros(nxx)
for i in range(nxx): frA[i] = self.fracGauss(sig[i],r=r,pscale=pscale)
index = [i for i in range(nxx-1) if (fr-frA[i])*(fr-frA[i+1])<=0.0][0]
while abs(frA[index]-fr)>1.0e-3:
sig = np.linspace(sig[index],sig[index+1],nxx)
for i in range(nxx): frA[i] = self.fracGauss(sig[i],r=r,pscale=pscale)
index = [i for i in range(nxx-1) if (fr-frA[i])*(fr-frA[i+1])<=0.0][0]
fwhm = 2.35482*sig[index]
return fwhm
def get_PSF(self, pos_img, chip=None, bandpass=None, folding_threshold=5.e-3):
dx = pos_img.x - self.chip.cen_pix_x
dy = pos_img.y - self.chip.cen_pix_y
return self.PSFspin(dx, dy)
def PSFspin(self, x, y):
"""
The PSF profile at a given image position relative to the axis center
Parameters:
theta : spin angles in a given exposure in unit of [arcsecond]
dx, dy: relative position to the axis center in unit of [pixels]
Return:
Spinned PSF: g1, g2 and axis ratio 'a/b'
"""
a2Rad = np.pi/(60.0*60.0*180.0)
ff = self.sigGauss * 0.107 * (1000.0/10.0) # in unit of [pixels]
rc = np.sqrt(x*x + y*y)
cpix = rc*(self.sigSpin*a2Rad)
beta = (np.arctan2(y,x) + np.pi/2)
ell = cpix**2/(2.0*ff**2+cpix**2)
#ell *= 10.0
qr = np.sqrt((1.0+ell)/(1.0-ell))
#psfShape = galsim.Shear(e=ell, beta=beta)
#g1, g2 = psfShape.g1, psfShape.g2
#qr = np.sqrt((1.0+ell)/(1.0-ell))
#return ell, beta, qr
PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
return self.psf.shear(PSFshear), PSFshear
\ No newline at end of file
ObservationSim/PSF/PSFInterp/PSFConfig.py 0 → 100644
View file @ 29ac5666
import sys
import numpy as np
import scipy.io
from itertools import islice
from PSF.PSFInterp.PSFUtil import *
###加载PSF信息###
def LoadPSF(iccd, iwave, ipsf, psfPath, InputMaxPixelPos=True, PSFCentroidWgt=False, PixSizeInMicrons=2.5, PSFBinning=False, PSFConvGauss=False):
"""
load psf informations from psf matrix.
Parameters:
iccd (int): ccd number [1,30].
iwave(int): wave-index [1,4].
ipsf (int): psf number [1, 100].
psfPath (int): path to psf matrix
InputMaxPixelPos(bool-optional): only True for 30*30 psf-matrix
PSFCentroidWgt(bool-optional): True for using psfMat with libCentroid.lib
Returns:
psfInfo (dirctionary)
"""
if iccd not in np.linspace(1, 30, 30, dtype='int'):
print('Error - iccd should be in [1, 30].')
sys.exit()
if iwave not in np.linspace(1, 4, 4, dtype='int'):
print('Error - iwave should be in [1, 4].')
sys.exit()
if ipsf not in np.linspace(1, 900, 900, dtype='int'):
print('Error - ipsf should be in [1, 900].')
sys.exit()
psfInfo = {}
fpath = psfPath +'/' +'ccd{:}'.format(iccd) +'/' + 'wave_{:}'.format(iwave)
#获取ipsf矩阵
'''
if not PSFCentroidWgt:
##读取PSF原数据
fpathMat = fpath +'/' +'5_psf_array' +'/' +'psf_{:}.mat'.format(ipsf)
data = scipy.io.loadmat(fpathMat)
psfInfo['psfMat'] = data['psf']
if PSFCentroidWgt:
##读取PSFCentroidWgt
ffpath = psfPath +'_proc/' +'ccd{:}'.format(iccd) +'/' + 'wave_{:}'.format(iwave)
ffpathMat = ffpath +'/' +'5_psf_array' +'/' +'psf_{:}_centroidWgt.mat'.format(ipsf)
data = scipy.io.loadmat(ffpathMat)
psfInfo['psfMat'] = data['psf']
'''
if not PSFCentroidWgt:
ffpath = fpath
ffpathMat = ffpath +'/5_psf_array/psf_{:}.mat'.format(ipsf)
if PSFCentroidWgt:
ffpath = psfPath +'_proc/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if PSFBinning and (not PSFConvGauss):
ffpath = psfPath +'_bin256x256/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if (not PSFBinning) and PSFConvGauss:
ffpath = psfPath +'_convGauss/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if PSFBinning and PSFConvGauss:
ffpath = psfPath +'_convGauss/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt_BC.mat'.format(ipsf)
data = scipy.io.loadmat(ffpathMat)
psfInfo['psfMat'] = data['psf']
psfInfo['pixsize'] = PixSizeInMicrons #microns
if PSFBinning:
psfInfo['pixsize'] = PixSizeInMicrons*2.0
#获取ipsf波长
fpathWave = fpath +'/' +'1_wavelength.txt'
f = open(fpathWave, 'r')
wavelength = np.float(f.readline())
f.close()
psfInfo['wavelength'] = wavelength
#获取ipsf位置
fpathCoordinate = fpath +'/' +'4_PSF_coordinate.txt'
f = open(fpathCoordinate, 'r')
header = f.readline()
for line in islice(f, ipsf-1, ipsf):
line = line.strip()
columns = line.split()
f.close()
icol = 0
psfInfo['field_x'] = float(columns[icol]) #deg, 视场采样位置
icol+= 1
psfInfo['field_y'] = float(columns[icol]) #deg
icol+= 1
psfInfo['centroid_x'] = float(columns[icol]) #mm, psf质心相对主光线的偏移量
icol+= 1
psfInfo['centroid_y'] = float(columns[icol]) #mm
icol+= 1
if InputMaxPixelPos == True:
psfInfo['max_x'] = float(columns[icol]) #mm, max pixel postion
icol+= 1
psfInfo['max_y'] = float(columns[icol]) #mm
icol+= 1
psfInfo['image_x'] = float(columns[icol]) #mm, 主光线位置
icol+= 1
psfInfo['image_y'] = float(columns[icol]) #mm
if PSFCentroidWgt:
psfInfo['centroid_x'] = data['cx'][0][0] #mm, psfCentroidWgt质心相对主光线的偏移量
psfInfo['centroid_y'] = data['cy'][0][0] #mm
if PSFBinning:
psfInfo['centroid_x'] = data['cx'][0][0] +0.0025/2 #binning引起的主光线漂移
psfInfo['centroid_y'] = data['cy'][0][0] +0.0025/2
return psfInfo
###插值PSF图像-IDW方法###
def psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=None, hoclist=None, PSFCentroidWgt=False):
"""
psf interpolation by IDW
Parameters:
px, py (float, float): position of the target
PSFMat (numpy.array): image
cen_col, cen_row (numpy.array, numpy.array): potions of the psf centers
IDWindex (int-optional): the power index of IDW
OnlyNeighbors (bool-optional): only neighbors are used for psf interpolation
Returns:
psfMaker (numpy.array)
"""
minimum_psf_weight = 1e-8
ref_col = px
ref_row = py
ngy, ngx = PSFMat[0, :, :].shape
npsf = PSFMat[:, :, :].shape[0]
psfWeight = np.zeros([npsf])
if OnlyNeighbors == True:
if hoc is None:
neigh = findNeighbors(px, py, cen_col, cen_row, dr=5., dn=4, OnlyDistance=False)
if hoc is not None:
neigh = findNeighbors_hoclist(cen_col, cen_row, tx=px,ty=py, dn=4, hoc=hoc, hoclist=hoclist)
neighFlag = np.zeros(npsf)
neighFlag[neigh] = 1
for ipsf in range(npsf):
if OnlyNeighbors == True:
if neighFlag[ipsf] != 1:
continue
dist = np.sqrt((ref_col - cen_col[ipsf])**2 + (ref_row - cen_row[ipsf])**2)
if IDWindex == 1:
psfWeight[ipsf] = dist
if IDWindex == 2:
psfWeight[ipsf] = dist**2
if IDWindex == 3:
psfWeight[ipsf] = dist**3
if IDWindex == 4:
psfWeight[ipsf] = dist**4
psfWeight[ipsf] = max(psfWeight[ipsf], minimum_psf_weight)
psfWeight[ipsf] = 1./psfWeight[ipsf]
psfWeight /= np.sum(psfWeight)
psfMaker = np.zeros([ngy, ngx], dtype=np.float32)
for ipsf in range(npsf):
if OnlyNeighbors == True:
if neighFlag[ipsf] != 1:
continue
iPSFMat = PSFMat[ipsf, :, :].copy()
ipsfWeight = psfWeight[ipsf]
psfMaker += iPSFMat * ipsfWeight
psfMaker /= np.nansum(psfMaker)
return psfMaker
###TEST###
if __name__ == '__main__':
iccd = 1
iwave= 1
ipsf = 1
psfPath = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
psfInfo = LoadPSF(iccd, iwave, ipsf, psfPath, InputMaxPixelPos=True, PSFCentroidWgt=True)
print('psfInfo.keys:', psfInfo.keys())
print(psfInfo)
ObservationSim/PSF/PSFInterp/PSFInterp.py 0 → 100644
View file @ 29ac5666
'''
PSF interpolation for CSST-Sim
NOTE: [iccd, iwave, ipsf] are counted from 1 to n, but [tccd, twave, tpsf] are counted from 0 to n-1
'''
import galsim
import numpy as np
import os
import time
import copy
import PSF.PSFInterp.PSFConfig as myConfig
import PSF.PSFInterp.PSFUtil as myUtil
from PSF.PSFModel import PSFModel
LOG_DEBUG = False #***#
NPSF = 900 #***# 30*30
iccdTest = 1 #***#
class PSFInterp(PSFModel):
# def __init__(self, PSF_data=None, params=None, PSF_data_file=None):
def __init__(self, chip, PSF_data=None, PSF_data_file=None, sigSpin=0, psfRa=0.15):
"""
The PSF data matrix is either given by a object parameter or read in from a file.
Parameters:
PSF_data: The PSF data matrix object
params: Other parameters?
PSF_data_file: The file for PSF data matrix (optional).
"""
if LOG_DEBUG:
print('===================================================')
print('DEBUG: psf module for csstSim ' \
+time.strftime("(%Y-%m-%d %H:%M:%S)", time.localtime()), flush=True)
print('===================================================')
self.sigSpin = sigSpin
self.sigGauss = psfRa
self.iccd = int(chip.getChipLabel(chipID=chip.chipID))
if PSF_data_file == None:
PSF_data_file = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
self.nwave= self._getPSFwave(self.iccd, PSF_data_file)
self.PSF_data = self._loadPSF(self.iccd, PSF_data_file)
if LOG_DEBUG:
print('nwave-{:} on ccd-{:}::'.format(self.nwave, self.iccd), flush=True)
print('self.PSF_data ... ok', flush=True)
print('Preparing self.[psfMat,cen_col,cen_row] for psfMaker ... ', end='', flush=True)
ngy, ngx = self.PSF_data[0][0]['psfMat'].shape
self.psfMat = np.zeros([self.nwave, NPSF, ngy, ngx], dtype=np.float32)
self.cen_col= np.zeros([self.nwave, NPSF], dtype=np.float32)
self.cen_row= np.zeros([self.nwave, NPSF], dtype=np.float32)
self.hoc =[]
self.hoclist=[]
for twave in range(self.nwave):
for tpsf in range(NPSF):
self.psfMat[twave, tpsf, :, :] = self.PSF_data[twave][tpsf]['psfMat']
self.PSF_data[twave][tpsf]['psfMat'] = 0 ###free psfMat
self.pixsize = self.PSF_data[twave][tpsf]['pixsize']*1e-3 ##mm
self.cen_col[twave, tpsf] = self.PSF_data[twave][tpsf]['image_x'] +0.*self.pixsize + self.PSF_data[twave][tpsf]['centroid_x']
self.cen_row[twave, tpsf] = self.PSF_data[twave][tpsf]['image_y'] +0.*self.pixsize + self.PSF_data[twave][tpsf]['centroid_y']
#hoclist on twave for neighborsFinding
hoc,hoclist = myUtil.findNeighbors_hoclist(self.cen_col[twave], self.cen_row[twave])
self.hoc.append(hoc)
self.hoclist.append(hoclist)
if LOG_DEBUG:
print('ok', flush=True)
def _getPSFwave(self, iccd, PSF_data_file):
"""
Get # of sampling waves on iccd
Parameters:
iccd: The chip of i-th ccd
PSF_data_file: The file for PSF data matrix
Returns:
nwave: The number of the sampling waves
"""
strs = os.listdir(PSF_data_file + '/ccd{:}'.format(iccd))
nwave = 0
for _ in strs:
if 'wave_' in _:
nwave += 1
return nwave
def _loadPSF(self, iccd, PSF_data_file):
"""
load psf-matrix on iccd
Parameters:
iccd: The chip of i-th ccd
PSF_data_file: The file for PSF data matrix
Returns:
psfSet: The matrix of the csst-psf
"""
psfSet = []
for ii in range(self.nwave):
iwave = ii+1
if LOG_DEBUG:
print('self._loadPSF: iwave::', iwave, flush=True)
psfWave = []
for jj in range(NPSF):
ipsf = jj+1
psfInfo = myConfig.LoadPSF(iccd, iwave, ipsf, PSF_data_file, InputMaxPixelPos=True, PSFCentroidWgt=True, PSFBinning=True, PSFConvGauss=True)
psfWave.append(psfInfo)
psfSet.append(psfWave)
if LOG_DEBUG:
print('psfSet has been loaded:', flush=True)
print('psfSet[iwave][ipsf][keys]:', psfSet[0][0].keys(), flush=True)
return psfSet
def _preprocessPSF(self):
pass
def _findWave(self, bandpass):
for twave in range(self.nwave):
bandwave = self.PSF_data[twave][0]['wavelength']
if bandpass.blue_limit < bandwave and bandwave < bandpass.red_limit:
return twave
return -1
def get_PSF(self, chip, pos_img, bandpass, pixSize=0.0184, galsimGSObject=True, findNeighMode='treeFind', folding_threshold=5.e-3):
"""
Get the PSF at a given image position
Parameters:
chip: A 'Chip' object representing the chip we want to extract PSF from.
pos_img: A 'galsim.Position' object representing the image position.
bandpass: A 'galsim.Bandpass' object representing the wavelength range.
pixSize: The pixels size of psf matrix
findNeighMode: 'treeFind' or 'hoclistFind'
Returns:
PSF: A 'galsim.GSObject'.
"""
pixSize = np.rad2deg(self.pixsize*1e-3/28)*3600 #set psf pixsize
#***# assert self.iccd == chip.chipID, 'ERROR: self.iccd != chip.chipID'
# twave = bandpass-1 #***# ??? #self.findWave(bandpass) ###twave=iwave-1 as that in NOTE
assert self.iccd == int(chip.getChipLabel(chipID=chip.chipID)), 'ERROR: self.iccd != chip.chipID'
twave = self._findWave(bandpass)
if twave == -1:
print("!!!PSF bandpass does not match.")
exit()
PSFMat = self.psfMat[twave]
cen_col= self.cen_col[twave]
cen_row= self.cen_row[twave]
# px = pos_img[0]
# py = pos_img[1]
px = (pos_img.x - chip.cen_pix_x)*0.01
py = (pos_img.y - chip.cen_pix_y)*0.01
if findNeighMode == 'treeFind':
imPSF = myConfig.psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, PSFCentroidWgt=True)
if findNeighMode == 'hoclistFind':
imPSF = myConfig.psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=self.hoc[twave], hoclist=self.hoclist[twave], PSFCentroidWgt=True)
if galsimGSObject:
img = galsim.ImageF(imPSF, scale=pixSize)
gsp = galsim.GSParams(folding_threshold=folding_threshold)
self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
gaussPSF = galsim.Gaussian(sigma=0.04, gsparams=gsp)
self.psf = galsim.Convolve(gaussPSF, self.psf)
return self.PSFspin(x=px/0.01, y=py/0.01)
return imPSF
def PSFspin(self, x, y):
"""
The PSF profile at a given image position relative to the axis center
Parameters:
theta : spin angles in a given exposure in unit of [arcsecond]
dx, dy: relative position to the axis center in unit of [pixels]
Return:
Spinned PSF: g1, g2 and axis ratio 'a/b'
"""
a2Rad = np.pi/(60.0*60.0*180.0)
ff = self.sigGauss * 0.107 * (1000.0/10.0) # in unit of [pixels]
rc = np.sqrt(x*x + y*y)
cpix = rc*(self.sigSpin*a2Rad)
beta = (np.arctan2(y,x) + np.pi/2)
ell = cpix**2/(2.0*ff**2+cpix**2)
qr = np.sqrt((1.0+ell)/(1.0-ell))
PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
return self.psf.shear(PSFshear), PSFshear
if __name__ == '__main__':
if True:
psfPath = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
psfCSST = PSFInterp(PSF_data_file = psfPath)
iwave= 1
ipsf = 665
pos_img = [psfCSST.cen_col[iwave, ipsf], psfCSST.cen_row[iwave, ipsf]]
img = psfCSST.get_PSF(1, pos_img, iwave, galsimGSObject=True)
print('haha')
if False:
#old version (discarded)
#plot check-1
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(18,5))
ax = plt.subplot(1,3,1)
plt.imshow(img)
plt.colorbar()
ax = plt.subplot(1,3,2)
imgx = psfCSST.itpPSF_data[iwave][ipsf]['psfMat']
imgx/= np.sum(imgx)
plt.imshow(imgx)
plt.colorbar()
ax = plt.subplot(1,3,3)
plt.imshow(img - imgx)
plt.colorbar()
plt.savefig('test/figs/test1.jpg')
if False:
#old version (discarded)
#plot check-2: 注意图像坐标和全局坐标
fig = plt.figure(figsize=(8,8), dpi = 200)
img = psfCSST.PSF_data[iwave][ipsf]['psfMat']
npix = img.shape[0]
dng = 105
imgg = img[dng:-dng, dng:-dng]
plt.imshow(imgg)
imgX = psfCSST.PSF_data[iwave][ipsf]['image_x'] #in mm
imgY = psfCSST.PSF_data[iwave][ipsf]['image_y'] #in mm
deltX= psfCSST.PSF_data[iwave][ipsf]['centroid_x'] #in mm
deltY= psfCSST.PSF_data[iwave][ipsf]['centroid_y'] #in mm
maxX = psfCSST.PSF_data[iwave][ipsf]['max_x']
maxY = psfCSST.PSF_data[iwave][ipsf]['max_y']
cenPix_X = npix/2 + deltX/0.005
cenPix_Y = npix/2 - deltY/0.005
maxPix_X = npix/2 + maxX/0.005-1
maxPix_Y = npix/2 - maxY/0.005-1
plt.plot([cenPix_X-dng],[cenPix_Y-dng], 'rx', ms = 20)
plt.plot([maxPix_X-dng],[maxPix_Y-dng], 'b+', ms=20)
from scipy import ndimage
y, x = ndimage.center_of_mass(img)
plt.plot([x-dng],[y-dng], 'rx', ms = 10, mew=4)
x, y = myUtil.findMaxPix(img)
plt.plot([x-dng],[y-dng], 'b+', ms = 10, mew=4)
plt.savefig('test/figs/test2.jpg')
ObservationSim/PSF/PSFInterp/PSFProcess.py 0 → 100644
View file @ 29ac5666
import os, sys
import numpy as np
import scipy.io
import mpi4py.MPI as MPI
#sys.path.append("/public/home/weichengliang/lnData/CSST_new_framwork/csstPSF_20201222")
import PSFConfig as myConfig
import PSFUtil as myUtil
def mkdir(path):
isExists = os.path.exists(path)
if not isExists:
os.mkdir(path)
############################################
comm = MPI.COMM_WORLD
ThisTask = comm.Get_rank()
NTasks = comm.Get_size()
npsf = 900
psfPath = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
npsfPerTasks = int(npsf/NTasks)
iStart= 0 + npsfPerTasks*ThisTask
iEnd = npsfPerTasks + npsfPerTasks*ThisTask
if ThisTask == NTasks:
iEnd = npsf
for iccd in range(1, 31):
iccdPath = psfPath + '_proc/ccd{:}'.format(iccd)
if ThisTask == 0:
mkdir(iccdPath)
comm.barrier()
for iwave in range(1, 5):
iwavePath = iccdPath + '/wave_{:}'.format(iwave)
if ThisTask == 0:
mkdir(iwavePath)
comm.barrier()
psfMatPath = iwavePath + '/5_psf_array'
if ThisTask == 0:
mkdir(psfMatPath)
comm.barrier()
for ii in range(iStart, iEnd):
ipsf = ii+1
if ThisTask ==0:
print('iccd-iwave-ipsf: {:4}{:4}{:4}'.format(iccd, iwave, ipsf), end='\r',flush=True)
#if iccd != 1 or iwave !=1 or ipsf != 1:
# continue
ipsfOutput = psfMatPath + '/psf_{:}_centroidWgt.mat'.format(ipsf)
psfInfo = myConfig.LoadPSF(iccd, iwave, ipsf, psfPath, InputMaxPixelPos=True)
ipsfMat = psfInfo['psfMat']
npix_y, npix_x = ipsfMat.shape
#if npsf == 100:
# ncut = 160
#if npsf == 900:
# ncut = 200
ncut = int(npix_y*0.90)
ncut = ncut + ncut%2 #even pixs
img, cx, cy = myUtil.centroidWgt(ipsfMat, nt=ncut)
dcx = cx - npix_x/2 #pixel coords -> global coords
dcy = cy - npix_y/2 #pixel coords -> global coords
dcx*= psfInfo['pixsize']*1e-3 #5e-3 #pixels -> mm
dcy*= psfInfo['pixsize']*1e-3 #5e-3 #pixels -> mm
nn = npix_y
dn = int((nn - ncut)/2)
imgt = np.zeros([nn, nn], dtype=np.float32)
imgt[dn:-dn, dn:-dn] = img
scipy.io.savemat(ipsfOutput, {'cx':dcx, 'cy':dcy, 'psf':imgt})
if iccd != 1 or iwave !=1 or ipsf != 1:
if ThisTask == 0:
print('CHECK::', dcx, dcy, psfInfo['centroid_x'],psfInfo['centroid_y'])
ObservationSim/PSF/PSFInterp/PSFUtil.py 0 → 100644
View file @ 29ac5666
import sys
import ctypes
import numpy as np
from scipy import ndimage
import scipy.spatial as spatial
###定义PSF像素的全局坐标###
def psfPixelLayout(nrows, ncols, cenPosRow, cenPosCol, pixSizeInMicrons=5.0):
"""
convert psf pixels to physical position
Parameters:
nrows, ncols (int, int): psf sampling with [nrows, ncols].
cenPosRow, cenPosCol (float, float): A physical position of the chief ray for a given psf.
pixSizeInMicrons (float-optional): The pixel size in microns from the psf sampling.
Returns:
psfPixelPos (numpy.array-float): [posx, posy] in mm for [irow, icol]
Notes:
1. show positions on ccd, but not position on image only [+/- dy]
"""
psfPixelPos = np.zeros([2, nrows, ncols])
if nrows % 2 != 0:
sys.exit()
if ncols % 2 != 0:
sys.exit()
cenPix_row = nrows/2 + 1 #中心主光线对应pixle [由长光定义]
cenPix_col = ncols/2 + 1
for irow in range(nrows):
for icol in range(ncols):
delta_row = ((irow + 1) - cenPix_row)*pixSizeInMicrons*1e-3
delta_col = ((icol + 1) - cenPix_col)*pixSizeInMicrons*1e-3
psfPixelPos[0, irow, icol] = cenPosCol + delta_col
psfPixelPos[1, irow, icol] = cenPosRow - delta_row #note-1:in CCD全局坐标
return psfPixelPos
###查找最大pixel位置###
def findMaxPix(img):
"""
get the pixel position of the maximum-value
Parameters:
img (numpy.array-float): image
Returns:
imgMaxPix_x, imgMaxPix_y (int, int): pixel position in columns & rows
"""
maxIndx = np.argmax(img)
maxIndx = np.unravel_index(maxIndx, np.array(img).shape)
imgMaxPix_x = maxIndx[1]
imgMaxPix_y = maxIndx[0]
return imgMaxPix_x, imgMaxPix_y
###查找neighbors位置###
def findNeighbors(tx, ty, px, py, dr=0.1, dn=1, OnlyDistance=True):
"""
find nearest neighbors by 2D-KDTree
Parameters:
tx, ty (float, float): a given position
px, py (numpy.array, numpy.array): position data for tree
dr (float-optional): distance
dn (int-optional): nearest-N
OnlyDistance (bool-optional): only use distance to find neighbors. Default: True
Returns:
dataq (numpy.array): index
"""
datax = px
datay = py
tree = spatial.KDTree(list(zip(datax.ravel(), datay.ravel())))
dataq=[]
rr = dr
if OnlyDistance == True:
dataq = tree.query_ball_point([tx, ty], rr)
if OnlyDistance == False:
while len(dataq) < dn:
dataq = tree.query_ball_point([tx, ty], rr)
rr += dr
dd = np.hypot(datax[dataq]-tx, datay[dataq]-ty)
ddSortindx = np.argsort(dd)
dataq = np.array(dataq)[ddSortindx[0:dn]]
return dataq
###查找neighbors位置-hoclist###
def hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy):
if np.max(partx) > nhocx*dhocx:
print('ERROR')
sys.exit()
if np.max(party) > nhocy*dhocy:
print('ERROR')
sys.exit()
npart = partx.size
hoclist= np.zeros(npart, dtype=np.int32)-1
hoc = np.zeros([nhocy, nhocx], dtype=np.int32)-1
for ipart in range(npart):
ix = int(partx[ipart]/dhocx)
iy = int(party[ipart]/dhocy)
hoclist[ipart] = hoc[iy, ix]
hoc[iy, ix] = ipart
return hoc, hoclist
def hocFind(px, py, dhocx, dhocy, hoc, hoclist):
ix = int(px/dhocx)
iy = int(py/dhocy)
neigh=[]
it = hoc[iy, ix]
while it != -1:
neigh.append(it)
it = hoclist[it]
return neigh
def findNeighbors_hoclist(px, py, tx=None,ty=None, dn=4, hoc=None, hoclist=None):
nhocy = nhocx = 20
pxMin = np.min(px)
pxMax = np.max(px)
pyMin = np.min(py)
pyMax = np.max(py)
dhocx = (pxMax - pxMin)/(nhocx-1)
dhocy = (pyMax - pyMin)/(nhocy-1)
partx = px - pxMin +dhocx/2
party = py - pyMin +dhocy/2
if hoc is None:
hoc, hoclist = hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy)
return hoc, hoclist
if hoc is not None:
tx = tx - pxMin +dhocx/2
ty = ty - pyMin +dhocy/2
itx = int(tx/dhocx)
ity = int(ty/dhocy)
ps = [-1, 0, 1]
neigh=[]
for ii in range(3):
for jj in range(3):
ix = itx + ps[ii]
iy = ity + ps[jj]
if ix < 0:
continue
if iy < 0:
continue
if ix > nhocx-1:
continue
if iy > nhocy-1:
continue
#neightt = myUtil.hocFind(ppx, ppy, dhocx, dhocy, hoc, hoclist)
it = hoc[iy, ix]
while it != -1:
neigh.append(it)
it = hoclist[it]
#neigh.append(neightt)
#ll = [i for k in neigh for i in k]
if dn != -1:
ptx = np.array(partx[neigh])
pty = np.array(party[neigh])
dd = np.hypot(ptx-tx, pty-ty)
idx = np.argsort(dd)
neigh= np.array(neigh)[idx[0:dn]]
return neigh
###PSF中心对齐###
def psfCentering(img, apSizeInArcsec=0.5, psfSampleSizeInMicrons=5, focalLengthInMeters=28, CenteringMode=1):
"""
centering psf within an aperture
Parameters:
img (numpy.array): image
apSizeInArcsec (float-optional): aperture size in arcseconds.
psfSampleSizeInMicrons (float-optional): psf pixel size in microns.
focalLengthInMeters (float-optional): csst focal length im meters.
CenteringMode (int-optional): how to center psf images
Returns:
imgT (numpy.array)
"""
if CenteringMode == 1:
imgMaxPix_x, imgMaxPix_y = findMaxPix(img)
if CenteringMode == 2:
y,x = ndimage.center_of_mass(img) #y-rows, x-cols
imgMaxPix_x = int(x)
imgMaxPix_y = int(y)
apSizeInMicrons = np.deg2rad(apSizeInArcsec/3600.)*focalLengthInMeters*1e6
apSizeInPix = apSizeInMicrons/psfSampleSizeInMicrons
apSizeInPix = np.int(np.ceil(apSizeInPix))
imgT = np.zeros_like(img)
ngy, ngx = img.shape
cy = int(ngy/2)
cx = int(ngx/2)
imgT[cy-apSizeInPix:cy+apSizeInPix+1,
cx-apSizeInPix:cx+apSizeInPix+1] = \
img[imgMaxPix_y-apSizeInPix:imgMaxPix_y+apSizeInPix+1,
imgMaxPix_x-apSizeInPix:imgMaxPix_x+apSizeInPix+1]
return imgT
###插值对齐-fft###
def psfCentering_FFT(image):
"""
centering psf within an aperture by FFT
"""
ny, nx = image.shape
py, px = ndimage.center_of_mass(image)
dx = (px - nx/2)
dy = (py - ny/2)
k=np.zeros((nx,ny,2),dtype=float)
fg=np.fft.fft2(image)
ge =np.zeros_like(fg)
inx = int(nx/2)
jny = int(ny/2)
#prepare for the phase multiply matrix
#left bottom
for i in range(inx+1):
for j in range(jny+1):
k[i][j][0]=i;
k[i][j][1]=j;
#top right
for i in range(inx-1):
for j in range(jny-1):
k[i+inx+1][j+jny+1][0]=(-(nx/2-1)+i)
k[i+inx+1][j+jny+1][1]=(-(ny/2-1)+j)
#bottom right
for i in range(inx+1):
for j in range(jny-1):
k[i][j+jny+1][0]=i
k[i][j+jny+1][1]=(-(ny/2-1)+j)
#top left
for i in range(inx-1):
for j in range(int(jny+1)):
k[i+inx+1][j][0]=(-(nx/2-1)+i)
k[i+inx+1][j][1]=j
for i in range(nx):
for j in range(ny):
ge[i][j]=fg[i][j]*np.exp(2.*np.pi*(dx*k[i][j][0]/nx+dy*k[i][j][1]/ny)*1j)
get=np.fft.ifft2(ge).real
return(get)
###图像叠加###
def psfStack(*psfMat):
"""
stacked image from the different psfs
Parameters:
*psfMat (numpy.array): the different psfs for stacking
Returns:
img (numpy.array): image
"""
nn = len(psfMat)
img = np.zeros_like(psfMat[0])
for ii in range(nn):
img += psfMat[ii]/np.sum(psfMat[ii])
img /= np.sum(img)
return img
###计算PSF椭率-接口###
def psfSizeCalculator(psfMat, psfSampleSize=2.5, CalcPSFcenter=True, SigRange=True, TailorScheme=2, cenPix=None):
"""
calculate psf size & ellipticity
Parameters:
psfMat (numpy.array): image
psfSampleSize (float-optional): psf size in microns.
CalcPSFcenter (bool-optional): whether calculate psf center. Default: True
SigRange (bool-optional): whether use psf tailor. Default: False
TailorScheme (int-optional): which method for psf tailor. Default: 1
Returns:
cenX, cenY (float, float): the pixel position of the mass center
sz (float): psf size
e1, e2 (float, float): psf ellipticity
REE80 (float): radius of REE80 in arcseconds
"""
psfSampleSize = psfSampleSize*1e-3 #mm
REE80 = -1.0 ##encircling 80% energy
if SigRange is True:
if TailorScheme == 1:
psfMat = imSigRange(psfMat, fraction=0.80)
psfInfo['psfMat'] = psfMat #set on/off
if TailorScheme == 2:
#img = psfTailor(psfMat, apSizeInArcsec=0.5)
imgX, REE80 = psfEncircle(psfMat, cenPix=cenPix)
#psfMat = img
REE80 = REE80[0]
if CalcPSFcenter is True:
img = psfMat/np.sum(psfMat)
y,x = ndimage.center_of_mass(img) #y-rows, x-cols
cenX = x
cenY = y
if CalcPSFcenter is False:
cenPix_X = psfMat.shape[1]/2 #90
cenPix_Y = psfMat.shape[0]/2 #90
cenX = cenPix_X + psfInfo['centroid_x']/psfSampleSize
cenY = cenPix_Y - psfInfo['centroid_y']/psfSampleSize
pixSize = 1
sz, e1, e2 = psfSecondMoments(psfMat, cenX, cenY, pixSize=pixSize)
return cenX, cenY, sz, e1, e2, REE80
###计算PSF椭率###
def psfSecondMoments(psfMat, cenX, cenY, pixSize=1):
"""
estimate the psf ellipticity by the second moment of surface brightness
Parameters:
psfMat (numpy.array-float): image
cenX, cenY (float, float): pixel position of the psf center
pixSize (float-optional): pixel size
Returns:
sz (float): psf size
e1, e2 (float, float): psf ellipticity
"""
apr = 0.5 #arcsec, 0.5角秒内测量
fl = 28. #meters
pxs = 5.0 #microns
apr = np.deg2rad(apr/3600.)*fl*1e6
apr = apr/pxs
apr = np.int(np.ceil(apr))
I = psfMat
ncol = I.shape[1]
nrow = I.shape[0]
w = 0.0
w11 = 0.0
w12 = 0.0
w22 = 0.0
for icol in range(ncol):
for jrow in range(nrow):
x = icol*pixSize - cenX
y = jrow*pixSize - cenY
rr = np.sqrt(x*x + y*y)
wgt= 0.0
if rr <= apr:
wgt = 1.0
w += I[jrow, icol]*wgt
w11 += x*x*I[jrow, icol]*wgt
w12 += x*y*I[jrow, icol]*wgt
w22 += y*y*I[jrow, icol]*wgt
w11 /= w
w12 /= w
w22 /= w
sz = w11 + w22
e1 = (w11 - w22)/sz
e2 = 2.0*w12/sz
return sz, e1, e2
###计算REE80###
def psfEncircle(img, fraction=0.8, psfSampleSizeInMicrons=2.5, focalLengthInMeters=28, cenPix=None):
"""
psf tailor within a given percentage.
Parameters:
img (numpy.array-float): image
fraction (float-optional): a percentage for psf tailor.
psfSampleSizeInMicrons (float-optional): psf pixel size in microns.
focalLengthInMeters (float-optional): csst focal length im meters.
Returns:
img*wgt (numpy.array-float): image
REE80 (float): radius of REE80 in arcseconds.
"""
#imgMaxPix_x, imgMaxPix_y = findMaxPix(img)
y,x = ndimage.center_of_mass(img) #y-rows, x-cols
imgMaxPix_x = x #int(x)
imgMaxPix_y = y #int(y)
if cenPix != None:
imgMaxPix_x = cenPix[0]
imgMaxPix_y = cenPix[1]
im1 = img.copy()
im1size = im1.shape
dis = np.zeros_like(img)
for irow in range(im1size[0]):
for icol in range(im1size[1]):
dx = icol - imgMaxPix_x
dy = irow - imgMaxPix_y
dis[irow, icol] = np.hypot(dx, dy)
nn = im1size[1]*im1size[0]
disX = dis.reshape(nn)
disXsortId = np.argsort(disX)
imgX = img.reshape(nn)
imgY = imgX[disXsortId]
psfFrac = np.cumsum(imgY)/np.sum(imgY)
ind = np.where(psfFrac > fraction)[0][0]
wgt = np.ones_like(dis)
#wgt[np.where(dis > dis[np.where(img == imgY[ind])])] = 0
REE80 = np.rad2deg(dis[np.where(img == imgY[ind])]*psfSampleSizeInMicrons*1e-6/focalLengthInMeters)*3600
return img*wgt, REE80
###图像能量百分比查找###
def imSigRange(img, fraction=0.80):
"""
extract the image within x-percent (DISCARD)
Parameters:
img (numpy.array-float): image
fraction (float-optional): a percentage
Returns:
im1 (numpy.array-float): image
"""
im1 = img.copy()
im1size = im1.shape
im2 = np.sort(im1.reshape(im1size[0]*im1size[1]))
im2 = im2[::-1]
im3 = np.cumsum(im2)/np.sum(im2)
loc = np.where(im3 > fraction)
#print(im3[loc[0][0]], im2[loc[0][0]])
im1[np.where(im1 <= im2[loc[0][0]])]=0
return im1
###孔径内图像裁剪###
def psfTailor(img, apSizeInArcsec=0.5, psfSampleSizeInMicrons=5, focalLengthInMeters=28, cenPix=None):
"""
psf tailor within a given aperture size
Parameters:
img (numpy.array-float): image
apSizeInArcsec (float-optional): aperture size in arcseconds.
psfSampleSizeInMicrons (float-optional): psf pixel size in microns.
focalLengthInMeters (float-optional): csst focal length im meters.
Returns:
imgT (numpy.array-float): image
"""
#imgMaxPix_x, imgMaxPix_y = findMaxPix(img)
y,x = ndimage.center_of_mass(img) #y-rows, x-cols
imgMaxPix_x = int(x)
imgMaxPix_y = int(y)
if cenPix != None:
imgMaxPix_x = int(cenPix[0])
imgMaxPix_y = int(cenPix[1])
apSizeInMicrons = np.deg2rad(apSizeInArcsec/3600.)*focalLengthInMeters*1e6
apSizeInPix = apSizeInMicrons/psfSampleSizeInMicrons
apSizeInPix = np.int(np.ceil(apSizeInPix))
print('apSizeInPix=',apSizeInPix)
imgT = np.zeros_like(img)
imgT[imgMaxPix_y-apSizeInPix:imgMaxPix_y+apSizeInPix+1,
imgMaxPix_x-apSizeInPix:imgMaxPix_x+apSizeInPix+1] = \
img[imgMaxPix_y-apSizeInPix:imgMaxPix_y+apSizeInPix+1,
imgMaxPix_x-apSizeInPix:imgMaxPix_x+apSizeInPix+1]
return imgT
###centroid with a window###
def centroidWgt(img, nt=160):
#libCentroid = ctypes.CDLL('/public/home/weichengliang/lnData/CSST_new_framwork/csstPSF/libCentroid/libCentroid.so') # CDLL加载库
libCentroid = ctypes.CDLL('/public/home/weichengliang/lnData/CSST_new_framwork/csstPSF_20210108/libCentroid/libCentroid.so') # CDLL加载库
libCentroid.centroidWgt.argtypes = [ctypes.POINTER(ctypes.c_float), ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_double)]
libCentroid.imSplint.argtypes = [ctypes.POINTER(ctypes.c_float), ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_float)]
imx = img/np.sum(img)
ny, nx = imx.shape
#imx centroid
nn = nx*ny
arr = (ctypes.c_float*nn)()
arr[:] = imx.reshape(nn)
para = (ctypes.c_double*10)()
libCentroid.centroidWgt(arr, ny, nx, para)
imx_cy = para[3] #irow
imx_cx = para[4] #icol
#imx -> imy
nxt=nyt=nt
nn=nxt*nyt
yat = (ctypes.c_float*nn)()
libCentroid.imSplint(arr, ny, nx, para, nxt, nyt, yat)
imy = np.array(yat[:]).reshape([nxt, nyt])
return imy, imx_cx, imx_cy
'''
def psfCentering_wgt(ipsfMat, psf_image_x, psf_image_y, psfSampleSizeInMicrons=5.0):
img, cx, cy = centroidWgt(ipsfMat, nt=160)
nrows, ncols = ipsfMat.shape
cyt = (cy + nrows/2)*psfSampleSizeInMicrons*1e-3 +psf_image_y
cxt = (cx + ncols/2)*psfSampleSizeInMicrons*1e-3 +psf_image_x
return img, cxt, cyt
'''
###binning image###
def binningPSF(img, ngg):
imgX = img.reshape(ngg, img.shape[0]//ngg, ngg, img.shape[1]//ngg).mean(-1).mean(1)
return imgX
ObservationSim/PSF/PSFInterp/__init__.py 0 → 100644
View file @ 29ac5666
from .PSFInterp import PSFInterp
\ No newline at end of file
ObservationSim/PSF/PSFInterp/libCentroid/Makefile 0 → 100644
View file @ 29ac5666
#OPTS += -D
CC = gcc
OPTIMIZE = -fPIC -g -O3 #-Wall -wd981 #-wd1419 -wd810
#GSLI = -I/home/alex/opt/gsl/include
#GSLL = -L/home/alex/opt/gsl/lib -lgsl -lgslcblas
#FFTWI = -I/home/alex/opt/fftw/include
#FFTWL = -L/home/alex/opt/fftw/lib -lfftw3 -lfftw3f
#HDF5I = -I/home/alex/opt/hdf5/include
#HDF5L = -L/home/alex/opt/hdf5/lib -lhdf5_hl -lhdf5
#FITSI = -I/home/alex/opt/cfitsio/include
#FITSL = -L/home/alex/opt/cfitsio/lib -lcfitsio
#EXTRACFLAGS =
#EXTRACLIB =
CLINK=$(CC)
CFLAGS=$(OPTIMIZE) #$(EXTRACFLAGS) $(OPTS)
CLIB= -shared -lm #$(EXTRACLIB)
OBJS = centroidWgt.o nrutil.o
EXEC = libCentroid.so
all: $(EXEC)
$(EXEC): $(OBJS)
$(CLINK) $(CFLAGS) -o $@ $(OBJS) $(CLIB)
$(OBJS): nrutil.h Makefile
.PHONY : clean
clean:
rm -f *.o $(EXEC)
ObservationSim/PSF/PSFInterp/libCentroid/centroidWgt.c 0 → 100644
View file @ 29ac5666
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include "nrutil.h"
//y is the input image, nx and ny are the pixels along x and y axis;
//yt and residu are the required matrix for the function
//para contains the needed parameters: centx=para[3], centy=para[4]
void star_gaus(float **y,int nx,int ny,double *para);
void Interp_bicubic(int nx,int ny,float **a0,int nbound, int nxt,int nyt, float **at,double *xcen);
void splie2(float **ya, int m, int n, float **y2a);
void spline(float y[], int n, float yp1, float ypn, float y2[]);
void splint(float ya[], float y2a[], int n, float x, float *y);
void centroidWgt(float *y, int nx, int ny, double *para)
{
int i, j, k;
float **yy;
double **basef;
double **coff;
double ccol, crow;
yy = matrix(0, nx-1, 0, ny-1);
for(i=0; i<nx; i++)
for(j=0; j<ny; j++)
{
k = j + i*nx;
yy[i][j] = y[k];
}
star_gaus(yy, nx, ny, para);
//ccol = para[4];
//crow = para[3];
}
void imSplint(float *y, int nx, int ny, double *para, int nxt, int nyt, float *yat)
{
int i, j, k;
float **yy;
double **basef;
double **coff;
double ccol, crow;
yy = matrix(0, nx-1, 0, ny-1);
for(i=0; i<nx; i++)
for(j=0; j<ny; j++)
{
k = j + i*nx;
yy[i][j] = y[k];
}
crow = para[3];
ccol = para[4];
int nbound;
float **at;
double xcen[2];
at = matrix(0, nxt-1, 0, nyt-1);
nbound = (int)((nx - nxt)/2);
xcen[0]= crow;
xcen[1]= ccol;
Interp_bicubic(nx, ny, yy, nbound, nxt, nyt, at, xcen);
for(i=0; i<nxt; i++)
for(j=0; j<nyt; j++)
{
k = j + i*nxt;
yat[k] = at[i][j];
}
}
//void star_gaus(float **y,int nx,int ny,float **yt,float **residu,double *para)
void star_gaus(float **y,int nx,int ny,double *para)
{
void gasfit_2D(double **x, double *y,int np,double *para,double bg0, int fbg);
double **xs,*ys,ymax;
int i,j,np,npt,im,jm,**xi,k;
double kc,bgc,sigmac,xc,yc,sigma2v,det;
double bg0=0;
int fbg=0;
np=nx*ny;
xs=dmatrix(0,np-1,0,1);
ys=dvector(0,np-1);
xi=imatrix(0,np-1,0,1);
ymax=y[0][0];
for(i=0;i<nx;i++)for(j=0;j<ny;j++){
if(ymax<y[i][j]){ymax=y[i][j];im=i;jm=j;}
}
int cutPix;
cutPix = 23;
npt=0;
for(i=-cutPix;i<=cutPix;i++){
for(j=-cutPix;j<=cutPix;j++){
xs[npt][0]=xi[npt][0]=i+im;
xs[npt][1]=xi[npt][1]=j+jm;
ys[npt]=y[im+i][jm+j];
npt++;
}
}
gasfit_2D(xs, ys,npt,para,bg0,fbg);
kc=para[0];sigmac=para[1];bgc=para[2];xc=para[3];yc=para[4];
// printf("%e %e %e %e %e\n",kc,sigmac,bgc,xc,yc);
/*
sigma2v=-1./(2.*sigmac*sigmac);
for(i=0;i<nx;i++){
for(j=0;j<ny;j++){
det=DSQR(i-xc)+DSQR(j-yc);
yt[i][j]=kc*exp(det*sigma2v)+bgc;
residu[i][j]=y[i][j]-yt[i][j];
}
}
*/
free_dmatrix(xs,0,np-1,0,1);
free_imatrix(xi,0,np-1,0,1);
free_dvector(ys,0,np-1);
}
void gasfit_2D(double **x, double *y,int np,double *para,double bg0,int fbg)
{
void search_2D(double **x,double *y,int np,double kc,double kd,double sigmac,
double sigmad,double bgc,double bgd,double xc, double xd,
double yc, double yd,double *para,double bg0,int fbg);
int i,j,k,imax=0,isigma;
double ymax,ymin,kc,kd,sigmac,sigmad,bgc,bgd,ysigma,xc,yc,xd,yd;
double det,dett;
ymin=ymax=y[imax];
for(i=1;i<np;i++){
if(ymax<y[i]){imax=i;ymax=y[i];}
if(ymin>y[i])ymin=y[i];
}
kc=ymax;kd=ymax/12.;
det=ysigma=kc*exp(-0.5);
for(i=0;i<np;i++){
dett=fabs(ysigma-y[i]);
if(dett<det && y[i]!=kc){det=dett;isigma=i;}
//if(dett<det){det=dett;isigma=i;}
}
xc=x[imax][0];yc=x[imax][1];
sigmac=sqrt(DSQR(xc-x[isigma][0])+DSQR(yc-x[isigma][1]))*1.;
xd=yd=sigmac*0.25;
sigmad=0.25*sigmac;
bgc=0.;bgd=fabs(ymin);
for(i=0;i<4;i++){
search_2D(x,y,np,kc,kd,sigmac,sigmad,bgc,bgd,xc,xd,yc,yd,para,bg0,fbg);
kd*=0.33;sigmad*=0.33;bgd*=0.33;xd*=0.33;yd*=0.33;
kc=para[0];sigmac=para[1];bgc=para[2];xc=para[3];yc=para[4];
}
if(fbg==0)para[2]=bg0;
}
void search_2D(double **x,double *y,int np,double kc,double kd,double sigmac,
double sigmad,double bgc,double bgd,double xc, double xd,
double yc, double yd,double *para,double bg0,int fbg)
{
double k,sigma,bg,k2,k20,sigma2v,det,xt,yt;
int i,j,l,m,p,q;
sigma2v=-1./(2.*sigmac*sigmac);
bg=bgc;
k20=0;
for(m=0;m<np;m++){
det=DSQR(x[m][0]-xc)+DSQR(x[m][1]-yc);
det=kc*exp(det*sigma2v)+bgc-y[m];
k20+=det*det;
}
for(i=-4;i<=4;i++){
k=kc+i*kd;
if(k>0){
for(j=-4;j<=4;j++){
sigma=sigmac+j*sigmad;
if(sigma>0){
sigma2v=-1./(2.*sigma*sigma);
for(p=-4;p<=4;p++){
xt=xc+p*xd;
for(q=-4;q<=4;q++){
yt=yc+q*yd;
k2=0;
if(fbg==0){
bg=bg0;
for(m=0;m<np;m++){
det=DSQR(x[m][0]-xt)+DSQR(x[m][1]-yt);
det=k*exp(det*sigma2v)+bg-y[m];
k2+=det*det;
}
}
else{
for(l=-4;l<=4;l++){
bg=bgc+l*bgd;
for(m=0;m<np;m++){
det=DSQR(x[m][0]-xt)+DSQR(x[m][1]-yt);
det=k*exp(det*sigma2v)+bg-y[m];
k2+=det*det;
}
}
}
//printf("k20=%e k2=%e\n",k20,k2);
if(k2<=k20){k20=k2;para[5]=k2;
para[0]=k;para[1]=sigma;para[2]=bg;para[3]=xt;para[4]=yt;}
}
}
}
}
}
}
}
/////////////////////////////
//#include <math.h>
//#include <stdio.h>
//#include <stdlib.h>
//#include "nrutil.h"
//nx,ny is the pixels of x and y direction
//a0 is the input image in nx*ny
//nbound is the boundary limit
//nxt,nyt is out put image dimentions
//at is the output image and cen represent the required center
void Interp_bicubic(int nx,int ny,float **a0,int nbound,
int nxt,int nyt, float **at,double *xcen)
{
void splie2(float **ya, int m, int n, float **y2a);
void spline(float y[], int n, float yp1, float ypn, float y2[]);
void splint(float ya[], float y2a[], int n, float x, float *y);
int i,j,m,n,ic,jc;
float *ytmp,*yytmp,**y2a,x1,x2,shift1,shift2;
y2a=matrix(0,nx,0,ny);
ytmp=vector(0,ny);
yytmp=vector(0,ny);
splie2(a0,nx,ny,y2a);
ic=nx*0.5;
jc=ny*0.5;
shift1=xcen[0]-ic;//-0.5;
shift2=xcen[1]-jc;//-0.5;
if(fabs(shift1)>nbound || fabs(shift2)>nbound){
printf("bicubic shifts too much %e %e\n",shift1,shift2);
getchar();
}
for (n=0;n<nyt;n++){
x2=n+nbound+shift2;
for(i=0;i<nx;i++){
splint(a0[i],y2a[i],ny,x2,&yytmp[i]);
spline(yytmp,ny,1.0e30,1.0e30,ytmp);
}
for (m=0;m<nxt;m++){
x1=m+nbound+shift1;
splint(yytmp,ytmp,ny,x1,&at[m][n]);
}
}
free_vector(yytmp,0,ny);
free_vector(ytmp,0,ny);
free_matrix(y2a,0,nx,0,ny);
}
void splie2(float **ya, int m, int n, float **y2a)
{
void spline(float y[], int n, float yp1, float ypn, float y2[]);
int j;
for (j=0;j<m;j++)spline(ya[j],n,1.0e30,1.0e30,y2a[j]);
}
void spline(float y[], int n, float yp1, float ypn, float y2[])
{
int i,k;
float p,qn,sig,un,*u;
u=vector(0,n-1);
if (yp1 > 0.99e30)
y2[0]=u[0]=0.0;
else {
y2[0] = -0.5;
u[0]=3.0*(y[1]-y[0]-yp1);
}
sig=0.5;
for (i=1;i<=n-2;i++) {
p=sig*y2[i-1]+2.0;
y2[i]=(sig-1.0)/p;
u[i]=(y[i+1]-2.*y[i]+y[i-1]);
u[i]=(3.0*u[i]-sig*u[i-1])/p;
}
if (ypn > 0.99e30)
qn=un=0.0;
else {
qn=0.5;
un=3.0*(ypn-y[n-1]+y[n-2]);
}
y2[n-1]=(un-qn*u[n-2])/(qn*y2[n-2]+1.0);
for (k=n-2;k>=0;k--)y2[k]=y2[k]*y2[k+1]+u[k];
free_vector(u,0,n-1);
}
void splint(float ya[], float y2a[], int n, float x, float *y)
{
void nrerror(char error_text[]);
int klo,khi,k;
float h,b,a;
klo=x;
if( klo<0)klo=0;
if(klo==n-1)klo=n-2;
khi=klo+1;
if (klo<0 || khi>=n) printf("error:klo, khi, n: %d %d %d\n", klo, khi, n);
if (klo<0 || khi>=n) nrerror("Bad xa input to routine splint");
a=(khi-x);
b=(x-klo);
*y=a*ya[klo]+b*ya[khi]+((a*a*a-a)*y2a[klo]+(b*b*b-b)*y2a[khi])/6.0;
}
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