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Commit df22b3b0 authored by Fang Yuedong's avatar Fang Yuedong
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Merge branch 'master' into 'release_v3.0' 

Release version v3.2.0

See merge request !33
parents 1b4f4012 428f2c1e
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observation_sim/instruments/chip/Chip.py
View file @ df22b3b0
......@@ -191,7 +191,7 @@ class Chip(FocalPlane):
"""Return the filter index and type for a given chip #(chipID)
"""
filter_type_list = _util.ALL_FILTERS
if chipID == None:
if chipID is None:
chipID = self.chipID
# updated configurations
......
observation_sim/instruments/chip/chip_utils.py
View file @ df22b3b0
......@@ -173,6 +173,13 @@ def get_flat(img, seed):
return flat_img, flat_normal
def get_innerflat(chip=None, filt=None):
from observation_sim.mock_objects import FlatLED
led_obj = FlatLED(chip, filt)
flat_img = led_obj.getInnerFlat()
return flat_img
def add_cosmic_rays(img, chip, exptime=150, seed=0):
cr_map, cr_event_num = effects.produceCR_Map(
xLen=chip.npix_x, yLen=chip.npix_y,
......@@ -206,7 +213,7 @@ def get_poisson(seed=0, sky_level=0.):
def get_base_img(img, chip, read_noise, readout_time, dark_noise, exptime=150., InputDark=None):
if InputDark == None:
if InputDark is None:
# base_level = read_noise**2 + dark_noise*(exptime+0.5*readout_time)
# base_level = dark_noise*(exptime+0.5*readout_time)
base_level = dark_noise*(exptime)
......@@ -237,7 +244,7 @@ def add_poisson(img, chip, exptime=150., seed=0, sky_level=0., poisson_noise=Non
img.addNoise(poisson_noise)
# img -= read_noise**2
if InputDark != None:
if InputDark is not None:
# "Instrument/data/dark/dark_1000s_example_0.fits"
hdu = fits.open(InputDark)
img += hdu[0].data/hdu[0].header['exptime']*exptime
......
observation_sim/instruments/chip/effects.py
View file @ df22b3b0
......@@ -3,7 +3,8 @@ from matplotlib.pyplot import flag
import numpy as np
from numpy.core.fromnumeric import mean, size
from numpy.random import Generator, PCG64
import math,copy
import math
import copy
from numba import jit
from astropy import stats
......@@ -22,7 +23,7 @@ def AddOverscan(GSImage, overscan=1000, gain=1, widthl=27, widthr=27, widtht=8,
NoiseOS = galsim.GaussianNoise(rng, sigma=read_noise)
newimg.addNoise(NoiseOS)
newimg = (newimg+overscan)/gain
newimg.array[widtht:(widtht+imgshape[0]),widthl:(widthl+imgshape[1])] = GSImage.array
newimg.array[widtht:(widtht+imgshape[0]), widthl:(widthl+imgshape[1])] = GSImage.array
newimg.wcs = GSImage.wcs
# if GSImage.wcs is not None:
# newwcs = GSImage.wcs.withOrigin(galsim.PositionD(widthl,widtht))
......@@ -37,11 +38,11 @@ def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=
# Hot Pixel > 20e-/s
# Dead Pixel < 70%*Mean
rgf = Generator(PCG64(int(seed*1.1)))
if IfHotPix==True and IfDeadPix==True:
if IfHotPix is True and IfDeadPix is True:
HotFraction = rgf.random() # fraction in total bad pixels
elif IfHotPix==False and IfDeadPix==False:
elif IfHotPix is False and IfDeadPix is False:
return GSImage
elif IfHotPix==True:
elif IfHotPix is True:
HotFraction = 1
else:
HotFraction = 0
......@@ -50,28 +51,28 @@ def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=
NPixBad = int(NPix*fraction)
NPixHot = int(NPix*fraction*HotFraction)
NPixDead = NPixBad-NPixHot
NPix_y,NPix_x = GSImage.array.shape
NPix_y, NPix_x = GSImage.array.shape
mean = np.mean(GSImage.array)
rgp = Generator(PCG64(int(seed)))
yxposfrac = rgp.random((NPixBad,2))
YPositHot = np.array(NPix_y*yxposfrac[0:NPixHot,0]).astype(np.int32)
XPositHot = np.array(NPix_x*yxposfrac[0:NPixHot,1]).astype(np.int32)
YPositDead = np.array(NPix_y*yxposfrac[NPixHot:,0]).astype(np.int32)
XPositDead = np.array(NPix_x*yxposfrac[NPixHot:,1]).astype(np.int32)
yxposfrac = rgp.random((NPixBad, 2))
YPositHot = np.array(NPix_y*yxposfrac[0:NPixHot, 0]).astype(np.int32)
XPositHot = np.array(NPix_x*yxposfrac[0:NPixHot, 1]).astype(np.int32)
YPositDead = np.array(NPix_y*yxposfrac[NPixHot:, 0]).astype(np.int32)
XPositDead = np.array(NPix_x*yxposfrac[NPixHot:, 1]).astype(np.int32)
rgh = Generator(PCG64(int(seed*1.2)))
rgd = Generator(PCG64(int(seed*1.3)))
if IfHotPix==True:
GSImage.array[YPositHot,XPositHot] += rgh.gamma(2,25*150,size=NPixHot)
if IfDeadPix==True:
GSImage.array[YPositDead,XPositDead] = rgd.random(NPixDead)*(mean-biaslevel)*0.7+biaslevel+rgp.standard_normal()*5
if IfHotPix is True:
GSImage.array[YPositHot, XPositHot] += rgh.gamma(2, 25*150, size=NPixHot)
if IfDeadPix is True:
GSImage.array[YPositDead, XPositDead] = rgd.random(NPixDead)*(mean-biaslevel)*0.7+biaslevel+rgp.standard_normal()*5
return GSImage
def BadColumns(GSImage, seed=20240309, chipid=1, logger=None):
# Set bad column values
ysize,xsize = GSImage.array.shape
ysize, xsize = GSImage.array.shape
subarr = GSImage.array[int(ysize*0.1):int(ysize*0.12), int(xsize*0.1):int(xsize*0.12)]
subarr = stats.sigma_clip(subarr, sigma=4, cenfunc='median', maxiters=3, masked=False)
meanimg = np.median(subarr)
......@@ -82,8 +83,8 @@ def BadColumns(GSImage, seed=20240309, chipid=1, logger=None):
rgxpos = Generator(PCG64(int(seed*1.2)))
rgdn = Generator(PCG64(int(seed*1.3)))
nbadsecA,nbadsecD = rgn.integers(low=1, high=5, size=2)
collen = rgcollen.integers(low=int(ysize*0.1), high=int(ysize*0.7), size=(nbadsecA+nbadsecD))
nbadsecA, nbadsecD = rgn.integers(low=1, high=5, size=2)
collen = rgcollen.integers(low=int(ysize*0.1), high=int(ysize*0.5), size=(nbadsecA+nbadsecD))
xposit = rgxpos.integers(low=int(xsize*0.05), high=int(xsize*0.95), size=(nbadsecA+nbadsecD))
if logger is not None:
logger.info(xposit+1)
......@@ -91,47 +92,47 @@ def BadColumns(GSImage, seed=20240309, chipid=1, logger=None):
print(xposit+1)
# signs = 2*rgdn.integers(0,2,size=(nbadsecA+nbadsecD))-1
# if meanimg>0:
dn = rgdn.integers(low=np.abs(meanimg)*1.3+50, high=np.abs(meanimg)*2+150, size=(nbadsecA+nbadsecD)) #*signs
dn = rgdn.integers(low=np.abs(meanimg)*1.3+50, high=np.abs(meanimg)*2+150, size=(nbadsecA+nbadsecD)) # *signs
# elif meanimg<0:
# dn = rgdn.integers(low=meanimg*2-150, high=meanimg*1.3-50, size=(nbadsecA+nbadsecD)) #*signs
for badcoli in range(nbadsecA):
GSImage.array[(ysize-collen[badcoli]):ysize,xposit[badcoli]:(xposit[badcoli]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli],1)))+dn[badcoli])
GSImage.array[(ysize-collen[badcoli]):ysize, xposit[badcoli]:(xposit[badcoli]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli], 1)))+dn[badcoli])
for badcoli in range(nbadsecD):
GSImage.array[0:collen[badcoli+nbadsecA],xposit[badcoli+nbadsecA]:(xposit[badcoli+nbadsecA]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli+nbadsecA],1)))+dn[badcoli+nbadsecA])
GSImage.array[0:collen[badcoli+nbadsecA], xposit[badcoli+nbadsecA]:(xposit[badcoli+nbadsecA]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli+nbadsecA], 1)))+dn[badcoli+nbadsecA])
return GSImage
def AddBiasNonUniform16(GSImage, bias_level = 500, nsecy = 2, nsecx=8, seed=202102, logger=None):
def AddBiasNonUniform16(GSImage, bias_level=500, nsecy=2, nsecx=8, seed=202102, logger=None):
# Generate Bias and its non-uniformity, and add the 16 bias values to the GS-Image
rg = Generator(PCG64(int(seed)))
Random16 = (rg.random(nsecy*nsecx)-0.5)*20
if int(bias_level)==0:
BiasLevel = np.zeros((nsecy,nsecx))
elif bias_level>0:
BiasLevel = Random16.reshape((nsecy,nsecx)) + bias_level
if int(bias_level) == 0:
BiasLevel = np.zeros((nsecy, nsecx))
elif bias_level > 0:
BiasLevel = Random16.reshape((nsecy, nsecx)) + bias_level
if logger is not None:
msg = str(" Biases of 16 channels: " + str(BiasLevel))
logger.info(msg)
else:
print(" Biases of 16 channels:\n",BiasLevel)
print(" Biases of 16 channels:\n", BiasLevel)
arrshape = GSImage.array.shape
secsize_x = int(arrshape[1]/nsecx)
secsize_y = int(arrshape[0]/nsecy)
for rowi in range(nsecy):
for coli in range(nsecx):
GSImage.array[rowi*secsize_y:(rowi+1)*secsize_y,coli*secsize_x:(coli+1)*secsize_x] += BiasLevel[rowi,coli]
GSImage.array[rowi*secsize_y:(rowi+1)*secsize_y, coli*secsize_x:(coli+1)*secsize_x] += BiasLevel[rowi, coli]
return GSImage
def MakeBiasNcomb(npix_x, npix_y, bias_level=500, ncombine=1, read_noise=5, gain=1, seed=202102, logger=None):
# Start with 0 value bias GS-Image
ncombine=int(ncombine)
ncombine = int(ncombine)
BiasSngImg0 = galsim.Image(npix_x, npix_y, init_value=0)
BiasSngImg = AddBiasNonUniform16(BiasSngImg0,
bias_level=bias_level,
nsecy = 2, nsecx=8,
seed=int(seed),
logger=logger)
BiasSngImg = AddBiasNonUniform16(BiasSngImg0,
bias_level=bias_level,
nsecy=2, nsecx=8,
seed=int(seed),
logger=logger)
BiasCombImg = BiasSngImg*ncombine
rng = galsim.UniformDeviate()
NoiseBias = galsim.GaussianNoise(rng=rng, sigma=read_noise*ncombine**0.5)
......@@ -139,7 +140,7 @@ def MakeBiasNcomb(npix_x, npix_y, bias_level=500, ncombine=1, read_noise=5, gain
if ncombine == 1:
BiasTag = 'Single'
pass
elif ncombine >1:
elif ncombine > 1:
BiasCombImg /= ncombine
BiasTag = 'Combine'
# BiasCombImg.replaceNegative(replace_value=0)
......@@ -147,32 +148,32 @@ def MakeBiasNcomb(npix_x, npix_y, bias_level=500, ncombine=1, read_noise=5, gain
return BiasCombImg, BiasTag
def ApplyGainNonUniform16(GSImage, gain=1, nsecy = 2, nsecx=8, seed=202102, logger=None):
def ApplyGainNonUniform16(GSImage, gain=1, nsecy=2, nsecx=8, seed=202102, logger=None):
# Generate Gain non-uniformity, and multipy the different factors (mean~1 with sigma~1%) to the GS-Image
rg = Generator(PCG64(int(seed)))
Random16 = (rg.random(nsecy*nsecx)-0.5)*0.04+1 # sigma~1%
Gain16 = Random16.reshape((nsecy,nsecx))/gain
Gain16 = Random16.reshape((nsecy, nsecx))/gain
gain_array = np.ones(nsecy*nsecx)*gain
if logger is not None:
msg = str("Gain of 16 channels: " + str(Gain16))
logger.info(msg)
else:
print("Gain of 16 channels: ",Gain16)
print("Gain of 16 channels: ", Gain16)
arrshape = GSImage.array.shape
secsize_x = int(arrshape[1]/nsecx)
secsize_y = int(arrshape[0]/nsecy)
for rowi in range(nsecy):
for coli in range(nsecx):
GSImage.array[rowi*secsize_y:(rowi+1)*secsize_y,coli*secsize_x:(coli+1)*secsize_x] *= Gain16[rowi,coli]
gain_array[rowi*nsecx+coli] = 1/Gain16[rowi,coli]
GSImage.array[rowi*secsize_y:(rowi+1)*secsize_y, coli*secsize_x:(coli+1)*secsize_x] *= Gain16[rowi, coli]
gain_array[rowi*nsecx+coli] = 1/Gain16[rowi, coli]
return GSImage, gain_array
def GainsNonUniform16(GSImage, gain=1, nsecy = 2, nsecx=8, seed=202102, logger=None):
def GainsNonUniform16(GSImage, gain=1, nsecy=2, nsecx=8, seed=202102, logger=None):
# Generate Gain non-uniformity, and multipy the different factors (mean~1 with sigma~1%) to the GS-Image
rg = Generator(PCG64(int(seed)))
Random16 = (rg.random(nsecy*nsecx)-0.5)*0.04+1 # sigma~1%
Gain16 = Random16.reshape((nsecy,nsecx))/gain
Gain16 = Random16.reshape((nsecy, nsecx))/gain
if logger is not None:
msg = str(seed-20210202, "Gains of 16 channels: " + str(Gain16))
logger.info(msg)
......@@ -190,22 +191,22 @@ def GainsNonUniform16(GSImage, gain=1, nsecy = 2, nsecx=8, seed=202102, logger=N
def MakeFlatSmooth(GSBounds, seed):
rg = Generator(PCG64(int(seed)))
r1,r2,r3,r4 = rg.random(4)
r1, r2, r3, r4 = rg.random(4)
a1 = -0.5 + 0.2*r1
a2 = -0.5 + 0.2*r2
a3 = r3+5
a4 = r4+5
xmin,xmax,ymin,ymax = GSBounds.getXMin(), GSBounds.getXMax(), GSBounds.getYMin(), GSBounds.getYMax()
xmin, xmax, ymin, ymax = GSBounds.getXMin(), GSBounds.getXMax(), GSBounds.getYMin(), GSBounds.getYMax()
Flty, Fltx = np.mgrid[ymin:(ymax+1), xmin:(xmax+1)]
rg = Generator(PCG64(int(seed)))
p1,p2,bg=rg.poisson(1000, 3)
p1, p2, bg = rg.poisson(1000, 3)
Fltz = 0.6*1e-7*(a1 * (Fltx-p1) ** 2 + a2 * (Flty-p2) ** 2 - a3*Fltx - a4*Flty) + bg*20
FlatImg = galsim.ImageF(Fltz)
return FlatImg
def MakeFlatNcomb(flat_single_image, ncombine=1, read_noise=5, gain=1, overscan=500, biaslevel=500, seed_bias=20210311, logger=None):
ncombine=int(ncombine)
ncombine = int(ncombine)
FlatCombImg = flat_single_image*ncombine
rng = galsim.UniformDeviate()
NoiseFlatPoi = galsim.PoissonNoise(rng=rng, sky_level=0)
......@@ -215,15 +216,15 @@ def MakeFlatNcomb(flat_single_image, ncombine=1, read_noise=5, gain=1, overscan=
# NoiseFlat = galsim.CCDNoise(rng, gain=gain, read_noise=read_noise*ncombine**0.5, sky_level=0)
for i in range(ncombine):
FlatCombImg = AddBiasNonUniform16(
FlatCombImg,
bias_level=biaslevel,
nsecy=2, nsecx=8,
FlatCombImg,
bias_level=biaslevel,
nsecy=2, nsecx=8,
seed=seed_bias,
logger=logger)
if ncombine == 1:
FlatTag = 'Single'
pass
elif ncombine >1:
elif ncombine > 1:
FlatCombImg /= ncombine
FlatTag = 'Combine'
# FlatCombImg.replaceNegative(replace_value=0)
......@@ -232,7 +233,7 @@ def MakeFlatNcomb(flat_single_image, ncombine=1, read_noise=5, gain=1, overscan=
def MakeDarkNcomb(npix_x, npix_y, overscan=500, bias_level=500, seed_bias=202102, darkpsec=0.02, exptime=150, ncombine=10, read_noise=5, gain=1, logger=None):
ncombine=int(ncombine)
ncombine = int(ncombine)
darkpix = darkpsec*exptime
DarkSngImg = galsim.Image(npix_x, npix_y, init_value=darkpix)
rng = galsim.UniformDeviate()
......@@ -243,15 +244,15 @@ def MakeDarkNcomb(npix_x, npix_y, overscan=500, bias_level=500, seed_bias=202102
DarkCombImg.addNoise(NoiseReadN)
for i in range(ncombine):
DarkCombImg = AddBiasNonUniform16(
DarkCombImg,
bias_level=bias_level,
nsecy = 2, nsecx=8,
DarkCombImg,
bias_level=bias_level,
nsecy=2, nsecx=8,
seed=int(seed_bias),
logger=logger)
if ncombine == 1:
DarkTag = 'Single'
pass
elif ncombine >1:
elif ncombine > 1:
DarkCombImg /= ncombine
DarkTag = 'Combine'
# DarkCombImg.replaceNegative(replace_value=0)
......@@ -261,27 +262,30 @@ def MakeDarkNcomb(npix_x, npix_y, overscan=500, bias_level=500, seed_bias=202102
def PRNU_Img(xsize, ysize, sigma=0.01, seed=202101):
rg = Generator(PCG64(int(seed)))
prnuarr = rg.normal(1, sigma, (ysize,xsize))
prnuarr = rg.normal(1, sigma, (ysize, xsize))
prnuimg = galsim.ImageF(prnuarr)
return prnuimg
def NonLinear_f(x, beta_1, beta_2):
return x - beta_1 * x * x + beta_2 * x * x * x
def NonLinearity(GSImage, beta1=5E-7, beta2=0):
NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x
# NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x
GSImage.applyNonlinearity(NonLinear_f, beta1, beta2)
return GSImage
######################################## Saturation & Bleeding Start ###############################
# Saturation & Bleeding Start#
def BleedingTrail(aa, yy):
if aa<0.2:
aa=0.2
if aa < 0.2:
aa = 0.2
else:
pass
try:
fy = 0.5*(math.exp(math.log(yy+1)**3/aa)+np.exp(-1*math.log(yy+1)**3/aa))
faa= 0.5*(math.e+1/math.e)
faa = 0.5*(math.e+1/math.e)
trail_frac = 1-0.1*(fy-1)/(faa-1)
except Exception as e:
print(e)
......@@ -289,79 +293,96 @@ def BleedingTrail(aa, yy):
return trail_frac
def MakeTrail(imgarr, satuyxtuple, charge, fullwell=9e4, direction='up', trailcutfrac=0.9):
'''
direction: "up" or "down". For "up", bleeds along Y-decreasing direction; for "down", bleeds along Y-increasing direction.
'''
yi,xi = satuyxtuple
yi, xi = satuyxtuple
aa = np.log(charge/fullwell)**3 # scale length of the bleeding trail
yy = 1
while charge>0:
if yi<0 or yi>imgarr.shape[0]-1:
while charge > 0:
if yi < 0 or yi > imgarr.shape[0]-1:
break
if yi==0 or yi==imgarr.shape[0]-1:
imgarr[yi,xi] = fullwell
if yi == 0 or yi == imgarr.shape[0]-1:
imgarr[yi, xi] = fullwell
break
if direction=='up':
if imgarr[yi-1,xi]>=fullwell:
imgarr[yi,xi] = fullwell
yi-=1
if direction == 'up':
if imgarr[yi-1, xi] >= fullwell:
imgarr[yi, xi] = fullwell
yi -= 1
# [TEST] charge in the middle
if yi == (imgarr.shape[0] // 2 - 1):
break
continue
elif direction=='down':
if imgarr[yi+1,xi]>=fullwell:
imgarr[yi,xi] = fullwell
yi+=1
elif direction == 'down':
if imgarr[yi+1, xi] >= fullwell:
imgarr[yi, xi] = fullwell
yi += 1
if yi == (imgarr.shape[0] // 2):
break
continue
if aa<=1:
while imgarr[yi,xi] >= fullwell:
imgarr[yi,xi] = fullwell
if direction=='up':
imgarr[yi-1,xi] += charge
charge = imgarr[yi-1,xi]-fullwell
yi-=1
if yi<0:
if aa <= 1:
while imgarr[yi, xi] >= fullwell:
imgarr[yi, xi] = fullwell
if direction == 'up':
imgarr[yi-1, xi] += charge
charge = imgarr[yi-1, xi]-fullwell
yi -= 1
# if yi < 0:
if yi < 0 or yi == (imgarr.shape[0]//2 - 1):
break
elif direction=='down':
imgarr[yi+1,xi] += charge
charge = imgarr[yi+1,xi]-fullwell
yi+=1
if yi>imgarr.shape[0]:
elif direction == 'down':
imgarr[yi+1, xi] += charge
charge = imgarr[yi+1, xi]-fullwell
yi += 1
# if yi > imgarr.shape[0]:
if yi > imgarr.shape[0] or yi == (imgarr.shape[0]//2):
break
else:
# calculate bleeding trail:
trail_frac = BleedingTrail(aa,yy)
trail_frac = BleedingTrail(aa, yy)
# put charge upwards
if trail_frac>=0.99:
imgarr[yi,xi] = fullwell
if direction=='up':
yi-=1
elif direction=='down':
yi+=1
if trail_frac >= 0.99:
imgarr[yi, xi] = fullwell
if direction == 'up':
yi -= 1
if yi == (imgarr.shape[0]//2 - 1):
break
elif direction == 'down':
yi += 1
if yi == (imgarr.shape[0]//2):
break
yy += 1
else:
if trail_frac<trailcutfrac:
if trail_frac < trailcutfrac:
break
charge = fullwell*trail_frac
imgarr[yi,xi] += charge
if imgarr[yi,xi]>fullwell:
imgarr[yi,xi] = fullwell
if direction=='up':
yi-=1
elif direction=='down':
yi+=1
imgarr[yi, xi] += charge
if imgarr[yi, xi] > fullwell:
imgarr[yi, xi] = fullwell
if direction == 'up':
yi -= 1
if yi == (imgarr.shape[0]//2 - 1):
break
elif direction == 'down':
yi += 1
if yi == (imgarr.shape[0]//2):
break
yy += 1
return imgarr
def ChargeFlow(imgarr, fullwell=9E4):
size_y,size_x = imgarr.shape
satupos_y,satupos_x = np.where(imgarr>fullwell)
size_y, size_x = imgarr.shape
satupos_y, satupos_x = np.where(imgarr > fullwell)
if satupos_y.shape[0]==0:
if satupos_y.shape[0] == 0:
# make no change for the image array
return imgarr
elif satupos_y.shape[0]/imgarr.size > 0.5:
......@@ -371,28 +392,28 @@ def ChargeFlow(imgarr, fullwell=9E4):
chargedict = {}
imgarrorig = copy.deepcopy(imgarr)
for yi,xi in zip(satupos_y,satupos_x):
yxidx = ''.join([str(yi),str(xi)])
chargedict[yxidx] = imgarrorig[yi,xi]-fullwell
for yi, xi in zip(satupos_y, satupos_x):
yxidx = ''.join([str(yi), str(xi)])
chargedict[yxidx] = imgarrorig[yi, xi]-fullwell
for yi,xi in zip(satupos_y,satupos_x):
yxidx = ''.join([str(yi),str(xi)])
for yi, xi in zip(satupos_y, satupos_x):
yxidx = ''.join([str(yi), str(xi)])
satcharge = chargedict[yxidx]
chargeup = ((np.random.random()-0.5)*0.05+0.5)*satcharge
chargedn = satcharge - chargeup
try:
# Charge Clump moves up
if yi>=0 and yi<imgarr.shape[0]:
imgarr = MakeTrail(imgarr, (yi,xi), chargeup, fullwell=9e4, direction='up', trailcutfrac=0.9)
if yi >= 0 and yi < imgarr.shape[0]:
imgarr = MakeTrail(imgarr, (yi, xi), chargeup, fullwell=9e4, direction='up', trailcutfrac=0.9)
# Charge Clump moves down
imgarr = MakeTrail(imgarr, (yi,xi), chargedn, fullwell=9e4, direction='down', trailcutfrac=0.9)
imgarr = MakeTrail(imgarr, (yi, xi), chargedn, fullwell=9e4, direction='down', trailcutfrac=0.9)
except Exception as e:
print(e,'@pix ',(yi+1,xi+1))
print(e, '@pix ', (yi+1, xi+1))
return imgarr
return imgarr
def SaturBloom(GSImage, nsect_x=1, nsect_y=1, fullwell=9e4):
"""
To simulate digital detector's saturation and blooming effect. The blooming is along the read-out direction, perpendicular to the charge transfer direction. Charge clumpy overflows the pixel well will flow to two oposite directions with nearly same charges.
......@@ -418,42 +439,42 @@ def SaturBloom(GSImage, nsect_x=1, nsect_y=1, fullwell=9e4):
return GSImage
################################# Saturation & Bleeding End ####################################
# Saturation & Bleeding End #
def readout16(GSImage, rowi=0, coli=0, overscan_value=0):
# readout image as 16 outputs of sub-images plus prescan & overscan.
# assuming image width and height sizes are both even.
# assuming image has 2 columns and 8 rows of output channels.
# assuming image has 2 columns and 8 rows of output channels.
# 00 01
# 10 11
# 20 21
# ...
# return: GS Image Object
npix_y,npix_x = GSImage.array.shape
npix_y, npix_x = GSImage.array.shape
subheight = int(8+npix_y/2+8)
subwidth = int(16+npix_x/8+27)
OutputSubimg = galsim.ImageUS(subwidth, subheight, init_value=overscan_value)
if rowi<4 and coli==0:
if rowi < 4 and coli == 0:
subbounds = galsim.BoundsI(1, int(npix_x/2), int(npix_y/8*rowi+1), int(npix_y/8*(rowi+1)))
subbounds = subbounds.shift(galsim.PositionI(GSImage.bounds.getXMin()-1, GSImage.bounds.getYMin()-1))
subimg = GSImage[subbounds]
OutputSubimg.array[27:int(npix_y/8)+27,8:int(npix_x/2)+8] = subimg.array
elif rowi<4 and coli==1:
OutputSubimg.array[27:int(npix_y/8)+27, 8:int(npix_x/2)+8] = subimg.array
elif rowi < 4 and coli == 1:
subbounds = galsim.BoundsI(npix_x/2+1, npix_x, npix_y/8*rowi+1, npix_y/8*(rowi+1))
subbounds = subbounds.shift(galsim.PositionI(GSImage.bounds.getXMin()-1, GSImage.bounds.getYMin()-1))
subimg = GSImage[subbounds]
OutputSubimg.array[27:int(npix_y/8)+27,8:int(npix_x/2)+8] = subimg.array
elif rowi>=4 and rowi<8 and coli==0:
OutputSubimg.array[27:int(npix_y/8)+27, 8:int(npix_x/2)+8] = subimg.array
elif rowi >= 4 and rowi < 8 and coli == 0:
subbounds = galsim.BoundsI(1, npix_x/2, npix_y/8*rowi+1, npix_y/8*(rowi+1))
subbounds = subbounds.shift(galsim.PositionI(GSImage.bounds.getXMin()-1, GSImage.bounds.getYMin()-1))
subimg = GSImage[subbounds]
OutputSubimg.array[16:int(npix_y/8)+16,8:int(npix_x/2)+8] = subimg.array
elif rowi>=4 and rowi<8 and coli==1:
OutputSubimg.array[16:int(npix_y/8)+16, 8:int(npix_x/2)+8] = subimg.array
elif rowi >= 4 and rowi < 8 and coli == 1:
subbounds = galsim.BoundsI(npix_x/2+1, npix_x, npix_y/8*rowi+1, npix_y/8*(rowi+1))
subbounds = subbounds.shift(galsim.PositionI(GSImage.bounds.getXMin()-1, GSImage.bounds.getYMin()-1))
subimg = GSImage[subbounds]
OutputSubimg.array[16 :int(npix_y/8)+16,8:int(npix_x/2)+8] = subimg.array
OutputSubimg.array[16:int(npix_y/8)+16, 8:int(npix_x/2)+8] = subimg.array
else:
print("\n\033[31mError: "+"Wrong rowi or coli assignment. Permitted: 0<=rowi<=7, 0<=coli<=1."+"\033[0m\n")
return OutputSubimg
......@@ -463,169 +484,163 @@ def readout16(GSImage, rowi=0, coli=0, overscan_value=0):
def CTE_Effect(GSImage, threshold=27, direction='column'):
# Devide the image into 4 sections and apply CTE effect with different trail directions.
# GSImage: a GalSim Image object.
size_y,size_x = GSImage.array.shape
size_y, size_x = GSImage.array.shape
size_sect_y = int(size_y/2)
size_sect_x = int(size_x/2)
imgarr = GSImage.array
if direction == 'column':
imgarr[0:size_sect_y,:] = CTEModelColRow(imgarr[0:size_sect_y,:], trail_direction='down', direction='column', threshold=threshold)
imgarr[size_sect_y:size_y,:] = CTEModelColRow(imgarr[size_sect_y:size_y,:], trail_direction='up', direction='column', threshold=threshold)
imgarr[0:size_sect_y, :] = CTEModelColRow(imgarr[0:size_sect_y, :], trail_direction='down', direction='column', threshold=threshold)
imgarr[size_sect_y:size_y, :] = CTEModelColRow(imgarr[size_sect_y:size_y, :], trail_direction='up', direction='column', threshold=threshold)
elif direction == 'row':
imgarr[:,0:size_sect_x] = CTEModelColRow(imgarr[:,0:size_sect_x], trail_direction='right', direction='row', threshold=threshold)
imgarr[:,size_sect_x:size_x] = CTEModelColRow(imgarr[:,size_sect_x:size_x], trail_direction='left', direction='row', threshold=threshold)
imgarr[:, 0:size_sect_x] = CTEModelColRow(imgarr[:, 0:size_sect_x], trail_direction='right', direction='row', threshold=threshold)
imgarr[:, size_sect_x:size_x] = CTEModelColRow(imgarr[:, size_sect_x:size_x], trail_direction='left', direction='row', threshold=threshold)
return GSImage
@jit()
def CTEModelColRow(img, trail_direction = 'up', direction='column', threshold=27):
def CTEModelColRow(img, trail_direction='up', direction='column', threshold=27):
#total trail flux vs (pixel flux)^1/2 is approximately linear
#total trail flux = trail_a * (pixel flux)^1/2 + trail_b
#trail pixel flux = pow(0.5,x)/0.5, normalize to 1
trail_a = 5.651803799619966
# total trail flux vs (pixel flux)^1/2 is approximately linear
# total trail flux = trail_a * (pixel flux)^1/2 + trail_b
# trail pixel flux = pow(0.5,x)/0.5, normalize to 1
trail_a = 5.651803799619966
trail_b = -2.671933068990729
sh1 = img.shape[0]
sh2 = img.shape[1]
n_img = img*0
idx = np.where(img<threshold)
idx = np.where(img < threshold)
if len(idx[0]) == 0:
pass
elif len(idx[0])>0:
elif len(idx[0]) > 0:
n_img[idx] = img[idx]
yidx,xidx = np.where(img>=threshold)
yidx, xidx = np.where(img >= threshold)
if len(yidx) == 0:
pass
elif len(yidx)>0:
elif len(yidx) > 0:
# print(index)
for i, j in zip(yidx,xidx):
f = img[i,j]
for i, j in zip(yidx, xidx):
f = img[i, j]
trail_f = (np.sqrt(f)*trail_a + trail_b)*0.5
# trail_f=5E-5*f**1.5
xy_num = 10
all_trail = np.zeros(xy_num)
xy_upstr = np.arange(1,xy_num,1)
xy_upstr = np.arange(1, xy_num, 1)
# all_trail_pix = np.sum(pow(0.5,xy_upstr)/0.5)
all_trail_pix = 0
for m in xy_upstr:
a1=12.97059491
b1=0.54286652
c1=0.69093105
a2=2.77298856
b2=0.11231055
c2=-0.01038675
a1 = 12.97059491
b1 = 0.54286652
c1 = 0.69093105
a2 = 2.77298856
b2 = 0.11231055
c2 = -0.01038675
# t_pow = 0
am=1
bm=1
am = 1
bm = 1
t_pow = am*np.exp(-bm*m)
# if m < 5:
# t_pow = a1*np.exp(-b1*m)+c1
# else:
# t_pow = a2*np.exp(-b2*m)+c2
if t_pow <0:
if t_pow < 0:
t_pow = 0
all_trail_pix += t_pow
all_trail[m] = t_pow
trail_pix_eff = trail_f/all_trail_pix
all_trail = trail_pix_eff*all_trail
all_trail[0] = f - trail_f
for m in np.arange(0,xy_num,1):
for m in np.arange(0, xy_num, 1):
if direction == 'column':
if trail_direction == 'down':
y_pos = i + m
elif trail_direction == 'up':
y_pos = i - m
if y_pos < 0 or y_pos >=sh1:
if y_pos < 0 or y_pos >= sh1:
break
n_img[y_pos,j] = n_img[y_pos,j] + all_trail[m]
n_img[y_pos, j] = n_img[y_pos, j] + all_trail[m]
elif direction == 'row':
if trail_direction == 'left':
x_pos = j - m
elif trail_direction == 'right':
x_pos = j + m
if x_pos < 0 or x_pos >=sh2:
if x_pos < 0 or x_pos >= sh2:
break
n_img[i,x_pos] = n_img[i,x_pos] + all_trail[m]
n_img[i, x_pos] = n_img[i, x_pos] + all_trail[m]
return n_img
#---------- For Cosmic-Ray Simulation ------------
#---------- Zhang Xin ----------------------------
# ---------- For Cosmic-Ray Simulation ------------
# ---------- Zhang Xin ----------------------------
def getYValue(collection, x):
index = 0;
index = 0
if (collection.shape[1] == 2):
while(x>collection[index,0] and index < collection.shape[0]):
index= index + 1;
while (x > collection[index, 0] and index < collection.shape[0]):
index = index + 1
if (index == collection.shape[0] or index == 0):
return 0;
return 0
deltX = collection[index, 0] - collection[index-1, 0];
deltY = collection[index, 1] - collection[index-1, 1];
deltX = collection[index, 0] - collection[index-1, 0]
deltY = collection[index, 1] - collection[index-1, 1]
if deltX == 0:
return (collection[index, 1] + collection[index-1, 1])/2.0
else:
a = deltY/deltX;
return a * (x - collection[index-1,0]) + collection[index-1, 1];
return 0;
a = deltY/deltX
return a * (x - collection[index-1, 0]) + collection[index-1, 1]
return 0
def selectCosmicRayCollection(attachedSizes, xLen, yLen,cr_pixelRatio,CR_max_size):
def selectCosmicRayCollection(attachedSizes, xLen, yLen, cr_pixelRatio, CR_max_size):
normalRay = 0.90
nnormalRay = 1-normalRay
max_nrayLen = 100
pixelNum = int(xLen * yLen * cr_pixelRatio * normalRay );
pixelNum_n = int(xLen * yLen * cr_pixelRatio * nnormalRay )
CRPixelNum = 0;
maxValue = max(attachedSizes[:,1])
maxValue += 0.1;
cr_event_num = 0;
CRs = np.zeros(pixelNum);
pixelNum = int(xLen * yLen * cr_pixelRatio * normalRay)
pixelNum_n = int(xLen * yLen * cr_pixelRatio * nnormalRay)
CRPixelNum = 0
maxValue = max(attachedSizes[:, 1])
maxValue += 0.1
cr_event_num = 0
CRs = np.zeros(pixelNum)
while (CRPixelNum < pixelNum):
x = CR_max_size * np.random.random();
y = maxValue * np.random.random();
x = CR_max_size * np.random.random()
y = maxValue * np.random.random()
if (y <= getYValue(attachedSizes, x)):
CRs[cr_event_num] = np.ceil(x);
cr_event_num = cr_event_num + 1;
CRPixelNum = CRPixelNum + round(x);
CRs[cr_event_num] = np.ceil(x)
cr_event_num = cr_event_num + 1
CRPixelNum = CRPixelNum + round(x)
while (CRPixelNum < pixelNum + pixelNum_n):
nx = np.random.random()*(max_nrayLen-CR_max_size)+CR_max_size
CRs[cr_event_num] = np.ceil(nx);
cr_event_num = cr_event_num + 1;
CRPixelNum = CRPixelNum + np.ceil(nx);
CRs[cr_event_num] = np.ceil(nx)
cr_event_num = cr_event_num + 1
CRPixelNum = CRPixelNum + np.ceil(nx)
return CRs[0:cr_event_num];
return CRs[0:cr_event_num]
def defineEnergyForCR(cr_event_size,seed = 12345):
def defineEnergyForCR(cr_event_size, seed=12345):
import random
sigma = 0.6 / 2.355;
mean = 3.3;
sigma = 0.6 / 2.355
mean = 3.3
random.seed(seed)
energys = np.zeros(cr_event_size);
energys = np.zeros(cr_event_size)
for i in np.arange(cr_event_size):
energy_index = random.normalvariate(mean,sigma);
energys[i] = pow(10, energy_index);
energy_index = random.normalvariate(mean, sigma)
energys[i] = pow(10, energy_index)
return energys
return energys;
def convCR(CRmap=None, addPSF=None, sp_n = 4):
def convCR(CRmap=None, addPSF=None, sp_n=4):
sh = CRmap.shape
# sp_n = 4
......@@ -634,23 +649,23 @@ def convCR(CRmap=None, addPSF=None, sp_n = 4):
for i in np.arange(sh[0]):
i_st = sp_n*i
for j in np.arange(sh[1]):
if CRmap[i,j] ==0:
if CRmap[i, j] == 0:
continue
j_st = sp_n*j
pix_v1 = CRmap[i,j]*pix_v0
pix_v1 = CRmap[i, j]*pix_v0
for m in np.arange(sp_n):
for n in np.arange(sp_n):
subCRmap[i_st+m, j_st + n] = pix_v1
m_size = addPSF.shape[0]
subCRmap_n = np.zeros(np.array(subCRmap.shape) + m_size -1)
subCRmap_n = np.zeros(np.array(subCRmap.shape) + m_size - 1)
for i in np.arange(subCRmap.shape[0]):
for j in np.arange(subCRmap.shape[1]):
if subCRmap[i,j]>0:
convPix = addPSF*subCRmap[i,j]
subCRmap_n[i:i+m_size,j:j+m_size]+=convPix
if subCRmap[i, j] > 0:
convPix = addPSF*subCRmap[i, j]
subCRmap_n[i:i+m_size, j:j+m_size] += convPix
CRmap_n = np.zeros((np.array(subCRmap_n.shape)/sp_n).astype(np.int32))
sh_n = CRmap_n.shape
......@@ -659,12 +674,12 @@ def convCR(CRmap=None, addPSF=None, sp_n = 4):
i_st = sp_n*i
for j in np.arange(sh_n[1]):
p_v = 0
j_st=sp_n*j
j_st = sp_n*j
for m in np.arange(sp_n):
for n in np.arange(sp_n):
p_v += subCRmap_n[i_st+m, j_st + n]
CRmap_n[i,j] = p_v
CRmap_n[i, j] = p_v
return CRmap_n
......@@ -673,15 +688,15 @@ def produceCR_Map(xLen, yLen, exTime, cr_pixelRatio, gain, attachedSizes, seed=2
# Return: an 2-D numpy array
# attachedSizes = np.loadtxt('./wfc-cr-attachpixel.dat');
np.random.seed(seed)
CR_max_size = 20.0;
cr_size = selectCosmicRayCollection(attachedSizes, xLen, yLen, cr_pixelRatio, CR_max_size);
CR_max_size = 20.0
cr_size = selectCosmicRayCollection(attachedSizes, xLen, yLen, cr_pixelRatio, CR_max_size)
cr_event_size = cr_size.shape[0];
cr_energys = defineEnergyForCR(cr_event_size,seed = seed);
cr_event_size = cr_size.shape[0]
cr_energys = defineEnergyForCR(cr_event_size, seed=seed)
CRmap = np.zeros([yLen, xLen]);
CRmap = np.zeros([yLen, xLen])
## produce conv kernel
# produce conv kernel
from astropy.modeling.models import Gaussian2D
o_size = 4
sp_n = 8
......@@ -694,28 +709,26 @@ def produceCR_Map(xLen, yLen, exTime, cr_pixelRatio, gain, attachedSizes, seed=2
addPSF = addPSF_(xp, yp)
convKernel = addPSF/addPSF.sum()
#################################
# ---------------------------------
for i in np.arange(cr_event_size):
xPos = round((xLen - 1)* np.random.random());
yPos = round((yLen - 1)* np.random.random());
cr_lens = int(cr_size[i]);
if cr_lens ==0:
continue;
pix_energy = cr_energys[i]/gain/cr_lens;
pos_angle = 1/2*math.pi*np.random.random();
xPos = round((xLen - 1) * np.random.random())
yPos = round((yLen - 1) * np.random.random())
cr_lens = int(cr_size[i])
if cr_lens == 0:
continue
pix_energy = cr_energys[i]/gain/cr_lens
pos_angle = 1/2*math.pi*np.random.random()
crMatrix = np.zeros([cr_lens+1, cr_lens + 1])
for j in np.arange(cr_lens):
x_n = int(np.cos(pos_angle)*j - np.sin(pos_angle)*0);
x_n = int(np.cos(pos_angle)*j - np.sin(pos_angle)*0)
if x_n < 0:
x_n = x_n + cr_lens+1
y_n = int(np.sin(pos_angle)*j + np.cos(pos_angle)*0);
if x_n<0 or x_n >cr_lens or y_n < 0 or y_n > cr_lens:
continue;
crMatrix[y_n,x_n] = pix_energy;
y_n = int(np.sin(pos_angle)*j + np.cos(pos_angle)*0)
if x_n < 0 or x_n > cr_lens or y_n < 0 or y_n > cr_lens:
continue
crMatrix[y_n, x_n] = pix_energy
crMatrix_n = convCR(crMatrix, convKernel, sp_n)
# crMatrix_n = crMatrix
......@@ -729,9 +742,7 @@ def produceCR_Map(xLen, yLen, exTime, cr_pixelRatio, gain, attachedSizes, seed=2
sly = slice(ypix[oky].min(), ypix[oky].max()+1)
slx = slice(xpix[okx].min(), xpix[okx].max()+1)
CRmap[sly, slx] += crMatrix_n[oky,:][:,okx]
CRmap[sly, slx] += crMatrix_n[oky, :][:, okx]
return CRmap.astype(np.int32), cr_event_size
......@@ -754,7 +765,7 @@ def ShutterEffectArr(GSImage, t_exp=150, t_shutter=1.3, dist_bearing=735, dt=1E-
s = np.zeros(SampleNumb)
s1 = np.zeros(SampleNumb)
s2 = np.zeros(SampleNumb)
brt = np.zeros(SampleNumb)
brt = np.zeros(SampleNumb)
idx = np.arange(SampleNumb)
sidx = np.zeros(SampleNumb)
s1idx = np.zeros(SampleNumb)
......@@ -770,9 +781,9 @@ def ShutterEffectArr(GSImage, t_exp=150, t_shutter=1.3, dist_bearing=735, dt=1E-
s2[i] = dist_bearing-DistHalf*np.cos(theta[i])
s1idx[i] = int(s1[i]/dist_bearing*(SampleNumb))
s2idx[i] = int(s2[i]/dist_bearing*(SampleNumb))
brt[(idx>s1idx[i]) & (idx<s2idx[i])] += dt
brt[(idx > s1idx[i]) & (idx < s2idx[i])] += dt
if t_exp>t_shutter*2:
if t_exp > t_shutter*2:
brt = brt*2+(t_exp-t_shutter*2)
else:
brt = brt*2
......@@ -785,16 +796,16 @@ def ShutterEffectArr(GSImage, t_exp=150, t_shutter=1.3, dist_bearing=735, dt=1E-
xmax = GSImage.bounds.getXMax()
ymin = GSImage.bounds.getYMin()
ymax = GSImage.bounds.getYMax()
if xmin<np.min(x) or xmax>np.max(x):
if xmin < np.min(x) or xmax > np.max(x):
raise LookupError("Out of focal-plane bounds in X-direction.")
if ymin<-25331 or ymax>25331:
if ymin < -25331 or ymax > 25331:
raise LookupError("Out of focal-plane bounds in Y-direction.")
sizex = xmax-xmin+1
sizey = ymax-ymin+1
xnewgrid = np.mgrid[xmin:(xmin+sizex)]
expeffect = interpolate.splev(xnewgrid, intp, der=0)
expeffect /= t_exp
exparrnormal = np.tile(expeffect, (sizey,1))
exparrnormal = np.tile(expeffect, (sizey, 1))
# GSImage *= exparrnormal
return exparrnormal
observation_sim/instruments/chip/libBF/diffusion_X1.c
View file @ df22b3b0
......@@ -37,7 +37,7 @@ void addEffects(int ngx_ima, int ngy_ima, float *arr_ima, float *arr_imc, int bi
{
printf("Adding BF effect...\n");
//setup BF correlation fliter
float neX;
float neX, neXtemp;
float neP1 = 50000;
float bfaP1[9]={0.9707182, 0.002143905, 0.004131103, 0.001149542, 0.0005501739, 0.0005469659, 0.0003726081, 0.0003795207, 0.0001633302};
float neP2 = 10000;
......@@ -56,15 +56,18 @@ void addEffects(int ngx_ima, int ngy_ima, float *arr_ima, float *arr_imc, int bi
neX = arr_ima[j+i*ny];
if(neX >= 10000)
{
neXtemp = neX;
if(neXtemp > 100000)
neXtemp = 100000;
bfa[0][0]=0; //linearInterp(neX, neP1, bfaP1[0], neP2, bfaP2[0]); //0;
bfa[0][1]=bfa[0][-1]=linearInterp(neX, neP1, bfaP1[1], neP2, bfaP2[1]); //0.01575;
bfa[-1][0]=bfa[1][0]=linearInterp(neX, neP1, bfaP1[2], neP2, bfaP2[2]); //0.00652;
bfa[-1][-1]=bfa[1][1]=bfa[-1][1]=bfa[1][-1]=linearInterp(neX, neP1, bfaP1[3], neP2, bfaP2[3]); //0.00335;
bfa[0][-2]=bfa[0][2]=linearInterp(neX, neP1, bfaP1[4], neP2, bfaP2[4]);
bfa[-2][0]=bfa[2][0]=linearInterp(neX, neP1, bfaP1[5], neP2, bfaP2[5]); //0.00118;
bfa[-2][-1]=bfa[-2][1]=bfa[2][1]=bfa[2][-1]=linearInterp(neX, neP1, bfaP1[6], neP2, bfaP2[6]);
bfa[-1][-2]=bfa[1][2]=bfa[-1][2]=bfa[1][-2]=linearInterp(neX, neP1, bfaP1[7], neP2, bfaP2[7]); //0.00083;
bfa[-2][-2]=bfa[-2][2]=bfa[2][-2]=bfa[2][2]=linearInterp(neX, neP1, bfaP1[8], neP2, bfaP2[8]); //0.00043;
bfa[0][1]=bfa[0][-1]=linearInterp(neXtemp, neP1, bfaP1[1], neP2, bfaP2[1]); //0.01575;
bfa[-1][0]=bfa[1][0]=linearInterp(neXtemp, neP1, bfaP1[2], neP2, bfaP2[2]); //0.00652;
bfa[-1][-1]=bfa[1][1]=bfa[-1][1]=bfa[1][-1]=linearInterp(neXtemp, neP1, bfaP1[3], neP2, bfaP2[3]); //0.00335;
bfa[0][-2]=bfa[0][2]=linearInterp(neXtemp, neP1, bfaP1[4], neP2, bfaP2[4]);
bfa[-2][0]=bfa[2][0]=linearInterp(neXtemp, neP1, bfaP1[5], neP2, bfaP2[5]); //0.00118;
bfa[-2][-1]=bfa[-2][1]=bfa[2][1]=bfa[2][-1]=linearInterp(neXtemp, neP1, bfaP1[6], neP2, bfaP2[6]);
bfa[-1][-2]=bfa[1][2]=bfa[-1][2]=bfa[1][-2]=linearInterp(neXtemp, neP1, bfaP1[7], neP2, bfaP2[7]); //0.00083;
bfa[-2][-2]=bfa[-2][2]=bfa[2][-2]=bfa[2][2]=linearInterp(neXtemp, neP1, bfaP1[8], neP2, bfaP2[8]); //0.00043;
}
else
{
......
observation_sim/instruments/chip/libCTI/CTI_modeling.py
View file @ df22b3b0
......@@ -31,7 +31,7 @@ def get_trap_map(seeds,nx,ny,nmax,rho_trap,beta,c,out_dir):
get_trap_h(seeds_p,c_int(int(nsp)),c_int(int(nx)),c_int(int(ny)),\
c_int(int(nmax)),rho_trap_p,c_float(beta),\
c_float(c),filename)
def bin2fits(bin_file,fits_dir,nsp,nx,ny,nmax):
data = np.fromfile(bin_file,dtype=np.float32)
data = data.reshape(nx,nsp,ny,nmax).transpose(1,3,2,0)
......@@ -101,6 +101,6 @@ if __name__ =='__main__':
image = fits.getdata("inputdata/image.fits").astype(np.int32)
get_trap_map(trap_seeds,nx,ny,nmax,rho_trap,beta,c,".")
bin2fits("trap.bin",".",nsp,nx,ny,nmax)
image_cti = CTI_sim(image,nx,ny,noverscan,nsp,nmax,beta,w,c,t,rho_trap,trap_seeds,release_seed)
image_cti = CTI_sim(image,nx,ny,noverscan,nsp,nmax,beta,w,c,t,rho_trap,trap_seeds,release_seed)
fits.writeto("output/image_CTI.fits",data=image_cti,overwrite=True)
"""
observation_sim/mock_objects/CatalogBase.py
View file @ df22b3b0
......@@ -12,6 +12,9 @@ class CatalogBase(metaclass=ABCMeta):
def __init__(self):
pass
def free_mem(self, **kward):
pass
@abstractmethod
def load_sed(self, obj, **kward):
pass
......@@ -29,8 +32,8 @@ class CatalogBase(metaclass=ABCMeta):
param = {
"star": -1,
"id": -1,
"ra": 0,
"dec": 0.,
"ra": -999.,
"dec": -999.,
"ra_orig": 0.,
"dec_orig": 0.,
"z": 0.,
......@@ -44,15 +47,15 @@ class CatalogBase(metaclass=ABCMeta):
"bfrac": 0.,
"av": 0.,
"redden": 0.,
"hlr_bulge": 0.,
"hlr_disk": 0.,
"hlr_bulge": -999.,
"hlr_disk": -999.,
"ell_bulge": 0.,
"ell_disk": 0.,
"ell_tot": 0.,
"e1_disk": 0.,
"e2_disk": 0.,
"e1_bulge": 0.,
"e2_bulge": 0.,
"e1_disk": -999.,
"e2_disk": -999.,
"e1_bulge": -999.,
"e2_bulge": -999.,
"teff": 0.,
"logg": 0.,
"feh": 0.,
......@@ -91,6 +94,8 @@ class CatalogBase(metaclass=ABCMeta):
bandpass = target_filt.bandpass_full
if norm_filt is not None:
if hasattr(norm_filt, 'bandpass_full'):
norm_filt = Table(np.array(np.array([norm_filt.bandpass_full.wave_list*10.0, norm_filt.bandpass_full.func(norm_filt.bandpass_full.wave_list)])).T, names=(['WAVELENGTH', 'SENSITIVITY']))
norm_thr_rang_ids = norm_filt['SENSITIVITY'] > 0.001
else:
norm_filt = Table(
......
observation_sim/mock_objects/FlatLED.py
View file @ df22b3b0
......@@ -50,6 +50,8 @@ fluxLED = {'LED1': 15, 'LED2': 15, 'LED3': 12.5, 'LED4': 9, 'LED5': 9,
# 'LED14': 10}
mirro_eff = {'GU': 0.61, 'GV': 0.8, 'GI': 0.8}
bandtoLed = {'NUV': ['LED1', 'LED2'], 'u': ['LED13', 'LED14'], 'g': ['LED3', 'LED4', 'LED5'], 'r': ['LED6', 'LED7'], 'i': ['LED8'], 'z': ['LED9', 'LED10'], 'y': ['LED10'], 'GU': ['LED1', 'LED2', 'LED13', 'LED14'], 'GV': ['LED3', 'LED4', 'LED5', 'LED6'], 'GI': ['LED7', 'LED8', 'LED9', 'LED10']}
# mirro_eff = {'GU':1, 'GV':1, 'GI':1}
......@@ -69,10 +71,18 @@ class FlatLED(MockObject):
with pkg_resources.path('observation_sim.mock_objects.data.led', "") as ledDir:
self.flatDir = ledDir.as_posix()
def getInnerFlat(self):
ledflats = bandtoLed[self.chip.filter_type]
iFlat = np.zeros([self.chip.npix_y, self.chip.npix_x])
for nled in ledflats:
iFlat = iFlat + self.getLEDImage1(led_type=nled, LED_Img_flag=False)
iFlat = iFlat/len(ledflats)
return iFlat
###
# return LED flat, e/s
###
def getLEDImage(self, led_type='LED1'):
def getLEDImage(self, led_type='LED1', LED_Img_flag=True):
# cwave = cwaves[led_type]
flat = fits.open(os.path.join(self.flatDir, 'model_' +
cwaves_name[led_type] + 'nm.fits'))
......@@ -102,7 +112,10 @@ class FlatLED(MockObject):
N[self.chip.npix_y * i:self.chip.npix_y * (i + 1), self.chip.npix_x * j:self.chip.npix_x * (j + 1)]),
method='linear')
U = U/np.mean(U)
flatImage = U*fluxLED[led_type]*1000
flatImage = U
if LED_Img_flag:
flatImage = flatImage*fluxLED[led_type]*1000
gc.collect()
return flatImage
......@@ -110,39 +123,42 @@ class FlatLED(MockObject):
# return LED flat, e/s
###
def getLEDImage1(self, led_type='LED1'):
def getLEDImage1(self, led_type='LED1', LED_Img_flag=True):
# cwave = cwaves[led_type]
flat = fits.open(os.path.join(self.flatDir, 'model_' +
cwaves_name[led_type] + 'nm.fits'))
xlen = flat[0].header['NAXIS1']
ylen = 601
i = self.chip.rowID - 1
j = self.chip.colID - 1
x = np.linspace(0, self.chip.npix_x, int(xlen/6.))
y = np.linspace(0, self.chip.npix_y, int(ylen/5.))
xx, yy = np.meshgrid(x, y)
a1 = flat[0].data[int(ylen*i/5.):int(ylen*i/5.)+int(ylen/5.),
int(xlen*j/6.):int(xlen*j/6.)+int(xlen/6.)]
# z = np.sin((xx+yy+xx**2+yy**2))
# fInterp = interp2d(xx, yy, z, kind='linear')
X_ = np.hstack((xx.flatten()[:, None], yy.flatten()[:, None]))
Z_ = a1.flatten()
n_x = np.arange(0, self.chip.npix_x, 1)
n_y = np.arange(0, self.chip.npix_y, 1)
M, N = np.meshgrid(n_x, n_y)
U = griddata(X_, Z_, (
M[0:self.chip.npix_y, 0:self.chip.npix_x],
N[0:self.chip.npix_y, 0:self.chip.npix_x]),
method='linear')
x_seg_len = 4
y_seg_len = 8
x_seg = int(self.chip.npix_x/x_seg_len)
y_seg = int(self.chip.npix_y/y_seg_len)
U = np.zeros([self.chip.npix_y, self.chip.npix_x], dtype=np.float32)
for y_seg_i in np.arange(y_seg_len):
for x_seg_i in np.arange(x_seg_len):
U[y_seg_i*y_seg:(y_seg_i+1)*y_seg, x_seg_i*x_seg:(x_seg_i+1)*x_seg] = griddata(X_, Z_, (M[y_seg_i*y_seg:(y_seg_i+1)*y_seg, x_seg_i*x_seg:(x_seg_i+1)*x_seg], N[y_seg_i*y_seg:(y_seg_i+1)*y_seg, x_seg_i*x_seg:(x_seg_i+1)*x_seg]), method='linear')
# U = griddata(X_, Z_, (
# M[0:self.chip.npix_y, 0:self.chip.npix_x],
# N[0:self.chip.npix_y, 0:self.chip.npix_x]),
# method='nearest').astype(np.float32)
U = U/np.mean(U)
flatImage = U*fluxLED[led_type]*1000
flatImage = U
if LED_Img_flag:
flatImage = U*fluxLED[led_type]*1000
gc.collect()
return flatImage
......
observation_sim/mock_objects/Galaxy.py
View file @ df22b3b0
......@@ -33,7 +33,7 @@ class Galaxy(MockObject):
raise ValueError(
"!!!The number of PSF profiles and the number of bandpasses must be equal.")
objs = []
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
# print("nphotons_tot = ", nphotons_tot)
......@@ -92,7 +92,7 @@ class Galaxy(MockObject):
return final
def drawObj_multiband(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0, exptime=150., fd_shear=None):
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
# print("nphotons_tot = ", nphotons_tot)
......@@ -115,7 +115,7 @@ class Galaxy(MockObject):
# Set Galsim Parameters
if self.getMagFilter(filt) <= 15 and (not big_galaxy):
folding_threshold = 5.e-4
folding_threshold = 5.e-8
else:
folding_threshold = 5.e-3
gsp = galsim.GSParams(folding_threshold=folding_threshold)
......@@ -170,8 +170,11 @@ class Galaxy(MockObject):
# print("nphotons_sub-band_%d = %.2f"%(i, nphotons))
# Get PSF model
EXTRA = False
if self.getMagFilter(filt) <= filt.mag_saturation-1.:
EXTRA = True
psf, pos_shear = psf_model.get_PSF(
chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold, extrapolate=EXTRA)
if self.bfrac == 0:
gal_temp = disk
......@@ -325,23 +328,28 @@ class Galaxy(MockObject):
galImg_List = []
try:
pos_img_local = [0,0]
pos_img_local = [0, 0]
x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
pos_img_local[0] = pos_img.x - x_start
pos_img_local[1] = pos_img.y - y_start
nnx = 0
nny = 0
for order in ["A","B"]:
for order in ["A", "B"]:
EXTRA = False
if self.getMagFilter(filt) <= filt.mag_saturation-2.:
EXTRA = True
psf, pos_shear = psf_model.get_PSF(
chip, pos_img_local=pos_img_local, bandNo=i+1, galsimGSObject=True, g_order=order, grating_split_pos=grating_split_pos)
chip, pos_img_local=pos_img_local, bandNo=i+1, galsimGSObject=True, g_order=order, grating_split_pos=grating_split_pos, extrapolate=EXTRA, ngg=3072)
star_p = galsim.Convolve(psf, gal)
if nnx == 0:
galImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset)
galImg = star_p.drawImage(
wcs=chip_wcs_local, offset=offset, method='no_pixel')
nnx = galImg.xmax - galImg.xmin + 1
nny = galImg.ymax - galImg.ymin + 1
else:
galImg = star_p.drawImage(nx = nnx, ny = nny, wcs=chip_wcs_local, offset=offset)
galImg = star_p.drawImage(
nx=nnx, ny=nny, wcs=chip_wcs_local, offset=offset, method='no_pixel')
galImg.setOrigin(0, 0)
# n1 = np.sum(np.isinf(galImg.array))
# n2 = np.sum(np.isnan(galImg.array))
......@@ -351,19 +359,23 @@ class Galaxy(MockObject):
# ERROR happens
return 2, pos_shear
galImg_List.append(galImg)
for order in ["C","D","E"]:
for order in ["C", "D", "E"]:
galImg_List.append(galImg)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
star_p = galsim.Convolve(psf, gal)
galImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset)
galImg.setOrigin(0, 0)
if np.sum(np.isnan(galImg.array)) > 0:
# ERROR happens
return 2, pos_shear
for order in ["A","B","C","D","E"]:
galImg_List.append(galImg)
try:
psf, pos_shear = psf_model.get_PSF(
chip=chip, pos_img=pos_img)
star_p = galsim.Convolve(psf, gal)
galImg = star_p.drawImage(
wcs=chip_wcs_local, offset=offset)
galImg.setOrigin(0, 0)
if np.sum(np.isnan(galImg.array)) > 0:
# ERROR happens
return 2, pos_shear
for order in ["A", "B", "C", "D", "E"]:
galImg_List.append(galImg)
except Exception as e:
continue
# starImg = gal.drawImage(
# wcs=chip_wcs_local, offset=offset, method='real_space')
......@@ -378,7 +390,7 @@ class Galaxy(MockObject):
subSlitPos = int(grating_split_pos_chip - gal_origin[1])
# part img disperse
star_p1s=[]
star_p1s = []
for galImg in galImg_List:
subImg_p1 = galImg.array[:, 0:subSlitPos]
......@@ -397,17 +409,18 @@ class Galaxy(MockObject):
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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)
star_p2s=[]
star_p2s = []
for galImg in galImg_List:
subImg_p2 = galImg.array[:,
subSlitPos:galImg.array.shape[1]]
subSlitPos:galImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0)
star_p2s.append(star_p2)
......@@ -423,7 +436,8 @@ class Galaxy(MockObject):
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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,
......@@ -439,7 +453,8 @@ class Galaxy(MockObject):
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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,
......@@ -453,7 +468,8 @@ class Galaxy(MockObject):
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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,
......@@ -465,7 +481,7 @@ class Galaxy(MockObject):
return 1, pos_shear
def getGSObj(self, psf, g1=0, g2=0, flux=None, filt=None, tel=None, exptime=150.):
if flux == None:
if flux is None:
flux = self.getElectronFluxFilt(filt, tel, exptime)
disk = galsim.Sersic(n=self.disk_sersic_idx,
half_light_radius=self.hlr_disk, flux=1.0)
......
observation_sim/mock_objects/MockObject.py
View file @ df22b3b0
......@@ -78,6 +78,8 @@ class MockObject(object):
(self.posImg.x, self.posImg.y), flush=True)
self.posImg, self.fd_shear = fdmodel.get_distorted(
chip=chip, pos_img=self.posImg)
if self.posImg is None:
return None, None, None, None, None
if verbose:
print("After field distortion:\n")
print("x = %.2f, y = %.2f\n" %
......@@ -108,7 +110,7 @@ class MockObject(object):
def drawObj_multiband(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., fd_shear=None):
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
# print("nphotons_tot = ", nphotons_tot)
......@@ -122,7 +124,7 @@ class MockObject(object):
return 2, None
# Set Galsim Parameters
if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4
folding_threshold = 5.e-8
else:
folding_threshold = 5.e-3
gsp = galsim.GSParams(folding_threshold=folding_threshold)
......@@ -160,14 +162,17 @@ class MockObject(object):
# print("nphotons_sub-band_%d = %.2f"%(i, nphotons))
# Get PSF model
EXTRA = False
if self.getMagFilter(filt) <= filt.mag_saturation-1.:
EXTRA = True
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass,
folding_threshold=folding_threshold)
folding_threshold=folding_threshold, extrapolate=EXTRA)
# star = galsim.DeltaFunction(gsparams=gsp)
# star = star.withFlux(nphotons)
# star = galsim.Convolve(psf, star)
star = psf.withFlux(nphotons)
stamp = star.drawImage(wcs=chip_wcs_local, offset=offset)
stamp = star.drawImage(method='no_pixel', wcs=chip_wcs_local, offset=offset)
if np.sum(np.isnan(stamp.array)) > 0:
continue
stamp.setCenter(x_nominal, y_nominal)
......@@ -235,14 +240,14 @@ class MockObject(object):
if chip.slsPSFOptim:
for k, v in spec_orders.items():
img_s = v[0]
nan_ids = np.isnan(img_s)
if img_s[nan_ids].shape[0] > 0:
img_s[nan_ids] = 0
print("DEBUG: specImg nan num is", img_s[nan_ids].shape[0])
#########################################################
# img_s, orig_off = convolveImg(img_s, psf_img_m)
orig_off = [0,0]
orig_off = [0, 0]
origin_order_x = v[1] - orig_off[0]
origin_order_y = v[2] - orig_off[1]
......@@ -257,15 +262,18 @@ class MockObject(object):
continue
# orders = {'A': 'order1', 'B': 'order0', 'C': 'order2', 'D': 'order-1', 'E': 'order-2'}
orders = {'A': 'order1', 'B': 'order0', 'C': 'order0', 'D': 'order0', 'E': 'order0'}
orders = {'A': 'order1', 'B': 'order0',
'C': 'order0', 'D': 'order0', 'E': 'order0'}
gratingN = chip_utils.getChipSLSGratingID(chip.chipID)[1]
if pos_img_local[0] < grating_split_pos:
gratingN = chip_utils.getChipSLSGratingID(chip.chipID)[0]
chip.img_stack[gratingN][orders[k]]['w' + str(bandNo)].setOrigin(0, 0)
chip.img_stack[gratingN][orders[k]]['w' + str(bandNo)][bounds] = chip.img_stack[gratingN][orders[k]]['w' + str(bandNo)][bounds] + specImg[bounds]
chip.img_stack[gratingN][orders[k]]['w' + str(bandNo)].setOrigin(chip.bound.xmin, chip.bound.ymin)
chip.img_stack[gratingN][orders[k]
]['w' + str(bandNo)].setOrigin(0, 0)
chip.img_stack[gratingN][orders[k]]['w' + str(
bandNo)][bounds] = chip.img_stack[gratingN][orders[k]]['w' + str(bandNo)][bounds] + specImg[bounds]
chip.img_stack[gratingN][orders[k]]['w' +
str(bandNo)].setOrigin(chip.bound.xmin, chip.bound.ymin)
else:
for k, v in spec_orders.items():
......@@ -313,11 +321,11 @@ class MockObject(object):
# specImg.wcs = local_wcs
# specImg.setOrigin(origin_order_x, origin_order_y)
# print('DEBUG: BEGIN -----------',bandNo,k)
img_s = v[0]
nan_ids = np.isnan(img_s)
if img_s[nan_ids].shape[0] > 0:
img_s[nan_ids] = 0
......@@ -330,9 +338,11 @@ class MockObject(object):
specImg.wcs = local_wcs
specImg.setOrigin(origin_order_x, origin_order_y)
try:
specImg = psf_model.get_PSF_AND_convolve_withsubImg(chip, cutImg=specImg, pos_img_local=pos_img_local, bandNo=bandNo, g_order=k, grating_split_pos=grating_split_pos)
specImg = psf_model.get_PSF_AND_convolve_withsubImg(
chip, cutImg=specImg, pos_img_local=pos_img_local, bandNo=bandNo, g_order=k, grating_split_pos=grating_split_pos)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
psf, pos_shear = psf_model.get_PSF(
chip=chip, pos_img=pos_img)
psf_img = psf.drawImage(nx=100, ny=100, wcs=local_wcs)
......@@ -376,7 +386,7 @@ class MockObject(object):
sedNormFactor = 1.
if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4
folding_threshold = 5.e-8
else:
folding_threshold = 5.e-3
gsp = galsim.GSParams(folding_threshold=folding_threshold)
......@@ -425,45 +435,51 @@ class MockObject(object):
# star = galsim.DeltaFunction(gsparams=gsp)
# star = star.withFlux(tel.pupil_area * exptime)
#psf list :["A","B","C","D","E"]
# psf list :["A","B","C","D","E"]
starImg_List = []
try:
pos_img_local = [0,0]
pos_img_local = [0, 0]
x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
pos_img_local[0] = pos_img.x - x_start
pos_img_local[1] = pos_img.y - y_start
nnx = 0
nny = 0
for order in ["A","B"]:
for order in ["A", "B"]:
EXTRA = False
if self.getMagFilter(filt) <= filt.mag_saturation-2.:
EXTRA = True
nnx = 2048
nny = 2048
psf, pos_shear = psf_model.get_PSF(
chip, pos_img_local=pos_img_local, bandNo=i+1, galsimGSObject=True, g_order=order, grating_split_pos=grating_split_pos)
chip, pos_img_local=pos_img_local, bandNo=i+1, galsimGSObject=True, g_order=order, grating_split_pos=grating_split_pos, extrapolate=EXTRA, ngg=3072)
# star_p = galsim.Convolve(psf, star)
star_p = psf.withFlux(tel.pupil_area * exptime)
star_p = psf.withFlux(tel.pupil_area * exptime)
if nnx == 0:
starImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset)
starImg = star_p.drawImage(
wcs=chip_wcs_local, offset=offset, method='no_pixel')
nnx = starImg.xmax - starImg.xmin + 1
nny = starImg.ymax - starImg.ymin + 1
else:
starImg = star_p.drawImage(nx = nnx, ny = nny, wcs=chip_wcs_local, offset=offset)
starImg = star_p.drawImage(
nx=nnx, ny=nny, wcs=chip_wcs_local, offset=offset, method='no_pixel')
# n1 = np.sum(np.isinf(starImg.array))
# n2 = np.sum(np.isnan(starImg.array))
# if n1>0 or n2 > 0:
# print("DEBUG: MockObject, inf:%d, nan:%d"%(n1, n2))
starImg.setOrigin(0, 0)
starImg_List.append(starImg)
for order in ["C","D","E"]:
for order in ["C", "D", "E"]:
starImg_List.append(starImg)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
# star_p = galsim.Convolve(psf, star)
star_p = psf.withFlux(tel.pupil_area * exptime)
starImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset)
star_p = psf.withFlux(tel.pupil_area * exptime)
starImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset, method='no_pixel')
starImg.setOrigin(0, 0)
for order in ["A","B","C","D","E"]:
for order in ["A", "B", "C", "D", "E"]:
starImg_List.append(starImg)
# psf_tmp = galsim.Gaussian(sigma=0.002)
# star = galsim.Convolve(psf_tmp, star)
......@@ -472,14 +488,14 @@ class MockObject(object):
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
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]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1])
# part img disperse
star_p1s=[]
star_p1s = []
for starImg in starImg_List:
subImg_p1 = starImg.array[:, 0:subSlitPos]
......@@ -498,17 +514,17 @@ class MockObject(object):
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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)
star_p2s=[]
star_p2s = []
for starImg in starImg_List:
subImg_p2 = starImg.array[:,
subSlitPos:starImg.array.shape[1]]
subSlitPos:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0)
star_p2s.append(star_p2)
......@@ -524,7 +540,8 @@ class MockObject(object):
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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)
......@@ -539,7 +556,8 @@ class MockObject(object):
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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)
......@@ -552,7 +570,8 @@ class MockObject(object):
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
self.addSLStoChipImage(
sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# 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)
......@@ -572,7 +591,7 @@ class MockObject(object):
def drawObj_PSF(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., fd_shear=None, chip_output=None):
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
# print("nphotons_tot = ", nphotons_tot)
......@@ -641,7 +660,7 @@ class MockObject(object):
os.makedirs(fn, exist_ok=True)
fn = fn + "/ccd_{:}".format(chip.chipID) + \
"_psf_"+str(self.param['id'])+".fits"
if fn != None:
if fn is not None:
if os.path.exists(fn):
os.remove(fn)
hdu = fitsio.PrimaryHDU()
......
observation_sim/mock_objects/Quasar.py
View file @ df22b3b0
......@@ -98,7 +98,7 @@ class Quasar(MockObject):
del self.sed
def getGSObj(self, psf, g1=0, g2=0, flux=None, filt=None, tel=None, exptime=150.):
if flux == None:
if flux is None:
flux = self.getElectronFluxFilt(filt, tel, exptime)
qso = galsim.Gaussian(sigma=1.e-8, flux=1.)
qso = qso.withFlux(flux)
......
observation_sim/mock_objects/SpecDisperser/SpecDisperser.py
View file @ df22b3b0
......@@ -50,8 +50,21 @@ def rotate90(array_orig=None, xc=0, yc=0, isClockwise=0):
class SpecDisperser(object):
def __init__(self, orig_img=None, xcenter=0, ycenter=0, origin=[100, 100], tar_spec=None, band_start=6200,
band_end=10000, isAlongY=0, conf='../param/CONF/csst.conf', gid=0, flat_cube=None, ignoreBeam=[]):
def __init__(
self,
orig_img=None,
xcenter=0,
ycenter=0,
origin=[100, 100],
tar_spec=None,
band_start=6200,
band_end=10000,
isAlongY=0,
conf="../param/CONF/csst.conf",
gid=0,
flat_cube=None,
ignoreBeam=[],
):
"""
orig_img: normal image,galsim image
xcenter, ycenter: the center of galaxy in orig_img
......@@ -66,23 +79,21 @@ class SpecDisperser(object):
# self.img_y = orig_img.shape[0]
# 5 orders, A, B ,
orderName=["A","B","C","D","E"]
orderName = ["A", "B", "C", "D", "E"]
self.orig_img_orders = OrderedDict()
if isinstance(orig_img, list):
orig_img_list = orig_img
list_len = len(orig_img_list)
if list_len < 5:
for i in np.arange(5-list_len):
orig_img_list.append(orig_img_list[list_len-1])
for i in np.arange(5 - list_len):
orig_img_list.append(orig_img_list[list_len - 1])
for i, k in enumerate(orig_img_list):
self.orig_img_orders[orderName[i]] = k
if isinstance(orig_img, galsim.Image):
for i in np.arange(5):
self.orig_img_orders[orderName[i]] = orig_img
orig_img_one = self.orig_img_orders["A"]
self.thumb_img = np.abs(orig_img_one.array)
......@@ -99,8 +110,12 @@ class SpecDisperser(object):
self.flat_cube = flat_cube
if self.isAlongY == 1:
for order in orderName:
self.orig_img_orders[order], self.thumb_x, self.thumb_y = rotate90(array_orig=self.orig_img_orders[order], xc=orig_img_one.center.x,
yc=orig_img_one.center.y, isClockwise=1)
self.orig_img_orders[order], self.thumb_x, self.thumb_y = rotate90(
array_orig=self.orig_img_orders[order],
xc=orig_img_one.center.x,
yc=orig_img_one.center.y,
isClockwise=1,
)
# self.thumb_img, self.thumb_x, self.thumb_y = rotate90(array_orig=self.thumb_img, xc=orig_img_one.center.x,
# yc=orig_img_one.center.y, isClockwise=1)
......@@ -139,6 +154,7 @@ class SpecDisperser(object):
from .disperse_c import interp
from .disperse_c import disperse
# from MockObject.disperse_c import disperse
self.thumb_img = np.abs(self.orig_img_orders[beam].array)
self.thumb_x = self.orig_img_orders[beam].center.x
......@@ -146,11 +162,12 @@ class SpecDisperser(object):
self.img_sh = self.orig_img_orders[beam].array.shape
dx = self.grating_conf.dxlam[beam]
xoff = 0
ytrace_beam, lam_beam = self.grating_conf.get_beam_trace(x=self.xcenter, y=self.ycenter, dx=(dx + xoff),
beam=beam)
ytrace_beam, lam_beam = self.grating_conf.get_beam_trace(
x=self.xcenter, y=self.ycenter, dx=(dx + xoff), beam=beam
)
# Account for pixel centering of the trace
yfrac_beam = ytrace_beam - floor(ytrace_beam+0.5)
yfrac_beam = ytrace_beam - floor(ytrace_beam + 0.5)
ysens = lam_beam * 0
lam_index = argsort(lam_beam)
......@@ -160,8 +177,8 @@ class SpecDisperser(object):
(self.band_end - self.band_start) / 0.1))
thri = interpolate.interp1d(
conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY'])
spci = interpolate.interp1d(self.spec['WAVELENGTH'], self.spec['FLUX'])
conf_sens["WAVELENGTH"], conf_sens["SENSITIVITY"])
spci = interpolate.interp1d(self.spec["WAVELENGTH"], self.spec["FLUX"])
beam_thr = thri(lam_intep)
spec_sample = spci(lam_intep)
......@@ -179,8 +196,9 @@ class SpecDisperser(object):
#
# self.writerSensitivityFile(conffile = self.grating_conf_file, beam = beam, w = lam_beam[lam_index], sens = ysensitivity[lam_index])
ysens[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep, bean_thr_spec, integrate=1, left=0,
right=0)
ysens[lam_index] = interp.interp_conserve_c(
lam_beam[lam_index], lam_intep, bean_thr_spec, integrate=1, left=0, right=0
)
sensitivity_beam = ysens
......@@ -196,7 +214,7 @@ class SpecDisperser(object):
dxpix = dx - dx[0] + x0[1]
dyc = cast[int](np.floor(ytrace_beam+0.5))
dyc = cast[int](np.floor(ytrace_beam + 0.5))
# dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5))
dypix = dyc - dyc[0] + x0[0]
......@@ -204,7 +222,7 @@ class SpecDisperser(object):
frac_ids = yfrac_beam < 0
dypix[frac_ids] = dypix[frac_ids] - 1
yfrac_beam[frac_ids] = 1+yfrac_beam[frac_ids]
yfrac_beam[frac_ids] = 1 + yfrac_beam[frac_ids]
flat_index = idx[dypix, dxpix]
......@@ -233,7 +251,7 @@ class SpecDisperser(object):
sub_flat_cube = zeros(
[len(self.flat_cube), beam_sh[0], beam_sh[1]])
sub_flat_cube[0] = sub_flat_cube[0] + 1.
sub_flat_cube[0] = sub_flat_cube[0] + 1.0
overlap_flag = 1
......@@ -260,33 +278,38 @@ class SpecDisperser(object):
overlap_flag = 0
if overlap_flag == 1:
sub_flat_cube[:, beam_y_s-originOut_y:beam_y_e-originOut_y+1, beam_x_s-originOut_x:beam_x_e -
originOut_x+1] = self.flat_cube[:, beam_y_s:beam_y_e+1, beam_x_s:beam_x_e+1]
sub_flat_cube[
:,
beam_y_s - originOut_y: beam_y_e - originOut_y + 1,
beam_x_s - originOut_x: beam_x_e - originOut_x + 1,
] = self.flat_cube[:, beam_y_s: beam_y_e + 1, beam_x_s: beam_x_e + 1]
for i in arange(0, len(self.flat_cube), 1):
beam_flat[:, i] = sub_flat_cube[i].flatten()
# beam_flat = zeros([len(modelf), len(self.flat_cube)])
# flat_sh = self.flat_cube[0].shape
# for i in arange(0, beam_sh[0], 1):
# for j in arange(0, beam_sh[1], 1):
# k = i * beam_sh[1] + j
# if originOut_y + i >= flat_sh[0] or originOut_y + i < 0 or originOut_x + j>= flat_sh[1] or originOut_x+j < 0:
# temp_bf = np.zeros_like(self.flat_cube[:, 0, 0])
# temp_bf[0] = 1.0
# beam_flat[k] = temp_bf
# else:
# beam_flat[k] = self.flat_cube[:, originOut_y + i, originOut_x + j]
status = disperse.disperse_grism_object(self.thumb_img.astype(np.float32),
flat_index[nonz],
yfrac_beam[nonz],
sensitivity_beam[nonz],
modelf, x0,
array(self.img_sh,
dtype=int64),
array(beam_sh, dtype=int64),
beam_flat,
lam_beam[lam_index][nonz])
# beam_flat = zeros([len(modelf), len(self.flat_cube)])
# flat_sh = self.flat_cube[0].shape
# for i in arange(0, beam_sh[0], 1):
# for j in arange(0, beam_sh[1], 1):
# k = i * beam_sh[1] + j
# if originOut_y + i >= flat_sh[0] or originOut_y + i < 0 or originOut_x + j>= flat_sh[1] or originOut_x+j < 0:
# temp_bf = np.zeros_like(self.flat_cube[:, 0, 0])
# temp_bf[0] = 1.0
# beam_flat[k] = temp_bf
# else:
# beam_flat[k] = self.flat_cube[:, originOut_y + i, originOut_x + j]
status = disperse.disperse_grism_object(
self.thumb_img.astype(np.float32),
flat_index[nonz],
yfrac_beam[nonz],
sensitivity_beam[nonz],
modelf,
x0,
array(self.img_sh, dtype=int64),
array(beam_sh, dtype=int64),
beam_flat,
lam_beam[lam_index][nonz],
)
model = modelf.reshape(beam_sh)
# n1 = np.sum(np.isinf(model))
......@@ -296,27 +319,27 @@ class SpecDisperser(object):
# if n1>0 or n2 > 0:
# print("DEBUG: SpecDisperser, inf:%d, nan:%d--------%d,%d"%(n1, n2, n3, n4))
# print(dypix)
# n1 = np.sum(np.isinf(self.thumb_img.astype(np.float32)))
# n2 = np.sum(np.isnan(self.thumb_img.astype(np.float32)))
# n3 = np.sum(np.isinf(yfrac_beam))
# n4 = np.sum(np.isnan(yfrac_beam))
# n5 = np.sum(np.isinf(sensitivity_beam))
# n6 = np.sum(np.isnan(sensitivity_beam))
# print("DEBUG: SpecDisperser, innput ---inf:%d, nan:%d, yfrac_beam:%d/%d, sensitivity_beam:%d/%d"%(n1, n2, n3, n4, n5, n6))
# n1 = np.sum(np.isinf(self.thumb_img.astype(np.float32)))
# n2 = np.sum(np.isnan(self.thumb_img.astype(np.float32)))
# n3 = np.sum(np.isinf(yfrac_beam))
# n4 = np.sum(np.isnan(yfrac_beam))
# n5 = np.sum(np.isinf(sensitivity_beam))
# n6 = np.sum(np.isnan(sensitivity_beam))
# print("DEBUG: SpecDisperser, innput ---inf:%d, nan:%d, yfrac_beam:%d/%d, sensitivity_beam:%d/%d"%(n1, n2, n3, n4, n5, n6))
self.beam_flux[beam] = sum(modelf)
if self.isAlongY == 1:
model, _, _ = rotate90(array_orig=model, isClockwise=0)
return model, originOut_x, originOut_y, dxpix, dypix, lam_beam, ysens
def writerSensitivityFile(self, conffile='', beam='', w=None, sens=None):
orders = {'A': '1st', 'B': '0st', 'C': '2st', 'D': '-1st', 'E': '-2st'}
def writerSensitivityFile(self, conffile="", beam="", w=None, sens=None):
orders = {"A": "1st", "B": "0st", "C": "2st", "D": "-1st", "E": "-2st"}
sens_file_name = conffile[0:-5] + \
'_sensitivity_' + orders[beam] + '.fits'
if not os.path.exists(sens_file_name) == True:
"_sensitivity_" + orders[beam] + ".fits"
if os.path.exists(sens_file_name) is False:
senstivity_out = Table(
array([w, sens]).T, names=('WAVELENGTH', 'SENSITIVITY'))
senstivity_out.write(sens_file_name, format='fits')
array([w, sens]).T, names=("WAVELENGTH", "SENSITIVITY"))
senstivity_out.write(sens_file_name, format="fits")
"""
......@@ -324,8 +347,8 @@ Demonstrate aXe trace polynomials.
"""
class aXeConf():
def __init__(self, conf_file='WFC3.IR.G141.V2.5.conf'):
class aXeConf:
def __init__(self, conf_file="WFC3.IR.G141.V2.5.conf"):
"""Read an aXe-compatible configuration file
Parameters
......@@ -340,17 +363,17 @@ class aXeConf():
self.count_beam_orders()
# Global XOFF/YOFF offsets
if 'XOFF' in self.conf.keys():
self.xoff = np.float(self.conf['XOFF'])
if "XOFF" in self.conf.keys():
self.xoff = np.float(self.conf["XOFF"])
else:
self.xoff = 0.
self.xoff = 0.0
if 'YOFF' in self.conf.keys():
self.yoff = np.float(self.conf['YOFF'])
if "YOFF" in self.conf.keys():
self.yoff = np.float(self.conf["YOFF"])
else:
self.yoff = 0.
self.yoff = 0.0
def read_conf_file(self, conf_file='WFC3.IR.G141.V2.5.conf'):
def read_conf_file(self, conf_file="WFC3.IR.G141.V2.5.conf"):
"""Read an aXe config file, convert floats and arrays
Parameters
......@@ -366,11 +389,11 @@ class aXeConf():
lines = open(conf_file).readlines()
for line in lines:
# empty / commented lines
if (line.startswith('#')) | (line.strip() == '') | ('"' in line):
if (line.startswith("#")) | (line.strip() == "") | ('"' in line):
continue
# split the line, taking out ; and # comments
spl = line.split(';')[0].split('#')[0].split()
spl = line.split(";")[0].split("#")[0].split()
param = spl[0]
if len(spl) > 2:
value = np.cast[float](spl[1:])
......@@ -385,22 +408,20 @@ class aXeConf():
return conf
def count_beam_orders(self):
"""Get the maximum polynomial order in DYDX or DLDP for each beam
"""
"""Get the maximum polynomial order in DYDX or DLDP for each beam"""
self.orders = {}
for beam in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']:
for beam in ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]:
order = 0
while 'DYDX_{0:s}_{1:d}'.format(beam, order) in self.conf.keys():
while "DYDX_{0:s}_{1:d}".format(beam, order) in self.conf.keys():
order += 1
while 'DLDP_{0:s}_{1:d}'.format(beam, order) in self.conf.keys():
while "DLDP_{0:s}_{1:d}".format(beam, order) in self.conf.keys():
order += 1
self.orders[beam] = order - 1
def get_beams(self):
"""Get beam parameters and read sensitivity curves
"""
"""Get beam parameters and read sensitivity curves"""
import os
from collections import OrderedDict
from astropy.table import Table, Column
......@@ -413,12 +434,15 @@ class aXeConf():
for beam in self.orders:
if self.orders[beam] > 0:
self.beams.append(beam)
self.dxlam[beam] = np.arange(self.conf['BEAM{0}'.format(beam)].min(),
self.conf['BEAM{0}'.format(beam)].max(), dtype=int)
self.dxlam[beam] = np.arange(
self.conf["BEAM{0}".format(beam)].min(), self.conf["BEAM{0}".format(beam)].max(), dtype=int
)
self.nx[beam] = int(self.dxlam[beam].max() -
self.dxlam[beam].min()) + 1
self.sens[beam] = Table.read(
'{0}/{1}'.format(os.path.dirname(self.conf_file), self.conf['SENSITIVITY_{0}'.format(beam)]))
"{0}/{1}".format(os.path.dirname(self.conf_file),
self.conf["SENSITIVITY_{0}".format(beam)])
)
# self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH'])
# self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY'])
......@@ -471,41 +495,41 @@ class aXeConf():
return a
def evaluate_dp(self, dx, dydx):
"""Evalate arc length along the trace given trace polynomial coefficients
# """Evalate arc length along the trace given trace polynomial coefficients
Parameters
----------
dx : array-like
x pixel to evaluate
# Parameters
# ----------
# dx : array-like
# x pixel to evaluate
dydx : array-like
Coefficients of the trace polynomial
# dydx : array-like
# Coefficients of the trace polynomial
Returns
-------
dp : array-like
Arc length along the trace at position `dx`.
# Returns
# -------
# dp : array-like
# Arc length along the trace at position `dx`.
For `dydx` polynomial orders 0, 1 or 2, integrate analytically.
Higher orders must be integrated numerically.
# For `dydx` polynomial orders 0, 1 or 2, integrate analytically.
# Higher orders must be integrated numerically.
**Constant:**
.. math:: dp = dx
# **Constant:**
# .. math:: dp = dx
**Linear:**
.. math:: dp = \sqrt{1+\mathrm{DYDX}[1]}\cdot dx
# **Linear:**
# .. math:: dp = \sqrt{1+\mathrm{DYDX}[1]}\cdot dx
**Quadratic:**
.. math:: u = \mathrm{DYDX}[1] + 2\ \mathrm{DYDX}[2]\cdot dx
# **Quadratic:**
# .. math:: u = \mathrm{DYDX}[1] + 2\ \mathrm{DYDX}[2]\cdot dx
.. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2])
# .. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2])
"""
# """
# dp is the arc length along the trace
# $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...
poly_order = len(dydx) - 1
if (poly_order == 2):
if poly_order == 2:
if np.abs(np.unique(dydx[2])).max() == 0:
poly_order = 1
......@@ -515,10 +539,9 @@ class aXeConf():
dp = np.sqrt(1 + dydx[1] ** 2) * (dx)
elif poly_order == 2: # quadratic trace
u0 = dydx[1] + 2 * dydx[2] * (0)
dp0 = (u0 * np.sqrt(1 + u0 ** 2) + np.arcsinh(u0)) / (4 * dydx[2])
dp0 = (u0 * np.sqrt(1 + u0**2) + np.arcsinh(u0)) / (4 * dydx[2])
u = dydx[1] + 2 * dydx[2] * (dx)
dp = (u * np.sqrt(1 + u ** 2) + np.arcsinh(u)) / \
(4 * dydx[2]) - dp0
dp = (u * np.sqrt(1 + u**2) + np.arcsinh(u)) / (4 * dydx[2]) - dp0
else:
# high order shape, numerical integration along trace
# (this can be slow)
......@@ -530,7 +553,7 @@ class aXeConf():
dyfull += i * dydx[i] * (xfull - 0.5) ** (i - 1)
# Integrate from 0 to dx / -dx
dpfull = xfull * 0.
dpfull = xfull * 0.0
lt0 = xfull < 0
if lt0.sum() > 1:
dpfull[lt0] = np.cumsum(
......@@ -548,7 +571,7 @@ class aXeConf():
return dp
def get_beam_trace(self, x=507, y=507, dx=0., beam='A'):
def get_beam_trace(self, x=507, y=507, dx=0.0, beam="A"):
"""Get an aXe beam trace for an input reference pixel and list of output x pixels `dx`
Parameters
......@@ -579,17 +602,17 @@ class aXeConf():
xi, yi = x - self.xoff, y - self.yoff
xoff_beam = self.field_dependent(
xi, yi, self.conf['XOFF_{0}'.format(beam)])
xi, yi, self.conf["XOFF_{0}".format(beam)])
yoff_beam = self.field_dependent(
xi, yi, self.conf['YOFF_{0}'.format(beam)])
xi, yi, self.conf["YOFF_{0}".format(beam)])
# y offset of trace (DYDX)
dydx = np.zeros(NORDER) # 0 #+1.e-80
dydx = [0] * NORDER
for i in range(NORDER):
if 'DYDX_{0:s}_{1:d}'.format(beam, i) in self.conf.keys():
coeffs = self.conf['DYDX_{0:s}_{1:d}'.format(beam, i)]
if "DYDX_{0:s}_{1:d}".format(beam, i) in self.conf.keys():
coeffs = self.conf["DYDX_{0:s}_{1:d}".format(beam, i)]
dydx[i] = self.field_dependent(xi, yi, coeffs)
# $dy = dydx_0+dydx_1 dx+dydx_2 dx^2+$ ...
......@@ -603,14 +626,20 @@ class aXeConf():
dldp = [0] * NORDER
for i in range(NORDER):
if 'DLDP_{0:s}_{1:d}'.format(beam, i) in self.conf.keys():
coeffs = self.conf['DLDP_{0:s}_{1:d}'.format(beam, i)]
if "DLDP_{0:s}_{1:d}".format(beam, i) in self.conf.keys():
coeffs = self.conf["DLDP_{0:s}_{1:d}".format(beam, i)]
dldp[i] = self.field_dependent(xi, yi, coeffs)
self.eval_input = {'x': x, 'y': y, 'beam': beam, 'dx': dx}
self.eval_output = {'xi': xi, 'yi': yi, 'dldp': dldp, 'dydx': dydx,
'xoff_beam': xoff_beam, 'yoff_beam': yoff_beam,
'dy': dy}
self.eval_input = {"x": x, "y": y, "beam": beam, "dx": dx}
self.eval_output = {
"xi": xi,
"yi": yi,
"dldp": dldp,
"dydx": dydx,
"xoff_beam": xoff_beam,
"yoff_beam": yoff_beam,
"dy": dy,
}
dp = self.evaluate_dp(dx - xoff_beam, dydx)
# ## dp is the arc length along the trace
......@@ -648,13 +677,13 @@ class aXeConf():
# dp = np.interp(dx-xoff_beam, xfull, dpfull)
# Evaluate dldp
lam = dp * 0.
lam = dp * 0.0
for i in range(NORDER):
lam += dldp[i] * dp ** i
lam += dldp[i] * dp**i
return dy, lam
def show_beams(self, beams=['E', 'D', 'C', 'B', 'A']):
def show_beams(self, beams=["E", "D", "C", "B", "A"]):
"""
Make a demo plot of the beams of a given configuration file
"""
......@@ -663,45 +692,47 @@ class aXeConf():
x0, x1 = 507, 507
dx = np.arange(-800, 1200)
if 'WFC3.UV' in self.conf_file:
if "WFC3.UV" in self.conf_file:
x0, x1 = 2073, 250
dx = np.arange(-1200, 1200)
if 'G800L' in self.conf_file:
if "G800L" in self.conf_file:
x0, x1 = 2124, 1024
dx = np.arange(-1200, 1200)
s = 200 # marker size
fig = plt.figure(figsize=[10, 3])
plt.scatter(0, 0, marker='s', s=s, color='black', edgecolor='0.8',
label='Direct')
plt.scatter(0, 0, marker="s", s=s, color="black",
edgecolor="0.8", label="Direct")
for beam in beams:
if 'XOFF_{0}'.format(beam) not in self.conf.keys():
if "XOFF_{0}".format(beam) not in self.conf.keys():
continue
xoff = self.field_dependent(
x0, x1, self.conf['XOFF_{0}'.format(beam)])
x0, x1, self.conf["XOFF_{0}".format(beam)])
dy, lam = self.get_beam_trace(x0, x1, dx=dx, beam=beam)
xlim = self.conf['BEAM{0}'.format(beam)]
xlim = self.conf["BEAM{0}".format(beam)]
ok = (dx >= xlim[0]) & (dx <= xlim[1])
plt.scatter(dx[ok] + xoff, dy[ok], c=lam[ok] / 1.e4, marker='s', s=s,
alpha=0.5, edgecolor='None')
plt.text(np.median(dx[ok]), np.median(dy[ok]) + 1, beam,
ha='center', va='center', fontsize=14)
print('Beam {0}, lambda=({1:.1f} - {2:.1f})'.format(beam,
plt.scatter(dx[ok] + xoff, dy[ok], c=lam[ok] / 1.0e4,
marker="s", s=s, alpha=0.5, edgecolor="None")
plt.text(np.median(dx[ok]), np.median(
dy[ok]) + 1, beam, ha="center", va="center", fontsize=14)
print("Beam {0}, lambda=({1:.1f} - {2:.1f})".format(beam,
lam[ok].min(), lam[ok].max()))
plt.grid()
plt.xlabel(r'$\Delta x$')
plt.ylabel(r'$\Delta y$')
plt.xlabel(r"$\Delta x$")
plt.ylabel(r"$\Delta y$")
cb = plt.colorbar(pad=0.01, fraction=0.05)
cb.set_label(r'$\lambda\,(\mu\mathrm{m})$')
cb.set_label(r"$\lambda\,(\mu\mathrm{m})$")
plt.title(self.conf_file)
plt.tight_layout()
plt.savefig('{0}.pdf'.format(self.conf_file))
plt.savefig("{0}.pdf".format(self.conf_file))
# def load_grism_config(conf_file):
# """Load parameters from an aXe configuration file
# Parameters
......
observation_sim/mock_objects/SpecDisperser/__init__.py
View file @ df22b3b0
from .SpecDisperser import *
from .disperse_c import disperse, interp
\ No newline at end of file
from .disperse_c import disperse, interp
observation_sim/mock_objects/SpecDisperser/setup_c.py
View file @ df22b3b0
......@@ -8,16 +8,16 @@ import numpy
extensions = [
Extension("disperse_c.interp", ["disperse_c/interp.pyx"],
include_dirs = [numpy.get_include()],
libraries=["m"]),
include_dirs=[numpy.get_include()],
libraries=["m"]),
Extension("disperse_c.disperse", ["disperse_c/disperse.pyx"],
include_dirs = [numpy.get_include()],
libraries=["m"]),
include_dirs=[numpy.get_include()],
libraries=["m"]),
]
setup(
name = "slssim_disperse",
ext_modules = cythonize(extensions),
name="slssim_disperse",
ext_modules=cythonize(extensions),
)
observation_sim/mock_objects/Stamp.py
View file @ df22b3b0
......@@ -21,7 +21,7 @@ class Stamp(MockObject):
del self.sed
def drawObj_multiband(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0, exptime=150., fd_shear=None):
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
try:
......@@ -92,7 +92,7 @@ class Stamp(MockObject):
else:
gal = gal + gal_temp
stamp = gal.drawImage(wcs=chip_wcs_local, offset=offset)
stamp = gal.drawImage(method='no_pixel', wcs=chip_wcs_local, offset=offset)
if np.sum(np.isnan(stamp.array)) > 0:
# ERROR happens
return 2, pos_shear
......
observation_sim/mock_objects/Star.py
View file @ df22b3b0
......@@ -22,7 +22,7 @@ class Star(MockObject):
del self.sed
def getGSObj(self, psf, g1=0, g2=0, flux=None, filt=None, tel=None, exptime=150.):
if flux == None:
if flux is None:
flux = self.getElectronFluxFilt(filt, tel, exptime)
# star = galsim.Gaussian(sigma=1.e-8, flux=1.)
star = galsim.DeltaFunction()
......@@ -35,7 +35,7 @@ class Star(MockObject):
raise ValueError(
"!!!The number of PSF profiles and the number of bandpasses must be equal.")
objs = []
if nphotons_tot == None:
if nphotons_tot is None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
try:
......
observation_sim/mock_objects/_util.py
View file @ df22b3b0
......@@ -58,7 +58,7 @@ def seds(sedlistn, seddir="./", unit="A"):
read SEDs and save into Python dictionary
Parameters:
sedlistn: filename of the sed template list and corresponding intrinsic
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
......@@ -420,7 +420,7 @@ def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0):
def eObs(e1, e2, g1, g2):
"""
Calculate the sheared (observed) ellipticity using the
Calculate the sheared (observed) ellipticity using the
intrinsic ellipticity and cosmic shear components.
Parameters:
......@@ -558,7 +558,7 @@ def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave
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)
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(
......
observation_sim/psf/FieldDistortion.py
View file @ df22b3b0
......@@ -62,7 +62,8 @@ class FieldDistortion(object):
the distored position.
"""
if not self.isContainObj_FD(chip=chip, pos_img=pos_img):
return galsim.PositionD(-1, -1), None
# return galsim.PositionD(-1, -1), None
return None, None
if not img_rot:
img_rot = np.radians(self.img_rot)
else:
......
observation_sim/psf/PSFGauss.py
View file @ df22b3b0
......@@ -50,7 +50,7 @@ class PSFGauss(PSFModel):
Return: the flux ratio
"""
if pscale == None:
if pscale is None:
pscale = self.pix_size
gaussx = galsim.Gaussian(flux=1.0, sigma=sig)
gaussImg = gaussx.drawImage(scale=pscale, method='no_pixel')
......@@ -68,7 +68,7 @@ class PSFGauss(PSFModel):
return the fwhm in arcsec
"""
if pscale == None:
if pscale is None:
pscale = self.pix_size
err = 1.0e-3
nxx = 100
......@@ -122,4 +122,4 @@ class PSFGauss(PSFModel):
# 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
return self.psf.shear(PSFshear), PSFshear
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