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Commit 45156a94 authored by JX's avatar JX 😵
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Merge remote-tracking branch 'origin/develop'

parents e360ee24 edffea7b
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observation_sim/ObservationSim.py
View file @ 45156a94
......@@ -23,7 +23,7 @@ class Observation(object):
self.filter_param = FilterParam()
self.Catalog = Catalog
def prepare_chip_for_exposure(self, chip, ra_cen, dec_cen, pointing, wcs_fp=None):
def prepare_chip_for_exposure(self, chip, ra_cen, dec_cen, pointing, wcs_fp=None, slsPSFOptim=False):
# Get WCS for the focal plane
if wcs_fp == None:
wcs_fp = self.focal_plane.getTanWCS(
......@@ -34,6 +34,27 @@ class Observation(object):
chip.img.setOrigin(chip.bound.xmin, chip.bound.ymin)
chip.img.wcs = wcs_fp
chip.slsPSFOptim = slsPSFOptim
if chip.chipID in [1, 2, 3, 4, 5, 10, 21, 26, 27, 28, 29, 30] and slsPSFOptim:
chip.img_stack = {}
for id1 in np.arange(2):
gn = chip_utils.getChipSLSGratingID(chip.chipID)[id1]
orders = {}
# for id2 in ['-2','-1','0','1','2']:
for id2 in ['0', '1']:
o_n = "order"+id2
allbands = {}
for id3 in ['1', '2', '3', '4']:
w_n = "w"+id3
allbands[w_n] = galsim.ImageF(chip.npix_x, chip.npix_y)
allbands[w_n].setOrigin(
chip.bound.xmin, chip.bound.ymin)
allbands[w_n].wcs = wcs_fp
orders[o_n] = allbands
chip.img_stack[gn] = orders
else:
chip.img_stack = {}
# Get random generators for this chip
chip.rng_poisson, chip.poisson_noise = chip_utils.get_poisson(
seed=int(self.config["random_seeds"]["seed_poisson"]) + pointing.id*30 + chip.chipID, sky_level=0.)
......@@ -91,15 +112,17 @@ class Observation(object):
input_date_str=date_str,
input_time_str=time_str
)
ra_cen, dec_cen = ra_cen[0], dec_cen[0]
ra_offset, dec_offset = pointing.ra - ra_cen, pointing.dec - dec_cen
ra_offset, dec_offset = pointing.ra - \
ra_cen[0], pointing.dec - dec_cen[0]
else:
ra_offset, dec_offset = 0., 0.
ra_cen = pointing.ra
dec_cen = pointing.dec
ra_offset, dec_offset = 0., 0.
slsPSFOpt = False
# Prepare necessary chip properties for simulation
chip = self.prepare_chip_for_exposure(chip, ra_cen, dec_cen, pointing)
chip = self.prepare_chip_for_exposure(
chip, ra_cen, dec_cen, pointing, slsPSFOptim=slsPSFOpt)
# Initialize SimSteps
sim_steps = SimSteps(overall_config=self.config,
......
observation_sim/config/ChipOutput.py
View file @ 45156a94
......@@ -78,12 +78,12 @@ class ChipOutput(object):
self.hdr += "\n"
self.cat.write(self.hdr)
def cat_add_obj(self, obj, pos_img, pos_shear):
def cat_add_obj(self, obj, pos_img, pos_shear, ra_offset=0., dec_offset=0.):
ximg = obj.real_pos.x + 1.0
yimg = obj.real_pos.y + 1.0
line = self.fmt % (
obj.id, int(self.chip_label), self.filt.filter_type, ximg, yimg, obj.ra, obj.dec, obj.ra_orig, obj.dec_orig, obj.z, obj.getMagFilter(
obj.id, int(self.chip_label), self.filt.filter_type, ximg, yimg, obj.ra + ra_offset, obj.dec + dec_offset, obj.ra_orig, obj.dec_orig, obj.z, obj.getMagFilter(
self.filt), obj.type,
obj.pmra, obj.pmdec, obj.rv, obj.parallax)
line += obj.additional_output_str
......
observation_sim/mock_objects/Galaxy.py
View file @ 45156a94
......@@ -323,28 +323,73 @@ class Galaxy(MockObject):
# # if fd_shear is not None:
# # gal = gal.shear(fd_shear)
starImg = gal.drawImage(
wcs=chip_wcs_local, offset=offset, method='real_space')
galImg_List = []
try:
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"]:
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)
star_p = galsim.Convolve(psf, gal)
if nnx == 0:
galImg = star_p.drawImage(wcs=chip_wcs_local, offset=offset)
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.setOrigin(0, 0)
# n1 = np.sum(np.isinf(galImg.array))
# n2 = np.sum(np.isnan(galImg.array))
# if n1>0 or n2 > 0:
# print("DEBUG: Galaxy, inf:%d, nan:%d"%(n1, n2))
if np.sum(np.isnan(galImg.array)) > 0:
# ERROR happens
return 2, pos_shear
galImg_List.append(galImg)
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)
# starImg = gal.drawImage(
# wcs=chip_wcs_local, offset=offset, method='real_space')
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0, 0)
origin_star = [y_nominal - (galImg.center.y - galImg.ymin),
x_nominal - (galImg.center.x - galImg.xmin)]
galImg.setOrigin(0, 0)
gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] -
1, origin_star[1] + starImg.array.shape[1] - 1]
gal_end = [origin_star[0] + galImg.array.shape[0] -
1, origin_star[1] + galImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
# part img disperse
subImg_p1 = starImg.array[:, 0:subSlitPos]
star_p1s=[]
for galImg in galImg_List:
subImg_p1 = galImg.array[:, 0:subSlitPos]
star_p1 = galsim.Image(subImg_p1)
star_p1.setOrigin(0, 0)
star_p1s.append(star_p1)
origin_p1 = origin_star
xcenter_p1 = min(x_nominal, grating_split_pos_chip-1) - 0
ycenter_p1 = y_nominal-0
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
sdp_p1 = SpecDisperser(orig_img=star_p1s, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED,
band_start=brange[0], band_end=brange[1],
......@@ -352,21 +397,25 @@ class Galaxy(MockObject):
isAlongY=0,
flat_cube=flat_cube)
# 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)
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=[]
for galImg in galImg_List:
subImg_p2 = starImg.array[:,
subSlitPos+1:starImg.array.shape[1]]
subImg_p2 = galImg.array[:,
subSlitPos + 1:galImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0)
star_p2s.append(star_p2)
origin_p2 = [origin_star[0], grating_split_pos_chip]
xcenter_p2 = max(x_nominal, grating_split_pos_chip - 1) - 0
ycenter_p2 = y_nominal - 0
sdp_p2 = SpecDisperser(orig_img=star_p2, xcenter=xcenter_p2,
sdp_p2 = SpecDisperser(orig_img=star_p2s, xcenter=xcenter_p2,
ycenter=ycenter_p2, origin=origin_p2,
tar_spec=normalSED,
band_start=brange[0], band_end=brange[1],
......@@ -374,41 +423,41 @@ class Galaxy(MockObject):
isAlongY=0,
flat_cube=flat_cube)
# 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)
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)
del sdp_p1
del sdp_p2
elif grating_split_pos_chip <= gal_origin[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
sdp = SpecDisperser(orig_img=galImg_List, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
# 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)
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)
del sdp
elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
sdp = SpecDisperser(orig_img=galImg_List, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
# 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)
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)
del sdp
# print(self.y_nominal, starImg.center.y, starImg.ymin)
......
observation_sim/mock_objects/MockObject.py
View file @ 45156a94
This diff is collapsed.
observation_sim/mock_objects/SpecDisperser/SpecDisperser.py
View file @ 45156a94
......@@ -65,10 +65,30 @@ class SpecDisperser(object):
# self.img_x = orig_img.shape[1]
# self.img_y = orig_img.shape[0]
self.thumb_img = np.abs(orig_img.array)
self.thumb_x = orig_img.center.x
self.thumb_y = orig_img.center.y
self.img_sh = orig_img.array.shape
# 5 orders, A, B ,
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, 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)
self.thumb_x = orig_img_one.center.x
self.thumb_y = orig_img_one.center.y
self.img_sh = orig_img_one.array.shape
self.id = gid
......@@ -78,10 +98,13 @@ class SpecDisperser(object):
self.isAlongY = isAlongY
self.flat_cube = flat_cube
if self.isAlongY == 1:
self.thumb_img, self.thumb_x, self.thumb_y = rotate90(array_orig=self.thumb_img, xc=orig_img.center.x,
yc=orig_img.center.y, isClockwise=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.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)
self.img_sh = orig_img.array.T.shape
self.img_sh = self.orig_img_orders[order].array.T.shape
self.xcenter = ycenter
self.ycenter = xcenter
......@@ -111,10 +134,16 @@ class SpecDisperser(object):
def compute_spec(self, beam):
# if beam == "B":
# return self.thumb_img, self.origin[1], self.origin[0], None, None, None
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
self.thumb_y = self.orig_img_orders[beam].center.y
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),
......@@ -169,7 +198,8 @@ class SpecDisperser(object):
dyc = cast[int](np.floor(ytrace_beam+0.5))
dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5))
# dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5))
dypix = dyc - dyc[0] + x0[0]
frac_ids = yfrac_beam < 0
......@@ -248,7 +278,8 @@ class SpecDisperser(object):
# 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],
flat_index[nonz],
yfrac_beam[nonz],
sensitivity_beam[nonz],
modelf, x0,
array(self.img_sh,
......@@ -258,11 +289,24 @@ class SpecDisperser(object):
lam_beam[lam_index][nonz])
model = modelf.reshape(beam_sh)
# n1 = np.sum(np.isinf(model))
# n2 = np.sum(np.isnan(model))
# n3 = np.sum(np.isinf(modelf))
# n4 = np.sum(np.isnan(modelf))
# 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))
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):
......
observation_sim/mock_objects/SpecDisperser/disperse_c/disperse.pyx
View file @ 45156a94
......@@ -21,6 +21,29 @@ cdef extern from "math.h":
double sqrt(double x)
double exp(double x)
def check_nan2D(np.ndarray[FTYPE_t, ndim=2] arr):
cdef int i, j
cdef int nrows = arr.shape[0]
cdef int ncols = arr.shape[1]
# 遍历数组的每个元素并检查是否存在 NaN
for i in range(nrows):
for j in range(ncols):
if np.isnan(arr[i, j]) | np.isinf(arr[i, j]):
return True
return False
def check_nan1d(np.ndarray[DTYPE_t, ndim=1] arr):
cdef int i
cdef int n = arr.shape[0]
# 遍历数组的每个元素并检查是否存在 NaN
for i in range(n):
if np.isnan(arr[i]) | np.isinf(arr[i]):
return True
return False
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.embedsignature(True)
......@@ -54,6 +77,18 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
nk = len(idxl)
nl = len(full)
#if check_nan2D(flam):
# print("DEBUG: disperse, input Array 'flam' contains NaN.")
#if check_nan1d(ysens):
# print("DEBUG: disperse, input Array 'ysens' contains NaN.")
#if check_nan1d(yfrac):
# print("DEBUG: disperse, input Array 'yfrac' contains NaN.")
#if check_nan1d(full):
# print("DEBUG: disperse, input Array 'full' contains NaN before processing.")
if (flat is not None):
nlamb = len(wlambda)
nflat = len(flat)
......@@ -95,14 +130,15 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
else:
for i in range(0-x0[1], x0[1]):
if (x0[1]+i < 0) | (x0[1]+i >= shd[1]):
x_pos = x0[1]+i
if (x_pos < 0) | (x_pos >= shd[1]):
continue
for j in range(0-x0[0], x0[0]):
if (x0[0]+j < 0) | (x0[0]+j >= shd[0]):
y_pos = x0[0]+j
if (y_pos < 0) | (y_pos >= shd[0]):
continue
fl_ij = flam[x0[0]+j, x0[1]+i] #/1.e-17
fl_ij = flam[y_pos, x_pos] #/1.e-17
if (fl_ij == 0):
continue
......@@ -110,11 +146,14 @@ def disperse_grism_object(np.ndarray[FTYPE_t, ndim=2] flam,
k1 = idxl[k]+j*shg[1]+i
if (k1 >= 0) & (k1 < nl):
full[k1] += ysens[k]*fl_ij*(1-yfrac[k])
k2 = idxl[k]+(j+1)*shg[1]+i
if (k2 >= 0) & (k2 < nl):
full[k2] += ysens[k]*fl_ij*yfrac[k]
#if (check_nan1d(full)):
# print("DEBUG: disperse, output Array 'full' contains NaN after processing.+++++++++++++++++++++++++++")
return True
@cython.boundscheck(False)
......
observation_sim/psf/PSFGauss.py
View file @ 45156a94
......@@ -17,7 +17,7 @@ class PSFGauss(PSFModel):
self.fwhm = self.fwhmGauss(r=psfRa)
self.sigGauss = psfRa # 80% light radius
self.sigSpin = sigSpin
self.psf = galsim.Gaussian(flux=1.0, fwhm=fwhm)
self.psf = galsim.Gaussian(flux=1.0, fwhm=self.fwhm)
def perfGauss(self, r, sig):
"""
......
observation_sim/psf/PSFInterpSLS.py
View file @ 45156a94
......@@ -20,8 +20,10 @@ import os
from astropy.io import fits
from astropy.modeling.models import Gaussian2D
from scipy import signal
from scipy import signal, interpolate
import datetime
import gc
# from jax import numpy as jnp
LOG_DEBUG = False # ***#
NPSF = 900 # ***# 30*30
......@@ -433,7 +435,16 @@ class PSFInterpSLS(PSFModel):
# PSF_int_trans[ids_szero] = 0
# print(PSF_int_trans[ids_szero].shape[0],PSF_int_trans.shape)
PSF_int_trans = PSF_int_trans/np.sum(PSF_int_trans)
###DEBGU
ids_szero = PSF_int_trans<0
n01 = PSF_int_trans[ids_szero].shape[0]
n1 = np.sum(np.isinf(PSF_int_trans))
n2 = np.sum(np.isnan(PSF_int_trans))
if n1>0 or n2>0:
print("DEBUG: PSFInterpSLS, inf:%d, nan:%d, 0 num:%d"%(n1, n2, n01))
####
# from astropy.io import fits
# fits.writeto(str(bandNo) + '_' + g_order+ '_psf_o.fits', PSF_int_trans)
......@@ -479,6 +490,215 @@ class PSFInterpSLS(PSFModel):
return PSF_int_trans, PSF_int
def get_PSF_AND_convolve_withsubImg(self, chip, cutImg=None, pos_img_local=[1000, 1000], bandNo=1, g_order='A', grating_split_pos=3685):
"""
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'.
"""
order_IDs = {'A': '1', 'B': '0', 'C': '0', 'D': '0', 'E': '0'}
contam_order_sigma = {'C': 0.28032344707964174,
'D': 0.39900182912061344, 'E': 1.1988309797685412} # arcsec
x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
# print(pos_img.x - x_start)
# pos_img_x = pos_img_local[0] + x_start
# pos_img_y = pos_img_local[1] + y_start
# pos_img = galsim.PositionD(pos_img_x, pos_img_y)
# centerPos_local = cutImg.ncol/2.
if pos_img_local[0] < grating_split_pos:
psf_data = self.grating1_data
else:
psf_data = self.grating2_data
grating_order = order_IDs[g_order]
# if grating_order in ['-2','-1','2']:
# grating_order = '1'
# if grating_order in ['0', '1']:
psf_order = psf_data['order'+grating_order]
psf_order_b = psf_order['band'+str(bandNo)]
psf_b_dat = psf_order_b['band_data']
# pos_p = psf_b_dat[1].data
pos_p = psf_b_dat[1].data/chip.pix_size - np.array([y_start, x_start])
pc_coeff = psf_b_dat[2].data
pcs = psf_b_dat[0].data
npc = 10
m_size = int(pcs.shape[0]**0.5)
sumImg = np.sum(cutImg.array)
tmp_img = cutImg*0
for j in np.arange(npc):
X_ = np.hstack((pos_p[:,1].flatten()[:, None], pos_p[:,0].flatten()[:, None]),dtype=np.float32)
Z_ = (pc_coeff[j].astype(np.float32)).flatten()
# print(pc_coeff[j].shape[0], pos_p[:,1].shape[0], pos_p[:,0].shape[0])
cx_len = int(chip.npix_x)
cy_len = int(chip.npix_y)
n_x = np.arange(0, cx_len, 1, dtype = int)
n_y = np.arange(0, cy_len, 1, dtype = int)
M, N = np.meshgrid(n_x, n_y)
# t1=datetime.datetime.now()
# U = interpolate.griddata(X_, Z_, (M[0:cy_len, 0:cx_len],N[0:cy_len, 0:cx_len]),
# method='nearest',fill_value=1.0)
b_img = galsim.Image(cx_len, cy_len)
b_img.setOrigin(0,0)
bounds = cutImg.bounds & b_img.bounds
if bounds.area() == 0:
continue
# ys = cutImg.ymin
# if ys < 0:
# ys = 0
# ye = cutImg.ymin+cutImg.nrow
# if ye >= cy_len-1:
# ye = cy_len-1
# if ye - ys <=0:
# continue
# xs = cutImg.xmin
# if xs < 0:
# xs = 0
# xe = cutImg.xmin+cutImg.ncol
# if xe >= cx_len-1:
# xe = cx_len-1
# if xe - xs <=0:
# continue
ys = bounds.ymin
ye = bounds.ymax+1
xs = bounds.xmin
xe = bounds.xmax+1
U = interpolate.griddata(X_, Z_, (M[ys:ye, xs:xe],N[ys:ye, xs:xe]),
method='nearest',fill_value=1.0)
# t2=datetime.datetime.now()
# print("time interpolate:", t2-t1)
# if U.shape != cutImg.array.shape:
# print('DEBUG:SHAPE',cutImg.ncol,cutImg.nrow,cutImg.xmin, cutImg.ymin)
# continue
img_tmp = cutImg
img_tmp[bounds] = img_tmp[bounds]*U
psf = pcs[:, j].reshape(m_size, m_size)
tmp_img = tmp_img + signal.fftconvolve(img_tmp.array, psf, mode='same', axes=None)
# t3=datetime.datetime.now()
# print("time convole:", t3-t2)
del U
del img_tmp
if np.sum(tmp_img.array)==0:
tmp_img = cutImg
else:
tmp_img = tmp_img/np.sum(tmp_img.array)*sumImg
return tmp_img
def convolveFullImgWithPCAPSF(self, chip, folding_threshold=5.e-3):
keys_L1= chip_utils.getChipSLSGratingID(chip.chipID)
# keys_L2 = ['order-2','order-1','order0','order1','order2']
keys_L2 = ['order0','order1']
keys_L3 = ['w1','w2','w3','w4']
npca = 10
x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
for i,gt in enumerate(keys_L1):
psfCo = self.grating1_data
if i > 0:
psfCo = self.grating2_data
for od in keys_L2:
psfCo_L2 = psfCo['order1']
if od in ['order-2','order-1','order0','order2']:
psfCo_L2 = psfCo['order0']
for w in keys_L3:
img = chip.img_stack[gt][od][w]
pcs = psfCo_L2['band'+w[1]]['band_data'][0].data
pos_p = psfCo_L2['band'+w[1]]['band_data'][1].data/chip.pix_size - np.array([y_start, x_start])
pc_coeff = psfCo_L2['band'+w[1]]['band_data'][2].data
# print("DEBUG-----------",np.max(pos_p[:,1]),np.min(pos_p[:,1]), np.max(pos_p[:,0]),np.min(pos_p[:,0]))
sum_img = np.sum(img.array)
# coeff_mat = np.zeros([npca, chip.npix_y, chip.npix_x])
# for m in np.arange(chip.npix_y):
# for n in np.arange(chip.npix_x):
# px = n
# py = m
# dist2 = (pos_p[:, 1] - px)*(pos_p[:, 1] - px) + (pos_p[:, 0] - py)*(pos_p[:, 0] - py)
# temp_sort_dist = np.zeros([dist2.shape[0], 2])
# temp_sort_dist[:, 0] = np.arange(0, dist2.shape[0], 1)
# temp_sort_dist[:, 1] = dist2
# # print(temp_sort_dist)
# dits2_sortlist = sorted(temp_sort_dist, key=lambda x: x[1])
# # print(dits2_sortlist)
# nearest4p = np.zeros([4, 3])
# pc_coeff_4p = np.zeros([npca, 4])
# for i in np.arange(4):
# smaller_ids = int(dits2_sortlist[i][0])
# nearest4p[i, 0] = pos_p[smaller_ids, 1]
# nearest4p[i, 1] = pos_p[smaller_ids, 0]
# # print(pos_p[smaller_ids, 1],pos_p[smaller_ids, 0])
# nearest4p[i, 2] = dits2_sortlist[i][1]
# pc_coeff_4p[:, i] = pc_coeff[npca, smaller_ids]
# # idw_dist = 1/(np.sqrt((px-nearest4p[:, 0]) * (px-nearest4p[:, 0]) + (
# # py-nearest4p[:, 1]) * (py-nearest4p[:, 1])))
# idw_dist = 1/(np.sqrt(nearest4p[:, 2]))
# coeff_int = np.zeros(npca)
# for i in np.arange(4):
# coeff_int = coeff_int + pc_coeff_4p[:, i]*idw_dist[i]
# coeff_mat[:, m, n] = coeff_int
m_size = int(pcs.shape[0]**0.5)
tmp_img = np.zeros_like(img.array,dtype=np.float32)
for j in np.arange(npca):
print(gt, od, w, j)
X_ = np.hstack((pos_p[:,1].flatten()[:, None], pos_p[:,0].flatten()[:, None]),dtype=np.float32)
Z_ = (pc_coeff[j].astype(np.float32)).flatten()
# print(pc_coeff[j].shape[0], pos_p[:,1].shape[0], pos_p[:,0].shape[0])
sub_size = 4
cx_len = int(chip.npix_x/sub_size)
cy_len = int(chip.npix_y/sub_size)
n_x = np.arange(0, chip.npix_x, sub_size, dtype = int)
n_y = np.arange(0, chip.npix_y, sub_size, dtype = int)
M, N = np.meshgrid(n_x, n_y)
t1=datetime.datetime.now()
# U = interpolate.griddata(X_, Z_, (M[0:cy_len, 0:cx_len],N[0:cy_len, 0:cx_len]),
# method='nearest',fill_value=1.0)
U1 = interpolate.griddata(X_, Z_, (M, N),
method='nearest',fill_value=1.0)
U = np.zeros_like(chip.img.array, dtype=np.float32)
for mi in np.arange(cy_len):
for mj in np.arange(cx_len):
U[mi*sub_size:(mi+1)*sub_size, mj*sub_size:(mj+1)*sub_size]=U1[mi,mj]
t2=datetime.datetime.now()
print("time interpolate:", t2-t1)
img_tmp = img.array*U
psf = pcs[:, j].reshape(m_size, m_size)
tmp_img = tmp_img + signal.fftconvolve(img_tmp, psf, mode='same', axes=None)
t3=datetime.datetime.now()
print("time convole:", t3-t2)
del U
del U1
chip.img = chip.img + tmp_img*sum_img/np.sum(tmp_img)
del tmp_img
gc.collect()
# pixSize = np.rad2deg(self.pixsize*1e-3/28)*3600 #set psf pixsize
#
# # assert self.iccd == int(chip.getChipLabel(chipID=chip.chipID)), 'ERROR: self.iccd != chip.chipID'
......
observation_sim/sim_steps/add_objects.py
View file @ 45156a94
......@@ -194,7 +194,8 @@ def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
if isUpdated == 1:
# TODO: add up stats
self.chip_output.cat_add_obj(obj, pos_img, pos_shear)
self.chip_output.cat_add_obj(
obj, pos_img, pos_shear, ra_offset=self.ra_offset, dec_offset=self.dec_offset)
pass
elif isUpdated == 0:
missed_obj += 1
......@@ -217,6 +218,26 @@ def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
obj.unload_SED()
del obj
# gc.collect()
if chip.survey_type == "spectroscopic" and not self.overall_config["run_option"]["out_cat_only"] and chip.slsPSFOptim:
# from observation_sim.instruments.chip import chip_utils as chip_utils
# gn = chip_utils.getChipSLSGratingID(chip.chipID)[0]
# img1 = np.zeros([2,chip.img.array.shape[0],chip.img.array.shape[1]])
# for id1 in np.arange(2):
# gn = chip_utils.getChipSLSGratingID(chip.chipID)[id1]
# img_i = 0
# for id2 in ['0','1']:
# o_n = "order"+id2
# for id3 in ['1','2','3','4']:
# w_n = "w"+id3
# img1[img_i] = img1[img_i] + chip.img_stack[gn][o_n][w_n].array
# img_i = img_i + 1
# from astropy.io import fits
# fits.writeto('order0.fits',img1[0],overwrite=True)
# fits.writeto('order1.fits',img1[1],overwrite=True)
psf_model.convolveFullImgWithPCAPSF(chip)
del psf_model
gc.collect()
......
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