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'''
PSF interpolation for CSST-Sim
NOTE: [iccd, iwave, ipsf] are counted from 1 to n, but [tccd, twave, tpsf] are counted from 0 to n-1
'''
import yaml
import sys
import time
import copy
import numpy as np
import scipy.spatial as spatial
import galsim
import h5py
from observation_sim.instruments import Filter, FilterParam, Chip
from observation_sim.instruments.chip import chip_utils
import os
from astropy.io import fits
from astropy.modeling.models import Gaussian2D
from scipy import signal, interpolate
import datetime
import gc
from astropy.io import fits
from observation_sim.psf._util import psf_extrapolate, psf_extrapolate1
LOG_DEBUG = False # ***#
NPSF = 900 # ***# 30*30
PIX_SIZE_MICRON = 5. # ***# in microns
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# def findNeighbors(tx, ty, px, py, dr=0.1, dn=1, OnlyDistance=True):
# """
# find nearest neighbors by 2D-KDTree
#
# Parameters:
# tx, ty (float, float): a given position
# px, py (numpy.array, numpy.array): position data for tree
# dr (float-optional): distance
# dn (int-optional): nearest-N
# OnlyDistance (bool-optional): only use distance to find neighbors. Default: True
#
# Returns:
# dataq (numpy.array): index
# """
# datax = px
# datay = py
# tree = spatial.KDTree(list(zip(datax.ravel(), datay.ravel())))
#
# dataq=[]
# rr = dr
# if OnlyDistance == True:
# dataq = tree.query_ball_point([tx, ty], rr)
# if OnlyDistance == False:
# while len(dataq) < dn:
# dataq = tree.query_ball_point([tx, ty], rr)
# rr += dr
# dd = np.hypot(datax[dataq]-tx, datay[dataq]-ty)
# ddSortindx = np.argsort(dd)
# dataq = np.array(dataq)[ddSortindx[0:dn]]
# return dataq
#
# ###find neighbors-hoclist###
# def hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy):
# if np.max(partx) > nhocx*dhocx:
# print('ERROR')
# sys.exit()
# if np.max(party) > nhocy*dhocy:
# print('ERROR')
# sys.exit()
#
# npart = partx.size
# hoclist= np.zeros(npart, dtype=np.int32)-1
# hoc = np.zeros([nhocy, nhocx], dtype=np.int32)-1
# for ipart in range(npart):
# ix = int(partx[ipart]/dhocx)
# iy = int(party[ipart]/dhocy)
# hoclist[ipart] = hoc[iy, ix]
# hoc[iy, ix] = ipart
# return hoc, hoclist
#
# def hocFind(px, py, dhocx, dhocy, hoc, hoclist):
# ix = int(px/dhocx)
# iy = int(py/dhocy)
#
# neigh=[]
# it = hoc[iy, ix]
# while it != -1:
# neigh.append(it)
# it = hoclist[it]
# return neigh
#
# def findNeighbors_hoclist(px, py, tx=None,ty=None, dn=4, hoc=None, hoclist=None):
# nhocy = nhocx = 20
#
# pxMin = np.min(px)
# pxMax = np.max(px)
# pyMin = np.min(py)
# pyMax = np.max(py)
#
# dhocx = (pxMax - pxMin)/(nhocx-1)
# dhocy = (pyMax - pyMin)/(nhocy-1)
# partx = px - pxMin +dhocx/2
# party = py - pyMin +dhocy/2
#
# if hoc is None:
# hoc, hoclist = hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy)
# return hoc, hoclist
#
# if hoc is not None:
# tx = tx - pxMin +dhocx/2
# ty = ty - pyMin +dhocy/2
# itx = int(tx/dhocx)
# ity = int(ty/dhocy)
#
# ps = [-1, 0, 1]
# neigh=[]
# for ii in range(3):
# for jj in range(3):
# ix = itx + ps[ii]
# iy = ity + ps[jj]
# if ix < 0:
# continue
# if iy < 0:
# continue
# if ix > nhocx-1:
# continue
# if iy > nhocy-1:
# continue
#
# #neightt = myUtil.hocFind(ppx, ppy, dhocx, dhocy, hoc, hoclist)
# it = hoc[iy, ix]
# while it != -1:
# neigh.append(it)
# it = hoclist[it]
# #neigh.append(neightt)
# #ll = [i for k in neigh for i in k]
# if dn != -1:
# ptx = np.array(partx[neigh])
# pty = np.array(party[neigh])
# dd = np.hypot(ptx-tx, pty-ty)
# idx = np.argsort(dd)
# neigh= np.array(neigh)[idx[0:dn]]
# return neigh
#
#
# ###PSF-IDW###
# def psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=None, hoclist=None, PSFCentroidWgt=False):
# """
# psf interpolation by IDW
#
# Parameters:
# px, py (float, float): position of the target
# PSFMat (numpy.array): image
# cen_col, cen_row (numpy.array, numpy.array): potions of the psf centers
# IDWindex (int-optional): the power index of IDW
# OnlyNeighbors (bool-optional): only neighbors are used for psf interpolation
#
# Returns:
# psfMaker (numpy.array)
# """
#
# minimum_psf_weight = 1e-8
# ref_col = px
# ref_row = py
#
# ngy, ngx = PSFMat[0, :, :].shape
# npsf = PSFMat[:, :, :].shape[0]
# psfWeight = np.zeros([npsf])
#
# if OnlyNeighbors == True:
# if hoc is None:
# neigh = findNeighbors(px, py, cen_col, cen_row, dr=5., dn=4, OnlyDistance=False)
# if hoc is not None:
# neigh = findNeighbors_hoclist(cen_col, cen_row, tx=px,ty=py, dn=4, hoc=hoc, hoclist=hoclist)
#
# neighFlag = np.zeros(npsf)
# neighFlag[neigh] = 1
#
# for ipsf in range(npsf):
# if OnlyNeighbors == True:
# if neighFlag[ipsf] != 1:
# continue
#
# dist = np.sqrt((ref_col - cen_col[ipsf])**2 + (ref_row - cen_row[ipsf])**2)
# if IDWindex == 1:
# psfWeight[ipsf] = dist
# if IDWindex == 2:
# psfWeight[ipsf] = dist**2
# if IDWindex == 3:
# psfWeight[ipsf] = dist**3
# if IDWindex == 4:
# psfWeight[ipsf] = dist**4
# psfWeight[ipsf] = max(psfWeight[ipsf], minimum_psf_weight)
# psfWeight[ipsf] = 1./psfWeight[ipsf]
# psfWeight /= np.sum(psfWeight)
#
# psfMaker = np.zeros([ngy, ngx], dtype=np.float32)
# for ipsf in range(npsf):
# if OnlyNeighbors == True:
# if neighFlag[ipsf] != 1:
# continue
#
# iPSFMat = PSFMat[ipsf, :, :].copy()
# ipsfWeight = psfWeight[ipsf]
#
# psfMaker += iPSFMat * ipsfWeight
# psfMaker /= np.nansum(psfMaker)
#
# return psfMaker
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class PSFInterpSLS(PSFModel):
def __init__(self, chip, filt, PSF_data_prefix="", sigSpin=0, psfRa=0.15, pix_size=0.005):
if LOG_DEBUG:
print('===================================================')
print('DEBUG: psf module for csstSim '
+ time.strftime("(%Y-%m-%d %H:%M:%S)", time.localtime()), flush=True)
print('===================================================')
self.sigSpin = sigSpin
self.sigGauss = psfRa
self.grating_ids = chip_utils.getChipSLSGratingID(chip.chipID)
_, self.grating_type = chip.getChipFilter(chipID=chip.chipID)
self.data_folder = PSF_data_prefix
self.getPSFDataFromFile(filt)
self.pixsize = pix_size # um
def getPSFDataFromFile(self, filt):
gratingInwavelist = {'GU': 0, 'GV': 1, 'GI': 2}
grating_orders = ['0', '1']
waveListFn = self.data_folder + '/wavelist.dat'
wavelists = np.loadtxt(waveListFn)
self.waveList = wavelists[:, gratingInwavelist[self.grating_type]]
bandranges = np.zeros([4, 2])
midBand = (self.waveList[0:3] + self.waveList[1:4])/2.*10000.
bandranges[0, 0] = filt.blue_limit
bandranges[1:4, 0] = midBand
bandranges[0:3, 1] = midBand
bandranges[3, 1] = filt.red_limit
self.bandranges = bandranges
self.grating1_data = {}
g_folder = self.data_folder + '/' + self.grating_ids[0] + '/'
for g_order in grating_orders:
g_folder_order = g_folder + 'PSF_Order_' + g_order + '/'
grating_order_data = {}
for bandi in [1, 2, 3, 4]:
subBand_data = {}
subBand_data['bandrange'] = bandranges[bandi-1]
final_folder = g_folder_order + str(bandi) + '/'
print(final_folder)
pca_fs = os.listdir(final_folder)
for fname in pca_fs:
if ('_PCs.fits' in fname) and (fname[0] != '.'):
fname_ = final_folder + fname
hdu = fits.open(fname_)
subBand_data['band_data'] = hdu
grating_order_data['band'+str(bandi)] = subBand_data
self.grating1_data['order'+g_order] = grating_order_data
self.grating2_data = {}
g_folder = self.data_folder + '/' + self.grating_ids[1] + '/'
for g_order in grating_orders:
g_folder_order = g_folder + 'PSF_Order_' + g_order + '/'
grating_order_data = {}
for bandi in [1, 2, 3, 4]:
subBand_data = {}
subBand_data['bandrange'] = bandranges[bandi - 1]
final_folder = g_folder_order + str(bandi) + '/'
print(final_folder)
pca_fs = os.listdir(final_folder)
for fname in pca_fs:
if ('_PCs.fits' in fname) and (fname[0] != '.'):
fname_ = final_folder + fname
hdu = fits.open(fname_)
subBand_data['band_data'] = hdu
grating_order_data['band' + str(bandi)] = subBand_data
self.grating2_data['order' + g_order] = grating_order_data
#
#
#
# def _getPSFwave(self, iccd, PSF_data_file, PSF_data_prefix):
# # fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_ccd{:}.h5'.format(iccd), 'r')
# fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_{:}.h5'.format(iccd), 'r')
# nwave = len(fq.keys())
# fq.close()
# return nwave
#
#
# def _loadPSF(self, iccd, PSF_data_file, PSF_data_prefix):
# psfSet = []
# # fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_ccd{:}.h5'.format(iccd), 'r')
# fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_{:}.h5'.format(iccd), 'r')
# for ii in range(self.nwave):
# iwave = ii+1
# psfWave = []
#
# fq_iwave = fq['w_{:}'.format(iwave)]
# for jj in range(self.npsf):
# ipsf = jj+1
# psfInfo = {}
# psfInfo['wavelength']= fq_iwave['wavelength'][()]
#
# fq_iwave_ipsf = fq_iwave['psf_{:}'.format(ipsf)]
# psfInfo['pixsize'] = PIX_SIZE_MICRON
# psfInfo['field_x'] = fq_iwave_ipsf['field_x'][()]
# psfInfo['field_y'] = fq_iwave_ipsf['field_y'][()]
# psfInfo['image_x'] = fq_iwave_ipsf['image_x'][()]
# psfInfo['image_y'] = fq_iwave_ipsf['image_y'][()]
# psfInfo['centroid_x']= fq_iwave_ipsf['cx'][()]
# psfInfo['centroid_y']= fq_iwave_ipsf['cy'][()]
# psfInfo['psfMat'] = fq_iwave_ipsf['psfMat'][()]
#
# psfWave.append(psfInfo)
# psfSet.append(psfWave)
# fq.close()
#
# if LOG_DEBUG:
# print('psfSet has been loaded:', flush=True)
# print('psfSet[iwave][ipsf][keys]:', psfSet[0][0].keys(), flush=True)
# return psfSet
#
#
# def _findWave(self, bandpass):
# if isinstance(bandpass,int):
# twave = bandpass
# return twave
#
# for twave in range(self.nwave):
# bandwave = self.PSF_data[twave][0]['wavelength']
# if bandpass.blue_limit < bandwave and bandwave < bandpass.red_limit:
# return twave
# return -1
#
#
def convolveWithGauss(self, img=None, sigma=1):
offset = int(np.ceil(sigma * 3))
g_size = 2 * offset + 1
m_cen = int(g_size / 2)
print('-----', g_size)
g_PSF_ = Gaussian2D(1, m_cen, m_cen, sigma, sigma)
yp, xp = np.mgrid[0:g_size, 0:g_size]
g_PSF = g_PSF_(xp, yp)
psf = g_PSF / g_PSF.sum()
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
convImg = convImg/np.sum(convImg)
return convImg
def get_PSF(self, chip, pos_img_local=[1000, 1000], bandNo=1, galsimGSObject=True, folding_threshold=5.e-3, g_order='A', grating_split_pos=3685, extrapolate=False, ngg=2048):
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"""
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)
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
pc_coeff = psf_b_dat[2].data
pcs = psf_b_dat[0].data
# print(max(pos_p[:,0]), min(pos_p[:,0]),max(pos_p[:,1]), min(pos_p[:,1]))
# print(chip.x_cen, chip.y_cen)
# print(pos_p)
px = pos_img.x*chip.pix_size
py = pos_img.y*chip.pix_size
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, 2])
pc_coeff_4p = np.zeros([pc_coeff.data.shape[0], 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]
pc_coeff_4p[:, i] = pc_coeff[:, smaller_ids]
idw_dist = 1/(np.sqrt((px-nearest4p[:, 0]) * (px-nearest4p[:, 0]) + (
py-nearest4p[:, 1]) * (py-nearest4p[:, 1])))
coeff_int = np.zeros(pc_coeff.data.shape[0])
for i in np.arange(4):
coeff_int = coeff_int + pc_coeff_4p[:, i]*idw_dist[i]
coeff_int = coeff_int / np.sum(coeff_int)
npc = 10
m_size = int(pcs.shape[0]**0.5)
PSF_int = np.dot(pcs[:, 0:npc], coeff_int[0:npc]
).reshape(m_size, m_size)
# PSF_int = PSF_int/np.sum(PSF_int)
PSF_int_trans = np.flipud(np.fliplr(PSF_int))
PSF_int_trans = np.fliplr(PSF_int_trans.T)
# PSF_int_trans = np.abs(PSF_int_trans)
# ids_szero = PSF_int_trans<0
# 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)
PSF_int_trans = PSF_int_trans-np.min(PSF_int_trans)
PSF_int_trans = PSF_int_trans/np.sum(PSF_int_trans)
# fits.writeto('/home/zhangxin/CSST_SIM/CSST_sim_develop/psf_test/psf.fits',PSF_int_trans)
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committed
n01 = PSF_int_trans[ids_szero].shape[0]
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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))
if extrapolate is True:
# for rep_i in np.arange(0, 2, 1):
# PSF_int_trans[rep_i,:] = 1e9*pow(10,rep_i)
# PSF_int_trans[-1-rep_i,:] = 1e9*pow(10,rep_i)
# PSF_int_trans[:,rep_i] = 1e9*pow(10,rep_i)
# PSF_int_trans[:,-1-rep_i] = 1e9*pow(10,rep_i)
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PSF_int_trans = psf_extrapolate1(PSF_int_trans, ngg=ngg)
# fits.writeto('/home/zhangxin/CSST_SIM/CSST_sim_develop/psf_test/psf_large.fits',PSF_int_trans)
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####
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# from astropy.io import fits
# fits.writeto(str(bandNo) + '_' + g_order+ '_psf_o.fits', PSF_int_trans)
# if g_order in ['C','D','E']:
# g_simgma = contam_order_sigma[g_order]/pixel_size_arc
# PSF_int_trans = self.convolveWithGauss(PSF_int_trans,g_simgma)
# n_m_size = int(m_size/2)
#
# n_PSF_int = np.zeros([n_m_size, n_m_size])
#
# for i in np.arange(n_m_size):
# for j in np.arange(n_m_size):
# n_PSF_int[i,j] = np.sum(PSF_int[2*i:2*i+2, 2*j:2*j+2])
#
# n_PSF_int = n_PSF_int/np.sum(n_PSF_int)
# chip.img = galsim.ImageF(chip.npix_x, chip.npix_y)
# chip.img.wcs = galsim.wcs.AffineTransform
if galsimGSObject:
# imPSFt = np.zeros([257,257])
# imPSFt[0:256, 0:256] = imPSF
# # imPSFt[120:130, 0:256] = 1.
pixel_size_arc = np.rad2deg(self.pixsize * 1e-3 / 28) * 3600
img = galsim.ImageF(PSF_int_trans, scale=pixel_size_arc)
gsp = galsim.GSParams(folding_threshold=folding_threshold)
# TEST: START
# Use sheared PSF to test the PSF orientation
# self.psf = galsim.InterpolatedImage(img, gsparams=gsp).shear(g1=0.8, g2=0.)
# TEST: END
self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
# if g_order in ['C','D','E']:
# add_psf = galsim.Gaussian(sigma=contam_order_sigma[g_order], flux=1.0)
# self.psf = galsim.Convolve(self.psf, add_psf)
wcs = chip.img.wcs.local(pos_img)
scale = galsim.PixelScale(0.074)
self.psf = wcs.toWorld(scale.toImage(
self.psf), image_pos=(pos_img))
# return self.PSFspin(x=px/0.01, y=py/0.01)
return self.psf, galsim.Shear(e=0., beta=(np.pi/2)*galsim.radians)
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)
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)
# 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)
bounds = cutImg.bounds & b_img.bounds
if bounds.area() == 0:
# ye = cy_len-1
# if ye - ys <=0:
# continue
# xs = cutImg.xmin
# 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)
# 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)
tmp_img = cutImg
else:
tmp_img = tmp_img/np.sum(tmp_img.array)*sumImg
def convolveFullImgWithPCAPSF(self, chip, folding_threshold=5.e-3):
# keys_L2 = ['order-2','order-1','order0','order1','order2']
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.
psfCo = self.grating1_data
if i > 0:
psfCo = self.grating2_data
for od in keys_L2:
psfCo_L2 = psfCo['order1']
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
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# 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)
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)
# 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)
U = np.zeros_like(chip.img.array, dtype=np.float32)
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)
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()
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# 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'
# twave = self._findWave(bandpass)
# if twave == -1:
# print("!!!PSF bandpass does not match.")
# exit()
# PSFMat = self.psfMat[twave]
# cen_col= self.cen_col[twave]
# cen_row= self.cen_row[twave]
#
# px = (pos_img.x - chip.cen_pix_x)*0.01
# py = (pos_img.y - chip.cen_pix_y)*0.01
# if findNeighMode == 'treeFind':
# imPSF = psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, PSFCentroidWgt=True)
# if findNeighMode == 'hoclistFind':
# assert(self.hoc != 0), 'hoclist should be built correctly!'
# imPSF = psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=self.hoc[twave], hoclist=self.hoclist[twave], PSFCentroidWgt=True)
#
# ############TEST: START
# TestGaussian = False
# if TestGaussian:
# gsx = galsim.Gaussian(sigma=0.04)
# #pointing_pa = -23.433333
# imPSF= gsx.shear(g1=0.8, g2=0.).rotate(0.*galsim.degrees).drawImage(nx = 256, ny=256, scale=pixSize).array
# ############TEST: END
#
# if galsimGSObject:
# imPSFt = np.zeros([257,257])
# imPSFt[0:256, 0:256] = imPSF
# # imPSFt[120:130, 0:256] = 1.
#
# img = galsim.ImageF(imPSFt, scale=pixSize)
# gsp = galsim.GSParams(folding_threshold=folding_threshold)
# ############TEST: START
# # Use sheared PSF to test the PSF orientation
# # self.psf = galsim.InterpolatedImage(img, gsparams=gsp).shear(g1=0.8, g2=0.)
# ############TEST: END
# self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
# wcs = chip.img.wcs.local(pos_img)
# scale = galsim.PixelScale(0.074)
# self.psf = wcs.toWorld(scale.toImage(self.psf), image_pos=(pos_img))
#
# # return self.PSFspin(x=px/0.01, y=py/0.01)
# return self.psf, galsim.Shear(e=0., beta=(np.pi/2)*galsim.radians)
# return imPSF
#
# def PSFspin(self, x, y):
# """
# The PSF profile at a given image position relative to the axis center
#
# Parameters:
# theta : spin angles in a given exposure in unit of [arcsecond]
# dx, dy: relative position to the axis center in unit of [pixels]
#
# Return:
# Spinned PSF: g1, g2 and axis ratio 'a/b'
# """
# a2Rad = np.pi/(60.0*60.0*180.0)
#
# ff = self.sigGauss * 0.107 * (1000.0/10.0) # in unit of [pixels]
# rc = np.sqrt(x*x + y*y)
# cpix = rc*(self.sigSpin*a2Rad)
#
# beta = (np.arctan2(y,x) + np.pi/2)
# ell = cpix**2/(2.0*ff**2+cpix**2)
# qr = np.sqrt((1.0+ell)/(1.0-ell))
# PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
# return self.psf.shear(PSFshear), PSFshear
if __name__ == '__main__':
configfn = '/Users/zhangxin/Work/SlitlessSim/CSST_SIM/CSST_new_sim/csst-simulation/config/config_C6_dev.yaml'
with open(configfn, "r") as stream:
try:
config = yaml.safe_load(stream)
for key, value in config.items():
print(key + " : " + str(value))
except yaml.YAMLError as exc:
print(exc)
chip = Chip(chipID=1, config=config)
filter_id, filter_type = chip.getChipFilter()
filt = Filter(filter_id=filter_id,
filter_type=filter_type,
filter_param=FilterParam())
psf_i = PSFInterpSLS(
chip, filt, PSF_data_prefix="/Volumes/EAGET/CSST_PSF_data/SLS_PSF_PCA_fp/")
pos_img = galsim.PositionD(x=25155, y=-22060)
psf_im = psf_i.get_PSF(chip, pos_img=pos_img, g_order='1')