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

for tests before release

See merge request !23
parents e0f2b9f7 1c35c0e2
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ObservationSim/MockObject/CatalogBase.py → observation_sim/mock_objects/CatalogBase.py
View file @ 38e2c452
......@@ -5,7 +5,7 @@ import cmath
from astropy.table import Table
from abc import abstractmethod, ABCMeta
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getABMAG
from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getABMAG
class CatalogBase(metaclass=ABCMeta):
......
ObservationSim/MockObject/FlatLED.py → observation_sim/mock_objects/FlatLED.py
View file @ 38e2c452
import galsim
import os, sys
import os
import sys
import numpy as np
import time
import math
......@@ -9,12 +10,11 @@ import astropy.constants as cons
from astropy.io import fits
from scipy.interpolate import griddata
from astropy.table import Table
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
from observation_sim.mock_objects.SpecDisperser import SpecDisperser
from scipy import interpolate
import gc
from ObservationSim.MockObject.MockObject import MockObject
# from ObservationSim.Straylight import calculateSkyMap_split_g
from observation_sim.mock_objects.MockObject import MockObject
try:
import importlib.resources as pkg_resources
......@@ -43,17 +43,18 @@ cwaves_fwhm = {'LED1': 110, 'LED2': 120, 'LED3': 200, 'LED4': 300, 'LED5': 300,
# e-/ms
fluxLED = {'LED1': 15, 'LED2': 15, 'LED3': 12.5, 'LED4': 9, 'LED5': 9,
'LED6': 9, 'LED7': 9, 'LED8': 9, 'LED9': 9, 'LED10': 12.5, 'LED11': 15, 'LED12':15, 'LED13': 12.5,
'LED6': 9, 'LED7': 9, 'LED8': 9, 'LED9': 9, 'LED10': 12.5, 'LED11': 15, 'LED12': 15, 'LED13': 12.5,
'LED14': 12.5}
# fluxLEDL = {'LED1': 10, 'LED2': 10, 'LED3': 10, 'LED4': 10, 'LED5': 10,
# 'LED6': 10, 'LED7': 10, 'LED8': 10, 'LED9': 10, 'LED10': 10, 'LED11': 10, 'LED12':10, 'LED13': 10,
# 'LED14': 10}
mirro_eff = {'GU':0.61, 'GV':0.8, 'GI':0.8}
mirro_eff = {'GU': 0.61, 'GV': 0.8, 'GI': 0.8}
# mirro_eff = {'GU':1, 'GV':1, 'GI':1}
class FlatLED(MockObject):
def __init__(self, chip,filt, flatDir = None, logger=None):
def __init__(self, chip, filt, flatDir=None, logger=None):
# self.led_type_list = led_type_list
self.filt = filt
self.chip = chip
......@@ -62,18 +63,19 @@ class FlatLED(MockObject):
self.flatDir = flatDir
else:
try:
with pkg_resources.files('ObservationSim.MockObject.data.led').joinpath("") as ledDir:
with pkg_resources.files('observation_sim.mock_objects.data.led').joinpath("") as ledDir:
self.flatDir = ledDir.as_posix()
except AttributeError:
with pkg_resources.path('ObservationSim.MockObject.data.led', "") as ledDir:
with pkg_resources.path('observation_sim.mock_objects.data.led', "") as ledDir:
self.flatDir = ledDir.as_posix()
###
### return LED flat, e/s
# return LED flat, e/s
###
def getLEDImage(self, led_type='LED1'):
# cwave = cwaves[led_type]
flat = fits.open(os.path.join(self.flatDir, 'model_' + cwaves_name[led_type] + 'nm.fits'))
flat = fits.open(os.path.join(self.flatDir, 'model_' +
cwaves_name[led_type] + 'nm.fits'))
xlen = flat[0].header['NAXIS1']
ylen = 601
x = np.linspace(0, self.chip.npix_x * 6, xlen)
......@@ -95,7 +97,8 @@ class FlatLED(MockObject):
i = self.chip.rowID - 1
j = self.chip.colID - 1
U = griddata(X_, Z_, (
M[self.chip.npix_y * i:self.chip.npix_y * (i + 1), self.chip.npix_x * j:self.chip.npix_x * (j + 1)],
M[self.chip.npix_y * i:self.chip.npix_y *
(i + 1), self.chip.npix_x * j:self.chip.npix_x * (j + 1)],
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)
......@@ -103,13 +106,14 @@ class FlatLED(MockObject):
gc.collect()
return flatImage
###
### return LED flat, e/s
# return LED flat, e/s
###
def getLEDImage1(self, led_type='LED1'):
# cwave = cwaves[led_type]
flat = fits.open(os.path.join(self.flatDir, 'model_' + cwaves_name[led_type] + 'nm.fits'))
flat = fits.open(os.path.join(self.flatDir, 'model_' +
cwaves_name[led_type] + 'nm.fits'))
xlen = flat[0].header['NAXIS1']
ylen = 601
......@@ -120,21 +124,22 @@ class FlatLED(MockObject):
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.)]
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_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 ]),
N[0:self.chip.npix_y, 0:self.chip.npix_x]),
method='linear')
U = U/np.mean(U)
flatImage = U*fluxLED[led_type]*1000
......@@ -143,12 +148,13 @@ class FlatLED(MockObject):
def drawObj_LEDFlat_img(self, led_type_list=['LED1'], exp_t_list=[0.1]):
if len(led_type_list) > len(exp_t_list):
return np.ones([self.chip.npix_y,self.chip.npix_x])
return np.ones([self.chip.npix_y, self.chip.npix_x])
ledFlat = np.zeros([self.chip.npix_y,self.chip.npix_x])
ledFlat = np.zeros([self.chip.npix_y, self.chip.npix_x])
ledStat = '00000000000000'
ledTimes = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
ledTimes = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
nledStat = '2'
for i in np.arange(len(led_type_list)):
......@@ -160,8 +166,10 @@ class FlatLED(MockObject):
led_wave = cwaves[led_type]
led_fwhm = cwaves_fwhm[led_type]
led_spec = self.gaussian1d_profile_led(led_wave, led_fwhm)
speci = interpolate.interp1d(led_spec['WAVELENGTH'], led_spec['FLUX'])
w_list = np.arange(self.filt.blue_limit, self.filt.red_limit, 0.5) #A
speci = interpolate.interp1d(
led_spec['WAVELENGTH'], led_spec['FLUX'])
w_list = np.arange(self.filt.blue_limit,
self.filt.red_limit, 0.5) # A
f_spec = speci(w_list)
ccd_bp = self.chip._getChipEffCurve(self.chip.filter_type)
......@@ -174,19 +182,21 @@ class FlatLED(MockObject):
# print("DEBUG1:---------------",np.mean(unitFlatImg))
ledFlat = ledFlat+unitFlatImg*exp_t
ledStat = ledStat[0:int(led_type[3:])-1]+nledStat+ledStat[int(led_type[3:]):]
ledStat = ledStat[0:int(led_type[3:])-1] + \
nledStat+ledStat[int(led_type[3:]):]
ledTimes[int(led_type[3:])-1] = exp_t * 1000
gc.collect()
return ledFlat, ledStat, ledTimes
def drawObj_LEDFlat_slitless(self, led_type_list=['LED1'], exp_t_list=[0.1]):
if len(led_type_list) != len(exp_t_list):
return np.ones([self.chip.npix_y,self.chip.npix_x])
return np.ones([self.chip.npix_y, self.chip.npix_x])
ledFlat = np.zeros([self.chip.npix_y,self.chip.npix_x])
ledFlat = np.zeros([self.chip.npix_y, self.chip.npix_x])
ledStat = '00000000000000'
ledTimes = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
ledTimes = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
nledStat = '2'
......@@ -213,7 +223,8 @@ class FlatLED(MockObject):
flat_cube=self.chip.flat_cube, led_spec=led_spec)
ledFlat = ledFlat + ledspec_map
ledStat = ledStat[0:int(led_type[3:])-1]+nledStat+ledStat[int(led_type[3:]):]
ledStat = ledStat[0:int(led_type[3:])-1] + \
nledStat+ledStat[int(led_type[3:]):]
ledTimes[int(led_type[3:])-1] = exp_t * 1000
return ledFlat, ledStat, ledTimes
......@@ -223,7 +234,6 @@ class FlatLED(MockObject):
elif self.chip.survey_type == "spectroscopic":
return self.drawObj_LEDFlat_slitless(led_type_list=led_type_list, exp_t_list=exp_t_list)
def gaussian1d_profile_led(self, xc=5050, fwhm=300):
sigma = fwhm/2.355
x_radii = int(5*sigma + 1)
......@@ -232,9 +242,10 @@ class FlatLED(MockObject):
xlist_[1:-1] = xlist
xlist_[0] = 2000
xlist_[-1] = 18000
ids1 = xlist>xc-fwhm
ids2 = xlist[ids1]<xc+fwhm
data = np.exp((-(xlist-xc)*(xlist-xc))/(2*sigma*sigma))/(np.sqrt(2*math.pi)*sigma)
ids1 = xlist > xc-fwhm
ids2 = xlist[ids1] < xc+fwhm
data = np.exp((-(xlist-xc)*(xlist-xc))/(2*sigma*sigma)) / \
(np.sqrt(2*math.pi)*sigma)
scale = 1/np.trapz(data[ids1][ids2], xlist[ids1][ids2])
data_ = np.zeros(len(xlist) + 2)
data_[1:-1] = data*scale
......@@ -297,7 +308,8 @@ class FlatLED(MockObject):
sub_x_s = k2
sub_x_e = sub_x_end_arr[j]
skyImg_sub = galsim.Image(skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
skyImg_sub = galsim.Image(
skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
origin_sub = [sub_y_s, sub_x_s]
sub_x_center = (sub_x_s + sub_x_e) / 2.
......@@ -321,11 +333,8 @@ class FlatLED(MockObject):
continue
fImg[bounds] = fImg[bounds] + ssImg[bounds]
else:
# sdp.compute_spec_orders()
y_len = skyMap.shape[0]
x_len = skyMap.shape[1]
......@@ -352,7 +361,8 @@ class FlatLED(MockObject):
sub_x_e = sub_x_end_arr[j]
# print(i,j,sub_y_s, sub_y_e,sub_x_s,sub_x_e)
T1 = time.time()
skyImg_sub = galsim.Image(skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
skyImg_sub = galsim.Image(
skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
origin_sub = [sub_y_s, sub_x_s]
sub_x_center = (sub_x_s + sub_x_e) / 2.
......@@ -398,7 +408,8 @@ class FlatLED(MockObject):
T1 = time.time()
skyImg_sub = galsim.Image(skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
skyImg_sub = galsim.Image(
skyImg.array[sub_y_s:sub_y_e, sub_x_s:sub_x_e])
origin_sub = [sub_y_s, sub_x_s]
sub_x_center = (sub_x_s + sub_x_e) / 2.
......@@ -426,14 +437,11 @@ class FlatLED(MockObject):
print('time: %s ms' % ((T2 - T1) * 1000))
if isAlongY == 1:
fimg, tmx, tmy = SpecDisperser.rotate90(array_orig=fImg.array, xc=0, yc=0, isClockwise=0)
fimg, tmx, tmy = SpecDisperser.rotate90(
array_orig=fImg.array, xc=0, yc=0, isClockwise=0)
else:
fimg = fImg.array
# fimg = fimg * pixelSize * pixelSize
return fimg
ObservationSim/MockObject/Galaxy.py → observation_sim/mock_objects/Galaxy.py
View file @ 38e2c452
......@@ -2,9 +2,9 @@ import numpy as np
import galsim
from astropy.table import Table
from ObservationSim.MockObject._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
from ObservationSim.MockObject.MockObject import MockObject
from observation_sim.mock_objects._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG
from observation_sim.mock_objects.SpecDisperser import SpecDisperser
from observation_sim.mock_objects.MockObject import MockObject
# import tracemalloc
......
ObservationSim/MockObject/MockObject.py → observation_sim/mock_objects/MockObject.py
View file @ 38e2c452
......@@ -6,10 +6,10 @@ from astropy import wcs
from astropy.table import Table
import astropy.io.fits as fitsio
from ObservationSim.MockObject._util import magToFlux, VC_A, convolveGaussXorders, convolveImg
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, \
from observation_sim.mock_objects._util import magToFlux, VC_A, convolveGaussXorders, convolveImg
from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, \
getABMAG
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
from observation_sim.mock_objects.SpecDisperser import SpecDisperser
class MockObject(object):
......@@ -24,14 +24,14 @@ class MockObject(object):
self.type = "star"
elif self.param["star"] == 2:
self.type = "quasar"
###mock_stamp_START
# mock_stamp_START
elif self.param["star"] == 3:
self.type = "stamp"
###mock_stamp_END
###for calibration
# mock_stamp_END
# for calibration
elif self.param["star"] == 4:
self.type = "calib"
###END
# END
self.sed = None
self.fd_shear = None
......@@ -72,11 +72,14 @@ class MockObject(object):
if verbose:
print("\n")
print("Before field distortion:\n")
print("x = %.2f, y = %.2f\n" % (self.posImg.x, self.posImg.y), flush=True)
self.posImg, self.fd_shear = fdmodel.get_distorted(chip=chip, pos_img=self.posImg)
print("x = %.2f, y = %.2f\n" %
(self.posImg.x, self.posImg.y), flush=True)
self.posImg, self.fd_shear = fdmodel.get_distorted(
chip=chip, pos_img=self.posImg)
if verbose:
print("After field distortion:\n")
print("x = %.2f, y = %.2f\n" % (self.posImg.x, self.posImg.y), flush=True)
print("x = %.2f, y = %.2f\n" %
(self.posImg.x, self.posImg.y), flush=True)
x, y = self.posImg.x + 0.5, self.posImg.y + 0.5
self.x_nominal = int(np.floor(x + 0.5))
......@@ -108,7 +111,8 @@ class MockObject(object):
# print("nphotons_tot = ", nphotons_tot)
try:
full = integrate_sed_bandpass(sed=self.sed, bandpass=filt.bandpass_full)
full = integrate_sed_bandpass(
sed=self.sed, bandpass=filt.bandpass_full)
except Exception as e:
print(e)
if self.logger:
......@@ -166,7 +170,8 @@ class MockObject(object):
if np.sum(np.isnan(stamp.array)) > 0:
continue
stamp.setCenter(x_nominal, y_nominal)
bounds = stamp.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
bounds = stamp.bounds & galsim.BoundsI(
0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() > 0:
chip.img.setOrigin(0, 0)
chip.img[bounds] += stamp[bounds]
......@@ -176,9 +181,9 @@ class MockObject(object):
if is_updated == 0:
# Return code 0: object has missed this detector
print("obj %s missed"%(self.id))
print("obj %s missed" % (self.id))
if self.logger:
self.logger.info("obj %s missed"%(self.id))
self.logger.info("obj %s missed" % (self.id))
return 0, pos_shear
return 1, pos_shear # Return code 1: draw sucesss
......@@ -193,7 +198,8 @@ class MockObject(object):
nan_ids = np.isnan(img_s)
if img_s[nan_ids].shape[0] > 0:
# img_s[nan_ids] = 0
print("DEBUG: before convolveGaussXorders specImg nan num is", img_s[nan_ids].shape[0])
print("DEBUG: before convolveGaussXorders specImg nan num is",
img_s[nan_ids].shape[0])
#########################################################
img_s, orig_off = convolveGaussXorders(img_s, xOrderSigPlus[k])
origin_order_x = v[1] - orig_off
......@@ -219,7 +225,8 @@ class MockObject(object):
stamp.wcs = local_wcs
stamp.setOrigin(origin_order_x, origin_order_y)
bounds = stamp.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
bounds = stamp.bounds & galsim.BoundsI(
0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() == 0:
continue
chip.img.setOrigin(0, 0)
......@@ -230,17 +237,18 @@ class MockObject(object):
del stamp
del spec_orders
def addSLStoChipImageWithPSF(self, sdp=None, chip=None, pos_img_local = [1,1], psf_model=None, bandNo = 1, grating_split_pos=3685, local_wcs=None, pos_img=None):
def addSLStoChipImageWithPSF(self, sdp=None, chip=None, pos_img_local=[1, 1], psf_model=None, bandNo=1, grating_split_pos=3685, local_wcs=None, pos_img=None):
spec_orders = sdp.compute_spec_orders()
for k, v in spec_orders.items():
img_s = v[0]
# print(bandNo,k)
try:
psf, pos_shear = psf_model.get_PSF(chip, pos_img_local = pos_img_local, bandNo = bandNo, galsimGSObject=True, g_order = k, grating_split_pos=grating_split_pos)
psf, pos_shear = psf_model.get_PSF(
chip, pos_img_local=pos_img_local, bandNo=bandNo, galsimGSObject=True, g_order=k, grating_split_pos=grating_split_pos)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
psf_img = psf.drawImage(nx=100, ny=100, wcs = local_wcs)
psf_img = psf.drawImage(nx=100, ny=100, wcs=local_wcs)
psf_img_m = psf_img.array
......@@ -263,7 +271,6 @@ class MockObject(object):
origin_order_x = v[1] - orig_off[0]
origin_order_y = v[2] - orig_off[1]
specImg = galsim.ImageF(img_s)
# photons = galsim.PhotonArray.makeFromImage(specImg)
# photons.x += origin_order_x
......@@ -278,7 +285,8 @@ class MockObject(object):
specImg.wcs = local_wcs
specImg.setOrigin(origin_order_x, origin_order_y)
bounds = specImg.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
bounds = specImg.bounds & galsim.BoundsI(
0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() == 0:
continue
chip.img.setOrigin(0, 0)
......@@ -297,7 +305,8 @@ class MockObject(object):
norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001
sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed,
norm_thr=normFilter,
sWave=np.floor(normFilter[norm_thr_rang_ids][0][0]),
sWave=np.floor(
normFilter[norm_thr_rang_ids][0][0]),
eWave=np.ceil(normFilter[norm_thr_rang_ids][-1][0]))
if sedNormFactor == 0:
return 2, None
......@@ -331,9 +340,9 @@ class MockObject(object):
'D': 5.5684364343742825, 'E': 16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list),2])
branges = np.zeros([len(bandpass_list), 2])
if hasattr(psf_model,'bandranges'):
if hasattr(psf_model, 'bandranges'):
if psf_model.bandranges is None:
return 2, None
if len(psf_model.bandranges) != len(bandpass_list):
......@@ -355,21 +364,23 @@ class MockObject(object):
psf_tmp = galsim.Gaussian(sigma=0.002)
star = galsim.Convolve(psf_tmp, star)
starImg = star.drawImage(nx=60, ny=60, wcs=chip_wcs_local, offset=offset)
starImg = star.drawImage(
nx=60, ny=60, wcs=chip_wcs_local, offset=offset)
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0,0)
starImg.setOrigin(0, 0)
gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] - 1, origin_star[1] + starImg.array.shape[1] - 1]
gal_end = [origin_star[0] + starImg.array.shape[0] -
1, origin_star[1] + starImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
## part img disperse
# part img disperse
subImg_p1 = starImg.array[:, 0:subSlitPos]
star_p1 = galsim.Image(subImg_p1)
origin_p1 = origin_star
star_p1.setOrigin(0,0)
star_p1.setOrigin(0, 0)
xcenter_p1 = min(x_nominal, grating_split_pos_chip - 1) - 0
ycenter_p1 = y_nominal - 0
......@@ -382,11 +393,12 @@ class MockObject(object):
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)
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)
subImg_p2 = starImg.array[:, subSlitPos + 1:starImg.array.shape[1]]
subImg_p2 = starImg.array[:,
subSlitPos + 1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0)
origin_p2 = [origin_star[0], grating_split_pos_chip]
......@@ -402,9 +414,9 @@ class MockObject(object):
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],
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp_p1
del sdp_p2
......@@ -417,9 +429,9 @@ class MockObject(object):
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],
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp
elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
......@@ -430,9 +442,9 @@ class MockObject(object):
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],
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp
# del psf
return 1, pos_shear
......@@ -447,8 +459,6 @@ class MockObject(object):
snr_obj = img_flux / sig_obj
return snr_obj
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:
......@@ -456,7 +466,8 @@ class MockObject(object):
# print("nphotons_tot = ", nphotons_tot)
try:
full = integrate_sed_bandpass(sed=self.sed, bandpass=filt.bandpass_full)
full = integrate_sed_bandpass(
sed=self.sed, bandpass=filt.bandpass_full)
except Exception as e:
print(e)
if self.logger:
......@@ -504,21 +515,21 @@ class MockObject(object):
folding_threshold=folding_threshold)
star_temp = psf.withFlux(nphotons)
if i==0:
if i == 0:
star = star_temp
else:
star = star+star_temp
pixelScale = 0.074
stamp = star.drawImage(wcs=chip_wcs_local, offset=offset)
#stamp = star.drawImage(nx=256, ny=256, scale=pixelScale)
# stamp = star.drawImage(nx=256, ny=256, scale=pixelScale)
if np.sum(np.isnan(stamp.array)) > 0:
return None
fn = chip_output.subdir + "/psfIDW"
os.makedirs(fn, exist_ok=True)
fn = fn + "/ccd_{:}".format(chip.chipID)+"_psf_"+str(self.param['id'])+".fits"
fn = fn + "/ccd_{:}".format(chip.chipID) + \
"_psf_"+str(self.param['id'])+".fits"
if fn != None:
if os.path.exists(fn):
os.remove(fn)
......
ObservationSim/MockObject/Quasar.py → observation_sim/mock_objects/Quasar.py
View file @ 38e2c452
......@@ -6,8 +6,8 @@ import astropy.constants as cons
from astropy.table import Table
from scipy import interpolate
from ObservationSim.MockObject.MockObject import MockObject
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG
from observation_sim.mock_objects.MockObject import MockObject
from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG
class Quasar(MockObject):
......
ObservationSim/MockObject/SpecDisperser/SpecDisperser.py → observation_sim/mock_objects/SpecDisperser/SpecDisperser.py
View file @ 38e2c452
......@@ -97,7 +97,6 @@ class SpecDisperser(object):
self.grating_conf_file = conf
self.ignoreBeam = ignoreBeam
def compute_spec_orders(self):
all_orders = OrderedDict()
......@@ -121,16 +120,18 @@ class SpecDisperser(object):
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
# Account for pixel centering of the trace
yfrac_beam = ytrace_beam - floor(ytrace_beam+0.5)
ysens = lam_beam * 0
lam_index = argsort(lam_beam)
conf_sens = self.grating_conf.sens[beam]
lam_intep = np.linspace(self.band_start, self.band_end, int((self.band_end - self.band_start) / 0.1))
lam_intep = np.linspace(self.band_start, self.band_end, int(
(self.band_end - self.band_start) / 0.1))
thri = interpolate.interp1d(conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY'])
thri = interpolate.interp1d(
conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY'])
spci = interpolate.interp1d(self.spec['WAVELENGTH'], self.spec['FLUX'])
beam_thr = thri(lam_intep)
......@@ -138,7 +139,7 @@ class SpecDisperser(object):
bean_thr_spec = beam_thr * spec_sample
###generate sensitivity file for aXe
# generate sensitivity file for aXe
# ysensitivity = lam_beam * 0
#
# ysensitivity[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep,
......@@ -155,7 +156,8 @@ class SpecDisperser(object):
sensitivity_beam = ysens
len_spec_x = len(dx)
len_spec_y = int(abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)
len_spec_y = int(
abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)
beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)
modelf = zeros(product(beam_sh), dtype=float)
......@@ -169,7 +171,7 @@ class SpecDisperser(object):
dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5))
frac_ids = yfrac_beam<0
frac_ids = yfrac_beam < 0
dypix[frac_ids] = dypix[frac_ids] - 1
yfrac_beam[frac_ids] = 1+yfrac_beam[frac_ids]
......@@ -199,7 +201,8 @@ class SpecDisperser(object):
else:
beam_flat = zeros([len(modelf), len(self.flat_cube)])
sub_flat_cube = zeros([len(self.flat_cube),beam_sh[0], beam_sh[1]])
sub_flat_cube = zeros(
[len(self.flat_cube), beam_sh[0], beam_sh[1]])
sub_flat_cube[0] = sub_flat_cube[0] + 1.
overlap_flag = 1
......@@ -210,23 +213,28 @@ class SpecDisperser(object):
sub_x_e = originOut_x + beam_sh[1] - 1
beam_x_s = max(sub_x_s, 0)
if beam_x_s > self.flat_cube[0].shape[1] - 1: overlap_flag = 0
if beam_x_s > self.flat_cube[0].shape[1] - 1:
overlap_flag = 0
if overlap_flag == 1:
beam_x_e = min(sub_x_e, self.flat_cube[0].shape[1] - 1)
if beam_x_e < 0: overlap_flag = 0
if beam_x_e < 0:
overlap_flag = 0
if overlap_flag == 1:
beam_y_s = max(sub_y_s, 0)
if beam_y_s > self.flat_cube[0].shape[0] - 1: overlap_flag = 0
if beam_y_s > self.flat_cube[0].shape[0] - 1:
overlap_flag = 0
if overlap_flag == 1:
beam_y_e = min(sub_y_e, self.flat_cube[0].shape[0] - 1)
if beam_y_e < 0: overlap_flag = 0
if beam_y_e < 0:
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[:, 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):
......@@ -243,7 +251,8 @@ class SpecDisperser(object):
flat_index[nonz], yfrac_beam[nonz],
sensitivity_beam[nonz],
modelf, x0,
array(self.img_sh, dtype=int64),
array(self.img_sh,
dtype=int64),
array(beam_sh, dtype=int64),
beam_flat,
lam_beam[lam_index][nonz])
......@@ -254,13 +263,15 @@ class SpecDisperser(object):
if self.isAlongY == 1:
model, _, _ = rotate90(array_orig=model, isClockwise=0)
return model, originOut_x, originOut_y, dxpix, dypix, lam_beam,ysens
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'}
sens_file_name = conffile[0:-5] + '_sensitivity_' + orders[beam] + '.fits'
sens_file_name = conffile[0:-5] + \
'_sensitivity_' + orders[beam] + '.fits'
if not os.path.exists(sens_file_name) == True:
senstivity_out = Table(array([w, sens]).T, names=('WAVELENGTH', 'SENSITIVITY'))
senstivity_out = Table(
array([w, sens]).T, names=('WAVELENGTH', 'SENSITIVITY'))
senstivity_out.write(sens_file_name, format='fits')
......@@ -284,7 +295,7 @@ class aXeConf():
self.conf_file = conf_file
self.count_beam_orders()
## Global XOFF/YOFF offsets
# Global XOFF/YOFF offsets
if 'XOFF' in self.conf.keys():
self.xoff = np.float(self.conf['XOFF'])
else:
......@@ -310,11 +321,11 @@ class aXeConf():
conf = OrderedDict()
lines = open(conf_file).readlines()
for line in lines:
## empty / commented lines
# empty / commented lines
if (line.startswith('#')) | (line.strip() == '') | ('"' in line):
continue
## split the line, taking out ; and # comments
# split the line, taking out ; and # comments
spl = line.split(';')[0].split('#')[0].split()
param = spl[0]
if len(spl) > 2:
......@@ -360,13 +371,14 @@ class aXeConf():
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.nx[beam] = int(self.dxlam[beam].max() - self.dxlam[beam].min()) + 1
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)]))
# self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH'])
# self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY'])
### Need doubles for interpolating functions
# Need doubles for interpolating functions
for col in self.sens[beam].colnames:
data = np.cast[np.double](self.sens[beam][col])
self.sens[beam].remove_column(col)
......@@ -394,22 +406,22 @@ class aXeConf():
Evaluated field-dependent coefficients
"""
## number of coefficients for a given polynomial order
## 1:1, 2:3, 3:6, 4:10, order:order*(order+1)/2
# number of coefficients for a given polynomial order
# 1:1, 2:3, 3:6, 4:10, order:order*(order+1)/2
if isinstance(coeffs, float):
order = 1
else:
order = int(-1 + np.sqrt(1 + 8 * len(coeffs))) // 2
## Build polynomial terms array
## $a = a_0+a_1x_i+a_2y_i+a_3x_i^2+a_4x_iy_i+a_5yi^2+$ ...
# Build polynomial terms array
# $a = a_0+a_1x_i+a_2y_i+a_3x_i^2+a_4x_iy_i+a_5yi^2+$ ...
xy = []
for p in range(order):
for px in range(p + 1):
# print 'x**%d y**%d' %(p-px, px)
xy.append(xi ** (p - px) * yi ** (px))
## Evaluate the polynomial, allowing for N-dimensional inputs
# Evaluate the polynomial, allowing for N-dimensional inputs
a = np.sum((np.array(xy).T * coeffs).T, axis=0)
return a
......@@ -445,26 +457,27 @@ class aXeConf():
.. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2])
"""
## dp is the arc length along the trace
## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...
# 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 np.abs(np.unique(dydx[2])).max() == 0:
poly_order = 1
if poly_order == 0: ## dy=0
if poly_order == 0: # dy=0
dp = dx
elif poly_order == 1: ## constant dy/dx
elif poly_order == 1: # constant dy/dx
dp = np.sqrt(1 + dydx[1] ** 2) * (dx)
elif poly_order == 2: ## quadratic trace
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])
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)
# high order shape, numerical integration along trace
# (this can be slow)
xmin = np.minimum((dx).min(), 0)
xmax = np.maximum((dx).max(), 0)
xfull = np.arange(xmin, xmax)
......@@ -472,11 +485,12 @@ class aXeConf():
for i in range(1, poly_order):
dyfull += i * dydx[i] * (xfull - 0.5) ** (i - 1)
## Integrate from 0 to dx / -dx
# Integrate from 0 to dx / -dx
dpfull = xfull * 0.
lt0 = xfull < 0
if lt0.sum() > 1:
dpfull[lt0] = np.cumsum(np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]
dpfull[lt0] = np.cumsum(
np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]
dpfull[lt0] *= -1
#
......@@ -520,10 +534,12 @@ class aXeConf():
NORDER = self.orders[beam] + 1
xi, yi = x - self.xoff, y - self.yoff
xoff_beam = self.field_dependent(xi, yi, self.conf['XOFF_{0}'.format(beam)])
yoff_beam = self.field_dependent(xi, yi, self.conf['YOFF_{0}'.format(beam)])
xoff_beam = self.field_dependent(
xi, yi, self.conf['XOFF_{0}'.format(beam)])
yoff_beam = self.field_dependent(
xi, yi, self.conf['YOFF_{0}'.format(beam)])
## y offset of trace (DYDX)
# y offset of trace (DYDX)
dydx = np.zeros(NORDER) # 0 #+1.e-80
dydx = [0] * NORDER
......@@ -538,7 +554,7 @@ class aXeConf():
for i in range(NORDER):
dy += dydx[i] * (dx - xoff_beam) ** i
## wavelength solution
# wavelength solution
dldp = np.zeros(NORDER)
dldp = [0] * NORDER
......@@ -587,7 +603,7 @@ class aXeConf():
#
# dp = np.interp(dx-xoff_beam, xfull, dpfull)
## Evaluate dldp
# Evaluate dldp
lam = dp * 0.
for i in range(NORDER):
lam += dldp[i] * dp ** i
......@@ -619,7 +635,8 @@ class aXeConf():
if 'XOFF_{0}'.format(beam) not in self.conf.keys():
continue
xoff = self.field_dependent(x0, x1, self.conf['XOFF_{0}'.format(beam)])
xoff = self.field_dependent(
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)]
ok = (dx >= xlim[0]) & (dx <= xlim[1])
......@@ -627,7 +644,8 @@ class aXeConf():
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()))
print('Beam {0}, lambda=({1:.1f} - {2:.1f})'.format(beam,
lam[ok].min(), lam[ok].max()))
plt.grid()
plt.xlabel(r'$\Delta x$')
......
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