add led dark simulation model

ObservationSim/Config/Header/ImageHeader.py

@@ -376,7 +376,7 @@ def generatePrimaryHeader(xlen = 9216, ylen = 9232, pointNum = '1', ra = 60, dec
     h_prim['OBJ_RA'] = ra
     h_prim['OBJ_DEC'] = dec
 
-    obs_type = {'SCI': '01', 'BIAS': '03', 'DARK': '07', 'FLAT': '11', 'CRS': '98', 'CRD': '99'}
+    obs_type = {'SCI': '01', 'BIAS': '04', 'DARK': '08', 'FLAT': '11', 'CRS': '98', 'CRD': '99', 'DARKPF':'09'}
 
     OBS_id = '1'+ obs_type[im_type] + str(int(project_cycle)) + pointNum.rjust(7,'0')
 
@@ -386,7 +386,7 @@ def generatePrimaryHeader(xlen = 9216, ylen = 9232, pointNum = '1', ra = 60, dec
     h_prim['EXPTIME'] = exptime
     
     # Define file types
-    file_type = {'SCI':'SCIE', 'BIAS':'BIAS', 'DARK':'DARK', 'FLAT':'FLAT', 'CRS':'CRS', 'CRD':'CRD','CALS':'CALS','CALF':'CALF'}
+    file_type = {'SCI':'SCIE', 'BIAS':'BIAS', 'DARK':'DARK', 'FLAT':'FLAT', 'CRS':'CRS', 'CRD':'CRD','CALS':'CALS','CALF':'CALF','DARKPF':'DARK'}
     h_prim['FILETYPE'] = file_type[im_type]
 
     co = coord.SkyCoord(ra, dec, unit='deg')

ObservationSim/MockObject/FlatLED.py

+
+
+import galsim
+import os, sys
+import numpy as np
+from astropy.io import fits
+from scipy.interpolate import griddata
+import math
+import astropy.constants as cons
+from astropy.table import Table
+from ObservationSim.MockObject.SpecDisperser import SpecDisperser
+import time
+from scipy import interpolate
+
+from ObservationSim.MockObject.MockObject import MockObject
+# from ObservationSim.Straylight import calculateSkyMap_split_g
+
+# flatDir = '/Volumes/EAGET/LED_FLAT/'
+LED_name = ['LED1', 'LED2', 'LED3', 'LED4', 'LED5', 'LED6', 'LED7', 'LED8', 'LED9', 'LED10', 'LED11', 'LED12', 'LED13',
+            'LED14']
+cwaves_name = {'LED1': '275', 'LED2': '310', 'LED3': '430', 'LED4': '505', 'LED5': '545', 'LED6': '590', 'LED7': '670',
+          'LED8': '760', 'LED9': '940', 'LED10': '940', 'LED11': '1050', 'LED12': '1050',
+          'LED13': '340', 'LED14': '365'}
+
+cwaves = {'LED1': 2750, 'LED2': 3100, 'LED3': 4300, 'LED4': 5050, 'LED5': 5250, 'LED6': 5900, 'LED7': 6700,
+          'LED8': 7600, 'LED9': 8800, 'LED10': 9400, 'LED11': 10500, 'LED12': 15500, 'LED13': 3400, 'LED14': 3650}
+cwaves_fwhm = {'LED1': 110, 'LED2': 120, 'LED3': 200, 'LED4': 300, 'LED5': 300, 'LED6': 130, 'LED7': 210,
+          'LED8': 260, 'LED9': 400, 'LED10': 370, 'LED11': 500, 'LED12': 1400, 'LED13': 90, 'LED14': 100}
+# LED_QE = {'LED1': 0.3, 'LED2': 0.4, 'LED13': 0.5, 'LED14': 0.5, 'LED10': 0.4}
+# e-/ms
+fluxLED = {'LED1': 0.16478729, 'LED2': 0.084220931, 'LED3': 2.263360617, 'LED4': 2.190623489, 'LED5': 0.703504768,
+           'LED6': 0.446117963, 'LED7': 0.647122098, 'LED8': 0.922313442,
+           'LED9': 0.987278143, 'LED10': 2.043989167, 'LED11': 0.612571429, 'LED12': 1.228915663, 'LED13': 0.17029384,
+           'LED14': 0.27842925}
+
+mirro_eff = {'GU':0.61, 'GV':0.8, 'GI':0.8}
+
+class FlatLED(object):
+    def __init__(self, chip,filt, flatDir = '/Users/zhangxin/Work/SlitlessSim/csst_sls_calibration/flat_field_cube/models/', logger=None):
+        # self.led_type_list = led_type_list
+        self.flatDir = flatDir
+        self.filt = filt
+        self.chip = chip
+        self.logger = logger
+
+    ###
+    ### return LED flat, e/s
+    ###
+    def getLEDImage(self, led_type='LED1'):
+        # cwave = cwaves[led_type]
+        flat = fits.open(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)
+        y = np.linspace(0, self.chip.npix_y * 5, ylen)
+        xx, yy = np.meshgrid(x, y)
+
+        a1 = flat[0].data[0:ylen, 0:xlen]
+        # 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 * 6, 1)
+        n_y = np.arange(0, self.chip.npix_y * 5, 1)
+
+        M, N = np.meshgrid(n_x, n_y)
+
+        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)],
+            N[self.chip.npix_y * i:self.chip.npix_y * (i + 1), self.chip.npix_x * j:self.chip.npix_x * (j + 1)]),
+                     method='cubic')
+        U = U/np.mean(U)
+        flatImage = U*fluxLED[led_type]*1000
+        return flatImage
+
+    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])
+
+        ledFlat = np.zeros([self.chip.npix_y,self.chip.npix_x])
+        for i in np.arange(len(led_type_list)):
+            led_type = led_type_list[i]
+            exp_t = exp_t_list[i]
+            unitFlatImg = self.getLEDImage(led_type=led_type)
+            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
+
+            f_spec = speci(w_list)
+            ccd_bp = self.chip._getChipEffCurve(self.chip.filter_type)
+            ccd_eff = ccd_bp.__call__(w_list / 10.)
+            filt_bp = self.filt.filter_bandpass
+            fil_eff = filt_bp.__call__(w_list / 10.)
+            t_spec = np.trapz(f_spec*ccd_eff*fil_eff, w_list)
+            print(i, np.mean(unitFlatImg), t_spec, exp_t)
+            unitFlatImg = unitFlatImg * t_spec
+
+            ledFlat = ledFlat+unitFlatImg*exp_t
+        return ledFlat
+
+    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])
+
+        ledFlat = np.zeros([self.chip.npix_y,self.chip.npix_x])
+        for i in np.arange(len(led_type_list)):
+            led_type = led_type_list[i]
+            exp_t = exp_t_list[i]
+            unitFlatImg = self.getLEDImage(led_type=led_type)
+            ledFlat_ = unitFlatImg*exp_t
+            ledFlat_ = ledFlat_ / mirro_eff[self.filt.filter_type]
+            ledFlat_.astype(np.float32)
+            led_wave = cwaves[led_type]
+            led_fwhm = cwaves_fwhm[led_type]
+            led_spec = self.gaussian1d_profile_led(led_wave, led_fwhm)
+            ledspec_map = self.calculateLEDSpec(
+                skyMap=ledFlat_,
+                blueLimit=self.filt.blue_limit,
+                redLimit=self.filt.red_limit,
+                conf=self.chip.sls_conf,
+                pixelSize=self.chip.pix_scale,
+                isAlongY=0,
+                flat_cube=self.chip.flat_cube, led_spec=led_spec)
+
+            ledFlat = ledFlat + ledspec_map
+
+        return ledFlat
+
+    def drawObj_LEDFlat(self, led_type_list=['LED1'], exp_t_list=[0.1]):
+        if self.chip.survey_type == "photometric":
+            return self.drawObj_LEDFlat_img(led_type_list=led_type_list, exp_t_list=exp_t_list)
+        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)
+        xlist = np.arange(xc-x_radii, xc+x_radii, 0.5)
+        xlist_ = np.zeros(len(xlist) + 2)
+        xlist_[1:-1] = xlist
+        xlist_[0] = 2550
+        xlist_[-1] = 10000
+        data = np.exp((-(xlist-xc)*(xlist-xc))/(2*sigma*sigma))/(np.sqrt(2*math.pi)*sigma)
+        data_ = np.zeros(len(xlist) + 2)
+        data_[1:-1] = data
+        return Table(np.array([xlist_.astype(np.float32), data_.astype(np.float32)]).T, names=('WAVELENGTH', 'FLUX'))
+
+    def calculateLEDSpec(self, skyMap=None, blueLimit=4200, redLimit=6500,
+                                conf=[''], pixelSize=0.074, isAlongY=0,
+                                split_pos=3685, flat_cube=None, led_spec=None):
+
+        conf1 = conf[0]
+        conf2 = conf[0]
+        if np.size(conf) == 2:
+            conf2 = conf[1]
+
+        skyImg = galsim.Image(skyMap, xmin=0, ymin=0)
+
+        tbstart = blueLimit
+        tbend = redLimit
+
+        fimg = np.zeros_like(skyMap)
+
+        fImg = galsim.Image(fimg)
+
+        spec = led_spec
+        if isAlongY == 0:
+            directParm = 0
+        if isAlongY == 1:
+            directParm = 1
+
+        if split_pos >= skyImg.array.shape[directParm]:
+            skyImg1 = galsim.Image(skyImg.array)
+            origin1 = [0, 0]
+            # sdp = specDisperser.specDisperser(orig_img=skyImg1, xcenter=skyImg1.center.x, ycenter=skyImg1.center.y,
+            #                                   full_img=fimg, tar_spec=spec, band_start=tbstart, band_end=tbend,
+            #                                   origin=origin1,
+            #                                   conf=conf1)
+            # sdp.compute_spec_orders()
+
+            y_len = skyMap.shape[0]
+            x_len = skyMap.shape[1]
+            delt_x = 100
+            delt_y = 100
+
+            sub_y_start_arr = np.arange(0, y_len, delt_y)
+            sub_y_end_arr = sub_y_start_arr + delt_y
+            sub_y_end_arr[-1] = min(sub_y_end_arr[-1], y_len)
+
+            sub_x_start_arr = np.arange(0, x_len, delt_x)
+            sub_x_end_arr = sub_x_start_arr + delt_x
+            sub_x_end_arr[-1] = min(sub_x_end_arr[-1], x_len)
+
+            for i, k1 in enumerate(sub_y_start_arr):
+                sub_y_s = k1
+                sub_y_e = sub_y_end_arr[i]
+
+                sub_y_center = (sub_y_s + sub_y_e) / 2.
+
+                for j, k2 in enumerate(sub_x_start_arr):
+                    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])
+                    origin_sub = [sub_y_s, sub_x_s]
+                    sub_x_center = (sub_x_s + sub_x_e) / 2.
+
+                    sdp = SpecDisperser(orig_img=skyImg_sub, xcenter=sub_x_center, ycenter=sub_y_center,
+                                        origin=origin_sub,
+                                        tar_spec=spec,
+                                        band_start=tbstart, band_end=tbend,
+                                        conf=conf2,
+                                        flat_cube=flat_cube, ignoreBeam=['D', 'E'])
+
+                    spec_orders = sdp.compute_spec_orders()
+
+                    for k, v in spec_orders.items():
+                        img_s = v[0]
+                        origin_order_x = v[1]
+                        origin_order_y = v[2]
+                        ssImg = galsim.ImageF(img_s)
+                        ssImg.setOrigin(origin_order_x, origin_order_y)
+                        bounds = ssImg.bounds & fImg.bounds
+                        if bounds.area() == 0:
+                            continue
+                        fImg[bounds] = fImg[bounds] + ssImg[bounds]
+
+
+
+        else:
+
+
+            # sdp.compute_spec_orders()
+            y_len = skyMap.shape[0]
+            x_len = skyMap.shape[1]
+            delt_x = 500
+            delt_y = y_len
+
+            sub_y_start_arr = np.arange(0, y_len, delt_y)
+            sub_y_end_arr = sub_y_start_arr + delt_y
+            sub_y_end_arr[-1] = min(sub_y_end_arr[-1], y_len)
+
+            delt_x = split_pos - 0
+            sub_x_start_arr = np.arange(0, split_pos, delt_x)
+            sub_x_end_arr = sub_x_start_arr + delt_x
+            sub_x_end_arr[-1] = min(sub_x_end_arr[-1], split_pos)
+
+            for i, k1 in enumerate(sub_y_start_arr):
+                sub_y_s = k1
+                sub_y_e = sub_y_end_arr[i]
+
+                sub_y_center = (sub_y_s + sub_y_e) / 2.
+
+                for j, k2 in enumerate(sub_x_start_arr):
+                    sub_x_s = k2
+                    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])
+                    origin_sub = [sub_y_s, sub_x_s]
+                    sub_x_center = (sub_x_s + sub_x_e) / 2.
+
+                    sdp = SpecDisperser(orig_img=skyImg_sub, xcenter=sub_x_center, ycenter=sub_y_center,
+                                        origin=origin_sub,
+                                        tar_spec=spec,
+                                        band_start=tbstart, band_end=tbend,
+                                        conf=conf1,
+                                        flat_cube=flat_cube)
+
+                    spec_orders = sdp.compute_spec_orders()
+
+                    for k, v in spec_orders.items():
+                        img_s = v[0]
+                        origin_order_x = v[1]
+                        origin_order_y = v[2]
+                        ssImg = galsim.ImageF(img_s)
+                        ssImg.setOrigin(origin_order_x, origin_order_y)
+                        bounds = ssImg.bounds & fImg.bounds
+                        if bounds.area() == 0:
+                            continue
+                        fImg[bounds] = fImg[bounds] + ssImg[bounds]
+
+                    T2 = time.time()
+
+                    print('time: %s ms' % ((T2 - T1) * 1000))
+
+            delt_x = x_len - split_pos
+            sub_x_start_arr = np.arange(split_pos, x_len, delt_x)
+            sub_x_end_arr = sub_x_start_arr + delt_x
+            sub_x_end_arr[-1] = min(sub_x_end_arr[-1], x_len)
+
+            for i, k1 in enumerate(sub_y_start_arr):
+                sub_y_s = k1
+                sub_y_e = sub_y_end_arr[i]
+
+                sub_y_center = (sub_y_s + sub_y_e) / 2.
+
+                for j, k2 in enumerate(sub_x_start_arr):
+                    sub_x_s = k2
+                    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])
+                    origin_sub = [sub_y_s, sub_x_s]
+                    sub_x_center = (sub_x_s + sub_x_e) / 2.
+
+                    sdp = SpecDisperser(orig_img=skyImg_sub, xcenter=sub_x_center, ycenter=sub_y_center,
+                                        origin=origin_sub,
+                                        tar_spec=spec,
+                                        band_start=tbstart, band_end=tbend,
+                                        conf=conf2,
+                                        flat_cube=flat_cube)
+
+                    spec_orders = sdp.compute_spec_orders()
+
+                    for k, v in spec_orders.items():
+                        img_s = v[0]
+                        origin_order_x = v[1]
+                        origin_order_y = v[2]
+                        ssImg = galsim.ImageF(img_s)
+                        ssImg.setOrigin(origin_order_x, origin_order_y)
+                        bounds = ssImg.bounds & fImg.bounds
+                        if bounds.area() == 0:
+                            continue
+                        fImg[bounds] = fImg[bounds] + ssImg[bounds]
+                    T2 = time.time()
+
+                    print('time: %s ms' % ((T2 - T1) * 1000))
+
+        if isAlongY == 1:
+            fimg, tmx, tmy = rotate90(array_orig=fImg.array, xc=0, yc=0, isClockwise=0)
+        else:
+            fimg = fImg.array
+
+        fimg = fimg * pixelSize * pixelSize
+
+        return fimg
+
+
+
+

ObservationSim/MockObject/SpecDisperser/SpecDisperser.py

@@ -239,7 +239,7 @@ class SpecDisperser(object):
 #                    else:
 #                        beam_flat[k] = self.flat_cube[:, originOut_y + i, originOut_x + j]
 
-        status = disperse.disperse_grism_object(self.thumb_img,
+        status = disperse.disperse_grism_object(self.thumb_img.astype(np.float32),
                                                 flat_index[nonz], yfrac_beam[nonz],
                                                 sensitivity_beam[nonz],
                                                 modelf, x0,

ObservationSim/MockObject/__init__.py

@@ -4,5 +4,6 @@ from .CatalogBase import CatalogBase
 from .Quasar import Quasar
 from .Star import Star
 from .Stamp import Stamp
+from .FlatLED import FlatLED
 # from .SkybackgroundMap import *
 # from .CosmicRay import CosmicRay

ObservationSim/ObservationSim.py

@@ -18,6 +18,7 @@ from ObservationSim.Straylight import calculateSkyMap_split_g
 from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
 from ObservationSim._util import get_shear_field, makeSubDir_PointingList
 from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
+from ObservationSim.MockObject import FlatLED
 
 class Observation(object):
     def __init__(self, config, Catalog, work_dir=None, data_dir=None):
@@ -202,8 +203,8 @@ class Observation(object):
                 obj = self.cat.objs[j]
 
                 # (DEBUG)
-                # if obj.getMagFilter(filt)>20:
-                #     continue
+                if obj.getMagFilter(filt)>20:
+                    continue
 
                 # load and convert SED; also caculate object's magnitude in all CSST bands
                 try:
@@ -412,6 +413,106 @@ class Observation(object):
 
         chip_output.Log_info("check running:2: pointing-%d chip-%d pid-%d memory-%6.2fGB"%(pointing.id, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ))
 
+    def run_one_chip_LEDlight(self, chip, filt, pointing, chip_output, wcs_fp=None, psf_model=None, cat_dir=None, sed_dir=None):
+
+
+
+
+        # # Get WCS for the focal plane
+        # if wcs_fp == None:
+        #     wcs_fp = self.focal_plane.getTanWCS(ra_cen, dec_cen, pointing.img_pa, chip.pix_scale)
+
+        # Create chip Image
+        chip.img = galsim.ImageF(chip.npix_x, chip.npix_y)
+        chip.img.setOrigin(chip.bound.xmin, chip.bound.ymin)
+        # chip.img.wcs = wcs_fp
+
+
+        led_obj = FlatLED(chip, filt)
+
+        led_flat = led_obj.drawObj_LEDFlat(led_type_list=self.config["obs_setting"]["LED_TYPE"], exp_t_list=self.config["obs_setting"]["LED_TIME"])
+        chip.img = chip.img + galsim.ImageF(led_flat)
+
+        # whether to output zero, dark, flat calibration images.
+        sky_map = np.zeros_like(chip.img.array)
+
+        self.config["ins_effects"]["shutter_effect"] = False
+
+        chip.img = chip.addEffects(
+            config=self.config,
+            img=chip.img,
+            chip_output=chip_output,
+            filt=filt,
+            ra_cen=pointing.ra,
+            dec_cen=pointing.dec,
+            img_rot=pointing.img_pa,
+            exptime=self.config["obs_setting"]["exp_time"],
+            pointing_ID=pointing.id,
+            timestamp_obs=pointing.timestamp,
+            pointing_type=pointing.pointing_type,
+            sky_map=sky_map, tel=self.tel,
+            logger=chip_output.logger)
+
+        if pointing.pointing_type == 'MS':
+            datetime_obs = datetime.utcfromtimestamp(pointing.timestamp)
+            date_obs = datetime_obs.strftime("%y%m%d")
+            time_obs = datetime_obs.strftime("%H%M%S")
+            h_prim = generatePrimaryHeader(
+                xlen=chip.npix_x,
+                ylen=chip.npix_y,
+                pointNum=str(pointing.id),
+                ra=pointing.ra,
+                dec=pointing.dec,
+                pixel_scale=chip.pix_scale,
+                date=date_obs,
+                time_obs=time_obs,
+                exptime=self.config["obs_setting"]["exp_time"],
+                im_type='DARKPF',
+                sat_pos=[pointing.sat_x, pointing.sat_y, pointing.sat_z],
+                sat_vel=[pointing.sat_vx, pointing.sat_vy, pointing.sat_vz],
+                chip_name=str(chip.chipID).rjust(2, '0'))
+            h_ext = generateExtensionHeader(
+                chip=chip,
+                xlen=chip.npix_x,
+                ylen=chip.npix_y,
+                ra=pointing.ra,
+                dec=pointing.dec,
+                pa=pointing.img_pa.deg,
+                gain=chip.gain,
+                readout=chip.read_noise,
+                dark=chip.dark_noise,
+                saturation=90000,
+                pixel_scale=chip.pix_scale,
+                pixel_size=chip.pix_size,
+                xcen=chip.x_cen,
+                ycen=chip.y_cen,
+                extName='SCI',
+                timestamp=pointing.timestamp,
+                exptime=self.config["obs_setting"]["exp_time"],
+                readoutTime=chip.readout_time)
+
+            chip.img = galsim.Image(chip.img.array, dtype=np.uint16)
+            hdu1 = fits.PrimaryHDU(header=h_prim)
+            hdu1.add_checksum()
+            hdu1.header.comments['CHECKSUM'] = 'HDU checksum'
+            hdu1.header.comments['DATASUM'] = 'data unit checksum'
+            hdu2 = fits.ImageHDU(chip.img.array, header=h_ext)
+            hdu2.add_checksum()
+            hdu2.header.comments['XTENSION'] = 'extension type'
+            hdu2.header.comments['CHECKSUM'] = 'HDU checksum'
+            hdu2.header.comments['DATASUM'] = 'data unit checksum'
+            hdu1 = fits.HDUList([hdu1, hdu2])
+            fname = os.path.join(chip_output.subdir, h_prim['FILENAME'] + '.fits')
+            hdu1.writeto(fname, output_verify='ignore', overwrite=True)
+
+            # chip_output.Log_info("# objects that are too bright %d out of %d" % (bright_obj, self.nobj))
+            # chip_output.Log_info("# objects that are too dim %d out of %d" % (dim_obj, self.nobj))
+            # chip_output.Log_info("# objects that are missed %d out of %d" % (missed_obj, self.nobj))
+        del chip.img
+
+        chip_output.Log_info("check running:2: pointing-%d chip-%d pid-%d memory-%6.2fGB" % (
+        pointing.id, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024)))
+
     def runExposure_MPI_PointingList(self, pointing_list,chips=None, use_mpi=False):
         if use_mpi:
             comm = MPI.COMM_WORLD
@@ -434,6 +535,7 @@ class Observation(object):
                     run_chips.append(chip)
                     run_filts.append(filt)
 
+
         for ipoint in range(len(pointing_list)):
             for ichip in range(nchips_per_fp):
                 i = ipoint*nchips_per_fp + ichip
@@ -461,11 +563,17 @@ class Observation(object):
                     subdir=sub_img_dir,
                     prefix=prefix)
                 chip_output.Log_info("running pointing#%d, chip#%d, at PID#%d..."%(pointing_ID, chip.chipID, pid))
-                self.run_one_chip(
-                    chip=chip, 
-                    filt=filt, 
-                    chip_output=chip_output, 
-                    pointing=pointing)
+                if self.config["obs_setting"]["survey_type"] == "LED":
+                    self.run_one_chip_LEDlight(chip=chip,
+                                               filt=filt,
+                                               chip_output=chip_output,
+                                               pointing=pointing)
+                else:
+                    self.run_one_chip(
+                        chip=chip,
+                        filt=filt,
+                        chip_output=chip_output,
+                        pointing=pointing)
                 chip_output.Log_info("finished running chip#%d..."%(chip.chipID))
                 for handler in chip_output.logger.handlers[:]:
                     chip_output.logger.removeHandler(handler)

config/config_C6_dev.yaml

@@ -64,7 +64,11 @@ obs_setting:
   # "Spectroscopic": simulate slitless spectroscopic chips only
   # "FGS": simulate FGS chips only (31-42)
   # "All": simulate full focal plane
-  survey_type: "Spectroscopic"
+  # "LED":
+  survey_type: "Photometric"
+
+  LED_TYPE: ["LED2","LED4","LED5"]
+  LED_TIME: [10.,10.,10.]
   
   # Exposure time [seconds]
   exp_time: 150.
@@ -100,7 +104,7 @@ obs_setting:
   # - give a list of indexes of chips: [ip_1, ip_2...]
   # - run all chips: null
   # Note: for all pointings
-  run_chips: [10]
+  run_chips: [8]
 
   # Whether to enable astrometric modeling
   enable_astrometric_model: False
@@ -125,7 +129,7 @@ psf_setting:
   # Which PSF model to use:
   # "Gauss": simple gaussian profile
   # "Interp": Interpolated PSF from sampled ray-tracing data
-  psf_model: "Interp"
+  psf_model: "Gauss"
 
   # PSF size [arcseconds]
   # radius of 80% energy encircled