add renamed files

observation_sim/sim_steps/add_objects.py

+import os
+import gc
+import psutil
+import traceback
+import numpy as np
+import galsim
+from observation_sim._util import get_shear_field
+from observation_sim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
+
+from astropy.time import Time
+from datetime import datetime, timezone
+
+
+def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
+
+    # Get exposure time
+    if (obs_param) and ("exptime" in obs_param) and (obs_param["exptime"] is not None):
+        exptime = obs_param["exptime"]
+    else:
+        exptime = pointing.exp_time
+
+    # Load catalogues
+    if catalog is None:
+        self.chip_output.Log_error(
+            "Catalog interface class must be specified for SCIE-OBS")
+        raise ValueError(
+            "Catalog interface class must be specified for SCIE-OBS")
+    cat = catalog(config=self.overall_config, chip=chip,
+                  pointing=pointing, chip_output=self.chip_output, filt=filt)
+
+    # Prepare output file(s) for this chip
+    # [NOTE] Headers of output .cat file may be updated by Catalog instance
+    # this should be called after the creation of Catalog instance
+    self.chip_output.create_output_file()
+
+    # Prepare the PSF model
+    if self.overall_config["psf_setting"]["psf_model"] == "Gauss":
+        psf_model = PSFGauss(
+            chip=chip, psfRa=self.overall_config["psf_setting"]["psf_rcont"])
+    elif self.overall_config["psf_setting"]["psf_model"] == "Interp":
+        if chip.survey_type == "spectroscopic":
+            psf_model = PSFInterpSLS(
+                chip, filt, PSF_data_prefix=self.overall_config["psf_setting"]["psf_sls_dir"])
+        else:
+            psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples,
+                                  PSF_data_file=self.overall_config["psf_setting"]["psf_pho_dir"])
+    else:
+        self.chip_output.Log_error("unrecognized PSF model type!!", flush=True)
+
+    # Apply field distortion model
+    if obs_param["field_dist"] == True:
+        fd_model = FieldDistortion(chip=chip, img_rot=pointing.img_pa.deg)
+    else:
+        fd_model = None
+
+    # Update limiting magnitudes for all filters based on the exposure time
+    # Get the filter which will be used for magnitude cut
+    for ifilt in range(len(self.all_filters)):
+        temp_filter = self.all_filters[ifilt]
+        temp_filter.update_limit_saturation_mags(
+            exptime=pointing.get_full_depth_exptime(temp_filter.filter_type), chip=chip)
+        if temp_filter.filter_type.lower() == self.overall_config["obs_setting"]["cut_in_band"].lower():
+            cut_filter = temp_filter
+
+    # Read in shear values from configuration file if the constant shear type is used
+    if self.overall_config["shear_setting"]["shear_type"] == "constant":
+        g1_field, g2_field, _ = get_shear_field(config=self.overall_config)
+        self.chip_output.Log_info(
+            "Use constant shear: g1=%.5f, g2=%.5f" % (g1_field, g2_field))
+
+    # Get chip WCS
+    if not hasattr(self, 'h_ext'):
+        _, _ = self.prepare_headers(chip=chip, pointing=pointing)
+
+    chip_wcs = galsim.FitsWCS(header=self.h_ext)
+
+    # Loop over objects
+    nobj = len(cat.objs)
+    missed_obj = 0
+    bright_obj = 0
+    dim_obj = 0
+    for j in range(nobj):
+        # # [DEBUG] [TODO]
+        # if j >= 10:
+        #     break
+        obj = cat.objs[j]
+
+        # load and convert SED; also caculate object's magnitude in all CSST bands
+        try:
+            sed_data = cat.load_sed(obj)
+            norm_filt = cat.load_norm_filt(obj)
+            obj.sed, obj.param["mag_%s" % filt.filter_type.lower()], obj.param["flux_%s" % filt.filter_type.lower()] = cat.convert_sed(
+                mag=obj.param["mag_use_normal"],
+                sed=sed_data,
+                target_filt=filt,
+                norm_filt=norm_filt,
+                mu=obj.mu
+            )
+            _, obj.param["mag_%s" % cut_filter.filter_type.lower()], obj.param["flux_%s" % cut_filter.filter_type.lower()] = cat.convert_sed(
+                mag=obj.param["mag_use_normal"],
+                sed=sed_data,
+                target_filt=cut_filter,
+                norm_filt=norm_filt,
+                mu=obj.mu
+            )
+        except Exception as e:
+            traceback.print_exc()
+            self.chip_output.Log_error(e)
+            continue
+
+        # [TODO] Testing
+        # self.chip_output.Log_info("mag_%s = %.3f"%(filt.filter_type.lower(), obj.param["mag_%s"%filt.filter_type.lower()]))
+
+        # Exclude very bright/dim objects (for now)
+        if cut_filter.is_too_bright(
+                mag=obj.param["mag_%s" %
+                              self.overall_config["obs_setting"]["cut_in_band"].lower()],
+                margin=self.overall_config["obs_setting"]["mag_sat_margin"]):
+            self.chip_output.Log_info("obj %s too birght!! mag_%s = %.3f" % (
+                obj.id, cut_filter.filter_type, obj.param["mag_%s" % self.overall_config["obs_setting"]["cut_in_band"].lower()]))
+            bright_obj += 1
+            obj.unload_SED()
+            continue
+        if filt.is_too_dim(
+                mag=obj.getMagFilter(filt),
+                margin=self.overall_config["obs_setting"]["mag_lim_margin"]):
+            self.chip_output.Log_info("obj %s too dim!! mag_%s = %.3f" % (
+                obj.id, filt.filter_type, obj.getMagFilter(filt)))
+            dim_obj += 1
+            obj.unload_SED()
+            continue
+
+        # Get corresponding shear values
+        if self.overall_config["shear_setting"]["shear_type"] == "constant":
+            if obj.type == 'star':
+                obj.g1, obj.g2 = 0., 0.
+            else:
+                # Figure out shear fields from overall configuration shear setting
+                obj.g1, obj.g2 = g1_field, g2_field
+        elif self.overall_config["shear_setting"]["shear_type"] == "catalog":
+            pass
+        else:
+            self.chip_output.Log_error("Unknown shear input")
+            raise ValueError("Unknown shear input")
+
+        # Get position of object on the focal plane
+        pos_img, _, _, _, fd_shear = obj.getPosImg_Offset_WCS(
+            img=chip.img, fdmodel=fd_model, chip=chip, verbose=False, chip_wcs=chip_wcs, img_header=self.h_ext)
+
+        # [TODO] For now, only consider objects which their centers (after field distortion) are projected within the focal plane
+        # Otherwise they will be considered missed objects
+        # if pos_img.x == -1 or pos_img.y == -1 or (not chip.isContainObj(x_image=pos_img.x, y_image=pos_img.y, margin=0.)):
+        if pos_img.x == -1 or pos_img.y == -1:
+            self.chip_output.Log_info('obj_ra = %.6f, obj_dec = %.6f, obj_ra_orig = %.6f, obj_dec_orig = %.6f' % (
+                obj.ra, obj.dec, obj.ra_orig, obj.dec_orig))
+            self.chip_output.Log_error("Objected missed: %s" % (obj.id))
+            missed_obj += 1
+            obj.unload_SED()
+            continue
+
+        # Draw object & update output catalog
+        try:
+            if self.overall_config["run_option"]["out_cat_only"]:
+                isUpdated = True
+                obj.real_pos = obj.getRealPos(
+                    chip.img, global_x=obj.posImg.x, global_y=obj.posImg.y, img_real_wcs=obj.chip_wcs)
+                pos_shear = 0.
+            elif chip.survey_type == "photometric" and not self.overall_config["run_option"]["out_cat_only"]:
+                isUpdated, pos_shear = obj.drawObj_multiband(
+                    tel=tel,
+                    pos_img=pos_img,
+                    psf_model=psf_model,
+                    bandpass_list=filt.bandpass_sub_list,
+                    filt=filt,
+                    chip=chip,
+                    g1=obj.g1,
+                    g2=obj.g2,
+                    exptime=exptime,
+                    fd_shear=fd_shear)
+
+            elif chip.survey_type == "spectroscopic" and not self.overall_config["run_option"]["out_cat_only"]:
+                isUpdated, pos_shear = obj.drawObj_slitless(
+                    tel=tel,
+                    pos_img=pos_img,
+                    psf_model=psf_model,
+                    bandpass_list=filt.bandpass_sub_list,
+                    filt=filt,
+                    chip=chip,
+                    g1=obj.g1,
+                    g2=obj.g2,
+                    exptime=exptime,
+                    normFilter=norm_filt,
+                    fd_shear=fd_shear)
+
+            if isUpdated == 1:
+                # TODO: add up stats
+                self.chip_output.cat_add_obj(obj, pos_img, pos_shear)
+                pass
+            elif isUpdated == 0:
+                missed_obj += 1
+                self.chip_output.Log_error("Objected missed: %s" % (obj.id))
+            else:
+                self.chip_output.Log_error(
+                    "Draw error, object omitted: %s" % (obj.id))
+                continue
+        except Exception as e:
+            traceback.print_exc()
+            self.chip_output.Log_error(e)
+            self.chip_output.Log_error(
+                "pointing: #%d, chipID: %d" % (pointing.id, chip.chipID))
+            if obj.type == "galaxy":
+                self.chip_output.Log_error("obj id: %s" % (obj.param['id']))
+                self.chip_output.Log_error("    e1: %.5f\n    e2: %.5f\n    size: %f\n    bfrac: %f\n    detA: %f\n    g1: %.5f\n    g2: %.5f\n" % (
+                    obj.param['e1'], obj.param['e2'], obj.param['size'], obj.param['bfrac'], obj.param['detA'], obj.param['g1'], obj.param['g2']))
+
+        # Unload SED:
+        obj.unload_SED()
+        del obj
+        gc.collect()
+    del psf_model
+    gc.collect()
+
+    self.chip_output.Log_info("Running checkpoint #1 (Object rendering finished): 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)))
+
+    self.chip_output.Log_info(
+        "# objects that are too bright %d out of %d" % (bright_obj, nobj))
+    self.chip_output.Log_info(
+        "# objects that are too dim %d out of %d" % (dim_obj, nobj))
+    self.chip_output.Log_info(
+        "# objects that are missed %d out of %d" % (missed_obj, nobj))
+
+    # Apply flat fielding (with shutter effects)
+    flat_normal = np.ones_like(chip.img.array)
+    if obs_param["flat_fielding"] == True:
+        flat_normal = flat_normal * chip.flat_img.array / \
+            np.mean(chip.flat_img.array)
+    if obs_param["shutter_effect"] == True:
+        flat_normal = flat_normal * chip.shutter_img
+        flat_normal = np.array(flat_normal, dtype='float32')
+        self.updateHeaderInfo(header_flag='ext', keys=[
+                              'SHTSTAT'], values=[True])
+    else:
+        self.updateHeaderInfo(header_flag='ext', keys=['SHTSTAT', 'SHTOPEN1', 'SHTCLOS0'], values=[
+                              True, self.h_ext['SHTCLOS1'], self.h_ext['SHTOPEN0']])
+    chip.img *= flat_normal
+    del flat_normal
+
+    # renew header info
+    datetime_obs = datetime.utcfromtimestamp(pointing.timestamp)
+    datetime_obs = datetime_obs.replace(tzinfo=timezone.utc)
+    t_obs = Time(datetime_obs)
+
+    # ccd刷新2s,等待0.s，开始曝光
+    t_obs_renew = Time(t_obs.mjd - (2.+0.) / 86400., format="mjd")
+
+    t_obs_utc = datetime.utcfromtimestamp(np.round(datetime.utcfromtimestamp(
+        t_obs_renew.unix).replace(tzinfo=timezone.utc).timestamp(), 1))
+    self.updateHeaderInfo(header_flag='prim', keys=[
+                          'DATE-OBS'], values=[t_obs_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-5]])
+
+    # dark time : 曝光时间+刷新后等带时间0.s+关快门后读出前等待0.s
+    self.updateHeaderInfo(header_flag='ext', keys=[
+                          'DARKTIME'], values=[0.+0.+pointing.exp_time])
+
+    return chip, filt, tel, pointing

observation_sim/sim_steps/add_pattern_noise.py

+from numpy.random import Generator, PCG64
+from observation_sim.instruments.chip import chip_utils
+from observation_sim.instruments.chip import effects
+
+
+def apply_PRNU(self, chip, filt, tel, pointing, catalog, obs_param):
+    chip.img *= chip.prnu_img
+    if self.overall_config["output_setting"]["prnu_output"] == True:
+        chip.prnu_img.write("%s/FlatImg_PRNU_%s.fits" %
+                            (self.chip_output.subdir, str(chip.chipID).rjust(2, '0')))
+    return chip, filt, tel, pointing
+
+
+def add_poisson_and_dark(self, chip, filt, tel, pointing, catalog, obs_param):
+    # Add dark current & Poisson noise
+    # Get exposure time
+    if (obs_param) and ("exptime" in obs_param) and (obs_param["exptime"] is not None):
+        exptime = obs_param["exptime"]
+    else:
+        exptime = pointing.exp_time
+
+    if obs_param["add_dark"] == True:
+        chip.img, _ = chip_utils.add_poisson(img=chip.img,
+                                             chip=chip,
+                                             exptime=pointing.exp_time,
+                                             poisson_noise=chip.poisson_noise,
+                                             InputDark=None)
+    else:
+        chip.img, _ = chip_utils.add_poisson(img=chip.img,
+                                             chip=self,
+                                             exptime=exptime,
+                                             poisson_noise=chip.poisson_noise,
+                                             dark_noise=0.)
+    return chip, filt, tel, pointing
+
+
+def add_detector_defects(self, chip, filt, tel, pointing, catalog, obs_param):
+    # Add Hot Pixels or/and Dead Pixels
+    rgbadpix = Generator(
+        PCG64(int(self.overall_config["random_seeds"]["seed_defective"]+chip.chipID)))
+    badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
+    chip.img = effects.DefectivePixels(
+        chip.img,
+        IfHotPix=obs_param["hot_pixels"],
+        IfDeadPix=obs_param["dead_pixels"],
+        fraction=badfraction,
+        seed=self.overall_config["random_seeds"]["seed_defective"]+chip.chipID, biaslevel=0)
+    # Apply Bad columns
+    if obs_param["bad_columns"] == True:
+        chip.img = effects.BadColumns(chip.img,
+                                      seed=self.overall_config["random_seeds"]["seed_badcolumns"],
+                                      chipid=chip.chipID)
+    return chip, filt, tel, pointing
+
+
+def add_nonlinearity(self, chip, filt, tel, pointing, catalog, obs_param):
+    self.chip_output.Log_info("  Applying Non-Linearity on the chip image")
+    chip.img = effects.NonLinearity(GSImage=chip.img,
+                                    beta1=5.e-7,
+                                    beta2=0)
+    return chip, filt, tel, pointing
+
+
+def add_blooming(self, chip, filt, tel, pointing, catalog, obs_param):
+    self.chip_output.Log_info("  Applying CCD Saturation & Blooming")
+    chip.img = effects.SaturBloom(GSImage=chip.img,
+                                  nsect_x=1,
+                                  nsect_y=1,
+                                  fullwell=int(chip.full_well))
+    return chip, filt, tel, pointing
+
+
+def add_bias(self, chip, filt, tel, pointing, catalog, obs_param):
+    self.chip_output.Log_info(
+        "  Adding Bias level and 16-channel non-uniformity")
+    if obs_param["bias_16channel"] == True:
+        chip.img = effects.AddBiasNonUniform16(chip.img,
+                                               bias_level=float(
+                                                   chip.bias_level),
+                                               nsecy=chip.nsecy,
+                                               nsecx=chip.nsecx,
+                                               seed=self.overall_config["random_seeds"]["seed_biasNonUniform"]+chip.chipID)
+    elif obs_param["bias_16channel"] == False:
+        chip.img += self.bias_level
+    return chip, filt, tel, pointing

observation_sim/sim_steps/add_sky_background.py

+import numpy as np
+import galsim
+from observation_sim.sky_background import calculateSkyMap_split_g
+from observation_sim.instruments import FilterParam
+
+from astropy.time import Time
+from datetime import datetime, timezone
+
+
+def add_sky_background_sci(self, chip, filt, tel, pointing, catalog, obs_param):
+
+    # Get exposure time
+    if (obs_param) and ("exptime" in obs_param) and (obs_param["exptime"] is not None):
+        exptime = obs_param["exptime"]
+    else:
+        exptime = pointing.exp_time
+
+    flat_normal = np.ones_like(chip.img.array)
+    if obs_param["flat_fielding"] == True:
+        flat_normal = flat_normal * chip.flat_img.array / \
+            np.mean(chip.flat_img.array)
+    if obs_param["shutter_effect"] == True:
+        flat_normal = flat_normal * chip.shutter_img
+        flat_normal = np.array(flat_normal, dtype='float32')
+        self.updateHeaderInfo(header_flag='ext', keys=[
+                              'SHTSTAT'], values=[True])
+    else:
+        self.updateHeaderInfo(header_flag='ext', keys=['SHTSTAT', 'SHTOPEN1', 'SHTCLOS0'], values=[
+                              True, self.h_ext['SHTCLOS1'], self.h_ext['SHTOPEN0']])
+
+    if obs_param["enable_straylight_model"]:
+        # Filter.sky_background, Filter.zodical_spec will be updated
+        filt.setFilterStrayLightPixel(
+            jtime=pointing.jdt,
+            sat_pos=np.array([pointing.sat_x, pointing.sat_y, pointing.sat_z]),
+            pointing_radec=np.array([pointing.ra, pointing.dec]),
+            sun_pos=np.array([pointing.sun_x, pointing.sun_y, pointing.sun_z]))
+        self.chip_output.Log_info(
+            "================================================")
+        self.chip_output.Log_info(
+            "sky background + stray light pixel flux value: %.5f" % (filt.sky_background))
+
+    if chip.survey_type == "photometric":
+        sky_map = filt.getSkyNoise(exptime=exptime)
+        sky_map = sky_map * np.ones_like(chip.img.array) * flat_normal
+        sky_map = galsim.Image(array=sky_map)
+    else:
+        # chip.loadSLSFLATCUBE(flat_fn='flat_cube.fits')
+        sky_map = calculateSkyMap_split_g(
+            skyMap=flat_normal,
+            blueLimit=filt.blue_limit,
+            redLimit=filt.red_limit,
+            conf=chip.sls_conf,
+            pixelSize=chip.pix_scale,
+            isAlongY=0,
+            flat_cube=chip.flat_cube,
+            zoldial_spec=filt.zodical_spec)
+        sky_map = (sky_map + filt.sky_background)*exptime
+
+    # sky_map = sky_map * tel.pupil_area * obs_param["exptime"]
+    chip.img += sky_map
+    return chip, filt, tel, pointing
+
+
+def add_sky_flat_calibration(self, chip, filt, tel, pointing, catalog, obs_param):
+
+    if not hasattr(self, 'h_ext'):
+        _, _ = self.prepare_headers(chip=chip, pointing=pointing)
+    chip_wcs = galsim.FitsWCS(header=self.h_ext)
+
+    # Get exposure time
+    if (obs_param) and ("exptime" in obs_param) and (obs_param["exptime"] is not None):
+        exptime = obs_param["exptime"]
+    else:
+        exptime = pointing.exp_time
+
+    skyback_level = obs_param["flat_level"]
+
+    filter_param = FilterParam()
+    sky_level_filt = obs_param["flat_level_filt"]
+    norm_scaler = skyback_level/exptime / filter_param.param[sky_level_filt][5]
+
+    flat_normal = np.ones_like(chip.img.array)
+    if obs_param["flat_fielding"] == True:
+        flat_normal = flat_normal * chip.flat_img.array / \
+            np.mean(chip.flat_img.array)
+    if obs_param["shutter_effect"] == True:
+        flat_normal = flat_normal * chip.shutter_img
+        flat_normal = np.array(flat_normal, dtype='float32')
+        # output 16-bit shutter effect image with pixel value <=65535
+        if self.overall_config["output_setting"]["shutter_output"] == True:
+            shutt_gsimg = galsim.ImageUS(chip.shutter_img*6E4)
+            shutt_gsimg.write("%s/ShutterEffect_%s_1.fits" %
+                              (self.chip_output.subdir, str(chip.chipID).rjust(2, '0')))
+        self.updateHeaderInfo(header_flag='ext', keys=[
+                              'SHTSTAT'], values=[True])
+    else:
+        self.updateHeaderInfo(header_flag='ext', keys=['SHTSTAT', 'SHTOPEN1', 'SHTCLOS0'], values=[
+                              True, self.h_ext['SHTCLOS1'], self.h_ext['SHTOPEN0']])
+
+    if chip.survey_type == "photometric":
+        sky_map = flat_normal * np.ones_like(chip.img.array) * norm_scaler * \
+            filter_param.param[chip.filter_type][5] / tel.pupil_area * exptime
+    elif chip.survey_type == "spectroscopic":
+        # flat_normal = np.ones_like(chip.img.array)
+        if obs_param["flat_fielding"] == True:
+
+            flat_normal = flat_normal * chip.flat_img.array / \
+                np.mean(chip.flat_img.array)
+        if obs_param["shutter_effect"] == True:
+
+            flat_normal = flat_normal * chip.shutter_img
+            flat_normal = np.array(flat_normal, dtype='float32')
+        sky_map = calculateSkyMap_split_g(
+            skyMap=flat_normal,
+            blueLimit=filt.blue_limit,
+            redLimit=filt.red_limit,
+            conf=chip.sls_conf,
+            pixelSize=chip.pix_scale,
+            isAlongY=0,
+            flat_cube=chip.flat_cube)
+        sky_map = sky_map * norm_scaler * exptime
+
+    chip.img += sky_map
+    return chip, filt, tel, pointing
+
+
+def add_sky_background(self, chip, filt, tel, pointing, catalog, obs_param):
+    if not hasattr(self, 'h_ext'):
+        _, _ = self.prepare_headers(chip=chip, pointing=pointing)
+    chip_wcs = galsim.FitsWCS(header=self.h_ext)
+
+    if "flat_level" not in obs_param or "flat_level_filt" not in obs_param:
+        chip, filt, tel, pointing = self.add_sky_background_sci(
+            chip, filt, tel, pointing, catalog, obs_param)
+    else:
+        if obs_param.get('flat_level') is None or obs_param.get('flat_level_filt') is None:
+            chip, filt, tel, pointing = self.add_sky_background_sci(
+                chip, filt, tel, pointing, catalog, obs_param)
+        else:
+            chip, filt, tel, pointing = self.add_sky_flat_calibration(
+                chip, filt, tel, pointing, catalog, obs_param)
+
+    # renew header info
+    datetime_obs = datetime.utcfromtimestamp(pointing.timestamp)
+    datetime_obs = datetime_obs.replace(tzinfo=timezone.utc)
+    t_obs = Time(datetime_obs)
+
+    # ccd刷新2s,等待0.5s，开始曝光
+    t_obs_renew = Time(t_obs.mjd - (2.+0.5) / 86400., format="mjd")
+
+    t_obs_utc = datetime.utcfromtimestamp(np.round(datetime.utcfromtimestamp(
+        t_obs_renew.unix).replace(tzinfo=timezone.utc).timestamp(), 1))
+    self.updateHeaderInfo(header_flag='prim', keys=[
+                          'DATE-OBS'], values=[t_obs_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-5]])
+
+    # dark time : 曝光时间+刷新后等带时间0.5s+关闭快门时间1.5s+管快门后读出前等待0.5s
+    self.updateHeaderInfo(header_flag='ext', keys=['DARKTIME'], values=[
+                          0.+0.0+0.0+pointing.exp_time])
+
+    return chip, filt, tel, pointing

observation_sim/sim_steps/prepare_headers.py

+from observation_sim.config.header import generatePrimaryHeader, generateExtensionHeader
+
+
+def prepare_headers(self, chip, pointing):
+    self.h_prim = generatePrimaryHeader(
+        xlen=chip.npix_x,
+        ylen=chip.npix_y,
+        pointing_id=pointing.obs_id,
+        pointing_type_code=pointing.pointing_type_code,
+        ra=pointing.ra,
+        dec=pointing.dec,
+        pixel_scale=chip.pix_scale,
+        time_pt=pointing.timestamp,
+        exptime=pointing.exp_time,
+        im_type=pointing.pointing_type,
+        sat_pos=[pointing.sat_x, pointing.sat_y, pointing.sat_z],
+        sat_vel=[pointing.sat_vx, pointing.sat_vy, pointing.sat_vz],
+        project_cycle=self.overall_config["project_cycle"],
+        run_counter=self.overall_config["run_counter"],
+        chip_name=str(chip.chipID).rjust(2, '0'))
+    self.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=pointing.pointing_type,
+        timestamp=pointing.timestamp,
+        exptime=pointing.exp_time,
+        readoutTime=chip.readout_time,
+        t_shutter_open=pointing.t_shutter_open,
+        t_shutter_close=pointing.t_shutter_close)
+
+    return self.h_prim, self.h_ext
+
+
+def updateHeaderInfo(self, header_flag='prim', keys=['key'], values=[0]):
+    if header_flag == 'prim':
+        for key, value in zip(keys, values):
+            self.h_prim[key] = value
+    if header_flag == 'ext':
+        for key, value in zip(keys, values):
+            self.h_ext[key] = value

observation_sim/sim_steps/readout_output.py

+import os
+import galsim
+import numpy as np
+from astropy.io import fits
+from observation_sim.instruments.chip import chip_utils
+from observation_sim.instruments.chip import effects
+
+from astropy.time import Time
+from datetime import datetime, timezone
+
+
+def add_prescan_overscan(self, chip, filt, tel, pointing, catalog, obs_param):
+    self.chip_output.Log_info("Apply pre/over-scan")
+    chip.img = chip_utils.AddPreScan(GSImage=chip.img,
+                                     pre1=chip.prescan_x,
+                                     pre2=chip.prescan_y,
+                                     over1=chip.overscan_x,
+                                     over2=chip.overscan_y)
+
+    if obs_param["add_dark"] == True:
+        ny = int(chip.npix_y/2)
+        base_dark = (ny-1)*(chip.readout_time/ny)*chip.dark_noise
+        chip.img.array[(chip.prescan_y+ny):-(chip.prescan_y+ny), :] = base_dark
+    return chip, filt, tel, pointing
+
+
+def add_readout_noise(self, chip, filt, tel, pointing, catalog, obs_param):
+    seed = int(self.overall_config["random_seeds"]
+               ["seed_readout"]) + pointing.id*30 + chip.chipID
+    rng_readout = galsim.BaseDeviate(seed)
+    readout_noise = galsim.GaussianNoise(
+        rng=rng_readout, sigma=chip.read_noise)
+    chip.img.addNoise(readout_noise)
+    return chip, filt, tel, pointing
+
+
+def apply_gain(self, chip, filt, tel, pointing, catalog, obs_param):
+    self.chip_output.Log_info("  Applying Gain")
+    if obs_param["gain_16channel"] == True:
+        chip.img, chip.gain_channel = effects.ApplyGainNonUniform16(chip.img,
+                                                                    gain=chip.gain,
+                                                                    nsecy=chip.nsecy,
+                                                                    nsecx=chip.nsecx,
+                                                                    seed=self.overall_config["random_seeds"]["seed_gainNonUniform"]+chip.chipID)
+    elif obs_param["gain_16channel"] == False:
+        chip.img /= chip.gain
+    return chip, filt, tel, pointing
+
+
+def quantization_and_output(self, chip, filt, tel, pointing, catalog, obs_param):
+
+    if not hasattr(self, 'h_ext'):
+        _, _ = self.prepare_headers(chip=chip, pointing=pointing)
+        self.updateHeaderInfo(header_flag='ext', keys=['SHTSTAT', 'SHTOPEN1', 'SHTCLOS0', 'SHTCLOS1', 'EXPTIME'], values=[
+                              False, self.h_ext['SHTOPEN0'], self.h_ext['SHTOPEN0'], self.h_ext['SHTOPEN0'], 0.0])
+        # renew header info
+        datetime_obs = datetime.utcfromtimestamp(pointing.timestamp)
+        datetime_obs = datetime_obs.replace(tzinfo=timezone.utc)
+        t_obs = Time(datetime_obs)
+
+        # ccd刷新2s,等待0.5s，开灯后等待0.5s,开始曝光
+        t_obs_renew = Time(t_obs.mjd - 2. / 86400., format="mjd")
+
+        t_obs_utc = datetime.utcfromtimestamp(np.round(datetime.utcfromtimestamp(
+            t_obs_renew.unix).replace(tzinfo=timezone.utc).timestamp(), 1))
+        self.updateHeaderInfo(header_flag='prim', keys=[
+                              'DATE-OBS'], values=[t_obs_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-5]])
+
+    gains1 = list(chip.gain_channel[0:8])
+    gains2 = list(chip.gain_channel[8:])
+    gains2.reverse()
+    gains = np.append(gains1, gains2)
+    self.updateHeaderInfo(header_flag='ext', keys=['GAIN01', 'GAIN02', 'GAIN03', 'GAIN04', 'GAIN05', 'GAIN06', 'GAIN07',
+                          'GAIN08', 'GAIN09', 'GAIN10', 'GAIN11', 'GAIN12', 'GAIN13', 'GAIN14', 'GAIN15', 'GAIN16'], values=gains)
+
+    if obs_param["format_output"] == True:
+        self.chip_output.Log_info("  Apply 1*16 format")
+        chip.img = chip_utils.formatOutput(GSImage=chip.img)
+        chip.nsecy = 1
+        chip.nsecx = 16
+
+    chip.img.array[chip.img.array > 65535] = 65535
+    chip.img.replaceNegative(replace_value=0)
+    chip.img.quantize()
+    chip.img = galsim.Image(chip.img.array, dtype=np.uint16)
+    fname = os.path.join(self.chip_output.subdir,
+                         self.h_prim['FILENAME'] + '.fits')
+
+    f_name_size = 68
+    if (len(self.h_prim['FILENAME']) > f_name_size):
+        self.updateHeaderInfo(header_flag='prim', keys=['FILENAME'], values=[
+                              self.h_prim['FILENAME'][0:f_name_size]])
+
+    hdu1 = fits.PrimaryHDU(header=self.h_prim)
+
+    self.updateHeaderInfo(header_flag='ext', keys=['DATASECT'], values=[
+                          str(chip.img.array.shape[1]) + 'x' + str(chip.img.array.shape[0])])
+    hdu2 = fits.ImageHDU(chip.img.array, header=self.h_ext)
+    hdu2.header.comments["XTENSION"] = "image extension"
+
+    hdu = fits.HDUList([hdu1, hdu2])
+    hdu[0].add_datasum(when='data unit checksum')
+    hdu[0].add_checksum(when='HDU checksum', override_datasum=True)
+    hdu[1].add_datasum(when='data unit checksum')
+    hdu[1].add_checksum(when='HDU checksum', override_datasum=True)
+    hdu.writeto(fname, output_verify='ignore', overwrite=True)
+
+    return chip, filt, tel, pointing

observation_sim/sky_background/SkybackgroundMap.py

+from observation_sim.mock_objects.SpecDisperser import SpecDisperser
+from observation_sim.mock_objects.SpecDisperser import rotate90
+
+import galsim
+import numpy as np
+from astropy.table import Table
+from scipy import interpolate
+
+import galsim
+import astropy.constants as cons
+
+import os
+
+import time
+
+try:
+    import importlib.resources as pkg_resources
+except ImportError:
+    # Try backported to PY<37 'importlib_resources'
+    import importlib_resources as pkg_resources
+
+
+# calculate sky map by sky SED
+
+def calculateSkyMap_split_g(skyMap=None, blueLimit=4200, redLimit=6500, skyfn='sky_emiss_hubble_50_50_A.dat', conf=[''], pixelSize=0.074, isAlongY=0,
+                            split_pos=3685, flat_cube=None, zoldial_spec=None):
+    # skyMap = np.ones([yLen, xLen], dtype='float32')
+    #
+    # if isAlongY == 1:
+    #     skyMap = np.ones([xLen, yLen], dtype='float32')
+
+    # for i in range(len(conf)):
+    #     conf[i] = os.path.join(SLSSIM_PATH, conf[i])
+    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)
+    try:
+        with pkg_resources.files('observation_sim.sky_background.data.sky').joinpath(skyfn) as data_path:
+            skySpec = np.loadtxt(data_path)
+    except AttributeError:
+        with pkg_resources.path('observation_sim.sky_background.data.sky', skyfn) as data_path:
+            skySpec = np.loadtxt(data_path)
+    # skySpec = np.loadtxt(skyfn)
+    spec = Table(np.array([skySpec[:, 0], skySpec[:, 1]]
+                          ).T, names=('WAVELENGTH', 'FLUX'))
+
+    if zoldial_spec is not None:
+        deltL = 0.5
+        lamb = np.arange(2000, 11000, deltL)
+
+        speci = interpolate.interp1d(
+            zoldial_spec['WAVELENGTH'], zoldial_spec['FLUX'])
+
+        y = speci(lamb)
+        # erg/s/cm2/A --> photo/s/m2/A
+        s_flux = y * lamb / (cons.h.value * cons.c.value) * 1e-13
+        spec = Table(np.array([lamb, s_flux]).T, names=('WAVELENGTH', 'FLUX'))
+    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]
+
+        # sdp = SpecDisperser(orig_img=skyImg1, xcenter=skyImg1.center.x, ycenter=skyImg1.center.y, origin=origin1,
+        #                 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:
+
+        # skyImg1 = galsim.Image(skyImg.array[:, 0:split_pos])
+        # origin1 = [0, 0]
+        # skyImg2 = galsim.Image(skyImg.array[:, split_pos:])
+        # origin2 = [0, split_pos]
+
+        # 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 = 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
+
+
+def calculateSkyMap(xLen=9232, yLen=9126, blueLimit=4200, redLimit=6500,
+                    skyfn='sky_emiss_hubble_50_50_A.dat', conf='', pixelSize=0.074, isAlongY=0):
+    skyMap = np.ones([yLen, xLen], dtype='float32')
+
+    if isAlongY == 1:
+        skyMap = np.ones([xLen, yLen], dtype='float32')
+
+    skyImg = galsim.Image(skyMap)
+
+    tbstart = blueLimit
+    tbend = redLimit
+
+    fimg = np.zeros_like(skyMap)
+    fImg = galsim.Image(fimg)
+    try:
+        with pkg_resources.files('observation_sim.sky_background.data.sky').joinpath(skyfn) as data_path:
+            skySpec = np.loadtxt(data_path)
+    except AttributeError:
+        with pkg_resources.path('observation_sim.sky_background.data.sky', skyfn) as data_path:
+            skySpec = np.loadtxt(data_path)
+    # skySpec = np.loadtxt(skyfn)
+
+    spec = Table(np.array([skySpec[:, 0], skySpec[:, 1]]
+                          ).T, names=('WAVELENGTH', 'FLUX'))
+
+    sdp = SpecDisperser(orig_img=skyImg, xcenter=skyImg.center.x, ycenter=skyImg.center.y, origin=[1, 1],
+                        tar_spec=spec,
+                        band_start=tbstart, band_end=tbend,
+                        conf=conf)
+
+    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
+        fImg[bounds] = fImg[bounds] + ssImg[bounds]
+
+    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

observation_sim/sky_background/Straylight.py

+import ctypes
+import numpy as np
+import astropy.constants as cons
+from scipy import interpolate
+import math
+from astropy.table import Table
+
+import astropy.coordinates as coord
+from astropy import units as u
+import sys
+
+try:
+    import importlib.resources as pkg_resources
+except ImportError:
+    # Try backported to PY<37 'importlib_resources'
+    import importlib_resources as pkg_resources
+
+filterPivotWave = {'nuv': 2875.5, 'u': 3629.6, 'g': 4808.4,
+                   'r': 6178.2, 'i': 7609.0, 'z': 9012.9, 'y': 9627.9}
+filterIndex = {'nuv': 0, 'u': 1, 'g': 2, 'r': 3, 'i': 4, 'z': 5, 'y': 6}
+filterCCD = {'nuv': 'UV0', 'u': 'UV0', 'g': 'Astro_MB',
+             'r': 'Astro_MB', 'i': 'Basic_NIR', 'z': 'Basic_NIR', 'y': 'Basic_NIR'}
+bandRange = {'nuv': [2504.0, 3230.0], 'u': [3190.0, 4039.0], 'g': [3989.0, 5498.0], 'r': [5438.0, 6956.0], 'i': [
+    6886.0, 8469.0], 'z': [8379.0, 10855.0], 'y': [9217.0, 10900.0], 'GU': [2550, 4000], 'GV': [4000, 6200], 'GI': [6200, 10000]}
+# Instrument_dir = '/Users/zhangxin/Work/SlitlessSim/CSST_SIM/CSST_C6/straylight/straylight/Instrument/'
+SpecOrder = ['-2', '-1', '0', '1', '2']
+
+filterMirrorEff = {'nuv': 0.54, 'u': 0.68, 'g': 0.8,
+                   'r': 0.8, 'i': 0.8, 'z': 0.8, 'y': 0.8}
+
+
+def transRaDec2D(ra, dec):
+    x1 = np.cos(dec / 57.2957795) * np.cos(ra / 57.2957795)
+    y1 = np.cos(dec / 57.2957795) * np.sin(ra / 57.2957795)
+    z1 = np.sin(dec / 57.2957795)
+    return np.array([x1, y1, z1])
+
+
+def getAngle132(x1=0, y1=0, z1=0, x2=0, y2=0, z2=0, x3=0, y3=0, z3=0):
+
+    cosValue = 0
+    angle = 0
+
+    x11 = x1-x3
+    y11 = y1-y3
+    z11 = z1-z3
+
+    x22 = x2-x3
+    y22 = y2-y3
+    z22 = z2-z3
+
+    tt = np.sqrt((x11*x11 + y11*y11 + z11 * z11)
+                 * (x22*x22 + y22*y22 + z22*z22))
+    if (tt == 0):
+        return 0
+
+    cosValue = (x11*x22+y11*y22+z11*z22)/tt
+
+    if (cosValue > 1):
+        cosValue = 1
+    if (cosValue < -1):
+        cosValue = -1
+    angle = math.acos(cosValue)
+    return angle * 360 / (2 * math.pi)
+
+
+def calculateAnglePwithEarth(sat=np.array([0, 0, 0]), pointing=np.array([0, 0, 0]), sun=np.array([0, 0, 0])):
+    modSat = np.sqrt(sat[0]*sat[0] + sat[1]*sat[1]+sat[2]*sat[2])
+    modPoint = np.sqrt(pointing[0]*pointing[0] +
+                       pointing[1]*pointing[1] + pointing[2]*pointing[2])
+    withLocalZenithAngle = (
+        pointing[0] * sat[0] + pointing[1] * sat[1] + pointing[2] * sat[2]) / (modPoint*modSat)
+
+    innerM_sat_sun = sat[0] * sun[0] + sat[1] * sun[1] + sat[2] * sun[2]
+    cosAngle = innerM_sat_sun / (modSat * cons.au.value/1000)
+    isInSunSide = 1
+    if (cosAngle < -0.3385737):  # cos109.79
+        isInSunSide = -1
+    elif cosAngle >= -0.3385737 and cosAngle <= 0.3385737:
+        isInSunSide = 0
+
+    return math.acos(withLocalZenithAngle)*180/math.pi, isInSunSide
+
+
+# /**
+#  * *eCoor = ra, *eCoor+1 = dec
+#  */
+def Cartesian2Equatorial(carCoor=np.array([0, 0, 0])):
+    eCoor = np.zeros(2)
+    if (carCoor[0] > 0 and carCoor[1] >= 0):
+        eCoor[0] = math.atan(carCoor[1] / carCoor[0]) * 360 / (2 * math.pi)
+    elif (carCoor[0] < 0):
+        eCoor[0] = (math.atan(carCoor[1] / carCoor[0]) +
+                    math.pi) * 360 / (2 * math.pi)
+    elif (carCoor[0] > 0 and carCoor[1] < 0):
+        eCoor[0] = (math.atan(carCoor[1] / carCoor[0]) +
+                    2 * math.pi) * 360 / (2 * math.pi)
+    elif (carCoor[0] == 0 and carCoor[1] < 0):
+        eCoor[0] = 270
+    elif (carCoor[0] == 0 and carCoor[1] > 0):
+        eCoor[0] = 90
+    eCoor[1] = math.atan(carCoor[2] / np.sqrt(carCoor[0] *
+                         carCoor[0] + carCoor[1] * carCoor[1])) * 360 / (2 * math.pi)
+    return eCoor
+
+
+class Straylight(object):
+    def __init__(self, jtime=2460843., sat_pos=np.array([0, 0, 0]), pointing_radec=np.array([0, 0]), sun_pos=np.array([0, 0, 0])):
+        self.jtime = jtime
+        self.sat = sat_pos
+        self.sun_pos = sun_pos
+        self.equator = coord.SkyCoord(
+            pointing_radec[0]*u.degree, pointing_radec[1]*u.degree, frame='icrs')
+        self.ecliptic = self.equator.transform_to('barycentrictrueecliptic')
+        self.pointing = transRaDec2D(pointing_radec[0], pointing_radec[1])
+        platForm = sys.platform
+        if platForm == 'darwin':
+            try:
+                with pkg_resources.files('observation_sim.sky_background.lib').joinpath('libstraylight.dylib') as dllFn:
+                    self.slcdll = ctypes.CDLL(dllFn)
+            except AttributeError:
+                with pkg_resources.path('observation_sim.sky_background.lib', 'libstraylight.dylib') as dllFn:
+                    self.slcdll = ctypes.CDLL(dllFn)
+        elif platForm == 'linux':
+            try:
+                with pkg_resources.files('observation_sim.sky_background.lib').joinpath('libstraylight.so') as dllFn:
+                    self.slcdll = ctypes.CDLL(dllFn)
+            except AttributeError:
+                with pkg_resources.path('observation_sim.sky_background.lib', 'libstraylight.so') as dllFn:
+                    self.slcdll = ctypes.CDLL(dllFn)
+        # self.slcdll=ctypes.CDLL('./libstraylight.dylib')
+        self.slcdll.Calculate.argtypes = [ctypes.c_double, ctypes.POINTER(ctypes.c_double),
+                                          ctypes.POINTER(ctypes.c_double), ctypes.POINTER(
+                                              ctypes.c_double),
+                                          ctypes.POINTER(ctypes.c_double), ctypes.c_char_p]
+
+        self.slcdll.PointSource.argtypes = [ctypes.c_double, ctypes.POINTER(ctypes.c_double),
+                                            ctypes.POINTER(ctypes.c_double), ctypes.POINTER(
+                                                ctypes.c_double),
+                                            ctypes.POINTER(ctypes.c_double), ctypes.c_char_p]
+
+        self.slcdll.EarthShine.argtypes = [ctypes.c_double, ctypes.POINTER(ctypes.c_double),
+                                           ctypes.POINTER(ctypes.c_double), ctypes.POINTER(
+                                               ctypes.c_double),
+                                           ctypes.POINTER(ctypes.c_double)]
+
+        self.slcdll.Zodiacal.argtypes = [ctypes.c_double, ctypes.POINTER(ctypes.c_double),
+                                         ctypes.POINTER(ctypes.c_double)]
+        self.slcdll.ComposeY.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.POINTER(ctypes.c_double),
+                                         ctypes.POINTER(ctypes.c_double)]
+        self.slcdll.Init.argtypes = [
+            ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
+        try:
+            with pkg_resources.files('observation_sim.sky_background.lib').joinpath('DE405') as tFn:
+                self.deFn = tFn.as_uri()[7:]
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.lib', 'DE405') as tFn:
+                self.deFn = tFn.as_uri()[7:]
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.lib').joinpath('PST') as tFn:
+                self.PSTFn = tFn.as_uri()[7:]
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.lib', 'PST') as tFn:
+                self.PSTFn = tFn.as_uri()[7:]
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.lib').joinpath('R') as tFn:
+                self.RFn = tFn.as_uri()[7:]
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.lib', 'R') as tFn:
+                self.RFn = tFn.as_uri()[7:]
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.lib').joinpath('Zodiacal') as tFn:
+                self.ZolFn = tFn.as_uri()[7:]
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.lib', 'Zodiacal') as tFn:
+                self.ZolFn = tFn.as_uri()[7:]
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.lib').joinpath('BrightGaia_with_csst_mag') as tFn:
+                self.brightStarTabFn = tFn.as_uri()[7:]
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.lib', 'BrightGaia_with_csst_mag') as tFn:
+                self.brightStarTabFn = tFn.as_uri()[7:]
+        print(self.deFn)
+        self.slcdll.Init(str.encode(self.deFn), str.encode(
+            self.PSTFn), str.encode(self.RFn), str.encode(self.ZolFn))
+
+    def getFilterAndCCD_Q(self, filter='i'):
+        try:
+            with pkg_resources.files('observation_sim.instruments.data.ccd').joinpath(filterCCD[filter] + '.txt') as ccd_fn:
+                q_ccd_f = np.loadtxt(ccd_fn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.instruments.data.ccd', filterCCD[filter] + '.txt') as ccd_fn:
+                q_ccd_f = np.loadtxt(ccd_fn)
+
+        try:
+            with pkg_resources.files('observation_sim.instruments.data.filters').joinpath(filter + '.txt') as filter_fn:
+                q_fil_f = np.loadtxt(filter_fn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.instruments.data.filters', filter + '.txt') as filter_fn:
+                q_fil_f = np.loadtxt(filter_fn)
+
+        band_s = 2000
+        band_e = 11000
+
+        q_ccd_f[:, 0] = q_ccd_f[:, 0]*10
+        q_ccd = np.zeros([q_ccd_f.shape[0]+2, q_ccd_f.shape[1]])
+        q_ccd[1:-1, :] = q_ccd_f
+        q_ccd[0] = [band_s, 0]
+        q_ccd[-1] = [band_e, 0]
+
+        q_fil = np.zeros([q_fil_f.shape[0]+2, q_fil_f.shape[1]])
+        q_fil[1:-1, :] = q_fil_f
+        q_fil[0] = [band_s, 0]
+        q_fil[-1] = [band_e, 0]
+
+        q_fil_i = interpolate.interp1d(q_fil[:, 0], q_fil[:, 1])
+        q_ccd_i = interpolate.interp1d(q_ccd[:, 0], q_ccd[:, 1])
+        bands = np.arange(bandRange[filter][0], bandRange[filter][1], 0.5)
+        q_ccd_fil = q_fil_i(bands)*q_ccd_i(bands)
+
+        return np.trapz(q_ccd_fil, bands)/(bandRange[filter][1]-bandRange[filter][0])
+
+    def calculateEarthShineFilter(self, filter='i', pixel_size_phy=10):
+        sat = (ctypes.c_double*3)()
+        sat[:] = self.sat
+        ob = (ctypes.c_double*3)()
+        ob[:] = self.pointing
+
+        py1 = (ctypes.c_double*3)()
+        py2 = (ctypes.c_double*3)()
+        self.slcdll.ComposeY(ob, py1, py2)
+
+        earth_e1 = (ctypes.c_double*7)()
+        self.slcdll.EarthShine(self.jtime, sat, ob, py1, earth_e1)
+        earth_e2 = (ctypes.c_double*7)()
+        self.slcdll.EarthShine(self.jtime, sat, ob, py2, earth_e2)
+
+        band_earth_e1 = earth_e1[:][filterIndex[filter]]
+        band_earth_e2 = earth_e2[:][filterIndex[filter]]
+
+        q = self.getFilterAndCCD_Q(filter=filter)
+        p_lambda = filterPivotWave[filter]
+        c = cons.c.value
+        h = cons.h.value
+        pix_earth_e1 = band_earth_e1 / \
+            (h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+        pix_earth_e2 = band_earth_e2 / \
+            (h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+
+        if pix_earth_e1 < pix_earth_e2:
+            return pix_earth_e1, py1[:]
+        else:
+            return pix_earth_e2, py2[:]
+
+    """
+    calculate zodiacal  call c++ program, seems to have some problem
+    """
+
+    def calculateZodiacalFilter1(self, filter='i', pixel_size_phy=10):
+        sat = (ctypes.c_double*3)()
+        sat[:] = self.sat
+        ob = (ctypes.c_double*3)()
+        ob[:] = self.pointing
+
+        zodical_e = (ctypes.c_double*7)()
+        self.slcdll.Zodiacal(self.jtime, ob, zodical_e)
+
+        ob1 = (ctypes.c_double*2)()
+        ob1[:] = np.array([self.ecliptic.lon.value, self.ecliptic.lat.value])
+        zodical_e1 = (ctypes.c_double*7)()
+        self.slcdll.Zodiacal1(ob1, zodical_e1)
+
+        band_zodical_e = zodical_e[:][filterIndex[filter]]
+
+        q = self.getFilterAndCCD_Q(filter=filter)
+        p_lambda = filterPivotWave[filter]
+        c = cons.c.value
+        h = cons.h.value
+        pix_zodical_e = band_zodical_e / \
+            (h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+
+        return pix_zodical_e, band_zodical_e
+
+    """
+    calculate zodiacal  use python
+    """
+
+    def calculateZodiacalFilter2(self, filter='i', aper=2, pixelsize=0.074, sun_pos=np.array([0, 0, 0])):
+
+        spec, v_mag = self.calculateZodicalSpec(
+            longitude=self.ecliptic.lon.value, latitude=self.ecliptic.lat.value, sun_pos=sun_pos)
+        # spec = self.calculateZodicalSpec(longitude = lon, latitude = lat)
+
+        try:
+            with pkg_resources.files('observation_sim.instruments.data.throughputs').joinpath(filter + '_throughput.txt') as throughputFn:
+                throughput = np.loadtxt(throughputFn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.instruments.data.throughputs', filter + '_throughput.txt') as throughputFn:
+                throughput = np.loadtxt(throughputFn)
+
+        deltL = 0.5
+        lamb = np.arange(bandRange[filter][0], bandRange[filter][1], deltL)
+
+        speci = interpolate.interp1d(spec['WAVELENGTH'], spec['FLUX'])
+
+        y = speci(lamb)
+        # erg/s/cm2/A --> photo/s/m2/A
+        flux = y * lamb / (cons.h.value * cons.c.value) * 1e-13
+
+        throughput_i = interpolate.interp1d(throughput[:, 0], throughput[:, 1])
+
+        throughput_ = throughput_i(lamb)
+
+        sky_pix = np.trapz(flux*throughput_, lamb) * \
+            math.pi * aper*aper/4 * pixelsize * pixelsize
+
+        # sky_pix_e = np.trapz(y, lamb) * math.pi * aper*aper/4 * pixelsize * pixelsize/(10*10*1e-6*1e-6)*1e-7*1e4
+
+        return sky_pix, v_mag  # ,  sky_pix_e
+
+    def calculateStarLightFilter(self, filter='i', pointYaxis=np.array([1, 1, 1]), pixel_size_phy=10):
+        sat = (ctypes.c_double*3)()
+        sat[:] = self.sat
+        ob = (ctypes.c_double*3)()
+        ob[:] = self.pointing
+
+        py = (ctypes.c_double*3)()
+        py[:] = pointYaxis
+
+        q = self.getFilterAndCCD_Q(filter=filter)
+        p_lambda = filterPivotWave[filter]
+        c = cons.c.value
+        h = cons.h.value
+
+        star_e1 = (ctypes.c_double*7)()
+        self.slcdll.PointSource(self.jtime, sat, ob, py,
+                                star_e1, str.encode(self.brightStarTabFn))
+
+        band_star_e1 = star_e1[:][filterIndex[filter]]
+
+        pix_star_e1 = band_star_e1 / \
+            (h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+
+        return pix_star_e1
+
+    def calculateEarthshineGrating(self, grating='GU', pixel_size_phy=10, normFilter='g',  aper=2, pixelsize=0.074):
+        sat = (ctypes.c_double*3)()
+        sat[:] = self.sat
+        ob = (ctypes.c_double*3)()
+        ob[:] = self.pointing
+
+        py1 = (ctypes.c_double*3)()
+        py2 = (ctypes.c_double*3)()
+        self.slcdll.ComposeY(ob, py1, py2)
+
+        earth_e1 = (ctypes.c_double*7)()
+        self.slcdll.EarthShine(self.jtime, sat, ob, py1, earth_e1)
+        earth_e2 = (ctypes.c_double*7)()
+        self.slcdll.EarthShine(self.jtime, sat, ob, py2, earth_e2)
+        # zodical_e = (ctypes.c_double*7)()
+        # self.slcdll.Zodiacal(self.jtime,ob,zodical_e)
+
+        band_earth_e1 = earth_e1[:][filterIndex[normFilter]]
+        band_earth_e2 = earth_e2[:][filterIndex[normFilter]]
+        band_earth_e = band_earth_e2
+        py = py2[:]
+        if band_earth_e1 < band_earth_e2:
+            band_earth_e = band_earth_e1
+            py = py1[:]
+        # band_earth_e = np.min([band_earth_e1, band_earth_e2])
+
+        # band_earth_e1 = 0
+        # band_earth_e2 = 0
+        # band_zodical_e = zodical_e[:][filterIndex[normFilter]]
+
+        q = self.getFilterAndCCD_Q(filter=normFilter)
+        p_lambda = filterPivotWave[normFilter]
+        c = cons.c.value
+        h = cons.h.value
+        pix_earth_e = band_earth_e / \
+            (h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+
+        # pix_earth_e2 = band_earth_e2/(h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+        # pix_zodical_e = band_zodical_e/(h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+        # pix_earth_e = np.min([pix_earth_e1, pix_earth_e2])
+
+        earthshine_v, earthshine_spec = self.calculatEarthshineByHSTSpec(
+            filter=normFilter, aper=aper, pixelsize=pixelsize)
+
+        lamb_earth = earthshine_spec['WAVELENGTH']
+        flux_earth = earthshine_spec['FLUX']*pix_earth_e/earthshine_v
+        # print(pix_earth_e,earthshine_v)
+        earth_v_grating = 0
+        for s_order in SpecOrder:
+            try:
+                with pkg_resources.files('observation_sim.instruments.data.sls_conf').joinpath(
+                        grating + '.Throughput.' + s_order + 'st.fits') as thpFn:
+                    thp_ = Table.read(thpFn)
+            except AttributeError:
+                with pkg_resources.path('observation_sim.instruments.data.sls_conf',
+                                        grating + '.Throughput.' + s_order + 'st.fits') as thpFn:
+                    thp_ = Table.read(thpFn)
+
+            thpFn_i = interpolate.interp1d(
+                thp_['WAVELENGTH'], thp_['SENSITIVITY'])
+            thp = thpFn_i(lamb_earth)
+            beamsEarth = np.trapz(flux_earth*thp, lamb_earth) * \
+                math.pi*aper*aper/4 * pixelsize * pixelsize
+            earth_v_grating = earth_v_grating + beamsEarth
+            # print(beamsEarth)
+
+        # print(earthshine_v, pix_earth_e, earth_v_grating)
+        return earth_v_grating, py
+
+    def calculateStarLightGrating(self, grating='GU', pointYaxis=np.array([1, 1, 1]), pixel_size_phy=10):
+        sat = (ctypes.c_double*3)()
+        sat[:] = self.sat
+        ob = (ctypes.c_double*3)()
+        ob[:] = self.pointing
+
+        py = (ctypes.c_double*3)()
+        py[:] = pointYaxis
+
+        # q=self.getFilterAndCCD_Q(filter=filter)
+        # p_lambda = filterPivotWave[filter]
+        c = cons.c.value
+        h = cons.h.value
+
+        star_e1 = (ctypes.c_double*7)()
+        self.slcdll.PointSource(self.jtime, sat, ob, py,
+                                star_e1, str.encode(self.brightStarTabFn))
+
+        filterPivotWaveList = np.zeros(7)
+        bandRangeList = np.zeros(7)
+        filterMirrorEffList = np.zeros(7)
+        filterNameList = list(filterPivotWave.keys())
+        for i in np.arange(7):
+            filterPivotWaveList[i] = filterPivotWave[filterNameList[i]]
+            filterMirrorEffList[i] = filterMirrorEff[filterNameList[i]]
+            brange = bandRange[filterNameList[i]]
+            bandRangeList[i] = brange[1] - brange[0]
+        filterFlux_lamb = star_e1[:]/bandRangeList / \
+            filterMirrorEffList/(h*c/(filterPivotWaveList*1e-10))
+        filterFlux_lambi = interpolate.interp1d(
+            filterPivotWaveList, filterFlux_lamb, fill_value="extrapolate")
+
+        lamb_g = np.arange(bandRange[grating][0], bandRange[grating][1], 1)
+        flux_g = filterFlux_lambi(lamb_g)
+        # flux_total_g = np.trapz(flux_g,lamb_g)
+        starLight_grating = 0
+        for s_order in SpecOrder:
+            try:
+                with pkg_resources.files('observation_sim.instruments.data.sls_conf').joinpath(
+                        grating + '.Throughput.' + s_order + 'st.fits') as thpFn:
+                    thp_ = Table.read(thpFn)
+            except AttributeError:
+                with pkg_resources.path('observation_sim.instruments.data.sls_conf',
+                                        grating + '.Throughput.' + s_order + 'st.fits') as thpFn:
+                    thp_ = Table.read(thpFn)
+
+            thpFn_i = interpolate.interp1d(
+                thp_['WAVELENGTH'], thp_['SENSITIVITY'])
+            thp = thpFn_i(lamb_g)
+            beamsZol = np.trapz(flux_g*thp, lamb_g) * \
+                pixel_size_phy*1e-6*pixel_size_phy*1e-6
+            starLight_grating = starLight_grating + beamsZol
+            # print(beamsZol)
+
+        # band_star_e1 = star_e1[:][filterIndex[filter]]
+
+        # pix_star_e1 = band_star_e1/(h*c/(p_lambda*1e-10))*pixel_size_phy*1e-6*pixel_size_phy*1e-6*q
+
+        return starLight_grating
+
+    def calculatEarthshineByHSTSpec(self, filter='g', aper=2, pixelsize=0.074, s=2000, e=11000):
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.data.sky').joinpath('earthShine.dat') as specFn:
+                spec = np.loadtxt(specFn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.data.sky',
+                                    'earthShine.dat') as specFn:
+                spec = np.loadtxt(specFn)
+
+        try:
+            with pkg_resources.files('observation_sim.instruments.data.throughputs').joinpath(filter + '_throughput.txt') as throughputFn:
+                throughput = np.loadtxt(throughputFn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.instruments.data.throughputs', filter + '_throughput.txt') as throughputFn:
+                throughput = np.loadtxt(throughputFn)
+
+        deltL = 0.5
+        lamb = np.arange(bandRange[filter][0], bandRange[filter][1], deltL)
+
+        speci = interpolate.interp1d(spec[:, 0], spec[:, 1])
+        y = speci(lamb)
+        # erg/s/cm2/A --> photo/s/m2/A
+        flux = y * lamb / (cons.h.value * cons.c.value) * 1e-13
+
+        throughput_i = interpolate.interp1d(throughput[:, 0], throughput[:, 1])
+
+        throughput_ = throughput_i(lamb)
+
+        sky_pix = np.trapz(flux*throughput_, lamb) * \
+            math.pi * aper*aper/4 * pixelsize * pixelsize
+
+        lamb = np.arange(s, e, deltL)
+        speci = interpolate.interp1d(spec[:, 0], spec[:, 1])
+        y = speci(lamb)
+        # erg/s/cm2/A --> photo/s/m2/A
+        flux = y * lamb / (cons.h.value * cons.c.value) * 1e-13
+
+        return sky_pix, Table(np.array([lamb, flux]).T, names=('WAVELENGTH', 'FLUX'))
+
+    def calculateZodicalSpec(self, longitude=50, latitude=60, sun_pos=np.array([0, 0, 0])):
+        from scipy.interpolate import interp2d
+        from scipy.interpolate import griddata
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.data').joinpath('Zodiacal_map1.dat') as z_map_fn:
+                ZL = np.loadtxt(z_map_fn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.data',
+                                    'Zodiacal_map1.dat') as z_map_fn:
+                ZL = np.loadtxt(z_map_fn)
+
+        # zl_sh = ZL.shape
+        # x = np.arange(0,zl_sh[1],1)
+        # y = np.arange(0,zl_sh[0],1)
+        x = ZL[0, 1:]
+        y = ZL[1:, 0]
+        X, Y = np.meshgrid(x, y)
+        # f_sur = interp2d(X,Y,ZL,kind='linear')
+        sun_radec = Cartesian2Equatorial(sun_pos)
+
+        sun_eclip = coord.SkyCoord(
+            sun_radec[0]*u.degree, sun_radec[1]*u.degree, frame='icrs')
+        sun_equtor = sun_eclip.transform_to('barycentrictrueecliptic')
+
+        longitude = longitude - (sun_equtor.lon*u.degree).value
+        longitude = np.abs(longitude)
+        # print((sun_equtor.lon*u.degree).value)
+
+        if (longitude > 180):
+            longitude = 360 - longitude
+
+        latitude = np.abs(latitude)
+        lo = longitude
+        la = latitude
+        zl = griddata((X.flatten(), Y.flatten()),
+                      ZL[1:, 1:].flatten(), (la, lo), method='cubic').min()
+        zl = zl*(math.pi*math.pi)/(180*180)/(3600*3600)*1e-4*1e7*1e-8*1e-4
+        # print(zl , '\n')
+
+        try:
+            with pkg_resources.files('observation_sim.sky_background.data.sky').joinpath('zodiacal.dat') as zodical_fn:
+                spec = np.loadtxt(zodical_fn)
+        except AttributeError:
+            with pkg_resources.path('observation_sim.sky_background.data.sky',
+                                    'zodiacal.dat') as zodical_fn:
+                spec = np.loadtxt(zodical_fn)
+
+        speci = interpolate.interp1d(spec[:, 0], spec[:, 1])
+        flux5000 = speci(5000)
+        f_ration = zl/flux5000
+
+        v_mag = np.log10(f_ration)*(-2.5)+22.1
+        # print("factor:", v_mag, lo, la)
+
+        return Table(np.array([spec[:, 0], spec[:, 1]*f_ration]).T, names=('WAVELENGTH', 'FLUX')), v_mag
+
+    def calculateStrayLightFilter(self, filter='i', pixel_size_phy=10, pixel_scale=0.074):
+        e1, py = self.calculateEarthShineFilter(
+            filter=filter, pixel_size_phy=pixel_size_phy)
+        e2, _ = self.calculateZodiacalFilter2(
+            filter=filter, sun_pos=self.sun_pos, pixelsize=pixel_scale)
+        e3 = self.calculateStarLightFilter(
+            filter=filter, pointYaxis=py, pixel_size_phy=pixel_size_phy)
+
+        return e1+e2+e3
+
+    def calculateStrayLightGrating(self, grating='GI', pixel_size_phy=10, normFilter_es='g'):
+        e1, py = self.calculateEarthshineGrating(
+            grating=grating, pixel_size_phy=pixel_size_phy, normFilter=normFilter_es)
+        e2 = self.calculateStarLightGrating(grating=grating, pointYaxis=py)
+        spec, _ = self.calculateZodicalSpec(
+            longitude=self.ecliptic.lon.value, latitude=self.ecliptic.lat.value, sun_pos=self.sun_pos)
+
+        return e1+e2, spec
+
+
+def testZodiacal(lon=285.04312526255366, lat=30.):
+    c_eclip = coord.SkyCoord(lon*u.degree, lat*u.degree,
+                             frame='barycentrictrueecliptic')
+    c_equtor = c_eclip.transform_to('icrs')
+
+    sl = Straylight(jtime=2459767.00354975, sat=np.array([]), radec=np.array(
+        [(c_equtor.ra*u.degree).value, (c_equtor.dec*u.degree).value]))
+    e_zol, v_mag = sl.calculateZodiacalFilter2(filter='i', sun_pos=np.array(
+        [-3.70939436e+07,  1.35334903e+08,  5.86673104e+07]))
+    print(e_zol)
+
+# ju=2.4608437604166665e+06
+# sat = (ctypes.c_double*3)()
+# sat[:] = np.array([5873.752, -1642.066, 2896.744])
+# ob = (ctypes.c_double*3)()
+# ob[:]=np.array([0.445256,0.76061,-0.47246])
+
+# sl = StrayLight(jtime = ju, sat = np.array([5873.752, -1642.066, 2896.744]), pointing = np.array([-0.445256,-0.76061,0.47246]))
+
+
+# fn = '/Users/zhangxin/Work/SurveyPlan/point/csst_survey_sim_20211028/E17.5_b17.5_beta_11.6_opt_transtime_1_CMG_1_dp_2_0.25_da_10_Texp_1.5_DEC60_500_0.1_800_1000_+5deg.dat'
+#
+# surveylist = np.loadtxt(fn)
+# sky_pix = np.zeros([surveylist.shape[0],7])
+#
+#
+# i = 693438
+# c_eclip = coord.SkyCoord(surveylist[:,2]*u.degree, surveylist[:,1]*u.degree,frame='barycentrictrueecliptic')
+# c_equtor = c_eclip.transform_to('icrs')
+#
+#
+# # pointing = transRaDec2D((c_equtor[i].ra*u.degree).value, (c_equtor[i].dec*u.degree).value)
+# #     # print(ju, pointing, surveylist[i,3:9])
+# # ju = surveylist[i,0]
+# # sl = StrayLight(jtime = ju, sat = surveylist[i,3:6], pointing = pointing)
+# # sl.caculateStrayLightGrating(grating = 'GI', pixel_size_phy = 10, normFilter = 'g')
+#
+# for i in np.arange(surveylist.shape[0]):
+#     print(i)
+#     if i > 300:
+#         break
+#     # if i != 300:
+#     #     continue
+#     # if i != 693438:
+#     #      continue
+#     ju = surveylist[i,0]
+#     pointing = transRaDec2D((c_equtor[i].ra*u.degree).value, (c_equtor[i].dec*u.degree).value)
+#     # print(ju, pointing, surveylist[i,3:9])
+#     sl = StrayLight(jtime = ju, sat = surveylist[i,3:6], radec = np.array([(c_equtor[i].ra*u.degree).value, (c_equtor[i].dec*u.degree).value]))
+#     # strayl_i,s_zoldical ,s_earth, s_earth1 = sl.caculateStrayLightFilter(filter = 'i')
+#     # print(i,strayl_i,s_zoldical,s_earth, s_earth1)
+#     p_cart= transRaDec2D((c_equtor[i].ra*u.degree).value, (c_equtor[i].dec*u.degree).value)
+#     sky_pix[i,6] = getAngle132(x1 = surveylist[i,6], y1 = surveylist[i,7], z1 = surveylist[i,8], x2 = p_cart[0], y2 = p_cart[1], z2 = p_cart[2], x3 = 0, y3 = 0, z3 = 0)
+#
+#     earthZenithAngle,isInSunSide = calculateAnglePwithEarth(sat = surveylist[i,3:6], pointing = pointing, sun = surveylist[i,6:9])
+#     sky_pix[i,4] = earthZenithAngle
+#     sky_pix[i,5] = isInSunSide
+#
+#     e1_,py = sl.caculateEarthShineFilter(filter = 'i')
+#     # e2, e2_ = sl.calculateZodiacalFilter1(filter = 'i')
+#     e3, v_mag = sl.calculateZodiacalFilter2(filter = 'i', sun_pos=surveylist[i,6:9])
+#     # e4 = sl.caculateStarLightFilter(filter = 'i',pointYaxis = py)
+#     # e4 = 0
+#
+#     e1,py = sl.caculateEarthshineGrating(grating = 'GI', pixel_size_phy = 10, normFilter = 'g')
+#
+#     # e2 = sl.caculateStarLightGrating(grating = 'GV', pointYaxis = py)
+#     e2 = sl.caculateStarLightGrating(grating = 'GI', pointYaxis = py)
+#     e4 = sl.caculateStarLightFilter(filter = 'i',pointYaxis = py)
+#
+#     e5=sl.caculateStrayLightFilter(filter = 'i', pixel_size_phy = 10, pixel_scale = 0.074, sun_pos = surveylist[i,6:9])
+#     e6,_=sl.caculateStrayLightGrating(grating = 'GI', normFilter_es = 'g', sun_pos = surveylist[i,6:9])
+#
+#     sky_pix[i,0] = e1
+#     sky_pix[i,1] = e2
+#     sky_pix[i,2] = e3
+#     sky_pix[i,3] = e4
+#     print(e1+e2,e1_+e3+e4,e5,e6)
+#
+#     # print(e1,e2,e3,e4)

observation_sim/sky_background/__init__.py

+from .Straylight import Straylight
+from .SkybackgroundMap import *

observation_sim/sky_background/data/Zodiacal_map1.dat

+0  0     5      10   15   20   25   30   45   60   75   90
+0  30000 150000 7000 3140 1610 985  640  275  150  100  76
+5  20000 12000	6000 2940 1540 945  625  271  150  100  76
+10 16000 8000   4740 2470 1370 865  590  264  148  100  76
+15 11500 6780   3440 1860 1110 755  525  251  146  100  76
+20 6400  4480   2410 1410 910  635  454  237  141  99   76
+25 3840  2830   1730 1100 749  545  410  223  136  97   76
+30 2480  1870   1220 845  615  467  365  207  131  95   76
+35 1650  1270   910  680  510  397  320  193  125  93   76
+40 1180  940    700  530  416  338  282  179  120  92   76
+45 910   730    555  442  356  292  250  166  116  90   76
+60 505   442    352  292  243  209  183  134  104  86   76
+75 338   317    269  227  196  172  151  116  93   82   76
+90 259   251    225  193  166  147  132  104  86   79   76
+105 212  210    197  170  150  133  119  96   82   77   76
+120 188  186    177  154  138  125  113  90   77   74   76
+135 179  178    166  147  134  122  110  90   77   73   76
+150 179  178    165  148  137  127  116  96   79   72   76
+165 196  192    179  165  151  141  131  104  82   72   76
+180 230  212    195  178  163  148  134  105  83   72   76
\ No newline at end of file

observation_sim/sky_background/data/sky/earthShine.dat

+#A     erg/s/A/arcsec^2/cm^2
+1000 2.41e-23
+1100 4.38e-22
+1200 4.01e-23
+1300 7.41e-25
+1400 4.29e-25
+1500 4.16e-25
+1600 2.55e-25
+1700 7.89e-25
+1800 9.33e-23
+1900 4.39e-22
+2000 1.01e-21
+2100 1.6e-21
+2200 7.49e-22
+2300 3.32e-22
+2400 2.5e-22
+2500 2.39e-22
+2600 5.62e-22
+2700 6.77e-21
+2800 2.03e-21
+2900 4.32e-20
+3000 9.34e-20
+3100 2.07e-19
+3200 3.6e-19
+3300 4.27e-19
+3400 6.4e-19
+3500 8.2e-19
+3600 1.06e-18
+3700 1.22e-18
+3800 1.23e-18
+3900 1.52e-18
+4000 2.38e-18
+4250 2.38e-18
+4500 2.86e-18
+4750 2.79e-18
+5000 2.63e-18
+5250 2.67e-18
+5500 2.58e-18
+5750 2.54e-18
+6000 2.42e-18
+6250 2.26e-18
+6500 2.17e-18
+6750 2.07e-18
+7000 1.93e-18
+7250 1.85e-18
+7500 1.74e-18
+7750 1.63e-18
+8000 1.56e-18
+8250 1.48e-18
+8500 1.35e-18
+8750 1.31e-18
+9000 1.22e-18
+9250 1.15e-18
+9500 1.1e-18
+9750 1.04e-18
+10000 1e-18
+10250 9.45e-19
+10500 9.04e-19
+10750 8.41e-19
+11000 8.03e-19

observation_sim/sky_background/data/sky/earthshine1.dat

+2000  7.69E-22
+2500  1.53E-21
+3000  1.43E-19
+3500  8.33E-19
+4000  1.66E-18
+4500  2.59E-18
+5000  2.63E-18
+5500  2.55E-18
+6000  2.42E-18
+7000  1.95E-18
+8000  1.56E-18
+9000  1.23E-18
+10000  9.97E-19
+11000  8.02E-19
+12000  6.65E-19
+13000  5.58E-19
+14000  4.70E-19
+15000  3.97E-19
+16000  3.35E-19
+17000  2.79E-19

observation_sim/sky_background/data/sky/sky.dat

+2000 7.69E-22 7.94E-20 8.02E-20
+2500 1.53E-21 3.83E-19 3.84E-19
+3000 1.43E-19 1.63E-18 1.77E-18
+3500 8.33E-19 2.72E-18 3.55E-18
+4000 1.66E-18 3.12E-18 4.78E-18
+4500 2.59E-18 4.97E-18 7.57E-18
+5000 2.63E-18 5.07E-18 7.70E-18
+5500 2.55E-18 5.17E-18 7.72E-18
+6000 2.42E-18 5.14E-18 7.56E-18
+7000 1.95E-18 4.48E-18 6.42E-18
+8000 1.56E-18 3.82E-18 5.38E-18
+9000 1.23E-18 3.18E-18 4.40E-18
+10000 9.97E-19 2.70E-18 3.70E-18
+11000 8.02E-19 2.26E-18 3.06E-18
+12000 6.65E-19 1.94E-18 2.61E-18
+13000 5.58E-19 1.68E-18 2.24E-18
+14000 4.70E-19 1.46E-18 1.93E-18
+15000 3.97E-19 1.26E-18 1.66E-18
+16000 3.35E-19 1.09E-18 1.43E-18
+17000 2.79E-19 9.27E-19 1.21E-18
\ No newline at end of file

observation_sim/sky_background/data/sky/sky_emiss_hubble_50_50_A.dat

+# huble skybackground mean level, earthshine + zodical light
+#wavelength(A)    flux(photon/s/A/arcsec^2/m^2)
+1000.0    6.062014541418706e-09
+1005.0    1.1324002246856417e-08
+1010.0    1.6638046446152143e-08
+1015.0    2.2004147139305884e-08
+1020.0    2.7422304326317643e-08
+1025.0    3.289251800718741e-08
+1030.0    3.8414788181915204e-08
+1035.0    4.398911485050101e-08
+1040.0    4.961549801294483e-08
+1045.0    5.5293937669246676e-08
+1050.0    6.102443381940653e-08
+1055.0    6.68069864634244e-08
+1060.0    7.26415956013003e-08
+1065.0    7.852826123303418e-08
+1070.0    8.446698335862612e-08
+1075.0    9.045776197807603e-08
+1080.0    9.650059709138398e-08
+1085.0    1.0259548869854993e-07
+1090.0    1.0874243679957394e-07
+1095.0    1.1494144139445595e-07
+1100.0    1.2119250248319594e-07
+1105.0    1.1621516866148182e-07
+1110.0    1.1118780580055496e-07
+1115.0    1.0611041390041543e-07
+1120.0    1.009829929610632e-07
+1125.0    9.580554298249834e-08
+1130.0    9.057806396472076e-08
+1135.0    8.530055590773051e-08
+1140.0    7.997301881152757e-08
+1145.0    7.459545267611195e-08
+1150.0    6.916785750148364e-08
+1155.0    6.369023328764268e-08
+1160.0    5.8162580034589e-08
+1165.0    5.2584897742322656e-08
+1170.0    4.695718641084362e-08
+1175.0    4.127944604015191e-08
+1180.0    3.5551676630247506e-08
+1185.0    2.977387818113043e-08
+1190.0    2.394605069280067e-08
+1195.0    1.8068194165258228e-08
+1200.0    1.2140308598503108e-08
+1205.0    1.1911173524700924e-08
+1210.0    1.1679717125645938e-08
+1215.0    1.144593940133815e-08
+1220.0    1.1209840351777559e-08
+1225.0    1.0971419976964167e-08
+1230.0    1.0730678276897972e-08
+1235.0    1.0487615251578977e-08
+1240.0    1.0242230901007181e-08
+1245.0    9.994525225182583e-09
+1250.0    9.744498224105179e-09
+1255.0    9.49214989777498e-09
+1260.0    9.237480246191975e-09
+1265.0    8.980489269356168e-09
+1270.0    8.721176967267561e-09
+1275.0    8.45954333992615e-09
+1280.0    8.19558838733194e-09
+1285.0    7.929312109484926e-09
+1290.0    7.660714506385111e-09
+1295.0    7.389795578032495e-09
+1300.0    7.116555324427076e-09
+1305.0    6.840993745568856e-09
+1310.0    6.5631108414578336e-09
+1315.0    6.282906612094011e-09
+1320.0    6.000381057477383e-09
+1325.0    5.715534177607957e-09
+1330.0    5.428365972485726e-09
+1335.0    5.138876442110697e-09
+1340.0    4.847065586482863e-09
+1345.0    4.552933405602229e-09
+1350.0    4.256479899468791e-09
+1355.0    3.9577050680825545e-09
+1360.0    3.656608911443514e-09
+1365.0    3.3531914295516723e-09
+1370.0    3.0474526224070294e-09
+1375.0    2.7393924900095834e-09
+1380.0    2.4290110323593374e-09
+1385.0    2.1163082494562875e-09
+1390.0    1.801284141300438e-09
+1395.0    1.483938707891785e-09
+1400.0    1.1642719492303297e-09
+1405.0    1.302827183295651e-09
+1410.0    1.442338980492367e-09
+1415.0    1.5828073408204775e-09
+1420.0    1.7242322642799823e-09
+1425.0    1.866613750870882e-09
+1430.0    2.009951800593176e-09
+1435.0    2.154246413446864e-09
+1440.0    2.2994975894319473e-09
+1445.0    2.445705328548425e-09
+1450.0    2.5928696307962963e-09
+1455.0    2.7409904961755633e-09
+1460.0    2.890067924686224e-09
+1465.0    3.040101916328279e-09
+1470.0    3.1910924711017297e-09
+1475.0    3.343039589006575e-09
+1480.0    3.495943270042813e-09
+1485.0    3.649803514210447e-09
+1490.0    3.804620321509474e-09
+1495.0    3.960393691939896e-09
+1500.0    4.117123625501715e-09
+1505.0    8.93940767390179e-09
+1510.0    1.3793642289099087e-08
+1515.0    1.8679827471093606e-08
+1520.0    2.3597963219885345e-08
+1525.0    2.8548049535474305e-08
+1530.0    3.353008641786049e-08
+1535.0    3.8544073867043887e-08
+1540.0    4.359001188302451e-08
+1545.0    4.866790046580235e-08
+1550.0    5.3777739615377417e-08
+1555.0    5.8919529331749705e-08
+1560.0    6.409326961491921e-08
+1565.0    6.929896046488596e-08
+1570.0    7.453660188164988e-08
+1575.0    7.980619386521105e-08
+1580.0    8.510773641556946e-08
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observation_sim/sky_background/data/sky/zodiacal.dat

+2000  7.94E-20
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+4000  3.12E-18
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observation_sim/sky_background/data/sky/zodiacal_.dat

+# A   erg/s/A/arcsec^2/cm^2
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