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 try: import importlib.resources as pkg_resources except ImportError: # Try backported to PY<37 'importlib_resources' import importlib_resources as pkg_resources # 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 = None, logger=None): # self.led_type_list = led_type_list self.filt = filt self.chip = chip self.logger = logger if flatDir is not None: self.flatDir = flatDir else: try: with pkg_resources.files('ObservationSim.MockObject.data.led').joinpath("") as ledDir: self.flatDir = ledDir.as_posix() except AttributeError: with pkg_resources.path('ObservationSim.MockObject.data.led', "") as ledDir: self.flatDir = ledDir.as_posix() ### ### return LED flat, e/s ### def getLEDImage(self, led_type='LED1'): # cwave = cwaves[led_type] flat = fits.open(os.path.join(self.flatDir, 'model_' + cwaves_name[led_type] + 'nm.fits')) 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