FlatLED.py 14.4 KB
Newer Older
Zhang Xin's avatar
Zhang Xin committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100


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)
Zhang Xin's avatar
Zhang Xin committed
101
            # print(i, np.mean(unitFlatImg), t_spec, exp_t)
Zhang Xin's avatar
Zhang Xin committed
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
            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