Merge branch 'master' into 'release_v3.0'

release v3.3.0

See merge request !35

observation_sim/instruments/data/throughputs/r_throughput.txt

-# r_cssc
+# r_csst
 # lambda_Angst  throughput
- 2000.00    0.00000
- 2010.00    0.00000
- 2020.00    0.00000
- 2030.00    0.00000
- 2040.00    0.00000
- 2050.00    0.00000
- 2060.00    0.00000
- 2070.00    0.00000
- 2080.00    0.00000
- 2090.00    0.00000
- 2100.00    0.00000
- 2110.00    0.00000
- 2120.00    0.00000
- 2130.00    0.00000
- 2140.00    0.00000
- 2150.00    0.00000
- 2160.00    0.00000
- 2170.00    0.00000
- 2180.00    0.00000
- 2190.00    0.00000
- 2200.00    0.00000
- 2210.00    0.00000
- 2220.00    0.00000
- 2230.00    0.00000
- 2240.00    0.00000
- 2250.00    0.00000
- 2260.00    0.00000
- 2270.00    0.00000
- 2280.00    0.00000
- 2290.00    0.00000
- 2300.00    0.00000
- 2310.00    0.00000
- 2320.00    0.00000
- 2330.00    0.00000
- 2340.00    0.00000
- 2350.00    0.00000
- 2360.00    0.00000
- 2370.00    0.00000
- 2380.00    0.00000
- 2390.00    0.00000
- 2400.00    0.00000
- 2410.00    0.00000
- 2420.00    0.00000
- 2430.00    0.00000
- 2440.00    0.00000
- 2450.00    0.00000
- 2460.00    0.00000
- 2470.00    0.00000
- 2480.00    0.00000
- 2490.00    0.00000
- 2500.00    0.00000
- 2510.00    0.00000
- 2520.00    0.00000
- 2530.00    0.00000
- 2540.00    0.00000
- 2550.00    0.00000
- 2560.00    0.00000
- 2570.00    0.00000
- 2580.00    0.00000
- 2590.00    0.00000
- 2600.00    0.00000
- 2610.00    0.00000
- 2620.00    0.00000
- 2630.00    0.00000
- 2640.00    0.00000
- 2650.00    0.00000
- 2660.00    0.00000
- 2670.00    0.00000
- 2680.00    0.00000
- 2690.00    0.00000
- 2700.00    0.00000
- 2710.00    0.00000
- 2720.00    0.00000
- 2730.00    0.00000
- 2740.00    0.00000
- 2750.00    0.00000
- 2760.00    0.00000
- 2770.00    0.00000
- 2780.00    0.00000
- 2790.00    0.00000
- 2800.00    0.00000
- 2810.00    0.00000
- 2820.00    0.00000
- 2830.00    0.00000
- 2840.00    0.00000
- 2850.00    0.00000
- 2860.00    0.00000
- 2870.00    0.00000
- 2880.00    0.00000
- 2890.00    0.00000
- 2900.00    0.00000
- 2910.00    0.00000
- 2920.00    0.00000
- 2930.00    0.00000
- 2940.00    0.00000
- 2950.00    0.00000
- 2960.00    0.00000
- 2970.00    0.00000
- 2980.00    0.00000
- 2990.00    0.00000
- 3000.00    0.00000
- 3010.00    0.00000
- 3020.00    0.00000
- 3030.00    0.00000
- 3040.00    0.00000
- 3050.00    0.00000
- 3060.00    0.00000
- 3070.00    0.00000
- 3080.00    0.00000
- 3090.00    0.00000
- 3100.00    0.00000
- 3110.00    0.00000
- 3120.00    0.00000
- 3130.00    0.00000
- 3140.00    0.00000
- 3150.00    0.00000
- 3160.00    0.00000
- 3170.00    0.00000
- 3180.00    0.00000
- 3190.00    0.00000
- 3200.00    0.00000
- 3210.00    0.00000
- 3220.00    0.00000
- 3230.00    0.00000
- 3240.00    0.00000
- 3250.00    0.00000
- 3260.00    0.00000
- 3270.00    0.00000
- 3280.00    0.00000
- 3290.00    0.00000
- 3300.00    0.00000
- 3310.00    0.00000
- 3320.00    0.00000
- 3330.00    0.00000
- 3340.00    0.00000
- 3350.00    0.00000
- 3360.00    0.00000
- 3370.00    0.00000
- 3380.00    0.00000
- 3390.00    0.00000
- 3400.00    0.00000
- 3410.00    0.00000
- 3420.00    0.00000
- 3430.00    0.00000
- 3440.00    0.00000
- 3450.00    0.00000
- 3460.00    0.00000
- 3470.00    0.00000
- 3480.00    0.00000
- 3490.00    0.00000
- 3500.00    0.00000
- 3510.00    0.00000
- 3520.00    0.00000
- 3530.00    0.00000
- 3540.00    0.00000
- 3550.00    0.00000
- 3560.00    0.00000
- 3570.00    0.00000
- 3580.00    0.00000
- 3590.00    0.00000
- 3600.00    0.00000
- 3610.00    0.00000
- 3620.00    0.00000
- 3630.00    0.00000
- 3640.00    0.00000
- 3650.00    0.00000
- 3660.00    0.00000
- 3670.00    0.00000
- 3680.00    0.00000
- 3690.00    0.00000
- 3700.00    0.00000
- 3710.00    0.00000
- 3720.00    0.00000
- 3730.00    0.00000
- 3740.00    0.00000
- 3750.00    0.00000
- 3760.00    0.00000
- 3770.00    0.00000
- 3780.00    0.00000
- 3790.00    0.00000
- 3800.00    0.00000
- 3810.00    0.00000
- 3820.00    0.00000
- 3830.00    0.00000
- 3840.00    0.00000
- 3850.00    0.00000
- 3860.00    0.00000
- 3870.00    0.00000
- 3880.00    0.00000
- 3890.00    0.00000
- 3900.00    0.00000
- 3910.00    0.00000
- 3920.00    0.00000
- 3930.00    0.00000
- 3940.00    0.00000
- 3950.00    0.00000
- 3960.00    0.00000
- 3970.00    0.00000
- 3980.00    0.00000
- 3990.00    0.00000
- 4000.00    0.00000
- 4010.00    0.00000
- 4020.00    0.00000
- 4030.00    0.00000
- 4040.00    0.00000
- 4050.00    0.00000
- 4060.00    0.00000
- 4070.00    0.00000
- 4080.00    0.00000
- 4090.00    0.00000
- 4100.00    0.00000
- 4110.00    0.00000
- 4120.00    0.00000
- 4130.00    0.00000
- 4140.00    0.00000
- 4150.00    0.00000
- 4160.00    0.00000
- 4170.00    0.00000
- 4180.00    0.00000
- 4190.00    0.00000
- 4200.00    0.00000
- 4210.00    0.00000
- 4220.00    0.00000
- 4230.00    0.00000
- 4240.00    0.00000
- 4250.00    0.00000
- 4260.00    0.00000
- 4270.00    0.00000
- 4280.00    0.00000
- 4290.00    0.00000
- 4300.00    0.00000
- 4310.00    0.00000
- 4320.00    0.00000
- 4330.00    0.00000
- 4340.00    0.00000
- 4350.00    0.00000
- 4360.00    0.00000
- 4370.00    0.00000
- 4380.00    0.00000
- 4390.00    0.00000
- 4400.00    0.00000
- 4410.00    0.00000
- 4420.00    0.00000
- 4430.00    0.00000
- 4440.00    0.00000
- 4450.00    0.00000
- 4460.00    0.00000
- 4470.00    0.00000
- 4480.00    0.00000
- 4490.00    0.00000
- 4500.00    0.00000
- 4510.00    0.00000
- 4520.00    0.00000
- 4530.00    0.00000
- 4540.00    0.00000
- 4550.00    0.00000
- 4560.00    0.00000
- 4570.00    0.00000
- 4580.00    0.00000
- 4590.00    0.00000
- 4600.00    0.00000
- 4610.00    0.00000
- 4620.00    0.00000
- 4630.00    0.00000
- 4640.00    0.00000
- 4650.00    0.00000
- 4660.00    0.00000
- 4670.00    0.00000
- 4680.00    0.00000
- 4690.00    0.00000
- 4700.00    0.00000
- 4710.00    0.00000
- 4720.00    0.00000
- 4730.00    0.00000
- 4740.00    0.00000
- 4750.00    0.00000
- 4760.00    0.00000
- 4770.00    0.00000
- 4780.00    0.00000
- 4790.00    0.00000
- 4800.00    0.00000
- 4810.00    0.00000
- 4820.00    0.00000
- 4830.00    0.00000
- 4840.00    0.00000
- 4850.00    0.00000
- 4860.00    0.00000
- 4870.00    0.00000
- 4880.00    0.00000
- 4890.00    0.00000
- 4900.00    0.00000
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- 4960.00    0.00000
- 4970.00    0.00000
- 4980.00    0.00000
- 4990.00    0.00000
- 5000.00    0.00000
- 5010.00    0.00000
- 5020.00    0.00000
- 5030.00    0.00000
- 5040.00    0.00000
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- 5060.00    0.00000
- 5070.00    0.00000
- 5080.00    0.00000
- 5090.00    0.00000
- 5100.00    0.00000
- 5110.00    0.00000
- 5120.00    0.00000
- 5130.00    0.00001
- 5140.00    0.00001
- 5150.00    0.00001
- 5160.00    0.00001
- 5170.00    0.00000
- 5180.00    0.00000
- 5190.00    0.00000
- 5200.00    0.00000
- 5210.00    0.00000
- 5220.00    0.00000
- 5230.00    0.00000
- 5240.00    0.00000
- 5250.00    0.00000
- 5260.00    0.00000
- 5270.00    0.00000
- 5280.00    0.00000
- 5290.00    0.00000
- 5300.00    0.00001
- 5310.00    0.00001
- 5320.00    0.00001
- 5330.00    0.00002
- 5340.00    0.00002
- 5350.00    0.00004
- 5360.00    0.00006
- 5370.00    0.00010
- 5380.00    0.00018
- 5390.00    0.00036
- 5400.00    0.00074
- 5410.00    0.00167
- 5420.00    0.00417
- 5430.00    0.01182
- 5440.00    0.03684
- 5450.00    0.10595
- 5460.00    0.22839
- 5470.00    0.35519
- 5480.00    0.43315
- 5490.00    0.47275
- 5500.00    0.51091
- 5510.00    0.55267
- 5520.00    0.58971
- 5530.00    0.61667
- 5540.00    0.63648
- 5550.00    0.65308
- 5560.00    0.66779
- 5570.00    0.67701
- 5580.00    0.67499
- 5590.00    0.66195
- 5600.00    0.64128
- 5610.00    0.62343
- 5620.00    0.61127
- 5630.00    0.61016
- 5640.00    0.61848
- 5650.00    0.63313
- 5660.00    0.65080
- 5670.00    0.66669
- 5680.00    0.67525
- 5690.00    0.67697
- 5700.00    0.67177
- 5710.00    0.66514
- 5720.00    0.65983
- 5730.00    0.65895
- 5740.00    0.66348
- 5750.00    0.66999
- 5760.00    0.67477
- 5770.00    0.67842
- 5780.00    0.67657
- 5790.00    0.66754
- 5800.00    0.65574
- 5810.00    0.64435
- 5820.00    0.63667
- 5830.00    0.63760
- 5840.00    0.64518
- 5850.00    0.65651
- 5860.00    0.66919
- 5870.00    0.68090
- 5880.00    0.68779
- 5890.00    0.68819
- 5900.00    0.68415
- 5910.00    0.67915
- 5920.00    0.67463
- 5930.00    0.67282
- 5940.00    0.67526
- 5950.00    0.68010
- 5960.00    0.68675
- 5970.00    0.69445
- 5980.00    0.70018
- 5990.00    0.70207
- 6000.00    0.70067
- 6010.00    0.69468
- 6020.00    0.68987
- 6030.00    0.68384
- 6040.00    0.68022
- 6050.00    0.67821
- 6060.00    0.67911
- 6070.00    0.68359
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- 6090.00    0.69103
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- 6140.00    0.70279
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- 6180.00    0.71235
- 6190.00    0.71174
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- 6210.00    0.71356
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- 6230.00    0.71588
- 6240.00    0.71584
- 6250.00    0.71758
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- 6370.00    0.71706
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observation_sim/instruments/data/throughputs/u_throughput.txt

-# u_cssc
+# u_csst
 # lambda_Angst  throughput
- 2000.00    0.00000
- 2010.00    0.00000
- 2020.00    0.00000
- 2030.00    0.00000
- 2040.00    0.00000
- 2050.00    0.00000
- 2060.00    0.00000
- 2070.00    0.00000
- 2080.00    0.00000
- 2090.00    0.00000
- 2100.00    0.00000
- 2110.00    0.00000
- 2120.00    0.00000
- 2130.00    0.00000
- 2140.00    0.00000
- 2150.00    0.00000
- 2160.00    0.00000
- 2170.00    0.00000
- 2180.00    0.00000
- 2190.00    0.00000
- 2200.00    0.00000
- 2210.00    0.00000
- 2220.00    0.00000
- 2230.00    0.00000
- 2240.00    0.00000
- 2250.00    0.00000
- 2260.00    0.00000
- 2270.00    0.00000
- 2280.00    0.00000
- 2290.00    0.00000
- 2300.00    0.00000
- 2310.00    0.00000
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- 2330.00    0.00000
- 2340.00    0.00000
- 2350.00    0.00000
- 2360.00    0.00000
- 2370.00    0.00000
- 2380.00    0.00000
- 2390.00    0.00000
- 2400.00    0.00000
- 2410.00    0.00000
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- 2450.00    0.00000
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- 2470.00    0.00000
- 2480.00    0.00000
- 2490.00    0.00000
- 2500.00    0.00000
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- 2570.00    0.00000
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- 2600.00    0.00000
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- 2930.00   -0.00000
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- 3080.00    0.00001
- 3090.00    0.00002
- 3100.00    0.00006
- 3110.00    0.00012
- 3120.00    0.00024
- 3130.00    0.00048
- 3140.00    0.00092
- 3150.00    0.00186
- 3160.00    0.00376
- 3170.00    0.00817
- 3180.00    0.01833
- 3190.00    0.04089
- 3200.00    0.07755
- 3210.00    0.12878
- 3220.00    0.18590
- 3230.00    0.22829
- 3240.00    0.25094
- 3250.00    0.26936
- 3260.00    0.28292
- 3270.00    0.29534
- 3280.00    0.30380
- 3290.00    0.31418
- 3300.00    0.32330
- 3310.00    0.33253
- 3320.00    0.34048
- 3330.00    0.33724
- 3340.00    0.33668
- 3350.00    0.33415
- 3360.00    0.33060
- 3370.00    0.33775
- 3380.00    0.34631
- 3390.00    0.34715
- 3400.00    0.34772
- 3410.00    0.34624
- 3420.00    0.34383
- 3430.00    0.34628
- 3440.00    0.34509
- 3450.00    0.33745
- 3460.00    0.33189
- 3470.00    0.33956
- 3480.00    0.34656
- 3490.00    0.35045
- 3500.00    0.35242
- 3510.00    0.35256
- 3520.00    0.35209
- 3530.00    0.34463
- 3540.00    0.34057
- 3550.00    0.34191
- 3560.00    0.34303
- 3570.00    0.34553
- 3580.00    0.34666
- 3590.00    0.34928
- 3600.00    0.35270
- 3610.00    0.35379
- 3620.00    0.35330
- 3630.00    0.35114
- 3640.00    0.35612
- 3650.00    0.35578
- 3660.00    0.35419
- 3670.00    0.35591
- 3680.00    0.36092
- 3690.00    0.36047
- 3700.00    0.36195
- 3710.00    0.36278
- 3720.00    0.36401
- 3730.00    0.36108
- 3740.00    0.36065
- 3750.00    0.37048
- 3760.00    0.37639
- 3770.00    0.37960
- 3780.00    0.38217
- 3790.00    0.37517
- 3800.00    0.37205
- 3810.00    0.37348
- 3820.00    0.37200
- 3830.00    0.37322
- 3840.00    0.37248
- 3850.00    0.37114
- 3860.00    0.37461
- 3870.00    0.37905
- 3880.00    0.38045
- 3890.00    0.37936
- 3900.00    0.37861
- 3910.00    0.38177
- 3920.00    0.38625
- 3930.00    0.38541
- 3940.00    0.38534
- 3950.00    0.38286
- 3960.00    0.37300
- 3970.00    0.34866
- 3980.00    0.30287
- 3990.00    0.24324
- 4000.00    0.17649
- 4010.00    0.11254
- 4020.00    0.06327
- 4030.00    0.03342
- 4040.00    0.01833
- 4050.00    0.01065
- 4060.00    0.00617
- 4070.00    0.00355
- 4080.00    0.00206
- 4090.00    0.00129
- 4100.00    0.00088
- 4110.00    0.00061
- 4120.00    0.00041
- 4130.00    0.00028
- 4140.00    0.00020
- 4150.00    0.00014
- 4160.00    0.00010
- 4170.00    0.00006
- 4180.00    0.00004
- 4190.00    0.00003
- 4200.00    0.00002
- 4210.00    0.00002
- 4220.00    0.00001
- 4230.00    0.00001
- 4240.00    0.00001
- 4250.00    0.00000
- 4260.00    0.00000
- 4270.00    0.00000
- 4280.00    0.00000
- 4290.00    0.00000
- 4300.00    0.00000
- 4310.00    0.00000
- 4320.00    0.00000
- 4330.00    0.00000
- 4340.00    0.00000
- 4350.00    0.00000
- 4360.00    0.00000
- 4370.00    0.00000
- 4380.00    0.00000
- 4390.00    0.00000
- 4400.00    0.00000
- 4410.00    0.00000
- 4420.00    0.00000
- 4430.00    0.00000
- 4440.00    0.00000
- 4450.00    0.00000
- 4460.00   -0.00000
- 4470.00   -0.00000
- 4480.00   -0.00000
- 4490.00   -0.00000
- 4500.00    0.00000
- 4510.00    0.00000
- 4520.00    0.00000
- 4530.00    0.00000
- 4540.00    0.00000
- 4550.00   -0.00000
- 4560.00   -0.00000
- 4570.00    0.00000
- 4580.00    0.00000
- 4590.00   -0.00000
- 4600.00   -0.00000
- 4610.00   -0.00000
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- 4630.00    0.00000
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- 4650.00    0.00000
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- 4670.00    0.00000
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- 4690.00    0.00000
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observation_sim/instruments/data/throughputs/y_throughput.txt

-# y_cssc
+# y_csst
 # lambda_Angst  throughput
- 2000.00    0.00000
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observation_sim/instruments/data/throughputs/z_throughput.txt

-# z_cssc
+# z_csst
 # lambda_Angst  throughput
- 2000.00   -0.00000
- 2010.00    0.00000
- 2020.00    0.00000
- 2030.00   -0.00000
- 2040.00    0.00000
- 2050.00   -0.00000
- 2060.00    0.00000
- 2070.00    0.00000
- 2080.00    0.00000
- 2090.00    0.00000
- 2100.00   -0.00000
- 2110.00   -0.00000
- 2120.00    0.00000
- 2130.00   -0.00000
- 2140.00    0.00000
- 2150.00    0.00000
- 2160.00   -0.00000
- 2170.00    0.00000
- 2180.00    0.00000
- 2190.00    0.00000
- 2200.00    0.00000
- 2210.00    0.00000
- 2220.00   -0.00000
- 2230.00    0.00000
- 2240.00    0.00000
- 2250.00    0.00000
- 2260.00    0.00000
- 2270.00    0.00000
- 2280.00    0.00000
- 2290.00    0.00000
- 2300.00   -0.00000
- 2310.00   -0.00000
- 2320.00    0.00000
- 2330.00   -0.00000
- 2340.00    0.00000
- 2350.00   -0.00000
- 2360.00    0.00000
- 2370.00    0.00000
- 2380.00    0.00000
- 2390.00   -0.00000
- 2400.00   -0.00000
- 2410.00   -0.00000
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- 2430.00    0.00000
- 2440.00    0.00000
- 2450.00   -0.00000
- 2460.00    0.00000
- 2470.00    0.00000
- 2480.00   -0.00000
- 2490.00    0.00000
- 2500.00   -0.00000
- 2510.00   -0.00000
- 2520.00    0.00000
- 2530.00    0.00000
- 2540.00    0.00000
- 2550.00   -0.00000
- 2560.00   -0.00000
- 2570.00   -0.00000
- 2580.00   -0.00000
- 2590.00    0.00000
- 2600.00    0.00000
- 2610.00   -0.00000
- 2620.00    0.00000
- 2630.00    0.00000
- 2640.00    0.00000
- 2650.00    0.00000
- 2660.00   -0.00000
- 2670.00    0.00000
- 2680.00    0.00000
- 2690.00   -0.00000
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- 2740.00    0.00000
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- 2790.00    0.00000
- 2800.00    0.00000
- 2810.00    0.00000
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- 2830.00    0.00000
- 2840.00    0.00000
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- 2900.00   -0.00000
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- 2940.00    0.00000
- 2950.00   -0.00000
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- 2980.00   -0.00000
- 2990.00   -0.00000
- 3000.00   -0.00000
- 3010.00    0.00000
- 3020.00   -0.00000
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- 3050.00    0.00000
- 3060.00    0.00000
- 3070.00   -0.00000
- 3080.00    0.00000
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- 3100.00    0.00000
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- 3150.00   -0.00000
- 3160.00   -0.00000
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- 3200.00   -0.00000
- 3210.00   -0.00000
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- 3230.00    0.00000
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- 3260.00    0.00000
- 3270.00    0.00000
- 3280.00   -0.00000
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- 3380.00    0.00000
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- 3400.00   -0.00000
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- 3500.00   -0.00000
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- 3540.00    0.00000
- 3550.00   -0.00000
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- 3580.00    0.00000
- 3590.00    0.00000
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- 3690.00    0.00000
- 3700.00    0.00000
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- 3730.00   -0.00000
- 3740.00    0.00000
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- 3900.00   -0.00000
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- 3950.00    0.00000
- 3960.00   -0.00000
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- 3980.00   -0.00000
- 3990.00   -0.00000
- 4000.00   -0.00000
- 4010.00   -0.00000
- 4020.00   -0.00000
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- 4040.00   -0.00000
- 4050.00    0.00000
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- 4080.00    0.00000
- 4090.00    0.00000
- 4100.00   -0.00000
- 4110.00   -0.00000
- 4120.00   -0.00000
- 4130.00    0.00000
- 4140.00    0.00000
- 4150.00    0.00000
- 4160.00   -0.00000
- 4170.00   -0.00000
- 4180.00    0.00000
- 4190.00   -0.00000
- 4200.00    0.00000
- 4210.00    0.00000
- 4220.00   -0.00000
- 4230.00   -0.00000
- 4240.00    0.00000
- 4250.00    0.00000
- 4260.00    0.00000
- 4270.00   -0.00000
- 4280.00    0.00000
- 4290.00   -0.00000
- 4300.00   -0.00000
- 4310.00   -0.00000
- 4320.00    0.00000
- 4330.00    0.00000
- 4340.00   -0.00000
- 4350.00    0.00000
- 4360.00    0.00000
- 4370.00    0.00000
- 4380.00    0.00000
- 4390.00    0.00000
- 4400.00    0.00000
- 4410.00    0.00000
- 4420.00    0.00000
- 4430.00   -0.00000
- 4440.00   -0.00000
- 4450.00    0.00000
- 4460.00    0.00000
- 4470.00   -0.00000
- 4480.00    0.00000
- 4490.00    0.00000
- 4500.00   -0.00000
- 4510.00   -0.00000
- 4520.00   -0.00000
- 4530.00    0.00000
- 4540.00   -0.00000
- 4550.00    0.00000
- 4560.00    0.00000
- 4570.00    0.00000
- 4580.00   -0.00000
- 4590.00    0.00000
- 4600.00    0.00000
- 4610.00   -0.00000
- 4620.00    0.00000
- 4630.00   -0.00000
- 4640.00    0.00000
- 4650.00    0.00000
- 4660.00    0.00000
- 4670.00    0.00000
- 4680.00    0.00000
- 4690.00    0.00001
- 4700.00    0.00000
- 4710.00    0.00000
- 4720.00    0.00001
- 4730.00    0.00001
- 4740.00    0.00001
- 4750.00    0.00001
- 4760.00    0.00001
- 4770.00    0.00001
- 4780.00    0.00001
- 4790.00    0.00002
- 4800.00    0.00003
- 4810.00    0.00005
- 4820.00    0.00006
- 4830.00    0.00005
- 4840.00    0.00003
- 4850.00    0.00001
- 4860.00    0.00000
- 4870.00    0.00000
- 4880.00    0.00000
- 4890.00   -0.00000
- 4900.00    0.00000
- 4910.00    0.00000
- 4920.00    0.00000
- 4930.00   -0.00000
- 4940.00   -0.00000
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+10140.000000	0.088199
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+10180.000000	0.075494
+10190.000000	0.072194
+10200.000000	0.068893
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+10240.000000	0.055898
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+10270.000000	0.046096
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+10290.000000	0.039411
+10300.000000	0.036030
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+11000.000000	0.000000

observation_sim/mock_objects/MockObject.py

@@ -172,7 +172,10 @@ class MockObject(object):
             # star = galsim.Convolve(psf, star)
             star = psf.withFlux(nphotons)
 
-            stamp = star.drawImage(method='no_pixel', wcs=chip_wcs_local, offset=offset)
+            if EXTRA:
+                stamp = star.drawImage(method='no_pixel', wcs=chip_wcs_local, offset=offset)
+            else:
+                stamp = star.drawImage(wcs=chip_wcs_local, offset=offset)
             if np.sum(np.isnan(stamp.array)) > 0:
                 continue
             stamp.setCenter(x_nominal, y_nominal)

observation_sim/mock_objects/SpecDisperser/SpecDisperser.py

@@ -18,6 +18,7 @@ import galsim
 
 import sys
 import os
+import math
 
 
 def rotate90(array_orig=None, xc=0, yc=0, isClockwise=0):

observation_sim/mock_objects/Stamp.py

@@ -92,7 +92,7 @@ class Stamp(MockObject):
             else:
                 gal = gal + gal_temp
 
-        stamp = gal.drawImage(method='no_pixel', wcs=chip_wcs_local, offset=offset)
+        stamp = gal.drawImage(wcs=chip_wcs_local, offset=offset)
         if np.sum(np.isnan(stamp.array)) > 0:
             # ERROR happens
             return 2, pos_shear

observation_sim/sim_steps/add_pattern_noise.py

@@ -38,7 +38,7 @@ 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)
+    badfraction = chip.badfraction*(rgbadpix.random()*0.5+0.7)  # 5E-5*(rgbadpix.random()*0.5+0.7)
     chip.img = effects.DefectivePixels(
         chip.img,
         IfHotPix=obs_param["hot_pixels"],

observation_sim/sim_steps/prepare_headers.py

@@ -15,10 +15,11 @@ def prepare_headers(self, chip, pointing):
         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],
+        pa=(pointing.img_pa.deg + 180) % 360 - 180,
         project_cycle=self.overall_config["project_cycle"],
         run_counter=self.overall_config["run_counter"],
         chip_name=str(chip.chipID).rjust(2, '0'),
-        obstype=pointing.survey_field_type,
+        obstype=pointing.pointing_type,
         dataset=pointing.dataset)
     self.h_ext = generateExtensionHeader(
         chip=chip,

setup.py

@@ -76,7 +76,7 @@ with open("requirements.txt", "r") as f:
     ]
 
 setup(name='csst_msc_sim',
-      version='3.2.1',
+      version='3.3.0',
       packages=find_packages(),
       # install_requires=[
       #     # 'numpy>=1.18.5',

tools/get_LED_Img.py

+import numpy as np
+from observation_sim.instruments import Chip, Filter, FilterParam
+import astropy.io.fits as fits
+from observation_sim.mock_objects import FlatLED
+
+# 输入打开的LED名字列表led_type_list和对应的打开时间列表exp_t_list
+
+
+def get_LED_Img(chipID=8, led_type_list=['LED1'], exp_t_list=[0.1]):
+    chip = Chip(chipID)
+    filter_id, filter_type = chip.getChipFilter()
+    filt = Filter(
+        filter_id=filter_id,
+        filter_type=filter_type,
+        filter_param=FilterParam())
+    led_obj = FlatLED(chip, filt)
+    led_flat, ledstat, letts = led_obj.drawObj_LEDFlat(
+        led_type_list=led_type_list, exp_t_list=exp_t_list)
+    return led_flat
+
+
+if __name__ == "__main__":
+    chipid = 7
+    led_type_list = ['LED5', 'LED6', 'LED7', 'LED8']
+    exp_t_list = [0.1, 8, 9, 10]
+
+    led_img = get_LED_Img(
+        chipID=chipid, led_type_list=led_type_list, exp_t_list=exp_t_list)
+    fits.writeto('test_led.fits', led_img)

tools/get_PSF_SLS.py

+import numpy as np
+import observation_sim.psf.PSFInterpSLS as PSFInterpSLS
+import observation_sim.psf.PSFInterp as PSFInterp
+from observation_sim.instruments import Chip, Filter, FilterParam
+import astropy.io.fits as fitsio
+
+from observation_sim.instruments import Chip, FilterParam, Filter
+import galsim
+
+# 计算 0级或1级光谱在某个波长位置的PSF, 返回值是过采样的PSF，像元大小有CSST图像像元大小的1/2
+# chipId 必须为[1,2,3,4,5,10,21,26,27,28,29,30]
+# order 只有 0 或 1
+# pos_img 为直接成像在图像上的位置,[x, y]
+# wave: 波长，单位A
+
+
+def get_SLS_PSF(chipID=1, pos_img=[6000, 4000], order=1, wave=8000, sls_psf_dir='/nfsdata/share/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp_cd/'):
+    orders = {0: 'B', 1: 'A'}
+    chip = Chip(chipID)
+    filter_id, filter_type = chip.getChipFilter()
+    filt = Filter(
+        filter_id=filter_id,
+        filter_type=filter_type,
+        filter_param=FilterParam())
+
+    psf_model = PSFInterpSLS(chip, filt, PSF_data_prefix=sls_psf_dir)
+    bandNo = 1
+    for i, brange in enumerate(psf_model.bandranges):
+        if wave >= brange[0] and wave < brange[1]:
+            bandNo = i + 1
+            break
+
+    psf1, _ = psf_model.get_PSF(
+        chip, pos_img_local=pos_img, bandNo=bandNo, galsimGSObject=False, g_order=orders[order])
+    fn = "psf.chip{:}.order{:}.wave{:}.fits".format(chipID, order, wave)
+    hdu = fitsio.PrimaryHDU()
+    hdu.data = psf1
+    hdu.header.set('pixScale', 5)
+    hdu.writeto(fn, overwrite=True)
+
+# 获得成像的PSF,PSF在波长区间分四段采样，返回4段的PSF，每个PSF代表不同band波长范围不同的四段psf，波长从小到大
+# chipId 必须为[6,7,8,9,11,12,13,14,15,16,17,18,19,20,22,23,24,25]
+# pos_img 为直接成像在图像上的位置,[x, y]
+
+
+def getPSFImage(chipID=6, pos_img=[6000, 4500], psf_pho_dir="/nfsdata/share/CSSOSDataProductsSims/data/psfcube/set1_dynamic/"):
+    chip = Chip(chipID=chipID)
+    print('chip.bound::', chip.bound.xmin, chip.bound.xmax,
+          chip.bound.ymin, chip.bound.ymax)
+
+    # Setup Position on focalplane
+    # try get the PSF at some location (1234, 1234) on the chip
+    pos_img = pos_img
+    x, y = pos_img
+    x = x+chip.bound.xmin
+    y = y+chip.bound.ymin
+
+    pos_img = galsim.PositionD(x, y)
+
+    # Setup sub-bandpass
+    # (There are 4 sub-bandpasses for each PSF sample)
+    filter_param = FilterParam()
+    filter_id, filter_type = chip.getChipFilter()
+    filt = Filter(
+        filter_id=filter_id,
+        filter_type=filter_type,
+        filter_param=filter_param,
+        ccd_bandpass=chip.effCurve)
+    bandpass_list = filt.bandpass_sub_list
+    for i in range(len(bandpass_list)):
+        print(i, end=" ", flush=True)
+        # say you want to access the PSF for the sub-bandpass at the blue end for that chip
+        bandpass = bandpass_list[i]
+
+        # Get corresponding PSF model
+        psf_model = PSFInterp(chip=chip, npsf=100,
+                              PSF_data_file=psf_pho_dir)
+        psf = psf_model.get_PSF(
+            chip=chip, pos_img=pos_img, bandpass=bandpass, galsimGSObject=False)
+        fn = "psf_chip{:}.wave{:}.fits".format(chipID, i)
+        hdu = fitsio.PrimaryHDU()
+        hdu.data = psf
+        hdu.header.set('pixScale', 5)
+        hdu.writeto(fn, overwrite=True)
+
+
+if __name__ == "__main__":
+    chipid = 2
+    pos_img = [6000, 4500]
+    order = 1
+    w = 5000
+    get_SLS_PSF(chipID=chipid, pos_img=pos_img, order=order, wave=w)
+
+    chipid = 7
+    pos_img = [6000, 4500]
+    getPSFImage(chipID=chipid, pos_img=pos_img)