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Commit 18bdecdb authored by Fang Yuedong's avatar Fang Yuedong
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encapsulate instrumental data

parent 72f88213
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ObservationSim/MockObject/SkybackgroundMap.py
View file @ 18bdecdb
......@@ -10,10 +10,16 @@ import galsim
import os
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='param/skybackground/sky_emiss_hubble_50_50_A.dat', conf=[''], pixelSize=0.074, isAlongY=0,
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):
# skyMap = np.ones([yLen, xLen], dtype='float32')
#
......@@ -36,7 +42,9 @@ def calculateSkyMap_split_g(skyMap=None, blueLimit=4200, redLimit=6500, skyfn='p
fImg = galsim.Image(fimg)
# skyfn = os.path.join(SLSSIM_PATH, skyfn)
skySpec = np.loadtxt(skyfn)
with pkg_resources.path('ObservationSim.MockObject.data', 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 isAlongY == 0:
......@@ -133,7 +141,7 @@ def calculateSkyMap_split_g(skyMap=None, blueLimit=4200, redLimit=6500, skyfn='p
return fimg
def calculateSkyMap(xLen=9232, yLen=9126, blueLimit=4200, redLimit=6500,
skyfn='param/skybackground/sky_emiss_hubble_50_50_A.dat', conf='', pixelSize=0.074, isAlongY=0):
skyfn='sky_emiss_hubble_50_50_A.dat', conf='', pixelSize=0.074, isAlongY=0):
skyMap = np.ones([yLen, xLen], dtype='float32')
if isAlongY == 1:
......@@ -146,7 +154,10 @@ def calculateSkyMap(xLen=9232, yLen=9126, blueLimit=4200, redLimit=6500,
fimg = np.zeros_like(skyMap)
fImg = galsim.Image(fimg)
skySpec = np.loadtxt(skyfn)
with pkg_resources.path('ObservationSim.MockObject.data', 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],
......
ObservationSim/MockObject/data/sky_emiss_hubble_50_50_A.dat 0 → 100644
View file @ 18bdecdb
# 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
1585.0 9.044122953272506e-08
1590.0 9.58066732166779e-08
1595.0 1.0120406746742795e-07
1600.0 1.0663341228497521e-07
1605.0 2.656467766234215e-07
1610.0 4.2564880223865926e-07
1615.0 5.866394891306888e-07
1620.0 7.486188372995099e-07
1625.0 9.115868467451226e-07
1630.0 1.075543517467527e-06
1635.0 1.2404888494667231e-06
1640.0 1.4064228427427105e-06
1645.0 1.5733454972954894e-06
1650.0 1.7412568131250607e-06
1655.0 1.9101567902314226e-06
1660.0 2.080045428614577e-06
1665.0 2.250922728274523e-06
1670.0 2.4227886892112605e-06
1675.0 2.595643311424789e-06
1680.0 2.76948659491511e-06
1685.0 2.9443185396822223e-06
1690.0 3.120139145726126e-06
1695.0 3.2969484130468213e-06
1700.0 3.474746341644308e-06
1705.0 3.6828985428956737e-06
1710.0 3.892211637748622e-06
1715.0 4.102685626203155e-06
1720.0 4.3143205082592715e-06
1725.0 4.527116283916972e-06
1730.0 4.741072953176257e-06
1735.0 4.956190516037125e-06
1740.0 5.172468972499578e-06
1745.0 5.389908322563615e-06
1750.0 5.608508566229232e-06
1755.0 5.828269703496438e-06
1760.0 6.0491917343652265e-06
1765.0 6.271274658835598e-06
1770.0 6.494518476907555e-06
1775.0 6.7189231885810926e-06
1780.0 6.944488793856216e-06
1785.0 7.171215292732926e-06
1790.0 7.399102685211216e-06
1795.0 7.6281509712910916e-06
1800.0 7.85836015097255e-06
1805.0 8.07091933107771e-06
1810.0 8.284535189309783e-06
1815.0 8.49920772566877e-06
1820.0 8.714936940154672e-06
1825.0 8.931722832767487e-06
1830.0 9.149565403507218e-06
1835.0 9.368464652373863e-06
1840.0 9.588420579367423e-06
1845.0 9.809433184487896e-06
1850.0 1.0031502467735282e-05
1855.0 1.0254628429109583e-05
1860.0 1.0478811068610799e-05
1865.0 1.0704050386238928e-05
1870.0 1.0930346381993973e-05
1875.0 1.115769905587593e-05
1880.0 1.1386108407884804e-05
1885.0 1.1615574438020591e-05
1890.0 1.184609714628329e-05
1895.0 1.2077676532672904e-05
1900.0 1.2310312597189432e-05
1905.0 1.2712087023747278e-05
1910.0 1.3115800446982773e-05
1915.0 1.3521452866895907e-05
1920.0 1.3929044283486693e-05
1925.0 1.433857469675512e-05
1930.0 1.4750044106701198e-05
1935.0 1.5163452513324916e-05
1940.0 1.5578799916626283e-05
1945.0 1.59960863166053e-05
1950.0 1.6415311713261957e-05
1955.0 1.6836476106596262e-05
1960.0 1.7259579496608216e-05
1965.0 1.7684621883297813e-05
1970.0 1.8111603266665053e-05
1975.0 1.8540523646709943e-05
1980.0 1.8971383023432478e-05
1985.0 1.940418139683266e-05
1990.0 1.9838918766910488e-05
1995.0 2.027559513366596e-05
2000.0 2.071421049709908e-05
2005.0 2.327957785004678e-05
2010.0 2.5857481770708722e-05
2015.0 2.8447922259084894e-05
2020.0 3.105089931517531e-05
2025.0 3.3666412938979966e-05
2030.0 3.6294463130498865e-05
2035.0 3.893504988973199e-05
2040.0 4.158817321667937e-05
2045.0 4.425383311134099e-05
2050.0 4.6932029573716845e-05
2055.0 4.9622762603806926e-05
2060.0 5.232603220161126e-05
2065.0 5.504183836712983e-05
2070.0 5.7770181100362636e-05
2075.0 6.051106040130968e-05
2080.0 6.326447626997097e-05
2085.0 6.60304287063465e-05
2090.0 6.880891771043627e-05
2095.0 7.159994328224026e-05
2100.0 7.440350542175852e-05
2105.0 7.745262372366961e-05
2110.0 8.051538557474235e-05
2115.0 8.359179097497673e-05
2120.0 8.668183992437276e-05
2125.0 8.978553242293042e-05
2130.0 9.290286847064974e-05
2135.0 9.60338480675307e-05
2140.0 9.917847121357329e-05
2145.0 0.00010233673790877756
2150.0 0.00010550864815314345
2155.0 0.00010869420194667097
2160.0 0.00011189339928936014
2165.0 0.00011510624018121096
2170.0 0.00011833272462222341
2175.0 0.00012157285261239751
2180.0 0.00012482662415173328
2185.0 0.00012809403924023065
2190.0 0.0001313750978778897
2195.0 0.00013466980006471039
2200.0 0.0001379781458006927
2205.0 0.00013697162005895603
2210.0 0.00013595910741274437
2215.0 0.00013494060786205768
2220.0 0.00013391612140689602
2225.0 0.00013288564804725934
2230.0 0.0001318491877831476
2235.0 0.00013080674061456097
2240.0 0.00012975830654149924
2245.0 0.00012870388556396258
2250.0 0.00012764347768195084
2255.0 0.00012657708289546412
2260.0 0.00012550470120450242
2265.0 0.00012442633260906571
2270.0 0.000123341977109154
2275.0 0.0001222516347047673
2280.0 0.00012115530539590555
2285.0 0.00012005298918256885
2290.0 0.00011894468606475708
2295.0 0.00011783039604247039
2300.0 0.00011671011911570866
2305.0 0.00011709491260915991
2310.0 0.00011748027476136693
2315.0 0.00011786620557232971
2320.0 0.00011825270504204823
2325.0 0.00011863977317052247
2330.0 0.00011902740995775252
2335.0 0.00011941561540373824
2340.0 0.00011980438950847977
2345.0 0.00012019373227197702
2350.0 0.00012058364369422999
2355.0 0.00012097412377523875
2360.0 0.00012136517251500323
2365.0 0.0001217567899135235
2370.0 0.00012214897597079947
2375.0 0.0001225417306868312
2380.0 0.00012293505406161867
2385.0 0.00012332894609516193
2390.0 0.0001237234067874609
2395.0 0.00012411843613851565
2400.0 0.0001245140341483261
2405.0 0.00012714023131933059
2410.0 0.0001297762696251924
2415.0 0.00013242214906591162
2420.0 0.00013507786964148816
2425.0 0.00013774343135192208
2430.0 0.00014041883419721335
2435.0 0.000143104078177362
2440.0 0.000145799163292368
2445.0 0.00014850408954223136
2450.0 0.00015121885692695206
2455.0 0.00015394346544653017
2460.0 0.00015667791510096563
2465.0 0.00015942220589025842
2470.0 0.0001621763378144086
2475.0 0.00016494031087341615
2480.0 0.000167714125067281
2485.0 0.00017049778039600328
2490.0 0.00017329127685958287
2495.0 0.00017609461445801988
2500.0 0.00017890779319131418
2505.0 0.00018094365741134615
2510.0 0.00018298622042831872
2515.0 0.00018503548224223207
2520.0 0.0001870914428530861
2525.0 0.0001891541022608809
2530.0 0.00019122346046561634
2535.0 0.00019329951746729256
2540.0 0.0001953822732659095
2545.0 0.00019747172786146707
2550.0 0.00019956788125396544
2555.0 0.00020167073344340452
2560.0 0.0002037802844297843
2565.0 0.00020589653421310478
2570.0 0.000208019482793366
2575.0 0.00021014913017056787
2580.0 0.00021228547634471055
2585.0 0.00021442852131579388
2590.0 0.00021657826508381795
2595.0 0.00021873470764878275
2600.0 0.00022089784901068826
2605.0 0.0002479953230516333
2610.0 0.00027519518738234857
2615.0 0.0003024974420028341
2620.0 0.00032990208691309003
2625.0 0.00035740912211311616
2630.0 0.00038501854760291255
2635.0 0.0004127303633824792
2640.0 0.00044054456945181607
2645.0 0.0004684611658109233
2650.0 0.0004964801524598007
2655.0 0.0005246015293984484
2660.0 0.0005528252966268666
2665.0 0.0005811514541450546
2670.0 0.0006095800019530132
2675.0 0.000638110940050742
2680.0 0.0006667442684382412
2685.0 0.0006954799871155105
2690.0 0.0007243180960825502
2695.0 0.0007532585953393599
2700.0 0.0007823014848859402
2705.0 0.0007556728768021461
2710.0 0.0007289404708827242
2715.0 0.0007021042671276747
2720.0 0.0006751642655369976
2725.0 0.0006481204661106927
2730.0 0.0006209728688487601
2735.0 0.0005937214737511999
2740.0 0.000566366280818012
2745.0 0.0005389072900491964
2750.0 0.0005113445014447532
2755.0 0.00048367791500468233
2760.0 0.0004559075307289837
2765.0 0.0004280333486176574
2770.0 0.00040005536867070347
2775.0 0.0003719735908881218
2780.0 0.00034378801526991243
2785.0 0.00031549864181607554
2790.0 0.0002871054705266108
2795.0 0.0002586085014015184
2800.0 0.00023000773444079842
2805.0 0.0003032463272527141
2810.0 0.0003767445559459658
2815.0 0.00045050242052055314
2820.0 0.0005245199209764765
2825.0 0.0005987970573137354
2830.0 0.0006733338295323304
2835.0 0.0007481302376322609
2840.0 0.0008231862816135275
2845.0 0.00089850196147613
2850.0 0.0009740772772200681
2855.0 0.0010499122288453423
2860.0 0.0011260068163519523
2865.0 0.0012023610397398977
2870.0 0.0012789748990091792
2875.0 0.0013558483941597966
2880.0 0.0014329815251917497
2885.0 0.0015103742921050388
2890.0 0.0015880266948996636
2895.0 0.0016659387335756243
2900.0 0.001744110408132921
2905.0 0.0017136314040109434
2910.0 0.001683037129351848
2915.0 0.0016523275841556356
2920.0 0.001621502768422306
2925.0 0.0015905626821518595
2930.0 0.0015595073253442955
2935.0 0.0015283366979996146
2940.0 0.0014970508001178162
2945.0 0.0014656496316989007
2950.0 0.0014341331927428679
2955.0 0.001402501483249718
2960.0 0.0013707545032194506
2965.0 0.001338892252652066
2970.0 0.0013069147315475647
2975.0 0.0012748219399059458
2980.0 0.0012426138777272093
2985.0 0.001210290545011356
2990.0 0.0011778519417583856
2995.0 0.0011452980679682978
3000.0 0.001112628923641093
3005.0 0.0011300853582804423
3010.0 0.0011475937132296088
3015.0 0.001165153988488592
3020.0 0.0011827661840573915
3025.0 0.0012004302999360085
3030.0 0.001218146336124442
3035.0 0.0012359142926226924
3040.0 0.0012537341694307594
3045.0 0.001271605966548643
3050.0 0.001289529683976344
3055.0 0.0013075053217138615
3060.0 0.0013255328797611957
3065.0 0.001343612358118347
3070.0 0.0013617437567853149
3075.0 0.0013799270757620995
3080.0 0.001398162315048701
3085.0 0.001416449474645119
3090.0 0.0014347885545513543
3095.0 0.0014531795547674065
3100.0 0.0014716224752932747
3105.0 0.0015154755320681617
3110.0 0.0015594621781252946
3115.0 0.0016035824134646728
3120.0 0.0016478362380862974
3125.0 0.0016922236519901674
3130.0 0.0017367446551762832
3135.0 0.001781399247644645
3140.0 0.0018261874293952524
3145.0 0.0018711092004281052
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ObservationSim/ObservationSim.py
View file @ 18bdecdb
......@@ -19,9 +19,11 @@ class Observation(object):
def __init__(self, config, Catalog, work_dir=None, data_dir=None):
self.path_dict = config_dir(config=config, work_dir=work_dir, data_dir=data_dir)
self.config = config
self.tel = Telescope(optEffCurve_path=self.path_dict["mirror_file"])
# self.tel = Telescope(optEffCurve_path=self.path_dict["mirror_file"])
self.tel = Telescope()
self.focal_plane = FocalPlane(survey_type=self.config["obs_setting"]["survey_type"])
self.filter_param = FilterParam(filter_dir=self.path_dict["filter_dir"])
# self.filter_param = FilterParam(filter_dir=self.path_dict["filter_dir"])
self.filter_param = FilterParam()
self.chip_list = []
self.filter_list = []
self.Catalog = Catalog
......@@ -42,10 +44,11 @@ class Observation(object):
# Make Chip & Filter lists
chip = Chip(
chipID=chipID,
ccdEffCurve_dir=self.path_dict["ccd_dir"],
CRdata_dir=self.path_dict["CRdata_dir"],
normalize_dir=self.path_dict["normalize_dir"],
sls_dir=self.path_dict["sls_dir"], config=self.config)
# ccdEffCurve_dir=self.path_dict["ccd_dir"],
# CRdata_dir=self.path_dict["CRdata_dir"],
# normalize_dir=self.path_dict["normalize_dir"],
# sls_dir=self.path_dict["sls_dir"],
config=self.config)
filter_id, filter_type = chip.getChipFilter()
filt = Filter(filter_id=filter_id,
filter_type=filter_type,
......@@ -107,7 +110,14 @@ class Observation(object):
dt=1E-3) # shutter effect normalized image for this chip
flat_normal = flat_normal*shuttimg
flat_normal = np.array(flat_normal,dtype='float32')
sky_map = calculateSkyMap_split_g(skyMap=flat_normal, blueLimit=filt.blue_limit, redLimit=filt.red_limit, skyfn=self.path_dict["sky_file"], conf=chip.sls_conf, pixelSize=chip.pix_scale, isAlongY=0)
sky_map = calculateSkyMap_split_g(
skyMap=flat_normal,
blueLimit=filt.blue_limit,
redLimit=filt.red_limit,
# skyfn=self.path_dict["sky_file"],
conf=chip.sls_conf,
pixelSize=chip.pix_scale,
isAlongY=0)
del flat_normal
if pointing.pointing_type == 'MS':
......
ObservationSim/PSF/PSFInterp/PSFConfig.py deleted 100644 → 0
View file @ 72f88213
import sys
import numpy as np
import scipy.io
from itertools import islice
# from PSF.PSFInterp.PSFUtil import *
# from PSFUtil import findNeighbors, findNeighbors_hoclist
###加载PSF信息###
def LoadPSF(iccd, iwave, ipsf, psfPath, InputMaxPixelPos=True, PSFCentroidWgt=False, PixSizeInMicrons=2.5, PSFBinning=False, PSFConvGauss=False):
"""
load psf informations from psf matrix.
Parameters:
iccd (int): ccd number [1,30].
iwave(int): wave-index [1,4].
ipsf (int): psf number [1, 100].
psfPath (int): path to psf matrix
InputMaxPixelPos(bool-optional): only True for 30*30 psf-matrix
PSFCentroidWgt(bool-optional): True for using psfMat with libCentroid.lib
Returns:
psfInfo (dirctionary)
"""
if iccd not in np.linspace(1, 30, 30, dtype='int'):
print('Error - iccd should be in [1, 30].')
sys.exit()
if iwave not in np.linspace(1, 4, 4, dtype='int'):
print('Error - iwave should be in [1, 4].')
sys.exit()
if ipsf not in np.linspace(1, 900, 900, dtype='int'):
print('Error - ipsf should be in [1, 900].')
sys.exit()
psfInfo = {}
fpath = psfPath +'/' +'ccd{:}'.format(iccd) +'/' + 'wave_{:}'.format(iwave)
#获取ipsf矩阵
'''
if not PSFCentroidWgt:
##读取PSF原数据
fpathMat = fpath +'/' +'5_psf_array' +'/' +'psf_{:}.mat'.format(ipsf)
data = scipy.io.loadmat(fpathMat)
psfInfo['psfMat'] = data['psf']
if PSFCentroidWgt:
##读取PSFCentroidWgt
ffpath = psfPath +'_proc/' +'ccd{:}'.format(iccd) +'/' + 'wave_{:}'.format(iwave)
ffpathMat = ffpath +'/' +'5_psf_array' +'/' +'psf_{:}_centroidWgt.mat'.format(ipsf)
data = scipy.io.loadmat(ffpathMat)
psfInfo['psfMat'] = data['psf']
'''
if not PSFCentroidWgt:
ffpath = fpath
ffpathMat = ffpath +'/5_psf_array/psf_{:}.mat'.format(ipsf)
if PSFCentroidWgt:
ffpath = psfPath +'_proc/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if PSFBinning and (not PSFConvGauss):
ffpath = psfPath +'_bin256x256/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if (not PSFBinning) and PSFConvGauss:
ffpath = psfPath +'_convGauss/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt.mat'.format(ipsf)
if PSFBinning and PSFConvGauss:
ffpath = psfPath +'_convGauss/ccd{:}/wave_{:}'.format(iccd, iwave)
ffpathMat = ffpath +'/5_psf_array/psf_{:}_centroidWgt_BC.mat'.format(ipsf)
data = scipy.io.loadmat(ffpathMat)
psfInfo['psfMat'] = data['psf']
psfInfo['pixsize'] = PixSizeInMicrons #microns
if PSFBinning:
psfInfo['pixsize'] = PixSizeInMicrons*2.0
#获取ipsf波长
fpathWave = fpath +'/' +'1_wavelength.txt'
f = open(fpathWave, 'r')
wavelength = np.float(f.readline())
f.close()
psfInfo['wavelength'] = wavelength
#获取ipsf位置
fpathCoordinate = fpath +'/' +'4_PSF_coordinate.txt'
f = open(fpathCoordinate, 'r')
header = f.readline()
for line in islice(f, ipsf-1, ipsf):
line = line.strip()
columns = line.split()
f.close()
icol = 0
psfInfo['field_x'] = float(columns[icol]) #deg, 视场采样位置
icol+= 1
psfInfo['field_y'] = float(columns[icol]) #deg
icol+= 1
psfInfo['centroid_x'] = float(columns[icol]) #mm, psf质心相对主光线的偏移量
icol+= 1
psfInfo['centroid_y'] = float(columns[icol]) #mm
icol+= 1
if InputMaxPixelPos == True:
psfInfo['max_x'] = float(columns[icol]) #mm, max pixel postion
icol+= 1
psfInfo['max_y'] = float(columns[icol]) #mm
icol+= 1
psfInfo['image_x'] = float(columns[icol]) #mm, 主光线位置
icol+= 1
psfInfo['image_y'] = float(columns[icol]) #mm
if PSFCentroidWgt:
psfInfo['centroid_x'] = data['cx'][0][0] #mm, psfCentroidWgt质心相对主光线的偏移量
psfInfo['centroid_y'] = data['cy'][0][0] #mm
# if PSFBinning:
# psfInfo['centroid_x'] = data['cx'][0][0] +0.0025/2 #binning引起的主光线漂移
# psfInfo['centroid_y'] = data['cy'][0][0] +0.0025/2
return psfInfo
###插值PSF图像-IDW方法###
def psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=None, hoclist=None, PSFCentroidWgt=False):
"""
psf interpolation by IDW
Parameters:
px, py (float, float): position of the target
PSFMat (numpy.array): image
cen_col, cen_row (numpy.array, numpy.array): potions of the psf centers
IDWindex (int-optional): the power index of IDW
OnlyNeighbors (bool-optional): only neighbors are used for psf interpolation
Returns:
psfMaker (numpy.array)
"""
minimum_psf_weight = 1e-8
ref_col = px
ref_row = py
ngy, ngx = PSFMat[0, :, :].shape
npsf = PSFMat[:, :, :].shape[0]
psfWeight = np.zeros([npsf])
from .PSFUtil import findNeighbors, findNeighbors_hoclist
if OnlyNeighbors == True:
if hoc is None:
neigh = findNeighbors(px, py, cen_col, cen_row, dr=5., dn=4, OnlyDistance=False)
if hoc is not None:
neigh = findNeighbors_hoclist(cen_col, cen_row, tx=px,ty=py, dn=4, hoc=hoc, hoclist=hoclist)
neighFlag = np.zeros(npsf)
neighFlag[neigh] = 1
for ipsf in range(npsf):
if OnlyNeighbors == True:
if neighFlag[ipsf] != 1:
continue
dist = np.sqrt((ref_col - cen_col[ipsf])**2 + (ref_row - cen_row[ipsf])**2)
if IDWindex == 1:
psfWeight[ipsf] = dist
if IDWindex == 2:
psfWeight[ipsf] = dist**2
if IDWindex == 3:
psfWeight[ipsf] = dist**3
if IDWindex == 4:
psfWeight[ipsf] = dist**4
psfWeight[ipsf] = max(psfWeight[ipsf], minimum_psf_weight)
psfWeight[ipsf] = 1./psfWeight[ipsf]
psfWeight /= np.sum(psfWeight)
psfMaker = np.zeros([ngy, ngx], dtype=np.float32)
for ipsf in range(npsf):
if OnlyNeighbors == True:
if neighFlag[ipsf] != 1:
continue
iPSFMat = PSFMat[ipsf, :, :].copy()
ipsfWeight = psfWeight[ipsf]
psfMaker += iPSFMat * ipsfWeight
psfMaker /= np.nansum(psfMaker)
return psfMaker
###TEST###
if __name__ == '__main__':
iccd = 1
iwave= 1
ipsf = 1
psfPath = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
psfInfo = LoadPSF(iccd, iwave, ipsf, psfPath, InputMaxPixelPos=True, PSFCentroidWgt=True)
print('psfInfo.keys:', psfInfo.keys())
print(psfInfo)
ObservationSim/PSF/PSFInterp/PSFInterp.py deleted 100644 → 0
View file @ 72f88213
'''
PSF interpolation for CSST-Sim
NOTE: [iccd, iwave, ipsf] are counted from 1 to n, but [tccd, twave, tpsf] are counted from 0 to n-1
'''
import galsim
import numpy as np
import os
import time
import copy
from ObservationSim.PSF.PSFInterp import PSFConfig as myConfig
from ObservationSim.PSF.PSFInterp import PSFUtil as myUtil
from ObservationSim.PSF.PSFModel import PSFModel
LOG_DEBUG = False #***#
NPSF = 900 #***# 30*30
iccdTest = 1 #***#
class PSFInterp(PSFModel):
# def __init__(self, PSF_data=None, params=None, PSF_data_file=None):
def __init__(self, chip, PSF_data=None, PSF_data_file=None, sigSpin=0, psfRa=0.15):
"""
The PSF data matrix is either given by a object parameter or read in from a file.
Parameters:
PSF_data: The PSF data matrix object
params: Other parameters?
PSF_data_file: The file for PSF data matrix (optional).
"""
from . import PSFUtil as myUtil
if LOG_DEBUG:
print('===================================================')
print('DEBUG: psf module for csstSim ' \
+time.strftime("(%Y-%m-%d %H:%M:%S)", time.localtime()), flush=True)
print('===================================================')
self.sigSpin = sigSpin
self.sigGauss = psfRa
self.iccd = int(chip.getChipLabel(chipID=chip.chipID))
if PSF_data_file == None:
PSF_data_file = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
self.nwave= self._getPSFwave(self.iccd, PSF_data_file)
self.PSF_data = self._loadPSF(self.iccd, PSF_data_file)
if LOG_DEBUG:
print('nwave-{:} on ccd-{:}::'.format(self.nwave, self.iccd), flush=True)
print('self.PSF_data ... ok', flush=True)
print('Preparing self.[psfMat,cen_col,cen_row] for psfMaker ... ', end='', flush=True)
ngy, ngx = self.PSF_data[0][0]['psfMat'].shape
self.psfMat = np.zeros([self.nwave, NPSF, ngy, ngx], dtype=np.float32)
self.cen_col= np.zeros([self.nwave, NPSF], dtype=np.float32)
self.cen_row= np.zeros([self.nwave, NPSF], dtype=np.float32)
self.hoc =[]
self.hoclist=[]
for twave in range(self.nwave):
for tpsf in range(NPSF):
self.psfMat[twave, tpsf, :, :] = self.PSF_data[twave][tpsf]['psfMat']
self.PSF_data[twave][tpsf]['psfMat'] = 0 ###free psfMat
self.pixsize = self.PSF_data[twave][tpsf]['pixsize']*1e-3 ##mm
self.cen_col[twave, tpsf] = self.PSF_data[twave][tpsf]['image_x'] +0.*self.pixsize + self.PSF_data[twave][tpsf]['centroid_x']
self.cen_row[twave, tpsf] = self.PSF_data[twave][tpsf]['image_y'] +0.*self.pixsize + self.PSF_data[twave][tpsf]['centroid_y']
#hoclist on twave for neighborsFinding
hoc,hoclist = myUtil.findNeighbors_hoclist(self.cen_col[twave], self.cen_row[twave])
self.hoc.append(hoc)
self.hoclist.append(hoclist)
if LOG_DEBUG:
print('ok', flush=True)
def _getPSFwave(self, iccd, PSF_data_file):
"""
Get # of sampling waves on iccd
Parameters:
iccd: The chip of i-th ccd
PSF_data_file: The file for PSF data matrix
Returns:
nwave: The number of the sampling waves
"""
strs = os.listdir(PSF_data_file + '/ccd{:}'.format(iccd))
nwave = 0
for _ in strs:
if 'wave_' in _:
nwave += 1
return nwave
def _loadPSF(self, iccd, PSF_data_file):
"""
load psf-matrix on iccd
Parameters:
iccd: The chip of i-th ccd
PSF_data_file: The file for PSF data matrix
Returns:
psfSet: The matrix of the csst-psf
"""
from . import PSFConfig as myConfig
psfSet = []
for ii in range(self.nwave):
iwave = ii+1
if LOG_DEBUG:
print('self._loadPSF: iwave::', iwave, flush=True)
psfWave = []
for jj in range(NPSF):
ipsf = jj+1
psfInfo = myConfig.LoadPSF(iccd, iwave, ipsf, PSF_data_file, InputMaxPixelPos=True, PSFCentroidWgt=True, PSFBinning=True, PSFConvGauss=True)
psfWave.append(psfInfo)
psfSet.append(psfWave)
if LOG_DEBUG:
print('psfSet has been loaded:', flush=True)
print('psfSet[iwave][ipsf][keys]:', psfSet[0][0].keys(), flush=True)
return psfSet
def _preprocessPSF(self):
pass
def _findWave(self, bandpass):
for twave in range(self.nwave):
bandwave = self.PSF_data[twave][0]['wavelength']
if bandpass.blue_limit < bandwave and bandwave < bandpass.red_limit:
return twave
return -1
def get_PSF(self, chip, pos_img, bandpass, pixSize=0.0184, galsimGSObject=True, findNeighMode='treeFind', folding_threshold=5.e-3):
"""
Get the PSF at a given image position
Parameters:
chip: A 'Chip' object representing the chip we want to extract PSF from.
pos_img: A 'galsim.Position' object representing the image position.
bandpass: A 'galsim.Bandpass' object representing the wavelength range.
pixSize: The pixels size of psf matrix
findNeighMode: 'treeFind' or 'hoclistFind'
Returns:
PSF: A 'galsim.GSObject'.
"""
from . import PSFConfig as myConfig
pixSize = np.rad2deg(self.pixsize*1e-3/28)*3600 #set psf pixsize
#***# assert self.iccd == chip.chipID, 'ERROR: self.iccd != chip.chipID'
# twave = bandpass-1 #***# ??? #self.findWave(bandpass) ###twave=iwave-1 as that in NOTE
assert self.iccd == int(chip.getChipLabel(chipID=chip.chipID)), 'ERROR: self.iccd != chip.chipID'
twave = self._findWave(bandpass)
if twave == -1:
print("!!!PSF bandpass does not match.")
exit()
PSFMat = self.psfMat[twave]
cen_col= self.cen_col[twave]
cen_row= self.cen_row[twave]
# px = pos_img[0]
# py = pos_img[1]
px = (pos_img.x - chip.cen_pix_x)*0.01
py = (pos_img.y - chip.cen_pix_y)*0.01
if findNeighMode == 'treeFind':
imPSF = myConfig.psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, PSFCentroidWgt=True)
if findNeighMode == 'hoclistFind':
imPSF = myConfig.psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=self.hoc[twave], hoclist=self.hoclist[twave], PSFCentroidWgt=True)
if galsimGSObject:
img = galsim.ImageF(imPSF, scale=pixSize)
gsp = galsim.GSParams(folding_threshold=folding_threshold)
self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
# gaussPSF = galsim.Gaussian(sigma=0.04, gsparams=gsp)
# self.psf = galsim.Convolve(gaussPSF, self.psf)
return self.PSFspin(x=px/0.01, y=py/0.01)
return imPSF
def PSFspin(self, x, y):
"""
The PSF profile at a given image position relative to the axis center
Parameters:
theta : spin angles in a given exposure in unit of [arcsecond]
dx, dy: relative position to the axis center in unit of [pixels]
Return:
Spinned PSF: g1, g2 and axis ratio 'a/b'
"""
a2Rad = np.pi/(60.0*60.0*180.0)
ff = self.sigGauss * 0.107 * (1000.0/10.0) # in unit of [pixels]
rc = np.sqrt(x*x + y*y)
cpix = rc*(self.sigSpin*a2Rad)
beta = (np.arctan2(y,x) + np.pi/2)
ell = cpix**2/(2.0*ff**2+cpix**2)
qr = np.sqrt((1.0+ell)/(1.0-ell))
PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
return self.psf.shear(PSFshear), PSFshear
if __name__ == '__main__':
if True:
psfPath = '/data/simudata/CSSOSDataProductsSims/data/csstPSFdata/CSSOS_psf_20210108/CSST_psf_ciomp_2p5um_cycle3_ccr90'
psfCSST = PSFInterp(PSF_data_file = psfPath)
iwave= 1
ipsf = 665
pos_img = [psfCSST.cen_col[iwave, ipsf], psfCSST.cen_row[iwave, ipsf]]
img = psfCSST.get_PSF(1, pos_img, iwave, galsimGSObject=True)
print('haha')
if False:
#old version (discarded)
#plot check-1
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(18,5))
ax = plt.subplot(1,3,1)
plt.imshow(img)
plt.colorbar()
ax = plt.subplot(1,3,2)
imgx = psfCSST.itpPSF_data[iwave][ipsf]['psfMat']
imgx/= np.sum(imgx)
plt.imshow(imgx)
plt.colorbar()
ax = plt.subplot(1,3,3)
plt.imshow(img - imgx)
plt.colorbar()
plt.savefig('test/figs/test1.jpg')
if False:
#old version (discarded)
#plot check-2: 注意图像坐标和全局坐标
fig = plt.figure(figsize=(8,8), dpi = 200)
img = psfCSST.PSF_data[iwave][ipsf]['psfMat']
npix = img.shape[0]
dng = 105
imgg = img[dng:-dng, dng:-dng]
plt.imshow(imgg)
imgX = psfCSST.PSF_data[iwave][ipsf]['image_x'] #in mm
imgY = psfCSST.PSF_data[iwave][ipsf]['image_y'] #in mm
deltX= psfCSST.PSF_data[iwave][ipsf]['centroid_x'] #in mm
deltY= psfCSST.PSF_data[iwave][ipsf]['centroid_y'] #in mm
maxX = psfCSST.PSF_data[iwave][ipsf]['max_x']
maxY = psfCSST.PSF_data[iwave][ipsf]['max_y']
cenPix_X = npix/2 + deltX/0.005
cenPix_Y = npix/2 - deltY/0.005
maxPix_X = npix/2 + maxX/0.005-1
maxPix_Y = npix/2 - maxY/0.005-1
plt.plot([cenPix_X-dng],[cenPix_Y-dng], 'rx', ms = 20)
plt.plot([maxPix_X-dng],[maxPix_Y-dng], 'b+', ms=20)
from scipy import ndimage
y, x = ndimage.center_of_mass(img)
plt.plot([x-dng],[y-dng], 'rx', ms = 10, mew=4)
from . import PSFUtil as myUtil
x, y = myUtil.findMaxPix(img)
plt.plot([x-dng],[y-dng], 'b+', ms = 10, mew=4)
plt.savefig('test/figs/test2.jpg')
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