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Commit ecd4d7aa authored by Feng Shuai's avatar Feng Shuai 💬
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Deleted gehong/__pycache__/__init__.cpython-38.pyc,... 

Deleted gehong/__pycache__/__init__.cpython-38.pyc, gehong/__pycache__/config.cpython-38.pyc, gehong/__pycache__/cube3d.cpython-38.pyc, gehong/__pycache__/map2d.cpython-38.pyc, gehong/__pycache__/spec1d.cpython-38.pyc, gehong/data/EMILES/model/HST_ch_iPp0.00.MAG, gehong/data/EMILES/model/HST_mag.dat, gehong/data/EMILES/model/out_mass_CH_PADOVA00, gehong/data/EMILES/.DS_Store, gehong/data/EMILES/Ech1.30Zm0.40T00.0631_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0708_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0794_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0891_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1000_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1122_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1259_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1413_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1585_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1778_iPp0.00_baseFe.fits, 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model mu Z Age M(*+remn) F275_wfc3 F336_WFC3 F435W_WFC F475W_WFC F555W_WFC F606W_WFC F775W_WFC F502N_WFC F550M_WFC F658N_WFC F660N_WFC F439W_WFPC2 F555W_WFPC2 F675W_WFPC2
Ech1.30Zm2.32T00.0631_iPp0.00_baseFe.fits 1.30 -2.32000 0.06310 0.79540 1.04380 1.49462 2.51491 2.58693 2.65098 2.66737 2.71491 2.65994 2.67854 2.62715 2.65877 2.53777 2.64360 2.70434
Ech1.30Zm2.32T00.0708_iPp0.00_baseFe.fits 1.30 -2.32000 0.07080 0.79540 1.18127 1.61475 2.58286 2.64903 2.70570 2.71787 2.74952 2.71667 2.73002 2.67326 2.70053 2.60447 2.69954 2.74509
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Ech1.30Zm2.32T00.0891_iPp0.00_baseFe.fits 1.30 -2.32000 0.08910 0.78320 1.40092 1.81871 2.70153 2.75810 2.80204 2.80691 2.80686 2.81699 2.82075 2.75160 2.77499 2.72141 2.79798 2.81682
Ech1.30Zm2.32T00.1000_iPp0.00_baseFe.fits 1.30 -2.32000 0.10000 0.77070 1.53307 1.92869 2.77557 2.82767 2.86574 2.86734 2.85392 2.88155 2.88208 2.80698 2.82935 2.79465 2.86268 2.86938
Ech1.30Zm2.32T00.1122_iPp0.00_baseFe.fits 1.30 -2.32000 0.11220 0.76400 1.65802 2.02151 2.83118 2.87883 2.91035 2.90826 2.88013 2.92795 2.92402 2.84100 2.86291 2.84982 2.90850 2.90185
Ech1.30Zm2.32T00.1259_iPp0.00_baseFe.fits 1.30 -2.32000 0.12590 0.75710 1.76001 2.10756 2.88703 2.92964 2.95373 2.94729 2.90184 2.97345 2.96475 2.87175 2.89340 2.90530 2.95326 2.93109
Ech1.30Zm2.32T00.1413_iPp0.00_baseFe.fits 1.30 -2.32000 0.14130 0.75050 1.85700 2.20238 2.95262 2.99019 3.00734 2.99706 2.93686 3.02898 3.01557 2.91517 2.93617 2.97025 3.00805 2.97221
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Ech1.30Zm2.32T00.1778_iPp0.00_baseFe.fits 1.30 -2.32000 0.17780 0.73840 2.06962 2.33542 2.84901 2.77199 2.59008 2.44381 1.94309 2.71735 2.51956 2.10996 2.14873 2.87662 2.62463 2.17083
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Ech1.30Zm2.32T00.3162_iPp0.00_baseFe.fits 1.30 -2.32000 0.31620 0.70610 2.75759 2.86666 3.20101 3.09832 2.87739 2.70633 2.13632 3.01897 2.78855 2.33558 2.38033 3.22888 2.91685 2.38746
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Ech1.30Zm2.32T00.5012_iPp0.00_baseFe.fits 1.30 -2.32000 0.50120 0.68200 3.25404 3.27494 3.54814 3.44881 3.24228 3.08263 2.54463 3.37039 3.15975 2.73521 2.77377 3.57197 3.27890 2.78133
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Ech1.30Zm2.32T00.7943_iPp0.00_baseFe.fits 1.30 -2.32000 0.79430 0.65930 3.74272 3.73344 3.92559 3.81513 3.60245 3.44541 2.91935 3.72706 3.52014 3.10604 3.14134 3.94702 3.64039 3.14939
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Ech1.30Zm2.32T01.7783_iPp0.00_baseFe.fits 1.30 -2.32000 1.77830 0.62050 4.93231 4.54367 4.66288 4.50684 4.23159 4.04571 3.46634 4.38243 4.13232 3.65652 3.69903 4.69224 4.27997 3.70975
Ech1.30Zm2.32T01.9953_iPp0.00_baseFe.fits 1.30 -2.32000 1.99530 0.61610 5.08087 4.72962 4.80846 4.65152 4.37853 4.19488 3.61929 4.52690 4.28033 3.81025 3.85162 4.83631 4.42641 3.86186
Ech1.30Zm2.32T02.2387_iPp0.00_baseFe.fits 1.30 -2.32000 2.23870 0.61150 5.32752 4.81089 4.91657 4.76024 4.48596 4.30113 3.72470 4.63473 4.38763 3.91346 3.95526 4.94572 4.53418 3.96657
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Ech1.30Zm2.32T02.8184_iPp0.00_baseFe.fits 1.30 -2.32000 2.81840 0.60330 5.64080 5.04277 5.16878 5.00628 4.72437 4.53752 3.96169 4.87477 4.62483 4.14485 4.18721 5.19968 4.77409 4.20010
Ech1.30Zm2.32T03.1623_iPp0.00_baseFe.fits 1.30 -2.32000 3.16230 0.59950 5.72830 5.11797 5.27251 5.10750 4.82131 4.63333 4.05809 4.97338 4.72092 4.23781 4.28095 5.30484 4.87199 4.29490
Ech1.30Zm2.32T03.5481_iPp0.00_baseFe.fits 1.30 -2.32000 3.54810 0.59540 5.82027 5.20801 5.38116 5.21078 4.91679 4.72550 4.14540 5.07203 4.81447 4.32378 4.36782 5.41523 4.96897 4.38256
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Ech1.30Zm2.32T08.9125_iPp0.00_baseFe.fits 1.30 -2.32000 8.91250 0.56820 7.00480 6.25092 6.32168 6.13356 5.82142 5.62398 5.01974 5.98281 5.71504 5.21074 5.25512 6.35710 5.87660 5.27213
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Ech1.30Zm2.32T11.2202_iPp0.00_baseFe.fits 1.30 -2.32000 11.22020 0.56310 7.22932 6.50664 6.52500 6.33674 6.02639 5.82958 5.22128 6.18676 5.92036 5.41649 5.46032 6.55919 6.08106 5.47734
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Ech1.30Zm1.71T00.1000_iPp0.00_baseFe.fits 1.30 -1.71000 0.10000 0.77360 1.53632 1.87715 2.60620 2.63433 2.63096 2.60630 2.49295 2.66303 2.63027 2.49076 2.51632 2.62582 2.63543 2.55074
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Ech1.30Zm1.71T00.2818_iPp0.00_baseFe.fits 1.30 -1.71000 0.28180 0.71620 2.73268 2.83157 3.16715 3.07168 2.86459 2.69989 2.13892 2.99768 2.78014 2.34325 2.38292 3.19225 2.90072 2.39011
Ech1.30Zm1.71T00.3162_iPp0.00_baseFe.fits 1.30 -1.71000 0.31620 0.71000 2.86801 2.94824 3.26744 3.17899 2.98410 2.82800 2.28612 3.10961 2.90471 2.48438 2.52249 3.29158 3.01860 2.53133
Ech1.30Zm1.71T00.3548_iPp0.00_baseFe.fits 1.30 -1.71000 0.35480 0.70390 2.95428 3.02851 3.34879 3.25618 3.05730 2.90009 2.35707 3.18393 2.97689 2.55579 2.59325 3.37259 3.09246 2.60188
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Ech1.30Zm1.71T00.4467_iPp0.00_baseFe.fits 1.30 -1.71000 0.44670 0.69180 3.19059 3.24930 3.53307 3.43280 3.22618 3.06621 2.51907 3.35483 3.14380 2.72000 2.75637 3.55643 3.26236 2.76466
Ech1.30Zm1.71T00.5012_iPp0.00_baseFe.fits 1.30 -1.71000 0.50120 0.68570 3.32606 3.37210 3.62884 3.52858 3.32558 3.16894 2.63502 3.45040 3.24506 2.82972 2.86467 3.65064 3.36109 2.87288
Ech1.30Zm1.71T00.5623_iPp0.00_baseFe.fits 1.30 -1.71000 0.56230 0.68010 3.47737 3.48671 3.72510 3.61929 3.40923 3.25024 2.71241 3.53768 3.32658 2.90817 2.94286 3.74748 3.44586 2.95107
Ech1.30Zm1.71T00.6310_iPp0.00_baseFe.fits 1.30 -1.71000 0.63100 0.67440 3.63992 3.63031 3.83809 3.72470 3.50512 3.34127 2.79513 3.63739 3.41974 2.99229 3.02732 3.86083 3.54319 3.03497
Ech1.30Zm1.71T00.7079_iPp0.00_baseFe.fits 1.30 -1.71000 0.70790 0.66870 3.72934 3.72826 3.94465 3.82878 3.60707 3.44346 2.90246 3.73895 3.52153 3.09431 3.12944 3.96755 3.64547 3.13793
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Ech1.30Zm1.71T00.8913_iPp0.00_baseFe.fits 1.30 -1.71000 0.89130 0.65790 3.98903 3.95686 4.15933 4.02588 3.78174 3.60875 3.05339 3.92218 3.69009 3.24455 3.28130 4.18430 3.82374 3.29020
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Ech1.30Zm1.71T01.1220_iPp0.00_baseFe.fits 1.30 -1.71000 1.12200 0.64740 4.31112 4.17350 4.31604 4.16441 3.89791 3.71487 3.13887 4.04768 3.80013 3.33401 3.37276 4.34290 3.94335 3.38216
Ech1.30Zm1.71T01.2589_iPp0.00_baseFe.fits 1.30 -1.71000 1.25890 0.64170 4.61053 4.29155 4.36833 4.19027 3.88837 3.68681 3.07286 4.05522 3.77950 3.27766 3.31982 4.39899 3.93898 3.32798
Ech1.30Zm1.71T01.4125_iPp0.00_baseFe.fits 1.30 -1.71000 1.41250 0.63400 4.75667 4.50122 4.61947 4.45481 4.17250 3.98246 3.39384 4.32854 4.07025 3.59005 3.63031 4.64794 4.22018 3.64005
Ech1.30Zm1.71T01.5849_iPp0.00_baseFe.fits 1.30 -1.71000 1.58490 0.62890 4.93647 4.61255 4.71230 4.54283 4.25502 4.06292 3.47026 4.41267 4.15156 3.66692 3.70769 4.74133 4.30362 3.71768
Ech1.30Zm1.71T01.7783_iPp0.00_baseFe.fits 1.30 -1.71000 1.77830 0.62390 5.10134 4.71034 4.81585 4.63721 4.33731 4.14052 3.53823 4.49941 4.23102 3.73604 3.77830 4.84739 4.38792 3.78897
Ech1.30Zm1.71T01.9953_iPp0.00_baseFe.fits 1.30 -1.71000 1.99530 0.61920 5.26140 4.80042 4.92463 4.74165 4.43570 4.23683 3.63177 4.60027 4.32807 3.82800 3.87129 4.95769 4.48733 3.88278
Ech1.30Zm1.71T02.2387_iPp0.00_baseFe.fits 1.30 -1.71000 2.23870 0.61470 5.37411 4.89193 5.03720 4.85138 4.54096 4.34044 3.73300 4.70747 4.43216 3.92811 3.97217 5.07151 4.59348 3.98417
Ech1.30Zm1.71T02.5119_iPp0.00_baseFe.fits 1.30 -1.71000 2.51190 0.60980 5.56547 5.03160 5.15317 4.96707 4.65794 4.45924 3.85543 4.82300 4.54981 4.04967 4.09335 5.18691 4.71033 4.10547
Ech1.30Zm1.71T02.8184_iPp0.00_baseFe.fits 1.30 -1.71000 2.81840 0.60530 5.69464 5.16001 5.24895 5.06071 4.75090 4.55273 3.95022 4.91512 4.64278 4.14479 4.18774 5.28198 4.80342 4.19957
Ech1.30Zm1.71T03.1623_iPp0.00_baseFe.fits 1.30 -1.71000 3.16230 0.60120 5.89821 5.29548 5.38001 5.18853 4.87482 4.67532 4.07069 5.04034 4.76577 4.26477 4.30810 5.41374 4.92804 4.32041
Ech1.30Zm1.71T03.5481_iPp0.00_baseFe.fits 1.30 -1.71000 3.54810 0.59730 6.04854 5.43685 5.51046 5.31463 4.99593 4.79436 4.18641 5.16368 4.88555 4.38042 4.42410 5.54483 5.04988 4.43660
Ech1.30Zm1.71T03.9811_iPp0.00_baseFe.fits 1.30 -1.71000 3.98110 0.59340 6.22480 5.58210 5.63500 5.43524 5.11276 4.91029 4.30047 5.28148 5.00163 4.49457 4.53845 5.66972 5.16736 4.55137
Ech1.30Zm1.71T04.4668_iPp0.00_baseFe.fits 1.30 -1.71000 4.46680 0.58950 6.38851 5.72741 5.76023 5.55609 5.22955 5.02609 4.41275 5.39923 5.11753 4.60888 4.65300 5.79531 5.28474 4.66581
Ech1.30Zm1.71T05.0119_iPp0.00_baseFe.fits 1.30 -1.71000 5.01190 0.58580 6.53080 5.84974 5.87615 5.66947 5.33947 5.13429 4.51859 5.51118 5.22648 4.71352 4.75806 5.91188 5.39524 4.77146
Ech1.30Zm1.71T05.6234_iPp0.00_baseFe.fits 1.30 -1.71000 5.62340 0.58240 6.71605 6.00535 5.99183 5.77946 5.44413 5.23718 4.61641 5.61764 5.32987 4.81382 4.85861 6.02780 5.50064 4.87183
Ech1.30Zm1.71T06.3096_iPp0.00_baseFe.fits 1.30 -1.71000 6.30960 0.57890 6.87366 6.14101 6.11244 5.89662 5.55782 5.34953 4.72586 5.73295 5.44266 4.92381 4.96884 6.14871 5.61475 4.98240
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Ech1.30Zm1.71T07.9433_iPp0.00_baseFe.fits 1.30 -1.71000 7.94330 0.57240 7.14645 6.37884 6.34863 6.12429 5.77595 5.56403 4.93382 5.95471 5.65863 5.13194 5.17755 6.38671 5.83434 5.19234
Ech1.30Zm1.71T08.9125_iPp0.00_baseFe.fits 1.30 -1.71000 8.91250 0.56940 7.28364 6.49236 6.46758 6.23941 5.88684 5.67337 5.04129 6.06720 5.76835 5.23860 5.28450 6.50621 5.94594 5.29978
Ech1.30Zm1.71T10.0000_iPp0.00_baseFe.fits 1.30 -1.71000 10.00000 0.56680 7.42763 6.61469 6.59977 6.37156 6.01942 5.80669 5.17847 6.19979 5.90095 5.37263 5.41811 6.63820 6.07844 5.43437
Ech1.30Zm1.71T11.2202_iPp0.00_baseFe.fits 1.30 -1.71000 11.22020 0.56420 7.53246 6.71724 6.71036 6.47905 6.12339 5.90974 5.27878 6.30542 6.00421 5.47368 5.51905 6.74981 6.18306 5.53611
Ech1.30Zm1.71T12.5893_iPp0.00_baseFe.fits 1.30 -1.71000 12.58930 0.55990 7.66243 6.82873 6.82452 6.58962 6.22986 6.01484 5.38112 6.41390 6.10954 5.57674 5.62221 6.86472 6.29016 5.63955
Ech1.30Zm1.71T14.1254_iPp0.00_baseFe.fits 1.30 -1.71000 14.12540 0.55190 7.72855 6.92637 6.92436 6.69609 6.34398 6.13176 5.50031 6.52456 6.22544 5.69644 5.74159 6.96315 6.40308 5.76009
Ech1.30Zm1.71T15.8489_iPp0.00_baseFe.fits 1.30 -1.71000 15.84890 0.54560 7.82183 7.02999 7.02610 6.79979 6.44983 6.23839 5.60830 6.62945 6.33166 5.80368 5.84855 7.06451 6.50859 5.86767
Ech1.30Zm1.71T17.7828_iPp0.00_baseFe.fits 1.30 -1.71000 17.78280 0.53780 7.86327 7.12015 7.12182 6.90195 6.55971 6.35125 5.72430 6.73604 6.44333 5.92064 5.96496 7.15894 6.61711 5.98414
Ech1.30Zm1.31T00.0631_iPp0.00_baseFe.fits 1.30 -1.31000 0.06310 0.79990 1.16450 1.54357 2.22608 2.23610 2.20026 2.15531 1.97701 2.24882 2.18714 1.99647 2.02583 2.24766 2.21082 2.06051
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Ech1.30Zm1.31T00.1000_iPp0.00_baseFe.fits 1.30 -1.31000 0.10000 0.77530 1.70024 1.99195 2.56990 2.56803 2.51874 2.46682 2.26023 2.56863 2.50103 2.29932 2.32588 2.58939 2.53134 2.35681
Ech1.30Zm1.31T00.1122_iPp0.00_baseFe.fits 1.30 -1.31000 0.11220 0.76950 1.88865 2.12430 2.57404 2.50165 2.32537 2.17898 1.65270 2.44658 2.25386 1.84319 1.88290 2.60094 2.35781 1.89640
Ech1.30Zm1.31T00.1259_iPp0.00_baseFe.fits 1.30 -1.31000 0.12590 0.76320 2.00421 2.23358 2.65496 2.57924 2.39998 2.25069 1.71087 2.52091 2.32765 1.91103 1.94960 2.68095 2.43283 1.96197
Ech1.30Zm1.31T00.1413_iPp0.00_baseFe.fits 1.30 -1.31000 0.14130 0.75670 2.17891 2.34631 2.73941 2.65629 2.46690 2.31090 1.75009 2.59187 2.39106 1.96136 1.99992 2.76546 2.50121 2.01084
Ech1.30Zm1.31T00.1585_iPp0.00_baseFe.fits 1.30 -1.31000 0.15850 0.75010 2.29124 2.44682 2.82491 2.73194 2.52854 2.36558 1.78448 2.66204 2.44805 2.00559 2.04454 2.85203 2.56507 2.05600
Ech1.30Zm1.31T00.1778_iPp0.00_baseFe.fits 1.30 -1.31000 0.17780 0.74380 2.43942 2.56364 2.93135 2.83425 2.62704 2.46440 1.88371 2.76322 2.54599 2.10597 2.14407 2.95851 2.66429 2.15723
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Ech1.30Zm1.31T00.2239_iPp0.00_baseFe.fits 1.30 -1.31000 0.22390 0.73100 2.67295 2.76789 3.13189 3.03881 2.84231 2.69024 2.13486 2.97297 2.76603 2.34920 2.38471 3.15772 2.87797 2.40130
Ech1.30Zm1.31T00.2512_iPp0.00_baseFe.fits 1.30 -1.31000 0.25120 0.72490 2.77881 2.86536 3.20602 3.11868 2.93367 2.79037 2.25906 3.05667 2.86188 2.46493 2.49824 3.23008 2.96753 2.51626
Ech1.30Zm1.31T00.2818_iPp0.00_baseFe.fits 1.30 -1.31000 0.28180 0.71880 2.89911 2.94987 3.28323 3.19770 3.01656 2.87563 2.35161 3.13673 2.94613 2.55515 2.58754 3.30647 3.04969 2.60526
Ech1.30Zm1.31T00.3162_iPp0.00_baseFe.fits 1.30 -1.31000 0.31620 0.71270 3.00368 3.04129 3.34909 3.26991 3.10198 2.96993 2.46690 3.21410 3.03629 2.66745 2.69653 3.36995 3.13278 2.71413
Ech1.30Zm1.31T00.3548_iPp0.00_baseFe.fits 1.30 -1.31000 0.35480 0.70690 3.13202 3.15094 3.42867 3.34896 3.18347 3.05377 2.56060 3.29253 3.11874 2.75774 2.78505 3.44808 3.21360 2.80167
Ech1.30Zm1.31T00.3981_iPp0.00_baseFe.fits 1.30 -1.31000 0.39810 0.70110 3.26240 3.26701 3.51686 3.42911 3.25270 3.11674 2.61350 3.36583 3.18445 2.81096 2.83911 3.53669 3.28436 2.85464
Ech1.30Zm1.31T00.4467_iPp0.00_baseFe.fits 1.30 -1.31000 0.44670 0.69500 3.39329 3.38280 3.60355 3.51100 3.32885 3.19036 2.68239 3.44442 3.25869 2.88157 2.90956 3.62324 3.36131 2.92445
Ech1.30Zm1.31T00.5012_iPp0.00_baseFe.fits 1.30 -1.31000 0.50120 0.68900 3.53305 3.51274 3.73124 3.62924 3.43382 3.28909 2.77273 3.55499 3.35971 2.96932 2.99907 3.75224 3.46841 3.01398
Ech1.30Zm1.31T00.5623_iPp0.00_baseFe.fits 1.30 -1.31000 0.56230 0.68350 3.69091 3.64855 3.83984 3.72696 3.51751 3.36636 2.83884 3.64462 3.43895 3.03534 3.06695 3.86155 3.55431 3.08165
Ech1.30Zm1.31T00.6310_iPp0.00_baseFe.fits 1.30 -1.31000 0.63100 0.67790 3.82014 3.78328 3.95322 3.83130 3.61083 3.45432 2.91494 3.74228 3.52872 3.11547 3.14726 3.97528 3.64916 3.16130
Ech1.30Zm1.31T00.7079_iPp0.00_baseFe.fits 1.30 -1.31000 0.70790 0.67220 4.00641 3.91546 4.07246 3.94465 3.71766 3.55939 3.01774 3.84984 3.63395 3.21777 3.24980 4.09489 3.75711 3.26346
Ech1.30Zm1.31T00.7943_iPp0.00_baseFe.fits 1.30 -1.31000 0.79430 0.66670 4.17894 4.04101 4.17139 4.03773 3.80444 3.64346 3.09632 3.93918 3.71891 3.29843 3.33055 4.19383 3.84469 3.34372
Ech1.30Zm1.31T00.8913_iPp0.00_baseFe.fits 1.30 -1.31000 0.89130 0.66150 4.37425 4.18042 4.27620 4.13760 3.89806 3.73368 3.17711 4.03545 3.81067 3.38304 3.41541 4.29889 3.93932 3.42880
Ech1.30Zm1.31T01.0000_iPp0.00_baseFe.fits 1.30 -1.31000 1.00000 0.65630 4.67809 4.33935 4.37529 4.22925 3.98221 3.81554 3.25742 4.12173 3.89267 3.46197 3.49482 4.39779 4.02456 3.50730
Ech1.30Zm1.31T01.1220_iPp0.00_baseFe.fits 1.30 -1.31000 1.12200 0.65100 4.73610 4.43353 4.45808 4.30825 4.05684 3.88792 3.32267 4.19788 3.96611 3.53051 3.56359 4.48091 4.09975 3.57622
Ech1.30Zm1.31T01.2589_iPp0.00_baseFe.fits 1.30 -1.31000 1.25890 0.64580 4.99658 4.52550 4.50565 4.33327 4.05249 3.86912 3.27325 4.20734 3.95283 3.48846 3.52459 4.53148 4.09988 3.53663
Ech1.30Zm1.31T01.4125_iPp0.00_baseFe.fits 1.30 -1.31000 1.41250 0.63740 5.14761 4.69045 4.64770 4.45838 4.15513 3.95947 3.32974 4.32165 4.04792 3.56091 3.59895 4.67578 4.20568 3.60932
Ech1.30Zm1.31T01.5849_iPp0.00_baseFe.fits 1.30 -1.31000 1.58490 0.63140 5.27547 4.78371 4.81307 4.62826 4.33034 4.14033 3.53043 4.49237 4.22569 3.74856 3.78689 4.84211 4.38061 3.79877
Ech1.30Zm1.31T01.7783_iPp0.00_baseFe.fits 1.30 -1.31000 1.77830 0.62610 5.47159 4.93693 4.93189 4.73890 4.43130 4.23689 3.61885 4.59747 4.32407 3.83739 3.87676 4.96206 4.48312 3.88890
Ech1.30Zm1.31T01.9953_iPp0.00_baseFe.fits 1.30 -1.31000 1.99530 0.62130 5.69126 5.11771 5.06093 4.85528 4.53399 4.33366 3.70518 4.70583 4.42288 3.92585 3.96595 5.09191 4.58773 3.97758
Ech1.30Zm1.31T02.2387_iPp0.00_baseFe.fits 1.30 -1.31000 2.23870 0.61660 5.84753 5.23297 5.17688 4.96620 4.63880 4.43653 3.80818 4.81300 4.52618 4.02484 4.06576 5.20912 4.69353 4.07803
Ech1.30Zm1.31T02.5119_iPp0.00_baseFe.fits 1.30 -1.31000 2.51190 0.61170 5.98900 5.36576 5.30119 5.08863 4.75999 4.55793 3.93165 4.93404 4.64742 4.14620 4.18732 5.33352 4.81496 4.19969
Ech1.30Zm1.31T02.8184_iPp0.00_baseFe.fits 1.30 -1.31000 2.81840 0.60710 6.07801 5.45463 5.39286 5.18152 4.85465 4.65360 4.02784 5.02770 4.74256 4.24379 4.28468 5.42482 4.90932 4.29670
Ech1.30Zm1.31T03.1623_iPp0.00_baseFe.fits 1.30 -1.31000 3.16230 0.60280 6.22139 5.57891 5.52212 5.30741 4.97631 4.77358 4.14517 5.15085 4.86317 4.36057 4.40196 5.55494 5.03169 4.41447
Ech1.30Zm1.31T03.5481_iPp0.00_baseFe.fits 1.30 -1.31000 3.54810 0.59890 6.37252 5.70544 5.64968 5.43105 5.09514 4.89072 4.25994 5.27230 4.98073 4.47416 4.51626 5.68349 5.15129 4.52969
Ech1.30Zm1.31T03.9811_iPp0.00_baseFe.fits 1.30 -1.31000 3.98110 0.59500 6.52439 5.82469 5.77012 5.54616 5.20380 4.99675 4.36029 5.38382 5.08765 4.57545 4.61832 5.80521 5.26092 4.63219
Ech1.30Zm1.31T04.4668_iPp0.00_baseFe.fits 1.30 -1.31000 4.46680 0.59120 6.66703 5.93698 5.88327 5.65371 5.30457 5.09426 4.45070 5.48758 5.18661 4.66749 4.71094 5.91955 5.36269 4.72500
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Ech1.30Zm1.31T05.6234_iPp0.00_baseFe.fits 1.30 -1.31000 5.62340 0.58390 6.95962 6.17041 6.11993 5.88109 5.52098 5.30574 4.65299 5.70866 5.40005 4.87012 4.91486 6.15818 5.58079 4.92950
Ech1.30Zm1.31T06.3096_iPp0.00_baseFe.fits 1.30 -1.31000 6.30960 0.58040 7.10232 6.28630 6.23872 5.99487 5.62898 5.41109 4.75309 5.81922 5.50652 4.97107 5.01642 6.27801 5.68961 5.03118
Ech1.30Zm1.31T07.0795_iPp0.00_baseFe.fits 1.30 -1.31000 7.07950 0.57710 7.26097 6.41887 6.37041 6.12302 5.75284 5.53273 4.87025 5.94496 5.62907 5.08877 5.13470 6.41053 5.81402 5.14951
Ech1.30Zm1.31T07.9433_iPp0.00_baseFe.fits 1.30 -1.31000 7.94330 0.57380 7.39214 6.53066 6.48819 6.23590 5.85937 5.63621 4.96860 6.05459 5.73360 5.18753 5.23396 6.52944 5.92154 5.24889
Ech1.30Zm1.31T08.9125_iPp0.00_baseFe.fits 1.30 -1.31000 8.91250 0.57080 7.53586 6.65390 6.60957 6.35306 5.97134 5.74556 5.07537 6.16931 5.84382 5.29293 5.33988 6.65168 6.03410 5.35489
Ech1.30Zm1.31T10.0000_iPp0.00_baseFe.fits 1.30 -1.31000 10.00000 0.56800 7.67228 6.77410 6.73884 6.47964 6.09446 5.86681 5.19327 6.29380 5.96585 5.41086 5.45811 6.78161 6.15767 5.47349
Ech1.30Zm1.31T11.2202_iPp0.00_baseFe.fits 1.30 -1.31000 11.22020 0.56540 7.81519 6.89622 6.85127 6.58794 6.19842 5.96842 5.28961 6.39986 6.06847 5.50788 5.55581 6.89471 6.26207 5.57159
Ech1.30Zm1.31T12.5893_iPp0.00_baseFe.fits 1.30 -1.31000 12.58930 0.56350 7.96145 7.02029 6.97480 6.70768 6.31369 6.08126 5.39498 6.51747 6.18225 5.61676 5.66521 7.01907 6.37787 5.68089
Ech1.30Zm1.31T14.1254_iPp0.00_baseFe.fits 1.30 -1.31000 14.12540 0.55500 8.06298 7.10119 7.05443 6.78641 6.39065 6.15697 5.47070 6.59573 6.25835 5.69066 5.73916 7.09897 6.45492 5.75540
Ech1.30Zm1.31T15.8489_iPp0.00_baseFe.fits 1.30 -1.31000 15.84890 0.54900 8.21845 7.25135 7.20605 6.94037 6.54797 6.31649 5.63287 6.75128 6.41654 5.85191 5.90045 7.25016 6.61164 5.91780
Ech1.30Zm1.31T17.7828_iPp0.00_baseFe.fits 1.30 -1.31000 17.78280 0.54080 8.24866 7.32691 7.27657 7.02238 6.64259 6.41554 5.73508 6.84033 6.51424 5.95717 6.00475 7.31826 6.70442 6.02150
Ech1.30Zm0.71T00.0631_iPp0.00_baseFe.fits 1.30 -0.71000 0.06310 0.80070 1.26522 1.60456 2.32327 2.32828 2.28065 2.22287 1.98801 2.34155 2.25942 2.03854 2.07093 2.34541 2.29196 2.10137
Ech1.30Zm0.71T00.0708_iPp0.00_baseFe.fits 1.30 -0.71000 0.07080 0.80070 1.39362 1.71743 2.40529 2.40625 2.35350 2.29242 2.04672 2.41531 2.33050 2.10463 2.13637 2.42699 2.36549 2.16344
Ech1.30Zm0.71T00.0794_iPp0.00_baseFe.fits 1.30 -0.71000 0.07940 0.79470 1.52797 1.82713 2.48761 2.48735 2.43559 2.37663 2.13320 2.49494 2.41349 2.19432 2.22459 2.50831 2.44738 2.25118
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Ech1.30Zm0.71T00.1000_iPp0.00_baseFe.fits 1.30 -0.71000 0.10000 0.77750 1.82467 2.04711 2.64079 2.63401 2.57602 2.51502 2.26669 2.63541 2.55213 2.33190 2.35965 2.66018 2.58862 2.38527
Ech1.30Zm0.71T00.1122_iPp0.00_baseFe.fits 1.30 -0.71000 0.11220 0.77170 1.93051 2.13882 2.70902 2.69387 2.62102 2.54711 2.22947 2.68883 2.59032 2.33779 2.36575 2.72897 2.63605 2.38897
Ech1.30Zm0.71T00.1259_iPp0.00_baseFe.fits 1.30 -0.71000 0.12590 0.76610 2.07890 2.26217 2.76232 2.71297 2.57636 2.44502 1.86262 2.68488 2.51544 2.12672 2.15905 2.78561 2.60095 2.17350
Ech1.30Zm0.71T00.1413_iPp0.00_baseFe.fits 1.30 -0.71000 0.14130 0.76000 2.19888 2.36797 2.84775 2.79944 2.66705 2.53922 1.95907 2.77167 2.60835 2.22730 2.25869 2.87014 2.69098 2.27191
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Ech1.30Zp0.22T05.0119_iPp0.00_baseFe.fits 1.30 0.22000 5.01190 0.61850 9.47196 7.64075 7.20197 6.81495 6.31200 6.03354 5.20937 6.59779 6.14023 5.50724 5.56106 7.26274 6.38270 5.58350
Ech1.30Zp0.22T05.6234_iPp0.00_baseFe.fits 1.30 0.22000 5.62340 0.61470 9.64709 7.79531 7.32573 6.93196 6.42208 6.13932 5.30516 6.71297 6.24741 5.60772 5.66210 7.38778 6.49310 5.68385
Ech1.30Zp0.22T06.3096_iPp0.00_baseFe.fits 1.30 0.22000 6.30960 0.61130 9.86192 7.96544 7.47548 7.07447 6.55716 6.27022 5.42661 6.85288 6.37964 5.73263 5.78747 7.53951 6.62859 5.80973
Ech1.30Zp0.22T07.0795_iPp0.00_baseFe.fits 1.30 0.22000 7.07950 0.60840 10.04410 8.10783 7.59524 7.18948 6.66765 6.37811 5.52811 6.96645 6.48869 5.83674 5.89213 7.66019 6.73936 5.91429
Ech1.30Zp0.22T07.9433_iPp0.00_baseFe.fits 1.30 0.22000 7.94330 0.60550 10.23907 8.26637 7.71449 7.30117 6.77180 6.47814 5.61945 7.07565 6.59009 5.93067 5.98650 7.78147 6.84395 6.00944
Ech1.30Zp0.22T08.9125_iPp0.00_baseFe.fits 1.30 0.22000 8.91250 0.60240 10.39559 8.41301 7.83138 7.41101 6.87457 6.57697 5.71210 7.18297 6.69032 6.02370 6.08018 7.90049 6.94703 6.10407
Ech1.30Zp0.22T10.0000_iPp0.00_baseFe.fits 1.30 0.22000 10.00000 0.59950 10.60790 8.57789 7.94942 7.52022 6.97494 6.67263 5.80034 7.28893 6.78736 6.11310 6.17018 8.02087 7.04788 6.19441
Ech1.30Zp0.22T11.2202_iPp0.00_baseFe.fits 1.30 0.22000 11.22020 0.59670 10.80733 8.74063 8.06668 7.62775 7.07271 6.76506 5.88068 7.39286 6.88149 6.19717 6.25475 8.14100 7.14632 6.28028
Ech1.30Zp0.22T12.5893_iPp0.00_baseFe.fits 1.30 0.22000 12.58930 0.59410 10.99865 8.89270 8.18836 7.74214 7.17924 6.86706 5.97290 7.50505 6.98492 6.29366 6.35194 8.26495 7.25320 6.37719
Ech1.30Zp0.22T14.1254_iPp0.00_baseFe.fits 1.30 0.22000 14.12540 0.59140 11.20960 9.03983 8.31023 7.85852 7.28887 6.97228 6.06897 7.62100 7.09136 6.39351 6.45238 8.38933 7.36293 6.47715
Ech1.30Zp0.22T15.8489_iPp0.00_baseFe.fits 1.30 0.22000 15.84890 0.58900 11.36825 9.19407 8.43951 7.98358 7.40924 7.08922 6.17511 7.74528 7.20963 6.50447 6.56379 8.52007 7.48347 6.58963
Ech1.30Zp0.22T17.7828_iPp0.00_baseFe.fits 1.30 0.22000 17.78280 0.58660 11.54960 9.34094 8.56480 8.10505 7.52639 7.20302 6.27933 7.86661 7.32461 6.61349 6.67354 8.64670 7.60054 6.69925
gehong/data/EMILES/model/HST_mag.dat deleted 100644 → 0
View file @ 50fc43d6
AGE METAL F555W F814W
0.7079 -1.71 8.425675688084176 8.964296538136548
17.7828 -0.4 11.85868651376804 11.919432348354617
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0.8913 -2.32 8.49927876951828 9.072954561790512
0.1585 -0.71 7.565838038648005 8.217447946381538
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0.7943 0.22 9.285798945760984 9.813467401120285
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gehong/data/EMILES/model/out_mass_CH_PADOVA00 deleted 100644 → 0
View file @ 50fc43d6
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Ch 1.30 -2.3152 0.1000 1.0000 0.7707 0.7096 0.0610 0.2293 0.132 0.121 2.903 5.84550230
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Ch 1.30 -1.7129 0.6310 1.0000 0.6744 0.5814 0.0930 0.3256 0.199 0.171 3.492 3.39665519
Ch 1.30 -1.7129 0.7079 1.0000 0.6687 0.5733 0.0954 0.3313 0.216 0.185 3.594 3.09245048
Ch 1.30 -1.7129 0.7943 1.0000 0.6631 0.5652 0.0979 0.3369 0.231 0.197 3.674 2.87299139
Ch 1.30 -1.7129 0.8913 1.0000 0.6579 0.5576 0.1004 0.3421 0.249 0.211 3.766 2.64022325
Ch 1.30 -1.7129 1.0000 1.0000 0.6526 0.5497 0.1029 0.3474 0.269 0.226 3.857 2.42790659
Ch 1.30 -1.7129 1.1220 1.0000 0.6474 0.5420 0.1055 0.3526 0.272 0.228 3.879 2.37915959
Ch 1.30 -1.7129 1.2589 1.0000 0.6417 0.5331 0.1086 0.3583 0.266 0.221 3.864 2.41199520
Ch 1.30 -1.7129 1.4125 1.0000 0.6340 0.5212 0.1128 0.3660 0.343 0.282 4.151 1.85103570
Ch 1.30 -1.7129 1.5849 1.0000 0.6289 0.5131 0.1158 0.3711 0.366 0.299 4.233 1.71673782
Ch 1.30 -1.7129 1.7783 1.0000 0.6239 0.5049 0.1190 0.3761 0.392 0.317 4.314 1.59348630
Ch 1.30 -1.7129 1.9953 1.0000 0.6192 0.4969 0.1223 0.3808 0.425 0.341 4.412 1.45618254
Ch 1.30 -1.7129 2.2387 1.0000 0.6147 0.4890 0.1256 0.3853 0.465 0.370 4.517 1.32229816
Ch 1.30 -1.7129 2.5119 1.0000 0.6098 0.4806 0.1292 0.3902 0.514 0.405 4.634 1.18690991
Ch 1.30 -1.7129 2.8184 1.0000 0.6053 0.4727 0.1326 0.3947 0.555 0.434 4.727 1.08967296
Ch 1.30 -1.7129 3.1623 1.0000 0.6012 0.4651 0.1361 0.3988 0.618 0.478 4.850 0.97253937
Ch 1.30 -1.7129 3.5481 1.0000 0.5973 0.4576 0.1397 0.4027 0.686 0.526 4.971 0.87036125
Ch 1.30 -1.7129 3.9811 1.0000 0.5934 0.4500 0.1433 0.4066 0.759 0.576 5.087 0.78184524
Ch 1.30 -1.7129 4.4668 1.0000 0.5895 0.4424 0.1471 0.4105 0.839 0.630 5.204 0.70234466
Ch 1.30 -1.7129 5.0119 1.0000 0.5858 0.4349 0.1510 0.4142 0.923 0.685 5.313 0.63493409
Ch 1.30 -1.7129 5.6234 1.0000 0.5824 0.4275 0.1548 0.4176 1.009 0.741 5.417 0.57689615
Ch 1.30 -1.7129 6.3096 1.0000 0.5789 0.4200 0.1589 0.4211 1.114 0.808 5.531 0.51969894
Ch 1.30 -1.7129 7.0795 1.0000 0.5756 0.4125 0.1631 0.4244 1.225 0.878 5.640 0.46993734
Ch 1.30 -1.7129 7.9433 1.0000 0.5724 0.4052 0.1672 0.4276 1.345 0.952 5.748 0.42553541
Ch 1.30 -1.7129 8.9125 1.0000 0.5694 0.3977 0.1717 0.4306 1.481 1.035 5.858 0.38440525
Ch 1.30 -1.7129 10.0000 1.0000 0.5668 0.3905 0.1763 0.4332 1.666 1.148 5.991 0.34016517
Ch 1.30 -1.7129 11.2202 1.0000 0.5642 0.3833 0.1809 0.4358 1.825 1.240 6.094 0.30920818
Ch 1.30 -1.7129 12.5893 1.0000 0.5599 0.3761 0.1838 0.4401 1.997 1.341 6.200 0.28043079
Ch 1.30 -1.7129 14.1254 1.0000 0.5519 0.3689 0.1830 0.4481 2.188 1.463 6.315 0.25223373
Ch 1.30 -1.7129 15.8489 1.0000 0.5456 0.3619 0.1838 0.4544 2.385 1.582 6.422 0.22875536
Ch 1.30 -1.7129 17.7828 1.0000 0.5378 0.3553 0.1825 0.4622 2.604 1.720 6.533 0.20653672
Ch 1.30 -1.3146 0.0631 1.0000 0.7999 0.7483 0.0516 0.2001 0.073 0.068 2.224 10.92709168
Ch 1.30 -1.3146 0.0708 1.0000 0.7932 0.7398 0.0534 0.2068 0.078 0.073 2.302 10.16621965
Ch 1.30 -1.3146 0.0794 1.0000 0.7872 0.7319 0.0553 0.2128 0.084 0.078 2.388 9.39655531
Ch 1.30 -1.3146 0.0891 1.0000 0.7814 0.7241 0.0573 0.2186 0.089 0.082 2.460 8.78863328
Ch 1.30 -1.3146 0.1000 1.0000 0.7753 0.7159 0.0594 0.2247 0.095 0.088 2.539 8.16980473
Ch 1.30 -1.3146 0.1122 1.0000 0.7695 0.7082 0.0613 0.2305 0.077 0.071 2.321 9.99360959
Ch 1.30 -1.3146 0.1259 1.0000 0.7632 0.7002 0.0629 0.2368 0.082 0.075 2.394 9.33782584
Ch 1.30 -1.3146 0.1413 1.0000 0.7567 0.6920 0.0647 0.2433 0.086 0.079 2.459 8.79550593
Ch 1.30 -1.3146 0.1585 1.0000 0.7501 0.6835 0.0666 0.2499 0.090 0.082 2.519 8.32575729
Ch 1.30 -1.3146 0.1778 1.0000 0.7438 0.6754 0.0684 0.2562 0.098 0.089 2.617 7.60483788
Ch 1.30 -1.3146 0.1995 1.0000 0.7373 0.6670 0.0703 0.2627 0.106 0.096 2.719 6.92340938
Ch 1.30 -1.3146 0.2239 1.0000 0.7310 0.6587 0.0723 0.2690 0.117 0.106 2.835 6.22166885
Ch 1.30 -1.3146 0.2512 1.0000 0.7249 0.6506 0.0743 0.2751 0.127 0.114 2.929 5.70862537
Ch 1.30 -1.3146 0.2818 1.0000 0.7188 0.6425 0.0764 0.2812 0.136 0.122 3.012 5.28644664
Ch 1.30 -1.3146 0.3162 1.0000 0.7127 0.6342 0.0785 0.2873 0.146 0.130 3.100 4.87691592
Ch 1.30 -1.3146 0.3548 1.0000 0.7069 0.6263 0.0806 0.2931 0.156 0.138 3.181 4.52333639
Ch 1.30 -1.3146 0.3981 1.0000 0.7011 0.6184 0.0827 0.2989 0.165 0.145 3.249 4.25053377
Ch 1.30 -1.3146 0.4467 1.0000 0.6950 0.6101 0.0849 0.3050 0.175 0.154 3.324 3.96597651
Ch 1.30 -1.3146 0.5012 1.0000 0.6890 0.6018 0.0872 0.3110 0.191 0.167 3.427 3.60674328
Ch 1.30 -1.3146 0.5623 1.0000 0.6835 0.5942 0.0893 0.3165 0.204 0.178 3.509 3.34562648
Ch 1.30 -1.3146 0.6310 1.0000 0.6779 0.5863 0.0916 0.3221 0.220 0.191 3.600 3.07490555
Ch 1.30 -1.3146 0.7079 1.0000 0.6722 0.5782 0.0940 0.3278 0.241 0.207 3.706 2.78897562
Ch 1.30 -1.3146 0.7943 1.0000 0.6667 0.5704 0.0963 0.3333 0.259 0.221 3.792 2.57703601
Ch 1.30 -1.3146 0.8913 1.0000 0.6615 0.5629 0.0987 0.3385 0.280 0.238 3.885 2.36630344
Ch 1.30 -1.3146 1.0000 1.0000 0.6563 0.5552 0.1011 0.3437 0.299 0.253 3.968 2.19238789
Ch 1.30 -1.3146 1.1220 1.0000 0.6510 0.5474 0.1037 0.3490 0.318 0.267 4.042 2.04763623
Ch 1.30 -1.3146 1.2589 1.0000 0.6458 0.5394 0.1064 0.3542 0.313 0.261 4.033 2.06405168
Ch 1.30 -1.3146 1.4125 1.0000 0.6374 0.5266 0.1109 0.3626 0.338 0.279 4.132 1.88431575
Ch 1.30 -1.3146 1.5849 1.0000 0.6314 0.5170 0.1143 0.3686 0.394 0.323 4.309 1.60176879
Ch 1.30 -1.3146 1.7783 1.0000 0.6261 0.5086 0.1175 0.3739 0.429 0.348 4.408 1.46116541
Ch 1.30 -1.3146 1.9953 1.0000 0.6213 0.5005 0.1208 0.3787 0.467 0.376 4.509 1.33166002
Ch 1.30 -1.3146 2.2387 1.0000 0.6166 0.4925 0.1242 0.3834 0.510 0.407 4.613 1.20974965
Ch 1.30 -1.3146 2.5119 1.0000 0.6117 0.4840 0.1277 0.3883 0.565 0.447 4.734 1.08196049
Ch 1.30 -1.3146 2.8184 1.0000 0.6071 0.4758 0.1312 0.3929 0.612 0.480 4.829 0.99143867
Ch 1.30 -1.3146 3.1623 1.0000 0.6028 0.4681 0.1347 0.3972 0.680 0.528 4.951 0.88671769
Ch 1.30 -1.3146 3.5481 1.0000 0.5989 0.4608 0.1382 0.4011 0.753 0.579 5.069 0.79510377
Ch 1.30 -1.3146 3.9811 1.0000 0.5950 0.4532 0.1418 0.4050 0.827 0.630 5.177 0.71985347
Ch 1.30 -1.3146 4.4668 1.0000 0.5912 0.4457 0.1455 0.4088 0.900 0.679 5.277 0.65655494
Ch 1.30 -1.3146 5.0119 1.0000 0.5874 0.4382 0.1492 0.4126 0.994 0.742 5.392 0.59073318
Ch 1.30 -1.3146 5.6234 1.0000 0.5839 0.4308 0.1531 0.4161 1.084 0.800 5.492 0.53859442
Ch 1.30 -1.3146 6.3096 1.0000 0.5804 0.4234 0.1571 0.4196 1.190 0.868 5.599 0.48791871
Ch 1.30 -1.3146 7.0795 1.0000 0.5771 0.4158 0.1612 0.4229 1.325 0.955 5.722 0.43553905
Ch 1.30 -1.3146 7.9433 1.0000 0.5738 0.4085 0.1653 0.4262 1.452 1.034 5.828 0.39516030
Ch 1.30 -1.3146 8.9125 1.0000 0.5708 0.4012 0.1696 0.4292 1.600 1.125 5.939 0.35666551
Ch 1.30 -1.3146 10.0000 1.0000 0.5680 0.3939 0.1741 0.4320 1.783 1.237 6.062 0.31857500
Ch 1.30 -1.3146 11.2202 1.0000 0.5654 0.3868 0.1787 0.4346 1.952 1.335 6.165 0.28963252
Ch 1.30 -1.3146 12.5893 1.0000 0.5635 0.3804 0.1831 0.4365 2.162 1.460 6.280 0.26060308
Ch 1.30 -1.3146 14.1254 1.0000 0.5550 0.3725 0.1825 0.4450 2.286 1.534 6.357 0.24283711
Ch 1.30 -1.3146 15.8489 1.0000 0.5490 0.3657 0.1833 0.4510 2.615 1.742 6.515 0.20993949
Ch 1.30 -1.3146 17.7828 1.0000 0.5408 0.3586 0.1822 0.4592 2.814 1.866 6.611 0.19218123
Ch 1.30 -0.7052 0.0631 1.0000 0.8007 0.7493 0.0513 0.1993 0.079 0.074 2.302 10.16839113
Ch 1.30 -0.7052 0.0708 1.0000 0.7947 0.7417 0.0530 0.2053 0.084 0.078 2.374 9.51705514
Ch 1.30 -0.7052 0.0794 1.0000 0.7887 0.7339 0.0548 0.2113 0.089 0.083 2.456 8.82237987
Ch 1.30 -0.7052 0.0891 1.0000 0.7832 0.7267 0.0566 0.2168 0.095 0.088 2.526 8.26944695
Ch 1.30 -0.7052 0.1000 1.0000 0.7775 0.7190 0.0586 0.2225 0.100 0.093 2.595 7.75930389
Ch 1.30 -0.7052 0.1122 1.0000 0.7717 0.7110 0.0608 0.2283 0.103 0.095 2.637 7.46667723
Ch 1.30 -0.7052 0.1259 1.0000 0.7661 0.7040 0.0622 0.2339 0.097 0.089 2.579 7.87948008
Ch 1.30 -0.7052 0.1413 1.0000 0.7600 0.6962 0.0638 0.2400 0.105 0.096 2.670 7.24335620
Ch 1.30 -0.7052 0.1585 1.0000 0.7537 0.6881 0.0655 0.2463 0.112 0.102 2.748 6.74192067
Ch 1.30 -0.7052 0.1778 1.0000 0.7479 0.6807 0.0672 0.2521 0.120 0.109 2.835 6.22251431
Ch 1.30 -0.7052 0.1995 1.0000 0.7419 0.6729 0.0689 0.2581 0.129 0.117 2.923 5.73777071
Ch 1.30 -0.7052 0.2239 1.0000 0.7358 0.6650 0.0708 0.2642 0.139 0.125 3.009 5.30034598
Ch 1.30 -0.7052 0.2512 1.0000 0.7301 0.6575 0.0726 0.2699 0.152 0.137 3.117 4.79941851
Ch 1.30 -0.7052 0.2818 1.0000 0.7244 0.6500 0.0745 0.2756 0.162 0.145 3.192 4.47940688
Ch 1.30 -0.7052 0.3162 1.0000 0.7191 0.6428 0.0762 0.2809 0.174 0.156 3.281 4.12705203
Ch 1.30 -0.7052 0.3548 1.0000 0.7135 0.6353 0.0782 0.2865 0.190 0.169 3.381 3.76245725
Ch 1.30 -0.7052 0.3981 1.0000 0.7080 0.6278 0.0802 0.2920 0.205 0.182 3.475 3.45200680
Ch 1.30 -0.7052 0.4467 1.0000 0.7027 0.6206 0.0821 0.2973 0.220 0.194 3.558 3.19623782
Ch 1.30 -0.7052 0.5012 1.0000 0.6974 0.6134 0.0840 0.3026 0.237 0.209 3.648 2.94177913
Ch 1.30 -0.7052 0.5623 1.0000 0.6921 0.6061 0.0860 0.3079 0.255 0.223 3.735 2.71742494
Ch 1.30 -0.7052 0.6310 1.0000 0.6869 0.5989 0.0880 0.3131 0.273 0.238 3.819 2.51457422
Ch 1.30 -0.7052 0.7079 1.0000 0.6814 0.5913 0.0901 0.3186 0.294 0.255 3.908 2.31685745
Ch 1.30 -0.7052 0.7943 1.0000 0.6760 0.5837 0.0923 0.3240 0.316 0.273 3.995 2.13782931
Ch 1.30 -0.7052 0.8913 1.0000 0.6705 0.5759 0.0947 0.3295 0.340 0.292 4.084 1.96985472
Ch 1.30 -0.7052 1.0000 1.0000 0.6651 0.5680 0.0971 0.3349 0.369 0.315 4.180 1.80302552
Ch 1.30 -0.7052 1.1220 1.0000 0.6596 0.5600 0.0996 0.3404 0.398 0.338 4.271 1.65877723
Ch 1.30 -0.7052 1.2589 1.0000 0.6542 0.5521 0.1022 0.3458 0.411 0.347 4.316 1.59060378
Ch 1.30 -0.7052 1.4125 1.0000 0.6487 0.5438 0.1049 0.3513 0.401 0.336 4.298 1.61764695
Ch 1.30 -0.7052 1.5849 1.0000 0.6380 0.5275 0.1105 0.3620 0.500 0.414 4.556 1.27542012
Ch 1.30 -0.7052 1.7783 1.0000 0.6325 0.5188 0.1137 0.3675 0.568 0.466 4.704 1.11320912
Ch 1.30 -0.7052 1.9953 1.0000 0.6276 0.5108 0.1167 0.3724 0.625 0.508 4.815 1.00468672
Ch 1.30 -0.7052 2.2387 1.0000 0.6229 0.5031 0.1198 0.3771 0.676 0.546 4.908 0.92178311
Ch 1.30 -0.7052 2.5119 1.0000 0.6181 0.4950 0.1231 0.3819 0.739 0.592 5.014 0.83607483
Ch 1.30 -0.7052 2.8184 1.0000 0.6133 0.4868 0.1264 0.3867 0.807 0.641 5.118 0.75969443
Ch 1.30 -0.7052 3.1623 1.0000 0.6091 0.4794 0.1297 0.3909 0.878 0.691 5.218 0.69338242
Ch 1.30 -0.7052 3.5481 1.0000 0.6050 0.4720 0.1330 0.3950 0.953 0.743 5.313 0.63502696
Ch 1.30 -0.7052 3.9811 1.0000 0.6009 0.4644 0.1365 0.3991 1.034 0.800 5.410 0.58088067
Ch 1.30 -0.7052 4.4668 1.0000 0.5970 0.4570 0.1400 0.4030 1.152 0.882 5.534 0.51826276
Ch 1.30 -0.7052 5.0119 1.0000 0.5930 0.4494 0.1437 0.4070 1.254 0.950 5.633 0.47291366
Ch 1.30 -0.7052 5.6234 1.0000 0.5892 0.4418 0.1474 0.4108 1.417 1.063 5.773 0.41580557
Ch 1.30 -0.7052 6.3096 1.0000 0.5859 0.4349 0.1510 0.4141 1.542 1.145 5.871 0.37993530
Ch 1.30 -0.7052 7.0795 1.0000 0.5824 0.4277 0.1547 0.4176 1.681 1.234 5.971 0.34656049
Ch 1.30 -0.7052 7.9433 1.0000 0.5791 0.4205 0.1586 0.4209 1.853 1.345 6.083 0.31258756
Ch 1.30 -0.7052 8.9125 1.0000 0.5760 0.4134 0.1626 0.4240 2.029 1.457 6.187 0.28381004
Ch 1.30 -0.7052 10.0000 1.0000 0.5729 0.4063 0.1666 0.4271 2.221 1.575 6.291 0.25791189
Ch 1.30 -0.7052 11.2202 1.0000 0.5700 0.3992 0.1708 0.4300 2.423 1.697 6.391 0.23524347
Ch 1.30 -0.7052 12.5893 1.0000 0.5673 0.3921 0.1752 0.4327 2.639 1.824 6.489 0.21498439
Ch 1.30 -0.7052 14.1254 1.0000 0.5647 0.3849 0.1798 0.4353 2.868 1.955 6.584 0.19690135
Ch 1.30 -0.7052 15.8489 1.0000 0.5604 0.3777 0.1827 0.4396 3.127 2.107 6.686 0.17923857
Ch 1.30 -0.7052 17.7828 1.0000 0.5543 0.3706 0.1837 0.4457 3.406 2.277 6.791 0.16275551
Ch 1.30 -0.3960 0.0631 1.0000 0.8015 0.7504 0.0511 0.1985 0.081 0.076 2.328 9.92768655
Ch 1.30 -0.3960 0.0708 1.0000 0.7954 0.7426 0.0528 0.2046 0.085 0.080 2.395 9.32986238
Ch 1.30 -0.3960 0.0794 1.0000 0.7899 0.7355 0.0544 0.2101 0.092 0.085 2.480 8.62839338
Ch 1.30 -0.3960 0.0891 1.0000 0.7846 0.7285 0.0561 0.2154 0.097 0.090 2.548 8.10889197
Ch 1.30 -0.3960 0.1000 1.0000 0.7790 0.7210 0.0581 0.2210 0.102 0.095 2.615 7.61789875
Ch 1.30 -0.3960 0.1122 1.0000 0.7735 0.7134 0.0601 0.2265 0.108 0.100 2.686 7.13634272
Ch 1.30 -0.3960 0.1259 1.0000 0.7686 0.7070 0.0616 0.2314 0.109 0.101 2.703 7.02444535
Ch 1.30 -0.3960 0.1413 1.0000 0.7627 0.6995 0.0632 0.2373 0.117 0.107 2.783 6.52943627
Ch 1.30 -0.3960 0.1585 1.0000 0.7569 0.6920 0.0649 0.2431 0.125 0.114 2.866 6.04659570
Ch 1.30 -0.3960 0.1778 1.0000 0.7515 0.6850 0.0666 0.2485 0.134 0.122 2.949 5.60475177
Ch 1.30 -0.3960 0.1995 1.0000 0.7460 0.6776 0.0683 0.2540 0.146 0.132 3.046 5.12164354
Ch 1.30 -0.3960 0.2239 1.0000 0.7407 0.6706 0.0701 0.2593 0.156 0.142 3.131 4.73872422
Ch 1.30 -0.3960 0.2512 1.0000 0.7355 0.6635 0.0719 0.2645 0.170 0.153 3.228 4.33339053
Ch 1.30 -0.3960 0.2818 1.0000 0.7301 0.6562 0.0739 0.2699 0.185 0.166 3.329 3.94889209
Ch 1.30 -0.3960 0.3162 1.0000 0.7251 0.6494 0.0758 0.2749 0.199 0.178 3.413 3.65284281
Ch 1.30 -0.3960 0.3548 1.0000 0.7200 0.6421 0.0778 0.2800 0.213 0.190 3.500 3.37333560
Ch 1.30 -0.3960 0.3981 1.0000 0.7152 0.6354 0.0798 0.2848 0.230 0.204 3.588 3.10989921
Ch 1.30 -0.3960 0.4467 1.0000 0.7103 0.6284 0.0819 0.2897 0.247 0.218 3.672 2.87958468
Ch 1.30 -0.3960 0.5012 1.0000 0.7057 0.6217 0.0840 0.2943 0.266 0.234 3.760 2.65373633
Ch 1.30 -0.3960 0.5623 1.0000 0.7007 0.6146 0.0861 0.2993 0.287 0.252 3.852 2.43895713
Ch 1.30 -0.3960 0.6310 1.0000 0.6958 0.6076 0.0882 0.3042 0.310 0.271 3.942 2.24492047
Ch 1.30 -0.3960 0.7079 1.0000 0.6906 0.6000 0.0905 0.3094 0.331 0.288 4.022 2.08507549
Ch 1.30 -0.3960 0.7943 1.0000 0.6854 0.5925 0.0929 0.3146 0.361 0.312 4.124 1.89921785
Ch 1.30 -0.3960 0.8913 1.0000 0.6804 0.5851 0.0953 0.3196 0.393 0.338 4.225 1.72928265
Ch 1.30 -0.3960 1.0000 1.0000 0.6749 0.5771 0.0978 0.3251 0.424 0.363 4.316 1.59049821
Ch 1.30 -0.3960 1.1220 1.0000 0.6697 0.5693 0.1004 0.3303 0.458 0.389 4.408 1.46167960
Ch 1.30 -0.3960 1.2589 1.0000 0.6643 0.5611 0.1032 0.3357 0.449 0.379 4.394 1.48006690
Ch 1.30 -0.3960 1.4125 1.0000 0.6534 0.5442 0.1092 0.3466 0.544 0.453 4.621 1.20129846
Ch 1.30 -0.3960 1.5849 1.0000 0.6471 0.5344 0.1127 0.3529 0.616 0.509 4.767 1.05032053
Ch 1.30 -0.3960 1.7783 1.0000 0.6419 0.5259 0.1159 0.3581 0.687 0.563 4.894 0.93413269
Ch 1.30 -0.3960 1.9953 1.0000 0.6373 0.5183 0.1190 0.3627 0.758 0.617 5.009 0.84060131
Ch 1.30 -0.3960 2.2387 1.0000 0.6327 0.5106 0.1220 0.3673 0.832 0.671 5.117 0.76080821
Ch 1.30 -0.3960 2.5119 1.0000 0.6280 0.5028 0.1252 0.3720 0.905 0.725 5.217 0.69381798
Ch 1.30 -0.3960 2.8184 1.0000 0.6236 0.4951 0.1285 0.3764 0.992 0.788 5.324 0.62842531
Ch 1.30 -0.3960 3.1623 1.0000 0.6194 0.4876 0.1318 0.3806 1.098 0.864 5.442 0.56406326
Ch 1.30 -0.3960 3.5481 1.0000 0.6155 0.4804 0.1351 0.3845 1.181 0.922 5.528 0.52100290
Ch 1.30 -0.3960 3.9811 1.0000 0.6116 0.4731 0.1386 0.3884 1.278 0.988 5.620 0.47864410
Ch 1.30 -0.3960 4.4668 1.0000 0.6076 0.4653 0.1423 0.3924 1.393 1.067 5.721 0.43617622
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gehong/data/filter/HST_WFPC2-PC.F555W.filter deleted 100644 → 0
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gehong/data/filter/SDSS_r.filter deleted 100644 → 0
View file @ 50fc43d6
# wavelength response
5360.0 0.0
5380.0 0.0002
5400.0 0.0018
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7060.0 0.0
\ No newline at end of file
gehong/data/filter/SLOAN_SDSS.g.filter deleted 100644 → 0
View file @ 50fc43d6
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gehong/data/filter/SLOAN_SDSS.r.filter deleted 100644 → 0
View file @ 50fc43d6
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gehong/map2d.py deleted 100644 → 0
View file @ 50fc43d6
from __future__ import division
import scipy.special as sp
import numpy as np
from astropy.io import fits
from skimage.transform import resize
def Sersic2D(x, y, mag = 12, r_eff = 1, n = 2, ellip = 0.5,
theta = 0, x_0 = 0, y_0 = 0, pixelscale = 0.01):
# Produce Sersic profile
bn = sp.gammaincinv(2. * n, 0.5)
a, b = r_eff, (1 - ellip) * r_eff
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = np.exp(-bn * (z ** (1 / n) - 1))
# Normalization
integral = a * b * 2 * np.pi * n * np.exp(bn) / (bn ** (2 * n)) * sp.gamma(2 * n)
prof_norm = profile / integral * pixelscale
# Calibration
total_flux = 10. ** ((22.5 - mag) * 0.4)
sb_mag = 22.5 - 2.5 * np.log10(prof_norm * total_flux / pixelscale)
return sb_mag
def VelMap2D(x, y, vmax = 200, rt = 1, ellip = 0.5,
theta = 0, x_0 = 0, y_0 = 0):
# Produce tanh profile
a, b = rt, (1 - ellip) * rt
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = vmax * np.tanh(z) * ((x_maj / a) / z)
return profile
def GradMap2D(x, y, a0 = 10, r_eff = 1, gred = -1, ellip = 0.5,
theta = 0, x_0 = 0, y_0 = 0):
# Produce gradiant profile
a, b = r_eff, (1 - ellip) * r_eff
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = a0 + z * gred
return profile
class Map2d(object):
"""
Generate an x, y grid in a rectangular region, sampled with xsamp and
ysamp spacings in the x and y directions, respectively. Origin is at the
upper-left corner to match numpy and matplotlib convention. astropy.io.fits
also assumes UL origin by default.
Parameters
----------
xsamp, ysamp : float, float
The sampling spacing in the x and y directions.
nx, ny: int, int
Number of samples in the x and y directions.
"""
def __init__(self, inst):
self.xsamp = inst.dpix
self.ysamp = inst.dpix
startx = -(inst.nx - 1) / 2.0 * self.xsamp
stopx = (inst.nx - 1) / 2.0 * self.xsamp
starty = -(inst.ny - 1) / 2.0 * self.ysamp
stopy = (inst.ny - 1) / 2.0 * self.ysamp
xvals = np.linspace(startx, stopx, num = inst.nx)
yvals = np.linspace(starty, stopy, num = inst.ny)
ones = np.ones((inst.ny, inst.nx))
x = ones * xvals
y = np.flipud(ones * yvals.reshape(int(inst.ny), 1))
self.nx = inst.nx
self.ny = inst.ny
self.x = x
self.y = y
self.row = xvals
# flip Y axis because we use Y increasing from bottom to top
self.col = yvals[::-1]
def sersic_map(self, mag = 12, r_eff = 2, n = 2.5, ellip = 0.5, theta = -50):
self.mag = mag
self.reff = r_eff / self.xsamp
self.n = n
self.ellip = ellip
self.theta = theta
self.map = Sersic2D(self.x, self.y, mag = self.mag,
r_eff = self.reff, n = self.n,
ellip = self.ellip, theta = self.theta,
pixelscale = self.xsamp * self.ysamp)
def tanh_map(self, vmax = 200, rt = 2, ellip = 0.5, theta = -50):
self.vmax = vmax
self.rt = rt / self.xsamp
self.ellip = ellip
self.theta = theta
self.map = VelMap2D(self.x, self.y, vmax = self.vmax, rt = self.rt,
ellip = self.ellip, theta = self.theta)
def gred_map(self, a0 = 10, r_eff = 1, gred = -1, ellip = 0.5, theta = 0):
self.a0 = a0
self.reff = r_eff / self.xsamp
self.gred = gred
self.ellip = ellip
self.theta = theta
self.map = GradMap2D(self.x, self.y, a0 = self.a0, r_eff = self.reff,
gred = self.gred, ellip = self.ellip, theta = self.theta)
def load_map(self, image):
if np.ndim(image) == 2:
self.map = resize(image, (self.nx, self.ny))
class StellarPopulationMap():
def __init__(self, inst, sbright = None, logage = None,
feh = None, vel = None, vdisp = None, ebv = None):
self.nx = inst.nx
self.ny = inst.ny
self.dpix = inst.dpix
self.fov_x = inst.fov_x
self.fov_y = inst.fov_y
self.sbright = sbright.map
self.logage = logage.map
self.feh = feh.map
self.vel = vel.map
self.vdisp = vdisp.map
self.ebv = ebv.map
self.mag = self.sbright + 2.5 * np.log10(self.dpix * self.dpix)
self.age = 10 ** self.logage / 1e9
self.vdisp[self.vdisp < 10] = 10
self.ebv[self.ebv < 0] = 0
class IonizedGasMap():
def __init__(self, inst, halpha = None, zh = None, vel = None, vdisp = None, ebv = None):
self.nx = inst.nx
self.ny = inst.ny
self.dpix = inst.dpix
self.fov_x = inst.fov_x
self.fov_y = inst.fov_y
self.halpha = halpha.map
self.zh = zh.map
self.vel = vel.map
self.vdisp = vdisp.map
self.ebv = ebv.map
self.vdisp[self.vdisp < 10] = 10
self.ebv[self.ebv < 0] = 0
\ No newline at end of file
gehong/spec1d.py deleted 100644 → 0
View file @ 50fc43d6
import os
import glob
import sys
from os import path
import numpy as np
import astropy.units as u
from astropy.io import fits
from scipy.stats import norm
from scipy.interpolate import interp1d
data_path = os.path.dirname(__file__)
def readcol(filename, **kwargs):
"""
Tries to reproduce the simplicity of the IDL procedure READCOL.
Given a file with some columns of strings and columns of numbers, this
function extract the columns from a file and places them in Numpy vectors
with the proper type:
name, mass = readcol('prova.txt', usecols=(0, 2))
where the file prova.txt contains the following:
##################
# name radius mass
##################
abc 25. 36.
cde 45. 56.
rdh 55 57.
qtr 75. 46.
hdt 47. 56.
##################
This function is a wrapper for numpy.genfromtxt() and accepts the same input.
See the following website for the full documentation
https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html
"""
f = np.genfromtxt(filename, dtype=None, **kwargs)
t = type(f[0])
if t == np.ndarray or t == np.void: # array or structured array
f = map(np.array, zip(*f))
# In Python 3.x all strings (e.g. name='NGC1023') are Unicode strings by defauls.
# However genfromtxt() returns byte strings b'NGC1023' for non-numeric columns.
# To have the same behaviour in Python 3 as in Python 2, I convert the Numpy
# byte string 'S' type into Unicode strings, which behaves like normal strings.
# With this change I can read the string a='NGC1023' from a text file and the
# test a == 'NGC1023' will give True as expected.
if sys.version >= '3':
f = [v.astype(str) if v.dtype.char=='S' else v for v in f]
return f
def log_rebin(lamRange, spec, oversample=False, velscale=None, flux=False):
"""
Logarithmically rebin a spectrum, while rigorously conserving the flux.
Basically the photons in the spectrum are simply redistributed according
to a new grid of pixels, with non-uniform size in the spectral direction.
When the flux keyword is set, this program performs an exact integration
of the original spectrum, assumed to be a step function within the
linearly-spaced pixels, onto the new logarithmically-spaced pixels.
The output was tested to agree with the analytic solution.
:param lamRange: two elements vector containing the central wavelength
of the first and last pixels in the spectrum, which is assumed
to have constant wavelength scale! E.g. from the values in the
standard FITS keywords: LAMRANGE = CRVAL1 + [0,CDELT1*(NAXIS1-1)].
It must be LAMRANGE[0] < LAMRANGE[1].
:param spec: input spectrum.
:param oversample: Oversampling can be done, not to loose spectral resolution,
especally for extended wavelength ranges and to avoid aliasing.
Default: OVERSAMPLE=1 ==> Same number of output pixels as input.
:param velscale: velocity scale in km/s per pixels. If this variable is
not defined, then it will contain in output the velocity scale.
If this variable is defined by the user it will be used
to set the output number of pixels and wavelength scale.
:param flux: (boolean) True to preserve total flux. In this case the
log rebinning changes the pixels flux in proportion to their
dLam so the following command will show large differences
beween the spectral shape before and after LOG_REBIN:
plt.plot(exp(logLam), specNew) # Plot log-rebinned spectrum
plt.plot(np.linspace(lamRange[0], lamRange[1], spec.size), spec)
By defaul, when this is False, the above two lines produce
two spectra that almost perfectly overlap each other.
:return: [specNew, logLam, velscale]
"""
lamRange = np.asarray(lamRange)
assert len(lamRange) == 2, 'lamRange must contain two elements'
assert lamRange[0] < lamRange[1], 'It must be lamRange[0] < lamRange[1]'
s = spec.shape
assert len(s) == 1, 'input spectrum must be a vector'
n = s[0]
if oversample:
m = int(n*oversample)
else:
m = int(n)
dLam = np.diff(lamRange)/(n - 1.) # Assume constant dLam
lim = lamRange/dLam + [-0.5, 0.5] # All in units of dLam
borders = np.linspace(*lim, num=n+1) # Linearly
logLim = np.log(lim)
c = 299792.458 # Speed of light in km/s
if velscale is None: # Velocity scale is set by user
velscale = np.diff(logLim)/m*c # Only for output
else:
logScale = velscale/c
m = int(np.diff(logLim)/logScale) # Number of output pixels
logLim[1] = logLim[0] + m*logScale
newBorders = np.exp(np.linspace(*logLim, num=m+1)) # Logarithmically
k = (newBorders - lim[0]).clip(0, n-1).astype(int)
specNew = np.add.reduceat(spec, k)[:-1] # Do analytic integral
specNew *= np.diff(k) > 0 # fix for design flaw of reduceat()
specNew += np.diff((newBorders - borders[k])*spec[k])
if not flux:
specNew /= np.diff(newBorders)
# Output log(wavelength): log of geometric mean
logLam = np.log(np.sqrt(newBorders[1:]*newBorders[:-1])*dLam)
return specNew, logLam, velscale
def gaussian_filter1d(spec, sig):
"""
Convolve a spectrum by a Gaussian with different sigma for every pixel.
If all sigma are the same this routine produces the same output as
scipy.ndimage.gaussian_filter1d, except for the border treatment.
Here the first/last p pixels are filled with zeros.
When creating a template library for SDSS data, this implementation
is 60x faster than a naive for loop over pixels.
:param spec: vector with the spectrum to convolve
:param sig: vector of sigma values (in pixels) for every pixel
:return: spec convolved with a Gaussian with dispersion sig
"""
sig = sig.clip(0.01) # forces zero sigmas to have 0.01 pixels
p = int(np.ceil(np.max(3*sig)))
m = 2*p + 1 # kernel size
x2 = np.linspace(-p, p, m)**2
n = spec.size
a = np.zeros((m, n))
for j in range(m): # Loop over the small size of the kernel
a[j, p:-p] = spec[j:n-m+j+1]
gau = np.exp(-x2[:, None]/(2*sig**2))
gau /= np.sum(gau, 0)[None, :] # Normalize kernel
conv_spectrum = np.sum(a*gau, 0)
return conv_spectrum
def calibrate(wave, flux, mag, filtername = 'SLOAN_SDSS.r'):
"""
Calibrate the spectra according to the magnitude.
:param wave: _description_
:type wave: _type_
:param flux: _description_
:type flux: _type_
:param mag: _description_
:type mag: _type_
:param filtername: _description_, defaults to './data/SLOAN_SDSS.r'
:type filtername: str, optional
:return: _description_
:rtype: _type_
"""
# Loading response curve
if filtername == '5100':
wave0 = np.linspace(3000,10000,7000)
response0 = np.zeros(7000)
response0[(wave0 > 5050) & (wave0 < 5150)] = 1.
else:
filter_file = data_path + '/data/filter/' + filtername+'.filter'
wave0, response0 = readcol(filter_file)
# Setting the response
func = interp1d(wave0, response0)
response = np.copy(wave)
ind_extra = (wave > max(wave0)) | (wave < min(wave0))
response[ind_extra] = 0
ind_inside = (wave < max(wave0)) & (wave > min(wave0))
response[ind_inside] = func(wave[ind_inside])
# Flux map of datacube for given filter band
preflux = np.sum(flux * response * np.mean(np.diff(wave))) / np.sum(response * np.mean(np.diff(wave)))
# Real flux from magnitude for given filter
realflux = (mag * u.STmag).to(u.erg/u.s/u.cm**2/u.AA).value
# Normalization
flux_ratio = realflux / preflux
flux_calibrate = flux * flux_ratio * 1e17 # Units: 10^-17 erg/s/A/cm^2
return flux_calibrate
# ----------------
# Reddening Module
def Calzetti_Law(wave, Rv = 4.05):
"""
Calzetti_Law
Dust Extinction Curve of Calzetti et al. (2000)
Parameters
----------
wave : float
Wavelength
Rv : float, optional
Extinction coefficient, by default 4.05
Returns
-------
float
Extinction value corresponding to the input wavelength
"""
wave_number = 1./(wave * 1e-4)
reddening_curve = np.zeros(len(wave))
idx = np.logical_and(wave >= 1200, wave <= 6300)
reddening_curve[idx] = 2.659 * ( -2.156 + 1.509 * wave_number[idx] - 0.198 * \
(wave_number[idx] ** 2)) + 0.011 * (wave_number[idx] **3 ) + Rv
idx = np.logical_and(wave >= 6300, wave <= 22000)
reddening_curve[idx] = 2.659 * ( -1.857 + 1.040 * wave_number[idx]) + Rv
return reddening_curve
def reddening(wave, flux, ebv = 0.0, law = 'calzetti', Rv = 4.05):
"""
Reddening an input spectra through a given reddening curve.
Parameters
----------
wave : float
Wavelength of input spectra
flux : float
Flux of input spectra
ebv : float, optional
E(B-V) value, by default 0
law : str, optional
Extinction curve, by default 'calzetti'
Rv : float, optional
Extinction coefficient, by default 4.05
Returns
-------
float
Flux of spectra after reddening
"""
curve = Calzetti_Law(wave, Rv = Rv)
fluxNew = flux / (10. ** (0.4 * ebv * curve))
return fluxNew
################
# Emission Lines
################
def SingleEmissinoLine(wave, line_wave, FWHM_inst):
"""
Profile of single emission line (Gaussian profile)
Parameters
----------
wave : float
Wavelength of
line_wave : float
Wavelength of emission line at the line center
FWHM_inst : float
Intrinsic broadening of emission line, units: A
Returns
-------
float
Spectra of single emission line
"""
sigma = FWHM_inst / 2.355
flux = norm.pdf(wave, line_wave, sigma)
return flux
class EmissionLineTemplate():
"""
Template for the emission lines
"""
def __init__(self, instrument, lam_range = [500, 15000], dlam = 0.1, model = 'hii'):
"""
__init__ _summary_
Parameters
----------
lam_range : list, optional
_description_, by default [500, 15000]
dlam : float, optional
_description_, by default 0.1
model : str, optional
_description_, by default 'nlr'
FWHM_inst : float, optional
_description_, by default 0.5
"""
self.lam_range = lam_range
self.wave = np.arange(lam_range[0], lam_range[1], 0.1)
self.FWHM_inst = instrument.inst_fwhm
self.model = model
# HII region model of fsps-cloudy
if model == 'hii':
# Loading emission line flux table
flux_table_file = data_path + '/data/fsps.nebular.fits'
line_table = fits.open(flux_table_file)
# Emission line list
line_list = line_table[1].data
line_wave = line_list['Wave']
line_names = line_list['Name']
w = (line_wave > lam_range[0]) & (line_wave < lam_range[1])
self.line_names = line_names[w]
self.line_wave = line_wave[w]
# Make parameter grid
grid = line_table[2].data
self.zh_grid = grid['logZ']
# Narrow line region model of
if model == 'nlr':
# Loading emission line flux table
flux_table_file = data_path + '/data/AGN.NLR.fits'
line_table = fits.open(flux_table_file)
# Emission line list
line_list = line_table[1].data
line_wave = line_list['Wave']
line_names = line_list['Name']
w = (line_wave > lam_range[0]) & (line_wave < lam_range[1])
self.line_names = line_names[w]
self.line_wave = line_wave[w]
# Make parameter grid
grid = line_table[2].data
self.zh_grid = grid['logZ']
# Flux ratio
flux_ratio = line_table[3].data
self.flux_ratio = flux_ratio
# Make emission line
nline = len(line_wave)
for i in range(nline):
if i==0:
emission_line = SingleEmissinoLine(self.wave, line_wave[i], self.FWHM_inst)
emission_lines = emission_line
else:
emission_line = SingleEmissinoLine(self.wave, line_wave[i], self.FWHM_inst)
emission_lines = np.vstack((emission_lines, emission_line))
self.emission_lines = emission_lines.T
class HII_Region():
"""
Class for the spectra of HII region
Parameters
----------
wave : _type_
_description_
temp : _type_
_description_
Halpha : int, optional
_description_, by default 100
logZ : bool, optional
_description_, by default False
vel : int, optional
_description_, by default 100
vdisp : int, optional
_description_, by default 120
Ebv : float, optional
_description_, by default 0.1
Raises
------
ValueError
_description_
"""
def __init__(self, inst, temp, halpha = 100, zh = 0,
vel = 100, vdisp = 120, ebv = 0.1):
if (zh > -2) & (zh < 0.5):
indz = np.argmin(np.abs(zh - temp.zh_grid))
flux_ratio = temp.flux_ratio[indz, :]
else:
raise ValueError('The range of logZ is not correct!')
# Make emission line spectra through adding emission lines
emlines = temp.emission_lines * flux_ratio
flux_combine = np.sum(emlines, axis = 1)
flux_calibrate = flux_combine * halpha # Units: erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(temp.wave, flux_calibrate, ebv = ebv)
# Broadening caused by Velocity Dispersion
velscale = 10
lam_range = [np.min(temp.wave), np.max(temp.wave)]
flux_logwave, logLam = log_rebin(lam_range, flux_dust, velscale=velscale)[:2]
sigma_gas = vdisp / velscale # in pixel
sigma_LSF = temp.FWHM_inst / (np.exp(logLam)) * 3e5 / velscale # in pixel
if sigma_gas>0:
sigma_dif = np.zeros(len(flux_logwave))
idx = (sigma_gas > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gas ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gas <= sigma_LSF)
sigma_dif[idx] = 0.1
flux_broad = gaussian_filter1d(flux_logwave, sigma_dif)
# Redshift
redshift = vel / 3e5
wave_r = np.exp(logLam) * (1 + redshift)
flux_red = np.interp(inst.wave, wave_r, flux_broad)
self.wave = inst.wave
self.flux = flux_red
#############
# AGN Spectra
#############
class AGN_NLR():
"""
Class for narrow line region of AGN
Parameters
----------
wave : float
Wavelength
temp : class
Class of emission line template
Halpha : float, optional
Total flux of Halpha emission line, units 1e-17 erg/s, by default 100
logZ : float, optional
Gas-phase metallicity, by default False
vel : float, optional
Line-of-sight velocity, by default 100
vdisp : float, optional
Velocity dispersion, by default 120
Ebv : float, optional
Dust extinction, by default 0.1
Raises
------
ValueError
_description_
"""
def __init__(self, temp, instrument, halpha = 100, zh = False,
vel = 100, vdisp = 120, ebv = 0.1):
if (zh > -2) & (zh < 0.5):
indz = np.argmin(np.abs(zh - temp.zh_grid))
flux_ratio = temp.flux_ratio[indz, :]
else:
raise ValueError('The value of logZ is not correct!')
# Make emission line spectra through adding emission lines
emlines = temp.emission_lines * flux_ratio
flux_combine = np.sum(emlines, axis = 1)
flux_calibrate = flux_combine * halpha # Units: 1e-17 erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(temp.wave, flux_calibrate, ebv = ebv)
# Broadening caused by Velocity Dispersion
velscale = 10
lam_range = [np.min(temp.wave), np.max(temp.wave)]
flux_logwave, logLam = log_rebin(lam_range, flux_dust, velscale=velscale)[:2]
sigma_gas = vdisp / velscale # in pixel
sigma_LSF = temp.FWHM_inst / (np.exp(logLam)) * 3e5 / velscale # in pixel
if sigma_gas>0:
sigma_dif = np.zeros(len(flux_logwave))
idx = (sigma_gas > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gas ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gas <= sigma_LSF)
sigma_dif[idx] = 0.1
flux_broad = gaussian_filter1d(flux_logwave, sigma_dif)
# Redshift
redshift = vel / 3e5
wave_r = np.exp(logLam) * (1 + redshift)
flux_red = np.interp(instrument.wave, wave_r, flux_broad)
self.wave = instrument.wave
self.flux = flux_red
class AGN_BLR():
"""
Class for the broad line region of AGN
"""
def __init__(self, instrument, hbeta_flux = 100, hbeta_fwhm = 2000, ebv = None, vel = 0.,
lam_range = [500, 15000]):
wave_rest = np.arange(lam_range[0], lam_range[1], 0.1)
line_names = ['Hepsilon', 'Hdelta', 'Hgamma', 'Hbeta', 'Halpha']
line_waves = [3970.079, 4101.742, 4340.471, 4861.333, 6562.819]
line_ratio = [0.101, 0.208, 0.405, 1.000, 2.579] # From Ilic et al. (2006)
# Make emission lines
for i in range(len(line_names)):
if i==0:
emission_line = SingleEmissinoLine(wave_rest, line_waves[i],
hbeta_fwhm / 3e5 * line_waves[i])
emission_lines = emission_line
else:
emission_line = SingleEmissinoLine(wave_rest, line_waves[i],
hbeta_fwhm / 3e5 * line_waves[i])
emission_lines = np.vstack((emission_lines, emission_line))
emlines = emission_lines.T * line_ratio
flux_combine = np.sum(emlines, axis = 1)
# Flux callibration
flux_calibrate = flux_combine * hbeta_flux # Units: 1e-17 erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(instrument.wave, wave_r, flux_dust)
self.wave = instrument.wave
self.flux = flux_red
class AGN_FeII():
"""
Class for FeII emission lines of AGN
"""
def __init__(self, instrument, hbeta_broad = 100, r4570 = 0.4, ebv = None, vel = 0.):
"""
_summary_
"""
filename = data_path + '/data/FeII.AGN.fits'
# Loading FeII template
hdulist = fits.open(filename)
data = hdulist[1].data
wave_rest = data['WAVE']
flux_model = data['FLUX']
# Determine the flux of FeII
Fe4570_temp = 100
Fe4570_model = hbeta_broad * r4570
Ratio_Fe4570 = Fe4570_model / Fe4570_temp
# Flux calibration
flux_calibrate = flux_model * Ratio_Fe4570
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(instrument.wave, wave_r, flux_dust)
self.wave = instrument.wave
self.flux = flux_red
class AGN_Powerlaw():
"""
AGN_Powerlaw _summary_
"""
def __init__(self, instrument, m5100 = 1000, alpha = -1.5, ebv = None, vel = 0.,):
"""
_summary_
"""
wave_rest = np.linspace(1000,20000,10000)
flux = wave_rest ** alpha
# Flux calibration
flux_calibrate = calibrate(wave_rest, flux, m5100, filtername='5100')
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(instrument.wave, wave_r, flux_dust)
self.wave = instrument.wave
self.flux = flux_red
class AGN():
def __init__(self, instrument, nlr_template, bhmass = 1e6, edd_ratio = 0.05,
halpha_broad = 100, halpha_narrow = 100, vdisp_broad = 2000, vdisp_narrow = 500,
vel = 1000, zh = 0, ebv = 0.1, dist = 20):
NLR = AGN_NLR(nlr_template, instrument, halpha = halpha_narrow, zh = zh,
vel = vel, vdisp = vdisp_narrow, ebv = ebv)
if halpha_broad > 0:
BLR = AGN_BLR(instrument, hbeta_flux = halpha_broad / 2.579,
hbeta_fwhm = vdisp_broad / 2.355, ebv = ebv, vel = vel)
m5100 = BHmass_to_M5100(bhmass, edd_ratio = edd_ratio, dist = dist)
PL = AGN_Powerlaw(instrument, m5100 = m5100, ebv = ebv, vel = vel)
Fe = AGN_FeII(instrument, hbeta_broad = halpha_broad / 2.579, ebv = ebv, vel = vel)
self.wave = instrument.wave
self.flux = NLR.flux + PL.flux + Fe.flux
if halpha_broad > 0:
self.flux = self.flux + BLR.flux
def BHmass_to_M5100(bhmass, edd_ratio = 0.05, dist = 21):
"""
Caculate luminosity at 5100A according to the black hole mass
Parameters
----------
BHmass : float
Black hole mass, unit: solar mass
Edd_ratio : float, optional
Eddtington ratio, by default 0.05
Dist : int, optional
Distance to the black hole, by default 21
Returns
-------
float
Luminosity at 5100A
"""
# Calculate bolometric luminosity
Ledd = 3e4 * bhmass
Lbol = Ledd * edd_ratio
# Convert bolometric luminosity to 5100A luminosity (Marconi et al. 2004)
L5100 = Lbol / 10.9
M5100 = 4.86 - 2.5 * np.log10(L5100)
m5100 = M5100 + 5. * np.log10(dist * 1e5)
return m5100
#################
# Stellar Spectra
#################
def age_metal(filename):
"""
Extract the age and metallicity from the name of a file of
the MILES library of Single Stellar Population models as
downloaded from http://miles.iac.es/ as of 2016
:param filename: string possibly including full path
(e.g. 'miles_library/Mun1.30Zm0.40T03.9811.fits')
:return: age (Gyr), [M/H]
"""
s = path.basename(filename)
age = float(s[s.find("T")+1:s.find("_iPp0.00_baseFe.fits")])
metal = s[s.find("Z")+1:s.find("T")]
if "m" in metal:
metal = -float(metal[1:])
elif "p" in metal:
metal = float(metal[1:])
else:
raise ValueError("This is not a standard MILES filename")
return age, metal
class StellarContinuumTemplate(object):
def __init__(self, instrument, velscale = 50,
pathname = data_path + '/data/EMILES/Ech*_baseFe.fits',
normalize = False):
"""
Produces an array of logarithmically-binned templates by reading
the spectra from the Single Stellar Population (SSP) library by
Vazdekis et al. (2010, MNRAS, 404, 1639) http://miles.iac.es/.
The code checks that the model specctra form a rectangular grid
in age and metallicity and properly sorts them in both parameters.
The code also returns the age and metallicity of each template
by reading these parameters directly from the file names.
The templates are broadened by a Gaussian with dispersion
sigma_diff = np.sqrt(、**2 - sigma_tem**2).
Thie script relies on the files naming convention adopted by
the MILES library, where SSP spectra have the form below
Mun1.30Zm0.40T03.9811.fits
This code can be easily adapted by the users to deal with other stellar
libraries, different IMFs or different abundances.
:param pathname: path with wildcards returning the list files to use
(e.g. 'miles_models/Mun1.30*.fits'). The files must form a Cartesian grid
in age and metallicity and the procedure returns an error if they are not.
:param velscale: desired velocity scale for the output templates library in km/s
(e.g. 60). This is generally the same or an integer fraction of the velscale
of the galaxy spectrum.
:param FWHM_gal: vector or scalar of the FWHM of the instrumental resolution of
the galaxy spectrum in Angstrom.
:param normalize: set to True to normalize each template to mean=1.
This is useful to compute light-weighted stellar population quantities.
:return: The following variables are stored as attributes of the miles class:
.templates: array has dimensions templates[npixels, n_ages, n_metals];
.log_lam_temp: natural np.log() wavelength of every pixel npixels;
.age_grid: (Gyr) has dimensions age_grid[n_ages, n_metals];
.metal_grid: [M/H] has dimensions metal_grid[n_ages, n_metals].
.n_ages: number of different ages
.n_metal: number of different metallicities
"""
FWHM_inst = instrument.inst_fwhm
files = glob.glob(pathname)
assert len(files) > 0, "Files not found %s" % pathname
all = [age_metal(f) for f in files]
all_ages, all_metals = np.array(all).T
ages, metals = np.unique(all_ages), np.unique(all_metals)
n_ages, n_metal = len(ages), len(metals)
assert set(all) == set([(a, b) for a in ages for b in metals]), \
'Ages and Metals do not form a Cartesian grid'
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SDSS galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
hdu = fits.open(files[0])
ssp = hdu[0].data
h2 = hdu[0].header
lam_range_temp = h2['CRVAL1'] + np.array([0, h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, log_lam_temp = log_rebin(lam_range_temp, ssp, velscale=velscale)[:2]
#wave=((np.arange(hdr['NAXIS1'])+1.0)-hdr['CRPIX1'])*hdr['CDELT1']+hdr['CRVAL1']
templates = np.empty((sspNew.size, n_ages, n_metal))
age_grid = np.empty((n_ages, n_metal))
metal_grid = np.empty((n_ages, n_metal))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the galaxy and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> galaxy
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
# FWHM of Emiles templates
Emile_wave = np.exp(log_lam_temp)
Emile_FWHM = np.zeros(h2['NAXIS1'])
Emile_FWHM[np.where(Emile_wave < 3060)] = 3.
Emile_FWHM[np.where((Emile_wave >= 3060) & (Emile_wave < 3540))] = 3.
Emile_FWHM[np.where((Emile_wave >= 3540) & (Emile_wave < 8950))] = 2.5
Lwave = Emile_wave[np.where(Emile_wave >= 8950)]
Emile_FWHM[np.where(Emile_wave >= 8950)]=60*2.35/3.e5*Lwave # sigma=60km/s at lambda > 8950
LSF = Emile_FWHM
FWHM_eff=Emile_FWHM.copy() # combined FWHM from stellar library and instrument(input)
if np.isscalar(FWHM_inst):
FWHM_eff[Emile_FWHM < FWHM_inst] = FWHM_inst
LSF[Emile_FWHM < FWHM_inst] = FWHM_inst
#else:
# FWHM_eff[Emile_FWHM < FWHM_inst] = FWHM_inst[Emile_FWHM < FWHM_inst]
# LSF[Emile_FWHM < FWHM_inst] = FWHM_inst[Emile_FWHM < FWHM_inst]
FWHM_dif = np.sqrt(FWHM_eff**2 - Emile_FWHM**2)
sigma_dif = FWHM_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
# Here we make sure the spectra are sorted in both [M/H] and Age
# along the two axes of the rectangular grid of templates.
for j, age in enumerate(ages):
for k, metal in enumerate(metals):
p = all.index((age, metal))
hdu = fits.open(files[p])
ssp = hdu[0].data
#if np.isscalar(FWHM_dif):
# ssp = ndimage.gaussian_filter1d(ssp, sigma_dif)
#else:
ssp = gaussian_filter1d(ssp, sigma_dif) # convolution with variable sigma
sspNew = log_rebin(lam_range_temp, ssp, velscale=velscale)[0]
#if normalize:
# sspNew /= np.mean(sspNew)
templates[:, j, k] = sspNew
age_grid[j, k] = age
metal_grid[j, k] = metal
self.templates = templates/np.median(templates) # Normalize by a scalar
self.log_lam_temp = log_lam_temp
self.age_grid = age_grid
self.metal_grid = metal_grid
self.n_ages = n_ages
self.n_metal = n_metal
self.lsf = log_rebin(lam_range_temp, LSF, velscale=velscale)[0]
self.velscale = velscale
def fmass_ssp(self):
isedpath = data_path + '/data/EMILES/model/'
massfile = isedpath + 'out_mass_CH_PADOVA00'
n_metal = self.n_metal
n_ages = self.n_ages
fage = self.age_grid[:,0]
fMs = np.zeros((n_ages,n_metal))
Metal, Age, Ms = readcol(massfile, usecols=(2, 3, 6))
for i in range(n_metal):
for j in range(self.n_ages):
locmin = np.argmin(abs(Metal - self.metal_grid[j, i])) & np.argmin(abs(Age - self.age_grid[j, i]))
fMs[j,i] = Ms[locmin]
return fMs
class StellarContinuum():
def __init__(self, template, instrument, mag = 15, age = 1, feh = 0, vel = 100, vdisp = 100, ebv = 0):
"""
Modelling the spectra of stellar population
Pars:
ssp - The class of simple stellar population
obswave - Wavelength, 1D-array
mag - Magnitude in r-band used for the calibration of spectra, scalar
Age - Mean age of stellar population used for determining the spectra profile, scalar (Gyr)
FeH - Mean FeH of stellar population used for determining the spectra profile, scalar
vel - Line of sight velocity used for determining the doppler effect, scalar (km/s)
vdisp - Line of sight velocity dispersion used for broadening the spectra, scalar (km/s)
Ebv - Extinction, scalar (mag), default 0 (no dust extinction)
dFeH - Range of FeH when searching the best templates, default 0.2 (searching the template
with -0.2 < FeH - dFeh < 0.2)
Attri:
wave - Wavelength is the same with obswave in Pars
flux - Flux of best model
"""
# -----------------
# Stellar Continuum
SSP_temp = template.templates
# Select metal bins
metals = template.metal_grid[0,:]
minloc = np.argmin(abs(feh - metals))
tpls = SSP_temp[:, :, minloc]
fmass = template.fmass_ssp()[:, minloc]
# Select age bins
Ages = template.age_grid[:,0]
minloc = np.argmin(abs(age-Ages))
Stellar = tpls[:, minloc]
wave = np.exp(template.log_lam_temp)
# Broadening caused by Velocity Dispersion
sigma_gal = vdisp / template.velscale # in pixel
sigma_LSF = template.lsf / template.velscale # in pixel
if sigma_gal>0:
sigma_dif = np.zeros(len(Stellar))
idx = (sigma_gal > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gal ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gal <= sigma_LSF)
sigma_dif[idx] = 0.1
flux0 = gaussian_filter1d(Stellar, sigma_dif)
# Dust Reddening
if np.isscalar(ebv):
flux0 = reddening(wave, flux0, ebv = ebv)
# Redshift
redshift = vel / 3e5
wave_r = wave * (1 + redshift)
flux = np.interp(instrument.wave, wave_r, flux0)
# Calibration
if np.isscalar(mag):
flux = calibrate(instrument.wave, flux, mag, filtername='SLOAN_SDSS.r')
# Convert to input wavelength
self.wave = instrument.wave
self.flux = flux
#####################
# Single Star Spectra
#####################
class SingleStarTemplate():
def __init__(self, inst, velscale = 20):
filename = data_path + '/data/Starlib.XSL.fits'
fwhm_inst = inst.inst_fwhm
hdulist = fits.open(filename)
lam = hdulist[1].data['Wave']
flux = hdulist[2].data
par = hdulist[3].data
lam_range_temp = np.array([3500, 12000])
TemNew, log_lam_temp = log_rebin(lam_range_temp, flux[1, :], velscale = velscale)[:2]
# FWHM of XLS templates
Temp_wave = np.exp(log_lam_temp)
Temp_FWHM = np.zeros(len(log_lam_temp))
Temp_FWHM[(Temp_wave < 5330)] = 13 * 2.35 / 3e5 * Temp_wave[(Temp_wave < 5330)] # sigma = 13km/s at lambda <5330
Temp_FWHM[(Temp_wave >= 5330) & (Temp_wave < 9440)] = 11 * 2.35 / 3e5 * Temp_wave[(Temp_wave >= 5330) & (Temp_wave < 9440)]
# sigma = 13km/s at 5330 < lambda < 9440
Temp_FWHM[(Temp_wave >= 9440)] = 16 * 2.35 / 3e5 * Temp_wave[(Temp_wave >= 9440)] # sigma=16km/s at lambda > 9440
LSF = Temp_FWHM
FWHM_eff = Temp_FWHM.copy() # combined FWHM from stellar library and instrument(input)
if np.isscalar(fwhm_inst):
FWHM_eff[Temp_FWHM < fwhm_inst] = fwhm_inst
LSF[Temp_FWHM < fwhm_inst] = fwhm_inst
#else:
# FWHM_eff[Temp_FWHM < FWHM_inst] = FWHM_inst[Temp_FWHM < FWHM_inst]
# LSF[Temp_FWHM < FWHM_inst] = FWHM_inst[Temp_FWHM < FWHM_inst]
FWHM_dif = np.sqrt(FWHM_eff ** 2 - Temp_FWHM ** 2)
sigma_dif = FWHM_dif / 2.355 / (lam[1] - lam[0]) # Sigma difference in pixels
temp = np.empty((TemNew.size, par.size))
for i in range(par.size):
temp0 = log_rebin(lam_range_temp, flux[i, :], velscale=velscale)[0]
#if np.isscalar(FWHM_dif):
# temp1 = ndimage.gaussian_filter1d(temp0, sigma_dif)
#else:
temp1 = gaussian_filter1d(temp0, sigma_dif) # convolution with variable sigma
tempNew = temp1 / np.mean(temp1)
temp[:, i] = tempNew
self.templates = temp
self.log_lam_temp = log_lam_temp
self.teff_grid = par['Teff']
self.feh_grid = par['FeH']
self.logg_grid = par['logg']
self.lsf = Temp_FWHM
self.velscale = velscale
class SingleStar():
def __init__(self, template, instrument, mag = 15, teff = 10000, feh = 0, vel = 100, ebv = 0):
StarTemp = template.templates
# Select metal bins
idx_feh = (np.abs(template.feh_grid - feh) < 0.5)
tpls = StarTemp[:, idx_feh]
# Select Teff bins
teff_feH = template.teff_grid[idx_feh]
minloc = np.argmin(abs(teff - teff_feH))
starspec = tpls[:, minloc]
wave = np.exp(template.log_lam_temp)
# Dust Reddening
if np.isscalar(ebv):
starspec = reddening(wave, starspec, ebv = ebv)
# Redshift
redshift = vel / 3e5
wave_r = wave * (1 + redshift)
flux = np.interp(instrument.wave, wave_r, starspec)
# Calibration
if np.isscalar(mag):
flux = calibrate(instrument.wave, flux, mag, filtername='SLOAN_SDSS.r')
# Convert to input wavelength
self.wave = instrument.wave
self.flux = flux
\ No newline at end of file
src/csst_ifs_gehong.egg-info/PKG-INFO deleted 100644 → 0
View file @ 50fc43d6
Metadata-Version: 2.1
Name: csst-ifs-gehong
Version: 1.0.0
Home-page: https://csst-ifs-gehong.readthedocs.io/en/latest/
Author: Shuai Feng
Author-email: sfeng@hebtu.edu.cn
License: MIT
Keywords: CSST-IFS
License-File: LICENSE
src/csst_ifs_gehong.egg-info/SOURCES.txt deleted 100644 → 0
View file @ 50fc43d6
LICENSE
README.md
setup.cfg
setup.py
src/csst_ifs_gehong.egg-info/PKG-INFO
src/csst_ifs_gehong.egg-info/SOURCES.txt
src/csst_ifs_gehong.egg-info/dependency_links.txt
src/csst_ifs_gehong.egg-info/requires.txt
src/csst_ifs_gehong.egg-info/top_level.txt
src/gehong/__init__.py
src/gehong/config.py
src/gehong/cube3d.py
src/gehong/map2d.py
src/gehong/spec1d.py
test/test_cube3d.py
test/test_map2d.py
test/test_spec1d.py
\ No newline at end of file
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