0415 first upload

gehong/data/EMILES/model/HST_ch_iPp0.00.MAG

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gehong/data/EMILES/model/out_mass_CH_PADOVA00

+Ch  1.30 -2.3152  0.0631   1.0000  0.7954  0.7426  0.0528  0.2046    0.112    0.105    2.694      7.08765287
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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 -2.3152  3.5481   1.0000  0.5954  0.4544  0.1410  0.4046    0.637    0.487    4.894      0.93395729
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+Ch  1.30 -2.3152  5.0119   1.0000  0.5840  0.4317  0.1523  0.4160    0.853    0.631    5.232      0.68444659
+Ch  1.30 -2.3152  5.6234   1.0000  0.5804  0.4240  0.1564  0.4196    0.929    0.679    5.331      0.62469185
+Ch  1.30 -2.3152  6.3096   1.0000  0.5773  0.4168  0.1604  0.4227    1.032    0.745    5.451      0.55928914
+Ch  1.30 -2.3152  7.0795   1.0000  0.5742  0.4097  0.1645  0.4258    1.139    0.813    5.564      0.50409306
+Ch  1.30 -2.3152  7.9433   1.0000  0.5711  0.4023  0.1688  0.4289    1.256    0.885    5.676      0.45473893
+Ch  1.30 -2.3152  8.9125   1.0000  0.5682  0.3949  0.1733  0.4318    1.397    0.971    5.797      0.40674943
+Ch  1.30 -2.3152 10.0000   1.0000  0.5656  0.3876  0.1780  0.4344    1.583    1.085    5.938      0.35722923
+Ch  1.30 -2.3152 11.2202   1.0000  0.5631  0.3806  0.1825  0.4369    1.672    1.130    6.002      0.33670405
+Ch  1.30 -2.3152 12.5893   1.0000  0.5571  0.3735  0.1835  0.4429    1.823    1.222    6.107      0.30560812
+Ch  1.30 -2.3152 14.1254   1.0000  0.5491  0.3663  0.1828  0.4509    2.096    1.399    6.275      0.26191565
+Ch  1.30 -2.3152 15.8489   1.0000  0.5428  0.3593  0.1835  0.4572    2.290    1.516    6.383      0.23701507
+Ch  1.30 -2.3152 17.7828   1.0000  0.5349  0.3528  0.1822  0.4651    2.432    1.604    6.464      0.21991659
+Ch  1.30 -1.7129  0.0631   1.0000  0.7984  0.7464  0.0520  0.2016    0.085    0.080    2.392      9.35656097
+Ch  1.30 -1.7129  0.0708   1.0000  0.7916  0.7377  0.0539  0.2084    0.091    0.085    2.471      8.70516497
+Ch  1.30 -1.7129  0.0794   1.0000  0.7859  0.7302  0.0557  0.2141    0.096    0.089    2.534      8.20777225
+Ch  1.30 -1.7129  0.0891   1.0000  0.7796  0.7217  0.0579  0.2204    0.100    0.093    2.591      7.79331798
+Ch  1.30 -1.7129  0.1000   1.0000  0.7736  0.7135  0.0601  0.2264    0.106    0.098    2.660      7.30918077
+Ch  1.30 -1.7129  0.1122   1.0000  0.7677  0.7060  0.0617  0.2323    0.074    0.068    2.287     10.31081629
+Ch  1.30 -1.7129  0.1259   1.0000  0.7612  0.6978  0.0635  0.2388    0.077    0.070    2.331      9.90345496
+Ch  1.30 -1.7129  0.1413   1.0000  0.7544  0.6890  0.0654  0.2456    0.080    0.073    2.388      9.39610790
+Ch  1.30 -1.7129  0.1585   1.0000  0.7480  0.6809  0.0671  0.2520    0.084    0.076    2.442      8.94012270
+Ch  1.30 -1.7129  0.1778   1.0000  0.7416  0.6725  0.0691  0.2584    0.088    0.080    2.505      8.43315373
+Ch  1.30 -1.7129  0.1995   1.0000  0.7350  0.6639  0.0710  0.2650    0.092    0.083    2.566      7.97372969
+Ch  1.30 -1.7129  0.2239   1.0000  0.7286  0.6555  0.0731  0.2714    0.100    0.090    2.663      7.28886623
+Ch  1.30 -1.7129  0.2512   1.0000  0.7224  0.6472  0.0751  0.2776    0.107    0.096    2.751      6.72221801
+Ch  1.30 -1.7129  0.2818   1.0000  0.7162  0.6389  0.0773  0.2838    0.117    0.104    2.853      6.11851870
+Ch  1.30 -1.7129  0.3162   1.0000  0.7100  0.6305  0.0795  0.2900    0.130    0.115    2.975      5.46945904
+Ch  1.30 -1.7129  0.3548   1.0000  0.7039  0.6223  0.0816  0.2961    0.138    0.122    3.047      5.11707834
+Ch  1.30 -1.7129  0.3981   1.0000  0.6981  0.6143  0.0838  0.3019    0.147    0.129    3.128      4.75213529
+Ch  1.30 -1.7129  0.4467   1.0000  0.6918  0.6058  0.0861  0.3082    0.158    0.138    3.215      4.38472841
+Ch  1.30 -1.7129  0.5012   1.0000  0.6857  0.5973  0.0884  0.3143    0.171    0.149    3.315      3.99870532
+Ch  1.30 -1.7129  0.5623   1.0000  0.6801  0.5895  0.0907  0.3199    0.184    0.159    3.398      3.70520507
+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
+Ch  1.30 -0.3960  5.0119   1.0000  0.6037  0.4576  0.1461  0.3963    1.559    1.182    5.850      0.38731083
+Ch  1.30 -0.3960  5.6234   1.0000  0.6001  0.4503  0.1498  0.3999    1.701    1.277    5.951      0.35275305
+Ch  1.30 -0.3960  6.3096   1.0000  0.5967  0.4432  0.1536  0.4033    1.907    1.416    6.081      0.31297591
+Ch  1.30 -0.3960  7.0795   1.0000  0.5937  0.4366  0.1571  0.4063    2.135    1.570    6.210      0.27803900
+Ch  1.30 -0.3960  7.9433   1.0000  0.5907  0.4300  0.1607  0.4093    2.336    1.701    6.313      0.25283300
+Ch  1.30 -0.3960  8.9125   1.0000  0.5877  0.4231  0.1645  0.4123    2.530    1.821    6.405      0.23231558
+Ch  1.30 -0.3960 10.0000   1.0000  0.5847  0.4162  0.1685  0.4153    2.735    1.947    6.495      0.21375564
+Ch  1.30 -0.3960 11.2202   1.0000  0.5819  0.4092  0.1727  0.4181    2.950    2.074    6.583      0.19724147
+Ch  1.30 -0.3960 12.5893   1.0000  0.5793  0.4022  0.1770  0.4207    3.230    2.243    6.686      0.17935574
+Ch  1.30 -0.3960 14.1254   1.0000  0.5768  0.3952  0.1816  0.4232    3.559    2.439    6.796      0.16206373
+Ch  1.30 -0.3960 15.8489   1.0000  0.5743  0.3881  0.1863  0.4257    4.004    2.705    6.928      0.14344893
+Ch  1.30 -0.3960 17.7828   1.0000  0.5703  0.3812  0.1891  0.4297    4.272    2.855    7.006      0.13350361
+Ch  1.30  0.0000  0.0631   1.0000  0.8011  0.7499  0.0512  0.1989    0.087    0.081    2.405      9.25043002
+Ch  1.30  0.0000  0.0708   1.0000  0.7959  0.7432  0.0527  0.2041    0.093    0.087    2.487      8.57805876
+Ch  1.30  0.0000  0.0794   1.0000  0.7905  0.7363  0.0543  0.2095    0.101    0.094    2.583      7.84939544
+Ch  1.30  0.0000  0.0891   1.0000  0.7852  0.7293  0.0559  0.2148    0.106    0.099    2.648      7.39204225
+Ch  1.30  0.0000  0.1000   1.0000  0.7803  0.7227  0.0576  0.2197    0.113    0.105    2.721      6.91335881
+Ch  1.30  0.0000  0.1122   1.0000  0.7753  0.7159  0.0594  0.2247    0.120    0.111    2.796      6.45187916
+Ch  1.30  0.0000  0.1259   1.0000  0.7706  0.7095  0.0610  0.2294    0.128    0.118    2.872      6.01294971
+Ch  1.30  0.0000  0.1413   1.0000  0.7652  0.7027  0.0625  0.2348    0.137    0.126    2.952      5.58982612
+Ch  1.30  0.0000  0.1585   1.0000  0.7600  0.6959  0.0640  0.2400    0.148    0.135    3.043      5.13745722
+Ch  1.30  0.0000  0.1778   1.0000  0.7550  0.6895  0.0655  0.2450    0.159    0.145    3.128      4.75114176
+Ch  1.30  0.0000  0.1995   1.0000  0.7497  0.6826  0.0672  0.2503    0.170    0.154    3.207      4.41822045
+Ch  1.30  0.0000  0.2239   1.0000  0.7452  0.6765  0.0687  0.2548    0.183    0.167    3.298      4.06246929
+Ch  1.30  0.0000  0.2512   1.0000  0.7406  0.6703  0.0703  0.2594    0.197    0.179    3.384      3.75224908
+Ch  1.30  0.0000  0.2818   1.0000  0.7357  0.6635  0.0721  0.2643    0.212    0.191    3.470      3.46647818
+Ch  1.30  0.0000  0.3162   1.0000  0.7310  0.6569  0.0740  0.2690    0.229    0.205    3.558      3.19867899
+Ch  1.30  0.0000  0.3548   1.0000  0.7262  0.6502  0.0760  0.2738    0.245    0.219    3.639      2.96749901
+Ch  1.30  0.0000  0.3981   1.0000  0.7218  0.6438  0.0780  0.2782    0.265    0.236    3.730      2.72839654
+Ch  1.30  0.0000  0.4467   1.0000  0.7172  0.6371  0.0801  0.2828    0.285    0.253    3.819      2.51522642
+Ch  1.30  0.0000  0.5012   1.0000  0.7127  0.6305  0.0822  0.2873    0.309    0.273    3.911      2.30987335
+Ch  1.30  0.0000  0.5623   1.0000  0.7082  0.6238  0.0844  0.2918    0.337    0.297    4.014      2.10039926
+Ch  1.30  0.0000  0.6310   1.0000  0.7036  0.6170  0.0866  0.2964    0.371    0.325    4.124      1.89816893
+Ch  1.30  0.0000  0.7079   1.0000  0.6989  0.6101  0.0888  0.3011    0.407    0.355    4.232      1.71870669
+Ch  1.30  0.0000  0.7943   1.0000  0.6942  0.6031  0.0910  0.3058    0.444    0.386    4.335      1.56246854
+Ch  1.30  0.0000  0.8913   1.0000  0.6892  0.5957  0.0935  0.3108    0.486    0.420    4.441      1.41726168
+Ch  1.30  0.0000  1.0000   1.0000  0.6844  0.5885  0.0959  0.3156    0.534    0.459    4.551      1.28147827
+Ch  1.30  0.0000  1.1220   1.0000  0.6795  0.5809  0.0986  0.3205    0.580    0.496    4.648      1.17219383
+Ch  1.30  0.0000  1.2589   1.0000  0.6745  0.5731  0.1014  0.3255    0.605    0.514    4.701      1.11568981
+Ch  1.30  0.0000  1.4125   1.0000  0.6696  0.5653  0.1043  0.3304    0.622    0.525    4.741      1.07584081
+Ch  1.30  0.0000  1.5849   1.0000  0.6600  0.5498  0.1102  0.3400    0.759    0.633    4.972      0.86913096
+Ch  1.30  0.0000  1.7783   1.0000  0.6546  0.5408  0.1138  0.3454    0.858    0.709    5.114      0.76290626
+Ch  1.30  0.0000  1.9953   1.0000  0.6497  0.5326  0.1171  0.3503    0.968    0.794    5.253      0.67094317
+Ch  1.30  0.0000  2.2387   1.0000  0.6452  0.5248  0.1203  0.3548    1.070    0.870    5.369      0.60307836
+Ch  1.30  0.0000  2.5119   1.0000  0.6408  0.5173  0.1235  0.3592    1.180    0.953    5.483      0.54296432
+Ch  1.30  0.0000  2.8184   1.0000  0.6364  0.5097  0.1268  0.3636    1.310    1.049    5.604      0.48590916
+Ch  1.30  0.0000  3.1623   1.0000  0.6324  0.5024  0.1300  0.3676    1.447    1.150    5.719      0.43691963
+Ch  1.30  0.0000  3.5481   1.0000  0.6285  0.4953  0.1332  0.3715    1.587    1.251    5.826      0.39601347
+Ch  1.30  0.0000  3.9811   1.0000  0.6245  0.4878  0.1366  0.3755    1.752    1.369    5.940      0.35633905
+Ch  1.30  0.0000  4.4668   1.0000  0.6204  0.4802  0.1402  0.3796    1.869    1.447    6.017      0.33192427
+Ch  1.30  0.0000  5.0119   1.0000  0.6169  0.4732  0.1437  0.3831    2.027    1.555    6.112      0.30436042
+Ch  1.30  0.0000  5.6234   1.0000  0.6136  0.4663  0.1473  0.3864    2.230    1.695    6.221      0.27509907
+Ch  1.30  0.0000  6.3096   1.0000  0.6103  0.4592  0.1511  0.3897    2.569    1.933    6.380      0.23757789
+Ch  1.30  0.0000  7.0795   1.0000  0.6072  0.4525  0.1547  0.3928    2.801    2.087    6.480      0.21679140
+Ch  1.30  0.0000  7.9433   1.0000  0.6044  0.4459  0.1584  0.3956    3.071    2.266    6.585      0.19679455
+Ch  1.30  0.0000  8.9125   1.0000  0.6017  0.4394  0.1622  0.3983    3.343    2.441    6.682      0.17998887
+Ch  1.30  0.0000 10.0000   1.0000  0.5988  0.4327  0.1662  0.4012    3.644    2.633    6.781      0.16436067
+Ch  1.30  0.0000 11.2202   1.0000  0.5961  0.4258  0.1703  0.4039    3.972    2.838    6.879      0.15005980
+Ch  1.30  0.0000 12.5893   1.0000  0.5933  0.4187  0.1745  0.4067    4.350    3.070    6.983      0.13637628
+Ch  1.30  0.0000 14.1254   1.0000  0.5906  0.4117  0.1789  0.4094    4.749    3.310    7.083      0.12437535
+Ch  1.30  0.0000 15.8489   1.0000  0.5879  0.4044  0.1835  0.4121    5.200    3.576    7.187      0.11307044
+Ch  1.30  0.0000 17.7828   1.0000  0.5854  0.3971  0.1883  0.4146    5.728    3.886    7.296      0.10218858
+Ch  1.30  0.2223  0.0631   1.0000  0.7977  0.7455  0.0522  0.2023    0.100    0.094    2.570      7.94588233
+Ch  1.30  0.2223  0.0708   1.0000  0.7925  0.7388  0.0537  0.2075    0.106    0.099    2.638      7.46024500
+Ch  1.30  0.2223  0.0794   1.0000  0.7879  0.7328  0.0551  0.2121    0.112    0.105    2.706      7.00908688
+Ch  1.30  0.2223  0.0891   1.0000  0.7831  0.7265  0.0567  0.2169    0.118    0.109    2.765      6.63856343
+Ch  1.30  0.2223  0.1000   1.0000  0.7780  0.7196  0.0584  0.2220    0.124    0.115    2.825      6.28024339
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gehong/data/filter/SDSS_r.filter

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gehong/data/filter/SLOAN_SDSS.g.filter

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gehong/data/filter/SLOAN_SDSS.r.filter

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gehong/map2d.py

+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

+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