SKA Data Challenge 2 Truth Catalog & Continuum Data¶
Truth catalog¶
truthcat=truthcat_prep(truthcat,wcs=wcs)
truthcat.head()
| id | ra | dec | hi_size | line_flux_integral | central_freq | pa | i | w20 | log_line_flux_integral | log_pa | central_freq_MHz | w20_MHz | w20_KHz | w20_px | line_start_MHz | line_end_MHz | line_center_px | ra_px | dec_px | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 180.191315 | -29.741030 | 4.451970 | 3.669104 | 1.120767e+09 | 268.105103 | 53.382599 | 145.950442 | 0.564560 | 2.428305 | 1120.766592 | -0.686770 | -686.769916 | 23.0 | 1120.079822 | 1121.453362 | 5684 | 428 | 975 |
| 1 | 1 | 180.051636 | -30.422684 | 4.398894 | 3.463066 | 1.114182e+09 | 280.521301 | 46.295010 | 255.653858 | 0.539461 | 2.447966 | 1114.181760 | -1.206541 | -1206.540706 | 40.0 | 1112.975219 | 1115.388301 | 5465 | 585 | 99 |
| 2 | 2 | 180.036743 | -29.837250 | 3.782270 | 3.698041 | 1.119836e+09 | 295.451538 | 87.650909 | 185.113345 | 0.567972 | 2.470486 | 1119.836160 | -0.872322 | -872.322327 | 29.0 | 1118.963838 | 1120.708482 | 5653 | 601 | 851 |
| 3 | 3 | 179.678726 | -29.798370 | 2.686259 | 3.795095 | 1.121970e+09 | 28.291687 | 63.669506 | 326.850396 | 0.579223 | 1.451659 | 1121.969664 | -1.543867 | -1543.867313 | 51.0 | 1120.425797 | 1123.513531 | 5724 | 1000 | 901 |
| 4 | 4 | 179.826416 | -29.689720 | 2.291830 | 3.693589 | 1.115078e+09 | 324.631927 | 18.434134 | 44.200626 | 0.567449 | 2.511391 | 1115.077504 | -0.204683 | -204.682994 | 7.0 | 1114.872821 | 1115.282187 | 5495 | 836 | 1041 |
| id | ra | dec | hi_size | line_flux_integral | central_freq | pa | i | w20 | log_line_flux_integral | log_pa | central_freq_MHz | w20_MHz | w20_KHz | w20_px | line_start_MHz | line_end_MHz | line_center_px | ra_px | dec_px | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 1.109100e+04 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 | 11091.000000 |
| mean | 5545.000000 | 179.972414 | -29.966005 | 6.118055 | 12.517398 | 1.037560e+09 | 180.711289 | 57.511148 | 252.100080 | 0.830318 | 2.126319 | 1037.560197 | -1.189703 | -1189.703036 | 39.655577 | 1036.370494 | 1038.749900 | 2914.451086 | 672.751961 | 685.166802 |
| std | 3201.840252 | 0.332330 | 0.281780 | 3.903841 | 22.411043 | 5.657355e+07 | 103.328386 | 21.470480 | 123.824019 | 0.434347 | 0.429552 | 56.573549 | 0.586674 | 586.673695 | 19.562054 | 56.626292 | 56.526846 | 1883.054079 | 370.143466 | 362.286564 |
| min | 0.000000 | 179.424271 | -30.493881 | 1.073070 | 1.340326 | 9.503012e+08 | 0.013977 | 0.371150 | 30.296076 | 0.127210 | -1.854584 | 950.301248 | -4.220918 | -4220.918283 | 5.000000 | 947.897431 | 950.621440 | 10.000000 | 7.000000 | 7.000000 |
| 25% | 2772.500000 | 179.677788 | -30.198223 | 3.561907 | 2.994294 | 9.873968e+08 | 90.743851 | 42.110619 | 170.006210 | 0.476294 | 1.957817 | 987.396768 | -1.560161 | -1560.161209 | 27.000000 | 986.233098 | 988.674568 | 1244.500000 | 355.000000 | 387.000000 |
| 50% | 5545.000000 | 179.955093 | -29.946529 | 5.115549 | 5.576763 | 1.031884e+09 | 182.674652 | 60.428268 | 221.884164 | 0.746382 | 2.261678 | 1031.883776 | -1.046541 | -1046.541128 | 35.000000 | 1030.610010 | 1033.076265 | 2726.000000 | 692.000000 | 710.000000 |
| 75% | 8317.500000 | 180.257912 | -29.717285 | 7.551074 | 12.928522 | 1.086454e+09 | 270.105148 | 75.569401 | 330.289404 | 1.111549 | 2.431533 | 1086.454080 | -0.800745 | -800.745271 | 52.000000 | 1085.478787 | 1087.819062 | 4542.000000 | 1001.000000 | 1005.000000 |
| max | 11090.000000 | 180.572922 | -29.503580 | 46.343719 | 582.329078 | 1.149487e+09 | 359.885406 | 89.999641 | 891.871827 | 2.765168 | 2.556164 | 1149.486720 | -0.138804 | -138.803743 | 141.000000 | 1148.631959 | 1150.471096 | 6640.000000 | 1280.000000 | 1280.000000 |
<matplotlib.axes._subplots.AxesSubplot at 0x7fe1832887f0>
Continuum cube¶
HI properties source vs non-source¶
Are sources visible among the noise?¶
Attempts at de-noising¶
test curvefit to ground truth spectral lines¶
vv=curve_fit(funcV,xvec,svec,p0=p0)
gg=curve_fit(funcG,xvec,svec,p0=pgauss)
ll=curve_fit(Lorentzian, xvec, svec,p0=plor)