Inf/Nan in saved lnp data

[karllark]

In the phat_small example, the saved lnp data has inf/nan values. Not sure this is expected/correct/handled.

for star 11 (e.g., using read_beast_data.read_lnp_data(), lnp_data['vals'][10])

lnp_data['vals'][10] = [        -inf         -inf -14.52056885 -13.67098999  -9.31604004

 -10.65057373 -40.64239502 -11.01411438 -16.07670593 -25.10942078
 -29.91729736  -4.2278595  -33.07322693         -inf  -8.46405029
 -15.84481812 -14.25837708 -32.72451782 -29.84307861 -22.21150208
 -11.51757812 -15.06747437  -5.56022644 -17.33676147 -11.8719635
 -10.1867981   -9.28611755 -21.74038696 -23.86488342 -17.86636353
 -10.87683105 -25.03875732 -29.36116028 -10.22483826 -27.44491577
 -22.59469604 -22.71034241 -12.56095886 -22.55511475 -24.80587769
 -31.91946411 -20.81546021 -24.07180786 -12.00401306 -29.77052307
 -17.01589966 -14.96948242 -17.44781494  -5.40725708 -22.33773804
 -13.35470581 -23.53224182 -10.66629028 -25.15463257 -24.44683838
 -31.56211853 -18.20074463 -21.46897888 -34.32868958 -14.89842224
 -20.51997375  -6.18809509 -21.77853394 -33.93466187 -24.41467285
 -35.1955719  -23.04743958 -27.2570343  -10.55079651 -26.78311157
  -2.10072327 -36.05075073 -23.9508667   -5.80871582  -8.19029236
 -14.44534302 -15.90348816 -12.22445679 -31.2414856          -inf
 -15.35185242 -17.26226807 -25.23768616 -21.58308411  -6.21963501
  -6.76751709 -23.06570435 -30.90133667 -15.95275879 -24.3944397
 -23.51104736 -21.15267944 -31.2636261  -11.34869385  -9.08224487
 -11.76669312 -19.22814941 -11.00666809 -25.29075623 -26.59324646
  -8.84820557 -20.42759705 -15.73077393 -16.82998657 -20.00735474
 -13.76686096  -3.29370117 -23.60916138  -3.23979187  -6.77680969
 -14.15246582  -9.40750122         -inf -47.24188232 -10.32223511
 -12.36685181 -20.140625   -14.44586182 -23.39547729 -22.03208923
  -4.28341675 -37.45126343 -13.50361633 -24.43670654 -24.61830139
 -30.68887329 -14.29042053 -17.31454468 -11.70449829 -13.55209351
 -18.85923767  -8.71893311  -3.54434204 -24.73576355 -13.01921082
 -24.58686829  -9.05516052 -14.62014771 -13.73208618  -9.81129456
 -18.8177948  -14.01272583 -17.17390442  -7.90835571 -16.4488678
 -11.20768738 -11.82859802  -9.50163269 -28.55551147 -31.03700256
 -24.0566864  -22.49705505 -34.04911804 -13.97171021  -5.83398438
 -12.5932312  -11.00498962 -16.63543701 -18.46710205 -19.70925903
 -25.42256165  -8.08688354 -11.51885986 -13.87168884 -14.01115417
 -13.58074951 -16.15388489 -19.3681488  -14.99661255 -21.0173645
 -23.34564209 -12.03205872  -9.84996033 -24.85736084 -28.5693512
 -21.33882141 -16.65423584 -11.33863831 -13.57423401 -12.98164368
 -22.33717346 -21.4334259  -21.7878418  -25.94195557 -14.08532715
 -15.3903656  -20.77851868 -14.66838074 -15.83825684 -25.61401367
 -23.42973328 -50.94456482 -16.08349609  -6.73297119 -13.59524536
 -23.30145264 -18.021698   -33.0798645  -32.61817932 -16.51777649
 -15.63142395 -19.31654358 -17.83164978 -16.81121826 -16.48934937
 -17.56677246 -21.42080688  -7.24447632 -14.66166687 -20.33155823
 -15.5332489  -11.19064331  -9.19073486  -8.97105408  -7.89593506
 -13.89692688 -29.33782959 -25.55531311  -9.25361633 -20.28901672
 -10.25602722 -18.86196899  -7.30917358 -12.48847961 -11.7277832
 -24.63317871 -41.50582886 -13.17098999 -38.93345642 -40.99975586
 -14.44476318 -15.83612061 -24.44992065 -14.07800293 -23.65501404
  -5.27674866 -22.67437744 -18.05703735 -11.00492859 -12.61436462
 -21.03144836 -34.97131348 -27.31080627 -13.29801941  -8.51242065
 -35.20088196 -78.75163269         -inf  -5.05256653 -11.33436584
  -8.68405151 -17.10354614 -18.39877319 -12.14797974 -18.78601074
 -14.42918396 -12.19488525 -12.95803833 -12.22106934 -16.94384766
 -17.33251953 -13.14562988 -19.18727112  -9.99243164 -32.84962463
 -15.39042664 -32.58805847 -29.15966797 -11.34272766]
lnp_data['indxs'][10] =  [   nan    nan  2021. 14521.  2340.   557.  4265.   540.  1176.   745.
  2614. 11398. 34493.    nan 23165.  4134. 35594.  1468.   745. 14518.
 20920.  8321.   879.  2609.  2104. 14040.  2412.  1173.  4877.   745.
  5212.  1467.   746.  6911.  1079.  3037.  1807.  1312.  1080.  5404.
  1078.  1467.   746. 27080.  1081.  4007.  3036.  7322. 10659.  1081.
  5520.  1467. 10112. 22787.  1076.  1080.  1807.  2250.  1807.  1173.
  2142.  8295.  1466.  3537.  1466.  4935.  1807.  1467. 13886.  2139.
 11787.   498.   496. 32418.  9702.   502. 10442. 18040.   495.    nan
 44114.   817.  1807.   495. 17925. 25484.   495.  4936.  3785.   497.
  5425.   818.  7731.  2051. 14685.  6571.   496.  7656.  1076.   741.
 24783.  4026.   499.  1158.   496.  4800. 24465. 26075.  2072.   814.
   498. 19926.    nan  3982.  5760. 26662.   499.   815.   871.   499.
   351.   492.   499.   498.   530. 10445.   496. 60988. 37050. 24395.
   496.   992.  5930.  2651.   496.  2588. 14091. 13268. 20345.   936.
  1140.  1142. 46358. 13858. 33176.  6256. 13514. 18361. 25485.  1499.
 14428. 16500. 11845.  9051. 24508.  1139.  1145. 35117.   657.  5519.
 12195. 17748.  6254.  1859.  5193. 16638. 10439.   989.   530.  2964.
  3127.  1856. 15235. 16998.  6273.   999.  3141. 18483. 21541.  2291.
  7494.  6269. 20526. 10111.  2331. 24357.  4469. 34120. 15478. 38112.
  2621. 57374.  4910.   235.   375.   569.   372.   373. 29329.  1181.
   693.   884. 18879. 15187.  1224.   569.   226.   230.   231.   885.
  4022. 35251.   844.   568.  5477.   230. 35112.   518.   232.  2014.
 13052.  1220. 22262.  3079.   842.  8270. 29031. 20314. 35110.  2579.
   365.   534.  1180.   843.  1224. 37035.  2678. 17347.  1218.   535.
   535.  1815. 29325.   516. 30411.  1474. 12917.    nan   515.   537.
 34654.   516. 19017.   513. 10623.   861.  9328.   516.  7284. 14442.
  2616.   514.  7601.  1176.  2613.   515.   221.   747. 18369.]