Report generated on 06-Apr-2022 at 08:48:43 by pytest-html v2.1.1
Packages | {"pluggy": "0.13.1", "py": "1.9.0", "pytest": "6.1.1"} |
Platform | Linux-5.4.0-105-generic-x86_64-with-glibc2.29 |
Plugins | {"anyio": "3.2.1", "html": "2.1.1", "hypothesis": "4.36.2", "metadata": "1.10.0", "typeguard": "2.12.1"} |
Python | 3.8.10 |
142 tests ran in 369.99 seconds.
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142 passed, 0 skipped, 0 failed, 0 errors, 0 expected failures, 0 unexpected passesResult | Test | Duration | Links |
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Passed | tests/test_clustering.py::test_clustering | 2.96 | |
No log output captured. | |||
Passed | tests/test_clustering.py::test_clusteringcase | 0.18 | |
------------------------------Captured stdout call------------------------------ maxradius: 11.45313971598804 | |||
Passed | tests/test_clustering.py::test_clusteringcase_eggbox | 0.01 | |
------------------------------Captured stdout call------------------------------ maxradius: 1.8439766380495826e-10 | |||
Passed | tests/test_clustering.py::test_overclustering_eggbox_txt | 0.50 | |
------------------------------Captured stdout call------------------------------ ==== TEST CASE 20 ===================== manual: r=2.989717e-03 nc=1 reclustered: nc=18 manual: r=2.989717e-03 nc=1 reclustered: nc=18 manual: r=2.270048e-03 nc=1 reclustered: nc=18 ==== TEST CASE 23 ===================== manual: r=1.212573e-05 nc=1 reclustered: nc=18 manual: r=1.353579e-05 nc=1 reclustered: nc=18 manual: r=1.235571e-05 nc=1 reclustered: nc=18 ==== TEST CASE 24 ===================== manual: r=3.947988e-05 nc=1 reclustered: nc=18 manual: r=4.172909e-05 nc=1 reclustered: nc=18 manual: r=7.273383e-05 nc=1 reclustered: nc=18 ==== TEST CASE 27 ===================== manual: r=1.978904e-08 nc=1 reclustered: nc=18 manual: r=1.890650e-08 nc=1 reclustered: nc=18 manual: r=1.605539e-08 nc=1 reclustered: nc=18 ==== TEST CASE 49 ===================== manual: r=6.815424e-05 nc=1 reclustered: nc=18 manual: r=4.236106e-05 nc=1 reclustered: nc=18 manual: r=5.657479e-05 nc=1 reclustered: nc=18 | |||
Passed | tests/test_clustering.py::test_overclustering_eggbox_update | 5.15 | |
------------------------------Captured stdout call------------------------------ ==== TEST CASE 20 ===================== loading... loading... done u0:960 -> u:960 : 881 points are common initialised with: r=3.861322e-01 nc=18 --- intermediate tests how create_new reacts --- updated to (with same data): r=3.861322e-01 nc=18 updated to (with new data): r=3.861322e-01 nc=19 --- end --- setting maxradiussq to None transitioned to : r=4.254452e-01 nc=18 True cluster 1/18: 82 points @ 0.40067 +- 0.01202 , 0.40022 +- 0.01511 cluster 2/18: 44 points @ 0.60378 +- 0.01234 , 0.98922 +- 0.00757 cluster 3/18: 89 points @ 0.59884 +- 0.01297 , 0.19993 +- 0.01399 cluster 4/18: 70 points @ 0.59770 +- 0.01321 , 0.59970 +- 0.01270 cluster 5/18: 66 points @ 0.79962 +- 0.01283 , 0.40189 +- 0.01364 cluster 6/18: 75 points @ 0.20262 +- 0.01346 , 0.20165 +- 0.01254 cluster 7/18: 32 points @ 0.19776 +- 0.01239 , 0.98809 +- 0.00597 cluster 8/18: 31 points @ 0.01257 +- 0.00703 , 0.39886 +- 0.01234 cluster 9/18: 67 points @ 0.79925 +- 0.01331 , 0.80076 +- 0.01356 cluster 10/18: 42 points @ 0.79877 +- 0.01327 , 0.01046 +- 0.00668 cluster 11/18: 41 points @ 0.99046 +- 0.00685 , 0.19675 +- 0.01388 cluster 12/18: 39 points @ 0.39798 +- 0.01322 , 0.00984 +- 0.00698 cluster 13/18: 23 points @ 0.01097 +- 0.00852 , 0.01041 +- 0.00711 cluster 14/18: 73 points @ 0.20029 +- 0.01323 , 0.59918 +- 0.01368 cluster 15/18: 44 points @ 0.98858 +- 0.00665 , 0.60129 +- 0.01259 cluster 16/18: 41 points @ 0.01238 +- 0.00658 , 0.80156 +- 0.01229 cluster 17/18: 84 points @ 0.40175 +- 0.01292 , 0.79791 +- 0.01272 cluster 18/18: 17 points @ 0.98546 +- 0.00732 , 0.98774 +- 0.00694 ==== TEST CASE 23 ===================== loading... loading... done u0:1040 -> u:1040 : 966 points are common initialised with: r=6.160003e-03 nc=1 --- intermediate tests how create_new reacts --- updated to (with same data): r=6.160003e-03 nc=1 updated to (with new data): r=6.160003e-03 nc=18 found lonely points 1039 18 (array([1]), array([1040])) --- end --- setting maxradiussq to None transitioned to : r=3.440933e-01 nc=18 True cluster 1/18: 73 points @ 0.60558 +- 0.03956 , 0.60161 +- 0.03742 cluster 2/18: 79 points @ 0.40034 +- 0.03940 , 0.40519 +- 0.03757 cluster 3/18: 41 points @ 0.97203 +- 0.02060 , 0.20253 +- 0.03872 cluster 4/18: 84 points @ 0.19581 +- 0.04039 , 0.60134 +- 0.03804 cluster 5/18: 87 points @ 0.20159 +- 0.03605 , 0.20771 +- 0.03841 cluster 6/18: 85 points @ 0.80668 +- 0.03802 , 0.40101 +- 0.03809 cluster 7/18: 39 points @ 0.03874 +- 0.02094 , 0.80033 +- 0.03636 cluster 8/18: 80 points @ 0.79850 +- 0.03921 , 0.79083 +- 0.03705 cluster 9/18: 40 points @ 0.96956 +- 0.02038 , 0.60841 +- 0.03341 cluster 10/18: 99 points @ 0.60042 +- 0.03532 , 0.19601 +- 0.03848 cluster 11/18: 19 points @ 0.04123 +- 0.02000 , 0.02673 +- 0.02228 cluster 12/18: 40 points @ 0.40610 +- 0.03892 , 0.03809 +- 0.02090 cluster 13/18: 87 points @ 0.40176 +- 0.03955 , 0.79885 +- 0.03542 cluster 14/18: 52 points @ 0.20797 +- 0.03841 , 0.96717 +- 0.02119 cluster 15/18: 38 points @ 0.59639 +- 0.03805 , 0.96716 +- 0.01828 cluster 16/18: 44 points @ 0.80236 +- 0.04018 , 0.03255 +- 0.01736 cluster 17/18: 21 points @ 0.96129 +- 0.01684 , 0.96388 +- 0.01766 cluster 18/18: 32 points @ 0.03640 +- 0.01610 , 0.39737 +- 0.03884 ==== TEST CASE 24 ===================== loading... loading... done u0:1080 -> u:1080 : 720 points are common initialised with: r=2.523267e-03 nc=1 --- intermediate tests how create_new reacts --- updated to (with same data): r=2.523267e-03 nc=1 updated to (with new data): r=2.523267e-03 nc=19 --- end --- setting maxradiussq to None transitioned to : r=7.393072e-01 nc=18 True cluster 1/18: 50 points @ 0.80133 +- 0.02367 , 0.40736 +- 0.02143 cluster 2/18: 67 points @ 0.40401 +- 0.02029 , 0.40261 +- 0.02217 cluster 3/18: 23 points @ 0.01998 +- 0.01209 , 0.81024 +- 0.01845 cluster 4/18: 60 points @ 0.20213 +- 0.02305 , 0.20032 +- 0.01965 cluster 5/18: 32 points @ 0.60086 +- 0.02176 , 0.98367 +- 0.01059 cluster 6/18: 47 points @ 0.80338 +- 0.02194 , 0.79993 +- 0.01991 cluster 7/18: 36 points @ 0.98306 +- 0.01107 , 0.59781 +- 0.02273 cluster 8/18: 76 points @ 0.59943 +- 0.01980 , 0.19632 +- 0.02156 cluster 9/18: 56 points @ 0.20016 +- 0.01965 , 0.59828 +- 0.02257 cluster 10/18: 9 points @ 0.01908 +- 0.01199 , 0.01513 +- 0.01132 cluster 11/18: 59 points @ 0.39933 +- 0.02184 , 0.80070 +- 0.02167 cluster 12/18: 29 points @ 0.98462 +- 0.01006 , 0.20604 +- 0.02154 cluster 13/18: 30 points @ 0.80514 +- 0.02107 , 0.01876 +- 0.00936 cluster 14/18: 14 points @ 0.98169 +- 0.01256 , 0.97917 +- 0.01043 cluster 15/18: 49 points @ 0.59988 +- 0.02080 , 0.59826 +- 0.02404 cluster 16/18: 22 points @ 0.02401 +- 0.00978 , 0.40533 +- 0.01845 cluster 17/18: 32 points @ 0.20429 +- 0.02222 , 0.97957 +- 0.00988 cluster 18/18: 29 points @ 0.39559 +- 0.02135 , 0.01376 +- 0.01155 ==== TEST CASE 27 ===================== loading... loading... done u0:1160 -> u:1160 : 790 points are common initialised with: r=4.168517e-03 nc=1 --- intermediate tests how create_new reacts --- updated to (with same data): r=4.168517e-03 nc=1 updated to (with new data): r=4.168517e-03 nc=18 found lonely points 879 18 (array([1]), array([1160])) --- end --- setting maxradiussq to None transitioned to : r=4.282518e-01 nc=18 True cluster 1/18: 28 points @ 0.02876 +- 0.01072 , 0.40418 +- 0.02504 cluster 2/18: 72 points @ 0.40427 +- 0.02589 , 0.40337 +- 0.02568 cluster 3/18: 66 points @ 0.80063 +- 0.03025 , 0.79903 +- 0.02314 cluster 4/18: 69 points @ 0.80137 +- 0.02777 , 0.40462 +- 0.02720 cluster 5/18: 31 points @ 0.02746 +- 0.01466 , 0.79860 +- 0.02690 cluster 6/18: 62 points @ 0.59600 +- 0.02452 , 0.59711 +- 0.02909 cluster 7/18: 64 points @ 0.20064 +- 0.02644 , 0.20221 +- 0.02651 cluster 8/18: 36 points @ 0.59915 +- 0.02534 , 0.97793 +- 0.01435 cluster 9/18: 39 points @ 0.97736 +- 0.01458 , 0.59960 +- 0.02338 cluster 10/18: 86 points @ 0.60015 +- 0.02393 , 0.19435 +- 0.02628 cluster 11/18: 32 points @ 0.98151 +- 0.01174 , 0.20348 +- 0.02462 cluster 12/18: 65 points @ 0.19863 +- 0.02526 , 0.59691 +- 0.02542 cluster 13/18: 15 points @ 0.02567 +- 0.01173 , 0.02191 +- 0.01523 cluster 14/18: 41 points @ 0.39375 +- 0.02798 , 0.02135 +- 0.01484 cluster 15/18: 77 points @ 0.40243 +- 0.02649 , 0.80397 +- 0.02748 cluster 16/18: 38 points @ 0.80371 +- 0.02372 , 0.02465 +- 0.01314 cluster 17/18: 18 points @ 0.97856 +- 0.01405 , 0.97604 +- 0.01338 cluster 18/18: 41 points @ 0.19975 +- 0.02576 , 0.97554 +- 0.01299 ==== TEST CASE 42 ===================== loading... loading... done u0:1640 -> u:1640 : 1324 points are common initialised with: r=2.155017e-01 nc=18 --- intermediate tests how create_new reacts --- updated to (with same data): r=2.155017e-01 nc=18 updated to (with new data): r=2.155017e-01 nc=20 --- end --- setting maxradiussq to None transitioned to : r=2.029861e-01 nc=18 True cluster 1/18: 134 points @ 0.20129 +- 0.00945 , 0.20101 +- 0.01049 cluster 2/18: 65 points @ 0.59936 +- 0.00932 , 0.99209 +- 0.00545 cluster 3/18: 85 points @ 0.99089 +- 0.00488 , 0.60043 +- 0.01046 cluster 4/18: 130 points @ 0.60003 +- 0.00928 , 0.20093 +- 0.01017 cluster 5/18: 129 points @ 0.59949 +- 0.00979 , 0.60100 +- 0.01016 cluster 6/18: 70 points @ 0.00947 +- 0.00504 , 0.79955 +- 0.00842 cluster 7/18: 130 points @ 0.40067 +- 0.00968 , 0.40022 +- 0.01000 cluster 8/18: 122 points @ 0.79990 +- 0.00981 , 0.80044 +- 0.00967 cluster 9/18: 65 points @ 0.19964 +- 0.00934 , 0.99144 +- 0.00504 cluster 10/18: 72 points @ 0.00860 +- 0.00543 , 0.40071 +- 0.01088 cluster 11/18: 128 points @ 0.19967 +- 0.01029 , 0.60159 +- 0.01024 cluster 12/18: 79 points @ 0.79941 +- 0.00983 , 0.00749 +- 0.00504 cluster 13/18: 76 points @ 0.99223 +- 0.00521 , 0.19896 +- 0.00995 cluster 14/18: 69 points @ 0.39964 +- 0.00971 , 0.00907 +- 0.00538 cluster 15/18: 126 points @ 0.40202 +- 0.00982 , 0.79926 +- 0.00912 cluster 16/18: 99 points @ 0.80119 +- 0.00999 , 0.39961 +- 0.00929 cluster 17/18: 29 points @ 0.98948 +- 0.00510 , 0.99242 +- 0.00478 cluster 18/18: 32 points @ 0.00711 +- 0.00510 , 0.00782 +- 0.00532 | |||
Passed | tests/test_flatnuts.py::test_detailed_balance | 4.10 | |
------------------------------Captured stdout call------------------------------ ---- seed=1 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [1.14374817e-04 3.02332573e-01] [-0.02893116 -0.02762223] -0.48840515457013567 BACKWARD SAMPLING FROM 4 [0.14168242 0.38811823] [0.02821641 0.02835197] -0.1665060917360781 BACKWARD SAMPLING FROM -4 [0.11583903 0.41282151] [-0.02893116 -0.02762223] -0.10171045505355579 BisectSampler ---- FORWARD SAMPLING FROM 0 [1.14374817e-04 3.02332573e-01] [-0.02893116 -0.02762223] -0.48840515457013567 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([1.14374817e-04, 3.02332573e-01]), array([-0.02893116, -0.02762223]), None, None, None, 10, array([0.28919725, 0.02611023]), array([ 0.02893116, -0.02762223]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([1.14374817e-04, 3.02332573e-01]), array([-0.02893116, -0.02762223]), 5, array([0.14454144, 0.1642214 ]), array([ 0.02893116, -0.02762223]), 10, array([0.28919725, 0.02611023]), array([ 0.02893116, -0.02762223]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([1.14374817e-04, 3.02332573e-01]), array([-0.02893116, -0.02762223]), 2, array([0.05774795, 0.2470881 ]), array([ 0.02893116, -0.02762223]), 5, array([0.14454144, 0.1642214 ]), array([ 0.02893116, -0.02762223]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([1.14374817e-04, 3.02332573e-01]), array([-0.02893116, -0.02762223]), 1, array([0.02881679, 0.27471034]), array([ 0.02893116, -0.02762223]), 2, array([0.05774795, 0.2470881 ]), array([ 0.02893116, -0.02762223]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.02881679 0.27471034] [ 0.02893116 -0.02762223] new direction: [0.02821641 0.02835197] reversing there [ 0.02893116 -0.02762223] making one step from [0.02881679 0.27471034] [ 0.02893116 -0.02762223] --> [0.0570332 0.30306231] [0.02821641 0.02835197] trying new point, [0.0570332 0.30306231] next() call -0.48643206112737647 goals: [('reflect-at', 1, array([0.0570332 , 0.30306231]), array([0.02821641, 0.02835197]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.09073879959282771 goals: [('bisect', 1, array([0.0570332 , 0.30306231]), array([0.02821641, 0.02835197]), None, None, None, 10, array([0.31098087, 0.55823006]), array([0.02821641, 0.02835197]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.31098087 0.55823006] [0.02821641 0.02835197] -0.09073879959282771 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.31098087, 0.55823006]), array([-0.02821641, -0.02835197]), None, None, None, 10, array([0.02881679, 0.27471034]), array([-0.02821641, -0.02835197]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.1016481498381896 goals: [('bisect', 0, array([0.31098087, 0.55823006]), array([-0.02821641, -0.02835197]), 5, array([0.16989883, 0.4164702 ]), array([-0.02821641, -0.02835197]), 10, array([0.02881679, 0.27471034]), array([-0.02821641, -0.02835197]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.25225605758357234 goals: [('bisect', 5, array([0.16989883, 0.4164702 ]), array([-0.02821641, -0.02835197]), 7, array([0.11346601, 0.35976626]), array([-0.02821641, -0.02835197]), 10, array([0.02881679, 0.27471034]), array([-0.02821641, -0.02835197]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3588980473806718 goals: [('bisect', 7, array([0.11346601, 0.35976626]), array([-0.02821641, -0.02835197]), 8, array([0.0852496 , 0.33141428]), array([-0.02821641, -0.02835197]), 10, array([0.02881679, 0.27471034]), array([-0.02821641, -0.02835197]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4864320611273768 goals: [('bisect', 8, array([0.0852496 , 0.33141428]), array([-0.02821641, -0.02835197]), 9, array([0.0570332 , 0.30306231]), array([-0.02821641, -0.02835197]), 10, array([0.02881679, 0.27471034]), array([-0.02821641, -0.02835197]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.02881679 0.27471034] [-0.02821641 -0.02835197] new direction: [-0.02893116 0.02762223] reversing there [-0.02821641 -0.02835197] making one step from [0.02881679 0.27471034] [-0.02821641 -0.02835197] --> [1.14374817e-04 3.02332573e-01] [0.02893116 0.02762223] trying new point, [1.14374817e-04 3.02332573e-01] next() call -0.4884051545701359 goals: [('reflect-at', 10, array([1.14374817e-04, 3.02332573e-01]), array([0.02893116, 0.02762223]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [1.14374817e-04 3.02332573e-01] [-0.02893116 -0.02762223] -0.48840515457013567 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [1.14374817e-04 3.02332573e-01] [-0.02893116 -0.02762223] -0.48840515457013567 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 sampling between (-3, 4) ---- seed=2 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.54966248 0.43532239] [0.00022913 0.03999934] -0.2033543310016853 BACKWARD SAMPLING FROM 4 [0.550579 0.59531977] [0.00022913 0.03999934] -0.2651418446056711 BACKWARD SAMPLING FROM -2 [0.54920422 0.35532371] [0.00022913 0.03999934] -0.4124530140569493 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.54966248 0.43532239] [0.00022913 0.03999934] -0.2033543310016853 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.54966248, 0.43532239]), array([0.00022913, 0.03999934]), None, None, None, 10, array([0.55195379, 0.83531583]), array([0.00022913, 0.03999934]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.3805855729471914 goals: [('bisect', 0, array([0.54966248, 0.43532239]), array([0.00022913, 0.03999934]), 5, array([0.55080813, 0.63531911]), array([0.00022913, 0.03999934]), 10, array([0.55195379, 0.83531583]), array([0.00022913, 0.03999934]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.55080813, 0.63531911]), array([0.00022913, 0.03999934]), 7, array([0.55126639, 0.7153178 ]), array([0.00022913, 0.03999934]), 10, array([0.55195379, 0.83531583]), array([0.00022913, 0.03999934]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.55080813, 0.63531911]), array([0.00022913, 0.03999934]), 6, array([0.55103726, 0.67531846]), array([0.00022913, 0.03999934]), 7, array([0.55126639, 0.7153178 ]), array([0.00022913, 0.03999934]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.55103726 0.67531846] [0.00022913 0.03999934] new direction: [-0.03532089 0.01877324] reversing there [0.00022913 0.03999934] making one step from [0.55103726 0.67531846] [0.00022913 0.03999934] --> [0.51571637 0.69409169] [-0.03532089 0.01877324] trying new point, [0.51571637 0.69409169] next() call None goals: [('reflect-at', 6, array([0.51571637, 0.69409169]), array([-0.03532089, 0.01877324]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 5... 5 steps to do at 5 -> [from 6, delta=5] targeting 1. goals: [('sample-at', 1)] target is on track, returning interpolation at 1... [0.54989161 0.47532174] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 5 [0.55080813 0.63531911] [0.00022913 0.03999934] -0.3805855729471914 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 5)] not done yet, continue expanding to 5... goals: [('expand-to', 5), ('sample-at', 5)] next() call -0.2033543310016853 goals: [('bisect', 0, array([0.55080813, 0.63531911]), array([-0.00022913, -0.03999934]), None, None, None, 5, array([0.54966248, 0.43532239]), array([-0.00022913, -0.03999934]), 1), ('sample-at', 5)] bisecting ... 0 None 5 successfully went all the way in one jump! goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.54966248 0.43532239] [0.00022913 0.03999934] -0.2033543310016853 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.54966248 0.43532239] [0.00022913 0.03999934] -0.2033543310016853 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-2, 1) ---- seed=3 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.29090474 0.51082761] [-0.01281883 0.03789034] -0.04377824648995989 BACKWARD SAMPLING FROM 4 [0.23962941 0.66238895] [-0.01281883 0.03789034] -0.3583382577658276 BACKWARD SAMPLING FROM -4 [0.34218007 0.35926626] [-0.01281883 0.03789034] -0.306118410872214 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.29090474 0.51082761] [-0.01281883 0.03789034] -0.04377824648995989 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.29090474, 0.51082761]), array([-0.01281883, 0.03789034]), None, None, None, 10, array([0.16271642, 0.88973096]), array([-0.01281883, 0.03789034]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.29090474, 0.51082761]), array([-0.01281883, 0.03789034]), 5, array([0.22681058, 0.70027928]), array([-0.01281883, 0.03789034]), 10, array([0.16271642, 0.88973096]), array([-0.01281883, 0.03789034]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.12894573017062852 goals: [('bisect', 0, array([0.29090474, 0.51082761]), array([-0.01281883, 0.03789034]), 2, array([0.26526707, 0.58660828]), array([-0.01281883, 0.03789034]), 5, array([0.22681058, 0.70027928]), array([-0.01281883, 0.03789034]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.22561386347891177 goals: [('bisect', 2, array([0.26526707, 0.58660828]), array([-0.01281883, 0.03789034]), 3, array([0.25244824, 0.62449861]), array([-0.01281883, 0.03789034]), 5, array([0.22681058, 0.70027928]), array([-0.01281883, 0.03789034]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.3583382577658276 goals: [('bisect', 3, array([0.25244824, 0.62449861]), array([-0.01281883, 0.03789034]), 4, array([0.23962941, 0.66238895]), array([-0.01281883, 0.03789034]), 5, array([0.22681058, 0.70027928]), array([-0.01281883, 0.03789034]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.22681058 0.70027928] [-0.01281883 0.03789034] new direction: [-0.03961561 -0.00553205] reversing there [-0.01281883 0.03789034] making one step from [0.22681058 0.70027928] [-0.01281883 0.03789034] --> [0.18719497 0.69474723] [-0.03961561 -0.00553205] trying new point, [0.18719497 0.69474723] next() call -0.4916020278270488 goals: [('reflect-at', 5, array([0.18719497, 0.69474723]), array([-0.03961561, -0.00553205]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3490349009639423 goals: [('bisect', 5, array([0.18719497, 0.69474723]), array([-0.03961561, -0.00553205]), None, None, None, 10, array([0.01088307, 0.66708697]), array([ 0.03961561, -0.00553205]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.01088307 0.66708697] [ 0.03961561 -0.00553205] -0.3490349009639423 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.01088307, 0.66708697]), array([-0.03961561, 0.00553205]), None, None, None, 10, array([0.38527301, 0.7224075 ]), array([0.03961561, 0.00553205]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4916020278270488 goals: [('bisect', 0, array([0.01088307, 0.66708697]), array([-0.03961561, 0.00553205]), 5, array([0.18719497, 0.69474723]), array([0.03961561, 0.00553205]), 10, array([0.38527301, 0.7224075 ]), array([0.03961561, 0.00553205]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.18719497, 0.69474723]), array([0.03961561, 0.00553205]), 7, array([0.26642619, 0.70581134]), array([0.03961561, 0.00553205]), 10, array([0.38527301, 0.7224075 ]), array([0.03961561, 0.00553205]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.18719497, 0.69474723]), array([0.03961561, 0.00553205]), 6, array([0.22681058, 0.70027928]), array([0.03961561, 0.00553205]), 7, array([0.26642619, 0.70581134]), array([0.03961561, 0.00553205]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.22681058 0.70027928] [0.03961561 0.00553205] new direction: [ 0.01281883 -0.03789034] reversing there [0.03961561 0.00553205] making one step from [0.22681058 0.70027928] [0.03961561 0.00553205] --> [0.23962941 0.66238895] [ 0.01281883 -0.03789034] trying new point, [0.23962941 0.66238895] next() call -0.3583382577658272 goals: [('reflect-at', 6, array([0.23962941, 0.66238895]), array([ 0.01281883, -0.03789034]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.04377824648995988 goals: [('bisect', 6, array([0.23962941, 0.66238895]), array([ 0.01281883, -0.03789034]), None, None, None, 10, array([0.29090474, 0.51082761]), array([ 0.01281883, -0.03789034]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.29090474 0.51082761] [-0.01281883 0.03789034] -0.04377824648995989 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.29090474 0.51082761] [-0.01281883 0.03789034] -0.04377824648995989 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-4, 3) ---- seed=4 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.96702984 0.54723225] [-0.03949306 -0.00634806] -0.49545942179887775 BACKWARD SAMPLING FROM 4 [0.80905758 0.52184001] [-0.03949306 -0.00634806] -0.33324941010291637 BACKWARD SAMPLING FROM 0 [0.96702984 0.54723225] [-0.03949306 -0.00634806] -0.49545942179887775 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.96702984 0.54723225] [-0.03949306 -0.00634806] -0.49545942179887775 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.16694885584599184 goals: [('bisect', 0, array([0.96702984, 0.54723225]), array([-0.03949306, -0.00634806]), None, None, None, 10, array([0.5720992 , 0.48375165]), array([-0.03949306, -0.00634806]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.5720992 0.48375165] [-0.03949306 -0.00634806] -0.16694885584599184 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.49545942179887775 goals: [('bisect', 0, array([0.5720992 , 0.48375165]), array([0.03949306, 0.00634806]), None, None, None, 10, array([0.96702984, 0.54723225]), array([0.03949306, 0.00634806]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.96702984 0.54723225] [-0.03949306 -0.00634806] -0.49545942179887775 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.96702984 0.54723225] [-0.03949306 -0.00634806] -0.49545942179887775 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 sampling between (-4, 3) ---- seed=5 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.48841119 0.61174386] [ 0.03653803 -0.01627797] -0.2753563808750551 BACKWARD SAMPLING FROM 4 [0.63456332 0.54663199] [ 0.03653803 -0.01627797] -0.22851708746536278 BACKWARD SAMPLING FROM -4 [0.34225906 0.67685573] [ 0.03653803 -0.01627797] -0.4495450053329357 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.48841119 0.61174386] [ 0.03653803 -0.01627797] -0.2753563808750551 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3970381430661767 goals: [('bisect', 0, array([0.48841119, 0.61174386]), array([ 0.03653803, -0.01627797]), None, None, None, 10, array([0.85379151, 0.44896419]), array([ 0.03653803, -0.01627797]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.85379151 0.44896419] [ 0.03653803 -0.01627797] -0.3970381430661767 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.27535638087505515 goals: [('bisect', 0, array([0.85379151, 0.44896419]), array([-0.03653803, 0.01627797]), None, None, None, 10, array([0.48841119, 0.61174386]), array([-0.03653803, 0.01627797]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.48841119 0.61174386] [ 0.03653803 -0.01627797] -0.2753563808750551 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.48841119 0.61174386] [ 0.03653803 -0.01627797] -0.2753563808750551 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -13..2 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-13, 2) ---- seed=6 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.10765668 0.59505206] [-0.036206 0.01700369] -0.11873116674075794 BACKWARD SAMPLING FROM 4 [0.03716733 0.66306682] [0.036206 0.01700369] -0.3330755453049499 BACKWARD SAMPLING FROM -4 [0.25248069 0.52703731] [-0.036206 0.01700369] -0.041010950598001826 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.10765668 0.59505206] [-0.036206 0.01700369] -0.11873116674075794 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.10765668, 0.59505206]), array([-0.036206 , 0.01700369]), None, None, None, 10, array([0.25440334, 0.76508895]), array([0.036206 , 0.01700369]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4080091653243474 goals: [('bisect', 0, array([0.10765668, 0.59505206]), array([-0.036206 , 0.01700369]), 5, array([0.07337333, 0.68007051]), array([0.036206 , 0.01700369]), 10, array([0.25440334, 0.76508895]), array([0.036206 , 0.01700369]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.07337333, 0.68007051]), array([0.036206 , 0.01700369]), 7, array([0.14578533, 0.71407788]), array([0.036206 , 0.01700369]), 10, array([0.25440334, 0.76508895]), array([0.036206 , 0.01700369]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4914817954950847 goals: [('bisect', 5, array([0.07337333, 0.68007051]), array([0.036206 , 0.01700369]), 6, array([0.10957933, 0.6970742 ]), array([0.036206 , 0.01700369]), 7, array([0.14578533, 0.71407788]), array([0.036206 , 0.01700369]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.14578533 0.71407788] [0.036206 0.01700369] new direction: [ 0.02481099 -0.03137538] reversing there [0.036206 0.01700369] making one step from [0.14578533 0.71407788] [0.036206 0.01700369] --> [0.17059633 0.6827025 ] [ 0.02481099 -0.03137538] trying new point, [0.17059633 0.6827025 ] next() call -0.4318041009131332 goals: [('reflect-at', 7, array([0.17059633, 0.6827025 ]), array([ 0.02481099, -0.03137538]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.12809180647624227 goals: [('bisect', 7, array([0.17059633, 0.6827025 ]), array([ 0.02481099, -0.03137538]), None, None, None, 10, array([0.2450293 , 0.58857635]), array([ 0.02481099, -0.03137538]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.2450293 0.58857635] [ 0.02481099 -0.03137538] -0.12809180647624227 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.2450293 , 0.58857635]), array([-0.02481099, 0.03137538]), None, None, None, 10, array([0.00308062, 0.90233018]), array([0.02481099, 0.03137538]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.2450293 , 0.58857635]), array([-0.02481099, 0.03137538]), 5, array([0.12097434, 0.74545327]), array([-0.02481099, 0.03137538]), 10, array([0.00308062, 0.90233018]), array([0.02481099, 0.03137538]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.30534071772163773 goals: [('bisect', 0, array([0.2450293 , 0.58857635]), array([-0.02481099, 0.03137538]), 2, array([0.19540732, 0.65132712]), array([-0.02481099, 0.03137538]), 5, array([0.12097434, 0.74545327]), array([-0.02481099, 0.03137538]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4318041009131337 goals: [('bisect', 2, array([0.19540732, 0.65132712]), array([-0.02481099, 0.03137538]), 3, array([0.17059633, 0.6827025 ]), array([-0.02481099, 0.03137538]), 5, array([0.12097434, 0.74545327]), array([-0.02481099, 0.03137538]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.17059633, 0.6827025 ]), array([-0.02481099, 0.03137538]), 4, array([0.14578533, 0.71407788]), array([-0.02481099, 0.03137538]), 5, array([0.12097434, 0.74545327]), array([-0.02481099, 0.03137538]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.14578533 0.71407788] [-0.02481099 0.03137538] new direction: [-0.036206 -0.01700369] reversing there [-0.02481099 0.03137538] making one step from [0.14578533 0.71407788] [-0.02481099 0.03137538] --> [0.10957933 0.6970742 ] [-0.036206 -0.01700369] trying new point, [0.10957933 0.6970742 ] next() call -0.4914817954950847 goals: [('reflect-at', 4, array([0.10957933, 0.6970742 ]), array([-0.036206 , -0.01700369]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.1187311667407582 goals: [('bisect', 4, array([0.10957933, 0.6970742 ]), array([-0.036206 , -0.01700369]), None, None, None, 10, array([0.10765668, 0.59505206]), array([ 0.036206 , -0.01700369]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.10765668 0.59505206] [-0.036206 0.01700369] -0.11873116674075794 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.10765668 0.59505206] [-0.036206 0.01700369] -0.11873116674075794 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-6, 1) ---- seed=7 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.97798951 0.53849587] [-0.02199973 0.03340676] -0.49675589327088776 BACKWARD SAMPLING FROM 0 [0.97798951 0.53849587] [-0.02199973 0.03340676] -0.49675589327088776 BACKWARD SAMPLING FROM -3 [0.91202793 0.42346309] [0.02932044 0.02720867] -0.48912121118231033 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.97798951 0.53849587] [-0.02199973 0.03340676] -0.49675589327088776 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), None, None, None, 10, array([0.75799217, 0.87256348]), array([-0.02199973, 0.03340676]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), 5, array([0.86799084, 0.70552968]), array([-0.02199973, 0.03340676]), 10, array([0.75799217, 0.87256348]), array([-0.02199973, 0.03340676]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), 2, array([0.93399004, 0.60530939]), array([-0.02199973, 0.03340676]), 5, array([0.86799084, 0.70552968]), array([-0.02199973, 0.03340676]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), 1, array([0.95598978, 0.57190263]), array([-0.02199973, 0.03340676]), 2, array([0.93399004, 0.60530939]), array([-0.02199973, 0.03340676]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.95598978 0.57190263] [-0.02199973 0.03340676] new direction: [-0.03029182 0.0261229 ] reversing there [-0.02199973 0.03340676] making one step from [0.95598978 0.57190263] [-0.02199973 0.03340676] --> [0.92569796 0.59802553] [-0.03029182 0.0261229 ] trying new point, [0.92569796 0.59802553] next() call None goals: [('reflect-at', 1, array([0.92569796, 0.59802553]), array([-0.03029182, 0.0261229 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 0... 10 steps to do at 0 -> [from 1, delta=10] targeting -9. goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), None, None, None, -9, array([0.82401288, 0.23783502]), array([0.02199973, 0.03340676]), -1), ('sample-at', -9)] bisecting ... 0 None -9 continue bisect at -5 next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), -5, array([0.91201182, 0.37146206]), array([0.02199973, 0.03340676]), -9, array([0.82401288, 0.23783502]), array([0.02199973, 0.03340676]), -1), ('sample-at', -9)] bisecting ... 0 -5 -9 continue bisect at -3 next() call None goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), -3, array([0.95601129, 0.43827559]), array([0.02199973, 0.03340676]), -5, array([0.91201182, 0.37146206]), array([0.02199973, 0.03340676]), -1), ('sample-at', -9)] bisecting ... 0 -3 -5 continue bisect at -2 next() call -0.4882763953959062 goals: [('bisect', 0, array([0.97798951, 0.53849587]), array([-0.02199973, 0.03340676]), -2, array([0.97801102, 0.47168235]), array([0.02199973, 0.03340676]), -3, array([0.95601129, 0.43827559]), array([0.02199973, 0.03340676]), -1), ('sample-at', -9)] bisecting ... 0 -2 -3 bisecting gave reflection point -3 [0.95601129 0.43827559] [0.02199973 0.03340676] new direction: [ 0.01936874 -0.03499789] reversing there [0.02199973 0.03340676] making one step from [0.95601129 0.43827559] [0.02199973 0.03340676] --> [0.97538002 0.4032777 ] [-0.01936874 0.03499789] trying new point, [0.97538002 0.4032777 ] next() call None goals: [('reflect-at', -3, array([0.97538002, 0.4032777 ]), array([-0.01936874, 0.03499789]), -1), ('sample-at', -9)] goals: [('sample-at', -9)] reversing at -2... -7 steps to do at -2 -> [from -3, delta=-7] targeting 4. goals: [('sample-at', 4)] reversing at 0... 4 steps to do at 0 -> [from 1, delta=4] targeting -3. goals: [('sample-at', -3)] reversing at -2... -1 steps to do at -2 -> [from -3, delta=-1] targeting -2. goals: [('sample-at', -2)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.97798951 0.53849587] [-0.02199973 0.03340676] -0.49675589327088776 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -2 [0.97801102 0.47168235] [0.02199973 0.03340676] -0.4882763953959062 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -2)] not done yet, continue expanding to -2... goals: [('expand-to', -2), ('sample-at', -2)] next() call -0.49675589327088776 goals: [('bisect', 0, array([0.97801102, 0.47168235]), array([-0.02199973, -0.03340676]), None, None, None, -2, array([0.97798951, 0.53849587]), array([ 0.02199973, -0.03340676]), -1), ('sample-at', -2)] bisecting ... 0 None -2 successfully went all the way in one jump! goals: [('sample-at', -2)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.97798951 0.53849587] [-0.02199973 0.03340676] -0.49675589327088776 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (-1, 0) ---- seed=8 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.86919454 0.53085569] [ 0.02962799 -0.02687344] -0.38965049563602216 BACKWARD SAMPLING FROM 3 [0.95807852 0.45023536] [ 0.02962799 -0.02687344] -0.48991371862053074 BACKWARD SAMPLING FROM -3 [0.78031056 0.61147602] [ 0.02962799 -0.02687344] -0.45977857566680347 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.86919454 0.53085569] [ 0.02962799 -0.02687344] -0.38965049563602216 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.86919454, 0.53085569]), array([ 0.02962799, -0.02687344]), None, None, None, 10, array([0.83452552, 0.26212126]), array([-0.02962799, -0.02687344]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.86919454, 0.53085569]), array([ 0.02962799, -0.02687344]), 5, array([0.98266549, 0.39648847]), array([-0.02962799, -0.02687344]), 10, array([0.83452552, 0.26212126]), array([-0.02962799, -0.02687344]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.4375602772906623 goals: [('bisect', 0, array([0.86919454, 0.53085569]), array([ 0.02962799, -0.02687344]), 2, array([0.92845053, 0.4771088 ]), array([ 0.02962799, -0.02687344]), 5, array([0.98266549, 0.39648847]), array([-0.02962799, -0.02687344]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.48991371862053074 goals: [('bisect', 2, array([0.92845053, 0.4771088 ]), array([ 0.02962799, -0.02687344]), 3, array([0.95807852, 0.45023536]), array([ 0.02962799, -0.02687344]), 5, array([0.98266549, 0.39648847]), array([-0.02962799, -0.02687344]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.95807852, 0.45023536]), array([ 0.02962799, -0.02687344]), 4, array([0.98770652, 0.42336192]), array([ 0.02962799, -0.02687344]), 5, array([0.98266549, 0.39648847]), array([-0.02962799, -0.02687344]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.98770652 0.42336192] [ 0.02962799 -0.02687344] new direction: [-0.03704999 -0.01507642] reversing there [ 0.02962799 -0.02687344] making one step from [0.98770652 0.42336192] [ 0.02962799 -0.02687344] --> [0.95065653 0.4082855 ] [-0.03704999 -0.01507642] trying new point, [0.95065653 0.4082855 ] next() call None goals: [('reflect-at', 4, array([0.95065653, 0.4082855 ]), array([-0.03704999, -0.01507642]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.45977857566680347 goals: [('bisect', 0, array([0.86919454, 0.53085569]), array([ 0.02962799, -0.02687344]), None, None, None, -3, array([0.78031056, 0.61147602]), array([ 0.02962799, -0.02687344]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.95807852 0.45023536] [ 0.02962799 -0.02687344] -0.48991371862053074 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.38965049563602216 goals: [('bisect', 0, array([0.95807852, 0.45023536]), array([-0.02962799, 0.02687344]), None, None, None, 3, array([0.86919454, 0.53085569]), array([-0.02962799, 0.02687344]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.78031056 0.61147602] [ 0.02962799 -0.02687344] -0.45977857566680347 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.38965049563602216 goals: [('bisect', 0, array([0.78031056, 0.61147602]), array([-0.02962799, 0.02687344]), None, None, None, -3, array([0.86919454, 0.53085569]), array([-0.02962799, 0.02687344]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.86919454 0.53085569] [ 0.02962799 -0.02687344] -0.38965049563602216 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-1, 2) ---- seed=9 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.01037415 0.50187459] [-0.03024815 -0.02617344] -9.773773097350387e-05 BACKWARD SAMPLING FROM 4 [0.11061846 0.39718082] [ 0.03024815 -0.02617344] -0.13826553062534178 BACKWARD SAMPLING FROM -4 [0.13136677 0.60656837] [-0.03024815 -0.02617344] -0.1505888292401249 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.01037415 0.50187459] [-0.03024815 -0.02617344] -9.773773097350387e-05 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.01037415, 0.50187459]), array([-0.03024815, -0.02617344]), None, None, None, 10, array([0.29210739, 0.24014015]), array([ 0.03024815, -0.02617344]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.2179104295369838 goals: [('bisect', 0, array([0.01037415, 0.50187459]), array([-0.03024815, -0.02617344]), 5, array([0.14086662, 0.37100737]), array([ 0.03024815, -0.02617344]), 10, array([0.29210739, 0.24014015]), array([ 0.03024815, -0.02617344]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.43132376818592777 goals: [('bisect', 5, array([0.14086662, 0.37100737]), array([ 0.03024815, -0.02617344]), 7, array([0.20136292, 0.31866048]), array([ 0.03024815, -0.02617344]), 10, array([0.29210739, 0.24014015]), array([ 0.03024815, -0.02617344]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.20136292, 0.31866048]), array([ 0.03024815, -0.02617344]), 8, array([0.23161108, 0.29248704]), array([ 0.03024815, -0.02617344]), 10, array([0.29210739, 0.24014015]), array([ 0.03024815, -0.02617344]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.23161108 0.29248704] [ 0.03024815 -0.02617344] new direction: [0.02396606 0.03202542] reversing there [ 0.03024815 -0.02617344] making one step from [0.23161108 0.29248704] [ 0.03024815 -0.02617344] --> [0.25557714 0.32451246] [0.02396606 0.03202542] trying new point, [0.25557714 0.32451246] next() call -0.41760827984962573 goals: [('reflect-at', 8, array([0.25557714, 0.32451246]), array([0.02396606, 0.03202542]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.20128562729667512 goals: [('bisect', 8, array([0.25557714, 0.32451246]), array([0.02396606, 0.03202542]), None, None, None, 10, array([0.30350927, 0.38856331]), array([0.02396606, 0.03202542]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.30350927 0.38856331] [0.02396606 0.03202542] -0.20128562729667512 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.30350927, 0.38856331]), array([-0.02396606, -0.03202542]), None, None, None, 10, array([0.06384864, 0.06830907]), array([-0.02396606, -0.03202542]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.30350927, 0.38856331]), array([-0.02396606, -0.03202542]), 5, array([0.18367895, 0.22843619]), array([-0.02396606, -0.03202542]), 10, array([0.06384864, 0.06830907]), array([-0.02396606, -0.03202542]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.41760827984962573 goals: [('bisect', 0, array([0.30350927, 0.38856331]), array([-0.02396606, -0.03202542]), 2, array([0.25557714, 0.32451246]), array([-0.02396606, -0.03202542]), 5, array([0.18367895, 0.22843619]), array([-0.02396606, -0.03202542]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.25557714, 0.32451246]), array([-0.02396606, -0.03202542]), 3, array([0.23161108, 0.29248704]), array([-0.02396606, -0.03202542]), 5, array([0.18367895, 0.22843619]), array([-0.02396606, -0.03202542]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.23161108 0.29248704] [-0.02396606 -0.03202542] new direction: [-0.03024815 0.02617344] reversing there [-0.02396606 -0.03202542] making one step from [0.23161108 0.29248704] [-0.02396606 -0.03202542] --> [0.20136292 0.31866048] [-0.03024815 0.02617344] trying new point, [0.20136292 0.31866048] next() call -0.43132376818592777 goals: [('reflect-at', 3, array([0.20136292, 0.31866048]), array([-0.03024815, 0.02617344]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -9.773773097349783e-05 goals: [('bisect', 3, array([0.20136292, 0.31866048]), array([-0.03024815, 0.02617344]), None, None, None, 10, array([0.01037415, 0.50187459]), array([0.03024815, 0.02617344]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.01037415 0.50187459] [-0.03024815 -0.02617344] -9.773773097350387e-05 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.01037415 0.50187459] [-0.03024815 -0.02617344] -9.773773097350387e-05 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 sampling between (0, 3) ---- seed=10 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.00394827 0.51219226] [0.00950643 0.03885393] -0.0018659354843499343 BACKWARD SAMPLING FROM 4 [0.041974 0.66760798] [0.00950643 0.03885393] -0.3520363250591377 BACKWARD SAMPLING FROM -4 [0.03407747 0.35677655] [-0.00950643 0.03885393] -0.25699258899589295 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.00394827 0.51219226] [0.00950643 0.03885393] -0.0018659354843499343 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.00394827, 0.51219226]), array([0.00950643, 0.03885393]), None, None, None, 10, array([0.0990126 , 0.90073154]), array([0.00950643, 0.03885393]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.00394827, 0.51219226]), array([0.00950643, 0.03885393]), 5, array([0.05148044, 0.7064619 ]), array([0.00950643, 0.03885393]), 10, array([0.0990126 , 0.90073154]), array([0.00950643, 0.03885393]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.10128899988595243 goals: [('bisect', 0, array([0.00394827, 0.51219226]), array([0.00950643, 0.03885393]), 2, array([0.02296113, 0.58990012]), array([0.00950643, 0.03885393]), 5, array([0.05148044, 0.7064619 ]), array([0.00950643, 0.03885393]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.2077471298760972 goals: [('bisect', 2, array([0.02296113, 0.58990012]), array([0.00950643, 0.03885393]), 3, array([0.03246757, 0.62875405]), array([0.00950643, 0.03885393]), 5, array([0.05148044, 0.7064619 ]), array([0.00950643, 0.03885393]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.3520363250591377 goals: [('bisect', 3, array([0.03246757, 0.62875405]), array([0.00950643, 0.03885393]), 4, array([0.041974 , 0.66760798]), array([0.00950643, 0.03885393]), 5, array([0.05148044, 0.7064619 ]), array([0.00950643, 0.03885393]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.05148044 0.7064619 ] [0.00950643 0.03885393] new direction: [0.03538475 0.01865259] reversing there [0.00950643 0.03885393] making one step from [0.05148044 0.7064619 ] [0.00950643 0.03885393] --> [0.08686519 0.7251145 ] [0.03538475 0.01865259] trying new point, [0.08686519 0.7251145 ] next() call None goals: [('reflect-at', 5, array([0.08686519, 0.7251145 ]), array([0.03538475, 0.01865259]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 4... 6 steps to do at 4 -> [from 5, delta=6] targeting -1. goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.008901001072892184 goals: [('bisect', 0, array([0.00394827, 0.51219226]), array([0.00950643, 0.03885393]), None, None, None, -1, array([0.00555817, 0.47333834]), array([-0.00950643, 0.03885393]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 4 [0.041974 0.66760798] [0.00950643 0.03885393] -0.3520363250591377 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 4)] not done yet, continue expanding to 4... goals: [('expand-to', 4), ('sample-at', 4)] next() call -0.001865935484349968 goals: [('bisect', 0, array([0.041974 , 0.66760798]), array([-0.00950643, -0.03885393]), None, None, None, 4, array([0.00394827, 0.51219226]), array([-0.00950643, -0.03885393]), 1), ('sample-at', 4)] bisecting ... 0 None 4 successfully went all the way in one jump! goals: [('sample-at', 4)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -1 [0.00555817 0.47333834] [-0.00950643 0.03885393] -0.008901001072892184 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.0018659354843499343 goals: [('bisect', 0, array([0.00555817, 0.47333834]), array([ 0.00950643, -0.03885393]), None, None, None, -1, array([0.00394827, 0.51219226]), array([-0.00950643, -0.03885393]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.00394827 0.51219226] [0.00950643 0.03885393] -0.0018659354843499343 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=11 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.4202036 0.4854271] [-0.01214282 -0.03811236] -0.09094015300190272 BACKWARD SAMPLING FROM 4 [0.37163231 0.33297767] [-0.01214282 -0.03811236] -0.4177610370777551 BACKWARD SAMPLING FROM -4 [0.4687749 0.63787653] [-0.01214282 -0.03811236] -0.3474991729078387 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.4202036 0.4854271] [-0.01214282 -0.03811236] -0.09094015300190272 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.4202036, 0.4854271]), array([-0.01214282, -0.03811236]), None, None, None, 10, array([0.29877537, 0.10430352]), array([-0.01214282, -0.03811236]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.4202036, 0.4854271]), array([-0.01214282, -0.03811236]), 5, array([0.35948949, 0.29486531]), array([-0.01214282, -0.03811236]), 10, array([0.29877537, 0.10430352]), array([-0.01214282, -0.03811236]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.18142810704210527 goals: [('bisect', 0, array([0.4202036, 0.4854271]), array([-0.01214282, -0.03811236]), 2, array([0.39591796, 0.40920238]), array([-0.01214282, -0.03811236]), 5, array([0.35948949, 0.29486531]), array([-0.01214282, -0.03811236]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.28136395006049925 goals: [('bisect', 2, array([0.39591796, 0.40920238]), array([-0.01214282, -0.03811236]), 3, array([0.38377513, 0.37109002]), array([-0.01214282, -0.03811236]), 5, array([0.35948949, 0.29486531]), array([-0.01214282, -0.03811236]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.4177610370777551 goals: [('bisect', 3, array([0.38377513, 0.37109002]), array([-0.01214282, -0.03811236]), 4, array([0.37163231, 0.33297767]), array([-0.01214282, -0.03811236]), 5, array([0.35948949, 0.29486531]), array([-0.01214282, -0.03811236]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.35948949 0.29486531] [-0.01214282 -0.03811236] new direction: [-0.0041129 0.03978799] reversing there [-0.01214282 -0.03811236] making one step from [0.35948949 0.29486531] [-0.01214282 -0.03811236] --> [0.35537659 0.3346533 ] [-0.0041129 0.03978799] trying new point, [0.35537659 0.3346533 ] next() call -0.4048904116037848 goals: [('reflect-at', 5, array([0.35537659, 0.3346533 ]), array([-0.0041129 , 0.03978799]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.07015589586681098 goals: [('bisect', 5, array([0.35537659, 0.3346533 ]), array([-0.0041129 , 0.03978799]), None, None, None, 10, array([0.3348121 , 0.53359324]), array([-0.0041129 , 0.03978799]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.3348121 0.53359324] [-0.0041129 0.03978799] -0.07015589586681098 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.3348121 , 0.53359324]), array([ 0.0041129 , -0.03978799]), None, None, None, 10, array([0.37594108, 0.13571335]), array([ 0.0041129 , -0.03978799]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.40489041160378453 goals: [('bisect', 0, array([0.3348121 , 0.53359324]), array([ 0.0041129 , -0.03978799]), 5, array([0.35537659, 0.3346533 ]), array([ 0.0041129 , -0.03978799]), 10, array([0.37594108, 0.13571335]), array([ 0.0041129 , -0.03978799]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.35537659, 0.3346533 ]), array([ 0.0041129 , -0.03978799]), 7, array([0.36360238, 0.25507732]), array([ 0.0041129 , -0.03978799]), 10, array([0.37594108, 0.13571335]), array([ 0.0041129 , -0.03978799]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.35537659, 0.3346533 ]), array([ 0.0041129 , -0.03978799]), 6, array([0.35948949, 0.29486531]), array([ 0.0041129 , -0.03978799]), 7, array([0.36360238, 0.25507732]), array([ 0.0041129 , -0.03978799]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.35948949 0.29486531] [ 0.0041129 -0.03978799] new direction: [0.01214282 0.03811236] reversing there [ 0.0041129 -0.03978799] making one step from [0.35948949 0.29486531] [ 0.0041129 -0.03978799] --> [0.37163231 0.33297767] [0.01214282 0.03811236] trying new point, [0.37163231 0.33297767] next() call -0.4177610370777547 goals: [('reflect-at', 6, array([0.37163231, 0.33297767]), array([0.01214282, 0.03811236]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.0909401530019027 goals: [('bisect', 6, array([0.37163231, 0.33297767]), array([0.01214282, 0.03811236]), None, None, None, 10, array([0.4202036, 0.4854271]), array([0.01214282, 0.03811236]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.4202036 0.4854271] [-0.01214282 -0.03811236] -0.09094015300190272 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.4202036 0.4854271] [-0.01214282 -0.03811236] -0.09094015300190272 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=12 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.26331502 0.53373939] [-0.03208755 -0.02388282] -0.04889673193185484 BACKWARD SAMPLING FROM 4 [0.13496481 0.43820812] [-0.03208755 -0.02388282] -0.05683571304926094 BACKWARD SAMPLING FROM -4 [0.39166522 0.62927067] [-0.03208755 -0.02388282] -0.2855871537311189 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.26331502 0.53373939] [-0.03208755 -0.02388282] -0.04889673193185484 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.26331502, 0.53373939]), array([-0.03208755, -0.02388282]), None, None, None, 10, array([0.05756049, 0.2949112 ]), array([ 0.03208755, -0.02388282]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.09704380253434214 goals: [('bisect', 0, array([0.26331502, 0.53373939]), array([-0.03208755, -0.02388282]), 5, array([0.10287726, 0.4143253 ]), array([-0.03208755, -0.02388282]), 10, array([0.05756049, 0.2949112 ]), array([ 0.03208755, -0.02388282]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.22332799455138033 goals: [('bisect', 5, array([0.10287726, 0.4143253 ]), array([-0.03208755, -0.02388282]), 7, array([0.03870216, 0.36655966]), array([-0.03208755, -0.02388282]), 10, array([0.05756049, 0.2949112 ]), array([ 0.03208755, -0.02388282]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3094040970833373 goals: [('bisect', 7, array([0.03870216, 0.36655966]), array([-0.03208755, -0.02388282]), 8, array([0.00661461, 0.34267684]), array([-0.03208755, -0.02388282]), 10, array([0.05756049, 0.2949112 ]), array([ 0.03208755, -0.02388282]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.41076953729758614 goals: [('bisect', 8, array([0.00661461, 0.34267684]), array([-0.03208755, -0.02388282]), 9, array([0.02547294, 0.31879402]), array([ 0.03208755, -0.02388282]), 10, array([0.05756049, 0.2949112 ]), array([ 0.03208755, -0.02388282]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.05756049 0.2949112 ] [ 0.03208755 -0.02388282] new direction: [ 0.02655384 -0.02991477] reversing there [ 0.03208755 -0.02388282] making one step from [0.05756049 0.2949112 ] [ 0.03208755 -0.02388282] --> [0.08411434 0.26499643] [ 0.02655384 -0.02991477] trying new point, [0.08411434 0.26499643] next() call None goals: [('reflect-at', 10, array([0.08411434, 0.26499643]), array([ 0.02655384, -0.02991477]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 9... 1 steps to do at 9 -> [from 10, delta=1] targeting 9. goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 9 [0.02547294 0.31879402] [ 0.03208755 -0.02388282] -0.41076953729758614 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 9)] not done yet, continue expanding to 9... goals: [('expand-to', 9), ('sample-at', 9)] next() call -0.04889673193185484 goals: [('bisect', 0, array([0.02547294, 0.31879402]), array([-0.03208755, 0.02388282]), None, None, None, 9, array([0.26331502, 0.53373939]), array([0.03208755, 0.02388282]), 1), ('sample-at', 9)] bisecting ... 0 None 9 successfully went all the way in one jump! goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.26331502 0.53373939] [-0.03208755 -0.02388282] -0.04889673193185484 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.26331502 0.53373939] [-0.03208755 -0.02388282] -0.04889673193185484 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=13 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.37285403 0.67984795] [0.03957308 0.00582848] -0.47382613413670277 BACKWARD SAMPLING FROM 4 [0.40042197 0.52612745] [-0.00300128 -0.03988724] -0.08870192535855582 BACKWARD SAMPLING FROM -4 [0.2145617 0.65653403] [0.03957308 0.00582848] -0.32930462797788607 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.37285403 0.67984795] [0.03957308 0.00582848] -0.47382613413670277 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.37285403, 0.67984795]), array([0.03957308, 0.00582848]), None, None, None, 10, array([0.76858485, 0.73813277]), array([0.03957308, 0.00582848]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.37285403, 0.67984795]), array([0.03957308, 0.00582848]), 5, array([0.57071944, 0.70899036]), array([0.03957308, 0.00582848]), 10, array([0.76858485, 0.73813277]), array([0.03957308, 0.00582848]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.37285403, 0.67984795]), array([0.03957308, 0.00582848]), 2, array([0.45200019, 0.69150491]), array([0.03957308, 0.00582848]), 5, array([0.57071944, 0.70899036]), array([0.03957308, 0.00582848]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.37285403, 0.67984795]), array([0.03957308, 0.00582848]), 1, array([0.41242711, 0.68567643]), array([0.03957308, 0.00582848]), 2, array([0.45200019, 0.69150491]), array([0.03957308, 0.00582848]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.41242711 0.68567643] [0.03957308 0.00582848] new direction: [-0.00300128 -0.03988724] reversing there [0.03957308 0.00582848] making one step from [0.41242711 0.68567643] [0.03957308 0.00582848] --> [0.40942582 0.64578919] [-0.00300128 -0.03988724] trying new point, [0.40942582 0.64578919] next() call -0.3494958457329673 goals: [('reflect-at', 1, array([0.40942582, 0.64578919]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 1, array([0.40942582, 0.64578919]), array([-0.00300128, -0.03988724]), None, None, None, 10, array([0.38241426, 0.28680399]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] bisecting ... 1 None 10 continue bisect at 5 next() call -0.08133824843659004 goals: [('bisect', 1, array([0.40942582, 0.64578919]), array([-0.00300128, -0.03988724]), 5, array([0.39742069, 0.48624021]), array([-0.00300128, -0.03988724]), 10, array([0.38241426, 0.28680399]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] bisecting ... 1 5 10 continue bisect at 7 next() call -0.18596233939691564 goals: [('bisect', 5, array([0.39742069, 0.48624021]), array([-0.00300128, -0.03988724]), 7, array([0.39141812, 0.40646572]), array([-0.00300128, -0.03988724]), 10, array([0.38241426, 0.28680399]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.297950107279207 goals: [('bisect', 7, array([0.39141812, 0.40646572]), array([-0.00300128, -0.03988724]), 8, array([0.38841683, 0.36657848]), array([-0.00300128, -0.03988724]), 10, array([0.38241426, 0.28680399]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4497216900962508 goals: [('bisect', 8, array([0.38841683, 0.36657848]), array([-0.00300128, -0.03988724]), 9, array([0.38541555, 0.32669123]), array([-0.00300128, -0.03988724]), 10, array([0.38241426, 0.28680399]), array([-0.00300128, -0.03988724]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.38241426 0.28680399] [-0.00300128 -0.03988724] new direction: [0.02675467 0.02973529] reversing there [-0.00300128 -0.03988724] making one step from [0.38241426 0.28680399] [-0.00300128 -0.03988724] --> [0.40916893 0.31653928] [0.02675467 0.02973529] trying new point, [0.40916893 0.31653928] next() call None goals: [('reflect-at', 10, array([0.40916893, 0.31653928]), array([0.02675467, 0.02973529]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 9... 1 steps to do at 9 -> [from 10, delta=1] targeting 9. goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 9 [0.38541555 0.32669123] [-0.00300128 -0.03988724] -0.4497216900962508 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 9)] not done yet, continue expanding to 9... goals: [('expand-to', 9), ('sample-at', 9)] next() call None goals: [('bisect', 0, array([0.38541555, 0.32669123]), array([0.00300128, 0.03988724]), None, None, None, 9, array([0.41242711, 0.68567643]), array([0.00300128, 0.03988724]), 1), ('sample-at', 9)] bisecting ... 0 None 9 continue bisect at 4 next() call -0.08133824843659004 goals: [('bisect', 0, array([0.38541555, 0.32669123]), array([0.00300128, 0.03988724]), 4, array([0.39742069, 0.48624021]), array([0.00300128, 0.03988724]), 9, array([0.41242711, 0.68567643]), array([0.00300128, 0.03988724]), 1), ('sample-at', 9)] bisecting ... 0 4 9 continue bisect at 6 next() call -0.13584941721527394 goals: [('bisect', 4, array([0.39742069, 0.48624021]), array([0.00300128, 0.03988724]), 6, array([0.40342325, 0.5660147 ]), array([0.00300128, 0.03988724]), 9, array([0.41242711, 0.68567643]), array([0.00300128, 0.03988724]), 1), ('sample-at', 9)] bisecting ... 4 6 9 continue bisect at 7 next() call -0.22278072400674448 goals: [('bisect', 6, array([0.40342325, 0.5660147 ]), array([0.00300128, 0.03988724]), 7, array([0.40642454, 0.60590194]), array([0.00300128, 0.03988724]), 9, array([0.41242711, 0.68567643]), array([0.00300128, 0.03988724]), 1), ('sample-at', 9)] bisecting ... 6 7 9 continue bisect at 8 next() call -0.3494958457329673 goals: [('bisect', 7, array([0.40642454, 0.60590194]), array([0.00300128, 0.03988724]), 8, array([0.40942582, 0.64578919]), array([0.00300128, 0.03988724]), 9, array([0.41242711, 0.68567643]), array([0.00300128, 0.03988724]), 1), ('sample-at', 9)] bisecting ... 7 8 9 bisecting gave reflection point 9 [0.41242711 0.68567643] [0.00300128 0.03988724] new direction: [-0.03957308 -0.00582848] reversing there [0.00300128 0.03988724] making one step from [0.41242711 0.68567643] [0.00300128 0.03988724] --> [0.37285403 0.67984795] [-0.03957308 -0.00582848] trying new point, [0.37285403 0.67984795] next() call -0.47382613413670327 goals: [('reflect-at', 9, array([0.37285403, 0.67984795]), array([-0.03957308, -0.00582848]), 1), ('sample-at', 9)] goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.37285403 0.67984795] [0.03957308 0.00582848] -0.47382613413670277 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.37285403 0.67984795] [0.03957308 0.00582848] -0.47382613413670277 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: 0..7 sampling between (0, 7) ---- seed=14 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.30009198 0.47490577] [-0.021002 0.03404285] -0.05289910562998988 BACKWARD SAMPLING FROM 4 [0.21608397 0.61107718] [-0.021002 0.03404285] -0.17757288486701345 BACKWARD SAMPLING FROM -4 [0.38409999 0.33873435] [-0.021002 0.03404285] -0.39884901225457176 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.30009198 0.47490577] [-0.021002 0.03404285] -0.05289910562998988 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.30009198, 0.47490577]), array([-0.021002 , 0.03404285]), None, None, None, 10, array([0.09007195, 0.8153343 ]), array([-0.021002 , 0.03404285]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.2822762805921456 goals: [('bisect', 0, array([0.30009198, 0.47490577]), array([-0.021002 , 0.03404285]), 5, array([0.19508196, 0.64512003]), array([-0.021002 , 0.03404285]), 10, array([0.09007195, 0.8153343 ]), array([-0.021002 , 0.03404285]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.19508196, 0.64512003]), array([-0.021002 , 0.03404285]), 7, array([0.15307796, 0.71320574]), array([-0.021002 , 0.03404285]), 10, array([0.09007195, 0.8153343 ]), array([-0.021002 , 0.03404285]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.41639365668362777 goals: [('bisect', 5, array([0.19508196, 0.64512003]), array([-0.021002 , 0.03404285]), 6, array([0.17407996, 0.67916288]), array([-0.021002 , 0.03404285]), 7, array([0.15307796, 0.71320574]), array([-0.021002 , 0.03404285]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.15307796 0.71320574] [-0.021002 0.03404285] new direction: [ 0.01597666 -0.03667078] reversing there [-0.021002 0.03404285] making one step from [0.15307796 0.71320574] [-0.021002 0.03404285] --> [0.16905462 0.67653496] [ 0.01597666 -0.03667078] trying new point, [0.16905462 0.67653496] next() call -0.40384711686066294 goals: [('reflect-at', 7, array([0.16905462, 0.67653496]), array([ 0.01597666, -0.03667078]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.07885688368045461 goals: [('bisect', 7, array([0.16905462, 0.67653496]), array([ 0.01597666, -0.03667078]), None, None, None, 10, array([0.21698461, 0.56652261]), array([ 0.01597666, -0.03667078]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.21698461 0.56652261] [ 0.01597666 -0.03667078] -0.07885688368045461 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.21698461, 0.56652261]), array([-0.01597666, 0.03667078]), None, None, None, 10, array([0.05721797, 0.93323043]), array([-0.01597666, 0.03667078]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.21698461, 0.56652261]), array([-0.01597666, 0.03667078]), 5, array([0.13710129, 0.74987652]), array([-0.01597666, 0.03667078]), 10, array([0.05721797, 0.93323043]), array([-0.01597666, 0.03667078]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.2616431298568962 goals: [('bisect', 0, array([0.21698461, 0.56652261]), array([-0.01597666, 0.03667078]), 2, array([0.18503128, 0.63986417]), array([-0.01597666, 0.03667078]), 5, array([0.13710129, 0.74987652]), array([-0.01597666, 0.03667078]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.40384711686066294 goals: [('bisect', 2, array([0.18503128, 0.63986417]), array([-0.01597666, 0.03667078]), 3, array([0.16905462, 0.67653496]), array([-0.01597666, 0.03667078]), 5, array([0.13710129, 0.74987652]), array([-0.01597666, 0.03667078]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.16905462, 0.67653496]), array([-0.01597666, 0.03667078]), 4, array([0.15307796, 0.71320574]), array([-0.01597666, 0.03667078]), 5, array([0.13710129, 0.74987652]), array([-0.01597666, 0.03667078]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.15307796 0.71320574] [-0.01597666 0.03667078] new direction: [ 0.021002 -0.03404285] reversing there [-0.01597666 0.03667078] making one step from [0.15307796 0.71320574] [-0.01597666 0.03667078] --> [0.17407996 0.67916288] [ 0.021002 -0.03404285] trying new point, [0.17407996 0.67916288] next() call -0.41639365668362777 goals: [('reflect-at', 4, array([0.17407996, 0.67916288]), array([ 0.021002 , -0.03404285]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05289910562998976 goals: [('bisect', 4, array([0.17407996, 0.67916288]), array([ 0.021002 , -0.03404285]), None, None, None, 10, array([0.30009198, 0.47490577]), array([ 0.021002 , -0.03404285]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.30009198 0.47490577] [-0.021002 0.03404285] -0.05289910562998988 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.30009198 0.47490577] [-0.021002 0.03404285] -0.05289910562998988 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -1..6 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -1..14 NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd sampling between (-1, 14) ---- seed=15 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.05436321 0.36153845] [-0.03996334 -0.00171217] -0.2411227034306628 BACKWARD SAMPLING FROM 4 [0.10549014 0.35468978] [ 0.03996334 -0.00171217] -0.2695023366962361 BACKWARD SAMPLING FROM -4 [0.21421657 0.36838711] [-0.03996334 -0.00171217] -0.23946877171984182 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.05436321 0.36153845] [-0.03996334 -0.00171217] -0.2411227034306628 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.36218247700975736 goals: [('bisect', 0, array([0.05436321, 0.36153845]), array([-0.03996334, -0.00171217]), None, None, None, 10, array([0.34527018, 0.34441678]), array([ 0.03996334, -0.00171217]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.34527018 0.34441678] [ 0.03996334 -0.00171217] -0.36218247700975736 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2411227034306628 goals: [('bisect', 0, array([0.34527018, 0.34441678]), array([-0.03996334, 0.00171217]), None, None, None, 10, array([0.05436321, 0.36153845]), array([0.03996334, 0.00171217]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.05436321 0.36153845] [-0.03996334 -0.00171217] -0.2411227034306628 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.05436321 0.36153845] [-0.03996334 -0.00171217] -0.2411227034306628 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-2, 1) ---- seed=16 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.22329108 0.52316334] [0.03673002 0.01584 ] -0.03163620782532942 BACKWARD SAMPLING FROM 4 [0.37021116 0.58652334] [0.03673002 0.01584 ] -0.16210675630294513 BACKWARD SAMPLING FROM -4 [0.076371 0.45980334] [0.03673002 0.01584 ] -0.0231134050599264 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.22329108 0.52316334] [0.03673002 0.01584 ] -0.03163620782532942 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.22329108, 0.52316334]), array([0.03673002, 0.01584 ]), None, None, None, 10, array([0.59059129, 0.68156334]), array([0.03673002, 0.01584 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.213778728689882 goals: [('bisect', 0, array([0.22329108, 0.52316334]), array([0.03673002, 0.01584 ]), 5, array([0.40694118, 0.60236334]), array([0.03673002, 0.01584 ]), 10, array([0.59059129, 0.68156334]), array([0.03673002, 0.01584 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.33998787578479583 goals: [('bisect', 5, array([0.40694118, 0.60236334]), array([0.03673002, 0.01584 ]), 7, array([0.48040122, 0.63404334]), array([0.03673002, 0.01584 ]), 10, array([0.59059129, 0.68156334]), array([0.03673002, 0.01584 ]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.41452505049277283 goals: [('bisect', 7, array([0.48040122, 0.63404334]), array([0.03673002, 0.01584 ]), 8, array([0.51713124, 0.64988334]), array([0.03673002, 0.01584 ]), 10, array([0.59059129, 0.68156334]), array([0.03673002, 0.01584 ]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4966839593077631 goals: [('bisect', 8, array([0.51713124, 0.64988334]), array([0.03673002, 0.01584 ]), 9, array([0.55386126, 0.66572334]), array([0.03673002, 0.01584 ]), 10, array([0.59059129, 0.68156334]), array([0.03673002, 0.01584 ]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.59059129 0.68156334] [0.03673002 0.01584 ] new direction: [-0.02554864 -0.0307777 ] reversing there [0.03673002 0.01584 ] making one step from [0.59059129 0.68156334] [0.03673002 0.01584 ] --> [0.56504265 0.65078563] [-0.02554864 -0.0307777 ] trying new point, [0.56504265 0.65078563] next() call -0.44384043934656114 goals: [('reflect-at', 10, array([0.56504265, 0.65078563]), array([-0.02554864, -0.0307777 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.56504265 0.65078563] [-0.02554864 -0.0307777 ] -0.44384043934656114 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.56504265, 0.65078563]), array([0.02554864, 0.0307777 ]), None, None, None, 10, array([0.82052904, 0.95856267]), array([0.02554864, 0.0307777 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.56504265, 0.65078563]), array([0.02554864, 0.0307777 ]), 5, array([0.69278584, 0.80467415]), array([0.02554864, 0.0307777 ]), 10, array([0.82052904, 0.95856267]), array([0.02554864, 0.0307777 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.56504265, 0.65078563]), array([0.02554864, 0.0307777 ]), 2, array([0.61613992, 0.71234104]), array([0.02554864, 0.0307777 ]), 5, array([0.69278584, 0.80467415]), array([0.02554864, 0.0307777 ]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.56504265, 0.65078563]), array([0.02554864, 0.0307777 ]), 1, array([0.59059129, 0.68156334]), array([0.02554864, 0.0307777 ]), 2, array([0.61613992, 0.71234104]), array([0.02554864, 0.0307777 ]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.59059129 0.68156334] [0.02554864 0.0307777 ] new direction: [-0.03673002 -0.01584 ] reversing there [0.02554864 0.0307777 ] making one step from [0.59059129 0.68156334] [0.02554864 0.0307777 ] --> [0.55386126 0.66572334] [-0.03673002 -0.01584 ] trying new point, [0.55386126 0.66572334] next() call -0.49668395930776266 goals: [('reflect-at', 1, array([0.55386126, 0.66572334]), array([-0.03673002, -0.01584 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.03163620782532951 goals: [('bisect', 1, array([0.55386126, 0.66572334]), array([-0.03673002, -0.01584 ]), None, None, None, 10, array([0.22329108, 0.52316334]), array([-0.03673002, -0.01584 ]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.22329108 0.52316334] [0.03673002 0.01584 ] -0.03163620782532942 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.22329108 0.52316334] [0.03673002 0.01584 ] -0.03163620782532942 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -13..2 NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd sampling between (-13, 2) ---- seed=17 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.294665 0.53058676] [-0.03402277 0.02103452] -0.055108102135022076 BACKWARD SAMPLING FROM 4 [0.15857392 0.61472483] [-0.03402277 0.02103452] -0.1770951824864543 BACKWARD SAMPLING FROM -4 [0.43075609 0.44644868] [-0.03402277 0.02103452] -0.12862220436569055 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.294665 0.53058676] [-0.03402277 0.02103452] -0.055108102135022076 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.294665 , 0.53058676]), array([-0.03402277, 0.02103452]), None, None, None, 10, array([0.04556272, 0.74093195]), array([0.03402277, 0.02103452]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.2381390123527663 goals: [('bisect', 0, array([0.294665 , 0.53058676]), array([-0.03402277, 0.02103452]), 5, array([0.12455114, 0.63575935]), array([-0.03402277, 0.02103452]), 10, array([0.04556272, 0.74093195]), array([0.03402277, 0.02103452]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.3968831438195334 goals: [('bisect', 5, array([0.12455114, 0.63575935]), array([-0.03402277, 0.02103452]), 7, array([0.0565056 , 0.67782839]), array([-0.03402277, 0.02103452]), 10, array([0.04556272, 0.74093195]), array([0.03402277, 0.02103452]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.49458344541998867 goals: [('bisect', 7, array([0.0565056 , 0.67782839]), array([-0.03402277, 0.02103452]), 8, array([0.02248283, 0.69886291]), array([-0.03402277, 0.02103452]), 10, array([0.04556272, 0.74093195]), array([0.03402277, 0.02103452]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.02248283, 0.69886291]), array([-0.03402277, 0.02103452]), 9, array([0.01153994, 0.71989743]), array([0.03402277, 0.02103452]), 10, array([0.04556272, 0.74093195]), array([0.03402277, 0.02103452]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.01153994 0.71989743] [0.03402277 0.02103452] new direction: [0.01297498 0.03783715] reversing there [0.03402277 0.02103452] making one step from [0.01153994 0.71989743] [0.03402277 0.02103452] --> [0.02451492 0.75773458] [0.01297498 0.03783715] trying new point, [0.02451492 0.75773458] next() call None goals: [('reflect-at', 9, array([0.02451492, 0.75773458]), array([0.01297498, 0.03783715]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 8... 2 steps to do at 8 -> [from 9, delta=2] targeting 7. goals: [('sample-at', 7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 8 [0.02248283 0.69886291] [-0.03402277 0.02103452] -0.49458344541998867 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 8)] not done yet, continue expanding to 8... goals: [('expand-to', 8), ('sample-at', 8)] next() call -0.055108102135022076 goals: [('bisect', 0, array([0.02248283, 0.69886291]), array([ 0.03402277, -0.02103452]), None, None, None, 8, array([0.294665 , 0.53058676]), array([ 0.03402277, -0.02103452]), 1), ('sample-at', 8)] bisecting ... 0 None 8 successfully went all the way in one jump! goals: [('sample-at', 8)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.294665 0.53058676] [-0.03402277 0.02103452] -0.055108102135022076 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.294665 0.53058676] [-0.03402277 0.02103452] -0.055108102135022076 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -7..8 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-7, 8) ---- seed=18 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.65037424 0.50545337] [-0.03976139 0.00436252] -0.21186506822282286 BACKWARD SAMPLING FROM 4 [0.49132867 0.52290346] [-0.03976139 0.00436252] -0.12725903771293967 BACKWARD SAMPLING FROM -4 [0.80941982 0.48800328] [-0.03976139 0.00436252] -0.3293792332040737 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.65037424 0.50545337] [-0.03976139 0.00436252] -0.21186506822282286 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.0620527440819295 goals: [('bisect', 0, array([0.65037424, 0.50545337]), array([-0.03976139, 0.00436252]), None, None, None, 10, array([0.25276031, 0.5490786 ]), array([-0.03976139, 0.00436252]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.25276031 0.5490786 ] [-0.03976139 0.00436252] -0.0620527440819295 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.21186506822282286 goals: [('bisect', 0, array([0.25276031, 0.5490786 ]), array([ 0.03976139, -0.00436252]), None, None, None, 10, array([0.65037424, 0.50545337]), array([ 0.03976139, -0.00436252]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.65037424 0.50545337] [-0.03976139 0.00436252] -0.21186506822282286 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.65037424 0.50545337] [-0.03976139 0.00436252] -0.21186506822282286 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -2..5 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-2, 5) ---- seed=19 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.21592326 0.54902743] [0.03668081 0.01595363] -0.05335753996074123 BACKWARD SAMPLING FROM 4 [0.36264649 0.61284195] [0.03668081 0.01595363] -0.22492257200551513 BACKWARD SAMPLING FROM -4 [0.06920002 0.48521291] [0.03668081 0.01595363] -0.005127547303597255 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.21592326 0.54902743] [0.03668081 0.01595363] -0.05335753996074123 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.21592326, 0.54902743]), array([0.03668081, 0.01595363]), None, None, None, 10, array([0.58273134, 0.70856374]), array([0.03668081, 0.01595363]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.28708492992102647 goals: [('bisect', 0, array([0.21592326, 0.54902743]), array([0.03668081, 0.01595363]), 5, array([0.3993273 , 0.62879559]), array([0.03668081, 0.01595363]), 10, array([0.58273134, 0.70856374]), array([0.03668081, 0.01595363]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.43453496563722904 goals: [('bisect', 5, array([0.3993273 , 0.62879559]), array([0.03668081, 0.01595363]), 7, array([0.47268891, 0.66070285]), array([0.03668081, 0.01595363]), 10, array([0.58273134, 0.70856374]), array([0.03668081, 0.01595363]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.47268891, 0.66070285]), array([0.03668081, 0.01595363]), 8, array([0.50936972, 0.67665648]), array([0.03668081, 0.01595363]), 10, array([0.58273134, 0.70856374]), array([0.03668081, 0.01595363]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.50936972 0.67665648] [0.03668081 0.01595363] new direction: [-0.02514398 -0.03110917] reversing there [0.03668081 0.01595363] making one step from [0.50936972 0.67665648] [0.03668081 0.01595363] --> [0.48422574 0.64554731] [-0.02514398 -0.03110917] trying new point, [0.48422574 0.64554731] next() call -0.38203753202294766 goals: [('reflect-at', 8, array([0.48422574, 0.64554731]), array([-0.02514398, -0.03110917]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.18094748307481168 goals: [('bisect', 8, array([0.48422574, 0.64554731]), array([-0.02514398, -0.03110917]), None, None, None, 10, array([0.43393777, 0.58332898]), array([-0.02514398, -0.03110917]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.43393777 0.58332898] [-0.02514398 -0.03110917] -0.18094748307481168 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.43393777, 0.58332898]), array([0.02514398, 0.03110917]), None, None, None, 10, array([0.68537759, 0.89442064]), array([0.02514398, 0.03110917]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.43393777, 0.58332898]), array([0.02514398, 0.03110917]), 5, array([0.55965768, 0.73887481]), array([0.02514398, 0.03110917]), 10, array([0.68537759, 0.89442064]), array([0.02514398, 0.03110917]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.38203753202294766 goals: [('bisect', 0, array([0.43393777, 0.58332898]), array([0.02514398, 0.03110917]), 2, array([0.48422574, 0.64554731]), array([0.02514398, 0.03110917]), 5, array([0.55965768, 0.73887481]), array([0.02514398, 0.03110917]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.48422574, 0.64554731]), array([0.02514398, 0.03110917]), 3, array([0.50936972, 0.67665648]), array([0.02514398, 0.03110917]), 5, array([0.55965768, 0.73887481]), array([0.02514398, 0.03110917]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.50936972 0.67665648] [0.02514398 0.03110917] new direction: [-0.03668081 -0.01595363] reversing there [0.02514398 0.03110917] making one step from [0.50936972 0.67665648] [0.02514398 0.03110917] --> [0.47268891 0.66070285] [-0.03668081 -0.01595363] trying new point, [0.47268891 0.66070285] next() call -0.434534965637229 goals: [('reflect-at', 3, array([0.47268891, 0.66070285]), array([-0.03668081, -0.01595363]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05335753996074122 goals: [('bisect', 3, array([0.47268891, 0.66070285]), array([-0.03668081, -0.01595363]), None, None, None, 10, array([0.21592326, 0.54902743]), array([-0.03668081, -0.01595363]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.21592326 0.54902743] [0.03668081 0.01595363] -0.05335753996074123 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.21592326 0.54902743] [0.03668081 0.01595363] -0.05335753996074123 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-5, 2) ---- seed=20 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.03588959 0.69175758] [ 0.03976161 -0.00436054] -0.4602811582030628 BACKWARD SAMPLING FROM 4 [0.19493603 0.67431541] [ 0.03976161 -0.00436054] -0.3988233188094961 BACKWARD SAMPLING FROM -1 [0.00387202 0.69611812] [-0.03976161 -0.00436054] -0.48078647770574867 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.03588959 0.69175758] [ 0.03976161 -0.00436054] -0.4602811582030628 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.36832687557067506 goals: [('bisect', 0, array([0.03588959, 0.69175758]), array([ 0.03976161, -0.00436054]), None, None, None, 10, array([0.43350569, 0.64815216]), array([ 0.03976161, -0.00436054]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.43350569 0.64815216] [ 0.03976161 -0.00436054] -0.36832687557067506 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4602811582030628 goals: [('bisect', 0, array([0.43350569, 0.64815216]), array([-0.03976161, 0.00436054]), None, None, None, 10, array([0.03588959, 0.69175758]), array([-0.03976161, 0.00436054]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.03588959 0.69175758] [ 0.03976161 -0.00436054] -0.4602811582030628 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.03588959 0.69175758] [ 0.03976161 -0.00436054] -0.4602811582030628 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 sampling between (-1, 0) ---- seed=21 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.30227189 0.66391029] [-0.01518562 -0.03700536] -0.38151645748992763 BACKWARD SAMPLING FROM 4 [0.2415294 0.51588884] [-0.01518562 -0.03700536] -0.032323918419140216 BACKWARD SAMPLING FROM -4 [0.17116112 0.6361286 ] [0.0365741 0.01619676] -0.24628550700212182 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.30227189 0.66391029] [-0.01518562 -0.03700536] -0.38151645748992763 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.30227189, 0.66391029]), array([-0.01518562, -0.03700536]), None, None, None, 10, array([0.15041567, 0.29385667]), array([-0.01518562, -0.03700536]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.031189595628649723 goals: [('bisect', 0, array([0.30227189, 0.66391029]), array([-0.01518562, -0.03700536]), 5, array([0.22634378, 0.47888348]), array([-0.01518562, -0.03700536]), 10, array([0.15041567, 0.29385667]), array([-0.01518562, -0.03700536]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.132317524420316 goals: [('bisect', 5, array([0.22634378, 0.47888348]), array([-0.01518562, -0.03700536]), 7, array([0.19597254, 0.40487276]), array([-0.01518562, -0.03700536]), 10, array([0.15041567, 0.29385667]), array([-0.01518562, -0.03700536]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.2345797760024729 goals: [('bisect', 7, array([0.19597254, 0.40487276]), array([-0.01518562, -0.03700536]), 8, array([0.18078691, 0.36786739]), array([-0.01518562, -0.03700536]), 10, array([0.15041567, 0.29385667]), array([-0.01518562, -0.03700536]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.3713075523755122 goals: [('bisect', 8, array([0.18078691, 0.36786739]), array([-0.01518562, -0.03700536]), 9, array([0.16560129, 0.33086203]), array([-0.01518562, -0.03700536]), 10, array([0.15041567, 0.29385667]), array([-0.01518562, -0.03700536]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.15041567 0.29385667] [-0.01518562 -0.03700536] new direction: [-0.02472709 0.03144155] reversing there [-0.01518562 -0.03700536] making one step from [0.15041567 0.29385667] [-0.01518562 -0.03700536] --> [0.12568857 0.32529821] [-0.02472709 0.03144155] trying new point, [0.12568857 0.32529821] next() call -0.3894077367200711 goals: [('reflect-at', 10, array([0.12568857, 0.32529821]), array([-0.02472709, 0.03144155]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.12568857 0.32529821] [-0.02472709 0.03144155] -0.3894077367200711 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.12568857, 0.32529821]), array([ 0.02472709, -0.03144155]), None, None, None, 10, array([0.37295952, 0.01088276]), array([ 0.02472709, -0.03144155]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.12568857, 0.32529821]), array([ 0.02472709, -0.03144155]), 5, array([0.24932405, 0.16809049]), array([ 0.02472709, -0.03144155]), 10, array([0.37295952, 0.01088276]), array([ 0.02472709, -0.03144155]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.12568857, 0.32529821]), array([ 0.02472709, -0.03144155]), 2, array([0.17514276, 0.26241512]), array([ 0.02472709, -0.03144155]), 5, array([0.24932405, 0.16809049]), array([ 0.02472709, -0.03144155]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.12568857, 0.32529821]), array([ 0.02472709, -0.03144155]), 1, array([0.15041567, 0.29385667]), array([ 0.02472709, -0.03144155]), 2, array([0.17514276, 0.26241512]), array([ 0.02472709, -0.03144155]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.15041567 0.29385667] [ 0.02472709 -0.03144155] new direction: [0.01518562 0.03700536] reversing there [ 0.02472709 -0.03144155] making one step from [0.15041567 0.29385667] [ 0.02472709 -0.03144155] --> [0.16560129 0.33086203] [0.01518562 0.03700536] trying new point, [0.16560129 0.33086203] next() call -0.3713075523755125 goals: [('reflect-at', 1, array([0.16560129, 0.33086203]), array([0.01518562, 0.03700536]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3815164574899263 goals: [('bisect', 1, array([0.16560129, 0.33086203]), array([0.01518562, 0.03700536]), None, None, None, 10, array([0.30227189, 0.66391029]), array([0.01518562, 0.03700536]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.30227189 0.66391029] [-0.01518562 -0.03700536] -0.38151645748992763 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.30227189 0.66391029] [-0.01518562 -0.03700536] -0.38151645748992763 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 sampling between (-3, 0) ---- seed=22 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.20846054 0.48168106] [-0.03805125 -0.01233299] -0.025922691544438898 BACKWARD SAMPLING FROM 4 [0.05625556 0.43234909] [-0.03805125 -0.01233299] -0.05879042044402399 BACKWARD SAMPLING FROM -4 [0.36066552 0.53101304] [-0.03805125 -0.01233299] -0.07706241401616516 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.20846054 0.48168106] [-0.03805125 -0.01233299] -0.025922691544438898 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.26560598511461014 goals: [('bisect', 0, array([0.20846054, 0.48168106]), array([-0.03805125, -0.01233299]), None, None, None, 10, array([0.17205191, 0.35835112]), array([ 0.03805125, -0.01233299]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.17205191 0.35835112] [ 0.03805125 -0.01233299] -0.26560598511461014 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.025922691544438898 goals: [('bisect', 0, array([0.17205191, 0.35835112]), array([-0.03805125, 0.01233299]), None, None, None, 10, array([0.20846054, 0.48168106]), array([0.03805125, 0.01233299]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.20846054 0.48168106] [-0.03805125 -0.01233299] -0.025922691544438898 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.20846054 0.48168106] [-0.03805125 -0.01233299] -0.025922691544438898 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-1, 2) ---- seed=23 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.22104536 0.68622209] [-0.03836626 0.01131504] -0.45791383918711764 BACKWARD SAMPLING FROM 0 [0.22104536 0.68622209] [-0.03836626 0.01131504] -0.45791383918711764 BACKWARD SAMPLING FROM -4 [0.3745104 0.64096191] [-0.03836626 0.01131504] -0.3185072622476324 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.22104536 0.68622209] [-0.03836626 0.01131504] -0.45791383918711764 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.22104536, 0.68622209]), array([-0.03836626, 0.01131504]), None, None, None, 10, array([0.16261722, 0.79937253]), array([0.03836626, 0.01131504]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.22104536, 0.68622209]), array([-0.03836626, 0.01131504]), 5, array([0.02921407, 0.74279731]), array([-0.03836626, 0.01131504]), 10, array([0.16261722, 0.79937253]), array([0.03836626, 0.01131504]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.22104536, 0.68622209]), array([-0.03836626, 0.01131504]), 2, array([0.14431285, 0.70885217]), array([-0.03836626, 0.01131504]), 5, array([0.02921407, 0.74279731]), array([-0.03836626, 0.01131504]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.22104536, 0.68622209]), array([-0.03836626, 0.01131504]), 1, array([0.18267911, 0.69753713]), array([-0.03836626, 0.01131504]), 2, array([0.14431285, 0.70885217]), array([-0.03836626, 0.01131504]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.18267911 0.69753713] [-0.03836626 0.01131504] new direction: [0.00426897 0.03977155] reversing there [-0.03836626 0.01131504] making one step from [0.18267911 0.69753713] [-0.03836626 0.01131504] --> [0.18694807 0.73730868] [0.00426897 0.03977155] trying new point, [0.18694807 0.73730868] next() call None goals: [('reflect-at', 1, array([0.18694807, 0.73730868]), array([0.00426897, 0.03977155]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 0... 10 steps to do at 0 -> [from 1, delta=10] targeting -9. goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.24938536408337458 goals: [('bisect', 0, array([0.22104536, 0.68622209]), array([-0.03836626, 0.01131504]), None, None, None, -9, array([0.56634169, 0.58438669]), array([-0.03836626, 0.01131504]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.22104536 0.68622209] [-0.03836626 0.01131504] -0.45791383918711764 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -9 [0.56634169 0.58438669] [-0.03836626 0.01131504] -0.24938536408337458 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.45791383918711764 goals: [('bisect', 0, array([0.56634169, 0.58438669]), array([ 0.03836626, -0.01131504]), None, None, None, -9, array([0.22104536, 0.68622209]), array([ 0.03836626, -0.01131504]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.22104536 0.68622209] [-0.03836626 0.01131504] -0.45791383918711764 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (-1, 0) ---- seed=24 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.13654458 0.38398001] [ 0.01933277 -0.03501777] -0.17758018669487663 BACKWARD SAMPLING FROM 4 [0.16900014 0.35473942] [-0.01277137 0.03790636] -0.2780384655059737 BACKWARD SAMPLING FROM -4 [0.05921351 0.52405108] [ 0.01933277 -0.03501777] -0.008983803050778645 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.13654458 0.38398001] [ 0.01933277 -0.03501777] -0.17758018669487663 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.13654458, 0.38398001]), array([ 0.01933277, -0.03501777]), None, None, None, 10, array([0.32987225, 0.03380233]), array([ 0.01933277, -0.03501777]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.13654458, 0.38398001]), array([ 0.01933277, -0.03501777]), 5, array([0.23320842, 0.20889117]), array([ 0.01933277, -0.03501777]), 10, array([0.32987225, 0.03380233]), array([ 0.01933277, -0.03501777]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.4480575304829881 goals: [('bisect', 0, array([0.13654458, 0.38398001]), array([ 0.01933277, -0.03501777]), 2, array([0.17521011, 0.31394447]), array([ 0.01933277, -0.03501777]), 5, array([0.23320842, 0.20889117]), array([ 0.01933277, -0.03501777]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.17521011, 0.31394447]), array([ 0.01933277, -0.03501777]), 3, array([0.19454288, 0.2789267 ]), array([ 0.01933277, -0.03501777]), 5, array([0.23320842, 0.20889117]), array([ 0.01933277, -0.03501777]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.19454288 0.2789267 ] [ 0.01933277 -0.03501777] new direction: [-0.01277137 0.03790636] reversing there [ 0.01933277 -0.03501777] making one step from [0.19454288 0.2789267 ] [ 0.01933277 -0.03501777] --> [0.18177151 0.31683306] [-0.01277137 0.03790636] trying new point, [0.18177151 0.31683306] next() call -0.43589702340056663 goals: [('reflect-at', 3, array([0.18177151, 0.31683306]), array([-0.01277137, 0.03790636]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.08868070867983059 goals: [('bisect', 3, array([0.18177151, 0.31683306]), array([-0.01277137, 0.03790636]), None, None, None, 10, array([0.0923719 , 0.58217758]), array([-0.01277137, 0.03790636]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.0923719 0.58217758] [-0.01277137 0.03790636] -0.08868070867983059 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.0923719 , 0.58217758]), array([ 0.01277137, -0.03790636]), None, None, None, 10, array([0.22008563, 0.20311399]), array([ 0.01277137, -0.03790636]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.156265316684782 goals: [('bisect', 0, array([0.0923719 , 0.58217758]), array([ 0.01277137, -0.03790636]), 5, array([0.15622876, 0.39264578]), array([ 0.01277137, -0.03790636]), 10, array([0.22008563, 0.20311399]), array([ 0.01277137, -0.03790636]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.43589702340056663 goals: [('bisect', 5, array([0.15622876, 0.39264578]), array([ 0.01277137, -0.03790636]), 7, array([0.18177151, 0.31683306]), array([ 0.01277137, -0.03790636]), 10, array([0.22008563, 0.20311399]), array([ 0.01277137, -0.03790636]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.18177151, 0.31683306]), array([ 0.01277137, -0.03790636]), 8, array([0.19454288, 0.2789267 ]), array([ 0.01277137, -0.03790636]), 10, array([0.22008563, 0.20311399]), array([ 0.01277137, -0.03790636]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.19454288 0.2789267 ] [ 0.01277137 -0.03790636] new direction: [-0.01933277 0.03501777] reversing there [ 0.01277137 -0.03790636] making one step from [0.19454288 0.2789267 ] [ 0.01277137 -0.03790636] --> [0.17521011 0.31394447] [-0.01933277 0.03501777] trying new point, [0.17521011 0.31394447] next() call -0.44805753048298835 goals: [('reflect-at', 8, array([0.17521011, 0.31394447]), array([-0.01933277, 0.03501777]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.17758018669487677 goals: [('bisect', 8, array([0.17521011, 0.31394447]), array([-0.01933277, 0.03501777]), None, None, None, 10, array([0.13654458, 0.38398001]), array([-0.01933277, 0.03501777]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.13654458 0.38398001] [ 0.01933277 -0.03501777] -0.17758018669487663 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.13654458 0.38398001] [ 0.01933277 -0.03501777] -0.17758018669487663 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-2, 1) ---- seed=25 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.87012414 0.58227693] [-0.02068224 -0.03423806] -0.46317666896799403 BACKWARD SAMPLING FROM 4 [0.78739516 0.44532469] [-0.02068224 -0.03423806] -0.34736293667574614 BACKWARD SAMPLING FROM 0 [0.87012414 0.58227693] [-0.02068224 -0.03423806] -0.46317666896799403 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.87012414 0.58227693] [-0.02068224 -0.03423806] -0.46317666896799403 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.87012414, 0.58227693]), array([-0.02068224, -0.03423806]), None, None, None, 10, array([0.66330169, 0.23989633]), array([-0.02068224, -0.03423806]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.3927441882386654 goals: [('bisect', 0, array([0.87012414, 0.58227693]), array([-0.02068224, -0.03423806]), 5, array([0.76671291, 0.41108663]), array([-0.02068224, -0.03423806]), 10, array([0.66330169, 0.23989633]), array([-0.02068224, -0.03423806]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.76671291, 0.41108663]), array([-0.02068224, -0.03423806]), 7, array([0.72534842, 0.34261051]), array([-0.02068224, -0.03423806]), 10, array([0.66330169, 0.23989633]), array([-0.02068224, -0.03423806]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.46785931365597694 goals: [('bisect', 5, array([0.76671291, 0.41108663]), array([-0.02068224, -0.03423806]), 6, array([0.74603067, 0.37684857]), array([-0.02068224, -0.03423806]), 7, array([0.72534842, 0.34261051]), array([-0.02068224, -0.03423806]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.72534842 0.34261051] [-0.02068224 -0.03423806] new direction: [-0.02832621 -0.02824227] reversing there [-0.02068224 -0.03423806] making one step from [0.72534842 0.34261051] [-0.02068224 -0.03423806] --> [0.69702221 0.31436824] [-0.02832621 -0.02824227] trying new point, [0.69702221 0.31436824] next() call None goals: [('reflect-at', 7, array([0.69702221, 0.31436824]), array([-0.02832621, -0.02824227]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 6... 4 steps to do at 6 -> [from 7, delta=4] targeting 3. goals: [('sample-at', 3)] target is on track, returning interpolation at 3... [0.8080774 0.47956275] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 6 [0.74603067 0.37684857] [-0.02068224 -0.03423806] -0.46785931365597694 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 6)] not done yet, continue expanding to 6... goals: [('expand-to', 6), ('sample-at', 6)] next() call -0.4631766689679939 goals: [('bisect', 0, array([0.74603067, 0.37684857]), array([0.02068224, 0.03423806]), None, None, None, 6, array([0.87012414, 0.58227693]), array([0.02068224, 0.03423806]), 1), ('sample-at', 6)] bisecting ... 0 None 6 successfully went all the way in one jump! goals: [('sample-at', 6)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.87012414 0.58227693] [-0.02068224 -0.03423806] -0.46317666896799403 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.87012414 0.58227693] [-0.02068224 -0.03423806] -0.46317666896799403 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (0, 3) ---- seed=26 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.30793495 0.51939148] [-0.03974779 0.00448475] -0.05211233589467034 BACKWARD SAMPLING FROM 4 [0.14894378 0.53733047] [-0.03974779 0.00448475] -0.028511673439282487 BACKWARD SAMPLING FROM -4 [0.46692612 0.50145249] [-0.03974779 0.00448475] -0.10903637436373395 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.30793495 0.51939148] [-0.03974779 0.00448475] -0.05211233589467034 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.055592009781843664 goals: [('bisect', 0, array([0.30793495, 0.51939148]), array([-0.03974779, 0.00448475]), None, None, None, 10, array([0.08954298, 0.56423895]), array([0.03974779, 0.00448475]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.08954298 0.56423895] [0.03974779 0.00448475] -0.055592009781843664 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.052112335894670306 goals: [('bisect', 0, array([0.08954298, 0.56423895]), array([-0.03974779, -0.00448475]), None, None, None, 10, array([0.30793495, 0.51939148]), array([ 0.03974779, -0.00448475]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.30793495 0.51939148] [-0.03974779 0.00448475] -0.05211233589467034 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.30793495 0.51939148] [-0.03974779 0.00448475] -0.05211233589467034 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -1..6 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-1, 6) ---- seed=27 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.18675584 0.32556672] [ 0.03615567 -0.01711045] -0.3977760002874148 BACKWARD SAMPLING FROM 1 [0.22291152 0.30845627] [ 0.03615567 -0.01711045] -0.48345727148351275 BACKWARD SAMPLING FROM -4 [0.04213315 0.3940085 ] [ 0.03615567 -0.01711045] -0.14131507902115056 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.18675584 0.32556672] [ 0.03615567 -0.01711045] -0.3977760002874148 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.18675584, 0.32556672]), array([ 0.03615567, -0.01711045]), None, None, None, 10, array([0.54831257, 0.15446226]), array([ 0.03615567, -0.01711045]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.18675584, 0.32556672]), array([ 0.03615567, -0.01711045]), 5, array([0.36753421, 0.24001449]), array([ 0.03615567, -0.01711045]), 10, array([0.54831257, 0.15446226]), array([ 0.03615567, -0.01711045]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.18675584, 0.32556672]), array([ 0.03615567, -0.01711045]), 2, array([0.25906719, 0.29134583]), array([ 0.03615567, -0.01711045]), 5, array([0.36753421, 0.24001449]), array([ 0.03615567, -0.01711045]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.48345727148351275 goals: [('bisect', 0, array([0.18675584, 0.32556672]), array([ 0.03615567, -0.01711045]), 1, array([0.22291152, 0.30845627]), array([ 0.03615567, -0.01711045]), 2, array([0.25906719, 0.29134583]), array([ 0.03615567, -0.01711045]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.25906719 0.29134583] [ 0.03615567 -0.01711045] new direction: [0.03637517 0.01663872] reversing there [ 0.03615567 -0.01711045] making one step from [0.25906719 0.29134583] [ 0.03615567 -0.01711045] --> [0.29544236 0.30798455] [0.03637517 0.01663872] trying new point, [0.29544236 0.30798455] next() call None goals: [('reflect-at', 2, array([0.29544236, 0.30798455]), array([0.03637517, 0.01663872]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 1... 9 steps to do at 1 -> [from 2, delta=9] targeting -7. goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.039546759765486834 goals: [('bisect', 0, array([0.18675584, 0.32556672]), array([ 0.03615567, -0.01711045]), None, None, None, -7, array([0.06633386, 0.44533984]), array([-0.03615567, -0.01711045]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 1 [0.22291152 0.30845627] [ 0.03615567 -0.01711045] -0.48345727148351275 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 1)] not done yet, continue expanding to 1... goals: [('expand-to', 1), ('sample-at', 1)] next() call -0.3977760002874148 goals: [('bisect', 0, array([0.22291152, 0.30845627]), array([-0.03615567, 0.01711045]), None, None, None, 1, array([0.18675584, 0.32556672]), array([-0.03615567, 0.01711045]), 1), ('sample-at', 1)] bisecting ... 0 None 1 successfully went all the way in one jump! goals: [('sample-at', 1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -7 [0.06633386 0.44533984] [-0.03615567 -0.01711045] -0.039546759765486834 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.39777600028741444 goals: [('bisect', 0, array([0.06633386, 0.44533984]), array([0.03615567, 0.01711045]), None, None, None, -7, array([0.18675584, 0.32556672]), array([-0.03615567, 0.01711045]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.18675584 0.32556672] [ 0.03615567 -0.01711045] -0.3977760002874148 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (-1, 0) ---- seed=28 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.72901374 0.5612396 ] [ 0.03990182 -0.0028009 ] -0.31260912938878843 BACKWARD SAMPLING FROM 4 [0.88862101 0.55003599] [ 0.03990182 -0.0028009 ] -0.4261186566116443 BACKWARD SAMPLING FROM -4 [0.56940648 0.57244321] [ 0.03990182 -0.0028009 ] -0.2277121021981689 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.72901374 0.5612396 ] [ 0.03990182 -0.0028009 ] -0.31260912938878843 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.39396757347735606 goals: [('bisect', 0, array([0.72901374, 0.5612396 ]), array([ 0.03990182, -0.0028009 ]), None, None, None, 10, array([0.87196809, 0.53323058]), array([-0.03990182, -0.0028009 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.87196809 0.53323058] [-0.03990182 -0.0028009 ] -0.39396757347735606 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3126091293887884 goals: [('bisect', 0, array([0.87196809, 0.53323058]), array([0.03990182, 0.0028009 ]), None, None, None, 10, array([0.72901374, 0.5612396 ]), array([-0.03990182, 0.0028009 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.72901374 0.5612396 ] [ 0.03990182 -0.0028009 ] -0.31260912938878843 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.72901374 0.5612396 ] [ 0.03990182 -0.0028009 ] -0.31260912938878843 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-7, 0) ---- seed=29 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.45271906 0.54229687] [-0.01950188 0.03492387] -0.12484009194327632 BACKWARD SAMPLING FROM 4 [0.37471154 0.68199235] [-0.01950188 0.03492387] -0.4842195850803701 BACKWARD SAMPLING FROM -4 [0.53072657 0.40260139] [-0.01950188 0.03492387] -0.25941645597282265 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.45271906 0.54229687] [-0.01950188 0.03492387] -0.12484009194327632 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.45271906, 0.54229687]), array([-0.01950188, 0.03492387]), None, None, None, 10, array([0.25770027, 0.89153558]), array([-0.01950188, 0.03492387]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.45271906, 0.54229687]), array([-0.01950188, 0.03492387]), 5, array([0.35520966, 0.71691623]), array([-0.01950188, 0.03492387]), 10, array([0.25770027, 0.89153558]), array([-0.01950188, 0.03492387]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.24278535636599288 goals: [('bisect', 0, array([0.45271906, 0.54229687]), array([-0.01950188, 0.03492387]), 2, array([0.4137153 , 0.61214461]), array([-0.01950188, 0.03492387]), 5, array([0.35520966, 0.71691623]), array([-0.01950188, 0.03492387]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.3480663501867237 goals: [('bisect', 2, array([0.4137153 , 0.61214461]), array([-0.01950188, 0.03492387]), 3, array([0.39421342, 0.64706848]), array([-0.01950188, 0.03492387]), 5, array([0.35520966, 0.71691623]), array([-0.01950188, 0.03492387]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.48421958508036966 goals: [('bisect', 3, array([0.39421342, 0.64706848]), array([-0.01950188, 0.03492387]), 4, array([0.37471154, 0.68199235]), array([-0.01950188, 0.03492387]), 5, array([0.35520966, 0.71691623]), array([-0.01950188, 0.03492387]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.35520966 0.71691623] [-0.01950188 0.03492387] new direction: [-0.01424107 0.03737903] reversing there [-0.01950188 0.03492387] making one step from [0.35520966 0.71691623] [-0.01950188 0.03492387] --> [0.34096859 0.75429526] [-0.01424107 0.03737903] trying new point, [0.34096859 0.75429526] next() call None goals: [('reflect-at', 5, array([0.34096859, 0.75429526]), array([-0.01424107, 0.03737903]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 4... 6 steps to do at 4 -> [from 5, delta=6] targeting -1. goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.1121758213412904 goals: [('bisect', 0, array([0.45271906, 0.54229687]), array([-0.01950188, 0.03492387]), None, None, None, -1, array([0.47222094, 0.507373 ]), array([-0.01950188, 0.03492387]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 4 [0.37471154 0.68199235] [-0.01950188 0.03492387] -0.48421958508036966 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 4)] not done yet, continue expanding to 4... goals: [('expand-to', 4), ('sample-at', 4)] next() call -0.12484009194327635 goals: [('bisect', 0, array([0.37471154, 0.68199235]), array([ 0.01950188, -0.03492387]), None, None, None, 4, array([0.45271906, 0.54229687]), array([ 0.01950188, -0.03492387]), 1), ('sample-at', 4)] bisecting ... 0 None 4 successfully went all the way in one jump! goals: [('sample-at', 4)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -1 [0.47222094 0.507373 ] [-0.01950188 0.03492387] -0.1121758213412904 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.12484009194327632 goals: [('bisect', 0, array([0.47222094, 0.507373 ]), array([ 0.01950188, -0.03492387]), None, None, None, -1, array([0.45271906, 0.54229687]), array([ 0.01950188, -0.03492387]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.45271906 0.54229687] [-0.01950188 0.03492387] -0.12484009194327632 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=30 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.64414354 0.38074849] [ 0.01245444 -0.03801167] -0.38522198158313303 BACKWARD SAMPLING FROM 0 [0.64414354 0.38074849] [ 0.01245444 -0.03801167] -0.38522198158313303 BACKWARD SAMPLING FROM -4 [0.59432576 0.53279516] [ 0.01245444 -0.03801167] -0.19005558113344231 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.64414354 0.38074849] [ 0.01245444 -0.03801167] -0.38522198158313303 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), None, None, None, 10, array([7.68687984e-01, 6.31822760e-04]), array([ 0.01245444, -0.03801167]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), 5, array([0.70641576, 0.19069016]), array([ 0.01245444, -0.03801167]), 10, array([7.68687984e-01, 6.31822760e-04]), array([ 0.01245444, -0.03801167]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), 2, array([0.66905243, 0.30472516]), array([ 0.01245444, -0.03801167]), 5, array([0.70641576, 0.19069016]), array([ 0.01245444, -0.03801167]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), 1, array([0.65659798, 0.34273682]), array([ 0.01245444, -0.03801167]), 2, array([0.66905243, 0.30472516]), array([ 0.01245444, -0.03801167]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.65659798 0.34273682] [ 0.01245444 -0.03801167] new direction: [-0.03320369 -0.02230504] reversing there [ 0.01245444 -0.03801167] making one step from [0.65659798 0.34273682] [ 0.01245444 -0.03801167] --> [0.62339429 0.32043178] [-0.03320369 -0.02230504] trying new point, [0.62339429 0.32043178] next() call None goals: [('reflect-at', 1, array([0.62339429, 0.32043178]), array([-0.03320369, -0.02230504]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 0... 10 steps to do at 0 -> [from 1, delta=10] targeting -9. goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call None goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), None, None, None, -9, array([0.53205353, 0.72285349]), array([ 0.01245444, -0.03801167]), -1), ('sample-at', -9)] bisecting ... 0 None -9 continue bisect at -5 next() call -0.2319571892828859 goals: [('bisect', 0, array([0.64414354, 0.38074849]), array([ 0.01245444, -0.03801167]), -5, array([0.58187131, 0.57080682]), array([ 0.01245444, -0.03801167]), -9, array([0.53205353, 0.72285349]), array([ 0.01245444, -0.03801167]), -1), ('sample-at', -9)] bisecting ... 0 -5 -9 continue bisect at -7 next() call -0.42459225549601265 goals: [('bisect', -5, array([0.58187131, 0.57080682]), array([ 0.01245444, -0.03801167]), -7, array([0.55696242, 0.64683016]), array([ 0.01245444, -0.03801167]), -9, array([0.53205353, 0.72285349]), array([ 0.01245444, -0.03801167]), -1), ('sample-at', -9)] bisecting ... -5 -7 -9 continue bisect at -8 next() call None goals: [('bisect', -7, array([0.55696242, 0.64683016]), array([ 0.01245444, -0.03801167]), -8, array([0.54450798, 0.68484182]), array([ 0.01245444, -0.03801167]), -9, array([0.53205353, 0.72285349]), array([ 0.01245444, -0.03801167]), -1), ('sample-at', -9)] bisecting ... -7 -8 -9 bisecting gave reflection point -8 [0.54450798 0.68484182] [ 0.01245444 -0.03801167] new direction: [-0.01383172 -0.03753243] reversing there [ 0.01245444 -0.03801167] making one step from [0.54450798 0.68484182] [ 0.01245444 -0.03801167] --> [0.53067626 0.64730939] [0.01383172 0.03753243] trying new point, [0.53067626 0.64730939] next() call -0.4120593466482403 goals: [('reflect-at', -8, array([0.53067626, 0.64730939]), array([0.01383172, 0.03753243]), -1), ('sample-at', -9)] goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.2842013875070083 goals: [('bisect', -8, array([0.53067626, 0.64730939]), array([0.01383172, 0.03753243]), None, None, None, -9, array([0.51684455, 0.60977695]), array([0.01383172, 0.03753243]), -1), ('sample-at', -9)] bisecting ... -8 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.64414354 0.38074849] [ 0.01245444 -0.03801167] -0.38522198158313303 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -9 [0.51684455 0.60977695] [0.01383172 0.03753243] -0.2842013875070083 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call None goals: [('bisect', 0, array([0.51684455, 0.60977695]), array([-0.01383172, -0.03753243]), None, None, None, -9, array([0.64132998, 0.94756887]), array([-0.01383172, -0.03753243]), -1), ('sample-at', -9)] bisecting ... 0 None -9 continue bisect at -5 next() call None goals: [('bisect', 0, array([0.51684455, 0.60977695]), array([-0.01383172, -0.03753243]), -5, array([0.58600312, 0.79743913]), array([-0.01383172, -0.03753243]), -9, array([0.64132998, 0.94756887]), array([-0.01383172, -0.03753243]), -1), ('sample-at', -9)] bisecting ... 0 -5 -9 continue bisect at -3 next() call None goals: [('bisect', 0, array([0.51684455, 0.60977695]), array([-0.01383172, -0.03753243]), -3, array([0.55833969, 0.72237426]), array([-0.01383172, -0.03753243]), -5, array([0.58600312, 0.79743913]), array([-0.01383172, -0.03753243]), -1), ('sample-at', -9)] bisecting ... 0 -3 -5 continue bisect at -2 next() call None goals: [('bisect', 0, array([0.51684455, 0.60977695]), array([-0.01383172, -0.03753243]), -2, array([0.54450798, 0.68484182]), array([-0.01383172, -0.03753243]), -3, array([0.55833969, 0.72237426]), array([-0.01383172, -0.03753243]), -1), ('sample-at', -9)] bisecting ... 0 -2 -3 continue bisect at -1 next() call -0.4120593466482403 goals: [('bisect', 0, array([0.51684455, 0.60977695]), array([-0.01383172, -0.03753243]), -1, array([0.53067626, 0.64730939]), array([-0.01383172, -0.03753243]), -2, array([0.54450798, 0.68484182]), array([-0.01383172, -0.03753243]), -1), ('sample-at', -9)] bisecting ... 0 -1 -2 bisecting gave reflection point -2 [0.54450798 0.68484182] [-0.01383172 -0.03753243] new direction: [ 0.01245444 -0.03801167] reversing there [-0.01383172 -0.03753243] making one step from [0.54450798 0.68484182] [-0.01383172 -0.03753243] --> [0.55696242 0.64683016] [-0.01245444 0.03801167] trying new point, [0.55696242 0.64683016] next() call -0.42459225549601265 goals: [('reflect-at', -2, array([0.55696242, 0.64683016]), array([-0.01245444, 0.03801167]), -1), ('sample-at', -9)] goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.38522198158313203 goals: [('bisect', -2, array([0.55696242, 0.64683016]), array([-0.01245444, 0.03801167]), None, None, None, -9, array([0.64414354, 0.38074849]), array([-0.01245444, 0.03801167]), -1), ('sample-at', -9)] bisecting ... -2 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.64414354 0.38074849] [ 0.01245444 -0.03801167] -0.38522198158313303 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -15..0 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-15, 0) ---- seed=31 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.07533272 0.54353469] [0.03312527 0.02242134] -0.026528374387011194 BACKWARD SAMPLING FROM 4 [0.20783379 0.63322006] [0.03312527 0.02242134] -0.24344226375583042 BACKWARD SAMPLING FROM -4 [0.05716835 0.45384931] [-0.03312527 0.02242134] -0.028257682631574084 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.07533272 0.54353469] [0.03312527 0.02242134] -0.026528374387011194 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.07533272, 0.54353469]), array([0.03312527, 0.02242134]), None, None, None, 10, array([0.4065854 , 0.76774813]), array([0.03312527, 0.02242134]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.33183373572512614 goals: [('bisect', 0, array([0.07533272, 0.54353469]), array([0.03312527, 0.02242134]), 5, array([0.24095906, 0.65564141]), array([0.03312527, 0.02242134]), 10, array([0.4065854 , 0.76774813]), array([0.03312527, 0.02242134]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.24095906, 0.65564141]), array([0.03312527, 0.02242134]), 7, array([0.30720959, 0.7004841 ]), array([0.03312527, 0.02242134]), 10, array([0.4065854 , 0.76774813]), array([0.03312527, 0.02242134]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4338904075452583 goals: [('bisect', 5, array([0.24095906, 0.65564141]), array([0.03312527, 0.02242134]), 6, array([0.27408433, 0.67806275]), array([0.03312527, 0.02242134]), 7, array([0.30720959, 0.7004841 ]), array([0.03312527, 0.02242134]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.30720959 0.7004841 ] [0.03312527 0.02242134] new direction: [0.01886481 0.03527207] reversing there [0.03312527 0.02242134] making one step from [0.30720959 0.7004841 ] [0.03312527 0.02242134] --> [0.32607441 0.73575617] [0.01886481 0.03527207] trying new point, [0.32607441 0.73575617] next() call None goals: [('reflect-at', 7, array([0.32607441, 0.73575617]), array([0.01886481, 0.03527207]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 6... 4 steps to do at 6 -> [from 7, delta=4] targeting 3. goals: [('sample-at', 3)] target is on track, returning interpolation at 3... [0.17470852 0.61079872] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 6 [0.27408433 0.67806275] [0.03312527 0.02242134] -0.4338904075452583 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 6)] not done yet, continue expanding to 6... goals: [('expand-to', 6), ('sample-at', 6)] next() call -0.026528374387011194 goals: [('bisect', 0, array([0.27408433, 0.67806275]), array([-0.03312527, -0.02242134]), None, None, None, 6, array([0.07533272, 0.54353469]), array([-0.03312527, -0.02242134]), 1), ('sample-at', 6)] bisecting ... 0 None 6 successfully went all the way in one jump! goals: [('sample-at', 6)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.07533272 0.54353469] [0.03312527 0.02242134] -0.026528374387011194 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.07533272 0.54353469] [0.03312527 0.02242134] -0.026528374387011194 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -2..5 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-2, 5) ---- seed=32 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.60910917 0.59655344] [-0.00874449 -0.03903247] -0.3020390652147834 BACKWARD SAMPLING FROM 4 [0.57413119 0.44042355] [-0.00874449 -0.03903247] -0.20918022823021926 BACKWARD SAMPLING FROM -1 [0.61785366 0.63558591] [-0.00874449 -0.03903247] -0.42066580339605575 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.60910917 0.59655344] [-0.00874449 -0.03903247] -0.3020390652147834 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.60910917, 0.59655344]), array([-0.00874449, -0.03903247]), None, None, None, 10, array([0.52166422, 0.20622872]), array([-0.00874449, -0.03903247]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.28137754791921 goals: [('bisect', 0, array([0.60910917, 0.59655344]), array([-0.00874449, -0.03903247]), 5, array([0.5653867 , 0.40139108]), array([-0.00874449, -0.03903247]), 10, array([0.52166422, 0.20622872]), array([-0.00874449, -0.03903247]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.5653867 , 0.40139108]), array([-0.00874449, -0.03903247]), 7, array([0.54789771, 0.32332614]), array([-0.00874449, -0.03903247]), 10, array([0.52166422, 0.20622872]), array([-0.00874449, -0.03903247]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.39173967918225305 goals: [('bisect', 5, array([0.5653867 , 0.40139108]), array([-0.00874449, -0.03903247]), 6, array([0.5566422 , 0.36235861]), array([-0.00874449, -0.03903247]), 7, array([0.54789771, 0.32332614]), array([-0.00874449, -0.03903247]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.54789771 0.32332614] [-0.00874449 -0.03903247] new direction: [-0.03993719 0.00224073] reversing there [-0.00874449 -0.03903247] making one step from [0.54789771 0.32332614] [-0.00874449 -0.03903247] --> [0.50796052 0.32556687] [-0.03993719 0.00224073] trying new point, [0.50796052 0.32556687] next() call None goals: [('reflect-at', 7, array([0.50796052, 0.32556687]), array([-0.03993719, 0.00224073]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 6... 4 steps to do at 6 -> [from 7, delta=4] targeting 3. goals: [('sample-at', 3)] target is on track, returning interpolation at 3... [0.58287569 0.47945602] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 6 [0.5566422 0.36235861] [-0.00874449 -0.03903247] -0.39173967918225305 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 6)] not done yet, continue expanding to 6... goals: [('expand-to', 6), ('sample-at', 6)] next() call -0.3020390652147834 goals: [('bisect', 0, array([0.5566422 , 0.36235861]), array([0.00874449, 0.03903247]), None, None, None, 6, array([0.60910917, 0.59655344]), array([0.00874449, 0.03903247]), 1), ('sample-at', 6)] bisecting ... 0 None 6 successfully went all the way in one jump! goals: [('sample-at', 6)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.60910917 0.59655344] [-0.00874449 -0.03903247] -0.3020390652147834 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.60910917 0.59655344] [-0.00874449 -0.03903247] -0.3020390652147834 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd sampling between (-1, 0) ---- seed=33 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.24851013 0.44997542] [-0.03413556 0.02085099] -0.06215937295762336 BACKWARD SAMPLING FROM 4 [0.1119679 0.53337938] [-0.03413556 0.02085099] -0.020195695214487927 BACKWARD SAMPLING FROM -4 [0.38505235 0.36657146] [-0.03413556 0.02085099] -0.2966723492317622 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.24851013 0.44997542] [-0.03413556 0.02085099] -0.06215937295762336 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.31828011334541667 goals: [('bisect', 0, array([0.24851013, 0.44997542]), array([-0.03413556, 0.02085099]), None, None, None, 10, array([0.09284544, 0.65848532]), array([0.03413556, 0.02085099]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.09284544 0.65848532] [0.03413556 0.02085099] -0.31828011334541667 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.062159372957623346 goals: [('bisect', 0, array([0.09284544, 0.65848532]), array([-0.03413556, -0.02085099]), None, None, None, 10, array([0.24851013, 0.44997542]), array([ 0.03413556, -0.02085099]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.24851013 0.44997542] [-0.03413556 0.02085099] -0.06215937295762336 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.24851013 0.44997542] [-0.03413556 0.02085099] -0.06215937295762336 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -2..5 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-2, 5) ---- seed=34 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.09270376 0.63289269] [-0.02461744 -0.03152747] -0.22505281963979504 BACKWARD SAMPLING FROM 4 [0.00576602 0.5067828 ] [ 0.02461744 -0.03152747] -0.0005917034778301611 BACKWARD SAMPLING FROM -2 [0.14193864 0.69594763] [-0.02461744 -0.03152747] -0.49001670522119156 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.09270376 0.63289269] [-0.02461744 -0.03152747] -0.22505281963979504 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.42756666673695237 goals: [('bisect', 0, array([0.09270376, 0.63289269]), array([-0.02461744, -0.03152747]), None, None, None, 10, array([0.15347069, 0.31761797]), array([ 0.02461744, -0.03152747]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.15347069 0.31761797] [ 0.02461744 -0.03152747] -0.42756666673695237 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.22505281963979504 goals: [('bisect', 0, array([0.15347069, 0.31761797]), array([-0.02461744, 0.03152747]), None, None, None, 10, array([0.09270376, 0.63289269]), array([0.02461744, 0.03152747]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.09270376 0.63289269] [-0.02461744 -0.03152747] -0.22505281963979504 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.09270376 0.63289269] [-0.02461744 -0.03152747] -0.22505281963979504 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -1..6 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-1, 6) ---- seed=35 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.14790391 0.51579028] [-0.00640292 0.03948421] -0.01405444290079977 BACKWARD SAMPLING FROM 4 [0.12229223 0.6737271 ] [-0.00640292 0.03948421] -0.3847415272663998 BACKWARD SAMPLING FROM -4 [0.17351559 0.35785345] [-0.00640292 0.03948421] -0.26762436346562096 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.14790391 0.51579028] [-0.00640292 0.03948421] -0.01405444290079977 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.14790391, 0.51579028]), array([-0.00640292, 0.03948421]), None, None, None, 10, array([0.08387471, 0.91063235]), array([-0.00640292, 0.03948421]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.14790391, 0.51579028]), array([-0.00640292, 0.03948421]), 5, array([0.11588931, 0.71321131]), array([-0.00640292, 0.03948421]), 10, array([0.08387471, 0.91063235]), array([-0.00640292, 0.03948421]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.12136585946729678 goals: [('bisect', 0, array([0.14790391, 0.51579028]), array([-0.00640292, 0.03948421]), 2, array([0.13509807, 0.59475869]), array([-0.00640292, 0.03948421]), 5, array([0.11588931, 0.71321131]), array([-0.00640292, 0.03948421]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.23354566196277238 goals: [('bisect', 2, array([0.13509807, 0.59475869]), array([-0.00640292, 0.03948421]), 3, array([0.12869515, 0.6342429 ]), array([-0.00640292, 0.03948421]), 5, array([0.11588931, 0.71321131]), array([-0.00640292, 0.03948421]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.3847415272663993 goals: [('bisect', 3, array([0.12869515, 0.6342429 ]), array([-0.00640292, 0.03948421]), 4, array([0.12229223, 0.6737271 ]), array([-0.00640292, 0.03948421]), 5, array([0.11588931, 0.71321131]), array([-0.00640292, 0.03948421]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.11588931 0.71321131] [-0.00640292 0.03948421] new direction: [-0.01879284 -0.03531047] reversing there [-0.00640292 0.03948421] making one step from [0.11588931 0.71321131] [-0.00640292 0.03948421] --> [0.09709647 0.67790084] [-0.01879284 -0.03531047] trying new point, [0.09709647 0.67790084] next() call -0.400322736376469 goals: [('reflect-at', 5, array([0.09709647, 0.67790084]), array([-0.01879284, -0.03531047]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -2.7636297378795028e-05 goals: [('bisect', 5, array([0.09709647, 0.67790084]), array([-0.01879284, -0.03531047]), None, None, None, 10, array([0.00313227, 0.5013485 ]), array([-0.01879284, -0.03531047]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.00313227 0.5013485 ] [-0.01879284 -0.03531047] -2.7636297378795028e-05 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.00313227, 0.5013485 ]), array([0.01879284, 0.03531047]), None, None, None, 10, array([0.19106067, 0.85445318]), array([0.01879284, 0.03531047]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.400322736376469 goals: [('bisect', 0, array([0.00313227, 0.5013485 ]), array([0.01879284, 0.03531047]), 5, array([0.09709647, 0.67790084]), array([0.01879284, 0.03531047]), 10, array([0.19106067, 0.85445318]), array([0.01879284, 0.03531047]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.09709647, 0.67790084]), array([0.01879284, 0.03531047]), 7, array([0.13468215, 0.74852178]), array([0.01879284, 0.03531047]), 10, array([0.19106067, 0.85445318]), array([0.01879284, 0.03531047]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.09709647, 0.67790084]), array([0.01879284, 0.03531047]), 6, array([0.11588931, 0.71321131]), array([0.01879284, 0.03531047]), 7, array([0.13468215, 0.74852178]), array([0.01879284, 0.03531047]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.11588931 0.71321131] [0.01879284 0.03531047] new direction: [ 0.00640292 -0.03948421] reversing there [0.01879284 0.03531047] making one step from [0.11588931 0.71321131] [0.01879284 0.03531047] --> [0.12229223 0.6737271 ] [ 0.00640292 -0.03948421] trying new point, [0.12229223 0.6737271 ] next() call -0.3847415272663989 goals: [('reflect-at', 6, array([0.12229223, 0.6737271 ]), array([ 0.00640292, -0.03948421]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.014054442900799765 goals: [('bisect', 6, array([0.12229223, 0.6737271 ]), array([ 0.00640292, -0.03948421]), None, None, None, 10, array([0.14790391, 0.51579028]), array([ 0.00640292, -0.03948421]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.14790391 0.51579028] [-0.00640292 0.03948421] -0.01405444290079977 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.14790391 0.51579028] [-0.00640292 0.03948421] -0.01405444290079977 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=36 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.72850719 0.60161421] [0.03837737 0.0112773 ] -0.39442946604858076 BACKWARD SAMPLING FROM 4 [0.7232834 0.53653594] [-0.02732618 -0.02921096] -0.27825536957064223 BACKWARD SAMPLING FROM -4 [0.57499771 0.55650503] [0.03837737 0.0112773 ] -0.20522140882168874 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.72850719 0.60161421] [0.03837737 0.0112773 ] -0.39442946604858076 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.72850719, 0.60161421]), array([0.03837737, 0.0112773 ]), None, None, None, 10, array([0.8877191 , 0.71438717]), array([-0.03837737, 0.0112773 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.72850719, 0.60161421]), array([0.03837737, 0.0112773 ]), 5, array([0.92039405, 0.65800069]), array([0.03837737, 0.0112773 ]), 10, array([0.8877191 , 0.71438717]), array([-0.03837737, 0.0112773 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.72850719, 0.60161421]), array([0.03837737, 0.0112773 ]), 2, array([0.80526193, 0.6241688 ]), array([0.03837737, 0.0112773 ]), 5, array([0.92039405, 0.65800069]), array([0.03837737, 0.0112773 ]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.4533621249956772 goals: [('bisect', 0, array([0.72850719, 0.60161421]), array([0.03837737, 0.0112773 ]), 1, array([0.76688456, 0.61289151]), array([0.03837737, 0.0112773 ]), 2, array([0.80526193, 0.6241688 ]), array([0.03837737, 0.0112773 ]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.80526193 0.6241688 ] [0.03837737 0.0112773 ] new direction: [-0.02732618 -0.02921096] reversing there [0.03837737 0.0112773 ] making one step from [0.80526193 0.6241688 ] [0.03837737 0.0112773 ] --> [0.77793575 0.59495785] [-0.02732618 -0.02921096] trying new point, [0.77793575 0.59495785] next() call -0.41530443247762705 goals: [('reflect-at', 2, array([0.77793575, 0.59495785]), array([-0.02732618, -0.02921096]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3969974267345493 goals: [('bisect', 2, array([0.77793575, 0.59495785]), array([-0.02732618, -0.02921096]), None, None, None, 10, array([0.55932632, 0.3612702 ]), array([-0.02732618, -0.02921096]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.55932632 0.3612702 ] [-0.02732618 -0.02921096] -0.3969974267345493 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.55932632, 0.3612702 ]), array([0.02732618, 0.02921096]), None, None, None, 10, array([0.83258811, 0.65337976]), array([0.02732618, 0.02921096]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.24284891598495364 goals: [('bisect', 0, array([0.55932632, 0.3612702 ]), array([0.02732618, 0.02921096]), 5, array([0.69595722, 0.50732498]), array([0.02732618, 0.02921096]), 10, array([0.83258811, 0.65337976]), array([0.02732618, 0.02921096]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.33574054173486656 goals: [('bisect', 5, array([0.69595722, 0.50732498]), array([0.02732618, 0.02921096]), 7, array([0.75060958, 0.56574689]), array([0.02732618, 0.02921096]), 10, array([0.83258811, 0.65337976]), array([0.02732618, 0.02921096]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4153044324776268 goals: [('bisect', 7, array([0.75060958, 0.56574689]), array([0.02732618, 0.02921096]), 8, array([0.77793575, 0.59495785]), array([0.02732618, 0.02921096]), 10, array([0.83258811, 0.65337976]), array([0.02732618, 0.02921096]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.77793575, 0.59495785]), array([0.02732618, 0.02921096]), 9, array([0.80526193, 0.6241688 ]), array([0.02732618, 0.02921096]), 10, array([0.83258811, 0.65337976]), array([0.02732618, 0.02921096]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.80526193 0.6241688 ] [0.02732618 0.02921096] new direction: [-0.03837737 -0.0112773 ] reversing there [0.02732618 0.02921096] making one step from [0.80526193 0.6241688 ] [0.02732618 0.02921096] --> [0.76688456 0.61289151] [-0.03837737 -0.0112773 ] trying new point, [0.76688456 0.61289151] next() call -0.4533621249956771 goals: [('reflect-at', 9, array([0.76688456, 0.61289151]), array([-0.03837737, -0.0112773 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.39442946604858065 goals: [('bisect', 9, array([0.76688456, 0.61289151]), array([-0.03837737, -0.0112773 ]), None, None, None, 10, array([0.72850719, 0.60161421]), array([-0.03837737, -0.0112773 ]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.72850719 0.60161421] [0.03837737 0.0112773 ] -0.39442946604858076 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.72850719 0.60161421] [0.03837737 0.0112773 ] -0.39442946604858076 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (-1, 0) ---- seed=37 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.9444966 0.46409817] [0.03939692 0.00691971] -0.46214868000795534 BACKWARD SAMPLING FROM 4 [0.8979157 0.491777 ] [-0.03939692 0.00691971] -0.40397152289437405 BACKWARD SAMPLING FROM -4 [0.7869089 0.43641935] [0.03939692 0.00691971] -0.360144051361222 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.9444966 0.46409817] [0.03939692 0.00691971] -0.46214868000795534 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.23267087672305614 goals: [('bisect', 0, array([0.9444966 , 0.46409817]), array([0.03939692, 0.00691971]), None, None, None, 10, array([0.66153415, 0.53329524]), array([-0.03939692, 0.00691971]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.66153415 0.53329524] [-0.03939692 0.00691971] -0.23267087672305614 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4621486800079553 goals: [('bisect', 0, array([0.66153415, 0.53329524]), array([ 0.03939692, -0.00691971]), None, None, None, 10, array([0.9444966 , 0.46409817]), array([-0.03939692, -0.00691971]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.9444966 0.46409817] [0.03939692 0.00691971] -0.46214868000795534 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.9444966 0.46409817] [0.03939692 0.00691971] -0.46214868000795534 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-3, 0) ---- seed=38 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.6336341 0.60596128] [-0.03324607 -0.02224182] -0.34109350056274995 BACKWARD SAMPLING FROM 4 [0.5006498 0.516994 ] [-0.03324607 -0.02224182] -0.12893506266001822 BACKWARD SAMPLING FROM -4 [0.65441475 0.53949241] [0.01523716 0.03698417] -0.23362496788797535 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.6336341 0.60596128] [-0.03324607 -0.02224182] -0.34109350056274995 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.21488037935387833 goals: [('bisect', 0, array([0.6336341 , 0.60596128]), array([-0.03324607, -0.02224182]), None, None, None, 10, array([0.30117336, 0.38354308]), array([-0.03324607, -0.02224182]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.30117336 0.38354308] [-0.03324607 -0.02224182] -0.21488037935387833 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.34109350056274995 goals: [('bisect', 0, array([0.30117336, 0.38354308]), array([0.03324607, 0.02224182]), None, None, None, 10, array([0.6336341 , 0.60596128]), array([0.03324607, 0.02224182]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.6336341 0.60596128] [-0.03324607 -0.02224182] -0.34109350056274995 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.6336341 0.60596128] [-0.03324607 -0.02224182] -0.34109350056274995 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 sampling between (-7, 0) ---- seed=39 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.60200201 0.52551458] [0.03992051 0.00252049] -0.1893406326438129 BACKWARD SAMPLING FROM 4 [0.76168405 0.53559656] [0.03992051 0.00252049] -0.3059202287204628 BACKWARD SAMPLING FROM -4 [0.44231997 0.51543261] [0.03992051 0.00252049] -0.10080054489174596 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.60200201 0.52551458] [0.03992051 0.00252049] -0.1893406326438129 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.60200201, 0.52551458]), array([0.03992051, 0.00252049]), None, None, None, 10, array([0.99879289, 0.55071952]), array([-0.03992051, 0.00252049]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.33944630091534117 goals: [('bisect', 0, array([0.60200201, 0.52551458]), array([0.03992051, 0.00252049]), 5, array([0.80160456, 0.53811705]), array([0.03992051, 0.00252049]), 10, array([0.99879289, 0.55071952]), array([-0.03992051, 0.00252049]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.41175585311595747 goals: [('bisect', 5, array([0.80160456, 0.53811705]), array([0.03992051, 0.00252049]), 7, array([0.88144558, 0.54315804]), array([0.03992051, 0.00252049]), 10, array([0.99879289, 0.55071952]), array([-0.03992051, 0.00252049]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4505393331216953 goals: [('bisect', 7, array([0.88144558, 0.54315804]), array([0.03992051, 0.00252049]), 8, array([0.92136609, 0.54567853]), array([0.03992051, 0.00252049]), 10, array([0.99879289, 0.55071952]), array([-0.03992051, 0.00252049]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4910752823977196 goals: [('bisect', 8, array([0.92136609, 0.54567853]), array([0.03992051, 0.00252049]), 9, array([0.9612866 , 0.54819902]), array([0.03992051, 0.00252049]), 10, array([0.99879289, 0.55071952]), array([-0.03992051, 0.00252049]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.99879289 0.55071952] [-0.03992051 0.00252049] new direction: [0.03741642 0.01414255] reversing there [-0.03992051 0.00252049] making one step from [0.99879289 0.55071952] [-0.03992051 0.00252049] --> [0.96379069 0.56486207] [-0.03741642 0.01414255] trying new point, [0.96379069 0.56486207] next() call None goals: [('reflect-at', 10, array([0.96379069, 0.56486207]), array([-0.03741642, 0.01414255]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 9... 1 steps to do at 9 -> [from 10, delta=1] targeting 9. goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 9 [0.9612866 0.54819902] [0.03992051 0.00252049] -0.4910752823977196 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 9)] not done yet, continue expanding to 9... goals: [('expand-to', 9), ('sample-at', 9)] next() call -0.1893406326438129 goals: [('bisect', 0, array([0.9612866 , 0.54819902]), array([-0.03992051, -0.00252049]), None, None, None, 9, array([0.60200201, 0.52551458]), array([-0.03992051, -0.00252049]), 1), ('sample-at', 9)] bisecting ... 0 None 9 successfully went all the way in one jump! goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.60200201 0.52551458] [0.03992051 0.00252049] -0.1893406326438129 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.60200201 0.52551458] [0.03992051 0.00252049] -0.1893406326438129 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -7..8 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-7, 8) ---- seed=40 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.52639952 0.62381221] [-0.02398644 0.03201016] -0.33016653033266774 BACKWARD SAMPLING FROM 4 [0.50385595 0.57055786] [ 0.00847644 -0.03909156] -0.18916555089029924 BACKWARD SAMPLING FROM -4 [0.6223453 0.49577156] [-0.02398644 0.03201016] -0.19388033318836445 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.52639952 0.62381221] [-0.02398644 0.03201016] -0.33016653033266774 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.52639952, 0.62381221]), array([-0.02398644, 0.03201016]), None, None, None, 10, array([0.28653508, 0.94391384]), array([-0.02398644, 0.03201016]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.52639952, 0.62381221]), array([-0.02398644, 0.03201016]), 5, array([0.4064673 , 0.78386302]), array([-0.02398644, 0.03201016]), 10, array([0.28653508, 0.94391384]), array([-0.02398644, 0.03201016]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.52639952, 0.62381221]), array([-0.02398644, 0.03201016]), 2, array([0.47842664, 0.68783254]), array([-0.02398644, 0.03201016]), 5, array([0.4064673 , 0.78386302]), array([-0.02398644, 0.03201016]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.42971710884526637 goals: [('bisect', 0, array([0.52639952, 0.62381221]), array([-0.02398644, 0.03201016]), 1, array([0.50241308, 0.65582238]), array([-0.02398644, 0.03201016]), 2, array([0.47842664, 0.68783254]), array([-0.02398644, 0.03201016]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.47842664 0.68783254] [-0.02398644 0.03201016] new direction: [ 0.00847644 -0.03909156] reversing there [-0.02398644 0.03201016] making one step from [0.47842664 0.68783254] [-0.02398644 0.03201016] --> [0.48690307 0.64874098] [ 0.00847644 -0.03909156] trying new point, [0.48690307 0.64874098] next() call -0.39508578257867283 goals: [('reflect-at', 2, array([0.48690307, 0.64874098]), array([ 0.00847644, -0.03909156]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.490019265835001 goals: [('bisect', 2, array([0.48690307, 0.64874098]), array([ 0.00847644, -0.03909156]), None, None, None, 10, array([0.55471457, 0.3360085 ]), array([ 0.00847644, -0.03909156]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.55471457 0.3360085 ] [ 0.00847644 -0.03909156] -0.490019265835001 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.55471457, 0.3360085 ]), array([-0.00847644, 0.03909156]), None, None, None, 10, array([0.4699502, 0.7269241]), array([-0.00847644, 0.03909156]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.14361883567172593 goals: [('bisect', 0, array([0.55471457, 0.3360085 ]), array([-0.00847644, 0.03909156]), 5, array([0.51233238, 0.5314663 ]), array([-0.00847644, 0.03909156]), 10, array([0.4699502, 0.7269241]), array([-0.00847644, 0.03909156]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.2729878665259479 goals: [('bisect', 5, array([0.51233238, 0.5314663 ]), array([-0.00847644, 0.03909156]), 7, array([0.49537951, 0.60964942]), array([-0.00847644, 0.03909156]), 10, array([0.4699502, 0.7269241]), array([-0.00847644, 0.03909156]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3950857825786728 goals: [('bisect', 7, array([0.49537951, 0.60964942]), array([-0.00847644, 0.03909156]), 8, array([0.48690307, 0.64874098]), array([-0.00847644, 0.03909156]), 10, array([0.4699502, 0.7269241]), array([-0.00847644, 0.03909156]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.48690307, 0.64874098]), array([-0.00847644, 0.03909156]), 9, array([0.47842664, 0.68783254]), array([-0.00847644, 0.03909156]), 10, array([0.4699502, 0.7269241]), array([-0.00847644, 0.03909156]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.47842664 0.68783254] [-0.00847644 0.03909156] new direction: [ 0.02398644 -0.03201016] reversing there [-0.00847644 0.03909156] making one step from [0.47842664 0.68783254] [-0.00847644 0.03909156] --> [0.50241308 0.65582238] [ 0.02398644 -0.03201016] trying new point, [0.50241308 0.65582238] next() call -0.42971710884526587 goals: [('reflect-at', 9, array([0.50241308, 0.65582238]), array([ 0.02398644, -0.03201016]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3301665303326673 goals: [('bisect', 9, array([0.50241308, 0.65582238]), array([ 0.02398644, -0.03201016]), None, None, None, 10, array([0.52639952, 0.62381221]), array([ 0.02398644, -0.03201016]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.52639952 0.62381221] [-0.02398644 0.03201016] -0.33016653033266774 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.52639952 0.62381221] [-0.02398644 0.03201016] -0.33016653033266774 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-6, 1) ---- seed=41 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.1164237 0.60386569] [-0.02717748 0.02934935] -0.14162825608964924 BACKWARD SAMPLING FROM 3 [0.03489126 0.69191375] [-0.02717748 0.02934935] -0.4609947924113364 BACKWARD SAMPLING FROM -4 [0.22513362 0.48646827] [-0.02717748 0.02934935] -0.027631419475083484 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.1164237 0.60386569] [-0.02717748 0.02934935] -0.14162825608964924 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.1164237 , 0.60386569]), array([-0.02717748, 0.02934935]), None, None, None, 10, array([0.1553511 , 0.89735923]), array([0.02717748, 0.02934935]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.1164237 , 0.60386569]), array([-0.02717748, 0.02934935]), 5, array([0.0194637 , 0.75061246]), array([0.02717748, 0.02934935]), 10, array([0.1553511 , 0.89735923]), array([0.02717748, 0.02934935]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.33226605088879657 goals: [('bisect', 0, array([0.1164237 , 0.60386569]), array([-0.02717748, 0.02934935]), 2, array([0.06206874, 0.6625644 ]), array([-0.02717748, 0.02934935]), 5, array([0.0194637 , 0.75061246]), array([0.02717748, 0.02934935]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4609947924113364 goals: [('bisect', 2, array([0.06206874, 0.6625644 ]), array([-0.02717748, 0.02934935]), 3, array([0.03489126, 0.69191375]), array([-0.02717748, 0.02934935]), 5, array([0.0194637 , 0.75061246]), array([0.02717748, 0.02934935]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.03489126, 0.69191375]), array([-0.02717748, 0.02934935]), 4, array([0.00771378, 0.7212631 ]), array([-0.02717748, 0.02934935]), 5, array([0.0194637 , 0.75061246]), array([0.02717748, 0.02934935]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.00771378 0.7212631 ] [-0.02717748 0.02934935] new direction: [ 0.03817639 -0.01193998] reversing there [-0.02717748 0.02934935] making one step from [0.00771378 0.7212631 ] [-0.02717748 0.02934935] --> [0.04589017 0.70932312] [ 0.03817639 -0.01193998] trying new point, [0.04589017 0.70932312] next() call None goals: [('reflect-at', 4, array([0.04589017, 0.70932312]), array([ 0.03817639, -0.01193998]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.022720784505758807 goals: [('bisect', 0, array([0.1164237 , 0.60386569]), array([-0.02717748, 0.02934935]), None, None, None, -3, array([0.19795614, 0.51581763]), array([-0.02717748, 0.02934935]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.03489126 0.69191375] [-0.02717748 0.02934935] -0.4609947924113364 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.14162825608964924 goals: [('bisect', 0, array([0.03489126, 0.69191375]), array([ 0.02717748, -0.02934935]), None, None, None, 3, array([0.1164237 , 0.60386569]), array([ 0.02717748, -0.02934935]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.19795614 0.51581763] [-0.02717748 0.02934935] -0.022720784505758807 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.14162825608964924 goals: [('bisect', 0, array([0.19795614, 0.51581763]), array([ 0.02717748, -0.02934935]), None, None, None, -3, array([0.1164237 , 0.60386569]), array([ 0.02717748, -0.02934935]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.1164237 0.60386569] [-0.02717748 0.02934935] -0.14162825608964924 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-2, 1) ---- seed=42 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.73199394 0.59865848] [-0.03066574 -0.02568292] -0.38957627172501086 BACKWARD SAMPLING FROM 4 [0.60933098 0.49592681] [-0.03066574 -0.02568292] -0.18584950597376992 BACKWARD SAMPLING FROM -4 [0.77492096 0.53144407] [0.00613482 0.03952675] -0.31261037020419746 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.73199394 0.59865848] [-0.03066574 -0.02568292] -0.38957627172501086 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4031802276107923 goals: [('bisect', 0, array([0.73199394, 0.59865848]), array([-0.03066574, -0.02568292]), None, None, None, 10, array([0.42533653, 0.3418293 ]), array([-0.03066574, -0.02568292]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.42533653 0.3418293 ] [-0.03066574 -0.02568292] -0.4031802276107923 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.38957627172501086 goals: [('bisect', 0, array([0.42533653, 0.3418293 ]), array([0.03066574, 0.02568292]), None, None, None, 10, array([0.73199394, 0.59865848]), array([0.03066574, 0.02568292]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.73199394 0.59865848] [-0.03066574 -0.02568292] -0.38957627172501086 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.73199394 0.59865848] [-0.03066574 -0.02568292] -0.38957627172501086 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd sampling between (0, 1) ---- seed=43 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.11505457 0.60906654] [ 0.03403177 -0.02101996] -0.15531265150360327 BACKWARD SAMPLING FROM 4 [0.25118164 0.52498669] [ 0.03403177 -0.02101996] -0.039350293891790226 BACKWARD SAMPLING FROM -4 [0.02107251 0.69314638] [-0.03403177 -0.02101996] -0.4665410967293263 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.11505457 0.60906654] [ 0.03403177 -0.02101996] -0.15531265150360327 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.23153067175015277 goals: [('bisect', 0, array([0.11505457, 0.60906654]), array([ 0.03403177, -0.02101996]), None, None, None, 10, array([0.45537225, 0.39886693]), array([ 0.03403177, -0.02101996]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.45537225 0.39886693] [ 0.03403177 -0.02101996] -0.23153067175015277 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.15531265150360327 goals: [('bisect', 0, array([0.45537225, 0.39886693]), array([-0.03403177, 0.02101996]), None, None, None, 10, array([0.11505457, 0.60906654]), array([-0.03403177, 0.02101996]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.11505457 0.60906654] [ 0.03403177 -0.02101996] -0.15531265150360327 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.11505457 0.60906654] [ 0.03403177 -0.02101996] -0.15531265150360327 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (0, 3) ---- seed=44 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.35931084 0.60923838] [0.03283351 0.02284646] -0.2137149365841216 BACKWARD SAMPLING FROM 4 [0.47062097 0.66599703] [-0.02002389 -0.03462721] -0.4551797092301754 BACKWARD SAMPLING FROM -4 [0.22797682 0.51785252] [0.03283351 0.02284646] -0.02997062159932815 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.35931084 0.60923838] [0.03283351 0.02284646] -0.2137149365841216 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.35931084, 0.60923838]), array([0.03283351, 0.02284646]), None, None, None, 10, array([0.68764589, 0.83770303]), array([0.03283351, 0.02284646]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.35931084, 0.60923838]), array([0.03283351, 0.02284646]), 5, array([0.52347836, 0.7234707 ]), array([0.03283351, 0.02284646]), 10, array([0.68764589, 0.83770303]), array([0.03283351, 0.02284646]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.39034946973740525 goals: [('bisect', 0, array([0.35931084, 0.60923838]), array([0.03283351, 0.02284646]), 2, array([0.42497785, 0.65493131]), array([0.03283351, 0.02284646]), 5, array([0.52347836, 0.7234707 ]), array([0.03283351, 0.02284646]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.49985733022926904 goals: [('bisect', 2, array([0.42497785, 0.65493131]), array([0.03283351, 0.02284646]), 3, array([0.45781135, 0.67777777]), array([0.03283351, 0.02284646]), 5, array([0.52347836, 0.7234707 ]), array([0.03283351, 0.02284646]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.45781135, 0.67777777]), array([0.03283351, 0.02284646]), 4, array([0.49064486, 0.70062424]), array([0.03283351, 0.02284646]), 5, array([0.52347836, 0.7234707 ]), array([0.03283351, 0.02284646]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.49064486 0.70062424] [0.03283351 0.02284646] new direction: [-0.02002389 -0.03462721] reversing there [0.03283351 0.02284646] making one step from [0.49064486 0.70062424] [0.03283351 0.02284646] --> [0.47062097 0.66599703] [-0.02002389 -0.03462721] trying new point, [0.47062097 0.66599703] next() call -0.455179709230175 goals: [('reflect-at', 4, array([0.47062097, 0.66599703]), array([-0.02002389, -0.03462721]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.08322252232880438 goals: [('bisect', 4, array([0.47062097, 0.66599703]), array([-0.02002389, -0.03462721]), None, None, None, 10, array([0.35047762, 0.45823376]), array([-0.02002389, -0.03462721]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.35047762 0.45823376] [-0.02002389 -0.03462721] -0.08322252232880438 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.35047762, 0.45823376]), array([0.02002389, 0.03462721]), None, None, None, 10, array([0.55071653, 0.80450587]), array([0.02002389, 0.03462721]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.3172442164483675 goals: [('bisect', 0, array([0.35047762, 0.45823376]), array([0.02002389, 0.03462721]), 5, array([0.45059708, 0.63136981]), array([0.02002389, 0.03462721]), 10, array([0.55071653, 0.80450587]), array([0.02002389, 0.03462721]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.45059708, 0.63136981]), array([0.02002389, 0.03462721]), 7, array([0.49064486, 0.70062424]), array([0.02002389, 0.03462721]), 10, array([0.55071653, 0.80450587]), array([0.02002389, 0.03462721]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.455179709230175 goals: [('bisect', 5, array([0.45059708, 0.63136981]), array([0.02002389, 0.03462721]), 6, array([0.47062097, 0.66599703]), array([0.02002389, 0.03462721]), 7, array([0.49064486, 0.70062424]), array([0.02002389, 0.03462721]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.49064486 0.70062424] [0.02002389 0.03462721] new direction: [-0.03283351 -0.02284646] reversing there [0.02002389 0.03462721] making one step from [0.49064486 0.70062424] [0.02002389 0.03462721] --> [0.45781135 0.67777777] [-0.03283351 -0.02284646] trying new point, [0.45781135 0.67777777] next() call -0.49985733022926904 goals: [('reflect-at', 7, array([0.45781135, 0.67777777]), array([-0.03283351, -0.02284646]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.21371493658412127 goals: [('bisect', 7, array([0.45781135, 0.67777777]), array([-0.03283351, -0.02284646]), None, None, None, 10, array([0.35931084, 0.60923838]), array([-0.03283351, -0.02284646]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.35931084 0.60923838] [0.03283351 0.02284646] -0.2137149365841216 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.35931084 0.60923838] [0.03283351 0.02284646] -0.2137149365841216 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-6, 1) ---- seed=45 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.4444695 0.47280797] [ 0.02554647 -0.0307795 ] -0.10801914778494684 BACKWARD SAMPLING FROM 4 [0.54665538 0.34968996] [ 0.02554647 -0.0307795 ] -0.4318299192036039 BACKWARD SAMPLING FROM -4 [0.34228361 0.59592598] [ 0.02554647 -0.0307795 ] -0.1736014655895373 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.4444695 0.47280797] [ 0.02554647 -0.0307795 ] -0.10801914778494684 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.4444695 , 0.47280797]), array([ 0.02554647, -0.0307795 ]), None, None, None, 10, array([0.6999342 , 0.16501293]), array([ 0.02554647, -0.0307795 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.4444695 , 0.47280797]), array([ 0.02554647, -0.0307795 ]), 5, array([0.57220185, 0.31891045]), array([ 0.02554647, -0.0307795 ]), 10, array([0.6999342 , 0.16501293]), array([ 0.02554647, -0.0307795 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.22125039734136934 goals: [('bisect', 0, array([0.4444695 , 0.47280797]), array([ 0.02554647, -0.0307795 ]), 2, array([0.49556244, 0.41124896]), array([ 0.02554647, -0.0307795 ]), 5, array([0.57220185, 0.31891045]), array([ 0.02554647, -0.0307795 ]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.31437162423426 goals: [('bisect', 2, array([0.49556244, 0.41124896]), array([ 0.02554647, -0.0307795 ]), 3, array([0.52110891, 0.38046946]), array([ 0.02554647, -0.0307795 ]), 5, array([0.57220185, 0.31891045]), array([ 0.02554647, -0.0307795 ]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.4318299192036037 goals: [('bisect', 3, array([0.52110891, 0.38046946]), array([ 0.02554647, -0.0307795 ]), 4, array([0.54665538, 0.34968996]), array([ 0.02554647, -0.0307795 ]), 5, array([0.57220185, 0.31891045]), array([ 0.02554647, -0.0307795 ]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.57220185 0.31891045] [ 0.02554647 -0.0307795 ] new direction: [-0.03976603 -0.00432004] reversing there [ 0.02554647 -0.0307795 ] making one step from [0.57220185 0.31891045] [ 0.02554647 -0.0307795 ] --> [0.53243582 0.31459041] [-0.03976603 -0.00432004] trying new point, [0.53243582 0.31459041] next() call None goals: [('reflect-at', 5, array([0.53243582, 0.31459041]), array([-0.03976603, -0.00432004]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 4... 6 steps to do at 4 -> [from 5, delta=6] targeting -1. goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.087909125121415 goals: [('bisect', 0, array([0.4444695 , 0.47280797]), array([ 0.02554647, -0.0307795 ]), None, None, None, -1, array([0.41892303, 0.50358747]), array([ 0.02554647, -0.0307795 ]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 4 [0.54665538 0.34968996] [ 0.02554647 -0.0307795 ] -0.4318299192036037 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 4)] not done yet, continue expanding to 4... goals: [('expand-to', 4), ('sample-at', 4)] next() call -0.10801914778494678 goals: [('bisect', 0, array([0.54665538, 0.34968996]), array([-0.02554647, 0.0307795 ]), None, None, None, 4, array([0.4444695 , 0.47280797]), array([-0.02554647, 0.0307795 ]), 1), ('sample-at', 4)] bisecting ... 0 None 4 successfully went all the way in one jump! goals: [('sample-at', 4)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -1 [0.41892303 0.50358747] [ 0.02554647 -0.0307795 ] -0.087909125121415 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.10801914778494684 goals: [('bisect', 0, array([0.41892303, 0.50358747]), array([-0.02554647, 0.0307795 ]), None, None, None, -1, array([0.4444695 , 0.47280797]), array([-0.02554647, 0.0307795 ]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.4444695 0.47280797] [ 0.02554647 -0.0307795 ] -0.10801914778494684 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-5, 2) ---- seed=46 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.04286545 0.4408672 ] [0.0271438 0.02938051] -0.04462731870887193 BACKWARD SAMPLING FROM 4 [0.15144064 0.55838923] [0.0271438 0.02938051] -0.05408341768692817 BACKWARD SAMPLING FROM -4 [0.06570974 0.32334517] [-0.0271438 0.02938051] -0.3922454873386127 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.04286545 0.4408672 ] [0.0271438 0.02938051] -0.04462731870887193 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.04286545, 0.4408672 ]), array([0.0271438 , 0.02938051]), None, None, None, 10, array([0.31430343, 0.73467228]), array([0.0271438 , 0.02938051]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.11224029674516076 goals: [('bisect', 0, array([0.04286545, 0.4408672 ]), array([0.0271438 , 0.02938051]), 5, array([0.17858444, 0.58776974]), array([0.0271438 , 0.02938051]), 10, array([0.31430343, 0.73467228]), array([0.0271438 , 0.02938051]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.2955054800380875 goals: [('bisect', 5, array([0.17858444, 0.58776974]), array([0.0271438 , 0.02938051]), 7, array([0.23287203, 0.64653076]), array([0.0271438 , 0.02938051]), 10, array([0.31430343, 0.73467228]), array([0.0271438 , 0.02938051]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4206137842727819 goals: [('bisect', 7, array([0.23287203, 0.64653076]), array([0.0271438 , 0.02938051]), 8, array([0.26001583, 0.67591127]), array([0.0271438 , 0.02938051]), 10, array([0.31430343, 0.73467228]), array([0.0271438 , 0.02938051]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.26001583, 0.67591127]), array([0.0271438 , 0.02938051]), 9, array([0.28715963, 0.70529177]), array([0.0271438 , 0.02938051]), 10, array([0.31430343, 0.73467228]), array([0.0271438 , 0.02938051]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.28715963 0.70529177] [0.0271438 0.02938051] new direction: [ 0.02281179 -0.03285761] reversing there [0.0271438 0.02938051] making one step from [0.28715963 0.70529177] [0.0271438 0.02938051] --> [0.30997142 0.67243417] [ 0.02281179 -0.03285761] trying new point, [0.30997142 0.67243417] next() call -0.41971042152871735 goals: [('reflect-at', 9, array([0.30997142, 0.67243417]), array([ 0.02281179, -0.03285761]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2988925458881023 goals: [('bisect', 9, array([0.30997142, 0.67243417]), array([ 0.02281179, -0.03285761]), None, None, None, 10, array([0.33278321, 0.63957656]), array([ 0.02281179, -0.03285761]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.33278321 0.63957656] [ 0.02281179 -0.03285761] -0.2988925458881023 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.33278321, 0.63957656]), array([-0.02281179, 0.03285761]), None, None, None, 10, array([0.1046653 , 0.96815261]), array([-0.02281179, 0.03285761]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.33278321, 0.63957656]), array([-0.02281179, 0.03285761]), 5, array([0.21872426, 0.80386459]), array([-0.02281179, 0.03285761]), 10, array([0.1046653 , 0.96815261]), array([-0.02281179, 0.03285761]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.33278321, 0.63957656]), array([-0.02281179, 0.03285761]), 2, array([0.28715963, 0.70529177]), array([-0.02281179, 0.03285761]), 5, array([0.21872426, 0.80386459]), array([-0.02281179, 0.03285761]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.41971042152871735 goals: [('bisect', 0, array([0.33278321, 0.63957656]), array([-0.02281179, 0.03285761]), 1, array([0.30997142, 0.67243417]), array([-0.02281179, 0.03285761]), 2, array([0.28715963, 0.70529177]), array([-0.02281179, 0.03285761]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.28715963 0.70529177] [-0.02281179 0.03285761] new direction: [-0.0271438 -0.02938051] reversing there [-0.02281179 0.03285761] making one step from [0.28715963 0.70529177] [-0.02281179 0.03285761] --> [0.26001583 0.67591127] [-0.0271438 -0.02938051] trying new point, [0.26001583 0.67591127] next() call -0.4206137842727819 goals: [('reflect-at', 2, array([0.26001583, 0.67591127]), array([-0.0271438 , -0.02938051]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.04462731870887185 goals: [('bisect', 2, array([0.26001583, 0.67591127]), array([-0.0271438 , -0.02938051]), None, None, None, 10, array([0.04286545, 0.4408672 ]), array([-0.0271438 , -0.02938051]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.04286545 0.4408672 ] [0.0271438 0.02938051] -0.04462731870887193 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.04286545 0.4408672 ] [0.0271438 0.02938051] -0.04462731870887193 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-3, 4) ---- seed=47 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.64556185 0.41459961] [-0.02119426 0.03392349] -0.2995403926532865 BACKWARD SAMPLING FROM 4 [0.56078481 0.55029357] [-0.02119426 0.03392349] -0.18885784622089788 BACKWARD SAMPLING FROM -1 [0.66675612 0.38067612] [-0.02119426 0.03392349] -0.40025922669221325 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.64556185 0.41459961] [-0.02119426 0.03392349] -0.2995403926532865 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.64556185, 0.41459961]), array([-0.02119426, 0.03392349]), None, None, None, 10, array([0.43361924, 0.75383452]), array([-0.02119426, 0.03392349]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.23423540704363024 goals: [('bisect', 0, array([0.64556185, 0.41459961]), array([-0.02119426, 0.03392349]), 5, array([0.53959055, 0.58421707]), array([-0.02119426, 0.03392349]), 10, array([0.43361924, 0.75383452]), array([-0.02119426, 0.03392349]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.4126483656060907 goals: [('bisect', 5, array([0.53959055, 0.58421707]), array([-0.02119426, 0.03392349]), 7, array([0.49720203, 0.65206405]), array([-0.02119426, 0.03392349]), 10, array([0.43361924, 0.75383452]), array([-0.02119426, 0.03392349]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.49720203, 0.65206405]), array([-0.02119426, 0.03392349]), 8, array([0.47600776, 0.68598754]), array([-0.02119426, 0.03392349]), 10, array([0.43361924, 0.75383452]), array([-0.02119426, 0.03392349]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.47600776 0.68598754] [-0.02119426 0.03392349] new direction: [-0.02911927 -0.02742386] reversing there [-0.02119426 0.03392349] making one step from [0.47600776 0.68598754] [-0.02119426 0.03392349] --> [0.44688849 0.65856368] [-0.02911927 -0.02742386] trying new point, [0.44688849 0.65856368] next() call -0.4141351863645779 goals: [('reflect-at', 8, array([0.44688849, 0.65856368]), array([-0.02911927, -0.02742386]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.20998692233081806 goals: [('bisect', 8, array([0.44688849, 0.65856368]), array([-0.02911927, -0.02742386]), None, None, None, 10, array([0.38864994, 0.60371597]), array([-0.02911927, -0.02742386]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.38864994 0.60371597] [-0.02911927 -0.02742386] -0.20998692233081806 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.38864994, 0.60371597]), array([0.02911927, 0.02742386]), None, None, None, 10, array([0.67984268, 0.87795454]), array([0.02911927, 0.02742386]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.38864994, 0.60371597]), array([0.02911927, 0.02742386]), 5, array([0.53424631, 0.74083525]), array([0.02911927, 0.02742386]), 10, array([0.67984268, 0.87795454]), array([0.02911927, 0.02742386]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.4141351863645779 goals: [('bisect', 0, array([0.38864994, 0.60371597]), array([0.02911927, 0.02742386]), 2, array([0.44688849, 0.65856368]), array([0.02911927, 0.02742386]), 5, array([0.53424631, 0.74083525]), array([0.02911927, 0.02742386]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.44688849, 0.65856368]), array([0.02911927, 0.02742386]), 3, array([0.47600776, 0.68598754]), array([0.02911927, 0.02742386]), 5, array([0.53424631, 0.74083525]), array([0.02911927, 0.02742386]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.47600776 0.68598754] [0.02911927 0.02742386] new direction: [ 0.02119426 -0.03392349] reversing there [0.02911927 0.02742386] making one step from [0.47600776 0.68598754] [0.02911927 0.02742386] --> [0.49720203 0.65206405] [ 0.02119426 -0.03392349] trying new point, [0.49720203 0.65206405] next() call -0.4126483656060907 goals: [('reflect-at', 3, array([0.49720203, 0.65206405]), array([ 0.02119426, -0.03392349]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2995403926532866 goals: [('bisect', 3, array([0.49720203, 0.65206405]), array([ 0.02119426, -0.03392349]), None, None, None, 10, array([0.64556185, 0.41459961]), array([ 0.02119426, -0.03392349]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.64556185 0.41459961] [-0.02119426 0.03392349] -0.2995403926532865 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.64556185 0.41459961] [-0.02119426 0.03392349] -0.2995403926532865 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-1, 2) ---- seed=48 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.86471039 0.44751263] [-0.03985609 -0.00338995] -0.40829857363895855 BACKWARD SAMPLING FROM 4 [0.70528601 0.43395284] [-0.03985609 -0.00338995] -0.3032420227989266 BACKWARD SAMPLING FROM -4 [0.90468504 0.44962923] [0.03979681 0.00402662] -0.44094269508821465 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.86471039 0.44751263] [-0.03985609 -0.00338995] -0.40829857363895855 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.20193125737173862 goals: [('bisect', 0, array([0.86471039, 0.44751263]), array([-0.03985609, -0.00338995]), None, None, None, 10, array([0.46614944, 0.41361315]), array([-0.03985609, -0.00338995]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.46614944 0.41361315] [-0.03985609 -0.00338995] -0.20193125737173862 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.40829857363895855 goals: [('bisect', 0, array([0.46614944, 0.41361315]), array([0.03985609, 0.00338995]), None, None, None, 10, array([0.86471039, 0.44751263]), array([0.03985609, 0.00338995]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.86471039 0.44751263] [-0.03985609 -0.00338995] -0.40829857363895855 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.86471039 0.44751263] [-0.03985609 -0.00338995] -0.40829857363895855 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 sampling between (-6, 1) ---- seed=49 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.68327676 0.56688466] [ 0.00178908 -0.03995997] -0.2893530363157967 BACKWARD SAMPLING FROM 4 [0.69043306 0.40704478] [ 0.00178908 -0.03995997] -0.3463573209308397 BACKWARD SAMPLING FROM -4 [0.55985875 0.65300213] [ 0.03994662 -0.00206584] -0.44934157693712723 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.68327676 0.56688466] [ 0.00178908 -0.03995997] -0.2893530363157967 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.68327676, 0.56688466]), array([ 0.00178908, -0.03995997]), None, None, None, 10, array([0.70116752, 0.16728496]), array([ 0.00178908, -0.03995997]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4604163445319175 goals: [('bisect', 0, array([0.68327676, 0.56688466]), array([ 0.00178908, -0.03995997]), 5, array([0.69222214, 0.36708481]), array([ 0.00178908, -0.03995997]), 10, array([0.70116752, 0.16728496]), array([ 0.00178908, -0.03995997]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.69222214, 0.36708481]), array([ 0.00178908, -0.03995997]), 7, array([0.69580029, 0.28716487]), array([ 0.00178908, -0.03995997]), 10, array([0.70116752, 0.16728496]), array([ 0.00178908, -0.03995997]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.69222214, 0.36708481]), array([ 0.00178908, -0.03995997]), 6, array([0.69401122, 0.32712484]), array([ 0.00178908, -0.03995997]), 7, array([0.69580029, 0.28716487]), array([ 0.00178908, -0.03995997]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.69401122 0.32712484] [ 0.00178908 -0.03995997] new direction: [-0.03993708 -0.00224268] reversing there [ 0.00178908 -0.03995997] making one step from [0.69401122 0.32712484] [ 0.00178908 -0.03995997] --> [0.65407414 0.32488216] [-0.03993708 -0.00224268] trying new point, [0.65407414 0.32488216] next() call None goals: [('reflect-at', 6, array([0.65407414, 0.32488216]), array([-0.03993708, -0.00224268]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 5... 5 steps to do at 5 -> [from 6, delta=5] targeting 1. goals: [('sample-at', 1)] target is on track, returning interpolation at 1... [0.68506584 0.52692469] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 5 [0.69222214 0.36708481] [ 0.00178908 -0.03995997] -0.4604163445319175 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 5)] not done yet, continue expanding to 5... goals: [('expand-to', 5), ('sample-at', 5)] next() call -0.2893530363157967 goals: [('bisect', 0, array([0.69222214, 0.36708481]), array([-0.00178908, 0.03995997]), None, None, None, 5, array([0.68327676, 0.56688466]), array([-0.00178908, 0.03995997]), 1), ('sample-at', 5)] bisecting ... 0 None 5 successfully went all the way in one jump! goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.68327676 0.56688466] [ 0.00178908 -0.03995997] -0.2893530363157967 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.68327676 0.56688466] [ 0.00178908 -0.03995997] -0.2893530363157967 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -2..5 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -10..5 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-10, 5) ---- seed=50 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.25547392 0.39632991] [-0.03142766 -0.02474474] -0.1669770580860475 BACKWARD SAMPLING FROM 4 [0.13808816 0.33647507] [0.00832488 0.03912411] -0.3437892113032023 BACKWARD SAMPLING FROM -4 [0.38118457 0.49530887] [-0.03142766 -0.02474474] -0.07292592188692902 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.25547392 0.39632991] [-0.03142766 -0.02474474] -0.1669770580860475 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.25547392, 0.39632991]), array([-0.03142766, -0.02474474]), None, None, None, 10, array([0.05880269, 0.14888252]), array([ 0.03142766, -0.02474474]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.25547392, 0.39632991]), array([-0.03142766, -0.02474474]), 5, array([0.09833562, 0.27260621]), array([-0.03142766, -0.02474474]), 10, array([0.05880269, 0.14888252]), array([ 0.03142766, -0.02474474]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.31177413021896305 goals: [('bisect', 0, array([0.25547392, 0.39632991]), array([-0.03142766, -0.02474474]), 2, array([0.1926186 , 0.34684043]), array([-0.03142766, -0.02474474]), 5, array([0.09833562, 0.27260621]), array([-0.03142766, -0.02474474]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4086155422937598 goals: [('bisect', 2, array([0.1926186 , 0.34684043]), array([-0.03142766, -0.02474474]), 3, array([0.16119094, 0.32209569]), array([-0.03142766, -0.02474474]), 5, array([0.09833562, 0.27260621]), array([-0.03142766, -0.02474474]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.16119094, 0.32209569]), array([-0.03142766, -0.02474474]), 4, array([0.12976328, 0.29735095]), array([-0.03142766, -0.02474474]), 5, array([0.09833562, 0.27260621]), array([-0.03142766, -0.02474474]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.12976328 0.29735095] [-0.03142766 -0.02474474] new direction: [0.00832488 0.03912411] reversing there [-0.03142766 -0.02474474] making one step from [0.12976328 0.29735095] [-0.03142766 -0.02474474] --> [0.13808816 0.33647507] [0.00832488 0.03912411] trying new point, [0.13808816 0.33647507] next() call -0.3437892113032023 goals: [('reflect-at', 4, array([0.13808816, 0.33647507]), array([0.00832488, 0.03912411]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.08108221261480617 goals: [('bisect', 4, array([0.13808816, 0.33647507]), array([0.00832488, 0.03912411]), None, None, None, 10, array([0.18803745, 0.57121976]), array([0.00832488, 0.03912411]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.18803745 0.57121976] [0.00832488 0.03912411] -0.08108221261480617 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.18803745, 0.57121976]), array([-0.00832488, -0.03912411]), None, None, None, 10, array([0.10478864, 0.17997861]), array([-0.00832488, -0.03912411]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.20416293012453088 goals: [('bisect', 0, array([0.18803745, 0.57121976]), array([-0.00832488, -0.03912411]), 5, array([0.14641304, 0.37559918]), array([-0.00832488, -0.03912411]), 10, array([0.10478864, 0.17997861]), array([-0.00832488, -0.03912411]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.14641304, 0.37559918]), array([-0.00832488, -0.03912411]), 7, array([0.12976328, 0.29735095]), array([-0.00832488, -0.03912411]), 10, array([0.10478864, 0.17997861]), array([-0.00832488, -0.03912411]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.3437892113032023 goals: [('bisect', 5, array([0.14641304, 0.37559918]), array([-0.00832488, -0.03912411]), 6, array([0.13808816, 0.33647507]), array([-0.00832488, -0.03912411]), 7, array([0.12976328, 0.29735095]), array([-0.00832488, -0.03912411]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.12976328 0.29735095] [-0.00832488 -0.03912411] new direction: [0.03142766 0.02474474] reversing there [-0.00832488 -0.03912411] making one step from [0.12976328 0.29735095] [-0.00832488 -0.03912411] --> [0.16119094 0.32209569] [0.03142766 0.02474474] trying new point, [0.16119094 0.32209569] next() call -0.4086155422937598 goals: [('reflect-at', 7, array([0.16119094, 0.32209569]), array([0.03142766, 0.02474474]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.16697705808604757 goals: [('bisect', 7, array([0.16119094, 0.32209569]), array([0.03142766, 0.02474474]), None, None, None, 10, array([0.25547392, 0.39632991]), array([0.03142766, 0.02474474]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.25547392 0.39632991] [-0.03142766 -0.02474474] -0.1669770580860475 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.25547392 0.39632991] [-0.03142766 -0.02474474] -0.1669770580860475 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-4, 3) ---- seed=51 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.34330367 0.64401973] [-0.0142563 0.03737322] -0.31819972792644835 BACKWARD SAMPLING FROM 1 [0.32904737 0.68139295] [-0.0142563 0.03737322] -0.4654286221899625 BACKWARD SAMPLING FROM -4 [0.40032887 0.49452683] [-0.0142563 0.03737322] -0.08050604831793806 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.34330367 0.64401973] [-0.0142563 0.03737322] -0.31819972792644835 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.34330367, 0.64401973]), array([-0.0142563 , 0.03737322]), None, None, None, 10, array([0.20074067, 0.98224803]), array([-0.0142563 , -0.03737322]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.34330367, 0.64401973]), array([-0.0142563 , 0.03737322]), 5, array([0.27202217, 0.83088585]), array([-0.0142563 , 0.03737322]), 10, array([0.20074067, 0.98224803]), array([-0.0142563 , -0.03737322]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.34330367, 0.64401973]), array([-0.0142563 , 0.03737322]), 2, array([0.31479107, 0.71876618]), array([-0.0142563 , 0.03737322]), 5, array([0.27202217, 0.83088585]), array([-0.0142563 , 0.03737322]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.4654286221899625 goals: [('bisect', 0, array([0.34330367, 0.64401973]), array([-0.0142563 , 0.03737322]), 1, array([0.32904737, 0.68139295]), array([-0.0142563 , 0.03737322]), 2, array([0.31479107, 0.71876618]), array([-0.0142563 , 0.03737322]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.31479107 0.71876618] [-0.0142563 0.03737322] new direction: [-0.039365 -0.00709908] reversing there [-0.0142563 0.03737322] making one step from [0.31479107 0.71876618] [-0.0142563 0.03737322] --> [0.27542607 0.7116671 ] [-0.039365 -0.00709908] trying new point, [0.27542607 0.7116671 ] next() call None goals: [('reflect-at', 2, array([0.27542607, 0.7116671 ]), array([-0.039365 , -0.00709908]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 1... 9 steps to do at 1 -> [from 2, delta=9] targeting -7. goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.27101878092937887 goals: [('bisect', 0, array([0.34330367, 0.64401973]), array([-0.0142563 , 0.03737322]), None, None, None, -7, array([0.44309777, 0.38240716]), array([-0.0142563 , 0.03737322]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 1 [0.32904737 0.68139295] [-0.0142563 0.03737322] -0.4654286221899625 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 1)] not done yet, continue expanding to 1... goals: [('expand-to', 1), ('sample-at', 1)] next() call -0.31819972792644835 goals: [('bisect', 0, array([0.32904737, 0.68139295]), array([ 0.0142563 , -0.03737322]), None, None, None, 1, array([0.34330367, 0.64401973]), array([ 0.0142563 , -0.03737322]), 1), ('sample-at', 1)] bisecting ... 0 None 1 successfully went all the way in one jump! goals: [('sample-at', 1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -7 [0.44309777 0.38240716] [-0.0142563 0.03737322] -0.27101878092937887 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.31819972792644835 goals: [('bisect', 0, array([0.44309777, 0.38240716]), array([ 0.0142563 , -0.03737322]), None, None, None, -7, array([0.34330367, 0.64401973]), array([ 0.0142563 , -0.03737322]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.34330367 0.64401973] [-0.0142563 0.03737322] -0.31819972792644835 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-6, 1) ---- seed=52 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.21077064 0.61842177] [ 0.037317 -0.01440282] -0.19750857452717718 BACKWARD SAMPLING FROM 4 [0.36003865 0.56081047] [ 0.037317 -0.01440282] -0.11103783451846358 BACKWARD SAMPLING FROM -4 [0.06150263 0.67603307] [ 0.037317 -0.01440282] -0.3892367900814762 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.21077064 0.61842177] [ 0.037317 -0.01440282] -0.19750857452717718 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.17868949115336583 goals: [('bisect', 0, array([0.21077064, 0.61842177]), array([ 0.037317 , -0.01440282]), None, None, None, 10, array([0.58394066, 0.47439353]), array([ 0.037317 , -0.01440282]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.58394066 0.47439353] [ 0.037317 -0.01440282] -0.17868949115336583 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.19750857452717718 goals: [('bisect', 0, array([0.58394066, 0.47439353]), array([-0.037317 , 0.01440282]), None, None, None, 10, array([0.21077064, 0.61842177]), array([-0.037317 , 0.01440282]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.21077064 0.61842177] [ 0.037317 -0.01440282] -0.19750857452717718 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.21077064 0.61842177] [ 0.037317 -0.01440282] -0.19750857452717718 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-5, 2) ---- seed=53 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.84666241 0.56116554] [-0.0304715 -0.02591308] -0.40518390734034826 BACKWARD SAMPLING FROM 4 [0.72477641 0.45751322] [-0.0304715 -0.02591308] -0.28521449853834674 BACKWARD SAMPLING FROM -1 [0.87713391 0.58707862] [-0.0304715 -0.02591308] -0.4794655207604886 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.84666241 0.56116554] [-0.0304715 -0.02591308] -0.40518390734034826 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.84666241, 0.56116554]), array([-0.0304715 , -0.02591308]), None, None, None, 10, array([0.54194741, 0.30203475]), array([-0.0304715 , -0.02591308]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.2995114075574864 goals: [('bisect', 0, array([0.84666241, 0.56116554]), array([-0.0304715 , -0.02591308]), 5, array([0.69430491, 0.43160014]), array([-0.0304715 , -0.02591308]), 10, array([0.54194741, 0.30203475]), array([-0.0304715 , -0.02591308]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.38125233905933353 goals: [('bisect', 5, array([0.69430491, 0.43160014]), array([-0.0304715 , -0.02591308]), 7, array([0.63336191, 0.37977398]), array([-0.0304715 , -0.02591308]), 10, array([0.54194741, 0.30203475]), array([-0.0304715 , -0.02591308]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4486963615420411 goals: [('bisect', 7, array([0.63336191, 0.37977398]), array([-0.0304715 , -0.02591308]), 8, array([0.60289041, 0.3538609 ]), array([-0.0304715 , -0.02591308]), 10, array([0.54194741, 0.30203475]), array([-0.0304715 , -0.02591308]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.60289041, 0.3538609 ]), array([-0.0304715 , -0.02591308]), 9, array([0.57241891, 0.32794783]), array([-0.0304715 , -0.02591308]), 10, array([0.54194741, 0.30203475]), array([-0.0304715 , -0.02591308]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.57241891 0.32794783] [-0.0304715 -0.02591308] new direction: [-0.00163041 -0.03996676] reversing there [-0.0304715 -0.02591308] making one step from [0.57241891 0.32794783] [-0.0304715 -0.02591308] --> [0.5707885 0.28798107] [-0.00163041 -0.03996676] trying new point, [0.5707885 0.28798107] next() call None goals: [('reflect-at', 9, array([0.5707885 , 0.28798107]), array([-0.00163041, -0.03996676]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 8... 2 steps to do at 8 -> [from 9, delta=2] targeting 7. goals: [('sample-at', 7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 8 [0.60289041 0.3538609 ] [-0.0304715 -0.02591308] -0.4486963615420411 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 8)] not done yet, continue expanding to 8... goals: [('expand-to', 8), ('sample-at', 8)] next() call -0.40518390734034826 goals: [('bisect', 0, array([0.60289041, 0.3538609 ]), array([0.0304715 , 0.02591308]), None, None, None, 8, array([0.84666241, 0.56116554]), array([0.0304715 , 0.02591308]), 1), ('sample-at', 8)] bisecting ... 0 None 8 successfully went all the way in one jump! goals: [('sample-at', 8)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.84666241 0.56116554] [-0.0304715 -0.02591308] -0.40518390734034826 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.84666241 0.56116554] [-0.0304715 -0.02591308] -0.40518390734034826 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd sampling between (-1, 0) ---- seed=54 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.42018297 0.3632395 ] [-0.03203303 0.0239559 ] -0.32206980281714764 BACKWARD SAMPLING FROM 4 [0.29205086 0.4590631 ] [-0.03203303 0.0239559 ] -0.06359472404742703 BACKWARD SAMPLING FROM -1 [0.45221599 0.3392836 ] [-0.03203303 0.0239559 ] -0.4251216837222281 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.42018297 0.3632395 ] [-0.03203303 0.0239559 ] -0.32206980281714764 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.13707944044464876 goals: [('bisect', 0, array([0.42018297, 0.3632395 ]), array([-0.03203303, 0.0239559 ]), None, None, None, 10, array([0.0998527 , 0.60279851]), array([-0.03203303, 0.0239559 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.0998527 0.60279851] [-0.03203303 0.0239559 ] -0.13707944044464876 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.32206980281714764 goals: [('bisect', 0, array([0.0998527 , 0.60279851]), array([ 0.03203303, -0.0239559 ]), None, None, None, 10, array([0.42018297, 0.3632395 ]), array([ 0.03203303, -0.0239559 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.42018297 0.3632395 ] [-0.03203303 0.0239559 ] -0.32206980281714764 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.42018297 0.3632395 ] [-0.03203303 0.0239559 ] -0.32206980281714764 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd sampling between (-1, 0) ---- seed=55 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.00913894 0.61831211] [-0.01800544 -0.0357184 ] -0.17501371251886214 BACKWARD SAMPLING FROM 4 [0.06288283 0.47543852] [ 0.01800544 -0.0357184 ] -0.00951795620141871 BACKWARD SAMPLING FROM -2 [0.04514983 0.68974891] [-0.01800544 -0.0357184 ] -0.45107737484070604 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.00913894 0.61831211] [-0.01800544 -0.0357184 ] -0.17501371251886214 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.00913894, 0.61831211]), array([-0.01800544, -0.0357184 ]), None, None, None, 10, array([0.17091548, 0.26112812]), array([ 0.01800544, -0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.04869226052335881 goals: [('bisect', 0, array([0.00913894, 0.61831211]), array([-0.01800544, -0.0357184 ]), 5, array([0.08088827, 0.43972012]), array([ 0.01800544, -0.0357184 ]), 10, array([0.17091548, 0.26112812]), array([ 0.01800544, -0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.2236987612488 goals: [('bisect', 5, array([0.08088827, 0.43972012]), array([ 0.01800544, -0.0357184 ]), 7, array([0.11689915, 0.36828332]), array([ 0.01800544, -0.0357184 ]), 10, array([0.17091548, 0.26112812]), array([ 0.01800544, -0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3595309576523012 goals: [('bisect', 7, array([0.11689915, 0.36828332]), array([ 0.01800544, -0.0357184 ]), 8, array([0.13490459, 0.33256492]), array([ 0.01800544, -0.0357184 ]), 10, array([0.17091548, 0.26112812]), array([ 0.01800544, -0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.13490459, 0.33256492]), array([ 0.01800544, -0.0357184 ]), 9, array([0.15291004, 0.29684652]), array([ 0.01800544, -0.0357184 ]), 10, array([0.17091548, 0.26112812]), array([ 0.01800544, -0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.15291004 0.29684652] [ 0.01800544 -0.0357184 ] new direction: [-0.03842475 0.01111479] reversing there [ 0.01800544 -0.0357184 ] making one step from [0.15291004 0.29684652] [ 0.01800544 -0.0357184 ] --> [0.11448528 0.30796131] [-0.03842475 0.01111479] trying new point, [0.11448528 0.30796131] next() call -0.46753918956341406 goals: [('reflect-at', 9, array([0.11448528, 0.30796131]), array([-0.03842475, 0.01111479]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.41206085167620243 goals: [('bisect', 9, array([0.11448528, 0.30796131]), array([-0.03842475, 0.01111479]), None, None, None, 10, array([0.07606053, 0.31907609]), array([-0.03842475, 0.01111479]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.07606053 0.31907609] [-0.03842475 0.01111479] -0.41206085167620243 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.07606053, 0.31907609]), array([ 0.03842475, -0.01111479]), None, None, None, 10, array([0.46030805, 0.20792822]), array([ 0.03842475, -0.01111479]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.07606053, 0.31907609]), array([ 0.03842475, -0.01111479]), 5, array([0.26818429, 0.26350216]), array([ 0.03842475, -0.01111479]), 10, array([0.46030805, 0.20792822]), array([ 0.03842475, -0.01111479]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.07606053, 0.31907609]), array([ 0.03842475, -0.01111479]), 2, array([0.15291004, 0.29684652]), array([ 0.03842475, -0.01111479]), 5, array([0.26818429, 0.26350216]), array([ 0.03842475, -0.01111479]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.46753918956341406 goals: [('bisect', 0, array([0.07606053, 0.31907609]), array([ 0.03842475, -0.01111479]), 1, array([0.11448528, 0.30796131]), array([ 0.03842475, -0.01111479]), 2, array([0.15291004, 0.29684652]), array([ 0.03842475, -0.01111479]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.15291004 0.29684652] [ 0.03842475 -0.01111479] new direction: [-0.01800544 0.0357184 ] reversing there [ 0.03842475 -0.01111479] making one step from [0.15291004 0.29684652] [ 0.03842475 -0.01111479] --> [0.13490459 0.33256492] [-0.01800544 0.0357184 ] trying new point, [0.13490459 0.33256492] next() call -0.3595309576523012 goals: [('reflect-at', 2, array([0.13490459, 0.33256492]), array([-0.01800544, 0.0357184 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.17501371251886214 goals: [('bisect', 2, array([0.13490459, 0.33256492]), array([-0.01800544, 0.0357184 ]), None, None, None, 10, array([0.00913894, 0.61831211]), array([0.01800544, 0.0357184 ]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.00913894 0.61831211] [-0.01800544 -0.0357184 ] -0.17501371251886214 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.00913894 0.61831211] [-0.01800544 -0.0357184 ] -0.17501371251886214 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-1, 2) ---- seed=56 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.24784955 0.45861133] [0.03582093 0.01780059] -0.05212748066477112 BACKWARD SAMPLING FROM 4 [0.39113326 0.5298137 ] [0.03582093 0.01780059] -0.08760332684710367 BACKWARD SAMPLING FROM -4 [0.10456585 0.38740895] [0.03582093 0.01780059] -0.16392632784250208 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.24784955 0.45861133] [0.03582093 0.01780059] -0.05212748066477112 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.41695714617072094 goals: [('bisect', 0, array([0.24784955, 0.45861133]), array([0.03582093, 0.01780059]), None, None, None, 10, array([0.60605882, 0.63661727]), array([0.03582093, 0.01780059]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.60605882 0.63661727] [0.03582093 0.01780059] -0.41695714617072094 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05212748066477112 goals: [('bisect', 0, array([0.60605882, 0.63661727]), array([-0.03582093, -0.01780059]), None, None, None, 10, array([0.24784955, 0.45861133]), array([-0.03582093, -0.01780059]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.24784955 0.45861133] [0.03582093 0.01780059] -0.05212748066477112 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.24784955 0.45861133] [0.03582093 0.01780059] -0.05212748066477112 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -5..10 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd new NUTS range: -5..26 NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd NUTS step: tree depth 5, rwd sampling between (-5, 26) ---- seed=57 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.56595589 0.5450637 ] [-0.00732732 0.03932315] -0.18553725262780735 BACKWARD SAMPLING FROM 3 [0.54397394 0.66303317] [-0.00732732 0.03932315] -0.4802014861520403 BACKWARD SAMPLING FROM -4 [0.59526516 0.38777109] [-0.00732732 0.03932315] -0.3346119116596618 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.56595589 0.5450637 ] [-0.00732732 0.03932315] -0.18553725262780735 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.56595589, 0.5450637 ]), array([-0.00732732, 0.03932315]), None, None, None, 10, array([0.49268272, 0.93829524]), array([-0.00732732, 0.03932315]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.56595589, 0.5450637 ]), array([-0.00732732, 0.03932315]), 5, array([0.52931931, 0.74167947]), array([-0.00732732, 0.03932315]), 10, array([0.49268272, 0.93829524]), array([-0.00732732, 0.03932315]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.34326862471080793 goals: [('bisect', 0, array([0.56595589, 0.5450637 ]), array([-0.00732732, 0.03932315]), 2, array([0.55130126, 0.62371001]), array([-0.00732732, 0.03932315]), 5, array([0.52931931, 0.74167947]), array([-0.00732732, 0.03932315]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4802014861520402 goals: [('bisect', 2, array([0.55130126, 0.62371001]), array([-0.00732732, 0.03932315]), 3, array([0.54397394, 0.66303317]), array([-0.00732732, 0.03932315]), 5, array([0.52931931, 0.74167947]), array([-0.00732732, 0.03932315]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.54397394, 0.66303317]), array([-0.00732732, 0.03932315]), 4, array([0.53664662, 0.70235632]), array([-0.00732732, 0.03932315]), 5, array([0.52931931, 0.74167947]), array([-0.00732732, 0.03932315]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.53664662 0.70235632] [-0.00732732 0.03932315] new direction: [-0.03991081 -0.00266972] reversing there [-0.00732732 0.03932315] making one step from [0.53664662 0.70235632] [-0.00732732 0.03932315] --> [0.49673582 0.69968659] [-0.03991081 -0.00266972] trying new point, [0.49673582 0.69968659] next() call None goals: [('reflect-at', 4, array([0.49673582, 0.69968659]), array([-0.03991081, -0.00266972]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.23927607150196617 goals: [('bisect', 0, array([0.56595589, 0.5450637 ]), array([-0.00732732, 0.03932315]), None, None, None, -3, array([0.58793784, 0.42709424]), array([-0.00732732, 0.03932315]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.54397394 0.66303317] [-0.00732732 0.03932315] -0.4802014861520402 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.18553725262780735 goals: [('bisect', 0, array([0.54397394, 0.66303317]), array([ 0.00732732, -0.03932315]), None, None, None, 3, array([0.56595589, 0.5450637 ]), array([ 0.00732732, -0.03932315]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.58793784 0.42709424] [-0.00732732 0.03932315] -0.23927607150196617 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.18553725262780735 goals: [('bisect', 0, array([0.58793784, 0.42709424]), array([ 0.00732732, -0.03932315]), None, None, None, -3, array([0.56595589, 0.5450637 ]), array([ 0.00732732, -0.03932315]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.56595589 0.5450637 ] [-0.00732732 0.03932315] -0.18553725262780735 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-5, 2) ---- seed=58 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.36510558 0.45120592] [ 0.03982568 -0.00373027] -0.09641182642477078 BACKWARD SAMPLING FROM 4 [0.52440832 0.43628484] [ 0.03982568 -0.00373027] -0.18824731253691152 BACKWARD SAMPLING FROM -4 [0.20580285 0.46612699] [ 0.03982568 -0.00373027] -0.03551966453010344 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.36510558 0.45120592] [ 0.03982568 -0.00373027] -0.09641182642477078 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3840192746128853 goals: [('bisect', 0, array([0.36510558, 0.45120592]), array([ 0.03982568, -0.00373027]), None, None, None, 10, array([0.76336242, 0.41390323]), array([ 0.03982568, -0.00373027]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.76336242 0.41390323] [ 0.03982568 -0.00373027] -0.3840192746128853 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.09641182642477081 goals: [('bisect', 0, array([0.76336242, 0.41390323]), array([-0.03982568, 0.00373027]), None, None, None, 10, array([0.36510558, 0.45120592]), array([-0.03982568, 0.00373027]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.36510558 0.45120592] [ 0.03982568 -0.00373027] -0.09641182642477078 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.36510558 0.45120592] [ 0.03982568 -0.00373027] -0.09641182642477078 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-5, 2) ---- seed=59 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.25628056 0.56398041] [ 0.03647074 -0.01642819] -0.08400852608514758 BACKWARD SAMPLING FROM 4 [0.4021635 0.49826765] [ 0.03647074 -0.01642819] -0.08090525343003006 BACKWARD SAMPLING FROM -4 [0.11039762 0.62969317] [ 0.03647074 -0.01642819] -0.2163478092623813 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.25628056 0.56398041] [ 0.03647074 -0.01642819] -0.08400852608514758 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.318567864176321 goals: [('bisect', 0, array([0.25628056, 0.56398041]), array([ 0.03647074, -0.01642819]), None, None, None, 10, array([0.62098791, 0.39969851]), array([ 0.03647074, -0.01642819]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.62098791 0.39969851] [ 0.03647074 -0.01642819] -0.318567864176321 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.08400852608514758 goals: [('bisect', 0, array([0.62098791, 0.39969851]), array([-0.03647074, 0.01642819]), None, None, None, 10, array([0.25628056, 0.56398041]), array([-0.03647074, 0.01642819]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.25628056 0.56398041] [ 0.03647074 -0.01642819] -0.08400852608514758 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.25628056 0.56398041] [ 0.03647074 -0.01642819] -0.08400852608514758 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -5..2 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-5, 2) ---- seed=60 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.32318268 0.66574957] [ 0.03997068 -0.00153133] -0.39563502198066103 BACKWARD SAMPLING FROM 4 [0.48306538 0.65962424] [ 0.03997068 -0.00153133] -0.43517479808588755 BACKWARD SAMPLING FROM -4 [0.16329997 0.6718749 ] [ 0.03997068 -0.00153133] -0.3825957187127649 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.32318268 0.66574957] [ 0.03997068 -0.00153133] -0.39563502198066103 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.32318268, 0.66574957]), array([ 0.03997068, -0.00153133]), None, None, None, 10, array([0.72288945, 0.65043624]), array([ 0.03997068, -0.00153133]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.44920044099302747 goals: [('bisect', 0, array([0.32318268, 0.66574957]), array([ 0.03997068, -0.00153133]), 5, array([0.52303606, 0.6580929 ]), array([ 0.03997068, -0.00153133]), 10, array([0.72288945, 0.65043624]), array([ 0.03997068, -0.00153133]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.482220565464306 goals: [('bisect', 5, array([0.52303606, 0.6580929 ]), array([ 0.03997068, -0.00153133]), 7, array([0.60297742, 0.65503024]), array([ 0.03997068, -0.00153133]), 10, array([0.72288945, 0.65043624]), array([ 0.03997068, -0.00153133]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.60297742, 0.65503024]), array([ 0.03997068, -0.00153133]), 8, array([0.64294809, 0.6534989 ]), array([ 0.03997068, -0.00153133]), 10, array([0.72288945, 0.65043624]), array([ 0.03997068, -0.00153133]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.64294809 0.6534989 ] [ 0.03997068 -0.00153133] new direction: [0.0021526 0.03994204] reversing there [ 0.03997068 -0.00153133] making one step from [0.64294809 0.6534989 ] [ 0.03997068 -0.00153133] --> [0.64510069 0.69344094] [0.0021526 0.03994204] trying new point, [0.64510069 0.69344094] next() call None goals: [('reflect-at', 8, array([0.64510069, 0.69344094]), array([0.0021526 , 0.03994204]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 7... 3 steps to do at 7 -> [from 8, delta=3] targeting 5. goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 7 [0.60297742 0.65503024] [ 0.03997068 -0.00153133] -0.482220565464306 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 7)] not done yet, continue expanding to 7... goals: [('expand-to', 7), ('sample-at', 7)] next() call -0.39563502198066103 goals: [('bisect', 0, array([0.60297742, 0.65503024]), array([-0.03997068, 0.00153133]), None, None, None, 7, array([0.32318268, 0.66574957]), array([-0.03997068, 0.00153133]), 1), ('sample-at', 7)] bisecting ... 0 None 7 successfully went all the way in one jump! goals: [('sample-at', 7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.32318268 0.66574957] [ 0.03997068 -0.00153133] -0.39563502198066103 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.32318268 0.66574957] [ 0.03997068 -0.00153133] -0.39563502198066103 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-3, 4) ---- seed=61 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.87732677 0.49955131] [-0.03835883 0.01134021] -0.38485364567003044 BACKWARD SAMPLING FROM 4 [0.72389146 0.54491217] [-0.03835883 0.01134021] -0.28722320791367995 BACKWARD SAMPLING FROM -4 [0.91257131 0.47071355] [ 0.03991597 -0.00259145] -0.4271143991277972 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.87732677 0.49955131] [-0.03835883 0.01134021] -0.38485364567003044 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2813698847877405 goals: [('bisect', 0, array([0.87732677, 0.49955131]), array([-0.03835883, 0.01134021]), None, None, None, 10, array([0.49373851, 0.61295345]), array([-0.03835883, 0.01134021]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.49373851 0.61295345] [-0.03835883 0.01134021] -0.2813698847877405 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.38485364567003044 goals: [('bisect', 0, array([0.49373851, 0.61295345]), array([ 0.03835883, -0.01134021]), None, None, None, 10, array([0.87732677, 0.49955131]), array([ 0.03835883, -0.01134021]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.87732677 0.49955131] [-0.03835883 0.01134021] -0.38485364567003044 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.87732677 0.49955131] [-0.03835883 0.01134021] -0.38485364567003044 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -1..6 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -1..14 NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd sampling between (-1, 14) ---- seed=62 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.03375471 0.48910751] [-0.00435504 -0.03976221] -0.0020527700218748433 BACKWARD SAMPLING FROM 4 [0.01633454 0.33005865] [-0.00435504 -0.03976221] -0.3611341698842485 BACKWARD SAMPLING FROM -4 [0.05117488 0.64815636] [-0.00435504 -0.03976221] -0.2756882768233669 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.03375471 0.48910751] [-0.00435504 -0.03976221] -0.0020527700218748433 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.03375471, 0.48910751]), array([-0.00435504, -0.03976221]), None, None, None, 10, array([0.00979571, 0.09148537]), array([ 0.00435504, -0.03976221]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.03375471, 0.48910751]), array([-0.00435504, -0.03976221]), 5, array([0.0119795 , 0.29029644]), array([-0.00435504, -0.03976221]), 10, array([0.00979571, 0.09148537]), array([ 0.00435504, -0.03976221]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.10250385662007835 goals: [('bisect', 0, array([0.03375471, 0.48910751]), array([-0.00435504, -0.03976221]), 2, array([0.02504463, 0.40958308]), array([-0.00435504, -0.03976221]), 5, array([0.0119795 , 0.29029644]), array([-0.00435504, -0.03976221]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.21204660991891755 goals: [('bisect', 2, array([0.02504463, 0.40958308]), array([-0.00435504, -0.03976221]), 3, array([0.02068959, 0.36982087]), array([-0.00435504, -0.03976221]), 5, array([0.0119795 , 0.29029644]), array([-0.00435504, -0.03976221]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.3611341698842485 goals: [('bisect', 3, array([0.02068959, 0.36982087]), array([-0.00435504, -0.03976221]), 4, array([0.01633454, 0.33005865]), array([-0.00435504, -0.03976221]), 5, array([0.0119795 , 0.29029644]), array([-0.00435504, -0.03976221]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.0119795 0.29029644] [-0.00435504 -0.03976221] new direction: [ 0.03630756 -0.01678574] reversing there [-0.00435504 -0.03976221] making one step from [0.0119795 0.29029644] [-0.00435504 -0.03976221] --> [0.04828706 0.2735107 ] [ 0.03630756 -0.01678574] trying new point, [0.04828706 0.2735107 ] next() call None goals: [('reflect-at', 5, array([0.04828706, 0.2735107 ]), array([ 0.03630756, -0.01678574]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 4... 6 steps to do at 4 -> [from 5, delta=6] targeting -1. goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.011144436722510533 goals: [('bisect', 0, array([0.03375471, 0.48910751]), array([-0.00435504, -0.03976221]), None, None, None, -1, array([0.03810975, 0.52886972]), array([-0.00435504, -0.03976221]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 4 [0.01633454 0.33005865] [-0.00435504 -0.03976221] -0.3611341698842485 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 4)] not done yet, continue expanding to 4... goals: [('expand-to', 4), ('sample-at', 4)] next() call -0.0020527700218748433 goals: [('bisect', 0, array([0.01633454, 0.33005865]), array([0.00435504, 0.03976221]), None, None, None, 4, array([0.03375471, 0.48910751]), array([0.00435504, 0.03976221]), 1), ('sample-at', 4)] bisecting ... 0 None 4 successfully went all the way in one jump! goals: [('sample-at', 4)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -1 [0.03810975 0.52886972] [-0.00435504 -0.03976221] -0.011144436722510533 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.0020527700218748585 goals: [('bisect', 0, array([0.03810975, 0.52886972]), array([0.00435504, 0.03976221]), None, None, None, -1, array([0.03375471, 0.48910751]), array([0.00435504, 0.03976221]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.03375471 0.48910751] [-0.00435504 -0.03976221] -0.0020527700218748433 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-4, 3) ---- seed=63 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.55394102 0.39624789] [-0.03667961 0.0159564 ] -0.2879815727116586 BACKWARD SAMPLING FROM 4 [0.4072226 0.46007348] [-0.03667961 0.0159564 ] -0.10284171548913772 BACKWARD SAMPLING FROM -4 [0.586297 0.36749354] [ 0.03884142 -0.00955742] -0.3913466099390096 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.55394102 0.39624789] [-0.03667961 0.0159564 ] -0.2879815727116586 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05644864986759578 goals: [('bisect', 0, array([0.55394102, 0.39624789]), array([-0.03667961, 0.0159564 ]), None, None, None, 10, array([0.18714497, 0.55581185]), array([-0.03667961, 0.0159564 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.18714497 0.55581185] [-0.03667961 0.0159564 ] -0.05644864986759578 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2879815727116586 goals: [('bisect', 0, array([0.18714497, 0.55581185]), array([ 0.03667961, -0.0159564 ]), None, None, None, 10, array([0.55394102, 0.39624789]), array([ 0.03667961, -0.0159564 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.55394102 0.39624789] [-0.03667961 0.0159564 ] -0.2879815727116586 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.55394102 0.39624789] [-0.03667961 0.0159564 ] -0.2879815727116586 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (0, 3) ---- seed=64 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.37909853 0.56709817] [0.00649753 0.03946875] -0.12813489647882234 BACKWARD SAMPLING FROM 4 [0.39993594 0.60551572] [ 0.00067241 -0.03999435] -0.21914395901276984 BACKWARD SAMPLING FROM -4 [0.35310841 0.40922317] [0.00649753 0.03946875] -0.16534817926836115 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.37909853 0.56709817] [0.00649753 0.03946875] -0.12813489647882234 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.37909853, 0.56709817]), array([0.00649753, 0.03946875]), None, None, None, 10, array([0.44407381, 0.96178565]), array([0.00649753, 0.03946875]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.37909853, 0.56709817]), array([0.00649753, 0.03946875]), 5, array([0.41158617, 0.76444191]), array([0.00649753, 0.03946875]), 10, array([0.44407381, 0.96178565]), array([0.00649753, 0.03946875]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.343448880085709 goals: [('bisect', 0, array([0.37909853, 0.56709817]), array([0.00649753, 0.03946875]), 2, array([0.39209358, 0.64603566]), array([0.00649753, 0.03946875]), 5, array([0.41158617, 0.76444191]), array([0.00649753, 0.03946875]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.39209358, 0.64603566]), array([0.00649753, 0.03946875]), 3, array([0.39859111, 0.68550441]), array([0.00649753, 0.03946875]), 5, array([0.41158617, 0.76444191]), array([0.00649753, 0.03946875]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.39859111 0.68550441] [0.00649753 0.03946875] new direction: [ 0.00067241 -0.03999435] reversing there [0.00649753 0.03946875] making one step from [0.39859111 0.68550441] [0.00649753 0.03946875] --> [0.39926352 0.64551007] [ 0.00067241 -0.03999435] trying new point, [0.39926352 0.64551007] next() call -0.3443704192313505 goals: [('reflect-at', 3, array([0.39926352, 0.64551007]), array([ 0.00067241, -0.03999435]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.30755732018362913 goals: [('bisect', 3, array([0.39926352, 0.64551007]), array([ 0.00067241, -0.03999435]), None, None, None, 10, array([0.40397041, 0.36554963]), array([ 0.00067241, -0.03999435]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.40397041 0.36554963] [ 0.00067241 -0.03999435] -0.30755732018362913 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.40397041, 0.36554963]), array([-0.00067241, 0.03999435]), None, None, None, 10, array([0.39724629, 0.76549311]), array([-0.00067241, 0.03999435]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.13390664748382503 goals: [('bisect', 0, array([0.40397041, 0.36554963]), array([-0.00067241, 0.03999435]), 5, array([0.40060835, 0.56552137]), array([-0.00067241, 0.03999435]), 10, array([0.39724629, 0.76549311]), array([-0.00067241, 0.03999435]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.3443704192313509 goals: [('bisect', 5, array([0.40060835, 0.56552137]), array([-0.00067241, 0.03999435]), 7, array([0.39926352, 0.64551007]), array([-0.00067241, 0.03999435]), 10, array([0.39724629, 0.76549311]), array([-0.00067241, 0.03999435]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.39926352, 0.64551007]), array([-0.00067241, 0.03999435]), 8, array([0.39859111, 0.68550441]), array([-0.00067241, 0.03999435]), 10, array([0.39724629, 0.76549311]), array([-0.00067241, 0.03999435]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.39859111 0.68550441] [-0.00067241 0.03999435] new direction: [-0.00649753 -0.03946875] reversing there [-0.00067241 0.03999435] making one step from [0.39859111 0.68550441] [-0.00067241 0.03999435] --> [0.39209358 0.64603566] [-0.00649753 -0.03946875] trying new point, [0.39209358 0.64603566] next() call -0.3434488800857094 goals: [('reflect-at', 8, array([0.39209358, 0.64603566]), array([-0.00649753, -0.03946875]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.12813489647882276 goals: [('bisect', 8, array([0.39209358, 0.64603566]), array([-0.00649753, -0.03946875]), None, None, None, 10, array([0.37909853, 0.56709817]), array([-0.00649753, -0.03946875]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.37909853 0.56709817] [0.00649753 0.03946875] -0.12813489647882234 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.37909853 0.56709817] [0.00649753 0.03946875] -0.12813489647882234 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (0, 1) ---- seed=65 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.23003963 0.50571517] [-0.03020213 0.02622654] -0.02686740398467679 BACKWARD SAMPLING FROM 4 [0.1092311 0.61062131] [-0.03020213 0.02622654] -0.1589291517496356 BACKWARD SAMPLING FROM -4 [0.35084815 0.40080902] [-0.03020213 0.02622654] -0.18453284519551352 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.23003963 0.50571517] [-0.03020213 0.02622654] -0.02686740398467679 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.23003963, 0.50571517]), array([-0.03020213, 0.02622654]), None, None, None, 10, array([0.07198169, 0.76798053]), array([0.03020213, 0.02622654]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.23721446196834403 goals: [('bisect', 0, array([0.23003963, 0.50571517]), array([-0.03020213, 0.02622654]), 5, array([0.07902897, 0.63684785]), array([-0.03020213, 0.02622654]), 10, array([0.07198169, 0.76798053]), array([0.03020213, 0.02622654]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.44810893033872173 goals: [('bisect', 5, array([0.07902897, 0.63684785]), array([-0.03020213, 0.02622654]), 7, array([0.0186247 , 0.68930092]), array([-0.03020213, 0.02622654]), 10, array([0.07198169, 0.76798053]), array([0.03020213, 0.02622654]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.0186247 , 0.68930092]), array([-0.03020213, 0.02622654]), 8, array([0.01157743, 0.71552746]), array([0.03020213, 0.02622654]), 10, array([0.07198169, 0.76798053]), array([0.03020213, 0.02622654]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.01157743 0.71552746] [0.03020213 0.02622654] new direction: [0.03498847 0.01938573] reversing there [0.03020213 0.02622654] making one step from [0.01157743 0.71552746] [0.03020213 0.02622654] --> [0.0465659 0.73491319] [0.03498847 0.01938573] trying new point, [0.0465659 0.73491319] next() call None goals: [('reflect-at', 8, array([0.0465659 , 0.73491319]), array([0.03498847, 0.01938573]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 7... 3 steps to do at 7 -> [from 8, delta=3] targeting 5. goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 7 [0.0186247 0.68930092] [-0.03020213 0.02622654] -0.44810893033872173 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 7)] not done yet, continue expanding to 7... goals: [('expand-to', 7), ('sample-at', 7)] next() call -0.026867403984676807 goals: [('bisect', 0, array([0.0186247 , 0.68930092]), array([ 0.03020213, -0.02622654]), None, None, None, 7, array([0.23003963, 0.50571517]), array([ 0.03020213, -0.02622654]), 1), ('sample-at', 7)] bisecting ... 0 None 7 successfully went all the way in one jump! goals: [('sample-at', 7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.23003963 0.50571517] [-0.03020213 0.02622654] -0.02686740398467679 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.23003963 0.50571517] [-0.03020213 0.02622654] -0.02686740398467679 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-6, 1) ---- seed=66 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.36268547 0.67910888] [ 0.01903098 -0.03518269] -0.46677023948045726 BACKWARD SAMPLING FROM 4 [0.43880939 0.53837811] [ 0.01903098 -0.03518269] -0.1146878297183696 BACKWARD SAMPLING FROM 0 [0.36268547 0.67910888] [ 0.01903098 -0.03518269] -0.46677023948045726 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.36268547 0.67910888] [ 0.01903098 -0.03518269] -0.46677023948045726 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.36268547, 0.67910888]), array([ 0.01903098, -0.03518269]), None, None, None, 10, array([0.55299527, 0.32728196]), array([ 0.01903098, -0.03518269]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.10493653469527497 goals: [('bisect', 0, array([0.36268547, 0.67910888]), array([ 0.01903098, -0.03518269]), 5, array([0.45784037, 0.50319542]), array([ 0.01903098, -0.03518269]), 10, array([0.55299527, 0.32728196]), array([ 0.01903098, -0.03518269]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.17935711354999778 goals: [('bisect', 5, array([0.45784037, 0.50319542]), array([ 0.01903098, -0.03518269]), 7, array([0.49590233, 0.43283003]), array([ 0.01903098, -0.03518269]), 10, array([0.55299527, 0.32728196]), array([ 0.01903098, -0.03518269]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.2635289874278154 goals: [('bisect', 7, array([0.49590233, 0.43283003]), array([ 0.01903098, -0.03518269]), 8, array([0.51493331, 0.39764734]), array([ 0.01903098, -0.03518269]), 10, array([0.55299527, 0.32728196]), array([ 0.01903098, -0.03518269]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.37900858427260387 goals: [('bisect', 8, array([0.51493331, 0.39764734]), array([ 0.01903098, -0.03518269]), 9, array([0.53396429, 0.36246465]), array([ 0.01903098, -0.03518269]), 10, array([0.55299527, 0.32728196]), array([ 0.01903098, -0.03518269]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.55299527 0.32728196] [ 0.01903098 -0.03518269] new direction: [-0.03590325 0.01763396] reversing there [ 0.01903098 -0.03518269] making one step from [0.55299527 0.32728196] [ 0.01903098 -0.03518269] --> [0.51709202 0.34491592] [-0.03590325 0.01763396] trying new point, [0.51709202 0.34491592] next() call -0.4343304840564407 goals: [('reflect-at', 10, array([0.51709202, 0.34491592]), array([-0.03590325, 0.01763396]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.51709202 0.34491592] [-0.03590325 0.01763396] -0.4343304840564407 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.51709202, 0.34491592]), array([ 0.03590325, -0.01763396]), None, None, None, 10, array([0.87612454, 0.16857633]), array([ 0.03590325, -0.01763396]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.51709202, 0.34491592]), array([ 0.03590325, -0.01763396]), 5, array([0.69660828, 0.25674612]), array([ 0.03590325, -0.01763396]), 10, array([0.87612454, 0.16857633]), array([ 0.03590325, -0.01763396]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.51709202, 0.34491592]), array([ 0.03590325, -0.01763396]), 2, array([0.58889852, 0.309648 ]), array([ 0.03590325, -0.01763396]), 5, array([0.69660828, 0.25674612]), array([ 0.03590325, -0.01763396]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.51709202, 0.34491592]), array([ 0.03590325, -0.01763396]), 1, array([0.55299527, 0.32728196]), array([ 0.03590325, -0.01763396]), 2, array([0.58889852, 0.309648 ]), array([ 0.03590325, -0.01763396]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.55299527 0.32728196] [ 0.03590325 -0.01763396] new direction: [-0.01903098 0.03518269] reversing there [ 0.03590325 -0.01763396] making one step from [0.55299527 0.32728196] [ 0.03590325 -0.01763396] --> [0.53396429 0.36246465] [-0.01903098 0.03518269] trying new point, [0.53396429 0.36246465] next() call -0.379008584272604 goals: [('reflect-at', 1, array([0.53396429, 0.36246465]), array([-0.01903098, 0.03518269]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.46677023948045687 goals: [('bisect', 1, array([0.53396429, 0.36246465]), array([-0.01903098, 0.03518269]), None, None, None, 10, array([0.36268547, 0.67910888]), array([-0.01903098, 0.03518269]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.36268547 0.67910888] [ 0.01903098 -0.03518269] -0.46677023948045726 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.36268547 0.67910888] [ 0.01903098 -0.03518269] -0.46677023948045726 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (0, 3) ---- seed=67 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.05999793 0.38627778] [-0.03313667 -0.02240449] -0.16345916433290608 BACKWARD SAMPLING FROM 4 [0.08924898 0.33300678] [0.01670024 0.03634697] -0.3525668892829215 BACKWARD SAMPLING FROM -4 [0.1925446 0.47589575] [-0.03313667 -0.02240449] -0.025799396299807646 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.05999793 0.38627778] [-0.03313667 -0.02240449] -0.16345916433290608 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.05999793, 0.38627778]), array([-0.03313667, -0.02240449]), None, None, None, 10, array([0.27136874, 0.16223286]), array([ 0.03313667, -0.02240449]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.05999793, 0.38627778]), array([-0.03313667, -0.02240449]), 5, array([0.10568541, 0.27425532]), array([ 0.03313667, -0.02240449]), 10, array([0.27136874, 0.16223286]), array([ 0.03313667, -0.02240449]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.31417147356422026 goals: [('bisect', 0, array([0.05999793, 0.38627778]), array([-0.03313667, -0.02240449]), 2, array([0.0062754, 0.3414688]), array([ 0.03313667, -0.02240449]), 5, array([0.10568541, 0.27425532]), array([ 0.03313667, -0.02240449]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4099982344835686 goals: [('bisect', 2, array([0.0062754, 0.3414688]), array([ 0.03313667, -0.02240449]), 3, array([0.03941207, 0.3190643 ]), array([ 0.03313667, -0.02240449]), 5, array([0.10568541, 0.27425532]), array([ 0.03313667, -0.02240449]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.03941207, 0.3190643 ]), array([ 0.03313667, -0.02240449]), 4, array([0.07254874, 0.29665981]), array([ 0.03313667, -0.02240449]), 5, array([0.10568541, 0.27425532]), array([ 0.03313667, -0.02240449]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.07254874 0.29665981] [ 0.03313667 -0.02240449] new direction: [0.01670024 0.03634697] reversing there [ 0.03313667 -0.02240449] making one step from [0.07254874 0.29665981] [ 0.03313667 -0.02240449] --> [0.08924898 0.33300678] [0.01670024 0.03634697] trying new point, [0.08924898 0.33300678] next() call -0.3525668892829215 goals: [('reflect-at', 4, array([0.08924898, 0.33300678]), array([0.01670024, 0.03634697]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05057127231465654 goals: [('bisect', 4, array([0.08924898, 0.33300678]), array([0.01670024, 0.03634697]), None, None, None, 10, array([0.1894504 , 0.55108859]), array([0.01670024, 0.03634697]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.1894504 0.55108859] [0.01670024 0.03634697] -0.05057127231465654 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.1894504 , 0.55108859]), array([-0.01670024, -0.03634697]), None, None, None, 10, array([0.02244803, 0.18761891]), array([-0.01670024, -0.03634697]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.218968162054107 goals: [('bisect', 0, array([0.1894504 , 0.55108859]), array([-0.01670024, -0.03634697]), 5, array([0.10594921, 0.36935375]), array([-0.01670024, -0.03634697]), 10, array([0.02244803, 0.18761891]), array([-0.01670024, -0.03634697]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.10594921, 0.36935375]), array([-0.01670024, -0.03634697]), 7, array([0.07254874, 0.29665981]), array([-0.01670024, -0.03634697]), 10, array([0.02244803, 0.18761891]), array([-0.01670024, -0.03634697]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.3525668892829218 goals: [('bisect', 5, array([0.10594921, 0.36935375]), array([-0.01670024, -0.03634697]), 6, array([0.08924898, 0.33300678]), array([-0.01670024, -0.03634697]), 7, array([0.07254874, 0.29665981]), array([-0.01670024, -0.03634697]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.07254874 0.29665981] [-0.01670024 -0.03634697] new direction: [-0.03313667 0.02240449] reversing there [-0.01670024 -0.03634697] making one step from [0.07254874 0.29665981] [-0.01670024 -0.03634697] --> [0.03941207 0.3190643 ] [-0.03313667 0.02240449] trying new point, [0.03941207 0.3190643 ] next() call -0.4099982344835688 goals: [('reflect-at', 7, array([0.03941207, 0.3190643 ]), array([-0.03313667, 0.02240449]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.16345916433290641 goals: [('bisect', 7, array([0.03941207, 0.3190643 ]), array([-0.03313667, 0.02240449]), None, None, None, 10, array([0.05999793, 0.38627778]), array([0.03313667, 0.02240449]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.05999793 0.38627778] [-0.03313667 -0.02240449] -0.16345916433290608 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.05999793 0.38627778] [-0.03313667 -0.02240449] -0.16345916433290608 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-3, 0) ---- seed=68 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.97282329 0.53608642] [-0.0176335 -0.03590348] -0.4894704551842365 BACKWARD SAMPLING FROM 3 [0.91992279 0.42837599] [-0.0176335 -0.03590348] -0.48725395469592275 BACKWARD SAMPLING FROM 0 [0.97282329 0.53608642] [-0.0176335 -0.03590348] -0.4894704551842365 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.97282329 0.53608642] [-0.0176335 -0.03590348] -0.4894704551842365 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), None, None, None, 10, array([0.79648829, 0.17705165]), array([-0.0176335 , -0.03590348]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), 5, array([0.88465579, 0.35656904]), array([-0.0176335 , -0.03590348]), 10, array([0.79648829, 0.17705165]), array([-0.0176335 , -0.03590348]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.4554553560620388 goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), 2, array([0.93755629, 0.46427947]), array([-0.0176335 , -0.03590348]), 5, array([0.88465579, 0.35656904]), array([-0.0176335 , -0.03590348]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.48725395469592286 goals: [('bisect', 2, array([0.93755629, 0.46427947]), array([-0.0176335 , -0.03590348]), 3, array([0.91992279, 0.42837599]), array([-0.0176335 , -0.03590348]), 5, array([0.88465579, 0.35656904]), array([-0.0176335 , -0.03590348]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.91992279, 0.42837599]), array([-0.0176335 , -0.03590348]), 4, array([0.90228929, 0.39247251]), array([-0.0176335 , -0.03590348]), 5, array([0.88465579, 0.35656904]), array([-0.0176335 , -0.03590348]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.90228929 0.39247251] [-0.0176335 -0.03590348] new direction: [-0.0084219 -0.03910335] reversing there [-0.0176335 -0.03590348] making one step from [0.90228929 0.39247251] [-0.0176335 -0.03590348] --> [0.8938674 0.35336917] [-0.0084219 -0.03910335] trying new point, [0.8938674 0.35336917] next() call None goals: [('reflect-at', 4, array([0.8938674 , 0.35336917]), array([-0.0084219 , -0.03910335]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call None goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), None, None, None, -3, array([0.97427621, 0.64379685]), array([ 0.0176335 , -0.03590348]), -1), ('sample-at', -3)] bisecting ... 0 None -3 continue bisect at -2 next() call None goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), -2, array([0.99190971, 0.60789338]), array([ 0.0176335 , -0.03590348]), -3, array([0.97427621, 0.64379685]), array([ 0.0176335 , -0.03590348]), -1), ('sample-at', -3)] bisecting ... 0 -2 -3 continue bisect at -1 next() call None goals: [('bisect', 0, array([0.97282329, 0.53608642]), array([-0.0176335 , -0.03590348]), -1, array([0.99045679, 0.5719899 ]), array([-0.0176335 , -0.03590348]), -2, array([0.99190971, 0.60789338]), array([ 0.0176335 , -0.03590348]), -1), ('sample-at', -3)] bisecting ... 0 -1 -2 bisecting gave reflection point -1 [0.99045679 0.5719899 ] [-0.0176335 -0.03590348] new direction: [-0.03469108 0.01991304] reversing there [-0.0176335 -0.03590348] making one step from [0.99045679 0.5719899 ] [-0.0176335 -0.03590348] --> [0.95576572 0.59190294] [ 0.03469108 -0.01991304] trying new point, [0.95576572 0.59190294] next() call None goals: [('reflect-at', -1, array([0.95576572, 0.59190294]), array([ 0.03469108, -0.01991304]), -1), ('sample-at', -3)] goals: [('sample-at', -3)] reversing at 0... -3 steps to do at 0 -> [from -1, delta=-3] targeting 2. goals: [('sample-at', 2)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.91992279 0.42837599] [-0.0176335 -0.03590348] -0.48725395469592286 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.4894704551842364 goals: [('bisect', 0, array([0.91992279, 0.42837599]), array([0.0176335 , 0.03590348]), None, None, None, 3, array([0.97282329, 0.53608642]), array([0.0176335 , 0.03590348]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.97282329 0.53608642] [-0.0176335 -0.03590348] -0.4894704551842365 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.97282329 0.53608642] [-0.0176335 -0.03590348] -0.4894704551842365 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 sampling between (-1, 0) ---- seed=69 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.07637632 0.3176806 ] [-0.0398791 0.00310763] -0.4184212317690846 BACKWARD SAMPLING FROM 4 [0.08314008 0.33011112] [0.0398791 0.00310763] -0.3642340475299841 BACKWARD SAMPLING FROM -4 [0.23379485 0.34519503] [ 0.00209787 -0.03994495] -0.32688726566612836 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.07637632 0.3176806 ] [-0.0398791 0.00310763] -0.4184212317690846 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3379065727944484 goals: [('bisect', 0, array([0.07637632, 0.3176806 ]), array([-0.0398791 , 0.00310763]), None, None, None, 10, array([0.32241468, 0.3487569 ]), array([0.0398791 , 0.00310763]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.32241468 0.3487569 ] [0.0398791 0.00310763] -0.3379065727944484 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4184212317690846 goals: [('bisect', 0, array([0.32241468, 0.3487569 ]), array([-0.0398791 , -0.00310763]), None, None, None, 10, array([0.07637632, 0.3176806 ]), array([ 0.0398791 , -0.00310763]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.07637632 0.3176806 ] [-0.0398791 0.00310763] -0.4184212317690846 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.07637632 0.3176806 ] [-0.0398791 0.00310763] -0.4184212317690846 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-2, 1) ---- seed=70 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.8595341 0.43370097] [ 0.03378467 -0.02141486] -0.42434395908116324 BACKWARD SAMPLING FROM 1 [0.89331877 0.41228611] [ 0.03378467 -0.02141486] -0.49518079847518337 BACKWARD SAMPLING FROM -4 [0.72439543 0.5193604 ] [ 0.03378467 -0.02141486] -0.2670596822248383 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.8595341 0.43370097] [ 0.03378467 -0.02141486] -0.42434395908116324 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.8595341 , 0.43370097]), array([ 0.03378467, -0.02141486]), None, None, None, 10, array([0.80261921, 0.21955238]), array([-0.03378467, -0.02141486]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.8595341 , 0.43370097]), array([ 0.03378467, -0.02141486]), 5, array([0.97154255, 0.32662667]), array([-0.03378467, -0.02141486]), 10, array([0.80261921, 0.21955238]), array([-0.03378467, -0.02141486]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.8595341 , 0.43370097]), array([ 0.03378467, -0.02141486]), 2, array([0.92710344, 0.39087125]), array([ 0.03378467, -0.02141486]), 5, array([0.97154255, 0.32662667]), array([-0.03378467, -0.02141486]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.49518079847518337 goals: [('bisect', 0, array([0.8595341 , 0.43370097]), array([ 0.03378467, -0.02141486]), 1, array([0.89331877, 0.41228611]), array([ 0.03378467, -0.02141486]), 2, array([0.92710344, 0.39087125]), array([ 0.03378467, -0.02141486]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.92710344 0.39087125] [ 0.03378467 -0.02141486] new direction: [-0.03988025 -0.00309289] reversing there [ 0.03378467 -0.02141486] making one step from [0.92710344 0.39087125] [ 0.03378467 -0.02141486] --> [0.88722319 0.38777836] [-0.03988025 -0.00309289] trying new point, [0.88722319 0.38777836] next() call None goals: [('reflect-at', 2, array([0.88722319, 0.38777836]), array([-0.03988025, -0.00309289]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 1... 9 steps to do at 1 -> [from 2, delta=9] targeting -7. goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.28146270933833795 goals: [('bisect', 0, array([0.8595341 , 0.43370097]), array([ 0.03378467, -0.02141486]), None, None, None, -7, array([0.62304142, 0.58360498]), array([ 0.03378467, -0.02141486]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 1 [0.89331877 0.41228611] [ 0.03378467 -0.02141486] -0.49518079847518337 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 1)] not done yet, continue expanding to 1... goals: [('expand-to', 1), ('sample-at', 1)] next() call -0.42434395908116324 goals: [('bisect', 0, array([0.89331877, 0.41228611]), array([-0.03378467, 0.02141486]), None, None, None, 1, array([0.8595341 , 0.43370097]), array([-0.03378467, 0.02141486]), 1), ('sample-at', 1)] bisecting ... 0 None 1 successfully went all the way in one jump! goals: [('sample-at', 1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -7 [0.62304142 0.58360498] [ 0.03378467 -0.02141486] -0.28146270933833795 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -7)] not done yet, continue expanding to -7... goals: [('expand-to', -7), ('sample-at', -7)] next() call -0.42434395908116324 goals: [('bisect', 0, array([0.62304142, 0.58360498]), array([-0.03378467, 0.02141486]), None, None, None, -7, array([0.8595341 , 0.43370097]), array([-0.03378467, 0.02141486]), -1), ('sample-at', -7)] bisecting ... 0 None -7 successfully went all the way in one jump! goals: [('sample-at', -7)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.8595341 0.43370097] [ 0.03378467 -0.02141486] -0.42434395908116324 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (0, 1) ---- seed=71 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.18557527 0.3865993 ] [ 0.01746073 -0.03598781] -0.17796556719379159 BACKWARD SAMPLING FROM 4 [0.25971764 0.3556196 ] [0.01088008 0.03849187] -0.29429788638027965 BACKWARD SAMPLING FROM -4 [0.11573234 0.53055056] [ 0.01746073 -0.03598781] -0.018363694539417472 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.18557527 0.3865993 ] [ 0.01746073 -0.03598781] -0.17796556719379159 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.18557527, 0.3865993 ]), array([ 0.01746073, -0.03598781]), None, None, None, 10, array([0.36018262, 0.02672116]), array([ 0.01746073, -0.03598781]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.18557527, 0.3865993 ]), array([ 0.01746073, -0.03598781]), 5, array([0.27287895, 0.20666023]), array([ 0.01746073, -0.03598781]), 10, array([0.36018262, 0.02672116]), array([ 0.01746073, -0.03598781]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.4538641787707029 goals: [('bisect', 0, array([0.18557527, 0.3865993 ]), array([ 0.01746073, -0.03598781]), 2, array([0.22049674, 0.31462368]), array([ 0.01746073, -0.03598781]), 5, array([0.27287895, 0.20666023]), array([ 0.01746073, -0.03598781]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call None goals: [('bisect', 2, array([0.22049674, 0.31462368]), array([ 0.01746073, -0.03598781]), 3, array([0.23795748, 0.27863586]), array([ 0.01746073, -0.03598781]), 5, array([0.27287895, 0.20666023]), array([ 0.01746073, -0.03598781]), 1), ('sample-at', 10)] bisecting ... 2 3 5 bisecting gave reflection point 3 [0.23795748 0.27863586] [ 0.01746073 -0.03598781] new direction: [0.01088008 0.03849187] reversing there [ 0.01746073 -0.03598781] making one step from [0.23795748 0.27863586] [ 0.01746073 -0.03598781] --> [0.24883756 0.31712773] [0.01088008 0.03849187] trying new point, [0.24883756 0.31712773] next() call -0.44898840843073634 goals: [('reflect-at', 3, array([0.24883756, 0.31712773]), array([0.01088008, 0.03849187]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.14649318477587447 goals: [('bisect', 3, array([0.24883756, 0.31712773]), array([0.01088008, 0.03849187]), None, None, None, 10, array([0.32499811, 0.5865708 ]), array([0.01088008, 0.03849187]), 1), ('sample-at', 10)] bisecting ... 3 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.32499811 0.5865708 ] [0.01088008 0.03849187] -0.14649318477587447 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.32499811, 0.5865708 ]), array([-0.01088008, -0.03849187]), None, None, None, 10, array([0.21619732, 0.20165213]), array([-0.01088008, -0.03849187]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.17676633722022025 goals: [('bisect', 0, array([0.32499811, 0.5865708 ]), array([-0.01088008, -0.03849187]), 5, array([0.27059772, 0.39411146]), array([-0.01088008, -0.03849187]), 10, array([0.21619732, 0.20165213]), array([-0.01088008, -0.03849187]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.44898840843073634 goals: [('bisect', 5, array([0.27059772, 0.39411146]), array([-0.01088008, -0.03849187]), 7, array([0.24883756, 0.31712773]), array([-0.01088008, -0.03849187]), 10, array([0.21619732, 0.20165213]), array([-0.01088008, -0.03849187]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call None goals: [('bisect', 7, array([0.24883756, 0.31712773]), array([-0.01088008, -0.03849187]), 8, array([0.23795748, 0.27863586]), array([-0.01088008, -0.03849187]), 10, array([0.21619732, 0.20165213]), array([-0.01088008, -0.03849187]), 1), ('sample-at', 10)] bisecting ... 7 8 10 bisecting gave reflection point 8 [0.23795748 0.27863586] [-0.01088008 -0.03849187] new direction: [-0.01746073 0.03598781] reversing there [-0.01088008 -0.03849187] making one step from [0.23795748 0.27863586] [-0.01088008 -0.03849187] --> [0.22049674 0.31462368] [-0.01746073 0.03598781] trying new point, [0.22049674 0.31462368] next() call -0.4538641787707029 goals: [('reflect-at', 8, array([0.22049674, 0.31462368]), array([-0.01746073, 0.03598781]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.1779655671937919 goals: [('bisect', 8, array([0.22049674, 0.31462368]), array([-0.01746073, 0.03598781]), None, None, None, 10, array([0.18557527, 0.3865993 ]), array([-0.01746073, 0.03598781]), 1), ('sample-at', 10)] bisecting ... 8 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.18557527 0.3865993 ] [ 0.01746073 -0.03598781] -0.17796556719379159 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.18557527 0.3865993 ] [ 0.01746073 -0.03598781] -0.17796556719379159 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -7..0 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-7, 0) ---- seed=72 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.1067382 0.68434263] [-0.02420434 0.03184572] -0.4304741024618556 BACKWARD SAMPLING FROM 0 [0.1067382 0.68434263] [-0.02420434 0.03184572] -0.4304741024618556 BACKWARD SAMPLING FROM -4 [0.20355555 0.55695974] [-0.02420434 0.03184572] -0.06127258297454431 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.1067382 0.68434263] [-0.02420434 0.03184572] -0.4304741024618556 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.1067382 , 0.68434263]), array([-0.02420434, 0.03184572]), None, None, None, 10, array([0.13530517, 0.99720014]), array([ 0.02420434, -0.03184572]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.1067382 , 0.68434263]), array([-0.02420434, 0.03184572]), 5, array([0.01428348, 0.84357125]), array([0.02420434, 0.03184572]), 10, array([0.13530517, 0.99720014]), array([ 0.02420434, -0.03184572]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.1067382 , 0.68434263]), array([-0.02420434, 0.03184572]), 2, array([0.05832953, 0.74803408]), array([-0.02420434, 0.03184572]), 5, array([0.01428348, 0.84357125]), array([0.02420434, 0.03184572]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.1067382 , 0.68434263]), array([-0.02420434, 0.03184572]), 1, array([0.08253387, 0.71618836]), array([-0.02420434, 0.03184572]), 2, array([0.05832953, 0.74803408]), array([-0.02420434, 0.03184572]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.08253387 0.71618836] [-0.02420434 0.03184572] new direction: [-0.03993916 0.0022054 ] reversing there [-0.02420434 0.03184572] making one step from [0.08253387 0.71618836] [-0.02420434 0.03184572] --> [0.04259471 0.71839376] [-0.03993916 0.0022054 ] trying new point, [0.04259471 0.71839376] next() call None goals: [('reflect-at', 1, array([0.04259471, 0.71839376]), array([-0.03993916, 0.0022054 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 0... 10 steps to do at 0 -> [from 1, delta=10] targeting -9. goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.1834117210951967 goals: [('bisect', 0, array([0.1067382 , 0.68434263]), array([-0.02420434, 0.03184572]), None, None, None, -9, array([0.32457724, 0.39773113]), array([-0.02420434, 0.03184572]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.1067382 0.68434263] [-0.02420434 0.03184572] -0.4304741024618556 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -9 [0.32457724 0.39773113] [-0.02420434 0.03184572] -0.1834117210951967 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.4304741024618556 goals: [('bisect', 0, array([0.32457724, 0.39773113]), array([ 0.02420434, -0.03184572]), None, None, None, -9, array([0.1067382 , 0.68434263]), array([ 0.02420434, -0.03184572]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.1067382 0.68434263] [-0.02420434 0.03184572] -0.4304741024618556 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 sampling between (0, 1) ---- seed=73 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.64268992 0.53862093] [0.02818502 0.02838318] -0.2251698677879107 BACKWARD SAMPLING FROM 4 [0.72214504 0.62997003] [-0.03328493 -0.02218362] -0.4718993295718914 BACKWARD SAMPLING FROM -4 [0.52994985 0.42508821] [0.02818502 0.02838318] -0.21057062182068897 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.64268992 0.53862093] [0.02818502 0.02838318] -0.2251698677879107 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.64268992, 0.53862093]), array([0.02818502, 0.02838318]), None, None, None, 10, array([0.92454007, 0.82245273]), array([0.02818502, 0.02838318]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.64268992, 0.53862093]), array([0.02818502, 0.02838318]), 5, array([0.78361499, 0.68053683]), array([0.02818502, 0.02838318]), 10, array([0.92454007, 0.82245273]), array([0.02818502, 0.02838318]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.3580765940944526 goals: [('bisect', 0, array([0.64268992, 0.53862093]), array([0.02818502, 0.02838318]), 2, array([0.69905995, 0.59538729]), array([0.02818502, 0.02838318]), 5, array([0.78361499, 0.68053683]), array([0.02818502, 0.02838318]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.45593173307845614 goals: [('bisect', 2, array([0.69905995, 0.59538729]), array([0.02818502, 0.02838318]), 3, array([0.72724496, 0.62377047]), array([0.02818502, 0.02838318]), 5, array([0.78361499, 0.68053683]), array([0.02818502, 0.02838318]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.72724496, 0.62377047]), array([0.02818502, 0.02838318]), 4, array([0.75542998, 0.65215365]), array([0.02818502, 0.02838318]), 5, array([0.78361499, 0.68053683]), array([0.02818502, 0.02838318]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.75542998 0.65215365] [0.02818502 0.02838318] new direction: [-0.03328493 -0.02218362] reversing there [0.02818502 0.02838318] making one step from [0.75542998 0.65215365] [0.02818502 0.02838318] --> [0.72214504 0.62997003] [-0.03328493 -0.02218362] trying new point, [0.72214504 0.62997003] next() call -0.4718993295718915 goals: [('reflect-at', 4, array([0.72214504, 0.62997003]), array([-0.03328493, -0.02218362]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.13659198800339803 goals: [('bisect', 4, array([0.72214504, 0.62997003]), array([-0.03328493, -0.02218362]), None, None, None, 10, array([0.52243544, 0.49686829]), array([-0.03328493, -0.02218362]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.52243544 0.49686829] [-0.03328493 -0.02218362] -0.13659198800339803 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.52243544, 0.49686829]), array([0.03328493, 0.02218362]), None, None, None, 10, array([0.85528478, 0.71870452]), array([0.03328493, 0.02218362]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.38248798494168457 goals: [('bisect', 0, array([0.52243544, 0.49686829]), array([0.03328493, 0.02218362]), 5, array([0.68886011, 0.6077864 ]), array([0.03328493, 0.02218362]), 10, array([0.85528478, 0.71870452]), array([0.03328493, 0.02218362]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.68886011, 0.6077864 ]), array([0.03328493, 0.02218362]), 7, array([0.75542998, 0.65215365]), array([0.03328493, 0.02218362]), 10, array([0.85528478, 0.71870452]), array([0.03328493, 0.02218362]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4718993295718915 goals: [('bisect', 5, array([0.68886011, 0.6077864 ]), array([0.03328493, 0.02218362]), 6, array([0.72214504, 0.62997003]), array([0.03328493, 0.02218362]), 7, array([0.75542998, 0.65215365]), array([0.03328493, 0.02218362]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.75542998 0.65215365] [0.03328493 0.02218362] new direction: [-0.02818502 -0.02838318] reversing there [0.03328493 0.02218362] making one step from [0.75542998 0.65215365] [0.03328493 0.02218362] --> [0.72724496 0.62377047] [-0.02818502 -0.02838318] trying new point, [0.72724496 0.62377047] next() call -0.4559317330784566 goals: [('reflect-at', 7, array([0.72724496, 0.62377047]), array([-0.02818502, -0.02838318]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2251698677879109 goals: [('bisect', 7, array([0.72724496, 0.62377047]), array([-0.02818502, -0.02838318]), None, None, None, 10, array([0.64268992, 0.53862093]), array([-0.02818502, -0.02838318]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.64268992 0.53862093] [0.02818502 0.02838318] -0.2251698677879107 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.64268992 0.53862093] [0.02818502 0.02838318] -0.2251698677879107 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: 0..7 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: 0..15 sampling between (0, 15) ---- seed=74 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.869972 0.47642782] [ 0.03934543 -0.00720676] -0.38537124082534857 BACKWARD SAMPLING FROM 4 [0.908303 0.46166811] [-0.03985264 0.00343029] -0.4308738435817744 BACKWARD SAMPLING FROM -4 [0.7125903 0.50525487] [ 0.03934543 -0.00720676] -0.2542376365289063 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.869972 0.47642782] [ 0.03934543 -0.00720676] -0.38537124082534857 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.38560758459709216 goals: [('bisect', 0, array([0.869972 , 0.47642782]), array([ 0.03934543, -0.00720676]), None, None, None, 10, array([0.73657373, 0.4043602 ]), array([-0.03934543, -0.00720676]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.73657373 0.4043602 ] [-0.03934543 -0.00720676] -0.38560758459709216 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3853712408253484 goals: [('bisect', 0, array([0.73657373, 0.4043602 ]), array([0.03934543, 0.00720676]), None, None, None, 10, array([0.869972 , 0.47642782]), array([-0.03934543, 0.00720676]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.869972 0.47642782] [ 0.03934543 -0.00720676] -0.38537124082534857 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.869972 0.47642782] [ 0.03934543 -0.00720676] -0.38537124082534857 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -14..1 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-14, 1) ---- seed=75 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.67969204 0.54427582] [0.03617356 0.01707259] -0.25549498455609526 BACKWARD SAMPLING FROM 4 [0.8243863 0.61256616] [0.03617356 0.01707259] -0.49819564920674286 BACKWARD SAMPLING FROM -4 [0.53499778 0.47598547] [0.03617356 0.01707259] -0.15032003434226449 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.67969204 0.54427582] [0.03617356 0.01707259] -0.25549498455609526 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.67969204, 0.54427582]), array([0.03617356, 0.01707259]), None, None, None, 10, array([0.95857231, 0.71500168]), array([-0.03617356, 0.01707259]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.67969204, 0.54427582]), array([0.03617356, 0.01707259]), 5, array([0.86055987, 0.62963875]), array([0.03617356, 0.01707259]), 10, array([0.95857231, 0.71500168]), array([-0.03617356, 0.01707259]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.3596546025768169 goals: [('bisect', 0, array([0.67969204, 0.54427582]), array([0.03617356, 0.01707259]), 2, array([0.75203917, 0.57842099]), array([0.03617356, 0.01707259]), 5, array([0.86055987, 0.62963875]), array([0.03617356, 0.01707259]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.42462744731562935 goals: [('bisect', 2, array([0.75203917, 0.57842099]), array([0.03617356, 0.01707259]), 3, array([0.78821274, 0.59549358]), array([0.03617356, 0.01707259]), 5, array([0.86055987, 0.62963875]), array([0.03617356, 0.01707259]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.49819564920674275 goals: [('bisect', 3, array([0.78821274, 0.59549358]), array([0.03617356, 0.01707259]), 4, array([0.8243863 , 0.61256616]), array([0.03617356, 0.01707259]), 5, array([0.86055987, 0.62963875]), array([0.03617356, 0.01707259]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.86055987 0.62963875] [0.03617356 0.01707259] new direction: [-0.01645837 -0.03645713] reversing there [0.03617356 0.01707259] making one step from [0.86055987 0.62963875] [0.03617356 0.01707259] --> [0.84410149 0.59318162] [-0.01645837 -0.03645713] trying new point, [0.84410149 0.59318162] next() call -0.46478885376243206 goals: [('reflect-at', 5, array([0.84410149, 0.59318162]), array([-0.01645837, -0.03645713]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3894210121323491 goals: [('bisect', 5, array([0.84410149, 0.59318162]), array([-0.01645837, -0.03645713]), None, None, None, 10, array([0.76180964, 0.41089599]), array([-0.01645837, -0.03645713]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.76180964 0.41089599] [-0.01645837 -0.03645713] -0.3894210121323491 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.76180964, 0.41089599]), array([0.01645837, 0.03645713]), None, None, None, 10, array([0.92639335, 0.77546725]), array([0.01645837, 0.03645713]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.46478885376243206 goals: [('bisect', 0, array([0.76180964, 0.41089599]), array([0.01645837, 0.03645713]), 5, array([0.84410149, 0.59318162]), array([0.01645837, 0.03645713]), 10, array([0.92639335, 0.77546725]), array([0.01645837, 0.03645713]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.84410149, 0.59318162]), array([0.01645837, 0.03645713]), 7, array([0.87701824, 0.66609588]), array([0.01645837, 0.03645713]), 10, array([0.92639335, 0.77546725]), array([0.01645837, 0.03645713]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.84410149, 0.59318162]), array([0.01645837, 0.03645713]), 6, array([0.86055987, 0.62963875]), array([0.01645837, 0.03645713]), 7, array([0.87701824, 0.66609588]), array([0.01645837, 0.03645713]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.86055987 0.62963875] [0.01645837 0.03645713] new direction: [-0.03617356 -0.01707259] reversing there [0.01645837 0.03645713] making one step from [0.86055987 0.62963875] [0.01645837 0.03645713] --> [0.8243863 0.61256616] [-0.03617356 -0.01707259] trying new point, [0.8243863 0.61256616] next() call -0.4981956492067427 goals: [('reflect-at', 6, array([0.8243863 , 0.61256616]), array([-0.03617356, -0.01707259]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.25549498455609504 goals: [('bisect', 6, array([0.8243863 , 0.61256616]), array([-0.03617356, -0.01707259]), None, None, None, 10, array([0.67969204, 0.54427582]), array([-0.03617356, -0.01707259]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.67969204 0.54427582] [0.03617356 0.01707259] -0.25549498455609526 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.67969204 0.54427582] [0.03617356 0.01707259] -0.25549498455609526 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -12..3 NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd NUTS step: tree depth 4, fwd sampling between (-12, 3) ---- seed=76 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.65630586 0.36945572] [0.0063244 0.03949686] -0.4283913100076723 BACKWARD SAMPLING FROM 4 [0.68160346 0.52744316] [0.0063244 0.03949686] -0.24170572618743733 BACKWARD SAMPLING FROM 0 [0.65630586 0.36945572] [0.0063244 0.03949686] -0.4283913100076723 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.65630586 0.36945572] [0.0063244 0.03949686] -0.4283913100076723 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.65630586, 0.36945572]), array([0.0063244 , 0.03949686]), None, None, None, 10, array([0.71954987, 0.76442432]), array([0.0063244 , 0.03949686]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.2926344475809864 goals: [('bisect', 0, array([0.65630586, 0.36945572]), array([0.0063244 , 0.03949686]), 5, array([0.68792786, 0.56694002]), array([0.0063244 , 0.03949686]), 10, array([0.71954987, 0.76442432]), array([0.0063244 , 0.03949686]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.68792786, 0.56694002]), array([0.0063244 , 0.03949686]), 7, array([0.70057666, 0.64593374]), array([0.0063244 , 0.03949686]), 10, array([0.71954987, 0.76442432]), array([0.0063244 , 0.03949686]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.3826032159139785 goals: [('bisect', 5, array([0.68792786, 0.56694002]), array([0.0063244 , 0.03949686]), 6, array([0.69425226, 0.60643688]), array([0.0063244 , 0.03949686]), 7, array([0.70057666, 0.64593374]), array([0.0063244 , 0.03949686]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.70057666 0.64593374] [0.0063244 0.03949686] new direction: [-0.03371121 0.02153031] reversing there [0.0063244 0.03949686] making one step from [0.70057666 0.64593374] [0.0063244 0.03949686] --> [0.66686545 0.66746405] [-0.03371121 0.02153031] trying new point, [0.66686545 0.66746405] next() call None goals: [('reflect-at', 7, array([0.66686545, 0.66746405]), array([-0.03371121, 0.02153031]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 6... 4 steps to do at 6 -> [from 7, delta=4] targeting 3. goals: [('sample-at', 3)] target is on track, returning interpolation at 3... [0.67527906 0.4879463 ] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 6 [0.69425226 0.60643688] [0.0063244 0.03949686] -0.3826032159139785 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 6)] not done yet, continue expanding to 6... goals: [('expand-to', 6), ('sample-at', 6)] next() call -0.4283913100076723 goals: [('bisect', 0, array([0.69425226, 0.60643688]), array([-0.0063244 , -0.03949686]), None, None, None, 6, array([0.65630586, 0.36945572]), array([-0.0063244 , -0.03949686]), 1), ('sample-at', 6)] bisecting ... 0 None 6 successfully went all the way in one jump! goals: [('sample-at', 6)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.65630586 0.36945572] [0.0063244 0.03949686] -0.4283913100076723 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.65630586 0.36945572] [0.0063244 0.03949686] -0.4283913100076723 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 sampling between (-1, 0) ---- seed=77 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.32615094 0.54106782] [-0.03291268 0.02273226] -0.07426929137401193 BACKWARD SAMPLING FROM 4 [0.19450023 0.63199687] [-0.03291268 0.02273226] -0.23670484165889305 BACKWARD SAMPLING FROM -4 [0.45780164 0.45013877] [-0.03291268 0.02273226] -0.1358679463834473 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.32615094 0.54106782] [-0.03291268 0.02273226] -0.07426929137401193 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.32615094, 0.54106782]), array([-0.03291268, 0.02273226]), None, None, None, 10, array([0.00297583, 0.76839044]), array([0.03291268, 0.02273226]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.3123190738073511 goals: [('bisect', 0, array([0.32615094, 0.54106782]), array([-0.03291268, 0.02273226]), 5, array([0.16158756, 0.65472913]), array([-0.03291268, 0.02273226]), 10, array([0.00297583, 0.76839044]), array([0.03291268, 0.02273226]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.16158756, 0.65472913]), array([-0.03291268, 0.02273226]), 7, array([0.0957622 , 0.70019366]), array([-0.03291268, 0.02273226]), 10, array([0.00297583, 0.76839044]), array([0.03291268, 0.02273226]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4019354437867032 goals: [('bisect', 5, array([0.16158756, 0.65472913]), array([-0.03291268, 0.02273226]), 6, array([0.12867488, 0.67746139]), array([-0.03291268, 0.02273226]), 7, array([0.0957622 , 0.70019366]), array([-0.03291268, 0.02273226]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.0957622 0.70019366] [-0.03291268 0.02273226] new direction: [-0.01742501 -0.03600513] reversing there [-0.03291268 0.02273226] making one step from [0.0957622 0.70019366] [-0.03291268 0.02273226] --> [0.07833719 0.66418853] [-0.01742501 -0.03600513] trying new point, [0.07833719 0.66418853] next() call -0.34004177980724587 goals: [('reflect-at', 7, array([0.07833719, 0.66418853]), array([-0.01742501, -0.03600513]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.039782409475446034 goals: [('bisect', 7, array([0.07833719, 0.66418853]), array([-0.01742501, -0.03600513]), None, None, None, 10, array([0.02606217, 0.55617315]), array([-0.01742501, -0.03600513]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.02606217 0.55617315] [-0.01742501 -0.03600513] -0.039782409475446034 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.02606217, 0.55617315]), array([0.01742501, 0.03600513]), None, None, None, 10, array([0.20031226, 0.91622441]), array([0.01742501, 0.03600513]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.02606217, 0.55617315]), array([0.01742501, 0.03600513]), 5, array([0.11318721, 0.73619878]), array([0.01742501, 0.03600513]), 10, array([0.20031226, 0.91622441]), array([0.01742501, 0.03600513]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.20724246552376055 goals: [('bisect', 0, array([0.02606217, 0.55617315]), array([0.01742501, 0.03600513]), 2, array([0.06091219, 0.62818341]), array([0.01742501, 0.03600513]), 5, array([0.11318721, 0.73619878]), array([0.01742501, 0.03600513]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.3400417798072463 goals: [('bisect', 2, array([0.06091219, 0.62818341]), array([0.01742501, 0.03600513]), 3, array([0.07833719, 0.66418853]), array([0.01742501, 0.03600513]), 5, array([0.11318721, 0.73619878]), array([0.01742501, 0.03600513]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.07833719, 0.66418853]), array([0.01742501, 0.03600513]), 4, array([0.0957622 , 0.70019366]), array([0.01742501, 0.03600513]), 5, array([0.11318721, 0.73619878]), array([0.01742501, 0.03600513]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.0957622 0.70019366] [0.01742501 0.03600513] new direction: [ 0.03291268 -0.02273226] reversing there [0.01742501 0.03600513] making one step from [0.0957622 0.70019366] [0.01742501 0.03600513] --> [0.12867488 0.67746139] [ 0.03291268 -0.02273226] trying new point, [0.12867488 0.67746139] next() call -0.4019354437867037 goals: [('reflect-at', 4, array([0.12867488, 0.67746139]), array([ 0.03291268, -0.02273226]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.07426929137401193 goals: [('bisect', 4, array([0.12867488, 0.67746139]), array([ 0.03291268, -0.02273226]), None, None, None, 10, array([0.32615094, 0.54106782]), array([ 0.03291268, -0.02273226]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.32615094 0.54106782] [-0.03291268 0.02273226] -0.07426929137401193 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.32615094 0.54106782] [-0.03291268 0.02273226] -0.07426929137401193 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-3, 4) ---- seed=78 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.04818123 0.68096301] [-0.02701408 -0.02949982] -0.4105058701928128 BACKWARD SAMPLING FROM 4 [0.05987511 0.56296374] [ 0.02701408 -0.02949982] -0.05134792603041273 BACKWARD SAMPLING FROM 0 [0.04818123 0.68096301] [-0.02701408 -0.02949982] -0.4105058701928128 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.04818123 0.68096301] [-0.02701408 -0.02949982] -0.4105058701928128 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.18718326659217294 goals: [('bisect', 0, array([0.04818123, 0.68096301]), array([-0.02701408, -0.02949982]), None, None, None, 10, array([0.22195962, 0.38596484]), array([ 0.02701408, -0.02949982]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.22195962 0.38596484] [ 0.02701408 -0.02949982] -0.18718326659217294 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.41050587019281287 goals: [('bisect', 0, array([0.22195962, 0.38596484]), array([-0.02701408, 0.02949982]), None, None, None, 10, array([0.04818123, 0.68096301]), array([0.02701408, 0.02949982]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.04818123 0.68096301] [-0.02701408 -0.02949982] -0.4105058701928128 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.04818123 0.68096301] [-0.02701408 -0.02949982] -0.4105058701928128 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 sampling between (-1, 0) ---- seed=79 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.50066813 0.46806743] [0.01781275 0.03581488] -0.13808040243633282 BACKWARD SAMPLING FROM 4 [0.57191913 0.61132696] [0.01781275 0.03581488] -0.3184669014578434 BACKWARD SAMPLING FROM -4 [0.42941712 0.32480789] [0.01781275 0.03581488] -0.4758529786384704 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.50066813 0.46806743] [0.01781275 0.03581488] -0.13808040243633282 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.50066813, 0.46806743]), array([0.01781275, 0.03581488]), None, None, None, 10, array([0.67879563, 0.82621627]), array([0.01781275, 0.03581488]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4445258817169163 goals: [('bisect', 0, array([0.50066813, 0.46806743]), array([0.01781275, 0.03581488]), 5, array([0.58973188, 0.64714185]), array([0.01781275, 0.03581488]), 10, array([0.67879563, 0.82621627]), array([0.01781275, 0.03581488]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.58973188, 0.64714185]), array([0.01781275, 0.03581488]), 7, array([0.62535738, 0.71877161]), array([0.01781275, 0.03581488]), 10, array([0.67879563, 0.82621627]), array([0.01781275, 0.03581488]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.58973188, 0.64714185]), array([0.01781275, 0.03581488]), 6, array([0.60754463, 0.68295673]), array([0.01781275, 0.03581488]), 7, array([0.62535738, 0.71877161]), array([0.01781275, 0.03581488]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.60754463 0.68295673] [0.01781275 0.03581488] new direction: [-0.03971424 -0.00477279] reversing there [0.01781275 0.03581488] making one step from [0.60754463 0.68295673] [0.01781275 0.03581488] --> [0.56783039 0.67818395] [-0.03971424 -0.00477279] trying new point, [0.56783039 0.67818395] next() call None goals: [('reflect-at', 6, array([0.56783039, 0.67818395]), array([-0.03971424, -0.00477279]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 5... 5 steps to do at 5 -> [from 6, delta=5] targeting 1. goals: [('sample-at', 1)] target is on track, returning interpolation at 1... [0.51848088 0.50388231] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 5 [0.58973188 0.64714185] [0.01781275 0.03581488] -0.4445258817169163 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 5)] not done yet, continue expanding to 5... goals: [('expand-to', 5), ('sample-at', 5)] next() call -0.13808040243633282 goals: [('bisect', 0, array([0.58973188, 0.64714185]), array([-0.01781275, -0.03581488]), None, None, None, 5, array([0.50066813, 0.46806743]), array([-0.01781275, -0.03581488]), 1), ('sample-at', 5)] bisecting ... 0 None 5 successfully went all the way in one jump! goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.50066813 0.46806743] [0.01781275 0.03581488] -0.13808040243633282 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.50066813 0.46806743] [0.01781275 0.03581488] -0.13808040243633282 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-3, 4) ---- seed=80 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.26986897 0.67448187] [-0.03941662 0.0068066 ] -0.41696368821130175 BACKWARD SAMPLING FROM 3 [0.1516191 0.69490167] [-0.03941662 0.0068066 ] -0.4863274153461391 BACKWARD SAMPLING FROM -4 [0.42753547 0.64725549] [-0.03941662 0.0068066 ] -0.36244551853049845 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.26986897 0.67448187] [-0.03941662 0.0068066 ] -0.41696368821130175 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.26986897, 0.67448187]), array([-0.03941662, 0.0068066 ]), None, None, None, 10, array([0.12429727, 0.74254784]), array([0.03941662, 0.0068066 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.26986897, 0.67448187]), array([-0.03941662, 0.0068066 ]), 5, array([0.07278585, 0.70851486]), array([-0.03941662, 0.0068066 ]), 10, array([0.12429727, 0.74254784]), array([0.03941662, 0.0068066 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.46049425869412336 goals: [('bisect', 0, array([0.26986897, 0.67448187]), array([-0.03941662, 0.0068066 ]), 2, array([0.19103572, 0.68809507]), array([-0.03941662, 0.0068066 ]), 5, array([0.07278585, 0.70851486]), array([-0.03941662, 0.0068066 ]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4863274153461391 goals: [('bisect', 2, array([0.19103572, 0.68809507]), array([-0.03941662, 0.0068066 ]), 3, array([0.1516191 , 0.69490167]), array([-0.03941662, 0.0068066 ]), 5, array([0.07278585, 0.70851486]), array([-0.03941662, 0.0068066 ]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.1516191 , 0.69490167]), array([-0.03941662, 0.0068066 ]), 4, array([0.11220248, 0.70170826]), array([-0.03941662, 0.0068066 ]), 5, array([0.07278585, 0.70851486]), array([-0.03941662, 0.0068066 ]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.11220248 0.70170826] [-0.03941662 0.0068066 ] new direction: [-0.03348855 0.02187504] reversing there [-0.03941662 0.0068066 ] making one step from [0.11220248 0.70170826] [-0.03941662 0.0068066 ] --> [0.07871393 0.7235833 ] [-0.03348855 0.02187504] trying new point, [0.07871393 0.7235833 ] next() call None goals: [('reflect-at', 4, array([0.07871393, 0.7235833 ]), array([-0.03348855, 0.02187504]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.37200718954009426 goals: [('bisect', 0, array([0.26986897, 0.67448187]), array([-0.03941662, 0.0068066 ]), None, None, None, -3, array([0.38811884, 0.65406208]), array([-0.03941662, 0.0068066 ]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.1516191 0.69490167] [-0.03941662 0.0068066 ] -0.4863274153461391 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.41696368821130175 goals: [('bisect', 0, array([0.1516191 , 0.69490167]), array([ 0.03941662, -0.0068066 ]), None, None, None, 3, array([0.26986897, 0.67448187]), array([ 0.03941662, -0.0068066 ]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.38811884 0.65406208] [-0.03941662 0.0068066 ] -0.37200718954009426 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.41696368821130175 goals: [('bisect', 0, array([0.38811884, 0.65406208]), array([ 0.03941662, -0.0068066 ]), None, None, None, -3, array([0.26986897, 0.67448187]), array([ 0.03941662, -0.0068066 ]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.26986897 0.67448187] [-0.03941662 0.0068066 ] -0.41696368821130175 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-1, 2) ---- seed=81 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.35541912 0.34084888] [-0.00924508 -0.03891694] -0.3797748752889402 BACKWARD SAMPLING FROM 4 [0.45306473 0.42098814] [0.02672267 0.02976405] -0.18066975294765303 BACKWARD SAMPLING FROM -4 [0.39239943 0.49651665] [-0.00924508 -0.03891694] -0.07714032966931968 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.35541912 0.34084888] [-0.00924508 -0.03891694] -0.3797748752889402 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.35541912, 0.34084888]), array([-0.00924508, -0.03891694]), None, None, None, 10, array([0.26296833, 0.04832056]), array([-0.00924508, 0.03891694]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.35541912, 0.34084888]), array([-0.00924508, -0.03891694]), 5, array([0.30919372, 0.14626416]), array([-0.00924508, -0.03891694]), 10, array([0.26296833, 0.04832056]), array([-0.00924508, 0.03891694]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.35541912, 0.34084888]), array([-0.00924508, -0.03891694]), 2, array([0.33692896, 0.26301499]), array([-0.00924508, -0.03891694]), 5, array([0.30919372, 0.14626416]), array([-0.00924508, -0.03891694]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.35541912, 0.34084888]), array([-0.00924508, -0.03891694]), 1, array([0.34617404, 0.30193193]), array([-0.00924508, -0.03891694]), 2, array([0.33692896, 0.26301499]), array([-0.00924508, -0.03891694]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.34617404 0.30193193] [-0.00924508 -0.03891694] new direction: [0.02672267 0.02976405] reversing there [-0.00924508 -0.03891694] making one step from [0.34617404 0.30193193] [-0.00924508 -0.03891694] --> [0.37289671 0.33169598] [0.02672267 0.02976405] trying new point, [0.37289671 0.33169598] next() call -0.4236039997490474 goals: [('reflect-at', 1, array([0.37289671, 0.33169598]), array([0.02672267, 0.02976405]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3120636527905234 goals: [('bisect', 1, array([0.37289671, 0.33169598]), array([0.02672267, 0.02976405]), None, None, None, 10, array([0.61340077, 0.59957245]), array([0.02672267, 0.02976405]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.61340077 0.59957245] [0.02672267 0.02976405] -0.3120636527905234 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.61340077, 0.59957245]), array([-0.02672267, -0.02976405]), None, None, None, 10, array([0.34617404, 0.30193193]), array([-0.02672267, -0.02976405]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.14541481093575562 goals: [('bisect', 0, array([0.61340077, 0.59957245]), array([-0.02672267, -0.02976405]), 5, array([0.4797874 , 0.45075219]), array([-0.02672267, -0.02976405]), 10, array([0.34617404, 0.30193193]), array([-0.02672267, -0.02976405]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.2387862650871675 goals: [('bisect', 5, array([0.4797874 , 0.45075219]), array([-0.02672267, -0.02976405]), 7, array([0.42634206, 0.39122409]), array([-0.02672267, -0.02976405]), 10, array([0.34617404, 0.30193193]), array([-0.02672267, -0.02976405]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3197643473542989 goals: [('bisect', 7, array([0.42634206, 0.39122409]), array([-0.02672267, -0.02976405]), 8, array([0.39961938, 0.36146004]), array([-0.02672267, -0.02976405]), 10, array([0.34617404, 0.30193193]), array([-0.02672267, -0.02976405]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4236039997490474 goals: [('bisect', 8, array([0.39961938, 0.36146004]), array([-0.02672267, -0.02976405]), 9, array([0.37289671, 0.33169598]), array([-0.02672267, -0.02976405]), 10, array([0.34617404, 0.30193193]), array([-0.02672267, -0.02976405]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.34617404 0.30193193] [-0.02672267 -0.02976405] new direction: [0.00924508 0.03891694] reversing there [-0.02672267 -0.02976405] making one step from [0.34617404 0.30193193] [-0.02672267 -0.02976405] --> [0.35541912 0.34084888] [0.00924508 0.03891694] trying new point, [0.35541912 0.34084888] next() call -0.37977487528894 goals: [('reflect-at', 10, array([0.35541912, 0.34084888]), array([0.00924508, 0.03891694]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.35541912 0.34084888] [-0.00924508 -0.03891694] -0.3797748752889402 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.35541912 0.34084888] [-0.00924508 -0.03891694] -0.3797748752889402 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-3, 4) ---- seed=82 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.27522567 0.63923076] [ 0.00848742 -0.03908918] -0.2801896445066674 BACKWARD SAMPLING FROM 4 [0.30917537 0.48287406] [ 0.00848742 -0.03908918] -0.051460926903102756 BACKWARD SAMPLING FROM -1 [0.26673825 0.67831994] [ 0.00848742 -0.03908918] -0.433049641425479 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.27522567 0.63923076] [ 0.00848742 -0.03908918] -0.2801896445066674 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.27522567, 0.63923076]), array([ 0.00848742, -0.03908918]), None, None, None, 10, array([0.36009991, 0.24833901]), array([ 0.00848742, -0.03908918]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.08995656502013205 goals: [('bisect', 0, array([0.27522567, 0.63923076]), array([ 0.00848742, -0.03908918]), 5, array([0.31766279, 0.44378488]), array([ 0.00848742, -0.03908918]), 10, array([0.36009991, 0.24833901]), array([ 0.00848742, -0.03908918]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.2817612222756952 goals: [('bisect', 5, array([0.31766279, 0.44378488]), array([ 0.00848742, -0.03908918]), 7, array([0.33463764, 0.36560653]), array([ 0.00848742, -0.03908918]), 10, array([0.36009991, 0.24833901]), array([ 0.00848742, -0.03908918]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4350702414142291 goals: [('bisect', 7, array([0.33463764, 0.36560653]), array([ 0.00848742, -0.03908918]), 8, array([0.34312507, 0.32651736]), array([ 0.00848742, -0.03908918]), 10, array([0.36009991, 0.24833901]), array([ 0.00848742, -0.03908918]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.34312507, 0.32651736]), array([ 0.00848742, -0.03908918]), 9, array([0.35161249, 0.28742818]), array([ 0.00848742, -0.03908918]), 10, array([0.36009991, 0.24833901]), array([ 0.00848742, -0.03908918]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.35161249 0.28742818] [ 0.00848742 -0.03908918] new direction: [-0.01853685 0.03544552] reversing there [ 0.00848742 -0.03908918] making one step from [0.35161249 0.28742818] [ 0.00848742 -0.03908918] --> [0.33307564 0.32287371] [-0.01853685 0.03544552] trying new point, [0.33307564 0.32287371] next() call -0.4476412362297438 goals: [('reflect-at', 9, array([0.33307564, 0.32287371]), array([-0.01853685, 0.03544552]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3003853287533051 goals: [('bisect', 9, array([0.33307564, 0.32287371]), array([-0.01853685, 0.03544552]), None, None, None, 10, array([0.31453879, 0.35831923]), array([-0.01853685, 0.03544552]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.31453879 0.35831923] [-0.01853685 0.03544552] -0.3003853287533051 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.31453879, 0.35831923]), array([ 0.01853685, -0.03544552]), None, None, None, 10, array([0.4999073 , 0.00386399]), array([ 0.01853685, -0.03544552]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.31453879, 0.35831923]), array([ 0.01853685, -0.03544552]), 5, array([0.40722304, 0.18109161]), array([ 0.01853685, -0.03544552]), 10, array([0.4999073 , 0.00386399]), array([ 0.01853685, -0.03544552]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.31453879, 0.35831923]), array([ 0.01853685, -0.03544552]), 2, array([0.35161249, 0.28742818]), array([ 0.01853685, -0.03544552]), 5, array([0.40722304, 0.18109161]), array([ 0.01853685, -0.03544552]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.4476412362297438 goals: [('bisect', 0, array([0.31453879, 0.35831923]), array([ 0.01853685, -0.03544552]), 1, array([0.33307564, 0.32287371]), array([ 0.01853685, -0.03544552]), 2, array([0.35161249, 0.28742818]), array([ 0.01853685, -0.03544552]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.35161249 0.28742818] [ 0.01853685 -0.03544552] new direction: [-0.00848742 0.03908918] reversing there [ 0.01853685 -0.03544552] making one step from [0.35161249 0.28742818] [ 0.01853685 -0.03544552] --> [0.34312507 0.32651736] [-0.00848742 0.03908918] trying new point, [0.34312507 0.32651736] next() call -0.43507024141422884 goals: [('reflect-at', 2, array([0.34312507, 0.32651736]), array([-0.00848742, 0.03908918]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2801896445066674 goals: [('bisect', 2, array([0.34312507, 0.32651736]), array([-0.00848742, 0.03908918]), None, None, None, 10, array([0.27522567, 0.63923076]), array([-0.00848742, 0.03908918]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.27522567 0.63923076] [ 0.00848742 -0.03908918] -0.2801896445066674 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.27522567 0.63923076] [ 0.00848742 -0.03908918] -0.2801896445066674 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-1, 2) ---- seed=83 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.25704243 0.68378758] [-0.03248384 0.02334096] -0.4552588508048801 BACKWARD SAMPLING FROM 0 [0.25704243 0.68378758] [-0.03248384 0.02334096] -0.4552588508048801 BACKWARD SAMPLING FROM -4 [0.38697777 0.59042374] [-0.03248384 0.02334096] -0.1770815538786539 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.25704243 0.68378758] [-0.03248384 0.02334096] -0.4552588508048801 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.25704243, 0.68378758]), array([-0.03248384, 0.02334096]), None, None, None, 10, array([0.06779592, 0.91719719]), array([0.03248384, 0.02334096]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.25704243, 0.68378758]), array([-0.03248384, 0.02334096]), 5, array([0.09462325, 0.80049239]), array([-0.03248384, 0.02334096]), 10, array([0.06779592, 0.91719719]), array([0.03248384, 0.02334096]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.25704243, 0.68378758]), array([-0.03248384, 0.02334096]), 2, array([0.19207476, 0.73046951]), array([-0.03248384, 0.02334096]), 5, array([0.09462325, 0.80049239]), array([-0.03248384, 0.02334096]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.25704243, 0.68378758]), array([-0.03248384, 0.02334096]), 1, array([0.22455859, 0.70712854]), array([-0.03248384, 0.02334096]), 2, array([0.19207476, 0.73046951]), array([-0.03248384, 0.02334096]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.22455859 0.70712854] [-0.03248384 0.02334096] new direction: [-0.03398668 0.02109279] reversing there [-0.03248384 0.02334096] making one step from [0.22455859 0.70712854] [-0.03248384 0.02334096] --> [0.19057191 0.72822133] [-0.03398668 0.02109279] trying new point, [0.19057191 0.72822133] next() call None goals: [('reflect-at', 1, array([0.19057191, 0.72822133]), array([-0.03398668, 0.02109279]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 0... 10 steps to do at 0 -> [from 1, delta=10] targeting -9. goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.15955218049839826 goals: [('bisect', 0, array([0.25704243, 0.68378758]), array([-0.03248384, 0.02334096]), None, None, None, -9, array([0.54939694, 0.47371893]), array([-0.03248384, 0.02334096]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.25704243 0.68378758] [-0.03248384 0.02334096] -0.4552588508048801 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -9 [0.54939694 0.47371893] [-0.03248384 0.02334096] -0.15955218049839826 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -9)] not done yet, continue expanding to -9... goals: [('expand-to', -9), ('sample-at', -9)] next() call -0.4552588508048801 goals: [('bisect', 0, array([0.54939694, 0.47371893]), array([ 0.03248384, -0.02334096]), None, None, None, -9, array([0.25704243, 0.68378758]), array([ 0.03248384, -0.02334096]), -1), ('sample-at', -9)] bisecting ... 0 None -9 successfully went all the way in one jump! goals: [('sample-at', -9)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.25704243 0.68378758] [-0.03248384 0.02334096] -0.4552588508048801 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 sampling between (0, 1) ---- seed=84 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.04604099 0.37024232] [-0.03477044 -0.01977415] -0.2115230931353383 BACKWARD SAMPLING FROM 4 [0.0588575 0.31191846] [-0.03418324 0.02077272] -0.4439154472871768 BACKWARD SAMPLING FROM -4 [0.18512274 0.4493389 ] [-0.03477044 -0.01977415] -0.049217056098525905 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.04604099 0.37024232] [-0.03477044 -0.01977415] -0.2115230931353383 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.04604099, 0.37024232]), array([-0.03477044, -0.01977415]), None, None, None, 10, array([0.30166336, 0.17250086]), array([ 0.03477044, -0.01977415]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.04604099, 0.37024232]), array([-0.03477044, -0.01977415]), 5, array([0.12781118, 0.27137159]), array([ 0.03477044, -0.01977415]), 10, array([0.30166336, 0.17250086]), array([ 0.03477044, -0.01977415]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.35858253304774335 goals: [('bisect', 0, array([0.04604099, 0.37024232]), array([-0.03477044, -0.01977415]), 2, array([0.02349988, 0.33069403]), array([ 0.03477044, -0.01977415]), 5, array([0.12781118, 0.27137159]), array([ 0.03477044, -0.01977415]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.4485888583524455 goals: [('bisect', 2, array([0.02349988, 0.33069403]), array([ 0.03477044, -0.01977415]), 3, array([0.05827031, 0.31091988]), array([ 0.03477044, -0.01977415]), 5, array([0.12781118, 0.27137159]), array([ 0.03477044, -0.01977415]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.05827031, 0.31091988]), array([ 0.03477044, -0.01977415]), 4, array([0.09304075, 0.29114574]), array([ 0.03477044, -0.01977415]), 5, array([0.12781118, 0.27137159]), array([ 0.03477044, -0.01977415]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.09304075 0.29114574] [ 0.03477044 -0.01977415] new direction: [-0.03418324 0.02077272] reversing there [ 0.03477044 -0.01977415] making one step from [0.09304075 0.29114574] [ 0.03477044 -0.01977415] --> [0.0588575 0.31191846] [-0.03418324 0.02077272] trying new point, [0.0588575 0.31191846] next() call -0.4439154472871768 goals: [('reflect-at', 4, array([0.0588575 , 0.31191846]), array([-0.03418324, 0.02077272]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.061009567312068635 goals: [('bisect', 4, array([0.0588575 , 0.31191846]), array([-0.03418324, 0.02077272]), None, None, None, 10, array([0.14624196, 0.43655477]), array([0.03418324, 0.02077272]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.14624196 0.43655477] [0.03418324 0.02077272] -0.061009567312068635 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.14624196, 0.43655477]), array([-0.03418324, -0.02077272]), None, None, None, 10, array([0.19559048, 0.22882758]), array([ 0.03418324, -0.02077272]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.35020744828671635 goals: [('bisect', 0, array([0.14624196, 0.43655477]), array([-0.03418324, -0.02077272]), 5, array([0.02467426, 0.33269117]), array([ 0.03418324, -0.02077272]), 10, array([0.19559048, 0.22882758]), array([ 0.03418324, -0.02077272]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.02467426, 0.33269117]), array([ 0.03418324, -0.02077272]), 7, array([0.09304075, 0.29114574]), array([ 0.03418324, -0.02077272]), 10, array([0.19559048, 0.22882758]), array([ 0.03418324, -0.02077272]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4439154472871768 goals: [('bisect', 5, array([0.02467426, 0.33269117]), array([ 0.03418324, -0.02077272]), 6, array([0.0588575 , 0.31191846]), array([ 0.03418324, -0.02077272]), 7, array([0.09304075, 0.29114574]), array([ 0.03418324, -0.02077272]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.09304075 0.29114574] [ 0.03418324 -0.02077272] new direction: [-0.03477044 0.01977415] reversing there [ 0.03418324 -0.02077272] making one step from [0.09304075 0.29114574] [ 0.03418324 -0.02077272] --> [0.05827031 0.31091988] [-0.03477044 0.01977415] trying new point, [0.05827031 0.31091988] next() call -0.4485888583524455 goals: [('reflect-at', 7, array([0.05827031, 0.31091988]), array([-0.03477044, 0.01977415]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.2115230931353383 goals: [('bisect', 7, array([0.05827031, 0.31091988]), array([-0.03477044, 0.01977415]), None, None, None, 10, array([0.04604099, 0.37024232]), array([0.03477044, 0.01977415]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.04604099 0.37024232] [-0.03477044 -0.01977415] -0.2115230931353383 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.04604099 0.37024232] [-0.03477044 -0.01977415] -0.2115230931353383 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -6..1 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-6, 1) ---- seed=85 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.62037381 0.50895307] [0.03756862 0.01373314] -0.19343380286879597 BACKWARD SAMPLING FROM 4 [0.77064828 0.56388562] [0.03756862 0.01373314] -0.3479665415854334 BACKWARD SAMPLING FROM -4 [0.47009935 0.45402052] [0.03756862 0.01373314] -0.13692311038869834 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.62037381 0.50895307] [0.03756862 0.01373314] -0.19343380286879597 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.62037381, 0.50895307]), array([0.03756862, 0.01373314]), None, None, None, 10, array([0.99605998, 0.64628445]), array([0.03756862, 0.01373314]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4019156709890521 goals: [('bisect', 0, array([0.62037381, 0.50895307]), array([0.03756862, 0.01373314]), 5, array([0.8082169 , 0.57761876]), array([0.03756862, 0.01373314]), 10, array([0.99605998, 0.64628445]), array([0.03756862, 0.01373314]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.8082169 , 0.57761876]), array([0.03756862, 0.01373314]), 7, array([0.88335413, 0.60508503]), array([0.03756862, 0.01373314]), 10, array([0.99605998, 0.64628445]), array([0.03756862, 0.01373314]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.4619911782824545 goals: [('bisect', 5, array([0.8082169 , 0.57761876]), array([0.03756862, 0.01373314]), 6, array([0.84578551, 0.5913519 ]), array([0.03756862, 0.01373314]), 7, array([0.88335413, 0.60508503]), array([0.03756862, 0.01373314]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.88335413 0.60508503] [0.03756862 0.01373314] new direction: [-0.01499532 -0.03708288] reversing there [0.03756862 0.01373314] making one step from [0.88335413 0.60508503] [0.03756862 0.01373314] --> [0.86835881 0.56800215] [-0.01499532 -0.03708288] trying new point, [0.86835881 0.56800215] next() call -0.43482716294154355 goals: [('reflect-at', 7, array([0.86835881, 0.56800215]), array([-0.01499532, -0.03708288]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3623496671674091 goals: [('bisect', 7, array([0.86835881, 0.56800215]), array([-0.01499532, -0.03708288]), None, None, None, 10, array([0.82337284, 0.4567535 ]), array([-0.01499532, -0.03708288]), 1), ('sample-at', 10)] bisecting ... 7 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.82337284 0.4567535 ] [-0.01499532 -0.03708288] -0.3623496671674091 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.82337284, 0.4567535 ]), array([0.01499532, 0.03708288]), None, None, None, 10, array([0.97332606, 0.82758234]), array([0.01499532, 0.03708288]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.82337284, 0.4567535 ]), array([0.01499532, 0.03708288]), 5, array([0.89834945, 0.64216792]), array([0.01499532, 0.03708288]), 10, array([0.97332606, 0.82758234]), array([0.01499532, 0.03708288]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.37606463005047236 goals: [('bisect', 0, array([0.82337284, 0.4567535 ]), array([0.01499532, 0.03708288]), 2, array([0.85336348, 0.53091927]), array([0.01499532, 0.03708288]), 5, array([0.89834945, 0.64216792]), array([0.01499532, 0.03708288]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.43482716294154355 goals: [('bisect', 2, array([0.85336348, 0.53091927]), array([0.01499532, 0.03708288]), 3, array([0.86835881, 0.56800215]), array([0.01499532, 0.03708288]), 5, array([0.89834945, 0.64216792]), array([0.01499532, 0.03708288]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.86835881, 0.56800215]), array([0.01499532, 0.03708288]), 4, array([0.88335413, 0.60508503]), array([0.01499532, 0.03708288]), 5, array([0.89834945, 0.64216792]), array([0.01499532, 0.03708288]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.88335413 0.60508503] [0.01499532 0.03708288] new direction: [-0.03756862 -0.01373314] reversing there [0.01499532 0.03708288] making one step from [0.88335413 0.60508503] [0.01499532 0.03708288] --> [0.84578551 0.5913519 ] [-0.03756862 -0.01373314] trying new point, [0.84578551 0.5913519 ] next() call -0.4619911782824549 goals: [('reflect-at', 4, array([0.84578551, 0.5913519 ]), array([-0.03756862, -0.01373314]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.19343380286879602 goals: [('bisect', 4, array([0.84578551, 0.5913519 ]), array([-0.03756862, -0.01373314]), None, None, None, 10, array([0.62037381, 0.50895307]), array([-0.03756862, -0.01373314]), 1), ('sample-at', 10)] bisecting ... 4 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.62037381 0.50895307] [0.03756862 0.01373314] -0.19343380286879597 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.62037381 0.50895307] [0.03756862 0.01373314] -0.19343380286879597 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-4, 3) ---- seed=86 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.22742677 0.66408196] [-0.00019397 0.03999953] -0.36239760340403976 BACKWARD SAMPLING FROM 4 [0.22997871 0.54410506] [ 0.00068648 -0.03999411] -0.05076080590070252 BACKWARD SAMPLING FROM -4 [0.22820267 0.50408385] [-0.00019397 0.03999953] -0.026246700683537846 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.22742677 0.66408196] [-0.00019397 0.03999953] -0.36239760340403976 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.22742677, 0.66408196]), array([-0.00019397, 0.03999953]), None, None, None, 10, array([0.22548702, 0.93592274]), array([-0.00019397, -0.03999953]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.22742677, 0.66408196]), array([-0.00019397, 0.03999953]), 5, array([0.22645689, 0.86407961]), array([-0.00019397, 0.03999953]), 10, array([0.22548702, 0.93592274]), array([-0.00019397, -0.03999953]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.22742677, 0.66408196]), array([-0.00019397, 0.03999953]), 2, array([0.22703882, 0.74408102]), array([-0.00019397, 0.03999953]), 5, array([0.22645689, 0.86407961]), array([-0.00019397, 0.03999953]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.22742677, 0.66408196]), array([-0.00019397, 0.03999953]), 1, array([0.22723279, 0.70408149]), array([-0.00019397, 0.03999953]), 2, array([0.22703882, 0.74408102]), array([-0.00019397, 0.03999953]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.22723279 0.70408149] [-0.00019397 0.03999953] new direction: [ 0.00068648 -0.03999411] reversing there [-0.00019397 0.03999953] making one step from [0.22723279 0.70408149] [-0.00019397 0.03999953] --> [0.22791927 0.66408738] [ 0.00068648 -0.03999411] trying new point, [0.22791927 0.66408738] next() call -0.362531970289674 goals: [('reflect-at', 1, array([0.22791927, 0.66408738]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 1, array([0.22791927, 0.66408738]), array([ 0.00068648, -0.03999411]), None, None, None, 10, array([0.23409759, 0.3041404 ]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] bisecting ... 1 None 10 continue bisect at 5 next() call -0.026814464192513657 goals: [('bisect', 1, array([0.22791927, 0.66408738]), array([ 0.00068648, -0.03999411]), 5, array([0.23066519, 0.50411095]), array([ 0.00068648, -0.03999411]), 10, array([0.23409759, 0.3041404 ]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] bisecting ... 1 5 10 continue bisect at 7 next() call -0.09888785040833842 goals: [('bisect', 5, array([0.23066519, 0.50411095]), array([ 0.00068648, -0.03999411]), 7, array([0.23203815, 0.42412273]), array([ 0.00068648, -0.03999411]), 10, array([0.23409759, 0.3041404 ]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.19490757833235206 goals: [('bisect', 7, array([0.23203815, 0.42412273]), array([ 0.00068648, -0.03999411]), 8, array([0.23272463, 0.38412862]), array([ 0.00068648, -0.03999411]), 10, array([0.23409759, 0.3041404 ]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.33091599613376643 goals: [('bisect', 8, array([0.23272463, 0.38412862]), array([ 0.00068648, -0.03999411]), 9, array([0.23341111, 0.34413451]), array([ 0.00068648, -0.03999411]), 10, array([0.23409759, 0.3041404 ]), array([ 0.00068648, -0.03999411]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.23409759 0.3041404 ] [ 0.00068648 -0.03999411] new direction: [0.03260973 0.02316475] reversing there [ 0.00068648 -0.03999411] making one step from [0.23409759 0.3041404 ] [ 0.00068648 -0.03999411] --> [0.26670732 0.32730515] [0.03260973 0.02316475] trying new point, [0.26670732 0.32730515] next() call -0.4083602779624293 goals: [('reflect-at', 10, array([0.26670732, 0.32730515]), array([0.03260973, 0.02316475]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.26670732 0.32730515] [0.03260973 0.02316475] -0.4083602779624293 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.26670732, 0.32730515]), array([-0.03260973, -0.02316475]), None, None, None, 10, array([0.05938997, 0.09565768]), array([ 0.03260973, -0.02316475]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.26670732, 0.32730515]), array([-0.03260973, -0.02316475]), 5, array([0.10365867, 0.21148142]), array([-0.03260973, -0.02316475]), 10, array([0.05938997, 0.09565768]), array([ 0.03260973, -0.02316475]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.26670732, 0.32730515]), array([-0.03260973, -0.02316475]), 2, array([0.20148786, 0.28097566]), array([-0.03260973, -0.02316475]), 5, array([0.10365867, 0.21148142]), array([-0.03260973, -0.02316475]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.26670732, 0.32730515]), array([-0.03260973, -0.02316475]), 1, array([0.23409759, 0.3041404 ]), array([-0.03260973, -0.02316475]), 2, array([0.20148786, 0.28097566]), array([-0.03260973, -0.02316475]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.23409759 0.3041404 ] [-0.03260973 -0.02316475] new direction: [-0.00068648 0.03999411] reversing there [-0.03260973 -0.02316475] making one step from [0.23409759 0.3041404 ] [-0.03260973 -0.02316475] --> [0.23341111 0.34413451] [-0.00068648 0.03999411] trying new point, [0.23341111 0.34413451] next() call -0.33091599613376643 goals: [('reflect-at', 1, array([0.23341111, 0.34413451]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 1, array([0.23341111, 0.34413451]), array([-0.00068648, 0.03999411]), None, None, None, 10, array([0.22723279, 0.70408149]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] bisecting ... 1 None 10 continue bisect at 5 next() call -0.026814464192513682 goals: [('bisect', 1, array([0.23341111, 0.34413451]), array([-0.00068648, 0.03999411]), 5, array([0.23066519, 0.50411095]), array([-0.00068648, 0.03999411]), 10, array([0.22723279, 0.70408149]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] bisecting ... 1 5 10 continue bisect at 7 next() call -0.11469583748629265 goals: [('bisect', 5, array([0.23066519, 0.50411095]), array([-0.00068648, 0.03999411]), 7, array([0.22929223, 0.58409917]), array([-0.00068648, 0.03999411]), 10, array([0.22723279, 0.70408149]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.21861955894928337 goals: [('bisect', 7, array([0.22929223, 0.58409917]), array([-0.00068648, 0.03999411]), 8, array([0.22860575, 0.62409328]), array([-0.00068648, 0.03999411]), 10, array([0.22723279, 0.70408149]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.3625319702896749 goals: [('bisect', 8, array([0.22860575, 0.62409328]), array([-0.00068648, 0.03999411]), 9, array([0.22791927, 0.66408738]), array([-0.00068648, 0.03999411]), 10, array([0.22723279, 0.70408149]), array([-0.00068648, 0.03999411]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.22723279 0.70408149] [-0.00068648 0.03999411] new direction: [ 0.00019397 -0.03999953] reversing there [-0.00068648 0.03999411] making one step from [0.22723279 0.70408149] [-0.00068648 0.03999411] --> [0.22742677 0.66408196] [ 0.00019397 -0.03999953] trying new point, [0.22742677 0.66408196] next() call -0.36239760340404076 goals: [('reflect-at', 10, array([0.22742677, 0.66408196]), array([ 0.00019397, -0.03999953]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.22742677 0.66408196] [-0.00019397 0.03999953] -0.36239760340403976 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.22742677 0.66408196] [-0.00019397 0.03999953] -0.36239760340403976 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 sampling between (0, 3) ---- seed=87 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.18291099 0.36246153] [ 0.03905665 -0.00863586] -0.25318859507313624 BACKWARD SAMPLING FROM 4 [0.3391376 0.32791809] [ 0.03905665 -0.00863586] -0.42765944711127135 BACKWARD SAMPLING FROM -4 [0.02668439 0.39700497] [ 0.03905665 -0.00863586] -0.13295572584667534 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.18291099 0.36246153] [ 0.03905665 -0.00863586] -0.25318859507313624 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.18291099, 0.36246153]), array([ 0.03905665, -0.00863586]), None, None, None, 10, array([0.5734775 , 0.27610293]), array([ 0.03905665, -0.00863586]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.4797518449351289 goals: [('bisect', 0, array([0.18291099, 0.36246153]), array([ 0.03905665, -0.00863586]), 5, array([0.37819425, 0.31928223]), array([ 0.03905665, -0.00863586]), 10, array([0.5734775 , 0.27610293]), array([ 0.03905665, -0.00863586]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.37819425, 0.31928223]), array([ 0.03905665, -0.00863586]), 7, array([0.45630755, 0.30201051]), array([ 0.03905665, -0.00863586]), 10, array([0.5734775 , 0.27610293]), array([ 0.03905665, -0.00863586]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.37819425, 0.31928223]), array([ 0.03905665, -0.00863586]), 6, array([0.4172509 , 0.31064637]), array([ 0.03905665, -0.00863586]), 7, array([0.45630755, 0.30201051]), array([ 0.03905665, -0.00863586]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.4172509 0.31064637] [ 0.03905665 -0.00863586] new direction: [-0.03724409 0.01459033] reversing there [ 0.03905665 -0.00863586] making one step from [0.4172509 0.31064637] [ 0.03905665 -0.00863586] --> [0.3800068 0.3252367] [-0.03724409 0.01459033] trying new point, [0.3800068 0.3252367] next() call -0.45398023973187507 goals: [('reflect-at', 6, array([0.3800068, 0.3252367]), array([-0.03724409, 0.01459033]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.19605535574242375 goals: [('bisect', 6, array([0.3800068, 0.3252367]), array([-0.03724409, 0.01459033]), None, None, None, 10, array([0.23103043, 0.383598 ]), array([-0.03724409, 0.01459033]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.23103043 0.383598 ] [-0.03724409 0.01459033] -0.19605535574242375 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.23103043, 0.383598 ]), array([ 0.03724409, -0.01459033]), None, None, None, 10, array([0.60347136, 0.23769474]), array([ 0.03724409, -0.01459033]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.23103043, 0.383598 ]), array([ 0.03724409, -0.01459033]), 5, array([0.4172509 , 0.31064637]), array([ 0.03724409, -0.01459033]), 10, array([0.60347136, 0.23769474]), array([ 0.03724409, -0.01459033]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.3115996729727667 goals: [('bisect', 0, array([0.23103043, 0.383598 ]), array([ 0.03724409, -0.01459033]), 2, array([0.30551862, 0.35441735]), array([ 0.03724409, -0.01459033]), 5, array([0.4172509 , 0.31064637]), array([ 0.03724409, -0.01459033]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.37943542516122514 goals: [('bisect', 2, array([0.30551862, 0.35441735]), array([ 0.03724409, -0.01459033]), 3, array([0.34276271, 0.33982702]), array([ 0.03724409, -0.01459033]), 5, array([0.4172509 , 0.31064637]), array([ 0.03724409, -0.01459033]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.45398023973187507 goals: [('bisect', 3, array([0.34276271, 0.33982702]), array([ 0.03724409, -0.01459033]), 4, array([0.3800068, 0.3252367]), array([ 0.03724409, -0.01459033]), 5, array([0.4172509 , 0.31064637]), array([ 0.03724409, -0.01459033]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.4172509 0.31064637] [ 0.03724409 -0.01459033] new direction: [-0.03905665 0.00863586] reversing there [ 0.03724409 -0.01459033] making one step from [0.4172509 0.31064637] [ 0.03724409 -0.01459033] --> [0.37819425 0.31928223] [-0.03905665 0.00863586] trying new point, [0.37819425 0.31928223] next() call -0.4797518449351292 goals: [('reflect-at', 5, array([0.37819425, 0.31928223]), array([-0.03905665, 0.00863586]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.25318859507313635 goals: [('bisect', 5, array([0.37819425, 0.31928223]), array([-0.03905665, 0.00863586]), None, None, None, 10, array([0.18291099, 0.36246153]), array([-0.03905665, 0.00863586]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.18291099 0.36246153] [ 0.03905665 -0.00863586] -0.25318859507313624 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.18291099 0.36246153] [ 0.03905665 -0.00863586] -0.25318859507313624 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-2, 1) ---- seed=88 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.64755105 0.50714969] [ 0.00604104 -0.03954119] -0.21030015627840412 BACKWARD SAMPLING FROM 3 [0.66567418 0.38852612] [ 0.00604104 -0.03954119] -0.3768913954170837 BACKWARD SAMPLING FROM -3 [0.62942792 0.62577326] [ 0.00604104 -0.03954119] -0.39582616718099384 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.64755105 0.50714969] [ 0.00604104 -0.03954119] -0.21030015627840412 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.64755105, 0.50714969]), array([ 0.00604104, -0.03954119]), None, None, None, 10, array([0.70796149, 0.11173778]), array([ 0.00604104, -0.03954119]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.64755105, 0.50714969]), array([ 0.00604104, -0.03954119]), 5, array([0.67775627, 0.30944373]), array([ 0.00604104, -0.03954119]), 10, array([0.70796149, 0.11173778]), array([ 0.00604104, -0.03954119]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.28223684347738265 goals: [('bisect', 0, array([0.64755105, 0.50714969]), array([ 0.00604104, -0.03954119]), 2, array([0.65963314, 0.42806731]), array([ 0.00604104, -0.03954119]), 5, array([0.67775627, 0.30944373]), array([ 0.00604104, -0.03954119]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.3768913954170836 goals: [('bisect', 2, array([0.65963314, 0.42806731]), array([ 0.00604104, -0.03954119]), 3, array([0.66567418, 0.38852612]), array([ 0.00604104, -0.03954119]), 5, array([0.67775627, 0.30944373]), array([ 0.00604104, -0.03954119]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.66567418, 0.38852612]), array([ 0.00604104, -0.03954119]), 4, array([0.67171523, 0.34898492]), array([ 0.00604104, -0.03954119]), 5, array([0.67775627, 0.30944373]), array([ 0.00604104, -0.03954119]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.67171523 0.34898492] [ 0.00604104 -0.03954119] new direction: [-0.03138488 -0.02479897] reversing there [ 0.00604104 -0.03954119] making one step from [0.67171523 0.34898492] [ 0.00604104 -0.03954119] --> [0.64033034 0.32418595] [-0.03138488 -0.02479897] trying new point, [0.64033034 0.32418595] next() call None goals: [('reflect-at', 4, array([0.64033034, 0.32418595]), array([-0.03138488, -0.02479897]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.3958261671809942 goals: [('bisect', 0, array([0.64755105, 0.50714969]), array([ 0.00604104, -0.03954119]), None, None, None, -3, array([0.62942792, 0.62577326]), array([ 0.00604104, -0.03954119]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.66567418 0.38852612] [ 0.00604104 -0.03954119] -0.3768913954170836 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.21030015627840412 goals: [('bisect', 0, array([0.66567418, 0.38852612]), array([-0.00604104, 0.03954119]), None, None, None, 3, array([0.64755105, 0.50714969]), array([-0.00604104, 0.03954119]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.62942792 0.62577326] [ 0.00604104 -0.03954119] -0.3958261671809942 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.21030015627840412 goals: [('bisect', 0, array([0.62942792, 0.62577326]), array([-0.00604104, 0.03954119]), None, None, None, -3, array([0.64755105, 0.50714969]), array([-0.00604104, 0.03954119]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.64755105 0.50714969] [ 0.00604104 -0.03954119] -0.21030015627840412 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (0, 3) ---- seed=89 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.15259523 0.48024773] [ 0.01413523 -0.03741918] -0.016519553788509094 BACKWARD SAMPLING FROM 4 [0.20913614 0.33057099] [ 0.01413523 -0.03741918] -0.38069631886308075 BACKWARD SAMPLING FROM -4 [0.09605432 0.62992447] [ 0.01413523 -0.03741918] -0.21561781084839587 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.15259523 0.48024773] [ 0.01413523 -0.03741918] -0.016519553788509094 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.15259523, 0.48024773]), array([ 0.01413523, -0.03741918]), None, None, None, 10, array([0.29394749, 0.10605589]), array([ 0.01413523, -0.03741918]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.15259523, 0.48024773]), array([ 0.01413523, -0.03741918]), 5, array([0.22327136, 0.29315181]), array([ 0.01413523, -0.03741918]), 10, array([0.29394749, 0.10605589]), array([ 0.01413523, -0.03741918]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.12819855855898762 goals: [('bisect', 0, array([0.15259523, 0.48024773]), array([ 0.01413523, -0.03741918]), 2, array([0.18086568, 0.40540936]), array([ 0.01413523, -0.03741918]), 5, array([0.22327136, 0.29315181]), array([ 0.01413523, -0.03741918]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.23684509426933237 goals: [('bisect', 2, array([0.18086568, 0.40540936]), array([ 0.01413523, -0.03741918]), 3, array([0.19500091, 0.36799018]), array([ 0.01413523, -0.03741918]), 5, array([0.22327136, 0.29315181]), array([ 0.01413523, -0.03741918]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.38069631886308075 goals: [('bisect', 3, array([0.19500091, 0.36799018]), array([ 0.01413523, -0.03741918]), 4, array([0.20913614, 0.33057099]), array([ 0.01413523, -0.03741918]), 5, array([0.22327136, 0.29315181]), array([ 0.01413523, -0.03741918]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.22327136 0.29315181] [ 0.01413523 -0.03741918] new direction: [-0.03612676 0.0171714 ] reversing there [ 0.01413523 -0.03741918] making one step from [0.22327136 0.29315181] [ 0.01413523 -0.03741918] --> [0.1871446 0.31032321] [-0.03612676 0.0171714 ] trying new point, [0.1871446 0.31032321] next() call -0.46722761628857196 goals: [('reflect-at', 5, array([0.1871446 , 0.31032321]), array([-0.03612676, 0.0171714 ]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.1347530535350935 goals: [('bisect', 5, array([0.1871446 , 0.31032321]), array([-0.03612676, 0.0171714 ]), None, None, None, 10, array([0.00651078, 0.39618021]), array([-0.03612676, 0.0171714 ]), 1), ('sample-at', 10)] bisecting ... 5 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.00651078 0.39618021] [-0.03612676 0.0171714 ] -0.1347530535350935 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.00651078, 0.39618021]), array([ 0.03612676, -0.0171714 ]), None, None, None, 10, array([0.36777841, 0.22446621]), array([ 0.03612676, -0.0171714 ]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.46722761628857196 goals: [('bisect', 0, array([0.00651078, 0.39618021]), array([ 0.03612676, -0.0171714 ]), 5, array([0.1871446 , 0.31032321]), array([ 0.03612676, -0.0171714 ]), 10, array([0.36777841, 0.22446621]), array([ 0.03612676, -0.0171714 ]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.1871446 , 0.31032321]), array([ 0.03612676, -0.0171714 ]), 7, array([0.25939812, 0.27598041]), array([ 0.03612676, -0.0171714 ]), 10, array([0.36777841, 0.22446621]), array([ 0.03612676, -0.0171714 ]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.1871446 , 0.31032321]), array([ 0.03612676, -0.0171714 ]), 6, array([0.22327136, 0.29315181]), array([ 0.03612676, -0.0171714 ]), 7, array([0.25939812, 0.27598041]), array([ 0.03612676, -0.0171714 ]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.22327136 0.29315181] [ 0.03612676 -0.0171714 ] new direction: [-0.01413523 0.03741918] reversing there [ 0.03612676 -0.0171714 ] making one step from [0.22327136 0.29315181] [ 0.03612676 -0.0171714 ] --> [0.20913614 0.33057099] [-0.01413523 0.03741918] trying new point, [0.20913614 0.33057099] next() call -0.3806963188630805 goals: [('reflect-at', 6, array([0.20913614, 0.33057099]), array([-0.01413523, 0.03741918]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.016519553788509028 goals: [('bisect', 6, array([0.20913614, 0.33057099]), array([-0.01413523, 0.03741918]), None, None, None, 10, array([0.15259523, 0.48024773]), array([-0.01413523, 0.03741918]), 1), ('sample-at', 10)] bisecting ... 6 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.15259523 0.48024773] [ 0.01413523 -0.03741918] -0.016519553788509094 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.15259523 0.48024773] [ 0.01413523 -0.03741918] -0.016519553788509094 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (-1, 2) ---- seed=90 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.33405116 0.64459187] [-0.03949695 0.00632385] -0.31713020215676097 BACKWARD SAMPLING FROM 4 [0.17606336 0.66988726] [-0.03949695 0.00632385] -0.37627016306758043 BACKWARD SAMPLING FROM -4 [0.49203895 0.61929648] [-0.03949695 0.00632385] -0.2989468009164582 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.33405116 0.64459187] [-0.03949695 0.00632385] -0.31713020215676097 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.33405116, 0.64459187]), array([-0.03949695, 0.00632385]), None, None, None, 10, array([0.06091833, 0.70783034]), array([0.03949695, 0.00632385]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.3974546157438025 goals: [('bisect', 0, array([0.33405116, 0.64459187]), array([-0.03949695, 0.00632385]), 5, array([0.13656641, 0.67621111]), array([-0.03949695, 0.00632385]), 10, array([0.06091833, 0.70783034]), array([0.03949695, 0.00632385]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.4475028760344699 goals: [('bisect', 5, array([0.13656641, 0.67621111]), array([-0.03949695, 0.00632385]), 7, array([0.05757252, 0.6888588 ]), array([-0.03949695, 0.00632385]), 10, array([0.06091833, 0.70783034]), array([0.03949695, 0.00632385]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.4763666836489148 goals: [('bisect', 7, array([0.05757252, 0.6888588 ]), array([-0.03949695, 0.00632385]), 8, array([0.01807557, 0.69518265]), array([-0.03949695, 0.00632385]), 10, array([0.06091833, 0.70783034]), array([0.03949695, 0.00632385]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.01807557, 0.69518265]), array([-0.03949695, 0.00632385]), 9, array([0.02142138, 0.70150649]), array([0.03949695, 0.00632385]), 10, array([0.06091833, 0.70783034]), array([0.03949695, 0.00632385]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.02142138 0.70150649] [0.03949695 0.00632385] new direction: [-0.02643714 0.03001795] reversing there [0.03949695 0.00632385] making one step from [0.02142138 0.70150649] [0.03949695 0.00632385] --> [0.00501576 0.73152444] [0.02643714 0.03001795] trying new point, [0.00501576 0.73152444] next() call None goals: [('reflect-at', 9, array([0.00501576, 0.73152444]), array([0.02643714, 0.03001795]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 8... 2 steps to do at 8 -> [from 9, delta=2] targeting 7. goals: [('sample-at', 7)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 8 [0.01807557 0.69518265] [-0.03949695 0.00632385] -0.4763666836489148 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 8)] not done yet, continue expanding to 8... goals: [('expand-to', 8), ('sample-at', 8)] next() call -0.31713020215676097 goals: [('bisect', 0, array([0.01807557, 0.69518265]), array([ 0.03949695, -0.00632385]), None, None, None, 8, array([0.33405116, 0.64459187]), array([ 0.03949695, -0.00632385]), 1), ('sample-at', 8)] bisecting ... 0 None 8 successfully went all the way in one jump! goals: [('sample-at', 8)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.33405116 0.64459187] [-0.03949695 0.00632385] -0.31713020215676097 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.33405116 0.64459187] [-0.03949695 0.00632385] -0.31713020215676097 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd new NUTS range: -11..4 NUTS step: tree depth 4, rwd NUTS step: tree depth 4, rwd sampling between (-11, 4) ---- seed=91 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.2010036 0.32902047] [-0.03987224 -0.00319445] -0.38562623380806715 BACKWARD SAMPLING FROM 4 [0.04151464 0.31624266] [-0.03987224 -0.00319445] -0.42294623725634856 BACKWARD SAMPLING FROM -4 [0.36049255 0.34179827] [-0.03987224 -0.00319445] -0.37782476710495483 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.2010036 0.32902047] [-0.03987224 -0.00319445] -0.38562623380806715 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.2010036 , 0.32902047]), array([-0.03987224, -0.00319445]), None, None, None, 10, array([0.1977188 , 0.29707595]), array([ 0.03987224, -0.00319445]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.43688850948485125 goals: [('bisect', 0, array([0.2010036 , 0.32902047]), array([-0.03987224, -0.00319445]), 5, array([0.0016424 , 0.31304821]), array([-0.03987224, -0.00319445]), 10, array([0.1977188 , 0.29707595]), array([ 0.03987224, -0.00319445]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.47030777958157655 goals: [('bisect', 5, array([0.0016424 , 0.31304821]), array([-0.03987224, -0.00319445]), 7, array([0.07810208, 0.3066593 ]), array([ 0.03987224, -0.00319445]), 10, array([0.1977188 , 0.29707595]), array([ 0.03987224, -0.00319445]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.48978477744979876 goals: [('bisect', 7, array([0.07810208, 0.3066593 ]), array([ 0.03987224, -0.00319445]), 8, array([0.11797432, 0.30346485]), array([ 0.03987224, -0.00319445]), 10, array([0.1977188 , 0.29707595]), array([ 0.03987224, -0.00319445]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call None goals: [('bisect', 8, array([0.11797432, 0.30346485]), array([ 0.03987224, -0.00319445]), 9, array([0.15784656, 0.3002704 ]), array([ 0.03987224, -0.00319445]), 10, array([0.1977188 , 0.29707595]), array([ 0.03987224, -0.00319445]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 9 [0.15784656 0.3002704 ] [ 0.03987224 -0.00319445] new direction: [0.02906527 0.02748108] reversing there [ 0.03987224 -0.00319445] making one step from [0.15784656 0.3002704 ] [ 0.03987224 -0.00319445] --> [0.18691183 0.32775148] [0.02906527 0.02748108] trying new point, [0.18691183 0.32775148] next() call -0.38833742025683327 goals: [('reflect-at', 9, array([0.18691183, 0.32775148]), array([0.02906527, 0.02748108]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.28529319268566455 goals: [('bisect', 9, array([0.18691183, 0.32775148]), array([0.02906527, 0.02748108]), None, None, None, 10, array([0.21597711, 0.35523256]), array([0.02906527, 0.02748108]), 1), ('sample-at', 10)] bisecting ... 9 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.21597711 0.35523256] [0.02906527 0.02748108] -0.28529319268566455 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.21597711, 0.35523256]), array([-0.02906527, -0.02748108]), None, None, None, 10, array([0.07467563, 0.08042175]), array([ 0.02906527, -0.02748108]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.21597711, 0.35523256]), array([-0.02906527, -0.02748108]), 5, array([0.07065074, 0.21782716]), array([-0.02906527, -0.02748108]), 10, array([0.07467563, 0.08042175]), array([ 0.02906527, -0.02748108]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.21597711, 0.35523256]), array([-0.02906527, -0.02748108]), 2, array([0.15784656, 0.3002704 ]), array([-0.02906527, -0.02748108]), 5, array([0.07065074, 0.21782716]), array([-0.02906527, -0.02748108]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call -0.38833742025683327 goals: [('bisect', 0, array([0.21597711, 0.35523256]), array([-0.02906527, -0.02748108]), 1, array([0.18691183, 0.32775148]), array([-0.02906527, -0.02748108]), 2, array([0.15784656, 0.3002704 ]), array([-0.02906527, -0.02748108]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 2 [0.15784656 0.3002704 ] [-0.02906527 -0.02748108] new direction: [-0.03987224 0.00319445] reversing there [-0.02906527 -0.02748108] making one step from [0.15784656 0.3002704 ] [-0.02906527 -0.02748108] --> [0.11797432 0.30346485] [-0.03987224 0.00319445] trying new point, [0.11797432 0.30346485] next() call -0.4897847774497991 goals: [('reflect-at', 2, array([0.11797432, 0.30346485]), array([-0.03987224, 0.00319445]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.3856262338080673 goals: [('bisect', 2, array([0.11797432, 0.30346485]), array([-0.03987224, 0.00319445]), None, None, None, 10, array([0.2010036 , 0.32902047]), array([0.03987224, 0.00319445]), 1), ('sample-at', 10)] bisecting ... 2 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.2010036 0.32902047] [-0.03987224 -0.00319445] -0.38562623380806715 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.2010036 0.32902047] [-0.03987224 -0.00319445] -0.38562623380806715 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (0, 3) ---- seed=92 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.42472813 0.47886965] [-0.0004355 -0.03999763] -0.09577814063769932 BACKWARD SAMPLING FROM 4 [0.42298615 0.31887913] [-0.0004355 -0.03999763] -0.49951826010720873 BACKWARD SAMPLING FROM -4 [0.42647012 0.63886016] [-0.0004355 -0.03999763] -0.3319651929892012 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.42472813 0.47886965] [-0.0004355 -0.03999763] -0.09577814063769932 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.42472813, 0.47886965]), array([-0.0004355 , -0.03999763]), None, None, None, 10, array([0.42037317, 0.07889335]), array([-0.0004355 , -0.03999763]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.42472813, 0.47886965]), array([-0.0004355 , -0.03999763]), 5, array([0.42255065, 0.2788815 ]), array([-0.0004355 , -0.03999763]), 10, array([0.42037317, 0.07889335]), array([-0.0004355 , -0.03999763]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.21765730389482751 goals: [('bisect', 0, array([0.42472813, 0.47886965]), array([-0.0004355 , -0.03999763]), 2, array([0.42385714, 0.39887439]), array([-0.0004355 , -0.03999763]), 5, array([0.42255065, 0.2788815 ]), array([-0.0004355 , -0.03999763]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.33859005788161145 goals: [('bisect', 2, array([0.42385714, 0.39887439]), array([-0.0004355 , -0.03999763]), 3, array([0.42342164, 0.35887676]), array([-0.0004355 , -0.03999763]), 5, array([0.42255065, 0.2788815 ]), array([-0.0004355 , -0.03999763]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call -0.49951826010720873 goals: [('bisect', 3, array([0.42342164, 0.35887676]), array([-0.0004355 , -0.03999763]), 4, array([0.42298615, 0.31887913]), array([-0.0004355 , -0.03999763]), 5, array([0.42255065, 0.2788815 ]), array([-0.0004355 , -0.03999763]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 5 [0.42255065 0.2788815 ] [-0.0004355 -0.03999763] new direction: [-0.03548521 0.01846077] reversing there [-0.0004355 -0.03999763] making one step from [0.42255065 0.2788815 ] [-0.0004355 -0.03999763] --> [0.38706544 0.29734227] [-0.03548521 0.01846077] trying new point, [0.38706544 0.29734227] next() call None goals: [('reflect-at', 5, array([0.38706544, 0.29734227]), array([-0.03548521, 0.01846077]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 4... 6 steps to do at 4 -> [from 5, delta=6] targeting -1. goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.09483173136735507 goals: [('bisect', 0, array([0.42472813, 0.47886965]), array([-0.0004355 , -0.03999763]), None, None, None, -1, array([0.42516363, 0.51886728]), array([-0.0004355 , -0.03999763]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 4 [0.42298615 0.31887913] [-0.0004355 -0.03999763] -0.49951826010720873 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 4)] not done yet, continue expanding to 4... goals: [('expand-to', 4), ('sample-at', 4)] next() call -0.09577814063769934 goals: [('bisect', 0, array([0.42298615, 0.31887913]), array([0.0004355 , 0.03999763]), None, None, None, 4, array([0.42472813, 0.47886965]), array([0.0004355 , 0.03999763]), 1), ('sample-at', 4)] bisecting ... 0 None 4 successfully went all the way in one jump! goals: [('sample-at', 4)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -1 [0.42516363 0.51886728] [-0.0004355 -0.03999763] -0.09483173136735507 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -1)] not done yet, continue expanding to -1... goals: [('expand-to', -1), ('sample-at', -1)] next() call -0.09577814063769935 goals: [('bisect', 0, array([0.42516363, 0.51886728]), array([0.0004355 , 0.03999763]), None, None, None, -1, array([0.42472813, 0.47886965]), array([0.0004355 , 0.03999763]), -1), ('sample-at', -1)] bisecting ... 0 None -1 successfully went all the way in one jump! goals: [('sample-at', -1)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.42472813 0.47886965] [-0.0004355 -0.03999763] -0.09577814063769932 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd new NUTS range: -4..3 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd sampling between (-4, 3) ---- seed=93 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.60635434 0.65751157] [ 0.03990013 -0.00282481] -0.4939564695757347 BACKWARD SAMPLING FROM 4 [0.515227 0.56286192] [-0.03275687 -0.02295621] -0.18212468401683823 BACKWARD SAMPLING FROM -4 [0.44675381 0.66881082] [ 0.03990013 -0.00282481] -0.4560081322130549 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.60635434 0.65751157] [ 0.03990013 -0.00282481] -0.4939564695757347 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.60635434, 0.65751157]), array([ 0.03990013, -0.00282481]), None, None, None, 10, array([0.99464435, 0.62926345]), array([-0.03990013, -0.00282481]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.60635434, 0.65751157]), array([ 0.03990013, -0.00282481]), 5, array([0.80585499, 0.64338751]), array([ 0.03990013, -0.00282481]), 10, array([0.99464435, 0.62926345]), array([-0.03990013, -0.00282481]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call None goals: [('bisect', 0, array([0.60635434, 0.65751157]), array([ 0.03990013, -0.00282481]), 2, array([0.6861546 , 0.65186194]), array([ 0.03990013, -0.00282481]), 5, array([0.80585499, 0.64338751]), array([ 0.03990013, -0.00282481]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 1 next() call None goals: [('bisect', 0, array([0.60635434, 0.65751157]), array([ 0.03990013, -0.00282481]), 1, array([0.64625447, 0.65468676]), array([ 0.03990013, -0.00282481]), 2, array([0.6861546 , 0.65186194]), array([ 0.03990013, -0.00282481]), 1), ('sample-at', 10)] bisecting ... 0 1 2 bisecting gave reflection point 1 [0.64625447 0.65468676] [ 0.03990013 -0.00282481] new direction: [-0.03275687 -0.02295621] reversing there [ 0.03990013 -0.00282481] making one step from [0.64625447 0.65468676] [ 0.03990013 -0.00282481] --> [0.6134976 0.63173055] [-0.03275687 -0.02295621] trying new point, [0.6134976 0.63173055] next() call -0.4051013634874946 goals: [('reflect-at', 1, array([0.6134976 , 0.63173055]), array([-0.03275687, -0.02295621]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.12085928392615417 goals: [('bisect', 1, array([0.6134976 , 0.63173055]), array([-0.03275687, -0.02295621]), None, None, None, 10, array([0.31868579, 0.42512465]), array([-0.03275687, -0.02295621]), 1), ('sample-at', 10)] bisecting ... 1 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.31868579 0.42512465] [-0.03275687 -0.02295621] -0.12085928392615417 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.31868579, 0.42512465]), array([0.03275687, 0.02295621]), None, None, None, 10, array([0.64625447, 0.65468676]), array([0.03275687, 0.02295621]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.1362945285525923 goals: [('bisect', 0, array([0.31868579, 0.42512465]), array([0.03275687, 0.02295621]), 5, array([0.48247013, 0.53990571]), array([0.03275687, 0.02295621]), 10, array([0.64625447, 0.65468676]), array([0.03275687, 0.02295621]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.24220254166073774 goals: [('bisect', 5, array([0.48247013, 0.53990571]), array([0.03275687, 0.02295621]), 7, array([0.54798386, 0.58581813]), array([0.03275687, 0.02295621]), 10, array([0.64625447, 0.65468676]), array([0.03275687, 0.02295621]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.31652810148428956 goals: [('bisect', 7, array([0.54798386, 0.58581813]), array([0.03275687, 0.02295621]), 8, array([0.58074073, 0.60877434]), array([0.03275687, 0.02295621]), 10, array([0.64625447, 0.65468676]), array([0.03275687, 0.02295621]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.40510136348749465 goals: [('bisect', 8, array([0.58074073, 0.60877434]), array([0.03275687, 0.02295621]), 9, array([0.6134976 , 0.63173055]), array([0.03275687, 0.02295621]), 10, array([0.64625447, 0.65468676]), array([0.03275687, 0.02295621]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.64625447 0.65468676] [0.03275687 0.02295621] new direction: [-0.03990013 0.00282481] reversing there [0.03275687 0.02295621] making one step from [0.64625447 0.65468676] [0.03275687 0.02295621] --> [0.60635434 0.65751157] [-0.03990013 0.00282481] trying new point, [0.60635434 0.65751157] next() call -0.49395646957573464 goals: [('reflect-at', 10, array([0.60635434, 0.65751157]), array([-0.03990013, 0.00282481]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.60635434 0.65751157] [ 0.03990013 -0.00282481] -0.4939564695757347 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.60635434 0.65751157] [ 0.03990013 -0.00282481] -0.4939564695757347 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -3..4 NUTS step: tree depth 3, fwd NUTS step: tree depth 3, fwd new NUTS range: -3..12 sampling between (-3, 12) ---- seed=94 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.71938579 0.60722118] [ 0.03572141 -0.01799947] -0.4024627109969381 BACKWARD SAMPLING FROM 4 [0.86227142 0.53522328] [ 0.03572141 -0.01799947] -0.38726449528400564 BACKWARD SAMPLING FROM -2 [0.64794297 0.64322012] [ 0.03572141 -0.01799947] -0.46631508651324616 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.71938579 0.60722118] [ 0.03572141 -0.01799947] -0.4024627109969381 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4925337817021134 goals: [('bisect', 0, array([0.71938579, 0.60722118]), array([ 0.03572141, -0.01799947]), None, None, None, 10, array([0.92340014, 0.42722645]), array([-0.03572141, -0.01799947]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.92340014 0.42722645] [-0.03572141 -0.01799947] -0.4925337817021134 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.4024627109969381 goals: [('bisect', 0, array([0.92340014, 0.42722645]), array([0.03572141, 0.01799947]), None, None, None, 10, array([0.71938579, 0.60722118]), array([-0.03572141, 0.01799947]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.71938579 0.60722118] [ 0.03572141 -0.01799947] -0.4024627109969381 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.71938579 0.60722118] [ 0.03572141 -0.01799947] -0.4024627109969381 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: -1..2 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd new NUTS range: -1..6 NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd NUTS step: tree depth 3, rwd sampling between (-1, 6) ---- seed=95 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.72423768 0.48208699] [-0.02922414 0.02731208] -0.26627105453876776 BACKWARD SAMPLING FROM 4 [0.60734111 0.59133529] [-0.02922414 0.02731208] -0.2887083100641112 BACKWARD SAMPLING FROM -4 [0.82122514 0.40753202] [ 0.01990911 -0.03469333] -0.4440844529643073 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.72423768 0.48208699] [-0.02922414 0.02731208] -0.26627105453876776 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.72423768, 0.48208699]), array([-0.02922414, 0.02731208]), None, None, None, 10, array([0.43199625, 0.75520775]), array([-0.02922414, 0.02731208]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.34307459325849876 goals: [('bisect', 0, array([0.72423768, 0.48208699]), array([-0.02922414, 0.02731208]), 5, array([0.57811696, 0.61864737]), array([-0.02922414, 0.02731208]), 10, array([0.43199625, 0.75520775]), array([-0.02922414, 0.02731208]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.57811696, 0.61864737]), array([-0.02922414, 0.02731208]), 7, array([0.51966868, 0.67327152]), array([-0.02922414, 0.02731208]), 10, array([0.43199625, 0.75520775]), array([-0.02922414, 0.02731208]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call -0.41694366417810785 goals: [('bisect', 5, array([0.57811696, 0.61864737]), array([-0.02922414, 0.02731208]), 6, array([0.54889282, 0.64595945]), array([-0.02922414, 0.02731208]), 7, array([0.51966868, 0.67327152]), array([-0.02922414, 0.02731208]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 7 [0.51966868 0.67327152] [-0.02922414 0.02731208] new direction: [-0.03257512 0.02321339] reversing there [-0.02922414 0.02731208] making one step from [0.51966868 0.67327152] [-0.02922414 0.02731208] --> [0.48709356 0.69648491] [-0.03257512 0.02321339] trying new point, [0.48709356 0.69648491] next() call None goals: [('reflect-at', 7, array([0.48709356, 0.69648491]), array([-0.03257512, 0.02321339]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 6... 4 steps to do at 6 -> [from 7, delta=4] targeting 3. goals: [('sample-at', 3)] target is on track, returning interpolation at 3... [0.63656525 0.56402322] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 6 [0.54889282 0.64595945] [-0.02922414 0.02731208] -0.41694366417810785 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 6)] not done yet, continue expanding to 6... goals: [('expand-to', 6), ('sample-at', 6)] next() call -0.2662710545387677 goals: [('bisect', 0, array([0.54889282, 0.64595945]), array([ 0.02922414, -0.02731208]), None, None, None, 6, array([0.72423768, 0.48208699]), array([ 0.02922414, -0.02731208]), 1), ('sample-at', 6)] bisecting ... 0 None 6 successfully went all the way in one jump! goals: [('sample-at', 6)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.72423768 0.48208699] [-0.02922414 0.02731208] -0.26627105453876776 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.72423768 0.48208699] [-0.02922414 0.02731208] -0.26627105453876776 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -2..1 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-2, 1) ---- seed=96 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.09240082 0.561974 ] [-0.03300675 -0.02259545] -0.05227866114907259 BACKWARD SAMPLING FROM 4 [0.0396262 0.47159221] [ 0.03300675 -0.02259545] -0.010872647013765053 BACKWARD SAMPLING FROM -4 [0.22442783 0.65235578] [-0.03300675 -0.02259545] -0.3153374835129045 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.09240082 0.561974 ] [-0.03300675 -0.02259545] -0.05227866114907259 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.36436264123928686 goals: [('bisect', 0, array([0.09240082, 0.561974 ]), array([-0.03300675, -0.02259545]), None, None, None, 10, array([0.23766673, 0.33601954]), array([ 0.03300675, -0.02259545]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 10 [0.23766673 0.33601954] [ 0.03300675 -0.02259545] -0.36436264123928686 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call -0.05227866114907242 goals: [('bisect', 0, array([0.23766673, 0.33601954]), array([-0.03300675, 0.02259545]), None, None, None, 10, array([0.09240082, 0.561974 ]), array([0.03300675, 0.02259545]), 1), ('sample-at', 10)] bisecting ... 0 None 10 successfully went all the way in one jump! goals: [('sample-at', 10)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.09240082 0.561974 ] [-0.03300675 -0.02259545] -0.05227866114907259 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.09240082 0.561974 ] [-0.03300675 -0.02259545] -0.05227866114907259 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd sampling between (0, 1) ---- seed=97 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.20467407 0.34514352] [-0.01012837 0.03869646] -0.3207023648241864 BACKWARD SAMPLING FROM 4 [0.1641606 0.49992937] [-0.01012837 0.03869646] -0.013474413804503925 BACKWARD SAMPLING FROM -1 [0.21480243 0.30644705] [-0.01012837 0.03869646] -0.4913543244490346 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.20467407 0.34514352] [-0.01012837 0.03869646] -0.3207023648241864 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.20467407, 0.34514352]), array([-0.01012837, 0.03869646]), None, None, None, 10, array([0.1033904 , 0.73210814]), array([-0.01012837, 0.03869646]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.030512397919510853 goals: [('bisect', 0, array([0.20467407, 0.34514352]), array([-0.01012837, 0.03869646]), 5, array([0.15403223, 0.53862583]), array([-0.01012837, 0.03869646]), 10, array([0.1033904 , 0.73210814]), array([-0.01012837, 0.03869646]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call -0.1772023323934378 goals: [('bisect', 5, array([0.15403223, 0.53862583]), array([-0.01012837, 0.03869646]), 7, array([0.1337755 , 0.61601875]), array([-0.01012837, 0.03869646]), 10, array([0.1033904 , 0.73210814]), array([-0.01012837, 0.03869646]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 8 next() call -0.3068542827523578 goals: [('bisect', 7, array([0.1337755 , 0.61601875]), array([-0.01012837, 0.03869646]), 8, array([0.12364714, 0.65471522]), array([-0.01012837, 0.03869646]), 10, array([0.1033904 , 0.73210814]), array([-0.01012837, 0.03869646]), 1), ('sample-at', 10)] bisecting ... 7 8 10 continue bisect at 9 next() call -0.4740442218592488 goals: [('bisect', 8, array([0.12364714, 0.65471522]), array([-0.01012837, 0.03869646]), 9, array([0.11351877, 0.69341168]), array([-0.01012837, 0.03869646]), 10, array([0.1033904 , 0.73210814]), array([-0.01012837, 0.03869646]), 1), ('sample-at', 10)] bisecting ... 8 9 10 bisecting gave reflection point 10 [0.1033904 0.73210814] [-0.01012837 0.03869646] new direction: [-0.03691991 0.01539222] reversing there [-0.01012837 0.03869646] making one step from [0.1033904 0.73210814] [-0.01012837 0.03869646] --> [0.0664705 0.74750036] [-0.03691991 0.01539222] trying new point, [0.0664705 0.74750036] next() call None goals: [('reflect-at', 10, array([0.0664705 , 0.74750036]), array([-0.03691991, 0.01539222]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 9... 1 steps to do at 9 -> [from 10, delta=1] targeting 9. goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 9 [0.11351877 0.69341168] [-0.01012837 0.03869646] -0.4740442218592488 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 9)] not done yet, continue expanding to 9... goals: [('expand-to', 9), ('sample-at', 9)] next() call -0.3207023648241862 goals: [('bisect', 0, array([0.11351877, 0.69341168]), array([ 0.01012837, -0.03869646]), None, None, None, 9, array([0.20467407, 0.34514352]), array([ 0.01012837, -0.03869646]), 1), ('sample-at', 9)] bisecting ... 0 None 9 successfully went all the way in one jump! goals: [('sample-at', 9)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.20467407 0.34514352] [-0.01012837 0.03869646] -0.3207023648241864 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.20467407 0.34514352] [-0.01012837 0.03869646] -0.3207023648241864 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd sampling between (0, 1) ---- seed=98 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.73226725 0.56718902] [ 0.01554863 -0.03685431] -0.3245372087629051 BACKWARD SAMPLING FROM 4 [0.79446178 0.41977178] [ 0.01554863 -0.03685431] -0.39604184352994004 BACKWARD SAMPLING FROM -2 [0.70116998 0.64089763] [ 0.01554863 -0.03685431] -0.4939714550399919 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.73226725 0.56718902] [ 0.01554863 -0.03685431] -0.3245372087629051 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.73226725, 0.56718902]), array([ 0.01554863, -0.03685431]), None, None, None, 10, array([0.88775357, 0.19864593]), array([ 0.01554863, -0.03685431]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call -0.49941240374695045 goals: [('bisect', 0, array([0.73226725, 0.56718902]), array([ 0.01554863, -0.03685431]), 5, array([0.81001041, 0.38291747]), array([ 0.01554863, -0.03685431]), 10, array([0.88775357, 0.19864593]), array([ 0.01554863, -0.03685431]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 7 next() call None goals: [('bisect', 5, array([0.81001041, 0.38291747]), array([ 0.01554863, -0.03685431]), 7, array([0.84110768, 0.30920886]), array([ 0.01554863, -0.03685431]), 10, array([0.88775357, 0.19864593]), array([ 0.01554863, -0.03685431]), 1), ('sample-at', 10)] bisecting ... 5 7 10 continue bisect at 6 next() call None goals: [('bisect', 5, array([0.81001041, 0.38291747]), array([ 0.01554863, -0.03685431]), 6, array([0.82555904, 0.34606317]), array([ 0.01554863, -0.03685431]), 7, array([0.84110768, 0.30920886]), array([ 0.01554863, -0.03685431]), 1), ('sample-at', 10)] bisecting ... 5 6 7 bisecting gave reflection point 6 [0.82555904 0.34606317] [ 0.01554863 -0.03685431] new direction: [-0.03412149 -0.02087401] reversing there [ 0.01554863 -0.03685431] making one step from [0.82555904 0.34606317] [ 0.01554863 -0.03685431] --> [0.79143756 0.32518916] [-0.03412149 -0.02087401] trying new point, [0.79143756 0.32518916] next() call None goals: [('reflect-at', 6, array([0.79143756, 0.32518916]), array([-0.03412149, -0.02087401]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 5... 5 steps to do at 5 -> [from 6, delta=5] targeting 1. goals: [('sample-at', 1)] target is on track, returning interpolation at 1... [0.74781588 0.53033471] None next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 5 [0.81001041 0.38291747] [ 0.01554863 -0.03685431] -0.49941240374695045 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 5)] not done yet, continue expanding to 5... goals: [('expand-to', 5), ('sample-at', 5)] next() call -0.3245372087629051 goals: [('bisect', 0, array([0.81001041, 0.38291747]), array([-0.01554863, 0.03685431]), None, None, None, 5, array([0.73226725, 0.56718902]), array([-0.01554863, 0.03685431]), 1), ('sample-at', 5)] bisecting ... 0 None 5 successfully went all the way in one jump! goals: [('sample-at', 5)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 0 [0.73226725 0.56718902] [ 0.01554863 -0.03685431] -0.3245372087629051 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 0)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.73226725 0.56718902] [ 0.01554863 -0.03685431] -0.3245372087629051 NUTS step: tree depth 0, fwd NUTS step: tree depth 0, fwd new NUTS range: 0..1 NUTS step: tree depth 1, fwd NUTS step: tree depth 1, fwd new NUTS range: 0..3 NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd NUTS step: tree depth 2, fwd sampling between (0, 3) ---- seed=99 ---- StepSampler ---- FORWARD SAMPLING FROM 0 [0.67227856 0.4880784 ] [0.00390489 0.03980894] -0.2277557872556839 BACKWARD SAMPLING FROM 3 [0.68399324 0.60750522] [0.00390489 0.03980894] -0.3783905365498532 BACKWARD SAMPLING FROM -3 [0.66056388 0.36865158] [0.00390489 0.03980894] -0.4338274302289816 BisectSampler ---- FORWARD SAMPLING FROM 0 [0.67227856 0.4880784 ] [0.00390489 0.03980894] -0.2277557872556839 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 10)] not done yet, continue expanding to 10... goals: [('expand-to', 10), ('sample-at', 10)] next() call None goals: [('bisect', 0, array([0.67227856, 0.4880784 ]), array([0.00390489, 0.03980894]), None, None, None, 10, array([0.71132748, 0.88616781]), array([0.00390489, 0.03980894]), 1), ('sample-at', 10)] bisecting ... 0 None 10 continue bisect at 5 next() call None goals: [('bisect', 0, array([0.67227856, 0.4880784 ]), array([0.00390489, 0.03980894]), 5, array([0.69180302, 0.68712311]), array([0.00390489, 0.03980894]), 10, array([0.71132748, 0.88616781]), array([0.00390489, 0.03980894]), 1), ('sample-at', 10)] bisecting ... 0 5 10 continue bisect at 2 next() call -0.28854490986652237 goals: [('bisect', 0, array([0.67227856, 0.4880784 ]), array([0.00390489, 0.03980894]), 2, array([0.68008834, 0.56769628]), array([0.00390489, 0.03980894]), 5, array([0.69180302, 0.68712311]), array([0.00390489, 0.03980894]), 1), ('sample-at', 10)] bisecting ... 0 2 5 continue bisect at 3 next() call -0.37839053654985255 goals: [('bisect', 2, array([0.68008834, 0.56769628]), array([0.00390489, 0.03980894]), 3, array([0.68399324, 0.60750522]), array([0.00390489, 0.03980894]), 5, array([0.69180302, 0.68712311]), array([0.00390489, 0.03980894]), 1), ('sample-at', 10)] bisecting ... 2 3 5 continue bisect at 4 next() call None goals: [('bisect', 3, array([0.68399324, 0.60750522]), array([0.00390489, 0.03980894]), 4, array([0.68789813, 0.64731416]), array([0.00390489, 0.03980894]), 5, array([0.69180302, 0.68712311]), array([0.00390489, 0.03980894]), 1), ('sample-at', 10)] bisecting ... 3 4 5 bisecting gave reflection point 4 [0.68789813 0.64731416] [0.00390489 0.03980894] new direction: [-0.01907752 0.03515748] reversing there [0.00390489 0.03980894] making one step from [0.68789813 0.64731416] [0.00390489 0.03980894] --> [0.66882061 0.68247164] [-0.01907752 0.03515748] trying new point, [0.66882061 0.68247164] next() call None goals: [('reflect-at', 4, array([0.66882061, 0.68247164]), array([-0.01907752, 0.03515748]), 1), ('sample-at', 10)] goals: [('sample-at', 10)] reversing at 3... 7 steps to do at 3 -> [from 4, delta=7] targeting -3. goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.4338274302289816 goals: [('bisect', 0, array([0.67227856, 0.4880784 ]), array([0.00390489, 0.03980894]), None, None, None, -3, array([0.66056388, 0.36865158]), array([0.00390489, 0.03980894]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM 3 [0.68399324 0.60750522] [0.00390489 0.03980894] -0.37839053654985255 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', 3)] not done yet, continue expanding to 3... goals: [('expand-to', 3), ('sample-at', 3)] next() call -0.22775578725568393 goals: [('bisect', 0, array([0.68399324, 0.60750522]), array([-0.00390489, -0.03980894]), None, None, None, 3, array([0.67227856, 0.4880784 ]), array([-0.00390489, -0.03980894]), 1), ('sample-at', 3)] bisecting ... 0 None 3 successfully went all the way in one jump! goals: [('sample-at', 3)] next() call None goals: [('sample-at', 0)] next() call None BACKWARD SAMPLING FROM -3 [0.66056388 0.36865158] [0.00390489 0.03980894] -0.4338274302289816 next() call None goals: [('sample-at', 0)] next() call None next() call None goals: [('sample-at', -3)] not done yet, continue expanding to -3... goals: [('expand-to', -3), ('sample-at', -3)] next() call -0.2277557872556839 goals: [('bisect', 0, array([0.66056388, 0.36865158]), array([-0.00390489, -0.03980894]), None, None, None, -3, array([0.67227856, 0.4880784 ]), array([-0.00390489, -0.03980894]), -1), ('sample-at', -3)] bisecting ... 0 None -3 successfully went all the way in one jump! goals: [('sample-at', -3)] next() call None goals: [('sample-at', 0)] next() call None NUTSSampler ---- FORWARD SAMPLING FROM 0 [0.67227856 0.4880784 ] [0.00390489 0.03980894] -0.2277557872556839 NUTS step: tree depth 0, rwd NUTS step: tree depth 0, rwd new NUTS range: -1..0 NUTS step: tree depth 1, rwd NUTS step: tree depth 1, rwd new NUTS range: -3..0 NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd NUTS step: tree depth 2, rwd sampling between (-3, 0) | |||
Passed | tests/test_flatnuts.py::test_singlejumper | 0.04 | |
------------------------------Captured stdout call------------------------------ make reflect make stuck | |||
Passed | tests/test_flatnuts.py::test_directjumper | 0.04 | |
------------------------------Captured stdout call------------------------------ make reflect make stuck | |||
Passed | tests/test_hotstart.py::test_hotstart_SLOW | 20.33 | |
------------------------------Captured stdout call------------------------------ -8963077118.743088 96.99934128550026 proposals: [[4.19976674e+01 1.01901282e-01 2.26584579e-02] [4.19936982e+01 9.70120971e-02 1.25468225e-01] [4.20043522e+01 8.81983708e-02 4.16063032e-01] [4.19910425e+01 9.23532542e-02 3.57120722e-01] [4.20010471e+01 1.04769832e-01 5.40934064e-02] [4.20004875e+01 8.44214056e-02 6.57794482e-01] [4.20114084e+01 8.71986004e-02 7.70800710e-01] [4.19997931e+01 1.17497505e-01 5.97616561e-01] [4.19793249e+01 1.16910821e-01 1.62802382e+00] [4.19777034e+01 1.00198605e-01 1.23013202e+00] [4.19809513e+01 9.73026870e-02 9.29402866e-01] [4.20035101e+01 8.39559746e-02 7.31648830e-01] [4.19818445e+01 8.91999970e-02 1.13587040e+00] [4.19944650e+01 8.89373681e-02 4.00203681e-01] [4.19879296e+01 9.87877365e-02 3.79163467e-01] [4.19993921e+01 1.05583972e-01 7.05549601e-02] [4.19958797e+01 1.00198250e-01 4.45624038e-02] [4.20028928e+01 1.13275091e-01 3.83072005e-01] [4.20075672e+01 8.60294765e-02 6.71506433e-01] [4.20000003e+01 1.32876001e-01 1.75531881e+00] [4.19941441e+01 1.20714239e-01 8.95917835e-01] [4.19973837e+01 1.02585211e-01 3.33463863e-02] [4.20109730e+01 9.76256032e-02 3.25050704e-01] [4.20249867e+01 1.01357574e-01 1.52720024e+00] [4.19795771e+01 1.01689723e-01 1.04810966e+00] [4.20151020e+01 9.49699778e-02 6.45034516e-01] [4.19711011e+01 9.64829396e-02 2.02124669e+00] [4.19779289e+01 9.01472311e-02 1.45803800e+00] [4.20283363e+01 1.50686758e-01 5.29231415e+00] [4.20036985e+01 1.13687102e-01 4.17933502e-01] [4.19861307e+01 1.11595948e-01 7.74029950e-01] [4.20008644e+01 9.20834520e-02 1.61590461e-01] [4.19749175e+01 9.45972273e-02 1.60648204e+00] [4.19978867e+01 1.40462497e-01 2.43829897e+00] [4.19934265e+01 1.00151410e-01 1.12874993e-01] [4.19958796e+01 9.06920226e-02 2.67799740e-01] [4.20091161e+01 1.15823745e-01 7.14187136e-01] [4.19660038e+01 9.54604067e-02 2.71598389e+00] [4.19911555e+01 1.13508293e-01 5.76293834e-01] [4.19706547e+01 1.03201725e-01 2.07074602e+00]] 40 [ultranest] Sampling 400 live points from prior ... Z=-inf(0.00%) | Like=77.54..101.10 [77.5411..97.9860] | it/evals=0/401 eff=0.0000% N=400 Z=88.4(0.00%) | Like=91.98..101.10 [77.5411..97.9860] | it/evals=40/444 eff=90.9091% N=400 Z=90.7(0.02%) | Like=93.90..101.11 [77.5411..97.9860] | it/evals=80/491 eff=87.9121% N=400 Z=91.2(0.03%) | Like=94.28..101.11 [77.5411..97.9860] | it/evals=90/506 eff=84.9057% N=400 Z=92.3(0.10%) | Like=95.13..101.11 [77.5411..97.9860] | it/evals=120/540 eff=85.7143% N=400 Z=93.5(0.31%) | Like=96.16..101.11 [77.5411..97.9860] | it/evals=160/587 eff=85.5615% N=400 Z=93.9(0.50%) | Like=96.58..101.11 [77.5411..97.9860] | it/evals=180/610 eff=85.7143% N=400 Z=94.3(0.75%) | Like=96.86..101.11 [77.5411..97.9860] | it/evals=200/631 eff=86.5801% N=400 Z=95.0(1.44%) | Like=97.36..101.11 [77.5411..97.9860] | it/evals=240/676 eff=86.9565% N=400 Z=95.5(2.52%) | Like=97.83..101.11 [77.5411..97.9860] | it/evals=280/725 eff=86.1538% N=400 Z=96.0(3.98%) | Like=98.18..101.11 [98.1782..98.1801]*| it/evals=320/786 eff=82.9016% N=400 Z=96.4(5.72%) | Like=98.49..101.11 [98.4874..98.4975] | it/evals=360/833 eff=83.1409% N=400 Z=96.7(7.90%) | Like=98.73..101.11 [98.7330..98.7367]*| it/evals=400/883 eff=82.8157% N=400 Z=96.9(10.53%) | Like=99.00..101.11 [99.0049..99.0065]*| it/evals=440/927 eff=83.4915% N=400 Z=97.0(11.16%) | Like=99.05..101.11 [99.0347..99.0489] | it/evals=450/942 eff=83.0258% N=400 Z=97.2(13.19%) | Like=99.20..101.11 [99.2022..99.2031]*| it/evals=480/977 eff=83.1889% N=400 Z=97.4(16.22%) | Like=99.35..101.11 [99.3491..99.3523]*| it/evals=520/1024 eff=83.3333% N=400 Z=97.5(18.84%) | Like=99.49..101.12 [99.4932..99.4953]*| it/evals=554/1066 eff=83.1832% N=400 Z=97.6(19.30%) | Like=99.52..101.12 [99.5178..99.5184]*| it/evals=560/1072 eff=83.3333% N=400 Z=97.7(22.62%) | Like=99.64..101.12 [99.6378..99.6380]*| it/evals=600/1118 eff=83.5655% N=400 Z=97.8(25.22%) | Like=99.74..101.12 [99.7364..99.7367]*| it/evals=630/1160 eff=82.8947% N=400 Z=97.9(26.14%) | Like=99.76..101.12 [99.7576..99.7651]*| it/evals=640/1170 eff=83.1169% N=400 Z=98.0(29.59%) | Like=99.88..101.12 [99.8754..99.8765]*| it/evals=680/1219 eff=83.0281% N=400 Z=98.1(33.21%) | Like=99.96..101.12 [99.9610..99.9618]*| it/evals=720/1267 eff=83.0450% N=400 Z=98.2(36.67%) | Like=100.08..101.12 [100.0795..100.0837]*| it/evals=760/1322 eff=82.4295% N=400 Z=98.3(40.21%) | Like=100.18..101.12 [100.1751..100.1761]*| it/evals=800/1367 eff=82.7301% N=400 Z=98.3(41.10%) | Like=100.20..101.12 [100.1987..100.1988]*| it/evals=810/1380 eff=82.6531% N=400 Z=98.4(43.67%) | Like=100.28..101.12 [100.2815..100.2828]*| it/evals=840/1418 eff=82.5147% N=400 Z=98.5(47.32%) | Like=100.37..101.12 [100.3733..100.3744]*| it/evals=880/1473 eff=82.0130% N=400 Z=98.5(49.03%) | Like=100.42..101.12 [100.4183..100.4192]*| it/evals=900/1496 eff=82.1168% N=400 [ultranest] Explored until L=1e+02 [ultranest] Likelihood function evaluations: 1508 [ultranest] logZ = 99.21 +- 0.03825 [ultranest] Effective samples strategy satisfied (ESS = 972.0, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5) [ultranest] logZ error budget: single: 0.05 bs:0.04 tail:0.41 total:0.41 required:<0.50 [ultranest] done iterating. logZ = 99.218 +- 0.413 single instance: logZ = 99.218 +- 0.049 bootstrapped : logZ = 99.210 +- 0.078 tail : logZ = +- 0.405 insert order U test : converged: True correlation: inf iterations mean : 41.9564│ ▁ ▁▁▁▁▁▁▁▂▂▃▄▇▅▆▆▇▇▅▇▆▆▅▅▄▃▂▂▁▁▁▁▁▁ ▁ │42.0216 41.9894 +- 0.0091 scatter : 0.0728│ ▁▁▁▁▁▁▂▃▄▄▅▆▇▆▇▅▅▄▄▄▃▃▂▂▂▁▁▁▁▁ ▁▁▁ ▁ │0.1198 0.0914 +- 0.0065 aux_logweight 0.80 +- 0.63 [ultranest] Sampling 400 live points from prior ... Z=-inf(0.00%) | Like=-4.1e+11..-2.8e+02 [-4.059e+11..-3.867e+06] | it/evals=0/401 eff=0.0000% N=400 Z=-1e+10(0.00%) | Like=-1.4e+10..-2.8e+02 [-4.059e+11..-3.867e+06] | it/evals=40/444 eff=90.9091% N=400 Z=-3e+09(0.00%) | Like=-2.6e+09..-2.8e+02 [-4.059e+11..-3.867e+06] | it/evals=80/488 eff=90.9091% N=400 Z=-6e+08(0.00%) | Like=-5.9e+08..-2.8e+02 [-4.059e+11..-3.867e+06] | it/evals=120/531 eff=91.6031% N=400 Z=-2e+08(0.00%) | Like=-1.8e+08..-2.8e+02 [-4.059e+11..-3.867e+06] | it/evals=160/576 eff=90.9091% N=400 Z=-37629480.8(0.00%) | Like=-37019184.92..-276.64 [-4.059e+11..-3.867e+06] | it/evals=200/627 eff=88.1057% N=400 Z=-12066612.5(0.00%) | Like=-11773456.96..-276.64 [-4.059e+11..-3.867e+06] | it/evals=240/671 eff=88.5609% N=400 Z=-5121053.0(0.00%) | Like=-5026605.32..-276.64 [-4.059e+11..-3.867e+06] | it/evals=270/708 eff=87.6623% N=400 Z=-4239114.9(0.00%) | Like=-4218156.66..-276.64 [-4.059e+11..-3.867e+06] | it/evals=280/720 eff=87.5000% N=400 Z=-2131925.2(0.00%) | Like=-2080057.46..-276.64 [-3713236.8402..-67574.4853] | it/evals=320/772 eff=86.0215% N=400 Z=-1014637.6(0.00%) | Like=-964900.30..-276.64 [-3713236.8402..-67574.4853] | it/evals=360/818 eff=86.1244% N=400 Z=-541870.3(0.00%) | Like=-535492.84..-276.64 [-3713236.8402..-67574.4853] | it/evals=400/876 eff=84.0336% N=400 Z=-300783.4(0.00%) | Like=-295422.19..-276.64 [-3713236.8402..-67574.4853] | it/evals=440/927 eff=83.4915% N=400 Z=-255114.9(0.00%) | Like=-251465.12..-276.64 [-3713236.8402..-67574.4853] | it/evals=450/939 eff=83.4879% N=400 Z=-151349.5(0.00%) | Like=-150202.02..-276.64 [-3713236.8402..-67574.4853] | it/evals=480/974 eff=83.6237% N=400 Z=-99997.7(0.00%) | Like=-98049.75..-276.64 [-3713236.8402..-67574.4853] | it/evals=520/1028 eff=82.8025% N=400 Z=-65129.4(0.00%) | Like=-65004.43..-276.64 [-67571.4782..-7758.7267] | it/evals=560/1081 eff=82.2320% N=400 Z=-55427.6(0.00%) | Like=-51004.27..-276.64 [-67571.4782..-7758.7267] | it/evals=576/1110 eff=81.1268% N=400 Z=-40261.6(0.00%) | Like=-40093.67..-276.64 [-67571.4782..-7758.7267] | it/evals=600/1149 eff=80.1068% N=400 Z=-29476.4(0.00%) | Like=-28904.97..-276.64 [-67571.4782..-7758.7267] | it/evals=640/1212 eff=78.8177% N=400 Z=-21665.4(0.00%) | Like=-21630.82..-276.64 [-67571.4782..-7758.7267] | it/evals=680/1275 eff=77.7143% N=400 Z=-15756.3(0.00%) | Like=-15544.83..-276.64 [-67571.4782..-7758.7267] | it/evals=720/1338 eff=76.7591% N=400 Z=-12015.4(0.00%) | Like=-12002.51..-276.64 [-67571.4782..-7758.7267] | it/evals=760/1402 eff=75.8483% N=400 Z=-9473.4(0.00%) | Like=-9362.21..-276.64 [-67571.4782..-7758.7267] | it/evals=800/1488 eff=73.5294% N=400 Z=-7725.4(0.00%) | Like=-7665.90..-276.64 [-7753.2372..-2005.4268] | it/evals=840/1571 eff=71.7336% N=400 Z=-6168.5(0.00%) | Like=-6136.23..-276.64 [-7753.2372..-2005.4268] | it/evals=880/1684 eff=68.5358% N=400 Z=-4952.2(0.00%) | Like=-4935.58..-249.74 [-7753.2372..-2005.4268] | it/evals=920/1775 eff=66.9091% N=400 Z=-4041.3(0.00%) | Like=-4003.76..-249.74 [-7753.2372..-2005.4268] | it/evals=960/1860 eff=65.7534% N=400 Z=-3518.0(0.00%) | Like=-3509.78..-249.74 [-7753.2372..-2005.4268] | it/evals=1000/1957 eff=64.2261% N=400 Z=-2883.7(0.00%) | Like=-2816.71..-249.74 [-7753.2372..-2005.4268] | it/evals=1040/2051 eff=62.9921% N=400 Z=-2411.5(0.00%) | Like=-2394.29..-249.74 [-7753.2372..-2005.4268] | it/evals=1080/2171 eff=60.9825% N=400 Z=-2097.1(0.00%) | Like=-2076.89..-249.74 [-7753.2372..-2005.4268] | it/evals=1120/2298 eff=59.0095% N=400 Z=-1817.5(0.00%) | Like=-1804.91..-249.74 [-2003.1952..-893.8641] | it/evals=1160/2387 eff=58.3795% N=400 Z=-1783.6(0.00%) | Like=-1754.08..-249.74 [-2003.1952..-893.8641] | it/evals=1170/2406 eff=58.3250% N=400 Z=-1566.1(0.00%) | Like=-1555.97..-249.74 [-2003.1952..-893.8641] | it/evals=1200/2475 eff=57.8313% N=400 Z=-1401.3(0.00%) | Like=-1389.84..-249.74 [-2003.1952..-893.8641] | it/evals=1240/2557 eff=57.4873% N=400 Z=-1337.9(0.00%) | Like=-1324.13..-249.74 [-2003.1952..-893.8641] | it/evals=1260/2604 eff=57.1688% N=400 Z=-1276.9(0.00%) | Like=-1266.90..-249.74 [-2003.1952..-893.8641] | it/evals=1280/2643 eff=57.0664% N=400 Z=-1120.2(0.00%) | Like=-1110.35..-241.79 [-2003.1952..-893.8641] | it/evals=1320/2726 eff=56.7498% N=400 Z=-1014.1(0.00%) | Like=-1001.54..-223.34 [-2003.1952..-893.8641] | it/evals=1350/2781 eff=56.6989% N=400 Z=-996.6(0.00%) | Like=-984.22..-223.34 [-2003.1952..-893.8641] | it/evals=1360/2795 eff=56.7850% N=400 Z=-915.5(0.00%) | Like=-904.91..-223.34 [-2003.1952..-893.8641] | it/evals=1400/2872 eff=56.6343% N=400 Z=-835.4(0.00%) | Like=-820.73..-176.99 [-892.8216..-562.4334] | it/evals=1440/2938 eff=56.7376% N=400 Z=-743.6(0.00%) | Like=-733.85..-176.99 [-892.8216..-562.4334] | it/evals=1480/3019 eff=56.5101% N=400 Z=-699.3(0.00%) | Like=-684.33..-115.14 [-892.8216..-562.4334] | it/evals=1520/3102 eff=56.2546% N=400 Z=-686.8(0.00%) | Like=-676.46..-115.14 [-892.8216..-562.4334] | it/evals=1530/3126 eff=56.1262% N=400 Z=-654.2(0.00%) | Like=-643.23..-115.14 [-892.8216..-562.4334] | it/evals=1560/3173 eff=56.2568% N=400 Z=-625.3(0.00%) | Like=-615.39..-115.14 [-892.8216..-562.4334] | it/evals=1600/3244 eff=56.2588% N=400 Z=-611.4(0.00%) | Like=-601.50..-115.14 [-892.8216..-562.4334] | it/evals=1620/3278 eff=56.2891% N=400 Z=-595.0(0.00%) | Like=-585.36..-115.14 [-892.8216..-562.4334] | it/evals=1640/3314 eff=56.2800% N=400 Z=-574.5(0.00%) | Like=-565.11..-115.14 [-892.8216..-562.4334] | it/evals=1680/3370 eff=56.5657% N=400 Z=-564.2(0.00%) | Like=-555.02..-87.70 [-561.9422..-496.7966] | it/evals=1710/3421 eff=56.6038% N=400 Z=-561.7(0.00%) | Like=-552.59..-87.70 [-561.9422..-496.7966] | it/evals=1720/3435 eff=56.6722% N=400 Z=-552.2(0.00%) | Like=-543.29..-72.54 [-561.9422..-496.7966] | it/evals=1760/3501 eff=56.7559% N=400 Z=-542.9(0.00%) | Like=-533.87..-72.54 [-561.9422..-496.7966] | it/evals=1800/3566 eff=56.8541% N=400 Z=-533.6(0.00%) | Like=-524.21..-72.54 [-561.9422..-496.7966] | it/evals=1840/3633 eff=56.9131% N=400 Z=-526.1(0.00%) | Like=-516.45..-72.54 [-561.9422..-496.7966] | it/evals=1880/3703 eff=56.9180% N=400 Z=-523.5(0.00%) | Like=-514.33..-72.54 [-561.9422..-496.7966] | it/evals=1890/3716 eff=56.9964% N=400 Z=-514.5(0.00%) | Like=-504.81..-72.54 [-561.9422..-496.7966] | it/evals=1920/3752 eff=57.2792% N=400 Z=-503.4(0.00%) | Like=-493.81..-72.54 [-496.7229..-425.4198] | it/evals=1960/3812 eff=57.4443% N=400 Z=-497.6(0.00%) | Like=-487.69..-72.54 [-496.7229..-425.4198] | it/evals=1980/3839 eff=57.5749% N=400 Z=-492.4(0.00%) | Like=-482.81..-72.54 [-496.7229..-425.4198] | it/evals=2000/3869 eff=57.6535% N=400 Z=-481.9(0.00%) | Like=-471.51..-72.54 [-496.7229..-425.4198] | it/evals=2040/3916 eff=58.0205% N=400 Z=-467.6(0.00%) | Like=-457.50..-72.54 [-496.7229..-425.4198] | it/evals=2080/3974 eff=58.1981% N=400 Z=-460.2(0.00%) | Like=-450.50..-72.54 [-496.7229..-425.4198] | it/evals=2120/4041 eff=58.2258% N=400 Z=-451.3(0.00%) | Like=-441.42..-48.30 [-496.7229..-425.4198] | it/evals=2160/4093 eff=58.4890% N=400 Z=-442.1(0.00%) | Like=-431.97..-48.30 [-496.7229..-425.4198] | it/evals=2200/4159 eff=58.5262% N=400 Z=-432.8(0.00%) | Like=-422.44..-48.30 [-424.9178..-365.3215] | it/evals=2240/4224 eff=58.5774% N=400 Z=-424.5(0.00%) | Like=-413.65..-48.30 [-424.9178..-365.3215] | it/evals=2280/4292 eff=58.5817% N=400 Z=-416.6(0.00%) | Like=-405.68..-48.30 [-424.9178..-365.3215] | it/evals=2320/4344 eff=58.8235% N=400 Z=-408.2(0.00%) | Like=-397.87..-48.30 [-424.9178..-365.3215] | it/evals=2360/4411 eff=58.8382% N=400 Z=-400.0(0.00%) | Like=-389.40..-48.30 [-424.9178..-365.3215] | it/evals=2400/4465 eff=59.0406% N=400 Z=-394.0(0.00%) | Like=-383.71..-48.30 [-424.9178..-365.3215] | it/evals=2430/4518 eff=59.0092% N=400 Z=-392.3(0.00%) | Like=-381.40..-48.30 [-424.9178..-365.3215] | it/evals=2440/4533 eff=59.0370% N=400 Z=-383.1(0.00%) | Like=-372.99..-48.30 [-424.9178..-365.3215] | it/evals=2480/4607 eff=58.9494% N=400 Z=-373.9(0.00%) | Like=-363.25..-48.30 [-365.1228..-305.6284] | it/evals=2520/4677 eff=58.9198% N=400 Z=-365.1(0.00%) | Like=-353.57..-48.30 [-365.1228..-305.6284] | it/evals=2560/4731 eff=59.1088% N=400 Z=-354.9(0.00%) | Like=-343.81..-36.34 [-365.1228..-305.6284] | it/evals=2600/4785 eff=59.2930% N=400 Z=-353.2(0.00%) | Like=-342.59..-36.34 [-365.1228..-305.6284] | it/evals=2610/4809 eff=59.1971% N=400 Z=-347.7(0.00%) | Like=-337.09..-31.02 [-365.1228..-305.6284] | it/evals=2640/4848 eff=59.3525% N=400 Z=-337.5(0.00%) | Like=-325.86..4.51 [-365.1228..-305.6284] | it/evals=2680/4910 eff=59.4235% N=400 Z=-333.4(0.00%) | Like=-322.27..4.51 [-365.1228..-305.6284] | it/evals=2700/4942 eff=59.4452% N=400 Z=-329.6(0.00%) | Like=-318.09..4.51 [-365.1228..-305.6284] | it/evals=2720/4966 eff=59.5707% N=400 Z=-320.1(0.00%) | Like=-308.11..5.94 [-365.1228..-305.6284] | it/evals=2760/5022 eff=59.7144% N=400 Z=-312.9(0.00%) | Like=-301.47..33.15 [-305.4729..-246.0365] | it/evals=2790/5068 eff=59.7686% N=400 Z=-310.4(0.00%) | Like=-298.96..33.15 [-305.4729..-246.0365] | it/evals=2800/5079 eff=59.8418% N=400 Z=-300.0(0.00%) | Like=-288.26..33.15 [-305.4729..-246.0365] | it/evals=2840/5135 eff=59.9789% N=400 Z=-291.1(0.00%) | Like=-278.53..33.15 [-305.4729..-246.0365] | it/evals=2880/5200 eff=60.0000% N=400 Z=-282.1(0.00%) | Like=-270.79..33.15 [-305.4729..-246.0365] | it/evals=2920/5260 eff=60.0823% N=400 Z=-274.2(0.00%) | Like=-262.16..33.15 [-305.4729..-246.0365] | it/evals=2960/5318 eff=60.1871% N=400 Z=-271.6(0.00%) | Like=-259.39..33.15 [-305.4729..-246.0365] | it/evals=2970/5335 eff=60.1824% N=400 Z=-265.2(0.00%) | Like=-252.95..33.15 [-305.4729..-246.0365] | it/evals=3000/5370 eff=60.3622% N=400 Z=-257.2(0.00%) | Like=-245.23..33.15 [-246.0052..-181.9655] | it/evals=3040/5425 eff=60.4975% N=400 Z=-252.8(0.00%) | Like=-240.18..33.15 [-246.0052..-181.9655] | it/evals=3060/5455 eff=60.5341% N=400 Z=-248.5(0.00%) | Like=-236.40..33.15 [-246.0052..-181.9655] | it/evals=3080/5478 eff=60.6538% N=400 Z=-239.8(0.00%) | Like=-227.58..33.15 [-246.0052..-181.9655] | it/evals=3120/5527 eff=60.8543% N=400 Z=-231.7(0.00%) | Like=-219.05..33.15 [-246.0052..-181.9655] | it/evals=3160/5583 eff=60.9686% N=400 Z=-222.5(0.00%) | Like=-209.88..58.38 [-246.0052..-181.9655] | it/evals=3200/5634 eff=61.1387% N=400 Z=-213.3(0.00%) | Like=-200.38..76.85 [-246.0052..-181.9655] | it/evals=3240/5700 eff=61.1321% N=400 Z=-203.3(0.00%) | Like=-190.75..84.22 [-246.0052..-181.9655] | it/evals=3280/5757 eff=61.2283% N=400 Z=-193.8(0.00%) | Like=-180.96..84.22 [-181.6575..-127.1559] | it/evals=3320/5814 eff=61.3225% N=400 Z=-191.5(0.00%) | Like=-178.74..84.22 [-181.6575..-127.1559] | it/evals=3330/5829 eff=61.3373% N=400 Z=-186.1(0.00%) | Like=-173.36..84.22 [-181.6575..-127.1559] | it/evals=3360/5861 eff=61.5272% N=400 Z=-176.2(0.00%) | Like=-162.69..84.22 [-181.6575..-127.1559] | it/evals=3400/5920 eff=61.5942% N=400 Z=-170.0(0.00%) | Like=-156.47..84.22 [-181.6575..-127.1559] | it/evals=3420/5947 eff=61.6549% N=400 Z=-165.8(0.00%) | Like=-152.80..84.22 [-181.6575..-127.1559] | it/evals=3440/5969 eff=61.7705% N=400 Z=-156.7(0.00%) | Like=-143.72..87.91 [-181.6575..-127.1559] | it/evals=3480/6018 eff=61.9438% N=400 Z=-148.9(0.00%) | Like=-136.08..87.91 [-181.6575..-127.1559] | it/evals=3520/6071 eff=62.0702% N=400 Z=-141.4(0.00%) | Like=-128.03..95.46 [-181.6575..-127.1559] | it/evals=3560/6128 eff=62.1508% N=400 Z=-135.4(0.00%) | Like=-122.57..95.46 [-127.0824..-76.9703] | it/evals=3600/6175 eff=62.3377% N=400 Z=-129.3(0.00%) | Like=-115.62..95.46 [-127.0824..-76.9703] | it/evals=3640/6234 eff=62.3929% N=400 Z=-122.0(0.00%) | Like=-108.30..95.46 [-127.0824..-76.9703] | it/evals=3680/6291 eff=62.4682% N=400 Z=-119.5(0.00%) | Like=-105.57..95.46 [-127.0824..-76.9703] | it/evals=3690/6307 eff=62.4683% N=400 Z=-114.5(0.00%) | Like=-101.14..95.46 [-127.0824..-76.9703] | it/evals=3720/6341 eff=62.6157% N=400 Z=-107.6(0.00%) | Like=-92.93..95.46 [-127.0824..-76.9703] | it/evals=3760/6392 eff=62.7503% N=400 Z=-99.8(0.00%) | Like=-86.14..95.46 [-127.0824..-76.9703] | it/evals=3800/6451 eff=62.7995% N=400 Z=-93.9(0.00%) | Like=-79.78..95.46 [-127.0824..-76.9703] | it/evals=3840/6504 eff=62.9096% N=400 Z=-87.5(0.00%) | Like=-73.71..95.46 [-76.8329..-31.0885] | it/evals=3880/6560 eff=62.9870% N=400 Z=-80.8(0.00%) | Like=-66.10..95.46 [-76.8329..-31.0885] | it/evals=3920/6611 eff=63.1138% N=400 Z=-72.7(0.00%) | Like=-58.43..95.46 [-76.8329..-31.0885] | it/evals=3960/6681 eff=63.0473% N=400 Z=-64.2(0.00%) | Like=-50.14..95.46 [-76.8329..-31.0885] | it/evals=4000/6731 eff=63.1812% N=400 Z=-56.6(0.00%) | Like=-42.25..95.46 [-76.8329..-31.0885] | it/evals=4040/6794 eff=63.1842% N=400 Z=-54.5(0.00%) | Like=-40.26..95.46 [-76.8329..-31.0885] | it/evals=4050/6809 eff=63.1924% N=400 Z=-50.8(0.00%) | Like=-36.34..95.46 [-76.8329..-31.0885] | it/evals=4080/6846 eff=63.2951% N=400 Z=-44.9(0.00%) | Like=-30.73..97.52 [-31.0687..8.7264] | it/evals=4120/6898 eff=63.4041% N=400 Z=-42.3(0.00%) | Like=-27.94..97.52 [-31.0687..8.7264] | it/evals=4140/6929 eff=63.4094% N=400 Z=-39.9(0.00%) | Like=-25.81..97.52 [-31.0687..8.7264] | it/evals=4160/6953 eff=63.4824% N=400 Z=-33.8(0.00%) | Like=-19.11..97.52 [-31.0687..8.7264] | it/evals=4200/7000 eff=63.6364% N=400 Z=-28.9(0.00%) | Like=-13.81..97.81 [-31.0687..8.7264] | it/evals=4230/7038 eff=63.7240% N=400 Z=-27.2(0.00%) | Like=-12.10..98.13 [-31.0687..8.7264] | it/evals=4240/7052 eff=63.7402% N=400 Z=-21.6(0.00%) | Like=-6.73..98.24 [-31.0687..8.7264] | it/evals=4280/7099 eff=63.8901% N=400 Z=-15.3(0.00%) | Like=-0.29..98.24 [-31.0687..8.7264] | it/evals=4320/7151 eff=63.9905% N=400 Z=-9.3(0.00%) | Like=5.94..98.24 [-31.0687..8.7264] | it/evals=4360/7203 eff=64.0894% N=400 Z=-4.0(0.00%) | Like=11.18..98.24 [8.7436..45.4062] | it/evals=4400/7263 eff=64.1119% N=400 Z=-2.0(0.00%) | Like=13.40..98.24 [8.7436..45.4062] | it/evals=4410/7274 eff=64.1548% N=400 Z=3.0(0.00%) | Like=18.46..98.24 [8.7436..45.4062] | it/evals=4440/7311 eff=64.2454% N=400 Z=9.0(0.00%) | Like=24.23..98.39 [8.7436..45.4062] | it/evals=4480/7358 eff=64.3863% N=400 Z=12.0(0.00%) | Like=27.45..98.39 [8.7436..45.4062] | it/evals=4500/7384 eff=64.4330% N=400 Z=14.7(0.00%) | Like=30.09..98.39 [8.7436..45.4062] | it/evals=4520/7409 eff=64.4885% N=400 Z=19.8(0.00%) | Like=35.70..98.39 [8.7436..45.4062] | it/evals=4560/7463 eff=64.5618% N=400 Z=23.4(0.00%) | Like=38.68..98.39 [8.7436..45.4062] | it/evals=4590/7503 eff=64.6206% N=400 Z=24.5(0.00%) | Like=39.70..98.39 [8.7436..45.4062] | it/evals=4600/7515 eff=64.6521% N=400 Z=28.0(0.00%) | Like=43.51..98.39 [8.7436..45.4062] | it/evals=4640/7565 eff=64.7592% N=400 Z=32.5(0.00%) | Like=47.97..98.39 [45.5706..67.5478] | it/evals=4680/7617 eff=64.8469% N=400 Z=35.7(0.00%) | Like=51.08..98.39 [45.5706..67.5478] | it/evals=4720/7668 eff=64.9422% N=400 Z=38.7(0.00%) | Like=54.16..98.39 [45.5706..67.5478] | it/evals=4760/7724 eff=64.9918% N=400 Z=39.5(0.00%) | Like=54.86..98.39 [45.5706..67.5478] | it/evals=4770/7737 eff=65.0129% N=400 Z=41.9(0.00%) | Like=57.55..98.39 [45.5706..67.5478] | it/evals=4800/7777 eff=65.0671% N=400 Z=45.3(0.00%) | Like=60.71..98.39 [45.5706..67.5478] | it/evals=4840/7829 eff=65.1501% N=400 Z=46.8(0.00%) | Like=62.54..98.41 [45.5706..67.5478] | it/evals=4860/7859 eff=65.1562% N=400 Z=48.3(0.00%) | Like=63.72..98.41 [45.5706..67.5478] | it/evals=4880/7881 eff=65.2319% N=400 Z=51.0(0.00%) | Like=66.65..98.41 [45.5706..67.5478] | it/evals=4920/7928 eff=65.3560% N=400 Z=53.4(0.00%) | Like=69.28..98.41 [67.5628..80.8524] | it/evals=4960/7977 eff=65.4613% N=400 Z=56.1(0.00%) | Like=71.64..98.41 [67.5628..80.8524] | it/evals=5000/8026 eff=65.5652% N=400 Z=57.9(0.00%) | Like=73.74..98.41 [67.5628..80.8524] | it/evals=5040/8078 eff=65.6421% N=400 Z=59.9(0.00%) | Like=75.54..98.41 [67.5628..80.8524] | it/evals=5080/8129 eff=65.7265% N=400 Z=61.7(0.00%) | Like=77.34..98.41 [67.5628..80.8524] | it/evals=5120/8182 eff=65.7929% N=400 Z=63.5(0.00%) | Like=79.26..98.41 [67.5628..80.8524] | it/evals=5160/8233 eff=65.8751% N=400 Z=65.1(0.00%) | Like=80.72..98.41 [67.5628..80.8524] | it/evals=5200/8291 eff=65.8979% N=400 Z=65.7(0.00%) | Like=81.30..98.41 [80.8533..89.6445] | it/evals=5220/8319 eff=65.9174% N=400 Z=66.5(0.00%) | Like=82.32..98.41 [80.8533..89.6445] | it/evals=5240/8345 eff=65.9534% N=400 Z=67.9(0.00%) | Like=83.86..98.41 [80.8533..89.6445] | it/evals=5280/8395 eff=66.0413% N=400 Z=69.0(0.00%) | Like=84.73..98.41 [80.8533..89.6445] | it/evals=5310/8433 eff=66.1023% N=400 Z=69.3(0.00%) | Like=85.11..98.41 [80.8533..89.6445] | it/evals=5320/8450 eff=66.0870% N=400 Z=70.5(0.00%) | Like=86.41..98.41 [80.8533..89.6445] | it/evals=5360/8501 eff=66.1647% N=400 Z=71.5(0.00%) | Like=87.38..98.41 [80.8533..89.6445] | it/evals=5400/8557 eff=66.2008% N=400 Z=72.5(0.00%) | Like=88.23..98.41 [80.8533..89.6445] | it/evals=5440/8610 eff=66.2607% N=400 Z=73.3(0.01%) | Like=89.27..98.41 [80.8533..89.6445] | it/evals=5480/8661 eff=66.3358% N=400 Z=73.5(0.01%) | Like=89.47..98.41 [80.8533..89.6445] | it/evals=5490/8675 eff=66.3444% N=400 Z=74.2(0.02%) | Like=90.07..98.49 [89.6801..90.9753] | it/evals=5520/8711 eff=66.4180% N=400 Z=75.0(0.05%) | Like=90.97..98.49 [89.6801..90.9753] | it/evals=5560/8765 eff=66.4674% N=400 Z=75.7(0.11%) | Like=91.67..98.49 [91.6401..91.6716] | it/evals=5600/8819 eff=66.5162% N=400 Z=76.4(0.21%) | Like=92.30..98.49 [92.3017..92.3752] | it/evals=5640/8867 eff=66.6116% N=400 Z=76.8(0.34%) | Like=92.77..98.49 [92.7745..92.7789]*| it/evals=5670/8909 eff=66.6353% N=400 Z=77.0(0.39%) | Like=92.95..98.49 [92.9521..92.9647] | it/evals=5680/8920 eff=66.6667% N=400 Z=77.5(0.68%) | Like=93.47..98.49 [93.4513..93.4659] | it/evals=5720/8968 eff=66.7600% N=400 Z=78.0(1.14%) | Like=93.92..98.49 [93.9203..93.9340] | it/evals=5760/9021 eff=66.8136% N=400 Z=78.5(1.76%) | Like=94.42..98.49 [94.4199..94.4316] | it/evals=5800/9067 eff=66.9205% N=400 Z=78.9(2.61%) | Like=94.80..98.49 [94.7710..94.7997] | it/evals=5840/9118 eff=66.9878% N=400 Z=78.9(2.87%) | Like=94.86..98.49 [94.8620..94.8792] | it/evals=5850/9130 eff=67.0103% N=400 Z=79.2(3.71%) | Like=95.08..98.49 [95.0837..95.0846]*| it/evals=5880/9170 eff=67.0468% N=400 Z=79.5(4.97%) | Like=95.37..98.49 [95.3544..95.3709] | it/evals=5920/9221 eff=67.1126% N=400 Z=79.6(5.74%) | Like=95.52..98.49 [95.5150..95.5168]*| it/evals=5940/9247 eff=67.1414% N=400 Z=79.8(6.59%) | Like=95.66..98.49 [95.6561..95.6587]*| it/evals=5960/9270 eff=67.1928% N=400 Z=80.0(8.27%) | Like=95.90..98.49 [95.9000..95.9189] | it/evals=6000/9317 eff=67.2872% N=400 Z=80.3(10.25%) | Like=96.11..98.49 [96.1056..96.1110]*| it/evals=6040/9376 eff=67.2906% N=400 Z=80.5(12.48%) | Like=96.30..98.49 [96.2989..96.3076]*| it/evals=6080/9423 eff=67.3834% N=400 Z=80.6(15.04%) | Like=96.48..98.49 [96.4812..96.4923] | it/evals=6120/9479 eff=67.4083% N=400 Z=80.8(17.97%) | Like=96.69..98.49 [96.6875..96.6906]*| it/evals=6160/9527 eff=67.4921% N=400 Z=81.0(20.93%) | Like=96.85..98.50 [96.8460..96.8468]*| it/evals=6200/9586 eff=67.4940% N=400 Z=81.0(21.78%) | Like=96.89..98.50 [96.8851..96.8930]*| it/evals=6210/9596 eff=67.5294% N=400 Z=81.1(24.15%) | Like=97.00..98.50 [96.9983..96.9996]*| it/evals=6240/9636 eff=67.5617% N=400 Z=81.2(27.57%) | Like=97.13..98.50 [97.1322..97.1413]*| it/evals=6280/9694 eff=67.5705% N=400 Z=81.3(29.36%) | Like=97.20..98.50 [97.1952..97.1957]*| it/evals=6300/9717 eff=67.6183% N=400 Z=81.3(31.00%) | Like=97.27..98.50 [97.2717..97.2766]*| it/evals=6320/9741 eff=67.6587% N=400 Z=81.5(34.66%) | Like=97.39..98.50 [97.3944..97.3960]*| it/evals=6360/9796 eff=67.6884% N=400 Z=81.5(37.34%) | Like=97.49..98.50 [97.4946..97.4947]*| it/evals=6390/9846 eff=67.6477% N=400 Z=81.6(38.27%) | Like=97.51..98.50 [97.5113..97.5147]*| it/evals=6400/9859 eff=67.6604% N=400 Z=81.6(42.06%) | Like=97.59..98.50 [97.5888..97.5898]*| it/evals=6440/9911 eff=67.7111% N=400 Z=81.7(45.73%) | Like=97.67..98.50 [97.6705..97.6731]*| it/evals=6480/9962 eff=67.7682% N=400 Z=81.8(49.08%) | Like=97.76..98.50 [97.7636..97.7671]*| it/evals=6520/10008 eff=67.8601% N=400 [ultranest] Explored until L=1e+02 [ultranest] Likelihood function evaluations: 10026 [ultranest] logZ = 82.54 +- 0.1468 [ultranest] Effective samples strategy satisfied (ESS = 989.4, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.43, need <0.5) [ultranest] logZ error budget: single: 0.19 bs:0.15 tail:0.41 total:0.43 required:<0.50 [ultranest] done iterating. logZ = 82.511 +- 0.480 single instance: logZ = 82.511 +- 0.193 bootstrapped : logZ = 82.536 +- 0.257 tail : logZ = +- 0.405 insert order U test : converged: True correlation: inf iterations mean : 41.9474│ ▁ ▁▁▁▁▁▁▁▁▁▁▂▃▃▄▆▅▆▇▆▅▆▅▅▄▄▂▂▁▁▁▁▁▁▁▁ │42.0252 41.9896 +- 0.0093 scatter : 0.0684│ ▁▁▁▁▁▁▂▂▃▄▆▆▆▆▇▅▅▅▄▃▃▂▂▁▁▁▁▁▁▁▁▁▁▁ ▁ │0.1256 0.0911 +- 0.0068 RECYCLING: ref: {'niter': 6931, 'logz': 82.51126797630579, 'logzerr': 0.48000210459420994, 'logz_bs': 82.53603482383392, 'logz_single': 82.51126797630579, 'logzerr_tail': 0.40526526018403786, 'logzerr_bs': 0.2572199240005233, 'ess': 989.4418387883029, 'H': 14.904885613395052, 'Herr': 0.14344533438670065, 'posterior': {'mean': [41.98963561307596, 0.09112288330816437], 'stdev': [0.009349186585804043, 0.006775975213110563], 'median': [41.98941172473769, 0.0906763001145217], 'errlo': [41.98058131149014, 0.08469946646382202], 'errup': [41.999107688907316, 0.09767389448562613], 'information_gain_bits': [4.14647919764959, 3.592890199934908]}, 'weighted_samples': {'upoints': array([[9.75177245e-01, 8.77828782e-04], [8.18524108e-02, 2.44479339e-03], [9.88480171e-02, 2.02700325e-02], ..., [5.20995235e-01, 2.39361007e-01], [5.20995032e-01, 2.39712725e-01], [5.20995165e-01, 2.39004262e-01]]), 'points': array([[ 9.50354489e+02, 1.00811787e-02], [-8.36295178e+02, 1.02277281e-02], [-8.02303966e+02, 1.20525830e-02], ..., [ 4.19904702e+01, 9.06659110e-02], [ 4.19900633e+01, 9.09600940e-02], [ 4.19903302e+01, 9.03684948e-02]]), 'weights': array([0. , 0. , 0. , ..., 0.00177121, 0.00177125, 0.00177555]), 'logw': array([ -5.99271429, -5.99521429, -5.99771429, ..., -22.31896455, -22.31896455, -22.31896455]), 'bootstrapped_weights': array([[0. , 0. , 0. , ..., 0. , 0. , 0. ], [0. , 0. , 0. , ..., 0. , 0. , 0. ], [0. , 0. , 0. , ..., 0. , 0. , 0. ], ..., [0. , 0.00274031, 0.00279516, ..., 0.00293349, 0.00267692, 0. ], [0.00273848, 0.00274037, 0.00279522, ..., 0.00293356, 0.00267697, 0. ], [0.00274512, 0. , 0.002802 , ..., 0. , 0.00268347, 0.0026264 ]]), 'logl': array([-4.05945790e+11, -3.68707854e+11, -2.45356023e+11, ..., 9.84941424e+01, 9.84941642e+01, 9.84965862e+01])}, 'samples': array([[42.00291904, 0.09197281], [41.9775074 , 0.08824589], [42.00658608, 0.08615179], ..., [42.00145679, 0.10275319], [41.98672294, 0.09255127], [41.98355338, 0.08669633]]), 'maximum_likelihood': {'logl': 98.49658620114383, 'point': [41.990330163570434, 0.09036849476345528], 'point_untransformed': [0.5209951650817852, 0.23900426211224726]}, 'ncall': 10026, 'paramnames': ['mean', 'scatter'], 'logzerr_single': 0.19303423021186586, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} skipping 15 weights: [0. 0. 0. ... 0.99755914 0.9975809 1. ] rec: {'ncall': 2048, 'niter': 6931, 'logz': 82.51126797630566, 'logzerr': 0.4800022347771922, 'ess': 989.4418387882997, 'posterior': {'mean': [41.98968202356687, 0.09114676119272457], 'stdev': [0.009297326255806548, 0.006793819118296698], 'median': [41.98947238149481, 0.09068695842395716], 'errlo': [41.98062812212561, 0.08466827339953377], 'errup': [41.999107688907316, 0.09770985563634536], 'information_gain_bits': [nan, 3.6621734305941214]}, 'weighted_samples': {'upoints': array([[9.75177245e-01, 8.77828782e-04], [8.18524108e-02, 2.44479339e-03], [9.88480171e-02, 2.02700325e-02], ..., [5.20995235e-01, 2.39361007e-01], [5.20995032e-01, 2.39712725e-01], [5.20995165e-01, 2.39004262e-01]]), 'points': array([[ 9.50354489e+02, 1.00811787e-02], [-8.36295178e+02, 1.02277281e-02], [-8.02303966e+02, 1.20525830e-02], ..., [ 4.19904702e+01, 9.06659110e-02], [ 4.19900633e+01, 9.09600940e-02], [ 4.19903302e+01, 9.03684948e-02]]), 'weights': array([0. , 0. , 0. , ..., 0.00177121, 0.00177125, 0.00177555]), 'logw': array([ -5.99271429, -5.99521429, -5.99771429, ..., -22.31896455, -22.31896455, -22.31896455]), 'logl': array([ -inf, -inf, -inf, ..., 98.49414236, 98.49416417, 98.4965862 ])}, 'samples': array([[41.99261818, 0.08729851], [41.99664389, 0.08319908], [41.97824608, 0.08718514], ..., [41.99975046, 0.10227979], [42.00241764, 0.08860842], [41.97018642, 0.08833478]]), 'maximum_likelihood': {'logl': 98.49658620114383, 'point': [41.990330163570434, 0.09036849476345528], 'point_untransformed': [0.5209951650817852, 0.23900426211224726]}, 'param_names': ['mean', 'scatter']} weights: [0.34167524 0.36965396 0.11394021 ... 0.11309269 0.90291318 0.65861225] rec2: {'ncall': 6931, 'niter': 6931, 'logz': 97.76985434512109, 'logzerr': 8.570688762427586e-05, 'ess': 5123.234668254743, 'posterior': {'mean': [41.989673533632136, 0.09059357409028677], 'stdev': [0.006543754165026486, 0.004596090599814409], 'median': [41.98941172473769, 0.09050115057035493], 'errlo': [41.983044750694944, 0.08599162931708715], 'errup': [41.996458031426755, 0.0952836649169508], 'information_gain_bits': [nan, 3.8393471248186173]}, 'weighted_samples': {'upoints': None, 'points': array([[42.00291904, 0.09197281], [41.9775074 , 0.08824589], [42.00658608, 0.08615179], ..., [42.00145679, 0.10275319], [41.98672294, 0.09255127], [41.98355338, 0.08669633]]), 'weights': array([1.01960801e-04, 1.10310053e-04, 3.40013973e-05, ..., 3.37484848e-05, 2.69442269e-04, 1.96539361e-04]), 'logw': array([-8.84375938, -8.84375938, -8.84375938, ..., -8.84375938, -8.84375938, -8.84375938]), 'logl': array([97.42269161, 97.50139824, 96.32450479, ..., 96.31703869, 98.39445732, 98.07896589])}, 'samples': array([[41.99542553, 0.09237804], [41.97980873, 0.09237831], [41.99767108, 0.09574967], ..., [41.9977458 , 0.09371036], [41.99336497, 0.09418241], [41.98304951, 0.09079366]]), 'maximum_likelihood': {'logl': 98.49658620114383, 'point': [41.990330163570434, 0.09036849476345528], 'point_untransformed': None}, 'param_names': ['mean', 'scatter']} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+1, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.50, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=77.54, Lmax=101.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=444, regioncalls=1760, ndraw=40, logz=88.39, remainder_fraction=99.9981%, Lmin=91.98, Lmax=101.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=491, regioncalls=3640, ndraw=40, logz=90.68, remainder_fraction=99.9808%, Lmin=93.90, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=506, regioncalls=4240, ndraw=40, logz=91.16, remainder_fraction=99.9690%, Lmin=94.28, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=540, regioncalls=5640, ndraw=40, logz=92.30, remainder_fraction=99.9044%, Lmin=95.13, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=587, regioncalls=7520, ndraw=40, logz=93.45, remainder_fraction=99.6886%, Lmin=96.16, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=610, regioncalls=8480, ndraw=40, logz=93.91, remainder_fraction=99.5043%, Lmin=96.58, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=631, regioncalls=9320, ndraw=40, logz=94.33, remainder_fraction=99.2526%, Lmin=96.86, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=676, regioncalls=11120, ndraw=40, logz=94.99, remainder_fraction=98.5635%, Lmin=97.36, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=725, regioncalls=13120, ndraw=40, logz=95.54, remainder_fraction=97.4838%, Lmin=97.83, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=786, regioncalls=15560, ndraw=40, logz=95.99, remainder_fraction=96.0244%, Lmin=98.18, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=833, regioncalls=17480, ndraw=40, logz=96.35, remainder_fraction=94.2789%, Lmin=98.49, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=883, regioncalls=19520, ndraw=40, logz=96.67, remainder_fraction=92.0986%, Lmin=98.73, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=927, regioncalls=21280, ndraw=40, logz=96.95, remainder_fraction=89.4688%, Lmin=99.00, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=942, regioncalls=21880, ndraw=40, logz=97.01, remainder_fraction=88.8368%, Lmin=99.05, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=977, regioncalls=23280, ndraw=40, logz=97.19, remainder_fraction=86.8106%, Lmin=99.20, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=1024, regioncalls=25160, ndraw=40, logz=97.39, remainder_fraction=83.7818%, Lmin=99.35, Lmax=101.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=554, ncalls=1066, regioncalls=26840, ndraw=40, logz=97.55, remainder_fraction=81.1584%, Lmin=99.49, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1072, regioncalls=27080, ndraw=40, logz=97.57, remainder_fraction=80.7011%, Lmin=99.52, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1118, regioncalls=28960, ndraw=40, logz=97.73, remainder_fraction=77.3810%, Lmin=99.64, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1160, regioncalls=30640, ndraw=40, logz=97.84, remainder_fraction=74.7842%, Lmin=99.74, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=1170, regioncalls=31040, ndraw=40, logz=97.87, remainder_fraction=73.8575%, Lmin=99.76, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=1219, regioncalls=33040, ndraw=40, logz=98.00, remainder_fraction=70.4083%, Lmin=99.88, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1267, regioncalls=35040, ndraw=40, logz=98.11, remainder_fraction=66.7902%, Lmin=99.96, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=1322, regioncalls=37320, ndraw=40, logz=98.21, remainder_fraction=63.3310%, Lmin=100.08, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1367, regioncalls=39200, ndraw=40, logz=98.31, remainder_fraction=59.7909%, Lmin=100.18, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1380, regioncalls=39720, ndraw=40, logz=98.33, remainder_fraction=58.8978%, Lmin=100.20, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1418, regioncalls=41280, ndraw=40, logz=98.39, remainder_fraction=56.3332%, Lmin=100.28, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1473, regioncalls=43600, ndraw=40, logz=98.47, remainder_fraction=52.6823%, Lmin=100.37, Lmax=101.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1496, regioncalls=44520, ndraw=40, logz=98.50, remainder_fraction=50.9723%, Lmin=100.42, Lmax=101.12 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=1e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 1508 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 99.21 +- 0.03825 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 972.0, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.41, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.05 bs:0.04 tail:0.41 total:0.41 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.50, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-405945789829.58, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=444, regioncalls=1760, ndraw=40, logz=-14543780120.47, remainder_fraction=100.0000%, Lmin=-14330281204.24, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=488, regioncalls=3520, ndraw=40, logz=-2695832923.91, remainder_fraction=100.0000%, Lmin=-2578922208.45, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=531, regioncalls=5240, ndraw=40, logz=-621093047.40, remainder_fraction=100.0000%, Lmin=-588559621.47, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=576, regioncalls=7040, ndraw=40, logz=-179312911.40, remainder_fraction=100.0000%, Lmin=-175123183.83, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=627, regioncalls=9080, ndraw=40, logz=-37629480.84, remainder_fraction=100.0000%, Lmin=-37019184.92, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=671, regioncalls=10840, ndraw=40, logz=-12066612.49, remainder_fraction=100.0000%, Lmin=-11773456.96, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=708, regioncalls=12320, ndraw=40, logz=-5121052.97, remainder_fraction=100.0000%, Lmin=-5026605.32, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=720, regioncalls=12800, ndraw=40, logz=-4239114.89, remainder_fraction=100.0000%, Lmin=-4218156.66, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=772, regioncalls=14880, ndraw=40, logz=-2131925.19, remainder_fraction=100.0000%, Lmin=-2080057.46, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=818, regioncalls=16720, ndraw=40, logz=-1014637.58, remainder_fraction=100.0000%, Lmin=-964900.30, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=876, regioncalls=19040, ndraw=40, logz=-541870.28, remainder_fraction=100.0000%, Lmin=-535492.84, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=927, regioncalls=21080, ndraw=40, logz=-300783.39, remainder_fraction=100.0000%, Lmin=-295422.19, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=939, regioncalls=21560, ndraw=40, logz=-255114.89, remainder_fraction=100.0000%, Lmin=-251465.12, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=974, regioncalls=22960, ndraw=40, logz=-151349.49, remainder_fraction=100.0000%, Lmin=-150202.02, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=1028, regioncalls=25120, ndraw=40, logz=-99997.74, remainder_fraction=100.0000%, Lmin=-98049.75, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1081, regioncalls=27240, ndraw=40, logz=-65129.44, remainder_fraction=100.0000%, Lmin=-65004.43, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=576, ncalls=1110, regioncalls=28400, ndraw=40, logz=-55427.63, remainder_fraction=100.0000%, Lmin=-51004.27, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1149, regioncalls=29960, ndraw=40, logz=-40261.62, remainder_fraction=100.0000%, Lmin=-40093.67, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=1212, regioncalls=32480, ndraw=40, logz=-29476.44, remainder_fraction=100.0000%, Lmin=-28904.97, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=1275, regioncalls=35000, ndraw=40, logz=-21665.40, remainder_fraction=100.0000%, Lmin=-21630.82, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1338, regioncalls=37520, ndraw=40, logz=-15756.30, remainder_fraction=100.0000%, Lmin=-15544.83, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=1402, regioncalls=40080, ndraw=40, logz=-12015.41, remainder_fraction=100.0000%, Lmin=-12002.51, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1488, regioncalls=43520, ndraw=40, logz=-9473.37, remainder_fraction=100.0000%, Lmin=-9362.21, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3]), array([393, 6, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1571, regioncalls=46840, ndraw=40, logz=-7725.36, remainder_fraction=100.0000%, Lmin=-7665.90, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1684, regioncalls=51360, ndraw=40, logz=-6168.48, remainder_fraction=100.0000%, Lmin=-6136.23, Lmax=-276.64 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3]), array([396, 1, 3])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1775, regioncalls=55000, ndraw=40, logz=-4952.17, remainder_fraction=100.0000%, Lmin=-4935.58, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1860, regioncalls=58400, ndraw=40, logz=-4041.29, remainder_fraction=100.0000%, Lmin=-4003.76, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3]), array([397, 1, 2])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1957, regioncalls=62280, ndraw=40, logz=-3517.98, remainder_fraction=100.0000%, Lmin=-3509.78, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=2051, regioncalls=66040, ndraw=40, logz=-2883.66, remainder_fraction=100.0000%, Lmin=-2816.71, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3]), array([397, 1, 2])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=2171, regioncalls=70840, ndraw=40, logz=-2411.47, remainder_fraction=100.0000%, Lmin=-2394.29, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=2298, regioncalls=75920, ndraw=40, logz=-2097.08, remainder_fraction=100.0000%, Lmin=-2076.89, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=2387, regioncalls=79480, ndraw=40, logz=-1817.46, remainder_fraction=100.0000%, Lmin=-1804.91, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=2406, regioncalls=80240, ndraw=40, logz=-1783.64, remainder_fraction=100.0000%, Lmin=-1754.08, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=2475, regioncalls=83080, ndraw=40, logz=-1566.13, remainder_fraction=100.0000%, Lmin=-1555.97, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=2557, regioncalls=86360, ndraw=40, logz=-1401.30, remainder_fraction=100.0000%, Lmin=-1389.84, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=2604, regioncalls=88240, ndraw=40, logz=-1337.91, remainder_fraction=100.0000%, Lmin=-1324.13, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=2643, regioncalls=89800, ndraw=40, logz=-1276.87, remainder_fraction=100.0000%, Lmin=-1266.90, Lmax=-249.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=2726, regioncalls=93160, ndraw=40, logz=-1120.19, remainder_fraction=100.0000%, Lmin=-1110.35, Lmax=-241.79 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2781, regioncalls=95360, ndraw=40, logz=-1014.13, remainder_fraction=100.0000%, Lmin=-1001.54, Lmax=-223.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=2795, regioncalls=95960, ndraw=40, logz=-996.61, remainder_fraction=100.0000%, Lmin=-984.22, Lmax=-223.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2872, regioncalls=99040, ndraw=40, logz=-915.47, remainder_fraction=100.0000%, Lmin=-904.91, Lmax=-223.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2938, regioncalls=101680, ndraw=40, logz=-835.37, remainder_fraction=100.0000%, Lmin=-820.73, Lmax=-176.99 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1480, ncalls=3019, regioncalls=105000, ndraw=40, logz=-743.56, remainder_fraction=100.0000%, Lmin=-733.85, Lmax=-176.99 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1520, ncalls=3102, regioncalls=108320, ndraw=40, logz=-699.33, remainder_fraction=100.0000%, Lmin=-684.33, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=3126, regioncalls=109320, ndraw=40, logz=-686.79, remainder_fraction=100.0000%, Lmin=-676.46, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=3173, regioncalls=111200, ndraw=40, logz=-654.22, remainder_fraction=100.0000%, Lmin=-643.23, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=3244, regioncalls=114040, ndraw=40, logz=-625.35, remainder_fraction=100.0000%, Lmin=-615.39, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=3278, regioncalls=115400, ndraw=40, logz=-611.42, remainder_fraction=100.0000%, Lmin=-601.50, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1640, ncalls=3314, regioncalls=116920, ndraw=40, logz=-595.00, remainder_fraction=100.0000%, Lmin=-585.36, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1680, ncalls=3370, regioncalls=119160, ndraw=40, logz=-574.51, remainder_fraction=100.0000%, Lmin=-565.11, Lmax=-115.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=3421, regioncalls=121240, ndraw=40, logz=-564.24, remainder_fraction=100.0000%, Lmin=-555.02, Lmax=-87.70 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1720, ncalls=3435, regioncalls=121800, ndraw=40, logz=-561.71, remainder_fraction=100.0000%, Lmin=-552.59, Lmax=-87.70 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1760, ncalls=3501, regioncalls=124480, ndraw=40, logz=-552.18, remainder_fraction=100.0000%, Lmin=-543.29, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=3566, regioncalls=127160, ndraw=40, logz=-542.89, remainder_fraction=100.0000%, Lmin=-533.87, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1840, ncalls=3633, regioncalls=129960, ndraw=40, logz=-533.59, remainder_fraction=100.0000%, Lmin=-524.21, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1880, ncalls=3703, regioncalls=132880, ndraw=40, logz=-526.06, remainder_fraction=100.0000%, Lmin=-516.45, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=3716, regioncalls=133480, ndraw=40, logz=-523.48, remainder_fraction=100.0000%, Lmin=-514.33, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=3752, regioncalls=135040, ndraw=40, logz=-514.54, remainder_fraction=100.0000%, Lmin=-504.81, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1960, ncalls=3812, regioncalls=137480, ndraw=40, logz=-503.35, remainder_fraction=100.0000%, Lmin=-493.81, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=3839, regioncalls=138600, ndraw=40, logz=-497.65, remainder_fraction=100.0000%, Lmin=-487.69, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=3869, regioncalls=140000, ndraw=40, logz=-492.38, remainder_fraction=100.0000%, Lmin=-482.81, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2040, ncalls=3916, regioncalls=141960, ndraw=40, logz=-481.86, remainder_fraction=100.0000%, Lmin=-471.51, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2080, ncalls=3974, regioncalls=144400, ndraw=40, logz=-467.59, remainder_fraction=100.0000%, Lmin=-457.50, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2120, ncalls=4041, regioncalls=147160, ndraw=40, logz=-460.25, remainder_fraction=100.0000%, Lmin=-450.50, Lmax=-72.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=4093, regioncalls=149440, ndraw=40, logz=-451.28, remainder_fraction=100.0000%, Lmin=-441.42, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=4159, regioncalls=152360, ndraw=40, logz=-442.09, remainder_fraction=100.0000%, Lmin=-431.97, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2240, ncalls=4224, regioncalls=155080, ndraw=40, logz=-432.78, remainder_fraction=100.0000%, Lmin=-422.44, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2280, ncalls=4292, regioncalls=157880, ndraw=40, logz=-424.47, remainder_fraction=100.0000%, Lmin=-413.65, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2320, ncalls=4344, regioncalls=160120, ndraw=40, logz=-416.64, remainder_fraction=100.0000%, Lmin=-405.68, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2360, ncalls=4411, regioncalls=162960, ndraw=40, logz=-408.18, remainder_fraction=100.0000%, Lmin=-397.87, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=4465, regioncalls=165200, ndraw=40, logz=-400.01, remainder_fraction=100.0000%, Lmin=-389.40, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=4518, regioncalls=167680, ndraw=40, logz=-394.00, remainder_fraction=100.0000%, Lmin=-383.71, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2440, ncalls=4533, regioncalls=168280, ndraw=40, logz=-392.25, remainder_fraction=100.0000%, Lmin=-381.40, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2480, ncalls=4607, regioncalls=171320, ndraw=40, logz=-383.10, remainder_fraction=100.0000%, Lmin=-372.99, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=4677, regioncalls=174320, ndraw=40, logz=-373.88, remainder_fraction=100.0000%, Lmin=-363.25, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2560, ncalls=4731, regioncalls=176640, ndraw=40, logz=-365.06, remainder_fraction=100.0000%, Lmin=-353.57, Lmax=-48.30 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=4785, regioncalls=178960, ndraw=40, logz=-354.85, remainder_fraction=100.0000%, Lmin=-343.81, Lmax=-36.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=4809, regioncalls=180000, ndraw=40, logz=-353.22, remainder_fraction=100.0000%, Lmin=-342.59, Lmax=-36.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2640, ncalls=4848, regioncalls=181760, ndraw=40, logz=-347.69, remainder_fraction=100.0000%, Lmin=-337.09, Lmax=-31.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2680, ncalls=4910, regioncalls=184320, ndraw=40, logz=-337.47, remainder_fraction=100.0000%, Lmin=-325.86, Lmax=4.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=4942, regioncalls=185720, ndraw=40, logz=-333.44, remainder_fraction=100.0000%, Lmin=-322.27, Lmax=4.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2720, ncalls=4966, regioncalls=186840, ndraw=40, logz=-329.57, remainder_fraction=100.0000%, Lmin=-318.09, Lmax=4.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2760, ncalls=5022, regioncalls=189240, ndraw=40, logz=-320.11, remainder_fraction=100.0000%, Lmin=-308.11, Lmax=5.94 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=5068, regioncalls=191160, ndraw=40, logz=-312.88, remainder_fraction=100.0000%, Lmin=-301.47, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=5079, regioncalls=191640, ndraw=40, logz=-310.36, remainder_fraction=100.0000%, Lmin=-298.96, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2840, ncalls=5135, regioncalls=193920, ndraw=40, logz=-299.95, remainder_fraction=100.0000%, Lmin=-288.26, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=5200, regioncalls=196560, ndraw=40, logz=-291.14, remainder_fraction=100.0000%, Lmin=-278.53, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2920, ncalls=5260, regioncalls=199040, ndraw=40, logz=-282.12, remainder_fraction=100.0000%, Lmin=-270.79, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2960, ncalls=5318, regioncalls=201400, ndraw=40, logz=-274.21, remainder_fraction=100.0000%, Lmin=-262.16, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=5335, regioncalls=202120, ndraw=40, logz=-271.64, remainder_fraction=100.0000%, Lmin=-259.39, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=5370, regioncalls=203640, ndraw=40, logz=-265.19, remainder_fraction=100.0000%, Lmin=-252.95, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3040, ncalls=5425, regioncalls=205960, ndraw=40, logz=-257.20, remainder_fraction=100.0000%, Lmin=-245.23, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=5455, regioncalls=207280, ndraw=40, logz=-252.78, remainder_fraction=100.0000%, Lmin=-240.18, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3080, ncalls=5478, regioncalls=208320, ndraw=40, logz=-248.52, remainder_fraction=100.0000%, Lmin=-236.40, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3120, ncalls=5527, regioncalls=210400, ndraw=40, logz=-239.80, remainder_fraction=100.0000%, Lmin=-227.58, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3160, ncalls=5583, regioncalls=212760, ndraw=40, logz=-231.74, remainder_fraction=100.0000%, Lmin=-219.05, Lmax=33.15 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=5634, regioncalls=214840, ndraw=40, logz=-222.54, remainder_fraction=100.0000%, Lmin=-209.88, Lmax=58.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=5700, regioncalls=217560, ndraw=40, logz=-213.29, remainder_fraction=100.0000%, Lmin=-200.38, Lmax=76.85 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3280, ncalls=5757, regioncalls=219920, ndraw=40, logz=-203.34, remainder_fraction=100.0000%, Lmin=-190.75, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3320, ncalls=5814, regioncalls=222280, ndraw=40, logz=-193.80, remainder_fraction=100.0000%, Lmin=-180.96, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3330, ncalls=5829, regioncalls=222920, ndraw=40, logz=-191.46, remainder_fraction=100.0000%, Lmin=-178.74, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3360, ncalls=5861, regioncalls=224280, ndraw=40, logz=-186.10, remainder_fraction=100.0000%, Lmin=-173.36, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=5920, regioncalls=226680, ndraw=40, logz=-176.23, remainder_fraction=100.0000%, Lmin=-162.69, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=5947, regioncalls=227800, ndraw=40, logz=-170.03, remainder_fraction=100.0000%, Lmin=-156.47, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3440, ncalls=5969, regioncalls=228920, ndraw=40, logz=-165.77, remainder_fraction=100.0000%, Lmin=-152.80, Lmax=84.22 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3480, ncalls=6018, regioncalls=230920, ndraw=40, logz=-156.66, remainder_fraction=100.0000%, Lmin=-143.72, Lmax=87.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3520, ncalls=6071, regioncalls=233040, ndraw=40, logz=-148.90, remainder_fraction=100.0000%, Lmin=-136.08, Lmax=87.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3560, ncalls=6128, regioncalls=235440, ndraw=40, logz=-141.40, remainder_fraction=100.0000%, Lmin=-128.03, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3600, ncalls=6175, regioncalls=237400, ndraw=40, logz=-135.44, remainder_fraction=100.0000%, Lmin=-122.57, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3640, ncalls=6234, regioncalls=239920, ndraw=40, logz=-129.28, remainder_fraction=100.0000%, Lmin=-115.62, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3680, ncalls=6291, regioncalls=242280, ndraw=40, logz=-121.98, remainder_fraction=100.0000%, Lmin=-108.30, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3690, ncalls=6307, regioncalls=243000, ndraw=40, logz=-119.49, remainder_fraction=100.0000%, Lmin=-105.57, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3720, ncalls=6341, regioncalls=244600, ndraw=40, logz=-114.52, remainder_fraction=100.0000%, Lmin=-101.14, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3760, ncalls=6392, regioncalls=246680, ndraw=40, logz=-107.63, remainder_fraction=100.0000%, Lmin=-92.93, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3800, ncalls=6451, regioncalls=249240, ndraw=40, logz=-99.84, remainder_fraction=100.0000%, Lmin=-86.14, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3840, ncalls=6504, regioncalls=251600, ndraw=40, logz=-93.94, remainder_fraction=100.0000%, Lmin=-79.78, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3880, ncalls=6560, regioncalls=253920, ndraw=40, logz=-87.53, remainder_fraction=100.0000%, Lmin=-73.71, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3920, ncalls=6611, regioncalls=256040, ndraw=40, logz=-80.84, remainder_fraction=100.0000%, Lmin=-66.10, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3960, ncalls=6681, regioncalls=258920, ndraw=40, logz=-72.71, remainder_fraction=100.0000%, Lmin=-58.43, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4000, ncalls=6731, regioncalls=261160, ndraw=40, logz=-64.17, remainder_fraction=100.0000%, Lmin=-50.14, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4040, ncalls=6794, regioncalls=264040, ndraw=40, logz=-56.55, remainder_fraction=100.0000%, Lmin=-42.25, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4050, ncalls=6809, regioncalls=264760, ndraw=40, logz=-54.51, remainder_fraction=100.0000%, Lmin=-40.26, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4080, ncalls=6846, regioncalls=266360, ndraw=40, logz=-50.77, remainder_fraction=100.0000%, Lmin=-36.34, Lmax=95.46 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4120, ncalls=6898, regioncalls=268640, ndraw=40, logz=-44.92, remainder_fraction=100.0000%, Lmin=-30.73, Lmax=97.52 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4140, ncalls=6929, regioncalls=270040, ndraw=40, logz=-42.30, remainder_fraction=100.0000%, Lmin=-27.94, Lmax=97.52 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4160, ncalls=6953, regioncalls=271000, ndraw=40, logz=-39.90, remainder_fraction=100.0000%, Lmin=-25.81, Lmax=97.52 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4200, ncalls=7000, regioncalls=272920, ndraw=40, logz=-33.76, remainder_fraction=100.0000%, Lmin=-19.11, Lmax=97.52 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4230, ncalls=7038, regioncalls=274560, ndraw=40, logz=-28.94, remainder_fraction=100.0000%, Lmin=-13.81, Lmax=97.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4240, ncalls=7052, regioncalls=275160, ndraw=40, logz=-27.24, remainder_fraction=100.0000%, Lmin=-12.10, Lmax=98.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4280, ncalls=7099, regioncalls=277080, ndraw=40, logz=-21.58, remainder_fraction=100.0000%, Lmin=-6.73, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4320, ncalls=7151, regioncalls=279240, ndraw=40, logz=-15.35, remainder_fraction=100.0000%, Lmin=-0.29, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4360, ncalls=7203, regioncalls=281360, ndraw=40, logz=-9.28, remainder_fraction=100.0000%, Lmin=5.94, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4400, ncalls=7263, regioncalls=283840, ndraw=40, logz=-3.98, remainder_fraction=100.0000%, Lmin=11.18, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4410, ncalls=7274, regioncalls=284360, ndraw=40, logz=-2.04, remainder_fraction=100.0000%, Lmin=13.40, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4440, ncalls=7311, regioncalls=285880, ndraw=40, logz=3.00, remainder_fraction=100.0000%, Lmin=18.46, Lmax=98.24 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4480, ncalls=7358, regioncalls=287760, ndraw=40, logz=8.96, remainder_fraction=100.0000%, Lmin=24.23, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4500, ncalls=7384, regioncalls=288920, ndraw=40, logz=11.95, remainder_fraction=100.0000%, Lmin=27.45, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4520, ncalls=7409, regioncalls=290000, ndraw=40, logz=14.74, remainder_fraction=100.0000%, Lmin=30.09, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4560, ncalls=7463, regioncalls=292240, ndraw=40, logz=19.80, remainder_fraction=100.0000%, Lmin=35.70, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4590, ncalls=7503, regioncalls=293880, ndraw=40, logz=23.43, remainder_fraction=100.0000%, Lmin=38.68, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4600, ncalls=7515, regioncalls=294360, ndraw=40, logz=24.45, remainder_fraction=100.0000%, Lmin=39.70, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4640, ncalls=7565, regioncalls=296400, ndraw=40, logz=28.05, remainder_fraction=100.0000%, Lmin=43.51, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4680, ncalls=7617, regioncalls=298480, ndraw=40, logz=32.49, remainder_fraction=100.0000%, Lmin=47.97, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4720, ncalls=7668, regioncalls=300600, ndraw=40, logz=35.71, remainder_fraction=100.0000%, Lmin=51.08, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4760, ncalls=7724, regioncalls=302880, ndraw=40, logz=38.70, remainder_fraction=100.0000%, Lmin=54.16, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4770, ncalls=7737, regioncalls=303480, ndraw=40, logz=39.50, remainder_fraction=100.0000%, Lmin=54.86, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4800, ncalls=7777, regioncalls=305360, ndraw=40, logz=41.88, remainder_fraction=100.0000%, Lmin=57.55, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4840, ncalls=7829, regioncalls=307480, ndraw=40, logz=45.28, remainder_fraction=100.0000%, Lmin=60.71, Lmax=98.39 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4860, ncalls=7859, regioncalls=308760, ndraw=40, logz=46.79, remainder_fraction=100.0000%, Lmin=62.54, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4880, ncalls=7881, regioncalls=309640, ndraw=40, logz=48.27, remainder_fraction=100.0000%, Lmin=63.72, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4920, ncalls=7928, regioncalls=311600, ndraw=40, logz=51.04, remainder_fraction=100.0000%, Lmin=66.65, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4960, ncalls=7977, regioncalls=313640, ndraw=40, logz=53.45, remainder_fraction=100.0000%, Lmin=69.28, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5000, ncalls=8026, regioncalls=315640, ndraw=40, logz=56.08, remainder_fraction=100.0000%, Lmin=71.64, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5040, ncalls=8078, regioncalls=317840, ndraw=40, logz=57.95, remainder_fraction=100.0000%, Lmin=73.74, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5080, ncalls=8129, regioncalls=319880, ndraw=40, logz=59.93, remainder_fraction=100.0000%, Lmin=75.54, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5120, ncalls=8182, regioncalls=322000, ndraw=40, logz=61.74, remainder_fraction=100.0000%, Lmin=77.34, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5160, ncalls=8233, regioncalls=324160, ndraw=40, logz=63.47, remainder_fraction=100.0000%, Lmin=79.26, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5200, ncalls=8291, regioncalls=326560, ndraw=40, logz=65.13, remainder_fraction=100.0000%, Lmin=80.72, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5220, ncalls=8319, regioncalls=327760, ndraw=40, logz=65.73, remainder_fraction=100.0000%, Lmin=81.30, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5240, ncalls=8345, regioncalls=328840, ndraw=40, logz=66.46, remainder_fraction=100.0000%, Lmin=82.32, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5280, ncalls=8395, regioncalls=330880, ndraw=40, logz=67.94, remainder_fraction=100.0000%, Lmin=83.86, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5310, ncalls=8433, regioncalls=332480, ndraw=40, logz=68.97, remainder_fraction=99.9999%, Lmin=84.73, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5320, ncalls=8450, regioncalls=333280, ndraw=40, logz=69.28, remainder_fraction=99.9998%, Lmin=85.11, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5360, ncalls=8501, regioncalls=335360, ndraw=40, logz=70.49, remainder_fraction=99.9994%, Lmin=86.41, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5400, ncalls=8557, regioncalls=337640, ndraw=40, logz=71.52, remainder_fraction=99.9984%, Lmin=87.38, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5440, ncalls=8610, regioncalls=339920, ndraw=40, logz=72.47, remainder_fraction=99.9959%, Lmin=88.23, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5480, ncalls=8661, regioncalls=342240, ndraw=40, logz=73.31, remainder_fraction=99.9900%, Lmin=89.27, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5490, ncalls=8675, regioncalls=342920, ndraw=40, logz=73.54, remainder_fraction=99.9876%, Lmin=89.47, Lmax=98.41 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5520, ncalls=8711, regioncalls=344440, ndraw=40, logz=74.18, remainder_fraction=99.9764%, Lmin=90.07, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5560, ncalls=8765, regioncalls=346600, ndraw=40, logz=74.98, remainder_fraction=99.9489%, Lmin=90.97, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5600, ncalls=8819, regioncalls=348760, ndraw=40, logz=75.71, remainder_fraction=99.8941%, Lmin=91.67, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5640, ncalls=8867, regioncalls=350720, ndraw=40, logz=76.38, remainder_fraction=99.7920%, Lmin=92.30, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5670, ncalls=8909, regioncalls=352560, ndraw=40, logz=76.84, remainder_fraction=99.6612%, Lmin=92.77, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5680, ncalls=8920, regioncalls=353000, ndraw=40, logz=76.99, remainder_fraction=99.6085%, Lmin=92.95, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5720, ncalls=8968, regioncalls=355080, ndraw=40, logz=77.53, remainder_fraction=99.3157%, Lmin=93.47, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5760, ncalls=9021, regioncalls=357200, ndraw=40, logz=78.02, remainder_fraction=98.8649%, Lmin=93.92, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5800, ncalls=9067, regioncalls=359120, ndraw=40, logz=78.47, remainder_fraction=98.2438%, Lmin=94.42, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5840, ncalls=9118, regioncalls=361200, ndraw=40, logz=78.86, remainder_fraction=97.3902%, Lmin=94.80, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5850, ncalls=9130, regioncalls=361720, ndraw=40, logz=78.95, remainder_fraction=97.1280%, Lmin=94.86, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5880, ncalls=9170, regioncalls=363320, ndraw=40, logz=79.20, remainder_fraction=96.2870%, Lmin=95.08, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5920, ncalls=9221, regioncalls=365400, ndraw=40, logz=79.50, remainder_fraction=95.0289%, Lmin=95.37, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5940, ncalls=9247, regioncalls=366480, ndraw=40, logz=79.64, remainder_fraction=94.2645%, Lmin=95.52, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5960, ncalls=9270, regioncalls=367400, ndraw=40, logz=79.78, remainder_fraction=93.4054%, Lmin=95.66, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6000, ncalls=9317, regioncalls=369280, ndraw=40, logz=80.03, remainder_fraction=91.7288%, Lmin=95.90, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6040, ncalls=9376, regioncalls=371640, ndraw=40, logz=80.26, remainder_fraction=89.7527%, Lmin=96.11, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6080, ncalls=9423, regioncalls=373560, ndraw=40, logz=80.46, remainder_fraction=87.5220%, Lmin=96.30, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6120, ncalls=9479, regioncalls=375840, ndraw=40, logz=80.64, remainder_fraction=84.9630%, Lmin=96.48, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6160, ncalls=9527, regioncalls=377840, ndraw=40, logz=80.80, remainder_fraction=82.0315%, Lmin=96.69, Lmax=98.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6200, ncalls=9586, regioncalls=380280, ndraw=40, logz=80.96, remainder_fraction=79.0690%, Lmin=96.85, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6210, ncalls=9596, regioncalls=380720, ndraw=40, logz=80.99, remainder_fraction=78.2190%, Lmin=96.89, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6240, ncalls=9636, regioncalls=382520, ndraw=40, logz=81.10, remainder_fraction=75.8492%, Lmin=97.00, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6280, ncalls=9694, regioncalls=384880, ndraw=40, logz=81.23, remainder_fraction=72.4337%, Lmin=97.13, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6300, ncalls=9717, regioncalls=385880, ndraw=40, logz=81.29, remainder_fraction=70.6438%, Lmin=97.20, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6320, ncalls=9741, regioncalls=386840, ndraw=40, logz=81.35, remainder_fraction=69.0033%, Lmin=97.27, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6360, ncalls=9796, regioncalls=389200, ndraw=40, logz=81.45, remainder_fraction=65.3357%, Lmin=97.39, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6390, ncalls=9846, regioncalls=391240, ndraw=40, logz=81.53, remainder_fraction=62.6597%, Lmin=97.49, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6400, ncalls=9859, regioncalls=391840, ndraw=40, logz=81.55, remainder_fraction=61.7254%, Lmin=97.51, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6440, ncalls=9911, regioncalls=393920, ndraw=40, logz=81.64, remainder_fraction=57.9415%, Lmin=97.59, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6480, ncalls=9962, regioncalls=396040, ndraw=40, logz=81.73, remainder_fraction=54.2676%, Lmin=97.67, Lmax=98.50 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6520, ncalls=10008, regioncalls=397920, ndraw=40, logz=81.80, remainder_fraction=50.9164%, Lmin=97.76, Lmax=98.50 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=1e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 10026 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 82.54 +- 0.1468 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 989.4, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.43, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.19 bs:0.15 tail:0.41 total:0.43 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_netiterintegrate.py::test_singleblock[100] | 3.47 | |
------------------------------Captured stdout call------------------------------ ================================================================================ NLIVE=100 Standard integrator 236.2 +- 0.2 in 740 iter 0.47s Graph integrator 236.1732 +- 0.2430 (main) 236.2408 +- 0.3325 (bs) 236.2 +- 0.2 in 842 iter 0.98s Vectorized graph integrator tree size: (822, 100) 236.1728 +- 0.2432 (main) 236.1499 +- 0.3864 (bs) 236.2 +- 0.2 in 822 iter 0.58s Vectorized graph integrator with insertion order test tree size: (822, 100) 236.1728 +- 0.2432 (main) 236.2381 +- 0.3834 (bs) insertion order: inf 236.2 +- 0.2 in 822 iter 0.71s | |||
Passed | tests/test_netiterintegrate.py::test_visualisation | 0.00 | |
------------------------------Captured stdout call------------------------------ testing tree visualisation... - Node: None - Node: 810 - Node: 790 - Node: 302 - Node: 830 - Node: 466 - Node: 584 - Node: 840 - Node: 137 - Node: 678 - Node: 140 - Node: 665 - Node: 900 - Node: 105 - Node: 78 - Node: 435 Empty Tree ║║║║╠╦╦╗ 665 ║║║║║O║║ 78 ║║║║║ O║ 105 ║║║║O ║ 435 ║+║║ ║ 790 ║║║║ ║ ║O║║ ║ 302 O ║║ ║ 810 ║\ \ ║ \ \ ╠╗║ ║ 830 ║O║ ║ 466 O ║ ║ 584 ║ \ ║ \ ║ \ ╠╦╗ ║ 840 ║║O ║ 137 O║ ║ 140 O ║ 678 O 900 | |||
Passed | tests/test_netiterintegrate.py::test_treedump | 0.07 | |
------------------------------Captured stdout call------------------------------ testing tree dumping... | |||
Passed | tests/test_ordertest.py::test_invalid_order | 0.00 | |
No log output captured. | |||
Passed | tests/test_ordertest.py::test_diff_expand | 0.00 | |
No log output captured. | |||
Passed | tests/test_ordertest.py::test_order_correctness | 0.20 | |
------------------------------Captured stdout call------------------------------ frac: 1 runlength: [] frac: 0.9 split after 551 split after 445 runlength: [551, 445] number of runs: 0 2 | |||
Passed | tests/test_popstepsampling.py::test_stepsampler_cubeslice | 47.65 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Z=-inf(0.00%) | Like=-29.02..-0.12 [-29.0187..-8.0090] | it/evals=0/536 eff=0.0000% N=400 Z=-30.0(0.00%) | Like=-24.57..-0.12 [-29.0187..-8.0090] | it/evals=10/954 eff=1.8051% N=400 Z=-27.3(0.00%) | Like=-22.93..-0.12 [-29.0187..-8.0090] | it/evals=20/1609 eff=1.6543% N=400 Z=-25.8(0.00%) | Like=-21.66..-0.12 [-29.0187..-8.0090] | it/evals=31/2267 eff=1.6604% N=400 Z=-24.7(0.00%) | Like=-20.86..-0.12 [-29.0187..-8.0090] | it/evals=40/3009 eff=1.5332% N=400 Z=-23.8(0.00%) | Like=-19.89..-0.12 [-29.0187..-8.0090] | it/evals=50/3720 eff=1.5060% N=400 Z=-22.7(0.00%) | Like=-18.39..-0.12 [-29.0187..-8.0090] | it/evals=59/4639 eff=1.3918% N=400 Z=-21.6(0.00%) | Like=-17.73..-0.12 [-29.0187..-8.0090] | it/evals=68/5492 eff=1.3354% N=400 Z=-20.7(0.00%) | Like=-16.60..-0.12 [-29.0187..-8.0090] | it/evals=78/6490 eff=1.2808% N=400 Z=-19.7(0.00%) | Like=-15.85..-0.12 [-29.0187..-8.0090] | it/evals=87/7314 eff=1.2583% N=400 Z=-19.5(0.00%) | Like=-15.75..-0.12 [-29.0187..-8.0090] | it/evals=90/7541 eff=1.2603% N=400 Z=-18.9(0.00%) | Like=-15.14..-0.12 [-29.0187..-8.0090] | it/evals=98/8287 eff=1.2426% N=400 Z=-18.7(0.00%) | Like=-15.08..-0.12 [-29.0187..-8.0090] | it/evals=100/8459 eff=1.2408% N=400 Z=-18.3(0.00%) | Like=-14.69..-0.12 [-29.0187..-8.0090] | it/evals=107/9217 eff=1.2136% N=400 Z=-17.8(0.00%) | Like=-14.13..-0.12 [-29.0187..-8.0090] | it/evals=115/9951 eff=1.2041% N=400 Z=-17.3(0.00%) | Like=-13.68..-0.07 [-29.0187..-8.0090] | it/evals=123/10701 eff=1.1941% N=400 Z=-16.8(0.00%) | Like=-13.04..-0.07 [-29.0187..-8.0090] | it/evals=130/11607 eff=1.1600% N=400 Z=-16.2(0.00%) | Like=-12.73..-0.07 [-29.0187..-8.0090] | it/evals=138/12366 eff=1.1533% N=400 Z=-15.9(0.00%) | Like=-12.39..-0.07 [-29.0187..-8.0090] | it/evals=145/13051 eff=1.1462% N=400 Z=-15.6(0.00%) | Like=-12.14..-0.07 [-29.0187..-8.0090] | it/evals=150/13647 eff=1.1323% N=400 Z=-15.0(0.00%) | Like=-11.38..-0.07 [-29.0187..-8.0090] | it/evals=160/14794 eff=1.1116% N=400 Z=-14.5(0.00%) | Like=-11.19..-0.07 [-29.0187..-8.0090] | it/evals=169/15707 eff=1.1041% N=400 Z=-14.0(0.00%) | Like=-10.62..-0.07 [-29.0187..-8.0090] | it/evals=180/16882 eff=1.0921% N=400 Z=-13.5(0.00%) | Like=-10.22..-0.07 [-29.0187..-8.0090] | it/evals=191/18165 eff=1.0751% N=400 Z=-13.2(0.00%) | Like=-9.97..-0.07 [-29.0187..-8.0090] | it/evals=200/19033 eff=1.0734% N=400 Z=-12.8(0.01%) | Like=-9.61..-0.07 [-29.0187..-8.0090] | it/evals=211/20202 eff=1.0655% N=400 Z=-12.5(0.01%) | Like=-9.27..-0.07 [-29.0187..-8.0090] | it/evals=221/21200 eff=1.0625% N=400 Z=-12.1(0.01%) | Like=-8.93..-0.07 [-29.0187..-8.0090] | it/evals=231/22294 eff=1.0551% N=400 Z=-11.8(0.02%) | Like=-8.63..-0.07 [-29.0187..-8.0090] | it/evals=241/23460 eff=1.0451% N=400 Z=-11.5(0.02%) | Like=-8.53..-0.07 [-29.0187..-8.0090] | it/evals=250/24541 eff=1.0356% N=400 Z=-11.3(0.03%) | Like=-8.34..-0.07 [-29.0187..-8.0090] | it/evals=259/25747 eff=1.0218% N=400 Z=-11.0(0.04%) | Like=-8.12..-0.07 [-29.0187..-8.0090] | it/evals=270/26673 eff=1.0277% N=400 Z=-10.9(0.05%) | Like=-7.96..-0.07 [-8.0021..-4.4217] | it/evals=278/27925 eff=1.0100% N=400 Z=-10.6(0.06%) | Like=-7.73..-0.07 [-8.0021..-4.4217] | it/evals=289/28998 eff=1.0106% N=400 Z=-10.4(0.08%) | Like=-7.58..-0.07 [-8.0021..-4.4217] | it/evals=298/30265 eff=0.9978% N=400 Z=-10.4(0.08%) | Like=-7.55..-0.07 [-8.0021..-4.4217] | it/evals=300/30355 eff=1.0015% N=400 Z=-10.2(0.09%) | Like=-7.39..-0.07 [-8.0021..-4.4217] | it/evals=308/31484 eff=0.9909% N=400 Z=-10.1(0.11%) | Like=-7.14..-0.07 [-8.0021..-4.4217] | it/evals=317/32546 eff=0.9861% N=400 Z=-9.9(0.13%) | Like=-6.94..-0.07 [-8.0021..-4.4217] | it/evals=326/33842 eff=0.9748% N=400 Z=-9.7(0.15%) | Like=-6.78..-0.07 [-8.0021..-4.4217] | it/evals=333/34837 eff=0.9670% N=400 Z=-9.6(0.19%) | Like=-6.66..-0.07 [-8.0021..-4.4217] | it/evals=342/36029 eff=0.9599% N=400 Z=-9.4(0.22%) | Like=-6.55..-0.07 [-8.0021..-4.4217] | it/evals=350/36856 eff=0.9601% N=400 Z=-9.2(0.25%) | Like=-6.43..-0.07 [-8.0021..-4.4217] | it/evals=359/38135 eff=0.9514% N=400 Z=-9.1(0.29%) | Like=-6.34..-0.07 [-8.0021..-4.4217] | it/evals=368/39080 eff=0.9514% N=400 Z=-9.0(0.34%) | Like=-6.23..-0.07 [-8.0021..-4.4217] | it/evals=377/40214 eff=0.9469% N=400 Z=-8.8(0.39%) | Like=-6.12..-0.07 [-8.0021..-4.4217] | it/evals=389/41683 eff=0.9423% N=400 Z=-8.6(0.45%) | Like=-6.04..-0.07 [-8.0021..-4.4217] | it/evals=400/42784 eff=0.9438% N=400 Z=-8.5(0.52%) | Like=-5.96..-0.07 [-8.0021..-4.4217] | it/evals=409/44082 eff=0.9363% N=400 Z=-8.4(0.58%) | Like=-5.84..-0.07 [-8.0021..-4.4217] | it/evals=418/45405 eff=0.9288% N=400 Z=-8.3(0.67%) | Like=-5.74..-0.07 [-8.0021..-4.4217] | it/evals=430/46806 eff=0.9266% N=400 Z=-8.2(0.75%) | Like=-5.65..-0.07 [-8.0021..-4.4217] | it/evals=441/48250 eff=0.9216% N=400 Z=-8.1(0.84%) | Like=-5.56..-0.07 [-8.0021..-4.4217] | it/evals=450/49284 eff=0.9205% N=400 Z=-8.0(0.92%) | Like=-5.45..-0.07 [-8.0021..-4.4217] | it/evals=459/50563 eff=0.9150% N=400 Z=-7.9(1.00%) | Like=-5.28..-0.07 [-8.0021..-4.4217] | it/evals=469/51808 eff=0.9123% N=400 Z=-7.8(1.07%) | Like=-5.16..-0.07 [-8.0021..-4.4217] | it/evals=477/53130 eff=0.9046% N=400 Z=-7.7(1.18%) | Like=-5.07..-0.07 [-8.0021..-4.4217] | it/evals=486/54407 eff=0.8999% N=400 Z=-7.6(1.31%) | Like=-4.93..-0.07 [-8.0021..-4.4217] | it/evals=495/55536 eff=0.8978% N=400 Z=-7.6(1.38%) | Like=-4.88..-0.07 [-8.0021..-4.4217] | it/evals=500/56392 eff=0.8930% N=400 Z=-7.5(1.48%) | Like=-4.83..-0.04 [-8.0021..-4.4217] | it/evals=508/57562 eff=0.8887% N=400 Z=-7.4(1.60%) | Like=-4.72..-0.04 [-8.0021..-4.4217] | it/evals=515/58695 eff=0.8834% N=400 Z=-7.3(1.76%) | Like=-4.60..-0.04 [-8.0021..-4.4217] | it/evals=525/60254 eff=0.8771% N=400 Z=-7.2(1.97%) | Like=-4.52..-0.04 [-8.0021..-4.4217] | it/evals=535/61475 eff=0.8760% N=400 Z=-7.2(2.09%) | Like=-4.45..-0.04 [-8.0021..-4.4217] | it/evals=541/62654 eff=0.8690% N=400 Z=-7.1(2.27%) | Like=-4.42..-0.04 [-4.4216..-3.3233] | it/evals=550/63774 eff=0.8679% N=400 Z=-7.0(2.45%) | Like=-4.35..-0.04 [-4.4216..-3.3233] | it/evals=559/65031 eff=0.8649% N=400 Z=-6.9(2.68%) | Like=-4.30..-0.04 [-4.4216..-3.3233] | it/evals=568/66384 eff=0.8608% N=400 Z=-6.8(2.90%) | Like=-4.23..-0.04 [-4.4216..-3.3233] | it/evals=579/67627 eff=0.8613% N=400 Z=-6.8(3.13%) | Like=-4.16..-0.04 [-4.4216..-3.3233] | it/evals=588/69251 eff=0.8540% N=400 Z=-6.7(3.38%) | Like=-4.10..-0.04 [-4.4216..-3.3233] | it/evals=598/70919 eff=0.8480% N=400 Z=-6.7(3.44%) | Like=-4.09..-0.04 [-4.4216..-3.3233] | it/evals=600/71025 eff=0.8496% N=400 Z=-6.6(3.67%) | Like=-4.04..-0.04 [-4.4216..-3.3233] | it/evals=608/72367 eff=0.8448% N=400 Z=-6.5(3.94%) | Like=-3.97..-0.04 [-4.4216..-3.3233] | it/evals=617/73756 eff=0.8411% N=400 Z=-6.5(4.22%) | Like=-3.89..-0.04 [-4.4216..-3.3233] | it/evals=627/75009 eff=0.8404% N=400 Z=-6.4(4.41%) | Like=-3.86..-0.04 [-4.4216..-3.3233] | it/evals=635/76037 eff=0.8395% N=400 Z=-6.4(4.65%) | Like=-3.79..-0.04 [-4.4216..-3.3233] | it/evals=642/77350 eff=0.8343% N=400 Z=-6.3(4.94%) | Like=-3.75..-0.04 [-4.4216..-3.3233] | it/evals=650/78496 eff=0.8323% N=400 Z=-6.3(5.35%) | Like=-3.69..-0.04 [-4.4216..-3.3233] | it/evals=661/79740 eff=0.8331% N=400 Z=-6.2(5.56%) | Like=-3.66..-0.04 [-4.4216..-3.3233] | it/evals=668/81014 eff=0.8286% N=400 Z=-6.2(5.88%) | Like=-3.60..-0.04 [-4.4216..-3.3233] | it/evals=678/82176 eff=0.8291% N=400 Z=-6.1(6.23%) | Like=-3.54..-0.04 [-4.4216..-3.3233] | it/evals=688/83669 eff=0.8262% N=400 Z=-6.0(6.57%) | Like=-3.48..-0.04 [-4.4216..-3.3233] | it/evals=697/84946 eff=0.8244% N=400 Z=-6.0(6.64%) | Like=-3.45..-0.04 [-4.4216..-3.3233] | it/evals=700/85135 eff=0.8261% N=400 Z=-6.0(6.95%) | Like=-3.39..-0.04 [-4.4216..-3.3233] | it/evals=708/86721 eff=0.8202% N=400 Z=-5.9(7.31%) | Like=-3.33..-0.04 [-4.4216..-3.3233] | it/evals=717/88032 eff=0.8182% N=400 Z=-5.9(7.46%) | Like=-3.31..-0.04 [-3.3224..-2.9811] | it/evals=720/88371 eff=0.8185% N=400 Z=-5.9(7.87%) | Like=-3.26..-0.04 [-3.3224..-2.9811] | it/evals=729/89765 eff=0.8158% N=400 Z=-5.8(8.25%) | Like=-3.22..-0.04 [-3.3224..-2.9811] | it/evals=737/90875 eff=0.8146% N=400 Z=-5.8(8.42%) | Like=-3.15..-0.04 [-3.3224..-2.9811] | it/evals=745/92092 eff=0.8125% N=400 Z=-5.7(8.70%) | Like=-3.13..-0.04 [-3.3224..-2.9811] | it/evals=750/92915 eff=0.8107% N=400 Z=-5.7(8.89%) | Like=-3.11..-0.04 [-3.3224..-2.9811] | it/evals=756/94000 eff=0.8077% N=400 Z=-5.7(9.29%) | Like=-3.06..-0.04 [-3.3224..-2.9811] | it/evals=764/95164 eff=0.8062% N=400 Z=-5.6(9.72%) | Like=-3.02..-0.04 [-3.3224..-2.9811] | it/evals=772/96298 eff=0.8050% N=400 Z=-5.6(10.14%) | Like=-2.96..-0.04 [-2.9692..-2.8625] | it/evals=779/97308 eff=0.8039% N=400 Z=-5.6(10.56%) | Like=-2.93..-0.04 [-2.9692..-2.8625] | it/evals=787/98439 eff=0.8027% N=400 Z=-5.5(10.91%) | Like=-2.87..-0.04 [-2.9692..-2.8625] | it/evals=794/99539 eff=0.8009% N=400 Z=-5.5(11.16%) | Like=-2.85..-0.04 [-2.8574..-2.8235] | it/evals=800/100506 eff=0.7992% N=400 Z=-5.5(11.61%) | Like=-2.80..-0.04 [-2.8234..-2.7987] | it/evals=809/101973 eff=0.7965% N=400 Z=-5.5(11.68%) | Like=-2.80..-0.04 [-2.7975..-2.7923]*| it/evals=810/102038 eff=0.7969% N=400 Z=-5.4(12.05%) | Like=-2.77..-0.04 [-2.7706..-2.7647]*| it/evals=817/103341 eff=0.7937% N=400 Z=-5.4(12.44%) | Like=-2.75..-0.04 [-2.7461..-2.7454]*| it/evals=826/104659 eff=0.7923% N=400 Z=-5.3(12.95%) | Like=-2.72..-0.04 [-2.7159..-2.7119]*| it/evals=834/106057 eff=0.7893% N=400 Z=-5.3(13.57%) | Like=-2.70..-0.04 [-2.6954..-2.6944]*| it/evals=845/107595 eff=0.7883% N=400 Z=-5.3(13.85%) | Like=-2.66..-0.04 [-2.6849..-2.6631] | it/evals=850/108444 eff=0.7867% N=400 Z=-5.3(14.28%) | Like=-2.64..-0.04 [-2.6406..-2.6382]*| it/evals=856/109634 eff=0.7836% N=400 Z=-5.2(14.84%) | Like=-2.62..-0.04 [-2.6247..-2.6231]*| it/evals=865/111288 eff=0.7801% N=400 Z=-5.2(15.25%) | Like=-2.58..-0.04 [-2.5826..-2.5823]*| it/evals=873/112488 eff=0.7789% N=400 Z=-5.2(15.76%) | Like=-2.56..-0.04 [-2.5602..-2.5505]*| it/evals=881/113649 eff=0.7779% N=400 Z=-5.1(16.32%) | Like=-2.52..-0.04 [-2.5194..-2.5141]*| it/evals=889/114884 eff=0.7765% N=400 Z=-5.1(16.82%) | Like=-2.49..-0.04 [-2.4927..-2.4922]*| it/evals=896/116144 eff=0.7741% N=400 Z=-5.1(17.09%) | Like=-2.48..-0.04 [-2.4828..-2.4795]*| it/evals=900/116779 eff=0.7733% N=400 Z=-5.1(17.52%) | Like=-2.47..-0.04 [-2.4686..-2.4683]*| it/evals=907/117816 eff=0.7725% N=400 Z=-5.0(17.68%) | Like=-2.46..-0.03 [-2.4623..-2.4573]*| it/evals=913/118915 eff=0.7704% N=400 Z=-5.0(18.15%) | Like=-2.43..-0.03 [-2.4261..-2.4242]*| it/evals=921/120090 eff=0.7695% N=400 Z=-5.0(18.72%) | Like=-2.39..-0.03 [-2.4029..-2.3893] | it/evals=929/121337 eff=0.7682% N=400 Z=-5.0(19.26%) | Like=-2.36..-0.03 [-2.3553..-2.3478]*| it/evals=937/122581 eff=0.7669% N=400 Z=-4.9(19.76%) | Like=-2.32..-0.03 [-2.3217..-2.3203]*| it/evals=945/123869 eff=0.7654% N=400 Z=-4.9(20.07%) | Like=-2.31..-0.03 [-2.3067..-2.3053]*| it/evals=950/124525 eff=0.7654% N=400 Z=-4.9(20.76%) | Like=-2.28..-0.03 [-2.2752..-2.2715]*| it/evals=959/125834 eff=0.7645% N=400 Z=-4.9(21.49%) | Like=-2.24..-0.03 [-2.2397..-2.2395]*| it/evals=969/127602 eff=0.7618% N=400 Z=-4.8(22.13%) | Like=-2.21..-0.03 [-2.2111..-2.2085]*| it/evals=978/129173 eff=0.7595% N=400 Z=-4.8(22.75%) | Like=-2.17..-0.03 [-2.1689..-2.1636]*| it/evals=987/130445 eff=0.7590% N=400 Z=-4.8(22.90%) | Like=-2.16..-0.03 [-2.1559..-2.1421] | it/evals=990/131028 eff=0.7579% N=400 Z=-4.8(23.67%) | Like=-2.12..-0.03 [-2.1239..-2.1174]*| it/evals=1000/132303 eff=0.7581% N=400 Z=-4.7(24.33%) | Like=-2.08..-0.03 [-2.0780..-2.0756]*| it/evals=1008/133697 eff=0.7562% N=400 Z=-4.7(25.05%) | Like=-2.05..-0.03 [-2.0511..-2.0457]*| it/evals=1017/135262 eff=0.7541% N=400 Z=-4.7(25.63%) | Like=-2.02..-0.03 [-2.0228..-2.0203]*| it/evals=1027/136574 eff=0.7542% N=400 Z=-4.7(26.30%) | Like=-2.00..-0.03 [-1.9990..-1.9960]*| it/evals=1036/138360 eff=0.7509% N=400 Z=-4.6(26.95%) | Like=-1.95..-0.03 [-1.9543..-1.9517]*| it/evals=1045/139778 eff=0.7498% N=400 Z=-4.6(27.38%) | Like=-1.94..-0.03 [-1.9375..-1.9336]*| it/evals=1050/140682 eff=0.7485% N=400 Z=-4.6(27.90%) | Like=-1.92..-0.03 [-1.9186..-1.9174]*| it/evals=1057/142229 eff=0.7453% N=400 Z=-4.6(28.40%) | Like=-1.88..-0.03 [-1.8805..-1.8791]*| it/evals=1065/143512 eff=0.7442% N=400 Z=-4.6(29.19%) | Like=-1.86..-0.03 [-1.8557..-1.8513]*| it/evals=1074/144921 eff=0.7431% N=400 Z=-4.6(29.68%) | Like=-1.82..-0.03 [-1.8219..-1.8170]*| it/evals=1081/146045 eff=0.7422% N=400 Z=-4.5(30.37%) | Like=-1.80..-0.03 [-1.7978..-1.7969]*| it/evals=1089/147510 eff=0.7403% N=400 Z=-4.5(31.06%) | Like=-1.78..-0.03 [-1.7772..-1.7753]*| it/evals=1099/149288 eff=0.7381% N=400 Z=-4.5(31.15%) | Like=-1.78..-0.03 [-1.7753..-1.7745]*| it/evals=1100/149295 eff=0.7388% N=400 Z=-4.5(31.72%) | Like=-1.75..-0.03 [-1.7506..-1.7506]*| it/evals=1108/151076 eff=0.7354% N=400 Z=-4.5(32.53%) | Like=-1.72..-0.03 [-1.7188..-1.7181]*| it/evals=1119/152419 eff=0.7361% N=400 Z=-4.4(33.10%) | Like=-1.69..-0.03 [-1.6925..-1.6916]*| it/evals=1127/154108 eff=0.7332% N=400 Z=-4.4(33.89%) | Like=-1.67..-0.03 [-1.6693..-1.6684]*| it/evals=1136/155670 eff=0.7316% N=400 Z=-4.4(34.55%) | Like=-1.64..-0.03 [-1.6356..-1.6295]*| it/evals=1145/157268 eff=0.7299% N=400 Z=-4.4(34.85%) | Like=-1.62..-0.03 [-1.6204..-1.6127]*| it/evals=1150/158021 eff=0.7296% N=400 Z=-4.4(35.53%) | Like=-1.59..-0.03 [-1.5940..-1.5934]*| it/evals=1159/159652 eff=0.7278% N=400 Z=-4.3(36.36%) | Like=-1.57..-0.03 [-1.5687..-1.5677]*| it/evals=1170/161116 eff=0.7280% N=400 Z=-4.3(37.07%) | Like=-1.55..-0.03 [-1.5498..-1.5495]*| it/evals=1179/162715 eff=0.7264% N=400 Z=-4.3(37.61%) | Like=-1.54..-0.03 [-1.5432..-1.5400]*| it/evals=1185/163604 eff=0.7261% N=400 Z=-4.3(37.89%) | Like=-1.52..-0.03 [-1.5214..-1.5177]*| it/evals=1190/164681 eff=0.7244% N=400 Z=-4.3(38.41%) | Like=-1.51..-0.03 [-1.5084..-1.5080]*| it/evals=1197/165337 eff=0.7257% N=400 Z=-4.3(38.68%) | Like=-1.50..-0.03 [-1.5038..-1.5029]*| it/evals=1200/166312 eff=0.7233% N=400 Z=-4.3(39.44%) | Like=-1.49..-0.03 [-1.4892..-1.4880]*| it/evals=1208/167575 eff=0.7226% N=400 Z=-4.2(40.13%) | Like=-1.47..-0.03 [-1.4687..-1.4684]*| it/evals=1217/169003 eff=0.7218% N=400 Z=-4.2(40.54%) | Like=-1.46..-0.03 [-1.4586..-1.4474] | it/evals=1225/170664 eff=0.7195% N=400 Z=-4.2(41.05%) | Like=-1.42..-0.03 [-1.4170..-1.4145]*| it/evals=1233/172104 eff=0.7181% N=400 Z=-4.2(41.54%) | Like=-1.39..-0.03 [-1.3878..-1.3791]*| it/evals=1239/173329 eff=0.7165% N=400 Z=-4.2(42.23%) | Like=-1.36..-0.03 [-1.3627..-1.3601]*| it/evals=1247/174627 eff=0.7157% N=400 Z=-4.2(42.48%) | Like=-1.36..-0.03 [-1.3573..-1.3572]*| it/evals=1250/174686 eff=0.7172% N=400 Z=-4.2(43.03%) | Like=-1.34..-0.03 [-1.3399..-1.3385]*| it/evals=1256/176256 eff=0.7142% N=400 Z=-4.2(43.31%) | Like=-1.33..-0.03 [-1.3305..-1.3289]*| it/evals=1260/176886 eff=0.7139% N=400 Z=-4.2(43.95%) | Like=-1.32..-0.03 [-1.3156..-1.3135]*| it/evals=1268/178116 eff=0.7135% N=400 Z=-4.1(44.43%) | Like=-1.31..-0.03 [-1.3062..-1.3051]*| it/evals=1274/179054 eff=0.7131% N=400 Z=-4.1(45.03%) | Like=-1.28..-0.03 [-1.2840..-1.2832]*| it/evals=1283/180321 eff=0.7131% N=400 Z=-4.1(45.42%) | Like=-1.27..-0.03 [-1.2719..-1.2719]*| it/evals=1289/181554 eff=0.7115% N=400 Z=-4.1(45.95%) | Like=-1.27..-0.03 [-1.2658..-1.2645]*| it/evals=1297/182780 eff=0.7112% N=400 Z=-4.1(46.21%) | Like=-1.26..-0.03 [-1.2611..-1.2610]*| it/evals=1300/183109 eff=0.7115% N=400 Z=-4.1(46.61%) | Like=-1.25..-0.03 [-1.2489..-1.2438]*| it/evals=1305/183992 eff=0.7108% N=400 Z=-4.1(47.00%) | Like=-1.24..-0.03 [-1.2406..-1.2389]*| it/evals=1311/184967 eff=0.7103% N=400 Z=-4.1(47.55%) | Like=-1.23..-0.03 [-1.2276..-1.2276]*| it/evals=1318/186168 eff=0.7095% N=400 Z=-4.1(48.25%) | Like=-1.20..-0.03 [-1.1986..-1.1985]*| it/evals=1326/187360 eff=0.7092% N=400 Z=-4.0(48.64%) | Like=-1.19..-0.03 [-1.1909..-1.1907]*| it/evals=1331/188312 eff=0.7083% N=400 Z=-4.0(49.12%) | Like=-1.18..-0.03 [-1.1807..-1.1738]*| it/evals=1339/189470 eff=0.7082% N=400 Z=-4.0(49.58%) | Like=-1.17..-0.03 [-1.1651..-1.1562]*| it/evals=1345/190490 eff=0.7076% N=400 Z=-4.0(50.01%) | Like=-1.15..-0.03 [-1.1466..-1.1435]*| it/evals=1350/191558 eff=0.7062% N=400 Z=-4.0(50.60%) | Like=-1.13..-0.03 [-1.1332..-1.1291]*| it/evals=1358/192836 eff=0.7057% N=400 Z=-4.0(51.28%) | Like=-1.12..-0.03 [-1.1181..-1.1107]*| it/evals=1366/194139 eff=0.7051% N=400 Z=-4.0(51.90%) | Like=-1.10..-0.03 [-1.0998..-1.0995]*| it/evals=1374/195480 eff=0.7043% N=400 Z=-4.0(52.30%) | Like=-1.09..-0.03 [-1.0898..-1.0887]*| it/evals=1381/196696 eff=0.7035% N=400 Z=-4.0(52.94%) | Like=-1.08..-0.03 [-1.0811..-1.0800]*| it/evals=1389/197982 eff=0.7030% N=400 Z=-4.0(53.57%) | Like=-1.06..-0.03 [-1.0607..-1.0600]*| it/evals=1397/199336 eff=0.7022% N=400 Z=-3.9(53.81%) | Like=-1.05..-0.03 [-1.0548..-1.0535]*| it/evals=1400/200002 eff=0.7014% N=400 Z=-3.9(54.37%) | Like=-1.05..-0.03 [-1.0462..-1.0455]*| it/evals=1408/201398 eff=0.7005% N=400 Z=-3.9(54.91%) | Like=-1.04..-0.03 [-1.0397..-1.0395]*| it/evals=1416/202794 eff=0.6996% N=400 Z=-3.9(55.48%) | Like=-1.03..-0.03 [-1.0303..-1.0266]*| it/evals=1424/204211 eff=0.6987% N=400 Z=-3.9(55.94%) | Like=-1.02..-0.03 [-1.0234..-1.0221]*| it/evals=1430/205191 eff=0.6983% N=400 Z=-3.9(56.45%) | Like=-1.01..-0.03 [-1.0145..-1.0115]*| it/evals=1437/206251 eff=0.6981% N=400 Z=-3.9(56.76%) | Like=-1.00..-0.03 [-1.0039..-1.0026]*| it/evals=1441/207192 eff=0.6968% N=400 Z=-3.9(57.27%) | Like=-0.98..-0.03 [-0.9838..-0.9837]*| it/evals=1448/208459 eff=0.6960% N=400 Z=-3.9(57.44%) | Like=-0.98..-0.03 [-0.9832..-0.9830]*| it/evals=1450/208561 eff=0.6966% N=400 Z=-3.9(57.86%) | Like=-0.98..-0.03 [-0.9801..-0.9787]*| it/evals=1456/209774 eff=0.6954% N=400 Z=-3.9(58.40%) | Like=-0.97..-0.03 [-0.9739..-0.9731]*| it/evals=1463/210553 eff=0.6962% N=400 Z=-3.9(58.80%) | Like=-0.97..-0.03 [-0.9697..-0.9687]*| it/evals=1468/211694 eff=0.6948% N=400 Z=-3.9(59.24%) | Like=-0.96..-0.03 [-0.9642..-0.9619]*| it/evals=1476/213036 eff=0.6941% N=400 Z=-3.8(59.73%) | Like=-0.95..-0.03 [-0.9500..-0.9496]*| it/evals=1484/214448 eff=0.6933% N=400 Z=-3.8(60.27%) | Like=-0.94..-0.03 [-0.9397..-0.9391]*| it/evals=1492/215921 eff=0.6923% N=400 Z=-3.8(60.69%) | Like=-0.93..-0.03 [-0.9294..-0.9288]*| it/evals=1499/216952 eff=0.6922% N=400 Z=-3.8(60.77%) | Like=-0.93..-0.03 [-0.9288..-0.9253]*| it/evals=1500/217236 eff=0.6918% N=400 Z=-3.8(61.38%) | Like=-0.91..-0.02 [-0.9142..-0.9136]*| it/evals=1508/218630 eff=0.6910% N=400 Z=-3.8(61.96%) | Like=-0.89..-0.02 [-0.8876..-0.8817]*| it/evals=1517/220431 eff=0.6894% N=400 Z=-3.8(62.44%) | Like=-0.88..-0.02 [-0.8757..-0.8748]*| it/evals=1525/221804 eff=0.6888% N=400 Z=-3.8(62.74%) | Like=-0.87..-0.02 [-0.8714..-0.8709]*| it/evals=1530/222727 eff=0.6882% N=400 Z=-3.8(63.23%) | Like=-0.86..-0.02 [-0.8601..-0.8586]*| it/evals=1538/224046 eff=0.6877% N=400 Z=-3.8(63.61%) | Like=-0.85..-0.02 [-0.8460..-0.8460]*| it/evals=1545/225297 eff=0.6870% N=400 Z=-3.8(63.94%) | Like=-0.84..-0.02 [-0.8410..-0.8380]*| it/evals=1550/226001 eff=0.6871% N=400 Z=-3.8(64.37%) | Like=-0.83..-0.02 [-0.8271..-0.8264]*| it/evals=1557/227211 eff=0.6865% N=400 Z=-3.8(64.82%) | Like=-0.81..-0.02 [-0.8135..-0.8135]*| it/evals=1565/228491 eff=0.6861% N=400 Z=-3.8(65.30%) | Like=-0.81..-0.02 [-0.8085..-0.8078]*| it/evals=1573/229851 eff=0.6855% N=400 Z=-3.7(65.63%) | Like=-0.80..-0.02 [-0.7991..-0.7978]*| it/evals=1579/230898 eff=0.6850% N=400 Z=-3.7(66.00%) | Like=-0.79..-0.02 [-0.7892..-0.7889]*| it/evals=1585/232292 eff=0.6835% N=400 Z=-3.7(66.41%) | Like=-0.78..-0.01 [-0.7797..-0.7784]*| it/evals=1593/233609 eff=0.6831% N=400 Z=-3.7(66.74%) | Like=-0.77..-0.01 [-0.7711..-0.7706]*| it/evals=1598/234717 eff=0.6820% N=400 Z=-3.7(66.87%) | Like=-0.77..-0.01 [-0.7704..-0.7694]*| it/evals=1600/234722 eff=0.6828% N=400 Z=-3.7(67.24%) | Like=-0.76..-0.01 [-0.7624..-0.7612]*| it/evals=1606/235930 eff=0.6819% N=400 Z=-3.7(67.79%) | Like=-0.75..-0.01 [-0.7543..-0.7542]*| it/evals=1615/237583 eff=0.6809% N=400 Z=-3.7(68.06%) | Like=-0.75..-0.01 [-0.7488..-0.7482]*| it/evals=1620/238351 eff=0.6808% N=400 Z=-3.7(68.51%) | Like=-0.74..-0.01 [-0.7409..-0.7378]*| it/evals=1628/239711 eff=0.6803% N=400 Z=-3.7(69.02%) | Like=-0.73..-0.01 [-0.7345..-0.7296]*| it/evals=1637/241658 eff=0.6785% N=400 Z=-3.7(69.49%) | Like=-0.72..-0.01 [-0.7234..-0.7228]*| it/evals=1645/243013 eff=0.6780% N=400 Z=-3.7(69.73%) | Like=-0.72..-0.01 [-0.7195..-0.7148]*| it/evals=1650/243713 eff=0.6781% N=400 Z=-3.7(70.24%) | Like=-0.71..-0.01 [-0.7063..-0.7043]*| it/evals=1659/245585 eff=0.6766% N=400 Z=-3.7(70.74%) | Like=-0.70..-0.01 [-0.6991..-0.6988]*| it/evals=1668/247247 eff=0.6757% N=400 Z=-3.7(71.18%) | Like=-0.69..-0.01 [-0.6852..-0.6840]*| it/evals=1676/248747 eff=0.6749% N=400 Z=-3.7(71.83%) | Like=-0.68..-0.01 [-0.6753..-0.6739]*| it/evals=1688/250556 eff=0.6748% N=400 Z=-3.7(72.41%) | Like=-0.66..-0.01 [-0.6648..-0.6648]*| it/evals=1699/252454 eff=0.6741% N=400 Z=-3.7(72.45%) | Like=-0.66..-0.01 [-0.6648..-0.6643]*| it/evals=1700/252456 eff=0.6745% N=400 Z=-3.6(72.93%) | Like=-0.65..-0.01 [-0.6541..-0.6538]*| it/evals=1710/253762 eff=0.6749% N=400 Z=-3.6(73.31%) | Like=-0.65..-0.01 [-0.6468..-0.6466]*| it/evals=1717/255416 eff=0.6733% N=400 Z=-3.6(73.73%) | Like=-0.63..-0.01 [-0.6297..-0.6271]*| it/evals=1725/256943 eff=0.6724% N=400 Z=-3.6(74.15%) | Like=-0.62..-0.01 [-0.6175..-0.6125]*| it/evals=1733/258359 eff=0.6718% N=400 Z=-3.6(74.63%) | Like=-0.60..-0.01 [-0.6048..-0.6041]*| it/evals=1742/259829 eff=0.6715% N=400 Z=-3.6(75.02%) | Like=-0.60..-0.01 [-0.5980..-0.5966]*| it/evals=1750/261074 eff=0.6713% N=400 Z=-3.6(75.38%) | Like=-0.59..-0.01 [-0.5934..-0.5918]*| it/evals=1757/262618 eff=0.6701% N=400 Z=-3.6(75.72%) | Like=-0.59..-0.01 [-0.5866..-0.5866]*| it/evals=1765/263914 eff=0.6698% N=400 Z=-3.6(76.17%) | Like=-0.58..-0.01 [-0.5821..-0.5818]*| it/evals=1774/265421 eff=0.6694% N=400 Z=-3.6(76.42%) | Like=-0.58..-0.01 [-0.5757..-0.5739]*| it/evals=1779/266217 eff=0.6693% N=400 Z=-3.6(76.79%) | Like=-0.57..-0.01 [-0.5654..-0.5652]*| it/evals=1787/267721 eff=0.6685% N=400 Z=-3.6(76.98%) | Like=-0.56..-0.01 [-0.5633..-0.5574]*| it/evals=1791/268429 eff=0.6682% N=400 Z=-3.6(77.36%) | Like=-0.55..-0.01 [-0.5532..-0.5526]*| it/evals=1799/269860 eff=0.6676% N=400 Z=-3.6(77.40%) | Like=-0.55..-0.01 [-0.5526..-0.5525]*| it/evals=1800/269863 eff=0.6680% N=400 Z=-3.6(77.68%) | Like=-0.55..-0.01 [-0.5499..-0.5468]*| it/evals=1806/271171 eff=0.6670% N=400 Z=-3.6(78.05%) | Like=-0.54..-0.01 [-0.5387..-0.5380]*| it/evals=1815/272994 eff=0.6658% N=400 Z=-3.6(78.40%) | Like=-0.53..-0.01 [-0.5318..-0.5317]*| it/evals=1823/274430 eff=0.6653% N=400 Z=-3.6(78.71%) | Like=-0.52..-0.01 [-0.5249..-0.5247]*| it/evals=1830/275651 eff=0.6648% N=400 Z=-3.6(78.88%) | Like=-0.52..-0.01 [-0.5214..-0.5185]*| it/evals=1834/276446 eff=0.6644% N=400 Z=-3.6(79.15%) | Like=-0.52..-0.01 [-0.5159..-0.5144]*| it/evals=1841/277600 eff=0.6641% N=400 Z=-3.6(79.45%) | Like=-0.51..-0.01 [-0.5071..-0.5060]*| it/evals=1849/278953 eff=0.6638% N=400 Z=-3.6(79.49%) | Like=-0.51..-0.01 [-0.5060..-0.5058]*| it/evals=1850/279113 eff=0.6638% N=400 Z=-3.6(79.79%) | Like=-0.50..-0.01 [-0.4984..-0.4958]*| it/evals=1857/280450 eff=0.6631% N=400 Z=-3.6(80.04%) | Like=-0.49..-0.01 [-0.4941..-0.4940]*| it/evals=1863/281381 eff=0.6630% N=400 Z=-3.5(80.30%) | Like=-0.49..-0.01 [-0.4887..-0.4876]*| it/evals=1869/282579 eff=0.6623% N=400 Z=-3.5(80.60%) | Like=-0.48..-0.00 [-0.4788..-0.4780]*| it/evals=1877/283796 eff=0.6623% N=400 Z=-3.5(80.92%) | Like=-0.47..-0.00 [-0.4732..-0.4720]*| it/evals=1885/285070 eff=0.6622% N=400 Z=-3.5(81.11%) | Like=-0.47..-0.00 [-0.4693..-0.4681]*| it/evals=1890/285727 eff=0.6624% N=400 Z=-3.5(81.34%) | Like=-0.46..-0.00 [-0.4626..-0.4623]*| it/evals=1896/287050 eff=0.6614% N=400 Z=-3.5(81.48%) | Like=-0.46..-0.00 [-0.4558..-0.4558]*| it/evals=1900/287684 eff=0.6614% N=400 Z=-3.5(81.84%) | Like=-0.45..-0.00 [-0.4501..-0.4493]*| it/evals=1910/288941 eff=0.6620% N=400 Z=-3.5(82.20%) | Like=-0.44..-0.00 [-0.4414..-0.4413]*| it/evals=1920/290776 eff=0.6612% N=400 Z=-3.5(82.53%) | Like=-0.43..-0.00 [-0.4331..-0.4331]*| it/evals=1929/292204 eff=0.6611% N=400 Z=-3.5(82.90%) | Like=-0.43..-0.00 [-0.4290..-0.4281]*| it/evals=1939/293967 eff=0.6605% N=400 Z=-3.5(83.17%) | Like=-0.42..-0.00 [-0.4242..-0.4232]*| it/evals=1947/295340 eff=0.6601% N=400 Z=-3.5(83.28%) | Like=-0.42..-0.00 [-0.4222..-0.4221]*| it/evals=1950/295613 eff=0.6605% N=400 Z=-3.5(83.48%) | Like=-0.42..-0.00 [-0.4182..-0.4171]*| it/evals=1956/296789 eff=0.6599% N=400 Z=-3.5(83.73%) | Like=-0.41..-0.00 [-0.4147..-0.4146]*| it/evals=1963/298049 eff=0.6595% N=400 Z=-3.5(84.00%) | Like=-0.41..-0.00 [-0.4086..-0.4078]*| it/evals=1971/299397 eff=0.6592% N=400 Z=-3.5(84.26%) | Like=-0.40..-0.00 [-0.4030..-0.4029]*| it/evals=1979/300648 eff=0.6591% N=400 Z=-3.5(84.30%) | Like=-0.40..-0.00 [-0.4029..-0.4023]*| it/evals=1980/300651 eff=0.6594% N=400 Z=-3.5(84.53%) | Like=-0.40..-0.00 [-0.3979..-0.3962]*| it/evals=1987/301913 eff=0.6590% N=400 Z=-3.5(84.75%) | Like=-0.39..-0.00 [-0.3945..-0.3942]*| it/evals=1993/303006 eff=0.6586% N=400 Z=-3.5(84.98%) | Like=-0.39..-0.00 [-0.3917..-0.3901]*| it/evals=2000/303776 eff=0.6592% N=400 Z=-3.5(85.14%) | Like=-0.39..-0.00 [-0.3883..-0.3868]*| it/evals=2005/304948 eff=0.6584% N=400 Z=-3.5(85.36%) | Like=-0.38..-0.00 [-0.3844..-0.3843]*| it/evals=2012/306273 eff=0.6578% N=400 Z=-3.5(85.63%) | Like=-0.38..-0.00 [-0.3776..-0.3768]*| it/evals=2021/307639 eff=0.6578% N=400 Z=-3.5(85.87%) | Like=-0.37..-0.00 [-0.3742..-0.3739]*| it/evals=2029/309251 eff=0.6570% N=400 Z=-3.5(86.21%) | Like=-0.37..-0.00 [-0.3675..-0.3674]*| it/evals=2040/310627 eff=0.6576% N=400 Z=-3.5(86.44%) | Like=-0.36..-0.00 [-0.3595..-0.3593]*| it/evals=2047/312307 eff=0.6563% N=400 Z=-3.5(86.52%) | Like=-0.36..-0.00 [-0.3582..-0.3582]*| it/evals=2050/312313 eff=0.6572% N=400 Z=-3.5(86.70%) | Like=-0.35..-0.00 [-0.3531..-0.3529]*| it/evals=2056/313856 eff=0.6559% N=400 Z=-3.5(86.95%) | Like=-0.35..-0.00 [-0.3462..-0.3458]*| it/evals=2065/315160 eff=0.6561% N=400 Z=-3.5(87.08%) | Like=-0.34..-0.00 [-0.3448..-0.3448]*| it/evals=2070/315963 eff=0.6560% N=400 Z=-3.5(87.30%) | Like=-0.34..-0.00 [-0.3400..-0.3398]*| it/evals=2078/317271 eff=0.6558% N=400 Z=-3.5(87.55%) | Like=-0.33..-0.00 [-0.3347..-0.3341]*| it/evals=2087/319125 eff=0.6548% N=400 Z=-3.5(87.76%) | Like=-0.33..-0.00 [-0.3312..-0.3311]*| it/evals=2095/320277 eff=0.6549% N=400 Z=-3.5(87.89%) | Like=-0.33..-0.00 [-0.3291..-0.3290]*| it/evals=2100/321161 eff=0.6547% N=400 Z=-3.5(88.07%) | Like=-0.33..-0.00 [-0.3255..-0.3253]*| it/evals=2107/322344 eff=0.6545% N=400 Z=-3.5(88.21%) | Like=-0.32..-0.00 [-0.3239..-0.3219]*| it/evals=2112/323076 eff=0.6545% N=400 Z=-3.5(88.29%) | Like=-0.32..-0.00 [-0.3202..-0.3199]*| it/evals=2115/324007 eff=0.6536% N=400 Z=-3.5(88.49%) | Like=-0.32..-0.00 [-0.3180..-0.3171]*| it/evals=2123/325270 eff=0.6535% N=400 Z=-3.5(88.64%) | Like=-0.31..-0.00 [-0.3149..-0.3147]*| it/evals=2129/326491 eff=0.6529% N=400 Z=-3.4(88.84%) | Like=-0.31..-0.00 [-0.3090..-0.3088]*| it/evals=2137/327927 eff=0.6525% N=400 Z=-3.4(89.03%) | Like=-0.31..-0.00 [-0.3070..-0.3064]*| it/evals=2145/329330 eff=0.6521% N=400 Z=-3.4(89.16%) | Like=-0.31..-0.00 [-0.3053..-0.3053]*| it/evals=2150/330154 eff=0.6520% N=400 Z=-3.4(89.33%) | Like=-0.30..-0.00 [-0.2999..-0.2995]*| it/evals=2157/331458 eff=0.6515% N=400 Z=-3.4(89.40%) | Like=-0.30..-0.00 [-0.2991..-0.2986]*| it/evals=2160/331682 eff=0.6520% N=400 Z=-3.4(89.55%) | Like=-0.30..-0.00 [-0.2967..-0.2944]*| it/evals=2167/333001 eff=0.6515% N=400 Z=-3.4(89.68%) | Like=-0.29..-0.00 [-0.2915..-0.2907]*| it/evals=2173/334173 eff=0.6510% N=400 Z=-3.4(89.84%) | Like=-0.29..-0.00 [-0.2883..-0.2872]*| it/evals=2180/335600 eff=0.6504% N=400 Z=-3.4(90.01%) | Like=-0.29..-0.00 [-0.2860..-0.2850]*| it/evals=2188/336929 eff=0.6502% N=400 Z=-3.4(90.19%) | Like=-0.28..-0.00 [-0.2823..-0.2819]*| it/evals=2196/338257 eff=0.6500% N=400 Z=-3.4(90.28%) | Like=-0.28..-0.00 [-0.2794..-0.2786]*| it/evals=2200/338933 eff=0.6499% N=400 Z=-3.4(90.47%) | Like=-0.27..-0.00 [-0.2742..-0.2738]*| it/evals=2209/340575 eff=0.6494% N=400 Z=-3.4(90.66%) | Like=-0.27..-0.00 [-0.2690..-0.2689]*| it/evals=2218/342183 eff=0.6489% N=400 Z=-3.4(90.85%) | Like=-0.26..-0.00 [-0.2639..-0.2637]*| it/evals=2227/343517 eff=0.6490% N=400 Z=-3.4(91.00%) | Like=-0.26..-0.00 [-0.2604..-0.2591]*| it/evals=2234/344612 eff=0.6490% N=400 Z=-3.4(91.15%) | Like=-0.25..-0.00 [-0.2545..-0.2542]*| it/evals=2242/346177 eff=0.6484% N=400 Z=-3.4(91.31%) | Like=-0.25..-0.00 [-0.2514..-0.2509]*| it/evals=2250/347629 eff=0.6480% N=400 Z=-3.4(91.42%) | Like=-0.25..-0.00 [-0.2479..-0.2478]*| it/evals=2256/348869 eff=0.6474% N=400 Z=-3.4(91.58%) | Like=-0.25..-0.00 [-0.2462..-0.2459]*| it/evals=2264/350130 eff=0.6474% N=400 Z=-3.4(91.75%) | Like=-0.24..-0.00 [-0.2439..-0.2426]*| it/evals=2273/351454 eff=0.6475% N=400 Z=-3.4(91.86%) | Like=-0.24..-0.00 [-0.2416..-0.2416]*| it/evals=2279/352749 eff=0.6468% N=400 Z=-3.4(92.00%) | Like=-0.24..-0.00 [-0.2401..-0.2398]*| it/evals=2287/354025 eff=0.6467% N=400 Z=-3.4(92.15%) | Like=-0.24..-0.00 [-0.2363..-0.2356]*| it/evals=2295/355293 eff=0.6467% N=400 Z=-3.4(92.24%) | Like=-0.23..-0.00 [-0.2347..-0.2344]*| it/evals=2300/356045 eff=0.6467% N=400 Z=-3.4(92.37%) | Like=-0.23..-0.00 [-0.2329..-0.2325]*| it/evals=2307/357269 eff=0.6465% N=400 Z=-3.4(92.50%) | Like=-0.23..-0.00 [-0.2307..-0.2306]*| it/evals=2315/358620 eff=0.6463% N=400 Z=-3.4(92.63%) | Like=-0.23..-0.00 [-0.2289..-0.2283]*| it/evals=2323/359956 eff=0.6461% N=400 Z=-3.4(92.76%) | Like=-0.23..-0.00 [-0.2263..-0.2261]*| it/evals=2331/361292 eff=0.6459% N=400 Z=-3.4(92.89%) | Like=-0.22..-0.00 [-0.2213..-0.2212]*| it/evals=2339/362561 eff=0.6458% N=400 Z=-3.4(92.91%) | Like=-0.22..-0.00 [-0.2212..-0.2211]*| it/evals=2340/362725 eff=0.6458% N=400 Z=-3.4(93.05%) | Like=-0.22..-0.00 [-0.2169..-0.2165]*| it/evals=2349/364118 eff=0.6458% N=400 Z=-3.4(93.07%) | Like=-0.22..-0.00 [-0.2165..-0.2164]*| it/evals=2350/364638 eff=0.6452% N=400 Z=-3.4(93.23%) | Like=-0.21..-0.00 [-0.2117..-0.2109]*| it/evals=2360/366371 eff=0.6449% N=400 Z=-3.4(93.37%) | Like=-0.21..-0.00 [-0.2077..-0.2075]*| it/evals=2369/367832 eff=0.6447% N=400 Z=-3.4(93.52%) | Like=-0.20..-0.00 [-0.2031..-0.2022]*| it/evals=2379/369269 eff=0.6449% N=400 Z=-3.4(93.64%) | Like=-0.20..-0.00 [-0.2005..-0.2003]*| it/evals=2387/370864 eff=0.6443% N=400 Z=-3.4(93.81%) | Like=-0.20..-0.00 [-0.1951..-0.1950]*| it/evals=2399/372770 eff=0.6443% N=400 Z=-3.4(93.82%) | Like=-0.20..-0.00 [-0.1950..-0.1943]*| it/evals=2400/372823 eff=0.6444% N=400 Z=-3.4(93.92%) | Like=-0.19..-0.00 [-0.1927..-0.1927]*| it/evals=2407/373948 eff=0.6444% N=400 Z=-3.4(94.05%) | Like=-0.19..-0.00 [-0.1874..-0.1870]*| it/evals=2416/375751 eff=0.6437% N=400 Z=-3.4(94.19%) | Like=-0.18..-0.00 [-0.1844..-0.1844]*| it/evals=2427/377177 eff=0.6441% N=400 Z=-3.4(94.23%) | Like=-0.18..-0.00 [-0.1842..-0.1840]*| it/evals=2430/377883 eff=0.6437% N=400 Z=-3.4(94.35%) | Like=-0.18..-0.00 [-0.1813..-0.1807]*| it/evals=2439/379459 eff=0.6434% N=400 Z=-3.4(94.47%) | Like=-0.18..-0.00 [-0.1789..-0.1785]*| it/evals=2448/381206 eff=0.6428% N=400 Z=-3.4(94.49%) | Like=-0.18..-0.00 [-0.1782..-0.1781]*| it/evals=2450/381211 eff=0.6434% N=400 Z=-3.4(94.58%) | Like=-0.17..-0.00 [-0.1746..-0.1744]*| it/evals=2457/382869 eff=0.6424% N=400 Z=-3.4(94.68%) | Like=-0.17..-0.00 [-0.1719..-0.1718]*| it/evals=2465/384268 eff=0.6421% N=400 Z=-3.4(94.73%) | Like=-0.17..-0.00 [-0.1713..-0.1709]*| it/evals=2469/384968 eff=0.6420% N=400 Z=-3.4(94.80%) | Like=-0.17..-0.00 [-0.1700..-0.1700]*| it/evals=2475/385831 eff=0.6421% N=400 Z=-3.4(94.83%) | Like=-0.17..-0.00 [-0.1695..-0.1693]*| it/evals=2477/386303 eff=0.6419% N=400 Z=-3.4(94.92%) | Like=-0.17..-0.00 [-0.1673..-0.1672]*| it/evals=2485/387619 eff=0.6418% N=400 Z=-3.4(95.01%) | Like=-0.17..-0.00 [-0.1660..-0.1655]*| it/evals=2493/388923 eff=0.6417% N=400 Z=-3.4(95.09%) | Like=-0.16..-0.00 [-0.1642..-0.1641]*| it/evals=2500/389953 eff=0.6418% N=400 Z=-3.4(95.16%) | Like=-0.16..-0.00 [-0.1620..-0.1618]*| it/evals=2506/391148 eff=0.6413% N=400 Z=-3.4(95.21%) | Like=-0.16..-0.00 [-0.1600..-0.1599]*| it/evals=2510/392250 eff=0.6406% N=400 Z=-3.4(95.30%) | Like=-0.16..-0.00 [-0.1594..-0.1594]*| it/evals=2518/393530 eff=0.6405% N=400 Z=-3.4(95.34%) | Like=-0.16..-0.00 [-0.1590..-0.1572]*| it/evals=2522/394145 eff=0.6405% N=400 Z=-3.4(95.41%) | Like=-0.16..-0.00 [-0.1557..-0.1551]*| it/evals=2528/395329 eff=0.6401% N=400 Z=-3.4(95.49%) | Like=-0.15..-0.00 [-0.1542..-0.1535]*| it/evals=2536/396656 eff=0.6400% N=400 Z=-3.4(95.57%) | Like=-0.15..-0.00 [-0.1529..-0.1526]*| it/evals=2544/397972 eff=0.6399% N=400 Z=-3.4(95.64%) | Like=-0.15..-0.00 [-0.1505..-0.1502]*| it/evals=2550/398948 eff=0.6398% N=400 Z=-3.4(95.72%) | Like=-0.15..-0.00 [-0.1493..-0.1490]*| it/evals=2558/400259 eff=0.6397% N=400 Z=-3.4(95.79%) | Like=-0.15..-0.00 [-0.1472..-0.1472]*| it/evals=2565/401596 eff=0.6393% N=400 Z=-3.4(95.86%) | Like=-0.15..-0.00 [-0.1455..-0.1453]*| it/evals=2573/402884 eff=0.6393% N=400 Z=-3.4(95.91%) | Like=-0.14..-0.00 [-0.1440..-0.1436]*| it/evals=2578/403904 eff=0.6389% N=400 Z=-3.4(95.99%) | Like=-0.14..-0.00 [-0.1401..-0.1396]*| it/evals=2586/405236 eff=0.6388% N=400 Z=-3.4(96.04%) | Like=-0.14..-0.00 [-0.1386..-0.1384]*| it/evals=2591/406208 eff=0.6385% N=400 Z=-3.4(96.12%) | Like=-0.13..-0.00 [-0.1350..-0.1348]*| it/evals=2600/407511 eff=0.6386% N=400 Z=-3.4(96.18%) | Like=-0.13..-0.00 [-0.1343..-0.1340]*| it/evals=2607/408762 eff=0.6384% N=400 Z=-3.4(96.21%) | Like=-0.13..-0.00 [-0.1337..-0.1337]*| it/evals=2610/409074 eff=0.6387% N=400 Z=-3.4(96.27%) | Like=-0.13..-0.00 [-0.1325..-0.1323]*| it/evals=2617/410438 eff=0.6382% N=400 Z=-3.4(96.33%) | Like=-0.13..-0.00 [-0.1311..-0.1308]*| it/evals=2624/411614 eff=0.6381% N=400 Z=-3.4(96.39%) | Like=-0.13..-0.00 [-0.1289..-0.1285]*| it/evals=2630/412934 eff=0.6375% N=400 Z=-3.4(96.45%) | Like=-0.13..-0.00 [-0.1273..-0.1269]*| it/evals=2638/414178 eff=0.6375% N=400 Z=-3.4(96.52%) | Like=-0.12..-0.00 [-0.1244..-0.1243]*| it/evals=2646/415441 eff=0.6375% N=400 Z=-3.4(96.55%) | Like=-0.12..-0.00 [-0.1240..-0.1238]*| it/evals=2650/416076 eff=0.6375% N=400 Z=-3.4(96.60%) | Like=-0.12..-0.00 [-0.1232..-0.1229]*| it/evals=2655/417191 eff=0.6370% N=400 Z=-3.4(96.66%) | Like=-0.12..-0.00 [-0.1217..-0.1216]*| it/evals=2663/418466 eff=0.6370% N=400 Z=-3.4(96.73%) | Like=-0.12..-0.00 [-0.1202..-0.1200]*| it/evals=2672/419873 eff=0.6370% N=400 Z=-3.4(96.79%) | Like=-0.12..-0.00 [-0.1190..-0.1190]*| it/evals=2680/421320 eff=0.6367% N=400 Z=-3.4(96.84%) | Like=-0.12..-0.00 [-0.1175..-0.1171]*| it/evals=2687/422859 eff=0.6360% N=400 Z=-3.4(96.90%) | Like=-0.12..-0.00 [-0.1154..-0.1154]*| it/evals=2695/424153 eff=0.6360% N=400 Z=-3.4(96.93%) | Like=-0.11..-0.00 [-0.1143..-0.1140]*| it/evals=2699/424813 eff=0.6359% N=400 Z=-3.4(96.94%) | Like=-0.11..-0.00 [-0.1140..-0.1137]*| it/evals=2700/424853 eff=0.6361% N=400 Z=-3.4(96.99%) | Like=-0.11..-0.00 [-0.1129..-0.1128]*| it/evals=2707/426081 eff=0.6359% N=400 Z=-3.4(97.05%) | Like=-0.11..-0.00 [-0.1113..-0.1112]*| it/evals=2716/427783 eff=0.6355% N=400 Z=-3.4(97.12%) | Like=-0.11..-0.00 [-0.1094..-0.1092]*| it/evals=2725/429087 eff=0.6357% N=400 Z=-3.4(97.17%) | Like=-0.11..-0.00 [-0.1077..-0.1075]*| it/evals=2733/430304 eff=0.6357% N=400 Z=-3.4(97.21%) | Like=-0.11..-0.00 [-0.1068..-0.1066]*| it/evals=2739/431493 eff=0.6354% N=400 Z=-3.4(97.26%) | Like=-0.11..-0.00 [-0.1055..-0.1055]*| it/evals=2746/432617 eff=0.6353% N=400 Z=-3.4(97.28%) | Like=-0.10..-0.00 [-0.1038..-0.1036]*| it/evals=2750/433209 eff=0.6354% N=400 Z=-3.4(97.34%) | Like=-0.10..-0.00 [-0.1026..-0.1018]*| it/evals=2758/434477 eff=0.6354% N=400 Z=-3.4(97.38%) | Like=-0.10..-0.00 [-0.1005..-0.1002]*| it/evals=2765/435709 eff=0.6352% N=400 Z=-3.4(97.42%) | Like=-0.10..-0.00 [-0.0987..-0.0985]*| it/evals=2772/437051 eff=0.6348% N=400 Z=-3.4(97.47%) | Like=-0.10..-0.00 [-0.0981..-0.0981]*| it/evals=2780/438312 eff=0.6348% N=400 Z=-3.4(97.52%) | Like=-0.10..-0.00 [-0.0960..-0.0958]*| it/evals=2788/439576 eff=0.6348% N=400 Z=-3.4(97.55%) | Like=-0.10..-0.00 [-0.0955..-0.0955]*| it/evals=2793/440510 eff=0.6346% N=400 Z=-3.4(97.59%) | Like=-0.09..-0.00 [-0.0947..-0.0944]*| it/evals=2800/441273 eff=0.6351% N=400 Z=-3.4(97.64%) | Like=-0.09..-0.00 [-0.0934..-0.0931]*| it/evals=2809/443009 eff=0.6346% N=400 Z=-3.4(97.69%) | Like=-0.09..-0.00 [-0.0925..-0.0921]*| it/evals=2818/444789 eff=0.6341% N=400 Z=-3.4(97.74%) | Like=-0.09..-0.00 [-0.0899..-0.0898]*| it/evals=2827/446079 eff=0.6343% N=400 Z=-3.4(97.79%) | Like=-0.09..-0.00 [-0.0887..-0.0887]*| it/evals=2835/447458 eff=0.6341% N=400 Z=-3.4(97.83%) | Like=-0.09..-0.00 [-0.0871..-0.0871]*| it/evals=2844/449158 eff=0.6337% N=400 Z=-3.4(97.87%) | Like=-0.09..-0.00 [-0.0860..-0.0860]*| it/evals=2850/449954 eff=0.6340% N=400 Z=-3.4(97.91%) | Like=-0.08..-0.00 [-0.0836..-0.0833]*| it/evals=2859/451243 eff=0.6341% N=400 Z=-3.4(97.94%) | Like=-0.08..-0.00 [-0.0827..-0.0825]*| it/evals=2865/452703 eff=0.6334% N=400 Z=-3.3(98.00%) | Like=-0.08..-0.00 [-0.0808..-0.0807]*| it/evals=2876/454571 eff=0.6332% N=400 Z=-3.3(98.02%) | Like=-0.08..-0.00 [-0.0804..-0.0803]*| it/evals=2880/455237 eff=0.6332% N=400 Z=-3.3(98.05%) | Like=-0.08..-0.00 [-0.0800..-0.0799]*| it/evals=2888/456550 eff=0.6331% N=400 Z=-3.3(98.09%) | Like=-0.08..-0.00 [-0.0792..-0.0791]*| it/evals=2896/457846 eff=0.6331% N=400 Z=-3.3(98.11%) | Like=-0.08..-0.00 [-0.0790..-0.0788]*| it/evals=2900/458521 eff=0.6330% N=400 Z=-3.3(98.14%) | Like=-0.08..-0.00 [-0.0782..-0.0779]*| it/evals=2907/459673 eff=0.6330% N=400 Z=-3.3(98.17%) | Like=-0.08..-0.00 [-0.0768..-0.0768]*| it/evals=2913/460862 eff=0.6326% N=400 Z=-3.3(98.20%) | Like=-0.08..-0.00 [-0.0762..-0.0761]*| it/evals=2921/462258 eff=0.6324% N=400 Z=-3.3(98.24%) | Like=-0.08..-0.00 [-0.0750..-0.0747]*| it/evals=2929/463659 eff=0.6323% N=400 Z=-3.3(98.27%) | Like=-0.07..-0.00 [-0.0735..-0.0735]*| it/evals=2937/465099 eff=0.6320% N=400 Z=-3.3(98.31%) | Like=-0.07..-0.00 [-0.0723..-0.0723]*| it/evals=2946/466609 eff=0.6319% N=400 Z=-3.3(98.33%) | Like=-0.07..-0.00 [-0.0716..-0.0715]*| it/evals=2950/467204 eff=0.6320% N=400 Z=-3.3(98.37%) | Like=-0.07..-0.00 [-0.0700..-0.0699]*| it/evals=2960/468947 eff=0.6317% N=400 Z=-3.3(98.40%) | Like=-0.07..-0.00 [-0.0690..-0.0690]*| it/evals=2970/470696 eff=0.6315% N=400 Z=-3.3(98.44%) | Like=-0.07..-0.00 [-0.0684..-0.0683]*| it/evals=2979/472180 eff=0.6314% N=400 Z=-3.3(98.48%) | Like=-0.07..-0.00 [-0.0669..-0.0668]*| it/evals=2990/474061 eff=0.6313% N=400 Z=-3.3(98.52%) | Like=-0.07..-0.00 [-0.0659..-0.0658]*| it/evals=3000/475462 eff=0.6315% N=400 Z=-3.3(98.55%) | Like=-0.06..-0.00 [-0.0649..-0.0648]*| it/evals=3009/477048 eff=0.6313% N=400 Z=-3.3(98.58%) | Like=-0.06..-0.00 [-0.0645..-0.0644]*| it/evals=3018/478692 eff=0.6310% N=400 Z=-3.3(98.61%) | Like=-0.06..-0.00 [-0.0639..-0.0637]*| it/evals=3026/480077 eff=0.6308% N=400 Z=-3.3(98.64%) | Like=-0.06..-0.00 [-0.0634..-0.0633]*| it/evals=3034/481345 eff=0.6308% N=400 Z=-3.3(98.66%) | Like=-0.06..-0.00 [-0.0627..-0.0627]*| it/evals=3042/482653 eff=0.6308% N=400 Z=-3.3(98.68%) | Like=-0.06..-0.00 [-0.0625..-0.0624]*| it/evals=3047/483720 eff=0.6304% N=400 Z=-3.3(98.69%) | Like=-0.06..-0.00 [-0.0619..-0.0618]*| it/evals=3050/484209 eff=0.6304% N=400 Z=-3.3(98.71%) | Like=-0.06..-0.00 [-0.0614..-0.0614]*| it/evals=3058/485486 eff=0.6304% N=400 Z=-3.3(98.72%) | Like=-0.06..-0.00 [-0.0614..-0.0613]*| it/evals=3060/485700 eff=0.6305% N=400 Z=-3.3(98.73%) | Like=-0.06..-0.00 [-0.0612..-0.0611]*| it/evals=3064/486716 eff=0.6300% N=400 Z=-3.3(98.75%) | Like=-0.06..-0.00 [-0.0607..-0.0607]*| it/evals=3071/488011 eff=0.6298% N=400 Z=-3.3(98.78%) | Like=-0.06..-0.00 [-0.0600..-0.0600]*| it/evals=3079/489356 eff=0.6297% N=400 Z=-3.3(98.80%) | Like=-0.06..-0.00 [-0.0597..-0.0597]*| it/evals=3087/490653 eff=0.6297% N=400 Z=-3.3(98.82%) | Like=-0.06..-0.00 [-0.0588..-0.0587]*| it/evals=3094/491929 eff=0.6295% N=400 Z=-3.3(98.84%) | Like=-0.06..-0.00 [-0.0580..-0.0579]*| it/evals=3099/492688 eff=0.6295% N=400 Z=-3.3(98.84%) | Like=-0.06..-0.00 [-0.0579..-0.0578]*| it/evals=3100/492690 eff=0.6297% N=400 Z=-3.3(98.86%) | Like=-0.06..-0.00 [-0.0576..-0.0575]*| it/evals=3106/493827 eff=0.6295% N=400 Z=-3.3(98.88%) | Like=-0.06..-0.00 [-0.0569..-0.0567]*| it/evals=3114/495151 eff=0.6294% N=400 Z=-3.3(98.89%) | Like=-0.06..-0.00 [-0.0563..-0.0562]*| it/evals=3118/495771 eff=0.6294% N=400 Z=-3.3(98.91%) | Like=-0.06..-0.00 [-0.0560..-0.0560]*| it/evals=3125/496783 eff=0.6296% N=400 Z=-3.3(98.92%) | Like=-0.06..-0.00 [-0.0558..-0.0558]*| it/evals=3128/497644 eff=0.6291% N=400 Z=-3.3(98.94%) | Like=-0.06..-0.00 [-0.0554..-0.0553]*| it/evals=3136/498850 eff=0.6292% N=400 Z=-3.3(98.95%) | Like=-0.05..-0.00 [-0.0549..-0.0547]*| it/evals=3143/499894 eff=0.6292% N=400 Z=-3.3(98.97%) | Like=-0.05..-0.00 [-0.0542..-0.0542]*| it/evals=3148/500818 eff=0.6291% N=400 Z=-3.3(98.97%) | Like=-0.05..-0.00 [-0.0541..-0.0541]*| it/evals=3150/501237 eff=0.6289% N=400 Z=-3.3(99.00%) | Like=-0.05..-0.00 [-0.0533..-0.0532]*| it/evals=3160/502865 eff=0.6289% N=400 [ultranest] Explored until L=-0.002 [ultranest] Likelihood function evaluations: 502865 [ultranest] logZ = -3.322 +- 0.04713 [ultranest] Effective samples strategy satisfied (ESS = 1862.2, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.07 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [ultranest] logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [ultranest] done iterating. logZ = -3.330 +- 0.087 single instance: logZ = -3.330 +- 0.068 bootstrapped : logZ = -3.322 +- 0.087 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▁▂▂▃▃▄▅▄▆▅▅▄▃▃▃▂▂▂▃▃▅▆▇▇▇▆▇▆▄▃▃▂▁▁▁▁│1.00 0.53 +- 0.22 param1 : 0.00 │▁▁▁▁▂▂▃▂▄▃▅▅▆▄▄▄▃▂▂▂▂▃▄▃▆▇▇▆▆▆▅▃▃▃▁▁▁▁▁│1.00 0.53 +- 0.22 param2 : 0.00 │▁▁▁▁▁▂▃▃▄▅▅▆▆▅▅▄▃▂▂▂▂▂▄▆▇▇▇▇▇▇▅▄▃▃▁▁▁▁▁│1.00 0.53 +- 0.22 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=536, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-29.02, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=954, regioncalls=0, ndraw=128, logz=-30.05, remainder_fraction=100.0000%, Lmin=-24.57, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=1609, regioncalls=0, ndraw=128, logz=-27.34, remainder_fraction=100.0000%, Lmin=-22.93, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=31, ncalls=2267, regioncalls=0, ndraw=128, logz=-25.80, remainder_fraction=100.0000%, Lmin=-21.66, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=3009, regioncalls=0, ndraw=128, logz=-24.69, remainder_fraction=100.0000%, Lmin=-20.86, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=3720, regioncalls=0, ndraw=128, logz=-23.83, remainder_fraction=100.0000%, Lmin=-19.89, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=59, ncalls=4639, regioncalls=0, ndraw=128, logz=-22.67, remainder_fraction=100.0000%, Lmin=-18.39, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=68, ncalls=5492, regioncalls=0, ndraw=128, logz=-21.65, remainder_fraction=100.0000%, Lmin=-17.73, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=78, ncalls=6490, regioncalls=0, ndraw=128, logz=-20.66, remainder_fraction=100.0000%, Lmin=-16.60, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=87, ncalls=7314, regioncalls=0, ndraw=128, logz=-19.74, remainder_fraction=100.0000%, Lmin=-15.85, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=7541, regioncalls=0, ndraw=128, logz=-19.48, remainder_fraction=100.0000%, Lmin=-15.75, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=98, ncalls=8287, regioncalls=0, ndraw=128, logz=-18.89, remainder_fraction=100.0000%, Lmin=-15.14, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=8459, regioncalls=0, ndraw=128, logz=-18.73, remainder_fraction=100.0000%, Lmin=-15.08, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=107, ncalls=9217, regioncalls=0, ndraw=128, logz=-18.25, remainder_fraction=100.0000%, Lmin=-14.69, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=115, ncalls=9951, regioncalls=0, ndraw=128, logz=-17.76, remainder_fraction=100.0000%, Lmin=-14.13, Lmax=-0.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=123, ncalls=10701, regioncalls=0, ndraw=128, logz=-17.27, remainder_fraction=99.9999%, Lmin=-13.68, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=11607, regioncalls=0, ndraw=128, logz=-16.79, remainder_fraction=99.9999%, Lmin=-13.04, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=138, ncalls=12366, regioncalls=0, ndraw=128, logz=-16.25, remainder_fraction=99.9998%, Lmin=-12.73, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=145, ncalls=13051, regioncalls=0, ndraw=128, logz=-15.86, remainder_fraction=99.9997%, Lmin=-12.39, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=13647, regioncalls=0, ndraw=128, logz=-15.61, remainder_fraction=99.9996%, Lmin=-12.14, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=14794, regioncalls=0, ndraw=128, logz=-15.01, remainder_fraction=99.9993%, Lmin=-11.38, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=169, ncalls=15707, regioncalls=0, ndraw=128, logz=-14.55, remainder_fraction=99.9989%, Lmin=-11.19, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=16882, regioncalls=0, ndraw=128, logz=-14.02, remainder_fraction=99.9980%, Lmin=-10.62, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=191, ncalls=18165, regioncalls=0, ndraw=128, logz=-13.54, remainder_fraction=99.9968%, Lmin=-10.22, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=19033, regioncalls=0, ndraw=128, logz=-13.20, remainder_fraction=99.9955%, Lmin=-9.97, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=211, ncalls=20202, regioncalls=0, ndraw=128, logz=-12.80, remainder_fraction=99.9935%, Lmin=-9.61, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=221, ncalls=21200, regioncalls=0, ndraw=128, logz=-12.46, remainder_fraction=99.9906%, Lmin=-9.27, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=231, ncalls=22294, regioncalls=0, ndraw=128, logz=-12.12, remainder_fraction=99.9865%, Lmin=-8.93, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=241, ncalls=23460, regioncalls=0, ndraw=128, logz=-11.79, remainder_fraction=99.9818%, Lmin=-8.63, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=24541, regioncalls=0, ndraw=128, logz=-11.53, remainder_fraction=99.9759%, Lmin=-8.53, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=259, ncalls=25747, regioncalls=0, ndraw=128, logz=-11.30, remainder_fraction=99.9692%, Lmin=-8.34, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=26673, regioncalls=0, ndraw=128, logz=-11.05, remainder_fraction=99.9600%, Lmin=-8.12, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=278, ncalls=27925, regioncalls=0, ndraw=128, logz=-10.87, remainder_fraction=99.9517%, Lmin=-7.96, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=289, ncalls=28998, regioncalls=0, ndraw=128, logz=-10.62, remainder_fraction=99.9385%, Lmin=-7.73, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=298, ncalls=30265, regioncalls=0, ndraw=128, logz=-10.43, remainder_fraction=99.9249%, Lmin=-7.58, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=30355, regioncalls=0, ndraw=128, logz=-10.39, remainder_fraction=99.9214%, Lmin=-7.55, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=308, ncalls=31484, regioncalls=0, ndraw=128, logz=-10.23, remainder_fraction=99.9082%, Lmin=-7.39, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=317, ncalls=32546, regioncalls=0, ndraw=128, logz=-10.06, remainder_fraction=99.8897%, Lmin=-7.14, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=326, ncalls=33842, regioncalls=0, ndraw=128, logz=-9.87, remainder_fraction=99.8662%, Lmin=-6.94, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=333, ncalls=34837, regioncalls=0, ndraw=128, logz=-9.73, remainder_fraction=99.8454%, Lmin=-6.78, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=342, ncalls=36029, regioncalls=0, ndraw=128, logz=-9.55, remainder_fraction=99.8146%, Lmin=-6.66, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=36856, regioncalls=0, ndraw=128, logz=-9.40, remainder_fraction=99.7829%, Lmin=-6.55, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=359, ncalls=38135, regioncalls=0, ndraw=128, logz=-9.25, remainder_fraction=99.7467%, Lmin=-6.43, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=368, ncalls=39080, regioncalls=0, ndraw=128, logz=-9.10, remainder_fraction=99.7069%, Lmin=-6.34, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=377, ncalls=40214, regioncalls=0, ndraw=128, logz=-8.96, remainder_fraction=99.6637%, Lmin=-6.23, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=389, ncalls=41683, regioncalls=0, ndraw=128, logz=-8.79, remainder_fraction=99.6053%, Lmin=-6.12, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=42784, regioncalls=0, ndraw=128, logz=-8.65, remainder_fraction=99.5452%, Lmin=-6.04, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=409, ncalls=44082, regioncalls=0, ndraw=128, logz=-8.54, remainder_fraction=99.4829%, Lmin=-5.96, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=418, ncalls=45405, regioncalls=0, ndraw=128, logz=-8.43, remainder_fraction=99.4174%, Lmin=-5.84, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=430, ncalls=46806, regioncalls=0, ndraw=128, logz=-8.30, remainder_fraction=99.3346%, Lmin=-5.74, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=441, ncalls=48250, regioncalls=0, ndraw=128, logz=-8.18, remainder_fraction=99.2511%, Lmin=-5.65, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=49284, regioncalls=0, ndraw=128, logz=-8.09, remainder_fraction=99.1649%, Lmin=-5.56, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=459, ncalls=50563, regioncalls=0, ndraw=128, logz=-7.99, remainder_fraction=99.0789%, Lmin=-5.45, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=469, ncalls=51808, regioncalls=0, ndraw=128, logz=-7.89, remainder_fraction=98.9956%, Lmin=-5.28, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=477, ncalls=53130, regioncalls=0, ndraw=128, logz=-7.81, remainder_fraction=98.9267%, Lmin=-5.16, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=486, ncalls=54407, regioncalls=0, ndraw=128, logz=-7.71, remainder_fraction=98.8205%, Lmin=-5.07, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=495, ncalls=55536, regioncalls=0, ndraw=128, logz=-7.62, remainder_fraction=98.6923%, Lmin=-4.93, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=56392, regioncalls=0, ndraw=128, logz=-7.57, remainder_fraction=98.6210%, Lmin=-4.88, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=508, ncalls=57562, regioncalls=0, ndraw=128, logz=-7.48, remainder_fraction=98.5173%, Lmin=-4.83, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=515, ncalls=58695, regioncalls=0, ndraw=128, logz=-7.41, remainder_fraction=98.4019%, Lmin=-4.72, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=525, ncalls=60254, regioncalls=0, ndraw=128, logz=-7.31, remainder_fraction=98.2373%, Lmin=-4.60, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=535, ncalls=61475, regioncalls=0, ndraw=128, logz=-7.21, remainder_fraction=98.0257%, Lmin=-4.52, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=541, ncalls=62654, regioncalls=0, ndraw=128, logz=-7.16, remainder_fraction=97.9094%, Lmin=-4.45, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=63774, regioncalls=0, ndraw=128, logz=-7.07, remainder_fraction=97.7294%, Lmin=-4.42, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=559, ncalls=65031, regioncalls=0, ndraw=128, logz=-6.99, remainder_fraction=97.5527%, Lmin=-4.35, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=568, ncalls=66384, regioncalls=0, ndraw=128, logz=-6.92, remainder_fraction=97.3223%, Lmin=-4.30, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=579, ncalls=67627, regioncalls=0, ndraw=128, logz=-6.83, remainder_fraction=97.1034%, Lmin=-4.23, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=588, ncalls=69251, regioncalls=0, ndraw=128, logz=-6.76, remainder_fraction=96.8657%, Lmin=-4.16, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=598, ncalls=70919, regioncalls=0, ndraw=128, logz=-6.68, remainder_fraction=96.6162%, Lmin=-4.10, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=71025, regioncalls=0, ndraw=128, logz=-6.67, remainder_fraction=96.5634%, Lmin=-4.09, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=608, ncalls=72367, regioncalls=0, ndraw=128, logz=-6.61, remainder_fraction=96.3328%, 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[35mDEBUG [0m ultranest:integrator.py:2491 iteration=668, ncalls=81014, regioncalls=0, ndraw=128, logz=-6.21, remainder_fraction=94.4361%, Lmin=-3.66, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=678, ncalls=82176, regioncalls=0, ndraw=128, logz=-6.15, remainder_fraction=94.1186%, Lmin=-3.60, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=688, ncalls=83669, regioncalls=0, ndraw=128, logz=-6.09, remainder_fraction=93.7668%, Lmin=-3.54, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=697, ncalls=84946, regioncalls=0, ndraw=128, logz=-6.04, remainder_fraction=93.4349%, Lmin=-3.48, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=85135, regioncalls=0, ndraw=128, logz=-6.02, remainder_fraction=93.3556%, Lmin=-3.45, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=708, ncalls=86721, regioncalls=0, ndraw=128, logz=-5.98, remainder_fraction=93.0479%, Lmin=-3.39, Lmax=-0.04 [35mDEBUG [0m 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iteration=756, ncalls=94000, regioncalls=0, ndraw=128, logz=-5.72, remainder_fraction=91.1140%, Lmin=-3.11, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=764, ncalls=95164, regioncalls=0, ndraw=128, logz=-5.68, remainder_fraction=90.7148%, Lmin=-3.06, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=772, ncalls=96298, regioncalls=0, ndraw=128, logz=-5.64, remainder_fraction=90.2795%, Lmin=-3.02, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=779, ncalls=97308, regioncalls=0, ndraw=128, logz=-5.60, remainder_fraction=89.8627%, Lmin=-2.96, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=787, ncalls=98439, regioncalls=0, ndraw=128, logz=-5.56, remainder_fraction=89.4369%, Lmin=-2.93, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=794, ncalls=99539, regioncalls=0, ndraw=128, logz=-5.53, remainder_fraction=89.0858%, Lmin=-2.87, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=100506, regioncalls=0, ndraw=128, logz=-5.50, remainder_fraction=88.8449%, Lmin=-2.85, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=809, ncalls=101973, regioncalls=0, ndraw=128, logz=-5.46, remainder_fraction=88.3910%, Lmin=-2.80, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=102038, regioncalls=0, ndraw=128, logz=-5.45, remainder_fraction=88.3182%, Lmin=-2.80, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=817, ncalls=103341, regioncalls=0, ndraw=128, logz=-5.42, remainder_fraction=87.9471%, Lmin=-2.77, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=826, ncalls=104659, regioncalls=0, ndraw=128, logz=-5.38, remainder_fraction=87.5584%, Lmin=-2.75, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=834, ncalls=106057, regioncalls=0, ndraw=128, logz=-5.35, remainder_fraction=87.0523%, Lmin=-2.72, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=845, ncalls=107595, regioncalls=0, ndraw=128, logz=-5.30, remainder_fraction=86.4287%, Lmin=-2.70, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=108444, regioncalls=0, ndraw=128, logz=-5.28, remainder_fraction=86.1534%, Lmin=-2.66, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=856, ncalls=109634, regioncalls=0, ndraw=128, logz=-5.26, remainder_fraction=85.7233%, Lmin=-2.64, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=865, ncalls=111288, regioncalls=0, ndraw=128, logz=-5.22, remainder_fraction=85.1582%, Lmin=-2.62, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=873, ncalls=112488, regioncalls=0, ndraw=128, logz=-5.19, remainder_fraction=84.7458%, Lmin=-2.58, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=881, ncalls=113649, regioncalls=0, ndraw=128, logz=-5.16, remainder_fraction=84.2403%, Lmin=-2.56, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=889, ncalls=114884, regioncalls=0, ndraw=128, logz=-5.13, remainder_fraction=83.6794%, Lmin=-2.52, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=896, ncalls=116144, regioncalls=0, ndraw=128, logz=-5.10, remainder_fraction=83.1768%, Lmin=-2.49, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=116779, regioncalls=0, ndraw=128, logz=-5.09, remainder_fraction=82.9070%, Lmin=-2.48, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=907, ncalls=117816, regioncalls=0, ndraw=128, logz=-5.06, remainder_fraction=82.4805%, Lmin=-2.47, Lmax=-0.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=913, ncalls=118915, regioncalls=0, ndraw=128, logz=-5.04, remainder_fraction=82.3198%, Lmin=-2.46, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=921, ncalls=120090, regioncalls=0, ndraw=128, logz=-5.02, remainder_fraction=81.8520%, Lmin=-2.43, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=929, ncalls=121337, regioncalls=0, ndraw=128, logz=-4.99, remainder_fraction=81.2827%, Lmin=-2.39, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=937, ncalls=122581, regioncalls=0, ndraw=128, logz=-4.97, remainder_fraction=80.7411%, Lmin=-2.36, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=945, ncalls=123869, regioncalls=0, ndraw=128, logz=-4.94, remainder_fraction=80.2450%, Lmin=-2.32, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=124525, regioncalls=0, ndraw=128, logz=-4.92, remainder_fraction=79.9262%, Lmin=-2.31, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=959, ncalls=125834, regioncalls=0, ndraw=128, logz=-4.89, remainder_fraction=79.2442%, Lmin=-2.28, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=969, ncalls=127602, regioncalls=0, ndraw=128, logz=-4.86, remainder_fraction=78.5054%, Lmin=-2.24, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=978, ncalls=129173, regioncalls=0, ndraw=128, logz=-4.84, 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remainder_fraction=54.5799%, Lmin=-1.27, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1297, ncalls=182780, regioncalls=0, ndraw=128, logz=-4.10, remainder_fraction=54.0516%, Lmin=-1.27, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=183109, regioncalls=0, ndraw=128, logz=-4.10, remainder_fraction=53.7885%, Lmin=-1.26, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1305, ncalls=183992, regioncalls=0, ndraw=128, logz=-4.09, remainder_fraction=53.3882%, Lmin=-1.25, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1311, ncalls=184967, regioncalls=0, ndraw=128, logz=-4.08, remainder_fraction=52.9967%, Lmin=-1.24, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1318, ncalls=186168, regioncalls=0, ndraw=128, logz=-4.07, remainder_fraction=52.4499%, Lmin=-1.23, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1326, ncalls=187360, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=51.7531%, Lmin=-1.20, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1331, ncalls=188312, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=51.3633%, Lmin=-1.19, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1339, ncalls=189470, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=50.8786%, Lmin=-1.18, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1345, ncalls=190490, regioncalls=0, ndraw=128, logz=-4.03, remainder_fraction=50.4198%, Lmin=-1.17, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=191558, regioncalls=0, ndraw=128, logz=-4.02, remainder_fraction=49.9925%, Lmin=-1.15, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1358, ncalls=192836, regioncalls=0, ndraw=128, logz=-4.01, remainder_fraction=49.3978%, Lmin=-1.13, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1366, ncalls=194139, regioncalls=0, ndraw=128, logz=-4.00, remainder_fraction=48.7245%, Lmin=-1.12, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1374, ncalls=195480, regioncalls=0, ndraw=128, logz=-3.98, remainder_fraction=48.0979%, Lmin=-1.10, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1381, ncalls=196696, regioncalls=0, ndraw=128, logz=-3.97, remainder_fraction=47.7005%, Lmin=-1.09, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1389, ncalls=197982, regioncalls=0, ndraw=128, logz=-3.96, remainder_fraction=47.0590%, Lmin=-1.08, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1397, ncalls=199336, regioncalls=0, ndraw=128, logz=-3.95, remainder_fraction=46.4306%, Lmin=-1.06, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=200002, regioncalls=0, ndraw=128, logz=-3.95, remainder_fraction=46.1871%, Lmin=-1.05, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1408, ncalls=201398, regioncalls=0, ndraw=128, logz=-3.94, remainder_fraction=45.6260%, Lmin=-1.05, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1416, ncalls=202794, regioncalls=0, ndraw=128, logz=-3.93, remainder_fraction=45.0946%, Lmin=-1.04, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1424, ncalls=204211, regioncalls=0, ndraw=128, logz=-3.92, remainder_fraction=44.5176%, Lmin=-1.03, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1430, ncalls=205191, regioncalls=0, ndraw=128, logz=-3.91, remainder_fraction=44.0650%, Lmin=-1.02, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1437, ncalls=206251, regioncalls=0, ndraw=128, logz=-3.90, remainder_fraction=43.5461%, Lmin=-1.01, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1441, ncalls=207192, regioncalls=0, ndraw=128, logz=-3.89, remainder_fraction=43.2448%, Lmin=-1.00, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1448, ncalls=208459, regioncalls=0, ndraw=128, logz=-3.89, remainder_fraction=42.7292%, Lmin=-0.98, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=208561, regioncalls=0, ndraw=128, logz=-3.88, remainder_fraction=42.5588%, Lmin=-0.98, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1456, ncalls=209774, regioncalls=0, ndraw=128, logz=-3.88, remainder_fraction=42.1375%, Lmin=-0.98, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1463, ncalls=210553, regioncalls=0, ndraw=128, logz=-3.87, remainder_fraction=41.5973%, Lmin=-0.97, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1468, ncalls=211694, regioncalls=0, ndraw=128, logz=-3.86, remainder_fraction=41.2037%, Lmin=-0.97, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1476, ncalls=213036, regioncalls=0, ndraw=128, logz=-3.85, remainder_fraction=40.7583%, Lmin=-0.96, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1484, ncalls=214448, regioncalls=0, ndraw=128, logz=-3.84, remainder_fraction=40.2693%, Lmin=-0.95, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1492, ncalls=215921, regioncalls=0, ndraw=128, logz=-3.84, remainder_fraction=39.7295%, Lmin=-0.94, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1499, ncalls=216952, regioncalls=0, ndraw=128, logz=-3.83, remainder_fraction=39.3106%, Lmin=-0.93, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=217236, regioncalls=0, ndraw=128, logz=-3.83, remainder_fraction=39.2266%, Lmin=-0.93, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1508, ncalls=218630, regioncalls=0, ndraw=128, logz=-3.82, remainder_fraction=38.6220%, Lmin=-0.91, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1517, ncalls=220431, regioncalls=0, ndraw=128, logz=-3.81, remainder_fraction=38.0412%, Lmin=-0.89, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1525, ncalls=221804, regioncalls=0, ndraw=128, logz=-3.80, remainder_fraction=37.5627%, Lmin=-0.88, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=222727, regioncalls=0, ndraw=128, logz=-3.80, remainder_fraction=37.2575%, Lmin=-0.87, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1538, ncalls=224046, regioncalls=0, ndraw=128, logz=-3.79, remainder_fraction=36.7704%, Lmin=-0.86, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1545, ncalls=225297, regioncalls=0, ndraw=128, logz=-3.78, remainder_fraction=36.3914%, Lmin=-0.85, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=226001, regioncalls=0, ndraw=128, logz=-3.78, remainder_fraction=36.0648%, Lmin=-0.84, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1557, ncalls=227211, regioncalls=0, ndraw=128, logz=-3.77, remainder_fraction=35.6278%, Lmin=-0.83, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1565, ncalls=228491, regioncalls=0, ndraw=128, logz=-3.76, remainder_fraction=35.1825%, Lmin=-0.81, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1573, ncalls=229851, regioncalls=0, ndraw=128, logz=-3.75, remainder_fraction=34.6997%, Lmin=-0.81, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1579, ncalls=230898, regioncalls=0, ndraw=128, logz=-3.75, remainder_fraction=34.3671%, Lmin=-0.80, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1585, ncalls=232292, regioncalls=0, ndraw=128, logz=-3.74, remainder_fraction=34.0029%, Lmin=-0.79, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1593, ncalls=233609, regioncalls=0, ndraw=128, logz=-3.74, remainder_fraction=33.5868%, Lmin=-0.78, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1598, ncalls=234717, regioncalls=0, ndraw=128, logz=-3.73, remainder_fraction=33.2607%, Lmin=-0.77, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=234722, regioncalls=0, ndraw=128, logz=-3.73, 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remainder_fraction=14.3698%, Lmin=-0.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2029, ncalls=309251, regioncalls=0, ndraw=128, logz=-3.48, remainder_fraction=14.1281%, Lmin=-0.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2040, ncalls=310627, regioncalls=0, ndraw=128, logz=-3.48, remainder_fraction=13.7872%, Lmin=-0.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2047, ncalls=312307, regioncalls=0, ndraw=128, logz=-3.48, remainder_fraction=13.5648%, Lmin=-0.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=312313, regioncalls=0, ndraw=128, logz=-3.47, remainder_fraction=13.4757%, Lmin=-0.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2056, ncalls=313856, regioncalls=0, ndraw=128, logz=-3.47, remainder_fraction=13.3044%, Lmin=-0.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2065, ncalls=315160, regioncalls=0, ndraw=128, logz=-3.47, remainder_fraction=13.0504%, Lmin=-0.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=315963, regioncalls=0, ndraw=128, logz=-3.47, remainder_fraction=12.9210%, Lmin=-0.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2078, ncalls=317271, regioncalls=0, ndraw=128, logz=-3.47, remainder_fraction=12.6991%, Lmin=-0.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2087, ncalls=319125, regioncalls=0, ndraw=128, logz=-3.46, remainder_fraction=12.4484%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2095, ncalls=320277, regioncalls=0, ndraw=128, logz=-3.46, remainder_fraction=12.2409%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=321161, regioncalls=0, ndraw=128, logz=-3.46, remainder_fraction=12.1107%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2107, ncalls=322344, regioncalls=0, ndraw=128, logz=-3.46, remainder_fraction=11.9295%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2112, ncalls=323076, regioncalls=0, ndraw=128, logz=-3.46, remainder_fraction=11.7945%, Lmin=-0.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2115, ncalls=324007, regioncalls=0, ndraw=128, logz=-3.45, remainder_fraction=11.7145%, Lmin=-0.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2123, ncalls=325270, regioncalls=0, ndraw=128, logz=-3.45, remainder_fraction=11.5126%, Lmin=-0.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2129, ncalls=326491, regioncalls=0, ndraw=128, logz=-3.45, remainder_fraction=11.3626%, Lmin=-0.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2137, ncalls=327927, regioncalls=0, ndraw=128, logz=-3.45, remainder_fraction=11.1650%, Lmin=-0.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2145, ncalls=329330, regioncalls=0, ndraw=128, logz=-3.45, remainder_fraction=10.9699%, Lmin=-0.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=330154, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.8411%, Lmin=-0.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2157, ncalls=331458, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.6688%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=331682, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.6029%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2167, ncalls=333001, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.4457%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2173, ncalls=334173, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.3192%, Lmin=-0.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2180, ncalls=335600, regioncalls=0, ndraw=128, logz=-3.44, remainder_fraction=10.1603%, Lmin=-0.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2188, ncalls=336929, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.9863%, Lmin=-0.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2196, ncalls=338257, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.8082%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=338933, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.7195%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2209, ncalls=340575, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.5347%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2218, ncalls=342183, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.3382%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2227, ncalls=343517, regioncalls=0, ndraw=128, logz=-3.43, remainder_fraction=9.1512%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2234, ncalls=344612, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=9.0049%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2242, ncalls=346177, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=8.8497%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=347629, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=8.6917%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2256, ncalls=348869, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=8.5758%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2264, ncalls=350130, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=8.4240%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2273, ncalls=351454, regioncalls=0, ndraw=128, logz=-3.42, remainder_fraction=8.2531%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2279, ncalls=352749, regioncalls=0, ndraw=128, logz=-3.41, remainder_fraction=8.1448%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2287, ncalls=354025, regioncalls=0, ndraw=128, logz=-3.41, remainder_fraction=8.0024%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2295, ncalls=355293, regioncalls=0, ndraw=128, logz=-3.41, remainder_fraction=7.8515%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=356045, regioncalls=0, ndraw=128, logz=-3.41, remainder_fraction=7.7606%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2307, ncalls=357269, regioncalls=0, ndraw=128, logz=-3.41, remainder_fraction=7.6345%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2315, ncalls=358620, regioncalls=0, ndraw=128, logz=-3.41, 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remainder_fraction=3.8796%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2607, ncalls=408762, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.8162%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=409074, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.7895%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2617, ncalls=410438, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.7267%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2624, ncalls=411614, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.6656%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2630, ncalls=412934, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.6137%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2638, ncalls=414178, regioncalls=0, ndraw=128, logz=-3.37, remainder_fraction=3.5450%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2646, ncalls=415441, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.4790%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=416076, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.4464%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2655, ncalls=417191, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.4046%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2663, ncalls=418466, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.3416%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2672, ncalls=419873, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.2713%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2680, ncalls=421320, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.2090%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2687, ncalls=422859, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.1555%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2695, ncalls=424153, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.0957%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2699, ncalls=424813, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.0666%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=424853, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.0593%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2707, ncalls=426081, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=3.0093%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2716, ncalls=427783, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.9460%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2725, ncalls=429087, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.8832%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2733, ncalls=430304, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.8294%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2739, ncalls=431493, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.7884%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2746, ncalls=432617, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.7414%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=433209, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.7156%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2758, ncalls=434477, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.6643%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2765, ncalls=435709, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.6196%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2772, ncalls=437051, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.5765%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2780, ncalls=438312, regioncalls=0, ndraw=128, logz=-3.36, remainder_fraction=2.5270%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2788, ncalls=439576, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.4786%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2793, ncalls=440510, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.4490%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=441273, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.4084%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2809, ncalls=443009, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.3570%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2818, ncalls=444789, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.3069%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2827, ncalls=446079, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.2567%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2835, ncalls=447458, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.2137%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2844, ncalls=449158, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.1662%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=449954, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.1349%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2859, ncalls=451243, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.0885%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2865, ncalls=452703, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.0584%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2876, ncalls=454571, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=2.0043%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=455237, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.9850%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2888, ncalls=456550, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.9471%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2896, ncalls=457846, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.9099%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=458521, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.8913%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2907, ncalls=459673, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.8592%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2913, ncalls=460862, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.8323%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2921, ncalls=462258, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.7972%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2929, ncalls=463659, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.7628%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2937, ncalls=465099, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.7289%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2946, ncalls=466609, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.6910%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=467204, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.6746%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2960, ncalls=468947, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.6342%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=470696, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.5954%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2979, ncalls=472180, regioncalls=0, ndraw=128, logz=-3.35, remainder_fraction=1.5605%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2990, ncalls=474061, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.5190%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=475462, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.4822%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3009, ncalls=477048, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.4500%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3018, ncalls=478692, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.4184%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3026, ncalls=480077, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.3912%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3034, ncalls=481345, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.3645%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3042, ncalls=482653, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.3381%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3047, ncalls=483720, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.3218%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=484209, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.3121%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3058, ncalls=485486, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.2867%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=485700, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.2804%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3064, ncalls=486716, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.2679%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3071, ncalls=488011, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.2465%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3079, ncalls=489356, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.2224%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3087, ncalls=490653, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1986%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3094, ncalls=491929, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1783%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3099, ncalls=492688, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1640%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=492690, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1611%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3106, ncalls=493827, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1442%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3114, ncalls=495151, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1221%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3118, ncalls=495771, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.1111%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3125, ncalls=496783, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0923%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3128, ncalls=497644, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0842%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3136, ncalls=498850, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0631%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3143, ncalls=499894, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0450%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3148, ncalls=500818, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0323%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=501237, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0273%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3160, ncalls=502865, regioncalls=0, ndraw=128, logz=-3.34, remainder_fraction=1.0023%, Lmin=-0.05, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.002 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 502865 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.322 +- 0.04713 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1862.2, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.07 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_popstepsampling.py::test_direction_proposals | 0.14 | |
------------------------------Captured stdout call------------------------------ test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.AffineLayer'> test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.MLFriends'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.RobustEllipsoidRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_cube_oriented_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_random_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_oriented_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> test of proposal: <built-in function generate_region_random_direction> with region: <class 'ultranest.mlfriends.SimpleRegion'> layer: <class 'ultranest.mlfriends.ScalingLayer'> | |||
Passed | tests/test_regionsampling.py::test_region_sampling_scaling | 0.12 | |
------------------------------Captured stdout call------------------------------ enlargement factor: 1.6413050458476675 0.6092712640650785 sampling_method: <bound method MLFriends.sample_from_transformed_boundingbox of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03b80>> sampling_method: <bound method MLFriends.sample_from_boundingbox of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03b80>> sampling_method: <bound method MLFriends.sample_from_points of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03b80>> sampling_method: <bound method MLFriends.sample_from_wrapping_ellipsoid of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03b80>> | |||
Passed | tests/test_regionsampling.py::test_region_sampling_affine | 0.12 | |
------------------------------Captured stdout call------------------------------ enlargement factor: 1.6413050458476683 0.6092712640650781 sampling_method: <bound method MLFriends.sample_from_transformed_boundingbox of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03070>> sampling_method: <bound method MLFriends.sample_from_boundingbox of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03070>> sampling_method: <bound method MLFriends.sample_from_points of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03070>> sampling_method: <bound method MLFriends.sample_from_wrapping_ellipsoid of <ultranest.mlfriends.MLFriends object at 0x7fa6c2e03070>> | |||
Passed | tests/test_regionsampling.py::test_region_ellipsoid | 0.04 | |
------------------------------Captured stdout call------------------------------ enlargement factor: 1.641305045847665 0.6092712640650795 | |||
Passed | tests/test_regionsampling.py::test_region_mean_distances | 0.31 | |
------------------------------Captured stdout call------------------------------ circle: 604 half-circle: 317 enlargement factor: 2.0237675941072473 0.49412788450204137 (0.872214351919518, 43685.72803024098, 50086, array([[ 5.90424248e-01, -1.28168190e-01], [ 5.68326216e-01, -7.99190323e-02], [ 5.73713827e-01, -1.40974066e-01], [-1.19565575e-01, 5.61077247e-01], [-3.96422558e-01, 6.82850299e-01], [-2.59345436e-01, -6.34084337e-01], [-9.34539440e-01, -6.90742582e-01], [-4.54118206e-02, -5.78179518e-01], [-4.02749950e-01, 6.58573234e-01], [ 8.13997364e-02, 4.94366661e-01], [-3.09850782e-01, 6.14532501e-01], [-9.99417973e-02, 5.57440104e-01], [-4.37625549e-01, -6.32032570e-01], [-7.81158726e-01, -7.00564790e-01], [ 1.75458891e-01, 4.95689547e-01], [ 1.72389906e-01, 4.68867481e-01], [-8.01773651e-01, -7.15223858e-01], [ 5.21338908e-01, -2.14590400e-01], [-1.07235208e+00, -7.36808791e-01], [ 4.73338580e-01, 2.56518765e-01], [ 5.63695919e-01, -1.86477123e-01], [-8.70841117e-01, -7.11677665e-01], [-1.82362061e-01, -5.69668582e-01], [ 1.73098757e-01, -4.92037144e-01], [-1.00384656e+00, 7.21061155e-01], [ 4.43217737e-01, 2.93713871e-01], [-4.67073777e-03, -5.54412686e-01], [-6.23929429e-02, -5.55287094e-01], [-3.44813772e-01, -6.27307923e-01], [-1.08124986e+00, 7.05926589e-01], [ 3.94361999e-01, -2.85080745e-01], [ 3.09949304e-01, 4.03277027e-01], [ 4.59621597e-01, -1.96075238e-01], [-1.52726042e-01, -5.65775188e-01], [-6.08038220e-01, 6.84700627e-01], [-9.12413897e-01, -7.18619243e-01], [ 4.86777463e-02, -4.86096498e-01], [ 2.91454928e-01, 4.23042620e-01], [ 2.31918395e-01, -4.62497450e-01], [-1.02176787e+00, -7.36473700e-01], [ 3.27742484e-01, 4.07938102e-01], [-3.10999361e-01, 6.64406143e-01], [-1.06863070e-01, 5.82251954e-01], [ 1.99001281e-01, -4.81705431e-01], [ 5.28069411e-01, 3.43009964e-02], [ 5.27707974e-01, 6.99940502e-02], [-7.79644952e-01, 6.92311241e-01], [-1.28175802e-01, -5.79235117e-01], [ 4.29640240e-01, 3.44927443e-01], [ 3.80488663e-01, -3.37014162e-01], [ 2.11391114e-01, 4.82592387e-01], [ 5.45453354e-01, -4.69543644e-02], [ 5.45493267e-01, -1.72467410e-01], [-1.11318383e-01, -5.94600729e-01], [ 7.36423960e-03, 5.66951095e-01], [ 2.99291218e-01, -3.50666656e-01], [ 7.68501828e-02, -5.19709187e-01], [ 4.77348651e-02, -5.05413071e-01], [ 2.50452451e-02, 5.39406983e-01], [-3.18730924e-01, -6.52554784e-01], [-2.39256777e-01, 6.33617282e-01], [-2.88466757e-01, -6.21474875e-01], [ 4.80469484e-01, -1.58504100e-01], [-7.81309751e-01, -6.99209221e-01], [-7.99809401e-01, -7.19966299e-01], [ 5.61260056e-01, 2.07443092e-01], [ 6.00598496e-01, 1.07023267e-01], [-8.28078470e-01, 7.00202304e-01], [-7.07008910e-01, -6.91468096e-01], [ 5.79643737e-01, -7.94768507e-02], [ 2.31601268e-01, 4.38441077e-01], [-5.88732825e-01, 6.90909274e-01], [ 3.63399194e-01, 3.49554643e-01], [-3.01868015e-01, -6.04559566e-01], [-6.72179399e-01, -6.88365529e-01], [-2.20656209e-01, 6.14106558e-01], [-3.15873900e-01, -6.34460723e-01], [-2.29395363e-02, 5.55827183e-01], [ 1.22068325e-01, -4.81068091e-01], [ 1.60053053e-01, 4.54050443e-01], [ 2.34926635e-01, 4.06907034e-01], [ 6.40572426e-02, 4.96910527e-01], [-1.07581862e+00, 7.10741072e-01], [-7.79059982e-02, 5.81010585e-01], [ 3.13990457e-01, 3.58865342e-01], [-1.06545701e+00, 7.36147059e-01], [ 1.94737500e-01, 4.59040723e-01], [ 5.10198700e-02, 5.48356580e-01], [ 5.67040396e-01, -5.71229985e-02], [-5.32353607e-01, 6.64346344e-01], [ 2.29408539e-01, 4.35118178e-01], [-4.70684001e-01, -6.53176034e-01], [-2.68907819e-01, -6.02284378e-01], [ 5.09058033e-01, -2.12102299e-01], [ 2.90072345e-01, 4.08084343e-01], [-1.12205944e+00, 7.28737094e-01], [ 6.53607804e-02, -5.28177982e-01], [ 5.28723809e-01, 1.93295404e-01], [-1.04125478e+00, -7.12877859e-01], [-4.13773799e-01, 6.76765319e-01], [-3.23951768e-01, 6.66693816e-01], [ 4.51926359e-01, -2.25259849e-01], [ 3.37355863e-01, -3.34292278e-01], [-1.72688147e-01, 6.08087321e-01], [-8.42767128e-01, -6.97224524e-01], [ 5.64114235e-01, -9.35664926e-02], [-5.17747415e-01, -6.87507349e-01], [ 2.85274291e-01, -4.18731788e-01], [ 3.73648106e-01, 3.13017175e-01], [ 5.24885792e-01, -4.80149396e-02], [ 5.74123528e-01, -1.31515348e-01], [ 5.10160640e-01, -1.86260602e-01], [-2.32128947e-01, -6.21459295e-01], [ 8.53606834e-02, -4.90521609e-01], [-1.22126323e-01, 5.71042591e-01], [ 5.61162366e-01, -4.02157071e-02], [ 5.81915438e-01, -1.33557124e-01], [ 2.54251411e-01, 4.42380525e-01], [-2.96656471e-01, 6.27301543e-01], [ 6.12618000e-01, -1.54673577e-02], [ 4.68414253e-01, 3.15996267e-01], [ 1.38443846e-01, 5.06144106e-01], [ 3.38969312e-01, -3.91504214e-01], [-7.36115829e-01, 7.00008604e-01], [ 5.58683694e-01, -6.26853177e-02], [-8.75338415e-02, -5.76960767e-01], [-3.02106731e-01, 6.39277939e-01], [-7.89936457e-01, -7.15965159e-01], [ 6.24131120e-01, 6.76807148e-02], [ 4.83833063e-01, 2.09116673e-01], [-8.76175360e-01, 7.13023744e-01], [-9.50047536e-01, 7.21264422e-01], [-9.40171485e-01, 7.21434643e-01], [ 3.61128428e-01, -3.23874595e-01], [ 5.28527861e-01, -2.07977696e-01], [ 5.91031086e-01, -8.78825272e-02], [-3.31900764e-01, 6.28744257e-01], [ 5.61592717e-01, -8.70877435e-02], [ 1.01711982e-01, -4.90711675e-01], [ 5.98208937e-01, 1.30210229e-01], [ 2.31300459e-01, 4.46747873e-01], [ 5.72396956e-01, 1.45443564e-01], [ 4.82526743e-01, -2.61214652e-01], [-4.65451960e-02, -5.51682756e-01], [-8.56937404e-02, -5.61988808e-01], [ 5.92962781e-01, 9.38652273e-02], [-9.62416037e-01, -7.23512874e-01], [ 6.20222332e-01, -5.16017176e-02], [ 4.16660129e-01, 3.52897971e-01], [ 2.41583904e-02, 5.23144069e-01], [ 6.00141659e-01, -5.75933780e-02], [ 1.56927888e-01, -5.02193794e-01], [-3.93974093e-01, -6.43049126e-01], [ 2.02410228e-01, -4.55275855e-01], [-9.70814676e-04, -5.15726743e-01], [ 4.88659015e-01, 2.92225534e-01], [-1.05525804e+00, -7.30188494e-01], [ 2.52474637e-01, -4.27979784e-01], [ 5.38151632e-01, 6.75691773e-02], [ 2.42572354e-01, -4.12351242e-01], [-1.30226587e-01, 5.75922329e-01], [ 2.11651622e-01, 4.31478420e-01], [-1.08228060e-01, -5.84436143e-01], [-6.84886675e-01, -6.85042919e-01], [-2.56262799e-01, 6.37474619e-01], [ 5.67490271e-01, 1.62710894e-01], [-3.54462948e-01, 6.46586395e-01], [-1.97719948e-01, -6.04179939e-01], [ 2.62341518e-01, 3.94453328e-01], [-6.58017071e-01, 6.83796252e-01], [ 4.15531976e-01, -2.50272400e-01], [-6.10285376e-01, -6.93448002e-01], [-1.30071704e-02, 5.25061960e-01], [ 5.59574319e-01, -1.05796261e-01], [ 5.19595457e-01, 1.24528793e-01], [-8.92188768e-01, 7.05666600e-01], [-1.11557743e-01, 5.69709296e-01], [ 2.79533678e-01, -3.70884022e-01], [ 4.34984565e-01, 3.08947351e-01], [ 3.45974468e-01, 4.12969133e-01], [ 3.13933302e-01, -3.76474657e-01], [-2.75965010e-01, -6.42767643e-01], [ 5.70043451e-01, 6.56624613e-02], [ 7.57441643e-02, -5.01435276e-01], [ 5.25428777e-01, 8.08138545e-02], [-1.01292415e+00, -7.14287399e-01], [ 5.56600088e-01, -1.38665935e-01], [-1.04322227e+00, 7.31390728e-01], [ 5.20570175e-01, -2.11254458e-01], [-1.34117300e-01, -5.84277058e-01], [ 5.63348931e-01, 1.94965923e-01], [ 5.48364164e-01, -1.09980390e-02], [-3.40410352e-01, -6.51432653e-01], [ 4.82640666e-01, -2.73811703e-01], [ 2.92085360e-01, -3.84111012e-01], [-2.97168170e-01, -6.23521430e-01], [-5.18881179e-01, 6.82609666e-01], [-2.18707800e-01, -5.92383081e-01], [ 4.87095196e-01, -1.77017544e-01], [ 4.53688054e-01, -2.82546282e-01], [ 4.31422443e-01, 2.40448711e-01], [-5.21397253e-01, 6.90676472e-01], [-4.21392816e-01, 6.47159091e-01], [-5.44044678e-01, -6.62331621e-01], [-5.07279361e-01, -6.78812897e-01], [-6.52363580e-01, 6.92848286e-01], [-1.42174039e-01, -5.67496779e-01], [ 2.44511627e-01, 4.52886989e-01], [ 6.02900576e-01, 7.79187700e-02], [ 6.19586949e-01, -3.68616455e-02], [-4.35034001e-01, 6.86075529e-01], [-6.80164001e-01, 7.14224230e-01], [-6.79619602e-01, 7.21281074e-01], [ 7.94947538e-02, 5.00351363e-01], [ 2.99037191e-01, 4.21834051e-01], [ 2.45326114e-02, 5.44212568e-01], [-2.36383526e-01, 6.10675449e-01], [ 5.87704961e-01, -1.22705612e-01], [-1.10073316e+00, 7.08902191e-01], [-1.44756302e-01, 5.85217696e-01], [-3.91285306e-01, 6.54446303e-01], [-6.13796840e-01, -6.58699733e-01], [ 5.24678767e-01, 9.66140744e-02], [ 8.40114247e-02, 4.91917261e-01], [ 3.30864872e-02, 5.16473540e-01], [ 2.46226604e-01, -4.09232238e-01], [-1.33034368e-01, -5.94658196e-01], [ 6.03624607e-01, -9.70782567e-02], [-2.47891108e-01, 6.07823792e-01], [ 4.62914363e-01, 2.64549784e-01], [-5.09349525e-01, -6.44150967e-01], [ 1.13591509e-01, -4.73745674e-01], [ 1.48362665e-01, 4.98802454e-01], [-4.05371772e-01, -6.44312188e-01], [ 4.13735449e-01, -3.22718762e-01], [ 5.86936799e-01, 5.59155101e-02], [ 3.40831098e-01, -3.24550582e-01], [-2.76232847e-01, 6.39569251e-01], [ 1.53254495e-01, 4.56171994e-01], [-3.91115920e-01, 6.81134606e-01], [-9.59210936e-01, -7.00127462e-01], [ 5.72735655e-01, -9.50147825e-02], [ 2.30824521e-01, -4.46012440e-01], [ 2.28204223e-01, -4.04798095e-01], [-7.33534876e-01, 6.87691758e-01], [ 4.54868884e-01, -2.82817284e-01], [ 5.46092204e-01, -3.54687801e-03], [ 5.71604916e-01, 1.99084806e-01], [ 4.62990471e-01, 2.71594295e-01], [ 5.19813366e-01, -7.16447092e-02], [-6.74531056e-01, -6.73661254e-01], [ 4.35519314e-01, 3.27782968e-01], [ 4.59759219e-01, -2.97495711e-01], [ 5.36479128e-01, 6.97558942e-02], [ 3.20911096e-01, -3.74970703e-01], [-6.36511978e-01, 7.08221962e-01], [-3.19237142e-01, -6.38166882e-01], [ 3.08440849e-01, -3.97843176e-01], [ 2.15161217e-01, -4.45416491e-01], [ 5.23742183e-01, -6.86027422e-02], [-1.43795898e-01, 6.03843241e-01], [ 4.17881991e-01, 3.38802795e-01], [-9.05525862e-01, -6.91147062e-01], [-9.73625579e-01, 7.37176331e-01], [-1.88283171e-01, 6.24337353e-01], [ 3.17412253e-01, 3.58107852e-01], [-3.08253490e-01, 6.55257540e-01], [ 2.74791820e-01, 4.25170087e-01], [-4.85959091e-01, -6.82191636e-01], [ 4.85531428e-01, -2.17692438e-01], [-2.61347209e-01, 6.06844450e-01], [ 4.52048239e-01, -2.65805090e-01], [ 1.92686620e-01, -4.25541620e-01], [ 5.81632240e-01, 1.05684070e-01], [ 5.44123310e-01, 1.20172110e-01], [ 5.26526195e-01, 2.06299651e-01], [-2.90002911e-01, 6.16411381e-01], [-1.27839148e-01, -5.58561629e-01], [ 3.77309343e-01, 3.88445326e-01], [-6.76423775e-01, 7.04823962e-01], [ 5.12102318e-01, 1.48736261e-01], [-5.02015320e-01, -6.58331246e-01], [ 3.27664480e-01, 4.11424738e-01], [ 1.27090010e-01, -4.72192408e-01], [ 2.01735278e-02, -5.45992810e-01], [ 4.33323589e-01, -2.75800974e-01], [-2.24522422e-01, -5.94273343e-01], [-1.12824059e+00, 7.13459556e-01], [ 1.50616445e-01, -4.67437074e-01], [ 4.74098962e-01, -1.97705703e-01], [-1.38830266e-02, -5.17257962e-01], [ 6.15599276e-01, -2.81078599e-02], [-8.49426337e-01, -6.95820872e-01], [ 9.91674042e-02, 5.28045181e-01], [-7.02400738e-01, 7.13589361e-01], [ 5.30543730e-01, 8.13631383e-02], [ 5.03460184e-01, -2.13761124e-01], [-9.50434114e-01, -7.13216797e-01], [ 2.15097760e-01, -4.14738256e-01], [-6.09043167e-01, 6.74068269e-01], [ 4.26416839e-01, 3.19455107e-01], [ 1.42980694e-01, 5.07039208e-01], [ 2.53103622e-01, 4.31004531e-01], [-7.51466786e-01, -6.98367551e-01], [ 6.06775012e-01, -5.69107304e-02], [-3.31556017e-01, 6.48084055e-01], [-7.20021723e-01, -7.05147717e-01], [ 3.42373787e-01, -3.13355433e-01], [ 5.41800018e-01, -1.43625112e-01], [ 5.92055711e-01, -1.06163740e-02], [ 5.41007418e-01, -1.62003709e-01], [ 6.11496879e-01, -9.55651797e-02], [-8.98704729e-02, -5.93749811e-01], [-5.27060656e-01, -6.66801199e-01], [-1.12398671e-01, -5.82338575e-01], [ 2.39603269e-01, 4.03478432e-01], [ 3.36486746e-01, 4.12896778e-01]])) | |||
Passed | tests/test_regionsampling.py::test_ellipsoids | 0.14 | |
------------------------------Captured stdout call------------------------------ Ellipsis 0.6 [[0.54588005 0.48698095 0.47412371] [0.41878209 0.49408853 0.51881007] [0.55644485 0.56641132 0.56933028] ... [0.5316981 0.5816578 0.58371003] [0.42296226 0.49268289 0.46002077] [0.58528253 0.41797998 0.45592654]] [[5. 4.86980947 4.74123712] [4. 4.94088526 5.18810074] [5. 5.66411324 5.69330284] ... [5. 5.81657797 5.83710035] [4. 4.92682889 4.60020773] [5. 4.17979978 4.55926543]] Ellipsis 0.5 [[0.44811508 0.58286561 0.53134876] [0.44125234 0.5305976 0.41308457] [0.41110681 0.51747073 0.53042648] ... [0.4107215 0.54104277 0.48401175] [0.44263425 0.58404411 0.54291817] [0.40006105 0.51007869 0.57174967]] [[4. 5.82865614 5.31348763] [4. 5.30597603 4.13084571] [4. 5.17470734 5.30426475] ... [4. 5.41042772 4.84011747] [4. 5.84044108 5.42918171] [4. 5.10078686 5.71749668]] [False True True] | |||
Passed | tests/test_run.py::test_run | 4.93 | |
------------------------------Captured stdout call------------------------------ Creating directory for new run logs/test/run369 [ultranest.integrator.NestedSampler] Num live points [400] [ultranest] Num live points [400] [ultranest.integrator.NestedSampler] Resuming... [ultranest] Resuming... [ultranest.integrator.NestedSampler] Starting sampling ... [ultranest] Starting sampling ... Mono-modal Volume: ~exp(-3.94) Expected Volume: exp(-5.99) Hinz: +0.0e+00|*********** ***** ** *** * * * * *** ** **| +1.0e+05 Kunz: +0.0e+00|***** ******** * * ** ** ** * ** * | +1.0e+05 Z=-1e+02+0.4 | Like=-1e+02..0.4 | it/evals=0/500 eff=0.0000% Z=-7e+01+0.3 | Like=-6e+01..0.4 | it/evals=50/500 eff=10.0000% Z=-6e+01+0.2 | Like=-5e+01..0.4 | it/evals=100/625 eff=16.0000% Z=-5e+01+0.06 | Like=-5e+01..0.4 | it/evals=150/625 eff=24.0000% Z=-4e+01+-0.06 | Like=-4e+01..0.4 | it/evals=200/739 eff=27.0636% Z=-4e+01+-0.2 | Like=-3e+01..0.4 | it/evals=250/739 eff=33.8295% Mono-modal Volume: ~exp(-4.81) Expected Volume: exp(-6.65) Hinz: +0.0e+00|******* * ** **** ** * * * * * +6.3e+04 | +1.0e+05 Kunz: +0.0e+00|************** * * * +4.8e+04 | +1.0e+05 Z=-3e+01+-0.2 | Like=-3e+01..0.5 | it/evals=300/848 eff=35.3774% Z=-3e+01+-0.3 | Like=-3e+01..0.5 | it/evals=350/934 eff=37.4732% Z=-3e+01+-0.5 | Like=-2e+01..0.5 | it/evals=400/1013 eff=39.4867% Z=-2e+01+-0.6 | Like=-2e+01..0.5 | it/evals=450/1084 eff=41.5129% Z=-2e+01+-0.7 | Like=-2e+01..0.5 | it/evals=500/1142 eff=43.7828% Z=-2e+01+-0.8 | Like=-2e+01..0.5 | it/evals=550/1203 eff=45.7190% Mono-modal Volume: ~exp(-5.44) Expected Volume: exp(-7.40) Hinz: +0.0e+00|************** * * +3.6e+03 | +1.0e+04 Kunz: +0.0e+00|******* ***** * * +3.3e+03 | +1.0e+04 Z=-2e+01+-1 | Like=-1e+01..0.5 | it/evals=600/1301 eff=46.1184% Z=-2e+01+-1 | Like=-1e+01..0.5 | it/evals=650/1344 eff=48.3631% Z=-2e+01+-1 | Like=-1e+01..0.5 | it/evals=700/1426 eff=49.0884% Z=-1e+01+-1 | Like=-1e+01..0.5 | it/evals=750/1514 eff=49.5376% Z=-1e+01+-1 | Like=-9..0.5 | it/evals=800/1582 eff=50.5689% Mono-modal Volume: ~exp(-6.24) Expected Volume: exp(-8.11) Hinz: +0.0e+00|************* ****** * ** * * +6.8e+02 | +1.0e+03 Kunz: +0.0e+00|********** ***** **** * ** * +5.2e+02 | +1.0e+03 Z=-1e+01+-2 | Like=-8..0.5 | it/evals=850/1677 eff=50.6857% Z=-1e+01+-2 | Like=-7..0.5 | it/evals=900/1738 eff=51.7837% Z=-1e+01+-2 | Like=-6..0.5 | it/evals=950/1816 eff=52.3128% Z=-9+-2 | Like=-5..0.6 | it/evals=1000/1900 eff=52.6316% Z=-9+-2 | Like=-4..0.6 | it/evals=1050/1967 eff=53.3808% Z=-8+-2 | Like=-4..0.6 | it/evals=1100/2052 eff=53.6062% Mono-modal Volume: ~exp(-6.85) Expected Volume: exp(-8.79) Hinz: +0.0e+00|******** +1.4e+02 | +1.0e+03 Kunz: +0.0e+00|******* +1.3e+02 | +1.0e+03 Z=-8+-2 | Like=-3..0.6 | it/evals=1150/2120 eff=54.2453% Z=-7+-2 | Like=-3..0.6 | it/evals=1200/2192 eff=54.7445% Z=-7+-3 | Like=-2..0.6 | it/evals=1250/2263 eff=55.2364% Z=-6+-3 | Like=-2..0.6 | it/evals=1300/2328 eff=55.8419% Z=-6+-3 | Like=-2..0.6 | it/evals=1350/2413 eff=55.9470% Z=-6+-3 | Like=-1..0.6 | it/evals=1400/2485 eff=56.3380% Mono-modal Volume: ~exp(-7.69) Expected Volume: exp(-9.51) Hinz: +0.0|*********************** *** +50.0 | +100.0 Kunz: +0.0|******************* **** +46.0 | +100.0 Z=-6+-3 | Like=-1..0.6 | it/evals=1450/2540 eff=57.0866% Z=-5+-3 | Like=-1..0.6 | it/evals=1500/2623 eff=57.1864% Z=-5+-3 | Like=-0.8..0.6 | it/evals=1550/2697 eff=57.4713% Z=-5+-3 | Like=-0.7..0.6 | it/evals=1600/2772 eff=57.7201% Z=-5+-4 | Like=-0.5..0.6 | it/evals=1650/2849 eff=57.9151% Mono-modal Volume: ~exp(-8.22) Expected Volume: exp(-10.20) Hinz: +0.0|************* +23.4 | +100.0 Kunz: +0.0|************ +21.7 | +100.0 Z=-5+-4 | Like=-0.3..0.6 | it/evals=1700/3031 eff=56.0871% Z=-5+-4 | Like=-0.2..0.6 | it/evals=1750/3031 eff=57.7367% Z=-4+-4 | Like=-0.1..0.6 | it/evals=1800/3140 eff=57.3248% Z=-4+-4 | Like=-0.05..0.6 | it/evals=1850/3233 eff=57.2224% Z=-4+-4 | Like=0.04..0.6 | it/evals=1900/3233 eff=58.7689% Mono-modal Volume: ~exp(-9.22) Expected Volume: exp(-10.77) Hinz: +0.0|********* +15.8 | +100.0 Kunz: +0.0|******** +14.2 | +100.0 Z=-4+-4 | Like=0.1..0.6 | it/evals=1950/3330 eff=58.5586% Z=-4+-4 | Like=0.2..0.6 | it/evals=2000/3370 eff=59.3472% Z=-4+-5 | Like=0.2..0.6 | it/evals=2050/3433 eff=59.7145% Z=-4+-5 | Like=0.3..0.6 | it/evals=2100/3509 eff=59.8461% Z=-4+-5 | Like=0.3..0.6 | it/evals=2150/3575 eff=60.1399% Z=-4+-5 | Like=0.3..0.6 | it/evals=2200/3641 eff=60.4230% Z=-4+-5 | Like=0.3..0.6 | it/evals=2250/3725 eff=60.4027% Mono-modal Volume: ~exp(-9.61) Expected Volume: exp(-11.63) Hinz: +0.0|****** +10.1 | +100.0 Kunz: +0.0| ****************************************** | +10.0 Z=-4+-5 | Like=0.4..0.6 | it/evals=2300/3860 eff=59.5855% Z=-4+-5 | Like=0.4..0.6 | it/evals=2350/3860 eff=60.8808% Z=-4+-5 | Like=0.4..0.6 | it/evals=2400/3982 eff=60.2712% Z=-4+-6 | Like=0.4..0.6 | it/evals=2450/4087 eff=59.9462% Z=-4+-6 | Like=0.5..0.6 | it/evals=2500/4087 eff=61.1696% Mono-modal Volume: ~exp(-10.38) Expected Volume: exp(-12.25) Hinz: +0.0| +2.3 ******************************* +8.0 | +10.0 Kunz: +0.0| +2.4 ***************************** +7.7 | +10.0 Z=-4+-6 | Like=0.5..0.6 | it/evals=2550/4176 eff=61.0632% Z=-4+-6 | Like=0.5..0.6 | it/evals=2600/4251 eff=61.1621% Z=-4+-6 | Like=0.5..0.6 | it/evals=2650/4340 eff=61.0599% Z=-4+-6 | Like=0.5..0.6 | it/evals=2700/4412 eff=61.1967% Z=-4+-6 | Like=0.5..0.6 | it/evals=2750/4473 eff=61.4800% Z=-4+-6 | Like=0.5..0.6 | it/evals=2800/4557 eff=61.4439% Mono-modal Volume: ~exp(-11.12) Expected Volume: exp(-13.03) Hinz: +0.0| +2.8 ********************* +6.5 | +10.0 Kunz: +0.0| +2.9 ******************** +6.4 | +10.0 Z=-4+-7 | Like=0.5..0.6 | it/evals=2850/4635 eff=61.4887% Z=-4+-7 | Like=0.5..0.6 | it/evals=2900/4702 eff=61.6759% Z=-4+-7 | Like=0.5..0.6 | it/evals=2950/4776 eff=61.7672% Z=-4+-7 | Like=0.6..0.6 | it/evals=3000/4848 eff=61.8812% Z=-4+-7 | Like=0.6..0.6 | it/evals=3050/4927 eff=61.9038% Mono-modal Volume: ~exp(-11.78) Expected Volume: exp(-13.69) Hinz: +0.0| +3.2 **************** +5.9 | +10.0 Kunz: +0.0| +3.2 ************** +5.8 | +10.0 Z=-4+-7 | Like=0.6..0.6 | it/evals=3100/5110 eff=60.6654% Z=-4+-7 | Like=0.6..0.6 | it/evals=3150/5110 eff=61.6438% Z=-4+-7 | Like=0.6..0.6 | it/evals=3200/5233 eff=61.1504% Z=-4+-8 | Like=0.6..0.6 | it/evals=3250/5233 eff=62.1059% Z=-4+-8 | Like=0.6..0.6 | it/evals=3300/5335 eff=61.8557% Mono-modal Volume: ~exp(-12.61) Expected Volume: exp(-14.30) Hinz: +0.0| +3.4 *********** +5.4 | +10.0 Kunz: +0.0| +3.5 *********** +5.3 | +10.0 Z=-4+-8 | Like=0.6..0.6 | it/evals=3350/5424 eff=61.7625% Z=-4+-8 | Like=0.6..0.6 | it/evals=3400/5460 eff=62.2711% Z=-4+-8 | Like=0.6..0.6 | it/evals=3450/5536 eff=62.3194% Z=-4+-8 | Like=0.6..0.6 | it/evals=3500/5611 eff=62.3775% Z=-4+-8 | Like=0.6..0.6 | it/evals=3550/5678 eff=62.5220% Z=-4+-8 | Like=0.6..0.6 | it/evals=3600/5751 eff=62.5978% Mono-modal Volume: ~exp(-12.61) Expected Volume: exp(-15.10) Hinz: +0.0| +3.7 ******** +5.0 | +10.0 Kunz: +0.0| +3.7 ******** +5.0 | +10.0 Z=-4+-9 | Like=0.6..0.6 | it/evals=3650/5833 eff=62.5750% Z=-4+-9 | Like=0.6..0.6 | it/evals=3700/5888 eff=62.8397% Z=-4+-9 | Like=0.6..0.6 | it/evals=3750/5964 eff=62.8773% Z=-4+-9 | Like=0.6..0.6 | it/evals=3800/6034 eff=62.9765% Z=-4+-9 | Like=0.6..0.6 | it/evals=3850/6103 eff=63.0837% Z=-4+-9 | Like=0.6..0.6 | it/evals=3900/6196 eff=62.9438% Mono-modal Volume: ~exp(-13.85) Expected Volume: exp(-15.78) Hinz: +0.0| +3.9 ****** +4.8 | +10.0 Kunz: +0.0| +3.9 ****** +4.8 | +10.0 Z=-4+-9 | Like=0.6..0.6 | it/evals=3950/6356 eff=62.1460% Z=-4+-9 | Like=0.6..0.6 | it/evals=4000/6356 eff=62.9327% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4050/6467 eff=62.6256% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4100/6568 eff=62.4239% Mono-modal Volume: ~exp(-14.50) Expected Volume: exp(-16.37) Hinz: +0.0| +4.0 **** +4.7 | +10.0 Kunz: +0.0| +4.0 **** +4.7 | +10.0 Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4150/6658 eff=62.3310% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4200/6658 eff=63.0820% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4250/6740 eff=63.0564% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4300/6801 eff=63.2260% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4350/6893 eff=63.1075% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4400/6972 eff=63.1096% Z=-4+-1e+01 | Like=0.6..0.6 | it/evals=4450/7038 eff=63.2282% niter: 4453 ncall: 7049 nsamples: 4853 logz: -3.627 +/- 0.090 h: 3.253 -------------------------------Captured log call-------------------------------- [32mINFO [0m ultranest.integrator.NestedSampler:integrator.py:513 Num live points [400] [32mINFO [0m ultranest.integrator.NestedSampler:integrator.py:569 Resuming... [32mINFO [0m ultranest.integrator.NestedSampler:integrator.py:660 Starting sampling ... [33mWARNING [0m root:corner.py:724 Too few points to create valid contours | |||
Passed | tests/test_run.py::test_dlogz_reactive_run_SLOW | 81.98 | |
------------------------------Captured stdout call------------------------------ running for ess [ultranest] To achieve the desired logz accuracy, min_num_live_points was increased to 64 [ultranest] Sampling 64 live points from prior ... Z=-inf(0.00%) | Like=-239030.00..-374.91 [-239030.0011..-76860.5203] | it/evals=0/192 eff=0.0000% N=64 Z=-167765.3(0.00%) | Like=-167569.18..-374.91 [-239030.0011..-76860.5203] | it/evals=6/192 eff=4.6875% N=64 Z=-159987.5(0.00%) | Like=-158707.55..-374.91 [-239030.0011..-76860.5203] | it/evals=12/192 eff=9.3750% N=64 Z=-152982.0(0.00%) | Like=-146805.66..-374.91 [-239030.0011..-76860.5203] | it/evals=15/192 eff=11.7188% N=64 Z=-135773.3(0.00%) | Like=-133343.06..-374.91 [-239030.0011..-76860.5203] | it/evals=18/192 eff=14.0625% N=64 Z=-116726.2(0.00%) | Like=-115537.65..-374.91 [-239030.0011..-76860.5203] | it/evals=24/192 eff=18.7500% N=64 Z=-104285.6(0.00%) | Like=-103229.62..-374.91 [-239030.0011..-76860.5203] | it/evals=30/192 eff=23.4375% N=64 Z=-92116.4(0.00%) | Like=-90564.32..-374.91 [-239030.0011..-76860.5203] | it/evals=36/192 eff=28.1250% N=64 Z=-84439.1(0.00%) | Like=-81037.80..-157.35 [-239030.0011..-76860.5203] | it/evals=42/192 eff=32.8125% N=64 Z=-75146.8(0.00%) | Like=-74579.84..-157.35 [-75141.9922..-33421.5180] | it/evals=45/192 eff=35.1562% N=64 Z=-72264.8(0.00%) | Like=-71294.47..-157.35 [-75141.9922..-33421.5180] | it/evals=48/192 eff=37.5000% N=64 Z=-62305.9(0.00%) | Like=-60408.15..-157.35 [-75141.9922..-33421.5180] | it/evals=54/192 eff=42.1875% N=64 Z=-57043.2(0.00%) | Like=-56215.59..-157.35 [-75141.9922..-33421.5180] | it/evals=60/192 eff=46.8750% N=64 Z=-53672.9(0.00%) | Like=-51615.69..-157.35 [-75141.9922..-33421.5180] | it/evals=66/192 eff=51.5625% N=64 Z=-46350.7(0.00%) | Like=-46165.68..-157.35 [-75141.9922..-33421.5180] | it/evals=72/264 eff=36.0000% N=64 Z=-45613.1(0.00%) | Like=-45447.46..-157.35 [-75141.9922..-33421.5180] | it/evals=75/264 eff=37.5000% N=64 Z=-43748.7(0.00%) | Like=-43729.99..-157.35 [-75141.9922..-33421.5180] | it/evals=78/264 eff=39.0000% N=64 Z=-41951.1(0.00%) | Like=-41145.48..-157.35 [-75141.9922..-33421.5180] | it/evals=84/264 eff=42.0000% N=64 Z=-34971.4(0.00%) | Like=-34282.89..-157.35 [-75141.9922..-33421.5180] | it/evals=90/264 eff=45.0000% N=64 Z=-29284.1(0.00%) | Like=-28841.35..-157.35 [-32560.1424..-17157.9418] | it/evals=96/264 eff=48.0000% N=64 Z=-25535.5(0.00%) | Like=-25492.11..-157.35 [-32560.1424..-17157.9418] | it/evals=102/264 eff=51.0000% N=64 Z=-24766.6(0.00%) | Like=-24687.49..-157.35 [-32560.1424..-17157.9418] | it/evals=105/264 eff=52.5000% N=64 Z=-24037.4(0.00%) | Like=-23851.07..-157.35 [-32560.1424..-17157.9418] | it/evals=108/293 eff=47.1616% N=64 Z=-21275.4(0.00%) | Like=-21176.67..-157.35 [-32560.1424..-17157.9418] | it/evals=114/293 eff=49.7817% N=64 Z=-20658.4(0.00%) | Like=-20121.17..-157.35 [-32560.1424..-17157.9418] | it/evals=120/293 eff=52.4017% N=64 Z=-17922.8(0.00%) | Like=-17704.96..-157.35 [-32560.1424..-17157.9418] | it/evals=126/323 eff=48.6486% N=64 Z=-17243.4(0.00%) | Like=-17218.44..-157.35 [-32560.1424..-17157.9418] | it/evals=132/323 eff=50.9653% N=64 Z=-15745.4(0.00%) | Like=-15672.95..-157.35 [-17137.4592..-9186.4957] | it/evals=138/323 eff=53.2819% N=64 Z=-15256.5(0.00%) | Like=-15069.40..-157.35 [-17137.4592..-9186.4957] | it/evals=144/351 eff=50.1742% N=64 Z=-13548.0(0.00%) | Like=-13529.83..-157.35 [-17137.4592..-9186.4957] | it/evals=150/351 eff=52.2648% N=64 Z=-12442.4(0.00%) | Like=-12376.29..-157.35 [-17137.4592..-9186.4957] | it/evals=156/351 eff=54.3554% N=64 Z=-11825.8(0.00%) | Like=-11474.84..-141.88 [-17137.4592..-9186.4957] | it/evals=162/368 eff=53.2895% N=64 Z=-11014.9(0.00%) | Like=-10928.06..-141.88 [-17137.4592..-9186.4957] | it/evals=165/368 eff=54.2763% N=64 Z=-10797.6(0.00%) | Like=-10295.04..-141.88 [-17137.4592..-9186.4957] | it/evals=168/380 eff=53.1646% N=64 Z=-9204.0(0.00%) | Like=-9175.90..-19.73 [-9175.9021..-4745.8915] | it/evals=174/395 eff=52.5680% N=64 Z=-8689.7(0.00%) | Like=-8620.79..-19.73 [-9175.9021..-4745.8915] | it/evals=180/395 eff=54.3807% N=64 Z=-8048.2(0.00%) | Like=-7766.75..-19.73 [-9175.9021..-4745.8915] | it/evals=186/404 eff=54.7059% N=64 Z=-7027.4(0.00%) | Like=-6996.63..-19.73 [-9175.9021..-4745.8915] | it/evals=192/416 eff=54.5455% N=64 Z=-6700.9(0.00%) | Like=-6549.97..-19.73 [-9175.9021..-4745.8915] | it/evals=195/425 eff=54.0166% N=64 Z=-6185.1(0.00%) | Like=-6157.18..-19.73 [-9175.9021..-4745.8915] | it/evals=198/434 eff=53.5135% N=64 Z=-5743.3(0.00%) | Like=-5689.94..-19.73 [-9175.9021..-4745.8915] | it/evals=204/454 eff=52.3077% N=64 Z=-5252.9(0.00%) | Like=-5171.12..-19.73 [-9175.9021..-4745.8915] | it/evals=210/461 eff=52.8967% N=64 Z=-5021.0(0.00%) | Like=-4973.40..-19.73 [-9175.9021..-4745.8915] | it/evals=216/476 eff=52.4272% N=64 Z=-4715.8(0.00%) | Like=-4689.04..-19.73 [-4708.1683..-2810.1404] | it/evals=222/490 eff=52.1127% N=64 Z=-4659.2(0.00%) | Like=-4511.01..-19.73 [-4708.1683..-2810.1404] | it/evals=225/490 eff=52.8169% N=64 Z=-4506.4(0.00%) | Like=-4497.00..-19.73 [-4708.1683..-2810.1404] | it/evals=228/500 eff=52.2936% N=64 Z=-4348.8(0.00%) | Like=-4166.14..-19.73 [-4708.1683..-2810.1404] | it/evals=234/511 eff=52.3490% N=64 Z=-3705.0(0.00%) | Like=-3564.17..-19.73 [-4708.1683..-2810.1404] | it/evals=240/519 eff=52.7473% N=64 Z=-3296.0(0.00%) | Like=-3265.15..-19.73 [-4708.1683..-2810.1404] | it/evals=246/526 eff=53.2468% N=64 Z=-3110.2(0.00%) | Like=-3088.84..-19.73 [-4708.1683..-2810.1404] | it/evals=252/541 eff=52.8302% N=64 Z=-3081.0(0.00%) | Like=-3047.18..-10.67 [-4708.1683..-2810.1404] | it/evals=255/549 eff=52.5773% N=64 Z=-2987.1(0.00%) | Like=-2976.46..-10.67 [-4708.1683..-2810.1404] | it/evals=258/549 eff=53.1959% N=64 Z=-2821.6(0.00%) | Like=-2806.92..-9.63 [-2806.9246..-1542.9080] | it/evals=264/561 eff=53.1187% N=64 Z=-2612.6(0.00%) | Like=-2579.54..-9.63 [-2806.9246..-1542.9080] | it/evals=270/572 eff=53.1496% N=64 Z=-2456.5(0.00%) | Like=-2448.04..-9.63 [-2806.9246..-1542.9080] | it/evals=276/587 eff=52.7725% N=64 Z=-2261.3(0.00%) | Like=-2211.97..-9.63 [-2806.9246..-1542.9080] | it/evals=282/596 eff=53.0075% N=64 Z=-2181.2(0.00%) | Like=-2166.01..-9.63 [-2806.9246..-1542.9080] | it/evals=285/607 eff=52.4862% N=64 Z=-2102.7(0.00%) | Like=-2074.01..-9.63 [-2806.9246..-1542.9080] | it/evals=288/607 eff=53.0387% N=64 Z=-1903.2(0.00%) | Like=-1829.12..-9.63 [-2806.9246..-1542.9080] | it/evals=294/616 eff=53.2609% N=64 Z=-1717.7(0.00%) | Like=-1708.05..-9.63 [-2806.9246..-1542.9080] | it/evals=300/627 eff=53.2860% N=64 Z=-1586.0(0.00%) | Like=-1538.04..-7.50 [-1538.0373..-663.6352] | it/evals=306/739 eff=45.3333% N=64 Z=-1413.5(0.00%) | Like=-1400.61..-7.50 [-1538.0373..-663.6352] | it/evals=312/739 eff=46.2222% N=64 Z=-1349.5(0.00%) | Like=-1334.92..-7.50 [-1538.0373..-663.6352] | it/evals=315/739 eff=46.6667% N=64 Z=-1300.7(0.00%) | Like=-1155.87..-7.50 [-1538.0373..-663.6352] | it/evals=318/739 eff=47.1111% N=64 Z=-1122.5(0.00%) | Like=-1092.65..-7.50 [-1538.0373..-663.6352] | it/evals=324/739 eff=48.0000% N=64 Z=-1007.1(0.00%) | Like=-994.33..-7.50 [-1538.0373..-663.6352] | it/evals=330/739 eff=48.8889% N=64 Z=-935.7(0.00%) | Like=-921.97..-7.50 [-1538.0373..-663.6352] | it/evals=336/739 eff=49.7778% N=64 Z=-815.6(0.00%) | Like=-768.65..-7.50 [-1538.0373..-663.6352] | it/evals=342/862 eff=42.8571% N=64 Z=-736.4(0.00%) | Like=-716.07..-7.50 [-1538.0373..-663.6352] | it/evals=345/862 eff=43.2331% N=64 Z=-715.6(0.00%) | Like=-705.57..-4.04 [-1538.0373..-663.6352] | it/evals=348/862 eff=43.6090% N=64 Z=-659.8(0.00%) | Like=-621.54..-4.04 [-652.0736..-369.3759] | it/evals=354/862 eff=44.3609% N=64 Z=-597.0(0.00%) | Like=-586.16..-4.04 [-652.0736..-369.3759] | it/evals=360/862 eff=45.1128% N=64 Z=-569.7(0.00%) | Like=-542.72..-4.04 [-652.0736..-369.3759] | it/evals=366/862 eff=45.8647% N=64 Z=-509.2(0.00%) | Like=-498.76..-4.04 [-652.0736..-369.3759] | it/evals=372/862 eff=46.6165% N=64 Z=-492.1(0.00%) | Like=-481.00..-4.04 [-652.0736..-369.3759] | it/evals=375/862 eff=46.9925% N=64 Z=-483.4(0.00%) | Like=-459.94..-4.04 [-652.0736..-369.3759] | it/evals=378/862 eff=47.3684% N=64 Z=-441.8(0.00%) | Like=-430.22..-4.04 [-652.0736..-369.3759] | it/evals=384/862 eff=48.1203% N=64 Z=-412.7(0.00%) | Like=-401.91..-4.04 [-652.0736..-369.3759] | it/evals=390/862 eff=48.8722% N=64 Z=-385.0(0.00%) | Like=-367.94..-4.04 [-367.9363..-206.3879] | it/evals=396/877 eff=48.7085% N=64 Z=-366.5(0.00%) | Like=-353.66..-4.04 [-367.9363..-206.3879] | it/evals=402/893 eff=48.4922% N=64 Z=-350.3(0.00%) | Like=-338.35..-4.04 [-367.9363..-206.3879] | it/evals=405/895 eff=48.7365% N=64 Z=-346.9(0.00%) | Like=-326.84..-4.04 [-367.9363..-206.3879] | it/evals=408/1005 eff=43.3581% N=64 Z=-309.5(0.00%) | Like=-296.86..-4.04 [-367.9363..-206.3879] | it/evals=414/1005 eff=43.9957% N=64 Z=-282.7(0.00%) | Like=-271.74..-4.04 [-367.9363..-206.3879] | it/evals=420/1005 eff=44.6334% N=64 Z=-265.7(0.00%) | Like=-249.89..-4.04 [-367.9363..-206.3879] | it/evals=426/1005 eff=45.2710% N=64 Z=-232.0(0.00%) | Like=-217.62..-4.04 [-367.9363..-206.3879] | it/evals=432/1005 eff=45.9086% N=64 Z=-225.7(0.00%) | Like=-214.01..-4.04 [-367.9363..-206.3879] | it/evals=435/1005 eff=46.2274% N=64 Z=-218.6(0.00%) | Like=-203.35..-4.04 [-203.3469..-108.6569] | it/evals=438/1005 eff=46.5462% N=64 Z=-192.3(0.00%) | Like=-177.59..-4.04 [-203.3469..-108.6569] | it/evals=444/1005 eff=47.1838% N=64 Z=-177.7(0.00%) | Like=-166.12..-4.04 [-203.3469..-108.6569] | it/evals=450/1005 eff=47.8215% N=64 Z=-166.8(0.00%) | Like=-155.89..-4.04 [-203.3469..-108.6569] | it/evals=456/1023 eff=47.5495% N=64 Z=-153.6(0.00%) | Like=-141.88..-4.04 [-203.3469..-108.6569] | it/evals=462/1040 eff=47.3361% N=64 Z=-148.8(0.00%) | Like=-136.90..-4.04 [-203.3469..-108.6569] | it/evals=465/1040 eff=47.6434% N=64 Z=-144.6(0.00%) | Like=-131.99..-4.04 [-203.3469..-108.6569] | it/evals=468/1040 eff=47.9508% N=64 Z=-135.5(0.00%) | Like=-123.22..-4.04 [-203.3469..-108.6569] | it/evals=474/1150 eff=43.6464% N=64 Z=-124.9(0.00%) | Like=-106.73..-4.04 [-106.7273..-57.0628] | it/evals=480/1150 eff=44.1989% N=64 Z=-112.7(0.00%) | Like=-97.41..-4.04 [-106.7273..-57.0628] | it/evals=486/1150 eff=44.7514% N=64 Z=-98.7(0.00%) | Like=-85.71..-4.04 [-106.7273..-57.0628] | it/evals=492/1150 eff=45.3039% N=64 Z=-93.5(0.00%) | Like=-80.91..-2.95 [-106.7273..-57.0628] | it/evals=495/1150 eff=45.5801% N=64 Z=-91.5(0.00%) | Like=-77.70..-2.95 [-106.7273..-57.0628] | it/evals=498/1150 eff=45.8564% N=64 Z=-84.7(0.00%) | Like=-72.37..-0.12 [-106.7273..-57.0628] | it/evals=504/1150 eff=46.4088% N=64 Z=-81.5(0.00%) | Like=-69.50..-0.12 [-106.7273..-57.0628] | it/evals=510/1150 eff=46.9613% N=64 Z=-75.2(0.00%) | Like=-64.02..-0.12 [-106.7273..-57.0628] | it/evals=516/1257 eff=43.2523% N=64 Z=-66.0(0.00%) | Like=-52.54..-0.12 [-55.6206..-24.8214] | it/evals=522/1257 eff=43.7552% N=64 Z=-62.4(0.00%) | Like=-49.01..-0.12 [-55.6206..-24.8214] | it/evals=525/1257 eff=44.0067% N=64 Z=-60.1(0.00%) | Like=-48.89..-0.12 [-55.6206..-24.8214] | it/evals=528/1257 eff=44.2582% N=64 Z=-54.1(0.00%) | Like=-41.84..-0.12 [-55.6206..-24.8214] | it/evals=534/1257 eff=44.7611% N=64 Z=-51.5(0.00%) | Like=-40.09..-0.12 [-55.6206..-24.8214] | it/evals=540/1257 eff=45.2640% N=64 Z=-49.0(0.00%) | Like=-35.46..-0.09 [-55.6206..-24.8214] | it/evals=546/1257 eff=45.7670% N=64 Z=-45.8(0.00%) | Like=-34.25..-0.09 [-55.6206..-24.8214] | it/evals=552/1384 eff=41.8182% N=64 Z=-42.9(0.00%) | Like=-30.19..-0.09 [-55.6206..-24.8214] | it/evals=558/1384 eff=42.2727% N=64 Z=-38.5(0.00%) | Like=-26.02..-0.09 [-55.6206..-24.8214] | it/evals=564/1384 eff=42.7273% N=64 Z=-34.6(0.00%) | Like=-21.47..-0.09 [-24.5559..-12.0865] | it/evals=570/1384 eff=43.1818% N=64 Z=-32.3(0.00%) | Like=-19.93..-0.09 [-24.5559..-12.0865] | it/evals=576/1384 eff=43.6364% N=64 Z=-30.2(0.00%) | Like=-18.05..-0.09 [-24.5559..-12.0865] | it/evals=582/1384 eff=44.0909% N=64 Z=-29.4(0.00%) | Like=-16.99..-0.09 [-24.5559..-12.0865] | it/evals=585/1384 eff=44.3182% N=64 Z=-28.5(0.00%) | Like=-16.38..-0.09 [-24.5559..-12.0865] | it/evals=588/1384 eff=44.5455% N=64 Z=-27.4(0.00%) | Like=-15.47..-0.09 [-24.5559..-12.0865] | it/evals=594/1384 eff=45.0000% N=64 Z=-26.4(0.00%) | Like=-14.06..-0.09 [-24.5559..-12.0865] | it/evals=600/1384 eff=45.4545% N=64 Z=-24.5(0.00%) | Like=-11.53..-0.09 [-11.5308..-6.1350] | it/evals=606/1389 eff=45.7358% N=64 Z=-22.9(0.00%) | Like=-10.70..-0.09 [-11.5308..-6.1350] | it/evals=612/1404 eff=45.6716% N=64 Z=-22.4(0.00%) | Like=-10.55..-0.09 [-11.5308..-6.1350] | it/evals=615/1419 eff=45.3875% N=64 Z=-22.0(0.00%) | Like=-9.88..-0.09 [-11.5308..-6.1350] | it/evals=618/1540 eff=41.8699% N=64 Z=-21.1(0.01%) | Like=-9.23..-0.09 [-11.5308..-6.1350] | it/evals=624/1540 eff=42.2764% N=64 Z=-20.4(0.02%) | Like=-8.64..-0.09 [-11.5308..-6.1350] | it/evals=630/1540 eff=42.6829% N=64 Z=-19.8(0.04%) | Like=-7.90..-0.09 [-11.5308..-6.1350] | it/evals=636/1540 eff=43.0894% N=64 Z=-19.1(0.07%) | Like=-7.13..-0.09 [-11.5308..-6.1350] | it/evals=642/1540 eff=43.4959% N=64 Z=-18.8(0.10%) | Like=-6.75..-0.09 [-11.5308..-6.1350] | it/evals=645/1540 eff=43.6992% N=64 Z=-18.5(0.15%) | Like=-6.17..-0.09 [-11.5308..-6.1350] | it/evals=648/1540 eff=43.9024% N=64 Z=-17.7(0.34%) | Like=-5.56..-0.09 [-5.8264..-3.1710] | it/evals=654/1540 eff=44.3089% N=64 Z=-17.1(0.62%) | Like=-4.95..-0.09 [-5.8264..-3.1710] | it/evals=660/1540 eff=44.7154% N=64 Z=-16.6(0.95%) | Like=-4.43..-0.09 [-5.8264..-3.1710] | it/evals=666/1555 eff=44.6680% N=64 Z=-16.1(1.50%) | Like=-4.16..-0.09 [-5.8264..-3.1710] | it/evals=672/1563 eff=44.8299% N=64 Z=-15.9(1.89%) | Like=-3.92..-0.09 [-5.8264..-3.1710] | it/evals=675/1577 eff=44.6134% N=64 Z=-15.7(2.31%) | Like=-3.58..-0.09 [-5.8264..-3.1710] | it/evals=678/1577 eff=44.8116% N=64 Z=-15.3(3.40%) | Like=-3.29..-0.09 [-5.8264..-3.1710] | it/evals=684/1596 eff=44.6475% N=64 Z=-15.0(4.41%) | Like=-3.09..-0.09 [-3.0995..-1.4504] | it/evals=690/1613 eff=44.5449% N=64 Z=-14.8(5.61%) | Like=-2.95..-0.06 [-3.0995..-1.4504] | it/evals=696/1613 eff=44.9322% N=64 Z=-14.5(7.14%) | Like=-2.60..-0.06 [-3.0995..-1.4504] | it/evals=702/1635 eff=44.6849% N=64 Z=-14.4(8.20%) | Like=-2.47..-0.06 [-3.0995..-1.4504] | it/evals=705/1635 eff=44.8759% N=64 Z=-14.3(9.17%) | Like=-2.30..-0.06 [-3.0995..-1.4504] | it/evals=708/1635 eff=45.0668% N=64 Z=-14.1(11.50%) | Like=-2.17..-0.06 [-3.0995..-1.4504] | it/evals=714/1647 eff=45.1042% N=64 Z=-13.9(14.49%) | Like=-1.87..-0.06 [-3.0995..-1.4504] | it/evals=720/1647 eff=45.4833% N=64 Z=-13.7(17.38%) | Like=-1.71..-0.06 [-3.0995..-1.4504] | it/evals=726/1761 eff=42.7814% N=64 Z=-13.5(20.25%) | Like=-1.66..-0.06 [-3.0995..-1.4504] | it/evals=732/1761 eff=43.1349% N=64 Z=-13.5(22.00%) | Like=-1.48..-0.06 [-3.0995..-1.4504] | it/evals=735/1761 eff=43.3117% N=64 Z=-13.4(23.44%) | Like=-1.42..-0.06 [-1.4446..-0.8160] | it/evals=738/1761 eff=43.4885% N=64 Z=-13.3(26.34%) | Like=-1.32..-0.03 [-1.4446..-0.8160] | it/evals=744/1761 eff=43.8421% N=64 Z=-13.1(29.56%) | Like=-1.21..-0.03 [-1.4446..-0.8160] | it/evals=750/1761 eff=44.1956% N=64 Z=-13.0(32.69%) | Like=-1.05..-0.02 [-1.4446..-0.8160] | it/evals=756/1761 eff=44.5492% N=64 Z=-12.9(35.84%) | Like=-0.98..-0.02 [-1.4446..-0.8160] | it/evals=762/1761 eff=44.9028% N=64 Z=-12.9(37.44%) | Like=-0.95..-0.02 [-1.4446..-0.8160] | it/evals=765/1761 eff=45.0796% N=64 Z=-12.8(39.20%) | Like=-0.91..-0.02 [-1.4446..-0.8160] | it/evals=768/1761 eff=45.2563% N=64 Z=-12.7(42.68%) | Like=-0.80..-0.01 [-0.8030..-0.3849] | it/evals=774/1761 eff=45.6099% N=64 Z=-12.7(46.26%) | Like=-0.75..-0.01 [-0.8030..-0.3849] | it/evals=780/1773 eff=45.6407% N=64 Z=-12.6(49.82%) | Like=-0.66..-0.01 [-0.8030..-0.3849] | it/evals=786/1780 eff=45.8042% N=64 Z=-12.5(52.94%) | Like=-0.59..-0.01 [-0.8030..-0.3849] | it/evals=792/1792 eff=45.8333% N=64 Z=-12.5(54.36%) | Like=-0.57..-0.00 [-0.8030..-0.3849] | it/evals=795/1803 eff=45.7159% N=64 Z=-12.5(55.92%) | Like=-0.53..-0.00 [-0.8030..-0.3849] | it/evals=798/1803 eff=45.8884% N=64 Z=-12.4(58.78%) | Like=-0.47..-0.00 [-0.8030..-0.3849] | it/evals=804/1813 eff=45.9691% N=64 Z=-12.4(61.79%) | Like=-0.45..-0.00 [-0.8030..-0.3849] | it/evals=810/1832 eff=45.8145% N=64 Z=-12.3(64.64%) | Like=-0.43..-0.00 [-0.8030..-0.3849] | it/evals=816/1832 eff=46.1538% N=64 Z=-12.3(67.32%) | Like=-0.40..-0.00 [-0.8030..-0.3849] | it/evals=822/1845 eff=46.1538% N=64 Z=-12.3(68.62%) | Like=-0.37..-0.00 [-0.3758..-0.1630] | it/evals=825/1845 eff=46.3223% N=64 Z=-12.3(69.70%) | Like=-0.35..-0.00 [-0.3758..-0.1630] | it/evals=828/1853 eff=46.2828% N=64 Z=-12.2(72.13%) | Like=-0.32..-0.00 [-0.3758..-0.1630] | it/evals=834/1874 eff=46.0773% N=64 Z=-12.2(74.16%) | Like=-0.29..-0.00 [-0.3758..-0.1630] | it/evals=840/1874 eff=46.4088% N=64 Z=-12.2(76.17%) | Like=-0.25..-0.00 [-0.3758..-0.1630] | it/evals=846/1888 eff=46.3816% N=64 Z=-12.2(78.06%) | Like=-0.23..-0.00 [-0.3758..-0.1630] | it/evals=852/1901 eff=46.3800% N=64 Z=-12.1(78.94%) | Like=-0.21..-0.00 [-0.3758..-0.1630] | it/evals=855/1901 eff=46.5433% N=64 Z=-12.1(79.83%) | Like=-0.18..-0.00 [-0.3758..-0.1630] | it/evals=858/1915 eff=46.3533% N=64 Z=-12.1(81.51%) | Like=-0.17..-0.00 [-0.3758..-0.1630] | it/evals=864/1915 eff=46.6775% N=64 Z=-12.1(83.04%) | Like=-0.16..-0.00 [-0.1628..-0.0927] | it/evals=870/1933 eff=46.5490% N=64 Z=-12.1(84.48%) | Like=-0.15..-0.00 [-0.1628..-0.0927] | it/evals=876/1933 eff=46.8700% N=64 Z=-12.1(85.74%) | Like=-0.14..-0.00 [-0.1628..-0.0927] | it/evals=882/1948 eff=46.8153% N=64 Z=-12.1(86.36%) | Like=-0.13..-0.00 [-0.1628..-0.0927] | it/evals=885/1965 eff=46.5544% N=64 Z=-12.0(86.96%) | Like=-0.13..-0.00 [-0.1628..-0.0927] | it/evals=888/1965 eff=46.7123% N=64 Z=-12.0(88.09%) | Like=-0.11..-0.00 [-0.1628..-0.0927] | it/evals=894/2091 eff=44.1046% N=64 Z=-12.0(89.09%) | Like=-0.11..-0.00 [-0.1628..-0.0927] | it/evals=900/2091 eff=44.4006% N=64 Z=-12.0(90.01%) | Like=-0.10..-0.00 [-0.1628..-0.0927] | it/evals=906/2091 eff=44.6966% N=64 Z=-12.0(90.86%) | Like=-0.09..-0.00 [-0.0927..-0.0481] | it/evals=912/2091 eff=44.9926% N=64 Z=-12.0(91.26%) | Like=-0.09..-0.00 [-0.0927..-0.0481] | it/evals=915/2091 eff=45.1406% N=64 Z=-12.0(91.65%) | Like=-0.08..-0.00 [-0.0927..-0.0481] | it/evals=918/2091 eff=45.2886% N=64 Z=-12.0(92.37%) | Like=-0.08..-0.00 [-0.0927..-0.0481] | it/evals=924/2091 eff=45.5846% N=64 Z=-12.0(93.03%) | Like=-0.07..-0.00 [-0.0927..-0.0481] | it/evals=930/2091 eff=45.8806% N=64 Z=-12.0(93.64%) | Like=-0.07..-0.00 [-0.0927..-0.0481] | it/evals=936/2091 eff=46.1766% N=64 Z=-12.0(94.19%) | Like=-0.06..-0.00 [-0.0927..-0.0481] | it/evals=942/2091 eff=46.4726% N=64 Z=-12.0(94.45%) | Like=-0.06..-0.00 [-0.0927..-0.0481] | it/evals=945/2106 eff=46.2782% N=64 Z=-12.0(94.70%) | Like=-0.06..-0.00 [-0.0927..-0.0481] | it/evals=948/2106 eff=46.4251% N=64 Z=-12.0(95.16%) | Like=-0.05..-0.00 [-0.0927..-0.0481] | it/evals=954/2106 eff=46.7189% N=64 Z=-11.9(95.59%) | Like=-0.05..-0.00 [-0.0927..-0.0481] | it/evals=960/2124 eff=46.6019% N=64 Z=-11.9(95.97%) | Like=-0.04..-0.00 [-0.0476..-0.0260] | it/evals=966/2124 eff=46.8932% N=64 Z=-11.9(96.32%) | Like=-0.04..-0.00 [-0.0476..-0.0260] | it/evals=972/2138 eff=46.8660% N=64 Z=-11.9(96.49%) | Like=-0.04..-0.00 [-0.0476..-0.0260] | it/evals=975/2158 eff=46.5616% N=64 Z=-11.9(96.65%) | Like=-0.04..-0.00 [-0.0476..-0.0260] | it/evals=978/2158 eff=46.7049% N=64 Z=-11.9(96.94%) | Like=-0.03..-0.00 [-0.0476..-0.0260] | it/evals=984/2158 eff=46.9914% N=64 Z=-11.9(97.21%) | Like=-0.03..-0.00 [-0.0476..-0.0260] | it/evals=990/2177 eff=46.8528% N=64 Z=-11.9(97.46%) | Like=-0.03..-0.00 [-0.0476..-0.0260] | it/evals=996/2296 eff=44.6237% N=64 Z=-11.9(97.68%) | Like=-0.03..-0.00 [-0.0250..-0.0163]*| it/evals=1002/2296 eff=44.8925% N=64 Z=-11.9(97.89%) | Like=-0.02..-0.00 [-0.0234..-0.0153]*| it/evals=1008/2296 eff=45.1613% N=64 Z=-11.9(98.08%) | Like=-0.02..-0.00 [-0.0216..-0.0128]*| it/evals=1014/2296 eff=45.4301% N=64 Z=-11.9(98.25%) | Like=-0.02..-0.00 [-0.0202..-0.0121]*| it/evals=1020/2296 eff=45.6989% N=64 Z=-11.9(98.40%) | Like=-0.02..-0.00 [-0.0181..-0.0105]*| it/evals=1026/2296 eff=45.9677% N=64 Z=-11.9(98.54%) | Like=-0.02..-0.00 [-0.0171..-0.0088]*| it/evals=1032/2296 eff=46.2366% N=64 Z=-11.9(98.61%) | Like=-0.02..-0.00 [-0.0170..-0.0081]*| it/evals=1035/2296 eff=46.3710% N=64 Z=-11.9(98.67%) | Like=-0.02..-0.00 [-0.0166..-0.0076]*| it/evals=1038/2420 eff=44.0577% N=64 Z=-11.9(98.79%) | Like=-0.02..-0.00 [-0.0160..-0.0067]*| it/evals=1044/2420 eff=44.3124% N=64 Z=-11.9(98.90%) | Like=-0.01..-0.00 [-0.0135..-0.0057]*| it/evals=1050/2420 eff=44.5671% N=64 Z=-11.9(99.00%) | Like=-0.01..-0.00 [-0.0124..-0.0056]*| it/evals=1056/2420 eff=44.8217% N=64 [ultranest] Explored until L=-4e-05 [ultranest] Likelihood function evaluations: 2420 [ultranest] logZ = -11.92 +- 0.2845 [ultranest] Effective samples strategy wants to improve: -15.38..-0.00 (ESS = 253.7, need >10000) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.14 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 62 minimum live points (dlogz from 0.23 to 0.73, need <0.5) [ultranest] logZ error budget: single: 0.41 bs:0.28 tail:0.01 total:0.28 required:<0.50 [ultranest] Widening from 64 to 128 live points before L=-2e+01... [ultranest] Will add 64 live points (x1) at L=-3e+03 ... [ultranest] Exploring (in particular: L=-3087.56..-0.00) ... Z=-3096.9(0.00%) | Like=-3087.56..-0.00 [-3087.5635..-0.0010] | it/evals=253/2427 eff=0.0000% N=64 Z=-1716.5(0.00%) | Like=-1651.25..-0.00 [-3087.5635..-0.0010] | it/evals=302/2429 eff=33.3333% N=66 Z=-1080.6(0.00%) | Like=-1047.57..-0.00 [-3087.5635..-0.0010] | it/evals=327/2429 eff=44.4444% N=67 Z=-715.6(0.00%) | Like=-705.57..-0.00 [-3087.5635..-0.0010] | it/evals=350/2541 eff=4.1322% N=67 Z=-403.8(0.00%) | Like=-392.22..-0.00 [-3087.5635..-0.0010] | it/evals=397/2541 eff=7.4380% N=69 Z=-309.5(0.00%) | Like=-297.04..-0.00 [-3087.5635..-0.0010] | it/evals=420/2541 eff=9.9174% N=70 Z=-256.6(0.00%) | Like=-240.63..-0.00 [-3087.5635..-0.0010] | it/evals=438/2541 eff=13.2231% N=70 Z=-183.8(0.00%) | Like=-172.05..-0.00 [-3087.5635..-0.0010] | it/evals=462/2541 eff=20.6612% N=75 Z=-157.1(0.00%) | Like=-142.25..-0.00 [-3087.5635..-0.0010] | it/evals=476/2541 eff=22.3140% N=76 Z=-144.8(0.00%) | Like=-133.40..-0.00 [-3087.5635..-0.0010] | it/evals=486/2541 eff=25.6198% N=77 Z=-125.0(0.00%) | Like=-106.99..-0.00 [-3087.5635..-0.0010] | it/evals=499/2541 eff=26.4463% N=77 Z=-95.7(0.00%) | Like=-81.66..-0.00 [-3087.5635..-0.0010] | it/evals=516/2553 eff=27.8195% N=79 Z=-93.6(0.00%) | Like=-81.45..-0.00 [-3087.5635..-0.0010] | it/evals=517/2553 eff=28.5714% N=80 Z=-78.3(0.00%) | Like=-64.32..-0.00 [-3087.5635..-0.0010] | it/evals=538/2658 eff=18.4874% N=83 Z=-70.6(0.00%) | Like=-57.67..-0.00 [-3087.5635..-0.0010] | it/evals=546/2658 eff=20.5882% N=86 Z=-65.9(0.00%) | Like=-53.38..-0.00 [-3087.5635..-0.0010] | it/evals=552/2658 eff=21.8487% N=86 Z=-60.2(0.00%) | Like=-47.30..-0.00 [-3087.5635..-0.0010] | it/evals=561/2658 eff=23.5294% N=88 Z=-51.3(0.00%) | Like=-39.65..-0.00 [-3087.5635..-0.0010] | it/evals=576/2658 eff=26.4706% N=93 Z=-49.2(0.00%) | Like=-36.02..-0.00 [-3087.5635..-0.0010] | it/evals=582/2658 eff=28.1513% N=95 Z=-46.4(0.00%) | Like=-34.40..-0.00 [-3087.5635..-0.0010] | it/evals=588/2658 eff=28.9916% N=96 Z=-44.8(0.00%) | Like=-31.78..-0.00 [-3087.5635..-0.0010] | it/evals=594/2658 eff=30.2521% N=97 Z=-42.4(0.00%) | Like=-30.37..-0.00 [-3087.5635..-0.0010] | it/evals=600/2658 eff=31.9328% N=98 Z=-41.4(0.00%) | Like=-29.09..-0.00 [-3087.5635..-0.0010] | it/evals=604/2754 eff=23.9521% N=99 Z=-38.6(0.00%) | Like=-26.63..-0.00 [-3087.5635..-0.0010] | it/evals=612/2754 eff=25.7485% N=102 Z=-37.3(0.00%) | Like=-25.48..-0.00 [-3087.5635..-0.0010] | it/evals=618/2754 eff=27.2455% N=102 Z=-35.9(0.00%) | Like=-22.87..-0.00 [-3087.5635..-0.0010] | it/evals=624/2754 eff=28.7425% N=104 Z=-34.4(0.00%) | Like=-21.99..-0.00 [-3087.5635..-0.0010] | it/evals=627/2754 eff=29.3413% N=105 Z=-33.6(0.00%) | Like=-21.32..-0.00 [-3087.5635..-0.0010] | it/evals=630/2754 eff=30.2395% N=107 Z=-32.4(0.00%) | Like=-20.26..-0.00 [-3087.5635..-0.0010] | it/evals=636/2754 eff=32.0359% N=111 Z=-31.2(0.00%) | Like=-18.95..-0.00 [-3087.5635..-0.0010] | it/evals=642/2754 eff=33.8323% N=115 Z=-29.1(0.00%) | Like=-17.05..-0.00 [-3087.5635..-0.0010] | it/evals=653/2754 eff=36.5269% N=118 Z=-29.0(0.00%) | Like=-16.99..-0.00 [-3087.5635..-0.0010] | it/evals=654/2769 eff=35.2436% N=118 Z=-28.2(0.00%) | Like=-16.38..-0.00 [-3087.5635..-0.0010] | it/evals=660/2769 eff=36.6762% N=120 Z=-27.7(0.00%) | Like=-16.08..-0.00 [-3087.5635..-0.0010] | it/evals=666/2777 eff=37.5350% N=125 Z=-26.6(0.00%) | Like=-14.46..-0.00 [-3087.5635..-0.0010] | it/evals=678/2796 eff=37.7660% N=128 Z=-25.9(0.00%) | Like=-13.81..-0.00 [-3087.5635..-0.0010] | it/evals=684/2814 eff=37.3096% N=128 Z=-25.2(0.00%) | Like=-12.93..-0.00 [-3087.5635..-0.0010] | it/evals=690/2814 eff=38.3249% N=128 Z=-24.6(0.00%) | Like=-12.52..-0.00 [-3087.5635..-0.0010] | it/evals=696/2833 eff=37.7724% N=128 Z=-24.1(0.00%) | Like=-11.62..-0.00 [-3087.5635..-0.0010] | it/evals=702/2833 eff=38.7409% N=128 Z=-23.4(0.00%) | Like=-11.40..-0.00 [-3087.5635..-0.0010] | it/evals=708/2833 eff=39.7094% N=128 Z=-23.0(0.00%) | Like=-11.03..-0.00 [-3087.5635..-0.0010] | it/evals=713/2833 eff=39.9516% N=128 Z=-22.9(0.00%) | Like=-10.95..-0.00 [-3087.5635..-0.0010] | it/evals=714/2833 eff=40.1937% N=128 Z=-22.4(0.00%) | Like=-10.52..-0.00 [-3087.5635..-0.0010] | it/evals=720/2833 eff=40.6780% N=128 Z=-20.5(0.02%) | Like=-8.37..-0.00 [-3087.5635..-0.0010] | it/evals=745/2961 eff=32.5323% N=128 Z=-19.4(0.07%) | Like=-7.29..-0.00 [-3087.5635..-0.0010] | it/evals=762/2961 eff=34.0111% N=128 Z=-18.6(0.15%) | Like=-6.34..-0.00 [-3087.5635..-0.0010] | it/evals=774/2961 eff=35.1201% N=128 Z=-17.5(0.43%) | Like=-5.33..-0.00 [-3087.5635..-0.0010] | it/evals=792/2961 eff=36.7837% N=128 Z=-16.8(0.85%) | Like=-4.75..-0.00 [-3087.5635..-0.0010] | it/evals=807/2961 eff=38.0776% N=128 Z=-16.7(0.95%) | Like=-4.62..-0.00 [-3087.5635..-0.0010] | it/evals=810/2961 eff=38.6322% N=128 Z=-16.0(1.83%) | Like=-4.03..-0.00 [-3087.5635..-0.0010] | it/evals=828/2961 eff=40.2957% N=128 Z=-15.8(2.24%) | Like=-3.87..-0.00 [-3087.5635..-0.0010] | it/evals=834/2961 eff=41.0351% N=128 Z=-15.8(2.38%) | Like=-3.83..-0.00 [-3087.5635..-0.0010] | it/evals=836/2976 eff=40.2878% N=128 Z=-15.7(2.66%) | Like=-3.70..-0.00 [-3087.5635..-0.0010] | it/evals=840/2976 eff=41.0072% N=128 Z=-15.3(3.70%) | Like=-3.50..-0.00 [-3087.5635..-0.0010] | it/evals=852/3103 eff=34.2606% N=128 Z=-15.2(4.24%) | Like=-3.30..-0.00 [-3087.5635..-0.0010] | it/evals=858/3103 eff=34.8463% N=128 Z=-15.1(4.66%) | Like=-3.22..-0.00 [-3087.5635..-0.0010] | it/evals=864/3103 eff=35.1391% N=128 Z=-14.7(6.69%) | Like=-2.88..-0.00 [-3087.5635..-0.0010] | it/evals=882/3103 eff=36.0176% N=128 Z=-14.6(7.54%) | Like=-2.84..-0.00 [-3087.5635..-0.0010] | it/evals=888/3103 eff=36.8960% N=128 Z=-14.5(8.42%) | Like=-2.60..-0.00 [-3087.5635..-0.0010] | it/evals=895/3103 eff=37.6281% N=128 Z=-14.3(10.33%) | Like=-2.42..-0.00 [-3087.5635..-0.0010] | it/evals=906/3103 eff=38.5066% N=128 Z=-14.2(11.50%) | Like=-2.31..-0.00 [-3087.5635..-0.0010] | it/evals=912/3103 eff=39.2387% N=128 Z=-14.0(14.05%) | Like=-2.12..-0.00 [-3087.5635..-0.0010] | it/evals=925/3103 eff=39.9707% N=128 Z=-13.9(14.97%) | Like=-2.00..-0.00 [-3087.5635..-0.0010] | it/evals=930/3103 eff=40.5564% N=128 Z=-13.6(20.56%) | Like=-1.68..-0.00 [-3087.5635..-0.0010] | it/evals=954/3208 eff=36.8020% N=128 Z=-13.6(22.03%) | Like=-1.63..-0.00 [-3087.5635..-0.0010] | it/evals=960/3208 eff=36.9289% N=128 Z=-13.5(23.67%) | Like=-1.57..-0.00 [-3087.5635..-0.0010] | it/evals=966/3208 eff=37.4365% N=128 Z=-13.3(28.41%) | Like=-1.37..-0.00 [-3087.5635..-0.0010] | it/evals=985/3208 eff=38.7056% N=128 Z=-13.3(29.85%) | Like=-1.30..-0.00 [-3087.5635..-0.0010] | it/evals=990/3208 eff=39.0863% N=128 Z=-13.1(34.83%) | Like=-1.18..-0.00 [-3087.5635..-0.0010] | it/evals=1008/3208 eff=40.6091% N=128 Z=-13.1(36.28%) | Like=-1.10..-0.00 [-3087.5635..-0.0010] | it/evals=1014/3208 eff=41.1168% N=128 Z=-13.0(39.44%) | Like=-0.99..-0.00 [-3087.5635..-0.0010] | it/evals=1026/3208 eff=41.6244% N=128 Z=-12.9(40.89%) | Like=-0.96..-0.00 [-3087.5635..-0.0010] | it/evals=1032/3208 eff=42.0051% N=128 Z=-12.9(43.76%) | Like=-0.89..-0.00 [-3087.5635..-0.0010] | it/evals=1043/3311 eff=37.8227% N=128 Z=-12.9(44.03%) | Like=-0.87..-0.00 [-3087.5635..-0.0010] | it/evals=1044/3311 eff=37.9349% N=128 Z=-12.8(47.36%) | Like=-0.76..-0.00 [-3087.5635..-0.0010] | it/evals=1056/3311 eff=38.3838% N=128 Z=-12.8(49.14%) | Like=-0.73..-0.00 [-3087.5635..-0.0010] | it/evals=1062/3311 eff=38.7205% N=128 Z=-12.7(50.79%) | Like=-0.70..-0.00 [-3087.5635..-0.0010] | it/evals=1068/3311 eff=39.2817% N=128 Z=-12.7(51.90%) | Like=-0.68..-0.00 [-3087.5635..-0.0010] | it/evals=1072/3311 eff=39.5062% N=128 Z=-12.6(56.97%) | Like=-0.58..-0.00 [-3087.5635..-0.0010] | it/evals=1092/3311 eff=40.6285% N=128 Z=-12.6(58.40%) | Like=-0.56..-0.00 [-3087.5635..-0.0010] | it/evals=1098/3311 eff=40.9652% N=128 Z=-12.6(59.96%) | Like=-0.54..-0.00 [-3087.5635..-0.0010] | it/evals=1104/3311 eff=41.5264% N=128 Z=-12.5(62.72%) | Like=-0.49..-0.00 [-3087.5635..-0.0010] | it/evals=1116/3311 eff=42.4242% N=128 Z=-12.5(65.48%) | Like=-0.45..-0.00 [-3087.5635..-0.0010] | it/evals=1128/3439 eff=37.4877% N=128 Z=-12.5(66.58%) | Like=-0.44..-0.00 [-3087.5635..-0.0010] | it/evals=1133/3439 eff=37.5859% N=128 Z=-12.4(69.28%) | Like=-0.41..-0.00 [-3087.5635..-0.0010] | it/evals=1146/3439 eff=38.2728% N=128 Z=-12.4(70.48%) | Like=-0.39..-0.00 [-3087.5635..-0.0010] | it/evals=1152/3439 eff=38.5672% N=128 Z=-12.4(71.62%) | Like=-0.37..-0.00 [-3087.5635..-0.0010] | it/evals=1158/3439 eff=38.9598% N=128 Z=-12.4(72.32%) | Like=-0.36..-0.00 [-3087.5635..-0.0010] | it/evals=1162/3439 eff=39.2542% N=128 Z=-12.4(72.69%) | Like=-0.36..-0.00 [-3087.5635..-0.0010] | it/evals=1164/3439 eff=39.4504% N=128 Z=-12.4(73.80%) | Like=-0.35..-0.00 [-3087.5635..-0.0010] | it/evals=1170/3439 eff=39.6467% N=128 Z=-12.3(74.86%) | Like=-0.34..-0.00 [-3087.5635..-0.0010] | it/evals=1176/3439 eff=40.1374% N=128 Z=-12.3(77.23%) | Like=-0.31..-0.00 [-3087.5635..-0.0010] | it/evals=1191/3439 eff=40.8243% N=128 Z=-12.3(79.47%) | Like=-0.28..-0.00 [-3087.5635..-0.0010] | it/evals=1206/3439 eff=41.9038% N=128 Z=-12.3(80.31%) | Like=-0.27..-0.00 [-3087.5635..-0.0010] | it/evals=1212/3439 eff=42.2964% N=128 Z=-12.3(81.11%) | Like=-0.26..-0.00 [-3087.5635..-0.0010] | it/evals=1218/3448 eff=42.4125% N=128 Z=-12.3(81.90%) | Like=-0.25..-0.00 [-3087.5635..-0.0010] | it/evals=1224/3460 eff=42.4038% N=128 Z=-12.2(82.65%) | Like=-0.24..-0.00 [-3087.5635..-0.0010] | it/evals=1230/3460 eff=42.6923% N=128 Z=-12.2(84.00%) | Like=-0.21..-0.00 [-3087.5635..-0.0010] | it/evals=1242/3472 eff=42.8707% N=128 Z=-12.2(84.65%) | Like=-0.20..-0.00 [-3087.5635..-0.0010] | it/evals=1248/3481 eff=42.9783% N=128 Z=-12.2(84.87%) | Like=-0.20..-0.00 [-3087.5635..-0.0010] | it/evals=1250/3481 eff=43.0726% N=128 Z=-12.2(85.28%) | Like=-0.19..-0.00 [-3087.5635..-0.0010] | it/evals=1254/3496 eff=42.8439% N=128 Z=-12.2(86.52%) | Like=-0.17..-0.00 [-3087.5635..-0.0010] | it/evals=1266/3496 eff=43.3086% N=128 Z=-12.2(87.07%) | Like=-0.17..-0.00 [-3087.5635..-0.0010] | it/evals=1272/3509 eff=43.2507% N=128 Z=-12.2(88.16%) | Like=-0.16..-0.00 [-3087.5635..-0.0010] | it/evals=1284/3526 eff=43.1284% N=128 Z=-12.2(89.16%) | Like=-0.15..-0.00 [-3087.5635..-0.0010] | it/evals=1296/3526 eff=43.6709% N=128 Z=-12.2(89.63%) | Like=-0.14..-0.00 [-3087.5635..-0.0010] | it/evals=1302/3539 eff=43.3423% N=128 Z=-12.2(90.07%) | Like=-0.14..-0.00 [-3087.5635..-0.0010] | it/evals=1308/3539 eff=43.6997% N=128 Z=-12.2(90.49%) | Like=-0.13..-0.00 [-3087.5635..-0.0010] | it/evals=1314/3539 eff=43.7891% N=128 Z=-12.1(90.91%) | Like=-0.12..-0.00 [-3087.5635..-0.0010] | it/evals=1320/3551 eff=43.6782% N=128 Z=-12.1(91.30%) | Like=-0.12..-0.00 [-3087.5635..-0.0010] | it/evals=1326/3551 eff=44.2087% N=128 Z=-12.1(91.68%) | Like=-0.12..-0.00 [-3087.5635..-0.0010] | it/evals=1332/3566 eff=43.9791% N=128 Z=-12.1(92.04%) | Like=-0.11..-0.00 [-3087.5635..-0.0010] | it/evals=1338/3566 eff=44.3281% N=128 Z=-12.1(92.60%) | Like=-0.11..-0.00 [-3087.5635..-0.0010] | it/evals=1348/3566 eff=44.5899% N=128 Z=-12.1(93.03%) | Like=-0.10..-0.00 [-3087.5635..-0.0010] | it/evals=1356/3691 eff=40.5193% N=128 Z=-12.1(94.04%) | Like=-0.08..-0.00 [-3087.5635..-0.0010] | it/evals=1377/3691 eff=41.1487% N=128 Z=-12.1(95.14%) | Like=-0.07..-0.00 [-3087.5635..-0.0010] | it/evals=1404/3691 eff=42.0928% N=128 Z=-12.1(95.21%) | Like=-0.07..-0.00 [-3087.5635..-0.0010] | it/evals=1406/3691 eff=42.1715% N=128 Z=-12.1(95.36%) | Like=-0.07..-0.00 [-3087.5635..-0.0010] | it/evals=1410/3691 eff=42.3289% N=128 Z=-12.1(95.76%) | Like=-0.06..-0.00 [-3087.5635..-0.0010] | it/evals=1422/3691 eff=42.7223% N=128 Z=-12.1(95.95%) | Like=-0.06..-0.00 [-3087.5635..-0.0010] | it/evals=1428/3691 eff=43.0370% N=128 Z=-12.1(96.19%) | Like=-0.06..-0.00 [-3087.5635..-0.0010] | it/evals=1436/3691 eff=43.1943% N=128 Z=-12.1(96.92%) | Like=-0.04..-0.00 [-3087.5635..-0.0010] | it/evals=1464/3691 eff=44.1385% N=128 Z=-12.1(96.99%) | Like=-0.04..-0.00 [-3087.5635..-0.0010] | it/evals=1467/3691 eff=44.2172% N=128 Z=-12.1(97.19%) | Like=-0.04..-0.00 [-3087.5635..-0.0010] | it/evals=1476/3691 eff=44.4532% N=128 Z=-12.1(97.32%) | Like=-0.04..-0.00 [-3087.5635..-0.0010] | it/evals=1482/3817 eff=40.8017% N=128 Z=-12.1(97.44%) | Like=-0.04..-0.00 [-3087.5635..-0.0010] | it/evals=1488/3817 eff=41.0880% N=128 Z=-12.1(97.55%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1494/3817 eff=41.4460% N=128 Z=-12.1(97.59%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1496/3817 eff=41.5891% N=128 Z=-12.1(97.66%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1500/3817 eff=41.8039% N=128 Z=-12.1(98.06%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1524/3817 eff=42.3765% N=128 Z=-12.1(98.12%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1528/3817 eff=42.4481% N=128 Z=-12.1(98.14%) | Like=-0.03..-0.00 [-3087.5635..-0.0010] | it/evals=1530/3817 eff=42.5197% N=128 Z=-12.1(98.31%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1542/3817 eff=42.8776% N=128 Z=-12.1(98.39%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1548/3817 eff=43.0923% N=128 Z=-12.1(98.46%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1554/3817 eff=43.2355% N=128 Z=-12.1(98.53%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1560/3817 eff=43.4503% N=128 Z=-12.1(98.60%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1566/3817 eff=43.5934% N=128 Z=-12.1(98.72%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1578/3817 eff=43.8797% N=128 Z=-12.1(98.81%) | Like=-0.02..-0.00 [-3087.5635..-0.0010] | it/evals=1588/3817 eff=44.0229% N=128 Z=-12.1(99.03%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1614/3932 eff=41.6667% N=128 Z=-12.1(99.05%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1617/3932 eff=41.7328% N=128 Z=-12.1(99.08%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1620/3932 eff=41.8651% N=128 Z=-12.1(99.16%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1632/3932 eff=42.2619% N=128 Z=-12.1(99.20%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1638/3932 eff=42.6587% N=128 Z=-12.1(99.23%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1644/3932 eff=43.0556% N=128 Z=-12.1(99.24%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1646/3932 eff=43.1878% N=128 Z=-12.1(99.27%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1650/3932 eff=43.4524% N=128 Z=-12.1(99.30%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1656/3932 eff=43.8492% N=128 Z=-12.1(99.33%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1662/3932 eff=44.2460% N=128 Z=-12.1(99.36%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1668/3932 eff=44.6429% N=128 Z=-12.1(99.39%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1674/3942 eff=44.7438% N=128 Z=-12.1(99.42%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1680/3962 eff=44.5525% N=128 Z=-12.1(99.45%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1686/3962 eff=44.9416% N=128 Z=-12.1(99.47%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1692/3978 eff=44.8652% N=128 Z=-12.1(99.50%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1698/3992 eff=44.8473% N=128 Z=-12.1(99.52%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1704/3992 eff=45.2290% N=128 Z=-12.1(99.54%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1710/4120 eff=42.1765% N=128 Z=-12.1(99.56%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1716/4120 eff=42.5294% N=128 Z=-12.1(99.58%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1722/4120 eff=42.8824% N=128 Z=-12.1(99.60%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1728/4120 eff=43.2353% N=128 Z=-12.1(99.62%) | Like=-0.01..-0.00 [-3087.5635..-0.0010] | it/evals=1734/4120 eff=43.5882% N=128 Z=-12.1(99.64%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1740/4120 eff=43.9412% N=128 Z=-12.1(99.65%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1746/4120 eff=44.2941% N=128 Z=-12.1(99.67%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1752/4120 eff=44.6471% N=128 Z=-12.1(99.68%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1758/4120 eff=45.0000% N=128 Z=-12.1(99.69%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1762/4120 eff=45.2353% N=128 Z=-12.1(99.70%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1764/4120 eff=45.3529% N=128 Z=-12.1(99.71%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1770/4120 eff=45.7059% N=128 Z=-12.1(99.73%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1776/4224 eff=43.4035% N=128 Z=-12.1(99.74%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1782/4224 eff=43.7361% N=128 Z=-12.1(99.75%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1788/4224 eff=44.0687% N=128 Z=-12.1(99.76%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1794/4224 eff=44.4013% N=128 Z=-12.1(99.77%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1800/4224 eff=44.7339% N=128 Z=-12.1(99.78%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1806/4224 eff=45.0665% N=128 Z=-12.1(99.79%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1812/4224 eff=45.3991% N=128 Z=-12.1(99.80%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1818/4224 eff=45.7317% N=128 Z=-12.1(99.81%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1820/4224 eff=45.8426% N=128 Z=-12.1(99.81%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1824/4224 eff=46.0643% N=128 Z=-12.1(99.82%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1830/4224 eff=46.3969% N=128 Z=-12.1(99.83%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1836/4329 eff=44.1592% N=128 Z=-12.1(99.84%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1842/4329 eff=44.4735% N=128 Z=-12.1(99.84%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1848/4329 eff=44.7878% N=128 Z=-12.1(99.84%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1849/4329 eff=44.8402% N=128 Z=-12.1(99.85%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1854/4329 eff=45.1021% N=128 Z=-12.1(99.86%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1860/4329 eff=45.4164% N=128 Z=-12.1(99.86%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1866/4329 eff=45.7307% N=128 Z=-12.1(99.87%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1872/4329 eff=46.0450% N=128 Z=-12.1(99.88%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1878/4329 eff=46.3594% N=128 Z=-12.1(99.88%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1884/4329 eff=46.6737% N=128 Z=-12.1(99.89%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1890/4342 eff=46.6701% N=128 Z=-12.1(99.89%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1896/4342 eff=46.9823% N=128 Z=-12.1(99.90%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1902/4353 eff=47.0253% N=128 Z=-12.1(99.90%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1907/4364 eff=47.0165% N=128 Z=-12.1(99.90%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1908/4364 eff=47.0679% N=128 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1914/4379 eff=47.0138% N=128 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1920/4379 eff=47.3201% N=128 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1926/4379 eff=47.6263% N=128 Z=-12.1(99.92%) | Like=-0.00..-0.00 [-3087.5635..-0.0010] | it/evals=1932/4395 eff=47.5443% N=128 [ultranest] Explored until L=-2e-05 [ultranest] Likelihood function evaluations: 4395 [ultranest] logZ = -12.03 +- 0.318 [ultranest] Effective samples strategy wants to improve: -9.70..-0.00 (ESS = 527.7, need >10000) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.16 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 31 minimum live points (dlogz from 0.26 to 0.86, need <0.5) [ultranest] logZ error budget: single: 0.29 bs:0.32 tail:0.00 total:0.32 required:<0.50 [ultranest] Widening from 80 to 256 live points before L=-1e+01... [ultranest] Will add 176 live points (x1) at L=-3e+03 ... [ultranest] Exploring (in particular: L=-2898.15..-0.00) ... Z=-2927.1(0.00%) | Like=-2898.15..-0.00 [-2898.1460..-0.0010] | it/evals=261/4398 eff=0.0000% N=65 Z=-1716.6(0.00%) | Like=-1651.25..-0.00 [-2898.1460..-0.0010] | it/evals=302/4398 eff=33.3333% N=67 Z=-1344.1(0.00%) | Like=-1325.21..-0.00 [-2898.1460..-0.0010] | it/evals=318/4403 eff=37.5000% N=69 Z=-715.6(0.00%) | Like=-705.57..-0.00 [-2898.1460..-0.0010] | it/evals=352/4404 eff=44.4444% N=69 Z=-427.5(0.00%) | Like=-416.12..-0.00 [-2898.1460..-0.0010] | it/evals=394/4405 eff=50.0000% N=72 Z=-316.3(0.00%) | Like=-304.90..-0.00 [-2898.1460..-0.0010] | it/evals=422/4526 eff=5.3435% N=73 Z=-234.2(0.00%) | Like=-222.88..-0.00 [-2898.1460..-0.0010] | it/evals=447/4526 eff=6.1069% N=74 Z=-133.2(0.00%) | Like=-121.24..-0.00 [-2898.1460..-0.0010] | it/evals=501/4526 eff=10.6870% N=85 Z=-78.4(0.00%) | Like=-64.58..-0.00 [-2898.1460..-0.0010] | it/evals=550/4526 eff=18.3206% N=95 Z=-76.1(0.00%) | Like=-64.08..-0.00 [-2898.1460..-0.0010] | it/evals=552/4526 eff=19.8473% N=97 Z=-63.7(0.00%) | Like=-51.73..-0.00 [-2898.1460..-0.0010] | it/evals=573/4537 eff=23.2394% N=101 Z=-62.5(0.00%) | Like=-50.86..-0.00 [-2898.1460..-0.0010] | it/evals=576/4537 eff=25.3521% N=103 Z=-60.3(0.00%) | Like=-47.54..-0.00 [-2898.1460..-0.0010] | it/evals=582/4663 eff=14.1791% N=105 Z=-53.2(0.00%) | Like=-41.14..-0.00 [-2898.1460..-0.0010] | it/evals=594/4663 eff=15.6716% N=109 Z=-52.2(0.00%) | Like=-40.24..-0.00 [-2898.1460..-0.0010] | it/evals=597/4663 eff=16.4179% N=111 Z=-49.8(0.00%) | Like=-37.18..-0.00 [-2898.1460..-0.0010] | it/evals=606/4663 eff=17.9104% N=117 Z=-44.6(0.00%) | Like=-31.78..-0.00 [-2898.1460..-0.0010] | it/evals=624/4663 eff=20.8955% N=123 Z=-42.8(0.00%) | Like=-30.52..-0.00 [-2898.1460..-0.0010] | it/evals=630/4663 eff=21.6418% N=125 Z=-37.3(0.00%) | Like=-25.30..-0.00 [-2898.1460..-0.0010] | it/evals=653/4663 eff=23.1343% N=131 Z=-35.8(0.00%) | Like=-23.24..-0.00 [-2898.1460..-0.0010] | it/evals=660/4663 eff=24.2537% N=133 Z=-33.3(0.00%) | Like=-21.08..-0.00 [-2898.1460..-0.0010] | it/evals=672/4663 eff=26.8657% N=139 Z=-31.7(0.00%) | Like=-19.62..-0.00 [-2898.1460..-0.0010] | it/evals=684/4663 eff=30.5970% N=150 Z=-30.9(0.00%) | Like=-18.66..-0.00 [-2898.1460..-0.0010] | it/evals=690/4663 eff=32.0896% N=154 Z=-30.1(0.00%) | Like=-17.92..-0.00 [-2898.1460..-0.0010] | it/evals=696/4772 eff=23.6074% N=157 Z=-29.3(0.00%) | Like=-17.27..-0.00 [-2898.1460..-0.0010] | it/evals=702/4772 eff=24.1379% N=159 Z=-28.8(0.00%) | Like=-16.91..-0.00 [-2898.1460..-0.0010] | it/evals=708/4772 eff=25.1989% N=162 Z=-28.3(0.00%) | Like=-16.38..-0.00 [-2898.1460..-0.0010] | it/evals=714/4772 eff=25.9947% N=164 Z=-27.8(0.00%) | Like=-16.14..-0.00 [-2898.1460..-0.0010] | it/evals=720/4772 eff=27.3210% N=172 Z=-27.4(0.00%) | Like=-15.68..-0.00 [-2898.1460..-0.0010] | it/evals=726/4772 eff=28.3820% N=176 Z=-27.1(0.00%) | Like=-15.32..-0.00 [-2898.1460..-0.0010] | it/evals=732/4772 eff=29.4430% N=179 Z=-26.7(0.00%) | Like=-15.03..-0.00 [-2898.1460..-0.0010] | it/evals=738/4772 eff=30.5040% N=180 Z=-26.4(0.00%) | Like=-14.34..-0.00 [-2898.1460..-0.0010] | it/evals=744/4772 eff=31.2997% N=181 Z=-26.0(0.00%) | Like=-13.97..-0.00 [-2898.1460..-0.0010] | it/evals=750/4772 eff=32.0955% N=182 Z=-25.5(0.00%) | Like=-13.32..-0.00 [-2898.1460..-0.0010] | it/evals=756/4772 eff=33.1565% N=185 Z=-25.1(0.00%) | Like=-12.93..-0.00 [-2898.1460..-0.0010] | it/evals=762/4772 eff=34.2175% N=185 Z=-24.7(0.00%) | Like=-12.72..-0.00 [-2898.1460..-0.0010] | it/evals=768/4772 eff=35.0133% N=187 Z=-24.3(0.00%) | Like=-12.36..-0.00 [-2898.1460..-0.0010] | it/evals=774/4772 eff=35.8090% N=189 Z=-24.0(0.00%) | Like=-11.78..-0.00 [-2898.1460..-0.0010] | it/evals=780/4772 eff=37.1353% N=190 Z=-23.3(0.00%) | Like=-11.13..-0.00 [-2898.1460..-0.0010] | it/evals=792/4772 eff=38.9920% N=194 Z=-22.6(0.00%) | Like=-10.55..-0.00 [-2898.1460..-0.0010] | it/evals=805/4772 eff=41.3793% N=198 Z=-22.4(0.00%) | Like=-10.39..-0.00 [-2898.1460..-0.0010] | it/evals=810/4889 eff=32.5911% N=201 Z=-22.1(0.00%) | Like=-9.89..-0.00 [-2898.1460..-0.0010] | it/evals=816/4889 eff=33.4008% N=203 Z=-21.8(0.01%) | Like=-9.56..-0.00 [-2898.1460..-0.0010] | it/evals=822/4889 eff=34.0081% N=204 Z=-20.3(0.03%) | Like=-8.00..-0.00 [-2898.1460..-0.0010] | it/evals=851/4889 eff=35.8300% N=204 Z=-20.3(0.03%) | Like=-7.96..-0.00 [-2898.1460..-0.0010] | it/evals=852/4889 eff=36.0324% N=204 Z=-19.7(0.05%) | Like=-7.63..-0.00 [-2898.1460..-0.0010] | it/evals=864/4889 eff=37.2470% N=204 Z=-19.3(0.07%) | Like=-7.25..-0.00 [-2898.1460..-0.0010] | it/evals=876/4889 eff=38.2591% N=204 Z=-19.1(0.09%) | Like=-6.85..-0.00 [-2898.1460..-0.0010] | it/evals=882/4889 eff=38.8664% N=204 Z=-18.5(0.17%) | Like=-6.10..-0.00 [-2898.1460..-0.0010] | it/evals=897/4889 eff=39.6761% N=204 Z=-17.7(0.36%) | Like=-5.50..-0.00 [-2898.1460..-0.0010] | it/evals=918/4889 eff=41.4980% N=204 Z=-17.1(0.65%) | Like=-4.90..-0.00 [-2898.1460..-0.0010] | it/evals=936/4889 eff=42.3077% N=204 Z=-17.0(0.76%) | Like=-4.82..-0.00 [-2898.1460..-0.0010] | it/evals=942/4889 eff=43.1174% N=204 Z=-16.9(0.83%) | Like=-4.77..-0.00 [-2898.1460..-0.0010] | it/evals=945/4889 eff=43.3198% N=204 Z=-16.7(1.05%) | Like=-4.51..-0.00 [-2898.1460..-0.0010] | it/evals=954/4889 eff=44.1296% N=204 Z=-16.5(1.18%) | Like=-4.41..-0.00 [-2898.1460..-0.0010] | it/evals=960/5005 eff=35.9016% N=204 Z=-15.8(2.41%) | Like=-3.77..-0.00 [-2898.1460..-0.0010] | it/evals=992/5005 eff=37.3770% N=204 Z=-15.7(2.59%) | Like=-3.70..-0.00 [-2898.1460..-0.0010] | it/evals=996/5005 eff=37.8689% N=204 Z=-15.6(2.92%) | Like=-3.65..-0.00 [-2898.1460..-0.0010] | it/evals=1002/5005 eff=38.1967% N=204 Z=-15.5(3.22%) | Like=-3.58..-0.00 [-2898.1460..-0.0010] | it/evals=1008/5005 eff=38.8525% N=204 Z=-15.3(3.85%) | Like=-3.33..-0.00 [-2898.1460..-0.0010] | it/evals=1020/5005 eff=39.0164% N=204 Z=-15.0(5.28%) | Like=-3.07..-0.00 [-2898.1460..-0.0010] | it/evals=1042/5005 eff=40.1639% N=204 Z=-15.0(5.44%) | Like=-3.04..-0.00 [-2898.1460..-0.0010] | it/evals=1044/5005 eff=40.3279% N=204 Z=-14.6(7.51%) | Like=-2.76..-0.00 [-2898.1460..-0.0010] | it/evals=1074/5005 eff=42.4590% N=204 Z=-14.5(8.88%) | Like=-2.50..-0.00 [-2898.1460..-0.0010] | it/evals=1089/5005 eff=43.2787% N=204 Z=-14.4(9.17%) | Like=-2.49..-0.00 [-2898.1460..-0.0010] | it/evals=1092/5005 eff=43.6066% N=204 Z=-14.2(11.95%) | Like=-2.19..-0.00 [-2898.1460..-0.0010] | it/evals=1116/5005 eff=44.5902% N=204 Z=-14.1(13.39%) | Like=-2.08..-0.00 [-2898.1460..-0.0010] | it/evals=1128/5005 eff=45.2459% N=204 Z=-14.0(14.47%) | Like=-1.97..-0.00 [-2898.1460..-0.0010] | it/evals=1136/5128 eff=38.0628% N=204 Z=-13.7(20.49%) | Like=-1.62..-0.00 [-2898.1460..-0.0010] | it/evals=1176/5128 eff=39.2906% N=204 Z=-13.6(21.91%) | Like=-1.53..-0.00 [-2898.1460..-0.0010] | it/evals=1185/5128 eff=39.6999% N=204 Z=-13.6(22.43%) | Like=-1.51..-0.00 [-2898.1460..-0.0010] | it/evals=1188/5128 eff=39.9727% N=204 Z=-13.5(24.39%) | Like=-1.43..-0.00 [-2898.1460..-0.0010] | it/evals=1200/5128 eff=40.5184% N=204 Z=-13.3(28.76%) | Like=-1.30..-0.00 [-2898.1460..-0.0010] | it/evals=1224/5128 eff=42.2920% N=204 Z=-13.3(29.84%) | Like=-1.27..-0.00 [-2898.1460..-0.0010] | it/evals=1230/5128 eff=42.7012% N=204 Z=-13.2(32.99%) | Like=-1.21..-0.00 [-2898.1460..-0.0010] | it/evals=1248/5128 eff=43.5198% N=204 Z=-13.1(36.04%) | Like=-1.10..-0.00 [-2898.1460..-0.0010] | it/evals=1266/5255 eff=38.1395% N=204 Z=-13.1(37.10%) | Like=-1.06..-0.00 [-2898.1460..-0.0010] | it/evals=1272/5255 eff=38.3721% N=204 Z=-13.0(38.19%) | Like=-1.04..-0.00 [-2898.1460..-0.0010] | it/evals=1278/5255 eff=38.8372% N=204 Z=-13.0(40.02%) | Like=-0.98..-0.00 [-2898.1460..-0.0010] | it/evals=1290/5255 eff=39.4186% N=204 Z=-13.0(41.01%) | Like=-0.96..-0.00 [-2898.1460..-0.0010] | it/evals=1296/5255 eff=39.7674% N=204 Z=-12.9(42.01%) | Like=-0.94..-0.00 [-2898.1460..-0.0010] | it/evals=1302/5255 eff=39.8837% N=204 Z=-12.8(45.93%) | Like=-0.83..-0.00 [-2898.1460..-0.0010] | it/evals=1326/5255 eff=40.8140% N=204 Z=-12.8(46.05%) | Like=-0.82..-0.00 [-2898.1460..-0.0010] | it/evals=1327/5255 eff=40.9302% N=204 Z=-12.8(49.28%) | Like=-0.73..-0.00 [-2898.1460..-0.0010] | it/evals=1344/5255 eff=41.6279% N=204 Z=-12.7(51.39%) | Like=-0.70..-0.00 [-2898.1460..-0.0010] | it/evals=1356/5255 eff=42.2093% N=204 Z=-12.7(54.47%) | Like=-0.65..-0.00 [-2898.1460..-0.0010] | it/evals=1374/5255 eff=42.9070% N=204 Z=-12.7(55.30%) | Like=-0.64..-0.00 [-2898.1460..-0.0010] | it/evals=1380/5255 eff=43.3721% N=204 Z=-12.7(56.33%) | Like=-0.62..-0.00 [-2898.1460..-0.0010] | it/evals=1386/5255 eff=43.6047% N=204 Z=-12.6(57.26%) | Like=-0.60..-0.00 [-2898.1460..-0.0010] | it/evals=1392/5255 eff=44.0698% N=204 Z=-12.6(58.16%) | Like=-0.58..-0.00 [-2898.1460..-0.0010] | it/evals=1398/5255 eff=44.1860% N=204 Z=-12.6(61.45%) | Like=-0.53..-0.00 [-2898.1460..-0.0010] | it/evals=1420/5383 eff=39.1700% N=204 Z=-12.6(61.77%) | Like=-0.52..-0.00 [-2898.1460..-0.0010] | it/evals=1422/5383 eff=39.2713% N=204 Z=-12.5(63.47%) | Like=-0.49..-0.00 [-2898.1460..-0.0010] | it/evals=1434/5383 eff=39.7773% N=204 Z=-12.5(68.75%) | Like=-0.42..-0.00 [-2898.1460..-0.0010] | it/evals=1473/5383 eff=40.8907% N=204 Z=-12.5(69.10%) | Like=-0.41..-0.00 [-2898.1460..-0.0010] | it/evals=1476/5383 eff=41.0931% N=204 Z=-12.4(69.82%) | Like=-0.40..-0.00 [-2898.1460..-0.0010] | it/evals=1482/5383 eff=41.2955% N=204 Z=-12.4(73.34%) | Like=-0.34..-0.00 [-2898.1460..-0.0010] | it/evals=1512/5383 eff=42.1053% N=204 Z=-12.4(74.25%) | Like=-0.33..-0.00 [-2898.1460..-0.0010] | it/evals=1520/5383 eff=42.3077% N=204 Z=-12.3(77.68%) | Like=-0.29..-0.00 [-2898.1460..-0.0010] | it/evals=1554/5383 eff=43.5223% N=204 Z=-12.3(78.77%) | Like=-0.27..-0.00 [-2898.1460..-0.0010] | it/evals=1566/5383 eff=43.7247% N=204 Z=-12.3(80.40%) | Like=-0.26..-0.00 [-2898.1460..-0.0010] | it/evals=1584/5383 eff=44.6356% N=204 Z=-12.3(80.92%) | Like=-0.25..-0.00 [-2898.1460..-0.0010] | it/evals=1590/5383 eff=44.8381% N=204 Z=-12.3(81.91%) | Like=-0.24..-0.00 [-2898.1460..-0.0010] | it/evals=1602/5397 eff=44.7106% N=204 Z=-12.3(82.81%) | Like=-0.23..-0.00 [-2898.1460..-0.0010] | it/evals=1614/5406 eff=44.8071% N=204 Z=-12.3(84.10%) | Like=-0.21..-0.00 [-2898.1460..-0.0010] | it/evals=1632/5517 eff=40.9982% N=204 Z=-12.2(84.93%) | Like=-0.19..-0.00 [-2898.1460..-0.0010] | it/evals=1644/5517 eff=41.5330% N=204 Z=-12.2(85.33%) | Like=-0.18..-0.00 [-2898.1460..-0.0010] | it/evals=1650/5517 eff=41.9786% N=204 Z=-12.2(86.02%) | Like=-0.18..-0.00 [-2898.1460..-0.0010] | it/evals=1661/5517 eff=42.5134% N=204 Z=-12.2(86.80%) | Like=-0.17..-0.00 [-2898.1460..-0.0010] | it/evals=1674/5517 eff=42.7807% N=204 Z=-12.2(87.84%) | Like=-0.16..-0.00 [-2898.1460..-0.0010] | it/evals=1692/5517 eff=43.2264% N=204 Z=-12.2(88.71%) | Like=-0.15..-0.00 [-2898.1460..-0.0010] | it/evals=1708/5517 eff=43.6720% N=204 Z=-12.2(89.12%) | Like=-0.15..-0.00 [-2898.1460..-0.0010] | it/evals=1716/5517 eff=43.9394% N=204 Z=-12.2(89.40%) | Like=-0.14..-0.00 [-2898.1460..-0.0010] | it/evals=1722/5517 eff=44.1176% N=204 Z=-12.2(89.69%) | Like=-0.14..-0.00 [-2898.1460..-0.0010] | it/evals=1728/5517 eff=44.2959% N=204 Z=-12.2(89.97%) | Like=-0.13..-0.00 [-2898.1460..-0.0010] | it/evals=1734/5517 eff=44.4742% N=204 Z=-12.2(90.51%) | Like=-0.12..-0.00 [-2898.1460..-0.0010] | it/evals=1746/5517 eff=44.7415% N=204 Z=-12.2(90.86%) | Like=-0.12..-0.00 [-2898.1460..-0.0010] | it/evals=1754/5517 eff=45.0089% N=204 Z=-12.2(91.74%) | Like=-0.11..-0.00 [-2898.1460..-0.0010] | it/evals=1776/5517 eff=45.7219% N=204 Z=-12.2(92.60%) | Like=-0.10..-0.00 [-2898.1460..-0.0010] | it/evals=1800/5644 eff=41.7134% N=204 Z=-12.2(93.39%) | Like=-0.09..-0.00 [-2898.1460..-0.0010] | it/evals=1824/5644 eff=42.5140% N=204 Z=-12.2(93.57%) | Like=-0.09..-0.00 [-2898.1460..-0.0010] | it/evals=1830/5644 eff=42.8343% N=204 Z=-12.1(93.75%) | Like=-0.08..-0.00 [-2898.1460..-0.0010] | it/evals=1836/5644 eff=42.9944% N=204 Z=-12.1(94.26%) | Like=-0.08..-0.00 [-2898.1460..-0.0010] | it/evals=1854/5644 eff=43.1545% N=204 Z=-12.1(94.42%) | Like=-0.08..-0.00 [-2898.1460..-0.0010] | it/evals=1860/5644 eff=43.3147% N=204 Z=-12.1(94.88%) | Like=-0.07..-0.00 [-2898.1460..-0.0010] | it/evals=1878/5644 eff=43.7950% N=204 Z=-12.1(95.41%) | Like=-0.06..-0.00 [-2898.1460..-0.0010] | it/evals=1901/5644 eff=44.3555% N=204 Z=-12.1(95.69%) | Like=-0.06..-0.00 [-2898.1460..-0.0010] | it/evals=1914/5644 eff=44.5957% N=204 Z=-12.1(95.93%) | Like=-0.05..-0.00 [-2898.1460..-0.0010] | it/evals=1926/5644 eff=44.9159% N=204 Z=-12.1(96.04%) | Like=-0.05..-0.00 [-2898.1460..-0.0010] | it/evals=1932/5644 eff=45.0761% N=204 Z=-12.1(96.15%) | Like=-0.05..-0.00 [-2898.1460..-0.0010] | it/evals=1938/5644 eff=45.3163% N=204 Z=-12.1(96.26%) | Like=-0.05..-0.00 [-2898.1460..-0.0010] | it/evals=1944/5644 eff=45.5564% N=204 Z=-12.1(96.33%) | Like=-0.05..-0.00 [-2898.1460..-0.0010] | it/evals=1948/5644 eff=45.7966% N=204 Z=-12.1(96.76%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=1974/5760 eff=42.5641% N=204 Z=-12.1(96.94%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=1986/5760 eff=42.9304% N=204 Z=-12.1(97.06%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=1994/5760 eff=43.2234% N=204 Z=-12.1(97.11%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=1998/5760 eff=43.4432% N=204 Z=-12.1(97.20%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2004/5760 eff=43.5897% N=204 Z=-12.1(97.28%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2010/5760 eff=43.8828% N=204 Z=-12.1(97.57%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2034/5760 eff=44.1758% N=204 Z=-12.1(97.65%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2041/5760 eff=44.3223% N=204 Z=-12.1(97.71%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2046/5760 eff=44.4689% N=204 Z=-12.1(97.78%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2052/5760 eff=44.7619% N=204 Z=-12.1(97.84%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2058/5760 eff=45.0549% N=204 Z=-12.1(97.90%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2064/5760 eff=45.2015% N=204 Z=-12.1(98.13%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2088/5760 eff=45.5678% N=204 Z=-12.1(98.18%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2094/5760 eff=45.7875% N=204 Z=-12.1(98.43%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2124/5760 eff=46.4469% N=204 Z=-12.1(98.48%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2130/5880 eff=42.8956% N=204 Z=-12.1(98.53%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2138/5880 eff=43.0976% N=204 Z=-12.1(98.60%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2148/5880 eff=43.3670% N=204 Z=-12.1(98.68%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2160/5880 eff=43.5017% N=204 Z=-12.1(98.72%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2166/5880 eff=43.7710% N=204 Z=-12.1(98.83%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2184/5880 eff=44.3098% N=204 Z=-12.1(98.86%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2190/5880 eff=44.4444% N=204 Z=-12.1(98.93%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2202/5880 eff=44.5791% N=204 Z=-12.1(98.99%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2214/5880 eff=44.7811% N=204 Z=-12.1(99.02%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2220/5880 eff=45.0505% N=204 Z=-12.1(99.04%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2226/5880 eff=45.1852% N=204 Z=-12.1(99.10%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2238/5880 eff=45.5219% N=204 Z=-12.1(99.24%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2274/5916 eff=45.1677% N=204 Z=-12.1(99.25%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2277/5916 eff=45.2334% N=204 Z=-12.1(99.29%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2286/5916 eff=45.2991% N=204 Z=-12.1(99.33%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2298/5916 eff=45.4306% N=204 Z=-12.1(99.38%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2316/6044 eff=42.4500% N=204 Z=-12.1(99.40%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2322/6044 eff=42.6319% N=204 Z=-12.1(99.40%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2323/6044 eff=42.6925% N=204 Z=-12.1(99.44%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2334/6044 eff=42.8745% N=204 Z=-12.1(99.45%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2340/6044 eff=43.0564% N=204 Z=-12.1(99.48%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2352/6044 eff=43.3596% N=204 Z=-12.1(99.50%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2358/6044 eff=43.6022% N=204 Z=-12.1(99.52%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2369/6044 eff=43.7235% N=204 Z=-12.1(99.59%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2400/6044 eff=44.5118% N=204 Z=-12.1(99.62%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2415/6044 eff=44.8150% N=204 Z=-12.1(99.63%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2418/6044 eff=44.9363% N=204 Z=-12.1(99.67%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2442/6044 eff=45.4215% N=204 Z=-12.1(99.69%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2454/6044 eff=45.6640% N=204 Z=-12.1(99.73%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2484/6163 eff=43.1561% N=204 Z=-12.1(99.74%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2496/6163 eff=43.3824% N=204 Z=-12.1(99.76%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2508/6163 eff=43.7783% N=204 Z=-12.1(99.77%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2514/6163 eff=43.8348% N=204 Z=-12.1(99.80%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2544/6163 eff=44.4005% N=204 Z=-12.1(99.81%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2556/6163 eff=44.5701% N=204 Z=-12.1(99.81%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2562/6163 eff=44.7964% N=204 Z=-12.1(99.83%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2580/6163 eff=45.3054% N=204 Z=-12.1(99.84%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2592/6163 eff=45.4186% N=204 Z=-12.1(99.85%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2604/6281 eff=42.8420% N=204 Z=-12.1(99.85%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2610/6281 eff=42.9480% N=204 Z=-12.1(99.86%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2616/6281 eff=43.1601% N=204 Z=-12.1(99.88%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2652/6281 eff=43.9024% N=204 Z=-12.1(99.88%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2658/6281 eff=44.0615% N=204 Z=-12.1(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2664/6281 eff=44.2206% N=204 Z=-12.1(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2670/6281 eff=44.2736% N=204 Z=-12.1(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2676/6281 eff=44.4327% N=204 Z=-12.1(99.90%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2698/6281 eff=44.9099% N=204 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2700/6281 eff=45.0159% N=204 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2706/6281 eff=45.2280% N=204 Z=-12.1(99.91%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=2718/6281 eff=45.5461% N=204 [ultranest] Explored until L=-1e-06 [ultranest] Likelihood function evaluations: 6293 [ultranest] logZ = -12.15 +- 0.2622 [ultranest] Effective samples strategy wants to improve: -9.86..-0.00 (ESS = 830.3, need >10000) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.15 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 20 minimum live points (dlogz from 0.21 to 0.63, need <0.5) [ultranest] logZ error budget: single: 0.23 bs:0.26 tail:0.00 total:0.26 required:<0.50 [ultranest] Widening from 80 to 408 live points before L=-1e+01... [ultranest] Will add 328 live points (x1) at L=-3e+03 ... [ultranest] Exploring (in particular: L=-2898.15..-0.00) ... Z=-2927.1(0.00%) | Like=-2898.15..-0.00 [-2898.1460..-0.0010] | it/evals=261/6295 eff=0.0000% N=65 Z=-2166.6(0.00%) | Like=-2116.36..-0.00 [-2898.1460..-0.0010] | it/evals=288/6295 eff=50.0000% N=68 Z=-1165.1(0.00%) | Like=-1152.31..-0.00 [-2898.1460..-0.0010] | it/evals=323/6299 eff=50.0000% N=71 Z=-583.3(0.00%) | Like=-571.05..-0.00 [-2898.1460..-0.0010] | it/evals=371/6436 eff=4.8951% N=75 Z=-335.6(0.00%) | Like=-318.05..-0.00 [-2898.1460..-0.0010] | it/evals=425/6436 eff=5.5944% N=76 Z=-234.2(0.00%) | Like=-222.88..-0.00 [-2898.1460..-0.0010] | it/evals=453/6436 eff=6.2937% N=77 Z=-147.3(0.00%) | Like=-135.63..-0.00 [-2898.1460..-0.0010] | it/evals=496/6436 eff=9.7902% N=93 Z=-113.0(0.00%) | Like=-99.20..-0.00 [-2898.1460..-0.0010] | it/evals=522/6436 eff=11.1888% N=94 Z=-96.4(0.00%) | Like=-84.40..-0.00 [-2898.1460..-0.0010] | it/evals=534/6436 eff=13.2867% N=94 Z=-88.2(0.00%) | Like=-76.05..-0.00 [-2898.1460..-0.0010] | it/evals=544/6436 eff=14.6853% N=101 Z=-80.9(0.00%) | Like=-69.04..-0.00 [-2898.1460..-0.0010] | it/evals=558/6436 eff=16.0839% N=107 Z=-76.8(0.00%) | Like=-64.32..-0.00 [-2898.1460..-0.0010] | it/evals=564/6436 eff=17.4825% N=107 Z=-75.2(0.00%) | Like=-63.37..-0.00 [-2898.1460..-0.0010] | it/evals=568/6543 eff=11.2000% N=116 Z=-70.4(0.00%) | Like=-58.57..-0.00 [-2898.1460..-0.0010] | it/evals=576/6543 eff=11.6000% N=116 Z=-66.7(0.00%) | Like=-54.70..-0.00 [-2898.1460..-0.0010] | it/evals=582/6543 eff=12.0000% N=116 Z=-61.9(0.00%) | Like=-49.18..-0.00 [-2898.1460..-0.0010] | it/evals=595/6543 eff=13.2000% N=119 Z=-51.9(0.00%) | Like=-40.09..-0.00 [-2898.1460..-0.0010] | it/evals=618/6543 eff=16.4000% N=134 Z=-49.9(0.00%) | Like=-37.18..-0.00 [-2898.1460..-0.0010] | it/evals=627/6543 eff=17.6000% N=140 Z=-48.6(0.00%) | Like=-35.46..-0.00 [-2898.1460..-0.0010] | it/evals=630/6543 eff=18.0000% N=143 Z=-46.7(0.00%) | Like=-34.74..-0.00 [-2898.1460..-0.0010] | it/evals=636/6543 eff=18.8000% N=146 Z=-44.7(0.00%) | Like=-31.78..-0.00 [-2898.1460..-0.0010] | it/evals=648/6543 eff=20.4000% N=150 Z=-40.1(0.00%) | Like=-27.88..-0.00 [-2898.1460..-0.0010] | it/evals=666/6543 eff=21.6000% N=156 Z=-33.3(0.00%) | Like=-21.14..-0.00 [-2898.1460..-0.0010] | it/evals=703/6660 eff=17.1662% N=171 Z=-32.1(0.00%) | Like=-19.95..-0.00 [-2898.1460..-0.0010] | it/evals=714/6660 eff=18.8011% N=182 Z=-31.4(0.00%) | Like=-19.55..-0.00 [-2898.1460..-0.0010] | it/evals=720/6660 eff=20.1635% N=191 Z=-29.0(0.00%) | Like=-16.99..-0.00 [-2898.1460..-0.0010] | it/evals=744/6660 eff=21.7984% N=201 Z=-28.7(0.00%) | Like=-16.62..-0.00 [-2898.1460..-0.0010] | it/evals=748/6660 eff=22.3433% N=206 Z=-27.4(0.00%) | Like=-15.47..-0.00 [-2898.1460..-0.0010] | it/evals=768/6660 eff=24.7956% N=227 Z=-26.7(0.00%) | Like=-14.83..-0.00 [-2898.1460..-0.0010] | it/evals=780/6660 eff=25.6131% N=234 Z=-25.7(0.00%) | Like=-13.50..-0.00 [-2898.1460..-0.0010] | it/evals=798/6769 eff=21.4286% N=242 Z=-25.6(0.00%) | Like=-13.49..-0.00 [-2898.1460..-0.0010] | it/evals=799/6769 eff=21.6387% N=244 Z=-24.7(0.00%) | Like=-12.64..-0.00 [-2898.1460..-0.0010] | it/evals=816/6769 eff=22.6891% N=248 Z=-24.4(0.00%) | Like=-12.40..-0.00 [-2898.1460..-0.0010] | it/evals=822/6769 eff=23.3193% N=252 Z=-24.1(0.00%) | Like=-12.13..-0.00 [-2898.1460..-0.0010] | it/evals=828/6769 eff=23.9496% N=254 Z=-22.9(0.00%) | Like=-10.81..-0.00 [-2898.1460..-0.0010] | it/evals=852/6769 eff=26.0504% N=267 Z=-22.7(0.00%) | Like=-10.67..-0.00 [-2898.1460..-0.0010] | it/evals=858/6769 eff=26.6807% N=269 Z=-22.4(0.00%) | Like=-10.48..-0.00 [-2898.1460..-0.0010] | it/evals=864/6769 eff=27.5210% N=275 Z=-22.2(0.00%) | Like=-10.27..-0.00 [-2898.1460..-0.0010] | it/evals=870/6769 eff=28.1513% N=279 Z=-22.0(0.00%) | Like=-10.03..-0.00 [-2898.1460..-0.0010] | it/evals=876/6769 eff=29.4118% N=281 Z=-21.6(0.01%) | Like=-9.66..-0.00 [-2898.1460..-0.0010] | it/evals=888/6769 eff=30.6723% N=283 Z=-21.2(0.01%) | Like=-9.23..-0.00 [-2898.1460..-0.0010] | it/evals=900/6889 eff=25.5034% N=283 Z=-21.0(0.01%) | Like=-9.06..-0.00 [-2898.1460..-0.0010] | it/evals=906/6889 eff=26.0067% N=283 Z=-20.9(0.01%) | Like=-8.82..-0.00 [-2898.1460..-0.0010] | it/evals=912/6889 eff=26.3423% N=283 Z=-20.6(0.02%) | Like=-8.63..-0.00 [-2898.1460..-0.0010] | it/evals=921/6889 eff=26.8456% N=283 Z=-20.5(0.02%) | Like=-8.49..-0.00 [-2898.1460..-0.0010] | it/evals=924/6889 eff=27.1812% N=283 Z=-19.4(0.06%) | Like=-7.36..-0.00 [-2898.1460..-0.0010] | it/evals=960/6889 eff=28.0201% N=283 Z=-18.9(0.09%) | Like=-6.77..-0.00 [-2898.1460..-0.0010] | it/evals=978/6889 eff=29.0268% N=283 Z=-18.7(0.12%) | Like=-6.46..-0.00 [-2898.1460..-0.0010] | it/evals=985/6889 eff=29.3624% N=283 Z=-18.2(0.20%) | Like=-5.95..-0.00 [-2898.1460..-0.0010] | it/evals=1002/6889 eff=30.0336% N=283 Z=-17.5(0.41%) | Like=-5.31..-0.00 [-2898.1460..-0.0010] | it/evals=1032/6889 eff=31.3758% N=283 Z=-17.0(0.63%) | Like=-4.87..-0.00 [-2898.1460..-0.0010] | it/evals=1050/6889 eff=32.0470% N=283 Z=-16.8(0.79%) | Like=-4.72..-0.00 [-2898.1460..-0.0010] | it/evals=1062/6889 eff=32.3826% N=283 Z=-16.7(0.90%) | Like=-4.60..-0.00 [-2898.1460..-0.0010] | it/evals=1068/6889 eff=32.7181% N=283 Z=-16.6(1.00%) | Like=-4.50..-0.00 [-2898.1460..-0.0010] | it/evals=1074/6889 eff=33.2215% N=283 Z=-16.4(1.11%) | Like=-4.42..-0.00 [-2898.1460..-0.0010] | it/evals=1080/6889 eff=33.3893% N=283 Z=-15.1(4.33%) | Like=-3.18..-0.00 [-2898.1460..-0.0010] | it/evals=1176/6918 eff=35.3600% N=283 Z=-15.1(4.44%) | Like=-3.14..-0.00 [-2898.1460..-0.0010] | it/evals=1178/6918 eff=35.5200% N=283 Z=-15.0(4.97%) | Like=-3.06..-0.00 [-2898.1460..-0.0010] | it/evals=1188/6918 eff=35.8400% N=283 Z=-14.0(12.62%) | Like=-2.06..-0.00 [-2898.1460..-0.0010] | it/evals=1302/6968 eff=37.3333% N=283 Z=-13.9(13.16%) | Like=-2.03..-0.00 [-2898.1460..-0.0010] | it/evals=1308/6968 eff=37.9259% N=283 Z=-13.9(13.76%) | Like=-1.97..-0.00 [-2898.1460..-0.0010] | it/evals=1314/6968 eff=38.0741% N=283 Z=-13.5(19.77%) | Like=-1.60..-0.00 [-2898.1460..-0.0010] | it/evals=1368/7075 eff=34.3990% N=283 Z=-13.5(20.55%) | Like=-1.56..-0.00 [-2898.1460..-0.0010] | it/evals=1374/7075 eff=34.6547% N=283 Z=-13.5(21.27%) | Like=-1.54..-0.00 [-2898.1460..-0.0010] | it/evals=1380/7075 eff=35.1662% N=283 Z=-13.4(22.08%) | Like=-1.52..-0.00 [-2898.1460..-0.0010] | it/evals=1386/7075 eff=35.5499% N=283 Z=-13.4(22.73%) | Like=-1.48..-0.00 [-2898.1460..-0.0010] | it/evals=1392/7075 eff=35.9335% N=283 Z=-13.4(23.40%) | Like=-1.46..-0.00 [-2898.1460..-0.0010] | it/evals=1398/7075 eff=36.0614% N=283 Z=-13.3(25.54%) | Like=-1.39..-0.00 [-2898.1460..-0.0010] | it/evals=1416/7075 eff=36.4450% N=283 Z=-13.2(27.12%) | Like=-1.32..-0.00 [-2898.1460..-0.0010] | it/evals=1428/7075 eff=36.8286% N=283 Z=-13.2(29.12%) | Like=-1.26..-0.00 [-2898.1460..-0.0010] | it/evals=1444/7075 eff=37.0844% N=283 Z=-13.1(29.44%) | Like=-1.26..-0.00 [-2898.1460..-0.0010] | it/evals=1446/7075 eff=37.2123% N=283 Z=-13.1(32.53%) | Like=-1.18..-0.00 [-2898.1460..-0.0010] | it/evals=1470/7075 eff=37.8517% N=283 Z=-13.0(33.96%) | Like=-1.14..-0.00 [-2898.1460..-0.0010] | it/evals=1482/7075 eff=38.3632% N=283 Z=-13.0(34.76%) | Like=-1.12..-0.00 [-2898.1460..-0.0010] | it/evals=1488/7075 eff=38.6189% N=283 Z=-12.8(40.70%) | Like=-0.94..-0.00 [-2898.1460..-0.0010] | it/evals=1536/7075 eff=40.0256% N=283 Z=-12.8(42.11%) | Like=-0.89..-0.00 [-2898.1460..-0.0010] | it/evals=1548/7083 eff=39.8734% N=283 Z=-12.7(44.28%) | Like=-0.86..-0.00 [-2898.1460..-0.0010] | it/evals=1566/7092 eff=40.4255% N=283 Z=-12.7(45.01%) | Like=-0.83..-0.00 [-2898.1460..-0.0010] | it/evals=1572/7105 eff=40.1478% N=283 Z=-12.7(48.19%) | Like=-0.75..-0.00 [-2898.1460..-0.0010] | it/evals=1596/7114 eff=40.8039% N=283 Z=-12.6(49.73%) | Like=-0.73..-0.00 [-2898.1460..-0.0010] | it/evals=1608/7127 eff=40.7674% N=283 Z=-12.5(54.24%) | Like=-0.65..-0.00 [-2898.1460..-0.0010] | it/evals=1645/7146 eff=40.9144% N=283 Z=-12.5(54.77%) | Like=-0.65..-0.00 [-2898.1460..-0.0010] | it/evals=1650/7146 eff=41.1489% N=283 Z=-12.4(61.90%) | Like=-0.51..-0.00 [-2898.1460..-0.0010] | it/evals=1714/7167 eff=41.7620% N=283 Z=-12.4(62.69%) | Like=-0.49..-0.00 [-2898.1460..-0.0010] | it/evals=1722/7167 eff=42.1053% N=283 Z=-12.4(65.72%) | Like=-0.45..-0.00 [-2898.1460..-0.0010] | it/evals=1752/7282 eff=37.8160% N=283 Z=-12.3(68.59%) | Like=-0.39..-0.00 [-2898.1460..-0.0010] | it/evals=1783/7282 eff=38.2204% N=283 Z=-12.3(69.58%) | Like=-0.38..-0.00 [-2898.1460..-0.0010] | it/evals=1794/7282 eff=38.5238% N=283 Z=-12.3(72.19%) | Like=-0.34..-0.00 [-2898.1460..-0.0010] | it/evals=1824/7282 eff=39.5349% N=283 Z=-12.2(73.19%) | Like=-0.33..-0.00 [-2898.1460..-0.0010] | it/evals=1836/7282 eff=39.8382% N=283 Z=-12.2(75.50%) | Like=-0.30..-0.00 [-2898.1460..-0.0010] | it/evals=1866/7282 eff=40.2427% N=283 Z=-12.2(77.65%) | Like=-0.27..-0.00 [-2898.1460..-0.0010] | it/evals=1896/7282 eff=40.9505% N=283 Z=-12.2(78.07%) | Like=-0.27..-0.00 [-2898.1460..-0.0010] | it/evals=1902/7282 eff=41.0516% N=283 Z=-12.2(80.09%) | Like=-0.24..-0.00 [-2898.1460..-0.0010] | it/evals=1932/7282 eff=41.7594% N=283 Z=-12.1(80.81%) | Like=-0.23..-0.00 [-2898.1460..-0.0010] | it/evals=1944/7282 eff=41.9616% N=283 Z=-12.1(81.52%) | Like=-0.22..-0.00 [-2898.1460..-0.0010] | it/evals=1956/7282 eff=42.3660% N=283 Z=-12.1(82.88%) | Like=-0.21..-0.00 [-2898.1460..-0.0010] | it/evals=1980/7408 eff=38.2063% N=283 Z=-12.1(83.27%) | Like=-0.20..-0.00 [-2898.1460..-0.0010] | it/evals=1987/7408 eff=38.5650% N=283 Z=-12.1(83.84%) | Like=-0.19..-0.00 [-2898.1460..-0.0010] | it/evals=1998/7408 eff=39.0135% N=283 Z=-12.1(85.03%) | Like=-0.18..-0.00 [-2898.1460..-0.0010] | it/evals=2022/7408 eff=39.2825% N=283 Z=-12.1(86.69%) | Like=-0.16..-0.00 [-2898.1460..-0.0010] | it/evals=2058/7408 eff=40.4484% N=283 Z=-12.1(87.45%) | Like=-0.15..-0.00 [-2898.1460..-0.0010] | it/evals=2076/7408 eff=41.2556% N=283 Z=-12.1(88.41%) | Like=-0.14..-0.00 [-2898.1460..-0.0010] | it/evals=2100/7408 eff=41.4350% N=283 Z=-12.0(89.11%) | Like=-0.13..-0.00 [-2898.1460..-0.0010] | it/evals=2119/7408 eff=41.7937% N=283 Z=-12.0(89.29%) | Like=-0.13..-0.00 [-2898.1460..-0.0010] | it/evals=2124/7419 eff=41.4742% N=283 Z=-12.0(89.50%) | Like=-0.13..-0.00 [-2898.1460..-0.0010] | it/evals=2130/7419 eff=41.9183% N=283 Z=-12.0(89.91%) | Like=-0.13..-0.00 [-2898.1460..-0.0010] | it/evals=2142/7434 eff=41.8931% N=283 Z=-12.0(91.22%) | Like=-0.11..-0.00 [-2898.1460..-0.0010] | it/evals=2184/7445 eff=42.4479% N=283 Z=-12.0(91.40%) | Like=-0.11..-0.00 [-2898.1460..-0.0010] | it/evals=2190/7445 eff=42.6215% N=283 Z=-12.0(91.57%) | Like=-0.11..-0.00 [-2898.1460..-0.0010] | it/evals=2196/7445 eff=42.7083% N=283 Z=-12.0(91.90%) | Like=-0.11..-0.00 [-2898.1460..-0.0010] | it/evals=2208/7445 eff=43.0556% N=283 Z=-12.0(92.37%) | Like=-0.10..-0.00 [-2898.1460..-0.0010] | it/evals=2226/7471 eff=42.6146% N=283 Z=-12.0(92.99%) | Like=-0.09..-0.00 [-2898.1460..-0.0010] | it/evals=2251/7471 eff=42.9542% N=283 Z=-12.0(93.11%) | Like=-0.09..-0.00 [-2898.1460..-0.0010] | it/evals=2256/7471 eff=43.2088% N=283 Z=-12.0(94.14%) | Like=-0.08..-0.00 [-2898.1460..-0.0010] | it/evals=2304/7586 eff=40.5259% N=283 Z=-12.0(94.39%) | Like=-0.07..-0.00 [-2898.1460..-0.0010] | it/evals=2317/7586 eff=40.7579% N=283 Z=-12.0(94.71%) | Like=-0.07..-0.00 [-2898.1460..-0.0010] | it/evals=2334/7586 eff=40.9899% N=283 Z=-12.0(94.82%) | Like=-0.07..-0.00 [-2898.1460..-0.0010] | it/evals=2340/7586 eff=41.0673% N=283 Z=-12.0(95.23%) | Like=-0.06..-0.00 [-2898.1460..-0.0010] | it/evals=2364/7586 eff=41.6087% N=283 Z=-12.0(95.32%) | Like=-0.06..-0.00 [-2898.1460..-0.0010] | it/evals=2370/7586 eff=41.7633% N=283 Z=-12.0(95.51%) | Like=-0.06..-0.00 [-2898.1460..-0.0010] | it/evals=2382/7586 eff=41.9180% N=283 Z=-12.0(96.35%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2442/7586 eff=42.6141% N=283 Z=-12.0(96.40%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2446/7586 eff=42.8461% N=283 Z=-12.0(96.78%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2478/7586 eff=43.3875% N=283 Z=-12.0(96.84%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2484/7586 eff=43.5422% N=283 Z=-12.0(97.04%) | Like=-0.04..-0.00 [-2898.1460..-0.0010] | it/evals=2502/7586 eff=43.7742% N=283 Z=-12.0(97.16%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2514/7713 eff=40.2817% N=283 Z=-12.0(97.27%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2526/7713 eff=40.5634% N=283 Z=-12.0(97.33%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2532/7713 eff=40.6338% N=283 Z=-12.0(97.38%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2538/7713 eff=40.8451% N=283 Z=-12.0(97.59%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2562/7713 eff=41.1972% N=283 Z=-12.0(97.71%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2576/7713 eff=41.3380% N=283 Z=-12.0(97.74%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2580/7713 eff=41.4789% N=283 Z=-12.0(97.79%) | Like=-0.03..-0.00 [-2898.1460..-0.0010] | it/evals=2586/7713 eff=41.6901% N=283 Z=-11.9(98.31%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2664/7713 eff=43.4507% N=283 Z=-11.9(98.38%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2676/7713 eff=43.8028% N=283 Z=-11.9(98.45%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2688/7713 eff=44.0141% N=283 Z=-11.9(98.55%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2706/7838 eff=40.7120% N=283 Z=-11.9(98.66%) | Like=-0.02..-0.00 [-2898.1460..-0.0010] | it/evals=2730/7838 eff=41.0356% N=283 Z=-11.9(98.80%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2760/7838 eff=41.6828% N=283 Z=-11.9(98.84%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2770/7838 eff=41.8770% N=283 Z=-11.9(98.89%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2784/7838 eff=42.0065% N=283 Z=-11.9(99.07%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2834/7838 eff=42.9773% N=283 Z=-11.9(99.14%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2856/7838 eff=43.4951% N=283 Z=-11.9(99.16%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2862/7838 eff=43.6893% N=283 Z=-11.9(99.19%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2874/7847 eff=43.7580% N=283 Z=-11.9(99.23%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2886/7856 eff=43.7620% N=283 Z=-11.9(99.26%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2900/7856 eff=44.0179% N=283 Z=-11.9(99.30%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2916/7863 eff=44.1401% N=283 Z=-11.9(99.32%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2922/7869 eff=44.0990% N=283 Z=-11.9(99.36%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2940/7869 eff=44.3528% N=283 Z=-11.9(99.37%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2946/7879 eff=44.2623% N=283 Z=-11.9(99.39%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2952/7879 eff=44.3253% N=283 Z=-11.9(99.40%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2958/7879 eff=44.3884% N=283 Z=-11.9(99.42%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=2970/7887 eff=44.4166% N=283 Z=-11.9(99.48%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=3000/7899 eff=44.7073% N=283 Z=-11.9(99.51%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=3018/7908 eff=44.7059% N=283 Z=-11.9(99.52%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=3024/7908 eff=44.8297% N=283 Z=-11.9(99.54%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=3035/7908 eff=45.0155% N=283 Z=-11.9(99.56%) | Like=-0.01..-0.00 [-2898.1460..-0.0010] | it/evals=3048/7923 eff=44.7853% N=283 Z=-11.9(99.61%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3078/7923 eff=45.2147% N=283 Z=-11.9(99.65%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3108/7935 eff=45.3715% N=283 Z=-11.9(99.67%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3126/7947 eff=45.1632% N=283 Z=-11.9(99.70%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3156/7955 eff=45.3069% N=283 Z=-11.9(99.71%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3162/7960 eff=45.2909% N=283 Z=-11.9(99.72%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3174/7970 eff=45.3787% N=283 Z=-11.9(99.74%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3192/7999 eff=45.0762% N=283 Z=-11.9(99.74%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3198/7999 eff=45.1934% N=283 Z=-11.9(99.75%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3210/8014 eff=45.0901% N=283 Z=-11.9(99.78%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3243/8014 eff=45.3806% N=283 Z=-11.9(99.78%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3246/8139 eff=42.4160% N=283 Z=-11.9(99.79%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3252/8139 eff=42.5785% N=283 Z=-11.9(99.82%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3300/8139 eff=43.2286% N=283 Z=-11.9(99.82%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3306/8139 eff=43.3369% N=283 Z=-11.9(99.83%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3309/8139 eff=43.3911% N=283 Z=-11.9(99.83%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3312/8139 eff=43.4453% N=283 Z=-11.9(99.84%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3336/8139 eff=43.9328% N=283 Z=-11.9(99.85%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3348/8139 eff=44.0412% N=283 Z=-11.9(99.86%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3375/8139 eff=44.3662% N=283 Z=-11.9(99.87%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3384/8139 eff=44.4745% N=283 Z=-11.9(99.87%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3402/8139 eff=44.8537% N=283 Z=-11.9(99.88%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3420/8139 eff=45.2329% N=283 Z=-11.9(99.88%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3426/8139 eff=45.2871% N=283 Z=-11.9(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3440/8237 eff=43.2099% N=283 Z=-11.9(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3444/8237 eff=43.3128% N=283 Z=-11.9(99.89%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3450/8237 eff=43.4156% N=283 Z=-11.9(99.90%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3456/8237 eff=43.4671% N=283 Z=-11.9(99.90%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3462/8237 eff=43.5700% N=283 Z=-11.9(99.91%) | Like=-0.00..-0.00 [-2898.1460..-0.0010] | it/evals=3504/8237 eff=44.2901% N=283 [ultranest] Explored until L=-1e-06 [ultranest] Likelihood function evaluations: 8237 [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. {'logzerr': 0.5361290324626915, 'logzerr_tail': 0.0008709137754472351, 'logzerr_bs': 0.5361283250851212, 'logzerr_single': 0.41281628156576655} running again for logz [ultranest] To achieve the desired logz accuracy, min_num_live_points was increased to 317 [ultranest] Widening roots to 317 live points (have 64 already) ... [ultranest] Sampling 253 live points from prior ... Z=-232898.6(0.00%) | Like=-232687.20..-141.17 [-239030.0011..-80793.4986] | it/evals=2/8618 eff=0.0000% N=317 Z=-141964.7(0.00%) | Like=-140774.44..-141.17 [-239030.0011..-80793.4986] | it/evals=64/8618 eff=36.7188% N=317 Z=-122913.8(0.00%) | Like=-122658.38..-141.17 [-239030.0011..-80793.4986] | it/evals=96/8618 eff=59.3750% N=317 Z=-113627.2(0.00%) | Like=-112577.69..-141.17 [-239030.0011..-80793.4986] | it/evals=128/8618 eff=78.9062% N=317 Z=-101136.6(0.00%) | Like=-100759.15..-141.17 [-239030.0011..-80793.4986] | it/evals=160/8746 eff=50.0000% N=317 Z=-90570.6(0.00%) | Like=-90155.82..-141.17 [-239030.0011..-80793.4986] | it/evals=192/8746 eff=60.5469% N=317 Z=-81918.9(0.00%) | Like=-81453.03..-141.17 [-239030.0011..-80793.4986] | it/evals=224/8746 eff=71.0938% N=317 Z=-73315.0(0.00%) | Like=-73233.35..-141.17 [-79828.1068..-35298.0035] | it/evals=256/8873 eff=54.8303% N=317 Z=-64900.6(0.00%) | Like=-64729.91..-141.17 [-79828.1068..-35298.0035] | it/evals=288/9001 eff=46.3796% N=317 Z=-57792.4(0.00%) | Like=-57466.70..-141.17 [-79828.1068..-35298.0035] | it/evals=320/9001 eff=51.0763% N=317 Z=-51000.9(0.00%) | Like=-50915.48..-141.17 [-79828.1068..-35298.0035] | it/evals=352/9129 eff=44.6009% N=317 Z=-46172.6(0.00%) | Like=-46062.57..-141.17 [-79828.1068..-35298.0035] | it/evals=384/9129 eff=48.6698% N=317 Z=-42114.1(0.00%) | Like=-42091.66..-141.17 [-79828.1068..-35298.0035] | it/evals=416/9257 eff=43.5463% N=317 Z=-38002.3(0.00%) | Like=-37913.90..-141.17 [-79828.1068..-35298.0035] | it/evals=448/9384 eff=40.3803% N=317 Z=-33774.4(0.00%) | Like=-33743.22..-141.17 [-35116.2974..-17784.4700] | it/evals=480/9384 eff=43.5123% N=317 Z=-30486.5(0.00%) | Like=-30401.49..-141.17 [-35116.2974..-17784.4700] | it/evals=512/9639 eff=36.3795% N=317 Z=-30408.9(0.00%) | Like=-30375.13..-141.17 [-35116.2974..-17784.4700] | it/evals=513/9639 eff=36.4665% N=317 Z=-27141.9(0.00%) | Like=-27055.13..-141.17 [-35116.2974..-17784.4700] | it/evals=544/9670 eff=37.7966% N=317 Z=-24695.1(0.00%) | Like=-24585.63..-124.59 [-35116.2974..-17784.4700] | it/evals=576/9701 eff=38.8109% N=317 Z=-20206.5(0.00%) | Like=-20167.79..-124.59 [-35116.2974..-17784.4700] | it/evals=640/9802 eff=39.6341% N=317 Z=-18456.8(0.00%) | Like=-18445.49..-124.59 [-35116.2974..-17784.4700] | it/evals=672/9834 eff=40.6994% N=317 Z=-16940.3(0.00%) | Like=-16930.51..-41.60 [-17759.0030..-9845.1769] | it/evals=704/9886 eff=40.7593% N=317 Z=-15494.3(0.00%) | Like=-15443.95..-41.60 [-17759.0030..-9845.1769] | it/evals=736/9943 eff=40.8809% N=317 Z=-13699.9(0.00%) | Like=-13632.37..-41.60 [-17759.0030..-9845.1769] | it/evals=768/10007 eff=40.9361% N=317 Z=-12717.2(0.00%) | Like=-12697.63..-37.32 [-17759.0030..-9845.1769] | it/evals=800/10087 eff=40.5135% N=317 Z=-10768.3(0.00%) | Like=-10673.69..-37.32 [-17759.0030..-9845.1769] | it/evals=864/10213 eff=40.3947% N=317 Z=-9765.6(0.00%) | Like=-9732.89..-37.32 [-9834.1571..-5013.4378] | it/evals=896/10339 eff=39.2104% N=317 Z=-8741.3(0.00%) | Like=-8700.94..-37.32 [-9834.1571..-5013.4378] | it/evals=928/10411 eff=38.9901% N=317 Z=-7899.3(0.00%) | Like=-7886.92..-37.32 [-9834.1571..-5013.4378] | it/evals=960/10530 eff=37.9412% N=317 Z=-7029.1(0.00%) | Like=-7016.88..-37.32 [-9834.1571..-5013.4378] | it/evals=992/10688 eff=36.3967% N=317 Z=-6251.1(0.00%) | Like=-6232.84..-37.32 [-9834.1571..-5013.4378] | it/evals=1024/10780 eff=36.1135% N=317 Z=-5724.0(0.00%) | Like=-5714.75..-37.32 [-9834.1571..-5013.4378] | it/evals=1056/10813 eff=36.6767% N=317 Z=-5279.6(0.00%) | Like=-5268.05..-37.32 [-9834.1571..-5013.4378] | it/evals=1088/10859 eff=37.1465% N=317 Z=-3727.0(0.00%) | Like=-3713.78..-27.54 [-5008.6289..-2520.4011] | it/evals=1216/11040 eff=38.3137% N=317 Z=-3460.1(0.00%) | Like=-3444.46..-27.54 [-5008.6289..-2520.4011] | it/evals=1248/11122 eff=38.1839% N=317 Z=-3082.6(0.00%) | Like=-3067.69..-27.54 [-5008.6289..-2520.4011] | it/evals=1276/11157 eff=38.2452% N=317 Z=-3050.8(0.00%) | Like=-3035.77..-27.54 [-5008.6289..-2520.4011] | it/evals=1280/11161 eff=38.3003% N=317 Z=-2841.3(0.00%) | Like=-2827.32..-27.54 [-5008.6289..-2520.4011] | it/evals=1305/11292 eff=37.0450% N=317 Z=-2718.8(0.00%) | Like=-2689.88..-27.54 [-5008.6289..-2520.4011] | it/evals=1312/11292 eff=37.1520% N=317 Z=-2498.2(0.00%) | Like=-2484.50..-27.54 [-2514.8837..-1268.1945] | it/evals=1344/11292 eff=38.0086% N=317 Z=-2298.1(0.00%) | Like=-2287.32..-27.54 [-2514.8837..-1268.1945] | it/evals=1376/11292 eff=38.9008% N=317 Z=-2004.5(0.00%) | Like=-1985.86..-27.54 [-2514.8837..-1268.1945] | it/evals=1408/11292 eff=39.7216% N=317 Z=-1830.7(0.00%) | Like=-1820.00..-27.54 [-2514.8837..-1268.1945] | it/evals=1440/11329 eff=40.1198% N=317 Z=-1632.1(0.00%) | Like=-1620.64..-27.54 [-2514.8837..-1268.1945] | it/evals=1469/11366 eff=40.1947% N=317 Z=-1627.4(0.00%) | Like=-1616.68..-27.54 [-2514.8837..-1268.1945] | it/evals=1472/11366 eff=40.2643% N=317 Z=-1345.1(0.00%) | Like=-1332.78..-27.54 [-2514.8837..-1268.1945] | it/evals=1536/11514 eff=40.0132% N=317 Z=-1216.0(0.00%) | Like=-1202.14..-27.54 [-1267.0232..-655.0000] | it/evals=1568/11514 eff=40.9722% N=317 Z=-936.8(0.00%) | Like=-922.82..-5.31 [-1267.0232..-655.0000] | it/evals=1664/11642 eff=41.7195% N=317 Z=-747.2(0.00%) | Like=-733.92..-3.45 [-1267.0232..-655.0000] | it/evals=1728/11898 eff=40.1995% N=317 Z=-714.1(0.00%) | Like=-701.10..-3.45 [-1267.0232..-655.0000] | it/evals=1748/11898 eff=40.5223% N=317 Z=-679.2(0.00%) | Like=-656.72..-3.45 [-1267.0232..-655.0000] | it/evals=1759/11898 eff=40.7864% N=317 Z=-668.0(0.00%) | Like=-655.91..-3.45 [-1267.0232..-655.0000] | it/evals=1760/11898 eff=40.8157% N=317 Z=-616.9(0.00%) | Like=-604.35..-3.45 [-654.5853..-360.5211] | it/evals=1792/12026 eff=39.9887% N=317 Z=-525.8(0.00%) | Like=-513.80..-3.45 [-654.5853..-360.5211] | it/evals=1856/12026 eff=41.3744% N=317 Z=-484.0(0.00%) | Like=-470.07..-3.45 [-654.5853..-360.5211] | it/evals=1888/12026 eff=42.0814% N=317 Z=-402.5(0.00%) | Like=-390.18..-1.02 [-654.5853..-360.5211] | it/evals=1952/12154 eff=41.8668% N=317 Z=-369.5(0.00%) | Like=-357.12..-1.02 [-358.9036..-180.3833] | it/evals=1982/12282 eff=41.0074% N=317 Z=-330.0(0.00%) | Like=-317.65..-1.02 [-358.9036..-180.3833] | it/evals=2016/12282 eff=41.6139% N=317 Z=-274.9(0.00%) | Like=-262.23..-1.02 [-358.9036..-180.3833] | it/evals=2080/12412 eff=41.4329% N=317 Z=-251.8(0.00%) | Like=-238.95..-0.81 [-358.9036..-180.3833] | it/evals=2111/12412 eff=42.0194% N=317 Z=-224.9(0.00%) | Like=-213.38..-0.81 [-358.9036..-180.3833] | it/evals=2144/12541 eff=41.1010% N=317 Z=-211.3(0.00%) | Like=-198.80..-0.81 [-358.9036..-180.3833] | it/evals=2164/12541 eff=41.4219% N=317 Z=-203.1(0.00%) | Like=-189.11..-0.81 [-358.9036..-180.3833] | it/evals=2179/12541 eff=41.6934% N=317 Z=-185.8(0.00%) | Like=-173.81..-0.81 [-179.0569..-87.3649] | it/evals=2208/12541 eff=42.2859% N=317 Z=-183.5(0.00%) | Like=-171.45..-0.81 [-179.0569..-87.3649] | it/evals=2212/12541 eff=42.3105% N=317 Z=-177.8(0.00%) | Like=-166.08..-0.81 [-179.0569..-87.3649] | it/evals=2227/12541 eff=42.5327% N=317 Z=-156.5(0.00%) | Like=-144.14..-0.13 [-179.0569..-87.3649] | it/evals=2272/12669 eff=42.0675% N=317 Z=-152.9(0.00%) | Like=-141.67..-0.13 [-179.0569..-87.3649] | it/evals=2283/12669 eff=42.2111% N=317 Z=-135.1(0.00%) | Like=-123.18..-0.13 [-179.0569..-87.3649] | it/evals=2336/12669 eff=43.0725% N=317 Z=-122.4(0.00%) | Like=-109.61..-0.13 [-179.0569..-87.3649] | it/evals=2368/12669 eff=43.6947% N=317 Z=-115.1(0.00%) | Like=-103.05..-0.13 [-179.0569..-87.3649] | it/evals=2385/12797 eff=42.6283% N=317 Z=-110.8(0.00%) | Like=-98.44..-0.08 [-179.0569..-87.3649] | it/evals=2400/12797 eff=42.8837% N=317 Z=-99.2(0.00%) | Like=-86.39..-0.08 [-86.8855..-39.7474] | it/evals=2432/12797 eff=43.4177% N=317 Z=-95.5(0.00%) | Like=-83.77..-0.08 [-86.8855..-39.7474] | it/evals=2449/12797 eff=43.6731% N=317 Z=-92.5(0.00%) | Like=-80.51..-0.08 [-86.8855..-39.7474] | it/evals=2462/12797 eff=43.8124% N=317 Z=-92.1(0.00%) | Like=-80.07..-0.08 [-86.8855..-39.7474] | it/evals=2464/12797 eff=43.8588% N=317 Z=-87.1(0.00%) | Like=-75.28..-0.08 [-86.8855..-39.7474] | it/evals=2485/12799 eff=44.0706% N=317 Z=-85.1(0.00%) | Like=-72.84..-0.08 [-86.8855..-39.7474] | it/evals=2496/12927 eff=43.0246% N=317 Z=-81.8(0.00%) | Like=-69.70..-0.08 [-86.8855..-39.7474] | it/evals=2514/12927 eff=43.1373% N=317 Z=-79.1(0.00%) | Like=-66.92..-0.08 [-86.8855..-39.7474] | it/evals=2528/12927 eff=43.3626% N=317 Z=-72.5(0.00%) | Like=-59.49..-0.08 [-86.8855..-39.7474] | it/evals=2555/12927 eff=43.4979% N=317 Z=-71.1(0.00%) | Like=-58.75..-0.08 [-86.8855..-39.7474] | it/evals=2560/12927 eff=43.5655% N=317 Z=-64.8(0.00%) | Like=-52.41..-0.08 [-86.8855..-39.7474] | it/evals=2592/12927 eff=43.9937% N=317 Z=-62.6(0.00%) | Like=-50.86..-0.08 [-86.8855..-39.7474] | it/evals=2607/12927 eff=44.1515% N=317 Z=-58.4(0.00%) | Like=-45.34..-0.08 [-86.8855..-39.7474] | it/evals=2631/12927 eff=44.2867% N=317 Z=-53.6(0.00%) | Like=-41.44..-0.08 [-86.8855..-39.7474] | it/evals=2661/13055 eff=43.4830% N=317 Z=-48.7(0.00%) | Like=-36.09..-0.08 [-39.6454..-18.6130] | it/evals=2698/13055 eff=43.5926% N=317 Z=-46.8(0.00%) | Like=-34.75..-0.08 [-39.6454..-18.6130] | it/evals=2712/13055 eff=43.6802% N=317 Z=-44.9(0.00%) | Like=-32.82..-0.08 [-39.6454..-18.6130] | it/evals=2730/13055 eff=43.7240% N=317 Z=-43.6(0.00%) | Like=-31.53..-0.08 [-39.6454..-18.6130] | it/evals=2744/13055 eff=43.9211% N=317 Z=-41.6(0.00%) | Like=-28.75..-0.08 [-39.6454..-18.6130] | it/evals=2763/13055 eff=44.0088% N=317 Z=-39.6(0.00%) | Like=-27.00..-0.08 [-39.6454..-18.6130] | it/evals=2776/13055 eff=44.1402% N=317 Z=-38.6(0.00%) | Like=-26.59..-0.08 [-39.6454..-18.6130] | it/evals=2785/13055 eff=44.1840% N=317 Z=-37.6(0.00%) | Like=-25.35..-0.08 [-39.6454..-18.6130] | it/evals=2797/13055 eff=44.2716% N=317 Z=-36.4(0.00%) | Like=-23.87..-0.08 [-39.6454..-18.6130] | it/evals=2810/13055 eff=44.3593% N=317 Z=-34.9(0.00%) | Like=-22.71..-0.08 [-39.6454..-18.6130] | it/evals=2824/13055 eff=44.4469% N=317 Z=-16.3(1.25%) | Like=-4.25..-0.08 [-4.3342..-2.6281] | it/evals=3353/13055 eff=44.7974% N=317 Z=-15.4(3.20%) | Like=-3.45..-0.08 [-4.3342..-2.6281] | it/evals=3424/13183 eff=43.6608% N=317 Z=-13.6(17.62%) | Like=-1.63..-0.05 [-1.6315..-1.6303]*| it/evals=3643/13183 eff=43.9591% N=317 Z=-13.3(25.20%) | Like=-1.34..-0.05 [-1.3362..-1.3311]*| it/evals=3712/13183 eff=44.0656% N=317 Z=-13.0(32.65%) | Like=-1.14..-0.05 [-1.1363..-1.1288]*| it/evals=3776/13183 eff=44.1296% N=317 Z=-12.8(42.73%) | Like=-0.84..-0.05 [-0.8449..-0.8405]*| it/evals=3866/13183 eff=44.2787% N=317 Z=-12.4(60.77%) | Like=-0.49..-0.03 [-0.4929..-0.4926]*| it/evals=4032/13311 eff=43.3935% N=317 Z=-12.3(71.84%) | Like=-0.33..-0.03 [-0.3328..-0.3323]*| it/evals=4160/13311 eff=43.7046% N=317 Z=-12.1(80.23%) | Like=-0.22..-0.01 [-0.2243..-0.2236]*| it/evals=4288/13311 eff=43.8498% N=317 Z=-12.1(83.53%) | Like=-0.18..-0.00 [-0.1819..-0.1819]*| it/evals=4352/13311 eff=43.9743% N=317 Z=-12.1(85.62%) | Like=-0.17..-0.00 [-0.1652..-0.1646]*| it/evals=4399/13439 eff=42.9178% N=317 Z=-12.1(87.55%) | Like=-0.15..-0.00 [-0.1452..-0.1451]*| it/evals=4448/13439 eff=43.0390% N=317 Z=-12.0(88.66%) | Like=-0.13..-0.00 [-0.1314..-0.1311]*| it/evals=4480/13439 eff=43.1198% N=317 Z=-12.0(93.62%) | Like=-0.08..-0.00 [-0.0768..-0.0763]*| it/evals=4672/13567 eff=42.3872% N=317 Z=-12.0(94.75%) | Like=-0.06..-0.00 [-0.0632..-0.0631]*| it/evals=4736/13567 eff=42.5842% N=317 Z=-12.0(96.94%) | Like=-0.03..-0.00 [-0.0349..-0.0349]*| it/evals=4911/13695 eff=41.9789% N=317 Z=-11.9(98.22%) | Like=-0.02..-0.00 [-0.0198..-0.0197]*| it/evals=5086/13823 eff=41.2526% N=317 Z=-11.9(98.97%) | Like=-0.01..-0.00 [-0.0118..-0.0117]*| it/evals=5261/13823 eff=41.7214% N=317 [ultranest] Explored until L=-1e-06 [ultranest] Likelihood function evaluations: 13823 logzerr in iteration 0 0.37757991226305077 [ultranest] logZ = -11.96 +- 0.1918 [ultranest] Effective samples strategy satisfied (ESS = 1255.1, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.19 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 633 minimum live points (dlogz from 0.17 to 0.38, need <0.1) [ultranest] logZ error budget: single: 0.20 bs:0.19 tail:0.00 total:0.19 required:<0.10 [ultranest] Widening roots to 633 live points (have 317 already) ... [ultranest] Sampling 316 live points from prior ... Z=-239036.5(0.00%) | Like=-236674.95..-0.00 [-239030.0011..-77877.9052] | it/evals=1/14267 eff=0.0000% N=633 Z=-184777.3(0.00%) | Like=-184465.78..-0.00 [-239030.0011..-77877.9052] | it/evals=32/14267 eff=9.3750% N=633 Z=-124668.4(0.00%) | Like=-124605.48..-0.00 [-239030.0011..-77877.9052] | it/evals=160/14267 eff=53.9062% N=633 Z=-117854.9(0.00%) | Like=-117419.09..-0.00 [-239030.0011..-77877.9052] | it/evals=192/14267 eff=64.8438% N=633 Z=-113375.4(0.00%) | Like=-112782.43..-0.00 [-239030.0011..-77877.9052] | it/evals=224/14267 eff=75.0000% N=633 Z=-107501.9(0.00%) | Like=-107419.50..-0.00 [-239030.0011..-77877.9052] | it/evals=256/14395 eff=44.1406% N=633 Z=-103236.5(0.00%) | Like=-102555.74..-0.00 [-239030.0011..-77877.9052] | it/evals=288/14395 eff=50.7812% N=633 Z=-91541.4(0.00%) | Like=-91368.67..-0.00 [-239030.0011..-77877.9052] | it/evals=352/14395 eff=63.6719% N=633 Z=-83201.4(0.00%) | Like=-82587.56..-0.00 [-239030.0011..-77877.9052] | it/evals=416/14523 eff=51.3021% N=633 Z=-75005.2(0.00%) | Like=-74914.94..-0.00 [-77775.3540..-36328.5828] | it/evals=480/14523 eff=60.1562% N=633 Z=-71842.1(0.00%) | Like=-71318.09..-0.00 [-77775.3540..-36328.5828] | it/evals=512/14651 eff=48.2422% N=633 Z=-60269.0(0.00%) | Like=-60189.44..-0.00 [-77775.3540..-36328.5828] | it/evals=608/14778 eff=46.7919% N=633 Z=-51489.4(0.00%) | Like=-51406.61..-0.00 [-77775.3540..-36328.5828] | it/evals=704/14906 eff=46.1538% N=633 Z=-48759.1(0.00%) | Like=-48610.22..-0.00 [-77775.3540..-36328.5828] | it/evals=736/14906 eff=48.1095% N=633 Z=-46325.3(0.00%) | Like=-46271.97..-0.00 [-77775.3540..-36328.5828] | it/evals=768/15034 eff=43.0168% N=633 Z=-36346.2(0.00%) | Like=-36328.58..-0.00 [-77775.3540..-36328.5828] | it/evals=921/15290 eff=40.1390% N=633 Z=-36142.9(0.00%) | Like=-36067.54..-0.00 [-36299.4399..-17237.7604] | it/evals=928/15290 eff=40.5734% N=633 Z=-30462.5(0.00%) | Like=-30404.46..-0.00 [-36299.4399..-17237.7604] | it/evals=1020/15546 eff=36.1052% N=633 Z=-28231.0(0.00%) | Like=-28018.93..-0.00 [-36299.4399..-17237.7604] | it/evals=1056/15546 eff=37.2424% N=633 Z=-26326.2(0.00%) | Like=-26313.19..-0.00 [-36299.4399..-17237.7604] | it/evals=1088/15579 eff=37.3611% N=633 Z=-20463.8(0.00%) | Like=-20446.62..-0.00 [-36299.4399..-17237.7604] | it/evals=1252/15687 eff=39.7287% N=633 Z=-19690.1(0.00%) | Like=-19625.02..-0.00 [-36299.4399..-17237.7604] | it/evals=1280/15719 eff=39.7468% N=633 Z=-18543.2(0.00%) | Like=-18532.40..-0.00 [-36299.4399..-17237.7604] | it/evals=1312/15748 eff=39.9627% N=633 Z=-16925.2(0.00%) | Like=-16864.41..-0.00 [-17237.2250..-8777.2609] | it/evals=1376/15803 eff=40.3245% N=633 Z=-14369.4(0.00%) | Like=-14344.06..-0.00 [-17237.2250..-8777.2609] | it/evals=1472/15877 eff=41.2543% N=633 Z=-13755.2(0.00%) | Like=-13731.07..-0.00 [-17237.2250..-8777.2609] | it/evals=1504/15927 eff=41.2192% N=633 Z=-13237.2(0.00%) | Like=-13152.90..-0.00 [-17237.2250..-8777.2609] | it/evals=1536/15985 eff=40.7909% N=633 Z=-11958.5(0.00%) | Like=-11926.11..-0.00 [-17237.2250..-8777.2609] | it/evals=1600/16011 eff=41.3462% N=633 Z=-10262.0(0.00%) | Like=-10195.20..-0.00 [-17237.2250..-8777.2609] | it/evals=1696/16156 eff=40.6049% N=633 Z=-8168.6(0.00%) | Like=-8156.34..-0.00 [-8761.5024..-4370.7002] | it/evals=1824/16406 eff=38.6855% N=633 Z=-6972.7(0.00%) | Like=-6950.02..-0.00 [-8761.5024..-4370.7002] | it/evals=1920/16622 eff=37.2936% N=633 Z=-6632.0(0.00%) | Like=-6610.82..-0.00 [-8761.5024..-4370.7002] | it/evals=1952/16758 eff=36.0443% N=633 Z=-5748.7(0.00%) | Like=-5738.03..-0.00 [-8761.5024..-4370.7002] | it/evals=2039/16965 eff=34.9257% N=633 Z=-5521.8(0.00%) | Like=-5508.25..-0.00 [-8761.5024..-4370.7002] | it/evals=2080/16989 eff=35.4035% N=633 Z=-5232.4(0.00%) | Like=-5204.66..-0.00 [-8761.5024..-4370.7002] | it/evals=2112/17008 eff=35.5525% N=633 Z=-5059.5(0.00%) | Like=-5035.35..-0.00 [-8761.5024..-4370.7002] | it/evals=2144/17022 eff=35.7614% N=633 Z=-4766.9(0.00%) | Like=-4733.88..-0.00 [-8761.5024..-4370.7002] | it/evals=2176/17040 eff=35.8842% N=633 Z=-4359.3(0.00%) | Like=-4347.86..-0.00 [-4365.8804..-2199.2855] | it/evals=2240/17082 eff=36.4254% N=633 Z=-4170.4(0.00%) | Like=-4145.14..-0.00 [-4365.8804..-2199.2855] | it/evals=2272/17116 eff=36.5133% N=633 Z=-3755.1(0.00%) | Like=-3735.82..-0.00 [-4365.8804..-2199.2855] | it/evals=2336/17183 eff=36.8594% N=633 Z=-3238.5(0.00%) | Like=-3224.47..-0.00 [-4365.8804..-2199.2855] | it/evals=2432/17280 eff=37.2493% N=633 Z=-3088.4(0.00%) | Like=-3074.99..-0.00 [-4365.8804..-2199.2855] | it/evals=2463/17328 eff=37.2531% N=633 Z=-2968.5(0.00%) | Like=-2958.09..-0.00 [-4365.8804..-2199.2855] | it/evals=2496/17351 eff=37.4844% N=633 Z=-2889.2(0.00%) | Like=-2872.85..-0.00 [-4365.8804..-2199.2855] | it/evals=2516/17355 eff=37.6555% N=633 Z=-2836.1(0.00%) | Like=-2820.06..-0.00 [-4365.8804..-2199.2855] | it/evals=2528/17366 eff=37.8680% N=633 Z=-2629.8(0.00%) | Like=-2619.15..-0.00 [-4365.8804..-2199.2855] | it/evals=2560/17385 eff=38.1392% N=633 Z=-2433.0(0.00%) | Like=-2401.13..-0.00 [-4365.8804..-2199.2855] | it/evals=2624/17419 eff=38.5976% N=633 Z=-2300.0(0.00%) | Like=-2288.74..-0.00 [-4365.8804..-2199.2855] | it/evals=2656/17439 eff=38.8182% N=633 Z=-2104.6(0.00%) | Like=-2082.43..-0.00 [-2196.1964..-1107.6827] | it/evals=2720/17492 eff=39.3677% N=633 Z=-1924.5(0.00%) | Like=-1910.85..-0.00 [-2196.1964..-1107.6827] | it/evals=2784/17628 eff=38.9510% N=633 Z=-1824.7(0.00%) | Like=-1799.25..-0.00 [-2196.1964..-1107.6827] | it/evals=2816/17628 eff=39.3522% N=633 Z=-1715.0(0.00%) | Like=-1703.31..-0.00 [-2196.1964..-1107.6827] | it/evals=2848/17628 eff=39.8108% N=633 Z=-1654.4(0.00%) | Like=-1643.76..-0.00 [-2196.1964..-1107.6827] | it/evals=2869/17756 eff=38.7338% N=633 Z=-1549.0(0.00%) | Like=-1533.28..-0.00 [-2196.1964..-1107.6827] | it/evals=2912/17756 eff=39.4526% N=633 Z=-1476.1(0.00%) | Like=-1460.45..-0.00 [-2196.1964..-1107.6827] | it/evals=2944/17756 eff=39.9502% N=633 Z=-1324.0(0.00%) | Like=-1312.80..-0.00 [-2196.1964..-1107.6827] | it/evals=3008/17777 eff=40.3244% N=633 Z=-1249.8(0.00%) | Like=-1238.52..-0.00 [-2196.1964..-1107.6827] | it/evals=3040/17796 eff=40.5250% N=633 Z=-1130.0(0.00%) | Like=-1118.75..-0.00 [-2196.1964..-1107.6827] | it/evals=3104/17934 eff=39.7892% N=633 Z=-976.6(0.00%) | Like=-964.69..-0.00 [-1105.0830..-573.3152] | it/evals=3200/17934 eff=40.8696% N=633 Z=-922.1(0.00%) | Like=-909.47..-0.00 [-1105.0830..-573.3152] | it/evals=3232/18062 eff=39.8929% N=633 Z=-792.9(0.00%) | Like=-779.33..-0.00 [-1105.0830..-573.3152] | it/evals=3328/18190 eff=40.0148% N=633 Z=-715.4(0.00%) | Like=-703.27..-0.00 [-1105.0830..-573.3152] | it/evals=3398/18190 eff=40.7307% N=633 Z=-684.2(0.00%) | Like=-671.97..-0.00 [-1105.0830..-573.3152] | it/evals=3425/18322 eff=39.8279% N=633 Z=-622.2(0.00%) | Like=-608.87..-0.00 [-1105.0830..-573.3152] | it/evals=3488/18322 eff=40.5929% N=633 Z=-571.1(0.00%) | Like=-558.96..-0.00 [-573.1550..-296.8204] | it/evals=3552/18322 eff=41.1905% N=633 Z=-484.2(0.00%) | Like=-472.59..-0.00 [-573.1550..-296.8204] | it/evals=3648/18323 eff=42.0650% N=633 Z=-437.3(0.00%) | Like=-425.89..-0.00 [-573.1550..-296.8204] | it/evals=3712/18454 eff=41.5064% N=633 Z=-386.0(0.00%) | Like=-374.06..-0.00 [-573.1550..-296.8204] | it/evals=3808/18454 eff=42.6419% N=633 Z=-369.7(0.00%) | Like=-357.81..-0.00 [-573.1550..-296.8204] | it/evals=3834/18582 eff=41.6835% N=633 Z=-305.5(0.00%) | Like=-294.11..-0.00 [-296.5950..-157.9816] | it/evals=3963/18710 eff=41.8508% N=633 Z=-275.8(0.00%) | Like=-263.14..-0.00 [-296.5950..-157.9816] | it/evals=4032/18710 eff=42.7259% N=633 Z=-263.3(0.00%) | Like=-251.59..-0.00 [-296.5950..-157.9816] | it/evals=4064/18710 eff=42.9884% N=633 Z=-252.4(0.00%) | Like=-239.84..-0.00 [-296.5950..-157.9816] | it/evals=4095/18838 eff=42.2217% N=633 Z=-251.9(0.00%) | Like=-239.74..-0.00 [-296.5950..-157.9816] | it/evals=4096/18838 eff=42.2430% N=633 Z=-240.2(0.00%) | Like=-228.12..-0.00 [-296.5950..-157.9816] | it/evals=4128/18838 eff=42.6261% N=633 Z=-233.7(0.00%) | Like=-222.51..-0.00 [-296.5950..-157.9816] | it/evals=4146/18838 eff=42.7963% N=633 Z=-229.4(0.00%) | Like=-217.38..-0.00 [-296.5950..-157.9816] | it/evals=4160/18838 eff=43.0517% N=633 Z=-226.1(0.00%) | Like=-214.06..-0.00 [-296.5950..-157.9816] | it/evals=4170/18838 eff=43.1581% N=633 Z=-212.1(0.00%) | Like=-199.30..-0.00 [-296.5950..-157.9816] | it/evals=4216/18969 eff=42.4845% N=633 Z=-209.7(0.00%) | Like=-198.05..-0.00 [-296.5950..-157.9816] | it/evals=4224/18969 eff=42.6087% N=633 Z=-203.5(0.00%) | Like=-191.01..-0.00 [-296.5950..-157.9816] | it/evals=4246/18969 eff=42.7950% N=633 Z=-199.3(0.00%) | Like=-187.42..-0.00 [-296.5950..-157.9816] | it/evals=4256/18969 eff=42.9400% N=633 Z=-190.7(0.00%) | Like=-178.51..-0.00 [-296.5950..-157.9816] | it/evals=4288/18969 eff=43.2298% N=633 Z=-183.9(0.00%) | Like=-171.49..-0.00 [-296.5950..-157.9816] | it/evals=4315/18969 eff=43.5404% N=633 Z=-179.0(0.00%) | Like=-167.30..-0.00 [-296.5950..-157.9816] | it/evals=4336/18969 eff=43.7060% N=633 Z=-154.1(0.00%) | Like=-142.31..-0.00 [-157.8032..-80.0660] | it/evals=4448/19098 eff=43.7588% N=633 Z=-153.1(0.00%) | Like=-141.59..-0.00 [-157.8032..-80.0660] | it/evals=4455/19098 eff=43.7790% N=633 Z=-147.5(0.00%) | Like=-135.95..-0.00 [-157.8032..-80.0660] | it/evals=4480/19098 eff=43.9605% N=633 Z=-135.4(0.00%) | Like=-123.40..-0.00 [-157.8032..-80.0660] | it/evals=4544/19098 eff=44.5453% N=633 Z=-128.1(0.00%) | Like=-116.14..-0.00 [-157.8032..-80.0660] | it/evals=4576/19226 eff=43.7586% N=633 Z=-121.9(0.00%) | Like=-109.85..-0.00 [-157.8032..-80.0660] | it/evals=4608/19226 eff=44.0338% N=633 Z=-116.7(0.00%) | Like=-104.55..-0.00 [-157.8032..-80.0660] | it/evals=4640/19226 eff=44.4466% N=633 Z=-115.1(0.00%) | Like=-103.41..-0.00 [-157.8032..-80.0660] | it/evals=4652/19226 eff=44.5646% N=633 Z=-112.5(0.00%) | Like=-100.22..-0.00 [-157.8032..-80.0660] | it/evals=4672/19226 eff=44.7415% N=633 Z=-107.8(0.00%) | Like=-95.74..-0.00 [-157.8032..-80.0660] | it/evals=4704/19226 eff=45.1543% N=633 Z=-97.6(0.00%) | Like=-85.64..-0.00 [-157.8032..-80.0660] | it/evals=4768/19354 eff=44.6980% N=633 Z=-95.9(0.00%) | Like=-83.97..-0.00 [-157.8032..-80.0660] | it/evals=4782/19354 eff=44.7363% N=633 Z=-93.3(0.00%) | Like=-81.27..-0.00 [-157.8032..-80.0660] | it/evals=4802/19354 eff=44.9281% N=633 Z=-88.0(0.00%) | Like=-76.08..-0.00 [-79.9098..-37.7207] | it/evals=4847/19354 eff=45.3883% N=633 Z=-87.5(0.00%) | Like=-75.65..-0.00 [-79.9098..-37.7207] | it/evals=4852/19354 eff=45.4075% N=633 Z=-82.4(0.00%) | Like=-70.22..-0.00 [-79.9098..-37.7207] | it/evals=4897/19482 eff=44.6191% N=633 Z=-79.1(0.00%) | Like=-66.97..-0.00 [-79.9098..-37.7207] | it/evals=4928/19482 eff=44.9186% N=633 Z=-74.9(0.00%) | Like=-62.27..-0.00 [-79.9098..-37.7207] | it/evals=4966/19482 eff=45.1806% N=633 Z=-68.5(0.00%) | Like=-56.04..-0.00 [-79.9098..-37.7207] | it/evals=5024/19482 eff=45.8731% N=633 Z=-65.0(0.00%) | Like=-52.64..-0.00 [-79.9098..-37.7207] | it/evals=5056/19610 eff=45.0923% N=633 Z=-62.9(0.00%) | Like=-50.93..-0.00 [-79.9098..-37.7207] | it/evals=5081/19610 eff=45.2568% N=633 Z=-62.4(0.00%) | Like=-50.72..-0.00 [-79.9098..-37.7207] | it/evals=5088/19610 eff=45.3116% N=633 Z=-60.6(0.00%) | Like=-48.59..-0.00 [-79.9098..-37.7207] | it/evals=5114/19610 eff=45.5310% N=633 Z=-60.1(0.00%) | Like=-47.65..-0.00 [-79.9098..-37.7207] | it/evals=5120/19610 eff=45.6407% N=633 Z=-59.1(0.00%) | Like=-46.85..-0.00 [-79.9098..-37.7207] | it/evals=5130/19610 eff=45.7138% N=633 Z=-54.6(0.00%) | Like=-42.83..-0.00 [-79.9098..-37.7207] | it/evals=5184/19610 eff=46.2621% N=633 Z=-53.8(0.00%) | Like=-41.66..-0.00 [-79.9098..-37.7207] | it/evals=5197/19610 eff=46.3718% N=633 Z=-51.5(0.00%) | Like=-39.52..-0.00 [-79.9098..-37.7207] | it/evals=5229/19738 eff=45.4367% N=633 Z=-50.9(0.00%) | Like=-39.03..-0.00 [-79.9098..-37.7207] | it/evals=5239/19738 eff=45.5260% N=633 Z=-50.4(0.00%) | Like=-38.57..-0.00 [-79.9098..-37.7207] | it/evals=5248/19738 eff=45.6510% N=633 Z=-49.0(0.00%) | Like=-37.08..-0.00 [-37.5640..-17.8529] | it/evals=5270/19738 eff=45.9546% N=633 Z=-48.5(0.00%) | Like=-36.57..-0.00 [-37.5640..-17.8529] | it/evals=5280/19738 eff=46.1154% N=633 Z=-46.9(0.00%) | Like=-35.04..-0.00 [-37.5640..-17.8529] | it/evals=5309/19738 eff=46.4190% N=633 Z=-46.8(0.00%) | Like=-34.95..-0.00 [-37.5640..-17.8529] | it/evals=5312/19738 eff=46.4726% N=633 Z=-45.9(0.00%) | Like=-34.20..-0.00 [-37.5640..-17.8529] | it/evals=5333/19738 eff=46.6333% N=633 Z=-45.7(0.00%) | Like=-33.68..-0.00 [-37.5640..-17.8529] | it/evals=5338/19738 eff=46.6690% N=633 Z=-45.4(0.00%) | Like=-33.51..-0.00 [-37.5640..-17.8529] | it/evals=5344/19738 eff=46.7405% N=633 Z=-43.7(0.00%) | Like=-31.68..-0.00 [-37.5640..-17.8529] | it/evals=5376/19866 eff=45.9752% N=633 Z=-42.1(0.00%) | Like=-29.98..-0.00 [-37.5640..-17.8529] | it/evals=5408/19866 eff=46.2197% N=633 Z=-39.1(0.00%) | Like=-26.98..-0.00 [-37.5640..-17.8529] | it/evals=5457/19866 eff=46.7959% N=633 Z=-38.6(0.00%) | Like=-26.58..-0.00 [-37.5640..-17.8529] | it/evals=5469/19866 eff=46.8483% N=633 Z=-37.5(0.00%) | Like=-25.36..-0.00 [-37.5640..-17.8529] | it/evals=5490/19866 eff=47.0229% N=633 Z=-36.7(0.00%) | Like=-24.40..-0.00 [-37.5640..-17.8529] | it/evals=5507/19866 eff=47.1276% N=633 Z=-34.6(0.00%) | Like=-22.58..-0.00 [-37.5640..-17.8529] | it/evals=5553/19994 eff=46.5414% N=633 Z=-34.0(0.00%) | Like=-22.05..-0.00 [-37.5640..-17.8529] | it/evals=5568/19994 eff=46.6781% N=633 Z=-33.3(0.00%) | Like=-21.42..-0.00 [-37.5640..-17.8529] | it/evals=5589/19994 eff=46.8488% N=633 Z=-32.9(0.00%) | Like=-20.96..-0.00 [-37.5640..-17.8529] | it/evals=5602/19994 eff=46.8830% N=633 Z=-32.3(0.00%) | Like=-20.38..-0.00 [-37.5640..-17.8529] | it/evals=5621/19994 eff=46.9172% N=633 Z=-31.6(0.00%) | Like=-19.84..-0.00 [-37.5640..-17.8529] | it/evals=5646/19994 eff=47.2075% N=633 Z=-31.2(0.00%) | Like=-19.48..-0.00 [-37.5640..-17.8529] | it/evals=5660/19994 eff=47.2417% N=633 Z=-31.1(0.00%) | Like=-19.32..-0.00 [-37.5640..-17.8529] | it/evals=5664/19994 eff=47.2929% N=633 Z=-30.7(0.00%) | Like=-18.71..-0.00 [-37.5640..-17.8529] | it/evals=5677/19994 eff=47.3954% N=633 Z=-30.3(0.00%) | Like=-18.07..-0.00 [-37.5640..-17.8529] | it/evals=5689/19994 eff=47.5149% N=633 Z=-29.9(0.00%) | Like=-17.85..-0.00 [-37.5640..-17.8529] | it/evals=5700/19994 eff=47.5491% N=633 Z=-29.0(0.00%) | Like=-17.05..-0.00 [-17.8431..-9.0950] | it/evals=5728/19994 eff=47.8736% N=633 Z=-28.7(0.00%) | Like=-16.69..-0.00 [-17.8431..-9.0950] | it/evals=5742/20122 eff=46.9163% N=633 Z=-28.4(0.00%) | Like=-16.41..-0.00 [-17.8431..-9.0950] | it/evals=5752/20122 eff=46.9664% N=633 Z=-27.8(0.00%) | Like=-15.86..-0.00 [-17.8431..-9.0950] | it/evals=5777/20122 eff=46.9831% N=633 Z=-27.5(0.00%) | Like=-15.57..-0.00 [-17.8431..-9.0950] | it/evals=5789/20122 eff=47.0500% N=633 Z=-27.2(0.00%) | Like=-15.32..-0.00 [-17.8431..-9.0950] | it/evals=5802/20122 eff=47.0834% N=633 Z=-26.9(0.00%) | Like=-15.10..-0.00 [-17.8431..-9.0950] | it/evals=5815/20122 eff=47.1670% N=633 Z=-26.7(0.00%) | Like=-14.92..-0.00 [-17.8431..-9.0950] | it/evals=5824/20122 eff=47.2840% N=633 Z=-26.4(0.00%) | Like=-14.60..-0.00 [-17.8431..-9.0950] | it/evals=5836/20122 eff=47.4010% N=633 Z=-26.0(0.00%) | Like=-14.23..-0.00 [-17.8431..-9.0950] | it/evals=5857/20122 eff=47.6350% N=633 Z=-25.7(0.00%) | Like=-13.80..-0.00 [-17.8431..-9.0950] | it/evals=5874/20122 eff=47.7520% N=633 Z=-25.5(0.00%) | Like=-13.67..-0.00 [-17.8431..-9.0950] | it/evals=5882/20122 eff=47.8188% N=633 Z=-25.3(0.00%) | Like=-13.44..-0.00 [-17.8431..-9.0950] | it/evals=5890/20122 eff=47.8690% N=633 Z=-24.7(0.00%) | Like=-12.76..-0.00 [-17.8431..-9.0950] | it/evals=5923/20122 eff=48.1197% N=633 Z=-24.5(0.00%) | Like=-12.58..-0.00 [-17.8431..-9.0950] | it/evals=5929/20122 eff=48.1531% N=633 Z=-24.3(0.00%) | Like=-12.41..-0.00 [-17.8431..-9.0950] | it/evals=5940/20250 eff=47.2099% N=633 Z=-23.9(0.00%) | Like=-11.97..-0.00 [-17.8431..-9.0950] | it/evals=5963/20250 eff=47.4227% N=633 Z=-23.5(0.00%) | Like=-11.48..-0.00 [-17.8431..-9.0950] | it/evals=5984/20250 eff=47.6027% N=633 Z=-23.3(0.00%) | Like=-11.34..-0.00 [-17.8431..-9.0950] | it/evals=5993/20250 eff=47.6354% N=633 Z=-23.1(0.00%) | Like=-11.19..-0.00 [-17.8431..-9.0950] | it/evals=6002/20250 eff=47.6845% N=633 Z=-23.0(0.00%) | Like=-11.08..-0.00 [-17.8431..-9.0950] | it/evals=6009/20250 eff=47.7500% N=633 Z=-22.9(0.00%) | Like=-10.95..-0.00 [-17.8431..-9.0950] | it/evals=6017/20250 eff=47.7663% N=633 Z=-22.4(0.00%) | Like=-10.63..-0.00 [-17.8431..-9.0950] | it/evals=6044/20250 eff=47.9791% N=633 Z=-22.0(0.00%) | Like=-10.13..-0.00 [-17.8431..-9.0950] | it/evals=6071/20250 eff=48.0281% N=633 Z=-21.9(0.00%) | Like=-9.96..-0.00 [-17.8431..-9.0950] | it/evals=6080/20250 eff=48.1263% N=633 Z=-21.7(0.01%) | Like=-9.84..-0.00 [-17.8431..-9.0950] | it/evals=6092/20250 eff=48.1591% N=633 Z=-21.4(0.01%) | Like=-9.41..-0.00 [-17.8431..-9.0950] | it/evals=6112/20250 eff=48.3227% N=633 Z=-20.1(0.03%) | Like=-8.16..-0.00 [-9.0897..-4.4237] | it/evals=6208/20378 eff=48.2289% N=633 Z=-19.3(0.06%) | Like=-7.33..-0.00 [-9.0897..-4.4237] | it/evals=6272/20378 eff=48.8219% N=633 Z=-18.9(0.09%) | Like=-6.82..-0.00 [-9.0897..-4.4237] | it/evals=6304/20506 eff=48.1231% N=633 Z=-17.4(0.41%) | Like=-5.45..-0.00 [-9.0897..-4.4237] | it/evals=6432/20634 eff=48.3449% N=633 Z=-17.1(0.54%) | Like=-5.16..-0.00 [-9.0897..-4.4237] | it/evals=6464/20634 eff=48.6990% N=633 Z=-16.0(1.66%) | Like=-4.13..-0.00 [-4.4200..-3.3507] | it/evals=6614/20890 eff=48.2003% N=633 Z=-15.9(1.76%) | Like=-4.03..-0.00 [-4.4200..-3.3507] | it/evals=6624/20890 eff=48.2891% N=633 Z=-15.4(3.09%) | Like=-3.52..-0.00 [-4.4200..-3.3507] | it/evals=6720/21018 eff=47.9721% N=633 Z=-15.2(3.67%) | Like=-3.32..-0.00 [-3.3442..-3.1044] | it/evals=6752/21146 eff=47.3669% N=633 Z=-14.9(4.96%) | Like=-3.01..-0.00 [-3.0137..-2.9935] | it/evals=6816/21274 eff=46.9657% N=633 Z=-14.7(5.68%) | Like=-2.90..-0.00 [-2.8952..-2.8916]*| it/evals=6848/21274 eff=47.1619% N=633 Z=-14.5(7.35%) | Like=-2.60..-0.00 [-2.6050..-2.5982]*| it/evals=6912/21402 eff=46.7851% N=633 Z=-14.0(11.74%) | Like=-2.11..-0.00 [-2.1138..-2.1099]*| it/evals=7034/21786 eff=45.2596% N=633 Z=-14.0(11.96%) | Like=-2.09..-0.00 [-2.0911..-2.0864]*| it/evals=7040/21786 eff=45.2988% N=633 Z=-13.9(13.30%) | Like=-2.01..-0.00 [-2.0124..-2.0064]*| it/evals=7072/21914 eff=44.7846% N=633 Z=-13.7(15.16%) | Like=-1.87..-0.00 [-1.8670..-1.8662]*| it/evals=7113/22042 eff=44.3376% N=633 Z=-13.7(16.29%) | Like=-1.80..-0.00 [-1.8044..-1.8013]*| it/evals=7136/22170 eff=43.8302% N=633 Z=-13.6(17.80%) | Like=-1.69..-0.00 [-1.6945..-1.6941]*| it/evals=7168/22170 eff=44.0667% N=633 Z=-13.5(19.44%) | Like=-1.63..-0.00 [-1.6280..-1.6256]*| it/evals=7200/22170 eff=44.2535% N=633 Z=-13.2(26.34%) | Like=-1.36..-0.00 [-1.3641..-1.3638]*| it/evals=7329/22298 eff=44.3437% N=633 Z=-13.1(28.17%) | Like=-1.29..-0.00 [-1.2913..-1.2886]*| it/evals=7360/22298 eff=44.5275% N=633 Z=-13.1(29.89%) | Like=-1.24..-0.00 [-1.2383..-1.2370]*| it/evals=7392/22298 eff=44.7236% N=633 Z=-13.0(31.74%) | Like=-1.19..-0.00 [-1.1865..-1.1862]*| it/evals=7424/22298 eff=44.9075% N=633 Z=-12.9(35.30%) | Like=-1.06..-0.00 [-1.0571..-1.0571]*| it/evals=7488/22426 eff=44.6000% N=633 Z=-12.9(37.10%) | Like=-1.00..-0.00 [-1.0035..-1.0029]*| it/evals=7520/22426 eff=44.7810% N=633 Z=-12.8(40.73%) | Like=-0.91..-0.00 [-0.9051..-0.9047]*| it/evals=7584/22554 eff=44.4563% N=633 Z=-12.7(42.57%) | Like=-0.87..-0.00 [-0.8680..-0.8679]*| it/evals=7616/22554 eff=44.6583% N=633 Z=-12.7(44.34%) | Like=-0.82..-0.00 [-0.8227..-0.8218]*| it/evals=7648/22554 eff=44.8366% N=633 Z=-12.6(46.14%) | Like=-0.78..-0.00 [-0.7849..-0.7846]*| it/evals=7680/22682 eff=44.4457% N=633 Z=-12.6(47.97%) | Like=-0.74..-0.00 [-0.7432..-0.7419]*| it/evals=7712/22682 eff=44.6330% N=633 Z=-12.6(49.74%) | Like=-0.71..-0.00 [-0.7099..-0.7090]*| it/evals=7744/22682 eff=44.8437% N=633 Z=-12.5(51.46%) | Like=-0.68..-0.00 [-0.6754..-0.6748]*| it/evals=7776/22810 eff=44.2971% N=633 Z=-12.5(53.17%) | Like=-0.65..-0.00 [-0.6483..-0.6482]*| it/evals=7808/22810 eff=44.4701% N=633 Z=-12.5(54.82%) | Like=-0.62..-0.00 [-0.6178..-0.6178]*| it/evals=7840/22810 eff=44.6546% N=633 Z=-12.4(56.43%) | Like=-0.58..-0.00 [-0.5783..-0.5759]*| it/evals=7872/22938 eff=44.1982% N=633 Z=-12.4(59.63%) | Like=-0.52..-0.00 [-0.5184..-0.5174]*| it/evals=7936/23066 eff=43.8893% N=633 Z=-12.3(62.67%) | Like=-0.47..-0.00 [-0.4708..-0.4696]*| it/evals=8000/23194 eff=43.6554% N=633 Z=-12.3(65.53%) | Like=-0.43..-0.00 [-0.4281..-0.4278]*| it/evals=8064/23322 eff=43.3736% N=633 Z=-12.3(66.84%) | Like=-0.41..-0.00 [-0.4056..-0.4043]*| it/evals=8096/23450 eff=42.9814% N=633 Z=-12.3(68.47%) | Like=-0.38..-0.00 [-0.3787..-0.3780]*| it/evals=8135/23578 eff=42.5787% N=633 Z=-12.2(73.82%) | Like=-0.30..-0.00 [-0.2960..-0.2957]*| it/evals=8274/23706 eff=42.5943% N=633 Z=-12.1(76.51%) | Like=-0.26..-0.00 [-0.2611..-0.2600]*| it/evals=8352/23706 eff=42.9915% N=633 Z=-12.1(77.55%) | Like=-0.25..-0.00 [-0.2489..-0.2488]*| it/evals=8384/23834 eff=42.5993% N=633 Z=-12.1(78.54%) | Like=-0.24..-0.00 [-0.2388..-0.2385]*| it/evals=8416/23834 eff=42.7540% N=633 Z=-12.1(80.39%) | Like=-0.21..-0.00 [-0.2143..-0.2125]*| it/evals=8480/23834 eff=43.0944% N=633 Z=-12.1(81.27%) | Like=-0.20..-0.00 [-0.2038..-0.2037]*| it/evals=8512/23962 eff=42.6855% N=633 Z=-12.1(82.10%) | Like=-0.19..-0.00 [-0.1927..-0.1926]*| it/evals=8544/23962 eff=42.8484% N=633 Z=-12.0(85.38%) | Like=-0.16..-0.00 [-0.1596..-0.1593]*| it/evals=8684/24090 eff=42.8701% N=633 Z=-12.0(86.46%) | Like=-0.15..-0.00 [-0.1462..-0.1458]*| it/evals=8736/24090 eff=43.1112% N=633 Z=-12.0(87.08%) | Like=-0.14..-0.00 [-0.1370..-0.1363]*| it/evals=8768/24218 eff=42.6927% N=633 Z=-12.0(87.67%) | Like=-0.13..-0.00 [-0.1305..-0.1305]*| it/evals=8800/24218 eff=42.8217% N=633 Z=-12.0(88.24%) | Like=-0.13..-0.00 [-0.1255..-0.1255]*| it/evals=8832/24218 eff=43.0102% N=633 Z=-12.0(88.79%) | Like=-0.12..-0.00 [-0.1199..-0.1198]*| it/evals=8864/24346 eff=42.5884% N=633 Z=-12.0(90.88%) | Like=-0.10..-0.00 [-0.0989..-0.0988]*| it/evals=9002/24602 eff=42.1676% N=633 Z=-12.0(91.59%) | Like=-0.09..-0.00 [-0.0914..-0.0913]*| it/evals=9056/24730 eff=41.8846% N=633 Z=-12.0(91.99%) | Like=-0.09..-0.00 [-0.0866..-0.0864]*| it/evals=9088/24730 eff=42.0546% N=633 Z=-12.0(92.37%) | Like=-0.08..-0.00 [-0.0821..-0.0821]*| it/evals=9120/24858 eff=41.7017% N=633 Z=-11.9(92.73%) | Like=-0.08..-0.00 [-0.0774..-0.0769]*| it/evals=9152/24986 eff=41.3017% N=633 Z=-11.9(92.76%) | Like=-0.08..-0.00 [-0.0768..-0.0764]*| it/evals=9155/24986 eff=41.3202% N=633 Z=-11.9(93.41%) | Like=-0.07..-0.00 [-0.0703..-0.0702]*| it/evals=9216/25114 eff=41.0569% N=633 Z=-11.9(93.72%) | Like=-0.07..-0.00 [-0.0676..-0.0673]*| it/evals=9248/25114 eff=41.2483% N=633 Z=-11.9(94.58%) | Like=-0.06..-0.00 [-0.0587..-0.0585]*| it/evals=9344/25114 eff=41.7312% N=633 Z=-11.9(95.32%) | Like=-0.05..-0.00 [-0.0492..-0.0491]*| it/evals=9440/25242 eff=41.6734% N=633 Z=-11.9(95.55%) | Like=-0.05..-0.00 [-0.0468..-0.0468]*| it/evals=9472/25242 eff=41.8536% N=633 Z=-11.9(95.97%) | Like=-0.04..-0.00 [-0.0417..-0.0416]*| it/evals=9536/25370 eff=41.7327% N=633 Z=-11.9(96.16%) | Like=-0.04..-0.00 [-0.0390..-0.0390]*| it/evals=9568/25370 eff=41.8306% N=633 Z=-11.9(96.35%) | Like=-0.04..-0.00 [-0.0378..-0.0378]*| it/evals=9600/25498 eff=41.5265% N=633 Z=-11.9(97.04%) | Like=-0.03..-0.00 [-0.0311..-0.0311]*| it/evals=9735/25626 eff=41.6558% N=633 Z=-11.9(97.15%) | Like=-0.03..-0.00 [-0.0302..-0.0301]*| it/evals=9760/25626 eff=41.7603% N=633 Z=-11.9(97.55%) | Like=-0.03..-0.00 [-0.0257..-0.0256]*| it/evals=9856/25882 eff=41.3097% N=633 Z=-11.9(97.78%) | Like=-0.02..-0.00 [-0.0232..-0.0232]*| it/evals=9920/26010 eff=41.1423% N=633 Z=-11.9(97.99%) | Like=-0.02..-0.00 [-0.0211..-0.0211]*| it/evals=9984/26138 eff=40.9701% N=633 Z=-11.9(98.09%) | Like=-0.02..-0.00 [-0.0200..-0.0200]*| it/evals=10016/26138 eff=41.1034% N=633 Z=-11.9(98.46%) | Like=-0.02..-0.00 [-0.0161..-0.0161]*| it/evals=10154/26522 eff=40.3456% N=633 Z=-11.9(98.51%) | Like=-0.02..-0.00 [-0.0156..-0.0155]*| it/evals=10174/26522 eff=40.4183% N=633 Z=-11.9(98.72%) | Like=-0.01..-0.00 [-0.0134..-0.0134]*| it/evals=10272/26650 eff=40.3805% N=633 Z=-11.9(98.84%) | Like=-0.01..-0.00 [-0.0122..-0.0122]*| it/evals=10336/26650 eff=40.6762% N=633 [ultranest] Explored until L=-1e-06 [ultranest] Likelihood function evaluations: 26778 logzerr in iteration 1 0.126852003011403 [ultranest] logZ = -11.87 +- 0.08615 [ultranest] Effective samples strategy satisfied (ESS = 2546.8, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.09 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 1265 minimum live points (dlogz from 0.07 to 0.18, need <0.1) [ultranest] logZ error budget: single: 0.19 bs:0.09 tail:0.00 total:0.09 required:<0.10 [ultranest] Widening roots to 1265 live points (have 633 already) ... [ultranest] Sampling 632 live points from prior ... Z=-232694.3(0.00%) | Like=-229880.30..-0.00 [-239030.0011..-74914.9361] | it/evals=4/27538 eff=0.0000% N=1265 Z=-180722.4(0.00%) | Like=-180448.19..-0.00 [-239030.0011..-74914.9361] | it/evals=64/27538 eff=18.7500% N=1265 Z=-166936.0(0.00%) | Like=-166678.50..-0.00 [-239030.0011..-74914.9361] | it/evals=96/27538 eff=32.0312% N=1265 Z=-158714.8(0.00%) | Like=-158575.03..-0.00 [-239030.0011..-74914.9361] | it/evals=128/27538 eff=44.5312% N=1265 Z=-145580.6(0.00%) | Like=-145370.01..-0.00 [-239030.0011..-74914.9361] | it/evals=192/27538 eff=67.1875% N=1265 Z=-131355.5(0.00%) | Like=-131338.18..-0.00 [-239030.0011..-74914.9361] | it/evals=256/27666 eff=49.6094% N=1265 Z=-127831.8(0.00%) | Like=-127348.45..-0.00 [-239030.0011..-74914.9361] | it/evals=288/27666 eff=55.4688% N=1265 Z=-112782.6(0.00%) | Like=-112601.97..-0.00 [-239030.0011..-74914.9361] | it/evals=426/27666 eff=78.1250% N=1265 Z=-110176.1(0.00%) | Like=-110152.51..-0.00 [-239030.0011..-74914.9361] | it/evals=448/27666 eff=82.0312% N=1265 Z=-107487.2(0.00%) | Like=-107453.27..-0.00 [-239030.0011..-74914.9361] | it/evals=480/27794 eff=58.3333% N=1265 Z=-100295.4(0.00%) | Like=-100155.39..-0.00 [-239030.0011..-74914.9361] | it/evals=576/27794 eff=71.6146% N=1265 Z=-94269.4(0.00%) | Like=-94248.07..-0.00 [-239030.0011..-74914.9361] | it/evals=640/27922 eff=60.1562% N=1265 Z=-84705.4(0.00%) | Like=-84544.09..-0.00 [-239030.0011..-74914.9361] | it/evals=773/27922 eff=71.4844% N=1265 Z=-76109.5(0.00%) | Like=-75948.76..-0.00 [-239030.0011..-74914.9361] | it/evals=891/28050 eff=65.9375% N=1265 Z=-74302.7(0.00%) | Like=-74287.97..-0.00 [-74914.7603..-35323.2110] | it/evals=928/28178 eff=57.6823% N=1265 Z=-70944.4(0.00%) | Like=-70928.31..-0.00 [-74914.7603..-35323.2110] | it/evals=992/28178 eff=61.5885% N=1265 Z=-67403.5(0.00%) | Like=-67373.31..-0.00 [-74914.7603..-35323.2110] | it/evals=1056/28306 eff=56.4732% N=1265 Z=-65399.1(0.00%) | Like=-65352.40..-0.00 [-74914.7603..-35323.2110] | it/evals=1088/28306 eff=58.4821% N=1265 Z=-61101.9(0.00%) | Like=-61026.38..-0.00 [-74914.7603..-35323.2110] | it/evals=1152/28434 eff=53.8086% N=1265 Z=-56740.0(0.00%) | Like=-56600.20..-0.00 [-74914.7603..-35323.2110] | it/evals=1248/28562 eff=52.2569% N=1265 Z=-54927.0(0.00%) | Like=-54869.07..-0.00 [-74914.7603..-35323.2110] | it/evals=1280/28562 eff=53.4722% N=1265 Z=-50619.4(0.00%) | Like=-50569.04..-0.00 [-74914.7603..-35323.2110] | it/evals=1376/28690 eff=51.6406% N=1265 Z=-49314.0(0.00%) | Like=-49149.87..-0.00 [-74914.7603..-35323.2110] | it/evals=1408/28818 eff=48.0114% N=1265 Z=-48032.7(0.00%) | Like=-48022.22..-0.00 [-74914.7603..-35323.2110] | it/evals=1440/28818 eff=49.2188% N=1265 Z=-46738.3(0.00%) | Like=-46669.50..-0.00 [-74914.7603..-35323.2110] | it/evals=1472/28946 eff=46.3542% N=1265 Z=-44608.3(0.00%) | Like=-44592.28..-0.00 [-74914.7603..-35323.2110] | it/evals=1536/29074 eff=44.5913% N=1265 Z=-42642.5(0.00%) | Like=-42632.57..-0.00 [-74914.7603..-35323.2110] | it/evals=1600/29202 eff=43.2478% N=1265 Z=-40865.1(0.00%) | Like=-40839.53..-0.00 [-74914.7603..-35323.2110] | it/evals=1664/29330 eff=42.0833% N=1265 Z=-38579.7(0.00%) | Like=-38557.65..-0.00 [-74914.7603..-35323.2110] | it/evals=1728/29458 eff=41.2109% N=1265 Z=-35866.4(0.00%) | Like=-35784.38..-0.00 [-74914.7603..-35323.2110] | it/evals=1824/29714 eff=38.7153% N=1265 Z=-34782.8(0.00%) | Like=-34765.34..-0.00 [-35313.1585..-17042.0710] | it/evals=1856/29842 eff=37.5411% N=1265 Z=-33007.2(0.00%) | Like=-32991.48..-0.00 [-35313.1585..-17042.0710] | it/evals=1920/29970 eff=36.9531% N=1265 Z=-32118.8(0.00%) | Like=-32101.00..-0.00 [-35313.1585..-17042.0710] | it/evals=1952/30098 eff=35.7515% N=1265 Z=-29372.2(0.00%) | Like=-29356.57..-0.00 [-35313.1585..-17042.0710] | it/evals=2041/30354 eff=34.1033% N=1265 Z=-26715.8(0.00%) | Like=-26639.82..-0.00 [-35313.1585..-17042.0710] | it/evals=2144/30404 eff=35.5043% N=1265 Z=-25975.4(0.00%) | Like=-25917.95..-0.00 [-35313.1585..-17042.0710] | it/evals=2176/30431 eff=35.5842% N=1265 Z=-25217.5(0.00%) | Like=-25159.41..-0.00 [-35313.1585..-17042.0710] | it/evals=2208/30431 eff=36.1470% N=1265 Z=-23290.4(0.00%) | Like=-23265.12..-0.00 [-35313.1585..-17042.0710] | it/evals=2304/30489 eff=36.7652% N=1265 Z=-22749.3(0.00%) | Like=-22720.00..-0.00 [-35313.1585..-17042.0710] | it/evals=2336/30521 eff=37.0942% N=1265 Z=-20477.6(0.00%) | Like=-20468.45..-0.00 [-35313.1585..-17042.0710] | it/evals=2458/30608 eff=37.7423% N=1265 Z=-20429.8(0.00%) | Like=-20417.05..-0.00 [-35313.1585..-17042.0710] | it/evals=2464/30636 eff=37.5387% N=1265 Z=-18458.1(0.00%) | Like=-18446.83..-0.00 [-35313.1585..-17042.0710] | it/evals=2584/30721 eff=38.2362% N=1265 Z=-18402.1(0.00%) | Like=-18340.63..-0.00 [-35313.1585..-17042.0710] | it/evals=2592/30753 eff=37.9898% N=1265 Z=-17881.6(0.00%) | Like=-17866.08..-0.00 [-35313.1585..-17042.0710] | it/evals=2624/30780 eff=38.2493% N=1265 Z=-17499.5(0.00%) | Like=-17481.54..-0.00 [-35313.1585..-17042.0710] | it/evals=2656/30809 eff=38.4819% N=1265 Z=-16392.8(0.00%) | Like=-16381.01..-0.00 [-17004.0821..-8729.9759] | it/evals=2752/30906 eff=38.9302% N=1265 Z=-15918.1(0.00%) | Like=-15871.37..-0.00 [-17004.0821..-8729.9759] | it/evals=2784/30966 eff=38.8358% N=1265 Z=-14495.0(0.00%) | Like=-14483.65..-0.00 [-17004.0821..-8729.9759] | it/evals=2907/31101 eff=39.2035% N=1265 Z=-14189.6(0.00%) | Like=-14153.66..-0.00 [-17004.0821..-8729.9759] | it/evals=2944/31128 eff=39.2953% N=1265 Z=-13529.4(0.00%) | Like=-13512.63..-0.00 [-17004.0821..-8729.9759] | it/evals=3008/31203 eff=39.2565% N=1265 Z=-13147.7(0.00%) | Like=-13125.68..-0.00 [-17004.0821..-8729.9759] | it/evals=3040/31226 eff=39.3606% N=1265 Z=-12815.4(0.00%) | Like=-12801.68..-0.00 [-17004.0821..-8729.9759] | it/evals=3072/31304 eff=38.9831% N=1265 Z=-12198.4(0.00%) | Like=-12186.36..-0.00 [-17004.0821..-8729.9759] | it/evals=3136/31361 eff=39.1800% N=1265 Z=-11414.0(0.00%) | Like=-11397.61..-0.00 [-17004.0821..-8729.9759] | it/evals=3232/31501 eff=39.0369% N=1265 Z=-10866.1(0.00%) | Like=-10853.56..-0.00 [-17004.0821..-8729.9759] | it/evals=3296/31590 eff=39.1148% N=1265 Z=-10310.8(0.00%) | Like=-10298.35..-0.00 [-17004.0821..-8729.9759] | it/evals=3360/31705 eff=38.7893% N=1265 Z=-9584.2(0.00%) | Like=-9564.22..-0.00 [-17004.0821..-8729.9759] | it/evals=3456/31897 eff=38.2884% N=1265 Z=-9412.9(0.00%) | Like=-9398.33..-0.00 [-17004.0821..-8729.9759] | it/evals=3488/31959 eff=38.2062% N=1265 Z=-9143.3(0.00%) | Like=-9128.92..-0.00 [-17004.0821..-8729.9759] | it/evals=3520/32016 eff=38.1242% N=1265 Z=-8660.9(0.00%) | Like=-8636.01..-0.00 [-8721.9353..-4315.2925] | it/evals=3584/32148 eff=37.8008% N=1265 Z=-8383.6(0.00%) | Like=-8359.37..-0.00 [-8721.9353..-4315.2925] | it/evals=3616/32193 eff=37.7587% N=1265 Z=-7909.3(0.00%) | Like=-7896.02..-0.00 [-8721.9353..-4315.2925] | it/evals=3680/32329 eff=37.3043% N=1265 Z=-7689.6(0.00%) | Like=-7677.79..-0.00 [-8721.9353..-4315.2925] | it/evals=3712/32460 eff=36.7129% N=1265 Z=-7502.2(0.00%) | Like=-7483.69..-0.00 [-8721.9353..-4315.2925] | it/evals=3744/32534 eff=36.5340% N=1265 Z=-7329.4(0.00%) | Like=-7319.56..-0.00 [-8721.9353..-4315.2925] | it/evals=3776/32606 eff=36.2779% N=1265 Z=-7133.7(0.00%) | Like=-7122.52..-0.00 [-8721.9353..-4315.2925] | it/evals=3808/32663 eff=36.2460% N=1265 Z=-6766.1(0.00%) | Like=-6755.31..-0.00 [-8721.9353..-4315.2925] | it/evals=3872/32834 eff=35.6563% N=1265 Z=-6632.2(0.00%) | Like=-6616.24..-0.00 [-8721.9353..-4315.2925] | it/evals=3904/32921 eff=35.4201% N=1265 Z=-6167.5(0.00%) | Like=-6143.27..-0.00 [-8721.9353..-4315.2925] | it/evals=4000/33092 eff=35.1813% N=1265 Z=-6008.1(0.00%) | Like=-5997.22..-0.00 [-8721.9353..-4315.2925] | it/evals=4032/33191 eff=34.8729% N=1265 Z=-5755.0(0.00%) | Like=-5743.99..-0.00 [-8721.9353..-4315.2925] | it/evals=4078/33326 eff=34.4828% N=1265 Z=-5578.4(0.00%) | Like=-5567.25..-0.00 [-8721.9353..-4315.2925] | it/evals=4128/33345 eff=34.6251% N=1265 Z=-5443.7(0.00%) | Like=-5432.08..-0.00 [-8721.9353..-4315.2925] | it/evals=4160/33367 eff=34.7322% N=1265 Z=-5278.2(0.00%) | Like=-5265.61..-0.00 [-8721.9353..-4315.2925] | it/evals=4192/33381 eff=34.9188% N=1265 Z=-5137.4(0.00%) | Like=-5125.38..-0.00 [-8721.9353..-4315.2925] | it/evals=4224/33393 eff=35.0493% N=1265 Z=-4724.2(0.00%) | Like=-4712.76..-0.00 [-8721.9353..-4315.2925] | it/evals=4334/33471 eff=35.5552% N=1265 Z=-4678.9(0.00%) | Like=-4665.97..-0.00 [-8721.9353..-4315.2925] | it/evals=4352/33488 eff=35.6203% N=1265 Z=-4274.5(0.00%) | Like=-4260.64..-0.00 [-4312.6667..-2155.9340] | it/evals=4481/33573 eff=36.0863% N=1265 Z=-4190.6(0.00%) | Like=-4176.30..-0.00 [-4312.6667..-2155.9340] | it/evals=4512/33594 eff=36.2549% N=1265 Z=-3872.7(0.00%) | Like=-3862.06..-0.00 [-4312.6667..-2155.9340] | it/evals=4608/33784 eff=36.0213% N=1265 Z=-3593.8(0.00%) | Like=-3581.84..-0.00 [-4312.6667..-2155.9340] | it/evals=4712/33784 eff=36.8215% N=1265 Z=-3382.7(0.00%) | Like=-3369.40..-0.00 [-4312.6667..-2155.9340] | it/evals=4800/33912 eff=36.7425% N=1265 Z=-3275.7(0.00%) | Like=-3264.24..-0.00 [-4312.6667..-2155.9340] | it/evals=4832/33912 eff=36.9732% N=1265 Z=-3195.4(0.00%) | Like=-3177.99..-0.00 [-4312.6667..-2155.9340] | it/evals=4864/34040 eff=36.5460% N=1265 Z=-3105.8(0.00%) | Like=-3094.61..-0.00 [-4312.6667..-2155.9340] | it/evals=4896/34040 eff=36.7873% N=1265 Z=-3095.9(0.00%) | Like=-3078.10..-0.00 [-4312.6667..-2155.9340] | it/evals=4903/34040 eff=36.8024% N=1265 Z=-2968.4(0.00%) | Like=-2957.18..-0.00 [-4312.6667..-2155.9340] | it/evals=4960/34040 eff=37.1493% N=1265 Z=-2903.4(0.00%) | Like=-2892.29..-0.00 [-4312.6667..-2155.9340] | it/evals=4991/34048 eff=37.2853% N=1265 Z=-2902.7(0.00%) | Like=-2889.17..-0.00 [-4312.6667..-2155.9340] | it/evals=4992/34048 eff=37.3004% N=1265 Z=-2835.1(0.00%) | Like=-2820.62..-0.00 [-4312.6667..-2155.9340] | it/evals=5024/34070 eff=37.4775% N=1265 Z=-2760.2(0.00%) | Like=-2749.76..-0.00 [-4312.6667..-2155.9340] | it/evals=5056/34100 eff=37.6532% N=1265 Z=-2695.2(0.00%) | Like=-2682.94..-0.00 [-4312.6667..-2155.9340] | it/evals=5088/34125 eff=37.8407% N=1265 Z=-2622.2(0.00%) | Like=-2608.72..-0.00 [-4312.6667..-2155.9340] | it/evals=5120/34146 eff=37.9602% N=1265 Z=-2393.7(0.00%) | Like=-2379.52..-0.00 [-4312.6667..-2155.9340] | it/evals=5250/34234 eff=38.3499% N=1265 Z=-2274.4(0.00%) | Like=-2263.09..-0.00 [-4312.6667..-2155.9340] | it/evals=5312/34389 eff=37.9424% N=1265 Z=-2220.4(0.00%) | Like=-2208.30..-0.00 [-4312.6667..-2155.9340] | it/evals=5344/34389 eff=38.2003% N=1265 Z=-2174.2(0.00%) | Like=-2161.52..-0.00 [-4312.6667..-2155.9340] | it/evals=5376/34389 eff=38.3866% N=1265 Z=-2126.8(0.00%) | Like=-2114.34..-0.00 [-2155.5918..-1075.2830] | it/evals=5408/34389 eff=38.6158% N=1265 Z=-1985.0(0.00%) | Like=-1972.72..-0.00 [-2155.5918..-1075.2830] | it/evals=5504/34517 eff=38.6661% N=1265 Z=-1913.0(0.00%) | Like=-1901.72..-0.00 [-2155.5918..-1075.2830] | it/evals=5568/34517 eff=39.0460% N=1265 Z=-1854.3(0.00%) | Like=-1839.90..-0.00 [-2155.5918..-1075.2830] | it/evals=5600/34517 eff=39.2852% N=1265 Z=-1803.8(0.00%) | Like=-1792.08..-0.00 [-2155.5918..-1075.2830] | it/evals=5632/34645 eff=38.8943% N=1265 Z=-1753.2(0.00%) | Like=-1740.87..-0.00 [-2155.5918..-1075.2830] | it/evals=5664/34645 eff=39.1154% N=1265 Z=-1713.1(0.00%) | Like=-1701.06..-0.00 [-2155.5918..-1075.2830] | it/evals=5696/34645 eff=39.3504% N=1265 Z=-1661.1(0.00%) | Like=-1650.07..-0.00 [-2155.5918..-1075.2830] | it/evals=5736/34773 eff=38.9515% N=1265 Z=-1636.3(0.00%) | Like=-1624.89..-0.00 [-2155.5918..-1075.2830] | it/evals=5760/34773 eff=39.1824% N=1265 Z=-1561.3(0.00%) | Like=-1548.44..-0.00 [-2155.5918..-1075.2830] | it/evals=5824/34773 eff=39.6034% N=1265 Z=-1420.5(0.00%) | Like=-1405.87..-0.00 [-2155.5918..-1075.2830] | it/evals=5938/34916 eff=39.6083% N=1265 Z=-1409.6(0.00%) | Like=-1397.13..-0.00 [-2155.5918..-1075.2830] | it/evals=5952/34916 eff=39.6882% N=1265 Z=-1274.4(0.00%) | Like=-1262.64..-0.00 [-2155.5918..-1075.2830] | it/evals=6063/35044 eff=39.7302% N=1265 Z=-1253.8(0.00%) | Like=-1241.96..-0.00 [-2155.5918..-1075.2830] | it/evals=6080/35044 eff=39.8480% N=1265 Z=-1221.4(0.00%) | Like=-1209.79..-0.00 [-2155.5918..-1075.2830] | it/evals=6112/35044 eff=40.0445% N=1265 Z=-1158.3(0.00%) | Like=-1144.74..-0.00 [-2155.5918..-1075.2830] | it/evals=6176/35044 eff=40.4637% N=1265 Z=-1085.9(0.00%) | Like=-1072.84..-0.00 [-1074.5176..-549.0999] | it/evals=6240/35172 eff=40.1829% N=1265 Z=-1008.8(0.00%) | Like=-997.18..-0.00 [-1074.5176..-549.0999] | it/evals=6336/35172 eff=40.7885% N=1265 Z=-986.5(0.00%) | Like=-975.09..-0.00 [-1074.5176..-549.0999] | it/evals=6368/35300 eff=40.2915% N=1265 Z=-946.1(0.00%) | Like=-934.39..-0.00 [-1074.5176..-549.0999] | it/evals=6432/35300 eff=40.7351% N=1265 Z=-920.6(0.00%) | Like=-908.69..-0.00 [-1074.5176..-549.0999] | it/evals=6464/35300 eff=40.9379% N=1265 Z=-896.4(0.00%) | Like=-883.53..-0.00 [-1074.5176..-549.0999] | it/evals=6496/35428 eff=40.4714% N=1265 Z=-877.4(0.00%) | Like=-866.57..-0.00 [-1074.5176..-549.0999] | it/evals=6528/35428 eff=40.6710% N=1265 Z=-854.1(0.00%) | Like=-842.19..-0.00 [-1074.5176..-549.0999] | it/evals=6560/35428 eff=40.9080% N=1265 Z=-832.8(0.00%) | Like=-820.23..-0.00 [-1074.5176..-549.0999] | it/evals=6592/35428 eff=41.1325% N=1265 Z=-815.6(0.00%) | Like=-804.08..-0.00 [-1074.5176..-549.0999] | it/evals=6624/35556 eff=40.6825% N=1265 Z=-775.5(0.00%) | Like=-762.60..-0.00 [-1074.5176..-549.0999] | it/evals=6688/35556 eff=41.0754% N=1265 Z=-754.8(0.00%) | Like=-740.50..-0.00 [-1074.5176..-549.0999] | it/evals=6720/35684 eff=40.5971% N=1265 Z=-716.2(0.00%) | Like=-705.07..-0.00 [-1074.5176..-549.0999] | it/evals=6787/35684 eff=40.9717% N=1265 Z=-706.4(0.00%) | Like=-694.05..-0.00 [-1074.5176..-549.0999] | it/evals=6816/35684 eff=41.1651% N=1265 Z=-686.6(0.00%) | Like=-675.24..-0.00 [-1074.5176..-549.0999] | it/evals=6842/35814 eff=40.6592% N=1265 Z=-683.5(0.00%) | Like=-671.90..-0.00 [-1074.5176..-549.0999] | it/evals=6848/35814 eff=40.7187% N=1265 Z=-663.7(0.00%) | Like=-651.98..-0.00 [-1074.5176..-549.0999] | it/evals=6880/35814 eff=40.9091% N=1265 Z=-647.0(0.00%) | Like=-635.33..-0.00 [-1074.5176..-549.0999] | it/evals=6912/35814 eff=41.0519% N=1265 Z=-618.5(0.00%) | Like=-606.69..-0.00 [-1074.5176..-549.0999] | it/evals=6976/35814 eff=41.4683% N=1265 Z=-604.3(0.00%) | Like=-591.81..-0.00 [-1074.5176..-549.0999] | it/evals=7008/35814 eff=41.6111% N=1265 Z=-543.3(0.00%) | Like=-531.71..-0.00 [-548.9876..-277.9305] | it/evals=7136/35945 eff=41.6872% N=1265 Z=-504.0(0.00%) | Like=-491.19..-0.00 [-548.9876..-277.9305] | it/evals=7232/36073 eff=41.6599% N=1265 Z=-468.3(0.00%) | Like=-456.59..-0.00 [-548.9876..-277.9305] | it/evals=7328/36073 eff=42.2486% N=1265 Z=-456.0(0.00%) | Like=-443.51..-0.00 [-548.9876..-277.9305] | it/evals=7360/36201 eff=41.7927% N=1265 Z=-411.4(0.00%) | Like=-399.39..-0.00 [-548.9876..-277.9305] | it/evals=7499/36329 eff=42.0002% N=1265 Z=-406.9(0.00%) | Like=-394.66..-0.00 [-548.9876..-277.9305] | it/evals=7520/36329 eff=42.1572% N=1265 Z=-400.2(0.00%) | Like=-388.31..-0.00 [-548.9876..-277.9305] | it/evals=7552/36329 eff=42.2917% N=1265 Z=-391.3(0.00%) | Like=-379.26..-0.00 [-548.9876..-277.9305] | it/evals=7584/36329 eff=42.4263% N=1265 Z=-379.9(0.00%) | Like=-367.45..-0.00 [-548.9876..-277.9305] | it/evals=7616/36329 eff=42.5832% N=1265 Z=-370.2(0.00%) | Like=-358.19..-0.00 [-548.9876..-277.9305] | it/evals=7652/36457 eff=42.2018% N=1265 Z=-363.9(0.00%) | Like=-350.82..-0.00 [-548.9876..-277.9305] | it/evals=7680/36457 eff=42.3345% N=1265 Z=-353.4(0.00%) | Like=-341.41..-0.00 [-548.9876..-277.9305] | it/evals=7712/36457 eff=42.5224% N=1265 Z=-343.0(0.00%) | Like=-330.09..-0.00 [-548.9876..-277.9305] | it/evals=7744/36585 eff=42.1035% N=1265 Z=-319.0(0.00%) | Like=-306.77..-0.00 [-548.9876..-277.9305] | it/evals=7840/36585 eff=42.6703% N=1265 Z=-304.1(0.00%) | Like=-291.61..-0.00 [-548.9876..-277.9305] | it/evals=7904/36585 eff=42.8883% N=1265 Z=-296.9(0.00%) | Like=-284.19..-0.00 [-548.9876..-277.9305] | it/evals=7936/36585 eff=43.0518% N=1265 Z=-290.2(0.00%) | Like=-278.03..-0.00 [-548.9876..-277.9305] | it/evals=7968/36713 eff=42.6744% N=1265 Z=-275.2(0.00%) | Like=-262.98..-0.00 [-277.8440..-144.6438] | it/evals=8032/36713 eff=42.9861% N=1265 Z=-261.8(0.00%) | Like=-250.25..-0.00 [-277.8440..-144.6438] | it/evals=8096/36713 eff=43.3086% N=1265 Z=-252.1(0.00%) | Like=-240.17..-0.00 [-277.8440..-144.6438] | it/evals=8155/36841 eff=43.0495% N=1265 Z=-251.3(0.00%) | Like=-239.37..-0.00 [-277.8440..-144.6438] | it/evals=8160/36841 eff=43.0813% N=1265 Z=-244.6(0.00%) | Like=-232.55..-0.00 [-277.8440..-144.6438] | it/evals=8192/36841 eff=43.2086% N=1265 Z=-234.2(0.00%) | Like=-222.53..-0.00 [-277.8440..-144.6438] | it/evals=8252/36841 eff=43.5373% N=1265 Z=-228.6(0.00%) | Like=-217.10..-0.00 [-277.8440..-144.6438] | it/evals=8288/36841 eff=43.7387% N=1265 Z=-226.6(0.00%) | Like=-214.39..-0.00 [-277.8440..-144.6438] | it/evals=8301/36971 eff=43.2068% N=1265 Z=-223.3(0.00%) | Like=-211.21..-0.00 [-277.8440..-144.6438] | it/evals=8320/36971 eff=43.2905% N=1265 Z=-213.0(0.00%) | Like=-201.27..-0.00 [-277.8440..-144.6438] | it/evals=8391/36971 eff=43.6670% N=1265 Z=-204.4(0.00%) | Like=-192.21..-0.00 [-277.8440..-144.6438] | it/evals=8448/36971 eff=43.9808% N=1265 Z=-203.5(0.00%) | Like=-191.56..-0.00 [-277.8440..-144.6438] | it/evals=8453/36971 eff=44.0017% N=1265 Z=-194.8(0.00%) | Like=-183.15..-0.00 [-277.8440..-144.6438] | it/evals=8512/37099 eff=43.7506% N=1265 Z=-191.8(0.00%) | Like=-180.04..-0.00 [-277.8440..-144.6438] | it/evals=8544/37099 eff=43.9571% N=1265 Z=-184.6(0.00%) | Like=-172.43..-0.00 [-277.8440..-144.6438] | it/evals=8607/37099 eff=44.3080% N=1265 Z=-184.5(0.00%) | Like=-172.07..-0.00 [-277.8440..-144.6438] | it/evals=8608/37099 eff=44.3183% N=1265 Z=-179.3(0.00%) | Like=-167.46..-0.00 [-277.8440..-144.6438] | it/evals=8649/37099 eff=44.5144% N=1265 Z=-169.4(0.00%) | Like=-157.73..-0.00 [-277.8440..-144.6438] | it/evals=8736/37227 eff=44.3720% N=1265 Z=-163.3(0.00%) | Like=-150.96..-0.00 [-277.8440..-144.6438] | it/evals=8800/37227 eff=44.6674% N=1265 Z=-153.7(0.00%) | Like=-141.88..-0.00 [-144.6070..-73.9235] | it/evals=8873/37227 eff=45.0138% N=1265 Z=-151.2(0.00%) | Like=-138.99..-0.00 [-144.6070..-73.9235] | it/evals=8896/37356 eff=44.5807% N=1265 Z=-147.9(0.00%) | Like=-136.30..-0.00 [-144.6070..-73.9235] | it/evals=8928/37356 eff=44.7416% N=1265 Z=-141.9(0.00%) | Like=-129.75..-0.00 [-144.6070..-73.9235] | it/evals=8992/37356 eff=45.0633% N=1265 Z=-128.3(0.00%) | Like=-116.15..-0.00 [-144.6070..-73.9235] | it/evals=9122/37484 eff=45.1261% N=1265 Z=-126.0(0.00%) | Like=-114.06..-0.00 [-144.6070..-73.9235] | it/evals=9152/37484 eff=45.2948% N=1265 Z=-120.6(0.00%) | Like=-108.45..-0.00 [-144.6070..-73.9235] | it/evals=9216/37484 eff=45.6621% N=1265 Z=-118.2(0.00%) | Like=-106.42..-0.00 [-144.6070..-73.9235] | it/evals=9248/37484 eff=45.8309% N=1265 Z=-115.4(0.00%) | Like=-103.56..-0.00 [-144.6070..-73.9235] | it/evals=9288/37612 eff=45.4421% N=1265 Z=-111.5(0.00%) | Like=-99.45..-0.00 [-144.6070..-73.9235] | it/evals=9344/37612 eff=45.7459% N=1265 Z=-103.9(0.00%) | Like=-91.79..-0.00 [-144.6070..-73.9235] | it/evals=9440/37741 eff=45.5909% N=1265 Z=-99.1(0.00%) | Like=-87.04..-0.00 [-144.6070..-73.9235] | it/evals=9504/37741 eff=45.9491% N=1265 Z=-96.9(0.00%) | Like=-84.93..-0.00 [-144.6070..-73.9235] | it/evals=9536/37741 eff=46.0846% N=1265 Z=-96.2(0.00%) | Like=-83.98..-0.00 [-144.6070..-73.9235] | it/evals=9547/37741 eff=46.1233% N=1265 Z=-93.4(0.00%) | Like=-81.38..-0.00 [-144.6070..-73.9235] | it/evals=9586/37741 eff=46.3072% N=1265 Z=-92.5(0.00%) | Like=-80.81..-0.00 [-144.6070..-73.9235] | it/evals=9600/37741 eff=46.3750% N=1265 Z=-88.3(0.00%) | Like=-76.18..-0.00 [-144.6070..-73.9235] | it/evals=9665/37869 eff=46.0656% N=1265 Z=-87.8(0.00%) | Like=-75.98..-0.00 [-144.6070..-73.9235] | it/evals=9673/37869 eff=46.0943% N=1265 Z=-86.7(0.00%) | Like=-74.91..-0.00 [-144.6070..-73.9235] | it/evals=9696/37869 eff=46.2377% N=1265 Z=-84.9(0.00%) | Like=-72.74..-0.00 [-73.4603..-35.3555] | it/evals=9728/37869 eff=46.3811% N=1265 Z=-82.8(0.00%) | Like=-70.62..-0.00 [-73.4603..-35.3555] | it/evals=9763/37869 eff=46.5341% N=1265 Z=-79.2(0.00%) | Like=-67.21..-0.00 [-73.4603..-35.3555] | it/evals=9824/37869 eff=46.8209% N=1265 Z=-77.5(0.00%) | Like=-65.63..-0.00 [-73.4603..-35.3555] | it/evals=9856/37997 eff=46.4154% N=1265 Z=-75.6(0.00%) | Like=-63.71..-0.00 [-73.4603..-35.3555] | it/evals=9900/37997 eff=46.6232% N=1265 Z=-74.5(0.00%) | Like=-62.22..-0.00 [-73.4603..-35.3555] | it/evals=9920/37997 eff=46.7838% N=1265 Z=-72.7(0.00%) | Like=-60.52..-0.00 [-73.4603..-35.3555] | it/evals=9952/37997 eff=46.9538% N=1265 Z=-71.1(0.00%) | Like=-59.10..-0.00 [-73.4603..-35.3555] | it/evals=9984/37997 eff=47.1238% N=1265 Z=-68.2(0.00%) | Like=-56.10..-0.00 [-73.4603..-35.3555] | it/evals=10048/38125 eff=46.8875% N=1265 Z=-66.7(0.00%) | Like=-54.47..-0.00 [-73.4603..-35.3555] | it/evals=10080/38125 eff=47.0275% N=1265 Z=-65.1(0.00%) | Like=-52.90..-0.00 [-73.4603..-35.3555] | it/evals=10112/38125 eff=47.2049% N=1265 Z=-63.5(0.00%) | Like=-51.44..-0.00 [-73.4603..-35.3555] | it/evals=10144/38125 eff=47.2982% N=1265 Z=-63.2(0.00%) | Like=-51.20..-0.00 [-73.4603..-35.3555] | it/evals=10151/38125 eff=47.3168% N=1265 Z=-62.3(0.00%) | Like=-50.23..-0.00 [-73.4603..-35.3555] | it/evals=10176/38125 eff=47.4288% N=1265 Z=-60.9(0.00%) | Like=-48.94..-0.00 [-73.4603..-35.3555] | it/evals=10207/38253 eff=46.9888% N=1265 Z=-59.0(0.00%) | Like=-47.01..-0.00 [-73.4603..-35.3555] | it/evals=10253/38253 eff=47.2471% N=1265 Z=-55.7(0.00%) | Like=-43.60..-0.00 [-73.4603..-35.3555] | it/evals=10333/38253 eff=47.6252% N=1265 Z=-55.6(0.00%) | Like=-43.44..-0.00 [-73.4603..-35.3555] | it/evals=10336/38253 eff=47.6344% N=1265 Z=-54.5(0.00%) | Like=-42.62..-0.00 [-73.4603..-35.3555] | it/evals=10368/38253 eff=47.7728% N=1265 Z=-53.9(0.00%) | Like=-41.95..-0.00 [-73.4603..-35.3555] | it/evals=10385/38253 eff=47.8558% N=1265 Z=-52.4(0.00%) | Like=-40.36..-0.00 [-73.4603..-35.3555] | it/evals=10428/38382 eff=47.4845% N=1265 Z=-52.2(0.00%) | Like=-40.14..-0.00 [-73.4603..-35.3555] | it/evals=10434/38382 eff=47.5027% N=1265 Z=-52.0(0.00%) | Like=-39.96..-0.00 [-73.4603..-35.3555] | it/evals=10440/38382 eff=47.5118% N=1265 Z=-51.3(0.00%) | Like=-39.48..-0.00 [-73.4603..-35.3555] | it/evals=10464/38382 eff=47.7032% N=1265 Z=-50.8(0.00%) | Like=-39.14..-0.00 [-73.4603..-35.3555] | it/evals=10481/38382 eff=47.7762% N=1265 Z=-49.2(0.00%) | Like=-37.12..-0.00 [-73.4603..-35.3555] | it/evals=10538/38382 eff=48.0131% N=1265 Z=-47.1(0.00%) | Like=-35.05..-0.00 [-35.3436..-17.2753] | it/evals=10610/38511 eff=47.7525% N=1265 Z=-46.7(0.00%) | Like=-34.79..-0.00 [-35.3436..-17.2753] | it/evals=10624/38511 eff=47.8065% N=1265 Z=-46.1(0.00%) | Like=-34.32..-0.00 [-35.3436..-17.2753] | it/evals=10649/38511 eff=47.8966% N=1265 Z=-45.9(0.00%) | Like=-34.18..-0.00 [-35.3436..-17.2753] | it/evals=10656/38511 eff=47.9416% N=1265 Z=-45.7(0.00%) | Like=-33.93..-0.00 [-35.3436..-17.2753] | it/evals=10665/38511 eff=47.9867% N=1265 Z=-44.0(0.00%) | Like=-32.18..-0.00 [-35.3436..-17.2753] | it/evals=10737/38511 eff=48.3650% N=1265 Z=-43.6(0.00%) | Like=-31.70..-0.00 [-35.3436..-17.2753] | it/evals=10756/38511 eff=48.4641% N=1265 Z=-43.0(0.00%) | Like=-30.95..-0.00 [-35.3436..-17.2753] | it/evals=10784/38639 eff=48.0274% N=1265 Z=-42.2(0.00%) | Like=-30.05..-0.00 [-35.3436..-17.2753] | it/evals=10812/38639 eff=48.1254% N=1265 Z=-42.1(0.00%) | Like=-29.96..-0.00 [-35.3436..-17.2753] | it/evals=10816/38639 eff=48.1521% N=1265 Z=-41.2(0.00%) | Like=-29.14..-0.00 [-35.3436..-17.2753] | it/evals=10848/38639 eff=48.3569% N=1265 Z=-40.3(0.00%) | Like=-28.22..-0.00 [-35.3436..-17.2753] | it/evals=10880/38639 eff=48.5172% N=1265 Z=-39.2(0.00%) | Like=-27.25..-0.00 [-35.3436..-17.2753] | it/evals=10927/38639 eff=48.7221% N=1265 Z=-38.8(0.00%) | Like=-26.78..-0.00 [-35.3436..-17.2753] | it/evals=10946/38639 eff=48.8111% N=1265 Z=-38.5(0.00%) | Like=-26.56..-0.00 [-35.3436..-17.2753] | it/evals=10956/38639 eff=48.8556% N=1265 Z=-38.1(0.00%) | Like=-26.18..-0.00 [-35.3436..-17.2753] | it/evals=10976/38767 eff=48.4371% N=1265 Z=-37.4(0.00%) | Like=-25.40..-0.00 [-35.3436..-17.2753] | it/evals=11011/38767 eff=48.6132% N=1265 Z=-36.8(0.00%) | Like=-24.57..-0.00 [-35.3436..-17.2753] | it/evals=11040/38767 eff=48.7541% N=1265 Z=-36.6(0.00%) | Like=-24.50..-0.00 [-35.3436..-17.2753] | it/evals=11046/38767 eff=48.7717% N=1265 Z=-36.0(0.00%) | Like=-24.03..-0.00 [-35.3436..-17.2753] | it/evals=11072/38767 eff=48.9302% N=1265 Z=-34.8(0.00%) | Like=-22.83..-0.00 [-35.3436..-17.2753] | it/evals=11138/38895 eff=48.6635% N=1265 Z=-33.7(0.00%) | Like=-21.73..-0.00 [-35.3436..-17.2753] | it/evals=11200/38895 eff=48.9247% N=1265 Z=-33.4(0.00%) | Like=-21.44..-0.00 [-35.3436..-17.2753] | it/evals=11215/38895 eff=48.9856% N=1265 Z=-33.2(0.00%) | Like=-21.25..-0.00 [-35.3436..-17.2753] | it/evals=11228/38895 eff=49.0118% N=1265 Z=-33.1(0.00%) | Like=-21.16..-0.00 [-35.3436..-17.2753] | it/evals=11232/38895 eff=49.0379% N=1265 Z=-32.8(0.00%) | Like=-20.91..-0.00 [-35.3436..-17.2753] | it/evals=11256/38895 eff=49.1772% N=1265 Z=-32.5(0.00%) | Like=-20.59..-0.00 [-35.3436..-17.2753] | it/evals=11271/38895 eff=49.2033% N=1265 Z=-31.7(0.00%) | Like=-19.87..-0.00 [-35.3436..-17.2753] | it/evals=11325/38895 eff=49.4471% N=1265 Z=-31.7(0.00%) | Like=-19.85..-0.00 [-35.3436..-17.2753] | it/evals=11328/39023 eff=48.9279% N=1265 Z=-31.4(0.00%) | Like=-19.57..-0.00 [-35.3436..-17.2753] | it/evals=11346/39023 eff=48.9796% N=1265 Z=-31.2(0.00%) | Like=-19.32..-0.00 [-35.3436..-17.2753] | it/evals=11360/39023 eff=49.0485% N=1265 Z=-30.9(0.00%) | Like=-18.94..-0.00 [-35.3436..-17.2753] | it/evals=11380/39023 eff=49.1088% N=1265 Z=-30.7(0.00%) | Like=-18.69..-0.00 [-35.3436..-17.2753] | it/evals=11392/39023 eff=49.2035% N=1265 Z=-30.4(0.00%) | Like=-18.10..-0.00 [-35.3436..-17.2753] | it/evals=11410/39023 eff=49.2638% N=1265 Z=-30.2(0.00%) | Like=-17.97..-0.00 [-35.3436..-17.2753] | it/evals=11421/39023 eff=49.3068% N=1265 Z=-30.1(0.00%) | Like=-17.96..-0.00 [-35.3436..-17.2753] | it/evals=11424/39023 eff=49.3326% N=1265 Z=-30.0(0.00%) | Like=-17.86..-0.00 [-35.3436..-17.2753] | it/evals=11431/39023 eff=49.3499% N=1265 Z=-29.6(0.00%) | Like=-17.65..-0.00 [-35.3436..-17.2753] | it/evals=11456/39023 eff=49.4790% N=1265 Z=-28.8(0.00%) | Like=-16.96..-0.00 [-17.2727..-9.0457] | it/evals=11511/39023 eff=49.7201% N=1265 Z=-28.8(0.00%) | Like=-16.90..-0.00 [-17.2727..-9.0457] | it/evals=11516/39023 eff=49.7288% N=1265 Z=-28.7(0.00%) | Like=-16.86..-0.00 [-17.2727..-9.0457] | it/evals=11520/39151 eff=49.2122% N=1265 Z=-28.5(0.00%) | Like=-16.50..-0.00 [-17.2727..-9.0457] | it/evals=11540/39151 eff=49.3144% N=1265 Z=-28.3(0.00%) | Like=-16.31..-0.00 [-17.2727..-9.0457] | it/evals=11555/39151 eff=49.3484% N=1265 Z=-28.1(0.00%) | Like=-16.21..-0.00 [-17.2727..-9.0457] | it/evals=11566/39151 eff=49.3740% N=1265 Z=-28.0(0.00%) | Like=-16.16..-0.00 [-17.2727..-9.0457] | it/evals=11574/39151 eff=49.4251% N=1265 Z=-28.0(0.00%) | Like=-16.12..-0.00 [-17.2727..-9.0457] | it/evals=11579/39151 eff=49.4336% N=1265 Z=-27.5(0.00%) | Like=-15.65..-0.00 [-17.2727..-9.0457] | it/evals=11621/39151 eff=49.6721% N=1265 Z=-27.3(0.00%) | Like=-15.43..-0.00 [-17.2727..-9.0457] | it/evals=11640/39151 eff=49.7658% N=1265 Z=-27.2(0.00%) | Like=-15.33..-0.00 [-17.2727..-9.0457] | it/evals=11645/39151 eff=49.7743% N=1265 Z=-27.0(0.00%) | Like=-15.14..-0.00 [-17.2727..-9.0457] | it/evals=11667/39151 eff=49.8595% N=1265 Z=-26.6(0.00%) | Like=-14.69..-0.00 [-17.2727..-9.0457] | it/evals=11709/39151 eff=50.0213% N=1265 Z=-26.5(0.00%) | Like=-14.66..-0.00 [-17.2727..-9.0457] | it/evals=11712/39151 eff=50.0468% N=1265 Z=-26.2(0.00%) | Like=-14.34..-0.00 [-17.2727..-9.0457] | it/evals=11744/39279 eff=49.6419% N=1265 Z=-26.1(0.00%) | Like=-14.30..-0.00 [-17.2727..-9.0457] | it/evals=11751/39279 eff=49.6588% N=1265 Z=-25.9(0.00%) | Like=-14.01..-0.00 [-17.2727..-9.0457] | it/evals=11776/39279 eff=49.7767% N=1265 Z=-25.8(0.00%) | Like=-13.93..-0.00 [-17.2727..-9.0457] | it/evals=11785/39279 eff=49.8104% N=1265 Z=-25.5(0.00%) | Like=-13.73..-0.00 [-17.2727..-9.0457] | it/evals=11809/39279 eff=49.9452% N=1265 Z=-25.3(0.00%) | Like=-13.44..-0.00 [-17.2727..-9.0457] | it/evals=11832/39279 eff=50.0632% N=1265 Z=-25.2(0.00%) | Like=-13.39..-0.00 [-17.2727..-9.0457] | it/evals=11840/39279 eff=50.1053% N=1265 Z=-24.7(0.00%) | Like=-12.85..-0.00 [-17.2727..-9.0457] | it/evals=11893/39279 eff=50.3075% N=1265 Z=-24.6(0.00%) | Like=-12.68..-0.00 [-17.2727..-9.0457] | it/evals=11904/39279 eff=50.3665% N=1265 Z=-24.6(0.00%) | Like=-12.60..-0.00 [-17.2727..-9.0457] | it/evals=11912/39279 eff=50.4086% N=1265 Z=-24.4(0.00%) | Like=-12.43..-0.00 [-17.2727..-9.0457] | it/evals=11926/39279 eff=50.4423% N=1265 Z=-24.0(0.00%) | Like=-12.11..-0.00 [-17.2727..-9.0457] | it/evals=11966/39407 eff=50.0458% N=1265 Z=-24.0(0.00%) | Like=-12.08..-0.00 [-17.2727..-9.0457] | it/evals=11968/39407 eff=50.0625% N=1265 Z=-23.7(0.00%) | Like=-11.63..-0.00 [-17.2727..-9.0457] | it/evals=12000/39407 eff=50.2042% N=1265 Z=-23.6(0.00%) | Like=-11.56..-0.00 [-17.2727..-9.0457] | it/evals=12006/39407 eff=50.2209% N=1265 Z=-23.6(0.00%) | Like=-11.52..-0.00 [-17.2727..-9.0457] | it/evals=12012/39407 eff=50.2459% N=1265 Z=-23.4(0.00%) | Like=-11.41..-0.00 [-17.2727..-9.0457] | it/evals=12027/39407 eff=50.3292% N=1265 Z=-23.4(0.00%) | Like=-11.38..-0.00 [-17.2727..-9.0457] | it/evals=12032/39407 eff=50.3376% N=1265 Z=-23.2(0.00%) | Like=-11.21..-0.00 [-17.2727..-9.0457] | it/evals=12049/39407 eff=50.4043% N=1265 Z=-23.1(0.00%) | Like=-11.10..-0.00 [-17.2727..-9.0457] | it/evals=12060/39407 eff=50.4376% N=1265 Z=-23.0(0.00%) | Like=-11.00..-0.00 [-17.2727..-9.0457] | it/evals=12071/39407 eff=50.4793% N=1265 Z=-22.6(0.00%) | Like=-10.67..-0.00 [-17.2727..-9.0457] | it/evals=12115/39407 eff=50.6377% N=1265 Z=-22.6(0.00%) | Like=-10.64..-0.00 [-17.2727..-9.0457] | it/evals=12121/39407 eff=50.6543% N=1265 Z=-22.5(0.00%) | Like=-10.61..-0.00 [-17.2727..-9.0457] | it/evals=12128/39407 eff=50.6877% N=1265 Z=-22.4(0.00%) | Like=-10.54..-0.00 [-17.2727..-9.0457] | it/evals=12137/39407 eff=50.7293% N=1265 Z=-22.4(0.00%) | Like=-10.45..-0.00 [-17.2727..-9.0457] | it/evals=12147/39407 eff=50.7377% N=1265 Z=-22.2(0.00%) | Like=-10.21..-0.00 [-17.2727..-9.0457] | it/evals=12171/39535 eff=50.3175% N=1265 Z=-22.0(0.00%) | Like=-10.03..-0.00 [-17.2727..-9.0457] | it/evals=12192/39535 eff=50.4165% N=1265 Z=-21.8(0.01%) | Like=-9.85..-0.00 [-17.2727..-9.0457] | it/evals=12219/39535 eff=50.5320% N=1265 Z=-21.7(0.01%) | Like=-9.80..-0.00 [-17.2727..-9.0457] | it/evals=12224/39535 eff=50.5649% N=1265 Z=-21.5(0.01%) | Like=-9.55..-0.00 [-17.2727..-9.0457] | it/evals=12256/39535 eff=50.7134% N=1265 Z=-21.2(0.01%) | Like=-9.27..-0.00 [-17.2727..-9.0457] | it/evals=12288/39535 eff=50.8454% N=1265 Z=-21.0(0.01%) | Like=-9.11..-0.00 [-17.2727..-9.0457] | it/evals=12320/39535 eff=50.9856% N=1265 Z=-20.4(0.02%) | Like=-8.44..-0.00 [-9.0429..-4.7326] | it/evals=12416/39663 eff=50.8529% N=1265 Z=-20.2(0.03%) | Like=-8.23..-0.00 [-9.0429..-4.7326] | it/evals=12448/39663 eff=50.9589% N=1265 Z=-19.8(0.04%) | Like=-7.80..-0.00 [-9.0429..-4.7326] | it/evals=12512/39663 eff=51.2446% N=1265 Z=-19.4(0.06%) | Like=-7.40..-0.00 [-9.0429..-4.7326] | it/evals=12576/39791 eff=50.9894% N=1265 Z=-18.8(0.10%) | Like=-6.80..-0.00 [-9.0429..-4.7326] | it/evals=12672/39791 eff=51.4175% N=1265 Z=-18.6(0.12%) | Like=-6.62..-0.00 [-9.0429..-4.7326] | it/evals=12704/39919 eff=51.0193% N=1265 Z=-18.5(0.15%) | Like=-6.41..-0.00 [-9.0429..-4.7326] | it/evals=12736/39919 eff=51.1871% N=1265 Z=-18.3(0.18%) | Like=-6.21..-0.00 [-9.0429..-4.7326] | it/evals=12768/39919 eff=51.3310% N=1265 Z=-18.1(0.21%) | Like=-6.04..-0.00 [-9.0429..-4.7326] | it/evals=12800/39919 eff=51.4430% N=1265 Z=-17.9(0.25%) | Like=-5.89..-0.00 [-9.0429..-4.7326] | it/evals=12832/39919 eff=51.5949% N=1265 Z=-17.8(0.30%) | Like=-5.77..-0.00 [-9.0429..-4.7326] | it/evals=12864/40047 eff=51.1830% N=1265 Z=-17.6(0.35%) | Like=-5.61..-0.00 [-9.0429..-4.7326] | it/evals=12896/40047 eff=51.3176% N=1265 Z=-17.3(0.46%) | Like=-5.35..-0.00 [-9.0429..-4.7326] | it/evals=12960/40047 eff=51.5629% N=1265 Z=-17.2(0.53%) | Like=-5.21..-0.00 [-9.0429..-4.7326] | it/evals=12992/40175 eff=51.1712% N=1265 Z=-16.6(0.93%) | Like=-4.66..-0.00 [-4.7215..-4.1007] | it/evals=13135/40303 eff=51.2216% N=1265 Z=-16.2(1.38%) | Like=-4.29..-0.00 [-4.7215..-4.1007] | it/evals=13248/40431 eff=51.1021% N=1265 Z=-16.1(1.53%) | Like=-4.19..-0.00 [-4.7215..-4.1007] | it/evals=13280/40431 eff=51.2403% N=1265 Z=-16.0(1.71%) | Like=-4.10..-0.00 [-4.0980..-3.9588] | it/evals=13312/40559 eff=50.9088% N=1265 Z=-15.8(2.09%) | Like=-3.87..-0.00 [-3.8728..-3.8720]*| it/evals=13376/40559 eff=51.1065% N=1265 Z=-15.7(2.31%) | Like=-3.77..-0.00 [-3.7688..-3.7685]*| it/evals=13408/40559 eff=51.2054% N=1265 Z=-15.5(2.76%) | Like=-3.57..-0.00 [-3.5709..-3.5707]*| it/evals=13472/40687 eff=50.9151% N=1265 Z=-15.4(3.02%) | Like=-3.48..-0.00 [-3.4760..-3.4712]*| it/evals=13504/40687 eff=51.0432% N=1265 Z=-15.2(3.61%) | Like=-3.29..-0.00 [-3.2908..-3.2890]*| it/evals=13568/40815 eff=50.8019% N=1265 Z=-15.0(4.69%) | Like=-3.05..-0.00 [-3.0514..-3.0468]*| it/evals=13675/41071 eff=50.2525% N=1265 Z=-14.8(5.67%) | Like=-2.87..-0.00 [-2.8728..-2.8679]*| it/evals=13760/41199 eff=50.0761% N=1265 Z=-14.7(6.08%) | Like=-2.80..-0.00 [-2.8022..-2.8011]*| it/evals=13792/41199 eff=50.1777% N=1265 Z=-14.6(6.50%) | Like=-2.73..-0.00 [-2.7275..-2.7275]*| it/evals=13824/41327 eff=49.8599% N=1265 Z=-14.6(6.97%) | Like=-2.66..-0.00 [-2.6563..-2.6526]*| it/evals=13856/41455 eff=49.5123% N=1265 Z=-14.5(7.47%) | Like=-2.59..-0.00 [-2.5857..-2.5821]*| it/evals=13888/41455 eff=49.6476% N=1265 Z=-14.4(7.97%) | Like=-2.50..-0.00 [-2.5034..-2.5022]*| it/evals=13920/41583 eff=49.3474% N=1265 Z=-14.3(9.07%) | Like=-2.39..-0.00 [-2.3868..-2.3844]*| it/evals=13984/41711 eff=49.1294% N=1265 Z=-14.3(9.62%) | Like=-2.33..-0.00 [-2.3338..-2.3315]*| it/evals=14016/41711 eff=49.2623% N=1265 Z=-14.1(10.82%) | Like=-2.22..-0.00 [-2.2156..-2.2155]*| it/evals=14080/41839 eff=49.0471% N=1265 Z=-14.1(11.49%) | Like=-2.17..-0.00 [-2.1652..-2.1642]*| it/evals=14112/41967 eff=48.7120% N=1265 Z=-14.0(12.16%) | Like=-2.10..-0.00 [-2.1042..-2.1004]*| it/evals=14144/42095 eff=48.4031% N=1265 Z=-13.9(13.49%) | Like=-2.01..-0.00 [-2.0137..-2.0125]*| it/evals=14208/42223 eff=48.1739% N=1265 Z=-13.9(14.56%) | Like=-1.94..-0.00 [-1.9415..-1.9409]*| it/evals=14257/42351 eff=47.9620% N=1265 Z=-13.8(14.89%) | Like=-1.93..-0.00 [-1.9253..-1.9246]*| it/evals=14272/42479 eff=47.6209% N=1265 Z=-13.8(15.61%) | Like=-1.87..-0.00 [-1.8733..-1.8730]*| it/evals=14304/42479 eff=47.7404% N=1265 Z=-13.7(17.13%) | Like=-1.77..-0.00 [-1.7684..-1.7683]*| it/evals=14368/42479 eff=47.9196% N=1265 Z=-13.7(17.86%) | Like=-1.72..-0.00 [-1.7244..-1.7241]*| it/evals=14400/42479 eff=48.0523% N=1265 Z=-13.6(19.44%) | Like=-1.65..-0.00 [-1.6476..-1.6473]*| it/evals=14464/42479 eff=48.2580% N=1265 Z=-13.5(20.25%) | Like=-1.61..-0.00 [-1.6088..-1.6078]*| it/evals=14496/42607 eff=47.9371% N=1265 Z=-13.5(21.13%) | Like=-1.57..-0.00 [-1.5717..-1.5704]*| it/evals=14528/42607 eff=48.0358% N=1265 Z=-13.4(23.99%) | Like=-1.45..-0.00 [-1.4503..-1.4486]*| it/evals=14637/42607 eff=48.3582% N=1265 Z=-13.3(24.50%) | Like=-1.43..-0.00 [-1.4288..-1.4286]*| it/evals=14656/42607 eff=48.4043% N=1265 Z=-13.3(25.37%) | Like=-1.39..-0.00 [-1.3928..-1.3919]*| it/evals=14688/42735 eff=48.1044% N=1265 Z=-13.2(28.30%) | Like=-1.28..-0.00 [-1.2762..-1.2744]*| it/evals=14796/42735 eff=48.4698% N=1265 Z=-13.1(30.68%) | Like=-1.21..-0.00 [-1.2071..-1.2034]*| it/evals=14880/42863 eff=48.3142% N=1265 Z=-13.1(31.60%) | Like=-1.18..-0.00 [-1.1753..-1.1748]*| it/evals=14912/42863 eff=48.3919% N=1265 Z=-13.1(32.43%) | Like=-1.14..-0.00 [-1.1406..-1.1383]*| it/evals=14944/42863 eff=48.4890% N=1265 Z=-13.0(35.02%) | Like=-1.06..-0.00 [-1.0562..-1.0561]*| it/evals=15035/42991 eff=48.4244% N=1265 Z=-13.0(35.16%) | Like=-1.05..-0.00 [-1.0496..-1.0486]*| it/evals=15040/42991 eff=48.4372% N=1265 Z=-13.0(36.07%) | Like=-1.03..-0.00 [-1.0259..-1.0253]*| it/evals=15072/42991 eff=48.5527% N=1265 Z=-12.9(37.01%) | Like=-1.00..-0.00 [-0.9969..-0.9959]*| it/evals=15104/42991 eff=48.6426% N=1265 Z=-12.9(37.92%) | Like=-0.97..-0.00 [-0.9685..-0.9681]*| it/evals=15136/43119 eff=48.3481% N=1265 Z=-12.9(39.71%) | Like=-0.92..-0.00 [-0.9245..-0.9244]*| it/evals=15200/43119 eff=48.5454% N=1265 Z=-12.8(40.63%) | Like=-0.90..-0.00 [-0.8989..-0.8981]*| it/evals=15232/43247 eff=48.2541% N=1265 Z=-12.8(41.54%) | Like=-0.88..-0.00 [-0.8756..-0.8751]*| it/evals=15264/43247 eff=48.3299% N=1265 Z=-12.7(44.20%) | Like=-0.81..-0.00 [-0.8084..-0.8083]*| it/evals=15360/43375 eff=48.2242% N=1265 Z=-12.7(45.13%) | Like=-0.79..-0.00 [-0.7878..-0.7863]*| it/evals=15392/43375 eff=48.3119% N=1265 Z=-12.7(46.04%) | Like=-0.76..-0.00 [-0.7630..-0.7624]*| it/evals=15424/43375 eff=48.4184% N=1265 Z=-12.7(47.90%) | Like=-0.72..-0.00 [-0.7199..-0.7179]*| it/evals=15488/43503 eff=48.1762% N=1265 Z=-12.6(48.78%) | Like=-0.70..-0.00 [-0.7002..-0.6998]*| it/evals=15520/43503 eff=48.2819% N=1265 Z=-12.6(49.67%) | Like=-0.69..-0.00 [-0.6867..-0.6866]*| it/evals=15552/43631 eff=48.0057% N=1265 Z=-12.6(52.29%) | Like=-0.65..-0.00 [-0.6467..-0.6466]*| it/evals=15648/43759 eff=47.9234% N=1265 Z=-12.5(54.03%) | Like=-0.62..-0.00 [-0.6165..-0.6165]*| it/evals=15712/43887 eff=47.7514% N=1265 Z=-12.5(54.86%) | Like=-0.60..-0.00 [-0.6009..-0.6003]*| it/evals=15744/43887 eff=47.8728% N=1265 Z=-12.5(57.12%) | Like=-0.56..-0.00 [-0.5570..-0.5561]*| it/evals=15832/44143 eff=47.4392% N=1265 Z=-12.5(58.11%) | Like=-0.54..-0.00 [-0.5389..-0.5389]*| it/evals=15872/44271 eff=47.1858% N=1265 Z=-12.5(58.92%) | Like=-0.53..-0.00 [-0.5261..-0.5254]*| it/evals=15904/44271 eff=47.2866% N=1265 Z=-12.4(61.12%) | Like=-0.49..-0.00 [-0.4914..-0.4913]*| it/evals=15993/44527 eff=46.8482% N=1265 Z=-12.4(62.06%) | Like=-0.47..-0.00 [-0.4743..-0.4741]*| it/evals=16032/44655 eff=46.6106% N=1265 Z=-12.4(64.23%) | Like=-0.44..-0.00 [-0.4418..-0.4413]*| it/evals=16128/44911 eff=46.1916% N=1265 Z=-12.4(64.95%) | Like=-0.43..-0.00 [-0.4314..-0.4307]*| it/evals=16160/44911 eff=46.2831% N=1265 Z=-12.4(65.65%) | Like=-0.42..-0.00 [-0.4198..-0.4196]*| it/evals=16192/45039 eff=46.0208% N=1265 Z=-12.3(66.32%) | Like=-0.41..-0.00 [-0.4092..-0.4091]*| it/evals=16224/45167 eff=45.7904% N=1265 Z=-12.3(66.99%) | Like=-0.40..-0.00 [-0.3991..-0.3984]*| it/evals=16256/45295 eff=45.5689% N=1265 Z=-12.3(67.84%) | Like=-0.39..-0.00 [-0.3853..-0.3852]*| it/evals=16296/45295 eff=45.6640% N=1265 Z=-12.3(69.65%) | Like=-0.36..-0.00 [-0.3550..-0.3548]*| it/evals=16384/45423 eff=45.6226% N=1265 Z=-12.3(70.93%) | Like=-0.34..-0.00 [-0.3376..-0.3375]*| it/evals=16448/45423 eff=45.7725% N=1265 Z=-12.3(71.53%) | Like=-0.33..-0.00 [-0.3276..-0.3275]*| it/evals=16480/45423 eff=45.8669% N=1265 Z=-12.3(72.12%) | Like=-0.32..-0.00 [-0.3157..-0.3155]*| it/evals=16512/45551 eff=45.6370% N=1265 Z=-12.2(73.90%) | Like=-0.29..-0.00 [-0.2944..-0.2940]*| it/evals=16608/45551 eff=45.9071% N=1265 Z=-12.2(74.47%) | Like=-0.29..-0.00 [-0.2878..-0.2878]*| it/evals=16640/45551 eff=46.0118% N=1265 Z=-12.2(75.56%) | Like=-0.27..-0.00 [-0.2739..-0.2736]*| it/evals=16704/45679 eff=45.8974% N=1265 Z=-12.2(76.10%) | Like=-0.27..-0.00 [-0.2677..-0.2676]*| it/evals=16736/45679 eff=45.9741% N=1265 Z=-12.2(76.63%) | Like=-0.26..-0.00 [-0.2606..-0.2603]*| it/evals=16768/45679 eff=46.0616% N=1265 Z=-12.2(77.66%) | Like=-0.25..-0.00 [-0.2493..-0.2492]*| it/evals=16832/45807 eff=45.9260% N=1265 Z=-12.2(78.64%) | Like=-0.24..-0.00 [-0.2395..-0.2394]*| it/evals=16896/45807 eff=46.1108% N=1265 Z=-12.2(79.10%) | Like=-0.23..-0.00 [-0.2339..-0.2339]*| it/evals=16928/45807 eff=46.1869% N=1265 Z=-12.2(80.02%) | Like=-0.22..-0.00 [-0.2217..-0.2217]*| it/evals=16992/45935 eff=46.0297% N=1265 Z=-12.1(80.91%) | Like=-0.21..-0.00 [-0.2092..-0.2092]*| it/evals=17056/45935 eff=46.2294% N=1265 Z=-12.1(81.35%) | Like=-0.20..-0.00 [-0.2041..-0.2041]*| it/evals=17088/46063 eff=45.9819% N=1265 Z=-12.1(81.76%) | Like=-0.20..-0.00 [-0.1987..-0.1985]*| it/evals=17120/46063 eff=46.0677% N=1265 Z=-12.1(82.57%) | Like=-0.19..-0.00 [-0.1904..-0.1901]*| it/evals=17184/46063 eff=46.2875% N=1265 Z=-12.1(82.97%) | Like=-0.19..-0.00 [-0.1854..-0.1854]*| it/evals=17216/46191 eff=46.0625% N=1265 Z=-12.1(83.74%) | Like=-0.18..-0.00 [-0.1775..-0.1774]*| it/evals=17280/46191 eff=46.2169% N=1265 Z=-12.1(84.84%) | Like=-0.17..-0.00 [-0.1652..-0.1652]*| it/evals=17376/46319 eff=46.1156% N=1265 Z=-12.1(85.93%) | Like=-0.15..-0.00 [-0.1520..-0.1518]*| it/evals=17479/46447 eff=46.0524% N=1265 Z=-12.1(86.51%) | Like=-0.15..-0.00 [-0.1454..-0.1453]*| it/evals=17536/46575 eff=45.8962% N=1265 Z=-12.1(86.82%) | Like=-0.14..-0.00 [-0.1414..-0.1408]*| it/evals=17568/46575 eff=45.9849% N=1265 Z=-12.1(87.74%) | Like=-0.13..-0.00 [-0.1297..-0.1296]*| it/evals=17666/46703 eff=45.9234% N=1265 Z=-12.1(88.29%) | Like=-0.12..-0.00 [-0.1243..-0.1242]*| it/evals=17728/46831 eff=45.7391% N=1265 Z=-12.0(89.11%) | Like=-0.12..-0.00 [-0.1165..-0.1163]*| it/evals=17825/46959 eff=45.6954% N=1265 Z=-12.0(89.36%) | Like=-0.11..-0.00 [-0.1140..-0.1140]*| it/evals=17856/47087 eff=45.4744% N=1265 Z=-12.0(89.85%) | Like=-0.11..-0.00 [-0.1096..-0.1095]*| it/evals=17920/47215 eff=45.3421% N=1265 Z=-12.0(90.33%) | Like=-0.10..-0.00 [-0.1046..-0.1046]*| it/evals=17984/47343 eff=45.2215% N=1265 Z=-12.0(91.09%) | Like=-0.10..-0.00 [-0.0955..-0.0954]*| it/evals=18093/47599 eff=44.8957% N=1265 Z=-12.0(91.77%) | Like=-0.09..-0.00 [-0.0877..-0.0876]*| it/evals=18198/47855 eff=44.5879% N=1265 Z=-12.0(92.21%) | Like=-0.08..-0.00 [-0.0827..-0.0826]*| it/evals=18272/48111 eff=44.2201% N=1265 Z=-12.0(92.56%) | Like=-0.08..-0.00 [-0.0780..-0.0780]*| it/evals=18332/48239 eff=44.0972% N=1265 Z=-12.0(92.58%) | Like=-0.08..-0.00 [-0.0778..-0.0777]*| it/evals=18336/48239 eff=44.1116% N=1265 Z=-12.0(92.76%) | Like=-0.08..-0.00 [-0.0758..-0.0758]*| it/evals=18368/48239 eff=44.1980% N=1265 Z=-12.0(92.94%) | Like=-0.07..-0.00 [-0.0735..-0.0735]*| it/evals=18400/48367 eff=43.9662% N=1265 Z=-12.0(93.27%) | Like=-0.07..-0.00 [-0.0693..-0.0692]*| it/evals=18464/48367 eff=44.0903% N=1265 Z=-12.0(93.59%) | Like=-0.07..-0.00 [-0.0667..-0.0666]*| it/evals=18528/48367 eff=44.2334% N=1265 Z=-12.0(93.90%) | Like=-0.06..-0.00 [-0.0636..-0.0634]*| it/evals=18592/48495 eff=44.1167% N=1265 Z=-12.0(94.19%) | Like=-0.06..-0.00 [-0.0607..-0.0607]*| it/evals=18656/48495 eff=44.2542% N=1265 Z=-12.0(94.33%) | Like=-0.06..-0.00 [-0.0591..-0.0590]*| it/evals=18688/48495 eff=44.3301% N=1265 Z=-12.0(94.47%) | Like=-0.06..-0.00 [-0.0576..-0.0576]*| it/evals=18720/48495 eff=44.4202% N=1265 Z=-12.0(94.60%) | Like=-0.06..-0.00 [-0.0557..-0.0556]*| it/evals=18752/48623 eff=44.2370% N=1265 Z=-12.0(94.99%) | Like=-0.05..-0.00 [-0.0517..-0.0517]*| it/evals=18848/48623 eff=44.5104% N=1265 Z=-12.0(95.39%) | Like=-0.05..-0.00 [-0.0480..-0.0479]*| it/evals=18955/48751 eff=44.4965% N=1265 Z=-12.0(95.57%) | Like=-0.05..-0.00 [-0.0456..-0.0456]*| it/evals=19008/48751 eff=44.6043% N=1265 Z=-12.0(95.79%) | Like=-0.04..-0.00 [-0.0429..-0.0429]*| it/evals=19072/48879 eff=44.4827% N=1265 Z=-12.0(95.89%) | Like=-0.04..-0.00 [-0.0418..-0.0418]*| it/evals=19104/48879 eff=44.5712% N=1265 Z=-12.0(95.99%) | Like=-0.04..-0.00 [-0.0408..-0.0408]*| it/evals=19136/48879 eff=44.6691% N=1265 Z=-12.0(96.09%) | Like=-0.04..-0.00 [-0.0396..-0.0396]*| it/evals=19168/49007 eff=44.4738% N=1265 Z=-12.0(96.40%) | Like=-0.04..-0.00 [-0.0367..-0.0366]*| it/evals=19275/49007 eff=44.6775% N=1265 Z=-12.0(96.54%) | Like=-0.04..-0.00 [-0.0353..-0.0353]*| it/evals=19328/49135 eff=44.5109% N=1265 Z=-12.0(96.79%) | Like=-0.03..-0.00 [-0.0325..-0.0325]*| it/evals=19424/49263 eff=44.4470% N=1265 Z=-12.0(96.87%) | Like=-0.03..-0.00 [-0.0316..-0.0316]*| it/evals=19456/49263 eff=44.5202% N=1265 Z=-12.0(97.02%) | Like=-0.03..-0.00 [-0.0302..-0.0301]*| it/evals=19520/49391 eff=44.3974% N=1265 Z=-12.0(97.17%) | Like=-0.03..-0.00 [-0.0288..-0.0287]*| it/evals=19584/49391 eff=44.5430% N=1265 Z=-12.0(97.24%) | Like=-0.03..-0.00 [-0.0279..-0.0279]*| it/evals=19616/49519 eff=44.3711% N=1265 Z=-12.0(97.31%) | Like=-0.03..-0.00 [-0.0271..-0.0271]*| it/evals=19648/49519 eff=44.4525% N=1265 Z=-12.0(97.49%) | Like=-0.03..-0.00 [-0.0254..-0.0253]*| it/evals=19739/49775 eff=44.1672% N=1265 Z=-12.0(97.62%) | Like=-0.02..-0.00 [-0.0240..-0.0240]*| it/evals=19808/49775 eff=44.2969% N=1265 Z=-12.0(97.74%) | Like=-0.02..-0.00 [-0.0228..-0.0228]*| it/evals=19872/49903 eff=44.1737% N=1265 Z=-12.0(97.79%) | Like=-0.02..-0.00 [-0.0223..-0.0222]*| it/evals=19904/50031 eff=43.9901% N=1265 Z=-12.0(97.90%) | Like=-0.02..-0.00 [-0.0212..-0.0212]*| it/evals=19968/50159 eff=43.8920% N=1265 Z=-12.0(98.05%) | Like=-0.02..-0.00 [-0.0197..-0.0197]*| it/evals=20064/50415 eff=43.6296% N=1265 Z=-12.0(98.10%) | Like=-0.02..-0.00 [-0.0193..-0.0193]*| it/evals=20096/50415 eff=43.7166% N=1265 Z=-11.9(98.24%) | Like=-0.02..-0.00 [-0.0178..-0.0178]*| it/evals=20192/50671 eff=43.4375% N=1265 Z=-11.9(98.28%) | Like=-0.02..-0.00 [-0.0174..-0.0174]*| it/evals=20224/50799 eff=43.2682% N=1265 Z=-11.9(98.33%) | Like=-0.02..-0.00 [-0.0170..-0.0170]*| it/evals=20256/50927 eff=43.0965% N=1265 Z=-11.9(98.45%) | Like=-0.02..-0.00 [-0.0156..-0.0156]*| it/evals=20352/51183 eff=42.8343% N=1265 Z=-11.9(98.47%) | Like=-0.02..-0.00 [-0.0155..-0.0155]*| it/evals=20368/51183 eff=42.8638% N=1265 Z=-11.9(98.49%) | Like=-0.02..-0.00 [-0.0152..-0.0152]*| it/evals=20384/51311 eff=42.6802% N=1265 Z=-11.9(98.60%) | Like=-0.01..-0.00 [-0.0142..-0.0142]*| it/evals=20483/51311 eff=42.9062% N=1265 Z=-11.9(98.63%) | Like=-0.01..-0.00 [-0.0139..-0.0139]*| it/evals=20512/51311 eff=42.9731% N=1265 Z=-11.9(98.66%) | Like=-0.01..-0.00 [-0.0136..-0.0136]*| it/evals=20544/51311 eff=43.0275% N=1265 Z=-11.9(98.73%) | Like=-0.01..-0.00 [-0.0130..-0.0130]*| it/evals=20608/51439 eff=42.9148% N=1265 Z=-11.9(98.79%) | Like=-0.01..-0.00 [-0.0124..-0.0124]*| it/evals=20672/51439 eff=43.0563% N=1265 Z=-11.9(98.85%) | Like=-0.01..-0.00 [-0.0119..-0.0119]*| it/evals=20736/51439 eff=43.1728% N=1265 Z=-11.9(98.88%) | Like=-0.01..-0.00 [-0.0115..-0.0115]*| it/evals=20768/51439 eff=43.2436% N=1265 Z=-11.9(98.91%) | Like=-0.01..-0.00 [-0.0112..-0.0112]*| it/evals=20800/51567 eff=43.0765% N=1265 Z=-11.9(98.93%) | Like=-0.01..-0.00 [-0.0108..-0.0108]*| it/evals=20832/51567 eff=43.1345% N=1265 Z=-11.9(98.99%) | Like=-0.01..-0.00 [-0.0103..-0.0103]*| it/evals=20896/51567 eff=43.2918% N=1265 [ultranest] Explored until L=-1e-06 [ultranest] Likelihood function evaluations: 51695 logzerr in iteration 2 0.09844300329360392 [ultranest] logZ = -11.95 +- 0.05704 [ultranest] Effective samples strategy satisfied (ESS = 5077.6, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.04 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 360 minimum live points (dlogz from 0.05 to 0.11, need <0.1) [ultranest] logZ error budget: single: 0.17 bs:0.06 tail:0.01 total:0.06 required:<0.10 {'logzerr': 0.09844300329360392, 'logzerr_tail': 0.0065940830284478835, 'logzerr_bs': 0.09822190675444276, 'logzerr_single': 0.09294762088512282} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=False, log_dir=None, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:2241 To achieve the desired logz accuracy, min_num_live_points was increased to 64 [32mINFO [0m ultranest:integrator.py:1339 Sampling 64 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=10000 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=3, min_num_live_points=64, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 64.0), (inf, 64.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=192, regioncalls=128, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-239030.00, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=6, ncalls=192, regioncalls=128, ndraw=128, logz=-167765.30, remainder_fraction=100.0000%, Lmin=-167569.18, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=12, ncalls=192, regioncalls=128, ndraw=128, logz=-159987.53, remainder_fraction=100.0000%, Lmin=-158707.55, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=15, ncalls=192, regioncalls=128, ndraw=128, logz=-152982.01, remainder_fraction=100.0000%, Lmin=-146805.66, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=18, ncalls=192, regioncalls=128, ndraw=128, logz=-135773.27, remainder_fraction=100.0000%, Lmin=-133343.06, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=24, ncalls=192, regioncalls=128, ndraw=128, logz=-116726.18, remainder_fraction=100.0000%, Lmin=-115537.65, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=192, regioncalls=128, ndraw=128, logz=-104285.61, remainder_fraction=100.0000%, Lmin=-103229.62, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=36, ncalls=192, regioncalls=128, ndraw=128, logz=-92116.38, remainder_fraction=100.0000%, Lmin=-90564.32, Lmax=-374.91 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=42, ncalls=192, regioncalls=128, ndraw=128, logz=-84439.05, remainder_fraction=100.0000%, Lmin=-81037.80, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=45, ncalls=192, regioncalls=128, ndraw=128, logz=-75146.85, remainder_fraction=100.0000%, Lmin=-74579.84, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=48, ncalls=192, regioncalls=128, ndraw=128, logz=-72264.78, remainder_fraction=100.0000%, Lmin=-71294.47, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=54, ncalls=192, regioncalls=128, ndraw=128, logz=-62305.89, remainder_fraction=100.0000%, Lmin=-60408.15, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=192, regioncalls=128, ndraw=128, logz=-57043.18, remainder_fraction=100.0000%, Lmin=-56215.59, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=66, ncalls=192, regioncalls=128, ndraw=128, logz=-53672.87, remainder_fraction=100.0000%, Lmin=-51615.69, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=72, ncalls=264, regioncalls=256, ndraw=128, logz=-46350.74, remainder_fraction=100.0000%, Lmin=-46165.68, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=75, ncalls=264, regioncalls=256, ndraw=128, logz=-45613.08, remainder_fraction=100.0000%, Lmin=-45447.46, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=78, ncalls=264, regioncalls=256, ndraw=128, logz=-43748.71, remainder_fraction=100.0000%, Lmin=-43729.99, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=84, ncalls=264, regioncalls=256, ndraw=128, logz=-41951.14, remainder_fraction=100.0000%, Lmin=-41145.48, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=264, regioncalls=256, ndraw=128, logz=-34971.44, remainder_fraction=100.0000%, Lmin=-34282.89, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=96, ncalls=264, regioncalls=256, ndraw=128, logz=-29284.15, remainder_fraction=100.0000%, Lmin=-28841.35, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=102, ncalls=264, regioncalls=256, ndraw=128, logz=-25535.50, remainder_fraction=100.0000%, Lmin=-25492.11, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=105, ncalls=264, regioncalls=256, ndraw=128, logz=-24766.63, remainder_fraction=100.0000%, Lmin=-24687.49, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=108, ncalls=293, regioncalls=384, ndraw=128, logz=-24037.37, remainder_fraction=100.0000%, Lmin=-23851.07, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=114, ncalls=293, regioncalls=384, ndraw=128, logz=-21275.36, remainder_fraction=100.0000%, Lmin=-21176.67, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=293, regioncalls=384, ndraw=128, logz=-20658.40, remainder_fraction=100.0000%, Lmin=-20121.17, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=126, ncalls=323, regioncalls=512, ndraw=128, logz=-17922.80, remainder_fraction=100.0000%, Lmin=-17704.96, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=132, ncalls=323, regioncalls=512, ndraw=128, logz=-17243.44, remainder_fraction=100.0000%, Lmin=-17218.44, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=138, ncalls=323, regioncalls=512, ndraw=128, logz=-15745.39, remainder_fraction=100.0000%, Lmin=-15672.95, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=144, ncalls=351, regioncalls=640, ndraw=128, logz=-15256.50, remainder_fraction=100.0000%, Lmin=-15069.40, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=351, regioncalls=640, ndraw=128, logz=-13548.01, remainder_fraction=100.0000%, Lmin=-13529.83, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=156, ncalls=351, regioncalls=640, ndraw=128, logz=-12442.38, remainder_fraction=100.0000%, Lmin=-12376.29, Lmax=-157.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=162, ncalls=368, regioncalls=768, ndraw=128, logz=-11825.80, remainder_fraction=100.0000%, Lmin=-11474.84, Lmax=-141.88 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=165, 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[35mDEBUG [0m ultranest:integrator.py:2491 iteration=372, ncalls=862, regioncalls=6784, ndraw=128, logz=-509.22, remainder_fraction=100.0000%, Lmin=-498.76, Lmax=-4.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=375, ncalls=862, regioncalls=6784, ndraw=128, logz=-492.10, remainder_fraction=100.0000%, Lmin=-481.00, Lmax=-4.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=378, ncalls=862, regioncalls=6784, ndraw=128, logz=-483.40, remainder_fraction=100.0000%, Lmin=-459.94, Lmax=-4.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=384, ncalls=862, regioncalls=6784, ndraw=128, logz=-441.79, remainder_fraction=100.0000%, Lmin=-430.22, Lmax=-4.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=862, regioncalls=6784, ndraw=128, logz=-412.72, remainder_fraction=100.0000%, Lmin=-401.91, Lmax=-4.04 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=396, ncalls=877, regioncalls=7168, ndraw=128, logz=-385.03, remainder_fraction=100.0000%, Lmin=-367.94, 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ncalls=1404, regioncalls=9600, ndraw=128, logz=-22.91, remainder_fraction=99.9984%, Lmin=-10.70, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=615, ncalls=1419, regioncalls=9728, ndraw=128, logz=-22.41, remainder_fraction=99.9972%, Lmin=-10.55, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=618, ncalls=1540, regioncalls=10240, ndraw=128, logz=-21.98, remainder_fraction=99.9957%, Lmin=-9.88, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=624, ncalls=1540, regioncalls=10240, ndraw=128, logz=-21.12, remainder_fraction=99.9902%, Lmin=-9.23, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1540, regioncalls=10240, ndraw=128, logz=-20.44, remainder_fraction=99.9796%, Lmin=-8.64, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=636, ncalls=1540, regioncalls=10240, ndraw=128, logz=-19.78, remainder_fraction=99.9643%, Lmin=-7.90, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=642, ncalls=1540, regioncalls=10240, ndraw=128, logz=-19.14, remainder_fraction=99.9262%, Lmin=-7.13, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=645, ncalls=1540, regioncalls=10240, ndraw=128, logz=-18.81, remainder_fraction=99.8958%, Lmin=-6.75, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=648, ncalls=1540, regioncalls=10240, ndraw=128, logz=-18.49, remainder_fraction=99.8533%, Lmin=-6.17, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=654, ncalls=1540, regioncalls=10240, ndraw=128, logz=-17.71, remainder_fraction=99.6567%, Lmin=-5.56, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=660, ncalls=1540, regioncalls=10240, ndraw=128, logz=-17.14, remainder_fraction=99.3838%, Lmin=-4.95, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=666, ncalls=1555, regioncalls=10496, ndraw=128, logz=-16.61, remainder_fraction=99.0476%, Lmin=-4.43, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=672, ncalls=1563, regioncalls=10624, ndraw=128, logz=-16.14, remainder_fraction=98.4981%, Lmin=-4.16, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=675, ncalls=1577, regioncalls=10752, ndraw=128, logz=-15.93, remainder_fraction=98.1052%, Lmin=-3.92, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=678, ncalls=1577, regioncalls=10752, ndraw=128, logz=-15.72, remainder_fraction=97.6925%, Lmin=-3.58, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=684, ncalls=1596, regioncalls=11008, ndraw=128, logz=-15.34, remainder_fraction=96.6047%, Lmin=-3.29, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=690, ncalls=1613, regioncalls=11136, ndraw=128, logz=-15.03, remainder_fraction=95.5867%, Lmin=-3.09, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=696, ncalls=1613, regioncalls=11136, ndraw=128, logz=-14.76, remainder_fraction=94.3897%, Lmin=-2.95, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=702, ncalls=1635, regioncalls=11264, ndraw=128, logz=-14.53, remainder_fraction=92.8589%, Lmin=-2.60, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=705, ncalls=1635, regioncalls=11264, ndraw=128, logz=-14.41, remainder_fraction=91.8029%, Lmin=-2.47, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=708, ncalls=1635, regioncalls=11264, ndraw=128, logz=-14.30, remainder_fraction=90.8326%, Lmin=-2.30, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=714, ncalls=1647, regioncalls=11520, ndraw=128, logz=-14.08, remainder_fraction=88.4987%, Lmin=-2.17, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1647, regioncalls=11520, ndraw=128, logz=-13.88, remainder_fraction=85.5129%, Lmin=-1.87, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=726, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.69, remainder_fraction=82.6155%, Lmin=-1.71, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=732, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.53, remainder_fraction=79.7476%, Lmin=-1.66, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=735, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.46, remainder_fraction=77.9952%, Lmin=-1.48, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=738, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.39, remainder_fraction=76.5556%, Lmin=-1.42, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=744, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.26, remainder_fraction=73.6593%, Lmin=-1.32, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.14, remainder_fraction=70.4393%, Lmin=-1.21, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=756, ncalls=1761, regioncalls=11776, ndraw=128, logz=-13.03, remainder_fraction=67.3144%, Lmin=-1.05, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=762, ncalls=1761, regioncalls=11776, ndraw=128, logz=-12.92, remainder_fraction=64.1635%, Lmin=-0.98, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=765, ncalls=1761, regioncalls=11776, ndraw=128, logz=-12.88, remainder_fraction=62.5648%, Lmin=-0.95, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=768, ncalls=1761, regioncalls=11776, ndraw=128, logz=-12.83, remainder_fraction=60.7967%, Lmin=-0.91, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=774, ncalls=1761, regioncalls=11776, ndraw=128, logz=-12.75, remainder_fraction=57.3183%, Lmin=-0.80, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=780, ncalls=1773, regioncalls=12032, ndraw=128, logz=-12.67, remainder_fraction=53.7441%, Lmin=-0.75, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=786, ncalls=1780, regioncalls=12160, ndraw=128, logz=-12.60, remainder_fraction=50.1845%, Lmin=-0.66, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=792, ncalls=1792, regioncalls=12288, ndraw=128, logz=-12.54, remainder_fraction=47.0649%, Lmin=-0.59, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=795, ncalls=1803, regioncalls=12416, ndraw=128, logz=-12.51, remainder_fraction=45.6416%, Lmin=-0.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=798, ncalls=1803, regioncalls=12416, ndraw=128, logz=-12.48, remainder_fraction=44.0814%, Lmin=-0.53, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=804, ncalls=1813, regioncalls=12672, ndraw=128, logz=-12.43, remainder_fraction=41.2170%, Lmin=-0.47, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1832, regioncalls=12800, ndraw=128, logz=-12.38, remainder_fraction=38.2120%, Lmin=-0.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=816, ncalls=1832, regioncalls=12800, ndraw=128, logz=-12.34, remainder_fraction=35.3629%, Lmin=-0.43, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=822, ncalls=1845, regioncalls=12928, ndraw=128, logz=-12.30, remainder_fraction=32.6850%, Lmin=-0.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=825, ncalls=1845, regioncalls=12928, ndraw=128, logz=-12.28, remainder_fraction=31.3789%, Lmin=-0.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=828, ncalls=1853, regioncalls=13184, ndraw=128, logz=-12.26, remainder_fraction=30.2968%, Lmin=-0.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=834, ncalls=1874, regioncalls=13312, ndraw=128, logz=-12.23, remainder_fraction=27.8721%, Lmin=-0.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1874, regioncalls=13312, ndraw=128, logz=-12.20, remainder_fraction=25.8426%, Lmin=-0.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=846, ncalls=1888, regioncalls=13440, ndraw=128, logz=-12.18, remainder_fraction=23.8308%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=852, ncalls=1901, regioncalls=13568, ndraw=128, logz=-12.15, remainder_fraction=21.9392%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=855, ncalls=1901, regioncalls=13568, ndraw=128, logz=-12.14, remainder_fraction=21.0571%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=858, ncalls=1915, regioncalls=14080, ndraw=128, logz=-12.13, remainder_fraction=20.1740%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=864, ncalls=1915, regioncalls=14080, ndraw=128, logz=-12.11, remainder_fraction=18.4870%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=870, ncalls=1933, regioncalls=14208, ndraw=128, logz=-12.09, remainder_fraction=16.9564%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=876, ncalls=1933, regioncalls=14208, ndraw=128, logz=-12.07, remainder_fraction=15.5213%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=882, ncalls=1948, regioncalls=14336, ndraw=128, logz=-12.06, remainder_fraction=14.2578%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=885, ncalls=1965, regioncalls=14464, ndraw=128, logz=-12.05, remainder_fraction=13.6421%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=888, ncalls=1965, regioncalls=14464, ndraw=128, logz=-12.04, remainder_fraction=13.0438%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=894, ncalls=2091, regioncalls=14720, ndraw=128, logz=-12.03, remainder_fraction=11.9101%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=2091, regioncalls=14720, ndraw=128, logz=-12.02, remainder_fraction=10.9119%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=906, ncalls=2091, regioncalls=14720, ndraw=128, logz=-12.01, remainder_fraction=9.9925%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=912, ncalls=2091, regioncalls=14720, ndraw=128, logz=-12.00, remainder_fraction=9.1407%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=915, ncalls=2091, regioncalls=14720, ndraw=128, logz=-12.00, remainder_fraction=8.7375%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=918, ncalls=2091, regioncalls=14720, ndraw=128, logz=-11.99, remainder_fraction=8.3497%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=924, ncalls=2091, regioncalls=14720, ndraw=128, logz=-11.98, remainder_fraction=7.6255%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=930, ncalls=2091, regioncalls=14720, ndraw=128, logz=-11.98, remainder_fraction=6.9664%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=936, ncalls=2091, regioncalls=14720, ndraw=128, logz=-11.97, remainder_fraction=6.3595%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=942, ncalls=2091, regioncalls=14720, ndraw=128, logz=-11.96, remainder_fraction=5.8110%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=945, ncalls=2106, regioncalls=14976, ndraw=128, logz=-11.96, remainder_fraction=5.5515%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=948, ncalls=2106, regioncalls=14976, ndraw=128, logz=-11.96, remainder_fraction=5.2993%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=954, ncalls=2106, regioncalls=14976, ndraw=128, logz=-11.95, remainder_fraction=4.8360%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=2124, regioncalls=15104, ndraw=128, logz=-11.95, remainder_fraction=4.4127%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=966, ncalls=2124, regioncalls=15104, ndraw=128, logz=-11.95, remainder_fraction=4.0277%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=972, ncalls=2138, regioncalls=15232, ndraw=128, logz=-11.94, remainder_fraction=3.6757%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=975, ncalls=2158, regioncalls=15744, ndraw=128, logz=-11.94, remainder_fraction=3.5098%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=978, ncalls=2158, regioncalls=15744, ndraw=128, logz=-11.94, remainder_fraction=3.3527%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=984, ncalls=2158, regioncalls=15744, ndraw=128, logz=-11.94, remainder_fraction=3.0572%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=2177, regioncalls=15872, ndraw=128, logz=-11.93, remainder_fraction=2.7872%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=996, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.93, remainder_fraction=2.5406%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1002, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.93, remainder_fraction=2.3154%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1008, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.93, remainder_fraction=2.1102%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1014, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.92, remainder_fraction=1.9227%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1020, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.92, remainder_fraction=1.7525%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1026, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.92, remainder_fraction=1.5973%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1032, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.92, remainder_fraction=1.4560%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1035, ncalls=2296, regioncalls=16256, ndraw=128, logz=-11.92, remainder_fraction=1.3899%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1038, ncalls=2420, regioncalls=16512, ndraw=128, logz=-11.92, remainder_fraction=1.3268%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1044, ncalls=2420, regioncalls=16512, ndraw=128, logz=-11.92, remainder_fraction=1.2089%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=2420, regioncalls=16512, ndraw=128, logz=-11.92, remainder_fraction=1.1017%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1056, ncalls=2420, regioncalls=16512, ndraw=128, logz=-11.91, remainder_fraction=1.0035%, Lmin=-0.01, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-4e-05 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 2420 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -11.92 +- 0.2845 [32mINFO [0m ultranest:integrator.py:1483 Effective samples strategy wants to improve: -15.38..-0.00 (ESS = 253.7, need >10000) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.14 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 67 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 63 and 63, most (780/781 iterations) have 62 [35mDEBUG [0m ultranest:integrator.py:1564 at least 62 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 62 minimum live points (dlogz from 0.23 to 0.73, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.41 bs:0.28 tail:0.01 total:0.28 required:<0.50 [32mINFO [0m ultranest:integrator.py:2614 Widening from 64 to 128 live points before L=-2e+01... [32mINFO [0m ultranest:integrator.py:1283 Will add 64 live points (x1) at L=-3e+03 ... [32mINFO [0m ultranest:integrator.py:2336 Exploring (in particular: L=-3087.56..-0.00) ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 64.0), (-3087.563473062326, 128.0), (-0.0010389913923224752, 128.0), (inf, 64.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=253, ncalls=2427, regioncalls=16640, ndraw=128, logz=-3096.94, remainder_fraction=100.0000%, Lmin=-3087.56, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=302, ncalls=2429, regioncalls=16768, ndraw=128, logz=-1716.55, remainder_fraction=100.0000%, Lmin=-1651.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=327, ncalls=2429, regioncalls=16768, ndraw=128, logz=-1080.64, remainder_fraction=100.0000%, Lmin=-1047.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2541, regioncalls=17152, ndraw=128, logz=-715.57, remainder_fraction=100.0000%, Lmin=-705.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=397, ncalls=2541, regioncalls=17152, ndraw=128, logz=-403.82, remainder_fraction=100.0000%, Lmin=-392.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=420, ncalls=2541, regioncalls=17152, ndraw=128, logz=-309.52, remainder_fraction=100.0000%, Lmin=-297.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=438, ncalls=2541, regioncalls=17152, ndraw=128, logz=-256.62, remainder_fraction=100.0000%, Lmin=-240.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=462, ncalls=2541, regioncalls=17152, ndraw=128, logz=-183.80, remainder_fraction=100.0000%, Lmin=-172.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=476, ncalls=2541, regioncalls=17152, ndraw=128, logz=-157.10, remainder_fraction=100.0000%, Lmin=-142.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=486, ncalls=2541, regioncalls=17152, ndraw=128, logz=-144.77, remainder_fraction=100.0000%, Lmin=-133.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=499, ncalls=2541, regioncalls=17152, ndraw=128, logz=-125.05, remainder_fraction=100.0000%, Lmin=-106.99, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=516, ncalls=2553, regioncalls=17408, ndraw=128, logz=-95.70, remainder_fraction=100.0000%, Lmin=-81.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=517, ncalls=2553, regioncalls=17408, ndraw=128, logz=-93.62, remainder_fraction=100.0000%, Lmin=-81.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=538, ncalls=2658, regioncalls=17664, ndraw=128, logz=-78.27, remainder_fraction=100.0000%, Lmin=-64.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=546, ncalls=2658, regioncalls=17664, ndraw=128, logz=-70.57, remainder_fraction=100.0000%, Lmin=-57.67, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=552, ncalls=2658, regioncalls=17664, ndraw=128, logz=-65.93, remainder_fraction=100.0000%, Lmin=-53.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=561, ncalls=2658, regioncalls=17664, ndraw=128, 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ndraw=128, logz=-41.35, remainder_fraction=100.0000%, Lmin=-29.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=612, ncalls=2754, regioncalls=18048, ndraw=128, logz=-38.58, remainder_fraction=100.0000%, Lmin=-26.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=618, ncalls=2754, regioncalls=18048, ndraw=128, logz=-37.29, remainder_fraction=100.0000%, Lmin=-25.48, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=624, ncalls=2754, regioncalls=18048, ndraw=128, logz=-35.86, remainder_fraction=100.0000%, Lmin=-22.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=627, ncalls=2754, regioncalls=18048, ndraw=128, logz=-34.43, remainder_fraction=100.0000%, Lmin=-21.99, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=2754, regioncalls=18048, ndraw=128, logz=-33.56, remainder_fraction=100.0000%, Lmin=-21.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=636, ncalls=2754, regioncalls=18048, ndraw=128, logz=-32.37, remainder_fraction=100.0000%, Lmin=-20.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=642, ncalls=2754, regioncalls=18048, ndraw=128, logz=-31.16, remainder_fraction=100.0000%, Lmin=-18.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=653, ncalls=2754, regioncalls=18048, ndraw=128, logz=-29.10, remainder_fraction=100.0000%, Lmin=-17.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=654, ncalls=2769, regioncalls=18304, ndraw=128, logz=-28.96, remainder_fraction=100.0000%, Lmin=-16.99, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=660, ncalls=2769, regioncalls=18304, ndraw=128, logz=-28.24, remainder_fraction=100.0000%, Lmin=-16.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=666, ncalls=2777, regioncalls=18432, ndraw=128, logz=-27.68, remainder_fraction=100.0000%, Lmin=-16.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=678, ncalls=2796, regioncalls=18560, ndraw=128, logz=-26.56, remainder_fraction=99.9999%, Lmin=-14.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=684, ncalls=2814, regioncalls=18688, ndraw=128, logz=-25.90, remainder_fraction=99.9999%, Lmin=-13.81, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=690, ncalls=2814, regioncalls=18688, ndraw=128, logz=-25.18, remainder_fraction=99.9998%, Lmin=-12.93, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=696, ncalls=2833, regioncalls=18816, ndraw=128, logz=-24.59, remainder_fraction=99.9997%, Lmin=-12.52, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=702, ncalls=2833, regioncalls=18816, ndraw=128, logz=-24.05, remainder_fraction=99.9995%, Lmin=-11.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=708, ncalls=2833, regioncalls=18816, ndraw=128, logz=-23.40, remainder_fraction=99.9990%, Lmin=-11.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=713, ncalls=2833, regioncalls=18816, ndraw=128, logz=-22.99, remainder_fraction=99.9984%, Lmin=-11.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=714, ncalls=2833, regioncalls=18816, ndraw=128, logz=-22.92, remainder_fraction=99.9983%, Lmin=-10.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=2833, regioncalls=18816, ndraw=128, logz=-22.44, remainder_fraction=99.9972%, Lmin=-10.52, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=745, ncalls=2961, regioncalls=19200, ndraw=128, logz=-20.53, remainder_fraction=99.9806%, Lmin=-8.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=762, ncalls=2961, regioncalls=19200, ndraw=128, logz=-19.35, remainder_fraction=99.9350%, Lmin=-7.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=774, ncalls=2961, regioncalls=19200, ndraw=128, logz=-18.58, remainder_fraction=99.8531%, Lmin=-6.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=792, ncalls=2961, regioncalls=19200, ndraw=128, logz=-17.49, remainder_fraction=99.5667%, Lmin=-5.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=807, ncalls=2961, regioncalls=19200, ndraw=128, logz=-16.81, remainder_fraction=99.1478%, Lmin=-4.75, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=2961, regioncalls=19200, ndraw=128, logz=-16.69, remainder_fraction=99.0489%, Lmin=-4.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=828, ncalls=2961, regioncalls=19200, ndraw=128, logz=-16.02, remainder_fraction=98.1735%, Lmin=-4.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=834, ncalls=2961, regioncalls=19200, ndraw=128, logz=-15.83, remainder_fraction=97.7614%, Lmin=-3.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=836, ncalls=2976, regioncalls=19584, ndraw=128, logz=-15.77, remainder_fraction=97.6220%, Lmin=-3.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=2976, regioncalls=19584, ndraw=128, logz=-15.66, remainder_fraction=97.3422%, Lmin=-3.70, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=852, ncalls=3103, regioncalls=19968, ndraw=128, logz=-15.35, remainder_fraction=96.3003%, Lmin=-3.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=858, ncalls=3103, regioncalls=19968, ndraw=128, logz=-15.21, remainder_fraction=95.7617%, Lmin=-3.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=864, ncalls=3103, regioncalls=19968, ndraw=128, logz=-15.07, remainder_fraction=95.3353%, Lmin=-3.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=882, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.71, remainder_fraction=93.3119%, Lmin=-2.88, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=888, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.60, remainder_fraction=92.4571%, Lmin=-2.84, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=895, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.48, remainder_fraction=91.5755%, Lmin=-2.60, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=906, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.30, remainder_fraction=89.6705%, Lmin=-2.42, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=912, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.20, remainder_fraction=88.4957%, Lmin=-2.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=925, ncalls=3103, regioncalls=19968, ndraw=128, logz=-14.01, remainder_fraction=85.9531%, Lmin=-2.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=930, ncalls=3103, regioncalls=19968, ndraw=128, logz=-13.94, remainder_fraction=85.0253%, Lmin=-2.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=954, ncalls=3208, regioncalls=20224, ndraw=128, logz=-13.63, remainder_fraction=79.4428%, Lmin=-1.68, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 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remainder_fraction=10.3734%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1308, ncalls=3539, regioncalls=22144, ndraw=128, logz=-12.16, remainder_fraction=9.9336%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1314, ncalls=3539, regioncalls=22144, ndraw=128, logz=-12.15, remainder_fraction=9.5067%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=3551, regioncalls=22400, ndraw=128, logz=-12.15, remainder_fraction=9.0922%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1326, ncalls=3551, regioncalls=22400, ndraw=128, logz=-12.15, remainder_fraction=8.6963%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1332, ncalls=3566, regioncalls=22528, ndraw=128, logz=-12.14, remainder_fraction=8.3224%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1338, ncalls=3566, regioncalls=22528, ndraw=128, logz=-12.14, remainder_fraction=7.9605%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1348, ncalls=3566, regioncalls=22528, ndraw=128, logz=-12.13, remainder_fraction=7.3962%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1356, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.13, remainder_fraction=6.9683%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1377, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.12, remainder_fraction=5.9589%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1404, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.10, remainder_fraction=4.8606%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1406, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.10, remainder_fraction=4.7858%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1410, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.10, remainder_fraction=4.6416%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1422, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.10, remainder_fraction=4.2409%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1428, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.10, remainder_fraction=4.0510%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1436, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.09, remainder_fraction=3.8113%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1464, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.09, remainder_fraction=3.0811%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1467, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.08, remainder_fraction=3.0115%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1476, ncalls=3691, regioncalls=22912, ndraw=128, logz=-12.08, remainder_fraction=2.8105%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1482, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.08, remainder_fraction=2.6841%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1488, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.08, remainder_fraction=2.5631%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1494, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.08, remainder_fraction=2.4478%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1496, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.08, remainder_fraction=2.4107%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.08, remainder_fraction=2.3377%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1524, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.9433%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1528, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.8839%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.8554%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1542, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.6912%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1548, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.6144%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1554, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.5412%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.4716%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1566, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.4047%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1578, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.2803%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1588, ncalls=3817, regioncalls=23168, ndraw=128, logz=-12.07, remainder_fraction=1.1852%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1614, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.9687%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1617, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.9464%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.9246%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1632, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.8423%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1638, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.8039%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1644, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.7673%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1646, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.7555%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.7324%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1656, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.6990%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1662, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.6672%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1668, ncalls=3932, regioncalls=23424, ndraw=128, logz=-12.06, remainder_fraction=0.6368%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1674, ncalls=3942, regioncalls=23680, ndraw=128, logz=-12.06, remainder_fraction=0.6077%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1680, ncalls=3962, regioncalls=23936, ndraw=128, logz=-12.06, remainder_fraction=0.5800%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1686, ncalls=3962, regioncalls=23936, ndraw=128, logz=-12.06, remainder_fraction=0.5536%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1692, ncalls=3978, regioncalls=24192, ndraw=128, logz=-12.06, remainder_fraction=0.5283%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1698, ncalls=3992, regioncalls=24320, ndraw=128, logz=-12.06, remainder_fraction=0.5042%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1704, ncalls=3992, regioncalls=24320, ndraw=128, logz=-12.06, remainder_fraction=0.4812%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.4592%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1716, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.4383%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1722, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.4183%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1728, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3991%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1734, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3809%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1740, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3635%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1746, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3469%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1752, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3311%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1758, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3160%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1762, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3063%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1764, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.3015%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1770, ncalls=4120, regioncalls=24576, ndraw=128, logz=-12.06, remainder_fraction=0.2878%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1776, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2746%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1782, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2621%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1788, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2501%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1794, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2386%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2277%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1806, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2173%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1812, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.2074%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1818, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.1979%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1820, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.1948%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1824, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.1888%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1830, ncalls=4224, regioncalls=24832, ndraw=128, logz=-12.06, remainder_fraction=0.1802%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1836, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1720%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1842, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1641%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1848, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1566%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1849, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1554%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1854, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1494%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1860, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1426%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1866, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1361%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1872, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1298%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1878, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1239%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1884, ncalls=4329, regioncalls=25216, ndraw=128, logz=-12.06, remainder_fraction=0.1182%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=4342, regioncalls=25472, ndraw=128, logz=-12.06, remainder_fraction=0.1128%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1896, ncalls=4342, regioncalls=25472, ndraw=128, logz=-12.06, remainder_fraction=0.1077%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1902, ncalls=4353, regioncalls=25600, ndraw=128, logz=-12.06, remainder_fraction=0.1027%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1907, ncalls=4364, regioncalls=25856, ndraw=128, logz=-12.06, remainder_fraction=0.0988%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1908, ncalls=4364, regioncalls=25856, ndraw=128, logz=-12.06, remainder_fraction=0.0980%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1914, ncalls=4379, regioncalls=25984, ndraw=128, logz=-12.06, remainder_fraction=0.0935%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=4379, regioncalls=25984, ndraw=128, logz=-12.06, remainder_fraction=0.0893%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1926, ncalls=4379, regioncalls=25984, ndraw=128, logz=-12.06, remainder_fraction=0.0852%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1932, ncalls=4395, regioncalls=26112, ndraw=128, logz=-12.06, remainder_fraction=0.0813%, Lmin=-0.00, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-2e-05 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 4395 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -12.03 +- 0.318 [32mINFO [0m ultranest:integrator.py:1483 Effective samples strategy wants to improve: -9.70..-0.00 (ESS = 527.7, need >10000) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.16 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 91 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 31 and 127, most (440/1112 iterations) have 126 [35mDEBUG [0m ultranest:integrator.py:1564 at least 31 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 31 minimum live points (dlogz from 0.26 to 0.86, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.29 bs:0.32 tail:0.00 total:0.32 required:<0.50 [32mINFO [0m ultranest:integrator.py:2614 Widening from 80 to 256 live points before L=-1e+01... [32mINFO [0m ultranest:integrator.py:1283 Will add 176 live points (x1) at L=-3e+03 ... [32mINFO [0m ultranest:integrator.py:2336 Exploring (in particular: L=-2898.15..-0.00) ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 64.0), (-3087.563473062326, 128.0), (-2898.1460207797504, 256.0), (-0.0010389913923224752, 256.0), (-0.001017387493617101, 256.0), (inf, 64.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=261, ncalls=4398, regioncalls=26240, ndraw=128, logz=-2927.13, remainder_fraction=100.0000%, Lmin=-2898.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=302, ncalls=4398, regioncalls=26240, ndraw=128, logz=-1716.55, remainder_fraction=100.0000%, Lmin=-1651.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=318, ncalls=4403, regioncalls=26368, ndraw=128, logz=-1344.07, remainder_fraction=100.0000%, Lmin=-1325.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=352, ncalls=4404, regioncalls=26496, ndraw=128, logz=-715.60, remainder_fraction=100.0000%, Lmin=-705.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=394, ncalls=4405, regioncalls=26624, ndraw=128, logz=-427.54, remainder_fraction=100.0000%, Lmin=-416.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=422, ncalls=4526, regioncalls=26880, ndraw=128, logz=-316.35, remainder_fraction=100.0000%, Lmin=-304.90, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=447, ncalls=4526, regioncalls=26880, ndraw=128, logz=-234.18, remainder_fraction=100.0000%, Lmin=-222.88, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=501, ncalls=4526, regioncalls=26880, ndraw=128, logz=-133.24, remainder_fraction=100.0000%, Lmin=-121.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=4526, regioncalls=26880, ndraw=128, logz=-78.35, remainder_fraction=100.0000%, Lmin=-64.58, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=552, ncalls=4526, regioncalls=26880, ndraw=128, logz=-76.14, remainder_fraction=100.0000%, Lmin=-64.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=573, ncalls=4537, regioncalls=27136, ndraw=128, logz=-63.66, remainder_fraction=100.0000%, Lmin=-51.73, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=576, ncalls=4537, regioncalls=27136, ndraw=128, logz=-62.55, remainder_fraction=100.0000%, Lmin=-50.86, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=582, ncalls=4663, regioncalls=27392, ndraw=128, logz=-60.25, remainder_fraction=100.0000%, Lmin=-47.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=594, ncalls=4663, regioncalls=27392, ndraw=128, logz=-53.19, remainder_fraction=100.0000%, Lmin=-41.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=597, ncalls=4663, regioncalls=27392, ndraw=128, logz=-52.22, remainder_fraction=100.0000%, Lmin=-40.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=606, ncalls=4663, regioncalls=27392, ndraw=128, logz=-49.83, remainder_fraction=100.0000%, Lmin=-37.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=624, ncalls=4663, regioncalls=27392, ndraw=128, logz=-44.64, remainder_fraction=100.0000%, Lmin=-31.78, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=4663, regioncalls=27392, ndraw=128, logz=-42.81, remainder_fraction=100.0000%, Lmin=-30.52, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=653, ncalls=4663, regioncalls=27392, ndraw=128, logz=-37.28, remainder_fraction=100.0000%, Lmin=-25.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=660, ncalls=4663, regioncalls=27392, ndraw=128, logz=-35.76, remainder_fraction=100.0000%, Lmin=-23.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=672, ncalls=4663, regioncalls=27392, ndraw=128, logz=-33.25, remainder_fraction=100.0000%, Lmin=-21.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=684, ncalls=4663, regioncalls=27392, ndraw=128, logz=-31.68, remainder_fraction=100.0000%, Lmin=-19.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=690, ncalls=4663, regioncalls=27392, ndraw=128, logz=-30.91, remainder_fraction=100.0000%, Lmin=-18.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=696, ncalls=4772, regioncalls=27648, ndraw=128, logz=-30.08, remainder_fraction=100.0000%, Lmin=-17.92, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=702, ncalls=4772, regioncalls=27648, ndraw=128, logz=-29.35, remainder_fraction=100.0000%, Lmin=-17.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=708, ncalls=4772, regioncalls=27648, ndraw=128, logz=-28.77, remainder_fraction=100.0000%, Lmin=-16.91, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=714, ncalls=4772, regioncalls=27648, ndraw=128, logz=-28.26, remainder_fraction=100.0000%, Lmin=-16.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=4772, regioncalls=27648, ndraw=128, logz=-27.84, remainder_fraction=100.0000%, Lmin=-16.14, Lmax=-0.00 [35mDEBUG [0m 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[35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=4889, regioncalls=27904, ndraw=128, logz=-22.36, remainder_fraction=99.9964%, Lmin=-10.39, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=816, ncalls=4889, regioncalls=27904, ndraw=128, logz=-22.08, remainder_fraction=99.9952%, Lmin=-9.89, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=822, ncalls=4889, regioncalls=27904, ndraw=128, logz=-21.79, remainder_fraction=99.9937%, Lmin=-9.56, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=851, ncalls=4889, regioncalls=27904, ndraw=128, logz=-20.30, remainder_fraction=99.9738%, Lmin=-8.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=852, ncalls=4889, regioncalls=27904, ndraw=128, logz=-20.25, remainder_fraction=99.9724%, Lmin=-7.96, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=864, ncalls=4889, regioncalls=27904, ndraw=128, logz=-19.74, remainder_fraction=99.9534%, Lmin=-7.63, Lmax=-0.00 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remainder_fraction=63.9598%, Lmin=-1.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1272, ncalls=5255, regioncalls=28800, ndraw=128, logz=-13.07, remainder_fraction=62.9009%, Lmin=-1.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1278, ncalls=5255, regioncalls=28800, ndraw=128, logz=-13.04, remainder_fraction=61.8145%, Lmin=-1.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1290, ncalls=5255, regioncalls=28800, ndraw=128, logz=-12.99, remainder_fraction=59.9784%, Lmin=-0.98, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1296, ncalls=5255, regioncalls=28800, ndraw=128, logz=-12.97, remainder_fraction=58.9899%, Lmin=-0.96, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1302, ncalls=5255, regioncalls=28800, ndraw=128, logz=-12.94, remainder_fraction=57.9871%, Lmin=-0.94, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1326, ncalls=5255, regioncalls=28800, ndraw=128, 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ncalls=5383, regioncalls=29056, ndraw=128, logz=-12.32, remainder_fraction=21.2304%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1584, ncalls=5383, regioncalls=29056, ndraw=128, logz=-12.30, remainder_fraction=19.6030%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1590, ncalls=5383, regioncalls=29056, ndraw=128, logz=-12.30, remainder_fraction=19.0821%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1602, ncalls=5397, regioncalls=29312, ndraw=128, logz=-12.29, remainder_fraction=18.0947%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1614, ncalls=5406, regioncalls=29440, ndraw=128, logz=-12.27, remainder_fraction=17.1893%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1632, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.26, remainder_fraction=15.8980%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1644, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.25, remainder_fraction=15.0730%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.24, remainder_fraction=14.6733%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1661, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.24, remainder_fraction=13.9788%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1674, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.23, remainder_fraction=13.1959%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1692, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.21, remainder_fraction=12.1565%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1708, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.20, remainder_fraction=11.2928%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1716, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.20, remainder_fraction=10.8838%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1722, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.20, remainder_fraction=10.5974%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1728, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.19, remainder_fraction=10.3104%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1734, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.19, remainder_fraction=10.0335%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1746, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.18, remainder_fraction=9.4900%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1754, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.18, remainder_fraction=9.1446%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1776, ncalls=5517, regioncalls=29824, ndraw=128, logz=-12.17, remainder_fraction=8.2613%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.16, remainder_fraction=7.3959%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1824, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.15, remainder_fraction=6.6114%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1830, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.15, remainder_fraction=6.4272%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1836, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.15, remainder_fraction=6.2487%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1854, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.14, remainder_fraction=5.7398%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1860, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.14, remainder_fraction=5.5789%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1878, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.14, remainder_fraction=5.1204%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1901, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.13, remainder_fraction=4.5922%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1914, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.13, remainder_fraction=4.3147%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1926, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.13, remainder_fraction=4.0744%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1932, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.13, remainder_fraction=3.9602%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1938, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.12, remainder_fraction=3.8476%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1944, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.12, remainder_fraction=3.7393%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1948, ncalls=5644, regioncalls=30208, ndraw=128, logz=-12.12, remainder_fraction=3.6681%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1974, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.12, remainder_fraction=3.2392%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1986, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.12, remainder_fraction=3.0571%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1994, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.9417%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1998, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.8858%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2004, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.8038%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2010, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.7246%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2034, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.4270%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2041, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.3462%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2046, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.2902%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2052, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.2249%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2058, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.1612%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2064, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.11, remainder_fraction=2.0995%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2088, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.10, remainder_fraction=1.8694%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2094, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.10, remainder_fraction=1.8160%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2124, ncalls=5760, regioncalls=30464, ndraw=128, logz=-12.10, remainder_fraction=1.5700%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2130, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.5248%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2138, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.4668%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2148, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.3974%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.3184%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2166, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.2805%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2184, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.1731%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2190, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.1394%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2202, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.10, remainder_fraction=1.0748%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2214, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.09, remainder_fraction=1.0138%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2220, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.09, remainder_fraction=0.9846%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2226, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.09, remainder_fraction=0.9561%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2238, ncalls=5880, regioncalls=30720, ndraw=128, logz=-12.09, remainder_fraction=0.9019%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2274, ncalls=5916, regioncalls=31232, ndraw=128, logz=-12.09, remainder_fraction=0.7567%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2277, ncalls=5916, regioncalls=31232, ndraw=128, logz=-12.09, remainder_fraction=0.7457%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2286, ncalls=5916, regioncalls=31232, ndraw=128, logz=-12.09, remainder_fraction=0.7136%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2298, ncalls=5916, regioncalls=31232, ndraw=128, logz=-12.09, remainder_fraction=0.6730%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2316, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.6164%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2322, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5986%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2323, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5957%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2334, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5646%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5483%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2352, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5170%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2358, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.5021%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2369, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.4758%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.4089%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2415, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.3800%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2418, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.3745%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2442, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.3330%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2454, ncalls=6044, regioncalls=31616, ndraw=128, logz=-12.09, remainder_fraction=0.3140%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2484, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.2711%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2496, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.2557%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2508, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.2411%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2514, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.2341%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2544, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.2021%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2556, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.1906%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2562, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.1851%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2580, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.1695%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2592, ncalls=6163, regioncalls=32000, ndraw=128, logz=-12.09, remainder_fraction=0.1598%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2604, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1507%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1463%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2616, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1421%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2652, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1191%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2658, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1156%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2664, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1123%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2670, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1090%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2676, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.1059%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2698, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.0951%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.0941%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2706, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.0914%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2718, ncalls=6281, regioncalls=32256, ndraw=128, logz=-12.09, remainder_fraction=0.0862%, Lmin=-0.00, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-1e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 6293 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -12.15 +- 0.2622 [32mINFO [0m ultranest:integrator.py:1483 Effective samples strategy wants to improve: -9.86..-0.00 (ESS = 830.3, need >10000) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.15 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 109 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 21 and 203, most (927/1746 iterations) have 202 [35mDEBUG [0m ultranest:integrator.py:1564 at least 20 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 20 minimum live points (dlogz from 0.21 to 0.63, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.23 bs:0.26 tail:0.00 total:0.26 required:<0.50 [32mINFO [0m ultranest:integrator.py:2614 Widening from 80 to 408 live points before L=-1e+01... [32mINFO [0m ultranest:integrator.py:1283 Will add 328 live points (x1) at L=-3e+03 ... [32mINFO [0m ultranest:integrator.py:2336 Exploring (in particular: L=-2898.15..-0.00) ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 64.0), (-3087.563473062326, 128.0), (-2898.1460207797504, 408.0), (-0.0010389913923224752, 408.0), (-0.001017387493617101, 408.0), (-0.0010013848420007648, 408.0), (inf, 64.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=261, ncalls=6295, regioncalls=32768, ndraw=128, logz=-2927.13, remainder_fraction=100.0000%, Lmin=-2898.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=288, ncalls=6295, regioncalls=32768, ndraw=128, logz=-2166.61, remainder_fraction=100.0000%, Lmin=-2116.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=323, ncalls=6299, regioncalls=32896, ndraw=128, logz=-1165.10, remainder_fraction=100.0000%, Lmin=-1152.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=371, ncalls=6436, regioncalls=33792, ndraw=128, logz=-583.27, remainder_fraction=100.0000%, Lmin=-571.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=425, ncalls=6436, regioncalls=33792, ndraw=129, logz=-335.55, remainder_fraction=100.0000%, Lmin=-318.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=453, ncalls=6436, regioncalls=33792, ndraw=129, logz=-234.22, remainder_fraction=100.0000%, Lmin=-222.88, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=496, ncalls=6436, regioncalls=33792, ndraw=128, logz=-147.29, remainder_fraction=100.0000%, Lmin=-135.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=522, ncalls=6436, regioncalls=33792, ndraw=128, logz=-112.95, remainder_fraction=100.0000%, Lmin=-99.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=534, ncalls=6436, regioncalls=33792, ndraw=128, logz=-96.44, remainder_fraction=100.0000%, Lmin=-84.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=544, ncalls=6436, regioncalls=33792, ndraw=128, logz=-88.18, remainder_fraction=100.0000%, Lmin=-76.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=558, ncalls=6436, regioncalls=33792, ndraw=128, logz=-80.92, remainder_fraction=100.0000%, Lmin=-69.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=564, ncalls=6436, regioncalls=33792, ndraw=128, logz=-76.81, remainder_fraction=100.0000%, Lmin=-64.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=568, ncalls=6543, regioncalls=34304, ndraw=128, logz=-75.16, remainder_fraction=100.0000%, Lmin=-63.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=576, ncalls=6543, regioncalls=34304, ndraw=128, logz=-70.36, remainder_fraction=100.0000%, Lmin=-58.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=582, ncalls=6543, regioncalls=34304, ndraw=128, logz=-66.74, remainder_fraction=100.0000%, Lmin=-54.70, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=595, ncalls=6543, regioncalls=34304, ndraw=128, logz=-61.92, remainder_fraction=100.0000%, Lmin=-49.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=618, ncalls=6543, regioncalls=34304, ndraw=128, logz=-51.93, remainder_fraction=100.0000%, Lmin=-40.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=627, ncalls=6543, regioncalls=34304, ndraw=128, logz=-49.92, remainder_fraction=100.0000%, Lmin=-37.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=6543, regioncalls=34304, ndraw=128, logz=-48.60, remainder_fraction=100.0000%, Lmin=-35.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=636, ncalls=6543, regioncalls=34304, ndraw=128, logz=-46.66, remainder_fraction=100.0000%, Lmin=-34.74, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=648, ncalls=6543, regioncalls=34304, ndraw=128, logz=-44.67, remainder_fraction=100.0000%, Lmin=-31.78, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=666, ncalls=6543, regioncalls=34304, ndraw=128, logz=-40.11, remainder_fraction=100.0000%, Lmin=-27.88, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=703, ncalls=6660, regioncalls=34688, ndraw=128, logz=-33.32, remainder_fraction=100.0000%, Lmin=-21.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=714, ncalls=6660, regioncalls=34688, ndraw=128, logz=-32.06, remainder_fraction=100.0000%, Lmin=-19.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=6660, regioncalls=34688, ndraw=128, logz=-31.45, remainder_fraction=100.0000%, Lmin=-19.55, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=744, ncalls=6660, regioncalls=34688, ndraw=128, logz=-29.00, remainder_fraction=100.0000%, Lmin=-16.99, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=748, ncalls=6660, regioncalls=34688, ndraw=128, logz=-28.69, remainder_fraction=100.0000%, Lmin=-16.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=768, ncalls=6660, regioncalls=34688, ndraw=128, logz=-27.36, remainder_fraction=100.0000%, Lmin=-15.47, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=780, ncalls=6660, regioncalls=34688, ndraw=128, logz=-26.72, remainder_fraction=100.0000%, Lmin=-14.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=798, ncalls=6769, regioncalls=34944, ndraw=128, logz=-25.67, remainder_fraction=99.9999%, Lmin=-13.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=799, ncalls=6769, regioncalls=34944, ndraw=128, logz=-25.61, remainder_fraction=99.9999%, Lmin=-13.49, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=816, ncalls=6769, regioncalls=34944, ndraw=128, logz=-24.66, remainder_fraction=99.9997%, Lmin=-12.64, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=822, ncalls=6769, regioncalls=34944, ndraw=128, logz=-24.36, remainder_fraction=99.9996%, Lmin=-12.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=828, ncalls=6769, regioncalls=34944, ndraw=128, logz=-24.08, remainder_fraction=99.9995%, Lmin=-12.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=852, ncalls=6769, regioncalls=34944, ndraw=128, logz=-22.91, remainder_fraction=99.9982%, Lmin=-10.81, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=858, ncalls=6769, regioncalls=34944, ndraw=128, logz=-22.66, remainder_fraction=99.9976%, Lmin=-10.67, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=864, ncalls=6769, regioncalls=34944, ndraw=128, logz=-22.43, remainder_fraction=99.9969%, Lmin=-10.48, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=870, ncalls=6769, regioncalls=34944, ndraw=128, logz=-22.22, remainder_fraction=99.9961%, Lmin=-10.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=876, ncalls=6769, regioncalls=34944, ndraw=128, logz=-22.01, remainder_fraction=99.9954%, Lmin=-10.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=888, ncalls=6769, regioncalls=34944, ndraw=128, logz=-21.62, remainder_fraction=99.9935%, Lmin=-9.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=6889, regioncalls=35200, ndraw=128, logz=-21.22, remainder_fraction=99.9902%, Lmin=-9.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=906, ncalls=6889, regioncalls=35200, ndraw=128, logz=-21.04, remainder_fraction=99.9880%, Lmin=-9.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=912, ncalls=6889, regioncalls=35200, ndraw=128, logz=-20.86, remainder_fraction=99.9855%, Lmin=-8.82, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=921, ncalls=6889, regioncalls=35200, ndraw=128, logz=-20.59, remainder_fraction=99.9813%, Lmin=-8.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=924, ncalls=6889, regioncalls=35200, ndraw=128, logz=-20.50, remainder_fraction=99.9802%, Lmin=-8.49, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=6889, regioncalls=35200, ndraw=128, logz=-19.44, remainder_fraction=99.9428%, Lmin=-7.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=978, ncalls=6889, regioncalls=35200, ndraw=128, logz=-18.94, remainder_fraction=99.9051%, Lmin=-6.77, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=985, ncalls=6889, regioncalls=35200, ndraw=128, logz=-18.74, remainder_fraction=99.8818%, Lmin=-6.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1002, ncalls=6889, regioncalls=35200, ndraw=128, logz=-18.22, remainder_fraction=99.8013%, Lmin=-5.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1032, ncalls=6889, regioncalls=35200, ndraw=128, logz=-17.45, remainder_fraction=99.5915%, Lmin=-5.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=6889, regioncalls=35200, ndraw=128, logz=-17.03, remainder_fraction=99.3732%, Lmin=-4.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1062, ncalls=6889, regioncalls=35200, ndraw=128, logz=-16.78, remainder_fraction=99.2066%, Lmin=-4.72, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1068, ncalls=6889, regioncalls=35200, ndraw=128, logz=-16.67, remainder_fraction=99.1000%, Lmin=-4.60, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1074, ncalls=6889, regioncalls=35200, ndraw=128, logz=-16.55, remainder_fraction=99.0016%, Lmin=-4.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=6889, regioncalls=35200, ndraw=128, logz=-16.44, remainder_fraction=98.8937%, Lmin=-4.42, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1176, ncalls=6918, regioncalls=35584, ndraw=128, logz=-15.08, remainder_fraction=95.6682%, Lmin=-3.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1178, ncalls=6918, regioncalls=35584, ndraw=128, logz=-15.06, remainder_fraction=95.5644%, Lmin=-3.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1188, ncalls=6918, regioncalls=35584, ndraw=128, logz=-14.95, remainder_fraction=95.0253%, Lmin=-3.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1302, ncalls=6968, regioncalls=36096, ndraw=128, logz=-13.97, remainder_fraction=87.3804%, Lmin=-2.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1308, ncalls=6968, regioncalls=36096, ndraw=128, logz=-13.93, remainder_fraction=86.8380%, Lmin=-2.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1314, ncalls=6968, regioncalls=36096, ndraw=128, logz=-13.89, remainder_fraction=86.2355%, Lmin=-1.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1368, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.54, remainder_fraction=80.2309%, Lmin=-1.60, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1374, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.50, remainder_fraction=79.4464%, Lmin=-1.56, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1380, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.47, remainder_fraction=78.7336%, Lmin=-1.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1386, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.44, remainder_fraction=77.9249%, Lmin=-1.52, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1392, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.40, remainder_fraction=77.2662%, Lmin=-1.48, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1398, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.37, remainder_fraction=76.6010%, Lmin=-1.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1416, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.28, remainder_fraction=74.4619%, Lmin=-1.39, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1428, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.23, remainder_fraction=72.8752%, Lmin=-1.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1444, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.16, remainder_fraction=70.8815%, Lmin=-1.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1446, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.15, remainder_fraction=70.5629%, Lmin=-1.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1470, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.05, remainder_fraction=67.4714%, Lmin=-1.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1482, ncalls=7075, regioncalls=36352, ndraw=128, logz=-13.01, remainder_fraction=66.0424%, Lmin=-1.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1488, ncalls=7075, regioncalls=36352, ndraw=128, logz=-12.98, remainder_fraction=65.2428%, Lmin=-1.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1536, ncalls=7075, regioncalls=36352, ndraw=128, logz=-12.82, remainder_fraction=59.2982%, Lmin=-0.94, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1548, ncalls=7083, regioncalls=36608, ndraw=128, logz=-12.79, remainder_fraction=57.8925%, Lmin=-0.89, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1566, ncalls=7092, regioncalls=36736, ndraw=128, logz=-12.74, remainder_fraction=55.7174%, Lmin=-0.86, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1572, ncalls=7105, regioncalls=36864, ndraw=128, logz=-12.72, remainder_fraction=54.9881%, Lmin=-0.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1596, ncalls=7114, regioncalls=36992, ndraw=128, logz=-12.66, remainder_fraction=51.8132%, Lmin=-0.75, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1608, ncalls=7127, regioncalls=37248, ndraw=128, logz=-12.63, remainder_fraction=50.2719%, Lmin=-0.73, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1645, ncalls=7146, regioncalls=37632, ndraw=128, logz=-12.54, remainder_fraction=45.7582%, Lmin=-0.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=7146, regioncalls=37632, ndraw=128, logz=-12.53, remainder_fraction=45.2274%, Lmin=-0.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1714, ncalls=7167, regioncalls=37760, ndraw=128, logz=-12.41, remainder_fraction=38.1034%, Lmin=-0.51, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1722, ncalls=7167, regioncalls=37760, ndraw=128, logz=-12.40, remainder_fraction=37.3136%, Lmin=-0.49, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1752, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.35, remainder_fraction=34.2791%, Lmin=-0.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1783, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.31, remainder_fraction=31.4105%, Lmin=-0.39, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1794, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.29, remainder_fraction=30.4159%, Lmin=-0.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1824, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.26, remainder_fraction=27.8071%, Lmin=-0.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1836, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.24, remainder_fraction=26.8079%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1866, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.21, remainder_fraction=24.4957%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1896, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.19, remainder_fraction=22.3463%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1902, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.18, remainder_fraction=21.9256%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1932, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.16, remainder_fraction=19.9118%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1944, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.15, remainder_fraction=19.1886%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1956, ncalls=7282, regioncalls=38016, ndraw=128, logz=-12.14, remainder_fraction=18.4781%, Lmin=-0.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.12, remainder_fraction=17.1218%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1987, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.12, remainder_fraction=16.7342%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1998, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.11, remainder_fraction=16.1619%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2022, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.09, remainder_fraction=14.9679%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2058, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.08, remainder_fraction=13.3081%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2076, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.07, remainder_fraction=12.5468%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.06, remainder_fraction=11.5858%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2119, ncalls=7408, regioncalls=38272, ndraw=128, logz=-12.05, remainder_fraction=10.8925%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2124, ncalls=7419, regioncalls=38528, ndraw=128, logz=-12.05, remainder_fraction=10.7148%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2130, ncalls=7419, regioncalls=38528, ndraw=128, logz=-12.04, remainder_fraction=10.5046%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2142, ncalls=7434, regioncalls=38656, ndraw=128, logz=-12.04, remainder_fraction=10.0916%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2184, ncalls=7445, regioncalls=38784, ndraw=128, logz=-12.02, remainder_fraction=8.7751%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2190, ncalls=7445, regioncalls=38784, ndraw=128, logz=-12.02, remainder_fraction=8.6002%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2196, ncalls=7445, regioncalls=38784, ndraw=128, logz=-12.02, remainder_fraction=8.4307%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2208, ncalls=7445, regioncalls=38784, ndraw=128, logz=-12.02, remainder_fraction=8.0999%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2226, ncalls=7471, regioncalls=39040, ndraw=128, logz=-12.01, remainder_fraction=7.6295%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2251, ncalls=7471, regioncalls=39040, ndraw=128, logz=-12.01, remainder_fraction=7.0119%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2256, ncalls=7471, regioncalls=39040, ndraw=128, logz=-12.00, remainder_fraction=6.8930%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2304, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.99, remainder_fraction=5.8606%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2317, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.99, remainder_fraction=5.6060%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2334, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.99, remainder_fraction=5.2896%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.99, remainder_fraction=5.1824%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2364, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.98, remainder_fraction=4.7740%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2370, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.98, remainder_fraction=4.6767%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2382, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.98, remainder_fraction=4.4875%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2442, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.97, remainder_fraction=3.6497%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2446, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.97, remainder_fraction=3.5990%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2478, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.97, remainder_fraction=3.2216%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2484, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.96, remainder_fraction=3.1556%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2502, ncalls=7586, regioncalls=39296, ndraw=128, logz=-11.96, remainder_fraction=2.9643%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2514, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.8439%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2526, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.7278%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2532, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.6715%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2538, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.6162%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2562, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.4064%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2576, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.2917%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2580, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.96, remainder_fraction=2.2599%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2586, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.95, remainder_fraction=2.2131%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2664, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.95, remainder_fraction=1.6852%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2676, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.95, remainder_fraction=1.6160%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2688, ncalls=7713, regioncalls=39552, ndraw=128, logz=-11.95, remainder_fraction=1.5495%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2706, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.95, remainder_fraction=1.4550%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2730, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.95, remainder_fraction=1.3375%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2760, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=1.2040%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2770, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=1.1625%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2784, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=1.1068%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2834, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=0.9284%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2856, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=0.8593%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2862, ncalls=7838, regioncalls=39808, ndraw=128, logz=-11.94, remainder_fraction=0.8413%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2874, ncalls=7847, regioncalls=40064, ndraw=128, logz=-11.94, remainder_fraction=0.8066%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2886, ncalls=7856, regioncalls=40192, ndraw=128, logz=-11.94, remainder_fraction=0.7733%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=7856, regioncalls=40192, ndraw=128, logz=-11.94, remainder_fraction=0.7362%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2916, ncalls=7863, regioncalls=40448, ndraw=128, logz=-11.94, remainder_fraction=0.6959%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2922, ncalls=7869, regioncalls=40576, ndraw=128, logz=-11.94, remainder_fraction=0.6813%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2940, ncalls=7869, regioncalls=40576, ndraw=128, logz=-11.94, remainder_fraction=0.6395%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2946, ncalls=7879, regioncalls=40704, ndraw=128, logz=-11.94, remainder_fraction=0.6261%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2952, ncalls=7879, regioncalls=40704, ndraw=128, logz=-11.94, remainder_fraction=0.6130%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2958, ncalls=7879, regioncalls=40704, ndraw=128, logz=-11.94, remainder_fraction=0.6002%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=7887, regioncalls=40832, ndraw=128, logz=-11.94, remainder_fraction=0.5754%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=7899, regioncalls=41088, ndraw=128, logz=-11.94, remainder_fraction=0.5177%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3018, ncalls=7908, regioncalls=41216, ndraw=128, logz=-11.94, remainder_fraction=0.4859%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3024, ncalls=7908, regioncalls=41216, ndraw=128, logz=-11.94, remainder_fraction=0.4757%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3035, ncalls=7908, regioncalls=41216, ndraw=128, logz=-11.94, remainder_fraction=0.4576%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3048, ncalls=7923, regioncalls=41600, ndraw=128, logz=-11.94, remainder_fraction=0.4371%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3078, ncalls=7923, regioncalls=41600, ndraw=128, logz=-11.94, remainder_fraction=0.3933%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3108, ncalls=7935, regioncalls=41728, ndraw=128, logz=-11.94, remainder_fraction=0.3538%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3126, ncalls=7947, regioncalls=41856, ndraw=128, logz=-11.94, remainder_fraction=0.3321%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3156, ncalls=7955, regioncalls=41984, ndraw=128, logz=-11.94, remainder_fraction=0.2987%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3162, ncalls=7960, regioncalls=42112, ndraw=128, logz=-11.94, remainder_fraction=0.2925%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3174, ncalls=7970, regioncalls=42240, ndraw=128, logz=-11.94, remainder_fraction=0.2803%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3192, ncalls=7999, regioncalls=42496, ndraw=128, logz=-11.94, remainder_fraction=0.2631%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3198, ncalls=7999, regioncalls=42496, ndraw=128, logz=-11.94, remainder_fraction=0.2576%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3210, ncalls=8014, regioncalls=42624, ndraw=128, logz=-11.94, remainder_fraction=0.2469%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3243, ncalls=8014, regioncalls=42624, ndraw=128, logz=-11.93, remainder_fraction=0.2198%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3246, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.2174%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3252, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.2129%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3300, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1797%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3306, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1759%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3309, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1741%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3312, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1723%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3336, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1583%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3348, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1517%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3375, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1379%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3384, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1336%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3402, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1254%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1176%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3426, ncalls=8139, regioncalls=42880, ndraw=128, logz=-11.93, remainder_fraction=0.1152%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3440, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.1096%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3444, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.1081%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3450, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.1058%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3456, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.1036%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3462, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.1014%, Lmin=-0.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3504, ncalls=8237, regioncalls=43136, ndraw=128, logz=-11.93, remainder_fraction=0.0874%, Lmin=-0.00, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-1e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 8237 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. [32mINFO [0m ultranest:integrator.py:2241 To achieve the desired logz accuracy, min_num_live_points was increased to 317 [32mINFO [0m ultranest:integrator.py:1299 Widening roots to 317 live points (have 64 already) ... [32mINFO [0m ultranest:integrator.py:1339 Sampling 253 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.1, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=10, min_num_live_points=317, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 317.0), (inf, 317.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2, ncalls=8618, regioncalls=43264, ndraw=128, logz=-232898.61, remainder_fraction=100.0000%, Lmin=-232687.20, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=64, ncalls=8618, regioncalls=43264, ndraw=128, logz=-141964.73, remainder_fraction=100.0000%, Lmin=-140774.44, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=96, ncalls=8618, regioncalls=43264, ndraw=128, logz=-122913.79, remainder_fraction=100.0000%, Lmin=-122658.38, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=128, ncalls=8618, regioncalls=43264, ndraw=128, logz=-113627.23, remainder_fraction=100.0000%, Lmin=-112577.69, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=8746, regioncalls=43392, ndraw=128, logz=-101136.56, remainder_fraction=100.0000%, Lmin=-100759.15, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=192, ncalls=8746, regioncalls=43392, ndraw=128, logz=-90570.64, remainder_fraction=100.0000%, Lmin=-90155.82, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=224, ncalls=8746, regioncalls=43392, ndraw=128, logz=-81918.95, remainder_fraction=100.0000%, Lmin=-81453.03, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=256, ncalls=8873, regioncalls=43520, ndraw=128, logz=-73314.99, remainder_fraction=100.0000%, Lmin=-73233.35, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=288, ncalls=9001, regioncalls=43648, ndraw=128, logz=-64900.58, remainder_fraction=100.0000%, Lmin=-64729.91, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=9001, regioncalls=43648, ndraw=128, logz=-57792.37, remainder_fraction=100.0000%, Lmin=-57466.70, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=352, ncalls=9129, regioncalls=43776, ndraw=128, logz=-51000.87, remainder_fraction=100.0000%, Lmin=-50915.48, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=384, ncalls=9129, regioncalls=43776, ndraw=128, logz=-46172.65, remainder_fraction=100.0000%, Lmin=-46062.57, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=416, ncalls=9257, regioncalls=43904, ndraw=128, logz=-42114.07, remainder_fraction=100.0000%, Lmin=-42091.66, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=448, ncalls=9384, regioncalls=44032, ndraw=128, logz=-38002.25, remainder_fraction=100.0000%, Lmin=-37913.90, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=9384, regioncalls=44032, ndraw=128, logz=-33774.36, remainder_fraction=100.0000%, Lmin=-33743.22, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=512, ncalls=9639, regioncalls=44288, ndraw=128, logz=-30486.48, remainder_fraction=100.0000%, Lmin=-30401.49, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=513, ncalls=9639, regioncalls=44288, ndraw=128, logz=-30408.87, remainder_fraction=100.0000%, Lmin=-30375.13, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=544, ncalls=9670, regioncalls=44416, ndraw=128, logz=-27141.87, remainder_fraction=100.0000%, Lmin=-27055.13, Lmax=-141.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=576, ncalls=9701, regioncalls=44544, ndraw=128, logz=-24695.06, remainder_fraction=100.0000%, Lmin=-24585.63, Lmax=-124.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=9802, regioncalls=44928, ndraw=128, logz=-20206.51, remainder_fraction=100.0000%, Lmin=-20167.79, Lmax=-124.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=672, ncalls=9834, regioncalls=45056, ndraw=128, logz=-18456.80, remainder_fraction=100.0000%, Lmin=-18445.49, Lmax=-124.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=704, ncalls=9886, regioncalls=45312, ndraw=128, logz=-16940.33, remainder_fraction=100.0000%, Lmin=-16930.51, Lmax=-41.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=736, ncalls=9943, regioncalls=45568, ndraw=128, logz=-15494.35, remainder_fraction=100.0000%, Lmin=-15443.95, Lmax=-41.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=768, ncalls=10007, regioncalls=45824, ndraw=128, logz=-13699.92, remainder_fraction=100.0000%, Lmin=-13632.37, Lmax=-41.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=10087, regioncalls=46080, ndraw=128, logz=-12717.24, remainder_fraction=100.0000%, Lmin=-12697.63, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=864, ncalls=10213, regioncalls=46592, ndraw=128, logz=-10768.26, remainder_fraction=100.0000%, Lmin=-10673.69, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=896, ncalls=10339, regioncalls=47104, ndraw=128, logz=-9765.58, remainder_fraction=100.0000%, Lmin=-9732.89, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=928, ncalls=10411, regioncalls=47360, ndraw=128, logz=-8741.31, remainder_fraction=100.0000%, Lmin=-8700.94, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=10530, regioncalls=47872, ndraw=128, logz=-7899.30, remainder_fraction=100.0000%, Lmin=-7886.92, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=992, ncalls=10688, regioncalls=48512, ndraw=128, logz=-7029.15, remainder_fraction=100.0000%, Lmin=-7016.88, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1024, ncalls=10780, regioncalls=49024, ndraw=128, logz=-6251.07, remainder_fraction=100.0000%, Lmin=-6232.84, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1056, ncalls=10813, regioncalls=49664, ndraw=128, logz=-5724.01, remainder_fraction=100.0000%, Lmin=-5714.75, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1088, ncalls=10859, regioncalls=50688, ndraw=128, logz=-5279.64, remainder_fraction=100.0000%, Lmin=-5268.05, Lmax=-37.32 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1216, ncalls=11040, regioncalls=54144, ndraw=128, logz=-3726.98, remainder_fraction=100.0000%, Lmin=-3713.78, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1248, ncalls=11122, regioncalls=55936, ndraw=128, logz=-3460.08, remainder_fraction=100.0000%, Lmin=-3444.46, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1276, ncalls=11157, regioncalls=56576, ndraw=128, logz=-3082.62, remainder_fraction=100.0000%, Lmin=-3067.69, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=11161, regioncalls=56704, ndraw=128, logz=-3050.81, remainder_fraction=100.0000%, Lmin=-3035.77, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1305, ncalls=11292, regioncalls=57216, ndraw=128, logz=-2841.29, remainder_fraction=100.0000%, Lmin=-2827.32, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1312, ncalls=11292, regioncalls=57216, ndraw=128, logz=-2718.79, remainder_fraction=100.0000%, Lmin=-2689.88, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1344, ncalls=11292, regioncalls=57216, ndraw=128, logz=-2498.24, remainder_fraction=100.0000%, Lmin=-2484.50, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1376, ncalls=11292, regioncalls=57216, ndraw=128, logz=-2298.11, remainder_fraction=100.0000%, Lmin=-2287.32, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1408, ncalls=11292, regioncalls=57216, ndraw=128, logz=-2004.48, remainder_fraction=100.0000%, Lmin=-1985.86, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=11329, regioncalls=58624, ndraw=128, logz=-1830.75, remainder_fraction=100.0000%, Lmin=-1820.00, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1469, ncalls=11366, regioncalls=59904, ndraw=128, logz=-1632.08, remainder_fraction=100.0000%, Lmin=-1620.64, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1472, ncalls=11366, regioncalls=59904, ndraw=128, logz=-1627.35, remainder_fraction=100.0000%, Lmin=-1616.68, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1536, ncalls=11514, regioncalls=61440, ndraw=128, logz=-1345.11, remainder_fraction=100.0000%, Lmin=-1332.78, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1568, ncalls=11514, regioncalls=61440, ndraw=128, logz=-1216.01, remainder_fraction=100.0000%, Lmin=-1202.14, Lmax=-27.54 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1664, ncalls=11642, regioncalls=61568, ndraw=128, logz=-936.80, remainder_fraction=100.0000%, Lmin=-922.82, Lmax=-5.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1728, ncalls=11898, regioncalls=61824, ndraw=128, logz=-747.21, remainder_fraction=100.0000%, Lmin=-733.92, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1748, ncalls=11898, regioncalls=61824, ndraw=128, logz=-714.06, remainder_fraction=100.0000%, Lmin=-701.10, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1759, ncalls=11898, regioncalls=61824, ndraw=128, logz=-679.23, remainder_fraction=100.0000%, Lmin=-656.72, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1760, ncalls=11898, regioncalls=61824, ndraw=128, logz=-668.03, remainder_fraction=100.0000%, Lmin=-655.91, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1792, ncalls=12026, regioncalls=62080, ndraw=128, logz=-616.91, remainder_fraction=100.0000%, Lmin=-604.35, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1856, ncalls=12026, regioncalls=62080, ndraw=128, logz=-525.77, remainder_fraction=100.0000%, Lmin=-513.80, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1888, ncalls=12026, regioncalls=62080, ndraw=128, logz=-484.00, remainder_fraction=100.0000%, Lmin=-470.07, Lmax=-3.45 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1952, ncalls=12154, regioncalls=62208, ndraw=128, logz=-402.52, remainder_fraction=100.0000%, Lmin=-390.18, Lmax=-1.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1982, ncalls=12282, regioncalls=62336, ndraw=128, logz=-369.51, remainder_fraction=100.0000%, Lmin=-357.12, Lmax=-1.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2016, ncalls=12282, regioncalls=62336, ndraw=128, logz=-329.98, remainder_fraction=100.0000%, Lmin=-317.65, Lmax=-1.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2080, ncalls=12412, regioncalls=62848, ndraw=128, logz=-274.86, remainder_fraction=100.0000%, Lmin=-262.23, Lmax=-1.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2111, ncalls=12412, regioncalls=62848, ndraw=128, logz=-251.77, remainder_fraction=100.0000%, Lmin=-238.95, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2144, ncalls=12541, regioncalls=63360, ndraw=128, logz=-224.91, remainder_fraction=100.0000%, Lmin=-213.38, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2164, ncalls=12541, regioncalls=63360, ndraw=128, logz=-211.28, remainder_fraction=100.0000%, Lmin=-198.80, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2179, ncalls=12541, regioncalls=63360, ndraw=128, logz=-203.14, remainder_fraction=100.0000%, Lmin=-189.11, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2208, ncalls=12541, regioncalls=63360, ndraw=128, logz=-185.83, remainder_fraction=100.0000%, Lmin=-173.81, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2212, ncalls=12541, regioncalls=63360, ndraw=128, logz=-183.45, remainder_fraction=100.0000%, Lmin=-171.45, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2227, ncalls=12541, regioncalls=63360, ndraw=128, logz=-177.82, remainder_fraction=100.0000%, Lmin=-166.08, Lmax=-0.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2272, ncalls=12669, regioncalls=63616, ndraw=128, logz=-156.53, remainder_fraction=100.0000%, Lmin=-144.14, Lmax=-0.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2283, ncalls=12669, regioncalls=63616, ndraw=128, logz=-152.91, remainder_fraction=100.0000%, Lmin=-141.67, Lmax=-0.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2336, ncalls=12669, regioncalls=63616, ndraw=128, logz=-135.08, remainder_fraction=100.0000%, Lmin=-123.18, Lmax=-0.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2368, ncalls=12669, regioncalls=63616, ndraw=128, logz=-122.37, remainder_fraction=100.0000%, Lmin=-109.61, Lmax=-0.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2385, ncalls=12797, regioncalls=63872, ndraw=128, logz=-115.12, remainder_fraction=100.0000%, Lmin=-103.05, Lmax=-0.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=12797, regioncalls=63872, ndraw=128, logz=-110.85, remainder_fraction=100.0000%, Lmin=-98.44, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2432, ncalls=12797, regioncalls=63872, ndraw=128, logz=-99.18, remainder_fraction=100.0000%, Lmin=-86.39, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2449, ncalls=12797, regioncalls=63872, ndraw=128, logz=-95.46, remainder_fraction=100.0000%, Lmin=-83.77, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2462, ncalls=12797, regioncalls=63872, ndraw=128, logz=-92.51, remainder_fraction=100.0000%, Lmin=-80.51, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2464, ncalls=12797, regioncalls=63872, ndraw=128, logz=-92.13, remainder_fraction=100.0000%, Lmin=-80.07, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2485, ncalls=12799, regioncalls=64000, ndraw=128, logz=-87.13, remainder_fraction=100.0000%, Lmin=-75.28, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2496, ncalls=12927, regioncalls=64896, ndraw=128, logz=-85.10, remainder_fraction=100.0000%, Lmin=-72.84, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2514, ncalls=12927, regioncalls=64896, ndraw=128, logz=-81.84, remainder_fraction=100.0000%, Lmin=-69.70, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2528, ncalls=12927, regioncalls=64896, ndraw=128, logz=-79.10, remainder_fraction=100.0000%, Lmin=-66.92, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2555, ncalls=12927, regioncalls=64896, ndraw=128, logz=-72.47, remainder_fraction=100.0000%, Lmin=-59.49, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2560, ncalls=12927, regioncalls=64896, ndraw=128, logz=-71.10, remainder_fraction=100.0000%, Lmin=-58.75, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2592, ncalls=12927, regioncalls=64896, ndraw=128, logz=-64.77, remainder_fraction=100.0000%, Lmin=-52.41, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2607, ncalls=12927, regioncalls=64896, ndraw=128, logz=-62.59, remainder_fraction=100.0000%, Lmin=-50.86, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2631, ncalls=12927, regioncalls=64896, ndraw=128, logz=-58.37, remainder_fraction=100.0000%, Lmin=-45.34, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2661, ncalls=13055, regioncalls=65152, ndraw=128, logz=-53.57, remainder_fraction=100.0000%, Lmin=-41.44, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2698, ncalls=13055, regioncalls=65152, ndraw=128, logz=-48.69, remainder_fraction=100.0000%, Lmin=-36.09, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2712, ncalls=13055, regioncalls=65152, ndraw=128, logz=-46.76, remainder_fraction=100.0000%, Lmin=-34.75, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2730, ncalls=13055, regioncalls=65152, ndraw=128, logz=-44.95, remainder_fraction=100.0000%, Lmin=-32.82, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2744, ncalls=13055, regioncalls=65152, ndraw=128, logz=-43.60, remainder_fraction=100.0000%, Lmin=-31.53, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2763, ncalls=13055, regioncalls=65152, ndraw=128, logz=-41.56, remainder_fraction=100.0000%, Lmin=-28.75, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2776, ncalls=13055, regioncalls=65152, ndraw=128, logz=-39.64, remainder_fraction=100.0000%, Lmin=-27.00, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2785, ncalls=13055, regioncalls=65152, ndraw=128, logz=-38.65, remainder_fraction=100.0000%, Lmin=-26.59, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2797, ncalls=13055, regioncalls=65152, ndraw=128, logz=-37.59, remainder_fraction=100.0000%, Lmin=-25.35, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2810, ncalls=13055, regioncalls=65152, ndraw=128, logz=-36.35, remainder_fraction=100.0000%, Lmin=-23.87, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2824, ncalls=13055, regioncalls=65152, ndraw=128, logz=-34.92, remainder_fraction=100.0000%, Lmin=-22.71, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3353, ncalls=13055, regioncalls=65152, ndraw=128, logz=-16.31, remainder_fraction=98.7508%, Lmin=-4.25, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3424, ncalls=13183, regioncalls=65536, ndraw=128, logz=-15.38, remainder_fraction=96.8008%, Lmin=-3.45, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3643, ncalls=13183, regioncalls=65536, ndraw=128, logz=-13.64, remainder_fraction=82.3802%, Lmin=-1.63, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3712, ncalls=13183, regioncalls=65536, ndraw=128, logz=-13.29, remainder_fraction=74.7982%, Lmin=-1.34, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3776, ncalls=13183, regioncalls=65536, ndraw=128, logz=-13.04, remainder_fraction=67.3520%, Lmin=-1.14, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3866, ncalls=13183, regioncalls=65536, ndraw=128, logz=-12.76, remainder_fraction=57.2660%, Lmin=-0.84, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4032, ncalls=13311, regioncalls=65920, ndraw=128, logz=-12.42, remainder_fraction=39.2299%, Lmin=-0.49, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4160, ncalls=13311, regioncalls=65920, ndraw=128, logz=-12.25, remainder_fraction=28.1578%, Lmin=-0.33, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4288, ncalls=13311, regioncalls=65920, ndraw=128, logz=-12.14, remainder_fraction=19.7718%, Lmin=-0.22, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4352, ncalls=13311, regioncalls=65920, ndraw=128, logz=-12.10, remainder_fraction=16.4738%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4399, ncalls=13439, regioncalls=66048, ndraw=128, logz=-12.08, remainder_fraction=14.3796%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4448, ncalls=13439, regioncalls=66048, ndraw=128, logz=-12.06, remainder_fraction=12.4540%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4480, ncalls=13439, regioncalls=66048, ndraw=128, logz=-12.04, remainder_fraction=11.3399%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4672, ncalls=13567, regioncalls=66304, ndraw=128, logz=-11.99, remainder_fraction=6.3824%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4736, ncalls=13567, regioncalls=66304, ndraw=128, logz=-11.98, remainder_fraction=5.2522%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4911, ncalls=13695, regioncalls=66432, ndraw=128, logz=-11.95, remainder_fraction=3.0634%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5086, ncalls=13823, regioncalls=66944, ndraw=128, logz=-11.94, remainder_fraction=1.7766%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5261, ncalls=13823, regioncalls=66944, ndraw=128, logz=-11.93, remainder_fraction=1.0273%, Lmin=-0.01, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-1e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 13823 [32mINFO [0m ultranest:integrator.py:2578 logZ = -11.96 +- 0.1918 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1255.1, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.19 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 792 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 1 and inf, most (2922/3615 iterations) have 315 [35mDEBUG [0m ultranest:integrator.py:1564 at least 633 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 633 minimum live points (dlogz from 0.17 to 0.38, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.20 bs:0.19 tail:0.00 total:0.19 required:<0.10 [32mINFO [0m ultranest:integrator.py:1299 Widening roots to 633 live points (have 317 already) ... [32mINFO [0m ultranest:integrator.py:1339 Sampling 316 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 633.0), (inf, 633.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1, ncalls=14267, regioncalls=67072, ndraw=128, logz=-239036.45, remainder_fraction=100.0000%, Lmin=-236674.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=32, ncalls=14267, regioncalls=67072, ndraw=128, logz=-184777.26, remainder_fraction=100.0000%, Lmin=-184465.78, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=14267, regioncalls=67072, ndraw=128, logz=-124668.37, remainder_fraction=100.0000%, Lmin=-124605.48, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=192, ncalls=14267, regioncalls=67072, ndraw=128, logz=-117854.95, remainder_fraction=100.0000%, Lmin=-117419.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=224, ncalls=14267, regioncalls=67072, ndraw=128, logz=-113375.40, remainder_fraction=100.0000%, Lmin=-112782.43, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=256, ncalls=14395, regioncalls=67200, ndraw=128, logz=-107501.89, remainder_fraction=100.0000%, Lmin=-107419.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=288, ncalls=14395, regioncalls=67200, ndraw=128, logz=-103236.52, remainder_fraction=100.0000%, Lmin=-102555.74, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=352, ncalls=14395, regioncalls=67200, ndraw=128, logz=-91541.39, remainder_fraction=100.0000%, Lmin=-91368.67, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=416, ncalls=14523, regioncalls=67328, ndraw=128, logz=-83201.44, remainder_fraction=100.0000%, Lmin=-82587.56, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=14523, regioncalls=67328, ndraw=128, logz=-75005.21, remainder_fraction=100.0000%, Lmin=-74914.94, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=512, ncalls=14651, regioncalls=67456, ndraw=128, logz=-71842.13, remainder_fraction=100.0000%, Lmin=-71318.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=608, ncalls=14778, regioncalls=67584, ndraw=128, logz=-60268.97, remainder_fraction=100.0000%, Lmin=-60189.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=704, ncalls=14906, regioncalls=67712, ndraw=128, logz=-51489.36, remainder_fraction=100.0000%, Lmin=-51406.61, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=736, ncalls=14906, regioncalls=67712, ndraw=128, logz=-48759.12, remainder_fraction=100.0000%, Lmin=-48610.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=768, ncalls=15034, regioncalls=67840, ndraw=128, logz=-46325.34, remainder_fraction=100.0000%, Lmin=-46271.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=921, ncalls=15290, regioncalls=68096, ndraw=128, logz=-36346.20, remainder_fraction=100.0000%, Lmin=-36328.58, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=928, ncalls=15290, regioncalls=68096, ndraw=128, logz=-36142.87, remainder_fraction=100.0000%, Lmin=-36067.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1020, ncalls=15546, regioncalls=68352, ndraw=128, logz=-30462.48, remainder_fraction=100.0000%, Lmin=-30404.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1056, ncalls=15546, regioncalls=68352, ndraw=128, logz=-28231.04, remainder_fraction=100.0000%, Lmin=-28018.93, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1088, ncalls=15579, regioncalls=68480, ndraw=128, logz=-26326.20, remainder_fraction=100.0000%, Lmin=-26313.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1252, ncalls=15687, regioncalls=68992, ndraw=128, logz=-20463.76, remainder_fraction=100.0000%, Lmin=-20446.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=15719, regioncalls=69120, ndraw=128, logz=-19690.06, remainder_fraction=100.0000%, Lmin=-19625.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1312, ncalls=15748, regioncalls=69248, ndraw=128, logz=-18543.17, remainder_fraction=100.0000%, Lmin=-18532.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1376, ncalls=15803, regioncalls=69504, ndraw=128, logz=-16925.20, remainder_fraction=100.0000%, Lmin=-16864.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1472, ncalls=15877, regioncalls=69888, ndraw=128, logz=-14369.41, remainder_fraction=100.0000%, Lmin=-14344.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1504, ncalls=15927, regioncalls=70144, ndraw=128, logz=-13755.18, remainder_fraction=100.0000%, Lmin=-13731.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1536, ncalls=15985, regioncalls=70400, ndraw=128, logz=-13237.21, remainder_fraction=100.0000%, Lmin=-13152.90, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=16011, regioncalls=70528, ndraw=128, logz=-11958.51, remainder_fraction=100.0000%, Lmin=-11926.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1696, ncalls=16156, regioncalls=71168, ndraw=128, logz=-10261.99, remainder_fraction=100.0000%, Lmin=-10195.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1824, ncalls=16406, regioncalls=72320, ndraw=128, logz=-8168.60, remainder_fraction=100.0000%, Lmin=-8156.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=16622, regioncalls=73344, ndraw=128, logz=-6972.68, remainder_fraction=100.0000%, Lmin=-6950.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1952, ncalls=16758, regioncalls=73984, ndraw=128, logz=-6632.03, remainder_fraction=100.0000%, Lmin=-6610.82, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2039, ncalls=16965, regioncalls=75136, ndraw=128, logz=-5748.66, remainder_fraction=100.0000%, Lmin=-5738.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2080, ncalls=16989, regioncalls=75904, ndraw=128, logz=-5521.82, remainder_fraction=100.0000%, Lmin=-5508.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2112, ncalls=17008, regioncalls=76416, ndraw=128, logz=-5232.39, remainder_fraction=100.0000%, Lmin=-5204.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2144, ncalls=17022, regioncalls=76672, ndraw=128, logz=-5059.52, remainder_fraction=100.0000%, Lmin=-5035.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2176, ncalls=17040, regioncalls=77056, ndraw=128, logz=-4766.87, remainder_fraction=100.0000%, Lmin=-4733.88, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2240, ncalls=17082, regioncalls=78080, ndraw=128, logz=-4359.33, remainder_fraction=100.0000%, Lmin=-4347.86, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2272, ncalls=17116, regioncalls=78848, ndraw=128, logz=-4170.37, remainder_fraction=100.0000%, Lmin=-4145.14, Lmax=-0.00 [35mDEBUG [0m 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remainder_fraction=100.0000%, Lmin=-2820.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2560, ncalls=17385, regioncalls=86656, ndraw=128, logz=-2629.85, remainder_fraction=100.0000%, Lmin=-2619.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2624, ncalls=17419, regioncalls=88192, ndraw=128, logz=-2433.04, remainder_fraction=100.0000%, Lmin=-2401.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2656, ncalls=17439, regioncalls=88832, ndraw=128, logz=-2300.04, remainder_fraction=100.0000%, Lmin=-2288.74, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2720, ncalls=17492, regioncalls=91008, ndraw=128, logz=-2104.62, remainder_fraction=100.0000%, Lmin=-2082.43, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2784, ncalls=17628, regioncalls=91648, ndraw=128, logz=-1924.52, remainder_fraction=100.0000%, Lmin=-1910.85, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2816, ncalls=17628, regioncalls=91648, ndraw=128, logz=-1824.74, remainder_fraction=100.0000%, Lmin=-1799.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2848, ncalls=17628, regioncalls=91648, ndraw=128, logz=-1715.05, remainder_fraction=100.0000%, Lmin=-1703.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2869, ncalls=17756, regioncalls=91776, ndraw=128, logz=-1654.38, remainder_fraction=100.0000%, Lmin=-1643.76, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2912, ncalls=17756, regioncalls=91776, ndraw=128, logz=-1548.98, remainder_fraction=100.0000%, Lmin=-1533.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2944, ncalls=17756, regioncalls=91776, ndraw=128, logz=-1476.13, remainder_fraction=100.0000%, Lmin=-1460.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3008, ncalls=17777, regioncalls=93184, ndraw=128, logz=-1324.02, remainder_fraction=100.0000%, Lmin=-1312.80, Lmax=-0.00 [35mDEBUG [0m 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Lmin=-703.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3425, ncalls=18322, regioncalls=96256, ndraw=128, logz=-684.21, remainder_fraction=100.0000%, Lmin=-671.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3488, ncalls=18322, regioncalls=96256, ndraw=128, logz=-622.20, remainder_fraction=100.0000%, Lmin=-608.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3552, ncalls=18322, regioncalls=96256, ndraw=128, logz=-571.06, remainder_fraction=100.0000%, Lmin=-558.96, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3648, ncalls=18323, regioncalls=96384, ndraw=128, logz=-484.23, remainder_fraction=100.0000%, Lmin=-472.59, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3712, ncalls=18454, regioncalls=96896, ndraw=128, logz=-437.28, remainder_fraction=100.0000%, Lmin=-425.89, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3808, ncalls=18454, regioncalls=96896, ndraw=128, logz=-386.00, remainder_fraction=100.0000%, Lmin=-374.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3834, ncalls=18582, regioncalls=97024, ndraw=128, logz=-369.75, remainder_fraction=100.0000%, Lmin=-357.81, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3963, ncalls=18710, regioncalls=97408, ndraw=128, logz=-305.52, remainder_fraction=100.0000%, Lmin=-294.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4032, ncalls=18710, regioncalls=97408, ndraw=128, logz=-275.82, remainder_fraction=100.0000%, Lmin=-263.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4064, ncalls=18710, regioncalls=97408, ndraw=128, logz=-263.26, remainder_fraction=100.0000%, Lmin=-251.59, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4095, ncalls=18838, regioncalls=97536, ndraw=128, logz=-252.42, remainder_fraction=100.0000%, Lmin=-239.84, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4096, 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Lmin=-167.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4448, ncalls=19098, regioncalls=99200, ndraw=128, logz=-154.14, remainder_fraction=100.0000%, Lmin=-142.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4455, ncalls=19098, regioncalls=99200, ndraw=128, logz=-153.08, remainder_fraction=100.0000%, Lmin=-141.59, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4480, ncalls=19098, regioncalls=99200, ndraw=128, logz=-147.48, remainder_fraction=100.0000%, Lmin=-135.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4544, ncalls=19098, regioncalls=99200, ndraw=128, logz=-135.44, remainder_fraction=100.0000%, Lmin=-123.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4576, ncalls=19226, regioncalls=99456, ndraw=128, logz=-128.10, remainder_fraction=100.0000%, Lmin=-116.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4608, ncalls=19226, regioncalls=99456, ndraw=128, 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logz=-12.61, remainder_fraction=52.0347%, Lmin=-0.74, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7744, ncalls=22682, regioncalls=105856, ndraw=128, logz=-12.57, remainder_fraction=50.2582%, Lmin=-0.71, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7776, ncalls=22810, regioncalls=105984, ndraw=128, logz=-12.54, remainder_fraction=48.5381%, Lmin=-0.68, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7808, ncalls=22810, regioncalls=105984, ndraw=128, logz=-12.50, remainder_fraction=46.8280%, Lmin=-0.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7840, ncalls=22810, regioncalls=105984, ndraw=128, logz=-12.47, remainder_fraction=45.1771%, Lmin=-0.62, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7872, ncalls=22938, regioncalls=106112, ndraw=128, logz=-12.44, remainder_fraction=43.5701%, Lmin=-0.58, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=7936, ncalls=23066, regioncalls=106240, ndraw=128, logz=-12.39, remainder_fraction=40.3708%, Lmin=-0.52, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8000, ncalls=23194, regioncalls=106368, ndraw=128, logz=-12.34, remainder_fraction=37.3340%, Lmin=-0.47, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8064, ncalls=23322, regioncalls=106496, ndraw=128, logz=-12.30, remainder_fraction=34.4661%, Lmin=-0.43, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8096, ncalls=23450, regioncalls=106624, ndraw=128, logz=-12.28, remainder_fraction=33.1573%, Lmin=-0.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8135, ncalls=23578, regioncalls=106752, ndraw=128, logz=-12.25, remainder_fraction=31.5291%, Lmin=-0.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8274, ncalls=23706, regioncalls=107008, ndraw=128, logz=-12.18, remainder_fraction=26.1758%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8352, ncalls=23706, regioncalls=107008, ndraw=128, logz=-12.14, remainder_fraction=23.4949%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8384, ncalls=23834, regioncalls=107136, ndraw=128, logz=-12.13, remainder_fraction=22.4453%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8416, ncalls=23834, regioncalls=107136, ndraw=128, logz=-12.12, remainder_fraction=21.4618%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8480, ncalls=23834, regioncalls=107136, ndraw=128, logz=-12.09, remainder_fraction=19.6142%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8512, ncalls=23962, regioncalls=107264, ndraw=128, logz=-12.08, remainder_fraction=18.7289%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8544, ncalls=23962, regioncalls=107264, ndraw=128, logz=-12.07, remainder_fraction=17.9035%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8684, ncalls=24090, regioncalls=107392, ndraw=128, logz=-12.03, remainder_fraction=14.6198%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8736, ncalls=24090, regioncalls=107392, ndraw=128, logz=-12.02, remainder_fraction=13.5403%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8768, ncalls=24218, regioncalls=107520, ndraw=128, logz=-12.01, remainder_fraction=12.9247%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8800, ncalls=24218, regioncalls=107520, ndraw=128, logz=-12.01, remainder_fraction=12.3271%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8832, ncalls=24218, regioncalls=107520, ndraw=128, logz=-12.00, remainder_fraction=11.7556%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=8864, ncalls=24346, regioncalls=107648, ndraw=128, logz=-11.99, remainder_fraction=11.2079%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9002, ncalls=24602, regioncalls=107904, ndraw=128, logz=-11.97, remainder_fraction=9.1225%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9056, ncalls=24730, regioncalls=108032, ndraw=128, logz=-11.96, remainder_fraction=8.4058%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9088, ncalls=24730, regioncalls=108032, ndraw=128, logz=-11.96, remainder_fraction=8.0082%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9120, ncalls=24858, regioncalls=108160, ndraw=128, logz=-11.95, remainder_fraction=7.6317%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9152, ncalls=24986, regioncalls=108288, ndraw=128, logz=-11.95, remainder_fraction=7.2698%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9155, ncalls=24986, regioncalls=108288, ndraw=128, logz=-11.95, remainder_fraction=7.2369%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9216, ncalls=25114, regioncalls=108672, ndraw=128, logz=-11.94, remainder_fraction=6.5939%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9248, ncalls=25114, regioncalls=108672, ndraw=128, logz=-11.94, remainder_fraction=6.2803%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9344, ncalls=25114, regioncalls=108672, ndraw=128, logz=-11.93, remainder_fraction=5.4226%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9440, ncalls=25242, regioncalls=108800, ndraw=128, logz=-11.92, remainder_fraction=4.6776%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9472, ncalls=25242, regioncalls=108800, ndraw=128, logz=-11.92, remainder_fraction=4.4521%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9536, ncalls=25370, regioncalls=108928, ndraw=128, logz=-11.92, remainder_fraction=4.0341%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9568, ncalls=25370, regioncalls=108928, ndraw=128, logz=-11.91, remainder_fraction=3.8383%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9600, ncalls=25498, regioncalls=109056, ndraw=128, logz=-11.91, remainder_fraction=3.6529%, Lmin=-0.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9735, ncalls=25626, regioncalls=109184, ndraw=128, logz=-11.90, remainder_fraction=2.9631%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9760, ncalls=25626, regioncalls=109184, ndraw=128, logz=-11.90, remainder_fraction=2.8499%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9856, ncalls=25882, regioncalls=109440, ndraw=128, logz=-11.90, remainder_fraction=2.4536%, Lmin=-0.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9920, ncalls=26010, regioncalls=109568, ndraw=128, logz=-11.90, remainder_fraction=2.2207%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=9984, ncalls=26138, regioncalls=109696, ndraw=128, logz=-11.89, remainder_fraction=2.0094%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10016, ncalls=26138, regioncalls=109696, ndraw=128, logz=-11.89, remainder_fraction=1.9113%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10154, ncalls=26522, regioncalls=110080, ndraw=128, logz=-11.89, remainder_fraction=1.5399%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10174, ncalls=26522, regioncalls=110080, ndraw=128, logz=-11.89, remainder_fraction=1.4923%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10272, ncalls=26650, regioncalls=110592, ndraw=128, logz=-11.89, remainder_fraction=1.2795%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10336, ncalls=26650, regioncalls=110592, ndraw=128, logz=-11.89, remainder_fraction=1.1573%, Lmin=-0.01, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-1e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 26778 [32mINFO [0m ultranest:integrator.py:2578 logZ = -11.87 +- 0.08615 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 2546.8, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.09 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 1083 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 1 and inf, most (8228/8602 iterations) have 631 [35mDEBUG [0m ultranest:integrator.py:1564 at least 1265 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 1265 minimum live points (dlogz from 0.07 to 0.18, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.19 bs:0.09 tail:0.00 total:0.09 required:<0.10 [32mINFO [0m ultranest:integrator.py:1299 Widening roots to 1265 live points (have 633 already) ... [32mINFO [0m ultranest:integrator.py:1339 Sampling 632 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 1265.0), (inf, 1265.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4, ncalls=27538, regioncalls=110848, ndraw=128, logz=-232694.35, remainder_fraction=100.0000%, Lmin=-229880.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=64, ncalls=27538, regioncalls=110848, ndraw=128, logz=-180722.44, remainder_fraction=100.0000%, Lmin=-180448.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=96, ncalls=27538, regioncalls=110848, ndraw=128, logz=-166935.95, remainder_fraction=100.0000%, Lmin=-166678.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=128, ncalls=27538, regioncalls=110848, ndraw=128, logz=-158714.79, remainder_fraction=100.0000%, Lmin=-158575.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=192, ncalls=27538, regioncalls=110848, ndraw=128, logz=-145580.58, remainder_fraction=100.0000%, Lmin=-145370.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=256, ncalls=27666, regioncalls=110976, ndraw=128, logz=-131355.47, remainder_fraction=100.0000%, Lmin=-131338.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=288, ncalls=27666, regioncalls=110976, ndraw=128, logz=-127831.84, remainder_fraction=100.0000%, Lmin=-127348.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=426, ncalls=27666, regioncalls=110976, ndraw=128, logz=-112782.58, remainder_fraction=100.0000%, Lmin=-112601.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=448, ncalls=27666, regioncalls=110976, ndraw=128, logz=-110176.14, remainder_fraction=100.0000%, Lmin=-110152.51, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=27794, regioncalls=111104, ndraw=128, logz=-107487.23, remainder_fraction=100.0000%, Lmin=-107453.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=576, ncalls=27794, regioncalls=111104, ndraw=128, logz=-100295.42, remainder_fraction=100.0000%, Lmin=-100155.39, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=27922, regioncalls=111232, ndraw=128, logz=-94269.39, remainder_fraction=100.0000%, Lmin=-94248.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=773, ncalls=27922, regioncalls=111232, ndraw=128, logz=-84705.44, remainder_fraction=100.0000%, Lmin=-84544.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=891, ncalls=28050, regioncalls=111360, ndraw=128, logz=-76109.53, remainder_fraction=100.0000%, Lmin=-75948.76, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=928, ncalls=28178, regioncalls=111488, ndraw=128, logz=-74302.68, remainder_fraction=100.0000%, Lmin=-74287.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=992, ncalls=28178, regioncalls=111488, ndraw=128, logz=-70944.35, remainder_fraction=100.0000%, Lmin=-70928.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1056, ncalls=28306, regioncalls=111616, ndraw=128, logz=-67403.50, remainder_fraction=100.0000%, Lmin=-67373.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1088, ncalls=28306, regioncalls=111616, ndraw=128, logz=-65399.10, remainder_fraction=100.0000%, Lmin=-65352.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1152, ncalls=28434, regioncalls=111744, ndraw=128, logz=-61101.86, remainder_fraction=100.0000%, Lmin=-61026.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1248, ncalls=28562, regioncalls=111872, ndraw=128, logz=-56739.96, remainder_fraction=100.0000%, Lmin=-56600.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=28562, regioncalls=111872, ndraw=128, logz=-54926.99, remainder_fraction=100.0000%, Lmin=-54869.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1376, ncalls=28690, regioncalls=112000, ndraw=128, logz=-50619.35, remainder_fraction=100.0000%, Lmin=-50569.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1408, ncalls=28818, regioncalls=112128, ndraw=128, logz=-49314.01, remainder_fraction=100.0000%, Lmin=-49149.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=28818, regioncalls=112128, ndraw=128, logz=-48032.74, remainder_fraction=100.0000%, Lmin=-48022.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1472, ncalls=28946, regioncalls=112256, ndraw=128, logz=-46738.28, remainder_fraction=100.0000%, Lmin=-46669.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1536, ncalls=29074, regioncalls=112384, ndraw=128, logz=-44608.31, remainder_fraction=100.0000%, Lmin=-44592.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=29202, regioncalls=112512, ndraw=128, logz=-42642.48, remainder_fraction=100.0000%, Lmin=-42632.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1664, ncalls=29330, regioncalls=112640, ndraw=128, logz=-40865.06, remainder_fraction=100.0000%, Lmin=-40839.53, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1728, ncalls=29458, regioncalls=112768, ndraw=128, logz=-38579.71, remainder_fraction=100.0000%, Lmin=-38557.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1824, ncalls=29714, regioncalls=113024, ndraw=128, logz=-35866.42, remainder_fraction=100.0000%, Lmin=-35784.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1856, ncalls=29842, regioncalls=113152, ndraw=128, logz=-34782.78, remainder_fraction=100.0000%, Lmin=-34765.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=29970, regioncalls=113280, ndraw=128, logz=-33007.20, remainder_fraction=100.0000%, Lmin=-32991.48, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1952, ncalls=30098, regioncalls=113408, ndraw=128, logz=-32118.76, remainder_fraction=100.0000%, Lmin=-32101.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2041, ncalls=30354, regioncalls=113664, ndraw=128, logz=-29372.19, remainder_fraction=100.0000%, Lmin=-29356.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2144, ncalls=30404, regioncalls=113920, ndraw=128, logz=-26715.83, remainder_fraction=100.0000%, Lmin=-26639.82, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2176, ncalls=30431, regioncalls=114048, ndraw=128, logz=-25975.43, remainder_fraction=100.0000%, Lmin=-25917.95, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2208, ncalls=30431, regioncalls=114048, ndraw=128, logz=-25217.49, remainder_fraction=100.0000%, Lmin=-25159.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2304, ncalls=30489, regioncalls=114304, ndraw=128, logz=-23290.42, remainder_fraction=100.0000%, Lmin=-23265.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2336, ncalls=30521, regioncalls=114432, ndraw=128, logz=-22749.34, remainder_fraction=100.0000%, Lmin=-22720.00, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2458, ncalls=30608, regioncalls=114816, ndraw=128, logz=-20477.59, remainder_fraction=100.0000%, Lmin=-20468.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2464, ncalls=30636, regioncalls=114944, ndraw=128, logz=-20429.83, remainder_fraction=100.0000%, Lmin=-20417.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2584, ncalls=30721, regioncalls=115328, ndraw=128, logz=-18458.10, remainder_fraction=100.0000%, Lmin=-18446.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2592, ncalls=30753, regioncalls=115456, ndraw=128, logz=-18402.09, remainder_fraction=100.0000%, Lmin=-18340.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2624, ncalls=30780, regioncalls=115584, ndraw=128, logz=-17881.57, remainder_fraction=100.0000%, Lmin=-17866.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2656, ncalls=30809, regioncalls=115712, ndraw=128, logz=-17499.46, remainder_fraction=100.0000%, Lmin=-17481.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2752, ncalls=30906, regioncalls=116224, ndraw=128, logz=-16392.82, remainder_fraction=100.0000%, Lmin=-16381.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2784, ncalls=30966, regioncalls=116480, ndraw=128, logz=-15918.12, remainder_fraction=100.0000%, Lmin=-15871.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2907, ncalls=31101, regioncalls=117120, ndraw=128, logz=-14495.05, remainder_fraction=100.0000%, Lmin=-14483.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2944, ncalls=31128, regioncalls=117248, ndraw=128, logz=-14189.59, remainder_fraction=100.0000%, Lmin=-14153.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3008, ncalls=31203, regioncalls=117632, ndraw=128, logz=-13529.38, remainder_fraction=100.0000%, Lmin=-13512.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3040, ncalls=31226, regioncalls=117760, ndraw=128, logz=-13147.65, remainder_fraction=100.0000%, Lmin=-13125.68, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3072, ncalls=31304, regioncalls=118144, ndraw=128, logz=-12815.35, remainder_fraction=100.0000%, Lmin=-12801.68, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3136, ncalls=31361, regioncalls=118400, ndraw=128, logz=-12198.44, remainder_fraction=100.0000%, Lmin=-12186.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3232, ncalls=31501, regioncalls=119040, ndraw=128, logz=-11413.95, remainder_fraction=100.0000%, Lmin=-11397.61, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3296, ncalls=31590, regioncalls=119424, ndraw=128, logz=-10866.06, remainder_fraction=100.0000%, Lmin=-10853.56, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3360, ncalls=31705, regioncalls=119936, ndraw=128, logz=-10310.84, 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regioncalls=171520, ndraw=128, logz=-11.96, remainder_fraction=2.3778%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=19872, ncalls=49903, regioncalls=171648, ndraw=128, logz=-11.95, remainder_fraction=2.2620%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=19904, ncalls=50031, regioncalls=171776, ndraw=128, logz=-11.95, remainder_fraction=2.2062%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=19968, ncalls=50159, regioncalls=171904, ndraw=128, logz=-11.95, remainder_fraction=2.0984%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20064, ncalls=50415, regioncalls=172160, ndraw=128, logz=-11.95, remainder_fraction=1.9465%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20096, ncalls=50415, regioncalls=172160, ndraw=128, logz=-11.95, remainder_fraction=1.8984%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20192, ncalls=50671, regioncalls=172416, ndraw=128, logz=-11.95, remainder_fraction=1.7608%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20224, ncalls=50799, regioncalls=172544, ndraw=128, logz=-11.95, remainder_fraction=1.7172%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20256, ncalls=50927, regioncalls=172672, ndraw=128, logz=-11.95, remainder_fraction=1.6746%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20352, ncalls=51183, regioncalls=172928, ndraw=128, logz=-11.95, remainder_fraction=1.5532%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20368, ncalls=51183, regioncalls=172928, ndraw=128, logz=-11.95, remainder_fraction=1.5338%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20384, ncalls=51311, regioncalls=173184, ndraw=128, logz=-11.95, remainder_fraction=1.5147%, Lmin=-0.02, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20483, ncalls=51311, regioncalls=173184, ndraw=128, logz=-11.95, remainder_fraction=1.4014%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20512, ncalls=51311, regioncalls=173184, ndraw=128, logz=-11.95, remainder_fraction=1.3699%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20544, ncalls=51311, regioncalls=173184, ndraw=128, logz=-11.94, remainder_fraction=1.3359%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20608, ncalls=51439, regioncalls=173312, ndraw=128, logz=-11.94, remainder_fraction=1.2704%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20672, ncalls=51439, regioncalls=173312, ndraw=128, logz=-11.94, remainder_fraction=1.2081%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20736, ncalls=51439, regioncalls=173312, ndraw=128, logz=-11.94, remainder_fraction=1.1489%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20768, ncalls=51439, regioncalls=173312, ndraw=128, logz=-11.94, remainder_fraction=1.1203%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20800, ncalls=51567, regioncalls=173440, ndraw=128, logz=-11.94, remainder_fraction=1.0925%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20832, ncalls=51567, regioncalls=173440, ndraw=128, logz=-11.94, remainder_fraction=1.0653%, Lmin=-0.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20896, ncalls=51567, regioncalls=173440, ndraw=128, logz=-11.94, remainder_fraction=1.0131%, Lmin=-0.01, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-1e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 51695 [32mINFO [0m ultranest:integrator.py:2578 logZ = -11.95 +- 0.05704 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 5077.6, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.04 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 1511 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 1 and inf, most (13571/13914 iterations) have 1263 [35mDEBUG [0m ultranest:integrator.py:1564 at least 360 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 360 minimum live points (dlogz from 0.05 to 0.11, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.17 bs:0.06 tail:0.01 total:0.06 required:<0.10 | |||
Passed | tests/test_run.py::test_reactive_run | 8.33 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.28) * Expected Volume: exp(0.00) Quality: ok Hinz: -5.0|*****************************************************| +5.0 Kunz: -5.0|*****************************************************| +5.0 Z=-inf(0.00%) | Like=-110.42..0.28 [-110.4238..-29.8742] | it/evals=0/401 eff=0.0000% N=400 Z=-67.0(0.00%) | Like=-62.58..0.43 [-110.4238..-29.8742] | it/evals=50/452 eff=96.1538% N=400 Mono-modal Volume: ~exp(-4.42) * Expected Volume: exp(-0.23) Quality: ok Hinz: -5.0| ***************************************************| +5.0 Kunz: -5.0| * *************************************************| +5.0 Z=-57.4(0.00%) | Like=-52.63..0.43 [-110.4238..-29.8742] | it/evals=90/494 eff=95.7447% N=400 Z=-54.9(0.00%) | Like=-50.20..0.43 [-110.4238..-29.8742] | it/evals=100/505 eff=95.2381% N=400 Z=-46.9(0.00%) | Like=-42.21..0.43 [-110.4238..-29.8742] | it/evals=150/567 eff=89.8204% N=400 Mono-modal Volume: ~exp(-4.60) * Expected Volume: exp(-0.45) Quality: ok Hinz: -5.0| ***********************************************| +5.0 Kunz: -5.0| ***********************************************| +5.0 Z=-42.2(0.00%) | Like=-37.96..0.43 [-110.4238..-29.8742] | it/evals=180/613 eff=84.5070% N=400 Z=-40.4(0.00%) | Like=-36.05..0.43 [-110.4238..-29.8742] | it/evals=200/634 eff=85.4701% N=400 Z=-36.2(0.00%) | Like=-31.84..0.43 [-110.4238..-29.8742] | it/evals=250/700 eff=83.3333% N=400 Mono-modal Volume: ~exp(-4.98) * Expected Volume: exp(-0.67) Quality: ok Hinz: -5.0| ****************************************** | +5.0 Kunz: -5.0| ****************************************** | +5.0 Z=-34.3(0.00%) | Like=-29.94..0.43 [-110.4238..-29.8742] | it/evals=270/735 eff=80.5970% N=400 Z=-31.7(0.00%) | Like=-27.71..0.43 [-29.8121..-13.5243] | it/evals=300/770 eff=81.0811% N=400 Z=-28.9(0.00%) | Like=-24.89..0.43 [-29.8121..-13.5243] | it/evals=350/834 eff=80.6452% N=400 Mono-modal Volume: ~exp(-4.98) Expected Volume: exp(-0.90) Quality: ok Hinz: -5.0| *************************************** | +5.0 Kunz: -5.0| -2.8 ************************************** | +5.0 Z=-25.4(0.00%) | Like=-20.82..0.49 [-29.8121..-13.5243] | it/evals=400/907 eff=78.8955% N=400 Mono-modal Volume: ~exp(-5.43) * Expected Volume: exp(-1.12) Quality: ok Hinz: -5.0| -2.5 ********************************** | +5.0 Kunz: -5.0| -2.5 ********************************** | +5.0 Z=-22.6(0.00%) | Like=-18.55..0.49 [-29.8121..-13.5243] | it/evals=450/976 eff=78.1250% N=400 Z=-20.5(0.00%) | Like=-16.26..0.49 [-29.8121..-13.5243] | it/evals=500/1043 eff=77.7605% N=400 Mono-modal Volume: ~exp(-5.80) * Expected Volume: exp(-1.35) Quality: ok Hinz: -5.0| -2.3 ******************************* | +5.0 Kunz: -5.0| -2.1 ****************************** | +5.0 Z=-18.7(0.00%) | Like=-14.60..0.51 [-29.8121..-13.5243] | it/evals=540/1105 eff=76.5957% N=400 Z=-18.3(0.00%) | Like=-14.26..0.55 [-29.8121..-13.5243] | it/evals=550/1118 eff=76.6017% N=400 Z=-16.6(0.00%) | Like=-12.62..0.55 [-13.5092..-7.0346] | it/evals=600/1179 eff=77.0218% N=400 Mono-modal Volume: ~exp(-6.11) * Expected Volume: exp(-1.57) Quality: ok Hinz: -5.0| -2.0 **************************** | +5.0 Kunz: -5.0| -1.9 **************************** | +5.0 Z=-15.7(0.00%) | Like=-11.69..0.55 [-13.5092..-7.0346] | it/evals=630/1225 eff=76.3636% N=400 Z=-15.2(0.00%) | Like=-11.26..0.55 [-13.5092..-7.0346] | it/evals=650/1250 eff=76.4706% N=400 Z=-13.8(0.00%) | Like=-9.78..0.55 [-13.5092..-7.0346] | it/evals=700/1314 eff=76.5864% N=400 Mono-modal Volume: ~exp(-6.11) Expected Volume: exp(-1.80) Quality: ok Hinz: -5.0| -1.7 ************************** +2.9 | +5.0 Kunz: -5.0| -1.6 ************************ +2.9 | +5.0 Z=-12.7(0.01%) | Like=-8.72..0.55 [-13.5092..-7.0346] | it/evals=750/1378 eff=76.6871% N=400 Z=-11.8(0.03%) | Like=-7.66..0.55 [-13.5092..-7.0346] | it/evals=800/1448 eff=76.3359% N=400 Mono-modal Volume: ~exp(-6.11) Expected Volume: exp(-2.02) Quality: ok Hinz: -5.0| -1.4 *********************** +2.6 | +5.0 Kunz: -5.0| -1.4 ********************* +2.5 | +5.0 Z=-10.7(0.07%) | Like=-6.56..0.55 [-7.0343..-2.9827] | it/evals=850/1516 eff=76.1649% N=400 Mono-modal Volume: ~exp(-6.54) * Expected Volume: exp(-2.25) Quality: ok Hinz: -5.0| -1.2 ******************** +2.5 | +5.0 Kunz: -5.0| -1.1 ******************** +2.5 | +5.0 Z=-9.8(0.18%) | Like=-5.80..0.56 [-7.0343..-2.9827] | it/evals=900/1588 eff=75.7576% N=400 Z=-9.1(0.37%) | Like=-4.92..0.57 [-7.0343..-2.9827] | it/evals=950/1654 eff=75.7576% N=400 Mono-modal Volume: ~exp(-6.86) * Expected Volume: exp(-2.47) Quality: ok Hinz: -5.0| -1.0 ****************** +2.3 | +5.0 Kunz: -5.0| -0.9 ****************** +2.2 | +5.0 Z=-8.5(0.69%) | Like=-4.29..0.57 [-7.0343..-2.9827] | it/evals=990/1707 eff=75.7460% N=400 Z=-8.4(0.80%) | Like=-4.17..0.57 [-7.0343..-2.9827] | it/evals=1000/1719 eff=75.8150% N=400 Z=-7.8(1.42%) | Like=-3.65..0.59 [-7.0343..-2.9827] | it/evals=1050/1787 eff=75.7030% N=400 Mono-modal Volume: ~exp(-7.25) * Expected Volume: exp(-2.70) Quality: ok Hinz: -5.0| -0.8 **************** +2.1 | +5.0 Kunz: -5.0| -0.8 **************** +2.0 | +5.0 Z=-7.4(1.95%) | Like=-3.36..0.59 [-7.0343..-2.9827] | it/evals=1080/1835 eff=75.2613% N=400 Z=-7.3(2.35%) | Like=-3.20..0.59 [-7.0343..-2.9827] | it/evals=1100/1859 eff=75.3941% N=400 Z=-6.8(3.59%) | Like=-2.70..0.59 [-2.9806..-2.0814] | it/evals=1150/1926 eff=75.3604% N=400 Mono-modal Volume: ~exp(-7.32) * Expected Volume: exp(-2.92) Quality: ok Hinz: -5.0| -0.7 **************** +2.0 | +5.0 Kunz: -5.0| -0.7 ************** +1.9 | +5.0 Z=-6.7(4.17%) | Like=-2.58..0.59 [-2.9806..-2.0814] | it/evals=1170/1958 eff=75.0963% N=400 Z=-6.4(5.26%) | Like=-2.40..0.59 [-2.9806..-2.0814] | it/evals=1200/1996 eff=75.1880% N=400 Z=-6.1(7.29%) | Like=-2.06..0.59 [-2.0779..-1.8415] | it/evals=1250/2050 eff=75.7576% N=400 Mono-modal Volume: ~exp(-7.62) * Expected Volume: exp(-3.15) Quality: ok Hinz: -5.0| -0.6 ************** +1.8 | +5.0 Kunz: -5.0| -0.5 ************* +1.7 | +5.0 Z=-6.0(7.81%) | Like=-2.00..0.59 [-2.0779..-1.8415] | it/evals=1260/2061 eff=75.8579% N=400 Z=-5.8(9.88%) | Like=-1.73..0.59 [-1.7370..-1.7261] | it/evals=1300/2108 eff=76.1124% N=400 Mono-modal Volume: ~exp(-7.62) Expected Volume: exp(-3.37) Quality: ok Hinz: -5.0| -0.4 ************ +1.7 | +5.0 Kunz: -5.0| -0.4 ************ +1.7 | +5.0 Z=-5.5(12.75%) | Like=-1.48..0.59 [-1.4844..-1.4839]*| it/evals=1350/2169 eff=76.3143% N=400 Z=-5.3(16.19%) | Like=-1.26..0.59 [-1.2643..-1.2571]*| it/evals=1400/2237 eff=76.2112% N=400 Mono-modal Volume: ~exp(-8.00) * Expected Volume: exp(-3.60) Quality: ok Hinz: -5.0| -0.4 ************ +1.6 | +5.0 Kunz: -5.0| -0.3 ********** +1.6 | +5.0 Z=-5.1(19.40%) | Like=-1.09..0.59 [-1.1004..-1.0902] | it/evals=1440/2295 eff=75.9894% N=400 Z=-5.1(20.14%) | Like=-1.04..0.59 [-1.0447..-1.0442]*| it/evals=1450/2306 eff=76.0756% N=400 Z=-4.9(23.91%) | Like=-0.91..0.59 [-0.9064..-0.9011]*| it/evals=1500/2374 eff=75.9878% N=400 Mono-modal Volume: ~exp(-8.00) Expected Volume: exp(-3.82) Quality: ok Hinz: -5.0| -0.2 ********** +1.5 | +5.0 Kunz: -5.0| -0.2 ********** +1.5 | +5.0 Z=-4.8(28.28%) | Like=-0.70..0.59 [-0.7041..-0.7009]*| it/evals=1550/2436 eff=76.1297% N=400 Z=-4.6(32.78%) | Like=-0.54..0.59 [-0.5372..-0.5353]*| it/evals=1600/2513 eff=75.7217% N=400 Mono-modal Volume: ~exp(-8.11) * Expected Volume: exp(-4.05) Quality: ok Hinz: -5.0| -0.1 ********** +1.4 | +5.0 Kunz: -5.0| -0.1 ********* +1.4 | +5.0 Z=-4.6(34.48%) | Like=-0.49..0.59 [-0.4910..-0.4874]*| it/evals=1620/2546 eff=75.4893% N=400 Z=-4.5(37.16%) | Like=-0.40..0.59 [-0.3961..-0.3956]*| it/evals=1650/2581 eff=75.6534% N=400 Z=-4.4(41.71%) | Like=-0.30..0.59 [-0.2959..-0.2954]*| it/evals=1700/2654 eff=75.4215% N=400 Mono-modal Volume: ~exp(-8.53) * Expected Volume: exp(-4.27) Quality: ok Hinz: -5.0e+00| -5.7e-02 ******** +1.3e+00 | +5.0e+00 Kunz: -5.0e+00| -4.2e-02 ******** +1.3e+00 | +5.0e+00 Z=-4.4(42.67%) | Like=-0.29..0.59 [-0.2858..-0.2837]*| it/evals=1710/2670 eff=75.3304% N=400 Z=-4.3(46.38%) | Like=-0.20..0.59 [-0.2049..-0.2046]*| it/evals=1750/2723 eff=75.3336% N=400 Mono-modal Volume: ~exp(-9.00) * Expected Volume: exp(-4.50) Quality: ok Hinz: +0.0e+00|************** +1.2e+00 | +5.0e+00 Kunz: +0.0e+00|************** +1.2e+00 | +5.0e+00 Z=-4.2(50.51%) | Like=-0.09..0.59 [-0.0892..-0.0827]*| it/evals=1800/2788 eff=75.3769% N=400 Z=-4.1(54.67%) | Like=-0.02..0.59 [-0.0215..-0.0205]*| it/evals=1850/2846 eff=75.6337% N=400 Mono-modal Volume: ~exp(-9.00) Expected Volume: exp(-4.73) Quality: ok Hinz: +0.0e+00|************* +1.2e+00 | +5.0e+00 Kunz: +0.0| ************ +1.2 | +5.0 Z=-4.0(58.61%) | Like=0.06..0.59 [0.0579..0.0582]*| it/evals=1900/2909 eff=75.7274% N=400 Z=-4.0(62.35%) | Like=0.14..0.59 [0.1411..0.1432]*| it/evals=1950/2986 eff=75.4060% N=400 Mono-modal Volume: ~exp(-9.22) * Expected Volume: exp(-4.95) Quality: ok Hinz: +0.0| *********** +1.1 | +5.0 Kunz: +0.0| *********** +1.1 | +5.0 Z=-4.0(64.48%) | Like=0.17..0.59 [0.1744..0.1747]*| it/evals=1980/3032 eff=75.2280% N=400 Z=-3.9(65.91%) | Like=0.20..0.59 [0.1955..0.1960]*| it/evals=2000/3058 eff=75.2445% N=400 Z=-3.9(69.23%) | Like=0.25..0.59 [0.2514..0.2534]*| it/evals=2050/3130 eff=75.0916% N=400 Mono-modal Volume: ~exp(-9.22) Expected Volume: exp(-5.18) Quality: ok Hinz: +0.0| ********** +1.1 | +5.0 Kunz: +0.0| ********** +1.0 | +5.0 Z=-3.8(72.35%) | Like=0.30..0.59 [0.3022..0.3037]*| it/evals=2100/3198 eff=75.0536% N=400 Z=-3.8(75.22%) | Like=0.34..0.59 [0.3430..0.3442]*| it/evals=2150/3266 eff=75.0174% N=400 Mono-modal Volume: ~exp(-9.28) * Expected Volume: exp(-5.40) Quality: ok Hinz: +0.0| ********* +1.0 | +5.0 Kunz: +0.0| +0.3 **************************************| +1.0 Z=-3.8(75.76%) | Like=0.35..0.59 [0.3487..0.3489]*| it/evals=2160/3281 eff=74.9740% N=400 Z=-3.8(77.80%) | Like=0.37..0.59 [0.3745..0.3752]*| it/evals=2200/3327 eff=75.1623% N=400 Mono-modal Volume: ~exp(-9.98) * Expected Volume: exp(-5.63) Quality: ok Hinz: +0.0| +0.3 ************************************* | +1.0 Kunz: +0.0| +0.3 *********************************** | +1.0 Z=-3.7(80.20%) | Like=0.40..0.59 [0.3974..0.3977]*| it/evals=2250/3400 eff=75.0000% N=400 Z=-3.7(82.32%) | Like=0.42..0.59 [0.4190..0.4195]*| it/evals=2300/3462 eff=75.1143% N=400 Mono-modal Volume: ~exp(-10.17) * Expected Volume: exp(-5.85) Quality: ok Hinz: +0.0| +0.3 ********************************* | +1.0 Kunz: +0.0| +0.4 ******************************* | +1.0 Z=-3.7(83.88%) | Like=0.43..0.59 [0.4339..0.4339]*| it/evals=2340/3515 eff=75.1204% N=400 Z=-3.7(84.26%) | Like=0.44..0.59 [0.4383..0.4383]*| it/evals=2350/3529 eff=75.1039% N=400 Z=-3.7(85.99%) | Like=0.45..0.59 [0.4536..0.4538]*| it/evals=2400/3594 eff=75.1409% N=400 Mono-modal Volume: ~exp(-10.48) * Expected Volume: exp(-6.08) Quality: ok Hinz: +0.0| +0.4 ****************************** | +1.0 Kunz: +0.0| +0.4 ***************************** | +1.0 Z=-3.7(86.93%) | Like=0.46..0.59 [0.4626..0.4627]*| it/evals=2430/3634 eff=75.1391% N=400 Z=-3.6(87.52%) | Like=0.47..0.59 [0.4695..0.4695]*| it/evals=2450/3658 eff=75.1995% N=400 Z=-3.6(88.90%) | Like=0.49..0.59 [0.4854..0.4862]*| it/evals=2500/3731 eff=75.0525% N=400 Mono-modal Volume: ~exp(-10.68) * Expected Volume: exp(-6.30) Quality: ok Hinz: +0.0| +0.4 *************************** | +1.0 Kunz: +0.0| +0.4 ************************* | +1.0 Z=-3.6(89.42%) | Like=0.49..0.59 [0.4901..0.4901]*| it/evals=2520/3759 eff=75.0223% N=400 Z=-3.6(90.14%) | Like=0.50..0.59 [0.4964..0.4965]*| it/evals=2550/3792 eff=75.1769% N=400 Z=-3.6(91.25%) | Like=0.51..0.59 [0.5081..0.5085]*| it/evals=2600/3852 eff=75.3187% N=400 Mono-modal Volume: ~exp(-10.69) * Expected Volume: exp(-6.53) Quality: ok Hinz: +0.0| +0.4 ************************ | +1.0 Kunz: +0.0| +0.4 *********************** | +1.0 Z=-3.6(91.46%) | Like=0.51..0.59 [0.5109..0.5110]*| it/evals=2610/3865 eff=75.3247% N=400 Z=-3.6(92.24%) | Like=0.52..0.59 [0.5188..0.5189]*| it/evals=2650/3919 eff=75.3055% N=400 Mono-modal Volume: ~exp(-11.24) * Expected Volume: exp(-6.75) Quality: ok Hinz: +0.0| +0.4 ********************* | +1.0 Kunz: +0.0| +0.5 ********************* | +1.0 Z=-3.6(93.12%) | Like=0.53..0.59 [0.5284..0.5285]*| it/evals=2700/3994 eff=75.1252% N=400 Z=-3.6(93.91%) | Like=0.54..0.59 [0.5365..0.5367]*| it/evals=2750/4066 eff=75.0136% N=400 Mono-modal Volume: ~exp(-11.24) Expected Volume: exp(-6.98) Quality: ok Hinz: +0.0| +0.5 ******************* +0.8 | +1.0 Kunz: +0.0| +0.5 ****************** +0.8 | +1.0 Z=-3.6(94.61%) | Like=0.54..0.59 [0.5416..0.5417]*| it/evals=2800/4137 eff=74.9264% N=400 Z=-3.6(95.23%) | Like=0.55..0.59 [0.5472..0.5473]*| it/evals=2850/4211 eff=74.7835% N=400 Mono-modal Volume: ~exp(-11.33) * Expected Volume: exp(-7.20) Quality: ok Hinz: +0.0| +0.5 ****************** +0.8 | +1.0 Kunz: +0.0| +0.5 ***************** +0.8 | +1.0 Z=-3.6(95.56%) | Like=0.55..0.59 [0.5497..0.5498]*| it/evals=2880/4259 eff=74.6307% N=400 Z=-3.6(95.78%) | Like=0.55..0.59 [0.5517..0.5518]*| it/evals=2900/4283 eff=74.6845% N=400 Z=-3.6(96.26%) | Like=0.56..0.59 [0.5568..0.5568]*| it/evals=2950/4349 eff=74.7025% N=400 Mono-modal Volume: ~exp(-12.05) * Expected Volume: exp(-7.43) Quality: ok Hinz: +0.0| +0.5 *************** +0.8 | +1.0 Kunz: +0.0| +0.5 *************** +0.8 | +1.0 Z=-3.5(96.44%) | Like=0.56..0.59 [0.5591..0.5592]*| it/evals=2970/4377 eff=74.6794% N=400 Z=-3.5(96.70%) | Like=0.56..0.59 [0.5616..0.5618]*| it/evals=3000/4417 eff=74.6826% N=400 Z=-3.5(97.08%) | Like=0.57..0.59 [0.5651..0.5652]*| it/evals=3050/4483 eff=74.7000% N=400 Mono-modal Volume: ~exp(-12.05) Expected Volume: exp(-7.65) Quality: ok Hinz: +0.0| +0.5 *************** +0.8 | +1.0 Kunz: +0.0| +0.5 ************* +0.8 | +1.0 Z=-3.5(97.42%) | Like=0.57..0.59 [0.5679..0.5679]*| it/evals=3100/4552 eff=74.6628% N=400 Mono-modal Volume: ~exp(-12.28) * Expected Volume: exp(-7.88) Quality: ok Hinz: +0.0| +0.5 ************* +0.7 | +1.0 Kunz: +0.0| +0.5 ************ +0.7 | +1.0 Z=-3.5(97.72%) | Like=0.57..0.59 [0.5706..0.5707]*| it/evals=3150/4629 eff=74.4857% N=400 Z=-3.5(97.98%) | Like=0.57..0.59 [0.5730..0.5730]*| it/evals=3200/4692 eff=74.5573% N=400 Mono-modal Volume: ~exp(-12.40) * Expected Volume: exp(-8.10) Quality: ok Hinz: +0.0| +0.5 *********** +0.7 | +1.0 Kunz: +0.0| +0.5 *********** +0.7 | +1.0 Z=-3.5(98.17%) | Like=0.57..0.59 [0.5749..0.5749]*| it/evals=3240/4743 eff=74.6028% N=400 Z=-3.5(98.22%) | Like=0.58..0.59 [0.5752..0.5752]*| it/evals=3250/4757 eff=74.5926% N=400 Z=-3.5(98.43%) | Like=0.58..0.59 [0.5772..0.5772]*| it/evals=3300/4822 eff=74.6269% N=400 Mono-modal Volume: ~exp(-12.82) * Expected Volume: exp(-8.33) Quality: ok Hinz: +0.0| +0.5 *********** +0.7 | +1.0 Kunz: +0.0| +0.5 ********** +0.7 | +1.0 Z=-3.5(98.54%) | Like=0.58..0.59 [0.5788..0.5788]*| it/evals=3330/4858 eff=74.6972% N=400 Z=-3.5(98.61%) | Like=0.58..0.59 [0.5793..0.5793]*| it/evals=3350/4882 eff=74.7434% N=400 Z=-3.5(98.77%) | Like=0.58..0.59 [0.5811..0.5811]*| it/evals=3400/4940 eff=74.8899% N=400 Mono-modal Volume: ~exp(-12.99) * Expected Volume: exp(-8.55) Quality: ok Hinz: +0.0| +0.6 ********* +0.7 | +1.0 Kunz: +0.0| +0.6 ********* +0.7 | +1.0 Z=-3.5(98.83%) | Like=0.58..0.59 [0.5817..0.5817]*| it/evals=3420/4973 eff=74.7868% N=400 Z=-3.5(98.92%) | Like=0.58..0.59 [0.5824..0.5824]*| it/evals=3450/5012 eff=74.8049% N=400 [ultranest] Explored until L=0.6 [ultranest] Likelihood function evaluations: 5053 [ultranest] logZ = -3.495 +- 0.06646 [ultranest] Effective samples strategy satisfied (ESS = 1629.9, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.07, need <0.5) [ultranest] logZ error budget: single: 0.09 bs:0.07 tail:0.01 total:0.07 required:<0.50 [ultranest] done iterating. ncalls: 5055 nunique: 5055 {'niter': 3883, 'logz': -3.512979739281697, 'logzerr': 0.15019082031188857, 'logz_bs': -3.4946153773041653, 'logz_single': -3.512979739281697, 'logzerr_tail': 0.00993867560773154, 'logzerr_bs': 0.14986162028058514, 'ess': 1629.911652892675, 'H': 3.093626125867617, 'Herr': 0.05327658224759624, 'posterior': {'mean': [0.6528641810663336, 0.6264357937278213], 'stdev': [0.5380843565640636, 0.5207584836042055], 'median': [0.6513496553807023, 0.6346605137507799], 'errlo': [0.11736651751957616, 0.08940050480431339], 'errup': [1.193722161567365, 1.137157590020708], 'information_gain_bits': [0.6859949672653227, 0.7756177953890441]}, 'weighted_samples': {'upoints': array([[0.01988013, 0.02621099], [0.01833264, 0.066725 ], [0.08504421, 0.03905478], ..., [0.56409333, 0.5633068 ], [0.56303122, 0.5635434 ], [0.56347491, 0.56317946]]), 'points': array([[-4.80119866, -4.73789013], [-4.81667357, -4.33275002], [-4.14955789, -4.60945217], ..., [ 0.64093333, 0.63306805], [ 0.63031216, 0.63543397], [ 0.63474906, 0.63179457]]), 'weights': array([9.26018546e-50, 2.47035294e-46, 4.83768179e-43, ..., 2.50932121e-05, 2.50942808e-05, 2.50947339e-05]), 'logw': array([ -5.99271429, -5.99521429, -5.99771429, ..., -14.69896455, -14.69896455, -14.69896455]), 'bootstrapped_weights': array([[1.40582785e-49, 1.35668842e-49, 0.00000000e+00, ..., 1.36553936e-49, 1.51693786e-49, 1.46025903e-49], [0.00000000e+00, 3.61422607e-46, 4.17988021e-46, ..., 3.63785987e-46, 0.00000000e+00, 3.88958952e-46], [0.00000000e+00, 7.06788512e-43, 8.17277585e-43, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], ..., [0.00000000e+00, 4.11559930e-05, 4.44503916e-05, ..., 0.00000000e+00, 0.00000000e+00, 3.81059410e-05], [4.47343654e-05, 4.11577458e-05, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 4.44530874e-05, ..., 0.00000000e+00, 4.46347506e-05, 0.00000000e+00]]), 'logl': array([-110.42379603, -102.5323187 , -94.94998881, ..., 0.59307162, 0.59311421, 0.59313227])}, 'samples': array([[ 1.29700997, 0.39691822], [-0.25159545, 1.1739252 ], [ 0.49331732, -0.01375432], ..., [ 1.50143774, 0.56239362], [ 0.97385436, 1.10514422], [ 0.65458513, 0.52712821]]), 'maximum_likelihood': {'logl': 0.5931322698047243, 'point': [0.6347490626876349, 0.6317945702385996], 'point_untransformed': [0.5634749062687635, 0.56317945702386]}, 'ncall': 5053, 'paramnames': ['Hinz', 'Kunz'], 'logzerr_single': 0.08794353480881378, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=False, log_dir=None, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-110.42, Lmax=0.28 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=452, regioncalls=2080, ndraw=40, logz=-66.99, remainder_fraction=100.0000%, Lmin=-62.58, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=494, regioncalls=3760, ndraw=40, logz=-57.38, remainder_fraction=100.0000%, Lmin=-52.63, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=505, regioncalls=4200, ndraw=40, logz=-54.91, remainder_fraction=100.0000%, Lmin=-50.20, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=567, regioncalls=6680, ndraw=40, logz=-46.91, remainder_fraction=100.0000%, Lmin=-42.21, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=613, regioncalls=8520, ndraw=40, logz=-42.21, remainder_fraction=100.0000%, Lmin=-37.96, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=634, regioncalls=9360, ndraw=40, logz=-40.41, remainder_fraction=100.0000%, Lmin=-36.05, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=700, regioncalls=12000, ndraw=40, logz=-36.16, remainder_fraction=100.0000%, Lmin=-31.84, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=735, regioncalls=13400, ndraw=40, logz=-34.25, remainder_fraction=100.0000%, Lmin=-29.94, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=770, regioncalls=14800, ndraw=40, logz=-31.69, remainder_fraction=100.0000%, Lmin=-27.71, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=834, regioncalls=17360, ndraw=40, logz=-28.93, remainder_fraction=100.0000%, Lmin=-24.89, Lmax=0.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=907, regioncalls=20280, ndraw=40, logz=-25.35, remainder_fraction=100.0000%, Lmin=-20.82, Lmax=0.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=976, regioncalls=23040, ndraw=40, logz=-22.62, remainder_fraction=100.0000%, Lmin=-18.55, Lmax=0.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=1043, regioncalls=25720, ndraw=40, logz=-20.49, remainder_fraction=100.0000%, Lmin=-16.26, Lmax=0.49 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1105, regioncalls=28200, ndraw=40, logz=-18.69, remainder_fraction=100.0000%, Lmin=-14.60, Lmax=0.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=1118, regioncalls=28720, ndraw=40, logz=-18.33, remainder_fraction=100.0000%, Lmin=-14.26, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1179, regioncalls=31160, ndraw=40, logz=-16.64, remainder_fraction=99.9998%, Lmin=-12.62, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1225, regioncalls=33000, ndraw=40, logz=-15.74, remainder_fraction=99.9995%, Lmin=-11.69, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=1250, regioncalls=34000, ndraw=40, logz=-15.21, remainder_fraction=99.9991%, Lmin=-11.26, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=1314, regioncalls=36560, ndraw=40, logz=-13.84, remainder_fraction=99.9968%, Lmin=-9.78, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1378, regioncalls=39120, ndraw=40, logz=-12.73, remainder_fraction=99.9903%, Lmin=-8.72, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1448, regioncalls=41920, ndraw=40, logz=-11.76, remainder_fraction=99.9742%, Lmin=-7.66, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1516, regioncalls=44640, ndraw=40, logz=-10.71, remainder_fraction=99.9267%, Lmin=-6.56, Lmax=0.55 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1588, regioncalls=47520, ndraw=40, logz=-9.83, remainder_fraction=99.8233%, Lmin=-5.80, Lmax=0.56 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1654, regioncalls=50160, ndraw=40, logz=-9.11, remainder_fraction=99.6334%, Lmin=-4.92, Lmax=0.57 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1707, regioncalls=52320, ndraw=40, logz=-8.49, remainder_fraction=99.3057%, Lmin=-4.29, Lmax=0.57 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1719, regioncalls=52840, ndraw=40, logz=-8.36, remainder_fraction=99.1963%, Lmin=-4.17, Lmax=0.57 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1787, regioncalls=55560, ndraw=40, logz=-7.76, remainder_fraction=98.5780%, Lmin=-3.65, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1835, regioncalls=57480, ndraw=40, logz=-7.44, remainder_fraction=98.0469%, Lmin=-3.36, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1859, regioncalls=58480, ndraw=40, logz=-7.26, remainder_fraction=97.6487%, Lmin=-3.20, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1926, regioncalls=61160, ndraw=40, logz=-6.82, remainder_fraction=96.4136%, Lmin=-2.70, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1958, regioncalls=62440, ndraw=40, logz=-6.65, remainder_fraction=95.8254%, Lmin=-2.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1996, regioncalls=64040, ndraw=40, logz=-6.43, remainder_fraction=94.7437%, Lmin=-2.40, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=2050, regioncalls=66360, ndraw=40, logz=-6.10, remainder_fraction=92.7134%, Lmin=-2.06, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=2061, regioncalls=66800, ndraw=40, logz=-6.04, remainder_fraction=92.1904%, Lmin=-2.00, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=2108, regioncalls=68720, ndraw=40, logz=-5.80, remainder_fraction=90.1182%, Lmin=-1.73, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2169, regioncalls=71160, ndraw=40, logz=-5.54, remainder_fraction=87.2523%, Lmin=-1.48, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2237, regioncalls=73880, ndraw=40, logz=-5.31, remainder_fraction=83.8089%, Lmin=-1.26, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2295, regioncalls=76200, ndraw=40, logz=-5.14, remainder_fraction=80.6009%, Lmin=-1.09, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=2306, regioncalls=76720, ndraw=40, logz=-5.11, remainder_fraction=79.8571%, Lmin=-1.04, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=2374, regioncalls=79440, ndraw=40, logz=-4.93, remainder_fraction=76.0914%, Lmin=-0.91, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=2436, regioncalls=81920, ndraw=40, logz=-4.77, remainder_fraction=71.7238%, Lmin=-0.70, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2513, regioncalls=85000, ndraw=40, logz=-4.62, remainder_fraction=67.2236%, Lmin=-0.54, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2546, regioncalls=86360, ndraw=40, logz=-4.57, remainder_fraction=65.5165%, Lmin=-0.49, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=2581, regioncalls=87760, ndraw=40, logz=-4.50, remainder_fraction=62.8444%, Lmin=-0.40, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=2654, regioncalls=90680, ndraw=40, logz=-4.38, remainder_fraction=58.2874%, Lmin=-0.30, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2670, regioncalls=91360, ndraw=40, logz=-4.36, remainder_fraction=57.3338%, Lmin=-0.29, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=2723, regioncalls=93480, ndraw=40, logz=-4.28, remainder_fraction=53.6176%, Lmin=-0.20, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2788, regioncalls=96120, ndraw=40, logz=-4.20, remainder_fraction=49.4894%, Lmin=-0.09, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=2846, regioncalls=98440, ndraw=40, logz=-4.12, remainder_fraction=45.3322%, Lmin=-0.02, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=2909, regioncalls=100960, ndraw=40, logz=-4.05, remainder_fraction=41.3899%, Lmin=0.06, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=2986, regioncalls=104040, ndraw=40, logz=-3.99, remainder_fraction=37.6453%, Lmin=0.14, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=3032, regioncalls=105920, ndraw=40, logz=-3.95, remainder_fraction=35.5167%, Lmin=0.17, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=3058, regioncalls=107000, ndraw=40, logz=-3.93, remainder_fraction=34.0860%, Lmin=0.20, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=3130, regioncalls=109960, ndraw=40, logz=-3.88, remainder_fraction=30.7692%, Lmin=0.25, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=3198, regioncalls=112680, ndraw=40, logz=-3.84, remainder_fraction=27.6476%, Lmin=0.30, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=3266, regioncalls=115400, ndraw=40, logz=-3.80, remainder_fraction=24.7819%, Lmin=0.34, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=3281, regioncalls=116040, ndraw=40, logz=-3.79, remainder_fraction=24.2430%, Lmin=0.35, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=3327, regioncalls=117960, ndraw=40, logz=-3.76, remainder_fraction=22.1990%, Lmin=0.37, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=3400, regioncalls=121000, ndraw=40, logz=-3.73, remainder_fraction=19.8044%, Lmin=0.40, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=3462, regioncalls=123480, ndraw=40, logz=-3.71, remainder_fraction=17.6802%, Lmin=0.42, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=3515, regioncalls=125680, ndraw=40, logz=-3.69, remainder_fraction=16.1212%, Lmin=0.43, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=3529, regioncalls=126320, ndraw=40, logz=-3.68, remainder_fraction=15.7410%, Lmin=0.44, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3594, regioncalls=128920, ndraw=40, logz=-3.66, remainder_fraction=14.0103%, Lmin=0.45, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3634, regioncalls=130560, ndraw=40, logz=-3.65, remainder_fraction=13.0726%, Lmin=0.46, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3658, regioncalls=131520, ndraw=40, logz=-3.65, remainder_fraction=12.4782%, Lmin=0.47, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3731, regioncalls=134440, ndraw=40, logz=-3.63, remainder_fraction=11.0953%, Lmin=0.49, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=3759, regioncalls=135600, ndraw=40, logz=-3.62, remainder_fraction=10.5813%, Lmin=0.49, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3792, regioncalls=136920, ndraw=40, logz=-3.62, remainder_fraction=9.8607%, Lmin=0.50, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3852, regioncalls=139320, ndraw=40, logz=-3.60, remainder_fraction=8.7482%, Lmin=0.51, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3865, regioncalls=139880, ndraw=40, logz=-3.60, remainder_fraction=8.5412%, Lmin=0.51, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3919, regioncalls=142160, ndraw=40, logz=-3.59, remainder_fraction=7.7618%, Lmin=0.52, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3994, regioncalls=145160, ndraw=40, logz=-3.58, remainder_fraction=6.8783%, Lmin=0.53, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=4066, regioncalls=148160, ndraw=40, logz=-3.58, remainder_fraction=6.0943%, Lmin=0.54, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=4137, regioncalls=151000, ndraw=40, logz=-3.57, remainder_fraction=5.3940%, Lmin=0.54, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=4211, regioncalls=153960, ndraw=40, logz=-3.56, remainder_fraction=4.7748%, Lmin=0.55, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=4259, regioncalls=155960, ndraw=40, logz=-3.56, remainder_fraction=4.4363%, Lmin=0.55, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=4283, regioncalls=156920, ndraw=40, logz=-3.56, remainder_fraction=4.2245%, Lmin=0.55, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=4349, regioncalls=159560, ndraw=40, logz=-3.55, remainder_fraction=3.7367%, Lmin=0.56, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=4377, regioncalls=160720, ndraw=40, logz=-3.55, remainder_fraction=3.5573%, Lmin=0.56, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=4417, regioncalls=162360, ndraw=40, logz=-3.55, remainder_fraction=3.3040%, Lmin=0.56, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=4483, regioncalls=165000, ndraw=40, logz=-3.54, remainder_fraction=2.9209%, Lmin=0.57, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=4552, regioncalls=167760, ndraw=40, logz=-3.54, remainder_fraction=2.5820%, Lmin=0.57, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=4629, regioncalls=170880, ndraw=40, logz=-3.54, remainder_fraction=2.2817%, Lmin=0.57, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=4692, regioncalls=173400, ndraw=40, logz=-3.53, remainder_fraction=2.0165%, Lmin=0.57, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=4743, regioncalls=175480, ndraw=40, logz=-3.53, remainder_fraction=1.8265%, Lmin=0.57, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3250, ncalls=4757, regioncalls=176040, ndraw=40, logz=-3.53, remainder_fraction=1.7818%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3300, ncalls=4822, regioncalls=178640, ndraw=40, logz=-3.53, remainder_fraction=1.5739%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3330, ncalls=4858, regioncalls=180280, ndraw=40, logz=-3.53, remainder_fraction=1.4610%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3350, ncalls=4882, regioncalls=181240, ndraw=40, logz=-3.53, remainder_fraction=1.3902%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=4940, regioncalls=183560, ndraw=40, logz=-3.53, remainder_fraction=1.2278%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=4973, regioncalls=184960, ndraw=40, logz=-3.52, remainder_fraction=1.1682%, Lmin=0.58, Lmax=0.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3450, ncalls=5012, regioncalls=186560, ndraw=40, logz=-3.52, remainder_fraction=1.0842%, Lmin=0.58, Lmax=0.59 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=0.6 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5053 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.495 +- 0.06646 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1629.9, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.07, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.07 tail:0.01 total:0.07 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:2775 Making corner plot ... [35mDEBUG [0m ultranest:integrator.py:2821 Making run plot ... [35mDEBUG [0m ultranest:integrator.py:2797 Making trace plot ... | |||
Passed | tests/test_run.py::test_reactive_run_extraparams | 6.84 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.28) * Expected Volume: exp(0.00) Quality: ok Hinz : -5.0|*********************************************| +5.0 Kunz : -5.0|*********************************************| +5.0 ctr_distance: +0.0e+00|******************************************** | +1.0e+01 Z=-inf(0.00%) | Like=-4.88..-0.03 [-4.8818..-2.5392] | it/evals=0/401 eff=0.0000% N=400 Z=-6.6(1.05%) | Like=-3.91..-0.03 [-4.8818..-2.5392] | it/evals=40/442 eff=95.2381% N=400 Z=-5.6(2.67%) | Like=-3.50..-0.03 [-4.8818..-2.5392] | it/evals=80/484 eff=95.2381% N=400 Mono-modal Volume: ~exp(-4.31) * Expected Volume: exp(-0.23) Quality: ok Hinz : -5.0|*********************************************| +5.0 Kunz : -5.0|*********************************************| +5.0 ctr_distance: +0.0e+00|******************************* +6.9e+00 | +1.0e+01 Z=-5.5(3.15%) | Like=-3.43..-0.03 [-4.8818..-2.5392] | it/evals=90/495 eff=94.7368% N=400 Z=-5.1(4.70%) | Like=-3.22..-0.03 [-4.8818..-2.5392] | it/evals=120/532 eff=90.9091% N=400 Z=-4.7(7.12%) | Like=-2.94..-0.03 [-4.8818..-2.5392] | it/evals=160/585 eff=86.4865% N=400 Mono-modal Volume: ~exp(-4.99) * Expected Volume: exp(-0.45) Quality: ok Hinz : -5.0| ********************************************| +5.0 Kunz : -5.0|*********************************************| +5.0 ctr_distance: +0.0e+00|************************** +5.7e+00 | +1.0e+01 Z=-4.5(8.42%) | Like=-2.86..-0.03 [-4.8818..-2.5392] | it/evals=180/611 eff=85.3081% N=400 Z=-4.3(9.82%) | Like=-2.77..-0.03 [-4.8818..-2.5392] | it/evals=200/636 eff=84.7458% N=400 Z=-4.1(12.76%) | Like=-2.64..-0.03 [-4.8818..-2.5392] | it/evals=240/686 eff=83.9161% N=400 Mono-modal Volume: ~exp(-4.99) Expected Volume: exp(-0.67) Quality: ok Hinz : -5.0| ********************************************| +5.0 Kunz : -5.0| ***************************************** *| +5.0 ctr_distance: +0.0e+00|*********************** +5.1e+00 | +1.0e+01 Z=-3.9(15.74%) | Like=-2.51..-0.03 [-2.5090..-2.5073]*| it/evals=280/738 eff=82.8402% N=400 Z=-3.7(18.75%) | Like=-2.38..-0.03 [-2.3841..-2.3821]*| it/evals=320/793 eff=81.4249% N=400 Mono-modal Volume: ~exp(-5.30) * Expected Volume: exp(-0.90) Quality: ok Hinz : -5.0| **************************************** | +5.0 Kunz : -5.0| ************************************ * | +5.0 ctr_distance: +0.0e+00|********************* +4.5e+00 | +1.0e+01 Z=-3.5(21.98%) | Like=-2.26..-0.03 [-2.2557..-2.2540]*| it/evals=360/850 eff=80.0000% N=400 Z=-3.4(25.37%) | Like=-2.15..-0.03 [-2.1517..-2.1515]*| it/evals=400/902 eff=79.6813% N=400 Z=-3.3(28.35%) | Like=-2.04..-0.03 [-2.0444..-2.0393]*| it/evals=440/961 eff=78.4314% N=400 Mono-modal Volume: ~exp(-5.30) Expected Volume: exp(-1.12) Quality: ok Hinz : -5.0| ************************************ | +5.0 Kunz : -5.0| ********************************** | +5.0 ctr_distance: +0.0e+00|******************* +4.0e+00 | +1.0e+01 Z=-3.2(31.62%) | Like=-1.93..-0.03 [-1.9328..-1.9317]*| it/evals=480/1017 eff=77.7958% N=400 Z=-3.1(34.96%) | Like=-1.86..-0.03 [-1.8607..-1.8595]*| it/evals=520/1073 eff=77.2660% N=400 Mono-modal Volume: ~exp(-5.42) * Expected Volume: exp(-1.35) Quality: ok Hinz : -5.0| ****************************** * | +5.0 Kunz : -5.0| ******************************** | +5.0 ctr_distance: +0.0e+00|***************** +3.6e+00 | +1.0e+01 Z=-3.0(36.35%) | Like=-1.82..-0.03 [-1.8240..-1.8218]*| it/evals=540/1096 eff=77.5862% N=400 Z=-3.0(37.93%) | Like=-1.77..-0.03 [-1.7742..-1.7725]*| it/evals=560/1127 eff=77.0289% N=400 Z=-2.9(41.05%) | Like=-1.65..-0.03 [-1.6518..-1.6494]*| it/evals=600/1185 eff=76.4331% N=400 Mono-modal Volume: ~exp(-5.42) Expected Volume: exp(-1.57) Quality: ok Hinz : -5.0| **************************** | +5.0 Kunz : -5.0| ************************** * | +5.0 ctr_distance: +0.0e+00|*************** +3.2e+00 | +1.0e+01 Z=-2.8(44.24%) | Like=-1.58..-0.03 [-1.5763..-1.5757]*| it/evals=640/1247 eff=75.5608% N=400 Z=-2.8(47.23%) | Like=-1.49..-0.03 [-1.4913..-1.4892]*| it/evals=680/1313 eff=74.4797% N=400 Mono-modal Volume: ~exp(-6.24) * Expected Volume: exp(-1.80) Quality: ok Hinz : -5.0| ************************* +2.6 | +5.0 Kunz : -5.0| ************************* +2.7 | +5.0 ctr_distance: +0.0e+00|************* +2.9e+00 | +1.0e+01 Z=-2.7(50.21%) | Like=-1.43..-0.03 [-1.4292..-1.4283]*| it/evals=720/1367 eff=74.4571% N=400 Z=-2.6(52.99%) | Like=-1.36..-0.03 [-1.3636..-1.3570]*| it/evals=760/1423 eff=74.2913% N=400 Z=-2.6(55.78%) | Like=-1.29..-0.03 [-1.2904..-1.2895]*| it/evals=800/1475 eff=74.4186% N=400 Mono-modal Volume: ~exp(-6.24) Expected Volume: exp(-2.02) Quality: ok Hinz : -5.0| -2.5 ********************** +2.3 | +5.0 Kunz : -5.0| -2.4 *********************** +2.4 | +5.0 ctr_distance: +0.0e+00|************ +2.5e+00 | +1.0e+01 Z=-2.5(58.45%) | Like=-1.23..-0.03 [-1.2319..-1.2316]*| it/evals=840/1533 eff=74.1395% N=400 Z=-2.5(61.03%) | Like=-1.16..-0.03 [-1.1554..-1.1510]*| it/evals=880/1594 eff=73.7018% N=400 Mono-modal Volume: ~exp(-6.24) Expected Volume: exp(-2.25) Quality: ok Hinz : -5.0| -2.1 ******************** +2.2 | +5.0 Kunz : -5.0| -2.1 ******************** +2.1 | +5.0 ctr_distance: +0.0e+00|*********** +2.3e+00 | +1.0e+01 Z=-2.5(63.43%) | Like=-1.11..-0.03 [-1.1067..-1.1061]*| it/evals=920/1650 eff=73.6000% N=400 Z=-2.4(65.86%) | Like=-1.05..-0.03 [-1.0508..-1.0507]*| it/evals=960/1709 eff=73.3384% N=400 Mono-modal Volume: ~exp(-6.98) * Expected Volume: exp(-2.47) Quality: ok Hinz : -5.0| -1.9 ******************* +1.9 | +5.0 Kunz : -5.0| -2.0 ******************* +1.9 | +5.0 ctr_distance: +0.0e+00|********** +2.0e+00 | +1.0e+01 Z=-2.4(67.48%) | Like=-1.02..-0.03 [-1.0220..-1.0201]*| it/evals=990/1759 eff=72.8477% N=400 Z=-2.4(67.95%) | Like=-1.00..-0.03 [-1.0042..-1.0040]*| it/evals=1000/1772 eff=72.8863% N=400 Z=-2.4(70.19%) | Like=-0.96..-0.03 [-0.9629..-0.9622]*| it/evals=1040/1829 eff=72.7782% N=400 Mono-modal Volume: ~exp(-7.09) * Expected Volume: exp(-2.70) Quality: ok Hinz : -5.0| -1.8 ***************** +1.8 | +5.0 Kunz : -5.0| -1.8 ***************** +1.7 | +5.0 ctr_distance: +0.0e+00|********* +1.8e+00 | +1.0e+01 Z=-2.3(72.28%) | Like=-0.92..-0.03 [-0.9249..-0.9246]*| it/evals=1080/1887 eff=72.6295% N=400 Z=-2.3(74.36%) | Like=-0.89..-0.03 [-0.8918..-0.8907]*| it/evals=1120/1943 eff=72.5859% N=400 Z=-2.3(76.26%) | Like=-0.86..-0.03 [-0.8639..-0.8631]*| it/evals=1160/1996 eff=72.6817% N=400 Mono-modal Volume: ~exp(-7.09) Expected Volume: exp(-2.92) Quality: ok Hinz : -5.0| -1.5 *************** +1.6 | +5.0 Kunz : -5.0| -1.6 *************** +1.6 | +5.0 ctr_distance: +0.0e+00|******** +1.7e+00 | +1.0e+01 Z=-2.3(77.94%) | Like=-0.83..-0.03 [-0.8279..-0.8265]*| it/evals=1200/2048 eff=72.8155% N=400 Z=-2.2(79.52%) | Like=-0.78..-0.03 [-0.7823..-0.7809]*| it/evals=1240/2106 eff=72.6846% N=400 Mono-modal Volume: ~exp(-7.14) * Expected Volume: exp(-3.15) Quality: ok Hinz : -5.0| -1.4 ************* +1.4 | +5.0 Kunz : -5.0| -1.4 ************* +1.4 | +5.0 ctr_distance: +0.0e+00|******* +1.5e+00 | +1.0e+01 Z=-2.2(80.32%) | Like=-0.76..-0.03 [-0.7590..-0.7563]*| it/evals=1260/2137 eff=72.5389% N=400 Z=-2.2(81.03%) | Like=-0.74..-0.03 [-0.7362..-0.7354]*| it/evals=1280/2162 eff=72.6447% N=400 Z=-2.2(82.50%) | Like=-0.69..-0.03 [-0.6928..-0.6909]*| it/evals=1320/2215 eff=72.7273% N=400 Mono-modal Volume: ~exp(-7.52) * Expected Volume: exp(-3.37) Quality: ok Hinz : -5.0| -1.2 ************ +1.2 | +5.0 Kunz : -5.0| -1.2 ************ +1.2 | +5.0 ctr_distance: +0.0e+00|******* +1.3e+00 | +1.0e+01 Z=-2.2(83.51%) | Like=-0.67..-0.03 [-0.6667..-0.6666]*| it/evals=1350/2259 eff=72.6197% N=400 Z=-2.2(83.83%) | Like=-0.66..-0.03 [-0.6598..-0.6594]*| it/evals=1360/2272 eff=72.6496% N=400 Z=-2.2(85.12%) | Like=-0.62..-0.03 [-0.6234..-0.6227]*| it/evals=1400/2322 eff=72.8408% N=400 Mono-modal Volume: ~exp(-7.61) * Expected Volume: exp(-3.60) Quality: ok Hinz : -5.0| -1.1 *********** +1.1 | +5.0 Kunz : -5.0| -1.1 *********** +1.1 | +5.0 ctr_distance: +0.0e+00|****** +1.2e+00 | +1.0e+01 Z=-2.2(86.25%) | Like=-0.59..-0.03 [-0.5932..-0.5929]*| it/evals=1440/2382 eff=72.6539% N=400 Z=-2.1(87.33%) | Like=-0.57..-0.03 [-0.5655..-0.5646]*| it/evals=1480/2436 eff=72.6916% N=400 Z=-2.1(88.35%) | Like=-0.54..-0.03 [-0.5440..-0.5435]*| it/evals=1520/2489 eff=72.7621% N=400 Mono-modal Volume: ~exp(-8.10) * Expected Volume: exp(-3.82) Quality: ok Hinz : -5.0| -0.9 ********** +1.0 | +5.0 Kunz : -1.0|***************************************** ***| +1.0 ctr_distance: +0.0e+00|***** +1.1e+00 | +1.0e+01 Z=-2.1(88.60%) | Like=-0.54..-0.03 [-0.5381..-0.5373]*| it/evals=1530/2506 eff=72.6496% N=400 Z=-2.1(89.30%) | Like=-0.52..-0.03 [-0.5191..-0.5189]*| it/evals=1560/2544 eff=72.7612% N=400 Z=-2.1(90.16%) | Like=-0.49..-0.03 [-0.4949..-0.4941]*| it/evals=1600/2595 eff=72.8929% N=400 Mono-modal Volume: ~exp(-8.18) * Expected Volume: exp(-4.05) Quality: ok Hinz : -1.0| ** *************************************** | +1.0 Kunz : -1.0| ** ************************************ * | +1.0 ctr_distance: +0.00| * ************************************** | +1.00 Z=-2.1(90.57%) | Like=-0.48..-0.03 [-0.4804..-0.4800]*| it/evals=1620/2627 eff=72.7436% N=400 Z=-2.1(90.95%) | Like=-0.46..-0.03 [-0.4644..-0.4639]*| it/evals=1640/2652 eff=72.8242% N=400 Z=-2.1(91.68%) | Like=-0.44..-0.03 [-0.4405..-0.4372]*| it/evals=1680/2701 eff=73.0117% N=400 Mono-modal Volume: ~exp(-8.39) * Expected Volume: exp(-4.27) Quality: ok Hinz : -1.0| ************************************ | +1.0 Kunz : -1.0| ********************************** * | +1.0 ctr_distance: +0.00| *** ******************************** | +1.00 Z=-2.1(92.21%) | Like=-0.42..-0.03 [-0.4157..-0.4143]*| it/evals=1710/2740 eff=73.0769% N=400 Z=-2.1(92.38%) | Like=-0.41..-0.03 [-0.4107..-0.4104]*| it/evals=1720/2753 eff=73.0982% N=400 Z=-2.1(93.02%) | Like=-0.39..-0.03 [-0.3928..-0.3912]*| it/evals=1760/2814 eff=72.9080% N=400 Mono-modal Volume: ~exp(-8.97) * Expected Volume: exp(-4.50) Quality: ok Hinz : -1.0| ********************************* | +1.0 Kunz : -1.0| ********************************* | +1.0 ctr_distance: +0.00| ********************************* +0.75 | +1.00 Z=-2.1(93.61%) | Like=-0.37..-0.01 [-0.3749..-0.3747]*| it/evals=1800/2876 eff=72.6979% N=400 Z=-2.1(94.15%) | Like=-0.36..-0.01 [-0.3577..-0.3575]*| it/evals=1840/2927 eff=72.8136% N=400 Z=-2.1(94.65%) | Like=-0.34..-0.01 [-0.3425..-0.3424]*| it/evals=1880/2980 eff=72.8682% N=400 Mono-modal Volume: ~exp(-8.97) Expected Volume: exp(-4.73) Quality: ok Hinz : -1.0| **************************** | +1.0 Kunz : -1.0| * **************************** | +1.0 ctr_distance: +0.00|******************************* +0.68 | +1.00 Z=-2.1(95.10%) | Like=-0.33..-0.01 [-0.3276..-0.3274]*| it/evals=1920/3035 eff=72.8653% N=400 Z=-2.1(95.51%) | Like=-0.31..-0.01 [-0.3138..-0.3135]*| it/evals=1960/3098 eff=72.6464% N=400 Mono-modal Volume: ~exp(-9.29) * Expected Volume: exp(-4.95) Quality: ok Hinz : -1.0| **************************** | +1.0 Kunz : -1.0| ************************* +0.5 | +1.0 ctr_distance: +0.00|**************************** +0.61 | +1.00 Z=-2.1(95.71%) | Like=-0.31..-0.01 [-0.3064..-0.3063]*| it/evals=1980/3122 eff=72.7406% N=400 Z=-2.0(95.90%) | Like=-0.30..-0.01 [-0.2985..-0.2975]*| it/evals=2000/3145 eff=72.8597% N=400 Z=-2.0(96.25%) | Like=-0.28..-0.01 [-0.2826..-0.2825]*| it/evals=2040/3201 eff=72.8311% N=400 Mono-modal Volume: ~exp(-9.50) * Expected Volume: exp(-5.18) Quality: ok Hinz : -1.0| ************************* +0.5 | +1.0 Kunz : -1.0| ************************* +0.5 | +1.0 ctr_distance: +0.00|************************* +0.55 | +1.00 Z=-2.0(96.50%) | Like=-0.27..-0.01 [-0.2733..-0.2733]*| it/evals=2070/3249 eff=72.6571% N=400 Z=-2.0(96.57%) | Like=-0.27..-0.01 [-0.2705..-0.2697]*| it/evals=2080/3261 eff=72.7019% N=400 Z=-2.0(96.87%) | Like=-0.26..-0.01 [-0.2592..-0.2592]*| it/evals=2120/3320 eff=72.6027% N=400 Z=-2.0(97.05%) | Like=-0.25..-0.01 [-0.2534..-0.2531]*| it/evals=2146/3360 eff=72.5000% N=400 Mono-modal Volume: ~exp(-9.50) Expected Volume: exp(-5.40) Quality: ok Hinz : -1.0| -0.5 *********************** +0.5 | +1.0 Kunz : -1.0| -0.5 ******************** ** +0.5 | +1.0 ctr_distance: +0.00|*********************** +0.50 | +1.00 Z=-2.0(97.14%) | Like=-0.25..-0.01 [-0.2492..-0.2489]*| it/evals=2160/3379 eff=72.5076% N=400 Z=-2.0(97.39%) | Like=-0.24..-0.01 [-0.2381..-0.2380]*| it/evals=2200/3434 eff=72.5115% N=400 Z=-2.0(97.62%) | Like=-0.23..-0.01 [-0.2264..-0.2263]*| it/evals=2240/3493 eff=72.4216% N=400 Mono-modal Volume: ~exp(-9.50) Expected Volume: exp(-5.63) Quality: ok Hinz : -1.0| -0.4 ********************* +0.4 | +1.0 Kunz : -1.0| -0.4 ******************** +0.4 | +1.0 ctr_distance: +0.00|********************* +0.44 | +1.00 Z=-2.0(97.83%) | Like=-0.21..-0.01 [-0.2140..-0.2140]*| it/evals=2280/3561 eff=72.1291% N=400 Z=-2.0(98.02%) | Like=-0.20..-0.01 [-0.2028..-0.2026]*| it/evals=2320/3621 eff=72.0273% N=400 Mono-modal Volume: ~exp(-9.95) * Expected Volume: exp(-5.85) Quality: ok Hinz : -1.0| -0.4 ****************** +0.4 | +1.0 Kunz : -1.0| -0.4 ***************** +0.3 | +1.0 ctr_distance: +0.00|****************** +0.39 | +1.00 Z=-2.0(98.11%) | Like=-0.20..-0.01 [-0.1958..-0.1951]*| it/evals=2340/3648 eff=72.0443% N=400 Z=-2.0(98.20%) | Like=-0.19..-0.01 [-0.1900..-0.1895]*| it/evals=2360/3673 eff=72.1051% N=400 Z=-2.0(98.36%) | Like=-0.18..-0.01 [-0.1819..-0.1812]*| it/evals=2400/3728 eff=72.1154% N=400 Mono-modal Volume: ~exp(-10.20) * Expected Volume: exp(-6.08) Quality: ok Hinz : -1.0| -0.3 *************** +0.3 | +1.0 Kunz : -1.0| -0.3 **************** +0.3 | +1.0 ctr_distance: +0.00|**************** +0.35 | +1.00 Z=-2.0(98.47%) | Like=-0.17..-0.01 [-0.1731..-0.1729]*| it/evals=2430/3770 eff=72.1068% N=400 Z=-2.0(98.51%) | Like=-0.17..-0.01 [-0.1710..-0.1709]*| it/evals=2440/3783 eff=72.1253% N=400 Z=-2.0(98.64%) | Like=-0.16..-0.01 [-0.1604..-0.1604]*| it/evals=2480/3829 eff=72.3243% N=400 Mono-modal Volume: ~exp(-10.66) * Expected Volume: exp(-6.30) Quality: ok Hinz : -1.0| -0.3 ************** +0.3 | +1.0 Kunz : -1.0| -0.3 ************* +0.3 | +1.0 ctr_distance: +0.00|************** +0.30 | +1.00 Z=-2.0(98.76%) | Like=-0.15..-0.01 [-0.1520..-0.1519]*| it/evals=2520/3890 eff=72.2063% N=400 Z=-2.0(98.88%) | Like=-0.14..-0.01 [-0.1423..-0.1422]*| it/evals=2560/3942 eff=72.2756% N=400 Z=-2.0(98.98%) | Like=-0.14..-0.01 [-0.1363..-0.1363]*| it/evals=2600/3993 eff=72.3629% N=400 [ultranest] Explored until L=-0.008 [ultranest] Likelihood function evaluations: 4006 [ultranest] logZ = -2.005 +- 0.0327 [ultranest] Effective samples strategy satisfied (ESS = 1721.5, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.10 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.03, need <0.5) [ultranest] logZ error budget: single: 0.03 bs:0.03 tail:0.01 total:0.03 required:<0.50 [ultranest] done iterating. -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+1, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-4.88, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=442, regioncalls=1680, ndraw=40, logz=-6.59, remainder_fraction=98.9465%, Lmin=-3.91, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=484, regioncalls=3360, ndraw=40, logz=-5.65, remainder_fraction=97.3258%, Lmin=-3.50, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=495, regioncalls=3800, ndraw=40, logz=-5.48, remainder_fraction=96.8546%, Lmin=-3.43, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=532, regioncalls=5280, ndraw=40, logz=-5.09, remainder_fraction=95.2983%, Lmin=-3.22, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=585, regioncalls=7400, ndraw=40, logz=-4.66, remainder_fraction=92.8770%, Lmin=-2.94, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=611, regioncalls=8440, ndraw=40, logz=-4.49, remainder_fraction=91.5760%, Lmin=-2.86, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=636, regioncalls=9440, ndraw=40, logz=-4.33, remainder_fraction=90.1842%, Lmin=-2.77, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=686, regioncalls=11440, ndraw=40, logz=-4.08, remainder_fraction=87.2440%, Lmin=-2.64, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=738, regioncalls=13520, ndraw=40, logz=-3.87, remainder_fraction=84.2565%, Lmin=-2.51, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=793, regioncalls=15720, ndraw=40, logz=-3.69, remainder_fraction=81.2539%, Lmin=-2.38, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=850, regioncalls=18000, ndraw=40, logz=-3.53, remainder_fraction=78.0225%, Lmin=-2.26, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=902, regioncalls=20080, ndraw=40, logz=-3.40, remainder_fraction=74.6320%, Lmin=-2.15, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=961, regioncalls=22440, ndraw=40, logz=-3.28, remainder_fraction=71.6459%, Lmin=-2.04, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2]), array([399, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=1017, regioncalls=24680, ndraw=40, logz=-3.17, remainder_fraction=68.3815%, Lmin=-1.93, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=1073, regioncalls=26920, ndraw=40, logz=-3.07, remainder_fraction=65.0380%, Lmin=-1.86, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1096, regioncalls=27840, ndraw=40, logz=-3.03, remainder_fraction=63.6525%, Lmin=-1.82, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1127, regioncalls=29080, ndraw=40, logz=-2.98, remainder_fraction=62.0725%, Lmin=-1.77, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1185, regioncalls=31400, ndraw=40, logz=-2.90, remainder_fraction=58.9521%, Lmin=-1.65, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=1247, regioncalls=33880, ndraw=40, logz=-2.83, remainder_fraction=55.7587%, Lmin=-1.58, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=1313, regioncalls=36520, ndraw=40, logz=-2.76, remainder_fraction=52.7681%, Lmin=-1.49, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1367, regioncalls=38680, ndraw=40, logz=-2.70, remainder_fraction=49.7942%, Lmin=-1.43, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=1423, regioncalls=40920, ndraw=40, logz=-2.64, remainder_fraction=47.0052%, Lmin=-1.36, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1475, regioncalls=43000, ndraw=40, logz=-2.59, remainder_fraction=44.2174%, Lmin=-1.29, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1533, regioncalls=45320, ndraw=40, logz=-2.54, remainder_fraction=41.5534%, Lmin=-1.23, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1594, regioncalls=47800, ndraw=40, logz=-2.50, remainder_fraction=38.9665%, Lmin=-1.16, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1650, regioncalls=50040, ndraw=40, logz=-2.46, remainder_fraction=36.5672%, Lmin=-1.11, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1709, regioncalls=52400, ndraw=40, logz=-2.42, remainder_fraction=34.1354%, Lmin=-1.05, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1759, regioncalls=54400, ndraw=40, logz=-2.40, remainder_fraction=32.5228%, Lmin=-1.02, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1772, regioncalls=54920, ndraw=40, logz=-2.39, remainder_fraction=32.0506%, Lmin=-1.00, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=1829, regioncalls=57200, ndraw=40, logz=-2.36, remainder_fraction=29.8139%, Lmin=-0.96, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1887, regioncalls=59520, ndraw=40, logz=-2.33, remainder_fraction=27.7163%, Lmin=-0.92, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=1943, regioncalls=61760, ndraw=40, logz=-2.30, remainder_fraction=25.6388%, Lmin=-0.89, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=1996, regioncalls=63960, ndraw=40, logz=-2.28, remainder_fraction=23.7363%, Lmin=-0.86, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=2048, regioncalls=66040, ndraw=40, logz=-2.26, remainder_fraction=22.0601%, Lmin=-0.83, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=2106, regioncalls=68400, ndraw=40, logz=-2.24, remainder_fraction=20.4776%, Lmin=-0.78, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=2137, regioncalls=69640, ndraw=40, logz=-2.23, remainder_fraction=19.6779%, Lmin=-0.76, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=2162, regioncalls=70840, ndraw=40, logz=-2.22, remainder_fraction=18.9708%, Lmin=-0.74, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=2215, regioncalls=72960, ndraw=40, logz=-2.20, remainder_fraction=17.5041%, Lmin=-0.69, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2259, regioncalls=74720, ndraw=40, logz=-2.19, remainder_fraction=16.4925%, Lmin=-0.67, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=2272, regioncalls=75360, ndraw=40, logz=-2.18, remainder_fraction=16.1721%, Lmin=-0.66, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2322, regioncalls=77360, ndraw=40, logz=-2.17, remainder_fraction=14.8845%, Lmin=-0.62, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2382, regioncalls=79760, ndraw=40, logz=-2.16, remainder_fraction=13.7459%, Lmin=-0.59, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1480, ncalls=2436, regioncalls=81960, ndraw=40, logz=-2.14, remainder_fraction=12.6704%, Lmin=-0.57, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1520, ncalls=2489, regioncalls=84080, ndraw=40, logz=-2.13, remainder_fraction=11.6511%, Lmin=-0.54, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2506, regioncalls=84800, ndraw=40, logz=-2.13, remainder_fraction=11.4027%, Lmin=-0.54, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=2544, regioncalls=86400, ndraw=40, logz=-2.12, remainder_fraction=10.6999%, Lmin=-0.52, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2595, regioncalls=88440, ndraw=40, logz=-2.11, remainder_fraction=9.8352%, Lmin=-0.49, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2627, regioncalls=89720, ndraw=40, logz=-2.11, remainder_fraction=9.4266%, Lmin=-0.48, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1640, ncalls=2652, regioncalls=90760, ndraw=40, logz=-2.10, remainder_fraction=9.0503%, Lmin=-0.46, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1680, ncalls=2701, regioncalls=92720, ndraw=40, logz=-2.10, remainder_fraction=8.3166%, Lmin=-0.44, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2740, regioncalls=94280, ndraw=40, logz=-2.09, remainder_fraction=7.7943%, Lmin=-0.42, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1720, ncalls=2753, regioncalls=94880, ndraw=40, logz=-2.09, remainder_fraction=7.6215%, Lmin=-0.41, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1760, ncalls=2814, regioncalls=97320, ndraw=40, logz=-2.08, remainder_fraction=6.9783%, Lmin=-0.39, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2876, regioncalls=99840, ndraw=40, logz=-2.07, remainder_fraction=6.3894%, Lmin=-0.37, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1840, ncalls=2927, regioncalls=101880, ndraw=40, logz=-2.07, remainder_fraction=5.8521%, Lmin=-0.36, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1880, ncalls=2980, regioncalls=104040, ndraw=40, logz=-2.06, remainder_fraction=5.3481%, Lmin=-0.34, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2]), array([399, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=3035, regioncalls=106240, ndraw=40, logz=-2.06, remainder_fraction=4.9017%, Lmin=-0.33, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1960, ncalls=3098, regioncalls=108760, ndraw=40, logz=-2.05, remainder_fraction=4.4883%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=3122, regioncalls=109760, ndraw=40, logz=-2.05, remainder_fraction=4.2872%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=3145, regioncalls=110680, ndraw=40, logz=-2.05, remainder_fraction=4.1007%, Lmin=-0.30, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2040, ncalls=3201, regioncalls=112920, ndraw=40, logz=-2.05, remainder_fraction=3.7481%, Lmin=-0.28, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=3249, regioncalls=114880, ndraw=40, logz=-2.04, remainder_fraction=3.5010%, Lmin=-0.27, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2080, ncalls=3261, regioncalls=115440, ndraw=40, logz=-2.04, remainder_fraction=3.4263%, Lmin=-0.27, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2120, ncalls=3320, regioncalls=117800, ndraw=40, logz=-2.04, remainder_fraction=3.1296%, Lmin=-0.26, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2146, ncalls=3360, regioncalls=119400, ndraw=40, logz=-2.04, remainder_fraction=2.9519%, Lmin=-0.25, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=3379, regioncalls=120160, ndraw=40, logz=-2.04, remainder_fraction=2.8591%, Lmin=-0.25, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=3434, regioncalls=122360, ndraw=40, logz=-2.03, remainder_fraction=2.6105%, Lmin=-0.24, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2240, ncalls=3493, regioncalls=124720, ndraw=40, logz=-2.03, remainder_fraction=2.3795%, Lmin=-0.23, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2280, ncalls=3561, regioncalls=127440, ndraw=40, logz=-2.03, remainder_fraction=2.1693%, Lmin=-0.21, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2320, ncalls=3621, regioncalls=129840, ndraw=40, logz=-2.03, remainder_fraction=1.9813%, Lmin=-0.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=3648, regioncalls=131000, ndraw=40, logz=-2.03, remainder_fraction=1.8922%, Lmin=-0.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2360, ncalls=3673, regioncalls=132000, ndraw=40, logz=-2.03, remainder_fraction=1.8049%, Lmin=-0.19, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3728, regioncalls=134200, ndraw=40, logz=-2.02, remainder_fraction=1.6435%, Lmin=-0.18, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3770, regioncalls=135920, ndraw=40, logz=-2.02, remainder_fraction=1.5314%, Lmin=-0.17, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2440, ncalls=3783, regioncalls=136440, ndraw=40, logz=-2.02, remainder_fraction=1.4950%, Lmin=-0.17, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2480, ncalls=3829, regioncalls=138280, ndraw=40, logz=-2.02, remainder_fraction=1.3586%, Lmin=-0.16, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=3890, regioncalls=140760, ndraw=40, logz=-2.02, remainder_fraction=1.2366%, Lmin=-0.15, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2560, ncalls=3942, regioncalls=142840, ndraw=40, logz=-2.02, remainder_fraction=1.1247%, Lmin=-0.14, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3993, regioncalls=144880, ndraw=40, logz=-2.02, remainder_fraction=1.0222%, Lmin=-0.14, Lmax=-0.01 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.008 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 4006 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -2.005 +- 0.0327 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1721.5, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.10 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.03, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.03 bs:0.03 tail:0.01 total:0.03 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:2775 Making corner plot ... [35mDEBUG [0m ultranest:integrator.py:2821 Making run plot ... [35mDEBUG [0m ultranest:integrator.py:2797 Making trace plot ... | |||
Passed | tests/test_run.py::test_return_summary | 7.31 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.01) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0000|********************************************************| +1.0000 Z=-inf(0.00%) | Like=-2802.23..10.12 [-2802.2272..-315.3455] | it/evals=0/401 eff=0.0000% N=400 Z=-2233.1(0.00%) | Like=-2221.10..10.12 [-2802.2272..-315.3455] | it/evals=40/443 eff=93.0233% N=400 Z=-1762.4(0.00%) | Like=-1743.34..10.12 [-2802.2272..-315.3455] | it/evals=80/492 eff=86.9565% N=400 Mono-modal Volume: ~exp(-4.29) * Expected Volume: exp(-0.23) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| ***********************************************| +1.0 Z=-1668.0(0.00%) | Like=-1655.20..10.12 [-2802.2272..-315.3455] | it/evals=90/502 eff=88.2353% N=400 Z=-1387.4(0.00%) | Like=-1362.31..10.12 [-2802.2272..-315.3455] | it/evals=120/535 eff=88.8889% N=400 Z=-1009.2(0.00%) | Like=-996.92..10.12 [-2802.2272..-315.3455] | it/evals=160/583 eff=87.4317% N=400 Mono-modal Volume: ~exp(-4.72) * Expected Volume: exp(-0.45) Quality: ok a: +1.0e-06|********************************************************| +1.0e+00 b: +0.0| +0.3 *************************************| +1.0 Z=-830.4(0.00%) | Like=-822.44..10.12 [-2802.2272..-315.3455] | it/evals=180/608 eff=86.5385% N=400 Z=-655.8(0.00%) | Like=-646.69..10.12 [-2802.2272..-315.3455] | it/evals=200/629 eff=87.3362% N=400 Z=-486.2(0.00%) | Like=-479.15..10.12 [-2802.2272..-315.3455] | it/evals=240/670 eff=88.8889% N=400 Mono-modal Volume: ~exp(-4.75) * Expected Volume: exp(-0.67) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| +0.5 *****************************| +1.0 Z=-357.0(0.00%) | Like=-332.40..10.12 [-2802.2272..-315.3455] | it/evals=270/703 eff=89.1089% N=400 Z=-307.8(0.00%) | Like=-301.43..10.12 [-314.8584..-76.2883] | it/evals=280/714 eff=89.1720% N=400 Z=-244.6(0.00%) | Like=-237.89..10.12 [-314.8584..-76.2883] | it/evals=320/760 eff=88.8889% N=400 Mono-modal Volume: ~exp(-5.17) * Expected Volume: exp(-0.90) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| +0.5 ************************ | +1.0 Z=-204.1(0.00%) | Like=-197.31..10.12 [-314.8584..-76.2883] | it/evals=360/810 eff=87.8049% N=400 Z=-170.4(0.00%) | Like=-162.86..10.12 [-314.8584..-76.2883] | it/evals=400/853 eff=88.3002% N=400 Z=-146.3(0.00%) | Like=-139.19..10.12 [-314.8584..-76.2883] | it/evals=440/908 eff=86.6142% N=400 Mono-modal Volume: ~exp(-5.43) * Expected Volume: exp(-1.12) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| +0.6 ******************** | +1.0 Z=-140.3(0.00%) | Like=-133.61..10.12 [-314.8584..-76.2883] | it/evals=450/919 eff=86.7052% N=400 Z=-120.1(0.00%) | Like=-112.87..10.12 [-314.8584..-76.2883] | it/evals=480/952 eff=86.9565% N=400 Z=-96.1(0.00%) | Like=-89.35..10.12 [-314.8584..-76.2883] | it/evals=520/1001 eff=86.5225% N=400 Mono-modal Volume: ~exp(-5.43) Expected Volume: exp(-1.35) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| +0.6 **************** | +1.0 Z=-80.0(0.00%) | Like=-72.59..10.12 [-76.2392..-15.0145] | it/evals=560/1049 eff=86.2866% N=400 Z=-66.0(0.00%) | Like=-60.32..10.12 [-76.2392..-15.0145] | it/evals=600/1101 eff=85.5920% N=400 Mono-modal Volume: ~exp(-5.79) * Expected Volume: exp(-1.57) Quality: ok a: +0.000|********************************************************| +1.000 b: +0.0| +0.6 ************** | +1.0 Z=-57.8(0.00%) | Like=-51.71..10.12 [-76.2392..-15.0145] | it/evals=630/1138 eff=85.3659% N=400 Z=-55.0(0.00%) | Like=-48.13..10.12 [-76.2392..-15.0145] | it/evals=640/1148 eff=85.5615% N=400 Z=-45.5(0.00%) | Like=-39.66..10.12 [-76.2392..-15.0145] | it/evals=680/1193 eff=85.7503% N=400 Mono-modal Volume: ~exp(-6.02) * Expected Volume: exp(-1.80) Quality: ok a: +0.0000|********************************************************| +1.0000 b: +0.0| +0.7 *********** | +1.0 Z=-36.9(0.00%) | Like=-30.63..10.12 [-76.2392..-15.0145] | it/evals=720/1243 eff=85.4093% N=400 Z=-31.2(0.00%) | Like=-25.10..10.12 [-76.2392..-15.0145] | it/evals=760/1288 eff=85.5856% N=400 Z=-24.6(0.00%) | Like=-18.15..10.12 [-76.2392..-15.0145] | it/evals=800/1334 eff=85.6531% N=400 Mono-modal Volume: ~exp(-6.65) * Expected Volume: exp(-2.02) Quality: ok a: +0.0000|********************************************************| +1.0000 b: +0.0| +0.7 ********** | +1.0 Z=-23.0(0.00%) | Like=-17.06..10.12 [-76.2392..-15.0145] | it/evals=810/1346 eff=85.6237% N=400 Z=-19.4(0.00%) | Like=-13.51..10.12 [-14.9895..-0.6265] | it/evals=840/1384 eff=85.3659% N=400 Z=-15.7(0.00%) | Like=-9.79..10.13 [-14.9895..-0.6265] | it/evals=880/1429 eff=85.5199% N=400 Mono-modal Volume: ~exp(-6.65) Expected Volume: exp(-2.25) Quality: ok a: +0.0000|********************************************************| +1.0000 b: +0.0| +0.7 ******** +0.8 | +1.0 Z=-12.7(0.00%) | Like=-7.29..10.13 [-14.9895..-0.6265] | it/evals=920/1479 eff=85.2641% N=400 Z=-10.7(0.00%) | Like=-5.20..10.13 [-14.9895..-0.6265] | it/evals=960/1532 eff=84.8057% N=400 Mono-modal Volume: ~exp(-6.65) Expected Volume: exp(-2.47) Quality: ok a: +0.00|***************************************************** **| +1.00 b: +0.0| +0.7 ****** +0.8 | +1.0 Z=-8.7(0.00%) | Like=-3.21..10.13 [-14.9895..-0.6265] | it/evals=1000/1580 eff=84.7458% N=400 Z=-7.2(0.00%) | Like=-1.91..10.13 [-14.9895..-0.6265] | it/evals=1040/1632 eff=84.4156% N=400 Mono-modal Volume: ~exp(-6.83) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| **************************************************** | +1.00 b: +0.0| +0.7 ****** +0.8 | +1.0 Z=-6.0(0.00%) | Like=-0.91..10.13 [-14.9895..-0.6265] | it/evals=1080/1692 eff=83.5913% N=400 Z=-4.9(0.00%) | Like=0.32..10.13 [-0.5972..5.1978] | it/evals=1120/1747 eff=83.1477% N=400 Z=-3.8(0.01%) | Like=1.32..10.13 [-0.5972..5.1978] | it/evals=1160/1805 eff=82.5623% N=400 Mono-modal Volume: ~exp(-6.99) * Expected Volume: exp(-2.92) Quality: ok a: +0.00| ********************************************** | +1.00 b: +0.0| +0.7 ****** +0.8 | +1.0 Z=-3.6(0.02%) | Like=1.59..10.13 [-0.5972..5.1978] | it/evals=1170/1823 eff=82.2207% N=400 Z=-2.9(0.04%) | Like=2.41..10.13 [-0.5972..5.1978] | it/evals=1200/1866 eff=81.8554% N=400 Z=-2.0(0.09%) | Like=3.32..10.13 [-0.5972..5.1978] | it/evals=1240/1923 eff=81.4183% N=400 Mono-modal Volume: ~exp(-7.66) * Expected Volume: exp(-3.15) Quality: ok a: +0.0| * ************************************** | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=-1.6(0.13%) | Like=3.64..10.13 [-0.5972..5.1978] | it/evals=1260/1949 eff=81.3428% N=400 Z=-1.2(0.19%) | Like=3.94..10.13 [-0.5972..5.1978] | it/evals=1280/1972 eff=81.4249% N=400 Z=-0.5(0.37%) | Like=4.54..10.13 [-0.5972..5.1978] | it/evals=1320/2022 eff=81.3810% N=400 Mono-modal Volume: ~exp(-7.96) * Expected Volume: exp(-3.37) Quality: ok a: +0.0| ************************************ +0.8 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=-0.1(0.55%) | Like=4.93..10.13 [-0.5972..5.1978] | it/evals=1350/2061 eff=81.2763% N=400 Z=0.0(0.64%) | Like=5.00..10.13 [-0.5972..5.1978] | it/evals=1360/2071 eff=81.3884% N=400 Z=0.5(1.02%) | Like=5.40..10.13 [5.1994..7.0957] | it/evals=1400/2115 eff=81.6327% N=400 Mono-modal Volume: ~exp(-7.96) Expected Volume: exp(-3.60) Quality: ok a: +0.0| +0.2 ******************************** +0.8 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=0.9(1.59%) | Like=5.93..10.13 [5.1994..7.0957] | it/evals=1440/2169 eff=81.4019% N=400 Z=1.3(2.34%) | Like=6.30..10.13 [5.1994..7.0957] | it/evals=1480/2218 eff=81.4081% N=400 Z=1.6(3.40%) | Like=6.68..10.13 [5.1994..7.0957] | it/evals=1520/2274 eff=81.1099% N=400 Mono-modal Volume: ~exp(-7.96) Expected Volume: exp(-3.82) Quality: ok a: +0.0| +0.2 ****************************** +0.8 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=2.0(4.77%) | Like=6.98..10.13 [5.1994..7.0957] | it/evals=1560/2332 eff=80.7453% N=400 Z=2.2(6.35%) | Like=7.21..10.13 [7.0997..7.5183] | it/evals=1600/2394 eff=80.2407% N=400 Mono-modal Volume: ~exp(-8.34) * Expected Volume: exp(-4.05) Quality: ok a: +0.0| +0.3 *************************** +0.7 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=2.4(7.15%) | Like=7.37..10.13 [7.0997..7.5183] | it/evals=1620/2419 eff=80.2377% N=400 Z=2.5(8.09%) | Like=7.51..10.13 [7.0997..7.5183] | it/evals=1640/2443 eff=80.2741% N=400 Z=2.7(10.20%) | Like=7.76..10.13 [7.7582..7.7858] | it/evals=1680/2491 eff=80.3443% N=400 Mono-modal Volume: ~exp(-8.69) * Expected Volume: exp(-4.27) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=2.9(12.12%) | Like=7.93..10.13 [7.9328..7.9409]*| it/evals=1710/2531 eff=80.2440% N=400 Z=3.0(12.67%) | Like=8.00..10.13 [7.9955..7.9964]*| it/evals=1720/2543 eff=80.2613% N=400 Z=3.2(15.50%) | Like=8.21..10.13 [8.1913..8.2090] | it/evals=1760/2595 eff=80.1822% N=400 Mono-modal Volume: ~exp(-8.69) Expected Volume: exp(-4.50) Quality: ok a: +0.0| +0.3 ********************** +0.7 | +1.0 b: +0.0| +0.7 **** +0.8 | +1.0 Z=3.3(18.25%) | Like=8.37..10.13 [8.3678..8.3717]*| it/evals=1800/2647 eff=80.1068% N=400 Z=3.5(21.38%) | Like=8.49..10.13 [8.4912..8.4929]*| it/evals=1840/2704 eff=79.8611% N=400 Z=3.6(24.76%) | Like=8.69..10.13 [8.6892..8.6892]*| it/evals=1880/2759 eff=79.6948% N=400 Mono-modal Volume: ~exp(-9.06) * Expected Volume: exp(-4.73) Quality: ok a: +0.0| +0.3 ******************** +0.7 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=3.7(25.65%) | Like=8.72..10.13 [8.7214..8.7264]*| it/evals=1890/2773 eff=79.6460% N=400 Z=3.8(28.16%) | Like=8.80..10.13 [8.7961..8.8014]*| it/evals=1920/2809 eff=79.7011% N=400 Z=3.9(31.66%) | Like=8.91..10.13 [8.9102..8.9133]*| it/evals=1960/2858 eff=79.7396% N=400 Mono-modal Volume: ~exp(-9.10) * Expected Volume: exp(-4.95) Quality: ok a: +0.0| +0.3 ****************** +0.7 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=3.9(33.27%) | Like=8.96..10.13 [8.9581..8.9649]*| it/evals=1980/2883 eff=79.7422% N=400 Z=4.0(35.12%) | Like=9.01..10.13 [9.0116..9.0201]*| it/evals=2000/2908 eff=79.7448% N=400 Z=4.1(38.55%) | Like=9.12..10.14 [9.1169..9.1185]*| it/evals=2040/2960 eff=79.6875% N=400 Mono-modal Volume: ~exp(-9.52) * Expected Volume: exp(-5.18) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.2(41.31%) | Like=9.19..10.14 [9.1881..9.1907]*| it/evals=2070/3003 eff=79.5236% N=400 Z=4.2(42.30%) | Like=9.21..10.14 [9.2098..9.2141]*| it/evals=2080/3016 eff=79.5107% N=400 Z=4.3(45.89%) | Like=9.29..10.14 [9.2945..9.2968]*| it/evals=2120/3067 eff=79.4901% N=400 Mono-modal Volume: ~exp(-9.59) * Expected Volume: exp(-5.40) Quality: ok a: +0.0| +0.4 ************** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.3(49.25%) | Like=9.37..10.14 [9.3718..9.3724]*| it/evals=2160/3120 eff=79.4118% N=400 Z=4.4(52.56%) | Like=9.44..10.14 [9.4367..9.4417]*| it/evals=2200/3170 eff=79.4224% N=400 Z=4.5(55.81%) | Like=9.51..10.14 [9.5059..9.5091]*| it/evals=2240/3236 eff=78.9845% N=400 Mono-modal Volume: ~exp(-9.83) * Expected Volume: exp(-5.63) Quality: ok a: +0.0| +0.4 ************** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.5(56.60%) | Like=9.52..10.14 [9.5193..9.5195]*| it/evals=2250/3250 eff=78.9474% N=400 Z=4.5(58.89%) | Like=9.56..10.14 [9.5566..9.5592]*| it/evals=2280/3286 eff=79.0021% N=400 Z=4.6(61.84%) | Like=9.64..10.14 [9.6372..9.6393]*| it/evals=2320/3336 eff=79.0191% N=400 Mono-modal Volume: ~exp(-9.83) Expected Volume: exp(-5.85) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.6(64.70%) | Like=9.68..10.14 [9.6769..9.6848]*| it/evals=2360/3393 eff=78.8507% N=400 Z=4.7(67.38%) | Like=9.73..10.14 [9.7264..9.7265]*| it/evals=2400/3451 eff=78.6627% N=400 Mono-modal Volume: ~exp(-10.33) * Expected Volume: exp(-6.08) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.7(69.33%) | Like=9.75..10.14 [9.7531..9.7531]*| it/evals=2430/3491 eff=78.6153% N=400 Z=4.7(69.95%) | Like=9.76..10.14 [9.7597..9.7598]*| it/evals=2440/3502 eff=78.6589% N=400 Z=4.7(72.33%) | Like=9.78..10.14 [9.7838..9.7838]*| it/evals=2480/3556 eff=78.5805% N=400 Mono-modal Volume: ~exp(-10.61) * Expected Volume: exp(-6.30) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.8(74.53%) | Like=9.82..10.14 [9.8194..9.8203]*| it/evals=2520/3606 eff=78.6026% N=400 Z=4.8(76.61%) | Like=9.85..10.14 [9.8488..9.8513]*| it/evals=2560/3662 eff=78.4795% N=400 Z=4.8(78.54%) | Like=9.88..10.14 [9.8771..9.8793]*| it/evals=2600/3714 eff=78.4550% N=400 Mono-modal Volume: ~exp(-10.92) * Expected Volume: exp(-6.53) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.8(78.99%) | Like=9.88..10.14 [9.8844..9.8865]*| it/evals=2610/3724 eff=78.5199% N=400 Z=4.8(80.31%) | Like=9.91..10.14 [9.9108..9.9110]*| it/evals=2640/3759 eff=78.5948% N=400 Z=4.9(81.99%) | Like=9.93..10.14 [9.9310..9.9310]*| it/evals=2680/3810 eff=78.5924% N=400 Mono-modal Volume: ~exp(-10.92) Expected Volume: exp(-6.75) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.9(83.55%) | Like=9.95..10.14 [9.9505..9.9513]*| it/evals=2720/3863 eff=78.5446% N=400 Z=4.9(84.99%) | Like=9.97..10.14 [9.9654..9.9655]*| it/evals=2760/3924 eff=78.3201% N=400 Mono-modal Volume: ~exp(-10.92) Expected Volume: exp(-6.98) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.9(86.31%) | Like=9.98..10.14 [9.9800..9.9811]*| it/evals=2800/3976 eff=78.2998% N=400 Z=4.9(87.52%) | Like=10.00..10.14 [9.9975..9.9977]*| it/evals=2840/4029 eff=78.2585% N=400 Mono-modal Volume: ~exp(-11.25) * Expected Volume: exp(-7.20) Quality: ok a: +0.0| +0.5 ****** +0.6 | +1.0 b: +0.0| +0.7 ** +0.8 | +1.0 Z=4.9(88.63%) | Like=10.01..10.14 [10.0119..10.0123]*| it/evals=2880/4087 eff=78.1123% N=400 Z=4.9(89.65%) | Like=10.02..10.14 [10.0245..10.0246]*| it/evals=2920/4133 eff=78.2213% N=400 Z=5.0(90.59%) | Like=10.04..10.14 [10.0356..10.0357]*| it/evals=2960/4189 eff=78.1209% N=400 Mono-modal Volume: ~exp(-11.47) * Expected Volume: exp(-7.43) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(90.81%) | Like=10.04..10.14 [10.0378..10.0379]*| it/evals=2970/4200 eff=78.1579% N=400 Z=5.0(91.44%) | Like=10.04..10.14 [10.0444..10.0444]*| it/evals=3000/4235 eff=78.2269% N=400 Z=5.0(92.22%) | Like=10.05..10.14 [10.0543..10.0547]*| it/evals=3040/4283 eff=78.2900% N=400 Mono-modal Volume: ~exp(-11.92) * Expected Volume: exp(-7.65) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(92.59%) | Like=10.06..10.14 [10.0583..10.0583]*| it/evals=3060/4314 eff=78.1809% N=400 Z=5.0(92.93%) | Like=10.06..10.14 [10.0635..10.0638]*| it/evals=3080/4338 eff=78.2123% N=400 Z=5.0(93.58%) | Like=10.07..10.14 [10.0704..10.0704]*| it/evals=3120/4394 eff=78.1172% N=400 Mono-modal Volume: ~exp(-12.00) * Expected Volume: exp(-7.88) Quality: ok a: +0.00| +0.47 **** +0.54 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(94.03%) | Like=10.08..10.14 [10.0760..10.0762]*| it/evals=3150/4436 eff=78.0476% N=400 Z=5.0(94.17%) | Like=10.08..10.14 [10.0775..10.0776]*| it/evals=3160/4450 eff=78.0247% N=400 Z=5.0(94.71%) | Like=10.08..10.14 [10.0837..10.0837]*| it/evals=3200/4513 eff=77.8021% N=400 Mono-modal Volume: ~exp(-12.34) * Expected Volume: exp(-8.10) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(95.20%) | Like=10.09..10.14 [10.0893..10.0893]*| it/evals=3240/4565 eff=77.7911% N=400 Z=5.0(95.64%) | Like=10.09..10.14 [10.0935..10.0935]*| it/evals=3280/4613 eff=77.8543% N=400 Z=5.0(96.05%) | Like=10.10..10.14 [10.0983..10.0986]*| it/evals=3320/4667 eff=77.8064% N=400 Mono-modal Volume: ~exp(-12.34) Expected Volume: exp(-8.33) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(96.42%) | Like=10.10..10.14 [10.1023..10.1024]*| it/evals=3360/4722 eff=77.7418% N=400 Z=5.0(96.75%) | Like=10.11..10.14 [10.1065..10.1068]*| it/evals=3400/4777 eff=77.6788% N=400 Mono-modal Volume: ~exp(-12.61) * Expected Volume: exp(-8.55) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(96.91%) | Like=10.11..10.14 [10.1082..10.1084]*| it/evals=3420/4803 eff=77.6743% N=400 Z=5.0(97.06%) | Like=10.11..10.14 [10.1095..10.1097]*| it/evals=3440/4823 eff=77.7753% N=400 Z=5.0(97.33%) | Like=10.11..10.14 [10.1127..10.1127]*| it/evals=3480/4867 eff=77.9046% N=400 Mono-modal Volume: ~exp(-13.42) * Expected Volume: exp(-8.78) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(97.52%) | Like=10.12..10.14 [10.1152..10.1153]*| it/evals=3510/4904 eff=77.9307% N=400 Z=5.0(97.59%) | Like=10.12..10.14 [10.1158..10.1158]*| it/evals=3520/4916 eff=77.9451% N=400 Z=5.0(97.81%) | Like=10.12..10.14 [10.1179..10.1179]*| it/evals=3560/4966 eff=77.9676% N=400 Mono-modal Volume: ~exp(-13.42) Expected Volume: exp(-9.00) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(98.02%) | Like=10.12..10.14 [10.1199..10.1199]*| it/evals=3600/5022 eff=77.8884% N=400 Z=5.0(98.21%) | Like=10.12..10.14 [10.1213..10.1214]*| it/evals=3640/5071 eff=77.9276% N=400 Z=5.0(98.38%) | Like=10.12..10.14 [10.1232..10.1232]*| it/evals=3680/5116 eff=78.0322% N=400 Mono-modal Volume: ~exp(-13.42) Expected Volume: exp(-9.23) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(98.53%) | Like=10.12..10.14 [10.1243..10.1243]*| it/evals=3720/5168 eff=78.0201% N=400 Z=5.0(98.67%) | Like=10.13..10.14 [10.1259..10.1259]*| it/evals=3760/5219 eff=78.0245% N=400 Mono-modal Volume: ~exp(-13.59) * Expected Volume: exp(-9.45) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(98.73%) | Like=10.13..10.14 [10.1266..10.1267]*| it/evals=3780/5246 eff=78.0025% N=400 Z=5.0(98.79%) | Like=10.13..10.14 [10.1270..10.1270]*| it/evals=3800/5268 eff=78.0608% N=400 Z=5.0(98.91%) | Like=10.13..10.14 [10.1283..10.1283]*| it/evals=3840/5323 eff=78.0012% N=400 Mono-modal Volume: ~exp(-14.25) * Expected Volume: exp(-9.68) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.0| +0.7 ** +0.8 | +1.0 Z=5.0(98.99%) | Like=10.13..10.14 [10.1291..10.1292]*| it/evals=3870/5366 eff=77.9299% N=400 [ultranest] Explored until L=1e+01 [ultranest] Likelihood function evaluations: 5372 [ultranest] logZ = 5.019 +- 0.08874 [ultranest] Effective samples strategy satisfied (ESS = 1633.3, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.13 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.09, need <0.5) [ultranest] logZ error budget: single: 0.10 bs:0.09 tail:0.01 total:0.09 required:<0.50 [ultranest] done iterating. {'niter': 4276, 'logz': 5.051020984427532, 'logzerr': 0.1870390834080764, 'logz_bs': 5.0192524487065855, 'logz_single': 5.051020984427532, 'logzerr_tail': 0.009937801641216382, 'logzerr_bs': 0.18677488808903941, 'ess': 1633.2654245369472, 'H': 4.044059825069967, 'Herr': 0.08056230037213652, 'posterior': {'mean': [0.504545750059854, 0.7499797583222729], 'stdev': [0.10141453370059575, 0.010283830981567805], 'median': [0.5049034235593031, 0.7499304758703297], 'errlo': [0.40184962890215203, 0.7397941603014832], 'errup': [0.6066846835192893, 0.7602927413146257], 'information_gain_bits': [-1.8648368810631866, 3.4380506997618148]}, 'weighted_samples': {'upoints': array([[0.15886171, 0.00079399], [0.2272687 , 0.00324989], [0.62540308, 0.00356035], ..., [0.50105346, 0.75004204], [0.5009434 , 0.75004626], [0.49921391, 0.75003359]]), 'points': array([[0.15886171, 0.00079399], [0.2272687 , 0.00324989], [0.62540308, 0.00356035], ..., [0.50105346, 0.75004204], [0.5009434 , 0.75004626], [0.49921391, 0.75003359]]), 'weights': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 2.50962173e-05, 2.50964462e-05, 2.50969145e-05]), 'logw': array([ -5.99271429, -5.99521429, -5.99771429, ..., -15.68146455, -15.68146455, -15.68146455]), 'bootstrapped_weights': array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], ..., [3.63399184e-05, 0.00000000e+00, 3.94511287e-05, ..., 4.16610046e-05, 3.52652273e-05, 3.65611499e-05], [0.00000000e+00, 3.95525720e-05, 3.94514887e-05, ..., 4.16613847e-05, 3.52655491e-05, 3.65614835e-05], [3.63409281e-05, 0.00000000e+00, 3.94522248e-05, ..., 4.16621621e-05, 0.00000000e+00, 0.00000000e+00]]), 'logl': array([-2802.22720399, -2781.75798828, -2776.50729349, ..., 10.1396921 , 10.13970123, 10.13971989])}, 'samples': array([[0.56085751, 0.74861932], [0.66554326, 0.75220207], [0.38160242, 0.75444785], ..., [0.46581213, 0.74817287], [0.48199728, 0.75396871], [0.56534532, 0.74581714]]), 'maximum_likelihood': {'logl': 10.13971988576595, 'point': [0.49921391107990154, 0.7500335934223238], 'point_untransformed': [0.49921391107990154, 0.7500335934223238]}, 'ncall': 5372, 'paramnames': ['a', 'b'], 'logzerr_single': 0.10054923949326976, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-2802.23, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=443, regioncalls=1720, ndraw=40, logz=-2233.09, remainder_fraction=100.0000%, Lmin=-2221.10, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=492, regioncalls=3680, ndraw=40, logz=-1762.37, remainder_fraction=100.0000%, Lmin=-1743.34, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=502, regioncalls=4080, ndraw=40, logz=-1667.98, remainder_fraction=100.0000%, Lmin=-1655.20, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=535, regioncalls=5400, ndraw=40, logz=-1387.40, remainder_fraction=100.0000%, Lmin=-1362.31, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=583, regioncalls=7320, ndraw=40, logz=-1009.24, remainder_fraction=100.0000%, Lmin=-996.92, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=608, regioncalls=8320, ndraw=40, logz=-830.42, remainder_fraction=100.0000%, Lmin=-822.44, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=629, regioncalls=9160, ndraw=40, logz=-655.79, remainder_fraction=100.0000%, Lmin=-646.69, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=670, regioncalls=10800, ndraw=40, logz=-486.25, remainder_fraction=100.0000%, Lmin=-479.15, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=703, regioncalls=12120, ndraw=40, logz=-357.04, remainder_fraction=100.0000%, Lmin=-332.40, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=714, regioncalls=12560, ndraw=40, logz=-307.85, remainder_fraction=100.0000%, Lmin=-301.43, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=760, regioncalls=14400, ndraw=40, logz=-244.63, remainder_fraction=100.0000%, Lmin=-237.89, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=810, regioncalls=16400, ndraw=40, logz=-204.06, remainder_fraction=100.0000%, Lmin=-197.31, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=853, regioncalls=18120, ndraw=40, logz=-170.45, remainder_fraction=100.0000%, Lmin=-162.86, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=908, regioncalls=20320, ndraw=40, logz=-146.29, remainder_fraction=100.0000%, Lmin=-139.19, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=919, regioncalls=20760, ndraw=40, logz=-140.26, remainder_fraction=100.0000%, Lmin=-133.61, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=952, regioncalls=22080, ndraw=40, logz=-120.13, remainder_fraction=100.0000%, Lmin=-112.87, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=1001, regioncalls=24040, ndraw=40, logz=-96.10, remainder_fraction=100.0000%, Lmin=-89.35, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1049, regioncalls=25960, ndraw=40, logz=-79.96, remainder_fraction=100.0000%, Lmin=-72.59, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1101, regioncalls=28040, ndraw=40, logz=-65.99, remainder_fraction=100.0000%, Lmin=-60.32, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1138, regioncalls=29520, ndraw=40, logz=-57.78, remainder_fraction=100.0000%, Lmin=-51.71, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=1148, regioncalls=29920, ndraw=40, logz=-55.04, remainder_fraction=100.0000%, Lmin=-48.13, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=1193, regioncalls=31720, ndraw=40, logz=-45.53, remainder_fraction=100.0000%, Lmin=-39.66, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1243, regioncalls=33720, ndraw=40, logz=-36.90, remainder_fraction=100.0000%, Lmin=-30.63, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=1288, regioncalls=35520, ndraw=40, logz=-31.21, remainder_fraction=100.0000%, Lmin=-25.10, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1334, regioncalls=37360, ndraw=40, logz=-24.59, remainder_fraction=100.0000%, Lmin=-18.15, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1346, regioncalls=37840, ndraw=40, logz=-23.02, remainder_fraction=100.0000%, Lmin=-17.06, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1384, regioncalls=39360, ndraw=40, logz=-19.39, remainder_fraction=100.0000%, Lmin=-13.51, Lmax=10.12 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1429, regioncalls=41160, ndraw=40, logz=-15.66, remainder_fraction=100.0000%, Lmin=-9.79, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1479, regioncalls=43200, ndraw=40, logz=-12.71, remainder_fraction=100.0000%, Lmin=-7.29, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1532, regioncalls=45320, ndraw=40, logz=-10.71, remainder_fraction=100.0000%, Lmin=-5.20, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1580, regioncalls=47240, ndraw=40, logz=-8.71, remainder_fraction=99.9999%, Lmin=-3.21, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=1632, regioncalls=49320, ndraw=40, logz=-7.16, remainder_fraction=99.9995%, Lmin=-1.91, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1692, regioncalls=51720, ndraw=40, logz=-5.99, remainder_fraction=99.9984%, Lmin=-0.91, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=1747, regioncalls=53960, ndraw=40, logz=-4.92, remainder_fraction=99.9954%, Lmin=0.32, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=1805, regioncalls=56280, ndraw=40, logz=-3.83, remainder_fraction=99.9863%, Lmin=1.32, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1823, regioncalls=57000, ndraw=40, logz=-3.60, remainder_fraction=99.9825%, Lmin=1.59, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1866, regioncalls=58880, ndraw=40, logz=-2.86, remainder_fraction=99.9640%, Lmin=2.41, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=1923, regioncalls=61160, ndraw=40, logz=-1.95, remainder_fraction=99.9108%, Lmin=3.32, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1949, regioncalls=62200, ndraw=40, logz=-1.55, remainder_fraction=99.8676%, Lmin=3.64, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=1972, regioncalls=63200, ndraw=40, logz=-1.19, remainder_fraction=99.8133%, Lmin=3.94, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=2022, regioncalls=65200, ndraw=40, logz=-0.54, remainder_fraction=99.6295%, Lmin=4.54, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2061, regioncalls=66800, ndraw=40, logz=-0.13, remainder_fraction=99.4461%, Lmin=4.93, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=2071, regioncalls=67200, ndraw=40, logz=0.00, remainder_fraction=99.3640%, Lmin=5.00, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2115, regioncalls=68960, ndraw=40, logz=0.47, remainder_fraction=98.9836%, Lmin=5.40, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2169, regioncalls=71120, 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remainder_fraction=78.6200%, Lmin=8.49, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1880, ncalls=2759, regioncalls=94800, ndraw=40, logz=3.64, remainder_fraction=75.2353%, Lmin=8.69, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=2773, regioncalls=95360, ndraw=40, logz=3.68, remainder_fraction=74.3477%, Lmin=8.72, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=2809, regioncalls=96920, ndraw=40, logz=3.77, remainder_fraction=71.8359%, Lmin=8.80, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1960, ncalls=2858, regioncalls=98880, ndraw=40, logz=3.89, remainder_fraction=68.3368%, Lmin=8.91, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=2883, regioncalls=99920, ndraw=40, logz=3.95, remainder_fraction=66.7323%, Lmin=8.96, Lmax=10.13 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=2908, regioncalls=100920, ndraw=40, logz=4.00, 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remainder_fraction=8.5587%, Lmin=10.04, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3040, ncalls=4283, regioncalls=156720, ndraw=40, logz=4.97, remainder_fraction=7.7770%, Lmin=10.05, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=4314, regioncalls=158000, ndraw=40, logz=4.97, remainder_fraction=7.4136%, Lmin=10.06, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3080, ncalls=4338, regioncalls=158960, ndraw=40, logz=4.98, remainder_fraction=7.0659%, Lmin=10.06, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3120, ncalls=4394, regioncalls=161200, ndraw=40, logz=4.98, remainder_fraction=6.4200%, Lmin=10.07, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=4436, regioncalls=162920, ndraw=40, logz=4.99, remainder_fraction=5.9732%, Lmin=10.08, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3160, ncalls=4450, regioncalls=163480, ndraw=40, logz=4.99, remainder_fraction=5.8308%, Lmin=10.08, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=4513, regioncalls=166000, ndraw=40, logz=5.00, remainder_fraction=5.2920%, Lmin=10.08, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=4565, regioncalls=168120, ndraw=40, logz=5.00, remainder_fraction=4.8026%, Lmin=10.09, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3280, ncalls=4613, regioncalls=170040, ndraw=40, logz=5.01, remainder_fraction=4.3555%, Lmin=10.09, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3320, ncalls=4667, regioncalls=172200, ndraw=40, logz=5.01, remainder_fraction=3.9487%, Lmin=10.10, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3360, ncalls=4722, regioncalls=174400, ndraw=40, logz=5.01, remainder_fraction=3.5809%, Lmin=10.10, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=4777, regioncalls=176600, ndraw=40, logz=5.02, remainder_fraction=3.2465%, Lmin=10.11, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=4803, regioncalls=177680, ndraw=40, logz=5.02, remainder_fraction=3.0902%, Lmin=10.11, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3440, ncalls=4823, regioncalls=178480, ndraw=40, logz=5.02, remainder_fraction=2.9415%, Lmin=10.11, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3480, ncalls=4867, regioncalls=180240, ndraw=40, logz=5.02, remainder_fraction=2.6654%, Lmin=10.11, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3510, ncalls=4904, regioncalls=181760, ndraw=40, logz=5.03, remainder_fraction=2.4751%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3520, ncalls=4916, regioncalls=182240, ndraw=40, logz=5.03, remainder_fraction=2.4148%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3560, ncalls=4966, regioncalls=184240, ndraw=40, logz=5.03, remainder_fraction=2.1874%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3600, ncalls=5022, regioncalls=186480, ndraw=40, logz=5.03, remainder_fraction=1.9810%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3640, ncalls=5071, regioncalls=188440, ndraw=40, logz=5.03, remainder_fraction=1.7942%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3680, ncalls=5116, regioncalls=190240, ndraw=40, logz=5.03, remainder_fraction=1.6249%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3720, ncalls=5168, regioncalls=192320, ndraw=40, logz=5.04, remainder_fraction=1.4714%, Lmin=10.12, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3760, ncalls=5219, regioncalls=194360, ndraw=40, logz=5.04, remainder_fraction=1.3323%, Lmin=10.13, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3780, ncalls=5246, regioncalls=195480, ndraw=40, logz=5.04, remainder_fraction=1.2678%, Lmin=10.13, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3800, ncalls=5268, regioncalls=196360, ndraw=40, logz=5.04, remainder_fraction=1.2063%, Lmin=10.13, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3840, ncalls=5323, regioncalls=198560, ndraw=40, logz=5.04, remainder_fraction=1.0922%, Lmin=10.13, Lmax=10.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3870, ncalls=5366, regioncalls=200360, ndraw=40, logz=5.04, remainder_fraction=1.0138%, Lmin=10.13, Lmax=10.14 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=1e+01 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5372 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 5.019 +- 0.08874 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1633.3, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.13 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.09, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.10 bs:0.09 tail:0.01 total:0.09 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_run_resume[2.0] | 7.70 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.32) * Expected Volume: exp(0.00) Quality: ok a: +0.0000|********************************************************| +1.0000 Z=-inf(0.00%) | Like=-1245.39..3.65 [-1245.3896..-308.0711] | it/evals=0/528 eff=0.0000% N=400 Z=-990.5(0.00%) | Like=-981.54..3.68 [-1245.3896..-308.0711] | it/evals=50/528 eff=39.0625% N=400 Mono-modal Volume: ~exp(-4.49) * Expected Volume: exp(-0.23) Quality: ok a: +0.00| ************************************************ | +1.00 Z=-861.8(0.00%) | Like=-854.74..3.68 [-1245.3896..-308.0711] | it/evals=90/528 eff=70.3125% N=400 Z=-823.2(0.00%) | Like=-812.27..3.68 [-1245.3896..-308.0711] | it/evals=100/528 eff=78.1250% N=400 Z=-630.1(0.00%) | Like=-620.09..3.68 [-1245.3896..-308.0711] | it/evals=150/633 eff=64.3777% N=400 Mono-modal Volume: ~exp(-4.58) * Expected Volume: exp(-0.45) Quality: ok a: +0.0| ************************************** | +1.0 Z=-552.1(0.00%) | Like=-543.77..3.69 [-1245.3896..-308.0711] | it/evals=180/633 eff=77.2532% N=400 Z=-513.4(0.00%) | Like=-505.92..3.69 [-1245.3896..-308.0711] | it/evals=200/633 eff=85.8369% N=400 Z=-393.4(0.00%) | Like=-383.84..3.69 [-1245.3896..-308.0711] | it/evals=250/728 eff=76.2195% N=400 Mono-modal Volume: ~exp(-4.58) Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.2 ****************************** +0.8 | +1.0 Z=-308.5(0.00%) | Like=-296.59..3.69 [-307.5524..-75.4168] | it/evals=300/792 eff=76.5306% N=400 Z=-241.3(0.00%) | Like=-233.94..3.69 [-307.5524..-75.4168] | it/evals=350/853 eff=77.2627% N=400 Mono-modal Volume: ~exp(-5.21) * Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-231.2(0.00%) | Like=-222.15..3.69 [-307.5524..-75.4168] | it/evals=360/853 eff=79.4702% N=400 Z=-195.0(0.00%) | Like=-187.11..3.69 [-307.5524..-75.4168] | it/evals=400/903 eff=79.5229% N=400 Mono-modal Volume: ~exp(-5.32) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ******************** +0.7 | +1.0 Z=-148.8(0.00%) | Like=-141.92..3.69 [-307.5524..-75.4168] | it/evals=450/951 eff=81.6697% N=400 Z=-119.5(0.00%) | Like=-113.36..3.69 [-307.5524..-75.4168] | it/evals=500/986 eff=85.3242% N=400 Mono-modal Volume: ~exp(-5.33) * Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 Z=-98.3(0.00%) | Like=-90.77..3.69 [-307.5524..-75.4168] | it/evals=540/1035 eff=85.0394% N=400 Z=-91.6(0.00%) | Like=-85.36..3.69 [-307.5524..-75.4168] | it/evals=550/1035 eff=86.6142% N=400 Z=-70.1(0.00%) | Like=-63.21..3.69 [-75.3486..-15.0754] | it/evals=600/1076 eff=88.7574% N=400 Mono-modal Volume: ~exp(-5.34) * Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 Z=-60.2(0.00%) | Like=-53.09..3.69 [-75.3486..-15.0754] | it/evals=630/1109 eff=88.8575% N=400 Z=-53.3(0.00%) | Like=-47.34..3.69 [-75.3486..-15.0754] | it/evals=650/1144 eff=87.3656% N=400 Z=-42.1(0.00%) | Like=-35.02..3.69 [-75.3486..-15.0754] | it/evals=700/1194 eff=88.1612% N=400 Mono-modal Volume: ~exp(-5.92) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 Z=-37.6(0.00%) | Like=-31.23..3.69 [-75.3486..-15.0754] | it/evals=720/1221 eff=87.6979% N=400 Z=-32.8(0.00%) | Like=-26.86..3.69 [-75.3486..-15.0754] | it/evals=750/1245 eff=88.7574% N=400 Z=-26.3(0.00%) | Like=-20.36..3.69 [-75.3486..-15.0754] | it/evals=800/1305 eff=88.3978% N=400 Mono-modal Volume: ~exp(-6.05) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-25.2(0.00%) | Like=-19.53..3.69 [-75.3486..-15.0754] | it/evals=810/1305 eff=89.5028% N=400 Z=-21.1(0.00%) | Like=-15.13..3.69 [-75.3486..-15.0754] | it/evals=850/1356 eff=88.9121% N=400 Mono-modal Volume: ~exp(-6.36) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-16.7(0.00%) | Like=-11.04..3.69 [-14.9390..-1.0650] | it/evals=900/1405 eff=89.5522% N=400 Z=-13.4(0.00%) | Like=-8.20..3.69 [-14.9390..-1.0650] | it/evals=950/1451 eff=90.3901% N=400 Mono-modal Volume: ~exp(-6.63) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-11.5(0.00%) | Like=-5.86..3.69 [-14.9390..-1.0650] | it/evals=990/1493 eff=90.5764% N=400 Z=-10.9(0.00%) | Like=-5.22..3.69 [-14.9390..-1.0650] | it/evals=1000/1513 eff=89.8473% N=400 Z=-8.3(0.03%) | Like=-2.98..3.69 [-14.9390..-1.0650] | it/evals=1050/1562 eff=90.3614% N=400 Mono-modal Volume: ~exp(-7.01) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-7.3(0.07%) | Like=-2.03..3.69 [-14.9390..-1.0650] | it/evals=1080/1597 eff=90.2256% N=400 Z=-6.7(0.12%) | Like=-1.69..3.69 [-14.9390..-1.0650] | it/evals=1100/1613 eff=90.6843% N=400 Z=-5.5(0.40%) | Like=-0.52..3.69 [-1.0420..1.4103] | it/evals=1150/1659 eff=91.3423% N=400 Mono-modal Volume: ~exp(-7.01) Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-4.5(1.08%) | Like=0.49..3.69 [-1.0420..1.4103] | it/evals=1200/1716 eff=91.1854% N=400 Z=-3.7(2.59%) | Like=1.31..3.69 [-1.0420..1.4103] | it/evals=1250/1767 eff=91.4411% N=400 Mono-modal Volume: ~exp(-7.48) * Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.5(3.02%) | Like=1.42..3.69 [1.4124..1.7038] | it/evals=1260/1774 eff=91.7031% N=400 Z=-3.0(5.15%) | Like=1.82..3.69 [1.8072..1.8177] | it/evals=1300/1821 eff=91.4849% N=400 Mono-modal Volume: ~exp(-7.48) Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.5(8.76%) | Like=2.20..3.69 [2.1997..2.2038]*| it/evals=1350/1868 eff=91.9619% N=400 Z=-2.0(13.34%) | Like=2.53..3.69 [2.5278..2.5282]*| it/evals=1400/1917 eff=92.2874% N=400 Mono-modal Volume: ~exp(-7.64) * Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.8(17.25%) | Like=2.72..3.69 [2.7240..2.7290]*| it/evals=1440/1963 eff=92.1305% N=400 Z=-1.7(18.31%) | Like=2.78..3.69 [2.7629..2.7785] | it/evals=1450/1973 eff=92.1805% N=400 Z=-1.4(23.98%) | Like=2.99..3.69 [2.9857..2.9912]*| it/evals=1500/2026 eff=92.2509% N=400 Mono-modal Volume: ~exp(-7.84) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.3(27.63%) | Like=3.09..3.69 [3.0871..3.0878]*| it/evals=1530/2055 eff=92.4471% N=400 Z=-1.2(30.05%) | Like=3.13..3.69 [3.1343..3.1384]*| it/evals=1550/2073 eff=92.6479% N=400 Z=-1.0(36.04%) | Like=3.23..3.69 [3.2338..3.2374]*| it/evals=1600/2231 eff=87.3839% N=400 Mono-modal Volume: ~exp(-8.21) * Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.0(38.38%) | Like=3.27..3.69 [3.2718..3.2781]*| it/evals=1620/2231 eff=88.4762% N=400 Z=-0.9(41.87%) | Like=3.33..3.69 [3.3257..3.3279]*| it/evals=1650/2231 eff=90.1147% N=400 Z=-0.8(47.43%) | Like=3.40..3.69 [3.4041..3.4052]*| it/evals=1700/2246 eff=92.0910% N=400 Mono-modal Volume: ~exp(-8.53) * Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.7(48.51%) | Like=3.42..3.69 [3.4158..3.4202]*| it/evals=1710/2257 eff=92.0840% N=400 Z=-0.7(52.66%) | Like=3.46..3.69 [3.4553..3.4578]*| it/evals=1750/2403 eff=87.3689% N=400 Mono-modal Volume: ~exp(-8.53) Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(57.51%) | Like=3.51..3.69 [3.5118..3.5138]*| it/evals=1800/2403 eff=89.8652% N=400 Z=-0.5(62.04%) | Like=3.55..3.69 [3.5533..3.5544]*| it/evals=1850/2505 eff=87.8860% N=400 Mono-modal Volume: ~exp(-8.59) * Expected Volume: exp(-4.73) Quality: ok a: +0.000| +0.495 ** +0.505 | +1.000 Z=-0.4(65.38%) | Like=3.57..3.69 [3.5735..3.5736]*| it/evals=1890/2505 eff=89.7862% N=400 Z=-0.4(66.17%) | Like=3.58..3.69 [3.5809..3.5814]*| it/evals=1900/2505 eff=90.2613% N=400 Z=-0.4(69.91%) | Like=3.61..3.69 [3.6080..3.6080]*| it/evals=1950/2537 eff=91.2494% N=400 Mono-modal Volume: ~exp(-8.91) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.3(71.98%) | Like=3.62..3.69 [3.6159..3.6159]*| it/evals=1980/2569 eff=91.2863% N=400 Z=-0.3(73.29%) | Like=3.63..3.69 [3.6253..3.6255]*| it/evals=2000/2705 eff=86.7679% N=400 Z=-0.3(76.32%) | Like=3.64..3.69 [3.6374..3.6374]*| it/evals=2050/2705 eff=88.9371% N=400 Mono-modal Volume: ~exp(-9.26) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.3(77.44%) | Like=3.64..3.69 [3.6435..3.6437]*| it/evals=2070/2705 eff=89.8048% N=400 Z=-0.2(79.03%) | Like=3.65..3.69 [3.6499..3.6499]*| it/evals=2100/2722 eff=90.4393% N=400 Z=-0.2(81.44%) | Like=3.66..3.69 [3.6581..3.6582]*| it/evals=2150/2758 eff=91.1790% N=400 Mono-modal Volume: ~exp(-9.26) * Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.2(81.89%) | Like=3.66..3.69 [3.6591..3.6591]*| it/evals=2160/2776 eff=90.9091% N=400 Z=-0.2(83.59%) | Like=3.66..3.69 [3.6638..3.6640]*| it/evals=2200/2904 eff=87.8594% N=400 Mono-modal Volume: ~exp(-9.67) * Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.2(85.49%) | Like=3.67..3.69 [3.6689..3.6690]*| it/evals=2250/2904 eff=89.8562% N=400 Z=-0.2(87.18%) | Like=3.67..3.69 [3.6731..3.6731]*| it/evals=2300/2926 eff=91.0530% N=400 Mono-modal Volume: ~exp(-10.05) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(88.39%) | Like=3.68..3.69 [3.6754..3.6754]*| it/evals=2340/2970 eff=91.0506% N=400 Z=-0.1(88.68%) | Like=3.68..3.69 [3.6759..3.6759]*| it/evals=2350/3090 eff=87.3606% N=400 Z=-0.1(90.00%) | Like=3.68..3.69 [3.6785..3.6786]*| it/evals=2400/3090 eff=89.2193% N=400 Mono-modal Volume: ~exp(-10.21) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(90.72%) | Like=3.68..3.69 [3.6795..3.6795]*| it/evals=2430/3090 eff=90.3346% N=400 Z=-0.1(91.17%) | Like=3.68..3.69 [3.6801..3.6802]*| it/evals=2450/3217 eff=86.9720% N=400 Z=-0.1(92.20%) | Like=3.68..3.69 [3.6816..3.6817]*| it/evals=2500/3217 eff=88.7469% N=400 Mono-modal Volume: ~exp(-10.21) Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(93.12%) | Like=3.68..3.69 [3.6826..3.6827]*| it/evals=2550/3217 eff=90.5218% N=400 Z=-0.1(93.92%) | Like=3.68..3.69 [3.6835..3.6835]*| it/evals=2600/3312 eff=89.2857% N=400 Mono-modal Volume: ~exp(-10.64) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(94.07%) | Like=3.68..3.69 [3.6837..3.6837]*| it/evals=2610/3312 eff=89.6291% N=400 Z=-0.1(94.64%) | Like=3.68..3.69 [3.6840..3.6840]*| it/evals=2650/3330 eff=90.4437% N=400 Mono-modal Volume: ~exp(-10.71) * Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(95.27%) | Like=3.68..3.69 [3.6845..3.6845]*| it/evals=2700/3384 eff=90.4826% N=400 Z=-0.1(95.82%) | Like=3.68..3.69 [3.6848..3.6848]*| it/evals=2750/3512 eff=88.3676% N=400 Mono-modal Volume: ~exp(-10.71) Expected Volume: exp(-6.98) Quality: ok a: +0.0000| +0.4995 ** +0.5005 | +1.0000 Z=-0.1(96.31%) | Like=3.69..3.69 [3.6851..3.6851]*| it/evals=2800/3512 eff=89.9743% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 3621 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = -0.0125 +- 0.06231 [ultranest] Effective samples strategy satisfied (ESS = 1260.2, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.07, need <2.0) [ultranest] logZ error budget: single: 0.09 bs:0.06 tail:0.03 total:0.07 required:<2.00 [ultranest] done iterating. logZ = -0.014 +- 0.110 single instance: logZ = -0.014 +- 0.090 bootstrapped : logZ = -0.013 +- 0.104 tail : logZ = +- 0.034 insert order U test : converged: True correlation: inf iterations a : 0.460 │ ▁ ▁▁▁▁▁▁▂▃▃▃▅▆▅▇▇▇▇▇▆▆▄▃▂▂▁▁▁▁▁▁▁ ▁ │0.547 0.500 +- 0.010 [ultranest] Resuming from 3531 stored points Mono-modal Volume: ~exp(-4.29) * Expected Volume: exp(0.00) Quality: ok a: +0.0000|********************************************************| +1.0000 Z=-inf(0.00%) | Like=-1245.39..3.65 [-1245.3896..-308.0711] | it/evals=0/3621 eff=inf% N=400 Z=-990.5(0.00%) | Like=-981.54..3.68 [-1245.3896..-308.0711] | it/evals=50/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-4.34) * Expected Volume: exp(-0.23) Quality: ok a: +0.00| ************************************************ | +1.00 Z=-861.8(0.00%) | Like=-854.74..3.68 [-1245.3896..-308.0711] | it/evals=90/3621 eff=inf% N=400 Z=-823.2(0.00%) | Like=-812.27..3.68 [-1245.3896..-308.0711] | it/evals=100/3621 eff=inf% N=400 Z=-630.1(0.00%) | Like=-620.09..3.68 [-1245.3896..-308.0711] | it/evals=150/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-4.34) Expected Volume: exp(-0.45) Quality: ok a: +0.0| ************************************** | +1.0 Z=-513.4(0.00%) | Like=-505.92..3.69 [-1245.3896..-308.0711] | it/evals=200/3621 eff=inf% N=400 Z=-393.4(0.00%) | Like=-383.84..3.69 [-1245.3896..-308.0711] | it/evals=250/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-4.93) * Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.2 ****************************** +0.8 | +1.0 Z=-354.3(0.00%) | Like=-347.23..3.69 [-1245.3896..-308.0711] | it/evals=270/3621 eff=inf% N=400 Z=-308.5(0.00%) | Like=-296.59..3.69 [-307.5524..-75.4168] | it/evals=300/3621 eff=inf% N=400 Z=-241.3(0.00%) | Like=-233.94..3.69 [-307.5524..-75.4168] | it/evals=350/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-4.93) Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-195.0(0.00%) | Like=-187.11..3.69 [-307.5524..-75.4168] | it/evals=400/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-5.32) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ******************** +0.7 | +1.0 Z=-148.8(0.00%) | Like=-141.92..3.69 [-307.5524..-75.4168] | it/evals=450/3621 eff=inf% N=400 Z=-119.5(0.00%) | Like=-113.36..3.69 [-307.5524..-75.4168] | it/evals=500/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-5.40) * Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 Z=-98.3(0.00%) | Like=-90.77..3.69 [-307.5524..-75.4168] | it/evals=540/3621 eff=inf% N=400 Z=-91.6(0.00%) | Like=-85.36..3.69 [-307.5524..-75.4168] | it/evals=550/3621 eff=inf% N=400 Z=-70.1(0.00%) | Like=-63.21..3.69 [-75.3486..-15.0754] | it/evals=600/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-5.40) Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 Z=-53.3(0.00%) | Like=-47.34..3.69 [-75.3486..-15.0754] | it/evals=650/3621 eff=inf% N=400 Z=-42.1(0.00%) | Like=-35.02..3.69 [-75.3486..-15.0754] | it/evals=700/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-6.18) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 Z=-37.6(0.00%) | Like=-31.23..3.69 [-75.3486..-15.0754] | it/evals=720/3621 eff=inf% N=400 Z=-32.8(0.00%) | Like=-26.86..3.69 [-75.3486..-15.0754] | it/evals=750/3621 eff=inf% N=400 Z=-26.3(0.00%) | Like=-20.36..3.69 [-75.3486..-15.0754] | it/evals=800/3621 eff=inf% N=400 Have 2 modes Volume: ~exp(-6.24) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 11111222 +0.6 | +1.0 Z=-25.2(0.00%) | Like=-19.53..3.69 [-75.3486..-15.0754] | it/evals=810/3621 eff=inf% N=400 Z=-21.1(0.00%) | Like=-15.13..3.69 [-75.3486..-15.0754] | it/evals=850/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-6.39) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-16.7(0.00%) | Like=-11.04..3.69 [-14.9390..-1.0650] | it/evals=900/3621 eff=inf% N=400 Z=-13.4(0.00%) | Like=-8.20..3.69 [-14.9390..-1.0650] | it/evals=950/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-6.52) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-11.5(0.00%) | Like=-5.86..3.69 [-14.9390..-1.0650] | it/evals=990/3621 eff=inf% N=400 Z=-10.9(0.00%) | Like=-5.22..3.69 [-14.9390..-1.0650] | it/evals=1000/3621 eff=inf% N=400 Z=-8.3(0.03%) | Like=-2.98..3.69 [-14.9390..-1.0650] | it/evals=1050/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-6.96) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-7.3(0.07%) | Like=-2.03..3.69 [-14.9390..-1.0650] | it/evals=1080/3621 eff=inf% N=400 Z=-6.7(0.12%) | Like=-1.69..3.69 [-14.9390..-1.0650] | it/evals=1100/3621 eff=inf% N=400 Z=-5.5(0.40%) | Like=-0.52..3.69 [-1.0420..1.4103] | it/evals=1150/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-6.97) * Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-5.1(0.60%) | Like=-0.15..3.69 [-1.0420..1.4103] | it/evals=1170/3621 eff=inf% N=400 Z=-4.5(1.08%) | Like=0.49..3.69 [-1.0420..1.4103] | it/evals=1200/3621 eff=inf% N=400 Z=-3.7(2.59%) | Like=1.31..3.69 [-1.0420..1.4103] | it/evals=1250/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-7.41) * Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.5(3.02%) | Like=1.42..3.69 [1.4124..1.7038] | it/evals=1260/3621 eff=inf% N=400 Z=-3.0(5.15%) | Like=1.82..3.69 [1.8072..1.8177] | it/evals=1300/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-7.73) * Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.5(8.76%) | Like=2.20..3.69 [2.1997..2.2038]*| it/evals=1350/3621 eff=inf% N=400 Z=-2.0(13.34%) | Like=2.53..3.69 [2.5278..2.5282]*| it/evals=1400/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-7.73) Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.7(18.31%) | Like=2.78..3.69 [2.7629..2.7785] | it/evals=1450/3621 eff=inf% N=400 Z=-1.4(23.98%) | Like=2.99..3.69 [2.9857..2.9912]*| it/evals=1500/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.24) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.3(27.63%) | Like=3.09..3.69 [3.0871..3.0878]*| it/evals=1530/3621 eff=inf% N=400 Z=-1.2(30.05%) | Like=3.13..3.69 [3.1343..3.1384]*| it/evals=1550/3621 eff=inf% N=400 Z=-1.0(36.04%) | Like=3.23..3.69 [3.2338..3.2374]*| it/evals=1600/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.24) Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.9(41.87%) | Like=3.33..3.69 [3.3257..3.3279]*| it/evals=1650/3621 eff=inf% N=400 Z=-0.8(47.43%) | Like=3.40..3.69 [3.4041..3.4052]*| it/evals=1700/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.29) * Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.7(48.51%) | Like=3.42..3.69 [3.4158..3.4202]*| it/evals=1710/3621 eff=inf% N=400 Z=-0.7(52.66%) | Like=3.46..3.69 [3.4553..3.4578]*| it/evals=1750/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.55) * Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(57.51%) | Like=3.51..3.69 [3.5118..3.5138]*| it/evals=1800/3621 eff=inf% N=400 Z=-0.5(62.04%) | Like=3.55..3.69 [3.5533..3.5544]*| it/evals=1850/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.55) Expected Volume: exp(-4.73) Quality: ok a: +0.000| +0.495 ** +0.505 | +1.000 Z=-0.4(66.17%) | Like=3.58..3.69 [3.5809..3.5814]*| it/evals=1900/3621 eff=inf% N=400 Z=-0.4(69.91%) | Like=3.61..3.69 [3.6080..3.6080]*| it/evals=1950/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-8.94) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.3(71.98%) | Like=3.62..3.69 [3.6159..3.6159]*| it/evals=1980/3621 eff=inf% N=400 Z=-0.3(73.29%) | Like=3.63..3.69 [3.6253..3.6255]*| it/evals=2000/3621 eff=inf% N=400 Z=-0.3(76.32%) | Like=3.64..3.69 [3.6374..3.6374]*| it/evals=2050/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-9.16) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.3(77.44%) | Like=3.64..3.69 [3.6435..3.6437]*| it/evals=2070/3621 eff=inf% N=400 Z=-0.2(79.03%) | Like=3.65..3.69 [3.6499..3.6499]*| it/evals=2100/3621 eff=inf% N=400 Z=-0.2(81.44%) | Like=3.66..3.69 [3.6581..3.6582]*| it/evals=2150/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-9.48) * Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.2(81.89%) | Like=3.66..3.69 [3.6591..3.6591]*| it/evals=2160/3621 eff=inf% N=400 Z=-0.2(83.59%) | Like=3.66..3.69 [3.6638..3.6640]*| it/evals=2200/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-9.67) * Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.2(85.49%) | Like=3.67..3.69 [3.6689..3.6690]*| it/evals=2250/3621 eff=inf% N=400 Z=-0.2(87.18%) | Like=3.67..3.69 [3.6731..3.6731]*| it/evals=2300/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-9.90) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(88.39%) | Like=3.68..3.69 [3.6754..3.6754]*| it/evals=2340/3621 eff=inf% N=400 Z=-0.1(88.68%) | Like=3.68..3.69 [3.6759..3.6759]*| it/evals=2350/3621 eff=inf% N=400 Z=-0.1(90.00%) | Like=3.68..3.69 [3.6785..3.6786]*| it/evals=2400/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-10.13) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(90.72%) | Like=3.68..3.69 [3.6795..3.6795]*| it/evals=2430/3621 eff=inf% N=400 Z=-0.1(91.17%) | Like=3.68..3.69 [3.6801..3.6802]*| it/evals=2450/3621 eff=inf% N=400 Z=-0.1(92.20%) | Like=3.68..3.69 [3.6816..3.6817]*| it/evals=2500/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-10.15) * Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(92.58%) | Like=3.68..3.69 [3.6821..3.6821]*| it/evals=2520/3621 eff=inf% N=400 Z=-0.1(93.12%) | Like=3.68..3.69 [3.6826..3.6827]*| it/evals=2550/3621 eff=inf% N=400 Z=-0.1(93.92%) | Like=3.68..3.69 [3.6835..3.6835]*| it/evals=2600/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-10.65) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(94.07%) | Like=3.68..3.69 [3.6837..3.6837]*| it/evals=2610/3621 eff=inf% N=400 Z=-0.1(94.64%) | Like=3.68..3.69 [3.6840..3.6840]*| it/evals=2650/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-10.65) Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(95.27%) | Like=3.68..3.69 [3.6845..3.6845]*| it/evals=2700/3621 eff=inf% N=400 Z=-0.1(95.82%) | Like=3.68..3.69 [3.6848..3.6848]*| it/evals=2750/3621 eff=inf% N=400 Mono-modal Volume: ~exp(-11.12) * Expected Volume: exp(-6.98) Quality: ok a: +0.0000| +0.4995 ** +0.5005 | +1.0000 Z=-0.1(96.22%) | Like=3.69..3.69 [3.6850..3.6850]*| it/evals=2790/3621 eff=inf% N=400 Z=-0.1(96.31%) | Like=3.69..3.69 [3.6851..3.6851]*| it/evals=2800/3621 eff=inf% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 3621 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = -0.02627 +- 0.05466 [ultranest] Effective samples strategy satisfied (ESS = 1260.2, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.11 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <2.0) [ultranest] logZ error budget: single: 0.09 bs:0.05 tail:0.03 total:0.06 required:<2.00 [ultranest] done iterating. logZ = -0.014 +- 0.090 single instance: logZ = -0.014 +- 0.090 bootstrapped : logZ = -0.026 +- 0.084 tail : logZ = +- 0.034 insert order U test : converged: True correlation: inf iterations a : 0.463 │ ▁▁▁▁▁▁▁▂▂▃▂▃▄▅▅▆▇▇▆▆▆▆▆▅▃▃▂▂▁▁▁▁▁▁ ▁▁ │0.540 0.500 +- 0.010 ran with dlogz: 2.0 first run gave: {'niter': 3220, 'logz': -0.013974183723283412, 'logzerr': 0.10971812979089553, 'logz_bs': -0.012503866119405762, 'logz_single': -0.013974183723283412, 'logzerr_tail': 0.034478189717984345, 'logzerr_bs': 0.10416008083033766, 'ess': 1260.1962170536074, 'H': 3.2089858433621017, 'Herr': 0.054320577938106775, 'posterior': {'mean': [0.50045629571788], 'stdev': [0.010089509001508892], 'median': [0.5005430939745008], 'errlo': [0.4901097186137824], 'errup': [0.5105344881403425], 'information_gain_bits': [3.4862496598894177]}, 'maximum_likelihood': {'logl': 3.6862314817507196, 'point': [0.5000058486357236], 'point_untransformed': [0.5000058486357236]}, 'ncall': 3621, 'paramnames': ['a'], 'logzerr_single': 0.08956821204202557, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} second run gave: {'niter': 3220, 'logz': -0.013974183723283412, 'logzerr': 0.0903799134925259, 'logz_bs': -0.0262673710346743, 'logz_single': -0.013974183723283412, 'logzerr_tail': 0.034478189717984345, 'logzerr_bs': 0.0835450967842347, 'ess': 1260.1962170536074, 'H': 3.2089858433621017, 'Herr': 0.050998096489361884, 'posterior': {'mean': [0.500204894544299], 'stdev': [0.010073574516258744], 'median': [0.5001395437994671], 'errlo': [0.4899393034588605], 'errup': [0.5105068093450308], 'information_gain_bits': [3.4862496598894177]}, 'maximum_likelihood': {'logl': 3.6862314817507196, 'point': [0.5000058486357236], 'point_untransformed': [0.5000058486357236]}, 'ncall': 3621, 'paramnames': ['a'], 'logzerr_single': 0.08956821204202557, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmpp_k136rn, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=2.0, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=528, regioncalls=128, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1245.39, Lmax=3.65 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=528, regioncalls=128, ndraw=128, logz=-990.55, remainder_fraction=100.0000%, Lmin=-981.54, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=528, regioncalls=128, ndraw=128, logz=-861.82, remainder_fraction=100.0000%, Lmin=-854.74, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=528, regioncalls=128, ndraw=128, logz=-823.22, remainder_fraction=100.0000%, Lmin=-812.27, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=633, regioncalls=256, ndraw=128, logz=-630.08, remainder_fraction=100.0000%, Lmin=-620.09, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=633, regioncalls=256, ndraw=128, logz=-552.14, remainder_fraction=100.0000%, Lmin=-543.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=633, regioncalls=256, ndraw=128, logz=-513.45, remainder_fraction=100.0000%, Lmin=-505.92, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=728, regioncalls=384, ndraw=128, logz=-393.37, remainder_fraction=100.0000%, Lmin=-383.84, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=792, regioncalls=512, ndraw=128, logz=-308.52, remainder_fraction=100.0000%, Lmin=-296.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=853, regioncalls=640, ndraw=128, logz=-241.25, remainder_fraction=100.0000%, Lmin=-233.94, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=853, regioncalls=640, ndraw=128, logz=-231.21, remainder_fraction=100.0000%, Lmin=-222.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=903, regioncalls=768, ndraw=128, logz=-195.03, remainder_fraction=100.0000%, Lmin=-187.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=951, regioncalls=896, ndraw=128, logz=-148.82, remainder_fraction=100.0000%, Lmin=-141.92, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=986, regioncalls=1024, ndraw=128, logz=-119.52, remainder_fraction=100.0000%, Lmin=-113.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1035, regioncalls=1152, ndraw=128, logz=-98.29, remainder_fraction=100.0000%, Lmin=-90.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=1035, regioncalls=1152, ndraw=128, logz=-91.55, remainder_fraction=100.0000%, Lmin=-85.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1076, regioncalls=1280, ndraw=128, logz=-70.08, remainder_fraction=100.0000%, Lmin=-63.21, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1109, regioncalls=1408, ndraw=128, logz=-60.19, remainder_fraction=100.0000%, Lmin=-53.09, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=1144, regioncalls=1536, ndraw=128, logz=-53.27, remainder_fraction=100.0000%, Lmin=-47.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=1194, regioncalls=1792, ndraw=128, logz=-42.12, remainder_fraction=100.0000%, Lmin=-35.02, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1221, regioncalls=1920, ndraw=128, logz=-37.57, remainder_fraction=100.0000%, Lmin=-31.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1245, regioncalls=2048, ndraw=128, logz=-32.76, remainder_fraction=100.0000%, Lmin=-26.86, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1305, regioncalls=2432, ndraw=128, logz=-26.30, remainder_fraction=100.0000%, Lmin=-20.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1305, regioncalls=2432, ndraw=128, logz=-25.21, remainder_fraction=100.0000%, Lmin=-19.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1356, regioncalls=2816, ndraw=128, logz=-21.13, remainder_fraction=100.0000%, Lmin=-15.13, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1405, regioncalls=3200, ndraw=128, logz=-16.70, remainder_fraction=100.0000%, Lmin=-11.04, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1451, regioncalls=3584, ndraw=128, logz=-13.44, remainder_fraction=99.9998%, Lmin=-8.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1493, regioncalls=3968, ndraw=128, logz=-11.50, remainder_fraction=99.9989%, Lmin=-5.86, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1513, regioncalls=4096, ndraw=128, logz=-10.90, remainder_fraction=99.9981%, Lmin=-5.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1562, regioncalls=4608, ndraw=128, logz=-8.32, remainder_fraction=99.9749%, Lmin=-2.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1597, regioncalls=5120, ndraw=128, logz=-7.28, remainder_fraction=99.9295%, Lmin=-2.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1613, regioncalls=5376, ndraw=128, logz=-6.74, remainder_fraction=99.8799%, Lmin=-1.69, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1659, regioncalls=6272, ndraw=128, logz=-5.54, remainder_fraction=99.6023%, Lmin=-0.52, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1716, regioncalls=7040, ndraw=128, logz=-4.54, remainder_fraction=98.9163%, Lmin=0.49, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=1767, regioncalls=8064, ndraw=128, logz=-3.67, remainder_fraction=97.4064%, Lmin=1.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1774, regioncalls=8320, ndraw=128, logz=-3.52, remainder_fraction=96.9803%, Lmin=1.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=1821, regioncalls=9344, ndraw=128, logz=-2.98, remainder_fraction=94.8457%, Lmin=1.82, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=1868, regioncalls=10752, ndraw=128, logz=-2.46, remainder_fraction=91.2395%, Lmin=2.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=1917, regioncalls=12416, ndraw=128, logz=-2.04, remainder_fraction=86.6574%, Lmin=2.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=1963, regioncalls=14208, ndraw=128, logz=-1.77, remainder_fraction=82.7497%, Lmin=2.72, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=1973, regioncalls=14464, ndraw=128, logz=-1.71, remainder_fraction=81.6948%, Lmin=2.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=2026, regioncalls=16640, ndraw=128, logz=-1.44, remainder_fraction=76.0250%, Lmin=2.99, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2055, regioncalls=17792, ndraw=128, logz=-1.30, remainder_fraction=72.3720%, Lmin=3.09, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=2073, regioncalls=18688, ndraw=128, logz=-1.22, remainder_fraction=69.9531%, Lmin=3.13, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2231, regioncalls=20480, ndraw=128, logz=-1.04, remainder_fraction=63.9580%, Lmin=3.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2231, regioncalls=20480, ndraw=128, logz=-0.97, remainder_fraction=61.6166%, Lmin=3.27, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=2231, regioncalls=20480, ndraw=128, logz=-0.89, remainder_fraction=58.1268%, Lmin=3.33, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=2246, regioncalls=21120, ndraw=128, logz=-0.76, remainder_fraction=52.5721%, Lmin=3.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2257, regioncalls=21248, ndraw=128, logz=-0.74, remainder_fraction=51.4935%, Lmin=3.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=2403, regioncalls=23040, ndraw=128, logz=-0.66, remainder_fraction=47.3443%, Lmin=3.46, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2403, regioncalls=23040, ndraw=128, logz=-0.57, remainder_fraction=42.4866%, Lmin=3.51, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=2505, regioncalls=23168, ndraw=128, logz=-0.49, remainder_fraction=37.9595%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=2505, regioncalls=23168, ndraw=128, logz=-0.44, remainder_fraction=34.6221%, Lmin=3.57, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=2505, regioncalls=23168, ndraw=128, logz=-0.43, remainder_fraction=33.8290%, Lmin=3.58, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=2537, regioncalls=24448, ndraw=128, logz=-0.37, remainder_fraction=30.0871%, Lmin=3.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=2569, regioncalls=25088, ndraw=128, logz=-0.34, remainder_fraction=28.0168%, Lmin=3.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=2705, regioncalls=26240, ndraw=128, logz=-0.32, remainder_fraction=26.7118%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=2705, regioncalls=26240, ndraw=128, logz=-0.28, remainder_fraction=23.6800%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=2705, regioncalls=26240, ndraw=128, logz=-0.27, remainder_fraction=22.5589%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=2722, regioncalls=26496, ndraw=128, logz=-0.25, remainder_fraction=20.9745%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=2758, regioncalls=27136, ndraw=128, logz=-0.22, remainder_fraction=18.5586%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=2776, regioncalls=27392, ndraw=128, logz=-0.21, remainder_fraction=18.1081%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=2904, regioncalls=27648, ndraw=128, logz=-0.19, remainder_fraction=16.4125%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=2904, regioncalls=27648, ndraw=128, logz=-0.17, remainder_fraction=14.5086%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=2926, regioncalls=28032, ndraw=128, logz=-0.15, remainder_fraction=12.8198%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=2970, regioncalls=28800, ndraw=128, logz=-0.14, remainder_fraction=11.6097%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=3090, regioncalls=29056, ndraw=128, logz=-0.13, remainder_fraction=11.3250%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3090, regioncalls=29056, ndraw=128, logz=-0.12, remainder_fraction=10.0021%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3090, regioncalls=29056, ndraw=128, logz=-0.11, remainder_fraction=9.2822%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3217, regioncalls=29312, ndraw=128, logz=-0.11, remainder_fraction=8.8316%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3217, regioncalls=29312, ndraw=128, logz=-0.10, remainder_fraction=7.7971%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3217, regioncalls=29312, ndraw=128, logz=-0.09, remainder_fraction=6.8836%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3312, regioncalls=29440, ndraw=128, logz=-0.08, remainder_fraction=6.0763%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3312, regioncalls=29440, ndraw=128, logz=-0.08, remainder_fraction=5.9265%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3330, regioncalls=29824, ndraw=128, logz=-0.07, remainder_fraction=5.3633%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3384, regioncalls=30592, ndraw=128, logz=-0.06, remainder_fraction=4.7339%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=3512, regioncalls=30848, ndraw=128, logz=-0.06, remainder_fraction=4.1782%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3512, regioncalls=30848, ndraw=128, logz=-0.05, remainder_fraction=3.6877%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 3621 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -0.0125 +- 0.06231 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1260.2, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.07, need <2.0) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.06 tail:0.03 total:0.07 required:<2.00 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmpp_k136rn, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [3.68518867 3.68614504 0. 0.50013161 0.50013161]: u=[0.50013161] -> p=[0.50013161] -> L=3.6861450405247025 [32mINFO [0m ultranest:integrator.py:2246 Resuming from 3531 stored points [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=2.0, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=3621, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1245.39, Lmax=3.65 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=3621, regioncalls=0, ndraw=128, logz=-990.55, remainder_fraction=100.0000%, Lmin=-981.54, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=3621, regioncalls=0, ndraw=128, logz=-861.82, remainder_fraction=100.0000%, Lmin=-854.74, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=3621, regioncalls=0, ndraw=128, logz=-823.22, remainder_fraction=100.0000%, Lmin=-812.27, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=3621, regioncalls=0, ndraw=128, logz=-630.08, remainder_fraction=100.0000%, Lmin=-620.09, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=3621, regioncalls=0, ndraw=128, logz=-513.45, remainder_fraction=100.0000%, Lmin=-505.92, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=3621, regioncalls=0, ndraw=128, logz=-393.37, remainder_fraction=100.0000%, Lmin=-383.84, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=3621, regioncalls=0, ndraw=128, logz=-354.34, remainder_fraction=100.0000%, Lmin=-347.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=3621, regioncalls=0, ndraw=128, logz=-308.52, remainder_fraction=100.0000%, Lmin=-296.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=3621, regioncalls=0, ndraw=128, logz=-241.25, remainder_fraction=100.0000%, Lmin=-233.94, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=3621, regioncalls=0, ndraw=128, logz=-195.03, remainder_fraction=100.0000%, Lmin=-187.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3621, regioncalls=0, ndraw=128, logz=-148.82, remainder_fraction=100.0000%, Lmin=-141.92, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3621, regioncalls=0, ndraw=128, logz=-119.52, remainder_fraction=100.0000%, Lmin=-113.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=3621, regioncalls=0, ndraw=128, logz=-98.29, remainder_fraction=100.0000%, Lmin=-90.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=3621, regioncalls=0, ndraw=128, logz=-91.55, remainder_fraction=100.0000%, Lmin=-85.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=3621, regioncalls=0, ndraw=128, logz=-70.08, remainder_fraction=100.0000%, Lmin=-63.21, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=3621, regioncalls=0, ndraw=128, logz=-53.27, remainder_fraction=100.0000%, Lmin=-47.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=3621, regioncalls=0, ndraw=128, logz=-42.12, remainder_fraction=100.0000%, Lmin=-35.02, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=3621, regioncalls=0, ndraw=128, logz=-37.57, remainder_fraction=100.0000%, Lmin=-31.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=3621, regioncalls=0, ndraw=128, logz=-32.76, remainder_fraction=100.0000%, Lmin=-26.86, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=3621, regioncalls=0, ndraw=128, logz=-26.30, remainder_fraction=100.0000%, Lmin=-20.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=3621, regioncalls=0, ndraw=128, logz=-25.21, remainder_fraction=100.0000%, Lmin=-19.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=3621, regioncalls=0, ndraw=128, logz=-21.13, remainder_fraction=100.0000%, Lmin=-15.13, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=3621, regioncalls=0, ndraw=128, logz=-16.70, remainder_fraction=100.0000%, Lmin=-11.04, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=3621, regioncalls=0, ndraw=128, logz=-13.44, remainder_fraction=99.9998%, Lmin=-8.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=3621, regioncalls=0, ndraw=128, logz=-11.50, remainder_fraction=99.9989%, Lmin=-5.86, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=3621, regioncalls=0, ndraw=128, logz=-10.90, remainder_fraction=99.9981%, Lmin=-5.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=3621, regioncalls=0, ndraw=128, logz=-8.32, remainder_fraction=99.9749%, Lmin=-2.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=3621, regioncalls=0, ndraw=128, logz=-7.28, remainder_fraction=99.9295%, Lmin=-2.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=3621, regioncalls=0, ndraw=128, logz=-6.74, remainder_fraction=99.8799%, Lmin=-1.69, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=3621, regioncalls=0, ndraw=128, logz=-5.54, remainder_fraction=99.6023%, Lmin=-0.52, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=3621, regioncalls=0, ndraw=128, logz=-5.13, remainder_fraction=99.4025%, Lmin=-0.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=3621, regioncalls=0, ndraw=128, logz=-4.54, remainder_fraction=98.9163%, Lmin=0.49, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=3621, regioncalls=0, ndraw=128, logz=-3.67, remainder_fraction=97.4064%, Lmin=1.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=3621, regioncalls=0, ndraw=128, logz=-3.52, remainder_fraction=96.9803%, Lmin=1.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=3621, regioncalls=0, ndraw=128, logz=-2.98, remainder_fraction=94.8457%, Lmin=1.82, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=3621, regioncalls=0, ndraw=128, logz=-2.46, remainder_fraction=91.2395%, Lmin=2.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=3621, regioncalls=0, ndraw=128, logz=-2.04, remainder_fraction=86.6574%, Lmin=2.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=3621, regioncalls=0, ndraw=128, logz=-1.71, remainder_fraction=81.6948%, Lmin=2.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=3621, regioncalls=0, ndraw=128, logz=-1.44, remainder_fraction=76.0250%, Lmin=2.99, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=3621, regioncalls=0, ndraw=128, logz=-1.30, remainder_fraction=72.3720%, Lmin=3.09, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=3621, regioncalls=0, ndraw=128, logz=-1.22, remainder_fraction=69.9531%, Lmin=3.13, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=3621, regioncalls=0, ndraw=128, logz=-1.04, remainder_fraction=63.9580%, Lmin=3.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.89, remainder_fraction=58.1268%, Lmin=3.33, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.76, remainder_fraction=52.5721%, Lmin=3.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.74, remainder_fraction=51.4935%, Lmin=3.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.66, remainder_fraction=47.3443%, Lmin=3.46, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.57, remainder_fraction=42.4866%, Lmin=3.51, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.49, remainder_fraction=37.9595%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.43, remainder_fraction=33.8290%, Lmin=3.58, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.37, remainder_fraction=30.0871%, Lmin=3.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.34, remainder_fraction=28.0168%, Lmin=3.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.32, remainder_fraction=26.7118%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.28, remainder_fraction=23.6800%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.27, remainder_fraction=22.5589%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.25, remainder_fraction=20.9745%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.22, remainder_fraction=18.5586%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.21, remainder_fraction=18.1081%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.19, remainder_fraction=16.4125%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.17, remainder_fraction=14.5086%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.15, remainder_fraction=12.8198%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.14, remainder_fraction=11.6097%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.13, remainder_fraction=11.3250%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.12, remainder_fraction=10.0021%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.11, remainder_fraction=9.2822%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.11, remainder_fraction=8.8316%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.10, remainder_fraction=7.7971%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.09, remainder_fraction=7.4181%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.09, remainder_fraction=6.8836%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.08, remainder_fraction=6.0763%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.08, remainder_fraction=5.9265%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.07, remainder_fraction=5.3633%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.06, remainder_fraction=4.7339%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.06, remainder_fraction=4.1782%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.05, remainder_fraction=3.7809%, Lmin=3.69, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3621, regioncalls=0, ndraw=128, logz=-0.05, remainder_fraction=3.6877%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 3621 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -0.02627 +- 0.05466 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1260.2, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.11 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.06, need <2.0) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.05 tail:0.03 total:0.06 required:<2.00 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_run_resume[0.5] | 8.45 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.31) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 Z=-inf(0.00%) | Like=-1229.24..3.69 [-1229.2436..-284.2420] | it/evals=0/528 eff=0.0000% N=400 Z=-947.8(0.00%) | Like=-936.03..3.69 [-1229.2436..-284.2420] | it/evals=50/528 eff=39.0625% N=400 Mono-modal Volume: ~exp(-4.46) * Expected Volume: exp(-0.23) Quality: ok a: +0.0| ******************************************** | +1.0 Z=-794.7(0.00%) | Like=-767.63..3.69 [-1229.2436..-284.2420] | it/evals=90/528 eff=70.3125% N=400 Z=-744.3(0.00%) | Like=-735.03..3.69 [-1229.2436..-284.2420] | it/evals=100/528 eff=78.1250% N=400 Z=-573.7(0.00%) | Like=-562.58..3.69 [-1229.2436..-284.2420] | it/evals=150/635 eff=63.8298% N=400 Mono-modal Volume: ~exp(-4.46) Expected Volume: exp(-0.45) Quality: ok a: +0.0| ************************************ +0.8 | +1.0 Z=-436.3(0.00%) | Like=-427.78..3.69 [-1229.2436..-284.2420] | it/evals=200/711 eff=64.3087% N=400 Z=-342.3(0.00%) | Like=-335.76..3.69 [-1229.2436..-284.2420] | it/evals=250/711 eff=80.3859% N=400 Mono-modal Volume: ~exp(-4.84) * Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.3 **************************** +0.7 | +1.0 Z=-312.3(0.00%) | Like=-306.34..3.69 [-1229.2436..-284.2420] | it/evals=270/780 eff=71.0526% N=400 Z=-277.8(0.00%) | Like=-269.37..3.69 [-282.2298..-67.3700] | it/evals=300/780 eff=78.9474% N=400 Z=-224.0(0.00%) | Like=-217.57..3.69 [-282.2298..-67.3700] | it/evals=350/829 eff=81.5851% N=400 Mono-modal Volume: ~exp(-4.84) Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-174.1(0.00%) | Like=-164.34..3.69 [-282.2298..-67.3700] | it/evals=400/877 eff=83.8574% N=400 Mono-modal Volume: ~exp(-5.14) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ******************** +0.7 | +1.0 Z=-139.6(0.00%) | Like=-130.59..3.69 [-282.2298..-67.3700] | it/evals=450/922 eff=86.2069% N=400 Z=-110.7(0.00%) | Like=-103.90..3.69 [-282.2298..-67.3700] | it/evals=500/964 eff=88.6525% N=400 Mono-modal Volume: ~exp(-5.61) * Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 Z=-90.3(0.00%) | Like=-83.12..3.69 [-282.2298..-67.3700] | it/evals=540/1002 eff=89.7010% N=400 Z=-84.0(0.00%) | Like=-76.16..3.69 [-282.2298..-67.3700] | it/evals=550/1043 eff=85.5365% N=400 Z=-69.0(0.00%) | Like=-63.34..3.69 [-67.2945..-16.1658] | it/evals=600/1070 eff=89.5522% N=400 Have 2 modes Volume: ~exp(-5.83) * Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 11111111111122 +0.6 | +1.0 Z=-62.3(0.00%) | Like=-56.62..3.69 [-67.2945..-16.1658] | it/evals=630/1104 eff=89.4886% N=400 Z=-57.4(0.00%) | Like=-51.00..3.69 [-67.2945..-16.1658] | it/evals=650/1131 eff=88.9193% N=400 Z=-44.8(0.00%) | Like=-38.45..3.69 [-67.2945..-16.1658] | it/evals=700/1175 eff=90.3226% N=400 Mono-modal Volume: ~exp(-6.01) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 Z=-40.3(0.00%) | Like=-34.53..3.69 [-67.2945..-16.1658] | it/evals=720/1196 eff=90.4523% N=400 Z=-36.3(0.00%) | Like=-30.32..3.69 [-67.2945..-16.1658] | it/evals=750/1236 eff=89.7129% N=400 Z=-28.4(0.00%) | Like=-22.50..3.69 [-67.2945..-16.1658] | it/evals=800/1287 eff=90.1917% N=400 Mono-modal Volume: ~exp(-6.12) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-27.1(0.00%) | Like=-21.15..3.69 [-67.2945..-16.1658] | it/evals=810/1287 eff=91.3191% N=400 Z=-23.0(0.00%) | Like=-17.36..3.69 [-67.2945..-16.1658] | it/evals=850/1332 eff=91.2017% N=400 Mono-modal Volume: ~exp(-6.50) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-18.9(0.00%) | Like=-13.36..3.69 [-16.1569..-1.6640] | it/evals=900/1385 eff=91.3706% N=400 Z=-15.5(0.00%) | Like=-10.11..3.69 [-16.1569..-1.6640] | it/evals=950/1437 eff=91.6104% N=400 Have 2 modes Volume: ~exp(-6.53) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.45 222211 +0.55 | +1.00 Z=-13.2(0.00%) | Like=-7.53..3.69 [-16.1569..-1.6640] | it/evals=990/1477 eff=91.9220% N=400 Z=-12.6(0.00%) | Like=-7.06..3.69 [-16.1569..-1.6640] | it/evals=1000/1500 eff=90.9091% N=400 Z=-10.2(0.00%) | Like=-4.66..3.69 [-16.1569..-1.6640] | it/evals=1050/1546 eff=91.6230% N=400 Have 2 modes Volume: ~exp(-6.53) Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.46 222211 +0.54 | +1.00 Z=-8.4(0.02%) | Like=-3.02..3.69 [-16.1569..-1.6640] | it/evals=1100/1596 eff=91.9732% N=400 Z=-6.8(0.13%) | Like=-1.40..3.69 [-1.6514..1.3709] | it/evals=1150/1649 eff=92.0737% N=400 Mono-modal Volume: ~exp(-6.85) * Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-6.2(0.22%) | Like=-0.98..3.69 [-1.6514..1.3709] | it/evals=1170/1672 eff=91.9811% N=400 Z=-5.6(0.43%) | Like=-0.44..3.69 [-1.6514..1.3709] | it/evals=1200/1699 eff=92.3788% N=400 Z=-4.7(1.06%) | Like=0.20..3.69 [-1.6514..1.3709] | it/evals=1250/1748 eff=92.7300% N=400 Mono-modal Volume: ~exp(-7.05) * Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-4.5(1.23%) | Like=0.50..3.69 [-1.6514..1.3709] | it/evals=1260/1757 eff=92.8519% N=400 Z=-3.9(2.32%) | Like=1.22..3.69 [-1.6514..1.3709] | it/evals=1300/1804 eff=92.5926% N=400 Mono-modal Volume: ~exp(-7.13) * Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.2(4.75%) | Like=1.77..3.69 [1.7333..1.7988] | it/evals=1350/1850 eff=93.1034% N=400 Z=-2.6(8.16%) | Like=2.17..3.69 [2.1689..2.1707]*| it/evals=1400/1904 eff=93.0851% N=400 Mono-modal Volume: ~exp(-7.62) * Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.3(11.57%) | Like=2.42..3.69 [2.4219..2.4221]*| it/evals=1440/1947 eff=93.0834% N=400 Z=-2.2(12.55%) | Like=2.47..3.69 [2.4730..2.4768]*| it/evals=1450/1958 eff=93.0680% N=400 Z=-1.9(17.45%) | Like=2.75..3.69 [2.7491..2.7498]*| it/evals=1500/2012 eff=93.0521% N=400 Mono-modal Volume: ~exp(-7.88) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.7(20.74%) | Like=2.91..3.69 [2.9003..2.9129] | it/evals=1530/2042 eff=93.1790% N=400 Z=-1.6(23.08%) | Like=2.98..3.69 [2.9833..2.9849]*| it/evals=1550/2064 eff=93.1490% N=400 Z=-1.4(29.11%) | Like=3.14..3.69 [3.1391..3.1391]*| it/evals=1600/2114 eff=93.3489% N=400 Mono-modal Volume: ~exp(-7.92) * Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.3(31.53%) | Like=3.18..3.69 [3.1790..3.1792]*| it/evals=1620/2135 eff=93.3718% N=400 Z=-1.2(35.15%) | Like=3.26..3.69 [3.2566..3.2569]*| it/evals=1650/2267 eff=88.3771% N=400 Z=-1.0(41.06%) | Like=3.34..3.69 [3.3424..3.3427]*| it/evals=1700/2267 eff=91.0552% N=400 Mono-modal Volume: ~exp(-8.13) * Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.0(42.20%) | Like=3.36..3.69 [3.3572..3.3583]*| it/evals=1710/2267 eff=91.5908% N=400 Z=-0.9(46.74%) | Like=3.42..3.69 [3.4174..3.4175]*| it/evals=1750/2278 eff=93.1842% N=400 Mono-modal Volume: ~exp(-8.31) * Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.8(52.11%) | Like=3.47..3.69 [3.4731..3.4751]*| it/evals=1800/2329 eff=93.3126% N=400 Z=-0.7(57.09%) | Like=3.52..3.69 [3.5250..3.5260]*| it/evals=1850/2457 eff=89.9368% N=400 Mono-modal Volume: ~exp(-8.55) * Expected Volume: exp(-4.73) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(60.83%) | Like=3.55..3.69 [3.5540..3.5540]*| it/evals=1890/2457 eff=91.8814% N=400 Z=-0.6(61.73%) | Like=3.56..3.69 [3.5611..3.5629]*| it/evals=1900/2457 eff=92.3675% N=400 Z=-0.6(65.92%) | Like=3.59..3.69 [3.5913..3.5926]*| it/evals=1950/2499 eff=92.9014% N=400 Mono-modal Volume: ~exp(-8.55) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.5(68.26%) | Like=3.60..3.69 [3.6037..3.6038]*| it/evals=1980/2526 eff=93.1326% N=400 Z=-0.5(69.72%) | Like=3.61..3.69 [3.6112..3.6113]*| it/evals=2000/2655 eff=88.6918% N=400 Z=-0.5(73.12%) | Like=3.63..3.69 [3.6294..3.6295]*| it/evals=2050/2655 eff=90.9091% N=400 Mono-modal Volume: ~exp(-9.02) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.4(74.39%) | Like=3.64..3.69 [3.6359..3.6360]*| it/evals=2070/2655 eff=91.7960% N=400 Z=-0.4(76.19%) | Like=3.64..3.69 [3.6438..3.6438]*| it/evals=2100/2666 eff=92.6743% N=400 Z=-0.4(78.92%) | Like=3.65..3.69 [3.6533..3.6534]*| it/evals=2150/2780 eff=90.3361% N=400 Mono-modal Volume: ~exp(-9.25) * Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.4(79.43%) | Like=3.65..3.69 [3.6544..3.6547]*| it/evals=2160/2780 eff=90.7563% N=400 Z=-0.4(81.35%) | Like=3.66..3.69 [3.6596..3.6596]*| it/evals=2200/2780 eff=92.4370% N=400 Mono-modal Volume: ~exp(-9.60) * Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.3(83.51%) | Like=3.66..3.69 [3.6638..3.6638]*| it/evals=2250/2835 eff=92.4025% N=400 Z=-0.3(85.43%) | Like=3.67..3.69 [3.6691..3.6691]*| it/evals=2300/2887 eff=92.4809% N=400 Mono-modal Volume: ~exp(-9.61) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.3(86.80%) | Like=3.67..3.69 [3.6717..3.6718]*| it/evals=2340/2931 eff=92.4536% N=400 Z=-0.3(87.12%) | Like=3.67..3.69 [3.6725..3.6726]*| it/evals=2350/2940 eff=92.5197% N=400 Z=-0.3(88.62%) | Like=3.68..3.69 [3.6752..3.6753]*| it/evals=2400/2994 eff=92.5212% N=400 Mono-modal Volume: ~exp(-10.21) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.3(89.44%) | Like=3.68..3.69 [3.6771..3.6771]*| it/evals=2430/3029 eff=92.4306% N=400 Z=-0.2(89.95%) | Like=3.68..3.69 [3.6780..3.6780]*| it/evals=2450/3148 eff=89.1557% N=400 Z=-0.2(91.13%) | Like=3.68..3.69 [3.6802..3.6802]*| it/evals=2500/3148 eff=90.9753% N=400 Have 2 modes Volume: ~exp(-10.22) * Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 11 +0.501 | +1.000 Z=-0.2(91.56%) | Like=3.68..3.69 [3.6807..3.6807]*| it/evals=2520/3148 eff=91.7031% N=400 Z=-0.2(92.16%) | Like=3.68..3.69 [3.6815..3.6815]*| it/evals=2550/3273 eff=88.7574% N=400 Z=-0.2(93.08%) | Like=3.68..3.69 [3.6826..3.6826]*| it/evals=2600/3273 eff=90.4977% N=400 Have 2 modes Volume: ~exp(-10.57) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 11 +0.501 | +1.000 Z=-0.2(93.25%) | Like=3.68..3.69 [3.6827..3.6827]*| it/evals=2610/3273 eff=90.8458% N=400 Z=-0.2(93.89%) | Like=3.68..3.69 [3.6836..3.6836]*| it/evals=2650/3383 eff=88.8367% N=400 Mono-modal Volume: ~exp(-10.92) * Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(94.61%) | Like=3.68..3.69 [3.6842..3.6842]*| it/evals=2700/3383 eff=90.5129% N=400 Z=-0.2(95.24%) | Like=3.68..3.69 [3.6847..3.6847]*| it/evals=2750/3503 eff=88.6239% N=400 Mono-modal Volume: ~exp(-11.05) * Expected Volume: exp(-6.98) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(95.70%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2790/3503 eff=89.9130% N=400 Z=-0.2(95.80%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2800/3503 eff=90.2353% N=400 Z=-0.2(96.29%) | Like=3.69..3.69 [3.6852..3.6852]*| it/evals=2850/3625 eff=88.3721% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 3625 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = -0.1303 +- 0.07431 [ultranest] Effective samples strategy satisfied (ESS = 1253.4, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.08, need <0.5) [ultranest] logZ error budget: single: 0.09 bs:0.07 tail:0.04 total:0.08 required:<0.50 [ultranest] done iterating. logZ = -0.144 +- 0.183 single instance: logZ = -0.144 +- 0.092 bootstrapped : logZ = -0.130 +- 0.180 tail : logZ = +- 0.036 insert order U test : converged: True correlation: inf iterations a : 0.4621│ ▁▁▁▁▁▁▁▁▁▂▃▃▅▅▆▆▇▇▇▇▆▅▅▄▃▂▃▂▁▁▁▁▁▁▁ ▁ │0.5400 0.4999 +- 0.0100 [ultranest] Resuming from 3528 stored points Mono-modal Volume: ~exp(-4.24) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 Z=-inf(0.00%) | Like=-1229.24..3.69 [-1229.2436..-284.2420] | it/evals=0/3625 eff=inf% N=400 Z=-947.8(0.00%) | Like=-936.03..3.69 [-1229.2436..-284.2420] | it/evals=50/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-4.53) * Expected Volume: exp(-0.23) Quality: ok a: +0.0| ******************************************** | +1.0 Z=-794.7(0.00%) | Like=-767.63..3.69 [-1229.2436..-284.2420] | it/evals=90/3625 eff=inf% N=400 Z=-744.3(0.00%) | Like=-735.03..3.69 [-1229.2436..-284.2420] | it/evals=100/3625 eff=inf% N=400 Z=-573.7(0.00%) | Like=-562.58..3.69 [-1229.2436..-284.2420] | it/evals=150/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-4.53) Expected Volume: exp(-0.45) Quality: ok a: +0.0| ************************************ +0.8 | +1.0 Z=-436.3(0.00%) | Like=-427.78..3.69 [-1229.2436..-284.2420] | it/evals=200/3625 eff=inf% N=400 Z=-342.3(0.00%) | Like=-335.76..3.69 [-1229.2436..-284.2420] | it/evals=250/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-4.84) * Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.3 **************************** +0.7 | +1.0 Z=-312.3(0.00%) | Like=-306.34..3.69 [-1229.2436..-284.2420] | it/evals=270/3625 eff=inf% N=400 Z=-277.8(0.00%) | Like=-269.37..3.69 [-282.2298..-67.3700] | it/evals=300/3625 eff=inf% N=400 Z=-224.0(0.00%) | Like=-217.57..3.69 [-282.2298..-67.3700] | it/evals=350/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-4.84) Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-174.1(0.00%) | Like=-164.34..3.69 [-282.2298..-67.3700] | it/evals=400/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-5.21) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ******************** +0.7 | +1.0 Z=-139.6(0.00%) | Like=-130.59..3.69 [-282.2298..-67.3700] | it/evals=450/3625 eff=inf% N=400 Z=-110.7(0.00%) | Like=-103.90..3.69 [-282.2298..-67.3700] | it/evals=500/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-5.34) * Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 Z=-90.3(0.00%) | Like=-83.12..3.69 [-282.2298..-67.3700] | it/evals=540/3625 eff=inf% N=400 Z=-84.0(0.00%) | Like=-76.16..3.69 [-282.2298..-67.3700] | it/evals=550/3625 eff=inf% N=400 Z=-69.0(0.00%) | Like=-63.34..3.69 [-67.2945..-16.1658] | it/evals=600/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-5.57) * Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 ************** +0.6 | +1.0 Z=-62.3(0.00%) | Like=-56.62..3.69 [-67.2945..-16.1658] | it/evals=630/3625 eff=inf% N=400 Z=-57.4(0.00%) | Like=-51.00..3.69 [-67.2945..-16.1658] | it/evals=650/3625 eff=inf% N=400 Z=-44.8(0.00%) | Like=-38.45..3.69 [-67.2945..-16.1658] | it/evals=700/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-5.90) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 Z=-40.3(0.00%) | Like=-34.53..3.69 [-67.2945..-16.1658] | it/evals=720/3625 eff=inf% N=400 Z=-36.3(0.00%) | Like=-30.32..3.69 [-67.2945..-16.1658] | it/evals=750/3625 eff=inf% N=400 Z=-28.4(0.00%) | Like=-22.50..3.69 [-67.2945..-16.1658] | it/evals=800/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-5.95) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-27.1(0.00%) | Like=-21.15..3.69 [-67.2945..-16.1658] | it/evals=810/3625 eff=inf% N=400 Z=-23.0(0.00%) | Like=-17.36..3.69 [-67.2945..-16.1658] | it/evals=850/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-6.15) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-18.9(0.00%) | Like=-13.36..3.69 [-16.1569..-1.6640] | it/evals=900/3625 eff=inf% N=400 Z=-15.5(0.00%) | Like=-10.11..3.69 [-16.1569..-1.6640] | it/evals=950/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-6.28) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.45 ****** +0.55 | +1.00 Z=-13.2(0.00%) | Like=-7.53..3.69 [-16.1569..-1.6640] | it/evals=990/3625 eff=inf% N=400 Z=-12.6(0.00%) | Like=-7.06..3.69 [-16.1569..-1.6640] | it/evals=1000/3625 eff=inf% N=400 Z=-10.2(0.00%) | Like=-4.66..3.69 [-16.1569..-1.6640] | it/evals=1050/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-6.66) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-9.1(0.01%) | Like=-3.77..3.69 [-16.1569..-1.6640] | it/evals=1080/3625 eff=inf% N=400 Z=-8.4(0.02%) | Like=-3.02..3.69 [-16.1569..-1.6640] | it/evals=1100/3625 eff=inf% N=400 Z=-6.8(0.13%) | Like=-1.40..3.69 [-1.6514..1.3709] | it/evals=1150/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-7.11) * Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-6.2(0.22%) | Like=-0.98..3.69 [-1.6514..1.3709] | it/evals=1170/3625 eff=inf% N=400 Z=-5.6(0.43%) | Like=-0.44..3.69 [-1.6514..1.3709] | it/evals=1200/3625 eff=inf% N=400 Z=-4.7(1.06%) | Like=0.20..3.69 [-1.6514..1.3709] | it/evals=1250/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-7.11) Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-3.9(2.32%) | Like=1.22..3.69 [-1.6514..1.3709] | it/evals=1300/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-7.39) * Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.2(4.75%) | Like=1.77..3.69 [1.7333..1.7988] | it/evals=1350/3625 eff=inf% N=400 Z=-2.6(8.16%) | Like=2.17..3.69 [2.1689..2.1707]*| it/evals=1400/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-7.51) * Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.3(11.57%) | Like=2.42..3.69 [2.4219..2.4221]*| it/evals=1440/3625 eff=inf% N=400 Z=-2.2(12.55%) | Like=2.47..3.69 [2.4730..2.4768]*| it/evals=1450/3625 eff=inf% N=400 Z=-1.9(17.45%) | Like=2.75..3.69 [2.7491..2.7498]*| it/evals=1500/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-7.81) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.7(20.74%) | Like=2.91..3.69 [2.9003..2.9129] | it/evals=1530/3625 eff=inf% N=400 Z=-1.6(23.08%) | Like=2.98..3.69 [2.9833..2.9849]*| it/evals=1550/3625 eff=inf% N=400 Z=-1.4(29.11%) | Like=3.14..3.69 [3.1391..3.1391]*| it/evals=1600/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-8.28) * Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.3(31.53%) | Like=3.18..3.69 [3.1790..3.1792]*| it/evals=1620/3625 eff=inf% N=400 Z=-1.2(35.15%) | Like=3.26..3.69 [3.2566..3.2569]*| it/evals=1650/3625 eff=inf% N=400 Z=-1.0(41.06%) | Like=3.34..3.69 [3.3424..3.3427]*| it/evals=1700/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-8.28) Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.9(46.74%) | Like=3.42..3.69 [3.4174..3.4175]*| it/evals=1750/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-8.49) * Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.8(52.11%) | Like=3.47..3.69 [3.4731..3.4751]*| it/evals=1800/3625 eff=inf% N=400 Z=-0.7(57.09%) | Like=3.52..3.69 [3.5250..3.5260]*| it/evals=1850/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-8.81) * Expected Volume: exp(-4.73) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(60.83%) | Like=3.55..3.69 [3.5540..3.5540]*| it/evals=1890/3625 eff=inf% N=400 Z=-0.6(61.73%) | Like=3.56..3.69 [3.5611..3.5629]*| it/evals=1900/3625 eff=inf% N=400 Z=-0.6(65.92%) | Like=3.59..3.69 [3.5913..3.5926]*| it/evals=1950/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-8.85) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.5(68.26%) | Like=3.60..3.69 [3.6037..3.6038]*| it/evals=1980/3625 eff=inf% N=400 Z=-0.5(69.72%) | Like=3.61..3.69 [3.6112..3.6113]*| it/evals=2000/3625 eff=inf% N=400 Z=-0.5(73.12%) | Like=3.63..3.69 [3.6294..3.6295]*| it/evals=2050/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-9.02) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.4(74.39%) | Like=3.64..3.69 [3.6359..3.6360]*| it/evals=2070/3625 eff=inf% N=400 Z=-0.4(76.19%) | Like=3.64..3.69 [3.6438..3.6438]*| it/evals=2100/3625 eff=inf% N=400 Z=-0.4(78.92%) | Like=3.65..3.69 [3.6533..3.6534]*| it/evals=2150/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-9.21) * Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.4(79.43%) | Like=3.65..3.69 [3.6544..3.6547]*| it/evals=2160/3625 eff=inf% N=400 Z=-0.4(81.35%) | Like=3.66..3.69 [3.6596..3.6596]*| it/evals=2200/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-9.35) * Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.3(83.51%) | Like=3.66..3.69 [3.6638..3.6638]*| it/evals=2250/3625 eff=inf% N=400 Z=-0.3(85.43%) | Like=3.67..3.69 [3.6691..3.6691]*| it/evals=2300/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-9.71) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.3(86.80%) | Like=3.67..3.69 [3.6717..3.6718]*| it/evals=2340/3625 eff=inf% N=400 Z=-0.3(87.12%) | Like=3.67..3.69 [3.6725..3.6726]*| it/evals=2350/3625 eff=inf% N=400 Z=-0.3(88.62%) | Like=3.68..3.69 [3.6752..3.6753]*| it/evals=2400/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-10.03) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.3(89.44%) | Like=3.68..3.69 [3.6771..3.6771]*| it/evals=2430/3625 eff=inf% N=400 Z=-0.2(89.95%) | Like=3.68..3.69 [3.6780..3.6780]*| it/evals=2450/3625 eff=inf% N=400 Z=-0.2(91.13%) | Like=3.68..3.69 [3.6802..3.6802]*| it/evals=2500/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-10.03) Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(92.16%) | Like=3.68..3.69 [3.6815..3.6815]*| it/evals=2550/3625 eff=inf% N=400 Z=-0.2(93.08%) | Like=3.68..3.69 [3.6826..3.6826]*| it/evals=2600/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-10.53) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(93.25%) | Like=3.68..3.69 [3.6827..3.6827]*| it/evals=2610/3625 eff=inf% N=400 Z=-0.2(93.89%) | Like=3.68..3.69 [3.6836..3.6836]*| it/evals=2650/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-10.57) * Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(94.61%) | Like=3.68..3.69 [3.6842..3.6842]*| it/evals=2700/3625 eff=inf% N=400 Z=-0.2(95.24%) | Like=3.68..3.69 [3.6847..3.6847]*| it/evals=2750/3625 eff=inf% N=400 Mono-modal Volume: ~exp(-11.06) * Expected Volume: exp(-6.98) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.2(95.70%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2790/3625 eff=inf% N=400 Z=-0.2(95.80%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2800/3625 eff=inf% N=400 Z=-0.2(96.29%) | Like=3.69..3.69 [3.6852..3.6852]*| it/evals=2850/3625 eff=inf% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 3625 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = -0.1498 +- 0.06794 [ultranest] Effective samples strategy satisfied (ESS = 1253.4, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.08, need <0.5) [ultranest] logZ error budget: single: 0.09 bs:0.07 tail:0.04 total:0.08 required:<0.50 [ultranest] done iterating. logZ = -0.144 +- 0.152 single instance: logZ = -0.144 +- 0.092 bootstrapped : logZ = -0.150 +- 0.147 tail : logZ = +- 0.036 insert order U test : converged: True correlation: inf iterations a : 0.454 │ ▁ ▁▁▁▁▁▁▁▂▂▄▄▅▆▆▇▇▇▇▆▆▄▃▂▃▂▁▁▁▁▁▁▁▁ │0.540 0.500 +- 0.010 ran with dlogz: 0.5 first run gave: {'niter': 3253, 'logz': -0.14379632207248289, 'logzerr': 0.18310347998957455, 'logz_bs': -0.13026009874484876, 'logz_single': -0.14379632207248289, 'logzerr_tail': 0.03611931453602765, 'logzerr_bs': 0.1795056531191707, 'ess': 1253.420550147623, 'H': 3.348925652085825, 'Herr': 0.06724216689953912, 'posterior': {'mean': [0.4999163415129056], 'stdev': [0.009984478489580171], 'median': [0.4998294515402933], 'errlo': [0.4897722799365294], 'errup': [0.5098152223068952], 'information_gain_bits': [3.463126483234424]}, 'maximum_likelihood': {'logl': 3.6862316474629644, 'point': [0.5000010315477932], 'point_untransformed': [0.5000010315477932]}, 'ncall': 3625, 'paramnames': ['a'], 'logzerr_single': 0.09150035043765986, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} second run gave: {'niter': 3253, 'logz': -0.14379632207248289, 'logzerr': 0.15165994819494222, 'logz_bs': -0.1497862378729898, 'logz_single': -0.14379632207248289, 'logzerr_tail': 0.03611931453602765, 'logzerr_bs': 0.1472960793909331, 'ess': 1253.420550147623, 'H': 3.348925652085825, 'Herr': 0.06614145370598383, 'posterior': {'mean': [0.4999572757254387], 'stdev': [0.010007165175666608], 'median': [0.4998524605198037], 'errlo': [0.48987184730524147], 'errup': [0.5098360100994238], 'information_gain_bits': [3.463126483234424]}, 'maximum_likelihood': {'logl': 3.6862316474629644, 'point': [0.5000010315477932], 'point_untransformed': [0.5000010315477932]}, 'ncall': 3625, 'paramnames': ['a'], 'logzerr_single': 0.09150035043765986, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmpqmkyy2q3, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=528, regioncalls=128, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1229.24, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=528, regioncalls=128, ndraw=128, logz=-947.80, remainder_fraction=100.0000%, Lmin=-936.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=528, regioncalls=128, ndraw=128, logz=-794.67, remainder_fraction=100.0000%, Lmin=-767.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=528, regioncalls=128, ndraw=128, logz=-744.35, remainder_fraction=100.0000%, Lmin=-735.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=635, regioncalls=256, ndraw=128, logz=-573.67, remainder_fraction=100.0000%, Lmin=-562.58, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=711, regioncalls=384, ndraw=128, logz=-436.35, remainder_fraction=100.0000%, Lmin=-427.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=711, regioncalls=384, ndraw=128, logz=-342.29, remainder_fraction=100.0000%, Lmin=-335.76, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=780, regioncalls=512, ndraw=128, logz=-312.30, remainder_fraction=100.0000%, Lmin=-306.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=780, regioncalls=512, ndraw=128, logz=-277.84, remainder_fraction=100.0000%, Lmin=-269.37, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=829, regioncalls=640, ndraw=128, logz=-224.03, remainder_fraction=100.0000%, Lmin=-217.57, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=877, regioncalls=768, ndraw=128, logz=-174.15, remainder_fraction=100.0000%, Lmin=-164.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=922, regioncalls=896, ndraw=128, logz=-139.64, remainder_fraction=100.0000%, Lmin=-130.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=964, regioncalls=1024, ndraw=128, logz=-110.71, remainder_fraction=100.0000%, Lmin=-103.90, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1002, regioncalls=1152, ndraw=128, logz=-90.31, remainder_fraction=100.0000%, Lmin=-83.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=1043, regioncalls=1280, ndraw=128, logz=-84.03, remainder_fraction=100.0000%, Lmin=-76.16, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1070, regioncalls=1408, ndraw=128, logz=-69.04, remainder_fraction=100.0000%, Lmin=-63.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1104, regioncalls=1536, ndraw=128, logz=-62.34, remainder_fraction=100.0000%, Lmin=-56.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=1131, regioncalls=1664, ndraw=128, logz=-57.37, remainder_fraction=100.0000%, Lmin=-51.00, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=1175, regioncalls=1920, ndraw=128, logz=-44.82, remainder_fraction=100.0000%, Lmin=-38.45, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1196, regioncalls=2048, ndraw=128, logz=-40.29, remainder_fraction=100.0000%, Lmin=-34.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1236, regioncalls=2304, ndraw=128, logz=-36.28, remainder_fraction=100.0000%, Lmin=-30.32, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1287, regioncalls=2688, ndraw=128, logz=-28.45, remainder_fraction=100.0000%, Lmin=-22.50, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1287, regioncalls=2688, ndraw=128, logz=-27.10, remainder_fraction=100.0000%, Lmin=-21.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1332, regioncalls=3072, ndraw=128, logz=-22.97, remainder_fraction=100.0000%, Lmin=-17.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1385, regioncalls=3456, ndraw=128, logz=-18.87, remainder_fraction=100.0000%, Lmin=-13.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1437, regioncalls=3968, ndraw=128, logz=-15.47, remainder_fraction=100.0000%, Lmin=-10.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1477, regioncalls=4480, ndraw=128, logz=-13.15, remainder_fraction=99.9998%, Lmin=-7.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1500, regioncalls=4736, ndraw=128, logz=-12.63, remainder_fraction=99.9996%, Lmin=-7.06, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1546, regioncalls=5376, ndraw=128, logz=-10.23, remainder_fraction=99.9961%, Lmin=-4.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1596, regioncalls=5888, ndraw=128, logz=-8.40, remainder_fraction=99.9751%, Lmin=-3.02, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1649, regioncalls=6528, ndraw=128, logz=-6.78, remainder_fraction=99.8748%, Lmin=-1.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1672, regioncalls=6784, ndraw=128, logz=-6.25, remainder_fraction=99.7835%, Lmin=-0.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1699, regioncalls=7168, ndraw=128, logz=-5.57, remainder_fraction=99.5664%, Lmin=-0.44, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=1748, regioncalls=8320, ndraw=128, logz=-4.70, remainder_fraction=98.9446%, Lmin=0.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1757, regioncalls=8448, ndraw=128, logz=-4.54, remainder_fraction=98.7719%, Lmin=0.50, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=1804, regioncalls=9344, ndraw=128, logz=-3.89, remainder_fraction=97.6829%, Lmin=1.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=1850, regioncalls=10496, ndraw=128, logz=-3.18, remainder_fraction=95.2463%, Lmin=1.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=1904, regioncalls=11776, ndraw=128, logz=-2.64, remainder_fraction=91.8392%, Lmin=2.17, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=1947, regioncalls=13056, ndraw=128, logz=-2.30, remainder_fraction=88.4330%, Lmin=2.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=1958, regioncalls=13440, ndraw=128, logz=-2.22, remainder_fraction=87.4548%, Lmin=2.47, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=2012, regioncalls=15104, ndraw=128, logz=-1.89, remainder_fraction=82.5508%, Lmin=2.75, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2042, regioncalls=16128, ndraw=128, logz=-1.72, remainder_fraction=79.2637%, Lmin=2.91, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=2064, regioncalls=16896, ndraw=128, logz=-1.61, remainder_fraction=76.9153%, Lmin=2.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2114, regioncalls=19328, ndraw=128, logz=-1.38, remainder_fraction=70.8937%, Lmin=3.14, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2135, regioncalls=20224, ndraw=128, logz=-1.30, remainder_fraction=68.4742%, Lmin=3.18, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=2267, regioncalls=21504, ndraw=128, logz=-1.19, remainder_fraction=64.8523%, Lmin=3.26, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=2267, regioncalls=21504, ndraw=128, logz=-1.03, remainder_fraction=58.9386%, Lmin=3.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2267, regioncalls=21504, ndraw=128, logz=-1.01, remainder_fraction=57.8027%, Lmin=3.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=2278, regioncalls=22272, ndraw=128, logz=-0.90, remainder_fraction=53.2588%, Lmin=3.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2329, regioncalls=24832, ndraw=128, logz=-0.80, remainder_fraction=47.8890%, Lmin=3.47, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=2457, regioncalls=25088, ndraw=128, logz=-0.70, remainder_fraction=42.9116%, Lmin=3.52, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=2457, regioncalls=25088, ndraw=128, logz=-0.64, remainder_fraction=39.1708%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=2457, regioncalls=25088, ndraw=128, logz=-0.63, remainder_fraction=38.2725%, Lmin=3.56, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=2499, regioncalls=25984, ndraw=128, logz=-0.56, remainder_fraction=34.0753%, Lmin=3.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=2526, regioncalls=26624, ndraw=128, logz=-0.53, remainder_fraction=31.7411%, Lmin=3.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=2655, regioncalls=27264, ndraw=128, logz=-0.50, remainder_fraction=30.2779%, Lmin=3.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=2655, regioncalls=27264, ndraw=128, logz=-0.46, remainder_fraction=26.8792%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=2655, regioncalls=27264, ndraw=128, logz=-0.44, remainder_fraction=25.6086%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=2666, regioncalls=28288, ndraw=128, logz=-0.42, remainder_fraction=23.8096%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=2780, regioncalls=29312, ndraw=128, logz=-0.38, remainder_fraction=21.0780%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=2780, regioncalls=29312, ndraw=128, logz=-0.37, remainder_fraction=20.5695%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=2780, regioncalls=29312, ndraw=128, logz=-0.35, remainder_fraction=18.6478%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=2835, regioncalls=30464, ndraw=128, logz=-0.32, remainder_fraction=16.4895%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=2887, regioncalls=31744, ndraw=128, logz=-0.30, remainder_fraction=14.5727%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=2931, regioncalls=32384, ndraw=128, logz=-0.29, remainder_fraction=13.2025%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=2940, regioncalls=32640, ndraw=128, logz=-0.28, remainder_fraction=12.8799%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=2994, regioncalls=33536, ndraw=128, logz=-0.26, remainder_fraction=11.3784%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3029, regioncalls=34176, ndraw=128, logz=-0.26, remainder_fraction=10.5619%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3148, regioncalls=34432, ndraw=128, logz=-0.25, remainder_fraction=10.0498%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3148, regioncalls=34432, ndraw=128, logz=-0.24, remainder_fraction=8.8742%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=3148, regioncalls=34432, ndraw=128, logz=-0.23, remainder_fraction=8.4432%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3273, regioncalls=34688, ndraw=128, logz=-0.23, remainder_fraction=7.8354%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3273, regioncalls=34688, ndraw=128, logz=-0.22, remainder_fraction=6.9173%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3273, regioncalls=34688, ndraw=128, logz=-0.21, remainder_fraction=6.7470%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3383, regioncalls=34944, ndraw=128, logz=-0.21, remainder_fraction=6.1063%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3383, regioncalls=34944, ndraw=128, logz=-0.20, remainder_fraction=5.3898%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=3503, regioncalls=35200, ndraw=128, logz=-0.19, remainder_fraction=4.7571%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=3503, regioncalls=35200, ndraw=128, logz=-0.19, remainder_fraction=4.3048%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3503, regioncalls=35200, ndraw=128, logz=-0.19, remainder_fraction=4.1986%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=3625, regioncalls=35456, ndraw=128, logz=-0.18, remainder_fraction=3.7056%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 3625 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -0.1303 +- 0.07431 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1253.4, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.08, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.07 tail:0.04 total:0.08 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmpqmkyy2q3, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [3.6851476 3.68609358 0. 0.49983383 0.49983383]: u=[0.49983383] -> p=[0.49983383] -> L=3.6860935822797853 [32mINFO [0m ultranest:integrator.py:2246 Resuming from 3528 stored points [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=3625, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1229.24, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=3625, regioncalls=0, ndraw=128, logz=-947.80, remainder_fraction=100.0000%, Lmin=-936.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=3625, regioncalls=0, ndraw=128, logz=-794.67, remainder_fraction=100.0000%, Lmin=-767.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=3625, regioncalls=0, ndraw=128, logz=-744.35, remainder_fraction=100.0000%, Lmin=-735.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=3625, regioncalls=0, ndraw=128, logz=-573.67, remainder_fraction=100.0000%, Lmin=-562.58, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=3625, regioncalls=0, ndraw=128, logz=-436.35, remainder_fraction=100.0000%, Lmin=-427.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=3625, regioncalls=0, ndraw=128, logz=-342.29, remainder_fraction=100.0000%, Lmin=-335.76, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=3625, regioncalls=0, ndraw=128, logz=-312.30, remainder_fraction=100.0000%, Lmin=-306.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=3625, regioncalls=0, ndraw=128, logz=-277.84, remainder_fraction=100.0000%, Lmin=-269.37, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=3625, regioncalls=0, ndraw=128, logz=-224.03, remainder_fraction=100.0000%, Lmin=-217.57, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=3625, regioncalls=0, ndraw=128, logz=-174.15, remainder_fraction=100.0000%, Lmin=-164.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3625, regioncalls=0, ndraw=128, logz=-139.64, remainder_fraction=100.0000%, Lmin=-130.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3625, regioncalls=0, ndraw=128, logz=-110.71, remainder_fraction=100.0000%, Lmin=-103.90, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=3625, regioncalls=0, ndraw=128, logz=-90.31, remainder_fraction=100.0000%, Lmin=-83.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=3625, regioncalls=0, ndraw=128, logz=-84.03, remainder_fraction=100.0000%, Lmin=-76.16, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=3625, regioncalls=0, ndraw=128, logz=-69.04, remainder_fraction=100.0000%, Lmin=-63.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=3625, regioncalls=0, ndraw=128, logz=-62.34, remainder_fraction=100.0000%, Lmin=-56.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=3625, regioncalls=0, ndraw=128, logz=-57.37, remainder_fraction=100.0000%, Lmin=-51.00, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=3625, regioncalls=0, ndraw=128, logz=-44.82, remainder_fraction=100.0000%, Lmin=-38.45, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=3625, regioncalls=0, ndraw=128, logz=-40.29, remainder_fraction=100.0000%, Lmin=-34.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=3625, regioncalls=0, ndraw=128, logz=-36.28, remainder_fraction=100.0000%, Lmin=-30.32, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=3625, regioncalls=0, ndraw=128, logz=-28.45, remainder_fraction=100.0000%, Lmin=-22.50, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=3625, regioncalls=0, ndraw=128, logz=-27.10, remainder_fraction=100.0000%, Lmin=-21.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=3625, regioncalls=0, ndraw=128, logz=-22.97, remainder_fraction=100.0000%, Lmin=-17.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=3625, regioncalls=0, ndraw=128, logz=-18.87, remainder_fraction=100.0000%, Lmin=-13.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=3625, regioncalls=0, ndraw=128, logz=-15.47, remainder_fraction=100.0000%, Lmin=-10.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=3625, regioncalls=0, ndraw=128, logz=-13.15, remainder_fraction=99.9998%, Lmin=-7.53, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=3625, regioncalls=0, ndraw=128, logz=-12.63, remainder_fraction=99.9996%, Lmin=-7.06, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=3625, regioncalls=0, ndraw=128, logz=-10.23, remainder_fraction=99.9961%, Lmin=-4.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=3625, regioncalls=0, ndraw=128, logz=-9.07, remainder_fraction=99.9874%, Lmin=-3.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=3625, regioncalls=0, ndraw=128, logz=-8.40, remainder_fraction=99.9751%, Lmin=-3.02, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=3625, regioncalls=0, ndraw=128, logz=-6.78, remainder_fraction=99.8748%, Lmin=-1.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=3625, regioncalls=0, ndraw=128, logz=-6.25, remainder_fraction=99.7835%, Lmin=-0.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=3625, regioncalls=0, ndraw=128, logz=-5.57, remainder_fraction=99.5664%, Lmin=-0.44, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=3625, regioncalls=0, ndraw=128, logz=-4.70, remainder_fraction=98.9446%, Lmin=0.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=3625, regioncalls=0, ndraw=128, logz=-3.89, remainder_fraction=97.6829%, Lmin=1.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=3625, regioncalls=0, ndraw=128, logz=-3.18, remainder_fraction=95.2463%, Lmin=1.77, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=3625, regioncalls=0, ndraw=128, logz=-2.64, remainder_fraction=91.8392%, Lmin=2.17, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=3625, regioncalls=0, ndraw=128, logz=-2.30, remainder_fraction=88.4330%, Lmin=2.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=3625, regioncalls=0, ndraw=128, logz=-2.22, remainder_fraction=87.4548%, Lmin=2.47, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.89, remainder_fraction=82.5508%, Lmin=2.75, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.72, remainder_fraction=79.2637%, Lmin=2.91, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.61, remainder_fraction=76.9153%, Lmin=2.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.38, remainder_fraction=70.8937%, Lmin=3.14, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.30, remainder_fraction=68.4742%, Lmin=3.18, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.19, remainder_fraction=64.8523%, Lmin=3.26, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=3625, regioncalls=0, ndraw=128, logz=-1.03, remainder_fraction=58.9386%, Lmin=3.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.90, remainder_fraction=53.2588%, Lmin=3.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.80, remainder_fraction=47.8890%, Lmin=3.47, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.70, remainder_fraction=42.9116%, Lmin=3.52, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.64, remainder_fraction=39.1708%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.63, remainder_fraction=38.2725%, Lmin=3.56, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.56, remainder_fraction=34.0753%, Lmin=3.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.53, remainder_fraction=31.7411%, Lmin=3.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.50, remainder_fraction=30.2779%, Lmin=3.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.46, remainder_fraction=26.8792%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.44, remainder_fraction=25.6086%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.42, remainder_fraction=23.8096%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.38, remainder_fraction=21.0780%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.37, remainder_fraction=20.5695%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.35, remainder_fraction=18.6478%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.32, remainder_fraction=16.4895%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.30, remainder_fraction=14.5727%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.29, remainder_fraction=13.2025%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.28, remainder_fraction=12.8799%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.26, remainder_fraction=11.3784%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.26, remainder_fraction=10.5619%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.25, remainder_fraction=10.0498%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.24, remainder_fraction=8.8742%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.23, remainder_fraction=7.8354%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.22, remainder_fraction=6.9173%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.21, remainder_fraction=6.7470%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.21, remainder_fraction=6.1063%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.20, remainder_fraction=5.3898%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.19, remainder_fraction=4.7571%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.19, remainder_fraction=4.3048%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.19, remainder_fraction=4.1986%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=3625, regioncalls=0, ndraw=128, logz=-0.18, remainder_fraction=3.7056%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 3625 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -0.1498 +- 0.06794 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1253.4, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.08 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.08, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.07 tail:0.04 total:0.08 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_run_resume[0.1] | 15.02 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.23) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 Z=-inf(0.00%) | Like=-1226.65..3.68 [-1226.6504..-307.9035] | it/evals=0/528 eff=0.0000% N=400 Z=-951.8(0.00%) | Like=-945.36..3.68 [-1226.6504..-307.9035] | it/evals=50/528 eff=39.0625% N=400 Mono-modal Volume: ~exp(-4.49) * Expected Volume: exp(-0.23) Quality: ok a: +0.0| ********************************************** | +1.0 Z=-786.4(0.00%) | Like=-779.24..3.68 [-1226.6504..-307.9035] | it/evals=90/528 eff=70.3125% N=400 Z=-746.9(0.00%) | Like=-740.63..3.68 [-1226.6504..-307.9035] | it/evals=100/528 eff=78.1250% N=400 Z=-573.3(0.00%) | Like=-561.83..3.69 [-1226.6504..-307.9035] | it/evals=150/629 eff=65.5022% N=400 Have 2 modes Volume: ~exp(-4.72) * Expected Volume: exp(-0.45) Quality: ok a: +0.0| 222222111111111111111111111111111111 +0.8 | +1.0 Z=-499.7(0.00%) | Like=-493.61..3.69 [-1226.6504..-307.9035] | it/evals=180/629 eff=78.6026% N=400 Z=-463.6(0.00%) | Like=-456.60..3.69 [-1226.6504..-307.9035] | it/evals=200/717 eff=63.0915% N=400 Z=-374.7(0.00%) | Like=-364.11..3.69 [-1226.6504..-307.9035] | it/evals=250/717 eff=78.8644% N=400 Have 2 modes Volume: ~exp(-4.91) * Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.2 222111111111111111111111111111 +0.8 | +1.0 Z=-327.4(0.00%) | Like=-320.54..3.69 [-1226.6504..-307.9035] | it/evals=270/783 eff=70.4961% N=400 Z=-278.7(0.00%) | Like=-269.85..3.69 [-307.6123..-69.3248] | it/evals=300/783 eff=78.3290% N=400 Z=-208.4(0.00%) | Like=-201.38..3.69 [-307.6123..-69.3248] | it/evals=350/842 eff=79.1855% N=400 Mono-modal Volume: ~exp(-5.13) * Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-201.8(0.00%) | Like=-195.89..3.69 [-307.6123..-69.3248] | it/evals=360/842 eff=81.4480% N=400 Z=-168.6(0.00%) | Like=-162.35..3.69 [-307.6123..-69.3248] | it/evals=400/890 eff=81.6327% N=400 Mono-modal Volume: ~exp(-5.45) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ****************** +0.7 | +1.0 Z=-127.9(0.00%) | Like=-120.12..3.69 [-307.6123..-69.3248] | it/evals=450/938 eff=83.6431% N=400 Z=-102.3(0.00%) | Like=-95.31..3.69 [-307.6123..-69.3248] | it/evals=500/983 eff=85.7633% N=400 Mono-modal Volume: ~exp(-5.45) Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 *************** +0.6 | +1.0 Z=-76.3(0.00%) | Like=-67.48..3.69 [-67.4783..-14.2446] | it/evals=550/1045 eff=85.2713% N=400 Z=-59.7(0.00%) | Like=-53.11..3.69 [-67.4783..-14.2446] | it/evals=600/1097 eff=86.0832% N=400 Mono-modal Volume: ~exp(-5.73) * Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 Z=-50.8(0.00%) | Like=-44.90..3.69 [-67.4783..-14.2446] | it/evals=630/1119 eff=87.6217% N=400 Z=-46.9(0.00%) | Like=-40.98..3.69 [-67.4783..-14.2446] | it/evals=650/1149 eff=86.7824% N=400 Z=-36.3(0.00%) | Like=-30.34..3.69 [-67.4783..-14.2446] | it/evals=700/1202 eff=87.2818% N=400 Have 2 modes Volume: ~exp(-5.93) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 1111111111 +0.6 | +1.0 Z=-33.3(0.00%) | Like=-27.63..3.69 [-67.4783..-14.2446] | it/evals=720/1220 eff=87.8049% N=400 Z=-28.5(0.00%) | Like=-22.72..3.69 [-67.4783..-14.2446] | it/evals=750/1241 eff=89.1795% N=400 Z=-22.7(0.00%) | Like=-17.23..3.69 [-67.4783..-14.2446] | it/evals=800/1304 eff=88.4956% N=400 Mono-modal Volume: ~exp(-5.99) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-21.8(0.00%) | Like=-15.95..3.69 [-67.4783..-14.2446] | it/evals=810/1321 eff=87.9479% N=400 Z=-18.2(0.00%) | Like=-12.48..3.69 [-14.1709..-1.0696] | it/evals=850/1358 eff=88.7265% N=400 Mono-modal Volume: ~exp(-6.39) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ****** +0.6 | +1.0 Z=-14.7(0.00%) | Like=-9.22..3.69 [-14.1709..-1.0696] | it/evals=900/1408 eff=89.2857% N=400 Z=-11.8(0.00%) | Like=-6.10..3.69 [-14.1709..-1.0696] | it/evals=950/1454 eff=90.1328% N=400 Mono-modal Volume: ~exp(-6.42) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-10.0(0.00%) | Like=-4.75..3.69 [-14.1709..-1.0696] | it/evals=990/1495 eff=90.4110% N=400 Z=-9.6(0.01%) | Like=-4.29..3.69 [-14.1709..-1.0696] | it/evals=1000/1510 eff=90.0901% N=400 Z=-7.8(0.04%) | Like=-2.56..3.69 [-14.1709..-1.0696] | it/evals=1050/1560 eff=90.5172% N=400 Mono-modal Volume: ~exp(-6.94) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-6.9(0.10%) | Like=-1.64..3.69 [-14.1709..-1.0696] | it/evals=1080/1589 eff=90.8326% N=400 Z=-6.3(0.18%) | Like=-1.10..3.69 [-14.1709..-1.0696] | it/evals=1100/1608 eff=91.0596% N=400 Z=-5.1(0.58%) | Like=-0.12..3.69 [-1.0251..1.4019] | it/evals=1150/1662 eff=91.1252% N=400 Mono-modal Volume: ~exp(-6.94) Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-4.1(1.56%) | Like=0.95..3.69 [-1.0251..1.4019] | it/evals=1200/1714 eff=91.3242% N=400 Z=-3.3(3.55%) | Like=1.65..3.69 [1.4141..1.7303] | it/evals=1250/1768 eff=91.3743% N=400 Mono-modal Volume: ~exp(-7.60) * Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.1(4.13%) | Like=1.78..3.69 [1.7755..1.7957] | it/evals=1260/1785 eff=90.9747% N=400 Z=-2.6(6.75%) | Like=2.07..3.69 [2.0671..2.0678]*| it/evals=1300/1820 eff=91.5493% N=400 Mono-modal Volume: ~exp(-7.60) Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.2(10.66%) | Like=2.32..3.69 [2.3213..2.3252]*| it/evals=1350/1878 eff=91.3396% N=400 Z=-1.8(15.17%) | Like=2.60..3.69 [2.5988..2.6022]*| it/evals=1400/1928 eff=91.6230% N=400 Mono-modal Volume: ~exp(-7.81) * Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.6(19.27%) | Like=2.78..3.69 [2.7802..2.7819]*| it/evals=1440/1969 eff=91.7782% N=400 Z=-1.5(20.35%) | Like=2.83..3.69 [2.8275..2.8304]*| it/evals=1450/1978 eff=91.8885% N=400 Z=-1.3(25.93%) | Like=3.03..3.69 [3.0340..3.0340]*| it/evals=1500/2029 eff=92.0810% N=400 Mono-modal Volume: ~exp(-8.19) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.2(29.56%) | Like=3.15..3.69 [3.1450..3.1456]*| it/evals=1530/2062 eff=92.0578% N=400 Z=-1.1(31.96%) | Like=3.20..3.69 [3.2018..3.2042]*| it/evals=1550/2080 eff=92.2619% N=400 Z=-0.9(38.04%) | Like=3.31..3.69 [3.3108..3.3130]*| it/evals=1600/2219 eff=87.9604% N=400 Mono-modal Volume: ~exp(-8.23) * Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.9(40.40%) | Like=3.34..3.69 [3.3439..3.3448]*| it/evals=1620/2219 eff=89.0599% N=400 Z=-0.8(43.85%) | Like=3.40..3.69 [3.3956..3.3965]*| it/evals=1650/2219 eff=90.7092% N=400 Z=-0.7(49.50%) | Like=3.45..3.69 [3.4511..3.4523]*| it/evals=1700/2336 eff=87.8099% N=400 Mono-modal Volume: ~exp(-8.53) * Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(50.57%) | Like=3.46..3.69 [3.4593..3.4621]*| it/evals=1710/2336 eff=88.3264% N=400 Z=-0.6(54.69%) | Like=3.50..3.69 [3.5027..3.5030]*| it/evals=1750/2336 eff=90.3926% N=400 Mono-modal Volume: ~exp(-8.57) * Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.5(59.50%) | Like=3.54..3.69 [3.5412..3.5415]*| it/evals=1800/2439 eff=88.2786% N=400 Z=-0.4(63.91%) | Like=3.57..3.69 [3.5739..3.5746]*| it/evals=1850/2439 eff=90.7308% N=400 Mono-modal Volume: ~exp(-8.57) Expected Volume: exp(-4.73) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.3(67.87%) | Like=3.60..3.69 [3.5972..3.5973]*| it/evals=1900/2479 eff=91.3901% N=400 Z=-0.3(71.47%) | Like=3.62..3.69 [3.6209..3.6212]*| it/evals=1950/2531 eff=91.5063% N=400 Mono-modal Volume: ~exp(-9.06) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.3(73.46%) | Like=3.63..3.69 [3.6274..3.6276]*| it/evals=1980/2559 eff=91.7091% N=400 Z=-0.2(74.71%) | Like=3.63..3.69 [3.6330..3.6334]*| it/evals=2000/2586 eff=91.4913% N=400 Z=-0.2(77.60%) | Like=3.65..3.69 [3.6475..3.6477]*| it/evals=2050/2641 eff=91.4770% N=400 Mono-modal Volume: ~exp(-9.50) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.2(78.67%) | Like=3.65..3.69 [3.6506..3.6507]*| it/evals=2070/2655 eff=91.7960% N=400 Z=-0.2(80.17%) | Like=3.66..3.69 [3.6553..3.6553]*| it/evals=2100/2687 eff=91.8233% N=400 Z=-0.1(82.46%) | Like=3.66..3.69 [3.6619..3.6620]*| it/evals=2150/2739 eff=91.9196% N=400 Mono-modal Volume: ~exp(-9.50) Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.1(84.49%) | Like=3.67..3.69 [3.6671..3.6671]*| it/evals=2200/2790 eff=92.0502% N=400 Have 2 modes Volume: ~exp(-9.64) * Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 21 +0.502 | +1.000 Z=-0.1(86.30%) | Like=3.67..3.69 [3.6702..3.6703]*| it/evals=2250/2849 eff=91.8742% N=400 Z=-0.1(87.89%) | Like=3.67..3.69 [3.6740..3.6740]*| it/evals=2300/2965 eff=89.6686% N=400 Mono-modal Volume: ~exp(-10.03) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(89.04%) | Like=3.68..3.69 [3.6757..3.6757]*| it/evals=2340/2965 eff=91.2281% N=400 Z=-0.1(89.31%) | Like=3.68..3.69 [3.6761..3.6761]*| it/evals=2350/2965 eff=91.6179% N=400 Z=-0.1(90.56%) | Like=3.68..3.69 [3.6781..3.6781]*| it/evals=2400/3088 eff=89.2857% N=400 Mono-modal Volume: ~exp(-10.26) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(91.23%) | Like=3.68..3.69 [3.6794..3.6795]*| it/evals=2430/3088 eff=90.4018% N=400 Z=-0.0(91.66%) | Like=3.68..3.69 [3.6800..3.6801]*| it/evals=2450/3088 eff=91.1458% N=400 Z=-0.0(92.64%) | Like=3.68..3.69 [3.6811..3.6812]*| it/evals=2500/3207 eff=89.0631% N=400 Mono-modal Volume: ~exp(-10.26) Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(93.50%) | Like=3.68..3.69 [3.6822..3.6822]*| it/evals=2550/3207 eff=90.8443% N=400 Z=-0.0(94.26%) | Like=3.68..3.69 [3.6832..3.6832]*| it/evals=2600/3311 eff=89.3164% N=400 Mono-modal Volume: ~exp(-10.31) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(94.40%) | Like=3.68..3.69 [3.6833..3.6833]*| it/evals=2610/3311 eff=89.6599% N=400 Z=-0.0(94.93%) | Like=3.68..3.69 [3.6836..3.6836]*| it/evals=2650/3311 eff=91.0340% N=400 Mono-modal Volume: ~exp(-10.83) * Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(95.53%) | Like=3.68..3.69 [3.6842..3.6842]*| it/evals=2700/3372 eff=90.8479% N=400 Z=0.0(96.05%) | Like=3.68..3.69 [3.6846..3.6846]*| it/evals=2750/3486 eff=89.1121% N=400 Mono-modal Volume: ~exp(-10.98) * Expected Volume: exp(-6.98) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=0.0(96.43%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2790/3486 eff=90.4083% N=400 Z=0.0(96.52%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2800/3486 eff=90.7323% N=400 Z=0.0(96.93%) | Like=3.69..3.69 [3.6852..3.6852]*| it/evals=2850/3537 eff=90.8511% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 3551 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = 0.04562 +- 0.06643 [ultranest] Effective samples strategy satisfied (ESS = 1262.2, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.06 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 572 minimum live points (dlogz from 0.05 to 0.16, need <0.1) [ultranest] logZ error budget: single: 0.09 bs:0.07 tail:0.03 total:0.07 required:<0.10 [ultranest] Widening roots to 572 live points (have 400 already) ... [ultranest] Sampling 172 live points from prior ... Mono-modal Volume: ~exp(-4.63) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 Z=-inf(0.00%) | Like=-1233.27..3.69 [-1233.2658..-318.2188] | it/evals=0/3851 eff=0.0000% N=572 Mono-modal Volume: ~exp(-4.66) * Expected Volume: exp(-0.22) Quality: ok a: +0.00| ********************************************** | +1.00 Z=-813.2(0.00%) | Like=-806.92..3.69 [-1233.2658..-318.2188] | it/evals=128/3851 eff=33.5938% N=572 Z=-623.8(0.00%) | Like=-616.62..3.69 [-1233.2658..-318.2188] | it/evals=200/3851 eff=53.1250% N=572 Mono-modal Volume: ~exp(-4.77) * Expected Volume: exp(-0.46) Quality: ok a: +0.0| ************************************ +0.8 | +1.0 Z=-511.7(0.00%) | Like=-503.81..3.69 [-1233.2658..-318.2188] | it/evals=264/3851 eff=67.9688% N=572 Mono-modal Volume: ~exp(-5.12) * Expected Volume: exp(-0.69) Quality: ok a: +0.0| +0.2 ****************************** +0.8 | +1.0 Z=-325.4(0.00%) | Like=-318.31..3.69 [-1233.2658..-318.2188] | it/evals=392/3925 eff=59.4059% N=572 Z=-265.5(0.00%) | Like=-258.66..3.69 [-318.1270..-72.8697] | it/evals=450/3925 eff=69.3069% N=572 Mono-modal Volume: ~exp(-5.23) * Expected Volume: exp(-0.91) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-203.2(0.00%) | Like=-196.97..3.69 [-318.1270..-72.8697] | it/evals=521/3987 eff=62.1212% N=572 Z=-188.1(0.00%) | Like=-181.88..3.69 [-318.1270..-72.8697] | it/evals=550/3987 eff=63.2576% N=572 Mono-modal Volume: ~exp(-5.66) * Expected Volume: exp(-1.14) Quality: ok a: +0.0| +0.3 ****************** +0.7 | +1.0 Z=-126.4(0.00%) | Like=-119.36..3.69 [-318.1270..-72.8697] | it/evals=651/4041 eff=62.5786% N=572 Mono-modal Volume: ~exp(-5.90) * Expected Volume: exp(-1.37) Quality: ok a: +0.0| +0.4 *************** +0.6 | +1.0 Z=-81.5(0.00%) | Like=-74.83..3.69 [-318.1270..-72.8697] | it/evals=783/4081 eff=67.3184% N=572 Z=-63.9(0.00%) | Like=-57.76..3.69 [-72.7839..-14.8005] | it/evals=850/4112 eff=67.6093% N=572 Mono-modal Volume: ~exp(-6.11) * Expected Volume: exp(-1.60) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 Z=-49.8(0.00%) | Like=-43.68..3.69 [-72.7839..-14.8005] | it/evals=914/4112 eff=71.2082% N=572 Mono-modal Volume: ~exp(-6.17) * Expected Volume: exp(-1.82) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 Z=-33.2(0.00%) | Like=-27.07..3.69 [-72.7839..-14.8005] | it/evals=1043/4159 eff=73.6239% N=572 Z=-27.4(0.00%) | Like=-21.42..3.69 [-72.7839..-14.8005] | it/evals=1100/4189 eff=73.1760% N=572 Mono-modal Volume: ~exp(-6.32) * Expected Volume: exp(-2.05) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-21.6(0.00%) | Like=-15.84..3.69 [-72.7839..-14.8005] | it/evals=1173/4209 eff=74.2798% N=572 Z=-19.8(0.00%) | Like=-13.99..3.69 [-14.7621..-1.1236] | it/evals=1200/4223 eff=73.8000% N=572 Mono-modal Volume: ~exp(-6.95) * Expected Volume: exp(-2.28) Quality: ok a: +0.0| +0.5 ****** +0.6 | +1.0 Z=-14.2(0.00%) | Like=-8.82..3.69 [-14.7621..-1.1236] | it/evals=1302/4238 eff=76.1165% N=572 Mono-modal Volume: ~exp(-6.95) Expected Volume: exp(-2.50) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-9.4(0.01%) | Like=-4.15..3.69 [-14.7621..-1.1236] | it/evals=1441/4294 eff=76.1821% N=572 Z=-8.0(0.03%) | Like=-2.75..3.69 [-14.7621..-1.1236] | it/evals=1500/4317 eff=76.5993% N=572 Mono-modal Volume: ~exp(-7.44) * Expected Volume: exp(-2.73) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-6.6(0.13%) | Like=-1.36..3.69 [-14.7621..-1.1236] | it/evals=1561/4348 eff=75.6800% N=572 Z=-5.0(0.62%) | Like=0.04..3.69 [-1.1141..1.0398] | it/evals=1650/4367 eff=76.5528% N=572 Mono-modal Volume: ~exp(-7.44) Expected Volume: exp(-2.95) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.7(2.42%) | Like=1.27..3.69 [1.0412..1.3033] | it/evals=1750/4416 eff=75.4690% N=572 Z=-3.1(4.09%) | Like=1.72..3.69 [1.6978..1.7207] | it/evals=1800/4451 eff=75.0000% N=572 Mono-modal Volume: ~exp(-7.44) Expected Volume: exp(-3.18) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-2.2(10.86%) | Like=2.33..3.69 [2.3306..2.3351]*| it/evals=1933/4496 eff=74.7736% N=572 Mono-modal Volume: ~exp(-8.13) * Expected Volume: exp(-3.41) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.1(11.75%) | Like=2.39..3.69 [2.3870..2.3880]*| it/evals=1948/4503 eff=74.3590% N=572 Z=-2.1(11.89%) | Like=2.39..3.69 [2.3894..2.3901]*| it/evals=1950/4503 eff=74.6154% N=572 Mono-modal Volume: ~exp(-8.13) Expected Volume: exp(-3.64) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.5(21.10%) | Like=2.86..3.69 [2.8617..2.8623]*| it/evals=2082/4550 eff=75.2116% N=572 Mono-modal Volume: ~exp(-8.47) * Expected Volume: exp(-3.86) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.1(31.51%) | Like=3.18..3.69 [3.1755..3.1764]*| it/evals=2210/4618 eff=74.7486% N=572 Z=-0.9(37.85%) | Like=3.30..3.69 [3.3045..3.3046]*| it/evals=2286/4756 eff=66.8925% N=572 Mono-modal Volume: ~exp(-8.47) Expected Volume: exp(-4.09) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.8(43.08%) | Like=3.38..3.69 [3.3774..3.3790]*| it/evals=2350/4756 eff=68.9255% N=572 Z=-0.6(50.85%) | Like=3.46..3.69 [3.4635..3.4638]*| it/evals=2450/4756 eff=71.4424% N=572 Mono-modal Volume: ~exp(-8.88) * Expected Volume: exp(-4.31) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(52.11%) | Like=3.48..3.69 [3.4765..3.4765]*| it/evals=2467/4756 eff=71.9264% N=572 Mono-modal Volume: ~exp(-9.09) * Expected Volume: exp(-4.54) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.5(60.97%) | Like=3.55..3.69 [3.5520..3.5533]*| it/evals=2598/4778 eff=74.1232% N=572 Z=-0.5(61.10%) | Like=3.55..3.69 [3.5539..3.5550]*| it/evals=2600/4778 eff=74.2180% N=572 Z=-0.4(64.93%) | Like=3.58..3.69 [3.5792..3.5794]*| it/evals=2664/4911 eff=67.3401% N=572 Mono-modal Volume: ~exp(-9.41) * Expected Volume: exp(-4.77) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.3(68.32%) | Like=3.60..3.69 [3.5992..3.5993]*| it/evals=2726/4911 eff=69.1077% N=572 Z=-0.3(74.24%) | Like=3.63..3.69 [3.6292..3.6295]*| it/evals=2850/4911 eff=72.7273% N=572 Mono-modal Volume: ~exp(-9.41) Expected Volume: exp(-4.99) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.2(76.33%) | Like=3.64..3.69 [3.6408..3.6409]*| it/evals=2900/4911 eff=73.9057% N=572 Z=-0.2(78.26%) | Like=3.65..3.69 [3.6484..3.6485]*| it/evals=2950/4936 eff=73.7840% N=572 Mono-modal Volume: ~exp(-9.50) * Expected Volume: exp(-5.22) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.2(79.52%) | Like=3.65..3.69 [3.6519..3.6521]*| it/evals=2985/4952 eff=73.3930% N=572 Z=-0.2(81.84%) | Like=3.66..3.69 [3.6594..3.6596]*| it/evals=3055/5079 eff=68.3628% N=572 Mono-modal Volume: ~exp(-9.95) * Expected Volume: exp(-5.44) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.1(83.59%) | Like=3.66..3.69 [3.6638..3.6638]*| it/evals=3114/5079 eff=69.8378% N=572 Mono-modal Volume: ~exp(-9.95) Expected Volume: exp(-5.67) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.1(86.84%) | Like=3.67..3.69 [3.6704..3.6704]*| it/evals=3242/5079 eff=72.8614% N=572 Z=-0.1(89.09%) | Like=3.68..3.69 [3.6751..3.6751]*| it/evals=3350/5113 eff=73.4532% N=572 Mono-modal Volume: ~exp(-10.30) * Expected Volume: exp(-5.90) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(89.59%) | Like=3.68..3.69 [3.6759..3.6760]*| it/evals=3377/5124 eff=73.5189% N=572 Z=-0.1(90.73%) | Like=3.68..3.69 [3.6778..3.6780]*| it/evals=3444/5253 eff=68.4314% N=572 Z=-0.1(90.83%) | Like=3.68..3.69 [3.6780..3.6781]*| it/evals=3450/5253 eff=68.6275% N=572 Z=-0.0(91.59%) | Like=3.68..3.69 [3.6796..3.6796]*| it/evals=3500/5253 eff=69.8039% N=572 Have 2 modes Volume: ~exp(-10.52) * Expected Volume: exp(-6.13) Quality: ok a: +0.000| +0.499 11 +0.501 | +1.000 Z=-0.0(91.71%) | Like=3.68..3.69 [3.6798..3.6798]*| it/evals=3508/5253 eff=69.9346% N=572 Z=-0.0(92.94%) | Like=3.68..3.69 [3.6812..3.6812]*| it/evals=3600/5253 eff=71.6340% N=572 Have 2 modes Volume: ~exp(-10.82) * Expected Volume: exp(-6.36) Quality: ok a: +0.000| +0.499 11 +0.501 | +1.000 Z=-0.0(93.37%) | Like=3.68..3.69 [3.6818..3.6818]*| it/evals=3636/5253 eff=72.6144% N=572 Mono-modal Volume: ~exp(-10.97) * Expected Volume: exp(-6.58) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(94.69%) | Like=3.68..3.69 [3.6833..3.6834]*| it/evals=3764/5272 eff=73.9832% N=572 Z=-0.0(95.02%) | Like=3.68..3.69 [3.6836..3.6836]*| it/evals=3800/5391 eff=69.0048% N=572 Z=-0.0(95.43%) | Like=3.68..3.69 [3.6840..3.6840]*| it/evals=3850/5391 eff=69.7242% N=572 Mono-modal Volume: ~exp(-11.17) * Expected Volume: exp(-6.81) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(95.76%) | Like=3.68..3.69 [3.6843..3.6843]*| it/evals=3893/5391 eff=70.5036% N=572 Z=0.0(96.16%) | Like=3.68..3.69 [3.6846..3.6847]*| it/evals=3950/5391 eff=71.5228% N=572 Z=0.0(96.49%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=4000/5391 eff=72.1823% N=572 Mono-modal Volume: ~exp(-11.26) * Expected Volume: exp(-7.05) Quality: ok a: +0.0000| +0.4995 ** +0.5005 | +1.0000 Z=0.0(96.68%) | Like=3.69..3.69 [3.6850..3.6850]*| it/evals=4032/5391 eff=72.6619% N=572 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 5391 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = 0.04684 +- 0.05136 [ultranest] Effective samples strategy satisfied (ESS = 1807.7, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 570 minimum live points (dlogz from 0.04 to 0.18, need <0.1) [ultranest] logZ error budget: single: 0.07 bs:0.05 tail:0.03 total:0.06 required:<0.10 [ultranest] done iterating. logZ = 0.041 +- 0.181 single instance: logZ = 0.041 +- 0.074 bootstrapped : logZ = 0.047 +- 0.178 tail : logZ = +- 0.030 insert order U test : converged: True correlation: inf iterations a : 0.459 │ ▁ ▁▁▁▁▁▁▁▁▂▃▄▄▅▆▇▇▇▇▇▆▅▅▄▃▃▃▁▁▁▁▁▁▁▁▁ │0.539 0.500 +- 0.010 [ultranest] Resuming from 5048 stored points Mono-modal Volume: ~exp(-4.31) * Expected Volume: exp(0.00) Quality: ok a: +0.000|********************************************************| +1.000 Z=-inf(0.00%) | Like=-1226.65..3.68 [-1226.6504..-307.9035] | it/evals=0/5391 eff=inf% N=400 Z=-951.8(0.00%) | Like=-945.36..3.68 [-1226.6504..-307.9035] | it/evals=50/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-4.41) * Expected Volume: exp(-0.23) Quality: ok a: +0.0| ********************************************** | +1.0 Z=-786.4(0.00%) | Like=-779.24..3.68 [-1226.6504..-307.9035] | it/evals=90/5391 eff=inf% N=400 Z=-746.9(0.00%) | Like=-740.63..3.68 [-1226.6504..-307.9035] | it/evals=100/5391 eff=inf% N=400 Z=-573.3(0.00%) | Like=-561.83..3.69 [-1226.6504..-307.9035] | it/evals=150/5391 eff=inf% N=400 Have 2 modes Volume: ~exp(-4.64) * Expected Volume: exp(-0.45) Quality: ok a: +0.0| 222222111111111111111111111111111111 +0.8 | +1.0 Z=-499.7(0.00%) | Like=-493.61..3.69 [-1226.6504..-307.9035] | it/evals=180/5391 eff=inf% N=400 Z=-463.6(0.00%) | Like=-456.60..3.69 [-1226.6504..-307.9035] | it/evals=200/5391 eff=inf% N=400 Z=-374.7(0.00%) | Like=-364.11..3.69 [-1226.6504..-307.9035] | it/evals=250/5391 eff=inf% N=400 Have 2 modes Volume: ~exp(-4.64) Expected Volume: exp(-0.67) Quality: ok a: +0.0| +0.2 222111111111111111111111111111 +0.8 | +1.0 Z=-278.7(0.00%) | Like=-269.85..3.69 [-307.6123..-69.3248] | it/evals=300/5391 eff=inf% N=400 Z=-208.4(0.00%) | Like=-201.38..3.69 [-307.6123..-69.3248] | it/evals=350/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-4.82) * Expected Volume: exp(-0.90) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 Z=-201.8(0.00%) | Like=-195.89..3.69 [-307.6123..-69.3248] | it/evals=360/5391 eff=inf% N=400 Z=-168.6(0.00%) | Like=-162.35..3.69 [-307.6123..-69.3248] | it/evals=400/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-5.28) * Expected Volume: exp(-1.12) Quality: ok a: +0.0| +0.3 ****************** +0.7 | +1.0 Z=-127.9(0.00%) | Like=-120.12..3.69 [-307.6123..-69.3248] | it/evals=450/5391 eff=inf% N=400 Z=-102.3(0.00%) | Like=-95.31..3.69 [-307.6123..-69.3248] | it/evals=500/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-5.28) Expected Volume: exp(-1.35) Quality: ok a: +0.0| +0.4 *************** +0.6 | +1.0 Z=-76.3(0.00%) | Like=-67.48..3.69 [-67.4783..-14.2446] | it/evals=550/5391 eff=inf% N=400 Z=-59.7(0.00%) | Like=-53.11..3.69 [-67.4783..-14.2446] | it/evals=600/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-5.89) * Expected Volume: exp(-1.57) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 Z=-50.8(0.00%) | Like=-44.90..3.69 [-67.4783..-14.2446] | it/evals=630/5391 eff=inf% N=400 Z=-46.9(0.00%) | Like=-40.98..3.69 [-67.4783..-14.2446] | it/evals=650/5391 eff=inf% N=400 Z=-36.3(0.00%) | Like=-30.34..3.69 [-67.4783..-14.2446] | it/evals=700/5391 eff=inf% N=400 Have 2 modes Volume: ~exp(-6.17) * Expected Volume: exp(-1.80) Quality: ok a: +0.0| +0.4 1111111111 +0.6 | +1.0 Z=-33.3(0.00%) | Like=-27.63..3.69 [-67.4783..-14.2446] | it/evals=720/5391 eff=inf% N=400 Z=-28.5(0.00%) | Like=-22.72..3.69 [-67.4783..-14.2446] | it/evals=750/5391 eff=inf% N=400 Z=-22.7(0.00%) | Like=-17.23..3.69 [-67.4783..-14.2446] | it/evals=800/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-6.29) * Expected Volume: exp(-2.02) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 Z=-21.8(0.00%) | Like=-15.95..3.69 [-67.4783..-14.2446] | it/evals=810/5391 eff=inf% N=400 Z=-18.2(0.00%) | Like=-12.48..3.69 [-14.1709..-1.0696] | it/evals=850/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-6.40) * Expected Volume: exp(-2.25) Quality: ok a: +0.0| +0.4 ****** +0.6 | +1.0 Z=-14.7(0.00%) | Like=-9.22..3.69 [-14.1709..-1.0696] | it/evals=900/5391 eff=inf% N=400 Z=-11.8(0.00%) | Like=-6.10..3.69 [-14.1709..-1.0696] | it/evals=950/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-6.75) * Expected Volume: exp(-2.47) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 Z=-10.0(0.00%) | Like=-4.75..3.69 [-14.1709..-1.0696] | it/evals=990/5391 eff=inf% N=400 Z=-9.6(0.01%) | Like=-4.29..3.69 [-14.1709..-1.0696] | it/evals=1000/5391 eff=inf% N=400 Z=-7.8(0.04%) | Like=-2.56..3.69 [-14.1709..-1.0696] | it/evals=1050/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-6.75) Expected Volume: exp(-2.70) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-6.3(0.18%) | Like=-1.10..3.69 [-14.1709..-1.0696] | it/evals=1100/5391 eff=inf% N=400 Z=-5.1(0.58%) | Like=-0.12..3.69 [-1.0251..1.4019] | it/evals=1150/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-7.01) * Expected Volume: exp(-2.92) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 Z=-4.7(0.87%) | Like=0.36..3.69 [-1.0251..1.4019] | it/evals=1170/5391 eff=inf% N=400 Z=-4.1(1.56%) | Like=0.95..3.69 [-1.0251..1.4019] | it/evals=1200/5391 eff=inf% N=400 Z=-3.3(3.55%) | Like=1.65..3.69 [1.4141..1.7303] | it/evals=1250/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-7.20) * Expected Volume: exp(-3.15) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 Z=-3.1(4.13%) | Like=1.78..3.69 [1.7755..1.7957] | it/evals=1260/5391 eff=inf% N=400 Z=-2.6(6.75%) | Like=2.07..3.69 [2.0671..2.0678]*| it/evals=1300/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-7.70) * Expected Volume: exp(-3.37) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 Z=-2.2(10.66%) | Like=2.32..3.69 [2.3213..2.3252]*| it/evals=1350/5391 eff=inf% N=400 Z=-1.8(15.17%) | Like=2.60..3.69 [2.5988..2.6022]*| it/evals=1400/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-7.72) * Expected Volume: exp(-3.60) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.6(19.27%) | Like=2.78..3.69 [2.7802..2.7819]*| it/evals=1440/5391 eff=inf% N=400 Z=-1.5(20.35%) | Like=2.83..3.69 [2.8275..2.8304]*| it/evals=1450/5391 eff=inf% N=400 Z=-1.3(25.93%) | Like=3.03..3.69 [3.0340..3.0340]*| it/evals=1500/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-7.95) * Expected Volume: exp(-3.82) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-1.2(29.56%) | Like=3.15..3.69 [3.1450..3.1456]*| it/evals=1530/5391 eff=inf% N=400 Z=-1.1(31.96%) | Like=3.20..3.69 [3.2018..3.2042]*| it/evals=1550/5391 eff=inf% N=400 Z=-0.9(38.04%) | Like=3.31..3.69 [3.3108..3.3130]*| it/evals=1600/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-8.34) * Expected Volume: exp(-4.05) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.9(40.40%) | Like=3.34..3.69 [3.3439..3.3448]*| it/evals=1620/5391 eff=inf% N=400 Z=-0.8(43.85%) | Like=3.40..3.69 [3.3956..3.3965]*| it/evals=1650/5391 eff=inf% N=400 Z=-0.7(49.50%) | Like=3.45..3.69 [3.4511..3.4523]*| it/evals=1700/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-8.56) * Expected Volume: exp(-4.27) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.6(50.57%) | Like=3.46..3.69 [3.4593..3.4621]*| it/evals=1710/5391 eff=inf% N=400 Z=-0.6(54.69%) | Like=3.50..3.69 [3.5027..3.5030]*| it/evals=1750/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-8.56) Expected Volume: exp(-4.50) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 Z=-0.5(59.50%) | Like=3.54..3.69 [3.5412..3.5415]*| it/evals=1800/5391 eff=inf% N=400 Z=-0.4(63.91%) | Like=3.57..3.69 [3.5739..3.5746]*| it/evals=1850/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-8.58) * Expected Volume: exp(-4.73) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 Z=-0.4(67.12%) | Like=3.59..3.69 [3.5915..3.5916]*| it/evals=1890/5391 eff=inf% N=400 Z=-0.3(67.87%) | Like=3.60..3.69 [3.5972..3.5973]*| it/evals=1900/5391 eff=inf% N=400 Z=-0.3(71.47%) | Like=3.62..3.69 [3.6209..3.6212]*| it/evals=1950/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-9.02) * Expected Volume: exp(-4.95) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.3(73.46%) | Like=3.63..3.69 [3.6274..3.6276]*| it/evals=1980/5391 eff=inf% N=400 Z=-0.2(74.71%) | Like=3.63..3.69 [3.6330..3.6334]*| it/evals=2000/5391 eff=inf% N=400 Z=-0.2(77.60%) | Like=3.65..3.69 [3.6475..3.6477]*| it/evals=2050/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-9.33) * Expected Volume: exp(-5.18) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 Z=-0.2(78.67%) | Like=3.65..3.69 [3.6506..3.6507]*| it/evals=2070/5391 eff=inf% N=400 Z=-0.2(80.17%) | Like=3.66..3.69 [3.6553..3.6553]*| it/evals=2100/5391 eff=inf% N=400 Z=-0.1(82.46%) | Like=3.66..3.69 [3.6619..3.6620]*| it/evals=2150/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-9.72) * Expected Volume: exp(-5.40) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.1(82.89%) | Like=3.66..3.69 [3.6628..3.6632]*| it/evals=2160/5391 eff=inf% N=400 Z=-0.1(84.49%) | Like=3.67..3.69 [3.6671..3.6671]*| it/evals=2200/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-9.72) Expected Volume: exp(-5.63) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 Z=-0.1(86.30%) | Like=3.67..3.69 [3.6702..3.6703]*| it/evals=2250/5391 eff=inf% N=400 Z=-0.1(87.89%) | Like=3.67..3.69 [3.6740..3.6740]*| it/evals=2300/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.05) * Expected Volume: exp(-5.85) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.1(89.04%) | Like=3.68..3.69 [3.6757..3.6757]*| it/evals=2340/5391 eff=inf% N=400 Z=-0.1(89.31%) | Like=3.68..3.69 [3.6761..3.6761]*| it/evals=2350/5391 eff=inf% N=400 Z=-0.1(90.56%) | Like=3.68..3.69 [3.6781..3.6781]*| it/evals=2400/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.19) * Expected Volume: exp(-6.08) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(91.23%) | Like=3.68..3.69 [3.6794..3.6795]*| it/evals=2430/5391 eff=inf% N=400 Z=-0.0(91.66%) | Like=3.68..3.69 [3.6800..3.6801]*| it/evals=2450/5391 eff=inf% N=400 Z=-0.0(92.64%) | Like=3.68..3.69 [3.6811..3.6812]*| it/evals=2500/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.27) * Expected Volume: exp(-6.30) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(92.99%) | Like=3.68..3.69 [3.6816..3.6817]*| it/evals=2520/5391 eff=inf% N=400 Z=-0.0(93.50%) | Like=3.68..3.69 [3.6822..3.6822]*| it/evals=2550/5391 eff=inf% N=400 Z=-0.0(94.26%) | Like=3.68..3.69 [3.6832..3.6832]*| it/evals=2600/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.64) * Expected Volume: exp(-6.53) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(94.40%) | Like=3.68..3.69 [3.6833..3.6833]*| it/evals=2610/5391 eff=inf% N=400 Z=-0.0(94.93%) | Like=3.68..3.69 [3.6836..3.6836]*| it/evals=2650/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.82) * Expected Volume: exp(-6.75) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=-0.0(95.53%) | Like=3.68..3.69 [3.6842..3.6842]*| it/evals=2700/5391 eff=inf% N=400 Z=0.0(96.05%) | Like=3.68..3.69 [3.6846..3.6846]*| it/evals=2750/5391 eff=inf% N=400 Mono-modal Volume: ~exp(-10.89) * Expected Volume: exp(-6.98) Quality: ok a: +0.000| +0.499 ** +0.501 | +1.000 Z=0.0(96.43%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2790/5391 eff=inf% N=400 Z=0.0(96.52%) | Like=3.68..3.69 [3.6849..3.6849]*| it/evals=2800/5391 eff=inf% N=400 Z=0.0(96.93%) | Like=3.69..3.69 [3.6852..3.6852]*| it/evals=2850/5391 eff=inf% N=400 [ultranest] Explored until L=4 [ultranest] Likelihood function evaluations: 5391 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] logZ = 0.05415 +- 0.07355 [ultranest] Effective samples strategy satisfied (ESS = 1262.2, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.08 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.05 to 0.28, need <0.1) [ultranest] logZ error budget: single: 0.09 bs:0.07 tail:0.03 total:0.08 required:<0.10 [ultranest] done iterating. logZ = 0.043 +- 0.277 single instance: logZ = 0.043 +- 0.089 bootstrapped : logZ = 0.054 +- 0.276 tail : logZ = +- 0.029 insert order U test : converged: True correlation: inf iterations a : 0.459 │ ▁ ▁▁▁▁▁▁▁▁▂▂▃▃▅▅▆▇▇▆▇▅▅▆▃▃▃▃▃▁▁▁▁▁▁▁▁ │0.537 0.500 +- 0.010 ran with dlogz: 0.1 first run gave: {'niter': 4663, 'logz': 0.04097491838788085, 'logzerr': 0.18063136711385278, 'logz_bs': 0.046839054946532954, 'logz_single': 0.04097491838788085, 'logzerr_tail': 0.029540945136971863, 'logzerr_bs': 0.1781993921028741, 'ess': 1807.664188236005, 'H': 3.154130512875934, 'Herr': 0.046845066579135605, 'posterior': {'mean': [0.49970846080869835], 'stdev': [0.01004709239352154], 'median': [0.49935731941479294], 'errlo': [0.4895958650446224], 'errup': [0.5098140852032222], 'information_gain_bits': [3.4619440240408434]}, 'maximum_likelihood': {'logl': 3.686231637806363, 'point': [0.4999982692743935], 'point_untransformed': [0.4999982692743935]}, 'ncall': 5391, 'paramnames': ['a'], 'logzerr_single': 0.07425775503844387, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} second run gave: {'niter': 3261, 'logz': 0.043086985075368095, 'logzerr': 0.2772812105360212, 'logz_bs': 0.05414593490827967, 'logz_single': 0.043086985075368095, 'logzerr_tail': 0.029467881253302547, 'logzerr_bs': 0.27571092414114207, 'ess': 1262.1858737131056, 'H': 3.154512692418824, 'Herr': 0.06253777219135659, 'posterior': {'mean': [0.4997429610264432], 'stdev': [0.010025904571764277], 'median': [0.4993018305586789], 'errlo': [0.490008435684678], 'errup': [0.5097540368099296], 'information_gain_bits': [3.4625603733966566]}, 'maximum_likelihood': {'logl': 3.686231637806363, 'point': [0.4999982692743935], 'point_untransformed': [0.4999982692743935]}, 'ncall': 5391, 'paramnames': ['a'], 'logzerr_single': 0.08880473935014425, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmp6hev7y52, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.1, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=528, regioncalls=128, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1226.65, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=528, regioncalls=128, ndraw=128, logz=-951.81, remainder_fraction=100.0000%, Lmin=-945.36, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=528, regioncalls=128, ndraw=128, logz=-786.35, remainder_fraction=100.0000%, Lmin=-779.24, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=528, regioncalls=128, ndraw=128, logz=-746.93, remainder_fraction=100.0000%, Lmin=-740.63, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=629, regioncalls=256, ndraw=128, logz=-573.25, remainder_fraction=100.0000%, Lmin=-561.83, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=629, regioncalls=256, ndraw=128, logz=-499.66, remainder_fraction=100.0000%, Lmin=-493.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=717, regioncalls=384, ndraw=128, logz=-463.56, remainder_fraction=100.0000%, Lmin=-456.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=717, regioncalls=384, ndraw=128, logz=-374.71, remainder_fraction=100.0000%, Lmin=-364.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=783, regioncalls=512, ndraw=128, logz=-327.38, remainder_fraction=100.0000%, Lmin=-320.54, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=783, regioncalls=512, ndraw=128, logz=-278.70, remainder_fraction=100.0000%, Lmin=-269.85, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=842, regioncalls=640, ndraw=128, logz=-208.44, remainder_fraction=100.0000%, Lmin=-201.38, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=842, regioncalls=640, ndraw=128, logz=-201.75, remainder_fraction=100.0000%, Lmin=-195.89, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=890, regioncalls=768, ndraw=128, logz=-168.63, remainder_fraction=100.0000%, Lmin=-162.35, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=938, regioncalls=896, ndraw=128, logz=-127.89, remainder_fraction=100.0000%, Lmin=-120.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=983, regioncalls=1024, ndraw=128, logz=-102.29, remainder_fraction=100.0000%, Lmin=-95.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=1045, regioncalls=1280, ndraw=128, logz=-76.26, remainder_fraction=100.0000%, Lmin=-67.48, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1097, regioncalls=1536, ndraw=128, logz=-59.67, remainder_fraction=100.0000%, Lmin=-53.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=1119, regioncalls=1664, ndraw=128, logz=-50.83, remainder_fraction=100.0000%, Lmin=-44.90, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=1149, regioncalls=1792, ndraw=128, logz=-46.91, remainder_fraction=100.0000%, Lmin=-40.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=1202, regioncalls=2048, ndraw=128, logz=-36.30, remainder_fraction=100.0000%, Lmin=-30.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1220, regioncalls=2176, ndraw=128, logz=-33.33, remainder_fraction=100.0000%, Lmin=-27.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1241, regioncalls=2304, ndraw=128, logz=-28.46, remainder_fraction=100.0000%, Lmin=-22.72, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1304, regioncalls=2688, ndraw=128, logz=-22.67, remainder_fraction=100.0000%, Lmin=-17.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1321, regioncalls=2816, ndraw=128, logz=-21.80, remainder_fraction=100.0000%, Lmin=-15.95, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1358, regioncalls=3072, ndraw=128, logz=-18.22, remainder_fraction=100.0000%, Lmin=-12.48, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1408, regioncalls=3584, ndraw=128, logz=-14.70, remainder_fraction=100.0000%, Lmin=-9.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1454, regioncalls=4096, ndraw=128, logz=-11.83, remainder_fraction=99.9993%, Lmin=-6.10, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1495, regioncalls=4480, ndraw=128, logz=-9.96, remainder_fraction=99.9955%, Lmin=-4.75, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1510, regioncalls=4608, ndraw=128, logz=-9.56, remainder_fraction=99.9931%, Lmin=-4.29, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1560, regioncalls=5248, ndraw=128, logz=-7.83, remainder_fraction=99.9619%, Lmin=-2.56, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1589, regioncalls=5760, ndraw=128, logz=-6.88, remainder_fraction=99.9019%, Lmin=-1.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1608, regioncalls=6016, ndraw=128, logz=-6.28, remainder_fraction=99.8203%, Lmin=-1.10, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1662, regioncalls=6912, ndraw=128, logz=-5.10, remainder_fraction=99.4200%, Lmin=-0.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1714, regioncalls=7936, ndraw=128, logz=-4.10, remainder_fraction=98.4353%, Lmin=0.95, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=1768, regioncalls=9088, ndraw=128, logz=-3.28, remainder_fraction=96.4486%, Lmin=1.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1785, regioncalls=9344, ndraw=128, logz=-3.13, remainder_fraction=95.8667%, Lmin=1.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=1820, regioncalls=10240, ndraw=128, logz=-2.63, remainder_fraction=93.2522%, Lmin=2.07, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=1878, regioncalls=11776, ndraw=128, logz=-2.18, remainder_fraction=89.3449%, Lmin=2.32, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=1928, regioncalls=13312, ndraw=128, logz=-1.83, remainder_fraction=84.8291%, Lmin=2.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=1969, regioncalls=14464, ndraw=128, logz=-1.60, remainder_fraction=80.7292%, Lmin=2.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=1978, regioncalls=14720, ndraw=128, logz=-1.54, remainder_fraction=79.6520%, Lmin=2.83, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=2029, regioncalls=16640, ndraw=128, logz=-1.30, remainder_fraction=74.0684%, Lmin=3.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2062, regioncalls=17408, ndraw=128, logz=-1.18, remainder_fraction=70.4449%, Lmin=3.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=2080, regioncalls=18048, ndraw=128, logz=-1.10, remainder_fraction=68.0352%, Lmin=3.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2219, regioncalls=19456, ndraw=128, logz=-0.92, remainder_fraction=61.9581%, Lmin=3.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2219, regioncalls=19456, ndraw=128, logz=-0.86, remainder_fraction=59.5960%, Lmin=3.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=2219, regioncalls=19456, ndraw=128, logz=-0.78, remainder_fraction=56.1467%, Lmin=3.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=2336, regioncalls=20352, ndraw=128, logz=-0.66, remainder_fraction=50.5050%, Lmin=3.45, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2336, regioncalls=20352, ndraw=128, logz=-0.64, remainder_fraction=49.4302%, Lmin=3.46, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=2336, regioncalls=20352, ndraw=128, logz=-0.56, remainder_fraction=45.3100%, Lmin=3.50, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2439, regioncalls=20736, ndraw=128, logz=-0.48, remainder_fraction=40.5034%, Lmin=3.54, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=2439, regioncalls=20736, ndraw=128, logz=-0.40, remainder_fraction=36.0867%, Lmin=3.57, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=2479, regioncalls=21632, ndraw=128, logz=-0.34, remainder_fraction=32.1257%, Lmin=3.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=2531, regioncalls=22528, ndraw=128, logz=-0.29, remainder_fraction=28.5291%, Lmin=3.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=2559, regioncalls=23296, ndraw=128, logz=-0.27, remainder_fraction=26.5391%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=2586, regioncalls=24448, ndraw=128, logz=-0.25, remainder_fraction=25.2935%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=2641, regioncalls=25344, ndraw=128, logz=-0.21, remainder_fraction=22.4028%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=2655, regioncalls=25600, ndraw=128, logz=-0.20, remainder_fraction=21.3341%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=2687, regioncalls=26112, ndraw=128, logz=-0.18, remainder_fraction=19.8255%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=2739, regioncalls=26880, ndraw=128, logz=-0.15, remainder_fraction=17.5384%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=2790, regioncalls=27648, ndraw=128, logz=-0.13, remainder_fraction=15.5068%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=2849, regioncalls=28416, ndraw=128, logz=-0.10, remainder_fraction=13.7044%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=2965, regioncalls=28672, ndraw=128, logz=-0.09, remainder_fraction=12.1072%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=2965, regioncalls=28672, ndraw=128, logz=-0.07, remainder_fraction=10.9631%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=2965, regioncalls=28672, ndraw=128, logz=-0.07, remainder_fraction=10.6942%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3088, regioncalls=29056, ndraw=128, logz=-0.06, remainder_fraction=9.4447%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3088, regioncalls=29056, ndraw=128, logz=-0.05, remainder_fraction=8.7657%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=3088, regioncalls=29056, ndraw=128, logz=-0.04, remainder_fraction=8.3401%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=3207, regioncalls=29440, ndraw=128, logz=-0.03, remainder_fraction=7.3634%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=3207, regioncalls=29440, ndraw=128, logz=-0.02, remainder_fraction=6.5007%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3311, regioncalls=29568, ndraw=128, logz=-0.02, remainder_fraction=5.7383%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3311, regioncalls=29568, ndraw=128, logz=-0.01, remainder_fraction=5.5969%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=3311, regioncalls=29568, ndraw=128, logz=-0.01, remainder_fraction=5.0652%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=3372, regioncalls=30848, ndraw=128, logz=-0.00, remainder_fraction=4.4709%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=3486, regioncalls=31232, ndraw=128, logz=0.00, remainder_fraction=3.9462%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=3486, regioncalls=31232, ndraw=128, logz=0.01, remainder_fraction=3.5710%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3486, regioncalls=31232, ndraw=128, logz=0.01, remainder_fraction=3.4829%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=3537, regioncalls=32128, ndraw=128, logz=0.01, remainder_fraction=3.0740%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 3551 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 0.04562 +- 0.06643 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1262.2, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.06 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 572 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 399 and 399, most (1767/1768 iterations) have 398 [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 572 minimum live points (dlogz from 0.05 to 0.16, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.07 tail:0.03 total:0.07 required:<0.10 [32mINFO [0m ultranest:integrator.py:1299 Widening roots to 572 live points (have 400 already) ... [32mINFO [0m ultranest:integrator.py:1339 Sampling 172 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 572.0), (inf, 572.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=3851, regioncalls=32512, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1233.27, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=128, ncalls=3851, regioncalls=32512, ndraw=128, logz=-813.16, remainder_fraction=100.0000%, Lmin=-806.92, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=3851, regioncalls=32512, ndraw=128, logz=-623.76, remainder_fraction=100.0000%, Lmin=-616.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=264, ncalls=3851, regioncalls=32512, ndraw=128, logz=-511.66, remainder_fraction=100.0000%, Lmin=-503.81, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=392, ncalls=3925, regioncalls=32640, ndraw=128, logz=-325.40, remainder_fraction=100.0000%, Lmin=-318.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3925, regioncalls=32640, ndraw=128, logz=-265.45, remainder_fraction=100.0000%, Lmin=-258.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=521, ncalls=3987, regioncalls=32768, ndraw=128, logz=-203.18, remainder_fraction=100.0000%, Lmin=-196.97, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=3987, regioncalls=32768, ndraw=128, logz=-188.11, remainder_fraction=100.0000%, Lmin=-181.88, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=651, ncalls=4041, regioncalls=32896, ndraw=128, logz=-126.37, remainder_fraction=100.0000%, Lmin=-119.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=783, ncalls=4081, regioncalls=33024, ndraw=128, logz=-81.55, remainder_fraction=100.0000%, Lmin=-74.83, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=4112, regioncalls=33152, ndraw=128, logz=-63.87, remainder_fraction=100.0000%, Lmin=-57.76, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=914, ncalls=4112, regioncalls=33152, ndraw=128, logz=-49.78, remainder_fraction=100.0000%, Lmin=-43.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1043, ncalls=4159, regioncalls=33408, ndraw=128, logz=-33.25, remainder_fraction=100.0000%, Lmin=-27.07, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=4189, regioncalls=33536, ndraw=128, logz=-27.41, remainder_fraction=100.0000%, Lmin=-21.42, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1173, ncalls=4209, regioncalls=33664, ndraw=128, logz=-21.63, remainder_fraction=100.0000%, Lmin=-15.84, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=4223, regioncalls=33792, ndraw=128, logz=-19.81, remainder_fraction=100.0000%, Lmin=-13.99, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1302, ncalls=4238, regioncalls=33920, ndraw=128, logz=-14.21, remainder_fraction=99.9999%, Lmin=-8.82, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1441, ncalls=4294, regioncalls=34560, ndraw=128, logz=-9.38, remainder_fraction=99.9917%, Lmin=-4.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=4317, regioncalls=34816, ndraw=128, logz=-7.98, remainder_fraction=99.9670%, Lmin=-2.75, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1561, ncalls=4348, regioncalls=35072, ndraw=128, logz=-6.64, remainder_fraction=99.8735%, Lmin=-1.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=4367, regioncalls=35456, ndraw=128, logz=-5.03, remainder_fraction=99.3755%, Lmin=0.04, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=4416, regioncalls=36224, ndraw=128, logz=-3.67, remainder_fraction=97.5793%, Lmin=1.27, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=4451, regioncalls=36736, ndraw=128, logz=-3.14, remainder_fraction=95.9120%, Lmin=1.72, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1933, ncalls=4496, regioncalls=37632, ndraw=128, logz=-2.17, remainder_fraction=89.1401%, Lmin=2.33, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1948, ncalls=4503, regioncalls=37760, ndraw=128, logz=-2.10, remainder_fraction=88.2499%, Lmin=2.39, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=4503, regioncalls=37760, ndraw=128, logz=-2.09, remainder_fraction=88.1147%, Lmin=2.39, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2082, ncalls=4550, regioncalls=39424, ndraw=128, logz=-1.51, remainder_fraction=78.8970%, Lmin=2.86, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2210, ncalls=4618, regioncalls=41216, ndraw=128, logz=-1.11, remainder_fraction=68.4922%, Lmin=3.18, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2286, ncalls=4756, regioncalls=42240, ndraw=128, logz=-0.93, remainder_fraction=62.1508%, Lmin=3.30, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=4756, regioncalls=42240, ndraw=128, logz=-0.80, remainder_fraction=56.9172%, Lmin=3.38, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=4756, regioncalls=42240, ndraw=128, logz=-0.63, remainder_fraction=49.1470%, Lmin=3.46, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2467, ncalls=4756, regioncalls=42240, ndraw=128, logz=-0.61, remainder_fraction=47.8918%, Lmin=3.48, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2598, ncalls=4778, regioncalls=43648, ndraw=128, logz=-0.45, remainder_fraction=39.0289%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=4778, regioncalls=43648, ndraw=128, logz=-0.45, remainder_fraction=38.9029%, Lmin=3.55, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2664, ncalls=4911, regioncalls=44672, ndraw=128, logz=-0.39, remainder_fraction=35.0665%, Lmin=3.58, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2726, ncalls=4911, regioncalls=44672, ndraw=128, logz=-0.34, remainder_fraction=31.6822%, Lmin=3.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=4911, regioncalls=44672, ndraw=128, logz=-0.26, remainder_fraction=25.7564%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=4911, regioncalls=44672, ndraw=128, logz=-0.23, remainder_fraction=23.6731%, Lmin=3.64, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=4936, regioncalls=45184, ndraw=128, logz=-0.20, remainder_fraction=21.7394%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2985, ncalls=4952, regioncalls=45312, ndraw=128, logz=-0.19, remainder_fraction=20.4793%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3055, ncalls=5079, regioncalls=45824, ndraw=128, logz=-0.16, remainder_fraction=18.1647%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3114, ncalls=5079, regioncalls=45824, ndraw=128, logz=-0.14, remainder_fraction=16.4127%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3242, ncalls=5079, regioncalls=45824, ndraw=128, logz=-0.10, remainder_fraction=13.1587%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3350, ncalls=5113, regioncalls=46464, ndraw=128, logz=-0.07, remainder_fraction=10.9120%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3377, ncalls=5124, regioncalls=46592, ndraw=128, logz=-0.07, remainder_fraction=10.4119%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3444, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.06, remainder_fraction=9.2679%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3450, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.06, remainder_fraction=9.1719%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3500, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.05, remainder_fraction=8.4078%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3508, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.05, remainder_fraction=8.2914%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3600, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.03, remainder_fraction=7.0639%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3636, ncalls=5253, regioncalls=47360, ndraw=128, logz=-0.03, remainder_fraction=6.6343%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3764, ncalls=5272, regioncalls=48128, ndraw=128, logz=-0.01, remainder_fraction=5.3069%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3800, ncalls=5391, regioncalls=48384, ndraw=128, logz=-0.01, remainder_fraction=4.9837%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3850, ncalls=5391, regioncalls=48384, ndraw=128, logz=-0.01, remainder_fraction=4.5672%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3893, ncalls=5391, regioncalls=48384, ndraw=128, logz=-0.00, remainder_fraction=4.2369%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3950, ncalls=5391, regioncalls=48384, ndraw=128, logz=0.00, remainder_fraction=3.8355%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4000, ncalls=5391, regioncalls=48384, ndraw=128, logz=0.01, remainder_fraction=3.5148%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4032, ncalls=5391, regioncalls=48384, ndraw=128, logz=0.01, remainder_fraction=3.3237%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5391 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 0.04684 +- 0.05136 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1807.7, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 683 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 571 and 571, most (3132/3133 iterations) have 570 [35mDEBUG [0m ultranest:integrator.py:1564 at least 570 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 570 minimum live points (dlogz from 0.04 to 0.18, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.05 tail:0.03 total:0.06 required:<0.10 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmp6hev7y52, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [3.68337942 3.68622715 0. 0.50003002 0.50003002]: u=[0.50003002] -> p=[0.50003002] -> L=3.686227145661632 [32mINFO [0m ultranest:integrator.py:2246 Resuming from 5048 stored points [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.1, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=5391, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-1226.65, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=5391, regioncalls=0, ndraw=128, logz=-951.81, remainder_fraction=100.0000%, Lmin=-945.36, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=5391, regioncalls=0, ndraw=128, logz=-786.35, remainder_fraction=100.0000%, Lmin=-779.24, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=5391, regioncalls=0, ndraw=128, logz=-746.93, remainder_fraction=100.0000%, Lmin=-740.63, Lmax=3.68 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=5391, regioncalls=0, ndraw=128, logz=-573.25, remainder_fraction=100.0000%, Lmin=-561.83, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=5391, regioncalls=0, ndraw=128, logz=-499.66, remainder_fraction=100.0000%, Lmin=-493.61, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=5391, regioncalls=0, ndraw=128, logz=-463.56, remainder_fraction=100.0000%, Lmin=-456.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=5391, regioncalls=0, ndraw=128, logz=-374.71, remainder_fraction=100.0000%, Lmin=-364.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=5391, regioncalls=0, ndraw=128, logz=-278.70, remainder_fraction=100.0000%, Lmin=-269.85, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=5391, regioncalls=0, ndraw=128, logz=-208.44, remainder_fraction=100.0000%, Lmin=-201.38, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=5391, regioncalls=0, ndraw=128, logz=-201.75, remainder_fraction=100.0000%, Lmin=-195.89, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=5391, regioncalls=0, ndraw=128, logz=-168.63, remainder_fraction=100.0000%, Lmin=-162.35, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=5391, regioncalls=0, ndraw=128, logz=-127.89, remainder_fraction=100.0000%, Lmin=-120.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=5391, regioncalls=0, ndraw=128, logz=-102.29, remainder_fraction=100.0000%, Lmin=-95.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=5391, regioncalls=0, ndraw=128, logz=-76.26, remainder_fraction=100.0000%, Lmin=-67.48, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=5391, regioncalls=0, ndraw=128, logz=-59.67, remainder_fraction=100.0000%, Lmin=-53.11, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=5391, regioncalls=0, ndraw=128, logz=-50.83, remainder_fraction=100.0000%, Lmin=-44.90, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=5391, regioncalls=0, ndraw=128, logz=-46.91, remainder_fraction=100.0000%, Lmin=-40.98, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=5391, regioncalls=0, ndraw=128, logz=-36.30, remainder_fraction=100.0000%, Lmin=-30.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=5391, regioncalls=0, ndraw=128, logz=-33.33, remainder_fraction=100.0000%, Lmin=-27.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=5391, regioncalls=0, ndraw=128, logz=-28.46, remainder_fraction=100.0000%, Lmin=-22.72, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=5391, regioncalls=0, ndraw=128, logz=-22.67, remainder_fraction=100.0000%, Lmin=-17.23, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=5391, regioncalls=0, ndraw=128, logz=-21.80, remainder_fraction=100.0000%, Lmin=-15.95, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=5391, regioncalls=0, ndraw=128, logz=-18.22, remainder_fraction=100.0000%, Lmin=-12.48, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=5391, regioncalls=0, ndraw=128, logz=-14.70, remainder_fraction=100.0000%, Lmin=-9.22, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=5391, regioncalls=0, ndraw=128, logz=-11.83, remainder_fraction=99.9993%, Lmin=-6.10, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=5391, regioncalls=0, ndraw=128, logz=-9.96, remainder_fraction=99.9955%, Lmin=-4.75, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=5391, regioncalls=0, ndraw=128, logz=-9.56, remainder_fraction=99.9931%, Lmin=-4.29, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=5391, regioncalls=0, ndraw=128, logz=-7.83, remainder_fraction=99.9619%, Lmin=-2.56, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=5391, regioncalls=0, ndraw=128, logz=-6.28, remainder_fraction=99.8203%, Lmin=-1.10, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=5391, regioncalls=0, ndraw=128, logz=-5.10, remainder_fraction=99.4200%, Lmin=-0.12, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=5391, regioncalls=0, ndraw=128, logz=-4.69, remainder_fraction=99.1264%, Lmin=0.36, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=5391, regioncalls=0, ndraw=128, logz=-4.10, remainder_fraction=98.4353%, Lmin=0.95, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=5391, regioncalls=0, ndraw=128, logz=-3.28, remainder_fraction=96.4486%, Lmin=1.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=5391, regioncalls=0, ndraw=128, logz=-3.13, remainder_fraction=95.8667%, Lmin=1.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=5391, regioncalls=0, ndraw=128, logz=-2.63, remainder_fraction=93.2522%, Lmin=2.07, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=5391, regioncalls=0, ndraw=128, logz=-2.18, remainder_fraction=89.3449%, Lmin=2.32, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.83, remainder_fraction=84.8291%, Lmin=2.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.60, remainder_fraction=80.7292%, Lmin=2.78, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.54, remainder_fraction=79.6520%, Lmin=2.83, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.30, remainder_fraction=74.0684%, Lmin=3.03, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.18, remainder_fraction=70.4449%, Lmin=3.15, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=5391, regioncalls=0, ndraw=128, logz=-1.10, remainder_fraction=68.0352%, Lmin=3.20, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.92, remainder_fraction=61.9581%, Lmin=3.31, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.86, remainder_fraction=59.5960%, Lmin=3.34, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.78, remainder_fraction=56.1467%, Lmin=3.40, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.66, remainder_fraction=50.5050%, Lmin=3.45, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.64, remainder_fraction=49.4302%, Lmin=3.46, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.56, remainder_fraction=45.3100%, Lmin=3.50, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.48, remainder_fraction=40.5034%, Lmin=3.54, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.40, remainder_fraction=36.0867%, Lmin=3.57, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.36, remainder_fraction=32.8840%, Lmin=3.59, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.34, remainder_fraction=32.1257%, Lmin=3.60, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.29, remainder_fraction=28.5291%, Lmin=3.62, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.27, remainder_fraction=26.5391%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.25, remainder_fraction=25.2935%, Lmin=3.63, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.21, remainder_fraction=22.4028%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.20, remainder_fraction=21.3341%, Lmin=3.65, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.18, remainder_fraction=19.8255%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.15, remainder_fraction=17.5384%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.14, remainder_fraction=17.1128%, Lmin=3.66, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.13, remainder_fraction=15.5068%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.10, remainder_fraction=13.7044%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.09, remainder_fraction=12.1072%, Lmin=3.67, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.07, remainder_fraction=10.9631%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.07, remainder_fraction=10.6942%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.06, remainder_fraction=9.4447%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.05, remainder_fraction=8.7657%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.04, remainder_fraction=8.3401%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.03, remainder_fraction=7.3634%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.03, remainder_fraction=7.0055%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.02, remainder_fraction=6.5007%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.02, remainder_fraction=5.7383%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.01, remainder_fraction=5.5969%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.01, remainder_fraction=5.0652%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=5391, regioncalls=0, ndraw=128, logz=-0.00, remainder_fraction=4.4709%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=5391, regioncalls=0, ndraw=128, logz=0.00, remainder_fraction=3.9462%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=5391, regioncalls=0, ndraw=128, logz=0.01, remainder_fraction=3.5710%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=5391, regioncalls=0, ndraw=128, logz=0.01, remainder_fraction=3.4829%, Lmin=3.68, Lmax=3.69 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=5391, regioncalls=0, ndraw=128, logz=0.01, remainder_fraction=3.0740%, Lmin=3.69, Lmax=3.69 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=4 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5391 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = 0.05415 +- 0.07355 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1262.2, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.08 nat, need <0.50 nat) [35mDEBUG [0m ultranest:integrator.py:1537 conservative estimate says at least 572 live points are needed to reach dlogz goal [35mDEBUG [0m ultranest:integrator.py:1553 number of live points vary between 399 and 399, most (1463/1464 iterations) have 398 [35mDEBUG [0m ultranest:integrator.py:1564 at least 398 live points are needed to reach dlogz goal [32mINFO [0m ultranest:integrator.py:1568 Evidency uncertainty strategy wants 398 minimum live points (dlogz from 0.05 to 0.28, need <0.1) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.09 bs:0.07 tail:0.03 total:0.08 required:<0.10 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_reactive_run_resume_eggbox[hdf5] | 1.81 | |
------------------------------Captured stdout call------------------------------ ====== Running Eggbox problem [1] ===== [ultranest] Sampling 100 live points from prior ... Mono-modal Volume: ~exp(-3.50) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|******************* ** * * ******* *** ** ** ********| +3.1e+01 b: +0.0|**** ***** * ***** *** * * ****** ** *********** *******| +31.4 Z=-inf(0.00%) | Like=1.20..233.34 [1.1994..29.4283] | it/evals=0/101 eff=0.0000% N=100 Z=-0.3(0.00%) | Like=2.82..233.34 [1.1994..29.4283] | it/evals=10/111 eff=90.9091% N=100 Z=1.8(0.00%) | Like=5.72..241.87 [1.1994..29.4283] | it/evals=20/123 eff=86.9565% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.23) Quality: ok a: +0.0|********************* ** ********* *** ** ** ****** *| +31.4 b: +0.0| *** **** ** **** *** * * ****** ************* *******| +31.4 Z=5.8(0.00%) | Like=9.88..241.87 [1.1994..29.4283] | it/evals=30/137 eff=81.0811% N=100 Z=10.9(0.00%) | Like=15.15..241.87 [1.1994..29.4283] | it/evals=40/149 eff=81.6327% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.46) Quality: ok a: +0.0|************** ****** ** **** ***** *** *** ** ****** *| +31.4 b: +0.0|**** ***** ** **** *** * * *** ** ********** ** ********| +31.4 Z=14.8(0.00%) | Like=19.62..241.87 [1.1994..29.4283] | it/evals=50/162 eff=80.6452% N=100 Z=21.2(0.00%) | Like=25.71..241.87 [1.1994..29.4283] | it/evals=60/175 eff=80.0000% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.69) Quality: ok a: +0.0|******** ***** ******** ********** ** **** * ********| +31.4 b: +0.0|********** ** **** ***** *** ** ********** ** ********| +31.4 Z=25.5(0.00%) | Like=29.64..241.87 [29.6178..62.1019] | it/evals=70/188 eff=79.5455% N=100 Z=28.4(0.00%) | Like=32.87..241.87 [29.6178..62.1019] | it/evals=80/206 eff=75.4717% N=100 Z=31.0(0.00%) | Like=35.17..241.87 [29.6178..62.1019] | it/evals=90/223 eff=73.1707% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.92) Quality: ok a: +0.0|******** ***** ** ***** **** ******* ** ****** ********| +31.4 b: +0.0|********** ******* *** ** ************ **** ***** *****| +31.4 Z=34.9(0.00%) | Like=39.71..241.87 [29.6178..62.1019] | it/evals=100/244 eff=69.4444% N=100 Z=39.4(0.00%) | Like=45.13..241.87 [29.6178..62.1019] | it/evals=110/284 eff=59.7826% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.15) Quality: ok a: +0.0| **** ** ***** * ****** *** * ***** ** ********** *****| +31.4 b: +0.0|***** **** **** * **** **** ********* **** ***** *****| +31.4 Z=43.4(0.00%) | Like=49.11..241.87 [29.6178..62.1019] | it/evals=120/309 eff=57.4163% N=100 Z=48.4(0.00%) | Like=54.54..241.87 [29.6178..62.1019] | it/evals=130/341 eff=53.9419% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.38) Quality: ok a: +0.0| *** * ******* ****** ** ******* * ********* ****| +31.4 b: +0.0|***** *** **** ******** ********* ********** ***| +31.4 Z=54.8(0.00%) | Like=60.32..241.87 [29.6178..62.1019] | it/evals=140/377 eff=50.5415% N=100 Z=63.7(0.00%) | Like=69.11..241.87 [62.2053..112.3934] | it/evals=150/396 eff=50.6757% N=100 Z=71.7(0.00%) | Like=77.89..241.87 [62.2053..112.3934] | it/evals=160/441 eff=46.9208% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.61) Quality: ok a: +0.0| *** * ****** ******* ********* ********* ****| +31.4 b: +0.0|**** ******** ******** ******* * ******* ***| +31.4 Z=77.8(0.00%) | Like=87.30..241.87 [62.2053..112.3934] | it/evals=170/502 eff=42.2886% N=100 Z=88.7(0.00%) | Like=94.79..241.87 [62.2053..112.3934] | it/evals=180/553 eff=39.7351% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.84) Quality: ok a: +0.0| ** * ****** ***** ******** ******** ****| +31.4 b: +0.0|*** ******** ******* ******* ****** ***| +31.4 Z=94.6(0.00%) | Like=100.05..241.87 [62.2053..112.3934] | it/evals=190/584 eff=39.2562% N=100 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 626 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 235.268 +- 1.038 single instance: logZ = 235.268 +- 0.231 bootstrapped : logZ = 231.619 +- 0.772 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 12.65 │ ▁ ▇ │19.30 18.98 +- 0.35 b : 5.3 │ ▇ ▁ │26.2 6.3 +- 1.1 pointstore: (300, 7) 626 626 0 ====== Running Eggbox problem [2] ===== [ultranest] Resuming from 300 stored points Mono-modal Volume: ~exp(-3.06) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|******************* ** * * ******* *** ** ** ********| +3.1e+01 b: +0.0|**** ***** * ***** *** * * ****** ** *********** *******| +31.4 Z=-inf(0.00%) | Like=1.20..233.34 [1.1994..29.4283] | it/evals=0/626 eff=inf% N=100 Z=-0.3(0.00%) | Like=2.82..233.34 [1.1994..29.4283] | it/evals=10/626 eff=inf% N=100 Z=1.8(0.00%) | Like=5.72..241.87 [1.1994..29.4283] | it/evals=20/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.06) Expected Volume: exp(-0.23) Quality: ok a: +0.0|********************* ** ********* *** ** ** ****** *| +31.4 b: +0.0| *** **** ** **** *** * * ****** ************* *******| +31.4 Z=5.8(0.00%) | Like=9.88..241.87 [1.1994..29.4283] | it/evals=30/626 eff=inf% N=100 Z=10.9(0.00%) | Like=15.15..241.87 [1.1994..29.4283] | it/evals=40/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.06) Expected Volume: exp(-0.46) Quality: ok a: +0.0|************** ****** ** **** ***** *** *** ** ****** *| +31.4 b: +0.0|**** ***** ** **** *** * * *** ** ********** ** ********| +31.4 Z=14.8(0.00%) | Like=19.62..241.87 [1.1994..29.4283] | it/evals=50/626 eff=inf% N=100 Z=21.2(0.00%) | Like=25.71..241.87 [1.1994..29.4283] | it/evals=60/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.06) Expected Volume: exp(-0.69) Quality: ok a: +0.0|******** ***** ******** ********** ** **** * ********| +31.4 b: +0.0|********** ** **** ***** *** ** ********** ** ********| +31.4 Z=25.5(0.00%) | Like=29.64..241.87 [29.6178..62.1019] | it/evals=70/626 eff=inf% N=100 Z=28.4(0.00%) | Like=32.87..241.87 [29.6178..62.1019] | it/evals=80/626 eff=inf% N=100 Z=31.0(0.00%) | Like=35.17..241.87 [29.6178..62.1019] | it/evals=90/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.06) Expected Volume: exp(-0.92) Quality: ok a: +0.0|******** ***** ** ***** **** ******* ** ****** ********| +31.4 b: +0.0|********** ******* *** ** ************ **** ***** *****| +31.4 Z=34.9(0.00%) | Like=39.71..241.87 [29.6178..62.1019] | it/evals=100/626 eff=inf% N=100 Z=39.4(0.00%) | Like=45.13..241.87 [29.6178..62.1019] | it/evals=110/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.27) * Expected Volume: exp(-1.15) Quality: ok a: +0.0| **** ** ***** * ****** *** * ***** ** ********** *****| +31.4 b: +0.0|***** **** **** * **** **** ********* **** ***** *****| +31.4 Z=41.4(0.00%) | Like=46.04..241.87 [29.6178..62.1019] | it/evals=115/626 eff=inf% N=100 Z=43.4(0.00%) | Like=49.11..241.87 [29.6178..62.1019] | it/evals=120/626 eff=inf% N=100 Z=48.4(0.00%) | Like=54.54..241.87 [29.6178..62.1019] | it/evals=130/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-1.38) Quality: ok a: +0.0| *** * ******* ****** ** ******* * ********* ****| +31.4 b: +0.0|***** *** **** ******** ********* ********** ***| +31.4 Z=54.8(0.00%) | Like=60.32..241.87 [29.6178..62.1019] | it/evals=140/626 eff=inf% N=100 Z=63.7(0.00%) | Like=69.11..241.87 [62.2053..112.3934] | it/evals=150/626 eff=inf% N=100 Z=71.7(0.00%) | Like=77.89..241.87 [62.2053..112.3934] | it/evals=160/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-1.61) Quality: ok a: +0.0| *** * ****** ******* ********* ********* ****| +31.4 b: +0.0|**** ******** ******** ******* * ******* ***| +31.4 Z=77.8(0.00%) | Like=87.30..241.87 [62.2053..112.3934] | it/evals=170/626 eff=inf% N=100 Z=88.7(0.00%) | Like=94.79..241.87 [62.2053..112.3934] | it/evals=180/626 eff=inf% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-1.84) Quality: ok a: +0.0| ** * ****** ***** ******** ******** ****| +31.4 b: +0.0|*** ******** ******* ******* ****** ***| +31.4 Z=94.6(0.00%) | Like=100.05..241.87 [62.2053..112.3934] | it/evals=190/626 eff=inf% N=100 Z=102.1(0.00%) | Like=108.02..241.87 [62.2053..112.3934] | it/evals=200/635 eff=2222.2222% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-2.07) Quality: ok a: +0.0| ** ******* ****** ****** ******* ****| +31.4 b: +0.0|**** ******* *** ** ******* ****** ***| +31.4 Z=107.6(0.00%) | Like=113.25..241.87 [112.8373..172.1226] | it/evals=210/686 eff=350.0000% N=100 Z=115.9(0.00%) | Like=122.26..241.87 [112.8373..172.1226] | it/evals=220/773 eff=149.6599% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-2.30) Quality: ok a: +0.0|*** ***** ******* ****** ****** ***| +31.4 b: +0.0|*** ****** ** ** ****** ****** ***| +31.4 Z=122.3(0.00%) | Like=129.85..241.87 [112.8373..172.1226] | it/evals=230/847 eff=104.0724% N=100 Z=129.8(0.00%) | Like=136.55..241.87 [112.8373..172.1226] | it/evals=240/970 eff=69.7674% N=100 Z=137.3(0.00%) | Like=144.23..241.87 [112.8373..172.1226] | it/evals=250/1051 eff=58.8235% N=100 Mono-modal Volume: ~exp(-3.27) Expected Volume: exp(-2.53) Quality: ok a: +3.1e-05|*** ***** ***** ***** ***** ***| +3.1e+01 b: +0.0|*** ****** ** ** ***** ****** ***| +31.4 Z=142.6(0.00%) | Like=149.88..241.87 [112.8373..172.1226] | it/evals=260/1113 eff=53.3881% N=100 Z=150.6(0.00%) | Like=158.76..242.08 [112.8373..172.1226] | it/evals=270/1240 eff=43.9739% N=100 Have 17 modes Volume: ~exp(-3.30) * Expected Volume: exp(-2.76) Quality: ok a: +3.1e-05|66 HAFAH 3343 522D5 11191 G77| +3.1e+01 b: +3.1e-05|66E 2H22C 94 44 55A75 31131 DDF| +3.1e+01 Z=155.4(0.00%) | Like=161.77..242.08 [112.8373..172.1226] | it/evals=276/1298 eff=41.0714% N=100 Z=157.4(0.00%) | Like=165.84..242.08 [112.8373..172.1226] | it/evals=280/1339 eff=39.2707% N=100 Z=163.4(0.00%) | Like=169.78..242.08 [112.8373..172.1226] | it/evals=290/1390 eff=37.9581% N=100 Have 11 modes Volume: ~exp(-3.97) * Expected Volume: exp(-2.99) Quality: ok a: +3.1e-05|61 1AB31 33432 22221 1112 87| +3.1e+01 b: +3.1e-05|61 12228 94221 173A1 31131 3B| +3.1e+01 Z=169.2(0.00%) | Like=176.32..242.08 [173.9486..199.8262] | it/evals=299/1456 eff=36.0241% N=100 Z=169.7(0.00%) | Like=176.48..242.08 [173.9486..199.8262] | it/evals=300/1464 eff=35.7995% N=100 Z=177.0(0.00%) | Like=183.98..242.08 [173.9486..199.8262] | it/evals=310/1544 eff=33.7691% N=100 Z=182.0(0.00%) | Like=189.03..242.08 [173.9486..199.8262] | it/evals=320/1653 eff=31.1587% N=100 Have 11 modes Volume: ~exp(-4.06) * Expected Volume: exp(-3.22) Quality: ok a: +3.1e-05|61 1A111 33432 22221 8112 87| +3.1e+01 b: +3.1e-05|61 12288 9421 173A3 1133 3B| +3.1e+01 Z=182.7(0.00%) | Like=189.52..242.08 [173.9486..199.8262] | it/evals=322/1678 eff=30.6084% N=100 Z=185.5(0.00%) | Like=192.14..242.08 [173.9486..199.8262] | it/evals=330/1823 eff=27.5689% N=100 Z=187.8(0.00%) | Like=194.77..242.08 [173.9486..199.8262] | it/evals=340/1954 eff=25.6024% N=100 Have 9 modes Volume: ~exp(-4.40) * Expected Volume: exp(-3.45) Quality: ok a: +3.1e-05|69 1311 3432 22221 1112 27| +3.1e+01 b: +3.1e-05|82 1221 42 17233 1139 9| +3.1e+01 Z=189.3(0.00%) | Like=196.24..242.08 [173.9486..199.8262] | it/evals=345/2018 eff=24.7845% N=100 Z=190.7(0.00%) | Like=197.56..242.08 [173.9486..199.8262] | it/evals=350/2076 eff=24.1379% N=100 Z=193.4(0.00%) | Like=201.00..242.08 [200.0805..218.4468] | it/evals=360/2164 eff=23.4070% N=100 Have 9 modes Volume: ~exp(-4.40) Expected Volume: exp(-3.68) Quality: ok a: +3.1e-05|69 9391 343 222 1112 27| +3.1e+01 b: +3.1e-05|82 221 420 7233 1133 9| +3.1e+01 Z=197.1(0.00%) | Like=204.14..242.08 [200.0805..218.4468] | it/evals=370/2261 eff=22.6300% N=100 Z=200.0(0.00%) | Like=206.82..242.86 [200.0805..218.4468] | it/evals=380/2354 eff=21.9907% N=100 Z=203.2(0.00%) | Like=210.59..242.86 [200.0805..218.4468] | it/evals=390/2534 eff=20.4403% N=100 Have 9 modes Volume: ~exp(-4.40) Expected Volume: exp(-3.91) Quality: ok a: +3.1e-05|69 9391 343 222 1111 27| +3.1e+01 b: +3.1e-05|82 221 430 723 113 09| +3.1e+01 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 2692 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 235.433 +- 0.793 single instance: logZ = 235.433 +- 0.246 bootstrapped : logZ = 235.293 +- 0.385 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 0.0 │▁ ▁ ▇ ▇ ▄ ▃│31.4 18.3 +- 7.5 b : 0 │▄ ▃ ▅ ▁▇ ▃│31 18 +- 11 pointstore: (500, 7) 2066 2692 626 sampler results: ******************** {'niter': 500, 'logz': 235.43269744807787, 'logzerr': 0.7927566606506654, 'logz_bs': 235.29290858222754, 'logz_single': 235.43269744807787, 'logzerr_tail': 0.6931471805598903, 'logzerr_bs': 0.38472081967040594, 'ess': 5.274154691031552, 'H': 6.033256737351138, 'Herr': 0.22556685337143384, 'posterior': {'mean': [18.307180915024208, 17.648001295331124], 'stdev': [7.461548357996892, 10.76980339796849], 'median': [18.865082871338988, 18.948817848540145], 'errlo': [12.522158632337275, 0.18220504961510647], 'errup': [25.226194363774848, 25.103965082907077], 'information_gain_bits': [2.585369753894852, 2.5350418776355]}, 'maximum_likelihood': {'logl': 242.85917012872116, 'point': [12.522158632337275, 25.103965082907077], 'point_untransformed': [0.398592688903465, 0.7990840268302006]}, 'ncall': 2692, 'paramnames': ['a', 'b'], 'logzerr_single': 0.24562688650372005, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} reader results: ******************** {'niter': 500, 'logz': 235.43269744807787, 'logzerr': 0.7656270015727977, 'logz_bs': 235.75786154760183, 'logz_single': 235.43269744807787, 'logzerr_tail': 0.6931471805598903, 'logzerr_bs': 0.32516409952395975, 'ess': 5.274154691031552, 'H': 6.033256737351138, 'Herr': 0.22000811918063334, 'posterior': {'mean': [18.307180915024208, 17.648001295331113], 'stdev': [7.461548357996893, 10.769803397968483], 'median': [18.865082871338988, 18.948817848540145], 'errlo': [12.522158632337275, 0.18220504961510647], 'errup': [25.226194363774848, 25.103965082907077], 'information_gain_bits': [2.585369753894852, 2.5350418776355]}, 'maximum_likelihood': {'logl': 242.85917012872116, 'point': [12.522158632337275, 25.103965082907077], 'point_untransformed': [0.398592688903465, 0.7990840268302006]}, 'insertion_order_MWW_test': {'independent_iterations': inf, 'converged': True}} niter () logz () skipping logzerr () skipping logz_bs () logz_single () skipping logzerr_tail () skipping logzerr_bs () ess () H () skipping Herr () weighted_samples :: upoints (500, 2) weighted_samples :: points (500, 2) weighted_samples :: weights (500,) weighted_samples :: logw (500,) weighted_samples :: logl (500,) maximum_likelihood :: logl () maximum_likelihood :: point (2,) maximum_likelihood :: point_untransformed (2,) insertion_order_MWW_test: {'independent_iterations': inf, 'converged': True} {'independent_iterations': inf, 'converged': True} -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpgdb95oxy, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=200, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.20, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=111, regioncalls=440, ndraw=40, logz=-0.29, remainder_fraction=100.0000%, Lmin=2.82, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=123, regioncalls=920, ndraw=40, logz=1.84, remainder_fraction=100.0000%, Lmin=5.72, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=137, regioncalls=1480, ndraw=40, logz=5.81, remainder_fraction=100.0000%, Lmin=9.88, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=149, regioncalls=1960, ndraw=40, logz=10.87, remainder_fraction=100.0000%, Lmin=15.15, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=162, regioncalls=2480, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.62, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=175, regioncalls=3000, ndraw=40, logz=21.16, remainder_fraction=100.0000%, Lmin=25.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=188, regioncalls=3520, ndraw=40, logz=25.45, remainder_fraction=100.0000%, Lmin=29.64, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=206, regioncalls=4240, ndraw=40, logz=28.39, remainder_fraction=100.0000%, Lmin=32.87, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=223, regioncalls=4920, ndraw=40, logz=31.00, remainder_fraction=100.0000%, Lmin=35.17, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=244, regioncalls=5760, ndraw=40, logz=34.92, remainder_fraction=100.0000%, Lmin=39.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=284, regioncalls=7360, ndraw=40, logz=39.44, remainder_fraction=100.0000%, Lmin=45.13, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=309, regioncalls=8360, ndraw=40, logz=43.44, remainder_fraction=100.0000%, Lmin=49.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=341, regioncalls=9640, ndraw=40, logz=48.37, remainder_fraction=100.0000%, Lmin=54.54, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5]), array([60, 22, 14, 3, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=377, regioncalls=11080, ndraw=40, logz=54.83, remainder_fraction=100.0000%, Lmin=60.32, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=396, regioncalls=11840, ndraw=40, logz=63.66, remainder_fraction=100.0000%, Lmin=69.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=441, regioncalls=13640, ndraw=40, logz=71.73, remainder_fraction=100.0000%, Lmin=77.89, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=502, regioncalls=16080, ndraw=40, logz=77.78, remainder_fraction=100.0000%, Lmin=87.30, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=553, regioncalls=18120, ndraw=40, logz=88.69, remainder_fraction=100.0000%, Lmin=94.79, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=584, regioncalls=19360, ndraw=40, logz=94.65, remainder_fraction=100.0000%, Lmin=100.05, Lmax=241.87 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 626 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpgdb95oxy, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [107.94292989 191.2912043 0. 0.57032947 0.98250479 17.91742875 30.86629839]: u=[0.57032947 0.98250479] -> p=[17.91742875 30.86629839] -> L=191.29120430116933 [32mINFO [0m ultranest:integrator.py:2246 Resuming from 300 stored points [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=400, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=626, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.20, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=626, regioncalls=0, ndraw=40, logz=-0.29, remainder_fraction=100.0000%, Lmin=2.82, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=626, regioncalls=0, ndraw=40, logz=1.84, remainder_fraction=100.0000%, Lmin=5.72, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=626, regioncalls=0, ndraw=40, logz=5.81, remainder_fraction=100.0000%, Lmin=9.88, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=626, regioncalls=0, ndraw=40, logz=10.87, remainder_fraction=100.0000%, Lmin=15.15, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=626, regioncalls=0, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.62, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=626, regioncalls=0, ndraw=40, logz=21.16, remainder_fraction=100.0000%, Lmin=25.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=626, regioncalls=0, ndraw=40, logz=25.45, remainder_fraction=100.0000%, Lmin=29.64, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=626, regioncalls=0, ndraw=40, logz=28.39, remainder_fraction=100.0000%, Lmin=32.87, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=626, regioncalls=0, ndraw=40, logz=31.00, remainder_fraction=100.0000%, Lmin=35.17, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=626, regioncalls=0, ndraw=40, logz=34.92, remainder_fraction=100.0000%, Lmin=39.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=626, regioncalls=0, ndraw=40, logz=39.44, remainder_fraction=100.0000%, Lmin=45.13, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=115, ncalls=626, regioncalls=0, ndraw=40, logz=41.41, remainder_fraction=100.0000%, Lmin=46.04, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=626, regioncalls=0, ndraw=40, logz=43.44, remainder_fraction=100.0000%, Lmin=49.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=626, regioncalls=0, ndraw=40, logz=48.37, remainder_fraction=100.0000%, Lmin=54.54, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=626, regioncalls=0, ndraw=40, logz=54.83, remainder_fraction=100.0000%, Lmin=60.32, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=626, regioncalls=0, ndraw=40, logz=63.66, remainder_fraction=100.0000%, Lmin=69.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=626, regioncalls=0, ndraw=40, logz=71.73, remainder_fraction=100.0000%, Lmin=77.89, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=626, regioncalls=0, ndraw=40, logz=77.78, remainder_fraction=100.0000%, Lmin=87.30, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=626, regioncalls=0, ndraw=40, logz=88.69, remainder_fraction=100.0000%, Lmin=94.79, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=626, regioncalls=0, ndraw=40, logz=94.65, remainder_fraction=100.0000%, Lmin=100.05, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=635, regioncalls=360, ndraw=40, logz=102.10, remainder_fraction=100.0000%, Lmin=108.02, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=210, ncalls=686, regioncalls=2400, ndraw=40, logz=107.58, remainder_fraction=100.0000%, Lmin=113.25, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=220, ncalls=773, regioncalls=5880, ndraw=40, logz=115.88, remainder_fraction=100.0000%, Lmin=122.26, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), array([13, 18, 18, 14, 9, 15, 4, 6, 2, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=230, ncalls=847, regioncalls=8840, ndraw=40, logz=122.33, remainder_fraction=100.0000%, Lmin=129.85, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=970, regioncalls=13760, ndraw=40, logz=129.76, remainder_fraction=100.0000%, Lmin=136.55, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1051, regioncalls=17000, ndraw=40, logz=137.33, remainder_fraction=100.0000%, Lmin=144.23, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]), array([ 8, 13, 5, 6, 8, 3, 8, 5, 8, 6, 9, 5, 5, 4, 6, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=260, ncalls=1113, regioncalls=19480, ndraw=40, logz=142.65, remainder_fraction=100.0000%, Lmin=149.88, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1240, regioncalls=24560, ndraw=40, logz=150.61, remainder_fraction=100.0000%, Lmin=158.76, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), array([ 7, 14, 8, 5, 8, 4, 6, 5, 8, 10, 3, 5, 4, 5, 2, 1, 5])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=276, ncalls=1298, regioncalls=26880, ndraw=40, logz=155.38, remainder_fraction=100.0000%, Lmin=161.77, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=1339, regioncalls=28520, ndraw=40, logz=157.41, remainder_fraction=100.0000%, Lmin=165.84, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=290, ncalls=1390, regioncalls=30560, ndraw=40, logz=163.43, remainder_fraction=100.0000%, Lmin=169.78, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), array([25, 33, 23, 1, 1, 1, 1, 8, 1, 1, 5])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=299, ncalls=1456, regioncalls=33200, ndraw=40, logz=169.15, remainder_fraction=100.0000%, Lmin=176.32, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=1464, regioncalls=33520, ndraw=40, logz=169.65, remainder_fraction=100.0000%, Lmin=176.48, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=310, ncalls=1544, regioncalls=36720, ndraw=40, logz=177.02, remainder_fraction=100.0000%, Lmin=183.98, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=1653, regioncalls=41080, ndraw=40, logz=182.02, remainder_fraction=100.0000%, Lmin=189.03, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), array([26, 35, 22, 1, 1, 1, 1, 7, 1, 1, 4])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=322, ncalls=1678, regioncalls=42080, ndraw=40, logz=182.71, remainder_fraction=100.0000%, Lmin=189.52, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=330, ncalls=1823, regioncalls=47880, ndraw=40, logz=185.51, remainder_fraction=100.0000%, Lmin=192.14, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=340, ncalls=1954, regioncalls=53120, ndraw=40, logz=187.85, remainder_fraction=100.0000%, Lmin=194.77, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([27, 34, 24, 1, 1, 1, 1, 5, 6])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=345, ncalls=2018, regioncalls=55680, ndraw=40, logz=189.30, remainder_fraction=100.0000%, Lmin=196.24, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2076, regioncalls=58000, ndraw=40, logz=190.69, remainder_fraction=100.0000%, Lmin=197.56, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=2164, regioncalls=61520, ndraw=40, logz=193.38, remainder_fraction=100.0000%, Lmin=201.00, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([29, 28, 28, 1, 1, 1, 1, 6, 5])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=370, ncalls=2261, regioncalls=65400, ndraw=40, logz=197.14, remainder_fraction=100.0000%, Lmin=204.14, Lmax=242.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=380, ncalls=2354, regioncalls=69120, ndraw=40, logz=200.04, remainder_fraction=100.0000%, Lmin=206.82, Lmax=242.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=2534, regioncalls=76320, ndraw=40, logz=203.21, remainder_fraction=100.0000%, Lmin=210.59, Lmax=242.86 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([35, 23, 27, 1, 1, 1, 1, 6, 5])) [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 2692 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_reactive_run_resume_eggbox[tsv] | 1.80 | |
------------------------------Captured stdout call------------------------------ ====== Running Eggbox problem [1] ===== [ultranest] Sampling 100 live points from prior ... Mono-modal Volume: ~exp(-3.50) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|******************* ** * * ******* *** ** ** ********| +3.1e+01 b: +0.0|**** ***** * ***** *** * * ****** ** *********** *******| +31.4 Z=-inf(0.00%) | Like=1.20..233.34 [1.1994..29.4283] | it/evals=0/101 eff=0.0000% N=100 Z=-0.3(0.00%) | Like=2.82..233.34 [1.1994..29.4283] | it/evals=10/111 eff=90.9091% N=100 Z=1.8(0.00%) | Like=5.72..241.87 [1.1994..29.4283] | it/evals=20/123 eff=86.9565% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.23) Quality: ok a: +0.0|********************* ** ********* *** ** ** ****** *| +31.4 b: +0.0| *** **** ** **** *** * * ****** ************* *******| +31.4 Z=5.8(0.00%) | Like=9.88..241.87 [1.1994..29.4283] | it/evals=30/137 eff=81.0811% N=100 Z=10.9(0.00%) | Like=15.15..241.87 [1.1994..29.4283] | it/evals=40/149 eff=81.6327% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.46) Quality: ok a: +0.0|************** ****** ** **** ***** *** *** ** ****** *| +31.4 b: +0.0|**** ***** ** **** *** * * *** ** ********** ** ********| +31.4 Z=14.8(0.00%) | Like=19.62..241.87 [1.1994..29.4283] | it/evals=50/162 eff=80.6452% N=100 Z=21.2(0.00%) | Like=25.71..241.87 [1.1994..29.4283] | it/evals=60/175 eff=80.0000% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.69) Quality: ok a: +0.0|******** ***** ******** ********** ** **** * ********| +31.4 b: +0.0|********** ** **** ***** *** ** ********** ** ********| +31.4 Z=25.5(0.00%) | Like=29.64..241.87 [29.6178..62.1019] | it/evals=70/188 eff=79.5455% N=100 Z=28.4(0.00%) | Like=32.87..241.87 [29.6178..62.1019] | it/evals=80/206 eff=75.4717% N=100 Z=31.0(0.00%) | Like=35.17..241.87 [29.6178..62.1019] | it/evals=90/223 eff=73.1707% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.92) Quality: ok a: +0.0|******** ***** ** ***** **** ******* ** ****** ********| +31.4 b: +0.0|********** ******* *** ** ************ **** ***** *****| +31.4 Z=34.9(0.00%) | Like=39.71..241.87 [29.6178..62.1019] | it/evals=100/244 eff=69.4444% N=100 Z=39.4(0.00%) | Like=45.13..241.87 [29.6178..62.1019] | it/evals=110/284 eff=59.7826% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.15) Quality: ok a: +0.0| **** ** ***** * ****** *** * ***** ** ********** *****| +31.4 b: +0.0|***** **** **** * **** **** ********* **** ***** *****| +31.4 Z=43.4(0.00%) | Like=49.11..241.87 [29.6178..62.1019] | it/evals=120/309 eff=57.4163% N=100 Z=48.4(0.00%) | Like=54.54..241.87 [29.6178..62.1019] | it/evals=130/341 eff=53.9419% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.38) Quality: ok a: +0.0| *** * ******* ****** ** ******* * ********* ****| +31.4 b: +0.0|***** *** **** ******** ********* ********** ***| +31.4 Z=54.8(0.00%) | Like=60.32..241.87 [29.6178..62.1019] | it/evals=140/377 eff=50.5415% N=100 Z=63.7(0.00%) | Like=69.11..241.87 [62.2053..112.3934] | it/evals=150/396 eff=50.6757% N=100 Z=71.7(0.00%) | Like=77.89..241.87 [62.2053..112.3934] | it/evals=160/441 eff=46.9208% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.61) Quality: ok a: +0.0| *** * ****** ******* ********* ********* ****| +31.4 b: +0.0|**** ******** ******** ******* * ******* ***| +31.4 Z=77.8(0.00%) | Like=87.30..241.87 [62.2053..112.3934] | it/evals=170/502 eff=42.2886% N=100 Z=88.7(0.00%) | Like=94.79..241.87 [62.2053..112.3934] | it/evals=180/553 eff=39.7351% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.84) Quality: ok a: +0.0| ** * ****** ***** ******** ******** ****| +31.4 b: +0.0|*** ******** ******* ******* ****** ***| +31.4 Z=94.6(0.00%) | Like=100.05..241.87 [62.2053..112.3934] | it/evals=190/584 eff=39.2562% N=100 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 626 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 235.268 +- 1.038 single instance: logZ = 235.268 +- 0.231 bootstrapped : logZ = 231.619 +- 0.772 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 12.65 │ ▁ ▇ │19.30 18.98 +- 0.35 b : 5.3 │ ▇ ▁ │26.2 6.3 +- 1.1 626 626 0 ====== Running Eggbox problem [2] ===== [ultranest] Sampling 100 live points from prior ... Mono-modal Volume: ~exp(-3.23) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|* **** ************** ************ ****** *** **********| +3.1e+01 b: +3.1e-05|**** ****** ** ***** ********** **** *** **** **********| +3.1e+01 Z=-inf(0.00%) | Like=1.05..221.09 [1.0467..28.5121] | it/evals=0/101 eff=0.0000% N=100 Z=0.1(0.00%) | Like=3.95..221.09 [1.0467..28.5121] | it/evals=10/111 eff=90.9091% N=100 Z=3.5(0.00%) | Like=6.71..221.09 [1.0467..28.5121] | it/evals=20/122 eff=90.9091% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.23) Quality: ok a: +3.1e-05|* ****************** *********** ***** * ********* **| +3.1e+01 b: +3.1e-05|**** ********* ****** ******* *** *** **** * ********| +3.1e+01 Z=6.4(0.00%) | Like=10.30..228.18 [1.0467..28.5121] | it/evals=30/133 eff=90.9091% N=100 Z=10.4(0.00%) | Like=14.59..228.18 [1.0467..28.5121] | it/evals=40/145 eff=88.8889% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.46) Quality: ok a: +3.1e-05|* *********** ****************** ***** ****** *******| +3.1e+01 b: +3.1e-05|**** **** **** ***** * ******* * ** *** **** *** ******| +3.1e+01 Z=14.8(0.00%) | Like=19.71..228.18 [1.0467..28.5121] | it/evals=50/156 eff=89.2857% N=100 Z=22.2(0.00%) | Like=26.91..228.18 [1.0467..28.5121] | it/evals=60/174 eff=81.0811% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.69) Quality: ok a: +3.1e-05|* ** ******* ****************** ************* ** ***| +3.1e+01 b: +3.1e-05|**** **** **** ***** ** ******* * *********** *** ******| +3.1e+01 Z=26.6(0.00%) | Like=31.35..228.18 [29.1828..62.4284] | it/evals=70/189 eff=78.6517% N=100 Z=29.7(0.00%) | Like=34.32..228.18 [29.1828..62.4284] | it/evals=80/213 eff=70.7965% N=100 Z=33.3(0.00%) | Like=38.77..228.18 [29.1828..62.4284] | it/evals=90/240 eff=64.2857% N=100 Mono-modal Volume: ~exp(-3.26) * Expected Volume: exp(-0.92) Quality: ok a: +3.1e-05|* * ******* * **************** ************* * * ***| +3.1e+01 b: +3.1e-05|**** ******** ***** * ******* * *********** *** *****| +3.1e+01 Z=34.5(0.00%) | Like=39.28..228.18 [29.1828..62.4284] | it/evals=92/246 eff=63.0137% N=100 Z=37.8(0.00%) | Like=42.61..228.18 [29.1828..62.4284] | it/evals=100/273 eff=57.8035% N=100 Z=42.8(0.00%) | Like=47.95..228.18 [29.1828..62.4284] | it/evals=110/297 eff=55.8376% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.15) Quality: ok a: +3.1e-05|** * ********* ********** **** *** ********** * ***| +3.1e+01 b: +3.1e-05|**** ******** * *********** ** * ***** ***** *** * ***| +3.1e+01 Z=48.9(0.00%) | Like=54.86..228.18 [29.1828..62.4284] | it/evals=120/326 eff=53.0973% N=100 Z=55.4(0.00%) | Like=61.66..228.18 [29.1828..62.4284] | it/evals=130/368 eff=48.5075% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.38) Quality: ok a: +3.1e-05|** * ********* ******** ***** *** ********* * ***| +3.1e+01 b: +3.1e-05|**** ******* * ********* ** * *** ***** *** *****| +3.1e+01 Z=60.1(0.00%) | Like=68.52..228.18 [62.6017..102.3957] | it/evals=140/402 eff=46.3576% N=100 Z=67.2(0.00%) | Like=72.18..228.18 [62.6017..102.3957] | it/evals=150/452 eff=42.6136% N=100 Z=71.3(0.00%) | Like=76.78..238.35 [62.6017..102.3957] | it/evals=160/496 eff=40.4040% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.61) Quality: ok a: +3.1e-05|** ******* ******* **** *** ********* ***| +3.1e+01 b: +3.1e-05|**** ******* * * ****** **** **** ********* ***| +3.1e+01 Z=77.7(0.00%) | Like=85.32..238.35 [62.6017..102.3957] | it/evals=170/544 eff=38.2883% N=100 Z=83.6(0.00%) | Like=91.87..238.35 [62.6017..102.3957] | it/evals=180/573 eff=38.0550% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.84) Quality: ok a: +3.1e-05|*** ****** ******* ******** ******** ****| +3.1e+01 b: +3.1e-05|**** ******* ******* ********* ******** ***| +3.1e+01 Z=92.1(0.00%) | Like=98.48..238.35 [62.6017..102.3957] | it/evals=190/595 eff=38.3838% N=100 Z=101.4(0.00%) | Like=108.11..240.89 [102.9564..147.0741] | it/evals=200/674 eff=34.8432% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.07) Quality: ok a: +0.0|*** ****** ******* **** ** ******* ****| +31.4 b: +3.1e-05|*** ****** ******* ******* ******** ***| +3.1e+01 Z=108.2(0.00%) | Like=114.20..240.89 [102.9564..147.0741] | it/evals=210/722 eff=33.7621% N=100 Z=114.0(0.00%) | Like=121.27..240.89 [102.9564..147.0741] | it/evals=220/778 eff=32.4484% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.30) Quality: ok a: +0.0|*** ****** ****** *** ** ******* ***| +31.4 b: +3.1e-05|*** ***** ***** ******* ****** ** | +3.1e+01 Z=122.3(0.00%) | Like=128.65..240.89 [102.9564..147.0741] | it/evals=230/849 eff=30.7076% N=100 Z=128.0(0.00%) | Like=134.37..240.89 [102.9564..147.0741] | it/evals=240/950 eff=28.2353% N=100 Z=132.5(0.00%) | Like=139.29..242.00 [102.9564..147.0741] | it/evals=250/1034 eff=26.7666% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.53) Quality: ok a: +0.0|*** ***** ****** *** ** ****** ***| +31.4 b: +3.1e-05|*** ***** ****** ******* ****** ** | +3.1e+01 Z=140.8(0.00%) | Like=147.11..242.37 [147.1142..186.4200] | it/evals=260/1135 eff=25.1208% N=100 Z=148.1(0.00%) | Like=155.97..242.37 [147.1142..186.4200] | it/evals=270/1279 eff=22.9008% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.76) Quality: ok a: +0.0|*** **** ****** *** * ***** **| +31.4 b: +3.1e-05|*** ***** ***** ***** ****** * *| +3.1e+01 Z=155.6(0.00%) | Like=163.08..242.37 [147.1142..186.4200] | it/evals=280/1411 eff=21.3577% N=100 Z=161.0(0.00%) | Like=168.19..242.37 [147.1142..186.4200] | it/evals=290/1566 eff=19.7817% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.99) Quality: ok a: +3.1e-05|** **** ***** ***** ***** **| +3.1e+01 b: +3.1e-05|** ***** ***** ***** ***** *| +3.1e+01 Z=166.0(0.00%) | Like=173.84..242.37 [147.1142..186.4200] | it/evals=300/1700 eff=18.7500% N=100 Z=171.7(0.00%) | Like=179.38..242.37 [147.1142..186.4200] | it/evals=310/1793 eff=18.3107% N=100 Z=177.1(0.00%) | Like=184.35..242.37 [147.1142..186.4200] | it/evals=320/1938 eff=17.4102% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.22) Quality: ok a: +3.1e-05|** **** **** ***** **** **| +3.1e+01 b: +3.1e-05|** ***** ***** **** **** *| +3.1e+01 Z=182.5(0.00%) | Like=189.50..242.37 [186.7319..216.1536] | it/evals=330/2226 eff=15.5221% N=100 Z=186.2(0.00%) | Like=193.29..242.37 [186.7319..216.1536] | it/evals=340/2654 eff=13.3125% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.45) Quality: ok a: +3.1e-05|** **** **** **** **** **| +3.1e+01 b: +3.1e-05|** **** **** **** *** *| +3.1e+01 Z=191.2(0.00%) | Like=198.42..242.37 [186.7319..216.1536] | it/evals=350/3009 eff=12.0316% N=100 Z=197.3(0.00%) | Like=205.11..242.37 [186.7319..216.1536] | it/evals=360/3318 eff=11.1871% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.68) Quality: ok a: +3.1e-05|** **** **** *** **** **| +3.1e+01 b: +3.1e-05|** **** *** *** *** *| +3.1e+01 Z=200.0(0.00%) | Like=206.86..242.37 [186.7319..216.1536] | it/evals=370/3667 eff=10.3729% N=100 Z=203.2(0.00%) | Like=210.98..242.51 [186.7319..216.1536] | it/evals=380/4187 eff=9.2978% N=100 Z=206.3(0.00%) | Like=213.44..242.60 [186.7319..216.1536] | it/evals=390/4799 eff=8.2996% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.91) Quality: ok a: +3.1e-05|** **** *** *** *** *| +3.1e+01 b: +3.1e-05|** **** *** *** *** *| +3.1e+01 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 5287 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 236.030 +- 0.815 single instance: logZ = 236.030 +- 0.247 bootstrapped : logZ = 236.020 +- 0.429 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 0.0 │▁ ▃▁ ▇ ▂ ▁▇ ▁│31.4 16.9 +- 7.9 b : 0 │▆ ▃▁ ▄▁ ▁ ▁▇ ▃│31 14 +- 11 5287 5287 0 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpepb6rurq, backend=tsv, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=200, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.20, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=111, regioncalls=440, ndraw=40, logz=-0.29, remainder_fraction=100.0000%, Lmin=2.82, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=123, regioncalls=920, ndraw=40, logz=1.84, remainder_fraction=100.0000%, Lmin=5.72, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=137, regioncalls=1480, ndraw=40, logz=5.81, remainder_fraction=100.0000%, Lmin=9.88, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=149, regioncalls=1960, ndraw=40, logz=10.87, remainder_fraction=100.0000%, Lmin=15.15, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=162, regioncalls=2480, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.62, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=175, regioncalls=3000, ndraw=40, logz=21.16, remainder_fraction=100.0000%, Lmin=25.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=188, regioncalls=3520, ndraw=40, logz=25.45, remainder_fraction=100.0000%, Lmin=29.64, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=206, regioncalls=4240, ndraw=40, logz=28.39, remainder_fraction=100.0000%, Lmin=32.87, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=223, regioncalls=4920, ndraw=40, logz=31.00, remainder_fraction=100.0000%, Lmin=35.17, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=244, regioncalls=5760, ndraw=40, logz=34.92, remainder_fraction=100.0000%, Lmin=39.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=284, regioncalls=7360, ndraw=40, logz=39.44, remainder_fraction=100.0000%, Lmin=45.13, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=309, regioncalls=8360, ndraw=40, logz=43.44, remainder_fraction=100.0000%, Lmin=49.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=341, regioncalls=9640, ndraw=40, logz=48.37, remainder_fraction=100.0000%, Lmin=54.54, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5]), array([60, 22, 14, 3, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=377, regioncalls=11080, ndraw=40, logz=54.83, remainder_fraction=100.0000%, Lmin=60.32, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=396, regioncalls=11840, ndraw=40, logz=63.66, remainder_fraction=100.0000%, Lmin=69.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=441, regioncalls=13640, ndraw=40, logz=71.73, remainder_fraction=100.0000%, Lmin=77.89, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=502, regioncalls=16080, ndraw=40, logz=77.78, remainder_fraction=100.0000%, Lmin=87.30, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=553, regioncalls=18120, ndraw=40, logz=88.69, remainder_fraction=100.0000%, Lmin=94.79, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=584, regioncalls=19360, ndraw=40, logz=94.65, remainder_fraction=100.0000%, Lmin=100.05, Lmax=241.87 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 626 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpepb6rurq, backend=tsv, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=400, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.05, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=111, regioncalls=440, ndraw=40, logz=0.06, remainder_fraction=100.0000%, Lmin=3.95, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=122, regioncalls=880, ndraw=40, logz=3.55, remainder_fraction=100.0000%, Lmin=6.71, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=133, regioncalls=1320, ndraw=40, logz=6.36, remainder_fraction=100.0000%, Lmin=10.30, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=145, regioncalls=1800, ndraw=40, logz=10.43, remainder_fraction=100.0000%, Lmin=14.59, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=156, regioncalls=2240, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.71, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=174, regioncalls=2960, ndraw=40, logz=22.21, remainder_fraction=100.0000%, Lmin=26.91, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=189, regioncalls=3560, ndraw=40, logz=26.59, remainder_fraction=100.0000%, Lmin=31.35, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=213, regioncalls=4520, ndraw=40, logz=29.74, remainder_fraction=100.0000%, Lmin=34.32, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=240, regioncalls=5600, ndraw=40, logz=33.34, remainder_fraction=100.0000%, Lmin=38.77, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=92, ncalls=246, regioncalls=5840, ndraw=40, logz=34.49, remainder_fraction=100.0000%, Lmin=39.28, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=273, regioncalls=6920, ndraw=40, logz=37.85, remainder_fraction=100.0000%, Lmin=42.61, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=297, regioncalls=7880, ndraw=40, logz=42.78, remainder_fraction=100.0000%, Lmin=47.95, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=326, regioncalls=9040, ndraw=40, logz=48.91, remainder_fraction=100.0000%, Lmin=54.86, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=368, regioncalls=10720, ndraw=40, logz=55.41, remainder_fraction=100.0000%, Lmin=61.66, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=402, regioncalls=12080, ndraw=40, logz=60.12, remainder_fraction=100.0000%, Lmin=68.52, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=452, regioncalls=14080, ndraw=40, logz=67.24, remainder_fraction=100.0000%, Lmin=72.18, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=496, regioncalls=15840, ndraw=40, logz=71.34, remainder_fraction=100.0000%, Lmin=76.78, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=544, regioncalls=17760, ndraw=40, logz=77.69, remainder_fraction=100.0000%, Lmin=85.32, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=573, regioncalls=18920, ndraw=40, logz=83.64, remainder_fraction=100.0000%, Lmin=91.87, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=595, regioncalls=19800, ndraw=40, logz=92.13, remainder_fraction=100.0000%, Lmin=98.48, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=674, regioncalls=22960, ndraw=40, logz=101.38, remainder_fraction=100.0000%, Lmin=108.11, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=210, ncalls=722, regioncalls=24880, ndraw=40, logz=108.20, remainder_fraction=100.0000%, Lmin=114.20, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=220, ncalls=778, regioncalls=27120, ndraw=40, logz=114.04, remainder_fraction=100.0000%, Lmin=121.27, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=230, ncalls=849, regioncalls=29960, ndraw=40, logz=122.34, remainder_fraction=100.0000%, Lmin=128.65, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=950, regioncalls=34000, ndraw=40, logz=128.02, remainder_fraction=100.0000%, Lmin=134.37, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1034, regioncalls=37360, ndraw=40, logz=132.48, remainder_fraction=100.0000%, Lmin=139.29, Lmax=242.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=260, ncalls=1135, regioncalls=41400, ndraw=40, logz=140.81, remainder_fraction=100.0000%, Lmin=147.11, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1279, regioncalls=47160, ndraw=40, logz=148.11, remainder_fraction=100.0000%, Lmin=155.97, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=1411, regioncalls=52440, ndraw=40, logz=155.63, remainder_fraction=100.0000%, Lmin=163.08, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=290, ncalls=1566, regioncalls=58640, ndraw=40, logz=161.05, remainder_fraction=100.0000%, Lmin=168.19, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=1700, regioncalls=64000, ndraw=40, logz=166.03, remainder_fraction=100.0000%, Lmin=173.84, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=310, ncalls=1793, regioncalls=67720, ndraw=40, logz=171.65, remainder_fraction=100.0000%, Lmin=179.38, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=1938, regioncalls=73520, ndraw=40, logz=177.14, remainder_fraction=100.0000%, Lmin=184.35, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=330, ncalls=2226, regioncalls=85040, ndraw=40, logz=182.54, remainder_fraction=100.0000%, Lmin=189.50, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=340, ncalls=2654, regioncalls=102160, ndraw=40, logz=186.24, remainder_fraction=100.0000%, Lmin=193.29, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=3009, regioncalls=116360, ndraw=40, logz=191.15, remainder_fraction=100.0000%, Lmin=198.42, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=3318, regioncalls=128720, ndraw=40, logz=197.32, remainder_fraction=100.0000%, Lmin=205.11, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=370, ncalls=3667, regioncalls=142680, ndraw=40, logz=200.02, remainder_fraction=100.0000%, Lmin=206.86, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=380, ncalls=4187, regioncalls=163480, ndraw=40, logz=203.15, remainder_fraction=100.0000%, Lmin=210.98, Lmax=242.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=4799, regioncalls=187960, ndraw=40, logz=206.25, remainder_fraction=100.0000%, Lmin=213.44, Lmax=242.60 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5287 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_reactive_run_resume_eggbox[csv] | 1.62 | |
------------------------------Captured stdout call------------------------------ ====== Running Eggbox problem [1] ===== [ultranest] Sampling 100 live points from prior ... Mono-modal Volume: ~exp(-3.50) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|******************* ** * * ******* *** ** ** ********| +3.1e+01 b: +0.0|**** ***** * ***** *** * * ****** ** *********** *******| +31.4 Z=-inf(0.00%) | Like=1.20..233.34 [1.1994..29.4283] | it/evals=0/101 eff=0.0000% N=100 Z=-0.3(0.00%) | Like=2.82..233.34 [1.1994..29.4283] | it/evals=10/111 eff=90.9091% N=100 Z=1.8(0.00%) | Like=5.72..241.87 [1.1994..29.4283] | it/evals=20/123 eff=86.9565% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.23) Quality: ok a: +0.0|********************* ** ********* *** ** ** ****** *| +31.4 b: +0.0| *** **** ** **** *** * * ****** ************* *******| +31.4 Z=5.8(0.00%) | Like=9.88..241.87 [1.1994..29.4283] | it/evals=30/137 eff=81.0811% N=100 Z=10.9(0.00%) | Like=15.15..241.87 [1.1994..29.4283] | it/evals=40/149 eff=81.6327% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.46) Quality: ok a: +0.0|************** ****** ** **** ***** *** *** ** ****** *| +31.4 b: +0.0|**** ***** ** **** *** * * *** ** ********** ** ********| +31.4 Z=14.8(0.00%) | Like=19.62..241.87 [1.1994..29.4283] | it/evals=50/162 eff=80.6452% N=100 Z=21.2(0.00%) | Like=25.71..241.87 [1.1994..29.4283] | it/evals=60/175 eff=80.0000% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.69) Quality: ok a: +0.0|******** ***** ******** ********** ** **** * ********| +31.4 b: +0.0|********** ** **** ***** *** ** ********** ** ********| +31.4 Z=25.5(0.00%) | Like=29.64..241.87 [29.6178..62.1019] | it/evals=70/188 eff=79.5455% N=100 Z=28.4(0.00%) | Like=32.87..241.87 [29.6178..62.1019] | it/evals=80/206 eff=75.4717% N=100 Z=31.0(0.00%) | Like=35.17..241.87 [29.6178..62.1019] | it/evals=90/223 eff=73.1707% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-0.92) Quality: ok a: +0.0|******** ***** ** ***** **** ******* ** ****** ********| +31.4 b: +0.0|********** ******* *** ** ************ **** ***** *****| +31.4 Z=34.9(0.00%) | Like=39.71..241.87 [29.6178..62.1019] | it/evals=100/244 eff=69.4444% N=100 Z=39.4(0.00%) | Like=45.13..241.87 [29.6178..62.1019] | it/evals=110/284 eff=59.7826% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.15) Quality: ok a: +0.0| **** ** ***** * ****** *** * ***** ** ********** *****| +31.4 b: +0.0|***** **** **** * **** **** ********* **** ***** *****| +31.4 Z=43.4(0.00%) | Like=49.11..241.87 [29.6178..62.1019] | it/evals=120/309 eff=57.4163% N=100 Z=48.4(0.00%) | Like=54.54..241.87 [29.6178..62.1019] | it/evals=130/341 eff=53.9419% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.38) Quality: ok a: +0.0| *** * ******* ****** ** ******* * ********* ****| +31.4 b: +0.0|***** *** **** ******** ********* ********** ***| +31.4 Z=54.8(0.00%) | Like=60.32..241.87 [29.6178..62.1019] | it/evals=140/377 eff=50.5415% N=100 Z=63.7(0.00%) | Like=69.11..241.87 [62.2053..112.3934] | it/evals=150/396 eff=50.6757% N=100 Z=71.7(0.00%) | Like=77.89..241.87 [62.2053..112.3934] | it/evals=160/441 eff=46.9208% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.61) Quality: ok a: +0.0| *** * ****** ******* ********* ********* ****| +31.4 b: +0.0|**** ******** ******** ******* * ******* ***| +31.4 Z=77.8(0.00%) | Like=87.30..241.87 [62.2053..112.3934] | it/evals=170/502 eff=42.2886% N=100 Z=88.7(0.00%) | Like=94.79..241.87 [62.2053..112.3934] | it/evals=180/553 eff=39.7351% N=100 Mono-modal Volume: ~exp(-3.50) Expected Volume: exp(-1.84) Quality: ok a: +0.0| ** * ****** ***** ******** ******** ****| +31.4 b: +0.0|*** ******** ******* ******* ****** ***| +31.4 Z=94.6(0.00%) | Like=100.05..241.87 [62.2053..112.3934] | it/evals=190/584 eff=39.2562% N=100 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 626 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 235.268 +- 1.038 single instance: logZ = 235.268 +- 0.231 bootstrapped : logZ = 231.619 +- 0.772 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 12.65 │ ▁ ▇ │19.30 18.98 +- 0.35 b : 5.3 │ ▇ ▁ │26.2 6.3 +- 1.1 626 626 0 ====== Running Eggbox problem [2] ===== [ultranest] Sampling 100 live points from prior ... Mono-modal Volume: ~exp(-3.23) * Expected Volume: exp(0.00) Quality: ok a: +3.1e-05|* **** ************** ************ ****** *** **********| +3.1e+01 b: +3.1e-05|**** ****** ** ***** ********** **** *** **** **********| +3.1e+01 Z=-inf(0.00%) | Like=1.05..221.09 [1.0467..28.5121] | it/evals=0/101 eff=0.0000% N=100 Z=0.1(0.00%) | Like=3.95..221.09 [1.0467..28.5121] | it/evals=10/111 eff=90.9091% N=100 Z=3.5(0.00%) | Like=6.71..221.09 [1.0467..28.5121] | it/evals=20/122 eff=90.9091% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.23) Quality: ok a: +3.1e-05|* ****************** *********** ***** * ********* **| +3.1e+01 b: +3.1e-05|**** ********* ****** ******* *** *** **** * ********| +3.1e+01 Z=6.4(0.00%) | Like=10.30..228.18 [1.0467..28.5121] | it/evals=30/133 eff=90.9091% N=100 Z=10.4(0.00%) | Like=14.59..228.18 [1.0467..28.5121] | it/evals=40/145 eff=88.8889% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.46) Quality: ok a: +3.1e-05|* *********** ****************** ***** ****** *******| +3.1e+01 b: +3.1e-05|**** **** **** ***** * ******* * ** *** **** *** ******| +3.1e+01 Z=14.8(0.00%) | Like=19.71..228.18 [1.0467..28.5121] | it/evals=50/156 eff=89.2857% N=100 Z=22.2(0.00%) | Like=26.91..228.18 [1.0467..28.5121] | it/evals=60/174 eff=81.0811% N=100 Mono-modal Volume: ~exp(-3.23) Expected Volume: exp(-0.69) Quality: ok a: +3.1e-05|* ** ******* ****************** ************* ** ***| +3.1e+01 b: +3.1e-05|**** **** **** ***** ** ******* * *********** *** ******| +3.1e+01 Z=26.6(0.00%) | Like=31.35..228.18 [29.1828..62.4284] | it/evals=70/189 eff=78.6517% N=100 Z=29.7(0.00%) | Like=34.32..228.18 [29.1828..62.4284] | it/evals=80/213 eff=70.7965% N=100 Z=33.3(0.00%) | Like=38.77..228.18 [29.1828..62.4284] | it/evals=90/240 eff=64.2857% N=100 Mono-modal Volume: ~exp(-3.26) * Expected Volume: exp(-0.92) Quality: ok a: +3.1e-05|* * ******* * **************** ************* * * ***| +3.1e+01 b: +3.1e-05|**** ******** ***** * ******* * *********** *** *****| +3.1e+01 Z=34.5(0.00%) | Like=39.28..228.18 [29.1828..62.4284] | it/evals=92/246 eff=63.0137% N=100 Z=37.8(0.00%) | Like=42.61..228.18 [29.1828..62.4284] | it/evals=100/273 eff=57.8035% N=100 Z=42.8(0.00%) | Like=47.95..228.18 [29.1828..62.4284] | it/evals=110/297 eff=55.8376% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.15) Quality: ok a: +3.1e-05|** * ********* ********** **** *** ********** * ***| +3.1e+01 b: +3.1e-05|**** ******** * *********** ** * ***** ***** *** * ***| +3.1e+01 Z=48.9(0.00%) | Like=54.86..228.18 [29.1828..62.4284] | it/evals=120/326 eff=53.0973% N=100 Z=55.4(0.00%) | Like=61.66..228.18 [29.1828..62.4284] | it/evals=130/368 eff=48.5075% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.38) Quality: ok a: +3.1e-05|** * ********* ******** ***** *** ********* * ***| +3.1e+01 b: +3.1e-05|**** ******* * ********* ** * *** ***** *** *****| +3.1e+01 Z=60.1(0.00%) | Like=68.52..228.18 [62.6017..102.3957] | it/evals=140/402 eff=46.3576% N=100 Z=67.2(0.00%) | Like=72.18..228.18 [62.6017..102.3957] | it/evals=150/452 eff=42.6136% N=100 Z=71.3(0.00%) | Like=76.78..238.35 [62.6017..102.3957] | it/evals=160/496 eff=40.4040% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.61) Quality: ok a: +3.1e-05|** ******* ******* **** *** ********* ***| +3.1e+01 b: +3.1e-05|**** ******* * * ****** **** **** ********* ***| +3.1e+01 Z=77.7(0.00%) | Like=85.32..238.35 [62.6017..102.3957] | it/evals=170/544 eff=38.2883% N=100 Z=83.6(0.00%) | Like=91.87..238.35 [62.6017..102.3957] | it/evals=180/573 eff=38.0550% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-1.84) Quality: ok a: +3.1e-05|*** ****** ******* ******** ******** ****| +3.1e+01 b: +3.1e-05|**** ******* ******* ********* ******** ***| +3.1e+01 Z=92.1(0.00%) | Like=98.48..238.35 [62.6017..102.3957] | it/evals=190/595 eff=38.3838% N=100 Z=101.4(0.00%) | Like=108.11..240.89 [102.9564..147.0741] | it/evals=200/674 eff=34.8432% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.07) Quality: ok a: +0.0|*** ****** ******* **** ** ******* ****| +31.4 b: +3.1e-05|*** ****** ******* ******* ******** ***| +3.1e+01 Z=108.2(0.00%) | Like=114.20..240.89 [102.9564..147.0741] | it/evals=210/722 eff=33.7621% N=100 Z=114.0(0.00%) | Like=121.27..240.89 [102.9564..147.0741] | it/evals=220/778 eff=32.4484% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.30) Quality: ok a: +0.0|*** ****** ****** *** ** ******* ***| +31.4 b: +3.1e-05|*** ***** ***** ******* ****** ** | +3.1e+01 Z=122.3(0.00%) | Like=128.65..240.89 [102.9564..147.0741] | it/evals=230/849 eff=30.7076% N=100 Z=128.0(0.00%) | Like=134.37..240.89 [102.9564..147.0741] | it/evals=240/950 eff=28.2353% N=100 Z=132.5(0.00%) | Like=139.29..242.00 [102.9564..147.0741] | it/evals=250/1034 eff=26.7666% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.53) Quality: ok a: +0.0|*** ***** ****** *** ** ****** ***| +31.4 b: +3.1e-05|*** ***** ****** ******* ****** ** | +3.1e+01 Z=140.8(0.00%) | Like=147.11..242.37 [147.1142..186.4200] | it/evals=260/1135 eff=25.1208% N=100 Z=148.1(0.00%) | Like=155.97..242.37 [147.1142..186.4200] | it/evals=270/1279 eff=22.9008% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.76) Quality: ok a: +0.0|*** **** ****** *** * ***** **| +31.4 b: +3.1e-05|*** ***** ***** ***** ****** * *| +3.1e+01 Z=155.6(0.00%) | Like=163.08..242.37 [147.1142..186.4200] | it/evals=280/1411 eff=21.3577% N=100 Z=161.0(0.00%) | Like=168.19..242.37 [147.1142..186.4200] | it/evals=290/1566 eff=19.7817% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-2.99) Quality: ok a: +3.1e-05|** **** ***** ***** ***** **| +3.1e+01 b: +3.1e-05|** ***** ***** ***** ***** *| +3.1e+01 Z=166.0(0.00%) | Like=173.84..242.37 [147.1142..186.4200] | it/evals=300/1700 eff=18.7500% N=100 Z=171.7(0.00%) | Like=179.38..242.37 [147.1142..186.4200] | it/evals=310/1793 eff=18.3107% N=100 Z=177.1(0.00%) | Like=184.35..242.37 [147.1142..186.4200] | it/evals=320/1938 eff=17.4102% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.22) Quality: ok a: +3.1e-05|** **** **** ***** **** **| +3.1e+01 b: +3.1e-05|** ***** ***** **** **** *| +3.1e+01 Z=182.5(0.00%) | Like=189.50..242.37 [186.7319..216.1536] | it/evals=330/2226 eff=15.5221% N=100 Z=186.2(0.00%) | Like=193.29..242.37 [186.7319..216.1536] | it/evals=340/2654 eff=13.3125% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.45) Quality: ok a: +3.1e-05|** **** **** **** **** **| +3.1e+01 b: +3.1e-05|** **** **** **** *** *| +3.1e+01 Z=191.2(0.00%) | Like=198.42..242.37 [186.7319..216.1536] | it/evals=350/3009 eff=12.0316% N=100 Z=197.3(0.00%) | Like=205.11..242.37 [186.7319..216.1536] | it/evals=360/3318 eff=11.1871% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.68) Quality: ok a: +3.1e-05|** **** **** *** **** **| +3.1e+01 b: +3.1e-05|** **** *** *** *** *| +3.1e+01 Z=200.0(0.00%) | Like=206.86..242.37 [186.7319..216.1536] | it/evals=370/3667 eff=10.3729% N=100 Z=203.2(0.00%) | Like=210.98..242.51 [186.7319..216.1536] | it/evals=380/4187 eff=9.2978% N=100 Z=206.3(0.00%) | Like=213.44..242.60 [186.7319..216.1536] | it/evals=390/4799 eff=8.2996% N=100 Mono-modal Volume: ~exp(-3.26) Expected Volume: exp(-3.91) Quality: ok a: +3.1e-05|** **** *** *** *** *| +3.1e+01 b: +3.1e-05|** **** *** *** *** *| +3.1e+01 [ultranest] Explored until L=2e+02 [ultranest] Likelihood function evaluations: 5287 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = 236.030 +- 0.815 single instance: logZ = 236.030 +- 0.247 bootstrapped : logZ = 236.020 +- 0.429 tail : logZ = +- 0.693 insert order U test : converged: True correlation: inf iterations a : 0.0 │▁ ▃▁ ▇ ▂ ▁▇ ▁│31.4 16.9 +- 7.9 b : 0 │▆ ▃▁ ▄▁ ▁ ▁▇ ▃│31 14 +- 11 5287 5287 0 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpsbp081uu, backend=csv, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=200, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.20, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=111, regioncalls=440, ndraw=40, logz=-0.29, remainder_fraction=100.0000%, Lmin=2.82, Lmax=233.34 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=123, regioncalls=920, ndraw=40, logz=1.84, remainder_fraction=100.0000%, Lmin=5.72, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=137, regioncalls=1480, ndraw=40, logz=5.81, remainder_fraction=100.0000%, Lmin=9.88, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=149, regioncalls=1960, ndraw=40, logz=10.87, remainder_fraction=100.0000%, Lmin=15.15, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=162, regioncalls=2480, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.62, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=175, regioncalls=3000, ndraw=40, logz=21.16, remainder_fraction=100.0000%, Lmin=25.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=188, regioncalls=3520, ndraw=40, logz=25.45, remainder_fraction=100.0000%, Lmin=29.64, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=206, regioncalls=4240, ndraw=40, logz=28.39, remainder_fraction=100.0000%, Lmin=32.87, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=223, regioncalls=4920, ndraw=40, logz=31.00, remainder_fraction=100.0000%, Lmin=35.17, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=244, regioncalls=5760, ndraw=40, logz=34.92, remainder_fraction=100.0000%, Lmin=39.71, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=284, regioncalls=7360, ndraw=40, logz=39.44, remainder_fraction=100.0000%, Lmin=45.13, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=309, regioncalls=8360, ndraw=40, logz=43.44, remainder_fraction=100.0000%, Lmin=49.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=341, regioncalls=9640, ndraw=40, logz=48.37, remainder_fraction=100.0000%, Lmin=54.54, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2, 3, 4, 5]), array([60, 22, 14, 3, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=377, regioncalls=11080, ndraw=40, logz=54.83, remainder_fraction=100.0000%, Lmin=60.32, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=396, regioncalls=11840, ndraw=40, logz=63.66, remainder_fraction=100.0000%, Lmin=69.11, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=441, regioncalls=13640, ndraw=40, logz=71.73, remainder_fraction=100.0000%, Lmin=77.89, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=502, regioncalls=16080, ndraw=40, logz=77.78, remainder_fraction=100.0000%, Lmin=87.30, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=553, regioncalls=18120, ndraw=40, logz=88.69, remainder_fraction=100.0000%, Lmin=94.79, Lmax=241.87 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=584, regioncalls=19360, ndraw=40, logz=94.65, remainder_fraction=100.0000%, Lmin=100.05, Lmax=241.87 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 626 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=/tmp/tmpsbp081uu, backend=csv, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=400, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=1.05, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=111, regioncalls=440, ndraw=40, logz=0.06, remainder_fraction=100.0000%, Lmin=3.95, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=122, regioncalls=880, ndraw=40, logz=3.55, remainder_fraction=100.0000%, Lmin=6.71, Lmax=221.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=133, regioncalls=1320, ndraw=40, logz=6.36, remainder_fraction=100.0000%, Lmin=10.30, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=145, regioncalls=1800, ndraw=40, logz=10.43, remainder_fraction=100.0000%, Lmin=14.59, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=156, regioncalls=2240, ndraw=40, logz=14.85, remainder_fraction=100.0000%, Lmin=19.71, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=174, regioncalls=2960, ndraw=40, logz=22.21, remainder_fraction=100.0000%, Lmin=26.91, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=189, regioncalls=3560, ndraw=40, logz=26.59, remainder_fraction=100.0000%, Lmin=31.35, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=213, regioncalls=4520, ndraw=40, logz=29.74, remainder_fraction=100.0000%, Lmin=34.32, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=240, regioncalls=5600, ndraw=40, logz=33.34, remainder_fraction=100.0000%, Lmin=38.77, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=92, ncalls=246, regioncalls=5840, ndraw=40, logz=34.49, remainder_fraction=100.0000%, Lmin=39.28, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=273, regioncalls=6920, ndraw=40, logz=37.85, remainder_fraction=100.0000%, Lmin=42.61, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=297, regioncalls=7880, ndraw=40, logz=42.78, remainder_fraction=100.0000%, Lmin=47.95, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=326, regioncalls=9040, ndraw=40, logz=48.91, remainder_fraction=100.0000%, Lmin=54.86, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=368, regioncalls=10720, ndraw=40, logz=55.41, remainder_fraction=100.0000%, Lmin=61.66, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=402, regioncalls=12080, ndraw=40, logz=60.12, remainder_fraction=100.0000%, Lmin=68.52, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=452, regioncalls=14080, ndraw=40, logz=67.24, remainder_fraction=100.0000%, Lmin=72.18, Lmax=228.18 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=496, regioncalls=15840, ndraw=40, logz=71.34, remainder_fraction=100.0000%, Lmin=76.78, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=544, regioncalls=17760, ndraw=40, logz=77.69, remainder_fraction=100.0000%, Lmin=85.32, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=573, regioncalls=18920, ndraw=40, logz=83.64, remainder_fraction=100.0000%, Lmin=91.87, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=595, regioncalls=19800, ndraw=40, logz=92.13, remainder_fraction=100.0000%, Lmin=98.48, Lmax=238.35 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=674, regioncalls=22960, ndraw=40, logz=101.38, remainder_fraction=100.0000%, Lmin=108.11, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=210, ncalls=722, regioncalls=24880, ndraw=40, logz=108.20, remainder_fraction=100.0000%, Lmin=114.20, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=220, ncalls=778, regioncalls=27120, ndraw=40, logz=114.04, remainder_fraction=100.0000%, Lmin=121.27, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=230, ncalls=849, regioncalls=29960, ndraw=40, logz=122.34, remainder_fraction=100.0000%, Lmin=128.65, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=950, regioncalls=34000, ndraw=40, logz=128.02, remainder_fraction=100.0000%, Lmin=134.37, Lmax=240.89 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1034, regioncalls=37360, ndraw=40, logz=132.48, remainder_fraction=100.0000%, Lmin=139.29, Lmax=242.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=260, ncalls=1135, regioncalls=41400, ndraw=40, logz=140.81, remainder_fraction=100.0000%, Lmin=147.11, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1279, regioncalls=47160, ndraw=40, logz=148.11, remainder_fraction=100.0000%, Lmin=155.97, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=1411, regioncalls=52440, ndraw=40, logz=155.63, remainder_fraction=100.0000%, Lmin=163.08, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=290, ncalls=1566, regioncalls=58640, ndraw=40, logz=161.05, remainder_fraction=100.0000%, Lmin=168.19, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=1700, regioncalls=64000, ndraw=40, logz=166.03, remainder_fraction=100.0000%, Lmin=173.84, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=310, ncalls=1793, regioncalls=67720, ndraw=40, logz=171.65, remainder_fraction=100.0000%, Lmin=179.38, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=1938, regioncalls=73520, ndraw=40, logz=177.14, remainder_fraction=100.0000%, Lmin=184.35, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=330, ncalls=2226, regioncalls=85040, ndraw=40, logz=182.54, remainder_fraction=100.0000%, Lmin=189.50, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=340, ncalls=2654, regioncalls=102160, ndraw=40, logz=186.24, remainder_fraction=100.0000%, Lmin=193.29, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=3009, regioncalls=116360, ndraw=40, logz=191.15, remainder_fraction=100.0000%, Lmin=198.42, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=3318, regioncalls=128720, ndraw=40, logz=197.32, remainder_fraction=100.0000%, Lmin=205.11, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=370, ncalls=3667, regioncalls=142680, ndraw=40, logz=200.02, remainder_fraction=100.0000%, Lmin=206.86, Lmax=242.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=380, ncalls=4187, regioncalls=163480, ndraw=40, logz=203.15, remainder_fraction=100.0000%, Lmin=210.98, Lmax=242.51 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=4799, regioncalls=187960, ndraw=40, logz=206.25, remainder_fraction=100.0000%, Lmin=213.44, Lmax=242.60 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=2e+02 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 5287 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_reactive_run_warmstart_gauss | 8.20 | |
------------------------------Captured stdout call------------------------------ ====== Running Gauss problem [1] ===== [ultranest] Sampling 100 live points from prior ... Z=-inf(0.00%) | Like=-5e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=0/101 eff=0.0000% N=100 Z=-4e+13(0.00%) | Like=-4.1e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=10/112 eff=83.3333% N=100 Z=-3e+13(0.00%) | Like=-3.3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=20/122 eff=90.9091% N=100 Z=-3e+13(0.00%) | Like=-3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=30/132 eff=93.7500% N=100 Z=-3e+13(0.00%) | Like=-2.6e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=40/143 eff=93.0233% N=100 Z=-2e+13(0.00%) | Like=-2.2e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=46/151 eff=90.1961% N=100 Z=-2e+13(0.00%) | Like=-2.1e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=50/155 eff=90.9091% N=100 Z=-2e+13(0.00%) | Like=-1.7e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=60/165 eff=92.3077% N=100 Z=-1e+13(0.00%) | Like=-1.4e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=69/174 eff=93.2432% N=100 Z=-1e+13(0.00%) | Like=-1.4e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=70/176 eff=92.1053% N=100 Z=-1e+13(0.00%) | Like=-1.3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=80/186 eff=93.0233% N=100 Z=-1e+13(0.00%) | Like=-9.6e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=90/197 eff=92.7835% N=100 Z=-8e+12(0.00%) | Like=-8.1e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=100/208 eff=92.5926% N=100 Z=-7e+12(0.00%) | Like=-7e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=110/220 eff=91.6667% N=100 Z=-7e+12(0.00%) | Like=-6.8e+12..-2.1e+09 [-1.232e+13..-4.452e+12] | it/evals=115/225 eff=92.0000% N=100 Z=-6e+12(0.00%) | Like=-6.2e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=120/230 eff=92.3077% N=100 Z=-5e+12(0.00%) | Like=-5e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=130/243 eff=90.9091% N=100 Z=-5e+12(0.00%) | Like=-4.5e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=140/257 eff=89.1720% N=100 Z=-3e+12(0.00%) | Like=-3.4e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=150/268 eff=89.2857% N=100 Z=-3e+12(0.00%) | Like=-2.9e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=160/278 eff=89.8876% N=100 Z=-3e+12(0.00%) | Like=-2.9e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=161/279 eff=89.9441% N=100 Z=-2e+12(0.00%) | Like=-2.1e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=170/289 eff=89.9471% N=100 Z=-2e+12(0.00%) | Like=-1.8e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=180/299 eff=90.4523% N=100 Z=-1e+12(0.00%) | Like=-1.5e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=190/309 eff=90.9091% N=100 Z=-1e+12(0.00%) | Like=-1.1e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=200/319 eff=91.3242% N=100 Z=-1e+12(0.00%) | Like=-1.1e+12..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=207/326 eff=91.5929% N=100 Z=-1e+12(0.00%) | Like=-1e+12..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=210/329 eff=91.7031% N=100 Z=-9e+11(0.00%) | Like=-8.4e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=220/342 eff=90.9091% N=100 Z=-7e+11(0.00%) | Like=-7.1e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=230/352 eff=91.2698% N=100 Z=-6e+11(0.00%) | Like=-5.8e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=240/364 eff=90.9091% N=100 Z=-5e+11(0.00%) | Like=-5e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=250/376 eff=90.5797% N=100 Z=-5e+11(0.00%) | Like=-4.6e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=253/379 eff=90.6810% N=100 Z=-4e+11(0.00%) | Like=-4.2e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=260/387 eff=90.5923% N=100 Z=-3e+11(0.00%) | Like=-3.3e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=270/401 eff=89.7010% N=100 Z=-3e+11(0.00%) | Like=-3e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=276/408 eff=89.6104% N=100 Z=-3e+11(0.00%) | Like=-2.7e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=280/412 eff=89.7436% N=100 Z=-2e+11(0.00%) | Like=-2.2e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=290/422 eff=90.0621% N=100 Z=-2e+11(0.00%) | Like=-1.9e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=299/431 eff=90.3323% N=100 Z=-2e+11(0.00%) | Like=-1.8e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=300/432 eff=90.3614% N=100 Z=-1e+11(0.00%) | Like=-1.3e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=310/446 eff=89.5954% N=100 Z=-1e+11(0.00%) | Like=-1.1e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=320/458 eff=89.3855% N=100 Z=-1e+11(0.00%) | Like=-1e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=322/460 eff=89.4444% N=100 Z=-9e+10(0.00%) | Like=-8.4e+10..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=330/468 eff=89.6739% N=100 Z=-8e+10(0.00%) | Like=-7.8e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=340/478 eff=89.9471% N=100 Z=-7e+10(0.00%) | Like=-6.5e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=350/488 eff=90.2062% N=100 Z=-5e+10(0.00%) | Like=-5.2e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=360/498 eff=90.4523% N=100 Z=-4e+10(0.00%) | Like=-4.5e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=368/506 eff=90.6404% N=100 Z=-4e+10(0.00%) | Like=-4.4e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=370/508 eff=90.6863% N=100 Z=-4e+10(0.00%) | Like=-3.8e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=380/518 eff=90.9091% N=100 Z=-3e+10(0.00%) | Like=-2.9e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=390/528 eff=91.1215% N=100 Z=-3e+10(0.00%) | Like=-2.9e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=391/529 eff=91.1422% N=100 Z=-3e+10(0.00%) | Like=-2.5e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=400/539 eff=91.1162% N=100 Z=-2e+10(0.00%) | Like=-2.2e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=410/549 eff=91.3140% N=100 Z=-2e+10(0.00%) | Like=-2e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=414/553 eff=91.3907% N=100 Z=-2e+10(0.00%) | Like=-1.7e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=420/559 eff=91.5033% N=100 Z=-1e+10(0.00%) | Like=-1.3e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=430/569 eff=91.6844% N=100 Z=-1e+10(0.00%) | Like=-1.1e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=440/581 eff=91.4761% N=100 Z=-9e+09(0.00%) | Like=-8.8e+09..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=450/591 eff=91.6497% N=100 Z=-7e+09(0.00%) | Like=-7.3e+09..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=460/603 eff=91.4513% N=100 Z=-6e+09(0.00%) | Like=-5.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=470/615 eff=91.2621% N=100 Z=-5e+09(0.00%) | Like=-4.5e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=480/625 eff=91.4286% N=100 Z=-4e+09(0.00%) | Like=-4.3e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=483/628 eff=91.4773% N=100 Z=-4e+09(0.00%) | Like=-3.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=490/635 eff=91.5888% N=100 Z=-3e+09(0.00%) | Like=-3.1e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=500/648 eff=91.2409% N=100 Z=-3e+09(0.00%) | Like=-2.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=506/656 eff=91.0072% N=100 Z=-2e+09(0.00%) | Like=-2.5e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=510/660 eff=91.0714% N=100 Z=-2e+09(0.00%) | Like=-2e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=520/671 eff=91.0683% N=100 Z=-2e+09(0.00%) | Like=-1.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=529/682 eff=90.8935% N=100 Z=-2e+09(0.00%) | Like=-1.6e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=530/685 eff=90.5983% N=100 Z=-1e+09(0.00%) | Like=-1.3e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=540/697 eff=90.4523% N=100 Z=-8e+08(0.00%) | Like=-8.4e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=550/710 eff=90.1639% N=100 Z=-8e+08(0.00%) | Like=-8.2e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=552/712 eff=90.1961% N=100 Z=-8e+08(0.00%) | Like=-7.4e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=560/721 eff=90.1771% N=100 Z=-6e+08(0.00%) | Like=-5.9e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=570/731 eff=90.3328% N=100 Z=-5e+08(0.00%) | Like=-5.2e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=580/741 eff=90.4836% N=100 Z=-4e+08(0.00%) | Like=-3.8e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=590/755 eff=90.0763% N=100 Z=-3e+08(0.00%) | Like=-3.1e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=598/763 eff=90.1961% N=100 Z=-3e+08(0.00%) | Like=-2.8e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=600/765 eff=90.2256% N=100 Z=-2e+08(0.00%) | Like=-2.4e+08..-2e+04 [-2.461e+08..-7.777e+07] | it/evals=610/775 eff=90.3704% N=100 Z=-2e+08(0.00%) | Like=-2.2e+08..-2e+04 [-2.461e+08..-7.777e+07] | it/evals=620/786 eff=90.3790% N=100 Z=-2e+08(0.00%) | Like=-1.8e+08..-2e+04 [-2.461e+08..-7.777e+07] | it/evals=630/796 eff=90.5172% N=100 Z=-1e+08(0.00%) | Like=-1.5e+08..-2e+04 [-2.461e+08..-7.777e+07] | it/evals=640/806 eff=90.6516% N=100 Z=-1e+08(0.00%) | Like=-1.4e+08..-6e+03 [-2.461e+08..-7.777e+07] | it/evals=644/810 eff=90.7042% N=100 Z=-1e+08(0.00%) | Like=-1.3e+08..-6e+03 [-2.461e+08..-7.777e+07] | it/evals=650/816 eff=90.7821% N=100 Z=-1e+08(0.00%) | Like=-1.1e+08..-6e+03 [-2.461e+08..-7.777e+07] | it/evals=660/827 eff=90.7840% N=100 Z=-99584971.5(0.00%) | Like=-99056746.97..-6022.96 [-2.461e+08..-7.777e+07] | it/evals=667/835 eff=90.7483% N=100 Z=-95148616.1(0.00%) | Like=-93692477.41..-6022.96 [-2.461e+08..-7.777e+07] | it/evals=670/838 eff=90.7859% N=100 Z=-78681625.8(0.00%) | Like=-76849640.99..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=680/849 eff=90.7877% N=100 Z=-67129640.5(0.00%) | Like=-66446442.40..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=690/860 eff=90.7895% N=100 Z=-52437072.4(0.00%) | Like=-52098992.93..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=700/872 eff=90.6736% N=100 Z=-42467034.5(0.00%) | Like=-40947576.24..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=710/883 eff=90.6769% N=100 Z=-40875016.2(0.00%) | Like=-40682265.05..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=713/886 eff=90.7125% N=100 Z=-34273953.3(0.00%) | Like=-34009302.56..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=720/893 eff=90.7945% N=100 Z=-28150952.9(0.00%) | Like=-27577294.66..-6022.96 [-76849640.9883..-21157237.0757] | it/evals=730/904 eff=90.7960% N=100 Z=-24903284.6(0.00%) | Like=-24585788.12..-26.00 [-76849640.9883..-21157237.0757] | it/evals=740/917 eff=90.5753% N=100 Z=-18109608.4(0.00%) | Like=-17736757.48..-26.00 [-19938210.0363..-4653930.8201] | it/evals=750/927 eff=90.6892% N=100 Z=-15169525.4(0.00%) | Like=-14227274.47..-26.00 [-19938210.0363..-4653930.8201] | it/evals=760/937 eff=90.8005% N=100 Z=-11387981.3(0.00%) | Like=-11171106.07..-26.00 [-19938210.0363..-4653930.8201] | it/evals=770/949 eff=90.6949% N=100 Z=-9540359.8(0.00%) | Like=-9254160.55..-26.00 [-19938210.0363..-4653930.8201] | it/evals=780/959 eff=90.8033% N=100 Z=-9246880.2(0.00%) | Like=-9220004.55..-26.00 [-19938210.0363..-4653930.8201] | it/evals=782/961 eff=90.8246% N=100 Z=-8202857.7(0.00%) | Like=-7968261.52..-26.00 [-19938210.0363..-4653930.8201] | it/evals=790/969 eff=90.9091% N=100 Z=-6151494.4(0.00%) | Like=-6110210.34..-26.00 [-19938210.0363..-4653930.8201] | it/evals=800/980 eff=90.9091% N=100 Z=-5595023.2(0.00%) | Like=-5544084.40..-26.00 [-19938210.0363..-4653930.8201] | it/evals=805/986 eff=90.8578% N=100 Z=-5168753.7(0.00%) | Like=-5104017.56..-26.00 [-19938210.0363..-4653930.8201] | it/evals=810/991 eff=90.9091% N=100 Z=-4348450.3(0.00%) | Like=-4124983.59..-26.00 [-4571171.0738..-801360.3533] | it/evals=820/1001 eff=91.0100% N=100 Z=-3288211.8(0.00%) | Like=-3052967.15..-26.00 [-4571171.0738..-801360.3533] | it/evals=828/1010 eff=90.9890% N=100 Z=-2816901.9(0.00%) | Like=-2813964.84..-26.00 [-4571171.0738..-801360.3533] | it/evals=830/1012 eff=91.0088% N=100 Z=-2391242.8(0.00%) | Like=-2352037.02..-26.00 [-4571171.0738..-801360.3533] | it/evals=840/1022 eff=91.1063% N=100 Z=-1694476.5(0.00%) | Like=-1688714.15..-26.00 [-4571171.0738..-801360.3533] | it/evals=850/1033 eff=91.1040% N=100 Z=-1688727.3(0.00%) | Like=-1641589.55..-26.00 [-4571171.0738..-801360.3533] | it/evals=851/1034 eff=91.1135% N=100 Z=-1391311.1(0.00%) | Like=-1378714.97..-26.00 [-4571171.0738..-801360.3533] | it/evals=860/1044 eff=91.1017% N=100 Z=-1016207.0(0.00%) | Like=-1005805.90..-26.00 [-4571171.0738..-801360.3533] | it/evals=870/1056 eff=91.0042% N=100 Z=-921148.4(0.00%) | Like=-899817.07..-26.00 [-4571171.0738..-801360.3533] | it/evals=874/1061 eff=90.9469% N=100 Z=-823593.3(0.00%) | Like=-820443.05..-26.00 [-4571171.0738..-801360.3533] | it/evals=880/1067 eff=91.0031% N=100 Z=-718128.1(0.00%) | Like=-716385.20..-26.00 [-793371.7270..-221963.8669] | it/evals=890/1078 eff=91.0020% N=100 Z=-596455.2(0.00%) | Like=-575764.61..-26.00 [-793371.7270..-221963.8669] | it/evals=900/1090 eff=90.9091% N=100 Z=-491331.9(0.00%) | Like=-490644.03..-26.00 [-793371.7270..-221963.8669] | it/evals=910/1101 eff=90.9091% N=100 Z=-403815.9(0.00%) | Like=-396540.01..-26.00 [-793371.7270..-221963.8669] | it/evals=920/1112 eff=90.9091% N=100 Z=-335464.4(0.00%) | Like=-335428.11..-4.83 [-793371.7270..-221963.8669] | it/evals=930/1122 eff=90.9980% N=100 Z=-281288.1(0.00%) | Like=-269279.55..-4.83 [-793371.7270..-221963.8669] | it/evals=940/1133 eff=90.9971% N=100 Z=-267112.8(0.00%) | Like=-259247.40..-4.83 [-793371.7270..-221963.8669] | it/evals=943/1137 eff=90.9354% N=100 Z=-232361.2(0.00%) | Like=-227851.27..-4.83 [-793371.7270..-221963.8669] | it/evals=950/1144 eff=90.9962% N=100 Z=-195082.6(0.00%) | Like=-192338.76..-4.83 [-221393.3591..-50063.0867] | it/evals=960/1154 eff=91.0816% N=100 Z=-158846.7(0.00%) | Like=-156838.84..-4.83 [-221393.3591..-50063.0867] | it/evals=970/1165 eff=91.0798% N=100 Z=-123015.8(0.00%) | Like=-122093.92..-4.83 [-221393.3591..-50063.0867] | it/evals=980/1176 eff=91.0781% N=100 Z=-98769.3(0.00%) | Like=-94308.81..-4.83 [-221393.3591..-50063.0867] | it/evals=989/1186 eff=91.0681% N=100 Z=-94323.3(0.00%) | Like=-93705.11..-4.83 [-221393.3591..-50063.0867] | it/evals=990/1187 eff=91.0764% N=100 Z=-75793.0(0.00%) | Like=-75159.71..-4.83 [-221393.3591..-50063.0867] | it/evals=1000/1198 eff=91.0747% N=100 Z=-69193.8(0.00%) | Like=-69178.34..-4.43 [-221393.3591..-50063.0867] | it/evals=1010/1209 eff=91.0730% N=100 Z=-68151.1(0.00%) | Like=-67031.91..-4.43 [-221393.3591..-50063.0867] | it/evals=1012/1212 eff=91.0072% N=100 Z=-59478.1(0.00%) | Like=-56316.63..-4.43 [-221393.3591..-50063.0867] | it/evals=1020/1221 eff=90.9902% N=100 Z=-49166.7(0.00%) | Like=-46314.73..-4.43 [-49739.3666..-12576.5127] | it/evals=1030/1232 eff=90.9894% N=100 Z=-42809.3(0.00%) | Like=-40722.23..-4.43 [-49739.3666..-12576.5127] | it/evals=1035/1238 eff=90.9490% N=100 Z=-38197.5(0.00%) | Like=-36533.18..-4.43 [-49739.3666..-12576.5127] | it/evals=1040/1243 eff=90.9886% N=100 Z=-33560.9(0.00%) | Like=-32411.75..-4.43 [-49739.3666..-12576.5127] | it/evals=1050/1253 eff=91.0668% N=100 Z=-28364.5(0.00%) | Like=-28235.71..-4.43 [-49739.3666..-12576.5127] | it/evals=1060/1263 eff=91.1436% N=100 Z=-23735.6(0.00%) | Like=-21704.04..-4.43 [-49739.3666..-12576.5127] | it/evals=1070/1273 eff=91.2191% N=100 Z=-17722.4(0.00%) | Like=-17494.12..-0.07 [-49739.3666..-12576.5127] | it/evals=1080/1283 eff=91.2933% N=100 Z=-17509.5(0.00%) | Like=-17033.55..-0.07 [-49739.3666..-12576.5127] | it/evals=1081/1284 eff=91.3007% N=100 Z=-13805.2(0.00%) | Like=-13616.77..-0.07 [-49739.3666..-12576.5127] | it/evals=1090/1293 eff=91.3663% N=100 Z=-11448.5(0.00%) | Like=-11430.06..-0.07 [-12572.1941..-3639.3566] | it/evals=1100/1304 eff=91.3621% N=100 Z=-10886.1(0.00%) | Like=-10406.01..-0.07 [-12572.1941..-3639.3566] | it/evals=1104/1308 eff=91.3907% N=100 Z=-9677.3(0.00%) | Like=-9405.45..-0.07 [-12572.1941..-3639.3566] | it/evals=1110/1314 eff=91.4333% N=100 Z=-7396.0(0.00%) | Like=-7330.88..-0.07 [-12572.1941..-3639.3566] | it/evals=1120/1325 eff=91.4286% N=100 Z=-6557.4(0.00%) | Like=-6502.58..-0.07 [-12572.1941..-3639.3566] | it/evals=1130/1337 eff=91.3500% N=100 Z=-4918.0(0.00%) | Like=-4673.62..-0.07 [-12572.1941..-3639.3566] | it/evals=1140/1348 eff=91.3462% N=100 Z=-4140.2(0.00%) | Like=-4068.57..-0.07 [-12572.1941..-3639.3566] | it/evals=1150/1358 eff=91.4149% N=100 Z=-3620.6(0.00%) | Like=-3375.59..-0.07 [-3604.3797..-802.6300] | it/evals=1160/1368 eff=91.4826% N=100 Z=-2712.2(0.00%) | Like=-2672.37..-0.00 [-3604.3797..-802.6300] | it/evals=1170/1380 eff=91.4062% N=100 Z=-2576.7(0.00%) | Like=-2536.55..-0.00 [-3604.3797..-802.6300] | it/evals=1173/1383 eff=91.4263% N=100 Z=-2224.7(0.00%) | Like=-2201.36..-0.00 [-3604.3797..-802.6300] | it/evals=1180/1391 eff=91.4020% N=100 Z=-1915.5(0.00%) | Like=-1898.60..-0.00 [-3604.3797..-802.6300] | it/evals=1190/1404 eff=91.2577% N=100 Z=-1600.7(0.00%) | Like=-1576.13..-0.00 [-3604.3797..-802.6300] | it/evals=1200/1417 eff=91.1162% N=100 Z=-1273.5(0.00%) | Like=-1210.46..-0.00 [-3604.3797..-802.6300] | it/evals=1210/1429 eff=91.0459% N=100 Z=-1035.2(0.00%) | Like=-936.01..-0.00 [-3604.3797..-802.6300] | it/evals=1220/1440 eff=91.0448% N=100 Z=-747.3(0.00%) | Like=-722.16..-0.00 [-753.4608..-154.1297] | it/evals=1230/1454 eff=90.8419% N=100 Z=-637.3(0.00%) | Like=-618.44..-0.00 [-753.4608..-154.1297] | it/evals=1240/1466 eff=90.7760% N=100 Z=-622.1(0.00%) | Like=-567.65..-0.00 [-753.4608..-154.1297] | it/evals=1242/1468 eff=90.7895% N=100 Z=-453.1(0.00%) | Like=-420.93..-0.00 [-753.4608..-154.1297] | it/evals=1250/1476 eff=90.8430% N=100 Z=-343.4(0.00%) | Like=-321.96..-0.00 [-753.4608..-154.1297] | it/evals=1260/1487 eff=90.8435% N=100 Z=-324.7(0.00%) | Like=-304.81..-0.00 [-753.4608..-154.1297] | it/evals=1265/1493 eff=90.8112% N=100 Z=-293.1(0.00%) | Like=-269.15..-0.00 [-753.4608..-154.1297] | it/evals=1270/1498 eff=90.8441% N=100 Z=-245.0(0.00%) | Like=-220.24..-0.00 [-753.4608..-154.1297] | it/evals=1280/1508 eff=90.9091% N=100 Z=-198.7(0.00%) | Like=-179.69..-0.00 [-753.4608..-154.1297] | it/evals=1288/1517 eff=90.8963% N=100 Z=-190.1(0.00%) | Like=-163.20..-0.00 [-753.4608..-154.1297] | it/evals=1290/1519 eff=90.9091% N=100 Z=-156.7(0.00%) | Like=-138.36..-0.00 [-153.8288..-33.2230] | it/evals=1300/1532 eff=90.7821% N=100 Z=-144.3(0.00%) | Like=-124.32..-0.00 [-153.8288..-33.2230] | it/evals=1310/1544 eff=90.7202% N=100 Z=-107.5(0.00%) | Like=-88.54..-0.00 [-153.8288..-33.2230] | it/evals=1320/1555 eff=90.7216% N=100 Z=-94.0(0.00%) | Like=-70.34..-0.00 [-153.8288..-33.2230] | it/evals=1330/1566 eff=90.7231% N=100 Z=-84.4(0.00%) | Like=-66.66..-0.00 [-153.8288..-33.2230] | it/evals=1334/1572 eff=90.6250% N=100 Z=-78.0(0.00%) | Like=-59.63..-0.00 [-153.8288..-33.2230] | it/evals=1340/1578 eff=90.6631% N=100 Z=-68.1(0.00%) | Like=-47.59..-0.00 [-153.8288..-33.2230] | it/evals=1350/1591 eff=90.5433% N=100 Z=-55.9(0.00%) | Like=-36.04..-0.00 [-153.8288..-33.2230] | it/evals=1357/1598 eff=90.5874% N=100 Z=-51.8(0.00%) | Like=-34.07..-0.00 [-153.8288..-33.2230] | it/evals=1360/1601 eff=90.6063% N=100 Z=-46.7(0.00%) | Like=-28.87..-0.00 [-32.3716..-7.9890] | it/evals=1370/1613 eff=90.5486% N=100 Z=-42.4(0.00%) | Like=-24.81..-0.00 [-32.3716..-7.9890] | it/evals=1380/1626 eff=90.4325% N=100 Z=-38.6(0.00%) | Like=-21.44..-0.00 [-32.3716..-7.9890] | it/evals=1390/1637 eff=90.4359% N=100 Z=-35.5(0.00%) | Like=-18.34..-0.00 [-32.3716..-7.9890] | it/evals=1400/1648 eff=90.4393% N=100 Z=-34.9(0.00%) | Like=-17.38..-0.00 [-32.3716..-7.9890] | it/evals=1403/1652 eff=90.3995% N=100 Z=-32.8(0.00%) | Like=-15.23..-0.00 [-32.3716..-7.9890] | it/evals=1410/1662 eff=90.2689% N=100 Z=-30.7(0.00%) | Like=-13.76..-0.00 [-32.3716..-7.9890] | it/evals=1420/1673 eff=90.2734% N=100 Z=-29.5(0.00%) | Like=-12.25..-0.00 [-32.3716..-7.9890] | it/evals=1426/1680 eff=90.2532% N=100 Z=-28.1(0.00%) | Like=-10.30..-0.00 [-32.3716..-7.9890] | it/evals=1430/1684 eff=90.2778% N=100 Z=-26.1(0.00%) | Like=-8.61..-0.00 [-32.3716..-7.9890] | it/evals=1440/1697 eff=90.1691% N=100 Z=-24.3(0.01%) | Like=-7.41..-0.00 [-7.8443..-4.9205] | it/evals=1450/1710 eff=90.0621% N=100 Z=-23.0(0.05%) | Like=-5.44..-0.00 [-7.8443..-4.9205] | it/evals=1460/1720 eff=90.1235% N=100 Z=-21.5(0.26%) | Like=-4.30..-0.00 [-4.3008..-4.1557] | it/evals=1470/1731 eff=90.1288% N=100 Z=-21.3(0.32%) | Like=-3.97..-0.00 [-3.9720..-3.8905] | it/evals=1472/1733 eff=90.1408% N=100 Z=-20.4(0.80%) | Like=-3.18..-0.00 [-3.1789..-3.0107] | it/evals=1480/1741 eff=90.1889% N=100 Z=-19.4(2.05%) | Like=-2.57..-0.00 [-2.6399..-2.5714] | it/evals=1490/1751 eff=90.2483% N=100 Z=-19.1(2.82%) | Like=-2.45..-0.00 [-2.4464..-2.3119] | it/evals=1495/1756 eff=90.2778% N=100 Z=-18.9(3.62%) | Like=-2.25..-0.00 [-2.2673..-2.2510] | it/evals=1500/1761 eff=90.3070% N=100 Z=-18.4(5.61%) | Like=-1.86..-0.00 [-1.8649..-1.8110] | it/evals=1510/1774 eff=90.2031% N=100 Z=-18.1(7.76%) | Like=-1.50..-0.00 [-1.4996..-1.4776] | it/evals=1518/1782 eff=90.2497% N=100 Z=-18.0(8.38%) | Like=-1.41..-0.00 [-1.4062..-1.3954] | it/evals=1520/1784 eff=90.2613% N=100 Z=-17.7(11.75%) | Like=-1.25..-0.00 [-1.2661..-1.2467] | it/evals=1530/1795 eff=90.2655% N=100 Z=-17.4(15.70%) | Like=-0.99..-0.00 [-1.0017..-0.9891] | it/evals=1540/1806 eff=90.2696% N=100 Z=-17.3(16.14%) | Like=-0.98..-0.00 [-0.9819..-0.9559] | it/evals=1541/1807 eff=90.2753% N=100 Z=-17.1(20.25%) | Like=-0.83..-0.00 [-0.8264..-0.8118] | it/evals=1550/1816 eff=90.3263% N=100 Z=-16.9(24.79%) | Like=-0.67..-0.00 [-0.6822..-0.6720] | it/evals=1560/1826 eff=90.3824% N=100 Z=-16.8(26.54%) | Like=-0.63..-0.00 [-0.6420..-0.6278] | it/evals=1564/1830 eff=90.4046% N=100 Z=-16.7(29.30%) | Like=-0.55..-0.00 [-0.5506..-0.5464]*| it/evals=1570/1837 eff=90.3857% N=100 Z=-16.6(33.97%) | Like=-0.42..-0.00 [-0.4191..-0.4065] | it/evals=1580/1849 eff=90.3373% N=100 Z=-16.5(37.23%) | Like=-0.38..-0.00 [-0.3823..-0.3738]*| it/evals=1587/1858 eff=90.2730% N=100 Z=-16.5(38.67%) | Like=-0.35..-0.00 [-0.3479..-0.3376] | it/evals=1590/1861 eff=90.2896% N=100 Z=-16.3(43.36%) | Like=-0.28..-0.00 [-0.2759..-0.2674]*| it/evals=1600/1872 eff=90.2935% N=100 Z=-16.2(47.93%) | Like=-0.20..-0.00 [-0.2117..-0.1995] | it/evals=1610/1883 eff=90.2973% N=100 [ultranest] Explored until L=-4e-06 [ultranest] Likelihood function evaluations: 1887 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = -15.512 +- 0.959 single instance: logZ = -15.512 +- 0.391 bootstrapped : logZ = -15.438 +- 0.869 tail : logZ = +- 0.404 insert order U test : converged: True correlation: inf iterations a : -0.00365│ ▁ ▁▁▁▁▁▂▁▂▃▃▃▄▆▄▇▅▇▇▇▆▆▃▃▄▄▂▂▁▁▁▁▁ ▁▁ │0.00350 0.00005 +- 0.00098 pointstore: (1715, 5) 1887 1887 0 ====== Running Gauss problem [2] ===== [ultranest] Trying to resume from previous run, but likelihood function gives different result: [0.50000001] gave -1.801637165560648e-05, now -0.004417743572744871 Exception as expected: Cannot resume because loglikelihood function changed, unless resume=resume-similar. To start from scratch, delete '/tmp/tmp_g0nidx0'. ====== Running Gauss problem [3] ===== [ultranest] Trying to resume from previous run, but likelihood function gives different result: [0.50000001] gave -1.801637165560648e-05, now -0.018817470773834135 [ultranest] trying to salvage points from previous, different run ... [ultranest] Resuming from 1344 stored points Z=-inf(0.00%) | Like=-5e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=0/1887 eff=inf% N=100 Z=-4e+13(0.00%) | Like=-4.1e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=10/1887 eff=inf% N=100 Z=-3e+13(0.00%) | Like=-3.3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=20/1887 eff=inf% N=100 Z=-3e+13(0.00%) | Like=-3.2e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=23/1887 eff=inf% N=100 Z=-3e+13(0.00%) | Like=-3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=30/1887 eff=inf% N=100 Z=-3e+13(0.00%) | Like=-2.6e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=40/1887 eff=inf% N=100 Z=-2e+13(0.00%) | Like=-2.1e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=50/1887 eff=inf% N=100 Z=-2e+13(0.00%) | Like=-1.7e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=60/1887 eff=inf% N=100 Z=-1e+13(0.00%) | Like=-1.4e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=69/1887 eff=inf% N=100 Z=-1e+13(0.00%) | Like=-1.4e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=70/1887 eff=inf% N=100 Z=-1e+13(0.00%) | Like=-1.3e+13..-1.4e+10 [-4.998e+13..-1.242e+13] | it/evals=80/1887 eff=inf% N=100 Z=-1e+13(0.00%) | Like=-9.6e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=90/1887 eff=inf% N=100 Z=-8e+12(0.00%) | Like=-8.1e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=100/1887 eff=inf% N=100 Z=-7e+12(0.00%) | Like=-7e+12..-1.4e+10 [-1.232e+13..-4.452e+12] | it/evals=110/1887 eff=inf% N=100 Z=-7e+12(0.00%) | Like=-6.8e+12..-2.1e+09 [-1.232e+13..-4.452e+12] | it/evals=115/1887 eff=inf% N=100 Z=-6e+12(0.00%) | Like=-6.2e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=120/1887 eff=inf% N=100 Z=-5e+12(0.00%) | Like=-5e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=130/1887 eff=inf% N=100 Z=-5e+12(0.00%) | Like=-4.6e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=138/1887 eff=inf% N=100 Z=-5e+12(0.00%) | Like=-4.5e+12..-2e+04 [-1.232e+13..-4.452e+12] | it/evals=140/1887 eff=inf% N=100 Z=-3e+12(0.00%) | Like=-3.4e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=150/1887 eff=inf% N=100 Z=-3e+12(0.00%) | Like=-2.9e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=160/1887 eff=inf% N=100 Z=-3e+12(0.00%) | Like=-2.9e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=161/1887 eff=inf% N=100 Z=-2e+12(0.00%) | Like=-2.1e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=170/1887 eff=inf% N=100 Z=-2e+12(0.00%) | Like=-1.8e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=180/1887 eff=inf% N=100 Z=-1e+12(0.00%) | Like=-1.5e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=190/1887 eff=inf% N=100 Z=-1e+12(0.00%) | Like=-1.1e+12..-2e+04 [-4.394e+12..-1.143e+12] | it/evals=200/1887 eff=inf% N=100 Z=-1e+12(0.00%) | Like=-1.1e+12..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=207/1887 eff=inf% N=100 Z=-1e+12(0.00%) | Like=-1e+12..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=210/1887 eff=inf% N=100 Z=-9e+11(0.00%) | Like=-8.4e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=220/1887 eff=inf% N=100 Z=-7e+11(0.00%) | Like=-7.1e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=230/1887 eff=inf% N=100 Z=-6e+11(0.00%) | Like=-5.8e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=240/1887 eff=inf% N=100 Z=-5e+11(0.00%) | Like=-5e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=250/1887 eff=inf% N=100 Z=-5e+11(0.00%) | Like=-4.6e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=253/1887 eff=inf% N=100 Z=-4e+11(0.00%) | Like=-4.2e+11..-2e+04 [-1.142e+12..-3.697e+11] | it/evals=260/1887 eff=inf% N=100 Z=-3e+11(0.00%) | Like=-3.3e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=270/1887 eff=inf% N=100 Z=-3e+11(0.00%) | Like=-2.7e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=280/1887 eff=inf% N=100 Z=-2e+11(0.00%) | Like=-2.2e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=290/1887 eff=inf% N=100 Z=-2e+11(0.00%) | Like=-1.9e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=299/1887 eff=inf% N=100 Z=-2e+11(0.00%) | Like=-1.8e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=300/1887 eff=inf% N=100 Z=-1e+11(0.00%) | Like=-1.3e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=310/1887 eff=inf% N=100 Z=-1e+11(0.00%) | Like=-1.1e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=320/1887 eff=inf% N=100 Z=-1e+11(0.00%) | Like=-1e+11..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=322/1887 eff=inf% N=100 Z=-9e+10(0.00%) | Like=-8.4e+10..-2e+04 [-3.628e+11..-8.168e+10] | it/evals=330/1887 eff=inf% N=100 Z=-8e+10(0.00%) | Like=-7.8e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=340/1887 eff=inf% N=100 Z=-7e+10(0.00%) | Like=-7.1e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=345/1887 eff=inf% N=100 Z=-7e+10(0.00%) | Like=-6.5e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=350/1887 eff=inf% N=100 Z=-5e+10(0.00%) | Like=-5.2e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=360/1887 eff=inf% N=100 Z=-4e+10(0.00%) | Like=-4.5e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=368/1887 eff=inf% N=100 Z=-4e+10(0.00%) | Like=-4.4e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=370/1887 eff=inf% N=100 Z=-4e+10(0.00%) | Like=-3.8e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=380/1887 eff=inf% N=100 Z=-3e+10(0.00%) | Like=-2.9e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=390/1887 eff=inf% N=100 Z=-3e+10(0.00%) | Like=-2.9e+10..-2e+04 [-8.065e+10..-2.652e+10] | it/evals=391/1887 eff=inf% N=100 Z=-3e+10(0.00%) | Like=-2.5e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=400/1887 eff=inf% N=100 Z=-2e+10(0.00%) | Like=-2.2e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=410/1887 eff=inf% N=100 Z=-2e+10(0.00%) | Like=-1.7e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=420/1887 eff=inf% N=100 Z=-1e+10(0.00%) | Like=-1.3e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=430/1887 eff=inf% N=100 Z=-1e+10(0.00%) | Like=-1.2e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=437/1887 eff=inf% N=100 Z=-1e+10(0.00%) | Like=-1.1e+10..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=440/1887 eff=inf% N=100 Z=-9e+09(0.00%) | Like=-8.8e+09..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=450/1887 eff=inf% N=100 Z=-7e+09(0.00%) | Like=-7.3e+09..-2e+04 [-2.652e+10..-7.121e+09] | it/evals=460/1887 eff=inf% N=100 Z=-6e+09(0.00%) | Like=-5.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=470/1887 eff=inf% N=100 Z=-5e+09(0.00%) | Like=-4.5e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=480/1887 eff=inf% N=100 Z=-4e+09(0.00%) | Like=-4.3e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=483/1887 eff=inf% N=100 Z=-4e+09(0.00%) | Like=-3.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=490/1887 eff=inf% N=100 Z=-3e+09(0.00%) | Like=-3.1e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=500/1887 eff=inf% N=100 Z=-2e+09(0.00%) | Like=-2.5e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=510/1887 eff=inf% N=100 Z=-2e+09(0.00%) | Like=-2e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=520/1887 eff=inf% N=100 Z=-2e+09(0.00%) | Like=-1.7e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=529/1887 eff=inf% N=100 Z=-2e+09(0.00%) | Like=-1.6e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=530/1887 eff=inf% N=100 Z=-1e+09(0.00%) | Like=-1.3e+09..-2e+04 [-6.93e+09..-1.231e+09] | it/evals=540/1887 eff=inf% N=100 Z=-8e+08(0.00%) | Like=-8.4e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=550/1887 eff=inf% N=100 Z=-8e+08(0.00%) | Like=-8.2e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=552/1887 eff=inf% N=100 Z=-8e+08(0.00%) | Like=-7.4e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=560/1887 eff=inf% N=100 Z=-6e+08(0.00%) | Like=-5.9e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=570/1887 eff=inf% N=100 Z=-6e+08(0.00%) | Like=-5.4e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=575/1887 eff=inf% N=100 Z=-5e+08(0.00%) | Like=-5.2e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=580/1887 eff=inf% N=100 Z=-4e+08(0.00%) | Like=-3.8e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=590/1887 eff=inf% N=100 Z=-3e+08(0.00%) | Like=-3.1e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=598/1887 eff=inf% N=100 Z=-3e+08(0.00%) | Like=-2.8e+08..-2e+04 [-1.215e+09..-2.487e+08] | it/evals=600/1887 eff=inf% N=100 Z=-2e+08(0.00%) | Like=-2.4e+08..-2e+04 [-2.462e+08..-7.776e+07] | it/evals=610/1887 eff=inf% N=100 Z=-2e+08(0.00%) | Like=-2.2e+08..-2e+04 [-2.462e+08..-7.776e+07] | it/evals=620/1887 eff=inf% N=100 Z=-2e+08(0.00%) | Like=-2.2e+08..-2e+04 [-2.462e+08..-7.776e+07] | it/evals=621/1887 eff=inf% N=100 Z=-2e+08(0.00%) | Like=-1.8e+08..-2e+04 [-2.462e+08..-7.776e+07] | it/evals=630/1887 eff=inf% N=100 Z=-1e+08(0.00%) | Like=-1.5e+08..-2e+04 [-2.462e+08..-7.776e+07] | it/evals=640/1887 eff=inf% N=100 Z=-1e+08(0.00%) | Like=-1.4e+08..-6e+03 [-2.462e+08..-7.776e+07] | it/evals=644/1887 eff=inf% N=100 Z=-1e+08(0.00%) | Like=-1.3e+08..-6e+03 [-2.462e+08..-7.776e+07] | it/evals=650/1887 eff=inf% N=100 Z=-1e+08(0.00%) | Like=-1.1e+08..-6e+03 [-2.462e+08..-7.776e+07] | it/evals=660/1887 eff=inf% N=100 Z=-99582148.9(0.00%) | Like=-99059562.04..-6001.03 [-2.462e+08..-7.776e+07] | it/evals=667/1887 eff=inf% N=100 Z=-95151375.1(0.00%) | Like=-93695215.20..-6001.03 [-2.462e+08..-7.776e+07] | it/evals=670/1887 eff=inf% N=100 Z=-78679116.9(0.00%) | Like=-76847161.50..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=680/1887 eff=inf% N=100 Z=-67131957.9(0.00%) | Like=-66448748.01..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=690/1887 eff=inf% N=100 Z=-52435024.3(0.00%) | Like=-52096951.40..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=700/1887 eff=inf% N=100 Z=-42465191.4(0.00%) | Like=-40949386.18..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=710/1887 eff=inf% N=100 Z=-40873207.9(0.00%) | Like=-40680461.02..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=713/1887 eff=inf% N=100 Z=-34272297.5(0.00%) | Like=-34010952.05..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=720/1887 eff=inf% N=100 Z=-28152453.6(0.00%) | Like=-27575809.36..-6001.03 [-76847161.4980..-21155936.6446] | it/evals=730/1887 eff=inf% N=100 Z=-24901873.1(0.00%) | Like=-24584385.70..-24.58 [-76847161.4980..-21155936.6446] | it/evals=740/1887 eff=inf% N=100 Z=-18110812.1(0.00%) | Like=-17737948.70..-24.58 [-19936947.1007..-4653936.2656] | it/evals=750/1887 eff=inf% N=100 Z=-15302856.8(0.00%) | Like=-15168411.63..-24.58 [-19936947.1007..-4653936.2656] | it/evals=759/1887 eff=inf% N=100 Z=-15168423.8(0.00%) | Like=-14226207.64..-24.58 [-19936947.1007..-4653936.2656] | it/evals=760/1887 eff=inf% N=100 Z=-11388935.8(0.00%) | Like=-11170160.74..-24.58 [-19936947.1007..-4653936.2656] | it/evals=770/1887 eff=inf% N=100 Z=-9539486.2(0.00%) | Like=-9255020.99..-24.58 [-19936947.1007..-4653936.2656] | it/evals=780/1887 eff=inf% N=100 Z=-9247740.3(0.00%) | Like=-9220863.41..-24.58 [-19936947.1007..-4653936.2656] | it/evals=782/1887 eff=inf% N=100 Z=-8203667.8(0.00%) | Like=-7969059.95..-24.58 [-19936947.1007..-4653936.2656] | it/evals=790/1887 eff=inf% N=100 Z=-6152195.9(0.00%) | Like=-6110909.52..-24.58 [-19936947.1007..-4653936.2656] | it/evals=800/1887 eff=inf% N=100 Z=-5168110.7(0.00%) | Like=-5104656.58..-24.58 [-19936947.1007..-4653936.2656] | it/evals=810/1887 eff=inf% N=100 Z=-4349040.1(0.00%) | Like=-4125558.07..-24.58 [-4570566.3677..-801613.5660] | it/evals=820/1887 eff=inf% N=100 Z=-2816427.2(0.00%) | Like=-2814439.32..-24.58 [-4570566.3677..-801613.5660] | it/evals=830/1887 eff=inf% N=100 Z=-2391680.2(0.00%) | Like=-2352470.82..-24.58 [-4570566.3677..-801613.5660] | it/evals=840/1887 eff=inf% N=100 Z=-1694108.3(0.00%) | Like=-1689081.73..-24.58 [-4570566.3677..-801613.5660] | it/evals=850/1887 eff=inf% N=100 Z=-1689094.8(0.00%) | Like=-1641951.96..-24.58 [-4570566.3677..-801613.5660] | it/evals=851/1887 eff=inf% N=100 Z=-1391644.8(0.00%) | Like=-1379047.10..-24.58 [-4570566.3677..-801613.5660] | it/evals=860/1887 eff=inf% N=100 Z=-1016250.7(0.00%) | Like=-1006089.59..-24.58 [-4570566.3677..-801613.5660] | it/evals=870/1887 eff=inf% N=100 Z=-920877.0(0.00%) | Like=-900085.39..-24.58 [-4570566.3677..-801613.5660] | it/evals=874/1887 eff=inf% N=100 Z=-823336.7(0.00%) | Like=-820186.88..-24.58 [-4570566.3677..-801613.5660] | it/evals=880/1887 eff=inf% N=100 Z=-718367.8(0.00%) | Like=-716624.62..-24.58 [-793119.8150..-221964.0581] | it/evals=890/1887 eff=inf% N=100 Z=-639630.0(0.00%) | Like=-639396.18..-24.58 [-793119.8150..-221964.0581] | it/evals=897/1887 eff=inf% N=100 Z=-596673.7(0.00%) | Like=-575979.25..-24.58 [-793119.8150..-221964.0581] | it/evals=900/1887 eff=inf% N=100 Z=-491530.1(0.00%) | Like=-490445.93..-24.58 [-793119.8150..-221964.0581] | it/evals=910/1887 eff=inf% N=100 Z=-403995.7(0.00%) | Like=-396361.92..-24.58 [-793119.8150..-221964.0581] | it/evals=920/1887 eff=inf% N=100 Z=-335628.3(0.00%) | Like=-335591.94..-4.23 [-793119.8150..-221964.0581] | it/evals=930/1887 eff=inf% N=100 Z=-281138.1(0.00%) | Like=-269426.35..-4.23 [-793119.8150..-221964.0581] | it/evals=940/1887 eff=inf% N=100 Z=-266966.7(0.00%) | Like=-259391.44..-4.23 [-793119.8150..-221964.0581] | it/evals=943/1887 eff=inf% N=100 Z=-232224.9(0.00%) | Like=-227986.30..-4.23 [-793119.8150..-221964.0581] | it/evals=950/1887 eff=inf% N=100 Z=-195207.5(0.00%) | Like=-192214.74..-4.23 [-221260.2947..-49999.8216] | it/evals=960/1887 eff=inf% N=100 Z=-172769.0(0.00%) | Like=-171647.55..-4.23 [-221260.2947..-49999.8216] | it/evals=966/1887 eff=inf% N=100 Z=-158733.9(0.00%) | Like=-156726.85..-4.23 [-221260.2947..-49999.8216] | it/evals=970/1887 eff=inf% N=100 Z=-123115.0(0.00%) | Like=-122192.77..-4.23 [-221260.2947..-49999.8216] | it/evals=980/1887 eff=inf% N=100 Z=-98858.2(0.00%) | Like=-94395.69..-4.23 [-221260.2947..-49999.8216] | it/evals=989/1887 eff=inf% N=100 Z=-94410.2(0.00%) | Like=-93791.71..-4.23 [-221260.2947..-49999.8216] | it/evals=990/1887 eff=inf% N=100 Z=-75715.2(0.00%) | Like=-75082.19..-4.23 [-221260.2947..-49999.8216] | it/evals=1000/1887 eff=inf% N=100 Z=-69119.4(0.00%) | Like=-69103.96..-3.86 [-221260.2947..-49999.8216] | it/evals=1010/1887 eff=inf% N=100 Z=-68077.3(0.00%) | Like=-66958.70..-3.86 [-221260.2947..-49999.8216] | it/evals=1012/1887 eff=inf% N=100 Z=-59547.1(0.00%) | Like=-56249.53..-3.86 [-221260.2947..-49999.8216] | it/evals=1020/1887 eff=inf% N=100 Z=-49104.0(0.00%) | Like=-46375.62..-3.86 [-49676.3061..-12608.2519] | it/evals=1030/1887 eff=inf% N=100 Z=-42750.8(0.00%) | Like=-40665.18..-3.86 [-49676.3061..-12608.2519] | it/evals=1035/1887 eff=inf% N=100 Z=-38252.8(0.00%) | Like=-36587.26..-3.86 [-49676.3061..-12608.2519] | it/evals=1040/1887 eff=inf% N=100 Z=-33612.8(0.00%) | Like=-32462.69..-3.86 [-49676.3061..-12608.2519] | it/evals=1050/1887 eff=inf% N=100 Z=-28541.5(0.00%) | Like=-28487.57..-3.86 [-49676.3061..-12608.2519] | it/evals=1058/1887 eff=inf% N=100 Z=-28412.2(0.00%) | Like=-28188.21..-3.86 [-49676.3061..-12608.2519] | it/evals=1060/1887 eff=inf% N=100 Z=-23692.0(0.00%) | Like=-21662.39..-3.86 [-49676.3061..-12608.2519] | it/evals=1070/1887 eff=inf% N=100 Z=-17760.0(0.00%) | Like=-17456.73..-0.01 [-49676.3061..-12608.2519] | it/evals=1080/1887 eff=inf% N=100 Z=-13838.4(0.00%) | Like=-13583.79..-0.01 [-49676.3061..-12608.2519] | it/evals=1090/1887 eff=inf% N=100 Z=-11478.8(0.00%) | Like=-11399.84..-0.01 [-12603.9281..-3639.2946] | it/evals=1100/1887 eff=inf% N=100 Z=-10856.6(0.00%) | Like=-10434.89..-0.01 [-12603.9281..-3639.2946] | it/evals=1104/1887 eff=inf% N=100 Z=-9705.1(0.00%) | Like=-9378.04..-0.01 [-12603.9281..-3639.2946] | it/evals=1110/1887 eff=inf% N=100 Z=-7371.7(0.00%) | Like=-7306.69..-0.01 [-12603.9281..-3639.2946] | it/evals=1120/1887 eff=inf% N=100 Z=-6854.9(0.00%) | Like=-6766.39..-0.01 [-12603.9281..-3639.2946] | it/evals=1127/1887 eff=inf% N=100 Z=-6580.3(0.00%) | Like=-6479.79..-0.01 [-12603.9281..-3639.2946] | it/evals=1130/1887 eff=inf% N=100 Z=-4937.8(0.00%) | Like=-4692.98..-0.01 [-12603.9281..-3639.2946] | it/evals=1140/1887 eff=inf% N=100 Z=-4122.1(0.00%) | Like=-4067.70..-0.01 [-12603.9281..-3639.2946] | it/evals=1150/1887 eff=inf% N=100 Z=-3637.6(0.00%) | Like=-3359.18..-0.01 [-3621.3806..-807.4065] | it/evals=1160/1887 eff=inf% N=100 Z=-2706.4(0.00%) | Like=-2657.76..-0.01 [-3621.3806..-807.4065] | it/evals=1170/1887 eff=inf% N=100 Z=-2238.0(0.00%) | Like=-2188.11..-0.01 [-3621.3806..-807.4065] | it/evals=1180/1887 eff=inf% N=100 Z=-1903.2(0.00%) | Like=-1886.29..-0.01 [-3621.3806..-807.4065] | it/evals=1190/1887 eff=inf% N=100 Z=-1581.5(0.00%) | Like=-1542.77..-0.01 [-3621.3806..-807.4065] | it/evals=1200/1887 eff=inf% N=100 Z=-1237.0(0.00%) | Like=-1202.19..-0.01 [-3621.3806..-807.4065] | it/evals=1210/1887 eff=inf% N=100 Z=-1026.2(0.00%) | Like=-935.24..-0.01 [-3621.3806..-807.4065] | it/evals=1219/1887 eff=inf% N=100 Z=-952.0(0.00%) | Like=-931.89..-0.01 [-3621.3806..-807.4065] | it/evals=1220/1887 eff=inf% N=100 Z=-743.4(0.00%) | Like=-707.99..-0.01 [-749.1139..-155.2214] | it/evals=1230/1887 eff=inf% N=100 Z=-629.1(0.00%) | Like=-611.43..-0.00 [-749.1139..-155.2214] | it/evals=1240/1887 eff=inf% N=100 Z=-591.4(0.00%) | Like=-540.26..-0.00 [-749.1139..-155.2214] | it/evals=1242/1887 eff=inf% N=100 Z=-443.8(0.00%) | Like=-423.44..-0.00 [-749.1139..-155.2214] | it/evals=1250/1894 eff=17857.1429% N=100 Z=-338.3(0.00%) | Like=-319.03..-0.00 [-749.1139..-155.2214] | it/evals=1260/1906 eff=6631.5789% N=100 Z=-320.4(0.00%) | Like=-299.89..-0.00 [-749.1139..-155.2214] | it/evals=1265/1911 eff=5270.8333% N=100 Z=-309.0(0.00%) | Like=-274.96..-0.00 [-749.1139..-155.2214] | it/evals=1270/1916 eff=4379.3103% N=100 Z=-258.4(0.00%) | Like=-236.24..-0.00 [-749.1139..-155.2214] | it/evals=1280/1927 eff=3200.0000% N=100 Z=-201.0(0.00%) | Like=-179.77..-0.00 [-749.1139..-155.2214] | it/evals=1288/1936 eff=2628.5714% N=100 Z=-193.8(0.00%) | Like=-169.63..-0.00 [-749.1139..-155.2214] | it/evals=1290/1938 eff=2529.4118% N=100 Z=-161.9(0.00%) | Like=-130.03..-0.00 [-154.4051..-26.4873] | it/evals=1300/1949 eff=2096.7742% N=100 Z=-122.9(0.00%) | Like=-100.59..-0.00 [-154.4051..-26.4873] | it/evals=1310/1962 eff=1746.6667% N=100 Z=-102.1(0.00%) | Like=-82.16..-0.00 [-154.4051..-26.4873] | it/evals=1320/1972 eff=1552.9412% N=100 Z=-84.0(0.00%) | Like=-65.28..-0.00 [-154.4051..-26.4873] | it/evals=1330/1982 eff=1400.0000% N=100 Z=-77.8(0.00%) | Like=-60.49..-0.00 [-154.4051..-26.4873] | it/evals=1334/1987 eff=1334.0000% N=100 Z=-74.6(0.00%) | Like=-54.73..-0.00 [-154.4051..-26.4873] | it/evals=1340/1993 eff=1264.1509% N=100 Z=-66.2(0.00%) | Like=-43.45..-0.00 [-154.4051..-26.4873] | it/evals=1350/2004 eff=1153.8462% N=100 Z=-50.5(0.00%) | Like=-31.91..-0.00 [-154.4051..-26.4873] | it/evals=1357/2012 eff=1085.6000% N=100 Z=-48.7(0.00%) | Like=-31.07..-0.00 [-154.4051..-26.4873] | it/evals=1360/2015 eff=1062.5000% N=100 Z=-43.6(0.00%) | Like=-26.08..-0.00 [-26.2958..-5.4409] | it/evals=1370/2027 eff=978.5714% N=100 Z=-39.1(0.00%) | Like=-21.30..-0.00 [-26.2958..-5.4409] | it/evals=1380/2037 eff=920.0000% N=100 Z=-35.6(0.00%) | Like=-18.42..-0.00 [-26.2958..-5.4409] | it/evals=1390/2049 eff=858.0247% N=100 Z=-32.6(0.00%) | Like=-14.44..-0.00 [-26.2958..-5.4409] | it/evals=1400/2059 eff=813.9535% N=100 Z=-28.9(0.00%) | Like=-11.08..-0.00 [-26.2958..-5.4409] | it/evals=1410/2070 eff=770.4918% N=100 Z=-26.6(0.00%) | Like=-9.33..-0.00 [-26.2958..-5.4409] | it/evals=1420/2082 eff=728.2051% N=100 Z=-25.7(0.00%) | Like=-8.50..-0.00 [-26.2958..-5.4409] | it/evals=1426/2089 eff=705.9406% N=100 Z=-24.9(0.01%) | Like=-7.55..-0.00 [-26.2958..-5.4409] | it/evals=1430/2094 eff=690.8213% N=100 Z=-23.4(0.03%) | Like=-6.42..-0.00 [-26.2958..-5.4409] | it/evals=1440/2104 eff=663.5945% N=100 Z=-22.1(0.12%) | Like=-4.83..-0.00 [-4.8274..-4.7523] | it/evals=1450/2114 eff=638.7665% N=100 Z=-20.9(0.43%) | Like=-3.80..-0.00 [-3.7974..-3.7761] | it/evals=1460/2124 eff=616.0338% N=100 Z=-20.0(1.03%) | Like=-3.40..-0.00 [-3.4030..-3.3651] | it/evals=1470/2134 eff=595.1417% N=100 Z=-19.9(1.19%) | Like=-3.36..-0.00 [-3.3576..-3.3270] | it/evals=1472/2136 eff=591.1647% N=100 Z=-19.5(1.79%) | Like=-2.90..-0.00 [-2.8996..-2.8747] | it/evals=1480/2144 eff=575.8755% N=100 Z=-19.0(2.98%) | Like=-2.28..-0.00 [-2.2828..-2.1849] | it/evals=1490/2155 eff=555.9701% N=100 Z=-18.4(5.21%) | Like=-1.61..-0.00 [-1.7463..-1.6139] | it/evals=1500/2167 eff=535.7143% N=100 Z=-17.9(8.23%) | Like=-1.33..-0.00 [-1.3875..-1.3339] | it/evals=1510/2178 eff=518.9003% N=100 Z=-17.6(11.17%) | Like=-1.18..-0.00 [-1.1805..-1.1732]*| it/evals=1518/2188 eff=504.3189% N=100 Z=-17.5(12.00%) | Like=-1.17..-0.00 [-1.1726..-1.1180] | it/evals=1520/2190 eff=501.6502% N=100 Z=-17.2(16.09%) | Like=-0.89..-0.00 [-0.8888..-0.8881]*| it/evals=1530/2201 eff=487.2611% N=100 Z=-17.0(20.67%) | Like=-0.77..-0.00 [-0.7738..-0.7474] | it/evals=1540/2211 eff=475.3086% N=100 Z=-17.0(21.17%) | Like=-0.75..-0.00 [-0.7738..-0.7474] | it/evals=1541/2212 eff=474.1538% N=100 Z=-16.8(25.27%) | Like=-0.66..-0.00 [-0.6833..-0.6582] | it/evals=1550/2222 eff=462.6866% N=100 Z=-16.6(29.85%) | Like=-0.54..-0.00 [-0.5355..-0.5234] | it/evals=1560/2236 eff=446.9914% N=100 Z=-16.5(34.80%) | Like=-0.41..-0.00 [-0.4471..-0.4112] | it/evals=1570/2246 eff=437.3259% N=100 Z=-16.3(39.66%) | Like=-0.35..-0.00 [-0.3542..-0.3495]*| it/evals=1580/2258 eff=425.8760% N=100 Z=-16.3(42.87%) | Like=-0.28..-0.00 [-0.2820..-0.2808]*| it/evals=1587/2267 eff=417.6316% N=100 Z=-16.2(44.25%) | Like=-0.26..-0.00 [-0.2561..-0.2521]*| it/evals=1590/2270 eff=415.1436% N=100 Z=-16.1(48.74%) | Like=-0.23..-0.00 [-0.2337..-0.2135] | it/evals=1600/2282 eff=405.0633% N=100 [ultranest] Explored until L=-2e-05 [ultranest] Likelihood function evaluations: 2284 [ultranest] Writing samples and results to disk ... [ultranest] Writing samples and results to disk ... done [ultranest] Reached maximum number of improvement loops. [ultranest] done iterating. logZ = -15.401 +- 0.741 single instance: logZ = -15.401 +- 0.390 bootstrapped : logZ = -15.459 +- 0.620 tail : logZ = +- 0.405 insert order U test : converged: True correlation: inf iterations a : -0.00331│ ▁▁▁▁▁▁▁▁▂▂▃▅▄▃▇▇▇▇▇▄▆▄▃▄▂▃▁▁ ▁▁▁ ▁ │0.00440 0.00025 +- 0.00098 pointstore: (1703, 5) 397 2284 1887 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=False, log_dir=/tmp/tmp_g0nidx0, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 100 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.50, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=101, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-49977128652622.04, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=112, regioncalls=480, ndraw=40, logz=-41932563448515.12, remainder_fraction=100.0000%, Lmin=-40508378723011.33, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=122, regioncalls=880, ndraw=40, logz=-33237565078343.30, remainder_fraction=100.0000%, Lmin=-33004788076548.36, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=132, regioncalls=1280, ndraw=40, logz=-29684133841641.27, remainder_fraction=100.0000%, Lmin=-29654731646156.86, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=143, regioncalls=1720, ndraw=40, logz=-26024311452003.38, remainder_fraction=100.0000%, Lmin=-25864311505807.76, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=46, ncalls=151, regioncalls=2040, ndraw=40, logz=-22394867389307.37, remainder_fraction=100.0000%, Lmin=-22267402908949.62, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=155, regioncalls=2200, ndraw=40, logz=-21618903469091.73, remainder_fraction=100.0000%, Lmin=-20657643852261.66, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=165, regioncalls=2600, ndraw=40, logz=-16736505395315.38, remainder_fraction=100.0000%, Lmin=-16631669273078.86, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=69, ncalls=174, regioncalls=2960, ndraw=40, logz=-14754875235796.48, remainder_fraction=100.0000%, Lmin=-13974010155653.97, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=176, regioncalls=3040, ndraw=40, logz=-13974010155659.27, remainder_fraction=100.0000%, Lmin=-13902692147308.39, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=186, regioncalls=3440, ndraw=40, logz=-12514435159574.19, remainder_fraction=100.0000%, Lmin=-12508279160474.38, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=197, regioncalls=3880, ndraw=40, logz=-10193267488313.77, remainder_fraction=100.0000%, Lmin=-9640968563235.02, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=208, regioncalls=4320, ndraw=40, logz=-8200640852030.98, remainder_fraction=100.0000%, Lmin=-8082128050878.53, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=220, regioncalls=4800, ndraw=40, logz=-7173863638817.49, remainder_fraction=100.0000%, Lmin=-6962107832262.44, Lmax=-14202345756.60 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=115, ncalls=225, regioncalls=5000, ndraw=40, logz=-6806896849592.72, remainder_fraction=100.0000%, Lmin=-6798572125612.82, Lmax=-2101560050.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=230, regioncalls=5200, ndraw=40, logz=-6322457704122.88, remainder_fraction=100.0000%, Lmin=-6247007877914.18, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=243, regioncalls=5720, ndraw=40, logz=-5036135578185.74, remainder_fraction=100.0000%, Lmin=-5013134155411.33, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=257, regioncalls=6280, ndraw=40, logz=-4566342513618.33, remainder_fraction=100.0000%, Lmin=-4489051812244.98, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=268, regioncalls=6720, ndraw=40, logz=-3426886969738.49, remainder_fraction=100.0000%, Lmin=-3417861015794.89, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=278, regioncalls=7120, ndraw=40, logz=-2961969420058.14, remainder_fraction=100.0000%, Lmin=-2922089236302.01, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=161, ncalls=279, regioncalls=7160, ndraw=40, logz=-2922089236308.22, remainder_fraction=100.0000%, Lmin=-2889249507418.14, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=289, regioncalls=7560, ndraw=40, logz=-2133520045718.24, remainder_fraction=100.0000%, Lmin=-2131391081808.14, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=299, regioncalls=7960, ndraw=40, logz=-1843764249986.19, remainder_fraction=100.0000%, Lmin=-1836388311150.23, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=309, regioncalls=8360, ndraw=40, logz=-1479338126812.93, remainder_fraction=100.0000%, Lmin=-1477274817822.07, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=319, regioncalls=8760, ndraw=40, logz=-1162164164059.82, remainder_fraction=100.0000%, Lmin=-1149513369148.13, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=207, ncalls=326, regioncalls=9040, ndraw=40, logz=-1098683740545.40, remainder_fraction=100.0000%, Lmin=-1085733203899.34, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=210, ncalls=329, regioncalls=9160, ndraw=40, logz=-1062199650048.03, remainder_fraction=100.0000%, Lmin=-1036687340045.72, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=220, ncalls=342, regioncalls=9680, ndraw=40, logz=-871674334253.59, remainder_fraction=100.0000%, Lmin=-839966514038.46, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=230, ncalls=352, regioncalls=10080, ndraw=40, logz=-715976483973.56, remainder_fraction=100.0000%, Lmin=-706773276409.40, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=364, regioncalls=10560, ndraw=40, logz=-593422182128.55, remainder_fraction=100.0000%, Lmin=-576444441337.76, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=376, regioncalls=11080, ndraw=40, logz=-511876960333.87, remainder_fraction=100.0000%, Lmin=-501758974682.00, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=253, ncalls=379, regioncalls=11200, ndraw=40, logz=-478675075304.45, remainder_fraction=100.0000%, Lmin=-455241053559.73, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=260, ncalls=387, regioncalls=11520, ndraw=40, logz=-423629485137.20, remainder_fraction=100.0000%, Lmin=-418959245625.39, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=401, regioncalls=12120, ndraw=40, logz=-337456305895.85, remainder_fraction=100.0000%, Lmin=-332415073979.82, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=276, ncalls=408, regioncalls=12400, ndraw=40, logz=-298475456646.05, remainder_fraction=100.0000%, Lmin=-298119333065.73, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=412, regioncalls=12600, ndraw=40, logz=-280041079556.47, remainder_fraction=100.0000%, Lmin=-266139199581.03, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=290, ncalls=422, regioncalls=13040, ndraw=40, logz=-219987159145.22, remainder_fraction=100.0000%, Lmin=-217170277506.50, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=299, ncalls=431, regioncalls=13400, ndraw=40, logz=-186287571926.49, remainder_fraction=100.0000%, Lmin=-186217150611.38, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=432, regioncalls=13440, ndraw=40, logz=-186217150618.98, remainder_fraction=100.0000%, Lmin=-176257223101.81, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=310, ncalls=446, regioncalls=14040, ndraw=40, logz=-136032296082.24, remainder_fraction=100.0000%, Lmin=-134941926834.72, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=458, regioncalls=14520, ndraw=40, logz=-106028758932.65, remainder_fraction=100.0000%, Lmin=-105574617114.81, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=322, ncalls=460, regioncalls=14600, ndraw=40, logz=-102631626890.59, remainder_fraction=100.0000%, Lmin=-101211365642.63, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=330, ncalls=468, regioncalls=14960, ndraw=40, logz=-85746886310.20, remainder_fraction=100.0000%, Lmin=-84170564177.13, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=340, ncalls=478, regioncalls=15360, ndraw=40, logz=-78454705852.75, remainder_fraction=100.0000%, Lmin=-77856855358.69, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=488, regioncalls=15760, ndraw=40, logz=-65217978077.06, remainder_fraction=100.0000%, Lmin=-65071379682.57, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=498, regioncalls=16160, ndraw=40, logz=-52784301112.17, remainder_fraction=100.0000%, Lmin=-52465515313.79, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=368, ncalls=506, regioncalls=16480, ndraw=40, logz=-44886006297.10, remainder_fraction=100.0000%, Lmin=-44881183821.51, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=370, ncalls=508, regioncalls=16600, ndraw=40, logz=-44337101807.05, remainder_fraction=100.0000%, Lmin=-43924418108.48, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=380, ncalls=518, regioncalls=17080, ndraw=40, logz=-38174116980.61, remainder_fraction=100.0000%, Lmin=-38166134224.94, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=528, regioncalls=17480, ndraw=40, logz=-30564921897.35, remainder_fraction=100.0000%, Lmin=-29014402527.49, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=391, ncalls=529, regioncalls=17520, ndraw=40, logz=-29014402536.00, remainder_fraction=100.0000%, Lmin=-28739121812.84, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=539, regioncalls=17960, ndraw=40, logz=-25254381195.53, remainder_fraction=100.0000%, Lmin=-25189070092.02, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=410, ncalls=549, regioncalls=18360, ndraw=40, logz=-21660404181.26, remainder_fraction=100.0000%, Lmin=-21548375173.69, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=414, ncalls=553, regioncalls=18560, ndraw=40, logz=-20472052660.36, remainder_fraction=100.0000%, Lmin=-20078668592.97, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=420, ncalls=559, regioncalls=18840, ndraw=40, logz=-17503276366.73, remainder_fraction=100.0000%, Lmin=-16696410598.10, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=430, ncalls=569, regioncalls=19240, ndraw=40, logz=-13957975006.86, remainder_fraction=100.0000%, Lmin=-13215545136.09, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=581, regioncalls=19720, ndraw=40, logz=-11354970506.81, remainder_fraction=100.0000%, Lmin=-11308593818.41, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=591, regioncalls=20120, ndraw=40, logz=-8954739289.76, remainder_fraction=100.0000%, Lmin=-8802399549.13, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=460, ncalls=603, regioncalls=20640, ndraw=40, logz=-7358620740.27, remainder_fraction=100.0000%, Lmin=-7312924005.82, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=470, ncalls=615, regioncalls=21120, ndraw=40, logz=-5913717188.92, remainder_fraction=100.0000%, Lmin=-5686842852.59, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=625, regioncalls=21520, ndraw=40, logz=-4561851573.73, remainder_fraction=100.0000%, Lmin=-4534652974.21, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=483, ncalls=628, regioncalls=21680, ndraw=40, logz=-4341443065.59, remainder_fraction=100.0000%, Lmin=-4317184186.04, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=490, ncalls=635, regioncalls=21960, ndraw=40, logz=-3683655620.41, remainder_fraction=100.0000%, Lmin=-3668713486.10, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=648, regioncalls=22520, ndraw=40, logz=-3119287299.14, remainder_fraction=100.0000%, Lmin=-3077114603.52, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=506, ncalls=656, regioncalls=22880, ndraw=40, logz=-2711784349.67, remainder_fraction=100.0000%, Lmin=-2672326802.74, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=510, ncalls=660, regioncalls=23040, ndraw=40, logz=-2471295503.35, remainder_fraction=100.0000%, Lmin=-2451644303.44, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=671, regioncalls=23480, ndraw=40, logz=-2046833928.20, remainder_fraction=100.0000%, Lmin=-1963025685.85, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=529, ncalls=682, regioncalls=23960, ndraw=40, logz=-1683061127.53, remainder_fraction=100.0000%, Lmin=-1655504010.18, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=530, ncalls=685, regioncalls=24080, ndraw=40, logz=-1655504020.08, remainder_fraction=100.0000%, Lmin=-1587929042.93, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=697, regioncalls=24560, ndraw=40, logz=-1345672698.85, remainder_fraction=100.0000%, Lmin=-1324107214.64, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=710, regioncalls=25080, ndraw=40, logz=-842704557.04, remainder_fraction=100.0000%, Lmin=-842098627.35, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=552, ncalls=712, regioncalls=25200, ndraw=40, logz=-829471104.02, remainder_fraction=100.0000%, Lmin=-821755543.39, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=721, regioncalls=25560, ndraw=40, logz=-768681282.57, remainder_fraction=100.0000%, Lmin=-741055451.88, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=570, ncalls=731, regioncalls=25960, ndraw=40, logz=-598461526.63, remainder_fraction=100.0000%, Lmin=-588030061.40, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=580, ncalls=741, regioncalls=26360, ndraw=40, logz=-523313989.11, remainder_fraction=100.0000%, Lmin=-520429923.57, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=590, ncalls=755, regioncalls=26920, ndraw=40, logz=-389302279.60, remainder_fraction=100.0000%, Lmin=-384227190.96, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=598, ncalls=763, regioncalls=27280, ndraw=40, logz=-330216148.35, remainder_fraction=100.0000%, Lmin=-310871070.23, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=765, regioncalls=27360, ndraw=40, logz=-291718407.06, remainder_fraction=100.0000%, Lmin=-277171076.01, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=610, ncalls=775, regioncalls=27760, ndraw=40, logz=-245515440.47, remainder_fraction=100.0000%, Lmin=-244117200.51, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=620, ncalls=786, regioncalls=28200, ndraw=40, logz=-222411923.28, remainder_fraction=100.0000%, Lmin=-218434467.04, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=796, regioncalls=28600, ndraw=40, logz=-179666648.72, remainder_fraction=100.0000%, Lmin=-179079251.49, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=806, regioncalls=29000, ndraw=40, logz=-147157505.55, remainder_fraction=100.0000%, Lmin=-146182858.52, Lmax=-19941.74 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=644, ncalls=810, regioncalls=29160, ndraw=40, logz=-139592730.12, remainder_fraction=100.0000%, Lmin=-139232509.33, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=816, regioncalls=29520, ndraw=40, logz=-128990960.00, remainder_fraction=100.0000%, Lmin=-127079723.98, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=660, ncalls=827, regioncalls=29960, ndraw=40, logz=-108091218.09, remainder_fraction=100.0000%, Lmin=-105743831.21, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=667, ncalls=835, regioncalls=30360, ndraw=40, logz=-99584971.45, remainder_fraction=100.0000%, Lmin=-99056746.97, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=670, ncalls=838, regioncalls=30480, ndraw=40, logz=-95148616.12, remainder_fraction=100.0000%, Lmin=-93692477.41, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=849, regioncalls=30920, ndraw=40, logz=-78681625.77, remainder_fraction=100.0000%, Lmin=-76849640.99, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=690, ncalls=860, regioncalls=31360, ndraw=40, logz=-67129640.49, remainder_fraction=100.0000%, Lmin=-66446442.40, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=872, regioncalls=31840, ndraw=40, logz=-52437072.43, remainder_fraction=100.0000%, Lmin=-52098992.93, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=710, ncalls=883, regioncalls=32280, ndraw=40, logz=-42467034.54, remainder_fraction=100.0000%, Lmin=-40947576.24, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=713, ncalls=886, regioncalls=32520, ndraw=40, logz=-40875016.23, remainder_fraction=100.0000%, Lmin=-40682265.05, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=893, regioncalls=32800, ndraw=40, logz=-34273953.31, remainder_fraction=100.0000%, Lmin=-34009302.56, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=730, ncalls=904, regioncalls=33240, ndraw=40, logz=-28150952.86, remainder_fraction=100.0000%, Lmin=-27577294.66, Lmax=-6022.96 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2]), array([99, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=740, ncalls=917, regioncalls=33760, ndraw=40, logz=-24903284.59, remainder_fraction=100.0000%, Lmin=-24585788.12, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=927, regioncalls=34160, ndraw=40, logz=-18109608.41, remainder_fraction=100.0000%, Lmin=-17736757.48, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=937, regioncalls=34640, ndraw=40, logz=-15169525.42, remainder_fraction=100.0000%, Lmin=-14227274.47, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=770, ncalls=949, regioncalls=35120, ndraw=40, logz=-11387981.32, remainder_fraction=100.0000%, Lmin=-11171106.07, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=780, ncalls=959, regioncalls=35520, ndraw=40, logz=-9540359.81, remainder_fraction=100.0000%, Lmin=-9254160.55, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=782, ncalls=961, regioncalls=35640, ndraw=40, logz=-9246880.24, remainder_fraction=100.0000%, Lmin=-9220004.55, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=790, ncalls=969, regioncalls=35960, ndraw=40, logz=-8202857.71, remainder_fraction=100.0000%, Lmin=-7968261.52, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=980, regioncalls=36440, ndraw=40, logz=-6151494.36, remainder_fraction=100.0000%, Lmin=-6110210.34, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=805, ncalls=986, regioncalls=36760, ndraw=40, logz=-5595023.23, remainder_fraction=100.0000%, Lmin=-5544084.40, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=991, regioncalls=36960, ndraw=40, logz=-5168753.73, remainder_fraction=100.0000%, Lmin=-5104017.56, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=820, ncalls=1001, regioncalls=37360, ndraw=40, logz=-4348450.29, remainder_fraction=100.0000%, Lmin=-4124983.59, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=828, ncalls=1010, regioncalls=37800, ndraw=40, logz=-3288211.79, remainder_fraction=100.0000%, Lmin=-3052967.15, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=830, ncalls=1012, regioncalls=37880, ndraw=40, logz=-2816901.87, remainder_fraction=100.0000%, Lmin=-2813964.84, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1022, regioncalls=38280, ndraw=40, logz=-2391242.81, remainder_fraction=100.0000%, Lmin=-2352037.02, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1033, regioncalls=38720, ndraw=40, logz=-1694476.49, remainder_fraction=100.0000%, Lmin=-1688714.15, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=851, ncalls=1034, regioncalls=38960, ndraw=40, logz=-1688727.26, remainder_fraction=100.0000%, Lmin=-1641589.55, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=860, ncalls=1044, regioncalls=39360, ndraw=40, logz=-1391311.12, remainder_fraction=100.0000%, Lmin=-1378714.97, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=870, ncalls=1056, regioncalls=39840, ndraw=40, logz=-1016206.97, remainder_fraction=100.0000%, Lmin=-1005805.90, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=874, ncalls=1061, regioncalls=40080, ndraw=40, logz=-921148.44, remainder_fraction=100.0000%, Lmin=-899817.07, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1067, regioncalls=40320, ndraw=40, logz=-823593.32, remainder_fraction=100.0000%, Lmin=-820443.05, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=890, ncalls=1078, regioncalls=40760, ndraw=40, logz=-718128.13, remainder_fraction=100.0000%, Lmin=-716385.20, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1090, regioncalls=41240, ndraw=40, logz=-596455.20, remainder_fraction=100.0000%, Lmin=-575764.61, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=910, ncalls=1101, regioncalls=41680, ndraw=40, logz=-491331.87, remainder_fraction=100.0000%, Lmin=-490644.03, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1112, regioncalls=42200, ndraw=40, logz=-403815.91, remainder_fraction=100.0000%, Lmin=-396540.01, Lmax=-26.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=930, ncalls=1122, regioncalls=42600, ndraw=40, logz=-335464.44, remainder_fraction=100.0000%, Lmin=-335428.11, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=940, ncalls=1133, regioncalls=43080, ndraw=40, logz=-281288.10, remainder_fraction=100.0000%, Lmin=-269279.55, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=943, ncalls=1137, regioncalls=43280, ndraw=40, logz=-267112.83, remainder_fraction=100.0000%, Lmin=-259247.40, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1144, regioncalls=43560, ndraw=40, logz=-232361.23, remainder_fraction=100.0000%, Lmin=-227851.27, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1154, regioncalls=43960, ndraw=40, logz=-195082.59, remainder_fraction=100.0000%, Lmin=-192338.76, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=970, ncalls=1165, regioncalls=44400, ndraw=40, logz=-158846.65, remainder_fraction=100.0000%, Lmin=-156838.84, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=980, ncalls=1176, regioncalls=44840, ndraw=40, logz=-123015.80, remainder_fraction=100.0000%, Lmin=-122093.92, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=989, ncalls=1186, regioncalls=45280, ndraw=40, logz=-98769.27, remainder_fraction=100.0000%, Lmin=-94308.81, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1187, regioncalls=45320, ndraw=40, logz=-94323.31, remainder_fraction=100.0000%, Lmin=-93705.11, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1198, regioncalls=45760, ndraw=40, logz=-75793.03, remainder_fraction=100.0000%, Lmin=-75159.71, Lmax=-4.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1010, ncalls=1209, regioncalls=46200, ndraw=40, logz=-69193.78, remainder_fraction=100.0000%, Lmin=-69178.34, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1012, ncalls=1212, regioncalls=46360, ndraw=40, logz=-68151.14, remainder_fraction=100.0000%, Lmin=-67031.91, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1020, ncalls=1221, regioncalls=46720, ndraw=40, logz=-59478.14, remainder_fraction=100.0000%, Lmin=-56316.63, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1030, ncalls=1232, regioncalls=47160, ndraw=40, logz=-49166.66, remainder_fraction=100.0000%, Lmin=-46314.73, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1035, ncalls=1238, regioncalls=47480, ndraw=40, logz=-42809.28, remainder_fraction=100.0000%, Lmin=-40722.23, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=1243, regioncalls=47680, ndraw=40, logz=-38197.53, remainder_fraction=100.0000%, Lmin=-36533.18, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1253, regioncalls=48080, ndraw=40, logz=-33560.94, remainder_fraction=100.0000%, Lmin=-32411.75, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1060, ncalls=1263, regioncalls=48480, ndraw=40, logz=-28364.53, remainder_fraction=100.0000%, Lmin=-28235.71, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1070, ncalls=1273, regioncalls=48880, ndraw=40, logz=-23735.59, remainder_fraction=100.0000%, Lmin=-21704.04, Lmax=-4.43 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1283, regioncalls=49280, ndraw=40, logz=-17722.35, remainder_fraction=100.0000%, Lmin=-17494.12, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1081, ncalls=1284, regioncalls=49400, ndraw=40, logz=-17509.53, remainder_fraction=100.0000%, Lmin=-17033.55, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1090, ncalls=1293, regioncalls=49760, ndraw=40, logz=-13805.21, remainder_fraction=100.0000%, Lmin=-13616.77, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1304, regioncalls=50200, ndraw=40, logz=-11448.52, remainder_fraction=100.0000%, Lmin=-11430.06, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1104, ncalls=1308, regioncalls=50400, ndraw=40, logz=-10886.12, remainder_fraction=100.0000%, Lmin=-10406.01, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1110, ncalls=1314, regioncalls=50640, ndraw=40, logz=-9677.28, remainder_fraction=100.0000%, Lmin=-9405.45, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=1325, regioncalls=51080, ndraw=40, logz=-7395.99, remainder_fraction=100.0000%, Lmin=-7330.88, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1130, ncalls=1337, regioncalls=51560, ndraw=40, logz=-6557.37, remainder_fraction=100.0000%, Lmin=-6502.58, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1140, ncalls=1348, regioncalls=52000, ndraw=40, logz=-4917.95, remainder_fraction=100.0000%, Lmin=-4673.62, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1358, regioncalls=52480, ndraw=40, logz=-4140.24, remainder_fraction=100.0000%, Lmin=-4068.57, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=1368, regioncalls=52880, ndraw=40, logz=-3620.58, remainder_fraction=100.0000%, Lmin=-3375.59, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1380, regioncalls=53360, ndraw=40, logz=-2712.22, remainder_fraction=100.0000%, Lmin=-2672.37, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1173, ncalls=1383, regioncalls=53520, ndraw=40, logz=-2576.74, remainder_fraction=100.0000%, Lmin=-2536.55, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1180, ncalls=1391, regioncalls=53840, ndraw=40, logz=-2224.70, remainder_fraction=100.0000%, Lmin=-2201.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1190, ncalls=1404, regioncalls=54360, ndraw=40, logz=-1915.51, remainder_fraction=100.0000%, Lmin=-1898.60, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1417, regioncalls=54880, ndraw=40, logz=-1600.73, remainder_fraction=100.0000%, Lmin=-1576.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1210, ncalls=1429, regioncalls=55360, ndraw=40, logz=-1273.46, remainder_fraction=100.0000%, Lmin=-1210.46, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1220, ncalls=1440, regioncalls=55800, ndraw=40, logz=-1035.19, remainder_fraction=100.0000%, Lmin=-936.01, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1230, ncalls=1454, regioncalls=56360, ndraw=40, logz=-747.28, remainder_fraction=100.0000%, Lmin=-722.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=1466, regioncalls=56840, ndraw=40, logz=-637.32, remainder_fraction=100.0000%, Lmin=-618.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1242, ncalls=1468, regioncalls=56960, ndraw=40, logz=-622.12, remainder_fraction=100.0000%, Lmin=-567.65, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=1476, regioncalls=57280, ndraw=40, logz=-453.14, remainder_fraction=100.0000%, Lmin=-420.93, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1487, regioncalls=57720, ndraw=40, logz=-343.36, remainder_fraction=100.0000%, Lmin=-321.96, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1265, ncalls=1493, regioncalls=58000, ndraw=40, logz=-324.71, remainder_fraction=100.0000%, Lmin=-304.81, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1270, ncalls=1498, regioncalls=58200, ndraw=40, logz=-293.07, remainder_fraction=100.0000%, Lmin=-269.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=1508, regioncalls=58600, ndraw=40, logz=-244.96, remainder_fraction=100.0000%, Lmin=-220.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1288, ncalls=1517, regioncalls=59040, ndraw=40, logz=-198.68, remainder_fraction=100.0000%, Lmin=-179.69, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1290, ncalls=1519, regioncalls=59120, ndraw=40, logz=-190.06, remainder_fraction=100.0000%, Lmin=-163.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=1532, regioncalls=59640, ndraw=40, logz=-156.72, remainder_fraction=100.0000%, Lmin=-138.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1310, ncalls=1544, regioncalls=60160, ndraw=40, logz=-144.27, remainder_fraction=100.0000%, Lmin=-124.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=1555, regioncalls=60600, ndraw=40, logz=-107.47, remainder_fraction=100.0000%, Lmin=-88.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1330, ncalls=1566, regioncalls=61040, ndraw=40, logz=-93.99, remainder_fraction=100.0000%, Lmin=-70.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1334, ncalls=1572, regioncalls=61320, ndraw=40, logz=-84.38, remainder_fraction=100.0000%, Lmin=-66.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1340, ncalls=1578, regioncalls=61560, ndraw=40, logz=-78.05, remainder_fraction=100.0000%, Lmin=-59.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=1591, regioncalls=62080, ndraw=40, logz=-68.06, remainder_fraction=100.0000%, Lmin=-47.59, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1357, ncalls=1598, regioncalls=62400, ndraw=40, logz=-55.87, remainder_fraction=100.0000%, Lmin=-36.04, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=1601, regioncalls=62520, ndraw=40, logz=-51.83, remainder_fraction=100.0000%, Lmin=-34.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1370, ncalls=1613, regioncalls=63000, ndraw=40, logz=-46.66, remainder_fraction=100.0000%, Lmin=-28.87, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1380, ncalls=1626, regioncalls=63560, ndraw=40, logz=-42.36, remainder_fraction=100.0000%, Lmin=-24.81, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1390, ncalls=1637, regioncalls=64000, ndraw=40, logz=-38.60, remainder_fraction=100.0000%, Lmin=-21.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=1648, regioncalls=64440, ndraw=40, logz=-35.49, remainder_fraction=100.0000%, Lmin=-18.34, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1403, ncalls=1652, regioncalls=64640, ndraw=40, logz=-34.86, remainder_fraction=100.0000%, Lmin=-17.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1410, ncalls=1662, regioncalls=65040, ndraw=40, logz=-32.76, remainder_fraction=100.0000%, Lmin=-15.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1420, ncalls=1673, regioncalls=65480, ndraw=40, logz=-30.69, remainder_fraction=100.0000%, Lmin=-13.76, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1426, ncalls=1680, regioncalls=65840, ndraw=40, logz=-29.49, remainder_fraction=99.9999%, Lmin=-12.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1430, ncalls=1684, regioncalls=66000, ndraw=40, logz=-28.14, remainder_fraction=99.9997%, Lmin=-10.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=1697, regioncalls=66520, ndraw=40, logz=-26.12, remainder_fraction=99.9977%, Lmin=-8.61, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=1710, regioncalls=67040, ndraw=40, logz=-24.32, remainder_fraction=99.9859%, Lmin=-7.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1460, ncalls=1720, regioncalls=67440, ndraw=40, logz=-23.03, remainder_fraction=99.9451%, Lmin=-5.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1470, ncalls=1731, regioncalls=67880, ndraw=40, logz=-21.49, remainder_fraction=99.7426%, Lmin=-4.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1472, ncalls=1733, regioncalls=68040, ndraw=40, logz=-21.26, remainder_fraction=99.6811%, Lmin=-3.97, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1480, ncalls=1741, regioncalls=68360, ndraw=40, logz=-20.36, remainder_fraction=99.1955%, Lmin=-3.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1490, ncalls=1751, regioncalls=68760, ndraw=40, logz=-19.45, remainder_fraction=97.9467%, Lmin=-2.57, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1495, ncalls=1756, regioncalls=69000, ndraw=40, logz=-19.13, remainder_fraction=97.1779%, Lmin=-2.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=1761, regioncalls=69200, ndraw=40, logz=-18.86, remainder_fraction=96.3847%, Lmin=-2.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1510, ncalls=1774, regioncalls=69720, ndraw=40, logz=-18.41, remainder_fraction=94.3893%, Lmin=-1.86, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1518, ncalls=1782, regioncalls=70080, ndraw=40, logz=-18.08, remainder_fraction=92.2357%, Lmin=-1.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1520, ncalls=1784, regioncalls=70160, ndraw=40, logz=-18.01, remainder_fraction=91.6178%, Lmin=-1.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=1795, regioncalls=70600, ndraw=40, logz=-17.66, remainder_fraction=88.2455%, Lmin=-1.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1540, ncalls=1806, regioncalls=71040, ndraw=40, logz=-17.37, remainder_fraction=84.3049%, Lmin=-0.99, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1541, ncalls=1807, regioncalls=71160, ndraw=40, logz=-17.34, remainder_fraction=83.8617%, Lmin=-0.98, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=1816, regioncalls=71520, ndraw=40, logz=-17.13, remainder_fraction=79.7528%, Lmin=-0.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=1826, regioncalls=71920, ndraw=40, logz=-16.92, remainder_fraction=75.2117%, Lmin=-0.67, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1564, ncalls=1830, regioncalls=72120, ndraw=40, logz=-16.85, remainder_fraction=73.4569%, Lmin=-0.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1570, ncalls=1837, regioncalls=72400, ndraw=40, logz=-16.75, remainder_fraction=70.7038%, Lmin=-0.55, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1580, ncalls=1849, regioncalls=72880, ndraw=40, logz=-16.59, remainder_fraction=66.0335%, Lmin=-0.42, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1587, ncalls=1858, regioncalls=73280, ndraw=40, logz=-16.50, remainder_fraction=62.7706%, Lmin=-0.38, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1590, ncalls=1861, regioncalls=73400, ndraw=40, logz=-16.46, remainder_fraction=61.3265%, Lmin=-0.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=1872, regioncalls=73840, ndraw=40, logz=-16.35, remainder_fraction=56.6426%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1610, ncalls=1883, regioncalls=74400, ndraw=40, logz=-16.25, remainder_fraction=52.0720%, Lmin=-0.20, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-4e-06 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 1887 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmp_g0nidx0, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [-1.94762221e-01 -1.80163717e-05 0.00000000e+00 5.00000005e-01 1.06002728e-04]: u=[0.50000001] -> p=[0.000106] -> L=-1.801637165560648e-05 [33mWARNING [0m ultranest:integrator.py:1188 Trying to resume from previous run, but likelihood function gives different result: [0.50000001] gave -1.801637165560648e-05, now -0.004417743572744871 [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=1+0, resume=True, log_dir=/tmp/tmp_g0nidx0, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [35mDEBUG [0m ultranest:integrator.py:1177 Testing resume consistency: [-1.94762221e-01 -1.80163717e-05 0.00000000e+00 5.00000005e-01 1.06002728e-04]: u=[0.50000001] -> p=[0.000106] -> L=-1.801637165560648e-05 [33mWARNING [0m ultranest:integrator.py:1188 Trying to resume from previous run, but likelihood function gives different result: [0.50000001] gave -1.801637165560648e-05, now -0.018817470773834135 [32mINFO [0m ultranest:integrator.py:1106 trying to salvage points from previous, different run ... [32mINFO [0m ultranest:integrator.py:2246 Resuming from 1344 stored points [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.50, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=0, min_num_live_points=100, cluster_num_live_points=0 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 100.0), (inf, 100.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=1887, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-49977130652164.57, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=10, ncalls=1887, regioncalls=0, ndraw=40, logz=-41932561616957.05, remainder_fraction=100.0000%, Lmin=-40508380523197.54, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=20, ncalls=1887, regioncalls=0, ndraw=40, logz=-33237566708988.97, remainder_fraction=100.0000%, Lmin=-33004789701473.93, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=23, ncalls=1887, regioncalls=0, ndraw=40, logz=-32543005101534.51, remainder_fraction=100.0000%, Lmin=-32265055588306.13, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=30, ncalls=1887, regioncalls=0, ndraw=40, logz=-29684135382657.41, remainder_fraction=100.0000%, Lmin=-29654730105904.14, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=1887, regioncalls=0, ndraw=40, logz=-26024312894898.02, remainder_fraction=100.0000%, Lmin=-25864312944260.04, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=1887, regioncalls=0, ndraw=40, logz=-21618902153982.53, remainder_fraction=100.0000%, Lmin=-20657642566722.29, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=60, ncalls=1887, regioncalls=0, ndraw=40, logz=-16736504238198.11, remainder_fraction=100.0000%, Lmin=-16631670426566.43, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=69, ncalls=1887, regioncalls=0, ndraw=40, logz=-14754876322254.05, remainder_fraction=100.0000%, Lmin=-13974009098336.24, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=70, ncalls=1887, regioncalls=0, ndraw=40, logz=-13974009098341.54, remainder_fraction=100.0000%, Lmin=-13902691092692.20, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=1887, regioncalls=0, ndraw=40, logz=-12514434158996.97, remainder_fraction=100.0000%, Lmin=-12508280160805.51, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=1887, regioncalls=0, ndraw=40, logz=-10193266585284.78, remainder_fraction=100.0000%, Lmin=-9640969441459.09, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=1887, regioncalls=0, ndraw=40, logz=-8200640042061.08, remainder_fraction=100.0000%, Lmin=-8082128854974.46, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=110, ncalls=1887, regioncalls=0, ndraw=40, logz=-7173864396385.38, remainder_fraction=100.0000%, Lmin=-6962108578565.77, Lmax=-14202379464.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=115, ncalls=1887, regioncalls=0, ndraw=40, logz=-6806897587530.23, remainder_fraction=100.0000%, Lmin=-6798571388126.72, Lmax=-2101547083.83 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=1887, regioncalls=0, ndraw=40, logz=-6322458415316.69, remainder_fraction=100.0000%, Lmin=-6247007170976.70, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=130, ncalls=1887, regioncalls=0, ndraw=40, logz=-5036134943448.93, remainder_fraction=100.0000%, Lmin=-5013134788697.02, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=138, ncalls=1887, regioncalls=0, ndraw=40, logz=-4680998274552.75, remainder_fraction=100.0000%, Lmin=-4635047605209.84, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=140, ncalls=1887, regioncalls=0, ndraw=40, logz=-4566343118025.00, remainder_fraction=100.0000%, Lmin=-4489051212975.32, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=1887, regioncalls=0, ndraw=40, logz=-3426887493332.78, remainder_fraction=100.0000%, Lmin=-3417861538699.18, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=1887, regioncalls=0, ndraw=40, logz=-2961968933275.30, remainder_fraction=100.0000%, Lmin=-2922088752807.32, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=161, ncalls=1887, regioncalls=0, ndraw=40, logz=-2922088752813.53, remainder_fraction=100.0000%, Lmin=-2889249026647.98, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=170, ncalls=1887, regioncalls=0, ndraw=40, logz=-2133520458854.56, remainder_fraction=100.0000%, Lmin=-2131391494738.28, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=1887, regioncalls=0, ndraw=40, 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iteration=520, ncalls=1887, regioncalls=0, ndraw=40, logz=-2046821131.87, remainder_fraction=100.0000%, Lmin=-1963038217.51, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=529, ncalls=1887, regioncalls=0, ndraw=40, logz=-1683072731.21, remainder_fraction=100.0000%, Lmin=-1655492501.92, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=530, ncalls=1887, regioncalls=0, ndraw=40, logz=-1655492511.82, remainder_fraction=100.0000%, Lmin=-1587917772.00, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1887, regioncalls=0, ndraw=40, logz=-1345683074.51, remainder_fraction=100.0000%, Lmin=-1324117506.82, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=1887, regioncalls=0, ndraw=40, logz=-842696346.31, remainder_fraction=100.0000%, Lmin=-842106835.16, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=552, ncalls=1887, regioncalls=0, ndraw=40, logz=-829479250.06, remainder_fraction=100.0000%, Lmin=-821747435.36, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1887, regioncalls=0, ndraw=40, logz=-768673440.75, remainder_fraction=100.0000%, Lmin=-741047752.26, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=570, ncalls=1887, regioncalls=0, ndraw=40, logz=-598454607.33, remainder_fraction=100.0000%, Lmin=-588036920.17, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=575, ncalls=1887, regioncalls=0, ndraw=40, logz=-561645953.60, remainder_fraction=100.0000%, Lmin=-542758608.59, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=580, ncalls=1887, regioncalls=0, ndraw=40, logz=-523320459.46, remainder_fraction=100.0000%, Lmin=-520436376.06, Lmax=-19901.81 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=590, ncalls=1887, regioncalls=0, ndraw=40, logz=-389296698.92, remainder_fraction=100.0000%, Lmin=-384221646.77, Lmax=-19901.81 [35mDEBUG [0m 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remainder_fraction=100.0000%, Lmin=-40680461.02, Lmax=-6001.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1887, regioncalls=0, ndraw=40, logz=-34272297.45, remainder_fraction=100.0000%, Lmin=-34010952.05, Lmax=-6001.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=730, ncalls=1887, regioncalls=0, ndraw=40, logz=-28152453.57, remainder_fraction=100.0000%, Lmin=-27575809.36, Lmax=-6001.03 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2]), array([99, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=740, ncalls=1887, regioncalls=0, ndraw=40, logz=-24901873.14, remainder_fraction=100.0000%, Lmin=-24584385.70, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=1887, regioncalls=0, ndraw=40, logz=-18110812.08, remainder_fraction=100.0000%, Lmin=-17737948.70, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=759, ncalls=1887, regioncalls=0, ndraw=40, 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iteration=800, ncalls=1887, regioncalls=0, ndraw=40, logz=-6152195.90, remainder_fraction=100.0000%, Lmin=-6110909.52, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1887, regioncalls=0, ndraw=40, logz=-5168110.71, remainder_fraction=100.0000%, Lmin=-5104656.58, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=820, ncalls=1887, regioncalls=0, ndraw=40, logz=-4349040.12, remainder_fraction=100.0000%, Lmin=-4125558.07, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=830, ncalls=1887, regioncalls=0, ndraw=40, logz=-2816427.18, remainder_fraction=100.0000%, Lmin=-2814439.32, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1887, regioncalls=0, ndraw=40, logz=-2391680.21, remainder_fraction=100.0000%, Lmin=-2352470.82, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=1887, regioncalls=0, ndraw=40, logz=-1694108.33, remainder_fraction=100.0000%, Lmin=-1689081.73, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=851, ncalls=1887, regioncalls=0, ndraw=40, logz=-1689094.84, remainder_fraction=100.0000%, Lmin=-1641951.96, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=860, ncalls=1887, regioncalls=0, ndraw=40, logz=-1391644.76, remainder_fraction=100.0000%, Lmin=-1379047.10, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=870, ncalls=1887, regioncalls=0, ndraw=40, logz=-1016250.69, remainder_fraction=100.0000%, Lmin=-1006089.59, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=874, ncalls=1887, regioncalls=0, ndraw=40, logz=-920877.00, remainder_fraction=100.0000%, Lmin=-900085.39, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1887, regioncalls=0, ndraw=40, logz=-823336.66, remainder_fraction=100.0000%, Lmin=-820186.88, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=890, ncalls=1887, regioncalls=0, ndraw=40, logz=-718367.84, remainder_fraction=100.0000%, Lmin=-716624.62, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=897, ncalls=1887, regioncalls=0, ndraw=40, logz=-639629.99, remainder_fraction=100.0000%, Lmin=-639396.18, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1887, regioncalls=0, ndraw=40, logz=-596673.66, remainder_fraction=100.0000%, Lmin=-575979.25, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=910, ncalls=1887, regioncalls=0, ndraw=40, logz=-491530.15, remainder_fraction=100.0000%, Lmin=-490445.93, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1887, regioncalls=0, ndraw=40, logz=-403995.67, remainder_fraction=100.0000%, Lmin=-396361.92, Lmax=-24.58 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=930, ncalls=1887, regioncalls=0, ndraw=40, logz=-335628.28, remainder_fraction=100.0000%, Lmin=-335591.94, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=940, ncalls=1887, regioncalls=0, ndraw=40, logz=-281138.11, remainder_fraction=100.0000%, Lmin=-269426.35, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=943, ncalls=1887, regioncalls=0, ndraw=40, logz=-266966.68, remainder_fraction=100.0000%, Lmin=-259391.44, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=1887, regioncalls=0, ndraw=40, logz=-232224.92, remainder_fraction=100.0000%, Lmin=-227986.30, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1887, regioncalls=0, ndraw=40, logz=-195207.53, remainder_fraction=100.0000%, Lmin=-192214.74, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=966, ncalls=1887, regioncalls=0, ndraw=40, logz=-172769.03, remainder_fraction=100.0000%, Lmin=-171647.55, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=970, ncalls=1887, regioncalls=0, ndraw=40, logz=-158733.95, remainder_fraction=100.0000%, Lmin=-156726.85, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=980, ncalls=1887, regioncalls=0, ndraw=40, logz=-123115.01, remainder_fraction=100.0000%, Lmin=-122192.77, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=989, ncalls=1887, regioncalls=0, ndraw=40, logz=-98858.17, remainder_fraction=100.0000%, Lmin=-94395.69, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1887, regioncalls=0, ndraw=40, logz=-94410.19, remainder_fraction=100.0000%, Lmin=-93791.71, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1887, regioncalls=0, ndraw=40, logz=-75715.19, remainder_fraction=100.0000%, Lmin=-75082.19, Lmax=-4.23 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1010, ncalls=1887, regioncalls=0, ndraw=40, logz=-69119.40, remainder_fraction=100.0000%, Lmin=-69103.96, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1012, ncalls=1887, regioncalls=0, ndraw=40, logz=-68077.33, remainder_fraction=100.0000%, Lmin=-66958.70, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1020, ncalls=1887, regioncalls=0, ndraw=40, logz=-59547.14, remainder_fraction=100.0000%, Lmin=-56249.53, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1030, ncalls=1887, regioncalls=0, ndraw=40, logz=-49103.98, remainder_fraction=100.0000%, Lmin=-46375.62, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1035, ncalls=1887, regioncalls=0, ndraw=40, logz=-42750.79, remainder_fraction=100.0000%, Lmin=-40665.18, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=1887, regioncalls=0, ndraw=40, logz=-38252.82, remainder_fraction=100.0000%, Lmin=-36587.26, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=1887, regioncalls=0, ndraw=40, logz=-33612.77, remainder_fraction=100.0000%, Lmin=-32462.69, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1058, ncalls=1887, regioncalls=0, ndraw=40, logz=-28541.51, remainder_fraction=100.0000%, Lmin=-28487.57, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1060, ncalls=1887, regioncalls=0, ndraw=40, logz=-28412.18, remainder_fraction=100.0000%, Lmin=-28188.21, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1070, ncalls=1887, regioncalls=0, ndraw=40, logz=-23692.05, remainder_fraction=100.0000%, Lmin=-21662.39, Lmax=-3.86 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1887, regioncalls=0, ndraw=40, logz=-17760.01, remainder_fraction=100.0000%, Lmin=-17456.73, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1090, ncalls=1887, regioncalls=0, ndraw=40, logz=-13838.44, remainder_fraction=100.0000%, Lmin=-13583.79, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=1887, regioncalls=0, ndraw=40, logz=-11478.78, remainder_fraction=100.0000%, Lmin=-11399.84, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1104, ncalls=1887, regioncalls=0, ndraw=40, logz=-10856.65, remainder_fraction=100.0000%, Lmin=-10434.89, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1110, ncalls=1887, regioncalls=0, ndraw=40, logz=-9705.10, remainder_fraction=100.0000%, Lmin=-9378.04, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=1887, regioncalls=0, ndraw=40, logz=-7371.71, remainder_fraction=100.0000%, Lmin=-7306.69, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1127, ncalls=1887, regioncalls=0, ndraw=40, logz=-6854.87, remainder_fraction=100.0000%, Lmin=-6766.39, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1130, ncalls=1887, regioncalls=0, ndraw=40, logz=-6580.27, remainder_fraction=100.0000%, Lmin=-6479.79, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1140, ncalls=1887, regioncalls=0, ndraw=40, logz=-4937.77, remainder_fraction=100.0000%, Lmin=-4692.98, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=1887, regioncalls=0, ndraw=40, logz=-4122.10, remainder_fraction=100.0000%, Lmin=-4067.70, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=1887, regioncalls=0, ndraw=40, logz=-3637.58, remainder_fraction=100.0000%, Lmin=-3359.18, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1887, regioncalls=0, ndraw=40, logz=-2706.36, remainder_fraction=100.0000%, Lmin=-2657.76, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1180, ncalls=1887, regioncalls=0, ndraw=40, logz=-2238.02, remainder_fraction=100.0000%, Lmin=-2188.11, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1190, ncalls=1887, regioncalls=0, ndraw=40, logz=-1903.20, remainder_fraction=100.0000%, Lmin=-1886.29, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1887, regioncalls=0, ndraw=40, logz=-1581.52, remainder_fraction=100.0000%, Lmin=-1542.77, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1210, ncalls=1887, regioncalls=0, ndraw=40, logz=-1237.02, remainder_fraction=100.0000%, Lmin=-1202.19, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1219, ncalls=1887, regioncalls=0, ndraw=40, logz=-1026.17, remainder_fraction=100.0000%, Lmin=-935.24, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1220, ncalls=1887, regioncalls=0, ndraw=40, logz=-952.04, remainder_fraction=100.0000%, Lmin=-931.89, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1230, ncalls=1887, regioncalls=0, ndraw=40, logz=-743.38, remainder_fraction=100.0000%, Lmin=-707.99, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=1887, regioncalls=0, ndraw=40, logz=-629.08, remainder_fraction=100.0000%, Lmin=-611.43, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1242, ncalls=1887, regioncalls=0, ndraw=40, logz=-591.43, remainder_fraction=100.0000%, Lmin=-540.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=1894, regioncalls=320, ndraw=40, logz=-443.82, remainder_fraction=100.0000%, Lmin=-423.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1906, regioncalls=840, ndraw=40, logz=-338.27, remainder_fraction=100.0000%, Lmin=-319.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1265, ncalls=1911, regioncalls=1080, ndraw=40, logz=-320.42, remainder_fraction=100.0000%, Lmin=-299.89, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1270, ncalls=1916, regioncalls=1280, ndraw=40, logz=-308.97, remainder_fraction=100.0000%, Lmin=-274.96, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=1927, regioncalls=1720, ndraw=40, logz=-258.39, remainder_fraction=100.0000%, Lmin=-236.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1288, ncalls=1936, regioncalls=2160, ndraw=40, logz=-200.98, remainder_fraction=100.0000%, Lmin=-179.77, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1290, ncalls=1938, regioncalls=2240, ndraw=40, logz=-193.77, remainder_fraction=100.0000%, Lmin=-169.63, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=1949, regioncalls=2680, ndraw=40, logz=-161.93, remainder_fraction=100.0000%, Lmin=-130.03, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1310, ncalls=1962, regioncalls=3200, ndraw=40, logz=-122.86, remainder_fraction=100.0000%, Lmin=-100.59, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=1972, regioncalls=3600, ndraw=40, logz=-102.14, remainder_fraction=100.0000%, Lmin=-82.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1330, ncalls=1982, regioncalls=4000, ndraw=40, logz=-83.98, remainder_fraction=100.0000%, Lmin=-65.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1334, ncalls=1987, regioncalls=4280, ndraw=40, logz=-77.82, remainder_fraction=100.0000%, Lmin=-60.49, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1340, ncalls=1993, regioncalls=4520, ndraw=40, logz=-74.57, remainder_fraction=100.0000%, Lmin=-54.73, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2004, regioncalls=4960, ndraw=40, logz=-66.17, remainder_fraction=100.0000%, Lmin=-43.45, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1357, ncalls=2012, regioncalls=5360, ndraw=40, logz=-50.51, remainder_fraction=100.0000%, Lmin=-31.91, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=2015, regioncalls=5480, ndraw=40, logz=-48.69, remainder_fraction=100.0000%, Lmin=-31.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1370, ncalls=2027, regioncalls=5960, ndraw=40, logz=-43.60, remainder_fraction=100.0000%, Lmin=-26.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1380, ncalls=2037, regioncalls=6400, ndraw=40, logz=-39.07, remainder_fraction=100.0000%, Lmin=-21.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1390, ncalls=2049, regioncalls=6880, ndraw=40, logz=-35.61, remainder_fraction=100.0000%, Lmin=-18.42, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2059, regioncalls=7320, ndraw=40, logz=-32.64, remainder_fraction=100.0000%, Lmin=-14.44, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1410, ncalls=2070, regioncalls=7760, ndraw=40, logz=-28.95, remainder_fraction=99.9999%, Lmin=-11.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1420, ncalls=2082, regioncalls=8240, ndraw=40, logz=-26.64, remainder_fraction=99.9987%, Lmin=-9.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1426, ncalls=2089, regioncalls=8560, ndraw=40, logz=-25.66, remainder_fraction=99.9964%, Lmin=-8.50, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1430, ncalls=2094, regioncalls=8760, ndraw=40, logz=-24.86, remainder_fraction=99.9918%, Lmin=-7.55, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2104, regioncalls=9160, ndraw=40, logz=-23.41, remainder_fraction=99.9664%, Lmin=-6.42, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=2114, regioncalls=9560, ndraw=40, logz=-22.13, remainder_fraction=99.8781%, Lmin=-4.83, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1460, ncalls=2124, regioncalls=9960, ndraw=40, logz=-20.87, remainder_fraction=99.5678%, Lmin=-3.80, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1470, ncalls=2134, regioncalls=10360, ndraw=40, logz=-20.02, remainder_fraction=98.9715%, Lmin=-3.40, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1472, ncalls=2136, regioncalls=10480, ndraw=40, logz=-19.89, remainder_fraction=98.8134%, Lmin=-3.36, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1480, ncalls=2144, regioncalls=10800, ndraw=40, logz=-19.47, remainder_fraction=98.2117%, Lmin=-2.90, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1490, ncalls=2155, regioncalls=11240, ndraw=40, logz=-18.95, remainder_fraction=97.0160%, Lmin=-2.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=2167, regioncalls=11720, ndraw=40, logz=-18.37, remainder_fraction=94.7928%, Lmin=-1.61, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1510, ncalls=2178, regioncalls=12160, ndraw=40, logz=-17.91, remainder_fraction=91.7658%, Lmin=-1.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1518, ncalls=2188, regioncalls=12640, ndraw=40, logz=-17.60, remainder_fraction=88.8301%, Lmin=-1.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1520, ncalls=2190, regioncalls=12720, ndraw=40, logz=-17.53, remainder_fraction=87.9963%, Lmin=-1.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2201, regioncalls=13160, ndraw=40, logz=-17.23, remainder_fraction=83.9101%, Lmin=-0.89, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1540, ncalls=2211, regioncalls=13560, ndraw=40, logz=-16.98, remainder_fraction=79.3301%, Lmin=-0.77, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1541, ncalls=2212, regioncalls=13640, ndraw=40, logz=-16.96, remainder_fraction=78.8284%, Lmin=-0.75, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=2222, regioncalls=14040, ndraw=40, logz=-16.78, remainder_fraction=74.7285%, Lmin=-0.66, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=2236, regioncalls=14600, ndraw=40, logz=-16.61, remainder_fraction=70.1478%, Lmin=-0.54, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1570, ncalls=2246, regioncalls=15000, ndraw=40, logz=-16.46, remainder_fraction=65.2006%, Lmin=-0.41, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1580, ncalls=2258, regioncalls=15480, ndraw=40, logz=-16.33, remainder_fraction=60.3402%, Lmin=-0.35, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1587, ncalls=2267, regioncalls=15880, ndraw=40, logz=-16.25, remainder_fraction=57.1348%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1590, ncalls=2270, regioncalls=16000, ndraw=40, logz=-16.22, remainder_fraction=55.7549%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2282, regioncalls=16480, ndraw=40, logz=-16.12, remainder_fraction=51.2608%, Lmin=-0.23, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-2e-05 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 2284 [32mINFO [0m ultranest:integrator.py:2655 Writing samples and results to disk ... [32mINFO [0m ultranest:integrator.py:2687 Writing samples and results to disk ... done [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2552 Reached maximum number of improvement loops. [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_run.py::test_run_compat | 11.82 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.35) * Expected Volume: exp(0.00) Quality: ok a: -5.0|********************************************************| +5.0 b: +0.1|************************************************** **** | +1.0 Z=-inf(0.00%) | Like=-150977.65..-2.59 [-150977.6465..-32592.6399] | it/evals=0/401 eff=0.0000% N=400 Z=-109183.8(0.00%) | Like=-108777.30..-2.59 [-150977.6465..-32592.6399] | it/evals=40/441 eff=97.5610% N=400 Z=-85687.7(0.00%) | Like=-85520.13..-2.59 [-150977.6465..-32592.6399] | it/evals=80/487 eff=91.9540% N=400 Mono-modal Volume: ~exp(-4.49) * Expected Volume: exp(-0.23) Quality: ok a: -5.0| ********************************************** | +5.0 b: +0.1|************************************************** *****| +1.0 Z=-83195.0(0.00%) | Like=-83115.79..-2.59 [-150977.6465..-32592.6399] | it/evals=90/497 eff=92.7835% N=400 Z=-73133.6(0.00%) | Like=-72859.93..-2.59 [-150977.6465..-32592.6399] | it/evals=120/530 eff=92.3077% N=400 Z=-59932.0(0.00%) | Like=-59560.06..-2.59 [-150977.6465..-32592.6399] | it/evals=160/578 eff=89.8876% N=400 Mono-modal Volume: ~exp(-4.49) Expected Volume: exp(-0.45) Quality: ok a: -5.0| -2.8 ************************************** | +5.0 b: +0.1|************************************************** *****| +1.0 Z=-47827.9(0.00%) | Like=-47560.27..-2.59 [-150977.6465..-32592.6399] | it/evals=200/622 eff=90.0901% N=400 Z=-38536.0(0.00%) | Like=-38387.85..-2.59 [-150977.6465..-32592.6399] | it/evals=240/672 eff=88.2353% N=400 Mono-modal Volume: ~exp(-4.84) * Expected Volume: exp(-0.67) Quality: ok a: -5.0| -2.0 ****************************** +3.0 | +5.0 b: +0.1|************************************************** *****| +1.0 Z=-32852.3(0.00%) | Like=-32755.76..-2.59 [-150977.6465..-32592.6399] | it/evals=270/710 eff=87.0968% N=400 Z=-32335.6(0.00%) | Like=-32149.80..-2.59 [-32520.9601..-9764.7099] | it/evals=280/721 eff=87.2274% N=400 Z=-28114.8(0.00%) | Like=-28005.17..-2.59 [-32520.9601..-9764.7099] | it/evals=320/778 eff=84.6561% N=400 Z=-25619.8(0.00%) | Like=-25477.55..-2.59 [-32520.9601..-9764.7099] | it/evals=349/814 eff=84.2995% N=400 Mono-modal Volume: ~exp(-5.02) * Expected Volume: exp(-0.90) Quality: ok a: -5.0| -1.7 ************************* +2.6 | +5.0 b: +0.1|************************************************** *****| +1.0 Z=-24018.0(0.00%) | Like=-23816.85..-2.59 [-32520.9601..-9764.7099] | it/evals=360/827 eff=84.3091% N=400 Z=-19170.2(0.00%) | Like=-19136.76..-2.59 [-32520.9601..-9764.7099] | it/evals=400/874 eff=84.3882% N=400 Z=-17055.9(0.00%) | Like=-16865.75..-2.59 [-32520.9601..-9764.7099] | it/evals=440/922 eff=84.2912% N=400 Mono-modal Volume: ~exp(-5.02) Expected Volume: exp(-1.12) Quality: ok a: -5.0| -1.2 ******************** +2.3 | +5.0 b: +0.1|******************************** ***** *********** *****| +1.0 Z=-13939.7(0.00%) | Like=-13916.65..-2.59 [-32520.9601..-9764.7099] | it/evals=480/973 eff=83.7696% N=400 Z=-11844.8(0.00%) | Like=-11723.06..-2.59 [-32520.9601..-9764.7099] | it/evals=520/1023 eff=83.4671% N=400 Mono-modal Volume: ~exp(-5.39) * Expected Volume: exp(-1.35) Quality: ok a: -5.0| -1.0 ***************** +1.9 | +5.0 b: +0.1|***************************** ******** *****************| +1.0 Z=-10599.4(0.00%) | Like=-10533.94..-2.59 [-32520.9601..-9764.7099] | it/evals=540/1049 eff=83.2049% N=400 Z=-9483.6(0.00%) | Like=-9406.72..-2.59 [-9748.2423..-2391.0040] | it/evals=560/1071 eff=83.4575% N=400 Z=-7528.0(0.00%) | Like=-7512.45..-2.59 [-9748.2423..-2391.0040] | it/evals=600/1125 eff=82.7586% N=400 Mono-modal Volume: ~exp(-5.39) Expected Volume: exp(-1.57) Quality: ok a: -5.0| -0.6 ************** +1.6 | +5.0 b: +0.1|********************************************************| +1.0 Z=-6041.6(0.00%) | Like=-5999.25..-2.59 [-9748.2423..-2391.0040] | it/evals=640/1173 eff=82.7943% N=400 Z=-4823.3(0.00%) | Like=-4812.08..-2.59 [-9748.2423..-2391.0040] | it/evals=680/1225 eff=82.4242% N=400 Mono-modal Volume: ~exp(-5.89) * Expected Volume: exp(-1.80) Quality: ok a: -5.0| -0.4 *********** +1.4 | +5.0 b: +0.1|********************************************************| +1.0 Z=-3980.2(0.00%) | Like=-3958.06..-1.82 [-9748.2423..-2391.0040] | it/evals=720/1279 eff=81.9113% N=400 Z=-3374.9(0.00%) | Like=-3363.91..-1.82 [-9748.2423..-2391.0040] | it/evals=760/1325 eff=82.1622% N=400 Z=-2891.2(0.00%) | Like=-2880.60..-1.82 [-9748.2423..-2391.0040] | it/evals=800/1374 eff=82.1355% N=400 Mono-modal Volume: ~exp(-6.09) * Expected Volume: exp(-2.02) Quality: ok a: -5.0| -0.2 ********* +1.2 | +5.0 b: +0.1|********************************************************| +1.0 Z=-2747.7(0.00%) | Like=-2723.35..-1.82 [-9748.2423..-2391.0040] | it/evals=810/1389 eff=81.9009% N=400 Z=-2376.7(0.00%) | Like=-2366.61..-1.82 [-2388.8221..-886.7129] | it/evals=840/1423 eff=82.1114% N=400 Z=-1987.2(0.00%) | Like=-1976.85..-1.82 [-2388.8221..-886.7129] | it/evals=880/1474 eff=81.9367% N=400 Mono-modal Volume: ~exp(-6.44) * Expected Volume: exp(-2.25) Quality: ok a: -5.0e+00| -8.6e-02 ******** +1.1e+00 | +5.0e+00 b: +0.1|********************************************************| +1.0 Z=-1808.0(0.00%) | Like=-1794.30..-1.82 [-2388.8221..-886.7129] | it/evals=900/1501 eff=81.7439% N=400 Z=-1665.3(0.00%) | Like=-1656.93..-1.82 [-2388.8221..-886.7129] | it/evals=920/1521 eff=82.0696% N=400 Z=-1493.5(0.00%) | Like=-1478.11..-1.82 [-2388.8221..-886.7129] | it/evals=960/1567 eff=82.2622% N=400 Mono-modal Volume: ~exp(-6.57) * Expected Volume: exp(-2.47) Quality: ok a: -5.0e+00| -5.5e-03 ******* +1.0e+00 | +5.0e+00 b: +0.1|***************************************************** **| +1.0 Z=-1356.6(0.00%) | Like=-1343.50..-1.82 [-2388.8221..-886.7129] | it/evals=990/1605 eff=82.1577% N=400 Z=-1320.7(0.00%) | Like=-1299.01..-1.82 [-2388.8221..-886.7129] | it/evals=1000/1617 eff=82.1693% N=400 Z=-1147.6(0.00%) | Like=-1139.05..-1.82 [-2388.8221..-886.7129] | it/evals=1040/1665 eff=82.2134% N=400 Mono-modal Volume: ~exp(-6.73) * Expected Volume: exp(-2.70) Quality: ok a: +0.00| ************************************************** | +1.00 b: +0.1|************************************************* ** | +1.0 Z=-1019.3(0.00%) | Like=-1005.21..3.44 [-2388.8221..-886.7129] | it/evals=1080/1715 eff=82.1293% N=400 Z=-911.4(0.00%) | Like=-901.85..3.44 [-2388.8221..-886.7129] | it/evals=1120/1763 eff=82.1717% N=400 Z=-832.4(0.00%) | Like=-823.80..7.31 [-885.2464..-475.5022] | it/evals=1160/1817 eff=81.8631% N=400 Mono-modal Volume: ~exp(-6.93) * Expected Volume: exp(-2.92) Quality: ok a: +0.0| ********************************************* | +1.0 b: +0.1|************************************************** | +1.0 Z=-815.5(0.00%) | Like=-806.74..7.31 [-885.2464..-475.5022] | it/evals=1170/1828 eff=81.9328% N=400 Z=-764.9(0.00%) | Like=-754.93..7.31 [-885.2464..-475.5022] | it/evals=1200/1863 eff=82.0232% N=400 Z=-709.8(0.00%) | Like=-700.80..7.31 [-885.2464..-475.5022] | it/evals=1240/1914 eff=81.9022% N=400 Mono-modal Volume: ~exp(-7.15) * Expected Volume: exp(-3.15) Quality: ok a: +0.0| **************************************** | +1.0 b: +0.1| ********************************************** | +1.0 Z=-682.7(0.00%) | Like=-669.47..7.31 [-885.2464..-475.5022] | it/evals=1260/1946 eff=81.5006% N=400 Z=-648.1(0.00%) | Like=-637.68..7.31 [-885.2464..-475.5022] | it/evals=1280/1967 eff=81.6847% N=400 Z=-595.1(0.00%) | Like=-586.71..7.31 [-885.2464..-475.5022] | it/evals=1320/2016 eff=81.6832% N=400 Mono-modal Volume: ~exp(-7.48) * Expected Volume: exp(-3.37) Quality: ok a: +0.0| ************************************* +0.8 | +1.0 b: +0.1| ***************************************** +0.8 | +1.0 Z=-555.1(0.00%) | Like=-541.33..7.31 [-885.2464..-475.5022] | it/evals=1350/2058 eff=81.4234% N=400 Z=-541.9(0.00%) | Like=-531.11..7.31 [-885.2464..-475.5022] | it/evals=1360/2073 eff=81.2911% N=400 Z=-495.6(0.00%) | Like=-483.82..7.31 [-885.2464..-475.5022] | it/evals=1400/2123 eff=81.2536% N=400 Mono-modal Volume: ~exp(-7.89) * Expected Volume: exp(-3.60) Quality: ok a: +0.0| +0.2 ********************************** +0.8 | +1.0 b: +0.1| ******************************** ***** +0.8 | +1.0 Z=-457.3(0.00%) | Like=-447.71..7.31 [-475.1294..-251.4282] | it/evals=1440/2173 eff=81.2183% N=400 Z=-427.3(0.00%) | Like=-417.32..7.31 [-475.1294..-251.4282] | it/evals=1480/2218 eff=81.4081% N=400 Z=-391.3(0.00%) | Like=-381.92..7.31 [-475.1294..-251.4282] | it/evals=1520/2268 eff=81.3704% N=400 Mono-modal Volume: ~exp(-7.99) * Expected Volume: exp(-3.82) Quality: ok a: +0.0| +0.2 ******************************** +0.8 | +1.0 b: +0.1| ********************************** +0.8 | +1.0 Z=-384.8(0.00%) | Like=-374.80..7.31 [-475.1294..-251.4282] | it/evals=1530/2285 eff=81.1671% N=400 Z=-355.9(0.00%) | Like=-346.44..7.31 [-475.1294..-251.4282] | it/evals=1560/2323 eff=81.1232% N=400 Z=-325.4(0.00%) | Like=-314.84..7.31 [-475.1294..-251.4282] | it/evals=1600/2375 eff=81.0127% N=400 Mono-modal Volume: ~exp(-8.18) * Expected Volume: exp(-4.05) Quality: ok a: +0.0| +0.3 **************************** +0.7 | +1.0 b: +0.1| ******************************* +0.7 | +1.0 Z=-308.9(0.00%) | Like=-298.52..7.31 [-475.1294..-251.4282] | it/evals=1620/2405 eff=80.7980% N=400 Z=-295.1(0.00%) | Like=-285.08..7.31 [-475.1294..-251.4282] | it/evals=1640/2432 eff=80.7087% N=400 Z=-270.5(0.00%) | Like=-260.85..7.31 [-475.1294..-251.4282] | it/evals=1680/2494 eff=80.2292% N=400 Mono-modal Volume: ~exp(-8.59) * Expected Volume: exp(-4.27) Quality: ok a: +0.0| +0.3 ************************** +0.7 | +1.0 b: +0.1| +0.3 *************************** +0.7 | +1.0 Z=-257.6(0.00%) | Like=-247.96..7.31 [-251.0704..-126.3626] | it/evals=1710/2541 eff=79.8692% N=400 Z=-250.3(0.00%) | Like=-240.30..7.31 [-251.0704..-126.3626] | it/evals=1720/2553 eff=79.8885% N=400 Z=-230.3(0.00%) | Like=-220.85..7.31 [-251.0704..-126.3626] | it/evals=1760/2601 eff=79.9637% N=400 Mono-modal Volume: ~exp(-8.70) * Expected Volume: exp(-4.50) Quality: ok a: +0.0| +0.3 ************************ +0.7 | +1.0 b: +0.1| +0.3 ************************** +0.7 | +1.0 Z=-206.7(0.00%) | Like=-196.43..7.31 [-251.0704..-126.3626] | it/evals=1800/2651 eff=79.9645% N=400 Z=-187.4(0.00%) | Like=-177.94..7.31 [-251.0704..-126.3626] | it/evals=1840/2710 eff=79.6537% N=400 Z=-171.9(0.00%) | Like=-161.92..7.31 [-251.0704..-126.3626] | it/evals=1880/2763 eff=79.5599% N=400 Mono-modal Volume: ~exp(-8.71) * Expected Volume: exp(-4.73) Quality: ok a: +0.0| +0.3 ********************** +0.7 | +1.0 b: +0.1| +0.3 *********************** +0.7 | +1.0 Z=-167.4(0.00%) | Like=-157.40..7.31 [-251.0704..-126.3626] | it/evals=1890/2777 eff=79.5120% N=400 Z=-157.3(0.00%) | Like=-147.61..7.31 [-251.0704..-126.3626] | it/evals=1920/2810 eff=79.6680% N=400 Z=-144.7(0.00%) | Like=-135.09..7.31 [-251.0704..-126.3626] | it/evals=1960/2865 eff=79.5132% N=400 Mono-modal Volume: ~exp(-9.31) * Expected Volume: exp(-4.95) Quality: ok a: +0.0| +0.3 ******************* +0.7 | +1.0 b: +0.1| +0.3 ********************* +0.7 | +1.0 Z=-136.9(0.00%) | Like=-127.90..7.31 [-251.0704..-126.3626] | it/evals=1980/2890 eff=79.5181% N=400 Z=-132.6(0.00%) | Like=-122.47..7.31 [-126.0965..-65.3225] | it/evals=2000/2917 eff=79.4597% N=400 Z=-118.0(0.00%) | Like=-108.02..7.31 [-126.0965..-65.3225] | it/evals=2040/2968 eff=79.4393% N=400 Mono-modal Volume: ~exp(-9.44) * Expected Volume: exp(-5.18) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 b: +0.1| +0.4 ******************* +0.6 | +1.0 Z=-111.0(0.00%) | Like=-101.24..7.31 [-126.0965..-65.3225] | it/evals=2070/3011 eff=79.2800% N=400 Z=-108.5(0.00%) | Like=-98.26..7.31 [-126.0965..-65.3225] | it/evals=2080/3022 eff=79.3288% N=400 Z=-99.8(0.00%) | Like=-89.47..7.31 [-126.0965..-65.3225] | it/evals=2120/3076 eff=79.2227% N=400 Mono-modal Volume: ~exp(-9.44) Expected Volume: exp(-5.40) Quality: ok a: +0.0| +0.4 **************** +0.6 | +1.0 b: +0.1| +0.4 ****************** +0.6 | +1.0 Z=-92.8(0.00%) | Like=-83.11..7.31 [-126.0965..-65.3225] | it/evals=2160/3129 eff=79.1499% N=400 Z=-85.5(0.00%) | Like=-75.77..7.31 [-126.0965..-65.3225] | it/evals=2200/3180 eff=79.1367% N=400 Z=-78.7(0.00%) | Like=-69.15..7.31 [-126.0965..-65.3225] | it/evals=2240/3239 eff=78.9010% N=400 Mono-modal Volume: ~exp(-9.86) * Expected Volume: exp(-5.63) Quality: ok a: +0.0| +0.4 ************** +0.6 | +1.0 b: +0.1| +0.4 **************** +0.6 | +1.0 Z=-77.2(0.00%) | Like=-67.34..7.31 [-126.0965..-65.3225] | it/evals=2250/3251 eff=78.9197% N=400 Z=-72.0(0.00%) | Like=-61.87..7.31 [-65.1911..-30.7735] | it/evals=2280/3289 eff=78.9200% N=400 Z=-66.2(0.00%) | Like=-56.57..7.31 [-65.1911..-30.7735] | it/evals=2320/3342 eff=78.8579% N=400 Mono-modal Volume: ~exp(-10.17) * Expected Volume: exp(-5.85) Quality: ok a: +0.0| +0.4 ************** +0.6 | +1.0 b: +0.1| +0.4 ************** +0.6 | +1.0 Z=-63.9(0.00%) | Like=-54.08..7.31 [-65.1911..-30.7735] | it/evals=2340/3367 eff=78.8675% N=400 Z=-60.8(0.00%) | Like=-50.36..7.31 [-65.1911..-30.7735] | it/evals=2360/3390 eff=78.9298% N=400 Z=-55.7(0.00%) | Like=-45.84..7.31 [-65.1911..-30.7735] | it/evals=2388/3424 eff=78.9683% N=400 Z=-54.6(0.00%) | Like=-44.92..7.31 [-65.1911..-30.7735] | it/evals=2400/3437 eff=79.0254% N=400 Mono-modal Volume: ~exp(-10.30) * Expected Volume: exp(-6.08) Quality: ok a: +0.0| +0.4 ************ +0.6 | +1.0 b: +0.1| +0.4 ************** +0.6 | +1.0 Z=-51.8(0.00%) | Like=-42.12..7.31 [-65.1911..-30.7735] | it/evals=2430/3473 eff=79.0758% N=400 Z=-51.1(0.00%) | Like=-41.05..7.31 [-65.1911..-30.7735] | it/evals=2440/3484 eff=79.1180% N=400 Z=-46.9(0.00%) | Like=-37.04..7.31 [-65.1911..-30.7735] | it/evals=2480/3531 eff=79.2079% N=400 Mono-modal Volume: ~exp(-10.61) * Expected Volume: exp(-6.30) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 b: +0.1| +0.4 ************ +0.6 | +1.0 Z=-42.9(0.00%) | Like=-32.82..7.31 [-65.1911..-30.7735] | it/evals=2520/3583 eff=79.1706% N=400 Z=-39.3(0.00%) | Like=-29.37..7.31 [-30.7054..-11.8397] | it/evals=2560/3630 eff=79.2570% N=400 Z=-35.5(0.00%) | Like=-25.54..7.31 [-30.7054..-11.8397] | it/evals=2600/3679 eff=79.2925% N=400 Mono-modal Volume: ~exp(-10.94) * Expected Volume: exp(-6.53) Quality: ok a: +0.0| +0.4 ********** +0.6 | +1.0 b: +0.1| +0.4 ********** +0.6 | +1.0 Z=-34.6(0.00%) | Like=-24.39..7.31 [-30.7054..-11.8397] | it/evals=2610/3693 eff=79.2590% N=400 Z=-32.2(0.00%) | Like=-21.71..7.31 [-30.7054..-11.8397] | it/evals=2640/3730 eff=79.2793% N=400 Z=-28.9(0.00%) | Like=-18.76..7.31 [-30.7054..-11.8397] | it/evals=2680/3780 eff=79.2899% N=400 Mono-modal Volume: ~exp(-10.94) Expected Volume: exp(-6.75) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 b: +0.1| +0.4 ********** +0.6 | +1.0 Z=-26.2(0.00%) | Like=-16.06..7.31 [-30.7054..-11.8397] | it/evals=2720/3830 eff=79.3003% N=400 Z=-24.2(0.00%) | Like=-14.15..7.31 [-30.7054..-11.8397] | it/evals=2760/3880 eff=79.3103% N=400 Mono-modal Volume: ~exp(-10.94) Expected Volume: exp(-6.98) Quality: ok a: +0.0| +0.4 ******** +0.6 | +1.0 b: +0.1| +0.4 ******** +0.6 | +1.0 Z=-21.9(0.00%) | Like=-11.82..7.31 [-11.8356..-1.3664] | it/evals=2800/3939 eff=79.1184% N=400 Z=-19.9(0.00%) | Like=-9.73..7.37 [-11.8356..-1.3664] | it/evals=2840/3986 eff=79.1969% N=400 Mono-modal Volume: ~exp(-11.47) * Expected Volume: exp(-7.20) Quality: ok a: +0.0| +0.4 ******* +0.6 | +1.0 b: +0.1| +0.4 ******** +0.6 | +1.0 Z=-18.1(0.00%) | Like=-8.16..7.37 [-11.8356..-1.3664] | it/evals=2880/4040 eff=79.1209% N=400 Z=-16.6(0.00%) | Like=-6.48..7.37 [-11.8356..-1.3664] | it/evals=2920/4093 eff=79.0685% N=400 Z=-15.2(0.00%) | Like=-5.26..7.37 [-11.8356..-1.3664] | it/evals=2960/4144 eff=79.0598% N=400 Mono-modal Volume: ~exp(-11.47) Expected Volume: exp(-7.43) Quality: ok a: +0.00| +0.45 ****** +0.55 | +1.00 b: +0.10| +0.45 ******* +0.55 | +1.00 Z=-13.9(0.00%) | Like=-3.73..7.37 [-11.8356..-1.3664] | it/evals=3000/4195 eff=79.0514% N=400 Z=-12.4(0.01%) | Like=-2.23..7.37 [-11.8356..-1.3664] | it/evals=3040/4244 eff=79.0843% N=400 Mono-modal Volume: ~exp(-11.74) * Expected Volume: exp(-7.65) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 b: +0.10| +0.46 ****** +0.54 | +1.00 Z=-11.8(0.01%) | Like=-1.82..7.37 [-11.8356..-1.3664] | it/evals=3060/4273 eff=79.0085% N=400 Z=-11.4(0.02%) | Like=-1.51..7.37 [-11.8356..-1.3664] | it/evals=3080/4299 eff=78.9946% N=400 Z=-10.5(0.04%) | Like=-0.53..7.37 [-1.3574..2.8381] | it/evals=3120/4350 eff=78.9873% N=400 Mono-modal Volume: ~exp(-12.26) * Expected Volume: exp(-7.88) Quality: ok a: +0.00| +0.46 ****** +0.54 | +1.00 b: +0.10| +0.46 ****** +0.54 | +1.00 Z=-9.9(0.07%) | Like=0.18..7.37 [-1.3574..2.8381] | it/evals=3150/4391 eff=78.9276% N=400 Z=-9.7(0.08%) | Like=0.34..7.37 [-1.3574..2.8381] | it/evals=3160/4404 eff=78.9211% N=400 Z=-8.9(0.18%) | Like=1.11..7.37 [-1.3574..2.8381] | it/evals=3200/4449 eff=79.0319% N=400 Mono-modal Volume: ~exp(-12.36) * Expected Volume: exp(-8.10) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 b: +0.10| +0.47 ***** +0.53 | +1.00 Z=-8.2(0.36%) | Like=1.70..7.37 [-1.3574..2.8381] | it/evals=3240/4496 eff=79.1016% N=400 Z=-7.7(0.59%) | Like=2.11..7.37 [-1.3574..2.8381] | it/evals=3280/4547 eff=79.0933% N=400 Z=-7.2(0.93%) | Like=2.50..7.37 [-1.3574..2.8381] | it/evals=3320/4601 eff=79.0288% N=400 Mono-modal Volume: ~exp(-12.64) * Expected Volume: exp(-8.33) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 b: +0.10| +0.47 **** +0.53 | +1.00 Z=-7.1(1.03%) | Like=2.67..7.37 [-1.3574..2.8381] | it/evals=3330/4612 eff=79.0598% N=400 Z=-6.8(1.41%) | Like=2.98..7.37 [2.8386..4.4296] | it/evals=3360/4651 eff=79.0402% N=400 Z=-6.4(2.06%) | Like=3.45..7.37 [2.8386..4.4296] | it/evals=3400/4698 eff=79.1066% N=400 Mono-modal Volume: ~exp(-12.87) * Expected Volume: exp(-8.55) Quality: ok a: +0.00| +0.47 **** +0.53 | +1.00 b: +0.10| +0.47 **** +0.53 | +1.00 Z=-6.2(2.49%) | Like=3.64..7.37 [2.8386..4.4296] | it/evals=3420/4729 eff=79.0021% N=400 Z=-6.0(2.91%) | Like=3.78..7.37 [2.8386..4.4296] | it/evals=3440/4750 eff=79.0805% N=400 Z=-5.8(3.99%) | Like=3.95..7.37 [2.8386..4.4296] | it/evals=3480/4802 eff=79.0550% N=400 Mono-modal Volume: ~exp(-13.05) * Expected Volume: exp(-8.78) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.10| +0.47 **** +0.52 | +1.00 Z=-5.5(4.86%) | Like=4.21..7.37 [2.8386..4.4296] | it/evals=3510/4846 eff=78.9474% N=400 Z=-5.5(5.28%) | Like=4.28..7.37 [2.8386..4.4296] | it/evals=3520/4860 eff=78.9238% N=400 Z=-5.2(6.65%) | Like=4.53..7.37 [4.4313..4.8872] | it/evals=3560/4910 eff=78.9357% N=400 Mono-modal Volume: ~exp(-13.24) * Expected Volume: exp(-9.00) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.10| +0.48 **** +0.52 | +1.00 Z=-5.0(8.46%) | Like=4.75..7.37 [4.4313..4.8872] | it/evals=3600/4961 eff=78.9301% N=400 Z=-4.8(10.42%) | Like=5.02..7.37 [4.9026..5.0190] | it/evals=3640/5006 eff=79.0274% N=400 Z=-4.6(12.40%) | Like=5.26..7.37 [5.2578..5.2617]*| it/evals=3680/5055 eff=79.0548% N=400 Mono-modal Volume: ~exp(-13.24) Expected Volume: exp(-9.23) Quality: ok a: +0.00| +0.48 **** +0.52 | +1.00 b: +0.10| +0.48 **** +0.52 | +1.00 Z=-4.4(14.97%) | Like=5.43..7.37 [5.4183..5.4305] | it/evals=3720/5104 eff=79.0816% N=400 Z=-4.2(17.90%) | Like=5.68..7.37 [5.6800..5.6852]*| it/evals=3760/5152 eff=79.1246% N=400 Mono-modal Volume: ~exp(-13.28) * Expected Volume: exp(-9.45) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 b: +0.10| +0.48 *** +0.52 | +1.00 Z=-4.2(19.47%) | Like=5.80..7.37 [5.7998..5.8042]*| it/evals=3780/5188 eff=78.9474% N=400 Z=-4.1(21.25%) | Like=5.86..7.37 [5.8590..5.8615]*| it/evals=3800/5209 eff=79.0185% N=400 Z=-3.9(24.60%) | Like=6.06..7.37 [6.0581..6.0593]*| it/evals=3840/5256 eff=79.0774% N=400 Mono-modal Volume: ~exp(-13.99) * Expected Volume: exp(-9.68) Quality: ok a: +0.00| +0.48 ** +0.52 | +1.00 b: +0.10| +0.48 *** +0.51 | +1.00 Z=-3.8(27.42%) | Like=6.16..7.37 [6.1580..6.1599]*| it/evals=3870/5295 eff=79.0603% N=400 Z=-3.8(28.35%) | Like=6.19..7.37 [6.1888..6.1897]*| it/evals=3880/5305 eff=79.1030% N=400 Z=-3.7(32.11%) | Like=6.29..7.37 [6.2904..6.2918]*| it/evals=3920/5350 eff=79.1919% N=400 Mono-modal Volume: ~exp(-14.14) * Expected Volume: exp(-9.90) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-3.6(35.87%) | Like=6.40..7.37 [6.4039..6.4061]*| it/evals=3960/5402 eff=79.1683% N=400 Z=-3.5(39.73%) | Like=6.50..7.37 [6.5003..6.5010]*| it/evals=4000/5450 eff=79.2079% N=400 Z=-3.4(43.54%) | Like=6.58..7.37 [6.5800..6.5832]*| it/evals=4040/5505 eff=79.1381% N=400 Mono-modal Volume: ~exp(-14.49) * Expected Volume: exp(-10.13) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-3.3(44.56%) | Like=6.60..7.37 [6.5971..6.5977]*| it/evals=4050/5521 eff=79.0861% N=400 Z=-3.3(47.30%) | Like=6.64..7.37 [6.6422..6.6432]*| it/evals=4080/5560 eff=79.0698% N=400 Z=-3.3(48.93%) | Like=6.68..7.37 [6.6806..6.6814]*| it/evals=4098/5581 eff=79.0967% N=400 Z=-3.2(50.53%) | Like=6.71..7.37 [6.7051..6.7085]*| it/evals=4116/5606 eff=79.0626% N=400 Z=-3.2(50.88%) | Like=6.72..7.37 [6.7156..6.7166]*| it/evals=4120/5611 eff=79.0635% N=400 Mono-modal Volume: ~exp(-14.54) * Expected Volume: exp(-10.35) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-3.2(52.60%) | Like=6.75..7.37 [6.7541..6.7554]*| it/evals=4140/5637 eff=79.0529% N=400 Z=-3.1(54.26%) | Like=6.79..7.37 [6.7904..6.7931]*| it/evals=4160/5668 eff=78.9674% N=400 Z=-3.1(57.66%) | Like=6.85..7.37 [6.8502..6.8511]*| it/evals=4200/5718 eff=78.9771% N=400 Mono-modal Volume: ~exp(-15.16) * Expected Volume: exp(-10.58) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-3.0(60.09%) | Like=6.88..7.37 [6.8843..6.8865]*| it/evals=4230/5758 eff=78.9474% N=400 Z=-3.0(60.85%) | Like=6.89..7.37 [6.8944..6.8955]*| it/evals=4240/5770 eff=78.9572% N=400 Z=-3.0(63.74%) | Like=6.94..7.37 [6.9388..6.9401]*| it/evals=4280/5820 eff=78.9668% N=400 Mono-modal Volume: ~exp(-15.16) Expected Volume: exp(-10.80) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.9(66.58%) | Like=6.98..7.37 [6.9763..6.9784]*| it/evals=4320/5868 eff=79.0051% N=400 Z=-2.9(69.28%) | Like=7.01..7.37 [7.0114..7.0117]*| it/evals=4360/5926 eff=78.8997% N=400 Z=-2.9(71.76%) | Like=7.04..7.37 [7.0442..7.0443]*| it/evals=4400/5976 eff=78.9096% N=400 Mono-modal Volume: ~exp(-15.41) * Expected Volume: exp(-11.02) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.9(72.35%) | Like=7.05..7.37 [7.0489..7.0490]*| it/evals=4410/5988 eff=78.9191% N=400 Z=-2.8(74.05%) | Like=7.07..7.37 [7.0695..7.0699]*| it/evals=4440/6019 eff=79.0176% N=400 Z=-2.8(76.23%) | Like=7.09..7.37 [7.0915..7.0916]*| it/evals=4480/6068 eff=79.0402% N=400 Mono-modal Volume: ~exp(-15.44) * Expected Volume: exp(-11.25) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.8(77.21%) | Like=7.11..7.37 [7.1055..7.1056]*| it/evals=4500/6090 eff=79.0861% N=400 Z=-2.8(78.18%) | Like=7.12..7.37 [7.1166..7.1177]*| it/evals=4520/6112 eff=79.1317% N=400 Z=-2.8(80.02%) | Like=7.14..7.37 [7.1440..7.1458]*| it/evals=4560/6163 eff=79.1255% N=400 Mono-modal Volume: ~exp(-15.56) * Expected Volume: exp(-11.47) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.7(81.32%) | Like=7.16..7.37 [7.1584..7.1586]*| it/evals=4590/6208 eff=79.0289% N=400 Z=-2.7(81.73%) | Like=7.16..7.37 [7.1620..7.1640]*| it/evals=4600/6220 eff=79.0378% N=400 Z=-2.7(83.33%) | Like=7.19..7.37 [7.1882..7.1882]*| it/evals=4640/6264 eff=79.1269% N=400 Mono-modal Volume: ~exp(-16.05) * Expected Volume: exp(-11.70) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.7(84.79%) | Like=7.20..7.37 [7.2012..7.2020]*| it/evals=4680/6318 eff=79.0808% N=400 Z=-2.7(86.14%) | Like=7.22..7.37 [7.2174..7.2174]*| it/evals=4720/6364 eff=79.1415% N=400 Z=-2.7(87.37%) | Like=7.23..7.37 [7.2310..7.2313]*| it/evals=4760/6420 eff=79.0698% N=400 Mono-modal Volume: ~exp(-16.14) * Expected Volume: exp(-11.92) Quality: ok a: +0.00| +0.49 ** +0.51 | +1.00 b: +0.10| +0.49 ** +0.51 | +1.00 Z=-2.7(87.65%) | Like=7.23..7.37 [7.2335..7.2343]*| it/evals=4770/6431 eff=79.0914% N=400 Z=-2.7(88.50%) | Like=7.24..7.37 [7.2417..7.2417]*| it/evals=4800/6469 eff=79.0905% N=400 Z=-2.6(89.53%) | Like=7.25..7.37 [7.2522..7.2534]*| it/evals=4840/6526 eff=79.0075% N=400 Mono-modal Volume: ~exp(-16.58) * Expected Volume: exp(-12.15) Quality: ok a: +0.000| +0.495 ** +0.505 | +1.000 b: +0.100| +0.495 ** +0.505 | +1.000 Z=-2.6(90.01%) | Like=7.26..7.37 [7.2564..7.2569]*| it/evals=4860/6553 eff=78.9859% N=400 Z=-2.6(90.47%) | Like=7.26..7.37 [7.2601..7.2602]*| it/evals=4880/6575 eff=79.0283% N=400 Z=-2.6(91.33%) | Like=7.27..7.37 [7.2704..7.2707]*| it/evals=4920/6629 eff=78.9854% N=400 Mono-modal Volume: ~exp(-16.58) Expected Volume: exp(-12.37) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 b: +0.100| +0.496 ** +0.504 | +1.000 Z=-2.6(92.12%) | Like=7.28..7.37 [7.2802..7.2803]*| it/evals=4960/6683 eff=78.9432% N=400 Z=-2.6(92.84%) | Like=7.29..7.37 [7.2899..7.2901]*| it/evals=5000/6740 eff=78.8644% N=400 Mono-modal Volume: ~exp(-16.58) Expected Volume: exp(-12.60) Quality: ok a: +0.000| +0.496 ** +0.504 | +1.000 b: +0.100| +0.496 ** +0.504 | +1.000 Z=-2.6(93.50%) | Like=7.30..7.37 [7.2970..7.2971]*| it/evals=5040/6792 eff=78.8486% N=400 Z=-2.6(94.10%) | Like=7.30..7.37 [7.3027..7.3029]*| it/evals=5080/6841 eff=78.8697% N=400 Z=-2.6(94.64%) | Like=7.31..7.37 [7.3090..7.3092]*| it/evals=5120/6897 eff=78.8056% N=400 Mono-modal Volume: ~exp(-17.14) * Expected Volume: exp(-12.82) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 b: +0.100| +0.497 ** +0.503 | +1.000 Z=-2.6(94.77%) | Like=7.31..7.37 [7.3111..7.3112]*| it/evals=5130/6909 eff=78.8139% N=400 Z=-2.6(95.14%) | Like=7.31..7.37 [7.3149..7.3150]*| it/evals=5160/6949 eff=78.7907% N=400 Z=-2.6(95.59%) | Like=7.32..7.37 [7.3203..7.3204]*| it/evals=5200/7003 eff=78.7521% N=400 Mono-modal Volume: ~exp(-17.22) * Expected Volume: exp(-13.05) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 b: +0.100| +0.497 ** +0.503 | +1.000 Z=-2.6(95.80%) | Like=7.32..7.37 [7.3235..7.3236]*| it/evals=5220/7033 eff=78.6974% N=400 Z=-2.6(96.00%) | Like=7.33..7.37 [7.3252..7.3254]*| it/evals=5240/7060 eff=78.6787% N=400 Z=-2.6(96.37%) | Like=7.33..7.37 [7.3300..7.3302]*| it/evals=5280/7105 eff=78.7472% N=400 Mono-modal Volume: ~exp(-17.46) * Expected Volume: exp(-13.27) Quality: ok a: +0.000| +0.497 ** +0.503 | +1.000 b: +0.100| +0.497 ** +0.503 | +1.000 Z=-2.6(96.63%) | Like=7.33..7.37 [7.3328..7.3329]*| it/evals=5310/7139 eff=78.7951% N=400 Z=-2.6(96.71%) | Like=7.33..7.37 [7.3338..7.3338]*| it/evals=5320/7150 eff=78.8148% N=400 Z=-2.6(97.02%) | Like=7.34..7.37 [7.3372..7.3373]*| it/evals=5360/7204 eff=78.7772% N=400 Mono-modal Volume: ~exp(-17.63) * Expected Volume: exp(-13.50) Quality: ok a: +0.000| +0.498 ** +0.503 | +1.000 b: +0.100| +0.497 ** +0.502 | +1.000 Z=-2.6(97.30%) | Like=7.34..7.37 [7.3405..7.3405]*| it/evals=5400/7261 eff=78.7057% N=400 Z=-2.6(97.55%) | Like=7.34..7.37 [7.3433..7.3433]*| it/evals=5440/7311 eff=78.7151% N=400 Z=-2.6(97.78%) | Like=7.35..7.37 [7.3464..7.3464]*| it/evals=5480/7355 eff=78.7922% N=400 Mono-modal Volume: ~exp(-18.08) * Expected Volume: exp(-13.72) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 b: +0.100| +0.498 ** +0.502 | +1.000 Z=-2.6(97.83%) | Like=7.35..7.37 [7.3470..7.3470]*| it/evals=5490/7367 eff=78.8001% N=400 Z=-2.6(97.99%) | Like=7.35..7.37 [7.3485..7.3485]*| it/evals=5520/7402 eff=78.8346% N=400 Z=-2.6(98.18%) | Like=7.35..7.37 [7.3511..7.3512]*| it/evals=5560/7448 eff=78.8876% N=400 Mono-modal Volume: ~exp(-18.08) Expected Volume: exp(-13.95) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 b: +0.100| +0.498 ** +0.502 | +1.000 Z=-2.6(98.35%) | Like=7.35..7.37 [7.3532..7.3532]*| it/evals=5600/7491 eff=78.9733% N=400 Z=-2.5(98.50%) | Like=7.36..7.37 [7.3553..7.3554]*| it/evals=5640/7536 eff=79.0359% N=400 Mono-modal Volume: ~exp(-18.58) * Expected Volume: exp(-14.17) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 b: +0.100| +0.498 * +0.502 | +1.000 Z=-2.5(98.61%) | Like=7.36..7.37 [7.3564..7.3565]*| it/evals=5670/7577 eff=79.0024% N=400 Z=-2.5(98.65%) | Like=7.36..7.37 [7.3568..7.3569]*| it/evals=5680/7588 eff=79.0206% N=400 Z=-2.5(98.77%) | Like=7.36..7.37 [7.3581..7.3581]*| it/evals=5720/7639 eff=79.0164% N=400 Mono-modal Volume: ~exp(-18.58) Expected Volume: exp(-14.40) Quality: ok a: +0.000| +0.498 ** +0.502 | +1.000 b: +0.100| +0.498 * +0.502 | +1.000 Z=-2.5(98.89%) | Like=7.36..7.37 [7.3594..7.3594]*| it/evals=5760/7688 eff=79.0340% N=400 Z=-2.5(98.99%) | Like=7.36..7.37 [7.3603..7.3604]*| it/evals=5800/7745 eff=78.9653% N=400 [ultranest] Explored until L=7 [ultranest] Likelihood function evaluations: 7747 [ultranest] logZ = -2.554 +- 0.1059 [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.11, need <0.5) [ultranest] logZ error budget: single: 0.15 bs:0.11 tail:0.01 total:0.11 required:<0.50 [ultranest] done iterating. logZ = -2.534 +- 0.148 single instance: logZ = -2.534 +- 0.149 bootstrapped : logZ = -2.554 +- 0.148 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations a : 0.4537│ ▁ ▁▁▁▁▁▁▁▁▁▂▃▃▅▅▇▇▇▇▇▆▅▄▃▃▂▁▁▁▁▁▁▁▁▁ │0.5393 0.4997 +- 0.0098 b : 0.460 │ ▁▁▁ ▁▁▁▁▁▂▂▂▃▄▅▇▆▇▇▇▇▇▆▅▅▄▃▂▁▂▁▁▁▁▁▁▁ │0.537 0.500 +- 0.010 evidence: -2.5 +- 0.1 parameter values: a : 0.500 +- 0.010 b : 0.500 +- 0.010 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=2+0, resume=True, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=0 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=401, regioncalls=40, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-150977.65, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=40, ncalls=441, regioncalls=1640, ndraw=40, logz=-109183.79, remainder_fraction=100.0000%, Lmin=-108777.30, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=80, ncalls=487, regioncalls=3480, ndraw=40, logz=-85687.72, remainder_fraction=100.0000%, Lmin=-85520.13, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=497, regioncalls=3880, ndraw=40, logz=-83195.03, remainder_fraction=100.0000%, Lmin=-83115.79, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=120, ncalls=530, regioncalls=5200, ndraw=40, logz=-73133.56, remainder_fraction=100.0000%, Lmin=-72859.93, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=160, ncalls=578, regioncalls=7120, ndraw=40, logz=-59931.99, remainder_fraction=100.0000%, Lmin=-59560.06, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=622, regioncalls=8880, ndraw=40, logz=-47827.87, remainder_fraction=100.0000%, Lmin=-47560.27, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=240, ncalls=672, regioncalls=10880, ndraw=40, logz=-38535.98, remainder_fraction=100.0000%, Lmin=-38387.85, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=710, regioncalls=12400, ndraw=40, logz=-32852.27, remainder_fraction=100.0000%, Lmin=-32755.76, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=280, ncalls=721, regioncalls=12840, ndraw=40, logz=-32335.60, remainder_fraction=100.0000%, Lmin=-32149.80, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=320, ncalls=778, regioncalls=15120, ndraw=40, logz=-28114.80, remainder_fraction=100.0000%, Lmin=-28005.17, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=349, ncalls=814, regioncalls=16560, ndraw=40, logz=-25619.76, remainder_fraction=100.0000%, Lmin=-25477.55, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=827, regioncalls=17080, ndraw=40, logz=-24018.04, remainder_fraction=100.0000%, Lmin=-23816.85, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=874, regioncalls=18960, ndraw=40, logz=-19170.19, remainder_fraction=100.0000%, Lmin=-19136.76, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=440, ncalls=922, regioncalls=20880, ndraw=40, logz=-17055.95, remainder_fraction=100.0000%, Lmin=-16865.75, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=480, ncalls=973, regioncalls=22920, ndraw=40, logz=-13939.71, remainder_fraction=100.0000%, Lmin=-13916.65, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=520, ncalls=1023, regioncalls=24920, ndraw=40, logz=-11844.84, remainder_fraction=100.0000%, Lmin=-11723.06, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=1049, regioncalls=25960, ndraw=40, logz=-10599.42, remainder_fraction=100.0000%, Lmin=-10533.94, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=560, ncalls=1071, regioncalls=26840, ndraw=40, logz=-9483.59, remainder_fraction=100.0000%, Lmin=-9406.72, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=1125, regioncalls=29000, ndraw=40, logz=-7527.96, remainder_fraction=100.0000%, Lmin=-7512.45, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:1880 clustering found some stray points [need_accept=False] (array([1, 2]), array([399, 1])) [35mDEBUG [0m ultranest:integrator.py:2491 iteration=640, ncalls=1173, regioncalls=30920, ndraw=40, logz=-6041.56, remainder_fraction=100.0000%, Lmin=-5999.25, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=680, ncalls=1225, regioncalls=33000, ndraw=40, logz=-4823.29, remainder_fraction=100.0000%, Lmin=-4812.08, Lmax=-2.59 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=1279, regioncalls=35160, ndraw=40, logz=-3980.23, remainder_fraction=100.0000%, Lmin=-3958.06, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=760, ncalls=1325, regioncalls=37040, ndraw=40, logz=-3374.90, remainder_fraction=100.0000%, Lmin=-3363.91, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=1374, regioncalls=39000, ndraw=40, logz=-2891.19, remainder_fraction=100.0000%, Lmin=-2880.60, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=1389, regioncalls=39600, ndraw=40, logz=-2747.67, remainder_fraction=100.0000%, Lmin=-2723.35, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=840, ncalls=1423, regioncalls=41040, ndraw=40, logz=-2376.74, remainder_fraction=100.0000%, Lmin=-2366.61, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=880, ncalls=1474, regioncalls=43080, ndraw=40, logz=-1987.24, remainder_fraction=100.0000%, Lmin=-1976.85, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=1501, regioncalls=44160, ndraw=40, logz=-1807.95, remainder_fraction=100.0000%, Lmin=-1794.30, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=920, ncalls=1521, regioncalls=44960, ndraw=40, logz=-1665.32, remainder_fraction=100.0000%, Lmin=-1656.93, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=960, ncalls=1567, regioncalls=46800, ndraw=40, logz=-1493.48, remainder_fraction=100.0000%, Lmin=-1478.11, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=1605, regioncalls=48400, ndraw=40, logz=-1356.55, remainder_fraction=100.0000%, Lmin=-1343.50, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=1617, regioncalls=48880, ndraw=40, logz=-1320.66, remainder_fraction=100.0000%, Lmin=-1299.01, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1040, ncalls=1665, regioncalls=50840, ndraw=40, logz=-1147.62, remainder_fraction=100.0000%, Lmin=-1139.05, Lmax=-1.82 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=1715, regioncalls=52840, ndraw=40, logz=-1019.28, remainder_fraction=100.0000%, Lmin=-1005.21, Lmax=3.44 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1120, ncalls=1763, regioncalls=54840, ndraw=40, logz=-911.40, remainder_fraction=100.0000%, Lmin=-901.85, Lmax=3.44 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1160, ncalls=1817, regioncalls=57000, ndraw=40, logz=-832.44, remainder_fraction=100.0000%, Lmin=-823.80, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=1828, regioncalls=57440, ndraw=40, logz=-815.50, remainder_fraction=100.0000%, Lmin=-806.74, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=1863, regioncalls=58920, ndraw=40, logz=-764.89, remainder_fraction=100.0000%, Lmin=-754.93, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1240, ncalls=1914, regioncalls=60960, ndraw=40, logz=-709.83, remainder_fraction=100.0000%, Lmin=-700.80, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=1946, regioncalls=62240, ndraw=40, logz=-682.73, remainder_fraction=100.0000%, Lmin=-669.47, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=1967, regioncalls=63160, ndraw=40, logz=-648.15, remainder_fraction=100.0000%, Lmin=-637.68, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1320, ncalls=2016, regioncalls=65240, ndraw=40, logz=-595.11, remainder_fraction=100.0000%, Lmin=-586.71, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=2058, regioncalls=66960, ndraw=40, logz=-555.14, remainder_fraction=100.0000%, Lmin=-541.33, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1360, ncalls=2073, regioncalls=67560, ndraw=40, logz=-541.88, remainder_fraction=100.0000%, Lmin=-531.11, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=2123, regioncalls=69600, ndraw=40, logz=-495.61, remainder_fraction=100.0000%, Lmin=-483.82, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=2173, regioncalls=71640, ndraw=40, logz=-457.27, remainder_fraction=100.0000%, Lmin=-447.71, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1480, ncalls=2218, regioncalls=73480, ndraw=40, logz=-427.29, remainder_fraction=100.0000%, Lmin=-417.32, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1520, ncalls=2268, regioncalls=75520, ndraw=40, logz=-391.31, remainder_fraction=100.0000%, Lmin=-381.92, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=2285, regioncalls=76200, ndraw=40, logz=-384.81, remainder_fraction=100.0000%, Lmin=-374.80, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1560, ncalls=2323, regioncalls=77840, ndraw=40, logz=-355.88, remainder_fraction=100.0000%, Lmin=-346.44, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=2375, regioncalls=80040, ndraw=40, logz=-325.36, remainder_fraction=100.0000%, Lmin=-314.84, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=2405, regioncalls=81280, ndraw=40, logz=-308.93, remainder_fraction=100.0000%, Lmin=-298.52, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1640, ncalls=2432, regioncalls=82360, ndraw=40, logz=-295.11, remainder_fraction=100.0000%, Lmin=-285.08, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1680, ncalls=2494, regioncalls=84920, ndraw=40, logz=-270.49, remainder_fraction=100.0000%, Lmin=-260.85, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=2541, regioncalls=86800, ndraw=40, logz=-257.65, remainder_fraction=100.0000%, Lmin=-247.96, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1720, ncalls=2553, regioncalls=87360, ndraw=40, logz=-250.35, remainder_fraction=100.0000%, Lmin=-240.30, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1760, ncalls=2601, regioncalls=89320, ndraw=40, logz=-230.29, remainder_fraction=100.0000%, Lmin=-220.85, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=2651, regioncalls=91440, ndraw=40, logz=-206.70, remainder_fraction=100.0000%, Lmin=-196.43, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1840, ncalls=2710, regioncalls=94000, ndraw=40, logz=-187.42, remainder_fraction=100.0000%, Lmin=-177.94, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1880, ncalls=2763, regioncalls=96120, ndraw=40, logz=-171.89, remainder_fraction=100.0000%, Lmin=-161.92, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=2777, regioncalls=96680, ndraw=40, logz=-167.38, remainder_fraction=100.0000%, Lmin=-157.40, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1920, ncalls=2810, regioncalls=98400, ndraw=40, logz=-157.29, remainder_fraction=100.0000%, Lmin=-147.61, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1960, ncalls=2865, regioncalls=100720, ndraw=40, logz=-144.74, remainder_fraction=100.0000%, Lmin=-135.09, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=2890, regioncalls=101720, ndraw=40, logz=-136.93, remainder_fraction=100.0000%, Lmin=-127.90, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=2917, regioncalls=102880, ndraw=40, logz=-132.59, remainder_fraction=100.0000%, Lmin=-122.47, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2040, ncalls=2968, regioncalls=104960, ndraw=40, logz=-117.98, remainder_fraction=100.0000%, Lmin=-108.02, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=3011, regioncalls=106880, ndraw=40, logz=-111.04, remainder_fraction=100.0000%, Lmin=-101.24, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2080, ncalls=3022, regioncalls=107320, ndraw=40, logz=-108.49, remainder_fraction=100.0000%, Lmin=-98.26, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2120, ncalls=3076, regioncalls=109640, ndraw=40, logz=-99.75, remainder_fraction=100.0000%, Lmin=-89.47, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=3129, regioncalls=111840, ndraw=40, logz=-92.82, remainder_fraction=100.0000%, Lmin=-83.11, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=3180, regioncalls=113920, ndraw=40, logz=-85.51, remainder_fraction=100.0000%, Lmin=-75.77, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2240, ncalls=3239, regioncalls=116360, ndraw=40, logz=-78.70, remainder_fraction=100.0000%, Lmin=-69.15, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=3251, regioncalls=116880, ndraw=40, logz=-77.22, remainder_fraction=100.0000%, Lmin=-67.34, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2280, ncalls=3289, regioncalls=118440, ndraw=40, logz=-72.05, remainder_fraction=100.0000%, Lmin=-61.87, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2320, ncalls=3342, regioncalls=120560, ndraw=40, logz=-66.15, remainder_fraction=100.0000%, Lmin=-56.57, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=3367, regioncalls=121640, ndraw=40, logz=-63.93, remainder_fraction=100.0000%, Lmin=-54.08, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2360, ncalls=3390, regioncalls=122640, ndraw=40, logz=-60.85, remainder_fraction=100.0000%, Lmin=-50.36, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2388, ncalls=3424, regioncalls=124040, ndraw=40, logz=-55.74, remainder_fraction=100.0000%, Lmin=-45.84, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=3437, regioncalls=124560, ndraw=40, logz=-54.56, remainder_fraction=100.0000%, Lmin=-44.92, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=3473, regioncalls=126040, ndraw=40, logz=-51.85, remainder_fraction=100.0000%, Lmin=-42.12, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2440, ncalls=3484, regioncalls=126480, ndraw=40, logz=-51.06, remainder_fraction=100.0000%, Lmin=-41.05, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2480, ncalls=3531, regioncalls=128360, ndraw=40, logz=-46.91, remainder_fraction=100.0000%, Lmin=-37.04, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=3583, regioncalls=130480, ndraw=40, logz=-42.88, remainder_fraction=100.0000%, Lmin=-32.82, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2560, ncalls=3630, regioncalls=132360, ndraw=40, logz=-39.28, remainder_fraction=100.0000%, Lmin=-29.37, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=3679, regioncalls=134360, ndraw=40, logz=-35.49, remainder_fraction=100.0000%, Lmin=-25.54, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=3693, regioncalls=134960, ndraw=40, logz=-34.63, remainder_fraction=100.0000%, Lmin=-24.39, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2640, ncalls=3730, regioncalls=136440, ndraw=40, logz=-32.16, remainder_fraction=100.0000%, Lmin=-21.71, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2680, ncalls=3780, regioncalls=138480, ndraw=40, logz=-28.85, remainder_fraction=100.0000%, Lmin=-18.76, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2720, ncalls=3830, regioncalls=140520, ndraw=40, logz=-26.21, remainder_fraction=100.0000%, Lmin=-16.06, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2760, ncalls=3880, regioncalls=142560, ndraw=40, logz=-24.16, remainder_fraction=100.0000%, Lmin=-14.15, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=3939, regioncalls=144960, ndraw=40, logz=-21.86, remainder_fraction=100.0000%, Lmin=-11.82, Lmax=7.31 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2840, ncalls=3986, regioncalls=146960, ndraw=40, logz=-19.95, remainder_fraction=100.0000%, Lmin=-9.73, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=4040, regioncalls=149200, ndraw=40, logz=-18.11, remainder_fraction=100.0000%, Lmin=-8.16, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2920, ncalls=4093, regioncalls=151360, ndraw=40, logz=-16.57, remainder_fraction=99.9999%, Lmin=-6.48, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2960, ncalls=4144, regioncalls=153400, ndraw=40, logz=-15.18, remainder_fraction=99.9997%, Lmin=-5.26, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=4195, regioncalls=155520, ndraw=40, logz=-13.87, remainder_fraction=99.9987%, Lmin=-3.73, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3040, ncalls=4244, regioncalls=157560, ndraw=40, logz=-12.38, remainder_fraction=99.9942%, Lmin=-2.23, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=4273, regioncalls=158760, ndraw=40, logz=-11.80, remainder_fraction=99.9898%, Lmin=-1.82, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3080, ncalls=4299, regioncalls=159800, ndraw=40, logz=-11.36, remainder_fraction=99.9839%, Lmin=-1.51, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3120, ncalls=4350, regioncalls=161840, ndraw=40, logz=-10.51, remainder_fraction=99.9643%, Lmin=-0.53, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=4391, regioncalls=163520, ndraw=40, logz=-9.86, remainder_fraction=99.9298%, Lmin=0.18, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3160, ncalls=4404, regioncalls=164040, ndraw=40, logz=-9.65, remainder_fraction=99.9151%, Lmin=0.34, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=4449, regioncalls=165840, ndraw=40, logz=-8.88, remainder_fraction=99.8189%, Lmin=1.11, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=4496, regioncalls=167760, ndraw=40, logz=-8.21, remainder_fraction=99.6370%, Lmin=1.70, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3280, ncalls=4547, regioncalls=169800, ndraw=40, logz=-7.68, remainder_fraction=99.4068%, Lmin=2.11, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3320, ncalls=4601, regioncalls=171960, ndraw=40, logz=-7.23, remainder_fraction=99.0675%, Lmin=2.50, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3330, ncalls=4612, regioncalls=172440, ndraw=40, logz=-7.12, remainder_fraction=98.9682%, Lmin=2.67, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3360, ncalls=4651, regioncalls=174080, ndraw=40, logz=-6.81, remainder_fraction=98.5895%, Lmin=2.98, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=4698, regioncalls=175960, ndraw=40, logz=-6.42, remainder_fraction=97.9359%, Lmin=3.45, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=4729, regioncalls=177240, ndraw=40, logz=-6.23, remainder_fraction=97.5103%, Lmin=3.64, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3440, ncalls=4750, regioncalls=178080, ndraw=40, logz=-6.05, remainder_fraction=97.0898%, Lmin=3.78, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3480, ncalls=4802, regioncalls=180240, ndraw=40, logz=-5.75, remainder_fraction=96.0074%, Lmin=3.95, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3510, ncalls=4846, regioncalls=182080, ndraw=40, logz=-5.55, remainder_fraction=95.1410%, Lmin=4.21, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3520, ncalls=4860, regioncalls=182640, ndraw=40, logz=-5.48, remainder_fraction=94.7243%, Lmin=4.28, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3560, ncalls=4910, regioncalls=184640, ndraw=40, logz=-5.24, remainder_fraction=93.3523%, Lmin=4.53, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3600, ncalls=4961, regioncalls=186800, ndraw=40, logz=-5.01, remainder_fraction=91.5362%, Lmin=4.75, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3640, ncalls=5006, regioncalls=188840, ndraw=40, logz=-4.80, remainder_fraction=89.5817%, Lmin=5.02, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3680, ncalls=5055, regioncalls=190800, ndraw=40, logz=-4.60, remainder_fraction=87.5983%, Lmin=5.26, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3720, ncalls=5104, regioncalls=192880, ndraw=40, logz=-4.42, remainder_fraction=85.0281%, Lmin=5.43, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3760, ncalls=5152, regioncalls=194960, ndraw=40, logz=-4.25, remainder_fraction=82.0980%, Lmin=5.68, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3780, ncalls=5188, regioncalls=196480, ndraw=40, logz=-4.16, remainder_fraction=80.5331%, Lmin=5.80, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3800, ncalls=5209, regioncalls=197320, ndraw=40, logz=-4.08, remainder_fraction=78.7519%, Lmin=5.86, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3840, ncalls=5256, regioncalls=199200, ndraw=40, logz=-3.93, remainder_fraction=75.3981%, Lmin=6.06, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3870, ncalls=5295, regioncalls=200880, ndraw=40, logz=-3.83, remainder_fraction=72.5802%, Lmin=6.16, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3880, ncalls=5305, regioncalls=201280, ndraw=40, logz=-3.79, remainder_fraction=71.6516%, Lmin=6.19, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3920, ncalls=5350, regioncalls=203080, ndraw=40, logz=-3.67, remainder_fraction=67.8946%, Lmin=6.29, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3960, ncalls=5402, regioncalls=205200, ndraw=40, logz=-3.56, remainder_fraction=64.1350%, Lmin=6.40, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4000, ncalls=5450, regioncalls=207120, ndraw=40, logz=-3.46, remainder_fraction=60.2720%, Lmin=6.50, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4040, ncalls=5505, regioncalls=209320, ndraw=40, logz=-3.37, remainder_fraction=56.4648%, Lmin=6.58, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4050, ncalls=5521, regioncalls=210000, ndraw=40, logz=-3.35, remainder_fraction=55.4409%, Lmin=6.60, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4080, ncalls=5560, regioncalls=211560, ndraw=40, logz=-3.29, remainder_fraction=52.7037%, Lmin=6.64, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4098, ncalls=5581, regioncalls=212400, ndraw=40, logz=-3.25, remainder_fraction=51.0661%, Lmin=6.68, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4116, ncalls=5606, regioncalls=213400, ndraw=40, logz=-3.22, remainder_fraction=49.4707%, Lmin=6.71, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4120, ncalls=5611, regioncalls=213600, ndraw=40, logz=-3.21, remainder_fraction=49.1213%, Lmin=6.72, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4140, ncalls=5637, regioncalls=214720, ndraw=40, logz=-3.18, remainder_fraction=47.4024%, Lmin=6.75, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4160, ncalls=5668, regioncalls=216040, ndraw=40, logz=-3.15, remainder_fraction=45.7411%, Lmin=6.79, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4200, ncalls=5718, regioncalls=218040, ndraw=40, logz=-3.09, remainder_fraction=42.3425%, Lmin=6.85, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4230, ncalls=5758, regioncalls=219680, ndraw=40, logz=-3.05, remainder_fraction=39.9087%, Lmin=6.88, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4240, ncalls=5770, regioncalls=220240, ndraw=40, logz=-3.03, remainder_fraction=39.1514%, Lmin=6.89, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4280, ncalls=5820, regioncalls=222280, ndraw=40, logz=-2.98, remainder_fraction=36.2556%, Lmin=6.94, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4320, ncalls=5868, regioncalls=224200, ndraw=40, logz=-2.94, remainder_fraction=33.4246%, Lmin=6.98, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4360, ncalls=5926, regioncalls=226640, ndraw=40, logz=-2.90, remainder_fraction=30.7187%, Lmin=7.01, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4400, ncalls=5976, regioncalls=228680, ndraw=40, logz=-2.87, remainder_fraction=28.2432%, Lmin=7.04, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4410, ncalls=5988, regioncalls=229280, ndraw=40, logz=-2.86, remainder_fraction=27.6494%, Lmin=7.05, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4440, ncalls=6019, regioncalls=230520, ndraw=40, logz=-2.83, remainder_fraction=25.9477%, Lmin=7.07, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4480, ncalls=6068, regioncalls=232480, ndraw=40, logz=-2.81, remainder_fraction=23.7715%, Lmin=7.09, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4500, ncalls=6090, regioncalls=233400, ndraw=40, logz=-2.79, remainder_fraction=22.7868%, Lmin=7.11, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4520, ncalls=6112, regioncalls=234280, ndraw=40, logz=-2.78, remainder_fraction=21.8244%, Lmin=7.12, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4560, ncalls=6163, regioncalls=236320, ndraw=40, logz=-2.76, remainder_fraction=19.9759%, Lmin=7.14, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4590, ncalls=6208, regioncalls=238160, ndraw=40, logz=-2.74, remainder_fraction=18.6784%, Lmin=7.16, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4600, ncalls=6220, regioncalls=238640, ndraw=40, logz=-2.74, remainder_fraction=18.2708%, Lmin=7.16, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4640, ncalls=6264, regioncalls=240480, ndraw=40, logz=-2.72, remainder_fraction=16.6729%, Lmin=7.19, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4680, ncalls=6318, regioncalls=242760, ndraw=40, logz=-2.70, remainder_fraction=15.2130%, Lmin=7.20, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4720, ncalls=6364, regioncalls=244600, ndraw=40, logz=-2.68, remainder_fraction=13.8629%, Lmin=7.22, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4760, ncalls=6420, regioncalls=246840, ndraw=40, logz=-2.67, remainder_fraction=12.6337%, Lmin=7.23, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4770, ncalls=6431, regioncalls=247320, ndraw=40, logz=-2.67, remainder_fraction=12.3473%, Lmin=7.23, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4800, ncalls=6469, regioncalls=248880, ndraw=40, logz=-2.66, remainder_fraction=11.5044%, Lmin=7.24, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4840, ncalls=6526, regioncalls=251200, ndraw=40, logz=-2.64, remainder_fraction=10.4712%, Lmin=7.25, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4860, ncalls=6553, regioncalls=252320, ndraw=40, logz=-2.64, remainder_fraction=9.9895%, Lmin=7.26, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4880, ncalls=6575, regioncalls=253200, ndraw=40, logz=-2.63, remainder_fraction=9.5289%, Lmin=7.26, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4920, ncalls=6629, regioncalls=255360, ndraw=40, logz=-2.62, remainder_fraction=8.6679%, Lmin=7.27, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=4960, ncalls=6683, regioncalls=257640, ndraw=40, logz=-2.62, remainder_fraction=7.8823%, Lmin=7.28, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5000, ncalls=6740, regioncalls=260360, ndraw=40, logz=-2.61, remainder_fraction=7.1604%, Lmin=7.29, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5040, ncalls=6792, regioncalls=262480, ndraw=40, logz=-2.60, remainder_fraction=6.5036%, Lmin=7.30, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5080, ncalls=6841, regioncalls=264720, ndraw=40, logz=-2.59, remainder_fraction=5.9035%, Lmin=7.30, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5120, ncalls=6897, regioncalls=267000, ndraw=40, logz=-2.59, remainder_fraction=5.3594%, Lmin=7.31, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5130, ncalls=6909, regioncalls=267520, ndraw=40, logz=-2.59, remainder_fraction=5.2318%, Lmin=7.31, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5160, ncalls=6949, regioncalls=269320, ndraw=40, logz=-2.58, remainder_fraction=4.8646%, Lmin=7.31, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5200, ncalls=7003, regioncalls=271520, ndraw=40, logz=-2.58, remainder_fraction=4.4128%, Lmin=7.32, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5220, ncalls=7033, regioncalls=272760, ndraw=40, logz=-2.58, remainder_fraction=4.2037%, Lmin=7.32, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5240, ncalls=7060, regioncalls=273960, ndraw=40, logz=-2.57, remainder_fraction=4.0044%, Lmin=7.33, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5280, ncalls=7105, regioncalls=275800, ndraw=40, logz=-2.57, remainder_fraction=3.6315%, Lmin=7.33, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5310, ncalls=7139, regioncalls=277200, ndraw=40, logz=-2.57, remainder_fraction=3.3744%, Lmin=7.33, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5320, ncalls=7150, regioncalls=277640, ndraw=40, logz=-2.57, remainder_fraction=3.2924%, Lmin=7.33, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5360, ncalls=7204, regioncalls=279800, ndraw=40, logz=-2.56, remainder_fraction=2.9847%, Lmin=7.34, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5400, ncalls=7261, regioncalls=282160, ndraw=40, logz=-2.56, remainder_fraction=2.7049%, Lmin=7.34, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5440, ncalls=7311, regioncalls=284240, ndraw=40, logz=-2.56, remainder_fraction=2.4509%, Lmin=7.34, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5480, ncalls=7355, regioncalls=286000, ndraw=40, logz=-2.56, remainder_fraction=2.2207%, Lmin=7.35, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5490, ncalls=7367, regioncalls=286520, ndraw=40, logz=-2.56, remainder_fraction=2.1664%, Lmin=7.35, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5520, ncalls=7402, regioncalls=287920, ndraw=40, logz=-2.55, remainder_fraction=2.0121%, Lmin=7.35, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5560, ncalls=7448, regioncalls=289800, ndraw=40, logz=-2.55, remainder_fraction=1.8226%, Lmin=7.35, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5600, ncalls=7491, regioncalls=291600, ndraw=40, logz=-2.55, remainder_fraction=1.6507%, Lmin=7.35, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5640, ncalls=7536, regioncalls=293600, ndraw=40, logz=-2.55, remainder_fraction=1.4951%, Lmin=7.36, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5670, ncalls=7577, regioncalls=295280, ndraw=40, logz=-2.55, remainder_fraction=1.3880%, Lmin=7.36, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5680, ncalls=7588, regioncalls=295720, ndraw=40, logz=-2.55, remainder_fraction=1.3541%, Lmin=7.36, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5720, ncalls=7639, regioncalls=297800, ndraw=40, logz=-2.55, remainder_fraction=1.2262%, Lmin=7.36, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5760, ncalls=7688, regioncalls=299760, ndraw=40, logz=-2.54, remainder_fraction=1.1104%, Lmin=7.36, Lmax=7.37 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=5800, ncalls=7745, regioncalls=302120, ndraw=40, logz=-2.54, remainder_fraction=1.0053%, Lmin=7.36, Lmax=7.37 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=7 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 7747 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -2.554 +- 0.1059 [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.11 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.11, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.15 bs:0.11 tail:0.01 total:0.11 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:2775 Making corner plot ... [35mDEBUG [0m ultranest:integrator.py:2821 Making run plot ... [35mDEBUG [0m ultranest:integrator.py:2797 Making trace plot ... | |||
Passed | tests/test_samplingpath.py::test_horizontal | 0.00 | |
------------------------------Captured stdout call------------------------------ (array([0.5, 0. ]), array([1])) (array([0.5, 1. ]), array([1])) (array([0. , 0.3]), array([0])) (array([1. , 0.3]), array([0])) | |||
Passed | tests/test_samplingpath.py::test_corner | 0.00 | |
------------------------------Captured stdout call------------------------------ starting ray: [0.6 0.5] [0.4 0.5] (array([0.2, 0. ]), array([1])) (array([1., 1.]), array([0, 1])) restarting ray: [1. 1.] [-0.4 -0.5] (array([1., 1.]), array([0, 1])) (array([0.2, 0. ]), array([1])) (array([0.2, 0. ]), array([1])) (array([0. , 0.25]), array([0])) | |||
Passed | tests/test_samplingpath.py::test_wrap | 0.01 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_random | 0.41 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_forward | 1.84 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_samplingpath | 0.00 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_samplingpath_cubereflect | 0.00 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_samplingpath_oddcase | 0.00 | |
No log output captured. | |||
Passed | tests/test_samplingpath.py::test_reversible_gradient | 1.11 | |
------------------------------Captured stdout call------------------------------ setting seed = 84 reflecting at [0.09304075 0.29114574] with direction [ 0.03477044 -0.01977415] chose normal [-0.17706516 0.98419913] 53 reflecting at [0.09304075 0.29114574] with direction [ 0.03477044 -0.01977415] reflecting with [-0.51082933 0.85968215] new direction [-0.00074369 0.03999309] re-reflecting gives direction [ 0.03477044 -0.01977415] FORWARD: [ 0.03477044 -0.01977415] [0.09304075 0.29114574] BACKWARD: [ 0.03477044 -0.01977415] [0.09304075 0.29114574] setting seed = 1 reflecting at [0.49345332 0.62079968] with direction [6.48453852e-06 3.99999995e-02] chose normal [-0.14594849 -0.98929219] 33 reflecting at [0.49345332 0.62079968] with direction [6.48453852e-06 3.99999995e-02] reflecting with [-0.84015394 -0.54234801] new direction [-0.03645513 0.01646278] re-reflecting gives direction [6.48453852e-06 3.99999995e-02] FORWARD: [6.48453852e-06 3.99999995e-02] [0.49345332 0.62079968] BACKWARD: [6.48453852e-06 3.99999995e-02] [0.49345332 0.62079968] setting seed = 2 reflecting at [0.58951993 0.53010934] with direction [-0.028441 0.02812667] chose normal [-0.55268598 -0.83338959] 75 reflecting at [0.58951993 0.53010934] with direction [-0.028441 0.02812667] reflecting with [-0.38762042 -0.92181908] new direction [-0.03999471 0.00065022] re-reflecting gives direction [-0.028441 0.02812667] FORWARD: [-0.028441 0.02812667] [0.58951993 0.53010934] BACKWARD: [-0.028441 0.02812667] [0.58951993 0.53010934] setting seed = 3 reflecting at [0.18920214 0.52959253] with direction [ 0.00939036 -0.03888214] chose normal [-0.96649142 0.25669891] 45 reflecting at [0.18920214 0.52959253] with direction [ 0.00939036 -0.03888214] reflecting with [-0.98467624 0.17439239] new direction [-0.02217288 -0.03329209] re-reflecting gives direction [ 0.00939036 -0.03888214] FORWARD: [ 0.00939036 -0.03888214] [0.18920214 0.52959253] BACKWARD: [ 0.00939036 -0.03888214] [0.18920214 0.52959253] setting seed = 4 reflecting at [0.9934771 0.55358031] with direction [ 0.03949306 -0.00634806] chose normal [-0.97238131 -0.23339792] 0 reflecting at [0.9934771 0.55358031] with direction [ 0.03949306 -0.00634806] reflecting with [-0.97238131 -0.23339792] new direction [-0.03230892 -0.02358248] re-reflecting gives direction [ 0.03949306 -0.00634806] FORWARD: [ 0.03949306 -0.00634806] [0.9934771 0.55358031] BACKWARD: [ 0.03949306 -0.00634806] [0.9934771 0.55358031] setting seed = 5 reflecting at [0.56643207 0.39286012] with direction [ 0.03173046 -0.02435524] chose normal [0.31678437 0.94849758] 12 reflecting at [0.56643207 0.39286012] with direction [ 0.03173046 -0.02435524] reflecting with [-0.33575666 0.94194876] new direction [0.0091709 0.03893449] re-reflecting gives direction [ 0.03173046 -0.02435524] FORWARD: [ 0.03173046 -0.02435524] [0.56643207 0.39286012] BACKWARD: [ 0.03173046 -0.02435524] [0.56643207 0.39286012] setting seed = 6 reflecting at [0.86938756 0.54911538] with direction [-0.03790637 -0.01277133] chose normal [0.83159266 0.55538603] 69 reflecting at [0.86938756 0.54911538] with direction [-0.03790637 -0.01277133] reflecting with [ 0.43246121 -0.90165254] new direction [-0.03368751 -0.02156737] re-reflecting gives direction [-0.03790637 -0.01277133] FORWARD: [-0.03790637 -0.01277133] [0.86938756 0.54911538] BACKWARD: [-0.03790637 -0.01277133] [0.86938756 0.54911538] setting seed = 7 reflecting at [0.93312443 0.57556133] with direction [0.02306847 0.0326779 ] chose normal [-0.99882119 0.04854104] 39 reflecting at [0.93312443 0.57556133] with direction [0.02306847 0.0326779 ] reflecting with [ 0.77093646 -0.63691206] new direction [0.02773823 0.02881997] re-reflecting gives direction [0.02306847 0.0326779 ] FORWARD: [0.02306847 0.0326779 ] [0.93312443 0.57556133] BACKWARD: [0.02306847 0.0326779 ] [0.93312443 0.57556133] setting seed = 8 reflecting at [0.00974824 0.44465421] with direction [ 0.03582603 -0.01779032] chose normal [0.34627653 0.93813249] 99 reflecting at [0.00974824 0.44465421] with direction [ 0.03582603 -0.01779032] reflecting with [0.34760793 0.93763998] new direction [ 0.03876506 -0.00986257] re-reflecting gives direction [ 0.03582603 -0.01779032] FORWARD: [ 0.03582603 -0.01779032] [0.00974824 0.44465421] BACKWARD: [ 0.03582603 -0.01779032] [0.00974824 0.44465421] setting seed = 9 reflecting at [0.26300846 0.47112128] with direction [-0.03826888 0.01164015] chose normal [ 0.99593603 -0.09006347] 4 reflecting at [0.26300846 0.47112128] with direction [-0.03826888 0.01164015] reflecting with [0.97389943 0.22697994] new direction [0.02917942 0.02735986] re-reflecting gives direction [-0.03826888 0.01164015] FORWARD: [-0.03826888 0.01164015] [0.26300846 0.47112128] BACKWARD: [-0.03826888 0.01164015] [0.26300846 0.47112128] setting seed = 10 reflecting at [0.00412903 0.35451302] with direction [0.0367006 0.01590805] chose normal [ 0.22487937 -0.97438661] 90 reflecting at [0.00412903 0.35451302] with direction [0.0367006 0.01590805] reflecting with [ 0.22487937 -0.97438661] new direction [0.03996017 0.00178454] re-reflecting gives direction [0.0367006 0.01590805] FORWARD: [0.0367006 0.01590805] [0.00412903 0.35451302] BACKWARD: [0.0367006 0.01590805] [0.00412903 0.35451302] setting seed = 11 reflecting at [0.32155555 0.41865196] with direction [-0.03895091 0.0091009 ] chose normal [0.54430488 0.83888747] 16 reflecting at [0.32155555 0.41865196] with direction [-0.03895091 0.0091009 ] reflecting with [0.82811727 0.56055489] new direction [0.0060231 0.03954393] re-reflecting gives direction [-0.03895091 0.0091009 ] FORWARD: [-0.03895091 0.0091009 ] [0.32155555 0.41865196] BACKWARD: [-0.03895091 0.0091009 ] [0.32155555 0.41865196] setting seed = 12 reflecting at [0.35995727 0.3150146 ] with direction [0.03186583 0.02417786] chose normal [-0.99011844 0.14023365] 82 reflecting at [0.35995727 0.3150146 ] with direction [0.03186583 0.02417786] reflecting with [-0.86634575 0.49944472] new direction [0.00495484 0.03969193] re-reflecting gives direction [0.03186583 0.02417786] FORWARD: [0.03186583 0.02417786] [0.35995727 0.3150146 ] BACKWARD: [0.03186583 0.02417786] [0.35995727 0.3150146 ] setting seed = 13 reflecting at [0.73099081 0.54090516] with direction [0.02100007 0.03404404] chose normal [-0.98685102 0.16163249] 5 reflecting at [0.73099081 0.54090516] with direction [0.02100007 0.03404404] reflecting with [-0.93229687 0.36169401] new direction [0.00745422 0.0392993 ] re-reflecting gives direction [0.02100007 0.03404404] FORWARD: [0.02100007 0.03404404] [0.73099081 0.54090516] BACKWARD: [0.02100007 0.03404404] [0.73099081 0.54090516] setting seed = 14 reflecting at [0.31165599 0.36756094] with direction [-0.02635429 0.03009072] chose normal [ 0.82456261 -0.56577072] 96 reflecting at [0.31165599 0.36756094] with direction [-0.02635429 0.03009072] reflecting with [0.77339836 0.63392033] new direction [-0.02433225 0.0317481 ] re-reflecting gives direction [-0.02635429 0.03009072] FORWARD: [-0.02635429 0.03009072] [0.31165599 0.36756094] BACKWARD: [-0.02635429 0.03009072] [0.31165599 0.36756094] setting seed = 15 reflecting at [0.02715254 0.60855026] with direction [-0.03253657 -0.0232674 ] chose normal [ 0.98353428 -0.18072169] 52 reflecting at [0.02715254 0.60855026] with direction [-0.03253657 -0.0232674 ] reflecting with [ 0.95341068 -0.30167545] new direction [ 0.01323002 -0.03774873] re-reflecting gives direction [-0.03253657 -0.0232674 ] FORWARD: [-0.03253657 -0.0232674 ] [0.02715254 0.60855026] BACKWARD: [-0.03253657 -0.0232674 ] [0.02715254 0.60855026] setting seed = 16 reflecting at [0.23486289 0.63976911] with direction [-0.03505464 -0.01926583] chose normal [0.98410666 0.17757839] 28 reflecting at [0.23486289 0.63976911] with direction [-0.03505464 -0.01926583] reflecting with [ 0.93475612 -0.35529003] new direction [ 0.01340794 -0.0376859 ] re-reflecting gives direction [-0.03505464 -0.01926583] FORWARD: [-0.03505464 -0.01926583] [0.23486289 0.63976911] BACKWARD: [-0.03505464 -0.01926583] [0.23486289 0.63976911] setting seed = 17 reflecting at [0.15079862 0.63070329] with direction [-0.02987327 -0.02660052] chose normal [0.77877968 0.62729755] 76 reflecting at [0.15079862 0.63070329] with direction [-0.02987327 -0.02660052] reflecting with [ 0.82081006 -0.57120123] new direction [-0.01456347 -0.0372546 ] re-reflecting gives direction [-0.02987327 -0.02660052] FORWARD: [-0.02987327 -0.02660052] [0.15079862 0.63070329] BACKWARD: [-0.02987327 -0.02660052] [0.15079862 0.63070329] setting seed = 18 reflecting at [0.31819943 0.33216186] with direction [0.03839271 0.01122497] chose normal [-0.33803901 -0.9411321 ] 41 reflecting at [0.31819943 0.33216186] with direction [0.03839271 0.01122497] reflecting with [-0.34524316 0.93851327] new direction [0.03651456 0.01633055] re-reflecting gives direction [0.03839271 0.01122497] FORWARD: [0.03839271 0.01122497] [0.31819943 0.33216186] BACKWARD: [0.03839271 0.01122497] [0.31819943 0.33216186] setting seed = 19 reflecting at [0.06335039 0.66864627] with direction [-0.00412734 -0.03978649] chose normal [0.93844564 0.34542696] 79 reflecting at [0.06335039 0.66864627] with direction [-0.00412734 -0.03978649] reflecting with [ 0.99641622 -0.08458558] new direction [-0.00263833 -0.0399129 ] re-reflecting gives direction [-0.00412734 -0.03978649] FORWARD: [-0.00412734 -0.03978649] [0.06335039 0.66864627] BACKWARD: [-0.00412734 -0.03978649] [0.06335039 0.66864627] setting seed = 20 reflecting at [0.6289178 0.59036263] with direction [0.03621666 0.01698097] chose normal [ 0.02238176 -0.9997495 ] 63 reflecting at [0.6289178 0.59036263] with direction [0.03621666 0.01698097] reflecting with [-0.98569607 0.16853264] new direction [-0.0285175 0.02804911] re-reflecting gives direction [0.03621666 0.01698097] FORWARD: [0.03621666 0.01698097] [0.6289178 0.59036263] BACKWARD: [0.03621666 0.01698097] [0.6289178 0.59036263] setting seed = 21 reflecting at [0.0479965 0.3622258] with direction [ 0.01189335 -0.03819094] chose normal [0.69255841 0.7213618 ] 45 reflecting at [0.0479965 0.3622258] with direction [ 0.01189335 -0.03819094] reflecting with [0.64446979 0.76462977] new direction [ 0.03965328 -0.00525524] re-reflecting gives direction [ 0.01189335 -0.03819094] FORWARD: [ 0.01189335 -0.03819094] [0.0479965 0.3622258] BACKWARD: [ 0.01189335 -0.03819094] [0.0479965 0.3622258] setting seed = 22 reflecting at [0.45364136 0.3073653 ] with direction [-0.03917472 -0.00808342] chose normal [0.73205365 0.68124698] 97 reflecting at [0.45364136 0.3073653 ] with direction [-0.03917472 -0.00808342] reflecting with [-0.09378428 0.99559254] new direction [-0.03999511 0.00062567] re-reflecting gives direction [-0.03917472 -0.00808342] FORWARD: [-0.03917472 -0.00808342] [0.45364136 0.3073653 ] BACKWARD: [-0.03917472 -0.00808342] [0.45364136 0.3073653 ] setting seed = 23 reflecting at [0.13591686 0.30336076] with direction [-0.00025999 0.03999916] setting seed = 24 reflecting at [0.47619867 0.49486522] with direction [-0.02598612 -0.03040923] chose normal [-0.53400133 0.84548364] 50 reflecting at [0.47619867 0.49486522] with direction [-0.02598612 -0.03040923] reflecting with [0.94624486 0.32345119] new direction [ 0.0391631 -0.00813953] re-reflecting gives direction [-0.02598612 -0.03040923] FORWARD: [-0.02598612 -0.03040923] [0.47619867 0.49486522] BACKWARD: [-0.02598612 -0.03040923] [0.47619867 0.49486522] setting seed = 25 reflecting at [0.37122823 0.26399608] with direction [0.03256293 0.02323049] chose normal [-0.97667717 0.21471306] 60 reflecting at [0.37122823 0.26399608] with direction [0.03256293 0.02323049] reflecting with [-0.63178483 0.77514381] new direction [0.02932087 0.02720821] re-reflecting gives direction [0.03256293 0.02323049] FORWARD: [0.03256293 0.02323049] [0.37122823 0.26399608] BACKWARD: [0.03256293 0.02323049] [0.37122823 0.26399608] setting seed = 26 reflecting at [0.39378009 0.46705711] with direction [ 0.03979792 -0.0040157 ] chose normal [-0.50091154 0.86549848] 85 reflecting at [0.39378009 0.46705711] with direction [ 0.03979792 -0.0040157 ] reflecting with [-0.85384138 0.52053328] new direction [-0.02180062 0.03353704] re-reflecting gives direction [ 0.03979792 -0.0040157 ] FORWARD: [ 0.03979792 -0.0040157 ] [0.39378009 0.46705711] BACKWARD: [ 0.03979792 -0.0040157 ] [0.39378009 0.46705711] setting seed = 27 reflecting at [0.68895564 0.43889187] with direction [-0.00127249 0.03997975] chose normal [ 0.92407929 -0.38220081] 25 reflecting at [0.68895564 0.43889187] with direction [-0.00127249 0.03997975] reflecting with [-0.97547221 -0.22012263] new direction [-0.01602001 0.03665187] re-reflecting gives direction [-0.00127249 0.03997975] FORWARD: [-0.00127249 0.03997975] [0.68895564 0.43889187] BACKWARD: [-0.00127249 0.03997975] [0.68895564 0.43889187] setting seed = 28 reflecting at [0.93475923 0.56225156] with direction [-0.03108561 0.02517309] chose normal [ 0.25028455 -0.96817232] 53 reflecting at [0.93475923 0.56225156] with direction [-0.03108561 0.02517309] reflecting with [-0.0753835 -0.99715462] new direction [-0.03451679 -0.02021364] re-reflecting gives direction [-0.03108561 0.02517309] FORWARD: [-0.03108561 0.02517309] [0.93475923 0.56225156] BACKWARD: [-0.03108561 0.02517309] [0.93475923 0.56225156] setting seed = 29 reflecting at [0.15001162 0.39313651] with direction [-0.02702503 0.02948979] chose normal [ 0.93787043 -0.34698568] 15 reflecting at [0.15001162 0.39313651] with direction [-0.02702503 0.02948979] reflecting with [0.8970119 0.44200639] new direction [-0.00691923 0.03939701] re-reflecting gives direction [-0.02702503 0.02948979] FORWARD: [-0.02702503 0.02948979] [0.15001162 0.39313651] BACKWARD: [-0.02702503 0.02948979] [0.15001162 0.39313651] setting seed = 30 reflecting at [0.79559868 0.6204715 ] with direction [-0.01106701 0.03843854] chose normal [-0.94320997 -0.33219716] 57 reflecting at [0.79559868 0.6204715 ] with direction [-0.01106701 0.03843854] reflecting with [-0.53412191 -0.84540746] new direction [-0.03946637 -0.00651194] re-reflecting gives direction [-0.01106701 0.03843854] FORWARD: [-0.01106701 0.03843854] [0.79559868 0.6204715 ] BACKWARD: [-0.01106701 0.03843854] [0.79559868 0.6204715 ] setting seed = 31 reflecting at [0.70465924 0.58796209] with direction [0.00622801 0.03951217] chose normal [ 0.91849248 -0.39543844] 40 reflecting at [0.70465924 0.58796209] with direction [0.00622801 0.03951217] reflecting with [-0.99751744 -0.0704199 ] new direction [-0.01171731 0.03824532] re-reflecting gives direction [0.00622801 0.03951217] FORWARD: [0.00622801 0.03951217] [0.70465924 0.58796209] BACKWARD: [0.00622801 0.03951217] [0.70465924 0.58796209] setting seed = 32 reflecting at [0.41358712 0.44153528] with direction [ 0.02149569 -0.0337333 ] chose normal [-0.86603501 0.49998337] 44 reflecting at [0.41358712 0.44153528] with direction [ 0.02149569 -0.0337333 ] reflecting with [0.78359729 0.6212691 ] new direction [ 0.02794233 -0.02862213] re-reflecting gives direction [ 0.02149569 -0.0337333 ] FORWARD: [ 0.02149569 -0.0337333 ] [0.41358712 0.44153528] BACKWARD: [ 0.02149569 -0.0337333 ] [0.41358712 0.44153528] setting seed = 33 reflecting at [0.18839637 0.583353 ] with direction [0.00876515 0.03902784] chose normal [ 0.94116632 -0.33794371] 14 reflecting at [0.18839637 0.583353 ] with direction [0.00876515 0.03902784] reflecting with [ 0.41089806 -0.91168129] new direction [ 0.03504567 -0.01928214] re-reflecting gives direction [0.00876515 0.03902784] FORWARD: [0.00876515 0.03902784] [0.18839637 0.583353 ] BACKWARD: [0.00876515 0.03902784] [0.18839637 0.583353 ] setting seed = 34 reflecting at [0.69837732 0.63815456] with direction [0.0248712 0.03132767] chose normal [-0.9662808 -0.2574906] 59 reflecting at [0.69837732 0.63815456] with direction [0.0248712 0.03132767] reflecting with [-0.99996226 -0.00868732] new direction [-0.02541174 0.03089083] re-reflecting gives direction [0.0248712 0.03132767] FORWARD: [0.0248712 0.03132767] [0.69837732 0.63815456] BACKWARD: [0.0248712 0.03132767] [0.69837732 0.63815456] setting seed = 35 reflecting at [0.65608784 0.48141142] with direction [-0.03633999 -0.01671541] chose normal [ 0.81909097 -0.57366365] 13 reflecting at [0.65608784 0.48141142] with direction [-0.03633999 -0.01671541] reflecting with [0.99064996 0.13642818] new direction [ 0.03950549 -0.00627028] re-reflecting gives direction [-0.03633999 -0.01671541] FORWARD: [-0.03633999 -0.01671541] [0.65608784 0.48141142] BACKWARD: [-0.03633999 -0.01671541] [0.65608784 0.48141142] setting seed = 36 reflecting at [0.61006515 0.4226952 ] with direction [-0.00656845 0.03945701] chose normal [ 0.99395085 -0.10982581] 7 reflecting at [0.61006515 0.4226952 ] with direction [-0.00656845 0.03945701] reflecting with [-0.98326229 -0.18219571] new direction [-0.00800478 0.03919086] re-reflecting gives direction [-0.00656845 0.03945701] FORWARD: [-0.00656845 0.03945701] [0.61006515 0.4226952 ] BACKWARD: [-0.00656845 0.03945701] [0.61006515 0.4226952 ] setting seed = 37 reflecting at [0.74783048 0.44838258] with direction [-0.03830604 -0.01151726] chose normal [0.24749873 0.96888822] 9 reflecting at [0.74783048 0.44838258] with direction [-0.03830604 -0.01151726] reflecting with [0.9968224 0.07965612] new direction [ 0.03964894 -0.00528788] re-reflecting gives direction [-0.03830604 -0.01151726] FORWARD: [-0.03830604 -0.01151726] [0.74783048 0.44838258] BACKWARD: [-0.03830604 -0.01151726] [0.74783048 0.44838258] setting seed = 38 reflecting at [0.62632487 0.50336921] with direction [ 0.00520512 -0.03965989] chose normal [-0.47920534 0.87770282] 91 reflecting at [0.62632487 0.50336921] with direction [ 0.00520512 -0.03965989] reflecting with [0.07106544 0.99747166] new direction [0.01077519 0.03852136] re-reflecting gives direction [ 0.00520512 -0.03965989] FORWARD: [ 0.00520512 -0.03965989] [0.62632487 0.50336921] BACKWARD: [ 0.00520512 -0.03965989] [0.62632487 0.50336921] setting seed = 39 reflecting at [0.37707531 0.58289575] with direction [0.03440183 0.02040867] chose normal [-0.96803752 0.25080543] 26 reflecting at [0.37707531 0.58289575] with direction [0.03440183 0.02040867] reflecting with [ 0.48195217 -0.87619753] new direction [0.03565683 0.01812707] re-reflecting gives direction [0.03440183 0.02040867] FORWARD: [0.03440183 0.02040867] [0.37707531 0.58289575] BACKWARD: [0.03440183 0.02040867] [0.37707531 0.58289575] setting seed = 40 reflecting at [0.40535643 0.66852681] with direction [-0.0084009 -0.03910786] chose normal [ 0.99762991 -0.06880816] 34 reflecting at [0.40535643 0.66852681] with direction [-0.0084009 -0.03910786] reflecting with [ 0.98422636 -0.17691375] new direction [-0.00574414 -0.03958541] re-reflecting gives direction [-0.0084009 -0.03910786] FORWARD: [-0.0084009 -0.03910786] [0.40535643 0.66852681] BACKWARD: [-0.0084009 -0.03910786] [0.40535643 0.66852681] setting seed = 41 reflecting at [0.4304033 0.37361026] with direction [ 0.02446896 -0.03164285] chose normal [-0.39185582 0.92002664] 4 reflecting at [0.4304033 0.37361026] with direction [ 0.02446896 -0.03164285] reflecting with [-0.80640044 0.59136988] new direction [-0.03753422 0.01382688] re-reflecting gives direction [ 0.02446896 -0.03164285] FORWARD: [ 0.02446896 -0.03164285] [0.4304033 0.37361026] BACKWARD: [ 0.02446896 -0.03164285] [0.4304033 0.37361026] setting seed = 42 reflecting at [0.78988169 0.54673494] with direction [-0.01964246 0.034845 ] chose normal [0.95525297 0.29579006] 28 reflecting at [0.78988169 0.54673494] with direction [-0.01964246 0.034845 ] reflecting with [-0.70398882 -0.71021105] new direction [-0.03501649 0.01933508] re-reflecting gives direction [-0.01964246 0.034845 ] FORWARD: [-0.01964246 0.034845 ] [0.78988169 0.54673494] BACKWARD: [-0.01964246 0.034845 ] [0.78988169 0.54673494] setting seed = 43 reflecting at [0.33809302 0.34038204] with direction [-0.03061347 -0.0257452 ] chose normal [-0.59139425 0.80638257] 29 reflecting at [0.33809302 0.34038204] with direction [-0.03061347 -0.0257452 ] reflecting with [-0.63872083 0.76943856] new direction [-0.03094035 -0.02535142] re-reflecting gives direction [-0.03061347 -0.0257452 ] FORWARD: [-0.03061347 -0.0257452 ] [0.33809302 0.34038204] BACKWARD: [-0.03061347 -0.0257452 ] [0.33809302 0.34038204] setting seed = 44 reflecting at [0.56836523 0.39005034] with direction [-0.01068318 -0.03854698] chose normal [ 0.99893451 -0.04615032] 29 reflecting at [0.56836523 0.39005034] with direction [-0.01068318 -0.03854698] reflecting with [-0.69018106 0.72363672] new direction [-0.03900915 -0.00884794] re-reflecting gives direction [-0.01068318 -0.03854698] FORWARD: [-0.01068318 -0.03854698] [0.56836523 0.39005034] BACKWARD: [-0.01068318 -0.03854698] [0.56836523 0.39005034] setting seed = 45 reflecting at [0.94160314 0.52051464] with direction [-0.0399819 -0.00120333] chose normal [ 0.06446741 -0.99791981] 84 reflecting at [0.94160314 0.52051464] with direction [-0.0399819 -0.00120333] reflecting with [ 0.06446741 -0.99791981] new direction [-0.03980439 -0.00395101] re-reflecting gives direction [-0.0399819 -0.00120333] FORWARD: [-0.0399819 -0.00120333] [0.94160314 0.52051464] BACKWARD: [-0.0399819 -0.00120333] [0.94160314 0.52051464] setting seed = 46 reflecting at [0.25975823 0.44354413] with direction [-0.02164327 0.0336388 ] chose normal [ 0.9478891 -0.31860046] 70 reflecting at [0.25975823 0.44354413] with direction [-0.02164327 0.0336388 ] reflecting with [0.99984603 0.01754771] new direction [0.02044956 0.03437754] re-reflecting gives direction [-0.02164327 0.0336388 ] FORWARD: [-0.02164327 0.0336388 ] [0.25975823 0.44354413] BACKWARD: [-0.02164327 0.0336388 ] [0.25975823 0.44354413] setting seed = 47 reflecting at [0.01926161 0.63431919] with direction [-0.03559232 -0.0182534 ] chose normal [0.74369889 0.66851474] 81 reflecting at [0.01926161 0.63431919] with direction [-0.03559232 -0.0182534 ] reflecting with [ 0.9436164 -0.33104091] new direction [ 0.0163875 -0.03648904] re-reflecting gives direction [-0.03559232 -0.0182534 ] FORWARD: [-0.03559232 -0.0182534 ] [0.01926161 0.63431919] BACKWARD: [-0.03559232 -0.0182534 ] [0.01926161 0.63431919] setting seed = 48 reflecting at [0.14141263 0.72381215] with direction [ 0.01008992 -0.03870651] setting seed = 49 reflecting at [0.16156443 0.4674537 ] with direction [-0.02888115 -0.02767453] chose normal [ 0.78315843 -0.62182223] 61 reflecting at [0.16156443 0.4674537 ] with direction [-0.02888115 -0.02767453] reflecting with [0.98231877 0.187216 ] new direction [ 0.0370356 -0.01511173] re-reflecting gives direction [-0.02888115 -0.02767453] FORWARD: [-0.02888115 -0.02767453] [0.16156443 0.4674537 ] BACKWARD: [-0.02888115 -0.02767453] [0.16156443 0.4674537 ] setting seed = 50 reflecting at [0.79282295 0.55686971] with direction [0.00526153 0.03965244] chose normal [-0.9091523 -0.4164638] 59 reflecting at [0.79282295 0.55686971] with direction [0.00526153 0.03965244] reflecting with [-0.95815198 -0.28626 ] new direction [-0.02615101 0.03026755] re-reflecting gives direction [0.00526153 0.03965244] FORWARD: [0.00526153 0.03965244] [0.79282295 0.55686971] BACKWARD: [0.00526153 0.03965244] [0.79282295 0.55686971] setting seed = 51 reflecting at [0.62787959 0.51878844] with direction [0.02807295 0.02849403] chose normal [-0.99167671 -0.12875285] 11 reflecting at [0.62787959 0.51878844] with direction [0.02807295 0.02849403] reflecting with [-0.91529383 0.4027868 ] new direction [0.00204569 0.03994765] re-reflecting gives direction [0.02807295 0.02849403] FORWARD: [0.02807295 0.02849403] [0.62787959 0.51878844] BACKWARD: [0.02807295 0.02849403] [0.62787959 0.51878844] setting seed = 52 reflecting at [0.10765197 0.43679951] with direction [-0.03566072 -0.01811941] chose normal [0.97430448 0.22523493] 14 reflecting at [0.10765197 0.43679951] with direction [-0.03566072 -0.01811941] reflecting with [0.49373504 0.86961239] new direction [-0.00271496 0.03990776] re-reflecting gives direction [-0.03566072 -0.01811941] FORWARD: [-0.03566072 -0.01811941] [0.10765197 0.43679951] BACKWARD: [-0.03566072 -0.01811941] [0.10765197 0.43679951] setting seed = 53 reflecting at [0.38071412 0.62351758] with direction [-0.01441546 -0.03731212] chose normal [-0.78010783 0.62564509] 68 reflecting at [0.38071412 0.62351758] with direction [-0.01441546 -0.03731212] reflecting with [ 0.99116492 -0.13263523] new direction [ 0.00409791 -0.03978954] re-reflecting gives direction [-0.01441546 -0.03731212] FORWARD: [-0.01441546 -0.03731212] [0.38071412 0.62351758] BACKWARD: [-0.01441546 -0.03731212] [0.38071412 0.62351758] setting seed = 54 reflecting at [0.35419835 0.39601134] with direction [-0.03646926 -0.01643148] chose normal [0.97346802 0.2288231 ] 2 reflecting at [0.35419835 0.39601134] with direction [-0.03646926 -0.01643148] reflecting with [ 0.89562093 -0.44481812] new direction [ 0.00894519 -0.03898697] re-reflecting gives direction [-0.03646926 -0.01643148] FORWARD: [-0.03646926 -0.01643148] [0.35419835 0.39601134] BACKWARD: [-0.03646926 -0.01643148] [0.35419835 0.39601134] setting seed = 55 reflecting at [0.41253863 0.54222452] with direction [-0.00327261 -0.0398659 ] chose normal [0.7228596 0.69099493] 3 reflecting at [0.41253863 0.54222452] with direction [-0.00327261 -0.0398659 ] reflecting with [-0.98267299 0.18534775] new direction [-0.01147429 -0.03831893] re-reflecting gives direction [-0.00327261 -0.0398659 ] FORWARD: [-0.00327261 -0.0398659 ] [0.41253863 0.54222452] BACKWARD: [-0.00327261 -0.0398659 ] [0.41253863 0.54222452] setting seed = 56 reflecting at [-0.00082259 0.55972911] with direction [ 0.02032516 -0.03445124] chose normal [0.6437137 0.7652664] 92 reflecting at [-0.00082259 0.55972911] with direction [ 0.02032516 -0.03445124] reflecting with [0.6437137 0.7652664] new direction [ 0.03742321 -0.01412455] re-reflecting gives direction [ 0.02032516 -0.03445124] FORWARD: [ 0.02032516 -0.03445124] [-0.00082259 0.55972911] BACKWARD: [ 0.02032516 -0.03445124] [-0.00082259 0.55972911] setting seed = 57 reflecting at [0.32852629 0.48443255] with direction [ 0.03482865 -0.01967144] chose normal [-0.9653418 -0.26098892] 66 reflecting at [0.32852629 0.48443255] with direction [ 0.03482865 -0.01967144] reflecting with [-0.95158108 -0.30739787] new direction [-0.01673815 -0.03632952] re-reflecting gives direction [ 0.03482865 -0.01967144] FORWARD: [ 0.03482865 -0.01967144] [0.32852629 0.48443255] BACKWARD: [ 0.03482865 -0.01967144] [0.32852629 0.48443255] setting seed = 58 reflecting at [0.00831403 0.22576864] with direction [0.03708157 0.01499858] setting seed = 59 reflecting at [0.55842175 0.63618532] with direction [-0.03817546 -0.01194295] chose normal [ 0.96677775 -0.25561844] 29 reflecting at [0.55842175 0.63618532] with direction [-0.03817546 -0.01194295] reflecting with [ 0.9754194 -0.22035653] new direction [ 0.02933405 -0.027194 ] re-reflecting gives direction [-0.03817546 -0.01194295] FORWARD: [-0.03817546 -0.01194295] [0.55842175 0.63618532] BACKWARD: [-0.03817546 -0.01194295] [0.55842175 0.63618532] setting seed = 60 reflecting at [0.30258664 0.49076018] with direction [ 0.0205427 -0.03432197] chose normal [-0.99186614 -0.12728533] 21 reflecting at [0.30258664 0.49076018] with direction [ 0.0205427 -0.03432197] reflecting with [0.11689192 0.99314464] new direction [0.02795024 0.02861441] re-reflecting gives direction [ 0.0205427 -0.03432197] FORWARD: [ 0.0205427 -0.03432197] [0.30258664 0.49076018] BACKWARD: [ 0.0205427 -0.03432197] [0.30258664 0.49076018] setting seed = 61 reflecting at [0.35821533 0.55032466] with direction [ 0.02436953 -0.03171949] chose normal [-0.96739827 -0.25325991] 26 reflecting at [0.35821533 0.55032466] with direction [ 0.02436953 -0.03171949] reflecting with [-0.96012904 -0.2795572 ] new direction [-0.00353274 -0.03984369] re-reflecting gives direction [ 0.02436953 -0.03171949] FORWARD: [ 0.02436953 -0.03171949] [0.35821533 0.55032466] BACKWARD: [ 0.02436953 -0.03171949] [0.35821533 0.55032466] setting seed = 62 reflecting at [0.23626136 0.3035377 ] with direction [-0.03993416 -0.00229416] chose normal [0.93863157 0.34492141] 54 reflecting at [0.23626136 0.3035377 ] with direction [-0.03993416 -0.00229416] reflecting with [0.98471485 0.17417424] new direction [0.03829817 0.0115434 ] re-reflecting gives direction [-0.03993416 -0.00229416] FORWARD: [-0.03993416 -0.00229416] [0.23626136 0.3035377 ] BACKWARD: [-0.03993416 -0.00229416] [0.23626136 0.3035377 ] setting seed = 63 reflecting at [0.77738168 0.40200899] with direction [ 0.02915488 -0.027386 ] chose normal [-0.99656293 0.08283919] 19 reflecting at [0.77738168 0.40200899] with direction [ 0.02915488 -0.027386 ] reflecting with [-0.99966777 -0.02577498] new direction [-0.02770486 -0.02885205] re-reflecting gives direction [ 0.02915488 -0.027386 ] FORWARD: [ 0.02915488 -0.027386 ] [0.77738168 0.40200899] BACKWARD: [ 0.02915488 -0.027386 ] [0.77738168 0.40200899] setting seed = 64 reflecting at [0.00700152 0.5434836 ] with direction [ 0.03157176 -0.02456061] chose normal [0.0269266 0.99963741] 88 reflecting at [0.00700152 0.5434836 ] with direction [ 0.03157176 -0.02456061] reflecting with [0.16834631 0.98572791] new direction [0.0379336 0.01269023] re-reflecting gives direction [ 0.03157176 -0.02456061] FORWARD: [ 0.03157176 -0.02456061] [0.00700152 0.5434836 ] BACKWARD: [ 0.03157176 -0.02456061] [0.00700152 0.5434836 ] setting seed = 65 reflecting at [0.23297922 0.47284723] with direction [-0.02999187 0.02646673] chose normal [ 0.87714796 -0.48022022] 19 reflecting at [0.23297922 0.47284723] with direction [-0.02999187 0.02646673] reflecting with [ 0.99603093 -0.08900784] new direction [0.03420944 0.02072954] re-reflecting gives direction [-0.02999187 0.02646673] FORWARD: [-0.02999187 0.02646673] [0.23297922 0.47284723] BACKWARD: [-0.02999187 0.02646673] [0.23297922 0.47284723] setting seed = 66 reflecting at [0.63307038 0.58558882] with direction [0.03311318 0.02243919] chose normal [ 0.37536664 -0.92687641] 71 reflecting at [0.63307038 0.58558882] with direction [0.03311318 0.02243919] reflecting with [-0.94506677 0.32687734] new direction [-0.0121731 0.0381027] re-reflecting gives direction [0.03311318 0.02243919] FORWARD: [0.03311318 0.02243919] [0.63307038 0.58558882] BACKWARD: [0.03311318 0.02243919] [0.63307038 0.58558882] setting seed = 67 reflecting at [0.11399496 0.36469248] with direction [-0.031706 -0.02438707] chose normal [0.98377071 0.17943018] 38 reflecting at [0.11399496 0.36469248] with direction [-0.031706 -0.02438707] reflecting with [0.95209698 0.30579625] new direction [ 0.03997674 -0.00136388] re-reflecting gives direction [-0.031706 -0.02438707] FORWARD: [-0.031706 -0.02438707] [0.11399496 0.36469248] BACKWARD: [-0.031706 -0.02438707] [0.11399496 0.36469248] setting seed = 68 reflecting at [0.85336104 0.43151834] with direction [0.03964926 0.00528548] chose normal [-0.79459132 0.60714466] 17 reflecting at [0.85336104 0.43151834] with direction [0.03964926 0.00528548] reflecting with [-0.43881146 0.89857916] new direction [0.0285481 0.02801796] re-reflecting gives direction [0.03964926 0.00528548] FORWARD: [0.03964926 0.00528548] [0.85336104 0.43151834] BACKWARD: [0.03964926 0.00528548] [0.85336104 0.43151834] setting seed = 69 reflecting at [0.34957318 0.4747182 ] with direction [0.0381706 0.01195847] chose normal [-0.7961656 -0.60507879] 1 reflecting at [0.34957318 0.4747182 ] with direction [0.0381706 0.01195847] reflecting with [-0.86697568 -0.49835046] new direction [-0.02954452 -0.02696519] re-reflecting gives direction [0.0381706 0.01195847] FORWARD: [0.0381706 0.01195847] [0.34957318 0.4747182 ] BACKWARD: [0.0381706 0.01195847] [0.34957318 0.4747182 ] setting seed = 70 reflecting at [0.03434818 0.6149331 ] with direction [0.0266774 0.02980464] chose normal [ 0.39947727 -0.9167431 ] 42 reflecting at [0.03434818 0.6149331 ] with direction [0.0266774 0.02980464] reflecting with [ 0.6826168 -0.73077651] new direction [0.03155141 0.02458676] re-reflecting gives direction [0.0266774 0.02980464] FORWARD: [0.0266774 0.02980464] [0.03434818 0.6149331 ] BACKWARD: [0.0266774 0.02980464] [0.03434818 0.6149331 ] setting seed = 71 reflecting at [0.3301237 0.63863365] with direction [-0.00888267 0.03900126] chose normal [-0.97291035 -0.23118274] 69 reflecting at [0.3301237 0.63863365] with direction [-0.00888267 0.03900126] reflecting with [-0.49751035 -0.86745804] new direction [-0.03814895 -0.01202737] re-reflecting gives direction [-0.00888267 0.03900126] FORWARD: [-0.00888267 0.03900126] [0.3301237 0.63863365] BACKWARD: [-0.00888267 0.03900126] [0.3301237 0.63863365] setting seed = 72 reflecting at [0.56607085 0.38288097] with direction [-0.03933418 -0.00726789] chose normal [ 0.99936155 -0.03572801] 44 reflecting at [0.56607085 0.38288097] with direction [-0.03933418 -0.00726789] reflecting with [0.20311685 0.97915451] new direction [-0.03319769 0.02231398] re-reflecting gives direction [-0.03933418 -0.00726789] FORWARD: [-0.03933418 -0.00726789] [0.56607085 0.38288097] BACKWARD: [-0.03933418 -0.00726789] [0.56607085 0.38288097] setting seed = 73 reflecting at [0.13597885 0.63503832] with direction [-0.03999972 0.0001487 ] chose normal [ 0.95068267 -0.31016523] 56 reflecting at [0.13597885 0.63503832] with direction [-0.03999972 0.0001487 ] reflecting with [ 0.98237383 -0.18692688] new direction [ 0.03725902 -0.01455216] re-reflecting gives direction [-0.03999972 0.0001487 ] FORWARD: [-0.03999972 0.0001487 ] [0.13597885 0.63503832] BACKWARD: [-0.03999972 0.0001487 ] [0.13597885 0.63503832] setting seed = 74 reflecting at [0.05551036 0.43775811] with direction [0.03895671 0.00907606] chose normal [-0.96711996 0.25432065] 26 reflecting at [0.05551036 0.43775811] with direction [0.03895671 0.00907606] reflecting with [-0.28345858 0.95898448] new direction [0.0376308 0.01356183] re-reflecting gives direction [0.03895671 0.00907606] FORWARD: [0.03895671 0.00907606] [0.05551036 0.43775811] BACKWARD: [0.03895671 0.00907606] [0.05551036 0.43775811] setting seed = 75 reflecting at [0.50973295 0.32522409] with direction [-0.02789929 0.02866409] chose normal [0.96607282 0.25826984] 98 reflecting at [0.50973295 0.32522409] with direction [-0.02789929 0.02866409] reflecting with [0.85293874 0.52201103] new direction [-0.01283057 0.03788636] re-reflecting gives direction [-0.02789929 0.02866409] FORWARD: [-0.02789929 0.02866409] [0.50973295 0.32522409] BACKWARD: [-0.02789929 0.02866409] [0.50973295 0.32522409] setting seed = 76 reflecting at [0.32690662 0.39635018] with direction [-0.02471798 -0.03144871] chose normal [0.99980546 0.01972428] 96 reflecting at [0.32690662 0.39635018] with direction [-0.02471798 -0.03144871] reflecting with [ 0.99668345 -0.08137624] new direction [ 0.01928923 -0.03504177] re-reflecting gives direction [-0.02471798 -0.03144871] FORWARD: [-0.02471798 -0.03144871] [0.32690662 0.39635018] BACKWARD: [-0.02471798 -0.03144871] [0.32690662 0.39635018] setting seed = 77 reflecting at [0.159784 0.66828224] with direction [-0.03865662 -0.0102794 ] chose normal [0.99421477 0.10741039] 67 reflecting at [0.159784 0.66828224] with direction [-0.03865662 -0.0102794 ] reflecting with [ 0.79437479 -0.60742794] new direction [ 0.00021031 -0.03999945] re-reflecting gives direction [-0.03865662 -0.0102794 ] FORWARD: [-0.03865662 -0.0102794 ] [0.159784 0.66828224] BACKWARD: [-0.03865662 -0.0102794 ] [0.159784 0.66828224] setting seed = 78 reflecting at [0.20239385 0.43885819] with direction [-0.03864408 0.01032644] chose normal [-0.00914844 -0.99995815] 42 reflecting at [0.20239385 0.43885819] with direction [-0.03864408 0.01032644] reflecting with [0.81401512 0.58084368] new direction [0.00280365 0.03990162] re-reflecting gives direction [-0.03864408 0.01032644] FORWARD: [-0.03864408 0.01032644] [0.20239385 0.43885819] BACKWARD: [-0.03864408 0.01032644] [0.20239385 0.43885819] setting seed = 79 reflecting at [0.02295645 0.52977859] with direction [ 0.01690173 -0.03625371] chose normal [0.11595433 0.99325455] 54 reflecting at [0.02295645 0.52977859] with direction [ 0.01690173 -0.03625371] reflecting with [-0.11517722 0.99334496] new direction [0.00815767 0.03915932] re-reflecting gives direction [ 0.01690173 -0.03625371] FORWARD: [ 0.01690173 -0.03625371] [0.02295645 0.52977859] BACKWARD: [ 0.01690173 -0.03625371] [0.02295645 0.52977859] setting seed = 80 reflecting at [0.37096745 0.70643199] with direction [ 0.00949935 -0.03885566] chose normal [-0.99209853 -0.1254612 ] 94 reflecting at [0.37096745 0.70643199] with direction [ 0.00949935 -0.03885566] reflecting with [-0.97468899 -0.22356515] new direction [ 0.00838404 -0.03911148] re-reflecting gives direction [ 0.00949935 -0.03885566] FORWARD: [ 0.00949935 -0.03885566] [0.37096745 0.70643199] BACKWARD: [ 0.00949935 -0.03885566] [0.37096745 0.70643199] setting seed = 81 reflecting at [0.78116639 0.59011031] with direction [-0.01447537 -0.03728892] chose normal [-0.58455495 0.81135412] 52 reflecting at [0.78116639 0.59011031] with direction [-0.01447537 -0.03728892] reflecting with [ 0.9366811 -0.35018355] new direction [-0.01353712 -0.03763969] re-reflecting gives direction [-0.01447537 -0.03728892] FORWARD: [-0.01447537 -0.03728892] [0.78116639 0.59011031] BACKWARD: [-0.01447537 -0.03728892] [0.78116639 0.59011031] setting seed = 82 reflecting at [0.90241618 0.37140442] with direction [0.00489491 0.03969937] chose normal [-0.99997753 0.00670296] 37 reflecting at [0.90241618 0.37140442] with direction [0.00489491 0.03969937] reflecting with [-0.99945421 -0.03303454] new direction [-0.0075057 0.0392895] re-reflecting gives direction [0.00489491 0.03969937] FORWARD: [0.00489491 0.03969937] [0.90241618 0.37140442] BACKWARD: [0.00489491 0.03969937] [0.90241618 0.37140442] setting seed = 83 reflecting at [0.73947248 0.50969906] with direction [ 0.02973828 -0.02675135] chose normal [-0.83995537 0.54265548] 16 reflecting at [0.73947248 0.50969906] with direction [ 0.02973828 -0.02675135] reflecting with [-0.94063007 0.33943345] new direction [-0.0399681 -0.00159728] re-reflecting gives direction [ 0.02973828 -0.02675135] FORWARD: [ 0.02973828 -0.02675135] [0.73947248 0.50969906] BACKWARD: [ 0.02973828 -0.02675135] [0.73947248 0.50969906] setting seed = 84 reflecting at [0.09304075 0.29114574] with direction [ 0.03477044 -0.01977415] chose normal [-0.17706516 0.98419913] 53 reflecting at [0.09304075 0.29114574] with direction [ 0.03477044 -0.01977415] reflecting with [-0.51082933 0.85968215] new direction [-0.00074369 0.03999309] re-reflecting gives direction [ 0.03477044 -0.01977415] FORWARD: [ 0.03477044 -0.01977415] [0.09304075 0.29114574] BACKWARD: [ 0.03477044 -0.01977415] [0.09304075 0.29114574] setting seed = 85 reflecting at [0.09269465 0.40624046] with direction [ 0.02781881 -0.02874219] chose normal [-0.96043218 0.27851395] 24 reflecting at [0.09269465 0.40624046] with direction [ 0.02781881 -0.02874219] reflecting with [0.63881082 0.76936385] new direction [ 0.03336656 -0.02206066] re-reflecting gives direction [ 0.02781881 -0.02874219] FORWARD: [ 0.02781881 -0.02874219] [0.09269465 0.40624046] BACKWARD: [ 0.02781881 -0.02874219] [0.09269465 0.40624046] setting seed = 86 reflecting at [0.0772825 0.7321639] with direction [-0.0017544 -0.03996151] setting seed = 87 reflecting at [0.3542791 0.40874116] with direction [ 0.03874902 -0.0099254 ] chose normal [-0.12256498 0.99246049] 84 reflecting at [0.3542791 0.40874116] with direction [ 0.03874902 -0.0099254 ] reflecting with [-0.96541456 -0.26071962] new direction [-0.02848461 -0.0280825 ] re-reflecting gives direction [ 0.03874902 -0.0099254 ] FORWARD: [ 0.03874902 -0.0099254 ] [0.3542791 0.40874116] BACKWARD: [ 0.03874902 -0.0099254 ] [0.3542791 0.40874116] setting seed = 88 reflecting at [0.67093864 0.56773172] with direction [ 0.03197838 -0.0240288 ] chose normal [-0.97498227 0.22228264] 41 reflecting at [0.67093864 0.56773172] with direction [ 0.03197838 -0.0240288 ] reflecting with [-0.97588769 -0.21827326] new direction [-0.01869452 -0.03536262] re-reflecting gives direction [ 0.03197838 -0.0240288 ] FORWARD: [ 0.03197838 -0.0240288 ] [0.67093864 0.56773172] BACKWARD: [ 0.03197838 -0.0240288 ] [0.67093864 0.56773172] setting seed = 89 reflecting at [0.80828603 0.53459992] with direction [0.02786633 0.02869612] chose normal [-0.73290905 0.68032663] 80 reflecting at [0.80828603 0.53459992] with direction [0.02786633 0.02869612] reflecting with [-0.99658198 -0.08260969] new direction [-0.03221094 0.02371614] re-reflecting gives direction [0.02786633 0.02869612] FORWARD: [0.02786633 0.02869612] [0.80828603 0.53459992] BACKWARD: [0.02786633 0.02869612] [0.80828603 0.53459992] setting seed = 90 reflecting at [0.15933834 0.65311074] with direction [-0.02409888 -0.0319256 ] chose normal [ 0.8841271 -0.46724648] 74 reflecting at [0.15933834 0.65311074] with direction [-0.02409888 -0.0319256 ] reflecting with [ 0.99881338 -0.04870138] new direction [ 0.02087861 -0.03411867] re-reflecting gives direction [-0.02409888 -0.0319256 ] FORWARD: [-0.02409888 -0.0319256 ] [0.15933834 0.65311074] BACKWARD: [-0.02409888 -0.0319256 ] [0.15933834 0.65311074] setting seed = 91 reflecting at [0.06220912 0.5649269 ] with direction [-0.01395947 0.0374851 ] chose normal [-0.92831744 -0.37178856] 63 reflecting at [0.06220912 0.5649269 ] with direction [-0.01395947 0.0374851 ] reflecting with [ 0.50709066 -0.86189272] new direction [ 0.02598594 -0.03040939] re-reflecting gives direction [-0.01395947 0.0374851 ] FORWARD: [-0.01395947 0.0374851 ] [0.06220912 0.5649269 ] BACKWARD: [-0.01395947 0.0374851 ] [0.06220912 0.5649269 ] setting seed = 92 reflecting at [0.86691228 0.47991436] with direction [-0.02693892 -0.02956847] chose normal [-0.19961852 0.97987369] 68 reflecting at [0.86691228 0.47991436] with direction [-0.02693892 -0.02956847] reflecting with [0.80447377 0.59398817] new direction [0.0361881 0.01704175] re-reflecting gives direction [-0.02693892 -0.02956847] FORWARD: [-0.02693892 -0.02956847] [0.86691228 0.47991436] BACKWARD: [-0.02693892 -0.02956847] [0.86691228 0.47991436] setting seed = 93 reflecting at [0.40847232 0.38353044] with direction [-0.02290158 0.03279509] chose normal [0.97810947 0.20809099] 55 reflecting at [0.40847232 0.38353044] with direction [-0.02290158 0.03279509] reflecting with [0.99871915 0.05059696] new direction [0.01946991 0.0349417 ] re-reflecting gives direction [-0.02290158 0.03279509] FORWARD: [-0.02290158 0.03279509] [0.40847232 0.38353044] BACKWARD: [-0.02290158 0.03279509] [0.40847232 0.38353044] setting seed = 94 reflecting at [0.18607047 0.43045606] with direction [0.03999576 0.00058272] chose normal [-0.99138772 -0.13095947] 38 reflecting at [0.18607047 0.43045606] with direction [0.03999576 0.00058272] reflecting with [-0.85869194 0.51249211] new direction [-0.01847325 0.03547871] re-reflecting gives direction [0.03999576 0.00058272] FORWARD: [0.03999576 0.00058272] [0.18607047 0.43045606] BACKWARD: [0.03999576 0.00058272] [0.18607047 0.43045606] setting seed = 95 reflecting at [0.1329267 0.27135336] with direction [0.00593512 0.03955723] setting seed = 96 reflecting at [0.57282441 0.43763627] with direction [0.02371945 0.0322085 ] chose normal [-0.98828152 0.15264217] 98 reflecting at [0.57282441 0.43763627] with direction [0.02371945 0.0322085 ] reflecting with [-0.96810281 0.25055328] new direction [-0.00511631 0.03967144] re-reflecting gives direction [0.02371945 0.0322085 ] FORWARD: [0.02371945 0.0322085 ] [0.57282441 0.43763627] BACKWARD: [0.02371945 0.0322085 ] [0.57282441 0.43763627] setting seed = 97 reflecting at [0.59701368 0.45505001] with direction [0.00858305 0.03906829] chose normal [ 0.97234847 -0.23353469] 26 reflecting at [0.59701368 0.45505001] with direction [0.00858305 0.03906829] reflecting with [-0.96293126 -0.26974691] new direction [-0.02762979 0.02892395] re-reflecting gives direction [0.00858305 0.03906829] FORWARD: [0.00858305 0.03906829] [0.59701368 0.45505001] BACKWARD: [0.00858305 0.03906829] [0.59701368 0.45505001] setting seed = 98 reflecting at [0.05036115 0.45117145] with direction [-0.0046699 -0.03972646] chose normal [0.50785771 0.86144097] 48 reflecting at [0.05036115 0.45117145] with direction [-0.0046699 -0.03972646] reflecting with [0.9858335 0.16772692] new direction [ 0.01754476 -0.03594693] re-reflecting gives direction [-0.0046699 -0.03972646] FORWARD: [-0.0046699 -0.03972646] [0.05036115 0.45117145] BACKWARD: [-0.0046699 -0.03972646] [0.05036115 0.45117145] setting seed = 99 reflecting at [0.50555553 0.58688913] with direction [-0.03999867 -0.00032601] chose normal [0.99595977 0.08980054] 53 reflecting at [0.50555553 0.58688913] with direction [-0.03999867 -0.00032601] reflecting with [ 0.8602646 -0.50984784] new direction [ 0.01891779 -0.03524368] re-reflecting gives direction [-0.03999867 -0.00032601] FORWARD: [-0.03999867 -0.00032601] [0.50555553 0.58688913] BACKWARD: [-0.03999867 -0.00032601] [0.50555553 0.58688913] | |||
Passed | tests/test_stepsampling.py::test_stepsampler_cubemh | 10.15 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.16) * Expected Volume: exp(0.00) Quality: ok param0: +1.0e-12|******************************* *******************| +1.0e+00 param1: +1.0e-12|***************************************************| +1.0e+00 param2: +1.0e-12|***************************************************| +1.0e+00 Z=-inf(0.00%) | Like=-30.04..-1.06 [-30.0369..-9.7206] | it/evals=0/412 eff=0.0000% N=400 ineffective proposal scale (1.17216). shrinking... ineffective proposal scale (1.08266). shrinking... ineffective proposal scale (1). shrinking... ineffective proposal scale (1.17216). shrinking... ineffective proposal scale (1.17216). shrinking... ineffective proposal scale (1.08266). shrinking... ineffective proposal scale (1.17216). shrinking... ineffective proposal scale (1.08266). shrinking... ineffective proposal scale (1.0913). shrinking... ineffective proposal scale (1.01601). shrinking... ineffective proposal scale (1.19093). shrinking... ineffective proposal scale (1.1). shrinking... ineffective proposal scale (1.01601). shrinking... ineffective proposal scale (1.21). shrinking... ineffective proposal scale (1.11761). shrinking... ineffective proposal scale (1.04051). shrinking... ineffective proposal scale (0.961066). shrinking... ineffective proposal scale (0.961066). shrinking... ineffective proposal scale (0.961066). shrinking... ineffective proposal scale (0.887686). shrinking... ineffective proposal scale (0.826446). shrinking... ineffective proposal scale (0.763345). shrinking... ineffective proposal scale (0.873696). shrinking... ineffective proposal scale (0.813422). shrinking... ineffective proposal scale (0.813422). shrinking... Z=-25.1(0.00%) | Like=-21.40..-1.06 [-30.0369..-9.7206] | it/evals=50/1012 eff=8.1699% N=400 ineffective proposal scale (0.846375). shrinking... ineffective proposal scale (0.794269). shrinking... ineffective proposal scale (0.800603). shrinking... ineffective proposal scale (0.901899). shrinking... ineffective proposal scale (0.769432). shrinking... ineffective proposal scale (0.710683). shrinking... ineffective proposal scale (0.656421). shrinking... ineffective proposal scale (0.606301). shrinking... ineffective proposal scale (0.560008). shrinking... Mono-modal Volume: ~exp(-4.22) * Expected Volume: exp(-0.23) Quality: ok param0: +0.0000|******************************************* *******| +1.0000 param1: +1.0e-12|*********************************************** ** | +1.0e+00 param2: +1.0e-12|******************************************** **** *| +1.0e+00 Z=-21.0(0.00%) | Like=-16.93..-1.06 [-30.0369..-9.7206] | it/evals=90/1492 eff=8.2418% N=400 Z=-20.0(0.00%) | Like=-16.02..-1.06 [-30.0369..-9.7206] | it/evals=100/1612 eff=8.2508% N=400 ineffective proposal scale (0.560008). shrinking... ineffective proposal scale (0.51725). shrinking... ineffective proposal scale (0.477757). shrinking... Z=-16.2(0.00%) | Like=-12.90..-0.56 [-30.0369..-9.7206] | it/evals=150/2212 eff=8.2781% N=400 Mono-modal Volume: ~exp(-4.22) Expected Volume: exp(-0.45) Quality: ok param0: +0.0000|******************************************* *******| +1.0000 param1: +1.0e-12|****************************************** **** ** | +1.0e+00 param2: +1.0e-12|************************************** ***** *** | +1.0e+00 ineffective proposal scale (0.217629). shrinking... Z=-14.5(0.00%) | Like=-11.46..-0.20 [-30.0369..-9.7206] | it/evals=196/2764 eff=8.2910% N=400 Z=-14.3(0.00%) | Like=-11.20..-0.20 [-30.0369..-9.7206] | it/evals=200/2812 eff=8.2919% N=400 ineffective proposal scale (0.170132). shrinking... Z=-12.9(0.01%) | Like=-9.98..-0.11 [-30.0369..-9.7206] | it/evals=250/3412 eff=8.3001% N=400 Mono-modal Volume: ~exp(-4.79) * Expected Volume: exp(-0.67) Quality: ok param0: +1.0e-12|*************************************** *** **** **| +1.0e+00 param1: +1.0e-12|****************************************** ** ** | +1.0e+00 param2: +1.0e-12|************************************** ********* | +1.0e+00 Z=-12.4(0.02%) | Like=-9.71..-0.11 [-9.7132..-6.0704] | it/evals=270/3652 eff=8.3026% N=400 Z=-11.9(0.04%) | Like=-9.09..-0.11 [-9.7132..-6.0704] | it/evals=300/4012 eff=8.3056% N=400 Z=-11.0(0.09%) | Like=-8.36..-0.11 [-9.7132..-6.0704] | it/evals=350/4612 eff=8.3096% N=400 Mono-modal Volume: ~exp(-4.83) * Expected Volume: exp(-0.90) Quality: ok param0: +0.0000|******************************************* * ** **| +1.0000 param1: +1.0e-12|****************************************** ** ** | +1.0e+00 param2: +0.0000|************************************** ******** | +1.0000 Z=-10.9(0.11%) | Like=-8.23..-0.11 [-9.7132..-6.0704] | it/evals=360/4732 eff=8.3102% N=400 Z=-10.4(0.19%) | Like=-7.67..-0.11 [-9.7132..-6.0704] | it/evals=397/5176 eff=8.3124% N=400 Z=-10.3(0.20%) | Like=-7.64..-0.11 [-9.7132..-6.0704] | it/evals=400/5212 eff=8.3126% N=400 Z=-9.7(0.31%) | Like=-7.11..-0.11 [-9.7132..-6.0704] | it/evals=447/5776 eff=8.3147% N=400 Mono-modal Volume: ~exp(-4.84) * Expected Volume: exp(-1.12) Quality: ok param0: +0.0000|************************************ ****** * ** *| +1.0000 param1: +1.0e-12|********************************************** ** | +1.0e+00 param2: +1.0e-12|*********************************************** | +1.0e+00 Z=-9.7(0.32%) | Like=-7.09..-0.11 [-9.7132..-6.0704] | it/evals=450/5812 eff=8.3149% N=400 Z=-9.3(0.51%) | Like=-6.67..-0.11 [-9.7132..-6.0704] | it/evals=492/6316 eff=8.3164% N=400 Z=-9.2(0.56%) | Like=-6.57..-0.11 [-9.7132..-6.0704] | it/evals=500/6412 eff=8.3167% N=400 Mono-modal Volume: ~exp(-5.32) * Expected Volume: exp(-1.35) Quality: ok param0: +1.0e-12|************************************ ****** * ** | +1.0e+00 param1: +1.0e-12|************************************************** | +1.0e+00 param2: +1.0e-12|*********************************************** | +1.0e+00 Z=-8.8(0.81%) | Like=-6.14..-0.11 [-9.7132..-6.0704] | it/evals=540/6892 eff=8.3179% N=400 Z=-8.7(0.88%) | Like=-6.02..-0.11 [-6.0299..-4.1927] | it/evals=550/7012 eff=8.3182% N=400 Z=-8.3(1.45%) | Like=-5.49..-0.11 [-6.0299..-4.1927] | it/evals=600/7612 eff=8.3195% N=400 Mono-modal Volume: ~exp(-5.79) * Expected Volume: exp(-1.57) Quality: ok param0: +1.0e-12|******************************************* * ** | +1.0e+00 param1: +0.000|******************************* ***************** | +1.000 param2: +1.0e-12|************************************* ********* | +1.0e+00 Z=-8.0(1.81%) | Like=-5.29..-0.11 [-6.0299..-4.1927] | it/evals=630/7972 eff=8.3201% N=400 Z=-7.9(2.09%) | Like=-5.14..-0.11 [-6.0299..-4.1927] | it/evals=650/8212 eff=8.3205% N=400 Z=-7.5(2.99%) | Like=-4.88..-0.11 [-6.0299..-4.1927] | it/evals=700/8812 eff=8.3214% N=400 Mono-modal Volume: ~exp(-5.79) Expected Volume: exp(-1.80) Quality: ok param0: +0.000|******************************************* * * | +1.000 param1: +0.000|******************************* ***************** | +1.000 param2: +1.0e-12|************************************* ******* * | +1.0e+00 Z=-7.3(3.80%) | Like=-4.60..-0.11 [-6.0299..-4.1927] | it/evals=746/9364 eff=8.3222% N=400 Z=-7.2(3.88%) | Like=-4.57..-0.11 [-6.0299..-4.1927] | it/evals=750/9412 eff=8.3222% N=400 Z=-7.0(5.09%) | Like=-4.31..-0.11 [-6.0299..-4.1927] | it/evals=800/10012 eff=8.3229% N=400 Mono-modal Volume: ~exp(-5.79) Expected Volume: exp(-2.02) Quality: ok param0: +0.000|******************************************* *** | +1.000 param1: +0.00| ******************************* **************** | +1.00 param2: +0.00| ******************************************** | +1.00 Z=-6.8(6.30%) | Like=-4.09..-0.06 [-4.1888..-3.3876] | it/evals=847/10576 eff=8.3235% N=400 Z=-6.7(6.40%) | Like=-4.09..-0.06 [-4.1888..-3.3876] | it/evals=850/10612 eff=8.3235% N=400 Mono-modal Volume: ~exp(-6.66) * Expected Volume: exp(-2.25) Quality: ok param0: +0.00| ***************************************** *** * | +1.00 param1: +0.00| *********************************************** | +1.00 param2: +0.00| ******************************************* * | +1.00 Z=-6.5(7.81%) | Like=-3.82..-0.06 [-4.1888..-3.3876] | it/evals=900/11212 eff=8.3241% N=400 Z=-6.3(9.79%) | Like=-3.54..-0.06 [-4.1888..-3.3876] | it/evals=950/11812 eff=8.3246% N=400 Z=-6.2(10.88%) | Like=-3.33..-0.06 [-3.3856..-3.0945] | it/evals=981/12184 eff=8.3248% N=400 Mono-modal Volume: ~exp(-6.68) * Expected Volume: exp(-2.47) Quality: ok param0: +0.00| ***************************************** *** | +1.00 param1: +0.00| ********************************************** | +1.00 param2: +0.00| ******************************************* * | +1.00 Z=-6.2(11.33%) | Like=-3.29..-0.06 [-3.3856..-3.0945] | it/evals=990/12292 eff=8.3249% N=400 Z=-6.2(11.73%) | Like=-3.22..-0.06 [-3.3856..-3.0945] | it/evals=1000/12412 eff=8.3250% N=400 Z=-6.0(14.31%) | Like=-3.03..-0.06 [-3.0815..-2.9597] | it/evals=1050/13012 eff=8.3254% N=400 Mono-modal Volume: ~exp(-6.68) Expected Volume: exp(-2.70) Quality: ok param0: +0.00| **************************************** * | +1.00 param1: +0.00| ************************* ************ ***** | +1.00 param2: +0.00| * **************************************** * | +1.00 Z=-5.9(16.23%) | Like=-2.88..-0.06 [-2.8824..-2.8808]*| it/evals=1084/13420 eff=8.3257% N=400 Z=-5.8(17.09%) | Like=-2.82..-0.06 [-2.8220..-2.8188]*| it/evals=1100/13612 eff=8.3258% N=400 Z=-5.7(19.80%) | Like=-2.64..-0.06 [-2.6395..-2.6337]*| it/evals=1147/14176 eff=8.3261% N=400 Z=-5.7(19.97%) | Like=-2.62..-0.06 [-2.6183..-2.6180]*| it/evals=1150/14212 eff=8.3261% N=400 Mono-modal Volume: ~exp(-6.68) Expected Volume: exp(-2.92) Quality: ok param0: +0.00| *************************************** * | +1.00 param1: +0.00| ************************************* ***** | +1.00 param2: +0.00| ***************************************** ** | +1.00 Z=-5.6(21.73%) | Like=-2.53..-0.06 [-2.5335..-2.5266]*| it/evals=1179/14560 eff=8.3263% N=400 Z=-5.5(22.87%) | Like=-2.46..-0.03 [-2.4581..-2.4551]*| it/evals=1200/14812 eff=8.3264% N=400 Z=-5.4(25.46%) | Like=-2.29..-0.03 [-2.2860..-2.2860]*| it/evals=1246/15364 eff=8.3267% N=400 Z=-5.4(25.76%) | Like=-2.28..-0.03 [-2.2759..-2.2634] | it/evals=1250/15412 eff=8.3267% N=400 Have 2 modes Volume: ~exp(-7.56) * Expected Volume: exp(-3.15) Quality: ok param0: +0.0| 11111111111111111111122222222222222222 | +1.0 param1: +0.00| 1111111111111111111111222222 222222222 2222 | +1.00 param2: +0.0| 111111111111111111112222222222222222222 | +1.0 Z=-5.4(26.52%) | Like=-2.24..-0.03 [-2.2375..-2.2365]*| it/evals=1260/15532 eff=8.3267% N=400 Z=-5.3(28.38%) | Like=-2.12..-0.03 [-2.1218..-2.1203]*| it/evals=1289/15880 eff=8.3269% N=400 Z=-5.3(28.98%) | Like=-2.09..-0.03 [-2.0884..-2.0785]*| it/evals=1300/16012 eff=8.3269% N=400 Z=-5.2(32.37%) | Like=-1.90..-0.03 [-1.9002..-1.8998]*| it/evals=1348/16588 eff=8.3272% N=400 Have 2 modes Volume: ~exp(-7.76) * Expected Volume: exp(-3.37) Quality: ok param0: +0.0| 11111111111111111111222222222222222222 | +1.0 param1: +0.0| 1111111111111111111 22222222 2222222 2 2 | +1.0 param2: +0.0| 1111111111111111111 222222222222222222 | +1.0 Z=-5.2(32.52%) | Like=-1.90..-0.03 [-1.8971..-1.8900]*| it/evals=1350/16612 eff=8.3272% N=400 Z=-5.0(36.49%) | Like=-1.74..-0.03 [-1.7391..-1.7344]*| it/evals=1399/17200 eff=8.3274% N=400 Z=-5.0(36.57%) | Like=-1.73..-0.03 [-1.7344..-1.7328]*| it/evals=1400/17212 eff=8.3274% N=400 Have 2 modes Volume: ~exp(-7.76) Expected Volume: exp(-3.60) Quality: ok param0: +0.0| 111111111111111111 22222222222222222 | +1.0 param1: +0.0| 111111111111111111 2222222202222222 2 | +1.0 param2: +0.0| 111111111111111111 22222222222222222 | +1.0 Z=-5.0(39.24%) | Like=-1.63..-0.03 [-1.6259..-1.6248]*| it/evals=1440/17692 eff=8.3276% N=400 Z=-5.0(39.85%) | Like=-1.61..-0.03 [-1.6099..-1.6064]*| it/evals=1448/17788 eff=8.3276% N=400 Z=-4.9(39.95%) | Like=-1.60..-0.03 [-1.6049..-1.5962]*| it/evals=1450/17812 eff=8.3276% N=400 Z=-4.9(43.72%) | Like=-1.47..-0.03 [-1.4722..-1.4714]*| it/evals=1500/18412 eff=8.3278% N=400 Have 2 modes Volume: ~exp(-7.76) Expected Volume: exp(-3.82) Quality: ok param0: +0.0| 111111111111111111 2222222222222 22 | +1.0 param1: +0.0| 11111111111111111 22022020222222202 | +1.0 param2: +0.0| 11111111111111111 0 02222222222202 | +1.0 Z=-4.8(47.09%) | Like=-1.39..-0.01 [-1.3923..-1.3917]*| it/evals=1542/18916 eff=8.3279% N=400 Z=-4.8(47.57%) | Like=-1.39..-0.01 [-1.3855..-1.3841]*| it/evals=1550/19012 eff=8.3280% N=400 Z=-4.7(50.85%) | Like=-1.29..-0.01 [-1.2927..-1.2916]*| it/evals=1600/19612 eff=8.3281% N=400 Have 2 modes Volume: ~exp(-8.08) * Expected Volume: exp(-4.05) Quality: ok param0: +0.0| 11111111111111111 22222222 22222 +0.8 | +1.0 param1: +0.0| 1111111111111111 222 222222222 | +1.0 param2: +0.0| 11111111111111111 2222222222222 | +1.0 Z=-4.7(52.25%) | Like=-1.25..-0.01 [-1.2530..-1.2526]*| it/evals=1620/19852 eff=8.3282% N=400 Z=-4.6(54.35%) | Like=-1.21..-0.01 [-1.2056..-1.2053]*| it/evals=1650/20214 eff=8.3274% N=400 Z=-4.6(57.55%) | Like=-1.14..-0.01 [-1.1414..-1.1366]*| it/evals=1700/20814 eff=8.3276% N=400 Have 2 modes Volume: ~exp(-8.08) Expected Volume: exp(-4.27) Quality: ok param0: +0.0| 111111111111111 2202222 22222 +0.8 | +1.0 param1: +0.0| 111111111111111 2220222222222 | +1.0 param2: +0.0| 111111111111111 02222222222222 | +1.0 Z=-4.5(60.25%) | Like=-1.05..-0.01 [-1.0534..-1.0492]*| it/evals=1742/21318 eff=8.3278% N=400 Z=-4.5(60.73%) | Like=-1.04..-0.01 [-1.0385..-1.0382]*| it/evals=1750/21414 eff=8.3278% N=400 Have 2 modes Volume: ~exp(-8.66) * Expected Volume: exp(-4.50) Quality: ok param0: +0.0| 11111111111111 222222222222 +0.8 | +1.0 param1: +0.0| 111111111111111 222222222222 +0.8 | +1.0 param2: +0.0| 111111111111111 2222222222222 | +1.0 Z=-4.5(63.79%) | Like=-0.94..-0.01 [-0.9390..-0.9389]*| it/evals=1800/22014 eff=8.3279% N=400 Z=-4.4(66.53%) | Like=-0.88..-0.01 [-0.8839..-0.8837]*| it/evals=1847/22578 eff=8.3281% N=400 Z=-4.4(66.73%) | Like=-0.88..-0.01 [-0.8812..-0.8805]*| it/evals=1850/22614 eff=8.3281% N=400 Have 2 modes Volume: ~exp(-8.79) * Expected Volume: exp(-4.73) Quality: ok param0: +0.0| 1111111111111 222222222222 +0.8 | +1.0 param1: +0.0| 11111111111111 222222222222 +0.8 | +1.0 param2: +0.0| 1111111111111 222222222222 | +1.0 Z=-4.4(68.99%) | Like=-0.81..-0.01 [-0.8129..-0.8128]*| it/evals=1890/23094 eff=8.3282% N=400 Z=-4.4(69.61%) | Like=-0.81..-0.01 [-0.8058..-0.8049]*| it/evals=1900/23214 eff=8.3282% N=400 Z=-4.4(72.25%) | Like=-0.75..-0.01 [-0.7493..-0.7492]*| it/evals=1948/23790 eff=8.3283% N=400 Z=-4.4(72.36%) | Like=-0.75..-0.01 [-0.7484..-0.7480]*| it/evals=1950/23814 eff=8.3284% N=400 Have 2 modes Volume: ~exp(-8.79) Expected Volume: exp(-4.95) Quality: ok param0: +0.0| 111111111111 222222222222 +0.8 | +1.0 param1: +0.0| 1111111111111 22222222222 +0.8 | +1.0 param2: +0.0| 111111111111 2222222222 +0.8 | +1.0 Z=-4.3(73.99%) | Like=-0.70..-0.01 [-0.7007..-0.7007]*| it/evals=1984/24223 eff=8.3281% N=400 Z=-4.3(74.76%) | Like=-0.68..-0.01 [-0.6830..-0.6808]*| it/evals=2000/24415 eff=8.3281% N=400 Z=-4.3(76.92%) | Like=-0.64..-0.01 [-0.6361..-0.6348]*| it/evals=2047/24979 eff=8.3282% N=400 Z=-4.3(77.04%) | Like=-0.63..-0.01 [-0.6341..-0.6341]*| it/evals=2050/25015 eff=8.3283% N=400 Have 2 modes Volume: ~exp(-9.25) * Expected Volume: exp(-5.18) Quality: ok param0: +0.0| 111111111111 22222222222 +0.8 | +1.0 param1: +0.0| 111111111111 2222222222 +0.8 | +1.0 param2: +0.0| 111111111111 22222222222 +0.8 | +1.0 Z=-4.3(77.92%) | Like=-0.62..-0.01 [-0.6168..-0.6164]*| it/evals=2070/25255 eff=8.3283% N=400 Z=-4.3(79.18%) | Like=-0.59..-0.01 [-0.5915..-0.5908]*| it/evals=2100/25615 eff=8.3284% N=400 Z=-4.2(81.12%) | Like=-0.55..-0.01 [-0.5485..-0.5477]*| it/evals=2148/26191 eff=8.3285% N=400 Z=-4.2(81.19%) | Like=-0.55..-0.01 [-0.5468..-0.5462]*| it/evals=2150/26215 eff=8.3285% N=400 Have 2 modes Volume: ~exp(-9.25) Expected Volume: exp(-5.40) Quality: ok param0: +0.0| 11111111111 2222222222 +0.8 | +1.0 param1: +0.0| 11111111111 022222222 2 +0.8 | +1.0 param2: +0.0| 11111111111 2222222222 +0.8 | +1.0 Z=-4.2(82.51%) | Like=-0.52..-0.01 [-0.5166..-0.5151]*| it/evals=2186/26647 eff=8.3286% N=400 Z=-4.2(83.00%) | Like=-0.50..-0.01 [-0.5043..-0.5043]*| it/evals=2200/26815 eff=8.3286% N=400 Have 2 modes Volume: ~exp(-9.58) * Expected Volume: exp(-5.63) Quality: ok param0: +0.0| 11111111111 222222222 +0.8 | +1.0 param1: +0.0| 1111111111 22222222 +0.8 | +1.0 param2: +0.0| 11111111111 222222222 +0.8 | +1.0 Z=-4.2(84.68%) | Like=-0.46..-0.01 [-0.4649..-0.4648]*| it/evals=2250/27415 eff=8.3287% N=400 Z=-4.2(86.16%) | Like=-0.43..-0.01 [-0.4325..-0.4324]*| it/evals=2300/28015 eff=8.3288% N=400 Have 2 modes Volume: ~exp(-9.63) * Expected Volume: exp(-5.85) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param2: +0.0| 1111111111 222222222 +0.8 | +1.0 Z=-4.2(87.28%) | Like=-0.40..-0.01 [-0.4025..-0.4017]*| it/evals=2340/28495 eff=8.3289% N=400 Z=-4.2(87.54%) | Like=-0.40..-0.01 [-0.3971..-0.3969]*| it/evals=2350/28615 eff=8.3289% N=400 Z=-4.1(88.80%) | Like=-0.37..-0.01 [-0.3664..-0.3656]*| it/evals=2400/29215 eff=8.3290% N=400 Have 2 modes Volume: ~exp(-9.67) * Expected Volume: exp(-6.08) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-4.1(89.49%) | Like=-0.35..-0.01 [-0.3506..-0.3505]*| it/evals=2430/29575 eff=8.3290% N=400 Z=-4.1(89.94%) | Like=-0.34..-0.01 [-0.3366..-0.3359]*| it/evals=2450/29815 eff=8.3291% N=400 Z=-4.1(90.98%) | Like=-0.31..-0.01 [-0.3102..-0.3096]*| it/evals=2500/30415 eff=8.3292% N=400 Have 2 modes Volume: ~exp(-10.18) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-4.1(91.36%) | Like=-0.30..-0.01 [-0.2977..-0.2975]*| it/evals=2520/30655 eff=8.3292% N=400 Z=-4.1(91.91%) | Like=-0.28..-0.01 [-0.2806..-0.2805]*| it/evals=2550/31015 eff=8.3293% N=400 Z=-4.1(92.77%) | Like=-0.26..-0.01 [-0.2624..-0.2624]*| it/evals=2600/31615 eff=8.3293% N=400 Have 2 modes Volume: ~exp(-10.18) * Expected Volume: exp(-6.53) Quality: ok param0: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 2222222 +0.7 | +1.0 param2: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 Z=-4.1(92.94%) | Like=-0.26..-0.01 [-0.2580..-0.2579]*| it/evals=2610/31735 eff=8.3293% N=400 Z=-4.1(93.55%) | Like=-0.24..-0.01 [-0.2415..-0.2406]*| it/evals=2650/32215 eff=8.3294% N=400 Z=-4.1(94.22%) | Like=-0.22..-0.01 [-0.2246..-0.2232]*| it/evals=2698/32791 eff=8.3295% N=400 Have 2 modes Volume: ~exp(-10.52) * Expected Volume: exp(-6.75) Quality: ok param0: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-4.1(94.24%) | Like=-0.22..-0.01 [-0.2232..-0.2227]*| it/evals=2700/32815 eff=8.3295% N=400 Z=-4.1(94.84%) | Like=-0.20..-0.01 [-0.2023..-0.2019]*| it/evals=2748/33391 eff=8.3295% N=400 Z=-4.1(94.87%) | Like=-0.20..-0.01 [-0.2014..-0.2007]*| it/evals=2750/33415 eff=8.3295% N=400 Have 2 modes Volume: ~exp(-11.07) * Expected Volume: exp(-6.98) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.7 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-4.1(95.32%) | Like=-0.19..-0.00 [-0.1892..-0.1889]*| it/evals=2790/33895 eff=8.3296% N=400 Z=-4.1(95.43%) | Like=-0.19..-0.00 [-0.1862..-0.1858]*| it/evals=2800/34015 eff=8.3296% N=400 Z=-4.1(95.91%) | Like=-0.17..-0.00 [-0.1717..-0.1715]*| it/evals=2848/34591 eff=8.3297% N=400 Z=-4.1(95.93%) | Like=-0.17..-0.00 [-0.1715..-0.1708]*| it/evals=2850/34615 eff=8.3297% N=400 Have 2 modes Volume: ~exp(-11.66) * Expected Volume: exp(-7.20) Quality: ok param0: +0.0| +0.2 111111 222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.7 | +1.0 param2: +0.0| +0.2 1111111 22222 +0.7 | +1.0 Z=-4.1(96.20%) | Like=-0.16..-0.00 [-0.1644..-0.1643]*| it/evals=2880/34975 eff=8.3297% N=400 Z=-4.1(96.37%) | Like=-0.16..-0.00 [-0.1583..-0.1583]*| it/evals=2900/35215 eff=8.3297% N=400 Z=-4.1(96.76%) | Like=-0.14..-0.00 [-0.1439..-0.1438]*| it/evals=2948/35791 eff=8.3298% N=400 Z=-4.1(96.78%) | Like=-0.14..-0.00 [-0.1435..-0.1434]*| it/evals=2950/35815 eff=8.3298% N=400 Have 2 modes Volume: ~exp(-11.66) Expected Volume: exp(-7.43) Quality: ok param0: +0.0| +0.2 111111 222222 +0.7 | +1.0 param1: +0.0| +0.3 111111 222222 +0.7 | +1.0 param2: +0.0| +0.2 111111 22222 +0.7 | +1.0 Z=-4.1(97.00%) | Like=-0.14..-0.00 [-0.1371..-0.1367]*| it/evals=2981/36187 eff=8.3298% N=400 Z=-4.1(97.13%) | Like=-0.13..-0.00 [-0.1339..-0.1332]*| it/evals=3000/36415 eff=8.3299% N=400 Z=-4.1(97.44%) | Like=-0.12..-0.00 [-0.1238..-0.1238]*| it/evals=3048/36991 eff=8.3299% N=400 Z=-4.1(97.45%) | Like=-0.12..-0.00 [-0.1233..-0.1232]*| it/evals=3050/37015 eff=8.3299% N=400 Have 2 modes Volume: ~exp(-11.73) * Expected Volume: exp(-7.65) Quality: ok param0: +0.0| +0.3 111111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 222222 +0.7 | +1.0 param2: +0.0| +0.3 111111 22222 +0.7 | +1.0 Z=-4.1(97.51%) | Like=-0.12..-0.00 [-0.1212..-0.1211]*| it/evals=3060/37135 eff=8.3299% N=400 Z=-4.1(97.74%) | Like=-0.11..-0.00 [-0.1138..-0.1127]*| it/evals=3100/37615 eff=8.3300% N=400 Have 2 modes Volume: ~exp(-12.08) * Expected Volume: exp(-7.88) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(97.99%) | Like=-0.10..-0.00 [-0.1045..-0.1044]*| it/evals=3150/38217 eff=8.3296% N=400 Z=-4.0(98.22%) | Like=-0.10..-0.00 [-0.0956..-0.0955]*| it/evals=3200/38817 eff=8.3296% N=400 Have 2 modes Volume: ~exp(-12.08) Expected Volume: exp(-8.10) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.38%) | Like=-0.09..-0.00 [-0.0901..-0.0900]*| it/evals=3240/39298 eff=8.3295% N=400 Z=-4.0(98.42%) | Like=-0.09..-0.00 [-0.0888..-0.0883]*| it/evals=3250/39418 eff=8.3295% N=400 Z=-4.0(98.60%) | Like=-0.08..-0.00 [-0.0813..-0.0812]*| it/evals=3300/40018 eff=8.3295% N=400 Have 2 modes Volume: ~exp(-12.47) * Expected Volume: exp(-8.33) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.70%) | Like=-0.08..-0.00 [-0.0774..-0.0773]*| it/evals=3330/40378 eff=8.3296% N=400 Z=-4.0(98.76%) | Like=-0.07..-0.00 [-0.0747..-0.0747]*| it/evals=3350/40618 eff=8.3296% N=400 Z=-4.0(98.90%) | Like=-0.07..-0.00 [-0.0675..-0.0675]*| it/evals=3400/41218 eff=8.3297% N=400 Have 2 modes Volume: ~exp(-12.47) Expected Volume: exp(-8.55) Quality: ok param0: +0.0| +0.3 11111 2222 +0.7 | +1.0 param1: +0.0| +0.3 11111 2222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(99.00%) | Like=-0.06..-0.00 [-0.0628..-0.0626]*| it/evals=3437/41663 eff=8.3295% N=400 [ultranest] Explored until L=-0.0002 [ultranest] Likelihood function evaluations: 41675 [ultranest] logZ = -4.031 +- 0.05895 [ultranest] Effective samples strategy satisfied (ESS = 2002.7, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.05 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [ultranest] logZ error budget: single: 0.08 bs:0.06 tail:0.01 total:0.06 required:<0.50 [ultranest] done iterating. logZ = -4.027 +- 0.106 single instance: logZ = -4.027 +- 0.077 bootstrapped : logZ = -4.031 +- 0.106 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▂▃▃▄▄▅▇▇▇▇▆▆▆▅▃▂▂▁▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁│1.00 0.35 +- 0.17 param1 : 0.00 │▁▁▂▂▂▃▅▅▆▇▇▇▇▇▅▄▃▃▁▂▁▁▁▁▁▂▂▂▁▁▁▁▁▁▁▁▁▁ │1.00 0.34 +- 0.18 param2 : 0.00 │▁▁▁▁▂▃▄▄▇▇▇▇▇▆▅▄▃▃▂▂▁▁▁▁▁▂▁▂▁▂▁▁▁▁▁▁▁▁ │1.00 0.35 +- 0.18 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=412, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-30.04, Lmax=-1.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=1012, regioncalls=0, ndraw=128, logz=-25.06, remainder_fraction=100.0000%, Lmin=-21.40, Lmax=-1.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=1492, regioncalls=0, ndraw=128, logz=-21.02, remainder_fraction=100.0000%, Lmin=-16.93, Lmax=-1.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=1612, regioncalls=0, ndraw=128, logz=-20.00, remainder_fraction=100.0000%, Lmin=-16.02, Lmax=-1.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=2212, regioncalls=0, ndraw=128, logz=-16.22, remainder_fraction=99.9994%, Lmin=-12.90, Lmax=-0.56 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=196, ncalls=2764, regioncalls=0, ndraw=128, logz=-14.48, remainder_fraction=99.9968%, Lmin=-11.46, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=2812, regioncalls=0, ndraw=128, logz=-14.34, remainder_fraction=99.9963%, Lmin=-11.20, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=3412, regioncalls=0, ndraw=128, logz=-12.87, remainder_fraction=99.9855%, Lmin=-9.98, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=3652, regioncalls=0, ndraw=128, logz=-12.43, remainder_fraction=99.9780%, Lmin=-9.71, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=4012, regioncalls=0, ndraw=128, logz=-11.87, remainder_fraction=99.9605%, Lmin=-9.09, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=4612, regioncalls=0, ndraw=128, logz=-11.04, remainder_fraction=99.9084%, Lmin=-8.36, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=4732, regioncalls=0, ndraw=128, logz=-10.89, remainder_fraction=99.8920%, Lmin=-8.23, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=397, ncalls=5176, regioncalls=0, ndraw=128, logz=-10.37, remainder_fraction=99.8117%, Lmin=-7.67, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=5212, regioncalls=0, ndraw=128, logz=-10.33, remainder_fraction=99.8031%, Lmin=-7.64, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=447, ncalls=5776, regioncalls=0, ndraw=128, logz=-9.75, remainder_fraction=99.6907%, Lmin=-7.11, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=5812, regioncalls=0, ndraw=128, logz=-9.71, remainder_fraction=99.6800%, Lmin=-7.09, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=492, ncalls=6316, regioncalls=0, ndraw=128, logz=-9.27, remainder_fraction=99.4866%, Lmin=-6.67, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=6412, regioncalls=0, ndraw=128, logz=-9.19, remainder_fraction=99.4440%, Lmin=-6.57, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=6892, regioncalls=0, ndraw=128, logz=-8.81, remainder_fraction=99.1854%, Lmin=-6.14, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=7012, regioncalls=0, ndraw=128, logz=-8.71, remainder_fraction=99.1150%, Lmin=-6.02, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=7612, regioncalls=0, ndraw=128, logz=-8.25, remainder_fraction=98.5485%, Lmin=-5.49, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=7972, regioncalls=0, ndraw=128, logz=-8.00, remainder_fraction=98.1910%, Lmin=-5.29, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=8212, regioncalls=0, ndraw=128, logz=-7.85, remainder_fraction=97.9083%, Lmin=-5.14, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=8812, regioncalls=0, ndraw=128, logz=-7.52, remainder_fraction=97.0112%, Lmin=-4.88, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=746, ncalls=9364, regioncalls=0, ndraw=128, logz=-7.25, remainder_fraction=96.1999%, Lmin=-4.60, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=9412, regioncalls=0, ndraw=128, logz=-7.23, remainder_fraction=96.1230%, Lmin=-4.57, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=10012, regioncalls=0, ndraw=128, logz=-6.97, remainder_fraction=94.9092%, Lmin=-4.31, Lmax=-0.11 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=847, ncalls=10576, regioncalls=0, ndraw=128, logz=-6.76, remainder_fraction=93.6954%, Lmin=-4.09, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=10612, regioncalls=0, ndraw=128, logz=-6.75, remainder_fraction=93.6019%, Lmin=-4.09, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=11212, regioncalls=0, ndraw=128, logz=-6.54, remainder_fraction=92.1873%, Lmin=-3.82, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=11812, regioncalls=0, ndraw=128, logz=-6.34, remainder_fraction=90.2091%, Lmin=-3.54, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=981, ncalls=12184, regioncalls=0, ndraw=128, logz=-6.23, remainder_fraction=89.1195%, Lmin=-3.33, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=12292, regioncalls=0, ndraw=128, logz=-6.19, remainder_fraction=88.6691%, Lmin=-3.29, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=12412, regioncalls=0, ndraw=128, logz=-6.15, remainder_fraction=88.2654%, Lmin=-3.22, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=13012, regioncalls=0, ndraw=128, logz=-5.97, remainder_fraction=85.6883%, Lmin=-3.03, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1084, ncalls=13420, regioncalls=0, ndraw=128, logz=-5.86, remainder_fraction=83.7686%, Lmin=-2.88, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=13612, regioncalls=0, ndraw=128, logz=-5.81, remainder_fraction=82.9091%, Lmin=-2.82, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1147, ncalls=14176, regioncalls=0, ndraw=128, logz=-5.66, remainder_fraction=80.2047%, Lmin=-2.64, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=14212, regioncalls=0, ndraw=128, logz=-5.65, remainder_fraction=80.0296%, Lmin=-2.62, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1179, ncalls=14560, regioncalls=0, ndraw=128, logz=-5.57, remainder_fraction=78.2662%, Lmin=-2.53, Lmax=-0.06 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=14812, regioncalls=0, ndraw=128, logz=-5.52, remainder_fraction=77.1306%, Lmin=-2.46, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1246, ncalls=15364, regioncalls=0, ndraw=128, logz=-5.40, remainder_fraction=74.5381%, Lmin=-2.29, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=15412, regioncalls=0, ndraw=128, logz=-5.39, remainder_fraction=74.2389%, Lmin=-2.28, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=15532, regioncalls=0, ndraw=128, logz=-5.36, remainder_fraction=73.4801%, Lmin=-2.24, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1289, ncalls=15880, regioncalls=0, ndraw=128, logz=-5.29, remainder_fraction=71.6228%, Lmin=-2.12, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=16012, regioncalls=0, ndraw=128, logz=-5.27, remainder_fraction=71.0158%, Lmin=-2.09, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1348, ncalls=16588, regioncalls=0, ndraw=128, logz=-5.16, remainder_fraction=67.6268%, Lmin=-1.90, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=16612, regioncalls=0, ndraw=128, logz=-5.15, remainder_fraction=67.4832%, Lmin=-1.90, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1399, ncalls=17200, regioncalls=0, ndraw=128, logz=-5.05, remainder_fraction=63.5150%, Lmin=-1.74, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=17212, regioncalls=0, ndraw=128, logz=-5.05, remainder_fraction=63.4313%, Lmin=-1.73, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=17692, regioncalls=0, ndraw=128, logz=-4.97, remainder_fraction=60.7556%, Lmin=-1.63, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1448, ncalls=17788, regioncalls=0, ndraw=128, logz=-4.95, remainder_fraction=60.1502%, Lmin=-1.61, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=17812, regioncalls=0, ndraw=128, logz=-4.95, remainder_fraction=60.0452%, Lmin=-1.60, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=18412, regioncalls=0, ndraw=128, logz=-4.86, remainder_fraction=56.2831%, Lmin=-1.47, Lmax=-0.03 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1542, ncalls=18916, regioncalls=0, ndraw=128, logz=-4.79, remainder_fraction=52.9109%, Lmin=-1.39, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=19012, regioncalls=0, ndraw=128, logz=-4.77, remainder_fraction=52.4290%, Lmin=-1.39, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=19612, regioncalls=0, ndraw=128, logz=-4.70, remainder_fraction=49.1489%, Lmin=-1.29, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=19852, regioncalls=0, ndraw=128, logz=-4.67, remainder_fraction=47.7467%, Lmin=-1.25, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=20214, regioncalls=0, ndraw=128, logz=-4.64, remainder_fraction=45.6547%, Lmin=-1.21, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=20814, regioncalls=0, ndraw=128, logz=-4.58, remainder_fraction=42.4477%, Lmin=-1.14, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1742, ncalls=21318, regioncalls=0, ndraw=128, logz=-4.53, remainder_fraction=39.7522%, Lmin=-1.05, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=21414, regioncalls=0, ndraw=128, logz=-4.52, remainder_fraction=39.2742%, Lmin=-1.04, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=22014, regioncalls=0, ndraw=128, logz=-4.47, remainder_fraction=36.2143%, Lmin=-0.94, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1847, ncalls=22578, regioncalls=0, ndraw=128, logz=-4.43, remainder_fraction=33.4746%, Lmin=-0.88, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=22614, regioncalls=0, ndraw=128, logz=-4.43, remainder_fraction=33.2721%, Lmin=-0.88, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=23094, regioncalls=0, ndraw=128, logz=-4.40, remainder_fraction=31.0105%, Lmin=-0.81, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=23214, regioncalls=0, ndraw=128, logz=-4.39, remainder_fraction=30.3880%, Lmin=-0.81, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1948, ncalls=23790, regioncalls=0, ndraw=128, logz=-4.35, remainder_fraction=27.7482%, Lmin=-0.75, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=23814, regioncalls=0, ndraw=128, logz=-4.35, remainder_fraction=27.6380%, Lmin=-0.75, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1984, ncalls=24223, regioncalls=0, ndraw=128, logz=-4.33, remainder_fraction=26.0145%, Lmin=-0.70, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=24415, regioncalls=0, ndraw=128, logz=-4.32, remainder_fraction=25.2361%, Lmin=-0.68, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2047, ncalls=24979, regioncalls=0, ndraw=128, logz=-4.29, remainder_fraction=23.0772%, Lmin=-0.64, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=25015, regioncalls=0, ndraw=128, logz=-4.29, remainder_fraction=22.9561%, Lmin=-0.63, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=25255, regioncalls=0, ndraw=128, logz=-4.28, remainder_fraction=22.0789%, Lmin=-0.62, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=25615, regioncalls=0, ndraw=128, logz=-4.26, remainder_fraction=20.8188%, Lmin=-0.59, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2148, ncalls=26191, regioncalls=0, ndraw=128, logz=-4.24, remainder_fraction=18.8849%, Lmin=-0.55, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=26215, regioncalls=0, ndraw=128, logz=-4.24, remainder_fraction=18.8076%, Lmin=-0.55, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2186, ncalls=26647, regioncalls=0, ndraw=128, logz=-4.22, remainder_fraction=17.4904%, Lmin=-0.52, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=26815, regioncalls=0, ndraw=128, logz=-4.21, remainder_fraction=16.9984%, Lmin=-0.50, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=27415, regioncalls=0, ndraw=128, logz=-4.19, remainder_fraction=15.3219%, Lmin=-0.46, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=28015, regioncalls=0, ndraw=128, logz=-4.18, remainder_fraction=13.8385%, Lmin=-0.43, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=28495, regioncalls=0, ndraw=128, logz=-4.16, remainder_fraction=12.7169%, Lmin=-0.40, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=28615, regioncalls=0, ndraw=128, logz=-4.16, remainder_fraction=12.4583%, Lmin=-0.40, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=29215, regioncalls=0, ndraw=128, logz=-4.15, remainder_fraction=11.2007%, Lmin=-0.37, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=29575, regioncalls=0, ndraw=128, logz=-4.14, remainder_fraction=10.5052%, Lmin=-0.35, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=29815, regioncalls=0, ndraw=128, logz=-4.13, remainder_fraction=10.0624%, Lmin=-0.34, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=30415, regioncalls=0, ndraw=128, logz=-4.12, remainder_fraction=9.0180%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=30655, regioncalls=0, ndraw=128, logz=-4.12, remainder_fraction=8.6427%, Lmin=-0.30, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=31015, regioncalls=0, ndraw=128, logz=-4.11, remainder_fraction=8.0937%, Lmin=-0.28, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=31615, regioncalls=0, ndraw=128, logz=-4.10, remainder_fraction=7.2269%, Lmin=-0.26, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=31735, regioncalls=0, ndraw=128, logz=-4.10, remainder_fraction=7.0617%, Lmin=-0.26, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=32215, regioncalls=0, ndraw=128, logz=-4.09, remainder_fraction=6.4500%, Lmin=-0.24, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2698, ncalls=32791, regioncalls=0, ndraw=128, logz=-4.09, remainder_fraction=5.7834%, Lmin=-0.22, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=32815, regioncalls=0, ndraw=128, logz=-4.09, remainder_fraction=5.7565%, Lmin=-0.22, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2748, ncalls=33391, regioncalls=0, ndraw=128, logz=-4.08, remainder_fraction=5.1562%, Lmin=-0.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=33415, regioncalls=0, ndraw=128, logz=-4.08, remainder_fraction=5.1326%, Lmin=-0.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=33895, regioncalls=0, ndraw=128, logz=-4.08, remainder_fraction=4.6763%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=34015, regioncalls=0, ndraw=128, logz=-4.07, remainder_fraction=4.5702%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2848, ncalls=34591, regioncalls=0, ndraw=128, logz=-4.07, remainder_fraction=4.0875%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=34615, regioncalls=0, ndraw=128, logz=-4.07, remainder_fraction=4.0697%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=34975, regioncalls=0, ndraw=128, logz=-4.07, remainder_fraction=3.7990%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=35215, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=3.6267%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2948, ncalls=35791, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=3.2398%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=35815, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=3.2241%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2981, ncalls=36187, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=2.9963%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=36415, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=2.8655%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3048, ncalls=36991, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=2.5577%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=37015, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=2.5458%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=37135, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=2.4872%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=37615, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=2.2610%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=38217, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=2.0051%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=38817, regioncalls=0, ndraw=128, logz=-4.05, remainder_fraction=1.7784%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=39298, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.6161%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3250, ncalls=39418, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.5776%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3300, ncalls=40018, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.3983%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3330, ncalls=40378, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.3003%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3350, ncalls=40618, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.2391%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=41218, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.0976%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3437, ncalls=41663, regioncalls=0, ndraw=128, logz=-4.04, remainder_fraction=1.0033%, Lmin=-0.06, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.0002 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 41675 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -4.031 +- 0.05895 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 2002.7, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.05 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.08 bs:0.06 tail:0.01 total:0.06 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_stepsampling.py::test_stepsampler_regionmh | 9.00 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.13) * Expected Volume: exp(0.00) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-inf(0.00%) | Like=-28.44..-0.20 [-28.4416..-8.9505] | it/evals=0/412 eff=0.0000% N=400 ineffective proposal scale (1.26906). shrinking... ineffective proposal scale (1.26906). shrinking... ineffective proposal scale (1.17216). shrinking... ineffective proposal scale (1.08266). shrinking... ineffective proposal scale (1.26906). shrinking... ineffective proposal scale (1.74364). shrinking... ineffective proposal scale (1.74364). shrinking... ineffective proposal scale (1.74364). shrinking... ineffective proposal scale (1.74364). shrinking... ineffective proposal scale (1.61051). shrinking... ineffective proposal scale (1.62335). shrinking... ineffective proposal scale (1.64935). shrinking... ineffective proposal scale (1.52341). shrinking... ineffective proposal scale (1.29966). shrinking... ineffective proposal scale (1.29966). shrinking... ineffective proposal scale (1.84333). shrinking... ineffective proposal scale (1.70259). shrinking... ineffective proposal scale (1.70259). shrinking... ineffective proposal scale (1.57259). shrinking... ineffective proposal scale (1.99571). shrinking... ineffective proposal scale (1.84333). shrinking... ineffective proposal scale (1.70259). shrinking... ineffective proposal scale (1.70259). shrinking... ineffective proposal scale (1.85803). shrinking... ineffective proposal scale (1.87285). shrinking... ineffective proposal scale (1.72985). shrinking... ineffective proposal scale (1.59777). shrinking... ineffective proposal scale (1.47577). shrinking... ineffective proposal scale (1.3631). shrinking... ineffective proposal scale (1.25902). shrinking... ineffective proposal scale (1.17216). shrinking... Z=-24.0(0.00%) | Like=-20.02..-0.20 [-28.4416..-8.9505] | it/evals=50/1012 eff=8.1699% N=400 ineffective proposal scale (1.53556). shrinking... ineffective proposal scale (1.44103). shrinking... ineffective proposal scale (1.57259). shrinking... ineffective proposal scale (1.58513). shrinking... ineffective proposal scale (1.35231). shrinking... ineffective proposal scale (1.61051). shrinking... ineffective proposal scale (1.62335). shrinking... ineffective proposal scale (1.4994). shrinking... ineffective proposal scale (1.77156). shrinking... ineffective proposal scale (1.78569). shrinking... ineffective proposal scale (1.82875). shrinking... ineffective proposal scale (1.70259). shrinking... ineffective proposal scale (1.71616). shrinking... ineffective proposal scale (1.59777). shrinking... ineffective proposal scale (1.27918). shrinking... Mono-modal Volume: ~exp(-4.53) * Expected Volume: exp(-0.23) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-20.2(0.00%) | Like=-16.40..-0.20 [-28.4416..-8.9505] | it/evals=90/1492 eff=8.2418% N=400 ineffective proposal scale (1.41832). shrinking... Z=-19.5(0.00%) | Like=-15.84..-0.20 [-28.4416..-8.9505] | it/evals=100/1612 eff=8.2508% N=400 ineffective proposal scale (1.22937). shrinking... ineffective proposal scale (0.923647). shrinking... ineffective proposal scale (0.880663). shrinking... Z=-16.5(0.00%) | Like=-12.89..-0.20 [-28.4416..-8.9505] | it/evals=150/2212 eff=8.2781% N=400 ineffective proposal scale (0.819908). shrinking... ineffective proposal scale (0.448344). shrinking... Mono-modal Volume: ~exp(-4.53) Expected Volume: exp(-0.45) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-14.2(0.00%) | Like=-10.76..-0.20 [-28.4416..-8.9505] | it/evals=190/2692 eff=8.2897% N=400 Z=-13.8(0.00%) | Like=-10.66..-0.20 [-28.4416..-8.9505] | it/evals=200/2812 eff=8.2919% N=400 Z=-12.4(0.01%) | Like=-9.47..-0.20 [-28.4416..-8.9505] | it/evals=249/3400 eff=8.3000% N=400 Z=-12.4(0.01%) | Like=-9.44..-0.20 [-28.4416..-8.9505] | it/evals=250/3412 eff=8.3001% N=400 Mono-modal Volume: ~exp(-4.53) Expected Volume: exp(-0.67) Quality: ok param0: +0.00|***************************************************| +1.00 param1: +0.000|************************************************** | +1.000 param2: +0.000|***************************************************| +1.000 Z=-11.5(0.03%) | Like=-8.50..-0.20 [-8.9485..-4.8498] | it/evals=289/3881 eff=8.3022% N=400 Z=-11.2(0.04%) | Like=-8.20..-0.20 [-8.9485..-4.8498] | it/evals=300/4013 eff=8.3033% N=400 Z=-10.1(0.14%) | Like=-7.23..-0.17 [-8.9485..-4.8498] | it/evals=350/4614 eff=8.3056% N=400 Mono-modal Volume: ~exp(-4.70) * Expected Volume: exp(-0.90) Quality: ok param0: +0.00|************************************************ **| +1.00 param1: +0.000|************************************************** | +1.000 param2: +0.000|***************************************************| +1.000 Z=-9.9(0.16%) | Like=-7.11..-0.17 [-8.9485..-4.8498] | it/evals=360/4734 eff=8.3064% N=400 Z=-9.3(0.29%) | Like=-6.72..-0.17 [-8.9485..-4.8498] | it/evals=400/5214 eff=8.3091% N=400 Mono-modal Volume: ~exp(-5.10) * Expected Volume: exp(-1.12) Quality: ok param0: +0.000|************************************************** | +1.000 param1: +0.000|************************************************** | +1.000 param2: +0.000|************************************************* | +1.000 Z=-8.7(0.55%) | Like=-6.05..-0.17 [-8.9485..-4.8498] | it/evals=450/5814 eff=8.3118% N=400 Z=-8.1(0.94%) | Like=-5.43..-0.10 [-8.9485..-4.8498] | it/evals=499/6402 eff=8.3139% N=400 Z=-8.1(0.95%) | Like=-5.43..-0.10 [-8.9485..-4.8498] | it/evals=500/6414 eff=8.3139% N=400 Mono-modal Volume: ~exp(-5.49) * Expected Volume: exp(-1.35) Quality: ok param0: +0.000|********************************************* **** | +1.000 param1: +0.000|************************************************** | +1.000 param2: +0.000|************************************************* | +1.000 Z=-7.7(1.45%) | Like=-4.92..-0.10 [-8.9485..-4.8498] | it/evals=540/6894 eff=8.3154% N=400 Z=-7.6(1.61%) | Like=-4.85..-0.10 [-4.8456..-3.4991] | it/evals=550/7014 eff=8.3157% N=400 Z=-7.1(2.65%) | Like=-4.40..-0.10 [-4.8456..-3.4991] | it/evals=600/7614 eff=8.3172% N=400 Mono-modal Volume: ~exp(-5.49) Expected Volume: exp(-1.57) Quality: ok param0: +0.00| ******************************************** **** | +1.00 param1: +0.00| *********************************************** * | +1.00 param2: +0.00| *********************************************** | +1.00 Z=-6.8(3.98%) | Like=-4.11..-0.10 [-4.8456..-3.4991] | it/evals=650/8214 eff=8.3184% N=400 Z=-6.4(5.33%) | Like=-3.80..-0.08 [-4.8456..-3.4991] | it/evals=700/8814 eff=8.3195% N=400 Mono-modal Volume: ~exp(-5.51) * Expected Volume: exp(-1.80) Quality: ok param0: +0.00| ********************************************** | +1.00 param1: +0.00| ********************************************* | +1.00 param2: +0.00| *********************************************** | +1.00 Z=-6.3(6.08%) | Like=-3.63..-0.08 [-4.8456..-3.4991] | it/evals=720/9054 eff=8.3199% N=400 Z=-6.1(7.27%) | Like=-3.45..-0.08 [-3.4967..-3.1168] | it/evals=750/9414 eff=8.3204% N=400 Z=-5.9(9.45%) | Like=-3.22..-0.02 [-3.4967..-3.1168] | it/evals=800/10015 eff=8.3203% N=400 Mono-modal Volume: ~exp(-5.89) * Expected Volume: exp(-2.02) Quality: ok param0: +0.00| ********************************************* | +1.00 param1: +0.00| ********************************************* | +1.00 param2: +0.00| ********************************************* | +1.00 Z=-5.8(9.89%) | Like=-3.15..-0.02 [-3.4967..-3.1168] | it/evals=810/10135 eff=8.3205% N=400 Z=-5.6(11.78%) | Like=-2.99..-0.02 [-3.1085..-2.9617] | it/evals=850/10615 eff=8.3211% N=400 Mono-modal Volume: ~exp(-6.00) * Expected Volume: exp(-2.25) Quality: ok positive degeneracy between param2 and param1: rho=0.75 param0: +0.00| ****************************************** | +1.00 param1: +0.00| ******************************************* | +1.00 param2: +0.00| ******************************************* | +1.00 Z=-5.4(14.48%) | Like=-2.74..-0.02 [-2.7355..-2.7280]*| it/evals=900/11215 eff=8.3218% N=400 Z=-5.3(17.63%) | Like=-2.50..-0.02 [-2.4997..-2.4984]*| it/evals=950/11815 eff=8.3224% N=400 Have 2 modes Volume: ~exp(-6.24) * Expected Volume: exp(-2.47) Quality: ok param0: +0.00| 11111111111111111111 222222222222222222222 | +1.00 param1: +0.00| 1111111111111111111112222222222222222222222 | +1.00 param2: +0.00| 111111111111111111111222222222222222222222 | +1.00 Z=-5.1(20.12%) | Like=-2.36..-0.02 [-2.3603..-2.3592]*| it/evals=990/12295 eff=8.3228% N=400 Z=-5.1(20.62%) | Like=-2.33..-0.02 [-2.3273..-2.3250]*| it/evals=1000/12415 eff=8.3229% N=400 Z=-4.9(23.80%) | Like=-2.15..-0.02 [-2.1541..-2.1518]*| it/evals=1050/13015 eff=8.3234% N=400 Have 2 modes Volume: ~exp(-6.64) * Expected Volume: exp(-2.70) Quality: ok positive degeneracy between param1 and param0: rho=0.76 param0: +0.0| 1111111111111111111 222222222222222222222 | +1.0 param1: +0.0| 11111111111111111111222222222222222222222 | +1.0 param2: +0.0| 11111111111111111111 2222222222222222222 | +1.0 Z=-4.8(25.85%) | Like=-2.07..-0.02 [-2.0652..-2.0598]*| it/evals=1080/13375 eff=8.3237% N=400 Z=-4.8(27.24%) | Like=-1.98..-0.02 [-1.9844..-1.9809]*| it/evals=1100/13615 eff=8.3239% N=400 Z=-4.7(30.66%) | Like=-1.85..-0.02 [-1.8495..-1.8307] | it/evals=1146/14168 eff=8.3236% N=400 Z=-4.7(30.95%) | Like=-1.83..-0.02 [-1.8261..-1.8197]*| it/evals=1150/14216 eff=8.3237% N=400 Have 2 modes Volume: ~exp(-6.64) Expected Volume: exp(-2.92) Quality: ok param0: +0.0| 1111111111111111110 22222222222222222222 | +1.0 param1: +0.0| 11111111111111111111 22222222222222222222 | +1.0 param2: +0.0| 1111111111111111111 2222222222222222222 | +1.0 Z=-4.6(33.35%) | Like=-1.74..-0.02 [-1.7387..-1.7340]*| it/evals=1187/14661 eff=8.3234% N=400 Z=-4.6(34.10%) | Like=-1.71..-0.02 [-1.7055..-1.7016]*| it/evals=1200/14817 eff=8.3235% N=400 Z=-4.5(37.86%) | Like=-1.57..-0.02 [-1.5676..-1.5654]*| it/evals=1247/15382 eff=8.3233% N=400 Z=-4.4(38.12%) | Like=-1.56..-0.02 [-1.5623..-1.5579]*| it/evals=1250/15418 eff=8.3233% N=400 Have 2 modes Volume: ~exp(-7.07) * Expected Volume: exp(-3.15) Quality: ok param0: +0.0| 111111111111111111 222222222222222222 | +1.0 param1: +0.0| 111111111111111111 222222222222222222 | +1.0 param2: +0.0| 111111111111111111 222222222222222222 | +1.0 Z=-4.4(38.86%) | Like=-1.54..-0.02 [-1.5369..-1.5335]*| it/evals=1260/15538 eff=8.3234% N=400 Z=-4.4(41.57%) | Like=-1.45..-0.02 [-1.4458..-1.4450]*| it/evals=1300/16018 eff=8.3237% N=400 Z=-4.3(44.97%) | Like=-1.32..-0.02 [-1.3240..-1.3234]*| it/evals=1347/16582 eff=8.3241% N=400 Have 2 modes Volume: ~exp(-7.39) * Expected Volume: exp(-3.37) Quality: ok param0: +0.0| 11111111111111111 222222222222222 | +1.0 param1: +0.0| 1111111111111111 2222222222222222 | +1.0 param2: +0.0| 1111111111111111 222222222222222 | +1.0 Z=-4.3(45.20%) | Like=-1.32..-0.02 [-1.3211..-1.3210]*| it/evals=1350/16618 eff=8.3241% N=400 Z=-4.2(48.65%) | Like=-1.25..-0.02 [-1.2466..-1.2461]*| it/evals=1396/17170 eff=8.3244% N=400 Z=-4.2(48.91%) | Like=-1.24..-0.02 [-1.2362..-1.2340]*| it/evals=1400/17218 eff=8.3244% N=400 Have 2 modes Volume: ~exp(-7.39) Expected Volume: exp(-3.60) Quality: ok param0: +0.0| 1111111111111111 222222222222222 | +1.0 param1: +0.0| 111111111111111 222222222222222 | +1.0 param2: +0.0| 111111111111111 22222222222222 | +1.0 Z=-4.1(51.81%) | Like=-1.14..-0.01 [-1.1366..-1.1351]*| it/evals=1440/17698 eff=8.3247% N=400 Z=-4.1(52.50%) | Like=-1.13..-0.01 [-1.1261..-1.1256]*| it/evals=1450/17818 eff=8.3247% N=400 Z=-4.1(55.58%) | Like=-1.04..-0.01 [-1.0416..-1.0394]*| it/evals=1498/18395 eff=8.3245% N=400 Z=-4.1(55.74%) | Like=-1.04..-0.01 [-1.0390..-1.0388]*| it/evals=1500/18419 eff=8.3245% N=400 Have 2 modes Volume: ~exp(-7.84) * Expected Volume: exp(-3.82) Quality: ok param0: +0.0| 111111111111111 222222222222222 | +1.0 param1: +0.0| 11111111111111 22222222222222 | +1.0 param2: +0.0| 11111111111111 22222222222222 | +1.0 Z=-4.0(57.85%) | Like=-0.99..-0.01 [-0.9895..-0.9878]*| it/evals=1530/18779 eff=8.3247% N=400 Z=-4.0(59.20%) | Like=-0.96..-0.01 [-0.9563..-0.9558]*| it/evals=1550/19019 eff=8.3248% N=400 Z=-3.9(62.31%) | Like=-0.86..-0.01 [-0.8642..-0.8608]*| it/evals=1600/19619 eff=8.3251% N=400 Have 2 modes Volume: ~exp(-8.48) * Expected Volume: exp(-4.05) Quality: ok param0: +0.0| 11111111111111 22222222222222 | +1.0 param1: +0.0| 111111111111 2222222222222 | +1.0 param2: +0.0| 1111111111111 2222222222222 | +1.0 Z=-3.9(63.51%) | Like=-0.83..-0.01 [-0.8293..-0.8276]*| it/evals=1620/19859 eff=8.3252% N=400 Z=-3.9(65.45%) | Like=-0.79..-0.01 [-0.7910..-0.7892]*| it/evals=1650/20219 eff=8.3253% N=400 Z=-3.9(68.48%) | Like=-0.72..-0.01 [-0.7167..-0.7149]*| it/evals=1700/20819 eff=8.3256% N=400 Have 2 modes Volume: ~exp(-8.48) Expected Volume: exp(-4.27) Quality: ok param0: +0.0| 111111111111 222222222222 | +1.0 param1: +0.0| 111111111111 22222222222 +0.8 | +1.0 param2: +0.0| 111111111111 022222222222 +0.8 | +1.0 Z=-3.8(70.96%) | Like=-0.66..-0.01 [-0.6568..-0.6550]*| it/evals=1744/21347 eff=8.3258% N=400 Z=-3.8(71.29%) | Like=-0.65..-0.01 [-0.6492..-0.6481]*| it/evals=1750/21419 eff=8.3258% N=400 Have 2 modes Volume: ~exp(-8.68) * Expected Volume: exp(-4.50) Quality: ok param0: +0.0| 11111111111 22222222222 +0.8 | +1.0 param1: +0.0| 11111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-3.8(73.94%) | Like=-0.59..-0.01 [-0.5916..-0.5905]*| it/evals=1800/22019 eff=8.3260% N=400 Z=-3.8(76.41%) | Like=-0.54..-0.01 [-0.5416..-0.5414]*| it/evals=1850/22619 eff=8.3262% N=400 Have 2 modes Volume: ~exp(-8.80) * Expected Volume: exp(-4.73) Quality: ok param0: +0.0| 1111111111 22222222222 +0.8 | +1.0 param1: +0.0| 1111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-3.7(78.20%) | Like=-0.50..-0.01 [-0.5016..-0.4995]*| it/evals=1890/23099 eff=8.3264% N=400 Z=-3.7(78.64%) | Like=-0.49..-0.01 [-0.4937..-0.4928]*| it/evals=1900/23219 eff=8.3264% N=400 Z=-3.7(80.74%) | Like=-0.44..-0.01 [-0.4427..-0.4397]*| it/evals=1950/23819 eff=8.3266% N=400 Have 2 modes Volume: ~exp(-9.34) * Expected Volume: exp(-4.95) Quality: ok param0: +0.0| 1111111111 2222222222 +0.8 | +1.0 param1: +0.0| 1111111111 222222222 +0.8 | +1.0 param2: +0.0| 1111111111 222222222 +0.8 | +1.0 Z=-3.7(81.89%) | Like=-0.42..-0.01 [-0.4196..-0.4193]*| it/evals=1980/24179 eff=8.3267% N=400 Z=-3.7(82.61%) | Like=-0.41..-0.01 [-0.4054..-0.4050]*| it/evals=2000/24419 eff=8.3267% N=400 Z=-3.7(84.27%) | Like=-0.37..-0.01 [-0.3709..-0.3709]*| it/evals=2047/24983 eff=8.3269% N=400 Z=-3.7(84.36%) | Like=-0.37..-0.01 [-0.3673..-0.3672]*| it/evals=2050/25019 eff=8.3269% N=400 Have 2 modes Volume: ~exp(-9.77) * Expected Volume: exp(-5.18) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-3.6(85.01%) | Like=-0.35..-0.01 [-0.3542..-0.3535]*| it/evals=2070/25259 eff=8.3270% N=400 Z=-3.6(85.96%) | Like=-0.34..-0.01 [-0.3374..-0.3365]*| it/evals=2100/25619 eff=8.3271% N=400 Z=-3.6(87.34%) | Like=-0.31..-0.01 [-0.3122..-0.3111]*| it/evals=2147/26183 eff=8.3272% N=400 Z=-3.6(87.42%) | Like=-0.31..-0.01 [-0.3103..-0.3103]*| it/evals=2150/26219 eff=8.3272% N=400 Have 2 modes Volume: ~exp(-9.77) Expected Volume: exp(-5.40) Quality: ok param0: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 111111110 22222222 +0.8 | +1.0 param2: +0.0| +0.2 111111110 022222220 +0.8 | +1.0 Z=-3.6(88.41%) | Like=-0.29..-0.01 [-0.2917..-0.2915]*| it/evals=2187/26663 eff=8.3273% N=400 Z=-3.6(88.75%) | Like=-0.29..-0.01 [-0.2857..-0.2855]*| it/evals=2200/26819 eff=8.3273% N=400 Z=-3.6(89.87%) | Like=-0.26..-0.01 [-0.2649..-0.2647]*| it/evals=2247/27383 eff=8.3275% N=400 Have 2 modes Volume: ~exp(-10.02) * Expected Volume: exp(-5.63) Quality: ok param0: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.6(89.93%) | Like=-0.26..-0.01 [-0.2637..-0.2637]*| it/evals=2250/27419 eff=8.3275% N=400 Z=-3.6(90.95%) | Like=-0.24..-0.01 [-0.2414..-0.2413]*| it/evals=2297/27983 eff=8.3276% N=400 Z=-3.6(91.01%) | Like=-0.24..-0.01 [-0.2396..-0.2388]*| it/evals=2300/28019 eff=8.3276% N=400 Have 2 modes Volume: ~exp(-10.24) * Expected Volume: exp(-5.85) Quality: ok param0: +0.0| +0.2 11111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.6(91.79%) | Like=-0.22..-0.01 [-0.2243..-0.2243]*| it/evals=2340/28499 eff=8.3277% N=400 Z=-3.6(91.98%) | Like=-0.22..-0.01 [-0.2186..-0.2184]*| it/evals=2350/28619 eff=8.3277% N=400 Z=-3.6(92.85%) | Like=-0.20..-0.01 [-0.2033..-0.2033]*| it/evals=2400/29219 eff=8.3278% N=400 Have 2 modes Volume: ~exp(-10.33) * Expected Volume: exp(-6.08) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.6(93.33%) | Like=-0.19..-0.01 [-0.1920..-0.1919]*| it/evals=2430/29579 eff=8.3279% N=400 Z=-3.5(93.64%) | Like=-0.18..-0.01 [-0.1837..-0.1836]*| it/evals=2450/29819 eff=8.3280% N=400 Z=-3.5(94.34%) | Like=-0.17..-0.01 [-0.1726..-0.1722]*| it/evals=2500/30419 eff=8.3281% N=400 Have 2 modes Volume: ~exp(-10.75) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.3 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.7 | +1.0 Z=-3.5(94.60%) | Like=-0.17..-0.01 [-0.1685..-0.1685]*| it/evals=2520/30659 eff=8.3281% N=400 Z=-3.5(94.97%) | Like=-0.16..-0.00 [-0.1610..-0.1610]*| it/evals=2550/31019 eff=8.3282% N=400 Z=-3.5(95.53%) | Like=-0.15..-0.00 [-0.1465..-0.1465]*| it/evals=2600/31619 eff=8.3283% N=400 Have 2 modes Volume: ~exp(-10.75) Expected Volume: exp(-6.53) Quality: ok param0: +0.0| +0.3 1111111 222222 +0.8 | +1.0 param1: +0.0| +0.3 111111 222222 +0.7 | +1.0 param2: +0.0| +0.3 111111 222222 +0.7 | +1.0 Z=-3.5(95.95%) | Like=-0.14..-0.00 [-0.1364..-0.1361]*| it/evals=2642/32123 eff=8.3283% N=400 Z=-3.5(96.02%) | Like=-0.14..-0.00 [-0.1352..-0.1352]*| it/evals=2650/32219 eff=8.3284% N=400 Have 2 modes Volume: ~exp(-11.05) * Expected Volume: exp(-6.75) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 111111 22222 +0.7 | +1.0 param2: +0.0| +0.3 111111 222222 +0.7 | +1.0 Z=-3.5(96.47%) | Like=-0.12..-0.00 [-0.1239..-0.1239]*| it/evals=2700/32819 eff=8.3284% N=400 Z=-3.5(96.86%) | Like=-0.12..-0.00 [-0.1160..-0.1157]*| it/evals=2750/33420 eff=8.3283% N=400 Have 2 modes Volume: ~exp(-11.22) * Expected Volume: exp(-6.98) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.15%) | Like=-0.11..-0.00 [-0.1109..-0.1106]*| it/evals=2790/33900 eff=8.3284% N=400 Z=-3.5(97.21%) | Like=-0.11..-0.00 [-0.1090..-0.1089]*| it/evals=2800/34020 eff=8.3284% N=400 Z=-3.5(97.53%) | Like=-0.10..-0.00 [-0.0989..-0.0985]*| it/evals=2850/34621 eff=8.3282% N=400 Have 2 modes Volume: ~exp(-11.71) * Expected Volume: exp(-7.20) Quality: ok positive degeneracy between param2 and param0: rho=0.75 param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.70%) | Like=-0.09..-0.00 [-0.0918..-0.0918]*| it/evals=2880/34981 eff=8.3283% N=400 Z=-3.5(97.81%) | Like=-0.09..-0.00 [-0.0889..-0.0885]*| it/evals=2900/35221 eff=8.3283% N=400 Z=-3.5(97.95%) | Like=-0.09..-0.00 [-0.0850..-0.0847]*| it/evals=2927/35545 eff=8.3284% N=400 Z=-3.5(97.97%) | Like=-0.08..-0.00 [-0.0840..-0.0837]*| it/evals=2932/35605 eff=8.3284% N=400 Z=-3.5(98.06%) | Like=-0.08..-0.00 [-0.0809..-0.0806]*| it/evals=2950/35821 eff=8.3284% N=400 Have 2 modes Volume: ~exp(-11.71) Expected Volume: exp(-7.43) Quality: ok param0: +0.0| +0.3 11111 2222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.22%) | Like=-0.08..-0.00 [-0.0773..-0.0767]*| it/evals=2987/36265 eff=8.3285% N=400 Z=-3.5(98.28%) | Like=-0.08..-0.00 [-0.0757..-0.0757]*| it/evals=3000/36421 eff=8.3285% N=400 Z=-3.5(98.46%) | Like=-0.07..-0.00 [-0.0698..-0.0696]*| it/evals=3047/36985 eff=8.3285% N=400 Z=-3.5(98.47%) | Like=-0.07..-0.00 [-0.0695..-0.0692]*| it/evals=3050/37021 eff=8.3286% N=400 Have 2 modes Volume: ~exp(-12.23) * Expected Volume: exp(-7.65) Quality: ok param0: +0.0| +0.3 1111 2222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.51%) | Like=-0.07..-0.00 [-0.0684..-0.0682]*| it/evals=3060/37141 eff=8.3286% N=400 Z=-3.5(98.65%) | Like=-0.06..-0.00 [-0.0642..-0.0640]*| it/evals=3100/37622 eff=8.3284% N=400 Z=-3.5(98.79%) | Like=-0.06..-0.00 [-0.0586..-0.0585]*| it/evals=3146/38174 eff=8.3285% N=400 Have 2 modes Volume: ~exp(-12.23) Expected Volume: exp(-7.88) Quality: ok positive degeneracy between param2 and param0: rho=0.75 param0: +0.0| +0.3 1111 2222 +0.7 | +1.0 param1: +0.0| +0.3 11111 2222 +0.7 | +1.0 param2: +0.0| +0.3 11111 2222 +0.7 | +1.0 Z=-3.5(98.80%) | Like=-0.06..-0.00 [-0.0584..-0.0584]*| it/evals=3150/38222 eff=8.3285% N=400 Z=-3.5(98.94%) | Like=-0.05..-0.00 [-0.0523..-0.0522]*| it/evals=3200/38822 eff=8.3286% N=400 [ultranest] Explored until L=-0.001 [ultranest] Likelihood function evaluations: 39099 [ultranest] logZ = -3.483 +- 0.05779 [ultranest] Effective samples strategy satisfied (ESS = 1904.7, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.45+-0.04 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [ultranest] logZ error budget: single: 0.07 bs:0.06 tail:0.01 total:0.06 required:<0.50 [ultranest] done iterating. logZ = -3.483 +- 0.142 single instance: logZ = -3.483 +- 0.071 bootstrapped : logZ = -3.483 +- 0.142 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▁▂▂▄▄▆▆▆▇▇▆▆▄▃▃▂▂▃▃▃▅▅▅▅▅▅▄▄▃▂▁▁▁▁▁▁│1.00 0.46 +- 0.22 param1 : 0.00 │▁▁▁▁▁▂▃▄▅▆▅▇▇▆▅▄▄▃▂▂▂▂▃▃▃▅▅▅▅▄▃▃▃▂▁▁▁▁▁│1.00 0.48 +- 0.22 param2 : 0.00 │▁▁▁▁▂▂▃▄▄▅▇▇▇▆▄▄▃▃▂▂▂▂▃▃▄▅▆▅▅▅▄▃▂▂▁▁▁▁ │1.00 0.47 +- 0.22 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=True, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=412, regioncalls=0, ndraw=128, logz=-inf, remainder_fraction=100.0000%, Lmin=-28.44, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=1012, regioncalls=0, ndraw=128, logz=-23.99, remainder_fraction=100.0000%, Lmin=-20.02, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=1492, regioncalls=0, ndraw=128, logz=-20.23, remainder_fraction=100.0000%, Lmin=-16.40, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=1612, regioncalls=0, ndraw=128, logz=-19.46, remainder_fraction=100.0000%, Lmin=-15.84, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=2212, regioncalls=0, ndraw=128, logz=-16.51, remainder_fraction=99.9998%, Lmin=-12.89, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=190, ncalls=2692, regioncalls=0, ndraw=128, logz=-14.21, remainder_fraction=99.9978%, Lmin=-10.76, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=2812, regioncalls=0, ndraw=128, logz=-13.79, remainder_fraction=99.9966%, Lmin=-10.66, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=249, ncalls=3400, regioncalls=0, ndraw=128, logz=-12.40, remainder_fraction=99.9871%, Lmin=-9.47, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=3412, regioncalls=0, ndraw=128, logz=-12.38, remainder_fraction=99.9868%, Lmin=-9.44, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=289, ncalls=3881, regioncalls=0, ndraw=128, logz=-11.47, remainder_fraction=99.9681%, Lmin=-8.50, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=4013, regioncalls=0, ndraw=128, logz=-11.21, remainder_fraction=99.9579%, Lmin=-8.20, Lmax=-0.20 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=4614, regioncalls=0, ndraw=128, logz=-10.08, remainder_fraction=99.8644%, Lmin=-7.23, Lmax=-0.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=4734, regioncalls=0, ndraw=128, logz=-9.91, remainder_fraction=99.8406%, Lmin=-7.11, Lmax=-0.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=5214, regioncalls=0, ndraw=128, logz=-9.32, remainder_fraction=99.7137%, Lmin=-6.72, Lmax=-0.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=5814, regioncalls=0, ndraw=128, logz=-8.70, remainder_fraction=99.4479%, Lmin=-6.05, Lmax=-0.17 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=499, ncalls=6402, regioncalls=0, ndraw=128, logz=-8.14, remainder_fraction=99.0644%, Lmin=-5.43, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=6414, regioncalls=0, ndraw=128, logz=-8.13, remainder_fraction=99.0523%, Lmin=-5.43, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=6894, regioncalls=0, ndraw=128, logz=-7.70, remainder_fraction=98.5505%, Lmin=-4.92, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=7014, regioncalls=0, ndraw=128, logz=-7.60, remainder_fraction=98.3922%, Lmin=-4.85, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=7614, regioncalls=0, ndraw=128, logz=-7.14, remainder_fraction=97.3546%, Lmin=-4.40, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=8214, regioncalls=0, ndraw=128, logz=-6.75, remainder_fraction=96.0229%, Lmin=-4.11, Lmax=-0.10 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=8814, regioncalls=0, ndraw=128, logz=-6.43, remainder_fraction=94.6659%, Lmin=-3.80, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=9054, regioncalls=0, ndraw=128, logz=-6.31, remainder_fraction=93.9151%, Lmin=-3.63, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=9414, regioncalls=0, ndraw=128, logz=-6.14, remainder_fraction=92.7343%, Lmin=-3.45, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=10015, regioncalls=0, ndraw=128, logz=-5.88, remainder_fraction=90.5501%, Lmin=-3.22, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=10135, regioncalls=0, ndraw=128, logz=-5.83, remainder_fraction=90.1072%, Lmin=-3.15, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=10615, regioncalls=0, ndraw=128, logz=-5.65, remainder_fraction=88.2198%, Lmin=-2.99, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=11215, regioncalls=0, ndraw=128, logz=-5.44, remainder_fraction=85.5171%, Lmin=-2.74, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=11815, regioncalls=0, ndraw=128, logz=-5.25, remainder_fraction=82.3721%, Lmin=-2.50, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=12295, regioncalls=0, ndraw=128, logz=-5.11, remainder_fraction=79.8787%, Lmin=-2.36, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=12415, regioncalls=0, ndraw=128, logz=-5.08, remainder_fraction=79.3756%, Lmin=-2.33, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=13015, regioncalls=0, ndraw=128, logz=-4.93, remainder_fraction=76.2016%, Lmin=-2.15, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=13375, regioncalls=0, ndraw=128, logz=-4.84, remainder_fraction=74.1510%, Lmin=-2.07, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=13615, regioncalls=0, ndraw=128, logz=-4.79, remainder_fraction=72.7610%, Lmin=-1.98, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1146, ncalls=14168, regioncalls=0, ndraw=128, logz=-4.67, remainder_fraction=69.3370%, Lmin=-1.85, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=14216, regioncalls=0, ndraw=128, logz=-4.66, remainder_fraction=69.0467%, Lmin=-1.83, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1187, ncalls=14661, regioncalls=0, ndraw=128, logz=-4.58, remainder_fraction=66.6520%, Lmin=-1.74, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=14817, regioncalls=0, ndraw=128, logz=-4.55, remainder_fraction=65.8998%, Lmin=-1.71, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1247, ncalls=15382, regioncalls=0, ndraw=128, logz=-4.45, remainder_fraction=62.1350%, Lmin=-1.57, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=15418, regioncalls=0, ndraw=128, logz=-4.45, remainder_fraction=61.8825%, Lmin=-1.56, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=15538, regioncalls=0, ndraw=128, logz=-4.43, remainder_fraction=61.1404%, Lmin=-1.54, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=16018, regioncalls=0, ndraw=128, logz=-4.35, remainder_fraction=58.4255%, Lmin=-1.45, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1347, ncalls=16582, regioncalls=0, ndraw=128, logz=-4.27, remainder_fraction=55.0265%, Lmin=-1.32, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=16618, regioncalls=0, ndraw=128, logz=-4.27, remainder_fraction=54.8003%, Lmin=-1.32, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1396, ncalls=17170, regioncalls=0, ndraw=128, logz=-4.20, remainder_fraction=51.3491%, Lmin=-1.25, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=17218, regioncalls=0, ndraw=128, logz=-4.19, remainder_fraction=51.0867%, Lmin=-1.24, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=17698, regioncalls=0, ndraw=128, logz=-4.14, remainder_fraction=48.1882%, Lmin=-1.14, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=17818, regioncalls=0, ndraw=128, logz=-4.12, remainder_fraction=47.4966%, Lmin=-1.13, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1498, ncalls=18395, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=44.4248%, Lmin=-1.04, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=18419, regioncalls=0, ndraw=128, logz=-4.06, remainder_fraction=44.2614%, Lmin=-1.04, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=18779, regioncalls=0, ndraw=128, logz=-4.02, remainder_fraction=42.1465%, Lmin=-0.99, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=19019, regioncalls=0, ndraw=128, logz=-4.00, remainder_fraction=40.8023%, Lmin=-0.96, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=19619, regioncalls=0, ndraw=128, logz=-3.95, remainder_fraction=37.6924%, Lmin=-0.86, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=19859, regioncalls=0, ndraw=128, logz=-3.93, remainder_fraction=36.4899%, Lmin=-0.83, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=20219, regioncalls=0, ndraw=128, logz=-3.90, remainder_fraction=34.5530%, Lmin=-0.79, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=20819, regioncalls=0, ndraw=128, logz=-3.86, remainder_fraction=31.5163%, Lmin=-0.72, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1744, ncalls=21347, regioncalls=0, ndraw=128, logz=-3.82, remainder_fraction=29.0430%, Lmin=-0.66, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=21419, regioncalls=0, ndraw=128, logz=-3.82, remainder_fraction=28.7098%, Lmin=-0.65, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=22019, regioncalls=0, ndraw=128, logz=-3.78, remainder_fraction=26.0552%, Lmin=-0.59, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=22619, regioncalls=0, ndraw=128, logz=-3.75, remainder_fraction=23.5926%, Lmin=-0.54, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=23099, regioncalls=0, ndraw=128, logz=-3.73, remainder_fraction=21.8028%, Lmin=-0.50, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=23219, regioncalls=0, ndraw=128, logz=-3.72, remainder_fraction=21.3594%, Lmin=-0.49, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=23819, regioncalls=0, ndraw=128, logz=-3.70, remainder_fraction=19.2614%, Lmin=-0.44, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=24179, regioncalls=0, ndraw=128, logz=-3.68, remainder_fraction=18.1081%, Lmin=-0.42, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=24419, regioncalls=0, ndraw=128, logz=-3.67, remainder_fraction=17.3866%, Lmin=-0.41, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2047, ncalls=24983, regioncalls=0, ndraw=128, logz=-3.65, remainder_fraction=15.7297%, Lmin=-0.37, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=25019, regioncalls=0, ndraw=128, logz=-3.65, remainder_fraction=15.6390%, Lmin=-0.37, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=25259, regioncalls=0, ndraw=128, logz=-3.64, remainder_fraction=14.9864%, Lmin=-0.35, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=25619, regioncalls=0, ndraw=128, logz=-3.63, remainder_fraction=14.0398%, Lmin=-0.34, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2147, ncalls=26183, regioncalls=0, ndraw=128, logz=-3.62, remainder_fraction=12.6624%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=26219, regioncalls=0, ndraw=128, logz=-3.62, remainder_fraction=12.5798%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2187, ncalls=26663, regioncalls=0, ndraw=128, logz=-3.61, remainder_fraction=11.5899%, Lmin=-0.29, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=26819, regioncalls=0, ndraw=128, logz=-3.60, remainder_fraction=11.2539%, Lmin=-0.29, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2247, ncalls=27383, regioncalls=0, ndraw=128, logz=-3.59, remainder_fraction=10.1296%, Lmin=-0.26, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=27419, regioncalls=0, ndraw=128, logz=-3.59, remainder_fraction=10.0661%, Lmin=-0.26, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2297, ncalls=27983, regioncalls=0, ndraw=128, logz=-3.58, remainder_fraction=9.0521%, Lmin=-0.24, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=28019, regioncalls=0, ndraw=128, logz=-3.58, remainder_fraction=8.9933%, Lmin=-0.24, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=28499, regioncalls=0, ndraw=128, logz=-3.57, remainder_fraction=8.2103%, Lmin=-0.22, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=28619, regioncalls=0, ndraw=128, logz=-3.57, remainder_fraction=8.0236%, Lmin=-0.22, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=29219, regioncalls=0, ndraw=128, logz=-3.56, remainder_fraction=7.1497%, Lmin=-0.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=29579, regioncalls=0, ndraw=128, logz=-3.55, remainder_fraction=6.6677%, Lmin=-0.19, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=29819, regioncalls=0, ndraw=128, logz=-3.55, remainder_fraction=6.3635%, Lmin=-0.18, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=30419, regioncalls=0, ndraw=128, logz=-3.54, remainder_fraction=5.6567%, Lmin=-0.17, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=30659, regioncalls=0, ndraw=128, logz=-3.54, remainder_fraction=5.3984%, Lmin=-0.17, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=31019, regioncalls=0, ndraw=128, logz=-3.53, remainder_fraction=5.0312%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=31619, regioncalls=0, ndraw=128, logz=-3.53, remainder_fraction=4.4748%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2642, ncalls=32123, regioncalls=0, ndraw=128, logz=-3.52, remainder_fraction=4.0511%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=32219, regioncalls=0, ndraw=128, logz=-3.52, remainder_fraction=3.9752%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=32819, regioncalls=0, ndraw=128, logz=-3.52, remainder_fraction=3.5342%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=33420, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=3.1380%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=33900, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=2.8535%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=34020, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=2.7862%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=34621, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=2.4729%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=34981, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=2.3022%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=35221, regioncalls=0, ndraw=128, logz=-3.51, remainder_fraction=2.1933%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2927, ncalls=35545, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=2.0543%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2932, ncalls=35605, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=2.0298%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=35821, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.9440%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2987, ncalls=36265, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.7773%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=36421, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.7223%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3047, ncalls=36985, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.5376%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=37021, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.5264%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=37141, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.4895%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=37622, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.3513%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3146, ncalls=38174, regioncalls=0, ndraw=128, logz=-3.50, remainder_fraction=1.2079%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=38222, regioncalls=0, ndraw=128, logz=-3.49, remainder_fraction=1.1963%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=38822, regioncalls=0, ndraw=128, logz=-3.49, remainder_fraction=1.0589%, Lmin=-0.05, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.001 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 39099 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.483 +- 0.05779 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1904.7, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.45+-0.04 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.06 tail:0.01 total:0.06 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_stepsampling.py::test_stepsampler_cubeslice | 24.39 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-3.47) * Expected Volume: exp(0.00) Quality: ok param0: +1.0e-12|******************************************** ******| +1.0e+00 param1: +1.0e-12|***************************************************| +1.0e+00 param2: +1.0e-12|***************************************************| +1.0e+00 Z=-inf(0.00%) | Like=-29.79..-0.38 [-29.7909..-8.4581] | it/evals=0/403 eff=0.0000% N=400 Z=-25.8(0.00%) | Like=-21.51..-0.38 [-29.7909..-8.4581] | it/evals=48/602 eff=23.7624% N=400 Z=-25.5(0.00%) | Like=-21.14..-0.38 [-29.7909..-8.4581] | it/evals=50/610 eff=23.8095% N=400 Mono-modal Volume: ~exp(-4.65) * Expected Volume: exp(-0.23) Quality: ok param0: +1.0e-12|******************************************** * ****| +1.0e+00 param1: +1.0e-12|*********************************************** ***| +1.0e+00 param2: +1.0e-12|********************************************** ***| +1.0e+00 Z=-21.2(0.00%) | Like=-17.35..-0.38 [-29.7909..-8.4581] | it/evals=90/795 eff=22.7848% N=400 Z=-20.4(0.00%) | Like=-16.81..-0.38 [-29.7909..-8.4581] | it/evals=100/837 eff=22.8833% N=400 Z=-17.1(0.00%) | Like=-13.42..-0.38 [-29.7909..-8.4581] | it/evals=144/1063 eff=21.7195% N=400 Z=-16.7(0.00%) | Like=-12.91..-0.38 [-29.7909..-8.4581] | it/evals=150/1090 eff=21.7391% N=400 Mono-modal Volume: ~exp(-4.83) * Expected Volume: exp(-0.45) Quality: ok param0: +1.0e-12|******************************************** * ****| +1.0e+00 param1: +1.0e-12|*********************************************** ***| +1.0e+00 param2: +1.0e-12|*********************************** ******** * ** | +1.0e+00 Z=-15.1(0.00%) | Like=-11.75..-0.38 [-29.7909..-8.4581] | it/evals=180/1249 eff=21.2014% N=400 Z=-14.2(0.00%) | Like=-10.89..-0.38 [-29.7909..-8.4581] | it/evals=200/1383 eff=20.3459% N=400 Z=-12.7(0.02%) | Like=-9.58..-0.38 [-29.7909..-8.4581] | it/evals=244/1639 eff=19.6933% N=400 Z=-12.5(0.02%) | Like=-9.35..-0.38 [-29.7909..-8.4581] | it/evals=250/1671 eff=19.6696% N=400 Z=-12.0(0.04%) | Like=-9.01..-0.09 [-29.7909..-8.4581] | it/evals=269/1808 eff=19.1051% N=400 Mono-modal Volume: ~exp(-5.10) * Expected Volume: exp(-0.67) Quality: ok param0: +1.0e-12|*********************************** * ***** * ****| +1.0e+00 param1: +1.0e-12|****************************************** **** ***| +1.0e+00 param2: +1.0e-12|*********************************** ******** ** | +1.0e+00 Z=-11.9(0.04%) | Like=-9.00..-0.09 [-29.7909..-8.4581] | it/evals=270/1813 eff=19.1083% N=400 Z=-11.4(0.06%) | Like=-8.52..-0.09 [-29.7909..-8.4581] | it/evals=294/1970 eff=18.7261% N=400 Z=-11.3(0.07%) | Like=-8.45..-0.09 [-8.4564..-5.6145] | it/evals=300/2027 eff=18.4388% N=400 Z=-10.5(0.15%) | Like=-7.86..-0.09 [-8.4564..-5.6145] | it/evals=343/2308 eff=17.9769% N=400 Z=-10.4(0.17%) | Like=-7.77..-0.09 [-8.4564..-5.6145] | it/evals=350/2363 eff=17.8299% N=400 Mono-modal Volume: ~exp(-5.24) * Expected Volume: exp(-0.90) Quality: ok param0: +1.0e-12|************************************ ******* ** | +1.0e+00 param1: +1.0e-12|****************************************** ********| +1.0e+00 param2: +1.0e-12|*********************************** ******** * ** | +1.0e+00 Z=-10.3(0.20%) | Like=-7.63..-0.09 [-8.4564..-5.6145] | it/evals=360/2429 eff=17.7427% N=400 Z=-10.0(0.26%) | Like=-7.41..-0.09 [-8.4564..-5.6145] | it/evals=378/2549 eff=17.5896% N=400 Z=-9.8(0.35%) | Like=-7.14..-0.09 [-8.4564..-5.6145] | it/evals=400/2696 eff=17.4216% N=400 Z=-9.3(0.51%) | Like=-6.79..-0.09 [-8.4564..-5.6145] | it/evals=441/2989 eff=17.0336% N=400 Mono-modal Volume: ~exp(-5.45) * Expected Volume: exp(-1.12) Quality: ok param0: +1.0e-12|************************************ ******* ** | +1.0e+00 param1: +1.0e-12|****************************************** ********| +1.0e+00 param2: +1.0e-12|*********************************** ******** *** | +1.0e+00 Z=-9.2(0.57%) | Like=-6.70..-0.09 [-8.4564..-5.6145] | it/evals=450/3055 eff=16.9492% N=400 Z=-9.0(0.68%) | Like=-6.51..-0.09 [-8.4564..-5.6145] | it/evals=468/3209 eff=16.6607% N=400 Z=-8.9(0.79%) | Like=-6.35..-0.09 [-8.4564..-5.6145] | it/evals=486/3346 eff=16.4969% N=400 Z=-8.7(0.91%) | Like=-6.24..-0.09 [-8.4564..-5.6145] | it/evals=500/3463 eff=16.3239% N=400 Z=-8.6(1.09%) | Like=-6.01..-0.09 [-8.4564..-5.6145] | it/evals=519/3636 eff=16.0383% N=400 Mono-modal Volume: ~exp(-5.67) * Expected Volume: exp(-1.35) Quality: ok param0: +1.0e-12|******************************************** **** | +1.0e+00 param1: +0.0000|******************************** ******** *********| +1.0000 param2: +1.0e-12|*********************************** ******** * *** | +1.0e+00 Z=-8.4(1.33%) | Like=-5.86..-0.09 [-8.4564..-5.6145] | it/evals=540/3829 eff=15.7480% N=400 Z=-8.3(1.45%) | Like=-5.77..-0.09 [-8.4564..-5.6145] | it/evals=550/3922 eff=15.6161% N=400 Z=-8.1(1.69%) | Like=-5.62..-0.09 [-8.4564..-5.6145] | it/evals=571/4093 eff=15.4617% N=400 Z=-8.1(1.82%) | Like=-5.54..-0.09 [-5.6135..-4.0672] | it/evals=582/4186 eff=15.3724% N=400 Z=-8.0(1.96%) | Like=-5.48..-0.09 [-5.6135..-4.0672] | it/evals=592/4278 eff=15.2656% N=400 Z=-7.9(2.07%) | Like=-5.38..-0.09 [-5.6135..-4.0672] | it/evals=600/4332 eff=15.2594% N=400 Z=-7.8(2.22%) | Like=-5.30..-0.09 [-5.6135..-4.0672] | it/evals=618/4487 eff=15.1211% N=400 Z=-7.7(2.35%) | Like=-5.22..-0.09 [-5.6135..-4.0672] | it/evals=629/4596 eff=14.9905% N=400 Mono-modal Volume: ~exp(-5.67) Expected Volume: exp(-1.57) Quality: ok param0: +0.0000|******************************** *********** * ** | +1.0000 param1: +0.0000|******************************** ******** *********| +1.0000 param2: +1.0e-12|******************************************** *** | +1.0e+00 Z=-7.7(2.36%) | Like=-5.21..-0.09 [-5.6135..-4.0672] | it/evals=630/4604 eff=14.9857% N=400 Z=-7.6(2.70%) | Like=-5.04..-0.09 [-5.6135..-4.0672] | it/evals=650/4777 eff=14.8504% N=400 Z=-7.5(2.90%) | Like=-4.94..-0.09 [-5.6135..-4.0672] | it/evals=663/4900 eff=14.7333% N=400 Z=-7.4(3.22%) | Like=-4.80..-0.09 [-5.6135..-4.0672] | it/evals=684/5109 eff=14.5254% N=400 Z=-7.3(3.56%) | Like=-4.69..-0.09 [-5.6135..-4.0672] | it/evals=700/5283 eff=14.3354% N=400 Mono-modal Volume: ~exp(-6.27) * Expected Volume: exp(-1.80) Quality: ok param0: +0.0000|******************************** *********** * ** | +1.0000 param1: +0.000|******************************** ***************** | +1.000 param2: +0.000|********************************* ********** * *** | +1.000 Z=-7.2(3.95%) | Like=-4.63..-0.09 [-5.6135..-4.0672] | it/evals=720/5475 eff=14.1872% N=400 Z=-7.1(4.38%) | Like=-4.57..-0.09 [-5.6135..-4.0672] | it/evals=739/5675 eff=14.0095% N=400 Z=-7.0(4.63%) | Like=-4.51..-0.09 [-5.6135..-4.0672] | it/evals=750/5779 eff=13.9431% N=400 Z=-7.0(4.96%) | Like=-4.46..-0.09 [-5.6135..-4.0672] | it/evals=765/5904 eff=13.8990% N=400 Z=-6.8(5.66%) | Like=-4.29..-0.09 [-5.6135..-4.0672] | it/evals=793/6212 eff=13.6442% N=400 Z=-6.8(5.90%) | Like=-4.26..-0.09 [-5.6135..-4.0672] | it/evals=800/6303 eff=13.5524% N=400 Mono-modal Volume: ~exp(-6.27) Expected Volume: exp(-2.02) Quality: ok param0: +0.00|******************************** ************* ** | +1.00 param1: +0.00| ******************************* ************** ** | +1.00 param2: +0.00| ******************************************* * * * | +1.00 Z=-6.8(6.17%) | Like=-4.22..-0.09 [-5.6135..-4.0672] | it/evals=810/6398 eff=13.5045% N=400 Z=-6.7(6.53%) | Like=-4.17..-0.09 [-5.6135..-4.0672] | it/evals=823/6537 eff=13.4105% N=400 Z=-6.6(7.32%) | Like=-4.02..-0.09 [-4.0647..-3.3420] | it/evals=850/6812 eff=13.2564% N=400 Z=-6.5(7.89%) | Like=-3.90..-0.09 [-4.0647..-3.3420] | it/evals=873/7060 eff=13.1081% N=400 Z=-6.4(8.53%) | Like=-3.76..-0.09 [-4.0647..-3.3420] | it/evals=892/7265 eff=12.9934% N=400 Mono-modal Volume: ~exp(-6.27) Expected Volume: exp(-2.25) Quality: ok param0: +0.00| ******************************************** * | +1.00 param1: +0.00| ******************************* ************ * * | +1.00 param2: +0.00| ****************************************** *** | +1.00 Z=-6.4(8.79%) | Like=-3.72..-0.09 [-4.0647..-3.3420] | it/evals=900/7355 eff=12.9403% N=400 Z=-6.3(9.70%) | Like=-3.61..-0.09 [-4.0647..-3.3420] | it/evals=921/7598 eff=12.7952% N=400 Z=-6.2(10.82%) | Like=-3.41..-0.09 [-4.0647..-3.3420] | it/evals=950/7916 eff=12.6397% N=400 Z=-6.1(11.65%) | Like=-3.30..-0.09 [-3.3410..-3.0931] | it/evals=971/8162 eff=12.5097% N=400 Have 2 modes Volume: ~exp(-6.67) * Expected Volume: exp(-2.47) Quality: ok param0: +0.00| 11111111111111111111111112122222222222222222 | +1.00 param1: +0.00| 111111111111111111111211112222 222222222222 2 | +1.00 param2: +0.00| 111111111111111111111121222222222 2222222 2 2 | +1.00 Z=-6.1(12.60%) | Like=-3.20..-0.09 [-3.3410..-3.0931] | it/evals=990/8392 eff=12.3874% N=400 Z=-6.0(13.11%) | Like=-3.16..-0.09 [-3.3410..-3.0931] | it/evals=1000/8504 eff=12.3396% N=400 Z=-5.9(14.46%) | Like=-3.00..-0.08 [-3.0121..-2.9853] | it/evals=1026/8822 eff=12.1824% N=400 Z=-5.9(15.68%) | Like=-2.92..-0.08 [-2.9200..-2.9077] | it/evals=1050/9085 eff=12.0898% N=400 Z=-5.8(16.96%) | Like=-2.81..-0.08 [-2.8090..-2.8058]*| it/evals=1076/9370 eff=11.9955% N=400 Have 2 modes Volume: ~exp(-6.67) Expected Volume: exp(-2.70) Quality: ok param0: +0.00| 101111111111111111111112202222222222222222 0 | +1.00 param1: +0.00| 11111111111111111111111122222022222222 222 2 | +1.00 param2: +0.00| 11111111111111111111112122222222202222222 2 2 | +1.00 Z=-5.7(17.20%) | Like=-2.79..-0.08 [-2.7868..-2.7816]*| it/evals=1082/9442 eff=11.9664% N=400 Z=-5.7(18.26%) | Like=-2.71..-0.08 [-2.7141..-2.7100]*| it/evals=1100/9668 eff=11.8688% N=400 Z=-5.6(19.67%) | Like=-2.61..-0.08 [-2.6064..-2.5986]*| it/evals=1125/9966 eff=11.7604% N=400 Z=-5.5(21.13%) | Like=-2.53..-0.08 [-2.5307..-2.5297]*| it/evals=1149/10262 eff=11.6508% N=400 Z=-5.5(21.21%) | Like=-2.53..-0.08 [-2.5297..-2.5250]*| it/evals=1150/10279 eff=11.6409% N=400 Have 2 modes Volume: ~exp(-6.67) Expected Volume: exp(-2.92) Quality: ok param0: +0.00| 011111111111111111111122 0222222222222222 | +1.00 param1: +0.00| 11111111111111111111111022 20222222220202 0 | +1.00 param2: +0.00| 11111111111111111111122222222222 2222022 | +1.00 Z=-5.5(22.48%) | Like=-2.44..-0.08 [-2.4397..-2.4269] | it/evals=1170/10530 eff=11.5499% N=400 Z=-5.4(24.23%) | Like=-2.35..-0.08 [-2.3488..-2.3391]*| it/evals=1195/10839 eff=11.4475% N=400 Z=-5.4(24.62%) | Like=-2.32..-0.08 [-2.3234..-2.3208]*| it/evals=1200/10907 eff=11.4210% N=400 Z=-5.3(26.39%) | Like=-2.23..-0.08 [-2.2288..-2.2244]*| it/evals=1225/11221 eff=11.3206% N=400 Z=-5.3(28.27%) | Like=-2.15..-0.08 [-2.1483..-2.1459]*| it/evals=1250/11508 eff=11.2532% N=400 Have 2 modes Volume: ~exp(-7.01) * Expected Volume: exp(-3.15) Quality: ok param0: +0.0| 1111111111111111111112222222222222222 22 | +1.0 param1: +0.0| 111111111111111111111 222 2222222222222 | +1.0 param2: +0.0| 1111111111111111111112222222222 2222222 | +1.0 Z=-5.2(29.10%) | Like=-2.10..-0.08 [-2.1042..-2.1039]*| it/evals=1260/11637 eff=11.2130% N=400 Z=-5.2(30.67%) | Like=-2.04..-0.08 [-2.0423..-2.0419]*| it/evals=1280/11888 eff=11.1421% N=400 Z=-5.2(32.29%) | Like=-2.00..-0.08 [-2.0004..-2.0000]*| it/evals=1299/12126 eff=11.0779% N=400 Z=-5.2(32.35%) | Like=-2.00..-0.08 [-2.0000..-1.9986]*| it/evals=1300/12138 eff=11.0751% N=400 Z=-5.1(34.34%) | Like=-1.95..-0.08 [-1.9473..-1.9447]*| it/evals=1327/12476 eff=10.9887% N=400 Z=-5.0(35.74%) | Like=-1.89..-0.08 [-1.8921..-1.8906]*| it/evals=1348/12742 eff=10.9221% N=400 Have 2 modes Volume: ~exp(-7.71) * Expected Volume: exp(-3.37) Quality: ok param0: +0.0| 1111111111111111111 22222222222222 22 | +1.0 param1: +0.0| 11111111111111111111 222 2222222222222 | +1.0 param2: +0.0| 11111111111111111111 2222222222 2222222 | +1.0 Z=-5.0(35.87%) | Like=-1.89..-0.08 [-1.8899..-1.8845]*| it/evals=1350/12761 eff=10.9214% N=400 Z=-5.0(37.50%) | Like=-1.81..-0.02 [-1.8133..-1.8124]*| it/evals=1379/13110 eff=10.8497% N=400 Z=-4.9(38.84%) | Like=-1.75..-0.02 [-1.7513..-1.7507]*| it/evals=1400/13360 eff=10.8025% N=400 Z=-4.9(40.42%) | Like=-1.70..-0.02 [-1.6983..-1.6976]*| it/evals=1425/13678 eff=10.7320% N=400 Have 2 modes Volume: ~exp(-7.92) * Expected Volume: exp(-3.60) Quality: ok param0: +0.0| 1111111111111111111 222222222222222222 | +1.0 param1: +0.0| 1111111111111111111 22222222222222 2 | +1.0 param2: +0.0| 111111111111111111 22222222222222222 | +1.0 Z=-4.9(41.59%) | Like=-1.66..-0.02 [-1.6556..-1.6554]*| it/evals=1440/13844 eff=10.7111% N=400 Z=-4.9(42.40%) | Like=-1.63..-0.02 [-1.6343..-1.6335]*| it/evals=1450/13973 eff=10.6830% N=400 Z=-4.8(43.82%) | Like=-1.58..-0.02 [-1.5850..-1.5845]*| it/evals=1469/14213 eff=10.6349% N=400 Z=-4.8(45.61%) | Like=-1.55..-0.02 [-1.5503..-1.5483]*| it/evals=1490/14493 eff=10.5726% N=400 Z=-4.8(46.29%) | Like=-1.53..-0.02 [-1.5326..-1.5303]*| it/evals=1500/14614 eff=10.5530% N=400 Z=-4.7(48.00%) | Like=-1.48..-0.02 [-1.4759..-1.4751]*| it/evals=1525/14914 eff=10.5071% N=400 Have 2 modes Volume: ~exp(-7.92) Expected Volume: exp(-3.82) Quality: ok param0: +0.0| 1111111111111111 2222222222222222 | +1.0 param1: +0.0| 11111111111111111 22222222222222 20 | +1.0 param2: +0.0| 11111111111111111 222222222222222 | +1.0 Z=-4.7(48.36%) | Like=-1.47..-0.02 [-1.4702..-1.4680]*| it/evals=1530/14979 eff=10.4945% N=400 Z=-4.7(49.28%) | Like=-1.45..-0.02 [-1.4456..-1.4450]*| it/evals=1545/15151 eff=10.4739% N=400 Z=-4.7(49.71%) | Like=-1.44..-0.02 [-1.4362..-1.4300]*| it/evals=1550/15211 eff=10.4652% N=400 Z=-4.7(51.20%) | Like=-1.37..-0.01 [-1.3703..-1.3672]*| it/evals=1575/15501 eff=10.4298% N=400 Z=-4.6(53.00%) | Like=-1.30..-0.01 [-1.3037..-1.3031]*| it/evals=1599/15806 eff=10.3791% N=400 Z=-4.6(53.06%) | Like=-1.30..-0.01 [-1.3031..-1.3008]*| it/evals=1600/15817 eff=10.3782% N=400 Have 2 modes Volume: ~exp(-8.30) * Expected Volume: exp(-4.05) Quality: ok param0: +0.0| 1111111111111111 222222222222222 | +1.0 param1: +0.0| 1111111111111111 2222222222222222 | +1.0 param2: +0.0| 1111111111111111 222222222222222 | +1.0 Z=-4.6(54.29%) | Like=-1.27..-0.01 [-1.2706..-1.2704]*| it/evals=1620/16080 eff=10.3316% N=400 Z=-4.6(55.22%) | Like=-1.25..-0.01 [-1.2529..-1.2526]*| it/evals=1634/16260 eff=10.3026% N=400 Z=-4.6(56.12%) | Like=-1.23..-0.01 [-1.2284..-1.2262]*| it/evals=1650/16452 eff=10.2791% N=400 Z=-4.6(57.62%) | Like=-1.17..-0.01 [-1.1736..-1.1721]*| it/evals=1674/16756 eff=10.2348% N=400 Z=-4.5(59.20%) | Like=-1.14..-0.01 [-1.1406..-1.1380]*| it/evals=1699/17061 eff=10.1975% N=400 Z=-4.5(59.27%) | Like=-1.14..-0.01 [-1.1380..-1.1364]*| it/evals=1700/17074 eff=10.1955% N=400 Have 2 modes Volume: ~exp(-8.47) * Expected Volume: exp(-4.27) Quality: ok param0: +0.0| 111111111111111 22222222222222 | +1.0 param1: +0.0| 1111111111111111 222222222222222 | +1.0 param2: +0.0| 111111111111111 22222222222222 | +1.0 Z=-4.5(59.87%) | Like=-1.12..-0.01 [-1.1197..-1.1195]*| it/evals=1710/17194 eff=10.1822% N=400 Z=-4.5(61.23%) | Like=-1.09..-0.01 [-1.0903..-1.0894]*| it/evals=1731/17468 eff=10.1418% N=400 Z=-4.5(62.46%) | Like=-1.05..-0.01 [-1.0464..-1.0432]*| it/evals=1750/17702 eff=10.1144% N=400 Z=-4.5(63.90%) | Like=-1.02..-0.01 [-1.0185..-1.0115]*| it/evals=1773/17995 eff=10.0767% N=400 Z=-4.4(65.43%) | Like=-0.97..-0.01 [-0.9722..-0.9717]*| it/evals=1798/18310 eff=10.0391% N=400 Have 2 modes Volume: ~exp(-8.60) * Expected Volume: exp(-4.50) Quality: ok param0: +0.0| 11111111111111 222222222222 | +1.0 param1: +0.0| 111111111111111 2222222222222 | +1.0 param2: +0.0| 111111111111111 22222222222222 | +1.0 Z=-4.4(65.56%) | Like=-0.97..-0.01 [-0.9690..-0.9681]*| it/evals=1800/18336 eff=10.0357% N=400 Z=-4.4(66.77%) | Like=-0.94..-0.01 [-0.9371..-0.9370]*| it/evals=1822/18593 eff=10.0148% N=400 Z=-4.4(67.58%) | Like=-0.92..-0.01 [-0.9181..-0.9124]*| it/evals=1836/18775 eff=9.9918% N=400 Z=-4.4(68.37%) | Like=-0.89..-0.01 [-0.8893..-0.8891]*| it/evals=1850/18945 eff=9.9757% N=400 Z=-4.4(69.37%) | Like=-0.86..-0.01 [-0.8616..-0.8606]*| it/evals=1869/19168 eff=9.9584% N=400 Z=-4.4(70.46%) | Like=-0.85..-0.01 [-0.8450..-0.8422]*| it/evals=1889/19421 eff=9.9311% N=400 Have 2 modes Volume: ~exp(-8.60) Expected Volume: exp(-4.73) Quality: ok param0: +0.0| 111111111111 22222222222 +0.8 | +1.0 param1: +0.0| 1111111111111 2222222222222 | +1.0 param2: +0.0| 1111111111111 22222222222 +0.8 | +1.0 Z=-4.4(70.49%) | Like=-0.84..-0.01 [-0.8422..-0.8382]*| it/evals=1890/19432 eff=9.9306% N=400 Z=-4.3(71.01%) | Like=-0.82..-0.01 [-0.8157..-0.8148]*| it/evals=1900/19562 eff=9.9155% N=400 Z=-4.3(72.21%) | Like=-0.78..-0.01 [-0.7786..-0.7783]*| it/evals=1923/19852 eff=9.8859% N=400 Z=-4.3(73.45%) | Like=-0.75..-0.01 [-0.7533..-0.7522]*| it/evals=1947/20156 eff=9.8552% N=400 Z=-4.3(73.59%) | Like=-0.75..-0.01 [-0.7512..-0.7507]*| it/evals=1950/20194 eff=9.8515% N=400 Z=-4.3(74.43%) | Like=-0.73..-0.01 [-0.7343..-0.7341]*| it/evals=1967/20411 eff=9.8296% N=400 Have 2 modes Volume: ~exp(-9.01) * Expected Volume: exp(-4.95) Quality: ok param0: +0.0| 111111111111 222222222222 | +1.0 param1: +0.0| 111111111111 2222222222222 | +1.0 param2: +0.0| 1111111111111 22222222222 +0.8 | +1.0 Z=-4.3(75.00%) | Like=-0.72..-0.01 [-0.7238..-0.7236]*| it/evals=1980/20581 eff=9.8112% N=400 Z=-4.3(75.95%) | Like=-0.70..-0.01 [-0.7031..-0.7008]*| it/evals=2000/20822 eff=9.7934% N=400 Z=-4.3(77.05%) | Like=-0.68..-0.01 [-0.6808..-0.6790]*| it/evals=2025/21134 eff=9.7666% N=400 Z=-4.3(77.59%) | Like=-0.67..-0.01 [-0.6710..-0.6709]*| it/evals=2039/21316 eff=9.7485% N=400 Z=-4.3(78.02%) | Like=-0.66..-0.01 [-0.6604..-0.6602]*| it/evals=2050/21453 eff=9.7373% N=400 Have 2 modes Volume: ~exp(-9.28) * Expected Volume: exp(-5.18) Quality: ok param0: +0.0| 111111111111 22222222222 +0.8 | +1.0 param1: +0.0| 111111111111 22222222222 +0.8 | +1.0 param2: +0.0| 111111111111 2222222222 +0.8 | +1.0 Z=-4.2(78.82%) | Like=-0.64..-0.01 [-0.6412..-0.6404]*| it/evals=2070/21704 eff=9.7165% N=400 Z=-4.2(79.60%) | Like=-0.63..-0.01 [-0.6260..-0.6255]*| it/evals=2089/21938 eff=9.6991% N=400 Z=-4.2(80.07%) | Like=-0.62..-0.01 [-0.6171..-0.6168]*| it/evals=2100/22076 eff=9.6881% N=400 Z=-4.2(80.99%) | Like=-0.60..-0.01 [-0.5979..-0.5978]*| it/evals=2124/22380 eff=9.6633% N=400 Z=-4.2(81.89%) | Like=-0.57..-0.01 [-0.5727..-0.5724]*| it/evals=2148/22691 eff=9.6362% N=400 Z=-4.2(81.97%) | Like=-0.57..-0.01 [-0.5715..-0.5713]*| it/evals=2150/22712 eff=9.6361% N=400 Have 2 modes Volume: ~exp(-9.46) * Expected Volume: exp(-5.40) Quality: ok param0: +0.0| 11111111111 22222222222 +0.8 | +1.0 param1: +0.0| 1111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-4.2(82.34%) | Like=-0.57..-0.01 [-0.5655..-0.5624]*| it/evals=2160/22847 eff=9.6227% N=400 Z=-4.2(83.18%) | Like=-0.54..-0.01 [-0.5411..-0.5406]*| it/evals=2184/23155 eff=9.5979% N=400 Z=-4.2(83.70%) | Like=-0.53..-0.01 [-0.5311..-0.5304]*| it/evals=2200/23365 eff=9.5798% N=400 Z=-4.2(84.45%) | Like=-0.50..-0.01 [-0.5050..-0.5042]*| it/evals=2224/23671 eff=9.5570% N=400 Z=-4.2(85.19%) | Like=-0.49..-0.01 [-0.4859..-0.4854]*| it/evals=2247/23973 eff=9.5321% N=400 Have 2 modes Volume: ~exp(-9.46) Expected Volume: exp(-5.63) Quality: ok param0: +0.0| 11111111111 22222222222 +0.8 | +1.0 param1: +0.0| 1111111111 2222222222 +0.8 | +1.0 param2: +0.0| 11111111111 2222222220 +0.8 | +1.0 Z=-4.2(85.26%) | Like=-0.48..-0.01 [-0.4844..-0.4836]*| it/evals=2250/24008 eff=9.5307% N=400 Z=-4.2(85.93%) | Like=-0.47..-0.01 [-0.4691..-0.4685]*| it/evals=2273/24303 eff=9.5093% N=400 Z=-4.1(86.62%) | Like=-0.45..-0.01 [-0.4519..-0.4515]*| it/evals=2297/24612 eff=9.4870% N=400 Z=-4.1(86.70%) | Like=-0.45..-0.01 [-0.4495..-0.4490]*| it/evals=2300/24649 eff=9.4849% N=400 Z=-4.1(87.38%) | Like=-0.43..-0.01 [-0.4294..-0.4292]*| it/evals=2324/24954 eff=9.4649% N=400 Have 2 modes Volume: ~exp(-9.95) * Expected Volume: exp(-5.85) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| 1111111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 2222222222 +0.8 | +1.0 Z=-4.1(87.78%) | Like=-0.42..-0.01 [-0.4204..-0.4200]*| it/evals=2340/25153 eff=9.4534% N=400 Z=-4.1(88.04%) | Like=-0.41..-0.01 [-0.4148..-0.4141]*| it/evals=2350/25275 eff=9.4472% N=400 Z=-4.1(88.77%) | Like=-0.40..-0.01 [-0.3959..-0.3955]*| it/evals=2379/25625 eff=9.4311% N=400 Z=-4.1(89.27%) | Like=-0.38..-0.01 [-0.3829..-0.3824]*| it/evals=2400/25898 eff=9.4125% N=400 Z=-4.1(89.73%) | Like=-0.37..-0.00 [-0.3725..-0.3715]*| it/evals=2420/26153 eff=9.3970% N=400 Have 2 modes Volume: ~exp(-9.95) Expected Volume: exp(-6.08) Quality: ok param0: +0.0| +0.2 111111111 222222220 +0.8 | +1.0 param1: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-4.1(89.94%) | Like=-0.37..-0.00 [-0.3685..-0.3681]*| it/evals=2430/26287 eff=9.3870% N=400 Z=-4.1(90.34%) | Like=-0.36..-0.00 [-0.3568..-0.3566]*| it/evals=2450/26553 eff=9.3680% N=400 Z=-4.1(90.84%) | Like=-0.34..-0.00 [-0.3423..-0.3419]*| it/evals=2475/26875 eff=9.3484% N=400 Z=-4.1(91.33%) | Like=-0.33..-0.00 [-0.3285..-0.3283]*| it/evals=2500/27193 eff=9.3308% N=400 Have 2 modes Volume: ~exp(-10.45) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-4.1(91.71%) | Like=-0.32..-0.00 [-0.3173..-0.3168]*| it/evals=2520/27464 eff=9.3113% N=400 Z=-4.1(92.15%) | Like=-0.31..-0.00 [-0.3096..-0.3096]*| it/evals=2545/27778 eff=9.2958% N=400 Z=-4.1(92.24%) | Like=-0.31..-0.00 [-0.3087..-0.3086]*| it/evals=2550/27830 eff=9.2964% N=400 Z=-4.1(92.53%) | Like=-0.30..-0.00 [-0.3024..-0.3023]*| it/evals=2567/28053 eff=9.2829% N=400 Z=-4.1(92.96%) | Like=-0.29..-0.00 [-0.2920..-0.2900]*| it/evals=2594/28400 eff=9.2643% N=400 Z=-4.1(93.04%) | Like=-0.29..-0.00 [-0.2881..-0.2873]*| it/evals=2600/28478 eff=9.2599% N=400 Have 2 modes Volume: ~exp(-10.62) * Expected Volume: exp(-6.53) Quality: ok param0: +0.0| +0.2 11111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 22222222 +0.8 | +1.0 Z=-4.1(93.20%) | Like=-0.28..-0.00 [-0.2850..-0.2847]*| it/evals=2610/28600 eff=9.2553% N=400 Z=-4.1(93.56%) | Like=-0.28..-0.00 [-0.2758..-0.2757]*| it/evals=2634/28905 eff=9.2405% N=400 Z=-4.1(93.79%) | Like=-0.27..-0.00 [-0.2716..-0.2709]*| it/evals=2650/29105 eff=9.2318% N=400 Z=-4.1(94.13%) | Like=-0.26..-0.00 [-0.2588..-0.2588]*| it/evals=2675/29422 eff=9.2171% N=400 Have 2 modes Volume: ~exp(-10.83) * Expected Volume: exp(-6.75) Quality: ok param0: +0.0| +0.2 11111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-4.1(94.45%) | Like=-0.25..-0.00 [-0.2466..-0.2462]*| it/evals=2700/29732 eff=9.2050% N=400 Z=-4.1(94.64%) | Like=-0.24..-0.00 [-0.2406..-0.2395]*| it/evals=2715/29929 eff=9.1944% N=400 Z=-4.1(94.96%) | Like=-0.23..-0.00 [-0.2298..-0.2291]*| it/evals=2743/30287 eff=9.1779% N=400 Z=-4.1(95.04%) | Like=-0.23..-0.00 [-0.2258..-0.2255]*| it/evals=2750/30391 eff=9.1694% N=400 Z=-4.1(95.33%) | Like=-0.22..-0.00 [-0.2152..-0.2151]*| it/evals=2776/30739 eff=9.1499% N=400 Have 2 modes Volume: ~exp(-11.14) * Expected Volume: exp(-6.98) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-4.1(95.48%) | Like=-0.21..-0.00 [-0.2126..-0.2123]*| it/evals=2790/30921 eff=9.1412% N=400 Z=-4.1(95.58%) | Like=-0.21..-0.00 [-0.2091..-0.2089]*| it/evals=2800/31041 eff=9.1381% N=400 Z=-4.0(95.77%) | Like=-0.20..-0.00 [-0.2026..-0.2024]*| it/evals=2819/31294 eff=9.1247% N=400 Z=-4.0(95.91%) | Like=-0.20..-0.00 [-0.1969..-0.1968]*| it/evals=2833/31464 eff=9.1199% N=400 Z=-4.0(96.06%) | Like=-0.19..-0.00 [-0.1903..-0.1902]*| it/evals=2849/31665 eff=9.1124% N=400 Z=-4.0(96.07%) | Like=-0.19..-0.00 [-0.1902..-0.1901]*| it/evals=2850/31675 eff=9.1127% N=400 Z=-4.0(96.32%) | Like=-0.18..-0.00 [-0.1834..-0.1833]*| it/evals=2879/32036 eff=9.1004% N=400 Have 2 modes Volume: ~exp(-11.42) * Expected Volume: exp(-7.20) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-4.0(96.33%) | Like=-0.18..-0.00 [-0.1833..-0.1832]*| it/evals=2880/32048 eff=9.1001% N=400 Z=-4.0(96.49%) | Like=-0.18..-0.00 [-0.1782..-0.1781]*| it/evals=2900/32311 eff=9.0878% N=400 Z=-4.0(96.69%) | Like=-0.17..-0.00 [-0.1730..-0.1727]*| it/evals=2925/32615 eff=9.0796% N=400 Z=-4.0(96.88%) | Like=-0.16..-0.00 [-0.1648..-0.1645]*| it/evals=2950/32923 eff=9.0705% N=400 Have 2 modes Volume: ~exp(-11.42) Expected Volume: exp(-7.43) Quality: ok param0: +0.0| +0.2 1111111 222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 222222 +0.7 | +1.0 Z=-4.0(97.02%) | Like=-0.16..-0.00 [-0.1614..-0.1611]*| it/evals=2970/33174 eff=9.0621% N=400 Z=-4.0(97.19%) | Like=-0.15..-0.00 [-0.1548..-0.1548]*| it/evals=2994/33485 eff=9.0494% N=400 Z=-4.0(97.23%) | Like=-0.15..-0.00 [-0.1539..-0.1538]*| it/evals=3000/33563 eff=9.0462% N=400 Z=-4.0(97.36%) | Like=-0.15..-0.00 [-0.1492..-0.1491]*| it/evals=3020/33846 eff=9.0295% N=400 Z=-4.0(97.53%) | Like=-0.14..-0.00 [-0.1428..-0.1427]*| it/evals=3048/34195 eff=9.0191% N=400 Z=-4.0(97.54%) | Like=-0.14..-0.00 [-0.1426..-0.1424]*| it/evals=3050/34221 eff=9.0181% N=400 Have 2 modes Volume: ~exp(-11.68) * Expected Volume: exp(-7.65) Quality: ok param0: +0.0| +0.3 111111 222222 +0.7 | +1.0 param1: +0.0| +0.3 111111 22222 +0.7 | +1.0 param2: +0.0| +0.3 111111 22222 +0.7 | +1.0 Z=-4.0(97.60%) | Like=-0.14..-0.00 [-0.1394..-0.1392]*| it/evals=3060/34354 eff=9.0122% N=400 Z=-4.0(97.70%) | Like=-0.14..-0.00 [-0.1353..-0.1352]*| it/evals=3079/34616 eff=8.9987% N=400 Z=-4.0(97.81%) | Like=-0.13..-0.00 [-0.1322..-0.1321]*| it/evals=3100/34883 eff=8.9899% N=400 Z=-4.0(97.95%) | Like=-0.13..-0.00 [-0.1264..-0.1261]*| it/evals=3127/35216 eff=8.9815% N=400 Have 2 modes Volume: ~exp(-11.73) * Expected Volume: exp(-7.88) Quality: ok param0: +0.0| +0.3 111111 222222 +0.7 | +1.0 param1: +0.0| +0.3 111111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.05%) | Like=-0.12..-0.00 [-0.1206..-0.1205]*| it/evals=3150/35507 eff=8.9726% N=400 Z=-4.0(98.16%) | Like=-0.11..-0.00 [-0.1146..-0.1144]*| it/evals=3173/35791 eff=8.9656% N=400 Z=-4.0(98.27%) | Like=-0.11..-0.00 [-0.1078..-0.1076]*| it/evals=3200/36124 eff=8.9576% N=400 Z=-4.0(98.37%) | Like=-0.10..-0.00 [-0.1044..-0.1043]*| it/evals=3224/36451 eff=8.9429% N=400 Have 2 modes Volume: ~exp(-12.22) * Expected Volume: exp(-8.10) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.43%) | Like=-0.10..-0.00 [-0.1010..-0.1010]*| it/evals=3240/36655 eff=8.9367% N=400 Z=-4.0(98.47%) | Like=-0.10..-0.00 [-0.0986..-0.0983]*| it/evals=3250/36780 eff=8.9335% N=400 Z=-4.0(98.57%) | Like=-0.09..-0.00 [-0.0947..-0.0945]*| it/evals=3278/37127 eff=8.9253% N=400 Z=-4.0(98.61%) | Like=-0.09..-0.00 [-0.0919..-0.0919]*| it/evals=3291/37295 eff=8.9199% N=400 Z=-4.0(98.64%) | Like=-0.09..-0.00 [-0.0902..-0.0897]*| it/evals=3300/37422 eff=8.9136% N=400 Z=-4.0(98.71%) | Like=-0.09..-0.00 [-0.0866..-0.0865]*| it/evals=3322/37711 eff=8.9035% N=400 Have 2 modes Volume: ~exp(-12.38) * Expected Volume: exp(-8.33) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.73%) | Like=-0.09..-0.00 [-0.0855..-0.0854]*| it/evals=3330/37809 eff=8.9016% N=400 Z=-4.0(98.79%) | Like=-0.08..-0.00 [-0.0826..-0.0826]*| it/evals=3347/38029 eff=8.8947% N=400 Z=-4.0(98.80%) | Like=-0.08..-0.00 [-0.0822..-0.0821]*| it/evals=3350/38060 eff=8.8954% N=400 Z=-4.0(98.86%) | Like=-0.08..-0.00 [-0.0787..-0.0786]*| it/evals=3371/38312 eff=8.8916% N=400 Z=-4.0(98.88%) | Like=-0.08..-0.00 [-0.0776..-0.0775]*| it/evals=3381/38440 eff=8.8880% N=400 Z=-4.0(98.92%) | Like=-0.08..-0.00 [-0.0759..-0.0759]*| it/evals=3396/38626 eff=8.8840% N=400 Z=-4.0(98.93%) | Like=-0.08..-0.00 [-0.0756..-0.0756]*| it/evals=3400/38675 eff=8.8831% N=400 Have 2 modes Volume: ~exp(-12.47) * Expected Volume: exp(-8.55) Quality: ok param0: +0.0| +0.3 1111 22222 +0.7 | +1.0 param1: +0.0| +0.3 1111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-4.0(98.98%) | Like=-0.07..-0.00 [-0.0736..-0.0734]*| it/evals=3420/38930 eff=8.8762% N=400 [ultranest] Explored until L=-0.0002 [ultranest] Likelihood function evaluations: 39002 [ultranest] logZ = -4.026 +- 0.0611 [ultranest] Effective samples strategy satisfied (ESS = 2022.3, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.08 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [ultranest] logZ error budget: single: 0.08 bs:0.06 tail:0.01 total:0.06 required:<0.50 [ultranest] done iterating. logZ = -4.005 +- 0.069 single instance: logZ = -4.005 +- 0.075 bootstrapped : logZ = -4.026 +- 0.068 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▂▂▃▄▅▇▇▇▇▆▆▅▄▂▂▂▁▁▁▁▁▂▂▂▂▂▁▁▁▁▁▁▁▁▁ │1.00 0.34 +- 0.18 param1 : 0.00 │▁▁▁▂▂▃▄▄▆▇▇▆▇▅▅▄▃▂▂▁▁▁▁▂▁▂▁▂▂▂▂▁▁▁▁▁▁▁▁│1.00 0.34 +- 0.18 param2 : 0.00 │▁▁▁▂▃▄▅▅▆▇▆▆▅▆▅▆▄▂▂▂▁▁▁▁▂▁▂▂▂▁▂▁▁▁▁▁▁ ▁│1.00 0.34 +- 0.19 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=403, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-29.79, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=48, ncalls=602, regioncalls=0, ndraw=40, logz=-25.77, remainder_fraction=100.0000%, Lmin=-21.51, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=610, regioncalls=0, ndraw=40, logz=-25.50, remainder_fraction=100.0000%, Lmin=-21.14, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=795, regioncalls=0, ndraw=40, logz=-21.17, remainder_fraction=100.0000%, Lmin=-17.35, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=837, regioncalls=0, ndraw=40, logz=-20.38, remainder_fraction=100.0000%, Lmin=-16.81, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=144, ncalls=1063, regioncalls=0, ndraw=40, logz=-17.12, remainder_fraction=99.9998%, Lmin=-13.42, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=1090, regioncalls=0, ndraw=40, logz=-16.72, remainder_fraction=99.9997%, Lmin=-12.91, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=1249, regioncalls=0, ndraw=40, logz=-15.11, remainder_fraction=99.9983%, Lmin=-11.75, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=1383, regioncalls=0, ndraw=40, logz=-14.23, remainder_fraction=99.9958%, Lmin=-10.89, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=244, ncalls=1639, regioncalls=0, ndraw=40, logz=-12.67, remainder_fraction=99.9805%, Lmin=-9.58, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1671, regioncalls=0, ndraw=40, logz=-12.49, remainder_fraction=99.9769%, Lmin=-9.35, Lmax=-0.38 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=269, ncalls=1808, regioncalls=0, ndraw=40, logz=-11.97, remainder_fraction=99.9617%, Lmin=-9.01, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1813, regioncalls=0, ndraw=40, logz=-11.95, remainder_fraction=99.9607%, Lmin=-9.00, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=294, ncalls=1970, regioncalls=0, ndraw=40, logz=-11.41, remainder_fraction=99.9397%, Lmin=-8.52, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=2027, regioncalls=0, ndraw=40, logz=-11.29, remainder_fraction=99.9307%, Lmin=-8.45, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=343, ncalls=2308, regioncalls=0, ndraw=40, logz=-10.54, remainder_fraction=99.8454%, Lmin=-7.86, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2363, regioncalls=0, ndraw=40, logz=-10.44, remainder_fraction=99.8265%, Lmin=-7.77, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=2429, regioncalls=0, ndraw=40, logz=-10.29, remainder_fraction=99.7982%, Lmin=-7.63, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=378, ncalls=2549, regioncalls=0, ndraw=40, logz=-10.04, remainder_fraction=99.7364%, Lmin=-7.41, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=2696, regioncalls=0, ndraw=40, logz=-9.75, remainder_fraction=99.6485%, Lmin=-7.14, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=441, ncalls=2989, regioncalls=0, ndraw=40, logz=-9.30, remainder_fraction=99.4873%, Lmin=-6.79, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3055, regioncalls=0, ndraw=40, logz=-9.21, remainder_fraction=99.4312%, Lmin=-6.70, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=468, ncalls=3209, regioncalls=0, ndraw=40, logz=-9.03, remainder_fraction=99.3227%, Lmin=-6.51, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=486, ncalls=3346, regioncalls=0, ndraw=40, logz=-8.86, remainder_fraction=99.2078%, Lmin=-6.35, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3463, regioncalls=0, ndraw=40, logz=-8.74, remainder_fraction=99.0936%, Lmin=-6.24, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=519, ncalls=3636, regioncalls=0, ndraw=40, logz=-8.57, remainder_fraction=98.9066%, Lmin=-6.01, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=3829, regioncalls=0, ndraw=40, logz=-8.39, remainder_fraction=98.6729%, Lmin=-5.86, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=3922, regioncalls=0, ndraw=40, logz=-8.31, remainder_fraction=98.5540%, Lmin=-5.77, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=571, ncalls=4093, regioncalls=0, ndraw=40, logz=-8.15, remainder_fraction=98.3125%, Lmin=-5.62, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=582, ncalls=4186, regioncalls=0, ndraw=40, logz=-8.07, remainder_fraction=98.1841%, Lmin=-5.54, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=592, ncalls=4278, regioncalls=0, ndraw=40, logz=-8.00, remainder_fraction=98.0450%, Lmin=-5.48, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=4332, regioncalls=0, ndraw=40, logz=-7.94, remainder_fraction=97.9334%, Lmin=-5.38, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=618, ncalls=4487, regioncalls=0, ndraw=40, logz=-7.82, remainder_fraction=97.7782%, Lmin=-5.30, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=629, ncalls=4596, regioncalls=0, ndraw=40, logz=-7.75, remainder_fraction=97.6492%, Lmin=-5.22, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=4604, regioncalls=0, ndraw=40, logz=-7.74, remainder_fraction=97.6390%, Lmin=-5.21, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=4777, regioncalls=0, ndraw=40, logz=-7.61, remainder_fraction=97.2967%, Lmin=-5.04, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=663, ncalls=4900, regioncalls=0, ndraw=40, logz=-7.53, remainder_fraction=97.0999%, Lmin=-4.94, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=684, ncalls=5109, regioncalls=0, ndraw=40, logz=-7.39, remainder_fraction=96.7831%, Lmin=-4.80, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=5283, regioncalls=0, ndraw=40, logz=-7.30, remainder_fraction=96.4418%, Lmin=-4.69, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=5475, regioncalls=0, ndraw=40, logz=-7.19, remainder_fraction=96.0516%, Lmin=-4.63, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=739, ncalls=5675, regioncalls=0, ndraw=40, logz=-7.09, remainder_fraction=95.6202%, Lmin=-4.57, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=5779, regioncalls=0, ndraw=40, logz=-7.04, remainder_fraction=95.3727%, Lmin=-4.51, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=765, ncalls=5904, regioncalls=0, ndraw=40, logz=-6.97, remainder_fraction=95.0431%, Lmin=-4.46, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=793, ncalls=6212, regioncalls=0, ndraw=40, logz=-6.84, remainder_fraction=94.3407%, Lmin=-4.29, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=6303, regioncalls=0, ndraw=40, logz=-6.81, remainder_fraction=94.1019%, Lmin=-4.26, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=6398, regioncalls=0, ndraw=40, logz=-6.77, remainder_fraction=93.8311%, Lmin=-4.22, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=823, ncalls=6537, regioncalls=0, ndraw=40, logz=-6.71, remainder_fraction=93.4674%, Lmin=-4.17, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=6812, regioncalls=0, ndraw=40, logz=-6.60, remainder_fraction=92.6813%, Lmin=-4.02, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=873, ncalls=7060, regioncalls=0, ndraw=40, logz=-6.52, remainder_fraction=92.1112%, Lmin=-3.90, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=892, ncalls=7265, regioncalls=0, ndraw=40, logz=-6.44, remainder_fraction=91.4709%, Lmin=-3.76, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=7355, regioncalls=0, ndraw=40, logz=-6.41, remainder_fraction=91.2110%, Lmin=-3.72, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=921, ncalls=7598, regioncalls=0, ndraw=40, logz=-6.33, remainder_fraction=90.2987%, Lmin=-3.61, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=7916, regioncalls=0, ndraw=40, logz=-6.22, remainder_fraction=89.1798%, Lmin=-3.41, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=971, ncalls=8162, regioncalls=0, ndraw=40, logz=-6.14, remainder_fraction=88.3481%, Lmin=-3.30, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=8392, regioncalls=0, ndraw=40, logz=-6.07, remainder_fraction=87.3989%, Lmin=-3.20, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=8504, regioncalls=0, ndraw=40, logz=-6.03, remainder_fraction=86.8950%, Lmin=-3.16, Lmax=-0.09 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1026, ncalls=8822, regioncalls=0, ndraw=40, logz=-5.94, remainder_fraction=85.5353%, Lmin=-3.00, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=9085, regioncalls=0, ndraw=40, logz=-5.85, remainder_fraction=84.3177%, Lmin=-2.92, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1076, ncalls=9370, regioncalls=0, ndraw=40, logz=-5.77, remainder_fraction=83.0432%, Lmin=-2.81, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1082, ncalls=9442, regioncalls=0, ndraw=40, logz=-5.75, remainder_fraction=82.8032%, Lmin=-2.79, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=9668, regioncalls=0, ndraw=40, logz=-5.69, remainder_fraction=81.7374%, Lmin=-2.71, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1125, ncalls=9966, regioncalls=0, ndraw=40, logz=-5.61, remainder_fraction=80.3296%, Lmin=-2.61, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1149, ncalls=10262, regioncalls=0, ndraw=40, logz=-5.54, remainder_fraction=78.8718%, Lmin=-2.53, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=10279, regioncalls=0, ndraw=40, logz=-5.54, remainder_fraction=78.7871%, Lmin=-2.53, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=10530, regioncalls=0, ndraw=40, logz=-5.48, remainder_fraction=77.5202%, Lmin=-2.44, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1195, ncalls=10839, regioncalls=0, ndraw=40, logz=-5.41, remainder_fraction=75.7679%, Lmin=-2.35, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=10907, regioncalls=0, ndraw=40, logz=-5.40, remainder_fraction=75.3778%, Lmin=-2.32, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1225, ncalls=11221, regioncalls=0, ndraw=40, logz=-5.33, remainder_fraction=73.6085%, Lmin=-2.23, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=11508, regioncalls=0, ndraw=40, logz=-5.27, remainder_fraction=71.7301%, Lmin=-2.15, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=11637, regioncalls=0, ndraw=40, logz=-5.25, remainder_fraction=70.9030%, Lmin=-2.10, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1280, ncalls=11888, regioncalls=0, ndraw=40, logz=-5.20, remainder_fraction=69.3250%, Lmin=-2.04, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1299, ncalls=12126, regioncalls=0, ndraw=40, logz=-5.15, remainder_fraction=67.7058%, Lmin=-2.00, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=12138, regioncalls=0, ndraw=40, logz=-5.15, remainder_fraction=67.6453%, Lmin=-2.00, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1327, ncalls=12476, regioncalls=0, ndraw=40, logz=-5.09, remainder_fraction=65.6583%, Lmin=-1.95, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1348, ncalls=12742, regioncalls=0, ndraw=40, logz=-5.05, remainder_fraction=64.2646%, Lmin=-1.89, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=12761, regioncalls=0, ndraw=40, logz=-5.04, remainder_fraction=64.1257%, Lmin=-1.89, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1379, ncalls=13110, regioncalls=0, ndraw=40, logz=-4.99, remainder_fraction=62.5023%, Lmin=-1.81, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=13360, regioncalls=0, ndraw=40, logz=-4.95, remainder_fraction=61.1587%, Lmin=-1.75, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1425, ncalls=13678, regioncalls=0, ndraw=40, logz=-4.90, remainder_fraction=59.5772%, Lmin=-1.70, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1440, ncalls=13844, regioncalls=0, ndraw=40, logz=-4.88, remainder_fraction=58.4052%, Lmin=-1.66, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=13973, regioncalls=0, ndraw=40, logz=-4.86, remainder_fraction=57.5990%, Lmin=-1.63, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1469, ncalls=14213, regioncalls=0, ndraw=40, logz=-4.83, remainder_fraction=56.1777%, Lmin=-1.58, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1490, ncalls=14493, regioncalls=0, ndraw=40, logz=-4.80, remainder_fraction=54.3936%, Lmin=-1.55, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=14614, regioncalls=0, ndraw=40, logz=-4.78, remainder_fraction=53.7100%, Lmin=-1.53, Lmax=-0.02 [35mDEBUG [0m 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regioncalls=0, ndraw=40, logz=-4.03, remainder_fraction=2.4050%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3079, ncalls=34616, regioncalls=0, ndraw=40, logz=-4.03, remainder_fraction=2.2994%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=34883, regioncalls=0, ndraw=40, logz=-4.03, remainder_fraction=2.1887%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3127, ncalls=35216, regioncalls=0, ndraw=40, logz=-4.03, remainder_fraction=2.0546%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=35507, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.9462%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3173, ncalls=35791, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.8424%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=36124, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.7281%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3224, ncalls=36451, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.6321%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3240, ncalls=36655, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.5707%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3250, ncalls=36780, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.5338%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3278, ncalls=37127, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.4338%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3291, ncalls=37295, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.3896%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3300, ncalls=37422, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.3601%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3322, ncalls=37711, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.2899%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3330, ncalls=37809, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.2652%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3347, ncalls=38029, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.2136%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3350, ncalls=38060, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.2049%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3371, ncalls=38312, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.1448%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3381, ncalls=38440, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.1172%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3396, ncalls=38626, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.0775%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3400, ncalls=38675, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.0670%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3420, ncalls=38930, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=1.0167%, Lmin=-0.07, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.0002 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 39002 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -4.026 +- 0.0611 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 2022.3, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.08 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.08 bs:0.06 tail:0.01 total:0.06 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_stepsampling.py::test_stepsampler_regionslice | 20.68 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.24) * Expected Volume: exp(0.00) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.0000|***************************************************| +1.0000 param2: +0.00|***************************************************| +1.00 Z=-inf(0.00%) | Like=-30.54..-0.16 [-30.5444..-8.3389] | it/evals=0/403 eff=0.0000% N=400 Z=-27.7(0.00%) | Like=-23.38..-0.16 [-30.5444..-8.3389] | it/evals=25/513 eff=22.1239% N=400 Z=-24.2(0.00%) | Like=-19.84..-0.16 [-30.5444..-8.3389] | it/evals=47/610 eff=22.3810% N=400 Z=-23.8(0.00%) | Like=-19.64..-0.16 [-30.5444..-8.3389] | it/evals=50/625 eff=22.2222% N=400 Z=-21.2(0.00%) | Like=-17.55..-0.16 [-30.5444..-8.3389] | it/evals=72/729 eff=21.8845% N=400 Mono-modal Volume: ~exp(-4.56) * Expected Volume: exp(-0.23) Quality: ok param0: +0.0000|***************************************************| +1.0000 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-19.9(0.00%) | Like=-16.25..-0.16 [-30.5444..-8.3389] | it/evals=90/815 eff=21.6867% N=400 Z=-19.0(0.00%) | Like=-15.25..-0.16 [-30.5444..-8.3389] | it/evals=100/859 eff=21.7865% N=400 Z=-16.2(0.00%) | Like=-12.56..-0.16 [-30.5444..-8.3389] | it/evals=135/1038 eff=21.1599% N=400 Z=-15.4(0.00%) | Like=-11.94..-0.16 [-30.5444..-8.3389] | it/evals=150/1114 eff=21.0084% N=400 Mono-modal Volume: ~exp(-4.56) Expected Volume: exp(-0.45) Quality: ok param0: +0.0000|***************************************************| +1.0000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-13.9(0.00%) | Like=-10.51..-0.16 [-30.5444..-8.3389] | it/evals=180/1256 eff=21.0280% N=400 Z=-13.1(0.01%) | Like=-9.90..-0.02 [-30.5444..-8.3389] | it/evals=200/1355 eff=20.9424% N=400 Z=-12.0(0.02%) | Like=-9.07..-0.02 [-30.5444..-8.3389] | it/evals=235/1534 eff=20.7231% N=400 Z=-11.7(0.03%) | Like=-8.71..-0.02 [-30.5444..-8.3389] | it/evals=250/1615 eff=20.5761% N=400 Z=-11.2(0.04%) | Like=-8.30..-0.02 [-8.3047..-4.8735] | it/evals=267/1713 eff=20.3351% N=400 Mono-modal Volume: ~exp(-4.97) * Expected Volume: exp(-0.67) Quality: ok param0: +0.0000|***************************************************| +1.0000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-11.2(0.05%) | Like=-8.28..-0.02 [-8.3047..-4.8735] | it/evals=270/1733 eff=20.2551% N=400 Z=-10.6(0.09%) | Like=-7.82..-0.02 [-8.3047..-4.8735] | it/evals=300/1921 eff=19.7239% N=400 Z=-9.9(0.16%) | Like=-7.00..-0.02 [-8.3047..-4.8735] | it/evals=333/2122 eff=19.3380% N=400 Z=-9.6(0.21%) | Like=-6.84..-0.02 [-8.3047..-4.8735] | it/evals=350/2211 eff=19.3263% N=400 Mono-modal Volume: ~exp(-5.10) * Expected Volume: exp(-0.90) Quality: ok param0: +0.00|************************************************** | +1.00 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-9.5(0.26%) | Like=-6.58..-0.02 [-8.3047..-4.8735] | it/evals=360/2267 eff=19.2823% N=400 Z=-8.9(0.44%) | Like=-6.17..-0.02 [-8.3047..-4.8735] | it/evals=395/2499 eff=18.8185% N=400 Z=-8.8(0.48%) | Like=-6.11..-0.02 [-8.3047..-4.8735] | it/evals=400/2529 eff=18.7882% N=400 Z=-8.4(0.76%) | Like=-5.71..-0.02 [-8.3047..-4.8735] | it/evals=435/2759 eff=18.4400% N=400 Mono-modal Volume: ~exp(-5.10) Expected Volume: exp(-1.12) Quality: ok param0: +0.00|************************************************** | +1.00 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-8.2(0.90%) | Like=-5.54..-0.02 [-8.3047..-4.8735] | it/evals=450/2853 eff=18.3449% N=400 Z=-8.0(1.12%) | Like=-5.37..-0.02 [-8.3047..-4.8735] | it/evals=468/2980 eff=18.1395% N=400 Z=-7.8(1.32%) | Like=-5.20..-0.02 [-8.3047..-4.8735] | it/evals=484/3090 eff=17.9926% N=400 Z=-7.7(1.50%) | Like=-5.11..-0.02 [-8.3047..-4.8735] | it/evals=500/3213 eff=17.7746% N=400 Z=-7.4(2.00%) | Like=-4.87..-0.02 [-4.8667..-3.5363] | it/evals=534/3479 eff=17.3433% N=400 Mono-modal Volume: ~exp(-5.15) * Expected Volume: exp(-1.35) Quality: ok param0: +0.00|***************************************************| +1.00 param1: +0.00| **************************************************| +1.00 param2: +0.00|***************************************************| +1.00 Z=-7.3(2.11%) | Like=-4.85..-0.02 [-4.8667..-3.5363] | it/evals=540/3522 eff=17.2966% N=400 Z=-7.3(2.25%) | Like=-4.79..-0.02 [-4.8667..-3.5363] | it/evals=550/3583 eff=17.2793% N=400 Z=-7.0(2.78%) | Like=-4.59..-0.02 [-4.8667..-3.5363] | it/evals=581/3831 eff=16.9338% N=400 Z=-6.9(3.26%) | Like=-4.37..-0.02 [-4.8667..-3.5363] | it/evals=600/3990 eff=16.7131% N=400 Mono-modal Volume: ~exp(-5.90) * Expected Volume: exp(-1.57) Quality: ok param0: +0.00| ************************************************* | +1.00 param1: +0.00| ********************************************** * | +1.00 param2: +0.00| ************************************************ | +1.00 Z=-6.7(4.01%) | Like=-4.16..-0.02 [-4.8667..-3.5363] | it/evals=630/4220 eff=16.4921% N=400 Z=-6.6(4.59%) | Like=-4.07..-0.02 [-4.8667..-3.5363] | it/evals=650/4402 eff=16.2419% N=400 Z=-6.4(5.36%) | Like=-3.85..-0.02 [-4.8667..-3.5363] | it/evals=681/4678 eff=15.9187% N=400 Z=-6.3(6.01%) | Like=-3.73..-0.02 [-4.8667..-3.5363] | it/evals=700/4842 eff=15.7587% N=400 Mono-modal Volume: ~exp(-6.01) * Expected Volume: exp(-1.80) Quality: ok param0: +0.00| *********************************************** | +1.00 param1: +0.00| *********************************************** | +1.00 param2: +0.00| * ********************************************** | +1.00 Z=-6.2(6.84%) | Like=-3.61..-0.02 [-4.8667..-3.5363] | it/evals=720/5010 eff=15.6182% N=400 Z=-6.0(7.92%) | Like=-3.44..-0.02 [-3.5317..-3.1184] | it/evals=750/5259 eff=15.4353% N=400 Z=-5.9(9.14%) | Like=-3.28..-0.02 [-3.5317..-3.1184] | it/evals=782/5542 eff=15.2081% N=400 Z=-5.8(9.83%) | Like=-3.16..-0.02 [-3.5317..-3.1184] | it/evals=800/5704 eff=15.0830% N=400 Mono-modal Volume: ~exp(-6.16) * Expected Volume: exp(-2.02) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param1: rho=0.75 param0: +0.00| * ******************************************** | +1.00 param1: +0.00| ********************************************* | +1.00 param2: +0.00| ********************************************* | +1.00 Z=-5.7(10.42%) | Like=-3.09..-0.02 [-3.1048..-2.9456] | it/evals=810/5789 eff=15.0306% N=400 Z=-5.6(11.97%) | Like=-2.91..-0.02 [-2.9426..-2.8975] | it/evals=839/6053 eff=14.8417% N=400 Z=-5.5(12.51%) | Like=-2.84..-0.02 [-2.8353..-2.8323]*| it/evals=850/6154 eff=14.7723% N=400 Z=-5.4(13.89%) | Like=-2.75..-0.02 [-2.7539..-2.7527]*| it/evals=878/6416 eff=14.5944% N=400 Have 2 modes Volume: ~exp(-6.49) * Expected Volume: exp(-2.25) Quality: ok positive degeneracy between param1 and param0: rho=0.75 positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| 222222222222222222221111111111111111111111 | +1.0 param1: +0.00| 2222222222222222222221111111111111111111111 | +1.00 param2: +0.00| 2 22222222222222222211111111111111111111111 | +1.00 Z=-5.3(15.20%) | Like=-2.66..-0.02 [-2.6627..-2.6606]*| it/evals=900/6627 eff=14.4532% N=400 Z=-5.3(16.37%) | Like=-2.57..-0.02 [-2.5723..-2.5635]*| it/evals=921/6843 eff=14.2946% N=400 Z=-5.2(17.92%) | Like=-2.46..-0.02 [-2.4569..-2.4441] | it/evals=950/7162 eff=14.0491% N=400 Z=-5.0(20.30%) | Like=-2.28..-0.02 [-2.2804..-2.2800]*| it/evals=988/7564 eff=13.7912% N=400 Have 2 modes Volume: ~exp(-6.85) * Expected Volume: exp(-2.47) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| 22222222222222222222111111111111111111111 | +1.0 param1: +0.00| 2222222222222222222221111111111111111111111 | +1.00 param2: +0.0| 2222222222222222222211 111111111111111111 | +1.0 Z=-5.0(20.47%) | Like=-2.26..-0.02 [-2.2800..-2.2623] | it/evals=990/7589 eff=13.7710% N=400 Z=-5.0(21.00%) | Like=-2.23..-0.02 [-2.2302..-2.2298]*| it/evals=1000/7701 eff=13.6968% N=400 Z=-4.9(23.25%) | Like=-2.11..-0.02 [-2.1118..-2.1065]*| it/evals=1037/8120 eff=13.4326% N=400 Z=-4.8(24.28%) | Like=-2.09..-0.02 [-2.0895..-2.0887]*| it/evals=1050/8276 eff=13.3316% N=400 Have 2 modes Volume: ~exp(-6.85) Expected Volume: exp(-2.70) Quality: ok positive degeneracy between param2 and param0: rho=0.78 param0: +0.0| 20222222222222222222 1111111111111111111 | +1.0 param1: +0.0| 2222222222222222222211111111111111111111 | +1.0 param2: +0.0| 222222222222222222221011111111111111111 | +1.0 Z=-4.8(26.27%) | Like=-1.97..-0.02 [-1.9672..-1.9657]*| it/evals=1080/8624 eff=13.1323% N=400 Z=-4.7(27.83%) | Like=-1.91..-0.01 [-1.9129..-1.9126]*| it/evals=1100/8869 eff=12.9885% N=400 Z=-4.6(30.82%) | Like=-1.83..-0.01 [-1.8262..-1.8208]*| it/evals=1137/9326 eff=12.7381% N=400 Z=-4.6(31.84%) | Like=-1.80..-0.01 [-1.7968..-1.7915]*| it/evals=1150/9487 eff=12.6554% N=400 Have 2 modes Volume: ~exp(-7.07) * Expected Volume: exp(-2.92) Quality: ok positive degeneracy between param2 and param0: rho=0.79 param0: +0.0| 2222222222222222222 111111111111111111 | +1.0 param1: +0.0| 222222222222222222 1111111111111111111 | +1.0 param2: +0.0| 2222222222222222222 1111111111111111111 | +1.0 Z=-4.5(33.40%) | Like=-1.73..-0.01 [-1.7405..-1.7299] | it/evals=1170/9717 eff=12.5577% N=400 Z=-4.5(35.63%) | Like=-1.65..-0.01 [-1.6475..-1.6397]*| it/evals=1200/10066 eff=12.4146% N=400 Z=-4.4(38.14%) | Like=-1.55..-0.01 [-1.5480..-1.5478]*| it/evals=1235/10501 eff=12.2265% N=400 Z=-4.4(39.40%) | Like=-1.52..-0.01 [-1.5153..-1.5151]*| it/evals=1250/10690 eff=12.1477% N=400 Have 2 modes Volume: ~exp(-7.36) * Expected Volume: exp(-3.15) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.78 param0: +0.0| 22222222222222222 1111111111111111 | +1.0 param1: +0.0| 2222222222222222 111111111111111111 | +1.0 param2: +0.0| 2222222222222222 11111111111111111 | +1.0 Z=-4.4(40.19%) | Like=-1.49..-0.01 [-1.4890..-1.4862]*| it/evals=1260/10813 eff=12.1003% N=400 Z=-4.3(42.65%) | Like=-1.43..-0.01 [-1.4252..-1.4225]*| it/evals=1292/11207 eff=11.9552% N=400 Z=-4.3(43.15%) | Like=-1.41..-0.01 [-1.4078..-1.3917] | it/evals=1300/11308 eff=11.9179% N=400 Z=-4.2(45.42%) | Like=-1.34..-0.01 [-1.3419..-1.3369]*| it/evals=1330/11695 eff=11.7751% N=400 Have 2 modes Volume: ~exp(-7.81) * Expected Volume: exp(-3.37) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.77 param0: +0.0| 222222222222222 1111111111111111 | +1.0 param1: +0.0| 222222222222222 1111111111111111 | +1.0 param2: +0.0| 222222222222222 1111111111111111 | +1.0 Z=-4.2(46.96%) | Like=-1.30..-0.01 [-1.2992..-1.2956]*| it/evals=1350/11939 eff=11.6995% N=400 Z=-4.2(47.87%) | Like=-1.27..-0.01 [-1.2666..-1.2653]*| it/evals=1362/12099 eff=11.6420% N=400 Z=-4.2(49.21%) | Like=-1.23..-0.01 [-1.2298..-1.2292]*| it/evals=1379/12311 eff=11.5775% N=400 Z=-4.1(50.32%) | Like=-1.20..-0.01 [-1.2049..-1.2044]*| it/evals=1396/12533 eff=11.5058% N=400 Z=-4.1(50.50%) | Like=-1.20..-0.01 [-1.2012..-1.1996]*| it/evals=1400/12583 eff=11.4914% N=400 Z=-4.1(51.55%) | Like=-1.17..-0.01 [-1.1748..-1.1724]*| it/evals=1414/12755 eff=11.4448% N=400 Z=-4.1(53.17%) | Like=-1.12..-0.01 [-1.1239..-1.1230]*| it/evals=1435/13004 eff=11.3853% N=400 Have 2 modes Volume: ~exp(-7.81) Expected Volume: exp(-3.60) Quality: ok positive degeneracy between param2 and param0: rho=0.78 param0: +0.0| 22222222222222 111111111111110 | +1.0 param1: +0.0| 22222222222222 111111111111111 | +1.0 param2: +0.0| 222222222222220 011111111111111 | +1.0 Z=-4.1(53.71%) | Like=-1.11..-0.01 [-1.1058..-1.1053]*| it/evals=1442/13094 eff=11.3597% N=400 Z=-4.1(54.29%) | Like=-1.09..-0.01 [-1.0913..-1.0910]*| it/evals=1450/13202 eff=11.3264% N=400 Z=-4.0(56.00%) | Like=-1.04..-0.01 [-1.0445..-1.0386]*| it/evals=1476/13509 eff=11.2594% N=400 Z=-4.0(57.05%) | Like=-1.01..-0.01 [-1.0062..-0.9998]*| it/evals=1492/13706 eff=11.2130% N=400 Z=-4.0(57.62%) | Like=-0.99..-0.01 [-0.9915..-0.9898]*| it/evals=1500/13802 eff=11.1924% N=400 Z=-4.0(58.81%) | Like=-0.96..-0.01 [-0.9629..-0.9620]*| it/evals=1518/14041 eff=11.1282% N=400 Have 2 modes Volume: ~exp(-8.41) * Expected Volume: exp(-3.82) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| 22222222222222 1111111111111 | +1.0 param1: +0.0| 2222222222222 111111111111111 | +1.0 param2: +0.0| 22222222222222 111111111111111 | +1.0 Z=-4.0(59.69%) | Like=-0.94..-0.01 [-0.9429..-0.9420]*| it/evals=1530/14187 eff=11.0974% N=400 Z=-3.9(60.84%) | Like=-0.91..-0.01 [-0.9129..-0.9116]*| it/evals=1548/14407 eff=11.0516% N=400 Z=-3.9(60.99%) | Like=-0.91..-0.01 [-0.9093..-0.9066]*| it/evals=1550/14429 eff=11.0485% N=400 Z=-3.9(62.34%) | Like=-0.87..-0.01 [-0.8710..-0.8671]*| it/evals=1571/14720 eff=10.9707% N=400 Z=-3.9(64.02%) | Like=-0.81..-0.01 [-0.8126..-0.8114]*| it/evals=1598/15053 eff=10.9056% N=400 Z=-3.9(64.14%) | Like=-0.81..-0.01 [-0.8112..-0.8112]*| it/evals=1600/15075 eff=10.9029% N=400 Z=-3.9(64.91%) | Like=-0.80..-0.01 [-0.7971..-0.7946]*| it/evals=1614/15259 eff=10.8621% N=400 Have 2 modes Volume: ~exp(-8.64) * Expected Volume: exp(-4.05) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| 2222222222222 1111111111111 | +1.0 param1: +0.0| 2222222222222 1111111111111 | +1.0 param2: +0.0| 2222222222222 1111111111111 | +1.0 Z=-3.9(65.31%) | Like=-0.79..-0.01 [-0.7893..-0.7882]*| it/evals=1620/15330 eff=10.8506% N=400 Z=-3.8(67.13%) | Like=-0.74..-0.01 [-0.7450..-0.7446]*| it/evals=1650/15708 eff=10.7787% N=400 Z=-3.8(68.00%) | Like=-0.72..-0.01 [-0.7206..-0.7206]*| it/evals=1665/15894 eff=10.7461% N=400 Z=-3.8(68.88%) | Like=-0.70..-0.01 [-0.6994..-0.6994]*| it/evals=1680/16091 eff=10.7068% N=400 Z=-3.8(69.75%) | Like=-0.68..-0.01 [-0.6807..-0.6797]*| it/evals=1696/16286 eff=10.6761% N=400 Z=-3.8(69.99%) | Like=-0.68..-0.01 [-0.6789..-0.6773]*| it/evals=1700/16343 eff=10.6630% N=400 Have 2 modes Volume: ~exp(-8.91) * Expected Volume: exp(-4.27) Quality: ok positive degeneracy between param2 and param0: rho=0.78 param0: +0.0| 222222222222 111111111111 | +1.0 param1: +0.0| 22222222222 1111111111111 | +1.0 param2: +0.0| 2222222222222 111111111111 | +1.0 Z=-3.8(70.50%) | Like=-0.67..-0.01 [-0.6704..-0.6673]*| it/evals=1710/16469 eff=10.6416% N=400 Z=-3.8(71.31%) | Like=-0.65..-0.01 [-0.6492..-0.6490]*| it/evals=1724/16640 eff=10.6158% N=400 Z=-3.8(72.33%) | Like=-0.64..-0.01 [-0.6366..-0.6355]*| it/evals=1743/16878 eff=10.5777% N=400 Z=-3.8(72.70%) | Like=-0.63..-0.01 [-0.6313..-0.6300]*| it/evals=1750/16966 eff=10.5638% N=400 Z=-3.7(74.05%) | Like=-0.61..-0.01 [-0.6059..-0.6054]*| it/evals=1776/17287 eff=10.5170% N=400 Have 2 modes Volume: ~exp(-8.91) Expected Volume: exp(-4.50) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.77 param0: +0.0| 222222222222 11111111111 +0.8 | +1.0 param1: +0.0| 22222222222 11111111111 +0.8 | +1.0 param2: +0.0| 22222222222 01111111111 +0.8 | +1.0 Z=-3.7(75.28%) | Like=-0.58..-0.01 [-0.5790..-0.5787]*| it/evals=1800/17586 eff=10.4736% N=400 Z=-3.7(75.93%) | Like=-0.57..-0.01 [-0.5656..-0.5655]*| it/evals=1814/17759 eff=10.4499% N=400 Z=-3.7(76.84%) | Like=-0.55..-0.01 [-0.5492..-0.5489]*| it/evals=1833/17988 eff=10.4219% N=400 Z=-3.7(77.53%) | Like=-0.53..-0.01 [-0.5334..-0.5330]*| it/evals=1849/18188 eff=10.3946% N=400 Z=-3.7(77.57%) | Like=-0.53..-0.01 [-0.5330..-0.5310]*| it/evals=1850/18198 eff=10.3944% N=400 Z=-3.7(78.32%) | Like=-0.51..-0.01 [-0.5071..-0.5064]*| it/evals=1867/18425 eff=10.3578% N=400 Z=-3.7(78.94%) | Like=-0.49..-0.01 [-0.4931..-0.4925]*| it/evals=1881/18593 eff=10.3391% N=400 Have 2 modes Volume: ~exp(-8.91) Expected Volume: exp(-4.73) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| 2222222222 1111111110 +0.8 | +1.0 param1: +0.0| 2222222222 1111111111 +0.8 | +1.0 param2: +0.0| 2222222222 01111111111 +0.8 | +1.0 Z=-3.7(79.32%) | Like=-0.49..-0.01 [-0.4882..-0.4882]*| it/evals=1890/18721 eff=10.3160% N=400 Z=-3.7(79.75%) | Like=-0.48..-0.01 [-0.4822..-0.4805]*| it/evals=1900/18845 eff=10.3009% N=400 Z=-3.7(80.71%) | Like=-0.46..-0.01 [-0.4623..-0.4619]*| it/evals=1924/19135 eff=10.2695% N=400 Z=-3.6(81.66%) | Like=-0.45..-0.01 [-0.4452..-0.4446]*| it/evals=1949/19449 eff=10.2315% N=400 Z=-3.6(81.70%) | Like=-0.44..-0.01 [-0.4446..-0.4440]*| it/evals=1950/19462 eff=10.2298% N=400 Have 2 modes Volume: ~exp(-9.24) * Expected Volume: exp(-4.95) Quality: ok positive degeneracy between param2 and param0: rho=0.75 param0: +0.0| 2222222222 111111111 +0.8 | +1.0 param1: +0.0| 2222222222 111111111 +0.8 | +1.0 param2: +0.0| +0.2 222222222 1111111111 +0.8 | +1.0 Z=-3.6(82.82%) | Like=-0.42..-0.01 [-0.4219..-0.4214]*| it/evals=1980/19839 eff=10.1857% N=400 Z=-3.6(83.53%) | Like=-0.41..-0.01 [-0.4100..-0.4083]*| it/evals=2000/20095 eff=10.1549% N=400 Z=-3.6(84.49%) | Like=-0.39..-0.01 [-0.3914..-0.3911]*| it/evals=2029/20470 eff=10.1096% N=400 Z=-3.6(85.15%) | Like=-0.38..-0.01 [-0.3806..-0.3792]*| it/evals=2050/20727 eff=10.0851% N=400 Have 2 modes Volume: ~exp(-9.49) * Expected Volume: exp(-5.18) Quality: ok positive degeneracy between param1 and param0: rho=0.76 param0: +0.0| +0.2 222222222 111111111 +0.8 | +1.0 param1: +0.0| +0.2 222222222 111111111 +0.8 | +1.0 param2: +0.0| +0.2 222222222 111111111 +0.8 | +1.0 Z=-3.6(85.76%) | Like=-0.37..-0.01 [-0.3668..-0.3667]*| it/evals=2070/20962 eff=10.0671% N=400 Z=-3.6(86.22%) | Like=-0.36..-0.01 [-0.3565..-0.3561]*| it/evals=2085/21141 eff=10.0526% N=400 Z=-3.6(86.65%) | Like=-0.35..-0.01 [-0.3478..-0.3475]*| it/evals=2100/21332 eff=10.0325% N=400 Z=-3.6(87.46%) | Like=-0.33..-0.01 [-0.3285..-0.3279]*| it/evals=2129/21717 eff=9.9873% N=400 Z=-3.6(87.94%) | Like=-0.32..-0.01 [-0.3184..-0.3181]*| it/evals=2147/21931 eff=9.9717% N=400 Z=-3.6(88.02%) | Like=-0.32..-0.01 [-0.3164..-0.3163]*| it/evals=2150/21966 eff=9.9694% N=400 Have 2 modes Volume: ~exp(-9.70) * Expected Volume: exp(-5.40) Quality: ok param0: +0.0| +0.2 22222222 111111111 +0.8 | +1.0 param1: +0.0| +0.2 222222222 111111111 +0.8 | +1.0 param2: +0.0| +0.2 222222222 111111111 +0.8 | +1.0 Z=-3.6(88.30%) | Like=-0.31..-0.01 [-0.3081..-0.3068]*| it/evals=2160/22098 eff=9.9548% N=400 Z=-3.6(88.99%) | Like=-0.30..-0.01 [-0.2979..-0.2974]*| it/evals=2188/22443 eff=9.9261% N=400 Z=-3.6(89.27%) | Like=-0.29..-0.01 [-0.2898..-0.2893]*| it/evals=2200/22587 eff=9.9157% N=400 Z=-3.6(89.56%) | Like=-0.28..-0.01 [-0.2823..-0.2810]*| it/evals=2212/22762 eff=9.8918% N=400 Z=-3.5(89.88%) | Like=-0.27..-0.01 [-0.2749..-0.2746]*| it/evals=2226/22930 eff=9.8802% N=400 Have 2 modes Volume: ~exp(-9.86) * Expected Volume: exp(-5.63) Quality: ok param0: +0.0| +0.2 22222222 1111111 +0.8 | +1.0 param1: +0.0| +0.2 22222222 1111111 +0.8 | +1.0 param2: +0.0| +0.2 2222222 111111111 +0.8 | +1.0 Z=-3.5(90.42%) | Like=-0.27..-0.01 [-0.2664..-0.2661]*| it/evals=2250/23226 eff=9.8572% N=400 Z=-3.5(90.98%) | Like=-0.26..-0.00 [-0.2554..-0.2554]*| it/evals=2277/23584 eff=9.8214% N=400 Z=-3.5(91.44%) | Like=-0.24..-0.00 [-0.2450..-0.2449]*| it/evals=2300/23866 eff=9.8014% N=400 Z=-3.5(91.84%) | Like=-0.24..-0.00 [-0.2371..-0.2365]*| it/evals=2321/24139 eff=9.7772% N=400 Have 2 modes Volume: ~exp(-10.15) * Expected Volume: exp(-5.85) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| +0.2 22222222 1111111 +0.8 | +1.0 param1: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 param2: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 Z=-3.5(92.18%) | Like=-0.23..-0.00 [-0.2282..-0.2277]*| it/evals=2340/24371 eff=9.7618% N=400 Z=-3.5(92.35%) | Like=-0.22..-0.00 [-0.2239..-0.2232]*| it/evals=2350/24502 eff=9.7502% N=400 Z=-3.5(92.84%) | Like=-0.21..-0.00 [-0.2141..-0.2139]*| it/evals=2379/24866 eff=9.7237% N=400 Z=-3.5(93.18%) | Like=-0.21..-0.00 [-0.2063..-0.2063]*| it/evals=2400/25122 eff=9.7080% N=400 Z=-3.5(93.62%) | Like=-0.20..-0.00 [-0.2018..-0.2018]*| it/evals=2429/25482 eff=9.6842% N=400 Have 2 modes Volume: ~exp(-10.37) * Expected Volume: exp(-6.08) Quality: ok positive degeneracy between param2 and param0: rho=0.75 param0: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 param1: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 param2: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 Z=-3.5(93.63%) | Like=-0.20..-0.00 [-0.2018..-0.2009]*| it/evals=2430/25495 eff=9.6832% N=400 Z=-3.5(93.82%) | Like=-0.20..-0.00 [-0.1954..-0.1953]*| it/evals=2443/25664 eff=9.6699% N=400 Z=-3.5(93.91%) | Like=-0.19..-0.00 [-0.1945..-0.1935]*| it/evals=2450/25764 eff=9.6594% N=400 Z=-3.5(94.31%) | Like=-0.18..-0.00 [-0.1820..-0.1820]*| it/evals=2479/26125 eff=9.6365% N=400 Z=-3.5(94.53%) | Like=-0.18..-0.00 [-0.1784..-0.1783]*| it/evals=2496/26344 eff=9.6207% N=400 Z=-3.5(94.58%) | Like=-0.18..-0.00 [-0.1771..-0.1769]*| it/evals=2500/26395 eff=9.6172% N=400 Have 2 modes Volume: ~exp(-10.61) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.2 222222 111111 +0.8 | +1.0 param1: +0.0| +0.2 2222222 1111111 +0.8 | +1.0 param2: +0.0| +0.2 222222 1111111 +0.7 | +1.0 Z=-3.5(94.83%) | Like=-0.17..-0.00 [-0.1710..-0.1709]*| it/evals=2520/26644 eff=9.6022% N=400 Z=-3.5(95.14%) | Like=-0.16..-0.00 [-0.1637..-0.1634]*| it/evals=2547/26984 eff=9.5810% N=400 Z=-3.5(95.18%) | Like=-0.16..-0.00 [-0.1632..-0.1631]*| it/evals=2550/27018 eff=9.5800% N=400 Z=-3.5(95.33%) | Like=-0.16..-0.00 [-0.1594..-0.1582]*| it/evals=2564/27176 eff=9.5757% N=400 Z=-3.5(95.47%) | Like=-0.15..-0.00 [-0.1547..-0.1547]*| it/evals=2577/27332 eff=9.5685% N=400 Z=-3.5(95.62%) | Like=-0.15..-0.00 [-0.1504..-0.1497]*| it/evals=2591/27514 eff=9.5559% N=400 Z=-3.5(95.71%) | Like=-0.15..-0.00 [-0.1470..-0.1469]*| it/evals=2600/27635 eff=9.5465% N=400 Have 2 modes Volume: ~exp(-10.71) * Expected Volume: exp(-6.53) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| +0.3 222222 111111 +0.8 | +1.0 param1: +0.0| +0.3 2222222 1111111 +0.7 | +1.0 param2: +0.0| +0.3 22222 111111 +0.7 | +1.0 Z=-3.5(95.81%) | Like=-0.15..-0.00 [-0.1456..-0.1455]*| it/evals=2610/27762 eff=9.5388% N=400 Z=-3.5(96.08%) | Like=-0.14..-0.00 [-0.1386..-0.1384]*| it/evals=2639/28125 eff=9.5185% N=400 Z=-3.5(96.18%) | Like=-0.14..-0.00 [-0.1366..-0.1366]*| it/evals=2650/28264 eff=9.5105% N=400 Z=-3.5(96.30%) | Like=-0.13..-0.00 [-0.1332..-0.1328]*| it/evals=2663/28442 eff=9.4965% N=400 Z=-3.5(96.48%) | Like=-0.13..-0.00 [-0.1289..-0.1289]*| it/evals=2684/28707 eff=9.4818% N=400 Have 2 modes Volume: ~exp(-11.00) * Expected Volume: exp(-6.75) Quality: ok positive degeneracy between param1 and param0: rho=0.75 positive degeneracy between param2 and param0: rho=0.76 positive degeneracy between param2 and param1: rho=0.75 param0: +0.0| +0.3 222222 111111 +0.7 | +1.0 param1: +0.0| +0.3 222222 11111 +0.7 | +1.0 param2: +0.0| +0.3 22222 111111 +0.7 | +1.0 Z=-3.5(96.61%) | Like=-0.12..-0.00 [-0.1246..-0.1245]*| it/evals=2700/28902 eff=9.4730% N=400 Z=-3.5(96.72%) | Like=-0.12..-0.00 [-0.1220..-0.1220]*| it/evals=2713/29056 eff=9.4675% N=400 Z=-3.5(96.82%) | Like=-0.12..-0.00 [-0.1204..-0.1203]*| it/evals=2726/29228 eff=9.4561% N=400 Z=-3.5(96.93%) | Like=-0.12..-0.00 [-0.1190..-0.1188]*| it/evals=2741/29407 eff=9.4494% N=400 Z=-3.5(96.99%) | Like=-0.12..-0.00 [-0.1164..-0.1162]*| it/evals=2750/29514 eff=9.4456% N=400 Z=-3.5(97.20%) | Like=-0.11..-0.00 [-0.1100..-0.1098]*| it/evals=2780/29886 eff=9.4282% N=400 Have 2 modes Volume: ~exp(-11.11) * Expected Volume: exp(-6.98) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.75 positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| +0.3 22222 11111 +0.7 | +1.0 param1: +0.0| +0.3 22222 11111 +0.7 | +1.0 param2: +0.0| +0.3 22222 11111 +0.7 | +1.0 Z=-3.5(97.27%) | Like=-0.11..-0.00 [-0.1081..-0.1080]*| it/evals=2790/30016 eff=9.4206% N=400 Z=-3.5(97.33%) | Like=-0.11..-0.00 [-0.1060..-0.1056]*| it/evals=2800/30134 eff=9.4168% N=400 Z=-3.5(97.46%) | Like=-0.10..-0.00 [-0.1015..-0.1014]*| it/evals=2821/30397 eff=9.4043% N=400 Z=-3.5(97.63%) | Like=-0.10..-0.00 [-0.0983..-0.0980]*| it/evals=2850/30769 eff=9.3846% N=400 Z=-3.5(97.72%) | Like=-0.10..-0.00 [-0.0957..-0.0956]*| it/evals=2865/30965 eff=9.3735% N=400 Have 2 modes Volume: ~exp(-11.79) * Expected Volume: exp(-7.20) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.75 positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| +0.3 22222 11111 +0.7 | +1.0 param1: +0.0| +0.3 22222 11111 +0.7 | +1.0 param2: +0.0| +0.3 22222 11111 +0.7 | +1.0 Z=-3.5(97.80%) | Like=-0.09..-0.00 [-0.0931..-0.0929]*| it/evals=2880/31161 eff=9.3625% N=400 Z=-3.5(97.90%) | Like=-0.09..-0.00 [-0.0895..-0.0894]*| it/evals=2900/31412 eff=9.3512% N=400 Z=-3.5(98.02%) | Like=-0.09..-0.00 [-0.0858..-0.0858]*| it/evals=2925/31744 eff=9.3319% N=400 Z=-3.5(98.14%) | Like=-0.08..-0.00 [-0.0833..-0.0833]*| it/evals=2950/32044 eff=9.3225% N=400 Have 2 modes Volume: ~exp(-11.83) * Expected Volume: exp(-7.43) Quality: ok positive degeneracy between param1 and param0: rho=0.76 positive degeneracy between param2 and param0: rho=0.75 positive degeneracy between param2 and param1: rho=0.75 param0: +0.0| +0.3 2222 11111 +0.7 | +1.0 param1: +0.0| +0.3 22222 11111 +0.7 | +1.0 param2: +0.0| +0.3 22222 11111 +0.7 | +1.0 Z=-3.5(98.23%) | Like=-0.08..-0.00 [-0.0814..-0.0812]*| it/evals=2970/32295 eff=9.3118% N=400 Z=-3.5(98.33%) | Like=-0.08..-0.00 [-0.0781..-0.0780]*| it/evals=2995/32632 eff=9.2920% N=400 Z=-3.5(98.35%) | Like=-0.08..-0.00 [-0.0774..-0.0773]*| it/evals=3000/32698 eff=9.2885% N=400 Z=-3.5(98.45%) | Like=-0.07..-0.00 [-0.0733..-0.0731]*| it/evals=3026/33024 eff=9.2754% N=400 Z=-3.5(98.54%) | Like=-0.07..-0.00 [-0.0704..-0.0704]*| it/evals=3050/33312 eff=9.2671% N=400 Have 2 modes Volume: ~exp(-12.21) * Expected Volume: exp(-7.65) Quality: ok positive degeneracy between param1 and param0: rho=0.75 positive degeneracy between param2 and param0: rho=0.76 positive degeneracy between param2 and param1: rho=0.75 param0: +0.0| +0.3 2222 11111 +0.7 | +1.0 param1: +0.0| +0.3 22222 11111 +0.7 | +1.0 param2: +0.0| +0.3 22222 11111 +0.7 | +1.0 Z=-3.5(98.58%) | Like=-0.07..-0.00 [-0.0699..-0.0694]*| it/evals=3060/33442 eff=9.2609% N=400 Z=-3.5(98.66%) | Like=-0.07..-0.00 [-0.0672..-0.0670]*| it/evals=3086/33769 eff=9.2481% N=400 Z=-3.5(98.71%) | Like=-0.07..-0.00 [-0.0654..-0.0653]*| it/evals=3100/33942 eff=9.2421% N=400 Z=-3.5(98.78%) | Like=-0.06..-0.00 [-0.0630..-0.0629]*| it/evals=3124/34259 eff=9.2265% N=400 Have 2 modes Volume: ~exp(-12.21) Expected Volume: exp(-7.88) Quality: ok positive degeneracy between param2 and param0: rho=0.76 param0: +0.0| +0.3 2222 11111 +0.7 | +1.0 param1: +0.0| +0.3 2222 1111 +0.7 | +1.0 param2: +0.0| +0.3 22222 11111 +0.7 | +1.0 Z=-3.5(98.86%) | Like=-0.06..-0.00 [-0.0608..-0.0608]*| it/evals=3150/34571 eff=9.2183% N=400 Z=-3.5(98.93%) | Like=-0.06..-0.00 [-0.0582..-0.0579]*| it/evals=3176/34895 eff=9.2071% N=400 Z=-3.5(98.99%) | Like=-0.06..-0.00 [-0.0557..-0.0557]*| it/evals=3200/35201 eff=9.1951% N=400 [ultranest] Explored until L=-0.001 [ultranest] Likelihood function evaluations: 35252 [ultranest] logZ = -3.422 +- 0.04953 [ultranest] Effective samples strategy satisfied (ESS = 1897.8, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [ultranest] logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [ultranest] done iterating. logZ = -3.440 +- 0.141 single instance: logZ = -3.440 +- 0.070 bootstrapped : logZ = -3.422 +- 0.141 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▁▂▂▃▄▅▇▆▇▆▇▆▅▃▃▃▂▃▃▃▅▅▆▆▇▇▅▄▃▃▂▂▁▁▁▁│1.00 0.50 +- 0.22 param1 : 0.00 │▁▁▁▁▂▂▂▃▅▆▆▆▇▇▅▅▃▃▂▃▃▃▃▅▆▆▆▆▆▅▅▄▃▂▂▁▁▁▁│1.00 0.50 +- 0.22 param2 : 0.00 │▁▁▁▁▂▂▃▄▆▆▇▇▇▇▅▅▄▃▂▃▃▂▃▆▆▆▆▇▆▇▅▄▅▃▁▁▁▁▁│1.00 0.50 +- 0.22 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=403, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-30.54, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=25, ncalls=513, regioncalls=0, ndraw=40, logz=-27.72, remainder_fraction=100.0000%, Lmin=-23.38, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=47, ncalls=610, regioncalls=0, ndraw=40, logz=-24.20, remainder_fraction=100.0000%, Lmin=-19.84, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=625, regioncalls=0, ndraw=40, logz=-23.75, remainder_fraction=100.0000%, Lmin=-19.64, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=72, ncalls=729, regioncalls=0, ndraw=40, logz=-21.16, remainder_fraction=100.0000%, Lmin=-17.55, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=815, regioncalls=0, ndraw=40, logz=-19.86, remainder_fraction=100.0000%, Lmin=-16.25, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=859, regioncalls=0, ndraw=40, logz=-19.04, remainder_fraction=100.0000%, Lmin=-15.25, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=135, ncalls=1038, regioncalls=0, ndraw=40, logz=-16.21, remainder_fraction=99.9997%, Lmin=-12.56, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=1114, regioncalls=0, ndraw=40, logz=-15.36, remainder_fraction=99.9993%, Lmin=-11.94, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=1256, regioncalls=0, ndraw=40, logz=-13.93, remainder_fraction=99.9969%, Lmin=-10.51, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=1355, regioncalls=0, ndraw=40, logz=-13.12, remainder_fraction=99.9934%, Lmin=-9.90, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=235, ncalls=1534, regioncalls=0, ndraw=40, logz=-12.05, remainder_fraction=99.9809%, Lmin=-9.07, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1615, regioncalls=0, ndraw=40, logz=-11.65, remainder_fraction=99.9720%, Lmin=-8.71, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=267, ncalls=1713, regioncalls=0, ndraw=40, logz=-11.24, remainder_fraction=99.9576%, Lmin=-8.30, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1733, regioncalls=0, ndraw=40, logz=-11.17, remainder_fraction=99.9545%, Lmin=-8.28, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=1921, regioncalls=0, ndraw=40, logz=-10.55, remainder_fraction=99.9136%, Lmin=-7.82, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=333, ncalls=2122, regioncalls=0, ndraw=40, logz=-9.93, remainder_fraction=99.8436%, Lmin=-7.00, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2211, regioncalls=0, ndraw=40, logz=-9.62, remainder_fraction=99.7876%, Lmin=-6.84, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=2267, regioncalls=0, ndraw=40, logz=-9.45, remainder_fraction=99.7447%, Lmin=-6.58, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=395, ncalls=2499, regioncalls=0, ndraw=40, logz=-8.90, remainder_fraction=99.5552%, Lmin=-6.17, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=2529, regioncalls=0, ndraw=40, logz=-8.83, remainder_fraction=99.5209%, Lmin=-6.11, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=435, ncalls=2759, regioncalls=0, ndraw=40, logz=-8.38, remainder_fraction=99.2418%, Lmin=-5.71, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=2853, regioncalls=0, ndraw=40, logz=-8.20, remainder_fraction=99.1042%, Lmin=-5.54, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=468, ncalls=2980, regioncalls=0, ndraw=40, logz=-7.99, remainder_fraction=98.8842%, Lmin=-5.37, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=484, ncalls=3090, regioncalls=0, ndraw=40, logz=-7.83, remainder_fraction=98.6827%, Lmin=-5.20, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3213, regioncalls=0, ndraw=40, logz=-7.67, remainder_fraction=98.4983%, Lmin=-5.11, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=534, ncalls=3479, regioncalls=0, ndraw=40, logz=-7.38, remainder_fraction=98.0033%, Lmin=-4.87, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=3522, regioncalls=0, ndraw=40, logz=-7.33, remainder_fraction=97.8854%, Lmin=-4.85, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=3583, regioncalls=0, ndraw=40, logz=-7.26, remainder_fraction=97.7533%, Lmin=-4.79, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=581, ncalls=3831, regioncalls=0, ndraw=40, logz=-7.04, remainder_fraction=97.2155%, Lmin=-4.59, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=3990, regioncalls=0, ndraw=40, logz=-6.90, remainder_fraction=96.7382%, Lmin=-4.37, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=4220, regioncalls=0, ndraw=40, logz=-6.70, remainder_fraction=95.9898%, Lmin=-4.16, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=4402, regioncalls=0, ndraw=40, logz=-6.58, remainder_fraction=95.4063%, Lmin=-4.07, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=681, ncalls=4678, regioncalls=0, ndraw=40, logz=-6.39, remainder_fraction=94.6384%, Lmin=-3.85, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=4842, regioncalls=0, ndraw=40, logz=-6.29, remainder_fraction=93.9898%, Lmin=-3.73, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=720, ncalls=5010, regioncalls=0, ndraw=40, logz=-6.18, remainder_fraction=93.1566%, Lmin=-3.61, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=750, ncalls=5259, regioncalls=0, ndraw=40, logz=-6.02, remainder_fraction=92.0791%, Lmin=-3.44, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=782, ncalls=5542, regioncalls=0, ndraw=40, logz=-5.86, remainder_fraction=90.8622%, Lmin=-3.28, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=800, ncalls=5704, regioncalls=0, ndraw=40, logz=-5.78, remainder_fraction=90.1682%, Lmin=-3.16, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=810, ncalls=5789, regioncalls=0, ndraw=40, logz=-5.73, remainder_fraction=89.5817%, Lmin=-3.09, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=839, ncalls=6053, regioncalls=0, ndraw=40, logz=-5.60, remainder_fraction=88.0311%, Lmin=-2.91, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=850, ncalls=6154, regioncalls=0, ndraw=40, logz=-5.55, remainder_fraction=87.4943%, Lmin=-2.84, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=878, ncalls=6416, regioncalls=0, ndraw=40, logz=-5.43, remainder_fraction=86.1060%, Lmin=-2.75, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=900, ncalls=6627, regioncalls=0, ndraw=40, logz=-5.34, remainder_fraction=84.7952%, Lmin=-2.66, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=921, ncalls=6843, regioncalls=0, ndraw=40, logz=-5.26, remainder_fraction=83.6298%, Lmin=-2.57, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=950, ncalls=7162, regioncalls=0, ndraw=40, logz=-5.16, remainder_fraction=82.0757%, Lmin=-2.46, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=988, ncalls=7564, regioncalls=0, ndraw=40, logz=-5.03, remainder_fraction=79.6979%, Lmin=-2.28, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=7589, regioncalls=0, ndraw=40, logz=-5.02, remainder_fraction=79.5328%, Lmin=-2.26, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=7701, regioncalls=0, ndraw=40, logz=-4.99, remainder_fraction=78.9966%, Lmin=-2.23, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1037, ncalls=8120, regioncalls=0, ndraw=40, logz=-4.88, remainder_fraction=76.7483%, Lmin=-2.11, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=8276, regioncalls=0, ndraw=40, logz=-4.84, remainder_fraction=75.7192%, Lmin=-2.09, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=8624, regioncalls=0, ndraw=40, logz=-4.76, remainder_fraction=73.7286%, Lmin=-1.97, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=8869, regioncalls=0, ndraw=40, logz=-4.70, remainder_fraction=72.1659%, Lmin=-1.91, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1137, ncalls=9326, regioncalls=0, ndraw=40, logz=-4.61, remainder_fraction=69.1837%, Lmin=-1.83, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=9487, regioncalls=0, ndraw=40, logz=-4.58, remainder_fraction=68.1617%, Lmin=-1.80, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=9717, regioncalls=0, ndraw=40, logz=-4.54, remainder_fraction=66.5956%, Lmin=-1.73, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1200, ncalls=10066, regioncalls=0, ndraw=40, logz=-4.47, remainder_fraction=64.3746%, Lmin=-1.65, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1235, ncalls=10501, regioncalls=0, ndraw=40, logz=-4.40, remainder_fraction=61.8610%, Lmin=-1.55, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1250, ncalls=10690, regioncalls=0, ndraw=40, logz=-4.37, remainder_fraction=60.6010%, Lmin=-1.52, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1260, ncalls=10813, regioncalls=0, ndraw=40, logz=-4.35, remainder_fraction=59.8144%, Lmin=-1.49, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1292, ncalls=11207, regioncalls=0, ndraw=40, logz=-4.29, remainder_fraction=57.3474%, Lmin=-1.43, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1300, ncalls=11308, regioncalls=0, ndraw=40, logz=-4.28, remainder_fraction=56.8509%, Lmin=-1.41, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1330, ncalls=11695, regioncalls=0, ndraw=40, logz=-4.23, remainder_fraction=54.5811%, Lmin=-1.34, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1350, ncalls=11939, regioncalls=0, ndraw=40, logz=-4.20, remainder_fraction=53.0365%, Lmin=-1.30, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1362, ncalls=12099, regioncalls=0, ndraw=40, logz=-4.18, remainder_fraction=52.1340%, Lmin=-1.27, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1379, ncalls=12311, regioncalls=0, ndraw=40, logz=-4.16, remainder_fraction=50.7931%, Lmin=-1.23, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1396, ncalls=12533, regioncalls=0, ndraw=40, logz=-4.13, remainder_fraction=49.6832%, Lmin=-1.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1400, ncalls=12583, regioncalls=0, ndraw=40, logz=-4.12, remainder_fraction=49.4982%, Lmin=-1.20, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1414, ncalls=12755, regioncalls=0, ndraw=40, logz=-4.11, remainder_fraction=48.4501%, Lmin=-1.17, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1435, ncalls=13004, regioncalls=0, ndraw=40, logz=-4.08, remainder_fraction=46.8333%, Lmin=-1.12, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1442, ncalls=13094, regioncalls=0, ndraw=40, logz=-4.07, remainder_fraction=46.2928%, Lmin=-1.11, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1450, ncalls=13202, regioncalls=0, ndraw=40, logz=-4.06, remainder_fraction=45.7130%, Lmin=-1.09, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1476, ncalls=13509, regioncalls=0, ndraw=40, logz=-4.02, remainder_fraction=43.9986%, Lmin=-1.04, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1492, ncalls=13706, regioncalls=0, ndraw=40, logz=-4.01, remainder_fraction=42.9535%, Lmin=-1.01, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1500, ncalls=13802, regioncalls=0, ndraw=40, logz=-4.00, remainder_fraction=42.3781%, Lmin=-0.99, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1518, ncalls=14041, regioncalls=0, ndraw=40, logz=-3.97, remainder_fraction=41.1871%, Lmin=-0.96, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1530, ncalls=14187, regioncalls=0, ndraw=40, logz=-3.96, remainder_fraction=40.3075%, Lmin=-0.94, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1548, ncalls=14407, regioncalls=0, ndraw=40, logz=-3.94, remainder_fraction=39.1566%, Lmin=-0.91, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1550, ncalls=14429, regioncalls=0, ndraw=40, logz=-3.94, remainder_fraction=39.0084%, Lmin=-0.91, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1571, ncalls=14720, regioncalls=0, ndraw=40, logz=-3.92, remainder_fraction=37.6580%, Lmin=-0.87, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1598, ncalls=15053, regioncalls=0, ndraw=40, logz=-3.89, remainder_fraction=35.9849%, Lmin=-0.81, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1600, ncalls=15075, regioncalls=0, ndraw=40, logz=-3.89, remainder_fraction=35.8639%, Lmin=-0.81, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1614, ncalls=15259, regioncalls=0, ndraw=40, logz=-3.87, remainder_fraction=35.0949%, Lmin=-0.80, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1620, ncalls=15330, regioncalls=0, ndraw=40, logz=-3.87, remainder_fraction=34.6938%, Lmin=-0.79, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1650, ncalls=15708, regioncalls=0, ndraw=40, logz=-3.84, remainder_fraction=32.8654%, Lmin=-0.74, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1665, ncalls=15894, regioncalls=0, ndraw=40, logz=-3.83, remainder_fraction=32.0000%, Lmin=-0.72, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1680, ncalls=16091, regioncalls=0, ndraw=40, logz=-3.81, remainder_fraction=31.1238%, Lmin=-0.70, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1696, ncalls=16286, regioncalls=0, ndraw=40, logz=-3.80, remainder_fraction=30.2520%, Lmin=-0.68, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1700, ncalls=16343, regioncalls=0, ndraw=40, logz=-3.80, remainder_fraction=30.0146%, Lmin=-0.68, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1710, ncalls=16469, regioncalls=0, ndraw=40, logz=-3.79, remainder_fraction=29.5022%, Lmin=-0.67, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1724, ncalls=16640, regioncalls=0, ndraw=40, logz=-3.78, remainder_fraction=28.6916%, Lmin=-0.65, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1743, ncalls=16878, regioncalls=0, ndraw=40, logz=-3.77, remainder_fraction=27.6660%, Lmin=-0.64, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1750, ncalls=16966, regioncalls=0, ndraw=40, logz=-3.76, remainder_fraction=27.2999%, Lmin=-0.63, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1776, ncalls=17287, regioncalls=0, ndraw=40, logz=-3.74, remainder_fraction=25.9529%, Lmin=-0.61, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1800, ncalls=17586, regioncalls=0, ndraw=40, logz=-3.73, remainder_fraction=24.7207%, Lmin=-0.58, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1814, ncalls=17759, regioncalls=0, ndraw=40, logz=-3.72, remainder_fraction=24.0659%, Lmin=-0.57, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1833, ncalls=17988, regioncalls=0, ndraw=40, logz=-3.71, remainder_fraction=23.1644%, Lmin=-0.55, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1849, ncalls=18188, regioncalls=0, ndraw=40, logz=-3.70, remainder_fraction=22.4706%, Lmin=-0.53, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1850, ncalls=18198, regioncalls=0, ndraw=40, logz=-3.70, remainder_fraction=22.4346%, Lmin=-0.53, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1867, ncalls=18425, regioncalls=0, ndraw=40, logz=-3.69, remainder_fraction=21.6835%, Lmin=-0.51, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1881, ncalls=18593, regioncalls=0, ndraw=40, logz=-3.68, remainder_fraction=21.0638%, Lmin=-0.49, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1890, ncalls=18721, regioncalls=0, ndraw=40, logz=-3.67, remainder_fraction=20.6846%, Lmin=-0.49, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1900, ncalls=18845, regioncalls=0, ndraw=40, logz=-3.67, remainder_fraction=20.2546%, Lmin=-0.48, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1924, ncalls=19135, regioncalls=0, ndraw=40, logz=-3.66, remainder_fraction=19.2936%, Lmin=-0.46, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1949, ncalls=19449, regioncalls=0, ndraw=40, logz=-3.64, remainder_fraction=18.3384%, Lmin=-0.45, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1950, ncalls=19462, regioncalls=0, ndraw=40, logz=-3.64, remainder_fraction=18.2996%, Lmin=-0.44, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1980, ncalls=19839, regioncalls=0, ndraw=40, logz=-3.63, remainder_fraction=17.1788%, Lmin=-0.42, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2000, ncalls=20095, regioncalls=0, ndraw=40, logz=-3.62, remainder_fraction=16.4663%, Lmin=-0.41, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2029, ncalls=20470, regioncalls=0, ndraw=40, logz=-3.61, remainder_fraction=15.5108%, Lmin=-0.39, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2050, ncalls=20727, regioncalls=0, ndraw=40, logz=-3.60, remainder_fraction=14.8478%, Lmin=-0.38, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2070, ncalls=20962, regioncalls=0, ndraw=40, logz=-3.59, remainder_fraction=14.2401%, Lmin=-0.37, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2085, ncalls=21141, regioncalls=0, ndraw=40, logz=-3.59, remainder_fraction=13.7817%, Lmin=-0.36, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2100, ncalls=21332, regioncalls=0, ndraw=40, logz=-3.58, remainder_fraction=13.3453%, Lmin=-0.35, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2129, ncalls=21717, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=12.5427%, Lmin=-0.33, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2147, ncalls=21931, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=12.0580%, Lmin=-0.32, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=21966, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=11.9752%, Lmin=-0.32, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2160, ncalls=22098, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=11.7028%, Lmin=-0.31, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2188, ncalls=22443, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=11.0117%, Lmin=-0.30, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=22587, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=10.7251%, Lmin=-0.29, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2212, ncalls=22762, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=10.4410%, Lmin=-0.28, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2226, ncalls=22930, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=10.1204%, Lmin=-0.27, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=23226, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=9.5834%, Lmin=-0.27, Lmax=-0.01 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2277, ncalls=23584, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=9.0173%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=23866, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=8.5645%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2321, ncalls=24139, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=8.1622%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=24371, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=7.8193%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=24502, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=7.6509%, Lmin=-0.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2379, ncalls=24866, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=7.1568%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=25122, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=6.8163%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2429, ncalls=25482, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=6.3806%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=25495, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=6.3662%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2443, ncalls=25664, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=6.1832%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=25764, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=6.0859%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2479, ncalls=26125, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=5.6901%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2496, ncalls=26344, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=5.4688%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=26395, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=5.4178%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=26644, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=5.1745%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2547, ncalls=26984, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=4.8598%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=27018, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=4.8242%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2564, ncalls=27176, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=4.6721%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2577, ncalls=27332, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=4.5302%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2591, ncalls=27514, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=4.3847%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=27635, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=4.2939%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=27762, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=4.1950%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2639, ncalls=28125, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=3.9183%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=28264, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=3.8177%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2663, ncalls=28442, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=3.7020%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2684, ncalls=28707, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=3.5217%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=28902, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=3.3910%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2713, ncalls=29056, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=3.2845%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2726, ncalls=29228, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=3.1840%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2741, ncalls=29407, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=3.0721%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=29514, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=3.0069%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2780, ncalls=29886, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=2.7992%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=30016, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=2.7336%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=30134, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=2.6690%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2821, ncalls=30397, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=2.5378%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=30769, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=2.3674%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2865, ncalls=30965, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=2.2838%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=31161, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=2.2025%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=31412, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=2.0995%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2925, ncalls=31744, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.9766%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=32044, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.8608%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=32295, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.7723%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2995, ncalls=32632, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.6681%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=32698, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.6481%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3026, ncalls=33024, regioncalls=0, ndraw=40, logz=-3.46, remainder_fraction=1.5473%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=33312, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.4594%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=33442, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.4240%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3086, ncalls=33769, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.3366%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=33942, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.2916%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3124, ncalls=34259, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.2179%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=34571, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.1434%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3176, ncalls=34895, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.0730%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=35201, regioncalls=0, ndraw=40, logz=-3.45, remainder_fraction=1.0118%, Lmin=-0.06, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.001 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 35252 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.422 +- 0.04953 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1897.8, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.05 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_stepsampling.py::test_stepsampler_variable_speed_SLOW | 42.46 | |
------------------------------Captured stdout call------------------------------ [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-4.34) * Expected Volume: exp(0.00) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-inf(0.00%) | Like=-28.83..-0.16 [-28.8263..-8.7269] | it/evals=0/408 eff=0.0000% N=400 Z=-26.9(0.00%) | Like=-22.62..-0.16 [-28.8263..-8.7269] | it/evals=25/531 eff=19.0840% N=400 Z=-24.2(0.00%) | Like=-20.35..-0.16 [-28.8263..-8.7269] | it/evals=50/652 eff=19.8413% N=400 Z=-21.8(0.00%) | Like=-18.15..-0.16 [-28.8263..-8.7269] | it/evals=76/783 eff=19.8433% N=400 Mono-modal Volume: ~exp(-4.53) * Expected Volume: exp(-0.23) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-20.7(0.00%) | Like=-17.02..-0.16 [-28.8263..-8.7269] | it/evals=90/855 eff=19.7802% N=400 Z=-20.0(0.00%) | Like=-16.30..-0.16 [-28.8263..-8.7269] | it/evals=100/917 eff=19.3424% N=400 Z=-18.3(0.00%) | Like=-14.75..-0.16 [-28.8263..-8.7269] | it/evals=124/1052 eff=19.0184% N=400 Z=-16.9(0.00%) | Like=-13.55..-0.16 [-28.8263..-8.7269] | it/evals=150/1204 eff=18.6567% N=400 Z=-15.7(0.00%) | Like=-11.86..-0.16 [-28.8263..-8.7269] | it/evals=174/1326 eff=18.7905% N=400 Mono-modal Volume: ~exp(-4.70) * Expected Volume: exp(-0.45) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-15.3(0.00%) | Like=-11.67..-0.16 [-28.8263..-8.7269] | it/evals=180/1379 eff=18.3861% N=400 Z=-14.3(0.00%) | Like=-10.99..-0.16 [-28.8263..-8.7269] | it/evals=200/1490 eff=18.3486% N=400 Z=-13.5(0.00%) | Like=-10.28..-0.16 [-28.8263..-8.7269] | it/evals=222/1637 eff=17.9466% N=400 Z=-12.6(0.01%) | Like=-9.52..-0.16 [-28.8263..-8.7269] | it/evals=247/1809 eff=17.5302% N=400 Z=-12.6(0.01%) | Like=-9.48..-0.16 [-28.8263..-8.7269] | it/evals=250/1831 eff=17.4703% N=400 Mono-modal Volume: ~exp(-4.89) * Expected Volume: exp(-0.67) Quality: ok param0: +0.00|***************************************************| +1.00 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-12.0(0.02%) | Like=-9.05..-0.16 [-28.8263..-8.7269] | it/evals=270/1977 eff=17.1211% N=400 Z=-11.3(0.04%) | Like=-8.32..-0.16 [-8.6993..-4.7523] | it/evals=300/2235 eff=16.3488% N=400 Z=-10.8(0.07%) | Like=-7.89..-0.16 [-8.6993..-4.7523] | it/evals=321/2385 eff=16.1713% N=400 Z=-10.3(0.12%) | Like=-7.47..-0.16 [-8.6993..-4.7523] | it/evals=347/2631 eff=15.5536% N=400 Z=-10.3(0.12%) | Like=-7.39..-0.16 [-8.6993..-4.7523] | it/evals=350/2662 eff=15.4730% N=400 Mono-modal Volume: ~exp(-5.23) * Expected Volume: exp(-0.90) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.0000|***************************************************| +1.0000 param2: +0.000|***************************************************| +1.000 Z=-10.1(0.15%) | Like=-7.24..-0.16 [-8.6993..-4.7523] | it/evals=360/2749 eff=15.3257% N=400 Z=-9.6(0.25%) | Like=-6.77..-0.16 [-8.6993..-4.7523] | it/evals=390/2997 eff=15.0173% N=400 Z=-9.4(0.29%) | Like=-6.61..-0.16 [-8.6993..-4.7523] | it/evals=400/3082 eff=14.9142% N=400 Z=-9.2(0.37%) | Like=-6.45..-0.16 [-8.6993..-4.7523] | it/evals=420/3243 eff=14.7731% N=400 Mono-modal Volume: ~exp(-5.27) * Expected Volume: exp(-1.12) Quality: ok param0: +0.000|** ************************************************| +1.000 param1: +0.0000|***************************************************| +1.0000 param2: +0.00|***************************************************| +1.00 Z=-8.8(0.55%) | Like=-6.09..-0.16 [-8.6993..-4.7523] | it/evals=450/3508 eff=14.4788% N=400 Z=-8.3(0.89%) | Like=-5.54..-0.16 [-8.6993..-4.7523] | it/evals=491/3880 eff=14.1092% N=400 Z=-8.2(0.97%) | Like=-5.48..-0.16 [-8.6993..-4.7523] | it/evals=500/3947 eff=14.0964% N=400 Mono-modal Volume: ~exp(-5.27) Expected Volume: exp(-1.35) Quality: ok param0: +0.000|* * ***********************************************| +1.000 param1: +0.00| **************************************************| +1.00 param2: +0.00| ************************************************* | +1.00 Z=-7.8(1.50%) | Like=-5.01..-0.16 [-8.6993..-4.7523] | it/evals=540/4283 eff=13.9068% N=400 Z=-7.7(1.64%) | Like=-4.97..-0.16 [-8.6993..-4.7523] | it/evals=550/4377 eff=13.8295% N=400 Z=-7.3(2.35%) | Like=-4.62..-0.16 [-4.7510..-3.5347] | it/evals=590/4790 eff=13.4396% N=400 Z=-7.2(2.54%) | Like=-4.55..-0.16 [-4.7510..-3.5347] | it/evals=600/4902 eff=13.3274% N=400 Mono-modal Volume: ~exp(-5.32) * Expected Volume: exp(-1.57) Quality: ok param0: +0.00| ********************************************** | +1.00 param1: +0.00| ************************************************* | +1.00 param2: +0.00| * *********************************************** | +1.00 Z=-7.0(3.17%) | Like=-4.36..-0.16 [-4.7510..-3.5347] | it/evals=630/5206 eff=13.1086% N=400 Z=-6.9(3.66%) | Like=-4.24..-0.16 [-4.7510..-3.5347] | it/evals=650/5391 eff=13.0234% N=400 Z=-6.6(4.63%) | Like=-4.02..-0.16 [-4.7510..-3.5347] | it/evals=688/5799 eff=12.7431% N=400 Z=-6.5(4.86%) | Like=-3.89..-0.16 [-4.7510..-3.5347] | it/evals=700/5910 eff=12.7042% N=400 Z=-6.5(4.96%) | Like=-3.87..-0.16 [-4.7510..-3.5347] | it/evals=705/5963 eff=12.6730% N=400 Mono-modal Volume: ~exp(-5.82) * Expected Volume: exp(-1.80) Quality: ok param0: +0.00| ******************************************** | +1.00 param1: +0.00| ************************************************ | +1.00 param2: +0.00| * ********************************************** | +1.00 Z=-6.4(5.40%) | Like=-3.77..-0.16 [-4.7510..-3.5347] | it/evals=720/6108 eff=12.6139% N=400 Z=-6.3(6.20%) | Like=-3.64..-0.16 [-4.7510..-3.5347] | it/evals=750/6423 eff=12.4523% N=400 Z=-6.2(6.92%) | Like=-3.50..-0.16 [-3.5248..-3.0127] | it/evals=771/6639 eff=12.3577% N=400 Z=-6.0(7.80%) | Like=-3.25..-0.16 [-3.5248..-3.0127] | it/evals=794/6870 eff=12.2720% N=400 Z=-6.0(8.08%) | Like=-3.22..-0.16 [-3.5248..-3.0127] | it/evals=800/6932 eff=12.2474% N=400 Mono-modal Volume: ~exp(-6.00) * Expected Volume: exp(-2.02) Quality: ok param0: +0.00| ********************************************* | +1.00 param1: +0.00| ********************************************* | +1.00 param2: +0.00| ********************************************* | +1.00 Z=-5.9(8.38%) | Like=-3.18..-0.16 [-3.5248..-3.0127] | it/evals=810/7022 eff=12.2320% N=400 Z=-5.8(9.37%) | Like=-3.08..-0.16 [-3.5248..-3.0127] | it/evals=835/7295 eff=12.1102% N=400 Z=-5.7(10.09%) | Like=-2.95..-0.16 [-3.0064..-2.8253] | it/evals=850/7452 eff=12.0533% N=400 Z=-5.6(11.85%) | Like=-2.80..-0.16 [-2.8188..-2.7817] | it/evals=881/7823 eff=11.8685% N=400 Mono-modal Volume: ~exp(-6.14) * Expected Volume: exp(-2.25) Quality: ok param0: +0.00| ******************************************* | +1.00 param1: +0.00| ******************************************** | +1.00 param2: +0.00| ******************************************* | +1.00 Z=-5.5(13.03%) | Like=-2.71..-0.16 [-2.7099..-2.6926] | it/evals=900/8035 eff=11.7878% N=400 Z=-5.4(14.19%) | Like=-2.59..-0.07 [-2.5863..-2.5852]*| it/evals=922/8305 eff=11.6635% N=400 Z=-5.3(15.30%) | Like=-2.53..-0.07 [-2.5312..-2.5204] | it/evals=942/8536 eff=11.5782% N=400 Z=-5.3(15.81%) | Like=-2.49..-0.07 [-2.4875..-2.4820]*| it/evals=950/8630 eff=11.5431% N=400 Z=-5.2(17.01%) | Like=-2.42..-0.07 [-2.4214..-2.4198]*| it/evals=969/8886 eff=11.4188% N=400 Z=-5.2(18.02%) | Like=-2.36..-0.07 [-2.3560..-2.3537]*| it/evals=988/9112 eff=11.3407% N=400 Have 2 modes Volume: ~exp(-6.44) * Expected Volume: exp(-2.47) Quality: ok param0: +0.00| 1 11111111111111111111222222222222222222222 | +1.00 param1: +0.0| 111111111111111111111222222222222222222222 | +1.0 param2: +0.00| 1111111111111111111112222222222222222222222 | +1.00 Z=-5.2(18.09%) | Like=-2.35..-0.07 [-2.3537..-2.3534]*| it/evals=990/9141 eff=11.3259% N=400 Z=-5.1(18.78%) | Like=-2.32..-0.07 [-2.3178..-2.3145]*| it/evals=1000/9257 eff=11.2905% N=400 Z=-5.0(20.56%) | Like=-2.23..-0.07 [-2.2337..-2.2314]*| it/evals=1025/9577 eff=11.1692% N=400 Z=-5.0(21.98%) | Like=-2.14..-0.07 [-2.1379..-2.1377]*| it/evals=1047/9834 eff=11.0982% N=400 Z=-5.0(22.21%) | Like=-2.13..-0.07 [-2.1288..-2.1266]*| it/evals=1050/9869 eff=11.0888% N=400 Z=-4.9(24.23%) | Like=-2.04..-0.07 [-2.0420..-2.0400]*| it/evals=1077/10189 eff=11.0021% N=400 Have 2 modes Volume: ~exp(-6.74) * Expected Volume: exp(-2.70) Quality: ok param0: +0.0| 11111111111111111111122222222222222222222 | +1.0 param1: +0.0| 1111111111111111111122222222222222222222 | +1.0 param2: +0.0| 1 111111111111111111 2222222222222222222 | +1.0 Z=-4.9(24.46%) | Like=-2.04..-0.07 [-2.0359..-2.0335]*| it/evals=1080/10222 eff=10.9957% N=400 Z=-4.8(25.95%) | Like=-1.95..-0.05 [-1.9594..-1.9475] | it/evals=1100/10463 eff=10.9311% N=400 Z=-4.7(27.57%) | Like=-1.86..-0.05 [-1.8554..-1.8498]*| it/evals=1125/10759 eff=10.8601% N=400 Z=-4.7(28.77%) | Like=-1.81..-0.05 [-1.8148..-1.8098]*| it/evals=1141/10946 eff=10.8193% N=400 Z=-4.7(29.42%) | Like=-1.80..-0.05 [-1.7963..-1.7901]*| it/evals=1150/11037 eff=10.8113% N=400 Have 2 modes Volume: ~exp(-7.25) * Expected Volume: exp(-2.92) Quality: ok positive degeneracy between param1 and param0: rho=0.76 param0: +0.0| 111111111111111111 222222222222222222 | +1.0 param1: +0.0| 1111111111111111111 2222222222222222222 | +1.0 param2: +0.0| 11111111111111111 222222222222222222 | +1.0 Z=-4.6(31.04%) | Like=-1.74..-0.05 [-1.7399..-1.7393]*| it/evals=1170/11290 eff=10.7438% N=400 Z=-4.6(31.89%) | Like=-1.71..-0.05 [-1.7079..-1.7051]*| it/evals=1182/11434 eff=10.7123% N=400 Z=-4.6(32.95%) | Like=-1.67..-0.05 [-1.6712..-1.6651]*| it/evals=1196/11598 eff=10.6805% N=400 Z=-4.6(33.32%) | Like=-1.66..-0.05 [-1.6552..-1.6543]*| it/evals=1200/11649 eff=10.6676% N=400 Z=-4.5(35.54%) | Like=-1.57..-0.05 [-1.5684..-1.5677]*| it/evals=1226/11952 eff=10.6129% N=400 Z=-4.5(36.93%) | Like=-1.52..-0.05 [-1.5184..-1.5141]*| it/evals=1242/12156 eff=10.5648% N=400 Z=-4.5(37.58%) | Like=-1.50..-0.05 [-1.4992..-1.4929]*| it/evals=1250/12253 eff=10.5459% N=400 Have 2 modes Volume: ~exp(-7.25) Expected Volume: exp(-3.15) Quality: ok positive degeneracy between param1 and param0: rho=0.75 param0: +0.0| 1111111111111111 222222222222222222 | +1.0 param1: +0.0| 011111111111111111 22222222222222222 | +1.0 param2: +0.0| 11111111111111111 22222222222222222 | +1.0 Z=-4.4(38.53%) | Like=-1.46..-0.05 [-1.4586..-1.4575]*| it/evals=1260/12366 eff=10.5298% N=400 Z=-4.4(40.46%) | Like=-1.40..-0.05 [-1.4001..-1.3998]*| it/evals=1287/12688 eff=10.4736% N=400 Z=-4.4(41.34%) | Like=-1.37..-0.02 [-1.3736..-1.3729]*| it/evals=1300/12818 eff=10.4687% N=400 Z=-4.3(43.36%) | Like=-1.31..-0.02 [-1.3102..-1.3097]*| it/evals=1327/13130 eff=10.4242% N=400 Have 2 modes Volume: ~exp(-7.59) * Expected Volume: exp(-3.37) Quality: ok param0: +0.0| 111111111111111 22222222222222222 | +1.0 param1: +0.0| 11111111111111111 222222222222222 | +1.0 param2: +0.0| 111111111111111 22222222222222222 | +1.0 Z=-4.3(45.17%) | Like=-1.24..-0.02 [-1.2446..-1.2442]*| it/evals=1350/13420 eff=10.3687% N=400 Z=-4.2(47.15%) | Like=-1.21..-0.02 [-1.2145..-1.2137]*| it/evals=1376/13784 eff=10.2809% N=400 Z=-4.2(48.86%) | Like=-1.17..-0.02 [-1.1739..-1.1729]*| it/evals=1399/14090 eff=10.2191% N=400 Z=-4.2(48.94%) | Like=-1.17..-0.02 [-1.1729..-1.1720]*| it/evals=1400/14103 eff=10.2167% N=400 Z=-4.1(50.40%) | Like=-1.13..-0.02 [-1.1254..-1.1249]*| it/evals=1422/14400 eff=10.1571% N=400 Have 2 modes Volume: ~exp(-7.67) * Expected Volume: exp(-3.60) Quality: ok param0: +0.0| 11111111111111 2222222222222222 | +1.0 param1: +0.0| 111111111111111 22222222222222 | +1.0 param2: +0.0| 111111111111111 222222222222222 | +1.0 Z=-4.1(51.66%) | Like=-1.10..-0.02 [-1.0988..-1.0917]*| it/evals=1440/14656 eff=10.1010% N=400 Z=-4.1(52.34%) | Like=-1.08..-0.02 [-1.0790..-1.0771]*| it/evals=1450/14798 eff=10.0708% N=400 Z=-4.1(53.40%) | Like=-1.05..-0.02 [-1.0479..-1.0463]*| it/evals=1464/14984 eff=10.0384% N=400 Z=-4.1(55.35%) | Like=-0.99..-0.02 [-0.9901..-0.9881]*| it/evals=1491/15341 eff=9.9793% N=400 Z=-4.0(56.01%) | Like=-0.97..-0.02 [-0.9728..-0.9710]*| it/evals=1500/15457 eff=9.9621% N=400 Z=-4.0(57.66%) | Like=-0.92..-0.02 [-0.9220..-0.9214]*| it/evals=1523/15774 eff=9.9063% N=400 Have 2 modes Volume: ~exp(-8.10) * Expected Volume: exp(-3.82) Quality: ok param0: +0.0| 1111111111111 22222222222222 | +1.0 param1: +0.0| 1111111111111 22222222222222 | +1.0 param2: +0.0| 11111111111111 22222222222222 | +1.0 Z=-4.0(58.24%) | Like=-0.91..-0.02 [-0.9133..-0.9124]*| it/evals=1530/15876 eff=9.8863% N=400 Z=-4.0(59.61%) | Like=-0.88..-0.02 [-0.8802..-0.8801]*| it/evals=1550/16146 eff=9.8438% N=400 Z=-4.0(61.22%) | Like=-0.86..-0.02 [-0.8573..-0.8561]*| it/evals=1572/16488 eff=9.7713% N=400 Z=-3.9(62.76%) | Like=-0.82..-0.02 [-0.8182..-0.8176]*| it/evals=1595/16801 eff=9.7250% N=400 Z=-3.9(63.06%) | Like=-0.81..-0.02 [-0.8119..-0.8102]*| it/evals=1600/16863 eff=9.7188% N=400 Have 2 modes Volume: ~exp(-8.32) * Expected Volume: exp(-4.05) Quality: ok param0: +0.0| 111111111111 2222222222222 | +1.0 param1: +0.0| 1111111111111 2222222222222 | +1.0 param2: +0.0| 1111111111111 222222222222 | +1.0 Z=-3.9(64.35%) | Like=-0.79..-0.02 [-0.7856..-0.7839]*| it/evals=1620/17112 eff=9.6936% N=400 Z=-3.9(65.67%) | Like=-0.76..-0.02 [-0.7576..-0.7575]*| it/evals=1643/17415 eff=9.6562% N=400 Z=-3.9(66.11%) | Like=-0.74..-0.02 [-0.7430..-0.7393]*| it/evals=1650/17513 eff=9.6418% N=400 Z=-3.9(67.07%) | Like=-0.72..-0.01 [-0.7193..-0.7190]*| it/evals=1665/17746 eff=9.5988% N=400 Z=-3.8(68.14%) | Like=-0.69..-0.01 [-0.6906..-0.6882]*| it/evals=1683/18022 eff=9.5506% N=400 Z=-3.8(69.13%) | Like=-0.67..-0.01 [-0.6714..-0.6700]*| it/evals=1700/18286 eff=9.5046% N=400 Have 2 modes Volume: ~exp(-8.58) * Expected Volume: exp(-4.27) Quality: ok param0: +0.0| 11111111111 22222222222 +0.8 | +1.0 param1: +0.0| 111111111111 22222222222 +0.8 | +1.0 param2: +0.0| 111111111111 222222222222 | +1.0 Z=-3.8(69.71%) | Like=-0.66..-0.01 [-0.6579..-0.6573]*| it/evals=1710/18415 eff=9.4921% N=400 Z=-3.8(71.17%) | Like=-0.63..-0.01 [-0.6285..-0.6272]*| it/evals=1735/18787 eff=9.4360% N=400 Z=-3.8(71.92%) | Like=-0.61..-0.01 [-0.6090..-0.6089]*| it/evals=1749/18969 eff=9.4189% N=400 Z=-3.8(71.97%) | Like=-0.61..-0.01 [-0.6089..-0.6086]*| it/evals=1750/18982 eff=9.4177% N=400 Z=-3.8(73.13%) | Like=-0.59..-0.01 [-0.5903..-0.5885]*| it/evals=1773/19301 eff=9.3805% N=400 Z=-3.8(73.84%) | Like=-0.58..-0.01 [-0.5800..-0.5800]*| it/evals=1786/19501 eff=9.3503% N=400 Z=-3.8(74.46%) | Like=-0.57..-0.01 [-0.5690..-0.5680]*| it/evals=1798/19659 eff=9.3359% N=400 Have 2 modes Volume: ~exp(-8.80) * Expected Volume: exp(-4.50) Quality: ok param0: +0.0| 11111111111 22222222222 +0.8 | +1.0 param1: +0.0| 11111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-3.8(74.56%) | Like=-0.57..-0.01 [-0.5666..-0.5665]*| it/evals=1800/19686 eff=9.3332% N=400 Z=-3.8(75.11%) | Like=-0.56..-0.01 [-0.5562..-0.5528]*| it/evals=1811/19846 eff=9.3130% N=400 Z=-3.7(76.03%) | Like=-0.54..-0.01 [-0.5357..-0.5357]*| it/evals=1830/20086 eff=9.2959% N=400 Z=-3.7(77.00%) | Like=-0.52..-0.01 [-0.5201..-0.5200]*| it/evals=1850/20380 eff=9.2593% N=400 Z=-3.7(78.03%) | Like=-0.49..-0.01 [-0.4948..-0.4942]*| it/evals=1873/20701 eff=9.2261% N=400 Have 2 modes Volume: ~exp(-8.81) * Expected Volume: exp(-4.73) Quality: ok param0: +0.0| 1111111111 22222222222 +0.8 | +1.0 param1: +0.0| 11111111111 2222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-3.7(78.76%) | Like=-0.48..-0.01 [-0.4809..-0.4805]*| it/evals=1890/20945 eff=9.1993% N=400 Z=-3.7(79.22%) | Like=-0.47..-0.01 [-0.4748..-0.4739]*| it/evals=1900/21094 eff=9.1814% N=400 Z=-3.7(80.18%) | Like=-0.46..-0.01 [-0.4575..-0.4571]*| it/evals=1923/21427 eff=9.1454% N=400 Z=-3.7(81.06%) | Like=-0.45..-0.01 [-0.4455..-0.4453]*| it/evals=1946/21756 eff=9.1122% N=400 Z=-3.7(81.23%) | Like=-0.44..-0.01 [-0.4430..-0.4430]*| it/evals=1950/21811 eff=9.1075% N=400 Z=-3.7(82.09%) | Like=-0.43..-0.01 [-0.4251..-0.4249]*| it/evals=1972/22139 eff=9.0713% N=400 Have 2 modes Volume: ~exp(-9.47) * Expected Volume: exp(-4.95) Quality: ok param0: +0.0| +0.2 111111111 2222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 2222222222 +0.8 | +1.0 param2: +0.0| 1111111111 2222222222 +0.8 | +1.0 Z=-3.7(82.39%) | Like=-0.42..-0.00 [-0.4213..-0.4212]*| it/evals=1980/22248 eff=9.0626% N=400 Z=-3.6(83.11%) | Like=-0.41..-0.00 [-0.4099..-0.4091]*| it/evals=2000/22514 eff=9.0440% N=400 Z=-3.6(83.89%) | Like=-0.39..-0.00 [-0.3941..-0.3921]*| it/evals=2023/22830 eff=9.0192% N=400 Z=-3.6(84.64%) | Like=-0.38..-0.00 [-0.3818..-0.3808]*| it/evals=2045/23141 eff=8.9926% N=400 Z=-3.6(84.80%) | Like=-0.38..-0.00 [-0.3805..-0.3804]*| it/evals=2050/23230 eff=8.9794% N=400 Have 2 modes Volume: ~exp(-9.47) Expected Volume: exp(-5.18) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-3.6(85.44%) | Like=-0.37..-0.00 [-0.3691..-0.3678]*| it/evals=2070/23490 eff=8.9649% N=400 Z=-3.6(86.12%) | Like=-0.35..-0.00 [-0.3482..-0.3482]*| it/evals=2093/23823 eff=8.9357% N=400 Z=-3.6(86.34%) | Like=-0.35..-0.00 [-0.3465..-0.3464]*| it/evals=2100/23916 eff=8.9301% N=400 Z=-3.6(86.92%) | Like=-0.33..-0.00 [-0.3332..-0.3323]*| it/evals=2120/24212 eff=8.9031% N=400 Z=-3.6(87.66%) | Like=-0.32..-0.00 [-0.3200..-0.3198]*| it/evals=2146/24579 eff=8.8755% N=400 Z=-3.6(87.77%) | Like=-0.32..-0.00 [-0.3165..-0.3164]*| it/evals=2150/24636 eff=8.8711% N=400 Have 2 modes Volume: ~exp(-9.61) * Expected Volume: exp(-5.40) Quality: ok param0: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-3.6(88.03%) | Like=-0.31..-0.00 [-0.3132..-0.3128]*| it/evals=2160/24786 eff=8.8575% N=400 Z=-3.6(88.43%) | Like=-0.30..-0.00 [-0.3031..-0.3029]*| it/evals=2175/24987 eff=8.8461% N=400 Z=-3.6(89.05%) | Like=-0.29..-0.00 [-0.2923..-0.2917]*| it/evals=2200/25324 eff=8.8268% N=400 Z=-3.6(89.53%) | Like=-0.28..-0.00 [-0.2819..-0.2814]*| it/evals=2220/25601 eff=8.8092% N=400 Z=-3.6(90.03%) | Like=-0.27..-0.00 [-0.2736..-0.2734]*| it/evals=2242/25914 eff=8.7873% N=400 Have 2 modes Volume: ~exp(-9.75) * Expected Volume: exp(-5.63) Quality: ok param0: +0.0| +0.2 1111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 111111111 222222222 +0.8 | +1.0 Z=-3.6(90.21%) | Like=-0.27..-0.00 [-0.2700..-0.2698]*| it/evals=2250/26022 eff=8.7815% N=400 Z=-3.6(90.47%) | Like=-0.27..-0.00 [-0.2652..-0.2649]*| it/evals=2262/26193 eff=8.7698% N=400 Z=-3.6(90.93%) | Like=-0.26..-0.00 [-0.2579..-0.2578]*| it/evals=2284/26511 eff=8.7473% N=400 Z=-3.6(91.25%) | Like=-0.25..-0.00 [-0.2519..-0.2516]*| it/evals=2300/26733 eff=8.7343% N=400 Z=-3.6(91.70%) | Like=-0.24..-0.00 [-0.2439..-0.2430]*| it/evals=2323/27057 eff=8.7144% N=400 Have 2 modes Volume: ~exp(-10.04) * Expected Volume: exp(-5.85) Quality: ok param0: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.5(92.01%) | Like=-0.24..-0.00 [-0.2357..-0.2355]*| it/evals=2340/27302 eff=8.6982% N=400 Z=-3.5(92.19%) | Like=-0.23..-0.00 [-0.2294..-0.2293]*| it/evals=2350/27437 eff=8.6918% N=400 Z=-3.5(92.57%) | Like=-0.22..-0.00 [-0.2219..-0.2219]*| it/evals=2372/27769 eff=8.6667% N=400 Z=-3.5(92.94%) | Like=-0.21..-0.00 [-0.2134..-0.2133]*| it/evals=2395/28087 eff=8.6503% N=400 Z=-3.5(93.02%) | Like=-0.21..-0.00 [-0.2105..-0.2104]*| it/evals=2400/28149 eff=8.6490% N=400 Z=-3.5(93.32%) | Like=-0.21..-0.00 [-0.2053..-0.2049]*| it/evals=2419/28412 eff=8.6356% N=400 Z=-3.5(93.46%) | Like=-0.20..-0.00 [-0.2003..-0.1999]*| it/evals=2428/28545 eff=8.6268% N=400 Have 2 modes Volume: ~exp(-10.19) * Expected Volume: exp(-6.08) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.5(93.49%) | Like=-0.20..-0.00 [-0.1993..-0.1991]*| it/evals=2430/28569 eff=8.6265% N=400 Z=-3.5(93.78%) | Like=-0.19..-0.00 [-0.1942..-0.1942]*| it/evals=2450/28857 eff=8.6095% N=400 Z=-3.5(94.14%) | Like=-0.19..-0.00 [-0.1890..-0.1889]*| it/evals=2476/29206 eff=8.5954% N=400 Z=-3.5(94.43%) | Like=-0.18..-0.00 [-0.1834..-0.1829]*| it/evals=2498/29531 eff=8.5751% N=400 Z=-3.5(94.46%) | Like=-0.18..-0.00 [-0.1825..-0.1822]*| it/evals=2500/29561 eff=8.5731% N=400 Have 2 modes Volume: ~exp(-10.24) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.2 1111111 222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.5(94.71%) | Like=-0.18..-0.00 [-0.1772..-0.1767]*| it/evals=2520/29854 eff=8.5557% N=400 Z=-3.5(95.00%) | Like=-0.17..-0.00 [-0.1708..-0.1705]*| it/evals=2544/30175 eff=8.5441% N=400 Z=-3.5(95.07%) | Like=-0.17..-0.00 [-0.1691..-0.1685]*| it/evals=2550/30250 eff=8.5427% N=400 Z=-3.5(95.33%) | Like=-0.16..-0.00 [-0.1626..-0.1624]*| it/evals=2573/30598 eff=8.5204% N=400 Z=-3.5(95.56%) | Like=-0.16..-0.00 [-0.1558..-0.1557]*| it/evals=2595/30910 eff=8.5054% N=400 Z=-3.5(95.62%) | Like=-0.16..-0.00 [-0.1551..-0.1551]*| it/evals=2600/30975 eff=8.5037% N=400 Have 2 modes Volume: ~exp(-11.06) * Expected Volume: exp(-6.53) Quality: ok param0: +0.0| +0.3 111111 222222 +0.8 | +1.0 param1: +0.0| +0.3 1111111 222222 +0.8 | +1.0 param2: +0.0| +0.3 111111 222222 +0.7 | +1.0 Z=-3.5(95.72%) | Like=-0.15..-0.00 [-0.1532..-0.1531]*| it/evals=2610/31115 eff=8.4975% N=400 Z=-3.5(95.94%) | Like=-0.15..-0.00 [-0.1480..-0.1479]*| it/evals=2633/31443 eff=8.4818% N=400 Z=-3.5(96.10%) | Like=-0.14..-0.00 [-0.1431..-0.1427]*| it/evals=2650/31682 eff=8.4713% N=400 Z=-3.5(96.29%) | Like=-0.14..-0.00 [-0.1375..-0.1370]*| it/evals=2671/32002 eff=8.4520% N=400 Z=-3.5(96.48%) | Like=-0.13..-0.00 [-0.1319..-0.1318]*| it/evals=2694/32317 eff=8.4406% N=400 Have 2 modes Volume: ~exp(-11.06) Expected Volume: exp(-6.75) Quality: ok param0: +0.0| +0.3 111111 222220 +0.7 | +1.0 param1: +0.0| +0.3 111111 222222 +0.7 | +1.0 param2: +0.0| +0.3 111111 222220 +0.7 | +1.0 Z=-3.5(96.54%) | Like=-0.13..-0.00 [-0.1309..-0.1299]*| it/evals=2700/32389 eff=8.4404% N=400 Z=-3.5(96.72%) | Like=-0.12..-0.00 [-0.1248..-0.1247]*| it/evals=2723/32712 eff=8.4272% N=400 Z=-3.5(96.89%) | Like=-0.12..-0.00 [-0.1185..-0.1184]*| it/evals=2746/33040 eff=8.4130% N=400 Z=-3.5(96.92%) | Like=-0.12..-0.00 [-0.1179..-0.1179]*| it/evals=2750/33112 eff=8.4067% N=400 Z=-3.5(97.09%) | Like=-0.11..-0.00 [-0.1136..-0.1136]*| it/evals=2773/33433 eff=8.3946% N=400 Have 2 modes Volume: ~exp(-11.40) * Expected Volume: exp(-6.98) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.20%) | Like=-0.11..-0.00 [-0.1103..-0.1101]*| it/evals=2790/33655 eff=8.3897% N=400 Z=-3.5(97.27%) | Like=-0.11..-0.00 [-0.1074..-0.1071]*| it/evals=2800/33784 eff=8.3873% N=400 Z=-3.5(97.41%) | Like=-0.10..-0.00 [-0.1037..-0.1036]*| it/evals=2822/34115 eff=8.3702% N=400 Z=-3.5(97.54%) | Like=-0.10..-0.00 [-0.0999..-0.0999]*| it/evals=2844/34437 eff=8.3556% N=400 Z=-3.5(97.58%) | Like=-0.10..-0.00 [-0.0994..-0.0992]*| it/evals=2850/34526 eff=8.3514% N=400 Z=-3.5(97.70%) | Like=-0.10..-0.00 [-0.0962..-0.0961]*| it/evals=2872/34838 eff=8.3396% N=400 Have 2 modes Volume: ~exp(-11.40) Expected Volume: exp(-7.20) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.76%) | Like=-0.09..-0.00 [-0.0944..-0.0940]*| it/evals=2883/34978 eff=8.3377% N=400 Z=-3.5(97.85%) | Like=-0.09..-0.00 [-0.0911..-0.0909]*| it/evals=2900/35213 eff=8.3302% N=400 Z=-3.5(97.96%) | Like=-0.09..-0.00 [-0.0862..-0.0850]*| it/evals=2923/35533 eff=8.3198% N=400 Z=-3.5(98.07%) | Like=-0.08..-0.00 [-0.0813..-0.0812]*| it/evals=2946/35854 eff=8.3094% N=400 Z=-3.5(98.09%) | Like=-0.08..-0.00 [-0.0810..-0.0809]*| it/evals=2950/35908 eff=8.3080% N=400 Have 2 modes Volume: ~exp(-11.74) * Expected Volume: exp(-7.43) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.18%) | Like=-0.08..-0.00 [-0.0779..-0.0777]*| it/evals=2970/36224 eff=8.2905% N=400 Z=-3.5(98.28%) | Like=-0.08..-0.00 [-0.0754..-0.0753]*| it/evals=2993/36543 eff=8.2810% N=400 Z=-3.5(98.31%) | Like=-0.07..-0.00 [-0.0742..-0.0741]*| it/evals=3000/36647 eff=8.2765% N=400 Z=-3.5(98.40%) | Like=-0.07..-0.00 [-0.0712..-0.0711]*| it/evals=3023/36973 eff=8.2657% N=400 Z=-3.5(98.49%) | Like=-0.07..-0.00 [-0.0691..-0.0689]*| it/evals=3047/37294 eff=8.2588% N=400 Z=-3.5(98.50%) | Like=-0.07..-0.00 [-0.0686..-0.0684]*| it/evals=3050/37329 eff=8.2591% N=400 Have 2 modes Volume: ~exp(-12.06) * Expected Volume: exp(-7.65) Quality: ok param0: +0.0| +0.3 1111 2222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 1111 22222 +0.7 | +1.0 Z=-3.5(98.54%) | Like=-0.07..-0.00 [-0.0670..-0.0670]*| it/evals=3060/37469 eff=8.2549% N=400 Z=-3.5(98.58%) | Like=-0.07..-0.00 [-0.0656..-0.0655]*| it/evals=3072/37630 eff=8.2514% N=400 Z=-3.5(98.66%) | Like=-0.06..-0.00 [-0.0633..-0.0628]*| it/evals=3096/37952 eff=8.2446% N=400 Z=-3.5(98.67%) | Like=-0.06..-0.00 [-0.0626..-0.0624]*| it/evals=3100/38015 eff=8.2414% N=400 Z=-3.5(98.73%) | Like=-0.06..-0.00 [-0.0597..-0.0596]*| it/evals=3118/38266 eff=8.2343% N=400 Z=-3.5(98.79%) | Like=-0.06..-0.00 [-0.0581..-0.0581]*| it/evals=3138/38556 eff=8.2241% N=400 Have 2 modes Volume: ~exp(-12.06) Expected Volume: exp(-7.88) Quality: ok param0: +0.0| +0.3 1111 2222 +0.7 | +1.0 param1: +0.0| +0.3 1111 02222 +0.7 | +1.0 param2: +0.0| +0.3 1111 2222 +0.7 | +1.0 Z=-3.5(98.83%) | Like=-0.06..-0.00 [-0.0569..-0.0568]*| it/evals=3150/38738 eff=8.2164% N=400 Z=-3.5(98.89%) | Like=-0.05..-0.00 [-0.0545..-0.0544]*| it/evals=3171/39030 eff=8.2086% N=400 Z=-3.5(98.94%) | Like=-0.05..-0.00 [-0.0525..-0.0525]*| it/evals=3192/39327 eff=8.2000% N=400 Z=-3.5(98.96%) | Like=-0.05..-0.00 [-0.0519..-0.0519]*| it/evals=3200/39438 eff=8.1971% N=400 [ultranest] Explored until L=-0.001 [ultranest] Likelihood function evaluations: 39658 [ultranest] logZ = -3.454 +- 0.05166 [ultranest] Effective samples strategy satisfied (ESS = 1850.6, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.47+-0.07 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [ultranest] logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [ultranest] done iterating. logZ = -3.464 +- 0.118 single instance: logZ = -3.464 +- 0.071 bootstrapped : logZ = -3.454 +- 0.118 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁ ▁▁▁▂▃▅▅▆▄▆▅▄▄▃▃▂▃▂▃▃▅▅▇▇▇▇▇▆▄▂▃▂▁▁▁▁│1.00 0.54 +- 0.22 param1 : 0.00 │▁▁▁▁▁▂▂▃▃▄▅▄▄▄▄▃▃▂▂▂▂▃▄▄▅▆▇▇▇▅▆▃▂▂▂▁▁▁▁│1.00 0.53 +- 0.22 param2 : 0.00 │▁▁▁▁▁▂▂▃▄▅▅▅▅▆▄▄▂▃▂▂▃▃▄▄▆▆▇▇▅▇▆▄▃▃▂▁▁▁▁│1.00 0.54 +- 0.22 [ultranest] Sampling 400 live points from prior ... Mono-modal Volume: ~exp(-3.77) * Expected Volume: exp(0.00) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-inf(0.00%) | Like=-30.72..-0.14 [-30.7155..-8.6130] | it/evals=0/408 eff=0.0000% N=400 Z=-25.9(0.00%) | Like=-21.40..-0.14 [-30.7155..-8.6130] | it/evals=32/565 eff=19.3939% N=400 Z=-23.5(0.00%) | Like=-19.79..-0.14 [-30.7155..-8.6130] | it/evals=50/643 eff=20.5761% N=400 Z=-20.9(0.00%) | Like=-17.47..-0.14 [-30.7155..-8.6130] | it/evals=81/805 eff=20.0000% N=400 Mono-modal Volume: ~exp(-4.50) * Expected Volume: exp(-0.23) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.000|***************************************************| +1.000 Z=-20.3(0.00%) | Like=-16.75..-0.14 [-30.7155..-8.6130] | it/evals=90/842 eff=20.3620% N=400 Z=-19.6(0.00%) | Like=-16.01..-0.14 [-30.7155..-8.6130] | it/evals=100/881 eff=20.7900% N=400 Z=-17.6(0.00%) | Like=-14.15..-0.14 [-30.7155..-8.6130] | it/evals=135/1088 eff=19.6221% N=400 Z=-16.7(0.00%) | Like=-13.30..-0.14 [-30.7155..-8.6130] | it/evals=150/1187 eff=19.0597% N=400 Mono-modal Volume: ~exp(-4.50) Expected Volume: exp(-0.45) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-15.2(0.00%) | Like=-11.73..-0.14 [-30.7155..-8.6130] | it/evals=180/1381 eff=18.3486% N=400 Z=-14.3(0.00%) | Like=-11.07..-0.14 [-30.7155..-8.6130] | it/evals=200/1518 eff=17.8891% N=400 Z=-13.0(0.01%) | Like=-9.86..-0.14 [-30.7155..-8.6130] | it/evals=234/1754 eff=17.2821% N=400 Z=-12.5(0.01%) | Like=-9.36..-0.08 [-30.7155..-8.6130] | it/evals=250/1856 eff=17.1703% N=400 Z=-12.1(0.02%) | Like=-8.94..-0.08 [-30.7155..-8.6130] | it/evals=264/1963 eff=16.8906% N=400 Mono-modal Volume: ~exp(-4.59) * Expected Volume: exp(-0.67) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|***************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-11.9(0.02%) | Like=-8.82..-0.08 [-30.7155..-8.6130] | it/evals=270/2017 eff=16.6976% N=400 Z=-11.2(0.04%) | Like=-8.15..-0.08 [-8.5729..-4.8890] | it/evals=300/2241 eff=16.2955% N=400 Z=-10.7(0.07%) | Like=-7.73..-0.08 [-8.5729..-4.8890] | it/evals=322/2379 eff=16.2708% N=400 Z=-10.1(0.12%) | Like=-7.25..-0.08 [-8.5729..-4.8890] | it/evals=350/2597 eff=15.9308% N=400 Mono-modal Volume: ~exp(-4.78) * Expected Volume: exp(-0.90) Quality: ok param0: +0.000|***************************************************| +1.000 param1: +0.000|* *************************************************| +1.000 param2: +0.00|***************************************************| +1.00 Z=-9.9(0.14%) | Like=-7.13..-0.08 [-8.5729..-4.8890] | it/evals=360/2686 eff=15.7480% N=400 Z=-9.5(0.22%) | Like=-6.81..-0.08 [-8.5729..-4.8890] | it/evals=390/2899 eff=15.6062% N=400 Z=-9.4(0.26%) | Like=-6.65..-0.08 [-8.5729..-4.8890] | it/evals=400/2991 eff=15.4381% N=400 Z=-9.0(0.39%) | Like=-6.32..-0.08 [-8.5729..-4.8890] | it/evals=428/3230 eff=15.1237% N=400 Mono-modal Volume: ~exp(-5.27) * Expected Volume: exp(-1.12) Quality: ok param0: +0.000|************************************************** | +1.000 param1: +0.000|* *************************************************| +1.000 param2: +0.00|************************************************ **| +1.00 Z=-8.7(0.47%) | Like=-6.15..-0.08 [-8.5729..-4.8890] | it/evals=450/3421 eff=14.8957% N=400 Z=-8.4(0.65%) | Like=-5.84..-0.08 [-8.5729..-4.8890] | it/evals=479/3664 eff=14.6752% N=400 Z=-8.2(0.80%) | Like=-5.58..-0.08 [-8.5729..-4.8890] | it/evals=500/3853 eff=14.4802% N=400 Z=-7.9(1.09%) | Like=-5.24..-0.08 [-8.5729..-4.8890] | it/evals=527/4115 eff=14.1857% N=400 Mono-modal Volume: ~exp(-5.49) * Expected Volume: exp(-1.35) Quality: ok param0: +0.000|************************************************** | +1.000 param1: +0.00| **************************************************| +1.00 param2: +0.000|************************************************** | +1.000 Z=-7.8(1.27%) | Like=-5.11..-0.08 [-8.5729..-4.8890] | it/evals=540/4239 eff=14.0662% N=400 Z=-7.7(1.39%) | Like=-5.07..-0.08 [-8.5729..-4.8890] | it/evals=550/4360 eff=13.8889% N=400 Z=-7.6(1.61%) | Like=-4.90..-0.08 [-8.5729..-4.8890] | it/evals=565/4468 eff=13.8889% N=400 Z=-7.3(2.13%) | Like=-4.66..-0.08 [-4.8759..-3.4495] | it/evals=597/4743 eff=13.7463% N=400 Z=-7.3(2.18%) | Like=-4.63..-0.08 [-4.8759..-3.4495] | it/evals=600/4774 eff=13.7174% N=400 Z=-7.1(2.54%) | Like=-4.46..-0.08 [-4.8759..-3.4495] | it/evals=619/4946 eff=13.6164% N=400 Mono-modal Volume: ~exp(-5.49) Expected Volume: exp(-1.57) Quality: ok param0: +0.00| ***** ******************************************* | +1.00 param1: +0.000|************************************************** | +1.000 param2: +0.00| ************************************************* | +1.00 Z=-7.1(2.75%) | Like=-4.41..-0.08 [-4.8759..-3.4495] | it/evals=630/5047 eff=13.5571% N=400 Z=-6.9(3.19%) | Like=-4.26..-0.08 [-4.8759..-3.4495] | it/evals=650/5228 eff=13.4631% N=400 Z=-6.8(3.75%) | Like=-4.12..-0.08 [-4.8759..-3.4495] | it/evals=675/5476 eff=13.2979% N=400 Z=-6.6(4.42%) | Like=-3.95..-0.08 [-4.8759..-3.4495] | it/evals=700/5694 eff=13.2225% N=400 Z=-6.5(4.93%) | Like=-3.83..-0.08 [-4.8759..-3.4495] | it/evals=718/5854 eff=13.1646% N=400 Mono-modal Volume: ~exp(-5.51) * Expected Volume: exp(-1.80) Quality: ok param0: +0.00| ************************************************ | +1.00 param1: +0.00| ************************************************ | +1.00 param2: +0.00| ********************************************** * | +1.00 Z=-6.5(5.01%) | Like=-3.81..-0.08 [-4.8759..-3.4495] | it/evals=720/5877 eff=13.1459% N=400 Z=-6.3(6.01%) | Like=-3.57..-0.08 [-4.8759..-3.4495] | it/evals=750/6185 eff=12.9646% N=400 Z=-6.1(7.14%) | Like=-3.46..-0.08 [-4.8759..-3.4495] | it/evals=778/6474 eff=12.8087% N=400 Z=-6.0(8.07%) | Like=-3.36..-0.08 [-3.4294..-3.0089] | it/evals=800/6703 eff=12.6924% N=400 Mono-modal Volume: ~exp(-5.75) * Expected Volume: exp(-2.02) Quality: ok positive degeneracy between param1 and param0: rho=0.75 param0: +0.00| ********************************************** | +1.00 param1: +0.00| *********************************************** | +1.00 param2: +0.00| ********************************************* | +1.00 Z=-6.0(8.46%) | Like=-3.31..-0.08 [-3.4294..-3.0089] | it/evals=810/6800 eff=12.6562% N=400 Z=-5.9(9.57%) | Like=-3.10..-0.08 [-3.4294..-3.0089] | it/evals=838/7082 eff=12.5412% N=400 Z=-5.8(10.21%) | Like=-3.02..-0.08 [-3.4294..-3.0089] | it/evals=850/7190 eff=12.5184% N=400 Z=-5.7(11.43%) | Like=-2.92..-0.08 [-3.0005..-2.8728] | it/evals=878/7490 eff=12.3836% N=400 Mono-modal Volume: ~exp(-6.34) * Expected Volume: exp(-2.25) Quality: ok positive degeneracy between param2 and param1: rho=0.77 param0: +0.00| ******************************************* | +1.00 param1: +0.00| ******************************************** | +1.00 param2: +0.00| ******************************************** | +1.00 Z=-5.6(12.57%) | Like=-2.82..-0.04 [-2.8656..-2.7934] | it/evals=900/7734 eff=12.2716% N=400 Z=-5.5(14.05%) | Like=-2.70..-0.04 [-2.7010..-2.6945]*| it/evals=927/8021 eff=12.1638% N=400 Z=-5.4(15.40%) | Like=-2.61..-0.04 [-2.6142..-2.6119]*| it/evals=950/8283 eff=12.0512% N=400 Z=-5.3(16.76%) | Like=-2.47..-0.04 [-2.4697..-2.4694]*| it/evals=976/8559 eff=11.9623% N=400 Have 2 modes Volume: ~exp(-6.69) * Expected Volume: exp(-2.47) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 111111111111111111112222222222222222222222 | +1.0 param1: +0.00| 1111111111111111111111222222222222222222222 | +1.00 param2: +0.00| 11 1111111111111111111222222222222222222222 | +1.00 Z=-5.2(17.59%) | Like=-2.36..-0.04 [-2.3628..-2.3616]*| it/evals=990/8721 eff=11.8976% N=400 Z=-5.2(18.24%) | Like=-2.31..-0.04 [-2.3115..-2.3053]*| it/evals=1000/8846 eff=11.8399% N=400 Z=-5.1(20.09%) | Like=-2.17..-0.02 [-2.1727..-2.1705]*| it/evals=1026/9132 eff=11.7499% N=400 Z=-5.0(21.93%) | Like=-2.09..-0.02 [-2.0868..-2.0846]*| it/evals=1050/9438 eff=11.6176% N=400 Z=-4.9(23.77%) | Like=-2.04..-0.02 [-2.0350..-2.0342]*| it/evals=1074/9736 eff=11.5039% N=400 Have 2 modes Volume: ~exp(-7.11) * Expected Volume: exp(-2.70) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 1111111111111111111 22222222222222222222 | +1.0 param1: +0.0| 11111111111111111111222222222222222222222 | +1.0 param2: +0.0| 111111111111111111 22222222222222222222 | +1.0 Z=-4.9(24.27%) | Like=-2.01..-0.02 [-2.0281..-2.0083] | it/evals=1080/9801 eff=11.4881% N=400 Z=-4.9(25.36%) | Like=-1.96..-0.02 [-1.9595..-1.9590]*| it/evals=1094/9977 eff=11.4232% N=400 Z=-4.9(25.84%) | Like=-1.93..-0.02 [-1.9348..-1.9280]*| it/evals=1100/10060 eff=11.3872% N=400 Z=-4.8(26.81%) | Like=-1.89..-0.02 [-1.8944..-1.8919]*| it/evals=1113/10215 eff=11.3398% N=400 Z=-4.8(28.67%) | Like=-1.83..-0.02 [-1.8324..-1.8321]*| it/evals=1138/10526 eff=11.2384% N=400 Z=-4.7(29.65%) | Like=-1.79..-0.02 [-1.7887..-1.7871]*| it/evals=1150/10677 eff=11.1900% N=400 Have 2 modes Volume: ~exp(-7.11) Expected Volume: exp(-2.92) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 111111111111111111 2222222222222222222 | +1.0 param1: +0.0| 11111111111111111111 2222222222222222222 | +1.0 param2: +0.0| 0111111111111111111 2222222222222222222 | +1.0 Z=-4.7(31.08%) | Like=-1.74..-0.02 [-1.7394..-1.7350]*| it/evals=1170/10969 eff=11.0701% N=400 Z=-4.6(32.82%) | Like=-1.69..-0.02 [-1.6947..-1.6921]*| it/evals=1193/11261 eff=10.9843% N=400 Z=-4.6(33.36%) | Like=-1.68..-0.02 [-1.6826..-1.6768]*| it/evals=1200/11350 eff=10.9589% N=400 Z=-4.5(35.23%) | Like=-1.62..-0.02 [-1.6211..-1.6179]*| it/evals=1224/11654 eff=10.8761% N=400 Z=-4.5(36.92%) | Like=-1.55..-0.02 [-1.5529..-1.5525]*| it/evals=1248/11972 eff=10.7847% N=400 Z=-4.5(37.03%) | Like=-1.54..-0.02 [-1.5525..-1.5411] | it/evals=1250/12003 eff=10.7731% N=400 Have 2 modes Volume: ~exp(-7.35) * Expected Volume: exp(-3.15) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 11111111111111111 222222222222222222 | +1.0 param1: +0.0| 1 111111111111111 2222222222222222222 | +1.0 param2: +0.0| 111111111111111111 222222222222222222 | +1.0 Z=-4.5(37.74%) | Like=-1.50..-0.02 [-1.5030..-1.5016]*| it/evals=1260/12125 eff=10.7463% N=400 Z=-4.4(39.55%) | Like=-1.44..-0.02 [-1.4403..-1.4357]*| it/evals=1284/12446 eff=10.6591% N=400 Z=-4.4(40.69%) | Like=-1.38..-0.02 [-1.3932..-1.3832] | it/evals=1300/12647 eff=10.6148% N=400 Z=-4.3(42.52%) | Like=-1.33..-0.02 [-1.3328..-1.3247]*| it/evals=1323/12938 eff=10.5519% N=400 Z=-4.3(43.81%) | Like=-1.29..-0.02 [-1.2931..-1.2927]*| it/evals=1338/13149 eff=10.4949% N=400 Have 2 modes Volume: ~exp(-7.35) Expected Volume: exp(-3.37) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 1111111111111111 2222222222222222 | +1.0 param1: +0.0| 111111111111111 22222222222222222 | +1.0 param2: +0.0| 1111111111111111 22222222222222222 | +1.0 Z=-4.3(44.84%) | Like=-1.27..-0.02 [-1.2720..-1.2711]*| it/evals=1350/13312 eff=10.4554% N=400 Z=-4.3(46.69%) | Like=-1.22..-0.02 [-1.2234..-1.2221]*| it/evals=1372/13595 eff=10.3979% N=400 Z=-4.2(48.28%) | Like=-1.19..-0.02 [-1.1909..-1.1897]*| it/evals=1393/13903 eff=10.3162% N=400 Z=-4.2(48.70%) | Like=-1.18..-0.02 [-1.1835..-1.1824]*| it/evals=1400/14001 eff=10.2934% N=400 Z=-4.2(50.45%) | Like=-1.15..-0.02 [-1.1453..-1.1435]*| it/evals=1422/14305 eff=10.2265% N=400 Have 2 modes Volume: ~exp(-7.56) * Expected Volume: exp(-3.60) Quality: ok positive degeneracy between param2 and param1: rho=0.76 param0: +0.0| 111111111111111 22222222222222 | +1.0 param1: +0.0| 111111111111111 2222222222222222 | +1.0 param2: +0.0| 111111111111111 222222222222222 | +1.0 Z=-4.2(51.68%) | Like=-1.11..-0.02 [-1.1101..-1.1077]*| it/evals=1440/14526 eff=10.1940% N=400 Z=-4.1(52.42%) | Like=-1.08..-0.02 [-1.0795..-1.0776]*| it/evals=1450/14675 eff=10.1576% N=400 Z=-4.1(54.25%) | Like=-1.04..-0.02 [-1.0377..-1.0369]*| it/evals=1473/15017 eff=10.0773% N=400 Z=-4.1(55.24%) | Like=-1.02..-0.02 [-1.0187..-1.0177]*| it/evals=1486/15194 eff=10.0446% N=400 Z=-4.1(56.27%) | Like=-0.99..-0.02 [-0.9862..-0.9841]*| it/evals=1500/15383 eff=10.0113% N=400 Z=-4.0(57.88%) | Like=-0.96..-0.02 [-0.9598..-0.9592]*| it/evals=1524/15726 eff=9.9439% N=400 Have 2 modes Volume: ~exp(-8.10) * Expected Volume: exp(-3.82) Quality: ok positive degeneracy between param2 and param1: rho=0.75 param0: +0.0| 111111111111111 22222222222222 | +1.0 param1: +0.0| 11111111111111 22222222222222 | +1.0 param2: +0.0| 1111111111111 222222222222222 | +1.0 Z=-4.0(58.31%) | Like=-0.96..-0.02 [-0.9583..-0.9577]*| it/evals=1530/15811 eff=9.9280% N=400 Z=-4.0(59.71%) | Like=-0.93..-0.02 [-0.9327..-0.9319]*| it/evals=1550/16084 eff=9.8827% N=400 Z=-4.0(60.84%) | Like=-0.90..-0.02 [-0.9029..-0.9021]*| it/evals=1567/16325 eff=9.8399% N=400 Z=-4.0(61.69%) | Like=-0.89..-0.02 [-0.8895..-0.8850]*| it/evals=1580/16512 eff=9.8064% N=400 Z=-4.0(62.53%) | Like=-0.87..-0.02 [-0.8721..-0.8714]*| it/evals=1593/16672 eff=9.7898% N=400 Z=-4.0(62.98%) | Like=-0.86..-0.02 [-0.8610..-0.8608]*| it/evals=1600/16777 eff=9.7698% N=400 Have 2 modes Volume: ~exp(-8.40) * Expected Volume: exp(-4.05) Quality: ok param0: +0.0| 11111111111111 2222222222222 | +1.0 param1: +0.0| 1111111111111 2222222222222 | +1.0 param2: +0.0| 1111111111111 2222222222222 | +1.0 Z=-3.9(64.21%) | Like=-0.83..-0.02 [-0.8319..-0.8302]*| it/evals=1620/17068 eff=9.7192% N=400 Z=-3.9(64.99%) | Like=-0.82..-0.02 [-0.8192..-0.8163]*| it/evals=1633/17245 eff=9.6943% N=400 Z=-3.9(65.85%) | Like=-0.79..-0.02 [-0.7931..-0.7876]*| it/evals=1647/17433 eff=9.6695% N=400 Z=-3.9(66.02%) | Like=-0.78..-0.02 [-0.7844..-0.7819]*| it/evals=1650/17474 eff=9.6638% N=400 Z=-3.9(67.20%) | Like=-0.76..-0.02 [-0.7583..-0.7565]*| it/evals=1670/17760 eff=9.6198% N=400 Z=-3.9(68.83%) | Like=-0.72..-0.02 [-0.7221..-0.7185]*| it/evals=1699/18168 eff=9.5621% N=400 Z=-3.9(68.88%) | Like=-0.72..-0.02 [-0.7185..-0.7171]*| it/evals=1700/18181 eff=9.5608% N=400 Have 2 modes Volume: ~exp(-8.40) Expected Volume: exp(-4.27) Quality: ok param0: +0.0| 011111111111 222222222222 | +1.0 param1: +0.0| 1111111111111 222222222222 | +1.0 param2: +0.0| 1111111111111 222222222222 | +1.0 Z=-3.9(69.60%) | Like=-0.70..-0.02 [-0.6976..-0.6975]*| it/evals=1714/18375 eff=9.5355% N=400 Z=-3.8(70.45%) | Like=-0.67..-0.02 [-0.6748..-0.6738]*| it/evals=1728/18562 eff=9.5144% N=400 Z=-3.8(71.50%) | Like=-0.66..-0.02 [-0.6574..-0.6564]*| it/evals=1747/18829 eff=9.4796% N=400 Z=-3.8(71.69%) | Like=-0.65..-0.02 [-0.6530..-0.6527]*| it/evals=1750/18873 eff=9.4733% N=400 Z=-3.8(72.48%) | Like=-0.63..-0.02 [-0.6346..-0.6346]*| it/evals=1765/19093 eff=9.4420% N=400 Z=-3.8(73.67%) | Like=-0.62..-0.01 [-0.6181..-0.6172]*| it/evals=1788/19413 eff=9.4041% N=400 Have 2 modes Volume: ~exp(-8.40) Expected Volume: exp(-4.50) Quality: ok param0: +0.0| 011111111101 22222222222 +0.8 | +1.0 param1: +0.0| 11111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 222222222222 | +1.0 Z=-3.8(74.33%) | Like=-0.61..-0.01 [-0.6079..-0.6078]*| it/evals=1800/19590 eff=9.3799% N=400 Z=-3.8(75.38%) | Like=-0.58..-0.01 [-0.5845..-0.5823]*| it/evals=1823/19916 eff=9.3411% N=400 Z=-3.8(76.15%) | Like=-0.57..-0.01 [-0.5710..-0.5708]*| it/evals=1839/20141 eff=9.3156% N=400 Z=-3.8(76.65%) | Like=-0.56..-0.01 [-0.5608..-0.5607]*| it/evals=1850/20308 eff=9.2927% N=400 Z=-3.7(77.84%) | Like=-0.55..-0.01 [-0.5452..-0.5451]*| it/evals=1876/20684 eff=9.2487% N=400 Have 2 modes Volume: ~exp(-8.40) Expected Volume: exp(-4.73) Quality: ok param0: +0.0| 11111111101 2222222222 +0.8 | +1.0 param1: +0.0| 11111111111 22222222222 +0.8 | +1.0 param2: +0.0| 11111111111 22222222222 +0.8 | +1.0 Z=-3.7(78.46%) | Like=-0.53..-0.01 [-0.5328..-0.5320]*| it/evals=1890/20890 eff=9.2240% N=400 Z=-3.7(78.88%) | Like=-0.52..-0.01 [-0.5217..-0.5216]*| it/evals=1900/21039 eff=9.2059% N=400 Z=-3.7(79.40%) | Like=-0.51..-0.01 [-0.5083..-0.5063]*| it/evals=1913/21216 eff=9.1900% N=400 Z=-3.7(80.14%) | Like=-0.50..-0.01 [-0.4957..-0.4954]*| it/evals=1931/21467 eff=9.1660% N=400 Z=-3.7(80.91%) | Like=-0.48..-0.01 [-0.4806..-0.4802]*| it/evals=1950/21739 eff=9.1382% N=400 Z=-3.7(81.79%) | Like=-0.46..-0.01 [-0.4634..-0.4623]*| it/evals=1973/22058 eff=9.1098% N=400 Have 2 modes Volume: ~exp(-9.10) * Expected Volume: exp(-4.95) Quality: ok param0: +0.0| 111111111 2222222222 +0.8 | +1.0 param1: +0.0| +0.2 111111111 22222222222 +0.8 | +1.0 param2: +0.0| 1111111111 2222222222 +0.8 | +1.0 Z=-3.7(82.04%) | Like=-0.45..-0.01 [-0.4530..-0.4516]*| it/evals=1980/22164 eff=9.0976% N=400 Z=-3.7(82.77%) | Like=-0.44..-0.01 [-0.4359..-0.4357]*| it/evals=2000/22437 eff=9.0756% N=400 Z=-3.7(83.59%) | Like=-0.42..-0.01 [-0.4203..-0.4198]*| it/evals=2023/22758 eff=9.0482% N=400 Z=-3.7(84.30%) | Like=-0.41..-0.01 [-0.4054..-0.4053]*| it/evals=2045/23088 eff=9.0136% N=400 Z=-3.7(84.46%) | Like=-0.40..-0.00 [-0.4038..-0.4036]*| it/evals=2050/23176 eff=9.0007% N=400 Have 2 modes Volume: ~exp(-9.55) * Expected Volume: exp(-5.18) Quality: ok param0: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 11111111 2222222222 +0.8 | +1.0 Z=-3.7(85.08%) | Like=-0.38..-0.00 [-0.3835..-0.3834]*| it/evals=2070/23468 eff=8.9735% N=400 Z=-3.6(85.80%) | Like=-0.37..-0.00 [-0.3673..-0.3669]*| it/evals=2093/23770 eff=8.9559% N=400 Z=-3.6(86.01%) | Like=-0.36..-0.00 [-0.3626..-0.3625]*| it/evals=2100/23855 eff=8.9533% N=400 Z=-3.6(86.74%) | Like=-0.35..-0.00 [-0.3479..-0.3478]*| it/evals=2124/24172 eff=8.9349% N=400 Z=-3.6(87.43%) | Like=-0.33..-0.00 [-0.3337..-0.3330]*| it/evals=2148/24488 eff=8.9173% N=400 Z=-3.6(87.48%) | Like=-0.33..-0.00 [-0.3325..-0.3324]*| it/evals=2150/24513 eff=8.9164% N=400 Have 2 modes Volume: ~exp(-9.55) Expected Volume: exp(-5.40) Quality: ok param0: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 param2: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 Z=-3.6(87.77%) | Like=-0.32..-0.00 [-0.3249..-0.3233]*| it/evals=2161/24662 eff=8.9069% N=400 Z=-3.6(88.39%) | Like=-0.31..-0.00 [-0.3074..-0.3073]*| it/evals=2185/24987 eff=8.8868% N=400 Z=-3.6(88.76%) | Like=-0.30..-0.00 [-0.3024..-0.3013]*| it/evals=2200/25200 eff=8.8710% N=400 Z=-3.6(89.34%) | Like=-0.29..-0.00 [-0.2891..-0.2889]*| it/evals=2224/25526 eff=8.8514% N=400 Z=-3.6(89.85%) | Like=-0.28..-0.00 [-0.2784..-0.2782]*| it/evals=2246/25804 eff=8.8411% N=400 Have 2 modes Volume: ~exp(-9.86) * Expected Volume: exp(-5.63) Quality: ok param0: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 11111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 11111111 222222222 +0.8 | +1.0 Z=-3.6(89.94%) | Like=-0.28..-0.00 [-0.2765..-0.2764]*| it/evals=2250/25854 eff=8.8395% N=400 Z=-3.6(90.52%) | Like=-0.27..-0.00 [-0.2661..-0.2661]*| it/evals=2276/26224 eff=8.8135% N=400 Z=-3.6(90.81%) | Like=-0.26..-0.00 [-0.2607..-0.2603]*| it/evals=2290/26418 eff=8.8016% N=400 Z=-3.6(91.01%) | Like=-0.26..-0.00 [-0.2586..-0.2575]*| it/evals=2300/26553 eff=8.7944% N=400 Z=-3.6(91.52%) | Like=-0.25..-0.00 [-0.2471..-0.2471]*| it/evals=2326/26927 eff=8.7684% N=400 Have 2 modes Volume: ~exp(-10.13) * Expected Volume: exp(-5.85) Quality: ok param0: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 22222222 +0.8 | +1.0 param2: +0.0| +0.2 11111111 2222222 +0.8 | +1.0 Z=-3.6(91.78%) | Like=-0.24..-0.00 [-0.2428..-0.2419]*| it/evals=2340/27133 eff=8.7532% N=400 Z=-3.6(91.97%) | Like=-0.24..-0.00 [-0.2390..-0.2385]*| it/evals=2350/27278 eff=8.7432% N=400 Z=-3.6(92.38%) | Like=-0.23..-0.00 [-0.2306..-0.2303]*| it/evals=2373/27590 eff=8.7275% N=400 Z=-3.6(92.76%) | Like=-0.22..-0.00 [-0.2205..-0.2202]*| it/evals=2396/27896 eff=8.7140% N=400 Z=-3.6(92.83%) | Like=-0.22..-0.00 [-0.2196..-0.2195]*| it/evals=2400/27946 eff=8.7127% N=400 Z=-3.6(93.26%) | Like=-0.21..-0.00 [-0.2078..-0.2075]*| it/evals=2427/28333 eff=8.6886% N=400 Have 2 modes Volume: ~exp(-10.13) Expected Volume: exp(-6.08) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 Z=-3.6(93.41%) | Like=-0.20..-0.00 [-0.2025..-0.2017]*| it/evals=2437/28476 eff=8.6800% N=400 Z=-3.6(93.60%) | Like=-0.20..-0.00 [-0.1984..-0.1984]*| it/evals=2450/28675 eff=8.6649% N=400 Z=-3.6(93.92%) | Like=-0.19..-0.00 [-0.1912..-0.1910]*| it/evals=2472/28961 eff=8.6552% N=400 Z=-3.6(94.13%) | Like=-0.19..-0.00 [-0.1876..-0.1874]*| it/evals=2487/29161 eff=8.6471% N=400 Z=-3.6(94.26%) | Like=-0.19..-0.00 [-0.1859..-0.1857]*| it/evals=2497/29307 eff=8.6380% N=400 Z=-3.6(94.30%) | Like=-0.19..-0.00 [-0.1856..-0.1851]*| it/evals=2500/29353 eff=8.6347% N=400 Have 2 modes Volume: ~exp(-10.43) * Expected Volume: exp(-6.30) Quality: ok param0: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param1: +0.0| +0.2 1111111 2222222 +0.8 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.7 | +1.0 Z=-3.5(94.56%) | Like=-0.18..-0.00 [-0.1800..-0.1799]*| it/evals=2520/29662 eff=8.6119% N=400 Z=-3.5(94.87%) | Like=-0.17..-0.00 [-0.1720..-0.1719]*| it/evals=2545/30033 eff=8.5884% N=400 Z=-3.5(94.93%) | Like=-0.17..-0.00 [-0.1693..-0.1687]*| it/evals=2550/30099 eff=8.5861% N=400 Z=-3.5(95.23%) | Like=-0.16..-0.00 [-0.1622..-0.1619]*| it/evals=2576/30455 eff=8.5710% N=400 Z=-3.5(95.42%) | Like=-0.16..-0.00 [-0.1567..-0.1567]*| it/evals=2593/30697 eff=8.5586% N=400 Z=-3.5(95.49%) | Like=-0.16..-0.00 [-0.1552..-0.1549]*| it/evals=2600/30778 eff=8.5588% N=400 Have 2 modes Volume: ~exp(-10.43) Expected Volume: exp(-6.53) Quality: ok param0: +0.0| +0.2 111111 222222 +0.8 | +1.0 param1: +0.0| +0.2 111111 2222222 +0.7 | +1.0 param2: +0.0| +0.2 1111111 2222222 +0.7 | +1.0 Z=-3.5(95.60%) | Like=-0.15..-0.00 [-0.1537..-0.1537]*| it/evals=2610/30919 eff=8.5520% N=400 Z=-3.5(95.89%) | Like=-0.15..-0.00 [-0.1483..-0.1482]*| it/evals=2639/31318 eff=8.5355% N=400 Z=-3.5(96.00%) | Like=-0.15..-0.00 [-0.1462..-0.1460]*| it/evals=2650/31473 eff=8.5283% N=400 Z=-3.5(96.30%) | Like=-0.14..-0.00 [-0.1391..-0.1387]*| it/evals=2683/31943 eff=8.5058% N=400 Have 2 modes Volume: ~exp(-10.43) Expected Volume: exp(-6.75) Quality: ok param0: +0.0| +0.3 11111 222222 +0.7 | +1.0 param1: +0.0| +0.3 11111 222222 +0.7 | +1.0 param2: +0.0| +0.3 011111 222222 +0.7 | +1.0 Z=-3.5(96.44%) | Like=-0.14..-0.00 [-0.1360..-0.1359]*| it/evals=2700/32188 eff=8.4938% N=400 Z=-3.5(96.61%) | Like=-0.13..-0.00 [-0.1318..-0.1316]*| it/evals=2720/32454 eff=8.4857% N=400 Z=-3.5(96.84%) | Like=-0.12..-0.00 [-0.1240..-0.1239]*| it/evals=2750/32861 eff=8.4717% N=400 Z=-3.5(97.08%) | Like=-0.12..-0.00 [-0.1190..-0.1187]*| it/evals=2784/33314 eff=8.4584% N=400 Have 2 modes Volume: ~exp(-10.43) Expected Volume: exp(-6.98) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 222222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.12%) | Like=-0.12..-0.00 [-0.1177..-0.1177]*| it/evals=2790/33402 eff=8.4540% N=400 Z=-3.5(97.19%) | Like=-0.12..-0.00 [-0.1161..-0.1159]*| it/evals=2800/33526 eff=8.4526% N=400 Z=-3.5(97.39%) | Like=-0.11..-0.00 [-0.1097..-0.1097]*| it/evals=2831/33961 eff=8.4354% N=400 Z=-3.5(97.51%) | Like=-0.11..-0.00 [-0.1053..-0.1051]*| it/evals=2850/34241 eff=8.4217% N=400 Have 2 modes Volume: ~exp(-10.65) * Expected Volume: exp(-7.20) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(97.68%) | Like=-0.10..-0.00 [-0.1002..-0.0999]*| it/evals=2880/34663 eff=8.4056% N=400 Z=-3.5(97.79%) | Like=-0.10..-0.00 [-0.0955..-0.0954]*| it/evals=2900/34946 eff=8.3946% N=400 Z=-3.5(97.96%) | Like=-0.09..-0.00 [-0.0898..-0.0897]*| it/evals=2932/35393 eff=8.3788% N=400 Z=-3.5(98.04%) | Like=-0.09..-0.00 [-0.0876..-0.0873]*| it/evals=2950/35641 eff=8.3709% N=400 Have 2 modes Volume: ~exp(-11.34) * Expected Volume: exp(-7.43) Quality: ok param0: +0.0| +0.3 11111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.13%) | Like=-0.08..-0.00 [-0.0850..-0.0839]*| it/evals=2970/35916 eff=8.3624% N=400 Z=-3.5(98.27%) | Like=-0.08..-0.00 [-0.0810..-0.0810]*| it/evals=3000/36340 eff=8.3472% N=400 Z=-3.5(98.40%) | Like=-0.08..-0.00 [-0.0763..-0.0763]*| it/evals=3033/36793 eff=8.3340% N=400 Z=-3.5(98.46%) | Like=-0.07..-0.00 [-0.0740..-0.0738]*| it/evals=3050/37033 eff=8.3258% N=400 Have 2 modes Volume: ~exp(-11.35) * Expected Volume: exp(-7.65) Quality: ok param0: +0.0| +0.3 1111 22222 +0.7 | +1.0 param1: +0.0| +0.3 11111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.50%) | Like=-0.07..-0.00 [-0.0726..-0.0725]*| it/evals=3060/37178 eff=8.3202% N=400 Z=-3.5(98.60%) | Like=-0.07..-0.00 [-0.0684..-0.0683]*| it/evals=3089/37567 eff=8.3111% N=400 Z=-3.5(98.64%) | Like=-0.07..-0.00 [-0.0675..-0.0674]*| it/evals=3100/37723 eff=8.3059% N=400 Z=-3.5(98.74%) | Like=-0.06..-0.00 [-0.0638..-0.0638]*| it/evals=3132/38151 eff=8.2965% N=400 Have 2 modes Volume: ~exp(-11.80) * Expected Volume: exp(-7.88) Quality: ok param0: +0.0| +0.3 1111 22222 +0.7 | +1.0 param1: +0.0| +0.3 1111 22222 +0.7 | +1.0 param2: +0.0| +0.3 11111 22222 +0.7 | +1.0 Z=-3.5(98.80%) | Like=-0.06..-0.00 [-0.0622..-0.0622]*| it/evals=3150/38416 eff=8.2860% N=400 Z=-3.5(98.87%) | Like=-0.06..-0.00 [-0.0601..-0.0596]*| it/evals=3177/38795 eff=8.2745% N=400 Z=-3.5(98.93%) | Like=-0.06..-0.00 [-0.0576..-0.0574]*| it/evals=3200/39125 eff=8.2634% N=400 [ultranest] Explored until L=-0.001 [ultranest] Likelihood function evaluations: 39467 [ultranest] logZ = -3.501 +- 0.06405 [ultranest] Effective samples strategy satisfied (ESS = 1847.9, need >400) [ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [ultranest] logZ error budget: single: 0.07 bs:0.06 tail:0.01 total:0.06 required:<0.50 [ultranest] done iterating. logZ = -3.493 +- 0.164 single instance: logZ = -3.493 +- 0.071 bootstrapped : logZ = -3.501 +- 0.163 tail : logZ = +- 0.010 insert order U test : converged: True correlation: inf iterations param0 : 0.00 │▁▁▁▁▁▁▂▂▃▄▅▅▅▄▄▄▃▂▁▂▂▂▃▃▄▇▇▆▆▆▄▄▃▂▂▁▁▁▁│1.00 0.54 +- 0.22 param1 : 0.00 │ ▁▁▁▁▂▃▂▄▅▄▅▄▄▃▄▃▂▂▁▃▃▃▄▅▇▆▇▆▄▄▄▃▃▂▁▁▁▁│1.00 0.53 +- 0.23 param2 : 0.00 │▁▁▁▁▁▁▂▄▅▅▄▅▅▅▄▃▃▃▂▂▂▃▃▄▅▇▇▇▇▇▆▅▃▂▁▁▁▁▁│1.00 0.53 +- 0.22 -------------------------------Captured log call-------------------------------- [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=408, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-28.83, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=25, ncalls=531, regioncalls=0, ndraw=40, logz=-26.85, remainder_fraction=100.0000%, Lmin=-22.62, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=652, regioncalls=0, ndraw=40, logz=-24.15, remainder_fraction=100.0000%, Lmin=-20.35, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=76, ncalls=783, regioncalls=0, ndraw=40, logz=-21.82, remainder_fraction=100.0000%, Lmin=-18.15, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=855, regioncalls=0, ndraw=40, logz=-20.66, remainder_fraction=100.0000%, Lmin=-17.02, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=917, regioncalls=0, ndraw=40, logz=-19.96, remainder_fraction=100.0000%, Lmin=-16.30, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=124, ncalls=1052, regioncalls=0, ndraw=40, logz=-18.26, remainder_fraction=100.0000%, Lmin=-14.75, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=1204, regioncalls=0, ndraw=40, logz=-16.87, remainder_fraction=99.9998%, Lmin=-13.55, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=174, ncalls=1326, regioncalls=0, ndraw=40, logz=-15.66, remainder_fraction=99.9994%, Lmin=-11.86, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=1379, regioncalls=0, ndraw=40, logz=-15.27, remainder_fraction=99.9992%, Lmin=-11.67, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=1490, regioncalls=0, ndraw=40, logz=-14.29, remainder_fraction=99.9978%, Lmin=-10.99, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=222, ncalls=1637, regioncalls=0, ndraw=40, logz=-13.52, remainder_fraction=99.9953%, Lmin=-10.28, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=247, ncalls=1809, regioncalls=0, ndraw=40, logz=-12.64, remainder_fraction=99.9886%, Lmin=-9.52, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1831, regioncalls=0, ndraw=40, logz=-12.55, remainder_fraction=99.9876%, Lmin=-9.48, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=1977, regioncalls=0, ndraw=40, logz=-12.03, remainder_fraction=99.9788%, Lmin=-9.05, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=2235, regioncalls=0, ndraw=40, logz=-11.29, remainder_fraction=99.9558%, Lmin=-8.32, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=321, ncalls=2385, regioncalls=0, ndraw=40, logz=-10.84, remainder_fraction=99.9295%, Lmin=-7.89, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=347, ncalls=2631, regioncalls=0, ndraw=40, logz=-10.34, remainder_fraction=99.8841%, Lmin=-7.47, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2662, regioncalls=0, ndraw=40, logz=-10.29, remainder_fraction=99.8768%, Lmin=-7.39, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=2749, regioncalls=0, ndraw=40, logz=-10.10, remainder_fraction=99.8502%, Lmin=-7.24, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=2997, regioncalls=0, ndraw=40, logz=-9.61, remainder_fraction=99.7498%, Lmin=-6.77, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=3082, regioncalls=0, ndraw=40, logz=-9.45, remainder_fraction=99.7066%, Lmin=-6.61, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=420, ncalls=3243, regioncalls=0, ndraw=40, logz=-9.17, remainder_fraction=99.6280%, Lmin=-6.45, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3508, regioncalls=0, ndraw=40, logz=-8.79, remainder_fraction=99.4495%, Lmin=-6.09, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=491, ncalls=3880, regioncalls=0, ndraw=40, logz=-8.30, remainder_fraction=99.1088%, Lmin=-5.54, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3947, regioncalls=0, ndraw=40, logz=-8.20, remainder_fraction=99.0254%, Lmin=-5.48, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=4283, regioncalls=0, ndraw=40, logz=-7.77, remainder_fraction=98.4951%, Lmin=-5.01, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=4377, regioncalls=0, ndraw=40, logz=-7.67, remainder_fraction=98.3560%, Lmin=-4.97, Lmax=-0.16 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=590, ncalls=4790, regioncalls=0, ndraw=40, logz=-7.31, 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iteration=950, ncalls=8630, regioncalls=0, ndraw=40, logz=-5.31, remainder_fraction=84.1905%, Lmin=-2.49, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=969, ncalls=8886, regioncalls=0, ndraw=40, logz=-5.23, remainder_fraction=82.9940%, Lmin=-2.42, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=988, ncalls=9112, regioncalls=0, ndraw=40, logz=-5.17, remainder_fraction=81.9846%, Lmin=-2.36, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=990, ncalls=9141, regioncalls=0, ndraw=40, logz=-5.16, remainder_fraction=81.9129%, Lmin=-2.35, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1000, ncalls=9257, regioncalls=0, ndraw=40, logz=-5.12, remainder_fraction=81.2186%, Lmin=-2.32, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1025, ncalls=9577, regioncalls=0, ndraw=40, logz=-5.04, remainder_fraction=79.4417%, Lmin=-2.23, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1047, ncalls=9834, regioncalls=0, ndraw=40, logz=-4.97, remainder_fraction=78.0185%, Lmin=-2.14, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1050, ncalls=9869, regioncalls=0, ndraw=40, logz=-4.96, remainder_fraction=77.7926%, Lmin=-2.13, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1077, ncalls=10189, regioncalls=0, ndraw=40, logz=-4.88, remainder_fraction=75.7737%, Lmin=-2.04, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1080, ncalls=10222, regioncalls=0, ndraw=40, logz=-4.87, remainder_fraction=75.5443%, Lmin=-2.04, Lmax=-0.07 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=10463, regioncalls=0, ndraw=40, logz=-4.82, remainder_fraction=74.0501%, Lmin=-1.95, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1125, ncalls=10759, regioncalls=0, ndraw=40, logz=-4.75, remainder_fraction=72.4272%, Lmin=-1.86, Lmax=-0.05 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1141, ncalls=10946, regioncalls=0, ndraw=40, 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iteration=2340, ncalls=27302, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=7.9888%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=27437, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=7.8140%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2372, ncalls=27769, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=7.4341%, Lmin=-0.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2395, ncalls=28087, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=7.0581%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=28149, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=6.9763%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2419, ncalls=28412, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=6.6802%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2428, ncalls=28545, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=6.5429%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2430, ncalls=28569, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=6.5113%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=28857, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=6.2211%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2476, ncalls=29206, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=5.8595%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2498, ncalls=29531, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=5.5689%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=29561, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=5.5427%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=29854, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=5.2867%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2544, ncalls=30175, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.9992%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=30250, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.9301%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2573, ncalls=30598, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.6713%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2595, ncalls=30910, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.4363%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=30975, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.3844%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=31115, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.2836%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2633, ncalls=31443, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=4.0592%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=31682, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.9029%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2671, ncalls=32002, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.7126%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2694, ncalls=32317, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.5152%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=32389, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.4640%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2723, ncalls=32712, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.2792%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2746, ncalls=33040, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=3.1064%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=33112, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=3.0774%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2773, ncalls=33433, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.9129%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=33655, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.7965%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=33784, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.7309%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2822, ncalls=34115, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.5908%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2844, ncalls=34437, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.4596%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=34526, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.4248%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2872, ncalls=34838, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.2997%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2883, ncalls=34978, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.2399%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=35213, regioncalls=0, ndraw=40, logz=-3.49, remainder_fraction=2.1510%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2923, ncalls=35533, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=2.0362%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2946, ncalls=35854, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.9255%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=35908, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.9070%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=36224, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.8178%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2993, ncalls=36543, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.7183%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=36647, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.6896%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3023, ncalls=36973, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.5987%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3047, ncalls=37294, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.5081%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=37329, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.4971%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=37469, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.4611%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3072, ncalls=37630, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.4190%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3096, ncalls=37952, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.3387%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=38015, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.3256%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3118, ncalls=38266, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.2690%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3138, ncalls=38556, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.2083%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=38738, regioncalls=0, ndraw=40, logz=-3.48, remainder_fraction=1.1733%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3171, ncalls=39030, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=1.1143%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3192, ncalls=39327, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=1.0584%, Lmin=-0.05, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=39438, regioncalls=0, ndraw=40, logz=-3.47, remainder_fraction=1.0380%, Lmin=-0.05, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.001 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 39658 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.454 +- 0.05166 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1850.6, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.47+-0.07 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.05, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.05 tail:0.01 total:0.05 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. [35mDEBUG [0m ultranest:integrator.py:1060 ReactiveNestedSampler: dims=3+0, resume=False, log_dir=None, backend=hdf5, vectorized=False, nbootstraps=30, ndraw=128..65536 [32mINFO [0m ultranest:integrator.py:1339 Sampling 400 live points from prior ... [35mDEBUG [0m ultranest:integrator.py:2277 run_iter dlogz=0.5, dKL=0.5, frac_remain=0.01, Lepsilon=0.0010, min_ess=400 [35mDEBUG [0m ultranest:integrator.py:2281 max_iters=-1, max_ncalls=-1, max_num_improvement_loops=-1, min_num_live_points=400, cluster_num_live_points=40 [35mDEBUG [0m ultranest:integrator.py:2340 minimal_widths_sequence: [(-inf, 400.0), (inf, 400.0)] [35mDEBUG [0m ultranest:integrator.py:2491 iteration=0, ncalls=408, regioncalls=0, ndraw=40, logz=-inf, remainder_fraction=100.0000%, Lmin=-30.72, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=32, ncalls=565, regioncalls=0, ndraw=40, logz=-25.89, remainder_fraction=100.0000%, Lmin=-21.40, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=50, ncalls=643, regioncalls=0, ndraw=40, logz=-23.52, remainder_fraction=100.0000%, Lmin=-19.79, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=81, ncalls=805, regioncalls=0, ndraw=40, logz=-20.93, remainder_fraction=100.0000%, Lmin=-17.47, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=90, ncalls=842, regioncalls=0, ndraw=40, logz=-20.30, remainder_fraction=100.0000%, Lmin=-16.75, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=100, ncalls=881, regioncalls=0, ndraw=40, logz=-19.61, remainder_fraction=100.0000%, Lmin=-16.01, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=135, ncalls=1088, regioncalls=0, ndraw=40, logz=-17.60, remainder_fraction=99.9999%, Lmin=-14.15, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=150, ncalls=1187, regioncalls=0, ndraw=40, logz=-16.71, remainder_fraction=99.9998%, Lmin=-13.30, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=180, ncalls=1381, regioncalls=0, ndraw=40, logz=-15.23, remainder_fraction=99.9993%, Lmin=-11.73, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=200, ncalls=1518, regioncalls=0, ndraw=40, logz=-14.31, remainder_fraction=99.9982%, Lmin=-11.07, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=234, ncalls=1754, regioncalls=0, ndraw=40, logz=-12.98, remainder_fraction=99.9931%, Lmin=-9.86, Lmax=-0.14 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=250, ncalls=1856, regioncalls=0, ndraw=40, logz=-12.50, remainder_fraction=99.9895%, Lmin=-9.36, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=264, ncalls=1963, regioncalls=0, ndraw=40, logz=-12.09, remainder_fraction=99.9837%, Lmin=-8.94, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=270, ncalls=2017, regioncalls=0, ndraw=40, logz=-11.92, remainder_fraction=99.9811%, Lmin=-8.82, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=300, ncalls=2241, regioncalls=0, ndraw=40, logz=-11.17, remainder_fraction=99.9593%, Lmin=-8.15, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=322, ncalls=2379, regioncalls=0, ndraw=40, logz=-10.67, remainder_fraction=99.9339%, Lmin=-7.73, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=350, ncalls=2597, regioncalls=0, ndraw=40, logz=-10.12, remainder_fraction=99.8843%, Lmin=-7.25, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=360, ncalls=2686, regioncalls=0, ndraw=40, logz=-9.94, remainder_fraction=99.8597%, Lmin=-7.13, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=390, ncalls=2899, regioncalls=0, ndraw=40, logz=-9.50, remainder_fraction=99.7782%, Lmin=-6.81, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=400, ncalls=2991, regioncalls=0, ndraw=40, logz=-9.36, remainder_fraction=99.7402%, Lmin=-6.65, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=428, ncalls=3230, regioncalls=0, ndraw=40, logz=-8.99, remainder_fraction=99.6144%, Lmin=-6.32, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=450, ncalls=3421, regioncalls=0, ndraw=40, logz=-8.74, remainder_fraction=99.5274%, Lmin=-6.15, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=479, ncalls=3664, regioncalls=0, ndraw=40, logz=-8.43, remainder_fraction=99.3474%, Lmin=-5.84, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=500, ncalls=3853, regioncalls=0, ndraw=40, logz=-8.22, remainder_fraction=99.1959%, Lmin=-5.58, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=527, ncalls=4115, regioncalls=0, ndraw=40, logz=-7.95, remainder_fraction=98.9056%, Lmin=-5.24, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=540, ncalls=4239, regioncalls=0, ndraw=40, logz=-7.82, remainder_fraction=98.7327%, Lmin=-5.11, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=550, ncalls=4360, regioncalls=0, ndraw=40, logz=-7.72, remainder_fraction=98.6113%, Lmin=-5.07, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=565, ncalls=4468, regioncalls=0, ndraw=40, logz=-7.59, remainder_fraction=98.3870%, Lmin=-4.90, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=597, ncalls=4743, regioncalls=0, ndraw=40, logz=-7.33, remainder_fraction=97.8736%, Lmin=-4.66, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=600, ncalls=4774, regioncalls=0, ndraw=40, logz=-7.30, remainder_fraction=97.8227%, Lmin=-4.63, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=619, ncalls=4946, regioncalls=0, ndraw=40, logz=-7.15, remainder_fraction=97.4556%, Lmin=-4.46, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=630, ncalls=5047, regioncalls=0, ndraw=40, logz=-7.06, remainder_fraction=97.2463%, Lmin=-4.41, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=650, ncalls=5228, regioncalls=0, ndraw=40, logz=-6.92, remainder_fraction=96.8062%, Lmin=-4.26, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=675, ncalls=5476, regioncalls=0, ndraw=40, logz=-6.75, remainder_fraction=96.2513%, Lmin=-4.12, Lmax=-0.08 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=700, ncalls=5694, regioncalls=0, ndraw=40, logz=-6.60, remainder_fraction=95.5797%, Lmin=-3.95, 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regioncalls=0, ndraw=40, logz=-4.92, remainder_fraction=75.7277%, Lmin=-2.01, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1094, ncalls=9977, regioncalls=0, ndraw=40, logz=-4.88, remainder_fraction=74.6352%, Lmin=-1.96, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1100, ncalls=10060, regioncalls=0, ndraw=40, logz=-4.86, remainder_fraction=74.1624%, Lmin=-1.93, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1113, ncalls=10215, regioncalls=0, ndraw=40, logz=-4.82, remainder_fraction=73.1916%, Lmin=-1.89, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1138, ncalls=10526, regioncalls=0, ndraw=40, logz=-4.75, remainder_fraction=71.3346%, Lmin=-1.83, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1150, ncalls=10677, regioncalls=0, ndraw=40, logz=-4.72, remainder_fraction=70.3481%, Lmin=-1.79, Lmax=-0.02 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=1170, ncalls=10969, regioncalls=0, ndraw=40, 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[35mDEBUG [0m ultranest:integrator.py:2491 iteration=2150, ncalls=24513, regioncalls=0, ndraw=40, logz=-3.63, remainder_fraction=12.5203%, Lmin=-0.33, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2161, ncalls=24662, regioncalls=0, ndraw=40, logz=-3.62, remainder_fraction=12.2323%, Lmin=-0.32, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2185, ncalls=24987, regioncalls=0, ndraw=40, logz=-3.62, remainder_fraction=11.6097%, Lmin=-0.31, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2200, ncalls=25200, regioncalls=0, ndraw=40, logz=-3.61, remainder_fraction=11.2407%, Lmin=-0.30, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2224, ncalls=25526, regioncalls=0, ndraw=40, logz=-3.61, remainder_fraction=10.6613%, Lmin=-0.29, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2246, ncalls=25804, regioncalls=0, ndraw=40, logz=-3.60, remainder_fraction=10.1487%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2250, ncalls=25854, regioncalls=0, ndraw=40, logz=-3.60, remainder_fraction=10.0608%, Lmin=-0.28, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2276, ncalls=26224, regioncalls=0, ndraw=40, logz=-3.59, remainder_fraction=9.4785%, Lmin=-0.27, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2290, ncalls=26418, regioncalls=0, ndraw=40, logz=-3.59, remainder_fraction=9.1863%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2300, ncalls=26553, regioncalls=0, ndraw=40, logz=-3.59, remainder_fraction=8.9903%, Lmin=-0.26, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2326, ncalls=26927, regioncalls=0, ndraw=40, logz=-3.58, remainder_fraction=8.4812%, Lmin=-0.25, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2340, ncalls=27133, regioncalls=0, ndraw=40, logz=-3.58, remainder_fraction=8.2171%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2350, ncalls=27278, regioncalls=0, ndraw=40, logz=-3.58, remainder_fraction=8.0294%, Lmin=-0.24, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2373, ncalls=27590, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=7.6220%, Lmin=-0.23, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2396, ncalls=27896, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=7.2365%, Lmin=-0.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2400, ncalls=27946, regioncalls=0, ndraw=40, logz=-3.57, remainder_fraction=7.1747%, Lmin=-0.22, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2427, ncalls=28333, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=6.7449%, Lmin=-0.21, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2437, ncalls=28476, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=6.5944%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2450, ncalls=28675, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=6.3952%, Lmin=-0.20, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2472, ncalls=28961, regioncalls=0, ndraw=40, logz=-3.56, remainder_fraction=6.0785%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2487, ncalls=29161, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.8714%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2497, ncalls=29307, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.7363%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2500, ncalls=29353, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.6963%, Lmin=-0.19, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2520, ncalls=29662, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.4385%, Lmin=-0.18, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2545, ncalls=30033, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.1318%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2550, ncalls=30099, regioncalls=0, ndraw=40, logz=-3.55, remainder_fraction=5.0740%, Lmin=-0.17, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2576, ncalls=30455, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=4.7726%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2593, ncalls=30697, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=4.5833%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2600, ncalls=30778, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=4.5081%, Lmin=-0.16, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2610, ncalls=30919, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=4.4010%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2639, ncalls=31318, regioncalls=0, ndraw=40, logz=-3.54, remainder_fraction=4.1085%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2650, ncalls=31473, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=4.0025%, Lmin=-0.15, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2683, ncalls=31943, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=3.7048%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2700, ncalls=32188, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=3.5598%, Lmin=-0.14, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2720, ncalls=32454, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=3.3947%, Lmin=-0.13, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2750, ncalls=32861, regioncalls=0, ndraw=40, logz=-3.53, remainder_fraction=3.1611%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2784, ncalls=33314, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.9158%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2790, ncalls=33402, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.8752%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2800, ncalls=33526, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.8080%, Lmin=-0.12, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2831, ncalls=33961, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.6075%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2850, ncalls=34241, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.4920%, Lmin=-0.11, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2880, ncalls=34663, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.3185%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2900, ncalls=34946, regioncalls=0, ndraw=40, logz=-3.52, remainder_fraction=2.2089%, Lmin=-0.10, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2932, ncalls=35393, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=2.0449%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2950, ncalls=35641, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.9576%, Lmin=-0.09, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=2970, ncalls=35916, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.8662%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3000, ncalls=36340, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.7348%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3033, ncalls=36793, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.6013%, Lmin=-0.08, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3050, ncalls=37033, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.5367%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3060, ncalls=37178, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.4998%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3089, ncalls=37567, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.3974%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3100, ncalls=37723, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.3603%, Lmin=-0.07, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3132, ncalls=38151, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.2581%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3150, ncalls=38416, regioncalls=0, ndraw=40, logz=-3.51, remainder_fraction=1.2040%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3177, ncalls=38795, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=1.1274%, Lmin=-0.06, Lmax=-0.00 [35mDEBUG [0m ultranest:integrator.py:2491 iteration=3200, ncalls=39125, regioncalls=0, ndraw=40, logz=-3.50, remainder_fraction=1.0656%, Lmin=-0.06, Lmax=-0.00 [32mINFO [0m ultranest:integrator.py:2535 Explored until L=-0.001 [32mINFO [0m ultranest:integrator.py:2633 Likelihood function evaluations: 39467 [35mDEBUG [0m ultranest:integrator.py:2190 did a run_iter pass! [32mINFO [0m ultranest:integrator.py:2578 logZ = -3.501 +- 0.06405 [32mINFO [0m ultranest:integrator.py:1486 Effective samples strategy satisfied (ESS = 1847.9, need >400) [32mINFO [0m ultranest:integrator.py:1517 Posterior uncertainty strategy is satisfied (KL: 0.46+-0.06 nat, need <0.50 nat) [32mINFO [0m ultranest:integrator.py:1572 Evidency uncertainty strategy is satisfied (dlogz=0.06, need <0.5) [32mINFO [0m ultranest:integrator.py:1576 logZ error budget: single: 0.07 bs:0.06 tail:0.01 total:0.06 required:<0.50 [32mINFO [0m ultranest:integrator.py:2193 done iterating. | |||
Passed | tests/test_stepsampling.py::test_stepsampler | 0.21 | |
------------------------------Captured stdout call------------------------------ [0.19669413 0.15875824 0.53255916] -0.533599805036657 [0.82144124 0.82377575 0.70716751] -0.7373961297911862 | |||
Passed | tests/test_stepsampling.py::test_stepsampler_adapt_when_stuck | 0.55 | |
------------------------------Captured stdout call------------------------------ CubeMHSampler rejected by region ineffective proposal scale (1). shrinking... rejected by region ineffective proposal scale (0.385543). shrinking... rejected by region ineffective proposal scale (0.148644). shrinking... rejected by region ineffective proposal scale (0.0573086). shrinking... rejected by region ineffective proposal scale (0.0220949). shrinking... CubeSliceSampler | |||
Passed | tests/test_stepsampling.py::test_stepsampler_regionmh_adapt | 0.67 | |
------------------------------Captured stdout call------------------------------ MHSampler(nsteps=3) ineffective proposal scale (1.21). shrinking... ineffective proposal scale (0.880663). shrinking... ineffective proposal scale (0.640965). shrinking... MHSampler(adaptive_nsteps=move-distance) MHSampler(adaptive_nsteps=proposal-total-distances) MHSampler(adaptive_nsteps=proposal-summed-distances) MHSampler(nsteps=3) ineffective proposal scale (1.21). shrinking... MHSampler(adaptive_nsteps=move-distance) MHSampler(adaptive_nsteps=proposal-total-distances) MHSampler(adaptive_nsteps=proposal-summed-distances) SliceSampler(nsteps=3) SliceSampler(adaptive_nsteps=move-distance) SliceSampler(adaptive_nsteps=proposal-total-distances) SliceSampler(adaptive_nsteps=proposal-summed-distances) SliceSampler(nsteps=3) SliceSampler(adaptive_nsteps=move-distance) SliceSampler(adaptive_nsteps=proposal-total-distances) SliceSampler(adaptive_nsteps=proposal-summed-distances) | |||
Passed | tests/test_stepsampling.py::test_ellipsoid_bracket | 0.68 | |
------------------------------Captured stdout call------------------------------ seed: 0 [0.50992628 0.49615106] 0.8846954103224107 [[0.01926479 0.00029866] [0.00029866 0.02059322]] [[51.91984507 -0.75298154] [-0.75298154 48.57059161]] [[-0.03014079 -0.13548551] [-0.14053046 0.02905876]] [[-7.05626717 1.45908831] [ 1.51341904 6.80295189]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.017174902905080985 0.23786423427151487 seed: 1 [0.50159445 0.47100674] 1.8743099471353246 [[ 0.33611853 -0.03266527] [-0.03266527 0.34105515]] [[3.00309364 0.28762753] [0.28762753 2.95962525]] [[ 0.41434574 -0.40550726] [-0.44683648 -0.37602169]] [[-1.32593047 -1.11579659] [-1.22951834 1.20329128]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6917900617926906 0.8846363293094491 seed: 2 [0.48812653 0.53575766] 20.38450684976214 [[ 0.02882855 -0.00164512] [-0.00164512 0.01039618]] [[35.00392418 5.53912476] [ 5.53912476 97.06571607]] [[-0.16955471 0.00893047] [ 0.01501439 0.10085012]] [[-0.87122291 -5.85191377] [-9.83855613 0.51819813]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6730808254214168 0.8527385288859411 seed: 3 [0.47230407 0.50830476] 2.0192802558538405 [[0.30519242 0.02950729] [0.02950729 0.30154492]] [[ 3.30791723 -0.32369199] [-0.32369199 3.34792997]] [[-0.42039895 -0.35840918] [-0.39521765 0.38124524]] [[ 1.30899395 -1.26271615] [-1.39239654 -1.18708124]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.7071890735938766 0.855064597755619 seed: 4 [0.50099605 0.49480303] 1.0789338425112094 [[ 0.02212739 -0.00207538] [-0.00207538 0.01904731]] [[45.65946589 4.9750119 ] [ 4.9750119 53.04291745]] [[-0.1359778 0.06031112] [ 0.06842332 0.11985642]] [[-3.35006084 -5.86826706] [-6.65758288 2.95288126]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.052565699606072866 0.24797734092031085 seed: 5 [0.49104398 0.55215679] 2.0230339684412333 [[0.37506221 0.01679208] [0.01679208 0.33671203]] [[ 2.67219079 -0.13326413] [-0.13326413 2.97654281]] [[-0.57805143 -0.20228385] [-0.21732937 0.53803344]] [[ 0.61224159 -1.5157015 ] [-1.62843674 -0.56985664]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.7799365812711072 0.9593158783831075 seed: 6 [0.52418964 0.50030038] 2.1457304801032278 [[ 0.018437 -0.00089551] [-0.00089551 0.0204369 ]] [[54.35445482 2.38173058] [ 2.38173058 49.03546901]] [[ 0.05147825 -0.12564627] [-0.13464496 -0.04803782]] [[-6.94384782 -2.47738415] [-2.65481249 6.47977157]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.07419300769210857 0.3204076821837687 seed: 7 [0.484248 0.51819016] 1.8539629878442634 [[0.3042633 0.01242581] [0.01242581 0.34076343]] [[ 3.29152877 -0.12002437] [-0.12002437 2.93896385]] [[-0.17284996 -0.52381885] [-0.56099455 0.16139563]] [[-1.74353623 0.5016076 ] [ 0.53720694 1.62799648]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6659778758876831 0.8349705094435842 seed: 8 [0.50408499 0.49428346] 0.9655037157675875 [[ 0.02323618 -0.00217529] [-0.00217529 0.01719814]] [[43.55202481 5.50863067] [ 5.50863067 58.8425811 ]] [[-0.14724143 0.03944802] [ 0.04752047 0.12222906]] [[-2.39135596 -6.15088949] [-7.40957792 1.98512876]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.044400678705535615 0.2448003575805587 seed: 9 [0.48904592 0.48752116] 1.5384778143104043 [[ 0.35830766 -0.02034459] [-0.02034459 0.3526576 ]] [[2.80006946 0.16153418] [0.16153418 2.84493043]] [[-0.46246098 0.38004934] [ 0.40268134 0.43646917]] [[-1.13466924 -1.22987608] [-1.3031154 1.07089714]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6534001039705724 0.8271676775022705 seed: 10 [0.48019733 0.48916557] 2.9213240856230063 [[ 0.02687924 -0.00160957] [-0.00160957 0.01361342]] [[37.46870819 4.43008875] [ 4.43008875 73.98069138]] [[-0.1633707 0.01375712] [ 0.01953864 0.11502898]] [[-1.02505008 -6.03473119] [-8.57086863 0.72173568]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.19872037150428723 0.3506413010364053 seed: 11 [0.59345747 0.50405991] 3.7306863239940204 [[ 0.44726794 -0.04617036] [-0.04617036 0.39179286]] [[2.26332908 0.26671929] [0.26671929 2.58380033]] [[-0.59882319 0.29778974] [ 0.33882562 0.52629845]] [[-0.81437012 -1.2649626 ] [-1.43927639 0.71574004]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -1.092686015122924 1.4746656432581782 seed: 12 [0.50346304 0.49828554] 0.7215776662713073 [[ 0.02197582 -0.00051243] [-0.00051243 0.01859843]] [[45.53382294 1.25457289] [ 1.25457289 53.80254874]] [[-0.14689036 0.01997595] [ 0.02179624 0.134623 ]] [[-1.07847582 -6.6611345 ] [-7.26812202 0.98840835]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.019765258396075982 0.22521924489885897 seed: 13 [0.52739261 0.49636814] 3.3812808083835133 [[0.15850363 0.01102533] [0.01102533 0.38344053]] [[ 6.32164766 -0.18177065] [-0.18177065 2.61319295]] [[-0.03026402 -0.39697319] [-0.61892143 0.0194112 ]] [[-2.51305304 0.07881674] [ 0.12288328 1.61186 ]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6029032189790895 0.8593349879630028 seed: 14 [0.49578687 0.49107959] 0.985344450508965 [[0.0229603 0.00218594] [0.00218594 0.01727914]] [[44.08438795 -5.57700003] [-5.57700003 58.57878206]] [[-0.14575598 -0.0414186 ] [-0.04959054 0.12173709]] [[ 2.50484132 -6.1489965 ] [-7.36220173 -2.0920726 ]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.04428553210487171 0.244441630592295 seed: 15 [0.48642319 0.66841006] 99.49049823321974 [[ 0.48236792 -0.18182732] [-0.18182732 0.36665513]] [[2.5497309 1.26443268] [1.26443268 3.35440117]] [[-0.6332046 0.28534166] [ 0.46300505 0.39023257]] [[-1.22096725 -1.02906262] [-1.6697919 0.75246009]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -6.087296453252871 6.4023793076344395 seed: 16 [0.49343334 0.47906169] 29.470736305310133 [[0.02519435 0.00144764] [0.00144764 0.01541777]] [[39.90673801 -3.74700043] [-3.74700043 65.21203037]] [[-0.15773825 -0.01769165] [-0.02286578 0.12204478]] [[ 1.16331785 -6.20914081] [-8.02507871 -0.90007894]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.7596099227288234 0.9551032647005249 seed: 17 [0.45822053 0.45620346] 3.867101221747955 [[ 0.31747032 -0.11633444] [-0.11633444 0.32983302]] [[3.61744326 1.27589782] [1.27589782 3.48185534]] [[ 0.45650634 -0.33026093] [-0.48140645 -0.31317861]] [[-1.59428407 -1.03716033] [-1.51182183 1.09373219]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.9980562454409576 1.065967262438615 seed: 18 [0.50867005 0.48928539] 1.987131194992184 [[ 0.01875748 -0.00245339] [-0.00245339 0.02174844]] [[54.11045954 6.10407145] [ 6.10407145 46.66888943]] [[ 0.0744627 -0.11494687] [-0.13259532 -0.06455171]] [[-6.61385603 -3.21983974] [-3.71420034 5.73355084]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.08414368416027168 0.29342603298305664 seed: 19 [0.51301403 0.48323565] 1.6197886971538265 [[ 0.36663328 -0.01367268] [-0.01367268 0.30642217]] [[2.73206723 0.12190594] [0.12190594 3.26891089]] [[-0.59418281 0.11653355] [ 0.12860559 0.53840763]] [[-0.38401258 -1.60766961] [-1.77421269 0.34796581]] from [0.4 0.525] in direction [1. 0.] ellipsoid bracket: -0.6574825285771522 0.8797835081854678 | |||
Passed | tests/test_stepsampling.py::test_crop_bracket | 0.00 | |
------------------------------Captured stdout call------------------------------ left: -1.9354937665716845 [1.93780732 1.70985572] right: 2.3302374240517256 [-1.45856722 -0.87105549] | |||
Passed | tests/test_store.py::test_text_store | 0.01 | |
------------------------------Captured stdout call------------------------------ [(1, [101.0, 155.0, 413.0, 213.0]), (2, [99.0, 156.0, 413.0, 213.0])] | |||
Passed | tests/test_store.py::test_hdf5_store | 0.08 | |
------------------------------Captured stdout call------------------------------ [(1, array([101., 155., 413., 213.])), (2, array([ 99., 156., 413., 213.]))] | |||
Passed | tests/test_store.py::test_nullstore | 0.00 | |
No log output captured. | |||
Passed | tests/test_store.py::test_storemany | 1.39 | |
------------------------------Captured stdout call------------------------------ ======== <class 'ultranest.store.TextPointStore'> N=1 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.5, 1.5] reading... stack[0]: [(0, [-inf, -0.1, -0.1]), (1, [0.0, 1.0, 1.0]), (2, [-inf, -0.1, -0.1]), (3, [1.0, 1.5, 1.5])] stack[1]: [(1, [0.0, 1.0, 1.0]), (2, [-inf, -0.1, -0.1]), (3, [1.0, 1.5, 1.5])] 0 0.1 reading: [0.0, 1.0, 1.0] stack[2]: [(2, [-inf, -0.1, -0.1]), (3, [1.0, 1.5, 1.5])] stack[3]: [(3, [1.0, 1.5, 1.5])] 1 1.1 reading: [1.0, 1.5, 1.5] ======== <class 'ultranest.store.TextPointStore'> N=2 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] reading... stack[0]: [(0, [-inf, -0.1, -0.1]), (1, [-inf, 0.9, 0.9]), (2, [0.0, 1.0, 1.0]), (3, [1.0, 2.0, 2.0]), (4, [-inf, -0.1, -0.1]), (5, [-inf, 0.9, 0.9]), (6, [1.0, 1.5, 1.5]), (7, [2.0, 2.5, 2.5])] stack[1]: [(2, [0.0, 1.0, 1.0]), (3, [1.0, 2.0, 2.0]), (4, [-inf, -0.1, -0.1]), (5, [-inf, 0.9, 0.9]), (6, [1.0, 1.5, 1.5]), (7, [2.0, 2.5, 2.5])] 0 0.1 reading: [0.0, 1.0, 1.0] 1 1.1 reading: [1.0, 2.0, 2.0] stack[2]: [(4, [-inf, -0.1, -0.1]), (5, [-inf, 0.9, 0.9]), (6, [1.0, 1.5, 1.5]), (7, [2.0, 2.5, 2.5])] stack[3]: [(6, [1.0, 1.5, 1.5]), (7, [2.0, 2.5, 2.5])] 1 1.1 reading: [1.0, 1.5, 1.5] 2 2.1 reading: [2.0, 2.5, 2.5] ======== <class 'ultranest.store.TextPointStore'> N=10 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 2 3 storing: [2, 2.1, 2.1] 3 4 storing: [3, 3.1, 3.1] 4 5 storing: [4, 4.1, 4.1] 5 6 storing: [5, 5.1, 5.1] 6 7 storing: [6, 6.1, 6.1] 7 8 storing: [7, 7.1, 7.1] 8 9 storing: [8, 8.1, 8.1] 9 10 storing: [9, 9.1, 9.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] 3 4 storing: [3, 3.5, 3.5] 4 5 storing: [4, 4.5, 4.5] 5 6 storing: [5, 5.5, 5.5] 6 7 storing: [6, 6.5, 6.5] 7 8 storing: [7, 7.5, 7.5] 8 9 storing: [8, 8.5, 8.5] 9 10 storing: [9, 9.5, 9.5] 10 11 storing: [10, 10.5, 10.5] reading... stack[0]: [(0, [-inf, -0.1, -0.1]), (1, [-inf, 0.9, 0.9]), (2, [-inf, 1.9, 1.9]), (3, [-inf, 2.9, 2.9]), (4, [-inf, 3.9, 3.9]), (5, [-inf, 4.9, 4.9]), (6, [-inf, 5.9, 5.9]), (7, [-inf, 6.9, 6.9]), (8, [-inf, 7.9, 7.9]), (9, [-inf, 8.9, 8.9]), (10, [0.0, 1.0, 1.0]), (11, [1.0, 2.0, 2.0]), (12, [2.0, 3.0, 3.0]), (13, [3.0, 4.0, 4.0]), (14, [4.0, 5.0, 5.0]), (15, [5.0, 6.0, 6.0]), (16, [6.0, 7.0, 7.0]), (17, [7.0, 8.0, 8.0]), (18, [8.0, 9.0, 9.0]), (19, [9.0, 10.0, 10.0]), (20, [-inf, -0.1, -0.1]), (21, [-inf, 0.9, 0.9]), (22, [-inf, 1.9, 1.9]), (23, [-inf, 2.9, 2.9]), (24, [-inf, 3.9, 3.9]), (25, [-inf, 4.9, 4.9]), (26, [-inf, 5.9, 5.9]), (27, [-inf, 6.9, 6.9]), (28, [-inf, 7.9, 7.9]), (29, [-inf, 8.9, 8.9]), (30, [1.0, 1.5, 1.5]), (31, [2.0, 2.5, 2.5]), (32, [3.0, 3.5, 3.5]), (33, [4.0, 4.5, 4.5]), (34, [5.0, 5.5, 5.5]), (35, [6.0, 6.5, 6.5]), (36, [7.0, 7.5, 7.5]), (37, [8.0, 8.5, 8.5]), (38, [9.0, 9.5, 9.5]), (39, [10.0, 10.5, 10.5])] stack[1]: [(10, [0.0, 1.0, 1.0]), (11, [1.0, 2.0, 2.0]), (12, [2.0, 3.0, 3.0]), (13, [3.0, 4.0, 4.0]), (14, [4.0, 5.0, 5.0]), (15, [5.0, 6.0, 6.0]), (16, [6.0, 7.0, 7.0]), (17, [7.0, 8.0, 8.0]), (18, [8.0, 9.0, 9.0]), (19, [9.0, 10.0, 10.0]), (20, [-inf, -0.1, -0.1]), (21, [-inf, 0.9, 0.9]), (22, [-inf, 1.9, 1.9]), (23, [-inf, 2.9, 2.9]), (24, [-inf, 3.9, 3.9]), (25, [-inf, 4.9, 4.9]), (26, [-inf, 5.9, 5.9]), (27, [-inf, 6.9, 6.9]), (28, [-inf, 7.9, 7.9]), (29, [-inf, 8.9, 8.9]), (30, [1.0, 1.5, 1.5]), (31, [2.0, 2.5, 2.5]), (32, [3.0, 3.5, 3.5]), (33, [4.0, 4.5, 4.5]), (34, [5.0, 5.5, 5.5]), (35, [6.0, 6.5, 6.5]), (36, [7.0, 7.5, 7.5]), (37, [8.0, 8.5, 8.5]), (38, [9.0, 9.5, 9.5]), (39, [10.0, 10.5, 10.5])] 0 0.1 reading: [0.0, 1.0, 1.0] 1 1.1 reading: [1.0, 2.0, 2.0] 2 2.1 reading: [2.0, 3.0, 3.0] 3 3.1 reading: [3.0, 4.0, 4.0] 4 4.1 reading: [4.0, 5.0, 5.0] 5 5.1 reading: [5.0, 6.0, 6.0] 6 6.1 reading: [6.0, 7.0, 7.0] 7 7.1 reading: [7.0, 8.0, 8.0] 8 8.1 reading: [8.0, 9.0, 9.0] 9 9.1 reading: [9.0, 10.0, 10.0] stack[2]: [(20, [-inf, -0.1, -0.1]), (21, [-inf, 0.9, 0.9]), (22, [-inf, 1.9, 1.9]), (23, [-inf, 2.9, 2.9]), (24, [-inf, 3.9, 3.9]), (25, [-inf, 4.9, 4.9]), (26, [-inf, 5.9, 5.9]), (27, [-inf, 6.9, 6.9]), (28, [-inf, 7.9, 7.9]), (29, [-inf, 8.9, 8.9]), (30, [1.0, 1.5, 1.5]), (31, [2.0, 2.5, 2.5]), (32, [3.0, 3.5, 3.5]), (33, [4.0, 4.5, 4.5]), (34, [5.0, 5.5, 5.5]), (35, [6.0, 6.5, 6.5]), (36, [7.0, 7.5, 7.5]), (37, [8.0, 8.5, 8.5]), (38, [9.0, 9.5, 9.5]), (39, [10.0, 10.5, 10.5])] stack[3]: [(30, [1.0, 1.5, 1.5]), (31, [2.0, 2.5, 2.5]), (32, [3.0, 3.5, 3.5]), (33, [4.0, 4.5, 4.5]), (34, [5.0, 5.5, 5.5]), (35, [6.0, 6.5, 6.5]), (36, [7.0, 7.5, 7.5]), (37, [8.0, 8.5, 8.5]), (38, [9.0, 9.5, 9.5]), (39, [10.0, 10.5, 10.5])] 1 1.1 reading: [1.0, 1.5, 1.5] 2 2.1 reading: [2.0, 2.5, 2.5] 3 3.1 reading: [3.0, 3.5, 3.5] 4 4.1 reading: [4.0, 4.5, 4.5] 5 5.1 reading: [5.0, 5.5, 5.5] 6 6.1 reading: [6.0, 6.5, 6.5] 7 7.1 reading: [7.0, 7.5, 7.5] 8 8.1 reading: [8.0, 8.5, 8.5] 9 9.1 reading: [9.0, 9.5, 9.5] 10 10.1 reading: [10.0, 10.5, 10.5] ======== <class 'ultranest.store.TextPointStore'> N=100 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 2 3 storing: [2, 2.1, 2.1] 3 4 storing: [3, 3.1, 3.1] 4 5 storing: [4, 4.1, 4.1] 5 6 storing: [5, 5.1, 5.1] 6 7 storing: [6, 6.1, 6.1] 7 8 storing: [7, 7.1, 7.1] 8 9 storing: [8, 8.1, 8.1] 9 10 storing: [9, 9.1, 9.1] 10 11 storing: [10, 10.1, 10.1] 11 12 storing: [11, 11.1, 11.1] 12 13 storing: [12, 12.1, 12.1] 13 14 storing: [13, 13.1, 13.1] 14 15 storing: [14, 14.1, 14.1] 15 16 storing: [15, 15.1, 15.1] 16 17 storing: [16, 16.1, 16.1] 17 18 storing: [17, 17.1, 17.1] 18 19 storing: [18, 18.1, 18.1] 19 20 storing: [19, 19.1, 19.1] 20 21 storing: [20, 20.1, 20.1] 21 22 storing: [21, 21.1, 21.1] 22 23 storing: [22, 22.1, 22.1] 23 24 storing: [23, 23.1, 23.1] 24 25 storing: [24, 24.1, 24.1] 25 26 storing: [25, 25.1, 25.1] 26 27 storing: [26, 26.1, 26.1] 27 28 storing: [27, 27.1, 27.1] 28 29 storing: [28, 28.1, 28.1] 29 30 storing: [29, 29.1, 29.1] 30 31 storing: [30, 30.1, 30.1] 31 32 storing: [31, 31.1, 31.1] 32 33 storing: [32, 32.1, 32.1] 33 34 storing: [33, 33.1, 33.1] 34 35 storing: [34, 34.1, 34.1] 35 36 storing: [35, 35.1, 35.1] 36 37 storing: [36, 36.1, 36.1] 37 38 storing: [37, 37.1, 37.1] 38 39 storing: [38, 38.1, 38.1] 39 40 storing: [39, 39.1, 39.1] 40 41 storing: [40, 40.1, 40.1] 41 42 storing: [41, 41.1, 41.1] 42 43 storing: [42, 42.1, 42.1] 43 44 storing: [43, 43.1, 43.1] 44 45 storing: [44, 44.1, 44.1] 45 46 storing: [45, 45.1, 45.1] 46 47 storing: [46, 46.1, 46.1] 47 48 storing: [47, 47.1, 47.1] 48 49 storing: [48, 48.1, 48.1] 49 50 storing: [49, 49.1, 49.1] 50 51 storing: [50, 50.1, 50.1] 51 52 storing: [51, 51.1, 51.1] 52 53 storing: [52, 52.1, 52.1] 53 54 storing: [53, 53.1, 53.1] 54 55 storing: [54, 54.1, 54.1] 55 56 storing: [55, 55.1, 55.1] 56 57 storing: [56, 56.1, 56.1] 57 58 storing: [57, 57.1, 57.1] 58 59 storing: [58, 58.1, 58.1] 59 60 storing: [59, 59.1, 59.1] 60 61 storing: [60, 60.1, 60.1] 61 62 storing: [61, 61.1, 61.1] 62 63 storing: [62, 62.1, 62.1] 63 64 storing: [63, 63.1, 63.1] 64 65 storing: [64, 64.1, 64.1] 65 66 storing: [65, 65.1, 65.1] 66 67 storing: [66, 66.1, 66.1] 67 68 storing: [67, 67.1, 67.1] 68 69 storing: [68, 68.1, 68.1] 69 70 storing: [69, 69.1, 69.1] 70 71 storing: [70, 70.1, 70.1] 71 72 storing: [71, 71.1, 71.1] 72 73 storing: [72, 72.1, 72.1] 73 74 storing: [73, 73.1, 73.1] 74 75 storing: [74, 74.1, 74.1] 75 76 storing: [75, 75.1, 75.1] 76 77 storing: [76, 76.1, 76.1] 77 78 storing: [77, 77.1, 77.1] 78 79 storing: [78, 78.1, 78.1] 79 80 storing: [79, 79.1, 79.1] 80 81 storing: [80, 80.1, 80.1] 81 82 storing: [81, 81.1, 81.1] 82 83 storing: [82, 82.1, 82.1] 83 84 storing: [83, 83.1, 83.1] 84 85 storing: [84, 84.1, 84.1] 85 86 storing: [85, 85.1, 85.1] 86 87 storing: [86, 86.1, 86.1] 87 88 storing: [87, 87.1, 87.1] 88 89 storing: [88, 88.1, 88.1] 89 90 storing: [89, 89.1, 89.1] 90 91 storing: [90, 90.1, 90.1] 91 92 storing: [91, 91.1, 91.1] 92 93 storing: [92, 92.1, 92.1] 93 94 storing: [93, 93.1, 93.1] 94 95 storing: [94, 94.1, 94.1] 95 96 storing: [95, 95.1, 95.1] 96 97 storing: [96, 96.1, 96.1] 97 98 storing: [97, 97.1, 97.1] 98 99 storing: [98, 98.1, 98.1] 99 100 storing: [99, 99.1, 99.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] 3 4 storing: [3, 3.5, 3.5] 4 5 storing: [4, 4.5, 4.5] 5 6 storing: [5, 5.5, 5.5] 6 7 storing: [6, 6.5, 6.5] 7 8 storing: [7, 7.5, 7.5] 8 9 storing: [8, 8.5, 8.5] 9 10 storing: [9, 9.5, 9.5] 10 11 storing: [10, 10.5, 10.5] 11 12 storing: [11, 11.5, 11.5] 12 13 storing: [12, 12.5, 12.5] 13 14 storing: [13, 13.5, 13.5] 14 15 storing: [14, 14.5, 14.5] 15 16 storing: [15, 15.5, 15.5] 16 17 storing: [16, 16.5, 16.5] 17 18 storing: [17, 17.5, 17.5] 18 19 storing: [18, 18.5, 18.5] 19 20 storing: [19, 19.5, 19.5] 20 21 storing: [20, 20.5, 20.5] 21 22 storing: [21, 21.5, 21.5] 22 23 storing: [22, 22.5, 22.5] 23 24 storing: [23, 23.5, 23.5] 24 25 storing: [24, 24.5, 24.5] 25 26 storing: [25, 25.5, 25.5] 26 27 storing: [26, 26.5, 26.5] 27 28 storing: [27, 27.5, 27.5] 28 29 storing: [28, 28.5, 28.5] 29 30 storing: [29, 29.5, 29.5] 30 31 storing: [30, 30.5, 30.5] 31 32 storing: [31, 31.5, 31.5] 32 33 storing: [32, 32.5, 32.5] 33 34 storing: [33, 33.5, 33.5] 34 35 storing: [34, 34.5, 34.5] 35 36 storing: [35, 35.5, 35.5] 36 37 storing: [36, 36.5, 36.5] 37 38 storing: [37, 37.5, 37.5] 38 39 storing: [38, 38.5, 38.5] 39 40 storing: [39, 39.5, 39.5] 40 41 storing: [40, 40.5, 40.5] 41 42 storing: [41, 41.5, 41.5] 42 43 storing: [42, 42.5, 42.5] 43 44 storing: [43, 43.5, 43.5] 44 45 storing: [44, 44.5, 44.5] 45 46 storing: [45, 45.5, 45.5] 46 47 storing: [46, 46.5, 46.5] 47 48 storing: [47, 47.5, 47.5] 48 49 storing: [48, 48.5, 48.5] 49 50 storing: [49, 49.5, 49.5] 50 51 storing: [50, 50.5, 50.5] 51 52 storing: [51, 51.5, 51.5] 52 53 storing: [52, 52.5, 52.5] 53 54 storing: [53, 53.5, 53.5] 54 55 storing: [54, 54.5, 54.5] 55 56 storing: [55, 55.5, 55.5] 56 57 storing: [56, 56.5, 56.5] 57 58 storing: [57, 57.5, 57.5] 58 59 storing: [58, 58.5, 58.5] 59 60 storing: [59, 59.5, 59.5] 60 61 storing: [60, 60.5, 60.5] 61 62 storing: [61, 61.5, 61.5] 62 63 storing: [62, 62.5, 62.5] 63 64 storing: [63, 63.5, 63.5] 64 65 storing: [64, 64.5, 64.5] 65 66 storing: [65, 65.5, 65.5] 66 67 storing: [66, 66.5, 66.5] 67 68 storing: [67, 67.5, 67.5] 68 69 storing: [68, 68.5, 68.5] 69 70 storing: [69, 69.5, 69.5] 70 71 storing: [70, 70.5, 70.5] 71 72 storing: [71, 71.5, 71.5] 72 73 storing: [72, 72.5, 72.5] 73 74 storing: [73, 73.5, 73.5] 74 75 storing: [74, 74.5, 74.5] 75 76 storing: [75, 75.5, 75.5] 76 77 storing: [76, 76.5, 76.5] 77 78 storing: [77, 77.5, 77.5] 78 79 storing: [78, 78.5, 78.5] 79 80 storing: [79, 79.5, 79.5] 80 81 storing: [80, 80.5, 80.5] 81 82 storing: [81, 81.5, 81.5] 82 83 storing: [82, 82.5, 82.5] 83 84 storing: [83, 83.5, 83.5] 84 85 storing: [84, 84.5, 84.5] 85 86 storing: [85, 85.5, 85.5] 86 87 storing: [86, 86.5, 86.5] 87 88 storing: [87, 87.5, 87.5] 88 89 storing: [88, 88.5, 88.5] 89 90 storing: [89, 89.5, 89.5] 90 91 storing: [90, 90.5, 90.5] 91 92 storing: [91, 91.5, 91.5] 92 93 storing: [92, 92.5, 92.5] 93 94 storing: [93, 93.5, 93.5] 94 95 storing: [94, 94.5, 94.5] 95 96 storing: [95, 95.5, 95.5] 96 97 storing: [96, 96.5, 96.5] 97 98 storing: [97, 97.5, 97.5] 98 99 storing: [98, 98.5, 98.5] 99 100 storing: [99, 99.5, 99.5] 100 101 storing: [100, 100.5, 100.5] reading... stack[0]: [(0, [-inf, -0.1, -0.1]), (1, [-inf, 0.9, 0.9]), (2, [-inf, 1.9, 1.9]), (3, [-inf, 2.9, 2.9]), (4, [-inf, 3.9, 3.9]), (5, [-inf, 4.9, 4.9]), (6, [-inf, 5.9, 5.9]), (7, [-inf, 6.9, 6.9]), (8, [-inf, 7.9, 7.9]), (9, [-inf, 8.9, 8.9]), (10, [-inf, 9.9, 9.9]), (11, [-inf, 10.9, 10.9]), (12, [-inf, 11.9, 11.9]), (13, [-inf, 12.9, 12.9]), (14, [-inf, 13.9, 13.9]), (15, [-inf, 14.9, 14.9]), (16, [-inf, 15.9, 15.9]), (17, [-inf, 16.9, 16.9]), (18, [-inf, 17.9, 17.9]), (19, [-inf, 18.9, 18.9]), (20, [-inf, 19.9, 19.9]), (21, [-inf, 20.9, 20.9]), (22, [-inf, 21.9, 21.9]), (23, [-inf, 22.9, 22.9]), (24, [-inf, 23.9, 23.9]), (25, [-inf, 24.9, 24.9]), (26, [-inf, 25.9, 25.9]), (27, [-inf, 26.9, 26.9]), (28, [-inf, 27.9, 27.9]), (29, [-inf, 28.9, 28.9]), (30, [-inf, 29.9, 29.9]), (31, [-inf, 30.9, 30.9]), (32, [-inf, 31.9, 31.9]), (33, [-inf, 32.9, 32.9]), (34, [-inf, 33.9, 33.9]), (35, [-inf, 34.9, 34.9]), (36, [-inf, 35.9, 35.9]), (37, [-inf, 36.9, 36.9]), (38, [-inf, 37.9, 37.9]), (39, [-inf, 38.9, 38.9]), (40, [-inf, 39.9, 39.9]), (41, [-inf, 40.9, 40.9]), (42, [-inf, 41.9, 41.9]), (43, [-inf, 42.9, 42.9]), (44, [-inf, 43.9, 43.9]), (45, [-inf, 44.9, 44.9]), (46, [-inf, 45.9, 45.9]), (47, [-inf, 46.9, 46.9]), (48, [-inf, 47.9, 47.9]), (49, [-inf, 48.9, 48.9]), (50, [-inf, 49.9, 49.9]), (51, [-inf, 50.9, 50.9]), (52, [-inf, 51.9, 51.9]), (53, [-inf, 52.9, 52.9]), (54, [-inf, 53.9, 53.9]), (55, [-inf, 54.9, 54.9]), (56, [-inf, 55.9, 55.9]), (57, [-inf, 56.9, 56.9]), (58, [-inf, 57.9, 57.9]), (59, [-inf, 58.9, 58.9]), (60, [-inf, 59.9, 59.9]), (61, [-inf, 60.9, 60.9]), (62, [-inf, 61.9, 61.9]), (63, [-inf, 62.9, 62.9]), (64, [-inf, 63.9, 63.9]), (65, [-inf, 64.9, 64.9]), (66, [-inf, 65.9, 65.9]), (67, [-inf, 66.9, 66.9]), (68, [-inf, 67.9, 67.9]), (69, [-inf, 68.9, 68.9]), (70, [-inf, 69.9, 69.9]), (71, [-inf, 70.9, 70.9]), (72, [-inf, 71.9, 71.9]), (73, [-inf, 72.9, 72.9]), (74, [-inf, 73.9, 73.9]), (75, [-inf, 74.9, 74.9]), (76, [-inf, 75.9, 75.9]), (77, [-inf, 76.9, 76.9]), (78, [-inf, 77.9, 77.9]), (79, [-inf, 78.9, 78.9]), (80, [-inf, 79.9, 79.9]), (81, [-inf, 80.9, 80.9]), (82, [-inf, 81.9, 81.9]), (83, [-inf, 82.9, 82.9]), (84, [-inf, 83.9, 83.9]), (85, [-inf, 84.9, 84.9]), (86, [-inf, 85.9, 85.9]), (87, [-inf, 86.9, 86.9]), (88, [-inf, 87.9, 87.9]), (89, [-inf, 88.9, 88.9]), (90, [-inf, 89.9, 89.9]), (91, [-inf, 90.9, 90.9]), (92, [-inf, 91.9, 91.9]), (93, [-inf, 92.9, 92.9]), (94, [-inf, 93.9, 93.9]), (95, [-inf, 94.9, 94.9]), (96, [-inf, 95.9, 95.9]), (97, [-inf, 96.9, 96.9]), (98, [-inf, 97.9, 97.9]), (99, [-inf, 98.9, 98.9]), (100, [0.0, 1.0, 1.0]), (101, [1.0, 2.0, 2.0]), (102, [2.0, 3.0, 3.0]), (103, [3.0, 4.0, 4.0]), (104, [4.0, 5.0, 5.0]), (105, [5.0, 6.0, 6.0]), (106, [6.0, 7.0, 7.0]), (107, [7.0, 8.0, 8.0]), (108, [8.0, 9.0, 9.0]), (109, [9.0, 10.0, 10.0]), (110, [10.0, 11.0, 11.0]), (111, [11.0, 12.0, 12.0]), (112, [12.0, 13.0, 13.0]), (113, [13.0, 14.0, 14.0]), (114, [14.0, 15.0, 15.0]), (115, [15.0, 16.0, 16.0]), (116, [16.0, 17.0, 17.0]), (117, [17.0, 18.0, 18.0]), (118, [18.0, 19.0, 19.0]), (119, [19.0, 20.0, 20.0]), (120, [20.0, 21.0, 21.0]), (121, [21.0, 22.0, 22.0]), (122, [22.0, 23.0, 23.0]), (123, [23.0, 24.0, 24.0]), (124, [24.0, 25.0, 25.0]), (125, [25.0, 26.0, 26.0]), (126, [26.0, 27.0, 27.0]), (127, [27.0, 28.0, 28.0]), (128, [28.0, 29.0, 29.0]), (129, [29.0, 30.0, 30.0]), (130, [30.0, 31.0, 31.0]), (131, [31.0, 32.0, 32.0]), (132, [32.0, 33.0, 33.0]), (133, [33.0, 34.0, 34.0]), (134, [34.0, 35.0, 35.0]), (135, [35.0, 36.0, 36.0]), (136, [36.0, 37.0, 37.0]), (137, [37.0, 38.0, 38.0]), (138, [38.0, 39.0, 39.0]), (139, [39.0, 40.0, 40.0]), (140, [40.0, 41.0, 41.0]), (141, [41.0, 42.0, 42.0]), (142, [42.0, 43.0, 43.0]), (143, [43.0, 44.0, 44.0]), (144, [44.0, 45.0, 45.0]), (145, [45.0, 46.0, 46.0]), (146, [46.0, 47.0, 47.0]), (147, [47.0, 48.0, 48.0]), (148, [48.0, 49.0, 49.0]), (149, [49.0, 50.0, 50.0]), (150, [50.0, 51.0, 51.0]), (151, [51.0, 52.0, 52.0]), (152, [52.0, 53.0, 53.0]), (153, [53.0, 54.0, 54.0]), (154, [54.0, 55.0, 55.0]), (155, [55.0, 56.0, 56.0]), (156, [56.0, 57.0, 57.0]), (157, [57.0, 58.0, 58.0]), (158, [58.0, 59.0, 59.0]), (159, [59.0, 60.0, 60.0]), (160, [60.0, 61.0, 61.0]), (161, [61.0, 62.0, 62.0]), (162, [62.0, 63.0, 63.0]), (163, [63.0, 64.0, 64.0]), (164, [64.0, 65.0, 65.0]), (165, [65.0, 66.0, 66.0]), (166, [66.0, 67.0, 67.0]), (167, [67.0, 68.0, 68.0]), (168, [68.0, 69.0, 69.0]), (169, [69.0, 70.0, 70.0]), (170, [70.0, 71.0, 71.0]), (171, [71.0, 72.0, 72.0]), (172, [72.0, 73.0, 73.0]), (173, [73.0, 74.0, 74.0]), (174, [74.0, 75.0, 75.0]), (175, [75.0, 76.0, 76.0]), (176, [76.0, 77.0, 77.0]), (177, [77.0, 78.0, 78.0]), (178, [78.0, 79.0, 79.0]), (179, [79.0, 80.0, 80.0]), (180, [80.0, 81.0, 81.0]), (181, [81.0, 82.0, 82.0]), (182, [82.0, 83.0, 83.0]), (183, [83.0, 84.0, 84.0]), (184, [84.0, 85.0, 85.0]), (185, [85.0, 86.0, 86.0]), (186, [86.0, 87.0, 87.0]), (187, [87.0, 88.0, 88.0]), (188, [88.0, 89.0, 89.0]), (189, [89.0, 90.0, 90.0]), (190, [90.0, 91.0, 91.0]), (191, [91.0, 92.0, 92.0]), (192, [92.0, 93.0, 93.0]), (193, [93.0, 94.0, 94.0]), (194, [94.0, 95.0, 95.0]), (195, [95.0, 96.0, 96.0]), (196, [96.0, 97.0, 97.0]), (197, [97.0, 98.0, 98.0]), (198, [98.0, 99.0, 99.0]), (199, [99.0, 100.0, 100.0]), (200, [-inf, -0.1, -0.1]), (201, [-inf, 0.9, 0.9]), (202, [-inf, 1.9, 1.9]), (203, [-inf, 2.9, 2.9]), (204, [-inf, 3.9, 3.9]), (205, [-inf, 4.9, 4.9]), (206, [-inf, 5.9, 5.9]), (207, [-inf, 6.9, 6.9]), (208, [-inf, 7.9, 7.9]), (209, [-inf, 8.9, 8.9]), (210, [-inf, 9.9, 9.9]), (211, [-inf, 10.9, 10.9]), (212, [-inf, 11.9, 11.9]), (213, [-inf, 12.9, 12.9]), (214, [-inf, 13.9, 13.9]), (215, [-inf, 14.9, 14.9]), (216, [-inf, 15.9, 15.9]), (217, [-inf, 16.9, 16.9]), (218, [-inf, 17.9, 17.9]), (219, [-inf, 18.9, 18.9]), (220, [-inf, 19.9, 19.9]), (221, [-inf, 20.9, 20.9]), (222, [-inf, 21.9, 21.9]), (223, [-inf, 22.9, 22.9]), (224, [-inf, 23.9, 23.9]), (225, [-inf, 24.9, 24.9]), (226, [-inf, 25.9, 25.9]), (227, [-inf, 26.9, 26.9]), (228, [-inf, 27.9, 27.9]), (229, [-inf, 28.9, 28.9]), (230, [-inf, 29.9, 29.9]), (231, [-inf, 30.9, 30.9]), (232, [-inf, 31.9, 31.9]), (233, [-inf, 32.9, 32.9]), (234, [-inf, 33.9, 33.9]), (235, [-inf, 34.9, 34.9]), (236, [-inf, 35.9, 35.9]), (237, [-inf, 36.9, 36.9]), (238, [-inf, 37.9, 37.9]), (239, [-inf, 38.9, 38.9]), (240, [-inf, 39.9, 39.9]), (241, [-inf, 40.9, 40.9]), (242, [-inf, 41.9, 41.9]), (243, [-inf, 42.9, 42.9]), (244, [-inf, 43.9, 43.9]), (245, [-inf, 44.9, 44.9]), (246, [-inf, 45.9, 45.9]), (247, [-inf, 46.9, 46.9]), (248, [-inf, 47.9, 47.9]), (249, [-inf, 48.9, 48.9]), (250, [-inf, 49.9, 49.9]), (251, [-inf, 50.9, 50.9]), (252, [-inf, 51.9, 51.9]), (253, [-inf, 52.9, 52.9]), (254, [-inf, 53.9, 53.9]), (255, [-inf, 54.9, 54.9]), (256, [-inf, 55.9, 55.9]), (257, [-inf, 56.9, 56.9]), (258, [-inf, 57.9, 57.9]), (259, [-inf, 58.9, 58.9]), (260, [-inf, 59.9, 59.9]), (261, [-inf, 60.9, 60.9]), (262, [-inf, 61.9, 61.9]), (263, [-inf, 62.9, 62.9]), (264, [-inf, 63.9, 63.9]), (265, [-inf, 64.9, 64.9]), (266, [-inf, 65.9, 65.9]), (267, [-inf, 66.9, 66.9]), (268, [-inf, 67.9, 67.9]), (269, [-inf, 68.9, 68.9]), (270, [-inf, 69.9, 69.9]), (271, [-inf, 70.9, 70.9]), (272, [-inf, 71.9, 71.9]), (273, [-inf, 72.9, 72.9]), (274, [-inf, 73.9, 73.9]), (275, [-inf, 74.9, 74.9]), (276, [-inf, 75.9, 75.9]), (277, [-inf, 76.9, 76.9]), (278, [-inf, 77.9, 77.9]), (279, [-inf, 78.9, 78.9]), (280, [-inf, 79.9, 79.9]), (281, [-inf, 80.9, 80.9]), (282, [-inf, 81.9, 81.9]), (283, [-inf, 82.9, 82.9]), (284, [-inf, 83.9, 83.9]), (285, [-inf, 84.9, 84.9]), (286, [-inf, 85.9, 85.9]), (287, [-inf, 86.9, 86.9]), (288, [-inf, 87.9, 87.9]), (289, [-inf, 88.9, 88.9]), (290, [-inf, 89.9, 89.9]), (291, [-inf, 90.9, 90.9]), (292, [-inf, 91.9, 91.9]), (293, [-inf, 92.9, 92.9]), (294, [-inf, 93.9, 93.9]), (295, [-inf, 94.9, 94.9]), (296, [-inf, 95.9, 95.9]), (297, [-inf, 96.9, 96.9]), (298, [-inf, 97.9, 97.9]), (299, [-inf, 98.9, 98.9]), (300, [1.0, 1.5, 1.5]), (301, [2.0, 2.5, 2.5]), (302, [3.0, 3.5, 3.5]), (303, [4.0, 4.5, 4.5]), (304, [5.0, 5.5, 5.5]), (305, [6.0, 6.5, 6.5]), (306, [7.0, 7.5, 7.5]), (307, [8.0, 8.5, 8.5]), (308, [9.0, 9.5, 9.5]), (309, [10.0, 10.5, 10.5]), (310, [11.0, 11.5, 11.5]), (311, [12.0, 12.5, 12.5]), (312, [13.0, 13.5, 13.5]), (313, [14.0, 14.5, 14.5]), (314, [15.0, 15.5, 15.5]), (315, [16.0, 16.5, 16.5]), (316, [17.0, 17.5, 17.5]), (317, [18.0, 18.5, 18.5]), (318, [19.0, 19.5, 19.5]), (319, [20.0, 20.5, 20.5]), (320, [21.0, 21.5, 21.5]), (321, [22.0, 22.5, 22.5]), (322, [23.0, 23.5, 23.5]), (323, [24.0, 24.5, 24.5]), (324, [25.0, 25.5, 25.5]), (325, [26.0, 26.5, 26.5]), (326, [27.0, 27.5, 27.5]), (327, [28.0, 28.5, 28.5]), (328, [29.0, 29.5, 29.5]), (329, [30.0, 30.5, 30.5]), (330, [31.0, 31.5, 31.5]), (331, [32.0, 32.5, 32.5]), (332, [33.0, 33.5, 33.5]), (333, [34.0, 34.5, 34.5]), (334, [35.0, 35.5, 35.5]), (335, [36.0, 36.5, 36.5]), (336, [37.0, 37.5, 37.5]), (337, [38.0, 38.5, 38.5]), (338, [39.0, 39.5, 39.5]), (339, [40.0, 40.5, 40.5]), (340, [41.0, 41.5, 41.5]), (341, [42.0, 42.5, 42.5]), (342, [43.0, 43.5, 43.5]), (343, [44.0, 44.5, 44.5]), (344, [45.0, 45.5, 45.5]), (345, [46.0, 46.5, 46.5]), (346, [47.0, 47.5, 47.5]), (347, [48.0, 48.5, 48.5]), (348, [49.0, 49.5, 49.5]), (349, [50.0, 50.5, 50.5]), (350, [51.0, 51.5, 51.5]), (351, [52.0, 52.5, 52.5]), (352, [53.0, 53.5, 53.5]), (353, [54.0, 54.5, 54.5]), (354, [55.0, 55.5, 55.5]), (355, [56.0, 56.5, 56.5]), (356, [57.0, 57.5, 57.5]), (357, [58.0, 58.5, 58.5]), (358, [59.0, 59.5, 59.5]), (359, [60.0, 60.5, 60.5]), (360, [61.0, 61.5, 61.5]), (361, [62.0, 62.5, 62.5]), (362, [63.0, 63.5, 63.5]), (363, [64.0, 64.5, 64.5]), (364, [65.0, 65.5, 65.5]), (365, [66.0, 66.5, 66.5]), (366, [67.0, 67.5, 67.5]), (367, [68.0, 68.5, 68.5]), (368, [69.0, 69.5, 69.5]), (369, [70.0, 70.5, 70.5]), (370, [71.0, 71.5, 71.5]), (371, [72.0, 72.5, 72.5]), (372, [73.0, 73.5, 73.5]), (373, [74.0, 74.5, 74.5]), (374, [75.0, 75.5, 75.5]), (375, [76.0, 76.5, 76.5]), (376, [77.0, 77.5, 77.5]), (377, [78.0, 78.5, 78.5]), (378, [79.0, 79.5, 79.5]), (379, [80.0, 80.5, 80.5]), (380, [81.0, 81.5, 81.5]), (381, [82.0, 82.5, 82.5]), (382, [83.0, 83.5, 83.5]), (383, [84.0, 84.5, 84.5]), (384, [85.0, 85.5, 85.5]), (385, [86.0, 86.5, 86.5]), (386, [87.0, 87.5, 87.5]), (387, [88.0, 88.5, 88.5]), (388, [89.0, 89.5, 89.5]), (389, [90.0, 90.5, 90.5]), (390, [91.0, 91.5, 91.5]), (391, [92.0, 92.5, 92.5]), (392, [93.0, 93.5, 93.5]), (393, [94.0, 94.5, 94.5]), (394, [95.0, 95.5, 95.5]), (395, [96.0, 96.5, 96.5]), (396, [97.0, 97.5, 97.5]), (397, [98.0, 98.5, 98.5]), (398, [99.0, 99.5, 99.5]), (399, [100.0, 100.5, 100.5])] stack[1]: [(100, [0.0, 1.0, 1.0]), (101, [1.0, 2.0, 2.0]), (102, [2.0, 3.0, 3.0]), (103, [3.0, 4.0, 4.0]), (104, [4.0, 5.0, 5.0]), (105, [5.0, 6.0, 6.0]), (106, [6.0, 7.0, 7.0]), (107, [7.0, 8.0, 8.0]), (108, [8.0, 9.0, 9.0]), (109, [9.0, 10.0, 10.0]), (110, [10.0, 11.0, 11.0]), (111, [11.0, 12.0, 12.0]), (112, [12.0, 13.0, 13.0]), (113, [13.0, 14.0, 14.0]), (114, [14.0, 15.0, 15.0]), (115, [15.0, 16.0, 16.0]), (116, [16.0, 17.0, 17.0]), (117, [17.0, 18.0, 18.0]), (118, [18.0, 19.0, 19.0]), (119, [19.0, 20.0, 20.0]), (120, [20.0, 21.0, 21.0]), (121, [21.0, 22.0, 22.0]), (122, [22.0, 23.0, 23.0]), (123, [23.0, 24.0, 24.0]), (124, [24.0, 25.0, 25.0]), (125, [25.0, 26.0, 26.0]), (126, [26.0, 27.0, 27.0]), (127, [27.0, 28.0, 28.0]), (128, [28.0, 29.0, 29.0]), (129, [29.0, 30.0, 30.0]), (130, [30.0, 31.0, 31.0]), (131, [31.0, 32.0, 32.0]), (132, [32.0, 33.0, 33.0]), (133, [33.0, 34.0, 34.0]), (134, [34.0, 35.0, 35.0]), (135, [35.0, 36.0, 36.0]), (136, [36.0, 37.0, 37.0]), (137, [37.0, 38.0, 38.0]), (138, [38.0, 39.0, 39.0]), (139, [39.0, 40.0, 40.0]), (140, [40.0, 41.0, 41.0]), (141, [41.0, 42.0, 42.0]), (142, [42.0, 43.0, 43.0]), (143, [43.0, 44.0, 44.0]), (144, [44.0, 45.0, 45.0]), (145, [45.0, 46.0, 46.0]), (146, [46.0, 47.0, 47.0]), (147, [47.0, 48.0, 48.0]), (148, [48.0, 49.0, 49.0]), (149, [49.0, 50.0, 50.0]), (150, [50.0, 51.0, 51.0]), (151, [51.0, 52.0, 52.0]), (152, [52.0, 53.0, 53.0]), (153, [53.0, 54.0, 54.0]), (154, [54.0, 55.0, 55.0]), (155, [55.0, 56.0, 56.0]), (156, [56.0, 57.0, 57.0]), (157, [57.0, 58.0, 58.0]), (158, [58.0, 59.0, 59.0]), (159, [59.0, 60.0, 60.0]), (160, [60.0, 61.0, 61.0]), (161, [61.0, 62.0, 62.0]), (162, [62.0, 63.0, 63.0]), (163, [63.0, 64.0, 64.0]), (164, [64.0, 65.0, 65.0]), (165, [65.0, 66.0, 66.0]), (166, [66.0, 67.0, 67.0]), (167, [67.0, 68.0, 68.0]), (168, [68.0, 69.0, 69.0]), (169, [69.0, 70.0, 70.0]), (170, [70.0, 71.0, 71.0]), (171, [71.0, 72.0, 72.0]), (172, [72.0, 73.0, 73.0]), (173, [73.0, 74.0, 74.0]), (174, [74.0, 75.0, 75.0]), (175, [75.0, 76.0, 76.0]), (176, [76.0, 77.0, 77.0]), (177, [77.0, 78.0, 78.0]), (178, [78.0, 79.0, 79.0]), (179, [79.0, 80.0, 80.0]), (180, [80.0, 81.0, 81.0]), (181, [81.0, 82.0, 82.0]), (182, [82.0, 83.0, 83.0]), (183, [83.0, 84.0, 84.0]), (184, [84.0, 85.0, 85.0]), (185, [85.0, 86.0, 86.0]), (186, [86.0, 87.0, 87.0]), (187, [87.0, 88.0, 88.0]), (188, [88.0, 89.0, 89.0]), (189, [89.0, 90.0, 90.0]), (190, [90.0, 91.0, 91.0]), (191, [91.0, 92.0, 92.0]), (192, [92.0, 93.0, 93.0]), (193, [93.0, 94.0, 94.0]), (194, [94.0, 95.0, 95.0]), (195, [95.0, 96.0, 96.0]), (196, [96.0, 97.0, 97.0]), (197, [97.0, 98.0, 98.0]), (198, [98.0, 99.0, 99.0]), (199, [99.0, 100.0, 100.0]), (200, [-inf, -0.1, -0.1]), (201, [-inf, 0.9, 0.9]), (202, [-inf, 1.9, 1.9]), (203, [-inf, 2.9, 2.9]), (204, [-inf, 3.9, 3.9]), (205, [-inf, 4.9, 4.9]), (206, [-inf, 5.9, 5.9]), (207, [-inf, 6.9, 6.9]), (208, [-inf, 7.9, 7.9]), (209, [-inf, 8.9, 8.9]), (210, [-inf, 9.9, 9.9]), (211, [-inf, 10.9, 10.9]), (212, [-inf, 11.9, 11.9]), (213, [-inf, 12.9, 12.9]), (214, [-inf, 13.9, 13.9]), (215, [-inf, 14.9, 14.9]), (216, [-inf, 15.9, 15.9]), (217, [-inf, 16.9, 16.9]), (218, [-inf, 17.9, 17.9]), (219, [-inf, 18.9, 18.9]), (220, [-inf, 19.9, 19.9]), (221, [-inf, 20.9, 20.9]), (222, [-inf, 21.9, 21.9]), (223, [-inf, 22.9, 22.9]), (224, [-inf, 23.9, 23.9]), (225, [-inf, 24.9, 24.9]), (226, [-inf, 25.9, 25.9]), (227, [-inf, 26.9, 26.9]), (228, [-inf, 27.9, 27.9]), (229, [-inf, 28.9, 28.9]), (230, [-inf, 29.9, 29.9]), (231, [-inf, 30.9, 30.9]), (232, [-inf, 31.9, 31.9]), (233, [-inf, 32.9, 32.9]), (234, [-inf, 33.9, 33.9]), (235, [-inf, 34.9, 34.9]), (236, [-inf, 35.9, 35.9]), (237, [-inf, 36.9, 36.9]), (238, [-inf, 37.9, 37.9]), (239, [-inf, 38.9, 38.9]), (240, [-inf, 39.9, 39.9]), (241, [-inf, 40.9, 40.9]), (242, [-inf, 41.9, 41.9]), (243, [-inf, 42.9, 42.9]), (244, [-inf, 43.9, 43.9]), (245, [-inf, 44.9, 44.9]), (246, [-inf, 45.9, 45.9]), (247, [-inf, 46.9, 46.9]), (248, [-inf, 47.9, 47.9]), (249, [-inf, 48.9, 48.9]), (250, [-inf, 49.9, 49.9]), (251, [-inf, 50.9, 50.9]), (252, [-inf, 51.9, 51.9]), (253, [-inf, 52.9, 52.9]), (254, [-inf, 53.9, 53.9]), (255, [-inf, 54.9, 54.9]), (256, [-inf, 55.9, 55.9]), (257, [-inf, 56.9, 56.9]), (258, [-inf, 57.9, 57.9]), (259, [-inf, 58.9, 58.9]), (260, [-inf, 59.9, 59.9]), (261, [-inf, 60.9, 60.9]), (262, [-inf, 61.9, 61.9]), (263, [-inf, 62.9, 62.9]), (264, [-inf, 63.9, 63.9]), (265, [-inf, 64.9, 64.9]), (266, [-inf, 65.9, 65.9]), (267, [-inf, 66.9, 66.9]), (268, [-inf, 67.9, 67.9]), (269, [-inf, 68.9, 68.9]), (270, [-inf, 69.9, 69.9]), (271, [-inf, 70.9, 70.9]), (272, [-inf, 71.9, 71.9]), (273, [-inf, 72.9, 72.9]), (274, [-inf, 73.9, 73.9]), (275, [-inf, 74.9, 74.9]), (276, [-inf, 75.9, 75.9]), (277, [-inf, 76.9, 76.9]), (278, [-inf, 77.9, 77.9]), (279, [-inf, 78.9, 78.9]), (280, [-inf, 79.9, 79.9]), (281, [-inf, 80.9, 80.9]), (282, [-inf, 81.9, 81.9]), (283, [-inf, 82.9, 82.9]), (284, [-inf, 83.9, 83.9]), (285, [-inf, 84.9, 84.9]), (286, [-inf, 85.9, 85.9]), (287, [-inf, 86.9, 86.9]), (288, [-inf, 87.9, 87.9]), (289, [-inf, 88.9, 88.9]), (290, [-inf, 89.9, 89.9]), (291, [-inf, 90.9, 90.9]), (292, [-inf, 91.9, 91.9]), (293, [-inf, 92.9, 92.9]), (294, [-inf, 93.9, 93.9]), (295, [-inf, 94.9, 94.9]), (296, [-inf, 95.9, 95.9]), (297, [-inf, 96.9, 96.9]), (298, [-inf, 97.9, 97.9]), (299, [-inf, 98.9, 98.9]), (300, [1.0, 1.5, 1.5]), (301, [2.0, 2.5, 2.5]), (302, [3.0, 3.5, 3.5]), (303, [4.0, 4.5, 4.5]), (304, [5.0, 5.5, 5.5]), (305, [6.0, 6.5, 6.5]), (306, [7.0, 7.5, 7.5]), (307, [8.0, 8.5, 8.5]), (308, [9.0, 9.5, 9.5]), (309, [10.0, 10.5, 10.5]), (310, [11.0, 11.5, 11.5]), (311, [12.0, 12.5, 12.5]), (312, [13.0, 13.5, 13.5]), (313, [14.0, 14.5, 14.5]), (314, [15.0, 15.5, 15.5]), (315, [16.0, 16.5, 16.5]), (316, [17.0, 17.5, 17.5]), (317, [18.0, 18.5, 18.5]), (318, [19.0, 19.5, 19.5]), (319, [20.0, 20.5, 20.5]), (320, [21.0, 21.5, 21.5]), (321, [22.0, 22.5, 22.5]), (322, [23.0, 23.5, 23.5]), (323, [24.0, 24.5, 24.5]), (324, [25.0, 25.5, 25.5]), (325, [26.0, 26.5, 26.5]), (326, [27.0, 27.5, 27.5]), (327, [28.0, 28.5, 28.5]), (328, [29.0, 29.5, 29.5]), (329, [30.0, 30.5, 30.5]), (330, [31.0, 31.5, 31.5]), (331, [32.0, 32.5, 32.5]), (332, [33.0, 33.5, 33.5]), (333, [34.0, 34.5, 34.5]), (334, [35.0, 35.5, 35.5]), (335, [36.0, 36.5, 36.5]), (336, [37.0, 37.5, 37.5]), (337, [38.0, 38.5, 38.5]), (338, [39.0, 39.5, 39.5]), (339, [40.0, 40.5, 40.5]), (340, [41.0, 41.5, 41.5]), (341, [42.0, 42.5, 42.5]), (342, [43.0, 43.5, 43.5]), (343, [44.0, 44.5, 44.5]), (344, [45.0, 45.5, 45.5]), (345, [46.0, 46.5, 46.5]), (346, [47.0, 47.5, 47.5]), (347, [48.0, 48.5, 48.5]), (348, [49.0, 49.5, 49.5]), (349, [50.0, 50.5, 50.5]), (350, [51.0, 51.5, 51.5]), (351, [52.0, 52.5, 52.5]), (352, [53.0, 53.5, 53.5]), (353, [54.0, 54.5, 54.5]), (354, [55.0, 55.5, 55.5]), (355, [56.0, 56.5, 56.5]), (356, [57.0, 57.5, 57.5]), (357, [58.0, 58.5, 58.5]), (358, [59.0, 59.5, 59.5]), (359, [60.0, 60.5, 60.5]), (360, [61.0, 61.5, 61.5]), (361, [62.0, 62.5, 62.5]), (362, [63.0, 63.5, 63.5]), (363, [64.0, 64.5, 64.5]), (364, [65.0, 65.5, 65.5]), (365, [66.0, 66.5, 66.5]), (366, [67.0, 67.5, 67.5]), (367, [68.0, 68.5, 68.5]), (368, [69.0, 69.5, 69.5]), (369, [70.0, 70.5, 70.5]), (370, [71.0, 71.5, 71.5]), (371, [72.0, 72.5, 72.5]), (372, [73.0, 73.5, 73.5]), (373, [74.0, 74.5, 74.5]), (374, [75.0, 75.5, 75.5]), (375, [76.0, 76.5, 76.5]), (376, [77.0, 77.5, 77.5]), (377, [78.0, 78.5, 78.5]), (378, [79.0, 79.5, 79.5]), (379, [80.0, 80.5, 80.5]), (380, [81.0, 81.5, 81.5]), (381, [82.0, 82.5, 82.5]), (382, [83.0, 83.5, 83.5]), (383, [84.0, 84.5, 84.5]), (384, [85.0, 85.5, 85.5]), (385, [86.0, 86.5, 86.5]), (386, [87.0, 87.5, 87.5]), (387, [88.0, 88.5, 88.5]), (388, [89.0, 89.5, 89.5]), (389, [90.0, 90.5, 90.5]), (390, [91.0, 91.5, 91.5]), (391, [92.0, 92.5, 92.5]), (392, [93.0, 93.5, 93.5]), (393, [94.0, 94.5, 94.5]), (394, [95.0, 95.5, 95.5]), (395, [96.0, 96.5, 96.5]), (396, [97.0, 97.5, 97.5]), (397, [98.0, 98.5, 98.5]), (398, [99.0, 99.5, 99.5]), (399, [100.0, 100.5, 100.5])] 0 0.1 reading: [0.0, 1.0, 1.0] 1 1.1 reading: [1.0, 2.0, 2.0] 2 2.1 reading: [2.0, 3.0, 3.0] 3 3.1 reading: [3.0, 4.0, 4.0] 4 4.1 reading: [4.0, 5.0, 5.0] 5 5.1 reading: [5.0, 6.0, 6.0] 6 6.1 reading: [6.0, 7.0, 7.0] 7 7.1 reading: [7.0, 8.0, 8.0] 8 8.1 reading: [8.0, 9.0, 9.0] 9 9.1 reading: [9.0, 10.0, 10.0] 10 10.1 reading: [10.0, 11.0, 11.0] 11 11.1 reading: [11.0, 12.0, 12.0] 12 12.1 reading: [12.0, 13.0, 13.0] 13 13.1 reading: [13.0, 14.0, 14.0] 14 14.1 reading: [14.0, 15.0, 15.0] 15 15.1 reading: [15.0, 16.0, 16.0] 16 16.1 reading: [16.0, 17.0, 17.0] 17 17.1 reading: [17.0, 18.0, 18.0] 18 18.1 reading: [18.0, 19.0, 19.0] 19 19.1 reading: [19.0, 20.0, 20.0] 20 20.1 reading: [20.0, 21.0, 21.0] 21 21.1 reading: [21.0, 22.0, 22.0] 22 22.1 reading: [22.0, 23.0, 23.0] 23 23.1 reading: [23.0, 24.0, 24.0] 24 24.1 reading: [24.0, 25.0, 25.0] 25 25.1 reading: [25.0, 26.0, 26.0] 26 26.1 reading: [26.0, 27.0, 27.0] 27 27.1 reading: [27.0, 28.0, 28.0] 28 28.1 reading: [28.0, 29.0, 29.0] 29 29.1 reading: [29.0, 30.0, 30.0] 30 30.1 reading: [30.0, 31.0, 31.0] 31 31.1 reading: [31.0, 32.0, 32.0] 32 32.1 reading: [32.0, 33.0, 33.0] 33 33.1 reading: [33.0, 34.0, 34.0] 34 34.1 reading: [34.0, 35.0, 35.0] 35 35.1 reading: [35.0, 36.0, 36.0] 36 36.1 reading: [36.0, 37.0, 37.0] 37 37.1 reading: [37.0, 38.0, 38.0] 38 38.1 reading: [38.0, 39.0, 39.0] 39 39.1 reading: [39.0, 40.0, 40.0] 40 40.1 reading: [40.0, 41.0, 41.0] 41 41.1 reading: [41.0, 42.0, 42.0] 42 42.1 reading: [42.0, 43.0, 43.0] 43 43.1 reading: [43.0, 44.0, 44.0] 44 44.1 reading: [44.0, 45.0, 45.0] 45 45.1 reading: [45.0, 46.0, 46.0] 46 46.1 reading: [46.0, 47.0, 47.0] 47 47.1 reading: [47.0, 48.0, 48.0] 48 48.1 reading: [48.0, 49.0, 49.0] 49 49.1 reading: [49.0, 50.0, 50.0] 50 50.1 reading: [50.0, 51.0, 51.0] 51 51.1 reading: [51.0, 52.0, 52.0] 52 52.1 reading: [52.0, 53.0, 53.0] 53 53.1 reading: [53.0, 54.0, 54.0] 54 54.1 reading: [54.0, 55.0, 55.0] 55 55.1 reading: [55.0, 56.0, 56.0] 56 56.1 reading: [56.0, 57.0, 57.0] 57 57.1 reading: [57.0, 58.0, 58.0] 58 58.1 reading: [58.0, 59.0, 59.0] 59 59.1 reading: [59.0, 60.0, 60.0] 60 60.1 reading: [60.0, 61.0, 61.0] 61 61.1 reading: [61.0, 62.0, 62.0] 62 62.1 reading: [62.0, 63.0, 63.0] 63 63.1 reading: [63.0, 64.0, 64.0] 64 64.1 reading: [64.0, 65.0, 65.0] 65 65.1 reading: [65.0, 66.0, 66.0] 66 66.1 reading: [66.0, 67.0, 67.0] 67 67.1 reading: [67.0, 68.0, 68.0] 68 68.1 reading: [68.0, 69.0, 69.0] 69 69.1 reading: [69.0, 70.0, 70.0] 70 70.1 reading: [70.0, 71.0, 71.0] 71 71.1 reading: [71.0, 72.0, 72.0] 72 72.1 reading: [72.0, 73.0, 73.0] 73 73.1 reading: [73.0, 74.0, 74.0] 74 74.1 reading: [74.0, 75.0, 75.0] 75 75.1 reading: [75.0, 76.0, 76.0] 76 76.1 reading: [76.0, 77.0, 77.0] 77 77.1 reading: [77.0, 78.0, 78.0] 78 78.1 reading: [78.0, 79.0, 79.0] 79 79.1 reading: [79.0, 80.0, 80.0] 80 80.1 reading: [80.0, 81.0, 81.0] 81 81.1 reading: [81.0, 82.0, 82.0] 82 82.1 reading: [82.0, 83.0, 83.0] 83 83.1 reading: [83.0, 84.0, 84.0] 84 84.1 reading: [84.0, 85.0, 85.0] 85 85.1 reading: [85.0, 86.0, 86.0] 86 86.1 reading: [86.0, 87.0, 87.0] 87 87.1 reading: [87.0, 88.0, 88.0] 88 88.1 reading: [88.0, 89.0, 89.0] 89 89.1 reading: [89.0, 90.0, 90.0] 90 90.1 reading: [90.0, 91.0, 91.0] 91 91.1 reading: [91.0, 92.0, 92.0] 92 92.1 reading: [92.0, 93.0, 93.0] 93 93.1 reading: [93.0, 94.0, 94.0] 94 94.1 reading: [94.0, 95.0, 95.0] 95 95.1 reading: [95.0, 96.0, 96.0] 96 96.1 reading: [96.0, 97.0, 97.0] 97 97.1 reading: [97.0, 98.0, 98.0] 98 98.1 reading: [98.0, 99.0, 99.0] 99 99.1 reading: [99.0, 100.0, 100.0] stack[2]: [(200, [-inf, -0.1, -0.1]), (201, [-inf, 0.9, 0.9]), (202, [-inf, 1.9, 1.9]), (203, [-inf, 2.9, 2.9]), (204, [-inf, 3.9, 3.9]), (205, [-inf, 4.9, 4.9]), (206, [-inf, 5.9, 5.9]), (207, [-inf, 6.9, 6.9]), (208, [-inf, 7.9, 7.9]), (209, [-inf, 8.9, 8.9]), (210, [-inf, 9.9, 9.9]), (211, [-inf, 10.9, 10.9]), (212, [-inf, 11.9, 11.9]), (213, [-inf, 12.9, 12.9]), (214, [-inf, 13.9, 13.9]), (215, [-inf, 14.9, 14.9]), (216, [-inf, 15.9, 15.9]), (217, [-inf, 16.9, 16.9]), (218, [-inf, 17.9, 17.9]), (219, [-inf, 18.9, 18.9]), (220, [-inf, 19.9, 19.9]), (221, [-inf, 20.9, 20.9]), (222, [-inf, 21.9, 21.9]), (223, [-inf, 22.9, 22.9]), (224, [-inf, 23.9, 23.9]), (225, [-inf, 24.9, 24.9]), (226, [-inf, 25.9, 25.9]), (227, [-inf, 26.9, 26.9]), (228, [-inf, 27.9, 27.9]), (229, [-inf, 28.9, 28.9]), (230, [-inf, 29.9, 29.9]), (231, [-inf, 30.9, 30.9]), (232, [-inf, 31.9, 31.9]), (233, [-inf, 32.9, 32.9]), (234, [-inf, 33.9, 33.9]), (235, [-inf, 34.9, 34.9]), (236, [-inf, 35.9, 35.9]), (237, [-inf, 36.9, 36.9]), (238, [-inf, 37.9, 37.9]), (239, [-inf, 38.9, 38.9]), (240, [-inf, 39.9, 39.9]), (241, [-inf, 40.9, 40.9]), (242, [-inf, 41.9, 41.9]), (243, [-inf, 42.9, 42.9]), (244, [-inf, 43.9, 43.9]), (245, [-inf, 44.9, 44.9]), (246, [-inf, 45.9, 45.9]), (247, [-inf, 46.9, 46.9]), (248, [-inf, 47.9, 47.9]), (249, [-inf, 48.9, 48.9]), (250, [-inf, 49.9, 49.9]), (251, [-inf, 50.9, 50.9]), (252, [-inf, 51.9, 51.9]), (253, [-inf, 52.9, 52.9]), (254, [-inf, 53.9, 53.9]), (255, [-inf, 54.9, 54.9]), (256, [-inf, 55.9, 55.9]), (257, [-inf, 56.9, 56.9]), (258, [-inf, 57.9, 57.9]), (259, [-inf, 58.9, 58.9]), (260, [-inf, 59.9, 59.9]), (261, [-inf, 60.9, 60.9]), (262, [-inf, 61.9, 61.9]), (263, [-inf, 62.9, 62.9]), (264, [-inf, 63.9, 63.9]), (265, [-inf, 64.9, 64.9]), (266, [-inf, 65.9, 65.9]), (267, [-inf, 66.9, 66.9]), (268, [-inf, 67.9, 67.9]), (269, [-inf, 68.9, 68.9]), (270, [-inf, 69.9, 69.9]), (271, [-inf, 70.9, 70.9]), (272, [-inf, 71.9, 71.9]), (273, [-inf, 72.9, 72.9]), (274, [-inf, 73.9, 73.9]), (275, [-inf, 74.9, 74.9]), (276, [-inf, 75.9, 75.9]), (277, [-inf, 76.9, 76.9]), (278, [-inf, 77.9, 77.9]), (279, [-inf, 78.9, 78.9]), (280, [-inf, 79.9, 79.9]), (281, [-inf, 80.9, 80.9]), (282, [-inf, 81.9, 81.9]), (283, [-inf, 82.9, 82.9]), (284, [-inf, 83.9, 83.9]), (285, [-inf, 84.9, 84.9]), (286, [-inf, 85.9, 85.9]), (287, [-inf, 86.9, 86.9]), (288, [-inf, 87.9, 87.9]), (289, [-inf, 88.9, 88.9]), (290, [-inf, 89.9, 89.9]), (291, [-inf, 90.9, 90.9]), (292, [-inf, 91.9, 91.9]), (293, [-inf, 92.9, 92.9]), (294, [-inf, 93.9, 93.9]), (295, [-inf, 94.9, 94.9]), (296, [-inf, 95.9, 95.9]), (297, [-inf, 96.9, 96.9]), (298, [-inf, 97.9, 97.9]), (299, [-inf, 98.9, 98.9]), (300, [1.0, 1.5, 1.5]), (301, [2.0, 2.5, 2.5]), (302, [3.0, 3.5, 3.5]), (303, [4.0, 4.5, 4.5]), (304, [5.0, 5.5, 5.5]), (305, [6.0, 6.5, 6.5]), (306, [7.0, 7.5, 7.5]), (307, [8.0, 8.5, 8.5]), (308, [9.0, 9.5, 9.5]), (309, [10.0, 10.5, 10.5]), (310, [11.0, 11.5, 11.5]), (311, [12.0, 12.5, 12.5]), (312, [13.0, 13.5, 13.5]), (313, [14.0, 14.5, 14.5]), (314, [15.0, 15.5, 15.5]), (315, [16.0, 16.5, 16.5]), (316, [17.0, 17.5, 17.5]), (317, [18.0, 18.5, 18.5]), (318, [19.0, 19.5, 19.5]), (319, [20.0, 20.5, 20.5]), (320, [21.0, 21.5, 21.5]), (321, [22.0, 22.5, 22.5]), (322, [23.0, 23.5, 23.5]), (323, [24.0, 24.5, 24.5]), (324, [25.0, 25.5, 25.5]), (325, [26.0, 26.5, 26.5]), (326, [27.0, 27.5, 27.5]), (327, [28.0, 28.5, 28.5]), (328, [29.0, 29.5, 29.5]), (329, [30.0, 30.5, 30.5]), (330, [31.0, 31.5, 31.5]), (331, [32.0, 32.5, 32.5]), (332, [33.0, 33.5, 33.5]), (333, [34.0, 34.5, 34.5]), (334, [35.0, 35.5, 35.5]), (335, [36.0, 36.5, 36.5]), (336, [37.0, 37.5, 37.5]), (337, [38.0, 38.5, 38.5]), (338, [39.0, 39.5, 39.5]), (339, [40.0, 40.5, 40.5]), (340, [41.0, 41.5, 41.5]), (341, [42.0, 42.5, 42.5]), (342, [43.0, 43.5, 43.5]), (343, [44.0, 44.5, 44.5]), (344, [45.0, 45.5, 45.5]), (345, [46.0, 46.5, 46.5]), (346, [47.0, 47.5, 47.5]), (347, [48.0, 48.5, 48.5]), (348, [49.0, 49.5, 49.5]), (349, [50.0, 50.5, 50.5]), (350, [51.0, 51.5, 51.5]), (351, [52.0, 52.5, 52.5]), (352, [53.0, 53.5, 53.5]), (353, [54.0, 54.5, 54.5]), (354, [55.0, 55.5, 55.5]), (355, [56.0, 56.5, 56.5]), (356, [57.0, 57.5, 57.5]), (357, [58.0, 58.5, 58.5]), (358, [59.0, 59.5, 59.5]), (359, [60.0, 60.5, 60.5]), (360, [61.0, 61.5, 61.5]), (361, [62.0, 62.5, 62.5]), (362, [63.0, 63.5, 63.5]), (363, [64.0, 64.5, 64.5]), (364, [65.0, 65.5, 65.5]), (365, [66.0, 66.5, 66.5]), (366, [67.0, 67.5, 67.5]), (367, [68.0, 68.5, 68.5]), (368, [69.0, 69.5, 69.5]), (369, [70.0, 70.5, 70.5]), (370, [71.0, 71.5, 71.5]), (371, [72.0, 72.5, 72.5]), (372, [73.0, 73.5, 73.5]), (373, [74.0, 74.5, 74.5]), (374, [75.0, 75.5, 75.5]), (375, [76.0, 76.5, 76.5]), (376, [77.0, 77.5, 77.5]), (377, [78.0, 78.5, 78.5]), (378, [79.0, 79.5, 79.5]), (379, [80.0, 80.5, 80.5]), (380, [81.0, 81.5, 81.5]), (381, [82.0, 82.5, 82.5]), (382, [83.0, 83.5, 83.5]), (383, [84.0, 84.5, 84.5]), (384, [85.0, 85.5, 85.5]), (385, [86.0, 86.5, 86.5]), (386, [87.0, 87.5, 87.5]), (387, [88.0, 88.5, 88.5]), (388, [89.0, 89.5, 89.5]), (389, [90.0, 90.5, 90.5]), (390, [91.0, 91.5, 91.5]), (391, [92.0, 92.5, 92.5]), (392, [93.0, 93.5, 93.5]), (393, [94.0, 94.5, 94.5]), (394, [95.0, 95.5, 95.5]), (395, [96.0, 96.5, 96.5]), (396, [97.0, 97.5, 97.5]), (397, [98.0, 98.5, 98.5]), (398, [99.0, 99.5, 99.5]), (399, [100.0, 100.5, 100.5])] stack[3]: [(300, [1.0, 1.5, 1.5]), (301, [2.0, 2.5, 2.5]), (302, [3.0, 3.5, 3.5]), (303, [4.0, 4.5, 4.5]), (304, [5.0, 5.5, 5.5]), (305, [6.0, 6.5, 6.5]), (306, [7.0, 7.5, 7.5]), (307, [8.0, 8.5, 8.5]), (308, [9.0, 9.5, 9.5]), (309, [10.0, 10.5, 10.5]), (310, [11.0, 11.5, 11.5]), (311, [12.0, 12.5, 12.5]), (312, [13.0, 13.5, 13.5]), (313, [14.0, 14.5, 14.5]), (314, [15.0, 15.5, 15.5]), (315, [16.0, 16.5, 16.5]), (316, [17.0, 17.5, 17.5]), (317, [18.0, 18.5, 18.5]), (318, [19.0, 19.5, 19.5]), (319, [20.0, 20.5, 20.5]), (320, [21.0, 21.5, 21.5]), (321, [22.0, 22.5, 22.5]), (322, [23.0, 23.5, 23.5]), (323, [24.0, 24.5, 24.5]), (324, [25.0, 25.5, 25.5]), (325, [26.0, 26.5, 26.5]), (326, [27.0, 27.5, 27.5]), (327, [28.0, 28.5, 28.5]), (328, [29.0, 29.5, 29.5]), (329, [30.0, 30.5, 30.5]), (330, [31.0, 31.5, 31.5]), (331, [32.0, 32.5, 32.5]), (332, [33.0, 33.5, 33.5]), (333, [34.0, 34.5, 34.5]), (334, [35.0, 35.5, 35.5]), (335, [36.0, 36.5, 36.5]), (336, [37.0, 37.5, 37.5]), (337, [38.0, 38.5, 38.5]), (338, [39.0, 39.5, 39.5]), (339, [40.0, 40.5, 40.5]), (340, [41.0, 41.5, 41.5]), (341, [42.0, 42.5, 42.5]), (342, [43.0, 43.5, 43.5]), (343, [44.0, 44.5, 44.5]), (344, [45.0, 45.5, 45.5]), (345, [46.0, 46.5, 46.5]), (346, [47.0, 47.5, 47.5]), (347, [48.0, 48.5, 48.5]), (348, [49.0, 49.5, 49.5]), (349, [50.0, 50.5, 50.5]), (350, [51.0, 51.5, 51.5]), (351, [52.0, 52.5, 52.5]), (352, [53.0, 53.5, 53.5]), (353, [54.0, 54.5, 54.5]), (354, [55.0, 55.5, 55.5]), (355, [56.0, 56.5, 56.5]), (356, [57.0, 57.5, 57.5]), (357, [58.0, 58.5, 58.5]), (358, [59.0, 59.5, 59.5]), (359, [60.0, 60.5, 60.5]), (360, [61.0, 61.5, 61.5]), (361, [62.0, 62.5, 62.5]), (362, [63.0, 63.5, 63.5]), (363, [64.0, 64.5, 64.5]), (364, [65.0, 65.5, 65.5]), (365, [66.0, 66.5, 66.5]), (366, [67.0, 67.5, 67.5]), (367, [68.0, 68.5, 68.5]), (368, [69.0, 69.5, 69.5]), (369, [70.0, 70.5, 70.5]), (370, [71.0, 71.5, 71.5]), (371, [72.0, 72.5, 72.5]), (372, [73.0, 73.5, 73.5]), (373, [74.0, 74.5, 74.5]), (374, [75.0, 75.5, 75.5]), (375, [76.0, 76.5, 76.5]), (376, [77.0, 77.5, 77.5]), (377, [78.0, 78.5, 78.5]), (378, [79.0, 79.5, 79.5]), (379, [80.0, 80.5, 80.5]), (380, [81.0, 81.5, 81.5]), (381, [82.0, 82.5, 82.5]), (382, [83.0, 83.5, 83.5]), (383, [84.0, 84.5, 84.5]), (384, [85.0, 85.5, 85.5]), (385, [86.0, 86.5, 86.5]), (386, [87.0, 87.5, 87.5]), (387, [88.0, 88.5, 88.5]), (388, [89.0, 89.5, 89.5]), (389, [90.0, 90.5, 90.5]), (390, [91.0, 91.5, 91.5]), (391, [92.0, 92.5, 92.5]), (392, [93.0, 93.5, 93.5]), (393, [94.0, 94.5, 94.5]), (394, [95.0, 95.5, 95.5]), (395, [96.0, 96.5, 96.5]), (396, [97.0, 97.5, 97.5]), (397, [98.0, 98.5, 98.5]), (398, [99.0, 99.5, 99.5]), (399, [100.0, 100.5, 100.5])] 1 1.1 reading: [1.0, 1.5, 1.5] 2 2.1 reading: [2.0, 2.5, 2.5] 3 3.1 reading: [3.0, 3.5, 3.5] 4 4.1 reading: [4.0, 4.5, 4.5] 5 5.1 reading: [5.0, 5.5, 5.5] 6 6.1 reading: [6.0, 6.5, 6.5] 7 7.1 reading: [7.0, 7.5, 7.5] 8 8.1 reading: [8.0, 8.5, 8.5] 9 9.1 reading: [9.0, 9.5, 9.5] 10 10.1 reading: [10.0, 10.5, 10.5] 11 11.1 reading: [11.0, 11.5, 11.5] 12 12.1 reading: [12.0, 12.5, 12.5] 13 13.1 reading: [13.0, 13.5, 13.5] 14 14.1 reading: [14.0, 14.5, 14.5] 15 15.1 reading: [15.0, 15.5, 15.5] 16 16.1 reading: [16.0, 16.5, 16.5] 17 17.1 reading: [17.0, 17.5, 17.5] 18 18.1 reading: [18.0, 18.5, 18.5] 19 19.1 reading: [19.0, 19.5, 19.5] 20 20.1 reading: [20.0, 20.5, 20.5] 21 21.1 reading: [21.0, 21.5, 21.5] 22 22.1 reading: [22.0, 22.5, 22.5] 23 23.1 reading: [23.0, 23.5, 23.5] 24 24.1 reading: [24.0, 24.5, 24.5] 25 25.1 reading: [25.0, 25.5, 25.5] 26 26.1 reading: [26.0, 26.5, 26.5] 27 27.1 reading: [27.0, 27.5, 27.5] 28 28.1 reading: [28.0, 28.5, 28.5] 29 29.1 reading: [29.0, 29.5, 29.5] 30 30.1 reading: [30.0, 30.5, 30.5] 31 31.1 reading: [31.0, 31.5, 31.5] 32 32.1 reading: [32.0, 32.5, 32.5] 33 33.1 reading: [33.0, 33.5, 33.5] 34 34.1 reading: [34.0, 34.5, 34.5] 35 35.1 reading: [35.0, 35.5, 35.5] 36 36.1 reading: [36.0, 36.5, 36.5] 37 37.1 reading: [37.0, 37.5, 37.5] 38 38.1 reading: [38.0, 38.5, 38.5] 39 39.1 reading: [39.0, 39.5, 39.5] 40 40.1 reading: [40.0, 40.5, 40.5] 41 41.1 reading: [41.0, 41.5, 41.5] 42 42.1 reading: [42.0, 42.5, 42.5] 43 43.1 reading: [43.0, 43.5, 43.5] 44 44.1 reading: [44.0, 44.5, 44.5] 45 45.1 reading: [45.0, 45.5, 45.5] 46 46.1 reading: [46.0, 46.5, 46.5] 47 47.1 reading: [47.0, 47.5, 47.5] 48 48.1 reading: [48.0, 48.5, 48.5] 49 49.1 reading: [49.0, 49.5, 49.5] 50 50.1 reading: [50.0, 50.5, 50.5] 51 51.1 reading: [51.0, 51.5, 51.5] 52 52.1 reading: [52.0, 52.5, 52.5] 53 53.1 reading: [53.0, 53.5, 53.5] 54 54.1 reading: [54.0, 54.5, 54.5] 55 55.1 reading: [55.0, 55.5, 55.5] 56 56.1 reading: [56.0, 56.5, 56.5] 57 57.1 reading: [57.0, 57.5, 57.5] 58 58.1 reading: [58.0, 58.5, 58.5] 59 59.1 reading: [59.0, 59.5, 59.5] 60 60.1 reading: [60.0, 60.5, 60.5] 61 61.1 reading: [61.0, 61.5, 61.5] 62 62.1 reading: [62.0, 62.5, 62.5] 63 63.1 reading: [63.0, 63.5, 63.5] 64 64.1 reading: [64.0, 64.5, 64.5] 65 65.1 reading: [65.0, 65.5, 65.5] 66 66.1 reading: [66.0, 66.5, 66.5] 67 67.1 reading: [67.0, 67.5, 67.5] 68 68.1 reading: [68.0, 68.5, 68.5] 69 69.1 reading: [69.0, 69.5, 69.5] 70 70.1 reading: [70.0, 70.5, 70.5] 71 71.1 reading: [71.0, 71.5, 71.5] 72 72.1 reading: [72.0, 72.5, 72.5] 73 73.1 reading: [73.0, 73.5, 73.5] 74 74.1 reading: [74.0, 74.5, 74.5] 75 75.1 reading: [75.0, 75.5, 75.5] 76 76.1 reading: [76.0, 76.5, 76.5] 77 77.1 reading: [77.0, 77.5, 77.5] 78 78.1 reading: [78.0, 78.5, 78.5] 79 79.1 reading: [79.0, 79.5, 79.5] 80 80.1 reading: [80.0, 80.5, 80.5] 81 81.1 reading: [81.0, 81.5, 81.5] 82 82.1 reading: [82.0, 82.5, 82.5] 83 83.1 reading: [83.0, 83.5, 83.5] 84 84.1 reading: [84.0, 84.5, 84.5] 85 85.1 reading: [85.0, 85.5, 85.5] 86 86.1 reading: [86.0, 86.5, 86.5] 87 87.1 reading: [87.0, 87.5, 87.5] 88 88.1 reading: [88.0, 88.5, 88.5] 89 89.1 reading: [89.0, 89.5, 89.5] 90 90.1 reading: [90.0, 90.5, 90.5] 91 91.1 reading: [91.0, 91.5, 91.5] 92 92.1 reading: [92.0, 92.5, 92.5] 93 93.1 reading: [93.0, 93.5, 93.5] 94 94.1 reading: [94.0, 94.5, 94.5] 95 95.1 reading: [95.0, 95.5, 95.5] 96 96.1 reading: [96.0, 96.5, 96.5] 97 97.1 reading: [97.0, 97.5, 97.5] 98 98.1 reading: [98.0, 98.5, 98.5] 99 99.1 reading: [99.0, 99.5, 99.5] 100 100.1 reading: [100.0, 100.5, 100.5] ======== <class 'ultranest.store.HDF5PointStore'> N=1 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.5, 1.5] reading... stack[0]: [(0, array([-inf, -0.1, -0.1])), (1, array([0., 1., 1.])), (2, array([-inf, -0.1, -0.1])), (3, array([1. , 1.5, 1.5]))] stack[1]: [(1, array([0., 1., 1.])), (2, array([-inf, -0.1, -0.1])), (3, array([1. , 1.5, 1.5]))] 0 0.1 reading: [0. 1. 1.] stack[2]: [(2, array([-inf, -0.1, -0.1])), (3, array([1. , 1.5, 1.5]))] stack[3]: [(3, array([1. , 1.5, 1.5]))] 1 1.1 reading: [1. 1.5 1.5] ======== <class 'ultranest.store.HDF5PointStore'> N=2 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] reading... stack[0]: [(0, array([-inf, -0.1, -0.1])), (1, array([-inf, 0.9, 0.9])), (2, array([0., 1., 1.])), (3, array([1., 2., 2.])), (4, array([-inf, -0.1, -0.1])), (5, array([-inf, 0.9, 0.9])), (6, array([1. , 1.5, 1.5])), (7, array([2. , 2.5, 2.5]))] stack[1]: [(2, array([0., 1., 1.])), (3, array([1., 2., 2.])), (4, array([-inf, -0.1, -0.1])), (5, array([-inf, 0.9, 0.9])), (6, array([1. , 1.5, 1.5])), (7, array([2. , 2.5, 2.5]))] 0 0.1 reading: [0. 1. 1.] 1 1.1 reading: [1. 2. 2.] stack[2]: [(4, array([-inf, -0.1, -0.1])), (5, array([-inf, 0.9, 0.9])), (6, array([1. , 1.5, 1.5])), (7, array([2. , 2.5, 2.5]))] stack[3]: [(6, array([1. , 1.5, 1.5])), (7, array([2. , 2.5, 2.5]))] 1 1.1 reading: [1. 1.5 1.5] 2 2.1 reading: [2. 2.5 2.5] ======== <class 'ultranest.store.HDF5PointStore'> N=10 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 2 3 storing: [2, 2.1, 2.1] 3 4 storing: [3, 3.1, 3.1] 4 5 storing: [4, 4.1, 4.1] 5 6 storing: [5, 5.1, 5.1] 6 7 storing: [6, 6.1, 6.1] 7 8 storing: [7, 7.1, 7.1] 8 9 storing: [8, 8.1, 8.1] 9 10 storing: [9, 9.1, 9.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] 3 4 storing: [3, 3.5, 3.5] 4 5 storing: [4, 4.5, 4.5] 5 6 storing: [5, 5.5, 5.5] 6 7 storing: [6, 6.5, 6.5] 7 8 storing: [7, 7.5, 7.5] 8 9 storing: [8, 8.5, 8.5] 9 10 storing: [9, 9.5, 9.5] 10 11 storing: [10, 10.5, 10.5] reading... stack[0]: [(0, array([-inf, -0.1, -0.1])), (1, array([-inf, 0.9, 0.9])), (2, array([-inf, 1.9, 1.9])), (3, array([-inf, 2.9, 2.9])), (4, array([-inf, 3.9, 3.9])), (5, array([-inf, 4.9, 4.9])), (6, array([-inf, 5.9, 5.9])), (7, array([-inf, 6.9, 6.9])), (8, array([-inf, 7.9, 7.9])), (9, array([-inf, 8.9, 8.9])), (10, array([0., 1., 1.])), (11, array([1., 2., 2.])), (12, array([2., 3., 3.])), (13, array([3., 4., 4.])), (14, array([4., 5., 5.])), (15, array([5., 6., 6.])), (16, array([6., 7., 7.])), (17, array([7., 8., 8.])), (18, array([8., 9., 9.])), (19, array([ 9., 10., 10.])), (20, array([-inf, -0.1, -0.1])), (21, array([-inf, 0.9, 0.9])), (22, array([-inf, 1.9, 1.9])), (23, array([-inf, 2.9, 2.9])), (24, array([-inf, 3.9, 3.9])), (25, array([-inf, 4.9, 4.9])), (26, array([-inf, 5.9, 5.9])), (27, array([-inf, 6.9, 6.9])), (28, array([-inf, 7.9, 7.9])), (29, array([-inf, 8.9, 8.9])), (30, array([1. , 1.5, 1.5])), (31, array([2. , 2.5, 2.5])), (32, array([3. , 3.5, 3.5])), (33, array([4. , 4.5, 4.5])), (34, array([5. , 5.5, 5.5])), (35, array([6. , 6.5, 6.5])), (36, array([7. , 7.5, 7.5])), (37, array([8. , 8.5, 8.5])), (38, array([9. , 9.5, 9.5])), (39, array([10. , 10.5, 10.5]))] stack[1]: [(10, array([0., 1., 1.])), (11, array([1., 2., 2.])), (12, array([2., 3., 3.])), (13, array([3., 4., 4.])), (14, array([4., 5., 5.])), (15, array([5., 6., 6.])), (16, array([6., 7., 7.])), (17, array([7., 8., 8.])), (18, array([8., 9., 9.])), (19, array([ 9., 10., 10.])), (20, array([-inf, -0.1, -0.1])), (21, array([-inf, 0.9, 0.9])), (22, array([-inf, 1.9, 1.9])), (23, array([-inf, 2.9, 2.9])), (24, array([-inf, 3.9, 3.9])), (25, array([-inf, 4.9, 4.9])), (26, array([-inf, 5.9, 5.9])), (27, array([-inf, 6.9, 6.9])), (28, array([-inf, 7.9, 7.9])), (29, array([-inf, 8.9, 8.9])), (30, array([1. , 1.5, 1.5])), (31, array([2. , 2.5, 2.5])), (32, array([3. , 3.5, 3.5])), (33, array([4. , 4.5, 4.5])), (34, array([5. , 5.5, 5.5])), (35, array([6. , 6.5, 6.5])), (36, array([7. , 7.5, 7.5])), (37, array([8. , 8.5, 8.5])), (38, array([9. , 9.5, 9.5])), (39, array([10. , 10.5, 10.5]))] 0 0.1 reading: [0. 1. 1.] 1 1.1 reading: [1. 2. 2.] 2 2.1 reading: [2. 3. 3.] 3 3.1 reading: [3. 4. 4.] 4 4.1 reading: [4. 5. 5.] 5 5.1 reading: [5. 6. 6.] 6 6.1 reading: [6. 7. 7.] 7 7.1 reading: [7. 8. 8.] 8 8.1 reading: [8. 9. 9.] 9 9.1 reading: [ 9. 10. 10.] stack[2]: [(20, array([-inf, -0.1, -0.1])), (21, array([-inf, 0.9, 0.9])), (22, array([-inf, 1.9, 1.9])), (23, array([-inf, 2.9, 2.9])), (24, array([-inf, 3.9, 3.9])), (25, array([-inf, 4.9, 4.9])), (26, array([-inf, 5.9, 5.9])), (27, array([-inf, 6.9, 6.9])), (28, array([-inf, 7.9, 7.9])), (29, array([-inf, 8.9, 8.9])), (30, array([1. , 1.5, 1.5])), (31, array([2. , 2.5, 2.5])), (32, array([3. , 3.5, 3.5])), (33, array([4. , 4.5, 4.5])), (34, array([5. , 5.5, 5.5])), (35, array([6. , 6.5, 6.5])), (36, array([7. , 7.5, 7.5])), (37, array([8. , 8.5, 8.5])), (38, array([9. , 9.5, 9.5])), (39, array([10. , 10.5, 10.5]))] stack[3]: [(30, array([1. , 1.5, 1.5])), (31, array([2. , 2.5, 2.5])), (32, array([3. , 3.5, 3.5])), (33, array([4. , 4.5, 4.5])), (34, array([5. , 5.5, 5.5])), (35, array([6. , 6.5, 6.5])), (36, array([7. , 7.5, 7.5])), (37, array([8. , 8.5, 8.5])), (38, array([9. , 9.5, 9.5])), (39, array([10. , 10.5, 10.5]))] 1 1.1 reading: [1. 1.5 1.5] 2 2.1 reading: [2. 2.5 2.5] 3 3.1 reading: [3. 3.5 3.5] 4 4.1 reading: [4. 4.5 4.5] 5 5.1 reading: [5. 5.5 5.5] 6 6.1 reading: [6. 6.5 6.5] 7 7.1 reading: [7. 7.5 7.5] 8 8.1 reading: [8. 8.5 8.5] 9 9.1 reading: [9. 9.5 9.5] 10 10.1 reading: [10. 10.5 10.5] ======== <class 'ultranest.store.HDF5PointStore'> N=100 ======== writing... 0 1 storing: [0, 0.1, 0.1] 1 2 storing: [1, 1.1, 1.1] 2 3 storing: [2, 2.1, 2.1] 3 4 storing: [3, 3.1, 3.1] 4 5 storing: [4, 4.1, 4.1] 5 6 storing: [5, 5.1, 5.1] 6 7 storing: [6, 6.1, 6.1] 7 8 storing: [7, 7.1, 7.1] 8 9 storing: [8, 8.1, 8.1] 9 10 storing: [9, 9.1, 9.1] 10 11 storing: [10, 10.1, 10.1] 11 12 storing: [11, 11.1, 11.1] 12 13 storing: [12, 12.1, 12.1] 13 14 storing: [13, 13.1, 13.1] 14 15 storing: [14, 14.1, 14.1] 15 16 storing: [15, 15.1, 15.1] 16 17 storing: [16, 16.1, 16.1] 17 18 storing: [17, 17.1, 17.1] 18 19 storing: [18, 18.1, 18.1] 19 20 storing: [19, 19.1, 19.1] 20 21 storing: [20, 20.1, 20.1] 21 22 storing: [21, 21.1, 21.1] 22 23 storing: [22, 22.1, 22.1] 23 24 storing: [23, 23.1, 23.1] 24 25 storing: [24, 24.1, 24.1] 25 26 storing: [25, 25.1, 25.1] 26 27 storing: [26, 26.1, 26.1] 27 28 storing: [27, 27.1, 27.1] 28 29 storing: [28, 28.1, 28.1] 29 30 storing: [29, 29.1, 29.1] 30 31 storing: [30, 30.1, 30.1] 31 32 storing: [31, 31.1, 31.1] 32 33 storing: [32, 32.1, 32.1] 33 34 storing: [33, 33.1, 33.1] 34 35 storing: [34, 34.1, 34.1] 35 36 storing: [35, 35.1, 35.1] 36 37 storing: [36, 36.1, 36.1] 37 38 storing: [37, 37.1, 37.1] 38 39 storing: [38, 38.1, 38.1] 39 40 storing: [39, 39.1, 39.1] 40 41 storing: [40, 40.1, 40.1] 41 42 storing: [41, 41.1, 41.1] 42 43 storing: [42, 42.1, 42.1] 43 44 storing: [43, 43.1, 43.1] 44 45 storing: [44, 44.1, 44.1] 45 46 storing: [45, 45.1, 45.1] 46 47 storing: [46, 46.1, 46.1] 47 48 storing: [47, 47.1, 47.1] 48 49 storing: [48, 48.1, 48.1] 49 50 storing: [49, 49.1, 49.1] 50 51 storing: [50, 50.1, 50.1] 51 52 storing: [51, 51.1, 51.1] 52 53 storing: [52, 52.1, 52.1] 53 54 storing: [53, 53.1, 53.1] 54 55 storing: [54, 54.1, 54.1] 55 56 storing: [55, 55.1, 55.1] 56 57 storing: [56, 56.1, 56.1] 57 58 storing: [57, 57.1, 57.1] 58 59 storing: [58, 58.1, 58.1] 59 60 storing: [59, 59.1, 59.1] 60 61 storing: [60, 60.1, 60.1] 61 62 storing: [61, 61.1, 61.1] 62 63 storing: [62, 62.1, 62.1] 63 64 storing: [63, 63.1, 63.1] 64 65 storing: [64, 64.1, 64.1] 65 66 storing: [65, 65.1, 65.1] 66 67 storing: [66, 66.1, 66.1] 67 68 storing: [67, 67.1, 67.1] 68 69 storing: [68, 68.1, 68.1] 69 70 storing: [69, 69.1, 69.1] 70 71 storing: [70, 70.1, 70.1] 71 72 storing: [71, 71.1, 71.1] 72 73 storing: [72, 72.1, 72.1] 73 74 storing: [73, 73.1, 73.1] 74 75 storing: [74, 74.1, 74.1] 75 76 storing: [75, 75.1, 75.1] 76 77 storing: [76, 76.1, 76.1] 77 78 storing: [77, 77.1, 77.1] 78 79 storing: [78, 78.1, 78.1] 79 80 storing: [79, 79.1, 79.1] 80 81 storing: [80, 80.1, 80.1] 81 82 storing: [81, 81.1, 81.1] 82 83 storing: [82, 82.1, 82.1] 83 84 storing: [83, 83.1, 83.1] 84 85 storing: [84, 84.1, 84.1] 85 86 storing: [85, 85.1, 85.1] 86 87 storing: [86, 86.1, 86.1] 87 88 storing: [87, 87.1, 87.1] 88 89 storing: [88, 88.1, 88.1] 89 90 storing: [89, 89.1, 89.1] 90 91 storing: [90, 90.1, 90.1] 91 92 storing: [91, 91.1, 91.1] 92 93 storing: [92, 92.1, 92.1] 93 94 storing: [93, 93.1, 93.1] 94 95 storing: [94, 94.1, 94.1] 95 96 storing: [95, 95.1, 95.1] 96 97 storing: [96, 96.1, 96.1] 97 98 storing: [97, 97.1, 97.1] 98 99 storing: [98, 98.1, 98.1] 99 100 storing: [99, 99.1, 99.1] 1 2 storing: [1, 1.5, 1.5] 2 3 storing: [2, 2.5, 2.5] 3 4 storing: [3, 3.5, 3.5] 4 5 storing: [4, 4.5, 4.5] 5 6 storing: [5, 5.5, 5.5] 6 7 storing: [6, 6.5, 6.5] 7 8 storing: [7, 7.5, 7.5] 8 9 storing: [8, 8.5, 8.5] 9 10 storing: [9, 9.5, 9.5] 10 11 storing: [10, 10.5, 10.5] 11 12 storing: [11, 11.5, 11.5] 12 13 storing: [12, 12.5, 12.5] 13 14 storing: [13, 13.5, 13.5] 14 15 storing: [14, 14.5, 14.5] 15 16 storing: [15, 15.5, 15.5] 16 17 storing: [16, 16.5, 16.5] 17 18 storing: [17, 17.5, 17.5] 18 19 storing: [18, 18.5, 18.5] 19 20 storing: [19, 19.5, 19.5] 20 21 storing: [20, 20.5, 20.5] 21 22 storing: [21, 21.5, 21.5] 22 23 storing: [22, 22.5, 22.5] 23 24 storing: [23, 23.5, 23.5] 24 25 storing: [24, 24.5, 24.5] 25 26 storing: [25, 25.5, 25.5] 26 27 storing: [26, 26.5, 26.5] 27 28 storing: [27, 27.5, 27.5] 28 29 storing: [28, 28.5, 28.5] 29 30 storing: [29, 29.5, 29.5] 30 31 storing: [30, 30.5, 30.5] 31 32 storing: [31, 31.5, 31.5] 32 33 storing: [32, 32.5, 32.5] 33 34 storing: [33, 33.5, 33.5] 34 35 storing: [34, 34.5, 34.5] 35 36 storing: [35, 35.5, 35.5] 36 37 storing: [36, 36.5, 36.5] 37 38 storing: [37, 37.5, 37.5] 38 39 storing: [38, 38.5, 38.5] 39 40 storing: [39, 39.5, 39.5] 40 41 storing: [40, 40.5, 40.5] 41 42 storing: [41, 41.5, 41.5] 42 43 storing: [42, 42.5, 42.5] 43 44 storing: [43, 43.5, 43.5] 44 45 storing: [44, 44.5, 44.5] 45 46 storing: [45, 45.5, 45.5] 46 47 storing: [46, 46.5, 46.5] 47 48 storing: [47, 47.5, 47.5] 48 49 storing: [48, 48.5, 48.5] 49 50 storing: [49, 49.5, 49.5] 50 51 storing: [50, 50.5, 50.5] 51 52 storing: [51, 51.5, 51.5] 52 53 storing: [52, 52.5, 52.5] 53 54 storing: [53, 53.5, 53.5] 54 55 storing: [54, 54.5, 54.5] 55 56 storing: [55, 55.5, 55.5] 56 57 storing: [56, 56.5, 56.5] 57 58 storing: [57, 57.5, 57.5] 58 59 storing: [58, 58.5, 58.5] 59 60 storing: [59, 59.5, 59.5] 60 61 storing: [60, 60.5, 60.5] 61 62 storing: [61, 61.5, 61.5] 62 63 storing: [62, 62.5, 62.5] 63 64 storing: [63, 63.5, 63.5] 64 65 storing: [64, 64.5, 64.5] 65 66 storing: [65, 65.5, 65.5] 66 67 storing: [66, 66.5, 66.5] 67 68 storing: [67, 67.5, 67.5] 68 69 storing: [68, 68.5, 68.5] 69 70 storing: [69, 69.5, 69.5] 70 71 storing: [70, 70.5, 70.5] 71 72 storing: [71, 71.5, 71.5] 72 73 storing: [72, 72.5, 72.5] 73 74 storing: [73, 73.5, 73.5] 74 75 storing: [74, 74.5, 74.5] 75 76 storing: [75, 75.5, 75.5] 76 77 storing: [76, 76.5, 76.5] 77 78 storing: [77, 77.5, 77.5] 78 79 storing: [78, 78.5, 78.5] 79 80 storing: [79, 79.5, 79.5] 80 81 storing: [80, 80.5, 80.5] 81 82 storing: [81, 81.5, 81.5] 82 83 storing: [82, 82.5, 82.5] 83 84 storing: [83, 83.5, 83.5] 84 85 storing: [84, 84.5, 84.5] 85 86 storing: [85, 85.5, 85.5] 86 87 storing: [86, 86.5, 86.5] 87 88 storing: [87, 87.5, 87.5] 88 89 storing: [88, 88.5, 88.5] 89 90 storing: [89, 89.5, 89.5] 90 91 storing: [90, 90.5, 90.5] 91 92 storing: [91, 91.5, 91.5] 92 93 storing: [92, 92.5, 92.5] 93 94 storing: [93, 93.5, 93.5] 94 95 storing: [94, 94.5, 94.5] 95 96 storing: [95, 95.5, 95.5] 96 97 storing: [96, 96.5, 96.5] 97 98 storing: [97, 97.5, 97.5] 98 99 storing: [98, 98.5, 98.5] 99 100 storing: [99, 99.5, 99.5] 100 101 storing: [100, 100.5, 100.5] reading... stack[0]: [(0, array([-inf, -0.1, -0.1])), (1, array([-inf, 0.9, 0.9])), (2, array([-inf, 1.9, 1.9])), (3, array([-inf, 2.9, 2.9])), (4, array([-inf, 3.9, 3.9])), (5, array([-inf, 4.9, 4.9])), (6, array([-inf, 5.9, 5.9])), (7, array([-inf, 6.9, 6.9])), (8, array([-inf, 7.9, 7.9])), (9, array([-inf, 8.9, 8.9])), (10, array([-inf, 9.9, 9.9])), (11, array([-inf, 10.9, 10.9])), (12, array([-inf, 11.9, 11.9])), (13, array([-inf, 12.9, 12.9])), (14, array([-inf, 13.9, 13.9])), (15, array([-inf, 14.9, 14.9])), (16, array([-inf, 15.9, 15.9])), (17, array([-inf, 16.9, 16.9])), (18, array([-inf, 17.9, 17.9])), (19, array([-inf, 18.9, 18.9])), (20, array([-inf, 19.9, 19.9])), (21, array([-inf, 20.9, 20.9])), (22, array([-inf, 21.9, 21.9])), (23, array([-inf, 22.9, 22.9])), (24, array([-inf, 23.9, 23.9])), (25, array([-inf, 24.9, 24.9])), (26, array([-inf, 25.9, 25.9])), (27, array([-inf, 26.9, 26.9])), (28, array([-inf, 27.9, 27.9])), (29, array([-inf, 28.9, 28.9])), (30, array([-inf, 29.9, 29.9])), (31, array([-inf, 30.9, 30.9])), (32, array([-inf, 31.9, 31.9])), (33, array([-inf, 32.9, 32.9])), (34, array([-inf, 33.9, 33.9])), (35, array([-inf, 34.9, 34.9])), (36, array([-inf, 35.9, 35.9])), (37, array([-inf, 36.9, 36.9])), (38, array([-inf, 37.9, 37.9])), (39, array([-inf, 38.9, 38.9])), (40, array([-inf, 39.9, 39.9])), (41, array([-inf, 40.9, 40.9])), (42, array([-inf, 41.9, 41.9])), (43, array([-inf, 42.9, 42.9])), (44, array([-inf, 43.9, 43.9])), (45, array([-inf, 44.9, 44.9])), (46, array([-inf, 45.9, 45.9])), (47, array([-inf, 46.9, 46.9])), (48, array([-inf, 47.9, 47.9])), (49, array([-inf, 48.9, 48.9])), (50, array([-inf, 49.9, 49.9])), (51, array([-inf, 50.9, 50.9])), (52, array([-inf, 51.9, 51.9])), (53, array([-inf, 52.9, 52.9])), (54, array([-inf, 53.9, 53.9])), (55, array([-inf, 54.9, 54.9])), (56, array([-inf, 55.9, 55.9])), (57, array([-inf, 56.9, 56.9])), (58, array([-inf, 57.9, 57.9])), (59, array([-inf, 58.9, 58.9])), (60, array([-inf, 59.9, 59.9])), (61, array([-inf, 60.9, 60.9])), (62, array([-inf, 61.9, 61.9])), (63, array([-inf, 62.9, 62.9])), (64, array([-inf, 63.9, 63.9])), (65, array([-inf, 64.9, 64.9])), (66, array([-inf, 65.9, 65.9])), (67, array([-inf, 66.9, 66.9])), (68, array([-inf, 67.9, 67.9])), (69, array([-inf, 68.9, 68.9])), (70, array([-inf, 69.9, 69.9])), (71, array([-inf, 70.9, 70.9])), (72, array([-inf, 71.9, 71.9])), (73, array([-inf, 72.9, 72.9])), (74, array([-inf, 73.9, 73.9])), (75, array([-inf, 74.9, 74.9])), (76, array([-inf, 75.9, 75.9])), (77, array([-inf, 76.9, 76.9])), (78, array([-inf, 77.9, 77.9])), (79, array([-inf, 78.9, 78.9])), (80, array([-inf, 79.9, 79.9])), (81, array([-inf, 80.9, 80.9])), (82, array([-inf, 81.9, 81.9])), (83, array([-inf, 82.9, 82.9])), (84, array([-inf, 83.9, 83.9])), (85, array([-inf, 84.9, 84.9])), (86, array([-inf, 85.9, 85.9])), (87, array([-inf, 86.9, 86.9])), (88, array([-inf, 87.9, 87.9])), (89, array([-inf, 88.9, 88.9])), (90, array([-inf, 89.9, 89.9])), (91, array([-inf, 90.9, 90.9])), (92, array([-inf, 91.9, 91.9])), (93, array([-inf, 92.9, 92.9])), (94, array([-inf, 93.9, 93.9])), (95, array([-inf, 94.9, 94.9])), (96, array([-inf, 95.9, 95.9])), (97, array([-inf, 96.9, 96.9])), (98, array([-inf, 97.9, 97.9])), (99, array([-inf, 98.9, 98.9])), (100, array([0., 1., 1.])), (101, array([1., 2., 2.])), (102, array([2., 3., 3.])), (103, array([3., 4., 4.])), (104, array([4., 5., 5.])), (105, array([5., 6., 6.])), (106, array([6., 7., 7.])), (107, array([7., 8., 8.])), (108, array([8., 9., 9.])), (109, array([ 9., 10., 10.])), (110, array([10., 11., 11.])), (111, array([11., 12., 12.])), (112, array([12., 13., 13.])), (113, array([13., 14., 14.])), (114, array([14., 15., 15.])), (115, array([15., 16., 16.])), (116, array([16., 17., 17.])), (117, array([17., 18., 18.])), (118, array([18., 19., 19.])), (119, array([19., 20., 20.])), (120, array([20., 21., 21.])), (121, array([21., 22., 22.])), (122, array([22., 23., 23.])), (123, array([23., 24., 24.])), (124, array([24., 25., 25.])), (125, array([25., 26., 26.])), (126, array([26., 27., 27.])), (127, array([27., 28., 28.])), (128, array([28., 29., 29.])), (129, array([29., 30., 30.])), (130, array([30., 31., 31.])), (131, array([31., 32., 32.])), (132, array([32., 33., 33.])), (133, array([33., 34., 34.])), (134, array([34., 35., 35.])), (135, array([35., 36., 36.])), (136, array([36., 37., 37.])), (137, array([37., 38., 38.])), (138, array([38., 39., 39.])), (139, array([39., 40., 40.])), (140, array([40., 41., 41.])), (141, array([41., 42., 42.])), 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(299, array([-inf, 98.9, 98.9])), (300, array([1. , 1.5, 1.5])), (301, array([2. , 2.5, 2.5])), (302, array([3. , 3.5, 3.5])), (303, array([4. , 4.5, 4.5])), (304, array([5. , 5.5, 5.5])), (305, array([6. , 6.5, 6.5])), (306, array([7. , 7.5, 7.5])), (307, array([8. , 8.5, 8.5])), (308, array([9. , 9.5, 9.5])), (309, array([10. , 10.5, 10.5])), (310, array([11. , 11.5, 11.5])), (311, array([12. , 12.5, 12.5])), (312, array([13. , 13.5, 13.5])), (313, array([14. , 14.5, 14.5])), (314, array([15. , 15.5, 15.5])), (315, array([16. , 16.5, 16.5])), (316, array([17. , 17.5, 17.5])), (317, array([18. , 18.5, 18.5])), (318, array([19. , 19.5, 19.5])), (319, array([20. , 20.5, 20.5])), (320, array([21. , 21.5, 21.5])), (321, array([22. , 22.5, 22.5])), (322, array([23. , 23.5, 23.5])), (323, array([24. , 24.5, 24.5])), (324, array([25. , 25.5, 25.5])), (325, array([26. , 26.5, 26.5])), (326, array([27. , 27.5, 27.5])), (327, array([28. , 28.5, 28.5])), (328, array([29. , 29.5, 29.5])), (329, array([30. , 30.5, 30.5])), (330, array([31. , 31.5, 31.5])), (331, array([32. , 32.5, 32.5])), (332, array([33. , 33.5, 33.5])), (333, array([34. , 34.5, 34.5])), (334, array([35. , 35.5, 35.5])), (335, array([36. , 36.5, 36.5])), (336, array([37. , 37.5, 37.5])), (337, array([38. , 38.5, 38.5])), (338, array([39. , 39.5, 39.5])), (339, array([40. , 40.5, 40.5])), (340, array([41. , 41.5, 41.5])), (341, array([42. , 42.5, 42.5])), (342, array([43. , 43.5, 43.5])), (343, array([44. , 44.5, 44.5])), (344, array([45. , 45.5, 45.5])), (345, array([46. , 46.5, 46.5])), (346, array([47. , 47.5, 47.5])), (347, array([48. , 48.5, 48.5])), (348, array([49. , 49.5, 49.5])), (349, array([50. , 50.5, 50.5])), (350, array([51. , 51.5, 51.5])), (351, array([52. , 52.5, 52.5])), (352, array([53. , 53.5, 53.5])), (353, array([54. , 54.5, 54.5])), (354, array([55. , 55.5, 55.5])), (355, array([56. , 56.5, 56.5])), (356, array([57. , 57.5, 57.5])), (357, array([58. , 58.5, 58.5])), (358, array([59. , 59.5, 59.5])), (359, array([60. , 60.5, 60.5])), (360, array([61. , 61.5, 61.5])), (361, array([62. , 62.5, 62.5])), (362, array([63. , 63.5, 63.5])), (363, array([64. , 64.5, 64.5])), (364, array([65. , 65.5, 65.5])), (365, array([66. , 66.5, 66.5])), (366, array([67. , 67.5, 67.5])), (367, array([68. , 68.5, 68.5])), (368, array([69. , 69.5, 69.5])), (369, array([70. , 70.5, 70.5])), (370, array([71. , 71.5, 71.5])), (371, array([72. , 72.5, 72.5])), (372, array([73. , 73.5, 73.5])), (373, array([74. , 74.5, 74.5])), (374, array([75. , 75.5, 75.5])), (375, array([76. , 76.5, 76.5])), (376, array([77. , 77.5, 77.5])), (377, array([78. , 78.5, 78.5])), (378, array([79. , 79.5, 79.5])), (379, array([80. , 80.5, 80.5])), (380, array([81. , 81.5, 81.5])), (381, array([82. , 82.5, 82.5])), (382, array([83. , 83.5, 83.5])), (383, array([84. , 84.5, 84.5])), (384, array([85. , 85.5, 85.5])), (385, array([86. , 86.5, 86.5])), (386, array([87. , 87.5, 87.5])), (387, array([88. , 88.5, 88.5])), (388, array([89. , 89.5, 89.5])), (389, array([90. , 90.5, 90.5])), (390, array([91. , 91.5, 91.5])), (391, array([92. , 92.5, 92.5])), (392, array([93. , 93.5, 93.5])), (393, array([94. , 94.5, 94.5])), (394, array([95. , 95.5, 95.5])), (395, array([96. , 96.5, 96.5])), (396, array([97. , 97.5, 97.5])), (397, array([98. , 98.5, 98.5])), (398, array([99. , 99.5, 99.5])), (399, array([100. , 100.5, 100.5]))] 0 0.1 reading: [0. 1. 1.] 1 1.1 reading: [1. 2. 2.] 2 2.1 reading: [2. 3. 3.] 3 3.1 reading: [3. 4. 4.] 4 4.1 reading: [4. 5. 5.] 5 5.1 reading: [5. 6. 6.] 6 6.1 reading: [6. 7. 7.] 7 7.1 reading: [7. 8. 8.] 8 8.1 reading: [8. 9. 9.] 9 9.1 reading: [ 9. 10. 10.] 10 10.1 reading: [10. 11. 11.] 11 11.1 reading: [11. 12. 12.] 12 12.1 reading: [12. 13. 13.] 13 13.1 reading: [13. 14. 14.] 14 14.1 reading: [14. 15. 15.] 15 15.1 reading: [15. 16. 16.] 16 16.1 reading: [16. 17. 17.] 17 17.1 reading: [17. 18. 18.] 18 18.1 reading: [18. 19. 19.] 19 19.1 reading: [19. 20. 20.] 20 20.1 reading: [20. 21. 21.] 21 21.1 reading: [21. 22. 22.] 22 22.1 reading: [22. 23. 23.] 23 23.1 reading: [23. 24. 24.] 24 24.1 reading: [24. 25. 25.] 25 25.1 reading: [25. 26. 26.] 26 26.1 reading: [26. 27. 27.] 27 27.1 reading: [27. 28. 28.] 28 28.1 reading: [28. 29. 29.] 29 29.1 reading: [29. 30. 30.] 30 30.1 reading: [30. 31. 31.] 31 31.1 reading: [31. 32. 32.] 32 32.1 reading: [32. 33. 33.] 33 33.1 reading: [33. 34. 34.] 34 34.1 reading: [34. 35. 35.] 35 35.1 reading: [35. 36. 36.] 36 36.1 reading: [36. 37. 37.] 37 37.1 reading: [37. 38. 38.] 38 38.1 reading: [38. 39. 39.] 39 39.1 reading: [39. 40. 40.] 40 40.1 reading: [40. 41. 41.] 41 41.1 reading: [41. 42. 42.] 42 42.1 reading: [42. 43. 43.] 43 43.1 reading: [43. 44. 44.] 44 44.1 reading: [44. 45. 45.] 45 45.1 reading: [45. 46. 46.] 46 46.1 reading: [46. 47. 47.] 47 47.1 reading: [47. 48. 48.] 48 48.1 reading: [48. 49. 49.] 49 49.1 reading: [49. 50. 50.] 50 50.1 reading: [50. 51. 51.] 51 51.1 reading: [51. 52. 52.] 52 52.1 reading: [52. 53. 53.] 53 53.1 reading: [53. 54. 54.] 54 54.1 reading: [54. 55. 55.] 55 55.1 reading: [55. 56. 56.] 56 56.1 reading: [56. 57. 57.] 57 57.1 reading: [57. 58. 58.] 58 58.1 reading: [58. 59. 59.] 59 59.1 reading: [59. 60. 60.] 60 60.1 reading: [60. 61. 61.] 61 61.1 reading: [61. 62. 62.] 62 62.1 reading: [62. 63. 63.] 63 63.1 reading: [63. 64. 64.] 64 64.1 reading: [64. 65. 65.] 65 65.1 reading: [65. 66. 66.] 66 66.1 reading: [66. 67. 67.] 67 67.1 reading: [67. 68. 68.] 68 68.1 reading: [68. 69. 69.] 69 69.1 reading: [69. 70. 70.] 70 70.1 reading: [70. 71. 71.] 71 71.1 reading: [71. 72. 72.] 72 72.1 reading: [72. 73. 73.] 73 73.1 reading: [73. 74. 74.] 74 74.1 reading: [74. 75. 75.] 75 75.1 reading: [75. 76. 76.] 76 76.1 reading: [76. 77. 77.] 77 77.1 reading: [77. 78. 78.] 78 78.1 reading: [78. 79. 79.] 79 79.1 reading: [79. 80. 80.] 80 80.1 reading: [80. 81. 81.] 81 81.1 reading: [81. 82. 82.] 82 82.1 reading: [82. 83. 83.] 83 83.1 reading: [83. 84. 84.] 84 84.1 reading: [84. 85. 85.] 85 85.1 reading: [85. 86. 86.] 86 86.1 reading: [86. 87. 87.] 87 87.1 reading: [87. 88. 88.] 88 88.1 reading: [88. 89. 89.] 89 89.1 reading: [89. 90. 90.] 90 90.1 reading: [90. 91. 91.] 91 91.1 reading: [91. 92. 92.] 92 92.1 reading: [92. 93. 93.] 93 93.1 reading: [93. 94. 94.] 94 94.1 reading: [94. 95. 95.] 95 95.1 reading: [95. 96. 96.] 96 96.1 reading: [96. 97. 97.] 97 97.1 reading: [97. 98. 98.] 98 98.1 reading: [98. 99. 99.] 99 99.1 reading: [ 99. 100. 100.] stack[2]: [(200, array([-inf, -0.1, -0.1])), (201, array([-inf, 0.9, 0.9])), (202, array([-inf, 1.9, 1.9])), (203, array([-inf, 2.9, 2.9])), (204, array([-inf, 3.9, 3.9])), (205, array([-inf, 4.9, 4.9])), (206, array([-inf, 5.9, 5.9])), (207, array([-inf, 6.9, 6.9])), (208, array([-inf, 7.9, 7.9])), (209, array([-inf, 8.9, 8.9])), (210, array([-inf, 9.9, 9.9])), (211, array([-inf, 10.9, 10.9])), (212, array([-inf, 11.9, 11.9])), (213, array([-inf, 12.9, 12.9])), (214, array([-inf, 13.9, 13.9])), (215, array([-inf, 14.9, 14.9])), (216, array([-inf, 15.9, 15.9])), (217, array([-inf, 16.9, 16.9])), (218, array([-inf, 17.9, 17.9])), (219, array([-inf, 18.9, 18.9])), (220, array([-inf, 19.9, 19.9])), (221, array([-inf, 20.9, 20.9])), (222, array([-inf, 21.9, 21.9])), (223, array([-inf, 22.9, 22.9])), (224, array([-inf, 23.9, 23.9])), (225, array([-inf, 24.9, 24.9])), (226, array([-inf, 25.9, 25.9])), (227, array([-inf, 26.9, 26.9])), (228, array([-inf, 27.9, 27.9])), (229, array([-inf, 28.9, 28.9])), (230, array([-inf, 29.9, 29.9])), (231, array([-inf, 30.9, 30.9])), (232, array([-inf, 31.9, 31.9])), (233, array([-inf, 32.9, 32.9])), (234, array([-inf, 33.9, 33.9])), (235, array([-inf, 34.9, 34.9])), (236, array([-inf, 35.9, 35.9])), (237, array([-inf, 36.9, 36.9])), (238, array([-inf, 37.9, 37.9])), (239, array([-inf, 38.9, 38.9])), (240, array([-inf, 39.9, 39.9])), (241, array([-inf, 40.9, 40.9])), (242, array([-inf, 41.9, 41.9])), (243, array([-inf, 42.9, 42.9])), (244, array([-inf, 43.9, 43.9])), (245, array([-inf, 44.9, 44.9])), (246, array([-inf, 45.9, 45.9])), (247, array([-inf, 46.9, 46.9])), (248, array([-inf, 47.9, 47.9])), (249, array([-inf, 48.9, 48.9])), (250, array([-inf, 49.9, 49.9])), (251, array([-inf, 50.9, 50.9])), (252, array([-inf, 51.9, 51.9])), (253, array([-inf, 52.9, 52.9])), (254, array([-inf, 53.9, 53.9])), (255, array([-inf, 54.9, 54.9])), (256, array([-inf, 55.9, 55.9])), (257, array([-inf, 56.9, 56.9])), (258, array([-inf, 57.9, 57.9])), (259, array([-inf, 58.9, 58.9])), (260, array([-inf, 59.9, 59.9])), (261, array([-inf, 60.9, 60.9])), (262, array([-inf, 61.9, 61.9])), (263, array([-inf, 62.9, 62.9])), (264, array([-inf, 63.9, 63.9])), (265, array([-inf, 64.9, 64.9])), (266, array([-inf, 65.9, 65.9])), (267, array([-inf, 66.9, 66.9])), (268, array([-inf, 67.9, 67.9])), (269, array([-inf, 68.9, 68.9])), (270, array([-inf, 69.9, 69.9])), (271, array([-inf, 70.9, 70.9])), (272, array([-inf, 71.9, 71.9])), (273, array([-inf, 72.9, 72.9])), (274, array([-inf, 73.9, 73.9])), (275, array([-inf, 74.9, 74.9])), (276, array([-inf, 75.9, 75.9])), (277, array([-inf, 76.9, 76.9])), (278, array([-inf, 77.9, 77.9])), (279, array([-inf, 78.9, 78.9])), (280, array([-inf, 79.9, 79.9])), (281, array([-inf, 80.9, 80.9])), (282, array([-inf, 81.9, 81.9])), (283, array([-inf, 82.9, 82.9])), (284, array([-inf, 83.9, 83.9])), (285, array([-inf, 84.9, 84.9])), (286, array([-inf, 85.9, 85.9])), (287, array([-inf, 86.9, 86.9])), (288, array([-inf, 87.9, 87.9])), (289, array([-inf, 88.9, 88.9])), (290, array([-inf, 89.9, 89.9])), (291, array([-inf, 90.9, 90.9])), (292, array([-inf, 91.9, 91.9])), (293, array([-inf, 92.9, 92.9])), (294, array([-inf, 93.9, 93.9])), (295, array([-inf, 94.9, 94.9])), (296, array([-inf, 95.9, 95.9])), (297, array([-inf, 96.9, 96.9])), (298, array([-inf, 97.9, 97.9])), (299, array([-inf, 98.9, 98.9])), (300, array([1. , 1.5, 1.5])), (301, array([2. , 2.5, 2.5])), (302, array([3. , 3.5, 3.5])), (303, array([4. , 4.5, 4.5])), (304, array([5. , 5.5, 5.5])), (305, array([6. , 6.5, 6.5])), (306, array([7. , 7.5, 7.5])), (307, array([8. , 8.5, 8.5])), (308, array([9. , 9.5, 9.5])), (309, array([10. , 10.5, 10.5])), (310, array([11. , 11.5, 11.5])), (311, array([12. , 12.5, 12.5])), (312, array([13. , 13.5, 13.5])), (313, array([14. , 14.5, 14.5])), (314, array([15. , 15.5, 15.5])), (315, array([16. , 16.5, 16.5])), (316, array([17. , 17.5, 17.5])), (317, array([18. , 18.5, 18.5])), (318, array([19. , 19.5, 19.5])), (319, array([20. , 20.5, 20.5])), (320, array([21. , 21.5, 21.5])), (321, array([22. , 22.5, 22.5])), (322, array([23. , 23.5, 23.5])), (323, array([24. , 24.5, 24.5])), (324, array([25. , 25.5, 25.5])), (325, array([26. , 26.5, 26.5])), (326, array([27. , 27.5, 27.5])), (327, array([28. , 28.5, 28.5])), (328, array([29. , 29.5, 29.5])), (329, array([30. , 30.5, 30.5])), (330, array([31. , 31.5, 31.5])), (331, array([32. , 32.5, 32.5])), (332, array([33. , 33.5, 33.5])), (333, array([34. , 34.5, 34.5])), (334, array([35. , 35.5, 35.5])), (335, array([36. , 36.5, 36.5])), (336, array([37. , 37.5, 37.5])), (337, array([38. , 38.5, 38.5])), (338, array([39. , 39.5, 39.5])), (339, array([40. , 40.5, 40.5])), (340, array([41. , 41.5, 41.5])), (341, array([42. , 42.5, 42.5])), (342, array([43. , 43.5, 43.5])), (343, array([44. , 44.5, 44.5])), (344, array([45. , 45.5, 45.5])), (345, array([46. , 46.5, 46.5])), (346, array([47. , 47.5, 47.5])), (347, array([48. , 48.5, 48.5])), (348, array([49. , 49.5, 49.5])), (349, array([50. , 50.5, 50.5])), (350, array([51. , 51.5, 51.5])), (351, array([52. , 52.5, 52.5])), (352, array([53. , 53.5, 53.5])), (353, array([54. , 54.5, 54.5])), (354, array([55. , 55.5, 55.5])), (355, array([56. , 56.5, 56.5])), (356, array([57. , 57.5, 57.5])), (357, array([58. , 58.5, 58.5])), (358, array([59. , 59.5, 59.5])), (359, array([60. , 60.5, 60.5])), (360, array([61. , 61.5, 61.5])), (361, array([62. , 62.5, 62.5])), (362, array([63. , 63.5, 63.5])), (363, array([64. , 64.5, 64.5])), (364, array([65. , 65.5, 65.5])), (365, array([66. , 66.5, 66.5])), (366, array([67. , 67.5, 67.5])), (367, array([68. , 68.5, 68.5])), (368, array([69. , 69.5, 69.5])), (369, array([70. , 70.5, 70.5])), (370, array([71. , 71.5, 71.5])), (371, array([72. , 72.5, 72.5])), (372, array([73. , 73.5, 73.5])), (373, array([74. , 74.5, 74.5])), (374, array([75. , 75.5, 75.5])), (375, array([76. , 76.5, 76.5])), (376, array([77. , 77.5, 77.5])), (377, array([78. , 78.5, 78.5])), (378, array([79. , 79.5, 79.5])), (379, array([80. , 80.5, 80.5])), (380, array([81. , 81.5, 81.5])), (381, array([82. , 82.5, 82.5])), (382, array([83. , 83.5, 83.5])), (383, array([84. , 84.5, 84.5])), (384, array([85. , 85.5, 85.5])), (385, array([86. , 86.5, 86.5])), (386, array([87. , 87.5, 87.5])), (387, array([88. , 88.5, 88.5])), (388, array([89. , 89.5, 89.5])), (389, array([90. , 90.5, 90.5])), (390, array([91. , 91.5, 91.5])), (391, array([92. , 92.5, 92.5])), (392, array([93. , 93.5, 93.5])), (393, array([94. , 94.5, 94.5])), (394, array([95. , 95.5, 95.5])), (395, array([96. , 96.5, 96.5])), (396, array([97. , 97.5, 97.5])), (397, array([98. , 98.5, 98.5])), (398, array([99. , 99.5, 99.5])), (399, array([100. , 100.5, 100.5]))] stack[3]: [(300, array([1. , 1.5, 1.5])), (301, array([2. , 2.5, 2.5])), (302, array([3. , 3.5, 3.5])), (303, array([4. , 4.5, 4.5])), (304, array([5. , 5.5, 5.5])), (305, array([6. , 6.5, 6.5])), (306, array([7. , 7.5, 7.5])), (307, array([8. , 8.5, 8.5])), (308, array([9. , 9.5, 9.5])), (309, array([10. , 10.5, 10.5])), (310, array([11. , 11.5, 11.5])), (311, array([12. , 12.5, 12.5])), (312, array([13. , 13.5, 13.5])), (313, array([14. , 14.5, 14.5])), (314, array([15. , 15.5, 15.5])), (315, array([16. , 16.5, 16.5])), (316, array([17. , 17.5, 17.5])), (317, array([18. , 18.5, 18.5])), (318, array([19. , 19.5, 19.5])), (319, array([20. , 20.5, 20.5])), (320, array([21. , 21.5, 21.5])), (321, array([22. , 22.5, 22.5])), (322, array([23. , 23.5, 23.5])), (323, array([24. , 24.5, 24.5])), (324, array([25. , 25.5, 25.5])), (325, array([26. , 26.5, 26.5])), (326, array([27. , 27.5, 27.5])), (327, array([28. , 28.5, 28.5])), (328, array([29. , 29.5, 29.5])), (329, array([30. , 30.5, 30.5])), (330, array([31. , 31.5, 31.5])), (331, array([32. , 32.5, 32.5])), (332, array([33. , 33.5, 33.5])), (333, array([34. , 34.5, 34.5])), (334, array([35. , 35.5, 35.5])), (335, array([36. , 36.5, 36.5])), (336, array([37. , 37.5, 37.5])), (337, array([38. , 38.5, 38.5])), (338, array([39. , 39.5, 39.5])), (339, array([40. , 40.5, 40.5])), (340, array([41. , 41.5, 41.5])), (341, array([42. , 42.5, 42.5])), (342, array([43. , 43.5, 43.5])), (343, array([44. , 44.5, 44.5])), (344, array([45. , 45.5, 45.5])), (345, array([46. , 46.5, 46.5])), (346, array([47. , 47.5, 47.5])), (347, array([48. , 48.5, 48.5])), (348, array([49. , 49.5, 49.5])), (349, array([50. , 50.5, 50.5])), (350, array([51. , 51.5, 51.5])), (351, array([52. , 52.5, 52.5])), (352, array([53. , 53.5, 53.5])), (353, array([54. , 54.5, 54.5])), (354, array([55. , 55.5, 55.5])), (355, array([56. , 56.5, 56.5])), (356, array([57. , 57.5, 57.5])), (357, array([58. , 58.5, 58.5])), (358, array([59. , 59.5, 59.5])), (359, array([60. , 60.5, 60.5])), (360, array([61. , 61.5, 61.5])), (361, array([62. , 62.5, 62.5])), (362, array([63. , 63.5, 63.5])), (363, array([64. , 64.5, 64.5])), (364, array([65. , 65.5, 65.5])), (365, array([66. , 66.5, 66.5])), (366, array([67. , 67.5, 67.5])), (367, array([68. , 68.5, 68.5])), (368, array([69. , 69.5, 69.5])), (369, array([70. , 70.5, 70.5])), (370, array([71. , 71.5, 71.5])), (371, array([72. , 72.5, 72.5])), (372, array([73. , 73.5, 73.5])), (373, array([74. , 74.5, 74.5])), (374, array([75. , 75.5, 75.5])), (375, array([76. , 76.5, 76.5])), (376, array([77. , 77.5, 77.5])), (377, array([78. , 78.5, 78.5])), (378, array([79. , 79.5, 79.5])), (379, array([80. , 80.5, 80.5])), (380, array([81. , 81.5, 81.5])), (381, array([82. , 82.5, 82.5])), (382, array([83. , 83.5, 83.5])), (383, array([84. , 84.5, 84.5])), (384, array([85. , 85.5, 85.5])), (385, array([86. , 86.5, 86.5])), (386, array([87. , 87.5, 87.5])), (387, array([88. , 88.5, 88.5])), (388, array([89. , 89.5, 89.5])), (389, array([90. , 90.5, 90.5])), (390, array([91. , 91.5, 91.5])), (391, array([92. , 92.5, 92.5])), (392, array([93. , 93.5, 93.5])), (393, array([94. , 94.5, 94.5])), (394, array([95. , 95.5, 95.5])), (395, array([96. , 96.5, 96.5])), (396, array([97. , 97.5, 97.5])), (397, array([98. , 98.5, 98.5])), (398, array([99. , 99.5, 99.5])), (399, array([100. , 100.5, 100.5]))] 1 1.1 reading: [1. 1.5 1.5] 2 2.1 reading: [2. 2.5 2.5] 3 3.1 reading: [3. 3.5 3.5] 4 4.1 reading: [4. 4.5 4.5] 5 5.1 reading: [5. 5.5 5.5] 6 6.1 reading: [6. 6.5 6.5] 7 7.1 reading: [7. 7.5 7.5] 8 8.1 reading: [8. 8.5 8.5] 9 9.1 reading: [9. 9.5 9.5] 10 10.1 reading: [10. 10.5 10.5] 11 11.1 reading: [11. 11.5 11.5] 12 12.1 reading: [12. 12.5 12.5] 13 13.1 reading: [13. 13.5 13.5] 14 14.1 reading: [14. 14.5 14.5] 15 15.1 reading: [15. 15.5 15.5] 16 16.1 reading: [16. 16.5 16.5] 17 17.1 reading: [17. 17.5 17.5] 18 18.1 reading: [18. 18.5 18.5] 19 19.1 reading: [19. 19.5 19.5] 20 20.1 reading: [20. 20.5 20.5] 21 21.1 reading: [21. 21.5 21.5] 22 22.1 reading: [22. 22.5 22.5] 23 23.1 reading: [23. 23.5 23.5] 24 24.1 reading: [24. 24.5 24.5] 25 25.1 reading: [25. 25.5 25.5] 26 26.1 reading: [26. 26.5 26.5] 27 27.1 reading: [27. 27.5 27.5] 28 28.1 reading: [28. 28.5 28.5] 29 29.1 reading: [29. 29.5 29.5] 30 30.1 reading: [30. 30.5 30.5] 31 31.1 reading: [31. 31.5 31.5] 32 32.1 reading: [32. 32.5 32.5] 33 33.1 reading: [33. 33.5 33.5] 34 34.1 reading: [34. 34.5 34.5] 35 35.1 reading: [35. 35.5 35.5] 36 36.1 reading: [36. 36.5 36.5] 37 37.1 reading: [37. 37.5 37.5] 38 38.1 reading: [38. 38.5 38.5] 39 39.1 reading: [39. 39.5 39.5] 40 40.1 reading: [40. 40.5 40.5] 41 41.1 reading: [41. 41.5 41.5] 42 42.1 reading: [42. 42.5 42.5] 43 43.1 reading: [43. 43.5 43.5] 44 44.1 reading: [44. 44.5 44.5] 45 45.1 reading: [45. 45.5 45.5] 46 46.1 reading: [46. 46.5 46.5] 47 47.1 reading: [47. 47.5 47.5] 48 48.1 reading: [48. 48.5 48.5] 49 49.1 reading: [49. 49.5 49.5] 50 50.1 reading: [50. 50.5 50.5] 51 51.1 reading: [51. 51.5 51.5] 52 52.1 reading: [52. 52.5 52.5] 53 53.1 reading: [53. 53.5 53.5] 54 54.1 reading: [54. 54.5 54.5] 55 55.1 reading: [55. 55.5 55.5] 56 56.1 reading: [56. 56.5 56.5] 57 57.1 reading: [57. 57.5 57.5] 58 58.1 reading: [58. 58.5 58.5] 59 59.1 reading: [59. 59.5 59.5] 60 60.1 reading: [60. 60.5 60.5] 61 61.1 reading: [61. 61.5 61.5] 62 62.1 reading: [62. 62.5 62.5] 63 63.1 reading: [63. 63.5 63.5] 64 64.1 reading: [64. 64.5 64.5] 65 65.1 reading: [65. 65.5 65.5] 66 66.1 reading: [66. 66.5 66.5] 67 67.1 reading: [67. 67.5 67.5] 68 68.1 reading: [68. 68.5 68.5] 69 69.1 reading: [69. 69.5 69.5] 70 70.1 reading: [70. 70.5 70.5] 71 71.1 reading: [71. 71.5 71.5] 72 72.1 reading: [72. 72.5 72.5] 73 73.1 reading: [73. 73.5 73.5] 74 74.1 reading: [74. 74.5 74.5] 75 75.1 reading: [75. 75.5 75.5] 76 76.1 reading: [76. 76.5 76.5] 77 77.1 reading: [77. 77.5 77.5] 78 78.1 reading: [78. 78.5 78.5] 79 79.1 reading: [79. 79.5 79.5] 80 80.1 reading: [80. 80.5 80.5] 81 81.1 reading: [81. 81.5 81.5] 82 82.1 reading: [82. 82.5 82.5] 83 83.1 reading: [83. 83.5 83.5] 84 84.1 reading: [84. 84.5 84.5] 85 85.1 reading: [85. 85.5 85.5] 86 86.1 reading: [86. 86.5 86.5] 87 87.1 reading: [87. 87.5 87.5] 88 88.1 reading: [88. 88.5 88.5] 89 89.1 reading: [89. 89.5 89.5] 90 90.1 reading: [90. 90.5 90.5] 91 91.1 reading: [91. 91.5 91.5] 92 92.1 reading: [92. 92.5 92.5] 93 93.1 reading: [93. 93.5 93.5] 94 94.1 reading: [94. 94.5 94.5] 95 95.1 reading: [95. 95.5 95.5] 96 96.1 reading: [96. 96.5 96.5] 97 97.1 reading: [97. 97.5 97.5] 98 98.1 reading: [98. 98.5 98.5] 99 99.1 reading: [99. 99.5 99.5] 100 100.1 reading: [100. 100.5 100.5] | |||
Passed | tests/test_transforms.py::test_transform | 0.07 | |
------------------------------Captured stdout call------------------------------ -0.999 1 [ 1. -0.999 -0.999 1. ] (1000, 2) -0.999 0.001 [ 1. -0.999 -0.999 1. ] (1000, 2) -0.8991 1 [ 1. -0.8991 -0.8991 1. ] (1000, 2) -0.8991 0.001 [ 1. -0.8991 -0.8991 1. ] (1000, 2) -0.7992 1 [ 1. -0.7992 -0.7992 1. ] (1000, 2) -0.7992 0.001 [ 1. -0.7992 -0.7992 1. ] (1000, 2) -0.6993 1 [ 1. -0.6993 -0.6993 1. ] (1000, 2) -0.6993 0.001 [ 1. -0.6993 -0.6993 1. ] (1000, 2) -0.5994 1 [ 1. -0.5994 -0.5994 1. ] (1000, 2) -0.5994 0.001 [ 1. -0.5994 -0.5994 1. ] (1000, 2) -0.4995000000000001 1 [ 1. -0.4995 -0.4995 1. ] (1000, 2) -0.4995000000000001 0.001 [ 1. -0.4995 -0.4995 1. ] (1000, 2) -0.3996000000000001 1 [ 1. -0.3996 -0.3996 1. ] (1000, 2) -0.3996000000000001 0.001 [ 1. -0.3996 -0.3996 1. ] (1000, 2) -0.29970000000000013 1 [ 1. -0.2997 -0.2997 1. ] (1000, 2) -0.29970000000000013 0.001 [ 1. -0.2997 -0.2997 1. ] (1000, 2) -0.19980000000000017 1 [ 1. -0.1998 -0.1998 1. ] (1000, 2) -0.19980000000000017 0.001 [ 1. -0.1998 -0.1998 1. ] (1000, 2) -0.0999000000000002 1 [ 1. -0.0999 -0.0999 1. ] (1000, 2) -0.0999000000000002 0.001 [ 1. -0.0999 -0.0999 1. ] (1000, 2) -2.2182256032010628e-16 1 [ 1.0000000e+00 -2.2182256e-16 -2.2182256e-16 1.0000000e+00] (1000, 2) -2.2182256032010628e-16 0.001 [ 1.0000000e+00 -2.2182256e-16 -2.2182256e-16 1.0000000e+00] (1000, 2) 0.09989999999999964 1 [1. 0.0999 0.0999 1. ] (1000, 2) 0.09989999999999964 0.001 [1. 0.0999 0.0999 1. ] (1000, 2) 0.19979999999999973 1 [1. 0.1998 0.1998 1. ] (1000, 2) 0.19979999999999973 0.001 [1. 0.1998 0.1998 1. ] (1000, 2) 0.2996999999999998 1 [1. 0.2997 0.2997 1. ] (1000, 2) 0.2996999999999998 0.001 [1. 0.2997 0.2997 1. ] (1000, 2) 0.3995999999999997 1 [1. 0.3996 0.3996 1. ] (1000, 2) 0.3995999999999997 0.001 [1. 0.3996 0.3996 1. ] (1000, 2) 0.49949999999999956 1 [1. 0.4995 0.4995 1. ] (1000, 2) 0.49949999999999956 0.001 [1. 0.4995 0.4995 1. ] (1000, 2) 0.5993999999999996 1 [1. 0.5994 0.5994 1. ] (1000, 2) 0.5993999999999996 0.001 [1. 0.5994 0.5994 1. ] (1000, 2) 0.6992999999999997 1 [1. 0.6993 0.6993 1. ] (1000, 2) 0.6992999999999997 0.001 [1. 0.6993 0.6993 1. ] (1000, 2) 0.7991999999999996 1 [1. 0.7992 0.7992 1. ] (1000, 2) 0.7991999999999996 0.001 [1. 0.7992 0.7992 1. ] (1000, 2) 0.8990999999999995 1 [1. 0.8991 0.8991 1. ] (1000, 2) 0.8990999999999995 0.001 [1. 0.8991 0.8991 1. ] (1000, 2) | |||
Passed | tests/test_transforms.py::test_affine_transform | 0.18 | |
------------------------------Captured stdout call------------------------------ settings: corr: 0 scaleratio: 1 covmatrix: [1. 0. 0. 1.] (400, 2) settings: corr: 0.6 scaleratio: 1 covmatrix: [1. 0.6 0.6 1. ] (400, 2) settings: corr: 0.95 scaleratio: 1 covmatrix: [1. 0.95 0.95 1. ] (400, 2) settings: corr: 0.999 scaleratio: 1 covmatrix: [1. 0.999 0.999 1. ] (400, 2) | |||
Passed | tests/test_transforms.py::test_wrap | 0.03 | |
------------------------------Captured stdout call------------------------------ Npoints=10 wrapped_dims=[] Npoints=10 wrapped_dims=[0] Npoints=10 wrapped_dims=[1] Npoints=10 wrapped_dims=[0, 1] Npoints=100 wrapped_dims=[] Npoints=100 wrapped_dims=[0] Npoints=100 wrapped_dims=[1] Npoints=100 wrapped_dims=[0, 1] Npoints=1000 wrapped_dims=[] Npoints=1000 wrapped_dims=[0] Npoints=1000 wrapped_dims=[1] Npoints=1000 wrapped_dims=[0, 1] | |||
Passed | tests/test_utils.py::test_vectorize | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_is_affine_transform | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_tau | 0.67 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_make_log_dirs | 0.01 | |
------------------------------Captured stdout call------------------------------ Creating directory for new run /tmp/tmpmpg8aswt/run1 Creating directory for new run /tmp/tmpmpg8aswt/run2 | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[0-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[4-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[10-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[17-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[31-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[100-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[1000-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-1] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-4] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-10] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-37] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-53] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-100] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-1000] | 0.00 | |
No log output captured. | |||
Passed | tests/test_utils.py::test_distributed_work_chunk_size[513-513] | 0.00 | |
No log output captured. | |||
Passed | tests/test_viz.py::test_rounding_pos | 0.01 | |
No log output captured. | |||
Passed | tests/test_viz.py::test_rounding_u | 0.00 | |
No log output captured. | |||
Passed | tests/test_viz.py::test_rounding_negpos | 0.00 | |
No log output captured. | |||
Passed | tests/test_viz.py::test_rounding_withguess | 0.00 | |
No log output captured. | |||
Passed | tests/test_viz.py::test_fmt | 0.00 | |
No log output captured. |