diff --git a/devtools/conda-envs/test_env_jax.yaml b/devtools/conda-envs/test_env_jax.yaml index c2dd795e..0ffe9f34 100644 --- a/devtools/conda-envs/test_env_jax.yaml +++ b/devtools/conda-envs/test_env_jax.yaml @@ -22,5 +22,6 @@ dependencies: - xlrd # Docs - numpydoc - - sphinx <7 + - sphinx + - sphinx-rtd-theme - sphinxcontrib-bibtex diff --git a/docs/conf.py b/docs/conf.py index 92a5919a..5fbe1a40 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -146,7 +146,7 @@ # a list of builtin themes. # html_theme = "default" on_rtd = os.environ.get("READTHEDOCS", None) == "True" - +html_theme = "sphinx_rtd_theme" if not on_rtd: # only import and set the theme if we're building docs locally import sphinx_rtd_theme diff --git a/examples/harmonic-oscillators/QQMBARobserve.pdf b/examples/harmonic-oscillators/QQMBARobserve.pdf index 442f0d67..bd720ca1 100644 Binary files a/examples/harmonic-oscillators/QQMBARobserve.pdf and b/examples/harmonic-oscillators/QQMBARobserve.pdf differ diff --git a/examples/harmonic-oscillators/QQdf.pdf b/examples/harmonic-oscillators/QQdf.pdf index a4e4a10e..25e6f3b0 100644 Binary files a/examples/harmonic-oscillators/QQdf.pdf and b/examples/harmonic-oscillators/QQdf.pdf differ diff --git a/examples/harmonic-oscillators/QQstandardobserve.pdf b/examples/harmonic-oscillators/QQstandardobserve.pdf index 76a8ef6b..af8a14c9 100644 Binary files a/examples/harmonic-oscillators/QQstandardobserve.pdf and b/examples/harmonic-oscillators/QQstandardobserve.pdf differ diff --git a/examples/harmonic-oscillators/cumulative_probability_comparison_curves.pdf b/examples/harmonic-oscillators/cumulative_probability_comparison_curves.pdf index d48354cb..86cdefd2 100644 Binary files a/examples/harmonic-oscillators/cumulative_probability_comparison_curves.pdf and b/examples/harmonic-oscillators/cumulative_probability_comparison_curves.pdf differ diff --git a/examples/harmonic-oscillators/harmonic-oscillators-distributions.py b/examples/harmonic-oscillators/harmonic-oscillators-distributions.py index 181bbaa3..91a1efe4 100644 --- a/examples/harmonic-oscillators/harmonic-oscillators-distributions.py +++ b/examples/harmonic-oscillators/harmonic-oscillators-distributions.py @@ -34,6 +34,11 @@ from pymbar import testsystems, MBAR, confidenceintervals from pymbar.utils import ParameterError, DataError +import logging +import sys + +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + # ============================================================================================= # PARAMETERS # ============================================================================================= diff --git a/examples/harmonic-oscillators/harmonic-oscillators-distributions.py_output.txt b/examples/harmonic-oscillators/harmonic-oscillators-distributions.py_output.txt index 66050a67..dba7364b 100644 --- a/examples/harmonic-oscillators/harmonic-oscillators-distributions.py_output.txt +++ b/examples/harmonic-oscillators/harmonic-oscillators-distributions.py_output.txt @@ -1,5 +1,5 @@ Gaussian widths: -[ 0.2 0.25 0.33333333 0.5 1. 1. ] +[0.2 0.25 0.33333333 0.5 1. 1. ] Computing dimensionless free energies analytically... This script will perform 200 replicates of an experiment where samples are drawn from 6 harmonic oscillators. The harmonic oscillators have equilibrium positions @@ -10,815 +10,1230 @@ and the following number of samples will be drawn from each (can be zero if no s [2000 2000 2000 2000 2000 0] Performing replicate 1 / 200 -[ 0.03872209 1.04685416 4.12103712 9.20415087 16.68377262 - 25.06687405] -[ 0.00119439 0.01076232 0.02579093 0.04837197 0.17128761 0.41789113] +INFO:pymbar.mbar:Explicitly overwriting maxiter=10000 with maximum_iterations=10000 +INFO:pymbar.mbar:Explicitly overwriting maxiter=10000 with maximum_iterations=10000 +WARNING:pymbar.mbar_solvers: +******* JAX 64-bit mode is now on! ******* +* JAX is now set to 64-bit mode! * +* This MAY cause problems with other * +* uses of JAX in the same code. * +****************************************** + +INFO:absl:Remote TPU is not linked into jax; skipping remote TPU. +INFO:absl:Unable to initialize backend 'tpu_driver': Could not initialize backend 'tpu_driver' +INFO:absl:Unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig' +INFO:absl:Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig' +INFO:absl:Unable to initialize backend 'tpu': module 'jaxlib.xla_extension' has no attribute 'get_tpu_client' +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03975702 1.06089938 4.15772256 9.2692962 17.27135584 26.08738426] +[0.00127191 0.01101887 0.02573263 0.04955805 0.17669491 0.48088857] Performing replicate 2 / 200 -[ 0.03879351 1.06011432 4.13567374 9.29859965 16.7879643 - 25.25336846] -[ 0.00118318 0.01057124 0.02609688 0.04868193 0.17017228 0.55549544] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03917819 1.03483151 4.08832227 9.24339671 16.83740492 26.7608673 ] +[0.00118675 0.01060477 0.02551253 0.04938048 0.1780329 0.7321336 ] Performing replicate 3 / 200 -[ 0.04061115 1.06267931 4.09118803 9.31631727 17.11739421 - 26.34770003] -[ 0.00121496 0.01094449 0.02609262 0.049216 0.1778032 0.52499722] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.04055724 1.06874178 4.08892222 9.2340339 16.84226148 25.66223138] +[0.00122743 0.01085683 0.0258039 0.04863907 0.17568319 0.46901425] Performing replicate 4 / 200 -[ 0.04023556 1.06041097 4.10851042 9.25855859 17.05498693 - 25.94615061] -[ 0.00122054 0.0107666 0.02572417 0.04917176 0.17527207 0.50158067] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03859996 1.04319894 4.07635159 9.2704043 17.02365352 25.7952182 ] +[0.00118064 0.01067002 0.02573458 0.04935639 0.17344164 0.679355 ] Performing replicate 5 / 200 -[ 0.03647946 1.05641694 4.10395192 9.26248942 16.92409026 - 26.70029366] -[ 0.0011224 0.01062546 0.02651769 0.04913448 0.17630464 0.72125854] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03999851 1.0581945 4.10142542 9.12112493 16.76162058 25.29394191] +[0.00126098 0.01080304 0.02574235 0.04837199 0.17156425 0.52544919] Performing replicate 6 / 200 -[ 0.04071179 1.0741896 4.10644023 9.25740792 17.26691921 - 26.38829607] -[ 0.00122768 0.0109696 0.02558388 0.05002294 0.17694455 0.54839682] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.041259 1.06963024 4.13498272 9.23802983 17.28424224 25.63355068] +[0.00128916 0.0110855 0.02590312 0.04896417 0.1746822 0.41399842] Performing replicate 7 / 200 -[ 0.03852271 1.07731379 4.05550188 9.14657717 16.98357886 - 25.68571325] -[ 0.00118577 0.0107274 0.02537818 0.04896032 0.17669887 0.45346642] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03998083 1.05360812 4.15578022 9.2317178 16.86969683 26.28916118] +[0.00125697 0.01091931 0.02603321 0.04961309 0.17438609 0.67755995] Performing replicate 8 / 200 -[ 0.04189319 1.0422559 4.14801407 9.32298719 16.95873926 - 25.52876044] -[ 0.00125463 0.01064661 0.02606403 0.04913193 0.17378307 0.45504856] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04034359 1.06466163 4.09885153 9.2462794 16.96509658 26.03139988] +[0.0012728 0.01086498 0.0253186 0.04981314 0.17643884 0.54609721] Performing replicate 9 / 200 -[ 0.03949911 1.05865144 4.10665833 9.26083539 16.77483914 - 25.4414167 ] -[ 0.0012102 0.01094915 0.02516846 0.05049548 0.16926067 0.66967896] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03827445 1.0696382 4.08177677 9.23257688 16.68740565 25.63095153] +[0.00116849 0.01092243 0.02583004 0.04874015 0.17440486 0.49292946] Performing replicate 10 / 200 -[ 0.04096164 1.06280468 4.05615563 9.25905453 16.81709043 - 25.69905597] -[ 0.00121286 0.01066403 0.02541121 0.04878758 0.17561748 0.44487128] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03952777 1.06343587 4.15480868 9.25953574 17.27640783 26.29958404] +[0.00115818 0.01094069 0.02534381 0.04939131 0.1774903 0.51067183] Performing replicate 11 / 200 -[ 0.0395926 1.05378135 4.12253523 9.30643066 16.86864261 - 25.84667359] -[ 0.0012157 0.01054471 0.02617439 0.04923988 0.17454875 0.52326162] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04254579 1.06233906 4.11197514 9.30154172 16.95132393 26.09176338] +[0.00129279 0.01089794 0.02639181 0.04849168 0.17521874 0.53253179] Performing replicate 12 / 200 -[ 0.04116302 1.05230261 4.11942716 9.2614536 17.20360418 - 27.36197648] -[ 0.00123948 0.01088472 0.0259626 0.04819764 0.18388569 0.74267001] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04046828 1.06779345 4.12316785 9.2144211 16.86481569 25.40279876] +[0.00123747 0.01094135 0.02563493 0.04897413 0.17386576 0.4258853 ] Performing replicate 13 / 200 -[ 0.03959043 1.07498064 4.12712509 9.25017716 17.03684815 - 25.44762484] -[ 0.00122506 0.01053447 0.02585077 0.04890638 0.17236186 0.4605473 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.55e-12 +[ 0.04099892 1.05880623 4.12562008 9.20952558 16.77615539 25.29570702] +[0.00123927 0.01108925 0.02569523 0.04881061 0.17428858 0.40893383] Performing replicate 14 / 200 -[ 0.03925093 1.07331923 4.08678058 9.23983598 16.87978644 - 25.41324602] -[ 0.00116227 0.01097319 0.02577474 0.04923042 0.17285919 0.45938337] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0389926 1.06484681 4.13849916 9.20016077 17.22484414 26.82485627] +[0.00123789 0.01087692 0.02592645 0.04918875 0.18099647 0.5793371 ] Performing replicate 15 / 200 -[ 0.03762626 1.04784302 4.03419136 9.33848968 17.40656179 - 26.36889993] -[ 0.00111624 0.01067943 0.02593927 0.05008926 0.17922841 0.44141555] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04098728 1.0636233 4.14960165 9.2277248 16.8589865 25.3040073 ] +[0.00123771 0.01099136 0.02585176 0.04872039 0.1702846 0.50315912] Performing replicate 16 / 200 -[ 0.03876492 1.06515491 4.09690167 9.27350331 16.77611792 - 25.01321228] -[ 0.00117713 0.01075901 0.02563043 0.0495557 0.16873891 0.47542929] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03923365 1.06870702 4.14508531 9.24062163 17.43467359 26.9107548 ] +[0.00119932 0.01091245 0.02550141 0.05070653 0.18124351 0.55588466] Performing replicate 17 / 200 -[ 0.04117979 1.03454658 4.08782507 9.33505631 16.80413583 - 25.06372215] -[ 0.00128456 0.01105017 0.02581496 0.0496832 0.16863834 0.46387006] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04066468 1.05613288 4.13959897 9.22756101 16.79427048 25.76637338] +[0.00125339 0.01084751 0.02593038 0.04898718 0.17259343 0.61412782] Performing replicate 18 / 200 -[ 0.04062881 1.07206111 4.0969758 9.24632326 17.31070092 - 26.92703323] -[ 0.00119615 0.01108858 0.02562033 0.04919329 0.18085265 0.62970446] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04007358 1.052336 4.12959595 9.26827986 17.02631146 25.85213568] +[0.00122838 0.01108661 0.02590686 0.04886313 0.17569385 0.47852126] Performing replicate 19 / 200 -[ 0.04098208 1.05961615 4.12128125 9.30962447 16.94397297 - 26.10232369] -[ 0.00130547 0.01062386 0.02598146 0.04859003 0.1754179 0.60580545] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04006776 1.05958605 4.1035124 9.23026567 17.041872 26.64703671] +[0.00123073 0.01066059 0.0260152 0.04879449 0.17911898 0.61418207] Performing replicate 20 / 200 -[ 0.04032528 1.0879548 4.10343721 9.27519979 17.10785345 - 24.92711846] -[ 0.00118806 0.01079223 0.02565946 0.05027662 0.16847159 0.40262822] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0406548 1.06954475 4.11681392 9.21439386 17.15610343 26.54183819] +[0.00124471 0.01088371 0.0253197 0.04956307 0.17889697 0.72991734] Performing replicate 21 / 200 -[ 0.04192865 1.08243361 4.10875391 9.13949752 16.73047727 - 25.25316306] -[ 0.00125237 0.01112332 0.02530055 0.04940065 0.17186284 0.47276263] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04049427 1.03817909 4.10857467 9.21315947 17.01920751 25.83964872] +[0.00121153 0.01078787 0.02540604 0.04946169 0.17563293 0.50729575] Performing replicate 22 / 200 -[ 0.04118777 1.06384005 4.06494675 9.23753441 16.91621066 - 25.3139594 ] -[ 0.00119204 0.01106297 0.02513047 0.04994734 0.17268006 0.41454497] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04048135 1.05300501 4.07589649 9.19926947 16.89512108 25.21236681] +[0.00122891 0.01072923 0.02541592 0.04943366 0.17233233 0.40822652] Performing replicate 23 / 200 -[ 0.0390993 1.05894713 4.14002099 9.36712507 17.47224873 - 26.89336801] -[ 0.00122367 0.01087071 0.0266841 0.05049485 0.17834154 0.59493525] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04018128 1.06153561 4.09577817 9.26513979 17.09166103 26.01713156] +[0.00125902 0.01082566 0.02643073 0.04909324 0.17731166 0.45066092] Performing replicate 24 / 200 -[ 0.03864583 1.05714593 4.06372096 9.25449474 17.15422843 - 25.97207072] -[ 0.00118399 0.01058439 0.02637373 0.04956499 0.177942 0.43099336] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03911189 1.04776702 4.12374546 9.22281215 17.14060401 26.52209084] +[0.00116586 0.01073569 0.02658485 0.04904049 0.17745086 0.61378532] Performing replicate 25 / 200 -[ 0.03948336 1.07547801 4.13191518 9.26925592 16.93290468 - 26.06858333] -[ 0.001216 0.01101932 0.02574317 0.04979459 0.17438801 0.5499484 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04076984 1.05042164 4.12561296 9.19035734 17.03753787 26.3690878 ] +[0.00124476 0.01110278 0.02505696 0.04895399 0.17827157 0.58499507] Performing replicate 26 / 200 -[ 0.03809543 1.05961283 4.1290362 9.34039516 17.10617443 - 25.64693642] -[ 0.00117995 0.01100063 0.02622283 0.04886901 0.17419206 0.45025915] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03898449 1.0546573 4.15180381 9.2626973 16.84376007 25.59697552] +[0.00115487 0.01085495 0.02606065 0.04797529 0.17361287 0.46441599] Performing replicate 27 / 200 -[ 0.03837429 1.06098132 4.13139397 9.23627861 16.98050859 - 25.9840404 ] -[ 0.00116184 0.01099719 0.02600496 0.04870765 0.17490437 0.56004253] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03999522 1.09303557 4.10408993 9.29081769 17.14832343 26.00134637] +[0.00120718 0.01082247 0.02600826 0.04983042 0.17559717 0.56912693] Performing replicate 28 / 200 -[ 0.04089901 1.06094596 4.1084931 9.18954094 17.03366022 - 27.19739676] -[ 1.22335536e-03 1.09111714e-02 2.55241975e-02 4.91532731e-02 - 1.79780353e-01 1.29700947e+00] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +[ 0.03940443 1.06044634 4.10938349 9.23038296 16.91251349 25.7359186 ] +[0.00116005 0.01078888 0.02591056 0.04780177 0.17374039 0.70441365] Performing replicate 29 / 200 -[ 0.03932373 1.06061798 4.14257651 9.25772679 17.1040141 - 26.89166801] -[ 0.0012619 0.01106427 0.02624291 0.04818845 0.18052819 0.58651533] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.04096845 1.04503172 4.09211164 9.29293921 17.12211553 26.24559952] +[0.00123564 0.01064254 0.02565263 0.0497363 0.17688862 0.5317508 ] Performing replicate 30 / 200 -[ 0.03830555 1.07675025 4.10593691 9.22379068 17.25475091 - 27.46908558] -[ 0.00119107 0.01069722 0.02492837 0.04989302 0.18135647 1.18037019] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03895806 1.06483468 4.15005824 9.26166183 16.80550488 24.82327146] +[0.00115856 0.01092314 0.02625955 0.04871588 0.16926657 0.38646173] Performing replicate 31 / 200 -[ 0.04098643 1.07165904 4.13401233 9.23907172 16.76914282 - 25.29474999] -[ 0.00123635 0.01066206 0.02597318 0.04915641 0.16994564 0.51959529] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04238008 1.05743743 4.1056308 9.32651482 16.92192634 25.39458344] +[0.00127338 0.01089547 0.02630208 0.0496117 0.1706588 0.47758793] Performing replicate 32 / 200 -[ 0.04214462 1.07084635 4.09901783 9.23740736 16.87358986 - 25.83382194] -[ 0.00129874 0.01103236 0.02595043 0.04924991 0.17238572 0.65428254] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04159484 1.05071225 4.12666821 9.26660577 16.81424835 25.9618781 ] +[0.00124964 0.01064769 0.02557691 0.05018687 0.17240914 0.66972394] Performing replicate 33 / 200 -[ 0.04094335 1.07237575 4.10376286 9.19839308 16.89793352 - 25.47194317] -[ 0.00128309 0.01119435 0.0261842 0.04887926 0.17367087 0.44363813] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04114856 1.08268687 4.12299115 9.24390106 16.85243354 25.35284803] +[0.00126143 0.01082248 0.02576048 0.04938534 0.17348859 0.42136653] Performing replicate 34 / 200 -[ 0.03943088 1.06345318 4.0892416 9.21663741 16.7218649 - 25.38903272] -[ 0.00122541 0.01092207 0.02559204 0.0492007 0.17321738 0.45618271] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04116927 1.07259926 4.15824019 9.27528849 16.94791638 25.73293514] +[0.00130898 0.01130228 0.02553292 0.04890238 0.17299947 0.59897261] Performing replicate 35 / 200 -[ 0.04149619 1.08217791 4.08063046 9.20549221 16.74606988 - 26.0494642 ] -[ 0.00125865 0.01093689 0.02551306 0.0487514 0.17804127 0.55025066] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +[ 0.03998254 1.06717449 4.14399697 9.23972496 16.9624028 26.96435759] +[0.00121966 0.0108802 0.02620668 0.04891376 0.17943201 0.65080366] Performing replicate 36 / 200 -[ 0.04155688 1.08030244 4.09280753 9.27662628 16.70768417 - 26.03504328] -[ 0.00134798 0.01093258 0.02565731 0.04920167 0.17433314 0.62904366] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.04e-12 +[ 0.04264123 1.05767547 4.11653449 9.20769141 17.04500785 26.10842793] +[0.00131076 0.01087422 0.02565996 0.04929654 0.17639253 0.52582893] Performing replicate 37 / 200 -[ 0.03974489 1.07335977 4.08413552 9.25952372 17.29665173 - 28.28563725] -[ 0.0011814 0.01110251 0.02563585 0.04962323 0.18547217 0.9038873 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04045021 1.06887039 4.11249878 9.23298414 17.00163821 26.54896778] +[0.00121786 0.01088548 0.02588108 0.04842863 0.17858701 0.5923331 ] Performing replicate 38 / 200 -[ 0.039329 1.08464059 4.10384123 9.24106683 16.80232203 - 25.42109822] -[ 0.00119081 0.0110945 0.02596492 0.04873169 0.17260663 0.46046607] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03993204 1.05563989 4.13946936 9.17553932 16.98970347 25.99006797] +[0.00121329 0.0107906 0.02626776 0.04774212 0.17667996 0.50637292] Performing replicate 39 / 200 -[ 0.03920473 1.05686959 4.11568943 9.3071148 17.04466949 - 25.61952895] -[ 0.00125859 0.01073203 0.02602774 0.04948098 0.1721275 0.55936092] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04086752 1.06424092 4.09857977 9.18226761 16.89390769 26.05426704] +[0.00127276 0.01061977 0.02580727 0.04910851 0.17532733 0.6752216 ] Performing replicate 40 / 200 -[ 0.04056849 1.04744608 4.10377396 9.31741397 16.84758713 - 26.29780248] -[ 0.00123547 0.01095544 0.02572242 0.04949721 0.17546794 0.59808464] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04092222 1.05957368 4.12101868 9.20050245 16.89137484 25.83559166] +[0.00131931 0.01091978 0.02611108 0.04812313 0.17558518 0.53834034] Performing replicate 41 / 200 -[ 0.03906703 1.05905044 4.12239641 9.18702067 16.82501777 - 27.10982459] -[ 0.00124812 0.01104048 0.0257842 0.04933316 0.17916364 0.73373553] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04151071 1.06513282 4.14839983 9.19586848 16.95203126 25.77890756] +[0.00120184 0.01122049 0.02581584 0.04866411 0.17564218 0.48069513] Performing replicate 42 / 200 -[ 0.0398643 1.0860701 4.10147125 9.24407548 16.61495954 - 25.66198849] -[ 0.00121349 0.01086776 0.02609446 0.04894354 0.1697657 0.87173028] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.03894117 1.06248328 4.10691162 9.237179 17.25767297 26.58383127] +[0.00116467 0.01090227 0.02522051 0.04984708 0.17973117 0.57114519] Performing replicate 43 / 200 -[ 0.04357665 1.05215961 4.10655597 9.17246431 17.33293253 - 26.43350197] -[ 0.00132046 0.01078445 0.02569501 0.04937881 0.18181417 0.47190957] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03893747 1.08998322 4.11294521 9.27011107 16.82152613 25.69162369] +[0.00119889 0.01080592 0.02559094 0.04974843 0.173105 0.54165814] Performing replicate 44 / 200 -[ 0.04005254 1.06836077 4.09590863 9.24323524 17.10990874 - 26.00920421] -[ 0.00121285 0.0109505 0.02576628 0.04892032 0.17786524 0.43780567] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.03969406 1.07588721 4.07855096 9.2164793 16.81727107 26.99420726] +[0.00121327 0.0111927 0.02550437 0.04889684 0.17977323 0.75025091] Performing replicate 45 / 200 -[ 0.04218812 1.05051556 4.08913382 9.29222086 16.94372273 - 25.70293783] -[ 0.00134885 0.01094122 0.02527946 0.05010525 0.17262884 0.55128067] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0409628 1.0498194 4.0570913 9.15863763 16.95732627 25.81274175] +[0.0012538 0.01087767 0.02555359 0.04913447 0.17661516 0.47995671] Performing replicate 46 / 200 -[ 0.03929456 1.06406797 4.10668074 9.28700209 16.95653585 - 25.52549522] -[ 0.0011973 0.01118786 0.02571773 0.05017221 0.17154892 0.49591939] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03784128 1.05376079 4.14024376 9.20812591 16.99591019 27.63504139] +[0.00118904 0.0109167 0.02527668 0.04916967 0.18216441 1.18168139] Performing replicate 47 / 200 -[ 0.03858335 1.03859111 4.12398683 9.30506514 17.10646775 - 26.93208427] -[ 0.00114838 0.01080016 0.02575895 0.04931567 0.17915994 0.67339173] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04072759 1.07355921 4.07755443 9.23753614 17.10142911 26.99060011] +[0.00124392 0.01095825 0.02589432 0.04923171 0.17872629 0.76186565] Performing replicate 48 / 200 -[ 0.04033213 1.07390753 4.11973596 9.23159698 16.91778576 - 25.36657204] -[ 0.00119412 0.01101255 0.02606908 0.0485239 0.17345891 0.44675537] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04099168 1.07061333 4.05300409 9.31466777 17.07636877 26.11788757] +[0.001288 0.01131441 0.02553209 0.04922798 0.17707894 0.46873487] Performing replicate 49 / 200 -[ 0.03871061 1.05666491 4.12859303 9.20442253 16.96974794 - 25.48725212] -[ 0.00114953 0.01109879 0.02532095 0.04958064 0.17279435 0.48974638] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.33e-12 +[ 0.04061109 1.0583099 4.1262091 9.32764957 16.77489422 24.89577443] +[0.00121727 0.01087055 0.02613011 0.04908808 0.16753859 0.45652736] Performing replicate 50 / 200 -[ 0.03817402 1.06825838 4.12405373 9.2687802 16.93024192 - 25.60551154] -[ 0.00114902 0.011056 0.0253581 0.04956405 0.17307368 0.53240711] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03819336 1.04388309 4.06009309 9.23127245 16.83779946 26.03470382] +[0.00115177 0.01070781 0.02585428 0.04860633 0.17578544 0.56278652] Performing replicate 51 / 200 -[ 0.03962726 1.06215511 4.09561533 9.23442119 16.86799682 - 26.40502022] -[ 0.0012121 0.01098618 0.0256175 0.04944702 0.17583399 0.77539775] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04296584 1.04995387 4.09454104 9.2908982 16.99548594 25.37116655] +[0.00129242 0.01076949 0.02564604 0.04997884 0.17230372 0.43027189] Performing replicate 52 / 200 -[ 0.03775153 1.08306049 4.08540509 9.22652442 16.96619078 - 25.61464196] -[ 0.00113408 0.01121581 0.02503859 0.04921422 0.17451926 0.45703963] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04018633 1.05320304 4.10449686 9.32575202 16.94249606 25.35534363] +[0.00122334 0.01101526 0.02538801 0.04877874 0.17110704 0.4582548 ] Performing replicate 53 / 200 -[ 0.03809178 1.063273 4.1281617 9.27912326 17.07750772 - 25.78878478] -[ 0.00112335 0.01085543 0.02552272 0.05002676 0.17362101 0.47762955] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.48e-12 +[ 0.04051422 1.07560024 4.06923639 9.23086576 16.99229678 25.27871149] +[0.00122544 0.01100907 0.02547858 0.05014651 0.17030146 0.49117984] Performing replicate 54 / 200 -[ 0.03988228 1.07086552 4.11554818 9.31917477 16.76564162 - 25.47704926] -[ 0.00124647 0.01092716 0.02542296 0.0488907 0.17320091 0.44892903] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.6e-12 +[ 0.03992816 1.07733951 4.11835866 9.2895425 17.07267027 26.34914377] +[0.00120233 0.01077134 0.02585971 0.0492027 0.17513452 0.64654949] Performing replicate 55 / 200 -[ 0.03886668 1.04589804 4.15400504 9.18556453 16.92029095 - 26.07227412] -[ 0.00118187 0.01084346 0.02535141 0.04877413 0.17799295 0.55600099] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.4e-12 +[ 0.04115035 1.06903961 4.13083836 9.25936527 17.15358012 26.20000776] +[0.00126149 0.01120576 0.02621346 0.04973738 0.17704942 0.52284951] Performing replicate 56 / 200 -[ 0.03940752 1.08780748 4.07378082 9.36011112 16.71736056 - 26.09256179] -[ 0.00119906 0.01085569 0.02559264 0.04941776 0.17490801 0.55308288] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03685002 1.0741703 4.11392391 9.21633026 16.99122725 26.11778758] +[0.00113625 0.01126669 0.02586411 0.04927455 0.17573413 0.52366967] Performing replicate 57 / 200 -[ 0.03976834 1.05531585 4.12090988 9.25871227 17.05469332 - 25.47384457] -[ 0.00116692 0.01091533 0.02545316 0.04961602 0.17314039 0.45619244] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03911325 1.06385719 4.10390873 9.24473572 16.97504659 25.88141365] +[0.00114384 0.01096787 0.0257023 0.04927856 0.17515362 0.52197954] Performing replicate 58 / 200 -[ 0.04022509 1.05304624 4.14279309 9.20411507 16.9114254 - 25.23909446] -[ 0.00123073 0.01062749 0.02592157 0.04935883 0.17137561 0.4372685 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04016675 1.05762963 4.09604062 9.2954218 16.91930423 25.21394622] +[0.00122563 0.01091803 0.02575721 0.04919718 0.17185306 0.41612643] Performing replicate 59 / 200 -[ 0.04198442 1.09190931 4.1003484 9.24980997 17.09171136 - 25.71404008] -[ 0.00126643 0.01104939 0.0254546 0.0495251 0.1747193 0.44425839] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 4.97e-13 +[ 0.0410047 1.05192772 4.09051153 9.24844476 17.07723005 26.33006755] +[0.00126101 0.0109102 0.02573663 0.04979432 0.1765535 0.64089134] Performing replicate 60 / 200 -[ 0.03836887 1.06013407 4.12801476 9.29639658 17.0862348 - 25.97428981] -[ 0.00115669 0.01106848 0.02634074 0.04874742 0.17621757 0.47391828] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04018384 1.06779432 4.10814664 9.25829043 16.73288108 25.3879595 ] +[0.00125703 0.0109299 0.02593901 0.04918681 0.17149163 0.51496746] Performing replicate 61 / 200 -[ 0.04150982 1.04670208 4.07322963 9.25497209 17.02406772 - 26.44909264] -[ 0.0012138 0.01079625 0.02563196 0.04881978 0.17865798 0.57299729] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.16e-13 +[ 0.03848274 1.06734914 4.07897815 9.17060981 16.76165313 25.57105989] +[0.00120044 0.01092374 0.02553448 0.04871647 0.1718974 0.60931151] Performing replicate 62 / 200 -[ 0.03934155 1.05389633 4.05520843 9.25180382 16.82299188 - 25.96934134] -[ 0.00122482 0.01077927 0.02552698 0.04931858 0.17203859 0.72561108] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03944376 1.06983228 4.13510527 9.27047291 16.97492616 26.24980573] +[0.00125554 0.01114837 0.02559724 0.04906641 0.17522364 0.68179652] Performing replicate 63 / 200 -[ 0.0394505 1.06810984 4.14807615 9.26106136 17.14652708 - 26.54316709] -[ 0.00119534 0.0110708 0.0262439 0.04923901 0.17967353 0.48537348] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04223143 1.0399216 4.07712503 9.24027004 17.05437249 26.10321778] +[0.00129543 0.01061344 0.02517787 0.04975664 0.17730922 0.54984479] Performing replicate 64 / 200 -[ 0.0386403 1.06666599 4.12295467 9.22205775 16.81061593 - 25.49891838] -[ 0.00116541 0.01088585 0.02538442 0.04898985 0.17387773 0.4558854 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 4.44e-13 +[ 0.03684021 1.07295583 4.14277152 9.23300616 16.9725578 25.71754873] +[0.00114692 0.01096672 0.0259681 0.04904269 0.17243528 0.60742236] Performing replicate 65 / 200 -[ 0.03716103 1.06737994 4.10813167 9.2499215 17.1217963 - 26.62964854] -[ 0.00118407 0.01068737 0.02567345 0.04938239 0.17844138 0.6512197 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +[ 0.03780477 1.07253211 4.08190003 9.26547105 16.86577347 26.38977367] +[0.00114406 0.01087566 0.02554689 0.04917291 0.17410123 0.79437419] Performing replicate 66 / 200 -[ 0.04031913 1.05198072 4.1507701 9.17412615 16.84593107 - 26.14885276] -[ 0.0013035 0.01093227 0.02562355 0.04907074 0.17765979 0.5370083 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.0401197 1.06244674 4.06151461 9.2801348 16.99890142 25.43138518] +[0.00118019 0.01108739 0.02592773 0.04945637 0.17213388 0.45842425] Performing replicate 67 / 200 -[ 0.03923131 1.06769487 4.10739247 9.27047662 16.59407627 - 25.41735903] -[ 0.00118793 0.01106223 0.02600184 0.0487558 0.17095783 0.55762204] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03747782 1.0521345 4.11481637 9.24637551 16.90047345 25.7987785 ] +[0.00113409 0.01082343 0.02623638 0.04860003 0.1751291 0.51218015] Performing replicate 68 / 200 -[ 0.03867774 1.05728148 4.13095151 9.31766963 17.1178145 - 25.38953995] -[ 0.00116589 0.01076585 0.02561717 0.04914847 0.17286508 0.39079478] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04066381 1.06937607 4.08621831 9.30465603 17.11315295 27.01104886] +[0.00119873 0.01084634 0.02528301 0.04934373 0.18104588 0.58261039] Performing replicate 69 / 200 -[ 0.03988349 1.05983514 4.13566499 9.22941366 16.73269908 - 25.64926831] -[ 0.00120628 0.01064541 0.02615612 0.04820797 0.17394324 0.5293269 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04045144 1.08188797 4.0943604 9.24750022 16.98975397 26.21395278] +[0.00125666 0.01105172 0.02650129 0.04884713 0.17232104 0.87316908] Performing replicate 70 / 200 -[ 0.03933935 1.05549177 4.11624344 9.2629141 16.86525097 - 25.08007774] -[ 0.00118636 0.01115085 0.0259921 0.04963559 0.16991464 0.45969049] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03870965 1.07632687 4.10441094 9.28654289 16.68615709 25.32723236] +[0.00119717 0.01119132 0.025579 0.04920554 0.17011347 0.49316719] Performing replicate 71 / 200 -[ 0.03882391 1.06580309 4.11033449 9.27573771 16.97549046 - 24.96251834] -[ 0.00121027 0.01088758 0.02607206 0.04916825 0.17026025 0.40913918] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.039386 1.06092591 4.09698883 9.27454209 16.74268043 25.81112868] +[0.00126535 0.01070808 0.02500017 0.04951477 0.17237893 0.63573176] Performing replicate 72 / 200 -[ 0.0409446 1.05733141 4.07181996 9.37976135 17.03248256 - 26.91117293] -[ 0.00125731 0.01059108 0.02552714 0.05026152 0.17300802 1.19786023] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04041988 1.07607761 4.13212894 9.24934236 17.27963073 27.03782815] +[0.00127993 0.01089502 0.02562989 0.04922584 0.1818504 0.59927437] Performing replicate 73 / 200 -[ 0.03811781 1.06789553 4.11516307 9.32446679 17.43640884 - 26.61405269] -[ 0.00116659 0.01094016 0.02570927 0.05062876 0.17742682 0.65746405] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04127929 1.06025579 4.12813558 9.26820445 17.21134527 26.47929213] +[0.00129223 0.01107509 0.02544493 0.05007894 0.17567822 0.68876105] Performing replicate 74 / 200 -[ 0.0381458 1.05887914 4.09796857 9.30892834 16.88784495 - 25.71487556] -[ 0.00112839 0.01085114 0.02572465 0.05028719 0.17065214 0.82761706] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04004994 1.0618723 4.11166654 9.29885398 16.7933635 25.44362532] +[0.00125764 0.01066891 0.02633063 0.04917732 0.17081283 0.52717857] Performing replicate 75 / 200 -[ 0.0401521 1.06236363 4.11017279 9.35329751 17.26570635 - 25.60108332] -[ 0.00120785 0.01071492 0.02592803 0.05015632 0.17163907 0.46673607] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04024531 1.05842191 4.06520495 9.29120638 16.89498188 25.3585223 ] +[0.00121932 0.01109536 0.02543826 0.04984881 0.17390731 0.38379606] Performing replicate 76 / 200 -[ 0.04208477 1.07481956 4.08766469 9.21995234 17.04520851 - 26.23069767] -[ 0.00128655 0.01111018 0.02506267 0.0485317 0.17912092 0.46343036] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03990177 1.07198513 4.15316475 9.21020854 16.78139041 25.12109985] +[0.00121383 0.01109648 0.0260966 0.04841383 0.17080527 0.44464204] Performing replicate 77 / 200 -[ 0.04039995 1.06145861 4.13692693 9.25592441 16.80346774 - 25.68093991] -[ 0.0012299 0.01062708 0.02570854 0.04899283 0.17400377 0.47454473] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03962819 1.07465776 4.06810035 9.18361782 16.87836588 26.08562736] +[0.00115638 0.0112079 0.02514235 0.04962527 0.17354188 0.72858282] Performing replicate 78 / 200 -[ 0.03910017 1.0564547 4.13071047 9.29914633 16.81259322 - 24.58244981] -[ 0.00116647 0.01073716 0.02630342 0.04940024 0.16571311 0.44709115] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04020658 1.05935004 4.08847299 9.21219507 17.00925361 25.78227613] +[0.00121536 0.01105801 0.02554771 0.0491247 0.17645846 0.47837605] Performing replicate 79 / 200 -[ 0.04034019 1.06444659 4.09987915 9.25370012 16.89709345 - 25.84146065] -[ 0.00120831 0.0109684 0.02552749 0.04958401 0.17512119 0.47899182] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03901763 1.07325807 4.10546415 9.18539543 17.24835558 26.12799148] +[0.00121656 0.01102838 0.02539859 0.04954287 0.17725647 0.63038033] Performing replicate 80 / 200 -[ 0.03983444 1.07391485 4.11666155 9.22797895 16.90323485 - 26.33098555] -[ 0.00122238 0.01078531 0.02588733 0.049162 0.17522583 0.65654687] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03772282 1.05135637 4.12105194 9.25839731 16.88947632 26.02692276] +[0.00111679 0.01074593 0.02571016 0.04870125 0.1744918 0.55717131] Performing replicate 81 / 200 -[ 0.03970533 1.06197558 4.11074636 9.19174751 17.07448554 - 26.01918528] -[ 0.00121541 0.01084445 0.02529696 0.04798404 0.17642051 0.58650156] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04113095 1.07498998 4.13351432 9.29589947 17.24507685 25.69092776] +[0.00126642 0.01098841 0.02603217 0.04910939 0.17458546 0.45797298] Performing replicate 82 / 200 -[ 0.03677122 1.06105984 4.11488616 9.23809298 16.73061564 - 25.71098515] -[ 0.00112503 0.01078944 0.02585207 0.04903198 0.17322011 0.5000389 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04029865 1.05870641 4.12277071 9.2852909 16.90164222 25.80287098] +[0.0012529 0.01098668 0.02586294 0.04893893 0.17566426 0.45041842] Performing replicate 83 / 200 -[ 0.04107467 1.0653355 4.13172002 9.25239326 17.01280283 - 26.27385885] -[ 0.00121333 0.01112249 0.02577792 0.04912166 0.17699378 0.54697921] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04064571 1.06262115 4.13519108 9.21648576 17.07434298 26.4193471 ] +[0.00127865 0.01086618 0.0260134 0.04779824 0.17970389 0.53661174] Performing replicate 84 / 200 -[ 0.03845724 1.05619663 4.10560125 9.25294263 17.11784669 - 26.88939422] -[ 0.00112274 0.01061671 0.02623139 0.04863759 0.17992137 0.74058104] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.04e-12 +[ 0.04075934 1.07784213 4.12345256 9.3411892 16.86490949 25.2841445 ] +[0.00127673 0.01084731 0.02537164 0.04981869 0.17003347 0.52723186] Performing replicate 85 / 200 -[ 0.03986881 1.05518542 4.12486834 9.25122772 16.89936544 - 25.57645412] -[ 0.00130498 0.01089083 0.02541555 0.04986187 0.17140671 0.57647852] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03797752 1.06345571 4.12669393 9.25996554 17.1828226 26.16356859] +[0.00116438 0.01103706 0.0256936 0.04924531 0.17782879 0.50250589] Performing replicate 86 / 200 -[ 0.04232009 1.0540681 4.08796043 9.34980818 17.1081857 - 26.46320241] -[ 0.00126153 0.01083321 0.02542775 0.04979714 0.17760273 0.76258026] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04049187 1.06699882 4.12169282 9.18043815 17.0033469 26.73044448] +[0.00127624 0.01112024 0.02556946 0.04882608 0.18120119 0.56101813] Performing replicate 87 / 200 -[ 0.03948211 1.05328782 4.12304039 9.19464567 16.9815505 - 26.28691147] -[ 0.00119577 0.01061199 0.02565895 0.04943264 0.17746581 0.58841538] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.29e-12 +[ 0.0377928 1.05184561 4.13376216 9.31883317 17.32670932 26.16292849] +[0.0011722 0.01088862 0.02623937 0.04991815 0.17661542 0.52676727] Performing replicate 88 / 200 -[ 0.03791471 1.06078185 4.12339037 9.29977958 17.15756274 - 25.90687533] -[ 0.0011291 0.01123224 0.0259291 0.04924572 0.17453538 0.48963679] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03771418 1.07214751 4.13077042 9.24600658 17.01166339 26.60593503] +[0.00117547 0.01123351 0.02609129 0.04839887 0.17481887 0.92303418] Performing replicate 89 / 200 -[ 0.04087765 1.0605252 4.12449644 9.29654138 16.9609525 - 25.24359057] -[ 0.00121727 0.01133554 0.02545894 0.05002876 0.17198602 0.36917528] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04090549 1.05584047 4.15149472 9.21704926 16.64378726 25.21268171] +[0.00122963 0.01100725 0.02582475 0.04917159 0.17128015 0.46514184] Performing replicate 90 / 200 -[ 0.03983795 1.04901499 4.08310429 9.31075973 16.93125085 - 25.88421139] -[ 0.00120117 0.01086426 0.02603724 0.04904788 0.17449422 0.47714371] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03985388 1.07908627 4.09697946 9.18453296 16.98945641 26.72844428] +[0.00121978 0.01107567 0.02523913 0.04934788 0.17722961 0.78307547] Performing replicate 91 / 200 -[ 0.03896253 1.05143522 4.11418666 9.33723489 17.06327832 - 25.48520278] -[ 0.00122054 0.01081596 0.0258668 0.05007231 0.17214854 0.46851934] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03803466 1.03544304 4.08860532 9.23777155 17.05713549 25.58193499] +[0.00115501 0.01076071 0.02497495 0.04967383 0.17374451 0.4872352 ] Performing replicate 92 / 200 -[ 0.04101405 1.048906 4.12878765 9.19697868 16.80726944 - 25.70100635] -[ 0.00126021 0.01064219 0.02535777 0.05032234 0.17214338 0.64169694] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.16e-13 +[ 0.04006347 1.04753573 4.14574212 9.23344396 17.48139416 26.61847895] +[0.00122157 0.01102464 0.02648074 0.04905509 0.18128044 0.468383 ] Performing replicate 93 / 200 -[ 0.03826325 1.04682516 4.13189649 9.30472316 16.77310672 - 25.9488829 ] -[ 0.00115417 0.01098602 0.02581776 0.04954723 0.1732355 0.57264166] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03872923 1.05476097 4.06366533 9.24674771 16.83094137 25.59076482] +[0.00115099 0.01058947 0.02547795 0.04870884 0.17438749 0.50809601] Performing replicate 94 / 200 -[ 0.04008388 1.05568891 4.15699522 9.23955533 16.75516876 - 25.43538515] -[ 0.00122172 0.01067529 0.02578736 0.04793826 0.17152036 0.61978994] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.72e-12 +[ 0.04261512 1.0710742 4.06930434 9.14893023 16.56455583 24.98794174] +[0.00133729 0.01129983 0.02499118 0.04830273 0.1721445 0.39420417] Performing replicate 95 / 200 -[ 0.03989147 1.06057191 4.12602383 9.28020459 17.1171829 - 26.9770861 ] -[ 0.00121633 0.01089386 0.02586022 0.04962115 0.17986809 0.60045646] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04111475 1.04710012 4.08189777 9.26389806 17.18444428 26.24120363] +[0.00128206 0.01096107 0.02530173 0.04955747 0.17587679 0.55822492] Performing replicate 96 / 200 -[ 0.04018372 1.06378407 4.09540792 9.28942704 17.15932367 - 26.73308559] -[ 0.0012441 0.01076856 0.02576526 0.0498375 0.17894144 0.62273269] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0394957 1.06438775 4.08469906 9.27085151 17.17478418 26.32068726] +[0.00122771 0.01057243 0.02531828 0.04920897 0.17793623 0.5146069 ] Performing replicate 97 / 200 -[ 0.04100587 1.06522803 4.13572988 9.22937296 17.25872487 - 26.82197994] -[ 0.00123723 0.01083285 0.02551154 0.04993346 0.17862635 0.78778591] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03921839 1.06520555 4.11028003 9.25724806 17.09487629 26.51208104] +[0.00118925 0.0105599 0.02588019 0.04949867 0.17707493 0.66313222] Performing replicate 98 / 200 -[ 0.04067516 1.08158748 4.13823673 9.23741068 16.9811306 - 25.23987472] -[ 0.00123256 0.01105371 0.02533448 0.04867907 0.17237308 0.40975343] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +[ 0.04032752 1.07070478 4.07819303 9.28741418 16.98281547 26.02644847] +[0.00123312 0.01113446 0.02577189 0.05048048 0.17371148 0.62760602] Performing replicate 99 / 200 -[ 0.04062395 1.05988377 4.12728124 9.27860033 16.58976787 - 25.02622718] -[ 0.00125277 0.01090675 0.02603776 0.04873989 0.16872912 0.47892528] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04137904 1.07148857 4.10642452 9.25845353 16.83459289 25.26143481] +[0.00129265 0.01085527 0.02629242 0.04884718 0.17024108 0.56396339] Performing replicate 100 / 200 -[ 0.03972257 1.09509281 4.15950308 9.30395591 17.09624035 - 25.84577678] -[ 0.00126001 0.01084742 0.02559193 0.04980253 0.17180177 0.61705271] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04080261 1.06565138 4.14621308 9.23741143 16.80942848 25.56848149] +[0.00119377 0.01106742 0.02596423 0.04853808 0.17287324 0.53779613] Performing replicate 101 / 200 -[ 0.04056107 1.06268142 4.14299783 9.27897378 17.25811858 - 26.43393499] -[ 0.00126907 0.01101612 0.02539773 0.049318 0.17830433 0.52030066] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03978013 1.05607595 4.13311754 9.25369428 16.87968506 26.53965032] +[0.00122858 0.01101825 0.02562545 0.04959701 0.17755548 0.75236531] Performing replicate 102 / 200 -[ 0.03971916 1.06481619 4.1697053 9.19906996 16.8642742 - 24.95901197] -[ 0.00123067 0.01087849 0.02553632 0.04840275 0.17076318 0.42680896] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03974699 1.05780552 4.13175776 9.29190721 16.64104573 24.70888587] +[0.00124861 0.01093375 0.0258383 0.04935298 0.16661574 0.46024764] Performing replicate 103 / 200 -[ 0.04019079 1.06933399 4.1502625 9.31834996 16.85776306 - 25.29384044] -[ 0.00121923 0.01105588 0.02564125 0.04967859 0.16801638 0.58912949] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03855655 1.05026406 4.11644115 9.29256212 16.80431068 25.31318868] +[0.00117736 0.01065185 0.02648942 0.04880783 0.16993571 0.74053195] Performing replicate 104 / 200 -[ 0.04044161 1.07193778 4.12030397 9.23031517 17.0894128 - 25.77459379] -[ 0.00119144 0.01091753 0.02562534 0.05004625 0.17423257 0.4822451 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03957732 1.07284817 4.09490115 9.1343354 16.80342512 25.11514265] +[0.00119039 0.01111583 0.02534771 0.04973067 0.17169597 0.45651428] Performing replicate 105 / 200 -[ 0.03966612 1.04732655 4.10900437 9.21560935 16.97641103 - 25.28823359] -[ 0.00113239 0.01083073 0.02572284 0.04849648 0.17266307 0.41216901] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04067764 1.05440673 4.0901626 9.23276269 16.79637418 25.97455848] +[0.00124436 0.01107529 0.02623409 0.04803925 0.17574689 0.56412655] Performing replicate 106 / 200 -[ 0.04058305 1.06194868 4.13960165 9.28330548 16.99237878 - 26.02023238] -[ 0.00121885 0.01099261 0.02627706 0.04942447 0.1745773 0.59194044] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03898233 1.04732805 4.11130212 9.32657383 17.1688408 26.28303926] +[0.00117093 0.01111487 0.02547272 0.04927551 0.17686225 0.49228761] Performing replicate 107 / 200 -[ 0.04016653 1.0586404 4.12374235 9.16855927 16.97353355 - 25.99918498] -[ 0.00125259 0.01097261 0.02556163 0.04937959 0.17768187 0.45470523] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03955718 1.06472468 4.11938321 9.23990651 16.87385903 26.43387135] +[0.00122283 0.01093417 0.02589904 0.04935481 0.17792168 0.62450403] Performing replicate 108 / 200 -[ 0.03879013 1.06590004 4.14027896 9.24574505 17.14800199 - 25.70161487] -[ 0.00121531 0.01092094 0.02568198 0.04976532 0.17439807 0.42686539] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.51e-12 +[ 0.03969704 1.05198413 4.09605366 9.28220511 16.96117849 25.93311487] +[0.00121722 0.0107081 0.02611258 0.04877787 0.1751717 0.52120009] Performing replicate 109 / 200 -[ 0.03920765 1.05489571 4.10904223 9.21155769 17.07863938 - 26.96082993] -[ 0.0012491 0.01076601 0.02613833 0.04872695 0.18377979 0.50876926] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04070641 1.06283547 4.0917313 9.24848488 17.06413444 26.05554485] +[0.00123605 0.01111822 0.02584615 0.0495416 0.17681684 0.48197416] Performing replicate 110 / 200 -[ 0.03783798 1.06178244 4.07315509 9.26740031 17.01632652 - 26.76811196] -[ 0.00114821 0.0109043 0.02578434 0.04800786 0.18239618 0.5642425 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.04067367 1.05260032 4.07163454 9.22974058 16.85616976 25.21889182] +[0.00125346 0.0105616 0.02612463 0.04934689 0.17127147 0.46938113] Performing replicate 111 / 200 -[ 0.04113307 1.06937907 4.10844611 9.21849545 16.98213462 - 27.69583133] -[ 0.00127659 0.01073172 0.02569224 0.04941777 0.18081823 1.10021637] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04035482 1.04090992 4.08679139 9.23546774 17.25817909 26.14352913] +[0.0012295 0.01070532 0.02594349 0.04961055 0.17741344 0.54740953] Performing replicate 112 / 200 -[ 0.0378916 1.06092494 4.12751179 9.2131647 16.72047501 - 25.73117883] -[ 0.00117006 0.01091807 0.0254063 0.04844317 0.17340027 0.59354293] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 4.97e-13 +[ 0.03851721 1.06374754 4.12898974 9.22815738 17.32625948 27.13681444] +[0.00117103 0.01088856 0.02546227 0.04930057 0.18009885 0.86785184] Performing replicate 113 / 200 -[ 0.03879842 1.05923812 4.15028344 9.24604497 17.1040684 - 25.89964035] -[ 0.00119105 0.01113864 0.02577114 0.04985394 0.17423649 0.55067381] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03975256 1.07684118 4.17131174 9.25676741 17.33894298 26.49662909] +[0.0012304 0.01111145 0.02604939 0.0496227 0.17781969 0.53898784] Performing replicate 114 / 200 -[ 0.03986741 1.04559998 4.09840359 9.20590113 17.03039231 - 26.27680404] -[ 0.00125042 0.0108496 0.02578333 0.04871789 0.17753269 0.53927288] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03933926 1.06547972 4.11104166 9.2645009 17.06956076 25.30489389] +[0.00121352 0.01117126 0.02570488 0.04891444 0.17389792 0.37255527] Performing replicate 115 / 200 -[ 0.03796941 1.07580756 4.0914414 9.22499807 17.18948799 - 25.9507876 ] -[ 0.00116507 0.01095556 0.02533575 0.05007476 0.17712813 0.44888495] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04082175 1.06947184 4.11489152 9.23544977 17.0222807 26.73544549] +[0.00125021 0.01106241 0.02574139 0.0492689 0.1801875 0.60605367] Performing replicate 116 / 200 -[ 0.04138133 1.04793324 4.10421298 9.34562954 17.40152011 - 26.0026261 ] -[ 0.00128974 0.01086486 0.02601164 0.04981699 0.17563692 0.47490909] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03969111 1.05697663 4.13590081 9.23563459 16.689505 24.89829 ] +[0.0012505 0.01073039 0.0257756 0.04798925 0.16868445 0.48805263] Performing replicate 117 / 200 -[ 0.04028955 1.07236332 4.14659559 9.31715083 17.03785533 - 26.28553595] -[ 0.00128571 0.0110412 0.0256319 0.04930334 0.17644276 0.60921994] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03899082 1.06118654 4.16664215 9.19085736 17.21477983 25.81952023] +[0.0012246 0.01059931 0.02584278 0.04872755 0.17608251 0.4701872 ] Performing replicate 118 / 200 -[ 0.0404798 1.0413664 4.11790418 9.26735945 17.34113028 - 25.83928645] -[ 0.00120244 0.01088345 0.02606468 0.04985296 0.17584872 0.46738874] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03938932 1.04182525 4.13906231 9.22861148 16.73108122 25.82951928] +[0.00122267 0.01074873 0.02551924 0.04798966 0.17680297 0.46121468] Performing replicate 119 / 200 -[ 0.04130042 1.07092674 4.10352487 9.23831951 17.17766571 - 25.96775538] -[ 0.00125836 0.01078906 0.02601971 0.04969393 0.17471911 0.58320999] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04085653 1.08523539 4.10752553 9.29106241 17.12397354 26.45965902] +[0.00126275 0.01095167 0.02614668 0.04909208 0.17891643 0.57904427] Performing replicate 120 / 200 -[ 0.03989094 1.06972924 4.13119807 9.22439206 17.34792591 - 26.65192436] -[ 0.00124709 0.0109513 0.02583579 0.04970208 0.1816552 0.51888228] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03966784 1.05275393 4.06334652 9.24052813 16.96513471 26.0018254 ] +[0.00125752 0.0109406 0.02580093 0.04954974 0.17610202 0.52701718] Performing replicate 121 / 200 -[ 0.03886831 1.05644038 4.16189491 9.27478227 17.05671415 - 26.03793544] -[ 0.0012069 0.01064922 0.02641444 0.04861642 0.17618984 0.570946 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0392278 1.0509059 4.14813816 9.27401765 16.95575595 25.65411279] +[0.00124328 0.01095879 0.02579234 0.04980574 0.17335403 0.45269733] Performing replicate 122 / 200 -[ 0.03845955 1.0692065 4.04336601 9.153531 16.97012872 - 26.07238461] -[ 0.00116889 0.01083338 0.02494681 0.04991075 0.1785819 0.47401595] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.49e-12 +[ 0.04030122 1.06736746 4.10581422 9.27326187 17.22882853 26.23765112] +[0.00128936 0.0108582 0.02544977 0.04997193 0.17704064 0.53586202] Performing replicate 123 / 200 -[ 0.04156581 1.07785273 4.11470621 9.35139327 17.10678998 - 26.07432017] -[ 0.00127914 0.01081712 0.02541242 0.05101825 0.17360648 0.53747748] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04098913 1.07027915 4.1308825 9.17640299 16.59387907 25.29020727] +[0.00126063 0.01104441 0.02555134 0.04796448 0.17176697 0.4881096 ] Performing replicate 124 / 200 -[ 0.0369523 1.06708374 4.09793758 9.2176403 17.17170717 - 27.35039341] -[ 1.12591222e-03 1.09168894e-02 2.56230242e-02 4.98695112e-02 - 1.78652167e-01 1.13871906e+00] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04062636 1.0738277 4.11539359 9.23544782 17.06929376 26.60251332] +[0.00132629 0.01075991 0.0261117 0.0489554 0.18072907 0.52337566] Performing replicate 125 / 200 -[ 0.03916859 1.05790806 4.0774769 9.22638289 17.1919002 - 26.92773245] -[ 0.00118327 0.01092405 0.02567421 0.04908701 0.18286606 0.54396684] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03937951 1.04991478 4.07657551 9.37451092 17.30034472 26.67726699] +[0.00124574 0.01077056 0.02617023 0.05046814 0.1755068 0.6622275 ] Performing replicate 126 / 200 -[ 0.03977586 1.04991045 4.148005 9.22468179 17.06792636 - 26.30053771] -[ 0.0012626 0.01095323 0.02563349 0.04980977 0.17653974 0.67672793] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04014618 1.06962094 4.13476275 9.27207376 17.01697019 25.94857751] +[0.00122715 0.01086642 0.02608751 0.0490112 0.17526211 0.59631582] Performing replicate 127 / 200 -[ 0.04142095 1.0559735 4.12704262 9.23582 16.8061218 - 25.95989429] -[ 0.00125754 0.01098718 0.02571506 0.04887964 0.17428845 0.55912737] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.84e-12 +[ 0.04012365 1.06959893 4.136795 9.30053741 16.81646147 25.59531474] +[0.00120238 0.01066103 0.02626163 0.0496726 0.17102142 0.49455065] Performing replicate 128 / 200 -[ 0.03719678 1.04752747 4.18203517 9.23396859 17.0634186 - 26.32968929] -[ 0.001157 0.01071383 0.02630357 0.04895444 0.17690081 0.60939269] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03907917 1.06535349 4.09006646 9.29775737 16.98873287 25.94891514] +[0.0012042 0.0109542 0.02565464 0.04945527 0.17557739 0.496429 ] Performing replicate 129 / 200 -[ 0.04199611 1.06797827 4.15426835 9.19160114 16.91904166 - 25.57800039] -[ 0.00127048 0.01105406 0.02589197 0.04924891 0.1746844 0.44325957] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04007009 1.0665576 4.09885144 9.26820275 16.97261825 25.51556333] +[0.00122057 0.01089746 0.02572118 0.04924192 0.17384807 0.41869537] Performing replicate 130 / 200 -[ 0.03810507 1.06550056 4.10015959 9.2487155 17.11834377 - 25.96126425] -[ 0.0011702 0.01135679 0.02531197 0.04916234 0.17644098 0.48780892] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03940556 1.06150568 4.124533 9.30757003 16.92021228 26.12372054] +[0.00120674 0.01093973 0.02615613 0.04808091 0.17450865 0.63318736] Performing replicate 131 / 200 -[ 0.03925455 1.04888968 4.09475578 9.25740188 16.76167382 - 25.37098742] -[ 0.00120104 0.01103548 0.02540658 0.04888858 0.17244616 0.51280446] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03920296 1.08025181 4.10822698 9.27723336 17.24483201 25.98370511] +[0.00123939 0.01104459 0.02560428 0.04956373 0.17458978 0.58584147] Performing replicate 132 / 200 -[ 0.03922079 1.06169897 4.12579203 9.17781116 17.01796859 - 25.45095861] -[ 0.00118245 0.01100145 0.02540074 0.049855 0.17311313 0.46057395] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03858646 1.07799773 4.13255904 9.27317447 16.95468385 25.56091087] +[0.00119266 0.01101825 0.02589311 0.04927594 0.17201834 0.54546328] Performing replicate 133 / 200 -[ 0.03952071 1.06816464 4.08847207 9.30036398 17.15135567 - 26.31653997] -[ 0.0011987 0.01096494 0.02614188 0.04864399 0.17963335 0.492792 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03911504 1.06471426 4.07818217 9.29121857 17.32718995 27.10973772] +[0.00122737 0.01112691 0.02537415 0.05077511 0.18009441 0.64181253] Performing replicate 134 / 200 -[ 0.04166397 1.05488153 4.05895388 9.19203898 17.18184885 - 26.77963559] -[ 0.00123061 0.01086966 0.02528654 0.05017073 0.18146952 0.52727654] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03993939 1.06159119 4.15834172 9.32156648 17.15021594 27.34403775] +[0.00124575 0.01097182 0.02620751 0.04838349 0.18149691 0.70304715] Performing replicate 135 / 200 -[ 0.04136422 1.06096963 4.11123148 9.20663531 16.67375264 - 25.52112358] -[ 0.00127832 0.01088189 0.02545053 0.04918635 0.17224972 0.53498091] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03997742 1.04670652 4.10441351 9.24844055 16.88659783 25.57793852] +[0.0012007 0.01062593 0.02601795 0.04978437 0.1709323 0.50156911] Performing replicate 136 / 200 -[ 0.04219685 1.04388749 4.12758996 9.2469164 17.27460208 - 27.39583162] -[ 0.00127735 0.01075889 0.02578018 0.04891266 0.18656228 0.51596832] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04227092 1.07948184 4.08094232 9.24616593 17.18148586 26.50789515] +[0.00130684 0.01104017 0.02546124 0.04986266 0.17654614 0.72951383] Performing replicate 137 / 200 -[ 0.03985634 1.06269733 4.11189872 9.25424476 17.21081873 - 26.19167471] -[ 0.00121413 0.01106653 0.02546953 0.04982476 0.17848555 0.44741422] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04186856 1.05911993 4.08421965 9.22783168 17.20832579 27.54204858] +[0.00126092 0.01083188 0.02558903 0.0498912 0.18237663 0.80552724] Performing replicate 138 / 200 -[ 0.04013466 1.06941419 4.08422282 9.16676778 16.99451755 - 26.30783196] -[ 0.00119747 0.01102637 0.02567901 0.04936201 0.17694132 0.66087289] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.31e-12 +[ 0.03830127 1.05652294 4.09234818 9.26098698 17.06382018 26.35469625] +[0.00113282 0.01088066 0.02521804 0.04924227 0.17654489 0.55652125] Performing replicate 139 / 200 -[ 0.03878843 1.06913422 4.07804802 9.27823834 17.10329262 - 25.88496333] -[ 0.00118057 0.01080404 0.02634294 0.04955651 0.17520674 0.48367742] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03931839 1.05496684 4.11669925 9.28456829 16.91975186 25.78198815] +[0.00117346 0.01066347 0.02586121 0.04907783 0.17525836 0.45948119] Performing replicate 140 / 200 -[ 0.04010342 1.06048116 4.16675989 9.26194476 16.81398259 - 25.07159596] -[ 0.00123591 0.01077064 0.02539761 0.04958305 0.1690769 0.49305708] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03889085 1.05844308 4.10712845 9.21635591 17.19083339 26.03355946] +[0.00122652 0.01071796 0.02597344 0.04941993 0.1767484 0.55869732] Performing replicate 141 / 200 -[ 0.03874465 1.05328165 4.0431712 9.27195769 16.99390768 - 25.6237777 ] -[ 0.00116937 0.01065705 0.02579101 0.0484069 0.17646992 0.40229584] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03899333 1.05838529 4.10508641 9.20548026 16.95892104 26.60010583] +[0.00120943 0.01082774 0.02536828 0.049502 0.17858898 0.62024083] Performing replicate 142 / 200 -[ 0.04215721 1.07142533 4.0808735 9.32323629 17.04227769 - 25.17598607] -[ 0.00127759 0.01080358 0.02566303 0.05021267 0.17035356 0.4006108 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03876535 1.07044924 4.10229425 9.25400499 17.29433168 26.3273031 ] +[0.00117899 0.01090084 0.02487081 0.05012054 0.17807981 0.51088585] Performing replicate 143 / 200 -[ 0.04043168 1.07069117 4.10204078 9.29240237 17.14369459 - 26.04797286] -[ 0.00120949 0.01053949 0.02594493 0.04934239 0.17456359 0.66125308] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03965197 1.07068087 4.07476155 9.28150572 16.96241989 25.56316921] +[0.0012036 0.01108448 0.02594581 0.04986659 0.17247068 0.51616133] Performing replicate 144 / 200 -[ 0.04088449 1.05318877 4.08067794 9.18244357 16.977117 - 25.95323729] -[ 0.00129047 0.01072145 0.02522834 0.04993846 0.17707853 0.45108695] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03782186 1.04819365 4.08654462 9.19758351 17.08139035 26.73805951] +[0.00123128 0.01076974 0.02528505 0.04940238 0.17878989 0.75919779] Performing replicate 145 / 200 -[ 0.04009235 1.06843982 4.09262506 9.2638655 17.31553256 - 26.89565361] -[ 0.00127346 0.01123077 0.02565637 0.04922175 0.18174126 0.61998037] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.59e-12 +[ 0.03955792 1.06748669 4.12274755 9.2876324 16.84191376 25.88427357] +[0.00122025 0.01097262 0.02566806 0.04836106 0.17422019 0.51345935] Performing replicate 146 / 200 -[ 0.03963124 1.07956863 4.13765331 9.20350867 17.16504076 - 25.86232222] -[ 0.00114865 0.0109487 0.02540278 0.04911431 0.17617338 0.43291386] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.73e-12 +[ 0.04090624 1.07061657 4.12248787 9.22129461 17.13767388 25.7251652 ] +[0.00124265 0.01090442 0.02619221 0.04934468 0.17545381 0.4455065 ] Performing replicate 147 / 200 -[ 0.03985924 1.04159808 4.08316946 9.3056996 16.75991648 - 26.11941344] -[ 0.00122513 0.01096303 0.0262326 0.04886815 0.17401069 0.78336073] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04247263 1.06088521 4.09710023 9.25313333 16.94604727 25.34985036] +[0.00130544 0.01107241 0.02547975 0.04953334 0.17200736 0.44466769] Performing replicate 148 / 200 -[ 0.03982678 1.07264906 4.10198652 9.22584286 16.81294905 - 25.75419853] -[ 0.0012693 0.01063751 0.02593565 0.0479869 0.17544967 0.51410323] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03948504 1.0417179 4.13473866 9.21575116 16.67783787 25.04794447] +[0.00122683 0.01074013 0.02610383 0.04831819 0.17180627 0.4315699 ] Performing replicate 149 / 200 -[ 0.03950524 1.05342774 4.08868466 9.28111385 17.03683543 - 26.26077134] -[ 0.0012436 0.01103079 0.02575514 0.04977335 0.17523492 0.66081985] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03967424 1.03616867 4.16001326 9.27205735 17.03401188 25.14820512] +[0.00119554 0.01086735 0.02583886 0.04975345 0.17223631 0.39545792] Performing replicate 150 / 200 -[ 0.04103854 1.06564037 4.10091067 9.24946845 16.85832655 - 25.77860494] -[ 0.00123911 0.01080355 0.02570082 0.04926861 0.17414684 0.53573897] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0402514 1.07549166 4.08710382 9.18316922 17.21452912 27.39192443] +[0.00125881 0.01079044 0.02507131 0.04979174 0.18320188 0.73624437] Performing replicate 151 / 200 -[ 0.0421703 1.07052286 4.15591606 9.26279647 17.0148897 - 26.87139248] -[ 0.00131449 0.01131113 0.02585465 0.04885976 0.17908127 0.63926408] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04075875 1.06446981 4.12598608 9.18657692 17.00410277 26.12698844] +[0.00124183 0.01094237 0.02523711 0.04957501 0.17589965 0.5859568 ] Performing replicate 152 / 200 -[ 0.04283881 1.06075603 4.0949236 9.19495223 17.11194677 - 25.93971633] -[ 0.00130255 0.01102937 0.02483143 0.04947497 0.1779809 0.43927266] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04101345 1.04763512 4.12009443 9.33151172 16.95590024 25.65889519] +[0.00122857 0.01056484 0.02617427 0.04952269 0.17279317 0.4897494 ] Performing replicate 153 / 200 -[ 0.04233202 1.05904998 4.11066753 9.2909831 17.09160179 - 26.4795255 ] -[ 0.0012714 0.01113896 0.02557989 0.04980546 0.17527974 0.6553202 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03963724 1.04923664 4.1293401 9.19422398 16.7462357 26.54123029] +[0.00119871 0.01100298 0.02539898 0.04932843 0.17533782 1.09747299] Performing replicate 154 / 200 -[ 0.04033525 1.06779141 4.10535313 9.30277389 16.95386003 - 25.50736905] -[ 0.00122583 0.01110686 0.02513268 0.0501842 0.17232498 0.4821687 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0396528 1.05154947 4.12766231 9.1728756 16.97018007 27.61954165] +[1.15938542e-03 1.07849564e-02 2.57724853e-02 4.88200586e-02 + 1.79156984e-01 1.65805535e+00] Performing replicate 155 / 200 -[ 0.04026612 1.09644873 4.16853794 9.24726474 16.90114481 - 25.24258986] -[ 0.0012139 0.01100923 0.02671356 0.04786345 0.1725853 0.3901067 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +[ 0.03900252 1.06805543 4.11252292 9.23330868 17.30291588 27.15713116] +[1.16619814e-03 1.08636843e-02 2.55805027e-02 5.05067515e-02 + 1.79027988e-01 1.28821076e+00] Performing replicate 156 / 200 -[ 0.03906261 1.09062096 4.14562783 9.22102642 16.81932758 - 25.80535977] -[ 0.00117694 0.0111168 0.02529832 0.04911709 0.176728 0.44752374] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.83e-12 +[ 0.03825126 1.05068269 4.12024422 9.26866967 17.29864136 25.28958687] +[0.00117603 0.01084838 0.02592029 0.04892434 0.17292209 0.38506866] Performing replicate 157 / 200 -[ 0.04070245 1.08349564 4.087405 9.22263818 16.65246868 - 25.01727819] -[ 0.0012579 0.01114315 0.02527657 0.04912778 0.17105104 0.42416007] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04074161 1.05983631 4.12708568 9.30425151 16.92876987 25.27429218] +[0.00122668 0.01126359 0.02658345 0.04887155 0.16952013 0.4885423 ] Performing replicate 158 / 200 -[ 0.038858 1.06436365 4.11346521 9.28805281 17.00700928 - 25.94096547] -[ 0.00119331 0.01087144 0.025526 0.04949209 0.17435592 0.48873057] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03952316 1.06076929 4.09873444 9.2072707 16.99238426 26.99255113] +[0.0011724 0.01109218 0.02559123 0.04922654 0.17896665 0.84293768] Performing replicate 159 / 200 -[ 0.03863534 1.05785545 4.12211697 9.19493196 16.84859109 - 26.38544488] -[ 0.00116276 0.01081988 0.02584204 0.04828893 0.1793468 0.51649675] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04013869 1.0586082 4.12627944 9.24362618 17.18610273 26.64651113] +[0.00120888 0.01085788 0.02516004 0.05004442 0.17832514 0.59038519] Performing replicate 160 / 200 -[ 0.04046122 1.04030794 4.11241122 9.20594922 16.65867485 - 25.24791553] -[ 0.00123475 0.01059827 0.02570171 0.04948124 0.17060838 0.48291271] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.0413015 1.0667056 4.12340787 9.19371776 16.79472638 25.86591302] +[0.00127358 0.01074239 0.02564696 0.04852287 0.1764254 0.55926236] Performing replicate 161 / 200 -[ 0.04140849 1.07138653 4.13329869 9.20288428 17.02143001 - 25.99473279] -[ 0.001329 0.01092943 0.02507844 0.04961145 0.17602735 0.49470899] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03988714 1.0546929 4.04783104 9.19355724 16.85821701 25.17722503] +[0.00119341 0.01093701 0.02571522 0.04821304 0.17349739 0.39659871] Performing replicate 162 / 200 -[ 0.04166616 1.0709341 4.10146952 9.17054636 16.87661293 - 26.45863914] -[ 0.00126014 0.01079852 0.02572087 0.0487759 0.17724193 0.71373954] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03922439 1.0758835 4.14769951 9.29178673 17.15649697 26.37184533] +[0.00126954 0.01115485 0.02541799 0.0497061 0.178426 0.52659086] Performing replicate 163 / 200 -[ 0.04008722 1.05413579 4.10799876 9.26795049 16.81063176 - 24.99624393] -[ 0.00125608 0.01100212 0.02566485 0.04938976 0.1703181 0.4161677 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03887405 1.0648428 4.14152851 9.28511942 16.8002774 25.32637094] +[0.00119245 0.01085298 0.02565599 0.04912642 0.17267789 0.45782726] Performing replicate 164 / 200 -[ 0.0387415 1.06646642 4.11179728 9.26236001 16.84517008 - 25.10163539] -[ 0.00120862 0.01098634 0.02581575 0.04820443 0.17220011 0.40708557] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03835591 1.04481619 4.0999079 9.27746767 16.94448182 25.70249017] +[0.00118826 0.01078933 0.02588856 0.04983371 0.17477258 0.42534349] Performing replicate 165 / 200 -[ 0.0410829 1.05283067 4.0949469 9.27363586 17.07724205 - 25.84018779] -[ 0.00125169 0.01102343 0.02632057 0.04843004 0.17503375 0.50340767] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03985571 1.06205082 4.10466417 9.28596488 16.80142765 25.07881475] +[0.00126311 0.01093044 0.02551212 0.05029934 0.17011149 0.41537416] Performing replicate 166 / 200 -[ 0.03964108 1.07658514 4.15483952 9.26922229 16.842489 25.5748169 ] -[ 0.00120427 0.0110258 0.02523857 0.04860376 0.17265797 0.48069811] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04085501 1.06088217 4.1127687 9.27350857 16.83044789 25.36641665] +[0.00124942 0.01108553 0.02551076 0.0492831 0.17092589 0.49817389] Performing replicate 167 / 200 -[ 0.04115877 1.04650405 4.09023549 9.23421001 16.84001286 - 26.0646407 ] -[ 0.00123244 0.01086916 0.02530394 0.04886357 0.17471357 0.6034998 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0417479 1.06398196 4.09452888 9.09345488 16.81847848 25.02200611] +[0.00132671 0.01086001 0.02557125 0.04891484 0.17269353 0.42676554] Performing replicate 168 / 200 -[ 0.03962136 1.04880228 4.11120813 9.28817162 16.95461118 - 26.0747367 ] -[ 0.00121454 0.0107909 0.02593557 0.0497015 0.1759755 0.52477491] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04132402 1.06306073 4.14543788 9.22425213 16.909052 25.88161833] +[0.00123794 0.01096245 0.02594427 0.04909392 0.17622597 0.49658656] Performing replicate 169 / 200 -[ 0.03933242 1.0652405 4.13821324 9.29361812 16.94895642 - 26.08980593] -[ 0.00118615 0.01101444 0.02588868 0.0496701 0.17644145 0.49070508] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04334952 1.07472803 4.11907721 9.23708481 16.93965848 25.5696248 ] +[0.00133345 0.01095409 0.02557324 0.04970075 0.17279429 0.52423431] Performing replicate 170 / 200 -[ 0.03924082 1.04870899 4.12174642 9.26434069 17.19187269 - 25.53024351] -[ 0.00121825 0.01085487 0.02595499 0.05007439 0.17263563 0.4424045 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03914368 1.04005093 4.11601888 9.20718734 17.33242682 26.4266945 ] +[0.0011637 0.01082222 0.02562728 0.05031351 0.1787362 0.49123531] Performing replicate 171 / 200 -[ 0.04032347 1.06789577 4.09306472 9.20194451 17.26685859 - 26.16562743] -[ 0.00124151 0.010981 0.02581321 0.05008271 0.17687769 0.52078582] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03997725 1.0628203 4.13770212 9.3071347 16.75367996 26.2006745 ] +[0.00123121 0.01090087 0.02541468 0.04923955 0.1742997 0.70244597] Performing replicate 172 / 200 -[ 0.0392444 1.07221821 4.1263054 9.24548735 17.05286745 - 25.95925097] -[ 0.00118465 0.01116946 0.02566478 0.04890527 0.17580603 0.48896266] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04065934 1.06302232 4.12713853 9.26509301 17.2235768 26.33786598] +[0.00123969 0.010962 0.02565631 0.04938522 0.1754707 0.82666499] Performing replicate 173 / 200 -[ 0.03971729 1.06171116 4.09255312 9.24492582 16.95772866 - 26.12282377] -[ 0.0012028 0.01098921 0.0258358 0.04998143 0.17547126 0.54453633] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03914283 1.05229203 4.14504394 9.33218867 17.01584477 25.97850581] +[0.00122253 0.0108431 0.02639148 0.04915235 0.17476396 0.57407044] Performing replicate 174 / 200 -[ 0.04082981 1.05727036 4.07320823 9.32041326 17.02462864 - 25.95365609] -[ 0.00124846 0.01082101 0.02537474 0.05062054 0.17437232 0.50413472] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.43e-12 +[ 0.04074304 1.07693259 4.10627348 9.22829772 16.84683766 25.82375379] +[0.00122681 0.01131475 0.02554377 0.04910134 0.17469788 0.54660858] Performing replicate 175 / 200 -[ 0.0391653 1.0826314 4.11553603 9.23761106 17.09492746 - 25.48363107] -[ 0.00119351 0.01098443 0.02564818 0.04919253 0.17424364 0.39502371] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03967976 1.05561579 4.10602651 9.20229081 16.8768591 25.57468398] +[0.00120809 0.01097673 0.02580072 0.04863555 0.17517975 0.47655956] Performing replicate 176 / 200 -[ 0.04162999 1.04539376 4.13621659 9.25199983 17.22479623 - 26.68295993] -[ 0.00126612 0.01073412 0.02545388 0.04960743 0.17992553 0.59287252] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03765345 1.06348833 4.13476548 9.22768176 16.75002859 25.67408393] +[0.00112222 0.01109048 0.0260727 0.04909964 0.17279091 0.568084 ] Performing replicate 177 / 200 -[ 0.040892 1.0671822 4.11941036 9.26315538 17.12733979 - 25.48019854] -[ 0.00121534 0.01087971 0.02631539 0.04951128 0.17169236 0.45976469] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03964787 1.07404957 4.08347621 9.25861092 17.23642968 25.99004317] +[0.00124437 0.01112153 0.02536146 0.05069958 0.17697784 0.46042888] Performing replicate 178 / 200 -[ 0.040593 1.04735719 4.11480803 9.18996328 16.91438114 - 25.61631068] -[ 0.00121006 0.0105619 0.02519179 0.048928 0.17699047 0.41568978] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04218991 1.04929403 4.12309822 9.19991365 16.86064208 25.55290092] +[0.00131511 0.01075854 0.02576016 0.04882433 0.17394164 0.47489437] Performing replicate 179 / 200 -[ 0.03949997 1.05983624 4.1161433 9.24289668 17.07069573 - 25.94581591] -[ 0.00123058 0.01101692 0.02611206 0.04851842 0.17539834 0.49882988] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04113675 1.05677132 4.07609167 9.27176595 16.91881427 25.51713621] +[0.00126032 0.01034274 0.02543807 0.0498377 0.17093882 0.6084041 ] Performing replicate 180 / 200 -[ 0.04268441 1.04306007 4.13891794 9.27704161 16.97015949 - 25.93576014] -[ 0.00127801 0.01066696 0.0258331 0.04922634 0.17440892 0.55836257] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04050788 1.05553897 4.1765127 9.19951095 17.13882675 26.16725938] +[0.00120329 0.01103227 0.02582179 0.04964033 0.17752509 0.49837713] Performing replicate 181 / 200 -[ 0.03997153 1.0679806 4.09171679 9.31222106 17.15616892 - 26.37041466] -[ 0.00123118 0.01070067 0.02609698 0.0496551 0.17606712 0.5655925 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03922528 1.03847993 4.07477377 9.24126504 17.01294043 25.85593086] +[0.00121071 0.01086685 0.02554272 0.04916098 0.17527238 0.48923393] Performing replicate 182 / 200 -[ 0.03916729 1.0462345 4.04675658 9.27222361 16.91813772 - 25.68211268] -[ 0.00122488 0.01057735 0.02650417 0.04849245 0.17562164 0.42935177] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.03917716 1.0628745 4.0761117 9.30997285 17.06776714 25.92724437] +[0.00125504 0.01088776 0.02579925 0.05033104 0.1762384 0.45361692] Performing replicate 183 / 200 -[ 0.04104305 1.08116288 4.093905 9.27785203 16.87299391 - 25.6468424 ] -[ 0.00126306 0.01103343 0.02565125 0.04860066 0.17202623 0.58639778] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.26e-12 +[ 0.04034922 1.06076492 4.11632747 9.33255042 17.12372612 25.71913407] +[0.00128512 0.01105564 0.02548206 0.05015886 0.17262883 0.57342372] Performing replicate 184 / 200 -[ 0.04017636 1.06085919 4.09406332 9.19915067 16.79677292 - 25.93005427] -[ 0.00120525 0.01083511 0.02512188 0.04932081 0.17543116 0.54611401] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03883601 1.05516603 4.03578921 9.22865944 16.69866365 25.0440894 ] +[0.0011963 0.01085288 0.02561758 0.04875598 0.17139263 0.41026031] Performing replicate 185 / 200 -[ 0.04198703 1.04706363 4.05060448 9.30064134 16.94518554 - 26.03703662] -[ 0.00130929 0.01083047 0.02541583 0.04960069 0.17732444 0.47394367] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04255949 1.0548528 4.12206072 9.20923324 16.9765651 25.76771089] +[0.00123714 0.01074133 0.02660145 0.04932054 0.17356895 0.62180151] Performing replicate 186 / 200 -[ 0.03995719 1.0812905 4.11385805 9.34922996 17.3113262 - 26.72903961] -[ 0.00123332 0.01089008 0.02645681 0.05054283 0.17738668 0.64498736] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03786707 1.0842681 4.1139317 9.26646697 16.89970607 25.64714825] +[0.00118771 0.01120414 0.02516133 0.04969422 0.17199565 0.56021551] Performing replicate 187 / 200 -[ 0.04178661 1.04738889 4.08764344 9.19222811 17.09856684 - 27.02318802] -[ 0.00124473 0.0108632 0.02530931 0.04930682 0.18064872 0.70583336] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03885175 1.06401962 4.0953245 9.22816814 17.15613398 26.26410598] +[0.00117114 0.01084865 0.02598598 0.04936978 0.17778601 0.49034042] Performing replicate 188 / 200 -[ 0.03815262 1.06627266 4.1017307 9.19193494 16.92183004 - 25.15194766] -[ 0.00115353 0.01122614 0.02492129 0.04904429 0.1714296 0.46277932] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.08e-12 +[ 0.03916907 1.06195567 4.09631427 9.20990299 17.15550258 26.2847235 ] +[0.00118885 0.01079848 0.02587778 0.04907878 0.18019443 0.44079388] Performing replicate 189 / 200 -[ 0.04185249 1.05044745 4.07419763 9.18283925 16.96688124 - 26.25445596] -[ 0.00127318 0.01111222 0.02580958 0.04904387 0.17461487 0.82896019] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03778221 1.0496314 4.14973278 9.33390347 17.35876632 26.06781097] +[0.0011929 0.01081494 0.0260036 0.04993709 0.17287074 0.65602686] Performing replicate 190 / 200 -[ 0.04013031 1.05129657 4.12751049 9.29690192 17.25226686 - 26.4353545 ] -[ 0.00125514 0.01080433 0.02645488 0.04910563 0.17889221 0.48625833] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +[ 0.04107742 1.08007828 4.08829975 9.14858776 16.81771564 26.13745563] +[0.00123871 0.01115557 0.02513659 0.0491685 0.17612711 0.86093237] Performing replicate 191 / 200 -[ 0.04245385 1.05526463 4.09175209 9.29643086 16.90897975 - 26.12822411] -[ 0.00128934 0.01080084 0.02594365 0.04952361 0.17444127 0.63108606] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.03964298 1.06450389 4.10744734 9.27163908 17.22632445 26.61916102] +[0.00122403 0.01101905 0.02528958 0.04988069 0.17977885 0.58241499] Performing replicate 192 / 200 -[ 0.03939741 1.06506177 4.12351384 9.31724781 17.38964044 - 26.23684384] -[ 0.00125671 0.0109246 0.02568841 0.0500429 0.17853105 0.49399573] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.42e-13 +[ 0.0398146 1.05284374 4.08416859 9.27625284 17.07926581 25.25671039] +[0.00123174 0.01092759 0.02558197 0.04874624 0.17273435 0.39190106] Performing replicate 193 / 200 -[ 0.03998072 1.07018011 4.11376889 9.28750939 16.9131847 - 25.48530037] -[ 0.00124424 0.01106459 0.02607634 0.04907463 0.1724432 0.46890669] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.68e-12 +[ 0.03846028 1.05082904 4.12306679 9.30995412 17.26432513 25.90934975] +[0.00118513 0.01067334 0.02554032 0.04956267 0.17369254 0.51562942] Performing replicate 194 / 200 -[ 0.03996269 1.05972543 4.11531239 9.28403013 16.62235584 - 24.77307246] -[ 0.00121992 0.01089392 0.02644425 0.04882868 0.1669324 0.45633012] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04241041 1.06226337 4.14106477 9.19670241 17.10338225 26.20609885] +[0.00130314 0.01102521 0.02514543 0.05043752 0.17725599 0.51428122] Performing replicate 195 / 200 -[ 0.0389021 1.07285365 4.09265839 9.291044 16.68839384 - 24.53532919] -[ 0.00120791 0.01105546 0.0260614 0.04977696 0.16517615 0.40374384] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.0389781 1.0717443 4.06721461 9.156016 17.15954345 25.9435764 ] +[0.00120403 0.0108145 0.02534289 0.04982075 0.17844693 0.4502166 ] Performing replicate 196 / 200 -[ 0.03820896 1.06562087 4.12246071 9.28038403 16.9777533 - 25.60066381] -[ 0.00114809 0.0108086 0.02602538 0.04933105 0.17182912 0.50621923] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04040425 1.08273101 4.04965734 9.28964361 16.81022322 25.57520469] +[0.00121953 0.01078653 0.02594205 0.0485129 0.1747217 0.44656067] Performing replicate 197 / 200 -[ 0.03961265 1.05123143 4.13552453 9.29155693 17.01434371 - 26.7809199 ] -[ 0.00123017 0.01086232 0.02591244 0.04999904 0.17486925 1.0911329 ] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.6e-12 +[ 0.04225357 1.08785083 4.13716652 9.26753743 17.04926272 25.41591015] +[0.00131668 0.01092811 0.02585182 0.04985847 0.17281623 0.4083112 ] Performing replicate 198 / 200 -[ 0.03889642 1.07099961 4.14408392 9.23450485 17.17766108 - 26.65097084] -[ 0.00117274 0.01102282 0.02536606 0.04985116 0.1773005 0.72667978] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.51e-12 +[ 0.04080864 1.05578917 4.12542644 9.26846494 17.2013009 26.12837111] +[0.00125068 0.01089593 0.02535943 0.04918443 0.17650576 0.51148002] Performing replicate 199 / 200 -[ 0.04127325 1.0568403 4.10224829 9.26011535 16.65155574 - 24.59976436] -[ 0.00123371 0.01066127 0.02576043 0.04933736 0.16771452 0.41132017] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04037436 1.0602233 4.12043147 9.30622192 16.90120418 26.38462644] +[0.00126113 0.01079958 0.0262764 0.04862392 0.17454117 1.07843245] Performing replicate 200 / 200 -[ 0.03843392 1.05559499 4.11173046 9.3156173 17.01134082 - 26.14293771] -[ 0.00117839 0.01084721 0.02509163 0.05022886 0.17425602 0.54584475] +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with adaptive +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 0 +[ 0.04202009 1.04714895 4.11384316 9.22402556 16.73935212 25.20267944] +[0.0012903 0.01061105 0.02615075 0.04939276 0.17100542 0.4336926 ] Free energies Anderson-Darling Metrics (see README.md) -[[ 0. 1.28950265 1.22372038 1.19571806 1.29453297 1.29706093] - [ 0.92104011 0. 1.11743234 0.38514148 0.39310714 0.7255426 ] - [ 0.76238837 0.73065943 0. 0.38600061 0.3219965 0.27862602] - [ 0.84943314 0.22877813 0.67881642 0. 0.37026049 1.22756765] - [ 0.95619074 0.23467976 0.60820073 0.48639343 0. 3.21878282] - [ 0.87460656 0.44539327 0.31457453 0.9575389 2.70933039 0. ]] -The uncertainty estimates are tested in this section. +[[0. 0.63948233 0.64238618 0.56374172 0.5247762 0.62654248] + [0.49202939 0. 0.57771277 0.64306131 0.50730763 0.81995768] + [0.4305121 0.40320521 0. 0.50130062 0.49338421 0.58437865] + [0.37731585 0.49778176 0.53050247 0. 0.93139412 1.16545921] + [0.28301623 0.28782605 0.33423671 0.59964086 0. 1.81114272] + [0.32242773 0.51940134 0.29650744 0.77617482 1.46701154 0. ]] +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -833,60 +1248,60 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.084610 ( 0.074922, 0.094823) 0.079656 - 0.2 -24.000000 0.161892 ( 0.148933, 0.175278) 0.158519 - 0.3 -10.111111 0.232845 ( 0.217899, 0.248128) 0.235823 - 0.4 -5.250000 0.300133 ( 0.283869, 0.316650) 0.310843 - 0.5 -3.000000 0.369420 ( 0.352242, 0.386764) 0.382925 - 0.6 -1.777778 0.436043 ( 0.418348, 0.453818) 0.451494 - 0.7 -1.040816 0.500666 ( 0.482784, 0.518548) 0.516073 - 0.8 -0.562500 0.558294 ( 0.540498, 0.576017) 0.576289 - 0.9 -0.234568 0.615923 ( 0.598455, 0.633244) 0.631880 - 1.0 -0.000000 0.666889 ( 0.649927, 0.683639) 0.682689 - 1.1 0.173554 0.710526 ( 0.694174, 0.726612) 0.728668 - 1.2 0.305556 0.751166 ( 0.735546, 0.766469) 0.769861 - 1.3 0.408284 0.798468 ( 0.783934, 0.812625) 0.806399 - 1.4 0.489796 0.832112 ( 0.818536, 0.845268) 0.838487 - 1.5 0.555556 0.854430 ( 0.841595, 0.866818) 0.866386 - 1.6 0.609375 0.880080 ( 0.868224, 0.891457) 0.890401 - 1.7 0.653979 0.900733 ( 0.889788, 0.911172) 0.910869 - 1.8 0.691358 0.920053 ( 0.910092, 0.929485) 0.928139 - 1.9 0.722992 0.935376 ( 0.926312, 0.943891) 0.942567 - 2.0 0.750000 0.946369 ( 0.938034, 0.954141) 0.954500 - 2.1 0.773243 0.955363 ( 0.947695, 0.962457) 0.964271 - 2.2 0.793388 0.965023 ( 0.958164, 0.971296) 0.972193 - 2.3 0.810964 0.972019 ( 0.965829, 0.977613) 0.978552 - 2.4 0.826389 0.978348 ( 0.972848, 0.983245) 0.983605 - 2.5 0.840000 0.984011 ( 0.979227, 0.988184) 0.987581 - 2.6 0.852071 0.989340 ( 0.985369, 0.992695) 0.990678 - 2.7 0.862826 0.991672 ( 0.988124, 0.994602) 0.993066 - 2.8 0.872449 0.993338 ( 0.990131, 0.995925) 0.994890 - 2.9 0.881094 0.995003 ( 0.992185, 0.997200) 0.996268 - 3.0 0.888889 0.997335 ( 0.995200, 0.998848) 0.997300 - 3.1 0.895942 0.997668 ( 0.995653, 0.999062) 0.998065 - 3.2 0.902344 0.997668 ( 0.995653, 0.999062) 0.998626 - 3.3 0.908173 0.998334 ( 0.996591, 0.999459) 0.999033 - 3.4 0.913495 0.998668 ( 0.997081, 0.999637) 0.999326 - 3.5 0.918367 0.999001 ( 0.997595, 0.999794) 0.999535 - 3.6 0.922840 0.999334 ( 0.998145, 0.999919) 0.999682 - 3.7 0.926954 0.999334 ( 0.998145, 0.999919) 0.999784 - 3.8 0.930748 0.999667 ( 0.998772, 0.999992) 0.999855 - 3.9 0.934254 0.999667 ( 0.998772, 0.999992) 0.999904 - 4.0 0.937500 0.999667 ( 0.998772, 0.999992) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- - 0 0.0000 0.0000 0.0000 0.0000 0.0000 - 1 -0.2149 0.0082 0.1620 0.1618 0.1554 - 2 -0.4987 0.0121 0.1856 0.1852 0.1768 - 3 -0.9060 0.0103 0.1902 0.1899 0.1825 - 4 -1.5992 0.0102 0.1917 0.1915 0.1838 - 5 -1.5971 0.0123 0.1935 0.1931 0.1881 -Totals: -1.5971 0.0123 0.1935 0.1931 0.1881 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.075949 ( 0.066745, 0.085688) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.147568 ( 0.135108, 0.160473) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.217855 ( 0.203271, 0.232795) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.287142 ( 0.271096, 0.303456) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.359094 ( 0.342026, 0.376340) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.431712 ( 0.414041, 0.449469) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.492672 ( 0.474796, 0.510556) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.558961 ( 0.541167, 0.576680) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.621252 ( 0.603828, 0.638524) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.681879 ( 0.665108, 0.698420) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.731179 ( 0.715178, 0.746888) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.766156 ( 0.750851, 0.781125) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.810127 ( 0.795905, 0.823956) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.842438 ( 0.829194, 0.855251) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.868754 ( 0.856447, 0.880596) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.888408 ( 0.876904, 0.899421) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.901732 ( 0.890835, 0.912122) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.919387 ( 0.909389, 0.928856) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.932378 ( 0.923129, 0.941083) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.945037 ( 0.936609, 0.952903) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.956029 ( 0.948413, 0.963070) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.968021 ( 0.961439, 0.974013) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.974350 ( 0.968404, 0.979699) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.981013 ( 0.975836, 0.985583) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.984677 ( 0.979986, 0.988757) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.989340 ( 0.985369, 0.992695) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.993005 ( 0.989726, 0.995663) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.993671 ( 0.990537, 0.996184) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.996003 ( 0.993451, 0.997932) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.997002 ( 0.994754, 0.998628) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.998334 ( 0.996591, 0.999459) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.999334 ( 0.998145, 0.999919) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.999334 ( 0.998145, 0.999919) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.999334 ( 0.998145, 0.999919) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.999667 ( 0.998772, 0.999992) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.999667 ( 0.998772, 0.999992) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.999667 ( 0.998772, 0.999992) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.999667 ( 0.998772, 0.999992) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.999667 ( 0.998772, 0.999992) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.999667 ( 0.998772, 0.999992) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals: 0 0.0000 0.0000 0.0000 0.0000 0.0000 +INFO:pymbar.confidenceintervals: 1 -0.2184 0.0047 0.1547 0.1546 0.1551 +INFO:pymbar.confidenceintervals: 2 -0.5048 0.0061 0.1802 0.1801 0.1765 +INFO:pymbar.confidenceintervals: 3 -0.9105 0.0058 0.1841 0.1840 0.1822 +INFO:pymbar.confidenceintervals: 4 -1.6024 0.0070 0.1840 0.1839 0.1835 +INFO:pymbar.confidenceintervals: 5 -1.6010 0.0084 0.1885 0.1883 0.1879 +INFO:pymbar.confidenceintervals:Totals: -1.6010 0.0084 0.1885 0.1883 0.1879 Standard ensemble averaged observables -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -901,61 +1316,61 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.081836 ( 0.065680, 0.099573) 0.079656 - 0.2 -24.000000 0.161677 ( 0.139544, 0.185088) 0.158519 - 0.3 -10.111111 0.239521 ( 0.213610, 0.266417) 0.235823 - 0.4 -5.250000 0.305389 ( 0.277262, 0.334253) 0.310843 - 0.5 -3.000000 0.368263 ( 0.338670, 0.398356) 0.382925 - 0.6 -1.777778 0.431138 ( 0.400626, 0.461910) 0.451494 - 0.7 -1.040816 0.505988 ( 0.475042, 0.536911) 0.516073 - 0.8 -0.562500 0.563872 ( 0.533068, 0.594435) 0.576289 - 0.9 -0.234568 0.606786 ( 0.576362, 0.636807) 0.631880 - 1.0 -0.000000 0.663673 ( 0.634132, 0.692594) 0.682689 - 1.1 0.173554 0.711577 ( 0.683148, 0.739205) 0.728668 - 1.2 0.305556 0.757485 ( 0.730482, 0.783514) 0.769861 - 1.3 0.408284 0.796407 ( 0.770937, 0.820757) 0.806399 - 1.4 0.489796 0.828343 ( 0.804397, 0.851048) 0.838487 - 1.5 0.555556 0.865269 ( 0.843462, 0.885697) 0.866386 - 1.6 0.609375 0.888224 ( 0.868004, 0.906975) 0.890401 - 1.7 0.653979 0.915170 ( 0.897159, 0.931612) 0.910869 - 1.8 0.691358 0.928144 ( 0.911371, 0.943298) 0.928139 - 1.9 0.722992 0.945110 ( 0.930195, 0.958343) 0.942567 - 2.0 0.750000 0.955090 ( 0.941438, 0.967024) 0.954500 - 2.1 0.773243 0.964072 ( 0.951706, 0.974687) 0.964271 - 2.2 0.793388 0.969062 ( 0.957491, 0.978863) 0.972193 - 2.3 0.810964 0.973054 ( 0.962172, 0.982151) 0.978552 - 2.4 0.826389 0.981038 ( 0.971729, 0.988534) 0.983605 - 2.5 0.840000 0.985030 ( 0.976645, 0.991589) 0.987581 - 2.6 0.852071 0.990020 ( 0.983001, 0.995199) 0.990678 - 2.7 0.862826 0.993014 ( 0.987000, 0.997184) 0.993066 - 2.8 0.872449 0.993014 ( 0.987000, 0.997184) 0.994890 - 2.9 0.881094 0.994012 ( 0.988382, 0.997797) 0.996268 - 3.0 0.888889 0.997006 ( 0.992801, 0.999382) 0.997300 - 3.1 0.895942 0.997006 ( 0.992801, 0.999382) 0.998065 - 3.2 0.902344 0.998004 ( 0.994447, 0.999758) 0.998626 - 3.3 0.908173 0.998004 ( 0.994447, 0.999758) 0.999033 - 3.4 0.913495 0.999002 ( 0.996322, 0.999975) 0.999326 - 3.5 0.918367 0.999002 ( 0.996322, 0.999975) 0.999535 - 3.6 0.922840 0.999002 ( 0.996322, 0.999975) 0.999682 - 3.7 0.926954 0.999002 ( 0.996322, 0.999975) 0.999784 - 3.8 0.930748 0.999002 ( 0.996322, 0.999975) 0.999855 - 3.9 0.934254 0.999002 ( 0.996322, 0.999975) 0.999904 - 4.0 0.937500 0.999002 ( 0.996322, 0.999975) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- - 0 0.0399 -0.0001 0.0013 0.0013 0.0013 - 1 1.0629 0.0004 0.0116 0.0116 0.0113 - 2 4.1105 -0.0006 0.0306 0.0306 0.0300 - 3 9.2578 0.0078 0.0664 0.0659 0.0675 - 4 16.9898 -0.0102 0.1829 0.1826 0.1816 -Totals: 16.9898 -0.0102 0.1829 0.1826 0.1816 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.076846 ( 0.061180, 0.094111) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.158683 ( 0.136727, 0.181928) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.234531 ( 0.208820, 0.261245) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.308383 ( 0.280172, 0.337319) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.389222 ( 0.359264, 0.419598) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.438124 ( 0.407542, 0.468940) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.509980 ( 0.479030, 0.540892) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.568862 ( 0.538090, 0.599374) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.632735 ( 0.602658, 0.662310) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.690619 ( 0.661660, 0.718857) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.732535 ( 0.704710, 0.759480) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.779441 ( 0.753263, 0.804563) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.819361 ( 0.794959, 0.842556) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.851297 ( 0.828627, 0.872640) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.874251 ( 0.853038, 0.894050) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.893214 ( 0.873372, 0.911569) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.912176 ( 0.893897, 0.928897) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.926148 ( 0.909176, 0.941510) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.944112 ( 0.929079, 0.957467) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.954092 ( 0.940306, 0.966163) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.965070 ( 0.952857, 0.975527) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.974052 ( 0.963352, 0.982964) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.980040 ( 0.970517, 0.987754) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.987026 ( 0.979153, 0.993067) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.991018 ( 0.984314, 0.995881) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.993014 ( 0.987000, 0.997184) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.995010 ( 0.989801, 0.998376) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.998004 ( 0.994447, 0.999758) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.999002 ( 0.996322, 0.999975) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.999002 ( 0.996322, 0.999975) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.999002 ( 0.996322, 0.999975) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.999002 ( 0.996322, 0.999975) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.999002 ( 0.996322, 0.999975) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.999002 ( 0.996322, 0.999975) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.999002 ( 0.996322, 0.999975) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.999002 ( 0.996322, 0.999975) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.999002 ( 0.996322, 0.999975) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.999002 ( 0.996322, 0.999975) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.999002 ( 0.996322, 0.999975) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.999002 ( 0.996322, 0.999975) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals: 0 0.0400 -0.0000 0.0012 0.0012 0.0013 +INFO:pymbar.confidenceintervals: 1 1.0616 -0.0009 0.0116 0.0115 0.0113 +INFO:pymbar.confidenceintervals: 2 4.1124 0.0013 0.0298 0.0298 0.0301 +INFO:pymbar.confidenceintervals: 3 9.2495 -0.0005 0.0593 0.0593 0.0675 +INFO:pymbar.confidenceintervals: 4 16.9911 -0.0089 0.1877 0.1875 0.1817 +INFO:pymbar.confidenceintervals:Totals: 16.9911 -0.0089 0.1877 0.1875 0.1817 Anderson-Darling Metrics (see README.md) -[ 2.13488553 0.79787475 0.11485075 1.7653048 0.68530093] +[0.41460745 0.44599936 0.93761647 1.3195594 0.68606603] MBAR ensemble averaged observables -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -970,64 +1385,63 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.077371 ( 0.062952, 0.093121) 0.079656 - 0.2 -24.000000 0.172213 ( 0.151402, 0.194057) 0.158519 - 0.3 -10.111111 0.247088 ( 0.223120, 0.271853) 0.235823 - 0.4 -5.250000 0.306156 ( 0.280423, 0.332501) 0.310843 - 0.5 -3.000000 0.375208 ( 0.348050, 0.402760) 0.382925 - 0.6 -1.777778 0.440932 ( 0.412973, 0.469076) 0.451494 - 0.7 -1.040816 0.508319 ( 0.480061, 0.536552) 0.516073 - 0.8 -0.562500 0.561564 ( 0.533433, 0.589501) 0.576289 - 0.9 -0.234568 0.613145 ( 0.585450, 0.640483) 0.631880 - 1.0 -0.000000 0.662230 ( 0.635254, 0.688694) 0.682689 - 1.1 0.173554 0.712146 ( 0.686233, 0.737391) 0.728668 - 1.2 0.305556 0.756240 ( 0.731581, 0.780091) 0.769861 - 1.3 0.408284 0.782862 ( 0.759125, 0.805707) 0.806399 - 1.4 0.489796 0.816972 ( 0.794629, 0.838315) 0.838487 - 1.5 0.555556 0.847754 ( 0.826914, 0.867498) 0.866386 - 1.6 0.609375 0.875208 ( 0.855952, 0.893282) 0.890401 - 1.7 0.653979 0.895175 ( 0.877253, 0.911851) 0.910869 - 1.8 0.691358 0.911814 ( 0.895154, 0.927176) 0.928139 - 1.9 0.722992 0.929285 ( 0.914137, 0.943080) 0.942567 - 2.0 0.750000 0.940932 ( 0.926931, 0.953544) 0.954500 - 2.1 0.773243 0.951747 ( 0.938944, 0.963128) 0.964271 - 2.2 0.793388 0.960899 ( 0.949240, 0.971107) 0.972193 - 2.3 0.810964 0.969218 ( 0.958742, 0.978218) 0.978552 - 2.4 0.826389 0.975042 ( 0.965505, 0.983085) 0.983605 - 2.5 0.840000 0.980033 ( 0.971402, 0.987155) 0.987581 - 2.6 0.852071 0.984193 ( 0.976416, 0.990449) 0.990678 - 2.7 0.862826 0.989185 ( 0.982612, 0.994224) 0.993066 - 2.8 0.872449 0.992512 ( 0.986917, 0.996568) 0.994890 - 2.9 0.881094 0.993344 ( 0.988028, 0.997120) 0.996268 - 3.0 0.888889 0.994176 ( 0.989158, 0.997654) 0.997300 - 3.1 0.895942 0.995840 ( 0.991495, 0.998647) 0.998065 - 3.2 0.902344 0.997504 ( 0.993998, 0.999485) 0.998626 - 3.3 0.908173 0.997504 ( 0.993998, 0.999485) 0.999033 - 3.4 0.913495 0.997504 ( 0.993998, 0.999485) 0.999326 - 3.5 0.918367 0.998336 ( 0.995370, 0.999798) 0.999535 - 3.6 0.922840 0.998336 ( 0.995370, 0.999798) 0.999682 - 3.7 0.926954 0.999168 ( 0.996933, 0.999979) 0.999784 - 3.8 0.930748 0.999168 ( 0.996933, 0.999979) 0.999855 - 3.9 0.934254 0.999168 ( 0.996933, 0.999979) 0.999904 - 4.0 0.937500 0.999168 ( 0.996933, 0.999979) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- - 0 0.0399 -0.0001 0.0013 0.0013 0.0012 - 1 1.0626 0.0001 0.0114 0.0114 0.0109 - 2 4.1113 0.0002 0.0260 0.0260 0.0257 - 3 9.2565 0.0065 0.0472 0.0468 0.0493 - 4 16.9905 -0.0095 0.1804 0.1801 0.1751 - 5 25.9544 -0.0456 0.6175 0.6158 0.5718 -Totals: 25.9544 -0.0456 0.6175 0.6158 0.5718 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.079035 ( 0.064466, 0.094930) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.155574 ( 0.135644, 0.176589) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.237937 ( 0.214294, 0.262405) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.306988 ( 0.281234, 0.333351) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.386023 ( 0.358698, 0.413708) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.437604 ( 0.409674, 0.465731) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.501664 ( 0.473412, 0.529911) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.562396 ( 0.534269, 0.590326) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.634775 ( 0.607360, 0.661766) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.682196 ( 0.655603, 0.708215) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.727121 ( 0.701599, 0.751928) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.766223 ( 0.741894, 0.789713) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.797837 ( 0.774681, 0.820055) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.830283 ( 0.808558, 0.850966) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.864393 ( 0.844481, 0.883156) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.882696 ( 0.863920, 0.900265) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.898502 ( 0.880821, 0.914928) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.915973 ( 0.899654, 0.930982) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.942596 ( 0.928770, 0.955027) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.955075 ( 0.942672, 0.966045) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.965058 ( 0.953971, 0.974682) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.969218 ( 0.958742, 0.978218) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.976705 ( 0.967459, 0.984453) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.982529 ( 0.974398, 0.989144) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.985857 ( 0.978455, 0.991733) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.990849 ( 0.984741, 0.995419) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.993344 ( 0.988028, 0.997120) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.995008 ( 0.990311, 0.998164) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.996672 ( 0.992718, 0.999092) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.997504 ( 0.993998, 0.999485) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.998336 ( 0.995370, 0.999798) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.998336 ( 0.995370, 0.999798) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.999168 ( 0.996933, 0.999979) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.999168 ( 0.996933, 0.999979) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.999168 ( 0.996933, 0.999979) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.999168 ( 0.996933, 0.999979) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.999168 ( 0.996933, 0.999979) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.999168 ( 0.996933, 0.999979) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.999168 ( 0.996933, 0.999979) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.999168 ( 0.996933, 0.999979) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals: 0 0.0400 -0.0000 0.0012 0.0012 0.0012 +INFO:pymbar.confidenceintervals: 1 1.0619 -0.0006 0.0111 0.0110 0.0109 +INFO:pymbar.confidenceintervals: 2 4.1114 0.0003 0.0266 0.0266 0.0257 +INFO:pymbar.confidenceintervals: 3 9.2475 -0.0025 0.0446 0.0446 0.0493 +INFO:pymbar.confidenceintervals: 4 16.9938 -0.0062 0.1791 0.1790 0.1752 +INFO:pymbar.confidenceintervals: 5 25.9694 -0.0306 0.5802 0.5794 0.5879 +INFO:pymbar.confidenceintervals:Totals: 25.9694 -0.0306 0.5802 0.5794 0.5879 Anderson-Darling Metrics (see README.md) -[ 0.88636271 0.66578887 0.33607562 2.32717013 0.68941846 - 12.15892596] +[0.6901644 0.1987652 0.63184093 0.91454922 1.07566274 3.77360317] ==== State 1 alone with MBAR ===== -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -1042,55 +1456,55 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.108911 ( 0.069877, 0.155265) 0.079656 - 0.2 -24.000000 0.153465 ( 0.107260, 0.206163) 0.158519 - 0.3 -10.111111 0.262376 ( 0.204181, 0.325028) 0.235823 - 0.4 -5.250000 0.336634 ( 0.273241, 0.403090) 0.310843 - 0.5 -3.000000 0.391089 ( 0.325057, 0.459164) 0.382925 - 0.6 -1.777778 0.475248 ( 0.406855, 0.544104) 0.451494 - 0.7 -1.040816 0.534653 ( 0.465785, 0.602871) 0.516073 - 0.8 -0.562500 0.589109 ( 0.520668, 0.655878) 0.576289 - 0.9 -0.234568 0.599010 ( 0.530738, 0.665425) 0.631880 - 1.0 -0.000000 0.623762 ( 0.556038, 0.689165) 0.682689 - 1.1 0.173554 0.648515 ( 0.581525, 0.712719) 0.728668 - 1.2 0.305556 0.698020 ( 0.633092, 0.759233) 0.769861 - 1.3 0.408284 0.742574 ( 0.680251, 0.800349) 0.806399 - 1.4 0.489796 0.792079 ( 0.733624, 0.845059) 0.838487 - 1.5 0.555556 0.826733 ( 0.771728, 0.875614) 0.866386 - 1.6 0.609375 0.881188 ( 0.833262, 0.921978) 0.890401 - 1.7 0.653979 0.891089 ( 0.844735, 0.930123) 0.910869 - 1.8 0.691358 0.905941 ( 0.862162, 0.942125) 0.928139 - 1.9 0.722992 0.930693 ( 0.891940, 0.961400) 0.942567 - 2.0 0.750000 0.945545 ( 0.910411, 0.972368) 0.954500 - 2.1 0.773243 0.960396 ( 0.929565, 0.982663) 0.964271 - 2.2 0.793388 0.965347 ( 0.936163, 0.985886) 0.972193 - 2.3 0.810964 0.970297 ( 0.942906, 0.988968) 0.978552 - 2.4 0.826389 0.970297 ( 0.942906, 0.988968) 0.983605 - 2.5 0.840000 0.975248 ( 0.949833, 0.991875) 0.987581 - 2.6 0.852071 0.995050 ( 0.981815, 0.999874) 0.990678 - 2.7 0.862826 0.995050 ( 0.981815, 0.999874) 0.993066 - 2.8 0.872449 0.995050 ( 0.981815, 0.999874) 0.994890 - 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 - 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 - 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 - 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 - 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 - 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 - 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 - 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 - 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 - 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 - 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 - 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- -Totals: -0.2149 0.0082 0.1620 0.1618 0.1554 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.064356 ( 0.034884, 0.101964) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.148515 ( 0.103022, 0.200592) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.227723 ( 0.172689, 0.287861) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.316832 ( 0.254634, 0.382465) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.391089 ( 0.325057, 0.459164) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.470297 ( 0.401989, 0.539163) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.509901 ( 0.441113, 0.578503) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.589109 ( 0.520668, 0.655878) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.658416 ( 0.591774, 0.722087) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.698020 ( 0.633092, 0.759233) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.752475 ( 0.690837, 0.809379) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.762376 ( 0.701466, 0.818367) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.811881 ( 0.755313, 0.862603) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.831683 ( 0.777230, 0.879921) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.856436 ( 0.804997, 0.901197) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.896040 ( 0.850513, 0.934154) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.900990 ( 0.856322, 0.938155) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.920792 ( 0.879901, 0.953817) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.935644 ( 0.898036, 0.965116) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.945545 ( 0.910411, 0.972368) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.950495 ( 0.916705, 0.975888) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.955446 ( 0.923085, 0.979324) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.965347 ( 0.936163, 0.985886) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.975248 ( 0.949833, 0.991875) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.980198 ( 0.957004, 0.994552) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.990099 ( 0.972594, 0.998793) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.990099 ( 0.972594, 0.998793) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.990099 ( 0.972594, 0.998793) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals:Totals: -0.2184 0.0047 0.1547 0.1546 0.1551 ==== State 2 alone with MBAR ===== -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -1105,55 +1519,55 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.069307 ( 0.038600, 0.108060) 0.079656 - 0.2 -24.000000 0.148515 ( 0.103022, 0.200592) 0.158519 - 0.3 -10.111111 0.207921 ( 0.154941, 0.266376) 0.235823 - 0.4 -5.250000 0.272277 ( 0.213271, 0.335554) 0.310843 - 0.5 -3.000000 0.351485 ( 0.287281, 0.418475) 0.382925 - 0.6 -1.777778 0.415842 ( 0.348906, 0.484356) 0.451494 - 0.7 -1.040816 0.480198 ( 0.411729, 0.549038) 0.516073 - 0.8 -0.562500 0.549505 ( 0.480671, 0.617411) 0.576289 - 0.9 -0.234568 0.628713 ( 0.561121, 0.693891) 0.631880 - 1.0 -0.000000 0.663366 ( 0.596910, 0.726759) 0.682689 - 1.1 0.173554 0.712871 ( 0.648728, 0.773022) 0.728668 - 1.2 0.305556 0.742574 ( 0.680251, 0.800349) 0.769861 - 1.3 0.408284 0.772277 ( 0.712139, 0.827311) 0.806399 - 1.4 0.489796 0.811881 ( 0.755313, 0.862603) 0.838487 - 1.5 0.555556 0.831683 ( 0.777230, 0.879921) 0.866386 - 1.6 0.609375 0.856436 ( 0.804997, 0.901197) 0.890401 - 1.7 0.653979 0.891089 ( 0.844735, 0.930123) 0.910869 - 1.8 0.691358 0.915842 ( 0.873949, 0.949958) 0.928139 - 1.9 0.722992 0.935644 ( 0.898036, 0.965116) 0.942567 - 2.0 0.750000 0.940594 ( 0.904191, 0.968774) 0.954500 - 2.1 0.773243 0.950495 ( 0.916705, 0.975888) 0.964271 - 2.2 0.793388 0.955446 ( 0.923085, 0.979324) 0.972193 - 2.3 0.810964 0.970297 ( 0.942906, 0.988968) 0.978552 - 2.4 0.826389 0.970297 ( 0.942906, 0.988968) 0.983605 - 2.5 0.840000 0.975248 ( 0.949833, 0.991875) 0.987581 - 2.6 0.852071 0.980198 ( 0.957004, 0.994552) 0.990678 - 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 - 2.8 0.872449 0.995050 ( 0.981815, 0.999874) 0.994890 - 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 - 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 - 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 - 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 - 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 - 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 - 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 - 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 - 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 - 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 - 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 - 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- -Totals: -0.4987 0.0121 0.1856 0.1852 0.1768 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.064356 ( 0.034884, 0.101964) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.118812 ( 0.078022, 0.166738) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.202970 ( 0.150535, 0.260973) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.272277 ( 0.213271, 0.335554) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.361386 ( 0.296680, 0.428692) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.420792 ( 0.353697, 0.489373) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.475248 ( 0.406855, 0.544104) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.559406 ( 0.490628, 0.627069) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.618812 ( 0.550964, 0.684431) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.688119 ( 0.622712, 0.749997) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.727723 ( 0.664446, 0.786729) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.767327 ( 0.706797, 0.822844) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.816832 ( 0.760770, 0.866955) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.851485 ( 0.799408, 0.896978) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.866337 ( 0.816237, 0.909575) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.881188 ( 0.833262, 0.921978) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.881188 ( 0.833262, 0.921978) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.900990 ( 0.856322, 0.938155) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.920792 ( 0.879901, 0.953817) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.935644 ( 0.898036, 0.965116) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.940594 ( 0.904191, 0.968774) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.970297 ( 0.942906, 0.988968) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.980198 ( 0.957004, 0.994552) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.985149 ( 0.964520, 0.996911) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.985149 ( 0.964520, 0.996911) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.990099 ( 0.972594, 0.998793) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals:Totals: -0.5048 0.0061 0.1802 0.1801 0.1765 ==== State 3 alone with MBAR ===== -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -1168,55 +1582,55 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.089109 ( 0.053940, 0.131963) 0.079656 - 0.2 -24.000000 0.183168 ( 0.133045, 0.239230) 0.158519 - 0.3 -10.111111 0.227723 ( 0.172689, 0.287861) 0.235823 - 0.4 -5.250000 0.277228 ( 0.217831, 0.340803) 0.310843 - 0.5 -3.000000 0.341584 ( 0.277913, 0.408226) 0.382925 - 0.6 -1.777778 0.396040 ( 0.329813, 0.464217) 0.451494 - 0.7 -1.040816 0.480198 ( 0.411729, 0.549038) 0.516073 - 0.8 -0.562500 0.544554 ( 0.475702, 0.612571) 0.576289 - 0.9 -0.234568 0.599010 ( 0.530738, 0.665425) 0.631880 - 1.0 -0.000000 0.633663 ( 0.566210, 0.698609) 0.682689 - 1.1 0.173554 0.688119 ( 0.622712, 0.749997) 0.728668 - 1.2 0.305556 0.757426 ( 0.696146, 0.813878) 0.769861 - 1.3 0.408284 0.797030 ( 0.739027, 0.849465) 0.806399 - 1.4 0.489796 0.831683 ( 0.777230, 0.879921) 0.838487 - 1.5 0.555556 0.861386 ( 0.810607, 0.905396) 0.866386 - 1.6 0.609375 0.881188 ( 0.833262, 0.921978) 0.890401 - 1.7 0.653979 0.896040 ( 0.850513, 0.934154) 0.910869 - 1.8 0.691358 0.925743 ( 0.885897, 0.957632) 0.928139 - 1.9 0.722992 0.935644 ( 0.898036, 0.965116) 0.942567 - 2.0 0.750000 0.945545 ( 0.910411, 0.972368) 0.954500 - 2.1 0.773243 0.945545 ( 0.910411, 0.972368) 0.964271 - 2.2 0.793388 0.950495 ( 0.916705, 0.975888) 0.972193 - 2.3 0.810964 0.960396 ( 0.929565, 0.982663) 0.978552 - 2.4 0.826389 0.965347 ( 0.936163, 0.985886) 0.983605 - 2.5 0.840000 0.975248 ( 0.949833, 0.991875) 0.987581 - 2.6 0.852071 0.980198 ( 0.957004, 0.994552) 0.990678 - 2.7 0.862826 0.985149 ( 0.964520, 0.996911) 0.993066 - 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 - 2.9 0.881094 0.990099 ( 0.972594, 0.998793) 0.996268 - 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 - 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 - 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 - 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 - 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 - 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 - 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 - 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 - 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 - 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 - 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- -Totals: -0.9060 0.0103 0.1902 0.1899 0.1825 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.059406 ( 0.031226, 0.095809) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.133663 ( 0.090425, 0.183763) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.227723 ( 0.172689, 0.287861) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.277228 ( 0.217831, 0.340803) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.351485 ( 0.287281, 0.418475) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.440594 ( 0.372931, 0.509372) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.534653 ( 0.465785, 0.602871) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.579208 ( 0.510627, 0.646303) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.613861 ( 0.545896, 0.679691) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.678218 ( 0.612367, 0.740726) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.742574 ( 0.680251, 0.800349) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.772277 ( 0.712139, 0.827311) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.787129 ( 0.728235, 0.840640) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.816832 ( 0.760770, 0.866955) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.846535 ( 0.793837, 0.892740) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.886139 ( 0.838985, 0.926064) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.905941 ( 0.862162, 0.942125) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.910891 ( 0.868037, 0.946060) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.925743 ( 0.885897, 0.957632) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.945545 ( 0.910411, 0.972368) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.955446 ( 0.923085, 0.979324) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.960396 ( 0.929565, 0.982663) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.980198 ( 0.957004, 0.994552) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.985149 ( 0.964520, 0.996911) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.985149 ( 0.964520, 0.996911) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.985149 ( 0.964520, 0.996911) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals:Totals: -0.9105 0.0058 0.1841 0.1840 0.1822 ==== State 4 alone with MBAR ===== -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -1231,55 +1645,55 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.089109 ( 0.053940, 0.131963) 0.079656 - 0.2 -24.000000 0.158416 ( 0.111516, 0.211716) 0.158519 - 0.3 -10.111111 0.222772 ( 0.168234, 0.282508) 0.235823 - 0.4 -5.250000 0.282178 ( 0.222400, 0.346042) 0.310843 - 0.5 -3.000000 0.336634 ( 0.273241, 0.403090) 0.382925 - 0.6 -1.777778 0.415842 ( 0.348906, 0.484356) 0.451494 - 0.7 -1.040816 0.480198 ( 0.411729, 0.549038) 0.516073 - 0.8 -0.562500 0.534653 ( 0.465785, 0.602871) 0.576289 - 0.9 -0.234568 0.613861 ( 0.545896, 0.679691) 0.631880 - 1.0 -0.000000 0.658416 ( 0.591774, 0.722087) 0.682689 - 1.1 0.173554 0.688119 ( 0.622712, 0.749997) 0.728668 - 1.2 0.305556 0.752475 ( 0.690837, 0.809379) 0.769861 - 1.3 0.408284 0.811881 ( 0.755313, 0.862603) 0.806399 - 1.4 0.489796 0.836634 ( 0.782749, 0.884211) 0.838487 - 1.5 0.555556 0.866337 ( 0.816237, 0.909575) 0.866386 - 1.6 0.609375 0.886139 ( 0.838985, 0.926064) 0.890401 - 1.7 0.653979 0.910891 ( 0.868037, 0.946060) 0.910869 - 1.8 0.691358 0.910891 ( 0.868037, 0.946060) 0.928139 - 1.9 0.722992 0.925743 ( 0.885897, 0.957632) 0.942567 - 2.0 0.750000 0.930693 ( 0.891940, 0.961400) 0.954500 - 2.1 0.773243 0.945545 ( 0.910411, 0.972368) 0.964271 - 2.2 0.793388 0.950495 ( 0.916705, 0.975888) 0.972193 - 2.3 0.810964 0.960396 ( 0.929565, 0.982663) 0.978552 - 2.4 0.826389 0.965347 ( 0.936163, 0.985886) 0.983605 - 2.5 0.840000 0.970297 ( 0.942906, 0.988968) 0.987581 - 2.6 0.852071 0.985149 ( 0.964520, 0.996911) 0.990678 - 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 - 2.8 0.872449 0.995050 ( 0.981815, 0.999874) 0.994890 - 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 - 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 - 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 - 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 - 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 - 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 - 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 - 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 - 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 - 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 - 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 - 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- -Totals: -1.5992 0.0102 0.1917 0.1915 0.1838 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.084158 ( 0.050042, 0.126051) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.148515 ( 0.103022, 0.200592) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.242574 ( 0.186122, 0.303854) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.306931 ( 0.245381, 0.372102) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.371287 ( 0.306109, 0.438879) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.440594 ( 0.372931, 0.509372) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.509901 ( 0.441113, 0.578503) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.574257 ( 0.505617, 0.641505) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.623762 ( 0.556038, 0.689165) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.693069 ( 0.627898, 0.754619) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.747525 ( 0.685539, 0.804869) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.767327 ( 0.706797, 0.822844) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.811881 ( 0.755313, 0.862603) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.821782 ( 0.766242, 0.871292) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.861386 ( 0.810607, 0.905396) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.876238 ( 0.827563, 0.917867) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.881188 ( 0.833262, 0.921978) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.910891 ( 0.868037, 0.946060) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.930693 ( 0.891940, 0.961400) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.945545 ( 0.910411, 0.972368) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.960396 ( 0.929565, 0.982663) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.970297 ( 0.942906, 0.988968) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.975248 ( 0.949833, 0.991875) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.985149 ( 0.964520, 0.996911) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.985149 ( 0.964520, 0.996911) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.990099 ( 0.972594, 0.998793) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.990099 ( 0.972594, 0.998793) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.990099 ( 0.972594, 0.998793) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals:Totals: -1.6024 0.0070 0.1840 0.1839 0.1835 ==== State 5 alone with MBAR ===== -The uncertainty estimates are tested in this section. +INFO:pymbar.confidenceintervals:The uncertainty estimates are tested in this section. If the error is normally distributed, the actual error will be less than a multiplier 'alpha' times the computed uncertainty 'sigma' a fraction of time given by: @@ -1294,49 +1708,49 @@ A weak lower bound that holds regardless of how the error is distributed is give by Chebyshev's inequality, and is listed as 'cheby' below. Uncertainty estimates are tested for both free energy differences and expectations. -Error vs. alpha -alpha cheby obs obs err normal - 0.1 -99.000000 0.079208 ( 0.046183, 0.120099) 0.079656 - 0.2 -24.000000 0.118812 ( 0.078022, 0.166738) 0.158519 - 0.3 -10.111111 0.207921 ( 0.154941, 0.266376) 0.235823 - 0.4 -5.250000 0.277228 ( 0.217831, 0.340803) 0.310843 - 0.5 -3.000000 0.331683 ( 0.268577, 0.397946) 0.382925 - 0.6 -1.777778 0.410891 ( 0.344122, 0.479332) 0.451494 - 0.7 -1.040816 0.495050 ( 0.426391, 0.563801) 0.516073 - 0.8 -0.562500 0.534653 ( 0.465785, 0.602871) 0.576289 - 0.9 -0.234568 0.608911 ( 0.540836, 0.674943) 0.631880 - 1.0 -0.000000 0.678218 ( 0.612367, 0.740726) 0.682689 - 1.1 0.173554 0.707921 ( 0.643507, 0.768435) 0.728668 - 1.2 0.305556 0.752475 ( 0.690837, 0.809379) 0.769861 - 1.3 0.408284 0.801980 ( 0.744442, 0.853858) 0.806399 - 1.4 0.489796 0.841584 ( 0.788284, 0.888484) 0.838487 - 1.5 0.555556 0.856436 ( 0.804997, 0.901197) 0.866386 - 1.6 0.609375 0.876238 ( 0.827563, 0.917867) 0.890401 - 1.7 0.653979 0.891089 ( 0.844735, 0.930123) 0.910869 - 1.8 0.691358 0.905941 ( 0.862162, 0.942125) 0.928139 - 1.9 0.722992 0.915842 ( 0.873949, 0.949958) 0.942567 - 2.0 0.750000 0.940594 ( 0.904191, 0.968774) 0.954500 - 2.1 0.773243 0.950495 ( 0.916705, 0.975888) 0.964271 - 2.2 0.793388 0.960396 ( 0.929565, 0.982663) 0.972193 - 2.3 0.810964 0.960396 ( 0.929565, 0.982663) 0.978552 - 2.4 0.826389 0.980198 ( 0.957004, 0.994552) 0.983605 - 2.5 0.840000 0.990099 ( 0.972594, 0.998793) 0.987581 - 2.6 0.852071 0.995050 ( 0.981815, 0.999874) 0.990678 - 2.7 0.862826 0.995050 ( 0.981815, 0.999874) 0.993066 - 2.8 0.872449 0.995050 ( 0.981815, 0.999874) 0.994890 - 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 - 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 - 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 - 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 - 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 - 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 - 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 - 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 - 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 - 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 - 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 - 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 - - i average bias rms_error stddev ave_analyt_std ---------------------------------------------------------------------- -Totals: -1.5971 0.0123 0.1935 0.1931 0.1881 +INFO:pymbar.confidenceintervals:Error vs. alpha +INFO:pymbar.confidenceintervals:alpha cheby obs obs err normal +INFO:pymbar.confidenceintervals: 0.1 -99.000000 0.108911 ( 0.069877, 0.155265) 0.079656 +INFO:pymbar.confidenceintervals: 0.2 -24.000000 0.158416 ( 0.111516, 0.211716) 0.158519 +INFO:pymbar.confidenceintervals: 0.3 -10.111111 0.232673 ( 0.177156, 0.293203) 0.235823 +INFO:pymbar.confidenceintervals: 0.4 -5.250000 0.301980 ( 0.240767, 0.366908) 0.310843 +INFO:pymbar.confidenceintervals: 0.5 -3.000000 0.386139 ( 0.320309, 0.454104) 0.382925 +INFO:pymbar.confidenceintervals: 0.6 -1.777778 0.450495 ( 0.382589, 0.519329) 0.451494 +INFO:pymbar.confidenceintervals: 0.7 -1.040816 0.519802 ( 0.450962, 0.588271) 0.516073 +INFO:pymbar.confidenceintervals: 0.8 -0.562500 0.579208 ( 0.510627, 0.646303) 0.576289 +INFO:pymbar.confidenceintervals: 0.9 -0.234568 0.638614 ( 0.571308, 0.703320) 0.631880 +INFO:pymbar.confidenceintervals: 1.0 -0.000000 0.698020 ( 0.633092, 0.759233) 0.682689 +INFO:pymbar.confidenceintervals: 1.1 0.173554 0.747525 ( 0.685539, 0.804869) 0.728668 +INFO:pymbar.confidenceintervals: 1.2 0.305556 0.787129 ( 0.728235, 0.840640) 0.769861 +INFO:pymbar.confidenceintervals: 1.3 0.408284 0.816832 ( 0.760770, 0.866955) 0.806399 +INFO:pymbar.confidenceintervals: 1.4 0.489796 0.836634 ( 0.782749, 0.884211) 0.838487 +INFO:pymbar.confidenceintervals: 1.5 0.555556 0.851485 ( 0.799408, 0.896978) 0.866386 +INFO:pymbar.confidenceintervals: 1.6 0.609375 0.871287 ( 0.821889, 0.913732) 0.890401 +INFO:pymbar.confidenceintervals: 1.7 0.653979 0.876238 ( 0.827563, 0.917867) 0.910869 +INFO:pymbar.confidenceintervals: 1.8 0.691358 0.896040 ( 0.850513, 0.934154) 0.928139 +INFO:pymbar.confidenceintervals: 1.9 0.722992 0.915842 ( 0.873949, 0.949958) 0.942567 +INFO:pymbar.confidenceintervals: 2.0 0.750000 0.940594 ( 0.904191, 0.968774) 0.954500 +INFO:pymbar.confidenceintervals: 2.1 0.773243 0.965347 ( 0.936163, 0.985886) 0.964271 +INFO:pymbar.confidenceintervals: 2.2 0.793388 0.980198 ( 0.957004, 0.994552) 0.972193 +INFO:pymbar.confidenceintervals: 2.3 0.810964 0.980198 ( 0.957004, 0.994552) 0.978552 +INFO:pymbar.confidenceintervals: 2.4 0.826389 0.980198 ( 0.957004, 0.994552) 0.983605 +INFO:pymbar.confidenceintervals: 2.5 0.840000 0.980198 ( 0.957004, 0.994552) 0.987581 +INFO:pymbar.confidenceintervals: 2.6 0.852071 0.985149 ( 0.964520, 0.996911) 0.990678 +INFO:pymbar.confidenceintervals: 2.7 0.862826 0.985149 ( 0.964520, 0.996911) 0.993066 +INFO:pymbar.confidenceintervals: 2.8 0.872449 0.990099 ( 0.972594, 0.998793) 0.994890 +INFO:pymbar.confidenceintervals: 2.9 0.881094 0.995050 ( 0.981815, 0.999874) 0.996268 +INFO:pymbar.confidenceintervals: 3.0 0.888889 0.995050 ( 0.981815, 0.999874) 0.997300 +INFO:pymbar.confidenceintervals: 3.1 0.895942 0.995050 ( 0.981815, 0.999874) 0.998065 +INFO:pymbar.confidenceintervals: 3.2 0.902344 0.995050 ( 0.981815, 0.999874) 0.998626 +INFO:pymbar.confidenceintervals: 3.3 0.908173 0.995050 ( 0.981815, 0.999874) 0.999033 +INFO:pymbar.confidenceintervals: 3.4 0.913495 0.995050 ( 0.981815, 0.999874) 0.999326 +INFO:pymbar.confidenceintervals: 3.5 0.918367 0.995050 ( 0.981815, 0.999874) 0.999535 +INFO:pymbar.confidenceintervals: 3.6 0.922840 0.995050 ( 0.981815, 0.999874) 0.999682 +INFO:pymbar.confidenceintervals: 3.7 0.926954 0.995050 ( 0.981815, 0.999874) 0.999784 +INFO:pymbar.confidenceintervals: 3.8 0.930748 0.995050 ( 0.981815, 0.999874) 0.999855 +INFO:pymbar.confidenceintervals: 3.9 0.934254 0.995050 ( 0.981815, 0.999874) 0.999904 +INFO:pymbar.confidenceintervals: 4.0 0.937500 0.995050 ( 0.981815, 0.999874) 0.999937 +INFO:pymbar.confidenceintervals: +INFO:pymbar.confidenceintervals: i average bias rms_error stddev ave_analyt_std +INFO:pymbar.confidenceintervals:--------------------------------------------------------------------- +INFO:pymbar.confidenceintervals:Totals: -1.6010 0.0084 0.1885 0.1883 0.1879 diff --git a/examples/harmonic-oscillators/harmonic-oscillators.py b/examples/harmonic-oscillators/harmonic-oscillators.py index 2d692a19..4c0ee372 100644 --- a/examples/harmonic-oscillators/harmonic-oscillators.py +++ b/examples/harmonic-oscillators/harmonic-oscillators.py @@ -26,6 +26,11 @@ from pymbar import testsystems, exp, exp_gauss, bar, MBAR, FES from pymbar.utils import ParameterError +import logging +import sys + +logging.basicConfig(stream=sys.stdout, level=logging.INFO) + # ============================================================================================= # HELPER FUNCTIONS # ============================================================================================= @@ -219,7 +224,7 @@ def get_analytical(beta, K, O, observables): for k in range(1, K): if N_k[k] != 0: w_R = u_kln[k, k - 1, 0 : N_k[k]] - u_kln[k, k, 0 : N_k[k]] # reverse work - df_exp, ddf_exp = exp(w_R) + results = exp(w_R) df_exp = -results["Delta_f"] ddf_exp = results["dDelta_f"] exp_analytical = f_k_analytical[k] - f_k_analytical[k - 1] @@ -805,7 +810,7 @@ def generate_fes_data( # Compute fre energy profile, first with histograms print("Solving for free energies of state to initialize free energy profile...") mbar_options = dict() -mbar_options["verbose"] = True +mbar_options["verbose"] = False fes = FES(u_kn, N_k, mbar_options=mbar_options) print("Computing free energy profile ...") histogram_parameters = dict() diff --git a/examples/harmonic-oscillators/harmonic-oscillators.py_output.txt b/examples/harmonic-oscillators/harmonic-oscillators.py_output.txt index 5765e48b..81635d69 100644 --- a/examples/harmonic-oscillators/harmonic-oscillators.py_output.txt +++ b/examples/harmonic-oscillators/harmonic-oscillators.py_output.txt @@ -1,48 +1,42 @@ Computing dimensionless free energies analytically... This script will draw samples from 6 harmonic oscillators. -The harmonic oscillators have equilibrium positions -[0 1 2 3 4 5] -and spring constants -[25 16 9 4 1 1] -and the following number of samples will be drawn from each (can be zero if no samples drawn): -[10000 10000 10000 10000 0 10000] - +The harmonic oscillators have equilibrium positions: [0 1 2 3 4 5] +and spring constants: [25 16 9 4 1 1] +and the following number of samples will be drawn from each (can be zero if no samples drawn): [10000 10000 10000 10000 0 10000] generating samples... ====================================== - Initializing MBAR + Initializing MBAR ====================================== Estimating relative free energies from simulation (this may take a while)... -K (total states) = 6, total samples = 50000 -N_k = -[10000 10000 10000 10000 0 10000] -There are 5 states with samples. -Initializing free energies to zero. -Initial dimensionless free energies with method zeros -f_k = -[ 0. 0. 0. 0. 0. 0.] -Determining dimensionless free energies by Newton-Raphson / self-consistent iteration. -self consistent iteration gradient norm is 9.7464e+05, Newton-Raphson gradient norm is 22996 -Choosing self-consistent iteration on iteration 0 -self consistent iteration gradient norm is 6.5206e+05, Newton-Raphson gradient norm is 10294 -Choosing self-consistent iteration for lower gradient on iteration 1 -self consistent iteration gradient norm is 4.5608e+05, Newton-Raphson gradient norm is 4961.3 -Newton-Raphson used on iteration 2 -self consistent iteration gradient norm is 3170.1, Newton-Raphson gradient norm is 0.42264 -Newton-Raphson used on iteration 3 -self consistent iteration gradient norm is 0.23362, Newton-Raphson gradient norm is 2.3376e-09 -Newton-Raphson used on iteration 4 -self consistent iteration gradient norm is 1.2212e-09, Newton-Raphson gradient norm is 1.2449e-22 -Newton-Raphson used on iteration 5 -self consistent iteration gradient norm is 0, Newton-Raphson gradient norm is 1.4421e-22 -Choosing self-consistent iteration for lower gradient on iteration 6 -Converged to tolerance of 3.405346e-15 in 7 iterations. -Of 7 iterations, 4 were Newton-Raphson iterations and 3 were self-consistent iterations -Final dimensionless free energies -f_k = -[ 0. -0.22821647 -0.49856217 -0.89211081 -1.57434696 -1.57231022] -MBAR initialization complete. +INFO:pymbar.mbar:K (total states) = 6, total samples = 50000 +INFO:pymbar.mbar:N_k = +INFO:pymbar.mbar:[10000 10000 10000 10000 0 10000] +INFO:pymbar.mbar:There are 5 states with samples. +INFO:pymbar.mbar:Initializing free energies to zero. +INFO:pymbar.mbar:Initial dimensionless free energies with method zeros +INFO:pymbar.mbar:f_k = +INFO:pymbar.mbar:[0. 0. 0. 0. 0. 0.] +WARNING:pymbar.mbar_solvers: +******* JAX 64-bit mode is now on! ******* +* JAX is now set to 64-bit mode! * +* This MAY cause problems with other * +* uses of JAX in the same code. * +****************************************** + +INFO:absl:Remote TPU is not linked into jax; skipping remote TPU. +INFO:absl:Unable to initialize backend 'tpu_driver': Could not initialize backend 'tpu_driver' +INFO:absl:Unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig' +INFO:absl:Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig' +INFO:absl:Unable to initialize backend 'tpu': module 'jaxlib.xla_extension' has no attribute 'get_tpu_client' +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.22e-11 +INFO:pymbar.mbar:Final dimensionless free energies +INFO:pymbar.mbar:f_k = +INFO:pymbar.mbar:[ 0. -0.22821647 -0.49856217 -0.89211081 -1.57434696 -1.57231022] +INFO:pymbar.mbar:MBAR initialization complete. ============================================= - Testing compute_free_energy_differences + Testing compute_free_energy_differences ============================================= Error in free energies is: [[ 0. -0.00507292 0.01226345 0.02417993 0.03509095 0.03712769] @@ -52,21 +46,21 @@ Error in free energies is: [-0.03509095 -0.04016388 -0.0228275 -0.01091103 0. 0.00203674] [-0.03712769 -0.04220061 -0.02486424 -0.01294776 -0.00203674 0. ]] Uncertainty in free energies is: -[[ 0. 0.07100713 0.08109591 0.08450777 0.08621464 0.08859106] - [ 0.07100713 0. 0.03654687 0.04359901 0.04684609 0.05106776] - [ 0.08109591 0.03654687 0. 0.01988063 0.02659403 0.03325712] - [ 0.08450777 0.04359901 0.01988063 0. 0.0150578 0.02393041] - [ 0.08621464 0.04684609 0.02659403 0.0150578 0. 0.01018445] - [ 0.08859106 0.05106776 0.03325712 0.02393041 0.01018445 0. ]] +[[0. 0.07100713 0.08109591 0.08450777 0.08621464 0.08859106] + [0.07100713 0. 0.03654687 0.04359901 0.04684609 0.05106776] + [0.08109591 0.03654687 0. 0.01988063 0.02659403 0.03325712] + [0.08450777 0.04359901 0.01988063 0. 0.0150578 0.02393041] + [0.08621464 0.04684609 0.02659403 0.0150578 0. 0.01018445] + [0.08859106 0.05106776 0.03325712 0.02393041 0.01018445 0. ]] Standard deviations away is: -[[ 0. 0.07144245 0.15122157 0.28612665 0.40701847 0.41909071] - [ 0.07144245 0. 0.47436002 0.6709521 0.85735808 0.82636498] - [ 0.15122157 0.47436002 0. 0.59940132 0.85836953 0.7476365 ] - [ 0.28612665 0.6709521 0.59940132 0. 0.72460967 0.54105905] - [ 0.40701847 0.85735808 0.85836953 0.72460967 0. 0.19998505] - [ 0.41909071 0.82636498 0.7476365 0.54105905 0.19998505 0. ]] +[[0. 0.07144245 0.15122157 0.28612665 0.40701847 0.41909071] + [0.07144245 0. 0.47436002 0.6709521 0.85735808 0.82636498] + [0.15122157 0.47436002 0. 0.59940132 0.85836953 0.7476365 ] + [0.28612665 0.6709521 0.59940132 0. 0.72460967 0.54105905] + [0.40701847 0.85735808 0.85836953 0.72460967 0. 0.19998505] + [0.41909071 0.82636498 0.7476365 0.54105905 0.19998505 0. ]] ============================================== - Testing computeBAR + Testing computeBAR ============================================== BAR estimator for reduced free energy from states 0 to 1 is -0.230716 +/- 0.071054 BAR estimator differs by 0.107 standard deviations from analytical @@ -77,7 +71,7 @@ BAR estimator differs by -0.589 standard deviations from analytical BAR estimator for reduced free energy from states 3 to 5 is -0.681267 +/- 0.023925 BAR estimator differs by -0.497 standard deviations from analytical ============================================== - Testing EXP + Testing EXP ============================================== EXP forward free energy df from states 0 to 1 is 1.557946 +/- 0.427853 @@ -89,16 +83,16 @@ df differs by -1.637 standard deviations from analytical df from states 3 to 4 is -0.559564 +/- 0.075615 df differs by -1.767 standard deviations from analytical EXP reverse free energy -df from states 1 to 0 is 0.559564 +/- 0.075615 -df differs by -10.351 standard deviations from analytical -df from states 2 to 1 is 0.559564 +/- 0.075615 -df differs by -11.205 standard deviations from analytical -df from states 3 to 2 is 0.559564 +/- 0.075615 -df differs by -12.762 standard deviations from analytical -df from states 5 to 4 is 0.559564 +/- 0.075615 -df differs by -7.400 standard deviations from analytical +df from states 1 to 0 is 0.671875 +/- 0.860890 +df differs by -1.040 standard deviations from analytical +df from states 2 to 1 is 0.174442 +/- 0.385955 +df differs by -1.197 standard deviations from analytical +df from states 3 to 2 is -0.308404 +/- 0.039600 +df differs by -2.451 standard deviations from analytical +df from states 5 to 4 is 0.004544 +/- 0.012931 +df differs by -0.351 standard deviations from analytical ============================================== - Testing computeGauss + Testing computeGauss ============================================== Gaussian forward estimate df for reduced free energy from states 0 to 1 is 2.865610 +/- 0.077715 @@ -122,7 +116,7 @@ df differs by 0.018 standard deviations from analytical Testing compute_expectations ====================================== ============================================ - Testing observable position + Testing observable 'position' ============================================ ------------------------------ Now testing 'averages' mode @@ -130,15 +124,15 @@ Now testing 'averages' mode Analytical estimator of position is [0 1 2 3 4 5] MBAR estimator of the position is -[ -3.48469785e-03 1.00188217e+00 1.99757918e+00 2.99873349e+00 - 3.99464907e+00 4.99760397e+00] +[-3.48469785e-03 1.00188217e+00 1.99757918e+00 2.99873349e+00 + 3.99464907e+00 4.99760397e+00] MBAR estimators differ by X standard deviations -[ 1.7664237 0.7750278 0.79363382 0.28297505 0.45542995 0.24537691] +[1.7664237 0.7750278 0.79363382 0.28297505 0.45542995 0.24537691] Standard estimator of position is (states with samples): -[ -3.68674403e-03 1.00274700e+00 1.99811239e+00 2.99899704e+00 - 4.99614443e+00] +[-3.68674403e-03 1.00274700e+00 1.99811239e+00 2.99899704e+00 + 4.99614443e+00] Standard estimators differ by X standard deviations (states with samples) -[ 1.86650558 1.10643016 0.56781776 0.19885105 0.38694585] +[1.86650558 1.10643016 0.56781776 0.19885105 0.38694585] ------------------------------ Now testing 'differences' mode ------------------------------ @@ -157,40 +151,44 @@ MBAR estimator of the differences of position is [-3.99813377 -2.9927669 -1.99706988 -0.99591558 0. 1.0029549 ] [-5.00108867 -3.9957218 -3.00002479 -1.99887048 -1.0029549 0. ]] MBAR estimators differ by X standard deviations -[[ 0. 1.72012888 0.29286683 0.45350738 0.15666452 0.10928238] - [ 1.72012888 0. 1.11883687 0.61829776 0.60456237 0.42521087] - [ 0.29286683 1.11883687 0. 0.22439893 0.24722984 0.00242555] - [ 0.45350738 0.61829776 0.22439893 0. 0.39352513 0.10841951] - [ 0.15666452 0.60456237 0.24722984 0.39352513 0. 0.31251719] - [ 0.10928238 0.42521087 0.00242555 0.10841951 0.31251719 0. ]] +[[0. 1.72012888 0.29286683 0.45350738 0.15666452 0.10928238] + [1.72012888 0. 1.11883687 0.61829776 0.60456237 0.42521087] + [0.29286683 1.11883687 0. 0.22439893 0.24722984 0.00242555] + [0.45350738 0.61829776 0.22439893 0. 0.39352513 0.10841951] + [0.15666452 0.60456237 0.24722984 0.39352513 0. 0.31251719] + [0.10928238 0.42521087 0.00242555 0.10841951 0.31251719 0. ]] ============================================ - Testing observable position^2 + Testing observable 'position^2' ============================================ ------------------------------ Now testing 'averages' mode ------------------------------ Analytical estimator of position^2 is -[ 0.04 1.0625 4.11111111 9.25 17. 26. ] +[ 0.04 1.0625 4.11111111 9.25 17. 26. ] MBAR estimator of the position^2 is -[ 0.03920353 1.06513239 4.10098618 9.24130475 16.96750717 - 25.96503621] +[ 0.03920353 1.06513239 4.10098618 9.24130475 16.96750717 25.96503621] MBAR estimators differ by X standard deviations -[ 1.48807638 0.54017097 0.84031142 0.32399816 0.35202229 0.35234942] +[1.48807638 0.54017097 0.84031142 0.32399816 0.35202229 0.35234942] Standard estimator of position^2 is (states with samples): -[ 0.03902431 1.0671364 4.10295378 9.24835486 25.95419371] +[ 0.03902431 1.0671364 4.10295378 9.24835486 25.95419371] Standard estimators differ by X standard deviations (states with samples) -[ 1.78255707 0.91423646 0.61164653 0.0542101 0.45734256] +[1.78255707 0.91423646 0.61164653 0.0542101 0.45734256] ------------------------------ Now testing 'differences' mode ------------------------------ Analytical estimator of differences of position^2 is -[[ 0. 1.0225 4.07111111 9.21 16.96 25.96 ] - [ -1.0225 0. 3.04861111 8.1875 15.9375 24.9375 ] +[[ 0. 1.0225 4.07111111 9.21 16.96 + 25.96 ] + [ -1.0225 0. 3.04861111 8.1875 15.9375 + 24.9375 ] [ -4.07111111 -3.04861111 0. 5.13888889 12.88888889 21.88888889] - [ -9.21 -8.1875 -5.13888889 0. 7.75 16.75 ] - [-16.96 -15.9375 -12.88888889 -7.75 0. 9. ] - [-25.96 -24.9375 -21.88888889 -16.75 -9. 0. ]] + [ -9.21 -8.1875 -5.13888889 0. 7.75 + 16.75 ] + [-16.96 -15.9375 -12.88888889 -7.75 0. + 9. ] + [-25.96 -24.9375 -21.88888889 -16.75 -9. + 0. ]] MBAR estimator of the differences of position^2 is [[ 0. 1.02592886 4.06178264 9.20210122 16.92830364 25.92583268] @@ -200,41 +198,43 @@ MBAR estimator of the differences of position^2 is 21.86405003] [ -9.20210122 -8.17617236 -5.14031858 0. 7.72620242 16.72373146] - [-16.92830364 -15.90237478 -12.866521 -7.72620242 0. 8.99752903] - [-25.92583268 -24.89990381 -21.86405003 -16.72373146 -8.99752903 0. ]] + [-16.92830364 -15.90237478 -12.866521 -7.72620242 0. + 8.99752903] + [-25.92583268 -24.89990381 -21.86405003 -16.72373146 -8.99752903 + 0. ]] MBAR estimators differ by X standard deviations -[[ 0. 0.69998503 0.77344586 0.29426201 0.34338893 0.34431798] - [ 0.69998503 0. 0.99080923 0.41526508 0.38021573 0.3784246 ] - [ 0.77344586 0.99080923 0. 0.05037748 0.24255773 0.24854723] - [ 0.29426201 0.41526508 0.05037748 0. 0.28352442 0.25937471] - [ 0.34338893 0.38021573 0.24255773 0.28352442 0. 0.02866031] - [ 0.34431798 0.3784246 0.24854723 0.25937471 0.02866031 0. ]] +[[0. 0.69998503 0.77344586 0.29426201 0.34338893 0.34431798] + [0.69998503 0. 0.99080923 0.41526508 0.38021573 0.3784246 ] + [0.77344586 0.99080923 0. 0.05037748 0.24255773 0.24854723] + [0.29426201 0.41526508 0.05037748 0. 0.28352442 0.25937471] + [0.34338893 0.38021573 0.24255773 0.28352442 0. 0.02866031] + [0.34431798 0.3784246 0.24854723 0.25937471 0.02866031 0. ]] ============================================ - Testing observable potential energy + Testing observable 'potential energy' ============================================ ------------------------------ Now testing 'averages' mode ------------------------------ Analytical estimator of potential energy is -[ 0.5 0.5 0.5 0.5 0.5 0.5] +[0.5 0.5 0.5 0.5 0.5 0.5] MBAR estimator of the potential energy is -[ 0.49004416 0.49094444 0.49801248 0.49780763 0.50515732 0.49449826] +[0.49004416 0.49094444 0.49801248 0.49780763 0.50515732 0.49449826] MBAR estimators differ by X standard deviations -[ 1.48807638 1.46761746 0.37868666 0.46027626 1.15479482 0.89160547] +[1.48807638 1.46761746 0.37868666 0.46027626 1.15479482 0.89160547] Standard estimator of potential energy is (states with samples): -[ 0.48780389 0.49313917 0.49726907 0.50874525 0.4963747 ] +[0.48780389 0.49313917 0.49726907 0.50874525 0.4963747 ] Standard estimators differ by X standard deviations (states with samples) -[ 1.78255707 0.97143252 0.38254977 1.18389252 0.51843273] +[1.78255707 0.97143252 0.38254977 1.18389252 0.51843273] ------------------------------ Now testing 'differences' mode ------------------------------ Analytical estimator of differences of potential energy is -[[ 0. 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0. 0.] - [ 0. 0. 0. 0. 0. 0.]] +[[0. 0. 0. 0. 0. 0.] + [0. 0. 0. 0. 0. 0.] + [0. 0. 0. 0. 0. 0.] + [0. 0. 0. 0. 0. 0.] + [0. 0. 0. 0. 0. 0.] + [0. 0. 0. 0. 0. 0.]] MBAR estimator of the differences of potential energy is [[ 0. 0.00090028 0.00796832 0.00776347 0.01511316 0.0044541 ] [-0.00090028 0. 0.00706804 0.00686319 0.01421287 0.00355381] @@ -243,28 +243,28 @@ MBAR estimator of the differences of potential energy is [-0.01511316 -0.01421287 -0.00714483 -0.00734969 0. -0.01065906] [-0.0044541 -0.00355381 0.00351422 0.00330937 0.01065906 0. ]] MBAR estimators differ by X standard deviations -[[ 0. 0.09600306 0.93710365 0.94529546 1.87413184 0.48936165] - [ 0.09600306 0. 0.79949111 0.87947924 1.8276872 0.40690928] - [ 0.93710365 0.79949111 0. 0.02527933 1.01031102 0.43220005] - [ 0.94529546 0.87947924 0.02527933 0. 1.11149477 0.38648332] - [ 1.87413184 1.8276872 1.01031102 1.11149477 0. 1.60969876] - [ 0.48936165 0.40690928 0.43220005 0.38648332 1.60969876 0. ]] +[[0. 0.09600306 0.93710365 0.94529546 1.87413184 0.48936165] + [0.09600306 0. 0.79949111 0.87947924 1.8276872 0.40690928] + [0.93710365 0.79949111 0. 0.02527933 1.01031102 0.43220005] + [0.94529546 0.87947924 0.02527933 0. 1.11149477 0.38648332] + [1.87413184 1.8276872 1.01031102 1.11149477 0. 1.60969876] + [0.48936165 0.40690928 0.43220005 0.38648332 1.60969876 0. ]] ============================================ - Testing observable RMS displacement + Testing observable 'RMS displacement' ============================================ ------------------------------ Now testing 'averages' mode ------------------------------ Analytical estimator of RMS displacement is -[ 0.2 0.25 0.33333333 0.5 1. 1. ] +[0.2 0.25 0.33333333 0.5 1. 1. ] MBAR estimator of the RMS displacement is -[ 0.19799882 0.24772577 0.33267017 0.49890261 1.00514409 0.99448304] +[0.19799882 0.24772577 0.33267017 0.49890261 1.00514409 0.99448304] MBAR estimators differ by X standard deviations -[ 1.48059417 1.46091155 0.37830958 0.4597706 1.15775738 0.88913919] +[1.48059417 1.46091155 0.37830958 0.4597706 1.15775738 0.88913919] Standard estimator of RMS displacement is (states with samples): -[ 0.19754572 0.24827887 0.33242178 0.50435367 0.9963681 ] +[0.19754572 0.24827887 0.33242178 0.50435367 0.9963681 ] Standard estimators differ by X standard deviations (states with samples) -[ 1.77155231 0.96807704 0.38202598 1.18902446 0.51748957] +[1.77155231 0.96807704 0.38202598 1.18902446 0.51748957] ------------------------------ Now testing 'differences' mode ------------------------------ @@ -274,49 +274,49 @@ Now testing 'differences' mode Averages for state 0 [-0.0034847 0.03920353] Uncertainties for state 0 -[ 0.00197274 0.00053523] +[0.00197274 0.00053523] Correlation matrix between observables for state 0 -[[ 0.00390199 0.00390822] - [ 0.00390822 0.00391601]] +[[0.00385412 0.00356351] + [0.00356351 0.00346764]] Averages for state 1 -[ 1.00188217 1.06513239] +[1.00188217 1.06513239] Uncertainties for state 1 -[ 0.00242852 0.00487326] +[0.00242852 0.00487326] Correlation matrix between observables for state 1 -[[ 0.00078801 0.00077538] - [ 0.00077538 0.00076499]] +[[0.00078402 0.00075975] + [0.00075975 0.0007458 ]] Averages for state 2 -[ 1.99757918 4.10098618] +[1.99757918 4.10098618] Uncertainties for state 2 -[ 0.00305029 0.01204903] +[0.00305029 0.01204903] Correlation matrix between observables for state 2 -[[ 0.00050758 0.00051107] - [ 0.00051107 0.00051697]] +[[0.00051009 0.00051456] + [0.00051456 0.00052241]] Averages for state 3 -[ 2.99873349 9.24130475] +[2.99873349 9.24130475] Uncertainties for state 3 -[ 0.0044757 0.02683734] +[0.0044757 0.02683734] Correlation matrix between observables for state 3 -[[ 0.00063392 0.00064386] - [ 0.00064386 0.00065663]] +[[0.00063767 0.00064802] + [0.00064802 0.00066131]] Averages for state 4 -[ 3.99464907 16.96750717] +[ 3.99464907 16.96750717] Uncertainties for state 4 -[ 0.01174919 0.09230332] +[0.01174919 0.09230332] Correlation matrix between observables for state 4 -[[ 0.00081138 0.00085314] - [ 0.00085314 0.00090454]] +[[0.00082498 0.00086563] + [0.00086563 0.00091518]] Averages for state 5 -[ 4.99760397 25.96503621] +[ 4.99760397 25.96503621] Uncertainties for state 5 -[ 0.0097647 0.09923045] +[0.0097647 0.09923045] Correlation matrix between observables for state 5 -[[ 0.00108505 0.00111078] - [ 0.00111078 0.00114157]] +[[0.00109229 0.00111674] + [0.00111674 0.0011458 ]] ============================================ Testing compute_entropy_and_enthalpy ============================================ -Computing average energy and entropy by MBAR. +INFO:pymbar.mbar:Computing average energy and entropy by MBAR. Free energies [[ 0. -0.22821647 -0.49856217 -0.89211081 -1.57434696 -1.57231022] [ 0.22821647 0. -0.2703457 -0.66389433 -1.34613049 -1.34409375] @@ -324,14 +324,14 @@ Free energies [ 0.89211081 0.66389433 0.39354863 0. -0.68223615 -0.68019942] [ 1.57434696 1.34613049 1.07578479 0.68223615 0. 0.00203674] [ 1.57231022 1.34409375 1.07374805 0.68019942 -0.00203674 0. ]] -[[ 0. 0.07100713 0.08109591 0.08450777 0.08621464 0.08859106] - [ 0.07100713 0. 0.03654687 0.04359901 0.04684609 0.05106776] - [ 0.08109591 0.03654687 0. 0.01988063 0.02659403 0.03325712] - [ 0.08450777 0.04359901 0.01988063 0. 0.0150578 0.02393041] - [ 0.08621464 0.04684609 0.02659403 0.0150578 0. 0.01018445] - [ 0.08859106 0.05106776 0.03325712 0.02393041 0.01018445 0. ]] -maximum difference between values computed here and in computeFreeEnergies is 0 -maximum difference between uncertainties computed here and in computeFreeEnergies is 1.52656e-16 +[[0. 0.07100713 0.08109591 0.08450777 0.08621464 0.08859106] + [0.07100713 0. 0.03654687 0.04359901 0.04684609 0.05106776] + [0.08109591 0.03654687 0. 0.01988063 0.02659403 0.03325712] + [0.08450777 0.04359901 0.01988063 0. 0.0150578 0.02393041] + [0.08621464 0.04684609 0.02659403 0.0150578 0. 0.01018445] + [0.08859106 0.05106776 0.03325712 0.02393041 0.01018445 0. ]] +maximum difference between values computed here and in computeFreeEnergies is 1.77636e-15 +maximum difference between uncertainties computed here and in computeFreeEnergies is 3.20577e-15 Energies [[ 0. 0.00090028 0.00796832 0.00776347 0.01511316 0.0044541 ] [-0.00090028 0. 0.00706804 0.00686319 0.01421287 0.00355381] @@ -339,12 +339,12 @@ Energies [-0.00776347 -0.00686319 0.00020485 0. 0.00734969 -0.00330937] [-0.01511316 -0.01421287 -0.00714483 -0.00734969 0. -0.01065906] [-0.0044541 -0.00355381 0.00351422 0.00330937 0.01065906 0. ]] -[[ 0. 0.00937766 0.00850314 0.00821275 0.00806408 0.00910186] - [ 0.00937766 0. 0.00884067 0.00780369 0.00777643 0.00873368] - [ 0.00850314 0.00884067 0. 0.00810356 0.00707191 0.00813101] - [ 0.00821275 0.00780369 0.00810356 0. 0.00661243 0.00856278] - [ 0.00806408 0.00777643 0.00707191 0.00661243 0. 0.00662177] - [ 0.00910186 0.00873368 0.00813101 0.00856278 0.00662177 0. ]] +[[0. 0.00937766 0.00850314 0.00821275 0.00806408 0.00910186] + [0.00937766 0. 0.00884067 0.00780369 0.00777643 0.00873368] + [0.00850314 0.00884067 0. 0.00810356 0.00707191 0.00813101] + [0.00821275 0.00780369 0.00810356 0. 0.00661243 0.00856278] + [0.00806408 0.00777643 0.00707191 0.00661243 0. 0.00662177] + [0.00910186 0.00873368 0.00813101 0.00856278 0.00662177 0. ]] maximum difference between values computed here and in compute_expectations is 0 Entropies [[ 0. 0.22911676 0.5065305 0.89987428 1.58946012 1.57676432] @@ -353,12 +353,12 @@ Entropies [-0.89987428 -0.67075752 -0.39334378 0. 0.68958584 0.67689005] [-1.58946012 -1.36034336 -1.08292962 -0.68958584 0. -0.0126958 ] [-1.57676432 -1.34764756 -1.07023383 -0.67689005 0.0126958 0. ]] -[[ 0. 0.06545724 0.07741524 0.08167947 0.08282737 0.08569158] - [ 0.06545724 0. 0.03091016 0.04026155 0.04276694 0.04788673] - [ 0.07741524 0.03091016 0. 0.01626418 0.02278371 0.03017337] - [ 0.08167947 0.04026155 0.01626418 0. 0.01193696 0.01966654] - [ 0.08282737 0.04276694 0.02278371 0.01193696 0. 0.00930038] - [ 0.08569158 0.04788673 0.03017337 0.01966654 0.00930038 0. ]] +[[0. 0.06545724 0.07741524 0.08167947 0.08282737 0.08569158] + [0.06545724 0. 0.03091016 0.04026155 0.04276694 0.04788673] + [0.07741524 0.03091016 0. 0.01626418 0.02278371 0.03017337] + [0.08167947 0.04026155 0.01626418 0. 0.01193696 0.01966654] + [0.08282737 0.04276694 0.02278371 0.01193696 0. 0.00930038] + [0.08569158 0.04788673 0.03017337 0.01966654 0.00930038 0. ]] Error in entropies is: [[ 0. -0.00507292 0.01226345 0.02417993 0.03509095 0.03712769] [ 0.00507292 0. 0.01733637 0.02925285 0.04016388 0.04220061] @@ -367,12 +367,12 @@ Error in entropies is: [-0.03509095 -0.04016388 -0.0228275 -0.01091103 0. 0.00203674] [-0.03712769 -0.04220061 -0.02486424 -0.01294776 -0.00203674 0. ]] Standard deviations away is: -[[ 0. 0.09125358 0.05548167 0.20098632 0.241198 0.38129289] - [ 0.09125358 0. 0.33219934 0.55610535 0.6068006 0.80704602] - [ 0.05548167 0.33219934 0. 0.74527734 0.68832802 0.94051367] - [ 0.20098632 0.55610535 0.74527734 0. 0.29834555 0.82663935] - [ 0.241198 0.6068006 0.68832802 0.29834555 0. 1.365084 ] - [ 0.38129289 0.80704602 0.94051367 0.82663935 1.365084 0. ]] +[[0. 0.09125358 0.05548167 0.20098632 0.241198 0.38129289] + [0.09125358 0. 0.33219934 0.55610535 0.6068006 0.80704602] + [0.05548167 0.33219934 0. 0.74527734 0.68832802 0.94051367] + [0.20098632 0.55610535 0.74527734 0. 0.29834555 0.82663935] + [0.241198 0.6068006 0.68832802 0.29834555 0. 1.365084 ] + [0.38129289 0.80704602 0.94051367 0.82663935 1.365084 0. ]] ============================================ Testing compute_perturbed_free_energies ============================================ @@ -384,397 +384,801 @@ Error in free energies is: [-0.00599306 -0.02890279 -0.00961168 0. 0.0041401 ] [-0.01013316 -0.03304289 -0.01375178 -0.0041401 0. ]] Standard deviations away is: -[[ 0. 0.53637234 0.06648965 0.10322785 0.16569804] - [ 0.53637234 0. 0.78121116 0.90335259 0.885147 ] - [ 0.06648965 0.78121116 0. 0.60637204 0.55405521] - [ 0.10322785 0.90335259 0.60637204 0. 0.32697043] - [ 0.16569804 0.885147 0.55405521 0.32697043 0. ]] +[[0. 0.53637234 0.06648965 0.10322785 0.16569804] + [0.53637234 0. 0.78121116 0.90335259 0.885147 ] + [0.06648965 0.78121116 0. 0.60637204 0.55405521] + [0.10322785 0.90335259 0.60637204 0. 0.32697043] + [0.16569804 0.885147 0.55405521 0.32697043 0. ]] ============================================ - Testing computeExpectation (new states) + Testing compute_expectation (new states) ============================================ ============================================ - Testing observable position + Testing observable 'position' ============================================ Analytical estimator of position is 3.5 MBAR estimator of the position is -[ 3.49236976] +[3.49236976] MBAR estimators differ by X standard deviations -[ 0.96613603] +[0.96613603] ============================================ - Testing observable position^2 + Testing observable 'position^2' ============================================ Analytical estimator of position^2 is 12.75 MBAR estimator of the position^2 is -[ 12.69624286] +[12.69624286] MBAR estimators differ by X standard deviations -[ 0.95883622] +[0.95883622] ============================================ - Testing observable potential energy + Testing observable 'potential energy' ============================================ -Warning: dim=3 for (state_dependent==True) matrices for observables and dim=2 for (state_dependent==False) observables are deprecated; we suggest you convert to NxK form instead of NxKxK form. +WARNING:pymbar.mbar:dim=3 for (state_dependent==True) matrices for observables and dim=2 for (state_dependent==False) observables are deprecated; we suggest you convert to NxK form instead of NxKxK form. Analytical estimator of potential energy is 0.5 MBAR estimator of the potential energy is -[ 0.49965453] +[0.49965453] MBAR estimators differ by X standard deviations -[ 0.0755382] +[0.0755382] ============================================ - Testing observable RMS displacement + Testing observable 'RMS displacement' ============================================ -Warning: dim=3 for (state_dependent==True) matrices for observables and dim=2 for (state_dependent==False) observables are deprecated; we suggest you convert to NxK form instead of NxKxK form. +WARNING:pymbar.mbar:dim=3 for (state_dependent==True) matrices for observables and dim=2 for (state_dependent==False) observables are deprecated; we suggest you convert to NxK form instead of NxKxK form. Analytical estimator of RMS displacement is -0.707106781187 +0.7071067811865476 MBAR estimator of the RMS displacement is -[ 0.70686245] +[0.70686245] MBAR estimators differ by X standard deviations -[ 0.07552515] +[0.07552515] ============================================ Testing compute_overlap ============================================ Overlap matrix output -[[ 9.80923356e-01 1.90695263e-02 3.85341439e-06 2.70013863e-07 - 0.00000000e+00 2.99419710e-06] - [ 1.90695263e-02 9.15803124e-01 6.34754161e-02 1.43042919e-03 - 0.00000000e+00 2.21504403e-04] - [ 3.85341439e-06 6.34754161e-02 7.69783980e-01 1.60215618e-01 - 0.00000000e+00 6.52113265e-03] - [ 2.70013863e-07 1.43042919e-03 1.60215618e-01 7.15406403e-01 - 0.00000000e+00 1.22947280e-01] - [ 2.06696791e-04 5.70397075e-03 6.06332286e-02 3.56530602e-01 - 0.00000000e+00 5.76925501e-01] - [ 2.99419710e-06 2.21504403e-04 6.52113265e-03 1.22947280e-01 - 0.00000000e+00 8.70307089e-01]] +[[9.80923356e-01 1.90695263e-02 3.85341439e-06 2.70013863e-07 + 0.00000000e+00 2.99419710e-06] + [1.90695263e-02 9.15803124e-01 6.34754161e-02 1.43042919e-03 + 0.00000000e+00 2.21504403e-04] + [3.85341439e-06 6.34754161e-02 7.69783980e-01 1.60215618e-01 + 0.00000000e+00 6.52113265e-03] + [2.70013863e-07 1.43042919e-03 1.60215618e-01 7.15406403e-01 + 0.00000000e+00 1.22947280e-01] + [2.06696791e-04 5.70397075e-03 6.06332286e-02 3.56530602e-01 + 0.00000000e+00 5.76925501e-01] + [2.99419710e-06 2.21504403e-04 6.52113265e-03 1.22947280e-01 + 0.00000000e+00 8.70307089e-01]] Sum of row 0 is 1.000000 (should be 1), looks like it is. Sum of row 1 is 1.000000 (should be 1), looks like it is. Sum of row 2 is 1.000000 (should be 1), looks like it is. Sum of row 3 is 1.000000 (should be 1), looks like it is. Sum of row 4 is 1.000000 (should be 1), looks like it is. Sum of row 5 is 1.000000 (should be 1), looks like it is. -Overlap eigenvalue output -[ 1. 0.98130895 0.91842724 0.8028765 0.54961126 0. ] -Overlap scalar output -0.0186910500542 +Eigenvalues of overlap matrix: +[1. 0.98130895 0.91842724 0.8028765 0.54961126 0. ] +Overlap scalar measure: (1-lambda_2) +0.018691050054206237 ============================================ Testing compute_effective_sample_number ============================================ -Effective number of sample in state 0 is 10194.476 -Efficiency for state 0 is 10194/50000 = 0.2039 -Effective number of sample in state 1 is 10919.377 -Efficiency for state 1 is 10919/50000 = 0.2184 -Effective number of sample in state 2 is 12990.657 -Efficiency for state 2 is 12990/50000 = 0.2598 -Effective number of sample in state 3 is 13978.069 -Efficiency for state 3 is 13978/50000 = 0.2796 -Effective number of sample in state 4 is 15504.952 -Efficiency for state 4 is 15504/50000 = 0.3101 -Effective number of sample in state 5 is 11490.197 -Efficiency for state 5 is 11490/50000 = 0.2298 +INFO:pymbar.mbar:Effective number of sample in state 0 is 10194.476 +INFO:pymbar.mbar:Efficiency for state 0 is 10194.476396/50000 = 0.2039 +INFO:pymbar.mbar:Effective number of sample in state 1 is 10919.377 +INFO:pymbar.mbar:Efficiency for state 1 is 10919.377470/50000 = 0.2184 +INFO:pymbar.mbar:Effective number of sample in state 2 is 12990.657 +INFO:pymbar.mbar:Efficiency for state 2 is 12990.657462/50000 = 0.2598 +INFO:pymbar.mbar:Effective number of sample in state 3 is 13978.069 +INFO:pymbar.mbar:Efficiency for state 3 is 13978.068920/50000 = 0.2796 +INFO:pymbar.mbar:Effective number of sample in state 4 is 15504.952 +INFO:pymbar.mbar:Efficiency for state 4 is 15504.951706/50000 = 0.3101 +INFO:pymbar.mbar:Effective number of sample in state 5 is 11490.197 +INFO:pymbar.mbar:Efficiency for state 5 is 11490.197116/50000 = 0.2298 Effective Sample number -[ 10194.47639596 10919.37747028 12990.65746169 13978.06891977 - 15504.95170617 11490.19711633] +[10194.47639596 10919.37747028 12990.65746169 13978.06891977 + 15504.95170617 11490.19711633] Compare stanadrd estimate of with the MBAR estimate of We should have that with MBAR, err_MBAR = sqrt(N_k/N_eff)*err_standard, so standard (scaled) results should be very close to MBAR results. No standard estimate exists for states that are not sampled. - 0 1 2 3 4 5 -MBAR : [ 0.00197274 0.00242852 0.00305029 0.0044757 0.01174919 0.0097647 ] -standard : [ 0.00197521 0.00248276 0.00332433 0.00504378 0. 0.0099641 ] -sqrt N_k/N_eff : [ 0.99041575 0.95697603 0.87737334 0.845817 0. 0.93290251] -Standard (scaled): [ 0.00195628 0.00237594 0.00291668 0.00426611 0. 0.00929554] + 0 1 2 3 4 5 +MBAR : [0.00197274 0.00242852 0.00305029 0.0044757 0.01174919 0.0097647 ] +standard : [0.00197521 0.00248276 0.00332433 0.00504378 0. 0.0099641 ] +sqrt N_k/N_eff : [0.99041575 0.95697603 0.87737334 0.845817 0. 0.93290251] +Standard (scaled): [0.00195628 0.00237594 0.00291668 0.00426611 0. 0.00929554] ============================================ - Testing PMF functions + Testing free energy surface functions ============================================ ============================================ - Test 1: 1D PMF + Test 1: 1D free energy profile ============================================ There are a total of 7 umbrellas. Constructing umbrellas... Generating 1000 samples for each of 7 umbrellas... -Solving for free energies of state to initialize PMF... -K (total states) = 7, total samples = 7000 -N_k = -[1000 1000 1000 1000 1000 1000 1000] -There are 7 states with samples. -Initializing free energies to zero. -Initial dimensionless free energies with method zeros -f_k = -[ 0. 0. 0. 0. 0. 0. 0.] -Determining dimensionless free energies by Newton-Raphson / self-consistent iteration. -self consistent iteration gradient norm is 2.5021e+05, Newton-Raphson gradient norm is 8956.8 -Choosing self-consistent iteration on iteration 0 -self consistent iteration gradient norm is 1.4849e+05, Newton-Raphson gradient norm is 903.11 -Choosing self-consistent iteration for lower gradient on iteration 1 -self consistent iteration gradient norm is 91450, Newton-Raphson gradient norm is 174.12 -Newton-Raphson used on iteration 2 -self consistent iteration gradient norm is 34.592, Newton-Raphson gradient norm is 5.3207e-05 -Newton-Raphson used on iteration 3 -self consistent iteration gradient norm is 1.4532e-05, Newton-Raphson gradient norm is 2.2375e-17 -Newton-Raphson used on iteration 4 -self consistent iteration gradient norm is 1.1835e-18, Newton-Raphson gradient norm is 8.8747e-25 -Newton-Raphson used on iteration 5 -self consistent iteration gradient norm is 0, Newton-Raphson gradient norm is 5.5467e-25 -Choosing self-consistent iteration for lower gradient on iteration 6 -Converged to tolerance of 5.417773e-16 in 7 iterations. -Of 7 iterations, 4 were Newton-Raphson iterations and 3 were self-consistent iterations -Final dimensionless free energies -f_k = -[ 0. -1.69527543 -2.7494364 -3.09260875 -2.77319623 -1.74604746 - -0.0768459 ] -MBAR initialization complete. -Computing PMF ... -1D PMF: -36 counts out of 7000 counts not in any bin - bin x N f true error df sigmas - 0 -0.65 102 4.328 4.268 -0.060 0.146 0.41 - 1 -0.56 350 3.145 3.136 -0.009 0.117 0.08 - 2 -0.47 508 2.221 2.178 -0.044 0.107 0.41 - 3 -0.37 544 1.441 1.394 -0.047 0.098 0.49 - 4 -0.28 570 0.768 0.784 0.016 0.087 0.19 - 5 -0.19 559 0.343 0.348 0.005 0.077 0.07 - 6 -0.09 578 0.034 0.087 0.053 0.065 0.81 - 7 0.00 548 0.000 0.000 0.000 0.000 0.00 - 8 0.09 556 0.060 0.087 0.027 0.065 0.41 - 9 0.19 587 0.271 0.348 0.077 0.076 1.02 - 10 0.28 541 0.779 0.784 0.005 0.087 0.06 - 11 0.37 559 1.355 1.394 0.039 0.097 0.40 - 12 0.47 512 2.155 2.178 0.023 0.107 0.21 - 13 0.56 350 3.067 3.136 0.069 0.117 0.59 - 14 0.65 100 4.281 4.268 -0.013 0.147 0.09 -============================================ - Test 2: 2D PMF +Solving for free energies of state ... +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.72e-11 +Solving for free energies of state to initialize free energy profile... +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.72e-11 +Computing free energy profile ... +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.31e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.22e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.33e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.19e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.09e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.73e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.11e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.33e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.09e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.69e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.1e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.01e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 3.96e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.64e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.47e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.51e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.6e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.06e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.44e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.37e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.42e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.16e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.17e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.16e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.17e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.35e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.69e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.15e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.88e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.32e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.88e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.38e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.14e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.09e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.02e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.31e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 6.66e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.28e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.35e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.44e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.48e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.54e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.78e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.42e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.87e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.54e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.01e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.48e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.55e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.42e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.04e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.49e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.52e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.29e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.02e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.42e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.44e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.41e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.31e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.37e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.01e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.42e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.64e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.15e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.37e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.72e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.06e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 4.37e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.96e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.44e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.18e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.63e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.75e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.37e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.2e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.14e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.79e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.22e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.02e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.09e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.42e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.09e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.13e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.15e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.91e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.44e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.32e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.32e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.04e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.16e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.11e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 3.14e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.48e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.39e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.44e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.42e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.39e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 7.45e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.24e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.94e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.22e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 9.42e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.29e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 3.15e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 8.67e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.04e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.1e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.28e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 4.97e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.51e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.02e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 2.14e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.79e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.9e-12 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.87e-13 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 1.49e-12 +1D free energy profile: +29 counts out of 7000 counts not in any bin + bin x N true f_hist err_hist df_hist sig_hist f_kde err_kde df_kde sig_kde + 1 -0.65 94 4.268 4.408 -0.140 0.149 0.94 4.405 -0.136 0.107 1.27 + 2 -0.56 331 3.136 3.189 -0.053 0.118 0.45 3.152 -0.016 0.048 0.32 + 3 -0.47 531 2.178 2.198 -0.020 0.107 0.19 2.180 -0.002 0.044 0.05 + 4 -0.37 568 1.394 1.409 -0.015 0.097 0.15 1.415 -0.021 0.024 0.87 + 5 -0.28 551 0.784 0.843 -0.059 0.087 0.68 0.842 -0.058 0.039 1.47 + 6 -0.19 551 0.348 0.395 -0.047 0.076 0.62 0.408 -0.059 0.027 2.20 + 7 -0.09 567 0.087 0.075 0.012 0.065 0.19 0.074 0.013 0.041 0.32 + 8 0.00 549 0.000 0.000 0.000 0.000 0.00 0.000 0.000 0.037 0.00 + 9 0.09 567 0.087 0.019 0.069 0.065 1.06 0.027 0.060 0.029 2.08 + 10 0.19 583 0.348 0.246 0.102 0.076 1.34 0.246 0.103 0.029 3.59 + 11 0.28 561 0.784 0.696 0.088 0.087 1.01 0.700 0.084 0.027 3.06 + 12 0.37 530 1.394 1.351 0.043 0.098 0.44 1.345 0.049 0.038 1.27 + 13 0.47 542 2.178 2.020 0.157 0.107 1.48 2.041 0.137 0.043 3.18 + 14 0.56 321 3.136 3.078 0.058 0.118 0.49 3.047 0.089 0.060 1.49 + 15 0.65 125 4.268 3.981 0.287 0.139 2.06 3.970 0.299 0.083 3.59 +============================================ + Test 2: 2D free energy surface ============================================ There are a total of 49 umbrellas. Constructing umbrellas... -Generating 300 samples for each of 49 umbrellas... +Generating 500 samples for each of 49 umbrellas... Solving for free energies of state ... -Computing PMF ... -2D PMF: -127 counts out of 14700 counts not in any bin - bin x y N f true error df sigmas - 0 -0.65 -0.65 2 8.912 8.537 -0.376 0.726 0.52 - 1 -0.65 -0.56 6 7.194 7.404 0.210 0.343 0.61 - 2 -0.65 -0.47 15 6.614 6.446 -0.168 0.343 0.49 - 3 -0.65 -0.37 20 5.260 5.662 0.402 0.267 1.51 - 4 -0.65 -0.28 18 5.407 5.052 -0.355 0.337 1.05 - 5 -0.65 -0.19 19 4.351 4.617 0.266 0.267 1.00 - 6 -0.65 -0.09 20 4.513 4.356 -0.157 0.313 0.50 - 7 -0.65 0.00 12 3.983 4.268 0.285 0.267 1.07 - 8 -0.65 0.09 19 3.910 4.356 0.445 0.249 1.79 - 9 -0.65 0.19 17 4.390 4.617 0.227 0.271 0.84 - 10 -0.65 0.28 13 4.814 5.052 0.239 0.281 0.85 - 11 -0.65 0.37 18 5.172 5.662 0.490 0.263 1.86 - 12 -0.65 0.47 13 6.301 6.446 0.145 0.294 0.49 - 13 -0.65 0.56 11 7.313 7.404 0.091 0.359 0.25 - 14 -0.65 0.65 7 8.720 8.537 -0.183 0.727 0.25 - 15 -0.56 -0.65 11 7.704 7.404 -0.299 0.441 0.68 - 16 -0.56 -0.56 40 5.859 6.272 0.413 0.226 1.83 - 17 -0.56 -0.47 59 5.023 5.314 0.291 0.207 1.41 - 18 -0.56 -0.37 62 4.487 4.530 0.043 0.209 0.20 - 19 -0.56 -0.28 53 3.538 3.920 0.382 0.187 2.04 - 20 -0.56 -0.19 65 3.236 3.484 0.249 0.189 1.32 - 21 -0.56 -0.09 45 3.416 3.223 -0.193 0.208 0.93 - 22 -0.56 0.00 62 2.912 3.136 0.224 0.187 1.20 - 23 -0.56 0.09 62 2.942 3.223 0.281 0.186 1.51 - 24 -0.56 0.19 59 3.605 3.484 -0.121 0.207 0.58 - 25 -0.56 0.28 66 3.830 3.920 0.090 0.198 0.46 - 26 -0.56 0.37 65 4.482 4.530 0.048 0.205 0.23 - 27 -0.56 0.47 56 4.999 5.314 0.315 0.201 1.57 - 28 -0.56 0.56 29 6.262 6.272 0.010 0.244 0.04 - 29 -0.56 0.65 19 6.733 7.404 0.671 0.294 2.28 - 30 -0.47 -0.65 11 6.233 6.446 0.213 0.305 0.70 - 31 -0.47 -0.56 54 4.935 5.314 0.379 0.203 1.87 - 32 -0.47 -0.47 99 3.865 4.356 0.491 0.180 2.72 - 33 -0.47 -0.37 70 3.325 3.572 0.247 0.181 1.37 - 34 -0.47 -0.28 100 2.838 2.962 0.123 0.179 0.69 - 35 -0.47 -0.19 80 2.476 2.526 0.050 0.178 0.28 - 36 -0.47 -0.09 88 2.255 2.265 0.010 0.175 0.05 - 37 -0.47 0.00 86 1.894 2.178 0.284 0.167 1.70 - 38 -0.47 0.09 81 2.276 2.265 -0.011 0.177 0.06 - 39 -0.47 0.19 74 2.332 2.526 0.195 0.172 1.13 - 40 -0.47 0.28 100 2.681 2.962 0.281 0.172 1.63 - 41 -0.47 0.37 98 3.522 3.572 0.050 0.185 0.27 - 42 -0.47 0.47 65 4.157 4.356 0.198 0.187 1.06 - 43 -0.47 0.56 48 5.227 5.314 0.086 0.213 0.41 - 44 -0.47 0.65 25 6.333 6.446 0.114 0.308 0.37 - 45 -0.37 -0.65 21 5.277 5.662 0.385 0.273 1.41 - 46 -0.37 -0.56 47 4.177 4.530 0.353 0.196 1.80 - 47 -0.37 -0.47 86 3.469 3.572 0.102 0.187 0.55 - 48 -0.37 -0.37 120 2.304 2.788 0.483 0.167 2.90 - 49 -0.37 -0.28 106 2.105 2.178 0.073 0.174 0.42 - 50 -0.37 -0.19 92 1.548 1.742 0.194 0.165 1.18 - 51 -0.37 -0.09 104 1.363 1.481 0.117 0.165 0.71 - 52 -0.37 0.00 87 1.516 1.394 -0.122 0.172 0.71 - 53 -0.37 0.09 90 1.556 1.481 -0.075 0.171 0.44 - 54 -0.37 0.19 90 1.385 1.742 0.357 0.160 2.23 - 55 -0.37 0.28 87 1.934 2.178 0.244 0.167 1.46 - 56 -0.37 0.37 98 2.637 2.788 0.151 0.172 0.87 - 57 -0.37 0.47 81 3.480 3.572 0.092 0.184 0.50 - 58 -0.37 0.56 70 4.429 4.530 0.101 0.200 0.50 - 59 -0.37 0.65 16 5.467 5.662 0.195 0.289 0.68 - 60 -0.28 -0.65 11 4.828 5.052 0.225 0.277 0.81 - 61 -0.28 -0.56 69 3.797 3.920 0.123 0.199 0.62 - 62 -0.28 -0.47 80 2.621 2.962 0.341 0.172 1.98 - 63 -0.28 -0.37 85 1.849 2.178 0.329 0.166 1.98 - 64 -0.28 -0.28 113 1.261 1.568 0.307 0.161 1.91 - 65 -0.28 -0.19 82 1.161 1.132 -0.029 0.167 0.17 - 66 -0.28 -0.09 84 0.887 0.871 -0.016 0.164 0.10 - 67 -0.28 0.00 103 0.633 0.784 0.151 0.157 0.96 - 68 -0.28 0.09 91 0.947 0.871 -0.076 0.165 0.46 - 69 -0.28 0.19 104 1.159 1.132 -0.026 0.167 0.16 - 70 -0.28 0.28 93 1.438 1.568 0.130 0.166 0.78 - 71 -0.28 0.37 89 2.115 2.178 0.063 0.171 0.37 - 72 -0.28 0.47 79 2.843 2.962 0.119 0.177 0.67 - 73 -0.28 0.56 57 3.694 3.920 0.226 0.191 1.18 - 74 -0.28 0.65 13 4.687 5.052 0.365 0.257 1.42 - 75 -0.19 -0.65 20 4.349 4.617 0.267 0.272 0.98 - 76 -0.19 -0.56 64 3.217 3.484 0.268 0.188 1.42 - 77 -0.19 -0.47 80 2.486 2.526 0.040 0.178 0.23 - 78 -0.19 -0.37 103 1.671 1.742 0.071 0.169 0.42 - 79 -0.19 -0.28 85 1.221 1.132 -0.088 0.168 0.53 - 80 -0.19 -0.19 85 0.780 0.697 -0.083 0.164 0.50 - 81 -0.19 -0.09 93 0.611 0.436 -0.176 0.164 1.07 - 82 -0.19 0.00 93 0.274 0.348 0.075 0.154 0.49 - 83 -0.19 0.09 91 0.419 0.436 0.017 0.157 0.11 - 84 -0.19 0.19 100 0.726 0.697 -0.030 0.163 0.18 - 85 -0.19 0.28 86 0.999 1.132 0.134 0.162 0.82 - 86 -0.19 0.37 83 1.578 1.742 0.165 0.166 0.99 - 87 -0.19 0.47 103 2.382 2.526 0.144 0.174 0.83 - 88 -0.19 0.56 66 3.468 3.484 0.016 0.197 0.08 - 89 -0.19 0.65 28 5.018 4.617 -0.401 0.340 1.18 - 90 -0.09 -0.65 13 4.124 4.356 0.231 0.265 0.87 - 91 -0.09 -0.56 42 3.331 3.223 -0.108 0.204 0.53 - 92 -0.09 -0.47 81 2.131 2.265 0.134 0.172 0.78 - 93 -0.09 -0.37 96 1.289 1.481 0.192 0.162 1.19 - 94 -0.09 -0.28 87 0.907 0.871 -0.036 0.165 0.22 - 95 -0.09 -0.19 77 0.413 0.436 0.023 0.157 0.14 - 96 -0.09 -0.09 91 0.181 0.174 -0.007 0.153 0.05 - 97 -0.09 0.00 93 0.017 0.087 0.070 0.149 0.47 - 98 -0.09 0.09 107 0.063 0.174 0.111 0.150 0.74 - 99 -0.09 0.19 94 0.573 0.436 -0.138 0.164 0.84 - 100 -0.09 0.28 97 0.759 0.871 0.112 0.160 0.70 - 101 -0.09 0.37 90 1.412 1.481 0.069 0.167 0.42 - 102 -0.09 0.47 82 2.166 2.265 0.099 0.171 0.58 - 103 -0.09 0.56 53 3.270 3.223 -0.047 0.197 0.24 - 104 -0.09 0.65 24 4.290 4.356 0.065 0.277 0.24 - 105 0.00 -0.65 20 4.606 4.268 -0.338 0.322 1.05 - 106 0.00 -0.56 65 2.982 3.136 0.154 0.188 0.82 - 107 0.00 -0.47 103 2.125 2.178 0.053 0.173 0.31 - 108 0.00 -0.37 78 1.424 1.394 -0.030 0.168 0.18 - 109 0.00 -0.28 106 0.639 0.784 0.145 0.157 0.92 - 110 0.00 -0.19 100 0.312 0.348 0.037 0.156 0.24 - 111 0.00 -0.09 98 0.053 0.087 0.034 0.150 0.23 - 112 0.00 0.00 90 0.000 0.000 0.034 0.000 0.00 - 113 0.00 0.09 102 0.198 0.087 -0.111 0.156 0.71 - 114 0.00 0.19 110 0.251 0.348 0.098 0.155 0.63 - 115 0.00 0.28 95 0.621 0.784 0.163 0.158 1.03 - 116 0.00 0.37 92 1.341 1.394 0.053 0.167 0.32 - 117 0.00 0.47 92 2.150 2.178 0.028 0.175 0.16 - 118 0.00 0.56 67 3.042 3.136 0.094 0.191 0.49 - 119 0.00 0.65 20 4.162 4.268 0.106 0.270 0.39 - 120 0.09 -0.65 24 4.199 4.356 0.156 0.271 0.58 - 121 0.09 -0.56 65 3.078 3.223 0.145 0.188 0.77 - 122 0.09 -0.47 76 2.287 2.265 -0.022 0.175 0.13 - 123 0.09 -0.37 80 1.486 1.481 -0.005 0.167 0.03 - 124 0.09 -0.28 84 0.845 0.871 0.027 0.162 0.16 - 125 0.09 -0.19 92 0.415 0.436 0.021 0.158 0.13 - 126 0.09 -0.09 101 -0.017 0.174 0.192 0.148 1.30 - 127 0.09 0.00 80 -0.075 0.087 0.162 0.147 1.11 - 128 0.09 0.09 90 0.134 0.174 0.041 0.154 0.26 - 129 0.09 0.19 79 0.287 0.436 0.149 0.155 0.96 - 130 0.09 0.28 100 0.603 0.871 0.268 0.156 1.72 - 131 0.09 0.37 91 1.500 1.481 -0.019 0.170 0.11 - 132 0.09 0.47 89 2.278 2.265 -0.013 0.176 0.07 - 133 0.09 0.56 53 3.139 3.223 0.084 0.193 0.44 - 134 0.09 0.65 24 3.956 4.356 0.400 0.254 1.58 - 135 0.19 -0.65 19 4.597 4.617 0.020 0.285 0.07 - 136 0.19 -0.56 44 3.373 3.484 0.111 0.192 0.58 - 137 0.19 -0.47 93 2.608 2.526 -0.081 0.180 0.45 - 138 0.19 -0.37 121 1.736 1.742 0.006 0.169 0.03 - 139 0.19 -0.28 85 1.014 1.132 0.119 0.161 0.74 - 140 0.19 -0.19 86 0.546 0.697 0.151 0.158 0.95 - 141 0.19 -0.09 77 0.345 0.436 0.091 0.156 0.58 - 142 0.19 0.00 96 0.096 0.348 0.253 0.150 1.68 - 143 0.19 0.09 98 0.486 0.436 -0.050 0.163 0.31 - 144 0.19 0.19 104 0.469 0.697 0.228 0.157 1.45 - 145 0.19 0.28 89 1.073 1.132 0.060 0.165 0.36 - 146 0.19 0.37 81 1.820 1.742 -0.078 0.175 0.44 - 147 0.19 0.47 105 2.264 2.526 0.262 0.170 1.55 - 148 0.19 0.56 58 3.437 3.484 0.047 0.198 0.24 - 149 0.19 0.65 20 4.316 4.617 0.301 0.267 1.13 - 150 0.28 -0.65 18 5.288 5.052 -0.236 0.318 0.74 - 151 0.28 -0.56 54 3.642 3.920 0.278 0.189 1.47 - 152 0.28 -0.47 101 2.714 2.962 0.248 0.172 1.44 - 153 0.28 -0.37 105 2.179 2.178 -0.001 0.173 0.00 - 154 0.28 -0.28 95 1.496 1.568 0.072 0.167 0.43 - 155 0.28 -0.19 97 1.175 1.132 -0.043 0.167 0.25 - 156 0.28 -0.09 97 0.748 0.871 0.123 0.160 0.77 - 157 0.28 0.00 101 0.692 0.784 0.092 0.161 0.57 - 158 0.28 0.09 111 0.664 0.871 0.207 0.159 1.30 - 159 0.28 0.19 90 1.059 1.132 0.073 0.166 0.44 - 160 0.28 0.28 100 1.338 1.568 0.230 0.164 1.40 - 161 0.28 0.37 98 1.960 2.178 0.218 0.169 1.29 - 162 0.28 0.47 77 2.638 2.962 0.324 0.173 1.88 - 163 0.28 0.56 53 3.970 3.920 -0.050 0.206 0.24 - 164 0.28 0.65 29 4.980 5.052 0.073 0.296 0.25 - 165 0.37 -0.65 22 5.502 5.662 0.160 0.283 0.56 - 166 0.37 -0.56 51 4.306 4.530 0.223 0.194 1.15 - 167 0.37 -0.47 78 3.375 3.572 0.197 0.177 1.11 - 168 0.37 -0.37 97 2.677 2.788 0.111 0.173 0.64 - 169 0.37 -0.28 91 2.176 2.178 0.002 0.172 0.01 - 170 0.37 -0.19 100 1.801 1.742 -0.059 0.173 0.34 - 171 0.37 -0.09 92 1.424 1.481 0.057 0.167 0.34 - 172 0.37 0.00 90 1.294 1.394 0.100 0.166 0.60 - 173 0.37 0.09 83 1.352 1.481 0.129 0.167 0.77 - 174 0.37 0.19 77 1.727 1.742 0.015 0.173 0.09 - 175 0.37 0.28 96 1.953 2.178 0.225 0.169 1.33 - 176 0.37 0.37 94 2.587 2.788 0.200 0.174 1.15 - 177 0.37 0.47 91 3.318 3.572 0.253 0.179 1.42 - 178 0.37 0.56 63 4.311 4.530 0.219 0.200 1.09 - 179 0.37 0.65 21 5.326 5.662 0.336 0.277 1.21 - 180 0.47 -0.65 17 6.581 6.446 -0.135 0.327 0.41 - 181 0.47 -0.56 63 5.137 5.314 0.177 0.206 0.86 - 182 0.47 -0.47 81 4.516 4.356 -0.160 0.196 0.82 - 183 0.47 -0.37 79 3.531 3.572 0.040 0.183 0.22 - 184 0.47 -0.28 86 2.929 2.962 0.033 0.180 0.18 - 185 0.47 -0.19 89 2.246 2.526 0.280 0.169 1.66 - 186 0.47 -0.09 90 2.225 2.265 0.040 0.174 0.23 - 187 0.47 0.00 82 1.985 2.178 0.192 0.171 1.13 - 188 0.47 0.09 78 2.071 2.265 0.194 0.172 1.13 - 189 0.47 0.19 102 2.095 2.526 0.431 0.168 2.56 - 190 0.47 0.28 98 2.873 2.962 0.089 0.181 0.49 - 191 0.47 0.37 92 3.299 3.572 0.273 0.179 1.53 - 192 0.47 0.47 68 4.240 4.356 0.115 0.194 0.59 - 193 0.47 0.56 59 5.060 5.314 0.254 0.209 1.21 - 194 0.47 0.65 26 6.003 6.446 0.443 0.275 1.61 - 195 0.56 -0.65 10 7.326 7.404 0.079 0.347 0.23 - 196 0.56 -0.56 30 6.374 6.272 -0.102 0.246 0.41 - 197 0.56 -0.47 47 5.386 5.314 -0.072 0.213 0.34 - 198 0.56 -0.37 57 4.247 4.530 0.282 0.191 1.48 - 199 0.56 -0.28 64 3.844 3.920 0.076 0.196 0.38 - 200 0.56 -0.19 53 3.221 3.484 0.264 0.187 1.41 - 201 0.56 -0.09 51 3.156 3.223 0.067 0.195 0.34 - 202 0.56 0.00 59 2.794 3.136 0.342 0.185 1.85 - 203 0.56 0.09 56 3.081 3.223 0.142 0.195 0.73 - 204 0.56 0.19 52 3.238 3.484 0.247 0.193 1.28 - 205 0.56 0.28 47 3.776 3.920 0.144 0.199 0.73 - 206 0.56 0.37 56 4.188 4.530 0.341 0.195 1.75 - 207 0.56 0.47 51 4.922 5.314 0.392 0.203 1.93 - 208 0.56 0.56 32 6.071 6.272 0.201 0.239 0.84 - 209 0.56 0.65 15 7.230 7.404 0.174 0.360 0.48 - 210 0.65 -0.65 3 8.421 8.537 0.116 0.527 0.22 - 211 0.65 -0.56 20 7.120 7.404 0.285 0.315 0.90 - 212 0.65 -0.47 21 6.137 6.446 0.309 0.275 1.12 - 213 0.65 -0.37 20 6.225 5.662 -0.563 0.373 1.51 - 214 0.65 -0.28 29 5.450 5.052 -0.397 0.326 1.22 - 215 0.65 -0.19 14 4.495 4.617 0.122 0.279 0.44 - 216 0.65 -0.09 24 4.253 4.356 0.103 0.275 0.37 - 217 0.65 0.00 20 4.327 4.268 -0.059 0.304 0.19 - 218 0.65 0.09 33 4.149 4.356 0.207 0.276 0.75 - 219 0.65 0.19 21 4.480 4.617 0.137 0.285 0.48 - 220 0.65 0.28 18 4.551 5.052 0.502 0.250 2.01 - 221 0.65 0.37 28 5.277 5.662 0.385 0.289 1.33 - 222 0.65 0.47 26 5.859 6.446 0.587 0.266 2.21 - 223 0.65 0.56 13 7.047 7.404 0.357 0.332 1.08 - 224 0.65 0.65 9 7.417 8.537 1.120 0.418 2.68 +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.5e-10 +Computing free energy surface ... +INFO:pymbar.mbar_solvers:Reached a solution to within tolerance with hybr +INFO:pymbar.mbar_solvers:Solution found within tolerance! +INFO:pymbar.mbar_solvers:Final gradient norm: 5.5e-10 +2D FES: +197 counts out of 24500 counts not in any bin +Uncertainties only calculated for histogram methods + bin x y N f_hist f_kde true err_hist err_kde df sigmas + 1 -0.65 -0.65 5 8.474 8.312 8.537 0.063 0.224 0.472 0.13 + 2 -0.65 -0.56 15 7.562 7.317 7.404 -0.158 0.088 0.297 0.53 + 3 -0.65 -0.47 21 6.584 6.277 6.446 -0.137 0.169 0.256 0.54 + 4 -0.65 -0.37 36 5.346 5.279 5.662 0.316 0.383 0.214 1.48 + 5 -0.65 -0.28 24 4.689 4.738 5.052 0.364 0.314 0.202 1.80 + 6 -0.65 -0.19 21 4.321 4.354 4.617 0.296 0.263 0.206 1.44 + 7 -0.65 -0.09 30 4.255 4.215 4.356 0.100 0.140 0.216 0.46 + 8 -0.65 0.00 21 4.534 4.231 4.268 -0.265 0.037 0.246 1.08 + 9 -0.65 0.09 33 4.313 4.233 4.356 0.043 0.122 0.230 0.19 + 10 -0.65 0.19 32 4.647 4.513 4.617 -0.030 0.104 0.228 0.13 + 11 -0.65 0.28 39 4.920 4.843 5.052 0.132 0.209 0.222 0.60 + 12 -0.65 0.37 33 5.652 5.486 5.662 0.010 0.177 0.240 0.04 + 13 -0.65 0.47 20 6.437 6.299 6.446 0.009 0.147 0.249 0.04 + 14 -0.65 0.56 21 6.828 6.934 7.404 0.577 0.471 0.235 2.46 + 15 -0.65 0.65 3 8.716 8.216 8.537 -0.180 0.321 0.519 0.35 + 16 -0.56 -0.65 14 7.419 7.322 7.404 -0.014 0.083 0.292 0.05 + 17 -0.56 -0.56 57 6.165 6.058 6.272 0.107 0.214 0.183 0.59 + 18 -0.56 -0.47 89 5.115 5.097 5.314 0.199 0.217 0.161 1.24 + 19 -0.56 -0.37 98 4.516 4.345 4.530 0.014 0.185 0.161 0.09 + 20 -0.56 -0.28 94 3.754 3.711 3.920 0.166 0.209 0.155 1.07 + 21 -0.56 -0.19 103 3.376 3.259 3.484 0.109 0.226 0.154 0.71 + 22 -0.56 -0.09 101 3.119 3.055 3.223 0.105 0.168 0.151 0.69 + 23 -0.56 0.00 95 3.100 2.988 3.136 0.036 0.148 0.153 0.24 + 24 -0.56 0.09 100 3.027 2.999 3.223 0.196 0.224 0.147 1.33 + 25 -0.56 0.19 88 3.342 3.265 3.484 0.143 0.219 0.151 0.94 + 26 -0.56 0.28 86 3.715 3.680 3.920 0.205 0.240 0.151 1.36 + 27 -0.56 0.37 92 4.353 4.292 4.530 0.177 0.238 0.155 1.14 + 28 -0.56 0.47 92 5.242 5.054 5.314 0.072 0.260 0.166 0.43 + 29 -0.56 0.56 52 5.886 5.930 6.272 0.386 0.342 0.172 2.25 + 30 -0.56 0.65 18 7.437 7.181 7.404 -0.033 0.224 0.282 0.12 + 31 -0.47 -0.65 21 6.682 6.236 6.446 -0.236 0.211 0.255 0.92 + 32 -0.47 -0.56 91 5.154 5.111 5.314 0.159 0.203 0.162 0.98 + 33 -0.47 -0.47 129 4.165 4.073 4.356 0.190 0.283 0.148 1.29 + 34 -0.47 -0.37 149 3.317 3.241 3.572 0.255 0.331 0.140 1.82 + 35 -0.47 -0.28 132 2.731 2.692 2.962 0.231 0.269 0.137 1.69 + 36 -0.47 -0.19 137 2.306 2.308 2.526 0.220 0.218 0.135 1.63 + 37 -0.47 -0.09 150 2.086 2.108 2.265 0.178 0.157 0.134 1.33 + 38 -0.47 0.00 134 1.984 2.015 2.178 0.193 0.163 0.134 1.45 + 39 -0.47 0.09 166 2.067 2.087 2.265 0.198 0.178 0.133 1.49 + 40 -0.47 0.19 161 2.416 2.392 2.526 0.110 0.134 0.136 0.81 + 41 -0.47 0.28 126 2.776 2.744 2.962 0.185 0.218 0.137 1.35 + 42 -0.47 0.37 141 3.416 3.353 3.572 0.156 0.219 0.141 1.11 + 43 -0.47 0.47 119 4.198 4.137 4.356 0.158 0.219 0.147 1.08 + 44 -0.47 0.56 101 5.242 5.139 5.314 0.072 0.174 0.164 0.44 + 45 -0.47 0.65 27 6.350 6.183 6.446 0.096 0.263 0.232 0.41 + 46 -0.37 -0.65 33 5.271 5.250 5.662 0.391 0.412 0.208 1.88 + 47 -0.37 -0.56 82 4.338 4.316 4.530 0.192 0.213 0.155 1.24 + 48 -0.37 -0.47 154 3.373 3.317 3.572 0.198 0.254 0.141 1.41 + 49 -0.37 -0.37 182 2.428 2.450 2.788 0.359 0.337 0.133 2.71 + 50 -0.37 -0.28 161 1.893 1.958 2.178 0.284 0.220 0.131 2.17 + 51 -0.37 -0.19 153 1.534 1.551 1.742 0.208 0.191 0.131 1.59 + 52 -0.37 -0.09 137 1.347 1.334 1.481 0.134 0.147 0.130 1.03 + 53 -0.37 0.00 150 1.198 1.259 1.394 0.195 0.135 0.129 1.52 + 54 -0.37 0.09 123 1.355 1.404 1.481 0.125 0.077 0.130 0.96 + 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