Gaussian widths:
[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
[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):
[2000 2000 2000 2000 2000    0]

Performing replicate 1 / 200
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.         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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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)
[0.41460745 0.44599936 0.93761647 1.3195594  0.68606603]
MBAR ensemble averaged observables
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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)
[0.6901644  0.1987652  0.63184093 0.91454922 1.07566274 3.77360317]

 ==== State 1 alone with MBAR ===== 
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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 ===== 
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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 ===== 
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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 ===== 
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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 ===== 
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:
P(error < alpha sigma) = erf(alpha / sqrt(2))
For example, the true error should be less than 1.0 * sigma
(one standard deviation) a total of 68% of the time, and
less than 2.0 * sigma (two standard deviations) 95% of the time.
The observed fraction of the time that error < alpha sigma, and its
uncertainty, is given as 'obs' (with uncertainty 'obs err') below.
This should be compared to the column labeled 'normal'.
A weak lower bound that holds regardless of how the error is distributed is given
by Chebyshev's inequality, and is listed as 'cheby' below.
Uncertainty estimates are tested for both free energy differences and expectations.

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
